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API Completeness

+

Narwhals has two different level of support for libraries: "full" and "interchange".

+

Libraries for which we have full support we intend to support the whole Narwhals API, however this is a work in progress.

+

In the following table it is possible to check which method is implemented for which backend.

+
+

Info

+
    +
  • "pandas-like" means pandas, cuDF and Modin
    • +
  • Polars supports all the methods (by design)
    • +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodpandas-likearrow
DataFrameclone✅✅
DataFramecollect_schema✅✅
DataFramecolumns✅✅
DataFramedrop✅✅
DataFramedrop_nulls✅✅
DataFramefilter✅✅
DataFramegather_every✅✅
DataFrameget_column✅✅
DataFramegroup_by✅✅
DataFramehead✅✅
DataFrameis_duplicated✅✅
DataFrameis_empty✅✅
DataFrameis_unique✅✅
DataFrameitem✅✅
DataFrameiter_rows✅✅
DataFramejoin✅✅
DataFramelazy✅✅
DataFramenull_count✅✅
DataFramepipe❌❌
DataFramerename✅✅
DataFramerows✅✅
DataFrameschema✅✅
DataFrameselect✅✅
DataFrameshape✅✅
DataFramesort✅✅
DataFrametail✅✅
DataFrameto_arrow✅✅
DataFrameto_dict✅✅
DataFrameto_numpy✅✅
DataFrameto_pandas✅✅
DataFrameunique✅✅
DataFramewith_columns✅✅
DataFramewith_row_index✅✅
DataFramewrite_csv✅✅
DataFramewrite_parquet✅✅
Exprabs✅✅
Expralias✅✅
Exprall✅✅
Exprany✅✅
Exprarg_true✅✅
Exprcast✅✅
Exprcat✅✅
Exprclip✅✅
Exprcount✅✅
Exprcum_sum✅✅
Exprdiff✅✅
Exprdrop_nulls✅✅
Exprdt✅✅
Exprfill_null✅✅
Exprfilter✅✅
Exprgather_every✅✅
Exprhead✅✅
Expris_between✅✅
Expris_duplicated✅✅
Expris_first_distinct✅✅
Expris_in✅✅
Expris_last_distinct✅✅
Expris_null✅✅
Expris_unique✅✅
Exprlen✅✅
Exprmax✅✅
Exprmean✅✅
Exprmin✅✅
Exprn_unique✅✅
Exprname✅✅
Exprnull_count✅✅
Exprover✅✅
Exprpipe❌❌
Exprquantile✅✅
Exprround✅✅
Exprsample✅✅
Exprshift✅✅
Exprsort✅✅
Exprstd✅✅
Exprstr✅✅
Exprsum✅✅
Exprtail✅✅
Exprunique✅✅
ExprCatNamespaceget_categories✅✅
ExprDateTimeNamespacedate✅✅
ExprDateTimeNamespaceday✅✅
ExprDateTimeNamespacehour✅✅
ExprDateTimeNamespacemicrosecond✅✅
ExprDateTimeNamespacemillisecond✅✅
ExprDateTimeNamespaceminute✅✅
ExprDateTimeNamespacemonth✅✅
ExprDateTimeNamespacenanosecond✅✅
ExprDateTimeNamespaceordinal_day✅✅
ExprDateTimeNamespacesecond✅✅
ExprDateTimeNamespaceto_string✅✅
ExprDateTimeNamespacetotal_microseconds✅✅
ExprDateTimeNamespacetotal_milliseconds✅✅
ExprDateTimeNamespacetotal_minutes✅✅
ExprDateTimeNamespacetotal_nanoseconds✅✅
ExprDateTimeNamespacetotal_seconds✅✅
ExprDateTimeNamespaceyear✅✅
ExprNameNamespacekeep✅✅
ExprNameNamespacemap✅✅
ExprNameNamespaceprefix✅✅
ExprNameNamespacesuffix✅✅
ExprNameNamespaceto_lowercase✅✅
ExprNameNamespaceto_uppercase✅✅
ExprStringNamespacecontains✅✅
ExprStringNamespaceends_with✅✅
ExprStringNamespacehead❌❌
ExprStringNamespacereplace✅✅
ExprStringNamespacereplace_all✅✅
ExprStringNamespaceslice✅✅
ExprStringNamespacestarts_with✅✅
ExprStringNamespacestrip_chars✅✅
ExprStringNamespacetail❌❌
ExprStringNamespaceto_datetime✅✅
ExprStringNamespaceto_lowercase✅✅
ExprStringNamespaceto_uppercase✅✅
LazyFrameclone✅✅
LazyFramecollect✅✅
LazyFramecollect_schema✅✅
LazyFramecolumns✅✅
LazyFramedrop✅✅
LazyFramedrop_nulls✅✅
LazyFramefilter✅✅
LazyFramegather_every✅✅
LazyFramegroup_by✅✅
LazyFramehead✅✅
LazyFramejoin✅✅
LazyFramelazy✅✅
LazyFramepipe❌❌
LazyFramerename✅✅
LazyFrameschema✅✅
LazyFrameselect✅✅
LazyFramesort✅✅
LazyFrametail✅✅
LazyFrameunique✅✅
LazyFramewith_columns✅✅
LazyFramewith_row_index✅✅
Seriesabs✅✅
Seriesalias✅✅
Seriesall✅✅
Seriesany✅✅
Seriesarg_true✅✅
Seriescast✅✅
Seriescat✅✅
Seriesclip✅✅
Seriescount✅✅
Seriescum_sum✅✅
Seriesdiff✅✅
Seriesdrop_nulls✅✅
Seriesdt✅✅
Seriesdtype✅✅
Seriesfill_null✅✅
Seriesfilter✅✅
Seriesgather_every✅✅
Serieshead✅✅
Seriesis_between✅✅
Seriesis_duplicated✅✅
Seriesis_empty✅✅
Seriesis_first_distinct✅✅
Seriesis_in✅✅
Seriesis_last_distinct✅✅
Seriesis_null✅✅
Seriesis_sorted✅✅
Seriesis_unique✅✅
Seriesitem✅✅
Serieslen✅✅
Seriesmax✅✅
Seriesmean✅✅
Seriesmin✅✅
Seriesn_unique✅✅
Seriesname✅✅
Seriesnull_count✅✅
Seriespipe❌❌
Seriesquantile✅✅
Seriesround✅✅
Seriessample✅✅
Seriesshape✅✅
Seriesshift✅✅
Seriessort✅✅
Seriesstd✅✅
Seriesstr✅✅
Seriessum✅✅
Seriestail✅✅
Seriesto_arrow✅✅
Seriesto_dummies✅✅
Seriesto_frame✅✅
Seriesto_list✅✅
Seriesto_numpy✅✅
Seriesto_pandas✅✅
Seriesunique✅✅
Seriesvalue_counts✅✅
Serieszip_with✅✅
SeriesCatNamespaceget_categories✅✅
SeriesDateTimeNamespacedate✅✅
SeriesDateTimeNamespaceday✅✅
SeriesDateTimeNamespacehour✅✅
SeriesDateTimeNamespacemicrosecond✅✅
SeriesDateTimeNamespacemillisecond✅✅
SeriesDateTimeNamespaceminute✅✅
SeriesDateTimeNamespacemonth✅✅
SeriesDateTimeNamespacenanosecond✅✅
SeriesDateTimeNamespaceordinal_day✅✅
SeriesDateTimeNamespacesecond✅✅
SeriesDateTimeNamespaceto_string✅✅
SeriesDateTimeNamespacetotal_microseconds✅✅
SeriesDateTimeNamespacetotal_milliseconds✅✅
SeriesDateTimeNamespacetotal_minutes✅✅
SeriesDateTimeNamespacetotal_nanoseconds✅✅
SeriesDateTimeNamespacetotal_seconds✅✅
SeriesDateTimeNamespaceyear✅✅
SeriesStringNamespacecontains✅✅
SeriesStringNamespaceends_with✅✅
SeriesStringNamespacehead❌❌
SeriesStringNamespacereplace✅✅
SeriesStringNamespacereplace_all✅✅
SeriesStringNamespaceslice✅✅
SeriesStringNamespacestarts_with✅✅
SeriesStringNamespacestrip_chars✅✅
SeriesStringNamespacetail❌❌
SeriesStringNamespaceto_lowercase✅✅
SeriesStringNamespaceto_uppercase✅✅
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+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/dataframe/index.html b/api-reference/dataframe/index.html new file mode 100644 index 0000000000..0a8363f1dd --- /dev/null +++ b/api-reference/dataframe/index.html @@ -0,0 +1,4863 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.DataFrame - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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narwhals.DataFrame

+ + +
+ + + + +
+ + +

Narwhals DataFrame, backed by a native dataframe.

+

The native dataframe might be pandas.DataFrame, polars.DataFrame, ...

+

This class is not meant to be instantiated directly - instead, use +narwhals.from_native.

+ + + + +
+ + + + + + + +
+ + + +

+ columns: list[str] + + + property + + +

+ + +
+ +

Get column names.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.columns
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+['foo', 'bar', 'ham']
+>>> func(df_pl)
+['foo', 'bar', 'ham']
+
+
+ +
+ +
+ + + +

+ schema: Schema + + + property + + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.schema
+
+

You can pass either pandas or Polars to func:

+ 
>>> df_pd_schema = func(df_pd)
+>>> df_pd_schema
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+ 
>>> df_pl_schema = func(df_pl)
+>>> df_pl_schema
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+
+ +
+ +
+ + + +

+ shape: tuple[int, int] + + + property + + +

+ + +
+ +

Get the shape of the DataFrame.

+ + +

Examples:

+

Construct pandas and polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3, 4, 5]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.shape
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+(5, 1)
+>>> func(df_pl)
+(5, 1)
+
+
+ +
+ + + +
+ + +

+ __arrow_c_stream__(requested_schema=None) + +

+ + +
+ +

Export a DataFrame via the Arrow PyCapsule Interface.

+
    +
  • if the underlying dataframe implements the interface, it'll return that
    • +
  • else, it'll call to_arrow and then defer to PyArrow's implementation
    • +
+

See PyCapsule Interface +for more.

+ +
+ +
+ +
+ + +

+ __getitem__(item) + +

+ + +
+ +

Extract column or slice of DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
item + str | slice | Sequence[int] | tuple[Sequence[int], str | int] | tuple[Sequence[int], Sequence[int] | Sequence[str] | slice] + +
+

how to slice dataframe. What happens depends on what is passed. It's easiest +to explain by example. Suppose we have a Dataframe df:

+
- `df['a']` extracts column `'a'` and returns a `Series`.
+- `df[0:2]` extracts the first two rows and returns a `DataFrame`.
+- `df[0:2, 'a']` extracts the first two rows from column `'a'` and returns
+    a `Series`.
+- `df[0:2, 0]` extracts the first two rows from the first column and returns
+    a `Series`.
+- `df[[0, 1], [0, 1, 2]]` extracts the first two rows and the first three columns
+    and returns a `DataFrame`
+- `df[0: 2, ['a', 'c']]` extracts the first two rows and columns `'a'` and `'c'` and
+    returns a `DataFrame`
+- `df[:, 0: 2]` extracts all rows from the first two columns and returns a `DataFrame`
+- `df[:, 'a': 'c']` extracts all rows and all columns positioned between `'a'` and `'c'`
+    _inclusive_ and returns a `DataFrame`. For example, if the columns are
+    `'a', 'b', 'c', 'd'`, then that would extract columns `'a'`, `'b'`, and `'c'`.
+
+
+ 		+ required +
+ + +
+ Notes +
    +
  • Integers are always interpreted as positions
    • +
  • Strings are always interpreted as column names.
    • +
+

In contrast with Polars, pandas allows non-string column names. +If you don't know whether the column name you're trying to extract +is definitely a string (e.g. df[df.columns[0]]) then you should +use DataFrame.get_column instead.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify(eager_only=True)
+... def func(df):
+...     return df["a"]
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+0    1
+1    2
+Name: a, dtype: int64
+>>> func(df_pl)
+shape: (2,)
+Series: 'a' [i64]
+[
+    1
+    2
+]
+
+ +
+ +
+ +
+ + +

+ clone() + +

+ + +
+ +

Create a copy of this DataFrame.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function in which we clone the DataFrame:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.clone()
+
+ 
>>> func(df_pd)
+   a  b
+0  1  3
+1  2  4
+
+ 
>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ collect_schema() + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.collect_schema()
+
+

You can pass either pandas or Polars to func:

+ 
>>> df_pd_schema = func(df_pd)
+>>> df_pd_schema
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+ 
>>> df_pl_schema = func(df_pl)
+>>> df_pl_schema
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+ +
+ +
+ +
+ + +

+ drop(*columns, strict=True) + +

+ + +
+ +

Remove columns from the dataframe.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*columns + str | Iterable[str] + +
+

Names of the columns that should be removed from the dataframe.

+
+ 		+ () +
strict + bool + +
+

Validate that all column names exist in the schema and throw an +exception if a column name does not exist in the schema.

+
+ 		+ True +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop("ham")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar
+0    1  6.0
+1    2  7.0
+2    3  8.0
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 1   ┆ 6.0 │
+│ 2   ┆ 7.0 │
+│ 3   ┆ 8.0 │
+└─────┴─────┘
+
+

Use positional arguments to drop multiple columns.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop("foo", "ham")
+
+ 
>>> func(df_pd)
+   bar
+0  6.0
+1  7.0
+2  8.0
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ bar │
+│ --- │
+│ f64 │
+╞═════╡
+│ 6.0 │
+│ 7.0 │
+│ 8.0 │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ drop_nulls(subset=None) + +

+ + +
+ +

Drop null values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
subset + str | list[str] | None + +
+

Column name(s) for which null values are considered. If set to None +(default), use all columns.

+
+ 		+ None +
+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1.0, 2.0, None], "ba": [1.0, None, 2.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop_nulls()
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+     a   ba
+0  1.0  1.0
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ ba  │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 1.0 ┆ 1.0 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ filter(*predicates) + +

+ + +
+ +

Filter the rows in the DataFrame based on one or more predicate expressions.

+

The original order of the remaining rows is preserved.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
predicates + IntoExpr | Iterable[IntoExpr] + +
+

Expression(s) that evaluates to a boolean Series.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function in which we filter on +one condition.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter(nw.col("foo") > 1)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar ham
+1    2    7   b
+2    3    8   c
+>>> func(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+
+

Filter on multiple conditions, combined with and/or operators:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter((nw.col("foo") < 3) & (nw.col("ham") == "a"))
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+
+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter((nw.col("foo") == 1) | (nw.col("ham") == "c"))
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+2    3    8   c
+>>> func(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+
+

Provide multiple filters using *args syntax:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     dframe = df.filter(
+...         nw.col("foo") <= 2,
+...         ~nw.col("ham").is_in(["b", "c"]),
+...     )
+...     return dframe
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth row in the DataFrame and return as a new DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Gather every n-th row.

+
+ 		+ required +
offset + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.gather_every(n=2, offset=1)
+
+ 
>>> func(df_pd)
+   a  b
+1  2  6
+3  4  8
+
+ 
>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 6   │
+│ 4   ┆ 8   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ get_column(name) + +

+ + +
+ +

Get a single column by name.

+ + +
+ Notes +

Although name is typed as str, pandas does allow non-string column +names, and they will work when passed to this function if the +narwhals.DataFrame is backed by a pandas dataframe with non-string +columns. This function can only be used to extract a column by name, so +there is no risk of ambiguity.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify(eager_only=True)
+... def func(df):
+...     name = df.columns[0]
+...     return df.get_column(name)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+0    1
+1    2
+Name: a, dtype: int64
+>>> func(df_pl)
+shape: (2,)
+Series: 'a' [i64]
+[
+    1
+    2
+]
+
+ +
+ +
+ +
+ + +

+ group_by(*keys) + +

+ + +
+ +

Start a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*keys + str | Iterable[str] + +
+

Column(s) to group by. Accepts multiple columns names as a list.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
GroupBy + GroupBy[Self] + +
+

Object which can be used to perform aggregations.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "a": ["a", "b", "a", "b", "c"],
+...     "b": [1, 2, 1, 3, 3],
+...     "c": [5, 4, 3, 2, 1],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function in which we group by one column +and call agg to compute the grouped sum of another column.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by("a").agg(nw.col("b").sum()).sort("a")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+
+

Group by multiple columns by passing a list of column names.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by(["a", "b"]).agg(nw.max("c")).sort("a", "b")
+>>> func(df_pd)
+   a  b  c
+0  a  1  5
+1  b  2  4
+2  b  3  2
+3  c  3  1
+>>> func(df_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 5   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of rows to return. If a negative value is passed, return all rows +except the last abs(n).

+
+ 		+ 5 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "foo": [1, 2, 3, 4, 5],
+...     "bar": [6, 7, 8, 9, 10],
+...     "ham": ["a", "b", "c", "d", "e"],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function that gets the first 3 rows.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.head(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+1    2    7   b
+2    3    8   c
+>>> func(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Get a mask of all duplicated rows in this DataFrame.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": [1, 2, 3, 1],
+...         "b": ["x", "y", "z", "x"],
+...     }
+... )
+>>> df_pl = pl.DataFrame(
+...     {
+...         "a": [1, 2, 3, 1],
+...         "b": ["x", "y", "z", "x"],
+...     }
+... )
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.is_duplicated()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+0     True
+1    False
+2    False
+3     True
+dtype: bool
+
+ 
>>> func(df_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    true
+    false
+    false
+    true
+]
+
+ +
+ +
+ +
+ + +

+ is_empty() + +

+ + +
+ +

Check if the dataframe is empty.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+

Let's define a dataframe-agnostic function that filters rows in which "foo" +values are greater than 10, and then checks if the result is empty or not:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter(nw.col("foo") > 10).is_empty()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> df_pd = pd.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
+>>> df_pl = pl.DataFrame({"foo": [1, 2, 3], "bar": [4, 5, 6]})
+>>> func(df_pd), func(df_pl)
+(True, True)
+
+ 
>>> df_pd = pd.DataFrame({"foo": [100, 2, 3], "bar": [4, 5, 6]})
+>>> df_pl = pl.DataFrame({"foo": [100, 2, 3], "bar": [4, 5, 6]})
+>>> func(df_pd), func(df_pl)
+(False, False)
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Get a mask of all unique rows in this DataFrame.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": [1, 2, 3, 1],
+...         "b": ["x", "y", "z", "x"],
+...     }
+... )
+>>> df_pl = pl.DataFrame(
+...     {
+...         "a": [1, 2, 3, 1],
+...         "b": ["x", "y", "z", "x"],
+...     }
+... )
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.is_unique()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+0    False
+1     True
+2     True
+3    False
+dtype: bool
+
+ 
>>> func(df_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    false
+     true
+     true
+    false
+]
+
+ +
+ +
+ +
+ + +

+ item(row=None, column=None) + +

+ + +
+ +

Return the DataFrame as a scalar, or return the element at the given row/column.

+ + +
+ Notes +

If row/col not provided, this is equivalent to df[0,0], with a check that the shape is (1,1). +With row/col, this is equivalent to df[row,col].

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that returns item at given row/column

+ 
>>> @nw.narwhalify
+... def func(df, row, column):
+...     return df.item(row, column)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd, 1, 1), func(df_pd, 2, "b")
+(5, 6)
+
+ 
>>> func(df_pl, 1, 1), func(df_pl, 2, "b")
+(5, 6)
+
+ +
+ +
+ +
+ + +

+ iter_rows(*, named=False, buffer_size=512) + +

+ + +
+ +

Returns an iterator over the DataFrame of rows of python-native values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
named + bool + +
+

By default, each row is returned as a tuple of values given +in the same order as the frame columns. Setting named=True will +return rows of dictionaries instead.

+
+ 		+ False +
buffer_size + int + +
+

Determines the number of rows that are buffered +internally while iterating over the data. +See https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.iter_rows.html

+
+ 		+ 512 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df, *, named):
+...     return df.iter_rows(named=named)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> [row for row in func(df_pd, named=False)]
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> [row for row in func(df_pd, named=True)]
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> [row for row in func(df_pl, named=False)]
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> [row for row in func(df_pl, named=True)]
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+
+ +
+ +
+ +
+ + +

+ join(other, *, how='inner', left_on=None, right_on=None) + +

+ + +
+ +

Join in SQL-like fashion.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
other + Self + +
+

DataFrame to join with.

+
+ 		+ required +
how + Literal['inner', 'left', 'cross', 'semi', 'anti'] + +
+

Join strategy.

+
    +
  • inner: Returns rows that have matching values in both tables.
    • +
  • cross: Returns the Cartesian product of rows from both tables.
    • +
  • semi: Filter rows that have a match in the right table.
    • +
  • anti: Filter rows that do not have a match in the right table.
    • +
+
+ 		+ 'inner' +
left_on + str | list[str] | None + +
+

Name(s) of the left join column(s).

+
+ 		+ None +
right_on + str | list[str] | None + +
+

Name(s) of the right join column(s).

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined DataFrame

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> data_other = {
+...     "apple": ["x", "y", "z"],
+...     "ham": ["a", "b", "d"],
+... }
+
+ 
>>> df_pd = pd.DataFrame(data)
+>>> other_pd = pd.DataFrame(data_other)
+
+ 
>>> df_pl = pl.DataFrame(data)
+>>> other_pl = pl.DataFrame(data_other)
+
+

Let's define a dataframe-agnostic function in which we join over "ham" column:

+ 
>>> @nw.narwhalify
+... def join_on_ham(df, other_any):
+...     return df.join(other_any, left_on="ham", right_on="ham")
+
+

We can now pass either pandas or Polars to the function:

+ 
>>> join_on_ham(df_pd, other_pd)
+   foo  bar ham apple
+0    1  6.0   a     x
+1    2  7.0   b     y
+
+ 
>>> join_on_ham(df_pl, other_pl)
+shape: (2, 4)
+┌─────┬─────┬─────┬───────┐
+│ foo ┆ bar ┆ ham ┆ apple │
+│ --- ┆ --- ┆ --- ┆ ---   │
+│ i64 ┆ f64 ┆ str ┆ str   │
+╞═════╪═════╪═════╪═══════╡
+│ 1   ┆ 6.0 ┆ a   ┆ x     │
+│ 2   ┆ 7.0 ┆ b   ┆ y     │
+└─────┴─────┴─────┴───────┘
+
+ +
+ +
+ +
+ + +

+ lazy() + +

+ + +
+ +

Lazify the DataFrame (if possible).

+

If a library does not support lazy execution, then this is a no-op.

+ + +

Examples:

+

Construct pandas and Polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.lazy()
+
+

Note that then, pandas dataframe stay eager, but Polars DataFrame becomes a Polars LazyFrame:

+ 
>>> func(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> func(df_pl)
+<LazyFrame ...>
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Create a new DataFrame that shows the null counts per column.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> df_pd = pd.DataFrame(
+...     {
+...         "foo": [1, None, 3],
+...         "bar": [6, 7, None],
+...         "ham": ["a", "b", "c"],
+...     }
+... )
+>>> df_pl = pl.DataFrame(
+...     {
+...         "foo": [1, None, 3],
+...         "bar": [6, 7, None],
+...         "ham": ["a", "b", "c"],
+...     }
+... )
+
+

Let's define a dataframe-agnostic function that returns the null count of +each columns:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.null_count()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar  ham
+0    1    1    0
+
+ 
>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ u32 ┆ u32 ┆ u32 │
+╞═════╪═════╪═════╡
+│ 1   ┆ 1   ┆ 0   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "ba": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.pipe(
+...         lambda _df: _df.select([x for x in _df.columns if len(x) == 1])
+...     )
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+   a
+0  1
+1  2
+2  3
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ rename(mapping) + +

+ + +
+ +

Rename column names.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
mapping + dict[str, str] + +
+

Key value pairs that map from old name to new name.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.rename({"foo": "apple"})
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   apple  bar ham
+0      1    6   a
+1      2    7   b
+2      3    8   c
+>>> func(df_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ apple ┆ bar ┆ ham │
+│ ---   ┆ --- ┆ --- │
+│ i64   ┆ i64 ┆ str │
+╞═══════╪═════╪═════╡
+│ 1     ┆ 6   ┆ a   │
+│ 2     ┆ 7   ┆ b   │
+│ 3     ┆ 8   ┆ c   │
+└───────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ rows(*, named=False) + +

+ + +
+ +

Returns all data in the DataFrame as a list of rows of python-native values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
named + bool + +
+

By default, each row is returned as a tuple of values given +in the same order as the frame columns. Setting named=True will +return rows of dictionaries instead.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df, *, named):
+...     return df.rows(named=named)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd, named=False)
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> func(df_pd, named=True)
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> func(df_pl, named=False)
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> func(df_pl, named=True)
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+
+ +
+ +
+ +
+ + +

+ select(*exprs, **named_exprs) + +

+ + +
+ +

Select columns from this DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*exprs + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to select, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, + other non-expression inputs are parsed as literals.

+
+ 		+ () +
**named_exprs + IntoExpr + +
+

Additional columns to select, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function in which we pass the name of a +column to select that column.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select("foo")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo
+0    1
+1    2
+2    3
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ foo │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+
+

Multiple columns can be selected by passing a list of column names.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(["foo", "bar"])
+>>> func(df_pd)
+   foo  bar
+0    1    6
+1    2    7
+2    3    8
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 6   │
+│ 2   ┆ 7   │
+│ 3   ┆ 8   │
+└─────┴─────┘
+
+

Multiple columns can also be selected using positional arguments instead of a +list. Expressions are also accepted.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("foo"), nw.col("bar") + 1)
+>>> func(df_pd)
+   foo  bar
+0    1    7
+1    2    8
+2    3    9
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+
+

Use keyword arguments to easily name your expression inputs.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(threshold=nw.col("foo") * 2)
+>>> func(df_pd)
+   threshold
+0          2
+1          4
+2          6
+>>> func(df_pl)
+shape: (3, 1)
+┌───────────┐
+│ threshold │
+│ ---       │
+│ i64       │
+╞═══════════╡
+│ 2         │
+│ 4         │
+│ 6         │
+└───────────┘
+
+ +
+ +
+ +
+ + +

+ sort(by, *more_by, descending=False) + +

+ + +
+ +

Sort the dataframe by the given columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
by + str | Iterable[str] + +
+

Column(s) names to sort by.

+
+ 		+ required +
*more_by + str + +
+

Additional columns to sort by, specified as positional + arguments.

+
+ 		+ () +
descending + bool | Sequence[bool] + +
+

Sort in descending order. When sorting by multiple + columns, can be specified per column by passing a + sequence of booleans.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "a": [1, 2, None],
+...     "b": [6.0, 5.0, 4.0],
+...     "c": ["a", "c", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function in which we sort by multiple +columns in different orders

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.sort("c", "a", descending=[False, True])
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a    b  c
+0  1.0  6.0  a
+2  NaN  4.0  b
+1  2.0  5.0  c
+>>> func(df_pl)
+shape: (3, 3)
+┌──────┬─────┬─────┐
+│ a    ┆ b   ┆ c   │
+│ ---  ┆ --- ┆ --- │
+│ i64  ┆ f64 ┆ str │
+╞══════╪═════╪═════╡
+│ 1    ┆ 6.0 ┆ a   │
+│ null ┆ 4.0 ┆ b   │
+│ 2    ┆ 5.0 ┆ c   │
+└──────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of rows to return. If a negative value is passed, return all rows +except the first abs(n).

+
+ 		+ 5 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "foo": [1, 2, 3, 4, 5],
+...     "bar": [6, 7, 8, 9, 10],
+...     "ham": ["a", "b", "c", "d", "e"],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function that gets the last 3 rows.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.tail(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar ham
+2    3    8   c
+3    4    9   d
+4    5   10   e
+>>> func(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 3   ┆ 8   ┆ c   │
+│ 4   ┆ 9   ┆ d   │
+│ 5   ┆ 10  ┆ e   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ to_arrow() + +

+ + +
+ +

Convert to arrow table.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2, 3], "bar": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that converts to arrow table:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.to_arrow()
+
+ 
>>> func(df_pd)
+pyarrow.Table
+foo: int64
+bar: string
+----
+foo: [[1,2,3]]
+bar: [["a","b","c"]]
+
+ 
>>> func(df_pl)
+pyarrow.Table
+foo: int64
+bar: large_string
+----
+foo: [[1,2,3]]
+bar: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ to_dict(*, as_series=True) + +

+ + +
+ +

Convert DataFrame to a dictionary mapping column name to values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
as_series + bool + +
+

If set to true True, then the values are Narwhals Series, + otherwise the values are Any.

+
+ 		+ True +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "A": [1, 2, 3, 4, 5],
+...     "fruits": ["banana", "banana", "apple", "apple", "banana"],
+...     "B": [5, 4, 3, 2, 1],
+...     "animals": ["beetle", "fly", "beetle", "beetle", "beetle"],
+...     "optional": [28, 300, None, 2, -30],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.to_dict(as_series=False)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+{'A': [1, 2, 3, 4, 5], 'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'], 'B': [5, 4, 3, 2, 1], 'animals': ['beetle', 'fly', 'beetle', 'beetle', 'beetle'], 'optional': [28.0, 300.0, nan, 2.0, -30.0]}
+>>> func(df_pl)
+{'A': [1, 2, 3, 4, 5], 'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'], 'B': [5, 4, 3, 2, 1], 'animals': ['beetle', 'fly', 'beetle', 'beetle', 'beetle'], 'optional': [28, 300, None, 2, -30]}
+
+ +
+ +
+ +
+ + +

+ to_numpy() + +

+ + +
+ +

Convert this DataFrame to a NumPy ndarray.

+ + +

Examples:

+

Construct pandas and polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.5, 7.0, 8.5], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.to_numpy()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+array([[1, 6.5, 'a'],
+       [2, 7.0, 'b'],
+       [3, 8.5, 'c']], dtype=object)
+>>> func(df_pl)
+array([[1, 6.5, 'a'],
+       [2, 7.0, 'b'],
+       [3, 8.5, 'c']], dtype=object)
+
+ +
+ +
+ +
+ + +

+ to_pandas() + +

+ + +
+ +

Convert this DataFrame to a pandas DataFrame.

+ + +

Examples:

+

Construct pandas and Polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.to_pandas()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> func(df_pl)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+
+ +
+ +
+ +
+ + +

+ unique(subset=None, *, keep='any', maintain_order=False) + +

+ + +
+ +

Drop duplicate rows from this dataframe.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
subset + str | list[str] | None + +
+

Column name(s) to consider when identifying duplicate rows.

+
+ 		+ None +
keep + Literal['any', 'first', 'last', 'none'] + +
+

{'first', 'last', 'any', 'none'} +Which of the duplicate rows to keep.

+
    +
  • 'any': Does not give any guarantee of which row is kept. + This allows more optimizations.
    • +
  • 'none': Don't keep duplicate rows.
    • +
  • 'first': Keep first unique row.
    • +
  • 'last': Keep last unique row.
    • +
+
+ 		+ 'any' +
maintain_order + bool + +
+

Keep the same order as the original DataFrame. This is more +expensive to compute. Settings this to True blocks the possibility +to run on the streaming engine for polars.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3, 1],
+...     "bar": ["a", "a", "a", "a"],
+...     "ham": ["b", "b", "b", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.unique(["bar", "ham"])
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo bar ham
+0    1   a   b
+>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ str ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ a   ┆ b   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ with_columns(*exprs, **named_exprs) + +

+ + +
+ +

Add columns to this DataFrame.

+

Added columns will replace existing columns with the same name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*exprs + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to add, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, other + non-expression inputs are parsed as literals.

+
+ 		+ () +
**named_exprs + IntoExpr + +
+

Additional columns to add, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
DataFrame + Self + +
+

A new DataFrame with the columns added.

+
+
+ + +
+ Note +

Creating a new DataFrame using this method does not create a new copy of +existing data.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "a": [1, 2, 3, 4],
+...     "b": [0.5, 4, 10, 13],
+...     "c": [True, True, False, True],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

Let's define a dataframe-agnostic function in which we pass an expression +to add it as a new column:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns((nw.col("a") * 2).alias("a*2"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a     b      c  a*2
+0  1   0.5   True    2
+1  2   4.0   True    4
+2  3  10.0  False    6
+3  4  13.0   True    8
+>>> func(df_pl)
+shape: (4, 4)
+┌─────┬──────┬───────┬─────┐
+│ a   ┆ b    ┆ c     ┆ a*2 │
+│ --- ┆ ---  ┆ ---   ┆ --- │
+│ i64 ┆ f64  ┆ bool  ┆ i64 │
+╞═════╪══════╪═══════╪═════╡
+│ 1   ┆ 0.5  ┆ true  ┆ 2   │
+│ 2   ┆ 4.0  ┆ true  ┆ 4   │
+│ 3   ┆ 10.0 ┆ false ┆ 6   │
+│ 4   ┆ 13.0 ┆ true  ┆ 8   │
+└─────┴──────┴───────┴─────┘
+
+ +
+ +
+ +
+ + +

+ with_row_index(name='index') + +

+ + +
+ +

Insert column which enumerates rows.

+ + +

Examples:

+

Construct pandas as polars DataFrames:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_row_index()
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+   index  a  b
+0      0  1  4
+1      1  2  5
+2      2  3  6
+>>> func(df_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ index ┆ a   ┆ b   │
+│ ---   ┆ --- ┆ --- │
+│ u32   ┆ i64 ┆ i64 │
+╞═══════╪═════╪═════╡
+│ 0     ┆ 1   ┆ 4   │
+│ 1     ┆ 2   ┆ 5   │
+│ 2     ┆ 3   ┆ 6   │
+└───────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ write_csv(file=None) + +

+ + +
+ +

Write dataframe to parquet file.

+ + +

Examples:

+

Construct pandas and Polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> def func(df):
+...     df = nw.from_native(df)
+...     return df.write_csv()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+'foo,bar,ham\n1,6.0,a\n2,7.0,b\n3,8.0,c\n'
+>>> func(df_pl)
+'foo,bar,ham\n1,6.0,a\n2,7.0,b\n3,8.0,c\n'
+
+

If we had passed a file name to write_csv, it would have been +written to that file.

+ +
+ +
+ +
+ + +

+ write_parquet(file) + +

+ + +
+ +

Write dataframe to parquet file.

+ + +

Examples:

+

Construct pandas and Polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+
+

We define a library agnostic function:

+ 
>>> def func(df):
+...     df = nw.from_native(df)
+...     df.write_parquet("foo.parquet")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+>>> func(df_pl)
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/dependencies/index.html b/api-reference/dependencies/index.html new file mode 100644 index 0000000000..5908a2dce5 --- /dev/null +++ b/api-reference/dependencies/index.html @@ -0,0 +1,2051 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.dependencies - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + +
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+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.dependencies

+ + +
+ + + + +
+ + + +
+ + + + + + + + + +
+ + +

+ get_cudf() + +

+ + +
+ +

Get cudf module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_modin() + +

+ + +
+ +

Get modin.pandas module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_pandas() + +

+ + +
+ +

Get pandas module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_polars() + +

+ + +
+ +

Get Polars module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_pyarrow() + +

+ + +
+ +

Get pyarrow module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ is_cudf_dataframe(df) + +

+ + +
+ +

Check whether df is a cudf DataFrame without importing cudf.

+ +
+ +
+ +
+ + +

+ is_cudf_series(ser) + +

+ + +
+ +

Check whether ser is a cudf Series without importing cudf.

+ +
+ +
+ +
+ + +

+ is_dask_dataframe(df) + +

+ + +
+ +

Check whether df is a Dask DataFrame without importing Dask.

+ +
+ +
+ +
+ + +

+ is_modin_dataframe(df) + +

+ + +
+ +

Check whether df is a modin DataFrame without importing modin.

+ +
+ +
+ +
+ + +

+ is_modin_series(ser) + +

+ + +
+ +

Check whether ser is a modin Series without importing modin.

+ +
+ +
+ +
+ + +

+ is_numpy_array(arr) + +

+ + +
+ +

Check whether arr is a NumPy Array without importing NumPy.

+ +
+ +
+ +
+ + +

+ is_pandas_dataframe(df) + +

+ + +
+ +

Check whether df is a pandas DataFrame without importing pandas.

+ +
+ +
+ +
+ + +

+ is_pandas_series(ser) + +

+ + +
+ +

Check whether ser is a pandas Series without importing pandas.

+ +
+ +
+ +
+ + +

+ is_polars_dataframe(df) + +

+ + +
+ +

Check whether df is a Polars DataFrame without importing Polars.

+ +
+ +
+ +
+ + +

+ is_polars_lazyframe(df) + +

+ + +
+ +

Check whether df is a Polars LazyFrame without importing Polars.

+ +
+ +
+ +
+ + +

+ is_polars_series(ser) + +

+ + +
+ +

Check whether ser is a Polars Series without importing Polars.

+ +
+ +
+ +
+ + +

+ is_pyarrow_chunked_array(ser) + +

+ + +
+ +

Check whether ser is a PyArrow ChunkedArray without importing PyArrow.

+ +
+ +
+ +
+ + +

+ is_pyarrow_table(df) + +

+ + +
+ +

Check whether df is a PyArrow Table without importing PyArrow.

+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/dtypes/index.html b/api-reference/dtypes/index.html new file mode 100644 index 0000000000..59593dd41a --- /dev/null +++ b/api-reference/dtypes/index.html @@ -0,0 +1,2375 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.dtypes - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.dtypes

+ + +
+ + + + +
+ + + +
+ + + + + + + + +
+ + + +

+ Int64 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int32 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int16 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int8 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt64 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt32 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt16 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt8 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Float64 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Float32 + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Boolean + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Categorical + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Enum + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ String + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Datetime + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Duration + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Object + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Unknown + + +

+ + +
+ + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr/index.html b/api-reference/expr/index.html new file mode 100644 index 0000000000..8a739f4e26 --- /dev/null +++ b/api-reference/expr/index.html @@ -0,0 +1,4675 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
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+
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+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ abs() + +

+ + +
+ +

Return absolute value of each element.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, -2], "b": [-3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").abs())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  3
+1  2  4
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ alias(name) + +

+ + +
+ +

Rename the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
name + str + +
+

The new name.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [4, 5]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select((nw.col("b") + 10).alias("c"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    c
+0  14
+1  15
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ c   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 14  │
+│ 15  │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ all() + +

+ + +
+ +

Return whether all values in the column are True.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [True, False], "b": [True, True]})
+>>> df_pl = pl.DataFrame({"a": [True, False], "b": [True, True]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").all())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a     b
+0  False  True
+>>> func(df_pl)
+shape: (1, 2)
+┌───────┬──────┐
+│ a     ┆ b    │
+│ ---   ┆ ---  │
+│ bool  ┆ bool │
+╞═══════╪══════╡
+│ false ┆ true │
+└───────┴──────┘
+
+ +
+ +
+ +
+ + +

+ any() + +

+ + +
+ +

Return whether any of the values in the column are True

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [True, False], "b": [True, True]})
+>>> df_pl = pl.DataFrame({"a": [True, False], "b": [True, True]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").any())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+      a     b
+0  True  True
+>>> func(df_pl)
+shape: (1, 2)
+┌──────┬──────┐
+│ a    ┆ b    │
+│ ---  ┆ ---  │
+│ bool ┆ bool │
+╞══════╪══════╡
+│ true ┆ true │
+└──────┴──────┘
+
+ +
+ +
+ +
+ + +

+ arg_true() + +

+ + +
+ +

Find elements where boolean expression is True.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"a": [1, None, None, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").is_null().arg_true())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a
+1  1
+2  2
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ u32 │
+╞═════╡
+│ 1   │
+│ 2   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ cast(dtype) + +

+ + +
+ +

Redefine an object's data type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dtype + Any + +
+

Data type that the object will be cast into.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from datetime import date
+>>> df_pd = pd.DataFrame({"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0]})
+>>> df_pl = pl.DataFrame({"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(
+...         nw.col("foo").cast(nw.Float32), nw.col("bar").cast(nw.UInt8)
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar
+0  1.0    6
+1  2.0    7
+2  3.0    8
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ f32 ┆ u8  │
+╞═════╪═════╡
+│ 1.0 ┆ 6   │
+│ 2.0 ┆ 7   │
+│ 3.0 ┆ 8   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ count() + +

+ + +
+ +

Returns the number of non-null elements in the column.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [None, 4, 4]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3], "b": [None, 4, 4]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().count())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  3  2
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 3   ┆ 2   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ cum_sum() + +

+ + +
+ +

Return cumulative sum.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]})
+>>> df_pl = pl.DataFrame({"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").cum_sum())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    a   b
+0   1   2
+1   2   6
+2   5  10
+3  10  16
+4  15  22
+>>> func(df_pl)
+shape: (5, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+│ 2   ┆ 6   │
+│ 5   ┆ 10  │
+│ 10  ┆ 16  │
+│ 15  ┆ 22  │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ diff() + +

+ + +
+ +

Returns the difference between each element and the previous one.

+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to calculate +the diff and fill missing values with 0 in a Int64 column, you could +do:

+
nw.col("a").diff().fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 1, 3, 5, 5]})
+>>> df_pl = pl.DataFrame({"a": [1, 1, 3, 5, 5]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(a_diff=nw.col("a").diff())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a_diff
+0     NaN
+1     0.0
+2     2.0
+3     2.0
+4     0.0
+>>> func(df_pl)
+shape: (5, 1)
+┌────────┐
+│ a_diff │
+│ ---    │
+│ i64    │
+╞════════╡
+│ null   │
+│ 0      │
+│ 2      │
+│ 2      │
+│ 0      │
+└────────┘
+
+ +
+ +
+ +
+ + +

+ drop_nulls() + +

+ + +
+ +

Remove missing values.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+ 
>>> df_pd = pd.DataFrame({"a": [2.0, 4.0, float("nan"), 3.0, None, 5.0]})
+>>> df_pl = pl.DataFrame({"a": [2.0, 4.0, float("nan"), 3.0, None, 5.0]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").drop_nulls())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a
+0  2.0
+1  4.0
+3  3.0
+5  5.0
+>>> func(df_pl)  # nan != null for polars
+shape: (5, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 2.0 │
+│ 4.0 │
+│ NaN │
+│ 3.0 │
+│ 5.0 │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ fill_null(value) + +

+ + +
+ +

Fill null values with given value.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame(
+...     {"a": [2, 4, None, 3, 5], "b": [2.0, 4.0, float("nan"), 3.0, 5.0]}
+... )
+>>> df_pl = pl.DataFrame(
+...     {"a": [2, 4, None, 3, 5], "b": [2.0, 4.0, float("nan"), 3.0, 5.0]}
+... )
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(nw.col("a", "b").fill_null(0))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a    b
+0  2.0  2.0
+1  4.0  4.0
+2  0.0  0.0
+3  3.0  3.0
+4  5.0  5.0
+
+ 
>>> func(df_pl)  # nan != null for polars
+shape: (5, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 2.0 │
+│ 4   ┆ 4.0 │
+│ 0   ┆ NaN │
+│ 3   ┆ 3.0 │
+│ 5   ┆ 5.0 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ filter(*predicates) + +

+ + +
+ +

Filters elements based on a condition, returning a new expression.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [2, 3, 4, 5, 6, 7], "b": [10, 11, 12, 13, 14, 15]})
+>>> df_pl = pl.DataFrame({"a": [2, 3, 4, 5, 6, 7], "b": [10, 11, 12, 13, 14, 15]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(
+...         nw.col("a").filter(nw.col("a") > 4),
+...         nw.col("b").filter(nw.col("b") < 13),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a   b
+3  5  10
+4  6  11
+5  7  12
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 5   ┆ 10  │
+│ 6   ┆ 11  │
+│ 7   ┆ 12  │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth value in the Series and return as new Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Gather every n-th row.

+
+ 		+ required +
offset + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").gather_every(n=2, offset=1))
+
+ 
>>> func(df_pd)
+   a
+1  2
+3  4
+
+ 
>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 4   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ head(n=10) + +

+ + +
+ +

Get the first n rows.

+

Arguments + n : int + Number of rows to return.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": list(range(10))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that returns the first 3 rows:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").head(3))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a
+0  0
+1  1
+2  2
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 0   │
+│ 1   │
+│ 2   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ clip(lower_bound=None, upper_bound=None) + +

+ + +
+ +

Clip values in the Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
lower_bound + Any | None + +
+

Lower bound value.

+
+ 		+ None +
upper_bound + Any | None + +
+

Upper bound value.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>>
+>>> s = [1, 2, 3]
+>>> df_pd = pd.DataFrame({"s": s})
+>>> df_pl = pl.DataFrame({"s": s})
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func_lower(df):
+...     return df.select(nw.col("s").clip(2))
+
+

We can then pass either pandas or Polars to func_lower:

+ 
>>> func_lower(df_pd)
+   s
+0  2
+1  2
+2  3
+>>> func_lower(df_pl)
+shape: (3, 1)
+┌─────┐
+│ s   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 2   │
+│ 3   │
+└─────┘
+
+

We define another library agnostic function:

+ 
>>> @nw.narwhalify
+... def func_upper(df):
+...     return df.select(nw.col("s").clip(upper_bound=2))
+
+

We can then pass either pandas or Polars to func_upper:

+ 
>>> func_upper(df_pd)
+   s
+0  1
+1  2
+2  2
+>>> func_upper(df_pl)
+shape: (3, 1)
+┌─────┐
+│ s   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 2   │
+└─────┘
+
+

We can have both at the same time

+ 
>>> s = [-1, 1, -3, 3, -5, 5]
+>>> df_pd = pd.DataFrame({"s": s})
+>>> df_pl = pl.DataFrame({"s": s})
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("s").clip(-1, 3))
+
+

We can pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   s
+0 -1
+1  1
+2 -1
+3  3
+4 -1
+5  3
+>>> func(df_pl)
+shape: (6, 1)
+┌─────┐
+│ s   │
+│ --- │
+│ i64 │
+╞═════╡
+│ -1  │
+│ 1   │
+│ -1  │
+│ 3   │
+│ -1  │
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ is_between(lower_bound, upper_bound, closed='both') + +

+ + +
+ +

Check if this expression is between the given lower and upper bounds.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
lower_bound + Any + +
+

Lower bound value.

+
+ 		+ required +
upper_bound + Any + +
+

Upper bound value.

+
+ 		+ required +
closed + str + +
+

Define which sides of the interval are closed (inclusive).

+
+ 		+ 'both' +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2, 3, 4, 5]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3, 4, 5]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").is_between(2, 4, "right"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a
+0  False
+1  False
+2   True
+3   True
+4  False
+>>> func(df_pl)
+shape: (5, 1)
+┌───────┐
+│ a     │
+│ ---   │
+│ bool  │
+╞═══════╡
+│ false │
+│ false │
+│ true  │
+│ true  │
+│ false │
+└───────┘
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Return a boolean mask indicating duplicated values.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().is_duplicated())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a      b
+0   True   True
+1  False   True
+2  False  False
+3   True  False
+>>> func(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ true  ┆ true  │
+│ false ┆ true  │
+│ false ┆ false │
+│ true  ┆ false │
+└───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ is_first_distinct() + +

+ + +
+ +

Return a boolean mask indicating the first occurrence of each distinct value.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().is_first_distinct())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a      b
+0   True   True
+1   True  False
+2   True   True
+3  False   True
+>>> func(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ true  ┆ true  │
+│ true  ┆ false │
+│ true  ┆ true  │
+│ false ┆ true  │
+└───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ is_in(other) + +

+ + +
+ +

Check if elements of this expression are present in the other iterable.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
other + Any + +
+

iterable

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2, 9, 10]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 9, 10]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(b=nw.col("a").is_in([1, 2]))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    a      b
+0   1   True
+1   2   True
+2   9  False
+3  10  False
+
+ 
>>> func(df_pl)
+shape: (4, 2)
+┌─────┬───────┐
+│ a   ┆ b     │
+│ --- ┆ ---   │
+│ i64 ┆ bool  │
+╞═════╪═══════╡
+│ 1   ┆ true  │
+│ 2   ┆ true  │
+│ 9   ┆ false │
+│ 10  ┆ false │
+└─────┴───────┘
+
+ +
+ +
+ +
+ + +

+ is_last_distinct() + +

+ + +
+ +

Return a boolean mask indicating the last occurrence of each distinct value.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().is_last_distinct())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a      b
+0  False  False
+1   True   True
+2   True   True
+3   True   True
+>>> func(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ false ┆ false │
+│ true  ┆ true  │
+│ true  ┆ true  │
+│ true  ┆ true  │
+└───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ is_null() + +

+ + +
+ +

Returns a boolean Series indicating which values are null.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame(
+...     {"a": [2, 4, None, 3, 5], "b": [2.0, 4.0, float("nan"), 3.0, 5.0]}
+... )
+>>> df_pl = pl.DataFrame(
+...     {"a": [2, 4, None, 3, 5], "b": [2.0, 4.0, float("nan"), 3.0, 5.0]}
+... )
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         a_is_null=nw.col("a").is_null(), b_is_null=nw.col("b").is_null()
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a    b  a_is_null  b_is_null
+0  2.0  2.0      False      False
+1  4.0  4.0      False      False
+2  NaN  NaN       True       True
+3  3.0  3.0      False      False
+4  5.0  5.0      False      False
+
+ 
>>> func(df_pl)  # nan != null for polars
+shape: (5, 4)
+┌──────┬─────┬───────────┬───────────┐
+│ a    ┆ b   ┆ a_is_null ┆ b_is_null │
+│ ---  ┆ --- ┆ ---       ┆ ---       │
+│ i64  ┆ f64 ┆ bool      ┆ bool      │
+╞══════╪═════╪═══════════╪═══════════╡
+│ 2    ┆ 2.0 ┆ false     ┆ false     │
+│ 4    ┆ 4.0 ┆ false     ┆ false     │
+│ null ┆ NaN ┆ true      ┆ false     │
+│ 3    ┆ 3.0 ┆ false     ┆ false     │
+│ 5    ┆ 5.0 ┆ false     ┆ false     │
+└──────┴─────┴───────────┴───────────┘
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Return a boolean mask indicating unique values.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().is_unique())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+       a      b
+0  False  False
+1   True  False
+2   True   True
+3  False   True
+>>> func(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ false ┆ false │
+│ true  ┆ false │
+│ true  ┆ true  │
+│ false ┆ true  │
+└───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in the column.

+

Null values count towards the total.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": ["x", "y", "z"], "b": [1, 2, 1]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that computes the len over different values of "b" column:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(
+...         nw.col("a").filter(nw.col("b") == 1).len().alias("a1"),
+...         nw.col("a").filter(nw.col("b") == 2).len().alias("a2"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a1  a2
+0   2   1
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a1  ┆ a2  │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 2   ┆ 1   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ max() + +

+ + +
+ +

Returns the maximum value(s) from a column(s).

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [10, 20], "b": [50, 100]})
+>>> df_pl = pl.DataFrame({"a": [10, 20], "b": [50, 100]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.max("a", "b"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    a    b
+0  20  100
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 20  ┆ 100 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ mean() + +

+ + +
+ +

Get mean value.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
+>>> df_pl = pl.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").mean())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a    b
+0  0.0  4.0
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 0.0 ┆ 4.0 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ min() + +

+ + +
+ +

Returns the minimum value(s) from a column(s).

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 3]})
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [4, 3]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.min("a", "b"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  3
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Count null values.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, None, 1], "b": ["a", None, "b", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all().null_count())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  2
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ n_unique() + +

+ + +
+ +

Returns count of unique values

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 1, 3, 3, 5]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 1, 3, 3, 5]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").n_unique())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  5  3
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 5   ┆ 3   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ over(*keys) + +

+ + +
+ +

Compute expressions over the given groups.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
keys + str | Iterable[str] + +
+

Names of columns to compute window expression over. + Must be names of columns, as opposed to expressions - + so, this is a bit less flexible than Polars' Expr.over.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3], "b": [1, 1, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(a_min_per_group=nw.col("a").min().over("b"))
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+   a  b  a_min_per_group
+0  1  1                1
+1  2  1                1
+2  3  2                3
+>>> func(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────────────────┐
+│ a   ┆ b   ┆ a_min_per_group │
+│ --- ┆ --- ┆ ---             │
+│ i64 ┆ i64 ┆ i64             │
+╞═════╪═════╪═════════════════╡
+│ 1   ┆ 1   ┆ 1               │
+│ 2   ┆ 1   ┆ 1               │
+│ 3   ┆ 2   ┆ 3               │
+└─────┴─────┴─────────────────┘
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Lets define a library-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").pipe(lambda x: x + 1))
+
+

We can then pass any supported library:

+ 
>>> func(df_pd)
+   a
+0  2
+1  3
+2  4
+3  5
+>>> func(df_pl)
+shape: (4, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 3   │
+│ 4   │
+│ 5   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ quantile(quantile, interpolation) + +

+ + +
+ +

Get quantile value.

+ + +
+ Note +

pandas and Polars may have implementation differences for a given interpolation method.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
quantile + +
+

float +Quantile between 0.0 and 1.0.

+
+ 		+ required +
interpolation + +
+

{'nearest', 'higher', 'lower', 'midpoint', 'linear'} +Interpolation method.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": list(range(50)), "b": list(range(50, 100))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").quantile(0.5, interpolation="linear"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+      a     b
+0  24.5  74.5
+
+ 
>>> func(df_pl)
+shape: (1, 2)
+┌──────┬──────┐
+│ a    ┆ b    │
+│ ---  ┆ ---  │
+│ f64  ┆ f64  │
+╞══════╪══════╡
+│ 24.5 ┆ 74.5 │
+└──────┴──────┘
+
+ +
+ +
+ +
+ + +

+ round(decimals=0) + +

+ + +
+ +

Round underlying floating point data by decimals digits.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
decimals + int + +
+

Number of decimals to round by.

+
+ 		+ 0 +
+ + +
+ Notes +

For values exactly halfway between rounded decimal values pandas behaves differently than Polars and Arrow.

+

pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, 3.5 and +4.5 to 4.0, etc..).

+

Polars and Arrow round away from 0 (e.g. -0.5 to -1.0, 0.5 to 1.0, 1.5 to 2.0, 2.5 to 3.0, etc..).

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1.12345, 2.56789, 3.901234]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that rounds to the first decimal:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").round(1))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a
+0  1.1
+1  2.6
+2  3.9
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 1.1 │
+│ 2.6 │
+│ 3.9 │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ sample(n=None, fraction=None, *, with_replacement=False) + +

+ + +
+ +

Sample randomly from this expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int | None + +
+

Number of items to return. Cannot be used with fraction.

+
+ 		+ None +
fraction + float | None + +
+

Fraction of items to return. Cannot be used with n.

+
+ 		+ None +
with_replacement + bool + +
+

Allow values to be sampled more than once.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+ 
>>> df_pd = pd.DataFrame({"a": [1, 2, 3]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").sample(fraction=1.0, with_replacement=True))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a
+2  3
+0  1
+2  3
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 2   │
+│ 3   │
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ shift(n) + +

+ + +
+ +

Shift values by n positions.

+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to shift +and fill missing values with 0 in a Int64 column, you could +do:

+
nw.col("a").shift(1).fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 1, 3, 5, 5]})
+>>> df_pl = pl.DataFrame({"a": [1, 1, 3, 5, 5]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(a_shift=nw.col("a").shift(n=1))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a_shift
+0      NaN
+1      1.0
+2      1.0
+3      3.0
+4      5.0
+>>> func(df_pl)
+shape: (5, 1)
+┌─────────┐
+│ a_shift │
+│ ---     │
+│ i64     │
+╞═════════╡
+│ null    │
+│ 1       │
+│ 1       │
+│ 3       │
+│ 5       │
+└─────────┘
+
+ +
+ +
+ +
+ + +

+ sort(*, descending=False, nulls_last=False) + +

+ + +
+ +

Sort this column. Place null values first.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
descending + bool + +
+

Sort in descending order.

+
+ 		+ False +
nulls_last + bool + +
+

Place null values last instead of first.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+ 
>>> df_pd = pd.DataFrame({"a": [5, None, 1, 2]})
+>>> df_pl = pl.DataFrame({"a": [5, None, 1, 2]})
+
+

Let's define dataframe-agnostic functions:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").sort())
+
+ 
>>> def func_descend(df):
+...     df = nw.from_native(df)
+...     df = df.select(nw.col("a").sort(descending=True))
+...     return nw.to_native(df)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a
+1  NaN
+2  1.0
+3  2.0
+0  5.0
+>>> func(df_pl)
+shape: (4, 1)
+┌──────┐
+│ a    │
+│ ---  │
+│ i64  │
+╞══════╡
+│ null │
+│ 1    │
+│ 2    │
+│ 5    │
+└──────┘
+
+ 
>>> func_descend(df_pd)
+     a
+1  NaN
+0  5.0
+3  2.0
+2  1.0
+>>> func_descend(df_pl)
+shape: (4, 1)
+┌──────┐
+│ a    │
+│ ---  │
+│ i64  │
+╞══════╡
+│ null │
+│ 5    │
+│ 2    │
+│ 1    │
+└──────┘
+
+ +
+ +
+ +
+ + +

+ std(*, ddof=1) + +

+ + +
+ +

Get standard deviation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
ddof + int + +
+

“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, + where N represents the number of elements. By default ddof is 1.

+
+ 		+ 1 +
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [20, 25, 60], "b": [1.5, 1, -1.4]})
+>>> df_pl = pl.DataFrame({"a": [20, 25, 60], "b": [1.5, 1, -1.4]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").std(ddof=0))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+          a         b
+0  17.79513  1.265789
+>>> func(df_pl)
+shape: (1, 2)
+┌──────────┬──────────┐
+│ a        ┆ b        │
+│ ---      ┆ ---      │
+│ f64      ┆ f64      │
+╞══════════╪══════════╡
+│ 17.79513 ┆ 1.265789 │
+└──────────┴──────────┘
+
+ +
+ +
+ +
+ + +

+ sum() + +

+ + +
+ +

Return the sum value.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [5, 10], "b": [50, 100]})
+>>> df_pl = pl.DataFrame({"a": [5, 10], "b": [50, 100]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").sum())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    a    b
+0  15  150
+>>> func(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 15  ┆ 150 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ tail(n=10) + +

+ + +
+ +

Get the last n rows.

+

Arguments + n : int + Number of rows to return.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": list(range(10))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function that returns the last 3 rows:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").tail(3))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a
+7  7
+8  8
+9  9
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 7   │
+│ 8   │
+│ 9   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ unique() + +

+ + +
+ +

Return unique values

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]})
+>>> df_pl = pl.DataFrame({"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a", "b").unique())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  2
+1  3  4
+2  5  6
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+│ 3   ┆ 4   │
+│ 5   ┆ 6   │
+└─────┴─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_cat/index.html b/api-reference/expr_cat/index.html new file mode 100644 index 0000000000..1b9ec0a91b --- /dev/null +++ b/api-reference/expr_cat/index.html @@ -0,0 +1,1491 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.cat - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.cat

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ get_categories() + +

+ + +
+ +

Get unique categories from column.

+ + +

Examples:

+

Let's create some dataframes:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["apple", "mango", "mango"]}
+>>> df_pd = pd.DataFrame(data, dtype="category")
+>>> df_pl = pl.DataFrame(data, schema={"fruits": pl.Categorical})
+
+

We define a dataframe-agnostic function to get unique categories +from column 'fruits':

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("fruits").cat.get_categories())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+  fruits
+0  apple
+1  mango
+>>> func(df_pl)
+shape: (2, 1)
+┌────────┐
+│ fruits │
+│ ---    │
+│ str    │
+╞════════╡
+│ apple  │
+│ mango  │
+└────────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_dt/index.html b/api-reference/expr_dt/index.html new file mode 100644 index 0000000000..53191652dc --- /dev/null +++ b/api-reference/expr_dt/index.html @@ -0,0 +1,2785 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.dt - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
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+ + + +
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+ + + +
+
+ + + + + + + +

narwhals.Expr.dt

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ date() + +

+ + +
+ +

Extract the date from underlying DateTime representation.

+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ NotImplementedError + +
+

If pandas default backend is being used.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {"a": [datetime(2012, 1, 7, 10, 20), datetime(2023, 3, 10, 11, 32)]}
+>>> df_pd = pd.DataFrame(data).convert_dtypes(
+...     dtype_backend="pyarrow"
+... )
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").dt.date())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+            a
+0  2012-01-07
+1  2023-03-10
+
+ 
>>> func(df_pl)  # docetst
+shape: (2, 1)
+┌────────────┐
+│ a          │
+│ ---        │
+│ date       │
+╞════════════╡
+│ 2012-01-07 │
+│ 2023-03-10 │
+└────────────┘
+
+ +
+ +
+ +
+ + +

+ year() + +

+ + +
+ +

Extract year from underlying DateTime representation.

+

Returns the year number in the calendar date.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(nw.col("datetime").dt.year().alias("year"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    datetime  year
+0 1978-06-01  1978
+1 2024-12-13  2024
+2 2065-01-01  2065
+>>> func(df_pl)
+shape: (3, 2)
+┌─────────────────────┬──────┐
+│ datetime            ┆ year │
+│ ---                 ┆ ---  │
+│ datetime[μs]        ┆ i32  │
+╞═════════════════════╪══════╡
+│ 1978-06-01 00:00:00 ┆ 1978 │
+│ 2024-12-13 00:00:00 ┆ 2024 │
+│ 2065-01-01 00:00:00 ┆ 2065 │
+└─────────────────────┴──────┘
+
+ +
+ +
+ +
+ + +

+ month() + +

+ + +
+ +

Extract month from underlying DateTime representation.

+

Returns the month number starting from 1. The return value ranges from 1 to 12.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.year().alias("year"),
+...         nw.col("datetime").dt.month().alias("month"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    datetime  year  month
+0 1978-06-01  1978      6
+1 2024-12-13  2024     12
+2 2065-01-01  2065      1
+>>> func(df_pl)
+shape: (3, 3)
+┌─────────────────────┬──────┬───────┐
+│ datetime            ┆ year ┆ month │
+│ ---                 ┆ ---  ┆ ---   │
+│ datetime[μs]        ┆ i32  ┆ i8    │
+╞═════════════════════╪══════╪═══════╡
+│ 1978-06-01 00:00:00 ┆ 1978 ┆ 6     │
+│ 2024-12-13 00:00:00 ┆ 2024 ┆ 12    │
+│ 2065-01-01 00:00:00 ┆ 2065 ┆ 1     │
+└─────────────────────┴──────┴───────┘
+
+ +
+ +
+ +
+ + +

+ day() + +

+ + +
+ +

Extract day from underlying DateTime representation.

+

Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.)

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.year().alias("year"),
+...         nw.col("datetime").dt.month().alias("month"),
+...         nw.col("datetime").dt.day().alias("day"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+    datetime  year  month  day
+0 1978-06-01  1978      6    1
+1 2024-12-13  2024     12   13
+2 2065-01-01  2065      1    1
+>>> func(df_pl)
+shape: (3, 4)
+┌─────────────────────┬──────┬───────┬─────┐
+│ datetime            ┆ year ┆ month ┆ day │
+│ ---                 ┆ ---  ┆ ---   ┆ --- │
+│ datetime[μs]        ┆ i32  ┆ i8    ┆ i8  │
+╞═════════════════════╪══════╪═══════╪═════╡
+│ 1978-06-01 00:00:00 ┆ 1978 ┆ 6     ┆ 1   │
+│ 2024-12-13 00:00:00 ┆ 2024 ┆ 12    ┆ 13  │
+│ 2065-01-01 00:00:00 ┆ 2065 ┆ 1     ┆ 1   │
+└─────────────────────┴──────┴───────┴─────┘
+
+ +
+ +
+ +
+ + +

+ ordinal_day() + +

+ + +
+ +

Get ordinal day.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(a_ordinal_day=nw.col("a").dt.ordinal_day())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+           a  a_ordinal_day
+0 2020-01-01              1
+1 2020-08-03            216
+>>> func(df_pl)
+shape: (2, 2)
+┌─────────────────────┬───────────────┐
+│ a                   ┆ a_ordinal_day │
+│ ---                 ┆ ---           │
+│ datetime[μs]        ┆ i16           │
+╞═════════════════════╪═══════════════╡
+│ 2020-01-01 00:00:00 ┆ 1             │
+│ 2020-08-03 00:00:00 ┆ 216           │
+└─────────────────────┴───────────────┘
+
+ +
+ +
+ +
+ + +

+ hour() + +

+ + +
+ +

Extract hour from underlying DateTime representation.

+

Returns the hour number from 0 to 23.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1),
+...         datetime(2024, 10, 13, 5),
+...         datetime(2065, 1, 1, 10),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(nw.col("datetime").dt.hour().alias("hour"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+             datetime  hour
+0 1978-01-01 01:00:00     1
+1 2024-10-13 05:00:00     5
+2 2065-01-01 10:00:00    10
+>>> func(df_pl)
+shape: (3, 2)
+┌─────────────────────┬──────┐
+│ datetime            ┆ hour │
+│ ---                 ┆ ---  │
+│ datetime[μs]        ┆ i8   │
+╞═════════════════════╪══════╡
+│ 1978-01-01 01:00:00 ┆ 1    │
+│ 2024-10-13 05:00:00 ┆ 5    │
+│ 2065-01-01 10:00:00 ┆ 10   │
+└─────────────────────┴──────┘
+
+ +
+ +
+ +
+ + +

+ minute() + +

+ + +
+ +

Extract minutes from underlying DateTime representation.

+

Returns the minute number from 0 to 59.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1),
+...         datetime(2024, 10, 13, 5, 30),
+...         datetime(2065, 1, 1, 10, 20),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour"),
+...         nw.col("datetime").dt.minute().alias("minute"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+             datetime  hour  minute
+0 1978-01-01 01:01:00     1       1
+1 2024-10-13 05:30:00     5      30
+2 2065-01-01 10:20:00    10      20
+>>> func(df_pl)
+shape: (3, 3)
+┌─────────────────────┬──────┬────────┐
+│ datetime            ┆ hour ┆ minute │
+│ ---                 ┆ ---  ┆ ---    │
+│ datetime[μs]        ┆ i8   ┆ i8     │
+╞═════════════════════╪══════╪════════╡
+│ 1978-01-01 01:01:00 ┆ 1    ┆ 1      │
+│ 2024-10-13 05:30:00 ┆ 5    ┆ 30     │
+│ 2065-01-01 10:20:00 ┆ 10   ┆ 20     │
+└─────────────────────┴──────┴────────┘
+
+ +
+ +
+ +
+ + +

+ second() + +

+ + +
+ +

Extract seconds from underlying DateTime representation.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1),
+...         datetime(2024, 10, 13, 5, 30, 14),
+...         datetime(2065, 1, 1, 10, 20, 30),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour"),
+...         nw.col("datetime").dt.minute().alias("minute"),
+...         nw.col("datetime").dt.second().alias("second"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+             datetime  hour  minute  second
+0 1978-01-01 01:01:01     1       1       1
+1 2024-10-13 05:30:14     5      30      14
+2 2065-01-01 10:20:30    10      20      30
+>>> func(df_pl)
+shape: (3, 4)
+┌─────────────────────┬──────┬────────┬────────┐
+│ datetime            ┆ hour ┆ minute ┆ second │
+│ ---                 ┆ ---  ┆ ---    ┆ ---    │
+│ datetime[μs]        ┆ i8   ┆ i8     ┆ i8     │
+╞═════════════════════╪══════╪════════╪════════╡
+│ 1978-01-01 01:01:01 ┆ 1    ┆ 1      ┆ 1      │
+│ 2024-10-13 05:30:14 ┆ 5    ┆ 30     ┆ 14     │
+│ 2065-01-01 10:20:30 ┆ 10   ┆ 20     ┆ 30     │
+└─────────────────────┴──────┴────────┴────────┘
+
+ +
+ +
+ +
+ + +

+ millisecond() + +

+ + +
+ +

Extract milliseconds from underlying DateTime representation.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 505000),
+...         datetime(2065, 1, 1, 10, 20, 30, 67000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour"),
+...         nw.col("datetime").dt.minute().alias("minute"),
+...         nw.col("datetime").dt.second().alias("second"),
+...         nw.col("datetime").dt.millisecond().alias("millisecond"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                 datetime  hour  minute  second  millisecond
+0 1978-01-01 01:01:01.000     1       1       1            0
+1 2024-10-13 05:30:14.505     5      30      14          505
+2 2065-01-01 10:20:30.067    10      20      30           67
+>>> func(df_pl)
+shape: (3, 5)
+┌─────────────────────────┬──────┬────────┬────────┬─────────────┐
+│ datetime                ┆ hour ┆ minute ┆ second ┆ millisecond │
+│ ---                     ┆ ---  ┆ ---    ┆ ---    ┆ ---         │
+│ datetime[μs]            ┆ i8   ┆ i8     ┆ i8     ┆ i32         │
+╞═════════════════════════╪══════╪════════╪════════╪═════════════╡
+│ 1978-01-01 01:01:01     ┆ 1    ┆ 1      ┆ 1      ┆ 0           │
+│ 2024-10-13 05:30:14.505 ┆ 5    ┆ 30     ┆ 14     ┆ 505         │
+│ 2065-01-01 10:20:30.067 ┆ 10   ┆ 20     ┆ 30     ┆ 67          │
+└─────────────────────────┴──────┴────────┴────────┴─────────────┘
+
+ +
+ +
+ +
+ + +

+ microsecond() + +

+ + +
+ +

Extract microseconds from underlying DateTime representation.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 505000),
+...         datetime(2065, 1, 1, 10, 20, 30, 67000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour"),
+...         nw.col("datetime").dt.minute().alias("minute"),
+...         nw.col("datetime").dt.second().alias("second"),
+...         nw.col("datetime").dt.microsecond().alias("microsecond"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                 datetime  hour  minute  second  microsecond
+0 1978-01-01 01:01:01.000     1       1       1            0
+1 2024-10-13 05:30:14.505     5      30      14       505000
+2 2065-01-01 10:20:30.067    10      20      30        67000
+>>> func(df_pl)
+shape: (3, 5)
+┌─────────────────────────┬──────┬────────┬────────┬─────────────┐
+│ datetime                ┆ hour ┆ minute ┆ second ┆ microsecond │
+│ ---                     ┆ ---  ┆ ---    ┆ ---    ┆ ---         │
+│ datetime[μs]            ┆ i8   ┆ i8     ┆ i8     ┆ i32         │
+╞═════════════════════════╪══════╪════════╪════════╪═════════════╡
+│ 1978-01-01 01:01:01     ┆ 1    ┆ 1      ┆ 1      ┆ 0           │
+│ 2024-10-13 05:30:14.505 ┆ 5    ┆ 30     ┆ 14     ┆ 505000      │
+│ 2065-01-01 10:20:30.067 ┆ 10   ┆ 20     ┆ 30     ┆ 67000       │
+└─────────────────────────┴──────┴────────┴────────┴─────────────┘
+
+ +
+ +
+ +
+ + +

+ nanosecond() + +

+ + +
+ +

Extract Nanoseconds from underlying DateTime representation

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 500000),
+...         datetime(2065, 1, 1, 10, 20, 30, 60000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour"),
+...         nw.col("datetime").dt.minute().alias("minute"),
+...         nw.col("datetime").dt.second().alias("second"),
+...         nw.col("datetime").dt.nanosecond().alias("nanosecond"),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                 datetime  hour  minute  second  nanosecond
+0 1978-01-01 01:01:01.000     1       1       1           0
+1 2024-10-13 05:30:14.500     5      30      14   500000000
+2 2065-01-01 10:20:30.060    10      20      30    60000000
+>>> func(df_pl)
+shape: (3, 5)
+┌─────────────────────────┬──────┬────────┬────────┬────────────┐
+│ datetime                ┆ hour ┆ minute ┆ second ┆ nanosecond │
+│ ---                     ┆ ---  ┆ ---    ┆ ---    ┆ ---        │
+│ datetime[μs]            ┆ i8   ┆ i8     ┆ i8     ┆ i32        │
+╞═════════════════════════╪══════╪════════╪════════╪════════════╡
+│ 1978-01-01 01:01:01     ┆ 1    ┆ 1      ┆ 1      ┆ 0          │
+│ 2024-10-13 05:30:14.500 ┆ 5    ┆ 30     ┆ 14     ┆ 500000000  │
+│ 2065-01-01 10:20:30.060 ┆ 10   ┆ 20     ┆ 30     ┆ 60000000   │
+└─────────────────────────┴──────┴────────┴────────┴────────────┘
+
+ +
+ +
+ +
+ + +

+ total_minutes() + +

+ + +
+ +

Get total minutes.

+ + +
+ Notes +

The function outputs the total minutes in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(a_total_minutes=nw.col("a").dt.total_minutes())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                a  a_total_minutes
+0 0 days 00:10:00               10
+1 0 days 00:20:40               20
+>>> func(df_pl)
+shape: (2, 2)
+┌──────────────┬─────────────────┐
+│ a            ┆ a_total_minutes │
+│ ---          ┆ ---             │
+│ duration[μs] ┆ i64             │
+╞══════════════╪═════════════════╡
+│ 10m          ┆ 10              │
+│ 20m 40s      ┆ 20              │
+└──────────────┴─────────────────┘
+
+ +
+ +
+ +
+ + +

+ total_seconds() + +

+ + +
+ +

Get total seconds.

+ + +
+ Notes +

The function outputs the total seconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(a_total_seconds=nw.col("a").dt.total_seconds())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                       a  a_total_seconds
+0        0 days 00:00:10               10
+1 0 days 00:00:20.040000               20
+>>> func(df_pl)
+shape: (2, 2)
+┌──────────────┬─────────────────┐
+│ a            ┆ a_total_seconds │
+│ ---          ┆ ---             │
+│ duration[μs] ┆ i64             │
+╞══════════════╪═════════════════╡
+│ 10s          ┆ 10              │
+│ 20s 40ms     ┆ 20              │
+└──────────────┴─────────────────┘
+
+ +
+ +
+ +
+ + +

+ total_milliseconds() + +

+ + +
+ +

Get total milliseconds.

+ + +
+ Notes +

The function outputs the total milliseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = {
+...     "a": [
+...         timedelta(milliseconds=10),
+...         timedelta(milliseconds=20, microseconds=40),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         a_total_milliseconds=nw.col("a").dt.total_milliseconds()
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                       a  a_total_milliseconds
+0 0 days 00:00:00.010000                    10
+1 0 days 00:00:00.020040                    20
+>>> func(df_pl)
+shape: (2, 2)
+┌──────────────┬──────────────────────┐
+│ a            ┆ a_total_milliseconds │
+│ ---          ┆ ---                  │
+│ duration[μs] ┆ i64                  │
+╞══════════════╪══════════════════════╡
+│ 10ms         ┆ 10                   │
+│ 20040µs      ┆ 20                   │
+└──────────────┴──────────────────────┘
+
+ +
+ +
+ +
+ + +

+ total_microseconds() + +

+ + +
+ +

Get total microseconds.

+ + +
+ Notes +

The function outputs the total microseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = {
+...     "a": [
+...         timedelta(microseconds=10),
+...         timedelta(milliseconds=1, microseconds=200),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         a_total_microseconds=nw.col("a").dt.total_microseconds()
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                       a  a_total_microseconds
+0 0 days 00:00:00.000010                    10
+1 0 days 00:00:00.001200                  1200
+>>> func(df_pl)
+shape: (2, 2)
+┌──────────────┬──────────────────────┐
+│ a            ┆ a_total_microseconds │
+│ ---          ┆ ---                  │
+│ duration[μs] ┆ i64                  │
+╞══════════════╪══════════════════════╡
+│ 10µs         ┆ 10                   │
+│ 1200µs       ┆ 1200                 │
+└──────────────┴──────────────────────┘
+
+ +
+ +
+ +
+ + +

+ total_nanoseconds() + +

+ + +
+ +

Get total nanoseconds.

+ + +
+ Notes +

The function outputs the total nanoseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
+>>> df_pd = pd.DataFrame({"a": pd.to_datetime(data)})
+>>> df_pl = pl.DataFrame({"a": data}).with_columns(
+...     pl.col("a").str.to_datetime(time_unit="ns")
+... )
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds()
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                              a  a_diff_total_nanoseconds
+0 2024-01-01 00:00:00.000000001                       NaN
+1 2024-01-01 00:00:00.000000002                       1.0
+>>> func(df_pl)
+shape: (2, 2)
+┌───────────────────────────────┬──────────────────────────┐
+│ a                             ┆ a_diff_total_nanoseconds │
+│ ---                           ┆ ---                      │
+│ datetime[ns]                  ┆ i64                      │
+╞═══════════════════════════════╪══════════════════════════╡
+│ 2024-01-01 00:00:00.000000001 ┆ null                     │
+│ 2024-01-01 00:00:00.000000002 ┆ 1                        │
+└───────────────────────────────┴──────────────────────────┘
+
+ +
+ +
+ +
+ + +

+ to_string(format) + +

+ + +
+ +

Convert a Date/Time/Datetime column into a String column with the given format.

+ + +
+ Notes +

Unfortunately, different libraries interpret format directives a bit +differently.

+
    +
  • Chrono, the library used by Polars, uses "%.f" for fractional seconds, + whereas pandas and Python stdlib use ".%f".
    • +
  • PyArrow interprets "%S" as "seconds, including fractional seconds" + whereas most other tools interpret it as "just seconds, as 2 digits".
    • +
+

Therefore, we make the following adjustments:

+
    +
  • for pandas-like libraries, we replace "%S.%f" with "%S%.f".
    • +
  • for PyArrow, we replace "%S.%f" with "%S".
    • +
+

Workarounds like these don't make us happy, and we try to avoid them as +much as possible, but here we feel like it's the best compromise.

+

If you just want to format a date/datetime Series as a local datetime +string, and have it work as consistently as possible across libraries, +we suggest using:

+
    +
  • "%Y-%m-%dT%H:%M:%S%.f" for datetimes
    • +
  • "%Y-%m-%d" for dates
    • +
+

though note that, even then, different tools may return a different number +of trailing zeros. Nonetheless, this is probably consistent enough for +most applications.

+

If you have an application where this is not enough, please open an issue +and let us know.

+
+ +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = [
+...     datetime(2020, 3, 1),
+...     datetime(2020, 4, 1),
+...     datetime(2020, 5, 1),
+... ]
+>>> df_pd = pd.DataFrame({"a": data})
+>>> df_pl = pl.DataFrame({"a": data})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                     a
+0  2020/03/01 00:00:00
+1  2020/04/01 00:00:00
+2  2020/05/01 00:00:00
+
+ 
>>> func(df_pl)
+shape: (3, 1)
+┌─────────────────────┐
+│ a                   │
+│ ---                 │
+│ str                 │
+╞═════════════════════╡
+│ 2020/03/01 00:00:00 │
+│ 2020/04/01 00:00:00 │
+│ 2020/05/01 00:00:00 │
+└─────────────────────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_name/index.html b/api-reference/expr_name/index.html new file mode 100644 index 0000000000..93f1ea1099 --- /dev/null +++ b/api-reference/expr_name/index.html @@ -0,0 +1,1890 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.name - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.name

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ keep() + +

+ + +
+ +

Keep the original root name of the expression.

+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("foo").alias("alias_for_foo").name.keep())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd).columns
+Index(['foo'], dtype='object')
+>>> func(df_pl).columns
+['foo']
+
+ +
+ +
+ +
+ + +

+ map(function) + +

+ + +
+ +

Rename the output of an expression by mapping a function over the root name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
function + Callable[[str], str] + +
+

Function that maps a root name to a new name.

+
+ 		+ required +
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> renaming_func = lambda s: s[::-1]  # reverse column name
+>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("foo", "BAR").name.map(renaming_func))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd).columns
+Index(['oof', 'RAB'], dtype='object')
+>>> func(df_pl).columns
+['oof', 'RAB']
+
+ +
+ +
+ +
+ + +

+ prefix(prefix) + +

+ + +
+ +

Add a prefix to the root column name of the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
prefix + str + +
+

Prefix to add to the root column name.

+
+ 		+ required +
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def add_colname_prefix(df, prefix):
+...     return df.select(nw.col("foo", "BAR").name.prefix(prefix))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> add_colname_prefix(df_pd, "with_prefix_").columns
+Index(['with_prefix_foo', 'with_prefix_BAR'], dtype='object')
+
+ 
>>> add_colname_prefix(df_pl, "with_prefix_").columns
+['with_prefix_foo', 'with_prefix_BAR']
+
+ +
+ +
+ +
+ + +

+ suffix(suffix) + +

+ + +
+ +

Add a suffix to the root column name of the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
suffix + str + +
+

Suffix to add to the root column name.

+
+ 		+ required +
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def add_colname_suffix(df, suffix):
+...     return df.select(nw.col("foo", "BAR").name.suffix(suffix))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> add_colname_suffix(df_pd, "_with_suffix").columns
+Index(['foo_with_suffix', 'BAR_with_suffix'], dtype='object')
+>>> add_colname_suffix(df_pl, "_with_suffix").columns
+['foo_with_suffix', 'BAR_with_suffix']
+
+ +
+ +
+ +
+ + +

+ to_lowercase() + +

+ + +
+ +

Make the root column name lowercase.

+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def to_lower(df):
+...     return df.select(nw.col("foo", "BAR").name.to_lowercase())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> to_lower(df_pd).columns
+Index(['foo', 'bar'], dtype='object')
+>>> to_lower(df_pl).columns
+['foo', 'bar']
+
+ +
+ +
+ +
+ + +

+ to_uppercase() + +

+ + +
+ +

Make the root column name uppercase.

+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def to_upper(df):
+...     return df.select(nw.col("foo", "BAR").name.to_uppercase())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> to_upper(df_pd).columns
+Index(['FOO', 'BAR'], dtype='object')
+>>> to_upper(df_pl).columns
+['FOO', 'BAR']
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_str/index.html b/api-reference/expr_str/index.html new file mode 100644 index 0000000000..7c6c53d0e5 --- /dev/null +++ b/api-reference/expr_str/index.html @@ -0,0 +1,2671 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.str - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.str

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ contains(pattern, *, literal=False) + +

+ + +
+ +

Check if string contains a substring that matches a pattern.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A Character sequence or valid regular expression pattern.

+
+ 		+ required +
literal + bool + +
+

If True, treats the pattern as a literal string. + If False, assumes the pattern is a regular expression.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"pets": ["cat", "dog", "rabbit and parrot", "dove", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(
+...         default_match=nw.col("pets").str.contains("parrot|Dove"),
+...         case_insensitive_match=nw.col("pets").str.contains("(?i)parrot|Dove"),
+...         literal_match=nw.col("pets").str.contains(
+...             "parrot|Dove", literal=True
+...         ),
+...     )
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+                pets default_match case_insensitive_match literal_match
+0                cat         False                  False         False
+1                dog         False                  False         False
+2  rabbit and parrot          True                   True         False
+3               dove         False                   True         False
+4               None          None                   None          None
+>>> func(df_pl)
+shape: (5, 4)
+┌───────────────────┬───────────────┬────────────────────────┬───────────────┐
+│ pets              ┆ default_match ┆ case_insensitive_match ┆ literal_match │
+│ ---               ┆ ---           ┆ ---                    ┆ ---           │
+│ str               ┆ bool          ┆ bool                   ┆ bool          │
+╞═══════════════════╪═══════════════╪════════════════════════╪═══════════════╡
+│ cat               ┆ false         ┆ false                  ┆ false         │
+│ dog               ┆ false         ┆ false                  ┆ false         │
+│ rabbit and parrot ┆ true          ┆ true                   ┆ false         │
+│ dove              ┆ false         ┆ true                   ┆ false         │
+│ null              ┆ null          ┆ null                   ┆ null          │
+└───────────────────┴───────────────┴────────────────────────┴───────────────┘
+
+ +
+ +
+ +
+ + +

+ ends_with(suffix) + +

+ + +
+ +

Check if string values end with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
suffix + str + +
+

suffix substring

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(has_suffix=nw.col("fruits").str.ends_with("ngo"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+  fruits has_suffix
+0  apple      False
+1  mango       True
+2   None       None
+
+ 
>>> func(df_pl)
+shape: (3, 2)
+┌────────┬────────────┐
+│ fruits ┆ has_suffix │
+│ ---    ┆ ---        │
+│ str    ┆ bool       │
+╞════════╪════════════╡
+│ apple  ┆ false      │
+│ mango  ┆ true       │
+│ null   ┆ null       │
+└────────┴────────────┘
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Take the first n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of elements to take. Negative indexing is not supported.

+
+ 		+ 5 +
+ + +
+ Notes +

If the length of the string has fewer than n characters, the full string is returned.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(lyrics_head=nw.col("lyrics").str.head())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+      lyrics lyrics_head
+0    Atatata       Atata
+1      taata       taata
+2  taatatata       taata
+3    zukkyun       zukky
+
+ 
>>> func(df_pl)
+shape: (4, 2)
+┌───────────┬─────────────┐
+│ lyrics    ┆ lyrics_head │
+│ ---       ┆ ---         │
+│ str       ┆ str         │
+╞═══════════╪═════════════╡
+│ Atatata   ┆ Atata       │
+│ taata     ┆ taata       │
+│ taatatata ┆ taata       │
+│ zukkyun   ┆ zukky       │
+└───────────┴─────────────┘
+
+ +
+ +
+ +
+ + +

+ slice(offset, length=None) + +

+ + +
+ +

Create subslices of the string values of an expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
offset + int + +
+

Start index. Negative indexing is supported.

+
+ 		+ required +
length + int | None + +
+

Length of the slice. If set to None (default), the slice is taken to the +end of the string.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"s": ["pear", None, "papaya", "dragonfruit"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(s_sliced=nw.col("s").str.slice(4, length=3))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+             s s_sliced
+0         pear
+1         None     None
+2       papaya       ya
+3  dragonfruit      onf
+
+ 
>>> func(df_pl)
+shape: (4, 2)
+┌─────────────┬──────────┐
+│ s           ┆ s_sliced │
+│ ---         ┆ ---      │
+│ str         ┆ str      │
+╞═════════════╪══════════╡
+│ pear        ┆          │
+│ null        ┆ null     │
+│ papaya      ┆ ya       │
+│ dragonfruit ┆ onf      │
+└─────────────┴──────────┘
+
+

Using negative indexes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(s_sliced=nw.col("s").str.slice(-3))
+
+ 
>>> func(df_pd)
+             s s_sliced
+0         pear      ear
+1         None     None
+2       papaya      aya
+3  dragonfruit      uit
+
+ 
>>> func(df_pl)
+shape: (4, 2)
+┌─────────────┬──────────┐
+│ s           ┆ s_sliced │
+│ ---         ┆ ---      │
+│ str         ┆ str      │
+╞═════════════╪══════════╡
+│ pear        ┆ ear      │
+│ null        ┆ null     │
+│ papaya      ┆ aya      │
+│ dragonfruit ┆ uit      │
+└─────────────┴──────────┘
+
+ +
+ +
+ +
+ + +

+ replace(pattern, value, *, literal=False, n=1) + +

+ + +
+ +

Replace first matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
value + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
literal + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
n + int + +
+

Number of matches to replace.

+
+ 		+ 1 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"foo": ["123abc", "abc abc123"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     df = df.with_columns(replaced=nw.col("foo").str.replace("abc", ""))
+...     return df.to_dict(as_series=False)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+{'foo': ['123abc', 'abc abc123'], 'replaced': ['123', ' abc123']}
+
+ 
>>> func(df_pl)
+{'foo': ['123abc', 'abc abc123'], 'replaced': ['123', ' abc123']}
+
+ +
+ +
+ +
+ + +

+ replace_all(pattern, value, *, literal=False) + +

+ + +
+ +

Replace all matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
value + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
literal + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"foo": ["123abc", "abc abc123"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     df = df.with_columns(replaced=nw.col("foo").str.replace_all("abc", ""))
+...     return df.to_dict(as_series=False)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+{'foo': ['123abc', 'abc abc123'], 'replaced': ['123', ' 123']}
+
+ 
>>> func(df_pl)
+{'foo': ['123abc', 'abc abc123'], 'replaced': ['123', ' 123']}
+
+ +
+ +
+ +
+ + +

+ starts_with(prefix) + +

+ + +
+ +

Check if string values start with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
prefix + str + +
+

prefix substring

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(has_prefix=nw.col("fruits").str.starts_with("app"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+  fruits has_prefix
+0  apple       True
+1  mango      False
+2   None       None
+
+ 
>>> func(df_pl)
+shape: (3, 2)
+┌────────┬────────────┐
+│ fruits ┆ has_prefix │
+│ ---    ┆ ---        │
+│ str    ┆ bool       │
+╞════════╪════════════╡
+│ apple  ┆ true       │
+│ mango  ┆ false      │
+│ null   ┆ null       │
+└────────┴────────────┘
+
+ +
+ +
+ +
+ + +

+ strip_chars(characters=None) + +

+ + +
+ +

Remove leading and trailing characters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
characters + str | None + +
+

The set of characters to be removed. All combinations of this set of characters will be stripped from the start and end of the string. If set to None (default), all leading and trailing whitespace is removed instead.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["apple", "\nmango"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     df = df.with_columns(stripped=nw.col("fruits").str.strip_chars())
+...     return df.to_dict(as_series=False)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
+
+ 
>>> func(df_pl)
+{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Take the last n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of elements to take. Negative indexing is not supported.

+
+ 		+ 5 +
+ + +
+ Notes +

If the length of the string has fewer than n characters, the full string is returned.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(lyrics_tail=nw.col("lyrics").str.tail())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+      lyrics lyrics_tail
+0    Atatata       atata
+1      taata       taata
+2  taatatata       atata
+3    zukkyun       kkyun
+
+ 
>>> func(df_pl)
+shape: (4, 2)
+┌───────────┬─────────────┐
+│ lyrics    ┆ lyrics_tail │
+│ ---       ┆ ---         │
+│ str       ┆ str         │
+╞═══════════╪═════════════╡
+│ Atatata   ┆ atata       │
+│ taata     ┆ taata       │
+│ taatatata ┆ atata       │
+│ zukkyun   ┆ kkyun       │
+└───────────┴─────────────┘
+
+ +
+ +
+ +
+ + +

+ to_datetime(format) + +

+ + +
+ +

Convert to Datetime dtype.

+ + +
+ Notes +

pandas defaults to nanosecond time unit, Polars to microsecond. +Prior to pandas 2.0, nanoseconds were the only time unit supported +in pandas, with no ability to set any other one. The ability to +set the time unit in pandas, if the version permits, will arrive.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
format + str + +
+

Format to parse strings with. Must be passed, as different + dataframe libraries have different ways of auto-inferring + formats.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": ["2020-01-01", "2020-01-02"]})
+>>> df_pl = pl.DataFrame({"a": ["2020-01-01", "2020-01-02"]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a").str.to_datetime(format="%Y-%m-%d"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+           a
+0 2020-01-01
+1 2020-01-02
+>>> func(df_pl)
+shape: (2, 1)
+┌─────────────────────┐
+│ a                   │
+│ ---                 │
+│ datetime[μs]        │
+╞═════════════════════╡
+│ 2020-01-01 00:00:00 │
+│ 2020-01-02 00:00:00 │
+└─────────────────────┘
+
+ +
+ +
+ +
+ + +

+ to_lowercase() + +

+ + +
+ +

Transform string to lowercase variant.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["APPLE", "MANGO", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(lower_col=nw.col("fruits").str.to_lowercase())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+  fruits lower_col
+0  APPLE     apple
+1  MANGO     mango
+2   None      None
+
+ 
>>> func(df_pl)
+shape: (3, 2)
+┌────────┬───────────┐
+│ fruits ┆ lower_col │
+│ ---    ┆ ---       │
+│ str    ┆ str       │
+╞════════╪═══════════╡
+│ APPLE  ┆ apple     │
+│ MANGO  ┆ mango     │
+│ null   ┆ null      │
+└────────┴───────────┘
+
+ +
+ +
+ +
+ + +

+ to_uppercase() + +

+ + +
+ +

Transform string to uppercase variant.

+ + +
+ Notes +

The PyArrow backend will convert 'ß' to 'ẞ' instead of 'SS'. +For more info see the related issue. +There may be other unicode-edge-case-related variations across implementations.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(upper_col=nw.col("fruits").str.to_uppercase())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+  fruits upper_col
+0  apple     APPLE
+1  mango     MANGO
+2   None      None
+
+ 
>>> func(df_pl)
+shape: (3, 2)
+┌────────┬───────────┐
+│ fruits ┆ upper_col │
+│ ---    ┆ ---       │
+│ str    ┆ str       │
+╞════════╪═══════════╡
+│ apple  ┆ APPLE     │
+│ mango  ┆ MANGO     │
+│ null   ┆ null      │
+└────────┴───────────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/group_by/index.html b/api-reference/group_by/index.html new file mode 100644 index 0000000000..d4c293c65c --- /dev/null +++ b/api-reference/group_by/index.html @@ -0,0 +1,1570 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.GroupBy - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.GroupBy

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ agg(*aggs, **named_aggs) + +

+ + +
+ +

Compute aggregations for each group of a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
aggs + IntoExpr | Iterable[IntoExpr] + +
+

Aggregations to compute for each group of the group by operation, +specified as positional arguments.

+
+ 		+ () +
named_aggs + IntoExpr + +
+

Additional aggregations, specified as keyword arguments.

+
+ 		+ {} +
+ + +

Examples:

+

Group by one column or by multiple columns and call agg to compute +the grouped sum of another column.

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "c"],
+...         "b": [1, 2, 1, 3, 3],
+...         "c": [5, 4, 3, 2, 1],
+...     }
+... )
+>>> df_pl = pl.DataFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "c"],
+...         "b": [1, 2, 1, 3, 3],
+...         "c": [5, 4, 3, 2, 1],
+...     }
+... )
+
+

We define library agnostic functions:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by("a").agg(nw.col("b").sum()).sort("a")
+
+ 
>>> @nw.narwhalify
+... def func_mult_col(df):
+...     return df.group_by("a", "b").agg(nw.sum("c")).sort("a", "b")
+
+

We can then pass either pandas or Polars to func and func_mult_col:

+ 
>>> func(df_pd)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> func_mult_col(df_pd)
+   a  b  c
+0  a  1  8
+1  b  2  4
+2  b  3  2
+3  c  3  1
+>>> func_mult_col(df_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 8   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/index.html b/api-reference/index.html new file mode 100644 index 0000000000..d80eda7c1f --- /dev/null +++ b/api-reference/index.html @@ -0,0 +1,1350 @@ + + + + + + + + + + + + + + + + + + + + + + + API Reference - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

API Reference

+

Anything documented in the API reference is intended to work consistently among +supported backends.

+

For example: +

import narwhals as nw
+
+df.with_columns(
+    a_mean=nw.col("a").mean(),
+    a_std=nw.col("a").std(),
+)
+
+is supported, as DataFrame.with_columns, narwhals.col, Expr.mean, and Expr.std are +all documented in the API reference.

+

However, +

import narwhals as nw
+
+df.with_columns(
+    a_ewm_mean=nw.col("a").ewm_mean(alpha=0.7),
+)
+
+is not - Expr.ewm_mean only appears in the Polars API reference, but not in the Narwhals +one.

+

In general, you should expect any fundamental dataframe operation to be supported - if +one that you need is not, please do open a feature request!

+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/lazyframe/index.html b/api-reference/lazyframe/index.html new file mode 100644 index 0000000000..f95cbe60f6 --- /dev/null +++ b/api-reference/lazyframe/index.html @@ -0,0 +1,3844 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.LazyFrame - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
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+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.LazyFrame

+ + +
+ + + + +
+ + +

Narwhals DataFrame, backed by a native dataframe.

+

The native dataframe might be pandas.DataFrame, polars.LazyFrame, ...

+

This class is not meant to be instantiated directly - instead, use +narwhals.from_native.

+ + + + +
+ + + + + + + +
+ + + +

+ columns: list[str] + + + property + + +

+ + +
+ +

Get column names.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> lf_pl = pl.LazyFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.columns
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+['foo', 'bar', 'ham']
+>>> func(lf_pl)
+['foo', 'bar', 'ham']
+
+
+ +
+ +
+ + + +

+ schema: Schema + + + property + + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import narwhals as nw
+>>> lf_pl = pl.LazyFrame(
+...     {
+...         "foo": [1, 2, 3],
+...         "bar": [6.0, 7.0, 8.0],
+...         "ham": ["a", "b", "c"],
+...     }
+... )
+>>> lf = nw.from_native(lf_pl)
+>>> lf.schema
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+
+ +
+ + + +
+ + +

+ clone() + +

+ + +
+ +

Create a copy of this DataFrame.

+
+
+
+

import narwhals as nw +import pandas as pd +import polars as pl +data = {"a": [1, 2], "b": [3, 4]} +df_pd = pd.DataFrame(data) +df_pl = pl.LazyFrame(data)

+
+
+
+

Let's define a dataframe-agnostic function in which we copy the DataFrame:

+
+
+
+

@nw.narwhalify +... def func(df): +... return df.clone()

+

func(df_pd) + a b +0 1 3 +1 2 4

+

func(df_pl).collect() +shape: (2, 2) +┌─────┬─────┐ +│ a ┆ b │ +│ --- ┆ --- │ +│ i64 ┆ i64 │ +╞═════╪═════╡ +│ 1 ┆ 3 │ +│ 2 ┆ 4 │ +└─────┴─────┘

+
+
+
+ +
+ +
+ +
+ + +

+ collect() + +

+ + +
+ +

Materialize this LazyFrame into a DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

DataFrame

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> lf_pl = pl.LazyFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "b", "c"],
+...         "b": [1, 2, 3, 4, 5, 6],
+...         "c": [6, 5, 4, 3, 2, 1],
+...     }
+... )
+>>> lf = nw.from_native(lf_pl)
+>>> lf
+┌───────────────────────────────────────────────┐
+| Narwhals LazyFrame                            |
+| Use `narwhals.to_native` to see native output |
+└───────────────────────────────────────────────┘
+>>> df = lf.group_by("a").agg(nw.all().sum()).collect()
+>>> nw.to_native(df).sort("a")
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 4   ┆ 10  │
+│ b   ┆ 11  ┆ 10  │
+│ c   ┆ 6   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ collect_schema() + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import narwhals as nw
+>>> lf_pl = pl.LazyFrame(
+...     {
+...         "foo": [1, 2, 3],
+...         "bar": [6.0, 7.0, 8.0],
+...         "ham": ["a", "b", "c"],
+...     }
+... )
+>>> lf = nw.from_native(lf_pl)
+>>> lf.collect_schema()
+Schema({'foo': Int64, 'bar': Float64, 'ham', String})
+
+ +
+ +
+ +
+ + +

+ drop(*columns, strict=True) + +

+ + +
+ +

Remove columns from the LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*columns + str | Iterable[str] + +
+

Names of the columns that should be removed from the dataframe.

+
+ 		+ () +
strict + bool + +
+

Validate that all column names exist in the schema and throw an +exception if a column name does not exist in the schema.

+
+ 		+ True +
+ + +
+ Warning +

strict argument is ignored for polars<1.0.0.

+

Please consider upgrading to a newer version or pass to eager mode.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop("ham")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar
+0    1  6.0
+1    2  7.0
+2    3  8.0
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 1   ┆ 6.0 │
+│ 2   ┆ 7.0 │
+│ 3   ┆ 8.0 │
+└─────┴─────┘
+
+

Use positional arguments to drop multiple columns.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop("foo", "ham")
+
+ 
>>> func(df_pd)
+   bar
+0  6.0
+1  7.0
+2  8.0
+>>> func(lf_pl).collect()
+shape: (3, 1)
+┌─────┐
+│ bar │
+│ --- │
+│ f64 │
+╞═════╡
+│ 6.0 │
+│ 7.0 │
+│ 8.0 │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ drop_nulls(subset=None) + +

+ + +
+ +

Drop null values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
subset + str | list[str] | None + +
+

Column name(s) for which null values are considered. If set to None +(default), use all columns.

+
+ 		+ None +
+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1.0, 2.0, None], "ba": [1.0, None, 2.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.drop_nulls()
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+     a   ba
+0  1.0  1.0
+>>> func(df_pl).collect()
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ ba  │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 1.0 ┆ 1.0 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ filter(*predicates) + +

+ + +
+ +

Filter the rows in the LazyFrame based on a predicate expression.

+

The original order of the remaining rows is preserved.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*predicates + IntoExpr | Iterable[IntoExpr] + +
+

Expression that evaluates to a boolean Series.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function in which we filter on +one condition.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter(nw.col("foo") > 1)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo  bar ham
+1    2    7   b
+2    3    8   c
+>>> func(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+
+

Filter on multiple conditions:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter((nw.col("foo") < 3) & (nw.col("ham") == "a"))
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+
+

Provide multiple filters using *args syntax:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     dframe = df.filter(
+...         nw.col("foo") == 1,
+...         nw.col("ham") == "a",
+...     )
+...     return dframe
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> func(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+
+

Filter on an OR condition:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.filter((nw.col("foo") == 1) | (nw.col("ham") == "c"))
+>>> func(df_pd)
+   foo  bar ham
+0    1    6   a
+2    3    8   c
+>>> func(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth row in the DataFrame and return as a new DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Gather every n-th row.

+
+ 		+ required +
offset + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> df_pd = pd.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.gather_every(n=2, offset=1)
+
+ 
>>> func(df_pd)
+   a  b
+1  2  6
+3  4  8
+
+ 
>>> func(lf_pl).collect()
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 6   │
+│ 4   ┆ 8   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ group_by(*keys) + +

+ + +
+ +

Start a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*keys + str | Iterable[str] + +
+

Column(s) to group by. Accepts expression input. Strings are +parsed as column names.

+
+ 		+ () +
+ + +

Examples:

+

Group by one column and call agg to compute the grouped sum of +another column.

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "a": ["a", "b", "a", "b", "c"],
+...     "b": [1, 2, 1, 3, 3],
+...     "c": [5, 4, 3, 2, 1],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+>>> lf_pl = pl.LazyFrame(df)
+
+

Let's define a dataframe-agnostic function in which we group by one column +and call agg to compute the grouped sum of another column.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by("a").agg(nw.col("b").sum()).sort("a")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+
+

Group by multiple columns by passing a list of column names.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by(["a", "b"]).agg(nw.max("c")).sort(["a", "b"])
+>>> func(df_pd)
+   a  b  c
+0  a  1  5
+1  b  2  4
+2  b  3  2
+3  c  3  1
+>>> func(df_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 5   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 5   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of rows to return.

+
+ 		+ 5 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "a": [1, 2, 3, 4, 5, 6],
+...     "b": [7, 8, 9, 10, 11, 12],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function that gets the first 3 rows.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.head(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  7
+1  2  8
+2  3  9
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ join(other, *, how='inner', left_on=None, right_on=None) + +

+ + +
+ +

Add a join operation to the Logical Plan.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
other + Self + +
+

Lazy DataFrame to join with.

+
+ 		+ required +
how + Literal['inner', 'left', 'cross', 'semi', 'anti'] + +
+

Join strategy.

+
    +
  • inner: Returns rows that have matching values in both tables.
    • +
  • cross: Returns the Cartesian product of rows from both tables.
    • +
  • semi: Filter rows that have a match in the right table.
    • +
  • anti: Filter rows that do not have a match in the right table.
    • +
+
+ 		+ 'inner' +
left_on + str | list[str] | None + +
+

Join column of the left DataFrame.

+
+ 		+ None +
right_on + str | list[str] | None + +
+

Join column of the right DataFrame.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined LazyFrame

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> data_other = {
+...     "apple": ["x", "y", "z"],
+...     "ham": ["a", "b", "d"],
+... }
+
+ 
>>> df_pd = pd.DataFrame(data)
+>>> other_pd = pd.DataFrame(data_other)
+
+ 
>>> df_pl = pl.LazyFrame(data)
+>>> other_pl = pl.LazyFrame(data_other)
+
+

Let's define a dataframe-agnostic function in which we join over "ham" column:

+ 
>>> @nw.narwhalify
+... def join_on_ham(df, other_any):
+...     return df.join(other_any, left_on="ham", right_on="ham")
+
+

We can now pass either pandas or Polars to the function:

+ 
>>> join_on_ham(df_pd, other_pd)
+   foo  bar ham apple
+0    1  6.0   a     x
+1    2  7.0   b     y
+
+ 
>>> join_on_ham(df_pl, other_pl).collect()
+shape: (2, 4)
+┌─────┬─────┬─────┬───────┐
+│ foo ┆ bar ┆ ham ┆ apple │
+│ --- ┆ --- ┆ --- ┆ ---   │
+│ i64 ┆ f64 ┆ str ┆ str   │
+╞═════╪═════╪═════╪═══════╡
+│ 1   ┆ 6.0 ┆ a   ┆ x     │
+│ 2   ┆ 7.0 ┆ b   ┆ y     │
+└─────┴─────┴─────┴───────┘
+
+ +
+ +
+ +
+ + +

+ lazy() + +

+ + +
+ +

Lazify the DataFrame (if possible).

+

If a library does not support lazy execution, then this is a no-op.

+ + +

Examples:

+

Construct pandas and Polars objects:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.LazyFrame(df)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.lazy()
+
+

Note that then, pandas dataframe stay eager, and the Polars LazyFrame stays lazy:

+ 
>>> func(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> func(df_pl)
+<LazyFrame ...>
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "ba": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.pipe(lambda _df: _df.select("a"))
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+   a
+0  1
+1  2
+2  3
+>>> func(df_pl).collect()
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ rename(mapping) + +

+ + +
+ +

Rename column names.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
mapping + dict[str, str] + +
+

Key value pairs that map from old name to new name, or a + function that takes the old name as input and returns the + new name.

+
+ 		+ required +
+ + +
+ Notes +

If existing names are swapped (e.g. 'A' points to 'B' and 'B' + points to 'A'), polars will block projection and predicate + pushdowns at this node.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.rename({"foo": "apple"})
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   apple  bar ham
+0      1    6   a
+1      2    7   b
+2      3    8   c
+>>> func(lf_pl).collect()
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ apple ┆ bar ┆ ham │
+│ ---   ┆ --- ┆ --- │
+│ i64   ┆ i64 ┆ str │
+╞═══════╪═════╪═════╡
+│ 1     ┆ 6   ┆ a   │
+│ 2     ┆ 7   ┆ b   │
+│ 3     ┆ 8   ┆ c   │
+└───────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ select(*exprs, **named_exprs) + +

+ + +
+ +

Select columns from this LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*exprs + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to select, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, + other non-expression inputs are parsed as literals.

+
+ 		+ () +
**named_exprs + IntoExpr + +
+

Additional columns to select, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+>>> lf_pl = pl.LazyFrame(df)
+
+

Let's define a dataframe-agnostic function in which we pass the name of a +column to select that column.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select("foo")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo
+0    1
+1    2
+2    3
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ foo │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+>>> func(lf_pl).collect()
+shape: (3, 1)
+┌─────┐
+│ foo │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+
+

Multiple columns can be selected by passing a list of column names.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(["foo", "bar"])
+>>> func(df_pd)
+   foo  bar
+0    1    6
+1    2    7
+2    3    8
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 6   │
+│ 2   ┆ 7   │
+│ 3   ┆ 8   │
+└─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 6   │
+│ 2   ┆ 7   │
+│ 3   ┆ 8   │
+└─────┴─────┘
+
+

Multiple columns can also be selected using positional arguments instead of a +list. Expressions are also accepted.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("foo"), nw.col("bar") + 1)
+>>> func(df_pd)
+   foo  bar
+0    1    7
+1    2    8
+2    3    9
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+
+

Use keyword arguments to easily name your expression inputs.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(threshold=nw.col("foo") * 2)
+>>> func(df_pd)
+   threshold
+0          2
+1          4
+2          6
+>>> func(df_pl)
+shape: (3, 1)
+┌───────────┐
+│ threshold │
+│ ---       │
+│ i64       │
+╞═══════════╡
+│ 2         │
+│ 4         │
+│ 6         │
+└───────────┘
+>>> func(lf_pl).collect()
+shape: (3, 1)
+┌───────────┐
+│ threshold │
+│ ---       │
+│ i64       │
+╞═══════════╡
+│ 2         │
+│ 4         │
+│ 6         │
+└───────────┘
+
+ +
+ +
+ +
+ + +

+ sort(by, *more_by, descending=False) + +

+ + +
+ +

Sort the LazyFrame by the given columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
by + str | Iterable[str] + +
+

Column(s) to sort by. Accepts expression input. Strings are + parsed as column names.

+
+ 		+ required +
*more_by + str + +
+

Additional columns to sort by, specified as positional + arguments.

+
+ 		+ () +
descending + bool | Sequence[bool] + +
+

Sort in descending order. When sorting by multiple + columns, can be specified per column by passing a + sequence of booleans.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "a": [1, 2, None],
+...     "b": [6.0, 5.0, 4.0],
+...     "c": ["a", "c", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_lf = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function in which we sort by multiple +columns in different orders

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.sort("c", "a", descending=[False, True])
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a    b  c
+0  1.0  6.0  a
+2  NaN  4.0  b
+1  2.0  5.0  c
+>>> func(df_lf).collect()
+shape: (3, 3)
+┌──────┬─────┬─────┐
+│ a    ┆ b   ┆ c   │
+│ ---  ┆ --- ┆ --- │
+│ i64  ┆ f64 ┆ str │
+╞══════╪═════╪═════╡
+│ 1    ┆ 6.0 ┆ a   │
+│ null ┆ 4.0 ┆ b   │
+│ 2    ┆ 5.0 ┆ c   │
+└──────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of rows to return.

+
+ 		+ 5 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = {
+...     "a": [1, 2, 3, 4, 5, 6],
+...     "b": [7, 8, 9, 10, 11, 12],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function that gets the last 3 rows.

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.tail(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a   b
+3  4  10
+4  5  11
+5  6  12
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 4   ┆ 10  │
+│ 5   ┆ 11  │
+│ 6   ┆ 12  │
+└─────┴─────┘
+>>> func(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 4   ┆ 10  │
+│ 5   ┆ 11  │
+│ 6   ┆ 12  │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ unique(subset=None, *, keep='any', maintain_order=False) + +

+ + +
+ +

Drop duplicate rows from this LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
subset + str | list[str] | None + +
+

Column name(s) to consider when identifying duplicate rows. + If set to None, use all columns.

+
+ 		+ None +
keep + Literal['any', 'first', 'last', 'none'] + +
+

{'first', 'last', 'any', 'none'} +Which of the duplicate rows to keep.

+
    +
  • 'any': Does not give any guarantee of which row is kept. + This allows more optimizations.
    • +
  • 'none': Don't keep duplicate rows.
    • +
  • 'first': Keep first unique row.
    • +
  • 'last': Keep last unique row.
    • +
+
+ 		+ 'any' +
maintain_order + bool + +
+

Keep the same order as the original DataFrame. This is more +expensive to compute. Settings this to True blocks the possibility +to run on the streaming engine for polars.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
LazyFrame + Self + +
+

LazyFrame with unique rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3, 1],
+...     "bar": ["a", "a", "a", "a"],
+...     "ham": ["b", "b", "b", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> lf_pl = pl.LazyFrame(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.unique(["bar", "ham"])
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   foo bar ham
+0    1   a   b
+>>> func(lf_pl).collect()
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ str ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ a   ┆ b   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ with_columns(*exprs, **named_exprs) + +

+ + +
+ +

Add columns to this LazyFrame.

+

Added columns will replace existing columns with the same name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
*exprs + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to add, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, other + non-expression inputs are parsed as literals.

+
+ 		+ () +
**named_exprs + IntoExpr + +
+

Additional columns to add, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
LazyFrame + Self + +
+

A new LazyFrame with the columns added.

+
+
+ + +
+ Note +

Creating a new LazyFrame using this method does not create a new copy of +existing data.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df = {
+...     "a": [1, 2, 3, 4],
+...     "b": [0.5, 4, 10, 13],
+...     "c": [True, True, False, True],
+... }
+>>> df_pd = pd.DataFrame(df)
+>>> df_pl = pl.DataFrame(df)
+>>> lf_pl = pl.LazyFrame(df)
+
+

Let's define a dataframe-agnostic function in which we pass an expression +to add it as a new column:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns((nw.col("a") * 2).alias("2a"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a     b      c  2a
+0  1   0.5   True   2
+1  2   4.0   True   4
+2  3  10.0  False   6
+3  4  13.0   True   8
+>>> func(df_pl)
+shape: (4, 4)
+┌─────┬──────┬───────┬─────┐
+│ a   ┆ b    ┆ c     ┆ 2a  │
+│ --- ┆ ---  ┆ ---   ┆ --- │
+│ i64 ┆ f64  ┆ bool  ┆ i64 │
+╞═════╪══════╪═══════╪═════╡
+│ 1   ┆ 0.5  ┆ true  ┆ 2   │
+│ 2   ┆ 4.0  ┆ true  ┆ 4   │
+│ 3   ┆ 10.0 ┆ false ┆ 6   │
+│ 4   ┆ 13.0 ┆ true  ┆ 8   │
+└─────┴──────┴───────┴─────┘
+>>> func(lf_pl).collect()
+shape: (4, 4)
+┌─────┬──────┬───────┬─────┐
+│ a   ┆ b    ┆ c     ┆ 2a  │
+│ --- ┆ ---  ┆ ---   ┆ --- │
+│ i64 ┆ f64  ┆ bool  ┆ i64 │
+╞═════╪══════╪═══════╪═════╡
+│ 1   ┆ 0.5  ┆ true  ┆ 2   │
+│ 2   ┆ 4.0  ┆ true  ┆ 4   │
+│ 3   ┆ 10.0 ┆ false ┆ 6   │
+│ 4   ┆ 13.0 ┆ true  ┆ 8   │
+└─────┴──────┴───────┴─────┘
+
+ +
+ +
+ +
+ + +

+ with_row_index(name='index') + +

+ + +
+ +

Insert column which enumerates rows.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_row_index()
+
+

We can then pass either pandas or Polars:

+ 
>>> func(df_pd)
+   index  a  b
+0      0  1  4
+1      1  2  5
+2      2  3  6
+>>> func(df_pl).collect()
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ index ┆ a   ┆ b   │
+│ ---   ┆ --- ┆ --- │
+│ u32   ┆ i64 ┆ i64 │
+╞═══════╪═════╪═════╡
+│ 0     ┆ 1   ┆ 4   │
+│ 1     ┆ 2   ┆ 5   │
+│ 2     ┆ 3   ┆ 6   │
+└───────┴─────┴─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/narwhals/index.html b/api-reference/narwhals/index.html new file mode 100644 index 0000000000..d189bf2d41 --- /dev/null +++ b/api-reference/narwhals/index.html @@ -0,0 +1,3569 @@ + + + + + + + + + + + + + + + + + + + + + + + Top-level functions - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Top-level functions

+

Here are the top-level functions available in Narwhals.

+ + +
+ + + + +
+ + + +
+ + + + + + + + + +
+ + +

+ all() + +

+ + +
+ +

Instantiate an expression representing all columns.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.all() * 2)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a   b
+0  2   8
+1  4  10
+2  6  12
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 8   │
+│ 4   ┆ 10  │
+│ 6   ┆ 12  │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ all_horizontal(*exprs) + +

+ + +
+ +

Compute the bitwise AND horizontally across columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
exprs + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts expression input.

+
+ 		+ () +
+ + +
+ Notes +

pandas and Polars handle null values differently.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {
+...     "a": [False, False, True, True, False, None],
+...     "b": [False, True, True, None, None, None],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select("a", "b", all=nw.all_horizontal("a", "b"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+       a      b    all
+0  False  False  False
+1  False   True  False
+2   True   True   True
+3   True   None  False
+4  False   None  False
+5   None   None  False
+
+ 
>>> func(df_pl)
+shape: (6, 3)
+┌───────┬───────┬───────┐
+│ a     ┆ b     ┆ all   │
+│ ---   ┆ ---   ┆ ---   │
+│ bool  ┆ bool  ┆ bool  │
+╞═══════╪═══════╪═══════╡
+│ false ┆ false ┆ false │
+│ false ┆ true  ┆ false │
+│ true  ┆ true  ┆ true  │
+│ true  ┆ null  ┆ null  │
+│ false ┆ null  ┆ false │
+│ null  ┆ null  ┆ null  │
+└───────┴───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ any_horizontal(*exprs) + +

+ + +
+ +

Compute the bitwise OR horizontally across columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
exprs + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts expression input.

+
+ 		+ () +
+ + +
+ Notes +

pandas and Polars handle null values differently.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {
+...     "a": [False, False, True, True, False, None],
+...     "b": [False, True, True, None, None, None],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select("a", "b", any=nw.any_horizontal("a", "b"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+       a      b    any
+0  False  False  False
+1  False   True   True
+2   True   True   True
+3   True   None   True
+4  False   None  False
+5   None   None  False
+
+ 
>>> func(df_pl)
+shape: (6, 3)
+┌───────┬───────┬───────┐
+│ a     ┆ b     ┆ any   │
+│ ---   ┆ ---   ┆ ---   │
+│ bool  ┆ bool  ┆ bool  │
+╞═══════╪═══════╪═══════╡
+│ false ┆ false ┆ false │
+│ false ┆ true  ┆ true  │
+│ true  ┆ true  ┆ true  │
+│ true  ┆ null  ┆ true  │
+│ false ┆ null  ┆ null  │
+│ null  ┆ null  ┆ null  │
+└───────┴───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ col(*names) + +

+ + +
+ +

Creates an expression that references one or more columns by their name(s).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
names + str | Iterable[str] + +
+

Name(s) of the columns to use in the aggregation function.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [3, 4]})
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.col("a") * nw.col("b"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+   a
+0  3
+1  8
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 3   │
+│ 8   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ concat(items, *, how='vertical') + +

+ + +
+ +
+ +
+ +
+ + +

+ from_dict(data, schema=None, *, native_namespace=None) + +

+ + +
+ +

Instantiate DataFrame from dictionary.

+ + +
+ Notes +

For pandas-like dataframes, conversion to schema is applied after dataframe +creation.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
data + dict[str, Any] + +
+

Dictionary to create DataFrame from.

+
+ 		+ required +
schema + dict[str, DType] | Schema | None + +
+

The DataFrame schema as Schema or dict of {name: type}.

+
+ 		+ None +
native_namespace + ModuleType | None + +
+

The native library to use for DataFrame creation. Only +necessary if inputs are not Narwhals Series.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     data = {"c": [5, 2], "d": [1, 4]}
+...     native_namespace = nw.get_native_namespace(df)
+...     return nw.from_dict(data, native_namespace=native_namespace)
+
+

Let's see what happens when passing pandas / Polars input:

+ 
>>> func(pd.DataFrame(data))
+   c  d
+0  5  1
+1  2  4
+>>> func(pl.DataFrame(data))
+shape: (2, 2)
+┌─────┬─────┐
+│ c   ┆ d   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 5   ┆ 1   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ from_native(native_object, *, strict=True, eager_only=None, eager_or_interchange_only=None, series_only=None, allow_series=None) + +

+ + +
+ +

Convert dataframe/series to Narwhals DataFrame, LazyFrame, or Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
native_object + Any + +
+

Raw object from user. +Depending on the other arguments, input object can be:

+
    +
  • pandas.DataFrame
    • +
  • polars.DataFrame
    • +
  • polars.LazyFrame
    • +
  • anything with a __narwhals_dataframe__ or __narwhals_lazyframe__ method
    • +
  • pandas.Series
    • +
  • polars.Series
    • +
  • anything with a __narwhals_series__ method
    • +
+
+ 		+ required +
strict + bool + +
+

Whether to raise if object can't be converted (default) or +to just leave it as-is.

+
+ 		+ True +
eager_only + bool | None + +
+

Whether to only allow eager objects.

+
+ 		+ None +
eager_or_interchange_only + bool | None + +
+

Whether to only allow eager objects or objects which +implement the Dataframe Interchange Protocol.

+
+ 		+ None +
series_only + bool | None + +
+

Whether to only allow series.

+
+ 		+ None +
allow_series + bool | None + +
+

Whether to allow series (default is only dataframe / lazyframe).

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

narwhals.DataFrame or narwhals.LazyFrame or narwhals.Series

+
+
+ +
+ +
+ +
+ + +

+ get_level(obj) + +

+ + +
+ +

Level of support Narwhals has for current object.

+

This can be one of:

+
    +
  • 'full': full Narwhals API support
    • +
  • 'metadata': only metadata operations are supported (df.schema)
    • +
+ +
+ +
+ +
+ + +

+ get_native_namespace(obj) + +

+ + +
+ +

Get native namespace from object.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df = nw.from_native(pd.DataFrame({"a": [1, 2, 3]}))
+>>> nw.get_native_namespace(df)
+<module 'pandas'...>
+>>> df = nw.from_native(pl.DataFrame({"a": [1, 2, 3]}))
+>>> nw.get_native_namespace(df)
+<module 'polars'...>
+
+ +
+ +
+ +
+ + +

+ is_ordered_categorical(series) + +

+ + +
+ +

Return whether indices of categories are semantically meaningful.

+

This is a convenience function to accessing what would otherwise be +the is_ordered property from the DataFrame Interchange Protocol, +see https://data-apis.org/dataframe-protocol/latest/API.html.

+
    +
  • For Polars:
    • +
  • Enums are always ordered.
    • +
  • Categoricals are ordered if dtype.ordering == "physical".
    • +
  • For pandas-like APIs:
    • +
  • Categoricals are ordered if dtype.cat.ordered == True.
    • +
  • For PyArrow table:
    • +
  • Categoricals are ordered if dtype.type.ordered == True.
    • +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = ["x", "y"]
+>>> s_pd = pd.Series(data, dtype=pd.CategoricalDtype(ordered=True))
+>>> s_pl = pl.Series(data, dtype=pl.Categorical(ordering="physical"))
+
+

Let's define a library-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return nw.is_ordered_categorical(s)
+
+

Then, we can pass any supported library to func:

+ 
>>> func(s_pd)
+True
+>>> func(s_pl)
+True
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of rows.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [5, 10]})
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [5, 10]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.len())
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   len
+0    2
+>>> func(df_pl)
+shape: (1, 1)
+┌─────┐
+│ len │
+│ --- │
+│ u32 │
+╞═════╡
+│ 2   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ lit(value, dtype=None) + +

+ + +
+ +

Return an expression representing a literal value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
value + Any + +
+

The value to use as literal.

+
+ 		+ required +
dtype + DType | None + +
+

The data type of the literal value. If not provided, the data type will be inferred.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pl = pl.DataFrame({"a": [1, 2]})
+>>> df_pd = pd.DataFrame({"a": [1, 2]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.with_columns(nw.lit(3).alias("b"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+   a  b
+0  1  3
+1  2  3
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i32 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 3   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ max(*columns) + +

+ + +
+ +

Return the maximum value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).max().

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
columns + str + +
+

Name(s) of the columns to use in the aggregation function.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [5, 10]})
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [5, 10]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.max("a"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   a
+0  2
+>>> func(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ maybe_align_index(lhs, rhs) + +

+ + +
+ +

Align lhs to the Index of `rhs, if they're both pandas-like.

+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, +for example if your library already aligns indices for users. +If you're designing a new library, we highly encourage you to not +rely on the Index. +For non-pandas-like inputs, this only checks that lhs and rhs +are the same length.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2]}, index=[3, 4])
+>>> s_pd = pd.Series([6, 7], index=[4, 3])
+>>> df = nw.from_native(df_pd)
+>>> s = nw.from_native(s_pd, series_only=True)
+>>> nw.to_native(nw.maybe_align_index(df, s))
+   a
+4  2
+3  1
+
+ +
+ +
+ +
+ + +

+ maybe_convert_dtypes(df, *args, **kwargs) + +

+ + +
+ +

Convert columns to the best possible dtypes using dtypes supporting pd.NA, if df is pandas-like.

+ + +
+ Notes +

For non-pandas-like inputs, this is a no-op. +Also, args and kwargs just get passed down to the underlying library as-is.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> import numpy as np
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
+...         "b": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
+...     }
+... )
+>>> df = nw.from_native(df_pd)
+>>> nw.to_native(nw.maybe_convert_dtypes(df)).dtypes
+a             Int32
+b           boolean
+dtype: object
+
+ +
+ +
+ +
+ + +

+ maybe_set_index(df, column_names) + +

+ + +
+ +

Set columns columns to be the index of df, if df is pandas-like.

+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, +for example if your library already aligns indices for users. +If you're designing a new library, we highly encourage you to not +rely on the Index. +For non-pandas-like inputs, this is a no-op.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
+>>> df = nw.from_native(df_pd)
+>>> nw.to_native(nw.maybe_set_index(df, "b"))
+   a
+b
+4  1
+5  2
+
+ +
+ +
+ +
+ + +

+ mean(*columns) + +

+ + +
+ +

Get the mean value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).mean()

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
columns + str + +
+

Name(s) of the columns to use in the aggregation function

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pl = pl.DataFrame({"a": [1, 8, 3]})
+>>> df_pd = pd.DataFrame({"a": [1, 8, 3]})
+
+

We define a dataframe agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.mean("a"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+     a
+0  4.0
+>>> func(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 4.0 │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ min(*columns) + +

+ + +
+ +

Return the minimum value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).min().

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
columns + str + +
+

Name(s) of the columns to use in the aggregation function.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [5, 10]})
+>>> df_pl = pl.DataFrame({"a": [1, 2], "b": [5, 10]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.min("b"))
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(df_pd)
+   b
+0  5
+>>> func(df_pl)
+shape: (1, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 5   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ narwhalify(func=None, *, strict=False, eager_only=False, eager_or_interchange_only=False, series_only=False, allow_series=True) + +

+ + +
+ +

Decorate function so it becomes dataframe-agnostic.

+

narwhalify will try to convert any dataframe/series-like object into the narwhal +respective DataFrame/Series, while leaving the other parameters as they are.

+

Similarly, if the output of the function is a narwhals DataFrame or Series, it will be +converted back to the original dataframe/series type, while if the output is another +type it will be left as is.

+

By setting strict=True, then every input and every output will be required to be a +dataframe/series-like object.

+

Instead of writing

+
import narwhals as nw
+
+
+def func(df):
+    df = nw.from_native(df, strict=False)
+    df = df.group_by("a").agg(nw.col("b").sum())
+    return nw.to_native(df)
+
+

you can just write

+
import narwhals as nw
+
+
+@nw.narwhalify
+def func(df):
+    return df.group_by("a").agg(nw.col("b").sum())
+
+

You can also pass in extra arguments, e.g.

+
@nw.narwhalify(eager_only=True)
+
+

that will get passed down to nw.from_native.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
func + Callable[..., Any] | None + +
+

Function to wrap in a from_native-to_native block.

+
+ 		+ None +
strict + bool + +
+

Whether to raise if object can't be converted or to just leave it as-is +(default).

+
+ 		+ False +
eager_only + bool | None + +
+

Whether to only allow eager objects.

+
+ 		+ False +
eager_or_interchange_only + bool | None + +
+

Whether to only allow eager objects or objects which +implement the Dataframe Interchange Protocol.

+
+ 		+ False +
series_only + bool | None + +
+

Whether to only allow series.

+
+ 		+ False +
allow_series + bool | None + +
+

Whether to allow series (default is only dataframe / lazyframe).

+
+ 		+ True +
+ +
+ +
+ +
+ + +

+ new_series(name, values, dtype=None, *, native_namespace) + +

+ + +
+ +

Instantiate Narwhals Series from raw data.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
name + str + +
+

Name of resulting Series.

+
+ 		+ required +
values + Any + +
+

Values of make Series from.

+
+ 		+ required +
dtype + DType | type[DType] | None + +
+

(Narwhals) dtype. If not provided, the native library +may auto-infer it from values.

+
+ 		+ None +
native_namespace + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     values = [4, 1, 2]
+...     native_namespace = nw.get_native_namespace(df)
+...     return nw.new_series("c", values, nw.Int32, native_namespace=native_namespace)
+
+

Let's see what happens when passing pandas / Polars input:

+ 
>>> func(pd.DataFrame(data))
+0    4
+1    1
+2    2
+Name: c, dtype: int32
+>>> func(pl.DataFrame(data))
+shape: (3,)
+Series: 'c' [i32]
+[
+   4
+   1
+   2
+]
+
+ +
+ +
+ +
+ + +

+ sum(*columns) + +

+ + +
+ +

Sum all values.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).sum()

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
columns + str + +
+

Name(s) of the columns to use in the aggregation function

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pl = pl.DataFrame({"a": [1, 2]})
+>>> df_pd = pd.DataFrame({"a": [1, 2]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.sum("a"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+   a
+0  3
+>>> func(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ sum_horizontal(*exprs) + +

+ + +
+ +

Sum all values horizontally across columns

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
exprs + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts expression input.

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3], "b": [5, 10, 15]})
+>>> df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [5, 10, 15]})
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(nw.sum_horizontal("a", "b"))
+
+

We can then pass either pandas or polars to func:

+ 
>>> func(df_pd)
+    a
+0   6
+1  12
+2  18
+>>> func(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 6   │
+│ 12  │
+│ 18  │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ show_versions() + +

+ + +
+ +

Print useful debugging information

+

Examples:

+
>>> from narwhals import show_versions
+>>> show_versions()  # doctest:+SKIP
+
+ +
+ +
+ +
+ + +

+ to_native(narwhals_object, *, strict=True) + +

+ + +
+ +

Convert Narwhals object to native one.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
narwhals_object + DataFrame[IntoFrameT] | LazyFrame[IntoFrameT] | Series + +
+

Narwhals object.

+
+ 		+ required +
strict + bool + +
+

whether to raise on non-Narwhals input.

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ IntoFrameT | Any + +
+

Object of class that user started with.

+
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/schema/index.html b/api-reference/schema/index.html new file mode 100644 index 0000000000..09b0a959ae --- /dev/null +++ b/api-reference/schema/index.html @@ -0,0 +1,1582 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Schema - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Schema

+ + +
+ + + + +
+ + +

Ordered mapping of column names to their data type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
schema + Mapping[str, DType] | Iterable[tuple[str, DType]] | None + +
+

Mapping[str, DType] | Iterable[tuple[str, DType]] | None +The schema definition given by column names and their associated. +instantiated Narwhals data type. Accepts a mapping or an iterable of tuples.

+
+ 		+ None +
+ + +

Examples:

+

Define a schema by passing instantiated data types.

+ 
>>> import narwhals as nw
+>>> schema = nw.Schema({"foo": nw.Int8(), "bar": nw.String()})
+>>> schema
+Schema({'foo': Int8, 'bar': String})
+
+

Access the data type associated with a specific column name.

+ 
>>> schema["foo"]
+Int8
+
+

Access various schema properties using the names, dtypes, and len methods.

+ 
>>> schema.names()
+['foo', 'bar']
+>>> schema.dtypes()
+[Int8, String]
+>>> schema.len()
+2
+
+ + + + +
+ + + + + + + + + +
+ + +

+ names() + +

+ + +
+ +

Get the column names of the schema.

+ +
+ +
+ +
+ + +

+ dtypes() + +

+ + +
+ +

Get the data types of the schema.

+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Get the number of columns in the schema.

+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/selectors/index.html b/api-reference/selectors/index.html new file mode 100644 index 0000000000..ef070ec0c2 --- /dev/null +++ b/api-reference/selectors/index.html @@ -0,0 +1,1803 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.selectors - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.selectors

+

The following selectors are all supported. In addition, just like in Polars, the following +set operations are supported:

+
    +
  • set intersection: &
    • +
  • set union: |
    • +
  • set difference: -
    • +
  • complement: ~
    • +
+ + +
+ + + + +
+ + + +
+ + + + + + + + + +
+ + +

+ boolean() + +

+ + +
+ +

Select boolean columns.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select boolean +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.boolean())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+       c
+0  False
+1   True
+>>> func(df_pl)
+shape: (2, 1)
+┌───────┐
+│ c     │
+│ ---   │
+│ bool  │
+╞═══════╡
+│ false │
+│ true  │
+└───────┘
+
+ +
+ +
+ +
+ + +

+ by_dtype(*dtypes) + +

+ + +
+ +

Select columns based on their dtype.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dtypes + Any + +
+

one or data types to select

+
+ 		+ () +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select int64 and float64 +dtypes and multiplies each value by 2:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.by_dtype(nw.Int64, nw.Float64) * 2)
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   a    c
+0  2  8.2
+1  4  4.6
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ c   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 8.2 │
+│ 4   ┆ 4.6 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ categorical() + +

+ + +
+ +

Select categorical columns.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data).astype({"b": "category"})
+>>> df_pl = pl.DataFrame(data, schema_overrides={"b": pl.Categorical})
+
+

Let's define a dataframe-agnostic function to select string +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.categorical())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   b
+0  x
+1  y
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ cat │
+╞═════╡
+│ x   │
+│ y   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ numeric() + +

+ + +
+ +

Select numeric columns.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select numeric +dtypes and multiplies each value by 2:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.numeric() * 2)
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   a    c
+0  2  8.2
+1  4  4.6
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ c   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 8.2 │
+│ 4   ┆ 4.6 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ string() + +

+ + +
+ +

Select string columns.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select string +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.string())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   b
+0  x
+1  y
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ str │
+╞═════╡
+│ x   │
+│ y   │
+└─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series/index.html b/api-reference/series/index.html new file mode 100644 index 0000000000..0bafaa8b8d --- /dev/null +++ b/api-reference/series/index.html @@ -0,0 +1,5559 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series

+ + +
+ + + + +
+ + +

Narwhals Series, backed by a native series.

+

The native dataframe might be pandas.Series, polars.Series, ...

+

This class is not meant to be instantiated directly - instead, use +narwhals.from_native, making sure to pass allow_series=True or +series_only=True.

+ + + + +
+ + + + + + + +
+ + + +

+ dtype: Any + + + property + + +

+ + +
+ +

Get the data type of the Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dtype
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+Int64
+>>> func(s_pl)
+Int64
+
+
+ +
+ +
+ + + +

+ name: str + + + property + + +

+ + +
+ +

Get the name of the Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="foo")
+>>> s_pl = pl.Series("foo", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.name
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+'foo'
+>>> func(s_pl)
+'foo'
+
+
+ +
+ +
+ + + +

+ shape: tuple[int] + + + property + + +

+ + +
+ +

Get the shape of the Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.shape
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+(3,)
+>>> func(s_pl)
+(3,)
+
+
+ +
+ + + +
+ + +

+ __arrow_c_stream__(requested_schema=None) + +

+ + +
+ +

Export a Series via the Arrow PyCapsule Interface.

+

Narwhals doesn't implement anything itself here:

+
    +
  • if the underlying series implements the interface, it'll return that
    • +
  • else, it'll call to_arrow and then defer to PyArrow's implementation
    • +
+

See PyCapsule Interface +for more.

+ +
+ +
+ +
+ + +

+ __getitem__(idx) + +

+ + +
+ +
+ +
+ +
+ + +

+ abs() + +

+ + +
+ +

Calculate the absolute value of each element.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [2, -4, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.abs()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2
+1    4
+2    3
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   4
+   3
+]
+
+ +
+ +
+ +
+ + +

+ alias(name) + +

+ + +
+ +

Rename the Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
name + str + +
+

The new name.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="foo")
+>>> s_pl = pl.Series("foo", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.alias("bar")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1
+1    2
+2    3
+Name: bar, dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: 'bar' [i64]
+[
+   1
+   2
+   3
+]
+
+ +
+ +
+ +
+ + +

+ all() + +

+ + +
+ +

Return whether all values in the Series are True.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [True, False, True]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.all()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.False_
+>>> func(s_pl)
+False
+
+ +
+ +
+ +
+ + +

+ any() + +

+ + +
+ +

Return whether any of the values in the Series are True.

+ + +
+ Notes +

Only works on Series of data type Boolean.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [False, True, False]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.any()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.True_
+>>> func(s_pl)
+True
+
+ +
+ +
+ +
+ + +

+ arg_true() + +

+ + +
+ +

Find elements where boolean Series is True.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = [1, None, None, 2]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_null().arg_true()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+1    1
+2    2
+Name: a, dtype: int64
+>>> func(s_pl)
+shape: (2,)
+Series: 'a' [u32]
+[
+   1
+   2
+]
+
+ +
+ +
+ +
+ + +

+ cast(dtype) + +

+ + +
+ +

Cast between data types.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
dtype + Any + +
+

Data type that the object will be cast into.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [True, False, True]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.cast(nw.Int64)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1
+1    0
+2    1
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   0
+   1
+]
+
+ +
+ +
+ +
+ + +

+ count() + +

+ + +
+ +

Returns the number of non-null elements in the Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.count()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.int64(3)
+>>> func(s_pl)
+3
+
+ +
+ +
+ +
+ + +

+ cum_sum() + +

+ + +
+ +

Calculate the cumulative sum.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [2, 4, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.cum_sum()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2
+1    6
+2    9
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   6
+   9
+]
+
+ +
+ +
+ +
+ + +

+ diff() + +

+ + +
+ +

Calculate the difference with the previous element, for each element.

+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to calculate +the diff and fill missing values with 0 in a Int64 column, you could +do:

+
s.diff().fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [2, 4, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.diff()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    NaN
+1    2.0
+2   -1.0
+dtype: float64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   null
+   2
+   -1
+]
+
+ +
+ +
+ +
+ + +

+ drop_nulls() + +

+ + +
+ +

Drop all null values.

+ + +
+ See Also +

drop_nans

+
+ +
+ Notes +

A null value is not the same as a NaN value. +To drop NaN values, use :func:drop_nans.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import numpy as np
+>>> import narwhals as nw
+>>> s_pd = pd.Series([2, 4, None, 3, 5])
+>>> s_pl = pl.Series("a", [2, 4, None, 3, 5])
+
+

Now define a dataframe-agnostic function with a column argument for the column to evaluate :

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.drop_nulls()
+
+

Then we can pass either Series (polars or pandas) to func:

+ 
>>> func(s_pd)
+0    2.0
+1    4.0
+3    3.0
+4    5.0
+dtype: float64
+>>> func(s_pl)
+shape: (4,)
+Series: 'a' [i64]
+[
+   2
+   4
+   3
+   5
+]
+
+ +
+ +
+ +
+ + +

+ fill_null(value) + +

+ + +
+ +

Fill null values using the specified value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
value + Any + +
+

Value used to fill null values.

+
+ 		+ required +
+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, None]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.fill_null(5)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1.0
+1    2.0
+2    5.0
+dtype: float64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   2
+   5
+]
+
+ +
+ +
+ +
+ + +

+ filter(other) + +

+ + +
+ +

Filter elements in the Series based on a condition.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [4, 10, 15, 34, 50]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.filter(s > 10)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+2    15
+3    34
+4    50
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   15
+   34
+   50
+]
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth value in the Series and return as new Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Gather every n-th row.

+
+ 		+ required +
offset + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = [1, 2, 3, 4]
+>>> s_pd = pd.Series(name="a", data=data)
+>>> s_pl = pl.Series(name="a", values=data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.gather_every(n=2, offset=1)
+
+ 
>>> func(s_pd)
+1    2
+3    4
+Name: a, dtype: int64
+
+ 
>>> func(s_pl)
+shape: (2,)
+Series: 'a' [i64]
+[
+   2
+   4
+]
+
+ +
+ +
+ +
+ + +

+ head(n=10) + +

+ + +
+ +

Get the first n rows.

+

Arguments + n : int + Number of rows to return.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = list(range(10))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

Let's define a dataframe-agnostic function that returns the first 3 rows:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.head(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    0
+1    1
+2    2
+dtype: int64
+
+ 
>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   0
+   1
+   2
+]
+
+ +
+ +
+ +
+ + +

+ is_between(lower_bound, upper_bound, closed='both') + +

+ + +
+ +

Get a boolean mask of the values that are between the given lower/upper bounds.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
lower_bound + Any + +
+

Lower bound value.

+
+ 		+ required +
upper_bound + Any + +
+

Upper bound value.

+
+ 		+ required +
closed + str + +
+

Define which sides of the interval are closed (inclusive).

+
+ 		+ 'both' +
+ + +
+ Notes +

If the value of the lower_bound is greater than that of the upper_bound, +then the values will be False, as no value can satisfy the condition.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s_pd = pd.Series([1, 2, 3, 4, 5])
+>>> s_pl = pl.Series([1, 2, 3, 4, 5])
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_between(2, 4, "right")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1    False
+2     True
+3     True
+4    False
+dtype: bool
+>>> func(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+   true
+   false
+]
+
+ +
+ +
+ +
+ + +

+ clip(lower_bound=None, upper_bound=None) + +

+ + +
+ +

Clip values in the Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
lower_bound + Any | None + +
+

Lower bound value.

+
+ 		+ None +
upper_bound + Any | None + +
+

Upper bound value.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>>
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func_lower(s):
+...     return s.clip(2)
+
+

We can then pass either pandas or Polars to func_lower:

+ 
>>> func_lower(s_pd)
+0    2
+1    2
+2    3
+dtype: int64
+>>> func_lower(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   2
+   3
+]
+
+

We define another library agnostic function:

+ 
>>> @nw.narwhalify
+... def func_upper(s):
+...     return s.clip(upper_bound=2)
+
+

We can then pass either pandas or Polars to func_upper:

+ 
>>> func_upper(s_pd)
+0    1
+1    2
+2    2
+dtype: int64
+>>> func_upper(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   2
+   2
+]
+
+

We can have both at the same time

+ 
>>> s = [-1, 1, -3, 3, -5, 5]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.clip(-1, 3)
+
+

We can pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0   -1
+1    1
+2   -1
+3    3
+4   -1
+5    3
+dtype: int64
+>>> func(s_pl)
+shape: (6,)
+Series: '' [i64]
+[
+   -1
+    1
+   -1
+    3
+   -1
+    3
+]
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Get a mask of all duplicated rows in the Series.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, 2, 3, 1])
+>>> s_pl = pl.Series([1, 2, 3, 1])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_duplicated()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0     True
+1    False
+2    False
+3     True
+dtype: bool
+>>> func(s_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    true
+    false
+    false
+    true
+]
+
+ +
+ +
+ +
+ + +

+ is_empty() + +

+ + +
+ +

Check if the series is empty.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+

Let's define a dataframe-agnostic function that filters rows in which "foo" +values are greater than 10, and then checks if the result is empty or not:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.filter(s > 10).is_empty()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> s_pd = pd.Series([1, 2, 3])
+>>> s_pl = pl.Series([1, 2, 3])
+>>> func(s_pd), func(s_pl)
+(True, True)
+
+ 
>>> s_pd = pd.Series([100, 2, 3])
+>>> s_pl = pl.Series([100, 2, 3])
+>>> func(s_pd), func(s_pl)
+(False, False)
+
+ +
+ +
+ +
+ + +

+ is_first_distinct() + +

+ + +
+ +

Return a boolean mask indicating the first occurrence of each distinct value.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, 1, 2, 3, 2])
+>>> s_pl = pl.Series([1, 1, 2, 3, 2])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_first_distinct()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0     True
+1    False
+2     True
+3     True
+4    False
+dtype: bool
+
+ 
>>> func(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+    true
+    false
+    true
+    true
+    false
+]
+
+ +
+ +
+ +
+ + +

+ is_in(other) + +

+ + +
+ +

Check if the elements of this Series are in the other sequence.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
other + Any + +
+

Sequence of primitive type.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s_pd = pd.Series([1, 2, 3])
+>>> s_pl = pl.Series([1, 2, 3])
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_in([3, 2, 8])
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1     True
+2     True
+dtype: bool
+>>> func(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   true
+   true
+]
+
+ +
+ +
+ +
+ + +

+ is_last_distinct() + +

+ + +
+ +

Return a boolean mask indicating the last occurrence of each distinct value.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, 1, 2, 3, 2])
+>>> s_pl = pl.Series([1, 1, 2, 3, 2])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_last_distinct()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1     True
+2    False
+3     True
+4     True
+dtype: bool
+
+ 
>>> func(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+    false
+    true
+    false
+    true
+    true
+]
+
+ +
+ +
+ +
+ + +

+ is_null() + +

+ + +
+ +

Returns a boolean Series indicating which values are null.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, None]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_null()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1    False
+2     True
+dtype: bool
+>>> func(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+]
+
+ +
+ +
+ +
+ + +

+ is_sorted(*, descending=False) + +

+ + +
+ +

Check if the Series is sorted.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
descending + bool + +
+

Check if the Series is sorted in descending order.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> unsorted_data = [1, 3, 2]
+>>> sorted_data = [3, 2, 1]
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s, descending=False):
+...     return s.is_sorted(descending=descending)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(pl.Series(unsorted_data))
+False
+>>> func(pl.Series(sorted_data), descending=True)
+True
+>>> func(pd.Series(unsorted_data))
+False
+>>> func(pd.Series(sorted_data), descending=True)
+True
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Get a mask of all unique rows in the Series.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, 2, 3, 1])
+>>> s_pl = pl.Series([1, 2, 3, 1])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.is_unique()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1     True
+2     True
+3    False
+dtype: bool
+
+ 
>>> func(s_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    false
+     true
+     true
+    false
+]
+
+ +
+ +
+ +
+ + +

+ item(index=None) + +

+ + +
+ +

Return the Series as a scalar, or return the element at the given index.

+

If no index is provided, this is equivalent to s[0], with a check +that the shape is (1,). With an index, this is equivalent to s[index].

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+

Let's define a dataframe-agnostic function that returns item at given index

+ 
>>> @nw.narwhalify
+... def func(s, index=None):
+...     return s.item(index)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(pl.Series("a", [1]), None), func(pd.Series([1]), None)
+(1, 1)
+
+ 
>>> func(pl.Series("a", [9, 8, 7]), -1), func(pl.Series([9, 8, 7]), -2)
+(7, 8)
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in the Series.

+

Null values count towards the total.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = [1, 2, None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

Let's define a dataframe-agnostic function that computes the len of the series:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.len()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+3
+>>> func(s_pl)
+3
+
+ +
+ +
+ +
+ + +

+ max() + +

+ + +
+ +

Get the maximum value in this Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.max()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.int64(3)
+>>> func(s_pl)
+3
+
+ +
+ +
+ +
+ + +

+ mean() + +

+ + +
+ +

Reduce this Series to the mean value.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.mean()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.float64(2.0)
+>>> func(s_pl)
+2.0
+
+ +
+ +
+ +
+ + +

+ min() + +

+ + +
+ +

Get the minimal value in this Series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.min()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.int64(1)
+>>> func(s_pl)
+1
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Create a new Series that shows the null counts per column.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, None, 3])
+>>> s_pl = pl.Series([1, None, None])
+
+

Let's define a dataframe-agnostic function that returns the null count of +the series:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.null_count()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+1
+>>> func(s_pl)
+2
+
+ +
+ +
+ +
+ + +

+ n_unique() + +

+ + +
+ +

Count the number of unique values.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.n_unique()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+3
+>>> func(s_pl)
+3
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> s_pd = pd.Series([1, 2, 3, 4])
+>>> s_pl = pl.Series([1, 2, 3, 4])
+
+

Lets define a function to pipe into

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.pipe(lambda x: x + 2)
+
+

Now apply it to the series

+ 
>>> func(s_pd)
+0    3
+1    4
+2    5
+3    6
+dtype: int64
+>>> func(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   3
+   4
+   5
+   6
+]
+
+ +
+ +
+ +
+ + +

+ quantile(quantile, interpolation) + +

+ + +
+ +

Get quantile value of the series.

+ + +
+ Note +

pandas and Polars may have implementation differences for a given interpolation method.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
quantile + +
+

float +Quantile between 0.0 and 1.0.

+
+ 		+ required +
interpolation + +
+

{'nearest', 'higher', 'lower', 'midpoint', 'linear'} +Interpolation method.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = list(range(50))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return [
+...         s.quantile(quantile=q, interpolation="nearest")
+...         for q in (0.1, 0.25, 0.5, 0.75, 0.9)
+...     ]
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+[5, 12, 24, 37, 44]
+
+ 
>>> func(s_pl)
+[5.0, 12.0, 25.0, 37.0, 44.0]
+
+ +
+ +
+ +
+ + +

+ round(decimals=0) + +

+ + +
+ +

Round underlying floating point data by decimals digits.

+

Arguments + decimals: Number of decimals to round by.

+ + +
+ Notes +

For values exactly halfway between rounded decimal values pandas behaves differently than Polars and Arrow.

+

pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, 3.5 and +4.5 to 4.0, etc..).

+

Polars and Arrow round away from 0 (e.g. -0.5 to -1.0, 0.5 to 1.0, 1.5 to 2.0, 2.5 to 3.0, etc..).

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = [1.12345, 2.56789, 3.901234]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

Let's define a dataframe-agnostic function that rounds to the first decimal:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.round(1)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1.1
+1    2.6
+2    3.9
+dtype: float64
+
+ 
>>> func(s_pl)
+shape: (3,)
+Series: '' [f64]
+[
+   1.1
+   2.6
+   3.9
+]
+
+ +
+ +
+ +
+ + +

+ sample(n=None, fraction=None, *, with_replacement=False) + +

+ + +
+ +

Sample randomly from this Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int | None + +
+

Number of items to return. Cannot be used with fraction.

+
+ 		+ None +
fraction + float | None + +
+

Fraction of items to return. Cannot be used with n.

+
+ 		+ None +
with_replacement + bool + +
+

Allow values to be sampled more than once.

+
+ 		+ False +
+ + +
+ Notes +

The sample method returns a Series with a specified number of +randomly selected items chosen from this Series. +The results are not consistent across libraries.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+
+ 
>>> s_pd = pd.Series([1, 2, 3, 4])
+>>> s_pl = pl.Series([1, 2, 3, 4])
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.sample(fraction=1.0, with_replacement=True)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+   a
+2  3
+1  2
+3  4
+3  4
+>>> func(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   1
+   4
+   3
+   4
+]
+
+ +
+ +
+ +
+ + +

+ shift(n) + +

+ + +
+ +

Shift values by n positions.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of indices to shift forward. If a negative value is passed, +values are shifted in the opposite direction instead.

+
+ 		+ required +
+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to shift +and fill missing values with 0 in a Int64 column, you could +do:

+
s.shift(1).fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [2, 4, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.shift(1)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    NaN
+1    2.0
+2    4.0
+dtype: float64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   null
+   2
+   4
+]
+
+ +
+ +
+ +
+ + +

+ sort(*, descending=False, nulls_last=False) + +

+ + +
+ +

Sort this Series. Place null values first.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
descending + bool + +
+

Sort in descending order.

+
+ 		+ False +
nulls_last + bool + +
+

Place null values last instead of first.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [5, None, 1, 2]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define library agnostic functions:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.sort()
+
+ 
>>> @nw.narwhalify
+... def func_descend(s):
+...     return s.sort(descending=True)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+1    NaN
+2    1.0
+3    2.0
+0    5.0
+dtype: float64
+>>> func(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   null
+   1
+   2
+   5
+]
+>>> func_descend(s_pd)
+1    NaN
+0    5.0
+3    2.0
+2    1.0
+dtype: float64
+>>> func_descend(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   null
+   5
+   2
+   1
+]
+
+ +
+ +
+ +
+ + +

+ std(*, ddof=1) + +

+ + +
+ +

Get the standard deviation of this Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
ddof + int + +
+

“Delta Degrees of Freedom”: the divisor used in the calculation is N - ddof, + where N represents the number of elements.

+
+ 		+ 1 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.std()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.float64(1.0)
+>>> func(s_pl)
+1.0
+
+ +
+ +
+ +
+ + +

+ sum() + +

+ + +
+ +

Reduce this Series to the sum value.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.sum()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+np.int64(6)
+>>> func(s_pl)
+6
+
+ +
+ +
+ +
+ + +

+ tail(n=10) + +

+ + +
+ +

Get the last n rows.

+

Arguments + n : int + Number of rows to return.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = list(range(10))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

Let's define a dataframe-agnostic function that returns the last 3 rows:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.tail(3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+7    7
+8    8
+9    9
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   7
+   8
+   9
+]
+
+ +
+ +
+ +
+ + +

+ to_arrow() + +

+ + +
+ +

Convert to arrow.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = [1, 2, 3, 4]
+>>> s_pd = pd.Series(name="a", data=data)
+>>> s_pl = pl.Series(name="a", values=data)
+
+

Let's define a dataframe-agnostic function that converts to arrow:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.to_arrow()
+
+ 
>>> func(s_pd)
+<pyarrow.lib.Int64Array object at ...>
+[
+    1,
+    2,
+    3,
+    4
+]
+
+ 
>>> func(s_pl)
+<pyarrow.lib.Int64Array object at ...>
+[
+    1,
+    2,
+    3,
+    4
+]
+
+ +
+ +
+ +
+ + +

+ to_dummies(*, separator='_', drop_first=False) + +

+ + +
+ +

Get dummy/indicator variables.

+

Arguments + separator: Separator/delimiter used when generating column names. + drop_first: Remove the first category from the variable being encoded.

+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+
+

Let's define a dataframe-agnostic function that rounds to the first decimal:

+ 
>>> @nw.narwhalify
+... def func(s, drop_first: bool = False):
+...     return s.to_dummies(drop_first=drop_first)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+   a_1  a_2  a_3
+0    1    0    0
+1    0    1    0
+2    0    0    1
+
+ 
>>> func(s_pd, drop_first=True)
+   a_2  a_3
+0    0    0
+1    1    0
+2    0    1
+
+ 
>>> func(s_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ a_1 ┆ a_2 ┆ a_3 │
+│ --- ┆ --- ┆ --- │
+│ u8  ┆ u8  ┆ u8  │
+╞═════╪═════╪═════╡
+│ 1   ┆ 0   ┆ 0   │
+│ 0   ┆ 1   ┆ 0   │
+│ 0   ┆ 0   ┆ 1   │
+└─────┴─────┴─────┘
+>>> func(s_pl, drop_first=True)
+shape: (3, 2)
+┌─────┬─────┐
+│ a_2 ┆ a_3 │
+│ --- ┆ --- │
+│ u8  ┆ u8  │
+╞═════╪═════╡
+│ 0   ┆ 0   │
+│ 1   ┆ 0   │
+│ 0   ┆ 1   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ to_frame() + +

+ + +
+ +

Convert to dataframe.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="a")
+>>> s_pl = pl.Series("a", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.to_frame()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+   a
+0  1
+1  2
+2  3
+>>> func(s_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ to_list() + +

+ + +
+ +

Convert to list.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="a")
+>>> s_pl = pl.Series("a", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.to_list()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+[1, 2, 3]
+>>> func(s_pl)
+[1, 2, 3]
+
+ +
+ +
+ +
+ + +

+ to_numpy() + +

+ + +
+ +

Convert to numpy.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="a")
+>>> s_pl = pl.Series("a", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.to_numpy()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+array([1, 2, 3]...)
+>>> func(s_pl)
+array([1, 2, 3]...)
+
+ +
+ +
+ +
+ + +

+ to_pandas() + +

+ + +
+ +

Convert to pandas.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [1, 2, 3]
+>>> s_pd = pd.Series(s, name="a")
+>>> s_pl = pl.Series("a", s)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.to_pandas()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1
+1    2
+2    3
+Name: a, dtype: int64
+>>> func(s_pl)
+0    1
+1    2
+2    3
+Name: a, dtype: int64
+
+ +
+ +
+ +
+ + +

+ unique() + +

+ + +
+ +

Returns unique values

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> s = [2, 4, 4, 6]
+>>> s_pd = pd.Series(s)
+>>> s_pl = pl.Series(s)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.unique()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2
+1    4
+2    6
+dtype: int64
+>>> func(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   4
+   6
+]
+
+ +
+ +
+ +
+ + +

+ value_counts(*, sort=False, parallel=False, name=None, normalize=False) + +

+ + +
+ +

Count the occurrences of unique values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
sort + bool + +
+

Sort the output by count in descending order. If set to False (default), +the order of the output is random.

+
+ 		+ False +
parallel + bool + +
+

Execute the computation in parallel. Used for Polars only.

+
+ 		+ False +
name + str | None + +
+

Give the resulting count column a specific name; if normalize is True +defaults to "proportion", otherwise defaults to "count".

+
+ 		+ None +
normalize + bool + +
+

If true gives relative frequencies of the unique values

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s_pd = pd.Series([1, 1, 2, 3, 2], name="s")
+>>> s_pl = pl.Series(values=[1, 1, 2, 3, 2], name="s")
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.value_counts(sort=True)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+   s  count
+0  1      2
+1  2      2
+2  3      1
+
+ 
>>> func(s_pl)
+shape: (3, 2)
+┌─────┬───────┐
+│ s   ┆ count │
+│ --- ┆ ---   │
+│ i64 ┆ u32   │
+╞═════╪═══════╡
+│ 1   ┆ 2     │
+│ 2   ┆ 2     │
+│ 3   ┆ 1     │
+└─────┴───────┘
+
+ +
+ +
+ +
+ + +

+ zip_with(mask, other) + +

+ + +
+ +

Take values from self or other based on the given mask.

+

Where mask evaluates true, take values from self. Where mask evaluates false, +take values from other.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
mask + Self + +
+

Boolean Series

+
+ 		+ required +
other + Self + +
+

Series of same type.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> s1_pl = pl.Series([1, 2, 3, 4, 5])
+>>> s2_pl = pl.Series([5, 4, 3, 2, 1])
+>>> mask_pl = pl.Series([True, False, True, False, True])
+>>> s1_pd = pd.Series([1, 2, 3, 4, 5])
+>>> s2_pd = pd.Series([5, 4, 3, 2, 1])
+>>> mask_pd = pd.Series([True, False, True, False, True])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s1_any, mask_any, s2_any):
+...     return s1_any.zip_with(mask_any, s2_any)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s1_pl, mask_pl, s2_pl)
+shape: (5,)
+Series: '' [i64]
+[
+   1
+   4
+   3
+   2
+   5
+]
+>>> func(s1_pd, mask_pd, s2_pd)
+0    1
+1    4
+2    3
+3    2
+4    5
+dtype: int64
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_cat/index.html b/api-reference/series_cat/index.html new file mode 100644 index 0000000000..0df54f41c9 --- /dev/null +++ b/api-reference/series_cat/index.html @@ -0,0 +1,1488 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.cat - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.cat

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ get_categories() + +

+ + +
+ +

Get unique categories from column.

+ + +

Examples:

+

Let's create some series:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["apple", "mango", "mango"]
+>>> s_pd = pd.Series(data, dtype="category")
+>>> s_pl = pl.Series(data, dtype=pl.Categorical)
+
+

We define a dataframe-agnostic function to get unique categories +from column 'fruits':

+ 
>>> @nw.narwhalify(series_only=True)
+... def func(s):
+...     return s.cat.get_categories()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    apple
+1    mango
+dtype: object
+>>> func(s_pl)
+shape: (2,)
+Series: '' [str]
+[
+   "apple"
+   "mango"
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_dt/index.html b/api-reference/series_dt/index.html new file mode 100644 index 0000000000..9058fac5ae --- /dev/null +++ b/api-reference/series_dt/index.html @@ -0,0 +1,2645 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.dt - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.dt

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ date() + +

+ + +
+ +

Get the date in a datetime series.

+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ NotImplementedError + +
+

If pandas default backend is being used.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2012, 1, 7, 10, 20), datetime(2023, 3, 10, 11, 32)]
+>>> s_pd = pd.Series(dates).convert_dtypes(
+...     dtype_backend="pyarrow"
+... )
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.date()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2012-01-07
+1    2023-03-10
+dtype: date32[day][pyarrow]
+
+ 
>>> func(s_pl)
+shape: (2,)
+Series: '' [date]
+[
+   2012-01-07
+   2023-03-10
+]
+
+ +
+ +
+ +
+ + +

+ year() + +

+ + +
+ +

Get the year in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2012, 1, 7), datetime(2023, 3, 10)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.year()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2012
+1    2023
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i32]
+[
+   2012
+   2023
+]
+
+ +
+ +
+ +
+ + +

+ month() + +

+ + +
+ +

Gets the month in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2023, 2, 1), datetime(2023, 8, 3)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.month()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2
+1    8
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   2
+   8
+]
+
+ +
+ +
+ +
+ + +

+ day() + +

+ + +
+ +

Extracts the day in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2022, 1, 1), datetime(2022, 1, 5)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.day()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    1
+1    5
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   1
+   5
+]
+
+ +
+ +
+ +
+ + +

+ ordinal_day() + +

+ + +
+ +

Get ordinal day.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> data = [datetime(2020, 1, 1), datetime(2020, 8, 3)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.ordinal_day()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0      1
+1    216
+dtype: int32
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i16]
+[
+   1
+   216
+]
+
+ +
+ +
+ +
+ + +

+ hour() + +

+ + +
+ +

Extracts the hour in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2022, 1, 1, 5, 3), datetime(2022, 1, 5, 9, 12)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.hour()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    5
+1    9
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   5
+   9
+]
+
+ +
+ +
+ +
+ + +

+ minute() + +

+ + +
+ +

Extracts the minute in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2022, 1, 1, 5, 3), datetime(2022, 1, 5, 9, 12)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.minute()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0     3
+1    12
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   3
+   12
+]
+
+ +
+ +
+ +
+ + +

+ second() + +

+ + +
+ +

Extracts the second(s) in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [datetime(2022, 1, 1, 5, 3, 10), datetime(2022, 1, 5, 9, 12, 4)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.second()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    10
+1     4
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   10
+    4
+]
+
+ +
+ +
+ +
+ + +

+ millisecond() + +

+ + +
+ +

Extracts the milliseconds in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [
+...     datetime(2023, 5, 21, 12, 55, 10, 400000),
+...     datetime(2023, 5, 21, 12, 55, 10, 600000),
+...     datetime(2023, 5, 21, 12, 55, 10, 800000),
+...     datetime(2023, 5, 21, 12, 55, 11, 0),
+...     datetime(2023, 5, 21, 12, 55, 11, 200000),
+... ]
+
+ 
>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.millisecond().alias("datetime")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    400
+1    600
+2    800
+3      0
+4    200
+Name: datetime, dtype: int...
+>>> func(s_pl)
+shape: (5,)
+Series: 'datetime' [i32]
+[
+    400
+    600
+    800
+    0
+    200
+]
+
+ +
+ +
+ +
+ + +

+ microsecond() + +

+ + +
+ +

Extracts the microseconds in a datetime series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [
+...     datetime(2023, 5, 21, 12, 55, 10, 400000),
+...     datetime(2023, 5, 21, 12, 55, 10, 600000),
+...     datetime(2023, 5, 21, 12, 55, 10, 800000),
+...     datetime(2023, 5, 21, 12, 55, 11, 0),
+...     datetime(2023, 5, 21, 12, 55, 11, 200000),
+... ]
+
+ 
>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.microsecond().alias("datetime")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    400000
+1    600000
+2    800000
+3         0
+4    200000
+Name: datetime, dtype: int...
+>>> func(s_pl)
+shape: (5,)
+Series: 'datetime' [i32]
+[
+   400000
+   600000
+   800000
+   0
+   200000
+]
+
+ +
+ +
+ +
+ + +

+ nanosecond() + +

+ + +
+ +

Extracts the nanosecond(s) in a date series.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import datetime
+>>> import narwhals as nw
+>>> dates = [
+...     datetime(2022, 1, 1, 5, 3, 10, 500000),
+...     datetime(2022, 1, 5, 9, 12, 4, 60000),
+... ]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.nanosecond()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    500000000
+1     60000000
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i32]
+[
+   500000000
+   60000000
+]
+
+ +
+ +
+ +
+ + +

+ total_minutes() + +

+ + +
+ +

Get total minutes.

+ + +
+ Notes +

The function outputs the total minutes in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.total_minutes()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    10
+1    20
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ +
+ +
+ +
+ + +

+ total_seconds() + +

+ + +
+ +

Get total seconds.

+ + +
+ Notes +

The function outputs the total seconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.total_seconds()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    10
+1    20
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ +
+ +
+ +
+ + +

+ total_milliseconds() + +

+ + +
+ +

Get total milliseconds.

+ + +
+ Notes +

The function outputs the total milliseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = [
+...     timedelta(milliseconds=10),
+...     timedelta(milliseconds=20, microseconds=40),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.total_milliseconds()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    10
+1    20
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ +
+ +
+ +
+ + +

+ total_microseconds() + +

+ + +
+ +

Get total microseconds.

+ + +
+ Notes +

The function outputs the total microseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = [
+...     timedelta(microseconds=10),
+...     timedelta(milliseconds=1, microseconds=200),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.total_microseconds()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0      10
+1    1200
+dtype: int...
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        1200
+]
+
+ +
+ +
+ +
+ + +

+ total_nanoseconds() + +

+ + +
+ +

Get total nanoseconds.

+ + +
+ Notes +

The function outputs the total nanoseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> from datetime import timedelta
+>>> import narwhals as nw
+>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
+>>> s_pd = pd.to_datetime(pd.Series(data))
+>>> s_pl = pl.Series(data).str.to_datetime(time_unit="ns")
+
+

We define a library agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.diff().dt.total_nanoseconds()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    NaN
+1    1.0
+dtype: float64
+>>> func(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        null
+        1
+]
+
+ +
+ +
+ +
+ + +

+ to_string(format) + +

+ + +
+ +

Convert a Date/Time/Datetime series into a String series with the given format.

+ + +
+ Notes +

Unfortunately, different libraries interpret format directives a bit +differently.

+
    +
  • Chrono, the library used by Polars, uses "%.f" for fractional seconds, + whereas pandas and Python stdlib use ".%f".
    • +
  • PyArrow interprets "%S" as "seconds, including fractional seconds" + whereas most other tools interpret it as "just seconds, as 2 digits".
    • +
+

Therefore, we make the following adjustments:

+
    +
  • for pandas-like libraries, we replace "%S.%f" with "%S%.f".
    • +
  • for PyArrow, we replace "%S.%f" with "%S".
    • +
+

Workarounds like these don't make us happy, and we try to avoid them as +much as possible, but here we feel like it's the best compromise.

+

If you just want to format a date/datetime Series as a local datetime +string, and have it work as consistently as possible across libraries, +we suggest using:

+
    +
  • "%Y-%m-%dT%H:%M:%S%.f" for datetimes
    • +
  • "%Y-%m-%d" for dates
    • +
+

though note that, even then, different tools may return a different number +of trailing zeros. Nonetheless, this is probably consistent enough for +most applications.

+

If you have an application where this is not enough, please open an issue +and let us know.

+
+ +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = [
+...     datetime(2020, 3, 1),
+...     datetime(2020, 4, 1),
+...     datetime(2020, 5, 1),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.dt.to_string("%Y/%m/%d")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    2020/03/01
+1    2020/04/01
+2    2020/05/01
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (3,)
+Series: '' [str]
+[
+   "2020/03/01"
+   "2020/04/01"
+   "2020/05/01"
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_str/index.html b/api-reference/series_str/index.html new file mode 100644 index 0000000000..e2b1bc262c --- /dev/null +++ b/api-reference/series_str/index.html @@ -0,0 +1,2402 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.str - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.str

+ + +
+ + + + +
+ + + + + +
+ + + + + + + + + +
+ + +

+ contains(pattern, *, literal=False) + +

+ + +
+ +

Check if string contains a substring that matches a pattern.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A Character sequence or valid regular expression pattern.

+
+ 		+ required +
literal + bool + +
+

If True, treats the pattern as a literal string. + If False, assumes the pattern is a regular expression.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> pets = ["cat", "dog", "rabbit and parrot", "dove", None]
+>>> s_pd = pd.Series(pets)
+>>> s_pl = pl.Series(pets)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.contains("parrot|dove")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1    False
+2     True
+3     True
+4     None
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+   true
+   null
+]
+
+ +
+ +
+ +
+ + +

+ ends_with(suffix) + +

+ + +
+ +

Check if string values end with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
suffix + str + +
+

suffix substring

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["apple", "mango", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.ends_with("ngo")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    False
+1     True
+2     None
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   true
+   null
+]
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Take the first n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of elements to take. Negative indexing is supported (see note (1.))

+
+ 		+ 5 +
+ + +
+ Notes +
    +
  1. When the n input is negative, head returns characters up to the n-th from the end of the string. + For example, if n = -3, then all characters except the last three are returned.
    2. +
  2. If the length of the string has fewer than n characters, the full string is returned.
    3. +
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> lyrics = ["Atatata", "taata", "taatatata", "zukkyun"]
+>>> s_pd = pd.Series(lyrics)
+>>> s_pl = pl.Series(lyrics)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.head()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    Atata
+1    taata
+2    taata
+3    zukky
+dtype: object
+>>> func(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   "Atata"
+   "taata"
+   "taata"
+   "zukky"
+]
+
+ +
+ +
+ +
+ + +

+ replace(pattern, value, *, literal=False, n=1) + +

+ + +
+ +

Replace first matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
value + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
literal + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
n + int + +
+

Number of matches to replace.

+
+ 		+ 1 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["123abc", "abc abc123"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     s = s.str.replace("abc", "")
+...     return s.to_list()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+['123', ' abc123']
+
+ 
>>> func(s_pl)
+['123', ' abc123']
+
+ +
+ +
+ +
+ + +

+ replace_all(pattern, value, *, literal=False) + +

+ + +
+ +

Replace all matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
pattern + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
value + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
literal + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["123abc", "abc abc123"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     s = s.str.replace_all("abc", "")
+...     return s.to_list()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+['123', ' 123']
+
+ 
>>> func(s_pl)
+['123', ' 123']
+
+ +
+ +
+ +
+ + +

+ slice(offset, length=None) + +

+ + +
+ +

Create subslices of the string values of a Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
offset + int + +
+

Start index. Negative indexing is supported.

+
+ 		+ required +
length + int | None + +
+

Length of the slice. If set to None (default), the slice is taken to the +end of the string.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["pear", None, "papaya", "dragonfruit"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.slice(4, length=3)
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0
+1    None
+2      ya
+3     onf
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   ""
+   null
+   "ya"
+   "onf"
+]
+
+

Using negative indexes:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.slice(-3)
+
+ 
>>> func(s_pd)
+0     ear
+1    None
+2     aya
+3     uit
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+    "ear"
+    null
+    "aya"
+    "uit"
+]
+
+ +
+ +
+ +
+ + +

+ starts_with(prefix) + +

+ + +
+ +

Check if string values start with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
prefix + str + +
+

prefix substring

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["apple", "mango", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.starts_with("app")
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0     True
+1    False
+2     None
+dtype: object
+
+ 
>>> func(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   true
+   false
+   null
+]
+
+ +
+ +
+ +
+ + +

+ strip_chars(characters=None) + +

+ + +
+ +

Remove leading and trailing characters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
characters + str | None + +
+

The set of characters to be removed. All combinations of this set of characters will be stripped from the start and end of the string. If set to None (default), all leading and trailing whitespace is removed instead.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["apple", "\nmango"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     s = s.str.strip_chars()
+...     return s.to_list()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+['apple', 'mango']
+
+ 
>>> func(s_pl)
+['apple', 'mango']
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Take the last n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
n + int + +
+

Number of elements to take. Negative indexing is supported (see note (1.))

+
+ 		+ 5 +
+ + +
+ Notes +
    +
  1. When the n input is negative, tail returns characters starting from the n-th from the beginning of + the string. For example, if n = -3, then all characters except the first three are returned.
    2. +
  2. If the length of the string has fewer than n characters, the full string is returned.
    3. +
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> lyrics = ["Atatata", "taata", "taatatata", "zukkyun"]
+>>> s_pd = pd.Series(lyrics)
+>>> s_pl = pl.Series(lyrics)
+
+

We define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return s.str.tail()
+
+

We can then pass either pandas or Polars to func:

+ 
>>> func(s_pd)
+0    atata
+1    taata
+2    atata
+3    kkyun
+dtype: object
+>>> func(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   "atata"
+   "taata"
+   "atata"
+   "kkyun"
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/api-reference/typing/index.html b/api-reference/typing/index.html new file mode 100644 index 0000000000..51842a469b --- /dev/null +++ b/api-reference/typing/index.html @@ -0,0 +1,1644 @@ + + + + + + + + + + + + + + + + + + + + + narwhals.typing - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ +
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+ + + + + + + +

narwhals.typing

+

Narwhals comes fully statically typed. In addition to nw.DataFrame, nw.Expr, +nw.Series, nw.LazyFrame, we also provide the following type hints:

+

DataFrameT

+

A TypeVar bound to nw.DataFrame. Use this when you have a function which +accepts a nw.DataFrame and returns a nw.DataFrame backed by the same backend, for example:

+
import narwhals as nw
+from narwhals.typing import DataFrameT
+
+
+@nw.narwhalify
+def func(df: DataFrameT) -> DataFrameT:
+    return df.with_columns(c=df["a"] + 1)
+
+

Frame

+

Either a nw.DataFrame or nw.LazyFrame. Use this if your function can work on +either and your function doesn't care about its backend, for example:

+
import narwhals as nw
+from narwhals.typing import Frame
+
+
+@nw.narwhalify
+def func(df: Frame) -> list[str]:
+    return df.columns
+
+

FrameT

+

A TypeVar bound to Frame. Use this if your function accepts either nw.DataFrame +or nw.LazyFrame and returns an object backed by the same backend, for example:

+
import narwhals as nw
+from narwhals.typing import FrameT
+
+
+@nw.narwhalify
+def func(df: FrameT) -> FrameT:
+    return df.with_columns(c=nw.col("a") + 1)
+
+

IntoDataFrame

+

An object which can be converted to nw.DataFrame (e.g. pd.DataFrame, pl.DataFrame). +Use this if your function accepts a narwhalifiable object but doesn't care about its backend:

+
from __future__ import annotations
+
+import narwhals as nw
+from narwhals.typing import IntoDataFrame
+
+
+def func(df_native: IntoDataFrame) -> tuple[int, int]:
+    df = nw.from_native(df_native, eager_only=True)
+    return df.shape
+
+

IntoDataFrameT

+

A TypeVar bound to IntoDataFrame. Use this if your function accepts +a function which can be converted to nw.DataFrame and returns an object of the same +class:

+
import narwhals as nw
+from narwhals.typing import IntoDataFrameT
+
+
+def func(df_native: IntoDataFrameT) -> IntoDataFrameT:
+    df = nw.from_native(df_native, eager_only=True)
+    return nw.to_native(df.with_columns(c=df["a"] + 1))
+
+

IntoExpr

+

Use this to mean "either a Narwhals expression, or something +which can be converted into one". For example, exprs in DataFrame.select is +typed to accept IntoExpr, as it can either accept a nw.Expr (e.g. df.select(nw.col('a'))) +or a string which will be interpreted as a nw.Expr, e.g. df.select('a').

+

IntoFrame

+

An object which can be converted to nw.DataFrame or nw.LazyFrame +(e.g. pd.DataFrame, pl.DataFrame, pl.LazyFrame). Use this if your function can accept +an object which can be converted to either nw.DataFrame or nw.LazyFrame and it doesn't +care about its backend:

+
import narwhals as nw
+from narwhals.typing import IntoFrame
+
+
+def func(df_native: IntoFrame) -> list[str]:
+    df = nw.from_native(df_native)
+    return df.columns
+
+

IntoFrameT

+

A TypeVar bound to IntoFrame. Use this if your function accepts an +object which is convertible to nw.DataFrame or nw.LazyFrame and returns an object +of the same type:

+
import narwhals as nw
+from narwhals.typing import IntoFrameT
+
+
+def func(df_native: IntoFrameT) -> IntoFrameT:
+    df = nw.from_native(df_native)
+    return nw.to_native(df.with_columns(c=nw.col("a") + 1))
+
+

nw.narwhalify, or nw.from_native?

+

Although the former is more readable, the latter is better at preserving type hints.

+

Here's an example: +

import polars as pl
+import narwhals as nw
+from narwhals.typing import IntoDataFrameT, DataFrameT
+
+df = pl.DataFrame({"a": [1, 2, 3]})
+
+
+def func(df: IntoDataFrameT) -> IntoDataFrameT:
+    df = nw.from_native(df, eager_only=True)
+    return nw.to_native(df.select(b=nw.col("a")))
+
+
+reveal_type(func(df))
+
+
+@nw.narwhalify(strict=True)
+def func_2(df: DataFrameT) -> DataFrameT:
+    return df.select(b=nw.col("a"))
+
+
+reveal_type(func_2(df))
+

+

Running mypy on it gives: +

$ mypy f.py 
+f.py:11: note: Revealed type is "polars.dataframe.frame.DataFrame"
+f.py:17: note: Revealed type is "Any"
+Success: no issues found in 1 source file
+

+

In the first case, mypy can infer that df is a polars.DataFrame. In the second case, it can't.

+

If you want to make the most out of type hints and preserve them as much as possible, we recommend +nw.from_native and nw.to_native - otherwise, nw.narwhalify. Type hints will still be respected +inside the function body if you type the arguments.

+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 0000000000..85449ec798 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,119 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Backward-compatibility: docstring section titles in bold. */ +.doc-section-title { + font-weight: bold; +} + +/* Symbols in Navigation and ToC. */ +:root, +[data-md-color-scheme="default"] { + --doc-symbol-attribute-fg-color: #953800; + --doc-symbol-function-fg-color: #8250df; + --doc-symbol-method-fg-color: #8250df; + --doc-symbol-class-fg-color: #0550ae; + --doc-symbol-module-fg-color: #5cad0f; + + --doc-symbol-attribute-bg-color: #9538001a; + --doc-symbol-function-bg-color: #8250df1a; + --doc-symbol-method-bg-color: #8250df1a; + --doc-symbol-class-bg-color: #0550ae1a; + --doc-symbol-module-bg-color: #5cad0f1a; +} + +[data-md-color-scheme="slate"] { + --doc-symbol-attribute-fg-color: #ffa657; + --doc-symbol-function-fg-color: #d2a8ff; + --doc-symbol-method-fg-color: #d2a8ff; + --doc-symbol-class-fg-color: #79c0ff; + --doc-symbol-module-fg-color: #baff79; + + --doc-symbol-attribute-bg-color: #ffa6571a; + --doc-symbol-function-bg-color: #d2a8ff1a; + --doc-symbol-method-bg-color: #d2a8ff1a; + --doc-symbol-class-bg-color: #79c0ff1a; + --doc-symbol-module-bg-color: #baff791a; +} + +code.doc-symbol { + border-radius: .1rem; + font-size: .85em; + padding: 0 .3em; + font-weight: bold; +} + +code.doc-symbol-attribute { + color: var(--doc-symbol-attribute-fg-color); + background-color: var(--doc-symbol-attribute-bg-color); +} + +code.doc-symbol-attribute::after { + content: "attr"; +} + +code.doc-symbol-function { + color: var(--doc-symbol-function-fg-color); + background-color: var(--doc-symbol-function-bg-color); +} + +code.doc-symbol-function::after { + content: "func"; +} + +code.doc-symbol-method { + color: var(--doc-symbol-method-fg-color); + background-color: var(--doc-symbol-method-bg-color); +} + +code.doc-symbol-method::after { + content: "meth"; +} + +code.doc-symbol-class { + color: var(--doc-symbol-class-fg-color); + background-color: var(--doc-symbol-class-bg-color); +} + +code.doc-symbol-class::after { + content: "class"; +} + +code.doc-symbol-module { + color: var(--doc-symbol-module-fg-color); + background-color: var(--doc-symbol-module-bg-color); +} + +code.doc-symbol-module::after { + content: "mod"; +} + +.doc-signature .autorefs { + color: inherit; + border-bottom: 1px dotted currentcolor; +} diff --git a/assets/image.png b/assets/image.png new file mode 100644 index 0000000000..578bd24213 Binary files /dev/null and b/assets/image.png differ diff --git a/assets/images/favicon.png b/assets/images/favicon.png new file mode 100644 index 0000000000..1cf13b9f9d Binary files /dev/null and b/assets/images/favicon.png differ diff --git a/assets/javascripts/bundle.471ce7a9.min.js b/assets/javascripts/bundle.471ce7a9.min.js new file mode 100644 index 0000000000..90d2d95af9 --- /dev/null +++ b/assets/javascripts/bundle.471ce7a9.min.js @@ -0,0 +1,29 @@ +"use strict";(()=>{var ji=Object.create;var gr=Object.defineProperty;var Wi=Object.getOwnPropertyDescriptor;var Ui=Object.getOwnPropertyNames,Vt=Object.getOwnPropertySymbols,Di=Object.getPrototypeOf,xr=Object.prototype.hasOwnProperty,io=Object.prototype.propertyIsEnumerable;var no=(e,t,r)=>t in e?gr(e,t,{enumerable:!0,configurable:!0,writable:!0,value:r}):e[t]=r,$=(e,t)=>{for(var r 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o!="undefined"?Ne(o):I({x:0,y:0}),i=O(et(t),$t(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ve(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function qn(e){let t=e.title;if(!t.length)return S;let r=`__tooltip_${Ya++}`,o=Rt(r,"inline"),n=R(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return 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I(...e).pipe(oe(o=>d(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Gn(e){let t=P("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Pt("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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p=o.pipe(E(()=>s.innerHTML=""),v(({items:l})=>O(I(...l.slice(0,10)),I(...l.slice(10)).pipe(Be(4),Vr(n),v(([f])=>f)))),m(Mn),pe());return p.subscribe(l=>s.appendChild(l)),p.pipe(oe(l=>{let f=fe("details",l);return typeof f=="undefined"?S:d(f,"toggle").pipe(U(o),m(()=>f))})).subscribe(l=>{l.open===!1&&l.offsetTop<=i.scrollTop&&i.scrollTo({top:l.offsetTop})}),t.pipe(b(dr),m(({data:l})=>l)).pipe(E(l=>o.next(l)),L(()=>o.complete()),m(l=>$({ref:e},l)))}function as(e,{query$:t}){return t.pipe(m(({value:r})=>{let o=xe();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function pi(e,t){let r=new g,o=r.pipe(X(),ne(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),d(e,"click").pipe(U(o)).subscribe(n=>n.preventDefault()),as(e,t).pipe(E(n=>r.next(n)),L(()=>r.complete()),m(n=>$({ref:e},n)))}function li(e,{worker$:t,keyboard$:r}){let o=new g,n=Se("search-query"),i=O(d(n,"keydown"),d(n,"focus")).pipe(ve(se),m(()=>n.value),K());return o.pipe(Ue(i),m(([{suggest:s},p])=>{let c=p.split(/([\s-]+)/);if(s!=null&&s.length&&c[c.length-1]){let l=s[s.length-1];l.startsWith(c[c.length-1])&&(c[c.length-1]=l)}else c.length=0;return c})).subscribe(s=>e.innerHTML=s.join("").replace(/\s/g," ")),r.pipe(b(({mode:s})=>s==="search")).subscribe(s=>{switch(s.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(b(dr),m(({data:s})=>s)).pipe(E(s=>o.next(s)),L(()=>o.complete()),m(()=>({ref:e})))}function mi(e,{index$:t,keyboard$:r}){let o=ye();try{let n=ii(o.search,t),i=Se("search-query",e),a=Se("search-result",e);d(e,"click").pipe(b(({target:p})=>p instanceof Element&&!!p.closest("a"))).subscribe(()=>Je("search",!1)),r.pipe(b(({mode:p})=>p==="search")).subscribe(p=>{let c=Re();switch(p.type){case"Enter":if(c===i){let l=new Map;for(let f of P(":first-child [href]",a)){let 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z([t,r.pipe(Q(xe()),b(o=>!!o.searchParams.get("h")))]).pipe(m(([o,n])=>ni(o.config)(n.searchParams.get("h"))),m(o=>{var a;let n=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let s=i.nextNode();s;s=i.nextNode())if((a=s.parentElement)!=null&&a.offsetHeight){let p=s.textContent,c=o(p);c.length>p.length&&n.set(s,c)}for(let[s,p]of n){let{childNodes:c}=x("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function ss(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return z([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Xr(e,o){var n=o,{header$:t}=n,r=ao(n,["header$"]);let i=R(".md-sidebar__scrollwrap",e),{y:a}=Ve(i);return C(()=>{let s=new g,p=s.pipe(X(),ne(!0)),c=s.pipe(Le(0,me));return 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r=`https://api.github.com/users/${e}`;return Ie(r).pipe(m(o=>({repositories:o.public_repos})),De({}))}}function di(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return st(Ie(`${r}/releases/permalink/latest`).pipe(de(()=>S),m(({tag_name:o})=>({version:o})),De({})),Ie(r).pipe(de(()=>S),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),De({}))).pipe(m(([o,n])=>$($({},o),n)))}function hi(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return ui(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return di(r,o)}return S}var cs;function ps(e){return cs||(cs=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return S}return hi(e.href).pipe(E(o=>__md_set("__source",o,sessionStorage)))}).pipe(de(()=>S),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),G(1)))}function bi(e){let t=R(":scope > :last-child",e);return C(()=>{let r=new g;return 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n=t.pipe(m(({offset:{y:a}})=>a),Be(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return z([i,n]).pipe(m(([a,s])=>!(a&&s)),K(),U(o.pipe(Ce(1))),ne(!0),ct({delay:250}),m(a=>({hidden:a})))}function xi(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(X(),ne(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(U(a),Z("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),d(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),fs(e,{viewport$:t,main$:o,target$:n}).pipe(E(s=>i.next(s)),L(()=>i.complete()),m(s=>$({ref:e},s)))}function yi({document$:e,viewport$:t}){e.pipe(v(()=>P(".md-ellipsis")),oe(r=>tt(r).pipe(U(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?mt(n,{viewport$:t}).pipe(U(e.pipe(Ce(1))),L(()=>n.removeAttribute("title"))):S})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>P(".md-status")),oe(r=>mt(r,{viewport$:t}))).subscribe()}function Ei({document$:e,tablet$:t}){e.pipe(v(()=>P(".md-toggle--indeterminate")),E(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>d(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),ee(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function us(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function wi({document$:e}){e.pipe(v(()=>P("[data-md-scrollfix]")),E(t=>t.removeAttribute("data-md-scrollfix")),b(us),oe(t=>d(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Ti({viewport$:e,tablet$:t}){z([ze("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),ee(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let n=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",n&&window.scrollTo(0,n)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let o=e[r];typeof 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t!="undefined"&<(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&<(r);break;case"Enter":let o=Re();o instanceof HTMLLabelElement&&o.click()}});yi({viewport$:Oe,document$:ot});Ei({document$:ot,tablet$:hr});wi({document$:ot});Ti({viewport$:Oe,tablet$:hr});var rt=Qn(Se("header"),{viewport$:Oe}),jt=ot.pipe(m(()=>Se("main")),v(e=>Bn(e,{viewport$:Oe,header$:rt})),G(1)),hs=O(...ae("consent").map(e=>yn(e,{target$:Mt})),...ae("dialog").map(e=>zn(e,{alert$:to})),...ae("header").map(e=>Kn(e,{viewport$:Oe,header$:rt,main$:jt})),...ae("palette").map(e=>Gn(e)),...ae("progress").map(e=>Jn(e,{progress$:ro})),...ae("search").map(e=>mi(e,{index$:Li,keyboard$:eo})),...ae("source").map(e=>bi(e))),bs=C(()=>O(...ae("announce").map(e=>xn(e)),...ae("content").map(e=>Nn(e,{viewport$:Oe,target$:Mt,print$:Mi})),...ae("content").map(e=>B("search.highlight")?fi(e,{index$:Li,location$:Wt}):S),...ae("header-title").map(e=>Yn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Oi,()=>Xr(e,{viewport$:Oe,header$:rt,main$:jt})):Nr(hr,()=>Xr(e,{viewport$:Oe,header$:rt,main$:jt}))),...ae("tabs").map(e=>vi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>gi(e,{viewport$:Oe,header$:rt,main$:jt,target$:Mt})),...ae("top").map(e=>xi(e,{viewport$:Oe,header$:rt,main$:jt,target$:Mt})))),_i=ot.pipe(v(()=>bs),Pe(hs),G(1));_i.subscribe();window.document$=ot;window.location$=Wt;window.target$=Mt;window.keyboard$=eo;window.viewport$=Oe;window.tablet$=hr;window.screen$=Oi;window.print$=Mi;window.alert$=to;window.progress$=ro;window.component$=_i;})(); 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"sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n __assign = Object.assign || function __assign(t) {\r\n for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n s = arguments[i];\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n }\r\n return t;\r\n }\r\n return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n var t = {};\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n t[p] = s[p];\r\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n t[p[i]] = s[p[i]];\r\n }\r\n return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n return new (P || (P = Promise))(function (resolve, reject) {\r\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n function verb(n) { return function (v) { return step([n, v]); }; }\r\n function step(op) {\r\n if (f) throw new TypeError(\"Generator is already executing.\");\r\n while (_) try {\r\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n if (y = 0, t) op = [op[0] & 2, t.value];\r\n switch (op[0]) {\r\n case 0: case 1: t = op; break;\r\n case 4: _.label++; return { value: op[1], done: false };\r\n case 5: _.label++; y = op[1]; op = [0]; continue;\r\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n default:\r\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n if (t[2]) _.ops.pop();\r\n _.trys.pop(); continue;\r\n }\r\n op = body.call(thisArg, _);\r\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n if (m) return m.call(o);\r\n if (o && typeof o.length === \"number\") return {\r\n next: function () {\r\n if (o && i >= o.length) o = void 0;\r\n return { value: o && o[i++], done: !o };\r\n }\r\n };\r\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n if (!m) return o;\r\n var i = m.call(o), r, ar = [], e;\r\n try {\r\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n }\r\n catch (error) { e = { error: error }; }\r\n finally {\r\n try {\r\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n }\r\n finally { if (e) throw e.error; }\r\n }\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n for (var ar = [], i = 0; i < arguments.length; i++)\r\n ar = ar.concat(__read(arguments[i]));\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n r[k] = a[j];\r\n return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n if (ar || !(i in from)) {\r\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n ar[i] = from[i];\r\n }\r\n }\r\n return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n function fulfill(value) { resume(\"next\", value); }\r\n function reject(value) { resume(\"throw\", value); }\r\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n var i, p;\r\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var m = o[Symbol.asyncIterator], i;\r\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n if (mod && mod.__esModule) return mod;\r\n var result = {};\r\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n __setModuleDefault(result, mod);\r\n return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an