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Added idxmin implementation (#34)
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@nipsn
nipsn authored Feb 13, 2024
1 parent bb1549a commit 7ef4142
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66 changes: 66 additions & 0 deletions docs/user-guide/advanced/Pandas_API.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2698,6 +2698,72 @@
"tab.max()"
]
},
{
"cell_type": "markdown",
"id": "d98b298c",
"metadata": {},
"source": [
"### Table.idxmin()\n",
"\n",
"```\n",
"Table.idxmax(axis=0, skipna=True, numeric_only=False)\n",
"```\n",
"\n",
"Return index of first occurrence of minimum over requested axis.\n",
"\n",
"**Parameters:**\n",
"\n",
"| Name | Type | Description | Default |\n",
"| :----------: | :--: | :------------------------------------------------------------------------------- | :-----: |\n",
"| axis | int | The axis to calculate the idxmin across 0 is columns, 1 is rows. | 0 |\n",
"| skipna | bool | Ignore any null values along the axis. | True |\n",
"| numeric_only | bool | Only use columns of the table that are of a numeric data type. | False |\n",
"\n",
"**Returns:**\n",
"\n",
"| Type | Description |\n",
"| :----------------: | :------------------------------------------------------------------- |\n",
"| Dictionary | A dictionary where the key represents the column name / row number and the values are the result of calling `idxmin` on that column / row. |"
]
},
{
"cell_type": "markdown",
"id": "143f5483",
"metadata": {},
"source": [
"**Examples:**\n",
"\n",
"Calculate the idxmin across the columns of a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da7cbf8f",
"metadata": {},
"outputs": [],
"source": [
"tab.idxmin()"
]
},
{
"cell_type": "markdown",
"id": "fb531e00",
"metadata": {},
"source": [
"Calculate the idxmin across the rows of a table using only columns thar are of a numeric data type"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9907226a",
"metadata": {},
"outputs": [],
"source": [
"tab.idxmin(axis=1, numeric_only=True)"
]
},
{
"cell_type": "markdown",
"id": "301ab2c2",
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26 changes: 19 additions & 7 deletions src/pykx/pandas_api/pandas_meta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ def preparse_computations(tab, axis=0, skipna=True, numeric_only=False, bool_onl
skipna,
axis
)
return (res, cols if axis == 0 else q.til(len(res)))
return (res, cols if axis == 0 else q.til(len(res)), cols)


# The simple computation functions all return a tuple of the results and the col names the results
Expand Down Expand Up @@ -259,33 +259,45 @@ def abs(self, numeric_only=False):

@convert_result
def all(self, axis=0, bool_only=False, skipna=True):
res, cols = preparse_computations(self, axis, skipna, bool_only=bool_only)
res, cols, _ = preparse_computations(self, axis, skipna, bool_only=bool_only)
return (q('{"b"$x}', [all(x) for x in res]), cols)

@convert_result
def any(self, axis=0, bool_only=False, skipna=True):
res, cols = preparse_computations(self, axis, skipna, bool_only=bool_only)
res, cols, _ = preparse_computations(self, axis, skipna, bool_only=bool_only)
return (q('{"b"$x}', [any(x) for x in res]), cols)

@convert_result
def max(self, axis=0, skipna=True, numeric_only=False):
res, cols = preparse_computations(self, axis, skipna, numeric_only)
res, cols, _ = preparse_computations(self, axis, skipna, numeric_only)
return (q(
'{[row] {$[11h=type x; {[x1; y1] $[x1 > y1; x1; y1]} over x; max x]} each row}',
res
), cols)

@convert_result
def min(self, axis=0, skipna=True, numeric_only=False):
res, cols = preparse_computations(self, axis, skipna, numeric_only)
res, cols, _ = preparse_computations(self, axis, skipna, numeric_only)
return (q(
'{[row] {$[11h=type x; {[x1; y1] $[x1 < y1; x1; y1]} over x; min x]} each row}',
res
), cols)

@convert_result
def idxmin(self, axis=0, skipna=True, numeric_only=False):
tab = self
axis = q('{$[11h~type x; `index`columns?x; x]}', axis)
res, cols, ix = preparse_computations(tab, axis, skipna, numeric_only)
return (q(
'''{[row;tab;axis]
row:{$[11h~type x; {[x1; y1] $[x1 < y1; x1; y1]} over x; min x]} each row;
m:$[0~axis; (::); flip] value flip tab;
$[0~axis; (::); cols tab] m {$[abs type y;x]?y}' row}
''', res, tab[ix], axis), cols)

@convert_result
def prod(self, axis=0, skipna=True, numeric_only=False, min_count=0):
res, cols = preparse_computations(self, axis, skipna, numeric_only)
res, cols, _ = preparse_computations(self, axis, skipna, numeric_only)
return (q(
'{[row; minc] {$[y > 0; $[y>count[x]; 0N; prd x]; prd x]}[;minc] each row}',
res,
Expand All @@ -294,7 +306,7 @@ def prod(self, axis=0, skipna=True, numeric_only=False, min_count=0):

@convert_result
def sum(self, axis=0, skipna=True, numeric_only=False, min_count=0):
res, cols = preparse_computations(self, axis, skipna, numeric_only)
res, cols, _ = preparse_computations(self, axis, skipna, numeric_only)
return (q(
'{[row; minc]'
'{$[y > 0;'
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23 changes: 23 additions & 0 deletions tests/test_pandas_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1964,6 +1964,29 @@ def test_pandas_max(q):
assert float(qmax[i]) == float(pmax[i])


def test_pandas_idxmin(q):
tab = q('([] sym: 100?`foo`bar`baz`qux; price: 250.0f - 100?500.0f; ints: 100 - 100?200)')
df = tab.pd()

p_m = df.idxmin()
q_m = tab.idxmin()
for c in q.key(q_m).py():
assert p_m[c] == q_m[c].py()

q_m = tab.idxmin(axis=1, numeric_only=True, skipna=True)
p_m = df.idxmin(axis=1, numeric_only=True, skipna=True)
for c in q.key(q_m).py():
assert p_m[c] == q_m[c].py()

tab = q('([]price: 250.0f - 100?500.0f; ints: 100 - 100?200)')
df = tab.pd()

q_m = tab.idxmin(axis=1)
p_m = df.idxmin(axis=1)
for c in q.key(q_m).py():
assert p_m[c] == q_m[c].py()


def test_pandas_all(q):
tab = q(
'([] sym: 100?`foo`bar`baz`qux; price: 250.0f - 100?500.0f; ints: 100 - 100?200;'
Expand Down

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