From 392fabd3bcfe5da6e3e86756172e3c308cb881cf Mon Sep 17 00:00:00 2001 From: Ishani Gupta Date: Tue, 29 Aug 2023 13:59:54 -0400 Subject: [PATCH] publishing pandas_1 --- .ipynb_checkpoints/_quarto-checkpoint.yml | 2 +- .../pandas_1/execute-results/html.json | 4 +- .../pandas_1/libs/bootstrap/bootstrap.min.css | 4 +- .quarto/xref/aaac88ef | 2 +- _quarto.yml | 2 +- ...nciples-and-Techniques-of-Data-Science.pdf | Bin 41871 -> 472655 bytes docs/index.html | 6 + docs/intro_lec/introduction.html | 10 + docs/pandas_1/images/df_elections.png | Bin 0 -> 155874 bytes docs/pandas_1/images/locgraphic.png | Bin 0 -> 78461 bytes docs/pandas_1/images/non-uniqueindex.png | Bin 0 -> 70135 bytes docs/pandas_1/images/uniqueindex.png | Bin 0 -> 73880 bytes {pandas_1 => docs/pandas_1}/pandas_1.html | 313 ++-- docs/search.json | 35 + pandas_1/pandas_1.ipynb | 1431 ----------------- 15 files changed, 271 insertions(+), 1538 deletions(-) create mode 100644 docs/pandas_1/images/df_elections.png create mode 100644 docs/pandas_1/images/locgraphic.png create mode 100644 docs/pandas_1/images/non-uniqueindex.png create mode 100644 docs/pandas_1/images/uniqueindex.png rename {pandas_1 => docs/pandas_1}/pandas_1.html (85%) delete mode 100644 pandas_1/pandas_1.ipynb diff --git a/.ipynb_checkpoints/_quarto-checkpoint.yml b/.ipynb_checkpoints/_quarto-checkpoint.yml index 27d73ac6..bb93146e 100644 --- a/.ipynb_checkpoints/_quarto-checkpoint.yml +++ b/.ipynb_checkpoints/_quarto-checkpoint.yml @@ -14,7 +14,7 @@ book: chapters: - index.md - intro_lec/introduction.qmd - # - pandas_1/pandas_1.qmd + - pandas_1/pandas_1.qmd # - pandas_2/pandas_2.qmd # - pandas_3/pandas_3.qmd # - eda/eda.qmd diff --git a/.quarto/_freeze/pandas_1/pandas_1/execute-results/html.json b/.quarto/_freeze/pandas_1/pandas_1/execute-results/html.json index 2b4606ab..96d15cb9 100644 --- a/.quarto/_freeze/pandas_1/pandas_1/execute-results/html.json +++ b/.quarto/_freeze/pandas_1/pandas_1/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "6656801baf458cf577896c4da88ac925", + "hash": "d49596f0b82372ac8942a5cb2468912f", "result": { - "markdown": "---\ntitle: Pandas I\nformat:\n html:\n toc: true\n toc-depth: 5\n toc-location: right\n code-fold: false\n theme:\n - cosmo\n - cerulean\n callout-icon: false\n---\n\n::: {.callout-note collapse=\"true\"}\n## Learning Outcomes\n\n- Build familiarity with basic `pandas` syntax\n- Learn the methods of selecting and filtering data from a DataFrame.\n- Understand the differences between DataFrames and Series\n:::\n\nData scientists work with data stored in a variety of formats. The primary focus of this class is in understanding tabular data -- one of the most widely used formats in data science. This note introduces DataFrames, which are among the most popular representations of tabular data. We’ll also introduce `pandas`, the standard Python package for manipulating data in DataFrames.\n\n## Introduction to Exploratory Data Analysis\n\nImagine you collected, or have been given a box of data. What do you do next? \n\nunderstand_data\n\nThe first step is to clean your data. **Data cleaning** often corrects issues in the structure and formatting of data, including missing values and unit conversions.\n\nData scientists have coined the term **exploratory data analysis (EDA)** to describe the process of transforming raw data to insightful observations. EDA is an *open-ended* analysis of transforming, visualizing, and summarizing patterns in data. In order to conduct EDA, we first need to familiarize ourselves with `pandas` -- an important programming tool.\n\n## Introduction to Pandas\n\n`pandas` is a data analysis library to make data cleaning and analysis fast and convenient in Python. \n\nThe `pandas` library adopts many coding idioms from `NumPy`. The biggest difference is that `pandas` is designed for working with tabular data, one of the most common data formats (and the focus of Data 100).\n\nBefore writing any code, we must import `pandas` into our Python environment.\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\n# `pd` is the conventional alias for Pandas, as `np` is for NumPy\nimport pandas as pd\n```\n:::\n\n\n## Series, DataFrames, and Indices\n\nThere are three fundamental data structures in `pandas`:\n\n1. **Series**: 1D labeled array data; best thought of as columnar data\n2. **DataFrame**: 2D tabular data with rows and columns\n3. **Index**: A sequence of row/column labels\n\nDataFrames, Series, and Indices can be represented visually in the following diagram.\n\n![](images/df_series_index.png)\n\nNotice how the **DataFrame** is a two dimensional object -- it contains both rows and columns. The **Series** above is a singular column of this DataFrame, namely the `Candidate` column. Both contain an **Index**, or a shared list of row labels (the integers from 0 to 5, inclusive).\n\n### Series\n\nA Series represents a column of a DataFrame; more generally, it can be any 1-dimensional array-like object containing values of the same type with associated data labels, called its index.\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\nimport pandas as pd\n\ns = pd.Series([-1, 10, 2])\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n0 -1\n1 10\n2 2\ndtype: int64\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\ns.array # Data contained within the Series\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n\n[-1, 10, 2]\nLength: 3, dtype: int64\n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\ns.index # The Index of the Series\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\nRangeIndex(start=0, stop=3, step=1)\n```\n:::\n:::\n\n\nBy default, row indices in `pandas` are a sequential list of integers beginning from 0. Optionally, a list of desired indices can be passed to the `index` argument.\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\ns = pd.Series([-1, 10, 2], index = [\"a\", \"b\", \"c\"])\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\na -1\nb 10\nc 2\ndtype: int64\n```\n:::\n:::\n\n\nIndices can also be changed after initialization.\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\ns.index = [\"first\", \"second\", \"third\"]\nprint(s)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\nfirst -1\nsecond 10\nthird 2\ndtype: int64\n```\n:::\n:::\n\n\n#### Selection in Series\n\nSimilar to an array, we can select a single value or a set of values from a Series. There are 3 primary methods of selecting data.\n\n1. A single index label\n2. A list of index labels\n3. A filtering condition\n\nLet's define the following Series `ser`.\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\nser = pd.Series([4, -2, 0, 6], index = [\"a\", \"b\", \"c\", \"d\"])\nprint(ser)\n```\n\n::: {.cell-output .cell-output-stdout}\n```\na 4\nb -2\nc 0\nd 6\ndtype: int64\n```\n:::\n:::\n\n\n##### A Single Index Label\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\nprint(ser[\"a\"]) # Notice how the return value is a single array element\n```\n\n::: {.cell-output .cell-output-stdout}\n```\n4\n```\n:::\n:::\n\n\n##### A List of Index Labels\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\nser[[\"a\", \"c\"]] # Notice how the return value is another Series\n```\n\n::: {.cell-output .cell-output-display execution_count=9}\n```\na 4\nc 0\ndtype: int64\n```\n:::\n:::\n\n\n##### A Filtering Condition\n\nPerhaps the most interesting (and useful) method of selecting data from a Series is with a filtering condition. \n\nWe first must apply a vectorized boolean operation to our Series that encodes the filter conditon.\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\nser > 0 # Filter condition: select all elements greater than 0\n```\n\n::: {.cell-output .cell-output-display execution_count=10}\n```\na True\nb False\nc False\nd True\ndtype: bool\n```\n:::\n:::\n\n\nUpon \"indexing\" in our Series with this condition, `pandas` selects only the rows with `True` values.\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\nser[ser > 0] \n```\n\n::: {.cell-output .cell-output-display execution_count=11}\n```\na 4\nd 6\ndtype: int64\n```\n:::\n:::\n\n\n### DataFrames\n\nIn Data 8, you encountered the `Table` class of the `datascience` library, which represented tabular data. In Data 100, we'll be using the `DataFrame` class of the `pandas` library.\n\nHere is an example of a DataFrame that contains election data.\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\nimport pandas as pd\n\nelections = pd.read_csv(\"data/elections.csv\")\nelections\n```\n\n::: {.cell-output .cell-output-display execution_count=12}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
41832Andrew JacksonDemocratic702735win54.574789
.....................
1772016Jill SteinGreen1457226loss1.073699
1782020Joseph BidenDemocratic81268924win51.311515
1792020Donald TrumpRepublican74216154loss46.858542
1802020Jo JorgensenLibertarian1865724loss1.177979
1812020Howard HawkinsGreen405035loss0.255731
\n

182 rows × 6 columns

\n
\n```\n:::\n:::\n\n\nLet's dissect the code above. \n\n1. We first import the ````pandas```` library into our Python environment, using the alias `pd`.
 ```` import pandas as pd ````\n\n2. There are a number of ways to read data into a DataFrame. In Data 100, our data are typically stored in a CSV (comma-seperated values) file format. We can import a CSV file into a DataFrame by passing the data path as an argument to the following ````pandas```` function. \n
 ```` pd.read_csv(\"elections.csv\") ```` \n\nThis code stores our DataFrame object in the ````elections```` variable. Upon inspection, our ````elections```` DataFrame has 182 rows and 6 columns (`Year`, `Candidate`, `Party`, `Popular Vote`, `Result`, `%`). Each row represents a single record -- in our example, a presedential candidate from some particular year. Each column represents a single attribute, or feature of the record.\n\nIn the example above, we constructed a DataFrame object using data from a CSV file. As we'll explore in the next section, we can create a DataFrame with data of our own.\n\n#### Creating a DataFrame\n\nThere are many ways to create a DataFrame. Here, we will cover the most popular approaches.\n\n1. Using a list and column names\n2. From a dictionary\n3. From a Series\n\n##### Using a List and Column Names\n\nConsider the following examples.\n\n::: {.cell execution_count=13}\n``` {.python .cell-code}\ndf_list = pd.DataFrame([1, 2, 3], columns=[\"Numbers\"])\ndf_list\n```\n\n::: {.cell-output .cell-output-display execution_count=13}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Numbers
01
12
23
\n
\n```\n:::\n:::\n\n\nThe first code cell creates a DataFrame with a single column `Numbers`, while the second creates a DataFrame with an additional column `Description`. Notice how a 2D list of values is required to initialize the second DataFrame -- each nested list represents a single row of data.\n\n::: {.cell execution_count=14}\n``` {.python .cell-code}\ndf_list = pd.DataFrame([[1, \"one\"], [2, \"two\"]], columns = [\"Number\", \"Description\"])\ndf_list\n```\n\n::: {.cell-output .cell-output-display execution_count=14}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NumberDescription
01one
12two
\n
\n```\n:::\n:::\n\n\n##### From a Dictionary\n\nA second (and more common) way to create a DataFrame is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.\n\n::: {.cell execution_count=15}\n``` {.python .cell-code}\ndf_dict = pd.DataFrame({\"Fruit\": [\"Strawberry\", \"Orange\"], \"Price\": [5.49, 3.99]})\ndf_dict\n```\n\n::: {.cell-output .cell-output-display execution_count=15}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FruitPrice
0Strawberry5.49
1Orange3.99
\n
\n```\n:::\n:::\n\n\n##### From a Series\n\nEarlier, we explained how a Series was synonymous to a column in a DataFrame. It follows then, that a DataFrame is equivalent to a collection of Series, which all share the same index. \n\nIn fact, we can initialize a DataFrame by merging two or more Series.\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\n# Notice how our indices, or row labels, are the same\n\ns_a = pd.Series([\"a1\", \"a2\", \"a3\"], index = [\"r1\", \"r2\", \"r3\"])\ns_b = pd.Series([\"b1\", \"b2\", \"b3\"], index = [\"r1\", \"r2\", \"r3\"])\n\npd.DataFrame({\"A-column\": s_a, \"B-column\": s_b})\n```\n\n::: {.cell-output .cell-output-display execution_count=16}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
A-columnB-column
r1a1b1
r2a2b2
r3a3b3
\n
\n```\n:::\n:::\n\n\n### Indices\n\nThe major takeaway: we can think of a **DataFrame** as a collection of **Series** that all share the same **Index**.\n\nOn a more technical note, an Index doesn't have to be an integer, nor does it have to be unique. For example, we can set the index of the `elections` Dataframe to be the name of presedential candidates. Selecting a new Series from this modified DataFrame yields the following.\n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\n# This sets the index to the \"Candidate\" column\nelections.set_index(\"Candidate\", inplace=True)\n```\n:::\n\n\n![](images/index_comparison_2.png)\n\nTo retrieve the indices of a DataFrame, simply use the `.index` attribute of the DataFrame class.\n\n::: {.cell execution_count=18}\n``` {.python .cell-code}\nelections.index\n```\n\n::: {.cell-output .cell-output-display execution_count=18}\n```\nIndex(['Andrew Jackson', 'John Quincy Adams', 'Andrew Jackson',\n 'John Quincy Adams', 'Andrew Jackson', 'Henry Clay', 'William Wirt',\n 'Hugh Lawson White', 'Martin Van Buren', 'William Henry Harrison',\n ...\n 'Darrell Castle', 'Donald Trump', 'Evan McMullin', 'Gary Johnson',\n 'Hillary Clinton', 'Jill Stein', 'Joseph Biden', 'Donald Trump',\n 'Jo Jorgensen', 'Howard Hawkins'],\n dtype='object', name='Candidate', length=182)\n```\n:::\n:::\n\n\n::: {.cell execution_count=19}\n``` {.python .cell-code}\n# This resets the index to be the default list of integers\nelections.reset_index(inplace=True) \n```\n:::\n\n\n## Slicing in DataFrames\n\nNow that we've learned how to create DataFrames, let's dive deeper into their capabilities. \n\nThe API (application programming interface) for the DataFrame class is enormous. In this section, we'll discuss several methods of the DataFrame API that allow us to extract subsets of data.\n\nThe simplest way to manipulate a DataFrame is to extract a subset of rows and columns, known as **slicing**. We will do so with three primary methods of the DataFrame class:\n\n1. `.loc`\n2. `.iloc`\n3. `[]`\n\n### Indexing with .loc\n\nThe `.loc` operator selects rows and columns in a DataFrame by their row and column label(s), respectively. The **row labels** (commonly referred to as the **indices**) are the bold text on the far *left* of a DataFrame, while the **column labels** are the column names found at the *top* of a DataFrame.\n\nTo grab data with `.loc`, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the `.loc` function; the column labels are the second. For example, we can select the the row labeled `0` and the column labeled `Candidate` from the `elections` DataFrame.\n\n::: {.cell execution_count=20}\n``` {.python .cell-code}\nelections.loc[0, 'Candidate']\n```\n\n::: {.cell-output .cell-output-display execution_count=20}\n```\n'Andrew Jackson'\n```\n:::\n:::\n\n\nTo select *multiple* rows and columns, we can use Python slice notation. Here, we select both the first four rows and columns.\n\n::: {.cell execution_count=21}\n``` {.python .cell-code}\nelections.loc[0:3, 'Year':'Popular vote']\n```\n\n::: {.cell-output .cell-output-display execution_count=21}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearPartyPopular vote
01824Democratic-Republican151271
11824Democratic-Republican113142
21828Democratic642806
31828National Republican500897
\n
\n```\n:::\n:::\n\n\nSuppose that instead, we wanted *every* column value for the first four rows in the `elections` DataFrame. The shorthand `:` is useful for this.\n\n::: {.cell execution_count=22}\n``` {.python .cell-code}\nelections.loc[0:3, :]\n```\n\n::: {.cell-output .cell-output-display execution_count=22}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPartyPopular voteResult%
0Andrew Jackson1824Democratic-Republican151271loss57.210122
1John Quincy Adams1824Democratic-Republican113142win42.789878
2Andrew Jackson1828Democratic642806win56.203927
3John Quincy Adams1828National Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\nThere are a couple of things we should note. Unlike conventional Python, Pandas allows us to slice string values (in our example, the column labels). Secondly, slicing with `.loc` is *inclusive*. Notice how our resulting DataFrame includes every row and column between and including the slice labels we specified.\n\nEquivalently, we can use a list to obtain multiple rows and columns in our `elections` DataFrame. \n\n::: {.cell execution_count=23}\n``` {.python .cell-code}\nelections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']]\n```\n\n::: {.cell-output .cell-output-display execution_count=23}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
\n
\n```\n:::\n:::\n\n\nLastly, we can interchange list and slicing notation.\n\n::: {.cell execution_count=24}\n``` {.python .cell-code}\nelections.loc[[0, 1, 2, 3], :]\n```\n\n::: {.cell-output .cell-output-display execution_count=24}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPartyPopular voteResult%
0Andrew Jackson1824Democratic-Republican151271loss57.210122
1John Quincy Adams1824Democratic-Republican113142win42.789878
2Andrew Jackson1828Democratic642806win56.203927
3John Quincy Adams1828National Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\n### Indexing with .iloc\n\nSlicing with `.iloc` works similarily to `.loc`, although `.iloc` uses the integer positions of rows and columns rather the labels. The arguments to the `.iloc` function also behave similarly - single values, lists, indices, and any combination of these are permitted. \n\nLet's begin reproducing our results from above. We'll begin by selecting for the first presedential candidate in our `elections` DataFrame:\n\n::: {.cell execution_count=25}\n``` {.python .cell-code}\n# elections.loc[0, \"Candidate\"] - Previous approach\nelections.iloc[0, 1]\n```\n\n::: {.cell-output .cell-output-display execution_count=25}\n```\n1824\n```\n:::\n:::\n\n\nNotice how the first argument to both `.loc` and `.iloc` are the same. This is because the row with a label of 0 is conveniently in the 0^th^ (or first) position of the `elections` DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.\n\nHowever, when we select the first four rows and columns using `.iloc`, we notice something.\n\n::: {.cell execution_count=26}\n``` {.python .cell-code}\n# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach\nelections.iloc[0:4, 0:4]\n```\n\n::: {.cell-output .cell-output-display execution_count=26}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPartyPopular vote
0Andrew Jackson1824Democratic-Republican151271
1John Quincy Adams1824Democratic-Republican113142
2Andrew Jackson1828Democratic642806
3John Quincy Adams1828National Republican500897
\n
\n```\n:::\n:::\n\n\nSlicing is no longer inclusive in `.iloc` - it's *exclusive*. This is one of Pandas syntatical subtleties; you'll get used to with practice.\n\nList behavior works just as expected.\n\n::: {.cell execution_count=27}\n``` {.python .cell-code}\n#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach\nelections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]\n```\n\n::: {.cell-output .cell-output-display execution_count=27}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPartyPopular vote
0Andrew Jackson1824Democratic-Republican151271
1John Quincy Adams1824Democratic-Republican113142
2Andrew Jackson1828Democratic642806
3John Quincy Adams1828National Republican500897
\n
\n```\n:::\n:::\n\n\nThis discussion begs the question: when should we use `.loc` vs `.iloc`? In most cases, `.loc` is generally safer to use. You can imagine `.iloc` may return incorrect values when applied to a dataset where the ordering of data can change. \n\n### Indexing with []\n\nThe `[]` selection operator is the most baffling of all, yet the commonly used. It only takes a single argument, which may be one of the following:\n\n1. A slice of row numbers\n2. A list of column labels\n3. A single column label\n\nThat is, `[]` is *context dependent*. Let's see some examples.\n\n#### A slice of row numbers\n\nSay we wanted the first four rows of our `elections` DataFrame.\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\nelections[0:4]\n```\n\n::: {.cell-output .cell-output-display execution_count=28}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPartyPopular voteResult%
0Andrew Jackson1824Democratic-Republican151271loss57.210122
1John Quincy Adams1824Democratic-Republican113142win42.789878
2Andrew Jackson1828Democratic642806win56.203927
3John Quincy Adams1828National Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\n#### A list of column labels\n\nSuppose we now want the first four columns.\n\n::: {.cell execution_count=29}\n``` {.python .cell-code}\nelections[[\"Year\", \"Candidate\", \"Party\", \"Popular vote\"]]\n```\n\n::: {.cell-output .cell-output-display execution_count=29}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
41832Andrew JacksonDemocratic702735
...............
1772016Jill SteinGreen1457226
1782020Joseph BidenDemocratic81268924
1792020Donald TrumpRepublican74216154
1802020Jo JorgensenLibertarian1865724
1812020Howard HawkinsGreen405035
\n

182 rows × 4 columns

\n
\n```\n:::\n:::\n\n\n#### A single column label\n\nLastly, if we only want the `Candidate` column.\n\n::: {.cell execution_count=30}\n``` {.python .cell-code}\nelections[\"Candidate\"]\n```\n\n::: {.cell-output .cell-output-display execution_count=30}\n```\n0 Andrew Jackson\n1 John Quincy Adams\n2 Andrew Jackson\n3 John Quincy Adams\n4 Andrew Jackson\n ... \n177 Jill Stein\n178 Joseph Biden\n179 Donald Trump\n180 Jo Jorgensen\n181 Howard Hawkins\nName: Candidate, Length: 182, dtype: object\n```\n:::\n:::\n\n\nThe output looks like a Series! In this course, we'll become very comfortable with `[]`, especially for selecting columns. In practice, `[]` is much more common than `.loc`.\n\n## Parting Note\n\nThe `pandas` library is enormous and contains many useful functions. Here is a link to [documentation](https://pandas.pydata.org/docs/).\n\nThe introductory `pandas` lectures will cover important data structures and methods you should be fluent in. However, we want you to get familiar with the real world programming practice of ...Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. \n\nWith that, let's move on to Pandas II.\n\n", + "markdown": "---\ntitle: Pandas I\nformat:\n html:\n toc: true\n toc-depth: 5\n toc-location: right\n code-fold: false\n theme:\n - cosmo\n - cerulean\n callout-icon: false\n---\n\n::: {.callout-note collapse=\"false\"}\n## Learning Outcomes\n\n- Build familiarity with `pandas` and `pandas` syntax.\n- Learn key data structures: `DataFrame`, `Series`, and `Index`.\n- Understand methods for extracting data: `.loc`, `.iloc`, and `[]`.\n:::\n\nIn this sequence of lectures, we will dive right into things by having you explore and manipulate real-world data. We'll first introduce `pandas`, a popular Python library for interacting with **tabular data**.\n\n## Tabular Data\n\nData scientists work with data stored in a variety of formats. The primary focus of this class is understanding *tabular data* — data that is stored in a table.\n\nTabular data is one of the most common systems that data scientists use to organize data. This is in large part due to the simplicity and flexibility of tables. Tables allow us to represent each **observation**, or instance of collecting data from an individual, as its own *row*. We can record each observation's distinct characteristics, or **features**, in separate *columns*.\n\nTo see this in action, we'll explore the `elections` dataset, which stores information about political candidates who ran for president of the United States in previous years.\n\n::: {.cell execution_count=1}\n``` {.python .cell-code code-fold=\"true\"}\nimport pandas as pd\npd.read_csv(\"data/elections.csv\")\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
41832Andrew JacksonDemocratic702735win54.574789
.....................
1772016Jill SteinGreen1457226loss1.073699
1782020Joseph BidenDemocratic81268924win51.311515
1792020Donald TrumpRepublican74216154loss46.858542
1802020Jo JorgensenLibertarian1865724loss1.177979
1812020Howard HawkinsGreen405035loss0.255731
\n

182 rows × 6 columns

\n
\n```\n:::\n:::\n\n\nIn the `elections` dataset, each row represents one instance of a candidate running for president in a particular year. For example, the first row represents Andrew Jackson running for president in the year 1824. Each column represents one characteristic piece of information about each presidential candidate. For example, the column named \"Result\" stores whether or not the candidate won the election. \n\nYour work in Data 8 helped you grow very familiar with using and interpreting data stored in a tabular format. Back then, you used the `Table` class of the `datascience` library, a special programming library created specifically for Data 8 students.\n\nIn Data 100, we will be working with the programming library `pandas`, which is generally accepted in the data science community as the industry- and academia-standard tool for manipulating tabular data (as well as the inspiration for Petey, our panda bear mascot).\n\nUsing `pandas`, we can\n\n- Arrange data in a tabular format.\n- Extract useful information filtered by specific conditions.\n- Operate on data to gain new insights.\n- Apply `NumPy` functions to our data (our friends from Data 8).\n- Perform vectorized computations to speed up our analysis (Lab 1).\n\n## `Series`, `DataFrame`s, and Indices\n\nTo begin our work in `pandas`, we must first import the library into our Python environment. This will allow us to use `pandas` data structures and methods in our code.\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# `pd` is the conventional alias for Pandas, as `np` is for NumPy\nimport pandas as pd\n```\n:::\n\n\nThere are three fundamental data structures in `pandas`:\n\n1. **Series**: 1D labeled array data; best thought of as columnar data.\n2. **DataFrame**: 2D tabular data with rows and columns.\n3. **Index**: A sequence of row/column labels.\n\n`DataFrame`s, `Series`, and Indices can be represented visually in the following diagram, which considers the first few rows of the `elections` dataset.\n\n![](images/df_elections.png)\n\nNotice how the **DataFrame** is a two-dimensional object — it contains both rows and columns. The **Series** above is a singular column of this `DataFrame`, namely the `Result` column. Both contain an **Index**, or a shared list of row labels (the integers from 0 to 4, inclusive).\n\n### Series\n\nA Series represents a column of a `DataFrame`; more generally, it can be any 1-dimensional array-like object. It contains:\n\n- A sequence of **values** of the same type.\n- A sequence of data labels called the **index**.\n\nIn the cell below, we create a `Series` named `s`.\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\ns = pd.Series([\"welcome\", \"to\", \"data 100\"])\ns\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n0 welcome\n1 to\n2 data 100\ndtype: object\n```\n:::\n:::\n\n\n::: {.cell execution_count=4}\n``` {.python .cell-code}\ns.values # Data values contained within the Series\n```\n\n::: {.cell-output .cell-output-display execution_count=4}\n```\narray(['welcome', 'to', 'data 100'], dtype=object)\n```\n:::\n:::\n\n\n::: {.cell execution_count=5}\n``` {.python .cell-code}\ns.index # The Index of the Series\n```\n\n::: {.cell-output .cell-output-display execution_count=5}\n```\nRangeIndex(start=0, stop=3, step=1)\n```\n:::\n:::\n\n\nBy default, the Index of a Series is a sequential list of integers beginning from 0. Optionally, a manually specified list of desired indices can be passed to the `index` argument.\n\n::: {.cell execution_count=6}\n``` {.python .cell-code}\ns = pd.Series([-1, 10, 2], index = [\"a\", \"b\", \"c\"])\ns\n```\n\n::: {.cell-output .cell-output-display execution_count=6}\n```\na -1\nb 10\nc 2\ndtype: int64\n```\n:::\n:::\n\n\n::: {.cell execution_count=7}\n``` {.python .cell-code}\ns.index\n```\n\n::: {.cell-output .cell-output-display execution_count=7}\n```\nIndex(['a', 'b', 'c'], dtype='object')\n```\n:::\n:::\n\n\nIndices can also be changed after initialization.\n\n::: {.cell execution_count=8}\n``` {.python .cell-code}\ns.index = [\"first\", \"second\", \"third\"]\ns\n```\n\n::: {.cell-output .cell-output-display execution_count=8}\n```\nfirst -1\nsecond 10\nthird 2\ndtype: int64\n```\n:::\n:::\n\n\n::: {.cell execution_count=9}\n``` {.python .cell-code}\ns.index\n```\n\n::: {.cell-output .cell-output-display execution_count=9}\n```\nIndex(['first', 'second', 'third'], dtype='object')\n```\n:::\n:::\n\n\n#### Selection in `Series`\n\nMuch like when working with `NumPy` arrays, we can select a single value or a set of values from a `Series`. To do so, there are three primary methods:\n\n1. A single label.\n2. A list of labels.\n3. A filtering condition.\n\nTo demonstrate this, let's define the Series `ser`.\n\n::: {.cell execution_count=10}\n``` {.python .cell-code}\nser = pd.Series([4, -2, 0, 6], index = [\"a\", \"b\", \"c\", \"d\"])\nser\n```\n\n::: {.cell-output .cell-output-display execution_count=10}\n```\na 4\nb -2\nc 0\nd 6\ndtype: int64\n```\n:::\n:::\n\n\n##### A Single Label\n\n::: {.cell execution_count=11}\n``` {.python .cell-code}\nser[\"a\"] # We return the value stored at the Index label \"a\"\n```\n\n::: {.cell-output .cell-output-display execution_count=11}\n```\n4\n```\n:::\n:::\n\n\n##### A List of Labels\n\n::: {.cell execution_count=12}\n``` {.python .cell-code}\nser[[\"a\", \"c\"]] # We return a *Series* of the values stored at the Index labels \"a\" and \"c\"\n```\n\n::: {.cell-output .cell-output-display execution_count=12}\n```\na 4\nc 0\ndtype: int64\n```\n:::\n:::\n\n\n##### A Filtering Condition\n\nPerhaps the most interesting (and useful) method of selecting data from a Series is by using a filtering condition. \n\nFirst, we apply a boolean operation to the `Series`. This creates **a new Series of boolean values**.\n\n::: {.cell execution_count=13}\n``` {.python .cell-code}\nser > 0 # Filter condition: select all elements greater than 0\n```\n\n::: {.cell-output .cell-output-display execution_count=13}\n```\na True\nb False\nc False\nd True\ndtype: bool\n```\n:::\n:::\n\n\nWe then use this boolean condition to index into our original `Series`. `pandas` will select only the entries in the original `Series` that satisfy the condition.\n\n::: {.cell execution_count=14}\n``` {.python .cell-code}\nser[ser > 0] \n```\n\n::: {.cell-output .cell-output-display execution_count=14}\n```\na 4\nd 6\ndtype: int64\n```\n:::\n:::\n\n\n### DataFrames\n\nTypically, we will work with `Series` using the perspective that they are columns in a `DataFrame`. We can think of a **DataFrame** as a collection of **Series** that all share the same **Index**. \n\nIn Data 8, you encountered the `Table` class of the `datascience` library, which represented tabular data. In Data 100, we'll be using the `DataFrame` class of the `pandas` library.\n\n#### Creating a `DataFrame`\n\nThere are many ways to create a `DataFrame`. Here, we will cover the most popular approaches:\n\n1. From a CSV file.\n2. Using a list and column name(s).\n3. From a dictionary.\n4. From a `Series`.\n\nMore generally, the syntax for creating a `DataFrame` is: `pandas.DataFrame(data, index, columns)`.\n\n##### From a CSV file\nIn Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a `DataFrame` by passing the data path as an argument to the following ````pandas```` function. \n
 ```` pd.read_csv(\"filename.csv\") ```` \n\nWith our new understanding of `pandas` in hand, let's return to the `elections` dataset from before. Now, we can recognize that it is represented as a `pandas` DataFrame.\n\n::: {.cell execution_count=15}\n``` {.python .cell-code}\nelections = pd.read_csv(\"data/elections.csv\")\nelections\n```\n\n::: {.cell-output .cell-output-display execution_count=15}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
41832Andrew JacksonDemocratic702735win54.574789
.....................
1772016Jill SteinGreen1457226loss1.073699
1782020Joseph BidenDemocratic81268924win51.311515
1792020Donald TrumpRepublican74216154loss46.858542
1802020Jo JorgensenLibertarian1865724loss1.177979
1812020Howard HawkinsGreen405035loss0.255731
\n

182 rows × 6 columns

\n
\n```\n:::\n:::\n\n\nThis code stores our `DataFrame` object in the ````elections```` variable. Upon inspection, our ````elections```` DataFrame has 182 rows and 6 columns (`Year`, `Candidate`, `Party`, `Popular Vote`, `Result`, `%`). Each row represents a single record — in our example, a presidential candidate from some particular year. Each column represents a single attribute or feature of the record.\n\n##### Using a List and Column Name(s)\n\nWe'll now explore creating a `DataFrame` with data of our own.\n\nConsider the following examples. The first code cell creates a `DataFrame` with a single column `Numbers`. The second creates a `DataFrame` with the columns `Numbers` and `Description`. Notice how a 2D list of values is required to initialize the second `DataFrame` — each nested list represents a single row of data.\n\n::: {.cell execution_count=16}\n``` {.python .cell-code}\ndf_list = pd.DataFrame([1, 2, 3], columns=[\"Numbers\"])\ndf_list\n```\n\n::: {.cell-output .cell-output-display execution_count=16}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Numbers
01
12
23
\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=17}\n``` {.python .cell-code}\ndf_list = pd.DataFrame([[1, \"one\"], [2, \"two\"]], columns = [\"Number\", \"Description\"])\ndf_list\n```\n\n::: {.cell-output .cell-output-display execution_count=17}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NumberDescription
01one
12two
\n
\n```\n:::\n:::\n\n\n##### From a Dictionary\n\nA third (and more common) way to create a `DataFrame` is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.\n\nBelow are two ways of implementing this approach. The first is based on specifying the columns of the `DataFrame`, whereas the second is based on specifying the rows of the `DataFrame`.\n\n::: {.cell execution_count=18}\n``` {.python .cell-code}\ndf_dict = pd.DataFrame({\"Fruit\": [\"Strawberry\", \"Orange\"], \"Price\": [5.49, 3.99]})\ndf_dict\n```\n\n::: {.cell-output .cell-output-display execution_count=18}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FruitPrice
0Strawberry5.49
1Orange3.99
\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=19}\n``` {.python .cell-code}\ndf_dict = pd.DataFrame([{\"Fruit\":\"Strawberry\", \"Price\":5.49}, {\"Fruit\": \"Orange\", \"Price\":3.99}])\ndf_dict\n```\n\n::: {.cell-output .cell-output-display execution_count=19}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
FruitPrice
0Strawberry5.49
1Orange3.99
\n
\n```\n:::\n:::\n\n\n##### From a `Series`\n\nEarlier, we explained how a `Series` was synonymous to a column in a `DataFrame`. It follows, then, that a `DataFrame` is equivalent to a collection of `Series`, which all share the same `Index`. \n\nIn fact, we can initialize a `DataFrame` by merging two or more `Series`.\n\n::: {.cell execution_count=20}\n``` {.python .cell-code}\n# Notice how our indices, or row labels, are the same\n\ns_a = pd.Series([\"a1\", \"a2\", \"a3\"], index = [\"r1\", \"r2\", \"r3\"])\ns_b = pd.Series([\"b1\", \"b2\", \"b3\"], index = [\"r1\", \"r2\", \"r3\"])\n\npd.DataFrame({\"A-column\": s_a, \"B-column\": s_b})\n```\n\n::: {.cell-output .cell-output-display execution_count=20}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
A-columnB-column
r1a1b1
r2a2b2
r3a3b3
\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=21}\n``` {.python .cell-code}\npd.DataFrame(s_a)\n```\n\n::: {.cell-output .cell-output-display execution_count=21}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
r1a1
r2a2
r3a3
\n
\n```\n:::\n:::\n\n\n::: {.cell execution_count=22}\n``` {.python .cell-code}\ns_a.to_frame()\n```\n\n::: {.cell-output .cell-output-display execution_count=22}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
0
r1a1
r2a2
r3a3
\n
\n```\n:::\n:::\n\n\n### Indices\n\nOn a more technical note, an `Index` doesn't have to be an integer, nor does it have to be unique. For example, we can set the index of the `elections` Dataframe to be the name of presidential candidates. \n\n::: {.cell execution_count=23}\n``` {.python .cell-code}\n# Creating a DataFrame from a CSV file and specifying the Index column\nelections = pd.read_csv(\"data/elections.csv\", index_col = \"Candidate\")\nelections\n```\n\n::: {.cell-output .cell-output-display execution_count=23}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearPartyPopular voteResult%
Candidate
Andrew Jackson1824Democratic-Republican151271loss57.210122
John Quincy Adams1824Democratic-Republican113142win42.789878
Andrew Jackson1828Democratic642806win56.203927
John Quincy Adams1828National Republican500897loss43.796073
Andrew Jackson1832Democratic702735win54.574789
..................
Jill Stein2016Green1457226loss1.073699
Joseph Biden2020Democratic81268924win51.311515
Donald Trump2020Republican74216154loss46.858542
Jo Jorgensen2020Libertarian1865724loss1.177979
Howard Hawkins2020Green405035loss0.255731
\n

182 rows × 5 columns

\n
\n```\n:::\n:::\n\n\nWe can also select a new column and set it as the index of the DataFrame. For example, we can set the index of the `elections` Dataframe to represent the candidate's party.\n\n::: {.cell execution_count=24}\n``` {.python .cell-code}\nelections.reset_index(inplace = True) # Resetting the index so we can set the Index again\n# This sets the index to the \"Party\" column\nelections.set_index(\"Party\")\n```\n\n::: {.cell-output .cell-output-display execution_count=24}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CandidateYearPopular voteResult%
Party
Democratic-RepublicanAndrew Jackson1824151271loss57.210122
Democratic-RepublicanJohn Quincy Adams1824113142win42.789878
DemocraticAndrew Jackson1828642806win56.203927
National RepublicanJohn Quincy Adams1828500897loss43.796073
DemocraticAndrew Jackson1832702735win54.574789
..................
GreenJill Stein20161457226loss1.073699
DemocraticJoseph Biden202081268924win51.311515
RepublicanDonald Trump202074216154loss46.858542
LibertarianJo Jorgensen20201865724loss1.177979
GreenHoward Hawkins2020405035loss0.255731
\n

182 rows × 5 columns

\n
\n```\n:::\n:::\n\n\nAnd, if we'd like, we can revert the index back to the default list of integers.\n\n::: {.cell execution_count=25}\n``` {.python .cell-code}\n# This resets the index to be the default list of integer\nelections.reset_index(inplace=True) \nelections.index\n```\n\n::: {.cell-output .cell-output-display execution_count=25}\n```\nRangeIndex(start=0, stop=182, step=1)\n```\n:::\n:::\n\n\nIt is also important to note that the row labels that constitute an index don't have to be unique. While index values can be unique and numeric, acting as a row number, they can also be named and non-unique. \n\nHere we see unique and numeric index values.\n![](images/uniqueindex.png)\n\nHowever, here the index values here are non-unique. \n![](images/non-uniqueindex.png)\n\n## `DataFrame` Attributes: Index, Columns, and Shape\n\nOn the other hand, column names in a `DataFrame` are almost always unique. Looking back to the `elections` dataset, it wouldn't make sense to have two columns named \"Candidate\".\n\nSometimes, you'll want to extract these different values, in particular, the list of row and column labels.\n\nFor index/row labels, use `DataFrame.index`:\n\n::: {.cell execution_count=26}\n``` {.python .cell-code}\nelections.set_index(\"Party\", inplace = True)\nelections.index\n```\n\n::: {.cell-output .cell-output-display execution_count=26}\n```\nIndex(['Democratic-Republican', 'Democratic-Republican', 'Democratic',\n 'National Republican', 'Democratic', 'National Republican',\n 'Anti-Masonic', 'Whig', 'Democratic', 'Whig',\n ...\n 'Constitution', 'Republican', 'Independent', 'Libertarian',\n 'Democratic', 'Green', 'Democratic', 'Republican', 'Libertarian',\n 'Green'],\n dtype='object', name='Party', length=182)\n```\n:::\n:::\n\n\nFor column labels, use `DataFrame.columns`:\n\n::: {.cell execution_count=27}\n``` {.python .cell-code}\nelections.columns\n```\n\n::: {.cell-output .cell-output-display execution_count=27}\n```\nIndex(['index', 'Candidate', 'Year', 'Popular vote', 'Result', '%'], dtype='object')\n```\n:::\n:::\n\n\nAnd for the shape of the DataFrame, we can use `DataFrame.shape`:\n\n::: {.cell execution_count=28}\n``` {.python .cell-code}\nelections.shape\n```\n\n::: {.cell-output .cell-output-display execution_count=28}\n```\n(182, 6)\n```\n:::\n:::\n\n\n## Slicing in `DataFrame`s\n\nNow that we've learned more about `DataFrame`s, let's dive deeper into their capabilities. \n\nThe API (Application Programming Interface) for the `DataFrame` class is enormous. In this section, we'll discuss several methods of the `DataFrame` API that allow us to extract subsets of data.\n\nThe simplest way to manipulate a `DataFrame` is to extract a subset of rows and columns, known as **slicing**. \n\nCommon ways we may want to extract data are grabbing:\n\n- The first or last `n` rows in the `DataFrame`.\n- Data with a certain label.\n- Data at a certain position.\n\nWe will do so with four primary methods of the DataFrame class:\n\n1. `.head` and `.tail`\n2. `.loc`\n3. `.iloc`\n4. `[]`\n\n### Extracting data with `.head` and `.tail`\n\nThe simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the `DataFrame`.\n\nTo extract the first `n` rows of a DataFrame `df`, we use the syntax `df.head(n)`.\n\n::: {.cell execution_count=29}\n``` {.python .cell-code}\nelections = pd.read_csv(\"data/elections.csv\")\n\n# Extract the first 5 rows of the DataFrame\nelections.head(5)\n```\n\n::: {.cell-output .cell-output-display execution_count=29}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
41832Andrew JacksonDemocratic702735win54.574789
\n
\n```\n:::\n:::\n\n\nSimilarly, calling `df.tail(n)` allows us to extract the last `n` rows of the DataFrame. \n\n::: {.cell execution_count=30}\n``` {.python .cell-code}\n# Extract the last 5 rows of the DataFrame\nelections.tail(5)\n```\n\n::: {.cell-output .cell-output-display execution_count=30}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
1772016Jill SteinGreen1457226loss1.073699
1782020Joseph BidenDemocratic81268924win51.311515
1792020Donald TrumpRepublican74216154loss46.858542
1802020Jo JorgensenLibertarian1865724loss1.177979
1812020Howard HawkinsGreen405035loss0.255731
\n
\n```\n:::\n:::\n\n\n### Label-based Extraction: Indexing with `.loc`\n\nFor the more complex task of extracting data with specific column or index labels, we can use `.loc`. The `.loc` accessor allows us to specify the ***labels*** of rows and columns we wish to extract. The **labels** (commonly referred to as the **indices**) are the bold text on the far *left* of a DataFrame, while the **column labels** are the column names found at the *top* of a DataFrame.\n\n![](images/locgraphic.png)\n\nTo grab data with `.loc`, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the `.loc` function; the column labels are the second.\n\nArguments to `.loc` can be:\n\n- A single value.\n- A slice.\n- A list.\n\nFor example, to select a single value, we can select the row labeled `0` and the column labeled `Candidate` from the `elections` `DataFrame`.\n\n::: {.cell execution_count=31}\n``` {.python .cell-code}\nelections.loc[0, 'Candidate']\n```\n\n::: {.cell-output .cell-output-display execution_count=31}\n```\n'Andrew Jackson'\n```\n:::\n:::\n\n\nKeep in mind that passing in just one argument as a single value will produce a `Series`. Below, we've extracted a subset of the `\"Popular vote\"` column as a `Series`.\n\n::: {.cell execution_count=32}\n``` {.python .cell-code}\nelections.loc[[87, 25, 179], \"Popular vote\"]\n```\n\n::: {.cell-output .cell-output-display execution_count=32}\n```\n87 15761254\n25 848019\n179 74216154\nName: Popular vote, dtype: int64\n```\n:::\n:::\n\n\nTo select *multiple* rows and columns, we can use Python slice notation. Here, we select the rows from labels `0` to `3` and the columns from labels `\"Year\"` to `\"Popular vote\"`. \n\n::: {.cell execution_count=33}\n``` {.python .cell-code}\nelections.loc[0:3, 'Year':'Popular vote']\n```\n\n::: {.cell-output .cell-output-display execution_count=33}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
\n
\n```\n:::\n:::\n\n\nSuppose that instead, we want to extract *all* column values for the first four rows in the `elections` DataFrame. The shorthand `:` is useful for this.\n\n::: {.cell execution_count=34}\n``` {.python .cell-code}\nelections.loc[0:3, :]\n```\n\n::: {.cell-output .cell-output-display execution_count=34}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\nWe can use the same shorthand to extract all rows. \n\n::: {.cell execution_count=35}\n``` {.python .cell-code}\nelections.loc[:, [\"Year\", \"Candidate\", \"Result\"]]\n```\n\n::: {.cell-output .cell-output-display execution_count=35}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidateResult
01824Andrew Jacksonloss
11824John Quincy Adamswin
21828Andrew Jacksonwin
31828John Quincy Adamsloss
41832Andrew Jacksonwin
............
1772016Jill Steinloss
1782020Joseph Bidenwin
1792020Donald Trumploss
1802020Jo Jorgensenloss
1812020Howard Hawkinsloss
\n

182 rows × 3 columns

\n
\n```\n:::\n:::\n\n\nThere are a couple of things we should note. Firstly, unlike conventional Python, `pandas` allows us to slice string values (in our example, the column labels). Secondly, slicing with `.loc` is *inclusive*. Notice how our resulting `DataFrame` includes every row and column between and including the slice labels we specified.\n\nEquivalently, we can use a list to obtain multiple rows and columns in our `elections` DataFrame. \n\n::: {.cell execution_count=36}\n``` {.python .cell-code}\nelections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']]\n```\n\n::: {.cell-output .cell-output-display execution_count=36}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
\n
\n```\n:::\n:::\n\n\nLastly, we can interchange list and slicing notation.\n\n::: {.cell execution_count=37}\n``` {.python .cell-code}\nelections.loc[[0, 1, 2, 3], :]\n```\n\n::: {.cell-output .cell-output-display execution_count=37}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\n### Integer-based Extraction: Indexing with `.iloc`\n\nSlicing with `.iloc` works similarly to `.loc`. However, `.iloc` uses the *index positions* of rows and columns rather than the labels (think to yourself: **l**oc uses **l**ables; **i**loc uses **i**ndices). The arguments to the `.iloc` function also behave similarly — single values, lists, indices, and any combination of these are permitted. \n\nLet's begin reproducing our results from above. We'll begin by selecting the first presidential candidate in our `elections` DataFrame:\n\n::: {.cell execution_count=38}\n``` {.python .cell-code}\n# elections.loc[0, \"Candidate\"] - Previous approach\nelections.iloc[0, 1]\n```\n\n::: {.cell-output .cell-output-display execution_count=38}\n```\n'Andrew Jackson'\n```\n:::\n:::\n\n\nNotice how the first argument to both `.loc` and `.iloc` are the same. This is because the row with a label of 0 is conveniently in the $0^{th}$ (equivalently, the first position) of the `elections` DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.\n\nAnd, as before, if we were to pass in only one single value argument, our result would be a `Series`.\n\n::: {.cell execution_count=39}\n``` {.python .cell-code}\nelections.iloc[[1,2,3],1]\n```\n\n::: {.cell-output .cell-output-display execution_count=39}\n```\n1 John Quincy Adams\n2 Andrew Jackson\n3 John Quincy Adams\nName: Candidate, dtype: object\n```\n:::\n:::\n\n\nHowever, when we select the first four rows and columns using `.iloc`, we notice something.\n\n::: {.cell execution_count=40}\n``` {.python .cell-code}\n# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach\nelections.iloc[0:4, 0:4]\n```\n\n::: {.cell-output .cell-output-display execution_count=40}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
\n
\n```\n:::\n:::\n\n\nSlicing is no longer inclusive in `.iloc` — it's *exclusive*. In other words, the right end of a slice is not included when using `.iloc`. This is one of the subtleties of `pandas` syntax; you will get used to it with practice.\n\nList behavior works just as expected.\n\n::: {.cell execution_count=41}\n``` {.python .cell-code}\n#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach\nelections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]\n```\n\n::: {.cell-output .cell-output-display execution_count=41}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
\n
\n```\n:::\n:::\n\n\nAnd just like with `.loc`, we can use a colon with `.iloc` to extract all rows or columns.\n\n::: {.cell execution_count=42}\n``` {.python .cell-code}\nelections.iloc[:, 0:3]\n```\n\n::: {.cell-output .cell-output-display execution_count=42}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidateParty
01824Andrew JacksonDemocratic-Republican
11824John Quincy AdamsDemocratic-Republican
21828Andrew JacksonDemocratic
31828John Quincy AdamsNational Republican
41832Andrew JacksonDemocratic
............
1772016Jill SteinGreen
1782020Joseph BidenDemocratic
1792020Donald TrumpRepublican
1802020Jo JorgensenLibertarian
1812020Howard HawkinsGreen
\n

182 rows × 3 columns

\n
\n```\n:::\n:::\n\n\nThis discussion begs the question: When should we use `.loc` vs. `.iloc`? In most cases, `.loc` is generally safer to use. You can imagine `.iloc` may return incorrect values when applied to a dataset where the ordering of data can change. However, `.iloc` can still be useful — for example, if you are looking at a `DataFrame` of sorted movie earnings and want to get the median earnings for a given year, you can use `.iloc` to index into the middle.\n\nOverall, it is important to remember that:\n\n- `.loc` performances **l**abel-based extraction.\n- `.iloc` performs **i**nteger-based extraction.\n\n### Context-dependent Extraction: Indexing with `[]`\n\nThe `[]` selection operator is the most baffling of all, yet the most commonly used. It only takes a single argument, which may be one of the following:\n\n1. A slice of row numbers.\n2. A list of column labels.\n3. A single-column label.\n\nThat is, `[]` is *context-dependent*. Let's see some examples.\n\n#### A slice of row numbers\n\nSay we wanted the first four rows of our `elections` DataFrame.\n\n::: {.cell execution_count=43}\n``` {.python .cell-code}\nelections[0:4]\n```\n\n::: {.cell-output .cell-output-display execution_count=43}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular voteResult%
01824Andrew JacksonDemocratic-Republican151271loss57.210122
11824John Quincy AdamsDemocratic-Republican113142win42.789878
21828Andrew JacksonDemocratic642806win56.203927
31828John Quincy AdamsNational Republican500897loss43.796073
\n
\n```\n:::\n:::\n\n\n#### A list of column labels\n\nSuppose we now want the first four columns.\n\n::: {.cell execution_count=44}\n``` {.python .cell-code}\nelections[[\"Year\", \"Candidate\", \"Party\", \"Popular vote\"]]\n```\n\n::: {.cell-output .cell-output-display execution_count=44}\n```{=html}\n
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearCandidatePartyPopular vote
01824Andrew JacksonDemocratic-Republican151271
11824John Quincy AdamsDemocratic-Republican113142
21828Andrew JacksonDemocratic642806
31828John Quincy AdamsNational Republican500897
41832Andrew JacksonDemocratic702735
...............
1772016Jill SteinGreen1457226
1782020Joseph BidenDemocratic81268924
1792020Donald TrumpRepublican74216154
1802020Jo JorgensenLibertarian1865724
1812020Howard HawkinsGreen405035
\n

182 rows × 4 columns

\n
\n```\n:::\n:::\n\n\n#### A single-column label\n\nLastly, `[]` allows us to extract only the `Candidate` column.\n\n::: {.cell execution_count=45}\n``` {.python .cell-code}\nelections[\"Candidate\"]\n```\n\n::: {.cell-output .cell-output-display execution_count=45}\n```\n0 Andrew Jackson\n1 John Quincy Adams\n2 Andrew Jackson\n3 John Quincy Adams\n4 Andrew Jackson\n ... \n177 Jill Stein\n178 Joseph Biden\n179 Donald Trump\n180 Jo Jorgensen\n181 Howard Hawkins\nName: Candidate, Length: 182, dtype: object\n```\n:::\n:::\n\n\nThe output is a `Series`! In this course, we'll become very comfortable with `[]`, especially for selecting columns. In practice, `[]` is much more common than `.loc`, especially since it is far more concise.\n\n## Parting Note\n\nThe `pandas` library is enormous and contains many useful functions. Here is a link to [documentation](https://pandas.pydata.org/docs/). We certainly don't expect you to memorize each and every method of the library.\n\nThe introductory Data 100 `pandas` lectures will provide a high-level view of the key data structures and methods that will form the foundation of your `pandas` knowledge. A goal of this course is to help you build your familiarity with the real-world programming practice of ...Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. Being able to search for, read, and implement documentation is an important life skill for any data scientist. \n\nWith that, we will move on to Pandas II.\n\n", "supporting": [ "pandas_1_files/figure-html" ], diff --git a/.quarto/_freeze/pandas_1/pandas_1/libs/bootstrap/bootstrap.min.css b/.quarto/_freeze/pandas_1/pandas_1/libs/bootstrap/bootstrap.min.css index 3bcb6e29..f346bf0f 100644 --- a/.quarto/_freeze/pandas_1/pandas_1/libs/bootstrap/bootstrap.min.css +++ b/.quarto/_freeze/pandas_1/pandas_1/libs/bootstrap/bootstrap.min.css @@ -3,8 +3,8 @@ * Copyright 2011-2021 The Bootstrap Authors * Copyright 2011-2021 Twitter, Inc. * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) - */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root{--bs-blue: #033c73;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: 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+{"options":{"chapters":true},"entries":[],"headings":["tabular-data","series-dataframes-and-indices","series","selection-in-series","a-single-label","a-list-of-labels","a-filtering-condition","dataframes","creating-a-dataframe","from-a-csv-file","using-a-list-and-column-names","from-a-dictionary","from-a-series","indices","dataframe-attributes-index-columns-and-shape","slicing-in-dataframes","extracting-data-with-.head-and-.tail","label-based-extraction-indexing-with-.loc","integer-based-extraction-indexing-with-.iloc","context-dependent-extraction-indexing-with","a-slice-of-row-numbers","a-list-of-column-labels","a-single-column-label","parting-note"]} \ No newline at end of file diff --git a/_quarto.yml b/_quarto.yml index 27d73ac6..bb93146e 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -14,7 +14,7 @@ book: chapters: - index.md - intro_lec/introduction.qmd - # - pandas_1/pandas_1.qmd + - pandas_1/pandas_1.qmd # - pandas_2/pandas_2.qmd # - pandas_3/pandas_3.qmd # - eda/eda.qmd diff 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  1. +
    + + 2  Pandas I +
    2. diff --git a/docs/intro_lec/introduction.html b/docs/intro_lec/introduction.html index 51a57a6e..d3b218c7 100644 --- a/docs/intro_lec/introduction.html +++ b/docs/intro_lec/introduction.html @@ -30,6 +30,7 @@ + @@ -117,6 +118,12 @@ 1  Introduction + +
  2. +
    + + 2  Pandas I +
    3. @@ -582,6 +589,9 @@

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      -

      In this sequence of lectures, we will dive right into things by having you explore and manipulate real-world data. We’ll first introduce pandas, a popular Python library for interacting with tabular data.

      -
      -

      Tabular Data

      +
      +

      2.1 Tabular Data

      Data scientists work with data stored in a variety of formats. The primary focus of this class is understanding tabular data — data that is stored in a table.

      Tabular data is one of the most common systems that data scientists use to organize data. This is in large part due to the simplicity and flexibility of tables. Tables allow us to represent each observation, or instance of collecting data from an individual, as its own row. We can record each observation’s distinct characteristics, or features, in separate columns.

      To see this in action, we’ll explore the elections dataset, which stores information about political candidates who ran for president of the United States in previous years.

      @@ -311,8 +401,8 @@

      Tabular Data

    1. Perform vectorized computations to speed up our analysis (Lab 1).
      2. -
      -

      Series, DataFrames, and Indices

      +
      +

      2.2 Series, DataFrames, and Indices

      To begin our work in pandas, we must first import the library into our Python environment. This will allow us to use pandas data structures and methods in our code.

      # `pd` is the conventional alias for Pandas, as `np` is for NumPy
@@ -327,8 +417,8 @@ 
      Series
 
      DataFrames, Series, and Indices can be represented visually in the following diagram, which considers the first few rows of the elections dataset.
      
 
      
 
      Notice how the DataFrame is a two-dimensional object — it contains both rows and columns. The Series above is a singular column of this DataFrame, namely the Result column. Both contain an Index, or a shared list of row labels (the integers from 0 to 4, inclusive).
      
-
      
-
      Series
      
+
      
+
      2.2.1 Series
      
 
      A Series represents a column of a DataFrame; more generally, it can be any 1-dimensional array-like object. It contains:
      
 
        
 
      • A sequence of values of the same type.
        • 
@@ -391,8 +481,8 @@ 
        Series
        
 
        Index(['first', 'second', 'third'], dtype='object')
      -
      -

      Selection in Series

      +
      +

      2.2.1.1 Selection in Series

      Much like when working with NumPy arrays, we can select a single value or a set of values from a Series. To do so, there are three primary methods:

      1. A single label.
        2. @@ -411,8 +501,8 @@

        Selection in Ser dtype: int64 -
        -
        A Single Label
        +
        +
        2.2.1.1.1 A Single Label
        ser["a"] # We return the value stored at the Index label "a"
        @@ -420,8 +510,8 @@
        A Single Label
        -
        -
        A List of Labels
        +
        +
        2.2.1.1.2 A List of Labels
        ser[["a", "c"]] # We return a *Series* of the values stored at the Index labels "a" and "c"
        @@ -431,8 +521,8 @@
        A List of Labels
        -
        -
        A Filtering Condition
        +
        +
        2.2.1.1.3 A Filtering Condition

        Perhaps the most interesting (and useful) method of selecting data from a Series is by using a filtering condition.

        First, we apply a boolean operation to the Series. This creates a new Series of boolean values.

        @@ -457,12 +547,12 @@
        A Filtering Conditio
        -
        -

        DataFrames

        +
        +

        2.2.2 DataFrames

        Typically, we will work with Series using the perspective that they are columns in a DataFrame. We can think of a DataFrame as a collection of Series that all share the same Index.

        In Data 8, you encountered the Table class of the datascience library, which represented tabular data. In Data 100, we’ll be using the DataFrame class of the pandas library.

        -
        -

        Creating a DataFrame

        +
        +

        2.2.2.1 Creating a DataFrame

        There are many ways to create a DataFrame. Here, we will cover the most popular approaches:

        1. From a CSV file.
          2. @@ -471,15 +561,13 @@

          Creating a Data
        2. From a Series.

        More generally, the syntax for creating a DataFrame is: pandas.DataFrame(data, index, columns).

        -
        -
        From a CSV file
        +
        +
        2.2.2.1.1 From a CSV file

        In Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a DataFrame by passing the data path as an argument to the following pandas function.
         pd.read_csv("filename.csv")

        With our new understanding of pandas in hand, let’s return to the elections dataset from before. Now, we can recognize that it is represented as a pandas DataFrame.

        -
        import pandas as pd
-
-elections = pd.read_csv("data/elections.csv")
-elections
        +
        elections = pd.read_csv("data/elections.csv")
+elections
        @@ -605,8 +693,8 @@
        From a CSV file

        This code stores our DataFrame object in the elections variable. Upon inspection, our elections DataFrame has 182 rows and 6 columns (Year, Candidate, Party, Popular Vote, Result, %). Each row represents a single record — in our example, a presidential candidate from some particular year. Each column represents a single attribute or feature of the record.

        -
        -
        Using a List and Column Name(s)
        +
        +
        2.2.2.1.2 Using a List and Column Name(s)

        We’ll now explore creating a DataFrame with data of our own.

        Consider the following examples. The first code cell creates a DataFrame with a single column Numbers. The second creates a DataFrame with the columns Numbers and Description. Notice how a 2D list of values is required to initialize the second DataFrame — each nested list represents a single row of data.

        @@ -675,8 +763,8 @@
        Using a List
        -
        -
        From a Dictionary
        +
        +
        2.2.2.1.3 From a Dictionary

        A third (and more common) way to create a DataFrame is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.

        Below are two ways of implementing this approach. The first is based on specifying the columns of the DataFrame, whereas the second is based on specifying the rows of the DataFrame.

        @@ -744,8 +832,8 @@
        From a Dictionary
        -
        -
        From a Series
        +
        +
        2.2.2.1.4 From a Series

        Earlier, we explained how a Series was synonymous to a column in a DataFrame. It follows, then, that a DataFrame is equivalent to a collection of Series, which all share the same Index.

        In fact, we can initialize a DataFrame by merging two or more Series.

        @@ -856,8 +944,8 @@
        From a Series
        -
        -

        Indices

        +
        +

        2.2.3 Indices

        On a more technical note, an Index doesn’t have to be an integer, nor does it have to be unique. For example, we can set the index of the elections Dataframe to be the name of presidential candidates.

        # Creating a DataFrame from a CSV file and specifying the Index column
@@ -1116,11 +1204,12 @@ 
        Indices

        It is also important to note that the row labels that constitute an index don’t have to be unique. While index values can be unique and numeric, acting as a row number, they can also be named and non-unique.

        -

        +

        Here we see unique and numeric index values.

        +

        However, here the index values here are non-unique.

        -
        -

        DataFrame Attributes: Index, Columns, and Shape

        +
        +

        2.3 DataFrame Attributes: Index, Columns, and Shape

        On the other hand, column names in a DataFrame are almost always unique. Looking back to the elections dataset, it wouldn’t make sense to have two columns named “Candidate”.

        Sometimes, you’ll want to extract these different values, in particular, the list of row and column labels.

        For index/row labels, use DataFrame.index:

        @@ -1153,12 +1242,17 @@

        -

        Slicing in DataFrames

        +
        +

        2.4 Slicing in DataFrames

        Now that we’ve learned more about DataFrames, let’s dive deeper into their capabilities.

        The API (Application Programming Interface) for the DataFrame class is enormous. In this section, we’ll discuss several methods of the DataFrame API that allow us to extract subsets of data.

        The simplest way to manipulate a DataFrame is to extract a subset of rows and columns, known as slicing.

        -

        Common ways we may want to extract data are grabbing: - The first or last n rows in the DataFrame. - Data with a certain label. - Data at a certain position.

        +

        Common ways we may want to extract data are grabbing:

        +
          +
        • The first or last n rows in the DataFrame.
          • +
        • Data with a certain label.
          • +
        • Data at a certain position.
          • +

        We will do so with four primary methods of the DataFrame class:

        1. .head and .tail
          2. @@ -1166,8 +1260,8 @@

          Slicing in Dat
        2. .iloc
        3. []

        -
        -

        Extracting data with .head and .tail

        +
        +

        2.4.1 Extracting data with .head and .tail

        The simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the DataFrame.

        To extract the first n rows of a DataFrame df, we use the syntax df.head(n).

        @@ -1316,12 +1410,17 @@

        Extra

        -
        -

        Label-based Extraction: Indexing with .loc

        +
        +

        2.4.2 Label-based Extraction: Indexing with .loc

        For the more complex task of extracting data with specific column or index labels, we can use .loc. The .loc accessor allows us to specify the labels of rows and columns we wish to extract. The labels (commonly referred to as the indices) are the bold text on the far left of a DataFrame, while the column labels are the column names found at the top of a DataFrame.

        To grab data with .loc, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the .loc function; the column labels are the second.

        -

        Arguments to .loc can be: - A single value. - A slice. - A list.

        +

        Arguments to .loc can be:

        +
          +
        • A single value.
          • +
        • A slice.
          • +
        • A list.
          • +

        For example, to select a single value, we can select the row labeled 0 and the column labeled Candidate from the elections DataFrame.

        elections.loc[0, 'Candidate']
        @@ -1659,8 +1758,8 @@

        -
        -

        Integer-based Extraction: Indexing with .iloc

        +
        +

        2.4.3 Integer-based Extraction: Indexing with .iloc

        Slicing with .iloc works similarly to .loc. However, .iloc uses the index positions of rows and columns rather than the labels (think to yourself: loc uses lables; iloc uses indices). The arguments to the .iloc function also behave similarly — single values, lists, indices, and any combination of these are permitted.

        Let’s begin reproducing our results from above. We’ll begin by selecting the first presidential candidate in our elections DataFrame:

        @@ -1879,10 +1978,14 @@

        -

        Context-dependent Extraction: Indexing with []

        +
        +

        2.4.4 Context-dependent Extraction: Indexing with []

        The [] selection operator is the most baffling of all, yet the most commonly used. It only takes a single argument, which may be one of the following:

        1. A slice of row numbers.
          2. @@ -1890,8 +1993,8 @@

          A single-column label.

        That is, [] is context-dependent. Let’s see some examples.

        -
        -

        A slice of row numbers

        +
        +

        2.4.4.1 A slice of row numbers

        Say we wanted the first four rows of our elections DataFrame.

        elections[0:4]
        @@ -1955,8 +2058,8 @@

        A slice of row numb

        -
        -

        A list of column labels

        +
        +

        2.4.4.2 A list of column labels

        Suppose we now want the first four columns.

        elections[["Year", "Candidate", "Party", "Popular vote"]]
        @@ -2060,8 +2163,8 @@

        A list of column l

        -
        -

        A single-column label

        +
        +

        2.4.4.3 A single-column label

        Lastly, [] allows us to extract only the Candidate column.

        elections["Candidate"]
        @@ -2084,15 +2187,16 @@

        A single-column labe

        -
        -

        Parting Note

        +
        +

        2.5 Parting Note

        The pandas library is enormous and contains many useful functions. Here is a link to documentation. We certainly don’t expect you to memorize each and every method of the library.

        The introductory Data 100 pandas lectures will provide a high-level view of the key data structures and methods that will form the foundation of your pandas knowledge. A goal of this course is to help you build your familiarity with the real-world programming practice of …Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. Being able to search for, read, and implement documentation is an important life skill for any data scientist.

        With that, we will move on to Pandas II.

        + +
        - - + + diff --git a/docs/search.json b/docs/search.json index 58d497d1..87a2f5ff 100644 --- a/docs/search.json +++ b/docs/search.json @@ -26,5 +26,40 @@ "title": "1  Introduction", "section": "1.2 Conclusion", "text": "1.2 Conclusion\nThe data science lifecycle is meant to be a set of general guidelines rather than a hard set of requirements. In our journey exploring the lifecycle, we’ll cover both the underlying theory and technologies used in data science. By the end of the course, we hope that you start to see yourself as a data scientist.\nWith that, we’ll begin by introducing one of the most important tools in exploratory data analysis: pandas." + }, + { + "objectID": "pandas_1/pandas_1.html#tabular-data", + "href": "pandas_1/pandas_1.html#tabular-data", + "title": "2  Pandas I", + "section": "2.1 Tabular Data", + "text": "2.1 Tabular Data\nData scientists work with data stored in a variety of formats. The primary focus of this class is understanding tabular data — data that is stored in a table.\nTabular data is one of the most common systems that data scientists use to organize data. This is in large part due to the simplicity and flexibility of tables. Tables allow us to represent each observation, or instance of collecting data from an individual, as its own row. We can record each observation’s distinct characteristics, or features, in separate columns.\nTo see this in action, we’ll explore the elections dataset, which stores information about political candidates who ran for president of the United States in previous years.\n\n\nCode\nimport pandas as pd\npd.read_csv(\"data/elections.csv\")\n\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n1457226\nloss\n1.073699\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\nwin\n51.311515\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\nloss\n46.858542\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\nloss\n1.177979\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 6 columns\n\n\n\nIn the elections dataset, each row represents one instance of a candidate running for president in a particular year. For example, the first row represents Andrew Jackson running for president in the year 1824. Each column represents one characteristic piece of information about each presidential candidate. For example, the column named “Result” stores whether or not the candidate won the election.\nYour work in Data 8 helped you grow very familiar with using and interpreting data stored in a tabular format. Back then, you used the Table class of the datascience library, a special programming library created specifically for Data 8 students.\nIn Data 100, we will be working with the programming library pandas, which is generally accepted in the data science community as the industry- and academia-standard tool for manipulating tabular data (as well as the inspiration for Petey, our panda bear mascot).\nUsing pandas, we can\n\nArrange data in a tabular format.\nExtract useful information filtered by specific conditions.\nOperate on data to gain new insights.\nApply NumPy functions to our data (our friends from Data 8).\nPerform vectorized computations to speed up our analysis (Lab 1)." + }, + { + "objectID": "pandas_1/pandas_1.html#series-dataframes-and-indices", + "href": "pandas_1/pandas_1.html#series-dataframes-and-indices", + "title": "2  Pandas I", + "section": "2.2 Series, DataFrames, and Indices", + "text": "2.2 Series, DataFrames, and Indices\nTo begin our work in pandas, we must first import the library into our Python environment. This will allow us to use pandas data structures and methods in our code.\n\n# `pd` is the conventional alias for Pandas, as `np` is for NumPy\nimport pandas as pd\n\nThere are three fundamental data structures in pandas:\n\nSeries: 1D labeled array data; best thought of as columnar data.\nDataFrame: 2D tabular data with rows and columns.\nIndex: A sequence of row/column labels.\n\nDataFrames, Series, and Indices can be represented visually in the following diagram, which considers the first few rows of the elections dataset.\n\nNotice how the DataFrame is a two-dimensional object — it contains both rows and columns. The Series above is a singular column of this DataFrame, namely the Result column. Both contain an Index, or a shared list of row labels (the integers from 0 to 4, inclusive).\n\n2.2.1 Series\nA Series represents a column of a DataFrame; more generally, it can be any 1-dimensional array-like object. It contains:\n\nA sequence of values of the same type.\nA sequence of data labels called the index.\n\nIn the cell below, we create a Series named s.\n\ns = pd.Series([\"welcome\", \"to\", \"data 100\"])\ns\n\n0 welcome\n1 to\n2 data 100\ndtype: object\n\n\n\ns.values # Data values contained within the Series\n\narray(['welcome', 'to', 'data 100'], dtype=object)\n\n\n\ns.index # The Index of the Series\n\nRangeIndex(start=0, stop=3, step=1)\n\n\nBy default, the Index of a Series is a sequential list of integers beginning from 0. Optionally, a manually specified list of desired indices can be passed to the index argument.\n\ns = pd.Series([-1, 10, 2], index = [\"a\", \"b\", \"c\"])\ns\n\na -1\nb 10\nc 2\ndtype: int64\n\n\n\ns.index\n\nIndex(['a', 'b', 'c'], dtype='object')\n\n\nIndices can also be changed after initialization.\n\ns.index = [\"first\", \"second\", \"third\"]\ns\n\nfirst -1\nsecond 10\nthird 2\ndtype: int64\n\n\n\ns.index\n\nIndex(['first', 'second', 'third'], dtype='object')\n\n\n\n2.2.1.1 Selection in Series\nMuch like when working with NumPy arrays, we can select a single value or a set of values from a Series. To do so, there are three primary methods:\n\nA single label.\nA list of labels.\nA filtering condition.\n\nTo demonstrate this, let’s define the Series ser.\n\nser = pd.Series([4, -2, 0, 6], index = [\"a\", \"b\", \"c\", \"d\"])\nser\n\na 4\nb -2\nc 0\nd 6\ndtype: int64\n\n\n\n2.2.1.1.1 A Single Label\n\nser[\"a\"] # We return the value stored at the Index label \"a\"\n\n4\n\n\n\n\n2.2.1.1.2 A List of Labels\n\nser[[\"a\", \"c\"]] # We return a *Series* of the values stored at the Index labels \"a\" and \"c\"\n\na 4\nc 0\ndtype: int64\n\n\n\n\n2.2.1.1.3 A Filtering Condition\nPerhaps the most interesting (and useful) method of selecting data from a Series is by using a filtering condition.\nFirst, we apply a boolean operation to the Series. This creates a new Series of boolean values.\n\nser > 0 # Filter condition: select all elements greater than 0\n\na True\nb False\nc False\nd True\ndtype: bool\n\n\nWe then use this boolean condition to index into our original Series. pandas will select only the entries in the original Series that satisfy the condition.\n\nser[ser > 0] \n\na 4\nd 6\ndtype: int64\n\n\n\n\n\n\n2.2.2 DataFrames\nTypically, we will work with Series using the perspective that they are columns in a DataFrame. We can think of a DataFrame as a collection of Series that all share the same Index.\nIn Data 8, you encountered the Table class of the datascience library, which represented tabular data. In Data 100, we’ll be using the DataFrame class of the pandas library.\n\n2.2.2.1 Creating a DataFrame\nThere are many ways to create a DataFrame. Here, we will cover the most popular approaches:\n\nFrom a CSV file.\nUsing a list and column name(s).\nFrom a dictionary.\nFrom a Series.\n\nMore generally, the syntax for creating a DataFrame is: pandas.DataFrame(data, index, columns).\n\n2.2.2.1.1 From a CSV file\nIn Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a DataFrame by passing the data path as an argument to the following pandas function.  pd.read_csv(\"filename.csv\")\nWith our new understanding of pandas in hand, let’s return to the elections dataset from before. Now, we can recognize that it is represented as a pandas DataFrame.\n\nelections = pd.read_csv(\"data/elections.csv\")\nelections\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n1457226\nloss\n1.073699\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\nwin\n51.311515\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\nloss\n46.858542\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\nloss\n1.177979\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 6 columns\n\n\n\nThis code stores our DataFrame object in the elections variable. Upon inspection, our elections DataFrame has 182 rows and 6 columns (Year, Candidate, Party, Popular Vote, Result, %). Each row represents a single record — in our example, a presidential candidate from some particular year. Each column represents a single attribute or feature of the record.\n\n\n2.2.2.1.2 Using a List and Column Name(s)\nWe’ll now explore creating a DataFrame with data of our own.\nConsider the following examples. The first code cell creates a DataFrame with a single column Numbers. The second creates a DataFrame with the columns Numbers and Description. Notice how a 2D list of values is required to initialize the second DataFrame — each nested list represents a single row of data.\n\ndf_list = pd.DataFrame([1, 2, 3], columns=[\"Numbers\"])\ndf_list\n\n\n\n\n\n\n\n\nNumbers\n\n\n\n\n0\n1\n\n\n1\n2\n\n\n2\n3\n\n\n\n\n\n\n\n\ndf_list = pd.DataFrame([[1, \"one\"], [2, \"two\"]], columns = [\"Number\", \"Description\"])\ndf_list\n\n\n\n\n\n\n\n\nNumber\nDescription\n\n\n\n\n0\n1\none\n\n\n1\n2\ntwo\n\n\n\n\n\n\n\n\n\n2.2.2.1.3 From a Dictionary\nA third (and more common) way to create a DataFrame is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.\nBelow are two ways of implementing this approach. The first is based on specifying the columns of the DataFrame, whereas the second is based on specifying the rows of the DataFrame.\n\ndf_dict = pd.DataFrame({\"Fruit\": [\"Strawberry\", \"Orange\"], \"Price\": [5.49, 3.99]})\ndf_dict\n\n\n\n\n\n\n\n\nFruit\nPrice\n\n\n\n\n0\nStrawberry\n5.49\n\n\n1\nOrange\n3.99\n\n\n\n\n\n\n\n\ndf_dict = pd.DataFrame([{\"Fruit\":\"Strawberry\", \"Price\":5.49}, {\"Fruit\": \"Orange\", \"Price\":3.99}])\ndf_dict\n\n\n\n\n\n\n\n\nFruit\nPrice\n\n\n\n\n0\nStrawberry\n5.49\n\n\n1\nOrange\n3.99\n\n\n\n\n\n\n\n\n\n2.2.2.1.4 From a Series\nEarlier, we explained how a Series was synonymous to a column in a DataFrame. It follows, then, that a DataFrame is equivalent to a collection of Series, which all share the same Index.\nIn fact, we can initialize a DataFrame by merging two or more Series.\n\n# Notice how our indices, or row labels, are the same\n\ns_a = pd.Series([\"a1\", \"a2\", \"a3\"], index = [\"r1\", \"r2\", \"r3\"])\ns_b = pd.Series([\"b1\", \"b2\", \"b3\"], index = [\"r1\", \"r2\", \"r3\"])\n\npd.DataFrame({\"A-column\": s_a, \"B-column\": s_b})\n\n\n\n\n\n\n\n\nA-column\nB-column\n\n\n\n\nr1\na1\nb1\n\n\nr2\na2\nb2\n\n\nr3\na3\nb3\n\n\n\n\n\n\n\n\npd.DataFrame(s_a)\n\n\n\n\n\n\n\n\n0\n\n\n\n\nr1\na1\n\n\nr2\na2\n\n\nr3\na3\n\n\n\n\n\n\n\n\ns_a.to_frame()\n\n\n\n\n\n\n\n\n0\n\n\n\n\nr1\na1\n\n\nr2\na2\n\n\nr3\na3\n\n\n\n\n\n\n\n\n\n\n\n2.2.3 Indices\nOn a more technical note, an Index doesn’t have to be an integer, nor does it have to be unique. For example, we can set the index of the elections Dataframe to be the name of presidential candidates.\n\n# Creating a DataFrame from a CSV file and specifying the Index column\nelections = pd.read_csv(\"data/elections.csv\", index_col = \"Candidate\")\nelections\n\n\n\n\n\n\n\n\nYear\nParty\nPopular vote\nResult\n%\n\n\nCandidate\n\n\n\n\n\n\n\n\n\nAndrew Jackson\n1824\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\nJohn Quincy Adams\n1824\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\nAndrew Jackson\n1828\nDemocratic\n642806\nwin\n56.203927\n\n\nJohn Quincy Adams\n1828\nNational Republican\n500897\nloss\n43.796073\n\n\nAndrew Jackson\n1832\nDemocratic\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n\n\nJill Stein\n2016\nGreen\n1457226\nloss\n1.073699\n\n\nJoseph Biden\n2020\nDemocratic\n81268924\nwin\n51.311515\n\n\nDonald Trump\n2020\nRepublican\n74216154\nloss\n46.858542\n\n\nJo Jorgensen\n2020\nLibertarian\n1865724\nloss\n1.177979\n\n\nHoward Hawkins\n2020\nGreen\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 5 columns\n\n\n\nWe can also select a new column and set it as the index of the DataFrame. For example, we can set the index of the elections Dataframe to represent the candidate’s party.\n\nelections.reset_index(inplace = True) # Resetting the index so we can set the Index again\n# This sets the index to the \"Party\" column\nelections.set_index(\"Party\")\n\n\n\n\n\n\n\n\nCandidate\nYear\nPopular vote\nResult\n%\n\n\nParty\n\n\n\n\n\n\n\n\n\nDemocratic-Republican\nAndrew Jackson\n1824\n151271\nloss\n57.210122\n\n\nDemocratic-Republican\nJohn Quincy Adams\n1824\n113142\nwin\n42.789878\n\n\nDemocratic\nAndrew Jackson\n1828\n642806\nwin\n56.203927\n\n\nNational Republican\nJohn Quincy Adams\n1828\n500897\nloss\n43.796073\n\n\nDemocratic\nAndrew Jackson\n1832\n702735\nwin\n54.574789\n\n\n...\n...\n...\n...\n...\n...\n\n\nGreen\nJill Stein\n2016\n1457226\nloss\n1.073699\n\n\nDemocratic\nJoseph Biden\n2020\n81268924\nwin\n51.311515\n\n\nRepublican\nDonald Trump\n2020\n74216154\nloss\n46.858542\n\n\nLibertarian\nJo Jorgensen\n2020\n1865724\nloss\n1.177979\n\n\nGreen\nHoward Hawkins\n2020\n405035\nloss\n0.255731\n\n\n\n\n182 rows × 5 columns\n\n\n\nAnd, if we’d like, we can revert the index back to the default list of integers.\n\n# This resets the index to be the default list of integer\nelections.reset_index(inplace=True) \nelections.index\n\nRangeIndex(start=0, stop=182, step=1)\n\n\nIt is also important to note that the row labels that constitute an index don’t have to be unique. While index values can be unique and numeric, acting as a row number, they can also be named and non-unique.\nHere we see unique and numeric index values. \nHowever, here the index values here are non-unique." + }, + { + "objectID": "pandas_1/pandas_1.html#dataframe-attributes-index-columns-and-shape", + "href": "pandas_1/pandas_1.html#dataframe-attributes-index-columns-and-shape", + "title": "2  Pandas I", + "section": "2.3 DataFrame Attributes: Index, Columns, and Shape", + "text": "2.3 DataFrame Attributes: Index, Columns, and Shape\nOn the other hand, column names in a DataFrame are almost always unique. Looking back to the elections dataset, it wouldn’t make sense to have two columns named “Candidate”.\nSometimes, you’ll want to extract these different values, in particular, the list of row and column labels.\nFor index/row labels, use DataFrame.index:\n\nelections.set_index(\"Party\", inplace = True)\nelections.index\n\nIndex(['Democratic-Republican', 'Democratic-Republican', 'Democratic',\n 'National Republican', 'Democratic', 'National Republican',\n 'Anti-Masonic', 'Whig', 'Democratic', 'Whig',\n ...\n 'Constitution', 'Republican', 'Independent', 'Libertarian',\n 'Democratic', 'Green', 'Democratic', 'Republican', 'Libertarian',\n 'Green'],\n dtype='object', name='Party', length=182)\n\n\nFor column labels, use DataFrame.columns:\n\nelections.columns\n\nIndex(['index', 'Candidate', 'Year', 'Popular vote', 'Result', '%'], dtype='object')\n\n\nAnd for the shape of the DataFrame, we can use DataFrame.shape:\n\nelections.shape\n\n(182, 6)" + }, + { + "objectID": "pandas_1/pandas_1.html#slicing-in-dataframes", + "href": "pandas_1/pandas_1.html#slicing-in-dataframes", + "title": "2  Pandas I", + "section": "2.4 Slicing in DataFrames", + "text": "2.4 Slicing in DataFrames\nNow that we’ve learned more about DataFrames, let’s dive deeper into their capabilities.\nThe API (Application Programming Interface) for the DataFrame class is enormous. In this section, we’ll discuss several methods of the DataFrame API that allow us to extract subsets of data.\nThe simplest way to manipulate a DataFrame is to extract a subset of rows and columns, known as slicing.\nCommon ways we may want to extract data are grabbing:\n\nThe first or last n rows in the DataFrame.\nData with a certain label.\nData at a certain position.\n\nWe will do so with four primary methods of the DataFrame class:\n\n.head and .tail\n.loc\n.iloc\n[]\n\n\n2.4.1 Extracting data with .head and .tail\nThe simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the DataFrame.\nTo extract the first n rows of a DataFrame df, we use the syntax df.head(n).\n\nelections = pd.read_csv(\"data/elections.csv\")\n\n# Extract the first 5 rows of the DataFrame\nelections.head(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\nwin\n54.574789\n\n\n\n\n\n\n\nSimilarly, calling df.tail(n) allows us to extract the last n rows of the DataFrame.\n\n# Extract the last 5 rows of the DataFrame\nelections.tail(5)\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n177\n2016\nJill Stein\nGreen\n1457226\nloss\n1.073699\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\nwin\n51.311515\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\nloss\n46.858542\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\nloss\n1.177979\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\nloss\n0.255731\n\n\n\n\n\n\n\n\n\n2.4.2 Label-based Extraction: Indexing with .loc\nFor the more complex task of extracting data with specific column or index labels, we can use .loc. The .loc accessor allows us to specify the labels of rows and columns we wish to extract. The labels (commonly referred to as the indices) are the bold text on the far left of a DataFrame, while the column labels are the column names found at the top of a DataFrame.\n\nTo grab data with .loc, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the .loc function; the column labels are the second.\nArguments to .loc can be:\n\nA single value.\nA slice.\nA list.\n\nFor example, to select a single value, we can select the row labeled 0 and the column labeled Candidate from the elections DataFrame.\n\nelections.loc[0, 'Candidate']\n\n'Andrew Jackson'\n\n\nKeep in mind that passing in just one argument as a single value will produce a Series. Below, we’ve extracted a subset of the \"Popular vote\" column as a Series.\n\nelections.loc[[87, 25, 179], \"Popular vote\"]\n\n87 15761254\n25 848019\n179 74216154\nName: Popular vote, dtype: int64\n\n\nTo select multiple rows and columns, we can use Python slice notation. Here, we select the rows from labels 0 to 3 and the columns from labels \"Year\" to \"Popular vote\".\n\nelections.loc[0:3, 'Year':'Popular vote']\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nSuppose that instead, we want to extract all column values for the first four rows in the elections DataFrame. The shorthand : is useful for this.\n\nelections.loc[0:3, :]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\nWe can use the same shorthand to extract all rows.\n\nelections.loc[:, [\"Year\", \"Candidate\", \"Result\"]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nResult\n\n\n\n\n0\n1824\nAndrew Jackson\nloss\n\n\n1\n1824\nJohn Quincy Adams\nwin\n\n\n2\n1828\nAndrew Jackson\nwin\n\n\n3\n1828\nJohn Quincy Adams\nloss\n\n\n4\n1832\nAndrew Jackson\nwin\n\n\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nloss\n\n\n178\n2020\nJoseph Biden\nwin\n\n\n179\n2020\nDonald Trump\nloss\n\n\n180\n2020\nJo Jorgensen\nloss\n\n\n181\n2020\nHoward Hawkins\nloss\n\n\n\n\n182 rows × 3 columns\n\n\n\nThere are a couple of things we should note. Firstly, unlike conventional Python, pandas allows us to slice string values (in our example, the column labels). Secondly, slicing with .loc is inclusive. Notice how our resulting DataFrame includes every row and column between and including the slice labels we specified.\nEquivalently, we can use a list to obtain multiple rows and columns in our elections DataFrame.\n\nelections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nLastly, we can interchange list and slicing notation.\n\nelections.loc[[0, 1, 2, 3], :]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\n\n\n2.4.3 Integer-based Extraction: Indexing with .iloc\nSlicing with .iloc works similarly to .loc. However, .iloc uses the index positions of rows and columns rather than the labels (think to yourself: loc uses lables; iloc uses indices). The arguments to the .iloc function also behave similarly — single values, lists, indices, and any combination of these are permitted.\nLet’s begin reproducing our results from above. We’ll begin by selecting the first presidential candidate in our elections DataFrame:\n\n# elections.loc[0, \"Candidate\"] - Previous approach\nelections.iloc[0, 1]\n\n'Andrew Jackson'\n\n\nNotice how the first argument to both .loc and .iloc are the same. This is because the row with a label of 0 is conveniently in the \\(0^{th}\\) (equivalently, the first position) of the elections DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.\nAnd, as before, if we were to pass in only one single value argument, our result would be a Series.\n\nelections.iloc[[1,2,3],1]\n\n1 John Quincy Adams\n2 Andrew Jackson\n3 John Quincy Adams\nName: Candidate, dtype: object\n\n\nHowever, when we select the first four rows and columns using .iloc, we notice something.\n\n# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach\nelections.iloc[0:4, 0:4]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nSlicing is no longer inclusive in .iloc — it’s exclusive. In other words, the right end of a slice is not included when using .iloc. This is one of the subtleties of pandas syntax; you will get used to it with practice.\nList behavior works just as expected.\n\n#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach\nelections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n\n\n\n\n\nAnd just like with .loc, we can use a colon with .iloc to extract all rows or columns.\n\nelections.iloc[:, 0:3]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n\n\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n\n\n178\n2020\nJoseph Biden\nDemocratic\n\n\n179\n2020\nDonald Trump\nRepublican\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n\n\n181\n2020\nHoward Hawkins\nGreen\n\n\n\n\n182 rows × 3 columns\n\n\n\nThis discussion begs the question: When should we use .loc vs. .iloc? In most cases, .loc is generally safer to use. You can imagine .iloc may return incorrect values when applied to a dataset where the ordering of data can change. However, .iloc can still be useful — for example, if you are looking at a DataFrame of sorted movie earnings and want to get the median earnings for a given year, you can use .iloc to index into the middle.\nOverall, it is important to remember that:\n\n.loc performances label-based extraction.\n.iloc performs integer-based extraction.\n\n\n\n2.4.4 Context-dependent Extraction: Indexing with []\nThe [] selection operator is the most baffling of all, yet the most commonly used. It only takes a single argument, which may be one of the following:\n\nA slice of row numbers.\nA list of column labels.\nA single-column label.\n\nThat is, [] is context-dependent. Let’s see some examples.\n\n2.4.4.1 A slice of row numbers\nSay we wanted the first four rows of our elections DataFrame.\n\nelections[0:4]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\nResult\n%\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\nloss\n57.210122\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\nwin\n42.789878\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\nwin\n56.203927\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\nloss\n43.796073\n\n\n\n\n\n\n\n\n\n2.4.4.2 A list of column labels\nSuppose we now want the first four columns.\n\nelections[[\"Year\", \"Candidate\", \"Party\", \"Popular vote\"]]\n\n\n\n\n\n\n\n\nYear\nCandidate\nParty\nPopular vote\n\n\n\n\n0\n1824\nAndrew Jackson\nDemocratic-Republican\n151271\n\n\n1\n1824\nJohn Quincy Adams\nDemocratic-Republican\n113142\n\n\n2\n1828\nAndrew Jackson\nDemocratic\n642806\n\n\n3\n1828\nJohn Quincy Adams\nNational Republican\n500897\n\n\n4\n1832\nAndrew Jackson\nDemocratic\n702735\n\n\n...\n...\n...\n...\n...\n\n\n177\n2016\nJill Stein\nGreen\n1457226\n\n\n178\n2020\nJoseph Biden\nDemocratic\n81268924\n\n\n179\n2020\nDonald Trump\nRepublican\n74216154\n\n\n180\n2020\nJo Jorgensen\nLibertarian\n1865724\n\n\n181\n2020\nHoward Hawkins\nGreen\n405035\n\n\n\n\n182 rows × 4 columns\n\n\n\n\n\n2.4.4.3 A single-column label\nLastly, [] allows us to extract only the Candidate column.\n\nelections[\"Candidate\"]\n\n0 Andrew Jackson\n1 John Quincy Adams\n2 Andrew Jackson\n3 John Quincy Adams\n4 Andrew Jackson\n ... \n177 Jill Stein\n178 Joseph Biden\n179 Donald Trump\n180 Jo Jorgensen\n181 Howard Hawkins\nName: Candidate, Length: 182, dtype: object\n\n\nThe output is a Series! In this course, we’ll become very comfortable with [], especially for selecting columns. In practice, [] is much more common than .loc, especially since it is far more concise." + }, + { + "objectID": "pandas_1/pandas_1.html#parting-note", + "href": "pandas_1/pandas_1.html#parting-note", + "title": "2  Pandas I", + "section": "2.5 Parting Note", + "text": "2.5 Parting Note\nThe pandas library is enormous and contains many useful functions. Here is a link to documentation. We certainly don’t expect you to memorize each and every method of the library.\nThe introductory Data 100 pandas lectures will provide a high-level view of the key data structures and methods that will form the foundation of your pandas knowledge. A goal of this course is to help you build your familiarity with the real-world programming practice of …Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. Being able to search for, read, and implement documentation is an important life skill for any data scientist.\nWith that, we will move on to Pandas II." } ] \ No newline at end of file diff --git a/pandas_1/pandas_1.ipynb b/pandas_1/pandas_1.ipynb deleted file mode 100644 index 21cf7784..00000000 --- a/pandas_1/pandas_1.ipynb +++ /dev/null @@ -1,1431 +0,0 @@ -{ - "cells": [ - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "---\n", - "title: Pandas I\n", - "format:\n", - " html:\n", - " toc: true\n", - " toc-depth: 5\n", - " toc-location: right\n", - " code-fold: false\n", - " theme:\n", - " - cosmo\n", - " - cerulean\n", - " callout-icon: false\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "::: {.callout-note collapse=\"false\"}\n", - "## Learning Outcomes\n", - "\n", - "- Build familiarity with `pandas` and `pandas` syntax.\n", - "- Learn key data structures: `DataFrame`, `Series`, and `Index`.\n", - "- Understand methods for extracting data: `.loc`, `.iloc`, and `[]`.\n", - ":::\n", - "\n", - "In this sequence of lectures, we will dive right into things by having you explore and manipulate real-world data. We'll first introduce `pandas`, a popular Python library for interacting with **tabular data**.\n", - "\n", - "## Tabular Data\n", - "\n", - "Data scientists work with data stored in a variety of formats. The primary focus of this class is understanding *tabular data* — data that is stored in a table.\n", - "\n", - "Tabular data is one of the most common systems that data scientists use to organize data. This is in large part due to the simplicity and flexibility of tables. Tables allow us to represent each **observation**, or instance of collecting data from an individual, as its own *row*. We can record each observation's distinct characteristics, or **features**, in separate *columns*.\n", - "\n", - "To see this in action, we'll explore the `elections` dataset, which stores information about political candidates who ran for president of the United States in previous years." - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        YearCandidatePartyPopular voteResult%
        01824Andrew JacksonDemocratic-Republican151271loss57.210122
        11824John Quincy AdamsDemocratic-Republican113142win42.789878
        21828Andrew JacksonDemocratic642806win56.203927
        31828John Quincy AdamsNational Republican500897loss43.796073
        41832Andrew JacksonDemocratic702735win54.574789
        .....................
        1772016Jill SteinGreen1457226loss1.073699
        1782020Joseph BidenDemocratic81268924win51.311515
        1792020Donald TrumpRepublican74216154loss46.858542
        1802020Jo JorgensenLibertarian1865724loss1.177979
        1812020Howard HawkinsGreen405035loss0.255731
        \n", - "

        182 rows × 6 columns

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        " - ], - "text/plain": [ - " Year Candidate Party Popular vote Result \\\n", - "0 1824 Andrew Jackson Democratic-Republican 151271 loss \n", - "1 1824 John Quincy Adams Democratic-Republican 113142 win \n", - "2 1828 Andrew Jackson Democratic 642806 win \n", - "3 1828 John Quincy Adams National Republican 500897 loss \n", - "4 1832 Andrew Jackson Democratic 702735 win \n", - ".. ... ... ... ... ... \n", - "177 2016 Jill Stein Green 1457226 loss \n", - "178 2020 Joseph Biden Democratic 81268924 win \n", - "179 2020 Donald Trump Republican 74216154 loss \n", - "180 2020 Jo Jorgensen Libertarian 1865724 loss \n", - "181 2020 Howard Hawkins Green 405035 loss \n", - "\n", - " % \n", - "0 57.210122 \n", - "1 42.789878 \n", - "2 56.203927 \n", - "3 43.796073 \n", - "4 54.574789 \n", - ".. ... \n", - "177 1.073699 \n", - "178 51.311515 \n", - "179 46.858542 \n", - "180 1.177979 \n", - "181 0.255731 \n", - "\n", - "[182 rows x 6 columns]" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#| code-fold: true\n", - "import pandas as pd\n", - "pd.read_csv(\"data/elections.csv\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the `elections` dataset, each row represents one instance of a candidate running for president in a particular year. For example, the first row represents Andrew Jackson running for president in the year 1824. Each column represents one characteristic piece of information about each presidential candidate. For example, the column named \"Result\" stores whether or not the candidate won the election. \n", - "\n", - "Your work in Data 8 helped you grow very familiar with using and interpreting data stored in a tabular format. Back then, you used the `Table` class of the `datascience` library, a special programming library created specifically for Data 8 students.\n", - "\n", - "In Data 100, we will be working with the programming library `pandas`, which is generally accepted in the data science community as the industry- and academia-standard tool for manipulating tabular data (as well as the inspiration for Petey, our panda bear mascot).\n", - "\n", - "Using `pandas`, we can\n", - "\n", - "- Arrange data in a tabular format.\n", - "- Extract useful information filtered by specific conditions.\n", - "- Operate on data to gain new insights.\n", - "- Apply `NumPy` functions to our data (our friends from Data 8).\n", - "- Perform vectorized computations to speed up our analysis (Lab 1).\n", - "\n", - "## `Series`, `DataFrame`s, and Indices\n", - "\n", - "To begin our work in `pandas`, we must first import the library into our Python environment. This will allow us to use `pandas` data structures and methods in our code." - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [], - "source": [ - "# `pd` is the conventional alias for Pandas, as `np` is for NumPy\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are three fundamental data structures in `pandas`:\n", - "\n", - "1. **Series**: 1D labeled array data; best thought of as columnar data.\n", - "2. **DataFrame**: 2D tabular data with rows and columns.\n", - "3. **Index**: A sequence of row/column labels.\n", - "\n", - "`DataFrame`s, `Series`, and Indices can be represented visually in the following diagram, which considers the first few rows of the `elections` dataset.\n", - "\n", - "![](images/df_elections.png)\n", - "\n", - "Notice how the **DataFrame** is a two-dimensional object — it contains both rows and columns. The **Series** above is a singular column of this `DataFrame`, namely the `Result` column. Both contain an **Index**, or a shared list of row labels (the integers from 0 to 4, inclusive).\n", - "\n", - "### Series\n", - "\n", - "A Series represents a column of a `DataFrame`; more generally, it can be any 1-dimensional array-like object. It contains:\n", - "\n", - "- A sequence of **values** of the same type.\n", - "- A sequence of data labels called the **index**.\n", - "\n", - "In the cell below, we create a `Series` named `s`." - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 welcome\n", - "1 to\n", - "2 data 100\n", - "dtype: object" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = pd.Series([\"welcome\", \"to\", \"data 100\"])\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['welcome', 'to', 'data 100'], dtype=object)" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.values # Data values contained within the Series" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RangeIndex(start=0, stop=3, step=1)" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.index # The Index of the Series" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, the Index of a Series is a sequential list of integers beginning from 0. Optionally, a manually specified list of desired indices can be passed to the `index` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "a -1\n", - "b 10\n", - "c 2\n", - "dtype: int64" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s = pd.Series([-1, 10, 2], index = [\"a\", \"b\", \"c\"])\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['a', 'b', 'c'], dtype='object')" - ] - }, - "execution_count": 104, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Indices can also be changed after initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "first -1\n", - "second 10\n", - "third 2\n", - "dtype: int64" - ] - }, - "execution_count": 105, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.index = [\"first\", \"second\", \"third\"]\n", - "s" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['first', 'second', 'third'], dtype='object')" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "s.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Selection in `Series`\n", - "\n", - "Much like when working with `NumPy` arrays, we can select a single value or a set of values from a `Series`. To do so, there are three primary methods:\n", - "\n", - "1. A single label.\n", - "2. A list of labels.\n", - "3. A filtering condition.\n", - "\n", - "To demonstrate this, let's define the Series `ser`." - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "a 4\n", - "b -2\n", - "c 0\n", - "d 6\n", - "dtype: int64" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ser = pd.Series([4, -2, 0, 6], index = [\"a\", \"b\", \"c\", \"d\"])\n", - "ser" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### A Single Label" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ser[\"a\"] # We return the value stored at the Index label \"a\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### A List of Labels" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "a 4\n", - "c 0\n", - "dtype: int64" - ] - }, - "execution_count": 109, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ser[[\"a\", \"c\"]] # We return a *Series* of the values stored at the Index labels \"a\" and \"c\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### A Filtering Condition\n", - "\n", - "Perhaps the most interesting (and useful) method of selecting data from a Series is by using a filtering condition. \n", - "\n", - "First, we apply a boolean operation to the `Series`. This creates **a new Series of boolean values**." - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "a True\n", - "b False\n", - "c False\n", - "d True\n", - "dtype: bool" - ] - }, - "execution_count": 110, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ser > 0 # Filter condition: select all elements greater than 0" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We then use this boolean condition to index into our original `Series`. `pandas` will select only the entries in the original `Series` that satisfy the condition." - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "a 4\n", - "d 6\n", - "dtype: int64" - ] - }, - "execution_count": 111, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ser[ser > 0] " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### DataFrames\n", - "\n", - "Typically, we will work with `Series` using the perspective that they are columns in a `DataFrame`. We can think of a **DataFrame** as a collection of **Series** that all share the same **Index**. \n", - "\n", - "In Data 8, you encountered the `Table` class of the `datascience` library, which represented tabular data. In Data 100, we'll be using the `DataFrame` class of the `pandas` library.\n", - "\n", - "#### Creating a `DataFrame`\n", - "\n", - "There are many ways to create a `DataFrame`. Here, we will cover the most popular approaches:\n", - "\n", - "1. From a CSV file.\n", - "2. Using a list and column name(s).\n", - "3. From a dictionary.\n", - "4. From a `Series`.\n", - "\n", - "More generally, the syntax for creating a `DataFrame` is: `pandas.DataFrame(data, index, columns)`.\n", - "\n", - "##### From a CSV file\n", - "In Data 100, our data are typically stored in a CSV (comma-separated values) file format. We can import a CSV file into a `DataFrame` by passing the data path as an argument to the following ````pandas```` function. \n", - "
         ```` pd.read_csv(\"filename.csv\") ```` \n", - "\n", - "With our new understanding of `pandas` in hand, let's return to the `elections` dataset from before. Now, we can recognize that it is represented as a `pandas` DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": 112, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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        YearCandidatePartyPopular voteResult%
        01824Andrew JacksonDemocratic-Republican151271loss57.210122
        11824John Quincy AdamsDemocratic-Republican113142win42.789878
        21828Andrew JacksonDemocratic642806win56.203927
        31828John Quincy AdamsNational Republican500897loss43.796073
        41832Andrew JacksonDemocratic702735win54.574789
        .....................
        1772016Jill SteinGreen1457226loss1.073699
        1782020Joseph BidenDemocratic81268924win51.311515
        1792020Donald TrumpRepublican74216154loss46.858542
        1802020Jo JorgensenLibertarian1865724loss1.177979
        1812020Howard HawkinsGreen405035loss0.255731
        \n", - "

        182 rows × 6 columns

        \n", - "
        " - ], - "text/plain": [ - " Year Candidate Party Popular vote Result \\\n", - "0 1824 Andrew Jackson Democratic-Republican 151271 loss \n", - "1 1824 John Quincy Adams Democratic-Republican 113142 win \n", - "2 1828 Andrew Jackson Democratic 642806 win \n", - "3 1828 John Quincy Adams National Republican 500897 loss \n", - "4 1832 Andrew Jackson Democratic 702735 win \n", - ".. ... ... ... ... ... \n", - "177 2016 Jill Stein Green 1457226 loss \n", - "178 2020 Joseph Biden Democratic 81268924 win \n", - "179 2020 Donald Trump Republican 74216154 loss \n", - "180 2020 Jo Jorgensen Libertarian 1865724 loss \n", - "181 2020 Howard Hawkins Green 405035 loss \n", - "\n", - " % \n", - "0 57.210122 \n", - "1 42.789878 \n", - "2 56.203927 \n", - "3 43.796073 \n", - "4 54.574789 \n", - ".. ... \n", - "177 1.073699 \n", - "178 51.311515 \n", - "179 46.858542 \n", - "180 1.177979 \n", - "181 0.255731 \n", - "\n", - "[182 rows x 6 columns]" - ] - }, - "execution_count": 112, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "elections = pd.read_csv(\"data/elections.csv\")\n", - "elections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This code stores our `DataFrame` object in the ````elections```` variable. Upon inspection, our ````elections```` DataFrame has 182 rows and 6 columns (`Year`, `Candidate`, `Party`, `Popular Vote`, `Result`, `%`). Each row represents a single record — in our example, a presidential candidate from some particular year. Each column represents a single attribute or feature of the record.\n", - "\n", - "##### Using a List and Column Name(s)\n", - "\n", - "We'll now explore creating a `DataFrame` with data of our own.\n", - "\n", - "Consider the following examples. The first code cell creates a `DataFrame` with a single column `Numbers`. The second creates a `DataFrame` with the columns `Numbers` and `Description`. Notice how a 2D list of values is required to initialize the second `DataFrame` — each nested list represents a single row of data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_list = pd.DataFrame([1, 2, 3], columns=[\"Numbers\"])\n", - "df_list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_list = pd.DataFrame([[1, \"one\"], [2, \"two\"]], columns = [\"Number\", \"Description\"])\n", - "df_list" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### From a Dictionary\n", - "\n", - "A third (and more common) way to create a `DataFrame` is with a dictionary. The dictionary keys represent the column names, and the dictionary values represent the column values.\n", - "\n", - "Below are two ways of implementing this approach. The first is based on specifying the columns of the `DataFrame`, whereas the second is based on specifying the rows of the `DataFrame`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_dict = pd.DataFrame({\"Fruit\": [\"Strawberry\", \"Orange\"], \"Price\": [5.49, 3.99]})\n", - "df_dict" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_dict = pd.DataFrame([{\"Fruit\":\"Strawberry\", \"Price\":5.49}, {\"Fruit\": \"Orange\", \"Price\":3.99}])\n", - "df_dict" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### From a `Series`\n", - "\n", - "Earlier, we explained how a `Series` was synonymous to a column in a `DataFrame`. It follows, then, that a `DataFrame` is equivalent to a collection of `Series`, which all share the same `Index`. \n", - "\n", - "In fact, we can initialize a `DataFrame` by merging two or more `Series`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Notice how our indices, or row labels, are the same\n", - "\n", - "s_a = pd.Series([\"a1\", \"a2\", \"a3\"], index = [\"r1\", \"r2\", \"r3\"])\n", - "s_b = pd.Series([\"b1\", \"b2\", \"b3\"], index = [\"r1\", \"r2\", \"r3\"])\n", - "\n", - "pd.DataFrame({\"A-column\": s_a, \"B-column\": s_b})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(s_a)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "s_a.to_frame()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indices\n", - "\n", - "On a more technical note, an `Index` doesn't have to be an integer, nor does it have to be unique. For example, we can set the index of the `elections` Dataframe to be the name of presidential candidates. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Creating a DataFrame from a CSV file and specifying the Index column\n", - "elections = pd.read_csv(\"data/elections.csv\", index_col = \"Candidate\")\n", - "elections" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also select a new column and set it as the index of the DataFrame. For example, we can set the index of the `elections` Dataframe to represent the candidate's party." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.reset_index(inplace = True) # Resetting the index so we can set the Index again\n", - "# This sets the index to the \"Party\" column\n", - "elections.set_index(\"Party\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And, if we'd like, we can revert the index back to the default list of integers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This resets the index to be the default list of integer\n", - "elections.reset_index(inplace=True) \n", - "elections.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is also important to note that the row labels that constitute an index don't have to be unique. While index values can be unique and numeric, acting as a row number, they can also be named and non-unique. \n", - "\n", - "Here we see unique and numeric index values.\n", - "![](images/uniqueindex.png)\n", - "\n", - "However, here the index values here are non-unique. \n", - "![](images/non-uniqueindex.png)\n", - "\n", - "## `DataFrame` Attributes: Index, Columns, and Shape\n", - "\n", - "On the other hand, column names in a `DataFrame` are almost always unique. Looking back to the `elections` dataset, it wouldn't make sense to have two columns named \"Candidate\".\n", - "\n", - "Sometimes, you'll want to extract these different values, in particular, the list of row and column labels.\n", - "\n", - "For index/row labels, use `DataFrame.index`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.set_index(\"Party\", inplace = True)\n", - "elections.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For column labels, use `DataFrame.columns`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And for the shape of the DataFrame, we can use `DataFrame.shape`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Slicing in `DataFrame`s\n", - "\n", - "Now that we've learned more about `DataFrame`s, let's dive deeper into their capabilities. \n", - "\n", - "The API (Application Programming Interface) for the `DataFrame` class is enormous. In this section, we'll discuss several methods of the `DataFrame` API that allow us to extract subsets of data.\n", - "\n", - "The simplest way to manipulate a `DataFrame` is to extract a subset of rows and columns, known as **slicing**. \n", - "\n", - "Common ways we may want to extract data are grabbing:\n", - "\n", - "- The first or last `n` rows in the `DataFrame`.\n", - "- Data with a certain label.\n", - "- Data at a certain position.\n", - "\n", - "We will do so with four primary methods of the DataFrame class:\n", - "\n", - "1. `.head` and `.tail`\n", - "2. `.loc`\n", - "3. `.iloc`\n", - "4. `[]`\n", - "\n", - "### Extracting data with `.head` and `.tail`\n", - "\n", - "The simplest scenario in which we want to extract data is when we simply want to select the first or last few rows of the `DataFrame`.\n", - "\n", - "To extract the first `n` rows of a DataFrame `df`, we use the syntax `df.head(n)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections = pd.read_csv(\"data/elections.csv\")\n", - "\n", - "# Extract the first 5 rows of the DataFrame\n", - "elections.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, calling `df.tail(n)` allows us to extract the last `n` rows of the DataFrame. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Extract the last 5 rows of the DataFrame\n", - "elections.tail(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Label-based Extraction: Indexing with `.loc`\n", - "\n", - "For the more complex task of extracting data with specific column or index labels, we can use `.loc`. The `.loc` accessor allows us to specify the ***labels*** of rows and columns we wish to extract. The **labels** (commonly referred to as the **indices**) are the bold text on the far *left* of a DataFrame, while the **column labels** are the column names found at the *top* of a DataFrame.\n", - "\n", - "![](images/locgraphic.png)\n", - "\n", - "To grab data with `.loc`, we must specify the row and column label(s) where the data exists. The row labels are the first argument to the `.loc` function; the column labels are the second.\n", - "\n", - "Arguments to `.loc` can be:\n", - "\n", - "- A single value.\n", - "- A slice.\n", - "- A list.\n", - "\n", - "For example, to select a single value, we can select the row labeled `0` and the column labeled `Candidate` from the `elections` `DataFrame`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[0, 'Candidate']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Keep in mind that passing in just one argument as a single value will produce a `Series`. Below, we've extracted a subset of the `\"Popular vote\"` column as a `Series`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[[87, 25, 179], \"Popular vote\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To select *multiple* rows and columns, we can use Python slice notation. Here, we select the rows from labels `0` to `3` and the columns from labels `\"Year\"` to `\"Popular vote\"`. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[0:3, 'Year':'Popular vote']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Suppose that instead, we want to extract *all* column values for the first four rows in the `elections` DataFrame. The shorthand `:` is useful for this." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[0:3, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the same shorthand to extract all rows. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[:, [\"Year\", \"Candidate\", \"Result\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are a couple of things we should note. Firstly, unlike conventional Python, `pandas` allows us to slice string values (in our example, the column labels). Secondly, slicing with `.loc` is *inclusive*. Notice how our resulting `DataFrame` includes every row and column between and including the slice labels we specified.\n", - "\n", - "Equivalently, we can use a list to obtain multiple rows and columns in our `elections` DataFrame. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, we can interchange list and slicing notation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.loc[[0, 1, 2, 3], :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Integer-based Extraction: Indexing with `.iloc`\n", - "\n", - "Slicing with `.iloc` works similarly to `.loc`. However, `.iloc` uses the *index positions* of rows and columns rather than the labels (think to yourself: **l**oc uses **l**ables; **i**loc uses **i**ndices). The arguments to the `.iloc` function also behave similarly — single values, lists, indices, and any combination of these are permitted. \n", - "\n", - "Let's begin reproducing our results from above. We'll begin by selecting the first presidential candidate in our `elections` DataFrame:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# elections.loc[0, \"Candidate\"] - Previous approach\n", - "elections.iloc[0, 1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the first argument to both `.loc` and `.iloc` are the same. This is because the row with a label of 0 is conveniently in the $0^{th}$ (equivalently, the first position) of the `elections` DataFrame. Generally, this is true of any DataFrame where the row labels are incremented in ascending order from 0.\n", - "\n", - "And, as before, if we were to pass in only one single value argument, our result would be a `Series`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.iloc[[1,2,3],1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, when we select the first four rows and columns using `.iloc`, we notice something." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# elections.loc[0:3, 'Year':'Popular vote'] - Previous approach\n", - "elections.iloc[0:4, 0:4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Slicing is no longer inclusive in `.iloc` — it's *exclusive*. In other words, the right end of a slice is not included when using `.iloc`. This is one of the subtleties of `pandas` syntax; you will get used to it with practice.\n", - "\n", - "List behavior works just as expected." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#elections.loc[[0, 1, 2, 3], ['Year', 'Candidate', 'Party', 'Popular vote']] - Previous Approach\n", - "elections.iloc[[0, 1, 2, 3], [0, 1, 2, 3]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And just like with `.loc`, we can use a colon with `.iloc` to extract all rows or columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections.iloc[:, 0:3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This discussion begs the question: When should we use `.loc` vs. `.iloc`? In most cases, `.loc` is generally safer to use. You can imagine `.iloc` may return incorrect values when applied to a dataset where the ordering of data can change. However, `.iloc` can still be useful — for example, if you are looking at a `DataFrame` of sorted movie earnings and want to get the median earnings for a given year, you can use `.iloc` to index into the middle.\n", - "\n", - "Overall, it is important to remember that:\n", - "\n", - "- `.loc` performances **l**abel-based extraction.\n", - "- `.iloc` performs **i**nteger-based extraction.\n", - "\n", - "### Context-dependent Extraction: Indexing with `[]`\n", - "\n", - "The `[]` selection operator is the most baffling of all, yet the most commonly used. It only takes a single argument, which may be one of the following:\n", - "\n", - "1. A slice of row numbers.\n", - "2. A list of column labels.\n", - "3. A single-column label.\n", - "\n", - "That is, `[]` is *context-dependent*. Let's see some examples.\n", - "\n", - "#### A slice of row numbers\n", - "\n", - "Say we wanted the first four rows of our `elections` DataFrame." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections[0:4]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### A list of column labels\n", - "\n", - "Suppose we now want the first four columns." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections[[\"Year\", \"Candidate\", \"Party\", \"Popular vote\"]]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### A single-column label\n", - "\n", - "Lastly, `[]` allows us to extract only the `Candidate` column." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "elections[\"Candidate\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output is a `Series`! In this course, we'll become very comfortable with `[]`, especially for selecting columns. In practice, `[]` is much more common than `.loc`, especially since it is far more concise.\n", - "\n", - "## Parting Note\n", - "\n", - "The `pandas` library is enormous and contains many useful functions. Here is a link to [documentation](https://pandas.pydata.org/docs/). We certainly don't expect you to memorize each and every method of the library.\n", - "\n", - "The introductory Data 100 `pandas` lectures will provide a high-level view of the key data structures and methods that will form the foundation of your `pandas` knowledge. A goal of this course is to help you build your familiarity with the real-world programming practice of ...Googling! Answers to your questions can be found in documentation, Stack Overflow, etc. Being able to search for, read, and implement documentation is an important life skill for any data scientist. \n", - "\n", - "With that, we will move on to Pandas II.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}

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