Overview

Pandas is a package of fast, efficient data analysis tools for Python. It is built on NumPy and provides easy-to-use data structures and data analysis tools.

Recap on Tabular Data in Python So Far...

data = [
    ["name", "city", "age"],
    ["Alice", "New York", 25],
    ["Bob", "Los Angeles", 30],
    ["Charlie", "Chicago", 35],
]

age_idx = data[0].index("age")
data[2][city_idx]

data = [
  {"name": "Alice", "city": "New York", "age": 25},
  {"name": "Bob", "city": "Los Angeles", "age": 30},
  {"name": "Charlie", "city": "Chicago", "age": 35},
]

data[1]["age"]

Importing pandas

First, import the pandas library.

import pandas as pd

By convention, we import pandas as pd. This is a common practice in the data science community.

Pandas Data Structures

Pandas provides two fundamental data structures:

1. Series
2. DataFrame

These structures handle the vast majority of typical use cases in finance, social sciences, statistics, and many other areas.

Pandas Series

A Series is a one-dimensional array-like object containing an array of data and an associated array of data labels, called its index.

s = pd.Series([0.25, 0.5, 0.75, 1.0])
print(s)

0    0.25
1    0.50
2    0.75
3    1.00
dtype: float64

Pandas DataFrame

A DataFrame is a two-dimensional array with both flexible row indices and flexible column names.

area = pd.Series({'CA': 423967, 'TX': 695662, 'NY': 141297, 'FL': 170312, 'IL': 149995})
population = pd.Series({'CA': 38332521, 'TX': 26448193, 'NY': 19651127, 'FL': 19552860, 'IL': 12882135})
df = pd.DataFrame({'area': area, 'population': population})
print(df)

        area  population
CA    423967    38332521
TX    695662    26448193
NY    141297    19651127
FL    170312    19552860
IL    149995    12882135

Variables for DataFrames are almost always called df or foo_df.

You Can Perform Math Between Series

Because of pandas is built on top of NumPy (a popular package for doing matrix math), we can do basic math operations on DataFrames and Series.

df['population'] / df['area']

CA     90.413926
TX     38.018740
NY    139.076746
FL    114.806121
IL     85.883763
dtype: float64

And Then We Can Add It To The DataFrame

DataFrames act like dictionaries in this way, so we can add a new column like this:

df['density'] = df['population'] / df['area']
print(df)

      area  population    density
CA  423967    38332521  90.413926
TX  695662    26448193  38.018740
NY  141297    19651127 138.195210
FL  170312    19552860 114.806121
IL  149995    12882135  85.883763

DataFrames Are The Best of Both Worlds

df["area"]

CA  423967
TX  695662
NY  141297
FL  170312
IL  149995

df["area"]["TX"]

695662

df.loc['TX']

area            695662
population    26448193
Name: TX, dtype: int64

df.loc['TX']['area']

695662

Pandas Can Read CSVs

The most common way we'll use pandas in this class is to read CSV files. It's a much better CSV parser than the csv package!

df = pd.read_csv('data.csv')

But you can use pandas to read Excel files, SQL databases, and more!

Viewing Data

Use .head() to view the first few rows.

df.head(2)

    name  age         city
0  Alice   25     New York
1    Bob   30  Los Angeles

Viewing Data

Use .info() to get a concise summary of the DataFrame.

df.info()

RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype 
---  ------  --------------  ----- 
 0   name    3 non-null      object
 1   age     3 non-null      int64 
 2   city    3 non-null      object
dtypes: int64(1), object(2)
memory usage: 200.0+ bytes

Series Methods

• In addition to the normal list stuff, Series have some handy methods for aggregation.

  df['age'].mean()
  

  30.0
  

• When you use these methods on a DataFrame, they return a Series

  df.mean(numeric_only=True)
  

  age          31.0
  salary    60000.0
  

Selecting Multiple Columns

Selecting multiple columns returns a DataFrame.

df[['name', 'city']]

      name         city
0    Alice     New York
1      Bob  Los Angeles
2  Charlie      Chicago

Filtering Data

In addition to selecting columns, you can Filter rows based on booleans (the results of conditionals):

df[df['age'] > 30]

      name  age     city
2  Charlie   35  Chicago

is_new_york = df['city'] == 'New York'
df[is_new_york]

    name  age      city
0  Alice   25  New York

Adding and Modifying Columns

Add a new column:

df['salary'] = [50000, None, 70000]

Modify an existing column:

df['age'] = df['age'] + 1
df

      name  age         city
0    Alice   26     New York
1      Bob   31  Los Angeles
2  Charlie   36      Chicago

Sorting Data

Sort by a column:

df.sort_values('age')

      name  age         city
0    Alice   26     New York
1      Bob   31  Los Angeles
2  Charlie   36     k Chicago