Data Analysis with Pandas and Python

Table of Contents

Chapter 1 : Installation and Setup

Introduction to Data Analysis with Pandas and Python

MacOS - Download the Anaconda Distribution, Our Python Development Environment

MacOS - Install Anaconda Distribution

MacOS - Access the Terminal Application

MacOS - Create Conda Environment and Install Pandas and Jupyter Notebook

MacOS - Unpack Course Materials + The Start and Shutdown Process

Windows - Download the Anaconda Distribution

Windows - Install Anaconda Distribution

Windows - Create Conda Environment and Install Pandas and Jupyter Notebook

Windows - Unpack Course Materials + The Start and Shutdown Process

Introduction to the Jupyter Notebook Interface

Cell Types and Cell Modes in Jupyter Notebook

Code Cell Execution in Jupyter Notebook

Popular Keyboard Shortcuts in Jupyter Notebook

Import Libraries into Jupyter Notebook

Chapter 2 : Bonus: Python Crash Course

Introduction to the Python Crash Course

Comments

Basic Data Types

Operators

Variables

Built-In Functions

Custom Functions

String Methods

Lists

Index Positions and Slicing

Dictionaries

Chapter 3 : Series

Create Jupyter Notebook for the Series Module

Create a Series Object from a Python List

Create a Series Object from a Python Dictionary

Introduction to Attributes on a Series Object

Introduction to Methods on a Series Object

Parameters and Arguments

Create Series from a Dataset with the pd.read_csv Method

Use the Head and Tail Methods to Return Rows from the Beginning and End of a Dataset

Passing Pandas Objects to Python Built-In Functions

Accessing More Series Attributes

Use the sort_values Method to Sort a Series in Ascending or Descending Order

Use the inplace Parameter to Permanently Mutate a Pandas Data Structure

Use the sort_index Method to Sort the Index of a Pandas Series Object

Use Python’s in Keyword to Check for Inclusion in Series Values or Index

Extract Series Values by Index Position

Extract Series Values by Index Label

Use the get Method to Retrieve a Value for an Index label in a Series

Math Methods on Series Objects

Use the idxmax and idxmin Methods to Find Index of Greatest or Smallest Value

Use the value_counts Method to See Counts of Unique Values within a Series

Use the apply Method to Invoke a Function on Every Series Values

The Series#map Method

Chapter 4 : DataFrames I: Introduction

Introduction to DataFrames I Module

Shared Methods and Attributes Between Series and DataFrames

Differences Between Shared Methods

Select One Column from a DataFrame

Select Two or More Columns from a DataFrame

Add a New Column to DataFrame

Broadcasting Operations on DataFrames

A Review of the value_counts Method

Drop DataFrame Rows with Null Values with the dropna Method

Fill in Null DataFrame Values with the fillna Method

Convert DataFrame Column Types with the astype Method

Sort a DataFrame with the sort_values Method, Part I

Sort a DataFrame with the sort_values Method, Part II

Sort DataFrame Index with the sort_index() Method

Rank Series Values with the rank Method

Chapter 5 : DataFrames II: Filtering Data

This Module’s Dataset + Memory Optimization

Filter a DataFrame Based on a Condition

Filter DataFrame with More than One Condition (AND - )

Filter DataFrame with More than One Condition (OR - |)

Check for Inclusion with the isin() Method

Check for Null and Present DataFrame Values with the isnull and notnull Methods

Check for Inclusion Within a Range of Values with the between Method

Check for Duplicate DataFrame Rows with the duplicated Method

Delete Duplicate DataFrame Rows with the drop_duplicates Method

Identify and Count Unique Values with the unique and nunique Methods

Chapter 6 : DataFrames III: Data Extraction

Introduction to the DataFrames III Module + Import Dataset

Use the set_index and reset_index Methods to Define a new DataFrame Index

Retrieve Rows by Index Label with loc Accessor

Retrieve Rows by Index Position with iloc Accessor

Passing Second Arguments to the loc and iloc Accessors

Set New Value for a Specific Cell or Cells in a Row

Set Multiple Values in a DataFrame

Rename Index Labels or Columns in a DataFrame

Delete Rows or Columns from a DataFrame

Create Random Sample with the sample Method

Use the nsmallest / nlargest Methods to Get Rows with Smallest / Largest Values.

Filter a DataFrame with the where() Method

Filter a DataFrame with the query Method

A Review of the apply Method on a Pandas Series Object

Apply a Function to Every DataFrame Row with the apply Method

Create a Copy of a DataFrame with the copy Method

Chapter 7 : Working with Text Data

Introduction to the Working with Text Data Section

Common String Methods - lower, upper, title, and len

Use the str.replace Method to Replace All Occurrences of a Character with Another

Filter a DataFrame’s Rows with String Methods

More DataFrame String Methods - strip, lstrip, and rstrip

Invoke String Methods on DataFrame Index and Columns

Split Strings by Characters with the str.split Method

More Practice with the str.split Method on a Series

Exploring the expand and n Parameters of the str.split Method

Chapter 8 : MultiIndex

Introduction to the MultiIndex Module

Create a MultiIndex on a DataFrame with the set_index Method

Extract Index Level Values with the get_level_values Method

Change Index Level Name with the set_names Method

The sort_index Method on a MultiIndex DataFrame

Extract Rows from a MultiIndex DataFrame

The transpose Method on a MultiIndex DataFrame

The .swaplevel() Method

The .stack() Method

The .unstack() Method, Part 1

The .unstack() Method, Part 2

The .unstack() Method, Part 3

The pivot Method

Use the pivot_table Method to Create an Aggregate Summary of a DataFrame

Use the pd.melt Method to Create a Narrow Dataset from a Wide One

Chapter 9 : The GroupBy Object

Introduction to the GroupBy Module

First Operations with the GroupBy Object

Retrieve a Group from a GroupBy Object with the get_group Method

Methods on the GroupBy Object and DataFrame Columns

Grouping by Multiple Columns

The .agg() Method

Iterating Through Groups

Chapter 10 : Merging, Joining, and Concatenating DataFrames

Introduction to the Merging, Joining, and Concatenating Section

The pd.concat Method, Part 1

The pd.concat Method, Part 2

The append Method on a DataFrame

Inner Joins, Part 1

Inner Joins, Part 2

Outer Joins

Left Joins

The left_on and right_on Parameters

Merging by Indexes with the left_index and right_index Parameters

The .join() Method

The pd.merge() Method

Chapter 11 : Working with Dates and Times in Datasets

Introduction to the Working with Dates and Times Module

Review of Python’s Datetime Module

The Pandas Timestamp Object

The Pandas DateTimeIndex Object

The pd.to_datetime() Method

Create Range of Dates with the pd.date_range() Method, Part 1

Create Range of Dates with the pd.date_range() Method, Part 2

Create Range of Dates with the pd.date_range() Method, Part 3

The .dt Accessor

Install Pandas-datareader Library

Import Financial Dataset with pandas_datareader Library

Selecting Rows from a DataFrame with a DateTimeIndex

Timestamp Object Attributes and Methods

The pd.DateOffset Object

Timeseries Offsets

The Timedelta Object

Timedeltas in a Dataset

Chapter 12 : Input and Output in Pandas

Introduction to the Input and Output Section

Pass a URL to the pd.read_csv Method

Quick Object Conversions

Export CSV File with the to_csv Method

Install xlrd and openpyxl Libraries to Read and Write Excel Files

Import Excel File into Pandas with the read_excel Method

Export Excel File with the to_excel Method

Chapter 13 : Visualization

Introduction to the Visualization Section

Use the plot Method to Render a Line Chart

Modifying Plot Aesthetics with matplotlib Templates

Creating Bar Graphs to Show Counts

Creating Pie Charts to Represent Proportions

Chapter 14 : Options and Settings in Pandas

Introduction to the Options and Settings Module

Changing Pandas Options with Attributes and Dot Syntax

Changing Pandas Options with Methods

The precision Option

Chapter 15 : Thank You

Conclusion