Data Analysis with Pandas and Python 

Introduction to Data analysis with Pandas and Python

About Me

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 and Install the Anaconda Distribution

Windows - Create conda Environment and Install pandas and Jupyter Notebook

Windows - Unpack Course Materials + The Startdown and Shutdown Process

Intro 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

Intro to the Python Crash Course

Comments

Basic Data Types

Operators

Variables

Built-in Functions

Custom Functions

String Methods

Lists

Index Positions and Slicing

Dictionaries

Create Jupyter Notebook for the Series Module

Create A Series Object from a Python List

Create A Series Object from a Python Dictionary

Intro to Methods

Intro to Attributes

Parameters and Arguments

Import Series with the pd.read_csv Function

Use the head and tail Methods to Return Rows from Beginning and End of Dataset

Passing Series to Python Built-In Functions

The sort_values Method

The sort_index Method

Extract Series Values by Index Position

Extract Series Values by Index Label

The get Method

Overwrite a Series Value

The copy Method

The inplace Parameter

Math Methods on Series Objects

Broadcasting

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 map Method

Intro to DataFrames I Module

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 New Column to DataFrame

Create New Column from Existing Column

A Review of the value_counts Method

Drop DataFrame Rows with Null Values with the dropna Method

Fill in Missing DataFrame Values with the fillna Method

The astype Method I

The astype Method II

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

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

Intro 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