Master Data Analysis with Python - Intro to Pandas

Description

In this course, you will learn :

• Best practises from Ted Petrou, author of Master Data Analysis with Python and a pandas expert.
• An overview of the pandas DataFrame and Series.
• Understanding the various data types that are available in a DataFrame.
• Accessing DataFrame components such as the index, columns, and values.
• In a DataFrame, create a meaningful index.
• Completing a five-step data exploration process.

Syllabus :

1. Course Contents

• Exploring the Course Contents
• Opening the Material with Jupyter Notebooks
• Introduction to Jupyter Notebooks
• Working through a Course Material
• Differences with Video Notebooks
• When to Open a New Notebook

 

2. What is pandas?

• What is pandas?
• Which version of pandas to use?
• Pandas examples

 

3. The DataFrame and Series

• Intro to the DataFrame and Series
• DataFrame Components
• Selecting a Series
• Components of a Series
• Getting Help in a Jupyter Notebook

 

4. Data Types and Missing Values

• Intro to Data Types and Missing Values
• Finding the Data Type of Each Column
• Getting More Metadata

 

5. Setting a Meaningful Index

• Setting an Index of a DataFrame
• Accessing the Index, Columns, and Data
• Accessing the Components of a Series
• The Default Index
• Setting an Index on Read
• Choosing a Good Index

 

6. Five-Step Process for Data Exploration

• Five-Step Process for Data Exploration

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