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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
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Five-Step Process for Data Exploration
