15 Jupyter Notebooks, with text, code, exercises, and solutions
5 Hours of Video
Nearly 50 Exercises
120-page pdf of all course material
The components of the DataFrame and Series
Common Data Types
Selecting subsets of data
Filtering data via boolean selection
Master Data Analysis with Python - Intro to Pandas targets those who want to completely master doing data analysis with pandas. This course provides an introduction to the components of the two primary pandas objects, the DataFrame and Series, and how to select subsets of data from them.
This course is taught by expert instructor Ted Petrou, author of the highly-rated book Pandas Cookbook. Ted has taught over 1,000 hours of live in-person data science courses that use the pandas library. Pandas is a difficult library to use effectively and is often taught incorrectly with poor practices. Ted is extremely adept at using pandas and is known for developing best practices on how to use the library.
There are nearly 50 exercises available to help practice the material taught from the lectures. Detailed video and text solutions for each of the exercises are available so that you can see exactly how Ted thinks through the exercises to arrive at a solution.
All of the material and exercises are written in Jupyter Notebooks, which you will be able to download. This allows you to read the notes, run the code, and write solutions to the exercises all in a single place. Additionally, the full contents of the course are available as a 120-page document giving you access to the material from anywhere.
This course targets those who have an interest in becoming experts and completely mastering the pandas library for data analysis in a professional environment. This course does not cover all of the pandas library, just a small and fundamental portion of it. If you are looking for a brief introduction of the entire pandas library, this course is not it. It takes many dozens of hours, lots of practice, and rigorous understanding to be successful using pandas for data analysis.
This course assumes no previous pandas experience. The only prerequisite knowledge is to understand the fundamentals of Python.
This course is the first from the 10-part series Master Data Analysis with Python. The second part is titled Master Data Analysis with Python - Essential Pandas Commands.
- 01 Downloading the Course Material
- Course Contents (Download)
- 02 Exploring the Course Contents
- 03 Opening the Material with Jupyter Notebooks
- 04 Jupyter Notebook Tips and Tricks
- 05 Working through a Notebook from the Course
- 06 About to Begin!
- 01 What is pandas
- 02 pandas examples
- 03 The DataFrame and Series
- 03b Exercise Solutions - The DataFrame and Series
- 04 Data Types and Missing Values
- 04b Exercise Solutions - Data Types and Missing Values
- 05 Setting a Meaningful Index
- 05b Exercise Solutions - Setting a Meaningful Index
- 06 Five Step Process for Data Exploration
- 07 Selecting Subsets of DataFrames with Just the Brackets
- 07b Exercise Solutions - Selecting Subsets of Data with Just the Brackets
- 08 Selecting Subsets of Data from DataFrames with loc
- 08b Exercise Solutions - Selecting Subsets of Data from DataFrames with loc
- 09 Selecting Subsets of Data with iloc
- 09b Exercise Solutions - Selecting Subsets of Data from DataFrames with iloc
- 10 Selecting Subsets of Data from a Series
- 10b Exercise Solutions - Selecting Subsets of Data from a Series
- 11 Boolean Indexing Single Conditions
- 11b Exercise Solutions - Boolean Indexing Single Conditions
- 12 Boolean Indexing Multiple Conditions
- 12b Exercise Solutions - Boolean Indexing Multiple Conditions
- 13 Boolean Indexing More
- 13b Exercise Solutions - Boolean Indexing More
- 14 Miscellaneous Subset Selection
- 14b Exercise Solutions - Miscellaneous Subset Selection
- Continue Mastering Data Analysis with Python
$100.00Complete Master Data Analysis with Python BundlePurchase the entire bundle which includes Exercise Python, Master Data Analysis with Python Volume 1 and all video courses for each for only $100. (Some videos are still being created)