Notes in IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python.
This book covers a beginner to intermediate range of knowledge and skills required for Data Analysis in Python. Unlike some of the other books in Data Science that I am reading, which focus too heavily on the math and science behind analytical concepts, it tries to put emphasis on the coding aspect of Data Analysis, which is a welcome aspect for a developer like me.
Although the Author tries to make it easy for newcomers in Python by building a good foundation before going into a deeper dive for any topics, I would suggest going through some introductory Python book or course with a focus on data analytics before starting this book. I have not covered the first two chapters on Python Basics and Built-in Data Structures as I already have some experience in it. The author covers every aspect of a Data Analysis operation, from input and cleaning of data with pandas, mathematical operations using NumPy to plotting and visualization using matplotlib and seaborn.
I especially enjoyed the Appendix section where the author covers advanced topics on NumPy, pandas and IPython system.
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I have only included the Jupyter notebooks containing my notes from the book. I have not included the datasets because some of them are quite large and take a whole lot of space in the repo. You can get the datasets at books's official GitHub Repository
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These notes are by no means close to the book's original content. I have created this repo because I want all of my notes in a single place with easy access. By all means refer to the notes if you want need quick guidance. But for in-depth knowledge, buy the book.
Attribution: Python for Data Analysis by Wes McKinney (O’Reilly). Copyright 2017 Wes McKinney, 978-1-491-95766-0.
The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.