Python for data analysis files

About the Open Edition

The 3rd edition of Python for Data Analysis is now available as an “Open Access” HTML version on this site https://wesmckinney.com/book in addition to the usual print and e-book formats. This edition was initially published in August 2022 and will have errata fixed periodically over the coming months and years. If you encounter any errata, please report them here.

In general, the content from this website may not be copied or reproduced. The code examples are MIT-licensed and can be found on GitHub or Gitee along with the supporting datasets.

If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook (in PDF and EPUB formats) to support the author.

This web version of the book was created with the Quarto publishing system.

What’s New in the 3rd Edition?

The book has been updated for pandas 1.4.0 and Python 3.10. The changes between the 2nd and 3rd editions are focused on bringing the content up-to-date with changes in pandas since 2017.

Update History

This website will be updated periodically as new early release content becomes available, and post-publication for errata fixes.

• April 12, 2023: Update to pandas 2.0.0 and fix some code examples.
• October 19, 2022: Fix a table link and add eBooks.com links.
• September 20, 2022: Website update after final publication including a couple of minor errata fixes.
• July 22, 2022: Incorporate copy-editing and other improvements for “QC1” stage of production en route to publication in print later this summer.
• May 18, 2022: Update open access edition with all chapters. Include edits from technical review feedback (thank you!), acknowledgements for the third edition, and other preparation to make the book ready for production on its way to print later in 2022.
• February 13, 2022: Update open access edition with chapters 7 through 10.
• January 23, 2022: First open access edition with chapters 1 through 6.

Источник

About the Open Edition

The 3rd edition of Python for Data Analysis is now available as an “Open Access” HTML version on this site https://wesmckinney.com/book in addition to the usual print and e-book formats. This edition was initially published in August 2022 and will have errata fixed periodically over the coming months and years. If you encounter any errata, please report them here.

In general, the content from this website may not be copied or reproduced. The code examples are MIT-licensed and can be found on GitHub or Gitee along with the supporting datasets.

If you find the online edition of the book useful, please consider ordering a paper copy or a DRM-free eBook (in PDF and EPUB formats) to support the author.

This web version of the book was created with the Quarto publishing system.

What’s New in the 3rd Edition?

The book has been updated for pandas 1.4.0 and Python 3.10. The changes between the 2nd and 3rd editions are focused on bringing the content up-to-date with changes in pandas since 2017.

Update History

This website will be updated periodically as new early release content becomes available, and post-publication for errata fixes.

• April 12, 2023: Update to pandas 2.0.0 and fix some code examples.
• October 19, 2022: Fix a table link and add eBooks.com links.
• September 20, 2022: Website update after final publication including a couple of minor errata fixes.
• July 22, 2022: Incorporate copy-editing and other improvements for “QC1” stage of production en route to publication in print later this summer.
• May 18, 2022: Update open access edition with all chapters. Include edits from technical review feedback (thank you!), acknowledgements for the third edition, and other preparation to make the book ready for production on its way to print later in 2022.
• February 13, 2022: Update open access edition with chapters 7 through 10.
• January 23, 2022: First open access edition with chapters 1 through 6.

Источник

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Materials and IPython notebooks for «Python for Data Analysis» by Wes McKinney, published by O’Reilly Media

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README.md

Course materials for a multi day course on data analysis with Python using Pandas based on materials from «Python for Data Analysis, 3rd Edition» by Wes McKinney, published by O’Reilly Media. Book content including updates and errata fixes can be found for free on the author’s website and is available for sale on Amazon.

The objective of this course is to provide students with an experimental approach, through practical experience, with data analysis using the Python programming language. The course is designed to provide students with practical experience with state-of-the-art data analysis tools that are widely used in industry.

This covers will cover the majority of Python for Data Analysis by Wes McKinney. On completion of this course students should be able to:

• Recognize and select data types used in Python for data analysis;
• Understand how to prepare data for further analysis using Pandas, Matplotlib, and Seaborn libraries;
• Understand and apply data modelling and analysis workflows in Python;
• Apply Python for real-world data analysis problems.

A whirlwind tutorial of the basics of the Python programming language. There module also covers a bit of IPython and Jupyter related topics sufficient to make learners comfortable with the programming environment prior to tackling the more advanced material presented in later modules. This material should be shared with students prior to the start of the course to review.

Tutorial	Open in Google Colab	Open in Kaggle
Python Language Basics
Built-in Data Structures, Functions, and Files

After completing this module learners should understand various data types used in data analysis in Python such as NumPy arrays, Pandas Series, and Pandas DataFrames. Learners should also be able to read (write) data from (to) storage in various formats using Pandas.

Tutorial	Open in Google Colab	Open in Kaggle
NumPy Basics
Advanced NumPy (optional)
Pandas Basics
Data Loading, Storage, and File Formats

After completing this module, learners should understand how to prepare (i.e., clean, manipulate, aggregate, and visualize) data for further analysis. Learners will develop a knowledge of the Pandas API as well as a basic knowledge of plotting and visualizing of data with Matplotlib and Seaborn.

Tutorial	Open in Google Colab	Open in Kaggle
Data Cleaning and Preparation
Data Wrangling
Plotting and Visualization
Data Aggregation and Group Operations
Time Series

After competing this module learners will understand how to develop basic data modelling and analysis pipelines using Patsy, Statsmodels and Scikit-Learn.

Tutorial	Open in Google Colab	Open in Kaggle
Defining Data Models using Patsy
Statistics Approach to Data Modeling with Statsmodels
Machine Learning Approach to Data Modeling with Scikit-Learn

Finally, learners will also have an opportunity to apply the skills that they have learned to analyze real data. Typically, instructors should select 3 of the following projects to cover over one day of instruction.

Tutorial	Open in Google Colab	Open in Kaggle
Data Analysis Example: Bitly Data from USA.gov
Data Analysis Example: MovieLens 1M
Data Analysis Example: US Baby Names
Data Analysis Example: USDA Food Database
Data Analysis Example: 2012 Federal Election Commission

To get the most out of this material learners should have completed Python Crash Course prior to attempting this course (but this is not a strict prerequesite).

Instructors have a few options for teaching the material.

1. Have the book open on an iPad (or similar); have the students open a new blank notebook; live code some (or all) the examples from the book and use the text of the book as speaking notes.
2. Have the students open the book in their browser; have students open a blank notebook in another browser window and the have them read through relevant chapters of the book and code up the examples. Lead instructor and any teaching assistants are available to troubleshoot and answer individual questions. Common questions should be answered to the group as a live demo.
3. Have the students open the book in their browser; have students open the provided notebooks in another browser window and the have them read through relevant chapters of the book and execute the provided code. Lead instructor and any teaching assistants are available to troubleshoot and answer individual questions. Common questions should be answered to the group as a live demo.
4. Some combination of the above.

Approach 1 is the most difficult for the lead instructor but likely the most engaging for learners; option 3 is easier for both lead instructor and the students but likely results in the least learning. Option 2 is a middle ground: easier for the lead instructor but still requires students to write their own code.

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.

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Materials and IPython notebooks for «Python for Data Analysis» by Wes McKinney, published by O’Reilly Media

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