- Mastering pandas
- Table of Contents
- Mastering pandas
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Introduction to pandas and Data Analysis
- 2. Installation of pandas and the Supporting Software
- 3. The pandas Data Structures
- 4. Operations in pandas, Part I – Indexing and Selecting
- 5. Operations in pandas, Part II – Grouping, Merging, and Reshaping of Data
- 6. Missing Data, Time Series, and Plotting Using Matplotlib
- 7. A Tour of Statistics – The Classical Approach
- 8. A Brief Tour of Bayesian Statistics
- Introduction to Bayesian statistics
- Mathematical framework for Bayesian statistics
- Probability distributions
- Bayesian statistics versus Frequentist statistics
- Conducting Bayesian statistical analysis
- Monte Carlo estimation of the likelihood function and PyMC
- References
- Summary
- 9. The pandas Library Architecture
- 10. R and pandas Compared
- 11. Brief Tour of Machine Learning
- Role of pandas in machine learning
- Installation of scikit-learn
- Introduction to machine learning
- Application of machine learning – Kaggle Titanic competition
- Data analysis and preprocessing using pandas
- A naïve approach to Titanic problem
- The scikit-learn ML/classifier interface
- Supervised learning algorithms
- Unsupervised learning algorithms
- Summary
- Index
Femi Anthony is a seasoned and knowledgeable software programmer, with over 15 years experience in a vast array of languages, including Perl, C, C++, Java, and Python. He has worked in both the Internet space and financial services space for many years and is now working for a well-known financial data company. He holds a bachelor's degree in mathematics with computer science from MIT and a master's degree from the University of Pennsylvania. His pet interests include data science, machine learning, and Python. Femi is working on a few side projects in these areas. His hobbies include reading, soccer, and road cycling. You can follow him at @dataphanatik, and for any queries, contact him at <[email protected]>.
First and foremost, I would like to thank my wife, Ene, for her support throughout my career and in writing this book. She has been my inspiration and motivation for continuing to improve my knowledge and helping me move ahead in my career. She is my rock, and I dedicate this book to her. I also thank my wonderful children, Femi, Lara, and our new addition, Temi, for always making me smile and for understanding on those days when I was writing this book instead of playing games with them.
I would also like to thank my book reviewers—Opeyemi Akinjayeju, who is a dear friend of mine, as well as Louis Hénault and Carlos Marin—for their invaluable feedback and input toward the completion of this book. Lastly, I would like to thank my parents, George and Katie Anthony, for instilling a strong work ethic in me from an early age.
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