- 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
For a more in-depth look at Bayesian statistics topics that we touched upon, please take a look at the following references:
- Probabilistic Programming and Bayesian Methods for Hackers at https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- Bayesian Data Analysis, Third Edition, Andrew Gelman at http://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science/dp/1439840954
- The Bayesian Choice, Christian P Robert (this is more theoretical) at http://www.springer.com/us/book/9780387952314
- PyMC documentation at http://pymc-devs.github.io/pymc/index.html
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