- 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
Conducting a Bayesian statistical analysis involves the following steps:
- Specifying a probability model: In this step, we fully describe the model using a probability distribution. Based on the distribution of a sample that we have taken, we try to fit a model to it and attempt to assign probabilities to unknown parameters.
- Calculating a posterior distribution: The posterior distribution is a distribution that we calculate in light of observed data. In this case, we will directly apply Bayes formula. It will be specified as a function of the probability model that we specified in the previous step.
- Checking our model: This is a necessary step where we review our model and its outputs before we make inferences. Bayesian inference methods use probability distributions to assign probabilities to possible outcomes.
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