1.Preliminaries

2.Python Language Basics, IPython, and Jupyter Notebooks

3.Built-in Data Structures, Functions, and Files

4. NumPy Basics: Arrays and Vectorized Computation

5.Getting Started with pandas

6.Data Loading, Storage, and File Formats

7. Data Cleaning and Preparation

8. Data Wrangling: Join, Combine, and Reshape

9. Plotting and Visualization

10.Data Aggregation and Group Operations

11.Time Series

12. Advanced pandas

13. Introduction to Modeling Libraries in Python

14. Data Analysis Examples

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. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples