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Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python? The second edition of this hands-on guide—updated for Python 3.5 and Pandas 1.0—is packed with practical cases studies that show you how to effectively solve a broad set of data analysis problems, using Python libraries such as NumPy, pandas, matplotlib, and IPython.
Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.
Table of Contents
Chapter 1 Preliminaries
Chapter 2 Python Language Basics, IPython, and Jupyter Notebooks
Chapter 3 Built-in Data Structures, Functions, and Files
Chapter 4 NumPy Basics: Arrays and Vectorized Computation
Chapter 5 Getting Started with pandas
Chapter 6 Data Loading, Storage, and File Formats
Chapter 7 Data Cleaning and Preparation
Chapter 8 Data Wrangling: Join, Combine, and Reshape
Chapter 9 Plotting and Visualization
Chapter 10 Data Aggregation and Group Operations
Chapter 11 Interlude: Data Analysis Examples
Chapter 12 Time Series
Chapter 13 Advanced NumPy
Chapter 14 Using Modeling Libraries with pandas
Chapter 15 Examples Data Sets
Appendix Advanced IPython and Jupyter