Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPythonGet 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.
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Contents
Chapter 1 Preliminaries | 1 |
Chapter 2 Python Language Basics IPython and Jupyter Notebooks | 15 |
Chapter 3 Builtin Data Structures Functions and Files | 51 |
Arrays and Vectorized Computation | 87 |
Chapter 5 Getting Started with pandas | 125 |
Chapter 6 Data Loading Storage and File Formats | 169 |
Chapter 7 Data Cleaning and Preparation | 195 |
Join Combine and Reshape | 225 |
Chapter 11 Time Series | 323 |
Chapter 12 Advanced pandas | 369 |
Chapter 13 Introduction to Modeling Libraries in Python | 389 |
Chapter 14 Data Analysis Examples | 409 |
Appendix A Advanced NumPy | 455 |
Appendix B More on the IPython System | 489 |
| 511 | |
About the Author | 529 |
Chapter 9 Plotting and Visualization | 257 |
Chapter 10 Data Aggregation and Group Operations | 293 |
Other editions - View all
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython Wes McKinney Limited preview - 2017 |
Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Ipython Wes McKinney No preview available - 2017 |
Common terms and phrases
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