Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third 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, 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 Jupyter notebook and IPython shell for exploratory computing
  • Learn basic and advanced features in NumPy
  • Get started with data analysis tools in the pandas library
  • Use flexible tools to load, clean, transform, merge, and reshape data
  • Create informative visualizations with matplotlib
  • Apply the pandas groupby facility to slice, dice, and summarize datasets
  • Analyze and manipulate regular and irregular time series data
  • Learn how to solve real-world data analysis problems with thorough, detailed examples

Table of Contents

  1. Preliminaries
    1. 1.1 What Is This Book About?
    2. What Kinds of Data?
    3. 1.2 Why Python for Data Analysis?
    4. Python as Glue
    5. Solving the “Two-Language” Problem
    6. Why Not Python?
    7. 1.3 Essential Python Libraries
    8. NumPy
    9. pandas
    10. matplotlib
    11. IPython and Jupyter
    12. SciPy
    13. scikit-learn
    14. statsmodels
    15. Other Packages
    16. 1.4 Installation and Setup
    17. Miniconda on Windows
    18. Miniconda on macOS
    19. GNU/Linux
    20. Installing Necessary Packages
    21. Integrated Development Environments (IDEs) and Text Editors
    22. 1.5 Community and Conferences
    23. 1.6 Navigating This Book
    24. Code Examples
    25. Data for Examples
    26. Import Conventions
  2. Python Language Basics, IPython, and Jupyter Notebooks
    1. 2.1 The Python Interpreter
    2. 2.2 IPython Basics
    3. Running the IPython Shell
    4. Running the Jupyter Notebook
    5. Tab Completion
    6. Introspection
    7. The %run Command
    8. Executing Code from the Clipboard
    9. Terminal Keyboard Shortcuts
    10. About Magic Commands
    11. Matplotlib Integration
    12. 2.3 Python Language Basics
    13. Language Semantics
    14. Scalar Types
    15. Control Flow
    16. 2.4 Conclusion