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Book Description
Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you’ll need to effectively solve a broad set of data analysis problems. This book is not an exposition on analytical methods using Python as the implementation language.
Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientificcomputing.
- Use the IPython interactive shell as your primary development environment
- Learn basic and advanced NumPy (Numerical Python) features
- Get started with data analysis tools in the pandas library
- Use high-performance tools to load, clean, transform, merge, and reshape data
- Create scatter plots and static or interactive visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
- Learn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
Table of Contents
Chapter 1. Preliminaries
Chapter 2. Introductory Examples
Chapter 3. IPython: An Interactive Computing and Development Environment
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 Wrangling: Clean, Transform, Merge, Reshape
Chapter 8. Plotting and Visualization
Chapter 9. Data Aggregation and Group Operations
Chapter 10. Time Series
Chapter 11. Financial and Economic Data Applications
Chapter 12. Advanced NumPy
Chapter 1. Preliminaries
Chapter 2. Introductory Examples
Chapter 3. IPython: An Interactive Computing and Development Environment
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 Wrangling: Clean, Transform, Merge, Reshape
Chapter 8. Plotting and Visualization
Chapter 9. Data Aggregation and Group Operations
Chapter 10. Time Series
Chapter 11. Financial and Economic Data Applications
Chapter 12. Advanced NumPy
Book Details
- Paperback: 470 pages
- Publisher: O’Reilly Media (October 2012)
- Language: English
- ISBN-10: 1449319793
- ISBN-13: 978-1449319793
- File Size: 14.0 MiB
