Python for Data Analysis
- Length: 470 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2012-10-29
- ISBN-10: 1449319793
- ISBN-13: 9781449319793
- Sales Rank: #186945 (See Top 100 Books)
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 scientific computing.
- 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
Donate to keep this site alive
How to download source code?
1. Go to: https://www.oreilly.com/
2. Search the book title: Python for Data Analysis
, sometime you may not get the results, please search the main title
3. Click the book title in the search results
3. Publisher resources
section, click Download Example Code
.
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.