|ISBN: 9781491957660||Number of Pages: 547|
|Publisher: O’Reilly Media||Book Title: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython|
|Publication Year: 2017||Target Audience: Trade|
|Author: William McKinney||Reading Age: 18+|
Still struggling to find a complete course of controlling, processing, sorting, and analyzing structured data with Python? Python for Data Analysis contains a lot of practical cases. You will learn how to use various Python libraries (including NumPy, pandas, matplotlib, and IPython) to solve differ data analysis problems.
Written by Wes McKinney, founder of the Pandas project, the book introduces the specific details and basic points of using Python to operate, process, clean and regularize data.
The 2nd version is a comprehensive revision and update of Python 3.6, covering the new version of pandas, NumPy, IPython, and Jupyter, and adding a number of actual cases, which can help you solve a series of data analysis problems efficiently.
Major updates in the 2nd version
- All codes, including the Python tutorials updated to Python 3.6 (Python 2.7 was used in the first version)
- Updated the installation guidelines for Python third-party release Anaconda and other required Python packages
- Update the Pandas library to the new version in 2017
- A new chapter about more advanced pandas tools and some tips
- A concise introduction to the use of new stats models and scikit ear
Python for Data Analysis is one of the Best Quantitative Trading Books for Beginners
About the Author
William McKinney, a senior data analysis expert, has made in-depth research on various Python libraries (including NumPy, pandas, matplotlib, and IPython), and has accumulated rich experience in lots of practices.
He has written many classic articles related to Python data analysis, which have been reprinted by major technical communities.
He is one of the recognized authoritative in Python and open-source technology communities.
Pandas, a famous open-source Python library for data analysis, has been developed by him. Before he founded Lambda Foundry, a company dedicated to enterprise data analysis, he was a quantitative analyst at AQR Capital Management.
Python for Data Analysis PDF version will come as soon as possible.
Table of Contents
Chapter 1 Preparations
Chapter 2 Python Language Foundation, IPython, and Jupyter notebook
Chapter 3 Built-in Data Structure, Function, and File
Chapter 4 NumPy Basis: Array and Vectorization
Chapter 5 Introduction to Pandas
Chapter 6 Data Loading, Storage, and File Format
Chapter 7 Data Cleaning and Preparation
Chapter 8 Data Regularization: Connection, Union, and Remodeling
Chapter 9 Drawing and Visualization
Chapter 10 Data Aggregation and Grouping Operation
Chapter 11 Time Series
Chapter 12 Advanced Pandas
Chapter 13 Introduction to Python Modeling Library
Chapter 14 Data Analysis Example
Appendix A High-order NumPy
Appendix B More about the IPython system