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Python for Data Analysis in pdf


Download This PDF Book :Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition by Wes McKinney, for free.

Get 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 Jupiter 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 IPython shell and Jupiter notebook for exploratory computing

Learn basic and advanced features in NumPy (Numerical Python)

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 group by 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.  

What Is This Book About?

This book is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. My goal is to offer a guide to the parts of the Python programming language and its data-oriented library ecosystem and tools that will equip you to become an effective data analyst. While 'data analysis' is in the title of the book, the focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis.

New for the Second Edition

The first edition of this book was published in 2012, during a time when open source data analysis libraries for Python (such as pandas) were very new and developing rapidly. In this updated and expanded second edition, I have overhauled the chapters to account both for incompatible changes and deprecations as well as new features that have occurred in the last five years.

I’ve also added fresh content to introduce tools that either did not exist in 2012 or had not matured enough to make the first cut. Finally, I have tried to avoid writing about new or cutting-edge open source projects that may not have had a chance to mature. I would like readers of this edition to find that the content is still almost as relevant in 2020 or 2021 as it is in 2017.

About the Author

Wes McKinney is a New York?based software developer and entrepreneur. After finishing his undergraduate degree in mathematics at MIT in 2007, he went on to do quantitative finance work at AQR Capital Management in Greenwich, CT. Frustrated by cumbersome data analysis tools, he learned Python and started building what would later become the pandas project. 

He's now an active member of the Python data community and is an advocate for the use of Python in data analysis, finance, and statistical computing applications.

Wes was later the co-founder and CEO of DataPad, whose technology assets and team were acquired by Cloudera in 2014. He has since become involved in big data technology, joining the Project Management Committees for the Apache Arrow and Apache Parquet projects in the Apache Software Foundation. 

In 2016, he joined Two Sigma Investments in New York City, where he continues working to make data analysis faster and easier through open source software.


1. Preliminaries

2. Python Language Basics, IPython, and Jupyter Notebooks

3. Built-in Data Structures, Functions, and Files

4. NumPy Basics: Arrays and Vectorized Computation

5. Getting Started with pandas

6. Data Loading, Storage, and File Formats

7. Data Cleaning and Preparation

8. Data Wrangling: Join, Combine, and Reshape

9. Plotting and Visualization

10. Data Aggregation and Group Operations

11. Time Series

12. Advanced pandas

13. Introduction to Modeling Libraries in Python

14. Data Analysis Examples

A. Advanced NumPy

B. More on the IPython System

About The Book:

Publisher ‏ : ‎ O'Reilly Media; 2nd edition (October 31, 2017)

Language ‏ : ‎ English

Pages ‏ : ‎ 550 

File : PDF,11 MB


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