Python for Data Analysis

Python for Data Analysis
PDF
  • eBook:
    Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd Edition
  • Author:
    Wes McKinney
  • Edition:
    2 edition
  • Categories:
  • Data:
    October 24, 2017
  • ISBN:
    1491957662
  • ISBN-13:
    9781491957660
  • Language:
    English
  • Pages:
    550 pages
  • Format:
    PDF

Book Description
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 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 IPython shell and Jupyter 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 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.

Content

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

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