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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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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
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Think Python
Think Python
In January 1999 I was preparing to teach an introductory programming class in Java. I had taught it three times and I was getting frustrated. The failure rate in the class was too high and, even for students who succeeded, the overall level of achievement was too low.

One of the problems I saw was the books. They were too
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Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning
Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

Key Features

  • Get a hands-on, fast-paced introduction to the Python data science stack
  • Explore ways to create useful metrics and statistics from...
Python for Data Mining Quick Syntax Reference
Python for Data Mining Quick Syntax Reference
?Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis.


Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian
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Python Data Analysis Cookbook
Python Data Analysis Cookbook

Key Features

  • Analyze Big Data sets, create attractive visualizations, and manipulate and process various data types
  • Packed with rich recipes to help you learn and explore amazing algorithms for statistics and machine learning
  • Authored by Ivan Idris, expert in python programming and proud...
Pro Python Best Practices: Debugging, Testing and Maintenance
Pro Python Best Practices: Debugging, Testing and Maintenance

Learn software engineering and coding best practices to write Python code right and error free. In this book you’ll see how to properly debug, organize, test, and maintain your code, all of which leads to better, more efficient coding.

Software engineering is difficult. Programs of any substantial length are...

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