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Python Data Science Handbook: Essential Tools for Working with Data

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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

  • IPython and Jupyter: provide computational environments for data scientists using Python
  • NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
  • Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
  • Matplotlib: includes capabilities for a flexible range of data visualizations in Python
  • Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Scientific Computing with Python 3
Scientific Computing with Python 3

Key Features

  • Your ultimate resource for getting up and running with Python numerical computations
  • Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules
  • A hands-on guide to implementing mathematics with Python, with complete...
Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition
Python Web Scraping: Hands-on data scraping and crawling using PyQT, Selnium, HTML and Python, 2nd Edition

Successfully scrape data from any website with the power of Python 3.x

Key Features

  • A hands-on guide to web scraping using Python with solutions to real-world problems
  • Create a number of different web scrapers in Python to extract information
  • This book...

Practical Data Science Cookbook - Second Edition
Practical Data Science Cookbook - Second Edition

Over 85 recipes to help you complete real-world data science projects in R and Python

About This Book

  • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
  • Get beyond the theory and implement real-world projects in data science using...
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on...
Python Data Analysis
Python Data Analysis

Key Features

  • Find, manipulate, and analyze your data using the Python 3.5 libraries
  • Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
  • An easy-to-follow guide with realistic examples that are frequently used in real-world data...
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