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| | Python Data Analysis
Key Features
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Find, manipulate, and analyze your data using the Python 3.5 libraries
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Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
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An easy-to-follow guide with realistic examples that are frequently used in real-world data
... | | Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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,... |
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NumPy 1.5 Beginner's Guide
An action packed guide for the easy-to-use, high performance, free open source NumPy mathematical library using real-world examples
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The first and only book that truly explores NumPy practically
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Perform high performance calculations with clean and efficient NumPy code
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Analyze large data sets...
| | Python Data Science Handbook: Essential Tools for Working with Data
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,... | | Python Crash Course: A Hands-On, Project-Based Introduction to Programming
Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time.
In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing ... |
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