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Author by : Wes McKinney Languange Used : en Release Date : 2017-09-25 Publisher by : "O'Reilly Media, Inc." ISBN : 9781491957615 File Size : 45,8 Mb Total Download : 644
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 sci
Author by : Jake VanderPlas Languange Used : en Release Date : 2016-11-21 Publisher by : "O'Reilly Media, Inc." ISBN : 9781491912133 File Size : 49,9 Mb Total Download : 538
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
Author by : Fabio Nelli Languange Used : en Release Date : 2018-09-27 Publisher by : Apress ISBN : 9781484239131 File Size : 50,6 Mb Total Download : 508
Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly