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nexusstc/Python Data Science Handbook/4943671270d6de48845d38cd01e90fe8.pdf
Python Data Science Handbook : Essential Tools for Working with Data 🔍
Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, O'Reilly Media, Sebastopol, CA, 2016
English [en] · PDF · 6.6MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save
description
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 allIPython, 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, youll 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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lgli/OR - Python Data Science Handbook 2016.pdf
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lgrsnf/OR - Python Data Science Handbook 2016.pdf
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zlib/Computers/Programming/Jake VanderPlas/Python Data Science Handbook_3380617.pdf
Alternative title
Python для сложных задач: наука о данных и машинное обучение: 16+
Alternative author
Дж. Вандер Плас; [перевела с английского И. Пальти]
Alternative author
Плас, Джейк Вандер
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Питер
Alternative edition
First edition, Beijing; Boston; Farnham; Sebastopol; Tokyo, 2016
Alternative edition
Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2020
Alternative edition
Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018
Alternative edition
United States, United States of America
Alternative edition
First edition, Sebastopol, CA, 2016
Alternative edition
1st Edition, Dec 10, 2016
Alternative edition
Beijing, 2017
Alternative edition
1, PS, 2017
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Alternative description
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.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.
Alternative description
**Revision History**
December 2016: First Edition
2016-11-17: First Release
date open sourced
2017-10-20
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