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nexusstc/Python Data Science Handbook: Essential Tools for Working With Data/9ec841ba57279c375e7a49ceb5912dcb.pdf
Python Data Science Handbook : Essential Tools for Working with Data 🔍
Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016
英语 [en] · PDF · 11.8MB · 2016 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
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
\*\*
About the Author
Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.
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lgli/Python_Data_Science_Handbook-o.pdf
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lgrsnf/Python_Data_Science_Handbook-o.pdf
备用文件名
zlib/Computers/Programming/Jake VanderPlas/Python Data Science Handbook: Essential Tools for Working with Data_3404428.pdf
备选标题
Python для сложных задач: наука о данных и машинное обучение: 16+
备选作者
Дж. Вандер Плас; [перевела с английского И. Пальти]
备选作者
Плас, Джейк Вандер
备用出版商
Creative Media Partners, LLC
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Питер
备用版本
First edition, Beijing; Boston; Farnham; Sebastopol; Tokyo, 2016
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Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2020
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Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018
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United States, United States of America
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O'Reilly Media, Sebastopol, CA, 2016
备用版本
First edition, Sebastopol, CA, 2016
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Beijing, 2017
备用版本
1, 20161121
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1, PS, 2017
元数据中的注释
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Фактическая дата выхода в свет - 2019
Пер.: Plas, Jake Vander Python data science handbook Beijing [etc.] : O'Reilly, cop. 2017 978-1491912058
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备用描述
This book is a reference for day-to-day Python-enabled science. It provides hundreds of short code recipes demonstrating solutions to problems common in computational and statistical approaches to science, making use of the breadth of free and open data software available for Python 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
备用描述
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.
备用描述
**Revision History**
December 2016: First Edition
2016-11-17: First Release
开源日期
2017-12-22
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