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nexusstc/Python Data Science Handbook: Essential Tools for Working with Data/696ac456dd05ac853f469fd205d847a9.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
英语 [en] · PDF · 7.6MB · 2016 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save
描述
The Python Data Science Handbook provides a reference to the breadth of computational and statistical methods that are central to data-intensive science, research, and discovery. People with a programming background who want to use Python effectively for data science tasks will learn how to face a variety of problems: e.g., how can I read this data format into my script? How can I manipulate, transform, and clean this data? How can I visualize this type of data? How can I use this data to gain insight, answer questions, or to build statistical or machine learning models?
This book is a reference for day-to-day Python-enabled data science, covering both the computational and statistical skills necessary to effectively work with . The discussion is augmented with frequent example applications, showing how the wide breadth of open source Python tools can be used together to analyze, manipulate, visualize, and learn from data.
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lgli/Python Data Science Handbook - Essential Tools for Working with Data.pdf
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lgrsnf/Python Data Science Handbook - Essential Tools for Working with Data.pdf
备用文件名
zlib/Computers/Programming/Jake VanderPlas/Python Data Science Handbook: Essential Tools for Working with Data_3335035.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
Питер
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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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First edition, Sebastopol, CA, 2016
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1st Edition, Dec 10, 2016
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Beijing, 2017
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1, PS, 2017
元数据中的注释
0
元数据中的注释
lg2093123
元数据中的注释
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元数据中的注释
Фактическая дата выхода в свет - 2019
Пер.: Plas, Jake Vander Python data science handbook Beijing [etc.] : O'Reilly, cop. 2017 978-1491912058
元数据中的注释
РГБ
元数据中的注释
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Пер.: Plas, Jake Vander Python data science handbook Beijing [etc.]: O'Reilly 978-1491912058
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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 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
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
2017-08-18
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