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Python Data Science Handbook : Essential Tools for Working with Data 🔍
Jacob T. Vanderplas; Jake VanderPlas O'Reilly Media, Incorporated, 1st Edition, Dec 10, 2016
English [en] · EPUB · 8.0MB · 2016 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/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 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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upload/newsarch_ebooks_2025_10/2019/07/25/1491912057.epub
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nexusstc/Python Data Science Handbook: Essential Tools for Working With Data/b72d6570421b823ba68c6d4b2f7bf2a4.epub
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lgli/1491912057.epub
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lgrsnf/1491912057.epub
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zlib/Computers/Programming/Jake VanderPlas/Python Data Science Handbook: Essential Tools for Working with Data_2838912.epub
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
Alternative publisher
Creative Media Partners, LLC
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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
O'Reilly Media, Sebastopol, CA, 2016
Alternative edition
First edition, Sebastopol, CA, 2016
Alternative edition
Beijing, 2017
Alternative edition
1, 20161121
Alternative edition
1, PS, 2017
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Alternative description
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
Alternative description
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.
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
2016-12-13
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