Python Data Science Handbook - Info and Reading Options
Essential Tools for Working with Data
By Jake VanderPlas
"Python Data Science Handbook" was published by O'Reilly Media, Incorporated in 2023, it has 550 pages and the language of the book is English.
“Python Data Science Handbook” Metadata:
- Title: Python Data Science Handbook
- Author: Jake VanderPlas
- Language: English
- Number of Pages: 550
- Is Family Friendly: Yes - No Mature Content
- Publisher: O'Reilly Media, Incorporated
- Publish Date: 2023
“Python Data Science Handbook” Subjects and Themes:
- Subjects: ➤ Python (Computer program language) - Data mining - COMPUTERS - General - Datenanalyse - Datenmanagement - Python
Edition Specifications:
- Pagination: 550
Edition Identifiers:
- Google Books ID: rimgEAAAQBAJ - h_UtzwEACAAJ
- The Open Library ID: OL38337600M - OL19545956W
- ISBN-13: 9781098121228
- ISBN-10: 1098121228
- All ISBNs: 9781098121228 - 1098121228
AI-generated Review of “Python Data Science Handbook”:
Snippets and Summary:
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.
"Python Data Science Handbook" Description:
Google Books:
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
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