Python dēta saiensu handobukku Jupyter, NumPy, pandas, Matplotlib, scikit-learn o tsukatta dēta bunseki, kikai gakushū / = Python data science handbook :

Python dēta saiensu handobukku Jupyter, NumPy, pandas, Matplotlib, scikit-learn o tsukatta dēta bunseki, kikai gakushū / = Python data science handbook :

Pythonデータサイエンスハンドブック : Jupyter, NumPy, pandas, Matplotlib, scikit-learnを使ったデータ分析, 機械学習 /
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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;...

Ausführliche Beschreibung

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Bibliographische Detailangaben
1. Verfasser: Vanderplas, Jacob T. (VerfasserIn)
Weitere Verfasser: Kikuchi, Akira (ÜbersetzerIn)
Format: Elektronisch E-Book
Sprache:Japanese
Veröffentlicht: Tōkyō-to Shinjuku-ku Orairī Japan 2024
Ausgabe:Dai 2-han.
Schlagworte:
Python (Computer program language) > Handbooks, manuals, etc
Data mining > Handbooks, manuals, etc > Statistical methods
Electronic data processing > Handbooks, manuals, etc
Python (Langage de programmation) ; Guides, manuels, etc
Online-Zugang:lizenzpflichtig
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Beschreibung
Zusammenfassung: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;Python, 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.
Beschreibung:Includes bibliographical references and index
Beschreibung:1 online resource (576 pages)
ISBN:9784814400638
4814400632

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