Python data science handbook: essential tools for working with data
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;...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Sebastopol, CA
O'Reilly Media, Incorporated
2023
|
| Ausgabe: | Second edition. |
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781098121211/?ar |
| 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. - Description based upon online resource; title from PDF title page (viewed Jan 4th, 2023) |
| Umfang: | 1 Online-Ressource (xxiv, 563 Seiten) illustrations |
| ISBN: | 9781098121198 1098121198 |
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| spelling | Vanderplas, Jacob T. VerfasserIn aut Python data science handbook essential tools for working with data Jake VanderPlas Second edition. Sebastopol, CA O'Reilly Media, Incorporated 2023 1 Online-Ressource (xxiv, 563 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references and index. - Description based upon online resource; title from PDF title page (viewed Jan 4th, 2023) 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. Python (Computer program language) Data mining Python (Langage de programmation) Exploration de données (Informatique) 1098121228 Erscheint auch als Druck-Ausgabe 1098121228 |
| spellingShingle | Vanderplas, Jacob T. Python data science handbook essential tools for working with data Python (Computer program language) Data mining Python (Langage de programmation) Exploration de données (Informatique) |
| title | Python data science handbook essential tools for working with data |
| title_auth | Python data science handbook essential tools for working with data |
| title_exact_search | Python data science handbook essential tools for working with data |
| title_full | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_fullStr | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_full_unstemmed | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_short | Python data science handbook |
| title_sort | python data science handbook essential tools for working with data |
| title_sub | essential tools for working with data |
| topic | Python (Computer program language) Data mining Python (Langage de programmation) Exploration de données (Informatique) |
| topic_facet | Python (Computer program language) Data mining Python (Langage de programmation) Exploration de données (Informatique) |
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