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Recommendation numbered Nº:  20022020p1


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📌 For your review and personal analysis, copy and paste the address of the book page in your browser. The educational and research community will also thank you for evaluating this information (above). The technical, ethical and labor implications of Artificial Intelligence (AI) will affect us all. And it is everyone’s responsibility.


Summary

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. (Source: Amazon).

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

1. IPython: Beyond Normal Python

  • Help and Documentation in IPython
  • Keyboard Shortcuts in the IPython Shell
  • IPython Magic Commands
  • Input and Output History
  • IPython and Shell Commands
  • Errors and Debugging
  • Profiling and Timing Code
  • More IPython Resources

2. Introduction to NumPy

  • Understanding Data Types in Python
  • The Basics of NumPy Arrays
  • Computation on NumPy Arrays: Universal Functions
  • Aggregations: Min, Max, and Everything In Between
  • Computation on Arrays: Broadcasting
  • Comparisons, Masks, and Boolean Logic
  • Fancy Indexing
  • Sorting Arrays
  • Structured Data: NumPy’s Structured Arrays

3. Data Manipulation with Pandas

  • Introducing Pandas Objects
  • Data Indexing and Selection
  • Operating on Data in Pandas
  • Handling Missing Data
  • Hierarchical Indexing
  • Combining Datasets: Concat and Append
  • Combining Datasets: Merge and Join
  • Aggregation and Grouping
  • Pivot Tables
  • Vectorized String Operations
  • Working with Time Series
  • High-Performance Pandas: eval() and query()
  • Further Resources

4. Visualization with Matplotlib

  • Simple Line Plots
  • Simple Scatter Plots
  • Visualizing Errors
  • Density and Contour Plots
  • Histograms, Binnings, and Density
  • Customizing Plot Legends
  • Customizing Colorbars
  • Multiple Subplots
  • Text and Annotation
  • Customizing Ticks
  • Customizing Matplotlib: Configurations and Stylesheets
  • Three-Dimensional Plotting in Matplotlib
  • Geographic Data with Basemap
  • Visualization with Seaborn
  • Further Resources

5. Machine Learning

  • What Is Machine Learning?
  • Introducing Scikit-Learn
  • Hyperparameters and Model Validation
  • Feature Engineering
  • In Depth: Naive Bayes Classification
  • In Depth: Linear Regression
  • In-Depth: Support Vector Machines
  • In-Depth: Decision Trees and Random Forests
  • In Depth: Principal Component Analysis
  • In-Depth: Manifold Learning
  • In Depth: k-Means Clustering
  • In Depth: Gaussian Mixture Models
  • In-Depth: Kernel Density Estimation
  • Application: A Face Detection Pipeline
  • Further Machine Learning Resources

Author

(Unofficial biography. For informational purposes only).

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Jake VanderPlas

Director of Open Software at the University of Washington’s eScience institute, and researches and teaches in a variety of areas, including Astronomy, Astrostatistics, Machine Learning, and Scalable Computation.


🔘 Book page

-Creative Commons address: http://bit.ly/2Io1a5f
-Paid version address: amzn.to/2wgNFBn

📌 For your review and personal analysis, copy and paste the address of the book page in your browser. The educational and research community will also thank you for evaluating this information (above). The technical, ethical and labor implications of Artificial Intelligence (AI) will affect us all. And it is everyone’s responsibility.


Please, give thank the author and Publisher

Thank you very much for this useful information to @OReillyMedia, @jakevdp, via @States_AI_IA #phyton #datascience #free #ebook #ai #artificialintelligence #thebibleai #openscience #openaccess #thanks


Sheet book
Internal Id20022020p1
AuthorJake VanderPlas
TitlePython Data Science Handbook
Book website1.- Creative Common Version: jakevdp.github.io/PythonDataScienceHandbook
2.- Paid version: amzn.to/2wgNFBn
Publisher (1) associated with ISBNOReilly
Book’s own website as an extra Publisher(2)jakevdp.github.io/PythonDataScienceHandbook
Publication dateNovember 2016
ISBN978-1491912058
ObservationsPages, 541
Reviews on the book (if applicable)Amazon
Available for sale at (if applicable)Amazon
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The use of the cover image is restricted to the unequivocal identification of the book to avoid errors.Warning 2
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We do not provide links (in general) with hypertext to any website. We do this to avoid suggesting any type of commercial or interest relationship of ours in the recommendation. There are no conflicts of interest between the recommender and the recommended. Also to comply with the Intellectual Property Law (IP)/ Copyright, and (finally) let the user that all decisions regarding the use of the material are always personal. And to ensure private use without commercial purposes.
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RxRegistration ID
R0Hash MD5 (of R3):22649a2110353c7143cc4ee1e69c2f31
R1Registration number (in the domain editorialia.com at WordPress):dmeditorialiawp.3278
R2Date-p-order (ddmmyyyypx): 20022020p1
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R4Resource official title:Python Data Science Handbook
R5Publisher:OReilly
R6Resource website (1) ( #OpenAccess | #Openscience ): jakevdp.github.io/PythonDataScienceHandbook
R7Resource website (2) (Editorial|company):oreilly.com/library/view/python-data-science/9781491912126/
R10ISBN13 (without “-“):9781491912126
R12Authors (separated by commas):Jake VanderPlas
R13ORCID’s (separated by commas):orcid.org/0000-0002-9623-3401
R14Keyword (selected 1 among the labels applied to this entry):=programming
R15QR code (of the linked url at WP):La imagen tiene un atributo ALT vacío; su nombre de archivo es chart
R16Time stamp URL:https://web.archive.org/web/20200220120415/https://editorialia.com/2020/02/20/python-data-science-handbook-essential-tools-for-working-with-data/
R17Digital signature URL:Pending signature