Recommendation numbered Nº: 20022020p1
-Creative Commons address: bit.ly/2Io1a5f -Paid version address: amzn.to/2wgNFBn
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).
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.
-Creative Commons address: http://bit.ly/2Io1a5f -Paid version address: amzn.to/2wgNFBn
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
Tweet
| Sheet book | |
|---|---|
| Internal Id | 20022020p1 |
| Author | Jake VanderPlas |
| Title | Python Data Science Handbook |
| Book website | 1.- Creative Common Version: jakevdp.github.io/PythonDataScienceHandbook 2.- Paid version: amzn.to/2wgNFBn |
| Publisher (1) associated with ISBN | OReilly |
| Book’s own website as an extra Publisher(2) | jakevdp.github.io/PythonDataScienceHandbook |
| Publication date | November 2016 |
| ISBN | 978-1491912058 |
| Observations | Pages, 541 |
| Reviews on the book (if applicable) | Amazon |
| Available for sale at (if applicable) | Amazon |
| Access to the book page is provided by the Publisher herself (or the author). Keep in mind that the policy of the Publisher or the author may change. You must observe and comply with the terms of use set by the publication. | Warning 1 1.- The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. 2.- Terms of Service: [oreilly. com/terms] |
| The use of the cover image is restricted to the unequivocal identification of the book to avoid errors. | Warning 2 The cover book is under the editorial O’Reilly. [(Source image: jakevdp.github.io/PythonDataScienceHandbook/figures/PDSH-cover.png). CC-BY-NC-ND license]. |
| 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. The aforementioned, supposes that there is no responsibility of ours regarding the uses of licensing that a specific user may make. | Warning 3 A particular policy |
| If an author, or an Editorial, considers that we must rectify, delete or modify elements in this recommendation, please let us know in the contact form. Our manifest interest is always to protect the author and the Publisher from piracy; on which we position ourselves totally against. | Warning 4 For the author and the publisher |
| If you notice any incidence (or related third party infringement) in the terms of use of this book review, please let us know in the contact form. (If it occurs, we will suspend the review of this book in a precautionary manner). | Warning 5 |
| Sheet book |
| Rx | Registration ID | Nº |
|---|---|---|
| R0 | Hash MD5 (of R3): | 22649a2110353c7143cc4ee1e69c2f31 |
| R1 | Registration number (in the domain editorialia.com at WordPress): | dmeditorialiawp.3278 |
| R2 | Date-p-order (ddmmyyyypx): | 20022020p1 |
| R3 | Cid (combined id R1+R2): | dmeditorialiawp.327820022020p1 |
| R4 | Resource official title: | Python Data Science Handbook |
| R5 | Publisher: | OReilly |
| R6 | Resource website (1) ( #OpenAccess | #Openscience ): | jakevdp.github.io/PythonDataScienceHandbook |
| R7 | Resource website (2) (Editorial|company): | oreilly.com/library/view/python-data-science/9781491912126/ |
| R10 | ISBN13 (without “-“): | 9781491912126 |
| R12 | Authors (separated by commas): | Jake VanderPlas |
| R13 | ORCID’s (separated by commas): | orcid.org/0000-0002-9623-3401 |
| R14 | Keyword (selected 1 among the labels applied to this entry): | =programming |
| R15 | QR code (of the linked url at WP): | |
| R16 | Time stamp URL: | https://web.archive.org/web/20200220120415/https://editorialia.com/2020/02/20/python-data-science-handbook-essential-tools-for-working-with-data/ |
| R17 | Digital signature URL: | Pending signature |
