NumPy Indexing and Slicing Arrays, Boolean Mask Arrays , Numpy Python Data Science

In this Python NumPy Tutorial on Data Science, We discuss Numpy Indexing and Slicing Arrays. We Learn Numpy Boolean Indexing. NumPy is the ultimate package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, tools for integrating C/C++ and Fortran code, sophisticated (broadcasting) functions, useful linear algebra, random number capabilities and Fourier transform.
Basic slicing ( 0:32 ) extends Python’s basic concept of slicing to N dimensions. Basic slicing occurs when obj is a slice object (constructed by start:stop:step notation inside of brackets) .
NumPy Boolean arrays ( 8:12 ) used as indices are treated in a different manner entirely than index arrays. Boolean arrays must be of the same shape as the initial dimensions of the array being indexed. In the most straightforward case, the boolean array has the same shape.

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NumPy Data Science Essential Training introduces the beginning to intermediate data scientist to NumPy, the Python library that supports numerical, scientific, and statistical programming, including machine learning. The library supports several aspects of data science, providing multidimensional array objects, derived objects (matrixes and masked arrays), and routines for math, logic, sorting, statistics, and random number generation. Jupyter Notebook, a browser-based tool for creating interactive documents with live code, annotations, and even visualizations such as plots. Learn how to create NumPy arrays, use NumPy statements and snippets, and index, slice, iterate, and otherwise manipulate arrays. Plus, learn how to plot data and combine NumPy arrays with Python classes, and get examples of NumPy in action: solving linear equations, finding patterns, performing statistics, generating magic cubes, and more.

Topics include:

1. Using Jupyter Notebook
2. Creating NumPy arrays from Python structures
3. Slicing arrays
4. Using Boolean masking and broadcasting techniques
5. Plotting in Jupyter notebooks
6. Joining and splitting arrays
7. Rearranging array elements
8. Creating universal functions
9. Finding patterns
10. Building magic squares and magic cubes with NumPy and Python

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[billwindsor]

Excellent video here on Python 2D and 3D multidimensional arrays, and multidimensional array indexing operations; *thank you!*

[santiagonicolasandradeeche]

thanks for the help, I'm from Ecuador and this information is very important.

[divyadarbe]

Sir, U explain the concept very well. But please increase the font size, we can't see the text..Thank you

[madhun]

Could you please upload the mission values and outliers videos.

[alokranjansanga]

please enlarge the font size, I can't see clearly

[sanandapodder]

getting error in zero_mod_7_mask showing the variable is undefined