Python for Data science: The latest guide for the novice data scientist. Learn the principles of Python language to analyze and manage data. Machine learning and Scikit-learn sections included. by William Dimick

Author:William Dimick [Dimick, William]
Language: eng
Format: azw3, epub
Published: 2020-09-24T00:00:00+00:00

large_array = np. arrange (0,100,2). reshape (5,10)

large_array # show

Out []: array ([[ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18],

[20, 22, 24, 26, 28, 30, 32, 34, 36, 38],

[40, 42, 44, 46, 48, 50, 52, 54, 56, 58],

[60, 62, 64, 66, 68, 70, 72, 74, 76, 78],

[80, 82, 84, 86, 88, 90, 92, 94, 96, 98]])

Tip: Try grabbing single elements and rows from random arrays you create. After getting very familiar with this, try selecting columns. The point is to try as many combinations as possible to get you familiar with the approach. If the slicing and indexing notations are confusing, try to revisit the section under list or string slicing and indexing.

Click this link to revisit the examples on slicing:

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