Python for Data Science: Data analysis and Deep learning with Python coding and programming. The latest beginner's guide with practical applications on Machine learning and Artificial intelligence. by William Wizner

Author:William Wizner [Wizner, William]
Language: eng
Format: azw3
Published: 2020-07-20T16:00:00+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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