September 2018
Intermediate to advanced
472 pages
12h 2m
English
book
Python Data Science Essentials - Third Edition
by Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti
Content preview from Python Data Science Essentials - Third Edition
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Slicing and indexing with NumPy arrays
Indexing allows us to take a view of a ndarray by pointing out either what slice of columns and rows to visualize, or an index:
- Let's define a working array:
In: import numpy as np M = np.arange(10*10, dtype=int).reshape(10,10)
- Our array is a 10 x 10 two-dimensional array. We can initially start by slicing it into a single dimension. The notation for a single dimension is the same as that in Python lists:
[start_index_included:end_index_exclude:steps]
- Let's say that we want to extract even rows from 2 to 8:
In: M[2:9:2,:] Out: array([[20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [60, 61, 62, 63, 64, 65, 66, 67, 68, 69], [80, 81, 82, 83, 84, 85, 86, 87, 88, ...
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