Advanced Indexing Techniques on NumPy Arrays - Learn NumPy Series
This video is apart of a full Learn NumPy Series-
• Introduction to NumPy Arrays for Begi...
In this one we'll look at how we can begin using advanced indexing methods on our NumPy Arrays
#Python #NumPy #Tutorial
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Full code from the video:
import numpy as np
row_1 = [1,2,3,4,5]
row_2 = [6,7,8,9,10]
row_3 = [11,12,13,14,15]
row_4 = [16,17,18,19,20]
row_5 = [21,22,23,24,25]
test_data = np.array([row_1,row_2,row_3,row_4,row_5])
print(test_data)
Using Python Slices
print(test_data[:,2:4:1])
Same Elements but reversed
print(test_data[:,-2:-4:-1])
#boolean index
greater_than_five = test_data != 5
returns one dimensional array
print(greater_than_five)
single line operation
print(test_data[greater_than_five])
print(test_data[test_data!=5])
But what if we wanted to retain shape?
drop_under_5_array = np.where(test_data != 5, test_data, 0)
print(drop_under_5_array)
Using Multiple Logic Conditions
drop_under_5_and_over_20 = np.logical_and(test_data!=5, test_data!=20)
YouTube Description doesn't allow angled brackets :(
print(drop_under_5_and_over_20)
print(test_data[drop_under_5_and_over_20])
https://github.com/Derrick-Sherrill/N...
Packages (& Versions) used in this video:
Python 3.7
NumPy 1.17
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Code from this tutorial and all my others can be found on my GitHub:
https://github.com/Derrick-Sherrill/D...
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