Predictive Data Analysis with Python/

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NumPy Array Indexing

NumPy Array Indexing

In this lesson, array indexing in NumPy is explained.

We'll cover the following...

Indexing for a 1-D array

Just like normal arrays, elements of a NumPy array can also be accessed and changed through indexing. Through NumPy indexing, we can also access and change elements in a specific range.

The following code snippet provides an example of all these functionalities:

Python 3.5
import numpy as np
arr = np.arange(0,10,1) # Generate array with numbers from 0 to 9
print("The Array")
print(arr)
print("\nElement at index 5")
print(arr[5]) # Fetch element at index 5
print("\nElements in a range of 0 to 6")
print(arr[0:6]) # Fetch elements in a range
arr[0:6] = 20 # Assign a value to a range of elements
print("\nNew array after changing elements in a range of 0 to 6")
print(arr)

Note: The : inside the [] defines the range. The value to the left is the starting index and is inclusive. Meanwhile, the value on the right defines the ending index, which is exclusive; exclusive means that the value at the end index will not be considered for the ...