NumPy Array Indexing
Knowing the basics of NumPy array indexing is important for analyzing and manipulating the array object. NumPy in Python offers many ways to do array indexing.
- Slicing: Just like lists in Python, NumPy arrays can be sliced. As arrays can be multidimensional, you need to specify a slice for each dimension of the array.
- Integer array indexing: In this method, lists are passed for indexing for each dimension. One-to-one mapping of corresponding elements is done to construct a new arbitrary array.
- Boolean array indexing: This method is used when we want to pick elements from the array which satisfy some condition.
Python3
# Python program to demonstrate # indexing in numpy import numpy as np # An exemplar array arr = np.array([[-1, 2, 0, 4], [4, -0.5, 6, 0], [2.6, 0, 7, 8], [3, -7, 4, 2.0]]) # Slicing array temp = arr[:2, ::2] print ("Array with first 2 rows and alternate" "columns(0 and 2):\n", temp) # Integer array indexing example temp = arr[[0, 1, 2, 3], [3, 2, 1, 0]] print ("\nElements at indices (0, 3), (1, 2), (2, 1)," "(3, 0):\n", temp) # boolean array indexing example cond = arr > 0 # cond is a boolean array temp = arr[cond] print ("\nElements greater than 0:\n", temp) |
Output:
Array with first 2 rows and alternatecolumns(0 and 2):
[[-1. 0.]
[ 4. 6.]]
Elements at indices (0, 3), (1, 2), (2, 1),(3, 0):
[ 4. 6. 0. 3.]
Elements greater than 0:
[ 2. 4. 4. 6. 2.6 7. 8. 3. 4. 2. ]
Introduction to NumPy
This article will help you get acquainted with the widely used array-processing library NumPy in Python.