Select Rows / Columns by Index in NumPy Array

In this article, we explore how to select elements, rows, columns, and sub-arrays from a 2D NumPy array, which is an essential skill in data analysis and manipulation.

Importing NumPy Module

First, we import NumPy, which is a fundamental package for scientific computing in Python:

import numpy as np

NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

Creating a 2D NumPy Array

We create a 2D array (matrix) using np.array() by passing a list of lists:

import numpy as np

nArr2D = np.array([[21, 22, 23],
                  [11, 22, 33],
                  [43, 77, 89]])

print(nArr2D)

Output:

[[21 22 23]
 [11 22 33]
 [43 77 89]]

Each inner list becomes a row in the 2D array. The above code creates a 3×3 matrix.

Select an Element from 2D NumPy Array

We can select a single element from the array using its row and column indices:

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

num = nArr2D[1][2]
print('Element at row index 1 & column index 2 is:', num)

Output:

Element at row index 1 & column index 2 is: 33

This code snippet accesses the second row (index 1 as indexing starts at 0) and the third column (index 2). The output is 33, the element at that position.

Alternatively, we can use a tuple of indices:

num = nArr2D[1, 2]
print('Element at row index 1 & column index 2 is:', num)

Output:

Element at row index 1 & column index 2 is: 33

This is a more concise way to achieve the same result.

Select Rows from 2D NumPy Array

• To select an entire row from a 2D Array, we can pass the row index in [] operator. Like arr[row_index]. It will return a complete row at given index.
• To select multiple rows use: arr[start_index: end_index , :]. It will return rows from start_index to end_index – 1 and will include all columns.

Let’s see an example, To select entire 2nd row from a 2D Array, we can pass the row index 1 in [] operator. Like this,

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

# Select row at index 1 i.e. 2nd row
row = nArr2D[1]

print('Row at Index 1:', row)

Output:

Row at Index 1: [11 22 33]

This snippet selects the second row of the array. Remember, in Python, indexing starts at 0.

For selecting multiple rows:

rows = nArr2D[1:3, :]

print('Rows from Index 1 to 2:\n', rows)

Output:

Rows from Index 1 to 2:
 [[11 22 33]
 [43 77 89]]

Here, 1:3 is a slice indicating the second and third rows (excluding row at index 3).

Select Columns from 2D NumPy Array

To select a column, pass the column index along with the rows information in the [] operator of NumPy Array.

arr[ : , column_index]

It will return a complete column at given index. To select multiple columns use,

ndArray[ : , start_index: end_index]

It will return columns from start_index to end_index – 1.

Let’s see an example,

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

# Select 2nd Column from 2D Array
column = nArr2D[:, 1]

print('Column at Index 1:', column)

Output:

Column at Index 1: [22 22 77]

The : means “select all rows,” and 1 specifies the second column.

For multiple columns:

columns = nArr2D[:, 1:3]

print('Columns from Index 1 to 2:\n', columns)

Output:

 [[22 23]
 [22 33]
 [77 89]]

This selects columns 2 and 3 (1:3 slice).

Select a Sub Matrix from a NumPy Array

To select sub 2d Numpy Array we can pass the row & column index range in [] operator i.e.

arr[start_row_index : end_row_index , start_column_index : end_column_index]

It will return a sub 2D Numpy Array for given row and column range.

Let’s use these, to select a sub-matrix from a 2D NumPy Array,

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

# Select sub-Array i.e. from rows 1 to 2 and columns 1 to 2
sub2DArr = nArr2D[1:3, 1:3]

print('Sub 2D Array:\n', sub2DArr)

Output:

Sub 2D Array:
 [[22 33]
 [77 89]]

This snippet selects rows 2 to 3 and columns 2 to 3, forming a smaller 2×2 matrix.

Selecting a View vs Selecting a Copy from a NumPy Array

Modifying a view will affect the original array:

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

nArr2D[1] = [100, 100, 100]

print('Modified Array:\n', nArr2D)
print('Original Array:\n', nArr2D)

Output:

Modified Array:
 [[ 21  22  23]
 [100 100 100]
 [ 43  77  89]]
Original Array:
 [[ 21  22  23]
 [100 100 100]
 [ 43  77  89]]

Changing the second row in the view alters the original array.

To avoid this, create a copy:

import numpy as np

nArr2D = np.array([[21, 22, 23],
                   [11, 22, 33],
                   [43, 77, 89]])

row_copy = nArr2D[1].copy()
row_copy[:] = 200

print('Modified Array:\n', row_copy)
print('Original Array:\n', nArr2D)

Output:

Modified Array:
 [200 200 200]
Original Array:
 [[21 22 23]
 [11 22 33]
 [43 77 89]]

Modifying row_copy doesn’t change nArr2D because row_copy is an independent copy.

Summary

Today, we learned how to select rows & columns from a 2D NumPy Array in Python.