Ways to Add Row/Columns in Numpy Array - Python

Last Updated : 24 Jun, 2025

Adding rows or columns to a NumPy array means appending new data along a specific axis. For example, if you have a 2D array like [[1, 2], [3, 4]] and you add a new row [5, 6], the array becomes [[1, 2], [3, 4], [5, 6]]. Similarly, adding a column [7, 8, 9] to a 3x2 array transforms it into a 3x3 array.

Let’s explore different ways to do this efficiently using NumPy.

Add columns in the Numpy array

We can add columns to a NumPy array using append(), concatenate(), insert(), column_stack() or hstack() with axis=1. Just make sure the new column has the same number of rows as the original array.

Using np.append()

np.append() adds values to a NumPy array along a specified axis or flattens if axis is not set.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([[1], [2], [3]])  c = np.append(a, b, axis=1) print(c)

Output

[[ 1  2  3  1]  [45  4  7  2]  [ 9  6 10  3]]

Explanation:

a is base array and b is array to append.
np.append(a, b, axis=1) adds b as a new column to a, resulting in a 3x4 matrix
whereas, axis=1 append along columns (i.e., horizontally).

Using np.concatenate()

np.concatenate() joins two or more arrays along a specified axis.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([[1], [2], [3]])  c = np.concatenate([a, b], axis=1) print (c)

Output

[[ 1  2  3  1]  [45  4  7  2]  [ 9  6 10  3]]

Explanation:

np.concatenate([a, b], axis=1) is joining list of two arrays [a, b].
axis=1 join along columns (horizontally).

Using np.insert()

np.insert() inserts values into a NumPy array at specified positions along a given axis.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([[1], [2], [3]])  c = np.insert(a, 3, b.flatten(), axis=1) print(c)

Output

[[ 1  2  3  1]  [45  4  7  2]  [ 9  6 10  3]]

Explanation:

b.flatten() converts b to a 1D array [1, 2, 3].
np.insert(a, 3, b.flatten(), axis=1) inserts b as the 4th column (index 3), one value per row.

Using np.hstack()

np.hstack() horizontally stacks arrays (adds columns side by side).

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([1, 2, 3])  c = np.hstack((a, np.atleast_2d(b).T)) print (c)

Output

[[ 1  2  3  1]  [45  4  7  2]  [ 9  6 10  3]]

Explanation:

np.atleast_2d(b).T converts b to a 3×1 column.
np.hstack() adds the column to the right of a.

Using np.column_stack()

np.column_stack() stacks 1D or 2D arrays as columns to form a 2D array.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([1, 2, 3])  c = np.column_stack((a, b)) print (c)

Output

[[ 1  2  3  1]  [45  4  7  2]  [ 9  6 10  3]]

Explanation: np.column_stack((a, b)) stacks b as a new column to the right of a.

Add row in Numpy array

To add rows to a NumPy array, use functions like np.r_, np.insert(), np.vstack() or np.append() with axis=0.

Using np.r_

np.r_ is a shortcut for stacking arrays vertically (row-wise).

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([1, 2, 3])  c = np.r_[a,[b]] print (c)

Output

[[ 1  2  3]  [45  4  7]  [ 9  6 10]  [ 1  2  3]]

Explanation: np.r_[a, [b]] appends b as a new row at the bottom of a.

Using np.insert()

np.insert() can add rows to a NumPy array by specifying the row index and using axis=0.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([1, 2, 3])  n = a.shape[0]   c = np.insert(a, n, b, axis=0) print(c)

Output

[[ 1  2  3]  [45  4  7]  [ 9  6 10]  [ 1  2  3]]

Explanation:

n = a.shape[0] gets row count (3).
np.insert(a, n, b, axis=0) inserts b as a new row at the end of a.

Using np.vstack()

np.vstack() adds arrays vertically, stacking them as new rows.

Python

import numpy as np a = np.array([[1, 2, 3], [45, 4, 7], [9, 6, 10]]) b = np.array([1, 2, 3])  c = np.vstack ((a, b)) print (c)

Output

[[ 1  2  3]  [45  4  7]  [ 9  6 10]  [ 1  2  3]]

Explanation: np.vstack((a, b)) vertically stacks b as a new row under a.

Using numpy.append()

You can add rows to an empty NumPy array using the numpy.append() function.

Example 1: Adding new rows to an empty 2-D array

Python

import numpy as np     a = np.empty((0,2), int) print("Empty array:\n", a)  a = np.append(a, np.array([[10,20]]), axis=0) a = np.append(a, np.array([[40,50]]), axis=0)  print("\nNew array:\n",a )

Output

Empty array:  []  New array:  [[10 20]  [40 50]]

Explanation:

np.empty((0,2), int) creates an empty array with 0 rows and 2 columns.
np.append(..., axis=0) appends new rows to a (row-wise), first adds [10, 20] then [40, 50].

Example 2: Adding new rows to an existing 2-D array.

Python

import numpy as np    a = np.array([[1, 2, 3], [4, 5, 6]]) print("Original array:\n", a)  b = np.array([[7, 8, 9], [10, 11, 12]]) c = np.append(a, b, axis=0) print("\nNew array:\n", c)

Output

Original array:  [[1 2 3]  [4 5 6]]  New array:  [[ 1  2  3]  [ 4  5  6]  [ 7  8  9]  [10 11 12]]

Explanation: a is a 2D array, and b has the same number of columns. Using np.append(axis=0), the new rows are added to the bottom of the original array.

Related Articles:
np.append()
np.concatenate
np.insert()
np.hstack()
np.column_stack()
np.vstack()

Ways to Convert a Python Dictionary to a NumPy Array

garg_ak0109

Improve

Article Tags :

Practice Tags :

python

Ways to Add Row/Columns in Numpy Array - Python

Add columns in the Numpy array

Using np.append()

Using np.concatenate()

Using np.insert()

Using np.hstack()

Using np.column_stack()

Add row in Numpy array

Using np.r_

Using np.insert()

Using np.vstack()

Using numpy.append()

Similar Reads