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Python NumPy: How to Add a New Value to a NumPy Array in Python

Learn how to add elements to a NumPy array in Python using np.append(), np.insert(), np.concatenate(), and axis-specific methods for 1D and 2D arrays.

Unlike standard Python lists where you can simply call .append(), NumPy arrays have a fixed size once created. Adding elements requires creating a new array that includes the additional values. NumPy provides several functions for this, each suited for different scenarios - appending to the end, inserting at a specific position, or combining multiple arrays.

This guide covers the main approaches with examples for both 1D and 2D arrays.

Using np.append() - Add to the End

The np.append() function adds one or more values to the end of an array. It returns a new array - the original is not modified.

import numpy as np

arr = np.array([1, 2, 3])

new_arr = np.append(arr, 4)
print("Single append:", new_arr)

new_arr = np.append(arr, [4, 5, 6])
print("Multiple append:", new_arr)

Output:

Single append: [1 2 3 4]
Multiple append: [1 2 3 4 5 6]

Appending to a 2D Array

For 2D arrays, specify the axis parameter to control whether you add a row or a column:

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6]])

new_row = np.array([[7, 8, 9]])
result = np.append(arr, new_row, axis=0)
print("New row:\n", result)

new_col = np.array([[10], [11]])
result = np.append(arr, new_col, axis=1)
print("New column:\n", result)

Output:

New row:
 [[1 2 3]
 [4 5 6]
 [7 8 9]]
New column:
 [[ 1  2  3 10]
 [ 4  5  6 11]]

Using np.insert() - Add at a Specific Position

The np.insert() function lets you insert values at any index within the array, not just at the end:

import numpy as np

arr = np.array([1, 2, 3, 4, 5])

new_arr = np.insert(arr, 2, 99)
print("Insert at index 2:", new_arr)

new_arr = np.insert(arr, 1, [10, 20])
print("Insert multiple at index 1:", new_arr)

Output:

Insert at index 2: [ 1  2 99  3  4  5]
Insert multiple at index 1: [ 1 10 20  2  3  4  5]

Inserting Rows and Columns in 2D Arrays

import numpy as np

arr = np.array([[1, 2, 3],
                [7, 8, 9]])

new_row = [4, 5, 6]
result = np.insert(arr, 1, new_row, axis=0)
print("Insert row:\n", result)

new_col = [10, 20]
result = np.insert(arr, 1, new_col, axis=1)
print("Insert column:\n", result)

Output:

Insert row:
 [[1 2 3]
 [4 5 6]
 [7 8 9]]
Insert column:
 [[ 1 10  2  3]
 [ 7 20  8  9]]

Using np.concatenate() - Combine Arrays

The np.concatenate() function joins two or more arrays along an existing axis. It's the most efficient choice when combining larger blocks of data:

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5])
arr3 = np.array([6, 7, 8])

result = np.concatenate((arr1, arr2, arr3))
print("Concatenated:", result)

Output:

Concatenated: [1 2 3 4 5 6 7 8]

Concatenating 2D Arrays

import numpy as np

arr = np.array([[1, 2],
                [3, 4]])

new_rows = np.array([[5, 6], [7, 8]])
result = np.concatenate((arr, new_rows), axis=0)
print("Add rows:\n", result)

new_cols = np.array([[10, 20], [30, 40]])
result = np.concatenate((arr, new_cols), axis=1)
print("Add columns:\n", result)

Output:

Add rows:
 [[1 2]
 [3 4]
 [5 6]
 [7 8]]
Add columns:
 [[ 1  2 10 20]
 [ 3  4 30 40]]

Using np.hstack() and np.vstack()

These convenience functions simplify horizontal and vertical stacking:

import numpy as np

arr = np.array([1, 2, 3])

result = np.hstack((arr, [4, 5]))
print("hstack:", result)

arr2d = np.array([[1, 2, 3]])
result = np.vstack((arr2d, [[4, 5, 6]]))
print("vstack:\n", result)

Output:

hstack: [1 2 3 4 5]
vstack:
 [[1 2 3]
 [4 5 6]]

Performance Considerations

Comparison of Methods

Method Position Best For
np.append() End of array Adding values to the end
np.insert() Any index Adding values at specific positions
np.concatenate() End (or start) Combining multiple arrays efficiently
np.hstack() Horizontal Adding columns or extending 1D arrays
np.vstack() Vertical Adding rows to 2D arrays

Conclusion

Adding values to NumPy arrays requires creating new arrays because NumPy arrays have fixed sizes.

  • Use np.append() for simple additions to the end,
  • Use np.insert() when you need to place values at specific indices
  • Use np.concatenate() for combining larger arrays efficiently.
  • For 2D operations, always specify the axis parameter to avoid unintended flattening.

Most importantly, avoid adding elements in a loop - build a Python list first and convert it to a NumPy array for the best performance.

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