NumPy Insert

In this article we will learn about NumPy Insert, we will explore their usage, benefits, and best practices, along with examples.

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What does NumPy Insert function do?

Insertion in NumPy involves adding elements or arrays into specified positions within an existing array. It allows users to expand array dimensions or incorporate new data seamlessly.

NumPy Insert

• np.insert():
  • The np.insert() function inserts values along a specified axis at given indices within an array.
  • It enables both single-value and array insertion, providing flexibility in data augmentation.
• Syntax
  • numpy.insert(arr, obj, values, axis=None)
    • arr: This is the input array where the insertion operation will take place.
    • obj: This specifies the index or indices before which values need to be inserted. It can be:
      • An integer: If obj is an integer, values are inserted before the objth element along the specified axis.
      • A slice object: If obj is a slice object, values are inserted before the elements indicated by the slice along the specified axis.
      • An array-like: If obj is an array-like object, values are inserted before the elements indicated by the array-like object along the specified axis.
    • values: These are the values to be inserted into the array. It can be a single value or an array of values. If values is a single value, it is broadcasted to fit the shape of the insertion.
    • axis: This is the axis along which the insertion should be performed. By default, axis is None, and the input array is flattened before the insertion. If axis is an integer, values are inserted along the specified axis. If axis is None, values are appended to the flattened input array.

Benefits and Applications of Insertion

• Dynamic Data Management:
  • Insertion facilitates dynamic expansion of array dimensions, accommodating additional data seamlessly.
  • It enables users to incorporate new elements or arrays into existing arrays, adapting to changing requirements.

Best Practices for Insertion

• Indexing Accuracy:
  • Ensure accurate indexing when performing insertion to avoid unintended data corruption.
  • Double-check indices and axes to prevent errors and maintain data integrity.

Practical Example for Insertion

Single value insertion

arr = np.array([1, 2, 3, 4, 5])
new_arr = np.insert(arr,1,45)
print(new_arr)

• np.insert(arr, 1, 45): This line uses the np.insert() function to insert the value 45 before the element at index 1 in the array arr.

[ 1 45  2  3  4  5]

• The value 45 is inserted before the element at index 1 (which is 2) in the original array arr.
• The resulting array new_arr contains the original elements of arr with the value 45 inserted at the specified index.

Multiple Value Insertion

arr = np.array([1, 2, 3, 4, 5])
new_values = np.array([10, 20])

inserted_arr = np.insert(arr, 2, new_values)
print(inserted_arr)

• We have an existing NumPy array arr containing the elements [1, 2, 3, 4, 5].
• We create a new NumPy array new_values containing the elements [10, 20].
• The np.insert() function inserts the values from new_values before the 2nd index of the array arr.

[ 1  2 10 20  3  4  5]

• The values [10, 20] are inserted between the elements 2 and 3.
• The resulting array becomes [1, 2, 10, 20, 3, 4, 5], where [10, 20] are inserted before the element 3 at the 3rd index of the original array.

Insertion in 2D array

axis = 0

arr = np.array([[1, 2, 3], [4, 5,6],[7,8,9]])
new_arr = np.insert(arr, 1, [120, 140, 180], axis = 0)
print(new_arr)

• We use the np.insert() function to insert a new row [120, 140, 180] before the row at index 1 along axis 0 (rows).

[[  1   2   3]
 [120 140 180]
 [  4   5   6]
 [  7   8   9]]

• This output shows the modified array new_arr after the insertion operation. The new row [120, 140, 180] has been inserted before the row [4, 5, 6] at index 1.

axis = 1

arr = np.array([[1, 2, 3], [4, 5,6],[7,8,9]])
new_arr = np.insert(arr, 1, [120, 140, 180], axis = 1)
print(new_arr)

• We use the np.insert() function to insert a new column [120, 140, 180] before the column at index 1 along axis 1 (columns).

[[  1 120   2   3]
 [  4 140   5   6]
 [  7 180   8   9]]

• This output shows the modified array new_arr after the insertion operation. The new column [120, 140, 180] has been inserted before the column [2, 5, 8] at index 1.

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Reference: np.insert()

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Also Read:

• NumPy Concatenation
• NumPy Copy v/s View