numpy.trim_zeros() – The NumPy trim_zeros Python Function

Introduction to NumPy trim_zeros

The numpy.trim_zeros() function is used to trim the leading and/or trailing zeros from a 1-D array or sequence.

Syntax:

numpy.trim_zeros(arr, trim)

Parameters:

  • arr: 1-D array or sequence
  • trim: trim is an optional parameter with a default value to be ‘fb’ (front and back) we can either select ‘f’ (front) or ‘b’ for the back.

Return: [trimmed]1-D array or sequence (without leading and/or trailing zeros as per user’s choice)

Code Examples of NumPy trim_zeros

Example 01: With default value trim=’fb’

# welcome to softhunt.net
import numpy as np

softhunt = np.array((0, 0, 0, 0, 1, 3, 5, 7, 0, 9, 0, 11, 0, 0))

# without trim parameter
# returns an array without leading and trailing zeros

res = np.trim_zeros(softhunt)
print(res)

Output:

[ 1  3  5  7  0  9  0 11]

Note: These programs will not run in online IDEs. Please test them on your systems to see how they operate.

Example 02: When trim=’f’

# welcome to softhunt.net
import numpy as np
softhunt = np.array((0, 0, 0, 0, 1, 3, 5, 7, 0, 9, 0, 11, 0, 0))

# without trim parameter
# returns an array without any leading zeros

res = np.trim_zeros(softhunt, 'f')
print(res)

Output:

[ 1  3  5  7  0  9  0 11  0  0]

Example 03: When trim=’b’

# welcome to softhunt.net
import numpy as np
softhunt = np.array((0, 0, 0, 0, 1, 3, 5, 7, 0, 9, 0, 11, 0, 0))

# without trim parameter
# returns an array without any leading zeros

res = np.trim_zeros(softhunt, 'b')
print(res)

Output:

[ 0  0  0  0  1  3  5  7  0  9  0 11]

Note: These programs will not run in online IDEs. Please test them on your systems to see how they operate.

FAQs

What is a NumPy Ndarray?

NumPy is a Python library for scientific and numerical applications, and it is the tool of choice for linear algebra computations.

The ndarray, which is a shorthand term for N-dimensional array, is the most important data structure in NumPy. The data in a ndarray is simply referred to as an array when dealing with NumPy. It’s a memory array with data of the same type, such as integers or floating-point values, that’s fixed in size.

How many dimensions can a NumPy array have?

Numpy arrays can have several dimensions in general. Starting with a 1-dimensional array and using the NumPy reshape() function to rearrange elements of that array into a new shape is one technique to generate such an array.

Conclusion

That’s all for this article, if you have any confusion contact us through our website or email us at [email protected] or by using LinkedIn. And make sure you check out our NumPy tutorials.

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