Use numpy.delete(), which returns a new array with sub-arrays along an axis deleted.
numpy.delete(a, index)
For your specific question:
import numpy as np
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
index = [2, 3, 6]
new_a = np.delete(a, index)
print(new_a)
# Output: [1, 2, 5, 6, 8, 9]
Note that numpy.delete() returns a new array since array scalars are immutable, similar to strings in Python, so each time a change is made to it, a new object is created. I.e., to quote the delete() docs:
"A copy of arr with the elements specified by obj removed. Note that delete does not occur in-place..."
If the code I post has output, it is the result of running the code.
Answer from Levon on Stack OverflowNumPy
numpy.org › doc › stable › reference › generated › numpy.delete.html
numpy.delete — NumPy v2.5 Manual
The axis along which to delete the subarray defined by obj.
Note.nkmk.me
note.nkmk.me › home › python › numpy
NumPy: Delete rows/columns from an array with np.delete() | note.nkmk.me
February 5, 2024 - a = np.arange(12).reshape(3, 4) print(a) # [[ 0 1 2 3] # [ 4 5 6 7] # [ 8 9 10 11]] print(np.delete(a, [0, 2], 0)) # [[4 5 6 7]] print(np.delete(a, [0, 3], 1)) # [[ 1 2] # [ 5 6] # [ 9 10]] ... Starting from NumPy version 1.19, a list or array of Boolean values can be treated as a mask, with the indexes corresponding to True being deleted.
Top answer 1 of 13
466
Use numpy.delete(), which returns a new array with sub-arrays along an axis deleted.
numpy.delete(a, index)
For your specific question:
import numpy as np
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
index = [2, 3, 6]
new_a = np.delete(a, index)
print(new_a)
# Output: [1, 2, 5, 6, 8, 9]
Note that numpy.delete() returns a new array since array scalars are immutable, similar to strings in Python, so each time a change is made to it, a new object is created. I.e., to quote the delete() docs:
"A copy of arr with the elements specified by obj removed. Note that delete does not occur in-place..."
If the code I post has output, it is the result of running the code.
2 of 13
123
Use np.setdiff1d:
import numpy as np
>>> a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> b = np.array([3,4,7])
>>> c = np.setdiff1d(a,b)
>>> c
array([1, 2, 5, 6, 8, 9])
GeeksforGeeks
geeksforgeeks.org › python › numpy-delete-python
numpy.delete() in Python - GeeksforGeeks
January 23, 2026 - The numpy.delete() function returns a new array with the deletion of sub-arrays along with the mentioned axis.
Programiz
programiz.com › python-programming › numpy › methods › delete
NumPy delete()
The delete() method deletes the values at specified indices. import numpy as np array1 = np.array([0, 1, 2, 3])
DataCamp
datacamp.com › doc › numpy › delete-numpy
NumPy delete()
The NumPy `delete()` function is used to remove elements from an array along a specified axis.
NumPy
numpy.org › doc › 2.3 › reference › generated › numpy.delete.html
numpy.delete — NumPy v2.3 Manual
The axis along which to delete the subarray defined by obj.
Kite
kite.com › python › answers › how-to-delete-values-from-a-numpy-array-in-python
Kite.com
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w3resource
w3resource.com › numpy › manipulation › delete.php
NumPy: numpy.delete() function - w3resource
April 25, 2026 - The numpy.delete() function is used to remove one or more elements from an array along a specified axis.
NumPy
numpy.org › doc › 2.2 › reference › generated › numpy.delete.html
numpy.delete — NumPy v2.2 Manual
The axis along which to delete the subarray defined by obj.
Note.nkmk.me
note.nkmk.me › home › python › numpy
NumPy: Extract or delete elements, rows, and columns that satisfy the conditions | note.nkmk.me
May 31, 2019 - numpy.where(): Manipulate elements depending on conditions · By combining these two functions, you can delete the rows and columns that satisfy the condition.
University of Texas at Austin
het.as.utexas.edu › HET › Software › Numpy › reference › generated › numpy.delete.html
numpy.delete — NumPy v1.9 Manual
Return a new array with sub-arrays along an axis deleted.
GitHub
github.com › numpy › numpy › issues › 18412
numpy delete where is giving wrong result i.e. always deleting the first element if there is atleast one True · Issue #18412 · numpy/numpy
February 14, 2021 - `xyz =np.array( [[[612. , 0.8679449]], [[612. , 0.7679449]], [[206., 0.338741 ]], [[62., 2.338741 ]]]) xyx = np.copy(xyz) np.delete(xyx, np.where([[False], [False],[False],[ True]]), axis=0)`
Author: numpy
Vultr Docs
docs.vultr.com › python › third party › numpy › delete()
Python Numpy delete() - Remove Elements
November 6, 2024 - Specify the axis along which the deletion is to take place: 0 for rows and 1 for columns. Apply the numpy.delete() function with the appropriate axis and index.
NumPy
numpy.org › doc › 1.25 › reference › generated › numpy.delete.html
numpy.delete — NumPy v1.25 Manual
The axis along which to delete the subarray defined by obj.
TutorialsPoint
tutorialspoint.com › numpy › numpy_delete.htm
Numpy delete() Function
axis: The axis along which to delete the given subarray. If not given the input array(arr) is flattened. Following is the basic example of Numpy delete() Function which deletes the element at index 5 −
Medium
medium.com › @heyamit10 › numpy-delete-in-numpy-90ffd785a5cf
Understanding numpy.delete() with Syntax and Parameters | by Hey Amit | Medium
February 8, 2025 - At its core, numpy.delete() is a simple tool to remove specific elements, rows, or columns from a NumPy array.
NumPy
numpy.org › doc › 2.0 › reference › generated › numpy.delete.html
numpy.delete — NumPy v2.0 Manual
The axis along which to delete the subarray defined by obj.
NumPy
numpy.org › devdocs › reference › generated › numpy.delete.html
numpy.delete — NumPy v2.6.dev0 Manual
The axis along which to delete the subarray defined by obj.
Top answer 1 of 6
53
You can find the index/indices of the object using np.argwhere, and then delete the object(s) using np.delete.
Example:
x = np.array([1,2,3,4,5])
index = np.argwhere(x==3)
y = np.delete(x, index)
print(x, y)
2 of 6
28
Cast it as a numpy array, and mask it out:
x = np.array(list("abcdef"))
x = x[x!='e'] # <-- THIS IS THE METHOD
print x
# array(['a', 'b', 'c', 'd', 'f'])
Doesn't have to be more complicated than this.