NumPy
numpy.org › doc › stable › reference › generated › numpy.all.html
numpy.all — NumPy v2.5 Manual
Test whether all array elements along a given axis evaluate to True.
NumPy
numpy.org › doc › stable › reference › generated › numpy.where.html
numpy.where — NumPy v2.5 Manual
The rest of this documentation covers only the case where all three arguments are provided. ... Where True, yield x, otherwise yield y. ... Values from which to choose. x, y and condition need to be broadcastable to some shape. ... An array with elements from x where condition is True, and elements from y elsewhere. ... Try it in your browser! >>> import numpy ...
Medium
medium.com › @heyamit10 › numpy-all-in-python-aae751f06e32
numpy.all() in Python. I understand that learning data science… | by Hey Amit | Medium
April 18, 2025 - Have you ever tried to check if ... a specific condition? Let’s say you’re trying to verify if all items are non-zero, or if every student in your class scored above a certain grade. Doing this manually is cumbersome, isn’t it? That’s where numpy.all() becomes your ...
Sharp Sight
sharpsight.ai › blog › numpy-all
Numpy All, Explained - Sharp Sight
July 24, 2021 - By default, the np.all returns a boolean value (i.e., True or False) that indicates whether all of the elements of the input are true, or meet a particular condition. However, if you use np.all with the axis parameter on a multi-dimensional ...
Codecademy
codecademy.com › docs › python:numpy › ndarray › .all()
Python:NumPy | ndarray | .all() | Codecademy
October 30, 2025 - Returns True if all elements in the array evaluate to True, or along a specified axis.
Note.nkmk.me
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NumPy: Extract or delete elements, rows, and columns that satisfy the conditions | note.nkmk.me
May 31, 2019 - This article describes how to extract or delete elements, rows, and columns that satisfy the condition from the NumPy array ndarray. Extract elements that satisfy the conditions Extract rows and colum ...
Codepointtech
codepointtech.com › home › mastering numpy: understanding all() & any() functions
Mastering NumPy: Understanding all() & any() Functions - codepointtech.com
July 4, 2026 - For more on NumPy operations, consider reading our guide on NumPy Boolean Indexing Explained. Choosing between all() and any() depends entirely on the logical question you”re trying to answer: Use all() when you need to confirm that a condition holds true for every single element.
Scaler
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What is the numpy.all() in Numpy? - Scaler Topics
May 4, 2023 - When we use the NumPy all() function on a two-dimensional array, it checks if every element of the two-dimensional array is greater than 2 (given condition).
SciPy
docs.scipy.org › doc › numpy-1.14.0 › reference › generated › numpy.all.html
numpy.all — NumPy v1.14 Manual
numpy.all(a, axis=None, out=None, keepdims=<class 'numpy._globals._NoValue'>)[source]¶ · Test whether all array elements along a given axis evaluate to True. See also · ndarray.all · equivalent method · any · Test whether any element along a given axis evaluates to True.
NumPy
numpy.org › devdocs › reference › generated › numpy.where.html
numpy.where — NumPy v2.6.dev0 Manual
The rest of this documentation covers only the case where all three arguments are provided. ... Where True, yield x, otherwise yield y. ... Values from which to choose. x, y and condition need to be broadcastable to some shape. ... An array with elements from x where condition is True, and elements from y elsewhere. ... Try it in your browser! >>> import numpy as np >>> a = np.arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.where(a < 5, a, 10*a) array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])
NumPy
numpy.org › doc › 2.1 › reference › generated › numpy.all.html
numpy.all — NumPy v2.1 Manual
Test whether all array elements along a given axis evaluate to True.
DigitalOcean
digitalocean.com › community › tutorials › python-numpy-where
How to use Python numpy.where() Method | DigitalOcean
Leverage NumPy’s where() function to efficiently select elements from arrays based on conditions, creating new arrays with tailored values.
NumPy
numpy.org › devdocs › reference › generated › numpy.all.html
numpy.all — NumPy v2.6.dev0 Manual
Test whether all array elements along a given axis evaluate to True.
Top answer 1 of 3
5
You can use np.all with axis=1 on a boolean array.
import numpy as np
arr = np.array([[0.8, 0.9], [0.1, 0.6], [0.2, 0.3]])
print(np.all(arr>0.5, axis=1))
>> [True False False]
2 of 3
2
import numpy as np
# Value Initialization
a = np.array([0.75, 0.25, 0.50])
y_predict = np.zeros((1, a.shape[0]))
#If the value is greater than 0.5, the value is 1; otherwise 0
y_predict = (a > 0.5).astype(float)
NumPy
numpy.org › doc › 2.2 › reference › generated › numpy.where.html
numpy.where — NumPy v2.2 Manual
The rest of this documentation covers only the case where all three arguments are provided. ... Where True, yield x, otherwise yield y. ... Values from which to choose. x, y and condition need to be broadcastable to some shape. ... An array with elements from x where condition is True, and elements from y elsewhere. ... >>> import numpy as np >>> a = np.arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.where(a < 5, a, 10*a) array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])
Project Shop
projectshop.in › home › blog › understanding a.any() and a.all() in python
Understanding a.any() and a.all() in Python - Project Shop
June 21, 2024 - The a.all() function is your go-to when you need to verify if all elements in an array meet a certain condition. ... import numpy as np # Create an array array = np.array([1, 1, 1, 1, 1]) # Check if all elements are non-zero result = array.all() print(result) # Output: True
NumPy
numpy.org › doc › 2.3 › reference › generated › numpy.all.html
numpy.all — NumPy v2.3 Manual
Test whether all array elements along a given axis evaluate to True.
Top answer 1 of 7
107
If you take a look at the result of valeur <= 0.6, you can see what’s causing this ambiguity:
>>> valeur <= 0.6
array([ True, False, False, False], dtype=bool)
So the result is another array that has in this case 4 boolean values. Now what should the result be? Should the condition be true when one value is true? Should the condition be true only when all values are true?
That’s exactly what numpy.any and numpy.all do. The former requires at least one true value, the latter requires that all values are true:
>>> np.any(valeur <= 0.6)
True
>>> np.all(valeur <= 0.6)
False
2 of 7
14
There is one more way you can get this
import numpy as np
a = np.array([1,2,3,4])
b = np.array([5,6,7,8])
c = np.array([1,2,3,4])
print((a == b ).all()) #False
print((a == c ).all()) # True
print((a == b ).any()) #False
print((a == c ).any()) #True
print((a > 3 ).all()) #False
Note.nkmk.me
note.nkmk.me › home › python › numpy
numpy.where(): Manipulate elements depending on conditions | note.nkmk.me
November 5, 2019 - You can apply multiple conditions with np.where() by enclosing each condition in () and using & or |. print(np.where((a > 2) & (a < 6), -1, 100)) # [[100 100 100] # [ -1 -1 -1] # [100 100 100]] print(np.where((a > 2) & (a < 6) | (a == 7), -1, ...
NumPy
numpy.org › doc › 2.4 › reference › generated › numpy.all.html
numpy.all — NumPy v2.4 Manual
Test whether all array elements along a given axis evaluate to True.