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 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])
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 - Absolutely! In fact, numpy.all() is a natural fit for boolean arrays. It evaluates the True or False values directly, making it perfect for situations where you’re working with logical conditions.
Sharp Sight
sharpsight.ai › blog › numpy-all
Numpy All, Explained - Sharp Sight
July 24, 2021 - So what’s happening, is that the conditional logic (i.e., the greater than operation) creates a Numpy array filled with boolean values. Then np.all operates on that to make a final True/False determination.
Codecademy
codecademy.com › docs › python:numpy › ndarray › .all()
Python:NumPy | ndarray | .all() | Codecademy
October 30, 2025 - In NumPy, the .all() method returns True if all elements in an ndarray evaluate to True, or if all elements along a specified axis evaluate to True.
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 - You can extract rows and columns that match the conditions in the same way as np.all().
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
scaler.com › home › topics › what is the numpy.all() in numpy?
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).
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.
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.
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 › 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.
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])
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.
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
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 › 2.3 › reference › generated › numpy.all.html
numpy.all — NumPy v2.3 Manual
Test whether all array elements along a given axis evaluate to True.