NumPy ndarray.all() Method

NumPy ndarray.all()

The numpy.ndarray.all() method checks whether all elements in a NumPy array evaluate to True. It can operate across the entire array or along a specified axis.

Syntax

ndarray.all(axis=None, out=None, keepdims=False, *, where=True)

Parameters

Parameter	Type	Description
axis	None, int, or tuple of ints, optional	Axis or axes along which a logical AND operation is performed. If None, it checks the entire array.
out	ndarray, optional	Alternative output array for storing the result. Must have the same shape as expected output.
keepdims	bool, optional	If True, the reduced dimensions are kept as size one, allowing proper broadcasting.
where	array_like of bool, optional	Specifies elements to include in the check for all True values.

Return Value

Returns a boolean value if axis=None, or an array of boolean values if an axis is specified. The result is True if all elements (or elements along the specified axis) evaluate to True, otherwise False.

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Examples

1. Checking if All Elements Are True in ndarray

In this example, we create a 2×2 boolean array and check if all its elements are True.

import numpy as np

arr = np.array([[True, False],
                [True, True]])

result = arr.all()
print(result)

Output:

False

Since there is at least one False value in the array, the all() method returns False.

2. Using the axis Parameter in ndarray.all()

Here, we check if all elements along a specific axis are True.

import numpy as np

arr = np.array([[True, False],
                [True, True]])

result_axis0 = arr.all(axis=0)
print(result_axis0)

result_axis1 = arr.all(axis=1)
print(result_axis1)

Output:

[ True False]
[False  True]

For axis=0 (columns), it checks if all elements in each column are True. The first column has all True values, but the second column has a False, so the result is [True, False].

For axis=1 (rows), it checks if all elements in each row are True. The first row has a False, so it returns False, but the second row is all True, so it returns True.

3. Keeping Dimensions with keepdims=True in ndarray.all()

In this example, we use keepdims=True to retain the reduced axis as a dimension of size one.

import numpy as np

arr = np.array([[True, True],
                [True, True]])

result = arr.all(axis=1, keepdims=True)
print(result)

Output:

[[ True]
 [ True]]

Even though the result is True for both rows, keeping dimensions ensures that the output retains the original shape, making it useful for broadcasting operations.

4. Using the where Parameter in ndarray.all()

The where parameter allows checking only specific elements for True values.

import numpy as np

arr = np.array([True, False, True])
mask = np.array([True, False, True])

result = arr.all(where=mask)
print(result)

Output:

True

Here, only the elements where mask is True are considered. Since the first and last elements are both True, the result is True, even though the middle element is False because it is ignored due to the mask.