bundles / numpy 2.5.0.dev0+git20251130.2de293a / numpy / all
_ArrayFunctionDispatcher
numpy:all
source: /dev/numpy/build-install/usr/lib/python3.14/site-packages/numpy/_core/fromnumeric.py :2548
Signature
def all ( a , axis = None , out = None , keepdims = <no value> , * , where = <no value> ) Summary
Test whether all array elements along a given axis evaluate to True.
Parameters
a: array_likeInput array or object that can be converted to an array.
axis: None or int or tuple of ints, optionalAxis or axes along which a logical AND reduction is performed. The default (
axis=None) is to perform a logical AND over all the dimensions of the input array.axismay be negative, in which case it counts from the last to the first axis. If this is a tuple of ints, a reduction is performed on multiple axes, instead of a single axis or all the axes as before.out: ndarray, optionalAlternate output array in which to place the result. It must have the same shape as the expected output and its type is preserved (e.g., if
dtype(out)is float, the result will consist of 0.0's and 1.0's). Seeufuncs-output-typefor more details.keepdims: bool, optionalIf this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then
keepdimswill not be passed through to the all method of sub-classes ofndarray, however any non-default value will be. If the sub-class' method does not implementkeepdimsany exceptions will be raised.where: array_like of bool, optionalElements to include in checking for all
Truevalues. See~numpy.ufunc.reducefor details.
Returns
all: ndarray, boolA new boolean or array is returned unless
outis specified, in which case a reference tooutis returned.
Notes
Not a Number (NaN), positive infinity and negative infinity evaluate to True because these are not equal to zero.
Examples
import numpy as np
✓np.all([[True,False],[True,True]])
✗np.all([[True,False],[True,True]], axis=0)
✓np.all([-1, 4, 5])
✗np.all([1.0, np.nan])
✗np.all([[True, True], [False, True]], where=[[True], [False]])
✗o=np.array(False) z=np.all([-1, 4, 5], out=o)✓
id(z), id(o), z
✗See also
- any
Test whether any element along a given axis evaluates to True.
- ndarray.all
equivalent method
Aliases
-
numpy.all