numpy.all#

numpy.all(a,axis=None,out=None,keepdims=<novalue>,*,where=<novalue>)[source]#

Test whether all array elements along a given axis evaluate to True.

Parameters:
aarray_like

Input array or object that can be converted to an array.

axisNone or int or tuple of ints, optional

Axis or axes along which a logical AND reduction is performed.The default (axis=None) is to perform a logical AND over allthe dimensions of the input array.axis may be negative, inwhich case it counts from the last to the first axis. If thisis a tuple of ints, a reduction is performed on multipleaxes, instead of a single axis or all the axes as before.

outndarray, optional

Alternate output array in which to place the result.It must have the same shape as the expected output and itstype is preserved (e.g., ifdtype(out) is float, the resultwill consist of 0.0’s and 1.0’s). SeeOutput type determinationfor more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are leftin 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, thenkeepdims will not bepassed through to theall method of sub-classes ofndarray, however any non-default value will be. If thesub-class’ method does not implementkeepdims anyexceptions will be raised.

wherearray_like of bool, optional

Elements to include in checking for allTrue values.Seereduce for details.

New in version 1.20.0.

Returns:
allndarray, bool

A new boolean or array is returned unlessout is specified,in which case a reference toout is returned.

See also

ndarray.all

equivalent method

any

Test whether any element along a given axis evaluates to True.

Notes

Not a Number (NaN), positive infinity and negative infinityevaluate toTrue because these are not equal to zero.

Changed in version 2.0:Before NumPy 2.0,all did not return booleans for object dtypeinput arrays.This behavior is still available vianp.logical_and.reduce.

Examples

>>>importnumpyasnp>>>np.all([[True,False],[True,True]])False
>>>np.all([[True,False],[True,True]],axis=0)array([ True, False])
>>>np.all([-1,4,5])True
>>>np.all([1.0,np.nan])True
>>>np.all([[True,True],[False,True]],where=[[True],[False]])True
>>>o=np.array(False)>>>z=np.all([-1,4,5],out=o)>>>id(z),id(o),z(28293632, 28293632, array(True)) # may vary
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