numpy.all¶

numpy.
all
(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)[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 all the dimensions of the input array. axis may be negative, in which case it counts from the last to the first axis.New in version 1.7.0.
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.
 outndarray, optional
Alternate 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). See Output type determination for more details. keepdimsbool, optional
If 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 keepdims will not be passed through to the
all
method of subclasses ofndarray
, however any nondefault value will be. If the subclass’ method does not implement keepdims any exceptions will be raised. wherearray_like of bool, optional
Elements to include in checking for all True values. See
reduce
for details.New in version 1.20.0.
 Returns
 allndarray, bool
A new boolean or array is returned unless out is specified, in which case a reference to out 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 infinity evaluate to True because these are not equal to zero.
Examples
>>> 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