nums.numpy.all

nums.numpy.all(a, axis=None, out=None, keepdims=False)[source]

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

This docstring was copied from numpy.all.

Some inconsistencies with the NumS version may exist.

Parameters

  • a (BlockArray) – Input array or object that can be converted to an array.

  • axis (None 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. 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 (BlockArray, 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 ufuncs-output-type for more details.

  • keepdims (bool, 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 sub-classes of BlockArray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

Returns

all – A new boolean or array is returned unless out is specified, in which case a reference to out is returned.

Return type

BlockArray, bool

See also

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.

‘out’ is currently not supported.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.all(nps.array([[True,False],[True,True]])).get()  
array(False)

>>> nps.all(nps.array([[True,False],[True,True]]), axis=0).get()  
array([ True, False])

>>> nps.all(nps.array([-1, 4, 5])).get()  
array(True)

>>> nps.all(nps.array([1.0, nps.nan])).get()  
array(True)

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