Document

all(a, axis=None, out=None, keepdims=<no value>, *, where=<no value>)

Notes

Not a Number (NaN), positive infinity and negative infinity evaluate to :None:None:`True` because these are not equal to zero.

Parameters

a : array_like

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. :None:None:`axis` may be negative, in which case it counts from the last to the first axis.

versionadded

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, 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 :None:None:`keepdims` will not be passed through to the all method of sub-classes of ndarray , however any non-default value will be. If the sub-class' method does not implement :None:None:`keepdims` any exceptions will be raised.

where : array_like of bool, optional

Elements to include in checking for all :None:None:`True` values. See :None:None:`~numpy.ufunc.reduce` for details.

versionadded

Returns

all : ndarray, bool: A new boolean or array is returned unless :None:None:`out` is specified, in which case a reference to :None:None:`out` is returned.

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

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.arrays.sparse.array.SparseArray.all dask.array.routines.fliplr dask.array.routines.flipud scipy.linalg._decomp_qr.qr numpy.all pandas.core.arrays.masked.BaseMaskedArray.all scipy.optimize._basinhopping.basinhopping numpy.ma.core.all dask.array.reductions.median dask.array.core.Array.all dask.array.reductions.all numpy.alltrue skimage.restoration.uft.ir2tf numpy.ma.core.allclose dask.array.reductions.nanmedian dask.array.ufunc.degrees skimage.restoration.uft.laplacian numpy.any dask.array.random.RandomState.uniform numpy.allclose numpy.ma.core.allequal numpy.matrixlib.defmatrix.matrix.all numpy.ma.core.MaskedArray.all

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them