The number of dimensions and items in an array is defined by its shape, which is a tuple of N non-negative integers that specify the sizes of each dimension. The type of items in the array is …

numpy.ndarray.ndim # attribute ndarray.ndim # Number of array dimensions. Examples Try it in your browser!

numpy.ndarray.size # attribute ndarray.size # Number of elements in the array. Equal to np.prod(a.shape), i.e., the product of the array’s dimensions. Notes a.size returns a standard …

NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers.

In NumPy, a dimension of an array is sometimes referred to as an “axis”. This terminology may be useful to disambiguate between the dimensionality of an array and the dimensionality of the …

numpy.size # numpy.size(a, axis=None) [source] # Return the number of elements along a given axis. Parameters: aarray_like Input data. axisNone or int or tuple of ints, optional Axis or axes …

Tuple of array dimensions. The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array …

General broadcasting rules # When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimension and works its way left. Two …

The ndarray creation functions can create arrays with any dimension by specifying how many dimensions and length along that dimension in a tuple or list. numpy.zeros will create an array …

numpy.expand_dims # numpy.expand_dims(a, axis) [source] # Expand the shape of an array. Insert a new axis that will appear at the axis position in the expanded array shape. …