Python NumPy numpy.shape() Function

Syntax of
numpy.shape()

Example Codes:
numpy.shape()

Example Codes:
numpy.shape()
to Pass a Simple Array 
Example Codes:
numpy.shape()
to Pass a MultiDimensional Array 
Example Codes:
numpy.shape()
to Call the Function Using Array’s Name
Python NumPy numpy.shape()
function finds the shape of an array
. By shape, we mean that it helps in finding the dimensions of an array
. It returns the shape in the form of a tuple
because we cannot alter a tuple
just like we cannot alter the dimensions of an array
.
Syntax of numpy.shape()
numpy.shape(a)
Parameters
a 
It is an array like structure. It is the input array to find the dimensions. 
Return
It returns the shape of an array
in the form of a tuple
of integers
. The values of the tuples show the length of the array
dimensions.
Example Codes: numpy.shape()
The parameter a
is a mandatory parameter. If we execute this function on an empty array
, it generates the following output.
import numpy as np
a = np.array([])
dimensions = np.shape(a)
print(dimensions)
Output:
(0,)
It has returned a tuple
with a single integer
0. It shows that the array
is one dimensional with zero elements.
Example Codes: numpy.shape()
to Pass a Simple Array
We will pass a simple onedimensional array
now.
import numpy as np
a = np.array([89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78])
dimensions = np.shape(a)
print(dimensions)
Output:
(20,)
The output shows that the array
is onedimensional and contains 20 elements.
Example Codes: numpy.shape()
to Pass a MultiDimensional Array
import numpy as np
a = np.array([[11, 12, 5], [15, 6,10], [10, 8, 12], [12,15,8], [34, 78, 90]])
dimensions = np.shape(a)
print(dimensions)
Output:
(5, 3)
Note that the output tuple now contains two integer
elements. It shows that the array
contains five rows and three columns.
Now we will pass a more complex array
.
import numpy as np
a = np.array([[[11, 12, 5], [15, 6,10]],
[[10, 8, 12], [12,15,8]],
[[34, 78, 90], [4, 8, 10]]
])
dimensions = np.shape(a)
print(dimensions)
Output:
(3, 2, 3)
We have passed an array
that contains three arrays of 2D arrays. The output tuple shows that the array
has three layers, two rows, and three columns.
Example Codes: numpy.shape()
to Call the Function Using Array’s Name
We can call this function using the array’s name as well. It generates the same output. The following code snippets implement this function using the array’s name.
We will pass a onedimensional array
first.
import numpy as np
a = np.array([89, 34, 56, 87, 90, 23, 45, 12, 65, 78, 9, 34, 12, 11, 2, 65, 78, 82, 28, 78])
dimensions = a.shape
print(dimensions)
Output:
(20,)
Note that it has generated the same output as the output generated using numpy.shape()
calling method.
import numpy as np
a = np.array([[[11, 12, 5], [15, 6,10]],
[[10, 8, 12], [12,15,8]],
[[34, 78, 90], [4, 8, 10]]
])
dimensions = a.shape
print(dimensions)
Output:
(3, 2, 3)