NumPy libraries help us with using arrays in Python with the help of array object called “ndarray”. Using array() function, we can create ndarray objects.
We can create various dimensional arrays using “ndarray”. Let’s see them all with an example.
1. A. Creating a 0-D array:
Syntax:
# creating 0-D array
arr = np.array(53)
print(arr)
Output:
53
B. Checking the dimension of array:
Using “[variablename].ndim” , find the dimension of an array.
Syntax:
# creating 0-D array
arr = np.array(53)
print(arr)
#checking dimensions
print(arr.ndim)
Output:
53
0
C. Checking the shape of an array:
Using “[variablename].shape”, find the shape of an array.
Syntax:
# creating 0-D array
arr = np.array(53)
print(arr)
#checking dimensions
print(arr.ndim)
#shape of an array
print ('shape of array:', arr. shape)
Output:
53
0
shape of array: () ---- since it’s a 0-dimensional array, there is no number displayed in it.
2. Creating a 1-D array, checking the dimension and shape of the 1-D array:
1 | 2 | 3 | 4 | 5 |
Syntax:
# creating 1-D array
arr1 = np.array([1, 2, 3, 4, 5])
print(arr1)
#checking dimensions
print(arr1.ndim)
#shape of an array
print('shape of array :', arr1.shape)
Output:
[1 2 3 4 5]
1
shape of array: (5,) --- This shows the array is having 1 dimension and
the first dimension has 5 elements.
3. Creating a 2-D array, checking the dimension and shape of the 2-D array:
1 | 2 | 3 | 4 | 5 |
6 | 7 | 8 | 9 | 4 |
Syntax:
# creating 2-D array
arr2 = np.array([[1, 2, 3, 4, 5],[6,7,8,9,4]])
print(arr2)
#checking dimensions
print(arr2.ndim)
#shape of an array
print('shape of array :', arr2.shape)
Output:
[[1 2 3 4 5]
[6 7 8 9 4]]
2
shape of array : (2, 5) –
This shows that 2 values inside a bracket represent it has 2 dimensions.
First dimension (row) has 2 values,
and the second dimension(column) has 5 values.
4. Creating a 3-D array, checking the dimension and shape of the 3-D array:
Syntax:
# creating 3-D array
arr3 = np.array([[[1, 2, 3, 4, 5],[6,7,8,9,4]],[[1, 2, 3, 4, 5],[6,7,8,9,4]]])
print(arr3)
#checking dimensions
print(arr3.ndim)
#Shape of an array
print('shape of array :', arr3.shape)
Output:
[[[1 2 3 4 5]
[6 7 8 9 4]]
[[1 2 3 4 5]
[6 7 8 9 4]]]
3
shape of array : (2, 2, 5)
This shows that 3 values inside a bracket represent it has 3 dimensions.
First dimension (i=number of sheets) has 2 sheets
second dimension (j=row) has 2 values,
and the third dimension(k=column) has 5 values.
5. Creating a 4-D array, checking the dimension and shape of the 4-D array:
Syntax:
# creating 4-D array
arr4 = np.array([[[[1, 2, 3, 4, 5],[6,7,8,9,4]],[[1, 2, 3, 4, 5],[6,7,8,9,4]]],[[[1, 2, 3, 4, 5],[6,7,8,9,4]],[[1, 2, 3, 4, 5],[6,7,8,9,4]]]])
print(arr4)
#checking dimensions
print(arr4.ndim)
#Shape of an array
print('shape of array :', arr4.shape)
Output:
[[[[1 2 3 4 5]
[6 7 8 9 4]]
[[1 2 3 4 5]
[6 7 8 9 4]]]
[[[1 2 3 4 5]
[6 7 8 9 4]]
[[1 2 3 4 5]
[6 7 8 9 4]]]]
4
shape of array : (2, 2, 2, 5)
This shows that 4 values inside a bracket represent it has 4 dimensions.
First dimension(r=number of arrays)has 2 arrays
Second dimension (i=number of sheets) has 2 sheets in each array
Third dimension (j=row) has 2 values,
and the Fourth dimension(k=column) has 5 values.
This is how we find the shape of an array in Python. Try doing different shapes and find its values.
Happy Analyzing!