# Python shape of an array

In this Python tutorial, we will learn the Python shape of an array, and also we will cover these topics:

• Python shape of a 2D array
• Python shape of a nested array
• Python shape and type of N-d array
• Python reshape of a multidimensional array
• Python shape of a 3D array
• Python index array
• Python print the shape of a multidimensional array
• Python transpose of 1D array
• Python change the shape of a 1D array to a 3D array
• Python change the shape of an array

## Python shape of a 2D array

Here, we can see shape of a 2D array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• I have taken a variable as an array and I have assigned an array as array = np.array([[1, 2, 3, 4, 5], [5, 6, 7, 8, 9]]).
• And I have used np.array to get the dimensions of an array, the shape property is used to get the current shape of an array. to get the output I have to print(array.shape).

Example:

``````import numpy as np
array = np.array([[1, 2, 3, 4, 5], [5, 6, 7, 8, 9]])
print(array.shape)``````

We can see the output (2, 5) because there is 2 array which consists of 5 elements in it, which is the shape of an array. You can refer to the below screenshot for the output.

## Python shape of a nested array

Here, we can see how to find the shape of a nested array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array and created a variable called an array.
• The variable array is assigned as array = np.zeros((5,3)), the np.zeros are used to get an array of a given size and shape filled with zeros.
• The ((5,3)) is the size and shape of an array, to get the output I have used print(array).

Example:

``````import numpy as np
array = np.zeros((5,3))
print(array)``````

In the below screenshot, you can see the array with size of (5,3) with zeros.

## Python shape and type of N-d array

Now we can see how to find the shape and type of N-d array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• Taken a variable as x and assigned an array as x = np.array([[1, 2, 4],[3, 4, 5],[4, 5, 6]]).
• The np.array is used to find the dimension of an array, and variable a = x.shape is used to find the shape of an array and variable b = x.dtype is used to find the type of an array.
• To get the output as a shape I have used print(a) and to get the output as a datatype, I have used print(b).

Example:

``````import numpy as np
x = np.array([[1, 2, 4],[3, 4, 5],[4, 5, 6]])
a = x.shape
b = x.dtype
print(a)
print(b)
``````

In the below screenshot, you can see the shape and datatype of an array as the output.

## Python reshape of a multidimensional array

Now, we can see how to reshape of a multidimensional array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• And assigned a variable x as x = np.array([[ 0, 1, 3, 4],[ 4, 5, 6, 7],[ 8, 9, 10, 11],[12,13,14,15,]]).
• The np.array is used to find the dimension of an array, and for the variable array, I have assigned it as array = x.reshape(4, 2, 2).
• The reshape() function is used to give a new shape for an array without changing the data.
• The (4,2,2) 4 is no of elements in the array 2 is the row and another 2 is a column, to get the output I have used print(array).

Example:

``````import numpy as np
x = np.array([[ 0,  1, 3, 4],[ 4,  5, 6, 7],[ 8,  9, 10, 11],[12,13,14,15,]])
array = x.reshape(4, 2 , 2)
print(array)
``````

In the below screenshot, you can see the output as reshaped array.

## Python shape of a 3D array

Here, we can see how to get the shape of a 3D array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• And assigned a variable array_3d as array_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]]).
• The np. array is used to get the dimension of the array, to get the output I have used print(array_3d.shape).

Example

``````import numpy as np
array_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]])
print(array_3d.shape)``````

We can see the shape 3d array as the output in the below screenshot.

## Python index array

Here, we can see index array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• And assigned a variable array as array = np.arange(20,2,-2).
• The np.arange is an inbuilt numpy function that returns a ndarray object containing a spaced value with a defined interval.
• The (20,2,-2) is the range given between 20 to 2 with the difference value -2.
• To get the output I have used print(array).

Example:

``````import numpy as np
array = np.arange(20,2,-2)
print(array)``````

We can see the output as the array from range 20 to 2 with 2 difference value between them. You can refer to the below screenshot for the output.

## Python print shape of a multidimensional array

Here, we can how to print the shape of a multidimensional array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• I have taken three variables as array1 = np.array([[1,2]]), and array2 = np.array([[1, 3], [2, 4]]), and array3 = np.array([[1, 3, 5, 7], [2, 4, 6, 8], [3, 6, 9, 12]]) with all the three with different dimensions.
• The np.array is used to get the dimension of an array.
• To get the ouput I have used print(array1.shape), print(array2.shape), print(array3.shape).

Example:

``````import numpy as np
array1 = np.array([[1,2]])
array2 = np.array([[1, 3], [2, 4]])
array3 = np.array([[1, 3, 5, 7], [2, 4, 6, 8],  [3, 6, 9, 12]])
print(array1.shape)
print(array2.shape)
print(array3.shape)``````

In the below screenshot, you can see the shape of multi dimensional array as the output.

## Python transpose of 1D array

Now, we can see how to transpose 1D array in python.

• In this example, I have imported a module called numpy as np. The NumPy library is used to work with an array.
• The variable array is assigned as array = np.arange(4).
• The np.arange is an inbuilt numpy function that returns a ndarray object containing a spaced value with a defined interval 4 is the range of an array.
• To transpose an array I have used the transpose() function. To get the output I have used print(array.transpose()).

Example:

``````import numpy as np
array = np.arange(4)
print(array.transpose())``````

You can see the array of range 4 as the output. You can refer to the below screenshot for the output.

## Python change shape of a 1D array to a 3D array

Now, we can see how to change the shape of a 1D array to a 3D array in python.

• In this example, I have imported a module called numpy as np and assigned, the variable array as array = np.array([2,4,6,8,10,12,14,16,18,20,22,24]). The np.array is used to get the dimension of an array.
• To change the shape of an array I have created another variable as array_3d and assigned array_3d = array.reshape(2, 3, 2).
• The reshape() function is used to get the new shape for an array without changing the data. To get the output I have used print(array_3d).

Example:

``````import numpy as np
array = np.array([2,4,6,8,10,12,14,16,18,20,22,24])
array_3d = array.reshape(2, 3, 2)
print(array_3d)``````

The input array was of 1d array and in the output you can see 3d array. You can refer to the below screenshot for the output.

## Python change shape of an array

Now, we can see how to change the shape of an array in python.

• In this example, I have imported a module called numpy as np and assigned, the variable array as array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]) .
• The np.array is used to get the dimension of an array.
• To change the shape of an array I have created another variable as newarray and assigned newarray = array.reshape(3, 4). Here 3 is the number of rows and 4 is the number of columns.
• The reshape() function is used to get the new shape for an array without changing the data. To get the output, I have used print(newarray).

Example:

``````import numpy as np
array = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarray = array.reshape(3, 4)
print(newarray)``````

The given array is changed to the array of range (3,4). You can refer to the below screenshot for the output.

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In this tutorial, we have learned about the Python shape of an array, and also we have covered these topics:

• Python shape of a 2D array
• Python shape of a nested array
• Python shape and type of N-d array
• Python reshape of a multidimensional array
• Python shape of a 3D array
• Python index array
• Python print the shape of a multidimensional array
• Python transpose of 1D array
• Python change the shape of a 1D array to a 3D array
• Python change the shape of an array