In this Python tutorial, I will discuss what the **NumPy shape in Python** is, and what is meant by shape 0 and shape 1. And, how shape is different from the reshape function in Python.

**To find the shape of an array, we can use the NumPy shape in Python. The NumPy shape() function returns a Python tuple indicating the size of the array in each dimension. the np.shape[0], and np.shape[1] returns the size of the row and the size of the column respectively.**

## NumPy shape in Python

In NumPy, the **shape** of an array is a Python tuple that indicates the size of the array in each dimension. It is a property that defines the structure of a Python array, that is, the number of rows, columns, and so forth, depending on the dimensionality of the array.

**Syntax:** The syntax for accessing the shape attribute in Python is as follows:

`array_name.shape`

- It consists of a few parameters:
**array_name**: input array, whose shapes we want to find.**Returns:**The values of the shape function always give the length of the adjacent np.array in Python.

**Example:** Let’s take an example to check how to implement **NumPy shape in Python**

```
import numpy as np
matrix = np.array([[1, 2, 3], [4, 5, 6]])
shape_of_matrix = matrix.shape
print("Shape of the matrix:", shape_of_matrix)
```

**Output:** In the above example the array returns (2,6) which means that the array has 2 dimensions, and each dimension has 6 values.

`Shape of the matrix: (2, 3)`

Here is the Screenshot of the following given Python code:

This is the basic use of the NumPy shape in Python.

## Python shape function in NumPy

- The NumPy module provides a
**shape function**to represent the shape and size of an array in Python. The shape of an array is the no. of elements in each dimension. - The shape attribute always returns a tuple, that represents the length of each dimension.

**Syntax:** Here is the Syntax of the Python numpy shape function

`numpy.shape(arr)`

- It consists of a few parameters.
**arr:**input array**Returns:**The values of the shape tuple give the lengths of the adjacent array dimensions.

**Example:** Let’s take an example to check how to implement the NumPy shape in Python.

```
import numpy as np
arr = np.array([2,3,4,5,6,7,8])
print(arr)
print('Array Shape = ', np.shape(arr))
```

**Output:** In the above code first, we import a NumPy library in Python and create a 1-D array, it is a tuple with one value instead of an integer value. A tuple with one element has a sequence comma.

```
[2 3 4 5 6 7 8]
Array Shape = (7,)
```

Here is the Screenshot of the following given Python code:

## NumPy shape 0 Python

**Shape[0]**in**np.shape**is a tuple that always gives dimensions of the array in Python. The shape is a tuple that gives us an indication of the no. of dimensions in the array.- The shape function for NumPy arrays returns the dimensions of the Python array.
- If Y has u rows and v columns, then Y.shape is (u,v). So Y.shape[0] is v.

**Example:** Let’s take an example to check how to implement numpy shape 0

```
import numpy as np
a = np.array([[2,3],[3,4]])
b = a.shape
print(b)
c = a.shape[0]
print(c)
```

**Output:** In the above example, we have to use the function **np.shape** to give:

```
(2, 2)
2
```

Here is the Screenshot of the following given Python code

## NumPy shape[1] Python

- In Python NumPy, some of the functions return in the format of
**shape(R,1)**but some return as**(R,)**. - This will make matrix multiplication more complex since an explicit reshape is required.
**Shape[1]**in**np.shape**is a tuple that always gives dimensions of the array in Python. The shape function is a tuple that gives us an arrangement of the number of dimensions in the array.- If Y has w rows and z columns, then Y.shape is (w,z). So Y.shape[1] is z.

**Example:** Let’s take an example to check how to implement **Python NumPy shape 1**

```
import numpy as np
a = np.array([[2,3],[3,4]])
b = np.shape(a)
print('Column shape=', a.shape[1])
print('Array Shape = ', np.shape(a))
```

**Output:** In the above code, we will import a NumPy library in Python and create an array using the function **numpy.array**. Now we can easily use the function **np.shape()** and pass the value 1 as a parameter. The output will display the columns in an array.

```
Column shape= 2
Array Shape = (2, 2)
```

Here is the Screenshot of the following given Python code

## Python numpy shape vs reshape

**np.reshape**will copy the data if it cannot make a proper view, whereas getting the shape will raise an error instead of copying the data.- The
**np.shape**function will always give a tuple of array dimensions in Python and can easily be used to change the dimensions of an array. - The reshape function gives a new shape to an array without changing its value. It creates a new array and does not update the original array itself.
- The shape function always returns a tuple that tells us the length of each dimension while in the case of the reshape function, returns a new value on the existing data if possible rather than creating a full copy of the original array in Python.

**Example:**

```
import numpy as np
a = np.array([2,3,4,5])
print('Array Reshape',np.reshape(a,(2,2)))# Reshape function
print('Array shape',np.shape(a)) # shape function
```

**Output:** In the above code, we will import a NumPy library and create an array using the function numpy. array. Now, we can use an **np.reshape()** function to display the new array in the form of a 2*2 matrix. Along with that, we will equate with the function and it will return the array of dimensions in the form shape.

```
Array Reshape [[2 3]
[4 5]]
Array shape (4,)
```

Here is the Screenshot of the following given Python code:

## Conclusion

Understanding the **NumPy shape in Python**, is a fundamental skill when working with data. It enables a clearer understanding of array dimensions, which is critical when performing array operations, reshaping arrays, and utilizing broadcasting.

Mastering NumPy shapes can lead to more efficient and readable code, especially when handling complex multi-dimensional data. By taking advantage of the numerous functions provided by NumPy to manipulate shapes, one can fully leverage the power and flexibility of this essential Python library.

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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.