# Python Numpy Array Shape

In this post, you will learn how to get the shape of an array using Python Numpy.

**NumPy** is the fundamental package for scientific computing with Python. It is a highly optimised library for numerical operations. The support of NumPy makes the task easier.

The Python **numpy** module provides a **shape** function to determine the shape and size of an array or matrix. The shape of an array is defined by the number of elements in each dimension.

## Python get shape of an array

In **NumPy** we will use an attribute called **shape** which returns a tuple. The components of the tuple give the lengths of the corresponding array dimensions. The one-dimensional array is a row vector and its shape is a single value sequence followed by a comma. One-dimensional arrays don't have rows and columns, so the shape function returns a single value tuple.

**Syntax of numpy array**

**numpy.shape(array)**

Here, an **array** is passed as a parameter. It returns a tuple whose elements give the lengths of the corresponding array dimensions.

## Examples of numpy shape

In a one-dimensional array, it is a tuple with one element instead of an integer value. A tuple with one element has a trailing comma.

```
import numpy as np
arr = np.array([5,22,13,64,23,93,10])
print(arr)
print('Array Shape = ', np.shape(arr))
```

**Output of the above code:**

```
[ 5 22 13 64 23 93 10]
Array Shape = (7,)
```

The example above returns (7,) which means that the array is one dimension and contains 7 elements.

In the case of a two-dimensional array, it will be a number of rows, number of columns.

```
import numpy as np
x = np.array([[10, 20, 30], [40, 50, 60]])
print(x)
print('Array Shape = ', np.shape(x))
print()
```

**Output of the above code:**

```
[[10 20 30]
[40 50 60]]
Array Shape = (2, 3)
```

The example above returns (2, 3), which means that the array has 2 dimensions and each dimension has 3 elements.

## Python numpy shape dimensions

The dimension of a matrix is the number of rows and columns in a matrix. We can get the number of dimensions of the NumPy array as an integer value **int** with the **ndim** attribute of the **numpy.ndarray**.

```
import numpy as np
x = np.array([[6,3],[3,5]])
y = x.ndim
print('Shape dimension: ',y)
print('Shape dimension type: ',type(x.ndim))
```

**Output of the above code:**

```
Shape dimension: 2
Shape dimension type: <class 'int'>
```

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