To get the shape of a Python NumPy array use `numpy.ndarray.shape`

property. The array shape can be defined as the number of elements in each dimension and dimension is defined as a number of indices or subscripts, that can specify an individual element of an array.

To get the size of the multi-dimensional array, use an attribute called shape which returns a tuple (x,y) where x is the number of rows and y is the number of columns in the array. In this article, I will explain how to get the shape of a NumPy array by using `numpy.ndarray.shape`

property with examples.

## 1. Quick Examples of Array Shape

If you are in a hurry, below are some quick examples of how to get the shape of an array. Use the size property to get the length of the NumPy array.

```
# Quick examples of numpy array shape
# Example 1: Get the shape of the array
arr = np.array([[3, 6, 7, 9], [2, 4, 6, 8]])
array_shape = arr.shape
# Example 2: Creating 3 dimension array
arr = np.array([[[2, 3],[3, 4]],[[5, 6], [8, 9]]])
array_shape = arr.shape
# Example 3: Using Multi dimension
arr = np.array([1, 3, 5, 7, 9], ndmin=6)
array_shape = arr.shape
```

## 2. Get NumPy Array shape

To get the shape of a NumPy array, you can use the `shape`

attribute of the NumPy array object. For example, the `shape`

attribute is used to obtain the shape of the NumPy array `arr`

.

Use `ndarray.shape`

to get the shape of the NumPy array. This returns a tuple with each index having the number of corresponding elements. The below examples return `(2,4)`

which means that the `arr`

has 2 dimensions and each dimension has 4 elements (2 rows & 4 columns).

```
# Import numpy
import numpy as np
# Create a NumPy array
arr = np.array([[3, 6, 7, 9], [2, 4, 6, 8]])
print("Original array:\n", arr)
# Get the shape of the array
array_shape = arr.shape
print("Array shape:\n", array_shape)
```

Yields below output.

Similarly, it creates a 3-dimensional NumPy array and then prints its shape. This means the array `arr`

has 2 dimensions along the first axis, 2 dimensions along the second axis, and 2 dimensions along the third axis.

```
# Creating a 3 dimensions array
arr = np.array([[[2, 3],[3, 4]],[[5, 6], [8, 9]]])
# Get the shape of the array
array_shape = arr.shape
print("Array shape:\n", array_shape)
# Output:
# Array shape:
# (2, 2, 2)
```

## 3. Get the Shape of a Multi-Dimensional Array

To create a multi-dimensional NumPy array with `ndmin=6`

, which means you’re specifying that the array should have a minimum of 6 dimensions. However, the array you’ve provided contains only 1 element. When you specify `ndmin=6`

for such a small array, NumPy will add additional singleton dimensions to meet the specified minimum dimensions.

In this case, the array has been expanded to have 6 dimensions with a shape of `(1, 1, 1, 1, 1, 5)`

because of the `ndmin=6`

argument.

```
# Create multi dimensional array
arr = np.array([1, 3, 5, 7, 9], ndmin=6)
print("Original array:", arr)
# Use shape of Multi-Dimensional Array
array_shape = arr.shape
print("Shape of multi dimensional array:", array_shape)
```

Yields below output.

```
# Output:
# Original array: [[[[[[1 3 5 7 9]]]]]]
# Shape of multi dimensional array: (1, 1, 1, 1, 1, 5)
```

## Frequently Asked Questions

**How do I get the shape of a NumPy array?**

To get the shape of a NumPy array, you can use the `shape`

attribute of the NumPy array object. For example, the `shape`

attribute is used to obtain the shape of the NumPy array `array`

.

**What does the shape of a NumPy array represent?**

The shape of a NumPy array is a tuple representing its dimensions. For a 2D array, the shape is `(rows, columns)`

. For a 3D array, the shape is `(depth, rows, columns)`

, and so on for higher dimensions.

**Can the shape of a NumPy array be modified?**

The shape of a NumPy array is immutable. You cannot change the shape of an existing array. However, you can create a new array with a different shape based on the original array’s data.

**What does the shape of (n,) mean in NumPy?**

A shape of `(n,)`

indicates a 1-dimensional array with `n`

elements. It’s a special case where the array has a single dimension with size `n`

.

**How do I get the number of dimensions of a NumPy array?**

To get the number of dimensions of a NumPy array, you can use the `ndim`

attribute. For instance, the `ndim`

attribute is used to obtain the number of dimensions of the NumPy array `array`

.

## 4. Conclusion

In this article, I have explained how to get the shape of a Python NumPy array by using `numpy.ndarray.shape`

properties with examples. This returns a tuple with the number of rows and columns in an array. The array shape can be defined as the number of elements in each dimension and dimension is defined as a number of indices or subscripts, that can specify an individual element of an array.

Happy Learning!!

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