NumPy is a library in Python that provides a high-performance multidimensional array objects, along with that it provides the tools for working with these arrays. An array is a central data structure of the NumPy library, and it is used for representing vectors, matrices, and higher-dimensional datasets.

## Table of Contents

## Introduction

To begin using NumPy, you first need to import the library. Once you have imported NumPy, you can start creating arrays. Hereâ€™s how you can create a simple one-dimensional (1D) array like this,

import numpy as np # Creating a one-dimensional NumPy array arr = np.array([11, 12, 13, 14]) print(arr)

**Output:**

[11 12 13 14]

Similarly, you can create a two-dimensional (2D) array or a three-dimensional (3D) array by passing a list of lists or a list of lists of lists, respectively, to the `np.array`

function.

## Exploring NumPy Array Attributes

Once you have created an array, there are several attributes that you can access to learn more about the array:

### NumPy Array Attribute – shape

**Shape**: This attribute returns a tuple with each index having the number of corresponding elements in a dimension. For example, for a 1D Array, it will give a tuple with one value only i.e.

import numpy as np # Creating a one-dimensional NumPy array arr = np.array([11, 12, 13, 14]) print(arr.shape)

**Output:**

(4,)

For a 2D NumPy Array shape attribute will return a tuple with 2 values. For example,

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import numpy as np # Creating a 2D NumPy array arr = np.array([[11, 12, 13, 14], [11, 12, 13, 14], [11, 12, 13, 14]]) print(arr.shape)

**Output:**

(3, 4)

First value in tuple is 3 i.e. Number of rows in the NumPy Array. Whereas, second value in NumPy Array is 4 i.e. number of columns in NumPy Array

### NumPy Array Attribute – size

**Size**: This gives you the total number of elements in the array.

import numpy as np # Creating a one-dimensional NumPy array arr = np.array([11, 12, 13, 14]) print(arr.size)

**Output:**

4

For 2D NumPy Array:

import numpy as np # Creating a 2D NumPy array arr = np.array([[11, 12, 13, 14], [11, 12, 13, 14], [11, 12, 13, 14]]) print(arr.size)

**Output:**

12

For 2D NumPy Array it will return total numbers of elements in array including all rows and columns.

### NumPy Array Attribute – dtype

**Data type (dtype)**: It tells you the data type of the elements in the array.

import numpy as np # Creating a one-dimensional NumPy array arr = np.array([11, 12, 13, 14]) print(arr.dtype)

**Output:**

int64

**Example 2:**

Here, in this example NumPy Array contains float values, so the dtype will give `float64`

import numpy as np # Creating a one-dimensional NumPy array arr = np.array([11, 12.1, 13.2, 14]) print(arr.dtype)

**Output:**

float64

### NumPy Array Attribute – ndim

**ndim**: This stands for the number of dimensions of the array.

For a 2D NumPy Array it will return 2 and for a 3D NumPy Array it will return 3. For example,

import numpy as np # Creating a 2D NumPy array arr = np.array([[11, 12, 13, 14], [11, 12, 13, 14], [11, 12, 13, 14]]) print(arr.ndim)

**Output:**

2

## Working with Different Array Dimensions

You can create arrays with various dimensions as needed:

- For a
**1D array**, it represents a simple list or vector of values. - A
**2D array**is akin to a matrix or a list of lists. - A
**3D array**can represent a volume or a list of matrices.

Hereâ€™s an example of creating and accessing the attributes of a 2D array, which we dicussed above:

import numpy as np # Creating a 2D NumPy array arr = np.array([[11, 12, 13, 14], [11, 12, 13, 14], [11, 12, 13, 14]]) print(arr) print('Shape:', arr.shape) # Output: Shape: (3, 4) print('Size:', arr.size) # Output: Size: 12 print('Data type:', arr.dtype) # Output: Data type: int64 print('Dimensions:', arr.ndim) # Output: Dimensions: 2

**Output:**

[[11 12 13 14] [11 12 13 14] [11 12 13 14]] Shape: (3, 4) Size: 12 Data type: int64 Dimensions: 2

In the above example, we used different attributes of NumPy Array on a 2D Array.

## Summary

Understanding how to manipulate NumPy Arrays is useful for tasks in machine learning, where you often deal with large and multidimensional datasets.