{"id":4790,"date":"2020-09-10T16:59:41","date_gmt":"2020-09-10T11:29:41","guid":{"rendered":"https:\/\/www.h2kinfosys.com\/blog\/?p=4790"},"modified":"2020-09-10T16:59:44","modified_gmt":"2020-09-10T11:29:44","slug":"getting-started-with-numpy","status":"publish","type":"post","link":"https:\/\/www.h2kinfosys.com\/blog\/getting-started-with-numpy\/","title":{"rendered":"Getting Started with NumPy"},"content":{"rendered":"\n

NumPy stands for Numerical Python<\/strong>. It is a tool for mathematical computing and data preparation in Python. It can be utilized to perform several mathematical operations on arrays such as trigonometric, statistical, and algebraic routines. This library provides many useful features including handling n-dimensional arrays, broadcasting, performing operations, data generation, etc., thus, it\u2019s the fundamental package for scientific computing with Python. It also provides a large collection of high-level mathematical functions to operate on arrays.<\/p>\n\n\n\n

Why use NumPy..?<\/strong><\/h2>\n\n\n\n

It is incredibly fast, as it has bindings to C libraries. In Python, we have lists that serve the purpose of arrays, but they are slow to process. Python list has fewer properties than the NumPy array. So using Numpy is more suggestible.<\/p>\n\n\n\n

Why is NumPy Faster Than Lists..?<\/strong><\/h2>\n\n\n\n

NumPy arrays are stored at one continuous place in memory unlike lists, so processes can access and manipulate them very efficiently. This behavior is called locality of reference in computer science. This is the main reason why NumPy is faster than lists. Also, it is optimized to work with the latest CPU architectures<\/a>.<\/p>\n\n\n\n

Numpy is surprisingly compact, fast, and easy to use, so let\u2019s dive into the installation.<\/p>\n\n\n\n

Installation of NumPy<\/strong><\/h2>\n\n\n\n

If you have Python and pip already installed on a system, then the installation of NumPy is very easy.<\/p>\n\n\n\n

Install it using this command:<\/p>\n\n\n\n

C:\\Users\\Your Name<\/em>>pip install numpyif<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

If you have anaconda already installed in your machine then it is easy to install this package with conda run:<\/p>\n\n\n\n

Type this command in Jupyter notebook:<\/p>\n\n\n\n

conda install -c anaconda numpy<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Once NumPy is installed, import it in your applications<\/p>\n\n\n\n

How to Import NumPy<\/strong><\/h2>\n\n\n\n

We can import Numpy into our application by using import keyword<\/p>\n\n\n\n

NumPy is usually imported under the np alias.<\/p>\n\n\n\n

What is an alias:<\/strong> In Python alias are an alternate name for referring to the same thing.<\/p>\n\n\n\n

Create an alias with the \u2018as\u2019 keyword while importing:<\/p>\n\n\n\n

import numpy as np<\/p>\n\n\n\n

Now the NumPy package can be referred to as np instead of numpy<\/p>\n\n\n\n

Numpy has many different built-in functions and capabilities. We will focus on some of the most important aspects in Numpy Now let\u2019s discuss the most important concept in numpy.<\/p>\n\n\n\n

NumPy Arrays<\/strong><\/h2>\n\n\n\n

So, what are arrays?<\/p>\n\n\n\n

An array is a data structure consisting of a collection of elements, each identified by at least one array index or key.<\/p>\n\n\n\n

NumPy is used to work with arrays. The array object in NumPy is called ndarray.<\/p>\n\n\n\n

We can create a NumPy ndarray object by using the array() function.<\/p>\n\n\n\n

The pre-built function np.array is the correct way to create an array.<\/p>\n\n\n\n

Example:<\/strong><\/p>\n\n\n\n

import numpy as np \narr = np.array([1, 2, 3, 4, 5])\nprint(arr)\nprint(type(arr)).<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
[1 2 3 4 5]
<class \u2018numpy.ndarray\u2019><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
2-D Arrays<\/strong><\/p>\n\n\n\n
An array of arrays is known as 2D array. The two dimensional (2D) array is also known as matrix. A matrix can be represented as a table of rows and columns. Before we discuss more about two Dimensional arrays let’s have a look at the following example.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\n
Create a 2-D array containing two arrays with the values 1,2,3 and 4,5,6<\/p>\n\n\n\n
import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
[[1 2 3]
[4 5 6]]<\/p>\n\n\n\n
3-D Arrays<\/strong><\/p>\n\n\n\n
A 3D array is a multi-dimensional array(array of arrays). A 3D array is a collection of 2D arrays . It is specified by using three subscripts <\/p>\n\n\n\n
Block size, Row size and Column size<\/p>\n\n\n\n
Example<\/strong><\/p>\n\n\n\n
Create a 3-D array with two 2-D arrays, both containing two arrays with the values 1,2,3 and 4,5,6:<\/p>\n\n\n\n
import numpy as np\narr = np.array([[[1, 2],[4, 5]],[[1, 2],[4, 5]]])\nprint(arr)<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
[[[1 2][4 5]]
[[1 2][4 5]]]<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Check Number of Dimensions?<\/p>\n\n\n\n
NumPy Arrays provides the ndim attribute that returns an integer that tells us how many dimensions the array have.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\n
Check how many dimensions the arrays have:<\/p>\n\n\n\n
import numpy as np\na = np.array(42)\nb = np.array([1, 2, 3])\nc = np.array([[1, 2], [4, 5]])\nd = np.array([[[1, 2], [4, 5]], [[1, 2], [4, 5]]])\n\nprint(a.ndim) \nprint(b.ndim) \nprint(c.ndim) \nprint(d.ndim)\n<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
0
1
2
3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
NumPy Array Indexing<\/strong><\/h2>\n\n\n\n
Access Array Elements<\/strong><\/p>\n\n\n\n
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1, etc.<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\n
import numpy as np\na = np.array([1,2,3,4])\nprint(arr[2])\n<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
3<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Access 2-D Arrays<\/strong><\/h2>\n\n\n\n
To access elements from 2-D arrays we can use comma-separated integers representing the dimension and the index of the element.<\/p>\n\n\n\n
The first element indicates the row and the second element indicates the column<\/p>\n\n\n\n
Example:<\/strong><\/p>\n\n\n\n
Access the 2nd element on 1st dim:<\/p>\n\n\n\n
import numpy as np\narr = np.array([[1,2,3], [6,7,8]])\nprint(arr[0,2])\nprint(arr[1,2])\n<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
3
8<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Negative Indexing<\/strong><\/h2>\n\n\n\n
import numpy as np\narr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\nprint('Last element from 2nd dim: ', arr[1, -1])\n<\/pre>\n\n\n\n
Output:<\/strong><\/p>\n\n\n\n
Last element from 2nd dim:  9<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
Built-In Methods<\/strong><\/h2>\n\n\n\n
Numpy allows us to use many built-in methods for generating arrays. Let\u2019s examine the most used from those, as well as their purpose:<\/p>\n\n\n\n