Forem: Abhay Parashar

10 Impressive Automation Scripts You Need To Try Using Python

Abhay Parashar — Fri, 15 Dec 2023 08:33:20 +0000

Automation allows the execution of certain tasks efficiently without or with less human intervention. Python is certainly the kind of automation when It comes to programming languages. It has many inbuilt functionalities with wide support of different packages that allow a developer to automate certain tasks efficiently with less effort. In this blog, We will look at 10 Fun automation scripts that you need to try using Python.

Auto ProofRead:

Description: Correct spelling and grammatical errors in your text using the lmproof library in Python. This script can enhance the quality of your written content, making it error-free and polished.

PseudoData:

Description: Generate realistic-looking artificial datasets using Python. This script is valuable for tasks such as testing database schemas, ML model development, and API testing.

Notifo:

Description: Combat forgetfulness and boost productivity with this reminder script. Set custom reminders with messages, and the script will notify you after the specified time, acting as your digital memory aid.

NATO Encryptions:

Description: Encode and decode messages using NATO phonetic alphabet encryption. This script adds a layer of fun or security to messages, making them readable only with the correct set of keys.

Briefit:

Description: Summarize articles using neural networks and web scraping. This script generates abstract summaries of articles, making it easier to grasp the main points when time is limited.

Image Exif:

Description: Extract and remove Exif data from images. This script helps protect your privacy by removing metadata such as camera settings, geolocation, and other details embedded in images.

RemoveBG:

Description: Remove the background from images locally using the rembg Python package. This script is handy for writers and others who frequently work with images and need them without a background.

FilterText:

Description: Use regular expressions to filter text data. This script includes filters for email addresses, HTML tags, web links, mentions, and hashtags, making it versatile for various text-processing tasks.

EmailHeaders:

Description: Parse and display important information from email headers. This script provides insights into an email's origin, transmission, and content, helping identify potential issues or suspicious activity.

TrendyStocks:

Description: Fetch and visualize Google Trends data for specified stocks. This script helps investors understand the current trends in the market, making it a valuable tool for decision-making.
investment decisions.

Check out the source code for all these scripts here.

10 Python Code Habits You Must Avoid for Efficient Programming

Abhay Parashar — Sun, 09 Jul 2023 23:57:28 +0000

Python has gained immense popularity among developers worldwide due to its readability and extensive range of libraries and frameworks. However, like any programming language, Python is not immune to mistakes and pitfalls.

In this article, we’re going to explore 10 examples of bad code habits in Python. These examples will showcase common writing errors, inefficient practices, and potential pitfalls that can cause problems and make your Python code less efficient. Whether you’re a coding newbie or a seasoned programmer, this article is packed with examples of what NOT to do when writing code in Python.

1. Mixing different naming conventions

Mixing different naming conventions to define different parts, such as using a camel case for functions and a Pascal case for variables makes the code inconsistent and harder to read.

def myFunction(num):
    MyVar = num/3.5
    return MyVar

It’s better to follow a consistent naming convention throughout the source code to improve code readability and maintainability. One of the most used and widely adopted conventions is snake_case where identifiers are written in lowercase with underscores separating different words.

def my_function(num):
    my_var = num/3.5
    return my_var

2. Ignoring code comments

Leaving incomplete or misleading comments, or not providing any documentation can impact the understandability of the code and create confusion among fellow developers.

import re
text = "abc@xyz.com for further information."+\
        "You can also give feedbacl at feedback@xyz.com"

emails = re.findall(r"[a-z0-9\.\-+_]+@[a-z0-9\.\-+_]+\.[a-z]+", text)
print (emails)

Clear comments and comprehensive documentation provide essential context and ensure smooth collaboration between developers. It also reduces the time and effort required for understanding the code. By documenting the code properly developers can ensure that future changes and updates can be implemented efficiently, which leads to a more robust and maintainable end product.

Read More

How To Explain Each Machine Learning Model In Brief

Abhay Parashar — Thu, 25 Aug 2022 17:30:30 +0000

In this blog, I want to share a resource that provides concise explanations of all machine learning models ranging from Simple Linear regression to XGBoost to Clustering techniques.

Models Covered

Linear Regression
Polynomial Regression
Ridge Regression
Lasso Regression
Elastic Regression
Logistic Regression
K Nearest-Neighbors
Naive Bayes
Support Vector Machines
Decision Trees
Random Forest
Extra Trees
Gradient Boost
Ada Boost
XGBoost
K Means Clustering
Hierarchical Clustering
DBSCAN Clustering
Apriori
PCA

10 Useful Automation Scripts You Need To Try Using Python

Abhay Parashar — Wed, 25 May 2022 16:04:24 +0000

Automation is changing the lifestyle of people around the world. It is helping industries grow much faster without the need for manpower. One of the biggest industries that are being upgraded by Automation is Software Industry. Developers Around the world create and share software that helps reduce the time consumption for a process. Python is a leading language for producing automation scripts. It provides syntax with lots of tools and libraries that support writing automation scripts. In this blog, We are going to take a look at 10 useful automation scripts you need to try using python.

Wikipedia Automated Article Summaries
The New Way To Share Social Links ♻
Track Spam Number
Make Your Images Better
HTML Email Sender
One-Click Is All You Need (To Run Python)
Automate Product Marketing
Encrypt Your Passwords
Edit Videos The Pythonic Way
Clean and Analyze Text Super Quick

Check The Full Article Here

Jupyter Notebook 101: Everything You Need To Know

Abhay Parashar — Wed, 12 Jan 2022 03:37:12 +0000

Jupyter Notebook is a web-based computing platform that allows developers to code, visualize, share and embed multimedia with explanatory text inside a single document. It is a famous tool for showcasing your work because You can see both the code and the results in the same file.

It is very famous among data scientists because you can get a better understanding of the data by executing each line of code separately. Jupyter Notebook allows its users to download the notebook in various file formats like PDF, HTML, Python, Markdown, or an. ipynb file.

There Are Two Essential Parts of a Jupyter Notebook — Markdown Cell and Code Cells.

1. Markdown Cell

A Markdown Cell represents your work by providing enough explanation to readers about the code block below them. Markdown Writing Is an essential skill for every data scientist. It displays text which is formatted using a markup language. By Default All The Cells Will Be Code Cells. To Convert a Code Cell Into a Markdown Cell Just Press ESC to enter in command mode and then press m to convert it into a markdown.

2. Code Cell

A Code Cell allows you to Create, Update, Read, Edit, and Delete Code With full syntax highlighting and tab completion. The Default Kernal of Jupyter Notebook is Ipython That Runs Python Code. When a Code Cell executes the code inside it, the flow goes to the kernel (IPython default) that runs the code. To Execute a Code Cell You Can Use Keyboard Keys Combination SHIFT+ENTER

To Install Jupyter Notebook Inside a python environment just run the command pip install jupyter and to open the web client-server just type jupyter notebook

After Running The Command A Web Page Will Open On Your Default Browser That is The Homepage of Jupyter Notebook. Simply click on New select python3 to create a notebook.

The Blue Rectangle In Above Picture Shows The Title that can be renamed by clicking on it. The yellow rectangle shows a code cell that can be changed into a markdown cell-like maroon color box using the command or with the dropdown. In the Top Right Corner, You Can Confirm The Kernal. Below The Title There is a Toolbar using which you can perform multiple tasks like saving the file, creating a new cell, converting the code cell into markdown, etc.

Now, That You Have a Brief Idea About Jupyter Notebook. Let’s Know Some of the tricks and hacks that are helpful for you.

1. ᴍᴀʀᴋᴅᴏᴡɴ ᴄᴇʟʟ ɢᴜɪᴅᴇ
   1.1 ᴏʀɢᴀɴɪᴢɪɴɢ ᴛᴇxᴛ
     1.1.1 ᴛᴇxᴛ ʟᴇᴠᴇʟꜱ
     1.1.2 ᴛᴇxᴛ ᴇᴍᴘʜᴀꜱɪꜱ
     1.1.3 ᴘᴀʀᴀɢʀᴀᴘʜ ʙʀᴇᴀᴋᴇʀꜱ
     1.1.4 ʙʟᴀᴄᴋQᴜᴏᴛᴇꜱ
     1.1.5 ʜɪɢʜʟɪɢʜᴛɪɴɢ & ᴄᴏᴅᴇʙʟᴏᴄᴋꜱ
   1.2 ᴀᴅᴅɪɴɢ ᴄᴜꜱᴛᴏᴍ ᴄꜱꜱ
     1.2.1 ᴛᴇxᴛ ᴄᴏʟᴏʀ
     1.2.2 ᴛᴇxᴛ ʙᴀᴄᴋɢʀᴏᴜɴᴅ ᴄᴏʟᴏʀ
     1.2.3 ꜰᴏɴᴛ ꜰᴀᴍɪʟʏ
     1.2.4 ꜰᴏɴᴛ ꜱɪᴢᴇ
     1.2.5 ᴄᴏʟᴏʀᴇᴅ ɴᴏᴛᴇ ʙᴏxᴇꜱ
   1.3 ᴄʀᴇᴀᴛɪɴɢ ᴛᴀʙʟᴇꜱ
   1.4 ʜʏᴘᴇʀʟɪɴᴋꜱ
   1.5 ɪᴍᴀɢᴇꜱ
   1.6 ɴᴀᴠɪɢᴀᴛɪᴏɴꜱ (ᴛᴀʙʟᴇ ᴏꜰ ᴄᴏɴᴛᴇɴᴛ)
2. ᴄᴏᴅᴇ ᴄᴇʟʟ ᴛʀɪᴄᴋꜱ
   2.1 ʀᴜɴɴɪɴɢ ꜱʜᴇʟʟ ᴄᴏᴍᴍᴀɴᴅꜱ ɪɴꜱɪᴅᴇ ᴛʜᴇ ɴᴏᴛᴇʙᴏᴏᴋ
   2.2 ᴍᴇᴀꜱᴜʀɪɴɢ ᴄᴇʟʟ ᴇxᴇᴄᴜᴛɪᴏɴ ᴛɪᴍᴇ
   2.3 ᴠɪᴇᴡɪɴɢ & ʟɪɴᴋɪɴɢ ᴅᴏᴄᴜᴍᴇɴᴛᴀᴛɪᴏɴ
   2.4 ᴀʟᴀʀᴍ ꜰᴏʀ ꜱʜᴇʟʟ ᴇxᴇᴄᴜᴛɪᴏɴ
   2.5 ʜɪᴅᴇ ᴀɴɴᴏʏɪɴɢ ᴏᴜᴛᴘᴜᴛ
   2.6 ꜱʜᴏᴡ ᴍᴜʟᴛɪᴘʟᴇ ᴏᴜᴛᴘᴜᴛꜱ
   2.7 ɴᴏᴛᴇʙᴏᴏᴋ ᴡɪᴅᴛʜ
   2.8 ᴇᴍʙᴇᴅᴅɪɴɢ ᴠɪᴅᴇᴏꜱ
3. ɪᴍᴘᴏʀᴛᴀɴᴛ ᴋᴇʏʙᴏᴀʀᴅ ꜱʜᴏʀᴛᴄᴜᴛꜱ
4. ᴇxᴛᴇɴꜱɪᴏɴꜱ & ᴛʜᴇᴍᴇꜱ

1. Markdown Cell Guide

Markdown Cells Display Text that is used to understand the code flow and thought process of the developer. The Text Written Inside Should Be Clear and Properly Organized.

These Cells Are Similar To Comments in any programming language with some extra powers. Like, Adding images, Changing Font Size, Font Family, Dividing Text into different levels, breaking paragraphs, giving text color, adding links, etc. Let’s See Each One By One.

Read The Complete Blog Post Here

Top 10 Datasets For Machine Learning Practitioners With Notebook Solutions

Abhay Parashar — Tue, 04 Jan 2022 16:16:44 +0000

Machine learning is the study of computer algorithms that can improve automatically through experience and by the use of data, as per Wikipedia. It is a branch of Artificial Intelligence. It is used for time series forecasting, fraud detection, spam filtration, Recommendations, Marketing, Healthcare, etc.

Datasets work as roots for machine learning projects. a dataset is a collection of rows and columns where each column represents a different variable and each row represents a record for the variables. Every Machine Learning Project Start and End Because of Datasets. In This Blog, I am Going To Share With You Over 20 Datasets From Different Domains Like Computer Vision, Time Series, Natural Language Processing, Predictive Analysis, and More.

“The Goal Is To Convert Data Into Information and Information Into Insights” — Carly Fiorina

The IRIS Dataset
The Mall Customer Dataset
The Boston House Prices Dataset
IMDB Reviews Dataset
Wine Quality Dataset
Titanic Dataset
Spam SMS Dataset
Movie Lens Dataset
MNIST Dataset
German Traffic Sign Recognition

Get a Sneak Peak of Datasets and Notebook Links Here.

10 Handy Automation Scripts You Should Try Using Python

Abhay Parashar — Thu, 23 Dec 2021 06:51:30 +0000

Automation is the process of completing a task without any human intervention. There are many programming languages that provide different ways to automate tasks but out of all python is a preferred and first choice. The reason is it offers a simple syntax, tons of useful packages to work with. In this blog, we are going to take a look at 10 handy automation scripts you should have using python.

Your Friend For Reading Articles 👁‍🗨
One-Click Sketching
Stay Up With Top Headlines
Stocks Updates On The Start
Bulk Email Sender
No Time For EDA
Smart Login To Different Sites
Be Safe & Watermark Your Images
Remember That (Reminder App)
Google Scraper

Check The Full Article Here

12 Unique Python Project Ideas for Your Resume

Abhay Parashar — Thu, 15 Jul 2021 17:39:17 +0000

There are two ways to get a decent job in IT:

With a certificate
With knowledge and the projects you have built

In my opinion, projects speak louder than certificates. In this article, we are going to see 12 Python project ideas for your resume. Every idea is from a different domain and contains some information about the different projects you can build in it.

First, remember one thing:
“A project is complete when it starts working for you, rather than you working for it.” — Scott Allen

1. Desktop Assistant

A desktop assistant can be a good project for your resume. It shows the interviewer how wise you are. You know how to use resources and make something good with them.
You don’t have to be very technical or an advanced Python developer to create one. It can be created with just the help of a few different Python packages.
There are many available in Python that you can use, like Pyttsx3 for reading text, os for adding facilities like playing music or starting an application, Wikipedia for finding answers on the web, and more. Keep one thing in mind: Every package you use must provide a feature to your assistant.
You can even make your assistant more advanced by adding web scraping and automation facilities. Write a script that can scrape Google Search results. You can add this to your script to make it look more advanced.
This project has no end to it. The more features you add, the more professional and useful it becomes.
To get some insight into how all this will work, check out this article.

Check Out Other 11 Unique Project Ideas By From Here.

Believe Me You Will Not Regret It.

Best Practices To Follow While Creating Classes In Python

Abhay Parashar — Tue, 06 Jul 2021 16:16:07 +0000

Object-Oriented Programming Is a programming paradigm based on the concepts of classes and objects. The Concept of oop First Introduced in 1967. The First-ever programming language that has the primary features of OOP is Simula Created by Alan Kay.
A Class in oop Works as a blueprint for the object. if we define a car as a class then different brands or types of cars will be objects.

Why OOP?

You Might Have a question in mind why do I need oop? what’s the need of creating it? Why it is used so much?

OOP Makes it so much easy to maintain and update existing code.
It provides code reusability.
Works Best With Real-Life Problems.
Have a Modular Structure.
Easy to debug.
In comparison to others (functional or structural), it is much faster and more efficient to use.

Best Practices For OOP

1. Use Inheritance Instead of redefining variables

Inheritance is one of the four pillars of OOP. It is the process by which one class inherits the properties of another class. The Class that inherits properties from another class is called child and the other one is called the Parent class.

Let’s Take a scenario, You have a Employee class that has parameters like name, age, experience, salary. Then You have other classes Like Developers and Designers that contains the information related to the particular field.

Now If You Don’t Use Inheritance then you have to define the name, age, experience, salary parameters for each class separately

The Above Code Looks Messy and shows a bad representation. Inheritance can help us to reduce the code and make it look more readable and professional.

In Python, to inherit a class, we use Child_class(Parent_class) it at the time of defining the class. The super() method helps a child class to access the members of the parent class. Developers class accesses name, age, exp, and salary information from the parent class. Let’s see how inheritance reduce the size of the code —
Read More

35 Must Known GitHub Repositories For Developers

Abhay Parashar — Fri, 25 Jun 2021 17:57:57 +0000

GitHub is a git hosting platform service. There is not a single thing that you can’t find on GitHub related to the software industry. It is a goldmine of resources for developers. Like every other mine in the world, you need to be a good miner to dig gold to get great resources from it.
If you are not in the mood to do a lot of research, don’t worry I am here for you. In this blog, we are going to see some of the best GitHub repositories for developers that you should bookmark right away.

1. The Algorithm

It is one of the best GitHub repositories for learning data structures and algorithms using different languages. Data Structures must be known for every computer science student. Whether you are a python developer, Java developer, Go developer, or some old-school C++ developer, there is something for everyone in this repository that you should learn. All the algorithms and data structures present here are explained very easily. They also have a website for easy access to all the code.
Stats : (111k+ ⭐) (30.4k+ Forked)

2. freeCodeCamp/freeCodeCamp

Freecodecamp is a nonprofit organization that provides a free learning platform for learning to code. Their Github repository is the backend for everything. In the repo, there is a README.md file that contains the links to each course available on the website. The Github Profile of FreeCodeCamp also contains many other useful repositories like boilerplates for different programming languages, how to contribute to open source, and many more.
Stats : (325k+ ⭐) (26k+ Forked)

3. EbookFoundation/free-programming-books

This repository is another GEM of Github. The repository provided by EbookFoundation contains a list of free programming books. You will find links to free books in 20+ languages. There are Over a thousand books that are covering over 100 programming languages and millions of concepts.
Stats : (194k+ ⭐) (43k+ Forked)

4. Avik-Jain/100-Days-Of-ML-Code

This repository is more than a repo, it shows the constant hard work and dedication of a group of people to contribute to open source. As the name of the repository suggests, it has a 100-day curriculum to learn Machine Learning. One of the best parts of this repo is the attractive banner images for every day. If you are a learner or a developer of ML this one is a must be forked for you.
Stats : (32.4k+ ⭐) (8.2k+ Forked)

5. tuvtran/project-based-learning

One of the best ways to learn to code is by building projects because in the end, all that matters is the number of projects and knowledge you have. Many companies are giving priority to projects more than certificates.
This repo has a collection of links to different projects on the internet to help you boost your learning in different areas of programming. Whether it is web development, Game Development, JavaScript, or Python you will find a project for everything.
Stats : (51.4k+ ⭐) (8k+ Forked)

Check Out Other 30 Valuable Repositories here

Master Logging In Python

Abhay Parashar — Sat, 15 May 2021 06:04:54 +0000

Logging is the process of capturing the flow of code as it executes. Logging helps in debugging the code easily by writing logs. Logs are usually written inside a file, called a log file. This File is used to troubleshoot any problem that occurs during the execution of the program. Logging makes it so much easier to debug the code even when it is in production. In Python, we have a Library named logging that is used to write logs onto a file.

Read The Complete Article Here.
https://medium.com/pythoneers/master-logging-in-python-73cd2ff4a7cb

Basics Of Natural Language Processing in 10 Minutes

Abhay Parashar — Sat, 28 Nov 2020 03:40:38 +0000

Hello, there
You are here because you also want to learn natural language processing as quickly as possible, like me.
Let’s start
The first thing we need is to install some dependency

Python >3.7
Download From Here
Download an IDE or install Jupyter notebook
To install Jupyter notebook, just open your cmd(terminal) and type pip install jupyter-notebook after that type jupyter notebook to run it then you can see that your notebook is open at http://127.0.0.1:8888/token.
Install packages
pip install nltk

NLTK: It is a python library that can we used to perform all the NLP tasks(stemming, lemmatization, etc..)
In this blog, we are going to learn about

Tokenization
Stopwords
Stemming
Lemmatizer
WordNet
Part of speech tagging
Bag of Words Before learning anything let’s first understand NLP.

Natural Language refers to the way we humans communicate with each other and processing is basically proceeding the data in an understandable form. so we can say that NLP (Natural Language Processing) is a way that helps computers to communicate with humans in their own language.
It is one of the broadest fields in research because there is a huge amount of data out there and from that data, a big amount of data is text data. So when there is so much data available so we need some technique threw which we can process the data and retrieve some useful information from it.
Now, we have an understanding of what is NLP, let’s start understanding each topic one by one.

1. Tokenization

Tokenization is the process of dividing the whole text into tokens.
It is mainly of two types:

Word Tokenizer (separated by words)
Sentence Tokenizer (separated by sentence)

import nltk
from nltk.tokenize import sent_tokenize,word_tokenize
example_text = "Hello there, how are you doing today? The weather is great today. The sky is blue. python is awsome"
print(sent_tokenize(example_text))
print(word_tokenize(example_text))

In the above code
First, we are importing nltk , in the second line, we are importing our tokenizers sent_tokenize,word_tokenize from library nltk.tokenize , then to use the tokenizer on a text we just need to pass the text as a parameter in the tokenizer.
The output will look something like this

##sent_tokenize (Separated by sentence)
['Hello there, how are you doing today?', 'The weather is great today.', 'The sky is blue.', 'python is awsome']

##word_tokenize (Separated by words)
['Hello', 'there', ',', 'how', 'are', 'you', 'doing', 'today', '?', 'The', 'weather', 'is', 'great', 'today', '.', 'The', 'sky', 'is', 'blue', '.', 'python', 'is', 'awsome']

2. Stopwords

In general stopwords are the words in any language which does not add much meaning to a sentence. In NLP stopwords are those words which are not important in analyzing the data.
Example : he,she,hi,and etc.

Our main task is to remove all the stopwords for the text to do any further processing.
There are a total of 179 stopwords in English, using NLTK we can see all the stopwords in English.

We Just need to import stopwords from the library nltk.corpus.

from nltk.corpus import stopwords
print(stopwords.words('english'))
######################
######OUTPUT##########
######################
['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', "you're", "you've", "you'll", "you'd", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', "she's", 'her', 'hers', 'herself', 'it', "it's", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', "that'll", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', "don't", 'should', "should've", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', "aren't", 'couldn', "couldn't", 'didn', "didn't", 'doesn', "doesn't", 'hadn', "hadn't", 'hasn', "hasn't", 'haven', "haven't", 'isn', "isn't", 'ma', 'mightn', "mightn't", 'mustn', "mustn't", 'needn', "needn't", 'shan', "shan't", 'shouldn', "shouldn't", 'wasn', "wasn't", 'weren', "weren't", 'won', "won't", 'wouldn', "wouldn't"]

To remove Stopwords for a particular text

from nltk.corpus import stopwords
text = 'he is a good boy. he is very good in coding'
text = word_tokenize(text)
text_with_no_stopwords = [word for word in text if word not in stopwords.words('english')]
text_with_no_stopwords
##########OUTPUT##########
['good', 'boy', '.', 'good', 'coding']

3. Stemming

Stemming is the process of reducing a word to its word stem that affixes to suffixes and prefixes or to the roots of words known as a lemma.
In simple words, we can say that stemming is the process of removing plural and adjectives from the word.
Example :
loved → love, learning →learn
In python, we can implement stemming by usingPorterStemmer . we can import it from the library nltk.stem .

One thing to remember from Stemming is that it works best with single words.

from nltk.stem import PorterStemmer
ps = PorterStemmer()    ## Creating an object for porterstemmer
example_words = ['earn',"earning","earned","earns"]  ##Example words
for w in example_words:
    print(ps.stem(w))    ##Using ps object stemming the word
##########OUTPUT##########
earn
earn
earn
earn
Here we can see that earning,earned and earns are stem to there lemma or root word earn.

4. Lemmatizing

Lemmatization usually refers to doing things properly with the use of vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma.
In simple words lemmatization does the same work as stemming, the difference is that lemmatization returns a meaningful word.
Example:
Stemming
history → histori
Lemmatizing
history → history

It is Mostly used when designing chatbots, Q&A bots, text prediction, etc.

from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer() ## Create object for lemmatizer
example_words = ['history','formality','changes']
for w in example_words:
    print(lemmatizer.lemmatize(w))

#########OUTPUT############
----Lemmatizer-----
history
formality
change
-----Stemming------
histori
formal
chang

5. WordNet

WordNet is the lexical database i.e. dictionary for the English language, specifically designed for natural language processing.
We can use wordnet for finding synonyms and antonyms.
In python, we can import wordnet from nltk.corpus .
Code For Finding Synonym and antonym for a given word

from nltk.corpus import wordnet
synonyms = []   ## Creaing an empty list for all the synonyms
antonyms =[]    ## Creaing an empty list for all the antonyms
for syn in wordnet.synsets("happy"): ## Giving word 
    for i in syn.lemmas():        ## Finding the lemma,matching 
        synonyms.append(i.name())  ## appending all the synonyms       
        if i.antonyms():
            antonyms.append(i.antonyms()[0].name()) ## antonyms
print(set(synonyms)) ## Converting them into set for unique values
print(set(antonyms))
#########OUTPUT##########
{'felicitous', 'well-chosen', 'happy', 'glad'}
{'unhappy'}

6. Part of Speech Tagging

It is a process of converting a sentence to forms — a list of words, a list of tuples (where each tuple is having a form (word, tag)). The tag in the case is a part-of-speech tag and signifies whether the word is a noun, adjective, verb, and so on.

Part of Speech Tag List

 CC coordinating conjunction
 CD cardinal digit
 DT determiner
 EX existential there (like: “there is” … think of it like “there”)
 FW foreign word
 IN preposition/subordinating conjunction
 JJ adjective ‘big’
 JJR adjective, comparative ‘bigger’
 JJS adjective, superlative ‘biggest’
 LS list marker 1)
 MD modal could, will
 NN noun, singular ‘desk’
 NNS noun plural ‘desks’
 NNP proper noun, singular ‘Harrison’
 NNPS proper noun, plural ‘Americans’
 PDT predeterminer ‘all the kids’
 POS possessive ending parent’s
 PRP personal pronoun I, he, she
 PRP possessive pronoun my, his, hers
 RB adverb very, silently,
 RBR adverb, comparative better
 RBS adverb, superlative best
 RP particle give up
 TO to go ‘to’ the store.
 UH interjection errrrrrrrm
 VB verb, base form take
 VBD verb, past tense took
 VBG verb, gerund/present participle taking
 VBN verb, past participle taken
 VBP verb, sing. present, non-3d take
 VBZ verb, 3rd person sing. present takes
 WDT wh-determiner which
 WP wh-pronoun who, what
 WP possessive wh-pronoun whose
 WRB wh-abverb where, when

In python, we can do pos tagging using nltk.pos_tag .

import nltk
nltk.download('averaged_perceptron_tagger')
sample_text = '''
An sincerity so extremity he additions. Her yet there truth merit. Mrs all projecting favourable now unpleasing. Son law garden chatty temper. Oh children provided to mr elegance marriage strongly. Off can admiration prosperous now devonshire diminution law.
'''
from nltk.tokenize import word_tokenize
words = word_tokenize(sample_text)
print(nltk.pos_tag(words))
################OUTPUT############
[('An', 'DT'), ('sincerity', 'NN'), ('so', 'RB'), ('extremity', 'NN'), ('he', 'PRP'), ('additions', 'VBZ'), ('.', '.'), ('Her', 'PRP$'), ('yet', 'RB'), ('there', 'EX'), ('truth', 'NN'), ('merit', 'NN'), ('.', '.'), ('Mrs', 'NNP'), ('all', 'DT'), ('projecting', 'VBG'), ('favourable', 'JJ'), ('now', 'RB'), ('unpleasing', 'VBG'), ('.', '.'), ('Son', 'NNP'), ('law', 'NN'), ('garden', 'NN'), ('chatty', 'JJ'), ('temper', 'NN'), ('.', '.'), ('Oh', 'UH'), ('children', 'NNS'), ('provided', 'VBD'), ('to', 'TO'), ('mr', 'VB'), ('elegance', 'NN'), ('marriage', 'NN'), ('strongly', 'RB'), ('.', '.'), ('Off', 'CC'), ('can', 'MD'), ('admiration', 'VB'), ('prosperous', 'JJ'), ('now', 'RB'), ('devonshire', 'VBP'), ('diminution', 'NN'), ('law', 'NN'), ('.', '.')]

7. Bag Of Words

Till now we have understood about tokenizing, stemming, and lemmatizing. all of these are the part of the text cleaning, now after cleaning the text we need to convert the text into some kind of numerical representation called vectors so that we can feed the data to a machine learning model for further processing.

For converting the data into vectors we make use of some predefined libraries in python.

Let’s see how vector representation works

sent1 = he is a good boy
sent2 = she is a good girl
sent3 = boy and girl are good 
        |
        |
  After removal of stopwords , lematization or stemming
sent1 = good boy
sent2 = good girl
sent3 = boy girl good  
        | ### Now we will calculate the frequency for each word by
        |     calculating the occurrence of each word
word  frequency
good     3
boy      2
girl     2
         | ## Then according to their occurrence we assign o or 1 
         |    according to their occurrence in the sentence
         | ## 1 for present and 0 fot not present
         f1  f2   f3
        girl good boy   
sent1    0    1    1     
sent2    1    0    1
sent3    1    1    1
### After this we pass the vector form to machine learning model

The above process can be done using a CountVectorizer in python, we can import the same from sklearn.feature_extraction.text .

CODE to implement `CountVectorizer` In python

import pandas as pd
sent = pd.DataFrame(['he is a good boy', 'she is a good girl', 'boy and girl are good'],columns=['text'])
corpus = []
for i in range(0,3):
    words = sent['text'][i]
    words  = word_tokenize(words)
    texts = [lemmatizer.lemmatize(word) for word in words if word not in set(stopwords.words('english'))]
    text = ' '.join(texts)
    corpus.append(text)
print(corpus)   #### Cleaned Data
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer() ## Creating Object for CountVectorizer
X = cv.fit_transform(corpus).toarray()
X  ## Vectorize Form 
############OUTPUT##############
['good boy', 'good girl', 'boy girl good']
array([[1, 0, 1],
       [0, 1, 1],
       [1, 1, 1]], dtype=int64)

Congratulations 👍, Now you know the basics of NLP