Top 10 Python Libraries

In this blog, I will talk about the famous 10 libraries of python with their features along with examples. 

    Opencv Python 
    Scikit Learn 

Opencv Python

Opencv or Open Source Computer Vision is a library for image processing, machine learning, and computer vision applications, etc. originally developed by Intel.

What You Can Do With OpenCV

Process images and videos to identify objects
Document field detection
Detection of specific color
Detect edges of an image
Cartoonize an image
Facial Landmarks and Face detection
Camera calibration and 3D reconstruction

Image blurring with Opencv Python

Here is an example of blurring an image using gaussian blurring with OpenCV library.

import cv2

import numpy as np


image = cv2.imread(‘got.png’)


cv2.imshow(‘Real Image’, imag)



# Gaussian Blurring

gauss = cv2.GaussianBlur(image, (8, 8), 0)

cv2.imshow(‘gaussian blurring’, Gaussian)



Output for the above program

With this documentation, you can start making your own detection models.


Google Brain team’s, Tensorflow can be used to implement machine learning, deep learning, or neural network applications. Tensorflow is most famous because of its processing distribution between GPUs and CPUs. Tensorflow supports high level APIs and low level APIs for distribution.

A tensor can be described as an array of 0,1,2,3 or a higher dimensional array with homogenous data, upon which the scientific calculations are done just like numpy arrays with three properties as shape, size, and type.

With Tensorflow you can create

Convolution Neural Networks(CNNs)
Natural Language Processing(NLP)
Recurrent Neural Network(RNN)

Example of creating tensor in Tensorflow

# Program to create tensor in tensorflow


import tensorflow as tf

with tf.compat.v1.Session() as sess:

     x = tf.range(12.0, 100.0, delta = 25.5)

     y= tf.range(80.0, delta = 25.5, name =”y”)






Output for the above program

Tensor(“range_1:0”, shape=(4,), dtype=float32)

[12. 37.5 63. 88.5]

Tensor(“y_1:0”, shape=(4,), dtype=float32)

[ 0. 25.5 51. 76.5]


Matplotlib is a 2-D plotting or data visualization library, originally written by John D. Hunter. Matplotlib can be used to embed graphs, diagrams, or plots in web applications with flask, Django, or desktop applications such as Pyqt, Tkinter,wxpython, etc.

What you can plot with matplotlib

bar charts
pie charts
Scatter plots
error charts
Power spectra
Stem plots

And all other charts that you want to visualize..

Example of plotting scatter plot using Matplotlib

import pandas as pd

import matplotlib.pyplot as plt

%inline matplotlib # to prevent opening of plot in a new window

df = pd.read_csv(‘percapita.csv’) # reading data from a csv file

plt.xlabel(‘years (1960-2016’)) # labeling x axis

plt.ylabel(‘per capita (dollars)’) # labeling y axis


# plotting mean values of year and capital


Output for the above program

<matplotlib.collections.PathCollection at 0x7f3d8322e898>


Numpy or Numerical python is a free and open-source library for working with n-dimensional arrays or ndarrays. It is often used with other libraries such as scipy, matplotlib, Pandas, scikit for scientific computations for various data science or machine learning applications. Numpy is partly written in Python and the rest with C and C++.

Use Of Numpy

Easily working with arrays of high dimension.
Mathematical operations can be effortlessly applied.
Applying statistical implementations across arrays.
Widely used in data science, machine learning projects. 

Example for converting an array of temperatures in Celsius to Fahrenheit using numpy.

cel_arr = np.array([20.2,20.4,22.9, 21.5,23.7, 25.3,21.8,24.2,20.9, 22.1])


feh_arr = cel_arr * (9 / 5) + 32


Output for the above program

[20.2 20.4 22.9 21.5 23.7 25.3 21.8 24.2 20.9 22.1]

[68.36 68.72 73.22 70.7 74.66 77.54 71.24 75.56 69.62 71.78]

Scikit Learn

Scikit Learn is a python library mostly used along with numpy and pandas for working with complicated or complex data.

It is used for cross validations such as checking the precision of unsupervised and supervised models with various algorithms.

Scikit learn is used for

Classification, grouping, or sorting of datasets.
Clustering and Model selection
For Regressions such as Linear, Logistic, Multiple, or binomial regression.
Extracting objects or features from images and documents.

Here is an example from scikit-learn exercises for cross-validation of the diabetes dataset.

import numpy as np

import matplotlib.pyplot as plt

from sklearn import datasets

from sklearn.linear_model import LassoCV

from sklearn.linear_model import Lasso

from sklearn.model_selection import KFold

from sklearn.model_selection import GridSearchCV

X, y = datasets.load_diabetes(return_X_y=True)

X = X[:150]

y = y[:150]

lasso = Lasso(random_state=0, max_iter=10000)

alphas = np.logspace(-4, -0.5, 30)

tuned_parameters = [{‘alpha’: alphas}]

n_folds = 5

clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False), y)

scores = clf.cv_results_[‘mean_test_score’]

scores_stdv = clf.cv_results_[‘std_test_score’]

plt.figure().set_size_inches(8, 6)

plt.semilogx(alphas, scores)

std_error = scores_stdv / np.sqrt(n_folds)

plt.semilogx(alphas, scores + std_error, ‘b–‘)

plt.semilogx(alphas, scores – std_error, ‘b–‘)

plt.fill_between(alphas, scores + std_error, scores – std_error, alpha=0.2)

plt.ylabel(‘CV score +/- std error’)


plt.axhline(np.max(scores), linestyle=’–‘, color=’.5′)

plt.xlim([alphas[0], alphas[-1]])

Output for the above program

Cross validation is a technique for testing the effectiveness of a model and to evaluate if we have enough data.

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Requests is one of the famous libraries for making Http requests using python, for human beings.

Requests is used in

Read the response from the requested url.
Send a GET, POST, PUT, DELETE requests with its methods.
Handle exceptions.
Customize headers and data of the url.

Example program for scraping the FITA website with requests.

import requests

x = requests.get(‘’)

print(x.status_code) # output: 200

print(x.headers[‘Date’]) # output: Wed, 23 Sep 2020 16:02:02 GMT

print(x.headers[‘Keep-alive’]) # output: timeout=5, max=100


Output for the above program

<!DOCTYPE html>

<html lang=”en-US”>


<meta charset=”UTF-8″>

<meta name=”viewport” content=”width=device-width, initial-scale=1″>

<link rel=”profile” href=”″>

<link rel=”pingback” href=””>

<link rel=”shortcut icon” type=”image/png” href=”/wp-content/uploads/2019/07/favicvon.png”/>

<!– This site is optimized with the Yoast SEO Premium plugin v14.9 – –>

<title>FITA : Java, Hadoop, Android, AngularJS, Selenium, Software Testing, PHP, German, Salesforce, SEO, AngularJS, AWS, Cloud Computing, RPA, DevOps, IoT, Blockchain, Data Science, Digital Marketing, Python, Ethical Hacking, Dot Net Training in Chennai, Coimbatore, Madurai &amp; Bangalore</title>

<meta name=”description” content=”FITA – Best Dot Net, JAVA, Selenium, Software Testing, PHP, SEO, Android, AngularJS, Hadoop, AWS, Cloud Computing, DevOps, Salesforce, RPA, Blockchain, Digital Marketing, Data Science, Ethical Hacking, Python, German &amp; Oracle Training in Chennai, Coimbatore, Madurai &amp; Bangalore” />

<meta name=”robots” content=”index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1″ />


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Beautiful Soup

Beautiful soup is a python library for parsing data from the websites or for web scraping. It removes all those tags and styles from the source code and only uses the data that we want without having to reload the page, like the search for a <a> tag and return only its href value.

Scraping data with Beautiful Soup involves the following steps

Get the URL of the site.
Send a request to the server
Read the response from the server
Inspect the page and select elements you want
Parse using a scraper and store the data.

Example program for scraping all the courses available at FITA using Beautiful Soup

import requests

from bs4 import BeautifulSoup

url = requests.get(‘’)

page = url.content

soup = BeautifulSoup(page,’html.parser’)

links = soup.find_all(‘div’,class_=’course-name’)

for i in links:

link = i.find(‘h4’)



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Sqlalchemy is a famous ORM or Object Relational mapper for Python, which converts the user defined classes to SQL database or tables. All the operations of raw SQL can be performed using Python classes(inheriting models from sqlalchemy) and will be mapped to the databases.

Companies using Sqlalchemy

The OpenStack Project
Survey Monkey

Example of creating a table with sqlalchemy

from sqlalchemy import create_engine

from sqlalchemy.ext.declarative import declarative_base

engine = create_engine(‘sqlite:///:memory:’, echo=True)

Base = declarative_base()

from sqlalchemy import Column, Integer, String

class User(Base):

__tablename__ = ‘Friends’

id=Column(Integer, primary_key=True)

fullname = Column(String)

nickname = Column(String)

def __repr__(self):

return “<User(fullname=’%s’, nickname=’%s’)>” % (

self.fullname, self.nickname)

Which will create a table as follows

Table(Friends’, MetaData(bind=None),

Column(‘id’, Integer(), table=<users>, primary_key=True,nullable=False),

Column(‘fullname’, String(), table=<users>),

Column(‘nickname’, String(), table=<users>), schema=None)


Pyqt5 is a library for creating desktop applications or interacting programs using GUI.PyQt5 is the latest version of pyQt, it lets you use the Qt GUI framework and Qt designer for making the layout of the application. An alternative to Pyqt would be Tkinter, which is lightweight and lets the developer decide the layout and components.

Here is an example picture of an application I made with pyqt for cricket score evaluation.

You can have a glimpse of the source code for this application here

Pytest: Helps you write a better program

Whether you are writing a small program or a complex one, a program for deployment or for development, testing the program is important in every stage, therefore pytest can help you code failures, fixtures, and much more.

Features of Pytest

Automatically find and run tests.
Supports parallel testing
Simple but powerful fixture model
Can generate test reports in various forms (html report,json report, etc)

You can find the full documentation of pytest here.

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