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  • 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)

    clf.fit(X, 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(‘https://www.fita.in/’)

    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=”https://gmpg.org/xfn/11″>

    <link rel=”pingback” href=”https://www.fita.in/xmlrpc.php”>

    <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 – https://yoast.com/wordpress/plugins/seo/ –>

    <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” />

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    Check out this Online Python Course by FITA. FITA provides a complete Python course that covers all the beginning and the advanced concepts of python including Django, along with hands on building real-time projects like Bitly and Twitter using Django Framework, along with placement support, and certification at an affordable price with an active placement cell, to make an industry required certified python and Django developer.

    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(‘https://www.fita.in’)

    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.

    To get in-depth knowledge of Python along with its various applications and real-time projects like twitter and bitly clone with Django, you can enroll in Python Training in Chennai or Python Training in Bangalore by FITA, which covers all the basics and advanced concepts of python including exception handlings, regular expressions, along with building real-time projects like Bitly and Twitter with Django, or enroll for a Data science course at Chennai or Data science course in Bangalore which includes Supervised, Unsupervised machine learning algorithms, Data Analysis Manipulation and visualization, reinforcement testing, hypothesis testing and much more to make an industry required data scientist at an affordable price, which includes certification, support with career guidance assistance and an active placement cell, to make you an industry required certified data scientist and python developer.

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