In this blog, I will talk about the famous 10 libraries of python with their features along with examples.
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
Image blurring with Opencv Python
Here is an example of blurring an image using gaussian blurring with OpenCV library.
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import cv2 import numpy as np
image = cv2.imread(‘got.png’)
cv2.imshow(‘Real Image’, imag) cv2.waitKey(0)
# Gaussian Blurring gauss = cv2.GaussianBlur(image, (8, 8), 0) cv2.imshow(‘gaussian blurring’, Gaussian) cv2.waitKey(0) cv2.destroyAllWindows() |
Output for the above program
With this documentation, you can start making your own detection models.
Tensorflow
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
Example of creating tensor in Tensorflow
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# 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”) print(x) print(sess.run(x)) print() print(y) print(sess.run(y)) |
Output for the above program
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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
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
And all other charts that you want to visualize..
Example of plotting scatter plot using Matplotlib
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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 plt.scatter(df.year,df.per_capita,color=blue) # plotting mean values of year and capital plt.scatter(np.mean(df.year),np.mean(df.per_capita),color=’red’) |
Output for the above program
<matplotlib.collections.PathCollection at 0x7f3d8322e898>
Numpy
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
Example for converting an array of temperatures in Celsius to Fahrenheit using numpy.
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cel_arr = np.array([20.2,20.4,22.9, 21.5,23.7, 25.3,21.8,24.2,20.9, 22.1]) print(cel_arr) feh_arr = cel_arr * (9 / 5) + 32 print(feh_arr) |
Output for the above program
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[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
Here is an example from scikit-learn exercises for cross-validation of the diabetes dataset.
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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.xlabel(‘alpha’) plt.axhline(np.max(scores), linestyle=’–‘, color=’.5′) plt.xlim([alphas[0], alphas[-1]]) plt.show() |
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
Requests is one of the famous libraries for making Http requests using python, for human beings.
Requests is used in
Example program for scraping the FITA website with requests.
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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 |
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print(x.text) |
Output for the above program
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<!DOCTYPE html> <html lang=”en-US”> <head> <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 & 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 & Oracle Training in Chennai, Coimbatore, Madurai & 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
Example program for scraping all the courses available at FITA using Beautiful Soup
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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’) print(link.text.strip()) |
Output
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Oracle Developer Training SQL Server Training MySQL Training Networking/CCNA Training |
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sqlalchemy
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
Example of creating a table with sqlalchemy
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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
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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
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
You can find the full documentation of pytest here.
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