Here in this example we will be knowing that how to show Line, Scatter and Bar Chart using Python. For this example i downloaded a csv file called BSESN.csv from here Lets have simple example as following # BSE market data analysis – 2021-2022 import pandas as pd #pip install plotly import plotly.express as px df […]
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In this tutorial we will be knowing how to reverse a given number using Python. First, let us take a variable and assign a any number with multiple digits to it. Eg: num=12345 then take another variable, rev_num and assign [0] to it. E.g. rev_num=0 then take a while loop to perform next step. E.g:
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Retrieve Live Market Price Accessing Share and Bombay Stock Exchange (BSE) Market price using Web scraping with the help of Python’s Beautiful Soup Package. In this example we covered TCS share price as an example. Here is the Python code to perform the task. #data science with Python – TCS share and BSE Market price
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Graphs [for Data Science] Graphs in Python : Graph represents graphical / pictorial representation of set of data. Due to continuous development of various packages / library files in python it is easy and sophisticated way to show data in various graph format. [pie / bar / scatter / column charts] Before you run these
Extract text from an image. Extracting text from an image using pytesseract tool and package using Python programming. install tesseract.exe from here. First, here is an image which contains some text as following..[saved as sample.jpg) And here is the full code to extract text from above image. from PIL import Image #Python Imaging Library (abbreviated
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MYSQL Joins with Python Inner join example to retrieve records from two table. Here are those two table: Now we will retrieve records from both tables based on regno column. Source code is: # pip install mysql-connector import mysql.connector as sql # pip install pandas import pandas as pd db_connection = sql.connect(host=’localhost’, database=’college_db’, user=’root’, password=”)
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Database connectivity using Python In this article we’ll be covering all database examples like SELECT, INSERT, UPDATE and DELETE operations using Python and MySQL database. First,, create database[college_db] and table[student_info] as per following image and insert few records [here we using wamp server] before you execute all programs in Python. a) SELECT operation # pip
Face detection In this machine learning example identify face using Python programming. Package used in this example is opencv [computer vision]. To install this package simply use following syntax: pip install opencv-python import cv2 #pip install opencv-python # Load the cascade face_cascade = cv2.CascadeClassifier(‘face_detector.xml’) # Read the input image img = cv2.imread(‘test2.jpg’) # Detect faces
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Extract live gold price using web scrap – Data Science in the following example we used three packages, they are a) bs4 [Beautiful Soup for web scraping] b) pandas [panel data for data representation ] c) request [to get source / url] #data science with Python – extracting Gold Price import bs4 import pandas as
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Retrieve current temperature using BeautifulSoup #extract current temp import requests import bs4 #url=”https://weather.com/en-IN/weather/today/l/12.96,77.59?par=google&temp=c” url=”https://weather.com/en-IN/weather/today/l/17.31,76.81?par=google&temp=c” r=requests.get(url) soup=bs4.BeautifulSoup(r.text, “html.parser”) val= soup.find(‘span’, “data-testid=’TemperatureValue'”, class_=’CurrentConditions–tempValue–3KcTQ’) print(“Current temp is : “, val.text) Output of the above program is : Current temp is : 30° 2. To check live price, title and ratings of particular product #data science with Python –
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Data science with using Python In the following example we will be retrieving live population records using BeautifulSoup package.[web scraping] Live Population record #data science with Python – world’s population stat import bs4 import pandas as pd import requests url = ‘https://www.worldometers.info/world-population/#:~:text=Population%20in%20the%20world%20is,it%20was%20at%20around%202%25.’ result = requests.get(url) soup = bs4.BeautifulSoup(result.text,’lxml’) pop = soup.find_all(‘table’ ,class_= ‘table table-striped table-bordered
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