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
import bs4 
import pandas as pd 
import requests 

url = 'https://www.google.com/finance/quote/TCS:NSE'
result = requests.get(url) 
soup = bs4.BeautifulSoup(result.text,'lxml') 

for row in soup.find_all('div',attrs={"class" : "YMlKec fxKbKc"}):
    print ("TCS Share price:", row.text)

url2='https://www.google.com/finance/quote/SENSEX:INDEXBOM'
result2 = requests.get(url2) 
soup2 = bs4.BeautifulSoup(result2.text,'lxml') 

for row2 in soup2.find_all('div',attrs={"class" : "YMlKec fxKbKc"}):
    print ("BSE Market price:", row2.text)

Output of the above code is :

TCS Share price: ₹3,562.20
BSE Market price: 56,988.59

These values are retrieved live on any particular day when the market is open otherwise it shows previous day’s values.

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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 examples, install following packages.

a) pip install matplotlib [basic graphs]
b) pip install numpy [numeric python]
c) pip install pandas [panel data]
d) pip install plotly [for advanced and most sophisticated graphs]

Here are some examples.

1. The following example shows a particular company stock performance in given period. [here we took hdfc bank ltd example]

#Analyzing particular share price in particular period 
import pandas as pd
#pip install plotly
import plotly.express as px
df = pd.read_csv('HBD.csv')
fig = px.line(df, x = 'HBD_x', y = 'HBD_y', title='HDFC Shares prices 2019-20')
fig.show()

To download HBD.csv file, click here or you can google it.

Output of above example is :

2.  Student performance  

import matplotlib.pyplot as plt
import csv
x=[]
y=[]
with open('student_data.csv', 'r') as csvfile:
    plots= csv.reader(csvfile, delimiter=',')
    for row in plots:
        x.append(str(row[0]))
        y.append(str(row[1]))
plt.plot(x,y, marker='o')
#plt.bar(x,y)
plt.title('Student Marks Information')
plt.xlabel('Names')
plt.ylabel('Marks')
plt.show()

Prepare .csv file as following for above example

Output of above example is :

3. Course enrollment details

#Data Visualizing using Python 
import numpy as np 
import matplotlib.pyplot as plt  
# creating the dataset 
data = {'C':20, 'C++':15, 'Java':30, 'Python':35} 
courses = list(data.keys()) 
values = list(data.values()) 
fig = plt.figure(figsize = (5, 2)) 
# creating the bar plot 
plt.bar(courses, values, color ='green', width = 0.5)
plt.xlabel("Courses offered") 
plt.ylabel("No. of students enrolled") 
plt.title("Students enrolled in different courses") 
plt.show() 

Output of above example is :

4. Following example shows BSE [Bombay Stock Exchange] Market performance during – 2019-2020

# BSE market data analysis - 2019-20
import pandas as pd
#pip install plotly
import plotly.express as px
df = pd.read_csv('BSESN.csv')
fig = px.line(df, x = 'Date', y='Value', title='BSE Market Price Analysis 2019-20') #use px.bar, px.scatter 
fig.show()

To download BSESN.csv file, click here or you can google it.

Output of above example is :

5.  The following example[pie chart] shows population records from few Asian countries.

#Data Analysis and Visualization using  pie chart 
import plotly.express as px 
import numpy 
  
# Random Data 
random_x = [1300000000, 1450000000, 120000000, 12000000, 180000000] 
countries= ['India', 'China', 'Japan', 'Bangladesh', 'Pakistan'] 
  
fig = px.pie(values=random_x, names=countries, title='Asian countries population') 
fig.show()

Output of above example is :

6. Now, following example shows India’s  increase in population, life expectancy , GDP / Per capita income etc from 1952 to 2007.

#population chart : lifexp, gdppercapita etc.
import plotly.express as px
data = px.data.gapminder()

data_india = data[data.country == 'India']
fig = px.bar(data_india, x='year', y='pop',
             hover_data=['lifeExp', 'gdpPercap'], color='lifeExp',
             labels={'pop':'population of India'}, height=400)
fig.show()

Output of above example is :

Thanks for scrolling !!!

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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 pd 
import requests 

url = 'https://www.goodreturns.in/gold-rates/bangalore.html'
result = requests.get(url) 
soup = bs4.BeautifulSoup(result.content,'html.parser') 
price = soup.find_all('div', {"id":"current-price"})
for pr in price:
    print("Price:", pr.text)
txt=pr.text

from tkinter import *
root=Tk()
root.title('Gold Price')
root.configure(bg='gold')
root.geometry('200x100')
w=Label(root, text=txt, bg='red', font='sans 14')
b=Button(root, text="Close", fg='red', bg='yellow', command=root.destroy)
w.pack()
b.pack()
root.mainloop()

Output of the above example is:

Click here for other programs on Data Science 

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Data science: is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from ocean of data.

Web scraping can be a solution to speed up the data collection process.
Instead of looking at the job site every day, you can use Python to help automate the repetitive parts of your job search.

1. Web scraping example using Python [extract title, head and h1 text from a website]

from bs4 import BeautifulSoup
import requests
url = "https://flipkart.com"
req = requests.get(url)
soup = BeautifulSoup(req.text, "lxml") #lxml=html/xml html.parser
print(soup.title)
print(soup.head)
print(soup.h1)

file1=open('test.txt', 'w')
file1.write(str(soup.h1))
file1.close()

Output of the above program is :

2. Web scrap example [check web scraping is allowed or not, if status code other than 200 then site is not allowed for scraping!]

import requests
from bs4 import BeautifulSoup 
r=requests.get("https://www.amazon.in")
print(r.status_code)

output:

200

3. Retrieve live Covid-19 data using web scraping and store in .csv format locally

import bs4 
import pandas as pd 
import requests 
url = 'https://www.worldometers.info/coronavirus/country/india/'
result = requests.get(url) 
soup = bs4.BeautifulSoup(result.text,'lxml') 
#search for maincounter-number class 
cases = soup.find_all('div' ,class_= 'maincounter-number') 
# to store data
data = [] 
for i in cases: 
  span = i.find('span') 
  data.append(span.string)
print('Cases', '\tDeaths', '\tRecovered')
print(data, end='\t') 

#dataframe  to visualize
df = pd.DataFrame({"CoronaData": data}) 
#creating coloumns 
df.index = ['TotalCases', ' Deaths', 'Recovered'] 
# storing into Excel 
df.to_csv('Corona_Data.csv')

Output of the above code is :

[ Cases       /     Deaths / Recovered ]
[‘9,857,380 ‘, ‘143,055’, ‘9,357,464’]

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