BiharElection2020/Election.py at main · DipadityaDas/BiharElection2020 · GitHub

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from bs4 import BeautifulSoup
import requests
import pandas as pd
import matplotlib.pyplot as plt

main_logo = """  ____  _ _                  _____ _           _   _
 | __ )(_) |__   __ _ _ __  | ____| | ___  ___| |_(_) ___  _ __
 |  _ \| | '_ \ / _` | '__| |  _| | |/ _ \/ __| __| |/ _ \| '_ \
 | |_) | | | | | (_| | |    | |___| |  __/ (__| |_| | (_) | | | |
 |____/|_|_| |_|\__,_|_|    |_____|_|\___|\___|\__|_|\___/|_| | |
"""

# Official Website of Election Commission of India
url = "https://results.eci.gov.in/ACTRENDS2020/partywiseresult-S04.htm"

# Make a GET request to fetch the raw HTML content
htm_content = requests.get(url).text

# Parse the html content
soup = BeautifulSoup(htm_content, "html.parser")

# print the parsed data of html
# print(soup.prettify())

# Scaning the Entire HTML and finding <table border="1"></table> and returns the result in HTML format
BE_table = soup.find("table", attrs={"border": "1"})

# Scaning all the tr tags inside the table tag
BE_table_data = BE_table.tbody.find_all("tr")

data = []
row = []

# Fetching all the Heading of the table
for th in BE_table_data[2].find_all("th"):
    row.append(th.text)

data.append(row)

# Fetching all the Party Data (Won, leading and Total)
for x in range(3, 16):
    row = []
    for td in BE_table_data[x].find_all("td"):
        row.append(td.text)
    data.append(row)

# Creating a Pandas DataFrame of all the raw data
raw_data = pd.DataFrame(data[1:15], columns=data[0])
raw_data.set_index(pd.Index(['AIMIM', 'BSP', 'BJP', 'CPI', 'CPI(M)', 'CPI(ML)',
                             'HAMS', 'IND', 'INC', 'JD(U)', 'LJSP', 'RJD', 'VSIP']), inplace=True)

# Creating another Pandas DataFrame df without Total
df = pd.DataFrame(data[1:14], columns=data[0])
df.set_index(pd.Index(['AIMIM', 'BSP', 'BJP', 'CPI', 'CPI(M)', 'CPI(ML)',
                       'HAMS', 'IND', 'INC', 'JD(U)', 'LJSP', 'RJD', 'VSIP']), inplace=True)

# Creating a Dataframe for Mahagatbandhan
Mahagatbandhan = df.loc[["CPI", "CPI(M)", "CPI(ML)", "INC", "RJD"], :]
Mahagatbandhan['Won'] = Mahagatbandhan['Won'].astype('int')
Mahagatbandhan['Leading'] = Mahagatbandhan['Leading'].astype('int')
Mahagatbandhan['Total'] = Mahagatbandhan['Total'].astype('int')

# Creating a Dataframe for NDA
NDA = df.loc[['BJP', 'HAMS', 'JD(U)', 'VSIP'], :]
NDA['Won'] = NDA['Won'].astype('int')
NDA['Leading'] = NDA['Leading'].astype('int')
NDA['Total'] = NDA['Total'].astype('int')

# Printing the logo and raw_data
print(main_logo, "="*90, raw_data, sep="\n")

# Printing the Results of MahaMahagatbandhan and NDA
print("{i}\n{x}\n{i}\n{y}\n{i}".format(i="-"*90, x=Mahagatbandhan, y=NDA))

# Printing the Results
print("Mahagatbandhan  --> Won: {},  Leading: {} and Total: {}\n\nNDA             --> Won: {},  Leading: {} and Total: {}".format(
    Mahagatbandhan.Won.sum(), Mahagatbandhan.Leading.sum(), Mahagatbandhan.Total.sum(), NDA.Won.sum(), NDA.Leading.sum(), NDA.Total.sum()))

# Creating a PIE Chart
col = ['#C8FD36', '#000078', '#FF6600', '#FF3366', '#B5307C', '#F538D0',
       '#B26A98', '#AA0078', '#117700', '#003366', '#0093DD', '#008000', '#8391AB']
plt.pie(df['Total'], labels=df.index, radius=1.5, colors=col)
plt.show()