Visualization of Coronavirus(COVID-19) Confirm Cases in India on Map

Coronavirus: The first case of the COVID-19 pandemic in India was reported on 30 January 2020, beginning from China. The infection rate of COVID-19 in India is reported to be 1.7 percent, significantly lower than in the worst affected countries.

In this article, we will see the spread of covid19 cases in India concerning states/UTs.

This is a small project I have done using python and ML @jupyter notebook

Geopanadas: GeoPandas is an open-source project to make working with geospatial data in python easier.

BeautifulSoup: Extracting the data (Basically Html Files) of any website is called web scraping. This saves us a lot of time because we get a lot of data (a huge amount of data) in a structured form.

PRACTICAL IMPLEMENTATION

#Importing libraries

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

import requests

from bs4 import BeautifulSoup

import geopandas as gpd

#this is just importing the required libraries which is required .

#reading Indian shapely file

data_map = gpd.read_file(‘Indian_States.shp’)

print(data_map.head())

#printing 25 number state

data_map[‘st_nm’].iloc[25]

st_nm                                           geometry
0  Andaman & Nicobar Island  MULTIPOLYGON (((93.71976 7.20707, 93.71909 7.2...
1        Arunanchal Pradesh  POLYGON ((96.16261 29.38078, 96.16860 29.37432...
2                     Assam  MULTIPOLYGON (((89.74323 26.30362, 89.74290 26...
3                     Bihar  MULTIPOLYGON (((84.50720 24.26323, 84.50355 24...
4                Chandigarh  POLYGON ((76.84147 30.75996, 76.83599 30.73623...

Out[31]:

'Punjab'

#Extracting data from Indian govt. site(web scrapping)

url = ‘https://www.mohfw.gov.in/'

#Request to extract data from site

web_content = requests.get(url).content

#applying html parser to parse html content

soup = BeautifulSoup(web_content, “html.parser”)

def extract_contents(row):
return [x.text.replace(‘\n’, ‘’) for x in row]

stats = []

all_rows = soup.find_all(‘tr’)

#print(all_rows)

for row in all_rows:

stat = extract_contents(row.find_all(‘td’))

if len(stat) == 6:

stats.append(stat)

#Converting html data into dataframe

new_cols = [“S. No.”, “Name of State / UT”,”Active Cases*”,”Cured/Discharged/Migrated*”,”Deaths**”,”Total Confirmed cases*”]

state_data = pd.DataFrame(data = stats, columns = new_cols)

state_data.head()

import matplotlib.pyplot as plt #matplotlib are used to deal with (graphs)
plt.figure(figsize=(28,15))
plt.barh(state_data[“Name of State / UT”],state_data[‘Active Cases*’],color=’red’)
plt.xticks(fontsize=25)
plt.yticks(fontsize=25)
plt.show()

#Correction required to match names in both datasets

data_map.rename(columns = {‘st_nm’:’Name of State / UT’}, inplace = True)

data_map[‘Name of State / UT’] =data_map[‘Name of State / UT’].str.replace(‘&’,’and’)

data_map[‘Name of State / UT’].iloc[0] = ‘Andaman and Nicobar Islands’

data_map[‘Name of State / UT’].iloc[1] = ‘Arunachal Pradesh’

data_map[‘Name of State / UT’].iloc[6] = ‘Dadar Nagar Haveli’

data_map[‘Name of State / UT’].iloc[23] = ‘Delhi’

data_map[‘Name of State / UT’].iloc[29]=’Telengana’

#Plotting map of The India

data_map.plot()

#Merging both datasets together

merged = pd.merge(data_map, state_data, on = ‘Name of State / UT’)

merged.drop(‘S. No.’, axis = 1, inplace = True)

merged.head()

#plotting coronavirus cases on map

fig, ax = plt.subplots(1, figsize=(20, 12))

ax.axis(‘off’)

#Set the title of the map

ax.set_title(‘Coronavirus Confirm cases in India’, fontsize=25)
merged.plot(column = ‘Active Cases*’,cmap=’plasma’,linewidth=0.8, ax=ax, edgecolor=’0.8',legend = True)

#Showing plot of India with coronavirus cases

plt.show()

GitHub link=> https://github.com/P-riyanka-prasad/Visualization-of-Coronavirus/blob/master/india.ipynb