Created
November 20, 2020 20:32
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from bs4 import BeautifulSoup | |
import requests | |
import pandas as pd | |
import numpy as np | |
import re | |
def get_ncdc_data(): | |
""" | |
This function extracts data from the NCDC website and stores it as a pandas dataframe' | |
""" | |
PAGE_URL = "https://covid19.ncdc.gov.ng/" | |
# Response Data | |
response_data = requests.get(PAGE_URL).text | |
# Initializing the BeautifulSoup package and the specifying the parser | |
soup = BeautifulSoup(response_data, 'lxml') | |
content_table = soup.find("table", id="custom1") | |
# Extracting the Table header names | |
table_headers = content_table.thead.findAll("tr") | |
for k in range(len(table_headers)): | |
data = table_headers[k].find_all("th") | |
column_names = [j.string.strip() for j in data] | |
# Extracting the data in the Table's body (values) | |
table_data = content_table.tbody.findAll('tr') | |
values = [] | |
keys = [] | |
data_dict = {} | |
for k in range(len(table_data)): | |
key = table_data[k].find_all("td")[0].string.strip() | |
value = [j.string.strip() for j in table_data[k].find_all("td")] | |
keys.append(key) | |
values.append(value) | |
data_dict[key] = value | |
#Convert dictionary to dataframe | |
data = pd.DataFrame(data_dict).T | |
data.columns = ['state', 'confirmed', 'active', 'recovered', 'deaths'] | |
data = data.sort_values(by = 'state', ignore_index=True) | |
data = data.reset_index(drop=True) | |
#Removing the commas ( , ) between the numbers e.g 6,239 | |
data['confirmed'] = data['confirmed'].apply(lambda x: int(re.sub("[^0-9]", "", x)) ) | |
data['active'] = data['active'].apply(lambda x: int(re.sub("[^0-9]", "", x)) ) | |
data['recovered'] = data['recovered'].apply(lambda x: int(re.sub("[^0-9]", "", x)) ) | |
data['deaths'] = data['deaths'].apply(lambda x: int(re.sub("[^0-9]", "", x)) ) | |
#join States coordinate for map plot | |
return data |
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