API data extraction

api_1.py

import requests
import pandas as pd
from IPython.display import display

api_2.py

def api_extract(country,n=-1):
    
'''
    Function to extract university information from an API "http://universities.hipolabs.com/search?country="
    param country: Country for which one needs the university data
    param n: Number of records to return. -1 returns all the data
'''
err=0 # initializing an error variable to 0
##################################
#       API REQUEST              #
##################################
    req = requests.get(f"http://universities.hipolabs.com/search?country={country.lower().replace(' ','%20')}") # API request, converting 'country' to lowercase and replacing spaces with %20 and sending the API request
if req.status_code!= 200: # if status code != 200 error variable is assigned to 1
        err = 1

    js = req.json() # Getting the request output in JSON format

    try:    
        if err==1:
            raise Exception() # raises an exception in case of an error
        df = pd.DataFrame()
##################################
#       DATA STRUCTURING         #
##################################
        for i in js[0].keys(): # Structuring the JSON output into a data frame
            df[i] = [j[i] for j in js]
    except:
        return "No Data Found" # Returning no data upon an error
    else:
        if n!=-1:
            return df.head(n) #returns top n records
        else:
            return df # returns the whole data frame if n=-1

api_3.py

# API request for university data in the US
api_extract("United States",10)

api_4.py

##################################
#       API REQUEST              #
##################################
req = requests.get("https://api.covid19api.com/summary") # sending request to API
# req.status_code #Need not check the status code since there is no scope for error unless the API in not functional
js = req.json() # extracting the API result inJSON format 


country_data = pd.DataFrame()
##################################
#       DATA STRUCTURING         #
##################################
for i in range(len(js["Countries"])):
    js["Countries"][i].pop("Premium")
    country_data = country_data.append(pd.DataFrame(js["Countries"][i],index=[i])).copy()

country_data_main = country_data.copy()
display(country_data)