https://twitter.com/CovidTracker22

There are 5 components to this project:

Used Beautiful Soup to obtain information from (https://www.worldometers.info/coronavirus/) about the "Total cases per 1M Population" for the following countries: Hong Kong, Germany, India, USA
Searched for elements with the class attribute of "mt_a" as they represented the country names
Filtered out the countries I was interested in, by looking at the .text attribute
Used find_parent() to get the whole row data of the country, and used find_siblings() to get all statistics
The 8th index of find_siblings() was the "Total cases per 1M Population" information
Stored the information in a dictionary and returned this dictionary in the function

Used Selenium and Chrome webdriver to obtain information from a dynamic website (https://www.incovid19.org/) about total confirmed cases and daily change in cases for the following cities/states: Chennai, Bangalore (Urban), Delhi
WARNING: The chromedriver on this repo is for Linux
Found all relevant elements to click and scrape data from using the full XPath
General process
1.) click on relevant element to sort the states by alphabetical order
2.) click on the state
3.) click on relevant element to sort the districts/cities of the state by alphabetical order
4.) scrape the relevant data from the particular district/city
5.) store this data in a dictionary
Returned the dictionary of total confirmed cases in respective function, and also returned daily change in cases for respective function

Since the values of the dictionaries were still strings, I had to first removed any special characters/punctuation such as commas or up arrows, and then convert the values to integer type. I achieved this by using a list comprehension of the items() of the original dictionary but with my changes, and then creating the new dictionary using dict(). Also used Python context managers during file handling, and json.dump() to "dump" the JSON object in the file.

Used Matplotlib to make graphs, then saved them locally, and returned filepath of the graph image for future use.

Retrieved my twitter developer keys/tokens from local JSON file (not on Github because of Gitignore)
Used Tweepy library and my keys/tokens to make it easier to create the "auth" using the OAuthHandler, set the access_token of auth, and finally create my API
Used the result dictionaries from above web scraping functions to output the relevant data in a tweet by updating status.
Similarly tweeted the matplotlib generated graph images by updating status.

Added decorators to measure the time taken for each particular data scraping function
Applied some Python unit testing on 12th April 2022 for one of the data scraping functions

1.) Deploy the python script using Cron on local Linux virtual machine or on the cloud using AWS Lambda, so I don't have to press run
2.) Add type annotations for better documentation
3.) Break down the large functions into many smaller functions and better variable/function names\