City-data

This app allows the user to get data on cities by consuming openweathermap.org API and webscraping from wikipedia. You enter a city and state and then the openweathermap.org api searches for the temperature. If the input is valid and a temperature is returned, the program generates a wikipedia url. If the url is valid and has enough structure for scraping with BeautifulSoup, the program scrapes the information from the page and shows you some data.
A GUI flask app (flask_app.py) and command line app (main.py) are available and both have the ability to show a graph of population density. Not all cities have the same information and wikipedia recently changed some of the structure of its city webpages - this results in some blank information.. The data is exported into a csv (for each session) and also a SQLite database for data persistence across sessions. You can read, update, and delete the entries from within the CLI. The program works best for American cities.

While running flask_app.py, an api is also running which accepts a single city/state. So, for example in Postman you could run

http://127.0.0.1:5000/api/city, state

which would return the city data for that particular city.

The flask app and CLI utilize the same order of operations (order.py) for their similar features.

Instructions:

Clone the repo
Create an api.py file in the main directory and copy and paste the openweathermap.org variable (ex: ' OPEN_WEATHER_API = "abc123" ') that is given to you into the file and save.
pip install -r requirements.txt
Run main.py for a command line program

or

run flask_app.py for GUI and to run batches with some autosuggestions. (Browser should automatically open to http://localhost:5000/). While flask_app.py is running, it will accept a GET request to http://localhost:5000/api/\<city, state> and will return a JSON object of the city's information.

Flask App: flask_app.py allows for entry and lookup of up to 20 cities in a webform at a time. Autosuggestions are provided for cities with high population levels in the US via javascript. Obvious duplicates are excluded from results. A graph (via bokeh) is provided for population density if that information is available. A city_data.csv link is provided for download with the information just obtained about the different cities in that session. Each city and resulting information is also placed in CITY.db. This SQLite db can be edited in the CLI mode.

Example of flask_app.py (click image for better quality gif)

Running flask_app.py also opens up a GET route for an API and returns a JSON object (see below). This data is also stored in the sqlite database for viewing and editing with the CLI.

**Example of Postman response to GET request **

CLI: main.py allows for individual entry of cities. Each city that returns valid information will be added to city_data.csv (obvious duplicates are excluded) and CITY.db . The city_data.csv is created for each new session. The CITY.db persists across sessions and also includes cities obtained from the flask app and API GET requests. In order to view the db, delete records, edit records, and export to a csv, at least one valid city most be entered via the CLI. One reason to edit a record could be to add "Demonym" information for a city in which wikipedia doesn't have this info. A graph (via matplotlib) is possible for population density.

Example of main.py (click image for better quality gif)