data_collection_and_storage

Data:

neighborhoods table:

name: name of the neighborhood neighborhood_id: neighborhood code

cabs table:

• cab_id - vehicle code
• vehicle_id - the vehicle's technical ID
• company_name - the company that owns the vehicle

trips table:

• trip_id - ride code
• cab_id - code of the vehicle operating the ride
• start_ts - date and time of the beginning of the ride (time rounded to the hour)
• end_ts - date and time of the end of the ride (time rounded to the hour)
• duration_seconds - ride duration in seconds
• distance_miles - ride distance in miles
• pickup_location_id - pickup neighborhood code
• dropoff_location_id - dropoff neighborhood code

weather_records table:

• record_id - weather record code
• ts - record date and time (time rounded to the hour)
• temperature - temperature when the record was taken
• description - brief description of weather conditions, e.g. "light rain" or "scattered clouds"

Goal:

You're working as an analyst for Zuber, a new ride-sharing company that's launching in Chicago. Your task is to find patterns in the available information. You want to understand passenger preferences and the impact of external factors on rides. Working with a database, you'll analyze data from competitors and test a hypothesis about the impact of weather on ride frequency.

Libraries used:

requests

BeautifulSoup

MySQL

pandas

matplotlib.pyplot

scipy

pandas_profiling