UNDERSTANDING OPEN (real world) DATA SETS of Chicago and connecting sql to jupyter .
To complete the project in this notebook we will be using three datasets that are available on the city of Chicago's Data Portal:
Socioeconomic Indicators in Chicago:- https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2
Chicago Public Schools :- https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t
Chicago Crime Data :- https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
I used SQLite3 as a library to connect SQL to Jupyter Notebooks for the following reasons:
Lightweight and Easy to Use: Minimal setup required, perfect for quick development. File-Based Storage: Simplifies data management and sharing as a single file. Seamless Integration: The sqlite3 library allows straightforward SQL commands execution within Jupyter. No Configuration Needed: No complex setup, enabling a smooth development experience. Ideal for Prototyping: Great for testing and developing applications before scaling.