Data Case Study

By David Trakhtenberg

1. Install sodapy dependency using pip install sodapy
2. Run food_inspections_etl.py
3. Run test_food_inspections_etl.py to unit test
4. Tables/files will be output to Food Inspections Data folder in the current working directory
5. Can also view line by line input/output in Food Inspections Notebook.ipynb
6. Use the transformed data to analyze away!

• I used the following technologies: Github, Jupyter Notebook, and Python 3.6.8.
• I used the following Python libraries: Sodapy, numpy, and pandas.

Use a language of your choice to develop a script that reads the dataset and pushes an output to separate csv files Dataset: link

Create 2 output tables such that:

1. Table A includes a list of all inspections
2. Table B includes a list of all violations and comments associated with each inspection

I used Python to extract the dataset, transform it with several steps of logic, and load it into three output csv files. I also created a unit test script to validate a few parts of the ETL code.

• food_inspections_etl.py
• test_food_inspections_etl.py
• Food Inspections Notebook.ipynb
• README.md