This project demonstrates the ability to extract, transform, and load (ETL) data for use in data analysis.

Languages and libraries used:

• Python
• Pandas
• SQLAlchemy
• Postgres

Clone the repository to your desktop and do the following:

1. Navigate to the cloned folder and launch a GitBash (Windows) or Terminal (Mac).

2. Type source activate PythonData and hit ENTER.

3. Launch Jupyter Notebook and open the file, etl_project.ipynb. This file is both the Python code and the required Technical Report.

4. Observe the Data Extraction section that shows the 2 CSV files were successfully imported.

5. Observe the Data Cleanup section which shows the data cleanup by removing unwanted columns, renaming columns, and deleting unwanted rows. This results in 2 cleaned dataframes.

6. In the Jupyter Notebook file, create the database connection by inserting username and password into this this code: conn = "<insert user name>:<insert password>@localhost:5432/etl_5". Verify that the connection was made showing two tables.

7. Launch and use pgAdmin4 to open the file, queries.sql to perform the queries on the tables, world_happiness and annual_work_hours .