This repo contains script for demonstrating a simple ETL data pipeline. Starting from extracting data from the source, transforming into a desired format, and loading into a SQLite file.
Then, perform simple analysis queries on the stored data. See: analysis notebook.
Note: The used data is about the US population and unemployment rate over the past decade.
First, install the dependencies
$ pip install -r requirements.txt
Note: preferable, run under a virtualenv
$ virtualenv .env -p python3 $ source .env/bin/activate $ pip install -r requirements.txt
Second, run the main pipeline file
$ python pipeline.py
Data Pipeline created
extracting data from source ....
formatting and transforming data ...
loading into database ...
Done. See: result in "db.sqlite"
- pandas
- xlrd (for reading excel file)
- Python >= 3.6
POPULATION BY METROPOLITAN AREA AND COUNTY
- cbsa-est2017-alldata.csv
- data source: https://www.census.gov/data/tables/2017/demo/popest/total-metro-and-micro-statistical-areas.html#par_textimage
- download link: https://www2.census.gov/programs-surveys/popest/datasets/2010-2017/metro/totals/cbsa-est2017-alldata.csv
- dataset description: https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2017/cbsa-est2017-alldata.pdf
UNEMPLOYMENT BY COUNTY
- Unemployment.xls
- data source: https://catalog.data.gov/dataset/county-level-data-sets https://www.ers.usda.gov/data-products/county-level-data-sets/download-data
- download link: https://www.ers.usda.gov/webdocs/DataFiles/48747/Unemployment.xls
- description:
see the sheet "Variable Descriptions" in the same
xlsfile