Retail Sales Data

The Project

This project explores 12 months of sales data at an electronic retail store. The dataset contains thousands of transactions broken down by date, time, product type, cost, purchase address, etc.

The data is contained within a series of flat files and is imported into a local Jupyter Notebook environment running on a Python kernel.

What We Learned

• Concatenating multiple CSV files together using pdf.concat()

• Creating new columns with calculations performed on existing columns (feature engineering)

• Parsing cells as strings to make new columns using the .str() method

• Using the .apply() method

The Code and the Report

• GitHub repository for the data can be found here

• the report can also be read here

References

• https://stackoverflow.com/questions/27298178/concatenate-strings-from-several-rows-using-pandas-groupby

• https://stackoverflow.com/questions/52195887/counting-unique-pairs-of-numbers-into-a-python-dictionary