Learn how Pandas handles dates and times using a vehicle sales dataset.
In this project, you will learn how Pandas handles dates and times by working with a light vehicle sales dataset from FRED (Federal Reserve Economic Data). You will not build any model; the focus is entirely on understanding time series data structures and the Pandas tools that come with them.
Load the vehicle sales CSV from FRED into a Pandas DataFrame
Parse the date column and set it as the index
Create a date range using pd.date_range() and understand frequency strings ( D , W , M )
Resample the data to monthly and quarterly totals
Shift the series by one period and compute period-over-period differences
Plot the original series and the rolling mean on the same chart
You will understand how DatetimeIndex works, how to use .resample() , .shift() , .rolling() , and .diff() , and why differencing is useful when working with financial or sales data. This project prepares you to work on any time series problem.
A full walkthrough of this project, including different tools to conduct time series in Python, is available on Towards Data Science: 🔗 Time Series From Scratch — Introduction with Pandas