Pandas Time Series

Project Description

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.

Project Requirements

• 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

Technologies to Use

• Python

• Pandas

• Matplotlib

• Jupyter Notebook

What You Will Learn

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.

Want to See a Solution?

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