Pandas Time Series
Learn how Pandas handles dates and times using a light vehicle sales dataset from FRED.
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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
