# How To Resample and Interpolate Your Time Series Data With Python

Last Updated on February 11, 2020

You may have observations at the wrong frequency.

Maybe they are too granular or not granular enough. The Pandas library in Python provides the capability to change the frequency of your time series data.

In this tutorial, you will discover how to use Pandas in Python to both increase and decrease the sampling frequency of time series data.

After completing this tutorial, you will know:

- About time series resampling, the two types of resampling, and the 2 main reasons why you need to use them.
- How to use Pandas to upsample time series data to a higher frequency and interpolate the new observations.
- How to use Pandas to downsample time series data to a lower frequency and summarize the higher frequency observations.

**Kick-start your project** with my new book Time Series Forecasting With Python, including *step-by-step tutorials* and the *Python source code* files for all examples.

Letâ€™s get started.

**Update Dec/2016**: Fixed definitions of upsample and downsample.**Updated Apr/2019**: Updated the link to dataset.