In this article, we’ll be going through some examples of resampling time-series data using Pandas resample() function. We will cover the following common problems and should help you get started with time-series data manipulation.

Time-series data is common in data science projects. Often, you may be interested in resampling your time-series data into the frequency that you want to analyze data or draw additional insights from data [1].

In this article, we’ll be going through some examples of resampling time-series data using Pandas `resample()`

function. We will cover the following common problems and should help you get started with time-series data manipulation.

- Downsampling and performing aggregation
- Downsampling with a custom base
- Upsampling and filling values
- A practical example

Downsampling is to resample a time-series dataset to a wider time frame. For example, from minutes to hours, from days to years. The result will have a reduced number of rows and values can be aggregated with `mean()`

, `min()`

, `max()`

, `sum()`

etc...

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.