Python pandas resample timeseries

Time series data often comes with irregular intervals or different frequencies. Resampling is a technique used to change the frequency of the time series data to make it more manageable or to align it with a specific frequency. Pandas, a popular data manipulation library in Python, provides a powerful resample method to perform resampling on time series data.
In this tutorial, we will explore how to use the resample method in Pandas to resample time series data. We will cover both upsampling (increasing the frequency) and downsampling (decreasing the frequency) along with different aggregation methods.
Make sure you have Pandas and any other necessary libraries installed. You can install them using the following command:
Let's start by importing the required libraries:
For demonstration purposes, let's create a sample time series DataFrame:
In this tutorial, we explored how to use Pandas for resampling time series data. We covered both downsampling and upsampling, along with different aggregation methods. Resampling is a powerful tool for manipulating time series data and aligning it with the desired frequency. Experiment with different frequencies and aggregation methods to suit the requirements of your specific analysis or visualization.
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