This tutorial explains how to identify date gaps in time series data with pyrasgo.
This tutorial uses:
Open a new Jupyter Notebook and import the following:
If you haven't done so already, head over to https://docs.rasgoml.com/rasgo-docs/onboarding/initial-setup and follow the steps outlined there to create your free account. This account gives you free access to the Rasgo API which will calculate dataframe profiles, generate feature importance score, and produce feature explainability for you analysis. In addition, this account allows you to maintain access to your analysis and share with your colleagues.
We will create a dataframe that contains multiple time series, one for each group.
Your dataframe should look like:
Next, drop some rows randomly to create gaps in the data.
The function evaluate.timeseries_gaps will identify date gaps in the data.
That should return something like:
Passing the series identifier (group in this case) into evaluate.timeseries_gaps using the partition_columns parameter checks for date gaps in each of the series independently.
Your dataframe should look like this: