How to find duplicate based upon multiple columns in a rolling window in pandas?

Question

Sample Data

{\"transaction\": {\"merchant\": \"merchantA\", \"amount\": 20, \"time\": \"2019-02-13T10:00:00.000Z\"}}
{\"transaction\": {\"me         


        

Answer 1

First, you could form rolling 120 second blocs of data. You could then apply;

block and evaluate using duplicated: df = df[df.duplicated(subset=['val1','val2',’val3’], keep=False)]

Or groupby: df.groupby(['val1','val2',’val3’]).count()

Or even a SQL distinct. https://www.w3schools.com/sql/sql_distinct.asp

Please post what you have tried. The above methods work for strings, floats, datetimes and integer data types.