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I have a use case where:

Data is of the form: Col1, Col2, Col3 and Timestamp.

Now, I just want to get the counts of the rows vs Timestamp Bins.

i.e. for every half hour bucket (even the ones which have no correponding rows), I need the counts of how many rows are there.

Timestamps are spread over a one year period, so I can't divide it into 24 buckets.

I have to bin them at 30 minutes interval.

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19

groupby via pd.Grouper

# optionally, if needed
# df['Timestamp'] = pd.to_datetime(df['Timestamp'], errors='coerce')  
df.groupby(pd.Grouper(key='Timestamp', freq='30min')).count()

resample

df.set_index('Timestamp').resample('30min').count()
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2 Comments

@COLDSPEED thanks a lot! it works! what does errors=coerce do? And one more question: resample does it sample all the rows?
@davidnadal it will convert invalid datetime strings to NaT (instead of throwing parser errors). Resample will sample all rows.

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