nikfio opened a new issue, #41049:
URL: https://github.com/apache/arrow/issues/41049
### Describe the enhancement requested
Hello everyone,
I was looking for a way to perform group_by on Table on equally spaced
intervals of data specified by a freq value to apply on the key column passed
as group_by first input described as 'Name of the grouped columns'.
On pandas can be done like this (most compact):
`
df = dataframe.resample('timestamp').interpolate(method='nearest')
`
On polars can be done like this (it would be the most similar option for
pyarrow Table group_by):
`
dataframe.group_by_dynamic(
timestamp,
every=tf).agg(col('value').first().alias('first')
)
`
Does someone have any suggestion?
I think is a needed functionality, Table should have this feature.
To implement this feature, I was thinking about :
1. partitioning the Table in equale parts following a specified `freq` input
value like polars has
2. execute the group_by on each partition
3. concatenate the partitions in one final Table
What do you think about it?
How could I do the point 1?
Thanks,
Nick
### Component(s)
Python
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