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https://issues.apache.org/jira/browse/ARROW-15474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17623355#comment-17623355
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Jacek Pliszka edited comment on ARROW-15474 at 10/24/22 7:25 PM:
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Lance - the code you have posted might not be very efficient - something like
below should be faster:
1. add column with sequential number - index
{code:python}
import numpy as np
import pyarrow.compute as pc
t1=t.append_column('i', pa.array(np.arange(len(t))))
{code}
2. Find first row indices
{code:python}
t2 = t1.group_by(['keys', 'values']).aggregate([('i', 'min')]).column('i_min')
{code}
3. select rows with first row indices:
{code:python}
t.filter(pc.is_in(t1.column('i'), t2))
{code}
On my PC your code is 1.19s while code above is 0.17s.
to_pandas.drop_duplicates was around 0.36s
was (Author: jacek.pliszka):
Lance - the code you have posted might not be very efficient - something like
below should be faster:
1. add column with sequential number - index
{code:python}
import numpy as np
import pyarrow.compute as pc
t1=t.append_column('i', pa.array(np.arange(len(t))))
{code}
2. Find first row indices
{code:python}
t2 = t1.group_by(['keys', 'values']).aggregate([('i', 'min')]).column('i_min')
{code}
3. select rows with first row indices:
{code:python}
t.filter(pc.is_in(t1.column('i'), t2))
{code}
On my PC your code is 1.19s while code above is 0.25s.
to_pandas.drop_duplicates was around .36s
> [Python] Possibility of a table.drop_duplicates() function?
> -----------------------------------------------------------
>
> Key: ARROW-15474
> URL: https://issues.apache.org/jira/browse/ARROW-15474
> Project: Apache Arrow
> Issue Type: Wish
> Components: Python
> Affects Versions: 6.0.1
> Reporter: Lance Dacey
> Priority: Major
>
> I noticed that there is a group_by() and sort_by() function in the 7.0.0
> branch. Is it possible to include a drop_duplicates() function as well?
> ||id||updated_at||
> |1|2022-01-01 04:23:57|
> |2|2022-01-01 07:19:21|
> |2|2022-01-10 22:14:01|
> Something like this which would return a table without the second row in the
> example above would be great.
> I usually am reading an append-only dataset and then I need to report on
> latest version of each row. To drop duplicates, I am temporarily converting
> the append-only table to a pandas DataFrame, and then I convert it back to a
> table and save a separate "latest-version" dataset.
> {code:python}
> table.sort_by(sorting=[("id", "ascending"), ("updated_at",
> "ascending")]).drop_duplicates(subset=["id"] keep="last")
> {code}
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