[Pandas-dev] Help replacing workflows that used DataFrame.select

[Paul Hobson]

Follow-up question for the whole group:

My recollection is that .loc returns a slice, but .select returns a copy.
Is this correct? Are there any implications of that distinction with long,
chained workflows switching away from .select?

-Paul

On Tue, Nov 28, 2017 at 8:52 AM, Paul Hobson <pmhobson at gmail.com> wrote:

> Joris,
>
> Thanks for the nudge. I didn't understand that the callable could be
> passed the entire dataframe. That's what I needed.
>
> I'll miss the .select() method when it's gone, but it appears my use cases
> are covered.
>
> Cheers,
>
> -Paul
>
> On Tue, Nov 28, 2017 at 2:31 AM, Joris Van den Bossche <
> jorisvandenbossche at gmail.com> wrote:
>
>> Hi Paul,
>>
>> That's a good question. I think you can do it with a lambda function,
>> like this:
>>
>> (data.
>>      ... (full pipeline)
>>      .loc[:, lambda df: complex_fxn_that_selects_a_few_cols(df.columns)]
>> )
>>
>> Does that work?
>>
>> But personally I am not sure if I find this really an usability
>> improvement compared to the select method.
>>
>> Best,
>> Joris
>>
>>
>>
>> 2017-11-28 2:21 GMT+01:00 Paul Hobson <pmhobson at gmail.com>:
>>
>>> Hey folks,
>>>
>>> I noticed that DataFrame.select is now deprecated in favor of
>>> DataFrame.loc[index.map(selector_fxn)]
>>>
>>> PR: https://github.com/pandas-dev/pandas/pull/17633
>>> Issue: https://github.com/pandas-dev/pandas/issues/12401
>>>
>>> I have a lot of work flows that look something like this:
>>>
>>>     res = (
>>>         data.resample(freq)
>>>             .agg(agg_dict)
>>>             .pipe(fxn_that_adds_many_cols)
>>>             .select(complex_fxn_that_selects_a_few_cols, axis='columns')
>>>     )
>>>
>>> It's not immediately clear to me how to access all of the e.g., columns
>>> in the middle or at the end of a chain of dataframe operations.
>>>
>>> Any tips?
>>>
>>> -Paul
>>>
>>> _______________________________________________
>>> Pandas-dev mailing list
>>> Pandas-dev at python.org
>>> https://mail.python.org/mailman/listinfo/pandas-dev
>>>
>>>
>>
>
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