Github user citoubest commented on the issue:
https://github.com/apache/spark/pull/15135
@davies, what do you think about this patch? Can you give me some advice?
Thanks
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Github user citoubest commented on the issue:
https://github.com/apache/spark/pull/15135
with pandas, the param for agg is the function not a str (function names).
In [13]: df
Out[13]:
a b c d
0 0.068300 0.263883 0.237335 1
1 0.226992
Github user rxin commented on the issue:
https://github.com/apache/spark/pull/15135
Pandas doesn't support this, does it?
```
>>> pd.read_csv('test.csv').groupby('a').agg('sum', 'avg')
Traceback (most recent call last):
File "", line 1, in
File "/Library/Py
Github user citoubest commented on the issue:
https://github.com/apache/spark/pull/15135
OK, because pandas dataframe support the added approach to agg, so I
suppose maybe spark dataframe should support, but it not. So I have tried to
add this patch. If you think this patch is not ne
Github user rxin commented on the issue:
https://github.com/apache/spark/pull/15135
I understand the reasons why you want to add this -- but I feel this is too
esoteric and if we add this one, there are also a lot of other cases that can
be added and I don't know where we would stop.
Github user citoubest commented on the issue:
https://github.com/apache/spark/pull/15135
@rxin @davies @srowen
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Github user citoubest commented on the issue:
https://github.com/apache/spark/pull/15135
@petermaxlee
In my opinion, list comprehension can reduce code length to some extent.
It's better if the agg method can support the easy way in api level.
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Github user petermaxlee commented on the issue:
https://github.com/apache/spark/pull/15135
Isn't it as simple as
```
cols = [x for x in df.columns if x != "key]
df.groupby("key").agg([F.min(x) for x in cols] + [F.max(x) for x in cols])
```
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Github user AmplabJenkins commented on the issue:
https://github.com/apache/spark/pull/15135
Can one of the admins verify this patch?
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