GitHub user citoubest opened a pull request:
https://github.com/apache/spark/pull/15135
[pyspark][group]pyspark GroupedData can't apply agg functions on all left
numeric columns.
## What changes were proposed in this pull request?
With pyspark dataframe, the agg method
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 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
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
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
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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