DOC:Pandas groupby agg example for k-hot encoding

Location of the documentation

https://pandas.pydata.org/pandas-docs/version/0.22.0/generated/pandas.DataFrame.aggregate.html#pandas.DataFrame.aggregate

[this should provide the location of the documentation, e.g. “pandas.read_csv” or the URL of the documentation, e.g. “https://dev.pandas.io/docs/reference/api/pandas.read_csv.html”]

Note: You can check the latest versions of the docs on master here.

Documentation problem

It is strange that even on stackoverflow reverse explode has only a single answer(that too is asked very recently). So adding a few more examples should not be bad.

[this should provide a description of what documentation you believe needs to be fixed/improved]

Suggested fix for documentation

The following example tells how to perform k-hot encoding with agg.

maximum = df.groupby("ImageId").agg({"ClassId":lambda x:x.tolist()})["ClassId"].max()[0]
df = df.groupby("ImageId").agg({"ClassId":lambda x:x.tolist()})
df["ClassId"] = df["ClassId"].apply(lambda x:[1 if i+1 in x else 0 for i in range(maximum)] )

[this should explain the suggested fix and why it’s better than the existing documentation]

[Teut2711]

Hmm, ok. I already have the dev environment setup so it should not be very difficult to get started.

[Teut2711]

Also can you share the knowledge which function would you use in place of apply for performing that encoding?It would increase my skills.

https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html

I already have seen the docs for get_dummies. I thought you meant some vectorization method to convert [1, 3] to [1, 0, 1, 0]