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Description
Problem description
The existing doc for groupby().first() (https://pandas-docs.github.io/pandas-docs-travis/reference/api/pandas.core.groupby.GroupBy.first.html?highlight=first#pandas.core.groupby.GroupBy.first) does not describe the behavior with respect to missing data. In particular, it does not mention the fact that the behavior is broadcasting columnwise.
The docs read: "Compute first of group values...Computed first of values within each group." I think the correct description is "For each column, compute the first non-null entry, possibly aggregating values from across multiple rows." We might also want a simple example to explain the behavior.
Code Sample, a copy-pastable example if possible
import pandas as pd
x = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3]))
print(x.groupby("A", as_index=False).first())
print(x.groupby("A", as_index=False).nth(0))
print(x.groupby("A", as_index=False).head(1))
[...]
A B C
0 1 5.0 1
1 3 6.0 3
A B C
0 1 NaN 1
2 3 6.0 3
A B C
0 1 NaN 1
2 3 6.0 3