SeriesGroupBy.first / last loses categorical dtype

On 1.0.3 and master:

import pandas as pd

df = pd.DataFrame({"a": [1, 2, 3]})
df["b"] = df["a"].astype("category")

print(df.groupby("a")["b"].first())
print(df.groupby("a")["b"].last())

gives

a
1    1
2    2
3    3
Name: b, dtype: int64
a
1    1
2    2
3    3
Name: b, dtype: int64

but the dtype should still be categorical and not int64. This seemingly wrong output is explicitly tested for here: https://github.com/pandas-dev/pandas/blob/master/pandas/tests/groupby/aggregate/test_aggregate.py#L461

[Puumanamana]

Not sure if I should create a new issue or not, but I also wanted to point out some inconsistencies regarding this bug. When using agg(), the output dtype changes depending on whether we use a dictionary notation or not (see below):

import pandas as pd # v1.0.4

# Same example data as above
df = pd.DataFrame({"a": [1, 2, 3]})
df["b"] = df["a"].astype("category")

print(df.groupby("a").agg("first").b) # Categorical dtype
print(df.groupby("a").agg({"b": "first"}).b) # int64 dtype

gives

a
1    1
2    2
3    3
Name: b, dtype: category
Categories (3, int64): [1, 2, 3]

a
1    1
2    2
3    3
Name: b, dtype: int64

[jbrockmendel]

Looks like this is fixed on master. could use bisection/test