Pandas selecting by label sometimes return Series, sometimes returns DataFrame

Granted that the behavior is inconsistent, but I think it's easy to imagine cases where this is convenient. Anyway, to get a DataFrame every time, just pass a list to loc. There are other ways, but in my opinion this is the cleanest.

In [2]: type(df.loc[[3]])
Out[2]: pandas.core.frame.DataFrame

In [3]: type(df.loc[[1]])
Out[3]: pandas.core.frame.DataFrame

The TLDR

When using loc

df.loc[:] = Dataframe

df.loc[int] = Dataframe if you have more than one column and Series if you have only 1 column in the dataframe

df.loc[:, ["col_name"]] = Dataframe if you have more than one row and Series if you have only 1 row in the selection

df.loc[:, "col_name"] = Series

Not using loc

df["col_name"] = Series

df[["col_name"]] = Dataframe

You have an index with three index items 3. For this reason df.loc[3] will return a dataframe.

The reason is that you don't specify the column. So df.loc[3] selects three items of all columns (which is column 0), while df.loc[3,0] will return a Series. E.g. df.loc[1:2] also returns a dataframe, because you slice the rows.

Selecting a single row (as df.loc[1]) returns a Series with the column names as the index.

If you want to be sure to always have a DataFrame, you can slice like df.loc[1:1]. Another option is boolean indexing (df.loc[df.index==1]) or the take method (df.take([0]), but this used location not labels!).