["python","pandas"]
Keep selected column as DataFrame instead of Series

When selecting a single column from a pandas DataFrame(say df.iloc[:, 0], df['A'], or df.A, etc), the resulting vector is automatically converted to a Series instead of a single-column DataFrame. However, I am writing some functions that takes a DataFrame as an input argument. Therefore, I prefer to deal with single-column DataFrame instead of Series so that the function can assume say df.columns is accessible. Right now I have to explicitly convert the Series into a DataFrame by using something like pd.DataFrame(df.iloc[:, 0]). This doesn't seem like the most clean method. Is there a more elegant way to index from a DataFrame directly so that the result is a single-column DataFrame instead of Series?
As @Jeff mentions there are a few ways to do this, but I recommend using loc/iloc to be more explicit (and raise errors early if you're trying something ambiguous): In [10]: df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 'B']) In [11]: df Out[11]: A B 0 1 2 1 3 4 In [12]: df[['A']] In [13]: df[[0]] In [14]: df.loc[:, ['A']] In [15]: df.iloc[:, [0]] Out[12-15]: # they all return the same thing: A 0 1 1 3 The latter two choices remove ambiguity in the case of integer column names (precisely why loc/iloc were created). For example: In [16]: df = pd.DataFrame([[1, 2], [3, 4]], columns=['A', 0]) In [17]: df Out[17]: A 0 0 1 2 1 3 4 In [18]: df[[0]] # ambiguous Out[18]: A 0 1 1 3