python - Indexing Pandas data frames: integer rows, named columns

Question

Say df is a pandas dataframe.

• df.loc[] only accepts names
• df.iloc[] only accepts integers (actual placements)
• df.ix[] accepts both names and integers:

When referencing rows, df.ix[row_idx, ] only wants to be given names. e.g.

df = pd.DataFrame({'a' : ['one', 'two', 'three','four', 'five', 'six'],
                   '1' : np.arange(6)})
df = df.ix[2:6]
print(df)

   1      a
2  2  three
3  3   four
4  4   five
5  5    six

df.ix[0, 'a']

throws an error, it doesn't give return 'two'.

When referencing columns, iloc is prefers integers, not names. e.g.

df.ix[2, 1]

returns 'three', not 2. (Although df.idx[2, '1'] does return 2).

Oddly, I'd like the exact opposite functionality. Usually my column names are very meaningful, so in my code I reference them directly. But due to a lot of observation cleaning, the row names in my pandas data frames don't usually correspond to range(len(df)).

I realize I can use:

df.iloc[0].loc['a'] # returns three

But it seems ugly! Does anyone know of a better way to do this, so that the code would look like this?

df.foo[0, 'a'] # returns three

In fact, is it possible to add on my own new method to pandas.core.frame.DataFrames, so e.g. df.idx(rows, cols) is in fact df.iloc[rows].loc[cols]?

Answer 1

It's a late answer, but @unutbu's comment is still valid and a great solution to this problem.

To index a DataFrame with integer rows and named columns (labeled columns):

df.loc[df.index[#], 'NAME'] where # is a valid integer index and NAME is the name of the column.