["python","pandas","dataframe"]
Indexing Pandas data frames: integer rows, named columns

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]?
A very late answer but it amazed me that pandas still doesn't have such a function after all these years. If it irks you a lot, you can monkey-patch a custom indexer into the DataFrame: class XLocIndexer: def __init__(self, frame): self.frame = frame def __getitem__(self, key): row, col = key return self.frame.iloc[row][col] pd.core.indexing.IndexingMixin.xloc = property(lambda frame: XLocIndexer(frame)) # Usage df.xloc[0, 'a'] # one