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I want to simply reverse the column order of a given DataFrame.
My DataFrame:
data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012], 'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions', 'Lions', 'Lions'], 'wins': [11, 8, 10, 15, 11, 6, 10, 4], 'losses': [5, 8, 6, 1, 5, 10, 6, 12]} football = pd.DataFrame(data, columns=['year', 'team', 'wins', 'losses']) Actual output:
year team wins losses 0 2010 Bears 11 5 1 2011 Bears 8 8 2 2012 Bears 10 6 3 2011 Packers 15 1 4 2012 Packers 11 5 5 2010 Lions 6 10 6 2011 Lions 10 6 7 2012 Lions 4 12 I thought this would work but it reverses the row order not column order:
football[::-1] I also tried:
football.columns = football.columns[::-1] but that reversed the column labels and not the entire column itself.
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A Python list has a reverse function. The [::-1] slice operation to reverse a Python sequence. The reversed built-in function returns a reverse iterator.
Use the T attribute or the transpose() method to swap (= transpose) the rows and columns of pandas. DataFrame . Neither method changes the original object but returns a new object with the rows and columns swapped (= transposed object).
We can sort it by using the dataframe. sort_index() function. Alternatively, you can sort the index in descending order by passing in the ascending=False the argument in the function above.
A solution close to what you have already tried is to use:
>>> football[football.columns[::-1]] losses wins team year 0 5 11 Bears 2010 1 8 8 Bears 2011 2 6 10 Bears 2012 3 1 15 Packers 2011 4 5 11 Packers 2012 5 10 6 Lions 2010 6 6 10 Lions 2011 7 12 4 Lions 2012 football.columns[::-1] reverses the order of the DataFrame's sequence of columns, and football[...] reindexes the DataFrame using this new sequence.
A more succinct way to achieve the same thing is with the iloc indexer:
football.iloc[:, ::-1] The first : means "take all rows", the ::-1 means step backwards through the columns.
The loc indexer mentioned in @PietroBattiston's answer works in the same way.
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Note: As of Pandas v0.20, .ix indexer is deprecated in favour of .iloc / .loc.
Close to EdChum's answer... but faster:
In [3]: %timeit football.ix[::,::-1] 1000 loops, best of 3: 255 µs per loop In [4]: %timeit football.ix[::,football.columns[::-1]] 1000 loops, best of 3: 491 µs per loop Also notice one colon is redundant:
In [5]: all(football.ix[:,::-1] == football.ix[::,::-1]) Out[5]: True EDIT: a further (minimal) improvement is brought by using .loc rather than .ix, as in football.loc[:,::-1].
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