Pandas drop a level from a multi-level column index

Question

If I ve got a multi-level column index    gt  gt  gt  cols   pd MultiIndex from tuples    a    b      a    c      gt  gt  gt  pd DataFrame   1 2    3 4    columns cols         a    --- --     b   c -- --- -- 0   1   2 1   3   4   How can I drop the  a  level of that index  so I end up with        b   c -- --- -- 0   1   2 1   3   4

User · Answer

You can use MultiIndex droplevel    gt  gt  gt  cols   pd MultiIndex from tuples    a    b      a    c      gt  gt  gt  df   pd DataFrame   1 2    3 4    columns cols   gt  gt  gt  df    a       b  c 0  1  2 1  3  4   2 rows x 2 columns   gt  gt  gt  df columns   df columns droplevel    gt  gt  gt  df    b  c 0  1  2 1  3  4   2 rows x 2 columns

User · Answer

As of Pandas 0 24 0  we can now use DataFrame droplevel     cols   pd MultiIndex from tuples    a    b      a    c     df   pd DataFrame   1 2    3 4    columns cols   df droplevel 0  axis 1        b  c  0  1  2  1  3  4   This is very useful if you want to keep your DataFrame method-chain rolling

User · Answer

You could also achieve that by renaming the columns   df columns     a    b    This involves a manual step but could be an option especially if you would eventually rename your data frame

User · Answer

Another way to do this is to reassign df based on a cross section of df  using the  xs method    gt  gt  gt  df      a     b   c 0   1   2 1   3   4   gt  gt  gt  df   df xs  a   axis 1  drop level True          a    key on which to get cross section       axis 1   get cross section of column       drop level True   returns cross section without the multilevel index   gt  gt  gt  df      b   c 0   1   2 1   3   4

User · Answer

A small trick using sum  with level 1 work when level 1 is all unique   df sum level 1 axis 1  Out 202       b  c 0  1  2 1  3  4     More common solution get level values  df columns df columns get level values 1  df Out 206       b  c 0  1  2 1  3  4

User · Answer

I have struggled with this problem since I don   t know why my droplevel   function does not work  Work through several and learn that    a    in your table is columns name and    b        c    are index  Do like this will help  df columns name   None df reset index    make index become label

User · Answer

Another way to drop the index is to use a list comprehension    df columns    col 1  for col in df columns      b  c 0  1  2 1  3  4   This strategy is also useful if you want to combine the names from both levels like in the example below where the bottom level contains two  y s   cols   pd MultiIndex from tuples    A    x      A    y      B    y     df   pd DataFrame   1 2  8     3 4  9    columns cols      A     B    x  y  y 0  1  2  8 1  3  4  9   Dropping the top level would leave two columns with the index  y   That can be avoided by joining the names with the list comprehension   df columns        join col  for col in df columns       A x A y B y 0   1   2   8 1   3   4   9   That s a problem I had after doing a groupby and it took a while to find this other question that solved it  I adapted that solution to the specific case here

[python] Pandas: drop a level from a multi-level column index?

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