Pandas - How to flatten a hierarchical index in columns

Question

I have a data frame with a hierarchical index in axis 1 (columns) (from a groupby.agg operation):

     USAF   WBAN  year  month  day  s_PC  s_CL         


        

Answer 1

After reading through all the answers, I came up with this:

def __my_flatten_cols(self, how="_".join, reset_index=True):
    how = (lambda iter: list(iter)[-1]) if how == "last" else how
    self.columns = [how(filter(None, map(str, levels))) for levels in self.columns.values] \
                    if isinstance(self.columns, pd.MultiIndex) else self.columns
    return self.reset_index() if reset_index else self
pd.DataFrame.my_flatten_cols = __my_flatten_cols

Usage:

Given a data frame:

df = pd.DataFrame({"grouper": ["x","x","y","y"], "val1": [0,2,4,6], 2: [1,3,5,7]}, columns=["grouper", "val1", 2])

  grouper  val1  2
0       x     0  1
1       x     2  3
2       y     4  5
3       y     6  7

• Single aggregation method: resulting variables named the same as source:

  df.groupby(by="grouper").agg("min").my_flatten_cols()
  

  • Same as df.groupby(by="grouper", as_index=False) or .agg(...).reset_index()

  • ----- before -----
               val1  2
      grouper         
    
    ------ after -----
      grouper  val1  2
    0       x     0  1
    1       y     4  5
    

• Single source variable, multiple aggregations: resulting variables named after statistics:

  df.groupby(by="grouper").agg({"val1": [min,max]}).my_flatten_cols("last")
  

  • Same as a = df.groupby(..).agg(..); a.columns = a.columns.droplevel(0); a.reset_index().

  • ----- before -----
                val1    
               min max
      grouper         
    
    ------ after -----
      grouper  min  max
    0       x    0    2
    1       y    4    6
    

• Multiple variables, multiple aggregations: resulting variables named (varname)_(statname):

  df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols()
  # you can combine the names in other ways too, e.g. use a different delimiter:
  #df.groupby(by="grouper").agg({"val1": min, 2:[sum, "size"]}).my_flatten_cols(" ".join)
  

  • Runs a.columns = ["_".join(filter(None, map(str, levels))) for levels in a.columns.values] under the hood (since this form of agg() results in MultiIndex on columns).
  • If you don't have the my_flatten_cols helper, it might be easier to type in the solution suggested by @Seigi: a.columns = ["_".join(t).rstrip("_") for t in a.columns.values], which works similarly in this case (but fails if you have numeric labels on columns)
  • To handle the numeric labels on columns, you could use the solution suggested by @jxstanford and @Nolan Conaway (a.columns = ["_".join(tuple(map(str, t))).rstrip("_") for t in a.columns.values]), but I don't understand why the tuple() call is needed, and I believe rstrip() is only required if some columns have a descriptor like ("colname", "") (which can happen if you reset_index() before trying to fix up .columns)

  • ----- before -----
               val1           2     
               min       sum    size
      grouper              
    
    ------ after -----
      grouper  val1_min  2_sum  2_size
    0       x         0      4       2
    1       y         4     12       2
    

• You want to name the resulting variables manually: (this is deprecated since pandas 0.20.0 with no adequate alternative as of 0.23)

  df.groupby(by="grouper").agg({"val1": {"sum_of_val1": "sum", "count_of_val1": "count"},
                                     2: {"sum_of_2":    "sum", "count_of_2":    "count"}}).my_flatten_cols("last")
  

  • Other suggestions include: setting the columns manually: res.columns = ['A_sum', 'B_sum', 'count'] or .join()ing multiple groupby statements.

  • ----- before -----
                       val1                      2         
              count_of_val1 sum_of_val1 count_of_2 sum_of_2
      grouper                                              
    
    ------ after -----
      grouper  count_of_val1  sum_of_val1  count_of_2  sum_of_2
    0       x              2            2           2         4
    1       y              2           10           2        12
    

Cases handled by the helper function

• level names can be non-string, e.g. Index pandas DataFrame by column numbers, when column names are integers, so we have to convert with map(str, ..)
• they can also be empty, so we have to filter(None, ..)
• for single-level columns (i.e. anything except MultiIndex), columns.values returns the names (str, not tuples)
• depending on how you used .agg() you may need to keep the bottom-most label for a column or concatenate multiple labels
• (since I'm new to pandas?) more often than not, I want reset_index() to be able to work with the group-by columns in the regular way, so it does that by default