Pandas: getting rid of the multiindex

Question

After grouping and counting a dataframe I'm trying to remove the multiindex like this:

df = df[['CID','FE', 'FID']].groupby(by=['CID','FE']).count()               .unstack().reset_index() 

Printing the columns (df.colums) shows that it is still a MultiIndex.

MultiIndex(levels=[['FID', 'CID'], [...]] 

I can't access the column CID via df['CID'].

Answer 1

I think you need if is necessary convert MultiIndex to Index:

df.columns = df.columns.map(''.join) 

Or if need remove level use droplevel:

df.columns = df.columns.droplevel(0) 

If need access to values is possible use xs:

df = df.xs('CID', axis=1, level=1) 

You can also check:

What is the difference between size and count in pandas?

EDIT:

For remove MultiIndex is another solution select by ['FID'].

df = df.groupby(by=['CID','FE'])['FID'].count().unstack().reset_index() 

Samples (also added rename_axis for nicer output):

df = pd.DataFrame({'CID':[2,2,3],                    'FE':[5,5,6],                    'FID':[1,7,9]})  print (df)    CID  FE  FID 0    2   5    1 1    2   5    7 2    3   6    9  df = df.groupby(by=['CID','FE'])['FID']        .count()        .unstack()        .reset_index()        .rename_axis(None, axis=1)  print (df)        CID    5    6 0    2  2.0  NaN 1    3  NaN  1.0