CODEWITHSUNDEEP
pythonpandasmulti-index
HappyPy
HappyPy

Reputation: 10697

drop multiindex level but keep names of columns - pandas

I have a df that looks like this

   a     b      c
              c1  c2
0  87    33   32  34
1  32    10   45  62
2  78    83   99  71

I'd like to drop the c level but keep all the other column names

   a     b    c1  c2
0  87    33   32  34
1  32    10   45  62
2  78    83   99  71

df.columns = df.columns.droplevel(0) works but the names of a and b disappear

              c1  c2
0  87    33   32  34
1  32    10   45  62
2  78    83   99  71

Upvotes: 11

Views: 14446

Answers (1)

jezrael
jezrael

Reputation: 862601

You can use list comprehension to select second-level values if c:

df.columns = [col2 if col1 == 'c' else col1 for col1, col2 in df.columns]
print (df)
    a   b  c1  c2
0  87  33  32  34
1  32  10  45  62
2  78  83  99  71

Previous solutions for old pandas versions:

I think you can use set_index + droplevel + reset_index:

df = df.set_index(['a','b'])
df.columns = df.columns.droplevel(0)
df = df.reset_index()
print (df)
    a   b  c1  c2
0  87  33  32  34
1  32  10  45  62
2  78  83  99  71

Another solution with select columns by ['c']:

df = df.set_index(['a','b'])['c'].reset_index()
print (df)
    a   b  c1  c2
0  87  33  32  34
1  32  10  45  62
2  78  83  99  71

But if get it from pivot_table solution is remove [] or add parameter values='c' if missing.

Upvotes: 8

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