通过两列的合并来重新索引熊猫数据框

Question

Probably a duplicate, but I'm not even sure what to search for.

If I have a pandas dataframe like so:

index RH  LH  Data1  Data2 . . . 
1     A1  A2  A      B
2     B1  NaN C      D
3     NaN C2  E      F

And I want to re-index as so:

index Data1  Data2
A1    A      B
A2    A      B
B1    C      D
C2    E      F

Is there a simple-ish way to do this? Or should I just do a pair of for loops?

Answer 1

You can use DataFrame.set_index with all columns without names defined in list and reshape by DataFrame.stack , then remove last level by DataFrame.reset_index with drop=True , convert all another levels to columns and create index by DataFrame.set_index :

cols = df.columns.difference(['RH','LH']).tolist()
df = (df.set_index(cols)
        .stack()
        .reset_index(len(cols), drop=True)
        .reset_index(name='idx')
        .set_index('idx'))
print (df)
    Data1 Data2
idx            
A1      A     B
A2      A     B
B1      C     D
C2      E     F

Or use DataFrame.melt with DataFrame.dropna , remove column variable and last create index by idx column:

df = (df.melt(cols, value_name='idx')
       .dropna(subset=['idx'])
       .drop('variable', axis=1)
       .set_index('idx'))
print (df)
    Data1 Data2
idx            
A1      A     B
B1      C     D
A2      A     B
C2      E     F