重新格式化奇怪的 Dataframe

Question

Name	place	pers_data
NaN	NaN	Nan
Smith John	NY	sjohn@gmail.com
NaN	Nan	0987 4567
NaN	NaN	0653 6734
Vic Stied	SA	0986 5332
NaN	NaN	vickie@hotmail.com

I would like to delete the NaN values and reformat the file like the following:

Name	Place	pers_data	other	other_2
Smith John	NY	sjohn@gmail.com	0987 4567	0653 6734
Vic Stied	SA	vickie@hotmail.com	0986 5332

Can someone help me with that, I tried some stuff but without understanding anything, I'd like to really get what I am doing.

Answer 1

This is a variation on a pivot :

idx = df['Name'].notna().cumsum()
out = (df
   .assign(col=df.groupby(idx).cumcount(),
           Name=df['Name'].groupby(idx).ffill(),
           place=df['place'].groupby(idx).ffill()
          )
   .pivot(index=['Name', 'place'], columns='col', values='pers_data')
   .add_prefix('other_').rename(columns={'other_0': 'pers_data'})
   .reset_index().rename_axis(columns=None)
   .dropna(how='all')
)

output:

         Name place        pers_data             other_1    other_2
1  Smith John    NY  sjohn@gmail.com           0987 4567  0653 6734
2   Vic Stied    SA        0986 5332  vickie@hotmail.com        NaN

Answer 2

df1.loc[~df1.isna().all(axis=1)].fillna(method='ffill')\
    .groupby(['Name','place']).agg(','.join)\
    .pers_data.str.split(',',expand=True).add_prefix('other_')\
    .rename(columns={'other_0':'pers_data'}).reset_index()
        
                Name place        pers_data             other_1    other_2
        0  Smith John    NY  sjohn@gmail.com           0987 4567  0653 6734
        1   Vic Stied    SA        0986 5332  vickie@hotmail.com       None