Transpose dataframe based on column list

I have a dataframe in the following structure:

cNames  | cValues   |  number  
[a,b,c] | [1,2,3]   |  10      
[a,b,d] | [55,66,77]|  20

I would like to transpose – create columns from the names in cNames.
But I can’t manage to achieve this with transpose because I want a column for each value in the list.
The needed output:

a   | b   | c   | d   |  number
1   | 2   | 3   | NaN | 10
55  | 66  | NaN | 77  | 20

How can I achieve this result?
Thanks!

The code to create the DF:

d = {'cNames': [['a','b','c'], ['a','b','d']], 'cValues': [[1,2,3], 
[55,66,77]], 'number': [10,20]}
df = pd.DataFrame(data=d)

Answer

One option is concat:

pd.concat([pd.Series(x['cValues'], x['cNames'], name=idx) 
               for idx, x in df.iterrows()], 
          axis=1
         ).T.join(df.iloc[:,2:])

Or a DataFrame construction:

pd.DataFrame({idx: dict(zip(x['cNames'], x['cValues']) )
              for idx, x in df.iterrows()
            }).T.join(df.iloc[:,2:])

Output:

      a     b    c     d  number
0   1.0   2.0  3.0   NaN      10
1  55.0  66.0  NaN  77.0      20

---

Update Performances sort by run time on sample data

DataFrame

%%timeit
pd.DataFrame({idx: dict(zip(x['cNames'], x['cValues']) )
              for idx, x in df.iterrows()
            }).T.join(df.iloc[:,2:])
1.29 ms ± 36.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

concat:

%%timeit
pd.concat([pd.Series(x['cValues'], x['cNames'], name=idx) 
               for idx, x in df.iterrows()], 
          axis=1
         ).T.join(df.iloc[:,2:])
2.03 ms ± 86.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) 

KJDII’s new series

%%timeit
df['series'] = df.apply(lambda x: dict(zip(x['cNames'], x['cValues'])), axis=1)
pd.concat([df['number'], df['series'].apply(pd.Series)], axis=1)

2.09 ms ± 65.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Scott’s apply(pd.Series.explode)

%%timeit
df.apply(pd.Series.explode)
  .set_index(['number', 'cNames'], append=True)['cValues']
  .unstack()
  .reset_index()
  .drop('level_0', axis=1)

4.9 ms ± 135 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

wwnde’s set_index.apply(explode)

%%timeit
g=df.set_index('number').apply(lambda x: x.explode()).reset_index()
g['cValues']=g['cValues'].astype(int)
pd.pivot_table(g, index=["number"],values=["cValues"],columns=["cNames"]).droplevel(0, axis=1).reset_index()

7.27 ms ± 162 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Celius’ double explode

%%timeit
df1 = df.explode('cNames').explode('cValues')
df1['cValues'] = pd.to_numeric(df1['cValues'])
df1.pivot_table(columns='cNames',index='number',values='cValues')

9.42 ms ± 189 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)