Q:

List of values to Columns in Pandas DataFrame

I have a DataFrame in which one of the column has the list of values ( each values is a value of a feature). Now I need to convert those list of values into each column.
Ex:  DataFrame is having two columns in which data column is list of values 

   data  ,  Time      
[1,2,3,4], 12:34   
[5,6,7,8], 12:36   
[9,1,2,3], 12:45

I need to convert then as
Ex:    
   data0 data1 data2 data3 Time
    1   ,  2  ,  3  ,  4  , 12:34    
    5   ,  6  ,  7  ,  8  , 12:36    
    9   ,  1  ,  2  ,  3  , 12:45

How can I do this efficiently?

A:

numpy
We get a very fast solution by using np.column_stack directly on values.  The only thing left to do is to stitch together the columns
v = np.column_stack([df.data.values.tolist(), df.Time.values])
c = ['data{}'.format(i) for i in range(v.shape[1] - 1)] + ['Time']
pd.DataFrame(v, df.index, c)

  data0 data1 data2 data3   Time
0     1     2     3     4  12:34
1     5     6     7     8  12:36
2     9     1     2     3  12:45

timeit 
%%timeit
pd.DataFrame(df['data'].values.tolist()).add_prefix('data').join(df['Time'])
1000 loops, best of 3: 1.13 ms per loop

%%timeit
v = np.column_stack([df.data.values.tolist(), df.Time.values])
c = ['data{}'.format(i) for i in range(v.shape[1] - 1)] + ['Time']
pd.DataFrame(v, df.index, c)
10000 loops, best of 3: 183 µs per loop

A:

You can use DataFrame constructor with converting column data to numpy array by values + tolist, add_prefix and last join column Time:
df = pd.DataFrame(df['data'].values.tolist()).add_prefix('data').join(df['Time'])
print (df)
   data0  data1  data2  data3   Time
0      1      2      3      4  12:34
1      5      6      7      8  12:36
2      9      1      2      3  12:45

