如何从 Pandas 系列中提取不同长度的行值到新列？

Question

Suppose I have a pandas dataframe with a series C where each value is a list. Since the length of each list is different, How do I slice and append this series to new columns of this DataFrame ?

Additional findings: Starting with [ , and ', each letter is appended to the whole list (blanc space included to separate the word)

What should I do to combine the letters into a single word then apply the solutions?

Sample df -

id   A     B    C                       
0    1     2    ['Alan', 'Rod', 'Ben']  
1    1     3    ['Jeff']                  
2    4     6    ['Pete', 'Joe']  

Intermediate df -

id   A     B    C                       N1   N2   N3  N4  ....
0    1     2    ['Alan', 'Rod', 'Ben']  [    '    A   l
1    1     3    ['Jeff']                [    '    J   e
2    4     6    ['Pete', 'Joe']         [    '    P   e

Expected df -

id   A     B    C                        N1     N2      N3  
0    1     2    ['Alan', 'Rod', 'Ben']  'Alan'  'Rod'   'Ben'   
1    1     3    ['Jeff']                'Jeff'   Nan     Nan   
2    4     6    ['Pete', 'Joe']         'Pete'   'Joe'   Nan

Answer 1

Convert the series to a list, so that you have a list of lists, and then convert it to a dataframe with pandas.DataFrame(listoflists) . You can then append or merge the new dataframe to the old one.

Answer 2

df.join(pd.DataFrame(df["C"].apply(pd.Series))).rename(columns={0:"N1",1:"N2",2:"N3"})

   A  B                 C    N1   N2   N3
0  1  2  [Alan, Rod, Ben]  Alan  Rod  Ben
1  1  3            [Jeff]  Jeff  NaN  NaN
2  4  6       [Pete, Joe]  Pete  Joe  NaN

Answer 3

The solution is a greatly simplified version of this question . Just put the lists of unequal length into the pd.DataFrame() constructor, and the number of new columns will be determined automatically.

import pandas as pd
import numpy as np

df = pd.DataFrame(
    [[1, 2,['Alan', 'Rod', 'Ben']],
     [1, 3,['Jeff']],
     [4, 6,['Pete', 'Joe']]],
    columns=['A', 'B','C']
)

# 1. unpack and reconstruct a dataframe   
df_unpack = pd.DataFrame(df["C"].to_list())
# optional: None to NaN
# df_unpack.fillna(np.nan)    

print(df_unpack)
      0     1     2
0  Alan   Rod   Ben
1  Jeff  None  None
2  Pete   Joe  None

# 2. concatenate the results
df_out = pd.concat([df, df_unpack], axis=1)

# 3. determine names
df_out.index.name = "id"
df_out.columns = ['A','B','C'] + [f"N{i+1}" for i in range(df_unpack.shape[1])]

print(df_out)
    A  B                 C    N1    N2    N3
id                                          
0   1  2  [Alan, Rod, Ben]  Alan   Rod   Ben
1   1  3            [Jeff]  Jeff  None  None
2   4  6       [Pete, Joe]  Pete   Joe  None

Answer 4

iterate over items and create new columns:

newdf = pd.DataFrame();
for i , row in df.iterrows():
    for j in range(len(row['C'])):
        row['ncol{}'.format(j)] = row['C'][j]
    newdf = newdf.append(row,ignore_index=True)