Pandas dataframe how to replace single column with multiple

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For example I have a dataframe like:

col1 col2 col3
0    2    1

and I want to replace it so that

{0: [a,b], 1: [c,d], 2: [e, f]}

So I want to end up with a dataframe like this:

col1 col1b col2 col2b col3 col3b
a    b     e    f     c    d

I want to feed this data into tensorflow after transforming it so the below might also be acceptable output if tensorflow would accept it?

col1  col2  col3
[a,b] [e,f] [c,d]

Below is my current code:

field_names = ["elo", "map", "c1", "c2", "c3", "c4", "c5", "e1", "e2", "e3", "e4", "e5", "result"]
df_train = pd.read_csv('input/match_results.csv', names=field_names, skiprows=1, usecols=range(2, 13))

for count in range(1, 6):
    str_count = str(count)
    df_train['c' + str_count] = df_train['c' + str_count].map(champ_dict)
pandas tensorflow python
1 Answers

IIUC, you can use .stack .map and .cumcount to reshape your dataframe and index.

import pandas as pd
from string import ascii_lowercase

col_dict = dict(enumerate(ascii_lowercase))
map_dict = {0: ['a','b'], 1: ['c','d'], 2: ['e', 'f']}

s = df.stack().map(map_dict).explode().reset_index()
s['level_1'] = s['level_1'] +  s.groupby(['level_1','level_0']).cumcount().map(col_dict)




df_new = s.set_index(['level_0','level_1']).unstack(1).droplevel(0,1).reset_index(drop=True)

print(df_new)

level_1  col1a col1b col2a col2b col3a col3b
0           a     b     e     f     c     d
user_93751020
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