How to convert a set of pandas dataframe columns to a list of dictionaries and save it into a new column?


I have a dataframe :

   col1 col2 col3
0     1    a    W
1     1    b    X
2     2    c    Y
3     2    d    Z

I need to convert it into something like this, combining based on column 1 values:

   col1 col2 col3                    dict_col
0     1    a    W  [{'col2': 'a', 'col3': 'W'}, {'col2': 'b', 'col3': 'X'}]
0     2    c    Y  [{'col2': 'c', 'col3': 'Y'}, {'col2': 'd', 'col3': 'Z'}]

This is what i tried doing:

import pandas as pd

data = {
    'col1': [1, 1, 2, 2],
    'col2': ['a', 'b', 'c', 'd'],
    'col3': ['W', 'X', 'Y', 'Z']}
df = pd.DataFrame(data)


cols_to_use = ['col2','col3']

df['dict_col'] = df[cols_to_use].apply(
    lambda x: {'col2': x['col2'], 'col3': x['col3']},

Asked By: snn



You can use groupby and then apply your function:

def group_rows(sub_df):
    dict_col = []
    for i, row in sub_df.iterrows():
    return pd.Series({
            "col2": dict_col[0]["col2"],
            "col3": dict_col[0]["col3"],
            "dict_col": dict_col

this will give you

     col2 col3                                                                        dict_col
1       a    W  [{'col1': 1, 'col2': 'a', 'col3': 'W'}, {'col1': 1, 'col2': 'b', 'col3': 'X'}]
2       c    Y  [{'col1': 2, 'col2': 'c', 'col3': 'Y'}, {'col1': 2, 'col2': 'd', 'col3': 'Z'}]
Answered By: zaki98

You can use:

g = df.groupby('col1')

df1 = g.apply(lambda x: x.drop(columns='col1').to_dict('records')).rename('dict_col')
out = pd.concat([g.first(), df1], axis=1).reset_index()

You can also do as alternative:

g = df.drop(columns='col1').groupby(df['col1'])

df1 = g.apply(lambda x: x.to_dict('records')).rename('dict_col')
out = pd.concat([g.first(), df1], axis=1).reset_index()


>>> out
   col1 col2 col3                                           dict_col
0     1    a    W  [{'col2': 'a', 'col3': 'W'}, {'col2': 'b', 'co...
1     2    c    Y  [{'col2': 'c', 'col3': 'Y'}, {'col2': 'd', 'co...
Answered By: Corralien
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