Btukfyl

I have a dataframe where i have a column "Name". Name have multiples values like sample1, sample2, sample3. I want to apply a function to all those groups where value in Name column is same.

Output:

   Name  Value  Result

0  Name1      2       5

1  Name1      3       5

2  Name2      1      11

3  Name2      4      11

4  Name2      6      11

5  Name3      8      10

6  Name3      2      10

Looks like you want a groupby.apply. Something like this should work:

import pandas as pd



df = # ... load your data



def group_sum(g):

    g["Result"] = g["Value"].sum()

    return g



df_grouped = df.groupby("Name").apply(group_sum)

Edit: Alexandre Nixon's answer is better for this use case.

You want to use transform for that, which returns a dataframe with the transformed values and the same dimension.

a['Result'] = a.groupby('Name').transform(lambda x: x.sum())

For example if you have:

df = pd.DataFrame({'Name':['Name2','Name2', 'Name3'], 

                   'Value':[1,2,4]},

                   columns = ['Name','Value'])

df['Result'] = df.groupby('Name').transform(lambda x: x.sum())



print(df)



      Name  Value  Result

0  Name2      1       3

1  Name2      2       3

2  Name3      4       4

Df.groupby('Name').apply(lambda x: function (x.value))

The will work, in x.value you can put your column name