Python pandas: Add column to grouped DataFrame with method chaining

Question

First let say that i'm new to pandas .

I am trying to make a new column in a DataFrame. I am able to do this as shown in my example. But I want to do this by chaining methods, so I don't have to assign new variables. Let me first show what I want to achieve, and what I have done this so far:

In [1]:
import numpy as np
from pandas import Series,DataFrame
import pandas as pd

In [2]:
np.random.seed(10)
df=pd.DataFrame(np.random.randint(1,5,size=(10, 3)), columns=list('ABC'))
df

Out [2]:
A  B  C
2  2  1
4  1  2
4  1  2
2  1  2
2  3  1
2  1  3
1  3  1
4  1  1
4  4  3
1  4  3
In [3]:
filtered_DF = df[df['B']<2].copy()
grouped_DF = filtered_DF.groupby('A')
filtered_DF['C_Share_By_Group'] =filtered_DF.C.div(grouped_DF.C.transform("sum"))
filtered_DF

Out [3]:
A  B  C  C_Share_By_Group
4  1  2               0.4
4  1  2               0.4
2  1  2               0.4
2  1  3               0.6
4  1  1               0.2

I want to achieve the same thing by chaining methods. In R with dplyr package, I would be able to do something like:

df %>% 
  filter(B<2) %>%
  group_by(A) %>% 
  mutate('C_Share_By_Group'=C/sum(C))

In the pandas documentation it says that mutate in R(dplyr) is equal to assign in pandas, but assign doesn't work on a grouped object. When I try to assign something to grouped dataframe, I get an error:

"AttributeError: Cannot access callable attribute 'assign' of 'DataFrameGroupBy' objects, try using the 'apply' method"

I have tried the following, but don't know how to add the new column, or if it is even possible to achieve this by chaining methods:

(df.loc[df.B<2]
   .groupby('A')
    #****WHAT GOES HERE?**** apply(something)?
)

Answer 1

You can try assign:

print df[df['B']<2].assign(C_Share_By_Group=lambda df: 
                       df.C
                         .div(df.groupby('A')
                           .C
                           .transform("sum")))

   A  B  C  C_Share_By_Group
1  4  1  2               0.4
2  4  1  2               0.4
3  2  1  2               0.4
5  2  1  3               0.6
7  4  1  1               0.2