DF fillna a column according to one hot columns mean

Question

I’m working with Titanic data set and wanted to fill age’s na values according to tittles that i get using the names. I want to fill that age value which is in the group(miss, mrs, mr)’s own age mean

age	Tittle_Mr	Tittle_Miss	Tittle_Mrs
22	1	0	0
38	0	1	0
26	0	0	1
35	1	0	0
NaN	1	0	0

I want that na to be Tittle_Mr’s age’s mean.

In this case 57/2 ~ 28

Asked By: berat duran

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Source

Answer 1

import pandas as pd
import numpy as np

df = pd.DataFrame({'age': [22 , 38,  26,35,np.nan],"Tittle_Mr":[1,0,0,1,1],"Tittle_Miss":[0,1,0,0,0],"Tittle_Mrs":[0,0,1,0,0]})

indexs=list(np.where(df['age'].isnull())[0])

for i in indexs:
   if df["Tittle_Mr"][i]==1:
        df["age"][i]=np.mean(df.where(df["Tittle_Mr"]==1))["age"]
   elif df["Tittle_Miss"][i]==1:
        df["age"][i]=np.mean(df.where(df["Tittle_Miss"]==1))["age"]
   else:
        df["age"][i]=np.mean(df.where(df["Tittle_Mrs"]==1))["age"]

 print(df)

please try this

Answered By: Niraj Gautam

Answer 2

import pandas as pd
import numpy as np

df = pd.DataFrame({"age" : [22, 38, 26, 35, np.nan], "Tittle_Mr":[1, 0,0, 1, 1],
                   "Tittle_Miss" : [0, 1, 0, 0, 0], "Tittle_Mrs" : [0, 0, 1, 0, 0]})
df.fillna(df.age.mean())

Answered By: Enrique Benito Casado

DF fillna a column according to one hot columns mean

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