Filling missing values of categorical values based on other categorical values in pandas dataframe

Question

I want to fill missing values of categorical values in Pandas data frame with the most frequent values on another category. For example,

import pandas as pd
impo         


        

Answer 1

IIUC

Using mode

---

Data input

import pandas as pd
import numpy as np
data = {'type': ['softdrink', 'juice', 'softdrink', 'softdrink',    'juice','juice','softdrink'],
    'product': ['coca', np.nan, 'pepsi', 'pepsi', 'orange','grape',np.nan],
    'price': [25, 94, 57, 62, 70,50,60]}
df = pd.DataFrame(data)

---

solution

df.groupby('type').product.transform(lambda x: x.fillna(x.mode()[0]))

Out[28]: 
0      coca
1     grape
2     pepsi
3     pepsi
4    orange
5     grape
6     pepsi
Name: product, dtype: object

---

New df

df['product']=df.groupby('type').product.transform(lambda x: x.fillna(x.mode()[0]))
df
Out[40]: 
   price product       type
0     25    coca  softdrink
1     94   grape      juice
2     57   pepsi  softdrink
3     62   pepsi  softdrink
4     70  orange      juice
5     50   grape      juice
6     60   pepsi  softdrink