How to replace certain rows by shared column values in pandas DataFrame?

Question

Let's say I have the following pandas DataFrame:

import pandas as pd

data = [['Alex',10],['Bob',12],['Clarke',13], ['Bob', '#'], ['Bob', '#'], ['Bob', '#']]

df = pd.DataFrame(data,columns=['Name','Age'], dtype=float)
print(df)
     Name Age
0    Alex  10
1     Bob  12
2  Clarke  13
3     Bob   #
4     Bob   #
5     Bob   #

So, there are odd rows in the DataFrame for Bob , namely rows 3, 4, and 5. These values are consistently # , not 12. Row 1 shows that Bob should be 12, not # .

In this example, it's straightforward to fix this with replace() :

df = df.replace("#", 12)
print(df)
     Name Age
0    Alex  10
1     Bob  12
2  Clarke  13
3     Bob   12
4     Bob   12
5     Bob   12

However, this wouldn't work for larger dataframes, eg

     Name Age
0    Alex  10
1     Bob  12
2  Clarke  13
3     Bob   #
4     Bob   #
5     Bob   #
6  Clarke   #

whereby row 6 should be 6 Clarke 13 .

How does one replace any row in Age with # with the correct integer as given in other rows, based on Name ? If # exists, check other rows with the same Name value and replace # .

Answer 1

You want to use the valid values to fill the invalid ones? In that case, use map :

v = df.assign(Age=pd.to_numeric(df['Age'], errors='coerce')).dropna()
df['Age'] = df['Name'].map(v.set_index('Name').Age)  

df
     Name   Age
0    Alex  10.0
1     Bob  12.0
2  Clarke  13.0
3     Bob  12.0
4     Bob  12.0
5     Bob  12.0
6  Clarke  13.0

Answer 2

try this,

d= df[df['Age']!='#'].set_index('Name')['Age']
df['Age']=df['Name'].replace(d)

O/P:

     Name Age
0    Alex  10
1     Bob  12
2  Clarke  13
3     Bob  12
4     Bob  12
5     Bob  12
6  Clarke  13