How to update values of a column according to the values of a column of the other datafreme with pandas?

I have one dataframe:

import pandas as pd 
df1 = pd.DataFrame([['Tom', 'good', 3],
                       ['Jack', 'bad', 6],
                       ['Tom', 'average', 9],
                       ['Jerry', 'good', 89],
                       ['Lucy', 'average', 11]
                       ],
                      columns=['name', 'text', 'day'])

and the other dataframe:

df2 = pd.DataFrame([['Tom', 'bad', 55],
                   ['Jack', 'good', 64],
                   ['Mary', 'bad', 92],
                   ['Lucy', 'average', 109]
                   ],
                  columns=['name', 'text', 'day'])

if df2['name'] is in df1['name'], then the value of ‘day‘ of df1 should be replaced by that of df2, which means, I have the following result:

result = pd.DataFrame([['Tom', 'good', 55],
                   ['Jack', 'bad', 64],
                   ['Tom', 'average', 55],
                   ['Jerry', 'good', 89],
                   ['Lucy', 'average', 109]
                   ],
                  columns=['name', 'text', 'day'])

I know update can do that, but I want a conditional replacement method.

>Solution :

Just do np.where

df1['day'] = np.where(df1['name'].isin(df2['name']), df1['name'].map(df2.set_index('name')['day']),df1['day'])
df1
Out[263]: 
    name     text    day
0    Tom     good   55.0
1   Jack      bad   64.0
2    Tom  average   55.0
3  Jerry     good   89.0
4   Lucy  average  109.0