Update a column value in a data frame from another matching column in another data frame in Pandas

Question

I have two data frames

 df
 city   mail
  a    satya
  b    def
  c    akash
  d    satya
  e    abc
  f    xyz
#Another Dataframe d as
 city   mail
 x      satya
 y      def
 z      akash
 u      ash

So, now I need to update the city in df from the updated values ​​in 'd', comparing mail, if any mail identifier is not found, it should remain as it is. Therefore it should look like

 df ### o/p should be like
 city   mail
  x    satya
  y    def
  z    akash
  x    satya  #repeated so same value should placed here
  e    abc     # not found so as it was
  f    xyz

I tried -

s = {'mail': ['satya', 'def', 'akash', 'satya', 'abc', 'xyz'],'city': ['a', 'b', 'c', 'd', 'e', 'f']}
s1 = {'mail': ['satya', 'def', 'akash', 'ash'],'city': ['x', 'y', 'z', 'u']}
df = pd.DataFrame(s)
d = pd.DataFrame(s1)
#from google i tried
df.loc[df.mail.isin(d.mail),['city']] = d['city']

# Reliable lasting result as

 city   mail
 x  satya
 y  def
 z  akash
 u  satya  ###this value should be for city 'x'
 e    abc
 f    xyz

I cannot merge here: = 'mail', how = 'left', since I have fewer clients in one data frame. So after the merger, how can I match the value of the inappropriate mail city in the combined one.

Please offer.

Answer 1

, city df city d. update , .

# Add extra columns to dataframe.
df['mobile_no'] = ['212-555-1111'] * len(df)
df['age'] = [20] * len(df)

# Update city values keyed on `mail`.
new_city = df[['mail', 'city']].set_index('mail')
new_city.update(d.set_index('mail'))
df['city'] = new_city.values

>>> df
  city   mail     mobile_no  age
0    x  satya  212-555-1111   20
1    y    def  212-555-1111   20
2    z  akash  212-555-1111   20
3    x  satya  212-555-1111   20
4    e    abc  212-555-1111   20
5    f    xyz  212-555-1111   20