CODEWITHSUNDEEP
pythonpandasdataframe
Archit
Archit

Reputation: 588

Replacing value in a column in pandas dataframe from a column value in another dataframe

I have two dataframes df1 and df2

s = {'id': [4735,46,2345,8768,807],'city': ['a', 'b', 'd', 'e', 'f']}
s1 = {'id': [4735],'city_in_mail': ['x']}
df1 = pd.DataFrame(s)
df2 = pd.DataFrame(s1)

df1 looks like

     id city
0  4735    a
1    46    b
2  2345    d
3  8768    e
4   807    f

and df2 looks like:

     id city_in_mail
0  4735            x

I want to replace the value of column city in dataframe df1 from the value of column city_in_mail from dataframe df2 for the row where the id value is same.

So my df1 should become:

     id city
0  4735    x
1    46    b
2  2345    d
3  8768    e
4   807    f

How can do this with pandas ?

Upvotes: 3

Views: 389

Answers (4)

Scott Boston
Scott Boston

Reputation: 153460

Try combine_first with rename to align column index:

df2.set_index('id')\
   .rename(columns={'city_in_mail':'city'})\
   .combine_first(df1.set_index('id'))\
   .reset_index()

Output:

       id city
0  4735.0    x
1    46.0    b
2  2345.0    d
3  8768.0    e
4   807.0    f

Note: You can reassign this back to df1 if you choose.

Upvotes: 3

ALollz
ALollz

Reputation: 59519

Also .map + .fillna (if 'id' is a unique key in df2)

df1['city'] = df1.id.map(df2.set_index('id').city_in_mail).fillna(df1.city)

print(df1)
#     id city
#0  4735    x
#1    46    b
#2  2345    d
#3  8768    e
#4   807    f

Upvotes: 3

BENY
BENY

Reputation: 323226

Using merge with .loc 

s=df1.merge(df2,how='outer')
s.loc[s.city_in_mail.notnull(),'city']=s.city_in_mail
s
  city    id city_in_mail
0    x  4735            x
1    b    46          NaN
2    d  2345          NaN
3    e  8768          NaN
4    f   807          NaN

Upvotes: 3

rafaelc
rafaelc

Reputation: 59274

Use indexes to match and then loc

df1 = df1.set_index('id')
df2 = df2.set_index('id')
df1.loc[df1.index.isin(df2.index), :] = df2.city_in_mail

Or use update

c = df1.city
c.update(df2.city_in_mail)
df1['city'] = c

All outputs

        city
id  
4735    x
46      b
2345    d
8768    e
807     f

Of course, feel free to do df1.reset_index() in the end to go back to previous structure.

Upvotes: 3

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