如何比较列上的两个数据框并用其他列值替换

Question

I am having two data frames that are df1 and df2

id      first       last  size
  A 1978-01-01 1979-01-01     2
  B 2000-01-01 2000-01-01     1
  C 1998-01-01 2000-01-01     3
  D 1998-01-01 1998-01-01     1
  E 1999-01-01 2000-01-01     2

  id  token       
  A     ZA.00 
  B     As.11
  C     SD.34

output

id          first       last        size
  ZA.00     1978-01-01 1979-01-01     2
  As.11     2000-01-01 2000-01-01     1
  SD.34     1998-01-01 2000-01-01     3
  D         1998-01-01 1998-01-01     1
  E         1999-01-01 2000-01-01     2

If df1 id is present in df2 then token value is to set df1 id value. How can i achieve this.

Answer 1

Using Merge and combine_first :

df = df1.merge(df2,how='outer')
df['id'] = df['token'].combine_first(df['id'] )
df.drop('token',inplace=True,axis=1)

Another way is to use replace with dictionary of df2.values , here the df1 dataframe changes.:

df1.id.replace(dict(df2.values),inplace=True)

---

        id  first   last    size
    0   ZA.00   1978-01-01  1979-01-01  2
    1   As.11   2000-01-01  2000-01-01  1
    2   SD.34   1998-01-01  2000-01-01  3
    3   D   1998-01-01  1998-01-01  1
    4   E   1999-01-01  2000-01-01  2

Answer 2

Use map and fillna :

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

Output:

      id       first        last  size
0  ZA.00  1978-01-01  1979-01-01     2
1  As.11  2000-01-01  2000-01-01     1
2  SD.34  1998-01-01  2000-01-01     3
3      D  1998-01-01  1998-01-01     1
4      E  1999-01-01  2000-01-01     2

You can use map with a series as an argument.

Answer 3

If you do not wish to merge your DataFrame, you could use apply function to solve this. Change your small DataFrame to dictionary and map it to the other DataFrame.

from io import StringIO #used to get string to df

import pandas as pd

id_ =list('ABC')
token = 'ZA.00 As.11 SD.34'.split()
dt = pd.DataFrame(list(zip(id_,token)),columns=['id','token'])

a ='''
id first last size
A 1978-01-01 1979-01-01 2
B 2000-01-01 2000-01-01 1
C 1998-01-01 2000-01-01 3
D 1998-01-01 1998-01-01 1
E 1999-01-01 2000-01-01 2
'''

df =pd.read_csv(StringIO(a), sep=' ')

# This last two lines are all you need
mp= {x:y for x,y in zip(dt.id.tolist(),dt.token.tolist())}

df.id.apply(lambda x: mp[x] if x in mp.keys() else x)