使用 Pandas 将特定列值替换为另一个数据框列值

Question

There are 2 dataframes, the 1st dataframe contains 3 columns emp_id,emp_name and old email_id. While the 2nd dataframe contains 2 columns emp_id and new email_id.

Task is to, lookup 2nd dataframe and replace certain values in the 1st dataframe.

Input Dataframe:

import pandas as pd
data = { 
'emp_id': [111, 222, 333, 444, 555],
'emp_name': ['Sam','Joe','Jake','Rob','Matt'],
'email_id': ['one@gmail.com','two@gmail.com','three@gmail.com','four@gmail.com','five@gmail.com']
}
data = pd.DataFrame(data)

Current Output:

     emp_id  emp_name email_id
0     111    Sam      one@gmail.com
1     222    Joe      two@gmail.com
2     333    Jake     three@gmail.com
3     444    Rob      four@gmailcom
4     555    Matt     five@gmail.com

#replace certain values with new values by looking up another dataframe
data1 = { 
'emp_id': [111, 333, 555],
'email_id': ['one@yahoo.com','three@yahoo.com','five@yahoo.com']
}
data1 = pd.DataFrame(data1)

Desired Output:

 data = 

     emp_id  emp_name email_id
0     111    Sam      one@yahoo.com
1     222    Joe      two@gmail.com
2     333    Jake     three@yahoo.com
3     444    Rob      four@gmailcom
4     555    Matt     five@yahoo.com

Original data contains 50k+ rows so merging them doesn't seem like the correct option. Any help would be appreciated.

Cheers !

Answer 1

Let us try update

out = data.set_index('emp_id')
out.update(data1.set_index('emp_id')[['email_id']])
out.reset_index(inplace=True)
out
   emp_id         email_id
0     111    one@yahoo.com
1     222    two@gmail.com
2     333  three@yahoo.com
3     444   four@gmail.com
4     555   five@yahoo.com

Answer 2

The solution I will provide makes use of Panda's Boolean Indexing .

# Compare the column of interest of the DataFrames. Returns a Pandas Series with True/False based on the inequality operation.
difference = (data1['email_id'] != data2['email_id'])

# Get the indexes of the different rows, i.e the rows that hold True for the inequality.
indexes_to_be_changed = data1[difference].index

# Replace the rows of the first DataFrame with the rows of the second DataFrame.
data1.iloc[indexes_to_be_changed] = data2.iloc[indexes_to_be_changed]