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update values in one dataframe based on another dataframe - Pandas
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update values in one dataframe based on another dataframe - Pandas
#1
Hello everyone

I have a simple problem but for the life of me I can't find any straightforward answer on how to do it. So l have one dataframe with a column email and a column acronym. Another dataframe has the same columns. My goal is now to update the acronym in the first dataframe based on a match between the email in the first and the second dataframe. To visually illustrate:

df1:
Email - Acronym
[email protected] A
[email protected] Nan

df2:
Email - Acronym
[email protected] A
[email protected] B
[email protected] C

My goal is to update df1's Acronym on the second row, based on whats found in df2. I have been delving in the documentation of pandas for a while, to no avail. Any helpers? Thanks!
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#2
Update: as if this forum gave me an epiphany, as soon as I posted the thread, the SQL lightbulb lit up. For the ones interested, the solution:

>>> pd.merge(df1,df2,on='Email',how='left')

Closed!

edit: not closed, it did not do what I expected. Open!
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#3
Assuming that the email values in one are present in the other
import pandas as pd

d = {'email': ['[email protected]', '[email protected]', '[email protected]'], 'acronym': ['radar', 'laser', 'cia']}
df1 = pd.DataFrame(data=d)
d2 = {'email': ['[email protected]', '[email protected]', '[email protected]'], 'acronym': ['radar', 'fbi','jupyter']}
df2 = pd.DataFrame(data=d2)

df1['acronym'] = df2[df2['email'] == df1['email']]['acronym'] 
df1
Output:
email acronym 0 [email protected] radar 1 [email protected] fbi 2 [email protected] jupyter
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