how to compare values between two pandas dataframes, one with list and one with single value, efficiently?

Question

I have two dataframes, df1 and df2 as follows:

#TWO DFs
df1 = {'uuids': [[01, 03], [02], [02,03]} 

df2 = {'uuid':[01, 02, 03]}

These are instances of originals. My question is how to efficiently (speedwise) print df2 if it finds a value 01 present in df1?

Currently, I am doing following-

for j in range(len(df1)):
    for i in df1['uuids'][j]:# GOES THROUGH EACH VALUE INSIDE LIST OF COLUMN uuids
        print (df2[df2['uuid'] == i])# PRINTS df2 IF THERE IS A MATCH

Thank you for your time and help!

@Erfan thanks for your reply and link. Each value inside df1['uuids'] is a list type. So, how to proceed with that? — Rishabh Sahrawat, Feb 11 '20 at 12:03
Reopened, please share a more clear example dataset. Right now you shared a dictionary and not a dataframe. — Erfan, Feb 11 '20 at 13:15
I edited the question (dataframes). Each value inside `uuids` is a list and is having variable lengths sometimes 1, 2, 3 etc. — Rishabh Sahrawat, Feb 11 '20 at 13:43

score 0 · Answer 1 · answered Feb 11 '20 at 13:50

0

If using pandas>=0.25, you can use explode and compare the values:

df2['uuid'].isin(df1.explode('uuids')['uuids'].values)

Out[1]:
0    True
1    True
2    True

answered Feb 11 '20 at 13:50

realr

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how to compare values between two pandas dataframes, one with list and one with single value, efficiently?

1 Answers1