pandas dataframe boolean indexing with multiple conditions from another df

Question

I'm trying to identify the rows between 2 df which shared the same values for some columns for the SAME row.

Example:

import pandas as pd
df = pd.DataFrame([{'energy': 'power', 'id': '123'}, {'energy': 'gas', 'id': '456'}])
df2 = pd.DataFrame([{'energy': 'power', 'id': '456'}, {'energy': 'power', 'id': '123'}])

df =

   energy   id
0  power  123
1    gas  456

df2 =

   energy     id
0  power    456
1  power    123

Therefore, I'm trying to get the rows from df where energy & id matches exactly in the same row in df2. If I do like this, I get a false result:

df2.loc[(df2['energy'].isin(df['energy'])) & (df2['id'].isin(df['id']))]

because this will match the 2 rows of df2 whereas I would expect only power / 123 to be matched

How should I do to do boolean indexing with multiple "dynamic" conditions based on another df rows and matching the values for the same rows in the other df ?

Hope it's clear

Looks like an inner merge: df.merge(df2) ? of course you can select the common keys to join — anky
– anky, Commented Mar 14, 2021 at 16:27

canon-ball · Accepted Answer · 2021-03-14 17:20:29Z

2

pd.merge(df, df2, on=['id','energy'], how='inner')

answered Mar 14, 2021 at 17:20

canon-ball

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1 Comment

yeye Over a year ago

Yes that works like this indeed, if you want to do both standard Boolean indexing and merge you need 2 lines instead of 1 but it works great I thought there would be another option but let’s keep it like this then. Thanks

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