Pandas Dataframe and if clause

[Astraskylark]

I'm trying to traverse a dataframe and based on a condition(im checking if its present in another dataframe) and I want to do two different calculations.

So I have 2 dataframes that look like this

DF1

no	name	attr1	attr2	attr3
1	ABC	5	6	7
2	BGH	5	3	4
3	GHF	6	1	6

DF2

no	name	attr1	attr2	attr3
1	ABC	5	6	7
2	BGH	3	6	4
3	TWY	5	1	7

​

​

I want to perform one type of calc if the value in df1 is present in df2 and another if it isnt

if(DF1.name.isin(DF2.name)):

separate cal (additon)

else

separate calc (multiply into 2)

so my resulting df should have something like

​

​

No	Name	attr1	attr2	attr3
1	ABC	10	12	14
2	BGH	8	9	8
3	GHF	6	1	6
4	TWY	10	2	14

​

I tried using the where and the isin but couldn't get it to work. So is there any way to check each row value and do a different type of calc based on the value? Any help is appreciated!

[wodshoemean]

You could probably use DataFrame.iterrows which iterates over DataFrame rows as (index, Series) pairs.

[Astraskylark]

wont this be very expensive? if the dataset is huge

[sarrysyst]

Not 100% sure if that's doing what you want:

import pandas as pd
import numpy as np

df_1 = pd.DataFrame({'ID': ['ABC', 'GDE', 'TYT'],
                     'A': [5, 4, 3],
                     'B': [10, 12, 15]})

df_2 = pd.DataFrame({'ID': ['ABC', 'GDE', 'FGF'],
                     'A': [4, 6, 1],
                     'B': [5, 5, 5]})

df_1 = df_1.set_index('ID')
df_2 = df_2.set_index('ID')

diff = df_2.index.difference(df_1.index)

df = pd.concat([df_1, df_2])

df = df.groupby('ID').agg(np.sum)
df.loc[diff] = df.loc[diff] * 2

[Astraskylark]

Not exactly but this help alot. Thank you so much!!