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I have a dataframe of race results and I am trying to see if the winner of the race is from the same location as the race.

round_loc column:

0 Val d'Allos, France 168 Les Deux Alpes, France 378 Winter Park, CO, USA 499 Whistler, BC, Canada ...

country column:

0 France 168 France 378 France 499 Australia 602 France ...

My Code:

winners_df = df.loc[df['finish_position'] == 1, ['country', 'round_loc']] hometown_win = winners_df['country'].isin(winners_df['round_loc']) # Also tried hometown_win = winners_df['country'].isin(winners_df['round_loc'].values) print(hometown_win)

My results:

0 False 168 False 378 False 499 False 602 False ...

Not sure what I'm doing wrong.

winners_df['country'][0] in winners_df['round_loc'][0]

Works fine. I'm sure I could just do it with a loop but I feel like I'm missing something here.

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    This won't work isin is looking for exact matches, you want to compare each row and use contains to test for membership/matches here Commented Apr 6, 2017 at 7:59

1 Answer 1

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print (winners_df) round_loc country 0 Val d'Allos, France France 168 Les Deux Alpes, France USA <-changed data sample 378 Winter Park, CO, USA France 499 Whistler, BC, Canada Australia

If need check if in column round_loc is one value from column country:

a = '|'.join(winners_df['country'].unique().tolist()) print (a) France|USA|Australia hometown_win = winners_df['round_loc'].str.contains(a) print(hometown_win) 0 True 168 True 378 True 499 False Name: round_loc, dtype: bool

If need check if in column round_loc is one value from column country, but per row:

hometown_win = winners_df.apply(lambda x: x['country'] in x['round_loc'],axis=1) print(hometown_win) 0 True 168 False 378 False 499 False dtype: bool
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2 Comments

Ahhh that makes sense. Theres seems like there might be a problem here if there is a race in France (round_loc) that isn't won by someone in France (country) as each row contains a racer.
That's perfect! Thank you! I didn't know you could check all the items on an axis like that!

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