Below is an answer code I received from Kaggle Pandas course. def stars(row): if row.country == 'Canada': return 3 elif row.points >= 95: return 3 elif row.points >= 85: return 2 else: return 1 star_ratings_2 = reviews.apply(stars, axis='columns') The question goes like this: We'd like to host these wine reviews on our website, but a rating system ranging from 80 to 100 points is too hard to understand - we'd like to translate them into simple star ratings. A score of 95 or higher counts as 3 stars, a score of at least 85 but less than 95 is 2 stars. Any other score is 1 star. Also, the Canadian Vintners Association bought a lot of ads on the site, so any wines from Canada should automatically get 3 stars, regardless of points. Create a series star_ratings with the number of stars corresponding to each review in the dataset. The dataset looks like this: Table My question is: star_ratings_2 = reviews.apply(stars, axis='columns') Why axis='columns instead of axis='rows'? since the stars() functions has to process country and points columns of a row, shouldn't we pass a row to the stars() function? I just didn't expect the correct answer will be axis='columns', I ve asked around including ChatGPT, but there is no good answer for me. ChatGPT even think that I am right where the axis='rows' should be correct. Continue reading...
