How does python pandas traverse each row and accumulate for conditional filtering

Today, I would like to share with you how python pandas traverses each line and accumulates the relevant knowledge points of conditional filtering. The content is detailed and the logic is clear. I believe most people still know too much about this knowledge, so share this article for your reference. I hope you can get something after reading this article.

Pandas traverses each row and accumulates conditional filtering

The main purpose of this record is to sort each row and retain the previous 80% preference.

Train of thought:

The probability of each row is sorted and then accumulated, and preferences with cumulative values less than or equal to 0.8 are retained to obtain a cumulative filtered dataframe, which is then mapped back to the original data to retain the preferences of each row. Next is the implementation of the code.

A = [0.2,0.35,0.45], [0.1,0.2,0.7], [0.3,0.5,0.2]] data = pd.DataFrame (a, index= ['user1','user2','user3'], columns= ["a", "b", "c"]) sum_df= [] for index Row in data.iterrows (): df = row.sort_values (ascending=False). Cumsum () if df [0] > 0.8: new_df = df [: 1] else: new_df = df [df0.8: new_df = df [: 1] else: new_df = df [df