我最初是从以下数据帧开始的：
数据集与用户回答具有多个答案选择的多个问题并且用户能够回答多个答案有关。
movie_id, user_id, rated_value, question_id, answer_id, genre, user_gender, user_ethnicity
101, 345, 3.5, 1, 1, comedy, male, white
101, 345, 3.5, 1, 2, comedy, male, white
101, 345, 3.5, 2, 1, comedy, male, white
125, 345, 4.5, 1, 4, drama, male, white
101, 233, 4.0, 1, 3, comedy, female, black
101, 233, 4.0, 2, 2, comedy, female, black
125, 233, 3.0, 1, 1, drama, female, black
125, 233, 3.0, 2, 2, drama, female, black
125, 333, 3.0, 1, 1, comedy, male, asian
125, 333, 3.0, 2, 2, comedy, male, asian
我想通过旋转将桌子弄平。我可以成功完成工作而无需引入genre, user_gender, user_ethnicity，如下所示：
pivoted_df = df_to_pivot.assign(val=1).pivot_table(
index=['movie_id',
'user_id',
'rated_value'],
columns=['question_id',
'answer_id'],
values=['question_id', 'answer_id'],
fill_value=0)
然后将问题和答案ID组合在一起，以便列将显示为1_1, 1_2
pivoted_df.columns = pivoted_df.columns.droplevel()
pivoted_df.columns = ['{}_{}'.format(l1, l2).strip() for l1, l2 in pivoted_df.columns.values]
pivoted_df = pivoted_df.reset_index()
movie_id user_id rating_value 1_1 1_2 1_3 1_4...
但是尝试添加genre, user_gender, user_ethnicity
pivoted_df = df_to_pivot.assign(val=1).pivot_table(
index=['movie_id',
'user_id',
'rated_value'],
columns=['question_id',
'answer_id', 'genre', 'user_gender','user_ethnicity'],
values=['question_id', 'answer_id', 'genre', 'user_gender','user_ethnicity'],
fill_value=0)
它实际上不起作用。
我的目标是像其余部分一样枢转genre, user_gender, user_ethnicity，以便将列
movie_id user_id rated_value 1_1 1_2 1_3 1_4...comedy, drama...,male, female, black, white, asian
output:
movie_id, user_id, rated_value , 1_1, 1_2, 1_3, 1_4, comedy, drama, male, female, white, black, asian
101, 345, 3.5, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0
目标是每行获取movie_id，user_id对，其他所有内容都反映为1和0。
答案 0 :(得分：1)
将question_id和answer_id合并为一列，然后使用pd.get_dummies
df['QandA'] = df['question_id'].astype(str) + '_' + df['answer_id'].astype(str)
pd.get_dummies(df, columns=['QandA','genre','user_gender','user_ethnicity'])
输出：
movie_id user_id rated_value question_id answer_id QandA_1_1 QandA_1_2 QandA_1_3 QandA_1_4 QandA_2_1 QandA_2_2 genre_comedy genre_drama user_gender_female \
0 101 345 3.5 1 1 1 0 0 0 0 0 1 0 0
1 101 345 3.5 1 2 0 1 0 0 0 0 1 0 0
2 101 345 3.5 2 1 0 0 0 0 1 0 1 0 0
3 125 345 4.5 1 4 0 0 0 1 0 0 0 1 0
4 101 233 4.0 1 3 0 0 1 0 0 0 1 0 1
5 101 233 4.0 2 2 0 0 0 0 0 1 1 0 1
6 125 233 3.0 1 1 1 0 0 0 0 0 0 1 1
7 125 233 3.0 2 2 0 0 0 0 0 1 0 1 1
8 125 333 3.0 1 1 1 0 0 0 0 0 1 0 0
9 125 333 3.0 2 2 0 0 0 0 0 1 1 0 0
user_gender_male user_ethnicity_asian user_ethnicity_black user_ethnicity_white
0 1 0 0 1
1 1 0 0 1
2 1 0 0 1
3 1 0 0 1
4 0 0 1 0
5 0 0 1 0
6 0 0 1 0
7 0 0 1 0
8 1 1 0 0
9 1 1 0 0
我认为您需要pd.get_dummies：
pd.get_dummies(df, columns=['genre','user_gender','user_ethnicity'])
输出：
movie_id user_id rated_value question_id answer_id genre_comedy genre_drama user_gender_female user_gender_male user_ethnicity_asian user_ethnicity_black \
0 101 345 3.5 1 1 1 0 0 1 0 0
1 101 345 3.5 1 2 1 0 0 1 0 0
2 101 345 3.5 2 1 1 0 0 1 0 0
3 125 345 4.5 1 4 0 1 0 1 0 0
4 101 233 4.0 1 3 1 0 1 0 0 1
5 101 233 4.0 2 2 1 0 1 0 0 1
6 125 233 3.0 1 1 0 1 1 0 0 1
7 125 233 3.0 2 2 0 1 1 0 0 1
8 125 333 3.0 1 1 1 0 0 1 1 0
9 125 333 3.0 2 2 1 0 0 1 1 0
user_ethnicity_white
0 1
1 1
2 1
3 1
4 0
5 0
6 0
7 0
8 0
9 0