我最初从以下数据帧开始：
数据集与用户回答具有多个答案选择的多个问题有关，并且用户具有回答多个答案的能力。
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
我想通过旋转来平整这张桌子。我无需输入
genre, user_gender, user_ethnicity就可以成功完成以下操作：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，以便列将反映为
1_1, 1_2pivoted_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...但是当尝试添加
genre, user_gender, user_ethnicitypivoted_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)
它实际上是行不通的。
我的目标是像其他一样旋转
genre, user_gender, user_ethnicity，因此列将movie_id user_id rated_value 1_1 1_2 1_3 1_4...comedy, drama...,male, female, black, white, asianoutput:
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，其他所有内容分别反映为1和0。
最佳答案
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
关于python - Pandas 枢轴:多列枢轴，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/51090321/