尝试从现有列的内容创建新的数据框列。用例子更容易解释。我想转换一下：
. Yr Month Class Cost
1 2015 1 L 19.2361
2 2015 1 M 29.4723
3 2015 1 S 48.5980
4 2015 1 T 169.7630
5 2015 2 L 19.1506
6 2015 2 M 30.0886
7 2015 2 S 49.3765
8 2015 2 T 167.0000
9 2015 3 L 19.3465
10 2015 3 M 29.1991
11 2015 3 S 46.2580
12 2015 3 T 157.7916
13 2015 4 L 18.3165
14 2015 4 M 28.2314
15 2015 4 S 44.5844
16 2015 4 T 162.3241
17 2015 5 L 17.4556
18 2015 5 M 27.0434
19 2015 5 S 42.8841
20 2015 5 T 159.3457
21 2015 6 L 16.5343
22 2015 6 M 24.9853
23 2015 6 S 40.5612
24 2015 6 T 153.4902
...进入以下内容，以便我可以绘制4个单独的行[L，M，S，T]：
. Yr Month L M S T
1 2015 1 19.2361 29.4723 48.5980 169.7630
2 2015 2 19.1506 30.0886 49.3765 167.0000
3 2015 3 19.3465 29.1991 46.2580 157.7916
4 2015 4 18.3165 28.2314 44.5844 162.3241
5 2015 5 17.4556 27.0434 42.8841 159.3457
6 2015 6 16.5343 24.9853 40.5612 153.4902
我能够以一种非常笨拙的方式解决这个问题，通过过滤“班级”中的数据框架。列...然后3个单独的合并。
list_class = ['L', 'M', 'S', 'T']
year = 'Yr'
month = 'Month'
df_class = pd.DataFrame()
df_class1 = pd.DataFrame()
df_class2 = pd.DataFrame()
df_class1 = merge(df[[month, year, 'Class','Cost']][df['Class']==list_class[0]], df[[month, year, 'Class','Cost']][df['Class']==list_class[1]], \
left_on=[month, year], right_on=[month, year])
df_class2 = merge(df[[month, year, 'Class','Cost']][df['Class']==list_class[2]], df[[month, year, 'Class','Cost']][df['Class']==list_class[3]], \
left_on=[month, year], right_on=[month, year])
df_class = merge(df_class1, df_class2, left_on=[month, year], right_on=[month, year]).groupby([year, month]).mean().plot(figsize(15,8))
必须有一种更有效的方式。感觉它应该用groupby来完成，但是我无法解决它。
答案 0 :(得分：5)
您可以先将df转换为多级索引类型，然后unstack级别Class将为您提供所需内容。假设df是帖子开头显示的原始数据框。
df.set_index(['Yr', 'Month', 'Class'])['Cost'].unstack('Class')
Out[29]:
Class L M S T
Yr Month
2015 1 19.2361 29.4723 48.5980 169.7630
2 19.1506 30.0886 49.3765 167.0000
3 19.3465 29.1991 46.2580 157.7916
4 18.3165 28.2314 44.5844 162.3241
5 17.4556 27.0434 42.8841 159.3457
6 16.5343 24.9853 40.5612 153.4902