Question

我有两个数据帧：dfDepas和df7;

dfDepas.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 7 entries, 0 to 6
Data columns (total 4 columns):
day_of_week    7 non-null object
P_ACT_KW       7 non-null float64
P_SOUSCR       7 non-null float64
depassement    7 non-null float64
dtypes: float64(3), object(1)
memory usage: 280.0+ bytes


df7.info()
<class 'pandas.core.frame.DataFrame'>
Index: 7 entries, Fri to Thurs
Data columns (total 6 columns):
ACT_TIME_AERATEUR_1_F1    7 non-null float64
ACT_TIME_AERATEUR_1_F3    7 non-null float64
ACT_TIME_AERATEUR_1_F5    7 non-null float64
ACT_TIME_AERATEUR_1_F6    7 non-null float64
ACT_TIME_AERATEUR_1_F7    7 non-null float64
ACT_TIME_AERATEUR_1_F8    7 non-null float64
dtypes: float64(6)
memory usage: 392.0+ bytes

我尝试根据['day_of_week']合并这两个数据帧，这是dfDepas数据帧中的索引。我不知道如何使用它：merged_df = pd.merge(dfDepas, df7, how='inner',on=['day_of_week'])

有什么好主意帮我吗？谢谢

亲切的问候

修改

dfDepas
day_of_week P_ACT_KW P_SOUSCR depassement 
Fri 157.258929 427.142857 0.0 
Mon 157.788110 426.875000 0.0 
Sat 166.989236 426.875000 0.0 
Sun 149.676215 426.875000 0.0 
Thurs 157.339286 427.142857 0.0 
Tues 151.122913 427.016021 0.0 
Weds 159.569444 427.142857 0.0


df7

ACT_TIME_AERATEUR_1_F1 ACT_TIME_AERATEUR_1_F3 ACT_TIME_AERATEUR_1_F5 ACT_TIME_AERATEUR_1_F6 ACT_TIME_AERATEUR_1_F7 ACT_TIME_AERATEUR_1_F8

Fri 0.326258 0.330253 0.791144 0.654682 3.204544 1.008550 
Sat -0.201327 -0.228196 0.044616 0.184003 -0.579214 0.292886 
Sun 5.068735 5.250199 5.407271 5.546657 7.823564 5.786713 
Mon -0.587129 -0.559986 -0.294890 -0.155503 2.013379 -0.131496 
Tues-1.244922 -1.510025 -0.788717 -1.098790 -0.996845 -0.718881 
Weds-3.264598 -3.391776 -3.188409 -3.041306 -4.846189 -4.668533 
Thurs -0.178179 0.011002 -1.907544 -2.084516 -6.119337

Answer 1

您可以使用reset_index并将0列重命名为day_of_week以进行匹配：

merged_df = pd.merge(dfDepas, 
                     df7.reset_index().rename(columns={0:'day_of_week'}),
                     on=['day_of_week'])

感谢Quickbeam2k1寻求其他解决方案：

merged_df = pd.merge(dfDepas.set_index('day_of_week'), 
                     df7,
                     right_index=True,
                     left_index =True)

使用python合并两个数据帧

1 个答案: