s = pd.Series({'Gender':'F','Height':188},name='new_row')
s=pd.Series(['F',188],index=['Gender','Height'],name ='new_row')
s
df_append.append(s)# s=pd.Series({'Gender':'F','Height':188},name='new_row')# df_append.append(s)
0 M
1 F
10 NaN
11 NaN
Name: Gender, dtype: object 0 NaN
1 NaN
10 M
11 F
Name: Gender, dtype: object
0 173.0
1 192.0
10 NaN
11 NaN
Name: Height, dtype: float64 0 NaN
1 NaN
10 161.0
11 175.0
Name: Height, dtype: float64
left = pd.DataFrame({'key1':['K0','K0','K1','K2'],'key2':['K0','K1','K0','K1'],'A':['A0','A1','A2','A3'],'B':['B0','B1','B2','B3']})
right = pd.DataFrame({'key1':['K0','K1','K1','K2'],'key2':['K0','K0','K0','K0'],'C':['C0','C1','C2','C3'],'D':['D0','D1','D2','D3']})
right2 = pd.DataFrame({'key1':['K0','K1','K1','K2'],'key2':['K0','K0','K0','K0'],'C':['C0','C1','C2','C3']})
left = pd.DataFrame({'A':[1,2],'B':[2,2]})
right = pd.DataFrame({'A':[4,5,6],'B':[2,3,4]})#pd.merge(left, right, on='B', how='outer',validate='one_to_one') #报错
left = pd.DataFrame({'A':[1,2],'B':[2,1]})
pd.merge(left, right, on='B', how='outer',validate='one_to_one')
left = pd.DataFrame({'A':['A0','A1','A2'],'B':['B0','B1','B2']},index=['K0','K1','K2'])
right = pd.DataFrame({'C':['C0','C2','C3'],'D':['D0','D2','D3']},index=['K0','K2','K3'])
display(left)
display(right)
display(left.join(right))
left=left.rename_axis(index={None:'indx'})
right=right.rename_axis(index={None:'indx'})# display(left)# display(right)
pd.merge(left,right,how='left',on='indx')
A
B
K0
A0
B0
K1
A1
B1
K2
A2
B2
C
D
K0
C0
D0
K2
C2
D2
K3
C3
D3
A
B
C
D
K0
A0
B0
C0
D0
K1
A1
B1
NaN
NaN
K2
A2
B2
C2
D2
A
B
C
D
indx
K0
A0
B0
C0
D0
K1
A1
B1
NaN
NaN
K2
A2
B2
C2
D2
对于many_to_one模式下的合并,往往join更为方便
同样可以指定key:
left = pd.DataFrame({'A':['A0','A1','A2','A3'],'B':['B0','B1','B2','B3'],'key':['K0','K1','K0','K1']})
right = pd.DataFrame({'C':['C0','C1'],'D':['D0','D1']},index=['K0','K1'])
left.join(right, on='key')
A
B
key
C
D
0
A0
B0
K0
C0
D0
1
A1
B1
K1
C1
D1
2
A2
B2
K0
C0
D0
3
A3
B3
K1
C1
D1
多层key:
left = pd.DataFrame({'A':['A0','A1','A2','A3'],'B':['B0','B1','B2','B3'],'key1':['K0','K0','K1','K2'],'key2':['K0','K1','K0','K1']})
index = pd.MultiIndex.from_tuples([('K0','K0'),('K1','K0'),('K2','K0'),('K2','K1')],names=['key1','key2'])
right = pd.DataFrame({'C':['C0','C1','C2','C3'],'D':['D0','D1','D2','D3']},index=index)
display(left)
display(right)
display(left.join(right, on=['key1','key2']))
pd.merge(left,right,on=['key1','key2'],how='left')
left = pd.DataFrame({'A':['A0','A1','A2','A3'],'B':['B0','B1','B2','B3'],'key1':['K0','K0','K1','K2'],'key2':['K0','K1','K0','K1']})
index = pd.MultiIndex.from_tuples([('K0','K0'),('K1','K0'),('K2','K0'),('K2','K1')],names=['key1','key2'])
right = pd.DataFrame({'C':['C0','C1','C2','C3'],'D':['D0','D1','D2','D3']},index=index)
display(left.join(right, on=['key1','key2']))
pd.merge(left,right,on=['key1','key2'],how='left')
df1 = pd.read_csv('data/Course1.csv')
df2 = pd.read_csv('data/Course2.csv')
df_a11,df_a12,df_a21,df_a22 =0,0,0,0
df_a11= df1.query('课程类别 in ["学科基础课","专业必修课","专业选修课"]')
df_a12= df1.query('课程类别 not in ["学科基础课","专业必修课","专业选修课"]')
df_a21= df2.query('课程类别 in ["学科基础课","专业必修课","专业选修课"]')
df_a22= df2.query('课程类别 not in ["学科基础课","专业必修课","专业选修课"]')
df_a11.head()
(b) 将两张专业课的分数表和两张非专业课的分数表分别合并。
special = pd.concat([df_a11,df_a21])
common = pd.concat([df_a12,df_a22])
special.query('课程类别 not in ["学科基础课","专业必修课","专业选修课"]')
df = pd.concat([df1,df2])
special2 = df.query('课程类别 in ["学科基础课","专业必修课","专业选修课"]')
common2 = df.query('课程类别 not in ["学科基础课","专业必修课","专业选修课"]')
(special.equals(special2),common.equals(common2))
文章目录 Abstract1. Introduction2. Related Work3. Proposed Method3.1. Real-World Super-Resolution3.2. Downsampling with Domain TranslationNetwork Architectures 3.3. Frequency SeparationLoss Functions 3.4. Frequency Separation for Super-Resolution 4. Experi…
今天用read the docs的时候出现了下面的错误:
Extension error:
md \u7684 source_parser \u5df2\u6ce8\u518c
Command exited with exit code: 2
The server will continue serving the build folder, but the contents being served are no longer in sync with …