Python：如何通过保留第一个数据框的信息来合并列上的两个数据框？
我有两个数据框df1和df2。df1包含人的年龄信息，而df2包含人的性别信息。并非所有人都在里面df1或里面df2
df1 Name Age
0 Tom 34
1 Sara 18
2 Eva 44
3 Jack 27
4 Laura 30
df2
Name Sex
0 Tom M
1 Paul M
2 Eva F
3 Jack M
4 Michelle F
我想有人民的性别的信息df1和设置NaN，如果我没有在这个信息df2。我尝试这样做，df1 = pd.merge(df1, df2, on = 'Name', how = 'outer')但是我保留了一些df2我不想要的信息。
df1
Name Age Sex
0 Tom 34 M
1 Sara 18 NaN
2 Eva 44 F
3 Jack 27 M
4 Laura 30 NaN
回答：
Sample：df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'],
'Age': [34, 18, 44, 27, 30]})
#print (df1)
df3 = df1.copy()
df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Jack', 'Michelle'],
'Sex': ['M', 'M', 'F', 'M', 'F']})
#print (df2)
使用map由Series创建人set_index：
df1['Sex'] = df1['Name'].map(df2.set_index('Name')['Sex'])print (df1)
Name Age Sex
0 Tom 34 M
1 Sara 18 NaN
2 Eva 44 F
3 Jack 27 M
4 Laura 30 NaN
merge左连接的替代解决方案：
df = df3.merge(df2[['Name','Sex']], on='Name', how='left')print (df)
Name Age Sex
0 Tom 34 M
1 Sara 18 NaN
2 Eva 44 F
3 Jack 27 M
4 Laura 30 NaN
如果需要通过多列映射（例如Year和Code），则需要merge左连接：
df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'], 'Year':[2000,2003,2003,2004,2007],
'Code':[1,2,3,4,4],
'Age': [34, 18, 44, 27, 30]})
print (df1)
Name Year Code Age
0 Tom 2000 1 34
1 Sara 2003 2 18
2 Eva 2003 3 44
3 Jack 2004 4 27
4 Laura 2007 4 30
df2 = pd.DataFrame({'Name': ['Tom', 'Paul', 'Eva', 'Jack', 'Michelle'],
'Sex': ['M', 'M', 'F', 'M', 'F'],
'Year':[2001,2003,2003,2004,2007],
'Code':[1,2,3,5,3],
'Val':[21,34,23,44,67]})
print (df2)
Name Sex Year Code Val
0 Tom M 2001 1 21
1 Paul M 2003 2 34
2 Eva F 2003 3 23
3 Jack M 2004 5 44
4 Michelle F 2007 3 67
#merge by all columnsdf = df1.merge(df2, on=['Year','Code'], how='left')
print (df)
Name_x Year Code Age Name_y Sex Val
0 Tom 2000 1 34 NaN NaN NaN
1 Sara 2003 2 18 Paul M 34.0
2 Eva 2003 3 44 Eva F 23.0
3 Jack 2004 4 27 NaN NaN NaN
4 Laura 2007 4 30 NaN NaN NaN
#specified columns - columns for join (Year, Code) need always + appended columns (Val)
df = df1.merge(df2[['Year','Code', 'Val']], on=['Year','Code'], how='left')
print (df)
Name Year Code Age Val
0 Tom 2000 1 34 NaN
1 Sara 2003 2 18 34.0
2 Eva 2003 3 44 23.0
3 Jack 2004 4 27 NaN
4 Laura 2007 4 30 NaN
如果获取错误map意味着按连接列重复，则在这里Name：
df1 = pd.DataFrame({'Name': ['Tom', 'Sara', 'Eva', 'Jack', 'Laura'], 'Age': [34, 18, 44, 27, 30]})
print (df1)
Name Age
0 Tom 34
1 Sara 18
2 Eva 44
3 Jack 27
4 Laura 30
df3, df4 = df1.copy(), df1.copy()
df2 = pd.DataFrame({'Name': ['Tom', 'Tom', 'Eva', 'Jack', 'Michelle'],
'Val': [1,2,3,4,5]})
print (df2)
Name Val
0 Tom 1 <-duplicated name Tom
1 Tom 2 <-duplicated name Tom
2 Eva 3
3 Jack 4
4 Michelle 5
s = df2.set_index('Name')['Val']
df1['New'] = df1['Name'].map(s)
print (df1)
InvalidIndexError：重新索引仅对唯一值的Index对象有效
解决方案通过删除重复项DataFrame.drop_duplicates，或dict在最后一次重复匹配中使用map by ：
#default keep first values = df2.drop_duplicates('Name').set_index('Name')['Val']
print (s)
Name
Tom 1
Eva 3
Jack 4
Michelle 5
Name: Val, dtype: int64
df1['New'] = df1['Name'].map(s)
print (df1)
Name Age New
0 Tom 34 1.0
1 Sara 18 NaN
2 Eva 44 3.0
3 Jack 27 4.0
4 Laura 30 NaN
#add parameter for keep last value s = df2.drop_duplicates('Name', keep='last').set_index('Name')['Val']
print (s)
Name
Tom 2
Eva 3
Jack 4
Michelle 5
Name: Val, dtype: int64
df3['New'] = df3['Name'].map(s)
print (df3)
Name Age New
0 Tom 34 2.0
1 Sara 18 NaN
2 Eva 44 3.0
3 Jack 27 4.0
4 Laura 30 NaN
#map by dictionaryd = dict(zip(df2['Name'], df2['Val']))
print (d)
{'Tom': 2, 'Eva': 3, 'Jack': 4, 'Michelle': 5}
df4['New'] = df4['Name'].map(d)
print (df4)
Name Age New
0 Tom 34 2.0
1 Sara 18 NaN
2 Eva 44 3.0
3 Jack 27 4.0
4 Laura 30 NaN
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