python - Pandas:如何通过保留第一个数据帧的信息来合并列上的两个数据帧？

Question

我有两个数据帧df1和df2。 df1包含人的年龄信息，而df2包含人的性别信息。并非所有的人都在 df1 或 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

我想在df1中获取人员的性别信息，如果在df2中没有此信息，则设置NaN 。我尝试执行 df1 = pd.merge(df1, df2, on = 'Name', how = 'outer') 但我在 df2 中保留了一些人的信息我不想要的。

df1
     Name   Age     Sex
0     Tom    34      M
1     Sara   18     NaN
2     Eva    44      F
3     Jack   27      M
4     Laura  30     NaN

Answer 1

示例:

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 set_index 创建的系列 :

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

替代解决方案 merge左连接:

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

<小时/>

如果需要按多列进行映射(例如年份和代码)，则需要使用左连接合并:

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 columns
df = 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

<小时/>

如果 map 出现错误，则意味着连接列重复，此处 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: Reindexing only valid with uniquely valued Index objects

解决方案已被 DataFrame.drop_duplicates 删除重复项，或者使用 map by dict 进行最后一次重复匹配:

#default keep first value
s = 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 dictionary
d = 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