Python 3.x 根据另一个数据帧替换数据帧中的值_Python 3.x_Pandas

Python 3.x 根据另一个数据帧替换数据帧中的值

python-3.x pandas

Python 3.x 根据另一个数据帧替换数据帧中的值,python-3.x,pandas,Python 3.x,Pandas,我有两个数据帧： >>> tab1 Value Sp_name 0 None ROOT 1 0.066 Genus_1_sp1 2 0.1044 Genus_2_sp1 3 0.0708 EDGE 4 0.0586 Genus_3_sp1 5 0.0083 Genus_4_sp1 其思想是解

我有两个数据帧：

   >>> tab1
        Value       Sp_name    
    0   None        ROOT  
    1   0.066       Genus_1_sp1
    2   0.1044      Genus_2_sp1
    3   0.0708      EDGE  
    4   0.0586      Genus_3_sp1
    5   0.0083      Genus_4_sp1

其思想是解析另一个数据帧，例如：

>>> tab2
Old_name             New_name
Genus_1_sp1_A     Genus_1_sp1
Genus_2_sp1_A     Genus_2_sp1
Genus_3_sp1_A     Genus_3_sp1
Genus_4_sp1_A     Genus_4_sp1

并在选项卡1中替换与

tab2

中的

Sp\u名称匹配的Sp\u名称
，然后将Sp\u名称
替换为相应的旧名称
在示例中，我应该得到：
>>> tab1
    Value       Sp_name    
0   None        ROOT  
1   0.066       Genus_1_sp1_A
2   0.1044      Genus_2_sp1_A
3   0.0708      EDGE  
4   0.0586      Genus_3_sp1_A
5   0.0083      Genus_4_sp1_A

到目前为止，我试过：
for i in tab1['Sp_name']:
    found= tab2[tab2['New_name'].str.contains(i)]
    if len(found) > 0:
        tab1.at[i,'Sp_name'] = str(row['Old_name'])

从tab2
创建名称词典，然后使用.replace
将其替换回tab1

name_dict = dict(zip(tab2.New_name, tab2.Old_name))
tab1['Sp_name'] = tab1['Sp_name'].replace(name_dict)

tab1

    Value        Sp_name
0    None           ROOT
1   0.066  Genus_1_sp1_A
2  0.1044  Genus_2_sp1_A
3  0.0708           EDGE
4  0.0586  Genus_3_sp1_A
5  0.0083  Genus_4_sp1_A

从tab2
创建名称词典，然后使用.replace
将其替换回tab1

name_dict = dict(zip(tab2.New_name, tab2.Old_name))
tab1['Sp_name'] = tab1['Sp_name'].replace(name_dict)

tab1

    Value        Sp_name
0    None           ROOT
1   0.066  Genus_1_sp1_A
2  0.1044  Genus_2_sp1_A
3  0.0708           EDGE
4  0.0586  Genus_3_sp1_A
5  0.0083  Genus_4_sp1_A

使用
输出
    Value      Sp_name
0    None         ROOT
1   0.066  Genus_1_sp1
2  0.1044  Genus_2_sp1
3  0.0708         EDGE
4  0.0586  Genus_3_sp1
5  0.0083  Genus_4_sp1

使用
输出
    Value      Sp_name
0    None         ROOT
1   0.066  Genus_1_sp1
2  0.1044  Genus_2_sp1
3  0.0708         EDGE
4  0.0586  Genus_3_sp1
5  0.0083  Genus_4_sp1

试试看。它是为这种要求而设计的。使用其他数据帧中的非NA值就地修改
以下是我的示例代码供您参考：
from io import StringIO
import pandas as pd
from pprint import pprint

tab1="""
Value,Sp_name
None,ROOT
0.066,Genus_1_sp1
0.1044,Genus_2_sp1
0.0708,EDGE
0.0586,Genus_3_sp1
0.0083,Genus_4_sp1
"""
tab2="""
Old_name,New_name
Genus_1_sp1_A,Genus_1_sp1
Genus_2_sp1_A,Genus_2_sp1
Genus_3_sp1_A,Genus_3_sp1
Genus_4_sp1_A,Genus_4_sp1A
"""
df1 = pd.read_csv(StringIO(tab1)).set_index("Sp_name",drop=False)

df2=  pd.read_csv(StringIO(tab2)).rename(columns={"Old_name":"Sp_name"}).set_index("New_name")
df1.index.name ='New_name'

new_df = df1.copy()
new_df.update(df2)

print("\nthis is table 1 ")
pprint(df1,)

print("\nthis is table 2 ")

pprint(df2,)

print("\nthis is updated table")

pprint(new_df.reset_index(drop=True),)

这是输出
this is table 1 
              Value      Sp_name
New_name                        
ROOT           None         ROOT
Genus_1_sp1   0.066  Genus_1_sp1
Genus_2_sp1  0.1044  Genus_2_sp1
EDGE         0.0708         EDGE
Genus_3_sp1  0.0586  Genus_3_sp1
Genus_4_sp1  0.0083  Genus_4_sp1

this is table 2 
                    Sp_name
New_name                   
Genus_1_sp1   Genus_1_sp1_A
Genus_2_sp1   Genus_2_sp1_A
Genus_3_sp1   Genus_3_sp1_A
Genus_4_sp1A  Genus_4_sp1_A

this is updated table
    Value        Sp_name
0    None           ROOT
1   0.066  Genus_1_sp1_A
2  0.1044  Genus_2_sp1_A
3  0.0708           EDGE
4  0.0586  Genus_3_sp1_A
5  0.0083    Genus_4_sp1

试试看。它是为这种要求而设计的。使用其他数据帧中的非NA值就地修改
以下是我的示例代码供您参考：
from io import StringIO
import pandas as pd
from pprint import pprint

tab1="""
Value,Sp_name
None,ROOT
0.066,Genus_1_sp1
0.1044,Genus_2_sp1
0.0708,EDGE
0.0586,Genus_3_sp1
0.0083,Genus_4_sp1
"""
tab2="""
Old_name,New_name
Genus_1_sp1_A,Genus_1_sp1
Genus_2_sp1_A,Genus_2_sp1
Genus_3_sp1_A,Genus_3_sp1
Genus_4_sp1_A,Genus_4_sp1A
"""
df1 = pd.read_csv(StringIO(tab1)).set_index("Sp_name",drop=False)

df2=  pd.read_csv(StringIO(tab2)).rename(columns={"Old_name":"Sp_name"}).set_index("New_name")
df1.index.name ='New_name'

new_df = df1.copy()
new_df.update(df2)

print("\nthis is table 1 ")
pprint(df1,)

print("\nthis is table 2 ")

pprint(df2,)

print("\nthis is updated table")

pprint(new_df.reset_index(drop=True),)

这是输出
this is table 1 
              Value      Sp_name
New_name                        
ROOT           None         ROOT
Genus_1_sp1   0.066  Genus_1_sp1
Genus_2_sp1  0.1044  Genus_2_sp1
EDGE         0.0708         EDGE
Genus_3_sp1  0.0586  Genus_3_sp1
Genus_4_sp1  0.0083  Genus_4_sp1

this is table 2 
                    Sp_name
New_name                   
Genus_1_sp1   Genus_1_sp1_A
Genus_2_sp1   Genus_2_sp1_A
Genus_3_sp1   Genus_3_sp1_A
Genus_4_sp1A  Genus_4_sp1_A

this is updated table
    Value        Sp_name
0    None           ROOT
1   0.066  Genus_1_sp1_A
2  0.1044  Genus_2_sp1_A
3  0.0708           EDGE
4  0.0586  Genus_3_sp1_A
5  0.0083    Genus_4_sp1

您可以使用来进行映射。它也是最矢量化的（IMO）：

您可以使用来进行映射。它也是最矢量化的（IMO）：

谢谢你的解释！欢迎。祝你今天愉快。谢谢你的解释！欢迎。祝您有个美好的一天。