映射2个数据帧并替换目标数据帧中匹配值的标头 [英] Mapping 2 dataframes and replacing header of matched values in target dataframe

问题描述

我有一个数据框：df1

I have a dataframe: df1

  SAP_Name  SAP_Class  SAP_Sec
  Avi       5          C 
  Rison     6          A 
  Slesh     7          B 
  San       8          C 
  Sud       7          B 

df2：

Name_Fi Class

Avi     5 
Rison   6 
Slesh   7 

我正在尝试将df2与df1匹配，以便匹配的值应将标头替换为df1 。

I am trying to match df2 to df1 such that the matching values should have the headers replaced same as df1.

SAP_Name  SAP_Class
 Ankan          5
 Rison          6
 Slesh          7

下面是我正在使用的代码：

Below is the code which I am using :

d = {}
for col2 in df2.columns:
    for col1 in df1.columns:
        cond = df2[col2].isin(df1[col1]).all()
        if cond:
           d[col2] = col1
df2 = df2.rename(columns=d)
print (df2)

我能为了在一个小文件中获得所需的输出，但是我的实际文件有112444行×446列，要更改的目标文件有3行×35列，在这种情况下，代码运行了很长时间。有人可以在这里帮助我吗？

I am able to get the desired output in a small file, however My actual file has 112444 rows × 446 columns and the target file to be changed has 3 rows × 35 columns , the code is running for a long long time in this case. Can anyone please help me here?

推荐答案

我认为如果性能很重要，请使用 issubset 的c $ c>：

In my opinion if performance is important use issubset with set:

d = {}
for col2 in df2.columns:
    for col1 in df1.columns:
        cond = set(df2[col2]).issubset(df1[col1])
        if cond:
           d[col2] = col1
df2 = df2.rename(columns=d)
print (df2)
  SAP_Name  SAP_Class
0      Avi          5
1    Rison          6
2    Slesh          7

编辑：

#create dictioanry of Series without dupes
dfs1 = {col1: df1[col1].drop_duplicates() for col1 in df1.columns}
#print (dfs1)

#create dictionary of sets
set2 = {col2: set(df2[col2]) for col2 in df2.columns}
#print (set2)

#loop buy both dictionaries and find columns for rename
d = {}
for col2, v2 in set2.items():
    for col1, v1 in dfs1.items():
        cond = v2.issubset(v1)
        if cond:
           d[col2] = col1
df2 = df2.rename(columns=d)
print (df2)
  SAP_Name  SAP_Class
0      Avi          5
1    Rison          6
2    Slesh          7

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