如何在使用 pandas 读取多个文件时重命名列

Question

I have two data frames (to excel files) with the below columns

File 1- columns

person_ID   Test_CODE   REGISTRATION_DATE   subject_CD   subject_DESCRIPTION    subject_TYPE

File 2- columns

person_ID   Test_CODE   REGISTRATION_DATE   subject_Code subject_DESCRIPTION    subject_Indicator

However, the columns subject_CD and subject_Code mean the same. Similarly, subject_TYPE and subject_Indicator mean the same. So, I would like to rename them when I read the excel file

I tried the below but it doesn't work

dfs = []       
for f in files:
    df = pd.read_excel(f, sep=",",low_memory=False)
    print(df.columns)
    df1 = df[df.columns.intersection(['person_ID','Test_CODE','REGISTRATION_DATE','subject_CD','subject_DESCRIPTION','subject_TYPE'])].rename(columns={'subject_TYPE':'subject_Indicator','subject_CD':'subject_Code'})
    dfs.append(df1)

Since, I would like to append/merge both the files, I expect the column names in my final data frame to be like as shown below

person_ID   Test_CODE   REGISTRATION_DATE   subject_Code subject_DESCRIPTION subject_Indicator

Can help me with this?

Answer 1

If you want to retain the columns of the first file which is read you can do something like this which stores the columns of the first iteration and assigns the column to the rest of the files:

dfs = []       
for e,f in enumerate(files):
    df = pd.read_excel(f)
    print(df.columns)
    if e == 0:
        col = df.columns
    df.columns=col
    dfs.append(df)


Index(['person_ID', 'Test_CODE', 'REGISTRATION_DATE', 'subject_CD',
       'subject_DESCRIPTION', 'subject_TYPE'],
      dtype='object')
Index(['person_ID', 'Test_CODE', 'REGISTRATION_DATE', 'subject_Code',
       'subject_DESCRIPTION', 'subject_Indicator'],
      dtype='object')

---

[df.columns for df in dfs] #pd.concat(dfs)

[Index(['person_ID', 'Test_CODE', 'REGISTRATION_DATE', 'subject_CD',
        'subject_DESCRIPTION', 'subject_TYPE'],
       dtype='object'),
 Index(['person_ID', 'Test_CODE', 'REGISTRATION_DATE', 'subject_CD',
        'subject_DESCRIPTION', 'subject_TYPE'],
       dtype='object')]

Answer 2

Rename 2 columns from particular df:

 df.rename({"subject_CD": "subject_Code", "subject_TYPE": "subject_Indicator"}, axis='columns', inplace =True) 

Also You can connect df1 and df2 - on the same columns:

frames = [df1, df2]
result = pd.concat(frames)