# get your files

files = glob.glob('*.xlsx')

# read your files set the index and locate the mass column

# use list comprehension to iterate through your dfs and concatenate them together

df = pd.concat([pd.read_excel(file).set_index('ID')['Mass'].rename(file.split('.')[0]) for file in files], axis=1)

上面的列表基本上是：

# iterate through your files

for file in files:
    # read each file into memory, set the index, select the Mass column,
    # then rename the column to the file name
    pd.read_excel(file).set_index('ID')['Mass'].rename(file.split('.'))[0]

使用merge and reduce-idea是获取所有Dataframe的子集，然后合并id列上的所有df。

from functools import reduce
use_cols =  ['ID', 'Mass']
data_frames =[df1,df2,df3,df4]
data_frames = [df[use_cols] for df in data_frames]
final_df = reduce(lambda left,right,: pd.merge(left,right,on=['ID'],
                                            how='outer'), data_frames)

要直接加载Dataframe列表中的Dataframe，请使用（在路径构造函数路径（'.'）中提供所需的路径表示当前目录）-

from pathlib import Path
data_frames = [pd.read_excel(xlsx_file,use_cols=['ID', 'Mass']) for xlsx_file in Path('.').glob('**/*.xlsx')] #you can convert this list comprehension to generator if required.

最后，要重命名列，可以使用-

new_cols  = [f'Column {i}' for i in range(len(final_df.columns.values[1:]))]
new_cols.insert(0,'ID')
final_df.columns = new_cols