你可以试试这样做。

# Importing libraries.

import os  # Misc OS interfaces.
import pandas as pd  # Data manipulation library.

# Constants.

PATH_DATA_FOLDER = ''  # Specify your data folder location.

# Let's get your filenames and only leave unique ones.

list_files = os.listdir(PATH_DATA_FOLDER)
list_filenames = list(pd.unique([file.split('_')[0] for file in list_files]))

# Now, when we have our filenames, we can loop through them, read files and build dataframes.

for filename in list_filenames:
    # Get list of columns using the first data file available and append the `File` column.
    list_columns = list(pd.read_csv(os.path.join(PATH_DATA_FOLDER, filename + '_1.csv')).columns) + ['ID', 'File']
    # Create a dataframe which we will fill with data from different data files.
    df_final = pd.DataFrame(columns=list_columns)
    # Loop through files of same type (test, filex, name...).
    # Here we will loop through indices from 1 to 7.
    # You might also calculate these values dynamically. 
    for x in range(1, 8):
        # Reading a data file.
        df = pd.read_csv(os.path.join(PATH_DATA_FOLDER, filename + '_{}.csv'.format(x)))
        # Filling the `File` column with the file index. 
        df['File'] = x
        # Creating an ID column to track duplicates in different files.
        df['ID'] = range(0, len(df))
        # Appending our final dataframe
        df_final = df_final.append(df)
    # Resseting the dataframe indices.
    # Removing duplicates using the initial columns (without the `File` column).
    df_final = df_final.reset_index(drop=True).drop_duplicates(subset=list_columns[0:-1])
    # Dropping the unused ID column.
    df_final = df_final.drop(['ID'], axis=1)
    # Printing out the dataframe.
    print(df_final)

你可以通过 pd.concat 和 keys -参数（文档）。

frames = [df1, df2, ...] # your dataframes
file_names = ['file1', 'file2', ...] # the file names

df = pd.concat(frames, keys=file_names)

输出

Node  Target  Char1 Var2  Start
file1 0      1       2   23.1   No      1
      1      1       3   12.4   No      1
      2      1       4   52.1  Yes      1
      3      1      12   14.5   No      1
file2 0      1       2   23.1   No      1
      1      1       3   12.4   No      1
      2      1       4   52.1  Yes      1
      3      1      12   14.5   No      1
      4      2       1   23.1   No      0
      5      2      41   12.4  Yes      0
      6      3      15    8.2   No      0
      7      3      12   63.1   No      0
file3 0      1       2   23.1   No      1
      1      1       3   12.4   No      1
      2      1       4   52.1  Yes      1
      3      1      12   14.5   No      1
      4      2       1   23.1   No      0
      5      2      41   12.4  Yes      0
      6      3      15    8.2   No      0
      7      3      12   63.1   No      0
      8     41       2   12.4  Yes      0
      9     15       3    8.2   No      0
      10    15       8   12.2   No      0
      11    12       3   63.1   No      0

为了在文件中保留重复项，我们可以临时将级别1索引设置为列，以便 drop_duplicates 仅在跨文件复制时匹配。

df = df.reset_index(level=1).drop_duplicates()

# get rid of the extra column

df = df.drop('level_1', axis=1)

# Set the file name index as new column

df = df.reset_index().rename(columns={'index':'File'})

输出

File  Node  Target  Char1 Var2  Start
0   file1     1       2   23.1   No      1
1   file1     1       3   12.4   No      1
2   file1     1       4   52.1  Yes      1
3   file1     1      12   14.5   No      1
4   file2     2       1   23.1   No      0
5   file2     2      41   12.4  Yes      0
6   file2     3      15    8.2   No      0
7   file2     3      12   63.1   No      0
8   file3    41       2   12.4  Yes      0
9   file3    15       3    8.2   No      0
10  file3    15       8   12.2   No      0
11  file3    12       3   63.1   No      0