Read multiple *.txt files into Pandas Dataframe with filename as the first column

Question

Currently I have these codes which only reads one specific txt file and split into different columns. Each txt file is stored in the same directory and looks like this:

0 0.712518 0.615250 0.439180 0.206500
1 0.635078 0.811750 0.292786 0.092500

The code I wrote:

spark.read.format('csv').options(header='false').load("/mnt/datasets/model1/train/labels/2a.txt").toPandas()
df_2a.columns = ['Value']
df_2a_split = df_2a['Value'].str.split(' ', n=0, expand=True)
df_2a_split.columns = ['class','c1','c2','c3','c4']
display(df_2a_split)

And the output is like this:

class   c1       c2       c3          c4
0   0.712518    0.61525 0.43918     0.2065
1   0.635078    0.81175 0.292786    0.0925

However, I want to ingest all txt.files in a directory including the filename as the first column in the pandas dataframe. The expected result looks like below

file_name class   c1       c2      c3          c4
2a.txt  0   0.712518    0.61525 0.43918     0.2065
2a.txt  1   0.635078    0.81175 0.292786    0.0925
2b.txt  2   0.551273    0.5705  0.30198     0.0922
2b.txt  0   0.550212    0.31125 0.486563    0.2455

Answer 1

import os
import pandas as pd
import spark

directory = '/mnt/datasets/model1/train/labels/'

# Get all the filenames within your directory
files = []
for file in os.listdir(directory):
    if os.path.isfile(os.path.join(directory, file)):
        files.append(file)

# Create an empty df and fill it by looping your files
df = pd.DataFrame()
for file in files:
    df_temp = spark.read.format('csv').options(header='false').load(directory + file).toPandas()
    df_temp.columns = ['file_name', 'Value']
    df_temp = df_temp['Value'].str.split(' ', n=0, expand=True)
    df_temp.columns = ['file_name', 'class','c1','c2','c3','c4']
    df = pd.concat([df, df_temp], ignore_index=True)

# Fill the filename column
df['file_name'] = files

display(df)