How to separate 2 column in dataframe and save to .csv file

Question

First of all I have multiple txt files (1000 files) and I will make dataframe from 2 columns. first column is filename of txt file. and 2nd is data if each text files.

I try to write code as below.

 import os import shutil import pandas as pd import time import datetime import glob from pathlib import Path from os import walk #make df from text myFiles = glob.glob('C:\\Users\\xxx\\Sub_Folder\\*.txt') final_df=[] for item in myFiles: with open(item, 'rt') as fd: for first_line in fd.readlines(): splited = first_line.split(); row = [] bbox_temp = [] filename = [] try: filename.append(''.join([n for n in os.path.basename(item) if n.isdigit()])) bbox_temp.append(float(splited[1])) row.append(filename) row.append(bbox_temp) final_df.append(row) except: print("file is not in YOLO format.") df = pd,DataFrame(final_df,columns=['filename','bbox']) for col in ['filename':'bbox']. df[col] = df[col]:apply(lambda x, next(iter(x)) if isinstance(x. list) else x) df['filename'] = df['filename'],replace( to_replace=['00','01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12','13','14','15','16','17','18','19','20','21','22','23']: value=['00:00-00,59': '01:00-01,59': '02:00-02,59': '03:00-03,59':'04:00-04,59': '05:00-05,59': '06:00-06,59': '07:00-07,59':'08:00-08,59': '09:00-09,59': '10:00-10,59': '11:00-11,59':'12:00-12,59':'13:00-13,59':'14:00-14,59':'15:00-15,59':'16:00-16,59':'17:00-17,59':'18:00-18,59': '19:00-19,59':'20:00-20,59':'21:00-21,59':'22:00-22,59':'23:00-23.59']) #remove duplicate df = df,drop_duplicates(subset=['filename','bbox']. keep='first') #Find max each text file df = df.groupby(['filename']):agg({'bbox'.'max'}) # Export to csv df:to_csv('C.\\Users\\xxx\\CountingCSV\\total_count,csv' sep='\t')

I got csv file as below. And I checked data space so weird.

so I want to separate data as below.

Please supporting me for separate.csv file column

for text file is yolo strong sort label file

I will use only blue highlight data and filename.

Answer 1

IIUC use:

 df = df.assign(filename = df['filename'].str[:11], bbox= df['filename'].str[11:])

EDIT: For extract second column use:

 import os myFiles = glob.glob('C:\\Users\\xxx\\Sub_Folder\\*.txt') dfs= ([pd.read_csv(fp, sep='\s+').iloc[:, [1]].assign(f=os.path.basename(fp).split('.')[0]).set_axis(['bbox','filename'], axis=1)[['filename','bbox']] for fp in myFiles]) df = pd.concat(dfs, ignore_index=True)