<jupyter_start><jupyter_text>Combining dataThis file combines all the scrapped files into a single csv file.<jupyter_code>import os
import pandas as pd
import numpy as np
import re
#Reading all the data into dataframe
os.chdir('scraped_data/')
cols=['index','name','details','size','address','available time','bed','bath','price','nearest MRT','distance']
data = pd.DataFrame(columns=cols)
for f in os.listdir('.'):
    if 'EW' in f:
        print('Reading',f,'...')
        temp= pd.read_csv(f,header=None,encoding = "ISO-8859-1",names=cols)
        data=data.append(temp)

data.reset_index(inplace=True,drop=True)
#output the data
os.chdir('..')
data.to_csv('data/data.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1671388101983003, 'fraction_numerical': 0.007082152974504249, 'mean_word_length': 5.669811320754717, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '339667', 'license': 'MIT', 'n_tokens_mistral': 253, 'n_tokens_neox': 243, 'n_words': 55, 'path': 'projects/group09/group09_notebook_03_CE9010_2018.ipynb', 'repo_name': 'ououkhin63/CE9010_2018', 'size': '706'}