<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import pandas as pd

pd.set_option("display.max_columns",None)
pd.set_option("display.max_rows",None)
from google.colab import drive
drive.mount('/content/drive')
path = '/content/drive/MyDrive/trans_500000.csv' 
path2 = '/content/drive/MyDrive/trans_550000.csv' 
path3 = '/content/drive/MyDrive/trans_600000.csv' 

data = pd.read_csv(path, header=None)
data2 = pd.read_csv(path2, header=None)
data3 = pd.read_csv(path3, header=None)
len(data), len(data2), len(data3)
data.tail()
data2.head()
data2.tail()
data3.head()
data3.tail()
data.columns = ['a', 'b', 'c', 'd', 'e']
data.columns
data.loc[data['a'] =='51807']
data.loc[data['a'] == '51811']
from google.colab import drive
drive.mount('/content/drive')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18272841051314143, 'fraction_numerical': 0.05006257822277847, 'mean_word_length': 6.547169811320755, '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': '37619', 'license': 'MIT', 'n_tokens_mistral': 336, 'n_tokens_neox': 312, 'n_words': 54, 'path': 'correction_notebook.ipynb', 'repo_name': 'GbotemiB/Housing_Project', 'size': '799'}