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# scripts/clean_text_data.py
import os
import datetime
def clean_data():
"""
Reads a raw text file, cleans it, and saves it to the processed data folder.
"""
# --- Configuration ---
# Construct the filename based on today's date, matching the scraper's output
current_date = datetime.datetime.now().strftime("%Y-%m-%d")
raw_filename = f"bbc_nepali_articles_{current_date}.txt"
cleaned_filename = f"bbc_nepali_articles_{current_date}_cleaned.txt"
# Define the paths using our project structure
raw_file_path = os.path.join("data", "raw", raw_filename)
processed_file_path = os.path.join("data", "processed", cleaned_filename)
# Simple rule: we'll discard any line that has fewer than this many words.
MIN_WORDS_PER_LINE = 5
# --- End Configuration ---
print("--- Starting data cleaning process ---")
# Check if the raw file exists before we start
if not os.path.exists(raw_file_path):
print(f"Error: Raw data file not found at '{raw_file_path}'")
print("Please run the scraping script first.")
return
print(f"Reading raw data from: {raw_file_path}")
# Read all lines from the raw file
with open(raw_file_path, "r", encoding="utf-8") as f:
lines = f.readlines()
cleaned_lines = []
for line in lines:
# 1. Strip leading/trailing whitespace from the line
text = line.strip()
# 2. Apply our cleaning rules
# We keep the line only if it's not empty AND has enough words
if text and len(text.split()) >= MIN_WORDS_PER_LINE:
cleaned_lines.append(text)
# 3. Save the cleaned lines to the new file
print(f"Saving cleaned data to: {processed_file_path}")
os.makedirs(os.path.dirname(processed_file_path), exist_ok=True)
with open(processed_file_path, "w", encoding="utf-8") as f:
f.write("\n".join(cleaned_lines))
# Print a summary report
print("\n--- Cleaning Summary ---")
print(f"Total lines read: {len(lines)}")
print(f"Lines after cleaning: {len(cleaned_lines)}")
print(f"Lines discarded: {len(lines) - len(cleaned_lines)}")
print("------------------------")
if __name__ == "__main__":
clean_data() |