<jupyter_start><jupyter_text>This will analyze the menu of PyRamen****<jupyter_code># Import Libraries
from pathlib import Path
import csv
from collections import Counter
menu_filepath = Path("menu_data.csv")
sales_filepath = Path("sales_data.csv")
# Initialize List Objects to Hold Menu and Sales Data
menu = []
sales = []
# Read the Menu Data
with open(menu_filepath, "r") as menu_file:
    csvreader_menu = csv.reader(menu_file, delimiter = ',')

    # Set Header
    menu_header = next(csvreader_menu)

    # Loop Over Data and Append to Menu List Object
    for row in csvreader_menu:
        item = row[0]
        menu.append(item)
        category = row[1]
        menu.append(category)
        description = row[2]
        menu.append(description)
        price = row[3]
        menu.append(price)
        cost = row[4]
        menu.append(cost)
# Read the Sales Data
with open(sales_filepath, "r") as sales_file:
    csvreader_sales = csv.reader(sales_file, delimiter = ',')

    # Set Header
    sales_header = next(csvreader_sales)

    # Loop Over Data and Append to Menu List Object
    for row in csvreader_sales:
        line_item_id = row[0]
        sales.append(line_item_id)
        date = row[1]
        sales.append(date)
        credit_card_number = row[2]
        sales.append(credit_card_number)
        quantity = row[3]
        sales.append(quantity)
        menu_item = row[4]
        sales.append(menu_item)
# Initialize Dict Object to Hold Key-Value Pairs of Items and Metrics
report = {}

# Initialize a Row Counter Variable
row_count = 0

# Loop Over Every Row in the Sales List Object
for row in sales:<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08839446782922429, 'fraction_numerical': 0.00661455201443175, 'mean_word_length': 3.0388349514563107, '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': '219702', 'license': 'MIT', 'n_tokens_mistral': 541, 'n_tokens_neox': 512, 'n_words': 154, 'path': 'PyRamen/main.ipynb', 'repo_name': 'ameghani10/python-homework', 'size': '1663'}