<jupyter_start><jupyter_text>Food Dataset ExampleUsing Autonormalize to normalize a kaggle dataset about food purchasing. You can find the dataset for this demo on kaggle at https://www.kaggle.com/roshansharma/world-food-production.<jupyter_code>import os

import pandas as pd
from demo.food import load_sample

from featuretools.autonormalize import autonormalize as an
food_df = load_sample()
food_df.head()
entityset = an.auto_entityset(food_df, name='Foods')
entityset
entityset.plot()
import featuretools as ft
fm, features = ft.dfs(entityset=entityset, target_dataframe_name='Area Code_Element Code_Item Code')
features<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07633587786259542, 'fraction_numerical': 0.0, 'mean_word_length': 7.2, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 1, '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': '370630', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 213, 'n_tokens_neox': 207, 'n_words': 59, 'path': 'docs/source/guides/kaggle_food_dataset.ipynb', 'repo_name': 'isabella232/autonormalize', 'size': '655'}