<jupyter_start><jupyter_text>Let's choose a dish from 101 dataset and extract different varieties of the same from 6000 dataset We all love varieties in our faood :-)<jupyter_code># Use process to extract top 20 matches for the recipe pongal
possibilities = process.extract("pongal", df_6000['TranslatedRecipeName'], limit=20, scorer=fuzz.partial_ratio)
# Get matches with partial ratio as 100
# Partial rtaio compare subsections of the string
extracted_matches = [possible for possible in possibilities if possible[1] == 100]
extracted_matches
# Extract only the recipe name from the result
get_recipe_name = [extracted_matches[i][0] for i in range(len(extracted_matches))]
# Get recipes of the extracted matches
df_6000[df_6000['TranslatedRecipeName'].isin(get_recipe_name)]<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07692307692307693, 'fraction_numerical': 0.038461538461538464, 'mean_word_length': 7.23469387755102, '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': '479521', 'license': 'Apache-2.0', 'n_tokens_mistral': 263, 'n_tokens_neox': 232, 'n_words': 86, 'path': 'combine-indian-food-datasets-get-similar-recipes.ipynb', 'repo_name': 'SSaishruthi/Kaggle_Notebooks', 'size': '806'}