<jupyter_start><jupyter_text>using groupby<jupyter_code>from itertools import groupby
{x[0]: list(x[1]) for x in groupby(apparels_list, key=lambda x : x.city)}
example = {'foo': [1,2,3], 'bar': [3, 4, 1]}
reduce(lambda res, key: res.union(example[key]), example, set())<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18729096989966554, 'fraction_numerical': 0.026755852842809364, 'mean_word_length': 8.67741935483871, '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': '122875', 'license': 'MIT', 'n_tokens_mistral': 123, 'n_tokens_neox': 121, 'n_words': 27, 'path': 'functional_programming/python-mapreduce-examples-master/max_value_by_store/solution.ipynb', 'repo_name': 'musram/Advanced-Python', 'size': '299'}