<jupyter_start><jupyter_text>candidate_cell_dict = tidy_df.groupby('symbol')    counts = candidate_cell_dict.count()['barcode'].to_dict()    candidate_cell_dict = candidate_cell_dict['barcode'].apply(frozenset)<jupyter_code>dat.groupby('symbol')['barcode'].count().to_dict()['NRBP1']
dat.head()
print(dat.shape)
s = time.time()
gather_gene_sets(dat, min_percent_cells=.01)
t = time.time()

print(str(t-s) + ' seconds')<jupyter_output>303 first candidates!
Generated next valid candidates! Size: 2
45753
(frozenset({'HCK'}), frozenset({'RIPK1'}))
50.04134488105774 seconds to count candidates
0.012686014175415039 seconds to compress
47.803678035736084 seconds to count expected values
Evaluated 5182 valid candidates!
created iter of size 26847942
{'fraction_non_alphanumeric': 0.13520749665327977, 'fraction_numerical': 0.10307898259705489, 'mean_word_length': 8.234567901234568, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '518657', 'license': 'MIT', 'n_tokens_mistral': 313, 'n_tokens_neox': 265, 'n_words': 51, 'path': 'demos/python_demo.ipynb', 'repo_name': 'isrobson/pyDataTools', 'size': '747'}