<jupyter_start><jupyter_text>list_mirna = []for i in list_name:    cat = np.unique(i['miRNA'].tolist()).tolist()    list_mirna += catcat = np.unique(list_mirna)dictionar = {}for i in cat:    print(i, end=': ')    count = 0    for j in range(len(list_name)):        k = list_name[j]        if i in k['miRNA'].tolist():            print(str_name[j], end=',')            count += 1    if count in dictionar:        dictionar[count] = list(dictionar[count]).append(i)        print(dictionar[count])    else:        dictionar[count] = i    print(2*'\n')<jupyter_code>key = list(dictionar.keys())
dictionar.values()
help(dict)<jupyter_output>Help on class dict in module builtins:

class dict(object)
 |  dict() -> new empty dictionary
 |  dict(mapping) -> new dictionary initialized from a mapping object's
 |      (key, value) pairs
 |  dict(iterable) -> new dictionary initialized as if via:
 |      d = {}
 |      for k, v in iterable:
 |          d[k] = v
 |  dict(**kwargs) -> new dictionary initialized with the name=value pairs
 |      in the keyword argument list.  For example:  dict(one=1, two=2)
 |  
 |  Methods defined here:
 |  
 |  __contains__(self, key, /)
 |      True if the dictionary has the specified key, else False.
 |  
 |  __delitem__(self, key, /)
 |      Delete self[key].
 |  
 |  __eq__(self, value, /)
 |      Return self==value.
 |  
 |  __ge__(self, value, /)
 |      Return self>=value.
 |  
 |  __getattribute__(self, name, /)
 |      Return getattr(self, name).
 |  
 |  __getitem__(...)
 |      x.__getitem__(y) <==> x[y]
 |  
 |  __gt__(self, value, /)
 |      Return self>value.
 |  
 |  __init__(self,[...]
{'fraction_non_alphanumeric': 0.15356489945155394, 'fraction_numerical': 0.0030469226081657527, 'mean_word_length': 2.6733780760626398, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 11, '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': '524930', 'license': 'CC0-1.0', 'n_tokens_mistral': 570, 'n_tokens_neox': 564, 'n_words': 141, 'path': 'TargetScan/For_St_Results_noexp.ipynb', 'repo_name': 'alex123012/computer-literacy-2020', 'size': '1641'}