<jupyter_start><jupyter_text>visualizing data<jupyter_code>import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline  
plt.figure(figsize=(25,10))
dataset['repository'].value_counts().plot(kind='bar',title='Repositories')
plt.show()
dataset['rule'].value_counts().plot(kind='bar',title='Rules')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17052023121387283, 'fraction_numerical': 0.011560693641618497, 'mean_word_length': 13.458333333333334, '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': '49237', 'license': 'MIT', 'n_tokens_mistral': 130, 'n_tokens_neox': 124, 'n_words': 14, 'path': 'notebooks/BadSmells Detection.ipynb', 'repo_name': 'egmaziero/CodeSmells', 'size': '346'}