<jupyter_start><jupyter_text>Pandas:<jupyter_code>columns = [f"col{i}" for i in range(ncolumns)]
df = pd.DataFrame(np.random.randn(10, ncolumns), columns=columns)
display(df)<jupyter_output><empty_output><jupyter_text>Matplotlib:<jupyter_code>plt.ioff()
fig = plt.figure()
plt.ion()
plt.plot(np.sin(multiplier * np.linspace(0, 10, 100)))
display(fig)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1736842105263158, 'fraction_numerical': 0.021052631578947368, 'mean_word_length': 10.90625, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '516927', 'license': 'MIT', 'n_tokens_mistral': 171, 'n_tokens_neox': 162, 'n_words': 19, 'path': 'papermill/plot.ipynb', 'repo_name': 'nbgallery/dashboards', 'size': '380'}