<jupyter_start><jupyter_text>**Histogram**<jupyter_code>n = np.random.randn(100000)
fig,axes = plt.subplots(1,2, figsize= (12,6))

axes[0].hist(n , color = "blue")
axes[0].set_title("deafault histogram")
axes[0].set_xlim(np.min(n),np.max(n))

axes[1].hist(n , cumulative = True ,bins= 50 ,color = "red")
axes[1].set_title("cumalative histogram")
axes[1].set_xlim(np.min(n),np.max(n))

fig.tight_layout()
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21621621621621623, 'fraction_numerical': 0.04279279279279279, 'mean_word_length': 8.081632653061224, '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': '221302', 'license': 'MIT', 'n_tokens_mistral': 219, 'n_tokens_neox': 196, 'n_words': 24, 'path': 'machine_learning/04-Matplotlib.ipynb', 'repo_name': 'imkaran23/Jupyter', 'size': '444'}