<jupyter_start><jupyter_text>Some exploratory data analysis Load<jupyter_code>import seaborn as sns
from sklearn.datasets import load_iris
df = load_iris(as_frame=True)['data']<jupyter_output><empty_output><jupyter_text>Clean<jupyter_code>df
df.shape
df = df[df['petal length (cm)'] > 2]
df.shape<jupyter_output><empty_output><jupyter_text>Plot<jupyter_code>sns.histplot(df['petal length (cm)'])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13176470588235295, 'fraction_numerical': 0.002352941176470588, 'mean_word_length': 13.2, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 14, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '56794', 'license': 'Apache-2.0', 'n_tokens_mistral': 178, 'n_tokens_neox': 173, 'n_words': 23, 'path': 'guides/refactor/eda.ipynb', 'repo_name': 'jramirez857/projects', 'size': '425'}