<jupyter_start><jupyter_text>Visualise dataset<jupyter_code>data.head()
if dataset_name == 'iris':
    sns.scatterplot(x='petal_length', y='petal_width', hue='species', data=data)
elif dataset_name == 'tips':
    sns.scatterplot(x='total_bill', y='tip', hue='sex', data=data)
elif dataset_name == 'titanic':
    sns.scatterplot(x='age', y='fare', hue='class', data=data)<jupyter_output><empty_output><jupyter_text>Record values<jupyter_code>count = len(data)
data_cols = list(data.columns)

pm.record('count', count)
pm.record('data_cols', data_cols)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17758620689655172, 'fraction_numerical': 0.0, 'mean_word_length': 9.375, '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': '397883', 'license': 'MIT', 'n_tokens_mistral': 231, 'n_tokens_neox': 223, 'n_words': 31, 'path': 'template.ipynb', 'repo_name': 'mexeniz/papermill-demo', 'size': '580'}