<jupyter_start><jupyter_text>Generating random numbers and setting theseed<jupyter_code>import random
print([random.uniform(0, 10) for x in range(3)])
print([random.uniform(0, 10) for x in range(3)])
random.seed(12345)
print([random.uniform(0, 10) for x in range(3)])
random.seed(12345)
print([random.uniform(0, 10) for x in range(3)])<jupyter_output>[4.166198725453412, 0.10169169457068361, 8.252065092537432]
{'fraction_non_alphanumeric': 0.14878048780487804, 'fraction_numerical': 0.18536585365853658, 'mean_word_length': 8.340909090909092, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '530389', 'license': 'MIT', 'n_tokens_mistral': 207, 'n_tokens_neox': 159, 'n_words': 35, 'path': 'Chapter02/Basic probability for predictive modeling.ipynb', 'repo_name': 'arifmudi/Predictive-Analytics-with-TensorFlow', 'size': '410'}