<jupyter_start><jupyter_text>1. Imports<jupyter_code>import pandas as pd<jupyter_output><empty_output><jupyter_text>2. Load data<jupyter_code>state_owned_ases = pd.read_csv("../../data/state-owned-ases/csv/state_owned_ases.csv", header="infer", keep_default_na=False, na_values=[''])<jupyter_output><empty_output><jupyter_text>3. Get insights of the dataset<jupyter_code>state_owned_ases.head()
print(f"The dataset cointains {state_owned_ases.shape[0]} rows and {state_owned_ases.shape[1]} columns")<jupyter_output>The dataset cointains 989 rows and 16 columns
{'fraction_non_alphanumeric': 0.13214285714285715, 'fraction_numerical': 0.017857142857142856, 'mean_word_length': 10.0, 'pattern_counts': {'":': 0, '<': 12, '<?xml version=': 0, '>': 12, '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': '336633', 'license': 'CC-BY-4.0', 'n_tokens_mistral': 215, 'n_tokens_neox': 208, 'n_words': 31, 'path': 'examples/state-owned-ases/load_from_single_table_csv_file.ipynb', 'repo_name': 'zhiyichenGT/state-owned-ases', 'size': '560'}