<jupyter_start><jupyter_text><jupyter_code>score = [[15, 16, 17],
         [25, 26, 27],
         [35, 36, 37]]
type(score)
import numpy as np
np_score = np.array(score)
np_score
np_score.strides
type(np_score)
import pandas as pd
df_score = pd.DataFrame(np_score, index=['A_class', 'B_class', 'C_class'], columns=['kor', 'eng', 'math'])
df_score
avr_kor = df_score['kor'].mean()
avr_kor
max_kor = df_score['kor'].max()
max_kor
new_np_array = df_score.values
df_score.index
df_score.columns
new_np_array
type(new_np_array)
new_list_score = new_np_array.tolist()
type(new_list_score)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1503267973856209, 'fraction_numerical': 0.029411764705882353, 'mean_word_length': 6.475609756097561, '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': '488121', 'license': 'MIT', 'n_tokens_mistral': 268, 'n_tokens_neox': 257, 'n_words': 48, 'path': 'python/data_type_conversion.ipynb', 'repo_name': 'skawns0724/KOSA-Big-Data_Vision', 'size': '612'}