<jupyter_start><jupyter_text>study on painting removal<jupyter_code># df_paint = pd.read_excel(r'./data_raw/grinding_cycles_study/Paint_removal_cleaned.xlsx')
# df_paint['sn_group'] = df_paint['partid'].apply(lambda x : str(x)[0])
# df_plot = df_paint.groupby(by='sn_group').mean()
# df_plot.drop(columns = 'partid',inplace= True)
# sns.lineplot(data = df_plot.T,dashes = False)
# df_plot = df_paint.groupby(by='sn_group').max()
# df_plot.drop(columns = 'partid',inplace= True)
# sns.lineplot(data = df_plot.T,dashes = False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.17297297297297298, 'fraction_numerical': 0.0018018018018018018, 'mean_word_length': 8.114754098360656, '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': '62901', 'license': 'MIT', 'n_tokens_mistral': 237, 'n_tokens_neox': 232, 'n_words': 26, 'path': '02_griding_cycles_ds.ipynb', 'repo_name': 'WangCHEN9/at36_sliding_effort', 'size': '555'}