<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
name = ['John','Paul','Jane','Mike','Tony','Mary','Ben','Lynda','Lynne','Tory','Judy','Carl']
score = [20,90,80,77,0,55,10,88,87,78,98,100]
df = pd.DataFrame({'name':name, 'score':score})
df
pd.cut(df.score,4)
pd.qcut(df.score,4)
df['Quartile'] = pd.qcut(df['score'],4,['1stQ','2ndQ','3rdQ','4thQ'])
df
df[df['Quartile'] == '4thQ']
pd.get_dummies(df, columns=df.columns[2:])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.28051391862955033, 'fraction_numerical': 0.07066381156316917, 'mean_word_length': 12.764705882352942, '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': '218446', 'license': 'MIT', 'n_tokens_mistral': 237, 'n_tokens_neox': 214, 'n_words': 21, 'path': 'QCut.ipynb', 'repo_name': 'SuperDataWorld/Python', 'size': '467'}