<jupyter_start><jupyter_text>Warning DataFrame<jupyter_code>import seaborn as sns
df = sns.load_dataset('mpg')
df.head()
dfsel = df[['origin', 'mpg']]
import numpy as np
dfsel['tipo coche'] = np.where(dfsel.mpg < 10, 'coche lento', 'coche rapido')
dfsel = df[['origin', 'mpg']].copy()
import numpy as np
dfsel['tipo coche'] = np.where(dfsel.mpg < 10, 'coche lento', 'coche rapido')
dfsel
a = ['hola']
a
b = a
b
b.append('adios')
b
a
a = ['hola']
a
b = a.copy()
b
b.append('adios')
b
a<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1926070038910506, 'fraction_numerical': 0.007782101167315175, 'mean_word_length': 5.602564102564102, 'pattern_counts': {'":': 0, '<': 7, '<?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': '468566', 'license': 'MIT', 'n_tokens_mistral': 236, 'n_tokens_neox': 231, 'n_words': 57, 'path': '00_Extras/01_warning-dataframe.ipynb', 'repo_name': 'valentin-gonzalez/resolving-python-data-science', 'size': '514'}