<jupyter_start><jupyter_text>魔法命令 run```py%run xxx.py```<jupyter_code>%run hello.py<jupyter_output>Hello 今晚打老虎 !<jupyter_text>timeit```%timeit L = [ i for i in range(1000) ]```<jupyter_code>%timeit L = [ i*2 for i in range(100000) ]
%%timeit
L = []
for i in range(100000):
    L.append(i*2)<jupyter_output>15.9 ms ± 1.29 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)<jupyter_text>time<jupyter_code>%time L = [ i for i in range(1000) ]<jupyter_output>CPU times: user 50 µs, sys: 1e+03 ns, total: 51 µs
Wall time: 53.9 µs<jupyter_text>lsmagic列出魔法命令<jupyter_code>%lsmagic<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15435139573070608, 'fraction_numerical': 0.06896551724137931, 'mean_word_length': 6.261904761904762, 'pattern_counts': {'":': 0, '<': 14, '<?xml version=': 0, '>': 14, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '571137', 'license': 'MIT', 'n_tokens_mistral': 318, 'n_tokens_neox': 289, 'n_words': 63, 'path': '01-magic-command.ipynb', 'repo_name': 'zhubinqiang/my-machine-learning', 'size': '609'}