<jupyter_start><jupyter_text>tecla de atalho Shift + tab<jupyter_code>np.arange(0, 10)
np.arange(0,10,3)
np.zeros(5)
np.zeros((3,4))
np.ones((5, 6))<jupyter_output><empty_output><jupyter_text>Matriz identidade<jupyter_code>np.eye(4,4)<jupyter_output><empty_output><jupyter_text>np.linspace(start, the end, quantos numero do intervalo irá a aparecer)<jupyter_code>np.linspace(0, 10, 2)
np.random.rand(3, 4 ,3)
np.random.rand(10) * 10
np.random.randn(3,4)
np.random.randint(0, 100, 10)
np.round(np.random.rand(5), 4)
vet = np.random.rand(25)
vet
vet.reshape(5,5)
vet2 = vet.reshape((5,5))
vet2
vet2.shape
vet2.max()
vet2.min()
vet2.argmax()
vet2.argmin()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18181818181818182, 'fraction_numerical': 0.07038123167155426, 'mean_word_length': 7.756410256410256, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, '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': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '549755', 'license': 'Apache-2.0', 'n_tokens_mistral': 348, 'n_tokens_neox': 329, 'n_words': 46, 'path': 'secao5 - Python para Analise de dados utizando Numpy/arrays e matrizes.ipynb', 'repo_name': 'IgoPereiraBarros/data-science-and-machine-learning', 'size': '682'}