<jupyter_start><jupyter_text>[View in Colaboratory](https://colab.research.google.com/github/partha1189/ml/blob/master/numpy1.ipynb)<jupyter_code>my_list = [1,2,3,4]
import numpy as np
arr = np.array(my_list)
print(arr)
help(np.arange)

np.arange(4,13,2)
np.zeros((4,3))
np.linspace(0,5,10)
np.eye(4)
np.random.rand(2,2)
np.random.randn(5)
arr1 = np.arange(25)
arr1
ranarr = np.random.randint(0,50,10)
ranarr<jupyter_output><empty_output><jupyter_text>RESHAPE<jupyter_code>arr1.reshape(5,5)
ranarr.max()
ranarr.argmax()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18032786885245902, 'fraction_numerical': 0.06375227686703097, 'mean_word_length': 8.322033898305085, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 1, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '73545', 'license': 'Apache-2.0', 'n_tokens_mistral': 276, 'n_tokens_neox': 265, 'n_words': 26, 'path': 'numpy1.ipynb', 'repo_name': 'partha1189/machine_learning', 'size': '549'}