<jupyter_start><jupyter_text><jupyter_code>import numpy as np
a = np.array([[1,53,31,523,3],[59,44,6464,7,444464646]])
print(a)
print(a[1,2])
print(a[:,1::2])
print(a[1,:])
a[:,1:3] = [[21,12],[468,965]]
print(a)
b = np.array([[[45,53],[0,21]],[[89,55],[79,66]]])<jupyter_output><empty_output><jupyter_text><jupyter_code>print(b)
b[0,1,1]
b[:,1,:] = [[55,66],[89,98]]
print(b)
b[1,0,1]=22
b
b[1,0,:] = [88888,9999]
print(b)
b[0,:,0] = [44,44]
print(b)
np.full((2,2),5)
np.full_like(a,5)
np.random.randint(4,7,size=(2,3))
arr = np.ones((3,3))
np.repeat(arr,5,axis = 1)
np.random.rand(3,3)<jupyter_output><empty_output>
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{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '359364', 'license': 'MIT', 'n_tokens_mistral': 385, 'n_tokens_neox': 341, 'n_words': 35, 'path': 'numpy1.ipynb', 'repo_name': 'ikramulkayes/Datascience_Learning', 'size': '617'}