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In [1]: import numpy.random\n \n In [2]: rng = np.random.default_rng()\n \n In [3]: %timeit -n 1 rng.standard_normal(100000)\n    ...: %timeit -n 1 numpy.random.standard_normal(100000)\n    ...: \n-1.39 ms +- 52.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.55 ms +- 334 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.48 ms +- 31.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.85 ms +- 16.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [4]: %timeit -n 1 rng.standard_exponential(100000)\n    ...: %timeit -n 1 numpy.random.standard_exponential(100000)\n    ...: \n-651 us +- 126 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.44 ms +- 108 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+701 us +- 19.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.04 ms +- 27.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)\n    ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)\n    ...: \n-3.15 ms +- 175 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.58 ms +- 277 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.85 ms +- 53.4 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+6.54 ms +- 975 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [6]: rng = np.random.default_rng()\n \n In [7]: rng.random(3, dtype=np.float64)\n-Out[7]: array([0.70547514, 0.08925294, 0.40291075])\n+Out[7]: array([0.1171073 , 0.60991664, 0.51373836])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.7055436 , 0.81983244, 0.7707724 ], dtype=float32)\n+Out[8]: array([0.14971465, 0.04142678, 0.91847485], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 48,  53, 194], dtype=uint8)\n+Out[9]: array([198, 105, 169], dtype=uint8)\n 
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