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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-936 us +- 42 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.52 ms +- 33 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+928 us +- 46.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.53 ms +- 32 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-805 us +- 25.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.76 ms +- 6.78 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+804 us +- 28.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.76 ms +- 15.4 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-2.34 ms +- 19.4 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.51 ms +- 57.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.35 ms +- 25.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.48 ms +- 16.6 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([0.99052012, 0.09317833, 0.33425345])\n+Out[7]: array([0.11183614, 0.99736855, 0.80213609])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.39850473, 0.32691497, 0.37080425], dtype=float32)\n+Out[8]: array([0.30092323, 0.8955594 , 0.8660922 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([155, 170,  45], dtype=uint8)\n+Out[9]: array([ 22,  27, 102], dtype=uint8)\n 
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