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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-1.02 ms +- 53.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.57 ms +- 68.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+935 us +- 46.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.54 ms +- 43.2 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-1 ms +- 15.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.88 ms +- 18.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+799 us +- 18.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.88 ms +- 14.3 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-2.47 ms +- 22.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-7.95 ms +- 3.34 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.34 ms +- 29.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.47 ms +- 19.3 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([0.26314191, 0.23305205, 0.21018643])\n+Out[7]: array([0.22978782, 0.08194945, 0.27917787])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.1394285 , 0.46245968, 0.24632704], dtype=float32)\n+Out[8]: array([0.0332737 , 0.40015244, 0.07163864], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([140, 155,   8], dtype=uint8)\n+Out[9]: array([ 42, 218,  66], dtype=uint8)\n 
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