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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-20 ms +- 537 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-45.4 ms +- 1.08 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.37 ms +- 57.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.63 ms +- 24.7 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-12 ms +- 380 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-27.9 ms +- 2.01 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+725 us +- 5.67 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.94 ms +- 13.9 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-6.52 ms +- 188 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-12.1 ms +- 528 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.67 ms +- 10.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.06 ms +- 14.5 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([0.61364353, 0.63834349, 0.22803551])\n+Out[7]: array([0.87262273, 0.47345332, 0.75515287])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.07393819, 0.24147004, 0.89591014], dtype=float32)\n+Out[8]: array([0.79836625, 0.9985497 , 0.73898697], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 65,  24, 184], dtype=uint8)\n+Out[9]: array([122,  29,  52], dtype=uint8)\n 
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