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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-934 us +- 45.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.5 ms +- 15.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 14.33 times longer than the fastest. This could mean that an intermediate result is being cached.\n+2.66 ms +- 4.21 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 7.45 times longer than the fastest. This could mean that an intermediate result is being cached.\n+10.3 ms +- 6.87 ms 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-800 us +- 15.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.77 ms +- 38.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 11.27 times longer than the fastest. This could mean that an intermediate result is being cached.\n+1.96 ms +- 2.82 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 5.58 times longer than the fastest. This could mean that an intermediate result is being cached.\n+4.65 ms +- 3.54 ms 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 +- 15.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.47 ms +- 14.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 4.49 times longer than the fastest. This could mean that an intermediate result is being cached.\n+5.24 ms +- 3.57 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+The slowest run took 5.48 times longer than the fastest. This could mean that an intermediate result is being cached.\n+12.5 ms +- 5.68 ms 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([0.04544089, 0.72676655, 0.10354143])\n+Out[7]: array([0.908983  , 0.3437659 , 0.20120097])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.2785715, 0.4328674, 0.3974651], dtype=float32)\n+Out[8]: array([0.09780961, 0.12884355, 0.51136214], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([ 47,  32, 233], dtype=uint8)\n+Out[9]: array([ 86,  67, 151], dtype=uint8)\n 
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