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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-939 us +- 44.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.42 ms +- 36.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.03 ms +- 39.6 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.43 ms +- 41.9 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-796 us +- 18.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.69 ms +- 22 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+851 us +- 18 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.7 ms +- 34.5 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-2.92 ms +- 1.41 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.1 ms +- 28.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.46 ms +- 34.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.1 ms +- 20.7 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([0.55792744, 0.10045431, 0.39091772])\n+Out[7]: array([0.12730094, 0.86732915, 0.29751739])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.7786327 , 0.44079942, 0.11658084], dtype=float32)\n+Out[8]: array([0.07399744, 0.87415296, 0.9917921 ], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([192, 232, 195], dtype=uint8)\n+Out[9]: array([217, 193, 195], dtype=uint8)\n 
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