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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-928 us +- 45.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-2.51 ms +- 30.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+932 us +- 43.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.51 ms +- 23 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-803 us +- 24.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-1.78 ms +- 60.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+805 us +- 17.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.76 ms +- 15 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-2.39 ms +- 155 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-4.54 ms +- 161 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.35 ms +- 24.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+4.47 ms +- 17 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([0.09982097, 0.64569913, 0.10945991])\n+Out[7]: array([0.92211853, 0.31063142, 0.39304349])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.32890356, 0.16721952, 0.09815568], dtype=float32)\n+Out[8]: array([0.4325282, 0.6530782, 0.8306809], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([46, 21, 19], dtype=uint8)\n+Out[9]: array([239, 174, 112], dtype=uint8)\n 
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