Compare sample efficiency

compare.ipynb

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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
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   "execution_count": 4,
   "id": "60e5cda8",
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   "source": [
    "s = pd.Series(np.random.randn(100000))"
   ]
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     "text": [
      "1.53 ms ± 26.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np.random.choice(s, 1000, replace=False)"
   ]
  },
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    {
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     "text": [
      "1.54 ms ± 20.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "s.sample(1000).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7fd08ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.46 µs ± 115 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np.random.choice(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73df188a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.52 ms ± 13.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "s.sample(1).item"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "50577784",
   "metadata": {},
   "outputs": [],
   "source": []
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