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    "# Benchmark data processing\n",
    "\n",
    "Contains the code required to plot the figures in the docs.\n",
    "\n",
    "* Build the benchmarks with CMake, using the `BOOST_MYSQL_BENCH` CMake option and the `boost_mysql_bench` CMake target.\n",
    "* Run the benchmarks with `tools/ci/run_benchmarks.py`, which leaves a .txt file with the results in the current directory.\n",
    "* Run this notebook to generate the graphs.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "df = pd.read_csv('../private/benchmark-results.txt', header=None) # Update file path as required\n",
    "df = df.set_index(df.index.map(lambda x: x % df.groupby(0).size()[0]))\n",
    "df = df.pivot(columns=[0], values=[1])\n",
    "df.columns = df.columns.get_level_values(0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_result(df: pd.DataFrame, bench: str, title: str, ax):\n",
    "    cols = [f'{bench}_boost', f'{bench}_libmysqlclient', f'{bench}_libmariadb']\n",
    "    df = df[cols].rename(columns={\n",
    "        f'{bench}_boost': 'Boost.MySQL',\n",
    "        f'{bench}_libmysqlclient': 'libmysqlclient',\n",
    "        f'{bench}_libmariadb': 'libmariadb'\n",
    "    })\n",
    "    mean_val = round(df.mean().mean())\n",
    "    df.plot.box(ylim=(mean_val-150, mean_val+150), ax=ax, title=title, ylabel='Time (ms)')\n",
    "\n",
    "\n",
    "fig, _ = plt.subplots(2, 2, figsize=(15, 15))\n",
    "\n",
    "plot_result(df, bench='one_small_row', title='Reading one small row', ax=fig.axes[0])\n",
    "plot_result(df, bench='one_big_row', title='Reading one big row', ax=fig.axes[1])\n",
    "plot_result(df, bench='many_rows', title='Reading 5k rows', ax=fig.axes[2])\n",
    "plot_result(df, bench='stmt_params', title='Statement with params', ax=fig.axes[3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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