{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IS5cu1ZYtW/TDDz/IMAy721/nz5/Xli1bNHPmTLuHC270dmzNmjWLvEYKCgqK3Dpv2LChcnNzbVd8SuPp6am+ffuqb9++slqtevLJJ7Vo0SI999xzRa44o/zjFhgqpIYNG2rw4MFatGiRMjIy7Jb17t1bkmxPb1wxZ84cSb8+heJqV/4yvvov4ezsbCUkJDi8zdatW6t169b6+9//rn/+8596/PHHVbly6X/DeHh4KDo6WqtXr1Zqaqqt/YcfftCGDRuuu89rHzeuXr26GjVqZPfIf7Vq1SQVDSSOmjdvnu3fhmFo3rx5qlKlirp27Srp1zdnDw8P29iMK956660i2yqptscee0yFhYWaNWtWkXUuX75s69+tWzdVqVJFb775pt3P8trXliOKe438+9//1q5duxzaXkhIiCIiIrR06VK720ObNm3S999/b9e3d+/eKiwstDvXkvT666/LYrGoV69eDtVQFt26dVOtWrW0YsUKrVixQm3btrW7VVjc+ZFu/Nw3bNiwyGtk8eLFRa4APfbYY9q1a1exvw9ZWVm2cX3X/i5UqlRJrVu3lqQiH4GBioErQKiw/vKXv+gf//iHDh48qBYtWtja27Rpo9jYWC1evFhZWVnq1KmTvvrqKy1dulT9+vVTly5dXF5bjx49bH8tjh49Wrm5uXr77bcVGBhY4uDtGzFkyBBNnDhRkm749tfMmTOVlJSkjh076sknn9Tly5f15ptvqkWLFtcdz9O8eXN17txZkZGRqlWrlvbs2aOPPvrIbqByZGSkJOnpp59WdHS0PDw89Pjjjzt0fN7e3kpKSlJsbKyioqK0fv16rV27VlOnTrXdlvL399fAgQP15ptvymKxqGHDhvrss89sYzWuVlJtnTp10ujRoxUfH6/k5GT16NFDVapU0aFDh7Ry5Uq98cYbevTRRxUQEKCJEyfaHvvu3bu39u3bp/Xr16tOnToOHeMVDz30kFatWqX+/furT58+OnbsmBYuXKjmzZsrNzfXoW3Gx8erT58+6tChg4YPH65z587ZftZXb7Nv377q0qWL/vKXvyglJUVt2rTRxo0btWbNGo0fP14NGza8qWO7EVWqVNGAAQO0fPly5eXl6dVXX7Vb7ufnpwcffFCvvPKKLl26pDvuuEMbN27UsWPHbmj7I0eO1B//+Ec98sgj6t69u7755htt2LChyM9t0qRJ+uSTT/TQQw9p6NChioyMVF5enr777jt99NFHSklJUZ06dTRy5EidO3dOv/nNb1S3bl0dP35cb775piIiIuyuNKMCcdPTZ8ANu/ox+GtdeXT66sfgDcMwLl26ZMycOdOoX7++UaVKFSMsLMyYMmWKcfHiRbt+9erVK/Yx7yuPWq9cufKGapk+fbohyfaotmEYxieffGK0bt3a8Pb2NsLDw42XX37ZWLJkSZHHdW/kMfgr0tPTDQ8PD6NJkyZFlpVm+/btRmRkpOHp6Wk0aNDAWLhwoa3mq137iPALL7xgtG3b1qhRo4bh4+NjNGvWzPjrX/9q99EAly9fNp566ikjICDAsFgstm1eOY7Zs2cXqaekx+CrVatmHDlyxOjRo4dRtWpVIygoyJg+fbrd4/+GYRinT582HnnkEaNq1apGzZo1jdGjRxv79+8vss2Sarti8eLFRmRkpOHj42P4+voarVq1Mp555hkjLS3N1qewsNCYOXOmERISYvj4+BidO3c29u/fX+RcFae0c2C1Wo0XX3zRqFevnuHl5WXcfffdxmeffWbExsbaPbJe2jZUzKPe//znP4277rrL8PLyMpo3b26sWrWqyDYNwzAuXLhg/N///Z8RGhpqVKlSxWjcuLExe/Zsu8f9r+xjzJgxN3RcJf3elGTTpk2GJMNisRgnTpwosvzkyZNG//79jRo1ahj+/v7GwIEDjbS0tCLHXdxj8IWFhcaf//xno06dOkbVqlWN6Oho4/Dhw8X+3C5cuGBMmTLFaNSokeHp6WnUqVPHaN++vfHqq6/aXusfffSR0aNHDyMwMNDw9PQ07rzzTmP06NFGenr6DR0ryh+LYdyCEXQAnOLMmTMKCQnRtGnT9Nxzz7m7HACosBgDBFQgiYmJKiwsdPmXiALA7Y4xQEAFsHXrVn3//ff661//qn79+tl9KB0AoOy4BQZUAJ07d9bOnTv1wAMPaNmyZaV+9xcA4PoIQAAAwHQYAwQAAEyHAAQAAEyHQdDFsFqtSktLk6+vr1M/5h8AALiOYRi6cOGCQkNDValS6dd4CEDFSEtLK/ItxwAAoGI4ceKE6tatW2ofAlAxrnw54IkTJ+Tn5+fmagAAwI3IyclRWFiY3Zf8loQAVIwrt738/PwIQAAAVDA3MnyFQdAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0Kru7AAC41cInr3V3CWWW8lIfd5cA3Fa4AgQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEzHrQFox44d6tu3r0JDQ2WxWLR69Wq75RaLpdhp9uzZJW5zxowZRfo3a9bMxUcCAAAqErcGoLy8PLVp00bz588vdnl6errdtGTJElksFj3yyCOlbrdFixZ2633xxReuKB8AAFRQbv0coF69eqlXr14lLg8ODrabX7Nmjbp06aIGDRqUut3KlSsXWRcAAOCKCjMGKDMzU2vXrtWIESOu2/fQoUMKDQ1VgwYN9MQTTyg1NbXU/vn5+crJybGbAADA7avCBKClS5fK19dXAwYMKLVfVFSUEhMTlZSUpAULFujYsWPq2LGjLly4UOI68fHx8vf3t01hYWHOLh8AAJQjFSYALVmyRE888YS8vb1L7derVy8NHDhQrVu3VnR0tNatW6esrCx9+OGHJa4zZcoUZWdn26YTJ044u3wAAFCOVIjvAvvXv/6lgwcPasWKFWVet0aNGmrSpIkOHz5cYh8vLy95eXndTIkAAKACqRBXgN555x1FRkaqTZs2ZV43NzdXR44cUUhIiAsqAwAAFZFbA1Bubq6Sk5OVnJwsSTp27JiSk5PtBi3n5ORo5cqVGjlyZLHb6Nq1q+bNm2ebnzhxorZv366UlBTt3LlT/fv3l4eHh2JiYlx6LAAAoOJw6y2wPXv2qEuXLrb5uLg4SVJsbKwSExMlScuXL5dhGCUGmCNHjujMmTO2+ZMnTyomJkZnz55VQECAOnTooN27dysgIMB1BwIAACoUi2EYhruLKG9ycnLk7++v7Oxs+fn5ubscAE4WPnmtu0sos5SX+ri7BKDcK8v7d4UYAwQAAOBMBCAAAGA6BCAAAGA6FeJzgACUXxVxPA0AcAUIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYTmV3FwAAuL7wyWvdXUKZpbzUx90lACXiChAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdtwagHTt2qG/fvgoNDZXFYtHq1avtlg8dOlQWi8Vu6tmz53W3O3/+fIWHh8vb21tRUVH66quvXHQEAACgInJrAMrLy1ObNm00f/78Evv07NlT6enptumDDz4odZsrVqxQXFycpk+frr1796pNmzaKjo7WqVOnnF0+AACooCq7c+e9evVSr169Su3j5eWl4ODgG97mnDlzNGrUKA0bNkyStHDhQq1du1ZLlizR5MmTb6peAABweyj3Y4A+//xzBQYGqmnTpvrTn/6ks2fPlti3oKBAX3/9tbp162Zrq1Spkrp166Zdu3aVuF5+fr5ycnLsJgAAcPsq1wGoZ8+eevfdd7Vlyxa9/PLL2r59u3r16qXCwsJi+585c0aFhYUKCgqyaw8KClJGRkaJ+4mPj5e/v79tCgsLc+pxAACA8sWtt8Cu5/HHH7f9u1WrVmrdurUaNmyozz//XF27dnXafqZMmaK4uDjbfE5ODiEIAIDbWLm+AnStBg0aqE6dOjp8+HCxy+vUqSMPDw9lZmbatWdmZpY6jsjLy0t+fn52EwAAuH1VqAB08uRJnT17ViEhIcUu9/T0VGRkpLZs2WJrs1qt2rJli9q1a3erygQAAOWcWwNQbm6ukpOTlZycLEk6duyYkpOTlZqaqtzcXE2aNEm7d+9WSkqKtmzZoocffliNGjVSdHS0bRtdu3bVvHnzbPNxcXF6++23tXTpUv3www/605/+pLy8PNtTYQAAAG4dA7Rnzx516dLFNn9lHE5sbKwWLFigb7/9VkuXLlVWVpZCQ0PVo0cPzZo1S15eXrZ1jhw5ojNnztjmBw0apNOnT2vatGnKyMhQRESEkpKSigyMBgAA5mUxDMNwdxHlTU5Ojvz9/ZWdnc14IOA6wievdXcJKKdSXurj7hJgMmV5/65QY4AAAACcgQAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMhwAEAABMp7K7CwDwP+GT17q7BAAwBa4AAQAA0yEAAQAA0yEAAQAA0yEAAQAA0yEAAQAA03FrANqxY4f69u2r0NBQWSwWrV692rbs0qVL+vOf/6xWrVqpWrVqCg0N1ZAhQ5SWllbqNmfMmCGLxWI3NWvWzMVHAgAAKhK3BqC8vDy1adNG8+fPL7Lsl19+0d69e/Xcc89p7969WrVqlQ4ePKjf/va3191uixYtlJ6ebpu++OILV5QPAAAqKLd+DlCvXr3Uq1evYpf5+/tr06ZNdm3z5s1T27ZtlZqaqjvvvLPE7VauXFnBwcFOrRUAANw+KtQYoOzsbFksFtWoUaPUfocOHVJoaKgaNGigJ554QqmpqaX2z8/PV05Ojt0EAABuXxUmAF28eFF//vOfFRMTIz8/vxL7RUVFKTExUUlJSVqwYIGOHTumjh076sKFCyWuEx8fL39/f9sUFhbmikMAAADlRIUIQJcuXdJjjz0mwzC0YMGCUvv26tVLAwcOVOvWrRUdHa1169YpKytLH374YYnrTJkyRdnZ2bbpxIkTzj4EAABQjpT77wK7En6OHz+urVu3lnr1pzg1atRQkyZNdPjw4RL7eHl5ycvL62ZLBQAAFUS5vgJ0JfwcOnRImzdvVu3atcu8jdzcXB05ckQhISEuqBAAAFREbg1Aubm5Sk5OVnJysiTp2LFjSk5OVmpqqi5duqRHH31Ue/bs0XvvvafCwkJlZGQoIyNDBQUFtm107dpV8+bNs81PnDhR27dvV0pKinbu3Kn+/fvLw8NDMTExt/rwAABAOeXWW2B79uxRly5dbPNxcXGSpNjYWM2YMUOffPKJJCkiIsJuvW3btqlz586SpCNHjujMmTO2ZSdPnlRMTIzOnj2rgIAAdejQQbt371ZAQIBrDwYAAFQYbg1AnTt3lmEYJS4vbdkVKSkpdvPLly+/2bIAAMBtrlyPAQIAAHAFAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdhwLQ0aNHnV0HAADALeNQAGrUqJG6dOmiZcuW6eLFi86uCQAAwKUshmEYZV0pOTlZCQkJ+uCDD1RQUKBBgwZpxIgRatu2rStqvOVycnLk7++v7Oxs+fn5ubscmEj45LXuLgEwtZSX+ri7BNyEsrx/O3QFKCIiQm+88YbS0tK0ZMkSpaenq0OHDmrZsqXmzJmj06dPO1Q4AADArXBTg6ArV66sAQMGaOXKlXr55Zd1+PBhTZw4UWFhYRoyZIjS09OdVScAAIDT3FQA2rNnj5588kmFhIRozpw5mjhxoo4cOaJNmzYpLS1NDz/8sLPqBAAAcJrKjqw0Z84cJSQk6ODBg+rdu7feffdd9e7dW5Uq/Zqn6tevr8TERIWHhzuzVgAAAKdwKAAtWLBAw4cP19ChQxUSElJsn8DAQL3zzjs3VRwAAIArOBSADh06dN0+np6eio2NdWTzAAAALuXQGKCEhAStXLmySPvKlSu1dOnSmy4KAADAlRwKQPHx8apTp06R9sDAQL344os3XRQAAIArORSAUlNTVb9+/SLt9erVU2pq6k0XBQAA4EoOBaDAwEB9++23Rdq/+eYb1a5d+6aLAgAAcCWHAlBMTIyefvppbdu2TYWFhSosLNTWrVs1btw4Pf74486uEQAAwKkcegps1qxZSklJUdeuXVW58q+bsFqtGjJkCGOAAABAuedQAPL09NSKFSs0a9YsffPNN/Lx8VGrVq1Ur149Z9cHAADgdA4FoCuaNGmiJk2aOKsWAACAW8KhAFRYWKjExERt2bJFp06dktVqtVu+detWpxQHAADgCg4Ngh43bpzGjRunwsJCtWzZUm3atLGbbtSOHTvUt29fhYaGymKxaPXq1XbLDcPQtGnTFBISIh8fH3Xr1u2GPoV6/vz5Cg8Pl7e3t6KiovTVV1+V9RABAMBtzKErQMuXL9eHH36o3r1739TO8/Ly1KZNGw0fPlwDBgwosvyVV17R3/72Ny1dulT169fXc889p+joaH3//ffy9vYudpsrVqxQXFycFi5cqKioKM2dO1fR0dE6ePCgAgMDb6peAABwe3DoCpCnp6caNWp00zvv1auXXnjhBfXv37/IMsMwNHfuXD377LN6+OGH1bp1a7377rtKS0srcqXoanPmzNGoUaM0bNgwNW/eXAsXLlTVqlW1ZMmSm64XAADcHhwKQBMmTNAbb7whwzCcXY/NsWPHlJGRoW7dutna/P39FRUVpV27dhW7TkFBgb7++mu7dSpVqqRu3bqVuI4k5efnKycnx24CAAC3L4dugX3xxRfatm2b1q9frxYtWqhKlSp2y1etWnXThWVkZEiSgoKC7NqDgoJsy6515swZFRYWFrvOjz/+WOK+4uPjNXPmzJusGAAAVBQOBaAaNWoUe9uqopoyZYri4uJs8zk5OQoLC3NjRQAAwJUcCkAJCQnOrqOI4OBgSVJmZqZCQkJs7ZmZmYqIiCh2nTp16sjDw0OZmZl27ZmZmbbtFcfLy0teXl43XzQAAKgQHBoDJEmXL1/W5s2btWjRIl24cEGSlJaWptzcXKcUVr9+fQUHB2vLli22tpycHP373/9Wu3btil3H09NTkZGRdutYrVZt2bKlxHUAAID5OHQF6Pjx4+rZs6dSU1OVn5+v7t27y9fXVy+//LLy8/O1cOHCG9pObm6uDh8+bJs/duyYkpOTVatWLd15550aP368XnjhBTVu3Nj2GHxoaKj69etnW6dr167q37+/xo4dK0mKi4tTbGys7r33XrVt21Zz585VXl6ehg0b5sihAgCA25BDAWjcuHG699579c0336h27dq29v79+2vUqFE3vJ09e/aoS5cutvkr43BiY2OVmJioZ555Rnl5efrDH/6grKwsdejQQUlJSXafAXTkyBGdOXPGNj9o0CCdPn1a06ZNU0ZGhiIiIpSUlFRkYDQAADAvi+HAs+y1a9fWzp071bRpU/n6+uqbb75RgwYNlJKSoubNm+uXX35xRa23TE5Ojvz9/ZWdnS0/Pz93lwMTCZ+81t0lAKaW8lIfd5eAm1CW92+HxgBZrVYVFhYWaT958qR8fX0d2SQAAMAt41AA6tGjh+bOnWubt1gsys3N1fTp02/66zEAAABczaExQK+99pqio6PVvHlzXbx4Ub/73e906NAh1alTRx988IGzawQAAHAqhwJQ3bp19c0332j58uX69ttvlZubqxEjRuiJJ56Qj4+Ps2sEAABwKocCkCRVrlxZgwcPdmYtAAAAt4RDAejdd98tdfmQIUMcKgYAAOBWcPhzgK526dIl/fLLL/L09FTVqlUJQAAAoFxz6Cmw8+fP2025ubk6ePCgOnTowCBoAABQ7jn8XWDXaty4sV566aUiV4cAAADKG6cFIOnXgdFpaWnO3CQAAIDTOTQG6JNPPrGbNwxD6enpmjdvnh544AGnFAYAAOAqDgWgq7+NXfr1k6ADAgL0m9/8Rq+99poz6gIAAHAZhwKQ1Wp1dh0AAAC3jFPHAAEAAFQEDl0BiouLu+G+c+bMcWQXAAAALuNQANq3b5/27dunS5cuqWnTppKkn376SR4eHrrnnnts/SwWi3OqBAAAcCKHAlDfvn3l6+urpUuXqmbNmpJ+/XDEYcOGqWPHjpowYYJTiwQAAHAmh8YAvfbaa4qPj7eFH0mqWbOmXnjhBZ4CAwAA5Z5DASgnJ0enT58u0n769GlduHDhposCAABwJYcCUP/+/TVs2DCtWrVKJ0+e1MmTJ/XPf/5TI0aM0IABA5xdIwAAgFM5NAZo4cKFmjhxon73u9/p0qVLv26ocmWNGDFCs2fPdmqBAAAAzuZQAKpatareeustzZ49W0eOHJEkNWzYUNWqVXNqcQAAAK5wUx+EmJ6ervT0dDVu3FjVqlWTYRjOqgsAAMBlHApAZ8+eVdeuXdWkSRP17t1b6enpkqQRI0bwCDwAACj3HApA//d//6cqVaooNTVVVatWtbUPGjRISUlJTisOAADAFRwaA7Rx40Zt2LBBdevWtWtv3Lixjh8/7pTCAAAAXMWhK0B5eXl2V36uOHfunLy8vG66KAAAAFdyKAB17NhR7777rm3eYrHIarXqlVdeUZcuXZxWHAAAgCs4dAvslVdeUdeuXbVnzx4VFBTomWee0YEDB3Tu3Dl9+eWXzq4RAADAqRy6AtSyZUv99NNP6tChgx5++GHl5eVpwIAB2rdvnxo2bOjsGgEAAJyqzFeALl26pJ49e2rhwoX6y1/+4oqaAAAAXKrMV4CqVKmib7/91hW1AAAA3BIO3QIbPHiw3nnnHWfXAgAAcEs4NAj68uXLWrJkiTZv3qzIyMgi3wE2Z84cpxQHAADgCmUKQEePHlV4eLj279+ve+65R5L0008/2fWxWCzOqw4AAMAFyhSAGjdurPT0dG3btk3Sr1998be//U1BQUEuKQ4AAMAVyjQG6Npve1+/fr3y8vKcWhAAAICrOTQI+oprAxEAAEBFUKYAZLFYiozxcfWYn/DwcNt+r57GjBlTbP/ExMQifb29vV1aIwAAqFjKNAbIMAwNHTrU9oWnFy9e1B//+MciT4GtWrXKaQX+5z//UWFhoW1+//796t69uwYOHFjiOn5+fjp48KBtnoHZAADgamUKQLGxsXbzgwcPdmoxxQkICLCbf+mll9SwYUN16tSpxHUsFouCg4NdXRoAAKigyhSAEhISXFXHDSkoKNCyZcsUFxdX6lWd3Nxc1atXT1arVffcc49efPFFtWjRosT++fn5ys/Pt83n5OQ4tW4AAFC+3NQg6Ftt9erVysrK0tChQ0vs07RpUy1ZskRr1qzRsmXLZLVa1b59e508ebLEdeLj4+Xv72+bwsLCXFA9AAAoLyxGBXqUKzo6Wp6envr0009veJ1Lly7prrvuUkxMjGbNmlVsn+KuAIWFhSk7O1t+fn43XTdwo8Inr3V3CYCppbzUx90l4Cbk5OTI39//ht6/HfoqDHc4fvy4Nm/eXOYB1lWqVNHdd9+tw4cPl9jHy8vLNrAbAADc/irMLbCEhAQFBgaqT5+ypfPCwkJ99913CgkJcVFlAACgoqkQAchqtSohIUGxsbGqXNn+otWQIUM0ZcoU2/zzzz+vjRs36ujRo9q7d68GDx6s48ePa+TIkbe6bAAAUE5ViFtgmzdvVmpqqoYPH15kWWpqqipV+l+OO3/+vEaNGqWMjAzVrFlTkZGR2rlzp5o3b34rSwYAAOVYhRoEfauUZRAV4EwMggbci0HQFVtZ3r8rxC0wAAAAZyIAAQAA06kQY4AAR3A7CUBZVcT/N7ht5xiuAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMp1wFoxowZslgsdlOzZs1KXWflypVq1qyZvL291apVK61bt+4WVQsAACqKch2AJKlFixZKT0+3TV988UWJfXfu3KmYmBiNGDFC+/btU79+/dSvXz/t37//FlYMAADKu3IfgCpXrqzg4GDbVKdOnRL7vvHGG+rZs6cmTZqku+66S7NmzdI999yjefPm3cKKAQBAeVfuA9ChQ4cUGhqqBg0a6IknnlBqamqJfXft2qVu3brZtUVHR2vXrl2l7iM/P185OTl2EwAAuH1VdncBpYmKilJiYqKaNm2q9PR0zZw5Ux07dtT+/fvl6+tbpH9GRoaCgoLs2oKCgpSRkVHqfuLj4zVz5kyn1n67CZ+81t0lAACKURH/f055qY+7SyjfV4B69eqlgQMHqnXr1oqOjta6deuUlZWlDz/80Kn7mTJlirKzs23TiRMnnLp9AABQvpTrK0DXqlGjhpo0aaLDhw8Xuzw4OFiZmZl2bZmZmQoODi51u15eXvLy8nJanQAAoHwr11eArpWbm6sjR44oJCSk2OXt2rXTli1b7No2bdqkdu3a3YryAABABVGuA9DEiRO1fft2paSkaOfOnerfv788PDwUExMjSRoyZIimTJli6z9u3DglJSXptdde048//qgZM2Zoz549Gjt2rLsOAQAAlEPl+hbYyZMnFRMTo7NnzyogIEAdOnTQ7t27FRAQIElKTU1VpUr/y3Dt27fX+++/r2effVZTp05V48aNtXr1arVs2dJdhwAAAMohi2EYhruLKG9ycnLk7++v7Oxs+fn5ubuccqEiPmUAACifXPUUWFnev8v1LTAAAABXIAABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTIQABAADTKdcBKD4+Xvfdd598fX0VGBiofv366eDBg6Wuk5iYKIvFYjd5e3vfoooBAEBFUK4D0Pbt2zVmzBjt3r1bmzZt0qVLl9SjRw/l5eWVup6fn5/S09Nt0/Hjx29RxQAAoCKo7O4CSpOUlGQ3n5iYqMDAQH399dd68MEHS1zPYrEoODj4hveTn5+v/Px823xOTk7ZiwUAABVGub4CdK3s7GxJUq1atUrtl5ubq3r16iksLEwPP/ywDhw4UGr/+Ph4+fv726awsDCn1QwAAMqfChOArFarxo8frwceeEAtW7YssV/Tpk21ZMkSrVmzRsuWLZPValX79u118uTJEteZMmWKsrOzbdOJEydccQgAAKCcKNe3wK42ZswY7d+/X1988UWp/dq1a6d27drZ5tu3b6+77rpLixYt0qxZs4pdx8vLS15eXk6tFwAAlF8VIgCNHTtWn332mXbs2KG6deuWad0qVaro7rvv1uHDh11UHQAAqGjK9S0wwzA0duxYffzxx9q6davq169f5m0UFhbqu+++U0hIiAsqBAAAFVG5vgI0ZswYvf/++1qzZo18fX2VkZEhSfL395ePj48kaciQIbrjjjsUHx8vSXr++ed1//33q1GjRsrKytLs2bN1/PhxjRw50m3HAQAAypdyHYAWLFggSercubNde0JCgoYOHSpJSk1NVaVK/7uQdf78eY0aNUoZGRmqWbOmIiMjtXPnTjVv3vxWlQ0AAMo5i2EYhruLKG9ycnLk7++v7Oxs+fn5ubucciF88lp3lwAAuE2kvNTHJdsty/t3uR4DBAAA4AoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDqV3V2AGYVPXuvuEgAAMDWuAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANOpEAFo/vz5Cg8Pl7e3t6KiovTVV1+V2n/lypVq1qyZvL291apVK61bt+4WVQoAACqCch+AVqxYobi4OE2fPl179+5VmzZtFB0drVOnThXbf+fOnYqJidGIESO0b98+9evXT/369dP+/ftvceUAAKC8shiGYbi7iNJERUXpvvvu07x58yRJVqtVYWFheuqppzR58uQi/QcNGqS8vDx99tlntrb7779fERERWrhw4Q3tMycnR/7+/srOzpafn59zDuQq4ZPXOn2bAABUFCkv9XHJdsvy/l3ZJRU4SUFBgb7++mtNmTLF1lapUiV169ZNu3btKnadXbt2KS4uzq4tOjpaq1evLnE/+fn5ys/Pt81nZ2dL+vVEuoI1/xeXbBcAgIrAVe+vV7Z7I9d2ynUAOnPmjAoLCxUUFGTXHhQUpB9//LHYdTIyMortn5GRUeJ+4uPjNXPmzCLtYWFhDlQNAABK4z/Xtdu/cOGC/P39S+1TrgPQrTJlyhS7q0ZWq1Xnzp1T7dq1ZbFY3FhZ+ZaTk6OwsDCdOHHCJbcKUTzO+63HOXcPzvutV9HPuWEYunDhgkJDQ6/bt1wHoDp16sjDw0OZmZl27ZmZmQoODi52neDg4DL1lyQvLy95eXnZtdWoUcOxok3Iz8+vQv6iVHSc91uPc+4enPdbryKf8+td+bmiXD8F5unpqcjISG3ZssXWZrVatWXLFrVr167Yddq1a2fXX5I2bdpUYn8AAGA+5foKkCTFxcUpNjZW9957r9q2bau5c+cqLy9Pw4YNkyQNGTJEd9xxh+Lj4yVJ48aNU6dOnfTaa6+pT58+Wr58ufbs2aPFixe78zAAAEA5Uu4D0KBBg3T69GlNmzZNGRkZioiIUFJSkm2gc2pqqipV+t+FrPbt2+v999/Xs88+q6lTp6px48ZavXq1WrZs6a5DuG15eXlp+vTpRW4fwrU477ce59w9OO+3npnOebn/HCAAAABnK9djgAAAAFyBAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAASnSElJ0YgRI1S/fn35+PioYcOGmj59ugoKCtxd2m3tr3/9q9q3b6+qVavy6eUuNH/+fIWHh8vb21tRUVH66quv3F3SbW3Hjh3q27evQkNDZbFYSv0yazhHfHy87rvvPvn6+iowMFD9+vXTwYMH3V2WSxGA4BQ//vijrFarFi1apAMHDuj111/XwoULNXXqVHeXdlsrKCjQwIED9ac//cndpdy2VqxYobi4OE2fPl179+5VmzZtFB0drVOnTrm7tNtWXl6e2rRpo/nz57u7FNPYvn27xowZo927d2vTpk26dOmSevTooby8PHeX5jJ8DhBcZvbs2VqwYIGOHj3q7lJue4mJiRo/fryysrLcXcptJyoqSvfdd5/mzZsn6dev4wkLC9NTTz2lyZMnu7m625/FYtHHH3+sfv36ubsUUzl9+rQCAwO1fft2Pfjgg+4uxyW4AgSXyc7OVq1atdxdBuCwgoICff311+rWrZutrVKlSurWrZt27drlxsoA18rOzpak2/r/cAIQXOLw4cN68803NXr0aHeXAjjszJkzKiwstH31zhVBQUHKyMhwU1WAa1mtVo0fP14PPPDAbf01UgQglGry5MmyWCylTj/++KPdOj///LN69uypgQMHatSoUW6qvOJy5JwDgLOMGTNG+/fv1/Lly91dikuV+y9DhXtNmDBBQ4cOLbVPgwYNbP9OS0tTly5d1L59ey1evNjF1d2eynrO4Tp16tSRh4eHMjMz7dozMzMVHBzspqoA1xk7dqw+++wz7dixQ3Xr1nV3OS5FAEKpAgICFBAQcEN9f/75Z3Xp0kWRkZFKSEhQpUpcYHREWc45XMvT01ORkZHasmWLbRCu1WrVli1bNHbsWPcWBziRYRh66qmn9PHHH+vzzz9X/fr13V2SyxGA4BQ///yzOnfurHr16unVV1/V6dOnbcv4S9l1UlNTde7cOaWmpqqwsFDJycmSpEaNGql69eruLe42ERcXp9jYWN17771q27at5s6dq7y8PA0bNszdpd22cnNzdfjwYdv8sWPHlJycrFq1aunOO+90Y2W3rzFjxuj999/XmjVr5Ovraxvj5u/vLx8fHzdX5xo8Bg+nSExMLPENgZeY6wwdOlRLly4t0r5t2zZ17tz51hd0m5o3b55mz56tjIwMRURE6G9/+5uioqLcXdZt6/PPP1eXLl2KtMfGxioxMfHWF2QCFoul2PaEhITr3pKvqAhAAADAdBikAQAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATOf/Abr9CZnwep0XAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "values = np.random.randn(100) # array of normally distributed random numbers\n",
    "s = pd.Series(values) # generate a pandas series\n",
    "s.plot(kind='hist', title='Normally distributed random values') # hist computes distribution\n",
    "plt.show()  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "llmops-course",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}