{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96f79485-6673-4211-b349-85c6ae06db88",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b641c3-56db-49bf-b4e0-af1cad3fe8c4",
   "metadata": {},
   "source": [
    "Visualizing data can be helpful on many occasions, such as investigating the data and its distribution, checking for outliers, evaluating results and more.\n",
    "\n",
    "Here we visualize some experimental data in different plots and provide some code for the most common plot types used.\n",
    "\n",
    "The `matplotlib` library provides a great starting point for learning how to visualize data in Python, you can find many more plot types [here](https://matplotlib.org/stable/plot_types/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "007e0292-8c5a-4958-8caa-64765bbd6fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flow type</th>\n",
       "      <th>water flow / air flow</th>\n",
       "      <th>0 kg/h</th>\n",
       "      <th>100 kg/h</th>\n",
       "      <th>200 kg/h</th>\n",
       "      <th>300 kg/h</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>small</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>small</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>small</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>small</td>\n",
       "      <td>80</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>small</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  flow type  water flow / air flow  0 kg/h  100 kg/h  200 kg/h  300 kg/h\n",
       "0     small                     15       1         2         3         3\n",
       "1     small                     30       2         2         5         3\n",
       "2     small                     50       2         3         6         5\n",
       "3     small                     80       3         4         9         9\n",
       "4     small                    100       3         5        13        13"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "packed_column = pd.read_csv('packed_column.csv', encoding='utf-8', sep=';')\n",
    "packed_column.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcfddcb-298a-4534-a734-e14e546966b3",
   "metadata": {},
   "source": [
    "Let's start with a standard line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "564f68d3-9ec0-49df-a0bc-a15eb4bf0050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5OY2NEaiaU15eTlJSktNlRC3df22n+659dP+1j+6/ttN91z5ffvnlTmNMU1e9AaI8nGVkZPDFF184XUbUWrJkCZMmTXK6jKil+6/tdN+1j+6/9tH913a679pHRJq6JFyIHtZUSimllOpGNJwppZRSSnUjGs6UUkoppbqRqO5zFonP5yMvL4+qqiqnS+n2UlNTWbVqVeh+fHw8WVlZxMTEOFiVUkop1bv1uHCWl5dHSkoKw4cPR0ScLqdbKy0tJSUlBQBjDIWFheTl5bHHHns4XJlSSinVe/W4w5pVVVX069dPg1kriQj9+vXTFkellFLKYT0unAEazNpI95tSSinlvB4Zzpz25ptvMmbMGHJycpgzZ07EZSZNmtTqMdry8/OZNm0aS5Ys4fjjj++IUpVSSinVyYoXL2btkVMYHxe/f0uW13DWwQKBAJdccglvvPEGK1eu5JlnnmHlypUdsu0333yTo48+ukO2pZRSSqnOV7x4Mfk33Yx/69YWr9Prw9lLX2/h0Dnvs8e1r3HonPd56est7dresmXLyMnJYcSIEcTGxnLmmWfy8ssvN7p8MBjknHPO4cYbbwRgwYIFjB49mgkTJvC73/2OSy+9NLTsm2++yTHHHANAWVkZp512GmPHjuXss8/GGNOuupVSSinV8bbPnYdpZX/uXh3OXvp6C9e98B1biioxwJaiSq574bt2BbQtW7aQnZ0dup+VlcWWLZG35/f7Ofvssxk1ahR33HEHW7du5fbbb+ezzz7jk08+YfXq1aFlA4EAa9asITc3F4Cvv/6aefPmsXLlStavX88nn3zS5pqVUkop1Tn8+fmtXqfHDaUR7rbFK1i5taTRx7/eVIQ3EKwzr9IX4Ornv+WZZZsirpM7pA+3nDC+Q+q74IILmDFjBjfccANgtbr9/Oc/Jz09HYDTTz+dH374AYDPP/+ciRMnhtadMGECWVlZAOyzzz5s2LCBww47rEPqUkoppVTHcPdNI7Brd6vW6dUtZ/WDWXPzWyIzM5PNmzeH7ufl5ZGZmRlx2UMOOYQPPvigRcNXvPHGG0yfPj10Py4uLjTtdrvx+/1trlkppZRSHW/XU09ZwayVoyH06Jaz5lq4Dp3zPluKKhvMz0xL4N8XHNym5zzwwANZu3YtP/30E5mZmTz77LM8/fTTEZc9//zz+fDDD5kxYwYvvPACBx54IJdffjm7d+8mJSWF//znP+y1114AvPfee1x99dVtqkkppZRSXccEg2y/9z52PfYYyZMnkzxlCjsfegg2bGjR+j06nDXnj0eP4boXvqPSFwjNS4hx88ejx7R5mx6Ph/nz53P00UcTCAQ477zzGD++8ZB45ZVXUlxczK9//Wueeuoprr/+eiZMmEB6ejpjx44lNTWVHTt2EB8fHxrNXymllFLdU7Cqiq3XXEvpW2/R9+yzybj+OsTtpu9pv2CFyJct2UavDmcn72sdbrz3rTVsLapkSFoCfzx6TGh+Wx177LEce+yxTS6zZMmS0PRtt90Wmj7rrLOYNWsWfr+fU045hZNPPpm33nqLadOmhZaZNGkSkyZNCt2fP39+u+pVSimlVPv5d+8m76KLqfzf/xh4zTWknzOzTQO89+pwBlZAa28Y60i33nor7777LlVVVUybNo2TTz5ZR+5XSimlujnvxo1smjULf/42MufOpc/0to9L2uvDWXdz3333OV2CUkoppVqh4quvybv4YgCGPv44ifvt267t9eqzNZVSSiml2qPkrbfZdM45uFL7MPzZZ9odzEDDmVJKKaVUqxljKHzscbZcfjnxubkMf/ZZYocP75Bt62FNpZRSSqlWMIEABXfdze6nniJl2jSG/PkeXPHxHbZ9DWdKKaWUUi0UrKhgyx/+SNn775N+7rkM/OMfEFfHHojUw5qd4LzzzmPgwIHsueeedebv2rWLqVOnMmrUKKZOncru3dblHIwx/P73vycnJ4e9996br776qsE2N2zY0GB7LTFnzhyeeuopzjnnHJ5//vm2vSCllFJK4d+5k40zz6FsyRIybryRjGuu7vBgBhrOOsU555zDm2++2WD+nDlzmDJlCmvXrmXKlCnMmTMHsC7NtHbtWtauXcsjjzzCRRdd1GG11B8jTSmllFKtV71+PRvOOJPqtWvJmv9X0n91dqc9l4azbxfB3D3h1jTr57eL2r3JI444InTx8nAvv/wyM2fOBGDmzJm89NJLofm/+c1vEBEOOuggioqKyG/iKvbr169n3333Zfny5VRUVDBjxgxyc3M55ZRTmDhxIl988QUAJSUleL1eBgwYAMCHH37IIYccwogRI7QVTSmllGqhiuXL2fDLswhWVTHsX0+QcuSRnfp8vbvP2beLYPHvwWdfX7N4s3UfYO8ZHf50BQUFDB48GIBBgwZRUFAAwJYtW8jOzg4tl5WVxZYtW0LLhluzZg1nnnkmjz/+OD/72c+477776Nu3LytXruT7779nn332CS377rvvMmXKlND9/Px8Pv74Y1avXs2JJ57I0Ue3fYA8pZRSqjcofvU18q+7jpisLLIffYTYrKxOf86eHc7euBa2fdf443nLIVBdd56vEl6+FL5cGHmdQXvBMXPaXZqItHrk/x07dnDSSSfxwgsvkJubC8DHH3/M7NmzAdhzzz3Ze++9Q8u/+eabnHvuuaH7J598Mi6Xi9zc3FAwVEoppVRDxhgKH/0HO+6/n8QDDiBr/l9xp6V1yXP37sOa9YNZc/PbKSMjI3S4Mj8/n4EDBwKQmZnJ5s2bQ8vl5eWRmdnwklKpqakMHTqUjz/+uEXPt2zZMiZMmBC6HxcXF5o2xrTpNSillFI9nfH72XbLrey4/376HHcc2f9c0GXBDHp6y1lzLVxz97QOZdaXmg3nvtbh5Zx44oksXLiQa6+9loULF3LSSSeF5s+fP58zzzyTzz//nNTU1IiHNGNjY3nxxRc5+uijSU5O5qyzzuLQQw9l0aJFTJ48mZUrV/Ldd1ZL4YoVKxg7dixut7vDX4dSSinVUwXKytlyxRWUf/QR/WbNYsDlszvljMym9Oxw1pwpN9ftcwYQk2DNb4df/vKXLFmyhJ07d5KVlcVtt93G+eefz7XXXsuMGTNYsGABw4YNY9Ei6+SDY489ltdff52cnBwSExN57LHHGt12UlISr776KlOnTiU5OZmLL76YmTNnkpuby9ixYxk/fjypqam8/PLLTJ8+vV2vQymllOpNfAXb2XzhhVT/8AODbruNvmd0fP/zlujd4aym0/97f4LiPEjNsoJZO08GeOaZZyLO79evH++9916D+SLCgw8+2OQ2hw8fzvfffw9AWloay5cvByAQCPDkk08SHx/Pjz/+yFFHHcWwYcN46623eOKJJ0LrP/7443W2V1ZWRmlpaWtellJKKdVjVf3wA5svuJBgcTHZf3uI5COOcKyW3h3OwApinXBmZlepqKhg8uTJ+Hw+jDE89NBDxMbG8s477zhdmlJKKRUVyj/9lLzLfo8rIYFhT/6LePukO6doOItyKSkpoXHNlFJKKdU6RS++RP5NNxG3xx5kP/x3YoYMcbqkzjtbU0SyReQDEVkpIitEZLY9P11E3hGRtfbPvvZ8EZG/iMg6EflWRPbrrNqUUkop1bsZY9gx/0Hyr7uOxAMPYNjTT3WLYAadO5SGH7jKGJMLHARcIiK5wLXAe8aYUcB79n2AY4BR9m0W8LdOrE0ppZRSvZTxesm//gZ2zp9P6sknM/Thh3GnpDhdVkinHdY0xuQD+fZ0qYisAjKBk4BJ9mILgSXANfb8J4w1ANdnIpImIoPt7SillFJKtVugtJS83/+eik8/o/+ll9L/kotbPSh8Z+uSPmciMhzYF/gcyAgLXNuADHs6EwgfdCzPnqfhTCmllFLt5svPZ/OsC6j+6ScG33UXaaee4nRJEXV6OBORZOA/wOXGmJLwdGqMMSLSqqHqRWQW1mFPBgwYwJIlS+o8npqa6ugQEXl5eVxwwQVs374dEeGcc87h4osvBmDXrl2ce+65bNy4kWHDhvH444/Tt29fjDFcffXVvP322yQmJvK3v/2tzjUyATZu3MiMGTP4/PPPW1XP/fffT2ZmJh988AHTp0/n5JNPDj0WCAQa7KuqqqoG+1RFVlZWpvuqjXTftY/uv/bR/dd20bzvPJs3kzb/QaS6muJLLqYgvS9019dijOm0GxADvAVcGTZvDTDYnh4MrLGnHwZ+GWm5xm6jR4829a1cubLBvK60detW8+WXXxpjjCkpKTGjRo0yK1asMMYY88c//tHcfffdxhhj7r77bnP11VcbY4x57bXXzPTp000wGDSffvqpmTBhQoPt/vTTT2b8+PGtrmfSpElm+/btZubMmea5556r81hJSUmD5Z3ef9Hkgw8+cLqEqKX7rn10/7WP7r+2i9Z9V/rhh2b1vvuZH34+yVSuXuNYHcAXpgX5qTPP1hRgAbDKGHN/2EOvADPt6ZnAy2Hzf2OftXkQUGy6oL/Za+tfY9rz09h74d5Me34ar61v32WbBg8ezH77WSeapqSkMG7cOLZs2QLAyy+/zMyZ1kufOXMmL730Umj+b37zG0SEgw46iKKiotA1OCNZv349++67L8uXL6eiooIZM2aQm5vLKaecwsSJE0NDa5SUlOD1ehkwYAAAH374IYcccggjRozg+eefb9frVEoppaLB7kWL2HzhRcQMG8bwf/+b+DGjnS6pWZ15WPNQ4NfAdyLyjT3vemAOsEhEzgc2AjUjwL4OHAusAyqAczuxNsAKZrcuvZWqQBUA+eX53Lr0VgCOG3Fcu7e/YcMGvv76ayZOnAhAQUFB6JqZgwYNoqCgAIAtW7aQnZ0dWi8rK4stW7ZEvL7mmjVrOPPMM3n88cf52c9+xn333Uffvn1ZuXIl33//fZ3Doe+++y5TpkwJ3c/Pz+fjjz9m9erVnHjiiRx99NHtfo1KKaVUd2SMYce8Byh8+GGSDj+czLlzcScnOV1Wi3Tm2ZofA42d/jCl/gy7ue+SjqzhnmX3sHrX6kYf/3bHt3iD3jrzqgJV3PzJzTz/Q+SWpbHpY7lmwjXNPndZWRm/+MUvmDdvHn369GnwuIi0+uyQHTt2cNJJJ/HCCy+Qa49e/PHHHzN79mwA9txzT/bee+/Q8m+++SbnnlubcU8++WRcLhe5ubmhYKiUUkr1NEF7qIySV18l7fTTGXTLzYgnesbd79rLrHcz9YNZc/Nbyufz8Ytf/IKzzz6bU089NTQ/IyMjdLgyPz+fgQMHApCZmcnmzbUnqubl5ZGZmdlgu6mpqQwdOpSPP/64RXUsW7aMCRMmhO7HxcWFpq0srJRSSvUsgaIiNp93PiWvvsqAK65g0J9ui6pgBj388k3NtXBNe34a+eUN+3YNThrMY9Mfa9NzGmM4//zzGTduHFdeeWWdx0488UQWLlzItddey8KFCznppJNC8+fPn8+ZZ57J559/TmpqasRDmrGxsbz44oscffTRJCcnc9ZZZ3HooYeyaNEiJk+ezMqVK/nuu+8AWLFiBWPHjsXtdrfpdSillFLRxpuXx+ZZF+DbvJkh995L6gnHO11Sm/TocNac2fvNrtPnDCDeHc/s/Wa3eZuffPIJ//rXv9hrr71C/b/uuusujj32WK699lpmzJjBggULGDZsGIsWLQLg2GOP5fXXXycnJ4fExEQee6zxYJiUlMSrr77K1KlTSU5O5uKLL2bmzJnk5uYyduxYxo8fT2pqKi+//DLTp09v8+tQSimloknld9+x+cKLMD4fQ/+5gMQDD3S6pDbr1eGsptP/A189wLbybQxKGsTs/Wa362SAww47rNFDhv369eO9995rMF9EePDBB5vc7vDhw/n+++8BSEtLY/ny5YA1VtmTTz5JfHw8P/74I0cddRTDhg3jrbfe4oknngit//jjj9fZXllZmaPjwSmllFIdpfT999ly1R/wpKeT/cRC4kaOdLqkdunV4QysgNYRZ2Y6paKigsmTJ+Pz+TDG8NBDDxEbG8s777zjdGlKKaVUp9v11FMU3HkX8bm5ZP/9b3j693e6pHbr9eEs2qWkpITGNVNKKaV6CxMMsv3e+9j12GMkT55M5v/dhysx0emyOoSGM6WUUkpFlWBVFVuvuZbSt96i79lnk3H9dUgPOgFOw5lSSimlooZ/927yLr6Eym++YeA115B+zsxWjxva3Wk4U0oppVRU8G7cyKZZs/DnbyNz7lz6TO+ZV7rRcKaUUkqpbq/i66/Ju+hiAIY+/jiJ++3rcEWdp1dfIaAzVFVVMWHCBH72s58xfvx4brnlltBjP/30ExMnTiQnJ4czzjgDr9e6EkF1dTVnnHEGOTk5TJw4kQ0bNjTY7pIlSzj++NYPpnfhhRfyySefMGnSJD1xQCmlVFQqeettNp1zLq7UPgx/9pkeHcxAw1mHi4uL4/333+d///sf33zzDW+++SafffYZANdccw1XXHEF69ato2/fvixYsACABQsW0LdvX9atW8cVV1zBNdc0f+3Olvrss8846KCDOmx7SimlVFcxxlD42ONsufxy4seNY/gzzxA7fLjTZXW6Xh/OihcvZu2RU1g1Lpe1R06hePHidm1PREhOTgasa2z6fD5EBGMM77//PqeddhoAM2fO5KWXXgLg5ZdfZubMmQCcdtppvPfee01e+3L58uXsu+++/Pjjj+zYsYOpU6cyfvx4fvvb3zJs2DB27twJwKpVqxg9enToEk7PPfccEyZMYPTo0Xz00Uftep1KKaVUZzKBAAV33Mn2e+4hZepUhj7+GJ70dKfL6hK9OpwVL15M/k0349+6FYzBv3Ur+Tfd3O6AFggE2GeffRg4cCBTp05l4sSJFBYWkpaWhse++GpWVhZbtmwBYMuWLWRnZwPg8XhITU2lsLAw4raXLl3KhRdeyMsvv8zIkSO57bbbOPLII1mxYgWnnXYamzZtCi37xhtv1LmEk9/vZ9myZcybN4/bbrutXa9RKaWU6izBigryLvs9u596ivRzzyVz3lxc8fFOl9VlevQJAdvuuovqVasbfbzyf//D2P2+apiqKvJvuJGiRc9FXCdu3FgGXX99k8/rdrv55ptvKCoq4pRTTuH7779n0KBBrX8B9axatYpZs2bx9ttvM2TIEAA+/vhjXnzxRQCmT59O3759Q8u/9dZbda7TeeqppwKw//77R+zXppRSSjnNv3Mnmy+6mKoVK8i48UbSf3W20yV1uV7dclY/mDU3v7XS0tKYPHkyb775Jv369aOoqAi/3w9AXl4emZmZAGRmZrJ582bAat0qLi6mX79+DbY3ePBg4uPj+frrr5t97oqKCoqKikIhDqz+cGCFx5o6lFJKqe6iev16NpxxJtVr15I1/6+9MphBD285a66Fa+2RU6xDmvV4hgxh2L+eiLBG83bs2EFMTAxpaWlUVlbyzjvvcM011yAiTJ48meeff54zzzyThQsXctJJJwFw4oknsnDhQg4++GCef/55jjzyyIgD6qWlpbFgwQKmTp1KUlISkyZN4tBDD2XRokVcc801vP322+zevRuADz74gMmTJ7fpNSillFJdrWL5cjZfehni8TDsX0+QsNdeTpfkmF7dcjbwisuResewJT6egVdc3uZt5ufnM3nyZPbee28OPPBApk6dGhoC45577uH+++8nJyeHwsJCzj//fADOP/98CgsLycnJ4f7772fOnDmNbj8jI4NXX32VSy65hM8//5xbbrmFt99+mz333JPnnnuOQYMGkZKS0qC/mVJKKdVdFb/6GpvOOx9PejrD//1srw5m0MNbzpqTesIJAGyfOw9/fj6ewYMZeMXlofltsffeezd62HHEiBEsW7aswfz4+Hieey5yH7cakyZNYtKkSQAMHTqUFStWANYYaW+99RYej4dPP/2U5cuXExcXx9KlS5k7d25o/SVLloSm+/fvz4YNGygtLW3lq1NKKaU6jjGGwkf/wY777yfxgAPImv9X3GlpTpfluF4dzsAKaO0JY07btGkTM2bMIBgMEhsby6OPPgrAV1995XBlSimlVOOM38+2P91O0aJF9DnuOAbffReu2Finy+oWen04i3ajRo1q0QkCSimlVHcRKCtny5VXUP7hR/SbNYsBl89GXL26p1UdGs6UUkop1WV8BdvZfNGFVK/5gUG33UbfM2Y4XVK30yPDmTEm4tmOqmlNXZVAKaWUaq+qH35g8wUXEiwuJvtvD5F8xBFOl9Qt9bg2xPj4eAoLCzVotJIxhsLCQuJ70QjMSimluk75p5+y8ayzwe9n2JP/0mDWhB7XcpaVlUVeXh47duxwupRur6qqqk4Yi4+PJysry8GKlFJK9URFL75E/k03EbfHcLIffpiYsAHSVUM9LpzFxMSwxx57OF1GVFiyZAn77ruv02UopZTqoYwx7HzwIXbOn0/iwQeR9Ze/4E5Jcbqsbq/HhTOllFJKOc94veTfcivFL75I6sknM/hPtyE6VEaLaDhTSimlVIcKlJayZfZsypd+Sv9LL6X/JRfriXqtoOFMKaWUUh3Gl5/P5lkXUP3TTwy+6y7STj3F6ZKijoYzpZRSSnWIqlWrrKEyKioY+sjDJB1yiNMlRaUeN5SGUkoppbpe2UcfsfHsX4HLxbCnntJg1g4azpRSSinVLrsXLWLzhRcRM2wYw//9b+LHjHa6pKimhzWVUkop1SbGGHbMe4DChx8m6fDDyZw7F3dyktNlRT0NZ0oppZRqtaDXS/71N1Dy6quknX46g265GfForOgIuheVUkop1SqB4mLyLr2MiuXLGXDFFfSb9TsdKqMDaThTSimlVJOKFy9m+9x5DNy6lbUDB2KMIVhUxJB77yX1hOOdLq/H0XCmlFJKqUYVL15M/k03Y6qqEMC/fTsA/S68UINZJ9GzNZVSSinVqO1z52GqqhrML37lFQeq6R00nCmllFKqUf78/FbNV+2n4UwppZRSjfIMHtyq+ar9NJwppZRSqlH9L7ywwTyJj2fgFZd3fTG9hIYzpZRSSjXK++OPALj798cAniFDGHz7n0g94QRnC+vB9GxNpZRSSkVUvXYtu558krTTT2fw7X9iyZIlTJo0yemyejxtOVNKKaVUA8YYtt15F66kJAboIcwupeFMKaWUUg2UvvU2FZ99xoDZv8eTnu50Ob2KhjOllFJK1RGsqKDgnnuIGzuWvmec4XQ5vU6nhTMR+aeIbBeR78Pm3SoiW0TkG/t2bNhj14nIOhFZIyJHd1ZdSimllGrazkcewZ+fz6Abb9CLmTugM1vOHgemR5g/1xizj317HUBEcoEzgfH2Og+JiLsTa1NKKaVUBN6NG9m14J/0OeEEEg84wOlyeqVOC2fGmA+BXS1c/CTgWWNMtTHmJ2AdMKGzalNKKaVUZAV3z0FiYhj4hz84XUqv5USfs0tF5Fv7sGdfe14msDlsmTx7nlJKKaW6SOmSJZQtWUL/Sy4mJmOg0+X0WmKM6byNiwwHXjXG7GnfzwB2Aga4HRhsjDlPROYDnxljnrSXWwC8YYx5PsI2ZwGzAAYMGLD/okWLOq3+nq6srIzk5GSny4hauv/aTvdd++j+ax/df43w+ej3p9vB5aLwphshQl8z3XftM3ny5C+NMc0eK+7SXn7GmIKaaRF5FHjVvrsFyA5bNMueF2kbjwCPAIwZM8boYHhtp4MJto/uv7bTfdc+uv/aR/dfZDv//jA7duwg+x//YK/DDo24jO67rtGlhzVFJPwqqacANWdyvgKcKSJxIrIHMApY1pW1KaWUUr2VLz+fnQ8/TMrUo0huJJiprtNpLWci8gwwCegvInnALcAkEdkH67DmBuACAGPMChFZBKwE/MAlxphAZ9WmlFJKqVoFf/4zBIMMvOZap0tRdGI4M8b8MsLsBU0sfydwZ2fVo5RSSqmGyj/7jNI33qT/pZcSm6Xn4nUHeoUApZRSqpcyPh8Fd95JTGYm/X57vtPlKJsO+6uUUkr1UruffprqtevIenA+rvh4p8tRNm05U0oppXoh/86d7PjrfJIOO4zkI490uhwVRsOZUkop1Qtt/7/7CVZXk3H99YiI0+WoMBrOlFJKqV6m4uuvKX7xRfqdM5O4EXs4XY6qR8OZUkop1YuYQICC2+/AM3Ag/S+80OlyVAQazpRSSqlepOj5/1C1ciUDr74aV1KS0+WoCDScKaWUUr2Ef/dudtx/P4kHHECf4451uhzVCA1nSimlVC+x4y9/IVBaSsZNN+pJAN2YhjOllFKqF6hauZKify+i71lnET9mjNPlqCZoOFNKKaV6OGMM2+64E3dqKgMuu9TpclQzNJwppZRSPVzJ4sVUfvUVA6+6EndqqtPlqGZoOFNKKaV6sEBZGQX33kv8XnuReuqpTpejWkCvramUUkr1YDsffIjAjp1kP/gg4tI2mWigvyWllFKqh6r+8Ud2/etfpJ72CxL23tvpclQLaThTSimleiBjDAV33okrMZGBV17pdDmqFTScKaWUUj1Q6dvvUL70UwZcdhme9HSny1Gt0OJwJiIJIqIDoyillFLdXLCykoJ75hA3ejR9f3mm0+WoVmpROBORE4BvgDft+/uIyCudWJdSSiml2qjw0Ufxb80n48YbEI+e+xdtWtpydiswASgCMMZ8A+zRKRUppZRSqs28mzdT+I8F9DnuOJImTHC6HNUGLQ1nPmNMcb15pqOLUUoppVT7FNw9BzweBl79R6dLUW3U0nC2QkTOAtwiMkpE/gos7cS6lFJKKdVKZR9+SNn779P/oguJychwuhzVRi0NZ5cB44Fq4GmgGLi8k2pSSimlVCsFvV4K7ryL2OHDSZ850+lyVDs020tQRNzAa8aYycANnV+SUkoppVpr1+ML8W7cSPajj+KKjXW6HNUOzbacGWMCQFBE9EqpSimlVDfk27aNnX/7G8lTppB8+GFOl6PaqaXn15YB34nIO0B5zUxjzO87pSqllFJKtdj2P98LgQAZ113rdCmqA7Q0nL1g35RSSinVjZR/voyS11+n/yWXEJuV5XQ5qgO0KJwZYxZ2diFKKaWUah3j91Nwxx3EDBlCv9/91ulyVAdpUTgTkZ+IMK6ZMWZEh1eklFJKqRbZ/fQzVK9dS+Zf/4IrPt7pclQHaelhzQPCpuOB0wG9iqpSSinlEP/Onez4y19IOuQQUo46yulyVAdq0ThnxpjCsNsWY8w84LjOLU0ppZRSjdl+/1yCVVXW9TNFnC5HdaCWHtbcL+yuC6slTa+kqpRSSjmg8n//o/iFF0g//zziRmgPo56mpQHr/8Km/cAGYEaHV6OUUkqpJplgkG2334FnwAD6X3Sx0+WoTtDSszUnd3YhSimllGpe0X/+Q9X33zPk3j/jTk5yuhzVCVrU50xEZotIH7H8Q0S+EpFpnV2cUkoppWoFiorY8X/3k7D//vQ5/niny1GdpKUXPj/PGFMCTAP6Ab8G5nRaVUoppZRqYMdf/kqgpIRBN92oJwH0YC0NZzXvgGOBJ4wxK8LmKaWUUqqTVa1eze5nn6XvmWcSP3as0+WoTtTScPaliLyNFc7eEpEUINh5ZSmllFKqhjGGbbffgTs1lQG/v8zpclQna+nZmucD+wDrjTEVIpIOnNtpVSmllFIqpOTVV6n88ksG/ek23GlpTpejOllLW84OBtYYY4pE5FfAjUBx55WllFJKKYBAWTnb/3wv8XvuSdovfuF0OaoLtDSc/Q2oEJGfAVcBPwJPdFpVSimllAJg598ewr9jh3USgNvtdDmqC7Q0nPmNMQY4CZhvjHkQSOm8spRSSilVvX49uxY+Qeqpp5Lws585XY7qIi3tc1YqItdhDaFxuIi4gJjOK0sppZTq3YwxFNxxJ66EBAZeeYXT5agu1NKWszOAaqzxzrYBWcC9nVaVUkop1cuVvvsu5UuXMuCyS/H07+90OaoLtSic2YHsP0CcPWsn8GJnFaWUUkr1ZsGqKrbfPYe4UaPoe9ZZTpejulhLL9/0O+B54GF7VibwUifVpJRSSvVqhY/+A9/WrWTceCPiaWkPJNVTtPSw5iXAoUAJgDFmLTCwqRVE5J8isl1Evg+bly4i74jIWvtnX3u+iMhfRGSdiHwrIvu17eUopZRS0c2bl0fho4/S59hjSJo4welylANaGs6qjTHemjsi4gFMM+s8DkyvN+9a4D1jzCjgPfs+wDHAKPs2C2voDqWUUqrXKZgzB9xuBl59tdOlKIe0NJz9V0SuBxJEZCrwHLC4qRWMMR8Cu+rNPglYaE8vBE4Om/+EsXwGpInI4BbWppRSSvUIZR99RNm779H/oouIGTTI6XKUQ1oazq4BdgDfARcAr2NdJaC1Mowx+fb0NiDDns4ENoctl2fPU0oppXoF4/VScOddxA4bRvo5M50uRzmo2V6GIuIGVhhjxgKPdtQTG2OMiDR3aDRSPbOwDn0yYMAAlixZ0lEl9TplZWW6/9pB91/b6b5rH91/7dNd91/iW2+TsmEDuy+9hM1LlzpdTkTddd/1NM2GM2NMQETWiMhQY8ymdj5fgYgMNsbk24ctt9vztwDZYctl2fMi1fMI8AjAmDFjzKRJk9pZUu+1ZMkSdP+1ne6/ttN91z66/9qnO+4/X0EBP155FUmTJzPu0kudLqdR3XHf9UQtPazZF1ghIu+JyCs1tzY83ytATVvtTODlsPm/sc/aPAgoDjv8qZRSSvVo2/98L/j9ZFx3bfMLqx6vpYOn3NTaDYvIM8AkoL+I5AG3AHOARSJyPrARmGEv/jpwLLAOqADObe3zKaWUUtGoYvlySl57jf4XX0Ts0KFOl6O6gSbDmYjEAxcCOVgnAywwxvhbsmFjzC8beWhKhGUN1lhqSimlVK9h/H623X4HniGD6fe73zldjuommms5Wwj4gI+wxiLLBWZ3dlFKKaVUb7D72X9T/cMPZD7wAK6EBKfLUd1Ec+Es1xizF4CILACWdX5JSimlVM/nLyxkx1/+QtIhB5MybarT5ahupLkTAnw1Ey09nKmUUkqp5m2fO5dgRQUZN9yAiDhdjupGmms5+5mIlNjTgnWFgBJ72hhj+nRqdUoppVQPVPnttxT/5wXSzzmHuJEjnS5HdTNNhjNjjLurClFKKaV6AxMMsu32O3D370f/Sy52uhzVDbV0nDOllFJKdYDiF16g6rvvyPjDH3AnJztdjuqGNJwppZRSXSRQXMz2++eSsN9+9DnxRKfLUd2UhjOllFKqi+z463wCRUUMuulGPQlANUrDmVJKKdUFqtasYffTT5N2xgzix41zuhzVjWk4U0oppTqZMYaC2+/A3acPA2frWO6qaRrOlFJKqU5W8trrVHzxBQMuvxx3WprT5ahuTsOZUkop1YmC5eVs//Ofic/NJe3005wuR0WB5gahVUoppVQ77Pz73/Fv307mA/MQtw4fqpqnLWdKKaVUJ6le/xOFjy8k9eSTSdx3X6fLUVFCw5lSSinVCYwxFNx1F664OAZedaXT5agoouFMKaWU6gRl779P+ccfM+CyS/EMGOB0OSqKaDhTSimlOliwqoqCu+4mNmckfc86y+lyVJTREwKUUkqpDla4YAG+LVsY+vhjSEyM0+WoKKMtZ0oppVQH8uZtofCRR0k5ZjpJBx3kdDkqCmk4U0oppTrQ9nvuAZeLjKuvdroUFaU0nCmllFIdpOzjTyh95x36X3ABMYMHO12OilIazpRSSqkOYLxeCu68k5ihQ0k/71yny1FRTE8IUEoppTrArn89ifenn8j6+99wxcY6XY6KYtpyppRSSrWTr2A7Ox98kORJk0iZNMnpclSU03CmlFJKtdP2++7D+HxkXHet06WoHkDDmVJKKdUOFV98QcnixaSffx6xw4Y5XY7qATScKaWUUm1k/H623X4HnsGD6T9rltPlqB5CTwhQSiml2mj3v/9N9Zo1ZM6biysx0elyVA+hLWdKKaVUG/h37WLHA38h8aCDSDn6aKfLUT2IhjOllFKqDXbMnUewooJBN96AiDhdjupBNJwppZRSrVT53fcUPf886WefTVxOjtPlqB5Gw5lSSinVCiYYZNsdt+Pu14/+l17idDmqB9JwppRSSrVC8YsvUfW/bxl41VW4U1KcLkf1QBrOlFJKqRYKlJSw/f77SdhnH1JPOtHpclQPpUNpKKWUUi20Y/58Art2kfHIw4hL2zdU59B3llJKKdUCVT/8wO6nniZtxgwSxo93uhzVg2k4U0oppZphjKHgjjtxJycz4PLZTpejejgNZ0oppVQzSt94g4plyxhwxeV4+vZ1uhzVw2k4U0oppZoQLC+n4J4/E5c7jrTTT3e6HNUL6AkBSimlVBN2PvwI/oICMufej7jdTpejegFtOVNKKaUa4d2wgcLHHiP1pJNI3G8/p8tRvYSGM6WUUioCYwzb7roLV2wsA/9wldPlqF5Ew5lSSikVQdkHSyj/8CP6X3opngEDnC5H9SIazpRSSql6gtXVFNx9N7EjR5L+q7OdLkf1MnpCgFJKKVXPrn/+E9/mzQz95wIkJsbpclQvoy1nSimlVBjfli3sfPgRUo4+mqRDDnG6HNULOdJyJiIbgFIgAPiNMQeISDrwb2A4sAGYYYzZ7UR9Simleq+Ce/4MQMY1VztcieqtnGw5m2yM2ccYc4B9/1rgPWPMKOA9+75SSinVZcqXLqX07bfpf8EsYoYMcboc1Ut1p8OaJwEL7emFwMnOlaKUUqq3MV4v2+64k5jsbNLPO8/pclQv5lQ4M8DbIvKliMyy52UYY/Lt6W1AhjOlKaWU6o12PfkU3vXrybjuOlxxcU6Xo3oxMcZ0/ZOKZBpjtojIQOAd4DLgFWNMWtgyu40xDa4ua4e5WQADBgzYf9GiRV1Udc9TVlZGcnKy02VELd1/baf7rn10/7VPpP3nKi6m3y234svJoeiSi0HEoeq6N33vtc/kyZO/DOvO1ShHTggwxmyxf24XkReBCUCBiAw2xuSLyGBgeyPrPgI8AjBmzBgzadKkLqq651myZAm6/9pO91/b6b5rH91/7RNp/2295hpKAgFG3XcvscOHO1JXNND3Xtfo8sOaIpIkIik108A04HvgFWCmvdhM4OWurk0ppVTvU/HVVxS//Arp556rwUx1itfWv8a056cRPzx+/5Ys70TLWQbwolhNxh7gaWPMmyKyHFgkIucDG4EZDtSmlFKqFzGBANtuvwPPoEH0v/ACp8tRPdBr61/j1qW3UhWoavE6XR7OjDHrgZ9FmF8ITOnqepRSSvVeRYsWUb1qFZn3/x+uxESny1E9RCAYYGPpRlYXrub2z25vVTADvXyTUkqpXsq/ezc75j1A4oQJpBxzjNPlqCjlC/j4sfhHVhWuYtWuVazetZrVu1ZT6a9s8zY1nCmllOqVdsx7gEBZGRk33oDo2ZmqBSr9lfyw+wdWF65m1S4rjK3dvRZf0AdAgieBseljOSXnFMamjyW3Xy6XvX8Z+eX5zWy5Lg1nSimlep3K71dQtGgRfX/9K+JHj3a6HNUNlXhLWLNrTZ0WsfXF6wmaIACpcamMTR/Lr8b9irHpYxnXbxxDU4bidrnrbGf2frO7f58zpZRSylHBIAV33IE7PZ0Bl17qdDWqGyisLAwFsJWFK1m9azWbSzeHHh+YMJCx/cYyZegUxqWPY1y/cQxOGtyiFtfjRhwHwANfPcA61rWoHg1nSimleoXixYvZPnceA7dupRJIPf103H36OF2W6kLGGLaVb2PlLiuA1bSKba+oHVo1KzmLcf3GhQ5Njus3jv4J/dv1vMeNOI7jRhyHnC5ftmR5DWdKKaV6vOLFi8m/6WZMVRU1bR0lixeTNOFAUk84wdHaVOcImiCbSjaF+oatKrRaxoqqiwBwiYs9+uzBgYMOtFrD0scxJn0MqXGpzhaOhjOllFK9wPa58zBVdfv8mKoqts+dp+GsB/AFfawvWh86NFkTxCr8FQB4XB5GpY3iyKFHhg5Lju47mgRPgsOVR6bhTCmlVI9ijMG3ZStVK1dQtXIlVatW4d+6NeKy/vzWnUWnnFflr2Lt7rV1WsTW7l6LN+gFrDMmx/Qdw4kjTyS3Xy5j08eSk5ZDjDvG4cpbTsOZUkqpqGUCAbwbN1K1YmUoiFWtWkWwuNhawO0mbuRIJCEBU9lw3CnP4MFdXLFqjTJvmdUSFtZZ/6finwiYAAApsSnkpufyy7G/ZFw/q0VsWMqwBmdMRhsNZ0oppaKC8Xqp/vFHK4StsIPY6tWh0CWxscSNGUOf6dOJHzeO+PG5xI0ahSs+vk6fsxoSH8/AKy536NWo+nZV7WJ14eo6nfU3lW4KPd4/oT/j0scxOXsyuf1yGddvHEOShvTIMeo0nCmllOp2gpWVVK9ZQ+VKq0WseuUqqteuxfiswT5dSUnEjRtL2umnET8ul/jcXOJG7IHERD50VdOvbPvcefi2biVmyBAGXnG59jdzgDGGgoqC0JmSNYcmCyoKQstkJmcyLn0cJ4480WoRSx/HgMQBDlbdtTScKaWUclSgpISqldbhSOvQ5Eq863+CoDXYpzstjfjcXNLPmWm1iOXmEjN0KOJytep5Uk84gdQTTmDJkiVMmjSpE16Jqi9oguSV5rFy18pQJ/1VhavYXb0bAEEYnjqc/TP2D3XUH5s+tlucMekkDWdKKaW6jH/nTiuArVwV6iPm21w72Kdn0CDix42jz9HTic+1gphn0KAeeegqmry2/jUe+OoB8svzGfz8YGbvNzs0uGoNf9DPT8U/hVrCavqJlfvKAeuMyZy0HCZlTwq1ho3uO5rEGL3gfH0azpRSSnU4Ywz+rVupXLmS6lWrQn3E/NtrB/uMGTaU+PHjSTv9dLtFbByefv0crFpF8tr61+pcfii/PJ9bl95KXmke6QnpoRaxH3b/QHWgGoB4dzyj00dz/Ijj65wxGeuOdfKlRA0NZ0oppdrFBIN4N2wMHZKs6SMWqDlj0uUibuQIkg4+yOobNm4c8ePG4U5JcbZw1awSbwn3fXFfg+tCVgWqmP/NfABSYlIY128cZ4w5I9QiNrzP8Kg/Y9JJGs6UUkq1mPH5rDMma86WXLmS6tWrCVZYg31KTAxxY8aQMm0a8eNziR83jrjRo3EldM/BPns7YwyFVYVsLt3MppJN1s/STeSV5rGpdBPF1cVNrv/6qa+TlZylh507mIYzpZRSEQWrqqhes6ZOH7HqH34InTEpiYnEjxtH6qmnEp+bS3zuOGtMsUbOmFTOCAQDbKvYFgpgNcFrc+lmNpduptJfO/6bS1wMThpMdko204ZNY2jKUP75/T9DHfjD1SynOp6GM6WUUgRKS2tbwmp+/ri+9ozJ1FTix+fS9ze/toLYuFxihw9r9RmTqnN4A17yyvKs4FWvBSyvLA9/0B9aNsYVQ1ZKFkNThjJh0ASyU7LJTslmaJ+hDEka0mAk/QGJA+r0OQOrT9ns/WZ32evrbTScKaVUL+MvLKxztmTVypX4NtUO9ukZOJD43FxSpk61g9g4PEN65mCf0aTcVx5q7aoJYDW3beXbMJjQskkxSWSnZDOqr3U9yaEpQ0MBbGDiQFzS8lBdc1Zm6GzNpMhna6omfLsI3vsT+w927d+SxTWcKaVUD2WMwZ+fbwWwsD5i/oLawT5jsrOJz80l7dRTQ33EPP37O1h172WMYXf17trQVbI51AK2uXQzu6p21Vk+PT6d7JRs9s/YP9T6VRPA+sb17dAwfdyI4zhuxHE6RlxbfLsIFv8efA0vH9YYDWdKKRUlihcvZvvceQzcupW19Ua4N8Eg3o0bQ4cka/qJBYqKrJVdLmJH7EHixAmhEfXjx43F3aePcy+oFwqaINsrtjfaAlbmKwstKwgZSRkMTRnK5OzJoUORNSEsOTbZwVei6vBXQ8lW+7bFvm2F4i2w9m0I+lq1OQ1nSikVBcKvDSmAf+tWtl5/A0UvvYypqqJ61arQGZPExBA/ahQpU48ibtw4EnJziRszRs+Y7CK+oI+tZVvrBLCaTvh5pXl4g97Qsh7xkJmSSXZKNvsM3Mdq+bIDWGZKJnHuOAdfiQLAVwWlW2vDVk3wCg9h5TsarhefCn0yWx3MQMOZUkp1WyYQwJeXR/WPP7LtT7fXuWg3AD4fFUuXkrDPPqSeckpoRP24kSORWB3sszNV+isbHH6sOQS5rXwbARMILZvgSSArJYs9UvfgiKwj6hyCHJQ0CI9Lv4od46tsvMWrZrpiZ8P14tOs4NVnCAzZt3a6T2btdJzdsjl3Tyje3HAbTdB3hFJKOcwEAvg2b6b6xx+pXrvO+rluHd716zHV1c2uP/yZp7ugyujXkksQhSuuLm708OOOyrotJalxqWQnZ7P3gL05bsRxdVrA+if015MpnBAKXlsab/GqKGy4XkLf2oCVuX/tdKodvFIG1wavlphys/Y5U0qp7ioUwtats29hIcwbdqhr8GDicnJImjiRuFE5xI0cSd7lV+Dftq3BNj2DB3flS4hajV2CqKS6hNHpoyN2wi/xltTZxsCEgWSlZHFo5qG1ne9ThpKVktXrL9Td5bwVYUFrK5Tk1baA1QSxyl0N10voC32yrLCVdWC91q5M6DMYYpM6tta9Z1g/3/sTsKpFq2g4U0qpDmb8frx2CPOGtYY1CGFD7BB28MHE5eQQlzOS2JEjcSc3/K984FVXhvqc1ZD4eAZecXlXvKSoFAgG2Fm5k20V27hn2T0RL0F017K7Qvfd4g4NrHrMHsfUOfyYlZJFgkf77HUJb3nd4BWp1auy4aC4JKRbrVupmZB9YN1DjKlZVotXrEMXWd97Buw9gy+vlC9bsriGM6WUaiPj9+PdtJnqdWsbhjBfbSfgmCFDiB2VQ9Ihh9SGsBEjcSe3/D/0mrMyt8+dh2/rVmLqna3Z2/iDfnZW7qSgooBt5dsoKC9gW4X1s2bezsqddfp+NebvR/2d7JRsBicPJsalVzeIyB6n6+fFefB1lnWorqZFqDW85Y0fYqyZX1XUcL3EflbQSs2G7Im1hxhDLV9DIKbnhGcNZ0op1Qzj81ktYWvXUf3jOrz2IUnvTz/VDWGZmVZL2GGHEpczirickcSNGIErqWMOk6SecAKpJ5zQ48eaqgle28q3NQhcBRUFFJQXsKNyB0ETrLNevDueQUmDyEjKYOLgiWQkZjAoaRCDkgZxy9Jb2FnZsGP34KTBHJp5aFe9tOgUNk6XgNW5ffHvrcfCA1p1WeOd6mvmV0W4Vmdifytc9R0Gww6u1+KVCSlDICa+K15pt6HhTCmlbMbnw7tpUyiEVa9bh3fdj1Rv2ADhISwri7icHJKPOJzYnBziRuYQN3IErkSHDplEEV/Qx86KnXXCVnjo2lZhtXjVD14JnoRQ2Dpo8EFkJFnTGYkZofl9Yvs02vH+Dwf8QS9B1BbBILx7a8PO7L5KWHw5fPtvO4RthUgXSU8aYAevPWDYobWHGPsMsW69MHi1hIYzpVSvY3w+a8BWu0O+1TdsHdUbNtaGMJHaEDbp58Tl5BA7Moe4EXtoCGuEL+hjR8WOOoca64evnVWRg9egpEEMShzEIUMOCYWtjMSMUAhLiUlp1xmPvf4SRP5qqCyy+mpV2T9Dt7D79R+rKoZ6v68QXzmU74R+I2GPw+3AFRa8+gwBj47T1hYazpRSPZbxeq0QVmeIirV4N2wEv30haBFisrPtEDaZuFE5xI60D0fqoK0hvoCP7ZXbrdatmrBVL4TtrNxZ5/qOAImexNChxZy0HCtsJVqHHmtCWHJMcpcMNRH1lyAyBqpLGw9SdUJWcd3HfBVNbFisAVMT+tq3NOg7vPb+skcj9wNLzYYL/tsZr7TX03CmlIp6xuulesOGOp3yq9etw7uxXggbmk1czihSjpwSGqIidsQIXPG9+7CKL+ALha3wjvXhIaywsrBB8EqOSQ4FrNHpo+u2eNUEL73EUEMBXxOtVUX1Wq7qPdbUCQ7uuLCA1RfShsHgn9UGrvi0ugGsZjouFVxNXAi9/+iG43TFJFgnBahOoeFMKRU1gl4v3p82WIcgw8cJ27gRAvaXlstFbHY2sTk5pBx1VO3ZkXvsEfUhrLWDqAJ4A95Q6IrUv6ugvIDCqoYDcabEpFitW0kZjE0f2+BQY0ZiRvQFr4464xCsVixvedNBqsFj9jxvWdPbjk+tG6RSs+uGqTohKyxoddbZimHjdJniPCS1nftONUvDmVKqyyxZ8CdiHlnEgOIAn6S68c2awaTzG/73bYWwn2r7g9WEsE2b6oawoUOJzRlJyrSpVqf8UTlWCIvref1cGhtEtbCykHH9xjXaz2tXVcOBOFNiU0Jha1z6uDqHGmt+JsV08ECcTmvsjMNgEEZNbWXIsqeD/safzx1bN0j1yYKMvZoIWfZ0fCq43J2+O1rNHqfrv9F6SNhhL329hXvfWkPsoJz9W7K8hjOlVJdYsuBPpM17hji7v316cYDquc/w8fYi9tprSp2xwrybN9eGMLeb2KFDicsZScr0o2tD2PDhzYawoAkSCAYIGOvmD/qteSZQZ34gGCBogviNv/F1wpdvap1gIOJ2GqwTDNaZH2k6aIKh5/8s/zOqA3Uv5VQVqOLeL+6tM69PbJ9QyBrff3zDzvWJg0iM6WEnNBhj9alqKkgteyTyGYcvXdD0tuP6hAWnNKuTe/3DgpGCVkwi6CWbFFYwu+6F76j0NT/mXg0NZ0qpdgsGg1SU7qKsaDtlu3dQWbSTypJdeEuK8JYW4ystpv9T74aCWY04P8QtfIOtvEHQBbsHJFCYkcCOKRlsz4ijYGAs2/t78LoNQZOHP7iBoPdtAt8H8H8bFrTCQ01YAOqOPOLBJS7cLjducdf+FDcuceFxeepMu8SFW9wNglm4R6c9GurnFdXBKxgI68he1LqWrIC38e26PE23ch3z58ghKz4V3Po1qWoZY6jyBan0Bajw+qn0BuzpQN1pX4BKr58Kb4B/fPRTq4IZaDhTqlcLBPxWqNq9nfKinVQU76SqeBfVJUX4SovxlZYQKCslWFYG5ZVIeSWuyircFV5iqvzEVvqJrwoS7zW4wvqKe4CUFtZggL9dNZqigQkQE1M3rLhc9LPDTHhQqR9q6q/TIACFLecS6/HwcFQzr8l17NDU7DquhkErfJ22mvb8NPLL8xvMH5w0mIMGH9Tm7XYKX2XLOrfXnx9pgNJwsSl2i1WaFZ4GjInc96p+yIpNgnl7WYcy60vNhonNtJ6p0GG5LUWVZH72Pn88egwn75vpdFkNBIOGKr8VlGpDUs20n0pvkAqvnyo7RFV4AxGm/fXWqzvdFTScKRWFAgE/5cU7KSvaQUXxTsqLdlJdvIsqO1T5S0sIlJXZoarCDlXVeCq9xFT6iK0MEFcdIL4awuNCjH2rH6yqYqAq3oU33o0vPgZ/QgyVacmUJ8ZDciKSlIgnOQV3SgqxKWnE9kklvk868anpJKX1JzltACtPOJb04oYfbLtT3fz1dy934t7qGWbvN7trB1ENBq1BRRttxar/M+xxf1Xj2xV33SCVPNA6G7DZkJUG7nZcWmnKzXrGYRvVPyy3paiS6174DqDVAS0YNKHWpaqwVqamWqFqAlOddSIu66fK18iYbE2I87hIjHWTGOshPsZFYqyHhFg3aYmxDElzkxDrJiHGTaL9MyHWEzbtrjOdEOsmMcYTmk6IcXPEnz9gS1Fl84WE0XCmVCu1tFN7JH6fl/KSQsqLdlBetIOK4kKrpap0N76SktpQVV4OZeVIRRXuimrcNaGqymqpSqh3BCfWvtUPVZWxUB1nh6qEGPwJsXj7JlOWmABJCbiSk3Anp+BJ6UNMSqoVqlLTSeiTTnLaAJL6DiCpT39iYtt/lqNv1gyqw/qcAVTHWPNV844bcRxs+owH1r/INhcMCsLsYdObH0TVX92ywUYbPFYE9YbOqCMmqW6Q6p/T+FmE4SErLsWZvlh6xmGLBELhyU+VN0iFz8+dr61q0GJU6Qtw88vfs35HWeTDevUCU024qva3PjyFAlO9MJSeFEtWXzfxdnCywlXNdO382vU8tSGrZn6MG5erc9+Pfzx6TKv7nIkxTfzxdXNjxowxa9ascbqMqBW1AzF2MV/AR4W3jIriQr5ZOI8h/3qP2LDuKz4XbDpgCK7sTIJl5UhZBVRU1oaqKn+joaoxlbE1LVUefPEeAolxBBLjMInxkJRoh6pkPCmpxPTpQ1xKX+L7pJOQWhOqBpLUpx+emNjO2SltVBNs04oDFLUy2PZaxlitPd88BW/fWLdVyh0DY46HtKywgFVUN2Q1NfiouOq2TDU3VEPosbSoHvk9mj/7AkHTsJWpwSE4f535ld4AFQ2m/bUtU/a8Cm8AbxvCU03giRSMGg1ModYoDwmxLhJiPHVDU83yns4PT12h5rDwFw9cQHX+2mZfkIazXiyaP6BqGGPwBX1U+itDtyp/Ve3P6jKqSnbjKyvBV1KMv7SUYFkpwfJyTFkFVFRAuR2kKuzDfnZfqtjqAPFVQRKrId7XfC1BoCoOquPctS1VibEEE+IwSQlWqEqyW6r6WC1V8X3SiOvTl8TUfiSlDSC570CSUvvj7uGdkHvCe69ZNWcQVpfatxKoKgm7Hza/upH5Ncs3NfAogCchQpBKa3y4hlBfrJSmBx/tYer0m0pL6JR+U/5AsG4QqhOeIvdlqm1t8jdoearfGuUNtC48iTQMTwmxHhLCDt8lNGhhqnv47qaXvqewvOF/lkNS4/nk2iO75OoOPYWIfGmMOaC55Xr2N4CKqC0DWbaVMQZv0BsKTJFCVMRgVV1OoKyUQGkZwfIyTHkFlFfgKq+yDvVVeomp9OKp9JNQHSShGhK8kFhtwqahb0tClYA3zo03wYM/3oM/IZ5A3ziCifFUJCVQkZSAJFktVRkL3iDSx5ABxq74rseHql6hZnDROkGpuOnw1OBmL9/YNQnDeRKsQ33ht77DG85799ZGNiBw47YO3AE9k9Vv6lsq7T5JW4oqueY/35JXVMHBI/o16Bwefliu0huk0udvEJoiBS9foHUNHiKQaAehhFhXbX+lGDepCTF1wlO83Z+pdtpdZzoUrMIO38V5XO0OT15/sMFhuYQYN1dPH6vBrJNEdctZwh4J5vB7D+9dF69tp9fWv8Zbj9zAae9X068ECvvA80fGcdi513N45uFUBuyg5KukKlDVoiAVPl3trcCUlUN5Ja6KKlwVVaHDeYl2aEqoNiRWh9+359nTidXWEAvNMSL4E2JCh/uM3Y9KkpPD+lKl2H2p0ojrk0Z8ajqxKam4kpNxJSfjTk5GEhKQFrYefDJxz4id2nelujn08+9b++vofewR2julz08waF2IOTwkVTUSqiK2VJVaYcvbwlAVk9gwQMX1sW/15senNrJsSss7uc/ds/GzDa/onu89YwzeQBCv376FTVfbt/rzvYFAnWW8gSDVvnrLhK1THZoO1N6vv01/kNLqtg2t4hIatjDVma5thYp8WK9mumGfqYQOCk9doStaHXuDXtNyVjNKNtCigBY+AGXNWEiRxkdqcgDJoL9Fg1fWTIevEz6oZKTBK+sPPll/nTYNnhm2Ts6yrfzu9QDx9ufUgBI499VqHvXfyryRrjqtTvVboRK8hsQqSPK5yPC6SPQKSV4hvtqQUBUkrjqIpwVnyhiXYJISEbtFypWWXNt/KqUPnpQ+oT5VrqRkXCnW4zWBypWUjDs5CUlM7PIPNd+sGVTPfaZOeKz2aKf2FmlshHZjYMwxLWipaixsldaGraY6sNeISWoYlJIHRg5VjYWtuD5dPv7V8pGXseeXN5IgtYeXKk0s34+8jAPt+8aYsLASOchU+wMRw1KdsFMnFDVcPlIAihy0Wt9/qTEelxDrcRHncRFbc3O7iPW4rfluKyClJdbMr7vsY59saHTbC8+b0OjZd7Hu6AhPne3kfTM5ed/M3tEloRuI6nA2Ih8efNDP05PKuSF4A/O+mlc34AQbhpb6F+7tDjzGRXzQTVzQTVzQRWxAiA+6iQ0KsQEXcQEXsUEhzi/EB4TYoBATEGID4AlAbEDw+CEmYIgJYE37DZ6Awe03eOyb22/o90OA2HoNP/F+uOxVAzTTr8XtDrU2uZKTcaXbLVRJybhSUuoGqprWq5SUUKCqeVwSEqL2w27SgWN5//Aq4r6MJ60EivpA9f5VHHng2PZt2BirtSYYsAbLNPbPYLDe/UCEZex5de777e2F3Y80zwQa2WYwbJ1mlmlpbVu/bjhQqK8SXpzVsn0Um9wwJKVkWBdtbhCeGmmtik3p0FAVDFotQ4211ngDgdBj1RHCUigE1V+nfktRIMjn64cy3fyWqz2LGCKFbDX9+LN/Bq9+mkXCF2/iDQRbfUitKTFuCQs57nqByLolx3mITawfluouH1dvnTrT9uPWMu468+sELLer3Z3C315REHE4g8y0BH4+ekC7tq1UR4vqcCZYLT8XvG4AL/1PnBh5dO36g0oieHyGGD94AlaIqQkvMX5w+YN4AkHcviBufxCX35p2+QO4fUHEH8DlC+Cq+ekLID4/4vcjvgB4fYjPDz4/4vWBzw8+H3h9GK8PfF6M14fxejFeb9MjW7d2n8TGRrjF4IqNQ+JiqQhsj7ieAQbdcEPdUBUWqFzJyUh8fNtDVSh8+K0v5Dpf+i0JCx0QDtocbmrn+X94hyMzquHYui8v8NLFuD/7WysCUPhr9zff4dspLo81LpXLY99c9ebVv++2bnXWcYMrFhPwNtpfT6bdERaiGglbLndtGPIFqY7QohNqxQkE8XqDeCvCg08R3sCuusvUC0z1Q1HtMoGGz+EP4g92XBiKFGDCW368gSCvcBiveA9rsO4ZBw6ts3xcpCDkrh+I3BHDUqy7Y8JQdxNpOIOEGDd/PHqMg1UpFVlUh7Ma8X646HVD2vYtocATfgs2CEMd9EUoUjcExcRYQcj+ac3zWC1FMR4kxoMrxo14PPZ9N+KxbzFuXB434nHVvbldiEcQt+DyuBA3iFvsefa0C1xuAy6DmECEVo/aIPPtuq+IKWvYv8qfHCQ95g2oDEBFALY1EVoihZ3mlumG4SOICyMujHgIigsjboK4CIoHIy4CuEM/g/ZjAwPVREoYroCP1SUxBCWeIG6C4iIobgwuAuLGuNwE3fZ86v0UD8Gw5zL2ulYtbnu52nVqH3eH/Yy8Tp1thrYTeR2D26q15n47RrOPZLY5lUzZ2WD+VtOfq1ceWK+fUSVefzle/9Y6oahDw5B9KKzR1hqPi9TYGGLdjYQd+35cjKvuMvVbgepts7Hg1Nw/PofOeb/Rlp+bT8jtsP3SU9X0j9J+UyoadLtwJiLTgQcAN/APY8yclqwX4zeUbVwBbgG3FWaIAYmj9r4rDpc7DnGBuA3iMrjcxrrvCuJyBe37QVwug8sVsOZJEJc7iMvlx+UK4paAdR8/bgKICeKijX0rgoDXvnWwYNiXbFA8GFwM3dPPluVpuAK1XwRBt2HonsVs27CKoB0mgtihBBeB0M1DgFgCuPHjImCs+f6aPWEEP278RvAba55fBL/LmufDhd+48BkX/qBYP40Lr71sEMFv3PZzucOe1xV6ziCusGVcEZe1aml6mQAuDE2HD7dLrJsIHpfgcgmvmYvIihAwtpj+nOu7psN/h50vaN86/zqUVb4ZzIn5B4lhfaYqTCz3+GZQ6Q0Q53GTmOipbd1p4lCYFYrczS8TCkR1w1KMW6Lu0Lq2/LSf9ptS0aJbhTMRcQMPAlOBPGC5iLxijFnZ3LoxiQEGHJFnx6V6X8im7hd8pC9sP26CoZDgqg0CEUJCIOgiEIy8bIP7JvI2GqxjGm6jbo32Oo3UGGmdSE08Hw79PVkUsf3bFPwVbjyJAQbuXUrp0CSO987BExZI3G77Z808lwu3C+ungMflwuWyftYuU3fdmlBT56c93+0WEkRIaWqZes/b+DIRbnbtngavQ+oFr9rXEfopRPzyvvWOX3G176EGAeMfsb/i0+umtP3N3wscOke4toQGfaa+7DOVTy4+1Onyuj1t+VGq9+hW4QyYAKwzxqwHEJFngZOAJsOZuIPE7g1pt23tghKj2613LOPqYQ8xanht37MKE8s/Yn7FNzdOc7Cy6LDPcbO4+UU/l5tnQwFjHmdy2HEt7NTei1ktP946faYSYtzcrS0/LaYtP0r1Dt1qnDMROQ2Yboz5rX3/18BEY8ylYcvMAmYBjI+L2//F3GxS965g6UHnkTZWWy6as3Srj50r3+dKV23rxf3BGfTPPZJDhrTjosK9yNKtPv7zg4/CqiD94l38YnSM7rsW0n3XMcrKykhOTna6jKil+6/tdN+1z+TJk3vmOGfGmEeARwAOGOIxKScaNu53MyefeIHDlUWHScBLX+dyxltT9dBIG00CrqeXXIKog01C911H0P3XPrr/2k73XdfobuFsC5Addj/LnhdRacpIBt26hkGdXlbPoodGlFJKqe6ru13xdjkwSkT2EJFY4EzgFYdrUkoppZTqMt2q5cwY4xeRS4G3sIbS+KcxZoXDZSmllFJKdZluFc4AjDGvA687XYdSSimllBO622FNpZRSSqleTcOZUkoppVQ3ouFMKaWUUqob0XCmlFJKKdWNaDhTSimllOpGNJwppZRSSnUjGs6UUkoppbqRbnXh89YSkVJgjdN1RLH+wE6ni4hiuv/aTvdd++j+ax/df22n+659xhhjUppbqNsNQttKa1pydXcVmYh8ofuv7XT/tZ3uu/bR/dc+uv/aTvdd+4jIFy1ZTg9rKqWUUkp1IxrOlFJKKaW6kWgPZ484XUCU0/3XPrr/2k73Xfvo/msf3X9tp/uufVq0/6L6hACllFJKqZ4m2lvOlFJKKaV6lKgMZyLyTxHZLiLfO11LtBGRbBH5QERWisgKEZntdE3RRETiRWSZiPzP3n+3OV1TNBIRt4h8LSKvOl1LtBGRDSLynYh809Izv5RFRNJE5HkRWS0iq0TkYKdrihYiMsZ+z9XcSkTkcqfriiYicoX9vfG9iDwjIvGNLhuNhzVF5AigDHjCGLOn0/VEExEZDAw2xnwlIinAl8DJxpiVDpcWFUREgCRjTJmIxAAfA7ONMZ85XFpUEZErgQOAPsaY452uJ5qIyAbgAGOMjjXVSiKyEPjIGPMPEYkFEo0xRQ6XFXVExA1sASYaYzY6XU80EJFMrO+LXGNMpYgsAl43xjweafmobDkzxnwI7HK6jmhkjMk3xnxlT5cCq4BMZ6uKHsZSZt+NsW/R9x+Og0QkCzgO+IfTtajeQ0RSgSOABQDGGK8GszabAvyowazVPECCiHiARGBrYwtGZThTHUNEhgP7Ap87XEpUsQ/JfQNsB94xxuj+a515wNVA0OE6opUB3haRL0VkltPFRJE9gB3AY/Yh9X+ISJLTRUWpM4FnnC4imhhjtgD3AZuAfKDYGPN2Y8trOOulRCQZ+A9wuTGmxOl6ookxJmCM2QfIAiaIiB5abyEROR7Yboz50ulaothhxpj9gGOAS+xuHqp5HmA/4G/GmH2BcuBaZ0uKPvbh4BOB55yuJZqISF/gJKx/EoYASSLyq8aW13DWC9l9pf4DPGWMecHpeqKVfUjkA2C6w6VEk0OBE+1+U88CR4rIk86WFF3s/8AxxmwHXgQmOFtR1MgD8sJaup/HCmuqdY4BvjLGFDhdSJQ5CvjJGLPDGOMDXgAOaWxhDWe9jN2hfQGwyhhzv9P1RBsRGSAiafZ0AjAVWO1oUVHEGHOdMSbLGDMc69DI+8aYRv97VHWJSJJ9Ig/2IblpgJ613gLGmG3AZhEZY8+aAuiJUK33S/SQZltsAg4SkUT7e3gKVp/viKIynInIM8CnwBgRyROR852uKYocCvwaq8Wi5pToY50uKooMBj4QkW+B5Vh9znQ4CNVVMoCPReR/wDLgNWPMmw7XFE0uA56y/373Ae5ytpzoYv9DMBWr1Ue1gt1i+zzwFfAdVv5q9GoBUTmUhlJKKaVUTxWVLWdKKaWUUj2VhjOllFJKqW5Ew5lSSimlVDei4UwppZRSqhvRcKaUUkop1Y1oOFNKRQ0RmSsil4fdf0tE/hF2///si6o3tv45IjKknTXEici79jA0Z4jIEhE5oD3bVEqpcBrOlFLR5BPsUbVFxAX0B8aHPX4IsLSJ9c/BunRKi9kXKQ63L4AxZh9jzL9bsy2llGoJDWdKqWiyFDjYnh6PNTp+qYj0FZE4YBzwlYjcLCLLReR7EXlELKcBB2ANQvqNiCSIyP4i8l/7IuJvichgALs1bJ6IfAHMrnlyERkIPAkcaG9jZHhxIvJLEfnOft577Hmni8j99vRsEVlvT48QkU86cV8ppaKUhjOlVNQwxmwF/CIyFKuV7FPgc6zAdgDwnTHGC8w3xhxojNkTSACON8Y8D3wBnG1fuN4P/BU4zRizP/BP4M6wp4s1xhxgjPm/sOffDvwW+MhuOfux5jH7cOk9wJFYo88fKCInAx8Bh9uLHQ4UikimPf1hh+0cpVSPUb+5XimlurulWMHsEOB+INOeLsY67AkwWUSuBhKBdGAFsLjedsYAewLvWJe6ww3khz3e2kOWBwJLjDE7AETkKeAIY8xLIpJsXxMzG3gaOAIrnOllcJRSDWg4U0pFm5p+Z3thHdbcDFwFlACPiUg88BBwgDFms4jcCsRH2I4AK4wxB0d4DKC8A2teCpwLrMFqSTsPq7Xvqg58DqVUD6GHNZVS0WYpcDywyxgTMMbsAtKwws5SaoPYThFJBk4LW7cUSLGn1wADRORgABGJEZHwkwtaaxnwcxHpLyJu4JfAf+3HPgL+gHUY82tgMlBtjClux/MppXoobTlTSkWb77DO0ny63rxkY8xOABF5FKtVbRuwPGy5x4G/i0glVpg7DfiLiKRifR7OwzoE2mrGmHwRuRb4AKtV7jVjzMv2wx9hHdL80BgTEJHNwOq2PI9SqucTY4zTNSillFJKKZse1lRKKaWU6kY0nCmllFJKdSMazpRSSimluhENZ0oppZRS3YiGM6WUUkqpbkTDmVJKKaVUN6LhTCmllFKqG9FwppRSSinVjfw/O+mBMJb1VMUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and set the figsize - you can play with the size until you are happy with the proportions\n",
    "plt.figure(figsize=([10,5]))\n",
    "\n",
    "# add four line plots, one for each water flow\n",
    "plt.plot(packed_column['0 kg/h'], label='0 kg/h', marker='o')\n",
    "plt.plot(packed_column['100 kg/h'], label='100 kg/h', marker='o')\n",
    "plt.plot(packed_column['200 kg/h'], label='200 kg/h', marker='o')\n",
    "plt.plot(packed_column['300 kg/h'], label='300 kg/h', marker='o')\n",
    "\n",
    "# Add title, labels, legend and a grid\n",
    "plt.title('Relative pressure drop in a packed column with various water flows')\n",
    "plt.xlabel('Water flow')\n",
    "plt.ylabel('Pressure')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "\n",
    "# specify x axis range\n",
    "plt.xlim([1, 8])\n",
    "\n",
    "# show figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cf9db0b-cb25-4476-8c53-d2294c2b7f42",
   "metadata": {},
   "source": [
    "Now let's make a scatter plot.\n",
    "\n",
    "We can specify [many parameters](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html), such as the color and the alpha (transparency)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac6f04c5-cb74-4d7e-8ce8-d321e6d33c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = [1, 3, 5, 8, 10, 13]\n",
    "temperature = [12, 14, 16, 30, 45, 50]\n",
    "\n",
    "# define type of plot - here we'll make a scatterplot\n",
    "plt.scatter(time, temperature, alpha=1, color='green')\n",
    "plt.scatter(time, [i+2 for i in temperature], alpha=0.5, color='green')\n",
    "plt.scatter(time, [i+4 for i in temperature], alpha=0.1, color='green')\n",
    "plt.title('Scatter plot of temperature over time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384c33d0-5c7d-46fa-90ba-90ffe24465b7",
   "metadata": {},
   "source": [
    "Note, you can also use `pandas` functions for a quick plot, but for more elaborate and nice plots, `matplotlib` or similar libraries are recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3212eb0c-cc3f-49c2-b59c-90ced675cfee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "packed_column['100 kg/h'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "827b675e-ea68-40fc-ac1d-8217f0bf362b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "packed_column['100 kg/h'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "05f8165b-4a50-42ec-8851-9dad1ed160c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "packed_column[['0 kg/h', '100 kg/h', '200 kg/h', '300 kg/h']].iloc[:-1, :].boxplot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}