Project_Workspace_.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "import warnings # 0 warnings :)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from matplotlib.pyplot import figure\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import tree\n",
    "import graphviz\n",
    "import json\n",
    "import iplotter\n",
    "from IPython.core.display import HTML\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from IPython.display import SVG\n",
    "import pydotplus\n",
    "from IPython.display import HTML, Javascript\n",
    "#http://markibrahim.me/musings/notebooks/beautiful_javascript_charts.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('electricity.csv')\n",
    "df.drop_duplicates()\n",
    "df = df.dropna()\n",
    "df['Utility.State'] = df['Utility.State'].astype('category')\n",
    "df['Utility.Type'] = df['Utility.Type'].astype('category')\n",
    "df = df.rename(columns={'Demand.Summer Peak': 'Demand.SummerPeak', 'Demand.Winter Peak': 'Demand.WinterPeak', 'Uses.No Charge':'Uses.NoCharge'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Winter Peak Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"838\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uses.Total &lt;= 455990.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"681\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uses.Total &lt;= 134098.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"996\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Retail.Total.Revenue &lt;= 48508.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"311\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sources.Total &lt;= 106835.5</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"681\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Retail.Total.Revenue &lt;= 37896.25</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"99\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 21.7%</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"311\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6.6%</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"523\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29.8%</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"739\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5.4%</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"996\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uses.Total &lt;= 611932.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1343\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uses.Total &lt;= 545994.0</text>\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x1825237a1c8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_WinterPeak = pd.DataFrame()\n",
    "df_WinterPeak['Ranking'] = pd.qcut(df['Demand.WinterPeak'], q =3, labels = [\"L\",\"M\",\"H\"])\n",
    "attributes = df[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_WinterPeak['Ranking']\n",
    "Tree = tree.DecisionTreeClassifier(max_depth = 3)\n",
    "Tree.fit(attributes, target)\n",
    "dot_data = tree.export_graphviz(Tree,\n",
    "                               out_file=None,\n",
    "                               feature_names=attributes.columns,\n",
    "                               class_names = [\"L\",\"M\",\"H\"],\n",
    "                               proportion=True,\n",
    "                               impurity=False,\n",
    "                               filled=True,\n",
    "                               rounded=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "#graph.render('WinterPeakTree')\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summer Peak Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"995\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Retail.Total.Revenue &lt;= 39525.15</text>\r\n",
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      ],
      "text/plain": [
       "<graphviz.files.Source at 0x18252221748>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SummerPeak = pd.DataFrame()\n",
    "df_SummerPeak['Ranking'] = pd.qcut(df['Demand.SummerPeak'], q =3, labels = [\"L\",\"M\",\"H\"])\n",
    "attributes = df[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_SummerPeak['Ranking']\n",
    "Tree = tree.DecisionTreeClassifier(max_depth = 3)\n",
    "Tree.fit(attributes, target)\n",
    "dot_data = tree.export_graphviz(Tree,\n",
    "                               out_file=None,\n",
    "                               feature_names=attributes.columns,\n",
    "                               class_names = [\"L\",\"M\",\"H\"],\n",
    "                               proportion=True,\n",
    "                               impurity=False,\n",
    "                               filled=True,\n",
    "                               rounded=True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "#graph.render('SummerPeakTree')\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find best winter peak tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M353.5,-53C353.5,-53 228.5,-53 228.5,-53 222.5,-53 216.5,-47 216.5,-41 216.5,-41 216.5,-12 216.5,-12 216.5,-6 222.5,-0 228.5,-0 228.5,-0 353.5,-0 353.5,-0 359.5,-0 365.5,-6 365.5,-12 365.5,-12 365.5,-41 365.5,-41 365.5,-47 359.5,-53 353.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"291\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.0%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"291\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.0, 0.0, 1.0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"291\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 3&#45;&gt;5 -->\r\n",
       "<g id=\"edge5\" class=\"edge\"><title>3&#45;&gt;5</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M291,-88.9485C291,-80.7153 291,-71.848 291,-63.4814\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"294.5,-63.2367 291,-53.2367 287.5,-63.2367 294.5,-63.2367\"/>\r\n",
       "</g>\r\n",
       "<!-- 7 -->\r\n",
       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
       "<path fill=\"#82efaf\" stroke=\"black\" d=\"M570,-53C570,-53 396,-53 396,-53 390,-53 384,-47 384,-41 384,-41 384,-12 384,-12 384,-6 390,-0 396,-0 396,-0 570,-0 570,-0 576,-0 582,-6 582,-12 582,-12 582,-41 582,-41 582,-47 576,-53 570,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"483\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5.1%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"483\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.009, 0.729, 0.262]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"483\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = M</text>\r\n",
       "</g>\r\n",
       "<!-- 6&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>6&#45;&gt;7</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M561.68,-88.9485C549.498,-79.4346 536.232,-69.074 524.124,-59.6175\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"525.989,-56.6335 515.954,-53.2367 521.681,-62.1504 525.989,-56.6335\"/>\r\n",
       "</g>\r\n",
       "<!-- 8 -->\r\n",
       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<path fill=\"#d3baf6\" stroke=\"black\" d=\"M770,-53C770,-53 612,-53 612,-53 606,-53 600,-47 600,-41 600,-41 600,-12 600,-12 600,-6 606,-0 612,-0 612,-0 770,-0 770,-0 776,-0 782,-6 782,-12 782,-12 782,-41 782,-41 782,-47 776,-53 770,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"691\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1.6%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"691\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.0, 0.394, 0.606]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"691\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 6&#45;&gt;8 -->\r\n",
       "<g id=\"edge8\" class=\"edge\"><title>6&#45;&gt;8</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M634.428,-88.9485C642.85,-79.8005 651.993,-69.8697 660.423,-60.7126\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"663.108,-62.9643 667.306,-53.2367 657.958,-58.2231 663.108,-62.9643\"/>\r\n",
       "</g>\r\n",
       "<!-- 10 -->\r\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
       "<path fill=\"#965ae9\" stroke=\"black\" d=\"M1183,-157C1183,-157 973,-157 973,-157 967,-157 961,-151 961,-145 961,-145 961,-101 961,-101 961,-95 967,-89 973,-89 973,-89 1183,-89 1183,-89 1189,-89 1195,-95 1195,-101 1195,-101 1195,-145 1195,-145 1195,-151 1189,-157 1183,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1078\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Retail.Total.Revenue &lt;= 13387.15</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1078\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29.8%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1078\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.033, 0.116, 0.851]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1078\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1078.68,-192.884C1078.6,-184.778 1078.51,-175.982 1078.43,-167.472\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1081.92,-167.265 1078.33,-157.299 1074.92,-167.333 1081.92,-167.265\"/>\r\n",
       "</g>\r\n",
       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\r\n",
       "<path fill=\"#d5bdf6\" stroke=\"black\" d=\"M1399,-157C1399,-157 1225,-157 1225,-157 1219,-157 1213,-151 1213,-145 1213,-145 1213,-101 1213,-101 1213,-95 1219,-89 1225,-89 1225,-89 1399,-89 1399,-89 1405,-89 1411,-95 1411,-101 1411,-101 1411,-145 1411,-145 1411,-151 1405,-157 1399,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1312\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Revenue.Total &lt;= 40411.35</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1312\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5.4%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1312\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.368, 0.053, 0.579]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1312\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>9&#45;&gt;13</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1154.65,-192.884C1177.76,-182.767 1203.32,-171.575 1227.01,-161.206\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1228.58,-164.341 1236.33,-157.125 1225.77,-157.929 1228.58,-164.341\"/>\r\n",
       "</g>\r\n",
       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
       "<path fill=\"#e3d3f9\" stroke=\"black\" d=\"M970,-53C970,-53 812,-53 812,-53 806,-53 800,-47 800,-41 800,-41 800,-12 800,-12 800,-6 806,-0 812,-0 812,-0 970,-0 970,-0 976,-0 982,-6 982,-12 982,-12 982,-41 982,-41 982,-47 976,-53 970,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2.3%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.0, 0.438, 0.562]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"891\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 10&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>10&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1012.6,-88.9485C992.502,-78.794 970.5,-67.675 950.801,-57.7203\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"952.136,-54.4732 941.632,-53.0867 948.978,-60.7208 952.136,-54.4732\"/>\r\n",
       "</g>\r\n",
       "<!-- 12 -->\r\n",
       "<g id=\"node13\" class=\"node\"><title>12</title>\r\n",
       "<path fill=\"#9254e9\" stroke=\"black\" d=\"M1186,-53C1186,-53 1012,-53 1012,-53 1006,-53 1000,-47 1000,-41 1000,-41 1000,-12 1000,-12 1000,-6 1006,-0 1012,-0 1012,-0 1186,-0 1186,-0 1192,-0 1198,-6 1198,-12 1198,-12 1198,-41 1198,-41 1198,-47 1192,-53 1186,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1099\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 27.5%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1099\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.036, 0.089, 0.875]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1099\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 10&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>10&#45;&gt;12</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1085.34,-88.9485C1087.19,-80.6238 1089.19,-71.6509 1091.07,-63.2027\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1094.53,-63.7579 1093.28,-53.2367 1087.69,-62.2393 1094.53,-63.7579\"/>\r\n",
       "</g>\r\n",
       "<!-- 14 -->\r\n",
       "<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
       "<path fill=\"#f9e4d5\" stroke=\"black\" d=\"M1394,-53C1394,-53 1228,-53 1228,-53 1222,-53 1216,-47 1216,-41 1216,-41 1216,-12 1216,-12 1216,-6 1222,-0 1228,-0 1228,-0 1394,-0 1394,-0 1400,-0 1406,-6 1406,-12 1406,-12 1406,-41 1406,-41 1406,-47 1400,-53 1394,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1311\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1.0%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1311\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.476, 0.19, 0.333]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1311\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 13&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>13&#45;&gt;14</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1311.65,-88.9485C1311.56,-80.7153 1311.47,-71.848 1311.38,-63.4814\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1314.88,-63.1991 1311.27,-53.2367 1307.88,-63.2732 1314.88,-63.1991\"/>\r\n",
       "</g>\r\n",
       "<!-- 15 -->\r\n",
       "<g id=\"node16\" class=\"node\"><title>15</title>\r\n",
       "<path fill=\"#c7a7f3\" stroke=\"black\" d=\"M1610,-53C1610,-53 1436,-53 1436,-53 1430,-53 1424,-47 1424,-41 1424,-41 1424,-12 1424,-12 1424,-6 1430,-0 1436,-0 1436,-0 1610,-0 1610,-0 1616,-0 1622,-6 1622,-12 1622,-12 1622,-41 1622,-41 1622,-47 1616,-53 1610,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1523\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4.4%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1523\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.344, 0.022, 0.634]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1523\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = H</text>\r\n",
       "</g>\r\n",
       "<!-- 13&#45;&gt;15 -->\r\n",
       "<g id=\"edge15\" class=\"edge\"><title>13&#45;&gt;15</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1385.8,-88.9485C1408.88,-78.6102 1434.2,-67.2722 1456.73,-57.1809\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1458.17,-60.3685 1465.87,-53.0867 1455.31,-53.9799 1458.17,-60.3685\"/>\r\n",
       "</g>\r\n",
       "<!-- 17 -->\r\n",
       "<g id=\"node18\" class=\"node\"><title>17</title>\r\n",
       "<path fill=\"#f6d2b8\" stroke=\"black\" d=\"M1405,-253.5C1405,-253.5 1231,-253.5 1231,-253.5 1225,-253.5 1219,-247.5 1219,-241.5 1219,-241.5 1219,-212.5 1219,-212.5 1219,-206.5 1225,-200.5 1231,-200.5 1231,-200.5 1405,-200.5 1405,-200.5 1411,-200.5 1417,-206.5 1417,-212.5 1417,-212.5 1417,-241.5 1417,-241.5 1417,-247.5 1411,-253.5 1405,-253.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1318\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4.1%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1318\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.529, 0.207, 0.264]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1318\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;17 -->\r\n",
       "<g id=\"edge17\" class=\"edge\"><title>16&#45;&gt;17</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1318,-296.884C1318,-286.326 1318,-274.597 1318,-263.854\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1321.5,-263.52 1318,-253.52 1314.5,-263.52 1321.5,-263.52\"/>\r\n",
       "</g>\r\n",
       "<!-- 18 -->\r\n",
       "<g id=\"node19\" class=\"node\"><title>18</title>\r\n",
       "<path fill=\"#e99457\" stroke=\"black\" d=\"M1623,-261C1623,-261 1449,-261 1449,-261 1443,-261 1437,-255 1437,-249 1437,-249 1437,-205 1437,-205 1437,-199 1443,-193 1449,-193 1449,-193 1623,-193 1623,-193 1629,-193 1635,-199 1635,-205 1635,-205 1635,-249 1635,-249 1635,-255 1629,-261 1623,-261\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sources.Total &lt;= 545994.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 32.4%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.864, 0.107, 0.029]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;18 -->\r\n",
       "<g id=\"edge18\" class=\"edge\"><title>16&#45;&gt;18</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1388.78,-296.884C1410.21,-286.856 1433.9,-275.773 1455.89,-265.482\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1457.63,-268.533 1465.2,-261.125 1454.66,-262.193 1457.63,-268.533\"/>\r\n",
       "</g>\r\n",
       "<!-- 19 -->\r\n",
       "<g id=\"node20\" class=\"node\"><title>19</title>\r\n",
       "<path fill=\"#f1bc96\" stroke=\"black\" d=\"M1610.5,-149.5C1610.5,-149.5 1461.5,-149.5 1461.5,-149.5 1455.5,-149.5 1449.5,-143.5 1449.5,-137.5 1449.5,-137.5 1449.5,-108.5 1449.5,-108.5 1449.5,-102.5 1455.5,-96.5 1461.5,-96.5 1461.5,-96.5 1610.5,-96.5 1610.5,-96.5 1616.5,-96.5 1622.5,-102.5 1622.5,-108.5 1622.5,-108.5 1622.5,-137.5 1622.5,-137.5 1622.5,-143.5 1616.5,-149.5 1610.5,-149.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2.4%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.66, 0.06, 0.28]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;19 -->\r\n",
       "<g id=\"edge19\" class=\"edge\"><title>18&#45;&gt;19</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1536,-192.884C1536,-182.326 1536,-170.597 1536,-159.854\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1539.5,-159.52 1536,-149.52 1532.5,-159.52 1539.5,-159.52\"/>\r\n",
       "</g>\r\n",
       "<!-- 20 -->\r\n",
       "<g id=\"node21\" class=\"node\"><title>20</title>\r\n",
       "<path fill=\"#e89254\" stroke=\"black\" d=\"M1816.5,-157C1816.5,-157 1653.5,-157 1653.5,-157 1647.5,-157 1641.5,-151 1641.5,-145 1641.5,-145 1641.5,-101 1641.5,-101 1641.5,-95 1647.5,-89 1653.5,-89 1653.5,-89 1816.5,-89 1816.5,-89 1822.5,-89 1828.5,-95 1828.5,-101 1828.5,-101 1828.5,-145 1828.5,-145 1828.5,-151 1822.5,-157 1816.5,-157\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Uses.Total &lt;= 99710752.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 30.0%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.88, 0.11, 0.009]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 18&#45;&gt;20 -->\r\n",
       "<g id=\"edge20\" class=\"edge\"><title>18&#45;&gt;20</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1600.61,-192.884C1620,-182.946 1641.41,-171.971 1661.34,-161.757\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1663.07,-164.801 1670.37,-157.125 1659.88,-158.571 1663.07,-164.801\"/>\r\n",
       "</g>\r\n",
       "<!-- 21 -->\r\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\r\n",
       "<path fill=\"#e89153\" stroke=\"black\" d=\"M1818,-53C1818,-53 1652,-53 1652,-53 1646,-53 1640,-47 1640,-41 1640,-41 1640,-12 1640,-12 1640,-6 1646,-0 1652,-0 1652,-0 1818,-0 1818,-0 1824,-0 1830,-6 1830,-12 1830,-12 1830,-41 1830,-41 1830,-47 1824,-53 1818,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 29.8%</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0.884, 0.106, 0.01]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1735\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = L</text>\r\n",
       "</g>\r\n",
       "<!-- 20&#45;&gt;21 -->\r\n",
       "<g id=\"edge21\" class=\"edge\"><title>20&#45;&gt;21</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1735,-88.9485C1735,-80.7153 1735,-71.848 1735,-63.4814\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1738.5,-63.2367 1735,-53.2367 1731.5,-63.2367 1738.5,-63.2367\"/>\r\n",
       "</g>\r\n",
       "<!-- 22 -->\r\n",
       "<g id=\"node23\" class=\"node\"><title>22</title>\r\n",
       "<path fill=\"#bdf6d5\" stroke=\"black\" d=\"M1985.5,-53C1985.5,-53 1860.5,-53 1860.5,-53 1854.5,-53 1848.5,-47 1848.5,-41 1848.5,-41 1848.5,-12 1848.5,-12 1848.5,-6 1854.5,-0 1860.5,-0 1860.5,-0 1985.5,-0 1985.5,-0 1991.5,-0 1997.5,-6 1997.5,-12 1997.5,-12 1997.5,-41 1997.5,-41 1997.5,-47 1991.5,-53 1985.5,-53\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"1923\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 0.2%</text>\r\n",
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       "</g>\r\n",
       "<!-- 20&#45;&gt;22 -->\r\n",
       "<g id=\"edge22\" class=\"edge\"><title>20&#45;&gt;22</title>\r\n",
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       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x18252272c48>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'max_depth': [2,3,4,5,6], 'min_samples_split':[50,60,70,80,85,86,87,88,89,90,91,92,93,95,100,110,120]}\n",
    "Tree = tree.DecisionTreeClassifier()\n",
    "TreeCV = GridSearchCV(Tree, param_grid = parameters, n_jobs = -1)\n",
    "### calculation\n",
    "attributes = df[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_WinterPeak['Ranking']\n",
    "TreeCV.fit(attributes, target)\n",
    "TreeCV.best_params_\n",
    "dot_data = tree.export_graphviz(TreeCV.best_estimator_,\n",
    "            out_file=None,\n",
    "            feature_names=attributes.columns,\n",
    "            class_names = df_WinterPeak['Ranking'].cat.categories,\n",
    "            proportion=True,\n",
    "            impurity=False,\n",
    "            filled=True,\n",
    "            rounded=True)\n",
    "winter_best = graphviz.Source(dot_data)\n",
    "# graph.render('WinterPeakUsage-best')\n",
    "winter_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the best Summer Peak Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"422\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Sources.Total &lt;= 452146.0</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"539\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Retail.Total.Revenue &lt;= 39525.15</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"750\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 34.7%</text>\r\n",
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       "<graphviz.files.Source at 0x18252225b88>"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {'max_depth': [1,2,3,4,5,6], 'min_samples_split':[1,2,3,4,5,6,7,10,20,30,40,50,60,70,80,85,86]}\n",
    "Tree = tree.DecisionTreeClassifier()\n",
    "TreeCV = GridSearchCV(Tree, param_grid = parameters, n_jobs = -1)\n",
    "### calculation\n",
    "attributes = df[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_SummerPeak['Ranking']\n",
    "TreeCV.fit(attributes, target)\n",
    "TreeCV.best_params_\n",
    "dot_data = tree.export_graphviz(TreeCV.best_estimator_,\n",
    "            out_file=None,\n",
    "            feature_names=attributes.columns,\n",
    "            class_names = df_SummerPeak['Ranking'].cat.categories,\n",
    "            proportion=True,\n",
    "            impurity=False,\n",
    "            filled=True,\n",
    "            rounded=True)\n",
    "summer_best = graphviz.Source(dot_data)\n",
    "#graph.render('SummerPeakUsage-best')\n",
    "summer_best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes on Summer Peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18252aaad08>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_resample = df.sample(frac=1.0, random_state=0).reset_index(drop=True)\n",
    "attributes = df_resample[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_resample['Demand.SummerPeak']\n",
    "target = pd.qcut(target,q=2,labels=['L','H'])\n",
    "for j in range(attributes.shape[1]):\n",
    "    attributes.iloc[:,j] = pd.qcut(attributes.iloc[:,j],q=3,labels=['S','M','L'])\n",
    "# attributes.head(3)\n",
    "attributes = pd.get_dummies(attributes)\n",
    "hparam = {'alpha':[10**(-10),0.1,0.2,0.3,0.4,1]}\n",
    "NBayes = MultinomialNB()\n",
    "NBayesCV = GridSearchCV(NBayes, param_grid = hparam)\n",
    "NBayesCV.fit(attributes, target)\n",
    "results = pd.DataFrame()\n",
    "results['hparam'] = hparam['alpha']\n",
    "results['error']  = 1 - NBayesCV.cv_results_['mean_test_score']\n",
    "results.plot.line(x='hparam',y='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Navie Bayes validation error rate:  0.153\n"
     ]
    }
   ],
   "source": [
    "NBayesCV.best_params_\n",
    "error_bayes = 1 - NBayesCV.best_score_\n",
    "print('Navie Bayes validation error rate: ', error_bayes.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression on Summer Peak "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x182522d4388>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgr = LogisticRegression()\n",
    "hparam = {'C' : [0.0001,0.001,0.01,0.1,1,2]}\n",
    "lgrCV = GridSearchCV(lgr, param_grid = hparam)\n",
    "lgrCV.fit(attributes, target)\n",
    "results = pd.DataFrame()\n",
    "results['hparam'] = hparam['C']\n",
    "results['error']  = 1 - lgrCV.cv_results_['mean_test_score']\n",
    "results.plot.line(x='hparam',y='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression validation error rate:  0.152\n"
     ]
    }
   ],
   "source": [
    "error_logistic = 1 - lgrCV.best_score_\n",
    "print('Logistic regression validation error rate: ', error_logistic.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree on Summer Peak "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18252b2e648>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vy+W6rHMIjBtPI5DeeJS0a6/ru8JscLltEW/UHqHUHuf0V1v0GAAXWoXqs7/0P49pmuzfv58XX3wRj8fD8uXL2b59O1OnTmXmzJlkZmYSCARYu3Ytb775JjNmzDhvH6WlpZSWlgZfNzQ0hHXsaOXxeC7rHCxnMqQNpHXfHtq/dEMfVhZ5l9sW8UbtEUrtcc7ltkVeXt4F3++xC8jtdgcHcAEaGxvJysoK66DZ2dkUFhaSm5uL0+lkwoQJfPjhhwBkZWVhGAZJSUmUlJRQU6OBzXAYhgE+DQSLyOXrMQCKioo4evQo9fX1BAIBKisrKS4uDmvnfr+ftrY2WlpaANi3b19w8Li5uRnovsKoqqqioKDgUs8h4RheP3xci3XmtN2liEgM67ELyOl08uCDD7J48WJM06SkpISCggI2b95MUVERxcXF1NTUUF5eTltbG7t27WLLli0sW7YMh8PB7Nmzef7557EsixEjRgS7c1auXBkMBq/Xy8MPP9y/ZxpHDJ+/ezqIIweh8Eq7yxGRGGVYF+rkj2J1dbE9H35f9GtajccxF34NY+YcHCVf6aPKIk99vKHUHqHUHufYNgYgUSjbA4MyQDODishlUADEoO6B4JEaCBaRy6IAiFGG1w91h7E6O+wuRURilAIgRhk+P1gmHP7Q7lJEJEYpAGKVtwgASzODisglUgDEKCPTDZnZoHEAEblECoBY5vXrCkBELpkCIIYZvpFw7GOsU+12lyIiMUgBEMO6B4ItOPSB3aWISAxSAMQy76drBKsbSEQugQIghhmDMsCdo4FgEbkkCoBY5/VjaUoIEbkECoAYZ/j8cPwvWG2tdpciIjFGARDjjE/HAdQNJCK9pQCIdWcHghUAItJLCoAYZ6QPhJwrNA4gIr2mAIgDhtcPuhVURHpJARAPfH5oOo7VcsLuSkQkhigA4oDhHdn9D7V6IlhEwqcAiAfDR4BhYNVqHEBEwqcAiAPGgDTIHaYpIUSkVxQAccLwjdSzACLSKwqAeOHzw4kmrBONdlciIjFCARAngk8EqxtIRMKkAIgXBSPAcOiJYBEJmwIgThgpKZBXoIFgEQmbAiCOGD4/1NZgWZbdpYhIDFAAxBPvSDj5CTQ12F2JiMQABUAcMXxnp4bWA2Ei0jMFQDzJLwSnS+MAIhIWBUAcMZKSYJhXU0OLSFgUAHFGA8EiEi4FQLzx+qG9DY7/xe5KRCTKucLZaM+ePaxfvx7TNJk2bRrTp08P+by6upoNGzZQW1vLggULmDhxYvCzhoYG1qxZQ2Nj9xQFZWVl5OTkUF9fz4oVK2htbaWwsJD58+fjcoVVjlyE4fNj0b1EpJFzhd3liEgU6/EvrmmarFu3jkWLFuF2uykrK6O4uJj8/PzgNh6Ph7lz57Jt27bzvv/yyy9zxx13MGbMGDo6OjAMA4Cf/OQn3HrrrUyePJlXXnmFt99+m5tvvrkPTy1B5Q0HV1L3lBDjb7S7GhGJYj12AdXU1DB06FByc3NxuVxMmjSJqqqqkG1ycnLwer3BP+5nHTlyhK6uLsaMGQNAamoqKSkpWJbF+++/H7xSmDJlynn7lEtjuJKgoFBTQohIj3q8AmhqasLtdgdfu91uDhwI7y6Turo60tPTKS8vp76+nquvvppZs2bR2tpKWloaTqcTgOzsbJqami64j4qKCioqKgBYsmQJHo8nrGNHK5fL1e/n0PLFq+nY/gvc2dkYjugd5olEW8QStUcotcc5/dUWPQbAhe4m+ewv/c9jmib79+/nxRdfxOPxsHz5crZv305xcXHYBZaWllJaWhp83dAQ20+5ejyefj8HMzcf61Q7De//AeOK/J6/YJNItEUsUXuEUnucc7ltkZeXd8H3e/x56Ha7gwO4AI2NjWRlZYV10OzsbAoLC8nNzcXpdDJhwgQ+/PBDBg0aRHt7O11dXUD3VUZ2dnZY+5SenX0iWEtEisjF9BgARUVFHD16lPr6egKBAJWVlWH/gvf7/bS1tdHS0gLAvn37yM/PxzAMrrrqKnbu3AnQ66sC6cHQfEhO0doAInJRPXYBOZ1OHnzwQRYvXoxpmpSUlFBQUMDmzZspKiqiuLiYmpoaysvLaWtrY9euXWzZsoVly5bhcDiYPXs2zz//PJZlMWLEiGB3zqxZs1ixYgWbNm2isLCQqVOn9vvJJgrD6YThIzQQLCIXZVgx9shoXV2d3SVclkj1a5qbfoT1n7/EsXJTdyBEIfXxhlJ7hFJ7nGPbGIDEKJ8fTnfCX47YXYmIRCkFQJwyfCMBNDOoiHwuBUC8ysmD1AGgmUFF5HMoAOKU4XCA16+BYBH5XAqAOGZ4/XD4I6zAGbtLEZEopACIZz4/BM5A3SG7KxGRKKQAiGOG99MngjUQLCIXoACIZ0OGQtpA0DiAiFyAAiCOGYYBPr+uAETkghQAcc7w+uHjg1hnTttdiohEGQVAnDN8fujqgiMH7S5FRKKMAiDeefVEsIhcmAIg3mV7YFAGaG0AEfkMBUCcMwyj+4lgXQGIyGcoABKA4fND3WGszk67SxGRKKIASACG1w+WCYc/tLsUEYkiCoBEEFwjWN1AInKOAiABGJluyMzW1NAiEkIBkCg0ECwin6EASBCGzw/HPsY61W53KSISJRQACcLwjgTLgkMaCBaRbgqAROEtAsDSA2Ei8ikFQIIwBmdC9hDQOICIfEoBkEh8I3UrqIgEKQASiOHzQ/1RrLZWu0sRkSigAEggZ5eI1AphIgIKgMTi1RPBInKOAiCBGOkDYchQPRAmIoACIOEYvpHqAhIRQAGQeLx+aKzHOvmJ3ZWIiM0UAAnG8HUvEamJ4UREAZBovEWQlo7507VY9UftrkZEbKQASDBG6gAcjz0PHe2YLy7E+rjW7pJExCaucDbas2cP69evxzRNpk2bxvTp00M+r66uZsOGDdTW1rJgwQImTpwY/Ozuu+9m+PDhAHg8Hp588kkAVq1aRXV1NWlpaQDMmzcPn8/XF+ckPTB8I3F85/uYy5/FfLEMx6PfxRjxBbvLEpEI6zEATNNk3bp1LFq0CLfbTVlZGcXFxeTn5we38Xg8zJ07l23btp33/eTkZF566aUL7nv27NkhYSGRY+QNx/HEku4QWPYMjnlPY/zNWLvLEpEI6rELqKamhqFDh5Kbm4vL5WLSpElUVVWFbJOTk4PX68UwjH4rVPqeMWQojieWgCcXc+VzWP+10+6SRCSCerwCaGpqwu12B1+73W4OHAj/DpIzZ86wcOFCnE4nt99+OxMmTAh+tnHjRl5//XVGjx7NrFmzSEpKOu/7FRUVVFRUALBkyRI8Hk/Yx45GLpcrus7B48H8/lpOfO9xzqz5AYPnP8WAKbdE5NBR1xY2U3uEUnuc019t0WMAWJZ13nu9+aW/evVqsrOzOXbsGM8//zzDhw9n6NChzJw5k8zMTAKBAGvXruXNN99kxowZ532/tLSU0tLS4OuGhoawjx2NPB5PVJ6DNf9ZWP2PtPzTC5w8dgzHtNv6/ZjR2hZ2UXuEUnucc7ltkZeXd8H3e+wCcrvdNDY2Bl83NjaSlZUV9oGzs7MByM3NZdSoURw8eBCArKwsDMMgKSmJkpISamr0dKqdjNQBOOY/A9dMxNr0CuZbmy4Y/iISP3oMgKKiIo4ePUp9fT2BQIDKykqKi4vD2nlraytnzpwBoKWlhT/96U/BwePm5mag+wqjqqqKgoKCSz0H6SNGUjKOOU9iXF+C9eZPsba8qhAQiWM9dgE5nU4efPBBFi9ejGmalJSUUFBQwObNmykqKqK4uJiamhrKy8tpa2tj165dbNmyhWXLlvHxxx/zyiuv4HA4ME2T6dOnBwNg5cqVtLS0AOD1enn44Yf790wlLIbTCfc/CmkDsSrehFNt8D/mYTicdpcmIn3MsGLsJ15dXZ3dJVyWWOnXtCwLa9tGrG2b4EuTcDz0OMYFBukvR6y0RaSoPUKpPc6xbQxAEpNhGDj+20yMu78GuysxX/4eVmeH3WWJSB9SAMhFOUpvx7j/W7D/95jLn9VykiJxRAEgPXJMLsUx5wmorcEsfwqrpdnukkSkDygAJCzGlyZ13yZafxTzB2VYjfV2lyQil0kBIGEzRo3rnkm09RPMHyzEOnrE7pJE5DIoAKRXDP/f4PjOP0JXoHs66doP7C5JRC6RAkB6zcgv7J5ELjkFc+nTWH9+3+6SROQSKADkkhi5eTie/AFkZGOu+C7W3vfsLklEekkBIJfMyPbgeOL7cEUB5qrFmFX/aXdJItILCgC5LMagDByPfw+Kvoj1o3LMX/+H3SWJSJgUAHLZjLR0HI/+A4y+Fut/r8b8jzfsLklEwqAAkD5hJKfgmFuGMf5GrDc2YP58g2YSFYlyYS0KLxIOw5UED30bBqRj/eINaG+DmXMwHPqdIRKNFADSpwyHE+57BNLSsf7jDTh1Ch54FMOl/9REoo3+r5Q+ZxgGxn//n5hp6Vg/fw2rox3HN57ASE6xuzQR+Su6Npd+47hlBsasR2Dve5j/9BzWqXa7SxKRv6IAkH7lmHILxte+DR/sx1y6COtki90licinFADS7xzX3YRj7lNQdwjzpTKs5ka7SxIRFAASIcaY8d3PCjQ3YP7gSaz62F7aUyQeKAAkYowvjMbxvxZD5ynMHyzkzMEau0sSSWhaFD7CtNA1WEcPYy57FqOjHSvLY3c5UcPpctEVCNhdRtRQe5zjdLmwHinDGDL0kr7/eYvC6zZQiTjjigIcTy4h+Vf/h45PTthdTtRwJafQdbrT7jKihtrjHFdyCmdcSX2/3z7fo0gYDE8uGfMXcSbBr4b+WqauDkOoPc7pr7bQGICISIJSAIiIJCgFgIhIglIAiIgkKAWAiEiCUgCIiCQoBYCISIJSAIiIJKiYmwpCRET6hq4AImzhwoV2lxA11Bah1B6h1B7n9FdbKABERBKUAkBEJEEpACKstLTU7hKihtoilNojlNrjnP5qCw0Ci4gkKF0BiIgkKAWAiEiC0oIwEdDQ0MCqVas4ceIEhmFQWlrKV77yFbvLsp1pmixcuJDs7OyEv+Wvra2NNWvWcPjwYQzD4JFHHuHKK6+0uyxbvPXWW7z99tsYhkFBQQFz584lOTnZ7rIiZvXq1ezevZuMjAyWLl0KQGtrK8uXL+f48eMMGTKExx57jIEDB172sRQAEeB0Opk9ezYjRozg1KlTLFy4kDFjxpCfn293abb693//d4YNG8apU6fsLsV269ev55prruHxxx8nEAjQ2ZmYSyE2NTXxi1/8guXLl5OcnMyyZcuorKxkypQpdpcWMVOmTOHv/u7vWLVqVfC9rVu3cvXVVzN9+nS2bt3K1q1bue+++y77WOoCioCsrCxGjBgBwIABAxg2bBhNTU02V2WvxsZGdu/ezbRp0+wuxXbt7e3s37+fqVOnAuByuUhPT7e5KvuYpsnp06fp6uri9OnTZGVl2V1SRI0aNeq8X/dVVVXcdNNNANx0001UVVX1ybF0BRBh9fX1fPTRR/j9frtLsdWPf/xj7rvvPv36p/u/icGDB7N69Wpqa2sZMWIE999/P6mpqXaXFnHZ2dl89atf5ZFHHiE5OZmxY8cyduxYu8uy3SeffBIMwqysLFpaWvpkv7oCiKCOjg6WLl3K/fffT1pamt3l2GbXrl1kZGQEr4oSXVdXFx999BE333wzL774IikpKWzdutXusmzR2tpKVVUVq1atYu3atXR0dPDrX//a7rLilgIgQgKBAEuXLuXGG2/kuuuus7scW/3pT3/ivffeY968eaxYsYJ9+/axcuVKu8uyjdvtxu12M3LkSAAmTpzIRx99ZHNV9ti7dy85OTkMHjwYl8vFddddx5///Ge7y7JdRkYGzc3NADQ3NzN48OA+2a+6gCLAsizWrFnDsGHDuO222+wux3YzZ85k5syZALz//vts27aNb33rWzZXZZ/MzEzcbjd1dXXk5eWxd+/ehL1BwOPxcODAATo7O0lOTmbv3r0UFRXZXZbtiouL2bFjB9OnT2fHjh2MHz++T/arJ4Ej4I9//CPPPvssw4cPxzAMAO69916+9KUv2VyZ/c4GQKLfBnrw4EHWrFlDIBAgJyeHuXPn9sltfrFoy5YtVFZW4u3eefQAAAO0SURBVHQ68fl8zJkzh6SkJLvLipgVK1ZQXV3NyZMnycjI4K677mL8+PEsX76choYGPB4P3/72t/vkvw8FgIhIgtIYgIhIglIAiIgkKAWAiEiCUgCIiCQoBYCISIJSAEhCmTdvHn/4wx/sLkMkKigAREQSlAJApI91dXXZXYJIWDQVhCScgwcP8tprr3H8+HGuueYa5s2bx4EDB/jhD3/IzTffzL/927+RmprKPffcw4033gjA7t272bRpE8eOHSMtLY2SkhLuuusuoHs2z29+85vMmTOHn/3sZ+Tk5PDcc8+xbNky9u/fz+nTp/H5fDz00EMUFBQAsGrVKlJSUqivr2f//v34fD4ef/xxtm7dyo4dO8jIyODRRx+lsLDQtnaS+KcrAEk47777Lk899RSrVq3i0KFDbN++HYATJ05w8uRJ1qxZw7x583jllVeoq6sDICUlhW9+85usX7+ehQsX8qtf/Yrf/e53Ifutrq5m+fLlPP300wBcc801rFy5kn/5l3+hsLDwvAnv3n33Xe655x7WrVuHy+Xi6aefprCwkHXr1jFx4kRee+21/m8MSWgKAEk4t9xyC9nZ2QwcOJBrr72WgwcPBj+7++67SUpKYtSoUYwbN47KykoArrrqKoYPH47D4cDr9TJ58mSqq6tD9nvnnXeSmpoaXL5w6tSpDBgwgKSkJO68805qa2tpb28Pbj9+/HhGjBhBcnIyEyZMIDk5mZtuugmHw8GkSZMSdkZQiRx1AUnCyczMDP5zcnJycHW29PT0kEVYhgwZEpyC98CBA/z0pz/l0KFDBAIBAoEAEydODNmv2+0O/rNpmmzcuJGdO3fS0tISnASwpaUluBbEZ+vIyMgIed3R0dFXpyxyQQoAkU+1tbXR0dERDIGGhoZgn/3KlSv527/9W8rKykhOTubHP/7xeasynf0jD/Cb3/yG9957j2eeeYYhQ4bQ3t7OAw88ELmTEQmDuoBE/sqWLVsIBALs37+f3bt3c/311wNw6tQpBg4cSHJyMjU1NfzmN7+56H5OnTqFy+Vi4MCBdHZ2snHjxkiUL9IrugIQ+VRmZiYDBw7kG9/4BsnJyXz9619n2LBhADz00EO89tprvPrqq4waNYrrr7+etra2z93XTTfdxO9//3vmzJnDwIEDufvuu/nlL38ZqVMRCYvWAxChe2GaH/7wh6xZs8buUkQiRl1AIiIJSgEgIpKg1AUkIpKgdAUgIpKgFAAiIglKASAikqAUACIiCUoBICKSoP4/z4Nq/6FmFqAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hparam = {'max_depth' : [1,2,3,4,5,6,7,8,9,10]}\n",
    "Tree = DecisionTreeClassifier()\n",
    "TreeCV = GridSearchCV(Tree, param_grid = hparam)\n",
    "TreeCV.fit(attributes, target)\n",
    "results = pd.DataFrame()\n",
    "results['hparam'] = hparam['max_depth']\n",
    "results['error']  = 1 - TreeCV.cv_results_['mean_test_score']\n",
    "results.plot.line(x='hparam',y='error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree validation error rate:  0.152\n"
     ]
    }
   ],
   "source": [
    "error_tree = 1- TreeCV.best_score_\n",
    "print('Tree validation error rate: ', error_tree.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying some interactive stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed144733ed944bd69f8f3b71f933e9ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='Max_depth:', max=10), Output()), _dom_classes=('widget-i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.identity(x)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_slider = widgets.IntSlider(value=3,min=0,max=10,description='Max_depth:')\n",
    "def identity(x):\n",
    "    return x\n",
    "interact(identity, x=input_slider)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a16c5d57f34741f687db4bbafced6e59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=3, description='Max_depth:', max=6, min=1), Output()), _dom_classes=('wi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.win_tree(x)>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## trying with a winter decision tree\n",
    "df_WinterPeak = pd.DataFrame()\n",
    "df_WinterPeak['Ranking'] = pd.qcut(df['Demand.WinterPeak'], q =2, labels = [\"L\",\"H\"])\n",
    "attributes = df[['Sources.Total','Uses.Total','Revenue.Total','Retail.Total.Revenue']]\n",
    "target = df_WinterPeak['Ranking']\n",
    "\n",
    "def win_tree(x):\n",
    "    Tree = tree.DecisionTreeClassifier(max_depth = x)\n",
    "    Tree.fit(attributes, target)\n",
    "    dot_data = tree.export_graphviz(Tree,\n",
    "                                   out_file=None,\n",
    "                                   feature_names=attributes.columns,\n",
    "                                   class_names = [\"L\",\"H\"],\n",
    "                                   proportion=True,\n",
    "                                   impurity=False,\n",
    "                                   filled=True,\n",
    "                                   rounded=True)\n",
    "    #src = graphviz.Source(dot_data)\n",
    "    #return display(SVG(src.pipe(format='svg')))\n",
    "    # return graph\n",
    "    pydot_graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "    pydot_graph.set_size('\"5,20!\"')\n",
    "    # return pydot_graph\n",
    "    gvz_graph = graphviz.Source(pydot_graph.to_string())\n",
    "    return gvz_graph\n",
    "    \n",
    "input_slider = widgets.IntSlider(value=3,min=1,max=6,description='Max_depth:')\n",
    "interact(win_tree, x=input_slider)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C3.js"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>index</th>\n",
       "      <th>11</th>\n",
       "      <th>13</th>\n",
       "      <th>23</th>\n",
       "      <th>45</th>\n",
       "      <th>49</th>\n",
       "      <th>55</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>...</th>\n",
       "      <th>1977</th>\n",
       "      <th>1997</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2036</th>\n",
       "      <th>2046</th>\n",
       "      <th>2054</th>\n",
       "      <th>2059</th>\n",
       "      <th>2080</th>\n",
       "      <th>2091</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Demand.SummerPeak</td>\n",
       "      <td>211.5</td>\n",
       "      <td>10967.0</td>\n",
       "      <td>538.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>504.7</td>\n",
       "      <td>5596.0</td>\n",
       "      <td>7367.0</td>\n",
       "      <td>4616.0</td>\n",
       "      <td>303.2</td>\n",
       "      <td>...</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>7161.0</td>\n",
       "      <td>312.1</td>\n",
       "      <td>580.3</td>\n",
       "      <td>373.9</td>\n",
       "      <td>1496.5</td>\n",
       "      <td>9239.0</td>\n",
       "      <td>462.0</td>\n",
       "      <td>773.5</td>\n",
       "      <td>774.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Demand.WinterPeak</td>\n",
       "      <td>232.0</td>\n",
       "      <td>10660.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>347.2</td>\n",
       "      <td>6967.0</td>\n",
       "      <td>4303.0</td>\n",
       "      <td>3735.0</td>\n",
       "      <td>320.1</td>\n",
       "      <td>...</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>6170.0</td>\n",
       "      <td>281.3</td>\n",
       "      <td>422.6</td>\n",
       "      <td>257.1</td>\n",
       "      <td>1437.0</td>\n",
       "      <td>7543.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>571.6</td>\n",
       "      <td>515.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 333 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index     11       13     23     45     49      55      57  \\\n",
       "0  Demand.SummerPeak  211.5  10967.0  538.0  562.0  504.7  5596.0  7367.0   \n",
       "1  Demand.WinterPeak  232.0  10660.0  531.0  344.0  347.2  6967.0  4303.0   \n",
       "\n",
       "       58     59  ...    1977    1997   2005   2006   2036    2046    2054  \\\n",
       "0  4616.0  303.2  ...  8000.0  7161.0  312.1  580.3  373.9  1496.5  9239.0   \n",
       "1  3735.0  320.1  ...  7500.0  6170.0  281.3  422.6  257.1  1437.0  7543.0   \n",
       "\n",
       "    2059   2080   2091  \n",
       "0  462.0  773.5  774.8  \n",
       "1  391.0  571.6  515.4  \n",
       "\n",
       "[2 rows x 333 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Su_Wi = df[['Demand.SummerPeak', 'Demand.WinterPeak']]\n",
    "### drop rows with all zero values\n",
    "df_Su_Wi = df_Su_Wi.loc[~(df_Su_Wi==0).all(axis=1)]\n",
    "### drop the rows with values that are too low\n",
    "df_Su_Wi_bound = 200\n",
    "df_Su_Wi = df_Su_Wi[(df_Su_Wi['Demand.SummerPeak'] >= df_Su_Wi_bound) & (df_Su_Wi['Demand.WinterPeak'] >= df_Su_Wi_bound)]\n",
    "df_Su_Wi = df_Su_Wi.T.reset_index()\n",
    "df_Su_Wi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_Su_wi_scatt = {\n",
    "    \"data\": {\n",
    "        \"columns\": [\n",
    "            df_Su_Wi.iloc[0,:].values.flatten().tolist(),\n",
    "            df_Su_Wi.iloc[1,:].values.flatten().tolist()\n",
    "        ],\n",
    "     \"type\": 'scatter'\n",
    "            },\n",
    "    \"axis\": {\n",
    "        \"x\": {\n",
    "            \"label\": 'Utilities',\n",
    "            \"tick\": {\n",
    "                \"fit\": \"false\"\n",
    "            }\n",
    "        },\n",
    "        \"y\": {\n",
    "            \"label\": 'PeakUsage'\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "                781.0,\n",
       "                870.6,\n",
       "                430.0,\n",
       "                674.0,\n",
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       "                3884.0,\n",
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       "                436.0,\n",
       "                512.0,\n",
       "                484.0,\n",
       "                664.1,\n",
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       "                3138.0,\n",
       "                1109.0,\n",
       "                28863.0,\n",
       "                429.1,\n",
       "                1836.5,\n",
       "                229.8,\n",
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       "                367.1,\n",
       "                689.1,\n",
       "                312.4,\n",
       "                337.0,\n",
       "                252.8,\n",
       "                292.0,\n",
       "                6278.0,\n",
       "                683.0,\n",
       "                595.6,\n",
       "                980.0,\n",
       "                340.0,\n",
       "                402.0,\n",
       "                1068.0,\n",
       "                275.6,\n",
       "                256.0,\n",
       "                338.0,\n",
       "                16618.0,\n",
       "                621.8,\n",
       "                206.7,\n",
       "                528.7,\n",
       "                403.5,\n",
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       "                3797.0,\n",
       "                418.2,\n",
       "                607.0,\n",
       "                556.0,\n",
       "                294.5,\n",
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       "                4240.0,\n",
       "                2100.0,\n",
       "                1788.0,\n",
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       "                251.8,\n",
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       "                453.1,\n",
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       "                676.4,\n",
       "                3928.0,\n",
       "                3282.0,\n",
       "                2663.7,\n",
       "                281.0,\n",
       "                2949.0,\n",
       "                299.0,\n",
       "                1941.0,\n",
       "                534.3,\n",
       "                6823.0,\n",
       "                211.9,\n",
       "                578.1,\n",
       "                438.6,\n",
       "                59650.0,\n",
       "                19647.0,\n",
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       "                7500.0,\n",
       "                6170.0,\n",
       "                281.3,\n",
       "                422.6,\n",
       "                257.1,\n",
       "                1437.0,\n",
       "                7543.0,\n",
       "                391.0,\n",
       "                571.6,\n",
       "                515.4\n",
       "            ]\n",
       "        ],\n",
       "        'type': 'scatter'\n",
       "    },\n",
       "    'axis': {\n",
       "        'x': {\n",
       "            'label': 'Utilities',\n",
       "            'tick': {\n",
       "                'fit': 'false'\n",
       "            }\n",
       "        },\n",
       "        'y': {\n",
       "            'label': 'PeakUsage'\n",
       "        }\n",
       "    }\n",
       "};\n",
       "            data['bindto']='#chart'\n",
       "            c3.generate(data);\n",
       "        </script>\n",
       "    \" src=\"\" width=\"800\" height=\"420\" frameborder=0 sandbox=\"allow-scripts\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c3_plotter = iplotter.C3Plotter()\n",
    "c3_plotter.plot(data_Su_wi_scatt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line plot - compare power generated and use loss¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Utility by State</th>\n",
       "      <th>Total Generation of Power (MWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>6916431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>94490013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>55715570.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Utility by State  Total Generation of Power (MWh)\n",
       "0               AK                        6916431.0\n",
       "1               AL                       94490013.0\n",
       "2               AR                       55715570.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwr_generated_tot = df[['Sources.Total','Utility.State']].groupby('Utility.State').sum()\n",
    "pwr_generated_tot.reset_index(inplace=True)\n",
    "pwr_generated_tot = pwr_generated_tot.rename(columns={'Sources.Total': 'Total Generation of Power (MWh)', 'Utility.State':'Utility by State'})\n",
    "pwr_generated_tot.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Utility by State</th>\n",
       "      <th>Power losses of all kinds measured in megawatt hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>357085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>4448349.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>3068421.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Utility by State  Power losses of all kinds measured in megawatt hours\n",
       "0               AK                                           357085.0   \n",
       "1               AL                                          4448349.0   \n",
       "2               AR                                          3068421.0   "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_loss_tot = df[['Uses.Losses','Utility.State']].groupby('Utility.State').sum()\n",
    "use_loss_tot.reset_index(inplace=True)\n",
    "use_loss_tot = use_loss_tot.rename(columns={'Uses.Losses': 'Power losses of all kinds measured in megawatt hours', 'Utility.State':'Utility by State'})\n",
    "use_loss_tot.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Total Generation of Power (MWh)\n",
       "1    Power losses of all kinds measured in megawatt...\n",
       "Name: index, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gen_los = pd.merge(pwr_generated_tot, use_loss_tot)\n",
    "df_gen_los = df_gen_los.drop(\"Utility by State\", axis = 1)\n",
    "## drop rows with two 0 entries\n",
    "df_gen_los = df_gen_los.loc[~(df_gen_los==0).all(axis=1)]\n",
    "df_gen_los = df_gen_los.T.reset_index()\n",
    "df_gen_los['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe srcdoc=\"\n",
       "        <!-- Load c3.css -->\n",
       "        <link href='https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.css' rel='stylesheet' type='text/css'/>\n",
       "\n",
       "        <!-- Load d3.js and c3.js -->\n",
       "        <script src='http://d3js.org/d3.v3.min.js' charset='utf-8'></script>\n",
       "        <script src='http://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.js'></script>\n",
       "\n",
       "    \n",
       "        <div id=chart style='width: 100%; height: 100%'></div>\n",
       "        <script>\n",
       "            var chart = document.getElementById('chart');\n",
       "            var data = {\n",
       "    'data': {\n",
       "        'columns': [\n",
       "            [\n",
       "                'Total Generation of Power (MWh)',\n",
       "                6916431.0,\n",
       "                94490013.0,\n",
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       "            ],\n",
       "            [\n",
       "                'Power losses of all kinds measured in megawatt hours',\n",
       "                357085.0,\n",
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       "                2266274.0,\n",
       "                4794.0,\n",
       "                359447.0\n",
       "            ]\n",
       "        ],\n",
       "        'types': {\n",
       "            'Total Generation of Power (MWh)': 'area-spline',\n",
       "            'Power losses of all kinds measured in megawatt hours': 'area-spline'\n",
       "        },\n",
       "        'groups': [\n",
       "            [\n",
       "                'Total Generation of Power (MWh)',\n",
       "                'Power losses of all kinds measured in megawatt hours'\n",
       "            ]\n",
       "        ]\n",
       "    }\n",
       "};\n",
       "            data['bindto']='#chart'\n",
       "            c3.generate(data);\n",
       "        </script>\n",
       "    \" src=\"\" width=\"800\" height=\"420\" frameborder=0 sandbox=\"allow-scripts\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Su_wi_scatt_lin = {\n",
    "    \"data\": {\n",
    "        \"columns\": [\n",
    "            df_gen_los.iloc[0,:].values.flatten().tolist(),\n",
    "            df_gen_los.iloc[1,:].values.flatten().tolist()\n",
    "        ],\n",
    "        \"type\": 'bar',\n",
    "        \"groups\": [[df_gen_los['index'][0], df_gen_los['index'][1]]]\n",
    "    }\n",
    "}\n",
    "c3_plotter.plot(data_Su_wi_scatt_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Utility.Number                        int64\n",
       "Utility.Name                         object\n",
       "Utility.State                      category\n",
       "Utility.Type                       category\n",
       "Demand.SummerPeak                   float64\n",
       "Demand.WinterPeak                   float64\n",
       "Sources.Generation                  float64\n",
       "Sources.Purchased                   float64\n",
       "Sources.Other                       float64\n",
       "Sources.Total                       float64\n",
       "Uses.Retail                         float64\n",
       "Uses.Resale                         float64\n",
       "Uses.NoCharge                       float64\n",
       "Uses.Consumed                       float64\n",
       "Uses.Losses                         float64\n",
       "Uses.Total                          float64\n",
       "Revenues.Retail                     float64\n",
       "Revenue.Delivery                    float64\n",
       "Revenue.Resale                      float64\n",
       "Revenue.Adjustments                 float64\n",
       "Revenue.Transmission                float64\n",
       "Revenue.Other                       float64\n",
       "Revenue.Total                       float64\n",
       "Retail.Residential.Revenue          float64\n",
       "Retail.Residential.Sales            float64\n",
       "Retail.Residential.Customers        float64\n",
       "Retail.Commercial.Revenue           float64\n",
       "Retail.Commercial.Sales             float64\n",
       "Retail.Commercial.Customers         float64\n",
       "Retail.Industrial.Revenue           float64\n",
       "Retail.Industrial.Sales             float64\n",
       "Retail.Industrial.Customers         float64\n",
       "Retail.Transportation.Revenue       float64\n",
       "Retail.Transportation.Sales         float64\n",
       "Retail.Transportation.Customers     float64\n",
       "Retail.Total.Revenue                float64\n",
       "Retail.Total.Sales                  float64\n",
       "Retail.Total.Customers              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Source_Average', 'value'], ['Sources.Generation', 1059884.64], ['Sources.Purchased', 1492859.43], ['Sources.Other', 23689.91]]\n"
     ]
    }
   ],
   "source": [
    "source_avg_data = [\n",
    "        ['Source_Average','value'], \n",
    "        ['Sources.Generation', round(df['Sources.Generation'].mean(), 2)], \n",
    "        ['Sources.Purchased', round(df['Sources.Purchased'].mean(), 2)],\n",
    "        ['Sources.Other', round(df['Sources.Other'].mean(), 2)]\n",
    "                  ]\n",
    "print(\"%s\" % source_avg_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "iframe {border:0;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove iFrame border for cleaner chart rendering\n",
    "# increase size of text explanations\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    "iframe {border:0;}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "html_code = \"\"\"\n",
    "<html>\n",
    "  <head>\n",
    "    <script type=\"text/javascript\" src=\"https://www.gstatic.com/charts/loader.js\"></script>\n",
    "    <script type=\"text/javascript\">\n",
    "      google.charts.load(\"current\", {packages:[\"corechart\"]});\n",
    "      google.charts.setOnLoadCallback(drawChart);\n",
    "      function drawChart() {\n",
    "        var data = google.visualization.arrayToDataTable([['Source_Average', 'value'], ['Sources.Generation', 1059884.64], ['Sources.Purchased', 1492859.43], ['Sources.Other', 23689.91]]);\n",
    "\n",
    "        var options = {\n",
    "          title: \"Power Source Break Down (Mean)\",\n",
    "          pieHole: 0.4,\n",
    "        };\n",
    "\n",
    "        var chart = new google.visualization.PieChart(document.getElementById('donutchart'));\n",
    "        chart.draw(data, options);\n",
    "      }\n",
    "    </script>\n",
    "  </head>\n",
    "  <body>\n",
    "    <div id=\"donutchart\" style=\"width: 900px; height: 520px;\"></div>\n",
    "  </body>\n",
    "</html>\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<html>\n",
       "  <head>\n",
       "    <script type=\"text/javascript\" src=\"https://www.gstatic.com/charts/loader.js\"></script>\n",
       "    <script type=\"text/javascript\">\n",
       "      google.charts.load(\"current\", {packages:[\"corechart\"]});\n",
       "      google.charts.setOnLoadCallback(drawChart);\n",
       "      function drawChart() {\n",
       "        var data = google.visualization.arrayToDataTable([['Source_Average', 'value'], ['Sources.Generation', 1059884.64], ['Sources.Purchased', 1492859.43], ['Sources.Other', 23689.91]]);\n",
       "\n",
       "        var options = {\n",
       "          title: \"Power Source Break Down (Mean)\",\n",
       "          pieHole: 0.4,\n",
       "        };\n",
       "\n",
       "        var chart = new google.visualization.PieChart(document.getElementById('donutchart'));\n",
       "        chart.draw(data, options);\n",
       "      }\n",
       "    </script>\n",
       "  </head>\n",
       "  <body>\n",
       "    <div id=\"donutchart\" style=\"width: 900px; height: 520px;\"></div>\n",
       "  </body>\n",
       "</html>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(html_code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"data\": {\"columns\": [[\"Total Generation of Power (MWh)\", 6916431.0, 94490013.0, 55715570.0, 96248652.0, 324458359.0, 6402865.0, 101486615.0, 39227668.0, 10122968.0, 10079771.0, 299967369.0, 143429124.0, 9599248.0, 74809687.0, 21298495.0, 67260218.0, 123425199.0, 46930797.0, 67384202.0, 100330610.0, 43465938.0, 514494283.0, 8151082.0, 103735792.0, 127976481.0, 117494993.0, 52309087.0, 12720255.0, 197828433.0, 48086025.0, 56140826.0, 6082632.0, 34625121.0, 20818048.0, 40479149.0, 159749752.0, 186302938.0, 102203341.0, 219755024.0, 225408245.0, 5858602.0, 68691139.0, 14413753.0, 248821711.0, 773840664.0, 7257731.0, 116058018.0, 6143610.0, 130682456.0, 92475537.0, 54392.0, 7448970.0], [\"Power losses of all kinds measured in megawatt hours\", 357085.0, 4448349.0, 3068421.0, 4303615.0, 14768014.0, 0.0, 4151622.0, 1858385.0, 898247.0, 803143.0, 13593034.0, 6118751.0, 440866.0, 1736513.0, 1399418.0, 1869434.0, 5318550.0, 1796911.0, 2830552.0, 3763123.0, 1055204.0, 2224176.0, 218749.0, 4226527.0, 3546689.0, 3920962.0, 2536053.0, 866517.0, 8381897.0, 769574.0, 2114446.0, 371019.0, 1831202.0, 1142254.0, 1338628.0, 5485845.0, 7257792.0, 3471658.0, 5547029.0, 11693898.0, 330047.0, 2218050.0, 569944.0, 6171703.0, 14716925.0, 266012.0, 1927003.0, 284297.0, 4839739.0, 2266274.0, 4794.0, 359447.0]], \"types\": {\"Total Generation of Power (MWh)\": \"area-spline\", \"Power losses of all kinds measured in megawatt hours\": \"area-spline\"}, \"groups\": [[\"Total Generation of Power (MWh)\", \"Power losses of all kinds measured in megawatt hours\"]]}}'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(data_Su_wi_scatt_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Utility by Type</th>\n",
       "      <th>Total Generation of Power (MWh)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Investor Owned</td>\n",
       "      <td>2.348078e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Retail Power Marketer</td>\n",
       "      <td>1.543204e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cooperative</td>\n",
       "      <td>4.940855e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Municipal</td>\n",
       "      <td>4.548476e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Federal</td>\n",
       "      <td>2.880341e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Political Subdivision</td>\n",
       "      <td>1.854295e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>State</td>\n",
       "      <td>9.120960e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wholesale Power Marketer</td>\n",
       "      <td>2.629211e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Community Choice Aggregator</td>\n",
       "      <td>1.182636e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Behind the Meter</td>\n",
       "      <td>6.151714e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Utility by Type  Total Generation of Power (MWh)\n",
       "4               Investor Owned                     2.348078e+09\n",
       "7        Retail Power Marketer                     1.543204e+09\n",
       "2                  Cooperative                     4.940855e+08\n",
       "5                    Municipal                     4.548476e+08\n",
       "3                      Federal                     2.880341e+08\n",
       "6        Political Subdivision                     1.854295e+08\n",
       "8                        State                     9.120960e+07\n",
       "9     Wholesale Power Marketer                     2.629211e+07\n",
       "1  Community Choice Aggregator                     1.182636e+07\n",
       "0             Behind the Meter                     6.151714e+06"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwr_generated_type_tot = df[['Sources.Total','Utility.Type']].groupby('Utility.Type').sum()\n",
    "pwr_generated_type_tot.reset_index(inplace=True)\n",
    "pwr_generated_type_tot = pwr_generated_type_tot.rename(columns={'Sources.Total': 'Total Generation of Power (MWh)', 'Utility.Type':'Utility by Type'})\n",
    "pwr_generated_type_tot = pwr_generated_type_tot.sort_values(by =['Total Generation of Power (MWh)'],ascending = False)\n",
    "pwr_generated_type_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe srcdoc=\"\n",
       "        <!-- Load c3.css -->\n",
       "        <link href='https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.css' rel='stylesheet' type='text/css'/>\n",
       "\n",
       "        <!-- Load d3.js and c3.js -->\n",
       "        <script src='http://d3js.org/d3.v3.min.js' charset='utf-8'></script>\n",
       "        <script src='http://cdnjs.cloudflare.com/ajax/libs/c3/0.4.10/c3.min.js'></script>\n",
       "\n",
       "    \n",
       "        <div id=chart style='width: 100%; height: 100%'></div>\n",
       "        <script>\n",
       "            var chart = document.getElementById('chart');\n",
       "            var data = {\n",
       "    'data': {\n",
       "        'columns': [\n",
       "            [\n",
       "                'Investor Owned',\n",
       "                2348077855.0\n",
       "            ],\n",
       "            [\n",
       "                'Retail Power Marketer',\n",
       "                1543203536.0\n",
       "            ],\n",
       "            [\n",
       "                'Cooperative',\n",
       "                494085488.0\n",
       "            ],\n",
       "            [\n",
       "                'Municipal',\n",
       "                454847628.0\n",
       "            ],\n",
       "            [\n",
       "                'Federal',\n",
       "                288034058.0\n",
       "            ],\n",
       "            [\n",
       "                'Political Subdivision',\n",
       "                185429548.0\n",
       "            ],\n",
       "            [\n",
       "                'State',\n",
       "                91209595.0\n",
       "            ],\n",
       "            [\n",
       "                'Wholesale Power Marketer',\n",
       "                26292106.0\n",
       "            ],\n",
       "            [\n",
       "                'Community Choice Aggregator',\n",
       "                11826361.0\n",
       "            ],\n",
       "            [\n",
       "                'Behind the Meter',\n",
       "                6151714.0\n",
       "            ]\n",
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       "        'type': 'bar'\n",
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       "};\n",
       "            data['bindto']='#chart'\n",
       "            c3.generate(data);\n",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwr_generated_type_tot_lin = {\n",
    "    \"data\": {\n",
    "        \"columns\": [\n",
    "            pwr_generated_type_tot.iloc[0,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[1,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[2,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[3,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[4,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[5,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[6,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[7,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[8,:].values.flatten().tolist(),\n",
    "            pwr_generated_type_tot.iloc[9,:].values.flatten().tolist()\n",
    "        ],\n",
    "        \"type\": \"bar\"\n",
    "    }\n",
    "}\n",
    "c3_plotter.plot(pwr_generated_type_tot_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"data\": {\"columns\": [[\"Investor Owned\", 2348077855.0], [\"Retail Power Marketer\", 1543203536.0], [\"Cooperative\", 494085488.0], [\"Municipal\", 454847628.0], [\"Federal\", 288034058.0], [\"Political Subdivision\", 185429548.0], [\"State\", 91209595.0], [\"Wholesale Power Marketer\", 26292106.0], [\"Community Choice Aggregator\", 11826361.0], [\"Behind the Meter\", 6151714.0]], \"type\": \"bar\"}}'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(pwr_generated_type_tot_lin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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