EEP147_Demo3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/ericvandusen/DSEP/EEP147'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ESG_table = Table.read_table('ESGPorfolios_forcsv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Group</th> <th>Group_num</th> <th>UNIT NAME</th> <th>Capacity_MW</th> <th>Heat_Rate_MMBTUperMWh</th> <th>Fuel_Price_USDperMMBTU</th> <th>Fuel_Cost_USDperMWH</th> <th>Var_OandM_USDperMWH</th> <th>Total_Var_Cost_USDperMWH</th> <th>Carbon_tonsperMWH</th> <th>FixedCst_OandM_perDay</th> <th>Unnamed: 11</th> <th>Unnamed: 12</th> <th>Unnamed: 13</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>FOUR CORNERS        </td> <td>1900       </td> <td>11.67                </td> <td>3                     </td> <td>35                 </td> <td>1.5                </td> <td>36.5                    </td> <td>1.1              </td> <td>$8,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>ALAMITOS 7          </td> <td>250        </td> <td>16.05                </td> <td>4.5                   </td> <td>72.22              </td> <td>1.5                </td> <td>73.72                   </td> <td>0.85             </td> <td>$0                   </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>HUNTINGTON BEACH 1&2</td> <td>300        </td> <td>8.67                 </td> <td>4.5                   </td> <td>39                 </td> <td>1.5                </td> <td>40.5                    </td> <td>0.46             </td> <td>$2,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>HUNTINGTON BEACH 5  </td> <td>150        </td> <td>14.44                </td> <td>4.5                   </td> <td>65                 </td> <td>1.5                </td> <td>66.5                    </td> <td>0.77             </td> <td>$2,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>REDONDO 5&6         </td> <td>350        </td> <td>8.99                 </td> <td>4.5                   </td> <td>40.44              </td> <td>1.5                </td> <td>41.94                   </td> <td>0.48             </td> <td>$3,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>REDONDO 7&8         </td> <td>950        </td> <td>8.99                 </td> <td>4.5                   </td> <td>40.44              </td> <td>1.5                </td> <td>41.94                   </td> <td>0.48             </td> <td>$5,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Gas </td> <td>2        </td> <td>EL SEGUNDO 1&2      </td> <td>400        </td> <td>9.63                 </td> <td>4.5                   </td> <td>43.33              </td> <td>1.5                </td> <td>44.83                   </td> <td>0.51             </td> <td>$1,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Gas </td> <td>2        </td> <td>EL SEGUNDO 3&4      </td> <td>650        </td> <td>8.83                 </td> <td>4.5                   </td> <td>39.72              </td> <td>1.5                </td> <td>41.22                   </td> <td>0.47             </td> <td>$1,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Gas </td> <td>2        </td> <td>LONG BEACH          </td> <td>550        </td> <td>11.56                </td> <td>4.5                   </td> <td>52                 </td> <td>0.5                </td> <td>52.5                    </td> <td>0.61             </td> <td>$2,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Gas </td> <td>2        </td> <td>NORTH ISLAND        </td> <td>150        </td> <td>14.44                </td> <td>4.5                   </td> <td>65                 </td> <td>0.5                </td> <td>65.5                    </td> <td>0.77             </td> <td>$0                   </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (32 rows omitted)</p"
      ],
      "text/plain": [
       "Group    | Group_num | UNIT NAME            | Capacity_MW | Heat_Rate_MMBTUperMWh | Fuel_Price_USDperMMBTU | Fuel_Cost_USDperMWH | Var_OandM_USDperMWH | Total_Var_Cost_USDperMWH | Carbon_tonsperMWH | FixedCst_OandM_perDay | Unnamed: 11 | Unnamed: 12 | Unnamed: 13\n",
       "Big Coal | 1         | FOUR CORNERS         | 1900        | 11.67                 | 3                      | 35                  | 1.5                 | 36.5                     | 1.1               | $8,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | ALAMITOS 7           | 250         | 16.05                 | 4.5                    | 72.22               | 1.5                 | 73.72                    | 0.85              | $0                    | nan         | nan         | nan\n",
       "Big Coal | 1         | HUNTINGTON BEACH 1&2 | 300         | 8.67                  | 4.5                    | 39                  | 1.5                 | 40.5                     | 0.46              | $2,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | HUNTINGTON BEACH 5   | 150         | 14.44                 | 4.5                    | 65                  | 1.5                 | 66.5                     | 0.77              | $2,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | REDONDO 5&6          | 350         | 8.99                  | 4.5                    | 40.44               | 1.5                 | 41.94                    | 0.48              | $3,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | REDONDO 7&8          | 950         | 8.99                  | 4.5                    | 40.44               | 1.5                 | 41.94                    | 0.48              | $5,000                | nan         | nan         | nan\n",
       "Big Gas  | 2         | EL SEGUNDO 1&2       | 400         | 9.63                  | 4.5                    | 43.33               | 1.5                 | 44.83                    | 0.51              | $1,000                | nan         | nan         | nan\n",
       "Big Gas  | 2         | EL SEGUNDO 3&4       | 650         | 8.83                  | 4.5                    | 39.72               | 1.5                 | 41.22                    | 0.47              | $1,000                | nan         | nan         | nan\n",
       "Big Gas  | 2         | LONG BEACH           | 550         | 11.56                 | 4.5                    | 52                  | 0.5                 | 52.5                     | 0.61              | $2,000                | nan         | nan         | nan\n",
       "Big Gas  | 2         | NORTH ISLAND         | 150         | 14.44                 | 4.5                    | 65                  | 0.5                 | 65.5                     | 0.77              | $0                    | nan         | nan         | nan\n",
       "... (32 rows omitted)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ESG_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ESG_sorted =  ESG_table.sort(\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Group</th> <th>Group_num</th> <th>UNIT NAME</th> <th>Capacity_MW</th> <th>Heat_Rate_MMBTUperMWh</th> <th>Fuel_Price_USDperMMBTU</th> <th>Fuel_Cost_USDperMWH</th> <th>Var_OandM_USDperMWH</th> <th>Total_Var_Cost_USDperMWH</th> <th>Carbon_tonsperMWH</th> <th>FixedCst_OandM_perDay</th> <th>Unnamed: 11</th> <th>Unnamed: 12</th> <th>Unnamed: 13</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>FOUR CORNERS        </td> <td>1900       </td> <td>11.67                </td> <td>3                     </td> <td>35                 </td> <td>1.5                </td> <td>36.5                    </td> <td>1.1              </td> <td>$8,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>HUNTINGTON BEACH 1&2</td> <td>300        </td> <td>8.67                 </td> <td>4.5                   </td> <td>39                 </td> <td>1.5                </td> <td>40.5                    </td> <td>0.46             </td> <td>$2,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>REDONDO 5&6         </td> <td>350        </td> <td>8.99                 </td> <td>4.5                   </td> <td>40.44              </td> <td>1.5                </td> <td>41.94                   </td> <td>0.48             </td> <td>$3,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>REDONDO 7&8         </td> <td>950        </td> <td>8.99                 </td> <td>4.5                   </td> <td>40.44              </td> <td>1.5                </td> <td>41.94                   </td> <td>0.48             </td> <td>$5,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>HUNTINGTON BEACH 5  </td> <td>150        </td> <td>14.44                </td> <td>4.5                   </td> <td>65                 </td> <td>1.5                </td> <td>66.5                    </td> <td>0.77             </td> <td>$2,000               </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "        <tr>\n",
       "            <td>Big Coal</td> <td>1        </td> <td>ALAMITOS 7          </td> <td>250        </td> <td>16.05                </td> <td>4.5                   </td> <td>72.22              </td> <td>1.5                </td> <td>73.72                   </td> <td>0.85             </td> <td>$0                   </td> <td>nan        </td> <td>nan        </td> <td>nan        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Group    | Group_num | UNIT NAME            | Capacity_MW | Heat_Rate_MMBTUperMWh | Fuel_Price_USDperMMBTU | Fuel_Cost_USDperMWH | Var_OandM_USDperMWH | Total_Var_Cost_USDperMWH | Carbon_tonsperMWH | FixedCst_OandM_perDay | Unnamed: 11 | Unnamed: 12 | Unnamed: 13\n",
       "Big Coal | 1         | FOUR CORNERS         | 1900        | 11.67                 | 3                      | 35                  | 1.5                 | 36.5                     | 1.1               | $8,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | HUNTINGTON BEACH 1&2 | 300         | 8.67                  | 4.5                    | 39                  | 1.5                 | 40.5                     | 0.46              | $2,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | REDONDO 5&6          | 350         | 8.99                  | 4.5                    | 40.44               | 1.5                 | 41.94                    | 0.48              | $3,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | REDONDO 7&8          | 950         | 8.99                  | 4.5                    | 40.44               | 1.5                 | 41.94                    | 0.48              | $5,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | HUNTINGTON BEACH 5   | 150         | 14.44                 | 4.5                    | 65                  | 1.5                 | 66.5                     | 0.77              | $2,000                | nan         | nan         | nan\n",
       "Big Coal | 1         | ALAMITOS 7           | 250         | 16.05                 | 4.5                    | 72.22               | 1.5                 | 73.72                    | 0.85              | $0                    | nan         | nan         | nan"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a subset of the data for just one group\n",
    "Big_Coal= ESG_sorted.where(\"Group\",\"Big Coal\")\n",
    "Big_Coal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gByQi84FDqro6wP0UZ/W2r9ffLiKrRGRVWVmZr1O6Pc84xLSJY2JSOiMcJo07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\nnyxBGGOM8ckShDHGGJ8sQRhjjPHp/wO4MEe/X/9hpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1044188d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#table.plot('days', 'price')\n",
    "ESG_sorted.plot(\"Capacity_MW\",\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r3FxS/7n8JwEvAOcV3YAafk5FgsjvHwGuqnLstPx1PLw6J9D2pARxDbBVxb3c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115a4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ESG_sorted.bar(\"Capacity_MW\",\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oPpLq+UREREwetRcMqmZpvblaKnQz4G7bj9i+oHfhRUTEoNReMEjSyyWdBdxPmYLjfkln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\n/X9gopxVNcAfrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1115a4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Big_Coal.bar(\"Capacity_MW\",\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Big_Coal.bar(\"Capacity_MW\",\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1900,  300,  350,  950,  150,  250])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Capacity_MW_BC =  Big_Coal.column(\"Capacity_MW\")\n",
    "Capacity_MW_BC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 36.5 ,  40.5 ,  41.94,  41.94,  66.5 ,  73.72])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TVC =  Big_Coal.column(\"Total_Var_Cost_USDperMWH\")\n",
    "TVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wqi7fljSL0kx2E/A2GPq6/FDS5sBDlLjukvQfE7AerX525k7AurwMOAC4snrO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\n/X9gopxVNcAfrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111950e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Big_Coal.bar(\"Capacity_MW\",\"Total_Var_Cost_USDperMWH\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1900,  300,  350,  950,  150,  250])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1159d3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = [1900,  300,  350,  950,  150,  250]\n",
    "width\n",
    "height = [ 36.5,  40.5  ,  41.94,  41.94,  66.5 ,  73.72]\n",
    "cumwidth2=[1900,2200,2550,3500,3650,3900]\n",
    "halfwidth3=[950,150,175,475,75,125]\n",
    "\n",
    "y_pos = [950,2050,2375,3025,3575,3775]\n",
    "\n",
    "# Make the plot\n",
    "plt.bar(y_pos, height, width=width)\n",
    "#plt.xticks(y_pos, bars)\n",
    "# Add title and axis names\n",
    "plt.title('Big Coal')\n",
    "plt.xlabel('Capacity_MW')\n",
    "plt.ylabel('Variable Cost')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}