high_granularity_power_generation_ca_iso.ipynb

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      "source": [
        "<h1><center> US electricity generation by energy source ( price , co2 emission by grids)<center><h1> \n",
        "  High granularity Power generation CA ISO ( California) "
      ]
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      "source": [
        "# Table of Contents\n",
        "* [1. DataSet Description](#section-one)\n",
        "* [2. Import and Preprocessing](#section-two)\n",
        "* [3. Filler and Filter](#section-three)\n",
        "* [4. Daily Grpahs](#section-four)\n",
        "* [5. Carbon Emission vs Fuel](#section-five)\n",
        "* [6. Price vs Fuel](#section-six)"
      ]
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      "source": [
        "<a id=\"section-one\"></a>\n",
        "# 1. DataSet Description\n",
        "\n",
        "Dataset is weekly reports from EDA  for CA ISO"
      ]
    },
    {
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      "source": [
        "<a id=\"section-two\"></a>\n",
        "# 2. Import & Preprocessing\n",
        "\n",
        "Let's have a first look at the data. For the sake of simplicity we are only looking at data of plant 1 (power generation and weather). Data of power plant 2 can then be explored in a similar manner."
      ]
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      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import matplotlib.dates as mdates \n",
        "xformatter = mdates.DateFormatter('%H:%M') # for time axis plots\n",
        "\n",
        "# import plotly.offline as py\n",
        "# py.init_notebook_mode(connected=True)\n",
        "\n",
        "import sklearn\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "# Import all available data \n",
        "# df = pd.read_csv(\"../input/us-electricity-by-energy-source-in-spring/930-data-export.csv\")\n",
        "\n",
        "df = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200706.csv\")\n",
        "df2 = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200707.csv\")\n",
        "df3 = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200708.csv\")\n",
        "df4 = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200709.csv\")\n",
        "df5 = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200710.csv\")\n",
        "df6 = pd.read_csv(\"/content/nyiso-april-2ndweek/CAISO-supply-20200711.csv\")\n",
        "\n",
        "df =  df.transpose()\n",
        "df2 =  df2.transpose()\n",
        "df3 =  df3.transpose()\n",
        "df4 =  df4.transpose()\n",
        "df5 =  df5.transpose()\n",
        "df6 =  df6.transpose()"
      ],
      "execution_count": null,
      "outputs": []
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        "df.rename(columns=df.iloc[0], inplace = True)\n",
        "df2.rename(columns=df2.iloc[0], inplace = True)\n",
        "df3.rename(columns=df3.iloc[0], inplace = True)\n",
        "df4.rename(columns=df4.iloc[0], inplace = True)\n",
        "df5.rename(columns=df5.iloc[0], inplace = True)\n",
        "df6.rename(columns=df6.iloc[0], inplace = True)\n",
        "df2.head()"
      ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Renewables</th>\n",
              "      <th>Natural gas</th>\n",
              "      <th>Large hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Supply 07/07/2020</th>\n",
              "      <td>Renewables</td>\n",
              "      <td>Natural gas</td>\n",
              "      <td>Large hydro</td>\n",
              "      <td>Imports</td>\n",
              "      <td>Batteries</td>\n",
              "      <td>Nuclear</td>\n",
              "      <td>Coal</td>\n",
              "      <td>Other</td>\n",
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              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>5977</td>\n",
              "      <td>7918</td>\n",
              "      <td>1946</td>\n",
              "      <td>8518</td>\n",
              "      <td>-4</td>\n",
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              "      <td>12</td>\n",
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              "      <th>0:05</th>\n",
              "      <td>5912</td>\n",
              "      <td>7628</td>\n",
              "      <td>1995</td>\n",
              "      <td>8673</td>\n",
              "      <td>-6</td>\n",
              "      <td>2274</td>\n",
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              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>5885</td>\n",
              "      <td>7525</td>\n",
              "      <td>1987</td>\n",
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              "      <td>-8</td>\n",
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              "      <th>0:15</th>\n",
              "      <td>5854</td>\n",
              "      <td>7453</td>\n",
              "      <td>1958</td>\n",
              "      <td>8726</td>\n",
              "      <td>-77</td>\n",
              "      <td>2273</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
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              "                   Renewables  Natural gas  Large hydro  ...  Nuclear  Coal  Other\n",
              "Supply 07/07/2020  Renewables  Natural gas  Large hydro  ...  Nuclear  Coal  Other\n",
              "0:00                     5977         7918         1946  ...     2273    12      0\n",
              "0:05                     5912         7628         1995  ...     2274    13      0\n",
              "0:10                     5885         7525         1987  ...     2273    12      0\n",
              "0:15                     5854         7453         1958  ...     2273    10      0\n",
              "\n",
              "[5 rows x 8 columns]"
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        "def df_manipulation(dfs):\n",
        "    dfs['Timestamp'] = dfs.index\n",
        "    date =  dfs.iloc[0].Timestamp.replace('Supply ','')\n",
        "    dfs['DATE'] = date\n",
        "    dfs = dfs[1:len(dfs)-1]\n",
        "    dfs['DATE'] =  pd.to_datetime(dfs['DATE'],format = '%d/%m/%Y').dt.date\n",
        "    dfs['TIME'] = pd.to_datetime(dfs['Timestamp'],format = '%H:%M').dt.time\n",
        "    dfs['HOURS'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.hour\n",
        "    dfs['MINUTES'] = pd.to_datetime(dfs['TIME'],format='%H:%M:%S').dt.minute\n",
        "    dfs['MINUTES_PASS'] = dfs['MINUTES'] + dfs['HOURS']*60\n",
        "    dfs['Timestamp'] = pd.to_datetime(dfs['DATE'].apply(str) +' '+ dfs['TIME'].apply(str), format = '%Y-%m-%d %H:%M')\n",
        "    return dfs"
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      "source": [
        "dff = df_manipulation(df)\n",
        "dff2 = df_manipulation(df2)\n",
        "dff3 = df_manipulation(df3)\n",
        "dff4 = df_manipulation(df4)\n",
        "dff5 = df_manipulation(df5)\n",
        "dff6 = df_manipulation(df6)\n",
        "\n",
        "dff.head()"
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              "      <th>Natural gas</th>\n",
              "      <th>Large hydro</th>\n",
              "      <th>Imports</th>\n",
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              "      <th>Nuclear</th>\n",
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              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
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              "      <th>MINUTES_PASS</th>\n",
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              "      <th>0:00</th>\n",
              "      <td>6487</td>\n",
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              "      <td>6482</td>\n",
              "      <td>6674</td>\n",
              "      <td>1883</td>\n",
              "      <td>8142</td>\n",
              "      <td>12</td>\n",
              "      <td>2266</td>\n",
              "      <td>11</td>\n",
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              "      <td>2020-06-07 00:05:00</td>\n",
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              "      <td>10</td>\n",
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              "      <th>0:15</th>\n",
              "      <td>6466</td>\n",
              "      <td>6588</td>\n",
              "      <td>1852</td>\n",
              "      <td>8037</td>\n",
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              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:15:00</td>\n",
              "      <td>2020-06-07</td>\n",
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              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
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              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>6422</td>\n",
              "      <td>6509</td>\n",
              "      <td>1860</td>\n",
              "      <td>8012</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:20:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
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            "text/plain": [
              "     Renewables Natural gas Large hydro  ... HOURS MINUTES MINUTES_PASS\n",
              "0:00       6487        6999        1859  ...     0       0            0\n",
              "0:05       6482        6674        1883  ...     0       5            5\n",
              "0:10       6487        6501        1844  ...     0      10           10\n",
              "0:15       6466        6588        1852  ...     0      15           15\n",
              "0:20       6422        6509        1860  ...     0      20           20\n",
              "\n",
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              "      <th>Natural gas</th>\n",
              "      <th>Large hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
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              "      <th>DATE</th>\n",
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              "      <td>-4</td>\n",
              "      <td>2273</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-07 00:00:00</td>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>5912</td>\n",
              "      <td>7628</td>\n",
              "      <td>1995</td>\n",
              "      <td>8673</td>\n",
              "      <td>-6</td>\n",
              "      <td>2274</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-07 00:05:00</td>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>5885</td>\n",
              "      <td>7525</td>\n",
              "      <td>1987</td>\n",
              "      <td>8651</td>\n",
              "      <td>-8</td>\n",
              "      <td>2273</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-07 00:10:00</td>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>5854</td>\n",
              "      <td>7453</td>\n",
              "      <td>1958</td>\n",
              "      <td>8726</td>\n",
              "      <td>-77</td>\n",
              "      <td>2273</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-07 00:15:00</td>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>5838</td>\n",
              "      <td>6999</td>\n",
              "      <td>1900</td>\n",
              "      <td>9103</td>\n",
              "      <td>-48</td>\n",
              "      <td>2272</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-07-07 00:20:00</td>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Renewables Natural gas Large hydro  ... HOURS MINUTES MINUTES_PASS\n",
              "0:00       5977        7918        1946  ...     0       0            0\n",
              "0:05       5912        7628        1995  ...     0       5            5\n",
              "0:10       5885        7525        1987  ...     0      10           10\n",
              "0:15       5854        7453        1958  ...     0      15           15\n",
              "0:20       5838        6999        1900  ...     0      20           20\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:50.975961Z",
          "iopub.execute_input": "2021-05-31T04:17:50.976338Z",
          "iopub.status.idle": "2021-05-31T04:17:51.005963Z",
          "shell.execute_reply.started": "2021-05-31T04:17:50.976307Z",
          "shell.execute_reply": "2021-05-31T04:17:51.003798Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "g2mNeuyrsUm8",
        "outputId": "cdcc12e5-465d-4294-c715-1edfba35af86"
      },
      "source": [
        "dff3.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Renewables</th>\n",
              "      <th>Natural gas</th>\n",
              "      <th>Large hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>5452</td>\n",
              "      <td>8185</td>\n",
              "      <td>2027</td>\n",
              "      <td>9029</td>\n",
              "      <td>-6</td>\n",
              "      <td>2275</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-08-07 00:00:00</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>5370</td>\n",
              "      <td>8076</td>\n",
              "      <td>2089</td>\n",
              "      <td>9036</td>\n",
              "      <td>9</td>\n",
              "      <td>2275</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-08-07 00:05:00</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>5315</td>\n",
              "      <td>7884</td>\n",
              "      <td>2112</td>\n",
              "      <td>9032</td>\n",
              "      <td>9</td>\n",
              "      <td>2276</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-08-07 00:10:00</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>5312</td>\n",
              "      <td>7630</td>\n",
              "      <td>2072</td>\n",
              "      <td>9178</td>\n",
              "      <td>-8</td>\n",
              "      <td>2274</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-08-07 00:15:00</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>5320</td>\n",
              "      <td>7412</td>\n",
              "      <td>2101</td>\n",
              "      <td>9268</td>\n",
              "      <td>-21</td>\n",
              "      <td>2274</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-08-07 00:20:00</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Renewables Natural gas Large hydro  ... HOURS MINUTES MINUTES_PASS\n",
              "0:00       5452        8185        2027  ...     0       0            0\n",
              "0:05       5370        8076        2089  ...     0       5            5\n",
              "0:10       5315        7884        2112  ...     0      10           10\n",
              "0:15       5312        7630        2072  ...     0      15           15\n",
              "0:20       5320        7412        2101  ...     0      20           20\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.007651Z",
          "iopub.execute_input": "2021-05-31T04:17:51.008080Z",
          "iopub.status.idle": "2021-05-31T04:17:51.029576Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.008048Z",
          "shell.execute_reply": "2021-05-31T04:17:51.028353Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "6kmwS3nPsUm8",
        "outputId": "81bc4f79-28ce-495a-d6a9-167714ab4731"
      },
      "source": [
        "dff4.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Renewables</th>\n",
              "      <th>Natural gas</th>\n",
              "      <th>Large hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>6086</td>\n",
              "      <td>8045</td>\n",
              "      <td>1804</td>\n",
              "      <td>8583</td>\n",
              "      <td>-67</td>\n",
              "      <td>2272</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-09-07 00:00:00</td>\n",
              "      <td>2020-09-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>6078</td>\n",
              "      <td>7622</td>\n",
              "      <td>1775</td>\n",
              "      <td>8889</td>\n",
              "      <td>11</td>\n",
              "      <td>2272</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-09-07 00:05:00</td>\n",
              "      <td>2020-09-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>6115</td>\n",
              "      <td>7684</td>\n",
              "      <td>2019</td>\n",
              "      <td>8503</td>\n",
              "      <td>-34</td>\n",
              "      <td>2272</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-09-07 00:10:00</td>\n",
              "      <td>2020-09-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>6151</td>\n",
              "      <td>7472</td>\n",
              "      <td>1908</td>\n",
              "      <td>8527</td>\n",
              "      <td>-83</td>\n",
              "      <td>2273</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-09-07 00:15:00</td>\n",
              "      <td>2020-09-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>6152</td>\n",
              "      <td>7190</td>\n",
              "      <td>1837</td>\n",
              "      <td>8833</td>\n",
              "      <td>-70</td>\n",
              "      <td>2272</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-09-07 00:20:00</td>\n",
              "      <td>2020-09-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Renewables Natural gas Large hydro  ... HOURS MINUTES MINUTES_PASS\n",
              "0:00       6086        8045        1804  ...     0       0            0\n",
              "0:05       6078        7622        1775  ...     0       5            5\n",
              "0:10       6115        7684        2019  ...     0      10           10\n",
              "0:15       6151        7472        1908  ...     0      15           15\n",
              "0:20       6152        7190        1837  ...     0      20           20\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.031509Z",
          "iopub.execute_input": "2021-05-31T04:17:51.031843Z",
          "iopub.status.idle": "2021-05-31T04:17:51.041485Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.031812Z",
          "shell.execute_reply": "2021-05-31T04:17:51.040587Z"
        },
        "trusted": true,
        "id": "0wgMpr-zsUm8"
      },
      "source": [
        "# df['Timestamp'] = df.index\n",
        "# df['DATE'] = df.iloc[0].Timestamp.replace('Supply ','')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.046399Z",
          "iopub.execute_input": "2021-05-31T04:17:51.046774Z",
          "iopub.status.idle": "2021-05-31T04:17:51.056675Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.046743Z",
          "shell.execute_reply": "2021-05-31T04:17:51.055444Z"
        },
        "trusted": true,
        "id": "SldyxlznsUm9"
      },
      "source": [
        "# Remove row 0 \n",
        "# df.drop([1], inplace = True)\n",
        "# df.columns = df.iloc[0]\n",
        "\n",
        "# df = df[1:len(df)-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.065341Z",
          "iopub.execute_input": "2021-05-31T04:17:51.065699Z",
          "iopub.status.idle": "2021-05-31T04:17:51.071465Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.065668Z",
          "shell.execute_reply": "2021-05-31T04:17:51.070434Z"
        },
        "trusted": true,
        "id": "7wJEUPJ3sUm9"
      },
      "source": [
        "# df['DATE'] =  pd.to_datetime(df['DATE'],format = '%m/%d/%Y', errors='coerce').dt.date\n",
        "# df['TIME'] = pd.to_datetime(df['Timestamp'],format = '%H:%M', errors='coerce').dt.time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.079270Z",
          "iopub.execute_input": "2021-05-31T04:17:51.079929Z",
          "iopub.status.idle": "2021-05-31T04:17:51.085886Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.079880Z",
          "shell.execute_reply": "2021-05-31T04:17:51.084870Z"
        },
        "trusted": true,
        "id": "Mf3JcnFqsUm9"
      },
      "source": [
        "# df.describe()\n",
        "# df.info()\n",
        "# print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.093088Z",
          "iopub.execute_input": "2021-05-31T04:17:51.093600Z",
          "iopub.status.idle": "2021-05-31T04:17:51.101045Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.093556Z",
          "shell.execute_reply": "2021-05-31T04:17:51.099889Z"
        },
        "trusted": true,
        "id": "d9QjuSIJsUm9"
      },
      "source": [
        "# # add hours and minutes for ml models\n",
        "# df['HOURS'] = pd.to_datetime(df['TIME'],format='%H:%M:%S').dt.hour\n",
        "# df['MINUTES'] = pd.to_datetime(df['TIME'],format='%H:%M:%S').dt.minute\n",
        "# df['MINUTES_PASS'] = df['MINUTES'] + df['HOURS']*60"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.125416Z",
          "iopub.execute_input": "2021-05-31T04:17:51.125905Z",
          "iopub.status.idle": "2021-05-31T04:17:51.152067Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.125873Z",
          "shell.execute_reply": "2021-05-31T04:17:51.150975Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6sHbQVFsUm-",
        "outputId": "16d0534e-a03b-4ffd-99f3-fc13d51f30b7"
      },
      "source": [
        "# append all \n",
        "\n",
        "df = dff.append(dff)\n",
        "df = df.append(dff2)\n",
        "df = df.append(dff3)\n",
        "df = df.append(dff4)\n",
        "df = df.append(dff5)\n",
        "df = df.append(dff6)\n",
        "len(df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2016"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.175426Z",
          "iopub.execute_input": "2021-05-31T04:17:51.175770Z",
          "iopub.status.idle": "2021-05-31T04:17:51.180041Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.175742Z",
          "shell.execute_reply": "2021-05-31T04:17:51.179081Z"
        },
        "trusted": true,
        "id": "vUmQBNXtsUm-"
      },
      "source": [
        "# Region = df['Region Code'].head(1).item()\n",
        "# Region\n",
        "\n",
        "Region=\"CA ISO\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQgFMARasUm-"
      },
      "source": [
        "<a id=\"section-three\"></a>\n",
        "## 3. Filter and filler "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.226955Z",
          "iopub.execute_input": "2021-05-31T04:17:51.227401Z",
          "iopub.status.idle": "2021-05-31T04:17:51.233047Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.227372Z",
          "shell.execute_reply": "2021-05-31T04:17:51.232388Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbwBlEZTsUm_",
        "outputId": "af7ab412-d2b5-4c03-ec60-7cf35fafb785"
      },
      "source": [
        "print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Renewables              object\n",
            "Natural gas             object\n",
            "Large hydro             object\n",
            "Imports                 object\n",
            "Batteries               object\n",
            "Nuclear                 object\n",
            "Coal                    object\n",
            "Other                   object\n",
            "Timestamp       datetime64[ns]\n",
            "DATE                    object\n",
            "TIME                    object\n",
            "HOURS                    int64\n",
            "MINUTES                  int64\n",
            "MINUTES_PASS             int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.268118Z",
          "iopub.execute_input": "2021-05-31T04:17:51.268668Z",
          "iopub.status.idle": "2021-05-31T04:17:51.288676Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.268633Z",
          "shell.execute_reply": "2021-05-31T04:17:51.287821Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "8ceeT5c1sUm_",
        "outputId": "04e48331-c216-44e1-f60c-836ffe9c38fc"
      },
      "source": [
        "# df['Hydro'] = df['Hydro'] + df ['Large hydro']\n",
        "# df['NaturalGas'] = df['NaturalGas'] + df ['Natural gas']\n",
        "df = df.rename(columns={\"Natural gas\": \"NaturalGas\",\n",
        "                        \"Renewables\" : \"Solar\",\n",
        "                        \"Large hydro\": \"Hydro\"\n",
        "                       })\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>6487</td>\n",
              "      <td>6999</td>\n",
              "      <td>1859</td>\n",
              "      <td>7899</td>\n",
              "      <td>-1</td>\n",
              "      <td>2266</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:00:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>6482</td>\n",
              "      <td>6674</td>\n",
              "      <td>1883</td>\n",
              "      <td>8142</td>\n",
              "      <td>12</td>\n",
              "      <td>2266</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:05:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>6487</td>\n",
              "      <td>6501</td>\n",
              "      <td>1844</td>\n",
              "      <td>8197</td>\n",
              "      <td>-6</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:10:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>6466</td>\n",
              "      <td>6588</td>\n",
              "      <td>1852</td>\n",
              "      <td>8037</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:15:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>6422</td>\n",
              "      <td>6509</td>\n",
              "      <td>1860</td>\n",
              "      <td>8012</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:20:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Solar NaturalGas Hydro Imports  ...      TIME HOURS MINUTES MINUTES_PASS\n",
              "0:00  6487       6999  1859    7899  ...  00:00:00     0       0            0\n",
              "0:05  6482       6674  1883    8142  ...  00:05:00     0       5            5\n",
              "0:10  6487       6501  1844    8197  ...  00:10:00     0      10           10\n",
              "0:15  6466       6588  1852    8037  ...  00:15:00     0      15           15\n",
              "0:20  6422       6509  1860    8012  ...  00:20:00     0      20           20\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDrpEmm8sUm_"
      },
      "source": [
        "## Fill up missing columns as 0 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.302224Z",
          "iopub.execute_input": "2021-05-31T04:17:51.302548Z",
          "iopub.status.idle": "2021-05-31T04:17:51.325988Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.302501Z",
          "shell.execute_reply": "2021-05-31T04:17:51.325243Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "NJMEXSwcsUnA",
        "outputId": "3b3ec5cc-37aa-4600-f594-e79013af5e52"
      },
      "source": [
        "column_list = (\"Wind\",\"Solar\",\"Hydro\",\"Other\", \n",
        "               \"Oil\", \"NaturalGas\",\"Coal\",\"Nuclear\")\n",
        "\n",
        "for col in column_list:\n",
        "    if col not in df.columns:\n",
        "        df[col] = 0\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Oil</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>6487</td>\n",
              "      <td>6999</td>\n",
              "      <td>1859</td>\n",
              "      <td>7899</td>\n",
              "      <td>-1</td>\n",
              "      <td>2266</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:00:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>6482</td>\n",
              "      <td>6674</td>\n",
              "      <td>1883</td>\n",
              "      <td>8142</td>\n",
              "      <td>12</td>\n",
              "      <td>2266</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:05:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>6487</td>\n",
              "      <td>6501</td>\n",
              "      <td>1844</td>\n",
              "      <td>8197</td>\n",
              "      <td>-6</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:10:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>6466</td>\n",
              "      <td>6588</td>\n",
              "      <td>1852</td>\n",
              "      <td>8037</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:15:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>6422</td>\n",
              "      <td>6509</td>\n",
              "      <td>1860</td>\n",
              "      <td>8012</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:20:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Solar NaturalGas Hydro Imports  ... MINUTES MINUTES_PASS Wind Oil\n",
              "0:00  6487       6999  1859    7899  ...       0            0    0   0\n",
              "0:05  6482       6674  1883    8142  ...       5            5    0   0\n",
              "0:10  6487       6501  1844    8197  ...      10           10    0   0\n",
              "0:15  6466       6588  1852    8037  ...      15           15    0   0\n",
              "0:20  6422       6509  1860    8012  ...      20           20    0   0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.327415Z",
          "iopub.execute_input": "2021-05-31T04:17:51.327875Z",
          "iopub.status.idle": "2021-05-31T04:17:51.345577Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.327846Z",
          "shell.execute_reply": "2021-05-31T04:17:51.344652Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HF44HtmHsUnA",
        "outputId": "7c246c11-724f-44fc-a66c-ce5952e04354"
      },
      "source": [
        "df['Nuclear'] = pd.to_numeric(df['Nuclear'], errors='coerce').fillna(0)\n",
        "df['Hydro'] = pd.to_numeric(df['Hydro'], errors='coerce').fillna(0)\n",
        "df['NaturalGas'] = pd.to_numeric(df['NaturalGas'], errors='coerce').fillna(0)\n",
        "df['Coal'] = pd.to_numeric(df['Coal'], errors='coerce').fillna(0)\n",
        "df['Solar'] = pd.to_numeric(df['Solar'], errors='coerce').fillna(0)\n",
        "df['Coal'] = pd.to_numeric(df['Coal'], errors='coerce').fillna(0)\n",
        "df['Oil'] = pd.to_numeric(df['Oil'], errors='coerce').fillna(0)\n",
        "df['Other'] = pd.to_numeric(df['Other'], errors='coerce').fillna(0)\n",
        "print(df.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Solar                    int64\n",
            "NaturalGas               int64\n",
            "Hydro                    int64\n",
            "Imports                 object\n",
            "Batteries               object\n",
            "Nuclear                  int64\n",
            "Coal                     int64\n",
            "Other                    int64\n",
            "Timestamp       datetime64[ns]\n",
            "DATE                    object\n",
            "TIME                    object\n",
            "HOURS                    int64\n",
            "MINUTES                  int64\n",
            "MINUTES_PASS             int64\n",
            "Wind                     int64\n",
            "Oil                      int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SiikMEBusUnB"
      },
      "source": [
        "## Timestamp  different utility Graphs  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.346863Z",
          "iopub.execute_input": "2021-05-31T04:17:51.347315Z",
          "iopub.status.idle": "2021-05-31T04:17:51.361926Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.347277Z",
          "shell.execute_reply": "2021-05-31T04:17:51.360808Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvOr2GxOsUnB",
        "outputId": "c7c88765-c12e-456b-930e-4a43b54621c5"
      },
      "source": [
        "dates = df['DATE'].unique()\n",
        "dates"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([datetime.date(2020, 6, 7), datetime.date(2020, 7, 7),\n",
              "       datetime.date(2020, 8, 7), datetime.date(2020, 9, 7),\n",
              "       datetime.date(2020, 10, 7), datetime.date(2020, 11, 7)],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.386143Z",
          "iopub.execute_input": "2021-05-31T04:17:51.386458Z",
          "iopub.status.idle": "2021-05-31T04:17:51.390473Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.386431Z",
          "shell.execute_reply": "2021-05-31T04:17:51.389695Z"
        },
        "trusted": true,
        "id": "Yph04796sUnC"
      },
      "source": [
        "# remove first day which doesnt have all hours \n",
        "# dates= dates[1:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.443465Z",
          "iopub.execute_input": "2021-05-31T04:17:51.443825Z",
          "iopub.status.idle": "2021-05-31T04:17:51.452835Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.443794Z",
          "shell.execute_reply": "2021-05-31T04:17:51.451924Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CMWozhbsUnC",
        "outputId": "e7ea1327-e818-453f-f1db-751d6c49124f"
      },
      "source": [
        "# all the hours in day\n",
        "day1 = df[df['DATE']==dates[0]]\n",
        "day1['HOURS'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20, 21, 22, 23])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.460904Z",
          "iopub.execute_input": "2021-05-31T04:17:51.461413Z",
          "iopub.status.idle": "2021-05-31T04:17:51.464950Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.461376Z",
          "shell.execute_reply": "2021-05-31T04:17:51.463771Z"
        },
        "trusted": true,
        "id": "LneHnBJ2sUnC"
      },
      "source": [
        "# df['Other'] = df['Other'].astype(int) + df['Wood'].astype(int) + df['Refuse'].astype(int) + df['LandfillGas'].astype(int)\n",
        "# df = df.drop(columns=['Wood','Refuse','Renewables','LandfillGas'])\n",
        "# df.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.474524Z",
          "iopub.execute_input": "2021-05-31T04:17:51.475215Z",
          "iopub.status.idle": "2021-05-31T04:17:51.478958Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.475175Z",
          "shell.execute_reply": "2021-05-31T04:17:51.477947Z"
        },
        "trusted": true,
        "id": "XTw-mdJ8sUnC"
      },
      "source": [
        "# Pie chart ( Fuel Mix)\n",
        "# df_nw.plot.pie(\n",
        "#     index=[\"Wind Generation (MWh)\",\"Solar Generation (MWh)\",\"Hydro Generation (MWh)\",\"Other Generation (MWh)\", \n",
        "#             \"Petroleum Generation (MWh)\", \"Natural gas Generation (MWh)\",\"Coal Generation (MWh)\",\"Nuclear Generation (MWh)\"],\n",
        "#     subplots=True, name='Fuel Distribution in Generation / Fuel Mix')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HzGEkAaqsUnD"
      },
      "source": [
        "### Inperpolate values for missing dates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.487933Z",
          "iopub.execute_input": "2021-05-31T04:17:51.488677Z",
          "iopub.status.idle": "2021-05-31T04:17:51.493400Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.488627Z",
          "shell.execute_reply": "2021-05-31T04:17:51.492461Z"
        },
        "trusted": true,
        "id": "578NDmqesUnD"
      },
      "source": [
        "# index = pd.date_range(datetime.date(2020, 7, 6), periods=1440, freq='T')\n",
        "# series = pd.Series(range(1440), index=index, name='Timestamp')\n",
        "# series = pd.to_datetime(series.index,format = '%Y-%d-%m %H:%M:%S')\n",
        "# # print(series)\n",
        "\n",
        "# dhours = df[df['DATE']==datetime.date(2020, 7, 6)]\n",
        "# dunique_minpass = dhours['Timestamp'].unique()\n",
        "# # dunique_minpass = pd.to_datetime(dunique_minpass ,format = '%Y-%d-%m %H:%M:%S')\n",
        "# # print(dunique_minpass)\n",
        "\n",
        "# for m in series: \n",
        "#     if m not in dunique_minpass:\n",
        "#         print(\"missing tiemstamp \", m)\n",
        "#         dhours = dhours.append({ \n",
        "#             'Timestamp': m,\n",
        "#             'Renewables': 0\n",
        "#         }, ignore_index = True) \n",
        "#     else :\n",
        "#         print(\"Exists tmestamp \", m) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.525313Z",
          "iopub.execute_input": "2021-05-31T04:17:51.525897Z",
          "iopub.status.idle": "2021-05-31T04:17:51.541217Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.525861Z",
          "shell.execute_reply": "2021-05-31T04:17:51.540259Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "oYtb-nKxsUnD",
        "outputId": "9104337a-70d2-494d-be3d-4560765b2a44"
      },
      "source": [
        "df.head()\n",
        "# len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Imports</th>\n",
              "      <th>Batteries</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Oil</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0:00</th>\n",
              "      <td>6487</td>\n",
              "      <td>6999</td>\n",
              "      <td>1859</td>\n",
              "      <td>7899</td>\n",
              "      <td>-1</td>\n",
              "      <td>2266</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:00:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:05</th>\n",
              "      <td>6482</td>\n",
              "      <td>6674</td>\n",
              "      <td>1883</td>\n",
              "      <td>8142</td>\n",
              "      <td>12</td>\n",
              "      <td>2266</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:05:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:10</th>\n",
              "      <td>6487</td>\n",
              "      <td>6501</td>\n",
              "      <td>1844</td>\n",
              "      <td>8197</td>\n",
              "      <td>-6</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:10:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:15</th>\n",
              "      <td>6466</td>\n",
              "      <td>6588</td>\n",
              "      <td>1852</td>\n",
              "      <td>8037</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:15:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:15:00</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0:20</th>\n",
              "      <td>6422</td>\n",
              "      <td>6509</td>\n",
              "      <td>1860</td>\n",
              "      <td>8012</td>\n",
              "      <td>-31</td>\n",
              "      <td>2266</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-06-07 00:20:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:20:00</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Solar  NaturalGas  Hydro Imports  ... MINUTES  MINUTES_PASS  Wind  Oil\n",
              "0:00   6487        6999   1859    7899  ...       0             0     0    0\n",
              "0:05   6482        6674   1883    8142  ...       5             5     0    0\n",
              "0:10   6487        6501   1844    8197  ...      10            10     0    0\n",
              "0:15   6466        6588   1852    8037  ...      15            15     0    0\n",
              "0:20   6422        6509   1860    8012  ...      20            20     0    0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:17:51.576198Z",
          "iopub.execute_input": "2021-05-31T04:17:51.576505Z",
          "iopub.status.idle": "2021-05-31T04:18:37.467024Z",
          "shell.execute_reply.started": "2021-05-31T04:17:51.576478Z",
          "shell.execute_reply": "2021-05-31T04:18:37.466078Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8EaceUhsUnD",
        "outputId": "f2b046d5-4c71-4d35-d0db-8b1e88fc8aed"
      },
      "source": [
        "df2 = pd.DataFrame()\n",
        "\n",
        "for d in dates:\n",
        "#     print(d)\n",
        "    dhours = df[df['DATE']==d]\n",
        "    dunique_minpass = dhours['Timestamp'].unique()\n",
        "    dunique_minpass = pd.to_datetime(dunique_minpass ,format = '%Y-%m-%d %H:%M')\n",
        "#     print(dunique_minpass)\n",
        "#     '2020-07-06 00:00:00' \n",
        "    index = pd.date_range(d, periods=1440, freq='T')\n",
        "    series = pd.Series(range(1440), index=index, name='Timestamp')\n",
        "    series = pd.to_datetime(series.index,format = '%Y-%m-%d %H:%M')\n",
        "#     print(series)\n",
        "#    '2020-06-07 00:00:00'\n",
        "    for m in series: \n",
        "        if m not in dunique_minpass:\n",
        "#           print(\"missing tiemstamp \" , m)\n",
        "            dhours = dhours.append({ \n",
        "                'Timestamp': m,\n",
        "                'Batteries': 0\n",
        "            }, ignore_index = True) \n",
        "#         else :\n",
        "#             print(\"Exists tmestamp \" , m) \n",
        "    dhours = dhours.sort_values(by='Timestamp')\n",
        "    dhours = dhours.interpolate(method ='linear', limit_direction ='forward')\n",
        "\n",
        "    # adding separate time and date columns\n",
        "    dhours[\"DATE\"] = pd.to_datetime(dhours[\"Timestamp\"]).dt.date # add new column with date\n",
        "    dhours[\"TIME\"] = pd.to_datetime(dhours[\"Timestamp\"]).dt.time # add new column with time\n",
        "\n",
        "    # add hours and minutes for ml models\n",
        "    dhours['HOURS'] = pd.to_datetime(dhours['TIME'],format='%H:%M:%S').dt.hour\n",
        "    dhours['MINUTES'] = pd.to_datetime(dhours['TIME'],format='%H:%M:%S').dt.minute\n",
        "    dhours['MINUTES_PASS'] = dhours['MINUTES'] + dhours['HOURS']*60\n",
        "    df2 = df2.append(dhours)\n",
        "\n",
        "df = df2\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8928"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.469250Z",
          "iopub.execute_input": "2021-05-31T04:18:37.469661Z",
          "iopub.status.idle": "2021-05-31T04:18:37.510449Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.469617Z",
          "shell.execute_reply": "2021-05-31T04:18:37.509538Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        },
        "id": "bhZpFAKisUnE",
        "outputId": "810014af-54da-4f74-d96a-13130fb9a573"
      },
      "source": [
        "df = df.replace('undefined', 0)\n",
        "df = df.fillna(0)\n",
        "df = df.replace(np.nan, 0)\n",
        "# drop Imports and Batteries \n",
        "df.drop(['Imports','Batteries'], axis=1, inplace=True)  \n",
        "df.head(15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Oil</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>6999.0</td>\n",
              "      <td>1859.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:00:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>6999.0</td>\n",
              "      <td>1859.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:00:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:00:00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>6486.0</td>\n",
              "      <td>6934.0</td>\n",
              "      <td>1863.8</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>9.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:01:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:01:00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>6485.0</td>\n",
              "      <td>6869.0</td>\n",
              "      <td>1868.6</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>9.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:02:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:02:00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>6484.0</td>\n",
              "      <td>6804.0</td>\n",
              "      <td>1873.4</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:03:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:03:00</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579</th>\n",
              "      <td>6483.0</td>\n",
              "      <td>6739.0</td>\n",
              "      <td>1878.2</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:04:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:04:00</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6482.0</td>\n",
              "      <td>6674.0</td>\n",
              "      <td>1883.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:05:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>289</th>\n",
              "      <td>6482.0</td>\n",
              "      <td>6674.0</td>\n",
              "      <td>1883.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:05:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:05:00</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>580</th>\n",
              "      <td>6483.0</td>\n",
              "      <td>6639.4</td>\n",
              "      <td>1875.2</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:06:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:06:00</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>581</th>\n",
              "      <td>6484.0</td>\n",
              "      <td>6604.8</td>\n",
              "      <td>1867.4</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:07:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:07:00</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>582</th>\n",
              "      <td>6485.0</td>\n",
              "      <td>6570.2</td>\n",
              "      <td>1859.6</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:08:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:08:00</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>583</th>\n",
              "      <td>6486.0</td>\n",
              "      <td>6535.6</td>\n",
              "      <td>1851.8</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:09:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:09:00</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>290</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>6501.0</td>\n",
              "      <td>1844.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:10:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6487.0</td>\n",
              "      <td>6501.0</td>\n",
              "      <td>1844.0</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:10:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:10:00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>584</th>\n",
              "      <td>6482.8</td>\n",
              "      <td>6518.4</td>\n",
              "      <td>1845.6</td>\n",
              "      <td>2266.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-06-07 00:11:00</td>\n",
              "      <td>2020-06-07</td>\n",
              "      <td>00:11:00</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Solar  NaturalGas   Hydro  Nuclear  ...  MINUTES  MINUTES_PASS Wind  Oil\n",
              "0    6487.0      6999.0  1859.0   2266.0  ...        0             0  0.0  0.0\n",
              "288  6487.0      6999.0  1859.0   2266.0  ...        0             0  0.0  0.0\n",
              "576  6486.0      6934.0  1863.8   2266.0  ...        1             1  0.0  0.0\n",
              "577  6485.0      6869.0  1868.6   2266.0  ...        2             2  0.0  0.0\n",
              "578  6484.0      6804.0  1873.4   2266.0  ...        3             3  0.0  0.0\n",
              "579  6483.0      6739.0  1878.2   2266.0  ...        4             4  0.0  0.0\n",
              "1    6482.0      6674.0  1883.0   2266.0  ...        5             5  0.0  0.0\n",
              "289  6482.0      6674.0  1883.0   2266.0  ...        5             5  0.0  0.0\n",
              "580  6483.0      6639.4  1875.2   2266.0  ...        6             6  0.0  0.0\n",
              "581  6484.0      6604.8  1867.4   2266.0  ...        7             7  0.0  0.0\n",
              "582  6485.0      6570.2  1859.6   2266.0  ...        8             8  0.0  0.0\n",
              "583  6486.0      6535.6  1851.8   2266.0  ...        9             9  0.0  0.0\n",
              "290  6487.0      6501.0  1844.0   2266.0  ...       10            10  0.0  0.0\n",
              "2    6487.0      6501.0  1844.0   2266.0  ...       10            10  0.0  0.0\n",
              "584  6482.8      6518.4  1845.6   2266.0  ...       11            11  0.0  0.0\n",
              "\n",
              "[15 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.511959Z",
          "iopub.execute_input": "2021-05-31T04:18:37.512235Z",
          "iopub.status.idle": "2021-05-31T04:18:37.531195Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.512207Z",
          "shell.execute_reply": "2021-05-31T04:18:37.530261Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "droZaRB9sUnE",
        "outputId": "d3afca5a-855a-42a7-a231-976af6cc9deb"
      },
      "source": [
        "df[\"total_generation\"] = df[\"Wind\"] + df[\"Solar\"] + df[\"Hydro\"] + df[\"Other\"] + df[\"Oil\"] + df[\"NaturalGas\"] + df[\"Coal\"] + df[\"Nuclear\"]\n",
        "df[[\"TIME\",\"total_generation\"]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TIME</th>\n",
              "      <th>total_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00:00:00</td>\n",
              "      <td>17620.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>288</th>\n",
              "      <td>00:00:00</td>\n",
              "      <td>17620.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>00:01:00</td>\n",
              "      <td>17559.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>577</th>\n",
              "      <td>00:02:00</td>\n",
              "      <td>17498.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>00:03:00</td>\n",
              "      <td>17437.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>287</th>\n",
              "      <td>23:55:00</td>\n",
              "      <td>21176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1436</th>\n",
              "      <td>23:56:00</td>\n",
              "      <td>21176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1437</th>\n",
              "      <td>23:57:00</td>\n",
              "      <td>21176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1438</th>\n",
              "      <td>23:58:00</td>\n",
              "      <td>21176.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1439</th>\n",
              "      <td>23:59:00</td>\n",
              "      <td>21176.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8928 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          TIME  total_generation\n",
              "0     00:00:00           17620.0\n",
              "288   00:00:00           17620.0\n",
              "576   00:01:00           17559.2\n",
              "577   00:02:00           17498.4\n",
              "578   00:03:00           17437.6\n",
              "...        ...               ...\n",
              "287   23:55:00           21176.0\n",
              "1436  23:56:00           21176.0\n",
              "1437  23:57:00           21176.0\n",
              "1438  23:58:00           21176.0\n",
              "1439  23:59:00           21176.0\n",
              "\n",
              "[8928 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ytPty2HsUnF"
      },
      "source": [
        "Add unix timestamp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.532490Z",
          "iopub.execute_input": "2021-05-31T04:18:37.532799Z",
          "iopub.status.idle": "2021-05-31T04:18:37.551418Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.532770Z",
          "shell.execute_reply": "2021-05-31T04:18:37.550398Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNugNBIZsUnF",
        "outputId": "e9855b0b-207e-4b08-9fa3-28f45ee67d8d"
      },
      "source": [
        "df_unix = pd.to_datetime(df[\"Timestamp\"],origin='unix')\n",
        "df[\"Timestamp (Unix)\"] = (df_unix - pd.Timestamp(\"1970-01-01\")) // pd.Timedelta('1s')\n",
        "df[\"Timestamp (Unix)\"].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1591488000\n",
              "288    1591488000\n",
              "576    1591488060\n",
              "577    1591488120\n",
              "578    1591488180\n",
              "Name: Timestamp (Unix), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IN0gaMmUsUnF"
      },
      "source": [
        "### output csv \n",
        "\n",
        "CSV outPut total_generation(MW) and total_generation(W) and \n",
        "one Timestamp (Unix) and total_generation(W)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.554764Z",
          "iopub.execute_input": "2021-05-31T04:18:37.555078Z",
          "iopub.status.idle": "2021-05-31T04:18:37.713294Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.555049Z",
          "shell.execute_reply": "2021-05-31T04:18:37.712297Z"
        },
        "trusted": true,
        "id": "7LpglizJsUnG"
      },
      "source": [
        "ot = df\n",
        "ot['total_generation(W)'] = ot['total_generation']*1000000\n",
        "ot.to_csv(Region+'.csv',index=False, header=True)\n",
        "ot.to_csv(Region+'_genunix.csv', columns=['Timestamp (Unix)','total_generation(W)'], index=False, header=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nqn-VgYAsUnG"
      },
      "source": [
        "Output date wise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.715636Z",
          "iopub.execute_input": "2021-05-31T04:18:37.715935Z",
          "iopub.status.idle": "2021-05-31T04:18:37.899051Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.715907Z",
          "shell.execute_reply": "2021-05-31T04:18:37.898108Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFPMQZsbsUnG",
        "outputId": "e04370e4-1f2d-40c8-d39c-720308148cb2"
      },
      "source": [
        "for d in dates:\n",
        "    print(d)\n",
        "    # all the hours in day\n",
        "    ot2 = df[df['DATE']==d]\n",
        "    ot2.to_csv(Region+\"_\"+str(d)+'.csv',index=False, header=True)\n",
        "    ot2.to_csv(Region+'_genunix_'+str(d)+'.csv', columns=['Timestamp (Unix)','total_generation(W)'],index=False, header=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-06-07\n",
            "2020-07-07\n",
            "2020-08-07\n",
            "2020-09-07\n",
            "2020-10-07\n",
            "2020-11-07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InAZxDi-sUnH"
      },
      "source": [
        "<a id=\"section-four\"></a>\n",
        "# 4.  Daily graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.900610Z",
          "iopub.execute_input": "2021-05-31T04:18:37.900893Z",
          "iopub.status.idle": "2021-05-31T04:18:37.905463Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.900868Z",
          "shell.execute_reply": "2021-05-31T04:18:37.904307Z"
        },
        "trusted": true,
        "id": "PTcTNbzWsUnH"
      },
      "source": [
        "# chats of all days in series \n",
        "\n",
        "# for pp in df_nw['DATE'].unique():\n",
        "# #     print(df_nw[pp])\n",
        "#     print(pp)\n",
        "#     sub_df=df_nw[df_nw['DATE']==pp]\n",
        "#     ax = sub_df.plot(x='HOURS', y=[\"total_generation\"], title = \"Total generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'))\n",
        "#     ax.axhline(y=sub_df[\"total_generation\"].mean(), xmin=-1, xmax=1, color='r', linestyle='--', lw=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:37.907073Z",
          "iopub.execute_input": "2021-05-31T04:18:37.907391Z",
          "iopub.status.idle": "2021-05-31T04:18:38.662392Z",
          "shell.execute_reply.started": "2021-05-31T04:18:37.907364Z",
          "shell.execute_reply": "2021-05-31T04:18:38.661402Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "2MM73ztnsUnH",
        "outputId": "00ec1c45-75fa-4ce9-f949-7c5f8fac5cb8"
      },
      "source": [
        "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
        "fig.set_size_inches(20, 10)\n",
        "fig.subplots_adjust(wspace=0.2)\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "\n",
        "for pp,ax in zip(dates,axs.flatten()):\n",
        "#     print(df_nw[pp])\n",
        "    print(pp)\n",
        "    sub_df=df[df['DATE']==pp]\n",
        "    sub_df.plot(x='HOURS', y=[\"total_generation\"],ax = ax, title = \"Total generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'))\n",
        "    \n",
        "    ax.set_ylabel(\"generation(Mw)\")\n",
        "    \n",
        "    mean_gen = round(sub_df[\"total_generation\"].mean())\n",
        "    max_gen = sub_df[\"total_generation\"].max()\n",
        "    maxh = sub_df.loc[sub_df[\"total_generation\"]== max_gen]\n",
        "    maxh = maxh[\"HOURS\"]\n",
        "    ax.axhline(y=mean_gen, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "    \n",
        "    ax.text(5,mean_gen+1000, \"Mean \" + str(mean_gen) + \"(MWh)\" )\n",
        "    \n",
        "    ax.get_legend().remove()\n",
        "    \n",
        "#     ax.annotate('Peak '+ str(max_gen)+ \"(MWh)\", xy=(maxh,max_gen), xytext=(maxh+1, max_gen+1000),\n",
        "#              arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)\n",
        "    \n",
        "    ax.set_ylim(bottom = 0,top=37000)\n",
        "\n",
        "# ax.axhline(y=df_nw[\"total_generation\"].median(), xmin=-1, xmax=1, color='g', linestyle='--', lw=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-06-07\n",
            "2020-07-07\n",
            "2020-08-07\n",
            "2020-09-07\n",
            "2020-10-07\n",
            "2020-11-07\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJsZs6DRsUnI"
      },
      "source": [
        "## Daily chart for the week "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:25:06.815134Z",
          "iopub.execute_input": "2021-05-31T04:25:06.815683Z",
          "iopub.status.idle": "2021-05-31T04:25:08.454636Z",
          "shell.execute_reply.started": "2021-05-31T04:25:06.815638Z",
          "shell.execute_reply": "2021-05-31T04:25:08.453486Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "9DY_f-W2sUnI",
        "outputId": "2ee05f9c-6672-4ed4-a130-252262b123dc"
      },
      "source": [
        "fig, axs = plt.subplots(nrows=3, ncols=2)\n",
        "fig.set_size_inches(20, 10)\n",
        "fig.subplots_adjust(wspace=0.2)\n",
        "fig.subplots_adjust(hspace=0.5)\n",
        "\n",
        "fig.legend(column_list)\n",
        "\n",
        "    \n",
        "for pp,ax in zip(dates,axs.flatten()):\n",
        "#     print(df_nw[pp])\n",
        "    print(pp)\n",
        "    sub_df = df[df['DATE']==pp]\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[0], ax = ax, title = \"Fuel Mix for generation in \"+ Region + \" on \"+ pp.strftime('%B %d, %Y'),legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[1], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[2], ax = ax,legend=False)    \n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[3], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[4], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[5], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[6], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y = column_list[7], ax = ax,legend=False)\n",
        "    sub_df.plot.line(x='MINUTES_PASS', y =['total_generation'], ax = ax,legend=False)\n",
        "    \n",
        "    ax.set_ylabel(\"generation(Mw)\")\n",
        "    \n",
        "    mean_gen = round(sub_df[\"total_generation\"].mean())\n",
        "    max_gen = sub_df[\"total_generation\"].max()\n",
        "    maxh = sub_df.loc[sub_df[\"total_generation\"]== max_gen]\n",
        "    maxh = maxh[\"MINUTES_PASS\"]\n",
        "    ax.axhline(y=mean_gen, xmin=-1, xmax=1, color='r', linestyle='--', lw=2)\n",
        "    \n",
        "    ax.text(5,mean_gen+1000, \"Mean \" + str(mean_gen) + \"(MW)\" )\n",
        "\n",
        "#     ax.get_legend().remove()\n",
        "    \n",
        "#     ax.annotate('Peak '+ str(max_gen)+ \"(MWh)\", xy=(maxh,max_gen), xytext=(maxh+1, max_gen+1000),\n",
        "#              arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)\n",
        "    \n",
        "    ax.set_ylim(bottom = 0,top=39000)\n",
        "    \n",
        "ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-06-07\n",
            "2020-07-07\n",
            "2020-08-07\n",
            "2020-09-07\n",
            "2020-10-07\n",
            "2020-11-07\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb36478b090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.028403Z",
          "iopub.execute_input": "2021-05-31T04:18:40.028846Z",
          "iopub.status.idle": "2021-05-31T04:18:40.034085Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.028799Z",
          "shell.execute_reply": "2021-05-31T04:18:40.032868Z"
        },
        "trusted": true,
        "id": "rt32s4uesUnJ"
      },
      "source": [
        "# # Line Chart ( Trend during the day)\n",
        "# plt = df.plot.line(x='HOURS',\n",
        "#                     y = column_list,\n",
        "#                     title = \"Generation in \"+ Region + \" on \"+ Date)\n",
        "# plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.035259Z",
          "iopub.execute_input": "2021-05-31T04:18:40.035554Z",
          "iopub.status.idle": "2021-05-31T04:18:40.053470Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.035500Z",
          "shell.execute_reply": "2021-05-31T04:18:40.052452Z"
        },
        "trusted": true,
        "id": "ZSFHpE2ysUnJ"
      },
      "source": [
        "# usage in one day \n",
        "# df_nw.plot(x='HOURS',  kind='hist')\n",
        "\n",
        "# df_nw.plot(x='HOURS', y=[\"Wind Generation (MWh)\",\"Solar Generation (MWh)\",\"Hydro Generation (MWh)\",\"Other Generation (MWh)\", \n",
        "#                         \"Petroleum Generation (MWh)\", \"Natural gas Generation (MWh)\",\"Coal Generation (MWh)\",\"Nuclear Generation (MWh)\"], kind='hist')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzEkYyxUsUnJ"
      },
      "source": [
        "## Peak Hour"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.054412Z",
          "iopub.execute_input": "2021-05-31T04:18:40.054706Z",
          "iopub.status.idle": "2021-05-31T04:18:40.080166Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.054677Z",
          "shell.execute_reply": "2021-05-31T04:18:40.079247Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjfBq1sJsUnJ",
        "outputId": "513bad61-8951-4399-d0f0-69657b7cb3f8"
      },
      "source": [
        "## Peak Min\n",
        "peakmin = df[df[\"total_generation\"] == df[\"total_generation\"].max()]\n",
        "peakmin = peakmin.reset_index(drop=True)\n",
        "peakmin"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Oil</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>Timestamp (Unix)</th>\n",
              "      <th>total_generation(W)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>20282.0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>2271.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:45:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:45:00</td>\n",
              "      <td>17</td>\n",
              "      <td>45</td>\n",
              "      <td>1065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37956.0</td>\n",
              "      <td>1604771100</td>\n",
              "      <td>3.795600e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Solar  NaturalGas  ...  Timestamp (Unix)  total_generation(W)\n",
              "0  11872.0     20282.0  ...        1604771100         3.795600e+10\n",
              "\n",
              "[1 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.081402Z",
          "iopub.execute_input": "2021-05-31T04:18:40.081696Z",
          "iopub.status.idle": "2021-05-31T04:18:40.137595Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.081669Z",
          "shell.execute_reply": "2021-05-31T04:18:40.136242Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kC3mM2E8sUnJ",
        "outputId": "cdce172c-9c9e-4bd5-ef0a-d3b617ae0fd0"
      },
      "source": [
        "## Peak Hour\n",
        "# df_nw= df_nw[df_nw[\"DATE\"].astype(str) == Date]\n",
        "peakhour = df[(df['HOURS'].values==peakmin.HOURS.values) & (df[\"DATE\"].values==peakmin.DATE.values) ]\n",
        "peakhour"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Solar</th>\n",
              "      <th>NaturalGas</th>\n",
              "      <th>Hydro</th>\n",
              "      <th>Nuclear</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Other</th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>DATE</th>\n",
              "      <th>TIME</th>\n",
              "      <th>HOURS</th>\n",
              "      <th>MINUTES</th>\n",
              "      <th>MINUTES_PASS</th>\n",
              "      <th>Wind</th>\n",
              "      <th>Oil</th>\n",
              "      <th>total_generation</th>\n",
              "      <th>Timestamp (Unix)</th>\n",
              "      <th>total_generation(W)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>12935.0</td>\n",
              "      <td>19138.0</td>\n",
              "      <td>3191.0</td>\n",
              "      <td>2271.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:00:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:00:00</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>1020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37546.0</td>\n",
              "      <td>1604768400</td>\n",
              "      <td>3.754600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1104</th>\n",
              "      <td>12920.8</td>\n",
              "      <td>19187.4</td>\n",
              "      <td>3162.2</td>\n",
              "      <td>2270.8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:01:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:01:00</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>1021</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37552.2</td>\n",
              "      <td>1604768460</td>\n",
              "      <td>3.755220e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>12906.6</td>\n",
              "      <td>19236.8</td>\n",
              "      <td>3133.4</td>\n",
              "      <td>2270.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:02:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:02:00</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>1022</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37558.4</td>\n",
              "      <td>1604768520</td>\n",
              "      <td>3.755840e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1106</th>\n",
              "      <td>12892.4</td>\n",
              "      <td>19286.2</td>\n",
              "      <td>3104.6</td>\n",
              "      <td>2270.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:03:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:03:00</td>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "      <td>1023</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37564.6</td>\n",
              "      <td>1604768580</td>\n",
              "      <td>3.756460e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1107</th>\n",
              "      <td>12878.2</td>\n",
              "      <td>19335.6</td>\n",
              "      <td>3075.8</td>\n",
              "      <td>2270.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:04:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:04:00</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "      <td>1024</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37570.8</td>\n",
              "      <td>1604768640</td>\n",
              "      <td>3.757080e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>12864.0</td>\n",
              "      <td>19385.0</td>\n",
              "      <td>3047.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:05:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:05:00</td>\n",
              "      <td>17</td>\n",
              "      <td>5</td>\n",
              "      <td>1025</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37577.0</td>\n",
              "      <td>1604768700</td>\n",
              "      <td>3.757700e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1108</th>\n",
              "      <td>12840.0</td>\n",
              "      <td>19435.6</td>\n",
              "      <td>3032.8</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:06:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:06:00</td>\n",
              "      <td>17</td>\n",
              "      <td>6</td>\n",
              "      <td>1026</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37589.2</td>\n",
              "      <td>1604768760</td>\n",
              "      <td>3.758920e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1109</th>\n",
              "      <td>12816.0</td>\n",
              "      <td>19486.2</td>\n",
              "      <td>3018.6</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:07:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:07:00</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>1027</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37601.4</td>\n",
              "      <td>1604768820</td>\n",
              "      <td>3.760140e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1110</th>\n",
              "      <td>12792.0</td>\n",
              "      <td>19536.8</td>\n",
              "      <td>3004.4</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:08:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:08:00</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>1028</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37613.6</td>\n",
              "      <td>1604768880</td>\n",
              "      <td>3.761360e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1111</th>\n",
              "      <td>12768.0</td>\n",
              "      <td>19587.4</td>\n",
              "      <td>2990.2</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:09:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:09:00</td>\n",
              "      <td>17</td>\n",
              "      <td>9</td>\n",
              "      <td>1029</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37625.8</td>\n",
              "      <td>1604768940</td>\n",
              "      <td>3.762580e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>12744.0</td>\n",
              "      <td>19638.0</td>\n",
              "      <td>2976.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:10:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:10:00</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>1030</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37638.0</td>\n",
              "      <td>1604769000</td>\n",
              "      <td>3.763800e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1112</th>\n",
              "      <td>12720.2</td>\n",
              "      <td>19651.6</td>\n",
              "      <td>2981.4</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:11:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:11:00</td>\n",
              "      <td>17</td>\n",
              "      <td>11</td>\n",
              "      <td>1031</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37633.4</td>\n",
              "      <td>1604769060</td>\n",
              "      <td>3.763340e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1113</th>\n",
              "      <td>12696.4</td>\n",
              "      <td>19665.2</td>\n",
              "      <td>2986.8</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:12:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:12:00</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>1032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37628.8</td>\n",
              "      <td>1604769120</td>\n",
              "      <td>3.762880e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1114</th>\n",
              "      <td>12672.6</td>\n",
              "      <td>19678.8</td>\n",
              "      <td>2992.2</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:13:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:13:00</td>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>1033</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37624.2</td>\n",
              "      <td>1604769180</td>\n",
              "      <td>3.762420e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1115</th>\n",
              "      <td>12648.8</td>\n",
              "      <td>19692.4</td>\n",
              "      <td>2997.6</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:14:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:14:00</td>\n",
              "      <td>17</td>\n",
              "      <td>14</td>\n",
              "      <td>1034</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37619.6</td>\n",
              "      <td>1604769240</td>\n",
              "      <td>3.761960e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>12625.0</td>\n",
              "      <td>19706.0</td>\n",
              "      <td>3003.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:15:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:15:00</td>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>1035</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37615.0</td>\n",
              "      <td>1604769300</td>\n",
              "      <td>3.761500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1116</th>\n",
              "      <td>12600.6</td>\n",
              "      <td>19717.0</td>\n",
              "      <td>3013.4</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:16:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:16:00</td>\n",
              "      <td>17</td>\n",
              "      <td>16</td>\n",
              "      <td>1036</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37612.0</td>\n",
              "      <td>1604769360</td>\n",
              "      <td>3.761200e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1117</th>\n",
              "      <td>12576.2</td>\n",
              "      <td>19728.0</td>\n",
              "      <td>3023.8</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:17:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:17:00</td>\n",
              "      <td>17</td>\n",
              "      <td>17</td>\n",
              "      <td>1037</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37609.0</td>\n",
              "      <td>1604769420</td>\n",
              "      <td>3.760900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1118</th>\n",
              "      <td>12551.8</td>\n",
              "      <td>19739.0</td>\n",
              "      <td>3034.2</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:18:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:18:00</td>\n",
              "      <td>17</td>\n",
              "      <td>18</td>\n",
              "      <td>1038</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37606.0</td>\n",
              "      <td>1604769480</td>\n",
              "      <td>3.760600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1119</th>\n",
              "      <td>12527.4</td>\n",
              "      <td>19750.0</td>\n",
              "      <td>3044.6</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:19:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:19:00</td>\n",
              "      <td>17</td>\n",
              "      <td>19</td>\n",
              "      <td>1039</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37603.0</td>\n",
              "      <td>1604769540</td>\n",
              "      <td>3.760300e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>12503.0</td>\n",
              "      <td>19761.0</td>\n",
              "      <td>3055.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:20:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:20:00</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>1040</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37600.0</td>\n",
              "      <td>1604769600</td>\n",
              "      <td>3.760000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1120</th>\n",
              "      <td>12483.2</td>\n",
              "      <td>19774.0</td>\n",
              "      <td>3077.6</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:21:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:21:00</td>\n",
              "      <td>17</td>\n",
              "      <td>21</td>\n",
              "      <td>1041</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37615.8</td>\n",
              "      <td>1604769660</td>\n",
              "      <td>3.761580e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1121</th>\n",
              "      <td>12463.4</td>\n",
              "      <td>19787.0</td>\n",
              "      <td>3100.2</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:22:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:22:00</td>\n",
              "      <td>17</td>\n",
              "      <td>22</td>\n",
              "      <td>1042</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37631.6</td>\n",
              "      <td>1604769720</td>\n",
              "      <td>3.763160e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1122</th>\n",
              "      <td>12443.6</td>\n",
              "      <td>19800.0</td>\n",
              "      <td>3122.8</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:23:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:23:00</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>1043</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37647.4</td>\n",
              "      <td>1604769780</td>\n",
              "      <td>3.764740e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1123</th>\n",
              "      <td>12423.8</td>\n",
              "      <td>19813.0</td>\n",
              "      <td>3145.4</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:24:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:24:00</td>\n",
              "      <td>17</td>\n",
              "      <td>24</td>\n",
              "      <td>1044</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37663.2</td>\n",
              "      <td>1604769840</td>\n",
              "      <td>3.766320e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>12404.0</td>\n",
              "      <td>19826.0</td>\n",
              "      <td>3168.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:25:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:25:00</td>\n",
              "      <td>17</td>\n",
              "      <td>25</td>\n",
              "      <td>1045</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604769900</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1124</th>\n",
              "      <td>12380.2</td>\n",
              "      <td>19831.8</td>\n",
              "      <td>3186.2</td>\n",
              "      <td>2269.8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:26:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:26:00</td>\n",
              "      <td>17</td>\n",
              "      <td>26</td>\n",
              "      <td>1046</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604769960</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1125</th>\n",
              "      <td>12356.4</td>\n",
              "      <td>19837.6</td>\n",
              "      <td>3204.4</td>\n",
              "      <td>2269.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:27:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:27:00</td>\n",
              "      <td>17</td>\n",
              "      <td>27</td>\n",
              "      <td>1047</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604770020</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1126</th>\n",
              "      <td>12332.6</td>\n",
              "      <td>19843.4</td>\n",
              "      <td>3222.6</td>\n",
              "      <td>2269.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:28:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:28:00</td>\n",
              "      <td>17</td>\n",
              "      <td>28</td>\n",
              "      <td>1048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604770080</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1127</th>\n",
              "      <td>12308.8</td>\n",
              "      <td>19849.2</td>\n",
              "      <td>3240.8</td>\n",
              "      <td>2269.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:29:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:29:00</td>\n",
              "      <td>17</td>\n",
              "      <td>29</td>\n",
              "      <td>1049</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604770140</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>210</th>\n",
              "      <td>12285.0</td>\n",
              "      <td>19855.0</td>\n",
              "      <td>3259.0</td>\n",
              "      <td>2269.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:30:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:30:00</td>\n",
              "      <td>17</td>\n",
              "      <td>30</td>\n",
              "      <td>1050</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37679.0</td>\n",
              "      <td>1604770200</td>\n",
              "      <td>3.767900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>12258.6</td>\n",
              "      <td>19877.2</td>\n",
              "      <td>3266.2</td>\n",
              "      <td>2269.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:31:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:31:00</td>\n",
              "      <td>17</td>\n",
              "      <td>31</td>\n",
              "      <td>1051</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37682.2</td>\n",
              "      <td>1604770260</td>\n",
              "      <td>3.768220e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1129</th>\n",
              "      <td>12232.2</td>\n",
              "      <td>19899.4</td>\n",
              "      <td>3273.4</td>\n",
              "      <td>2269.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:32:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:32:00</td>\n",
              "      <td>17</td>\n",
              "      <td>32</td>\n",
              "      <td>1052</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37685.4</td>\n",
              "      <td>1604770320</td>\n",
              "      <td>3.768540e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1130</th>\n",
              "      <td>12205.8</td>\n",
              "      <td>19921.6</td>\n",
              "      <td>3280.6</td>\n",
              "      <td>2269.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:33:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:33:00</td>\n",
              "      <td>17</td>\n",
              "      <td>33</td>\n",
              "      <td>1053</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37688.6</td>\n",
              "      <td>1604770380</td>\n",
              "      <td>3.768860e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1131</th>\n",
              "      <td>12179.4</td>\n",
              "      <td>19943.8</td>\n",
              "      <td>3287.8</td>\n",
              "      <td>2269.8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:34:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:34:00</td>\n",
              "      <td>17</td>\n",
              "      <td>34</td>\n",
              "      <td>1054</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37691.8</td>\n",
              "      <td>1604770440</td>\n",
              "      <td>3.769180e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>211</th>\n",
              "      <td>12153.0</td>\n",
              "      <td>19966.0</td>\n",
              "      <td>3295.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:35:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:35:00</td>\n",
              "      <td>17</td>\n",
              "      <td>35</td>\n",
              "      <td>1055</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37695.0</td>\n",
              "      <td>1604770500</td>\n",
              "      <td>3.769500e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1132</th>\n",
              "      <td>12125.6</td>\n",
              "      <td>20006.6</td>\n",
              "      <td>3318.8</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:36:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:36:00</td>\n",
              "      <td>17</td>\n",
              "      <td>36</td>\n",
              "      <td>1056</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37731.8</td>\n",
              "      <td>1604770560</td>\n",
              "      <td>3.773180e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1133</th>\n",
              "      <td>12098.2</td>\n",
              "      <td>20047.2</td>\n",
              "      <td>3342.6</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:37:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:37:00</td>\n",
              "      <td>17</td>\n",
              "      <td>37</td>\n",
              "      <td>1057</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37768.6</td>\n",
              "      <td>1604770620</td>\n",
              "      <td>3.776860e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1134</th>\n",
              "      <td>12070.8</td>\n",
              "      <td>20087.8</td>\n",
              "      <td>3366.4</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:38:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:38:00</td>\n",
              "      <td>17</td>\n",
              "      <td>38</td>\n",
              "      <td>1058</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37805.4</td>\n",
              "      <td>1604770680</td>\n",
              "      <td>3.780540e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1135</th>\n",
              "      <td>12043.4</td>\n",
              "      <td>20128.4</td>\n",
              "      <td>3390.2</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:39:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:39:00</td>\n",
              "      <td>17</td>\n",
              "      <td>39</td>\n",
              "      <td>1059</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37842.2</td>\n",
              "      <td>1604770740</td>\n",
              "      <td>3.784220e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>12016.0</td>\n",
              "      <td>20169.0</td>\n",
              "      <td>3414.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:40:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:40:00</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>1060</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37879.0</td>\n",
              "      <td>1604770800</td>\n",
              "      <td>3.787900e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1136</th>\n",
              "      <td>11987.2</td>\n",
              "      <td>20191.6</td>\n",
              "      <td>3435.4</td>\n",
              "      <td>2270.2</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:41:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:41:00</td>\n",
              "      <td>17</td>\n",
              "      <td>41</td>\n",
              "      <td>1061</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37894.4</td>\n",
              "      <td>1604770860</td>\n",
              "      <td>3.789440e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1137</th>\n",
              "      <td>11958.4</td>\n",
              "      <td>20214.2</td>\n",
              "      <td>3456.8</td>\n",
              "      <td>2270.4</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:42:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:42:00</td>\n",
              "      <td>17</td>\n",
              "      <td>42</td>\n",
              "      <td>1062</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37909.8</td>\n",
              "      <td>1604770920</td>\n",
              "      <td>3.790980e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1138</th>\n",
              "      <td>11929.6</td>\n",
              "      <td>20236.8</td>\n",
              "      <td>3478.2</td>\n",
              "      <td>2270.6</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:43:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:43:00</td>\n",
              "      <td>17</td>\n",
              "      <td>43</td>\n",
              "      <td>1063</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37925.2</td>\n",
              "      <td>1604770980</td>\n",
              "      <td>3.792520e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1139</th>\n",
              "      <td>11900.8</td>\n",
              "      <td>20259.4</td>\n",
              "      <td>3499.6</td>\n",
              "      <td>2270.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:44:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:44:00</td>\n",
              "      <td>17</td>\n",
              "      <td>44</td>\n",
              "      <td>1064</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37940.6</td>\n",
              "      <td>1604771040</td>\n",
              "      <td>3.794060e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>20282.0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>2271.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:45:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:45:00</td>\n",
              "      <td>17</td>\n",
              "      <td>45</td>\n",
              "      <td>1065</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37956.0</td>\n",
              "      <td>1604771100</td>\n",
              "      <td>3.795600e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1140</th>\n",
              "      <td>11839.6</td>\n",
              "      <td>20289.0</td>\n",
              "      <td>3520.6</td>\n",
              "      <td>2270.8</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:46:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:46:00</td>\n",
              "      <td>17</td>\n",
              "      <td>46</td>\n",
              "      <td>1066</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37930.2</td>\n",
              "      <td>1604771160</td>\n",
              "      <td>3.793020e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1141</th>\n",
              "      <td>11807.2</td>\n",
              "      <td>20296.0</td>\n",
              "      <td>3520.2</td>\n",
              "      <td>2270.6</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:47:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:47:00</td>\n",
              "      <td>17</td>\n",
              "      <td>47</td>\n",
              "      <td>1067</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37904.4</td>\n",
              "      <td>1604771220</td>\n",
              "      <td>3.790440e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>11774.8</td>\n",
              "      <td>20303.0</td>\n",
              "      <td>3519.8</td>\n",
              "      <td>2270.4</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:48:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:48:00</td>\n",
              "      <td>17</td>\n",
              "      <td>48</td>\n",
              "      <td>1068</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37878.6</td>\n",
              "      <td>1604771280</td>\n",
              "      <td>3.787860e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1143</th>\n",
              "      <td>11742.4</td>\n",
              "      <td>20310.0</td>\n",
              "      <td>3519.4</td>\n",
              "      <td>2270.2</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:49:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:49:00</td>\n",
              "      <td>17</td>\n",
              "      <td>49</td>\n",
              "      <td>1069</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37852.8</td>\n",
              "      <td>1604771340</td>\n",
              "      <td>3.785280e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>11710.0</td>\n",
              "      <td>20317.0</td>\n",
              "      <td>3519.0</td>\n",
              "      <td>2270.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:50:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:50:00</td>\n",
              "      <td>17</td>\n",
              "      <td>50</td>\n",
              "      <td>1070</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37827.0</td>\n",
              "      <td>1604771400</td>\n",
              "      <td>3.782700e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>11671.8</td>\n",
              "      <td>20325.6</td>\n",
              "      <td>3527.6</td>\n",
              "      <td>2269.8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:51:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:51:00</td>\n",
              "      <td>17</td>\n",
              "      <td>51</td>\n",
              "      <td>1071</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37805.8</td>\n",
              "      <td>1604771460</td>\n",
              "      <td>3.780580e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1145</th>\n",
              "      <td>11633.6</td>\n",
              "      <td>20334.2</td>\n",
              "      <td>3536.2</td>\n",
              "      <td>2269.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:52:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:52:00</td>\n",
              "      <td>17</td>\n",
              "      <td>52</td>\n",
              "      <td>1072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37784.6</td>\n",
              "      <td>1604771520</td>\n",
              "      <td>3.778460e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1146</th>\n",
              "      <td>11595.4</td>\n",
              "      <td>20342.8</td>\n",
              "      <td>3544.8</td>\n",
              "      <td>2269.4</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:53:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:53:00</td>\n",
              "      <td>17</td>\n",
              "      <td>53</td>\n",
              "      <td>1073</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37763.4</td>\n",
              "      <td>1604771580</td>\n",
              "      <td>3.776340e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1147</th>\n",
              "      <td>11557.2</td>\n",
              "      <td>20351.4</td>\n",
              "      <td>3553.4</td>\n",
              "      <td>2269.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:54:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:54:00</td>\n",
              "      <td>17</td>\n",
              "      <td>54</td>\n",
              "      <td>1074</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37742.2</td>\n",
              "      <td>1604771640</td>\n",
              "      <td>3.774220e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>215</th>\n",
              "      <td>11519.0</td>\n",
              "      <td>20360.0</td>\n",
              "      <td>3562.0</td>\n",
              "      <td>2269.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:55:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:55:00</td>\n",
              "      <td>17</td>\n",
              "      <td>55</td>\n",
              "      <td>1075</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37721.0</td>\n",
              "      <td>1604771700</td>\n",
              "      <td>3.772100e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1148</th>\n",
              "      <td>11477.0</td>\n",
              "      <td>20402.6</td>\n",
              "      <td>3557.6</td>\n",
              "      <td>2269.2</td>\n",
              "      <td>10.8</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:56:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:56:00</td>\n",
              "      <td>17</td>\n",
              "      <td>56</td>\n",
              "      <td>1076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37717.2</td>\n",
              "      <td>1604771760</td>\n",
              "      <td>3.771720e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1149</th>\n",
              "      <td>11435.0</td>\n",
              "      <td>20445.2</td>\n",
              "      <td>3553.2</td>\n",
              "      <td>2269.4</td>\n",
              "      <td>10.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:57:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:57:00</td>\n",
              "      <td>17</td>\n",
              "      <td>57</td>\n",
              "      <td>1077</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37713.4</td>\n",
              "      <td>1604771820</td>\n",
              "      <td>3.771340e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1150</th>\n",
              "      <td>11393.0</td>\n",
              "      <td>20487.8</td>\n",
              "      <td>3548.8</td>\n",
              "      <td>2269.6</td>\n",
              "      <td>10.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:58:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:58:00</td>\n",
              "      <td>17</td>\n",
              "      <td>58</td>\n",
              "      <td>1078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37709.6</td>\n",
              "      <td>1604771880</td>\n",
              "      <td>3.770960e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1151</th>\n",
              "      <td>11351.0</td>\n",
              "      <td>20530.4</td>\n",
              "      <td>3544.4</td>\n",
              "      <td>2269.8</td>\n",
              "      <td>10.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2020-11-07 17:59:00</td>\n",
              "      <td>2020-11-07</td>\n",
              "      <td>17:59:00</td>\n",
              "      <td>17</td>\n",
              "      <td>59</td>\n",
              "      <td>1079</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>37705.8</td>\n",
              "      <td>1604771940</td>\n",
              "      <td>3.770580e+10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Solar  NaturalGas  ...  Timestamp (Unix)  total_generation(W)\n",
              "204   12935.0     19138.0  ...        1604768400         3.754600e+10\n",
              "1104  12920.8     19187.4  ...        1604768460         3.755220e+10\n",
              "1105  12906.6     19236.8  ...        1604768520         3.755840e+10\n",
              "1106  12892.4     19286.2  ...        1604768580         3.756460e+10\n",
              "1107  12878.2     19335.6  ...        1604768640         3.757080e+10\n",
              "205   12864.0     19385.0  ...        1604768700         3.757700e+10\n",
              "1108  12840.0     19435.6  ...        1604768760         3.758920e+10\n",
              "1109  12816.0     19486.2  ...        1604768820         3.760140e+10\n",
              "1110  12792.0     19536.8  ...        1604768880         3.761360e+10\n",
              "1111  12768.0     19587.4  ...        1604768940         3.762580e+10\n",
              "206   12744.0     19638.0  ...        1604769000         3.763800e+10\n",
              "1112  12720.2     19651.6  ...        1604769060         3.763340e+10\n",
              "1113  12696.4     19665.2  ...        1604769120         3.762880e+10\n",
              "1114  12672.6     19678.8  ...        1604769180         3.762420e+10\n",
              "1115  12648.8     19692.4  ...        1604769240         3.761960e+10\n",
              "207   12625.0     19706.0  ...        1604769300         3.761500e+10\n",
              "1116  12600.6     19717.0  ...        1604769360         3.761200e+10\n",
              "1117  12576.2     19728.0  ...        1604769420         3.760900e+10\n",
              "1118  12551.8     19739.0  ...        1604769480         3.760600e+10\n",
              "1119  12527.4     19750.0  ...        1604769540         3.760300e+10\n",
              "208   12503.0     19761.0  ...        1604769600         3.760000e+10\n",
              "1120  12483.2     19774.0  ...        1604769660         3.761580e+10\n",
              "1121  12463.4     19787.0  ...        1604769720         3.763160e+10\n",
              "1122  12443.6     19800.0  ...        1604769780         3.764740e+10\n",
              "1123  12423.8     19813.0  ...        1604769840         3.766320e+10\n",
              "209   12404.0     19826.0  ...        1604769900         3.767900e+10\n",
              "1124  12380.2     19831.8  ...        1604769960         3.767900e+10\n",
              "1125  12356.4     19837.6  ...        1604770020         3.767900e+10\n",
              "1126  12332.6     19843.4  ...        1604770080         3.767900e+10\n",
              "1127  12308.8     19849.2  ...        1604770140         3.767900e+10\n",
              "210   12285.0     19855.0  ...        1604770200         3.767900e+10\n",
              "1128  12258.6     19877.2  ...        1604770260         3.768220e+10\n",
              "1129  12232.2     19899.4  ...        1604770320         3.768540e+10\n",
              "1130  12205.8     19921.6  ...        1604770380         3.768860e+10\n",
              "1131  12179.4     19943.8  ...        1604770440         3.769180e+10\n",
              "211   12153.0     19966.0  ...        1604770500         3.769500e+10\n",
              "1132  12125.6     20006.6  ...        1604770560         3.773180e+10\n",
              "1133  12098.2     20047.2  ...        1604770620         3.776860e+10\n",
              "1134  12070.8     20087.8  ...        1604770680         3.780540e+10\n",
              "1135  12043.4     20128.4  ...        1604770740         3.784220e+10\n",
              "212   12016.0     20169.0  ...        1604770800         3.787900e+10\n",
              "1136  11987.2     20191.6  ...        1604770860         3.789440e+10\n",
              "1137  11958.4     20214.2  ...        1604770920         3.790980e+10\n",
              "1138  11929.6     20236.8  ...        1604770980         3.792520e+10\n",
              "1139  11900.8     20259.4  ...        1604771040         3.794060e+10\n",
              "213   11872.0     20282.0  ...        1604771100         3.795600e+10\n",
              "1140  11839.6     20289.0  ...        1604771160         3.793020e+10\n",
              "1141  11807.2     20296.0  ...        1604771220         3.790440e+10\n",
              "1142  11774.8     20303.0  ...        1604771280         3.787860e+10\n",
              "1143  11742.4     20310.0  ...        1604771340         3.785280e+10\n",
              "214   11710.0     20317.0  ...        1604771400         3.782700e+10\n",
              "1144  11671.8     20325.6  ...        1604771460         3.780580e+10\n",
              "1145  11633.6     20334.2  ...        1604771520         3.778460e+10\n",
              "1146  11595.4     20342.8  ...        1604771580         3.776340e+10\n",
              "1147  11557.2     20351.4  ...        1604771640         3.774220e+10\n",
              "215   11519.0     20360.0  ...        1604771700         3.772100e+10\n",
              "1148  11477.0     20402.6  ...        1604771760         3.771720e+10\n",
              "1149  11435.0     20445.2  ...        1604771820         3.771340e+10\n",
              "1150  11393.0     20487.8  ...        1604771880         3.770960e+10\n",
              "1151  11351.0     20530.4  ...        1604771940         3.770580e+10\n",
              "\n",
              "[60 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.139144Z",
          "iopub.execute_input": "2021-05-31T04:18:40.139439Z",
          "iopub.status.idle": "2021-05-31T04:18:40.149293Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.139411Z",
          "shell.execute_reply": "2021-05-31T04:18:40.148329Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3buCSjYBsUnK",
        "outputId": "02713486-d727-4c85-dcad-e5912e2ce251"
      },
      "source": [
        "## Average generation \n",
        "average = peakhour[\"total_generation\"].mean()\n",
        "average"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37706.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.150429Z",
          "iopub.execute_input": "2021-05-31T04:18:40.150828Z",
          "iopub.status.idle": "2021-05-31T04:18:40.340747Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.150787Z",
          "shell.execute_reply": "2021-05-31T04:18:40.339604Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRBsQkRzsUnK",
        "outputId": "c0aabbd8-b193-4753-baf4-e336c09e9986"
      },
      "source": [
        "peakhour.plot(y = ['Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal'])\n",
        "ax.hlines(23, -.5,.5, linestyles='dashed')\n",
        "ax.annotate('average',(-0.4, average))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(-0.4, 37706.2, 'average')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8uj2qYpsUnK"
      },
      "source": [
        "pick value in order off release fromm peakhour"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.342028Z",
          "iopub.execute_input": "2021-05-31T04:18:40.342303Z",
          "iopub.status.idle": "2021-05-31T04:18:40.355972Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.342277Z",
          "shell.execute_reply": "2021-05-31T04:18:40.354860Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVeJgvxOsUnK",
        "outputId": "bfdedf1e-a8fb-4bac-dc03-d8da113e60d5"
      },
      "source": [
        "# peakhour =  peakhour.iloc[: , column_list].copy()  \n",
        "peakt= peakmin[['Wind', 'Solar', 'Hydro', 'Other', 'Oil', 'NaturalGas', 'Coal', 'Nuclear']].transpose()\n",
        "\n",
        "# # peakhourt=peakhour2.reindex([\"Nuclear Generation (MW)\", \"Wind Generation (MW)\", \"Solar Generation (MW)\",\"Hydro Generation (MW)\" ,  \n",
        "# #                          \"Coal Generation (MW)\", \"Natural gas Generation (MW)\",\"Petroleum Generation (MW)\",\"Other Generation (MW)\"])\n",
        "\n",
        "for col in peakt.columns:\n",
        "    peakt = peakt.rename(columns={col:'generation'})\n",
        "    \n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation\n",
              "Wind               0.0\n",
              "Solar          11872.0\n",
              "Hydro           3521.0\n",
              "Other              0.0\n",
              "Oil                0.0\n",
              "NaturalGas     20282.0\n",
              "Coal              10.0\n",
              "Nuclear         2271.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.357470Z",
          "iopub.execute_input": "2021-05-31T04:18:40.357797Z",
          "iopub.status.idle": "2021-05-31T04:18:40.374637Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.357768Z",
          "shell.execute_reply": "2021-05-31T04:18:40.373657Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rz-42JysUnL",
        "outputId": "71a6113a-95a8-40b8-d888-62f897735102"
      },
      "source": [
        "# pie chart Percentage for Contribution by fuel during peak hour\n",
        "peakt[\"percent\"] = (peakt['generation'] / peakt['generation'].sum()) * 100\n",
        "peakt[\"percent\"] = peakt[\"percent\"].round(1)\n",
        "\n",
        "# peakt = peakt.mask(peakhour2< 0, 0)\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>31.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "      <td>9.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "      <td>53.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation  percent\n",
              "Wind               0.0      0.0\n",
              "Solar          11872.0     31.3\n",
              "Hydro           3521.0      9.3\n",
              "Other              0.0      0.0\n",
              "Oil                0.0      0.0\n",
              "NaturalGas     20282.0     53.4\n",
              "Coal              10.0      0.0\n",
              "Nuclear         2271.0      6.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.376041Z",
          "iopub.execute_input": "2021-05-31T04:18:40.376312Z",
          "iopub.status.idle": "2021-05-31T04:18:40.523840Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.376285Z",
          "shell.execute_reply": "2021-05-31T04:18:40.522865Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqOM70ApsUnL",
        "outputId": "4d0590c7-f38f-46fb-de27-4a2abfb3478d"
      },
      "source": [
        "peakt[\"generation\"].plot.bar(stacked=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb36465f950>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.524945Z",
          "iopub.execute_input": "2021-05-31T04:18:40.525182Z",
          "iopub.status.idle": "2021-05-31T04:18:40.629403Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.525159Z",
          "shell.execute_reply": "2021-05-31T04:18:40.628267Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vgb0eRVSsUnL",
        "outputId": "11380ba9-91a8-47f3-d591-7d5b5774e4cf"
      },
      "source": [
        "pie_peakhour= peakt.plot.pie(y='percent',title=\"Fuel Mix in \"+ Region + \" on \"+ str(peakmin.DATE.values[0])+\" \"+str(peakmin.Timestamp.values[0]) , figsize=(5, 5))\n",
        "pie_peakhour.legend(bbox_to_anchor=(1,1), loc=\"centre right\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb3645d9110>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.630986Z",
          "iopub.execute_input": "2021-05-31T04:18:40.631360Z",
          "iopub.status.idle": "2021-05-31T04:18:40.637039Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.631318Z",
          "shell.execute_reply": "2021-05-31T04:18:40.636056Z"
        },
        "trusted": true,
        "id": "P2PGjcZtsUnL"
      },
      "source": [
        "peakt = peakt.drop(columns='percent')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.638458Z",
          "iopub.execute_input": "2021-05-31T04:18:40.639001Z",
          "iopub.status.idle": "2021-05-31T04:18:40.648972Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.638958Z",
          "shell.execute_reply": "2021-05-31T04:18:40.647804Z"
        },
        "trusted": true,
        "id": "kudPn8JDsUnM"
      },
      "source": [
        "# Cummulative_generation\n",
        "# peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.650421Z",
          "iopub.execute_input": "2021-05-31T04:18:40.650811Z",
          "iopub.status.idle": "2021-05-31T04:18:40.662347Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.650783Z",
          "shell.execute_reply": "2021-05-31T04:18:40.661360Z"
        },
        "trusted": true,
        "id": "r49XSiybsUnM"
      },
      "source": [
        "# peakt.plot(x=\"Cummulative_generation\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.663378Z",
          "iopub.execute_input": "2021-05-31T04:18:40.663755Z",
          "iopub.status.idle": "2021-05-31T04:18:40.673509Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.663728Z",
          "shell.execute_reply": "2021-05-31T04:18:40.672732Z"
        },
        "trusted": true,
        "id": "FsC1W_YGsUnM"
      },
      "source": [
        "# draw graph for cummultaive consumption \n",
        "# peakhour2.plot(\"Cummulative_consumption\")\n",
        "\n",
        "# peakhour2.plot.bar(peakhour2[\"Cummulative_consumption\"], drawstyle='steps')\n",
        "# peakhour2.reset_index().plot.line(peakhour2[\"Cummulative_consumption\"], peakhour2.index, drawstyle='steps')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qznqWGQRsUnM"
      },
      "source": [
        "<a id=\"section-five\"></a>\n",
        "# 5. Carbon Emission vs Fuel\n",
        "\n",
        "Now merge dataset and look at data distributions and correlations for Co2 and fuel sources "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.675088Z",
          "iopub.execute_input": "2021-05-31T04:18:40.675680Z",
          "iopub.status.idle": "2021-05-31T04:18:40.700110Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.675634Z",
          "shell.execute_reply": "2021-05-31T04:18:40.698976Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "anNkkGWgsUnM",
        "outputId": "a8302ac0-7efb-47e5-8b26-72f14797ad1e"
      },
      "source": [
        "df_carbon = pd.read_csv(\"/content/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Carbon2.csv\")\n",
        "df_carbon"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Fuel Type</th>\n",
              "      <th>Life cycle CO2 equivalent  ( kgCO2eq/MWh)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nuclear</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Solar</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Other</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Petroleum</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Oil</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Natural gas</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaturalGas</td>\n",
              "      <td>490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Coal</td>\n",
              "      <td>820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hydro</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Fuel Type  Life cycle CO2 equivalent  ( kgCO2eq/MWh)\n",
              "0      Nuclear                                         12\n",
              "1        Solar                                         48\n",
              "2        Other                                         24\n",
              "3    Petroleum                                        490\n",
              "4          Oil                                        490\n",
              "5  Natural gas                                        490\n",
              "6   NaturalGas                                        490\n",
              "7         Coal                                        820\n",
              "8        Hydro                                          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.703598Z",
          "iopub.execute_input": "2021-05-31T04:18:40.703939Z",
          "iopub.status.idle": "2021-05-31T04:18:40.722302Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.703907Z",
          "shell.execute_reply": "2021-05-31T04:18:40.721168Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3gTofI8QsUnN",
        "outputId": "00c8f039-7de8-4d8f-df84-5f1fd09e12f8"
      },
      "source": [
        "df_carbon.info()\n",
        "df_carbon['Life cycle CO2 equivalent  ( kgCO2eq/MWh)']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9 entries, 0 to 8\n",
            "Data columns (total 2 columns):\n",
            " #   Column                                     Non-Null Count  Dtype \n",
            "---  ------                                     --------------  ----- \n",
            " 0   Fuel Type                                  9 non-null      object\n",
            " 1   Life cycle CO2 equivalent  ( kgCO2eq/MWh)  9 non-null      int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 272.0+ bytes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     12\n",
              "1     48\n",
              "2     24\n",
              "3    490\n",
              "4    490\n",
              "5    490\n",
              "6    490\n",
              "7    820\n",
              "8      0\n",
              "Name: Life cycle CO2 equivalent  ( kgCO2eq/MWh), dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.724090Z",
          "iopub.execute_input": "2021-05-31T04:18:40.724449Z",
          "iopub.status.idle": "2021-05-31T04:18:40.887628Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.724419Z",
          "shell.execute_reply": "2021-05-31T04:18:40.886812Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "tCoShyHksUnN",
        "outputId": "639579aa-ea2c-496b-bfd6-efbd04b2019b"
      },
      "source": [
        "df_carbon.sort_values('Life cycle CO2 equivalent  ( kgCO2eq/MWh)',ascending=True).plot( x='Fuel Type',kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb36ee61d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsdcjEm2sUnN"
      },
      "source": [
        "### Time vs consumption"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.888595Z",
          "iopub.execute_input": "2021-05-31T04:18:40.888956Z",
          "iopub.status.idle": "2021-05-31T04:18:40.912032Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.888930Z",
          "shell.execute_reply": "2021-05-31T04:18:40.910815Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nz-saUCosUnN",
        "outputId": "23178b48-3dd5-465a-e529-71058dc9b22c"
      },
      "source": [
        "# for i, row in peakhour2.iterrows():\n",
        "#     print(i)\n",
        "            \n",
        "# for j,lcoe in df_lcoe.iterrows():\n",
        "#         print(lcoe.values[0])\n",
        "\n",
        "peakt['carbon']=0\n",
        "for i, row in peakt.iterrows():\n",
        "    for j,carbon in df_carbon.iterrows():\n",
        "        if i in carbon[0]:\n",
        "            print(\"Match between two tables for \"+i+ \" and \"+ carbon[0])\n",
        "            generation=row[0]\n",
        "            print(str(generation) + ' * '+ str(carbon[1]))\n",
        "            peakt.at[i,'carbon'] = generation * carbon[1]\n",
        "            \n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Match between two tables for Solar and Solar\n",
            "11872.0 * 48\n",
            "Match between two tables for Hydro and Hydro\n",
            "3521.0 * 0\n",
            "Match between two tables for Other and Other\n",
            "0.0 * 24\n",
            "Match between two tables for Oil and Oil\n",
            "0.0 * 490\n",
            "Match between two tables for NaturalGas and NaturalGas\n",
            "20282.0 * 490\n",
            "Match between two tables for Coal and Coal\n",
            "10.0 * 820\n",
            "Match between two tables for Nuclear and Nuclear\n",
            "2271.0 * 12\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "      <th>carbon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>569856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "      <td>9938180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "      <td>27252</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon\n",
              "Wind               0.0        0\n",
              "Solar          11872.0   569856\n",
              "Hydro           3521.0        0\n",
              "Other              0.0        0\n",
              "Oil                0.0        0\n",
              "NaturalGas     20282.0  9938180\n",
              "Coal              10.0     8200\n",
              "Nuclear         2271.0    27252"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.915347Z",
          "iopub.execute_input": "2021-05-31T04:18:40.915675Z",
          "iopub.status.idle": "2021-05-31T04:18:40.926334Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.915647Z",
          "shell.execute_reply": "2021-05-31T04:18:40.925291Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cF1eqURtsUnO",
        "outputId": "ebedb7f9-7c2b-42b9-e836-4d493648ff16"
      },
      "source": [
        "# sort by ascending order of carbon emission\n",
        "peakt = peakt.sort_values(by=['carbon'])\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "      <th>carbon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "      <td>27252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>569856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "      <td>9938180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon\n",
              "Wind               0.0        0\n",
              "Hydro           3521.0        0\n",
              "Other              0.0        0\n",
              "Oil                0.0        0\n",
              "Coal              10.0     8200\n",
              "Nuclear         2271.0    27252\n",
              "Solar          11872.0   569856\n",
              "NaturalGas     20282.0  9938180"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.928363Z",
          "iopub.execute_input": "2021-05-31T04:18:40.928849Z",
          "iopub.status.idle": "2021-05-31T04:18:40.936473Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.928803Z",
          "shell.execute_reply": "2021-05-31T04:18:40.935409Z"
        },
        "trusted": true,
        "id": "A4YsvAI0sUnO"
      },
      "source": [
        "# peakt[\"total_carbon(metric tons)\"] = (peakt[\"generation\"] * peakt[\"carbon\"]) / 1000\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.937818Z",
          "iopub.execute_input": "2021-05-31T04:18:40.938097Z",
          "iopub.status.idle": "2021-05-31T04:18:40.956696Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.938057Z",
          "shell.execute_reply": "2021-05-31T04:18:40.955786Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EVMbeUe7sUnO",
        "outputId": "6eaab6d5-60f5-4f1e-9567-c18d423762c9"
      },
      "source": [
        "# Cummulative_generation with respect to nw ordering of carbon \n",
        "peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "      <th>carbon</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8200</td>\n",
              "      <td>3531.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "      <td>27252</td>\n",
              "      <td>5802.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>569856</td>\n",
              "      <td>17674.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "      <td>9938180</td>\n",
              "      <td>37956.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon  Cummulative_generation\n",
              "Wind               0.0        0                     0.0\n",
              "Hydro           3521.0        0                  3521.0\n",
              "Other              0.0        0                  3521.0\n",
              "Oil                0.0        0                  3521.0\n",
              "Coal              10.0     8200                  3531.0\n",
              "Nuclear         2271.0    27252                  5802.0\n",
              "Solar          11872.0   569856                 17674.0\n",
              "NaturalGas     20282.0  9938180                 37956.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.957897Z",
          "iopub.execute_input": "2021-05-31T04:18:40.958139Z",
          "iopub.status.idle": "2021-05-31T04:18:40.976506Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.958114Z",
          "shell.execute_reply": "2021-05-31T04:18:40.975307Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CNMa17VbsUnO",
        "outputId": "ce124396-807e-4c30-f236-68e46d9a2fd3"
      },
      "source": [
        "# Cummulative_carbon emission \n",
        "peakt['Cummulative_carbon(kg)']  = peakt[\"carbon\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>generation</th>\n",
              "      <th>carbon</th>\n",
              "      <th>Cummulative_generation</th>\n",
              "      <th>Cummulative_carbon(kg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wind</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hydro</th>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oil</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3521.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Coal</th>\n",
              "      <td>10.0</td>\n",
              "      <td>8200</td>\n",
              "      <td>3531.0</td>\n",
              "      <td>8200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nuclear</th>\n",
              "      <td>2271.0</td>\n",
              "      <td>27252</td>\n",
              "      <td>5802.0</td>\n",
              "      <td>35452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Solar</th>\n",
              "      <td>11872.0</td>\n",
              "      <td>569856</td>\n",
              "      <td>17674.0</td>\n",
              "      <td>605308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaturalGas</th>\n",
              "      <td>20282.0</td>\n",
              "      <td>9938180</td>\n",
              "      <td>37956.0</td>\n",
              "      <td>10543488</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            generation   carbon  Cummulative_generation  Cummulative_carbon(kg)\n",
              "Wind               0.0        0                     0.0                       0\n",
              "Hydro           3521.0        0                  3521.0                       0\n",
              "Other              0.0        0                  3521.0                       0\n",
              "Oil                0.0        0                  3521.0                       0\n",
              "Coal              10.0     8200                  3531.0                    8200\n",
              "Nuclear         2271.0    27252                  5802.0                   35452\n",
              "Solar          11872.0   569856                 17674.0                  605308\n",
              "NaturalGas     20282.0  9938180                 37956.0                10543488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.981247Z",
          "iopub.execute_input": "2021-05-31T04:18:40.981591Z",
          "iopub.status.idle": "2021-05-31T04:18:40.987816Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.981551Z",
          "shell.execute_reply": "2021-05-31T04:18:40.986422Z"
        },
        "trusted": true,
        "id": "OHfWw9jGsUnP"
      },
      "source": [
        "def def_plot_limity(ymax,ypos):\n",
        "    if (ypos < ymax ):\n",
        "        return ypos\n",
        "    return (ymax - (0.2*ymax))\n",
        "\n",
        "def def_plot_limitx(xmax,xpos):\n",
        "    if (xpos < xmax ):\n",
        "        return xpos\n",
        "    return xmax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:40.989279Z",
          "iopub.execute_input": "2021-05-31T04:18:40.989582Z",
          "iopub.status.idle": "2021-05-31T04:18:41.005203Z",
          "shell.execute_reply.started": "2021-05-31T04:18:40.989524Z",
          "shell.execute_reply": "2021-05-31T04:18:41.004334Z"
        },
        "trusted": true,
        "id": "uphN9E1tsUnP"
      },
      "source": [
        "## standard plot consumption vs total carbon \n",
        "# carbonplot = peakt.plot(x=\"generation\",y=\"carbon\",grid=True, style='.-', \n",
        "#                             title='carbon vs consumption in '+ Region + \" on \" + str(peakmin.DATE.values) ,legend=True)\n",
        "# carbonplot.set_xlabel(\"generation(MW)\")\n",
        "# carbonplot.set_ylabel(\"carbon(kg)\")\n",
        "\n",
        "# index=0\n",
        "# colcount=len(peakt.columns)\n",
        "# for i, row in peakt.iterrows():\n",
        "#     index=index+1\n",
        "#     generation = row.values[0]\n",
        "#     carbon = row.values[1]\n",
        "#     print(generation, carbon)\n",
        "#     carbonplot.annotate(i, xy=(generation, carbon), \n",
        "#                         xytext=(generation+(colcount-index)*1000, carbon+(colcount-index)*220000),\n",
        "#                         arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=45)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.006395Z",
          "iopub.execute_input": "2021-05-31T04:18:41.006884Z",
          "iopub.status.idle": "2021-05-31T04:18:41.365754Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.006855Z",
          "shell.execute_reply": "2021-05-31T04:18:41.364994Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReZQVHJxsUnP",
        "outputId": "2f3bc487-32f7-43f1-88d3-7ce1ce47d8a0"
      },
      "source": [
        "## Cumulative plot \n",
        "carbonplot = peakt.plot(x=\"Cummulative_generation\",y=\"Cummulative_carbon(kg)\", \n",
        "                            title='Cummulative_carbon vs Cummulative_generation in \\n'\n",
        "                        + Region + \" on \" + str(peakmin.DATE.values[0])+ \" \" +str(peakmin.Timestamp.values[0]) ,\n",
        "                        grid=True, style='.-',legend=True)\n",
        "carbonplot.set_xlabel(\"Cumulative generation (MW) \")\n",
        "carbonplot.set_ylabel(\"Cummulative_carbon(kg)\")\n",
        "\n",
        "xmax = peakt['Cummulative_generation'].max()\n",
        "ymax = peakt['Cummulative_carbon(kg)'].max()\n",
        "index=0\n",
        "colcount=len(peakt.columns)\n",
        "\n",
        "for i, row in peakt.iterrows():\n",
        "    index = index+1\n",
        "    x = row.values[2]\n",
        "    y = row.values[3]\n",
        "    xpos = def_plot_limitx(xmax, x+index*1000)\n",
        "    ypos = def_plot_limity(ymax, y+index*460000)\n",
        "    carbonplot.annotate(i, xy=(x, y), \n",
        "                         xytext=(xpos, ypos), ha='center', va='center',\n",
        "                         arrowprops=dict(arrowstyle='-|>') , rotation=0)\n",
        "    \n",
        "#     carbonplot.annotate(i, xy=(row.values[3], row.values[5]), ha='left', rotation=60)\n",
        "# xytext=(row.values[3]+(index+1000)*randint(1,5), row.values[3]+(index+1000)*randint(1,5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.366895Z",
          "iopub.execute_input": "2021-05-31T04:18:41.367353Z",
          "iopub.status.idle": "2021-05-31T04:18:41.370910Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.367322Z",
          "shell.execute_reply": "2021-05-31T04:18:41.370093Z"
        },
        "trusted": true,
        "id": "rlc5rBmEsUnP"
      },
      "source": [
        "\n",
        "# cplot = peakhour3.plot(y=\"commulative_CO2 emissions\",x=\"commulative_demand\",grid=True, style='.-', \n",
        "#                             title='commulative_CO2 emissions vs commulative_demand',legend=True)\n",
        "# cplot.set_xlabel(\"commulative_demand (kW) \")\n",
        "# cplot.set_ylabel(\"commulative_CO2 emissions (kg/kWh)\")\n",
        "\n",
        "# index=0\n",
        "# colcount=len(df.columns)\n",
        "# for i, row in peakhour3.iterrows():\n",
        "#     index=index+1\n",
        "#     print(row.values[3])\n",
        "#     tag= row.values[0], row.values[3], row.values[5]\n",
        "#     cplot.annotate(tag, \n",
        "#                     xy=(row.values[3], row.values[5]), \n",
        "#                     xytext=(row.values[3]+(colcount-index), row.values[5]+(colcount-index)),\n",
        "#                     arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqO66ImasUnQ"
      },
      "source": [
        "<a id=\"section-four\"></a>\n",
        "# 4.Cost vs Fuel Type\n",
        "LCOE (Levelized Cost Of generating Electricity) - price per MWh\n",
        "Now that we have a merged dataset we can take a closer look at data distributions and correlations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.372494Z",
          "iopub.execute_input": "2021-05-31T04:18:41.372984Z",
          "iopub.status.idle": "2021-05-31T04:18:41.396737Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.372943Z",
          "shell.execute_reply": "2021-05-31T04:18:41.395988Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "3dgYAoS2sUnQ",
        "outputId": "6650491a-bb32-400d-adb3-7ede0d81b394"
      },
      "source": [
        "df_cost = pd.read_csv(\"/content/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Price2.csv\")\n",
        "df_cost\n",
        "## Data source : IPCC ANEX III Technology-specific Cost and Performance Parameters -2014\n",
        "# Schlömer S., T. Bruckner, L. Fulton, E. Hertwich, A. McKinnon, D. Perczyk, J. Roy, R. Schaeffer, R. Sims, P. Smith, and R. Wiser,\n",
        "# 2014: Annex III: Technology-specific cost and performance parameters. In: Climate Change 2014: Mitigation of Climate\n",
        "# Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate\n",
        "# Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P.\n",
        "# Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University\n",
        "# Press, Cambridge, United Kingdom and New York, NY, USA."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-117-c3177547298a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_cost\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Price2.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf_cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m## Data source : IPCC ANEX III Technology-specific Cost and Performance Parameters -2014\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Schlömer S., T. Bruckner, L. Fulton, E. Hertwich, A. McKinnon, D. Perczyk, J. Roy, R. Schaeffer, R. Sims, P. Smith, and R. Wiser,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 2014: Annex III: Technology-specific cost and performance parameters. In: Climate Change 2014: Mitigation of Climate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../input/lcoe-co2/US Utility grids - LCOE Co2 Emissions - Price2.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.397801Z",
          "iopub.execute_input": "2021-05-31T04:18:41.398160Z",
          "iopub.status.idle": "2021-05-31T04:18:41.407525Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.398119Z",
          "shell.execute_reply": "2021-05-31T04:18:41.406836Z"
        },
        "trusted": true,
        "id": "md9YKVgssUnQ"
      },
      "source": [
        "# df_cost['LCOE / Marginal Cost ( with subsidy)  ( $/Mwh)']\n",
        "df_cost[['Fuel Type','LCOE from IEA 2020 ($/MWh) ']]\n",
        "\n",
        "# df_cost.info()\n",
        "## Data source : https://www.iea.org/articles/levelised-cost-of-electricity-calculator LCOE estimates from Projected Costs of Generating Electricity 2020"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.408390Z",
          "iopub.execute_input": "2021-05-31T04:18:41.408756Z",
          "iopub.status.idle": "2021-05-31T04:18:41.566796Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.408729Z",
          "shell.execute_reply": "2021-05-31T04:18:41.566089Z"
        },
        "trusted": true,
        "id": "VGZJBVr9sUnR"
      },
      "source": [
        "df_cost.sort_values('LCOE from IEA 2020 ($/MWh) ',ascending=True).plot(x=\"Fuel Type\",y='LCOE from IEA 2020 ($/MWh) ', kind='bar')\n",
        "# df_cost.xticks(rotation=90)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48syvETBsUnR"
      },
      "source": [
        "IEA LCOE estimates from Projected Costs of Generating Electricity 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.567707Z",
          "iopub.execute_input": "2021-05-31T04:18:41.568064Z",
          "iopub.status.idle": "2021-05-31T04:18:41.570897Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.568036Z",
          "shell.execute_reply": "2021-05-31T04:18:41.570269Z"
        },
        "trusted": true,
        "id": "trigoteYsUnR"
      },
      "source": [
        "# Assumptions \n",
        "# Discount rate 7% Central case\n",
        "# Carbon price 30.00 USD/ton Default assumption\n",
        "# Heat price 37.06 USD/MWh Default assumption\n",
        "# Coal price 100% Default assumption\n",
        "# Gas price 100% Default assumption"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.571733Z",
          "iopub.execute_input": "2021-05-31T04:18:41.572129Z",
          "iopub.status.idle": "2021-05-31T04:18:41.585350Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.572101Z",
          "shell.execute_reply": "2021-05-31T04:18:41.584587Z"
        },
        "trusted": true,
        "id": "GikKF_aZsUnR"
      },
      "source": [
        "peakt = peakt.drop(columns=['Cummulative_generation','carbon','Cummulative_carbon(kg)'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.586391Z",
          "iopub.execute_input": "2021-05-31T04:18:41.586809Z",
          "iopub.status.idle": "2021-05-31T04:18:41.618004Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.586781Z",
          "shell.execute_reply": "2021-05-31T04:18:41.616897Z"
        },
        "trusted": true,
        "id": "L2dnI5YXsUnS"
      },
      "source": [
        "peakt['price']=0\n",
        "for i, row in peakt.iterrows():\n",
        "#     print(i)\n",
        "    for j,cost in df_cost.iterrows():\n",
        "#         print(lcoe.values[0])\n",
        "        if i in cost[0]:\n",
        "            print(\"Match between two tables for \"+i+ \" and \"+ cost[0])\n",
        "            print(cost.values[2])\n",
        "            print(row.values[0])\n",
        "            peakt.at[i,'price']= cost[2] * row[0]\n",
        "\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.619325Z",
          "iopub.execute_input": "2021-05-31T04:18:41.619627Z",
          "iopub.status.idle": "2021-05-31T04:18:41.631088Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.619590Z",
          "shell.execute_reply": "2021-05-31T04:18:41.630273Z"
        },
        "trusted": true,
        "id": "AZYbzf-tsUnS"
      },
      "source": [
        "# sort by ascending order of price\n",
        "peakt = peakt.sort_values(by=['price'])\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.632082Z",
          "iopub.execute_input": "2021-05-31T04:18:41.632440Z",
          "iopub.status.idle": "2021-05-31T04:18:41.654009Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.632412Z",
          "shell.execute_reply": "2021-05-31T04:18:41.653040Z"
        },
        "trusted": true,
        "id": "V-xOz3HvsUnT"
      },
      "source": [
        "# Cummulative_generation with respect to nw ordering of cost \n",
        "peakt['Cummulative_generation']  = peakt[\"generation\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.655264Z",
          "iopub.execute_input": "2021-05-31T04:18:41.655618Z",
          "iopub.status.idle": "2021-05-31T04:18:41.661040Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.655504Z",
          "shell.execute_reply": "2021-05-31T04:18:41.659967Z"
        },
        "trusted": true,
        "id": "wM7mOu3wsUnT"
      },
      "source": [
        "# peakt[\"total_price($thousand)\"] = (peakt[\"generation\"] * peakt[\"price\"]) / 1000\n",
        "# peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.662195Z",
          "iopub.execute_input": "2021-05-31T04:18:41.662573Z",
          "iopub.status.idle": "2021-05-31T04:18:41.680612Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.662543Z",
          "shell.execute_reply": "2021-05-31T04:18:41.679668Z"
        },
        "trusted": true,
        "id": "Ox1jSK_osUnT"
      },
      "source": [
        "# Cummulative price\n",
        "peakt['Cummulative_price($)']  = peakt[\"price\"].cumsum()\n",
        "peakt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.682198Z",
          "iopub.execute_input": "2021-05-31T04:18:41.682501Z",
          "iopub.status.idle": "2021-05-31T04:18:41.695280Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.682472Z",
          "shell.execute_reply": "2021-05-31T04:18:41.694184Z"
        },
        "trusted": true,
        "id": "ACB7gZFksUnU"
      },
      "source": [
        "# Plot price vs generation\n",
        "\n",
        "# costplot = peakt.plot(y=\"price\",x=\"generation\",grid=True, style='.-', \n",
        "#                             title='price vs generation in ' + Region ,legend=True)\n",
        "# costplot.set_xlabel(\"generation (MW) \")\n",
        "# costplot.set_ylabel(\"Price LCOE ($/MWh)\")\n",
        "\n",
        "# index =0\n",
        "# colcount=len(peakt.columns)\n",
        "# for i, row in peakt.iterrows():\n",
        "#     index=index+1\n",
        "# #     print(row.values[6])\n",
        "# #     costplot.text(row.values[2]+index*10, row.values[6]+index*30, str(i))\n",
        "#     costplot.annotate(i, xy=(row.values[0], row.values[1]), \n",
        "#                         xytext=(row.values[0]+(colcount-index)*1000, row.values[1]+(colcount-index)*3000),\n",
        "#                         arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-05-31T04:18:41.697289Z",
          "iopub.execute_input": "2021-05-31T04:18:41.697814Z",
          "iopub.status.idle": "2021-05-31T04:18:42.066929Z",
          "shell.execute_reply.started": "2021-05-31T04:18:41.697766Z",
          "shell.execute_reply": "2021-05-31T04:18:42.065914Z"
        },
        "trusted": true,
        "id": "22kBLjAosUnU"
      },
      "source": [
        "# Plot Cummulative_price vs Cummulative_generation\n",
        "\n",
        "costplot = peakt.plot(x=\"Cummulative_generation\",y='Cummulative_price($)', \n",
        "                            title='Cummulative_price vs Cummulative_generation in \\n'\n",
        "                       + Region + \" on \" + str(peakmin.DATE.values[0])+ \" \" +str(peakmin.Timestamp.values[0]) ,\n",
        "                      grid=True, style='.-',legend=True)\n",
        "costplot.set_xlabel(\"Cumulative generation(MW)\")\n",
        "costplot.set_ylabel(\"Cummulative_price($)\")\n",
        "\n",
        "xmax = peakt['Cummulative_generation'].max()\n",
        "ymax = peakt['Cummulative_price($)'].max()\n",
        "index =0\n",
        "colcount=len(peakt.columns)\n",
        "for i, row in peakt.iterrows():\n",
        "    index=index+1\n",
        "    x = row.values[2]\n",
        "    y = row.values[3]\n",
        "    xpos = def_plot_limitx(xmax, x+index*1000)\n",
        "    ypos = def_plot_limity(ymax, y+index*5500)\n",
        "    costplot.annotate(i, xy=(x,y), \n",
        "                        xytext=(xpos, ypos),\n",
        "                        arrowprops=dict(arrowstyle='-|>') ,ha='left', rotation=0)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}