PJM_data_analysis.ipynb

{
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  {
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   "id": "34312e9d",
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   "source": [
    "# PJM 10 YEAR DATA ANALYSIS\n",
    "\n",
    "**DATA INCLUDES 10+ YEARS OF HOURLY ENERGY CONSUMPTIONIN MEGAWATTSA (from PJM's website)\n",
    "\n",
    "**PJM Interconnection LLC (PJM) is a regional transmission organization (RTO) in the United States\n",
    "\n",
    "It is part of the Eastern Interconnection grid operating an electric transmission system serving all or parts of Delaware, Illinois, Indiana, Kentucky, Maryland, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and the District of Columbia.\n",
    "\n",
    "**The regions have changed over the years so data may only appear for certain dates per region.\n",
    "\n",
    "**goals of working on this dataset:\n",
    "Split the last year into a test set- How to build a model to predict energy consumption?\n",
    "Find trends in energy consumption \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10301d89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import os\n",
    "import datetime \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d37a21d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>AEP_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-12-31 01:00:00</td>\n",
       "      <td>13478.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-12-31 02:00:00</td>\n",
       "      <td>12865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-12-31 03:00:00</td>\n",
       "      <td>12577.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-12-31 04:00:00</td>\n",
       "      <td>12517.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-12-31 05:00:00</td>\n",
       "      <td>12670.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              Datetime   AEP_MW\n",
       "0  2004-12-31 01:00:00  13478.0\n",
       "1  2004-12-31 02:00:00  12865.0\n",
       "2  2004-12-31 03:00:00  12577.0\n",
       "3  2004-12-31 04:00:00  12517.0\n",
       "4  2004-12-31 05:00:00  12670.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"pjm-data/AEP_hourly.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1804b1ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 121273 entries, 0 to 121272\n",
      "Data columns (total 2 columns):\n",
      " #   Column    Non-Null Count   Dtype  \n",
      "---  ------    --------------   -----  \n",
      " 0   Datetime  121273 non-null  object \n",
      " 1   AEP_MW    121273 non-null  float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 1.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6bf7ed2e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AEP_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>121273.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15499.513717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2591.399065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9581.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13630.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15310.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17200.000000</td>\n",
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       "      <th>max</th>\n",
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       "              AEP_MW\n",
       "count  121273.000000\n",
       "mean    15499.513717\n",
       "std      2591.399065\n",
       "min      9581.000000\n",
       "25%     13630.000000\n",
       "50%     15310.000000\n",
       "75%     17200.000000\n",
       "max     25695.000000"
      ]
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8b93a41e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#seperate date and time \n",
    "df[\"New_Date\"] = pd.to_datetime(df[\"Datetime\"]).dt.date\n",
    "df[\"New_Time\"] = pd.to_datetime(df[\"Datetime\"]).dt.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25e1b4e0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>AEP_MW</th>\n",
       "      <th>New_Date</th>\n",
       "      <th>New_Time</th>\n",
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       "      <th>0</th>\n",
       "      <td>2004-12-31 01:00:00</td>\n",
       "      <td>13478.0</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-12-31 02:00:00</td>\n",
       "      <td>12865.0</td>\n",
       "      <td>2004-12-31</td>\n",
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      "text/plain": [
       "              Datetime   AEP_MW    New_Date  New_Time\n",
       "0  2004-12-31 01:00:00  13478.0  2004-12-31  01:00:00\n",
       "1  2004-12-31 02:00:00  12865.0  2004-12-31  02:00:00"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "df1 = df\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f4f1506",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>AEP_MW</th>\n",
       "      <th>New_Date</th>\n",
       "      <th>New_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30221</th>\n",
       "      <td>2008-10-20 14:00:00</td>\n",
       "      <td>25695.0</td>\n",
       "      <td>2008-10-20</td>\n",
       "      <td>14:00:00</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                  Datetime   AEP_MW    New_Date  New_Time\n",
       "30221  2008-10-20 14:00:00  25695.0  2008-10-20  14:00:00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the max consumption \n",
    "df1[df1[\"AEP_MW\"] == df[\"AEP_MW\"].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f2089c3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>AEP_MW</th>\n",
       "      <th>New_Date</th>\n",
       "      <th>New_Time</th>\n",
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       "    <tr>\n",
       "      <th>100759</th>\n",
       "      <td>2016-10-02 05:00:00</td>\n",
       "      <td>9581.0</td>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>05:00:00</td>\n",
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      "text/plain": [
       "                   Datetime  AEP_MW    New_Date  New_Time\n",
       "100759  2016-10-02 05:00:00  9581.0  2016-10-02  05:00:00"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find the min consumption\n",
    "df1[df1[\"AEP_MW\"] == df[\"AEP_MW\"].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78ec2a6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aj/energyENV/lib/python3.6/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='AEP_MW', ylabel='Density'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot and visualization\n",
    "sns.distplot(df1[\"AEP_MW\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca19f901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc56d22f358>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df1[\"AEP_MW\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2584ba14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Datetime</th>\n",
       "      <th>AEP_MW</th>\n",
       "      <th>New_Date</th>\n",
       "      <th>New_Time</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-12-31 01:00:00</td>\n",
       "      <td>13478.0</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-12-31 02:00:00</td>\n",
       "      <td>12865.0</td>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>02:00:00</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Datetime   AEP_MW    New_Date  New_Time  Year\n",
       "0  2004-12-31 01:00:00  13478.0  2004-12-31  01:00:00  2004\n",
       "1  2004-12-31 02:00:00  12865.0  2004-12-31  02:00:00  2004"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#extract date and time\n",
    "df1[\"Year\"] = pd.DatetimeIndex(df['New_Date']).year\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1adf95c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015, 2016, 2017, 2018])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[\"Year\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5e66d66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Datetime    8758\n",
       "AEP_MW      5601\n",
       "New_Date     365\n",
       "New_Time      24\n",
       "Year           1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1[\"Year\"] == 2013].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8453a6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year', ylabel='AEP_MW'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#relationship of consumption and year\n",
    "sns.lineplot(x=df1[\"Year\"], y=df1[\"AEP_MW\"], data=df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7386e4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fc51506d128>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Regression\n",
    "sns.jointplot(x=df1[\"Year\"],\n",
    "              y=df1[\"AEP_MW\"],\n",
    "              data=df1,\n",
    "            kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f297cbb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7fc513f5beb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=df1[\"Year\"],\n",
    "              y=df1[\"AEP_MW\"],\n",
    "              data=df1,\n",
    "            kind=\"kde\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4febea7",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Invalid object type at position 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid object type",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-fc23b079d725>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#relation between consumption and time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"New_Time\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"AEP_MW\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\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      3\u001b[0m \u001b[0;31m#df1.head(2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\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---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(x, y, hue, size, style, data, palette, hue_order, hue_norm, sizes, size_order, size_norm, dashes, markers, style_order, units, estimator, ci, n_boot, seed, sort, err_style, err_kws, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m    708\u001b[0m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_attach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\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    711\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m    469\u001b[0m         \u001b[0;31m# Loop over the semantic subsets and add to the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m         \u001b[0mgrouping_vars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"size\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"style\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0msub_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrouping_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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[0m\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36miter_data\u001b[0;34m(self, grouping_vars, reverse, from_comp_data)\u001b[0m\n\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomp_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\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    985\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/seaborn/_core.py\u001b[0m in \u001b[0;36mcomp_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m                     \u001b[0morig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\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   1056\u001b[0m                 \u001b[0mcomp_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1057\u001b[0;31m                 \u001b[0mcomp_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig\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[0m\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"log\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/energyENV/lib/python3.6/site-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             values = lib.maybe_convert_numeric(\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcoerce_numeric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             )\n\u001b[1;32m    155\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\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;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Invalid object type at position 0"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#relation between consumption and time\n",
    "sns.lineplot(x=df1[\"New_Time\"], y=df1[\"AEP_MW\"], data=df1)\n",
    "#df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7162927",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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