{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.display import display, HTML\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import acf\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "from bokeh.plotting import figure, show, output_notebook\n",
    "from bokeh.models import HoverTool, ColumnDataSource\n",
    "from bokeh.models import LinearColorMapper, ColorBar, BasicTicker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.options.display.max_columns = None\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "# pd.set_option('display.float_format',lambda x: '%.4f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Chicago Historical temperature data came from NOAA](https://www.ncdc.noaa.gov/cdo-web/datasets). I pulled in data from 4 separate weather stations around Chicago. Chicago is not large enough that there will be massive monthly temperature differences between different weather stations in the city, and all weather stations in the NOAA database are reputable (ie calibrate their instruments, so same-year comparisons should have the same accuracy, and measurements shouldn't be off by more than 1 degree F."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>STATION</th>\n",
       "      <th>NAME</th>\n",
       "      <th>DATE</th>\n",
       "      <th>CDSD</th>\n",
       "      <th>DSND</th>\n",
       "      <th>DSNW</th>\n",
       "      <th>EMNT</th>\n",
       "      <th>EMSD</th>\n",
       "      <th>EMSN</th>\n",
       "      <th>EMXP</th>\n",
       "      <th>EMXT</th>\n",
       "      <th>HDSD</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNOW</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>TMAX</th>\n",
       "      <th>TMIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>USC00111577</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "      <td>1928-03-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.36</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.39</td>\n",
       "      <td>6.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>44.8</td>\n",
       "      <td>27.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USC00111577</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "      <td>1928-04-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.90</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.7</td>\n",
       "      <td>44.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USC00111577</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "      <td>1928-05-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.76</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.2</td>\n",
       "      <td>70.1</td>\n",
       "      <td>46.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>USC00111577</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "      <td>1928-06-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.56</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>72.9</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>USC00111577</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "      <td>1928-11-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.29</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.06</td>\n",
       "      <td>0.1</td>\n",
       "      <td>40.6</td>\n",
       "      <td>47.2</td>\n",
       "      <td>33.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       STATION                                NAME       DATE  CDSD  DSND  \\\n",
       "0  USC00111577  CHICAGO MIDWAY AIRPORT 3 SW, IL US 1928-03-01   NaN   1.0   \n",
       "1  USC00111577  CHICAGO MIDWAY AIRPORT 3 SW, IL US 1928-04-01   NaN   0.0   \n",
       "2  USC00111577  CHICAGO MIDWAY AIRPORT 3 SW, IL US 1928-05-01   NaN   0.0   \n",
       "3  USC00111577  CHICAGO MIDWAY AIRPORT 3 SW, IL US 1928-06-01   NaN   0.0   \n",
       "4  USC00111577  CHICAGO MIDWAY AIRPORT 3 SW, IL US 1928-11-01   NaN   0.0   \n",
       "\n",
       "   DSNW  EMNT  EMSD  EMSN  EMXP  EMXT  HDSD  PRCP  SNOW  TAVG  TMAX  TMIN  \n",
       "0   2.0   8.0   1.0   4.2  0.36  77.0   NaN  1.39   6.0  36.2  44.8  27.5  \n",
       "1   0.0  21.0   0.0   0.6  0.90  74.0   NaN  2.01   0.7  44.0  53.7  34.2  \n",
       "2   0.0  31.0   0.0   0.0  0.76  89.0   NaN  1.69   0.0  58.2  70.1  46.2  \n",
       "3   0.0  41.0   0.0   0.0  2.56  87.0   NaN  6.64   0.0  63.0  72.9  53.0  \n",
       "4   0.0  19.0   0.0   0.1  1.29  68.0   NaN  4.06   0.1  40.6  47.2  33.9  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_PATH = os.path.join('data', 'weather', 'chicago_hist.csv')\n",
    "chi = pd.read_csv(CSV_PATH, encoding='latin1', parse_dates=['DATE']) \n",
    "chi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>DATE</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>NAME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1928-03-01</td>\n",
       "      <td>36.2</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1928-04-01</td>\n",
       "      <td>44.0</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1928-05-01</td>\n",
       "      <td>58.2</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1928-06-01</td>\n",
       "      <td>63.0</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1928-11-01</td>\n",
       "      <td>40.6</td>\n",
       "      <td>CHICAGO MIDWAY AIRPORT 3 SW, IL US</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DATE  TAVG                                NAME\n",
       "0 1928-03-01  36.2  CHICAGO MIDWAY AIRPORT 3 SW, IL US\n",
       "1 1928-04-01  44.0  CHICAGO MIDWAY AIRPORT 3 SW, IL US\n",
       "2 1928-05-01  58.2  CHICAGO MIDWAY AIRPORT 3 SW, IL US\n",
       "3 1928-06-01  63.0  CHICAGO MIDWAY AIRPORT 3 SW, IL US\n",
       "4 1928-11-01  40.6  CHICAGO MIDWAY AIRPORT 3 SW, IL US"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_chi = chi[['DATE', 'TAVG', 'NAME']]\n",
    "temp_chi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">TAVG</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAME</th>\n",
       "      <th>CHICAGO MIDWAY AIRPORT 3 SW, IL US</th>\n",
       "      <th>CHICAGO UNIVERSITY, IL US</th>\n",
       "      <th>CHICAGO WEATHER BUREAU CITY 2, IL US</th>\n",
       "      <th>JOLIET, IL US</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1893-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         TAVG                            \\\n",
       "NAME       CHICAGO MIDWAY AIRPORT 3 SW, IL US CHICAGO UNIVERSITY, IL US   \n",
       "DATE                                                                      \n",
       "1893-11-01                                NaN                       NaN   \n",
       "1893-12-01                                NaN                       NaN   \n",
       "1894-01-01                                NaN                       NaN   \n",
       "1894-02-01                                NaN                       NaN   \n",
       "1894-03-01                                NaN                       NaN   \n",
       "\n",
       "                                                               \n",
       "NAME       CHICAGO WEATHER BUREAU CITY 2, IL US JOLIET, IL US  \n",
       "DATE                                                           \n",
       "1893-11-01                                  NaN          37.9  \n",
       "1893-12-01                                  NaN          28.5  \n",
       "1894-01-01                                  NaN          30.7  \n",
       "1894-02-01                                  NaN          24.7  \n",
       "1894-03-01                                  NaN          45.6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_chi = temp_chi.pivot(index='DATE', columns='NAME')\n",
    "temp_chi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_chi = pd.DataFrame(temp_chi.mean(axis=1))\n",
    "temp_chi.columns = ['Avg_Temp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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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>Avg_Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1893-11-01</th>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893-12-01</th>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-01-01</th>\n",
       "      <td>30.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-02-01</th>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894-03-01</th>\n",
       "      <td>45.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Avg_Temp\n",
       "DATE                \n",
       "1893-11-01      37.9\n",
       "1893-12-01      28.5\n",
       "1894-01-01      30.7\n",
       "1894-02-01      24.7\n",
       "1894-03-01      45.6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_chi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now that we have a continuous series of Chicago temperature data, we can plot that data and look for trends.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23a901b5438>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a9006c630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temp_chi.plot(figsize=(12,8))\n",
    "ax.set_title('Chicago Monthly Temperature from 1984 to Present', fontsize=16)\n",
    "ax.set_xlabel('Year', fontsize=14)\n",
    "ax.set_ylabel('Monthly Temperature [°F]', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x23a9028e198>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a904c1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chi_temp_by_month = temp_chi.groupby(temp_chi.index.month).mean()\n",
    "ax = chi_temp_by_month.plot(fontsize=12, figsize=(8,6))\n",
    "ax.set_title('Average Chicago Temperature by Month', fontsize=16)\n",
    "ax.set_xlabel('Month', fontsize=14)\n",
    "ax.set_ylabel('Average Temperature [°F]', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Our data is monthly, and as you can see from the two plots below, the average monthly temperature cycles every year. This is what we expect, but all that variation makes it hard to determine if any significant trends are present. To We'll decompose our temperature data into 3 separate time-series; trend, seasonality, and residual noise. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decomposition_to_df(decomp):\n",
    "    \"\"\"Helper Method that makes a DataFrame from decomposition data.\n",
    "    Args: \n",
    "        decomp:   the DecomposeResult returned from statsmodels\n",
    "                seasonal_decompose() method.\n",
    "    Returns:\n",
    "        A pandas dataframe containing 3 series.\"\"\"\n",
    "    trend = decomp.trend\n",
    "    trend.columns = ['Trend']\n",
    "    seasonality = decomp.seasonal\n",
    "    seasonality.columns = ['Seasonality']\n",
    "    residuals = decomp.resid\n",
    "    residuals.columns = ['Residuals']\n",
    "    return trend.join(seasonality).join(residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "decomposition = sm.tsa.seasonal_decompose(temp_chi)\n",
    "decomposed = decomposition_to_df(decomposition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a904cc630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = decomposed.plot(subplots=True,\n",
    "                     figsize=(12, 12), \n",
    "                     fontsize=12, \n",
    "                     sharey=False, \n",
    "                     sharex=False, \n",
    "                     legend=False)\n",
    "\n",
    "for i in range(len(decomposed.columns.values)):\n",
    "    ax[i].set_title(decomposed.columns.values[i], fontsize=16)\n",
    "    ax[i].set_ylabel('Temperature [F]', fontsize=12)\n",
    "    ax[i].set_xlabel('Year', fontsize=12)\n",
    "    ax[i].xaxis.set_label_coords(0.5, -0.01)\n",
    "    \n",
    "# ax[2].xaxis.set_label_coords(0.5, -0.1)\n",
    "    \n",
    "plt.suptitle('Decomposition of Chicago Temperature Data', x=.52, y=0.95, fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seasonality max value: 23.97°F\n",
      "Seasonality min value: -25.78°F\n",
      "Average Trend value from 1895 to 1920: 49.94°F\n",
      "Average Trend value from 1930 to 1970: 50.60°F\n",
      "Average Trend value from 1980 to 2018: 51.92°F\n"
     ]
    }
   ],
   "source": [
    "print('Seasonality max value: {:2.2f}°F'.format(decomposed['Seasonality'].max()))\n",
    "print('Seasonality min value: {:2.2f}°F'.format(decomposed['Seasonality'].min()))\n",
    "print('Average Trend value from 1895 to 1920: {:2.2f}°F'\n",
    "      .format(decomposed.loc['1895':'1920', 'Trend'].mean()))\n",
    "print('Average Trend value from 1930 to 1970: {:2.2f}°F'\n",
    "      .format(decomposed.loc['1940':'1960', 'Trend'].mean()))\n",
    "print('Average Trend value from 1980 to 2018: {:2.2f}°F'\n",
    "      .format(decomposed.loc['1980':'2018', 'Trend'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Looking at the plots of the parts of the decomposed temperature data, we can quickly see several things:**\n",
    "* the seasonality component is extremely consistent and swings between about 24 °F and -26 °F.\n",
    "* the level of residual noise is fairly consistent over the entire span, (which is good as we expect the noise to be random and not vary with time).\n",
    "* the center of the trend line slowly trends up from around 50.0 °F aroun 1900, to around 50.6 °F in the middle of the century to around 51.9 °F  over the past ~40 years. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What's causing this temperature increase?\n",
    "\n",
    "To better understand the rising air temperatures, we have to look at the composition of air, how these air components affect the air temperature, and how the composition of air changes over time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is our atmosphere made of? \n",
    "\n",
    "### Dry Air Composition\n",
    "\n",
    "The table below shows [the molar mass, the volume percentage, and total mass for the 16 most common dry air components](https://www.atmos.washington.edu/~dennis/321/Chapter_01_Tables.pdf), and I've plotted those relative composition values below. From this plot, you can quickly see that air composition is dominated by $\\text{N}_2$ (78.08%), $\\text{O}_2$ (20.95%), and $\\text{Ar}$ (0.934%), with **all other components making up the remaining 0.036% (!) of the dry air in the atmosphere.** If the air composition matters to air temperature, we should take a close look at the major players.   \n",
    "\n",
    "#### Why dry? What about H2O?\n",
    "Also, I should note that we specify **Dry** air composition as the amount of $\\text{H}_2\\text{O}$ (water) in the air depends on where you are. The percent of $\\text{H}_2\\text{O}$ varies between 0% and 5% of the air (by volume) depending on the measurement location's temperature, recent precipitation, and proximity to bodies of water. When the temperature is at or below 32 °F, the percentage of $\\text{H}_2\\text{O}$ in the air drops down to 0%, and when the temperature increases, the $\\text{H}_2\\text{O}$ percentage increases. As the percentage increase of $\\text{H}_2\\text{O}$ in the air is a result of increasing temperatures, we can rule out $\\text{H}_2\\text{O}$ as a cause of increasing temperatures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_PATH = os.path.join('data', 'weather', 'air_comp_washington.csv')\n",
    "air_comp = pd.read_csv(CSV_PATH, header=1, index_col='Formula')\n",
    "dry_air_comp = air_comp[~air_comp.index.isin(['H2O', 'Dry', 'O3'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mattt\\Anaconda3\\envs\\py36\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "for row in zip(dry_air_comp.index.values,dry_air_comp['pct_vol']):\n",
    "    tokens = row[1].split()\n",
    "    if len(tokens) == 1:\n",
    "        dry_air_comp.loc[row[0],'pct_vol'] = float(tokens[0])\n",
    "    elif tokens[1] == 'ppmv':\n",
    "        dry_air_comp.loc[row[0],'pct_vol'] = float(tokens[0]) * 10e-5\n",
    "    elif tokens[1] == 'ppbv':\n",
    "        dry_air_comp.loc[row[0],'pct_vol'] = float(tokens[0]) * 10e-8\n",
    "    elif tokens[1] == 'pptv':\n",
    "        dry_air_comp.loc[row[0],'pct_vol'] = float(tokens[0]) * 10e-11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Name</th>\n",
       "      <th>Molar_Mass</th>\n",
       "      <th>pct_vol</th>\n",
       "      <th>Total_Mass_g</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Formula</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N2</th>\n",
       "      <td>Nitrogen</td>\n",
       "      <td>28.013</td>\n",
       "      <td>78.08</td>\n",
       "      <td>3870000000000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>Oxygen</td>\n",
       "      <td>31.999</td>\n",
       "      <td>20.95</td>\n",
       "      <td>1185000000000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ar</th>\n",
       "      <td>Argon</td>\n",
       "      <td>39.948</td>\n",
       "      <td>0.934</td>\n",
       "      <td>65900000000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <td>Carbon dioxide</td>\n",
       "      <td>44.010</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>2760000000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ne</th>\n",
       "      <td>Neon</td>\n",
       "      <td>20.183</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>64800000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kr</th>\n",
       "      <td>Krypton</td>\n",
       "      <td>83.800</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>16900000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>He</th>\n",
       "      <td>Helium</td>\n",
       "      <td>4.003</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>3710000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH4</th>\n",
       "      <td>Methane</td>\n",
       "      <td>16.043</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>4900000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xe</th>\n",
       "      <td>Xenon</td>\n",
       "      <td>131.300</td>\n",
       "      <td>8.7e-06</td>\n",
       "      <td>2020000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N2O</th>\n",
       "      <td>Nitrous oxide</td>\n",
       "      <td>44.013</td>\n",
       "      <td>3.1e-05</td>\n",
       "      <td>2300000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>Carbon monoxide</td>\n",
       "      <td>28.010</td>\n",
       "      <td>1.2e-05</td>\n",
       "      <td>590000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2</th>\n",
       "      <td>Hydrogen</td>\n",
       "      <td>2.016</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>180000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>Ammonia</td>\n",
       "      <td>17.030</td>\n",
       "      <td>1e-05</td>\n",
       "      <td>30000000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>Nitrogen dioxide</td>\n",
       "      <td>46.000</td>\n",
       "      <td>1e-07</td>\n",
       "      <td>8100000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>Sulfur dioxide</td>\n",
       "      <td>64.060</td>\n",
       "      <td>2e-08</td>\n",
       "      <td>2300000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2S</th>\n",
       "      <td>Hydrogen sulfide</td>\n",
       "      <td>34.080</td>\n",
       "      <td>2e-08</td>\n",
       "      <td>1200000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Name  Molar_Mass   pct_vol            Total_Mass_g\n",
       "Formula                                                                \n",
       "N2               Nitrogen      28.013     78.08  3870000000000000000000\n",
       "O2                 Oxygen      31.999     20.95  1185000000000000000000\n",
       "Ar                  Argon      39.948     0.934    65900000000000000000\n",
       "CO2        Carbon dioxide      44.010    0.0353     2760000000000000000\n",
       "Ne                   Neon      20.183  0.001818       64800000000000000\n",
       "Kr                Krypton      83.800  0.000114       16900000000000000\n",
       "He                 Helium       4.003  0.000524        3710000000000000\n",
       "CH4               Methane      16.043  0.000172        4900000000000000\n",
       "Xe                  Xenon     131.300   8.7e-06        2020000000000000\n",
       "N2O         Nitrous oxide      44.013   3.1e-05        2300000000000000\n",
       "CO        Carbon monoxide      28.010   1.2e-05         590000000000000\n",
       "H2               Hydrogen       2.016     5e-05         180000000000000\n",
       "NH3               Ammonia      17.030     1e-05          30000000000000\n",
       "NO2      Nitrogen dioxide      46.000     1e-07           8100000000000\n",
       "SO2        Sulfur dioxide      64.060     2e-08           2300000000000\n",
       "H2S      Hydrogen sulfide      34.080     2e-08           1200000000000"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_air_comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\text{N}_2$, $\\text{O}_2$, and $\\text{Ar}$ make up 99.964% of the volume of all dry air on earth. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7807476899047764"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = 407 - 340\n",
    "1000000*.7808 / (1000000+diff)\n",
    "# 1000000*.00934 / (1000000+diff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.964"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_air_comp.loc[['N2','O2','Ar'],'pct_vol'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.00203984000001"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just over 100%. I'll attribute this to unstated rounding error\n",
    "dry_air_comp['pct_vol'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a936f2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dry_air_comp['pct_vol'].plot(kind='bar', figsize=(8,6))\n",
    "ax.set_ylabel('Percent of Atmospheric Composition', fontsize=14)\n",
    "ax.set_xlabel('Air Component', fontsize=14)\n",
    "ax.set_title('Composition of Dry Air (percentage by volume)', fontsize=16)\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the minor components of air (ie total composition without $\\text{N}_2$, $\\text{O}_2$, and $\\text{Ar}$), we see that $\\text{CO}_2$ is the dominant remaining gas.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a9234c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dry_air_comp.loc['CO2':,'pct_vol'].plot(kind='bar', figsize=(8,6))\n",
    "ax.set_ylabel('Percent of Atmospheric Composition', fontsize=14)\n",
    "ax.set_xlabel('Air Component', fontsize=14)\n",
    "ax.set_title('Minor Components of Dry Air (percentage by volume)', fontsize=16)\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rising $\\text{CO}_2$ Levels\n",
    "This data was from 1990. The values for mono-atomic components (eg $\\text{N}_2$, $\\text{O}_2$, and $\\text{Ar}$) are still accurate as the supply of these elements on Earth is constant and no massive-scale chemical reactions are changing $\\text{N}_2$, $\\text{O}_2$, or $\\text{Ar}$ into new compounds, but as the plot of (global monthly $ \\text{CO}_2$ measurements)[https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html#global] below shows, the measured concentration of $\\text{CO}_2$ has been steadily increasing from 340 parts per million (ppm) in 1980 to 407 ppm today. While that difference changes the percents of $\\text{N}_2$, $\\text{O}_2$, and $\\text{Ar}$ by less than 0.01%, **it's a 20% increase in the amount of $\\text{CO}_2$ in the air, in just 40 years!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "TXT_PATH = os.path.join('data', 'weather', 'mlo', 'co2_mm_gl.txt')\n",
    "names = ['year','month','decimal','average','trend']\n",
    "global_co2 = pd.read_table(TXT_PATH,\n",
    "                        header=None, \n",
    "                        skiprows=58,\n",
    "                        delim_whitespace=True)\n",
    "global_co2.columns = names\n",
    "global_co2[['year','month']] = global_co2[['year','month']].astype(str)\n",
    "global_co2['date'] = global_co2['year'] + '-' + global_co2['month']\n",
    "global_co2['date'] = pd.to_datetime(global_co2['date'])\n",
    "global_co2.set_index('date', inplace=True)\n",
    "global_co2.drop(['year','month', 'decimal'], axis=1, inplace=True)\n",
    "# global_co2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23a9b6c9f28>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a9c1fb978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = global_co2.plot(figsize=(12,8), fontsize=12)\n",
    "ax.set_title('Global Monthly Mean ${CO}_2$', fontsize=16)\n",
    "ax.set_xlabel('Date', fontsize=14)\n",
    "ax.set_ylabel('$CO_2$ Parts Per Million', fontsize=14)\n",
    "ax.legend(fontsize=14, loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO: pick up from here. Maybe implement a Bokeh stacked area plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What could be causing the $\\text{CO}_2$ level to rise?\n",
    "\n",
    "As I mentioned above, excluding nuclear reactions, chemical reactions are the only way to change the concentrations of atmospheric gases. The main chemical reaction that produces $\\text{CO}_2$ is the combustion reaction, in which fuel (like wood, gasoline, alcohol, coal, or any other hydrocarbon) and oxygen react and release a lot of energy. Hydrocarbon combustion reactions will always have the form below\n",
    "\n",
    "$$\\text{C}_x\\text{H}_y + z\\text{O}_2 \\longrightarrow x\\text{CO}_2 + \\frac{y}{2}\\text{H}_2\\text{O}$$\n",
    "where $\\text{C}_x\\text{H}_y$ is the general form for any hydrocarbon. For example, gasoline is $\\text{C}_8\\text{H}_18$\n",
    "\n",
    "https://www.engineeringtoolbox.com/co2-emission-fuels-d_1085.html\n",
    "\n",
    "http://vaclavsmil.com/2016/12/14/energy-transitions-global-and-national-perspectives-second-expanded-and-updated-edition/  (data prior to 1965)\n",
    "http://www.bp.com/statisticalreview (data from 1965 and beyond)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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>Year</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Crude_oil</th>\n",
       "      <th>Natural_gas</th>\n",
       "      <th>Traditional_biofuels</th>\n",
       "      <th>Other_renewables</th>\n",
       "      <th>Hydropower</th>\n",
       "      <th>Nuclear</th>\n",
       "      <th>Wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1800-01-01</td>\n",
       "      <td>97.22230</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5555.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1810-01-01</td>\n",
       "      <td>127.77788</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5833.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1820-01-01</td>\n",
       "      <td>152.77790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6111.111111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1830-01-01</td>\n",
       "      <td>263.88910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6388.888889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1840-01-01</td>\n",
       "      <td>355.55584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6944.444444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year       Coal  Solar  Crude_oil  Natural_gas  Traditional_biofuels  \\\n",
       "0 1800-01-01   97.22230    0.0        0.0          0.0           5555.555556   \n",
       "1 1810-01-01  127.77788    0.0        0.0          0.0           5833.333333   \n",
       "2 1820-01-01  152.77790    0.0        0.0          0.0           6111.111111   \n",
       "3 1830-01-01  263.88910    0.0        0.0          0.0           6388.888889   \n",
       "4 1840-01-01  355.55584    0.0        0.0          0.0           6944.444444   \n",
       "\n",
       "   Other_renewables  Hydropower  Nuclear  Wind  \n",
       "0               0.0         0.0      0.0   0.0  \n",
       "1               0.0         0.0      0.0   0.0  \n",
       "2               0.0         0.0      0.0   0.0  \n",
       "3               0.0         0.0      0.0   0.0  \n",
       "4               0.0         0.0      0.0   0.0  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_PATH = os.path.join('data', 'weather', 'global-primary-energy.csv')\n",
    "energy_sources = pd.read_csv(CSV_PATH, encoding='latin1', parse_dates=['Year']) \n",
    "# energy_sources = pd.read_csv(CSV_PATH, encoding='latin1', parse_dates=['Year'], index_col='Year') \n",
    "energy_sources.columns = [x.replace(' (terawatt-hours)','') for x in energy_sources.columns]\n",
    "energy_sources.drop(['Entity', 'Code'], axis=1, inplace=True)\n",
    "energy_sources.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stacked(df):\n",
    "    df_top = df.cumsum(axis=1)\n",
    "    df_bottom = df_top.shift(axis=1).fillna({'y0': 0})[::-1]\n",
    "    df_stack = pd.concat([df_bottom, df_top], ignore_index=True)\n",
    "    return df_stack\n",
    "\n",
    "from bokeh.palettes import brewer\n",
    "\n",
    "def stacked_area(df, **kwargs):\n",
    "    \"\"\"Return a stacked area plot.\"\"\"\n",
    "    columns = df.columns\n",
    "    series_noun = df.columns\n",
    "    areas = stacked(df)\n",
    "    x2 = np.hstack((df.index[::-1], df.index))\n",
    "\n",
    "    p = figure(\n",
    "        width=900, height=600, tools=[\n",
    "            HoverTool(tooltips=[('Coal', \"@Coal\"), \n",
    "                                ('Solar', '@Solar'), \n",
    "                                ('Crude oil', '@Crude_oil'), \n",
    "                                ('Natural Gas', '@Natural_gas'), \n",
    "                                ('Traditional Biofuels', '@Traditional_biofuels'), \n",
    "                                ('Other Renewables', '@Other_renewables'),\n",
    "                                ('Hydropower', '@Hydropower'), \n",
    "                                ('Nuclear', '@Nuclear'), \n",
    "                                ('Wind', '@Wind')])\n",
    "        ],\n",
    "        **kwargs\n",
    "    )\n",
    "    p.grid.minor_grid_line_color = '#eeeeee'\n",
    "\n",
    "    ys = []\n",
    "    xs = []\n",
    "    names = []\n",
    "\n",
    "    for col in columns:\n",
    "        ys.append(areas[col])\n",
    "        xs.append(x2)\n",
    "        names.append(col)\n",
    "\n",
    "    source = ColumnDataSource(data=dict(\n",
    "        x=xs,\n",
    "        y=ys,\n",
    "        name=names,\n",
    "    ))\n",
    "\n",
    "    p.patches(\n",
    "        'x', 'y', source=source, color=brewer['Spectral'][areas.shape[1]]\n",
    "    )\n",
    "    p.xaxis.formatter = DatetimeTickFormatter(\n",
    "        formats=dict(days=[\"%a\\n%d %b\"])\n",
    "    )\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Coal', 'Solar', 'Crude_oil', 'Natural_gas', 'Traditional_biofuels',\n",
       "       'Other_renewables', 'Hydropower', 'Nuclear', 'Wind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_sources.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "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>Coal</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Crude oil</th>\n",
       "      <th>Natural gas</th>\n",
       "      <th>Traditional biofuels</th>\n",
       "      <th>Other renewables</th>\n",
       "      <th>Hydropower</th>\n",
       "      <th>Nuclear</th>\n",
       "      <th>Wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>43403.142830</td>\n",
       "      <td>43736.195323</td>\n",
       "      <td>95120.415533</td>\n",
       "      <td>132384.586723</td>\n",
       "      <td>143387.802523</td>\n",
       "      <td>143949.468587</td>\n",
       "      <td>147972.406356</td>\n",
       "      <td>150588.946863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>44015.530330</td>\n",
       "      <td>44271.721259</td>\n",
       "      <td>94757.048189</td>\n",
       "      <td>131353.707009</td>\n",
       "      <td>142464.818119</td>\n",
       "      <td>143001.229065</td>\n",
       "      <td>146904.511029</td>\n",
       "      <td>149479.768100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45233.992180</td>\n",
       "      <td>45430.325167</td>\n",
       "      <td>94913.968858</td>\n",
       "      <td>130652.799408</td>\n",
       "      <td>141872.863827</td>\n",
       "      <td>142369.378754</td>\n",
       "      <td>146255.436374</td>\n",
       "      <td>148796.679510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>45205.498280</td>\n",
       "      <td>45343.059434</td>\n",
       "      <td>94431.565354</td>\n",
       "      <td>129953.829154</td>\n",
       "      <td>141283.915254</td>\n",
       "      <td>141742.658196</td>\n",
       "      <td>145539.854175</td>\n",
       "      <td>148031.803995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>44395.062240</td>\n",
       "      <td>44495.096147</td>\n",
       "      <td>93064.122617</td>\n",
       "      <td>128076.395807</td>\n",
       "      <td>139517.582437</td>\n",
       "      <td>139944.804923</td>\n",
       "      <td>143622.100042</td>\n",
       "      <td>146093.622097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>44277.591120</td>\n",
       "      <td>44342.286232</td>\n",
       "      <td>92324.566232</td>\n",
       "      <td>126357.651322</td>\n",
       "      <td>137911.027922</td>\n",
       "      <td>138309.583310</td>\n",
       "      <td>141811.231852</td>\n",
       "      <td>144463.389419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>42282.467300</td>\n",
       "      <td>42316.283554</td>\n",
       "      <td>89829.741464</td>\n",
       "      <td>123257.238244</td>\n",
       "      <td>134923.904914</td>\n",
       "      <td>135300.401547</td>\n",
       "      <td>138741.656886</td>\n",
       "      <td>141507.790671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40427.431150</td>\n",
       "      <td>40448.349069</td>\n",
       "      <td>86452.593789</td>\n",
       "      <td>117568.948939</td>\n",
       "      <td>129316.385599</td>\n",
       "      <td>129653.355988</td>\n",
       "      <td>132909.025335</td>\n",
       "      <td>135620.976065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41035.159780</td>\n",
       "      <td>41047.751011</td>\n",
       "      <td>87862.007510</td>\n",
       "      <td>119821.433620</td>\n",
       "      <td>131650.199450</td>\n",
       "      <td>131963.706950</td>\n",
       "      <td>135230.757068</td>\n",
       "      <td>137968.616345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40474.289930</td>\n",
       "      <td>40481.992043</td>\n",
       "      <td>87489.630063</td>\n",
       "      <td>118635.795353</td>\n",
       "      <td>130546.453413</td>\n",
       "      <td>130841.588237</td>\n",
       "      <td>133925.319975</td>\n",
       "      <td>136671.801050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38307.768360</td>\n",
       "      <td>38313.550935</td>\n",
       "      <td>84649.708045</td>\n",
       "      <td>114573.525195</td>\n",
       "      <td>126566.642435</td>\n",
       "      <td>126839.668986</td>\n",
       "      <td>129878.009108</td>\n",
       "      <td>132684.150349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>36451.830290</td>\n",
       "      <td>36456.060207</td>\n",
       "      <td>82235.461347</td>\n",
       "      <td>111301.084147</td>\n",
       "      <td>123377.231437</td>\n",
       "      <td>123632.367341</td>\n",
       "      <td>126552.554518</td>\n",
       "      <td>129319.607720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33987.208680</td>\n",
       "      <td>33990.218155</td>\n",
       "      <td>79163.418485</td>\n",
       "      <td>107401.943335</td>\n",
       "      <td>119561.695505</td>\n",
       "      <td>119795.922364</td>\n",
       "      <td>122599.643494</td>\n",
       "      <td>125359.438239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31804.607490</td>\n",
       "      <td>31806.901732</td>\n",
       "      <td>75327.497962</td>\n",
       "      <td>102589.849232</td>\n",
       "      <td>114918.551602</td>\n",
       "      <td>115135.386751</td>\n",
       "      <td>117762.093051</td>\n",
       "      <td>120405.152696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29258.898310</td>\n",
       "      <td>29260.705570</td>\n",
       "      <td>71770.426720</td>\n",
       "      <td>98288.698610</td>\n",
       "      <td>110758.698610</td>\n",
       "      <td>110963.312207</td>\n",
       "      <td>113598.694071</td>\n",
       "      <td>116296.258696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28174.870740</td>\n",
       "      <td>28176.296448</td>\n",
       "      <td>70324.363298</td>\n",
       "      <td>96125.896918</td>\n",
       "      <td>108625.896918</td>\n",
       "      <td>108816.456488</td>\n",
       "      <td>111403.943330</td>\n",
       "      <td>114058.910824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27734.283470</td>\n",
       "      <td>27735.434324</td>\n",
       "      <td>69482.742744</td>\n",
       "      <td>94856.180894</td>\n",
       "      <td>107356.180894</td>\n",
       "      <td>107540.086857</td>\n",
       "      <td>110196.621871</td>\n",
       "      <td>112778.776670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26808.322770</td>\n",
       "      <td>26809.287327</td>\n",
       "      <td>68146.127607</td>\n",
       "      <td>92540.054677</td>\n",
       "      <td>104954.110407</td>\n",
       "      <td>105132.263955</td>\n",
       "      <td>107744.508198</td>\n",
       "      <td>110268.373270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26669.065930</td>\n",
       "      <td>26669.945655</td>\n",
       "      <td>67180.547705</td>\n",
       "      <td>90975.993865</td>\n",
       "      <td>103184.977415</td>\n",
       "      <td>103353.777036</td>\n",
       "      <td>105948.252741</td>\n",
       "      <td>108381.833311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26868.040010</td>\n",
       "      <td>26868.797082</td>\n",
       "      <td>67048.551862</td>\n",
       "      <td>90439.348342</td>\n",
       "      <td>102505.374322</td>\n",
       "      <td>102666.816096</td>\n",
       "      <td>105230.018341</td>\n",
       "      <td>107621.613667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26827.282000</td>\n",
       "      <td>26827.986925</td>\n",
       "      <td>66034.160995</td>\n",
       "      <td>89439.413925</td>\n",
       "      <td>101364.156265</td>\n",
       "      <td>101515.545236</td>\n",
       "      <td>104033.687928</td>\n",
       "      <td>106442.395683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26166.420260</td>\n",
       "      <td>26167.061860</td>\n",
       "      <td>64519.759770</td>\n",
       "      <td>86845.150090</td>\n",
       "      <td>98630.263110</td>\n",
       "      <td>98776.356497</td>\n",
       "      <td>101261.377047</td>\n",
       "      <td>103585.415729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26027.212370</td>\n",
       "      <td>26027.813362</td>\n",
       "      <td>63910.138782</td>\n",
       "      <td>85483.432202</td>\n",
       "      <td>97130.550852</td>\n",
       "      <td>97270.850790</td>\n",
       "      <td>99630.469323</td>\n",
       "      <td>101858.335564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25881.255390</td>\n",
       "      <td>25881.812186</td>\n",
       "      <td>62955.007706</td>\n",
       "      <td>84376.030296</td>\n",
       "      <td>95886.770376</td>\n",
       "      <td>96021.601157</td>\n",
       "      <td>98362.844992</td>\n",
       "      <td>100549.208643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25754.677430</td>\n",
       "      <td>25755.146243</td>\n",
       "      <td>63155.774603</td>\n",
       "      <td>84368.682013</td>\n",
       "      <td>95744.640403</td>\n",
       "      <td>95875.086991</td>\n",
       "      <td>98082.781297</td>\n",
       "      <td>100196.600663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25837.703150</td>\n",
       "      <td>25838.209610</td>\n",
       "      <td>62618.570730</td>\n",
       "      <td>83729.681370</td>\n",
       "      <td>94972.436270</td>\n",
       "      <td>95094.172940</td>\n",
       "      <td>97305.286558</td>\n",
       "      <td>99402.859935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26125.717520</td>\n",
       "      <td>26126.104083</td>\n",
       "      <td>62880.240693</td>\n",
       "      <td>83435.611583</td>\n",
       "      <td>94546.722693</td>\n",
       "      <td>94663.162720</td>\n",
       "      <td>96817.561327</td>\n",
       "      <td>98819.403826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26428.587780</td>\n",
       "      <td>26428.850003</td>\n",
       "      <td>62762.879163</td>\n",
       "      <td>82773.148403</td>\n",
       "      <td>93767.806823</td>\n",
       "      <td>93872.123183</td>\n",
       "      <td>95958.580296</td>\n",
       "      <td>97905.257056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26164.303970</td>\n",
       "      <td>26164.314166</td>\n",
       "      <td>61901.120946</td>\n",
       "      <td>81036.733456</td>\n",
       "      <td>91916.159706</td>\n",
       "      <td>91980.626391</td>\n",
       "      <td>94071.359657</td>\n",
       "      <td>95964.473530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25407.056500</td>\n",
       "      <td>25407.067103</td>\n",
       "      <td>59974.995963</td>\n",
       "      <td>78262.853163</td>\n",
       "      <td>89028.254953</td>\n",
       "      <td>89090.148214</td>\n",
       "      <td>91125.774169</td>\n",
       "      <td>92861.838872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>25407.05650</td>\n",
       "      <td>25407.067103</td>\n",
       "      <td>59974.995963</td>\n",
       "      <td>78262.853163</td>\n",
       "      <td>89028.254953</td>\n",
       "      <td>89090.148214</td>\n",
       "      <td>91125.774169</td>\n",
       "      <td>92861.838872</td>\n",
       "      <td>92862.034249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>26164.30397</td>\n",
       "      <td>26164.314166</td>\n",
       "      <td>61901.120946</td>\n",
       "      <td>81036.733456</td>\n",
       "      <td>91916.159706</td>\n",
       "      <td>91980.626391</td>\n",
       "      <td>94071.359657</td>\n",
       "      <td>95964.473530</td>\n",
       "      <td>95964.805110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>26428.58778</td>\n",
       "      <td>26428.850003</td>\n",
       "      <td>62762.879163</td>\n",
       "      <td>82773.148403</td>\n",
       "      <td>93767.806823</td>\n",
       "      <td>93872.123183</td>\n",
       "      <td>95958.580296</td>\n",
       "      <td>97905.257056</td>\n",
       "      <td>97907.906833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>26125.71752</td>\n",
       "      <td>26126.104083</td>\n",
       "      <td>62880.240693</td>\n",
       "      <td>83435.611583</td>\n",
       "      <td>94546.722693</td>\n",
       "      <td>94663.162720</td>\n",
       "      <td>96817.561327</td>\n",
       "      <td>98819.403826</td>\n",
       "      <td>98823.036296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>25837.70315</td>\n",
       "      <td>25838.209610</td>\n",
       "      <td>62618.570730</td>\n",
       "      <td>83729.681370</td>\n",
       "      <td>94972.436270</td>\n",
       "      <td>95094.172940</td>\n",
       "      <td>97305.286558</td>\n",
       "      <td>99402.859935</td>\n",
       "      <td>99406.946042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>25754.67743</td>\n",
       "      <td>25755.146243</td>\n",
       "      <td>63155.774603</td>\n",
       "      <td>84368.682013</td>\n",
       "      <td>95744.640403</td>\n",
       "      <td>95875.086991</td>\n",
       "      <td>98082.781297</td>\n",
       "      <td>100196.600663</td>\n",
       "      <td>100201.333475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>25881.25539</td>\n",
       "      <td>25881.812186</td>\n",
       "      <td>62955.007706</td>\n",
       "      <td>84376.030296</td>\n",
       "      <td>95886.770376</td>\n",
       "      <td>96021.601157</td>\n",
       "      <td>98362.844992</td>\n",
       "      <td>100549.208643</td>\n",
       "      <td>100554.904811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>26027.21237</td>\n",
       "      <td>26027.813362</td>\n",
       "      <td>63910.138782</td>\n",
       "      <td>85483.432202</td>\n",
       "      <td>97130.550852</td>\n",
       "      <td>97270.850790</td>\n",
       "      <td>99630.469323</td>\n",
       "      <td>101858.335564</td>\n",
       "      <td>101865.461133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>26166.42026</td>\n",
       "      <td>26167.061860</td>\n",
       "      <td>64519.759770</td>\n",
       "      <td>86845.150090</td>\n",
       "      <td>98630.263110</td>\n",
       "      <td>98776.356497</td>\n",
       "      <td>101261.377047</td>\n",
       "      <td>103585.415729</td>\n",
       "      <td>103593.678812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>26827.28200</td>\n",
       "      <td>26827.986925</td>\n",
       "      <td>66034.160995</td>\n",
       "      <td>89439.413925</td>\n",
       "      <td>101364.156265</td>\n",
       "      <td>101515.545236</td>\n",
       "      <td>104033.687928</td>\n",
       "      <td>106442.395683</td>\n",
       "      <td>106451.575253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>26868.04001</td>\n",
       "      <td>26868.797082</td>\n",
       "      <td>67048.551862</td>\n",
       "      <td>90439.348342</td>\n",
       "      <td>102505.374322</td>\n",
       "      <td>102666.816096</td>\n",
       "      <td>105230.018341</td>\n",
       "      <td>107621.613667</td>\n",
       "      <td>107633.606562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>26669.06593</td>\n",
       "      <td>26669.945655</td>\n",
       "      <td>67180.547705</td>\n",
       "      <td>90975.993865</td>\n",
       "      <td>103184.977415</td>\n",
       "      <td>103353.777036</td>\n",
       "      <td>105948.252741</td>\n",
       "      <td>108381.833311</td>\n",
       "      <td>108397.752100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>26808.32277</td>\n",
       "      <td>26809.287327</td>\n",
       "      <td>68146.127607</td>\n",
       "      <td>92540.054677</td>\n",
       "      <td>104954.110407</td>\n",
       "      <td>105132.263955</td>\n",
       "      <td>107744.508198</td>\n",
       "      <td>110268.373270</td>\n",
       "      <td>110289.611118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>27734.28347</td>\n",
       "      <td>27735.434324</td>\n",
       "      <td>69482.742744</td>\n",
       "      <td>94856.180894</td>\n",
       "      <td>107356.180894</td>\n",
       "      <td>107540.086857</td>\n",
       "      <td>110196.621871</td>\n",
       "      <td>112778.776670</td>\n",
       "      <td>112810.279571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>28174.87074</td>\n",
       "      <td>28176.296448</td>\n",
       "      <td>70324.363298</td>\n",
       "      <td>96125.896918</td>\n",
       "      <td>108625.896918</td>\n",
       "      <td>108816.456488</td>\n",
       "      <td>111403.943330</td>\n",
       "      <td>114058.910824</td>\n",
       "      <td>114097.550106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>29258.89831</td>\n",
       "      <td>29260.705570</td>\n",
       "      <td>71770.426720</td>\n",
       "      <td>98288.698610</td>\n",
       "      <td>110758.698610</td>\n",
       "      <td>110963.312207</td>\n",
       "      <td>113598.694071</td>\n",
       "      <td>116296.258696</td>\n",
       "      <td>116349.237987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>31804.60749</td>\n",
       "      <td>31806.901732</td>\n",
       "      <td>75327.497962</td>\n",
       "      <td>102589.849232</td>\n",
       "      <td>114918.551602</td>\n",
       "      <td>115135.386751</td>\n",
       "      <td>117762.093051</td>\n",
       "      <td>120405.152696</td>\n",
       "      <td>120468.543702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>33987.20868</td>\n",
       "      <td>33990.218155</td>\n",
       "      <td>79163.418485</td>\n",
       "      <td>107401.943335</td>\n",
       "      <td>119561.695505</td>\n",
       "      <td>119795.922364</td>\n",
       "      <td>122599.643494</td>\n",
       "      <td>125359.438239</td>\n",
       "      <td>125445.018315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>36451.83029</td>\n",
       "      <td>36456.060207</td>\n",
       "      <td>82235.461347</td>\n",
       "      <td>111301.084147</td>\n",
       "      <td>123377.231437</td>\n",
       "      <td>123632.367341</td>\n",
       "      <td>126552.554518</td>\n",
       "      <td>129319.607720</td>\n",
       "      <td>129423.827031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>38307.76836</td>\n",
       "      <td>38313.550935</td>\n",
       "      <td>84649.708045</td>\n",
       "      <td>114573.525195</td>\n",
       "      <td>126566.642435</td>\n",
       "      <td>126839.668986</td>\n",
       "      <td>129878.009108</td>\n",
       "      <td>132684.150349</td>\n",
       "      <td>132817.186101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>40474.28993</td>\n",
       "      <td>40481.992043</td>\n",
       "      <td>87489.630063</td>\n",
       "      <td>118635.795353</td>\n",
       "      <td>130546.453413</td>\n",
       "      <td>130841.588237</td>\n",
       "      <td>133925.319975</td>\n",
       "      <td>136671.801050</td>\n",
       "      <td>136842.643474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>41035.15978</td>\n",
       "      <td>41047.751011</td>\n",
       "      <td>87862.007510</td>\n",
       "      <td>119821.433620</td>\n",
       "      <td>131650.199450</td>\n",
       "      <td>131963.706950</td>\n",
       "      <td>135230.757068</td>\n",
       "      <td>137968.616345</td>\n",
       "      <td>138187.675776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>40427.43115</td>\n",
       "      <td>40448.349069</td>\n",
       "      <td>86452.593789</td>\n",
       "      <td>117568.948939</td>\n",
       "      <td>129316.385599</td>\n",
       "      <td>129653.355988</td>\n",
       "      <td>132909.025335</td>\n",
       "      <td>135620.976065</td>\n",
       "      <td>135898.875137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>42282.46730</td>\n",
       "      <td>42316.283554</td>\n",
       "      <td>89829.741464</td>\n",
       "      <td>123257.238244</td>\n",
       "      <td>134923.904914</td>\n",
       "      <td>135300.401547</td>\n",
       "      <td>138741.656886</td>\n",
       "      <td>141507.790671</td>\n",
       "      <td>141849.207948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>44277.59112</td>\n",
       "      <td>44342.286232</td>\n",
       "      <td>92324.566232</td>\n",
       "      <td>126357.651322</td>\n",
       "      <td>137911.027922</td>\n",
       "      <td>138309.583310</td>\n",
       "      <td>141811.231852</td>\n",
       "      <td>144463.389419</td>\n",
       "      <td>144899.930010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>44395.06224</td>\n",
       "      <td>44495.096147</td>\n",
       "      <td>93064.122617</td>\n",
       "      <td>128076.395807</td>\n",
       "      <td>139517.582437</td>\n",
       "      <td>139944.804923</td>\n",
       "      <td>143622.100042</td>\n",
       "      <td>146093.622097</td>\n",
       "      <td>146620.503525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>45205.49828</td>\n",
       "      <td>45343.059434</td>\n",
       "      <td>94431.565354</td>\n",
       "      <td>129953.829154</td>\n",
       "      <td>141283.915254</td>\n",
       "      <td>141742.658196</td>\n",
       "      <td>145539.854175</td>\n",
       "      <td>148031.803995</td>\n",
       "      <td>148676.214234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>45233.99218</td>\n",
       "      <td>45430.325167</td>\n",
       "      <td>94913.968858</td>\n",
       "      <td>130652.799408</td>\n",
       "      <td>141872.863827</td>\n",
       "      <td>142369.378754</td>\n",
       "      <td>146255.436374</td>\n",
       "      <td>148796.679510</td>\n",
       "      <td>149505.977282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>44015.53033</td>\n",
       "      <td>44271.721259</td>\n",
       "      <td>94757.048189</td>\n",
       "      <td>131353.707009</td>\n",
       "      <td>142464.818119</td>\n",
       "      <td>143001.229065</td>\n",
       "      <td>146904.511029</td>\n",
       "      <td>149479.768100</td>\n",
       "      <td>150307.803276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>43403.14283</td>\n",
       "      <td>43736.195323</td>\n",
       "      <td>95120.415533</td>\n",
       "      <td>132384.586723</td>\n",
       "      <td>143387.802523</td>\n",
       "      <td>143949.468587</td>\n",
       "      <td>147972.406356</td>\n",
       "      <td>150588.946863</td>\n",
       "      <td>151548.476369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Coal         Solar     Crude oil    Natural gas  \\\n",
       "0            NaN  43403.142830  43736.195323   95120.415533   \n",
       "1            NaN  44015.530330  44271.721259   94757.048189   \n",
       "2            NaN  45233.992180  45430.325167   94913.968858   \n",
       "3            NaN  45205.498280  45343.059434   94431.565354   \n",
       "4            NaN  44395.062240  44495.096147   93064.122617   \n",
       "5            NaN  44277.591120  44342.286232   92324.566232   \n",
       "6            NaN  42282.467300  42316.283554   89829.741464   \n",
       "7            NaN  40427.431150  40448.349069   86452.593789   \n",
       "8            NaN  41035.159780  41047.751011   87862.007510   \n",
       "9            NaN  40474.289930  40481.992043   87489.630063   \n",
       "10           NaN  38307.768360  38313.550935   84649.708045   \n",
       "11           NaN  36451.830290  36456.060207   82235.461347   \n",
       "12           NaN  33987.208680  33990.218155   79163.418485   \n",
       "13           NaN  31804.607490  31806.901732   75327.497962   \n",
       "14           NaN  29258.898310  29260.705570   71770.426720   \n",
       "15           NaN  28174.870740  28176.296448   70324.363298   \n",
       "16           NaN  27734.283470  27735.434324   69482.742744   \n",
       "17           NaN  26808.322770  26809.287327   68146.127607   \n",
       "18           NaN  26669.065930  26669.945655   67180.547705   \n",
       "19           NaN  26868.040010  26868.797082   67048.551862   \n",
       "20           NaN  26827.282000  26827.986925   66034.160995   \n",
       "21           NaN  26166.420260  26167.061860   64519.759770   \n",
       "22           NaN  26027.212370  26027.813362   63910.138782   \n",
       "23           NaN  25881.255390  25881.812186   62955.007706   \n",
       "24           NaN  25754.677430  25755.146243   63155.774603   \n",
       "25           NaN  25837.703150  25838.209610   62618.570730   \n",
       "26           NaN  26125.717520  26126.104083   62880.240693   \n",
       "27           NaN  26428.587780  26428.850003   62762.879163   \n",
       "28           NaN  26164.303970  26164.314166   61901.120946   \n",
       "29           NaN  25407.056500  25407.067103   59974.995963   \n",
       "..           ...           ...           ...            ...   \n",
       "108  25407.05650  25407.067103  59974.995963   78262.853163   \n",
       "109  26164.30397  26164.314166  61901.120946   81036.733456   \n",
       "110  26428.58778  26428.850003  62762.879163   82773.148403   \n",
       "111  26125.71752  26126.104083  62880.240693   83435.611583   \n",
       "112  25837.70315  25838.209610  62618.570730   83729.681370   \n",
       "113  25754.67743  25755.146243  63155.774603   84368.682013   \n",
       "114  25881.25539  25881.812186  62955.007706   84376.030296   \n",
       "115  26027.21237  26027.813362  63910.138782   85483.432202   \n",
       "116  26166.42026  26167.061860  64519.759770   86845.150090   \n",
       "117  26827.28200  26827.986925  66034.160995   89439.413925   \n",
       "118  26868.04001  26868.797082  67048.551862   90439.348342   \n",
       "119  26669.06593  26669.945655  67180.547705   90975.993865   \n",
       "120  26808.32277  26809.287327  68146.127607   92540.054677   \n",
       "121  27734.28347  27735.434324  69482.742744   94856.180894   \n",
       "122  28174.87074  28176.296448  70324.363298   96125.896918   \n",
       "123  29258.89831  29260.705570  71770.426720   98288.698610   \n",
       "124  31804.60749  31806.901732  75327.497962  102589.849232   \n",
       "125  33987.20868  33990.218155  79163.418485  107401.943335   \n",
       "126  36451.83029  36456.060207  82235.461347  111301.084147   \n",
       "127  38307.76836  38313.550935  84649.708045  114573.525195   \n",
       "128  40474.28993  40481.992043  87489.630063  118635.795353   \n",
       "129  41035.15978  41047.751011  87862.007510  119821.433620   \n",
       "130  40427.43115  40448.349069  86452.593789  117568.948939   \n",
       "131  42282.46730  42316.283554  89829.741464  123257.238244   \n",
       "132  44277.59112  44342.286232  92324.566232  126357.651322   \n",
       "133  44395.06224  44495.096147  93064.122617  128076.395807   \n",
       "134  45205.49828  45343.059434  94431.565354  129953.829154   \n",
       "135  45233.99218  45430.325167  94913.968858  130652.799408   \n",
       "136  44015.53033  44271.721259  94757.048189  131353.707009   \n",
       "137  43403.14283  43736.195323  95120.415533  132384.586723   \n",
       "\n",
       "     Traditional biofuels  Other renewables     Hydropower        Nuclear  \\\n",
       "0           132384.586723     143387.802523  143949.468587  147972.406356   \n",
       "1           131353.707009     142464.818119  143001.229065  146904.511029   \n",
       "2           130652.799408     141872.863827  142369.378754  146255.436374   \n",
       "3           129953.829154     141283.915254  141742.658196  145539.854175   \n",
       "4           128076.395807     139517.582437  139944.804923  143622.100042   \n",
       "5           126357.651322     137911.027922  138309.583310  141811.231852   \n",
       "6           123257.238244     134923.904914  135300.401547  138741.656886   \n",
       "7           117568.948939     129316.385599  129653.355988  132909.025335   \n",
       "8           119821.433620     131650.199450  131963.706950  135230.757068   \n",
       "9           118635.795353     130546.453413  130841.588237  133925.319975   \n",
       "10          114573.525195     126566.642435  126839.668986  129878.009108   \n",
       "11          111301.084147     123377.231437  123632.367341  126552.554518   \n",
       "12          107401.943335     119561.695505  119795.922364  122599.643494   \n",
       "13          102589.849232     114918.551602  115135.386751  117762.093051   \n",
       "14           98288.698610     110758.698610  110963.312207  113598.694071   \n",
       "15           96125.896918     108625.896918  108816.456488  111403.943330   \n",
       "16           94856.180894     107356.180894  107540.086857  110196.621871   \n",
       "17           92540.054677     104954.110407  105132.263955  107744.508198   \n",
       "18           90975.993865     103184.977415  103353.777036  105948.252741   \n",
       "19           90439.348342     102505.374322  102666.816096  105230.018341   \n",
       "20           89439.413925     101364.156265  101515.545236  104033.687928   \n",
       "21           86845.150090      98630.263110   98776.356497  101261.377047   \n",
       "22           85483.432202      97130.550852   97270.850790   99630.469323   \n",
       "23           84376.030296      95886.770376   96021.601157   98362.844992   \n",
       "24           84368.682013      95744.640403   95875.086991   98082.781297   \n",
       "25           83729.681370      94972.436270   95094.172940   97305.286558   \n",
       "26           83435.611583      94546.722693   94663.162720   96817.561327   \n",
       "27           82773.148403      93767.806823   93872.123183   95958.580296   \n",
       "28           81036.733456      91916.159706   91980.626391   94071.359657   \n",
       "29           78262.853163      89028.254953   89090.148214   91125.774169   \n",
       "..                    ...               ...            ...            ...   \n",
       "108          89028.254953      89090.148214   91125.774169   92861.838872   \n",
       "109          91916.159706      91980.626391   94071.359657   95964.473530   \n",
       "110          93767.806823      93872.123183   95958.580296   97905.257056   \n",
       "111          94546.722693      94663.162720   96817.561327   98819.403826   \n",
       "112          94972.436270      95094.172940   97305.286558   99402.859935   \n",
       "113          95744.640403      95875.086991   98082.781297  100196.600663   \n",
       "114          95886.770376      96021.601157   98362.844992  100549.208643   \n",
       "115          97130.550852      97270.850790   99630.469323  101858.335564   \n",
       "116          98630.263110      98776.356497  101261.377047  103585.415729   \n",
       "117         101364.156265     101515.545236  104033.687928  106442.395683   \n",
       "118         102505.374322     102666.816096  105230.018341  107621.613667   \n",
       "119         103184.977415     103353.777036  105948.252741  108381.833311   \n",
       "120         104954.110407     105132.263955  107744.508198  110268.373270   \n",
       "121         107356.180894     107540.086857  110196.621871  112778.776670   \n",
       "122         108625.896918     108816.456488  111403.943330  114058.910824   \n",
       "123         110758.698610     110963.312207  113598.694071  116296.258696   \n",
       "124         114918.551602     115135.386751  117762.093051  120405.152696   \n",
       "125         119561.695505     119795.922364  122599.643494  125359.438239   \n",
       "126         123377.231437     123632.367341  126552.554518  129319.607720   \n",
       "127         126566.642435     126839.668986  129878.009108  132684.150349   \n",
       "128         130546.453413     130841.588237  133925.319975  136671.801050   \n",
       "129         131650.199450     131963.706950  135230.757068  137968.616345   \n",
       "130         129316.385599     129653.355988  132909.025335  135620.976065   \n",
       "131         134923.904914     135300.401547  138741.656886  141507.790671   \n",
       "132         137911.027922     138309.583310  141811.231852  144463.389419   \n",
       "133         139517.582437     139944.804923  143622.100042  146093.622097   \n",
       "134         141283.915254     141742.658196  145539.854175  148031.803995   \n",
       "135         141872.863827     142369.378754  146255.436374  148796.679510   \n",
       "136         142464.818119     143001.229065  146904.511029  149479.768100   \n",
       "137         143387.802523     143949.468587  147972.406356  150588.946863   \n",
       "\n",
       "              Wind  \n",
       "0    150588.946863  \n",
       "1    149479.768100  \n",
       "2    148796.679510  \n",
       "3    148031.803995  \n",
       "4    146093.622097  \n",
       "5    144463.389419  \n",
       "6    141507.790671  \n",
       "7    135620.976065  \n",
       "8    137968.616345  \n",
       "9    136671.801050  \n",
       "10   132684.150349  \n",
       "11   129319.607720  \n",
       "12   125359.438239  \n",
       "13   120405.152696  \n",
       "14   116296.258696  \n",
       "15   114058.910824  \n",
       "16   112778.776670  \n",
       "17   110268.373270  \n",
       "18   108381.833311  \n",
       "19   107621.613667  \n",
       "20   106442.395683  \n",
       "21   103585.415729  \n",
       "22   101858.335564  \n",
       "23   100549.208643  \n",
       "24   100196.600663  \n",
       "25    99402.859935  \n",
       "26    98819.403826  \n",
       "27    97905.257056  \n",
       "28    95964.473530  \n",
       "29    92861.838872  \n",
       "..             ...  \n",
       "108   92862.034249  \n",
       "109   95964.805110  \n",
       "110   97907.906833  \n",
       "111   98823.036296  \n",
       "112   99406.946042  \n",
       "113  100201.333475  \n",
       "114  100554.904811  \n",
       "115  101865.461133  \n",
       "116  103593.678812  \n",
       "117  106451.575253  \n",
       "118  107633.606562  \n",
       "119  108397.752100  \n",
       "120  110289.611118  \n",
       "121  112810.279571  \n",
       "122  114097.550106  \n",
       "123  116349.237987  \n",
       "124  120468.543702  \n",
       "125  125445.018315  \n",
       "126  129423.827031  \n",
       "127  132817.186101  \n",
       "128  136842.643474  \n",
       "129  138187.675776  \n",
       "130  135898.875137  \n",
       "131  141849.207948  \n",
       "132  144899.930010  \n",
       "133  146620.503525  \n",
       "134  148676.214234  \n",
       "135  149505.977282  \n",
       "136  150307.803276  \n",
       "137  151548.476369  \n",
       "\n",
       "[138 rows x 9 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "\nSupplying a user-defined data source AND iterable values to glyph methods is\nnot possibe. Either:\n\nPass all data directly as literals:\n\n    p.circe(x=a_list, y=an_array, ...)\n\nOr, put all data in a ColumnDataSource and pass column names:\n\n    source = ColumnDataSource(data=dict(x=a_list, y=an_array))\n    p.circe(x='x', y='y', source=source, ...)\n\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-240-2c2e05e42abf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# areas = stacked(energy_sources)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mstacked_area\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menergy_sources\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-238-e1b1ecd317dc>\u001b[0m in \u001b[0;36mstacked_area\u001b[1;34m(df, **kwargs)\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m     p.patches(\n\u001b[1;32m---> 48\u001b[1;33m         \u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'y'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbrewer\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Spectral'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mareas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     49\u001b[0m     )\n\u001b[0;32m     50\u001b[0m     p.xaxis.formatter = DatetimeTickFormatter(\n",
      "\u001b[1;32mfakesource\u001b[0m in \u001b[0;36mpatches\u001b[1;34m(self, xs, ys, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\bokeh\\plotting\\helpers.py\u001b[0m in \u001b[0;36mfunc\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    728\u001b[0m         \u001b[0mglyph_ca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_pop_colors_and_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    729\u001b[0m         \u001b[0m_process_sequence_literals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_user_source\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 730\u001b[1;33m         \u001b[0m_process_sequence_literals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglyphclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglyph_ca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_user_source\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    731\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    732\u001b[0m         \u001b[1;31m# handle the nonselection glyph, we always set one\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\bokeh\\plotting\\helpers.py\u001b[0m in \u001b[0;36m_process_sequence_literals\u001b[1;34m(glyphclass, kwargs, source, is_user_source)\u001b[0m\n\u001b[0;32m    296\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_user_source\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 298\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_GLYPH_SOURCE_MSG\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    300\u001b[0m         \u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: \nSupplying a user-defined data source AND iterable values to glyph methods is\nnot possibe. Either:\n\nPass all data directly as literals:\n\n    p.circe(x=a_list, y=an_array, ...)\n\nOr, put all data in a ColumnDataSource and pass column names:\n\n    source = ColumnDataSource(data=dict(x=a_list, y=an_array))\n    p.circe(x='x', y='y', source=source, ...)\n\n"
     ]
    }
   ],
   "source": [
    "# areas = stacked(energy_sources)\n",
    "stacked_area(energy_sources)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"f65eaa41-1c16-4afa-a3b2-7612eca73871\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f65eaa41-1c16-4afa-a3b2-7612eca73871' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'f65eaa41-1c16-4afa-a3b2-7612eca73871' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"f65eaa41-1c16-4afa-a3b2-7612eca73871\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"e7b4fca9-818d-49a5-bcd6-be77e0d86d16\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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{\"id\":\"b925b807-2be0-4638-8625-461459c585a9\",\"type\":\"LinearAxis\"},{\"id\":\"6cf051fb-424d-4ed0-83b2-352242201c96\",\"type\":\"Grid\"},{\"id\":\"ff8571e9-b6c9-481e-af2e-8cb23a75b6e4\",\"type\":\"LinearAxis\"},{\"id\":\"8c62840f-5225-4467-8cc0-b150ba791340\",\"type\":\"Grid\"},{\"id\":\"bec5da20-2495-4dd9-a69f-42236d54194e\",\"type\":\"BoxAnnotation\"},{\"id\":\"3b7b9000-bd82-46e0-bc25-86aa905c2257\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"dbe56d45-ec29-42f0-abcf-f594ed1dc7b2\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"4c841d26-150c-4674-ab7c-3b498c078984\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"bc867fdf-303c-47bc-9c52-e6459927e69b\",\"type\":\"DataRange1d\"},\"x_scale\":{\"id\":\"503e4bb0-1137-4714-8de2-f65c36d5162c\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"c3481089-d596-4f63-9ec0-3ff7cf25a1b6\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"5c0263e0-3be3-4aea-80ce-4462e077752d\",\"type\":\"LinearScale\"}},\"id\":\"78aad5d0-a740-418c-b3be-5cda0e8544c7\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"dimension\":1,\"minor_grid_line_color\":{\"value\":\"#eeeeee\"},\"plot\":{\"id\":\"78aad5d0-a740-418c-b3be-5cda0e8544c7\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"642a4dba-ae03-43b0-bc87-dff4843899f0\",\"type\":\"BasicTicker\"}},\"id\":\"8c62840f-5225-4467-8cc0-b150ba791340\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"c614b25a-4333-46bc-8f31-afba138735af\",\"type\":\"Selection\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"dbe56d45-ec29-42f0-abcf-f594ed1dc7b2\",\"type\":\"Title\"},{\"attributes\":{\"formatter\":{\"id\":\"74462709-fc56-4d36-bc74-8e8c7c7f91f8\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"78aad5d0-a740-418c-b3be-5cda0e8544c7\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"a94cea42-3d0a-4efc-aec6-29d1484db82a\",\"type\":\"BasicTicker\"}},\"id\":\"b925b807-2be0-4638-8625-461459c585a9\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"78aad5d0-a740-418c-b3be-5cda0e8544c7\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"8cf0a3fe-6490-4348-917e-bf1dfd95513b\",\"elementid\":\"e7b4fca9-818d-49a5-bcd6-be77e0d86d16\",\"modelid\":\"78aad5d0-a740-418c-b3be-5cda0e8544c7\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "78aad5d0-a740-418c-b3be-5cda0e8544c7"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = brewer['Spectral'][areas.shape[1]]\n",
    "x2 = np.hstack((df.index[::-1], df.index))\n",
    "\n",
    "p = figure()\n",
    "p.grid.minor_grid_line_color = '#eeeeee'\n",
    "\n",
    "p.patches([x2] * areas.shape[1], [areas[c].values for c in areas],\n",
    "          color=colors, alpha=0.8, line_color=None)\n",
    "\n",
    "output_notebook()\n",
    "\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "cdf = ColumnDataSource(energy_sources)\n",
    "p = figure(plot_height=500, plot_width=500, tools=\"\", toolbar_location=None,\n",
    "           title=\"Female Imprisonment Rates vs Male Imprisonment Rates in {}\"\n",
    "           .format(yr))\n",
    "p.background_fill_color=\"#f5f5f5\"\n",
    "p.grid.grid_line_color=\"white\"\n",
    "p.xaxis.axis_label = 'Male Imprisonment Rate [per 100k State male pop.]'\n",
    "p.yaxis.axis_label = 'Female Imprisonment Rate [per 100k State female pop.]'\n",
    "p.scatter(x='Male_rate',\n",
    "          y='Female_rate',\n",
    "          source=cdf,\n",
    "          fill_alpha=0.6,\n",
    "          size=12,\n",
    "          line_color=None)\n",
    "\n",
    "p.add_tools(HoverTool(\n",
    "    tooltips=[\n",
    "        ('State', '@Code'),\n",
    "        ('Male Rate', '@Male_rate'),\n",
    "        ('Female Rate', '@Female_rate'),\n",
    "    ],\n",
    "))\n",
    "\n",
    "output_notebook()\n",
    "show(p)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>y0</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "      <th>y5</th>\n",
       "      <th>y6</th>\n",
       "      <th>y7</th>\n",
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       "      <th>y9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>55</td>\n",
       "      <td>85</td>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>92</td>\n",
       "      <td>13</td>\n",
       "      <td>55</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>81</td>\n",
       "      <td>40</td>\n",
       "      <td>59</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>93</td>\n",
       "      <td>97</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>79</td>\n",
       "      <td>60</td>\n",
       "      <td>83</td>\n",
       "      <td>64</td>\n",
       "      <td>20</td>\n",
       "      <td>88</td>\n",
       "      <td>97</td>\n",
       "      <td>21</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>62</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>48</td>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>48</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>96</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "      <td>88</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47</td>\n",
       "      <td>61</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>75</td>\n",
       "      <td>53</td>\n",
       "      <td>89</td>\n",
       "      <td>27</td>\n",
       "      <td>92</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>45</td>\n",
       "      <td>88</td>\n",
       "      <td>63</td>\n",
       "      <td>56</td>\n",
       "      <td>66</td>\n",
       "      <td>15</td>\n",
       "      <td>85</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66</td>\n",
       "      <td>65</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>93</td>\n",
       "      <td>42</td>\n",
       "      <td>77</td>\n",
       "      <td>50</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>54</td>\n",
       "      <td>95</td>\n",
       "      <td>44</td>\n",
       "      <td>12</td>\n",
       "      <td>67</td>\n",
       "      <td>40</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "      <td>52</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>29</td>\n",
       "      <td>78</td>\n",
       "      <td>21</td>\n",
       "      <td>84</td>\n",
       "      <td>13</td>\n",
       "      <td>87</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>35</td>\n",
       "      <td>78</td>\n",
       "      <td>38</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>65</td>\n",
       "      <td>82</td>\n",
       "      <td>93</td>\n",
       "      <td>40</td>\n",
       "      <td>22</td>\n",
       "      <td>81</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29</td>\n",
       "      <td>65</td>\n",
       "      <td>48</td>\n",
       "      <td>89</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>34</td>\n",
       "      <td>82</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>97</td>\n",
       "      <td>80</td>\n",
       "      <td>12</td>\n",
       "      <td>33</td>\n",
       "      <td>56</td>\n",
       "      <td>63</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>69</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>78</td>\n",
       "      <td>98</td>\n",
       "      <td>16</td>\n",
       "      <td>62</td>\n",
       "      <td>83</td>\n",
       "      <td>37</td>\n",
       "      <td>95</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>90</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>99</td>\n",
       "      <td>54</td>\n",
       "      <td>30</td>\n",
       "      <td>76</td>\n",
       "      <td>45</td>\n",
       "      <td>78</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16</td>\n",
       "      <td>65</td>\n",
       "      <td>54</td>\n",
       "      <td>85</td>\n",
       "      <td>67</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>98</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>99</td>\n",
       "      <td>70</td>\n",
       "      <td>59</td>\n",
       "      <td>63</td>\n",
       "      <td>19</td>\n",
       "      <td>94</td>\n",
       "      <td>26</td>\n",
       "      <td>89</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>44</td>\n",
       "      <td>57</td>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>73</td>\n",
       "      <td>17</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    y0  y1  y2  y3  y4  y5  y6  y7  y8  y9\n",
       "0   30  60  55  85  10  46  63  31  11  97\n",
       "1   32  92  13  55  26  24  81  40  59  12\n",
       "2   32  44  19  93  97  93  23  37  41  52\n",
       "3   79  60  83  64  20  88  97  21  32  16\n",
       "4   33  62  18  18  48  37  22  10  68  79\n",
       "5   48  27  77  42  34  96  40  70  88  74\n",
       "6   47  61  25  43  75  53  89  27  92  13\n",
       "7   45  88  63  56  66  15  85  62  22  66\n",
       "8   66  65  35  41  93  42  77  50  45  18\n",
       "9   54  95  44  12  67  40  72  74  52  23\n",
       "10  10  85  85  29  78  21  84  13  87  85\n",
       "11  35  78  38  54  76  69  18  29  66  87\n",
       "12  65  82  93  40  22  81  47  41  32  77\n",
       "13  29  65  48  89  14  24  43  34  82  52\n",
       "14  97  80  12  33  56  63  40  35  69  92\n",
       "15  78  98  16  62  83  37  95  31  54  40\n",
       "16  90  31  30  99  54  30  76  45  78  87\n",
       "17  16  65  54  85  67  28  28  20  98  14\n",
       "18  99  70  59  63  19  94  26  89  40  52\n",
       "19  44  57  19  51  73  17  71  65  79  18"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randint(10, 100, size=(20, 10))).add_prefix('y')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Fuel</th>\n",
       "      <th>liq_dens</th>\n",
       "      <th>spec_c_cont</th>\n",
       "      <th colspan=\"2\" halign=\"left\">spec_en</th>\n",
       "      <th colspan=\"6\" halign=\"left\">spec_CO2_em</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>units</th>\n",
       "      <th>kg/l</th>\n",
       "      <th>kgC/kgfuel</th>\n",
       "      <th>kWh/kgfuel</th>\n",
       "      <th>Btu/lbfuel</th>\n",
       "      <th>KgCO2/kgfuel</th>\n",
       "      <th>KgCO2/galfuel</th>\n",
       "      <th>lbCO2/galfuel</th>\n",
       "      <th>kgCO2/kWh</th>\n",
       "      <th>kgCO2/GJ</th>\n",
       "      <th>lbCO2/mill Btu</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Methane(natural gas)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>15.4</td>\n",
       "      <td>23900.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.18</td>\n",
       "      <td>49.600</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Propane</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.82</td>\n",
       "      <td>13.8</td>\n",
       "      <td>21300.0</td>\n",
       "      <td>2.99</td>\n",
       "      <td>5.78</td>\n",
       "      <td>12.7</td>\n",
       "      <td>0.22</td>\n",
       "      <td>60.400</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Butane</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.83</td>\n",
       "      <td>13.6</td>\n",
       "      <td>21100.0</td>\n",
       "      <td>3.03</td>\n",
       "      <td>6.47</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.22</td>\n",
       "      <td>61.700</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LPG</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.82</td>\n",
       "      <td>13.7</td>\n",
       "      <td>21200.0</td>\n",
       "      <td>3.01</td>\n",
       "      <td>6.12</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.22</td>\n",
       "      <td>61.000</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gasoline</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.90</td>\n",
       "      <td>12.9</td>\n",
       "      <td>19900.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>9.20</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.071</td>\n",
       "      <td>165.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kerosene (Jet)</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.82</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18500.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.33</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.070</td>\n",
       "      <td>162.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diesel</td>\n",
       "      <td>0.846</td>\n",
       "      <td>0.86</td>\n",
       "      <td>12.7</td>\n",
       "      <td>19605.0</td>\n",
       "      <td>3.15</td>\n",
       "      <td>10.10</td>\n",
       "      <td>22.3</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.069</td>\n",
       "      <td>160.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Heavy fuel oil</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.85</td>\n",
       "      <td>11.6</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>3.11</td>\n",
       "      <td>11.60</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.075</td>\n",
       "      <td>173.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Petroleum coke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.89</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14500.0</td>\n",
       "      <td>3.26</td>\n",
       "      <td>14.70</td>\n",
       "      <td>32.4</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.097</td>\n",
       "      <td>225.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Coal:</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>227.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Anthracite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>3.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.104</td>\n",
       "      <td>229.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bituminous</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.4</td>\n",
       "      <td>13000.0</td>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.079</td>\n",
       "      <td>231.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Subbituminous</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.8</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>1.47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.060</td>\n",
       "      <td>233.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lignite</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.079</td>\n",
       "      <td>236.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Coke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.77</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>2.82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.108</td>\n",
       "      <td>251.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Peat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.52</td>\n",
       "      <td>4.7</td>\n",
       "      <td>7300.0</td>\n",
       "      <td>1.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.112</td>\n",
       "      <td>260.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Ethanol fuel</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.52</td>\n",
       "      <td>8.3</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>1.91</td>\n",
       "      <td>5.71</td>\n",
       "      <td>12.6</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.064</td>\n",
       "      <td>149.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Methanol fuel</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.37</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8500.0</td>\n",
       "      <td>1.37</td>\n",
       "      <td>4.11</td>\n",
       "      <td>9.1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.070</td>\n",
       "      <td>162.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Biodiesel</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.78</td>\n",
       "      <td>11.3</td>\n",
       "      <td>17400.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>9.48</td>\n",
       "      <td>20.9</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.070</td>\n",
       "      <td>162.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wood</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.113</td>\n",
       "      <td>263.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Fuel liq_dens spec_c_cont    spec_en             \\\n",
       "                   units     kg/l  kgC/kgfuel kWh/kgfuel Btu/lbfuel   \n",
       "0   Methane(natural gas)      NaN        0.75       15.4    23900.0   \n",
       "1                Propane    0.510        0.82       13.8    21300.0   \n",
       "2                 Butane    0.564        0.83       13.6    21100.0   \n",
       "3                    LPG    0.537        0.82       13.7    21200.0   \n",
       "4               Gasoline    0.737        0.90       12.9    19900.0   \n",
       "5         Kerosene (Jet)    0.821        0.82       12.0    18500.0   \n",
       "6                 Diesel    0.846        0.86       12.7    19605.0   \n",
       "7         Heavy fuel oil    0.980        0.85       11.6    18000.0   \n",
       "8         Petroleum coke      NaN        0.89        9.4    14500.0   \n",
       "9                  Coal:      NaN         NaN        NaN        NaN   \n",
       "10            Anthracite      NaN        0.92        9.0    14000.0   \n",
       "11            Bituminous      NaN        0.65        8.4    13000.0   \n",
       "12         Subbituminous      NaN        0.40        6.8    10500.0   \n",
       "13               Lignite      NaN        0.30        3.9     6000.0   \n",
       "14                  Coke      NaN        0.77        7.2    11200.0   \n",
       "15                  Peat      NaN        0.52        4.7     7300.0   \n",
       "16          Ethanol fuel    0.789        0.52        8.3    12800.0   \n",
       "17         Methanol fuel    0.791        0.37        5.5     8500.0   \n",
       "18             Biodiesel    0.880        0.78       11.3    17400.0   \n",
       "19                  Wood      NaN        0.50        4.5     7000.0   \n",
       "\n",
       "    spec_CO2_em                                                                \n",
       "   KgCO2/kgfuel KgCO2/galfuel lbCO2/galfuel kgCO2/kWh kgCO2/GJ lbCO2/mill Btu  \n",
       "0          2.75           NaN           NaN      0.18   49.600          115.0  \n",
       "1          2.99          5.78          12.7      0.22   60.400          140.0  \n",
       "2          3.03          6.47          14.3      0.22   61.700          144.0  \n",
       "3          3.01          6.12          13.5      0.22   61.000          142.0  \n",
       "4          3.30          9.20          20.3      0.26    0.071          165.3  \n",
       "5          3.00          9.33          20.6      0.25    0.070          162.5  \n",
       "6          3.15         10.10          22.3      0.25    0.069          160.8  \n",
       "7          3.11         11.60          25.5      0.27    0.075          173.3  \n",
       "8          3.26         14.70          32.4      0.35    0.097          225.1  \n",
       "9           NaN           NaN           NaN       NaN    0.000          227.3  \n",
       "10         3.37           NaN           NaN      0.37    0.104          229.5  \n",
       "11         2.38           NaN           NaN      0.28    0.079          231.7  \n",
       "12         1.47           NaN           NaN      0.22    0.060          233.9  \n",
       "13         1.10           NaN           NaN      0.28    0.079          236.1  \n",
       "14         2.82           NaN           NaN      0.39    0.108          251.5  \n",
       "15         1.91           NaN           NaN      0.40    0.112          260.7  \n",
       "16         1.91          5.71          12.6      0.23    0.064          149.6  \n",
       "17         1.37          4.11           9.1      0.25    0.070          162.2  \n",
       "18         2.85          9.48          20.9      0.25    0.070          162.8  \n",
       "19         1.83           NaN           NaN      0.41    0.113          263.1  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://www.engineeringtoolbox.com/co2-emission-fuels-d_1085.html\n",
    "CSV_PATH = os.path.join('data', 'weather', 'specific_co2.csv')\n",
    "specific_co2 = pd.read_csv(CSV_PATH, encoding='latin1', header=[0,1])\n",
    "# specific_co2.drop()\n",
    "specific_co2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Fuel', 'liq_dens_kg/l', 'spec_c_cont_kgC/kgfuel', 'spec_en_kWh/kgfuel',\n",
       "       'spec_en_Btu/lbfuel', 'spec_CO2em_KgCO2/kgfuel',\n",
       "       'spec_CO2em_KgCO2/galfuel', 'spec_CO2em_lbCO2/galfuel',\n",
       "       'spec_CO2em_kgCO2/kWh', 'spec_CO2em_kgCO2/GJ',\n",
       "       'spec_CO2em_lbCO2/mill_Btu'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specific_co2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O, cm-3</th>\n",
       "      <th>N2, cm-3</th>\n",
       "      <th>O2, cm-3</th>\n",
       "      <th>He, cm-3</th>\n",
       "      <th>Ar, cm-3</th>\n",
       "      <th>H, cm-3</th>\n",
       "      <th>N, cm-3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Height [km]</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.053000e+19</td>\n",
       "      <td>5.508000e+18</td>\n",
       "      <td>1.378000e+14</td>\n",
       "      <td>2.456000e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.839000e+19</td>\n",
       "      <td>4.932000e+18</td>\n",
       "      <td>1.234000e+14</td>\n",
       "      <td>2.199000e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.655000e+19</td>\n",
       "      <td>4.440000e+18</td>\n",
       "      <td>1.111000e+14</td>\n",
       "      <td>1.980000e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.494000e+19</td>\n",
       "      <td>4.008000e+18</td>\n",
       "      <td>1.003000e+14</td>\n",
       "      <td>1.787000e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.349000e+19</td>\n",
       "      <td>3.619000e+18</td>\n",
       "      <td>9.053000e+13</td>\n",
       "      <td>1.614000e+17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             O, cm-3      N2, cm-3      O2, cm-3      He, cm-3      Ar, cm-3  \\\n",
       "Height [km]                                                                    \n",
       "0.0              0.0  2.053000e+19  5.508000e+18  1.378000e+14  2.456000e+17   \n",
       "1.0              0.0  1.839000e+19  4.932000e+18  1.234000e+14  2.199000e+17   \n",
       "2.0              0.0  1.655000e+19  4.440000e+18  1.111000e+14  1.980000e+17   \n",
       "3.0              0.0  1.494000e+19  4.008000e+18  1.003000e+14  1.787000e+17   \n",
       "4.0              0.0  1.349000e+19  3.619000e+18  9.053000e+13  1.614000e+17   \n",
       "\n",
       "             H, cm-3  N, cm-3  \n",
       "Height [km]                    \n",
       "0.0              0.0      0.0  \n",
       "1.0              0.0      0.0  \n",
       "2.0              0.0      0.0  \n",
       "3.0              0.0      0.0  \n",
       "4.0              0.0      0.0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TXT_PATH = os.path.join('data', 'weather', 'nasa_msis_atmosphere_composition_data_12_08.txt')\n",
    "names = ['Height [km]', 'O, cm-3', 'N2, cm-3', 'O2, cm-3', 'Mass Density', \n",
    "         'Temperature_neutral', 'Temperature_exospheric', 'He, cm-3',\n",
    "         'Ar, cm-3', 'H, cm-3','N, cm-3', 'F10_7_daily']\n",
    "atmos_comp = pd.read_table(TXT_PATH, \n",
    "                           header=None, \n",
    "                           skiprows=39,\n",
    "                           delim_whitespace=True)\n",
    "\n",
    "atmos_comp.columns = names\n",
    "atmos_comp.drop(['Temperature_neutral', 'Temperature_exospheric', 'Mass Density', 'F10_7_daily'], axis=1, inplace=True)\n",
    "atmos_comp.set_index('Height [km]', inplace=True)\n",
    "atmos_comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a9c2a5d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,10))\n",
    "ax = fig.add_subplot(111)\n",
    "# ax.plot(atmos_comp['O, cm-3'].values, atmos_comp['O, cm-3'].index, label='O, $cm^{-3}$')\n",
    "ax.plot(atmos_comp['N2, cm-3'].values, atmos_comp['N2, cm-3'].index, label='N$_2$, $cm^{-3}$')\n",
    "ax.plot(atmos_comp['O2, cm-3'].values, atmos_comp['O2, cm-3'].index, label='O$_2$, $cm^{-3}$')\n",
    "# ax.plot(atmos_comp['He, cm-3'].values, atmos_comp['He, cm-3'].index, label='He, $cm^{-3}$')\n",
    "ax.plot(atmos_comp['Ar, cm-3'].values, atmos_comp['Ar, cm-3'].index, label='Ar, $cm^{-3}$')\n",
    "# ax.plot(atmos_comp['H, cm-3'].values, atmos_comp['H, cm-3'].index, label='H, $cm^{-3}$')\n",
    "# ax.plot(atmos_comp['N, cm-3'].values, atmos_comp['N, cm-3'].index, label='N, $cm^{-3}$')\n",
    "ax.set(ylim=[0, 50])\n",
    "ax.set_ylabel('Height [km]', fontsize=14)\n",
    "ax.set_xlabel('Molecules per Cubic Centimeter', fontsize=14)\n",
    "ax.set_title('Atmospheric Air Composition and Density', fontsize=16)\n",
    "ax.legend(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At a height of 0 km, N2 makes up 78.1091% of the dry atmosphere at that height\n",
      "At a height of 0 km, O2 makes up 20.9559% of the dry atmosphere at that height\n",
      "At a height of 0 km, Ar makes up 0.934418% of the dry atmosphere at that height\n",
      "At a height of 0 km, He makes up 0.000524279% of the dry atmosphere at that height\n"
     ]
    }
   ],
   "source": [
    "print('At a height of 0 km, N2 makes up {:2.6}% of the dry atmosphere at that height'\n",
    "      .format(atmos_comp.loc[0,'N2, cm-3'] * 100 / (atmos_comp.loc[0,:].sum()+0.0407)))\n",
    "print('At a height of 0 km, O2 makes up {:2.6}% of the dry atmosphere at that height'\n",
    "      .format(atmos_comp.loc[0,'O2, cm-3'] * 100 / (atmos_comp.loc[0,:].sum()+0.0407)))\n",
    "print('At a height of 0 km, Ar makes up {:2.6}% of the dry atmosphere at that height'\n",
    "      .format(atmos_comp.loc[0,'Ar, cm-3'] * 100 / (atmos_comp.loc[0,:].sum()+0.0407)))\n",
    "print('At a height of 0 km, He makes up {:2.6}% of the dry atmosphere at that height'\n",
    "      .format(atmos_comp.loc[0,'He, cm-3'] * 100 / (atmos_comp.loc[0,:].sum()+0.0407)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/co2/in-situ/surface/\n",
    "\n",
    "ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23a90637898>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a90ed0dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "moving_avg = temp_chi.rolling(window=60).mean()\n",
    "span_avg = moving_avg['Avg_Temp'].mean()\n",
    "moving_avg = moving_avg - span_avg\n",
    "moving_std = temp_chi.rolling(window=12).std()\n",
    "moving_avg['upper'] = moving_avg['Avg_Temp'] + (2 * moving_std['Avg_Temp']) \n",
    "moving_avg['lower'] = moving_avg['Avg_Temp'] - (2 * moving_std['Avg_Temp'])\n",
    "moving_avg.plot(figsize=(7,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.lmplot(data=moving_avg,\n",
    "#            x=moving_avg.index,\n",
    "#            y='Avg_Temp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'co2_levels' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-1bc4e29b0be8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mco2_levels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwindow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m52\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mmstd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mco2_levels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrolling\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwindow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m52\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'upper'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'co2'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmstd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'co2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'lower'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'co2'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmstd\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'co2'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'black'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'#ca0020'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'#0571b0'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'co2_levels' is not defined"
     ]
    }
   ],
   "source": [
    "ma = co2_levels.rolling(window=52).mean()\n",
    "mstd = co2_levels.rolling(window=52).std()\n",
    "ma['upper'] = ma['co2'] + (2 * mstd['co2'])\n",
    "ma['lower'] = ma['co2'] - (2 * mstd['co2'])\n",
    "ax = ma.plot(linewidth=0.8, fontsize=6, figsize=(8,6), color=['black', '#ca0020', '#0571b0'])\n",
    "\n",
    "# Specify labels, legend, and show the plot\n",
    "ax.set_xlabel('Date', fontsize=12)\n",
    "ax.set_ylabel('CO2 levels in Mauai Hawaii', fontsize=12)\n",
    "ax.tick_params(labelsize=12)\n",
    "ax.set_title('Rolling mean and variance of CO2 levels\\nin Mauai Hawaii from 1958 to 2001', fontsize=14)\n",
    "ax.legend(fontsize=14, loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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