Marcellus forecasts and projections (for Github).ipynb

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    "This notebook displays an interactive graphic made with Bokeh, which shows a variety of forecasts and projections for the Marcellus shale gas play in the northeast United States."
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    "import pandas as pd\n",
    "import numpy as np\n",
    "from bokeh.palettes import Blues5, Greys9, Reds6\n",
    "from bokeh.plotting import figure, show, output_notebook, ColumnDataSource\n",
    "from bokeh.models import HoverTool, TapTool"
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      "text/plain": [
       "            Navigant 2014-08  WoodMac 2014-02  Rice Model 2014-04  \\\n",
       "date                                                                \n",
       "2000-07-01               NaN              NaN                 NaN   \n",
       "2001-07-01               NaN              NaN                 NaN   \n",
       "2002-07-01               NaN              NaN                 NaN   \n",
       "2003-07-01               NaN              NaN                 NaN   \n",
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       "\n",
       "            Medlock & Jaffe 2011  UT $4/Mcf  UT EIA price case  UT $6/Mcf  \\\n",
       "date                                                                        \n",
       "2000-07-01                   NaN        NaN                NaN        NaN   \n",
       "2001-07-01                   NaN        NaN                NaN        NaN   \n",
       "2002-07-01                   NaN        NaN                NaN        NaN   \n",
       "2003-07-01                   NaN        NaN                NaN        NaN   \n",
       "2004-07-01                   NaN        NaN                NaN        NaN   \n",
       "\n",
       "            EIA AEO 2016  EIA AEO 2015  EIA AEO 2014  EIA AEO 2013  \\\n",
       "date                                                                 \n",
       "2000-07-01           NaN           NaN           NaN           NaN   \n",
       "2001-07-01           NaN           NaN           NaN           NaN   \n",
       "2002-07-01           NaN           NaN           NaN           NaN   \n",
       "2003-07-01           NaN           NaN           NaN           NaN   \n",
       "2004-07-01           NaN           NaN           NaN           NaN   \n",
       "\n",
       "            EIA AEO 2012  historical  \n",
       "date                                  \n",
       "2000-07-01           NaN         NaN  \n",
       "2001-07-01           NaN         NaN  \n",
       "2002-07-01           NaN         NaN  \n",
       "2003-07-01           NaN         NaN  \n",
       "2004-07-01           NaN         NaN  "
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     "execution_count": 12,
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   "source": [
    "forecasting_path = '/Users/mason/Dropbox/Documents [DB]/work/forecasting project - Together'\n",
    "file1 = 'Marcellus forecasts - all 2016-08 for Github.csv'\n",
    "all_forecasts = pd.read_csv(forecasting_path+'/'+file1,\n",
    "                     index_col=0, parse_dates=True)\n",
    "all_forecasts.head()"
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       "Index(['Navigant 2014-08', 'WoodMac 2014-02', 'Rice Model 2014-04',\n",
       "       'Medlock & Jaffe 2011', 'UT $4/Mcf', 'UT EIA price case', 'UT $6/Mcf',\n",
       "       'EIA AEO 2016', 'EIA AEO 2015', 'EIA AEO 2014', 'EIA AEO 2013',\n",
       "       'EIA AEO 2012', 'historical'],\n",
       "      dtype='object')"
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     "execution_count": 13,
     "metadata": {},
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    "all_forecasts.columns"
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    "numlines = len(all_forecasts.columns)\n",
    "\n",
    "all_forecasts_dict = {\n",
    "    'xs': [all_forecasts.index.values] * numlines,\n",
    "    'ys': [all_forecasts[name].values for name in all_forecasts],\n",
    "    'name': [name for name in all_forecasts]\n",
    "}"
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       "                   name                                                 xs  \\\n",
       "0      Navigant 2014-08  [2000-07-01T00:00:00.000000000, 2001-07-01T00:...   \n",
       "1       WoodMac 2014-02  [2000-07-01T00:00:00.000000000, 2001-07-01T00:...   \n",
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       "\n",
       "                                                  ys  \n",
       "0  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
       "1  [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...  \n",
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   "source": [
    "# the above code converts the df into a list of lists\n",
    "# for the transformed data in df format again, see below\n",
    "all_forecasts_dict_df = pd.DataFrame(all_forecasts_dict)\n",
    "all_forecasts_dict_df.head()"
   ]
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     "data": {
      "text/plain": [
       "0         Navigant 2014-08\n",
       "1          WoodMac 2014-02\n",
       "2       Rice Model 2014-04\n",
       "3     Medlock & Jaffe 2011\n",
       "4                UT $4/Mcf\n",
       "5        UT EIA price case\n",
       "6                UT $6/Mcf\n",
       "7             EIA AEO 2016\n",
       "8             EIA AEO 2015\n",
       "9             EIA AEO 2014\n",
       "10            EIA AEO 2013\n",
       "11            EIA AEO 2012\n",
       "12              historical\n",
       "Name: name, dtype: object"
      ]
     },
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   "source": [
    "all_forecasts_dict_df['name']"
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       "(13, 13)"
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   "source": [
    "# append additional columns to all_forecasts_dict for styling\n",
    "# do this *after* steps above, \n",
    "# otherwise it gets complicated to separate data points from meta-data\n",
    "# and doing it here by passing in a dict for specifying styling\n",
    "# will make it easier to experiment with styling\n",
    "\n",
    "mypalette = [Greys9[3], Greys9[3], Greys9[3], Greys9[3], \n",
    "             Blues5[0], Blues5[1], Blues5[2],\n",
    "             Reds6[0], Reds6[1], Reds6[2], Reds6[3], Reds6[4], \n",
    "             'black'\n",
    "            ]\n",
    "\n",
    "# check lengths match\n",
    "len(all_forecasts_dict_df['name']), len(mypalette)"
   ]
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       "          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(\"ac356c34-c5bc-493b-abaa-2b76bee55c20\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'ac356c34-c5bc-493b-abaa-2b76bee55c20' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
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       "            var render_items = [{\"docid\":\"b54c5c5e-bd8c-48e6-b23b-65cbbd4e3ec0\",\"elementid\":\"ac356c34-c5bc-493b-abaa-2b76bee55c20\",\"modelid\":\"8cf7c940-8cdc-4b3c-9708-b03d643c1cc1\",\"notebook_comms_target\":\"8b4e9ab3-e06b-4e72-9729-65fc7755aa88\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
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       "      load_libs(js_urls, function() {\n",
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       "        run_inline_js();\n",
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      ]
     },
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    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[21]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x10f7d2e80>"
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "source = ColumnDataSource(all_forecasts_dict)\n",
    "\n",
    "p = figure(title='Marcellus natural gas production outlooks', \n",
    "            width=500, height=500, x_axis_type=\"datetime\",\n",
    "            tools=[HoverTool(line_policy='next',\n",
    "                             tooltips='@name',\n",
    "                             ),\n",
    "                   TapTool(),\n",
    "                  ],\n",
    "            )\n",
    "\n",
    "p.multi_line(source=source,\n",
    "              xs='xs',\n",
    "              ys='ys',\n",
    "              line_color=mypalette, hover_line_color='mediumspringgreen',\n",
    "              line_width=2,\n",
    "              line_alpha=0.7, hover_line_alpha=1,\n",
    "             )\n",
    "\n",
    "p.xaxis.axis_label = \"years\"\n",
    "p.yaxis.axis_label = \"gas production (Bcf/d)\"\n",
    "\n",
    "show(p)"
   ]
  },
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
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