Social Cops Mandi Prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "msp_data = pd.read_csv(\"CMO_MSP_Mandi.csv\")\n",
    "monthly_data_cmo = pd.read_csv(\"Monthly_data_cmo.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         commodity  year          Type  msprice  msp_filter\n",
      "0   PADDY-UNHUSKED  2012  Kharif Crops   1250.0           1\n",
      "1  RICE(PADDY-HUS)  2012  Kharif Crops   1280.0           1\n",
      "2     Jowar_Hybrid  2012  Kharif Crops   1500.0           1\n",
      "3    SORGUM(JAWAR)  2012  Kharif Crops   1520.0           1\n",
      "4            BAJRI  2012  Kharif Crops   1175.0           1\n",
      "\n",
      "The shape of data frame is :  (155, 5)\n",
      "\n",
      "There are total 3 type of crops :  ['Kharif Crops' 'Rabi Crops' 'Other Crops']\n",
      "\n",
      "The data is for 5 years :  [2012 2013 2014 2015 2016]\n",
      "\n",
      "The are total 32 commodities :  \n",
      "                        commodity\n",
      "Ragi_Maldandi                   5\n",
      "Gram_Yellow                     5\n",
      "SPILT GERRN GRAM                5\n",
      "SAFFLOWER                       5\n",
      "RICE(PADDY-HUS)                 5\n",
      "PADDY-UNHUSKED                  5\n",
      "SORGUM(JAWAR)                   5\n",
      "Jowar_Hybrid                    5\n",
      "WHEAT(HUSKED)                   5\n",
      "NIGER-SEED                      5\n",
      "PIGEON PEA (TUR)                5\n",
      "WHEAT(UNHUSKED)                 5\n",
      "BAJRI                           5\n",
      "Jute_Yellow                     5\n",
      "COCONUT                         5\n",
      "MUSTARD                         5\n",
      "COTTON                          5\n",
      "SPLIT BLACK GRAM                5\n",
      "Soyabean_Black                  5\n",
      "BARLI                           5\n",
      "Copra (Ball)_Yellow             5\n",
      "SUNFLOWER                       5\n",
      "SOYABEAN                        5\n",
      "Toria_Yellow                    5\n",
      "Cotton_Long Staple              5\n",
      "GR.NUT KERNELS                  5\n",
      "SESAMUM                         5\n",
      "Copra (Milling)_Yellow          5\n",
      "Masur_Yellow                    5\n",
      "MAIZE                           5\n",
      "SUGAR-CANE                      4\n",
      "SUGARCANE                       1\n"
     ]
    }
   ],
   "source": [
    "### Undersanding MSP data\n",
    "print(msp_data.head())\n",
    "print()\n",
    "print(\"The shape of data frame is : \",msp_data.shape)\n",
    "print()\n",
    "print(\"There are total {0} type of crops : \".format(len(msp_data.Type.unique())),msp_data.Type.unique())\n",
    "print()\n",
    "print(\"The data is for {0} years : \".format(len(msp_data.year.unique())),msp_data.year.unique())\n",
    "print()\n",
    "print(\"The are total {0} commodities :  \".format(len(msp_data.commodity.unique())))\n",
    "print(pd.DataFrame(msp_data.commodity.value_counts()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      commodity  year         Type  msprice  msp_filter\n",
      "30   SUGAR-CANE  2012  Other Crops    170.0           1\n",
      "61   SUGAR-CANE  2013  Other Crops    210.0           1\n",
      "92   SUGAR-CANE  2014  Other Crops    220.0           1\n",
      "123  SUGAR-CANE  2015  Other Crops    230.0           1\n",
      "154   SUGARCANE  2016  Other Crops    230.0           1\n"
     ]
    }
   ],
   "source": [
    "print(msp_data[(msp_data[\"commodity\"]==\"SUGAR-CANE\") | (msp_data[\"commodity\"]==\"SUGARCANE\") ])\n",
    "### We can see Sugarcane has been mispelled as Sugar-CANE and SUGARCANE\n",
    "msp_data_1 = msp_data.copy()\n",
    "msp_data_1.commodity = msp_data.commodity.replace(\"SUGAR-CANE\",\"SUGARCANE\")\n",
    "msp_data_1.to_csv('CMO_MSP_MANDI_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Lets convert all commodity names to lower case\n",
    "msp_data_2 = msp_data_1.copy()\n",
    "msp_data_2.commodity = msp_data_2.commodity.str.lower()\n",
    "msp_data_2.to_csv('CMO_MSP_MANDI_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Converting Commidity to lower case\n",
    "monthly_data_cmo_1 = monthly_data_cmo.copy()\n",
    "monthly_data_cmo_1.Commodity = monthly_data_cmo_1.Commodity.str.lower()\n",
    "monthly_data_cmo_1.Commodity = monthly_data_cmo_1.Commodity.replace({'maize(corn.)':'maize (corn.)',\n",
    "                                                                    'soybean':'soyabean',\n",
    "                                                                    'kardai' : 'safflower',\n",
    "                                                                    'sarsav' : 'mustard',\n",
    "                                                                    'nachani' : 'ragi_maldandi'})\n",
    "\n",
    "monthly_data_cmo_1.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We merge the entries for same APMC and Commodity for a same date\n",
    "### The resason for this anomaly is not yet clear, But i assume this is an error in data collection\n",
    "monthly_data_cmo_2 = monthly_data_cmo_1.groupby(['state_name','district_name','APMC','Commodity','Year','Month','date'])\\\n",
    "    .agg({\n",
    "    'arrivals_in_qtl' : 'sum',\n",
    "    'min_price' : 'min',\n",
    "    'max_price' : 'max',\n",
    "    'modal_price' : 'mean'\n",
    "}).reset_index()\n",
    "monthly_data_cmo_2.head()\n",
    "monthly_data_cmo_2.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    state_name district_name        APMC Commodity  Year      Month     date  \\\n",
      "0  Maharashtra    Ahmadnagar  Ahmednagar     bajri  2014   November  2014-11   \n",
      "1  Maharashtra    Ahmadnagar  Ahmednagar     bajri  2014  September  2014-09   \n",
      "\n",
      "   arrivals_in_qtl  min_price  max_price  modal_price  \n",
      "0              395       1507       1750       1629.0  \n",
      "1               24       1400       1750       1575.0  \n",
      "\n",
      "The shape of data frame is :  (62387, 11)\n",
      "\n",
      "There are total 33 Districts  \n",
      "\n",
      "There are total 349 APMC  \n",
      "\n",
      "There are total 201 Commodities  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "### understanding monthly mandi price\n",
    "print(monthly_data_cmo_2.head(2))\n",
    "print()\n",
    "print(\"The shape of data frame is : \",monthly_data_cmo_2.shape)\n",
    "print()\n",
    "print(\"There are total {0} Districts  \".format(len(monthly_data_cmo_2.district_name.unique())))\n",
    "print()\n",
    "print(\"There are total {0} APMC  \".format(len(monthly_data_cmo_2.APMC.unique())))\n",
    "print()\n",
    "print(\"There are total {0} Commodities  \".format(len(monthly_data_cmo_2.Commodity.unique())))\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust Prices For Inflation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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></th>\n",
       "      <th>General index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2013</th>\n",
       "      <th>January</th>\n",
       "      <td>105.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>105.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>106.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>107.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               General index\n",
       "Year Month                  \n",
       "2013 January           105.1\n",
       "     February          105.8\n",
       "     March             106.0\n",
       "     April             106.4\n",
       "     May               107.2"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Read Consumer Index File\n",
    "cpi_data = pd.read_csv('All_India_Index_April-2018.csv')\n",
    "columns_to_keep = ['Sector','Year','Month','General index']\n",
    "cpi_data = cpi_data[columns_to_keep]\n",
    "cpi_data = cpi_data[cpi_data['Sector']=='Rural']\n",
    "cpi_data.drop(columns='Sector',inplace=True)\n",
    "cpi_data.set_index(['Year','Month'],inplace=True)\n",
    "\n",
    "cpi_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_price_adjusted = []\n",
    "max_price_adjusted = []\n",
    "modal_price_adjusted = []\n",
    "\n",
    "for index,row in monthly_data_cmo_2.iterrows():\n",
    "    \n",
    "    year = row['Year']\n",
    "    month = row['Month']\n",
    "    index = cpi_data.loc[(year,month),'General index']\n",
    "    min_price_adjusted.append(row['min_price']*index/100)\n",
    "    max_price_adjusted.append(row['max_price']*index/100)\n",
    "    modal_price_adjusted.append(row['modal_price']*index/100)\n",
    "    \n",
    "monthly_data_cmo_3 = monthly_data_cmo_2.copy()\n",
    "monthly_data_cmo_3['min_price_adjust'] = min_price_adjusted\n",
    "monthly_data_cmo_3['max_price_adjust'] = max_price_adjusted\n",
    "monthly_data_cmo_3['modal_price_normalised'] = modal_price_adjusted\n",
    "    \n",
    "monthly_data_cmo_3.head()\n",
    "monthly_data_cmo_3.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_data_cmo_4 = monthly_data_cmo_3\\\n",
    "    .assign(\n",
    "    q1_apmc_date_comm=monthly_data_cmo_3.groupby(['Commodity','date']).modal_price_normalised.transform(np.percentile,q=25),\n",
    "    q3_apmc_date_comm=monthly_data_cmo_3.groupby(['Commodity','date']).modal_price_normalised.transform(np.percentile,q=75)\n",
    ")\n",
    "\n",
    "outliers = []\n",
    "for index,row in monthly_data_cmo_4.iterrows():\n",
    "    modal_price = row['modal_price_normalised']\n",
    "    q1 = row['q1_apmc_date_comm']\n",
    "    q3 = row['q3_apmc_date_comm']\n",
    "    iqr = q3-q1\n",
    "    if((modal_price > q3 + 1.5 * iqr) or (modal_price < q1 - 1.5 * iqr) ):\n",
    "        outliers.append(True)\n",
    "    else:\n",
    "        outliers.append(False)\n",
    "\n",
    "        \n",
    "monthly_data_cmo_4['outlier'] = outliers\n",
    "monthly_data_cmo_4.head()\n",
    "monthly_data_cmo_4.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### According to this we have 5 % outliers\n",
    "#### We can closely examine these values and further reduce this number\n",
    "##### For now we remove all the outlier values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_data_cmo_5 = monthly_data_cmo_4[monthly_data_cmo_4.outlier==False]\n",
    "monthly_data_cmo_5.to_csv('Monthly_data_cmo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing out assumptions about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pune          5866\n",
       "Ahmadnagar    4458\n",
       "Nagpur        4243\n",
       "Solapur       4130\n",
       "Nasik         3495\n",
       "Satara        2632\n",
       "Buldhana      2612\n",
       "Amaravathi    2506\n",
       "Jalgaon       2465\n",
       "Aurangabad    2244\n",
       "Beed          1860\n",
       "Osmanabad     1676\n",
       "Thane         1583\n",
       "Kolhapur      1557\n",
       "Mumbai        1553\n",
       "Jalna         1456\n",
       "Latur         1372\n",
       "Yewatmal      1371\n",
       "Dhule         1266\n",
       "Parbhani      1246\n",
       "Chandrapur    1163\n",
       "Akola         1056\n",
       "Sangli        1031\n",
       "Nanded        1018\n",
       "Wasim          949\n",
       "Nandurbar      935\n",
       "Hingoli        752\n",
       "Wardha         620\n",
       "Raigad         587\n",
       "Bhandara       519\n",
       "Ratnagiri      399\n",
       "Gadchiroli     343\n",
       "Gondiya        125\n",
       "Name: district_name, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Assumption is there are equal mandis in all Districts\n",
    "monthly_data_cmo_5.district_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
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       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" 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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    }
   ],
   "source": [
    "def count_unique(data):\n",
    "    return len(set(data))\n",
    "\n",
    "### All mandis have all the commodities \n",
    "mandi_commodity_count = pd.DataFrame(monthly_data_cmo_5.groupby('APMC').Commodity.unique())\n",
    "\n",
    "count = []\n",
    "for index,row in mandi_commodity_count.iterrows():\n",
    "    count.append(len(row['Commodity']))\n",
    "\n",
    "monthly_data_cmo_5 = monthly_data_cmo_5.assign(\n",
    "    mandi_size = pd.cut(\n",
    "        monthly_data_cmo_5.groupby('APMC').Commodity.transform(count_unique),labels=['small','medium','large'],bins=[-0.1,10,40,100] ))\n",
    "\n",
    "plt.figure()\n",
    "sns.distplot(count)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We can see that this graph is positively skewed\n",
    "#### This shows that there are more small mandis as compared to large mandis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## All Combination of mandi and commodities have same number of months data\n",
    "monthly_data_cmo_5 = monthly_data_cmo_5.assign(date_count = monthly_data_cmo_5.groupby(['APMC','mandi_size','Commodity']).date.transform('count'))\n",
    "\n",
    "plt.figure()\n",
    "ax = sns.boxenplot(data= monthly_data_cmo_5,x='mandi_size',y='date_count')\n",
    "ax.set_ylabel('Number Of Observation per Commodity')\n",
    "ax.set_xlabel('Mandi Size')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For the purpose of this analysis i only select those APMC_Mandi which have atleast 20 observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Now We are left with only 122 Commodities\n",
    "monthly_data_cmo_6 = monthly_data_cmo_5[monthly_data_cmo_5['date_count']>=20].drop('mandi_size',axis=1)\n",
    "\n",
    "\n",
    "from datetime import datetime\n",
    "def convert_to_date(date_str):\n",
    "    return datetime.strptime(date_str,'%Y-%m').date()\n",
    "\n",
    "date_list = pd.date_range(start='2014-09-01',end='2016-11-01',freq='MS').date\n",
    "\n",
    "monthly_data_cmo_6.date = monthly_data_cmo_6.date.apply(convert_to_date)\n",
    "\n",
    "temp = monthly_data_cmo_6.set_index(['state_name','district_name','APMC','Commodity'])\n",
    "\n",
    "missing_dates_data = []\n",
    "\n",
    "date_com_mean = monthly_data_cmo_6.groupby(['date','Commodity']).mean()\n",
    "for index,row in temp.groupby(level=[0,1,2,3]):\n",
    "    dates = row['date'].values\n",
    "  \n",
    "    missing_dates = [date for date in date_list if date not in dates]\n",
    "\n",
    "    \n",
    "    for date in missing_dates:\n",
    "        if (date,index[3]) in date_com_mean.index:\n",
    "            missing_dates_data.append(index+(date.year,date.month,date)+tuple(date_com_mean.loc[(date,index[3])][1:]))\n",
    "            \n",
    "        else:\n",
    "            missing_dates_data.append(index+(date.year,date.month,date,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,np.nan))\n",
    "  \n",
    "        \n",
    "     \n",
    "\n",
    "monthly_data_cmo_7 = monthly_data_cmo_6.append(pd.DataFrame(data= missing_dates_data,columns=monthly_data_cmo_6.columns))\n",
    "monthly_data_cmo_7 = monthly_data_cmo_7.fillna(method='ffill')\n",
    "monthly_data_cmo_7.head()\n",
    "monthly_data_cmo_7.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Currently we have used ffill a more suitable way would be to use the average of the previous and the next month.\n",
    "#### But for now we leave it to the next iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us detect the seasonality type of each APMC-Comm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data provided has been collected for only two years, it is difficult to identify the seasonality type with visual inspection or statistical test.\n",
    "\n",
    "My approach was to try both additive and multiplicative methods, keep the one which has less autocorelation after deseasonalising.\n",
    "\n",
    "For Now, i have assumed that all seasonalities are additive in nature, and used Holt Winter's method for deseasonalizing the prices.\n",
    "\n",
    "Also we do not account for trend because it is very difficult to get general trend with just two years of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: 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",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "from seasonal import fit_seasons, adjust_seasons\n",
    "\n",
    "monthly_data_cmo_8 = monthly_data_cmo_7.sort_values(by=['district_name','APMC','Commodity','date'])\\\n",
    "    .set_index(['state_name','district_name','APMC','Commodity'])\n",
    "\n",
    "\n",
    "adjusted_modal_price = []\n",
    "\n",
    "for index,row in monthly_data_cmo_8.groupby(level=[0,1,2,3]):\n",
    "    row = row.reset_index()\n",
    "    temp = row[['date','modal_price_normalised']]\n",
    "    temp['date'] = pd.to_datetime(temp['date'])\n",
    "    temp  = temp.set_index('date')\n",
    "    seasons,trend = fit_seasons(temp.modal_price_normalised)\n",
    "   \n",
    "    \n",
    "    adjusted = temp.modal_price_normalised.values\n",
    "    \n",
    "    if seasons is not None:\n",
    "        adjusted = adjust_seasons(list(temp.modal_price_normalised.values),seasons=seasons) \n",
    "   \n",
    "    adjusted_modal_price = adjusted_modal_price+(adjusted.tolist())\n",
    "    \n",
    "monthly_data_cmo_9 = monthly_data_cmo_8.copy()\n",
    "monthly_data_cmo_9['adjusted_modal_price'] = adjusted_modal_price    \n",
    "monthly_data_cmo_9.to_csv('Monthly_data_cmo_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def coeff_of_var(x):\n",
    "    if np.mean(x)==0:\n",
    "        return x\n",
    "    \n",
    "    return np.var(x) / np.mean(x)\n",
    "\n",
    "c_var_APMC_comm = []\n",
    "\n",
    "for index,row in monthly_data_cmo_9.groupby(level=[0,1,2,3]):\n",
    "    temp = list(index) + [coeff_of_var(row.modal_price_normalised)]\n",
    "    c_var_APMC_comm.append(temp)\n",
    "\n",
    "c_var_APMC_comm = pd.DataFrame(columns=['state_name','district_name','APMC','Commodity','c_var'],data=c_var_APMC_comm)\n",
    "c_var_APMC = c_var_APMC_comm.groupby('APMC').agg({'c_var':'mean'})\n",
    "temp = monthly_data_cmo_9.reset_index()\n",
    "c_var_comm = temp.groupby(['Commodity','date']).agg({'adjusted_modal_price' : coeff_of_var}).reset_index().groupby('Commodity').mean()\n",
    "c_var_comm.columns = ['c_var']\n",
    "\n",
    "c_var_average_comm = c_var_APMC_comm.groupby('Commodity').agg({'c_var':'mean'})\n",
    "\n",
    "def is_higVar(row):\n",
    "    if row[0] <=q1:\n",
    "        return -1\n",
    "    else:\n",
    "        if (row[0] >=q3):\n",
    "            return 1\n",
    "        else:\n",
    "            return 0\n",
    "q1,q3 = c_var_APMC.c_var.quantile(q=[0.25,0.75])\n",
    "c_var_APMC['is_HighVarAPMC'] = c_var_APMC.c_var >q3\n",
    "c_var_APMC['is_LowVarAPMC'] = c_var_APMC.c_var <q1\n",
    "\n",
    "\n",
    "q1,q3 = c_var_comm.c_var.quantile(q=[0.25,0.75])\n",
    "c_var_comm['is_HighVarComm'] = c_var_comm.c_var >q3\n",
    "c_var_comm['is_LowVarComm'] = c_var_comm.c_var <q1\n",
    "\n",
    "q1,q3 = c_var_APMC_comm.c_var.quantile(q=[0.25,0.75])\n",
    "c_var_APMC_comm['is_HighVarAPMCComm'] = c_var_APMC_comm.c_var >q3\n",
    "c_var_APMC_comm['is_LowVarAPMCComm'] = c_var_APMC_comm.c_var <q1\n",
    "\n",
    "monthly_data_cmo_10 = monthly_data_cmo_9.reset_index().set_index(['APMC','Commodity']).copy()\\\n",
    ".join(c_var_APMC_comm.set_index(['APMC','Commodity'])[['is_HighVarAPMCComm','is_LowVarAPMCComm']],on=['APMC','Commodity'])\\\n",
    ".reset_index()\\\n",
    ".join(c_var_APMC[['is_HighVarAPMC','is_LowVarAPMC']],on=['APMC'])\\\n",
    ".set_index('Commodity')\\\n",
    ".join(c_var_comm[['is_HighVarComm','is_LowVarComm']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counts of Commdity Types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,(ax1,ax2,ax3) = plt.subplots(nrows=1,ncols=3,figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig,(ax1,ax2,ax3) = plt.subplots(nrows=1,ncols=3,figsize=(12,8))\n",
    "sns.boxenplot(c_var_APMC.c_var,ax=ax1)\n",
    "ax1.set_title('Coeff. of Var of APMC')\n",
    "ax1.spines['top'].set_visible(False)\n",
    "ax1.spines['right'].set_visible(False)\n",
    "ax1.spines['left'].set_visible(False)\n",
    "\n",
    "\n",
    "sns.boxenplot(c_var_comm.c_var,ax=ax2)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['left'].set_visible(False)\n",
    "ax2.set_title('Coeff. of Var of Comm.')\n",
    "\n",
    "\n",
    "sns.boxenplot(c_var_APMC_comm.c_var,ax=ax3)\n",
    "ax3.set_title('Coeff. of Var of Comm. in APMC')\n",
    "ax3.spines['top'].set_visible(False)\n",
    "ax3.spines['right'].set_visible(False)\n",
    "ax3.spines['left'].set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Custom Created Crop grouping\n",
    "main_comm_grouping = pd.read_csv('main_commodity.csv')\n",
    "main_comm_grouping.dropna(inplace=True,subset=['Commodity'])\n",
    "main_comm_grouping.MSP.fillna(inplace=True,value=False)\n",
    "c_var_average_comm_2 = c_var_average_comm.join(main_comm_grouping.set_index('Commodity') )\n",
    "plt.figure()\n",
    "sns.boxplot(data = c_var_average_comm_2[(c_var_average_comm_2['Crop Type - Season']=='Kharif Crops')|(c_var_average_comm_2['Crop Type - Season']=='Rabi Crops')]\n",
    "            ,y='c_var',x='Crop Type - Season',orient='v')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that Rabi Crops show more variation then Kharif Crops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.boxplot(data = c_var_average_comm_2[(c_var_average_comm_2['Crop Type - Season']=='Kharif Crops')|(c_var_average_comm_2['Crop Type - Season']=='Rabi Crops')]\n",
    "            ,y='c_var',x='MSP',orient='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that there is much less varition for crops for which we have the MSP declared. But this could be due the fact that there are very less commodities for which the MSP has been declared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.boxplot(data=c_var_APMC.join(monthly_data_cmo_5[['APMC','mandi_size']].drop_duplicates().set_index('APMC'),\n",
    "               on='APMC'),x='mandi_size',y='c_var')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that larger mandi show greater variation in price, but again we can attribute this to the fact thre are more crops in larger mandis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpi_data_2 = cpi_data.reset_index().groupby('Year').agg({'General index':'mean'})\n",
    "msp_data_3 = msp_data_2.sort_values(by=['commodity','year']).fillna(method='ffill')\n",
    "msp_adjusted = []\n",
    "for index,row in msp_data_3.iterrows():\n",
    " \n",
    "    year = row['year']\n",
    "    if year ==2012:\n",
    "        index =100\n",
    "    else:\n",
    "        index = cpi_data_2.loc[year,'General index']\n",
    "    msp_adjusted.append(row['msprice']*index/100)\n",
    "       \n",
    "msp_data_4 = msp_data_3.copy()\n",
    "msp_data_4['msp_normalised'] = msp_adjusted\n",
    "msp_data_4.commodity.replace({\n",
    "    'ragi_maldandi':'ragi'\n",
    "},inplace=True)\n",
    "\n",
    "msp_data_4.rename(columns={\n",
    "    'commodity':'Commodity',\n",
    "    'year':'Year'\n",
    "},inplace=True)\n",
    "monthly_data_cmo_10 = monthly_data_cmo_9.reset_index().set_index(['Commodity','Year'])\\\n",
    "    .join(msp_data_4.set_index(['Commodity','Year']))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_error_adjusted_price = (np.abs(monthly_data_cmo_10.adjusted_modal_price -monthly_data_cmo_10.msp_normalised)/monthly_data_cmo_10.msp_normalised).dropna()\n",
    "mean_error_adjusted_price = mean_error_adjusted_price.groupby('Commodity').mean().sort_values(ascending=False)\n",
    "\n",
    "mean_error_normalised_price = (np.abs(monthly_data_cmo_10.modal_price_normalised -monthly_data_cmo_10.msp_normalised)/monthly_data_cmo_10.msp_normalised).dropna()\n",
    "mean_error_normalised_price = mean_error_normalised_price.groupby('Commodity').mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_square_error =pd.DataFrame(mean_error_normalised_price).join(pd.DataFrame(mean_error_adjusted_price),lsuffix='normalised',rsuffix='adjusted')\n",
    "mean_square_error.columns = ['mean_error_normalised','mean_error_adjusted']\n",
    "mean_square_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_data_cmo_11 = monthly_data_cmo_10.reset_index().copy()\n",
    "\n",
    "plt.figure()\n",
    "sns.lineplot(data=monthly_data_cmo_11[monthly_data_cmo_11['Commodity']=='split black gram'].sort_values(by='date'),\n",
    "            x='date',\n",
    "            y='modal_price',\n",
    "            label='Split Black Gram')\n",
    "sns.lineplot(data=monthly_data_cmo_11[monthly_data_cmo_11['Commodity']=='mustard'].sort_values(by='date'),\n",
    "            x='date',\n",
    "            y='modal_price',\n",
    "            label='Split Black Gram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we can compare the demand and supply for commodities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corelation = []\n",
    "for index,row in monthly_data_cmo_11.set_index(['APMC','Commodity'])[['arrivals_in_qtl','modal_price']].groupby(level=[0,1]):\n",
    "    corelation.append(list(index)+[row.corr().iloc[0,1]])\n",
    "\n",
    "corelation = pd.DataFrame(corelation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "sns.boxplot(corelation.iloc[:,2],orient='v')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that for a majority of APMC and comm have negative corelation for demand and supply. \n",
    "Also some commodities| have positive corelation. This is a very interesting observation and can be stuided in detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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