{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Makeover Monday, 9 April 2018\n",
    "\n",
    "Analysis and visualisation of Arctic sea ice extent for [Makeover Monday](http://www.makeovermonday.co.uk).\n",
    "\n",
    "Data from [US National Snow & Ice data centre](https://nsidc.org/) via [Makeover Monday](http://www.makeovermonday.co.uk/data/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "from datetime import datetime\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read the dataset\n",
    "Rename the columns while we're here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>date</th>\n",
       "      <th>extent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1978-10-26</td>\n",
       "      <td>10.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1978-10-28</td>\n",
       "      <td>10.420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1978-10-30</td>\n",
       "      <td>10.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1978-11-01</td>\n",
       "      <td>10.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1978-11-03</td>\n",
       "      <td>10.777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  extent\n",
       "0 1978-10-26  10.231\n",
       "1 1978-10-28  10.420\n",
       "2 1978-10-30  10.557\n",
       "3 1978-11-01  10.670\n",
       "4 1978-11-03  10.777"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_raw = pd.read_excel('Arctic Sea Ice Extent.xlsx')\n",
    "sea_ice_raw.columns = ['date', 'extent']\n",
    "sea_ice_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>date</th>\n",
       "      <th>extent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12746</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>14.256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12747</th>\n",
       "      <td>2018-03-28</td>\n",
       "      <td>14.302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12748</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>14.238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749</th>\n",
       "      <td>2018-03-30</td>\n",
       "      <td>14.232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12750</th>\n",
       "      <td>2018-03-31</td>\n",
       "      <td>14.271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  extent\n",
       "12746 2018-03-27  14.256\n",
       "12747 2018-03-28  14.302\n",
       "12748 2018-03-29  14.238\n",
       "12749 2018-03-30  14.232\n",
       "12750 2018-03-31  14.271"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_raw.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      datetime64[ns]\n",
       "extent           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f91fa5563c8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sea_ice_raw.extent.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare for reshaping\n",
    "Annotate each entry in the dataset with the year, month, and day of year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>date</th>\n",
       "      <th>extent</th>\n",
       "      <th>year</th>\n",
       "      <th>doy</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1978-10-26</td>\n",
       "      <td>10.231</td>\n",
       "      <td>1978</td>\n",
       "      <td>299</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1978-10-28</td>\n",
       "      <td>10.420</td>\n",
       "      <td>1978</td>\n",
       "      <td>301</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1978-10-30</td>\n",
       "      <td>10.557</td>\n",
       "      <td>1978</td>\n",
       "      <td>303</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1978-11-01</td>\n",
       "      <td>10.670</td>\n",
       "      <td>1978</td>\n",
       "      <td>305</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1978-11-03</td>\n",
       "      <td>10.777</td>\n",
       "      <td>1978</td>\n",
       "      <td>307</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  extent  year  doy  month\n",
       "0 1978-10-26  10.231  1978  299     10\n",
       "1 1978-10-28  10.420  1978  301     10\n",
       "2 1978-10-30  10.557  1978  303     10\n",
       "3 1978-11-01  10.670  1978  305     11\n",
       "4 1978-11-03  10.777  1978  307     11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_raw['year'] = sea_ice_raw['date'].dt.year\n",
    "sea_ice_raw['doy'] = sea_ice_raw['date'].dt.dayofyear\n",
    "sea_ice_raw['month'] = sea_ice_raw['date'].dt.month\n",
    "sea_ice_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One column per year, one row per day\n",
    "Create a DataFrame with one column per year, and one row per day number.\n",
    "\n",
    "Plot the data, with each line being a separate year. Pick out the first and last years in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_ice_day_year = pd.pivot_table(sea_ice_raw,index='doy',columns='year',values='extent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f91fa40e588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sea_ice_day_year.loc[:, 1989:2016].plot(legend=None, label='_nolegend_', color='lightgrey')\n",
    "sea_ice_day_year[1988].plot(color='blue', ax=ax, legend=True)\n",
    "sea_ice_day_year[2017].plot(color='red', ax=ax, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One column per year, one row per month\n",
    "Aggregate with median, to give an overall value for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_ice_month_year = pd.pivot_table(sea_ice_raw,index='month',columns='year',values='extent', aggfunc=np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>year</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>14.8940</td>\n",
       "      <td>14.8730</td>\n",
       "      <td>15.2080</td>\n",
       "      <td>14.9650</td>\n",
       "      <td>14.3880</td>\n",
       "      <td>14.7100</td>\n",
       "      <td>14.9860</td>\n",
       "      <td>14.7970</td>\n",
       "      <td>...</td>\n",
       "      <td>14.1150</td>\n",
       "      <td>13.8200</td>\n",
       "      <td>13.4890</td>\n",
       "      <td>13.7030</td>\n",
       "      <td>13.8230</td>\n",
       "      <td>13.6310</td>\n",
       "      <td>13.7520</td>\n",
       "      <td>13.5490</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>13.1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.2745</td>\n",
       "      <td>16.0415</td>\n",
       "      <td>15.6375</td>\n",
       "      <td>15.8555</td>\n",
       "      <td>16.0275</td>\n",
       "      <td>15.3350</td>\n",
       "      <td>15.4475</td>\n",
       "      <td>15.8405</td>\n",
       "      <td>15.9645</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8475</td>\n",
       "      <td>14.6685</td>\n",
       "      <td>14.4320</td>\n",
       "      <td>14.6750</td>\n",
       "      <td>14.7675</td>\n",
       "      <td>14.3730</td>\n",
       "      <td>14.4145</td>\n",
       "      <td>14.1800</td>\n",
       "      <td>14.1560</td>\n",
       "      <td>13.8905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.4355</td>\n",
       "      <td>15.9910</td>\n",
       "      <td>15.6280</td>\n",
       "      <td>15.9685</td>\n",
       "      <td>16.0570</td>\n",
       "      <td>15.5700</td>\n",
       "      <td>15.9355</td>\n",
       "      <td>16.0180</td>\n",
       "      <td>15.7800</td>\n",
       "      <td>...</td>\n",
       "      <td>14.9620</td>\n",
       "      <td>15.1340</td>\n",
       "      <td>14.5450</td>\n",
       "      <td>15.1870</td>\n",
       "      <td>15.0370</td>\n",
       "      <td>14.7200</td>\n",
       "      <td>14.3560</td>\n",
       "      <td>14.3870</td>\n",
       "      <td>14.2730</td>\n",
       "      <td>14.2890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15.5000</td>\n",
       "      <td>15.5090</td>\n",
       "      <td>15.0860</td>\n",
       "      <td>15.4910</td>\n",
       "      <td>15.1450</td>\n",
       "      <td>15.0850</td>\n",
       "      <td>15.4450</td>\n",
       "      <td>15.0190</td>\n",
       "      <td>15.3740</td>\n",
       "      <td>...</td>\n",
       "      <td>14.5190</td>\n",
       "      <td>14.6050</td>\n",
       "      <td>14.1885</td>\n",
       "      <td>14.6600</td>\n",
       "      <td>14.3455</td>\n",
       "      <td>14.0930</td>\n",
       "      <td>13.9875</td>\n",
       "      <td>13.8180</td>\n",
       "      <td>13.7275</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.8360</td>\n",
       "      <td>13.7570</td>\n",
       "      <td>13.8855</td>\n",
       "      <td>14.0030</td>\n",
       "      <td>13.4675</td>\n",
       "      <td>13.5540</td>\n",
       "      <td>14.0770</td>\n",
       "      <td>13.3010</td>\n",
       "      <td>13.7350</td>\n",
       "      <td>...</td>\n",
       "      <td>13.1700</td>\n",
       "      <td>12.8640</td>\n",
       "      <td>12.6580</td>\n",
       "      <td>13.0080</td>\n",
       "      <td>13.0470</td>\n",
       "      <td>12.6760</td>\n",
       "      <td>12.5120</td>\n",
       "      <td>11.9100</td>\n",
       "      <td>12.6050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.5480</td>\n",
       "      <td>12.2690</td>\n",
       "      <td>12.4650</td>\n",
       "      <td>12.6520</td>\n",
       "      <td>12.2760</td>\n",
       "      <td>12.1920</td>\n",
       "      <td>12.2170</td>\n",
       "      <td>11.9650</td>\n",
       "      <td>12.4820</td>\n",
       "      <td>...</td>\n",
       "      <td>11.4410</td>\n",
       "      <td>10.7205</td>\n",
       "      <td>10.7685</td>\n",
       "      <td>10.5215</td>\n",
       "      <td>11.4820</td>\n",
       "      <td>11.1400</td>\n",
       "      <td>10.8890</td>\n",
       "      <td>10.5065</td>\n",
       "      <td>10.7325</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.3285</td>\n",
       "      <td>10.0745</td>\n",
       "      <td>10.3540</td>\n",
       "      <td>10.3500</td>\n",
       "      <td>10.7670</td>\n",
       "      <td>9.9290</td>\n",
       "      <td>9.7705</td>\n",
       "      <td>10.2110</td>\n",
       "      <td>10.4185</td>\n",
       "      <td>...</td>\n",
       "      <td>8.5120</td>\n",
       "      <td>8.1890</td>\n",
       "      <td>7.5490</td>\n",
       "      <td>7.6060</td>\n",
       "      <td>8.0400</td>\n",
       "      <td>7.9990</td>\n",
       "      <td>8.3980</td>\n",
       "      <td>7.8310</td>\n",
       "      <td>7.7940</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.9710</td>\n",
       "      <td>7.7650</td>\n",
       "      <td>7.7640</td>\n",
       "      <td>8.0270</td>\n",
       "      <td>8.0105</td>\n",
       "      <td>7.7570</td>\n",
       "      <td>7.3310</td>\n",
       "      <td>7.9100</td>\n",
       "      <td>7.2650</td>\n",
       "      <td>...</td>\n",
       "      <td>6.1430</td>\n",
       "      <td>5.9280</td>\n",
       "      <td>5.4380</td>\n",
       "      <td>4.6190</td>\n",
       "      <td>5.9340</td>\n",
       "      <td>6.0220</td>\n",
       "      <td>5.5390</td>\n",
       "      <td>5.2620</td>\n",
       "      <td>5.3100</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0370</td>\n",
       "      <td>7.5800</td>\n",
       "      <td>7.0530</td>\n",
       "      <td>7.2330</td>\n",
       "      <td>7.3210</td>\n",
       "      <td>6.6970</td>\n",
       "      <td>6.6570</td>\n",
       "      <td>7.3280</td>\n",
       "      <td>7.1835</td>\n",
       "      <td>...</td>\n",
       "      <td>5.2270</td>\n",
       "      <td>4.8100</td>\n",
       "      <td>4.5410</td>\n",
       "      <td>3.5335</td>\n",
       "      <td>5.1585</td>\n",
       "      <td>5.1905</td>\n",
       "      <td>4.5105</td>\n",
       "      <td>4.3600</td>\n",
       "      <td>4.7555</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.420</td>\n",
       "      <td>8.7540</td>\n",
       "      <td>9.2450</td>\n",
       "      <td>9.0370</td>\n",
       "      <td>9.7025</td>\n",
       "      <td>9.4530</td>\n",
       "      <td>8.3540</td>\n",
       "      <td>8.6305</td>\n",
       "      <td>9.5280</td>\n",
       "      <td>9.0400</td>\n",
       "      <td>...</td>\n",
       "      <td>6.8560</td>\n",
       "      <td>7.0180</td>\n",
       "      <td>6.1910</td>\n",
       "      <td>5.8730</td>\n",
       "      <td>7.5410</td>\n",
       "      <td>7.0490</td>\n",
       "      <td>6.9760</td>\n",
       "      <td>5.8570</td>\n",
       "      <td>6.6010</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.539</td>\n",
       "      <td>10.9200</td>\n",
       "      <td>11.2170</td>\n",
       "      <td>10.9650</td>\n",
       "      <td>11.6980</td>\n",
       "      <td>11.4730</td>\n",
       "      <td>10.7950</td>\n",
       "      <td>11.0410</td>\n",
       "      <td>11.7350</td>\n",
       "      <td>11.3690</td>\n",
       "      <td>...</td>\n",
       "      <td>9.7745</td>\n",
       "      <td>9.6665</td>\n",
       "      <td>9.7790</td>\n",
       "      <td>9.3055</td>\n",
       "      <td>9.7560</td>\n",
       "      <td>10.1320</td>\n",
       "      <td>9.8970</td>\n",
       "      <td>8.6140</td>\n",
       "      <td>9.4720</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.728</td>\n",
       "      <td>13.3220</td>\n",
       "      <td>13.6970</td>\n",
       "      <td>13.5265</td>\n",
       "      <td>13.8400</td>\n",
       "      <td>13.3255</td>\n",
       "      <td>13.0745</td>\n",
       "      <td>13.0650</td>\n",
       "      <td>13.1000</td>\n",
       "      <td>12.5405</td>\n",
       "      <td>...</td>\n",
       "      <td>12.3440</td>\n",
       "      <td>11.9740</td>\n",
       "      <td>12.2450</td>\n",
       "      <td>11.8750</td>\n",
       "      <td>12.2070</td>\n",
       "      <td>12.3950</td>\n",
       "      <td>12.1160</td>\n",
       "      <td>11.7250</td>\n",
       "      <td>11.8790</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year     1978     1979     1980     1981     1982     1983     1984     1985  \\\n",
       "month                                                                          \n",
       "1         NaN  15.5000  14.8940  14.8730  15.2080  14.9650  14.3880  14.7100   \n",
       "2         NaN  16.2745  16.0415  15.6375  15.8555  16.0275  15.3350  15.4475   \n",
       "3         NaN  16.4355  15.9910  15.6280  15.9685  16.0570  15.5700  15.9355   \n",
       "4         NaN  15.5000  15.5090  15.0860  15.4910  15.1450  15.0850  15.4450   \n",
       "5         NaN  13.8360  13.7570  13.8855  14.0030  13.4675  13.5540  14.0770   \n",
       "6         NaN  12.5480  12.2690  12.4650  12.6520  12.2760  12.1920  12.2170   \n",
       "7         NaN  10.3285  10.0745  10.3540  10.3500  10.7670   9.9290   9.7705   \n",
       "8         NaN   7.9710   7.7650   7.7640   8.0270   8.0105   7.7570   7.3310   \n",
       "9         NaN   7.0370   7.5800   7.0530   7.2330   7.3210   6.6970   6.6570   \n",
       "10     10.420   8.7540   9.2450   9.0370   9.7025   9.4530   8.3540   8.6305   \n",
       "11     11.539  10.9200  11.2170  10.9650  11.6980  11.4730  10.7950  11.0410   \n",
       "12     13.728  13.3220  13.6970  13.5265  13.8400  13.3255  13.0745  13.0650   \n",
       "\n",
       "year      1986     1987   ...        2009     2010     2011     2012     2013  \\\n",
       "month                     ...                                                   \n",
       "1      14.9860  14.7970   ...     14.1150  13.8200  13.4890  13.7030  13.8230   \n",
       "2      15.8405  15.9645   ...     14.8475  14.6685  14.4320  14.6750  14.7675   \n",
       "3      16.0180  15.7800   ...     14.9620  15.1340  14.5450  15.1870  15.0370   \n",
       "4      15.0190  15.3740   ...     14.5190  14.6050  14.1885  14.6600  14.3455   \n",
       "5      13.3010  13.7350   ...     13.1700  12.8640  12.6580  13.0080  13.0470   \n",
       "6      11.9650  12.4820   ...     11.4410  10.7205  10.7685  10.5215  11.4820   \n",
       "7      10.2110  10.4185   ...      8.5120   8.1890   7.5490   7.6060   8.0400   \n",
       "8       7.9100   7.2650   ...      6.1430   5.9280   5.4380   4.6190   5.9340   \n",
       "9       7.3280   7.1835   ...      5.2270   4.8100   4.5410   3.5335   5.1585   \n",
       "10      9.5280   9.0400   ...      6.8560   7.0180   6.1910   5.8730   7.5410   \n",
       "11     11.7350  11.3690   ...      9.7745   9.6665   9.7790   9.3055   9.7560   \n",
       "12     13.1000  12.5405   ...     12.3440  11.9740  12.2450  11.8750  12.2070   \n",
       "\n",
       "year      2014     2015     2016     2017     2018  \n",
       "month                                               \n",
       "1      13.6310  13.7520  13.5490  13.0000  13.1230  \n",
       "2      14.3730  14.4145  14.1800  14.1560  13.8905  \n",
       "3      14.7200  14.3560  14.3870  14.2730  14.2890  \n",
       "4      14.0930  13.9875  13.8180  13.7275      NaN  \n",
       "5      12.6760  12.5120  11.9100  12.6050      NaN  \n",
       "6      11.1400  10.8890  10.5065  10.7325      NaN  \n",
       "7       7.9990   8.3980   7.8310   7.7940      NaN  \n",
       "8       6.0220   5.5390   5.2620   5.3100      NaN  \n",
       "9       5.1905   4.5105   4.3600   4.7555      NaN  \n",
       "10      7.0490   6.9760   5.8570   6.6010      NaN  \n",
       "11     10.1320   9.8970   8.6140   9.4720      NaN  \n",
       "12     12.3950  12.1160  11.7250  11.8790      NaN  \n",
       "\n",
       "[12 rows x 41 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_month_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Medians for each month\n",
    "These are the median values of ice extent for each month, over the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     14.38800\n",
       "2     15.29450\n",
       "3     15.39250\n",
       "4     14.59650\n",
       "5     13.13000\n",
       "6     11.71600\n",
       "7      9.32600\n",
       "8      7.20900\n",
       "9      6.19700\n",
       "10     8.38000\n",
       "11    10.63475\n",
       "12    12.72050\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_month_year.median(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now find the difference of each month's extent from the median for that month over all years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.11200</td>\n",
       "      <td>0.50600</td>\n",
       "      <td>0.48500</td>\n",
       "      <td>0.82000</td>\n",
       "      <td>0.57700</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.32200</td>\n",
       "      <td>0.59800</td>\n",
       "      <td>0.40900</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.27300</td>\n",
       "      <td>-0.56800</td>\n",
       "      <td>-0.89900</td>\n",
       "      <td>-0.68500</td>\n",
       "      <td>-0.56500</td>\n",
       "      <td>-0.75700</td>\n",
       "      <td>-0.63600</td>\n",
       "      <td>-0.83900</td>\n",
       "      <td>-1.38800</td>\n",
       "      <td>-1.2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.98000</td>\n",
       "      <td>0.74700</td>\n",
       "      <td>0.34300</td>\n",
       "      <td>0.56100</td>\n",
       "      <td>0.73300</td>\n",
       "      <td>0.04050</td>\n",
       "      <td>0.15300</td>\n",
       "      <td>0.54600</td>\n",
       "      <td>0.67000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.44700</td>\n",
       "      <td>-0.62600</td>\n",
       "      <td>-0.86250</td>\n",
       "      <td>-0.61950</td>\n",
       "      <td>-0.52700</td>\n",
       "      <td>-0.92150</td>\n",
       "      <td>-0.88000</td>\n",
       "      <td>-1.11450</td>\n",
       "      <td>-1.13850</td>\n",
       "      <td>-1.4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.04300</td>\n",
       "      <td>0.59850</td>\n",
       "      <td>0.23550</td>\n",
       "      <td>0.57600</td>\n",
       "      <td>0.66450</td>\n",
       "      <td>0.17750</td>\n",
       "      <td>0.54300</td>\n",
       "      <td>0.62550</td>\n",
       "      <td>0.38750</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.43050</td>\n",
       "      <td>-0.25850</td>\n",
       "      <td>-0.84750</td>\n",
       "      <td>-0.20550</td>\n",
       "      <td>-0.35550</td>\n",
       "      <td>-0.67250</td>\n",
       "      <td>-1.03650</td>\n",
       "      <td>-1.00550</td>\n",
       "      <td>-1.11950</td>\n",
       "      <td>-1.1035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.90350</td>\n",
       "      <td>0.91250</td>\n",
       "      <td>0.48950</td>\n",
       "      <td>0.89450</td>\n",
       "      <td>0.54850</td>\n",
       "      <td>0.48850</td>\n",
       "      <td>0.84850</td>\n",
       "      <td>0.42250</td>\n",
       "      <td>0.77750</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.07750</td>\n",
       "      <td>0.00850</td>\n",
       "      <td>-0.40800</td>\n",
       "      <td>0.06350</td>\n",
       "      <td>-0.25100</td>\n",
       "      <td>-0.50350</td>\n",
       "      <td>-0.60900</td>\n",
       "      <td>-0.77850</td>\n",
       "      <td>-0.86900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.70600</td>\n",
       "      <td>0.62700</td>\n",
       "      <td>0.75550</td>\n",
       "      <td>0.87300</td>\n",
       "      <td>0.33750</td>\n",
       "      <td>0.42400</td>\n",
       "      <td>0.94700</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.60500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>-0.26600</td>\n",
       "      <td>-0.47200</td>\n",
       "      <td>-0.12200</td>\n",
       "      <td>-0.08300</td>\n",
       "      <td>-0.45400</td>\n",
       "      <td>-0.61800</td>\n",
       "      <td>-1.22000</td>\n",
       "      <td>-0.52500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.83200</td>\n",
       "      <td>0.55300</td>\n",
       "      <td>0.74900</td>\n",
       "      <td>0.93600</td>\n",
       "      <td>0.56000</td>\n",
       "      <td>0.47600</td>\n",
       "      <td>0.50100</td>\n",
       "      <td>0.24900</td>\n",
       "      <td>0.76600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.27500</td>\n",
       "      <td>-0.99550</td>\n",
       "      <td>-0.94750</td>\n",
       "      <td>-1.19450</td>\n",
       "      <td>-0.23400</td>\n",
       "      <td>-0.57600</td>\n",
       "      <td>-0.82700</td>\n",
       "      <td>-1.20950</td>\n",
       "      <td>-0.98350</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00250</td>\n",
       "      <td>0.74850</td>\n",
       "      <td>1.02800</td>\n",
       "      <td>1.02400</td>\n",
       "      <td>1.44100</td>\n",
       "      <td>0.60300</td>\n",
       "      <td>0.44450</td>\n",
       "      <td>0.88500</td>\n",
       "      <td>1.09250</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.81400</td>\n",
       "      <td>-1.13700</td>\n",
       "      <td>-1.77700</td>\n",
       "      <td>-1.72000</td>\n",
       "      <td>-1.28600</td>\n",
       "      <td>-1.32700</td>\n",
       "      <td>-0.92800</td>\n",
       "      <td>-1.49500</td>\n",
       "      <td>-1.53200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.76200</td>\n",
       "      <td>0.55600</td>\n",
       "      <td>0.55500</td>\n",
       "      <td>0.81800</td>\n",
       "      <td>0.80150</td>\n",
       "      <td>0.54800</td>\n",
       "      <td>0.12200</td>\n",
       "      <td>0.70100</td>\n",
       "      <td>0.05600</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.06600</td>\n",
       "      <td>-1.28100</td>\n",
       "      <td>-1.77100</td>\n",
       "      <td>-2.59000</td>\n",
       "      <td>-1.27500</td>\n",
       "      <td>-1.18700</td>\n",
       "      <td>-1.67000</td>\n",
       "      <td>-1.94700</td>\n",
       "      <td>-1.89900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.84000</td>\n",
       "      <td>1.38300</td>\n",
       "      <td>0.85600</td>\n",
       "      <td>1.03600</td>\n",
       "      <td>1.12400</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>1.13100</td>\n",
       "      <td>0.98650</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.97000</td>\n",
       "      <td>-1.38700</td>\n",
       "      <td>-1.65600</td>\n",
       "      <td>-2.66350</td>\n",
       "      <td>-1.03850</td>\n",
       "      <td>-1.00650</td>\n",
       "      <td>-1.68650</td>\n",
       "      <td>-1.83700</td>\n",
       "      <td>-1.44150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.04000</td>\n",
       "      <td>0.37400</td>\n",
       "      <td>0.86500</td>\n",
       "      <td>0.65700</td>\n",
       "      <td>1.32250</td>\n",
       "      <td>1.07300</td>\n",
       "      <td>-0.02600</td>\n",
       "      <td>0.25050</td>\n",
       "      <td>1.14800</td>\n",
       "      <td>0.66000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.52400</td>\n",
       "      <td>-1.36200</td>\n",
       "      <td>-2.18900</td>\n",
       "      <td>-2.50700</td>\n",
       "      <td>-0.83900</td>\n",
       "      <td>-1.33100</td>\n",
       "      <td>-1.40400</td>\n",
       "      <td>-2.52300</td>\n",
       "      <td>-1.77900</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.90425</td>\n",
       "      <td>0.28525</td>\n",
       "      <td>0.58225</td>\n",
       "      <td>0.33025</td>\n",
       "      <td>1.06325</td>\n",
       "      <td>0.83825</td>\n",
       "      <td>0.16025</td>\n",
       "      <td>0.40625</td>\n",
       "      <td>1.10025</td>\n",
       "      <td>0.73425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.86025</td>\n",
       "      <td>-0.96825</td>\n",
       "      <td>-0.85575</td>\n",
       "      <td>-1.32925</td>\n",
       "      <td>-0.87875</td>\n",
       "      <td>-0.50275</td>\n",
       "      <td>-0.73775</td>\n",
       "      <td>-2.02075</td>\n",
       "      <td>-1.16275</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00750</td>\n",
       "      <td>0.60150</td>\n",
       "      <td>0.97650</td>\n",
       "      <td>0.80600</td>\n",
       "      <td>1.11950</td>\n",
       "      <td>0.60500</td>\n",
       "      <td>0.35400</td>\n",
       "      <td>0.34450</td>\n",
       "      <td>0.37950</td>\n",
       "      <td>-0.18000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.37650</td>\n",
       "      <td>-0.74650</td>\n",
       "      <td>-0.47550</td>\n",
       "      <td>-0.84550</td>\n",
       "      <td>-0.51350</td>\n",
       "      <td>-0.32550</td>\n",
       "      <td>-0.60450</td>\n",
       "      <td>-0.99550</td>\n",
       "      <td>-0.84150</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year      1978     1979     1980     1981     1982     1983     1984     1985  \\\n",
       "month                                                                           \n",
       "1          NaN  1.11200  0.50600  0.48500  0.82000  0.57700  0.00000  0.32200   \n",
       "2          NaN  0.98000  0.74700  0.34300  0.56100  0.73300  0.04050  0.15300   \n",
       "3          NaN  1.04300  0.59850  0.23550  0.57600  0.66450  0.17750  0.54300   \n",
       "4          NaN  0.90350  0.91250  0.48950  0.89450  0.54850  0.48850  0.84850   \n",
       "5          NaN  0.70600  0.62700  0.75550  0.87300  0.33750  0.42400  0.94700   \n",
       "6          NaN  0.83200  0.55300  0.74900  0.93600  0.56000  0.47600  0.50100   \n",
       "7          NaN  1.00250  0.74850  1.02800  1.02400  1.44100  0.60300  0.44450   \n",
       "8          NaN  0.76200  0.55600  0.55500  0.81800  0.80150  0.54800  0.12200   \n",
       "9          NaN  0.84000  1.38300  0.85600  1.03600  1.12400  0.50000  0.46000   \n",
       "10     2.04000  0.37400  0.86500  0.65700  1.32250  1.07300 -0.02600  0.25050   \n",
       "11     0.90425  0.28525  0.58225  0.33025  1.06325  0.83825  0.16025  0.40625   \n",
       "12     1.00750  0.60150  0.97650  0.80600  1.11950  0.60500  0.35400  0.34450   \n",
       "\n",
       "year      1986     1987   ...       2009     2010     2011     2012     2013  \\\n",
       "month                     ...                                                  \n",
       "1      0.59800  0.40900   ...   -0.27300 -0.56800 -0.89900 -0.68500 -0.56500   \n",
       "2      0.54600  0.67000   ...   -0.44700 -0.62600 -0.86250 -0.61950 -0.52700   \n",
       "3      0.62550  0.38750   ...   -0.43050 -0.25850 -0.84750 -0.20550 -0.35550   \n",
       "4      0.42250  0.77750   ...   -0.07750  0.00850 -0.40800  0.06350 -0.25100   \n",
       "5      0.17100  0.60500   ...    0.04000 -0.26600 -0.47200 -0.12200 -0.08300   \n",
       "6      0.24900  0.76600   ...   -0.27500 -0.99550 -0.94750 -1.19450 -0.23400   \n",
       "7      0.88500  1.09250   ...   -0.81400 -1.13700 -1.77700 -1.72000 -1.28600   \n",
       "8      0.70100  0.05600   ...   -1.06600 -1.28100 -1.77100 -2.59000 -1.27500   \n",
       "9      1.13100  0.98650   ...   -0.97000 -1.38700 -1.65600 -2.66350 -1.03850   \n",
       "10     1.14800  0.66000   ...   -1.52400 -1.36200 -2.18900 -2.50700 -0.83900   \n",
       "11     1.10025  0.73425   ...   -0.86025 -0.96825 -0.85575 -1.32925 -0.87875   \n",
       "12     0.37950 -0.18000   ...   -0.37650 -0.74650 -0.47550 -0.84550 -0.51350   \n",
       "\n",
       "year      2014     2015     2016     2017    2018  \n",
       "month                                              \n",
       "1     -0.75700 -0.63600 -0.83900 -1.38800 -1.2650  \n",
       "2     -0.92150 -0.88000 -1.11450 -1.13850 -1.4040  \n",
       "3     -0.67250 -1.03650 -1.00550 -1.11950 -1.1035  \n",
       "4     -0.50350 -0.60900 -0.77850 -0.86900     NaN  \n",
       "5     -0.45400 -0.61800 -1.22000 -0.52500     NaN  \n",
       "6     -0.57600 -0.82700 -1.20950 -0.98350     NaN  \n",
       "7     -1.32700 -0.92800 -1.49500 -1.53200     NaN  \n",
       "8     -1.18700 -1.67000 -1.94700 -1.89900     NaN  \n",
       "9     -1.00650 -1.68650 -1.83700 -1.44150     NaN  \n",
       "10    -1.33100 -1.40400 -2.52300 -1.77900     NaN  \n",
       "11    -0.50275 -0.73775 -2.02075 -1.16275     NaN  \n",
       "12    -0.32550 -0.60450 -0.99550 -0.84150     NaN  \n",
       "\n",
       "[12 rows x 41 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_month_year_diff = sea_ice_month_year.subtract(sea_ice_month_year.median(axis=1), axis=0)\n",
    "sea_ice_month_year_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>...</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.66300</td>\n",
       "      <td>0.62300</td>\n",
       "      <td>0.35200</td>\n",
       "      <td>0.01600</td>\n",
       "      <td>0.22600</td>\n",
       "      <td>0.59700</td>\n",
       "      <td>0.36900</td>\n",
       "      <td>0.23100</td>\n",
       "      <td>-0.23800</td>\n",
       "      <td>0.02900</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.53900</td>\n",
       "      <td>-0.27300</td>\n",
       "      <td>-0.56800</td>\n",
       "      <td>-0.89900</td>\n",
       "      <td>-0.68500</td>\n",
       "      <td>-0.56500</td>\n",
       "      <td>-0.75700</td>\n",
       "      <td>-0.63600</td>\n",
       "      <td>-0.83900</td>\n",
       "      <td>-1.38800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.18150</td>\n",
       "      <td>0.17650</td>\n",
       "      <td>0.27400</td>\n",
       "      <td>0.06500</td>\n",
       "      <td>0.17150</td>\n",
       "      <td>0.41450</td>\n",
       "      <td>0.25050</td>\n",
       "      <td>-0.06700</td>\n",
       "      <td>-0.10850</td>\n",
       "      <td>0.16900</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.37350</td>\n",
       "      <td>-0.44700</td>\n",
       "      <td>-0.62600</td>\n",
       "      <td>-0.86250</td>\n",
       "      <td>-0.61950</td>\n",
       "      <td>-0.52700</td>\n",
       "      <td>-0.92150</td>\n",
       "      <td>-0.88000</td>\n",
       "      <td>-1.11450</td>\n",
       "      <td>-1.13850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.56750</td>\n",
       "      <td>-0.00450</td>\n",
       "      <td>0.56850</td>\n",
       "      <td>0.04450</td>\n",
       "      <td>0.10050</td>\n",
       "      <td>0.39150</td>\n",
       "      <td>0.15750</td>\n",
       "      <td>-0.12150</td>\n",
       "      <td>-0.27450</td>\n",
       "      <td>0.09550</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.20850</td>\n",
       "      <td>-0.43050</td>\n",
       "      <td>-0.25850</td>\n",
       "      <td>-0.84750</td>\n",
       "      <td>-0.20550</td>\n",
       "      <td>-0.35550</td>\n",
       "      <td>-0.67250</td>\n",
       "      <td>-1.03650</td>\n",
       "      <td>-1.00550</td>\n",
       "      <td>-1.11950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.56150</td>\n",
       "      <td>-0.28950</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.23350</td>\n",
       "      <td>0.08650</td>\n",
       "      <td>0.58950</td>\n",
       "      <td>0.26800</td>\n",
       "      <td>-0.19250</td>\n",
       "      <td>-0.41000</td>\n",
       "      <td>-0.13150</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.23850</td>\n",
       "      <td>-0.07750</td>\n",
       "      <td>0.00850</td>\n",
       "      <td>-0.40800</td>\n",
       "      <td>0.06350</td>\n",
       "      <td>-0.25100</td>\n",
       "      <td>-0.50350</td>\n",
       "      <td>-0.60900</td>\n",
       "      <td>-0.77850</td>\n",
       "      <td>-0.86900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.45900</td>\n",
       "      <td>-0.10300</td>\n",
       "      <td>0.14700</td>\n",
       "      <td>0.35000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.24000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>-0.02300</td>\n",
       "      <td>-0.08000</td>\n",
       "      <td>0.02400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.08200</td>\n",
       "      <td>0.04000</td>\n",
       "      <td>-0.26600</td>\n",
       "      <td>-0.47200</td>\n",
       "      <td>-0.12200</td>\n",
       "      <td>-0.08300</td>\n",
       "      <td>-0.45400</td>\n",
       "      <td>-0.61800</td>\n",
       "      <td>-1.22000</td>\n",
       "      <td>-0.52500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.29500</td>\n",
       "      <td>0.60600</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.37350</td>\n",
       "      <td>0.45850</td>\n",
       "      <td>0.15850</td>\n",
       "      <td>0.30100</td>\n",
       "      <td>-0.20000</td>\n",
       "      <td>0.41400</td>\n",
       "      <td>0.12600</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.48750</td>\n",
       "      <td>-0.27500</td>\n",
       "      <td>-0.99550</td>\n",
       "      <td>-0.94750</td>\n",
       "      <td>-1.19450</td>\n",
       "      <td>-0.23400</td>\n",
       "      <td>-0.57600</td>\n",
       "      <td>-0.82700</td>\n",
       "      <td>-1.20950</td>\n",
       "      <td>-0.98350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.41200</td>\n",
       "      <td>0.83900</td>\n",
       "      <td>-0.16600</td>\n",
       "      <td>0.13200</td>\n",
       "      <td>1.00700</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.62900</td>\n",
       "      <td>-0.36800</td>\n",
       "      <td>0.85200</td>\n",
       "      <td>-0.00400</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.63800</td>\n",
       "      <td>-0.81400</td>\n",
       "      <td>-1.13700</td>\n",
       "      <td>-1.77700</td>\n",
       "      <td>-1.72000</td>\n",
       "      <td>-1.28600</td>\n",
       "      <td>-1.32700</td>\n",
       "      <td>-0.92800</td>\n",
       "      <td>-1.49500</td>\n",
       "      <td>-1.53200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.65600</td>\n",
       "      <td>0.67800</td>\n",
       "      <td>-0.48600</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.58500</td>\n",
       "      <td>0.03800</td>\n",
       "      <td>0.32400</td>\n",
       "      <td>-0.53400</td>\n",
       "      <td>0.96300</td>\n",
       "      <td>0.06600</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.31600</td>\n",
       "      <td>-1.06600</td>\n",
       "      <td>-1.28100</td>\n",
       "      <td>-1.77100</td>\n",
       "      <td>-2.59000</td>\n",
       "      <td>-1.27500</td>\n",
       "      <td>-1.18700</td>\n",
       "      <td>-1.67000</td>\n",
       "      <td>-1.94700</td>\n",
       "      <td>-1.89900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.08800</td>\n",
       "      <td>0.77500</td>\n",
       "      <td>-0.06500</td>\n",
       "      <td>0.23450</td>\n",
       "      <td>1.12450</td>\n",
       "      <td>0.11550</td>\n",
       "      <td>0.88700</td>\n",
       "      <td>-0.12150</td>\n",
       "      <td>1.31000</td>\n",
       "      <td>0.47400</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.53550</td>\n",
       "      <td>-0.97000</td>\n",
       "      <td>-1.38700</td>\n",
       "      <td>-1.65600</td>\n",
       "      <td>-2.66350</td>\n",
       "      <td>-1.03850</td>\n",
       "      <td>-1.00650</td>\n",
       "      <td>-1.68650</td>\n",
       "      <td>-1.83700</td>\n",
       "      <td>-1.44150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.96000</td>\n",
       "      <td>0.41100</td>\n",
       "      <td>0.17100</td>\n",
       "      <td>0.54400</td>\n",
       "      <td>1.03000</td>\n",
       "      <td>0.52800</td>\n",
       "      <td>0.40100</td>\n",
       "      <td>-0.50500</td>\n",
       "      <td>0.89700</td>\n",
       "      <td>-0.05800</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.92100</td>\n",
       "      <td>-1.52400</td>\n",
       "      <td>-1.36200</td>\n",
       "      <td>-2.18900</td>\n",
       "      <td>-2.50700</td>\n",
       "      <td>-0.83900</td>\n",
       "      <td>-1.33100</td>\n",
       "      <td>-1.40400</td>\n",
       "      <td>-2.52300</td>\n",
       "      <td>-1.77900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.81925</td>\n",
       "      <td>0.23775</td>\n",
       "      <td>0.42675</td>\n",
       "      <td>0.22775</td>\n",
       "      <td>0.61475</td>\n",
       "      <td>0.75225</td>\n",
       "      <td>0.39725</td>\n",
       "      <td>0.05775</td>\n",
       "      <td>-0.33325</td>\n",
       "      <td>-0.05775</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.35775</td>\n",
       "      <td>-0.86025</td>\n",
       "      <td>-0.96825</td>\n",
       "      <td>-0.85575</td>\n",
       "      <td>-1.32925</td>\n",
       "      <td>-0.87875</td>\n",
       "      <td>-0.50275</td>\n",
       "      <td>-0.73775</td>\n",
       "      <td>-2.02075</td>\n",
       "      <td>-1.16275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.02650</td>\n",
       "      <td>0.61450</td>\n",
       "      <td>0.50550</td>\n",
       "      <td>0.24850</td>\n",
       "      <td>0.70850</td>\n",
       "      <td>0.69350</td>\n",
       "      <td>0.58050</td>\n",
       "      <td>0.20250</td>\n",
       "      <td>0.22350</td>\n",
       "      <td>0.31450</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.34350</td>\n",
       "      <td>-0.37650</td>\n",
       "      <td>-0.74650</td>\n",
       "      <td>-0.47550</td>\n",
       "      <td>-0.84550</td>\n",
       "      <td>-0.51350</td>\n",
       "      <td>-0.32550</td>\n",
       "      <td>-0.60450</td>\n",
       "      <td>-0.99550</td>\n",
       "      <td>-0.84150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year      1988     1989     1990     1991     1992     1993     1994     1995  \\\n",
       "month                                                                           \n",
       "1      0.66300  0.62300  0.35200  0.01600  0.22600  0.59700  0.36900  0.23100   \n",
       "2      0.18150  0.17650  0.27400  0.06500  0.17150  0.41450  0.25050 -0.06700   \n",
       "3      0.56750 -0.00450  0.56850  0.04450  0.10050  0.39150  0.15750 -0.12150   \n",
       "4      0.56150 -0.28950  0.00000  0.23350  0.08650  0.58950  0.26800 -0.19250   \n",
       "5      0.45900 -0.10300  0.14700  0.35000  0.00000  0.24000  0.53800 -0.02300   \n",
       "6      0.29500  0.60600  0.00000  0.37350  0.45850  0.15850  0.30100 -0.20000   \n",
       "7      0.41200  0.83900 -0.16600  0.13200  1.00700  0.00000  0.62900 -0.36800   \n",
       "8      0.65600  0.67800 -0.48600  0.25500  0.58500  0.03800  0.32400 -0.53400   \n",
       "9      1.08800  0.77500 -0.06500  0.23450  1.12450  0.11550  0.88700 -0.12150   \n",
       "10     0.96000  0.41100  0.17100  0.54400  1.03000  0.52800  0.40100 -0.50500   \n",
       "11     0.81925  0.23775  0.42675  0.22775  0.61475  0.75225  0.39725  0.05775   \n",
       "12     1.02650  0.61450  0.50550  0.24850  0.70850  0.69350  0.58050  0.20250   \n",
       "\n",
       "year      1996     1997   ...        2008     2009     2010     2011     2012  \\\n",
       "month                     ...                                                   \n",
       "1     -0.23800  0.02900   ...    -0.53900 -0.27300 -0.56800 -0.89900 -0.68500   \n",
       "2     -0.10850  0.16900   ...    -0.37350 -0.44700 -0.62600 -0.86250 -0.61950   \n",
       "3     -0.27450  0.09550   ...    -0.20850 -0.43050 -0.25850 -0.84750 -0.20550   \n",
       "4     -0.41000 -0.13150   ...    -0.23850 -0.07750  0.00850 -0.40800  0.06350   \n",
       "5     -0.08000  0.02400   ...    -0.08200  0.04000 -0.26600 -0.47200 -0.12200   \n",
       "6      0.41400  0.12600   ...    -0.48750 -0.27500 -0.99550 -0.94750 -1.19450   \n",
       "7      0.85200 -0.00400   ...    -0.63800 -0.81400 -1.13700 -1.77700 -1.72000   \n",
       "8      0.96300  0.06600   ...    -1.31600 -1.06600 -1.28100 -1.77100 -2.59000   \n",
       "9      1.31000  0.47400   ...    -1.53550 -0.97000 -1.38700 -1.65600 -2.66350   \n",
       "10     0.89700 -0.05800   ...    -0.92100 -1.52400 -1.36200 -2.18900 -2.50700   \n",
       "11    -0.33325 -0.05775   ...    -0.35775 -0.86025 -0.96825 -0.85575 -1.32925   \n",
       "12     0.22350  0.31450   ...    -0.34350 -0.37650 -0.74650 -0.47550 -0.84550   \n",
       "\n",
       "year      2013     2014     2015     2016     2017  \n",
       "month                                               \n",
       "1     -0.56500 -0.75700 -0.63600 -0.83900 -1.38800  \n",
       "2     -0.52700 -0.92150 -0.88000 -1.11450 -1.13850  \n",
       "3     -0.35550 -0.67250 -1.03650 -1.00550 -1.11950  \n",
       "4     -0.25100 -0.50350 -0.60900 -0.77850 -0.86900  \n",
       "5     -0.08300 -0.45400 -0.61800 -1.22000 -0.52500  \n",
       "6     -0.23400 -0.57600 -0.82700 -1.20950 -0.98350  \n",
       "7     -1.28600 -1.32700 -0.92800 -1.49500 -1.53200  \n",
       "8     -1.27500 -1.18700 -1.67000 -1.94700 -1.89900  \n",
       "9     -1.03850 -1.00650 -1.68650 -1.83700 -1.44150  \n",
       "10    -0.83900 -1.33100 -1.40400 -2.52300 -1.77900  \n",
       "11    -0.87875 -0.50275 -0.73775 -2.02075 -1.16275  \n",
       "12    -0.51350 -0.32550 -0.60450 -0.99550 -0.84150  \n",
       "\n",
       "[12 rows x 30 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_ice_month_year_diff.loc[:, 1988:2017]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick plot of each year\n",
    "How each year varies against the median values for each month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sea_ice_month_year_diff.plot(legend=None, subplots=True, layout=(7, 6), sharey=True, figsize=(15, 15), kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot just the last 30 complete years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sea_ice_month_year_diff.loc[:, 1988:2017].plot(legend=None, subplots=True, layout=(6, 5), sharey=True, figsize=(15, 15), kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar plot, with each month in a subplot, trend over years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sea_ice_month_year_diff.T.plot(legend=None, subplots=True, layout=(3, 4), sharey=True, figsize=(15, 15), kind='line');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colouring the data\n",
    "Pandas doesn't make it easy to have each bar in a chart have a different colour depending on its value. Therefore, we'll create a separate dataframe with the colour that each data point should be plotted with.\n",
    "\n",
    "To start with, normalise the data, so that the data ranges from 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>year</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.804426</td>\n",
       "      <td>0.676712</td>\n",
       "      <td>0.672287</td>\n",
       "      <td>0.742887</td>\n",
       "      <td>0.691675</td>\n",
       "      <td>0.570074</td>\n",
       "      <td>0.637935</td>\n",
       "      <td>0.696101</td>\n",
       "      <td>0.656270</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512540</td>\n",
       "      <td>0.450369</td>\n",
       "      <td>0.380611</td>\n",
       "      <td>0.425711</td>\n",
       "      <td>0.451001</td>\n",
       "      <td>0.410537</td>\n",
       "      <td>0.436038</td>\n",
       "      <td>0.393256</td>\n",
       "      <td>0.277555</td>\n",
       "      <td>0.303477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.776607</td>\n",
       "      <td>0.727503</td>\n",
       "      <td>0.642360</td>\n",
       "      <td>0.688303</td>\n",
       "      <td>0.724552</td>\n",
       "      <td>0.578609</td>\n",
       "      <td>0.602318</td>\n",
       "      <td>0.685142</td>\n",
       "      <td>0.711275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475869</td>\n",
       "      <td>0.438145</td>\n",
       "      <td>0.388303</td>\n",
       "      <td>0.439515</td>\n",
       "      <td>0.459009</td>\n",
       "      <td>0.375869</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.335195</td>\n",
       "      <td>0.330137</td>\n",
       "      <td>0.274183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.789884</td>\n",
       "      <td>0.696207</td>\n",
       "      <td>0.619705</td>\n",
       "      <td>0.691465</td>\n",
       "      <td>0.710116</td>\n",
       "      <td>0.607482</td>\n",
       "      <td>0.684510</td>\n",
       "      <td>0.701897</td>\n",
       "      <td>0.651739</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479347</td>\n",
       "      <td>0.515595</td>\n",
       "      <td>0.391465</td>\n",
       "      <td>0.526765</td>\n",
       "      <td>0.495153</td>\n",
       "      <td>0.428346</td>\n",
       "      <td>0.351633</td>\n",
       "      <td>0.358166</td>\n",
       "      <td>0.334141</td>\n",
       "      <td>0.337513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.760485</td>\n",
       "      <td>0.762381</td>\n",
       "      <td>0.673235</td>\n",
       "      <td>0.758588</td>\n",
       "      <td>0.685669</td>\n",
       "      <td>0.673024</td>\n",
       "      <td>0.748894</td>\n",
       "      <td>0.659115</td>\n",
       "      <td>0.733930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.553741</td>\n",
       "      <td>0.571865</td>\n",
       "      <td>0.484089</td>\n",
       "      <td>0.583456</td>\n",
       "      <td>0.517176</td>\n",
       "      <td>0.463962</td>\n",
       "      <td>0.441728</td>\n",
       "      <td>0.406006</td>\n",
       "      <td>0.386934</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.718862</td>\n",
       "      <td>0.702213</td>\n",
       "      <td>0.729294</td>\n",
       "      <td>0.754057</td>\n",
       "      <td>0.641201</td>\n",
       "      <td>0.659431</td>\n",
       "      <td>0.769652</td>\n",
       "      <td>0.606112</td>\n",
       "      <td>0.697576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.578504</td>\n",
       "      <td>0.514015</td>\n",
       "      <td>0.470601</td>\n",
       "      <td>0.544362</td>\n",
       "      <td>0.552582</td>\n",
       "      <td>0.474394</td>\n",
       "      <td>0.439831</td>\n",
       "      <td>0.312961</td>\n",
       "      <td>0.459431</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.745416</td>\n",
       "      <td>0.686617</td>\n",
       "      <td>0.727924</td>\n",
       "      <td>0.767334</td>\n",
       "      <td>0.688093</td>\n",
       "      <td>0.670390</td>\n",
       "      <td>0.675659</td>\n",
       "      <td>0.622550</td>\n",
       "      <td>0.731507</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512118</td>\n",
       "      <td>0.360274</td>\n",
       "      <td>0.370390</td>\n",
       "      <td>0.318335</td>\n",
       "      <td>0.520759</td>\n",
       "      <td>0.448683</td>\n",
       "      <td>0.395785</td>\n",
       "      <td>0.315174</td>\n",
       "      <td>0.362803</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.781349</td>\n",
       "      <td>0.727819</td>\n",
       "      <td>0.786723</td>\n",
       "      <td>0.785880</td>\n",
       "      <td>0.873762</td>\n",
       "      <td>0.697155</td>\n",
       "      <td>0.663751</td>\n",
       "      <td>0.756586</td>\n",
       "      <td>0.800316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.398525</td>\n",
       "      <td>0.330453</td>\n",
       "      <td>0.195574</td>\n",
       "      <td>0.207587</td>\n",
       "      <td>0.299052</td>\n",
       "      <td>0.290411</td>\n",
       "      <td>0.374499</td>\n",
       "      <td>0.255005</td>\n",
       "      <td>0.247208</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.730664</td>\n",
       "      <td>0.687250</td>\n",
       "      <td>0.687039</td>\n",
       "      <td>0.742466</td>\n",
       "      <td>0.738988</td>\n",
       "      <td>0.685564</td>\n",
       "      <td>0.595785</td>\n",
       "      <td>0.717808</td>\n",
       "      <td>0.581876</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345416</td>\n",
       "      <td>0.300105</td>\n",
       "      <td>0.196839</td>\n",
       "      <td>0.024236</td>\n",
       "      <td>0.301370</td>\n",
       "      <td>0.319916</td>\n",
       "      <td>0.218124</td>\n",
       "      <td>0.159747</td>\n",
       "      <td>0.169863</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.747102</td>\n",
       "      <td>0.861538</td>\n",
       "      <td>0.750474</td>\n",
       "      <td>0.788409</td>\n",
       "      <td>0.806955</td>\n",
       "      <td>0.675448</td>\n",
       "      <td>0.667018</td>\n",
       "      <td>0.808430</td>\n",
       "      <td>0.777977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365648</td>\n",
       "      <td>0.277766</td>\n",
       "      <td>0.221075</td>\n",
       "      <td>0.008746</td>\n",
       "      <td>0.351212</td>\n",
       "      <td>0.357956</td>\n",
       "      <td>0.214647</td>\n",
       "      <td>0.182929</td>\n",
       "      <td>0.266280</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.648894</td>\n",
       "      <td>0.752371</td>\n",
       "      <td>0.708535</td>\n",
       "      <td>0.848788</td>\n",
       "      <td>0.796207</td>\n",
       "      <td>0.564594</td>\n",
       "      <td>0.622866</td>\n",
       "      <td>0.812013</td>\n",
       "      <td>0.709168</td>\n",
       "      <td>...</td>\n",
       "      <td>0.248894</td>\n",
       "      <td>0.283035</td>\n",
       "      <td>0.108746</td>\n",
       "      <td>0.041728</td>\n",
       "      <td>0.393256</td>\n",
       "      <td>0.289568</td>\n",
       "      <td>0.274183</td>\n",
       "      <td>0.038356</td>\n",
       "      <td>0.195153</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.760643</td>\n",
       "      <td>0.630190</td>\n",
       "      <td>0.692782</td>\n",
       "      <td>0.639673</td>\n",
       "      <td>0.794152</td>\n",
       "      <td>0.746733</td>\n",
       "      <td>0.603846</td>\n",
       "      <td>0.655690</td>\n",
       "      <td>0.801949</td>\n",
       "      <td>0.724816</td>\n",
       "      <td>...</td>\n",
       "      <td>0.388778</td>\n",
       "      <td>0.366017</td>\n",
       "      <td>0.389726</td>\n",
       "      <td>0.289937</td>\n",
       "      <td>0.384879</td>\n",
       "      <td>0.464120</td>\n",
       "      <td>0.414594</td>\n",
       "      <td>0.144204</td>\n",
       "      <td>0.325026</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.782403</td>\n",
       "      <td>0.696839</td>\n",
       "      <td>0.775869</td>\n",
       "      <td>0.739937</td>\n",
       "      <td>0.806006</td>\n",
       "      <td>0.697576</td>\n",
       "      <td>0.644679</td>\n",
       "      <td>0.642677</td>\n",
       "      <td>0.650053</td>\n",
       "      <td>0.532139</td>\n",
       "      <td>...</td>\n",
       "      <td>0.490727</td>\n",
       "      <td>0.412750</td>\n",
       "      <td>0.469863</td>\n",
       "      <td>0.391886</td>\n",
       "      <td>0.461855</td>\n",
       "      <td>0.501475</td>\n",
       "      <td>0.442677</td>\n",
       "      <td>0.360274</td>\n",
       "      <td>0.392729</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year       1978      1979      1980      1981      1982      1983      1984  \\\n",
       "month                                                                         \n",
       "1           NaN  0.804426  0.676712  0.672287  0.742887  0.691675  0.570074   \n",
       "2           NaN  0.776607  0.727503  0.642360  0.688303  0.724552  0.578609   \n",
       "3           NaN  0.789884  0.696207  0.619705  0.691465  0.710116  0.607482   \n",
       "4           NaN  0.760485  0.762381  0.673235  0.758588  0.685669  0.673024   \n",
       "5           NaN  0.718862  0.702213  0.729294  0.754057  0.641201  0.659431   \n",
       "6           NaN  0.745416  0.686617  0.727924  0.767334  0.688093  0.670390   \n",
       "7           NaN  0.781349  0.727819  0.786723  0.785880  0.873762  0.697155   \n",
       "8           NaN  0.730664  0.687250  0.687039  0.742466  0.738988  0.685564   \n",
       "9           NaN  0.747102  0.861538  0.750474  0.788409  0.806955  0.675448   \n",
       "10     1.000000  0.648894  0.752371  0.708535  0.848788  0.796207  0.564594   \n",
       "11     0.760643  0.630190  0.692782  0.639673  0.794152  0.746733  0.603846   \n",
       "12     0.782403  0.696839  0.775869  0.739937  0.806006  0.697576  0.644679   \n",
       "\n",
       "year       1985      1986      1987    ...         2009      2010      2011  \\\n",
       "month                                  ...                                    \n",
       "1      0.637935  0.696101  0.656270    ...     0.512540  0.450369  0.380611   \n",
       "2      0.602318  0.685142  0.711275    ...     0.475869  0.438145  0.388303   \n",
       "3      0.684510  0.701897  0.651739    ...     0.479347  0.515595  0.391465   \n",
       "4      0.748894  0.659115  0.733930    ...     0.553741  0.571865  0.484089   \n",
       "5      0.769652  0.606112  0.697576    ...     0.578504  0.514015  0.470601   \n",
       "6      0.675659  0.622550  0.731507    ...     0.512118  0.360274  0.370390   \n",
       "7      0.663751  0.756586  0.800316    ...     0.398525  0.330453  0.195574   \n",
       "8      0.595785  0.717808  0.581876    ...     0.345416  0.300105  0.196839   \n",
       "9      0.667018  0.808430  0.777977    ...     0.365648  0.277766  0.221075   \n",
       "10     0.622866  0.812013  0.709168    ...     0.248894  0.283035  0.108746   \n",
       "11     0.655690  0.801949  0.724816    ...     0.388778  0.366017  0.389726   \n",
       "12     0.642677  0.650053  0.532139    ...     0.490727  0.412750  0.469863   \n",
       "\n",
       "year       2012      2013      2014      2015      2016      2017      2018  \n",
       "month                                                                        \n",
       "1      0.425711  0.451001  0.410537  0.436038  0.393256  0.277555  0.303477  \n",
       "2      0.439515  0.459009  0.375869  0.384615  0.335195  0.330137  0.274183  \n",
       "3      0.526765  0.495153  0.428346  0.351633  0.358166  0.334141  0.337513  \n",
       "4      0.583456  0.517176  0.463962  0.441728  0.406006  0.386934       NaN  \n",
       "5      0.544362  0.552582  0.474394  0.439831  0.312961  0.459431       NaN  \n",
       "6      0.318335  0.520759  0.448683  0.395785  0.315174  0.362803       NaN  \n",
       "7      0.207587  0.299052  0.290411  0.374499  0.255005  0.247208       NaN  \n",
       "8      0.024236  0.301370  0.319916  0.218124  0.159747  0.169863       NaN  \n",
       "9      0.008746  0.351212  0.357956  0.214647  0.182929  0.266280       NaN  \n",
       "10     0.041728  0.393256  0.289568  0.274183  0.038356  0.195153       NaN  \n",
       "11     0.289937  0.384879  0.464120  0.414594  0.144204  0.325026       NaN  \n",
       "12     0.391886  0.461855  0.501475  0.442677  0.360274  0.392729       NaN  \n",
       "\n",
       "[12 rows x 41 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sid_max = sea_ice_month_year_diff.max().max()\n",
    "sid_min = sea_ice_month_year_diff.min().min()\n",
    "sea_ice_month_year_dnorm = (sea_ice_month_year_diff - sid_min) / (sid_max - sid_min)\n",
    "sea_ice_month_year_dnorm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use the `matplotlib` color map to find the colour of each value.\n",
    "\n",
    "Note that we're doing `1 - value` to make the map scale from blue at high values to magenta at low ones, the opposite of what it normally does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>1980</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.19607843137254902, 0.803921568627451, 1.0, ...</td>\n",
       "      <td>(0.32156862745098036, 0.6784313725490196, 1.0,...</td>\n",
       "      <td>(0.3254901960784314, 0.6745098039215687, 1.0, ...</td>\n",
       "      <td>(0.2549019607843137, 0.7450980392156863, 1.0, ...</td>\n",
       "      <td>(0.3058823529411765, 0.6941176470588235, 1.0, ...</td>\n",
       "      <td>(0.43137254901960786, 0.5686274509803921, 1.0,...</td>\n",
       "      <td>(0.3607843137254902, 0.6392156862745098, 1.0, ...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>(0.3411764705882353, 0.6588235294117647, 1.0, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.48627450980392156, 0.5137254901960784, 1.0,...</td>\n",
       "      <td>(0.5490196078431373, 0.4509803921568627, 1.0, ...</td>\n",
       "      <td>(0.6196078431372549, 0.3803921568627451, 1.0, ...</td>\n",
       "      <td>(0.5764705882352941, 0.42352941176470593, 1.0,...</td>\n",
       "      <td>(0.5490196078431373, 0.4509803921568627, 1.0, ...</td>\n",
       "      <td>(0.5882352941176471, 0.4117647058823529, 1.0, ...</td>\n",
       "      <td>(0.5647058823529412, 0.43529411764705883, 1.0,...</td>\n",
       "      <td>(0.6078431372549019, 0.3921568627450981, 1.0, ...</td>\n",
       "      <td>(0.7215686274509804, 0.2784313725490196, 1.0, ...</td>\n",
       "      <td>(0.6980392156862745, 0.3019607843137255, 1.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.22352941176470587, 0.7764705882352941, 1.0,...</td>\n",
       "      <td>(0.27058823529411763, 0.7294117647058824, 1.0,...</td>\n",
       "      <td>(0.3568627450980392, 0.6431372549019607, 1.0, ...</td>\n",
       "      <td>(0.30980392156862746, 0.6901960784313725, 1.0,...</td>\n",
       "      <td>(0.27450980392156865, 0.7254901960784313, 1.0,...</td>\n",
       "      <td>(0.4196078431372549, 0.580392156862745, 1.0, 1.0)</td>\n",
       "      <td>(0.396078431372549, 0.603921568627451, 1.0, 1.0)</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.28627450980392155, 0.7137254901960784, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.5254901960784314, 0.4745098039215686, 1.0, ...</td>\n",
       "      <td>(0.5607843137254902, 0.4392156862745098, 1.0, ...</td>\n",
       "      <td>(0.611764705882353, 0.388235294117647, 1.0, 1.0)</td>\n",
       "      <td>(0.5607843137254902, 0.4392156862745098, 1.0, ...</td>\n",
       "      <td>(0.5411764705882353, 0.45882352941176474, 1.0,...</td>\n",
       "      <td>(0.6235294117647059, 0.3764705882352941, 1.0, ...</td>\n",
       "      <td>(0.615686274509804, 0.38431372549019605, 1.0, ...</td>\n",
       "      <td>(0.6666666666666666, 0.33333333333333337, 1.0,...</td>\n",
       "      <td>(0.6705882352941176, 0.3294117647058824, 1.0, ...</td>\n",
       "      <td>(0.7254901960784313, 0.27450980392156865, 1.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.20784313725490194, 0.7921568627450981, 1.0,...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>(0.3803921568627451, 0.6196078431372549, 1.0, ...</td>\n",
       "      <td>(0.3058823529411765, 0.6941176470588235, 1.0, ...</td>\n",
       "      <td>(0.2901960784313725, 0.7098039215686275, 1.0, ...</td>\n",
       "      <td>(0.39215686274509803, 0.607843137254902, 1.0, ...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.2980392156862745, 0.7019607843137254, 1.0, ...</td>\n",
       "      <td>(0.34901960784313724, 0.6509803921568628, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.5215686274509804, 0.4784313725490196, 1.0, ...</td>\n",
       "      <td>(0.48627450980392156, 0.5137254901960784, 1.0,...</td>\n",
       "      <td>(0.6078431372549019, 0.3921568627450981, 1.0, ...</td>\n",
       "      <td>(0.4745098039215686, 0.5254901960784314, 1.0, ...</td>\n",
       "      <td>(0.5058823529411764, 0.49411764705882355, 1.0,...</td>\n",
       "      <td>(0.5725490196078431, 0.4274509803921569, 1.0, ...</td>\n",
       "      <td>(0.6470588235294118, 0.3529411764705882, 1.0, ...</td>\n",
       "      <td>(0.6431372549019607, 0.3568627450980393, 1.0, ...</td>\n",
       "      <td>(0.6666666666666666, 0.33333333333333337, 1.0,...</td>\n",
       "      <td>(0.6627450980392157, 0.33725490196078434, 1.0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.2392156862745098, 0.7607843137254902, 1.0, ...</td>\n",
       "      <td>(0.23529411764705882, 0.7647058823529411, 1.0,...</td>\n",
       "      <td>(0.3254901960784314, 0.6745098039215687, 1.0, ...</td>\n",
       "      <td>(0.2392156862745098, 0.7607843137254902, 1.0, ...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.3254901960784314, 0.6745098039215687, 1.0, ...</td>\n",
       "      <td>(0.25098039215686274, 0.7490196078431373, 1.0,...</td>\n",
       "      <td>(0.3411764705882353, 0.6588235294117647, 1.0, ...</td>\n",
       "      <td>(0.26666666666666666, 0.7333333333333334, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.44705882352941173, 0.5529411764705883, 1.0,...</td>\n",
       "      <td>(0.42745098039215684, 0.5725490196078431, 1.0,...</td>\n",
       "      <td>(0.5176470588235293, 0.48235294117647065, 1.0,...</td>\n",
       "      <td>(0.4156862745098039, 0.5843137254901961, 1.0, ...</td>\n",
       "      <td>(0.4823529411764706, 0.5176470588235293, 1.0, ...</td>\n",
       "      <td>(0.5372549019607843, 0.4627450980392157, 1.0, ...</td>\n",
       "      <td>(0.5568627450980392, 0.44313725490196076, 1.0,...</td>\n",
       "      <td>(0.596078431372549, 0.403921568627451, 1.0, 1.0)</td>\n",
       "      <td>(0.611764705882353, 0.388235294117647, 1.0, 1.0)</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.2784313725490196, 0.7215686274509804, 1.0, ...</td>\n",
       "      <td>(0.2980392156862745, 0.7019607843137254, 1.0, ...</td>\n",
       "      <td>(0.27058823529411763, 0.7294117647058824, 1.0,...</td>\n",
       "      <td>(0.24313725490196078, 0.7568627450980392, 1.0,...</td>\n",
       "      <td>(0.3568627450980392, 0.6431372549019607, 1.0, ...</td>\n",
       "      <td>(0.3411764705882353, 0.6588235294117647, 1.0, ...</td>\n",
       "      <td>(0.22745098039215686, 0.7725490196078432, 1.0,...</td>\n",
       "      <td>(0.39215686274509803, 0.607843137254902, 1.0, ...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.4196078431372549, 0.580392156862745, 1.0, 1.0)</td>\n",
       "      <td>(0.48627450980392156, 0.5137254901960784, 1.0,...</td>\n",
       "      <td>(0.5294117647058824, 0.47058823529411764, 1.0,...</td>\n",
       "      <td>(0.4549019607843137, 0.5450980392156863, 1.0, ...</td>\n",
       "      <td>(0.44705882352941173, 0.5529411764705883, 1.0,...</td>\n",
       "      <td>(0.5254901960784314, 0.4745098039215686, 1.0, ...</td>\n",
       "      <td>(0.5607843137254902, 0.4392156862745098, 1.0, ...</td>\n",
       "      <td>(0.6862745098039216, 0.3137254901960784, 1.0, ...</td>\n",
       "      <td>(0.5411764705882353, 0.45882352941176474, 1.0,...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.2549019607843137, 0.7450980392156863, 1.0, ...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.27058823529411763, 0.7294117647058824, 1.0,...</td>\n",
       "      <td>(0.23137254901960785, 0.7686274509803921, 1.0,...</td>\n",
       "      <td>(0.30980392156862746, 0.6901960784313725, 1.0,...</td>\n",
       "      <td>(0.32941176470588235, 0.6705882352941177, 1.0,...</td>\n",
       "      <td>(0.3254901960784314, 0.6745098039215687, 1.0, ...</td>\n",
       "      <td>(0.3764705882352941, 0.6235294117647059, 1.0, ...</td>\n",
       "      <td>(0.26666666666666666, 0.7333333333333334, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.48627450980392156, 0.5137254901960784, 1.0,...</td>\n",
       "      <td>(0.6392156862745098, 0.36078431372549025, 1.0,...</td>\n",
       "      <td>(0.6313725490196078, 0.3686274509803922, 1.0, ...</td>\n",
       "      <td>(0.6823529411764706, 0.3176470588235294, 1.0, ...</td>\n",
       "      <td>(0.4784313725490196, 0.5215686274509804, 1.0, ...</td>\n",
       "      <td>(0.5529411764705883, 0.44705882352941173, 1.0,...</td>\n",
       "      <td>(0.6039215686274509, 0.39607843137254906, 1.0,...</td>\n",
       "      <td>(0.6862745098039216, 0.3137254901960784, 1.0, ...</td>\n",
       "      <td>(0.6392156862745098, 0.36078431372549025, 1.0,...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.21568627450980393, 0.7843137254901961, 1.0,...</td>\n",
       "      <td>(0.27058823529411763, 0.7294117647058824, 1.0,...</td>\n",
       "      <td>(0.21176470588235294, 0.788235294117647, 1.0, ...</td>\n",
       "      <td>(0.21176470588235294, 0.788235294117647, 1.0, ...</td>\n",
       "      <td>(0.12549019607843137, 0.8745098039215686, 1.0,...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>(0.33725490196078434, 0.6627450980392157, 1.0,...</td>\n",
       "      <td>(0.24313725490196078, 0.7568627450980392, 1.0,...</td>\n",
       "      <td>(0.2, 0.8, 1.0, 1.0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.6, 0.4, 1.0, 1.0)</td>\n",
       "      <td>(0.6705882352941176, 0.3294117647058824, 1.0, ...</td>\n",
       "      <td>(0.803921568627451, 0.196078431372549, 1.0, 1.0)</td>\n",
       "      <td>(0.792156862745098, 0.207843137254902, 1.0, 1.0)</td>\n",
       "      <td>(0.7019607843137254, 0.29803921568627456, 1.0,...</td>\n",
       "      <td>(0.7098039215686275, 0.2901960784313725, 1.0, ...</td>\n",
       "      <td>(0.6274509803921569, 0.37254901960784315, 1.0,...</td>\n",
       "      <td>(0.7450980392156863, 0.2549019607843137, 1.0, ...</td>\n",
       "      <td>(0.7529411764705882, 0.24705882352941178, 1.0,...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.26666666666666666, 0.7333333333333334, 1.0,...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.2549019607843137, 0.7450980392156863, 1.0, ...</td>\n",
       "      <td>(0.2588235294117647, 0.7411764705882353, 1.0, ...</td>\n",
       "      <td>(0.3137254901960784, 0.6862745098039216, 1.0, ...</td>\n",
       "      <td>(0.403921568627451, 0.596078431372549, 1.0, 1.0)</td>\n",
       "      <td>(0.2823529411764706, 0.7176470588235294, 1.0, ...</td>\n",
       "      <td>(0.4196078431372549, 0.580392156862745, 1.0, 1.0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.6549019607843137, 0.34509803921568627, 1.0,...</td>\n",
       "      <td>(0.7019607843137254, 0.29803921568627456, 1.0,...</td>\n",
       "      <td>(0.803921568627451, 0.196078431372549, 1.0, 1.0)</td>\n",
       "      <td>(0.9764705882352941, 0.02352941176470591, 1.0,...</td>\n",
       "      <td>(0.6980392156862745, 0.3019607843137255, 1.0, ...</td>\n",
       "      <td>(0.6823529411764706, 0.3176470588235294, 1.0, ...</td>\n",
       "      <td>(0.7843137254901961, 0.21568627450980393, 1.0,...</td>\n",
       "      <td>(0.8431372549019608, 0.1568627450980392, 1.0, ...</td>\n",
       "      <td>(0.8313725490196078, 0.16862745098039222, 1.0,...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.25098039215686274, 0.7490196078431373, 1.0,...</td>\n",
       "      <td>(0.13725490196078433, 0.8627450980392157, 1.0,...</td>\n",
       "      <td>(0.24705882352941178, 0.7529411764705882, 1.0,...</td>\n",
       "      <td>(0.21176470588235294, 0.788235294117647, 1.0, ...</td>\n",
       "      <td>(0.19215686274509802, 0.807843137254902, 1.0, ...</td>\n",
       "      <td>(0.3254901960784314, 0.6745098039215687, 1.0, ...</td>\n",
       "      <td>(0.3333333333333333, 0.6666666666666667, 1.0, ...</td>\n",
       "      <td>(0.19215686274509802, 0.807843137254902, 1.0, ...</td>\n",
       "      <td>(0.2196078431372549, 0.7803921568627451, 1.0, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.6352941176470588, 0.3647058823529412, 1.0, ...</td>\n",
       "      <td>(0.7215686274509804, 0.2784313725490196, 1.0, ...</td>\n",
       "      <td>(0.7803921568627451, 0.2196078431372549, 1.0, ...</td>\n",
       "      <td>(0.9921568627450981, 0.007843137254901933, 1.0...</td>\n",
       "      <td>(0.6509803921568628, 0.34901960784313724, 1.0,...</td>\n",
       "      <td>(0.6431372549019607, 0.3568627450980393, 1.0, ...</td>\n",
       "      <td>(0.788235294117647, 0.21176470588235297, 1.0, ...</td>\n",
       "      <td>(0.8196078431372549, 0.18039215686274512, 1.0,...</td>\n",
       "      <td>(0.7333333333333333, 0.2666666666666667, 1.0, ...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "      <td>(0.34901960784313724, 0.6509803921568628, 1.0,...</td>\n",
       "      <td>(0.24705882352941178, 0.7529411764705882, 1.0,...</td>\n",
       "      <td>(0.2901960784313725, 0.7098039215686275, 1.0, ...</td>\n",
       "      <td>(0.14901960784313725, 0.8509803921568627, 1.0,...</td>\n",
       "      <td>(0.20392156862745098, 0.7960784313725491, 1.0,...</td>\n",
       "      <td>(0.43529411764705883, 0.5647058823529412, 1.0,...</td>\n",
       "      <td>(0.3764705882352941, 0.6235294117647059, 1.0, ...</td>\n",
       "      <td>(0.18823529411764706, 0.8117647058823529, 1.0,...</td>\n",
       "      <td>(0.2901960784313725, 0.7098039215686275, 1.0, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.7529411764705882, 0.24705882352941178, 1.0,...</td>\n",
       "      <td>(0.7176470588235294, 0.2823529411764706, 1.0, ...</td>\n",
       "      <td>(0.8941176470588235, 0.10588235294117654, 1.0,...</td>\n",
       "      <td>(0.9607843137254902, 0.039215686274509776, 1.0...</td>\n",
       "      <td>(0.6078431372549019, 0.3921568627450981, 1.0, ...</td>\n",
       "      <td>(0.7098039215686275, 0.2901960784313725, 1.0, ...</td>\n",
       "      <td>(0.7254901960784313, 0.27450980392156865, 1.0,...</td>\n",
       "      <td>(0.9647058823529412, 0.03529411764705881, 1.0,...</td>\n",
       "      <td>(0.807843137254902, 0.19215686274509802, 1.0, ...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(0.2392156862745098, 0.7607843137254902, 1.0, ...</td>\n",
       "      <td>(0.3686274509803922, 0.6313725490196078, 1.0, ...</td>\n",
       "      <td>(0.3058823529411765, 0.6941176470588235, 1.0, ...</td>\n",
       "      <td>(0.3607843137254902, 0.6392156862745098, 1.0, ...</td>\n",
       "      <td>(0.20392156862745098, 0.7960784313725491, 1.0,...</td>\n",
       "      <td>(0.25098039215686274, 0.7490196078431373, 1.0,...</td>\n",
       "      <td>(0.396078431372549, 0.603921568627451, 1.0, 1.0)</td>\n",
       "      <td>(0.34509803921568627, 0.6549019607843137, 1.0,...</td>\n",
       "      <td>(0.19607843137254902, 0.803921568627451, 1.0, ...</td>\n",
       "      <td>(0.27450980392156865, 0.7254901960784313, 1.0,...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.611764705882353, 0.388235294117647, 1.0, 1.0)</td>\n",
       "      <td>(0.6352941176470588, 0.3647058823529412, 1.0, ...</td>\n",
       "      <td>(0.611764705882353, 0.388235294117647, 1.0, 1.0)</td>\n",
       "      <td>(0.7098039215686275, 0.2901960784313725, 1.0, ...</td>\n",
       "      <td>(0.615686274509804, 0.38431372549019605, 1.0, ...</td>\n",
       "      <td>(0.5372549019607843, 0.4627450980392157, 1.0, ...</td>\n",
       "      <td>(0.5843137254901961, 0.4156862745098039, 1.0, ...</td>\n",
       "      <td>(0.8588235294117647, 0.14117647058823535, 1.0,...</td>\n",
       "      <td>(0.6745098039215687, 0.3254901960784313, 1.0, ...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(0.21568627450980393, 0.7843137254901961, 1.0,...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>(0.22352941176470587, 0.7764705882352941, 1.0,...</td>\n",
       "      <td>(0.2588235294117647, 0.7411764705882353, 1.0, ...</td>\n",
       "      <td>(0.19215686274509802, 0.807843137254902, 1.0, ...</td>\n",
       "      <td>(0.30196078431372547, 0.6980392156862745, 1.0,...</td>\n",
       "      <td>(0.3529411764705882, 0.6470588235294118, 1.0, ...</td>\n",
       "      <td>(0.3568627450980392, 0.6431372549019607, 1.0, ...</td>\n",
       "      <td>(0.34901960784313724, 0.6509803921568628, 1.0,...</td>\n",
       "      <td>(0.4666666666666667, 0.5333333333333333, 1.0, ...</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.5098039215686274, 0.4901960784313726, 1.0, ...</td>\n",
       "      <td>(0.5882352941176471, 0.4117647058823529, 1.0, ...</td>\n",
       "      <td>(0.5294117647058824, 0.47058823529411764, 1.0,...</td>\n",
       "      <td>(0.6078431372549019, 0.3921568627450981, 1.0, ...</td>\n",
       "      <td>(0.5372549019607843, 0.4627450980392157, 1.0, ...</td>\n",
       "      <td>(0.4980392156862745, 0.5019607843137255, 1.0, ...</td>\n",
       "      <td>(0.5568627450980392, 0.44313725490196076, 1.0,...</td>\n",
       "      <td>(0.6392156862745098, 0.36078431372549025, 1.0,...</td>\n",
       "      <td>(0.6078431372549019, 0.3921568627450981, 1.0, ...</td>\n",
       "      <td>(0.0, 1.0, 1.0, 1.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year                                                1978  \\\n",
       "month                                                      \n",
       "1                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "2                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "3                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "4                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "5                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "6                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "7                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "8                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "9                                   (0.0, 1.0, 1.0, 1.0)   \n",
       "10                                  (0.0, 1.0, 1.0, 1.0)   \n",
       "11     (0.2392156862745098, 0.7607843137254902, 1.0, ...   \n",
       "12     (0.21568627450980393, 0.7843137254901961, 1.0,...   \n",
       "\n",
       "year                                                1979  \\\n",
       "month                                                      \n",
       "1      (0.19607843137254902, 0.803921568627451, 1.0, ...   \n",
       "2      (0.22352941176470587, 0.7764705882352941, 1.0,...   \n",
       "3      (0.20784313725490194, 0.7921568627450981, 1.0,...   \n",
       "4      (0.2392156862745098, 0.7607843137254902, 1.0, ...   \n",
       "5      (0.2784313725490196, 0.7215686274509804, 1.0, ...   \n",
       "6      (0.2549019607843137, 0.7450980392156863, 1.0, ...   \n",
       "7      (0.21568627450980393, 0.7843137254901961, 1.0,...   \n",
       "8      (0.26666666666666666, 0.7333333333333334, 1.0,...   \n",
       "9      (0.25098039215686274, 0.7490196078431373, 1.0,...   \n",
       "10     (0.34901960784313724, 0.6509803921568628, 1.0,...   \n",
       "11     (0.3686274509803922, 0.6313725490196078, 1.0, ...   \n",
       "12     (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "\n",
       "year                                                1980  \\\n",
       "month                                                      \n",
       "1      (0.32156862745098036, 0.6784313725490196, 1.0,...   \n",
       "2      (0.27058823529411763, 0.7294117647058824, 1.0,...   \n",
       "3      (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "4      (0.23529411764705882, 0.7647058823529411, 1.0,...   \n",
       "5      (0.2980392156862745, 0.7019607843137254, 1.0, ...   \n",
       "6      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "7      (0.27058823529411763, 0.7294117647058824, 1.0,...   \n",
       "8      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "9      (0.13725490196078433, 0.8627450980392157, 1.0,...   \n",
       "10     (0.24705882352941178, 0.7529411764705882, 1.0,...   \n",
       "11     (0.3058823529411765, 0.6941176470588235, 1.0, ...   \n",
       "12     (0.22352941176470587, 0.7764705882352941, 1.0,...   \n",
       "\n",
       "year                                                1981  \\\n",
       "month                                                      \n",
       "1      (0.3254901960784314, 0.6745098039215687, 1.0, ...   \n",
       "2      (0.3568627450980392, 0.6431372549019607, 1.0, ...   \n",
       "3      (0.3803921568627451, 0.6196078431372549, 1.0, ...   \n",
       "4      (0.3254901960784314, 0.6745098039215687, 1.0, ...   \n",
       "5      (0.27058823529411763, 0.7294117647058824, 1.0,...   \n",
       "6      (0.27058823529411763, 0.7294117647058824, 1.0,...   \n",
       "7      (0.21176470588235294, 0.788235294117647, 1.0, ...   \n",
       "8      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "9      (0.24705882352941178, 0.7529411764705882, 1.0,...   \n",
       "10     (0.2901960784313725, 0.7098039215686275, 1.0, ...   \n",
       "11     (0.3607843137254902, 0.6392156862745098, 1.0, ...   \n",
       "12     (0.2588235294117647, 0.7411764705882353, 1.0, ...   \n",
       "\n",
       "year                                                1982  \\\n",
       "month                                                      \n",
       "1      (0.2549019607843137, 0.7450980392156863, 1.0, ...   \n",
       "2      (0.30980392156862746, 0.6901960784313725, 1.0,...   \n",
       "3      (0.3058823529411765, 0.6941176470588235, 1.0, ...   \n",
       "4      (0.2392156862745098, 0.7607843137254902, 1.0, ...   \n",
       "5      (0.24313725490196078, 0.7568627450980392, 1.0,...   \n",
       "6      (0.23137254901960785, 0.7686274509803921, 1.0,...   \n",
       "7      (0.21176470588235294, 0.788235294117647, 1.0, ...   \n",
       "8      (0.2549019607843137, 0.7450980392156863, 1.0, ...   \n",
       "9      (0.21176470588235294, 0.788235294117647, 1.0, ...   \n",
       "10     (0.14901960784313725, 0.8509803921568627, 1.0,...   \n",
       "11     (0.20392156862745098, 0.7960784313725491, 1.0,...   \n",
       "12     (0.19215686274509802, 0.807843137254902, 1.0, ...   \n",
       "\n",
       "year                                                1983  \\\n",
       "month                                                      \n",
       "1      (0.3058823529411765, 0.6941176470588235, 1.0, ...   \n",
       "2      (0.27450980392156865, 0.7254901960784313, 1.0,...   \n",
       "3      (0.2901960784313725, 0.7098039215686275, 1.0, ...   \n",
       "4      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "5      (0.3568627450980392, 0.6431372549019607, 1.0, ...   \n",
       "6      (0.30980392156862746, 0.6901960784313725, 1.0,...   \n",
       "7      (0.12549019607843137, 0.8745098039215686, 1.0,...   \n",
       "8      (0.2588235294117647, 0.7411764705882353, 1.0, ...   \n",
       "9      (0.19215686274509802, 0.807843137254902, 1.0, ...   \n",
       "10     (0.20392156862745098, 0.7960784313725491, 1.0,...   \n",
       "11     (0.25098039215686274, 0.7490196078431373, 1.0,...   \n",
       "12     (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "\n",
       "year                                                1984  \\\n",
       "month                                                      \n",
       "1      (0.43137254901960786, 0.5686274509803921, 1.0,...   \n",
       "2      (0.4196078431372549, 0.580392156862745, 1.0, 1.0)   \n",
       "3      (0.39215686274509803, 0.607843137254902, 1.0, ...   \n",
       "4      (0.3254901960784314, 0.6745098039215687, 1.0, ...   \n",
       "5      (0.3411764705882353, 0.6588235294117647, 1.0, ...   \n",
       "6      (0.32941176470588235, 0.6705882352941177, 1.0,...   \n",
       "7      (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "8      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "9      (0.3254901960784314, 0.6745098039215687, 1.0, ...   \n",
       "10     (0.43529411764705883, 0.5647058823529412, 1.0,...   \n",
       "11      (0.396078431372549, 0.603921568627451, 1.0, 1.0)   \n",
       "12     (0.3529411764705882, 0.6470588235294118, 1.0, ...   \n",
       "\n",
       "year                                                1985  \\\n",
       "month                                                      \n",
       "1      (0.3607843137254902, 0.6392156862745098, 1.0, ...   \n",
       "2       (0.396078431372549, 0.603921568627451, 1.0, 1.0)   \n",
       "3      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "4      (0.25098039215686274, 0.7490196078431373, 1.0,...   \n",
       "5      (0.22745098039215686, 0.7725490196078432, 1.0,...   \n",
       "6      (0.3254901960784314, 0.6745098039215687, 1.0, ...   \n",
       "7      (0.33725490196078434, 0.6627450980392157, 1.0,...   \n",
       "8       (0.403921568627451, 0.596078431372549, 1.0, 1.0)   \n",
       "9      (0.3333333333333333, 0.6666666666666667, 1.0, ...   \n",
       "10     (0.3764705882352941, 0.6235294117647059, 1.0, ...   \n",
       "11     (0.34509803921568627, 0.6549019607843137, 1.0,...   \n",
       "12     (0.3568627450980392, 0.6431372549019607, 1.0, ...   \n",
       "\n",
       "year                                                1986  \\\n",
       "month                                                      \n",
       "1      (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "2      (0.3137254901960784, 0.6862745098039216, 1.0, ...   \n",
       "3      (0.2980392156862745, 0.7019607843137254, 1.0, ...   \n",
       "4      (0.3411764705882353, 0.6588235294117647, 1.0, ...   \n",
       "5      (0.39215686274509803, 0.607843137254902, 1.0, ...   \n",
       "6      (0.3764705882352941, 0.6235294117647059, 1.0, ...   \n",
       "7      (0.24313725490196078, 0.7568627450980392, 1.0,...   \n",
       "8      (0.2823529411764706, 0.7176470588235294, 1.0, ...   \n",
       "9      (0.19215686274509802, 0.807843137254902, 1.0, ...   \n",
       "10     (0.18823529411764706, 0.8117647058823529, 1.0,...   \n",
       "11     (0.19607843137254902, 0.803921568627451, 1.0, ...   \n",
       "12     (0.34901960784313724, 0.6509803921568628, 1.0,...   \n",
       "\n",
       "year                                                1987  \\\n",
       "month                                                      \n",
       "1      (0.3411764705882353, 0.6588235294117647, 1.0, ...   \n",
       "2      (0.28627450980392155, 0.7137254901960784, 1.0,...   \n",
       "3      (0.34901960784313724, 0.6509803921568628, 1.0,...   \n",
       "4      (0.26666666666666666, 0.7333333333333334, 1.0,...   \n",
       "5      (0.30196078431372547, 0.6980392156862745, 1.0,...   \n",
       "6      (0.26666666666666666, 0.7333333333333334, 1.0,...   \n",
       "7                                   (0.2, 0.8, 1.0, 1.0)   \n",
       "8      (0.4196078431372549, 0.580392156862745, 1.0, 1.0)   \n",
       "9      (0.2196078431372549, 0.7803921568627451, 1.0, ...   \n",
       "10     (0.2901960784313725, 0.7098039215686275, 1.0, ...   \n",
       "11     (0.27450980392156865, 0.7254901960784313, 1.0,...   \n",
       "12     (0.4666666666666667, 0.5333333333333333, 1.0, ...   \n",
       "\n",
       "year                         ...                          \\\n",
       "month                        ...                           \n",
       "1                            ...                           \n",
       "2                            ...                           \n",
       "3                            ...                           \n",
       "4                            ...                           \n",
       "5                            ...                           \n",
       "6                            ...                           \n",
       "7                            ...                           \n",
       "8                            ...                           \n",
       "9                            ...                           \n",
       "10                           ...                           \n",
       "11                           ...                           \n",
       "12                           ...                           \n",
       "\n",
       "year                                                2009  \\\n",
       "month                                                      \n",
       "1      (0.48627450980392156, 0.5137254901960784, 1.0,...   \n",
       "2      (0.5254901960784314, 0.4745098039215686, 1.0, ...   \n",
       "3      (0.5215686274509804, 0.4784313725490196, 1.0, ...   \n",
       "4      (0.44705882352941173, 0.5529411764705883, 1.0,...   \n",
       "5      (0.4196078431372549, 0.580392156862745, 1.0, 1.0)   \n",
       "6      (0.48627450980392156, 0.5137254901960784, 1.0,...   \n",
       "7                                   (0.6, 0.4, 1.0, 1.0)   \n",
       "8      (0.6549019607843137, 0.34509803921568627, 1.0,...   \n",
       "9      (0.6352941176470588, 0.3647058823529412, 1.0, ...   \n",
       "10     (0.7529411764705882, 0.24705882352941178, 1.0,...   \n",
       "11      (0.611764705882353, 0.388235294117647, 1.0, 1.0)   \n",
       "12     (0.5098039215686274, 0.4901960784313726, 1.0, ...   \n",
       "\n",
       "year                                                2010  \\\n",
       "month                                                      \n",
       "1      (0.5490196078431373, 0.4509803921568627, 1.0, ...   \n",
       "2      (0.5607843137254902, 0.4392156862745098, 1.0, ...   \n",
       "3      (0.48627450980392156, 0.5137254901960784, 1.0,...   \n",
       "4      (0.42745098039215684, 0.5725490196078431, 1.0,...   \n",
       "5      (0.48627450980392156, 0.5137254901960784, 1.0,...   \n",
       "6      (0.6392156862745098, 0.36078431372549025, 1.0,...   \n",
       "7      (0.6705882352941176, 0.3294117647058824, 1.0, ...   \n",
       "8      (0.7019607843137254, 0.29803921568627456, 1.0,...   \n",
       "9      (0.7215686274509804, 0.2784313725490196, 1.0, ...   \n",
       "10     (0.7176470588235294, 0.2823529411764706, 1.0, ...   \n",
       "11     (0.6352941176470588, 0.3647058823529412, 1.0, ...   \n",
       "12     (0.5882352941176471, 0.4117647058823529, 1.0, ...   \n",
       "\n",
       "year                                                2011  \\\n",
       "month                                                      \n",
       "1      (0.6196078431372549, 0.3803921568627451, 1.0, ...   \n",
       "2       (0.611764705882353, 0.388235294117647, 1.0, 1.0)   \n",
       "3      (0.6078431372549019, 0.3921568627450981, 1.0, ...   \n",
       "4      (0.5176470588235293, 0.48235294117647065, 1.0,...   \n",
       "5      (0.5294117647058824, 0.47058823529411764, 1.0,...   \n",
       "6      (0.6313725490196078, 0.3686274509803922, 1.0, ...   \n",
       "7       (0.803921568627451, 0.196078431372549, 1.0, 1.0)   \n",
       "8       (0.803921568627451, 0.196078431372549, 1.0, 1.0)   \n",
       "9      (0.7803921568627451, 0.2196078431372549, 1.0, ...   \n",
       "10     (0.8941176470588235, 0.10588235294117654, 1.0,...   \n",
       "11      (0.611764705882353, 0.388235294117647, 1.0, 1.0)   \n",
       "12     (0.5294117647058824, 0.47058823529411764, 1.0,...   \n",
       "\n",
       "year                                                2012  \\\n",
       "month                                                      \n",
       "1      (0.5764705882352941, 0.42352941176470593, 1.0,...   \n",
       "2      (0.5607843137254902, 0.4392156862745098, 1.0, ...   \n",
       "3      (0.4745098039215686, 0.5254901960784314, 1.0, ...   \n",
       "4      (0.4156862745098039, 0.5843137254901961, 1.0, ...   \n",
       "5      (0.4549019607843137, 0.5450980392156863, 1.0, ...   \n",
       "6      (0.6823529411764706, 0.3176470588235294, 1.0, ...   \n",
       "7       (0.792156862745098, 0.207843137254902, 1.0, 1.0)   \n",
       "8      (0.9764705882352941, 0.02352941176470591, 1.0,...   \n",
       "9      (0.9921568627450981, 0.007843137254901933, 1.0...   \n",
       "10     (0.9607843137254902, 0.039215686274509776, 1.0...   \n",
       "11     (0.7098039215686275, 0.2901960784313725, 1.0, ...   \n",
       "12     (0.6078431372549019, 0.3921568627450981, 1.0, ...   \n",
       "\n",
       "year                                                2013  \\\n",
       "month                                                      \n",
       "1      (0.5490196078431373, 0.4509803921568627, 1.0, ...   \n",
       "2      (0.5411764705882353, 0.45882352941176474, 1.0,...   \n",
       "3      (0.5058823529411764, 0.49411764705882355, 1.0,...   \n",
       "4      (0.4823529411764706, 0.5176470588235293, 1.0, ...   \n",
       "5      (0.44705882352941173, 0.5529411764705883, 1.0,...   \n",
       "6      (0.4784313725490196, 0.5215686274509804, 1.0, ...   \n",
       "7      (0.7019607843137254, 0.29803921568627456, 1.0,...   \n",
       "8      (0.6980392156862745, 0.3019607843137255, 1.0, ...   \n",
       "9      (0.6509803921568628, 0.34901960784313724, 1.0,...   \n",
       "10     (0.6078431372549019, 0.3921568627450981, 1.0, ...   \n",
       "11     (0.615686274509804, 0.38431372549019605, 1.0, ...   \n",
       "12     (0.5372549019607843, 0.4627450980392157, 1.0, ...   \n",
       "\n",
       "year                                                2014  \\\n",
       "month                                                      \n",
       "1      (0.5882352941176471, 0.4117647058823529, 1.0, ...   \n",
       "2      (0.6235294117647059, 0.3764705882352941, 1.0, ...   \n",
       "3      (0.5725490196078431, 0.4274509803921569, 1.0, ...   \n",
       "4      (0.5372549019607843, 0.4627450980392157, 1.0, ...   \n",
       "5      (0.5254901960784314, 0.4745098039215686, 1.0, ...   \n",
       "6      (0.5529411764705883, 0.44705882352941173, 1.0,...   \n",
       "7      (0.7098039215686275, 0.2901960784313725, 1.0, ...   \n",
       "8      (0.6823529411764706, 0.3176470588235294, 1.0, ...   \n",
       "9      (0.6431372549019607, 0.3568627450980393, 1.0, ...   \n",
       "10     (0.7098039215686275, 0.2901960784313725, 1.0, ...   \n",
       "11     (0.5372549019607843, 0.4627450980392157, 1.0, ...   \n",
       "12     (0.4980392156862745, 0.5019607843137255, 1.0, ...   \n",
       "\n",
       "year                                                2015  \\\n",
       "month                                                      \n",
       "1      (0.5647058823529412, 0.43529411764705883, 1.0,...   \n",
       "2      (0.615686274509804, 0.38431372549019605, 1.0, ...   \n",
       "3      (0.6470588235294118, 0.3529411764705882, 1.0, ...   \n",
       "4      (0.5568627450980392, 0.44313725490196076, 1.0,...   \n",
       "5      (0.5607843137254902, 0.4392156862745098, 1.0, ...   \n",
       "6      (0.6039215686274509, 0.39607843137254906, 1.0,...   \n",
       "7      (0.6274509803921569, 0.37254901960784315, 1.0,...   \n",
       "8      (0.7843137254901961, 0.21568627450980393, 1.0,...   \n",
       "9      (0.788235294117647, 0.21176470588235297, 1.0, ...   \n",
       "10     (0.7254901960784313, 0.27450980392156865, 1.0,...   \n",
       "11     (0.5843137254901961, 0.4156862745098039, 1.0, ...   \n",
       "12     (0.5568627450980392, 0.44313725490196076, 1.0,...   \n",
       "\n",
       "year                                                2016  \\\n",
       "month                                                      \n",
       "1      (0.6078431372549019, 0.3921568627450981, 1.0, ...   \n",
       "2      (0.6666666666666666, 0.33333333333333337, 1.0,...   \n",
       "3      (0.6431372549019607, 0.3568627450980393, 1.0, ...   \n",
       "4       (0.596078431372549, 0.403921568627451, 1.0, 1.0)   \n",
       "5      (0.6862745098039216, 0.3137254901960784, 1.0, ...   \n",
       "6      (0.6862745098039216, 0.3137254901960784, 1.0, ...   \n",
       "7      (0.7450980392156863, 0.2549019607843137, 1.0, ...   \n",
       "8      (0.8431372549019608, 0.1568627450980392, 1.0, ...   \n",
       "9      (0.8196078431372549, 0.18039215686274512, 1.0,...   \n",
       "10     (0.9647058823529412, 0.03529411764705881, 1.0,...   \n",
       "11     (0.8588235294117647, 0.14117647058823535, 1.0,...   \n",
       "12     (0.6392156862745098, 0.36078431372549025, 1.0,...   \n",
       "\n",
       "year                                                2017  \\\n",
       "month                                                      \n",
       "1      (0.7215686274509804, 0.2784313725490196, 1.0, ...   \n",
       "2      (0.6705882352941176, 0.3294117647058824, 1.0, ...   \n",
       "3      (0.6666666666666666, 0.33333333333333337, 1.0,...   \n",
       "4       (0.611764705882353, 0.388235294117647, 1.0, 1.0)   \n",
       "5      (0.5411764705882353, 0.45882352941176474, 1.0,...   \n",
       "6      (0.6392156862745098, 0.36078431372549025, 1.0,...   \n",
       "7      (0.7529411764705882, 0.24705882352941178, 1.0,...   \n",
       "8      (0.8313725490196078, 0.16862745098039222, 1.0,...   \n",
       "9      (0.7333333333333333, 0.2666666666666667, 1.0, ...   \n",
       "10     (0.807843137254902, 0.19215686274509802, 1.0, ...   \n",
       "11     (0.6745098039215687, 0.3254901960784313, 1.0, ...   \n",
       "12     (0.6078431372549019, 0.3921568627450981, 1.0, ...   \n",
       "\n",
       "year                                                2018  \n",
       "month                                                     \n",
       "1      (0.6980392156862745, 0.3019607843137255, 1.0, ...  \n",
       "2      (0.7254901960784313, 0.27450980392156865, 1.0,...  \n",
       "3      (0.6627450980392157, 0.33725490196078434, 1.0,...  \n",
       "4                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "5                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "6                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "7                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "8                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "9                                   (0.0, 1.0, 1.0, 1.0)  \n",
       "10                                  (0.0, 1.0, 1.0, 1.0)  \n",
       "11                                  (0.0, 1.0, 1.0, 1.0)  \n",
       "12                                  (0.0, 1.0, 1.0, 1.0)  \n",
       "\n",
       "[12 rows x 41 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmap = cm.get_cmap('cool')\n",
    "sea_ice_month_year_colour = (1 - sea_ice_month_year_dnorm).applymap(cmap)\n",
    "sea_ice_month_year_colour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick check it works for one chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 12 artists>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(sea_ice_month_year_diff.index, sea_ice_month_year_diff[2017], color=sea_ice_month_year_colour[2017])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now replot the data for each year, but using the colours we manually specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sea_ice_month_year_diff.loc[:, 1988:2017].plot(legend=None, subplots=True, layout=(6, 5), sharey=True, figsize=(15, 15), kind='bar', color=sea_ice_month_year_colour);\n",
    "f, axes = plt.subplots(6, 5, sharey=True, figsize=(15, 15), facecolor='darkslateblue')\n",
    "plt.suptitle('Changes in sea ice extent vs 30-year average\\nEach bar is one month', color='lightsteelblue', fontsize=24)\n",
    "for r, row in enumerate(axes):\n",
    "    for c, ax in enumerate(row):\n",
    "        yr = 1988 + r * 5 + c\n",
    "        ax.bar(sea_ice_month_year_diff.index, \n",
    "               sea_ice_month_year_diff[yr], \n",
    "               color=sea_ice_month_year_colour[yr])\n",
    "        ax.set_frame_on(False)\n",
    "        ax.set_axis_off()\n",
    "        ax.set_title(str(yr), color='lightsteelblue')\n",
    "#         ax.set_facecolor('darkslateblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.savefig(\"changes-per-year.png\", facecolor=f.get_facecolor(), transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And again, plot for each month, one bar per year. \n",
    "\n",
    "Restrict to just the past 30 years of full data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sea_ice_month_year_diff.loc[:, 1988:2017].plot(legend=None, subplots=True, layout=(6, 5), sharey=True, figsize=(15, 15), kind='bar', color=sea_ice_month_year_colour);\n",
    "f, axes = plt.subplots(3, 4, sharey=True, figsize=(15, 15), facecolor='darkslateblue')\n",
    "for r, row in enumerate(axes):\n",
    "    for c, ax in enumerate(row):\n",
    "        mt = r * 4 + c + 1\n",
    "        ax.bar(sea_ice_month_year_diff.loc[:, 1988:2017].T.index, \n",
    "               sea_ice_month_year_diff.loc[:, 1988:2017].T[mt], \n",
    "               color=sea_ice_month_year_colour.loc[:, 1988:2017].T[mt])\n",
    "        ax.set_frame_on(False)\n",
    "        ax.set_axis_off()\n",
    "        ax.set_title(datetime(1900, mt, 1).strftime('%B'), color='lightsteelblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}