Forest fires 2018-2020 visualisation.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import calmap\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>numOfFires</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       date  numOfFires\n",
       "0   1 2018-01-01           0\n",
       "1   2 2018-01-02           0\n",
       "2   3 2018-01-03           0\n",
       "3   4 2018-01-04           0\n",
       "4   5 2018-01-05           0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('source/fires_for_python2018_2020.csv', parse_dates = ['date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>numOfFires</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        date  numOfFires\n",
       "0 2018-01-01           0\n",
       "1 2018-01-02           0\n",
       "2 2018-01-03           0\n",
       "3 2018-01-04           0\n",
       "4 2018-01-05           0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['id'], axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1096 entries, 0 to 1095\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   date        1096 non-null   datetime64[ns]\n",
      " 1   numOfFires  1096 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(1)\n",
      "memory usage: 17.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-01-01 00:00:00\n",
      "2020-12-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(df.date.min())\n",
    "print(df.date.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numOfFires</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            numOfFires  year\n",
       "date                        \n",
       "2018-01-01           0  2018\n",
       "2018-01-02           0  2018\n",
       "2018-01-03           0  2018\n",
       "2018-01-04           0  2018\n",
       "2018-01-05           0  2018"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'] = df.date.dt.year\n",
    "df.set_index('date', inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>numOfFires</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>0</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            numOfFires  year\n",
       "date                        \n",
       "2020-01-01           0  2020\n",
       "2020-01-02           0  2020\n",
       "2020-01-03           0  2020\n",
       "2020-01-04           0  2020\n",
       "2020-01-05           0  2020"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df18 = df.query('year == 2018')\n",
    "df19 = df.query('year == 2019')\n",
    "df20 = df.query('year == 2020')\n",
    "df20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df19.numOfFires.max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-79-133eadf3e016>:3: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  calmap.calendarplot(df18['2018']['numOfFires'], cmap = 'magma_r'\n",
      "<ipython-input-79-133eadf3e016>:6: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  calmap.calendarplot(df19['2019']['numOfFires'], cmap = 'magma_r'\n",
      "<ipython-input-79-133eadf3e016>:9: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  fig, ax = calmap.calendarplot(df20['2020']['numOfFires'], cmap = 'magma_r'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 32480x28000 with 0 Axes>"
      ]
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (116, 100), dpi = 280)\n",
    "\n",
    "calmap.calendarplot(df18['2018']['numOfFires'], cmap = 'magma_r'\n",
    "                   , fig_kws={'figsize': (16,10)} \n",
    "                   , yearlabel_kws = {'color':'black', 'fontsize':20})\n",
    "calmap.calendarplot(df19['2019']['numOfFires'], cmap = 'magma_r'\n",
    "                   , fig_kws={'figsize': (16,10)} \n",
    "                   , yearlabel_kws = {'color':'black', 'fontsize':20})\n",
    "fig, ax = calmap.calendarplot(df20['2020']['numOfFires'], cmap = 'magma_r'\n",
    "                   , fig_kws={'figsize': (16,10)} \n",
    "                   , yearlabel_kws = {'color':'black', 'fontsize':20})\n",
    "\n",
    "fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist(), orientation='horizontal').ax.tick_params(labelsize = 18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_plot(df, k):\n",
    "    plt.figure(figsize = (16, 10), dpi = 300)\n",
    "    fig, ax = calmap.calendarplot(df, cmap = 'magma_r'\n",
    "                   , fig_kws={'figsize': (16,10)} \n",
    "                   , yearlabel_kws = {'color':'black', 'fontsize':20})\n",
    "    n = [num*3 for num in range(k)]\n",
    "    fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n",
    "                 , orientation='vertical'\n",
    "                 , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
    "    return plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-122-f6167903dc89>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  get_plot(df18['2018']['numOfFires'], df18.numOfFires.max())\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'tick_params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-122-f6167903dc89>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf18\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2018'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'numOfFires'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf18\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumOfFires\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-121-110ce6799a7c>\u001b[0m in \u001b[0;36mget_plot\u001b[1;34m(df, k)\u001b[0m\n\u001b[0;32m      5\u001b[0m                    , yearlabel_kws = {'color':'black', 'fontsize':20})\n\u001b[0;32m      6\u001b[0m     \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n\u001b[0m\u001b[0;32m      8\u001b[0m                  \u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m                  , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'tick_params'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " get_plot(df18['2018']['numOfFires'], df18.numOfFires.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-123-79ed5aa3741b>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  get_plot(df19['2019']['numOfFires'], df19.numOfFires.max())\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'tick_params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-123-79ed5aa3741b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf19\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2019'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'numOfFires'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf19\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumOfFires\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-121-110ce6799a7c>\u001b[0m in \u001b[0;36mget_plot\u001b[1;34m(df, k)\u001b[0m\n\u001b[0;32m      5\u001b[0m                    , yearlabel_kws = {'color':'black', 'fontsize':20})\n\u001b[0;32m      6\u001b[0m     \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n\u001b[0m\u001b[0;32m      8\u001b[0m                  \u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m                  , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'tick_params'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_plot(df19['2019']['numOfFires'], df19.numOfFires.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-124-b6b644cda541>:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  get_plot(df20['2020']['numOfFires'], df20.numOfFires.max())\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'tick_params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-124-b6b644cda541>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf20\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'2020'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'numOfFires'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf20\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumOfFires\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-121-110ce6799a7c>\u001b[0m in \u001b[0;36mget_plot\u001b[1;34m(df, k)\u001b[0m\n\u001b[0;32m      5\u001b[0m                    , yearlabel_kws = {'color':'black', 'fontsize':20})\n\u001b[0;32m      6\u001b[0m     \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n\u001b[0m\u001b[0;32m      8\u001b[0m                  \u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m                  , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'tick_params'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_plot(df20['2020']['numOfFires'], df20.numOfFires.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-129-55ec4c97ad15>:2: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
      "  fig, ax = calmap.calendarplot(df19['2019']['numOfFires'], cmap = 'magma_r'\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'tick_params'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-129-55ec4c97ad15>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m                    , yearlabel_kws = {'color':'black', 'fontsize':20})\n\u001b[0;32m      5\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mnum\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf19\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumOfFires\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n\u001b[0m\u001b[0;32m      7\u001b[0m                  \u001b[1;33m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'vertical'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m                  , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'tick_params'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4800x3000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16, 10), dpi = 300)\n",
    "fig, ax = calmap.calendarplot(df19['2019']['numOfFires'], cmap = 'magma_r'\n",
    "                   , fig_kws={'figsize': (16,10)} \n",
    "                   , yearlabel_kws = {'color':'black', 'fontsize':20})\n",
    "n = [num*2 for num in range(df19.numOfFires.max())]\n",
    "fig.colorbar(ax[0].get_children()[1], ax=ax.ravel().tolist()\n",
    "                 , orientation='vertical'\n",
    "                 , shrink = 0.18).set_ticks(n).tick_params(labelsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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