Chapter08/chapter_08.ipynb

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   "source": [
    "Group By\n",
    "========\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pyarrow as pa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by basics\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>group_b</td>\n",
       "      <td>5</td>\n",
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      "text/plain": [
       "     group  value\n",
       "0  group_a      0\n",
       "1  group_a      2\n",
       "2  group_b      1\n",
       "3  group_b      3\n",
       "4  group_b      5"
      ]
     },
     "execution_count": 2,
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   ],
   "source": [
    "df = pd.DataFrame([\n",
    "    [\"group_a\", 0],\n",
    "    [\"group_a\", 2],\n",
    "    [\"group_b\", 1],\n",
    "    [\"group_b\", 3],\n",
    "    [\"group_b\", 5],\n",
    "], columns=[\"group\", \"value\"])\n",
    "df = df.convert_dtypes(dtype_backend=\"numpy_nullable\")\n",
    "\n",
    "df"
   ]
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       "         value\n",
       "group         \n",
       "group_a      2\n",
       "group_b      9"
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    "df.groupby(\"group\").sum()"
   ]
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       "         value\n",
       "group         \n",
       "group_a      2\n",
       "group_b      9"
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    "df.groupby(\"group\").agg(\"sum\")"
   ]
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   "execution_count": 5,
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   "outputs": [
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      "text/plain": [
       "   value\n",
       "0      2\n",
       "1      2\n",
       "2      9\n",
       "3      9\n",
       "4      9"
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    "df.groupby(\"group\").transform(\"sum\")"
   ]
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.111111</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.333333</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.555556</td>\n",
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       "      value\n",
       "0       0.0\n",
       "1       1.0\n",
       "2  0.111111\n",
       "3  0.333333\n",
       "4  0.555556"
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    "df[[\"value\"]].div(df.groupby(\"group\").transform(\"sum\"))"
   ]
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   "execution_count": 7,
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       "     group  value\n",
       "0  group_a      2\n",
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    "df.groupby(\"group\", as_index=False).sum()"
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       "      <td>5</td>\n",
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       "     group  value\n",
       "0  group_a      0\n",
       "1  group_a      2\n",
       "2  group_b      1\n",
       "3  group_b      3\n",
       "4  group_b      5"
      ]
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     "execution_count": 8,
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    "df"
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       "      <td>9</td>\n",
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       "         value\n",
       "group         \n",
       "group_a      2\n",
       "group_b      9"
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   "source": [
    "df.groupby(\"group\").sum()"
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   "execution_count": 10,
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       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_a</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "         value\n",
       "group         \n",
       "group_a      0\n",
       "group_b      1"
      ]
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df.groupby(\"group\").min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sum_of_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_a</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>group_b</th>\n",
       "      <td>9</td>\n",
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      "text/plain": [
       "         sum_of_value\n",
       "group                \n",
       "group_a             2\n",
       "group_b             9"
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df.groupby(\"group\").agg(sum_of_value=pd.NamedAgg(column=\"value\", aggfunc=\"sum\"))"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more&#x2026;\n",
    "\n"
   ]
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   "execution_count": 12,
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       "      <th></th>\n",
       "      <th>value</th>\n",
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       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>group_a</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>group_b</th>\n",
       "      <td>5</td>\n",
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       "         value\n",
       "group         \n",
       "group_a      2\n",
       "group_b      5"
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     "execution_count": 12,
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   "source": [
    "df.groupby(\"group\").max()"
   ]
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       "\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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_a</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_b</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         value\n",
       "group         \n",
       "group_a      2\n",
       "group_b      5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"group\").agg(\"max\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   value\n",
       "0      2\n",
       "1      2\n",
       "2      5\n",
       "3      5\n",
       "4      5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"group\").transform(\"max\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping and calculating multiple columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>region</th>\n",
       "      <th>widget</th>\n",
       "      <th>month</th>\n",
       "      <th>sales</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>North</td>\n",
       "      <td>Widget A</td>\n",
       "      <td>Jan</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North</td>\n",
       "      <td>Widget B</td>\n",
       "      <td>Jan</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>Widget A</td>\n",
       "      <td>Jan</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South</td>\n",
       "      <td>Widget B</td>\n",
       "      <td>Jan</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North</td>\n",
       "      <td>Widget A</td>\n",
       "      <td>Feb</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>North</td>\n",
       "      <td>Widget B</td>\n",
       "      <td>Feb</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>South</td>\n",
       "      <td>Widget A</td>\n",
       "      <td>Feb</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>South</td>\n",
       "      <td>Widget B</td>\n",
       "      <td>Feb</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  region    widget month  sales  returns\n",
       "0  North  Widget A   Jan     10        2\n",
       "1  North  Widget B   Jan      4        0\n",
       "2  South  Widget A   Jan      8        3\n",
       "3  South  Widget B   Jan     12        8\n",
       "4  North  Widget A   Feb      3        0\n",
       "5  North  Widget B   Feb      7        0\n",
       "6  South  Widget A   Feb     11        2\n",
       "7  South  Widget B   Feb     13        4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([\n",
    "    [\"North\", \"Widget A\", \"Jan\", 10, 2],\n",
    "    [\"North\", \"Widget B\", \"Jan\", 4, 0],\n",
    "    [\"South\", \"Widget A\", \"Jan\", 8, 3],\n",
    "    [\"South\", \"Widget B\", \"Jan\", 12, 8],\n",
    "    [\"North\", \"Widget A\", \"Feb\", 3, 0],\n",
    "    [\"North\", \"Widget B\", \"Feb\", 7, 0],\n",
    "    [\"South\", \"Widget A\", \"Feb\", 11, 2],\n",
    "    [\"South\", \"Widget B\", \"Feb\", 13, 4],\n",
    "], columns=[\"region\", \"widget\", \"month\", \"sales\", \"returns\"])\n",
    "df = df.convert_dtypes(dtype_backend=\"numpy_nullable\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>region</th>\n",
       "      <th>month</th>\n",
       "      <th>sales</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widget</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Widget A</th>\n",
       "      <td>NorthSouthNorthSouth</td>\n",
       "      <td>JanJanFebFeb</td>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widget B</th>\n",
       "      <td>NorthSouthNorthSouth</td>\n",
       "      <td>JanJanFebFeb</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        region         month  sales  returns\n",
       "widget                                                      \n",
       "Widget A  NorthSouthNorthSouth  JanJanFebFeb     32        7\n",
       "Widget B  NorthSouthNorthSouth  JanJanFebFeb     36       12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"widget\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NorthSouthNorthSouth'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"North\" + \"South\" + \"North\" + \"South\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widget</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Widget A</th>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widget B</th>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sales  returns\n",
       "widget                  \n",
       "Widget A     32        7\n",
       "Widget B     36       12"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"widget\")[[\"sales\", \"returns\"]].agg(\"sum\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>sales_total</th>\n",
       "      <th>returns_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widget</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Widget A</th>\n",
       "      <td>32</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Widget B</th>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sales_total  returns_total\n",
       "widget                              \n",
       "Widget A           32              7\n",
       "Widget B           36             12"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"widget\").agg(\n",
    "    sales_total=pd.NamedAgg(column=\"sales\", aggfunc=\"sum\"),\n",
    "    returns_total=pd.NamedAgg(column=\"returns\", aggfunc=\"sum\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sales_total</th>\n",
       "      <th>returns_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widget</th>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Widget A</th>\n",
       "      <th>North</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Widget B</th>\n",
       "      <th>North</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sales_total  returns_total\n",
       "widget   region                            \n",
       "Widget A North            13              2\n",
       "         South            19              5\n",
       "Widget B North            11              0\n",
       "         South            25             12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"widget\", \"region\"]).agg(\n",
    "    sales_total=pd.NamedAgg(\"sales\", \"sum\"),\n",
    "    returns_total=pd.NamedAgg(\"returns\", \"sum\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sales_total</th>\n",
       "      <th>returns_total</th>\n",
       "      <th>sales_min</th>\n",
       "      <th>returns_min</th>\n",
       "      <th>sales_mean</th>\n",
       "      <th>returns_mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widget</th>\n",
       "      <th>region</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 rowspan=\"2\" valign=\"top\">Widget A</th>\n",
       "      <th>North</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Widget B</th>\n",
       "      <th>North</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South</th>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sales_total  returns_total  sales_min  returns_min  \\\n",
       "widget   region                                                       \n",
       "Widget A North            13              2          3            0   \n",
       "         South            19              5          8            2   \n",
       "Widget B North            11              0          4            0   \n",
       "         South            25             12         12            4   \n",
       "\n",
       "                 sales_mean  returns_mean  \n",
       "widget   region                            \n",
       "Widget A North          6.5           1.0  \n",
       "         South          9.5           2.5  \n",
       "Widget B North          5.5           0.0  \n",
       "         South         12.5           6.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"widget\", \"region\"]).agg(\n",
    "    sales_total=pd.NamedAgg(\"sales\", \"sum\"),\n",
    "    returns_total=pd.NamedAgg(\"returns\", \"sum\"),\n",
    "    sales_min=pd.NamedAgg(\"sales\", \"min\"),\n",
    "    returns_min=pd.NamedAgg(\"returns\", \"min\"),\n",
    "    sales_mean=pd.NamedAgg(\"sales\", \"mean\"),\n",
    "    returns_mean=pd.NamedAgg(\"returns\", \"mean\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more&#x2026;\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([0, 1, 1]).mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([0, 1, 1, 2, 2]).mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>group</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group_a</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_a</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>group_a</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>group_a</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>group_b</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     group  value\n",
       "0  group_a     42\n",
       "1  group_a    555\n",
       "2  group_a     42\n",
       "3  group_a    555\n",
       "4  group_b      0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([\n",
    "    [\"group_a\", 42],\n",
    "    [\"group_a\", 555],\n",
    "    [\"group_a\", 42],\n",
    "    [\"group_a\", 555],\n",
    "    [\"group_b\", 0],\n",
    "], columns=[\"group\", \"value\"])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "group_a    [42, 555]\n",
       "group_b            0\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([[42, 555], 0], index=pd.Index([\"group_a\", \"group_b\"], name=\"group\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "group_a     42\n",
       "group_a    555\n",
       "group_b      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(\n",
    "    [42, 555, 0],\n",
    "    index=pd.Index([\"group_a\", \"group_a\", \"group_b\"], name=\"group\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scalar_or_list_mode(ser: pd.Series):\n",
    "    result = ser.mode()\n",
    "    if len(result) > 1:\n",
    "        return result.tolist()\n",
    "    elif len(result) == 1:\n",
    "        return result.iloc[0]\n",
    "\n",
    "    return pd.NA\n",
    "\n",
    "def scalar_or_bust_mode(ser: pd.Series):\n",
    "    result = ser.mode()\n",
    "    if len(result) == 0:\n",
    "        return pd.NA\n",
    "\n",
    "    return result.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>scalar_or_list</th>\n",
       "      <th>scalar_or_bust</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_a</th>\n",
       "      <td>[42, 555]</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_b</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        scalar_or_list  scalar_or_bust\n",
       "group                                 \n",
       "group_a      [42, 555]              42\n",
       "group_b              0               0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"group\").agg(\n",
    "    scalar_or_list=pd.NamedAgg(column=\"value\", aggfunc=scalar_or_list_mode),\n",
    "    scalar_or_bust=pd.NamedAgg(column=\"value\", aggfunc=scalar_or_bust_mode),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by apply\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>group</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>group_a</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>group_a</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>group_a</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>group_a</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>group_b</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     group  value\n",
       "0  group_a     42\n",
       "1  group_a    555\n",
       "2  group_a     42\n",
       "3  group_a    555\n",
       "4  group_b      0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([\n",
    "    [\"group_a\", 42],\n",
    "    [\"group_a\", 555],\n",
    "    [\"group_a\", 42],\n",
    "    [\"group_a\", 555],\n",
    "    [\"group_b\", 0],\n",
    "], columns=[\"group\", \"value\"])\n",
    "df = df.convert_dtypes(dtype_backend=\"numpy_nullable\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "group_a     42\n",
       "group_a    555\n",
       "group_b      0\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(\n",
    "    [42, 555, 0],\n",
    "    index=pd.Index([\"group_a\", \"group_a\", \"group_b\"], name=\"group\"),\n",
    "    dtype=pd.Int64Dtype(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group     \n",
       "group_a  0     42\n",
       "         1    555\n",
       "group_b  0      0\n",
       "Name: value, dtype: Int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mode_for_apply(df: pd.DataFrame):\n",
    "    return df[\"value\"].mode()\n",
    "\n",
    "df.groupby(\"group\").apply(mode_for_apply, include_groups=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The data passed to apply is:\n",
      "   value\n",
      "0     42\n",
      "1    555\n",
      "2     42\n",
      "3    555\n",
      "\n",
      "The data passed to apply is:\n",
      "   value\n",
      "4      0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "group     \n",
       "group_a  0     42\n",
       "         1    555\n",
       "group_b  0      0\n",
       "Name: value, dtype: Int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mode_for_apply(df: pd.DataFrame):\n",
    "    print(f\"\\nThe data passed to apply is:\\n{df}\")\n",
    "    return df[\"value\"].mode()\n",
    "\n",
    "df.groupby(\"group\").apply(mode_for_apply, include_groups=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "group_a    1194\n",
       "group_b       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum_values(df: pd.DataFrame):\n",
    "    return df[\"value\"].sum()\n",
    "\n",
    "df.groupby(\"group\").apply(sum_values, include_groups=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Window operations\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     2\n",
       "3     4\n",
       "4     8\n",
       "5    16\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series([0, 1, 2, 4, 8, 16], dtype=pd.Int64Dtype())\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     NaN\n",
       "1     1.0\n",
       "2     3.0\n",
       "3     6.0\n",
       "4    12.0\n",
       "5    24.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.rolling(2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1       1\n",
       "2       3\n",
       "3       6\n",
       "4      12\n",
       "5      24\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.rolling(2).sum().astype(pd.Int64Dtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     1\n",
       "2     3\n",
       "3     6\n",
       "4    12\n",
       "5    24\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.rolling(2, min_periods=1).sum().astype(pd.Int64Dtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1    <NA>\n",
       "2       3\n",
       "3       7\n",
       "4      14\n",
       "5      28\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.rolling(3).sum().astype(pd.Int64Dtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    <NA>\n",
       "1       3\n",
       "2       7\n",
       "3      14\n",
       "4      28\n",
       "5    <NA>\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.rolling(3, center=True).sum().astype(pd.Int64Dtype())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.0\n",
       "1         0.5\n",
       "2         1.0\n",
       "3        1.75\n",
       "4         3.0\n",
       "5    5.166667\n",
       "dtype: Float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.expanding().mean().astype(pd.Float64Dtype())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There's more&#x2026;\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>59.977501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>59.017502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>59.264999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>59.982498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>60.095001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-22</th>\n",
       "      <td>488.299988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>492.790009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>494.170013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>495.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>495.220001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1006 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close\n",
       "Date                  \n",
       "2020-01-02   59.977501\n",
       "2020-01-03   59.017502\n",
       "2020-01-06   59.264999\n",
       "2020-01-07   59.982498\n",
       "2020-01-08   60.095001\n",
       "...                ...\n",
       "2023-12-22  488.299988\n",
       "2023-12-26  492.790009\n",
       "2023-12-27  494.170013\n",
       "2023-12-28  495.220001\n",
       "2023-12-29  495.220001\n",
       "\n",
       "[1006 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"data/NVDA.csv\",\n",
    "    usecols=[\"Date\", \"Close\"],\n",
    "    parse_dates=[\"Date\"],\n",
    "    dtype_backend=\"numpy_nullable\",\n",
    ").set_index(\"Date\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.ion()\n",
    "\n",
    "df.assign(\n",
    "    ma30=df[\"Close\"].rolling(30).mean().astype(pd.Float64Dtype()),\n",
    "    ma60=df[\"Close\"].rolling(60).mean().astype(pd.Float64Dtype()),\n",
    "    ma90=df[\"Close\"].rolling(90).mean().astype(pd.Float64Dtype()),\n",
    ").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2020-01-01</th>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>59.977501</td>\n",
       "      <td>59.977501</td>\n",
       "      <td>59.977501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>59.017502</td>\n",
       "      <td>59.977501</td>\n",
       "      <td>59.497501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>59.017502</td>\n",
       "      <td>59.977501</td>\n",
       "      <td>59.420001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>59.017502</td>\n",
       "      <td>59.982498</td>\n",
       "      <td>59.560625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>59.017502</td>\n",
       "      <td>60.095001</td>\n",
       "      <td>59.667500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023-01-01</th>\n",
       "      <th>2023-12-22</th>\n",
       "      <td>142.649994</td>\n",
       "      <td>504.089996</td>\n",
       "      <td>363.600610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-26</th>\n",
       "      <td>142.649994</td>\n",
       "      <td>504.089996</td>\n",
       "      <td>364.123644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-27</th>\n",
       "      <td>142.649994</td>\n",
       "      <td>504.089996</td>\n",
       "      <td>364.648024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-28</th>\n",
       "      <td>142.649994</td>\n",
       "      <td>504.089996</td>\n",
       "      <td>365.172410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-29</th>\n",
       "      <td>142.649994</td>\n",
       "      <td>504.089996</td>\n",
       "      <td>365.692600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1006 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Close                        \n",
       "                              min         max        mean\n",
       "Date       Date                                          \n",
       "2020-01-01 2020-01-02   59.977501   59.977501   59.977501\n",
       "           2020-01-03   59.017502   59.977501   59.497501\n",
       "           2020-01-06   59.017502   59.977501   59.420001\n",
       "           2020-01-07   59.017502   59.982498   59.560625\n",
       "           2020-01-08   59.017502   60.095001   59.667500\n",
       "...                           ...         ...         ...\n",
       "2023-01-01 2023-12-22  142.649994  504.089996  363.600610\n",
       "           2023-12-26  142.649994  504.089996  364.123644\n",
       "           2023-12-27  142.649994  504.089996  364.648024\n",
       "           2023-12-28  142.649994  504.089996  365.172410\n",
       "           2023-12-29  142.649994  504.089996  365.692600\n",
       "\n",
       "[1006 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(freq=\"YS\")).expanding().agg(\n",
    "    [\"min\", \"max\", \"mean\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(freq=\"YS\")).expanding().agg(\n",
    "    [\"min\", \"max\", \"mean\"]\n",
    ").droplevel(axis=1, level=0).reset_index(level=0, drop=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(freq=\"QS\")).expanding().agg(\n",
    "    [\"min\", \"max\", \"mean\"]\n",
    ").reset_index(level=0, drop=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(pd.Grouper(freq=\"MS\")).expanding().agg(\n",
    "    [\"min\", \"max\", \"mean\"]\n",
    ").reset_index(level=0, drop=True).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the highest rated movies by year\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: Episode VII - The Force Awakens</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4911</th>\n",
       "      <td>Signed Sealed Delivered</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4912</th>\n",
       "      <td>The Following</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4913</th>\n",
       "      <td>A Plague So Pleasant</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4914</th>\n",
       "      <td>Shanghai Calling</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4915</th>\n",
       "      <td>My Date with Drew</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4916 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     movie_title  title_year  imdb_score\n",
       "0                                         Avatar      2009.0         7.9\n",
       "1       Pirates of the Caribbean: At World's End      2007.0         7.1\n",
       "2                                        Spectre      2015.0         6.8\n",
       "3                          The Dark Knight Rises      2012.0         8.5\n",
       "4     Star Wars: Episode VII - The Force Awakens        <NA>         7.1\n",
       "...                                          ...         ...         ...\n",
       "4911                     Signed Sealed Delivered      2013.0         7.7\n",
       "4912                               The Following        <NA>         7.5\n",
       "4913                        A Plague So Pleasant      2013.0         6.3\n",
       "4914                            Shanghai Calling      2012.0         6.3\n",
       "4915                           My Date with Drew      2004.0         6.6\n",
       "\n",
       "[4916 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"data/movie.csv\",\n",
    "    usecols=[\"movie_title\", \"title_year\", \"imdb_score\"],\n",
    "    dtype_backend=\"numpy_nullable\",\n",
    ")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>2007</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                movie_title  title_year  imdb_score\n",
       "0                                    Avatar        2009         7.9\n",
       "1  Pirates of the Caribbean: At World's End        2007         7.1\n",
       "2                                   Spectre        2015         6.8"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"title_year\"] = df[\"title_year\"].astype(pd.Int16Dtype())\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>2009</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>2007</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Spectre</td>\n",
       "      <td>2015</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                movie_title  title_year  imdb_score\n",
       "0                                    Avatar        2009         7.9\n",
       "1  Pirates of the Caribbean: At World's End        2007         7.1\n",
       "2                                   Spectre        2015         6.8"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    \"data/movie.csv\",\n",
    "    usecols=[\"movie_title\", \"title_year\", \"imdb_score\"],\n",
    "    dtype={\"title_year\": pd.Int16Dtype()},\n",
    "    dtype_backend=\"numpy_nullable\",\n",
    ")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top_rated_movie</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Intolerance: Love's Struggle Throughout the Ages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Over the Hill to the Poorhouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>The Big Parade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>Metropolis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Pandora's Box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>Django Unchained</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>Butterfly Girl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Running Forever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             top_rated_movie\n",
       "title_year                                                  \n",
       "1916        Intolerance: Love's Struggle Throughout the Ages\n",
       "1920                          Over the Hill to the Poorhouse\n",
       "1925                                          The Big Parade\n",
       "1927                                              Metropolis\n",
       "1929                                           Pandora's Box\n",
       "...                                                      ...\n",
       "2012                                        Django Unchained\n",
       "2013                 Batman: The Dark Knight Returns, Part 2\n",
       "2014                                          Butterfly Girl\n",
       "2015                                         Running Forever\n",
       "2016                                    Kickboxer: Vengeance\n",
       "\n",
       "[91 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"title_year\", \"imdb_score\"]).groupby(\n",
    "    \"title_year\"\n",
    ")[[\"movie_title\"]].agg(top_rated_movie=pd.NamedAgg(\"movie_title\", \"last\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top_rated_movie</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Intolerance: Love's Struggle Throughout the Ages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Over the Hill to the Poorhouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>The Big Parade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>Metropolis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Pandora's Box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>Queen of the Mountains</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Running Forever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             top_rated_movie\n",
       "title_year                                                  \n",
       "1916        Intolerance: Love's Struggle Throughout the Ages\n",
       "1920                          Over the Hill to the Poorhouse\n",
       "1925                                          The Big Parade\n",
       "1927                                              Metropolis\n",
       "1929                                           Pandora's Box\n",
       "...                                                      ...\n",
       "2012                                   The Dark Knight Rises\n",
       "2013                 Batman: The Dark Knight Returns, Part 2\n",
       "2014                                  Queen of the Mountains\n",
       "2015                                         Running Forever\n",
       "2016                                    Kickboxer: Vengeance\n",
       "\n",
       "[91 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(\"movie_title\").groupby(\"title_year\").agg(\n",
    "    top_rated_movie=pd.NamedAgg(\"imdb_score\", \"idxmax\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_title</th>\n",
       "      <th>title_year</th>\n",
       "      <th>imdb_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>2012</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>2012</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4369</th>\n",
       "      <td>Queen of the Mountains</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4804</th>\n",
       "      <td>Butterfly Girl</td>\n",
       "      <td>2014</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 movie_title  title_year  imdb_score\n",
       "3      The Dark Knight Rises        2012         8.5\n",
       "293         Django Unchained        2012         8.5\n",
       "4369  Queen of the Mountains        2014         8.7\n",
       "4804          Butterfly Girl        2014         8.7"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"movie_title\"].isin({\n",
    "    \"Django Unchained\",\n",
    "    \"The Dark Knight Rises\",\n",
    "    \"Butterfly Girl\",\n",
    "    \"Queen of the Mountains\",\n",
    "})]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>top_rated_movie(s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title_year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>Intolerance: Love's Struggle Throughout the Ages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>Over the Hill to the Poorhouse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>The Big Parade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1927</th>\n",
       "      <td>Metropolis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1929</th>\n",
       "      <td>Pandora's Box</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>[The Dark Knight Rises, Django Unchained]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>[Queen of the Mountains, Butterfly Girl]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Running Forever</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>Kickboxer: Vengeance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          top_rated_movie(s)\n",
       "title_year                                                  \n",
       "1916        Intolerance: Love's Struggle Throughout the Ages\n",
       "1920                          Over the Hill to the Poorhouse\n",
       "1925                                          The Big Parade\n",
       "1927                                              Metropolis\n",
       "1929                                           Pandora's Box\n",
       "...                                                      ...\n",
       "2012               [The Dark Knight Rises, Django Unchained]\n",
       "2013                 Batman: The Dark Knight Returns, Part 2\n",
       "2014                [Queen of the Mountains, Butterfly Girl]\n",
       "2015                                         Running Forever\n",
       "2016                                    Kickboxer: Vengeance\n",
       "\n",
       "[91 rows x 1 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def top_rated(df: pd.DataFrame):\n",
    "    top_rating = df[\"imdb_score\"].max()\n",
    "    top_rated = df[df[\"imdb_score\"] == top_rating][\"movie_title\"].unique()\n",
    "\n",
    "    if len(top_rated) == 1:\n",
    "        return top_rated[0]\n",
    "    else:\n",
    "        return top_rated\n",
    "\n",
    "df.groupby(\"title_year\").apply(\n",
    "    top_rated, include_groups=False\n",
    ").to_frame().rename(columns={0: \"top_rated_movie(s)\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the best hitter in baseball across years\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to do it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>game</th>\n",
       "      <th>starttime</th>\n",
       "      <th>visiting_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th>id</th>\n",
       "      <th>side</th>\n",
       "      <th>pos</th>\n",
       "      <th>seq</th>\n",
       "      <th>ab</th>\n",
       "      <th>...</th>\n",
       "      <th>sh</th>\n",
       "      <th>sf</th>\n",
       "      <th>hpb</th>\n",
       "      <th>bb</th>\n",
       "      <th>ibb</th>\n",
       "      <th>k</th>\n",
       "      <th>sb</th>\n",
       "      <th>cs</th>\n",
       "      <th>gidp</th>\n",
       "      <th>int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>ANA201504100</td>\n",
       "      <td>7:12PM</td>\n",
       "      <td>KCA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>escoa003</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>ANA201504100</td>\n",
       "      <td>7:12PM</td>\n",
       "      <td>KCA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>mousm001</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>ANA201504100</td>\n",
       "      <td>7:12PM</td>\n",
       "      <td>KCA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>cainl001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>ANA201504100</td>\n",
       "      <td>7:12PM</td>\n",
       "      <td>KCA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>hosme001</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>ANA201504100</td>\n",
       "      <td>7:12PM</td>\n",
       "      <td>KCA</td>\n",
       "      <td>ANA</td>\n",
       "      <td>morak001</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630995</th>\n",
       "      <td>2013</td>\n",
       "      <td>WAS201309222</td>\n",
       "      <td>7:06PM</td>\n",
       "      <td>MIA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>lombs002</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630996</th>\n",
       "      <td>2013</td>\n",
       "      <td>WAS201309222</td>\n",
       "      <td>7:06PM</td>\n",
       "      <td>MIA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>stamc001</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630997</th>\n",
       "      <td>2013</td>\n",
       "      <td>WAS201309222</td>\n",
       "      <td>7:06PM</td>\n",
       "      <td>MIA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>clipt001</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630998</th>\n",
       "      <td>2013</td>\n",
       "      <td>WAS201309222</td>\n",
       "      <td>7:06PM</td>\n",
       "      <td>MIA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>tracc001</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630999</th>\n",
       "      <td>2013</td>\n",
       "      <td>WAS201309222</td>\n",
       "      <td>7:06PM</td>\n",
       "      <td>MIA</td>\n",
       "      <td>WAS</td>\n",
       "      <td>sorir001</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1631000 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         year          game starttime visiting_team home_team        id  side  \\\n",
       "0        2015  ANA201504100    7:12PM           KCA       ANA  escoa003     0   \n",
       "1        2015  ANA201504100    7:12PM           KCA       ANA  mousm001     0   \n",
       "2        2015  ANA201504100    7:12PM           KCA       ANA  cainl001     0   \n",
       "3        2015  ANA201504100    7:12PM           KCA       ANA  hosme001     0   \n",
       "4        2015  ANA201504100    7:12PM           KCA       ANA  morak001     0   \n",
       "...       ...           ...       ...           ...       ...       ...   ...   \n",
       "1630995  2013  WAS201309222    7:06PM           MIA       WAS  lombs002     1   \n",
       "1630996  2013  WAS201309222    7:06PM           MIA       WAS  stamc001     1   \n",
       "1630997  2013  WAS201309222    7:06PM           MIA       WAS  clipt001     1   \n",
       "1630998  2013  WAS201309222    7:06PM           MIA       WAS  tracc001     1   \n",
       "1630999  2013  WAS201309222    7:06PM           MIA       WAS  sorir001     1   \n",
       "\n",
       "         pos  seq  ab  ...  sh  sf  hpb  bb  ibb  k  sb  cs  gidp  int  \n",
       "0          1    1   3  ...   1   0    0   1    0  0   0   0     0    0  \n",
       "1          2    1   3  ...   0   0    2   0    0  0   0   0     0    0  \n",
       "2          3    1   4  ...   0   0    0   1    0  2   0   0     0    0  \n",
       "3          4    1   5  ...   0   0    0   0    0  1   0   0     0    0  \n",
       "4          5    1   5  ...   0   0    0   0    0  1   0   0     0    0  \n",
       "...      ...  ...  ..  ...  ..  ..  ...  ..  ... ..  ..  ..   ...  ...  \n",
       "1630995    9    2   1  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "1630996    9    3   0  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "1630997    9    4   0  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "1630998    9    5   1  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "1630999    9    6   0  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "\n",
       "[1631000 rows x 26 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_parquet(\"data/mlb_batting_lines.parquet\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>game</th>\n",
       "      <th>starttime</th>\n",
       "      <th>visiting_team</th>\n",
       "      <th>home_team</th>\n",
       "      <th>id</th>\n",
       "      <th>side</th>\n",
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       "      <th>...</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2383</th>\n",
       "      <td>2015</td>\n",
       "      <td>BAL201504100</td>\n",
       "      <td>3:11PM</td>\n",
       "      <td>TOR</td>\n",
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       "      <th>2384</th>\n",
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       "      <td>BAL201504100</td>\n",
       "      <td>3:11PM</td>\n",
       "      <td>TOR</td>\n",
       "      <td>BAL</td>\n",
       "      <td>pompd001</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2015</td>\n",
       "      <td>BAL201504100</td>\n",
       "      <td>3:11PM</td>\n",
       "      <td>TOR</td>\n",
       "      <td>BAL</td>\n",
       "      <td>bautj002</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2386</th>\n",
       "      <td>2015</td>\n",
       "      <td>BAL201504100</td>\n",
       "      <td>3:11PM</td>\n",
       "      <td>TOR</td>\n",
       "      <td>BAL</td>\n",
       "      <td>tolls001</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2387</th>\n",
       "      <td>2015</td>\n",
       "      <td>BAL201504100</td>\n",
       "      <td>3:11PM</td>\n",
       "      <td>TOR</td>\n",
       "      <td>BAL</td>\n",
       "      <td>encae001</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year          game starttime visiting_team home_team        id  side  \\\n",
       "2383  2015  BAL201504100    3:11PM           TOR       BAL  reyej001     0   \n",
       "2384  2015  BAL201504100    3:11PM           TOR       BAL  pompd001     0   \n",
       "2385  2015  BAL201504100    3:11PM           TOR       BAL  bautj002     0   \n",
       "2386  2015  BAL201504100    3:11PM           TOR       BAL  tolls001     0   \n",
       "2387  2015  BAL201504100    3:11PM           TOR       BAL  encae001     0   \n",
       "\n",
       "      pos  seq  ab  ...  sh  sf  hpb  bb  ibb  k  sb  cs  gidp  int  \n",
       "2383    1    1   5  ...   0   1    0   0    0  1   0   0     0    0  \n",
       "2384    2    1   6  ...   0   0    0   0    0  0   0   0     0    0  \n",
       "2385    3    1   3  ...   0   0    0   1    0  0   0   0     0    0  \n",
       "2386    3    2   1  ...   0   0    0   0    0  1   0   0     0    0  \n",
       "2387    4    1   4  ...   0   1    0   0    0  2   0   0     0    0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bal = df[df[\"game\"] == \"BAL201504100\"]\n",
    "bal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ab    75\n",
       "h     29\n",
       "hr     2\n",
       "dtype: Int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bal[[\"ab\", \"h\", \"hr\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_ab</th>\n",
       "      <th>total_h</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2000</th>\n",
       "      <th>abboj002</th>\n",
       "      <td>215</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbok002</th>\n",
       "      <td>157</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbop001</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abreb001</th>\n",
       "      <td>576</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acevj001</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>zavas001</th>\n",
       "      <td>175</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zerpa001</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zimmb002</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zunig001</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zunim001</th>\n",
       "      <td>124</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31508 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_ab  total_h\n",
       "year id                         \n",
       "2000 abboj002       215       59\n",
       "     abbok002       157       34\n",
       "     abbop001         5        2\n",
       "     abreb001       576      182\n",
       "     acevj001         1        0\n",
       "...                 ...      ...\n",
       "2023 zavas001       175       30\n",
       "     zerpa001         0        0\n",
       "     zimmb002         0        0\n",
       "     zunig001         0        0\n",
       "     zunim001       124       22\n",
       "\n",
       "[31508 rows x 2 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"year\", \"id\"]).agg(\n",
    "    total_ab=pd.NamedAgg(column=\"ab\", aggfunc=\"sum\"),\n",
    "    total_h=pd.NamedAgg(column=\"h\", aggfunc=\"sum\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2000</th>\n",
       "      <th>abboj002</th>\n",
       "      <td>0.274419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbok002</th>\n",
       "      <td>0.216561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abbop001</th>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abreb001</th>\n",
       "      <td>0.315972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acevj001</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>zavas001</th>\n",
       "      <td>0.171429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zerpa001</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zimmb002</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zunig001</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zunim001</th>\n",
       "      <td>0.177419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31508 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    avg\n",
       "year id                \n",
       "2000 abboj002  0.274419\n",
       "     abbok002  0.216561\n",
       "     abbop001       0.4\n",
       "     abreb001  0.315972\n",
       "     acevj001       0.0\n",
       "...                 ...\n",
       "2023 zavas001  0.171429\n",
       "     zerpa001       NaN\n",
       "     zimmb002       NaN\n",
       "     zunig001       NaN\n",
       "     zunim001  0.177419\n",
       "\n",
       "[31508 rows x 1 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.groupby([\"year\", \"id\"]).agg(\n",
    "        total_ab=pd.NamedAgg(column=\"ab\", aggfunc=\"sum\"),\n",
    "        total_h=pd.NamedAgg(column=\"h\", aggfunc=\"sum\"))\n",
    "    .assign(avg=lambda x: x[\"total_h\"] / x[\"total_ab\"])\n",
    "    .drop(columns=[\"total_ab\", \"total_h\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2000</th>\n",
       "      <th>abreb001</th>\n",
       "      <td>0.315972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alfoe001</th>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alicl001</th>\n",
       "      <td>0.294444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alomr001</th>\n",
       "      <td>0.309836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aloum001</th>\n",
       "      <td>0.354626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>walkc002</th>\n",
       "      <td>0.257732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>walkj003</th>\n",
       "      <td>0.27619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wittb002</th>\n",
       "      <td>0.276131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yelic001</th>\n",
       "      <td>0.278182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yoshm002</th>\n",
       "      <td>0.288641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4147 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    avg\n",
       "year id                \n",
       "2000 abreb001  0.315972\n",
       "     alfoe001  0.323529\n",
       "     alicl001  0.294444\n",
       "     alomr001  0.309836\n",
       "     aloum001  0.354626\n",
       "...                 ...\n",
       "2023 walkc002  0.257732\n",
       "     walkj003   0.27619\n",
       "     wittb002  0.276131\n",
       "     yelic001  0.278182\n",
       "     yoshm002  0.288641\n",
       "\n",
       "[4147 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    df.groupby([\"year\", \"id\"]).agg(\n",
    "        total_ab=pd.NamedAgg(column=\"ab\", aggfunc=\"sum\"),\n",
    "        total_h=pd.NamedAgg(column=\"h\", aggfunc=\"sum\"))\n",
    "    .loc[lambda df: df[\"total_ab\"] > 400]\n",
    "    .assign(avg=lambda x: x[\"total_h\"] / x[\"total_ab\"])\n",
    "    .drop(columns=[\"total_ab\", \"total_h\"])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>league_mean_avg</th>\n",
       "      <th>league_max_avg</th>\n",
       "      <th>batting_champion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>0.284512</td>\n",
       "      <td>0.372414</td>\n",
       "      <td>(2000, heltt001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>0.277945</td>\n",
       "      <td>0.350101</td>\n",
       "      <td>(2001, walkl001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.275713</td>\n",
       "      <td>0.369727</td>\n",
       "      <td>(2002, bondb001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>0.279268</td>\n",
       "      <td>0.358714</td>\n",
       "      <td>(2003, pujoa001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.281307</td>\n",
       "      <td>0.372159</td>\n",
       "      <td>(2004, suzui001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.27735</td>\n",
       "      <td>0.335017</td>\n",
       "      <td>(2005, lee-d002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.283609</td>\n",
       "      <td>0.347409</td>\n",
       "      <td>(2006, mauej001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.281354</td>\n",
       "      <td>0.363025</td>\n",
       "      <td>(2007, ordom001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.277991</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>(2008, jonec004)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.27801</td>\n",
       "      <td>0.365201</td>\n",
       "      <td>(2009, mauej001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.271227</td>\n",
       "      <td>0.359073</td>\n",
       "      <td>(2010, hamij003)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.269997</td>\n",
       "      <td>0.344406</td>\n",
       "      <td>(2011, cabrm001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.269419</td>\n",
       "      <td>0.346405</td>\n",
       "      <td>(2012, cabrm002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.268789</td>\n",
       "      <td>0.347748</td>\n",
       "      <td>(2013, cabrm001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.267409</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>(2014, altuj001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.268417</td>\n",
       "      <td>0.337995</td>\n",
       "      <td>(2015, cabrm001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.270181</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>(2016, lemad001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.268651</td>\n",
       "      <td>0.345763</td>\n",
       "      <td>(2017, altuj001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.261824</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>(2018, bettm001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.269233</td>\n",
       "      <td>0.335341</td>\n",
       "      <td>(2019, andet001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.262239</td>\n",
       "      <td>0.327731</td>\n",
       "      <td>(2021, turnt001)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.255169</td>\n",
       "      <td>0.326454</td>\n",
       "      <td>(2022, mcnej002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0.261457</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>(2023, arral001)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_mean_avg  league_max_avg  batting_champion\n",
       "year                                                   \n",
       "2000         0.284512        0.372414  (2000, heltt001)\n",
       "2001         0.277945        0.350101  (2001, walkl001)\n",
       "2002         0.275713        0.369727  (2002, bondb001)\n",
       "2003         0.279268        0.358714  (2003, pujoa001)\n",
       "2004         0.281307        0.372159  (2004, suzui001)\n",
       "2005          0.27735        0.335017  (2005, lee-d002)\n",
       "2006         0.283609        0.347409  (2006, mauej001)\n",
       "2007         0.281354        0.363025  (2007, ordom001)\n",
       "2008         0.277991        0.364465  (2008, jonec004)\n",
       "2009          0.27801        0.365201  (2009, mauej001)\n",
       "2010         0.271227        0.359073  (2010, hamij003)\n",
       "2011         0.269997        0.344406  (2011, cabrm001)\n",
       "2012         0.269419        0.346405  (2012, cabrm002)\n",
       "2013         0.268789        0.347748  (2013, cabrm001)\n",
       "2014         0.267409        0.340909  (2014, altuj001)\n",
       "2015         0.268417        0.337995  (2015, cabrm001)\n",
       "2016         0.270181        0.347826  (2016, lemad001)\n",
       "2017         0.268651        0.345763  (2017, altuj001)\n",
       "2018         0.261824        0.346154  (2018, bettm001)\n",
       "2019         0.269233        0.335341  (2019, andet001)\n",
       "2021         0.262239        0.327731  (2021, turnt001)\n",
       "2022         0.255169        0.326454  (2022, mcnej002)\n",
       "2023         0.261457        0.353659  (2023, arral001)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averages = (\n",
    "    df.groupby([\"year\", \"id\"]).agg(\n",
    "        total_ab=pd.NamedAgg(column=\"ab\", aggfunc=\"sum\"),\n",
    "        total_h=pd.NamedAgg(column=\"h\", aggfunc=\"sum\"))\n",
    "    .loc[lambda df: df[\"total_ab\"] > 400]\n",
    "    .assign(avg=lambda x: x[\"total_h\"] / x[\"total_ab\"])\n",
    "    .drop(columns=[\"total_ab\", \"total_h\"])\n",
    ")\n",
    "\n",
    "averages.groupby(\"year\").agg(\n",
    "    league_mean_avg=pd.NamedAgg(column=\"avg\", aggfunc=\"mean\"),\n",
    "    league_max_avg=pd.NamedAgg(column=\"avg\", aggfunc=\"max\"),\n",
    "    batting_champion=pd.NamedAgg(column=\"avg\", aggfunc=\"idxmax\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x710345bc7880>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "      <th>avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>abreb001</td>\n",
       "      <td>0.315972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000</td>\n",
       "      <td>alfoe001</td>\n",
       "      <td>0.323529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000</td>\n",
       "      <td>alicl001</td>\n",
       "      <td>0.294444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>alomr001</td>\n",
       "      <td>0.309836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>aloum001</td>\n",
       "      <td>0.354626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>2023</td>\n",
       "      <td>walkc002</td>\n",
       "      <td>0.257732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4143</th>\n",
       "      <td>2023</td>\n",
       "      <td>walkj003</td>\n",
       "      <td>0.27619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>2023</td>\n",
       "      <td>wittb002</td>\n",
       "      <td>0.276131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>2023</td>\n",
       "      <td>yelic001</td>\n",
       "      <td>0.278182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4146</th>\n",
       "      <td>2023</td>\n",
       "      <td>yoshm002</td>\n",
       "      <td>0.288641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4147 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year        id       avg\n",
       "0     2000  abreb001  0.315972\n",
       "1     2000  alfoe001  0.323529\n",
       "2     2000  alicl001  0.294444\n",
       "3     2000  alomr001  0.309836\n",
       "4     2000  aloum001  0.354626\n",
       "...    ...       ...       ...\n",
       "4142  2023  walkc002  0.257732\n",
       "4143  2023  walkj003   0.27619\n",
       "4144  2023  wittb002  0.276131\n",
       "4145  2023  yelic001  0.278182\n",
       "4146  2023  yoshm002  0.288641\n",
       "\n",
       "[4147 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns_df = averages.reset_index()\n",
    "years = sns_df[\"year\"].unique()\n",
    "cat = pd.CategoricalDtype(sorted(years), ordered=True)\n",
    "sns_df[\"year\"] = sns_df[\"year\"].astype(cat)\n",
    "\n",
    "sns_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4zUzxihOQV0lhW87BjOLkLjV7mq1oTrvVjiJVCpfcneb1+hAKdyAUCiEUCiEYDNYdB4NBtLa2wufzUXBEyGWiYIoQsiQURUFbWxva2trmPM9a1zVbIWeryHM8nkAmc4nRLsBMH6E4IKwmOyEUDUJ2AIoGIVtNNQv0SiqErACSAiEp5igYk69sCtLKUyS4mXCR69W+mozRqJiLr3mluhh7smTJZLHfIiR9fguyZUUxA6HWbrS2ttYFSbV9MBikdUaELCEKpgghDcUYs9fRrF+/fs5zdV1HOp1GIpGwU0ckk0mk02k7jUQ6nTZbJoOJzAKyJNuJUquJVMHqAywriaNVVkRwgBtX/n4wcxkFWvzw+7vg8/nsKbXavnbqzev10igSIU2AgilCyIqhKIo9NTUfuq4jm80il8vV9YVCwU6oWigUUCgU7JqcpVIJ5XLZLnZtFcOuzZLPOZ+W2VlV1bo2NWGjleCxNuljbV4jr9fMek0IWXkomCKErFqKoqC1tRWtra2NvhRCyCq21ovSE0IIIYQsCAVThBBCCCELQMEUIYQQQsgCUDBFCCGEELIAFEwRQgghhCwABVOEEEIIIQtAwRQhhBBCyAJQMEUIIYQQsgAUTBFCCCGELAAFU4QQQgghC0DBFCGEEELIAlBtPkJI0xFCIJ/PY3x8HMlkEul0GqlUCul0uq5AcT6fR6VSQaVSga7r0HUdAMAYA2DW5tM0zW5WoWGr+f1+tLS02C0YDMLhcDTyoxNCViAKpgghy04IgUQigeHhYYyOjiISiSAajSISiWBsbAzj4+MoFouX96KSDDDJeoNqz812GTweD0KhENra2tDe3o729naEw2GEw2F0dHSgo6MDbrf78q6NELKqUTBFCFky2WwWg4ODGBwcxNDQEC5evIihoSEMDw+jVCrN+ByhusFVL3hLGEJ1Q6hOCKXaVCeErEHIKiCrEJJaH0TN+IIC4AYgdDBDBzPKYEYZMCpgetFslRKYXoBUKSBTySM3GsPg4OCsL+nz+dDZ2WkHV7XHHR0dCAQC9ugYIWT1o2CKELIgnHNEo1E7aLLawMAAUqnUtPOFrIE7fODBbrN3+CEcXnDNC6F5zOBoMTEGyAoABUIBxHyfxw2wSgFSOQdWnoBUngAr5yCVJpAq55A9dwGnT5+e8akOhwPt7WGEw+326FZbW5vdWltb0draCk3TFutTEkIaiIIpQsi8FItFDA0NYXBwEBcvXrQDpsGLF1GeOsrEGLjDB6OlD9wZgHAGwJ1+81hxmgFOs5NkCIcXhsM78+NCAEYZUjkHqZSrBlpm4GWUc7gYHcfQ0MU538Lj8aC1tRV+vx9+vx+BQAA+nw8ejwderxcejwdutxsul8tuTqcTDocDDocDTqcTmqbRKBghDUbBFCHEVi6XEYlEMDw8jOHh4bqpuWg0Ov0JkgLDGQAP9oC7AuDOFnBXC7jDt/gjTM2GMUBxgCsOcHdo5nO4AVbJQyrnwSp5sHIeUiUPVimAVQrI6EVkowlII6PmVOQVUlUVmsMBTdPgrPaOmt4KwJxOZ11g5na77YDNCuB8Ph98Ph/cbjcFaYTMEwVThKwhhmEgHo8jGo0iGo1iZGQEkUgEo6OjGBkZwVgsBsGnL9jmmgfc3w3uDNQ1oXlWxihTo0gyhMMHw+Gb+7zqui6mF8GMEphRAYwymF4G4xXA0MF4xbyf62DcqOsNYaDIDSCvg+WyYMKwH7vcBfj2pUsSfD4/AoHJUbNAIGDvfLSOa+9zOp1X9F6ErHQUTBGyShiGgWQyifHxccRiMbsfGxuz+7GxMRjGzCMgXPOAezrAnT4Ihx/c4a8GTT5Aoq+KJVVd1yVkLwRmmVa8UoKbgRrXzQDM0IFqYGYGZxUzaKsuzGd6qRrIlZAolZAajYFdHJpXUKZpDrS0mMGVFYBNncL0+/326JfP54PX64Wi0M8XWdnoJ5iQJpfP55FMJpFIJOzeavF4HPF4HOPxOJLJ5IyjSgDMNUyqG9wVgtC84A5vtfeBO3xLs/CbNAcmAbJk7oDEZSzAr1VdH8b0Us0OyCKkmmOml2DoBRTTBYzFk4BRmffLO51OeL3eaa127VhtbwVhVqOF/KTRGhpMHThwAK+++ipOnjwJl8uFW265Bc899xyuvvpq+5xisYjHHnsML7/8MkqlEnbv3o2DBw+io6PDPmdwcBD9/f1499134fV6sWfPHhw4cMD+a2d0dBSPPfYY/vmf/xlnzpzBl770JfzBH/zBcn9cQgCYP9NWAkorGeXUlkwm7TZbCgGbpJiBkicMobogNA+46oHQ3OCaB0LzQKiuudMHLDP3sdfAKpeZR4pcFqE6kd/5C4vzYtX1YUJxQMA/v+dww5yyrJQmpy/1MpheBPTy5G2jhAmjjHyujFg6CqYPXvbUpMPhsEe6po58WbetvnaEzOVy0bowsigaGkwdOXIEe/fuxU033QRd1/Hkk0/ijjvuwIkTJ+DxeAAAjzzyCN5880288sorCAQC2LdvH+6++2588MEHAMypjTvvvBOdnZ348MMPMTo6ivvvvx+qquKZZ54BAJRKJbS3t+PLX/4yvva1rzXs85LVo1KpIJfLYWJiwu6z2SxyuRyy2azdMpkMstmsHThlMhmUy+VLv4EkgysuCNUH4QyDqy4zUKpp5n1uoDrisJKYoxqFRl8GDh48CAB46KGHGnwli+/KVkotIkmGkNzmz+jlEAIQRnXKsWyOiFnrx6wcYfaxGZAZRgmFbBmx1CiYcWEyaeslyIoCf02gVbsAf6ZRMqu53W643W44nU4KxggAgAkxz5+6ZRCLxRAOh3HkyBHcdtttSKfTaG9vx6FDh3DvvfcCAE6ePIlrrrkGR48exc0334y33noLd911F0ZGRuzRqhdeeAGPP/44YrHYtOHfz372s7j++usva2Qqk8kgEAggnU7D75/nX2VkwTjnczbDMOqOpzarvEjtsVV6xGrlctlupVLJbsVi0W6FQgHFYhH5fN4uZWKVLZkvoTgg5Opf9orT7GuSUfK6xJQuc43SKv6S9vzwJQqmlhhXXJi44VcafRnLT4jqOrDSZDNKU25bI2RmMCZVH7/cETHGGJxOF9zuybQVVl+7g9Ixww5LTdPMXZg1vaIoUFUVqqrax4qiQJZlKIpiN1mW6xoFdLNbrt/fTbVmKp1OAwCCwSAA4OOPP0alUsHtt99un7Nt2zb09fXZwdTRo0dx7bXX1k377d69G/39/Th+/DhuuOGGy74O6xeqJZPJXOlHWhWi0Sj279+PsbGxRl9KwwiwanBT7SUVYAyi9j4m2cei5rhuek1wc4t8JQ80PpZoGKbTFN9SY3oR7uOvN/oymp8kg0tuQHVXR7QEIASY4PaxeT+3H2fVXgiBfLGEfLEIiAQAgF3ZqrSGuOuuu/C//tf/avRlrApNE0xxzvHwww/j1ltvxc6dOwEAkUgEmqahpaWl7tyOjg5EIhH7nNpAynrceuxKHDhwAE899dQVPXc1KhaLazqQAqpfkHa9t9r7CWlODAJyPt7oyyBN7Ep/R5LpmiaY2rt3L44dO4b333+/0ZeCJ554Ao8++qh9O5PJoLe3t4FX1Fjr16/H4cOHL/t5QghwziGEsI8B2Fvzrfusc2qPhRD2NF7t8UxTfC+++CKi0SisGeva3mpTb9e2qdc4ta+9risxdZRK2KNV0vT7mmiR+FKS8okV9Rf8SiTAwN3BRl9GY4na0aTqcc2IE6t53Dp3IT+XkiRBkiQwxiBJEpxOJ7Zu3WpnrLem+FRVtfvaqT5rWq92ym+mKb7aqT5Jkuqm/KxrmNqsa6IpwaXRFMHUvn378MYbb+C9995DT0+PfX9nZyfK5TJSqVTd6FQ0GkVnZ6d9zkcffVT3elamZuucy2X94JOFYYxBlpd+u/3v//7vL/l7AGYwZq2dstZP1S5Ct5q1ED2TySCTySCdziCTzWAilwNw6dEsoTjAq2unrDVU9qJzpX4R+kpNZ+D54UtgTbBmajUTihP5HT/X6Mu4ctZCdN0qTD11IXppciG6vQ6qVLcwfb7M3YAt0/Jf1bbaLPHWAvTaUj+UK2tta+j/+0II7N+/H6+99hoOHz6MDRs21D1+4403QlVVvPPOO7jnnnsAAKdOncLg4CB27doFANi1axeefvppjI2NIRwOAwDefvtt+P1+bN++fXk/EFnVGGP2F2coNEv5kDnoul63s89qyWTSTpGQTCYn+/TY7HmjqoTisHf1CdVtHmvVtAiqlRbB2XQjXkJ1Nn63GYAvfukx80BxNfZCloBQmyAbuRBmolC9OGUx+GTgAztFwgy79i5zQbjL5Ya/xQe/v9MOiKzEobWpEqbepj+eyUI1NJjau3cvDh06hNdffx0+n8+evw0EAnC5XAgEAvjCF76ARx99FMFgEH6/H/v378euXbtw8803AwDuuOMObN++Hffddx+ef/55RCIRfPnLX8bevXvr/oH86Ec/AgDkcjnEYjH86Ec/gqZpFHCRZaMoClpbW9Ha2jqv8znnyGQydr6p2ZJ2jsfjyKRHZn8hJpl5qKo5pyYTdnqrCTu9yz7CtWj5j8jy4nwyaaedrHPqbTOvlFTtLycgcrpc8Po88HqD0xJ3zpa0s/aYRodIozQ0NcJsc7cvvvgiHnjgAQCTSTtfeumluqSdtVN4AwMD6O/vx+HDh+HxeLBnzx48++yzdf+wZnqv9evX48KFC5e8TkqNQJpdpVJBIpHA+Ph4XYvFYohGoxgbG8P4+PjspWRUtxlYOf3VrOh+cKfZIFN26VVD8BlKypTrawHWJNOczHg+mVLgUhhj8Hq9dt2+2pIyM5WSsXqPx0PBEFl0y/X7u6nyTDUrCqbIajC1yLFV4Nhq0WjU3iRQS6guGHadvgC4q1rk2OFruunDFc8q22IHOWXAqEwpdqxXk1rqgLCKHVcLHwujWoevphCyMOvymbX5Zg6m5+JwOOAPBBDw++2Cxv7qcW2RY+u2z+dblrWShMzHmswzRQhZOrIsIxwOIxwO49prr532uK7rGBsbw/DwsN0uXryIoaEhjI6OwshF65/AJDPIcrXUBFrm8UrMyr5kBAerFMDKE5AqebByvjolVgCrFCbr2+nWoumF/X3LGKtLDDk1eaS1aNrpdNrZvK1+6qhRIBCg9USEzAONTM0DjUyRtU7XdYyOjmJwcBAXL17E4OAgBgcHceHCAHK57LTzueapBlgt9kgWd/jNgsqraWt2dSRJKuUglXNg5RykUs4MnKqNVQqYK0BSFAV+fwCBgL+urMnUnWNWVm2Xy2XvOJ665d7aUk/b3wkx0cgUIaRpKIqC3t7eafnWhBBIpVIYGBjA4OBgXT82NgJkpiyMlxQYDp+5Hsvhh7CONa8ZaDVbqgfBzZEkOziyAqYJsHIOcjkHGJUZn6qqKsLhMNrbt6KtrQ3t7e1oa2tDKBRCMBi0NyN4PB4KfghZ4SiYIoRcMcaYHRRcf/31dY8VCgUMDQ3Zo1lDQ0O4ePEiLl68iHxyYMbXE9Vdh1zzmOkeNBe46q6paegEZA1CVq8s8LLrtlXXJdVOsVWn3VglD6naWHn2USWfz4fOnqvQ0dGBjo4OdHZ22scdHR0IBAIUJBGyRlAwRQhZEi6XC1u2bMGWLVvq7hdCIJ1OY3h4GCMjIxgZGbEXxEciEYyNjUGfiF36DZhkBlVMhpAkCCZXF8QLO/4xM1ybC7KZMGYdRZpKczjQ1hZCOLwV7e3t9lqz2mDJ7XZf5n8RQshqRcEUIWRZMcbs3V87duyY9rgQAplMBvF4HLFYDKlUqi7J6cTEBCYmJpDP51EoFFAul6HrOiqVCioV3azQY/4PFFm11xKpqmovtraav2aHWiAQQDAYRCgUoqk3QshloWCKENJUGGN2fqKNGzc2+nIIIeSSKEkMIYQQQsgCUDBFCCGEELIAFEwRQgghhCwABVOEEEIIIQtAwRQhhBBCyAJQMEUIIYQQsgAUTBFCCCGELAAFU4QQQgghC0DBFCGEEELIAlAwRQghhBCyAFROhhDS1DjnKJVKKBaLKBaL9nG5XEa5XEapVEKlUrFvVyoV6LoOwzCg6zo45+CcQwgBIcwKyJIkQZIkMMYgyzJUVYWqqlAUBQ6HA06n0261tfzcbjckif4GJYTUo2CKELIkyuUystksMpkMstkscrmcXaTYKlRc2wqFQl3L5wsoFgsolUqN/ig2xhg8Hi8CAT/8fn9doWSrBYNBBINBtLa2oqWlBYpCX7OErHb0r5wQMm+GYSAej2NsbAzj4+OIx+N2S6VSde2KgiBJhpBUCEmBkBVA8UNoCiArEJICSFYvT/bMui0DTAKYDMEkgLHq7eoxAMDqBSDEZC8MMM6rvQFwHYxXAEMHMypgRhmo9mm9hEwsBWk0CnB9zo/DGIPfH0AoFKwLsmqPraArEAhQ4EXICkX/cgkhdXK5HIaGhjA0NISRkRGMjo5iZGQEkUgEsVgMnPPZnywp4IoTQvVBONsgZAeE4oCQNQjFAcgahKyat2UVkFUzeKoeg62wKTSug1WKYHoRrFIA04uQKgXzuNoSpQJSg8M4d+7cnC/FGIPX67UDq0AgYI9++f1+BAIBeL1e+P1++Hw+u7ndbjA7WCSENAIFU4SsQUIIJBIJXLhwAQMDAzh//jwGBwcxODiIZDI5/QmMgasecE8YQvWAOzwQqhtCdYNrbgjVBaG6AGmNfaVICoTDC+HwXvpcbtgBF6vkq0FXEUy3Aq8iUnoRmUgcbGgYEHMErTVkWYbH60WgJvCygq+Zph+DwSCNgBGyyOhfFCGrXKFQwPnz53Hu3Lm6lslk6k9kDFzzgQd6wZ1+cGcA3OEDd/ggNC9AC68XRpLrAi9jrnOFAHgFTC/VtCKYUZ68bZQBvQRdL6FcLiF9GUFYINCCcLgd7e3tCIfDaG9vR2dnp92CwSCNdhFyGSiYImSVEEJgfHwcZ86cwdmzZ3HmzBmcOXMWw8ND9i42E4Ph9IO3XgXuagF3tZq9ww9IcsOun9RgrDolqkE4fPN/nhDm1KMVfOlFe/Rrcvoxj0SpgPS5Czh9+vSML6M5HFjX3Y3u7m6sW7fObr29vWhvb6cdjYRMQcEUIStQqVTCwMAAzp49i7Nnz+LcuXM4c+bMtNEmoThgeDvB3UEY7qAdOK256bi1gjFzHZqszm/q0ShDKk+AlXLVPguplIVRyuHcxRGcP39+2lM0TcO6devQ09ODnp4e9Pb2oqenB+vWraMRLbJm0TcqIU2sUqlgeHgYFy5cwIULF+zpuqHhYYgpC8G50w+j9Spwd8gMnNxBCNVds5ONkClkDdylAa7Wmacd9RKkYgZSKWP2xQyMUhrnBodnDLScLhd61q1Dd3VUq6urC11dXejo6EBHRwecTueSfyRCGqGhwdSBAwfw6quv4uTJk3C5XLjlllvw3HPP4eqrr7bPKRaLeOyxx/Dyyy+jVCph9+7dOHjwIDo6OuxzBgcH0d/fj3fffRderxd79uzBgQMH7EWWr776Kr7xjW/gRz/6EUqlEnbs2IGvfvWr2L1797J/ZkKm0nUdsVgMIyMjGBkZwcWLFzE0NITBwYsYHR2BYdT/mhOKA4YnbI4yuYMwXOaIE+Tm+dvIfew1sEqx0ZexagjVifzOX1j+N1Yc4N52cG/7lAsS5lRiMT0l0MrgzPkBnDlzZsaX8wcC6AiH0dbWhra2NoRCIYRCIbS0tNSliPB4PDSVSFaUhn77HjlyBHv37sVNN90EXdfx5JNP4o477sCJEyfg8XgAAI888gjefPNNvPLKKwgEAti3bx/uvvtufPDBBwDMvDd33nknOjs78eGHH2J0dBT3338/VFXFM888AwB477338J/+03/CM888g5aWFrz44ov4r//1v+IHP/gBbrjhhoZ9frK6CSFQKBSQSqWQSCSQSCQQj8cxPj6OsbExu0Wj0RnTDQjVBcPVVl3X1ALuNNc3CdXV9KNNrFKEpBcafRlzOnjwIADgoYceavCVXNr89vUtI8YgVCeE6gT3ddQ/JgRglCAVzSlDqZQFK5vTiMlSDpk51mpNvryZHNXv98Hr9cLtdtvN5XLVZal3OBzQNA0OhwMOhwOqqkLTtLrealNvq6pKQRtZFEzUr0xtqFgshnA4jCNHjuC2225DOp1Ge3s7Dh06hHvvvRcAcPLkSVxzzTU4evQobr75Zrz11lu46667MDIyYo9WvfDCC3j88ccRi8WgadqM77Vjxw788i//Mr7yla9c8roymQwCgQDS6TT8fv/ifWCybIQQdlkRzjkMw4BhGPZxbfkRq1UqFbvVli6pLW1iZeuuze6dy+WQSqWRyaSh63MkdWQMXHWDa14Ip9/cOad5zV10Tj+gOJbvP9Ai8/zwJQqmFhFXXJi44VcafRmLx6iAlfOQKhM16SGqvV67Y7EEiVcAvQJgaX5VyYpiBlo1AdfUYGym+2YLzmqboih1TZblutuSJEGWZciybJc4qm2MMTvYY4zVNeu+2n6+ap839di6bb3/TO+7kizX7+/mmRcAkE6nAQDBYBAA8PHHH6NSqeD222+3z9m2bRv6+vrsYOro0aO49tpr66b9du/ejf7+fhw/fnzGkSfOObLZrP0+U1m/MC3TtpCTJff3f//3+P3f//1GX8YVEWCT2bdlFQJSXSZuwSRz11w1QSXjOlg+ASmfaPCVLx6m0xTfYmJ6Ee7jrzf6MpYXYxCKEwacgIb6jPXV3vzVbt0303G1r95mtberr6NzQC+VUSiWpzwGsCUK4MjMvv71r2PHjh2Nvowr0jTBFOccDz/8MG699Vbs3LkTABCJRKBpGlpaWurO7ejoQCQSsc+pDaSsx63HZvJ7v/d7yOVy+KVf+qUZHz9w4ACeeuqphXwcskDlcrnRl3DFmF2ihFdvE7IwDAJyPt7oyyBkSc05kt/kmiaY2rt3L44dO4b3339/Sd/n0KFDeOqpp/D6668jHA7PeM4TTzyBRx991L6dyWTQ29u7pNdF6t1zzz245557rvj51rSeEMKezrOOa6f4aqf5pk711U7zvfzyy4jFYhBCTJsyrH2P2inE+cygC6uWnGT2Zq05uZogc+WGYVI+QX/VLyIBBu6eeSR9xRIcEBys2lt/gDDrGMK+fzl+lmaa0praZpv6qm2tra342Z/9WXuaT1XVadN71tRe7RSfLMtgjNn91Km+2abmZvssl3N7ptefa7qv9r7a565lTRFM7du3D2+88Qbee+899PT02Pd3dnaiXC4jlUrVjU5Fo1F0dnba53z00Ud1rxeNRu3Har388st48MEH8corr9RNHU5lLWQkK5f1pQRgUUpn/PRP//RlP0fXdWQymTkXoEejUaRSKbAZVhib2cf9dmJNo7oIHcrM6wCbieeHL4E1+ZqplUQoTuR3/FyjL2P+DB1SOWvnrZJKObDyBKTqQnRWufTPhqIo8Plb4PV44PF47MXnVrMWn9c2a23TTAvPrcBmpsesAIaQK9XQYEoIgf379+O1117D4cOHsWHDhrrHb7zxRqiqinfeeccepTh16hQGBwexa9cuAMCuXbvw9NNPY2xszB5pevvtt+H3+7F9+3b7tV566SV8/vOfx8svv4w777xzmT4hWcsURbFroW3cuHHW8wqFAiKRCEZHRzE8PIyhoSFcvHgRg4ODGB8fBjLDdedzzVMNrszUCNwVBHcGmqrci1CdzbcDbYovfukx80BxNfZC5kGoTZifyahU0yKkqzv3MmDFDORSZtZgSXM40NFplq8JhUJoa2tDMBi00yJYqRF8Ph8cDgcFOGTFaOhuvoceegiHDh3C66+/XpdbKhAIwOUyv+D6+/vx3e9+F9/+9rfh9/uxf/9+AMCHH34IwEyNcP3116O7uxvPP/88IpEI7rvvPjz44IN2aoRDhw5hz549+MM//EPcfffd9vu4XC4EAoFLXift5iONks/nMTg4WFeQ+OzZc4jFxupPZJIdXBmuoJ3xfCXvCCRNgOvmyFLRDJSkUhqSFTCV89NOl2UZnZ2ddhmaqUk7A4EABUhkWS3X7++GBlOz/aN68cUX8cADDwCYTNr50ksv1SXtrJ3CGxgYQH9/Pw4fPgyPx4M9e/bg2Weftad3PvvZz+LIkSPT3mfPnj349re/fcnrpGCKNJtsNmtnQ7fq8J07d65uFypgjWIF7YzohisI4fTZOwnJGsc5WGWippRMDlI5Zx4XM5Aq0wMmJkno7OiYVkqmp6cHHR0dizKtTshiWRPB1EpBwRRZCQzDwMjISE2R4zM4c/YsxmOx+hMlGYZzssCxUe2F5m36ZKBkHgQ3a+5VagsdF8Eq+Wqh4wKkSn7OtUuSJCHc0WGXhqmtxdfV1TVr/j5Cmg0FU02EgimykqXTabsQsjWadf78+WmjWJAUGA4/uNNfTRwagHD4wJ0+CKX5s66vOpyDGdXklXoR0Ms1t0vV43L9cfXxS3E4HAiHzbVL7e3tCIfD6OrqQmdnJzo7OxEOh2mEiawKFEw1EQqmyGrDOcfo6CgGBgbsIsoDAwMYHLyIQmH61A4kBYbmhXB4wDUPhOY1e9UFobrNXnFSwDUXq8xKpZrtu5Kvy/4t2VnAzeCJGfPPtaZpDvj9PgQCAfj9fvh8PrveXSAQQEtLC4LBIEKhEILBINxuN61dImsCBVNNhIIpslYIIZBIJOxiyyMjI/Yuw0g0iky1SsGMqhmrueKAUJzVpkHIDkBxQMgahKxCyBpQcyxkFZDUptqNOG9WwV+9aE6hWb01nabXHFcKdiLX2aiqau9oswKj2gDJ6mub1+ulVC6EzGJNlpMhhDQWYwyhUAihUAjXX3/9tMeLxaKdI2t8fBzxeNzOn5VMJpFKpZBMpZDNROeVtLSOJENICoSkQsgKUD2GpNTcNhsk2b5dl+iUyRBWqR4mmUlRrfI+5ies9pNlSSaTRnIwbgDcAOM6wHWwal04xstgegXMsKbVivYU26VqxjkcDoTaQggGN9ipMqx0ALWtpaUFLpeLRowIWYEomCKEzJvT6URfXx/6+vrmPM8wDExMTCCbzSKbzSKTydQVgs7lcigUCsjn85iYmLALRlv3FQoFFIs5FCear8afoijw+wMIBELw+/1102lWYGQFS9aUGiFkdaNgihCy6GRZht/vX/CwOufcLjxeKBRQKpVQLBbt+0qlEsrlst2s8j9TSwQBkyWGrOuzymFYJT6s8h9WZm2n0wmn0wm32w2v1wtPNRO30+mk0SNCSB0KpgghTUuSJLt8yNSC54QQ0ixW4IpPQgghhJDmQcEUIYQQQsgCUDBFCCGEELIAFEwRQgghhCwABVOEEEIIIQtAwRQhhBBCyAJQMEUIIYQQsgAUTBFCCCGELAAFU4QQQgghC0AZ0Akha4IQYloZGqvcDOccnHNIkgRJkuwyM5qm2c3pdEJR6CuTEDIdfTMQQlakUqmEeDyORCJR19LptN0ymYxZWHliAvl8HqJam+9KKYpil7exavV5vd665vP57Ob3++uOHQ7HIn16QkgzoWCKENJ0yuUyYrEYxsbGEI1GMTY2Zt+OxWIYHx9HJpO55OsIxQEhqRCKBuHxApIKIcmAJENICsAkgDEAzOyFqD5RAIKDCQPgZmNch84rKOkVpNN5sEQazKgAYv4Bmqpp8M8QZE09ru39fj/cbjcVVyakiVEwRQhZdvl8HpFIxG7RaNRukWgUyURi1ucKWQPX3BD+dRCaG1x1QahuCNVlNsVZbZoZLC0lIcxAyyiDGSUwvQxUe/N27XEZhl5CaaKI8fQomHFhMni7BFlR4Pf5EAgEEAgE0NLSYrfW1la7BYNBBINBuN3upf3chJA6FEwRQhadYRiIxWIYGRmx2+joKEZHRzEyOopMOj3zEyUZXPOA+7vBNS+E5gHXPNXeC6G5AVld3g8zF8YAWYGQFQhcZgAjBMArUwKumla9Db0EXS9ivFBCIhsBuzAAYO4gzOl0IhQKIRQKoa2tDW1tbWhvb7dbOBxGMBiELMtX/tkJITYKpgghV0QIgVgshqGhIVy8eBFDQ0MYGhrC8PAwRkZGoOv69CdJCgzNCxHoAXf4zADJ4bV7oTir025rAGOArEHIGsTlLKUS3Ay+9CJYpTCtlyp5TJQLyI8lMTwyMuvolyzLaG9vR0dHBzo7O+2+s7MTXV1daG9vpwX3hMwT/UshhMwpn8/j4sWLdhscHLSPS6XStPOF4gR3BMEDPjNgcvjBHT4Ipw9Cca2dYGmpMAlCdUKoTsDVMve5goNVimCVPKTyBFi1SeUJ6OUcRhJZRCJR/PjHP572VEmSEA6H0dXVha6uLnR3d9c1v9+/NJ+PkBWIgilCCMrlMiKRiD26NBk4XUQiEZ/+BFmF4fCDB9eBOwPgTr/ZO3yAQjvWmgaTIDS3ubbM0zbzOYKDlfOQyjmwUhZSKQeplAWzg60IfvjDH057msfrxbrubqxbtw7dNX13dzfa2togSZTGkKwdFEwRsgZwzpFMJhGNRjE6OopIJGKuXxoZwfDwMMZisRnSBjBwhxc80FMNmCabUGmEadVgEoTDC8PhBXyd0x/nBljZDLCkUhZSMQtWyiBTymLizDl8+umn056iahq6p4xmWaNbXV1dlCKCrDoUTBGywhUKhbp8S/F4HOPj43YKgbGxMYyNjc28hgkwF3x7OszRJYcfonaUSVq+BcruY6+BVYrL9n6riVCdyO/8haV5cUmGcAZgOAMwpr2xANMLdoBlBlsZGKUsLgxHMTAwMONLtra22tOHHR0ddeu2wuEw7UYkK05Dg6kDBw7g1VdfxcmTJ+FyuXDLLbfgueeew9VXX22fUywW8dhjj+Hll19GqVTC7t27cfDgQXR0dNjnDA4Oor+/H++++y68Xi/27NmDAwcO2Isn33//fTz++OM4efIk8vk81q9fj9/4jd/AI488suyfmZC5GIaBbDaLTCaDTCZjJ55Mp9NIpVJIpVL2cTKZRDKZnHHd0iQGobnMNUx+D4TmNUebHD5wzQfh8ABSc/xNxSpFSHqh0ZdhO3jwIADgoYceavCVXNrCUpEuAGMQqhuG6gZ8HdMfN8qQiuaIFrNGtkpZxAtZJD85iRMnTsz4sh6vFx3hMMLhsL0T0dqVaO1Q9Pv9NJVImkZDv0WPHDmCvXv34qabboKu63jyySdxxx134MSJE/B4PACARx55BG+++SZeeeUVBAIB7Nu3D3fffTc++OADAOYvnzvvvBOdnZ348MMPMTo6ivvvvx+qquKZZ54BAHg8Huzbtw/XXXcdPB4P3n//ffzGb/wGPB4P/uf//J8N+/xkdapUKpioZtzO5XJ1LZvNIpvN2seZTMbu05kMJnK5+b2JpIArTgjVB+EMV3MtWc1t5mHSPNXpOPqFQxpE1sA9IXBPaPpjQoBV8mClHKRyrrpmy1wcnynnkBscxrlz52Z/aUVBcEp+rdr8W4FAwE566vf74fV6KfEpWTJMiHlmjVsGsVgM4XAYR44cwW233YZ0Oo329nYcOnQI9957LwDg5MmTuOaaa3D06FHcfPPNeOutt3DXXXdhZGTEHq164YUX8PjjjyMWi0HTtBnf6+6774bH48F3vvOdaY9ZtbssmUwGvb29SKfTtIOlQYQQEEKAc2731rFVW6322GrWbcMw6tqf/umfIhKJwPrxt15/tma9/kzXMPneHJxPmwiZ3+djEuws3EyCYAyAZN8GY+Y5tfevMlI+AXaJ/EnLaSWNTAkwcHew0Zex+OxM9NzMNF9tbOrxPDBJgsftqWab98LtdsPj8cDlcsHtdsPpdNY1VVXtuoyKokBRFMiyDEVR7BqOVh1HK0izeuu7ovZ46vfI1PunPq/u2mtev7bVXkdtk2UZsizPeF/t/dbx1HNXU9CZyWQQCASW/Pd3c4zvV6WrifyCQfNL4eOPP0alUsHtt99un7Nt2zb09fXZwdTRo0dx7bXX1k377d69G/39/Th+/DhuuOGGae/zwx/+EB9++CF+93d/d8brOHDgAJ566qnF/Ghr3smTJ/HFL36x0ZfRtOxfCNXv0dXzVUaWA4OAnJ9h1+UaYbiCKGz+D2auLb1YnTIuAnYS1CKYUUZaLyMTT0MaiwFGpdGXvSL95E/+JH7v936v0ZfRdJommOKc4+GHH8att96KnTt3AgAikQg0TUNLS0vduR0dHYhEIvY5tYGU9bj1WK2enh7EYjHouo6vfvWrePDBB2e8lieeeAKPPvqofdsamSJXzvrLhy+w0GyzEtao0pS+foTJut8aeZocdSLNNzK1kqzakal54g4vmDDAqjUUGdeBam82o+4YVziCTEA7MWfRNMHU3r17cezYMbz//vtL9h7f//73kcvl8E//9E/4zd/8TWzevBm/8iu/Mu08h8NBPzCLbMuWLfje9763qK85daptpum9uab6DMOArut1rVKp1LVyuWw3a/q3VCqhWCyiWCyiUCjYzVonNTExYQeN8wuTmFmIV3aYhXnt5qzpq8fq5PFqWwvl+eFLYE20AH0lEYoT+R0/1+jLWFxCVEeZ8tVEo/nqcR5SJV/N9m5mfpfzcajJmXcOWhwOR3WKLwSPxwOv15zqs5rT6YTD4YDL5bKn96ymqqo9zVc7VWZNtc00LVY7jQdg2lKBqfdNneITQkx7Xev9ao9lWa47nmkKb6bpvKnHZGGaIpjat28f3njjDbz33nvo6emx7+/s7ES5XEYqlaobnYpGo+js7LTP+eijj+peLxqN2o/V2rBhAwDg2muvRTQaxVe/+tUZgymyMjDG7C+KZiKEQLlcnrb4fOoidKtZi9DNnXspVCbmM/3AIFQnuOKAUCYXn3Or4K/mMRehq01Wy24OQnU2blfaDL74pcfMA8XV2AuZB6E6G30Jl48bZpBUylazs+cglXJ2hnapMjHnCJLL5UZbZ8hegG4Ve7YKQVsL0AOBAHw+36zrZwlZDA0NpoQQ2L9/P1577TUcPnzYDnYsN954I1RVxTvvvIN77rkHAHDq1CkMDg5i165dAIBdu3bh6aefxtjYGMLhMADg7bffht/vx/bt22d9b875JbaUE3JlGGP26GYoNMMupksoFot2OoSpqRFqUyQkk0kkUylkM5EZF61ahOIAVz0QDrNYsFkHzyr14gOU5vgls2R5kkhjVHNQseJkSgQ7RUI5B6k8MePTGGMIhUIIh3vqijPXpkYIhUJwuZo/yCVrR0ODqb179+LQoUN4/fXX4fP57DVOgUAALpcLgUAAX/jCF/Doo48iGAzC7/dj//792LVrF26++WYAwB133IHt27fjvvvuw/PPP49IJIIvf/nL2Lt3rz1V9/Wvfx19fX3Ytm0bAOC9997D7/3e7+FLX/pSYz44IXOwdhNZfxxciq7rSKVSdsJOK2mnlbgzFoshGo1iIpWY8flCcVTr5/mrZWH8dokYyM0RaJEmxXlNdvRMXfJOuZQF+PREsZIkIdzRge6uq+3CylbSznA4TAWWyYrU0NQIs22/fPHFF/HAAw8AmEza+dJLL9Ul7aydwhsYGEB/fz8OHz4Mj8eDPXv24Nlnn7X/Qf7RH/0R/viP/xjnz5+HoijYtGkTfv3Xfx2/8Ru/Ma+kb8u1tZKQpZTL5TA2NoZIJIKRkRG7nIzVKpXp04tCdcGYUkqGOwMQDu+qW7NFZmGUa8rIZCcDp+oIE2b4FVJbt6+2SHJXVxcFS2RZLdfv76bKM9WsKJgiqx3nHLFYDMPDw7h48aJd7HhwcBCjkcj0un1MguHwmYGVHWRVy9FQ3b6VxajUr1myih2Xq9Ny+szLIUKhUF1x43Xr1tm36XuSNIs1mWeKENIYkiTZNdJ+4id+ou6xSqWCkZERDA4O4uLFi3YbHBxEJjU4w4spMKpThrVrs7jDB6F5ASoBsny4Prn7rbqwm1WbVJ4ws47PEixpDge61nXaNfS6a0aaurq64HSuwEXvhCwRCqYIIXNSVRXr16/H+vXrpz2WTqftUayhoSEMDw9XR7eGUEjOsEaLMXDVYwZWDnMxPHd47ZqBQvVQsHUpXAerFGsSVBbMJJWVgrngu5IHKxcg6YVZAyXAXJvXsW5yzZK1bslqwWBwVWXCJmQpUTBFCLligUAAgUAAO3bsqLtfCIF0Oo3h4WF7Tdbo6KjdYuNRiOzoDK/IwDU3uFYtyqx5zKBL9Zi1BjVPNcfWKvglzw0wowSml82gx7Cydde0uvuKkPTSjIu6pwoEWhDqXmfvgLN2w7W3t9uLvKlWHSGLh4IpQsiiY4zZBWenBlqAOXUYi8UQiUQwOjpqL4yPRqOIRqMYi8Vg5MZmfnFJBldd1QDLzKVl5tiq5tuyk5w6AXmJv+I4BzPKgFEGM8o1AVDZ7jE1KDLK8w6KLG63G4GQmTeptpBvS0uLnWep9pgWeBOyvOhfHCFk2amqai9cngnnHMlk0gysxsYwNjaGWCxm97HxcSTi4zBylygLIskQsgYhq2YvqeZ9kgJI8vSi0kIAEGaOJMEBYZYeMUuQ6GC8AmZYfeWyAiLGGLxeL3yt5mie1+uF3++vZuX2TTu2bvv9fgqOCGly9C+UENJ0JEmykzPOlnzXCrgSiQQSiYR9bCU7tRKeTkxM2NnnixPFBV2Ty+2G2+OCy9UCj8dT16xAyOfzzRgoeTyeeaViIYSsPBRMEUJWpNqAa76sUj9WfUWrRqNVI622xpksy9Nqs9EaI0LITCiYIoSsGbWlfigXEiFksdCYMyGEEELIAlAwRQghhBCyABRMEUIIIYQsAAVThBBCCCELQMEUIYQQQsgCUDBFCCGEELIAlBqBELKmVSoV5PN55PN5FItFVCoVuwkh7PMYY1BV1W4OhwNutxtutxuapjXwExBCGo2CKULIqmMVWrbq/lllaOLxOJLJJNLpNJKpFLLZLCrl8oLfT1EUeL3eupp5wWAQ7e3tdpHhzs5OtLe3U2kYQlYh+ldNCFmxyuUyhoaGcOHCBQwMDGBgYADDw8MYGhpGoZCf9XlC1iBUJ4QagHBatftUQFIBSYJgZt0+1GY8FwIQHEwYAOdmrT6jAsbLMIwKSsUykkMRsAsXZn1fSZLQ0dGBrq4u9PT0oK+vD729vejr60NHRweVmyFkhaJgihCyImSzWZw+fRqnT5/GmTNncObMGQwMDIBzXn+irMLQfOCt7RAOH7jmhdA84JoHQnVDKE5gKYMWwcH0ElilAFbJQypPgJUnzL6UxXA8g9HRUfzLv/xL3dOcTieuuuoqbNiwARs2bMCmTZuwadMmtLS0LN21EkIWBQVThJCmUyqVcPr0aXzyySc4deoUPjl5EsNDQ3XnCFmD4W4Hd7WCu1rM5myBUF31I0rLjUkQqsu8DgRhzHQO1yEVs5CK6WpLwSikcPLT0zh58mTdqaFQCFu2bMHmzZuxefNmbNmyBd3d3VQnkJAmwkTtCksyo0wmg0AggHQ6TfW8CFlkQggMDQ3hk08+wYkTJ3DixAmcPXsWhjEZhgjFAcPdBsPTBu4OwfCEIDRvY4OmpSA4WCkLOZ+EVEhAyicg5xOQyrm609xuN7Zs2YItW7Zg69at2Lx5M/r6+mg9FiFTLNfvb/qXRwhZVslkEidPnsTJkyfxySef4JNPPkE2m508QZJhuEMwPO1m87avzsBpJkyCcAagOwMArpq8Xy+ZQVU+DjkfRzafwI9//K/48Y9/bJ+iqio2bNiAzZs3Y9OmTdi4cSM2btyIQCCw7B+DkLWGgilCyJIQQiAWi+Hs2bM4ffo0Tp06hU8/PY1YbKzuPO7wwwhtguENw/C0g7uCS7umaSVSHDD8XTD8XahY9xk6pEICcj4OKZ+AkU/g0zPn8Omnn9Y9NRQKYcOGDVi/fr3d9/b2IhAI0FQhIYuEpvnmgab5CJmdEAKJRAKDg4P2jrpz587h7NlzyOWydedyzQPDHQL3tFVHntoAxdGgK1+FBIdUzFSnCJOQq/3UaUIA8Hp96OvrRW9vL7q7u+22bt06CrTIqkHTfISQplEulzE2NoZIJIJIJILR0VEMDw/PkYaAwXAGwIMbwF1BGO4guKetuiibLBkm2YvxEay536iYC90LKbMV00gX0zjxyUmcOHFi2ss4nU50dnbaraOjA+FwGOFwGB0dHQgGg7Q+i5Aa9K+BkDUun88jFothfHzcbrFYzG5jY2NIpVIzP1lSYDj84K3h6m66gN1Doq+XpiGr4J42cE9b/f2Cm2kbihlIpSykUhaslIVRyuL80CguzJIzi0kS2kIhtLe3IxwO231bW1tdU1V16T8bIU2Avu0IWaUqlUpdgGS1eDxu97HxcRQLhdlfRFJgaB4If7eZr8nhneydATNn0zJNB7mPvQZWKS7LezU7oTqR3/kLC38hJkE4fDAcvplTOBgVSOUcWClXzZeVs/NmRTMTiI3PPLJlCQRa0NYWsoOrUCiEYDBot9bWVrS2tsLlctG0IlnRGhpMHThwAK+++ipOnjwJl8uFW265Bc899xyuvvpq+5xisYjHHnsML7/8MkqlEnbv3o2DBw+io6PDPmdwcBD9/f1499134fV6sWfPHhw4cGDGYegPPvgA//7f/3vs3LkTP/rRj5bjYxKyqDjnSKfTdlA0tZmjTHGk06k5XoVBqC5w1QPe0m7mRdI84KobQnNDqG5wzQPIWtPsomOVIiR9jsBviR08eBAA8NBDDzXsGiz80qcsDlkFd7UCrtaZgy0hwPQiWDkPVpmoSVCaB6vkkShNIHXhIs6ePTvn26iqipbWVgT8fvj9fgQCAfj9fvh8Pni9Xng8Hni9XrjdbrhcLrsmotPptBtljyeN1NBg6siRI9i7dy9uuukm6LqOJ598EnfccQdOnDgBj8cDAHjkkUfw5ptv4pVXXkEgEMC+fftw991344MPPgAAGIaBO++8E52dnfjwww8xOjqK+++/H6qq4plnnql7v1Qqhfvvvx//8T/+R0Sj0WX/vITMhHOOiYkJ5HI5ZDIZpNNppFIpu08mk0gkEkgmk3ZtudocTFMJWTODIn93NShyQ1Szf9tZwFWnWS6FkIVgrCZBaWjmgAsADN3MBl/JVzPDm9nhWaUIphdhVAqIpguIxVOAcWW1EjXNAafLCafDYQdYDofDbtbt2n6m5nK57L72WJblK/yPRNaCptrNF4vFEA6HceTIEdx2221Ip9Nob2/HoUOHcO+99wIATp48iWuuuQZHjx7FzTffjLfeegt33XUXRkZG7NGqF154AY8//jhisVhdNffPfe5z2LJlC2RZxt/+7d/OOjJVKpVQKpXs25lMBr29vbSbr8kJISCEsMuLGIZh37Z667j2McMw6u63zrMes+6zjg3DgK7rdceVSgW6rkPXdZTLZbtZP0vFYhGFQgHFYhH5fN5uudwEJvITEFNLosyIQTBWrRknQVT76bebYyRpsUn5BBga93XVTCNTAgzcHbz0iSuV4GYtRAiwmuO6Xojqz4N1P+oeN/8VLN7Pi6qqdcHW1OPawM1qmqbZTVVVu1dVFbIsQ1VVKIoCWZbtJklSXWOMgTFmj7zNNR1q/Tqv7Wtb7WPzYb33TM26ttoewLT7Zzp3Oa3J3XzpdBoAEAyaXxIff/wxKpUKbr/9dvucbdu2oa+vzw6mjh49imuvvbZu2m/37t3o7+/H8ePHccMNNwAAXnzxRZw7dw5/8Rd/gd/93d+d8zoOHDiAp556arE/HrlC1mhjpVK59MkrhJBkCMVlFtj1hCFkDVAcELLDLMCrOCEUB7jqMo9VF9wnvws5Hzd/0QBYnSETmQ8GYf4skEXFVTcq7VvBjIpZyJrrgFEB4zoMQ0dRryCdLoAl0mB66dIvSK7Iz/zMz+D/+//+v0ZfxmVpmmCKc46HH34Yt956K3bu3AkAiEQi0DRtWqHPjo4ORCIR+5zaQMp63HoMAE6fPo3f/M3fxPe///15bed94okn8Oijj9q3rZEp0hiapiEcDmN4eLjRl7JoGDfAyhMQigbBK2YQxSuArANCB7jZJG5AcANCGOAOX6Mvu6EaPTLVTFb9yNRiEFNGrSDA7PvM21NHu4SkQCpP2AEUuD4ZWBkVMKGDGbr9Bw1ZGu3t7Y2+hMvWNMHU3r17cezYMbz//vuL+rqGYeBXf/VX8dRTT2Hr1q3zeo41REuaQygUwl/+5V8u2evXTuvVTu/V9lOn+azpPWu6z5rqs1rtVF+xWLRboVBAoVDAxMQE8vk8JiYmkM1mkc1mkc/MY6SBSeZoleo2e80NoXrMdVHVxeNc9QCyuuqm+zw/fAmsgQvQm4lQnMjv+LlGX8biEsIMYowSmF5tRglML4MZZcAomwGNUakGOxUzsOFm4MO4YQdAVxzs6EXIpYx9U5IkcxrP44Lb5Ztxis/hcMDlctnrsDRNu+Q0n6Io9lSfLMtQFAWKokCSpLqpvqnTZZf/n3T6NF/t/VNZ71H7XlOn98jMmiKY2rdvH9544w2899576Onpse/v7OxEuVxGKpWqG52KRqPo7Oy0z/noo4/qXs9aXN7Z2YlsNot//ud/xg9/+EPs27cPAOz1MYqi4B//8R/xH/7Df1jiT0iamfXF1Wi6riOXyyGdTtuLz60F6NYi9EQiYe/iq0zEZn8xSQHX3OZCdCvYmhp4qS5AWjmLaoXqXL5dbDP44pceMw+UxiceFaqz0ZcwP0IAvGLv7rMWnkv2IvQCmF6EVF2IfrlBkLWOyelywTVl7ZIV9FjBTe3apqmLzq2AaOo6KE3TVnQAQQHQ8mloMCWEwP79+/Haa6/h8OHD2LBhQ93jN954I1RVxTvvvIN77rkHAHDq1CkMDg5i165dAIBdu3bh6aefxtjYGMLhMADg7bffht/vx/bt26GqKv7t3/6t7nUPHjyI733ve/ibv/mbae9JSKMoioKWlpZp09ozEUIgm83Omh7BTJEwjmQyOueCU6E47XQIU9MiCM0NrnmbJj3CouRVIotHCDC9AFbO2ykRWHnC3LFXTY8gVfLmSNEcvF4fWjtDCAQCdrPSIlipEaz0CG63Gx6Px95pR1nYSbO4rJ9EIQQuXryIcDgMp3Phfxnt3bsXhw4dwuuvvw6fz2evcQoEAnC5XAgEAvjCF76ARx99FMFgEH6/H/v378euXbtw8803AwDuuOMObN++Hffddx+ef/55RCIRfPnLX8bevXvtqTprDZbFuv6p9xOyUjDG4K/m5JnrDwJd15FIJCaTdMZi0wOweBwT6cTsb2aNcmk+cIcXQvOCO7zgDj+400+19VarmRJ2lnLVQMkMmmYbSWKMobU1iLa2jWhvb0coZCbutJJ1hkIhO2EnZUknq8FlB1ObN2/G8ePHsWXLlgW/+Te+8Q0AwGc/+9m6+1988UU88MADAICvfe1rkCQJ99xzT13STossy3jjjTfQ39+PXbt2wePxYM+ePfjt3/7tBV8fISudoih2TbW5FAqFaUGWVUrGasnkzBsAhOIAd/jAnWZNOKPaC4evKUa0yCyEAKsUIBXTYKUspNJkSRmplDOn3WYgSRLa2toQDl9VV06mvb0dbW1taG9vp9p9ZM257DxTO3bswLe+9S17ZGgtWK48FYQ0s1KphGg0img0ipGREYyMjNQUOx6anrpCVmE4W2G4W8HdIRjuELi7lWr2LTfBzdp7hRSk4mShY7mUAYzp6UY0zYGurvoix1YLh8MIhUKUwJKsGMv1+/uyg6m///u/x/PPP49vfOMba2aajIIpQuZmGAYikQgGBgYwMDCA8+fP4+zZs7gwMABDr1kzwyQYrhYYnjZwTzsMTzu4q4WysS8SVilAysch5ZOQCwmzL6amTcc5HA709vait7cXPT09WLduHbq7u7Fu3ToEg0FatExWjaYNplpbW5HP56HrOjRNg8tVv7MlkZhj7cUKRcEUIVdG13UMDg7i008/xenTp3Hq1CmcPn26rsIAJAW6p80MrLztMDxhCM3duIteCYQAK2Uh5+OQ8nHI+TjkfAKsUp86wulyYeOGDdiwYQOuuuoqXHXVVVi/fj3a29spYCJrQtNmQP+DP/iDJbgMQshqpCgKNm7ciI0bN+Jnf/ZnAUwGWJ988glOnjyJEydO4Nz581CyEft5XPPAsEauPG0wPG1m7qy1iHNzeq4aNEn5hBk4Talh19XVhS1bbsKmTZvs1tHR0RRpPwhZ7ZqqNl+zopEpQpZWoVDAp59+ihMnTuCTTz7BiRMnMD4+XneO4WwB94RguNvA3UEY7uDq2klYTTUg5ZOQCknI+QSkQgJyIVU3TSdJEtavX4+tW7di8+bN2Lp1KzZt2gSv19u4ayekSTXtNF+tYrGIcrn+r6PVGGxQMEXI8ovFYvbo1alTp3Dy1ClM5HJ153DNA+5qBXe1wnC1mDsKmz1dA+dmmoFiuq7JheS0em9OpxMbN27Epk2bsHXrVmzZsgUbNmygCg2EzFPTBlMTExN4/PHH8dd//deIx6eXvzAMY9EurllQMEVI4wkhMDw8jDNnztS1qSNYQDVdg+YDd/ogNI+ZhNTK/l4tHA1JWfzUDVwHq2bzZpWCncxSquTNfE2lrFn7bUqNQUmS0NPTY69t2rBhAzZv3ozu7m6apiNkAZp2zdT/+T//B++++y6+8Y1v4L777sPXv/51DA8P44//+I/x7LPPLsU1EkIIGGPo6elBT09PXW66XC5n7yK8cOHCZMqGkRGUE9MDLZskm0GXpAGyAiFrEJJi7iyUZAgmAagPtpgwAG4Agpu14IyyXSdO0ktzZvtmjKG9vR3d3ZvR1dWFnp4e9PX1oa+vD93d3ZS8kpAV7LJHpvr6+vDnf/7n+OxnPwu/349/+Zd/webNm/Gd73wHL730Er773e8u1bU2DI1MEbLyCCGQSCQQi8XqWm3dw2w2i4mJPPL5CRSLMyepnIumOeDxmCVOPB6PXQ4oEAggGAxOS2ZJARMhy6tpR6YSiQQ2btwIwFwfZaVC+Omf/mn09/cv7tURQsgVYowhFAohFAph27Ztlzxf13WUy2VUKhW7Tf1bU1VVqKoKTdOgaRpl+SaEALiCYGrjxo04f/48+vr6sG3bNvz1X/81fuqnfgp///d/P68CrYQQ0owURaHgiBByRS57ZeOv/dqv4cc//jEA4Dd/8zfx9a9/HU6nE4888gj+9//+34t+gYQQQgghzWzBeaYGBgbw8ccfY/PmzbjuuusW67qaCq2ZIoQQQlaepl0zVatYLGL9+vVYv379Yl0PIYQQQsiKctnTfIZh4Hd+53ewbt06eL1enDt3DgDwW7/1W/jWt7616BdICCGEENLMLjuYevrpp/Htb38bzz//PDRNs+/fuXMnvvnNby7qxRFCCCGENLvLDqb+/M//HH/yJ3+C//7f/ztkWbbv/8xnPoOTJ08u6sURQgghhDS7y14zNTw8jM2bN0+7n3OOSqWyKBdFCCHzValUUCgUUCgUUCwWUSgUUC6XUSqVUCqV6vJG6boOwzCg6zo45zAMA5xzCCHsvrYBmHZsmW3vDmMMjDFIkgRZliHLMhRFsXNTORwOuN1uuFwueDwe+Hw+BAIBeDweKh1DyAp12cHU9u3b8f3vf3/aovO/+Zu/wQ033LBoF0YIWVuKxSISiQSSyaSdoTydTiOTySCbzSKbzSKXy022iQnkJyag67OXcFlJJElCS0srgsFWhEIhhMNhhMNhdHR0oKurC93d3QgGg2CLXU+QELJglx1MfeUrX8GePXswPDwMzjleffVVnDp1Cn/+53+ON954YymukRCywlmlXYaHhzE6OopIJIJIJIKxsTGMj49jbCyGQiE/vxeTNXBZhZA1wBmEkFQIWQUkBUJWzF6q9kyerLMnyWbdPSZBMGYeg1WLHbOaosfMLEM8LWip3p5PMCMEAAEmuHksDIBzMKED3AAzKmBGBTDKYEYJTC+D6UXE8gXE04M4c+bMjC/rdDrR29tr1/WziiL39PRQwlFCGuiK8kx9//vfx2//9m/jxz/+MXK5HH7iJ34CX/nKV3DHHXcsxTU2HOWZImR+OOcYHR3FuXPncOHCBVy4cAEDAwMYGhqatfadUJzgmhtCdYOrLgjVBaG4IFQnhOKAUJxmEWLFAcja/IKZlc7QwcoTkMo5SOUcWDELqZSFVMpALqanFVSWFQXr+/qwadMmbNq0CZs3b8bmzZupKgVZ85br9/dlB1N79uzBF77wBdx2221LdU1Nh4IpQqYzDAMDAwM4deoUPv30U3z66ac4e+4cioVC/YmSAsMZAHf4wZ1+cIcPwuED1zwQmsccMSLzJwRYJQ+pmIZUSEIqpCAXkpAKSXO0q0Zbezuu3roVW7ZswdatW3H11VcjFAo16MIJWX5Nm7QznU7j9ttvx/r16/Frv/ZreOCBB9Dd3b0U10YIaSLxeBzHjx/HiRMn8Mknn+DUqVP1o01MguFsAQ+tg+FuBXe1gjtbzIBpLYwmLRfGIDQPDM0Dw1/z3SsEWDkHOZ+AlI9Dyicwlo5j/IMP8MEHH9inBYMhbNt2Na6++mo7wAoGgw34IISsHlc0zReLxfCd73wHf/Znf4YTJ07g9ttvx+c//3n8/M//PFRVXYrrbCgamSJrTaVSwdmzZ3Hs2DEcP34cx48fx9jYWM0ZDIarFYa3DdzdBsPTBu5qpVGmJsQqBUj5OOSJOKT8uNmXc3XnWCNYVoC1ZcsWGsEiq0LTTvNN9S//8i948cUX8c1vfhNerxf/43/8Dzz00EPYsmXLYl1jw1EwRVYzIQRisZg94nT8+HGc+vRTVMrlyXNUF3RPGNzbDsMbhuEOAfLq+8NprZgMsMYhTYxDzschlSfqzmkNBrF1yxZs3LgRmzdvxsaNG9Hb20sL3cmK0rTTfLVGR0fx9ttv4+2334Ysy/gv/+W/4N/+7d+wfft2PP/883jkkUcW6zoJIYvA2lVnrXH69NNPceKTT5BMJCZPYhIMVxBGSzVw8oYhNC9N1a0iQnXBCPTACPTY97FKYTKwyicQn4jjBz/4AX7wgx/Y58iKgt6eHmzYsAF9fX3o7e1Fb2+vXV6MkLXqskemKpUK/u7v/g4vvvgi/vEf/xHXXXcdHnzwQfzqr/6qHfW99tpr+PznP49kMrkkF73caGSKrETZbBaDg4P2jrqzZ8/izJmzSKdTdedxzQPD0w7D2w7uaYfhbgPk5R19cB97Dawy826/tUyoTuR3/kLjLkAvQy4kIOUT1cXuSciFFJhRnnaqPxDAuu5udHZ2oqOjAx0dHWhvb0d7ezva2trQ0tJSVzWDkOXQtCNTXV1d4JzjV37lV/DRRx/h+uuvn3bOz/zMz8xrS+6BAwfw6quv4uTJk3C5XLjlllvw3HPP4eqrr7bPKRaLeOyxx/Dyyy+jVCph9+7dOHjwIDo6OuxzBgcH0d/fj3fffRderxd79uzBgQMH7OHow4cP42d+5memvf/o6Cg6Ozsv9z8BIQ1nGAZSqRQSiQTi8Tii0SjGxsYQjUYxPDyMoaEhZLPZac/jDj+M1qvA3UFznZM7BKG6GvAJ6rFKEZJeuPSJS+jgwYMAgIceeqih11GLN/oCFA2GrxOGr+Z7UghzFKuYhlRMQSpmIJUySBazyJz6FJ988smML8UkCS2BAILBIFpaWuzm9/vt5vP57Ob1euHxeFblOlyy+lx2MPW1r30Nv/iLvwin0znrOS0tLTh//vwlX+vIkSPYu3cvbrrpJui6jieffBJ33HEHTpw4AY/HAwB45JFH8Oabb+KVV15BIBDAvn37cPfdd9u7UwzDwJ133onOzk58+OGHGB0dxf333w9VVfHMM8/Uvd+pU6fqItNwOHy5H5+QaaxSSla5Equ3mlW+xDAM+7j2sdpyJ1YZFKssSrFYxMTEBCYmJpDL5ZDJZJBKp5HNZiH4LL9qJRmGwwfe0gfhDMBwtYA7W8BdLbTOiSwcYxCaG4bmhuHvqn9MCDC9CFbKQipPgJXzZl+ZAKsUMV7IIzFwEezs2Xm/naY54PV64PHUN7fbDY/HY5flcbvdszaXywWXy0XZ48mSWfAC9MUUi8UQDodx5MgR3HbbbUin02hvb8ehQ4dw7733AgBOnjyJa665BkePHsXNN9+Mt956C3fddRdGRkbs0aoXXngBjz/+OGKxGDRNs0emksnkvEbMrJpelkwmg97eXprmazCrnpoQoq6mmhWk1LbagMUKcMrlcl3QUtus/8/L5TKKxeKMxxcvXkSpVKrWcRMQgs9an22pCFQzd1czeItqRm/72MryvcJI+YSVd7xhmnFkSoCBu1dp2gLBAcHBhDCPIarZ4s1j834BwMoiLxb0M8IkCS6nEy6Xa1qQ5azebx07HA67t2oq1jZVVaEoit1b9RcVRbFrMlo9YwyyLEPTtMX6L0cuQ9NO8y2ldDoNAHbOk48//hiVSgW33367fc62bdvQ19dnB1NHjx7FtddeWzftt3v3bvT39+P48eN19QKvv/56lEol7Ny5E1/96ldx6623zngdBw4cwFNPPbUUH5Fcob/6q7/CN77xjUZfRsMxVEuTCOs2Wc0YBOR8vNGXseIJSYXuCUE3KshlCmDx+LL/2+nv78cv//IvL/O7kuXSNMEU5xwPP/wwbr31VuzcuRMAEIlEoGnatNGkjo4ORCIR+5zaQMp63HoMMNd5vfDCC/jJn/xJlEolfPOb38RnP/tZ/OAHP8BP/MRPTLuWJ554Ao8++qh92xqZIo3T1dV16ZMaREyr2cZqutqv7JlquzEINvWxKcfV3q4nV3vOKtEMI1PNaFWPTF2Oaq3DyZqHNbdrR69mfMwc7VLycWBKhvjlROtzV7emCab27t2LY8eO4f3331/017766qvrFrXfcsstOHv2LL72ta/hO9/5zrTzHQ4HHA7Hol8HuXK33XYbDh8+fNnPq53yq53emzrVZ03lWeuVZjqu7WuPa9c8We9TqejQDR2GrkM3jNnXN+EKwyJZBVdcZi07zQ2hecA1L7jmNUu1OH2A1DT/vC/J88OXwBq8AL0ZCcWJ/I6fa/RlLB5DB9MLYJWCubaqUgTTS2YzrL482Rtls0SOuLKl+M7qlJ6nZlpv6hRfbbOm9lwul/17oHaqT1VVaJpmT/FJ0sqbUidLoym+bfft24c33ngD7733Hnp6JvOedHZ2olwuI5VK1Y1ORaNRO8rv7OzERx99VPd60WjUfmw2P/VTP7UkgRtpLrIsQ5blhgfHnHNwzmdcmD7bAnRrEfrUBejpdBrpdBrxeBzJZAw8N/MvGq55zFp4zlZwl7kA3XAFAaX51m4I1dnwnWtf/NJj5oHS+N2NFqHOvtGn6Rg6pHIWrFQtzlyagFSZMAs2V/KQKoV5jww5XS74Aj54PW3weDzwer3wer32ovPaBehWYOT1eusWozudTgp2yLJpaDAlhMD+/fvx2muv4fDhw9iwYUPd4zfeeCNUVcU777yDe+65B4C5I29wcBC7du0CAOzatQtPP/00xsbG7N15b7/9Nvx+P7Zv3z7re//oRz9q6qkjsrpIkgRJkhY9e7SVIiEWiyEajSISiWB0dBTDw8O4ODSEaCQCkRmtew53+GC4guCeEAyPWQoGSmN/aTc0lxKZPyHMnXrFFKRC2kyPUMpALmbAKvkZn8IkCa0trWhr60QwGERra+uMqRECgYAdNFGWdbLSNPQndu/evTh06BBef/11+Hw+e41TIBCAy+VCIBDAF77wBTz66KMIBoPw+/3Yv38/du3ahZtvvhkAcMcdd2D79u2477778PzzzyMSieDLX/4y9u7da49G/MEf/AE2bNiAHTt2oFgs4pvf/Ca+973v4R//8R8b9tkJWQyyLCMUCiEUCmHbtm3THi+VSnbiznPnztmJOxOJASA1YJ/HHT4zcaeVvNMdojp7a51eqhZNTtiJO+ViGuB63WmSJKGzsxPr1u1A9wxJO4PBIAVHZNVraGqE2XJ+vPjii3jggQcATCbtfOmll+qSdtZO4Q0MDKC/vx+HDx+Gx+PBnj178Oyzz9r/gJ9//nn8yZ/8CYaHh+F2u3HdddfhK1/5yoyJPGdCGdDJahOPx/Hpp5/i1KlTOHnyJE588gky1d20AMxcVa6QXU7GLCnjbtwFkyVVV0pmIg65EIdUqi+GrGkOXHXVemzYsAHr16/H+vXr0dfXh66uLgqWSNNaMYWO1wIKpshqJ4RAJBLBiRMncOLECRw/fhynT5+GYRj2OWbZGSu4ajd3ma2gRe7ExCpFSPnxapHjOOT8+LQix6FQCFu2bMHmzZuxefNmbNq0Cd3d3VQOhqw4FEw1EQqmyFpUKpVw6tQpnDhxAseOHcPxEydmKIjcapelMTxtZpZ1CrCaBqsUIOXjkCfi1X4cUrl+xKmtrc3e8bx161Zs2bIFoVCoQVdMyOJak0k7CSHNw+Fw4LrrrsN1110HwBy9ikajOHHihDk1eOIEPv30NMqxmqSSjMFwBsBdQXCXuYvQcLZAOHwA7axaOoKDlbLmGqdCEnJ1qo6V6xeFtwaD2Hb1LmzduhXbtm3D1q1bKXAiZBHQyNQ80MgUITPTdR2Dg4M4deoUTp8+XV3gfgYTE/XTRmDMzIPl9IM7fOCaD8LhBdc8EKrbXI+1AsvgLDvOq7vp0pCLKUiFlBk8zbAwPBwOY8uWLfZo09atW9HW1tagCyekMWiar4lQMEXI/AkhMDY2hgsXLuDChQsYGBjA0NAQhoaGkUjMVhqFmbmmFKcZXKlOCMVqjskmaxCyBlj9ahvtMirm1Fwlb+ZnsvI1lTKQqsWDpyaw1BwOXLV+PTZt2oSNGzdi48aN2Lx5MwKBQIM+BCHNg6b5CCErEmPM3hr/7/7dv6t7LJ/P1+XDisViGB8fRywWQyKRQDyRwERmeP5vJskQkgohKxCSCkgKhKRUe9l8nMl2AejawtBmsWgGAamm9M9MJX9qb892f1Xt36bCKtDLwQQ3aypyA4zrZlZvXgEzKnYGcEkvThtdqhUKhbBu3Qb09vba7aqrrkJXVxclpySkwSiYIoQsG7fbjQ0bNkxL0FurXC4jnU4jlUohnU4jk8kgm80im80il8vZbWJiAvl83m6FYhHFQgblcmkZP9HCeTwe+EN++P3r7KSWoVAI4XAY4XAYHR0d6OzshNO5grKhE7LGUDBFCGkqmqahvb0d7e3tV/R8zjkqlQqKxSJKpZJdM7FcLtfVarRK/BiGASEEOOcQQtgNwLTb1n21j9VijNnNKmVkZb636rs5HI66+nCUo4mQlY/+FRNCVhVJkqhYOSFkWdFEOyGEEELIAlAwRQghhBCyABRMEUIIIYQsAAVThBBCCCELQMEUIYQQQsgCUDBFCCGEELIAlBqBEEKm0HUd8XgcyWQSiUTCTiCay+WQzWYxMTGBYrGIcrmMUqlk56yy8k6pqmrnlnK73fB4PPB4PGhpaUFraytaW1vR1taGjo4OeDyeBn9aQshCUTBFCFlzdF1HJBLB8PAwRkZGEIlEEIlEEI1GMTYWQzKZmJaQc1ZWaRqY5WmsEjJTa+jNxuP1oruryy4R09fXh40bN6K3t5cSehKyQtC/VELIqpVKpexiyxcvXrRbJBIB5zMEO5ICrrnBvZ0QmhvcLrrsMntZg5AdgKKaNQCZMnuxZSGqtfgqgFEG08tgetEsZKwXwMp5SKUcMuUcJs6ex+nTp+uerqoqrrrqKmzZsgXbtm3Dtm3bsHHjRgqwCGlCTMz7z6+1a7mqThNCrkw6nca5c+dw4cIFnD9/HhcuXMCFCxeQyWSmnStUNwynH9zhg3CYvXnshVCc04sXLwchwCp5SMU0pEIKUiEBOZ+EXEjWFT/WNA3btm3Djh07cO2112LHjh0IBALLf72ErBDL9fubgql5oGCKkOag6zoGBwdx7tw5nD171m7xeLz+RMZgOPzgrhZwZ0u1D4A7/YCsNebir4TgZoA1MQ55IgY5F4NcSJijXlXr16/Hddddh+uuuw6f+cxnEA6HG3jBhDQXCqaaCAVThCy/fD6Ps2fP4syZMzhz5gxOnz6N8+fPo1Kp1J3HHV5wVysMVyu4K2gHTpDkBl35EjMqkCfGIeeikLNRKBNjgDH536Szs9MOrq699lr09fWBNWK0jZAmQMFUE6FgipCllUql7IDp9OnT+PTTTzE8PFy/CFySYbhaYbiD4O5QNYAKAsoKGmlaCoJDyicgZ6OQcxEo2SiYXrQf9gcC2LF9O3bu3Int27fj6quvhtvtbuAFE7J8KJhqIhRMEbI4OOeIRCL2aNOZM2fw6enTGI/F6s4TigOGOwTuDto9dwbMnXNkbkJAKqbtkSs5F4VUytoPM8bQ19eHbdu2YevWrdi0aRM2bdoEn8/XwIsmZGlQMNVEKJgi5PJls1mcP38e58+fr1njdA6FQr7uPK55JgMmdwiGOwSheRqzEHyVYpUC5NwYpNwY5Pw45Ik4mFGuO6e9PYyrrlqPvr4+rF+/Hr29veju7kZ7ezuk2XYsEtLkKJhqIhRMETIzIQQSiQQGBwcxODiIgYEBDAwM4MKFCzMsCpdgOAP1o02uIITqbMzFr2VCgJUykPNxc4own4RUSEAqT0w7VVVVdHR0oqMjjI6ODoTDYbS1tdktFAohEAhQwEWa0nL9/qaEJYSQOem6jlgshkgkgpGREbsNDw9j8OJFFAuFac/hmhc80FtdFN4yOU23WheFrzSMQTgD0J0BILhx8n6jMpmeoZSBVMrCKGZwMTqOoaGLs76cJEloaWlFMNhqZ3hvaWlBIBCws74HAgH7tsfjoUXxZFWhYIqQNUzXdSSTScTjccRiMYyPjyMWi2FsbKyuzZbg0nAGwIOd4HYagoAZNMnqsly/+9hrYJXipU9cpYTqRH7nLyzeC8oquKcN3NM2/TFugJUnIJUnzL6SByvnwSoFML2A8Xwe8cwg2Jkzl3wbRVHswMoKsmZrfr8fgUAATieNYJLm1dBg6sCBA3j11Vdx8uRJuFwu3HLLLXjuuedw9dVX2+cUi0U89thjePnll1EqlbB7924cPHgQHR0d9jmDg4Po7+/Hu+++C6/Xiz179uDAgQN1mYJLpRJ++7d/G3/xF3+BSCSCrq4ufOUrX8HnP//5Zf3MhCwlwzCQzWaRyWSQyWSQTqeRSqXs2nLJZNJu8Xgc6XR69rIpjIGrHnB3O4TDZ6YgcPjMY6cfQnE1fF0TqxQh6dNHxpbTwYMHAQAPPfTQsr/3/ArWLBJJhnD6YTgvMVXCDTvTu9kXzb7mWNeLKE8UMZ4eAtPPzuvtVU2D3+eD3++Hz+eDz+eD1+u1jz0eD7xer10H0aqJ6Ha74XK54HQ6aSqSLJmGBlNHjhzB3r17cdNNN0HXdTz55JO44447cOLECbv45yOPPII333wTr7zyCgKBAPbt24e7774bH3zwAQDzl8edd96Jzs5OfPjhhxgdHcX9998PVVXxzDPP2O/1S7/0S4hGo/jWt76FzZs3Y3R0dOa/tglZZpxzlMtlFItFlEolFItFFAoFFItF5PN5FAoF5PN5u+VyObu3mhVA5fP5S78hzN1yXHFBeDshVDeE5gJXPZMlVBxeCNVFu+fI5ZNkCM1jbiKYD8HB9FJNsFWaDL70kt0MvYTiRAnj6VEw40Jd4tL5YIzB4XDA5XLZzel01jXrcYfDYd+ees5MzeVyQVVVmrpcw5pqAXosFkM4HMaRI0dw2223IZ1Oo729HYcOHcK9994LADh58iSuueYaHD16FDfffDPeeust3HXXXRgZGbFHq1544QU8/vjjiMVi0DQN//AP/4DPfe5zOHfuHILB4CWvo1QqoVQq2bczmQx6e3tpAXoTKJVK0HUdQgi7cc7BOYcQAoZh2PcZhjFrX9t0Xbf72uPZ+tnum6lVKhW7r1QqKJfLqFQqKJXKKJfLKJVLqJTLl/7gc2IQtYV2mVS9Ldm3wRgEk+qL8q4CUj4BhsZ+hTVyZEqAgbsv/Z22KgkBwCwqzezjagO3H697DALmj4t5ezF/diRJgsPhhMs1GWBZAdvUIE3TNPt+TdOmNVVVoSiK3cuybPdWkyTJ7hljUBQFXq930T7ParEmF6Cn02kAsAOejz/+GJVKBbfffrt9zrZt29DX12cHU0ePHsW1115bN+23e/du9Pf34/jx47jhhhvwd3/3d/jJn/xJPP/88/jOd74Dj8eD//bf/ht+53d+By6Xa9p1HDhwAE899dQSf1pyub773e/i+eefb/RlLAohyRCyA0JxQ3hbAVmDkGRAUiBkBZDMQrpq7BSkyqVGm6xfGID1u2F1hErkUhgE5Hz80ieSWRnuEPLb/ku1ILUOxvVqgWod4Hp9X33cvK8CZliPVcD0EvRKEflUGownGvJZvvjFL+Jzn/tcQ957rWuaYIpzjocffhi33nordu7cCQCIRCLQNA0tLS1153Z0dCASidjn1AZS1uPWYwBw7tw5vP/++3A6nXjttdcwPj6Ohx56CPF4HC+++OK0a3niiSfw6KOP2retkSnSWD09PZAkaVVMzzJugPE8YAVKTDIDKUmGkBQISTEXcQsBIWuYPvo0eSyYhNpRqbWkGUamGmlNj0xdtvpRKeuPECGrUNJD9YGTHSQZZtBkB1i1AVRNYGXoQBP8HF511VWNvoQ1q2mCqb179+LYsWN4//33F/21OedgjOEv//Iv7Qrr//f//l/ce++9OHjw4LTRKYfDAYfDsejXQRbmuuuuw/e+971Ff92p033zmc6bet5M50yd4qud5iuXy3azppWtNVOFQqHastB13b7OeY82ySq44oCQnRCqA0JxTjbVBa46IRQXhGq2lZyuwPPDl8AavAC9kYTiRH7HzzX6MpaeEGYAU7uOyijbPfSyebvaYFSqx5VqAFQBxMx/hEmlDJRsZN6XYk3ROZ31666mrsGaa02Wpml1U34zTfHR+quVpSmCqX379uGNN97Ae++9h56eHvv+zs5OlMtlpFKputGpaDSKzs5O+5yPPvqo7vWi0aj9GAB0dXVh3bp1diAFANdccw2EEBgaGsKWLVuW6qORFcBag6BpzVfjTdd1ewH6xMSE3edyObu3FqDncjlkMhlks1mk02mkMxkU0+OXfA+hOMxF55rbXoQuNC+45gHXvBAODyA1xVfFNEJ1Lu+Othl88UuPmQfK9CUDS21FJjytDYxqd/rVLjyvlMCMkrlb0zCDp9mCoZnIsmzu5gt44Xa77R19U9tMwc5st60/smV55f7xQZZOQ78hhRDYv38/XnvtNRw+fBgbNmyoe/zGG2+Eqqp45513cM899wAATp06hcHBQezatQsAsGvXLjz99NMYGxtDOBwGALz99tvw+/3Yvn07AODWW2/FK6+8glwuZy/Q+/TTTyFJUl3wRkizURTF3vp9JUqlkhlYVVMk1KZGsNIjxONxjI+PI5senvV1hOoyE3E6fGZz+qspEgIQirNhKRIWNccSuXJcN1MhVNMhSJVZUiPoRUh6EeDGJV9SkiT4fH4EAu12rqnatAhT0yNYqRG8Xi80TaORHbKsGrqb76GHHsKhQ4fw+uuv1+WWCgQC9tRbf38/vvvd7+Lb3/42/H4/9u/fDwD48MMPAZhTNNdffz26u7vx/PPPIxKJ4L777sODDz5op0bI5XK45pprcPPNN+Opp57C+Pg4HnzwQfz7f//v8f/+3/+75HVSORmyFpRKpWlJO6PRKCKRiN0qlcq05wlZA3f6qwk7W8BdLTBcLRAO35pbw7UqGWVIpRxYOVftaxN25iFXCoBx6R2pbrcbLS0tdqtNzDlT8k6Px0N5ociCrYnafLP95fDiiy/igQceADCZtPOll16qS9ppTeEBwMDAAPr7+3H48GF4PB7s2bMHzz77bF3SzpMnT2L//v344IMPEAqF8Eu/9Ev43d/93Rl3801FwRQh5trDeDxul5MZGhrCxYsXMTQ0hKGhIZSnpniQZBh2cBUEd7dWa/E1PtknmUJwSMWMWUammDJLyhTNcjJMnznDPGMMLS0taGtrQzAYtMvIBIPBuhIyVqDUjNPoZPVbE8HUSkHBFCFz45wjGo3WFTq+cOECzp8/j2Kx/pexUJww3EFwd8juudNPo1jLhetmceOJ8clCx8XUtKk3VVXt9aadnZ3o6OhAZ2enXeg4GAzW/cFKSDOiYKqJUDBFyJXhnCMSieDcuXM4d+4czpw5g7Nnz2J4eMr6LEmB4WqF4Q6Be0Jm72pp2oXvK4YQYKUs5NyY2SbGIBeSddnDNYcDmzZuxMaNG3HVVVehr68P69evRzgcpmk2suJRMNVEKJgiZHHl83mcPXsWZ86cwenTp3H69GmcP3++LhUEmGQWUnZXgyt3EIY7CCiUtmRWgpsjTdko5FwUSi4KVplMHeFwOLB161Zs27YN27Ztw5YtW7Bu3TraoUZWLQqmmggFU4QsvUqlgoGBAZw+fbomyDqDQqE+AzzXPGZg5QravXCu0cXuXDen67JRyLkIlFysbjF4OBzGddddhx07dmDHjh3YuHEjTc2RNWVNlpMhhKxdqqpi8+bN2Lx5s30f5xwjIyN1o1hnzpzB+PhFKKmLk0+2F7u3wnC1grvMY6F5VtVid1YpQMrFIOeikHNjUPLjdWud1q9fj8985jO47rrrppXZIoQsHRqZmgcamSKkuaTTaXsN1vnz56trss6jXC7Vnygp5lShq6WausFq/uZfj6WXIOcTkPLj5ujTxDikUtZ+WJIkbN261Q6crr322mmltwhZ62iar4lQMEVI8zMMA6Ojo/ZOwgsXLpi7CwcHUS6Vpp0vNDcMhx/c4TMTkGpeCIeZ+V2o7uUpsyOEOdpUylbTEaTN1AT5BKTyRN2pgUALrrlmG3bu3IkdO3Zg27Zt80rtQshaRsFUE6FgipCVy0rbcPHiRbsNDw9jaGgI0Wh01sLZQnWBV+sZmrUMHRCyA0LWIBQNqBalNke4aopPQ5ilTwQ3C+VaNeKMcjUbuJkpXCrnIZVzM5ZJaWtvx+ZNm7Bp0yZs2bIF27ZtQ0dHB2X1JuQyUTDVRCiYImR10nV9Wpb3WCxmt0QiiVwue+kXukyMMbS2BtHVNZm/qa+vD729vejr67vi8kGEkHq0AJ0QQpaYoihYt24d1q1bN+s5lUoFqVQK6XTaLiw9MTGBYrGIcrmMYrEIzrndJEmCoihQVRWqqsLj8cDj8djlVKzM4JSOgJDVg4IpQgiZg6qqaG9vR3t7e6MvhRDSpNZgYhZCCCGEkMVDwRQhhBBCyAJQMEUIIYQQsgAUTBFCCCGELAAFU4QQQgghC0DBFCGEEELIAlBqBEIImYEQAuVy2c4tlc/nUSgU7FYqlVAqlVAul1Eul2EYBgzDsPNNMcbAGIMkSZAkCZqmweFwQNM0OJ1OuN1u+Hw+u7W0tEDTtEZ/bELIFaBgihCyZhQKBcTjccTjcSQSCSSTSSSTSaRSKTsxZyaTQTqdRjabha7ry3p9Pp8PoVAIbW1tCIfD6Orqsltvby9VYCCkSVEwRQhZFcrlMsbGxhCNRu1mlYUZGxtDLBZDPp+f+0UYg5Ad4IoDcAQhPJpZi0/WIGQVkFUISQVkpVqXT4ZgslkUmUkQYACTqnX6AAgBQIBVa/WB62BcB7gBppfNen1GCUwvgVWKSFXyyAyZxZpnEgi0YP36PvT19WHDhg3YuHEjNm7ciEAgsKj/LQkhl4eCKULIiqDrOmKxGEZHRzE6OopIJFLXx+PxWZ8rFCe45oEIhMA1F4Tqrilg7AJXnRCKE5C1yUCokbgBVs5BKuUglbJgpSykYhqJYhrpf/s3/Ou//mvd6aFQCFu2bMHmzZuxZcsWbNmyBV1dXVQYmZBlQoWO54EKHROy9AzDQDweRyQSQTQatQOlSCSCkdFRxMbGYBjG9CcyCVzzgju84JoXwuE1AyfN6t2AtIr+buQGpFIGUiEJKZ+EVEhCzicglXN1p3m8XmytBlZW6+3tpZqAZE1Zrt/fFEzNAwVThCyMEAKZTMaedqudeotEIhgbG8NYLAZjljVKXPNUAyWfGTQ5fOax5jWDJUYbk6GXzKAqH4c8ETf7YhrA5Fe8pjmwadNGbN68GRs3bsSmTZuwYcMG+Hy+xl03IUtouX5/r6I/1wghy61UKiGVStkLuROJRF2Lx+MYHx9HPB5HpVKZ5VUYuOaGcAbtESYrUOIOL4TmNdckkbkpDhj+Lhj+Ltj/pQ0dUiEBOR+HNBGHkY/jk5On8Mknn9Q9ta2tDevXr8f69evR19eH3t5edHd3IxwO00gWIfNAI1PzQCNTZC0QQiCfzyOdTts726zealbglEqZx4XCpRZ0S+CqG0J1V9cqeeypN2GNNqluQKKRpWXDOaRiGlIhASmfhFxIQiomIZVy006VFQVdnZ3o6upCOBy2W1tbG4LBIFpbWxEIBCjgIk2LRqYIIQtiBUfW1v/aFABTmxUozSsVgCSDK05z8XYgCKE4JxdxWwu7rcXdiqM5FnSTSZIE7m4Fd7cCoZr7DR1SKQ2pkIJUzEAqZWCUsrgYjWNoaGjWl2OSBL/Pj0DAD7/fj0AgAJ/PB6/XC4/HY/dut9vu3W43XC4XXC4X3G43VFVd+s9NyBKiYIqQFYRzjkwmY0+rWVNrtbeTySQSySRSyeQcU2uThKJByE4IRxDca+5qE4qjGiRZt6v3qS5zMfcyB0juY6+BVYrL+p7NTqhO5Hf+wuK9oKyAu0Pg7tD0x7gOVp6AVJ6o9nmwSgFML4BVCkiUikiNxsAuDpkpIC77rRW4a4KrqW1qMGYFabUBm8/ng6ZptIORNERDg6kDBw7g1VdfxcmTJ+FyuXDLLbfgueeew9VXX22fUywW8dhjj+Hll19GqVTC7t27cfDgQXR0dNjnDA4Oor+/H++++y68Xi/27NmDAwcOQFHMj/fAAw/gz/7sz6a9//bt23H8+PGl/6CEzELXdWQyGbvVTqfVNjNISiGdTs28o62WpICrLgitBdzjqgZErsnASHXVBEjOFTHFxipFSHqh0ZdR5+DBgwCAhx56qCHvf/khywJICoQzAMN5iXxWQgC8UpNDqwxYx7wCZlQAowxm6GC8AhjmfTqvoKxXkE4XwBIZ8zF+iZ/zGSiKYgdWczW/32+fZx07HI4r/I9DSIODqSNHjmDv3r246aaboOs6nnzySdxxxx04ceIEPB4PAOCRRx7Bm2++iVdeeQWBQAD79u3D3XffjQ8++ACAuZ36zjvvRGdnJz788EOMjo7i/vvvh6qqeOaZZwAAf/iHf4hnn33Wfl9d1/GZz3wGv/iLv7j8H5qsWEIIVCoVlMvlujIiVmmRYrGIYrGIQqFQV3rEKkUyMTFhlybJZLPIZrMoFuYXIAjFYU6tudomp9GsoEh1mcFT9TZkmjIhDcIYYCU5XehrcW4GZkZNEFYTqJmBWDVQq95nGCWUimUkJ+JgwyOXFZBpmgavzwd/dYqytnk8nro2deTM6XTC5XLB6XTS+rE1qqkWoMdiMYTDYRw5cgS33XYb0uk02tvbcejQIdx7770AgJMnT+Kaa67B0aNHcfPNN+Ott97CXXfdhZGREXu06oUXXsDjjz+OWCw2Y62rv/3bv8Xdd9+N8+fPY/369dMet2puWTKZDHp7e2kB+ixSqfrREiEEODf/buacQwhh32f1szWrtlltnTPruLbpuj7jsXXbape6XdsqlUrdsRk4VVCplO3bi8HMks2AmmzZglWP7fukyfusc9cwKZ8AW/iv50XV6JEpAQbuDjbkvVcOYY6WiWoWevv25LGZnV5Ub5vHC/lZU1UVDocTTqcDTqfTrsdoNVVV7aYoClRVhSzLUBTFbrIsT2uSJNn91ONLtdoakVNvW8dz9dYxgGnHVpuJNW3bSGtyAXo6nQYABIPmF8THH3+MSqWC22+/3T5n27Zt6Ovrs4Opo0eP4tprr62b9tu9ezf6+/tx/Phx3HDDDdPe51vf+hZuv/32GQMpwJx+fOqppxbzo61af/VXf4VvfOMbjb6MJSUkxfxL2+k2/+pWNAjZMVlORFLMaRDrtqwCkgohq3AM/lM1188kZn2hA4AwqvcRcnkYBOT87FnfyZUz3CHkr7mzZhRsctrSHCWrGTGzRs/0IlilAF4poDyRQy6XbfTHaAp/+qd/io0bNzb6MpZc0wRTnHM8/PDDuPXWW7Fz504AQCQSgaZpaGlpqTu3o6MDkUjEPqc2kLIetx6bamRkBG+99RYOHTo067U88cQTePTRR+3b1sgUme6aa66BLMuXXsezgjGrntrUgSm7LpsZSAlJrgZRSrV+mwoIAa64zNElezSKQdijUNaIFJlLM45MNRqNTC2imtEpJgSEpECNn62u9ypVA6fa6cVqs9aB8eUtiL1SfOYzn0EoNMOGhlWoaYKpvXv34tixY3j//feX9H3+7M/+DC0tLfj5n//5Wc9xOBy0GHGerrvuOrzzzjvL+p6c87opu5mm/xZrmm9yuq9s97Vrpuy1UqUSSsUiCoXclU0Hyhq44pjcRWc11Wmuh6pdOK661lwSS88PXwJrsgXojSYUJ/I7fq7Rl9EchJgcPbICoGpvB0R6qeaxEphRgqSbo05TSaUMlFx0xrdijMHlcsHj98DtbrVTPFhrpqzmcJjTfJqm1U31zTbNZ92eaYpv6lQfY6xumo80XlMEU/v27cMbb7yB9957Dz09Pfb9nZ2dKJfLSKVSdaNT0WgUnZ2d9jkfffRR3etFo1H7sVpCCPzpn/4p7rvvvhnXUpGVQZKkpv7/T9d1e/G5tfB86gL0bHUBejabnbaTrzQxfsn3ELJmBlqKa/YF6XbgtfypDBabUJ3Lu3ttHr74pcfMA8XVkPcXqrMh77uouFG/yJzr06fP7B2AFXuECDVTbpIxc0A0F7fbDV+LDz5f57SdftYuv9kWoLtcLkq/QKZpaDAlhMD+/fvx2muv4fDhw9iwYUPd4zfeeCNUVcU777yDe+65BwBw6tQpDA4OYteuXQCAXbt24emnn8bY2BjC4TAA4O2334bf78f27dvrXu/IkSM4c+YMvvCFLyzDpyNrlaIo9hfzlSgWi9PSI9Qm3rTySpm5pGL2Yv9ZSYq5E3BKagTeRHmkLmVR8ymRxcN5NddU0VwzVB3xMfsKYI8Q1QZIZXPq3KhcUU4qwPyDyuPxwNvih9frqQuAPB7PnGkRvF6vnTaHkMXS0N18Dz30EA4dOoTXX3+9LrdUIBCAy2X+tdff34/vfve7+Pa3vw2/34/9+/cDAD788EMAZmqE66+/Ht3d3Xj++ecRiURw33334cEHH7RTI1juu+8+nD59Gv/0T/90WddJ5WRIs5opiWdtqw3EEolE3S7VWUkyuOyYEmhZgZejLmcVV12ArDVd8EUWgeBmgs5SFlIpW5e0k5XzkPQCmD6Pn6cqVVXhdnvg8dRnQK9N1Dn1tnWf1+u1k3Va59DoEJmP5fr93dBgarZ/DC+++CIeeOABAJNJO1966aW6pJ21U3gDAwPo7+/H4cOH4fF4sGfPHjz77LN1f32k02l0dXXhD//wD/Hrv/7rl3WdFEyR1aJYLM5aTmamhKETExOXflEmVQOr6qiX6p6cftSqt1U3hOY2R71Ic9FLkAspSMUUpELa7IsZSOXcrCNHgUALQiGzNp/V/P65y8lQyRjSCGsimFopKJgia5Wu63VBVjqdRjKZtPvaachEInHJ4EsojmpgZRU8rhY7dnjN26pnRWRkX5EEN4OkfBxSPmEWOC4kIZWn/38WDAbR3d2NdevWobu7u67QcVtbW1OvWSSk1prMM0UIaS6KoiAUCs17e3OpVLIDq3g8jkQigfHxccTjcYyPjyMWi2EsFsNEerbCuQxcc4M7fBCaF9zhrTn2maNblEri0rgBqZCCnI9DyschT8QhFxLTtvCHw2Fs3HgtNmzYgPXr12P9+vXo7e2F1+tt0IUTsjJRMEUIWTQOhwOdnZ3TdtJOlc/nEYvFEIvFEIlEMDY2hmg0ikgkgkgkglhsDDw7PU8cmASuecAdPnCHF0LzTR47fGY5nbW2lsYoQ84nqyNOccjVUafaKTpZUbBx0wZs2bIFmzdvxubNm7Fhw4Yr3iRBCKlHwRQhZNm53W57JGQmuq7bgVYkEsHo6KjdRkZGkUiMzPzCkmwGW5rXnD6smU4UmmdlL5g3KpCKaUjV9U1yIQkpnzTXNtVwOBzYvP0abNmyBVu3bsWWLVtw1VVX0ZolQpYQBVOEkKajKAq6urrQ1dU14+OlUskOtGoDrmg0ikg0iuRswRZgpoqwc3FZObpq0kPYvQNC1sxM9ks9tShEtRxJHqxSMHfLlXPmLrpSDnIpC1bJT3taW1sbNm7cjo0bN9qjTj09PVRsl5BlRsEUIWTFcTgcc45slUolexoxFothbGwMsVgM8XjcXL9VXc9l6PMsAyKr4NV6i1bJIFglhJhcLS1UW6i6OvIlBAAOCIAJw0xKyXWAG5PZuKtZuzFLuRxZlhEOh9HXdy36+vrstmHDBtoQQ0iToGCKELLqOBwO9PT01FVUmEoIgVwuV5eTqzYbvZWt3mr5fB75QgGFfB7FXAEL3QjtdDrhC/jh94Xh9XoRDAbtFgqF0NHRga6uLrS1tVGSSUKaHP0LJYSsSYwxOyt2X1/fZT1XCIFKpWLXaCyXy3Z9SM45OOdgjIExZtdPq63R5nA4aCqOkFWEgilCCLlMjDG7cC0hhFDCFkIIIYSQBaBgihBCCCFkASiYIoQQQghZAAqmCCGEEEIWgIIpQgghhJAFoGCKEEIIIWQBKJgihBBCCFkAyjNFCCFTcM5RLBZRqVSg6zp0XQfnHJIk2Yk4FUWxk3BSAk5C1jYKpggha0I+n0ckEqmr2ZdIJJBKpZBKpaolZCaQz0+gWCxe1msrigK32w2PxwO32w2v1wufzwe/3w+/34+WlhYEAgG0tLSgtbUVwWAQra2tUFV1iT4tIWQ5UTBFCFk1isUiLl68WNdGRkYwMjKCVCo1+xOZBKE4wGUNkL0QvlazqDGTalpN8WIhAGFUixcbMLiOUqWCVDwLFkvMWbi4ls/ns2vxhUIhtLW1zdhTpnVCmhsFU4SQFWdiYgIDAwMYGBjAhQsXzH5gANFIZHoBYibBcPggAj3gDi+E5gXXPBCqG1xzQyhOQNYmg6XFIARglMH0UrUVzVYpQtKLYJUCWKWAVCWPzHAUAwMDc75cINCCcLgd7e3tCIfDaG9vR0dHB8LhMDo7OxEKhWiqkZAGomCKENKUOOcYHx/H0NAQBgcHMTg4aAdQ4+Pj084XmhuGrwvc2QLu9IM7A+DOAITmWdxAaT4YAxQHhOKYx/gUAM7B9AJYOQ+pkger5M3j8gRYJY9EKY/0uQs4ffr0jE+XZRnhcBhdXV3o6upCd3c3uru7sW7dOvT09MDtdi/qxyOE1KNgihDSEEIIZLNZjI2NYWxsDJFIBKOjoxgZGcHo6CiGhoZRLpemPY9rXvBAD7irBYazBdzVAu5sAZQVPBUmSRCaB0LzgM91nlE2A6zyBKRSzuzLOeilHIbjGYyOjs74tNbWVvT19U1rHR0dkCTa1E3IQlEwRQhZNJxzTExMIJ1O2y2ZTCKVSiGZTCIejyORSCCRSCAWi8260FvImjm65O2pjjL5zaDJEQDkNfy1JWvgLg1wtcKY6XFugJVzkEpZSMUMpFIGUjGDeD6D5L/+K3784x/Xne50OrF+/XpcddVV2LBhg93a29vBlns0j5AVbA1/KxFC5mIYBjKZjL3TzWqZTAaZTMY+zmaz5mPVY8HnHFsBwCBUF7jqAW8JT65hcnjBHT5whw9QHMvyGVcdSYZwBmA4AzACUx7jRjXISkEqpCEVUzAKKZw6fRanTp2qO9Xj8WDDhg3YuHFjXZAVCEx9UUIIQMEUIWuKYRhIpVL26FBtSyaTSCaT5nEqhWwmM30x90yYBKE4wRUNwt0OoTrNtUKys3rshFBdZq+ZPRhNLS07STZH91wtQGvN/YKDlXKQC0lI1ZYppHDs+AkcO3as7iVag0FsqI5iWSNafX19CAQCNJJF1jQm5vVtubZlMhkEAgGk02n4/f5GXw4h0+TzeTsQslrtlFo8Hkc8nkAylbzkyJFQnOCqE0L5/9u78+AoyvQP4N/umZ4zc+WaSUISgiLxgMUTAUFdWJFFvKtcpQSrViwj8lsVXRWvdXeVQ2u11hVd8WB3S8iuLq5VXrWKRktlYaXQVS4BwZBkMrknc1/9/v7o6c5MZoAkM8lMkudT9Vb3TPd0unnTk4f3fft59X3BkDoeIKUstQCvHvkB3mT4iTGpqzDQBT7QAz7QLQVcIU/KrgUFJlRXV6GyshITJkxQBr6Xl5fDaDTm4OQJkYzU329qmSIkzzDGEAwGk7rYEpfd3d1wu91Kwsmuri6EQqkDtZOoNIgJejBjqdRKJBjiXW3yel/rUS5bjQzfvQUuMriEmeMVE3Twn3XN8P0AXgXRYINosCW/H4uCD7rj3YXd4ANuuINu7Nm7D3v27Ek5jMlkUp4yLCsrQ2lpqZLWoaSkhFq1yJiQ02BqzZo12Lp1K/bv3w+9Xo9Zs2Zh3bp1mDJlirJPMBjEqlWrUF9fj1AohAULFmDDhg2w2+3KPo2Njairq8Mnn3yCgoICLFu2DGvWrIFa3Xd5r7/+OtavX4+DBw/CYrFg4cKFeOqpp1BUVDSi10zGl3A4DI/HA4/Ho4wvksccpSvy2KNIOHzyg/MqiGo9mGAC05fG1xOLQQmWRsugbSkPUyDXp5Fiw4YNAIA77rgjx2fS52Qj04aNSg3RWATR2O+7M95dKA96l5Ye9IQ98Bz6Ad9//33awwmCgOLiYiVBaVFREQoLC5Vis9lgs9lgtVopYzzJWzn9hv3000+xYsUKnH/++YhGo1i9ejUuu+wy7N27V2kavvvuu/Huu+/ijTfegMViwZ133olrr70WX3zxBQBpDMiiRYvgcDjw5Zdfwul0YunSpRAEAU8++SQA4IsvvsDSpUvxzDPPYPHixWhubsbtt9+O5cuXY+vWrTm7fpL/otEoAoEAvF4vfD6fUrxeL7xeLzwej7KU1+WgyePxnLzFKIE0zkgLJljA9AndaYIOotzVljAGibrXSF7heDCdGTGdOXXwO2NS0tKQN57awQs+7AUX9iMW9ktpHVpdADtxiGgwGGC1WpXpecxms7I0mUxKKSgogNFohNFoREFBATQaDbV+kWGVV2Om2tvbUVpaik8//RRz586F2+1GSUkJNm/ejOuvvx4AsH//fpx++unYvn07LrzwQrz//vu44oor0NLSorRWvfjii7j//vvR3t4OjUaDp59+Gi+88AIOHz6s/KznnnsO69atQ1NTU8p5hEKhpD+Cvb29qKyspDFTeSAajaKxsRGiKColFosllcTJaSORCMLhsLIMhULKUi6BQEBZBgIB+P1++AMB+H2+QQVDMgZOCnI4HoivM2U6Ek6ZmoQlbKcB2RLe3wVuYGkuR1Q+tkwxcBANhbk+jexiotTCxcT4lD1pXoNJrwdBpVIpcyfq9XrodDplqdVqlQmrtVot1Go1NBoN1Go1BEGASqWCSqWCWq0Gz/NQqVTKZNcAwHGcUuQ/p4yxlHW5iPExi6IoKq8Tl4lF/nw6cnCY+PPl8+q/lNdVKlXKulzSbeu/nnisgQSnHMehuro6p7nMxuWYKbfbDQAoLJS+IHbt2oVIJIL58+cr+9TW1qKqqkoJprZv346pU6cmdfstWLAAdXV12LNnD84++2zMnDkTq1evxnvvvYeFCxeira0Nb775Jn7+85+nPY81a9bg8ccfH8YrJUP1xBNP4JNPPhmxnycKBimRosYIpjGAqTRS65FKANRaaI/9F3yoN+kzHFjCF7/8HiHZxYFB5e/M9WnkFVFjRKS0FohGwEcD8ZYwL1jYr7QWk5G1YMECPPjgg7k+jWGXN8GUKIq46667MHv2bJx11lkAgNbWVmg0Glit1qR97XY7WltblX0SAyl5u7wNAGbPno3XX38dN9xwA4LBIKLRKBYvXoznn38+7bk8+OCDuOeee5TXcssUyb2LLrpoRIMpPuIHIn6ofO3SGyoNRJUgBVVqLSBGpeCKi7cwId4CRS1PQ5KvLVP5aEy2TB1PvGVKaaGCmPBeX4sVomFoXHvBxcKAmDatKRlhF110Ua5PYUTkTTC1YsUKfPfdd/j888+zfuy9e/fiV7/6FR599FEsWLAATqcT9913H26//Xa88sorKfvLzb4k/8ybNw/z5s0b0mdjsVhKV18wGFRKUjef349AIACfzwe/3580Zsrr9cHj8cDncytpBgbU8qTSQIwHYcr4KHX/kpx6IOsT8OY54+4t4PJwAHo+Ymod/GdelevTGDomShM+h33gw/H5CCN+8BFpjkIuEpAmhY4G4gHUianVapjMJpjjY6YSx00ZjUYYDAYYDIbjdvPJXX2CIChFrVYr3XojJV1XX+K2/ufSv5uP5EZeBFN33nkn3nnnHXz22WeYMGGC8r7D4UA4HEZPT09S65TL5YLD4VD22blzZ9LxXC6Xsg2Quu1mz56N++67DwAwbdo0GI1GzJkzB7///e9RVlY2nJdH8oRKpYJer4der8/K8Rhj8Pv9SYPQEweg9x+MLj+x5/F44OsdYPcMx4OptfEB6Nq+3E9yEkxBl/QUH1Sj+2knJuhy95TaCdz+f6ukFXV2fneygQm6XJ/CiYli39Q2IU98vW8+QT7iP2GQZDQaUegoUp7kk5cWiyVlALrJZIJOpxsTwQQFRaNTToMpxhhWrlyJt956Cw0NDaipqUnafu6550IQBGzbtg3XXXcdAODAgQNobGzEzJkzAQAzZ87EE088gba2NpSWlgIAPvzwQ5jNZpxxxhkApISGiWkSAOkPq3wOhAwFx3HK/3oHKxqNwuv1wu12K4FW4nQties9PT3ocbvh6XWd/PeVV0NMyh+lj+eTMvTll8rjLOTDmjeJZB9jUnAUdCeUeFqEsDdtsKRSqVBaWorS0lNRUlKC4uJilJSUoKioSEmPUFhYSL0DZFTJ6dN8d9xxBzZv3oy33347KbeUxWJRWg/q6urw3nvvYdOmTTCbzVi5ciUA4MsvvwQgdd1Mnz4d5eXlWL9+PVpbW3HzzTfj1ltvVVIjbNq0CcuXL8cf//hHpZvvrrvuAs/z2LFjx0nPkzKgk3wQi8Xg8XiUiYPlpJ3KFDD9MqDHYicaM8JJrUCJOankaV8SWr2YOh545fBpHJIHGJNaluQs6AFpfj9VsAcQoym7FxYWobJSyoBeVlYGh8OhLAsLC5X/zBIy3Ebq73dOg6njNWW+9tpruOWWWwD0Je3csmVLUtJOuQsPAH788UfU1dWhoaEBRqMRy5Ytw9q1a5Nao5577jm8+OKLOHLkCKxWK376059i3bp1qKioOOl5UjBFRhtRFNHb25sytYw0rUxnUtDl8/lOfkCVBmK/aWSS1lWp71EerFEong+K98tTyHRBFeiGKtCTEjRptFpUV1WhuroalZWVqKqSppOpqKjIWlc6IZkaF8HUaEHBFBnLQqFQ0kTHcsuXvJS7HHvcbrh7ehAeSHZ2IJ6hXZfUypXU/agxSCknBCO1fOUAFwkmTAkjz73XAy6aPJ2PIAiYOHEiJsYnOJaXdrs9p/mDCBmIcZlnihAy8rRarTJv2kDI8wbKY7r6T5EjB19y6e7uQcB94gH3TDBA1BZA1JqUwrRmiDpzfHwXtXANiSiCC3ukcUwJ45pUQTe4SPJTkxzPY0JFBWpqalBTU4NJkyahpqYGFRUV1C1HyElQMEUIGRSdTgedTqc88DEQoVAIPT09Sd2M7e3taG9vR1tbG1wuF1xtbYh521I+y1QaiDozRJ0Vot4CUWdFTG8F05rychD9iGIMXDTU96Rc/Mk5qaQfBM7zPMrLy1FdXY2amhpUV1dj4sSJqKqqokHfhAwRBVOEkGGn1Wpht9tTEuwmisVi6OjogNPpREtLC5qamtDU1IRjx46hqakJEV9H8gc4HjGdBaLeGg+04kudGeDHQEsKY0AsAj7ik/IuhX3xee36lqqwN+0AcCA+CLx2GioqKjBhwgRlXFN5eTlNGExIllEwRQjJCyqVSgm4pk+fnrQtFovB5XLhxx9/xI8//ojGxkYcPXoUR48ehb/rSL8jcVKXoc4iFbnrUGcG0xTkPtBiDIiFwUekxJRcPEll32s/+Pjr4wVKAGCxWGGvmgSHwwGHwwG73Z709JxOl+d5qAgZQ2gA+gDQAHRC8hNjDB0dHUqQJQdajY3H0NWVfpyWnHdLGvxuiA+Ol4sG4AUwlQDGq6XAi+PjE1Xz8S4zqXCiCIhRcCwGiDFwsTC4WASIhaX1aCihBKUSCYKPBpPmbeyP43kU2mwoLi5WcjDJpbi4GKWlpSgpKaEuOUIGgAagE0LISXAcpwQa5513XtI2n8+HY8eOKd2GTqcTTqdTGacV7BnZSYKNBQWw2QthtVpRWFioFJvNhqKiIqXYbDYa8E3IKEPBFCFkTDIajaitrUVtbW3KNsYYfD4fOjs7408cSmkg5HkZ5bkZo9GoUmKxmDJPG8dxEARBmc9Nq9XCYDAoc8AZjUaYzWaYTCZlyhMap0TI2EXBFCFk3OE4TpkMlxBCMjXOnysmhBBCCMkMBVOEEEIIIRmgYIoQQgghJAMUTBFCCCGEZICCKUIIIYSQDFAwRQghhBCSAQqmCCGEEEIyQMEUIYQQQkgGKJgihBBCCMkAZUAnhIwpjDGEw2FEo1FEIhFEIpGkaWHkqWHSFVEUIYoiGGNJJRHHceB5XikqlQpqtVpZCoIAjUYDjUajTDmj1WohCAI4jsvRvwohZDhRMEUIyblQKAS3243e3l6leL1epfh8PgQCgaQSCoUQDAYRDAYRCoURDocRiUhBVD7ieB5arRZ6nQ56vT6p9J/XTy4FBQUwmUwwmUwoKCiAxWKBXq+noIyQPEPBFCFk2IRCIbS3t6OtrQ3t7e3o6OhAZ2cnOjo60NXVhe7ubnR3d8Pv9w/uwBwP8GowXg3Gq8B4FcAZwHQFAK8COBUYx8fXeWmdk9bBcfHX0rryHuKvwcWX6FsmkluqGAMggmMMYKJURBEciwEsJq2LMYBFpaUYRVSMwheIgvO5wYmd4GIR6XODoFarYTZbYLVaYLVaUVhYCJvNhsLCQhQVFaG4uBjFxcUoKSmBXq8f3L8rIWRIKJgihAxZNBpFa2srWlpa4HQ64XQ60draitbWVjidrXC7e47/YY6HKOjB1Howiw1MrYsXrVRUGqVArQHjBTCVAPBqKUgaK8QYIEbAxSLgYmEpwIqFwUXD8ddhcNEQuFgYiIYQjQYR8ofQ2dsM7ocfTnhos9kMu90Ou90Oh8OB8vJyVFRUoKysDGVlZRAEYYQukpCxjYIpQsgJRSIROJ1ONDU1obm5Gc3NzWhqakJTczNcLheYmKZlhVcjpjGCmSsgaoxgGmN8aQATDBA1BkClTd/yM97wKoBXSYHkYD8riuCiQXDRALhIAHzYDy7sAxfxgw/70B32oveHozh48GDKRzmeR5nDgQkTJqCyshKVlZWoqqpCVVUVioqKqCuRkEGgYIoQglgshra2NilI6lecTifENAGTqDFCNJZC1JrAtGaIWhNEbQGY1gSm1lGgNBJ4XgpQNQYAQCzdPowBsTD4kCdeesEFpWVzew9aWlqwc+fOpI/o9QZUV1cpwZUcaFVUVECj0Qz/dREyylAwRcg4EQqF4HK50NLSklSamprQ0tKSduA2EwyIGUshas1gOjNEnRmi1gJRawJU9PUxKnAcoNZCVGshGotTt8ci4IO94INu8MEe8EE3YgE39n9/CPv3708+VLw1Sw6sJkyYoJSSkhKo1fQ7QcYn+s0nZAxgjMHtdqOjo0MZ8O1yudDW1iaNaXI60dXZmf6zah1EbSFEixwsmSHqLBB1ZkBFY2rGPJUA0VgE0ViU/D5j4MJeKcgK9MSDLbfSmpVyGJUKdrsdFRUVcDgccDgcyngtu92OwsJCCrbImEW/2YTkKcYYfD4f3G43enp60N3drSw7OzvR1dWFrq4u5em4SCSS/kAcD1FTANFcrnTDiUq3nBlQ567bxvDdW+AiwZz9/LGCCTr4z7omuwflODCtCTGtCTHLhORtsUg8uOoFH+oFH+xFNOQ5bqAFSK1aRYWFKCkpQXFxsfLkYVFRUdITiVarlYIuMurk9Dd2zZo12Lp1K/bv3w+9Xo9Zs2Zh3bp1mDJlirJPMBjEqlWrUF9fj1AohAULFmDDhg2w2+3KPo2Njairq8Mnn3yCgoICLFu2DGvWrEm6IZ9//nn86U9/wtGjR1FVVYWHHnoIS5cuHdHrJeNXOByGx+OBx+NBb2+vspTX5RxL8rLH7Ybb7UbsZDmT5CfiNFaIBcb44G4jmNYIUVMApikAE3TS4/95iIsEwUcDuT6N49qwYQMA4I477sjxmZzY4JIrZIFKgGgsPk63YRR82Cu1aoXiy7APXNiPNo8PHV3fA/v2nfDwJpMJNpsNNpsNFouUAkJems1mmM1mWCwWmM1mmEwmGI1G8Hx+/o6T8SGnwdSnn36KFStW4Pzzz0c0GsXq1atx2WWXYe/evTAajQCAu+++G++++y7eeOMNWCwW3Hnnnbj22mvxxRdfAJAGzi5atAgOhwNffvklnE4nli5dCkEQ8OSTTwIAXnjhBTz44IPYuHEjzj//fOzcuRPLly+HzWbD4sWLc3b9ZHSIRqMIBoPw+/3w+/1KAsnEhJI+ny8pyaQcOEnFi3A4NOCfp6QG0BVClNMFCDophYCgAxP0YIIeomAAVBoa6E3yi0oNUW8F9NYTD4iP+MGF/eAifimojvjBRaSnEnuiQfQ629F47FhfXq8T4HgeBUZjUoLTgoICJfGpnAxVTpAqL3U6XVLRarXQaDQUmJFB41j/uRJyqL29HaWlpfj0008xd+5cuN1ulJSUYPPmzbj++usBAPv378fpp5+O7du348ILL8T777+PK664Ai0tLUpr1Ysvvoj7778f7e3t0Gg0mDVrFmbPno2nnnpK+VmrVq3Cjh078Pnnn6ecRygUQijU98evt7cXlZWVcLvdMJvNw/yvML6EQiHs27dPmbIj3dQd6TDGIIpiylQg8lQh/acPkacUiUQiCIfDSUWubzmjdlNTE0KhUNK0IkPB5OSPHAcgMVmknBgy4T1l3/H1Jc77u8ANPiHAiBktLVMMHERDYa5PY3gwURq/xUQATHmtvJ/4HuT9MsfzPDiOS1sAnHA9cZm4bjKZMGPGDGVKosRlYpGDucT14+3Xf73//idayuVE+50oRYa8Ld13ZP/PGQwGnHbaaSOecqO3txcWi2XY/37nVce02+0GABQWSl8Ku3btQiQSwfz585V9amtrUVVVpQRT27dvx9SpU5O6/RYsWIC6ujrs2bMHZ599NkKhEHQ6XdLP0uv12LlzJyKRSEriujVr1uDxxx8frsskCR566CF89dVXuT6NYSF9yTNIsUIs/h4h2ceBQeVP/4ABGZp06UAy5Xa70dTUlPXjjhYrV67Eddddl+vTGBZ5E0yJooi77roLs2fPxllnnQUAaG1thUajgdVqTdrXbrejtbVV2ScxkJK3y9sAKbh6+eWXcfXVV+Occ87Brl278PLLLyMSiaCjowNlZWVJn3/wwQdxzz33KK/llimSfYsWLRq1wZTS8tSvBYpxXErrE5DQKiVvI3nfMjVajOmWqeOJT+cjtVrFW6eU/8AkTvXD+t6PL+l3buRVVlbi3HPPzfVpDJu8CaZWrFiB7777Lm23W6YeeeQRtLa24sILLwRjDHa7HcuWLcP69evT9o3Ls7yT4XfppZfi0ksvHbGfJ4oiIpEIIpEIQqFQSlefPHFuMBhEIBBQloFAQBkr5fP5lLFT0hgpH3x+H1hM+p/sgMIkXg1RnjIlcQoVtTbhdfJ4qbGY18m4ewu4PB6APlowtQ7+M6/K9WkMXeI4qkhQGjsVDUolEpCm04kGpbFVsRC4aGhQcxqqVCoYC6SJpOUxU3JJHC+l0+mg0WhSiiAIUKvVyjKxqFSqlJLYhSZ3l8nvH68rTV4no1NefDvfeeedeOedd/DZZ59hwoS+R3AdDgfC4TB6enqSWqdcLhccDoeyT//svS6XS9kGSF16r776Kv785z/D5XKhrKwML730EkwmE0pKSob56kg+4XleCZYLCgqydlzGWNKg9MQB6P1fJz/F1wuft3Vg47J4NcR4YCUKejDB0DcYXWMAE+LTtYyi7ONM0I38k2iDcPv/rZJW1Pk9YTATdCffKVdiEXBhP/iID1zYpzzZx0X8yqBzPhI4aXDE8TzMJjOs1tKkJ/pMJhPMZjMKCgrSDkA3Go3QaDQUqJBhldNgijGGlStX4q233kJDQwNqamqStp977rkQBAHbtm1T+lkPHDiAxsZGzJw5EwAwc+ZMPPHEE2hra0NpaSkA4MMPP4TZbMYZZ5yRdDxBEJRgrb6+HldccQU9tUGyguM45X+98u/hQMViMXi93qTUCO54aoSenp6k0t3dje7uboR97cc/IK+CKMTnwtMWSNO+aE1gmoL40pA3A92znhuJjDxRBBf29E1XE+xV0iLwYR+46PHziGm0WhQXF6GwsAaFhYVKnik5LYLValVKQUEBfV+TvJXTp/nuuOMObN68GW+//XZSbimLxQK9XvqfYF1dHd577z1s2rQJZrMZK1euBAB8+eWXAKQ/RNOnT0d5eTnWr1+P1tZW3Hzzzbj11luV1Ajff/89du7ciRkzZqC7uxt/+MMf8OGHH2LXrl2YOHHiSc9zpJ4GIGQg5GSeiUk729vb0dHRgY6ODrS1taGtrQ2dnZ3pW7zkJJ46OXlnPPO5zgymMQH0B4v0x5jUkqRkQ+8FH4on7Qx706Yv0Ol0sNvtcDgcKC0tRUlJSVIpKiqC0WikFiMyrEbq73dOg6nj3USvvfYabrnlFgB9STu3bNmSlLRT7sIDgB9//BF1dXVoaGiA0WjEsmXLsHbtWiVp5759+3DTTTfhwIEDEAQBl156aUpy0BOhYIqMRvIDFq2trUppaWmB0+lEc3Mzuru7Uz/EcfFAy5IytQzTGPOmRYsMEzGaME+fG3xAmq9PFeoFYqkZ9gsLi1BZOQEVFRUoLy9XisPhgMVioUCJ5Ny4CKZGCwqmyFgUCATQ0tKC5uZmNDU1oampCc3NzTh2rAldXWkes+dViGlMUmClNUlBltYkdR1qCyjQGi2YKI1dUqaCcfcFUCFPyu5arRZVVVWoqqpCZWWlsqyoqIDBYMjBBRAycOMyzxQhZOTo9XqccsopOOWUU1K2+f3+eGB1TAmyGhsb0dzcjN6extSDyS1a2gKIGim4ksZnxcduCUbqPhwpoihlFQ/7wIe90tQuIV/fmKawN+1g76KiIlSfeY4SLFVXV6OqqgrFxcU0VomQk6BgihCSwmAwYPLkyZg8eXLKNo/Ho7RmtbS0KKW5uRmdnU4AzrTHZIIhPo+gAaJg6HsaMZ7+QVTrAbUWTK2hVq5ETJSeiIuG+lIEKKkCAuDl9AGRgPJ03PHYCgtRMfkMlJeXY8KECUmFWpkIGToKpgghg2IymVBbW4va2tqUbeFwGG1tbcoYrba2NrhcLrS3t6OtrQ0dHR0I9pw8UzdTa8FUAphKXmoAlQDGC4BKDcYLYLwa4FXxZXyd4wFeHV+qAI4H41RSqxjHx1/zCclUExOpZkCZ3kQqnBgDWExaitGEZVRaxiLKkhMjUrAUL4iFwcULH389EHq9ASVlJSguLkZxcTFKSkpgt9uTivxgDyEkuyiYIoRkjUajUVo6jsfv96OjowOdnZ1J6R7ktBByagiv1wuvzwe/t2vI8yMOHJecnV5ZxjPdJ+6ZklFbTPs021DxPC9N0Gvty50kL+XcShaLBWazWUkhYLPZUqbMIoSMHAqmCCEjymAwKAOaB0IURSULvVz8fn9S1vr+k1cnTmwtl8QJsOX1/hNly0WeSFv++f0nrU3Mai0XORu2IAhJ2bPlJLFykbNuyxm45fxkctHpdPQUHCGjDAVThJC8JrfUGI3GXJ8KIYSkRaM8CSGEEEIyQMEUIYQQQkgGKJgihBBCCMkABVOEEEIIIRmgAegDID+W3dvbm+MzIYQQQshAyX+3hzu9CgVTA9DZKSUZrKyszPGZEEIIIWSwOjs7YbFYhu34FEwNQGFhIQCgsbFxWCuDDExvby8qKytx7Ngxmng6x6gu8gfVRf6gusgfbrcbVVVVyt/x4ULB1ADIk3zKWYdJfjCbzVQfeYLqIn9QXeQPqov8MdyTddMAdEIIIYSQDFAwRQghhBCSAQqmBkCr1eKxxx6DVqvN9akQUH3kE6qL/EF1kT+oLvLHSNUFx4Z/OnZCCCGEkDGLWqYIIYQQQjJAwRQhhBBCSAYomCKEEEIIyQAFU4QQQgghGRi3wdTzzz+PiRMnQqfTYcaMGdi5c+dx992zZw+uu+46TJw4ERzH4dlnn03Z5ze/+Q04jksqtbW1w3gFY8dg6mLjxo2YM2cObDYbbDYb5s+fn7I/YwyPPvooysrKoNfrMX/+fBw8eHC4L2NMyHZd3HLLLSn3xeWXXz7clzFmDKY+tm7divPOOw9WqxVGoxHTp0/H3/72t6R96N4YumzXBd0bQzeYukhUX18PjuNw9dVXJ72flfuCjUP19fVMo9GwV199le3Zs4ctX76cWa1W5nK50u6/c+dOdu+997ItW7Ywh8PBnnnmmZR9HnvsMXbmmWcyp9OplPb29mG+ktFvsHVx0003seeff57t3r2b7du3j91yyy3MYrGwpqYmZZ+1a9cyi8XC/vWvf7FvvvmGXXnllaympoYFAoGRuqxRaTjqYtmyZezyyy9Pui+6urpG6pJGtcHWxyeffMK2bt3K9u7dyw4dOsSeffZZplKp2AcffKDsQ/fG0AxHXdC9MTSDrQvZkSNHWEVFBZszZw676qqrkrZl474Yl8HUBRdcwFasWKG8jsVirLy8nK1Zs+akn62urj5uMPWTn/wki2c5PmRSF4wxFo1GmclkYn/5y18YY4yJosgcDgd76qmnlH16enqYVqtlW7Zsye7JjzHZrgvGpD8Y/b+4yMBkWh+MMXb22Wezhx9+mDFG90Ymsl0XjNG9MVRDqYtoNMpmzZrFXn755ZR/92zdF+Oumy8cDmPXrl2YP3++8h7P85g/fz62b9+e0bEPHjyI8vJyTJo0CUuWLEFjY2OmpzumZaMu/H4/IpGIMonlkSNH0NramnRMi8WCGTNmZFy/Y9lw1IWsoaEBpaWlmDJlCurq6tDZ2ZnVcx+LMq0Pxhi2bduGAwcOYO7cuQDo3hiq4agLGd0bgzPUuvjtb3+L0tJS/PKXv0zZlq37YtxNdNzR0YFYLAa73Z70vt1ux/79+4d83BkzZmDTpk2YMmUKnE4nHn/8ccyZMwffffcdTCZTpqc9JmWjLu6//36Ul5crN0Jra6tyjP7HlLeRVMNRFwBw+eWX49prr0VNTQ0OHz6M1atXY+HChdi+fTtUKlVWr2EsGWp9uN1uVFRUIBQKQaVSYcOGDfjZz34GgO6NoRqOugDo3hiKodTF559/jldeeQVff/112u3Zui/GXTA1XBYuXKisT5s2DTNmzEB1dTX+8Y9/pI2GSebWrl2L+vp6NDQ0QKfT5fp0xrXj1cUvfvELZX3q1KmYNm0aTjnlFDQ0NGDevHm5ONUxzWQy4euvv4bX68W2bdtwzz33YNKkSbjkkktyfWrjzsnqgu6N4efxeHDzzTdj48aNKC4uHtafNe6CqeLiYqhUKrhcrqT3XS4XHA5H1n6O1WrFaaedhkOHDmXtmGNNJnXx9NNPY+3atfjoo48wbdo05X35cy6XC2VlZUnHnD59evZOfowZjrpIZ9KkSSguLsahQ4foD8YJDLU+eJ7HqaeeCgCYPn069u3bhzVr1uCSSy6he2OIhqMu0qF74+QGWxeHDx/G0aNHsXjxYuU9URQBAGq1GgcOHMjafTHuxkxpNBqce+652LZtm/KeKIrYtm0bZs6cmbWf4/V6cfjw4aTKIcmGWhfr16/H7373O3zwwQc477zzkrbV1NTA4XAkHbO3txc7duzIav2ONcNRF+k0NTWhs7OT7ouTyNb3lCiKCIVCAOjeGKrhqIt06N44ucHWRW1tLb799lt8/fXXSrnyyitx6aWX4uuvv0ZlZWX27ovBjKIfK+rr65lWq2WbNm1ie/fuZbfddhuzWq2stbWVMcbYzTffzB544AFl/1AoxHbv3s12797NysrK2L333st2797NDh48qOyzatUq1tDQwI4cOcK++OILNn/+fFZcXMza2tpG/PpGk8HWxdq1a5lGo2Fvvvlm0iPFHo8naR+r1crefvtt9r///Y9dddVV9Pj3AGS7LjweD7v33nvZ9u3b2ZEjR9hHH33EzjnnHDZ58mQWDAZzco2jyWDr48knn2T//ve/2eHDh9nevXvZ008/zdRqNdu4caOyD90bQ5PtuqB7Y+gGWxf9pXuKMhv3xbgMphhj7LnnnmNVVVVMo9GwCy64gP3nP/9Rtl188cVs2bJlyusjR44wACnl4osvVva54YYbWFlZGdNoNKyiooLdcMMN7NChQyN4RaPXYOqiuro6bV089thjyj6iKLJHHnmE2e12ptVq2bx589iBAwdG8IpGr2zWhd/vZ5dddhkrKSlhgiCw6upqtnz5cuVLj5zcYOrjoYceYqeeeirT6XTMZrOxmTNnsvr6+qTj0b0xdNmsC7o3MjOYuugvXTCVjfuCY4yxwTW0EUIIIYQQ2bgbM0UIIYQQkk0UTBFCCCGEZICCKUIIIYSQDFAwRQghhBCSAQqmCCGEEEIyQMEUIYQQQkgGKJgihBBCCMkABVOEEEIIIRmgYIoQQgghJAMUTBFCCCGEZICCKUIIIYSQDFAwRQgZkz744ANcdNFFsFqtKCoqwhVXXIHDhw8DAGbNmoX7778/af/29nYIgoDPPvsMAOB0OrFo0SLo9XrU1NRg8+bNmDhxIp599tmRvhRCSJ6jYIoQMib5fD7cc889+Oqrr7Bt2zbwPI9rrrkGoihiyZIlqK+vR+I873//+99RXl6OOXPmAACWLl2KlpYWNDQ04J///CdeeukltLW15epyCCF5jGOJ3yaEEDJGdXR0oKSkBN9++y3sdjvKy8vx8ccfK8HTrFmzMHfuXKxduxb79+/H6aefjv/+978477zzAACHDh3C5MmT8cwzz+Cuu+7K4ZUQQvINtUwRQsakgwcP4sYbb8SkSZNgNpsxceJEAEBjYyNKSkpw2WWX4fXXXwcAHDlyBNu3b8eSJUsAAAcOHIBarcY555yjHO/UU0+FzWYb8esghOQ/CqYIIWPS4sWL0dXVhY0bN2LHjh3YsWMHACAcDgMAlixZgjfffBORSASbN2/G1KlTMXXq1FyeMiFklKJgihAy5nR2duLAgQN4+OGHMW/ePJx++uno7u5O2ueqq65CMBjEBx98gM2bNyutUgAwZcoURKNR7N69W3nv0KFDKccghBAAUOf6BAghJNtsNhuKiorw0ksvoaysDI2NjXjggQeS9jEajbj66qvxyCOPYN++fbjxxhuVbbW1tZg/fz5uu+02vPDCCxAEAatWrYJerwfHcSN9OYSQPEctU4SQMYfnedTX12PXrl0466yzcPfdd+Opp55K2W/JkiX45ptvMGfOHFRVVSVt++tf/wq73Y65c+fimmuuwfLly2EymaDT6UbqMgghowQ9zUcIIQPQ1NSEyspKfPTRR5g3b16uT4cQkkcomCKEkDQ+/vhjeL1eTJ06FU6nE7/+9a/R3NyM77//HoIg5Pr0CCF5hMZMEUJIGpFIBKtXr8YPP/wAk8mEWbNm4fXXX6dAihCSglqmCCGEEEIyQAPQCSGEEEIyQMEUIYQQQkgGKJgihBBCCMkABVOEEEIIIRmgYIoQQgghJAMUTBFCCCGEZICCKUIIIYSQDFAwRQghhBCSgf8HcCiEcPGwdG0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = (sns_df[\"year\"] >= 2000) & (sns_df[\"year\"] < 2010)\n",
    "fig, ax = plt.subplots()\n",
    "sns.violinplot(\n",
    "    data=sns_df[mask],\n",
    "    ax=ax,\n",
    "    x=\"avg\",\n",
    "    y=\"year\",\n",
    "    order=sns_df.loc[mask, \"year\"].unique(),\n",
    ")\n",
    "ax.set_xlim(0.15, 0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YnhqO6PKbrfpzogmCEDBdlGXZM2t0DRtdU0caKSIIYqYhMUUQxKzAGPPETltbW7O7QxAEMW3QVzaCIAiCIIgpQGKKIAiCIAhiCpCYIgiCIAiCmAIkpgiCIAiCIKYAiSmCIAiCIIgpQGKKIAiCIAhiCpCYIgiCIAiCmALkM0UQxLTgmnJWq1Wv6LoOTdMCtWma0HUdhmHANM0RhXMOy7ICdS1uLjtBEMAY8ww9/UWSJEiSBFmWvVpRlEDt+l4pigJFUShHHkEQk4LEFEEQAADDMJDJZJDNZpHL5QLFdS1362KxiHK5jHK5jFKphHK5DE3T6gqf+QJjDLKsIBwedlFXFCXgrO4XX/X2+YtftLlFURRP2Lkizz2HnNoJYv5CYoogFjCWZSGbzaK/vx8DAwMYHBz0arfYOfQyKBTyjV+YCXbCYkECF2RAioMrMiCI4ILk1CLA/G0BnDkJjQXB2RYAOImNGbMTI0Nw8hvb284NazrgJj2223biYw7AsmtugXEL4NZwMmRu2cmQ/YmRuS9BsmXCsAyUdQOsWgUyRTA+fGymcVPkuGIsFAp5o2euoHNT5EQiEUQiEUSjUUQiEcTjcS/PoJtbMBaL0UgbQcwSJKYIYh7COUexWMTAwAD6+/vrl4EBDA4MwDTHEAJMAJfCsKQweOJKcCnslFCwiCFwUQEkBVyUAWGRvXVw7hNdhiPEjIAQY5YJ8FqR5u6rFXSWb7/T5hZ0y0DJsADdAMtXHTFnApbuE4+NIUoSWlIptLa2IpVKYcmSJWhrawvU7e3tSKVSNCpGEFNkkb0jEsTcxi+S3FEkf3FHmPr7+1GpVEa/kCDCkiOw1CXgcgRcicCSI+Cy6hSnLYV8oz/EqDAGiBK4OPyWOesTmpYFWDqYZYCZOmBqYKbuFA0wqmBGFcysgukVGEYZl/MV9A+9BpjaqJeVJAlLly5Fe3s7Ojo60N7ejs7OTm976dKliEQis/ecBDEPaaqY2rt3Lx577DGcPXsWqqri5ptvxr59+3Ddddd551QqFezZswePPvooqtUqbrvtNuzfvx8dHR3eOTt37sTJkyfR3d2NtWvX4vTp04H7VCoV3H333Th16hR+/vOf413vehe+853vzNJTEosdzjkKhYIznTYUKIODg17tFk0b/YMPYOCyCkuOwkq1B4WSEgGXo7CUCCAq0yaSIt1HwPQxhBsx7XA5jNK6dwd3CgIghMARmriQswwwvQxBK4LpJTDNaWtFmFoRFwdy6OlJYzSJGIvF0dHRjvb2dixduhRLlizxanekKx6P07QisWhpqpg6ceIEurq6sH79ehiGgfvvvx9btmzBmTNnEI1GAQC7du3C448/jsOHDyOZTGL79u244447cPLkycC17rzzTjz//PN48cUXR9zHNE2oqoqdO3fiH/7hH2bl2YiFS7VaRT6fRy6XQzabrVsymYxXhjIZmIYx9kUFEZakgstJWBHfyJGswlIiznYEXA7b8UWzCNMrEIzyrN5zKuzfvx8AcM899zS5J5PHmu4LChJ4KA4zFB/9HG6BaSUIWgFMK9p1tQhBKyKrFZB//Q288soro75clCS0trSgtbUVLS0tSCQSSCaTSCQSSCQSiMViiMViiEajiMViiEQiCIfDCIfDkGWZhBgxr2mqmHriiScC24888gja29tx6tQpbNq0CdlsFg8//DAOHjyIW2+9FQBw4MABrF27Fs899xxuuukmAMBDDz0EAOjr66srpqLRKL70pS8BAE6ePIlMJjODT0XMNTjnME3TW65fqVQCpVwue6vTisUiSqVSYPWaW3K5HPL5/DgjRz5EBZYUhhVuBZdUcNmJR/Km2lQ7XklWp3UkiSAmBRPAQzGYodjo55i6N6LF9JLTLoEZZRh6Gb25MvoGXrHjyiaAIAje6kl/8L3f2sK1u3CtL/x2GIIgjFlqzx+vuLYafnsNURS9vtRabtQekySJ4tAWGXMqZiqbzQIAWltbAQCnTp2CruvYvHmzd86aNWuwYsUKPPvss56YmgncD16XXC4HAPiN3/gNSNKc+rERM427ck0KgctJcNUJyJZCEPM9dryKsyKNM9/KNGcFGrNMMK0AaIWmPsZkYQZN8c02zKgg8rOjze7GhOGiAlNUnGB5d1WlXXsrMJ1jzDuHwwRHsVxFsVyFN9XIOdjsR6bNez71qU/hlltuaXY3Fh1zRhVYloV7770XGzduxLp16wAA6XQaiqIglUoFzu3o6EA6nZ7R/uzduxcPPPDAjN6DmJtYUhhcicEKxcAVOwaJiyFAsle0+Ve5RX7+OASt2OwuEwsMBg6xNNDsbhDzkO9+97skpprAnBFTXV1d6O7uxjPPPNPsrgAA7rvvPuzevdvbzuVyWL58Of7iL/4CsZg9DM45H3eefzQTw0ZeV3tO7b6ZPKfR/jTCdPeZc+4VwzA8J223ret6oLiu3G7tn95z61KphGKxhEqlDMGoAEYFYqm/kacb4ZXEPc8kAYB/nzAvp/KE0iCNEMwyHAxWpLXZ3WiMgK+XOwplt4P7+JwdbXLfWxhjXqnddvepqoprr73Wm95TFKXuNF9tqZ2mrDdl6a/9/QIae+8EgF/4hV+YsZ8TMTpzQkxt374dx44dw9NPP41ly5Z5+zs7O6FpGjKZTGB0qre3F52dnTPaJ9fVuJa3v/3tSCQSM3pvonlYloVKpTIiZiqfzwfiprLZrFdnsznkclkUi/YI1ZhyiQn2yJcvbsoOPA8WS44AojwnxFf0J4fA5lEA+kKAS2GUbri92d0ATA1CtQimFbxYqeEVgSWIehEw9XEvE4lEvAB0NwjdH4DumpH6neTrxUy5ImS0eKmx4qdqz6e4JmI6aaqY4pxjx44dOHLkCI4fP47Vq1cHjt94442QZRlPPfUUtm7dCgB46aWXcOHCBWzYsKEZXSYWOIIgeO7SE8UwjLqr+VxLBL81wuDgEMrZcaZxBAmWrMLyfKFqbRAitg2CMLOii8vh6V9dNoPcvXOP3ZDU5nZkCnA5PPM3sUxfEHlxeBWfXzyN4U+VSqWwdPkqLFmyBK2trV5paWlBS0tLYCUfxZkSC52m/oZ3dXXh4MGDOHr0KOLxuBcHlUwmoaoqkskk7rrrLuzevRutra1IJBLYsWMHNmzYEAg+P3fuHAqFAtLpNMrlsuczdf3110NRFADAmTNnoGkaBgcHkc/nvXPe+ta3zuYjEwsYSZLQ1taGtra2hs6vVqsjfKaGhoY8g87BwUHbxXxwEGbh8ugXEiRYnilntK7g4rI6adfyEX5HxNyFc9vM06hA0MtgegXMKIPpZcdnqmQLKL0Epo8+2hiLxdGxannAyNM18Gxvb0dbWxtkWZ7FByOIuQ3jTcxMOlqczoEDB7Bt2zYAw6adhw4dCph2+qf5brnlFpw4cWLEdc6fP49Vq1YBAFatWoXXX399xDmNPn4ul0MymUQ2m6VpPmJWsSwLuVxuhAO6P5VMX18/hjJD4NboY0hcCjmjXCq4FHGmEx3LBsemwbZuCC++dDEzjbuKzUkbw5xUMuAmmDWcTsZNP8Oc3IHgwylsmGUCZo0DuuU4oBua7XxuVMftSiQS8cw229vbvbJ06VJPOJHjObGQmI3P76aKqfkEiSlirmMYBoaGhgL5+WrT0AwMDCKbzYx/MUGC5a1eDAfy8nFRsbfdRMeiDAiyk+BYspMae7UIMHFOxH55OMmPwf159QxfTj0juN80avLwOcKmJidfbY4+5hNHM5koORwOI55IIOFLdJxIJLzptpaWlkBuPhJKxGJjNj6/6esnQSwQ3BxrS5cuHfM8V3S5uf/cGC83riubzSKXyzkO7zkUC+mGR3BHhQmAIHqrGoM1g7vq0W1zBtgrJF0RVivGhr2Iaj2LwC3f6rKa2nKSDU8zgiBAcYKnQ4ptOOkPpHaLoigjDB/9+/yvc1/rBmmHQiGv7QZwq6oKURSn/XkIgpgYJKYIYpHRqOhyMU3TsY4oeqscXcd4t5RKJc/o1rWf0DQNmqZ59hSuVYVpmtB13bGxMGBZJkzT3m8aJjjnsCzLrjmvO3XpLVcXBAiMgTHfai1RgCgIkOVQXeHiFyqu27a7iswVLO6+etv+c91CAdYEsbihdwCCIMZEFEXE43HE42PkdSMIgljEkMkGQRAEQRDEFCAxRRAEQRAEMQVITBEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFCAxRRAEQRAEMQVITBEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFCAxRRAEQRAEMQUonQxBEE3FsqxAXj+3uLn9/G1/rj9/vj+32Pn+grVpml5dWyzLChQA4JyPSOzs5gIUBAGMMTsHoCBAkiSIojgi958sy4G8fuFw2EtMHIlEEI1GEY1GEY/HEYvFEI1GIctyM378BEFMAySmCIJoGMMwvKTHbu0mOnaLPwGyv1QqlZGlWoWuabP3AEwAGLNrMHDGADB7HxjAAA5W/7Wcg4EDnANOzbgFuGWKhFUViXgcyWQS8XgcqVQKyWQSqVTKKy0tLV4di8XA2Ch9JQhiViExRRCLAM45KpUKisXiiFIoFOrud4+5oqlYLEHTqpPvBBPARQkQJHAmgQsyIIfBw84+wT0mAoLobIvggggw0TlHtK/j7RPBmQC4+5ldQxDs/f7iiaYZgDsii5tglglwE7AMMMsALBPM1O22qdltU3faTjE0FI0qStkSLg9kAHN8gSlJElpaWtDS0oLW1tZA8e8n4UUQMw+JKYKY41iWNabIGatt10WUSkVvGmtCiDIsUXGETwI8rICLMrgoA6LTFmTA2ee1BcmruSgDjjBasDBXqAn28zrw0V8xNpYFZlbA9CqYUQHTy3bttvUyTL2C3lwZfQOvAtbLY15OlmWkWlrQ6hNZtSNdyWTSq2nKkSAmBokpgphhDMNAoVCYUHHFUKFQRLlcmsRdGbhkiyAuyuCRpY74UcAlGVxUhrdFd1t2thVnW3JGdGwi3UcgVAvT94MhAnA5jNK6d9sbggAuRMDlSGMvNnVPZAl6GcwoO9sVML1kC69sGX39g4BljHu5aDQamGJMJpNIJBIjan8hAUYsZpoqpvbu3YvHHnsMZ8+ehaqquPnmm7Fv3z5cd9113jmVSgV79uzBo48+imq1ittuuw379+9HR0eHd87OnTtx8uRJdHd3Y+3atTh9+nTgPsePH8eDDz6If/3Xf0Uul8O1116Lj370o3j/+98/W49KzGN0Xa87HeYXP/l8foQgyufzyBcKqJTLE7shE3yiRgWPJ21xIw0Ln6D48Ysjpy1I0z6lxfQKBGOCz9Jk9u/fDwC45557mtyT8ZlS1JU7MhhOjH8dU/eNcNn/p0x3R70qYEYZOaOKfH8Wl9K9gGU21IVwOIx4PI5EIoFYLIZ4PO4F2NcWNwDfbUciEUgSfbcn5i9N/e09ceIEurq6sH79ehiGgfvvvx9btmzBmTNnEI1GAQC7du3C448/jsOHDyOZTGL79u244447cPLkycC17rzzTjz//PN48cUXR9znRz/6EX7xF38RH//4x9HR0YFjx47hAx/4AJLJJN71rnfNyrMSMwfn3FvN5V8B5q4Cc9v1AqDd4OjaAOpSqWSLp1Jp4gHSfjEkRcETrfYHnRQaFj2SEhwFco7ZQkicudgegnCFVygOABhTKnFux34ZFTCjOiy4zKqz7ewzNBTNKkq5Ci4P5cCMicfWKUoI0WgEkUiwqKrqlXA47NVuUVXVWzGpKApCoZBX3NWVJNSImYbx2jXATaSvrw/t7e04ceIENm3ahGw2i6VLl+LgwYN4z3veAwA4e/Ys1q5di2effRY33XRT4PWf+tSn8J3vfGfEyFQ9fvM3fxMdHR342te+Vve4+wHsksvlsHz5cmSzWSQSick/5Czz2muv4dVXXx2x1LsejQaocs5hmiY457AsK7DM3F/727VL0/1L1l944QUUCgXv2v6l6f7tYAE4txp6rolTb5VX7T7BFyfjHJvpIOcmI5QG7dVs84j5NDLFwWBFWpvdjemDcwCWs+rRXQFpBVZD1lshCfjOn0Zce4tGi/81Y7VTqRR+7dd+zbPLcGu3uNt+Sw3/60d7D5vogoGx3gvb29uxbt26RbsIIZfLIZlMzujn95yS69lsFgDQ2mq/oZw6dQq6rmPz5s3eOWvWrMGKFSvqiqmJ3mvt2rWjHt+7dy8eeOCBSV9/LtDT04Nt27Y1uxvzEPfNHIH388X5NkTMFgwcYmmg2d1YsNTzD5sOBgcH8eqrr077daebT3ziE7j11lub3Y0Fy5wRU5Zl4d5778XGjRuxbt06AEA6nYaiKEilUoFzOzo6kE6nJ32vb3/72/i3f/s3fOUrXxn1nPvuuw+7d+/2tt2RqfnE0qVL8Tu/8zv4zne+0+yuzCl4YPRorNGn4ChU4JjbXkTMx5Gp+cSCG5kajXqjUNwK7BsxQuXV8Lbpd7Fx1q9fj//8n/9zs7uxoJkzYqqrqwvd3d145plnZvQ+P/zhD/GHf/iH+D//5//ghhtuGPU8d859PiNJEu69917ce++9ze5KXdzpQv+UX23bX1xHa7ddGyfld8eujZdyY6aq1apnIFkuV5x22fvGOiF5JIjgogJrRBxUyGmHnKBxN34qGDPlXyk3H4j+5BDYPAtAn09wKYzSDbc3uxuNYRmBeClmVL04KnjtYQ8t5vPUmqzBqT9Gqrbtd5sfLW7KdaZ3i39bkiSv1G6LoghRFBftFBnRGHNCTG3fvh3Hjh3D008/jWXLlnn7Ozs7oWkaMplMYHSqt7cXnZ2dE77PiRMn8Fu/9Vt48MEH8YEPfGA6uk5MAcaY94bVTFxDS3/wud/hu57Bpb/O5wvIF7LQixMMVBck28NJclfnhZxAdQVB6wLfKj2fOJuJFXtjweXw1FacNYG7d+6xG5La3I40AJfDTbqxFQwuNyoj255wqkIwqg3ZK7iEVRXxRAyxWJu3eq92VZ+7os9fotFoIPBcEObXlw9icdHUTzHOOXbs2IEjR47g+PHjWL16deD4jTfeCFmW8dRTT2Hr1q0AgJdeegkXLlzAhg0bJnSv48eP413vehf27duHD3/4w9P2DMT8hzHmvWlPhWq16hNY+VGtE/L5fMBaIZfPo1gchGk0/gHlISqwfBYJfnuEWrsEz07BL8hEueERMs8DiZj7WJbPa8rxn3ItEDwfKtcWoYpGgr2j0SiSS5IjPKbc9Dd+SwS3HYvFmv5liSBmg6b+lnd1deHgwYM4evQo4vG4FweVTCahqiqSySTuuusu7N69G62trUgkEtixYwc2bNgQCD4/d+4cCoUC0uk0yuWyt5rv+uuvh6Io+OEPf4h3vetd+MhHPoKtW7d691EUxQt2J4ip4k4rTOZ3inMeEGN+887RXM8Dx4rFyQsyQXKmK+VxjDz9Ys11Oh9uz7dpy3mHZfj8oOoIJP++BqwJEokEWto7Ak7o/pJMJj3jzkQiQaKIIMagqdYIo81BHzhwwFuF5pp2Hjp0KGDa6Z/mu+WWW3DixIkR1zl//jxWrVqFbdu24etf//qI47/6q7+K48ePN9TX2VhaSRBTgXMOTdPqii7PN2uMHHxu6plJ599zUsdwQXJElgT4a0ECRF8OPn8+PtGfl0+qn5vPTVI833Fy9dXm5vOm01wPp9pptnHy9THGkEgk0dbWOiJnn7vt7ksmkySOiEXDbHx+zymfqbkMiSlisWAYRkCA1caOueam/nat6albjMmMlI0Kc8SVADDREV5ucmPRl9zYTXrsS3IsCM5qTGFYlDHmW9lZs0LTL9q8t0jXLsMCOMBgOW27MMtyEhybYNzyJTo2wEy7PdEAbNlZzZxyRoj8ufRqkxmnUikSSARRh0XnM0UQRPORJMmb4pkquq4HnObL5XJgZaW/1jTN2651sXe3a1dw6roOTdeha1XoFW2axdvUYIwhFA4jHAohHI4FHL0jkYgXX+QGYtfGH6VSKYTDYVpFRhDzABJTBEHMGO7S83g8Piv389tt6Lo+wmbD787vuvi7Tv21Lvt+XMdqv7O1IAiB5fPucnv/knsSQgSxOCAxRRDEgsFvtxEON8lqgCCIRQctvyEIgiAIgpgCJKYIgiAIgiCmAIkpgiAIgiCIKUBiiiAIgiAIYgqQmCIIgiAIgpgCJKYIgiAIgiCmAIkpgiAIgiCIKUBiiiAIgiAIYgqQmCIIgiAIgpgC5IBOEMSswzmHpmlerj63+HPzufn43La/uDn63OJPGeNPF+OmivGni/EjCIKXJkYURS81jCiKXmoYt4RCIaiqinA4DFVVoaoqotFooMTjcaiqSmlkCGKRQWKKIIgxsSwL5XIZxWIR5XIZpVLJa7ulVCoFtv0lkOi4UkHVEUyzAwOYUzCawOEA5wC3puWOoigiGosh4SQsdhMY15ZUKoVkMomWlhbEYjESYAQxjyExRRALGM45qtUq8vk88vk8CoUCCoWC1y4Wi96+QqHgCSWvlEqolMtT64QggYsyuCCCCzIgJcAVyd4vSIAgOrUELogja+YeF5y2CAgiwARwZtcQfG3G7NoVUo3/sGALKwvgHMwyAW4ClmG3LQPMMgDTALN0MFMHTN1uGxqYqQGmBsOwR9OyvYNgl3oAyxz31qIoIplMoaUlhVRquLS0tATargiLRqMkvghiDkFiiiDmCZxzlMtlZDIZZDIZZLNZr3ZLLpcLlHw+D13XJ3YjV/yIMrgYBY+nwEUZcPcJbluxRY6zH4IMLkrDxx2BNCFB00zc0Stmh5JyUfYO8VFe0hCmAWZUwIyqUztFH24begV9pQoGcm+CvfLKuJeUJMkTVu4IV23xj4jF43GEw2ESYAQxQ5CYIogmYpqmJ4oGBwcxNDQUKJlMJtDWNG38izIGLoZgSSEg1AIeCYFLCrgYrCEqtiASFVskSQogKPYIEDF9iBK4GAMPxRo737J8gqvsE15lT4CZRgV9hQr6s280JL4AQJZlxOMJJBJxxGIxxONxr8RisUCJRqOIRCKIxWKIRCKIRqNQFIXEGEGMQlPF1N69e/HYY4/h7NmzUFUVN998M/bt24frrrvOO6dSqWDPnj149NFHUa1Wcdttt2H//v3o6Ojwztm5cydOnjyJ7u5urF27FqdPnw7c56WXXsLdd9+NM2fOIJvN4sorr8T73vc+fPKTn4QsyyCI6cQ/euSWWpE0NDSEwcFBZHM5cGucWB1RhiWFwOUkuBqGJYfBpTC4W3slBC6FAFGZkdGgSPcRML0y7dddbHA5jNK6d49+giCAKxFwJdLYBS0LzKwGRrqGR8GqXjHNKiqlKgZyaXtKcoIxYqIkIeoIq0gk4oksNxDfv6+29oszCtAnFiJNFVMnTpxAV1cX1q9fD8MwcP/992PLli04c+YMotEoAGDXrl14/PHHcfjwYSSTSWzfvh133HEHTp48GbjWnXfeieeffx4vvvjiiPvIsowPfOADePvb345UKoUXXngBH/rQh2BZFj772c/OyrMS8wt3Ss2NKfLHG7nTZ9ls1quz2Swy2SyymSw0bfzgai6FYEkqeLQdXFLBZdUWR7IKS1YdsWTXEOeG4Gd6BYIxxfipWWT//v0AgHvuuafJPQkyPWHuPgQBXLB/hxqGczsGzKiCmcPxXm7sl110e5/TNkwdmq4hM5AH6xu0j08iaJ8Jgi2sotERo2FRZ1/Ud6x2fzQaRSgUIkFGzCmaKqaeeOKJwPYjjzyC9vZ2nDp1Cps2bUI2m8XDDz+MgwcP4tZbbwUAHDhwAGvXrsVzzz2Hm266CQDw0EMPAQD6+vrqiqmrr74aV199tbe9cuVKHD9+HP/yL/8yU49GjIO7TN1yRmXctrvf3a4t7lJ30zRHLIc3DMNbJu8uma9dYu8uv3eX4PtXo7kr0gqFIkqlote3hp5HVOyRITkBrtqjRpZPELlCiUuqPXokiDP1oyWI8WFsOAZustfgHODmcCC+qduizBpuDwsy55hT50wN+aEi2EAGzNAw0ag0URQDo1+qqnq1a1/hWliEQqFAURQFsiwHalEUIcsyJEkK2GO4bUEQPPsMt00QfuZUzFQ2mwUAtLa2AgBOnToFXdexefNm75w1a9ZgxYoVePbZZz0xNVHOnTuHJ554Anfccceo57i+Ny65XG5S93L5t3/7N3z0ox+d0jWImYWDBYOQBdGOP/KvDPOtFOPeyjHfsntugeklML204BxxmUFTfNMBMyqI/Oxos7vRZJj95QMhQIFvJSUH45bXDtpWcDBubxucI1coIVco2q+Z2hKBRcOnP/1pbNq0qdndWJDMGTFlWRbuvfdebNy4EevWrQMApNNpKIqCVCoVOLejowPpdHrC97j55pvx4x//GNVqFR/+8Ifx6U9/etRz9+7diwceeGDC9xiNCxcuTNu1iJmBeW/gCHxRpskEYjph4BBLA83uxoKDCyK4FLZjx/j4dhSLkVdffZXE1AwxZ8RUV1cXuru78cwzz8zYPb71rW8hn8/jhRdewEc/+lH81V/9FT72sY/VPfe+++7D7t27ve1cLofly5dP+t5bt27F7bff7m37p7NG21fvHHcqbKy2/3W1xT91Nd79xuqj2/ZT71jttepd95vf/CYuX7486jXHq0e7tv+Za9v+eiLTeYG+uSNYzmgVZwyAz+fIHdVyz5nnskwoDdIIwDTAwWBFWpvdjZnDP6LkG00CrJrRJj78BcY3GjXZ3zGBW4gqAtRUG9QGp/nctju9J8uyN8XnTvO503v+KT63uO75/m23NLINDLvwAwjsr227pZFt/z63DdiWGsTMMCd+stu3b8exY8fw9NNPY9myZd7+zs5OaJqGTCYTGJ3q7e1FZ2fnhO/jiqHrr78epmniwx/+MPbs2QNRHBm/4v7hTSf0i1yfX/qlX2p2F6Bpmhcz5Tet9BtausaX+Xze83FyvZ0s0xhXKnFJAZfCduC5G3Duaw/vV+ekP1P0J4fA5lEA+lyFS2GUbrh9/BNnEzcg3Q1GN4YDz71YJ8sfB6WPOCY4+yfjyhVW1boB6bUB6H6rBv9qwkgkAkVRpv/nQhAN0tRPd845duzYgSNHjuD48eNYvXp14PiNN94IWZbx1FNPYevWrQBsm4MLFy5gw4YNU7q3ZVnQdR2WZdUVU8Tiwv2WWjul3AiccxQKheFVfT5LhGw2a1sgZLOOHcIQMpm+8UfDBMkJYHdW+Em1gew+OwQ5bIuvGYbL4elfiTaD3L1zj92QJrDKbRbgcngGL86dVXn17RHs1XtVZ9Wer57kyjxFURxLhKRne1BrjeAXQP7i97OiL5rEfKepv8FdXV04ePAgjh49ing87sVBJZNJqKqKZDKJu+66C7t370ZraysSiQR27NiBDRs2BILPz507h0KhgHQ6jXK57PlMXX/99VAUBd/85jchyzJ+4Rd+AaFQCP/+7/+O++67D7/3e79HPlPElGGMeeaH/pHV0bAsC/l8PuA35XpR1TPu1Ir943dCkGwvKq+EHXPOYaNOSCFn1aHPuFOQGx4BG9MbiZgZ/AaeI0w8/S7qVQhO3ejIUCgUcn5v2+qadtaWSB2PKRJBBGHDeG2QymzefJQ38QMHDmDbtm0Ahk07Dx06FDDt9E/z3XLLLThx4sSI65w/fx6rVq3Ct771LXzuc5/Dyy+/DM45Vq5cid///d/Hrl27EA439i0xl8shmUwim80ikUhM/GEJYhK4fleuA3rtqJd/NCybzSGXy6JYLE7sHpICLjgu6KLrjC57zujeEnpRcVLGyMP7BTeVjER2D41gWWCmK4KqAVfzWpfzYXE0NoIg1E0n46aUicfjgbZb6IsksViYjc/vpoqp+QSJKWK+YBiGF9dVG+dVL9GxPzasWJyYv1YAJji5+9z8fE4tSE7OPskWY4LkHBsr2bGzzWranhVFk3BjiyzdzrkXiCvSRk6f1U61meOnAxIEAclUCq1OYmN/smN/Lj53fzQaJd8jghiD2fj8pjFaglhgSJKElpYWtLS0TPi1nHNUq1VPZLlmpv52balUKl7wfqBUiqgUZyBgXZDABQFgorNaUvR5fgnOykq/B5jgE2BsuOJw/uFOZdk+YU4NboFZpm1MaZlglgFYxoS7GwqFkEgmkUx0eELIn5y4VizFYjESRwQxzyAxRRCEB2PMc49ua2ub8vVccVYul1GpVFAulwMu9G7RNM1zpfe71bvt2hJwuXdc7w1dh2HYTvimYUxqhM1eAi9BkkTIigIlFEbIWZwQCoUCDttusLU/pigejyORSHiJhBOJxLSvCiYIYu5BYoogiBnDL86agT8NERD0IfP7BDHGIIoi5XsjCGJSkJgiCGLB4pop0qozgiBmEpqYJwiCIAiCmAIkpgiCIAiCIKYAiSmCIAiCIIgpQGKKIAiCIAhiCpCYIgiCIAiCmAIkpgiCIAiCIKYAiSmCIAiCIIgpQOYrBEEsGDjnqFQqXq5Bf9obv/u666zuOqmbpumZeQK22agkSRBFEZIkIRQKea7nqqoiFot5iYMTiQQSiQSlgCGIRQyJKYIg5hymaXoJmd2EzblcLlBqkznn8nkUCwXP7Xw2EUURLS0taG1tRVtbGzo6OtDe3o6Ojg50dnbiyiuvRCqVIod1gligkJgiCGLa4ZyjXC57I0TFYtGr3XY+n/dqf9sVRQ0jSLBEBVxSwNUlgKSAi26RAVG224IECDK4IDrJkkUvMTKYAO5Lgsw4Bzh3Eh7biY5hGWCWDmYagFkFM6pghgZmVGDoZVzOl9E/dB54+eW63QyrKq684gosW7YMK1aswLJly7B8+XKsWLEC8Xh8Gn7qBEE0CxJTBEHURdO0ESNDrugZsxSLKJVK4BNNNCwqjigKgcfjdi0p4GLYaYdsoSSFwcWQdxzC9L+N8am82NQgaEWwasGp8xCqeZjVPF55/U28+uqrI16SSqWwcuVKrFixIlA6Ojpo+pAg5gEkpghiEWCaJnK5HLLZLDKZDDKZDLLZbN2SyWSQy+VQqVQmcAfmCB9nFCgaAxcVwDdCxCUFEENOOzQ8eiTZ54EtENEgKrBUBVBbMGLCkXMwowKhkgWr5CBWsmCVLAYrGWRefBEvvPBC4HRFCWHZsquwYsUKLF++HMuWLcOyZctw1VVXIZlM0rQhQcwRSEwRxDzDHTHyYoWcGKJsNuu1/WIpk8kgn897wdVjIiqwpBC4FANPtvlGhRRnNMiuEdh2Rofog318GAOXVZiyCsQ7YfiPWSaEah5COQOhkoVQycCsZPHK62/UHc1S1QiuvPIKdHZ24oorrkBHRweWLl2K9vZ2tLe3I5VKUYJngpglmvqXtnfvXjz22GM4e/YsVFXFzTffjH379uG6667zzqlUKtizZw8effRRVKtV3Hbbbdi/fz86Ojq8c3bu3ImTJ0+iu7sba9euxenTp0e957lz5/C2t70Noigik8nM4NMRhI1pmqhUKqhUKiiXyyPa5XIZ5XLZW3HmX4HmxhgVi0Xk8/Y0mqZVG7sxE8ClECwpDB7rtKfH5LBdSyGnrTpiyd4HQax7qUj3ETB9IiNVRC1cDqO07t2jnyCIsNQULDVV80IOppdtgVXNgVVyECo5mNU8zr32Bl555ZW6l2OMIZlMoq2tDS0tLUilUkgmk0gmk0gkEojFYl6JRCKIRqOIRCJQVZVEGEFMkKb+xZw4cQJdXV1Yv349DMPA/fffjy1btuDMmTOIRqMAgF27duHxxx/H4cOHkUwmsX37dtxxxx04efJk4Fp33nknnn/+ebz44ouj3k/Xdbz3ve/Fr/zKr+BHP/rRjD4bMf9wRY9f1LhCZ6wyukiqoFqtQNf1qXVMEMGdeCLICfCwUjNaFAoIIrsOA6I8baNFTK9AMMrTcq3ZZP/+/QCAe+65p8k9ASYYQTYMY+BKBKYSgYkrgsc4t2O0qnk7PksrejXTyxiolDH0+ptgowiu0ZBlGaqqenYQ4XAY4XDYs4gIhUJeqd3vnuvf9l8jHA5DURSaoiQWFE0VU0888URg+5FHHkF7eztOnTqFTZs2IZvN4uGHH8bBgwdx6623AgAOHDiAtWvX4rnnnsNNN90EAHjooYcAAH19fWOKqT//8z/HmjVr8M53vpPE1DyDc+55Ammahmq16hVN06Bpmucf5PcT8pfRxJA9ClRufMRnNJjgrBJziwyEVHBVclaSSeCiu6JMAkT7HC7KzjHZPiZKwyvRBAWgAGRiNBgDpBAsKQQrumT08yzTWX1YcYoGZmqAUQUzq2CmvVIRpgZm6jAtAxVdB6uWwYbyzmpG3RZv04AgCLawUlVEfEKrVnS526FQCKqqeuJMUZSAoFMUxSuyLHuFRBsxW8ypsdxsNgsAaG1tBQCcOnUKuq5j8+bN3jlr1qzBihUr8Oyzz3piqhF+8IMf4PDhwzh9+jQee+yxcc93P6hdcrlcw/ea6wwODuLw4cMYGhoCAO/Npl5MzVjH/MdrqT2fcw7TNGGaJgzD8IppmtB13RNKPT090DTNM1DknMNyl6lPMxzMGblxakGGvTqegfv316lHHrdfNwLLXlbPMEWh1mSYQVN8U4UZFUR+drTZ3WgcJniLBIbhzlJH92/SMTl1297fqe84H3mOCY5iuYpiuYIBDNlTmVNbQzn2ozA2otTu92+rqoo1a9ZAFMVA8b/fjSfSxnvPnOh7KgBIkoTf/d3fxfLly8e8NzH7zBkxZVkW7r33XmzcuBHr1q0DAKTTaSiKglQqFTi3o6MD6XS64WsPDAxg27Zt+Pu//3skEomGXrN371488MADDd9jPvGVr3wF//RP/9TsbjQd5n/z57XHCGJ6YeAQSwPN7saixO9u3wilUmlEKMlc4ac//SkeeeSRZneDqGHOiKmuri50d3fjmWeemfZrf+hDH8L73vc+bNq0qeHX3Hfffdi9e7e3ncvlFsy3gfe///0YGhrCm2++6e3zfxvinAe+qfnfhPzH/K91z6k9Ptr57jFv+s4wYOg6iqUSrBl2sB4xIuXUo+13R55GP77wEUqDMzpysBjgYLAirc3uxvQSGH2qN0rlG8ny7WPel5jac2d2hMrPWCNUkiShpaXFO2Z3dfT3strj9Ubmxzq30etGo1F0dXVN8omJmWROiKnt27fj2LFjePrpp7Fs2TJvf2dnJzRNQyaTCYxO9fb2orOzs+Hr/+AHP8A//uM/4q/+6q8A2L+clmVBkiR89atfxZ133jniNe5c/EJkxYoV+NznPtfsboyJK7I0TfPipPxtN1aqNnaqNk6qNl7Ka7uB5qUSLMdcctKyyIl3cmOl3LgpOybKiZ2q2QfvfLlmn+y5ds8l36XoTw6BzcMA9LkEl8Io3XB7EzvAAVN3YqaqYKZmx0sZmr3f0sFM3dc2PNd3e7pat6esLXv/dE29y7KMcFhFWFWhjhI7NVrwuxsv5Y+bcmOlamOnJEnyCsVREdNNU8UU5xw7duzAkSNHcPz4caxevTpw/MYbb4Qsy3jqqaewdetWAMBLL72ECxcuYMOGDQ3f59lnnw3k6zp69Cj27duHH/3oR7jqqqum52GIaYUx5r0RziScc+i6XnflnmtP4K7wc9v+437BViqVUKlWUSnnph7MDjgiTYHlpUQJeeaXtteTs4qvzoq+0SwOJguXw5NfjdZE7t65x25IanM7AvtnODMX5rY48lbzFcC0EgSjDKaVbFsFowxmVO30OBOAMeZboRcLrNZzRYxf7PhX7ymKMmpAuX8/2TAQC4Gm/hZ3dXXh4MGDOHr0KOLxuBcHlUwmoaoqkskk7rrrLuzevRutra1IJBLYsWMHNmzYEAg+P3fuHAqFAtLpNMrlsuczdf3110NRFKxduzZw33//93+HIAhebBaxeGGMed9oa2PzpoJlWXVHx/wrDkfzmXLbRScti53TroBKsb/xDnjmm8NWCa7HlBXwm2rMRmFMfyRi5uGWLZQqOQjVnFezagGiVgDM0e03orEYWpcs8XymEokEkslkwGfK9Zhyi6qq3uo5GsUhiPFpqpj60pe+BAC45ZZbAvsPHDiAbdu2AQAefPBBCIKArVu3Bkw7/fzRH/0RTpw44W2/7W1vAwCcP38eq1atmrH+E8RoCILgfTBNF4ZhePnvanPm+Yvf+TyTyUJrRIQxwRFatZ5VwyNfqHFBt/PiTZ+XFQHA1G1zTs8F3S5iNQdYI2MJo7EYrrx61QgH9KVLl3pmnQs1XIEg5hKMT2SJwyIml8shmUwim802vCKQIOYClUplRD4+/3Ztnr6GU88AsHPyyeDCcJ49OyefPJx7T1QcIeYr0nDevumekpzzcO6MMmVHFq044vRoLIYVy5djxYoVuOqqq7zcfFdeeSXi8XgTHoAg5hez8flNk9UEscAJh8Po7OxseNGGZVkoFAreSFc+n0c2m/VGxNxSLBYDI2WFYhGlYgbcmmB0lSDBEpXh5MeSEw/m7hslNmxO5wM0dTt2qVqAoBUgVHJg1TyEar7uKBNjDB0dHVi+/HqsXLkSK1euxIoVK7BixQqkUimaaiOIOQ6JKYIgAgiCgEQiMalvcJxzlMtlT2T5cwu6++qVfD6PXD6PQr7PW105Lo6ppCWF7FEuT3DZ7vFcDDkjZK7LvM95nomOGBPAmWCvnKzVK9yyY5UsC+CmvdLNMsBM3V4JZ1SHi14GM5xgb71kr5CrQzQaxfK3XIvly5dj+fLlWLZsGVasWIFly5YhHJ6hAHWCIGYcElMEQUwbjDEvVqy9vX3Cr3fFmD8ezF+705BubJg9gpZHPp+BXpxiDsRpIJlMYcmVy9He3u7FMF1xxRW48sorceWVV1KIAEEsUEhMEQQxZ/CLsYl4yQF2bFjtCJi7ItJdIel6kpXLZS+FkZvaqDZOzO9L5C7zd5f0x+NxxONxJBIJxONxtLa2oqWlhZb5E8Qihf7yCYJYELi+RW1tbc3uCkEQi4y5Y7FMEARBEAQxDyExRRAEQRAEMQVITBEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFCAxRRAEQRAEMQXIGoEgiKbgN+h0vaDK5TKq1Wpd/yfGGARBgCzLkCQJsiwjFAp5lgiuP1UkEoEoLrJ8fwRBNBUSUwRBTCuWZWFoaAi9vb24fPky+vv70dfXh4GBAQwODmJoaAhDQ0PI5/MwTXP8C04C11gzFosFDDb9JZlMjmjLsjwj/SEIYmFDYoogiAlTKBTQ09ODixcvoqenJ1B6e3uh66OnduFSCJakgqtLhpMaCzK4KA3nzxOE4bx5/qR53AJzcubBMsG4CZg6mGWAWQZgamCmjqKpoZSr4vJQHsx8DahxNx+NsKoi6Ygrv8N5bYnFYl6JRqOIRqPkfk4Qixj66ycIYgSGYaCvrw89PT24dOlSoL548SLy+Xzd13FZhaWkYMVi4KEYLCUKrkRgyXbNJdUWSrMJ54ClBxMTG5W620WjilKmiN7+IcCsn6x4NEKhEKLRGKLRiCewotEoIpFIoHbbrhDzC7NQKATGajMuEwQx1yExRRCLDM458vm8N/3W19fnTcml02mk02n09fXBsqyRLxYkmKEYeGo5rFACVigOKxQHD8VhhWKAMAffUhgDRAVcVMBD8cZfZ1lgZtUe7XJFl9s2NTBTAwzNa5umhlJJx0B+AMzsASxjwl2VJCkwIuafhnRLS0uLV7e0tEBV1QnfhyCI6WUOvvMRBDFRqtUqcrmcV7LZLLLZLIaGhpDJZDA4OIjBwUEMDAxiYHAAWrU6ypUYLCUKK9oOrsR8YikGK5QAl1VbnCwGBAFcUAFZRWOThDVwy56CNHVHcOneNKQnxtxtwxVkVVTLGgYLl8GMN+xrjEM4HEZLSyva2lrR2hosbW1taGtrQ2trK1KpFE1FEsQMQX9ZBNFk3FVthUJhRCkWi17x73PrfD6PfD4/ZoySBxNgySq4HIeldoArUXA5AisUA1eisJQYuBxpeBou0n0ETK9M8ekXH1wOo7Tu3Q2cyAHL8E1DVsB0ty6DGRUIegVFvYzSQBY96fSY4osJAlLJpCeyWltb0dLS4gmvlpYWpFIptLa2Ih6P04pIgpgATRVTe/fuxWOPPYazZ89CVVXcfPPN2LdvH6677jrvnEqlgj179uDRRx9FtVrFbbfdhv3796Ojo8M7Z+fOnTh58iS6u7uxdu1anD59OnCf1157DatXrx5x/2effRY33XTTjD0fsfioVqveqJBbakeM8vm8t53P51EoFOpPqY2FIIKLCixRAZQW8IgCLoWcErantGTVbsthWHIEEJVpHVViegWCUZ62602G/fv3AwDuueeepvZjIjT8P80YIMrgogweio1/PueAqUHQS7bY0stgWimw3V8uYfD1N/DKK6+MfWtBQDweR0sqhVQqFZhm9K+IrA3KD4VCjT4dQSwomiqmTpw4ga6uLqxfvx6GYeD+++/Hli1bcObMGUSjUQDArl278Pjjj+Pw4cNIJpPYvn077rjjDpw8eTJwrTvvvBPPP/88XnzxxVHv98///M+44YYbvO22traZeTBi3lOtVr0RIFfw+EVQ7XSaWyqVBkdqBAmW6AigaDu4qNhxPZIT2+OU4D7Zi/2BQKMGRA2MAVIIlhQC1Jaxz7WMYcGlV4YFlzf6VcaQVkH20mWw1y8ADU50KooSWOXoX+3oD8JXVRWqqiISiXhtVVURDoehqipCoRBkWaZgfGLe0FQx9cQTTwS2H3nkEbS3t+PUqVPYtGkTstksHn74YRw8eBC33norAODAgQNYu3YtnnvuOW9U6aGHHgIA9PX1jSmm2tra0NnZ2VDfqtUqqr64klwuN6FnIyYP5xymacKyrEDtFtfM0d82DAOapnlmj5qmjSiVSgU//OEPkc1mYVkWLMsC59xr2/exYFkT9z7iTLA/zAQZYGx4213azwRwd5sx+Jf7MyeuBihO289wNmAGTfFNBmZUEPnZ0WZ3Y3yYaI9oyhF71ItbALhjTeFscx7YVzUsVIeyGBgcAptcpFmwC4x5Zq312mMV/+tH2wcAS5cuxe/93u9BFEVIkgRJkiCKorftr90iCEKg9u9njEEURRKCi4w5FTOVzWYBAK2trQCAU6dOQdd1bN682TtnzZo1WLFixaSm6H77t38blUoFb3nLW/Cxj30Mv/3bvz3quXv37sUDDzwwiacgXAzDQFdXF1566aVmd2XGsT9MAMAWYvQ2SowGA4dYGmh2N+YFnHPvC89MkU6n8dOf/nTGrj8Rrr76anz5y1+GoijN7goxQeaMmLIsC/feey82btyIdevWAbB/yRVFQSqVCpzb0dGBdDrd8LVjsRg+//nPY+PGjRAEAf/wD/+A3/md38F3vvOdUQXVfffdh927d3vbuVwOy5cvn/iDLXLm0+ohDjY8auSMInn7Rhtl8l6z+BBKg9My+rDY4GCwIq3N7sbs4IxcDY9g+bZrjoFz5/eJO19MfMecay3037f59H5JBJkz/3NdXV3o7u7GM888M+3XXrJkSUAYrV+/HpcuXcL//J//c1QxFQqFKJhyikiShC9+8YuTfn29ab7a6T23rev6iOJO71WrVa+uVquoVCqoVCpeLji3lEolO06qWESlbAdWNySTBBGW6AR+y04thYeDwevUEKR5L8KiPzkE1uQA9PkIl8Io3XB7s7vROJYFZlZqDE41wG173ltVz+LBs4JowNphNGRZhhIK2/kXQyEoioKQU8uy7NW1xc3b6E7Z+Yt/Km+0bf8UX+00nyAIgfMEQRgx5UfTe4uTOSGmtm/fjmPHjuHpp5/GsmXLvP2dnZ3QNA2ZTCYwOtXb29tw7NNovOMd78CTTz45pWsQM4v7RtUMTNNEqVQKBJ4XCgVvBZ67Ss+/Yi+TyaKQG2zsBkyw06p4Qeah4cBzSRk2mXQCz7moAL7g9Lkgxrgcbnxl2gxx9849dkOaP8aVXA43uQOO5YJeguAFoZeHLRf0MgQ3EN10hFODqGoEsWQM8djSQPB5rfu7G4QeiUS8oHM3AD0ctgUUWTMQ84mmiinOOXbs2IEjR47g+PHjI+wLbrzxRsiyjKeeegpbt24FALz00ku4cOECNmzYMKV7nz59GldcccWUrkEsXERR9JZ8T+T3xDAMT2xls1lkMpnAyj9XjA3XeeTzGejFBnyi/DAWWPVni7FQ0B5BDg/Xjk2CPTU5PTTklUTMHtyyR4jclXmaXyyVPPEk6OVx3dlFSUIqmUQqtTRgjeBPCu1PIO2u4KNpKmKx0tTf/K6uLhw8eBBHjx5FPB734qCSySRUVUUymcRdd92F3bt3o7W1FYlEAjt27MCGDRsCwefnzp1DoVBAOp1GuVz2fKauv/56KIqCr3/961AUBW9729sAAI899hi+9rWv4W//9m9n/ZmJhY0kSV6aj4lQrVbHNez0155hpzNyVsk1EtDMHM8pFVyOwlIijllndNi0U4mS7cJcwfGNGs4jWHG8vYbtC2pHksayMBBFES0tLWhruxJtbW0jDDv9xp2xWIymqwhiAjDOG0ynPhM3H+WP9cCBA9i2bRuAYdPOQ4cOBUw7/dN8t9xyC06cODHiOufPn8eqVavw9a9/Hfv27cPrr78OSZKwZs0afPSjH8V73vOehvuay+WQTCaRzWaRSCQm9qAEMcMYhoFCoVAz7ZhBJpPB0NAQhoaGvJQy/f39KJVKo16LyxFYSgxWKFaTey8OrkSmdXRrQWOZTsqY4ZQy8KWSCaSYMTQ75sjfNkZL+RMkGouhtaUl4GruTyPjOp4nEommTZsTRDOZjc/vpoqp+QSJKWIhUSqVvETHly9fxuXLl0ckOzaMOlNBTLCFVjg+QmhZobjtsr5QsAxb2HjB1b6kx57ocfPraYEcfMzUJhV8HQ6HEYvFkEwmvWk0/xRbynEkT6VS3igSLaMniLGZjc9vmuAmiEVIJBLBihUrsGLFirrHLctCf38/0uk0Ll26hJ6eHq++ePEihoYu1n0dl0K22FJidnJkJQquRIanFWXVDp6fTbg1LIICK9Lqbdu1YFaBCZi3MsbswOpkFLHY0kDAtRt0XbvPLX7HcFmWZ/AHQRDETEFiiiCIEQiCgPb2drS3t+MXf/EXRxwvl8sBgeUv6d5eVDJjxHCJMixJdQLlFTtwXlTsPHSCBC5IdrJlJnheX15AALe8wrjpTKXpXs0s31SaoUFwRo4agTGGaDSGZGtLIPdcvRx0bu2KIVVVaQqNIBYxJKYIgpgwqqri6quvxtVXXz3iGOcc+XzemzZ0pxP7+/sD8Vu5XA5asbG4oInABAGRSASJtmHxU7v6bLSkvbQcnyCIyUBiiiCIaYUx5gmUa6+9dsxz/asYXSPVarXq5VisjdtijEFRFM+YMRQKeb5E7nRaOBymlWgEQcwqJKYIgmgabqaBtra2ZneFIAhi0tAkP0EQBEEQxBQgMUUQBEEQBDEFJiSmOOe4cOECKpXKTPWHIAiCIAhiXjFhMXXNNdfgjTfemKn+EARBEARBzCsmJKYEQcC1116LgYFG8oARBEEQBEEsfCYcM/WXf/mX+OhHP4ru7u6Z6A9BEARBEMS8YsK5+VpaWlAqlWAYBhRFgaqqgeODg4PT2sG5AuXmIwiCIIj5x5zMzfeFL3xhBrpBEMRigXMOTdO8Uq1Wvdpt+/fXFl3XA8U0TRiGAdM0YZomLMsC5xyWNZxomDHmpXsRRRGSJAWKLMtQFMUrrhmo+4UxHA4jHA5DVVVvW1VVKIpCBqEEQUxcTH3wgx+ciX4QBDEHsCwLpVIJ+XzecyYvl8uBUqlUvDKa+NF13atdR3P/sYWCIAhQIxFEVNVLZlxbXGd2f9JjN6+fv025/Qhi/jIlB/RKpQJNCyYRpSkwgphbGIaBwcFB9PX1YXBwcETJZrNeyefzmODM/0iYAAgiOBPB3bYgAiwMKFHwsOjsk+xExoIECKL3GgiSfb5bM9F3Dbd27yHYxxmzr8UYAKf4R4w4B2A/F+OWvV2TMBncArNMwDJ8tTFcmwaYpTvt4Vq3dORzVbBMwUm6bNT7qYz9I2PME1ZuImV/8SdddnMKurWiKFP7/yIIYspMWEwVi0V8/OMfx7e//e26q/pM05yWjhEEMT6ccxQKBaTTaaTTaS+5sFdfvoyhoSFw35TXCJgALoVhSSHwWAe4GAIkBVx0iwyIMrggg4uSI3L8tRgQPWBze4SlVipOUTrWuYFliy1TB7P04bap2WLL1Lw2MzV729CQMzXkM0WwgQyY0XgCaFWNIJVKIZVKIpVKoaWlxdlOedtuSSaTkCTKIkYQ082E/6o+9rGP4Yc//CG+9KUv4Q/+4A/wxS9+ERcvXsRXvvIV/OVf/uVM9JEgFi2ccwwNDaG3t9crrmhKp9Po6UmjXC7Vf7EgwpKjsKLt4EoUlhwBVyLgcgRcVmHJKrikAqIcHMUhpgYTACkELoUmL9Q4d0RW1RZeRtVpO7VRAdPtumBUUBzIoqf38rijYnYS6iRaW21x1draGqjddmtrKwkvgpgAE17Nt2LFCnzjG9/ALbfcgkQigR//+Me45ppr8Hd/93c4dOgQvve97zV8rb179+Kxxx7D2bNnoaoqbr75Zuzbtw/XXXedd06lUsGePXvw6KOPolqt4rbbbsP+/fvR0dHhnbNz506cPHkS3d3dWLt2LU6fPj3iXpxzfP7zn8dXv/pVvP7661iyZAnuuece/I//8T8a6iut5iOmG8uykMlk0N/f75W+vj5cvnzZqy9fvjxiKt2FSyFYShSWEgMPxexaicEKxcCVKLgUnnaRFOk+AqZTBoTpgMthlNa9e3ovauqO0KqAGWUIegVML4MZZbt2tgWjPO7oF2MM8UQCrb6RrdpRL3+huC9irjInV/MNDg7i6quvBmDHR7lWCP/lv/wX/Mmf/MmErnXixAl0dXVh/fr1MAwD999/P7Zs2YIzZ84gGo0CAHbt2oXHH38chw8fRjKZxPbt23HHHXfg5MmTgWvdeeedeP755/Hiiy/WvddHPvIRfP/738df/dVf4Rd+4Re8eBGCmC4sy0KxWEQ2m0Uul0Mul0M2m0Umk/HK0NAQBgcHMTAwgKGhocCKs1q4HIGlJGFFY+AhWzTZQsmuIc5+rAzTKxCM8qzfdzLs378fAHDPPfc0uSf1GWPidfKIMrgog4fiAIAxgy4syyeyymBGBYJe8gQXM8oY0irIvpnGa6+9Nu6tBUFAIpFAKpUKxHS5xR/75caGUfA9sVCYsJi6+uqrcf78eaxYsQJr1qzBt7/9bfzyL/8yvvvd7yKVSk3oWk888URg+5FHHkF7eztOnTqFTZs2IZvN4uGHH8bBgwdx6623AgAOHDiAtWvX4rnnnsNNN90EAHjooYcAAH19fXXF1M9//nN86UtfQnd3tzfqtXr16ok+OrGA4JzDNM3Acny3XalUvFVr7gq2Uqnk1cViMVDy+Tzy+QKKpeLYsUkuogxLUmFFltpTbkoEljv9pkRhKVFwOWLHHxHETCEI9gimEh3/XG7Z04uO6HJHvuzaLVUMVCoYejMNZr7uBP2PD2MMqqoiGosh6lv9qPpWSPqtKcLhMEKhUKDIsuzZW8iyHLC9cK0wRFGEKIoQBIHsLIhpZ8Ji6g//8A/xwgsv4Fd/9VfxZ3/2Z/it3/ot/O///b+h6zr++q//ekqdyWazAIDW1lYAwKlTp6DrOjZv3uyds2bNGqxYsQLPPvusJ6bG47vf/S6uvvpqHDt2DP/1v/5XcM6xefNmfO5zn/PuVYv7weqSy+Um+1jTQm9vL/7mb/4GZ8+erXu83mztdL1hjDYTPFtvSOPd3z3OGBtzJRrnfHiJvq43JnzGhQ2vIGMCIErOtuCsMHNXmTnb7n4AzDLAqjmgmsN8kU3MoCm+6YIZFUR+drTZ3ZheBBGWEgUQHV4xCe5bQckBDLcZODi3UCxXUSxXvH2zBRMEKLIMQRAhCAyMCc6frAAGBkGw/4YZY/C/2/nf+/zvP7X434/Ger+cSLTNRN53Oee4+uqr8ZGPfATLli1r+HXExJmwmNq1a5fX3rx5M86ePYtTp07hmmuuwS/+4i9OuiOWZeHee+/Fxo0bsW7dOgBAOp2GoigjRrw6OjqQTqcbvvarr76K119/HYcPH8Y3vvENmKaJXbt24T3veQ9+8IMf1H3N3r178cADD0z6eaabf/zHf8QzzzzT7G4QI+Bg7huhU9F3XqIRGDjEEuU5bSbcsgJfmhciAwMDOHz4cOCzm5h+puwztXLlSqxcuXLKHenq6kJ3d/eMCAbL+YP5xje+gbe85S0AgIcffhg33ngjXnrppUDAu8t9992H3bt3e9u5XA7Lly+f9r41yvvf/37EYjH09vZO+7X9Izy137jGG+3xX2My/kSTvd9o93VdsF1XbLf4HbNd88je3l5omgbOuVemAndHp9wRKTi+R0zw9nOvPT9jRITS4KyOHCxkOBisSP2R8XmNOwoVGJGyR6jAffucbXBr2n+n3PcV5o4q+bb9dSgUwhVXXOFN/bl1bdu/D8Co57vboigG9rlFFKdnDLreyNhotLS04N3vnuaFDsQIJiymTNPEZz/7WXz5y19Gb28vXn75ZVx99dX4//6//w+rVq3CXXfdNeFObN++HceOHcPTTz8dGIrs7OyEpmnIZDKB0ane3l50dnY2fP0rrrgCkiR5QgoA1q5dCwC4cOFCXTHlzsXPFSKRCN773vc2uxsLGs75iJipSqUSiJcql8soFApe7JTrEl4oFJDP573Ac9O0l6iPNUrFpbBtT1BjW2ApUTvIXIkC0twyZIz+5BDYPAlAn+twKYzSDbc3uxtjwzlgVmtWBVZ8cVPu/ioEJ26qUecuWZYRiycQi0a9QHS3qDWO8mPFTLkpgNy4qekSLAQxESYspj7zmc/g61//Oj73uc/hQx/6kLd/3bp1+MIXvjAhMcU5x44dO3DkyBEcP358RFD4jTfeCFmW8dRTT2Hr1q0AgJdeegkXLlzAhg0bGr7Pxo0bYRgGXnnlFfyn//SfAAAvv/wyAEzLqBqxMGCMeW/aE11M4YdzjlKpFFjNNzQ0FFjN567oGxgYRCH75ujXEpVhcRWKeiv5bDuE+IzYH4z5bHJ4ZlahzQB379xjNyR17BObBJfDTboxrwkiHy6Ct7LPsVUwKs6o0ugIgoBkMolkstOph4u7ei+ZTHqr+dxVfHPpyypBTJUJ+0xdc801+MpXvoJ3vvOdiMfjeOGFF3D11Vfj7Nmz2LBhA4aGhhq+1j333IODBw/i6NGjgdGhZDIJVbXfAP/kT/4E3/ve9/DII48gkUhgx44dAIAf/ehH3vnnzp1DoVDAl7/8Zfzwhz/Et771LQDA9ddfD0VRYFkW1q9fj1gshi984QuwLAtdXV1IJBL4/ve/31BfyWeKmCkqlUrAa8r1l+rr63PMOXtRKOTrv1gQYQb8pewRLdt3KgouRwFadr7wMY3hVXW+FXdCYDTJFUjlcVfaRaNRz8gzlUqhtbXVc1NPJpOeqWdLSwtisRhZGxBzmjnpM3Xx4kVcc801I/ZbljXhBKZf+tKXAAC33HJLYP+BAwewbds2AMCDDz4IQRCwdevWgGmnnz/6oz/CiRMnvO23ve1tAIDz589j1apVEAQB3/3ud7Fjxw5s2rQJ0WgUv/7rv47Pf/7zE+ovQcwE4XAYy5YtG3O1TalUQjqdxuXLlz0H9J6eHs8JfWjo4qivtd3OI57lApdVe58SAZdU26lbDgMCOaE3Hcv0HM9R1/ncX2zxJBjVhvIBRmMxtHa2BdzO/Y7n/n00akQQE2PCI1M33ngjdu3ahd///d8PjEx9+tOfxpNPPol/+Zd/mam+NhUamSLmMtVq1RNW7shWb28v+vr6vFKpjGNrwATbVV1UAFEBd/PzCfKwGaQg27n4RNmXrNifnNhNVuxPSiz6gvIXKGPk4/PvH87F5+bqs48LbnsCSZIjkQiSSTsfnzutVpuPz5+rjxIiE4uVOTky9YlPfAIf/OAHcfHiRViWhcceewwvvfQSvvGNb+DYsWMz0UeCIMYhFAphxYoVWLFiRd3jnHMUi0UMDAx4MVtuyeVyyGQyyGazyGazTmB9FlqxfhqbycECCZG5IIAzO0ny8H6fIHMTKLvCTBC98zkTvITK3EusPLxqknuizS/eHC+gwMoyy15JZlkAN+1RIbe2DMAywCzTrk0DsHTbG8xr62CmYYsna3IJ3mVFQSwWQzy2BFEnEDsWiyEej3ul1j3cjUWSZXkq/yEEQUwjEx6ZAoB/+Zd/wac//Wm88MILKBQKePvb345PfOIT2LJly0z0cU5AI1PEYqNaraJYLAZWMpbLZW/FY6VS8dzj/Q7ytRYUmqZ52+5xTdNQ1TRozmsMo/ERmWYjK4q92kxVvVVn/tp18PYXd59/xVo0GiVBRBCzwJwcmfrgBz+Iu+66C08++eRM9IcgiDnCbNqDWJblia3a2i/EXGHmL7XeYq5nmGVZnneZv7jpRdwUI7WpSGpTldQuyZekKdnzEQSxAJnwu0I2m8XmzZuxcuVK/OEf/iG2bduGK6+8cib6RhDEIkEQBM+WgiAIYr4x4fWs3/nOd3Dx4kX8yZ/8Cb71rW9h5cqV+PVf/3UcPnx4wqv5CIIgCIIg5juTMgdZunQpdu/ejRdeeAHPP/88rrnmGnzgAx/AlVdeiV27duE//uM/prufBEEQBEEQc5IpOa319PTgySefxJNPPglRFPEbv/Eb+OlPf4rrr78eDz744HT1kSAIgiAIYs4yYTGl6zr+4R/+Ae9617uwcuVKHD58GPfeey8uXbqEr3/96/jnf/5nfPvb38anP/3pmegvQRAEQRDEnGLCAehXXHEFLMvCe9/7Xvzrv/4r3vrWt44459d+7demlNuMIAiCIAhivjBhMfXggw/iv/23/zbmqptUKoXz589PqWMEQRAEQRDzgQmLqT/4gz+YiX4QBEEQBEHMSyjVN0EQBEEQxBQgK1+CIOYEhmF4qWpq09AYhgFd12EYBgzDgGmasCzLq2thjEEQBK+4jueyLEOSJCiKEnA5dx3O2UJOxkwQxIxBYoogiClhGAby+TxyuRwKhQJyuRzy+TwKhQKKxWKguDn+SqUSKpWKV1cqFZjm5JIFTxeMMYTCYUSc/HqxWMzLoTda4mE36XAikYCiKE3tP0EQzYPEFEEQASzLQi6Xw+DgIAYHBzE0NOSVTCbjK1nkclkUi8UJ3oEBogxLkABRAhfC4GoMECRwQQQECRBEu83cWgCYAM7cNrMLmL1/5B0A7oxYcQvgHIybdtsy7bZlApYB5hSYBgxLR7GkYyA/AGb2AFbjCZhVNYJUKolkMumJLH/tF15uofQ5BLEwIDFFEIsEzjny+Tz6+/vR19eHgYEB9Pf3B+qBgQEMDg3BNMYREYIESwqBS2HwRBJcCoGLIbuWFHAxBIiK01bARdneFmRAEB0hNA+wLDBTA8wqmKGBmVUwo16poGBUUBzIo+dyP2A2llpLURTE4nEkfCNf8XgcsVgsMDLmtmOxGCKRiLc/FArR1CRBzAFITBHEAqBarY4QR/7S19eH/v5+aJo2+kUECZaswgq3gcsquBwBl8PgsgpLVm3h5NQQ5dl7uGYiCOBCGJDDI0a/xsQyPJEVrKu+bQ2mUUG5qGEgmwYzLwyPpjXcPQFqJIJoJOKJrEZqvyiLxWJQVZVEGUFMARJTBDEH4ZyjXC4jm83WTK1lAtNvrnAac6qNMViSCq4kYEWi4EoEXInAkiPgShRcjsBSIoAgjzliFOk+AqZXZuBpFydcDqO07t2+HdwWYabmjYLB1MBM3ds3vK2DWRpg6tBNHflsGWwwB2bp9vTlBGGCgGgkilgsOArmFv9omdv27yMxRix2miqm9u7di8ceewxnz56Fqqq4+eabsW/fPlx33XXeOZVKBXv27MGjjz6KarWK2267Dfv370dHR4d3zs6dO3Hy5El0d3dj7dq1OH36dOA+n/rUp/DAAw+MuH8kEplEvAdBjIRz7q08q1arXu2WcrmMcrnsBV2Xy2UvILtQKARKNptFLp8ff6oNAJfCtihKttijR7IrlKL2KJISBZfDdpzRFGF6BYJRnvJ1Zov9+/cDAO65554m96Q+I8agmB1LxkUZXIlO4cImYOnDIszUAV/bnrb0t+06a2jIDRbA+ofAjOqEbimKImKxOOLxoNjyT1uOVkejUQgCufQQ85umiqkTJ06gq6sL69evh2EYuP/++7FlyxacOXMG0aj9ZrJr1y48/vjjOHz4MJLJJLZv34477rgDJ0+eDFzrzjvvxPPPP48XX3xxxH3+9E//FHfffXdg3zvf+U6sX79+5h6OmBU4596S+npCxr+vtrjix2272257vKJpOnR9+PwpwwRwKQRLVMDDrfZ0mhSCJYXt6TZ3mk1WwSV12kQSscAQRDuAX5rg1KQfzn0CzCmGM1LmxY65x6owDA2aVkWmdxDsUnpCgfuMMW/q0V/8U5K105RuUVXVq0OhEERRnOwTE8SUaKqYeuKJJwLbjzzyCNrb23Hq1Cls2rQJ2WwWDz/8MA4ePIhbb70VAHDgwAGsXbsWzz33HG666SYAwEMPPQQA6Ovrqyum3D9OlxdeeAFnzpzBl7/85VH75n4Yu+Ryuck/aJMwTRO6roNzDs7tt1W3XW+bc+559liW5W27tVtM0/T2maYZ8PzxF9cPyG27xfULevLJJzE0NDSiD/WK24/atvscswGHM43hTWf4tgXJ3mb2fg42fJ636sypnTb37/eOOS+xTDCtCGjFOeOsywya4ptOmFFB5GdHm92N6YOJsGQVkFUA3BZknAPgYM6KSnt7uM3AwbmFQqmCQqkM9F4Gm7wEBBiD4HiMsTp1bXH32y8NHvPvc9u1dXt7O37/938fgiBAFMWAt1m9+/trAIF+ufvGao/Wn9H2hUKhyf8siQkxp2KmstksAKC1tRUAcOrUKei6js2bN3vnrFmzBitWrMCzzz7riamJ8rd/+7d4y1vegl/5lV8Z9Zy9e/fWnRqcL+i6jv/+3/87BgYGmt2VBYP3Jl8r4Oq891P0CDEeDBxiif4+pxXOYfm+FM40Fy9exE9+8pNZuddkeO9734s//uM/bnY3FgVzRkxZloV7770XGzduxLp16wAA6XQaiqIglUoFzu3o6EA6nZ7UfSqVCr75zW/iz/7sz8Y877777sPu3bu97Vwuh+XLl0/qns1AkiS84x3vwPe+971md2VWCYwIjdLmvhEkBNqoP+rkbPPa46NdY4EilAanNmpABOBgsCKtze7GHGCUUawR+4OjWu5x96+aGIk//piYWeaMmOrq6kJ3dzeeeeaZGb3PkSNHkM/n8cEPfnDM89w0E/MVxhg+9rGP4WMf+1jT+uCf6qs3zee2g3FI2ojajXvSdb1uHFS9+Ch7nx3HUWsHMKOShwmO4aQE7hpOCpLtsyRI4IJkBxkLsuO9JNs+TI43k922/ZogKnPKjyn6k0Ng8ygAfa7DpTBKN9ze7G5MHc6dgPaR8VQwqiPiq9xViYIbhzVBOwgACKsqImoEkchwzFQ4HPaKG0PlL24KIVmWoSiKV/vTDMmyDFEUvfRDoih627VTebR6kfAzJ8TU9u3bcezYMTz99NNYtmyZt7+zsxOapiGTyQRGp3p7e9HZ2Tmpe/3t3/4t3vWudwVWAxIzg/tG1GzqrbSbTAC6K/5qz/Ffq1bkVapVVCtllItD4BOdemAMXKwNQA87/k+qbXcgq7bVgaQCM7wiisvhkSvQ5jB379xjNyS1uR0ZBS7PEfdzvyVDwIIhKJBQI4gC509gZEhRQs6qv5YRQeejBaD7jUpVVaXVf8Sco6liinOOHTt24MiRIzh+/DhWr14dOH7jjTdClmU89dRT2Lp1KwDgpZdewoULF7Bhw4YJ3+/8+fP44Q9/iH/8x3+clv4T8wPGGBRFsd2mfQsRZhPOOTRNa9gawc11l81mMTSUQS5/eVwx5tkkeB5SEcdDyvGWkqP2iNckv1EHPJGI5hMQQUH7A9sawb/fv615bWbpExZDgJ06J5aMIRFvH9UGYTQrBMphSCxEmiqmurq6cPDgQRw9ehTxeNyLg0omk1BVFclkEnfddRd2796N1tZWJBIJ7NixAxs2bAgEn587dw6FQgHpdBrlctnzmbr++usDf7hf+9rXcMUVV+DXf/3XZ/U5CcJdWRMKhZBMJif8etM0kc/nA3nyXPPO2pQwxezgGB0RYDkmnVyO1ggv26fKktU5N8W4IOGWMwpUrWs3AP/oj6mBGY4QsnQIjiiaDIIg2KacqXjddDX+uja1TTxuv0aS5sSkBkHMGRifzbXltTcf5c36wIED2LZtG4Bh085Dhw4FTDv903y33HILTpw4MeI658+fx6pVqwDYAe4rV67EBz7wAXzmM5+ZcF9zuRySySSy2SwSicSEX08Qs0W5XMbAwEAg/56bTsafl880x3DKFkRvGtFyva3clDL+aUc3vktY5B+ulgVmVsD02hQytelknNqNHZoAsiwj4riUuyKonveSf7teOhnK50csNmbj87upYmo+QWKKWEhYloVMJuMlN/YnOh4YGMDg4CAGBgYwNDTUmCGpm/jYDaAXFUAK2W7eop3sGKICLkp2smPX6VuQAEEGFyWASTMe9zUqnAOWaadjqecWblQ948pAMSsQnBijRlCUEJLJBBIJu8Tjca+ul+jYn+yYpscIYnLMxuf3Iv86SRCLE0EQ0NraitbWVlx77bWjnsc5R7FY9KYW/TkCs9msF9eVy+WQy+WQz+dRKGQmb6YaWA0pAO6KSCbYDvGC6Ew/Cp7ZKQ/YU3gdt20cOGAvp7fs0SPutg3bGJWbYJYBmBN3sA+Hw0gkE0gkOpFKpZBMJpFIJALtRCKBZDLplfm8QpggiNEhMUUQxKgwxrwRkkZ91izLQrFYRKFQQLFYDBQ3+L5UKqFSqXg5C8vl8oh8hsOrLXVoWhmmY6cxWUNGUZIgSxJESUIorCAUinhL5t3l9W7xxw25sUL+kkgkaKSIIAgPElMEQUwrgiB4omMm8Kc1ctv+kTDOeSCthyAIkCSJ4oQIgpgxSEwRBDGv8AskgiCIuQA5nxEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFCAxRRAEQRAEMQVITBEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFCCjFoJYxBiG4eXhGxgYCKSGyWazKJVKAddyTdOg6zp0XYdhmDBMAwDAnFwujAGSJEGSZciSBFmWoSgKQqEQwuGwV2oT87qO4/6SSCSgqiqZbRIEMechMUUQC5hKpYJLly7h0qVLSKfTSKfT6O3tdcplZLOZxi7EBDsxMRMBQbBrJgDMn2vOyYVnWGClCsDdXHgGmGnYOfEmiCiKiMXiSCSGEwInk0kvpcto+yKRCIkwgiBmDRJTBDHPMQwDly5dwhtvvOGVN998E2+++SYGBgbqv0iUYcpR8MQV4HIElhwBl1VwKQwuhXy1AggyIIhT76ibYNg0AEsHM3UwUwMzdcDUwAzN2a6CGRpgajCMKjStikx6AOziJcAyG7qVKIoBceWKLn+JxWKIx+MjRsVoNIwgiIlCYoog5gn5fB4XLlwYUS5dugTTrBEZjMFSYrASV8EKxWGF4+ChuL0vFAPEkD0nN5swARAVcNFOEMzHOX0EnAOW6Yitii2+DLddBTOrgFEFM6q2CKtUMVTsA3vjzQmNijFBQERVEYlGEYtGvSTI/mTI/ilLVVW9aczxakqBQxALE/rLJog5hGEYSKfTuHDhAt5880288cYbnmgaGhoacT6XwjDVJbDCyUDhodj0jCbNJRgDRAlclMCVaOOv49weETOq9siXJ7zckTB3VMw+DlNHztSRz1ZweTAHZukNj4iNhyhJCDvCyi/OXLEWjUahqmrdODJ3RC2RSCAUCtHoGUHMIUhMEcQsUy6X0dPTg3Q6jZ6eHly8eBEXL17Em2++iXQ6PXKUCcweXUothxVOwQonYYaTsNQkIIWb8gzzCsYAUbZjviZ7DW4BpjM1aRnOdKVu1+62ZdZsG75pTRPMMmBYBjTLQL6og+UH7GOmjomO08myjHg8gWTSnsJMJpMjSiqV8upUKoVQKDT+hQmCmBRNFVN79+7FY489hrNnz0JVVdx8883Yt28frrvuOu+cSqWCPXv24NFHH0W1WsVtt92G/fv3o6Ojwztn586dOHnyJLq7u7F27VqcPn16xL3+6Z/+CZ/85Cfxs5/9DOFwGJs2bcLnP/95rFq1ahaelFgsVKtV9Pf3Y2BgAP39/ejv70dvby/6+vq8wO9MJlP3tVxWYapLwMOJ4EhTKD4jo0yR7iNgemXar7uY4HIYpXXvnuJFOMBNJ3ZsOJbMbleDI2fO1KZpVlEpVTGQ6wEzXkMjYiwcDiOVSqGlpSUgsmpFl7tNsWME0ThNFVMnTpxAV1cX1q9fD8MwcP/992PLli04c+YMolF7GH/Xrl14/PHHcfjwYSSTSWzfvh133HEHTp48GbjWnXfeieeffx4vvvjiiPucP38et99+O3bv3o1vfvObyGaz2LVrF+644w78+Mc/npVnJeYXlmWhVCqhWCyiUCigWCwin897tgH5fB7ZbBaZTAZDQ0NeXSwWR7+oIMFUouDJZbBCMViKE8cUjsMKJQBRnr0HBMD0CgSjPKv39LN//34AwD333NO0PkyVia9PrANjAJPABQmQ1cnFkpnacOyYXgEzKhCMitdmRgVFvYLSYAHpy/2AZYx7WUmSPJHljn7VBvS7NQXwE4udpoqpJ554IrD9yCOPoL29HadOncKmTZuQzWbx8MMP4+DBg7j11lsBAAcOHMDatWvx3HPP4aabbgIAPPTQQwCAvr6+umLq1KlTME0Tf/EXfwFBsH1K//RP/xS33347dF2HLM/uhxgxeQzD8HyOdF2Hpmmjlmq16hVN01CpVFCtVlGpVLxSLpdRLpdRqVRs8eT4KlXKExAZTACXw7CkMHiyxV4Zp0SGV8mForCUGCAqsx/0TSx8GAOkkL36stHXmLotsnxiyy/CYFRhGhX0FSvoz74JZlQb7o4gCIjFYog6wfv+WDA3eL82aN/1IlMUxfMmc9uSJEFyPMtEUYQkSV4tSRIEQfAKiTiiWcypmKlsNgsAaG1tBWCLIF3XsXnzZu+cNWvWYMWKFXj22Wc9MTUeN954IwRBwIEDB7Bt2zYUCgX83d/9HTZv3jyqkHI/hF1yuRwA4Dd+4zdoRc4ChYM5YocBguS0BXDm288EgDFwJgS2XZhehqiXgdIolgRzCGbQFN9UYUYFkZ8dbXY3ZgwuyOCKDCgxO26Mc9s/DNxrw2kzp21yjmy+iGy+YO+ffKQaMQne/va34/7778eSJUua3ZVFxZxRBZZl4d5778XGjRuxbt06AEA6nYaiKEilUoFzOzo6kE6nG7726tWr8f3vfx+/+7u/iz/+4z+GaZrYsGEDvve97436mr179+KBBx6Y1LMQ8xPvAwIIhKDQd11iNBg4xHkgnInFw49//GP87Gc/w6/+6q82uyuLijkjprq6utDd3Y1nnnlm2q+dTqfxoQ99CB/84Afx3ve+F/l8Hp/4xCfwnve8B08++WTdoeH77rsPu3fv9rZzuRyWL1+OT37yk3XdlTnnEx5iHu819Y436zXjbU8UzjlM04RlWTBN0yuGYQSKruswTTMwteef4qs31VetVlF1as2Z1ps6zB6hCoxOue3hUSrubs+D6QahNEijBlOEg8GKtDa7G9ML5wCs4dEmdwTK2c8C29zenuLvkTtF5y/+aTv/fv82AMTjcfzyL/8yRFGEIAheXa+41629X7371/ah3jm1fXNfU6/Pk3kPncw5ra2tWLNmTcM/e2J6mBNiavv27Th27BiefvppLFu2zNvf2dkJTdOQyWQCo1O9vb3o7Oxs+Ppf/OIXkUwm8bnPfc7b9/d///dYvnw5nn/++brThaFQqO5S4ne84x1IJBIN35toPpxzGIYxasyUP3bKzUXnBp/XC0B3g8zHfIsTZZiyHTfFlSisUMyulSisUBxcab4PVPQnh8CaGIC+EOBSGKUbbm92N0bHF5wu1ImPCsZMlSGY1YY9taLRaN0AdDdeyi3+PIxuvJRreBoKhSCKC8wPjViUNFVMcc6xY8cOHDlyBMePH8fq1asDx2+88UbIsoynnnoKW7duBQC89NJLuHDhAjZs2NDwfUqlkvdtwcX9A7asaVmPQ8xhGGOQZdnx5olP+XqGYaBQKNRdzTc4OOizRhjA0FAanNf/1m7JEVtYheKwwglYoYRdhxN2sPoMw+Xw9KxGmyR379xjNyS1ib2YGlyeRZ8vTxhVPePRgAO8UQHTq544Epz9jbi/R6NRpNpbAjYJrl+V38fKXckXi8UodpQgfDT1r6GrqwsHDx7E0aNHEY/HvTgo1+MkmUzirrvuwu7du9Ha2opEIoEdO3Zgw4YNgdGkc+fOoVAoIJ1Oo1wuez5T119/PRRFwW/+5m/iwQcfxKc//Wlvmu/+++/HypUr8ba3va0Zj07MYyRJ8vx4Vq5cOea5hmGgr68v4DPV09Pjld7eXliF3hGv47JqG3PWczZnQp07TZwp+yMRY+Omv6k18aw1/TR122Xd85hychV6uQurdtvQ0Mh0GmMM8XgcqaVLA/5RbrvWZyqZTNKKZoKYIoyP9rV5Nm4+ylywu+oOGDbtPHToUMC00z/Nd8stt+DEiRMjrnP+/HnPlPPRRx/F5z73Obz88suIRCLYsGED9u3b1/Dcci6XQzKZRDabpWk+YtowDAOXL1/Gm2++6bmgX7x4ERcuXEBPOg1eO3IqiDBDjqmnmgoIrdn2qVpQeA7nmi/5si8Rs6WDmX7Xcx0w6wgkr5gNeTmNBWMMkWgUcd/0md/fKRaLBRzPE4kEUqkU4vE4TZ0RhI/Z+PxuqpiaT5CYImYbXddx6dKlusmN65mDciUCM+SKK3u6kIcSdmJjYRFMyXBuix53GmzEdJiTey+Qo0+D4IwETRZRkuwYoDrJjd1S663k5uZz9/tz87k5+6LR6IjwBIIgJs5sfH4vgndYgpifyLKMlStXjphK5JxjcHBwhMB648030ZtOg+d7RlxrOPB9OADeUqK2sagSmVuGom5sUCA+SBshkODbFsyKPQ3WQHwQADBBQCwaRbwlWTd42i9sXHHjT0zsCiJVVSl2iCAIElMEMd9gjKGtrQ1tbW0jYv6q1SouXbrkTRe65dKlS7h8+TKs/ChiQxBhSSq4FLadtL1aAQTJThIsyHabCYAg+GwhHDyPLss2duQWwM2auCHTlzBYD06tGRqYpTmxQY0hiqITFN2BeDzuTXfF43FvOsyf/sQtkUiERn0Igpg2SEwRxAIiFAph9erVI1bGAnZ8lpt42S0DAwOBFYjZbBaVXP+s9tmLDWoNjhDVCqDafHCJRKKu5xtBEMRsQ2KKIBYJkiShs7NzXI82TdO8hM7FYtHz33I9uQzD8IxUDcMImBe693Fzqbl1vXghdxotHA7TKBFBEPMaElMEQQRQFAVLliyh3F4EQRANQl8HCYIgCIIgpgCJKYIgCIIgiClAYoogCIIgCGIKkJgiCIIgCIKYAiSmCIIgCIIgpgCJKYIgCIIgiClAYoogCIIgCGIKkM8UQRAzQqVSwdDQEAYHBz0T0Fwuh0KhgFKp5BmCVioVVKtVVKtVzwjULZZleaVeTnbGmFcABsYAQRAC+93i7vfXoiiCMQZRFCGKomc46pqNyrIcSFbsGo66iYhjsRji8ThisRiSySRUVSVHdoJYhJCYIghiwpTLZS8lTTqdxuXLl9HX14e+vj709/ejr78flXJ5YhcVRICJdu4/JoA7AgleDXAMCxUGDnj6ig/X3LfN3VcMb9ttDuaex7mXT3CqSJKEZDKJVCqF1tZWtLS0oLW11cul6JqhtrW1IRQKTfl+BEHMDUhMEQQxAsMw0NfXh56eHly6dAnpdBqXLl1CT08Penp6kMlkRn0tl1VYcgQ82QZLVsElFVx2kieLIUBS7MTJopNEWZAdITUHRnRcUcUtOykzt4YTNFtGTYJm3UvQDLMKZlRhGBVoxQr6s2+AvfLKmLdKJJNoX7oU7e3tdcuSJUsgSfQWTRDzAfpLJYhFiK7rI5Iep9NppNNp9PT0oLe3F5ZVZ6RGEGEqMfDkVbCUOHgoBkuJwVKi4EoUXI4A8znPnjMqBgBcHN49coKxASwTTC97RdBLYHoJTCtC0EoYqhaRO38B586dG6UrAlpbWjxxtXTpUq/QCBdBzC1ITBHEAoFzjkKhgGw2i0wmg0wmg8HBQQwODmJgYAADAwP2FFxfHzKZTN0YJACwlCisaDu4EoMVisMKxcHdWlbnxgjSfEAQwUMx8FAMAGCOdp6pQdCKYNWCXWtFr+4rFDEw9DJ+/vOfj3qbWCyOtrZWb1rRLalUCslk0iuJRALxeJxGuwhiBqC/KoKYI5imiVKphHK5jGKx6AVpu6VQKHjtfD6PQqGAfD6PfD6PbDaLfD4P0xz1I9tGlGHKEfB4py2WlKgjmuwRJq5E7Sm3CRDpPgKmV6bw5IsDLodRWvfukQdEBZaqAGpLfcHFOZhR8Y1qFcE0e5RL0EvIaiXkL13G66+/3lA/wqqKeDyOeCyGmFOi0ahXIpEIYrEYIpGIV9z9bq0oCgXaE4SPpoqpvXv34rHHHsPZs2ehqipuvvlm7Nu3D9ddd513TqVSwZ49e/Doo4+iWq3itttuw/79+9HR0eGds3PnTpw8eRLd3d1Yu3YtTp8+PeJe3/72t/HZz34WL7/8MpYuXYrt27fjox/96Gw8JrGAMQwD5XIZpVKpoeKuYHPFktsuFouoVqsT7wATwKUQLDEEri4Bl0LgctiOU5LC4LI6HMOkqICoTPvPgOkVCMYEg81nmP379wMA7rnnnib3ZJhJh7cz5v0/ItI2+ggXt8D0iiO8ynZtVMCMqrOvCmZWUTQ0lLJlXB7MgpmaE5Q/MURJQtQnrlzx5Rdl7mrHetuuSBPFiQl3gpirNFVMnThxAl1dXVi/fj0Mw8D999+PLVu24MyZM4hGowCAXbt24fHHH8fhw4eRTCaxfft23HHHHTh58mTgWnfeeSeef/55vPjiiyPu8//+3//D+9//fvzN3/wNtmzZgp///Of40Ic+BFVVsX379ll5VqJ5WJblLb2vVqveUnx/7S/lctlbsu+23TIsgEool0vQNG3yHRNlWIIMLsqAFAcPtYILCiDKwwHaTj1iWwp5Adw07UYAsIW1EgFXIo2/hnMnsF4DM3XA1Hxt3WsPB93b24apQ9M1ZAbyYH2DkxZlqhpBNDosylRVDYyIqaoaKOFw2KvD4bBnWeGvZVmmUTNi1mF8tMCJJtDX14f29nacOHECmzZtQjabxdKlS3Hw4EG85z3vAQCcPXsWa9euxbPPPoubbrop8PpPfepT+M53vjNiZOp973sfdF3H4cOHvX1/8zd/g8997nO4cOFC3T8894PXJZfLYfny5chms0gkEtP41PMfzjl++tOfIpvNArC9fxr5tTJNc0TxewwZhuH5Dum67rU1TYOu63VrTdO8/7tsLgfTMKb3WQNL9YP1yKX84xxbIAilQdumYA4xF0emOBisSGuzuzFzuLYT3LWecCwnfPvAnf1w7Sh8+6fxd6iev1goFMLKlSuhKAoURfF8xPxt11/M7zcmSVLAg8z1JBNFEYIgeNd33/Maff8DgM7OTlx77bXT9txEfXK5HJLJ5Ix+fs+pmCn3w7i11X7DOXXqFHRdx+bNm71z1qxZgxUrVtQVU6NRrVYRiQS/ramqijfffBOvv/46Vq1aNeI1e/fuxQMPPDDJJ1lcHDt2DJ///Oeb3Y1ZgXkfDBixxGvhyCNiJmDgEEsDze7GooBz7gkaN45Q0zR0d3c3s1t1+cIXvoC3vvWtze4GMUXmjJiyLAv33nsvNm7ciHXr1gEA0uk0FEVBKpUKnNvR0YF0Ot3wtW+77Tbs2rUL27Ztw6/92q/h3Llz3od/T09PXTF13333Yffu3d62OzJFjOTtb3873vKWt+Dll19udlemBK8xiBx31Gnc44uDuTgyNRdZ8CNTY+Kao/pGrWr2uaNYgWPAjI1ezQV+5Vd+BVdffXWzu0FMA3NGTHV1daG7uxvPPPPMtF/7Qx/6EF555RW8613vgq7rSCQS+MhHPoJPfepTEEbxxHFTSBDjc9VVV+GrX/3qjN+Hc+5N+blTeu4Un7uvWq0Gak3TvNgof3u0eCl/nJR/mnfi0ojZMVGibJtSenFQMiC4cVAS4MZDCf6YKOc85xjYHDG0HIXoTw6BzbEA9LkIl8Io3XB7s7sxMbgVNCg1dcDSa2Ksho/7Y678NaxxVpmOQigUGjNuyh8rVVsURfHq2uJO6/mn+SRJolgrYtLMCTG1fft2HDt2DE8//TSWLVvm7e/s7ISmachkMoHRqd7eXnR2djZ8fcYY9u3bh89+9rNIp9NYunQpnnrqKQCgbwXzCMaY9wZYO207E1iWNWoQulvqbddbqVcq5aAVJ7FaD7ADi31B6W6BE4TOJcddXFKc2lnFJ4U8A8qZhMvhya9UmyHu3rnHbkhqczvig8vhJtzUclbz2cV1ameGK3rsAkMDszQwQ7drRyDBmlzMYTgcdlbvtdRdyVfbrg06d2ta7UfMF5oqpjjn2LFjB44cOYLjx49j9erVgeM33ngjZFnGU089ha1btwIAXnrpJVy4cAEbNmyY8P1EUcRVV10FADh06BA2bNiApUuXTv1BiAWJIAjeG/x04LdRcC0SRrNLKBQKATHm+kwVikWUCkMNB7hyKWyndJFVcDkCS4mAyxFwJer5Stmia/LfyOt6JxEzR8B3quQ4qw87rbvWCIJjizARwqqKaDSCWKylrseUqqoB8eP3pnIFk6qqZAxKLDqa+hvf1dWFgwcP4ujRo4jH414clJt9PZlM4q677sLu3bvR2tqKRCKBHTt2YMOGDYHg83PnzqFQKCCdTqNcLnur+a6//nooioL+/n783//7f3HLLbegUqngwIEDOHz4ME6cONGMxyYWKZIk2WaJ8fiUrmNZ1rC4KhSQy+W8ks1mkc1mMTQ05DmgDwwMIJ+7NPoFBclOEeMad4aGnc+tUNz2pqLpj9nBFUpaEUK1MGzSqRe9NDSCXhozKbMgCEimUmhJXRVwQY/H40gkEojFYojH455hp3+UiEaCCGJyNNUaYbT56QMHDmDbtm0Ahk07Dx06FDDt9E/z3XLLLXWF0fnz57Fq1Sr09/fjt37rt/DTn/4UnHNs2LABn/nMZ/COd7yj4b7OxtJKgpgpNE3D4OAg+vv7vZQyly9f9uXl60U2m6n7Wi4qjrCKgSt27aWYUaKAKM/uw8xXPKFUAtNtYeRPHSNoBVsojRJfJIoilixZ4uXna2trw5IlS9Da2oq2tja0ttopZeLx+KixoASxGJmNz+855TM1lyExRSx0yuUyent70dPTg56eHqTTaVy8eBE9PT241NODSrl+kDmXQr5kxxFwOepMJ6q2E7sbvzXBNDVzHs6HA64NLeg27rqQ62UIetlJ/VIedUSJCQLaWlvR3t6Ojo4OLF261Etw7CY5bmlpIZFEEJNg0flMEQTRPFRVxapVq+pahXDOkc1mkU6nA2LLHtWy60pmHA8lQYLlBs2LirNaUQIXJNvJXRDBmWgHzQsCOIQGrSa4z/PLv5ze8QXzjo9caj9sJOkYSHLLDtrmpt22TDDLtF3CLdNZyaZDsAzAbMz9XpZlLFmyBEuWXI22tjYsXbo0MMLU3t6OtrY2ijMiiHkM/fUSBDEujDGkUimkUimsWbNmxHHOOYrFojeFODAwgKGhIQwODmJoaAjZbNaL67ITNmdhWXNtDeDoCIKAUCgMNRJGJBL3Vpu5MXCxWAyJRCIQo9Ta2oqWFns1Gy25J4iFDYkpgiCmDGPMC2auN7JVC+ccmqZ5CZ5dDzB/CiE3xZBlWQFHa865lyrEvXfttjsdVq/tpv9w04G4bTdliCiKnv+QJEkIh8M0akQQxJjQOwRBELOOmyuNjHEJglgIUDQjQRAEQRDEFCAxRRAEQRAEMQVITBEEQRAEQUwBElMEQRAEQRBTgMQUQRAEQRDEFKDVfARBNJ1yuYx8Pu/lGyyXy6hWq6hWq9B1PWCNIIoiFEWBoihe4t1oNOrlniOXcIIgZhsSUwRBzBiWZWFoaAjpdBrpdBp9fX2B4iZjrlar03I/QRCQamlBW2ur5y7e2dmJzs5OXHXVVbjqqqsQiUSm5V4EQRAuJKYIgpgSmqbZ+fsuXcLFixdx6dIlr/T09EDX9fovFERYUhhcjoOH28HdVDOSAi7IdnoZJ81MAG7Z6V8sE8zSAcPNj1cF08voL5YxmHkd//Ef/1H3ti0tLVixYoVXVq5ciVWrVmHp0qXkVE4QxKQgMUUQxLhUKpWAWLp48aJXei9fBq+TGoZLYVihJKx4HJYSAw/FnWTIdiJkiKFxcu5NEUODoBXBtAKEah5CNQehksdAKYuhF1/ECy+8EDg9Eolg9erVWL16NVatWuXVra2tJLIIghgTElMEQUDXdVy+fBmXL18OJDLu6enBxUuXMDQ4WPd1lhKFFe2AFYqDhxOwQglY4TisUAIQ5Vl+ihokBZakAJEWmLXHLNMWV+UshEoGQnkI+XIGPzvzc/zsZz8LnJpIJDxhtXLlSqxcuRIrVqzAkiVLSGQRBAGAxBRBLGjcBMSDg4MYGBjAwMCAl4zYLZcvX8bg4KAX4B2ACbCUGKzkVbBCtkiyQgnwcBxWKA4I8/QtRBBhqS2w1JbgfstyRNaQUzIYKg8hV2ckK6yqWLF8OZYvX44rr7wSV155Ja666ip0dnaira0NolgzPUkQxIJlnr4TEsTiw7Isb9WbW3K5XKBks1lkMhlkMhkMDQ1haGho9JglwBYVcgRWrBM8FLOFU8idkouBKxGALaLVcYIAS03BUlMAVg/vt0wIFXcUKwOhkoVZyeLlc6/i5ZdfHnEZURS9APglS5ZgyZIlaGtrQ0tLC1paWpBKpZBMJpFIJBAOh2ft8QiCmBlITBHELGEYhrf0v1gsem3/tn+/f18+X0CxVKwbm1QXQbKDu5UUrKgKLqvgcgRcicCSVXAlCi5HwaUZjltaKAgirEgrrEhrcD/nYHoJQjUPVsnZsVlaAUa1gEuDeaR7e4F6I34+FEXx7B3cOhKJQFVVr4RCIYTDYYTDYYRCISiK4iWKdrfdff5jiqLQVCRBzAJNFVN79+7FY489hrNnz0JVVdx8883Yt28frrvuOu+cSqWCPXv24NFHH0W1WsVtt92G/fv3o6OjAwDwwgsv4C//8i/xzDPPoL+/H6tWrcLdd9+Nj3zkI941enp6sGfPHvz7v/87zp07h507d+ILX/jCbD8usQDwCyJ3ZKhQKCCXy3k+Sf62N4pUKKBSLk/4fvbKNsVe5RZtBxcVQFTslW/e6rfw8LYUBpfD0zL9Fuk+AqZXpnydhQCXwyite/fIA4yBK1GYShSId9Z5oQVmVMC0ki269AqYXrb3GRUwQ4NpVFAuaRjI94OZPYBlTFu/GWO2uAqHEQ6FoKoqwuFwoI5EIoESjUa94hd5sVgMoVBo2vpGEAuJpoqpEydOoKurC+vXr4dhGLj//vuxZcsWnDlzBtFoFACwa9cuPP744zh8+DCSySS2b9+OO+64AydPngQAnDp1Cu3t7fj7v/97LF++HD/60Y/w4Q9/GKIoYvv27QCAarWKpUuX4s///M/x4IMPNu15iebCOUe5XEa5XEapVPKKO/rjFv+IkX9KLZ/Po1QqNX5DxmyxI4bApSh4osURPyGfKHKEkijb274CUW7qqBHTKxCMiQvA6Wb//v0AgHvuuadpfWhwPHAkTLBHBGXb22pEIHzdm1mApYOZum39YJlglgGYOphlApYBxk3AdGrLcM4xAW445zivsQwYloGSZoJVymBDee9ak8EdRYvH44jH44jFYkgkEl7b3e9u+4uqqjRKRixYGK8bddoc+vr60N7ejhMnTmDTpk3IZrNYunQpDh48iPe85z0AgLNnz2Lt2rV49tlncdNNN9W9TldXF37+85/jBz/4wYhjt9xyC9761rdOeGQql8shmUwim80ikUhM+NkWO5xzmKYJ0zRhGAZ0XYdpmtB1HYZh1N2naRp0XfeKpmnePk3TUK1WvX2VSsXbdt2zK5WKLZ6culqp1A+yHq/vnuAJOe3hGoERIrdtH4fQXDE0VaI/OURiysGSVBTf9t6m3X/a4XzYp8sRbcx02qZmCzlTA0wNzPXxcry8YGoQ3DZvXGYKgoBIJIpYLOpNZUaj0cB0Zu20Zr2pTEVRIMtyoEiSFCgk2gg/s/H5PadiprLZLACgtdWOSzh16hR0XcfmzZu9c9asWYMVK1aMKaay2ax3jcniprJwyeVyU7refKFUKuH+++/H6dOnm92VqeEYPtpFBoQoeCwJiDK4IHsjQWL2TftDgQmO8GHgjDltYYQYYob94YLpMeye0zCDpvhcmFFB5GdHm92NuYEgwRIkQI448WDcMVJ129wRWXbb3m/BYCKypozcYAGsf8gWbxMQY3ORjRs34oEHHoAkzamPUqIJzJnfAMuycO+992Ljxo1Yt24dACCdTkNRFKRSqcC5HR0dSKfTda/zox/9CN/61rfw+OOPT6k/e/fuxQMPPDCla8xHisXivBRSXJCCcUOCDAgSuCg5tS2iIMqBtpi5AEGfwNQdsShh4BBLA83uxryGyzKM5DIwS/NGwewYsiKYXrGnLOcZ//qv/4pyuYx4PN7srhBNZs6Iqa6uLnR3d+OZZ56Z9DW6u7tx++2345Of/CS2bNkypf7cd9992L17t7edy+WwfPnyKV1zPrB06VJ8//vfR7VaBecclrN6zG1blhVoW5YF0zRHbPuLYRgjan/xT+35a//03mhTff4pPrtkYZbqG0w2CoczMsUEwGlz/0iV7xivOW8hIZQGwTBnogCaCgcbuZJvoTLqaJM9wuQfbfL2c2vc3xVBLyGUfjGwjzGGUDiMSDyFSE1w/GSm+URRDEz3iaIYKIIgjFsYY15xt92+usXddu9NEHPit2D79u04duwYnn76aSxbtszb39nZCU3TkMlkAqNTvb296OwMrpw5c+YM3vnOd+LDH/4w/vzP/3zKfXL/gBcj7hvWfMUwDFQqFa+4Qee1geflcnlE4LltQ5D3tjVNAwA0JJOYAC6FYIkhQKoXY2XHVQUCzSV/sPnc8nOK/uQQ2ByImZoLcCmM0g23N7kTdpzTcJB5Tc3doHPT2+8PRGdOgWk4ge2GF+TO3H0TDEwXBAGx+MjA89raLf6VgpFIBOFwGIIwt37vCWIyNFVMcc6xY8cOHDlyBMePH8fq1asDx2+88UbIsoynnnoKW7duBQC89NJLuHDhAjZs2OCd97Of/Qy33norPvjBD+Izn/nMrD4DMfeQJMl7854q1Wp11JV9tbYI/v35wiBMY4JL3EUZ1igr+7jks0QQlREB7zOx8o/L4cmvYptG7t65x25IatP6wOVpMNa0HJsEo2xPaxkVOzmzL7Cb+QLAh8WO4Qmi6YQxhnBYRSSqIhpNBawRIpFIQADVCiNXOEUiEQr2Jgg0WUx1dXXh4MGDOHr0KOLxuBcHlUwmoaoqkskk7rrrLuzevRutra1IJBLYsWMHNmzY4AWfd3d349Zbb8Vtt92G3bt3e9dwHYhd3DigQqGAvr4+nD59Goqi4Prrr5/dhybmFe4IZVtb24RexzlHpVLx/Kb8gswd9fK3/fYM9vYgjImIMd+oGJfDnt8UlyN2wmE5Yht3KnbdyChYXV8loj6c2yKpYpt2CtWCnWBZKzr+UmUwvfFRPkUJOR5QcW/Kq3bay2/QWWvU6a6G87fd17sr5sjQkyCmj6ZaI4z2h3zgwAFs27YNwLBp56FDhwKmne4036c+9am6geIrV67Ea6+9Nua9as8ZC7JGIGYb/6iYX3TVjpBls1kvnUwmk0UunxvXKZ3Lqp2kWI6CK1FYoSi4YqeT4aEYuBRecDFg04KpDSdHrmQhVHIQKlmI1fyoI0fRaNRLJ9Pa2opUKoWWlhYkk0nE43EkEonAKFAkEqE4HIKYRmbj83tO+UzNZUhMEfMF0zSRz+e9/HyDg4MjEh339/ej9/JlaNVRPB4ECabi5OgLxWE5iY15KOEkOF7gSXxN3Ut0LDq1UMlA0IojTo1Go7jqqqtw1VVX4YorrsAVV1yBjo4OdHR0YOnSpYhEIk14AIIgXBadzxRBEFNHFEWkUimkUimsWrVq1PM45ygUCujt7cXly5eRTqfR29uLdDqNnp4e9PT0IJ99o+5rLSUKK5SAFU7ACiXAw7bIskIJQJxHbyum4SQvdoWT09YKI07t7OzEypX/f3t3HxzFXf8B/L0Pt/eUu8tBnkNyCQSSSME+KUIFa6FQRyuWOtbKKB21tBbQFjq21irUnwrazljHKR1btVUrpFapOOJ0nFIBKbRqB6oQiAXpYMnTJZfc8+Pu9/fH3m7ukgskubvcJfm8pt/Zvb3N3i7bvXzyffh8r4LL5UJ9fT3q6+tRV1eH0tJSai4jZIabQt96hJBc4jhO70jc1NSUcZ9AIICuri50dnbq5dKlS3jvvffgdneD+btG/AwzWCAbbUO1WsYStQnRaAOTLJM/alFR1P5LUZ/aLBf1JZvqvBmDprLycjQ2tKKxsVEv9fX1VMNECBkVBVOEkFGVlJRg/vz5mD9//oj3YrEYurq6cOnSpbTS2dmJ7u5uyIGeDEfkkv21LGp/LYM52VHenJZCAoIBjBPUhKt6kyKn/qcoAJP16VC06U6QiILXRsrFQ2rn71gQfCwEZMiBVF5eAZerBS6XC42NjWhoaIDL5aIEjISQcaNgihAyIZIkweVyweVyjXgvkUjA7Xajq6sL3d3deunt7YXb7Ybb7UYs2JfX8xNEERXl5aiqWoCqqiq9X1NtbS3mzJlDNU2EkJyhYIoQknOiKOqdsTPR+msNDAzoRUsj4ff79cmqtSz32s8wxmAwGPQs2CaTCTabDVarFTabDU6nE7NmzYLT6YTT6aSEkISQSUHBFCFk0qX216qvry/06RBCSFbozzZCCCGEkCxQMEUIIYQQkgUKpgghhBBCskDBFCGEEEJIFiiYIoQQQgjJAgVThBBCCCFZoNQIhMwgsiwjFAohGAwiEokgEokgHA4jEokgHo/reZ0SiQRkWdaXiqIg05zoHMfpRRAEcBwHnuchCMKoRRRFvQiCAIPBAFEUIUkSRFFMyyOlbaO57wghxYyCKUKmKC3xpcfjgcfjweDgIAYGBvSlz+dLK4FAEOFwqNCnPW4cx0GSJBhNJhiNRpiMRpjNZphMJpjNZpjNZlgsFlitVlgsFpSUlKQVm80Gu90Ou90Ok8lU6MshhExDFEwRUmQYYwiFQujr60NfXx/6+/szLj0ej54d/LLHE41gggQmmMFsDjBRAngDmGAA40V1HjxenQcPnKCucxzA8WAcD3VCvJQeARwHpNVSseRrBjCAg6K+ZgxgCsAUcBhaB1PAMWXofUVOvlbn3Eubey+5TCgyQrEEuEgEnBIApyQAJT7sPK5MkiTY7XaUlpbC4XCgtLR0RHE6nfrSYrFQrRgh5IoomCJkkjDGEAwG0d/fr9cmacHR8EApEomMfiCOh2IwgxkcUCxmMIMlOVmwBUw0gRlMUEQzIKoTB6cFQtNJMhjj5DigxMHJMXVdjumTH3NyDFwiCi4RAZeIQk5EEfFH4R64qE6OfAUGgwGlTiecwwIuh8ORVrSaL7vdDlGkr1VCZhp66gnJgizL8Pl8GBwc1EvqfHMDAwPweDz68vI1SRyYwQzFUAKltEINjiQLFIMFzGAGk6zJgMmo1g7NdBw3VJMGE8ZXRwVAUZJBVrLEw+Di2mt1XY6H0eMNw90/CIwh+AIAk9kMW7J5saSkBFarFVarVV+3WCwwm81p66lNliaTSS80tyAhU0NBg6mdO3di3759OHv2LMxmM5YtW4Yf/OAHaG5u1veJRCLYtm0b2traEI1GsWbNGuzevRuVlZUAgLfffhu7du3C0aNH0dfXh4aGBtx777342te+ph9j3759ePrpp3Hy5ElEo1EsXLgQO3bswJo1ayb9mklxYowhEonA7/fD7/fD5/Ppy9Ti9XrTit/vz9gxOw0vQBGH1ySZ1cBJsiRfq7VLE61Fspx6GVz8MrVZ5IqYwYTQVbeNvoOSSAZb0ZQALAJOjqbVfgUTMYR8UfQO+NTar3E2RaaSJGMyyDKlBVyZisViSStasKYFcmazmWrNCMmTgj5Zhw8fxqZNm/CBD3wAiUQCjzzyCFavXo329nZYrVYAwAMPPIADBw7gpZdegsPhwObNm7Fu3Tq8/vrrAIC33noLFRUVeOGFF1BXV4djx45h48aNEAQBmzdvBgAcOXIEN998M77//e+jtLQUzz33HG699Va8+eabuOaaawp2/SQ3GGOIxWIIhUJ6CQaDGUsgEEAgENDXteDJHwhATiTG9oEcDyaaoIhGsJIqvWlNXZqHmtqSARN4Q95rkrh4BHwinNfPGK/du3cDAO67774Cn8nYKFfagRfBjCVgxpKxH5QxNQiT1WZIJJsih5om4+CUOCAn1H5gchycktC3yUoC4VgCXCQATh5M9hUb4/+nGZhMJr1jfmptWeoytTZN25YanAmCMOHPJ2S64tgV/6yePG63GxUVFTh8+DBWrFgBr9eL8vJy7NmzB5/+9KcBAGfPnkVrayuOHz+OD33oQxmPs2nTJpw5cwavvfbaqJ+1cOFC3HHHHfj2t789pnPz+XxwOBzwer2w2+3jv7gZSpZlxOPxtGH3w0skEkE0GkUsFkM0GtWH7EejUX3YvrbU1sPhsBo0hUKIhMOQZXn8J8cLYIIRiiCpfYsEo9pZW5TA9HWtmNRO3AZzsqN2cTWzWU/spWAqS4poRvCaOwt9GlemddrXAzIt+EoNzoaCtqEgLgYukexbpsTBJaLqscbJaDTCYrXCmqwBG15DltpMadJGYCaXkiSNWGrFYDDo6TC0NBuE5MJk/P4uqjpfr9cLAJg1axYAtdYpHo9j1apV+j4tLS2or6+/bDDl9Xr1Y2SiKAr8fv9l94lGo4hGo/prn88HAEgkEjhw4AA6Ojr098by0I8Ws47nC2P4MSb6ZaPlDFIURV+XZVnfpuUV0oosy2nl4sWLiEaj+vkwxlIKAAy9zhcGLhnQJJfJ2h+Wuk1f8iPf09aTOEUGlBC4+NRLHQAAXIKa+LLFJSKwnN5f6NPIOyaoIzlhsEAfiZkcjclpIyzTRmgmtyfXI/EEIgNeeDwDyScoP8+5lr8M4GAwiJg9e7aehyw1V9nwvGWpSy1HWabvSsYYOI4bNX+atn34PtqxUrfdcMMNWLJkSY7/BchUUjTBlKIouP/++3HDDTfgqquuAgB0d3dDkiSUlpam7VtZWYnu7u6Mxzl27BhefPFFHDhwYNTPeuKJJxAIBPCZz3xm1H127tyJxx57bMT2N954A48//vgYrojkE6d/2SPtu5z+liUTxYFBCPUX+jRIUuofZNGojM7OzgKf0ej++Mc/4ve//z1mz55d6FMhBVI0wdSmTZtw6tQpHD16dMLHOHXqFNauXYvt27dj9erVGffZs2cPHnvsMezfvx8VFRWjHusb3/gGtm7dqr/2+Xyoq6vDtddei9tvvx1HjhxJ2z/1Lx/tL57U16lS39f+6hnt5y93rCv97HjP5XKvGWNQGIOckBEMBvRmtclrJebUmClDzRMbXts0bMk4LU+Stm364UMe7V+ITBADB8Uyem31tKfXPilpNVFDtVdqjVXG95I/n+//BzmOg2gwQBREGAwiBFGEmOzDlfqdOfx7LPV9bZ9UmfZP/TlFUUZ8Z6e+/5nPfOayLR1k+iuKYGrz5s3405/+hCNHjmDOnDn69qqqKsRiMQwODqbVTvX09KCqqirtGO3t7Vi5ciU2btyIRx99NOPntLW14ctf/jJeeumltKbDTIxGI4xG44jtFosFW7ZswZYtW8ZxhdMbYwyJRCKtxONxfRmLxUYstf5R2jK1pPaX0l5r/aQikQhC4TDCoRAURe3vMe7wSJDUflLJvlJMkNScTII01EdqRJ8p46R0JJ8o64m94Iqsz9RUw0QTQgvXFvo0xkfr4K51Ytf6SClD/aU4OQYkhtb1XFzJwidfT4TJbIYlOZJQG22oLbW+Ulp/qdSS2ldqtH5T2ro21ZDWdEdIMSro/5mMMWzZsgUvv/wyDh06hMbGxrT3r7vuOhgMBhw8eBC33347AKCjowMXL17E0qVL9f1Onz6Nm266CRs2bMD3vve9jJ+1d+9efPGLX0RbWxs+/vGP5++iZiCO4/QvvsmijeDTOqJrJRwOIxgMIhQKIRAI6CP7tBF8QyP6gvD7/QgGB8GUMXbC5Xgw0aiO4hOTo/e0dYMJTDQPWxonLWEmM5iuPBptkt371W3qimgu7ImMETPkeaoZpgx1Ek8dzaetK4nkqD61U7keJCmJ5OvkSD9tn+Tov4kQBEEdqed0ZBzdN3w9Nc2CtqQ8WIQMKehovvvuuw979uzB/v3703JLORwOmM3qF/BXvvIV/PnPf8bzzz8Pu92u1wgdO3YMgNq0d9NNN2HNmjVpfZkEQUB5eTkAtWlvw4YN+PGPf4x169bp+5jNZjgcjjGdK43mm54URUEoFILf79dTJQzPMeX3+/W8UmquKR98ft8YgjAumY3cpOeVYgaLnjKBGSx6nikIkxeIkixoNUFajqmElncqmpJvKpocOaeOouO12qEsUhoAgCCKMJlMah6plJFzlmGj6rSARwt6hpeSkhIYjUYaLUdmjMn4/V3QYGq0h/m5557DXXfdBWAoaefevXvTknZqzXw7duzI2FHc5XLh3XffBQDceOONOHz48Ih9NmzYgOeff35M50rBFEmlKAoCgYCe9dzr9WbMfq5mPh9AIOC//AEFAxSDOZntXMt8bgWTtABM3QaemjlyTk/GmZ4JndfW9cApDD4RHXNQZDAYUGKzoSRDrqbUACi1iSxTegFtGzVxETIx0z6YmkoomCLZ0Pr+eTwefS6+1HV1UuN+DAwOXLbGi4nGZM2WJUNNl7kgTYxFgTF1kmR9br6h2qG0GqNEegZzPhEdU1OZyWyGs9SJ0lJH2iTJdrtdn5vP4XDAZrPBbrfrtT+EkMKbcXmmCJmuJElCRUXFZUeQAmoes8HBwbRJj7WiBV7uvj74vFceJj6iI73W6V6QgGSuIcaLgCCqS05QE5lyAsDzah+xZI6u4Xm5Uj4luUjJU5Q6+kubjBjaa7VwipL+mikAk9X58pic3EcGFFkNkpSEmqhS0foSpfQvSmYYH08CSqPRmAyKauBwODBr1iw9QHI6nXA6nfqkxk6nkwIjQshlUTBFSBERRRFlZWUoKyu77H5aTVdqDVfqRMuDg4P6XII+nxex4MRGaxUjjuNgNJlgNptgNjvS+glpS22SYZvNptcWpRaTKc+dzQkhMwoFU4RMQWOt6dJEo9G0eQm1kY9a2gktTYVWEokEFEXRU12kZ7lnelZprd8jz/NpRRAEvfA8P2p2au21JElp2a21ofHakHltmL32mjpPE0KKCQVThMwAWn4fytBMCCG5N4N6qBJCCCGE5B4FU4QQQgghWaBgihBCCCEkCxRMEUIIIYRkgYIpQgghhJAs0Gg+QkhR0SaxjkQieonFYpBlGYqiQJZlAEhLvyBJkj4NC029QgiZbPSNQwjJO1mW4fF40jK6p85fODg4mDbJdCKR3aTAJrMZjuQUL1qG8/LycpSVlaG8vBzV1dWoqqqCxWLJ0RUSQmYyCqYIIVlLJBLo7e1FV1cXuru70dXVhZ6eHvT09KC7uxt9fX1QLjPnIDhenXdQkADTLHXaG14EeBFMUKe6YRw/bFqb5PQ1TElONRPXp5gJJmIIDQTR0+e57Nx7TqcTtbW1qK+v10tjYyOqqqooMSghZMwomCKEjEkgEEBnZ+fI0tWF3p6ezMESx0ExWKFYysEkK5hkhWKwgEnqRM1KcnJmCIZkoJQHigwuHgIfC4GLB8HFQuCjfvBRP/rCfgycbsepU6fSfsRstmDevLmYN28empqasGDBAjQ2NkKSpPycIyFkSqNgihACQG2K6+vrQ2dnJ7q6utICpkuXLsHv92f8OUWyQrFWgBltUKQSKEYbmLEEilQCJlkArsDjXHgBzGiDbLRlfp8p4KJ+COFB8BEv+PAA5JAHp4YFWYIoYm5jI1paWtDc3IyWlhY0NDRQ/yxCCDjGGCv0SUwFPp8PDocDXq8Xdru90KdDyLgxxjA4OKg3w6UuOzs70d3TAzlTXyVegCzZ1GDJZINitCcDJhsUYwnAT9NgQpHBRwbBhzwQgn0QQv0QQh5AGfo3koxGLJg/H+973/vQ0tKC1tZWaiIkpMhMxu9vCqbGiIIpUuxkWUZ/fz96e3v1vkqp/Za6u7sRjUYz/iwzWKAY1VolrajBkh3MYM5fE9xUwxTw4UHwwT4IQbcaZIUH1H5bSXa7Pa32asGCBSgrK6MAi5ACoWCqiFAwRQqJMQav1wu3243e3l69uN1u9PT0oLe3F319fXragBE/L5qSTXAleo2SIpVM/9qlyaAk1NqrgFsPsPioL20Xp9OJBQsWYP78+Zg3bx7mzp2L2tpaaiIkZBJQMFVEKJgi+cAYQyAQgMfjQX9/v54+oL+/Py2NgNvtHj1dAMdDMVigSFYwqQSKUVuWqB2+pRK1gzeZPImoWmsV7AMf6ocQ6gcfTe9zZjAY4HK59FJfX485c+agpqaGUjYQkkOT8fu7oH8W7dy5E/v27cPZs2dhNpuxbNky/OAHP0Bzc7O+TyQSwbZt29DW1oZoNIo1a9Zg9+7dqKysBAC8/fbb2LVrF44ePYq+vj40NDTg3nvvxde+9jX9GEePHsVDDz2Es2fPIhQKweVy4Z577sEDDzww6ddMpi/GGMLhMPx+f1rx+Xzwer160fIqaTmWLptTiePVEW+mWclRcNZk0DS0VJvhct/J23LqZXDxSM6PW+yYwYTQVbdldxDRCNlRC9lRO7QtEYUQ8oAPD4APeSCHPTj333dx7ty5ET/ucJSitrYGlZWVqKioQEVFBcrLyzF79mw4nU44nU6YzebszpEQkjMFDaYOHz6MTZs24QMf+AASiQQeeeQRrF69Gu3t7bBarQCABx54AAcOHMBLL70Eh8OBzZs3Y926dXj99dcBAG+99RYqKirwwgsvoK6uDseOHcPGjRshCAI2b94MALBardi8eTMWL14Mq9WKo0eP4p577oHVasXGjRsLdv1k8jHGIMsyYrEYotGovhxeUrNvRyIRhMPhtBIKhRAKhRAMBhEMBhEIBBEMBcEul0splWCAIprBjLOglJjBJDOYQUsXYFHXJQuYaCpYfyUuHgGfCBfkswFg9+7dAID77rtvUj93jHdw/EQjZHs1ZHv10DbG1LQN2kjCiA981A9P1Afv2Q60t7ePejiTyQSHwwG73Q6bzQa73Q6r1aoXi8UCs9msZ4bXiiRJMJlMMBqNkCQJkiTBaDRCEIR8XTkh015RNfO53W5UVFTg8OHDWLFiBbxeL8rLy7Fnzx58+tOfBgCcPXsWra2tOH78OD70oQ9lPM6mTZtw5swZvPbaa6N+1rp162C1WvHrX/96TOdGzXwjMcagKIo+xcfwkkgk0payLCMej6dtj8fjadu1Zep66jJ131gspm9P3aa9jsfjiMViiMXjiMfiiMfV9TEHPFfCccnkkgZ1KUpgggQIEphoVIu+bkoWI5jBNCX6KFlP7J2ZwZRoRvCaOyf1MzNiDFwiDC4aBB8PgosFwcXD4OJh8PGIui5HwCdigBzL+uMEQYBBC66SS60YDAZ9mVpEUdSX2tQ+2jrP82kFADiO08vQZTK9DH+tfcdk2m+41GNzHKd/Zuo5pJ6Xtp46LdHwbZl+TnutfQbP8/p6puVESuq5p26jQQwTM+2b+Ybzer0AgFmzZgFQa53i8ThWrVql79PS0oL6+vrLBlNer1c/RiYnTpzAsWPH8N3vfnfUfbQaCo3P5xt13+niZz/7GV544YVCn0Z2OF7NK8TxgJwAoH3xcskaHvV9NYm2mk2bpazr27VM28klA9K3pWXiTn6CHAcnxwEE836Zk4FLzLwmPkC9bsvp/YU+jTFhghGyYEy+UADGoGeGB9NfDy2hLsHApayDAQnGEI8pCIIHF4kALASOyeAUGVDktBGLZHqxWCx45plnMGfOnEKfypRVNMGUoii4//77ccMNN+Cqq64CAHR3d0OSJJSWlqbtW1lZie7u7ozHOXbsGF588UUcOHBgxHtz5szRO/Lu2LEDX/7yl0c9n507d+Kxxx6b+AVNQZed7mOq4HioAQ8P9RdGyjWNUgdLf+uRVBwYhFB/oU+jMHiAgQPj+GRAxoFxHDiOp2BqGlMUZdQaPzI2RRNMbdq0CadOncLRo0cnfIxTp05h7dq12L59O1avXj3i/b/97W8IBAJ444038PDDD6OpqQl33pm5Ov8b3/gGtm7dqr/2+Xyoq6ub8LlNBRs3bhxzH7LUJr7Rmvm0przUZr2xlNRmu8s1+Q1fT30di8XQ09ODeDyun2s+vizY8FqsZDDHuPTX4PjktuHzyxUvPuTR6uRmFAYOimX0mu1JxRQ1QztTAEUZeo1kLVSyNipX90mAArsYh0GSYDJaRzTvDW/q05r5Upv3RFEc0Tw2vBlrxGVeoalveBn+s8OPPbypLVMzXWqT3pWa/UZr7hve1JepeS+1eW60pr9MzXpXep8Ul6IIpjZv3ow//elPOHLkSFo1Y1VVFWKxGAYHB9Nqp3p6elBVVZV2jPb2dqxcuRIbN27Eo48+mvFzGhsbAQCLFi1CT08PduzYMWowZTQaYTQas7yy6Uv7gppqnVYZY3q/Kq0DutYJPRKJIBaLIRwO68vhndC1juehUAjhcFjvgB4MBhEKhcCS2bGv9FXHBAnMYFI7oRtSi0UdvSclO6EXsAO69cRecAXsM1UoTDQhtHDtJH0YU/tARbQO6F7wUT+4iB9CzK82r41CkoxwlM6CPdn53GazoaSkBBaLBVarVe98rhWj0ah3Qte+37RO6FoHdPolTcjEFDSYYoxhy5YtePnll3Ho0CE92NFcd911MBgMOHjwIG6//XYAQEdHBy5evIilS5fq+50+fRo33XQTNmzYgO9973tj+mxFUUbNBk2mL47j9F8iJSUlOT22oigIhULw+/0IBALw+XwjUiMMDg7q6RHUFAl9kAOj/8JMzyFlATOMlhoh978EmcGUv5FtY3DvV7epK+LkpgBgBlN+DizH0lIjCKEB8JFBcBk6j9vtdtQ0zEdVVZWeFiE1NcKsWbNgNpsp+CGkSBQ0mNq0aRP27NmD/fv3w2az6f2gHA4HzGYzHA4HvvSlL2Hr1q2YNWsW7HY7tmzZgqVLl+qdz0+dOoWbbroJa9aswdatW/VjCIKA8vJyAMBTTz2F+vp6tLS0AACOHDmCJ554Al/96lcLcNVkuuJ5HiUlJeMK0hRFgc/ng8fj0cvwpJ1utxv9/ZcJujgeimRJTixsTWY2VzOcq+vWCY0ezDrX0kyWiEEIpSTtDPaPyIouiCJcrno9YWd9fT3q6upQU1OT80CfEJJfBU2NMNpfVc899xzuuusuAENJO/fu3ZuWtFNr5tuxY0fGjuIulwvvvvsuAOAnP/kJfvrTn+LChQsQRRHz5s3D3XffjXvuuUdvi74SSo1ACklRFAwMDMDtdutFm4NPm1Kmr79/1LQPzGCGIlmhSLb0KWWMNjCpRB3hSCZGm05Gn6/PDT6SHjjZHQ40L1iApqYmNDU1obGxEfX19TSdDCGTgKaTKSIUTJFil0gk0NfXh97e3rRJjvWJjnt6EI9lzkekGCwpkxunTHZsshe031bRYYrarynYl5yLrw9C2JM20q2kxIbWVnWiY62Ul5dTkxwhBTLj8kwRQiZOFEVUVVWhqqoKixcvHvE+Ywwej0cNrLq70dXVhe7ubnR2dqKrqws9PT1QAj0jD8yLkLXgymiDYrIPBVvTuVZLSQZOoT61mS7UDzHUDyhD0/8YDAYseF8rWlvV0tzcjNraWgqcCJlhKJgiZIbgOA6zZ8/G7NmzsXDhwhHvJxIJuN1udHV1obOzUw+yLl26hEudnQgOXsx0VLX50JjafGhL9tWy5a1zfE4xBVw0oI6oC3shhAfAhz0QwoNpNU6CIKChsQHNzc1oaWlBa2srGhsbqamOEELBFCFEJYoiqqurUV1djWuvvXbE+z6fTw+yhgdbbnc3mD9DjwGOT/bVKgGTLFAMyVGJkiUtLQR4MX9Bl6KAi4fUOfBiIXCxIPioX01BEM2cgsBkMmFuawvmzZuHpqYmLFiwAHPnzqV0KYSQjCiYIoSMid1uh91u10fFporFYujt7dWbDrVmQ6309feA+S+TaIEXwQQJiiiBCUZ1fkNBVLfzojpFEPj0qXz0hJUKoMjqVD5KApwSB5eIgUtEwMlRcInR561zOEpR07gALpdLH1XncrlQU1Mz5sEphBBCwRQhJGuSJGHOnDmjzu2VSCTg8Xjgdrv1lA8DAwPweDwYHBzEwMBAMjeXH4FAH2T5Mrm3xsBoNMLucMBhr4TdbkdZWZleysvL9Ro4q9Wa1ecQQghAwRQhZBKIooiKigpUVFRccV/GGCKRCKLRKMLhsD7peOrURYwxffoSnudhNBphNpv1DN+SJE3CVRFCiIqCqTHSMkj4fL4r7EkIyQWe52G1Wsdde6RN/0MIIcDQ7+18ZoKiYGqM+vvVWeSn+2THhBBCyHTU398Ph8ORl2NTMDVGs2aps8hfvHgxbzeDjI3P50NdXR3+97//UQLVAqN7UTzoXhQPuhfFxev1or6+Xv89ng8UTI2RNrLH4XDQw1EktNFlpPDoXhQPuhfFg+5FccnnCF0a+0sIIYQQkgUKpgghhBBCskDB1BgZjUZs376dMiAXAboXxYPuRfGge1E86F4Ul8m4HxzL51hBQgghhJBpjmqmCCGEEEKyQMEUIYQQQkgWKJgihBBCCMkCBVOEEEIIIVmYscHUU089hYaGBphMJixZsgR///vfR9339OnTuP3229HQ0ACO4/Dkk0+O2GfHjh3gOC6ttLS05PEKpo/x3Itnn30Wy5cvh9PphNPpxKpVq0bszxjDt7/9bVRXV8NsNmPVqlV455138n0Z00Ku78Vdd9014rm45ZZb8n0Z08Z47se+fftw/fXXo7S0FFarFVdffTV+/etfp+1Dz8bE5fpe0LMxceO5F6na2trAcRw+9alPpW3PyXPBZqC2tjYmSRL7xS9+wU6fPs3uvvtuVlpaynp6ejLu//e//509+OCDbO/evayqqor96Ec/GrHP9u3b2cKFC1lXV5de3G53nq9k6hvvvfjc5z7HnnrqKXbixAl25swZdtdddzGHw8Hee+89fZ9du3Yxh8PB/vCHP7C3336bffKTn2SNjY0sHA5P1mVNSfm4Fxs2bGC33HJL2nPh8Xgm65KmtPHej7/+9a9s3759rL29nZ07d449+eSTTBAE9sorr+j70LMxMfm4F/RsTMx474XmwoULrLa2li1fvpytXbs27b1cPBczMpj64Ac/yDZt2qS/lmWZ1dTUsJ07d17xZ10u16jB1Pvf//4cnuXMkM29YIyxRCLBbDYb++Uvf8kYY0xRFFZVVcUef/xxfZ/BwUFmNBrZ3r17c3vy00yu7wVj6i+M4V9cZGyyvR+MMXbNNdewRx99lDFGz0Y2cn0vGKNnY6Imci8SiQRbtmwZ+9nPfjbi3z1Xz8WMa+aLxWJ46623sGrVKn0bz/NYtWoVjh8/ntWx33nnHdTU1GDu3LlYv349Ll68mO3pTmu5uBehUAjxeFyfwPLChQvo7u5OO6bD4cCSJUuyvr/TWT7uhebQoUOoqKhAc3MzvvKVr6C/vz+n5z4dZXs/GGM4ePAgOjo6sGLFCgD0bExUPu6Fhp6N8ZnovfjOd76DiooKfOlLXxrxXq6eixk30XFfXx9kWUZlZWXa9srKSpw9e3bCx12yZAmef/55NDc3o6urC4899hiWL1+OU6dOwWazZXva01Iu7sVDDz2Empoa/UHo7u7WjzH8mNp7ZKR83AsAuOWWW7Bu3To0Njbi/PnzeOSRR/Cxj30Mx48fhyAIOb2G6WSi98Pr9aK2thbRaBSCIGD37t24+eabAdCzMVH5uBcAPRsTMZF7cfToUfz85z/HyZMnM76fq+dixgVT+fKxj31MX1+8eDGWLFkCl8uF3/72txmjYZK9Xbt2oa2tDYcOHYLJZCr06cxoo92Lz372s/r6okWLsHjxYsybNw+HDh3CypUrC3Gq05rNZsPJkycRCARw8OBBbN26FXPnzsWNN95Y6FObca50L+jZyD+/34/Pf/7zePbZZ1FWVpbXz5pxwVRZWRkEQUBPT0/a9p6eHlRVVeXsc0pLS7FgwQKcO3cuZ8ecbrK5F0888QR27dqFV199FYsXL9a3az/X09OD6urqtGNeffXVuTv5aSYf9yKTuXPnoqysDOfOnaNfGJcx0fvB8zyampoAAFdffTXOnDmDnTt34sYbb6RnY4LycS8yoWfjysZ7L86fP493330Xt956q75NURQAgCiK6OjoyNlzMeP6TEmShOuuuw4HDx7UtymKgoMHD2Lp0qU5+5xAIIDz58+n3RySbqL34oc//CH+7//+D6+88gquv/76tPcaGxtRVVWVdkyfz4c333wzp/d3usnHvcjkvffeQ39/Pz0XV5Cr7ylFURCNRgHQszFR+bgXmdCzcWXjvRctLS3497//jZMnT+rlk5/8JD760Y/i5MmTqKury91zMZ5e9NNFW1sbMxqN7Pnnn2ft7e1s48aNrLS0lHV3dzPGGPv85z/PHn74YX3/aDTKTpw4wU6cOMGqq6vZgw8+yE6cOMHeeecdfZ9t27axQ4cOsQsXLrDXX3+drVq1ipWVlbHe3t5Jv76pZLz3YteuXUySJPa73/0ubUix3+9P26e0tJTt37+f/etf/2Jr166l4d9jkOt74ff72YMPPsiOHz/OLly4wF599VV27bXXsvnz57NIJFKQa5xKxns/vv/977O//OUv7Pz586y9vZ098cQTTBRF9uyzz+r70LMxMbm+F/RsTNx478VwmUZR5uK5mJHBFGOM/eQnP2H19fVMkiT2wQ9+kL3xxhv6ex/5yEfYhg0b9NcXLlxgAEaUj3zkI/o+d9xxB6uurmaSJLHa2lp2xx13sHPnzk3iFU1d47kXLpcr473Yvn27vo+iKOxb3/oWq6ysZEajka1cuZJ1dHRM4hVNXbm8F6FQiK1evZqVl5czg8HAXC4Xu/vuu/UvPXJl47kf3/zmN1lTUxMzmUzM6XSypUuXsra2trTj0bMxcbm8F/RsZGc892K4TMFULp4LjjHGxlfRRgghhBBCNDOuzxQhhBBCSC5RMEUIIYQQkgUKpgghhBBCskDBFCGEEEJIFiiYIoQQQgjJAgVThBBCCCFZoGCKEEIIISQLFEwRQgghhGSBgilCCCGEkCxQMEUIIYQQkgUKpgghhBBCskDBFCFk2nrllVfw4Q9/GKWlpZg9ezY+8YlP4Pz58wCAZcuW4aGHHkrb3+12w2Aw4MiRIwCArq4ufPzjH4fZbEZjYyP27NmDhoYGPPnkk5N9KYSQIkbBFCFk2goGg9i6dSv++c9/4uDBg+B5HrfddhsURcH69evR1taG1LneX3zxRdTU1GD58uUAgC984Qvo7OzEoUOH8Pvf/x7PPPMMent7C3U5hJAixbHUbxJCCJnG+vr6UF5ejn//+9+orKxETU0NXnvtNT14WrZsGVasWIFdu3bh7NmzaG1txT/+8Q9cf/31AIBz585h/vz5+NGPfoT777+/gFdCCCkmVDNFCJm23nnnHdx5552YO3cu7HY7GhoaAAAXL15EeXk5Vq9ejd/85jcAgAsXLuD48eNYv349AKCjowOiKOLaa6/Vj9fU1ASn0znp10EIKW4UTBFCpq1bb70VHo8Hzz77LN588028+eabAIBYLAYAWL9+PX73u98hHo9jz549WLRoERYtWlTIUyaETEEUTBFCpqX+/n50dHTg0UcfxcqVK9Ha2oqBgYG0fdauXYtIJIJXXnkFe/bs0WulAKC5uRmJRAInTpzQt507d27EMQghRCz0CRBCSD44nU7Mnj0bzzzzDKqrq3Hx4kU8/PDDaftYrVZ86lOfwre+9S2cOXMGd955p/5eS0sLVq1ahY0bN+Lpp5+GwWDAtm3bYDabwXHcZF8OIaSIUc0UIWRa4nkebW1teOutt3DVVVfhgQcewOOPPz5iv/Xr1+Ptt9/G8uXLUV9fn/ber371K1RWVmLFihW47bbbcPfdd8Nms8FkMk3WZRBCpgAazUcIIWP03nvvoa6uDq+++ipWrlxZ6NMhhBQJCqYIIWQUr732GgKBABYtWoSuri58/etfx6VLl/Cf//wHBoOh0KdHCCkS1GeKEEJGEY/H8cgjj+C///0vbDYbli1bht/85jcUSBFC0lDNFCGEEEJIFqgDOiGEEEJIFiiYIoQQQgjJAgVThBBCCCFZoGCKEEIIISQLFEwRQgghhGSBgilCCCGEkCxQMEUIIYQQkgUKpgghhBBCsvD/I2rpS5sQqNoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = sns_df[\"year\"] >= 2010\n",
    "fig, ax = plt.subplots()\n",
    "sns.violinplot(\n",
    "    data=sns_df[mask],\n",
    "    ax=ax,\n",
    "    x=\"avg\",\n",
    "    y=\"year\",\n",
    "    order=sns_df.loc[mask, \"year\"].unique(),\n",
    ")\n",
    "ax.set_xlim(0.15, 0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>league_mean_avg</th>\n",
       "      <th>league_max_avg</th>\n",
       "      <th>batting_champion</th>\n",
       "      <th>max_normalized_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>0.261457</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>(2023, arral001)</td>\n",
       "      <td>3.714121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.281307</td>\n",
       "      <td>0.372159</td>\n",
       "      <td>(2004, suzui001)</td>\n",
       "      <td>3.699129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.275713</td>\n",
       "      <td>0.369727</td>\n",
       "      <td>(2002, bondb001)</td>\n",
       "      <td>3.553521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.271227</td>\n",
       "      <td>0.359073</td>\n",
       "      <td>(2010, hamij003)</td>\n",
       "      <td>3.379203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.277991</td>\n",
       "      <td>0.364465</td>\n",
       "      <td>(2008, jonec004)</td>\n",
       "      <td>3.320429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      league_mean_avg  league_max_avg  batting_champion  max_normalized_avg\n",
       "year                                                                       \n",
       "2023         0.261457        0.353659  (2023, arral001)            3.714121\n",
       "2004         0.281307        0.372159  (2004, suzui001)            3.699129\n",
       "2002         0.275713        0.369727  (2002, bondb001)            3.553521\n",
       "2010         0.271227        0.359073  (2010, hamij003)            3.379203\n",
       "2008         0.277991        0.364465  (2008, jonec004)            3.320429"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def normalize(ser: pd.Series) -> pd.Series:\n",
    "    return (ser - ser.mean()) / ser.std()\n",
    "\n",
    "(\n",
    "    averages.assign(\n",
    "        normalized_avg=averages.groupby(\"year\").transform(normalize)\n",
    "    )\n",
    "    .groupby(\"year\").agg(\n",
    "        league_mean_avg=pd.NamedAgg(column=\"avg\", aggfunc=\"mean\"),\n",
    "        league_max_avg=pd.NamedAgg(column=\"avg\", aggfunc=\"max\"),\n",
    "        batting_champion=pd.NamedAgg(column=\"avg\", aggfunc=\"idxmax\"),\n",
    "        max_normalized_avg=pd.NamedAgg(column=\"normalized_avg\", aggfunc=\"max\"),\n",
    "    )\n",
    "    .sort_values(by=\"max_normalized_avg\", ascending=False)\n",
    ").head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Pandas-Cookbook-Third-Edition",
   "language": "python",
   "name": "pandas-cookbook-third-edition"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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