005 - SeasonsAnalysis.ipynb

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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>matches</th>\n",
       "      <th>mmr</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>kolemoen</td>\n",
       "      <td>DE</td>\n",
       "      <td>431</td>\n",
       "      <td>10484</td>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>kams134</td>\n",
       "      <td>PL</td>\n",
       "      <td>923</td>\n",
       "      <td>10477</td>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>TailBot</td>\n",
       "      <td>PL</td>\n",
       "      <td>538</td>\n",
       "      <td>10472</td>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Pajabol</td>\n",
       "      <td>PL</td>\n",
       "      <td>820</td>\n",
       "      <td>10471</td>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Adzikov</td>\n",
       "      <td>PL</td>\n",
       "      <td>1105</td>\n",
       "      <td>10442</td>\n",
       "      <td>M2_01 Wolf 2020</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14790</td>\n",
       "      <td>MagCCG</td>\n",
       "      <td>RU</td>\n",
       "      <td>4</td>\n",
       "      <td>389</td>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>14791</td>\n",
       "      <td>可乐加冰.</td>\n",
       "      <td>CN</td>\n",
       "      <td>4</td>\n",
       "      <td>386</td>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>14792</td>\n",
       "      <td>RomanTrooper</td>\n",
       "      <td>RU</td>\n",
       "      <td>4</td>\n",
       "      <td>386</td>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14793</td>\n",
       "      <td>Naveless</td>\n",
       "      <td>DE</td>\n",
       "      <td>4</td>\n",
       "      <td>384</td>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>14794</td>\n",
       "      <td>Pargali</td>\n",
       "      <td>PL</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182799 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rank          name country  matches    mmr           season\n",
       "0        1      kolemoen      DE      431  10484  M2_01 Wolf 2020\n",
       "1        2       kams134      PL      923  10477  M2_01 Wolf 2020\n",
       "2        3       TailBot      PL      538  10472  M2_01 Wolf 2020\n",
       "3        4       Pajabol      PL      820  10471  M2_01 Wolf 2020\n",
       "4        5       Adzikov      PL     1105  10442  M2_01 Wolf 2020\n",
       "..     ...           ...     ...      ...    ...              ...\n",
       "15   14790        MagCCG      RU        4    389  M4_02 Love 2022\n",
       "41   14791         可乐加冰.      CN        4    386  M4_02 Love 2022\n",
       "109  14792  RomanTrooper      RU        4    386  M4_02 Love 2022\n",
       "20   14793      Naveless      DE        4    384  M4_02 Love 2022\n",
       "55   14794       Pargali      PL        1     96  M4_02 Love 2022\n",
       "\n",
       "[182799 rows x 6 columns]"
      ]
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       "                var nbb_unformatted_code = \"%load_ext nb_black\\nimport pandas as pd\\nimport numpy as np\\nfrom os import listdir, path\\n\\noutput_path = \\\"./output\\\"\\n\\nplayers_df = (\\n    pd.concat(\\n        pd.read_excel(path.join(output_path, f))\\n        for f in listdir(\\\"./output\\\")\\n        if f.startswith(\\\"season_\\\")\\n    )\\n    .drop(columns=[\\\"Unnamed: 0\\\"])\\n    .dropna()\\n).sort_values([\\\"season\\\", \\\"rank\\\"])\\nplayers_df\";\n",
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    "%load_ext nb_black\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from os import listdir, path\n",
    "\n",
    "output_path = \"./output\"\n",
    "\n",
    "players_df = (\n",
    "    pd.concat(\n",
    "        pd.read_excel(path.join(output_path, f))\n",
    "        for f in listdir(\"./output\")\n",
    "        if f.startswith(\"season_\")\n",
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    "    .drop(columns=[\"Unnamed: 0\"])\n",
    "    .dropna()\n",
    ").sort_values([\"season\", \"rank\"])\n",
    "players_df"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>matches</th>\n",
       "      <th>mmr</th>\n",
       "      <th>season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>3259</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>142</td>\n",
       "      <td>9617</td>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>1138</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
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       "      <td>9746</td>\n",
       "      <td>M2_06 Magic 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>1167</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>260</td>\n",
       "      <td>9820</td>\n",
       "      <td>M2_07 Griffin 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1255</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>214</td>\n",
       "      <td>9779</td>\n",
       "      <td>M2_08 Draconid 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>12816</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>97</td>\n",
       "      <td>3360</td>\n",
       "      <td>M2_09 Dryad 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056</th>\n",
       "      <td>1057</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>351</td>\n",
       "      <td>9846</td>\n",
       "      <td>M2_10 Cat 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1638</th>\n",
       "      <td>1639</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>154</td>\n",
       "      <td>9785</td>\n",
       "      <td>M2_11 Mahakam 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>12856</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>80</td>\n",
       "      <td>7407</td>\n",
       "      <td>M2_12 Wild Hunt 2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>1467</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>175</td>\n",
       "      <td>9724</td>\n",
       "      <td>M3_01 Wolf 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>406</td>\n",
       "      <td>9903</td>\n",
       "      <td>M3_02 Love 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1784</th>\n",
       "      <td>9353</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>63</td>\n",
       "      <td>5384</td>\n",
       "      <td>M3_03 Bear 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>14501</td>\n",
       "      <td>sepro</td>\n",
       "      <td>BE</td>\n",
       "      <td>40</td>\n",
       "      <td>2836</td>\n",
       "      <td>M3_04 Elf 2021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rank   name country  matches   mmr                season\n",
       "2084   3259  sepro      BE      142  9617      M2_05 Viper 2020\n",
       "1137   1138  sepro      BE      254  9746      M2_06 Magic 2020\n",
       "1166   1167  sepro      BE      260  9820    M2_07 Griffin 2020\n",
       "1254   1255  sepro      BE      214  9779   M2_08 Draconid 2020\n",
       "2237  12816  sepro      BE       97  3360      M2_09 Dryad 2020\n",
       "1056   1057  sepro      BE      351  9846        M2_10 Cat 2020\n",
       "1638   1639  sepro      BE      154  9785    M2_11 Mahakam 2020\n",
       "2515  12856  sepro      BE       80  7407  M2_12 Wild Hunt 2020\n",
       "1466   1467  sepro      BE      175  9724       M3_01 Wolf 2021\n",
       "679     680  sepro      BE      406  9903       M3_02 Love 2021\n",
       "1784   9353  sepro      BE       63  5384       M3_03 Bear 2021\n",
       "2553  14501  sepro      BE       40  2836        M3_04 Elf 2021"
      ]
     },
     "execution_count": 2,
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    "players_df[players_df[\"name\"] == \"sepro\"]"
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       "                var nbb_unformatted_code = \"def sdi(data) -> float:\\n    \\\"\\\"\\\"\\n    Given a list of counts, calculate the Shannon Diversity Index\\n\\n    :param data: list of integers\\n    :return: Shannon Diversity Index\\n    \\\"\\\"\\\"\\n\\n    def p(n, N):\\n        return n / N * np.log(n / N)\\n\\n    x = np.array([d for d in data if d != 0])\\n\\n    N = np.sum(x.astype(\\\"float64\\\"))\\n\\n    if N == 0:\\n        raise ZeroDivisionError(\\n            \\\"Cannot calculate Shannon Diversity Index when the sum of all observations is zero\\\"\\n        )\\n\\n    return 0 - np.sum(np.array([p(n, N) for n in x]))\\n\\n\\ndef dominance(data) -> float:\\n    N = np.sum(np.array(data).astype(\\\"float64\\\"))\\n\\n    if N > 0:\\n        proportions = data / N\\n\\n        return np.sum(np.square(proportions))\\n    else:\\n        return np.nan\\n\\n\\ndef simpson(data) -> float:\\n    d = dominance(data)\\n\\n    if np.isnan(d):\\n        return np.nan\\n    else:\\n        return 1 - d\";\n",
       "                var nbb_formatted_code = \"def sdi(data) -> float:\\n    \\\"\\\"\\\"\\n    Given a list of counts, calculate the Shannon Diversity Index\\n\\n    :param data: list of integers\\n    :return: Shannon Diversity Index\\n    \\\"\\\"\\\"\\n\\n    def p(n, N):\\n        return n / N * np.log(n / N)\\n\\n    x = np.array([d for d in data if d != 0])\\n\\n    N = np.sum(x.astype(\\\"float64\\\"))\\n\\n    if N == 0:\\n        raise ZeroDivisionError(\\n            \\\"Cannot calculate Shannon Diversity Index when the sum of all observations is zero\\\"\\n        )\\n\\n    return 0 - np.sum(np.array([p(n, N) for n in x]))\\n\\n\\ndef dominance(data) -> float:\\n    N = np.sum(np.array(data).astype(\\\"float64\\\"))\\n\\n    if N > 0:\\n        proportions = data / N\\n\\n        return np.sum(np.square(proportions))\\n    else:\\n        return np.nan\\n\\n\\ndef simpson(data) -> float:\\n    d = dominance(data)\\n\\n    if np.isnan(d):\\n        return np.nan\\n    else:\\n        return 1 - d\";\n",
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    "def sdi(data) -> float:\n",
    "    \"\"\"\n",
    "    Given a list of counts, calculate the Shannon Diversity Index\n",
    "\n",
    "    :param data: list of integers\n",
    "    :return: Shannon Diversity Index\n",
    "    \"\"\"\n",
    "\n",
    "    def p(n, N):\n",
    "        return n / N * np.log(n / N)\n",
    "\n",
    "    x = np.array([d for d in data if d != 0])\n",
    "\n",
    "    N = np.sum(x.astype(\"float64\"))\n",
    "\n",
    "    if N == 0:\n",
    "        raise ZeroDivisionError(\n",
    "            \"Cannot calculate Shannon Diversity Index when the sum of all observations is zero\"\n",
    "        )\n",
    "\n",
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    "\n",
    "\n",
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    "\n",
    "    if N > 0:\n",
    "        proportions = data / N\n",
    "\n",
    "        return np.sum(np.square(proportions))\n",
    "    else:\n",
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    "\n",
    "\n",
    "def simpson(data) -> float:\n",
    "    d = dominance(data)\n",
    "\n",
    "    if np.isnan(d):\n",
    "        return np.nan\n",
    "    else:\n",
    "        return 1 - d"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>country</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>AE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>AM</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>AR</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>AT</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>AU</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2116</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>US</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2117</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>UZ</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2118</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>VN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2119</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>ZA</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2120</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>ZW</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               season country  count\n",
       "0     M2_01 Wolf 2020      AE      1\n",
       "1     M2_01 Wolf 2020      AM      1\n",
       "2     M2_01 Wolf 2020      AR      4\n",
       "3     M2_01 Wolf 2020      AT     11\n",
       "4     M2_01 Wolf 2020      AU     20\n",
       "...               ...     ...    ...\n",
       "2116  M4_02 Love 2022      US    101\n",
       "2117  M4_02 Love 2022      UZ      3\n",
       "2118  M4_02 Love 2022      VN      6\n",
       "2119  M4_02 Love 2022      ZA      7\n",
       "2120  M4_02 Love 2022      ZW      1\n",
       "\n",
       "[2121 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
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       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
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       "                var nbb_formatted_code = \"season_country_df = (\\n    players_df[players_df[\\\"rank\\\"] <= 2000]\\n    .groupby([\\\"season\\\", \\\"country\\\"])\\n    .agg(count=pd.NamedAgg(\\\"name\\\", \\\"count\\\"))\\n    .reset_index()\\n)\\nseason_country_df\";\n",
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    "    .groupby([\"season\", \"country\"])\n",
    "    .agg(count=pd.NamedAgg(\"name\", \"count\"))\n",
    "    .reset_index()\n",
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>richness</th>\n",
       "      <th>shannon_diversity</th>\n",
       "      <th>dominance</th>\n",
       "      <th>simpson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>79</td>\n",
       "      <td>2.911451</td>\n",
       "      <td>0.109716</td>\n",
       "      <td>0.890284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_02 Love 2020</td>\n",
       "      <td>73</td>\n",
       "      <td>2.784907</td>\n",
       "      <td>0.124585</td>\n",
       "      <td>0.875415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_03 Bear 2020</td>\n",
       "      <td>74</td>\n",
       "      <td>2.770397</td>\n",
       "      <td>0.123829</td>\n",
       "      <td>0.876171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_04 Elf 2020</td>\n",
       "      <td>78</td>\n",
       "      <td>2.921609</td>\n",
       "      <td>0.105208</td>\n",
       "      <td>0.894792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "      <td>82</td>\n",
       "      <td>2.975021</td>\n",
       "      <td>0.106955</td>\n",
       "      <td>0.893045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M2_06 Magic 2020</td>\n",
       "      <td>79</td>\n",
       "      <td>3.028216</td>\n",
       "      <td>0.100404</td>\n",
       "      <td>0.899596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M2_07 Griffin 2020</td>\n",
       "      <td>80</td>\n",
       "      <td>3.085621</td>\n",
       "      <td>0.092351</td>\n",
       "      <td>0.907649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2_08 Draconid 2020</td>\n",
       "      <td>83</td>\n",
       "      <td>3.043551</td>\n",
       "      <td>0.097257</td>\n",
       "      <td>0.902743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M2_09 Dryad 2020</td>\n",
       "      <td>83</td>\n",
       "      <td>3.071002</td>\n",
       "      <td>0.093658</td>\n",
       "      <td>0.906342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M2_10 Cat 2020</td>\n",
       "      <td>82</td>\n",
       "      <td>3.028427</td>\n",
       "      <td>0.101374</td>\n",
       "      <td>0.898626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M2_11 Mahakam 2020</td>\n",
       "      <td>83</td>\n",
       "      <td>2.981676</td>\n",
       "      <td>0.110916</td>\n",
       "      <td>0.889084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M2_12 Wild Hunt 2020</td>\n",
       "      <td>76</td>\n",
       "      <td>3.040627</td>\n",
       "      <td>0.099488</td>\n",
       "      <td>0.900512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M3_01 Wolf 2021</td>\n",
       "      <td>86</td>\n",
       "      <td>3.088299</td>\n",
       "      <td>0.098081</td>\n",
       "      <td>0.901919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M3_02 Love 2021</td>\n",
       "      <td>84</td>\n",
       "      <td>3.045043</td>\n",
       "      <td>0.101261</td>\n",
       "      <td>0.898739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M3_03 Bear 2021</td>\n",
       "      <td>86</td>\n",
       "      <td>3.086869</td>\n",
       "      <td>0.090533</td>\n",
       "      <td>0.909467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M3_04 Elf 2021</td>\n",
       "      <td>83</td>\n",
       "      <td>3.124564</td>\n",
       "      <td>0.091177</td>\n",
       "      <td>0.908823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M3_05 Viper 2021</td>\n",
       "      <td>82</td>\n",
       "      <td>3.147269</td>\n",
       "      <td>0.086137</td>\n",
       "      <td>0.913863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M3_06 Magic 2021</td>\n",
       "      <td>82</td>\n",
       "      <td>3.162725</td>\n",
       "      <td>0.084816</td>\n",
       "      <td>0.915184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M3_07 Griffin 2021</td>\n",
       "      <td>84</td>\n",
       "      <td>3.083937</td>\n",
       "      <td>0.098090</td>\n",
       "      <td>0.901910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M3_08 Draconid 2021</td>\n",
       "      <td>82</td>\n",
       "      <td>3.023176</td>\n",
       "      <td>0.103806</td>\n",
       "      <td>0.896194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>86</td>\n",
       "      <td>3.009823</td>\n",
       "      <td>0.108551</td>\n",
       "      <td>0.891449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M3_10 Cat 2021</td>\n",
       "      <td>89</td>\n",
       "      <td>3.132842</td>\n",
       "      <td>0.091885</td>\n",
       "      <td>0.908115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M3_11 Mahakam 2021</td>\n",
       "      <td>82</td>\n",
       "      <td>2.989644</td>\n",
       "      <td>0.110278</td>\n",
       "      <td>0.889722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M3_12 Wild Hunt 2021</td>\n",
       "      <td>80</td>\n",
       "      <td>3.101439</td>\n",
       "      <td>0.092863</td>\n",
       "      <td>0.907137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M4_01 Wolf 2022</td>\n",
       "      <td>84</td>\n",
       "      <td>3.012610</td>\n",
       "      <td>0.112893</td>\n",
       "      <td>0.887107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>79</td>\n",
       "      <td>3.030534</td>\n",
       "      <td>0.104149</td>\n",
       "      <td>0.895851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  season  richness  shannon_diversity  dominance   simpson\n",
       "0        M2_01 Wolf 2020        79           2.911451   0.109716  0.890284\n",
       "1        M2_02 Love 2020        73           2.784907   0.124585  0.875415\n",
       "2        M2_03 Bear 2020        74           2.770397   0.123829  0.876171\n",
       "3         M2_04 Elf 2020        78           2.921609   0.105208  0.894792\n",
       "4       M2_05 Viper 2020        82           2.975021   0.106955  0.893045\n",
       "5       M2_06 Magic 2020        79           3.028216   0.100404  0.899596\n",
       "6     M2_07 Griffin 2020        80           3.085621   0.092351  0.907649\n",
       "7    M2_08 Draconid 2020        83           3.043551   0.097257  0.902743\n",
       "8       M2_09 Dryad 2020        83           3.071002   0.093658  0.906342\n",
       "9         M2_10 Cat 2020        82           3.028427   0.101374  0.898626\n",
       "10    M2_11 Mahakam 2020        83           2.981676   0.110916  0.889084\n",
       "11  M2_12 Wild Hunt 2020        76           3.040627   0.099488  0.900512\n",
       "12       M3_01 Wolf 2021        86           3.088299   0.098081  0.901919\n",
       "13       M3_02 Love 2021        84           3.045043   0.101261  0.898739\n",
       "14       M3_03 Bear 2021        86           3.086869   0.090533  0.909467\n",
       "15        M3_04 Elf 2021        83           3.124564   0.091177  0.908823\n",
       "16      M3_05 Viper 2021        82           3.147269   0.086137  0.913863\n",
       "17      M3_06 Magic 2021        82           3.162725   0.084816  0.915184\n",
       "18    M3_07 Griffin 2021        84           3.083937   0.098090  0.901910\n",
       "19   M3_08 Draconid 2021        82           3.023176   0.103806  0.896194\n",
       "20      M3_09 Dryad 2021        86           3.009823   0.108551  0.891449\n",
       "21        M3_10 Cat 2021        89           3.132842   0.091885  0.908115\n",
       "22    M3_11 Mahakam 2021        82           2.989644   0.110278  0.889722\n",
       "23  M3_12 Wild Hunt 2021        80           3.101439   0.092863  0.907137\n",
       "24       M4_01 Wolf 2022        84           3.012610   0.112893  0.887107\n",
       "25       M4_02 Love 2022        79           3.030534   0.104149  0.895851"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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       "                var nbb_formatted_code = \"season_diversity_df = (\\n    season_country_df.groupby([\\\"season\\\"])\\n    .agg(\\n        richness=pd.NamedAgg(\\\"count\\\", \\\"count\\\"),\\n        shannon_diversity=pd.NamedAgg(\\\"count\\\", sdi),\\n        dominance=pd.NamedAgg(\\\"count\\\", dominance),\\n        simpson=pd.NamedAgg(\\\"count\\\", simpson),\\n    )\\n    .reset_index()\\n)\\nseason_diversity_df.set_index(\\\"season\\\").to_excel(\\n    \\\"./output/seasonal_diversity_of_nations.xlsx\\\"\\n)\\nseason_diversity_df\";\n",
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    "season_diversity_df = (\n",
    "    season_country_df.groupby([\"season\"])\n",
    "    .agg(\n",
    "        richness=pd.NamedAgg(\"count\", \"count\"),\n",
    "        shannon_diversity=pd.NamedAgg(\"count\", sdi),\n",
    "        dominance=pd.NamedAgg(\"count\", dominance),\n",
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    "    \"./output/seasonal_diversity_of_nations.xlsx\"\n",
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>players_found</th>\n",
       "      <th>players_total</th>\n",
       "      <th>players_captured</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>2905</td>\n",
       "      <td>2997</td>\n",
       "      <td>96.930264</td>\n",
       "      <td>32.846715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_02 Love 2020</td>\n",
       "      <td>3984</td>\n",
       "      <td>4883</td>\n",
       "      <td>81.589187</td>\n",
       "      <td>55.561048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_03 Bear 2020</td>\n",
       "      <td>4772</td>\n",
       "      <td>6632</td>\n",
       "      <td>71.954162</td>\n",
       "      <td>50.689290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_04 Elf 2020</td>\n",
       "      <td>6070</td>\n",
       "      <td>10209</td>\n",
       "      <td>59.457342</td>\n",
       "      <td>43.679412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "      <td>5977</td>\n",
       "      <td>10079</td>\n",
       "      <td>59.301518</td>\n",
       "      <td>43.177725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M2_06 Magic 2020</td>\n",
       "      <td>6104</td>\n",
       "      <td>9919</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>45.955518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M2_07 Griffin 2020</td>\n",
       "      <td>7372</td>\n",
       "      <td>14791</td>\n",
       "      <td>49.841120</td>\n",
       "      <td>37.817450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2_08 Draconid 2020</td>\n",
       "      <td>7037</td>\n",
       "      <td>13800</td>\n",
       "      <td>50.992754</td>\n",
       "      <td>38.180987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M2_09 Dryad 2020</td>\n",
       "      <td>7056</td>\n",
       "      <td>14554</td>\n",
       "      <td>48.481517</td>\n",
       "      <td>35.881649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M2_10 Cat 2020</td>\n",
       "      <td>6987</td>\n",
       "      <td>16011</td>\n",
       "      <td>43.638748</td>\n",
       "      <td>31.381644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M2_11 Mahakam 2020</td>\n",
       "      <td>6644</td>\n",
       "      <td>16752</td>\n",
       "      <td>39.660936</td>\n",
       "      <td>27.238699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M2_12 Wild Hunt 2020</td>\n",
       "      <td>7806</td>\n",
       "      <td>22464</td>\n",
       "      <td>34.748932</td>\n",
       "      <td>25.229545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M3_01 Wolf 2021</td>\n",
       "      <td>6197</td>\n",
       "      <td>13428</td>\n",
       "      <td>46.149836</td>\n",
       "      <td>31.576457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M3_02 Love 2021</td>\n",
       "      <td>7119</td>\n",
       "      <td>16016</td>\n",
       "      <td>44.449301</td>\n",
       "      <td>32.373062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M3_03 Bear 2021</td>\n",
       "      <td>7012</td>\n",
       "      <td>13636</td>\n",
       "      <td>51.422705</td>\n",
       "      <td>38.530067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M3_04 Elf 2021</td>\n",
       "      <td>10107</td>\n",
       "      <td>16049</td>\n",
       "      <td>62.975886</td>\n",
       "      <td>54.947305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M3_05 Viper 2021</td>\n",
       "      <td>7771</td>\n",
       "      <td>17623</td>\n",
       "      <td>44.095784</td>\n",
       "      <td>33.265596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M3_06 Magic 2021</td>\n",
       "      <td>10899</td>\n",
       "      <td>17442</td>\n",
       "      <td>62.487100</td>\n",
       "      <td>55.129612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M3_07 Griffin 2021</td>\n",
       "      <td>7771</td>\n",
       "      <td>16772</td>\n",
       "      <td>46.333174</td>\n",
       "      <td>35.300460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M3_08 Draconid 2021</td>\n",
       "      <td>8333</td>\n",
       "      <td>17523</td>\n",
       "      <td>47.554642</td>\n",
       "      <td>37.325240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>7561</td>\n",
       "      <td>17486</td>\n",
       "      <td>43.240307</td>\n",
       "      <td>32.141392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M3_10 Cat 2021</td>\n",
       "      <td>8023</td>\n",
       "      <td>14048</td>\n",
       "      <td>57.111333</td>\n",
       "      <td>46.147658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M3_11 Mahakam 2021</td>\n",
       "      <td>9147</td>\n",
       "      <td>19902</td>\n",
       "      <td>45.960205</td>\n",
       "      <td>36.891210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M3_12 Wild Hunt 2021</td>\n",
       "      <td>9566</td>\n",
       "      <td>21651</td>\n",
       "      <td>44.182717</td>\n",
       "      <td>35.687297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M4_01 Wolf 2022</td>\n",
       "      <td>7607</td>\n",
       "      <td>15520</td>\n",
       "      <td>49.014175</td>\n",
       "      <td>37.496051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>2972</td>\n",
       "      <td>14794</td>\n",
       "      <td>20.089225</td>\n",
       "      <td>0.938495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  season  players_found  players_total  players_captured  \\\n",
       "0        M2_01 Wolf 2020           2905           2997         96.930264   \n",
       "1        M2_02 Love 2020           3984           4883         81.589187   \n",
       "2        M2_03 Bear 2020           4772           6632         71.954162   \n",
       "3         M2_04 Elf 2020           6070          10209         59.457342   \n",
       "4       M2_05 Viper 2020           5977          10079         59.301518   \n",
       "5       M2_06 Magic 2020           6104           9919         61.538462   \n",
       "6     M2_07 Griffin 2020           7372          14791         49.841120   \n",
       "7    M2_08 Draconid 2020           7037          13800         50.992754   \n",
       "8       M2_09 Dryad 2020           7056          14554         48.481517   \n",
       "9         M2_10 Cat 2020           6987          16011         43.638748   \n",
       "10    M2_11 Mahakam 2020           6644          16752         39.660936   \n",
       "11  M2_12 Wild Hunt 2020           7806          22464         34.748932   \n",
       "12       M3_01 Wolf 2021           6197          13428         46.149836   \n",
       "13       M3_02 Love 2021           7119          16016         44.449301   \n",
       "14       M3_03 Bear 2021           7012          13636         51.422705   \n",
       "15        M3_04 Elf 2021          10107          16049         62.975886   \n",
       "16      M3_05 Viper 2021           7771          17623         44.095784   \n",
       "17      M3_06 Magic 2021          10899          17442         62.487100   \n",
       "18    M3_07 Griffin 2021           7771          16772         46.333174   \n",
       "19   M3_08 Draconid 2021           8333          17523         47.554642   \n",
       "20      M3_09 Dryad 2021           7561          17486         43.240307   \n",
       "21        M3_10 Cat 2021           8023          14048         57.111333   \n",
       "22    M3_11 Mahakam 2021           9147          19902         45.960205   \n",
       "23  M3_12 Wild Hunt 2021           9566          21651         44.182717   \n",
       "24       M4_01 Wolf 2022           7607          15520         49.014175   \n",
       "25       M4_02 Love 2022           2972          14794         20.089225   \n",
       "\n",
       "    extra_players_captured  \n",
       "0                32.846715  \n",
       "1                55.561048  \n",
       "2                50.689290  \n",
       "3                43.679412  \n",
       "4                43.177725  \n",
       "5                45.955518  \n",
       "6                37.817450  \n",
       "7                38.180987  \n",
       "8                35.881649  \n",
       "9                31.381644  \n",
       "10               27.238699  \n",
       "11               25.229545  \n",
       "12               31.576457  \n",
       "13               32.373062  \n",
       "14               38.530067  \n",
       "15               54.947305  \n",
       "16               33.265596  \n",
       "17               55.129612  \n",
       "18               35.300460  \n",
       "19               37.325240  \n",
       "20               32.141392  \n",
       "21               46.147658  \n",
       "22               36.891210  \n",
       "23               35.687297  \n",
       "24               37.496051  \n",
       "25                0.938495  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"summary_df = (\\n    players_df.groupby([\\\"season\\\"])\\n    .agg(\\n        players_found=pd.NamedAgg(\\\"name\\\", \\\"count\\\"),\\n        players_total=pd.NamedAgg(\\\"rank\\\", \\\"max\\\"),\\n    )\\n    .reset_index()\\n)\\n\\nsummary_df[\\\"players_captured\\\"] = (\\n    summary_df[\\\"players_found\\\"] * 100 / summary_df[\\\"players_total\\\"]\\n)\\nsummary_df[\\\"extra_players_captured\\\"] = (\\n    (summary_df[\\\"players_found\\\"] - 2860) * 100 / (summary_df[\\\"players_total\\\"] - 2860)\\n)\\nsummary_df.set_index(\\\"season\\\").to_excel(\\\"./output/seasonal_player_counts.xlsx\\\")\\nsummary_df\";\n",
       "                var nbb_formatted_code = \"summary_df = (\\n    players_df.groupby([\\\"season\\\"])\\n    .agg(\\n        players_found=pd.NamedAgg(\\\"name\\\", \\\"count\\\"),\\n        players_total=pd.NamedAgg(\\\"rank\\\", \\\"max\\\"),\\n    )\\n    .reset_index()\\n)\\n\\nsummary_df[\\\"players_captured\\\"] = (\\n    summary_df[\\\"players_found\\\"] * 100 / summary_df[\\\"players_total\\\"]\\n)\\nsummary_df[\\\"extra_players_captured\\\"] = (\\n    (summary_df[\\\"players_found\\\"] - 2860) * 100 / (summary_df[\\\"players_total\\\"] - 2860)\\n)\\nsummary_df.set_index(\\\"season\\\").to_excel(\\\"./output/seasonal_player_counts.xlsx\\\")\\nsummary_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_df = (\n",
    "    players_df.groupby([\"season\"])\n",
    "    .agg(\n",
    "        players_found=pd.NamedAgg(\"name\", \"count\"),\n",
    "        players_total=pd.NamedAgg(\"rank\", \"max\"),\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "summary_df[\"players_captured\"] = (\n",
    "    summary_df[\"players_found\"] * 100 / summary_df[\"players_total\"]\n",
    ")\n",
    "summary_df[\"extra_players_captured\"] = (\n",
    "    (summary_df[\"players_found\"] - 2860) * 100 / (summary_df[\"players_total\"] - 2860)\n",
    ")\n",
    "summary_df.set_index(\"season\").to_excel(\"./output/seasonal_player_counts.xlsx\")\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>season</th>\n",
       "      <th>players_found</th>\n",
       "      <th>players_total</th>\n",
       "      <th>players_captured</th>\n",
       "      <th>extra_players_captured</th>\n",
       "      <th>richness</th>\n",
       "      <th>shannon_diversity</th>\n",
       "      <th>dominance</th>\n",
       "      <th>simpson</th>\n",
       "      <th>top500_cutoff</th>\n",
       "      <th>top200_cutoff</th>\n",
       "      <th>top64_cutoff</th>\n",
       "      <th>low_estimate</th>\n",
       "      <th>high_estimate</th>\n",
       "      <th>mean_estimate</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M2_01 Wolf 2020</td>\n",
       "      <td>2905</td>\n",
       "      <td>2997</td>\n",
       "      <td>96.930264</td>\n",
       "      <td>32.846715</td>\n",
       "      <td>79</td>\n",
       "      <td>2.911451</td>\n",
       "      <td>0.109716</td>\n",
       "      <td>0.890284</td>\n",
       "      <td>9749</td>\n",
       "      <td>9872</td>\n",
       "      <td>10061</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3117.636364</td>\n",
       "      <td>124.944153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2_02 Love 2020</td>\n",
       "      <td>3984</td>\n",
       "      <td>4883</td>\n",
       "      <td>81.589187</td>\n",
       "      <td>55.561048</td>\n",
       "      <td>73</td>\n",
       "      <td>2.784907</td>\n",
       "      <td>0.124585</td>\n",
       "      <td>0.875415</td>\n",
       "      <td>9832</td>\n",
       "      <td>9952</td>\n",
       "      <td>10117</td>\n",
       "      <td>4566.666667</td>\n",
       "      <td>7100.0</td>\n",
       "      <td>5620.242424</td>\n",
       "      <td>441.315936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M2_03 Bear 2020</td>\n",
       "      <td>4772</td>\n",
       "      <td>6632</td>\n",
       "      <td>71.954162</td>\n",
       "      <td>50.689290</td>\n",
       "      <td>74</td>\n",
       "      <td>2.770397</td>\n",
       "      <td>0.123829</td>\n",
       "      <td>0.876171</td>\n",
       "      <td>9867</td>\n",
       "      <td>9995</td>\n",
       "      <td>10204</td>\n",
       "      <td>6036.363636</td>\n",
       "      <td>10300.0</td>\n",
       "      <td>7329.272727</td>\n",
       "      <td>760.229978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M2_04 Elf 2020</td>\n",
       "      <td>6070</td>\n",
       "      <td>10209</td>\n",
       "      <td>59.457342</td>\n",
       "      <td>43.679412</td>\n",
       "      <td>78</td>\n",
       "      <td>2.921609</td>\n",
       "      <td>0.105208</td>\n",
       "      <td>0.894792</td>\n",
       "      <td>9952</td>\n",
       "      <td>10087</td>\n",
       "      <td>10293</td>\n",
       "      <td>9927.272727</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>12319.454545</td>\n",
       "      <td>1494.370140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M2_05 Viper 2020</td>\n",
       "      <td>5977</td>\n",
       "      <td>10079</td>\n",
       "      <td>59.301518</td>\n",
       "      <td>43.177725</td>\n",
       "      <td>82</td>\n",
       "      <td>2.975021</td>\n",
       "      <td>0.106955</td>\n",
       "      <td>0.893045</td>\n",
       "      <td>9910</td>\n",
       "      <td>10028</td>\n",
       "      <td>10255</td>\n",
       "      <td>7766.666667</td>\n",
       "      <td>11400.0</td>\n",
       "      <td>9372.060606</td>\n",
       "      <td>727.332197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M2_06 Magic 2020</td>\n",
       "      <td>6104</td>\n",
       "      <td>9919</td>\n",
       "      <td>61.538462</td>\n",
       "      <td>45.955518</td>\n",
       "      <td>79</td>\n",
       "      <td>3.028216</td>\n",
       "      <td>0.100404</td>\n",
       "      <td>0.899596</td>\n",
       "      <td>9896</td>\n",
       "      <td>10002</td>\n",
       "      <td>10191</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>9800.0</td>\n",
       "      <td>8320.181818</td>\n",
       "      <td>618.201230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M2_07 Griffin 2020</td>\n",
       "      <td>7372</td>\n",
       "      <td>14791</td>\n",
       "      <td>49.841120</td>\n",
       "      <td>37.817450</td>\n",
       "      <td>80</td>\n",
       "      <td>3.085621</td>\n",
       "      <td>0.092351</td>\n",
       "      <td>0.907649</td>\n",
       "      <td>9978</td>\n",
       "      <td>10100</td>\n",
       "      <td>10289</td>\n",
       "      <td>12836.363636</td>\n",
       "      <td>19900.0</td>\n",
       "      <td>14683.272727</td>\n",
       "      <td>1331.618216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M2_08 Draconid 2020</td>\n",
       "      <td>7037</td>\n",
       "      <td>13800</td>\n",
       "      <td>50.992754</td>\n",
       "      <td>38.180987</td>\n",
       "      <td>83</td>\n",
       "      <td>3.043551</td>\n",
       "      <td>0.097257</td>\n",
       "      <td>0.902743</td>\n",
       "      <td>9946</td>\n",
       "      <td>10061</td>\n",
       "      <td>10246</td>\n",
       "      <td>9566.666667</td>\n",
       "      <td>13300.0</td>\n",
       "      <td>11186.242424</td>\n",
       "      <td>696.853229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>M2_09 Dryad 2020</td>\n",
       "      <td>7056</td>\n",
       "      <td>14554</td>\n",
       "      <td>48.481517</td>\n",
       "      <td>35.881649</td>\n",
       "      <td>83</td>\n",
       "      <td>3.071002</td>\n",
       "      <td>0.093658</td>\n",
       "      <td>0.906342</td>\n",
       "      <td>9946</td>\n",
       "      <td>10046</td>\n",
       "      <td>10183</td>\n",
       "      <td>9733.333333</td>\n",
       "      <td>12580.0</td>\n",
       "      <td>11218.666667</td>\n",
       "      <td>458.757501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>M2_10 Cat 2020</td>\n",
       "      <td>6987</td>\n",
       "      <td>16011</td>\n",
       "      <td>43.638748</td>\n",
       "      <td>31.381644</td>\n",
       "      <td>82</td>\n",
       "      <td>3.028427</td>\n",
       "      <td>0.101374</td>\n",
       "      <td>0.898626</td>\n",
       "      <td>9977</td>\n",
       "      <td>10067</td>\n",
       "      <td>10176</td>\n",
       "      <td>12800.000000</td>\n",
       "      <td>14620.0</td>\n",
       "      <td>13774.181818</td>\n",
       "      <td>369.275995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>M2_11 Mahakam 2020</td>\n",
       "      <td>6644</td>\n",
       "      <td>16752</td>\n",
       "      <td>39.660936</td>\n",
       "      <td>27.238699</td>\n",
       "      <td>83</td>\n",
       "      <td>2.981676</td>\n",
       "      <td>0.110916</td>\n",
       "      <td>0.889084</td>\n",
       "      <td>10000</td>\n",
       "      <td>10090</td>\n",
       "      <td>10216</td>\n",
       "      <td>12995.454545</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>16041.757576</td>\n",
       "      <td>976.287680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>M2_12 Wild Hunt 2020</td>\n",
       "      <td>7806</td>\n",
       "      <td>22464</td>\n",
       "      <td>34.748932</td>\n",
       "      <td>25.229545</td>\n",
       "      <td>76</td>\n",
       "      <td>3.040627</td>\n",
       "      <td>0.099488</td>\n",
       "      <td>0.900512</td>\n",
       "      <td>10070</td>\n",
       "      <td>10172</td>\n",
       "      <td>10313</td>\n",
       "      <td>12995.454545</td>\n",
       "      <td>35900.0</td>\n",
       "      <td>23051.757576</td>\n",
       "      <td>3678.743988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>M3_01 Wolf 2021</td>\n",
       "      <td>6197</td>\n",
       "      <td>13428</td>\n",
       "      <td>46.149836</td>\n",
       "      <td>31.576457</td>\n",
       "      <td>86</td>\n",
       "      <td>3.088299</td>\n",
       "      <td>0.098081</td>\n",
       "      <td>0.901919</td>\n",
       "      <td>9916</td>\n",
       "      <td>10044</td>\n",
       "      <td>10295</td>\n",
       "      <td>7968.181818</td>\n",
       "      <td>13700.0</td>\n",
       "      <td>9929.636364</td>\n",
       "      <td>1046.105394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>M3_02 Love 2021</td>\n",
       "      <td>7119</td>\n",
       "      <td>16016</td>\n",
       "      <td>44.449301</td>\n",
       "      <td>32.373062</td>\n",
       "      <td>84</td>\n",
       "      <td>3.045043</td>\n",
       "      <td>0.101261</td>\n",
       "      <td>0.898739</td>\n",
       "      <td>9975</td>\n",
       "      <td>10097</td>\n",
       "      <td>10325</td>\n",
       "      <td>11645.454545</td>\n",
       "      <td>19500.0</td>\n",
       "      <td>13977.757576</td>\n",
       "      <td>1430.087791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M3_03 Bear 2021</td>\n",
       "      <td>7012</td>\n",
       "      <td>13636</td>\n",
       "      <td>51.422705</td>\n",
       "      <td>38.530067</td>\n",
       "      <td>86</td>\n",
       "      <td>3.086869</td>\n",
       "      <td>0.090533</td>\n",
       "      <td>0.909467</td>\n",
       "      <td>9914</td>\n",
       "      <td>10026</td>\n",
       "      <td>10230</td>\n",
       "      <td>7466.666667</td>\n",
       "      <td>10900.0</td>\n",
       "      <td>9195.333333</td>\n",
       "      <td>715.137594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>M3_04 Elf 2021</td>\n",
       "      <td>10107</td>\n",
       "      <td>16049</td>\n",
       "      <td>62.975886</td>\n",
       "      <td>54.947305</td>\n",
       "      <td>83</td>\n",
       "      <td>3.124564</td>\n",
       "      <td>0.091177</td>\n",
       "      <td>0.908823</td>\n",
       "      <td>9992</td>\n",
       "      <td>10102</td>\n",
       "      <td>10323</td>\n",
       "      <td>11586.363636</td>\n",
       "      <td>20300.0</td>\n",
       "      <td>14814.606061</td>\n",
       "      <td>1527.809485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>M3_05 Viper 2021</td>\n",
       "      <td>7771</td>\n",
       "      <td>17623</td>\n",
       "      <td>44.095784</td>\n",
       "      <td>33.265596</td>\n",
       "      <td>82</td>\n",
       "      <td>3.147269</td>\n",
       "      <td>0.086137</td>\n",
       "      <td>0.913863</td>\n",
       "      <td>9998</td>\n",
       "      <td>10106</td>\n",
       "      <td>10300</td>\n",
       "      <td>12990.909091</td>\n",
       "      <td>20900.0</td>\n",
       "      <td>16159.515152</td>\n",
       "      <td>1322.547676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M3_06 Magic 2021</td>\n",
       "      <td>10899</td>\n",
       "      <td>17442</td>\n",
       "      <td>62.487100</td>\n",
       "      <td>55.129612</td>\n",
       "      <td>82</td>\n",
       "      <td>3.162725</td>\n",
       "      <td>0.084816</td>\n",
       "      <td>0.915184</td>\n",
       "      <td>9974</td>\n",
       "      <td>10082</td>\n",
       "      <td>10278</td>\n",
       "      <td>11263.636364</td>\n",
       "      <td>17200.0</td>\n",
       "      <td>13526.060606</td>\n",
       "      <td>1034.558786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>M3_07 Griffin 2021</td>\n",
       "      <td>7771</td>\n",
       "      <td>16772</td>\n",
       "      <td>46.333174</td>\n",
       "      <td>35.300460</td>\n",
       "      <td>84</td>\n",
       "      <td>3.083937</td>\n",
       "      <td>0.098090</td>\n",
       "      <td>0.901910</td>\n",
       "      <td>9958</td>\n",
       "      <td>10067</td>\n",
       "      <td>10287</td>\n",
       "      <td>10140.909091</td>\n",
       "      <td>15800.0</td>\n",
       "      <td>12241.515152</td>\n",
       "      <td>1040.267969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>M3_08 Draconid 2021</td>\n",
       "      <td>8333</td>\n",
       "      <td>17523</td>\n",
       "      <td>47.554642</td>\n",
       "      <td>37.325240</td>\n",
       "      <td>82</td>\n",
       "      <td>3.023176</td>\n",
       "      <td>0.103806</td>\n",
       "      <td>0.896194</td>\n",
       "      <td>9954</td>\n",
       "      <td>10079</td>\n",
       "      <td>10281</td>\n",
       "      <td>11200.000000</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>12854.545455</td>\n",
       "      <td>1051.176279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>7561</td>\n",
       "      <td>17486</td>\n",
       "      <td>43.240307</td>\n",
       "      <td>32.141392</td>\n",
       "      <td>86</td>\n",
       "      <td>3.009823</td>\n",
       "      <td>0.108551</td>\n",
       "      <td>0.891449</td>\n",
       "      <td>9968</td>\n",
       "      <td>10078</td>\n",
       "      <td>10250</td>\n",
       "      <td>11868.181818</td>\n",
       "      <td>15900.0</td>\n",
       "      <td>13442.969697</td>\n",
       "      <td>746.264455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M3_10 Cat 2021</td>\n",
       "      <td>8023</td>\n",
       "      <td>14048</td>\n",
       "      <td>57.111333</td>\n",
       "      <td>46.147658</td>\n",
       "      <td>89</td>\n",
       "      <td>3.132842</td>\n",
       "      <td>0.091885</td>\n",
       "      <td>0.908115</td>\n",
       "      <td>9879</td>\n",
       "      <td>9974</td>\n",
       "      <td>10083</td>\n",
       "      <td>5100.000000</td>\n",
       "      <td>7770.0</td>\n",
       "      <td>6559.939394</td>\n",
       "      <td>575.186402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M3_11 Mahakam 2021</td>\n",
       "      <td>9147</td>\n",
       "      <td>19902</td>\n",
       "      <td>45.960205</td>\n",
       "      <td>36.891210</td>\n",
       "      <td>82</td>\n",
       "      <td>2.989644</td>\n",
       "      <td>0.110278</td>\n",
       "      <td>0.889722</td>\n",
       "      <td>10012</td>\n",
       "      <td>10122</td>\n",
       "      <td>10252</td>\n",
       "      <td>12981.818182</td>\n",
       "      <td>23300.0</td>\n",
       "      <td>17920.363636</td>\n",
       "      <td>1647.708090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M3_12 Wild Hunt 2021</td>\n",
       "      <td>9566</td>\n",
       "      <td>21651</td>\n",
       "      <td>44.182717</td>\n",
       "      <td>35.687297</td>\n",
       "      <td>80</td>\n",
       "      <td>3.101439</td>\n",
       "      <td>0.092863</td>\n",
       "      <td>0.907137</td>\n",
       "      <td>10032</td>\n",
       "      <td>10140</td>\n",
       "      <td>10271</td>\n",
       "      <td>12981.818182</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>19567.696970</td>\n",
       "      <td>2331.781053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M4_01 Wolf 2022</td>\n",
       "      <td>7607</td>\n",
       "      <td>15520</td>\n",
       "      <td>49.014175</td>\n",
       "      <td>37.496051</td>\n",
       "      <td>84</td>\n",
       "      <td>3.012610</td>\n",
       "      <td>0.112893</td>\n",
       "      <td>0.887107</td>\n",
       "      <td>9941</td>\n",
       "      <td>10093</td>\n",
       "      <td>10340</td>\n",
       "      <td>8690.909091</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>12063.515152</td>\n",
       "      <td>1789.893240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M4_02 Love 2022</td>\n",
       "      <td>2972</td>\n",
       "      <td>14794</td>\n",
       "      <td>20.089225</td>\n",
       "      <td>0.938495</td>\n",
       "      <td>79</td>\n",
       "      <td>3.030534</td>\n",
       "      <td>0.104149</td>\n",
       "      <td>0.895851</td>\n",
       "      <td>9932</td>\n",
       "      <td>10068</td>\n",
       "      <td>10312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  season  players_found  players_total  players_captured  \\\n",
       "0        M2_01 Wolf 2020           2905           2997         96.930264   \n",
       "1        M2_02 Love 2020           3984           4883         81.589187   \n",
       "2        M2_03 Bear 2020           4772           6632         71.954162   \n",
       "3         M2_04 Elf 2020           6070          10209         59.457342   \n",
       "4       M2_05 Viper 2020           5977          10079         59.301518   \n",
       "5       M2_06 Magic 2020           6104           9919         61.538462   \n",
       "6     M2_07 Griffin 2020           7372          14791         49.841120   \n",
       "7    M2_08 Draconid 2020           7037          13800         50.992754   \n",
       "8       M2_09 Dryad 2020           7056          14554         48.481517   \n",
       "9         M2_10 Cat 2020           6987          16011         43.638748   \n",
       "10    M2_11 Mahakam 2020           6644          16752         39.660936   \n",
       "11  M2_12 Wild Hunt 2020           7806          22464         34.748932   \n",
       "12       M3_01 Wolf 2021           6197          13428         46.149836   \n",
       "13       M3_02 Love 2021           7119          16016         44.449301   \n",
       "14       M3_03 Bear 2021           7012          13636         51.422705   \n",
       "15        M3_04 Elf 2021          10107          16049         62.975886   \n",
       "16      M3_05 Viper 2021           7771          17623         44.095784   \n",
       "17      M3_06 Magic 2021          10899          17442         62.487100   \n",
       "18    M3_07 Griffin 2021           7771          16772         46.333174   \n",
       "19   M3_08 Draconid 2021           8333          17523         47.554642   \n",
       "20      M3_09 Dryad 2021           7561          17486         43.240307   \n",
       "21        M3_10 Cat 2021           8023          14048         57.111333   \n",
       "22    M3_11 Mahakam 2021           9147          19902         45.960205   \n",
       "23  M3_12 Wild Hunt 2021           9566          21651         44.182717   \n",
       "24       M4_01 Wolf 2022           7607          15520         49.014175   \n",
       "25       M4_02 Love 2022           2972          14794         20.089225   \n",
       "\n",
       "    extra_players_captured  richness  shannon_diversity  dominance   simpson  \\\n",
       "0                32.846715        79           2.911451   0.109716  0.890284   \n",
       "1                55.561048        73           2.784907   0.124585  0.875415   \n",
       "2                50.689290        74           2.770397   0.123829  0.876171   \n",
       "3                43.679412        78           2.921609   0.105208  0.894792   \n",
       "4                43.177725        82           2.975021   0.106955  0.893045   \n",
       "5                45.955518        79           3.028216   0.100404  0.899596   \n",
       "6                37.817450        80           3.085621   0.092351  0.907649   \n",
       "7                38.180987        83           3.043551   0.097257  0.902743   \n",
       "8                35.881649        83           3.071002   0.093658  0.906342   \n",
       "9                31.381644        82           3.028427   0.101374  0.898626   \n",
       "10               27.238699        83           2.981676   0.110916  0.889084   \n",
       "11               25.229545        76           3.040627   0.099488  0.900512   \n",
       "12               31.576457        86           3.088299   0.098081  0.901919   \n",
       "13               32.373062        84           3.045043   0.101261  0.898739   \n",
       "14               38.530067        86           3.086869   0.090533  0.909467   \n",
       "15               54.947305        83           3.124564   0.091177  0.908823   \n",
       "16               33.265596        82           3.147269   0.086137  0.913863   \n",
       "17               55.129612        82           3.162725   0.084816  0.915184   \n",
       "18               35.300460        84           3.083937   0.098090  0.901910   \n",
       "19               37.325240        82           3.023176   0.103806  0.896194   \n",
       "20               32.141392        86           3.009823   0.108551  0.891449   \n",
       "21               46.147658        89           3.132842   0.091885  0.908115   \n",
       "22               36.891210        82           2.989644   0.110278  0.889722   \n",
       "23               35.687297        80           3.101439   0.092863  0.907137   \n",
       "24               37.496051        84           3.012610   0.112893  0.887107   \n",
       "25                0.938495        79           3.030534   0.104149  0.895851   \n",
       "\n",
       "    top500_cutoff  top200_cutoff  top64_cutoff  low_estimate  high_estimate  \\\n",
       "0            9749           9872         10061   2900.000000         3600.0   \n",
       "1            9832           9952         10117   4566.666667         7100.0   \n",
       "2            9867           9995         10204   6036.363636        10300.0   \n",
       "3            9952          10087         10293   9927.272727        18000.0   \n",
       "4            9910          10028         10255   7766.666667        11400.0   \n",
       "5            9896          10002         10191   6800.000000         9800.0   \n",
       "6            9978          10100         10289  12836.363636        19900.0   \n",
       "7            9946          10061         10246   9566.666667        13300.0   \n",
       "8            9946          10046         10183   9733.333333        12580.0   \n",
       "9            9977          10067         10176  12800.000000        14620.0   \n",
       "10          10000          10090         10216  12995.454545        18900.0   \n",
       "11          10070          10172         10313  12995.454545        35900.0   \n",
       "12           9916          10044         10295   7968.181818        13700.0   \n",
       "13           9975          10097         10325  11645.454545        19500.0   \n",
       "14           9914          10026         10230   7466.666667        10900.0   \n",
       "15           9992          10102         10323  11586.363636        20300.0   \n",
       "16           9998          10106         10300  12990.909091        20900.0   \n",
       "17           9974          10082         10278  11263.636364        17200.0   \n",
       "18           9958          10067         10287  10140.909091        15800.0   \n",
       "19           9954          10079         10281  11200.000000        16900.0   \n",
       "20           9968          10078         10250  11868.181818        15900.0   \n",
       "21           9879           9974         10083   5100.000000         7770.0   \n",
       "22          10012          10122         10252  12981.818182        23300.0   \n",
       "23          10032          10140         10271  12981.818182        27500.0   \n",
       "24           9941          10093         10340   8690.909091        18900.0   \n",
       "25           9932          10068         10312           NaN            NaN   \n",
       "\n",
       "    mean_estimate      std_err  \n",
       "0     3117.636364   124.944153  \n",
       "1     5620.242424   441.315936  \n",
       "2     7329.272727   760.229978  \n",
       "3    12319.454545  1494.370140  \n",
       "4     9372.060606   727.332197  \n",
       "5     8320.181818   618.201230  \n",
       "6    14683.272727  1331.618216  \n",
       "7    11186.242424   696.853229  \n",
       "8    11218.666667   458.757501  \n",
       "9    13774.181818   369.275995  \n",
       "10   16041.757576   976.287680  \n",
       "11   23051.757576  3678.743988  \n",
       "12    9929.636364  1046.105394  \n",
       "13   13977.757576  1430.087791  \n",
       "14    9195.333333   715.137594  \n",
       "15   14814.606061  1527.809485  \n",
       "16   16159.515152  1322.547676  \n",
       "17   13526.060606  1034.558786  \n",
       "18   12241.515152  1040.267969  \n",
       "19   12854.545455  1051.176279  \n",
       "20   13442.969697   746.264455  \n",
       "21    6559.939394   575.186402  \n",
       "22   17920.363636  1647.708090  \n",
       "23   19567.696970  2331.781053  \n",
       "24   12063.515152  1789.893240  \n",
       "25            NaN          NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"season_df = pd.read_excel(\\\"./output/seasonal_stats.xlsx\\\").drop(\\n    columns=[\\\"Unnamed: 0\\\", \\\"min_mmr\\\", \\\"max_mmr\\\", \\\"num_matches\\\"]\\n)\\nestimates_df = pd.read_excel(\\\"./output/player_estimates.xlsx\\\").drop(\\n    columns=[\\\"Unnamed: 0\\\"]\\n)\\nmerged_df = pd.merge(summary_df, season_diversity_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, season_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, estimates_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df\";\n",
       "                var nbb_formatted_code = \"season_df = pd.read_excel(\\\"./output/seasonal_stats.xlsx\\\").drop(\\n    columns=[\\\"Unnamed: 0\\\", \\\"min_mmr\\\", \\\"max_mmr\\\", \\\"num_matches\\\"]\\n)\\nestimates_df = pd.read_excel(\\\"./output/player_estimates.xlsx\\\").drop(\\n    columns=[\\\"Unnamed: 0\\\"]\\n)\\nmerged_df = pd.merge(summary_df, season_diversity_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, season_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df = pd.merge(merged_df, estimates_df, how=\\\"left\\\", on=\\\"season\\\")\\nmerged_df\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_df = pd.read_excel(\"./output/seasonal_stats.xlsx\").drop(\n",
    "    columns=[\"Unnamed: 0\", \"min_mmr\", \"max_mmr\", \"num_matches\"]\n",
    ")\n",
    "estimates_df = pd.read_excel(\"./output/player_estimates.xlsx\").drop(\n",
    "    columns=[\"Unnamed: 0\"]\n",
    ")\n",
    "merged_df = pd.merge(summary_df, season_diversity_df, how=\"left\", on=\"season\")\n",
    "merged_df = pd.merge(merged_df, season_df, how=\"left\", on=\"season\")\n",
    "merged_df = pd.merge(merged_df, estimates_df, how=\"left\", on=\"season\")\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"merged_df[\\\"series\\\"] = merged_df.season.apply(lambda x: x.split(\\\"_\\\")[0])\";\n",
       "                var nbb_formatted_code = \"merged_df[\\\"series\\\"] = merged_df.season.apply(lambda x: x.split(\\\"_\\\")[0])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df[\"series\"] = merged_df.season.apply(lambda x: x.split(\"_\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uBc6q6jgRWQz8BLg7iu8JcAJNZ4OKMcZ0pZqaGs6dO0d1dXWXnjdqLRlVPaaqm93HZcAuYBhwC/B7d7PfA7e6j28BnlfValU9AOwFZorIECBLVdeq06Z7qsk+/mP9EVjob+UYY4xxsoeUlJRw4sSJLg8w0EUd/+5trCuB9cAgVT0GTiASkYHuZsNwWip+xW5Zrfu4abl/n8PusepEpBToB5yOzjsxxpieob6+nvPnz+Pz+aLa59KeqAcZEckA/gR8XVXPt9HQaOkFbaO8rX2a1uE+nNttjBw5sr0qG2NMz3N6D5zajSamUpU9lpLqhJgGF7+oBhkRScQJMM+o6p/d4hMiMsRtxQwBTrrlxcCIoN2HA0fd8uEtlAfvUywiCUA2cKZpPVT118CvAXJzc2N/1Y0xJpKKN8LTt0J1GQIkD5tJVsF/UUpWrGsW1dFlAjwO7FLVnwW99BrwOffx54BXg8oXuyPGRgPjgQ3urbUyEZntHvOzTfbxH+sOYLl2h9BtjDFdpaYSXfljqC4LFMUd2UDK6W0xrNQF0Zwncy3wGeB6Ednift0I/BjIE5E9QJ77HFXdCbwIvA8UAve7I8sAvgr8FmcwwD6ckWXgBLF+IrIX+AbOSLUeqb1U/6+++ipTp04NzJGxtDLGmKqqKsrOHEdO7Gj2Wty5w3SHcVBRu12mqm/Rcp8JwMJW9vkX4F9aKN8ITGmhvAq4sxPV7DbaS/W/cOFCbr75ZkSEbdu2cdddd/HBBx/EsMbGmFipqamhtLSUqqoqEuKTSJt8G/Hr/7fRNvWDp3aLPhmb8d8R216E/5wCj+Q437e9GJHDtpXqPyMjI/Bfic/n6xb/oRhjulZtbS2nT5/mxIkTVFVVAVBX30DllL+h4VLnrgeJadQtfJSyrO6RfsqCTLi2vQh/eQBKDwPqfP/LAxEJNO2l+n/55ZeZOHEiH/vYx3jiiSc6fT5jTM9QV1fHmTNnOH78OJWVzXMnniWHknk/wvf5FZR/dgknx95JZUNiSMeuqKhg9erVka5ygCXIDNeyR6G2yQ+5ttIpn3pXpw7dXqr/2267jdtuu43Vq1fz3e9+l6VLl3bqfMaY7i2cuS5V9UJV/AD/ju0e+8SJEyxZsoTVq1dTVVXF2LFjueSSSyJR7UYsyISrtJUVKVsrD1Nbqf795s6dy759+zh9+jT9+/ePyHmNMd1HfX095eXllJeX09DQELHjqiq7d++msLCQ9957LxC4UlJS2LdvnwWZbiF7uHurrIXyCGgt1f/evXsZO3YsIsLmzZupqamxNWWMucg0NDRQXl5OWVlZRINLXV0d69atw+v18tFHHwXK+/XrR15eHnfffTc5OTkRO18wCzLhWvg9pw8m+JZZYqpTHgGtpfr/05/+xFNPPUViYiKpqam88MIL1vlvzEXCn3r//PnzEQ0uZWVlLF++nKVLl1JaWhooHzduHB6Ph9zcXOLj48nIyIjYOZuS7jDErSvl5uZq0zT5u3btYtKkSaEfZNuLTh9MabHTgln4vU73x0RK2O/FGBMz/kXDzp8/T30I/SihKi4uxuv18s4771BbWwtAXFwcV111FQUFBYwdO7bR9kOGDCEhoe02h4hsUtWwF7KylkxHTL2r2wQVY0zPVFFRQWlpacTWdGloaGDHjh0UFhayY8eFyZlpaWksWLCARYsWxeQWuwUZY4zpQpWVlZSWlgZaGJ1VXV3N22+/jdfr5dixY4HyQYMG4fF4uO6660hJSYnIuTrCgowxxkRaQwOc2e/kE8sZAen9G83Sj4QzZ86wbNkyli9fjs/nC5RPnjwZj8fDFVdcQVxc7KdCWpAxxphIqvHBlmeh6P9BXRXa/1Jqb3mMEwyIyOH3799PUVER69evD/TjJCQkcPXVV+PxeLrdciYWZIwxJpKOb4c3Hgo8ldO7SVj6XdI8/0tFfcdaFg0NDWzatAmv18uHH34YKM/KymLhwoVcf/31ZGdnd7rq0WBBxhhjIqjhzIFm+briPnqL5PoyKggvEPhTvhQVFXH69IUFf0eMGIHH42H27NkkJSVFoNbRY0GmmxARPv3pT/P0008DzuSpIUOGMGvWLP76178Gtnv33XeZPXs2L7zwAnfccUesqmuMaaK2tpZz586RldyX5Cav6YBJ1ManQoijlJumfPGbNm0aBQUFTJo0qcfMk7Mg0020l+ofnFQT3/rWt/B4PDGqpTGmqfr6ekpLSwOd73GZY0iY/gXiN//O2SApg9r8H1Ne33aLw5/yxev1snnz5kDKl6SkJObOnUteXh5DhgyJ6nuJBgsyHfD6/tf5+eafc9x3nMHpg3lw+oN8bMzHOn1cf6r/O+64I5Dqf82aNYHXf/GLX/CJT3yCd999t9PnMsZ0Tl1dHefPn6eioqJR8soKTUFnPEjGpNuR6lLqskZyLr6/M+KsleOsX78er9fLwYMHA+V9+/YlLy+P+fPnk56eHu23EzUWZML0+v7XeeSdR6iqd5qwx3zHeOSdRwA6HWgWL17Mo48+yk033cS2bdu45557AkHmyJEjvPzyyyxfvtyCjDEx1FpwCVbZkEBlymjwT09pIcCUlZWxYsUKli5dyrlz5wLlY8aMoaCggNzc3HZn4fcEPf8ddLGfb/55IMD4VdVX8fPNP+90kGkr1f/Xv/51fvKTnxAfH9+pcxhjOsZ/W6yt4BKKI0eO4PV6efvttwMTMkWEq666Co/Hw/jx4yNV5W7BgkyYjvuOh1UertZS/W/cuJHFixcDcPr0ad544w0SEhK49dZbI3JeY0zL6uvrKSsro7y8vMPBRVXZvn07Xq+X7du3B8rT0tKYN28eeXl5MVm2IzExkfT09Kj+82pBJkyD0wdzzHesxfJIaC3V/4EDBwKPP//5z3PTTTdZgDEmivxrupSVlXU4uNTU1ARSvhw9ejRQPnDgQPLz85kzZw6pqamRqnJI4uPjSUtLIy0trUuGP1uQCdOD0x9s1CcDkBKfwoPTm6fn74jWUv0bY7pGQ0NDoOXS0bT7Z8+eZenSpaxYsYLy8vJA+aRJkygoKOjylC9xcXGkpqaSlpbW5XnMLMiEyd/vEunRZcG/iH7z589n/vz5zcqffPLJTp3LGNNcJILLgQMH8Hq93SblS0pKCmlpaaSmpsYsj5kFmQ742JiPRWTIsjEm9jq71HFDQwObN2/G6/Wye/fuQHlmZmYg5Uu0Vp1sib+fJS0trVsMFLIgY4zplTrbcqmsrGTVqlUsWbKEU6dOBcqHDx+Ox+Ph6quv7rKUL13dzxKOdoOMiKQDlaraICITgInAm6oamcUQuglV7TFpGlrT21Y5NSbAd9qZi5I5sN1N/atRlpaWdii4nDp1iqKiIlatWtUo5csVV1xBQUEBkydP7pLPklj2s4QjlJbMamCOiPQBlgEbgbuBT0WzYl0pJSWFkpIS+vXr12MDjapSUlLSrX/ZjGnP6bJqEhPiyE5NDG2H6jLY/SYs/yHUV8N134Apn4D05sOBO7PUsary4Ycf4vV62bRpU6OUL3PmzCE/P7/LUr4kJyeTnp4e036WcIQSZERVK0TkXuAXqvpvIvJetCvWlYYPH05xcXGjJm9PlJKSwvDhw2NdDWPCdqK0kj9tPsLv3jlIv/RE/rFgIteN609SQjt9CofWwZ+/dOH5m/8IyVkw7ZONNvMHl3CXOq6rq2PDhg14vd5G0wj69OkTSPmSkZER1jE7Ij4+nvT0dNLT03tcFoCQgoyIXI3Tcrk3jP16jMTEREaPHh3rahjTa/35vSP8m9fpND9VVs29v9/IS1++mtxRfdvecddfmpdtfNxpzSQkdXip47KyMlauXMnSpUs5e/ZsoLyrU76kpKQEWi099S5LKFfpQeBh4GVV3SkiY4AV0a2WMaa3OFNezVNrP2pUpgpbDp9rP8hkt9By73MJ1XX1lJ45SXV1dVh1OXr0aCDlS01NDdA45cu4ceOi/mHf3UaHdVabQUZE4oGPq+rN/jJV3Q88EO2KGWN6h6SEOAZkJHOstHFOwJy0EPplLr0R1v0vVLqtjYQUaqffy8lTp9veL4iqsnPnTgoLC9m2bVugPDU1lfnz53dJypekpCRSU1NJTU0lMTHE/qgeos0go6r1IjKjqypjjOl9MlISechzKZ//3QYa3AGSQ7JTmD6yT/s7D54C9xTSULyZhrpqavtP5kzS8FbT6gerqanhnXfewev1cuTIkUB5V6V8SUxMDAw77mn9LOEI5Z29JyKvAS8BPn+hqv45arUyxvQqV4/txx+/eg3bikvJSklg2ogcxgxou0NdVamsrOR8fQ61/edceKGdAHPu3LlAypeysrJA+cSJE/F4PFx55ZVRG7XVneezREsoQaYvUAJcH1SmgAUZY0xEJMbHMX1kn5BaLw0NDfh8PsrLy8MaLXbw4EG8Xi/r1q0LDGGOj49n9uzZeDweRo0a1dHqt0lEAoGlN04xaDfIqOoXuqIixhjTlo6kf2loaOC9997D6/XywQcfBMozMzNZsGABixYtilrKl+6QN6w7CGXG/wTgV8AgVZ0iIlOBm1X1h1GvnTGm1wtlJcqmKisrWbNmDUVFRZw8eTJQPmzYMDweD9dcc01UblddbCPDIiGU22W/Ab4J/B+Aqm4TkWeBNoOMiDwB3AScVNUpbtkjwJcA/6zHf1LVN9zXHsaZh1MPPKCqXrd8BvAkkAq8ATyoqioiycBTwAyc23l3q+rBkN61Mabbq6mpoaysjIqKipD3OX36NEuWLGHVqlWN9ps6dSoej4cpU6ZEfAhyb+xnCUcoQSZNVTc0+cGEciP0SeCXOIEg2H+q6r8HF4jIZGAxcBkwFFgqIhNUtR6nFXUfsA4nyBQAb+IEpLOqOk5EFgM/wUl3Y4zpDqp9cHq3k1eszyXQfwKE8AFfXV3N+fPnG+UFa4uqsnfvXgoLC9m4cWOjlC/XXnst+fn5DBs2rFNvpamekjesOwglyJwWkbE4nf2IyB1A86Uhm1DV1SIyKsR63AI8r6rVwAER2QvMFJGDQJaqrnXP/RRwK06QuQV4xN3/j8AvRUTUskQa0yFnfNUcOlNJamI8o/untZ/SpS3VZfD2z2H1T53nCSmw+BkYt6jFzf0jxcrKygKTINtTV1fHxo0bKSwsZP/+/YHyPn36sGjRIubPn09mZmbH30MLrJ8lfKEEmfuBXwMTReQIcIDOJcf8WxH5LE6izX9Q1bPAMJyWil+xW1brPm5ajvv9MICq1olIKdAPaDYLS0Tuw2kNdfmiQcb0BHtOlPHA8++x61gZ8XHCV+eN4YtzxpCT1sHbPyfevxBgAOqq4NX74UsrIetCIsmGhoZAZ36oSSvLy8sDKV/OnDkTKB89ejQej4eZM2dGdN6J9bN0Tig/iY9UdZGb8j9OVcva3aN1vwL+GadV9M/AfwD3AC21obWNctp5rXGh6q9xAiW5ubnW0jEmSHVdPb9csZddx5w/7foG5Zcr9jFzdF/mTmg/dX6Lyo83Lys77szMzxpCXV1doL8l1JFix44dw+v18tZbbzVK+TJjxgw8Hg8TJkyIWH9LXFxcILBYP0vnhBJk9orIH4EnVHVXZ06mqif8j0XkN8Bf3afFwIigTYcDR93y4S2UB+9TLCIJQDZwBmNMWM5V1LJi98lm5ftO+Zg7oYMHzbnE6X8JvnvdfwJ1qf0oLSkJuTPfn/LF6/WydevWQHlKSgrz5s0jPz+fAQMGdLCSjYlIo36WnpqQsrsJJchMxemUf1xE4oAncPpPzod7MhEZoqr+/pzbgB3u49eAZ0XkZzgd/+OBDW5amzIRmQ2sBz4L/CJon88Ba4E7gOXWH2NM+LJSEskd2YfluxsvdTGiT1rHDzpgEtzyK3jjH6DGBzkjqb3plxwvrQHa73Opqalh7dq1eL1eiosv3DEfMGAAeXl5zJs3L2IpX6yfJbpCmYxZhjOM+TciMhd4DvhPt3Xzz6q6t6X9ROQ5YD7QX0SKge8D80VkGs5trYPAl91z7BSRF4H3cUau3e+OLAP4KheGML/pfgE8DjztDhI4gxMIjTFhSk2K5xv5l7LtSCmny50AcOu0oVwxIrvjB01MRqfeTd2Q6dT7SqhOHkCZpjZu2bTg3LlzLFu2jOXLlzdK+XLppZfi8XiYPn16RAKB9bN0HWnvn383E/PHgC8Ao4CngWeAOcC/qmpHG9QxkZubqxs3box1NYzpdorPVnDgtI/0pATGDcwgK9TVKZuor6/H5/NRW3GelKqTNMQnURbXh/o2+l4++uijQMoXf6qY+Ph4Zs2ahcfjich6TzafpXNEZJOq5oa7Xyi3y/bgrB/zU1V9J6j8j27LxhhzERjeJ43hnbhFVldXR3l5OT6fj5z6EjLe+lfiPnwTkjJInf//ODPSQ7VeCFwNDQ1s2bIFr9fLrl0XunvT09O5/vrrWbRoEX36hJCJuR0pKSlkZGRYP0uMhNQno6rlLb2gqraujDG9XNOZ+YnxQurW3zkBBqCmnISib5O9eBwn0ydSVVXF6tWrm6V8GTp0aCDlS3JycqfqFBcXR0ZGRo9crvhiE8rVrxOR+3Fm4wemtqrqPVGrlTGm22ttZn5qQwVxu15ptv3pvZt5ftcWVq5c2SzlS35+PpdffnmnWxoXw3LFF5tQgszTwAeAB3gUZyJmp4YyG2N6Lv/M/NaWNq6OT6em70SSKpy76ztL03npyEDWrPHS4K5KlpiYyHXXXdfhlC/JcUpGzQloqKUmfRiSmmOtlm4qlJ/IOFW9U0RuUdXfu8kxvdGumDGm+1DVwBoutbW1bW5bUpfI4UsfoHTbfv54MJtdZen+o5CTk8OiRYtYsGBBh1O+ZFBB9o4/EJeaBfU1pCVnw5h5kH15h45noiuUIOP/jTonIlOA4zijzIwxFzn/AmFlZWUhpX3x+XysXFXIn197nbqKoYHyrAFDyLvpNj42J7dTrY2kpCSyTr5LXPZQWPI9qHfn3AyYDH/zHPQZ1eFjm+gI5af9axHpA3wXZwJkBvC9qNbKGBNT4S4QduzYMYqKilizZk2jBJe+PuOpHJbLp+9cwKIxGcRp6CtZ+sXHxwfmtCQmJsJHZ2D7ixcCDMCp9+HwBgsy3VAokzF/6z5cBYyJbnWMMbHkzynm8/naXSBMVdm1axeFhYVs3bo1sL0/5cuC/BuRzAGkJsbRN6mehvrQA0xwipdmM/v7jIJzh5vvVH6qeZmJuVaDjIh8o60dVfVnka+OMSYWwlkgrKamhnXr1uH1ejl8+MKHff/+/cnPz2fu3Lmkpfnn2ziBpSG0BMskJycHJky2OrN/4CSY9HHY+ETj8qFXhHYS06XaaslEdiEGY0y3095IsWClpaUsX76cZcuWcf78hdSFEyZMwOPxMGPGjA6lfElMTCQtLY309PTQUrxkD4NZX3Ei15ZnILUPLHoEBk8N+9wm+loNMqr6AwAR6a+qzdZoMcb0TKpKRUUFZWVl7Y4UAzh06BBer5e1a9c2Svkyc+ZMPB4PY8aEfxc9Li4uEFg6lOIle7gTVK7tD1WlziAAiYcrFoe0+qbpOm3dLrsJ+B1QKyINwF1N0soYY3oQ/zDk8+fPtztSrKGhga1bt+L1enn//fcD5f6ULwsXLqRv375h1yFikyVP7XYyPAd74x9gxCzoZ13H3Ulbt8v+FZijqh+IyCzg34B5XVMtY0ykhDNSrLq6mjVr1uD1ejlxIrD8E0OGDMHj8XDttdeGnfLFvwBYenq6MzosEnwlzctqfM6iaKZbaSvI1KnqBwCqul5ErI/GmB6ktrY20Jnf3kixkpISli5dyooVKxp1/k+ZMgWPx8Pll18edn9LVFO89BkJCSnOss5+2cOd/hrTrbQVZAY2GWHW6LmNLjPdXXVtPRsOnuGZdR8RHxfHp2ePZMYlfUlKuLgXpqqsrKS8vLxZTrGW7Nu3D6/Xy4YNGwKtnMTERK655ho8Hg/Dhw9v5wiN+ee0dCjFiyqc/QjqqyF7JCS1sShZ/wmw+Bl49X5nWed+4+G2xyBzcHjnNFHX1m/Bb2g8wqzpc2O6tQ0Hz/CZxzcEnr+x4xjPf2k2s8b0i2GtoiOctC/19fVs2rSJwsJC9u69sOZgdnZ2IOVLVlZWWOfvdDr9qvPOSLHl/wy1FTDpFlj0fejbSv+KCIxbBF9aCZVnIGMQpPcP/7wm6todXWZMT6SqPL32oyZl8PJ7Ry6qIBNOf4vP52PVqlUsWbKEkpILfRojR46koKCAWbNmhdxnovFKcV0xB8oP0D+tP5enXt655ZCPbITCb194/v4rzu2vvH+Gtm7TZQ1xvky3ZSlLzUUrLq75f9RxF8nwVv/kycrKynb7W06cOEFRURGrV68OzIcREa688koKCgq49NJLw2p9pKamsvbcWv7p7X8KlE3uO5n/vv6/GZQ+qGNv6OiW5mU7/gjXPggZAzt2TNMtWJAxFyUR4XNXX4J35/HAsvJxArdN79kdwxUVFZSXl7c7eVJV+eCDDygsLGTLli2NUr7MnTuXvLw8Bg0KPSAE97WcqTnDT5f+tNHr7595n11ndnU8yOSMbF42cAok2R36ns6CjLlozbikD89+cTZ/3HSYxPg4PjFjOFeOyIl1tcJWX1+Pz+fD5/MFJkO2pra2NpDy5dChQ4Hy/v37k5eXx9y5c0lPT2/jCBe0lj+suqKa8zXnm21fUdt+SppWjZgJw2dB8XrneVIGLHi47c5/0yNY7jJz0UpKiOfqsf24emzP7IOpqamhvLw8pCHI58+fD6R8KS0tDZSPHz8+kPIlpJQtOMElLS2NrKysFkeIDU4fzMfHfpxX9r4SKEuMS2RsztjQ3lhLckbC3U/BiZ1Ox/+AidB/fMePZ7qNUHKXXQpchZPmH+DjwOpoVsqY3iycIciHDx8OpHzxjyqLi4sLpHwZOzb0D/64uDgyMjLIyMhoMyAlxify5alfJiMxg7/s/wsjM0fy9zP+ngl9JoR8rhZlDrYhyBchae8/JBEpAj6hqmXu80zgJVUt6IL6RVxubq5u3Lgx1tUwppGGhoZAf0t7Q5AbGhrYtm0bXq+XnTt3BsrT09OZP38+eXl5YaV8SUxMJCMjo+3Mxy2ob6inpKqEtIQ0MpIyQt7P9EwisklVc8PdL5Q+mZFA0OpA1GArYxoTEXV1dZSXl+Pz+UJK+fLWW29RVFTEsWPHAuWDBw8mPz+fOfMW0JCYRnK8Nl7QqxWalM5xXwNJGs+Y+OSwZ/THx8UzMM1Gfpm2hRJkngY2iMjLgAK3AU9FtVbGXOTCWb/lzJkzLFmyhJUrV+Lz+QLlkydPpqCggKlTp3KeVP7zrcMs3bWLiYMz+ZZnHJek1TcLXP7+lpLaBB56aRtbi53+m0WTBvKDm6cwrI91tJvICmVlzH8RkTeBOW7RF1T1vehWy5iLTziz8uFCypd33303kDXZn/IlPz+fESNGAFAfn8Sjr+3l3YNOcsitxaV88ektvHDPdHLinaHOTftbnvDuDgQYgKW7TpJ/2Wnuyh0R6bdterlQhzCnAedV9XciMkBERqvqgWhWzJiLRX19fWBJ4/ZuiflTvni9Xvbs2RMoz87OZuHChVx//fXNUr6cqdRAgPGrqKnn0Lkq+g9y+lvS09MDt8OqautZsftks3NvOHDGgoyJuHaDjIh8H8jFGWX2OyAR+ANwbXSrZkzPFs4tsYqKikDKl9OnL6wROHLkSDweD7Nnz2415UtyQhypifFU1jZeI6ZPegqDBw9oNps/JTGe6ycOZOfRxnNdZo4Of30YY9oTSkvmNuBKYDOAqh61tP/GtExVA0sa19S03/l+4sQJlixZwurVqwNDlkWEadOm4fF4mDRpUrspX3ISavlm/jgefX13oCx/8iAmDs1udd/bpg9jzZ7TbDl8LrD9deMswaSJvFCCTI2qqogogIiENl3YmF7En6jS5/O1u+qkqrJ79268Xi+bN28OTLRMTk5mzpw55OfnM3hw6PNFVJUbJvZl0tBZHCypYGBmMpcPy6ZveuuLi43pn8ETn7+KA6fLSYiLY3T/dLJSI7SgmDFBQgkyL4rI/wE5IvIl4B7gt9GtljE9Q3V1NeXl5SElqqyrqwukfPnoowsZovv168eiRYuYP39+yClfwGnxpKenk5WVRXx8PEMGwNVhTLrvm55E33S7RWaiK5TRZf8uInnAeZx+me+p6pKo18yYbiqciZMAZWVlLF++nKVLlzZK+TJu3DgKCgrCSvkCTnDJyMggMzMzrP2MiYVQOv5/oqrfApa0UGZMr1FbWxtIVNneKDGA4uJivF4v77zzTqdTvoAFF9MzhXK7LA9oGlBuaKHMmIuOqlJVVRVyLrGGhga2b9+O1+tlx44dgfK0tDQWLFjAokWL6NcvvISdTW+LGdOTtJWF+avA14AxIrIt6KVM4O32DiwiTwA3ASdVdYpb1hd4ASctzUHgLlU96772MHAvUA88oKpet3wG8CSQCrwBPOgOREjGyTwwAygB7lbVgyG+b2PaVFdXF2i1tNeRD07fzNtvv43X622U8mXQoEF4PB6uu+46UlJSwqpDq8Gl/KTzldYXsoaGdUxjulpbLZlngTeBHwFB66JSpqpnQjj2k8AvaZyC5tvAMlX9sYh8233+LRGZDCwGLgOGAktFZIKq1gO/Au4D1uEEmQK3XvcCZ1V1nIgsBn4C3B1CvYxpkX/4sc/nC6nVAk7Kl2XLlrF8+fJmKV88Hg9XXHFF2DnB2my5HFoPL98HZw86GYtv+RWMXeCseW9MN9RqkFHVUqAU+CSAiAwEUoAMEclQ1UOt7evuv1pERjUpvgWY7z7+PbAS57bbLcDzqloNHBCRvcBMETkIZKnqWrcOTwG34gSZW4BH3GP9EfiliIi2N8THmCbC7WsB2L9/P16vlw0bNgRaOgkJCVx99dV4PB5GjmxhpccQpKenk52d3fJtsdIj8OJnoPyE87zsOLzwKfjyGug/rkPnMybaQun4/zjwM5wWxkngEmAXTqsjXINU9RiAqh5zAxfAMJyWil+xW1brPm5a7t/nsHusOhEpBfoBp2lCRO7DaQ11+I/fXFw60mppaGhg06ZNFBYWNkr5kpWVFUj5kp2d3aH6pKSkkJOT0+qsfgDOF18IMH61FVB6yIKM6bZC6fj/ITAbWKqqV4rIAtzWTQS11NbXNsrb2qd5oeqvgV+Ds55MRypoLg7hTJr0q6ysZNWqVRQVFTVK+TJixIhAypekpKQO1SclJYWsrCySk1ufOBmQ2hcSUqAuKCiKQFrPXPnT9A6hBJlaVS0RkTgRiVPVFSLykw6e74SIDHFbMUNwWkbgtFCCM/MNB4665cNbKA/ep1hEEoBsIJS+ItMLHCut5MApH8mJ8YwbmEGy1OPz+UKaNOl36tQpioqKWLVqVaPWzrRp0ygoKAgp5Utrwgoufn3Hwo3/Dn/5O/C/h+u/D/07uSKlMVEUSpA5JyIZOEsuPyMiJ4G6Dp7vNeBzwI/d768GlT8rIv7bcuOBDapaLyJlIjIbWA98FvhFk2OtBe4Allt/jAHYdew89/7+XY6ecwJDwWWD+Pt5I0iTEBbyUuXDDz+ksLCwUcqXpKQk5s6dS15eHkOGDOlw3VJTU8nKyupYyycuDi6/CwZfDqWHnY7/AZOd1o0x3VQoQeYWoAr4e+BTOC2GR9vbSUSew+nk7y8ixcD3cYLLiyJyL3AIuBNAVXeKyIvA+zgB7H53ZBnAV7kwhPlN9wvgceBpd5DAGZzRaaaXq66r41cr9wYCDEDhzhPccNlApg9ovdVRV1fHhg0bKCws5ODBg4Hyvn37kpeXF3bKl6bS0tLIyspqu88lFInJMHSa81VTAR+9De8+DompcNW9MGIWxFsOMtN9hJJWxgcgIlnAX0I9sKq21m+zsJXt/wX4lxbKNwJTWiivwg1SxtTU1FBeXs65yrpma6sA7DvlI3dQVrPRY2VlZaxYsYJly5Zx9uyF/caOHYvH4yE3N5eEhFCXXWouNTWV7OzszgeXlnz0Njxzx4Xn778CX3gDRl4d+XMZ00GhjC77Mk7LpRJowOlwV2BMdKtmTNv8OcR8Pl8grX5SfALzxvfn+Y3FjbadODijUYA5cuQIRUVFvPXWW4GULyLCVVddhcfjYfz48Z2qW6dui4Wivg7W/k/jMm2Ana9YkDHdSij/oj0EXKaqzYYGGxML/lZLZWVls5aJ1tfxqauGsPPYebYfOY8IfHb2SMb3TUS1hh07duD1etm27UISi7S0NObNm0deXh79+3duTZWI3RYLRVwLf75xdqvMdC+hBJl9QPtL+xkTRfX19YFWS3uZj3Piqvnv2ydwwtdAUoKQLdW8vaKIoqIijhw5Ethu4MCB5OfnM2fOHFJTUztVv5SUFLKzs6PXcmkqPgGu/hrsDUqIHpcAk2/umvMbE6JQgszDwDsish6o9heq6gNRq5UxrsrKSioqKsIaegyQ0FBDWtXZQMqX8vLywGuTJk3C4/Ewbdq0sFO+NNXlwSXYyGvgs3+Brc9CQipccTcMm9H19TCmDaEEmf8DlgPbcfpkjImq2traQKsl1AmTwQ4cOIDX62X9+vWNUr7MmjWLgoICLrnkkk7XMTk5maysrLCTXkZUYgqMmet8GdNNhRJk6lT1G1GvienVWurED3f/zVu28NfXC9m/54NAeWZmZiDlS05OTqfrmZKSQmZmZmyDizE9SChBZoWb++svNL5dZrPrTaf5O/ErKirCuh3mV1lZyerVqylasoRTJ08GyjVjIHffcRv5110VkVtZycnJZGdnhzdD3xgTUpD5G/f7w0FlNoTZdJg/OWVZWVmHWi1wIeXL6tWrqaysDJRX5IymdHAuVVkjWVs7gI+lpkN9+0sktyYxMZHs7OxODwwwprcKZTLm6K6oiLn4VVVVBTrxQ02pH0xV2bNnD16vl40bNzZK+TL2ymtZUjma2tS+ge3f2V9CZf1o0jpQ1/j4eLKzszs1y98Y0/bKmNer6nIRub2l11X1z9GrlrkYqCrV1dWdCixwIeWL1+vlwIEDgfI+ffoEUr4cqc/ijT9sabTftBHZpMQ3OGuthiguLo7MzEwyMzM7nPzSGHNBWy2ZeTijyj7ewmsKWJAxLepIOv2WlJWVsXLlSpYuXdoo5cuYMWPweDxcddVVgZQvo4jjM7NG8PT6wwD0TU/iH/PGEVcf2u04ESEjI4PMzMyWFwwzxnSItNfZKiKjVfVAe2U9RW5urm7cuDHW1bgodXROS1NHjx4NpHzx99mICLm5uRQUFDBu3LgWWxn1cYmcqFDKq+sZmpVEZlxNSPVoczVKYwwAIrJJVXPD3S+Ujv8/AdOblP0RsFlfhrq6usDSxZ1ptagqO3fupLCwsFHKl9TUVObPnx9Sypf4hlqGpuAsEk417cWXmE6kNKaXaKtPZiLOEsvZTfplsnD/jE3v5J/TUlFRQXV1dfs7tKGmpoZ33nkHr9cbtZQvTSUlJZGdnW1zXYzpAm21ZC4FbgJyaNwvUwZ8KYp1MlF2sqyKdw+eZdPBs0wZlsWs0f0Y1qf9D/JI3Q4DOHfuXCDlS1lZWaB84sSJeDwerrzyyk6nfGnKhiMb0/VaDTKq+irwqohcrapru7BOJoqqauv472V7+MO6Q4GyeRMG8F+Lp9Enrflto5qamkCrpTO3w/wOHjyI1+tl3bp1gePFx8cze/ZsPB4Po0aN6vQ5mkpISCArK8uGIxsTA6H0ydwmIjtx1pMpBK4Avq6qf4hqzUxUHDhdwTPrDzUqW/XhKfaeLOeqUc4ck3AyHoeioaGB9957D6/XywcfNE75smDBAhYtWhSRlC9NxcfHB4KLDUc2JjZCCTL5qvqPInIbUIyzGuUKwIJMD1Rb39Bih3hN3YXcYdXV1Z2+HQbO7bU1a9ZQVFTEyaCUL8OGDSM/P59rr702Kp3uFlyM6T5CCTL+VZBuBJ5T1TP2h9tzXdIvnZmj+rLh4IXUcyP7pjIwRSkpKYnIOU6dOsXSpUtZtWoVFRUXliKaOnUqBQUFXHbZZVH58I+LiyMrK4uMjAwLLsZ0E6EEmb+IyAc4t8u+JiIDgKroVstES3ZqIj+6fQovbjzM0l0nmTmqD3dPH0KKdu5Hqqrs3buXwsLCZilfrrvuOvLy8hg2bFgk3kIzIhKYpR/pwQLGmM5pdzImgIj0Ac6rar2IpAOZqno86rWLgt46GbPpsOP4+HhqNZ54aUDr6zp83Lq6Ot599128Xi/79+8PlPfp04dFixYxf/58MjMzI/EWWmQTKdtQVw3lJyEpA9L6xLo2poeL+GRMEflHVf039+kiVX0JQFV9IvId4J86VlXTVVSVqqoqfD4fVVVVjfpZ6uvriaOejva8+Hw+VqxYwdKlSzlz5sKtt1GjRlFQUMDMmTMDKV+iITU1lezsbBITbU37Fp3eAyv+FXa9Cn3HwY0/hdFzwW4jmi7WaktGRDar6vSmj1t63pP0hpZMTU0NPp+PioqKDielbM2xY8coKipizZo1jVK+zJgxA4/Hw4QJE6LaH2Kz9ENQ44MXPw97iy6UxSfBl1bA4Ckxq5bp2aKRVkZaedzScxNj9fX1gcASiWHHwVSV999/n8LCQrZu3RooT0lJYd68eeTn5zNgwICInrMpm6UfhvNHGgcYgPoaKNlrQcZ0ubaCjLbyuKXnJgb8i3/5b4dFWk1NDWvXrqWoqIjDhw8HygcMGEBeXh7z5s2L+ux5m6XfAYlpkNoHKs82Lk/Jjk19TK/WVpC5QkTO47RaUt3HuM/t38kYqq6uxufzdWqNlraUlpYGUr6cP38+UH7ppZfi8XiYPn161EdxJSYmkpWVRVpaR5Yc6+Wyh8MNP4E/33ehbFw+DLosdnUyvVZbaWVsuE43Es3bYX6HDh3C6/Wydu1a6uqcEWfx8fHMmjULj8fD6NHRXyTVJlJGyKSb4Z5RULIH0gfA4CsgY2Csa2V6oegN/zGdFu3bYeAMbd66dSuFhYXs2rUrUJ6ens7111/PwoUL6du3bxtHiAybSBlhiakwcpbzZUwMWZDpJsqrahGB9OTEqN8OA6iqqgqkfDlx4kSgfMiQIRQUFHDNNdeQnJwclXMHi4uLC6xIaRMpjbn4WJCJsfKqOlZ9eIr/WbEHEeErc0Zx+YAE4rXjEyTbcvr0aZYsWdIs5cvll1+Ox+NhypQpXfJh75+ln5GRYRMpjbmIWZCJIVVl7b5T3P/s5kDZ372wjf/71DQm50T2XHv27MHr9bJx48ZA6ygxMZHrrruO/Pz8qKV8aUpESE9PJysry4KLMb2ABZkYqKqqoqKigrq6Op5/93Cz11/ecowr8oZ3uoO/vr4+kPJl3759gfKcnBwWLVrEggULoprypSl/cIlmJgBjTPdif+1dxD8Lv7KyMrBYV0JCAv3Sm89c75ee1KlU+z6fj5UrV7JkyZJGKV8uueQSCgoKmDVrVpd+0FsKGGN6LwsyUdTesOO6ujpunzaYV7Yco6beuYWVnBDHxy8fSF1d+K2Y48ePU1RUxOrVqxulfJk+fToej4dLL720S0duJScnk52d3SUDCIwx3VNMgoyIHATKgHqgTlVzRaQv8AIwCjgI3KWqZ93tHwbudbd/QFW9bvkM4EkgFXgDeFAjsdpWJ/iTUpaXl4c07HhEWj3PfGE67x4qBeCqkVkMSalrcWGx1s63a9euQMoX/9tPSUlh7ty55OXlMWjQoA6/n46wFDDGGL9YtmQWqOrpoOffBpap6o9F5Nvu82+JyGRgMXAZMBRYKiITVLUe+BVwH7AOJ8gUAG925Zvwq6mpCaTS998OC0VDQwODk2u4eYIzs121NqQAU1NTw7p16/B6vY1SvvTv35/8/Hzmzp3b5bPlLQWMMaap7nS77BZgvvv498BK4Ftu+fOqWg0cEJG9wEy3NZSlqmsBROQp4Fa6MMjU1tYGAot/hnxHhdoAO3/+PMuWLWPZsmWNUr5MmDABj8fDjBkzuny+iaWAMca0JlZBRoEiEVHg/1T118AgVT0GoKrHRMSfA2MYTkvFr9gtq3UfNy1vRkTuw2nxMHLkyE5VvL6+noqKCnw+X9TSu7Tk8OHDFBYWtpjyJT8/nzFjxnRZXfwsBYwxpj2xCjLXqupRN5AscZd3bk1Ln17aRnnzQieI/Rqc9WTCrWxwepfq6upOjfwKhz/li9fr5f333w+Ud3XKl6bi4uICyx1bcDHGtCUmQUZVj7rfT4rIy8BM4ISIDHFbMUOAk+7mxcCIoN2HA0fd8uEtlEeMfz5LNNO7tKS6ujqQ8uX48QurXA8ZMgSPx8O1114bsxFbttyxMSYcXR5kRCQdiFPVMvdxPvAo8BrwOeDH7vdX3V1eA54VkZ/hdPyPBzaoar2IlInIbGA98FngF52tX21tbWDYcTgd+JFQUlLC0qVLWbFiRaOUL1OmTKGgoKDLUr60JCUlhZycHJvrYowJSyxaMoOAl93bLAnAs6paKCLvAi+KyL3AIeBOAFXdKSIvAu8DdcD97sgygK9yYQjzm0Sg07+kpKRL+1oA9u3bh9frZcOGDY1SvlxzzTV4PB6GDx/ezhGix0aMGWM6Q2I8raTL5ebm6saNG1t9/fjx410SZOrr69m4cSNer5e9e/cGyrOzswMpX7KysqJej9bExcWRnZ1NRkZGzOpgjOk+RGSTquaGu193GsLcK/h8PlatWsWSJUsoKSkJlI8cOTKQ8iWWt6T82ZEt9b4xJhIsyHSREydO4PV6WbNmDdXV1YDzgX7llVfi8XiYOHFizEdqWQJLY0yk2adJFKkqH3zwAYWFhWzZsiUw9Dk5OZm5c+eSn5/f5SlfWpKSkkJ2djZJSc2TdRpjTGdYkImC2traQMqXQ4cOBcr79esXSPmSnp4ewxo6EhMTycnJsRxjxpiosSATQefPn2f58uUsW7aM0tLSQPn48eMDKV+6w/yS+Ph4srOzu0WgM8Zc3CzIREBxcTFer5d33nknMDItLi6OmTNn4vF4GDt2bIxr6IiLiyMrK4uMjIyY9/8YY3oHCzId1NDQwPbt2/F6vezYsSNQnpaWxoIFC8jLy4tJypeWiAgZGRlkZmZ2i5aUMab3sCDjqqiuY99pH6VlyqD0ZFKobnG76upq3n77bbxeL8eOHQuUDx48GI/Hw3XXXdetFumyNDDGmFiyIAOUlFfz86V7eGrdRwAM75PKL++aQr+EC4HmzJkzgZQvPp8vUD558mQKCgqYOnVqt5pXYiPGjDHdgQUZYFtxaSDAABSfreRXaz7i/y0awf49uwMpX/y5zBITE7n66qvxeDyMGDGitcPGhI0YM8Z0JxZkgAOnfY0LtIFN69/hB2+9z749HwaKs7OzWbhwIddff31MU760JCEhgezsbFs4zBjTrViQAUb3d4bySl01mae2kXX8PRJrzrPPfX3EiBEUFBQwe/bsbpeF2EaMGWO6MwsywNQR2dw9JZN1v/s5cQ3OEGQRYdq0aXg8HiZNmtTtPsDj4uICI8a6U1+QMcYEsyAD9EtP5nt3Xs39K0dx9PAhrp07nxvzFzJ48OBYV60ZG45sjOlJLMi40pMT+cF3v0NtbW23GoLsJyKkpaXZcGRjTI9iQSbIJZdc0mXryYTDsiMbY3oq+9TqxtLS0sjKyup2gw2MMSZUFmS6IZtIaYy5WFiQ6UaSkpLIycnpln1CxhjTERZkuoH4+HhycnJsIqUx5qJjQSaG/HNdsrKyut08HGOMiQQLMjFi2ZGNMb2BBZkulpycTE5OjnXqG2N6BQsyXcQSWBpjeiMLMlFmCSyNMb2ZBZkoEZHATH3rdzHG9FYWZKLAn2PM0sAYY3o7+xSMIJupb4wxjVmQiYCkpCSys7NtyWNjjGnCgkwnJCYmkpWVZSPGjDGmFRZkOiA+Pp6srCzS09NtxJgxxrTBgkwYbDiyMcaEx4JMCESEzMxMMjMziYuLi3V1jDGmx7Ag0wab62KMMZ3T4/8tF5ECEdktIntF5NuROm5aWhqDBw+mT58+FmCMMaaDenRLRkTigf8B8oBi4F0ReU1V3+/oMVNSUujbt6/NdTHGmAjo6S2ZmcBeVd2vqjXA88AtnTmgZUg2xpjI6elBZhhwOOh5sVvWiIjcJyIbRWTjqVOnuqxyxhjT2/X0INPSOGJtVqD6a1XNVdXcAQMGdEG1jDHGQM8PMsXAiKDnw4GjMaqLMcaYJnp6kHkXGC8io0UkCVgMvBbjOhljjHH16NFlqlonIn8LeIF44AlV3RnjahljjHH16CADoKpvAG/Euh7GGGOa6+m3y4wxxnRjFmSMMcZEjQUZY4wxUSOqzaaVXNREpAzYHet6tKM/cDrWlWiH1bHzunv9wOoYKRdDHS9R1bAnGvb4jv8O2K2qubGuRFtEZKPVsfO6ex27e/3A6hgpvbmOdrvMGGNM1FiQMcYYEzW9Mcj8OtYVCIHVMTK6ex27e/3A6hgpvbaOva7j3xhjTNfpjS0ZY4wxXcSCjDHGmKjp8UFGREaIyAoR2SUiO0XkQbf8ERE5IiJb3K8bg/Z5WET2ishuEfEElc8Qke3ua/8tIi2tV9PReh50j71FRDa6ZX1FZImI7HG/94lVHUXk0qBrtUVEzovI12N9HUXkCRE5KSI7gsoidt1EJFlEXnDL14vIqAjV8aci8oGIbBORl0Ukxy0fJSKVQdfzsWjXsZX6ReznGsVr+EJQ/Q6KyJZYXUP3GK191nSb38c26hi730dV7dFfwBBguvs4E/gQmAw8AjzUwvaTga1AMjAa2AfEu69tAK7GWQztTeCGCNbzINC/Sdm/Ad92H38b+Eks6xhUr3jgOHBJrK8jMBeYDuyIxnUDvgY85j5eDLwQoTrmAwnu458E1XFU8HZNjhOVOrZSv4j9XKN1DZu8/h/A92J1Dd39Wvus6Ta/j23UMWa/jz2+JaOqx1R1s/u4DNhFC0swB7kFeF5Vq1X1ALAXmCkiQ4AsVV2rztV7Crg1urXnFuD37uPfB50v1nVcCOxT1Y/aqXvU66iqq4EzLZw7Utct+Fh/BBb6/2PrTB1VtUhV69yn63AW1GtVNOvYyjVsTbe5hn7use4CnmvrGF1Qx9Y+a7rN72NrdYzl72OPDzLB3GbblcB6t+hv3ebhE0FN2GHA4aDdit2yYe7jpuWRokCRiGwSkfvcskGqegycXw5gYIzr6LeYxn/Q3ek6QmSvW2Af94+wFOgX4freg/OfoN9oEXlPRFaJyJygenR1HSP1c432NZwDnFDVPUFlMb2GTT5ruuXvYwufh35d+vt40QQZEckA/gR8XVXPA78CxgLTgGM4zW1wmn5NaRvlkXKtqk4HbgDuF5G5bWwbqzoizgqjNwMvuUXd7Tq2pSN1imp9ReQ7QB3wjFt0DBipqlcC3wCeFZGsGNQxkj/XaP/MP0njf3pieg1b+KxpddNWzhn1erZWx1j8Pl4UQUZEEnEu6DOq+mcAVT2hqvWq2gD8Bpjpbl4MjAjafThw1C0f3kJ5RKjqUff7SeBltz4n3Gapv3l6MpZ1dN0AbFbVE259u9V1dEXyugX2EZEEIJvQby21SUQ+B9wEfMq95YB766TEfbwJ5z79hK6uY4R/rtG8hgnA7cALQXWP2TVs6bOGbvb72EodY/b72OODjHsv8HFgl6r+LKh8SNBmtwH+USuvAYvdERKjgfHABreZWyYis91jfhZ4NUJ1TBeRTP9jnE64HW5dPudu9rmg83V5HYM0+q+xO13HIJG8bsHHugNY7v8D7AwRKQC+BdysqhVB5QNEJN59PMat4/6urmOEf65RuYauRcAHqhq4dROra9jaZw3d6Pexjc/D2P0+apgjLLrbF3AdTlNtG7DF/boReBrY7pa/BgwJ2uc7OBF7N0Ejn4BcnD+2fcAvcTMiRKCOY3BGmWwFdgLfccv7AcuAPe73vrGqo3vsNKAEyA4qi+l1xAl4x4BanP+g7o3kdQNScG4N7sUZTTMmQnXci3Pf2v876R+N8wn3d2ArsBn4eLTr2Er9IvZzjdY1dMufBL7SZNsuv4buMVr7rOk2v49t1DFmv4+WVsYYY0zU9PjbZcYYY7ovCzLGGGOixoKMMcaYqLEgY4wxJmosyBhjjIkaCzLmoiQiKiL/EfT8IRF5JELHflJE7ojEsdo5z53iZNNd0aQ8OHPu+yLymIjEueU7WjueMbFgQcZcrKqB20Wkf6wrEsw/8S1E9wJfU9UFLby2T1WnAVNxsuze2vnatS3MuhsDWJAxF686nDXL/77pC01bIiJS7n6f7yYJfFFEPhSRH4vIp0RkgzjraowNOswiEVnjbneTu3+8OOt2vCtO4skvBx13hYg8izMBsml9Pukef4eI/MQt+x7OxLrHROSnrb1JdRIUvgOMa3LMUW79Nrtf17jlT4vILUHbPSMiN4dadzd7xesistWt791t/hRMr5cQ6woYE0X/A2wTkX8LY58rgEk4uZj2A79V1ZniLP70d8DX3e1GAfNwkkyuEJFxOKk3SlX1KhFJBt4WkSJ3+5nAFHVSvgeIyFCc9T1mAGdxMnXfqqqPisj1OGu+bGytsiKShrM0w/eavHQSyFPVKhEZjzOjPhf4LU7gfVVEsoFrcFKE3BtK3UXkE8BRVf2Ye/7sdq+o6dWsJWMuWupkn30KeCCM3d5VZ02Oapx0Gv4P2u04gcXvRVVtUCf9/H5gIk5Ous+Ks4Ljepx0I+Pd7Tc0DTCuq4CVqnrKbZU8g7OAV3vGuud5G3hdVd9s8noi8BsR2Y6TAmQygKquAsaJyECcPHV/cs8bat2347TifiIic1S1NIS6ml7MWjLmYvdfODmZfhdUVof7D5ab/C8p6LXqoMcNQc8baPz30jQfkz+F+9+pqjf4BRGZD/haqV9YC2cF8ffJtObvgRM4LbM4oCrotaeBT+GsG3RPUD3arbuqfigiM3DyYf1IRIpU9dEOvgfTC1hLxlzUVPUM8CLO7SC/gzi3p8BZ5S+xA4e+0x3RNRYnAepuwAt8VZxU64jIBHGybrdlPTBPRPq7HeufBFZ1oD5NZQPH1Enl/xmcJbX9nsS97aeqO92ykOru3t6rUNU/AP+Os2SyMa2ylozpDf4D+Nug57/B6ZPYgJM1t7VWRlt24wSDQThZgqtE5Lc4t9Q2uy2kU7Qz6ktVj4nIw8AKnNbEG6oaiaUR/hf4k4jc6R47uDVyQkR2Aa8EbR9q3S8HfioiDTgZk78agbqai5hlYTaml3EHC2wHplufiok2u11mTC8iIouAD4BfWIAxXcFaMsYYY6LGWjLGGGOixoKMMcaYqLEgY4wxJmosyBhjjIkaCzLGGGOi5v8DgJEQWt6QLrkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n\\nsns.regplot(\\n    data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Estimated\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Estimated Players\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n\\nsns.regplot(\\n    data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"mean_estimate\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Estimated\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Estimated Players\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "sns.regplot(\n",
    "    data=merged_df, x=\"players_total\", y=\"mean_estimate\", scatter=False, color=\".25\"\n",
    ")\n",
    "sns.scatterplot(data=merged_df, x=\"players_total\", y=\"mean_estimate\", hue=\"series\")\n",
    "plt.title(\"Actual Players vs Estimated\")\n",
    "plt.xlabel(\"Number of Players\")\n",
    "plt.ylabel(\"Estimated Players\")\n",
    "plt.xlim(2500, 23000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\nsns.regplot(\\n    data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Diversity\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Shannon Diversity\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"import seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\nsns.regplot(\\n    data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", scatter=False, color=\\\".25\\\"\\n)\\nsns.scatterplot(data=merged_df, x=\\\"players_total\\\", y=\\\"shannon_diversity\\\", hue=\\\"series\\\")\\nplt.title(\\\"Actual Players vs Diversity\\\")\\nplt.xlabel(\\\"Number of Players\\\")\\nplt.ylabel(\\\"Shannon Diversity\\\")\\nplt.xlim(2500, 23000)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.regplot(\n",
    "    data=merged_df, x=\"players_total\", y=\"shannon_diversity\", scatter=False, color=\".25\"\n",
    ")\n",
    "sns.scatterplot(data=merged_df, x=\"players_total\", y=\"shannon_diversity\", hue=\"series\")\n",
    "plt.title(\"Actual Players vs Diversity\")\n",
    "plt.xlabel(\"Number of Players\")\n",
    "plt.ylabel(\"Shannon Diversity\")\n",
    "plt.xlim(2500, 23000)\n",
    "plt.show()"
   ]
  }
 ],
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