007 - LifeTime Achievements.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gwent \"LifeTime\" Achievements\n",
    "\n",
    "Here we'll summerize data per player for every season since the beginning of Masters 02."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 1;\n",
       "                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\\\"])\";\n",
       "                var nbb_formatted_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\\\"])\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "            "
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   "source": [
    "%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",
    "    )\n",
    "    .drop(columns=[\"Unnamed: 0\"])\n",
    "    .dropna()\n",
    ").sort_values([\"season\", \"rank\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        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>rank</th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>matches</th>\n",
       "      <th>mmr</th>\n",
       "      <th>season</th>\n",
       "      <th>surplus_mmr</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",
       "      <td>884</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",
       "      <td>877</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",
       "      <td>872</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",
       "      <td>871</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",
       "      <td>842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4315</th>\n",
       "      <td>17482</td>\n",
       "      <td>TLGsuckMyD</td>\n",
       "      <td>PL</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>17483</td>\n",
       "      <td>drago95</td>\n",
       "      <td>PL</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>17484</td>\n",
       "      <td>Ciumeck</td>\n",
       "      <td>MD</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>17485</td>\n",
       "      <td>DuncanMacLe0d</td>\n",
       "      <td>RU</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4089</th>\n",
       "      <td>17486</td>\n",
       "      <td>NoWiSh-</td>\n",
       "      <td>TH</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>M3_09 Dryad 2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145484 rows × 7 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",
       "4315  17482     TLGsuckMyD      PL        2    192  M3_09 Dryad 2021   \n",
       "777   17483        drago95      PL        2    192  M3_09 Dryad 2021   \n",
       "2231  17484        Ciumeck      MD        1     96  M3_09 Dryad 2021   \n",
       "286   17485  DuncanMacLe0d      RU        1     96  M3_09 Dryad 2021   \n",
       "4089  17486        NoWiSh-      TH        1     96  M3_09 Dryad 2021   \n",
       "\n",
       "      surplus_mmr  \n",
       "0             884  \n",
       "1             877  \n",
       "2             872  \n",
       "3             871  \n",
       "4             842  \n",
       "...           ...  \n",
       "4315            0  \n",
       "777             0  \n",
       "2231            0  \n",
       "286             0  \n",
       "4089            0  \n",
       "\n",
       "[145484 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"def get_surplus(mmr):\\n    return max(0, mmr - 9600)\\n\\n\\nplayers_df[\\\"surplus_mmr\\\"] = players_df[\\\"mmr\\\"].apply(get_surplus)\\nplayers_df\";\n",
       "                var nbb_formatted_code = \"def get_surplus(mmr):\\n    return max(0, mmr - 9600)\\n\\n\\nplayers_df[\\\"surplus_mmr\\\"] = players_df[\\\"mmr\\\"].apply(get_surplus)\\nplayers_df\";\n",
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       "            }, 500);\n",
       "            "
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   "source": [
    "def get_surplus(mmr):\n",
    "    return max(0, mmr - 9600)\n",
    "\n",
    "\n",
    "players_df[\"surplus_mmr\"] = players_df[\"mmr\"].apply(get_surplus)\n",
    "players_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"players_stats_df = players_df.groupby([\\\"name\\\", \\\"country\\\"]).agg(\\n    appearances=pd.NamedAgg(\\\"season\\\", \\\"count\\\"),\\n    placements=pd.NamedAgg(\\\"mmr\\\", lambda x: (x >= 9600).sum()),\\n    total_matches=pd.NamedAgg(\\\"matches\\\", \\\"sum\\\"),\\n    total_mmr=pd.NamedAgg(\\\"mmr\\\", \\\"sum\\\"),\\n    total_surplus_mmr=pd.NamedAgg(\\\"surplus_mmr\\\", \\\"sum\\\"),\\n)\\n\\nplayers_stats_df[\\\"appearances_rank\\\"] = players_stats_df[\\\"appearances\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_matches_rank\\\"] = players_stats_df[\\\"total_matches\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_mmr_rank\\\"] = players_stats_df[\\\"total_mmr\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_surplus_mmr_rank\\\"] = players_stats_df[\\\"total_surplus_mmr\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\n\\nplayers_stats_df[\\\"appearances_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"appearances\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\nplayers_stats_df[\\\"total_matches_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"total_matches\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\nplayers_stats_df[\\\"total_mmr_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"total_mmr\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\n\\nplayers_stats_df[\\\"total_surplus_mmr_national_rank\\\"] = players_stats_df.groupby(\\n    [\\\"country\\\"]\\n)[\\\"total_surplus_mmr\\\"].rank(method=\\\"first\\\", ascending=False)\\n\\nplayers_stats_df = players_stats_df.reset_index()\";\n",
       "                var nbb_formatted_code = \"players_stats_df = players_df.groupby([\\\"name\\\", \\\"country\\\"]).agg(\\n    appearances=pd.NamedAgg(\\\"season\\\", \\\"count\\\"),\\n    placements=pd.NamedAgg(\\\"mmr\\\", lambda x: (x >= 9600).sum()),\\n    total_matches=pd.NamedAgg(\\\"matches\\\", \\\"sum\\\"),\\n    total_mmr=pd.NamedAgg(\\\"mmr\\\", \\\"sum\\\"),\\n    total_surplus_mmr=pd.NamedAgg(\\\"surplus_mmr\\\", \\\"sum\\\"),\\n)\\n\\nplayers_stats_df[\\\"appearances_rank\\\"] = players_stats_df[\\\"appearances\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_matches_rank\\\"] = players_stats_df[\\\"total_matches\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_mmr_rank\\\"] = players_stats_df[\\\"total_mmr\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\nplayers_stats_df[\\\"total_surplus_mmr_rank\\\"] = players_stats_df[\\\"total_surplus_mmr\\\"].rank(\\n    method=\\\"first\\\", ascending=False\\n)\\n\\nplayers_stats_df[\\\"appearances_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"appearances\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\nplayers_stats_df[\\\"total_matches_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"total_matches\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\nplayers_stats_df[\\\"total_mmr_national_rank\\\"] = players_stats_df.groupby([\\\"country\\\"])[\\n    \\\"total_mmr\\\"\\n].rank(method=\\\"first\\\", ascending=False)\\n\\nplayers_stats_df[\\\"total_surplus_mmr_national_rank\\\"] = players_stats_df.groupby(\\n    [\\\"country\\\"]\\n)[\\\"total_surplus_mmr\\\"].rank(method=\\\"first\\\", ascending=False)\\n\\nplayers_stats_df = players_stats_df.reset_index()\";\n",
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   "source": [
    "players_stats_df = players_df.groupby([\"name\", \"country\"]).agg(\n",
    "    appearances=pd.NamedAgg(\"season\", \"count\"),\n",
    "    placements=pd.NamedAgg(\"mmr\", lambda x: (x >= 9600).sum()),\n",
    "    total_matches=pd.NamedAgg(\"matches\", \"sum\"),\n",
    "    total_mmr=pd.NamedAgg(\"mmr\", \"sum\"),\n",
    "    total_surplus_mmr=pd.NamedAgg(\"surplus_mmr\", \"sum\"),\n",
    ")\n",
    "\n",
    "players_stats_df[\"appearances_rank\"] = players_stats_df[\"appearances\"].rank(\n",
    "    method=\"first\", ascending=False\n",
    ")\n",
    "players_stats_df[\"total_matches_rank\"] = players_stats_df[\"total_matches\"].rank(\n",
    "    method=\"first\", ascending=False\n",
    ")\n",
    "players_stats_df[\"total_mmr_rank\"] = players_stats_df[\"total_mmr\"].rank(\n",
    "    method=\"first\", ascending=False\n",
    ")\n",
    "players_stats_df[\"total_surplus_mmr_rank\"] = players_stats_df[\"total_surplus_mmr\"].rank(\n",
    "    method=\"first\", ascending=False\n",
    ")\n",
    "\n",
    "players_stats_df[\"appearances_national_rank\"] = players_stats_df.groupby([\"country\"])[\n",
    "    \"appearances\"\n",
    "].rank(method=\"first\", ascending=False)\n",
    "players_stats_df[\"total_matches_national_rank\"] = players_stats_df.groupby([\"country\"])[\n",
    "    \"total_matches\"\n",
    "].rank(method=\"first\", ascending=False)\n",
    "players_stats_df[\"total_mmr_national_rank\"] = players_stats_df.groupby([\"country\"])[\n",
    "    \"total_mmr\"\n",
    "].rank(method=\"first\", ascending=False)\n",
    "\n",
    "players_stats_df[\"total_surplus_mmr_national_rank\"] = players_stats_df.groupby(\n",
    "    [\"country\"]\n",
    ")[\"total_surplus_mmr\"].rank(method=\"first\", ascending=False)\n",
    "\n",
    "players_stats_df = players_stats_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>country</th>\n",
       "      <th>appearances</th>\n",
       "      <th>placements</th>\n",
       "      <th>total_matches</th>\n",
       "      <th>total_mmr</th>\n",
       "      <th>total_surplus_mmr</th>\n",
       "      <th>appearances_rank</th>\n",
       "      <th>total_matches_rank</th>\n",
       "      <th>total_mmr_rank</th>\n",
       "      <th>total_surplus_mmr_rank</th>\n",
       "      <th>appearances_national_rank</th>\n",
       "      <th>total_matches_national_rank</th>\n",
       "      <th>total_mmr_national_rank</th>\n",
       "      <th>total_surplus_mmr_national_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>DoctorLantern</td>\n",
       "      <td>BE</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>3931</td>\n",
       "      <td>174601</td>\n",
       "      <td>3842</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8981</th>\n",
       "      <td>NotsoGodlyDog</td>\n",
       "      <td>BE</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>2880</td>\n",
       "      <td>147073</td>\n",
       "      <td>2141</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>2881.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3623</th>\n",
       "      <td>Elriel</td>\n",
       "      <td>BE</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>6575</td>\n",
       "      <td>134778</td>\n",
       "      <td>378</td>\n",
       "      <td>2671.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>8420.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5629</th>\n",
       "      <td>Intrepid_Agent</td>\n",
       "      <td>BE</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2386</td>\n",
       "      <td>127079</td>\n",
       "      <td>318</td>\n",
       "      <td>1446.0</td>\n",
       "      <td>3972.0</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>9593.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18310</th>\n",
       "      <td>sllugaes</td>\n",
       "      <td>BE</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>5158</td>\n",
       "      <td>120099</td>\n",
       "      <td>492</td>\n",
       "      <td>2531.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>2252.0</td>\n",
       "      <td>6850.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4230</th>\n",
       "      <td>FractlZero</td>\n",
       "      <td>BE</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>224</td>\n",
       "      <td>11650</td>\n",
       "      <td>0</td>\n",
       "      <td>16368.0</td>\n",
       "      <td>20314.0</td>\n",
       "      <td>19131.0</td>\n",
       "      <td>20808.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9623</th>\n",
       "      <td>Plattekees</td>\n",
       "      <td>BE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>259</td>\n",
       "      <td>9878</td>\n",
       "      <td>278</td>\n",
       "      <td>20218.0</td>\n",
       "      <td>19764.0</td>\n",
       "      <td>19309.0</td>\n",
       "      <td>10478.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>Alph4Charlie</td>\n",
       "      <td>BE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>339</td>\n",
       "      <td>9686</td>\n",
       "      <td>86</td>\n",
       "      <td>19338.0</td>\n",
       "      <td>18516.0</td>\n",
       "      <td>20468.0</td>\n",
       "      <td>18352.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>BuccaneerMongoose</td>\n",
       "      <td>BE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>217</td>\n",
       "      <td>9673</td>\n",
       "      <td>73</td>\n",
       "      <td>19435.0</td>\n",
       "      <td>20407.0</td>\n",
       "      <td>20636.0</td>\n",
       "      <td>18993.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12817</th>\n",
       "      <td>Viexes</td>\n",
       "      <td>BE</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>272</td>\n",
       "      <td>9604</td>\n",
       "      <td>4</td>\n",
       "      <td>20534.0</td>\n",
       "      <td>19583.0</td>\n",
       "      <td>21199.0</td>\n",
       "      <td>20656.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name country  appearances  placements  total_matches  \\\n",
       "3189       DoctorLantern      BE           18          17           3931   \n",
       "8981       NotsoGodlyDog      BE           18           7           2880   \n",
       "3623              Elriel      BE           14          14           6575   \n",
       "5629      Intrepid_Agent      BE           17           2           2386   \n",
       "18310           sllugaes      BE           15           3           5158   \n",
       "...                  ...     ...          ...         ...            ...   \n",
       "4230          FractlZero      BE            2           0            224   \n",
       "9623          Plattekees      BE            1           1            259   \n",
       "818         Alph4Charlie      BE            1           1            339   \n",
       "1924   BuccaneerMongoose      BE            1           1            217   \n",
       "12817             Viexes      BE            1           1            272   \n",
       "\n",
       "       total_mmr  total_surplus_mmr  appearances_rank  total_matches_rank  \\\n",
       "3189      174601               3842            1077.0              1541.0   \n",
       "8981      147073               2141            1171.0              2881.0   \n",
       "3623      134778                378            2671.0               414.0   \n",
       "5629      127079                318            1446.0              3972.0   \n",
       "18310     120099                492            2531.0               819.0   \n",
       "...          ...                ...               ...                 ...   \n",
       "4230       11650                  0           16368.0             20314.0   \n",
       "9623        9878                278           20218.0             19764.0   \n",
       "818         9686                 86           19338.0             18516.0   \n",
       "1924        9673                 73           19435.0             20407.0   \n",
       "12817       9604                  4           20534.0             19583.0   \n",
       "\n",
       "       total_mmr_rank  total_surplus_mmr_rank  appearances_national_rank  \\\n",
       "3189            554.0                   604.0                        1.0   \n",
       "8981           1225.0                  1402.0                        3.0   \n",
       "3623           1622.0                  8420.0                        7.0   \n",
       "5629           1950.0                  9593.0                        4.0   \n",
       "18310          2252.0                  6850.0                        6.0   \n",
       "...               ...                     ...                        ...   \n",
       "4230          19131.0                 20808.0                       51.0   \n",
       "9623          19309.0                 10478.0                       63.0   \n",
       "818           20468.0                 18352.0                       61.0   \n",
       "1924          20636.0                 18993.0                       62.0   \n",
       "12817         21199.0                 20656.0                       64.0   \n",
       "\n",
       "       total_matches_national_rank  total_mmr_national_rank  \\\n",
       "3189                           3.0                      1.0   \n",
       "8981                           4.0                      2.0   \n",
       "3623                           1.0                      3.0   \n",
       "5629                           9.0                      4.0   \n",
       "18310                          2.0                      5.0   \n",
       "...                            ...                      ...   \n",
       "4230                          63.0                     60.0   \n",
       "9623                          61.0                     61.0   \n",
       "818                           58.0                     62.0   \n",
       "1924                          64.0                     63.0   \n",
       "12817                         60.0                     64.0   \n",
       "\n",
       "       total_surplus_mmr_national_rank  \n",
       "3189                               1.0  \n",
       "8981                               2.0  \n",
       "3623                              25.0  \n",
       "5629                              31.0  \n",
       "18310                             18.0  \n",
       "...                                ...  \n",
       "4230                              64.0  \n",
       "9623                              37.0  \n",
       "818                               60.0  \n",
       "1924                              61.0  \n",
       "12817                             63.0  \n",
       "\n",
       "[64 rows x 15 columns]"
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     },
     "execution_count": 4,
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