operators.ipynb

{
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  {
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    "# Rainbow Six Siege Analysis and Prediction\n",
    "\n",
    "## Research Topics\n",
    "\n",
    "- Exporatory Analysis\n",
    "    - Player behaviour\n",
    "    - Game Balance\n",
    "        - Map Balance (Attacker vs Defender)\n",
    "- Prediction\n",
    "    - predict the outcome of a match (win/lose)\n",
    "    - (operator pick recomandation)\n",
    "\n",
    "## Research Concept\n",
    "- Focus on PC platform and gamemode \"Bomb\"\n",
    "- Find correlations and biases in the dataset\n",
    "- Make use of these correlations and biases to generate suitable input vectors for ML models\n",
    "\n",
    "## ToDo's\n",
    "- Data Encoding for Prediction\n",
    "    - One Hot Encoding\n",
    "- Baseline Performance with Naive Bias\n",
    "- Use a simple MLP in Comparison"
   ],
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  {
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ],
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     "name": "#%%\n"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": "  platform    dateid skillrank      role       operator primaryweapon  \\\n0       PC  20170210    Bronze  Attacker   BOPE-CAPITAO          M249   \n1       PC  20170210    Bronze  Attacker   BOPE-CAPITAO          M249   \n2       PC  20170210    Bronze  Attacker   BOPE-CAPITAO      PARA-308   \n3       PC  20170210    Bronze  Attacker   BOPE-CAPITAO      PARA-308   \n4       PC  20170210    Bronze  Attacker  G.E.O.-JACKAL           C7E   \n\n  secondaryweapon secondarygadget  nbwins  nbkills  nbdeaths  nbpicks  \n0           PRB92        CLAYMORE       6        6         6        8  \n1           PRB92    STUN GRENADE       4        2         6        8  \n2           PRB92        CLAYMORE      37       40        49       70  \n3           PRB92    STUN GRENADE      17       24        31       41  \n4          ITA12S   BREACH CHARGE      74      123       127      174  ",
      "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>platform</th>\n      <th>dateid</th>\n      <th>skillrank</th>\n      <th>role</th>\n      <th>operator</th>\n      <th>primaryweapon</th>\n      <th>secondaryweapon</th>\n      <th>secondarygadget</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PC</td>\n      <td>20170210</td>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>M249</td>\n      <td>PRB92</td>\n      <td>CLAYMORE</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PC</td>\n      <td>20170210</td>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>M249</td>\n      <td>PRB92</td>\n      <td>STUN GRENADE</td>\n      <td>4</td>\n      <td>2</td>\n      <td>6</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PC</td>\n      <td>20170210</td>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>PARA-308</td>\n      <td>PRB92</td>\n      <td>CLAYMORE</td>\n      <td>37</td>\n      <td>40</td>\n      <td>49</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PC</td>\n      <td>20170210</td>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>PARA-308</td>\n      <td>PRB92</td>\n      <td>STUN GRENADE</td>\n      <td>17</td>\n      <td>24</td>\n      <td>31</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>PC</td>\n      <td>20170210</td>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>C7E</td>\n      <td>ITA12S</td>\n      <td>BREACH CHARGE</td>\n      <td>74</td>\n      <td>123</td>\n      <td>127</td>\n      <td>174</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operators = pd.read_csv(\"./data/datadump_s5_summary_operator_loadout.csv\")\n",
    "operators.head()"
   ],
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     "text": [
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": "  skillrank      role       operator  nbwins  nbkills  nbdeaths  nbpicks\n0    Bronze  Attacker   BOPE-CAPITAO    7131     9507     11290    15383\n1    Bronze  Attacker  G.E.O.-JACKAL   21358    32434     32760    45492\n2    Bronze  Attacker  GIGN-MONTAGNE   12175     9773     19389    26981\n3    Bronze  Attacker   GIGN-RESERVE     377      432       701      946\n4    Bronze  Attacker    GIGN-TWITCH   25733    35115     36318    53941",
      "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>skillrank</th>\n      <th>role</th>\n      <th>operator</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>7131</td>\n      <td>9507</td>\n      <td>11290</td>\n      <td>15383</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>21358</td>\n      <td>32434</td>\n      <td>32760</td>\n      <td>45492</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>GIGN-MONTAGNE</td>\n      <td>12175</td>\n      <td>9773</td>\n      <td>19389</td>\n      <td>26981</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>GIGN-RESERVE</td>\n      <td>377</td>\n      <td>432</td>\n      <td>701</td>\n      <td>946</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bronze</td>\n      <td>Attacker</td>\n      <td>GIGN-TWITCH</td>\n      <td>25733</td>\n      <td>35115</td>\n      <td>36318</td>\n      <td>53941</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "operators = operators.loc[operators['platform'] == ('PC')]\n",
    "operators = operators.drop(['dateid','platform','primaryweapon','secondaryweapon','secondarygadget'], axis=1)\n",
    "operators = operators.groupby(['skillrank','role', 'operator'], as_index=False)['nbwins', 'nbkills', 'nbdeaths', 'nbpicks'].sum()\n",
    "operators.head()"
   ],
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   "cell_type": "code",
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     "text": [
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
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     "data": {
      "text/plain": "        role             operator  nbwins  nbkills  nbdeaths  nbpicks  \\\n0   Attacker         BOPE-CAPITAO   65205    89647    101216   138254   \n1   Attacker        G.E.O.-JACKAL  193793   304650    291541   405461   \n2   Attacker        GIGN-MONTAGNE   98517    81475    155090   216753   \n4   Attacker          GIGN-TWITCH  227325   328186    317090   469867   \n5   Attacker           GSG9-BLITZ   45829    52784     76280    99824   \n6   Attacker              GSG9-IQ   64406    89407    101472   138740   \n8   Attacker            JTF2-BUCK  120327   169379    187398   255529   \n9   Attacker  NAVYSEAL-BLACKBEARD  134465   216468    189879   278612   \n11  Attacker           SAS-SLEDGE  182531   269781    282424   384025   \n12  Attacker         SAS-THATCHER  190594   252021    276596   395212   \n13  Attacker           SAT-HIBANA  259165   385322    368381   533111   \n14  Attacker        SPETSNAZ-FUZE  233038   363138    354549   493859   \n15  Attacker        SPETSNAZ-GLAZ  216694   366726    314764   453586   \n17  Attacker             SWAT-ASH  316987   537066    469432   654668   \n19  Attacker        SWAT-THERMITE  234739   312823    347763   491555   \n20  Defender         BOPE-CAVEIRA  224757   314489    296823   414296   \n21  Defender          G.E.O.-MIRA  226284   293029    282491   423166   \n22  Defender             GIGN-DOC  159420   206250    207479   301116   \n24  Defender            GIGN-ROOK  312685   384297    386406   568988   \n25  Defender          GSG9-BANDIT  289819   408088    386628   539873   \n26  Defender           GSG9-JAGER  325485   477720    432621   604988   \n28  Defender           JTF2-FROST  197539   246423    245028   356571   \n29  Defender    NAVYSEAL-VALKYRIE  242487   331203    310124   446115   \n30  Defender             SAS-MUTE  235142   268555    312492   445153   \n32  Defender            SAS-SMOKE  216367   274319    290591   411145   \n33  Defender             SAT-ECHO   74435    91846     88730   139139   \n34  Defender      SPETSNAZ-KAPKAN  120771   155032    165644   232114   \n36  Defender    SPETSNAZ-TACHANKA   25913    28358     37080    52099   \n37  Defender          SWAT-CASTLE   99671   110238    136760   192941   \n38  Defender           SWAT-PULSE  166553   224512    232647   319275   \n\n    k/d_ratio  win_ratio  \n0    0.885700   0.471632  \n1    1.044965   0.477957  \n2    0.525340   0.454513  \n4    1.034993   0.483807  \n5    0.691977   0.459098  \n6    0.881100   0.464221  \n8    0.903846   0.470894  \n9    1.140031   0.482625  \n11   0.955234   0.475310  \n12   0.911152   0.482258  \n13   1.045988   0.486137  \n14   1.024225   0.471872  \n15   1.165082   0.477735  \n17   1.144076   0.484195  \n19   0.899529   0.477544  \n20   1.059517   0.542503  \n21   1.037304   0.534741  \n22   0.994077   0.529431  \n24   0.994542   0.549546  \n25   1.055506   0.536828  \n26   1.104246   0.538002  \n28   1.005693   0.553996  \n29   1.067970   0.543553  \n30   0.859398   0.528227  \n32   0.944004   0.526255  \n33   1.035118   0.534969  \n34   0.935935   0.520309  \n36   0.764779   0.497380  \n37   0.806069   0.516588  \n38   0.965033   0.521660  ",
      "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>role</th>\n      <th>operator</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n      <th>k/d_ratio</th>\n      <th>win_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>65205</td>\n      <td>89647</td>\n      <td>101216</td>\n      <td>138254</td>\n      <td>0.885700</td>\n      <td>0.471632</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>193793</td>\n      <td>304650</td>\n      <td>291541</td>\n      <td>405461</td>\n      <td>1.044965</td>\n      <td>0.477957</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Attacker</td>\n      <td>GIGN-MONTAGNE</td>\n      <td>98517</td>\n      <td>81475</td>\n      <td>155090</td>\n      <td>216753</td>\n      <td>0.525340</td>\n      <td>0.454513</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Attacker</td>\n      <td>GIGN-TWITCH</td>\n      <td>227325</td>\n      <td>328186</td>\n      <td>317090</td>\n      <td>469867</td>\n      <td>1.034993</td>\n      <td>0.483807</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Attacker</td>\n      <td>GSG9-BLITZ</td>\n      <td>45829</td>\n      <td>52784</td>\n      <td>76280</td>\n      <td>99824</td>\n      <td>0.691977</td>\n      <td>0.459098</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Attacker</td>\n      <td>GSG9-IQ</td>\n      <td>64406</td>\n      <td>89407</td>\n      <td>101472</td>\n      <td>138740</td>\n      <td>0.881100</td>\n      <td>0.464221</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Attacker</td>\n      <td>JTF2-BUCK</td>\n      <td>120327</td>\n      <td>169379</td>\n      <td>187398</td>\n      <td>255529</td>\n      <td>0.903846</td>\n      <td>0.470894</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Attacker</td>\n      <td>NAVYSEAL-BLACKBEARD</td>\n      <td>134465</td>\n      <td>216468</td>\n      <td>189879</td>\n      <td>278612</td>\n      <td>1.140031</td>\n      <td>0.482625</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Attacker</td>\n      <td>SAS-SLEDGE</td>\n      <td>182531</td>\n      <td>269781</td>\n      <td>282424</td>\n      <td>384025</td>\n      <td>0.955234</td>\n      <td>0.475310</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Attacker</td>\n      <td>SAS-THATCHER</td>\n      <td>190594</td>\n      <td>252021</td>\n      <td>276596</td>\n      <td>395212</td>\n      <td>0.911152</td>\n      <td>0.482258</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Attacker</td>\n      <td>SAT-HIBANA</td>\n      <td>259165</td>\n      <td>385322</td>\n      <td>368381</td>\n      <td>533111</td>\n      <td>1.045988</td>\n      <td>0.486137</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-FUZE</td>\n      <td>233038</td>\n      <td>363138</td>\n      <td>354549</td>\n      <td>493859</td>\n      <td>1.024225</td>\n      <td>0.471872</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-GLAZ</td>\n      <td>216694</td>\n      <td>366726</td>\n      <td>314764</td>\n      <td>453586</td>\n      <td>1.165082</td>\n      <td>0.477735</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Attacker</td>\n      <td>SWAT-ASH</td>\n      <td>316987</td>\n      <td>537066</td>\n      <td>469432</td>\n      <td>654668</td>\n      <td>1.144076</td>\n      <td>0.484195</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Attacker</td>\n      <td>SWAT-THERMITE</td>\n      <td>234739</td>\n      <td>312823</td>\n      <td>347763</td>\n      <td>491555</td>\n      <td>0.899529</td>\n      <td>0.477544</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Defender</td>\n      <td>BOPE-CAVEIRA</td>\n      <td>224757</td>\n      <td>314489</td>\n      <td>296823</td>\n      <td>414296</td>\n      <td>1.059517</td>\n      <td>0.542503</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Defender</td>\n      <td>G.E.O.-MIRA</td>\n      <td>226284</td>\n      <td>293029</td>\n      <td>282491</td>\n      <td>423166</td>\n      <td>1.037304</td>\n      <td>0.534741</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Defender</td>\n      <td>GIGN-DOC</td>\n      <td>159420</td>\n      <td>206250</td>\n      <td>207479</td>\n      <td>301116</td>\n      <td>0.994077</td>\n      <td>0.529431</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Defender</td>\n      <td>GIGN-ROOK</td>\n      <td>312685</td>\n      <td>384297</td>\n      <td>386406</td>\n      <td>568988</td>\n      <td>0.994542</td>\n      <td>0.549546</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Defender</td>\n      <td>GSG9-BANDIT</td>\n      <td>289819</td>\n      <td>408088</td>\n      <td>386628</td>\n      <td>539873</td>\n      <td>1.055506</td>\n      <td>0.536828</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Defender</td>\n      <td>GSG9-JAGER</td>\n      <td>325485</td>\n      <td>477720</td>\n      <td>432621</td>\n      <td>604988</td>\n      <td>1.104246</td>\n      <td>0.538002</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Defender</td>\n      <td>JTF2-FROST</td>\n      <td>197539</td>\n      <td>246423</td>\n      <td>245028</td>\n      <td>356571</td>\n      <td>1.005693</td>\n      <td>0.553996</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Defender</td>\n      <td>NAVYSEAL-VALKYRIE</td>\n      <td>242487</td>\n      <td>331203</td>\n      <td>310124</td>\n      <td>446115</td>\n      <td>1.067970</td>\n      <td>0.543553</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Defender</td>\n      <td>SAS-MUTE</td>\n      <td>235142</td>\n      <td>268555</td>\n      <td>312492</td>\n      <td>445153</td>\n      <td>0.859398</td>\n      <td>0.528227</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Defender</td>\n      <td>SAS-SMOKE</td>\n      <td>216367</td>\n      <td>274319</td>\n      <td>290591</td>\n      <td>411145</td>\n      <td>0.944004</td>\n      <td>0.526255</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Defender</td>\n      <td>SAT-ECHO</td>\n      <td>74435</td>\n      <td>91846</td>\n      <td>88730</td>\n      <td>139139</td>\n      <td>1.035118</td>\n      <td>0.534969</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-KAPKAN</td>\n      <td>120771</td>\n      <td>155032</td>\n      <td>165644</td>\n      <td>232114</td>\n      <td>0.935935</td>\n      <td>0.520309</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-TACHANKA</td>\n      <td>25913</td>\n      <td>28358</td>\n      <td>37080</td>\n      <td>52099</td>\n      <td>0.764779</td>\n      <td>0.497380</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Defender</td>\n      <td>SWAT-CASTLE</td>\n      <td>99671</td>\n      <td>110238</td>\n      <td>136760</td>\n      <td>192941</td>\n      <td>0.806069</td>\n      <td>0.516588</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Defender</td>\n      <td>SWAT-PULSE</td>\n      <td>166553</td>\n      <td>224512</td>\n      <td>232647</td>\n      <td>319275</td>\n      <td>0.965033</td>\n      <td>0.521660</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator_groups = operators.groupby(['role', 'operator'], as_index=False)['nbwins', 'nbkills', 'nbdeaths', 'nbpicks'].sum()\n",
    "\n",
    "operator_groups['k/d_ratio'] = pd.to_numeric(operator_groups['nbkills']) / pd.to_numeric(operator_groups['nbdeaths'])\n",
    "operator_groups['win_ratio'] = pd.to_numeric(operator_groups['nbwins']) / pd.to_numeric(operator_groups['nbpicks'])\n",
    "\n",
    "operator_groups = operator_groups.loc[~operator_groups[\"operator\"].str.contains(\"RESERVE\")]\n",
    "operator_groups"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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aJntSEkhwphNhc1A2OALDMPK7WnIRKMESEREREbmItiQeYdbuDRzPSMXAwMQkyh5Eu3I3cX148fyunuQxdREUEREREblItiQeYcz2n4l3phNktREUYCPIaiPemc6Y7T+zJfFIfldR8pgSLBERERGRi8A0TWbt3oDVsBBoDfB1CTQMg0BrAFbDwuzdGzFNM59rKnlJCZaIiIiISHZME0vcMax7DmCJOwY5SIb2pCRwPCMVh8Wa5XaHxcqxjBT2pCTkVW2lANA7WCIiIiIiWbDuP4T9h7VYTqZgGgaGaeIJCyajzu24r7rynMcnONMxMLIdzMIwvNsSnOl5XXXJR2rBEhERERH5D+v+QwR+/i1GUiqm3QZ2G6bdhpGUSuDn32Ldf+icZUTYHJiY2XYBNE3vtgibI6+rL/lICZaIiIiIyOlME/v3azENC9htcKoFyjC8iZZhwf7DL+fsLlg2OIIoexDpHneW29M9boragykbHJHXVyD5SAmWiIiIiMhpLEeOY0lKAVs2b9PYArCcTMZy9PhZyzEMg3blbsJtekhzu3wtWaZpkuZ24TY9tC13o+bDKmSUYImIiIiInMZITcM0jH9brs7YwcA0wEhJO2dZ14cXp0flOyhiCyTV7fT9V8QWSI/Kd2gerEJIg1yIiIiIiJzGDArE+Of9qCyTLNPEMMEMDjyv8q4PL86wG+5hT0oCCc50ImwOygZHqOWqkFKCJSIiIiJyGk/xKDyhwRjJqd53sP7L6cITFoKnWNR5l2kYBuVCIvOuklJgqYugiIiIiMjpDIOMurdjmB7IcP47mIVpQoYTw/SQUee27LsQymVNCZaIiIiIyH+4r7qStIb3YIYGY2Q4ISMDI8OJGRpMWsN7zmseLLk8qYugiIiIiEgW3FddSWqrR7AcPY6RkoYZHOjtFqiWKzkLJVgiIiIiItkxDDzFi+Z3LcSPXJQugh6PhwEDBtC8eXPatGnDnj17Mm2fMWMGDRo0oE2bNrRp04adO3f6th07dow6derw119/XYyqiYiIiIiIXDQXpQVrxYoVZGRkMG/ePGJiYhg+fDiTJk3ybY+NjWXEiBFUq1Yt03FOp5MBAwYQGHh+Q16KiIiIiIgUJBelBWv9+vXUrl0bgOrVq7N58+ZM22NjY5k6dSotW7ZkypQpvvUjRoygRYsWlChR4mJUS0RERERE5KK6KC1YSUlJhIaG+patVisul4uAAO/pGjRoQKtWrQgNDSU6OprvvvuOEydOEBUVRe3atZk6dep5ncdqNYiMDP7POssZ66RgUqz8i+LlPxQr/6J4+Q/Fyn8oVv6lsMXroiRYoaGhJCcn+5Y9Ho8vuTJNk3bt2hEWFgZAnTp12LJlC6tXr8YwDNasWcMff/xB7969mTRpEsWLF8/2PG63SXx8SqZ1kZHBZ6yTgkmx8i+Kl/9QrPyL4uU/FCv/oVj5F3+NV/HiYVmuvyhdBGvUqMHKlSsBiImJoXLlyr5tSUlJNGzYkOTkZEzTZO3atVSrVo3333+fuXPnMmfOHK677jpGjBhx1uRKREREREQKN9M02Z/8F1sTfmN/8l+YpyZ9LsAuSgtW/fr1WbVqFS1atMA0TYYOHcqSJUtISUmhefPmdO/enbZt22K326lZsyZ16tS5GNUQERERERE/tSNxEwv3TSM+4xgGBiYeIu3FaHJ1RyqF35Df1cuWYfpDGpgNp9OtLoJ+TLHyL4qX/1Cs/Ivi5T8UK/+hWPmXrOK1I3ETM/8ahgUrdksghmFgmiYZnjQ8uHmqYp98T7IuaRdBERERERGR3DBNk8/2TsOCFYc1CMMwADAMA4c1CAtWFu57t8B2F1SCJSIiIiIiBcaBlJ0kOI9ht2Q9N67dEkh8xlEOpu66xDU7PxflHSwRERERkYLONE3ik3eSnpGAwx5BZEgFX2uJ5J8kVwIGRraxMAwDA4OTzvhLW7HzpARLRERERC47cfGbiNkxjdT0Y2AYYHoIchSjeqWOlIgsuAMoXA5CAyIw8WCaZpZJlmmamJiE2SIvfeXOg7oIioiIiMhlJS5+E2tih5GWcYIAazA2azAB1hDSMk6wJnYYcfGb8ruKl7XSwRWIsBUjw5OW5fYMTxqR9mKUCip/iWt2fpRgiYiIiMhlwzRNYnZMwzCsBPxnAAXvspWYvwruAAqXA8MweLRMRzy4SXen+mJhmibp7lQ8uGly9TMFtjunEiwRERERuWzEJ+8kNf0Y1mwGULBaAklNO0pCcsEcQOFyUSn8Bp6q2IdwexRpnhTS3CmkeVIIt0cViCHaz0bvYImIiIjIZSM9IwGMcwygYBikZcRf2orJGSqF38BL14/lYOouTjrjCbNFUiqofIFtuTpFCZaIiIiIXDYc9ggwzzGAgmkSaI+89JWTMxiGQengCvldjRxRF0ERERERyROmabLnpIdNxzzsOekpkO8xRYZUIMhRDHc2Ayi4PWkEBRYjIqRgDqAgBZ9asERERETkgv1xwsP7f7o5nm5iACYQ5TB4spKF6z17MFITMIMiMIuW9w6Lnk8Mw6B6pY6siR2Gy52K1RKIYRiYponbk4ZpuqleseAOoCAFnxIsEREREbkgf5zwMH6zCwsQZMWXsMSnpDH+50R6nJhNVddf3q55IcVw3fUMnlL5N0hBicgbqFm1DzF/vUtq2lFffYMCi1G94jOaB0suiBIsEREREck10zR5/083FiAw4N9WH8OVRtDJQ6RZApkd/jjDT76NYZoYKcexfT0c5/2v5HuSVb/GWBKSd5GWEU+gPZKIkII/gIIUfHoHS0RERERybW+SyfF0E4c183oj6QgYBg6cHLNGssd6pbdroC0IDAsBq96FfH5HyzAMIkMrUDKqBpGhFZRcSZ5QgiUiIiIiuZaYAQZkTk5c6eB2A4Z3m+khwQj9d3tAIEbyMYxjmmtKCh8lWCIiIiKSa+F274AWmUYM9LhPZV3ebYaFCDPp3+2G4Z2LKjXhEtdW5OJTgiUiIiIiuVYm1CDKYZDuPm2lxert/meapGOnqCeesu5D/27/Z5sZFHHJ6ytysSnBEhEREZFcMwyDJ6+x4gHSXN5JeglwYFoDSDPseAwLbVM+J9PbTa40zJCi3iHbRQoZJVgiIiIickGuK2Lh+WoBRDoMUt3eRCsl6AqKuBPoHj+dqs6/vDuaJjhTwfTguuuZfJ0PS+Ri0TDtIiIiInLBriti4fVbDfYmmSRmQLg9gHKJBrbVxyE51ZtMmSZmSNF8nwdL5GJSgiUiIiIiecIwDMqG/dsqZYbdQMbjYzGO7cJITcAMivB2C1TLlRRiSrBERERE5OIxDMxiFcjfGa9ELh29gyUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB45rwQrPj6ejRs3cvz48YtdHxEREREREb8VcK4dli5dyrhx46hYsSJ//vkn0dHRNG7c+KzHeDweBg4cyLZt27Db7QwZMoSyZcv6ts+YMYMFCxYQFRUFwKBBgyhbtiz9+vVj165dWK1Whg0bRpkyZS7w8kRERET+ZZomzoRduNMTsDoisEWUxzCM/K6WiBQi50ywZs2axaeffkpISAhJSUm0a9funAnWihUryMjIYN68ecTExDB8+HAmTZrk2x4bG8uIESOoVq1apmMAPvroI9auXcuwYcMyHSMiIiJyIdKObOLExndxpx4DwwDTgzWoGEVufIbA4jfkd/VEpJA4Z4JlGAYhISEAhIaG4nA4zlno+vXrqV27NgDVq1dn8+bNmbbHxsYydepUjhw5Qt26denUqRP33XcfdevWBeDgwYMUK1Ysp9ciIiIikqW0I5s4unY4GBaMgCAMw8A0Tdxpxzm6djjFbn9FSZaI5IlzJlhlypRh+PDh3HLLLaxbt+68uu0lJSURGhrqW7ZarbhcLgICvKdr0KABrVq1IjQ0lOjoaL777jvq1atHQEAAvXv3Zvny5bz99tvnPI/VahAZGfyfdZYz1knBpFj5F8XLfyhW/kXxuvhM0+TID+9hDQjAYgvKvNEWgseZysktM7jikUln7S6oWPkPxcq/FLZ4nTPBGjp0KPPmzWP16tVUrFiRl1566ZyFhoaGkpyc7Fv2eDy+5Mo0Tdq1a0dYWBgAderUYcuWLdSrVw+AESNG0LNnT5544gm++OILgoOz/7DdbpP4+JRM6yIjg89YJwWTYuVf8ipepmmyJyWZRGcG4TY7ZYND9P5DHtOz5V8Ur4svI34n6SePYAQEYbo8Z2w3sZN+Mo4je2KxR1bIthzFyn8oVv7FX+NVvHhYluuzHUVw06ZNAPz888+ULVuWe+65h7Jly7J27dpznqxGjRqsXLkSgJiYGCpXruzblpSURMOGDUlOTsY0TdauXUu1atVYuHAhU6ZMASAoyNt0b7Vaz/8KRaTA25IQT5+N6xkau4F3/tzKG7Eb6LNxPVsS4vO7aiJSiLnTE8Awsv0xxzAMMAzvfiIiFyjbFqw1a9Zwww038MUXX5yxrVatWmcttH79+qxatYoWLVpgmiZDhw5lyZIlpKSk0Lx5c7p3707btm2x2+3UrFmTOnXqkJKSQp8+fXjyySdxuVz07dv3vN73EhH/sCUhnre2xWI1DIKsVt/7D/EZGby1LZbu11bl+ojI/K6miBRCVkcEmB5M08wyyTJNE0zTu5+IyAUyTNM0z7bD/PnzadasmW959uzZtG3b9qJX7Hw4nW51EfRjipV/uZB4mabJKxvXk5CRQWAWLdNpbjeRdgfDbqyh7oJ5QM+Wf1G8Lj7TNPn72xdxpx3HEhB0xnaPKxVrYBQl7xl71u8gxcp/KFb+xV/jlV0XwWxbsD7//HO+/fZb1q5dy88//wx436Xavn17gUmwRMQ/7ElJ5kR6OkHZdPt1WCwcT09jT0oy5UJCs9xHRCS3DMOgyI3PcHTtcDyuVAxroK8V3XSngemhyI3P6AceEckT2SZYtWvXpnjx4sTHx9O8eXMALBYLV1999SWrnIgUDonODIxzvP9gGAaJzoxLXDMRuVwEFr+BYre/4psHyzQMb7fAoKKaB0tE8lS2CVZERAS33347t99+O3FxcbhcLkzT5ODBg1xxxRWXso4i4ufCbXY8pnnW9x9M0yTcZs+H2onI5SKw+A2UvGcszoRduNMTsDoisEWUV8uViOSpcw7T3rdvX2JiYkhNTSU1NZUyZcrw8ccfX4q6iUghUTY4hCiHI9t3sNI9HqIcgZQNDsmH2onI5cQwjLMOxS4icqGyHab9lJ07d/LFF19Qq1Ytli5dqpH9RCTHDMOgXblKuE2TNLebU2PrmP8su02TtuUq6ldk8VumabI7KY2NJ5LYnZTGOcaPEhGRQuycLVghId5JQFNSUoiKisLpdF6KeolIIXN9RCTdr63K7N1/cTw9zfeCeZQjkLblKmqIdvFbW+KTmbvrMMfTnRiAByjqsNG6/BVcH6lWWRGRy805E6yqVasyffp0SpQoQffu3XG5XJeiXiJSCF0fEcmwG2uwJyWZRGcG4TY7ZYND1HIlfmtLfDLjtu7/Z343y2nzu7kYt3U/3apcpSRLROQyc84Eq0ePHiQlJREYGMjKlSu54QaNsiMiuWcYhoZil0LBNE3m7Pwbq2EQaP23x71hGARaDdLcHubuOswb1TWIgojI5STbd7BOnjzJjBkz+OSTTwgODiYgIIBSpUrx/PPPX8r6iYiIFEh7ktM5keHCYck6eXJYDI6lO9mbnH6JayYiIvkp2xasbt26Ua1aNbZs2cKhQ4coVqwYEyZMoHfv3peyfiIiIgVSotOFAWed380CJDjVtV5E5HKSbYKVnJxMjx49ME2TBx98kNKlS7No0SKKFi16KesnIiJSIIXbAvDAWed38wARtnP2xhcRkUIk2299u9074adhGDgcDiZNmqQh2kVERP5RNsRBlD2ABKebQOuZCVa6x6Sow0aZEP3bKSJyOcn2HazTf42LjIxUciUiInIawzBoU6HkP/O7ef4zv5sHt2nSuvwVGuBCROQyk20LVmxsLC1atMA0TXbs2OH7/4Zh8NFHH13KOoqIiBRI10eG0K3KVczddZhj6U4saB4sEZHLXbYJ1uLFiy9lPURERPzS9ZEhvFG9PHuT00lwuoiwBVAmxKGWKxGRy1S2CVbp0qUvZT1ERET8lmEYlA0NzO9qiIhIAZDtO1giIiIiIiKSM0qwRERERERE8sg5J+f4448/mDdvHunp/85EP2zYsItaKREREREREX90zgTrlVdeoXXr1pQsWfJS1EdERERERMRvnTPBKlasGM2aNbsUdREREREREfFr50ywSpcuzdSpU7nuuut8Q87WqlXroldMRERERETE35wzwXI6nezatYtdu3b51inBEhER8XOmiSUuBSPFiRlsw1MiGDR3l4jIBcs2wXK5XAQEBDBo0KBLWR8RERG5yCx7E7F/swfLyQxMAwwTPGF2Mu4ti6dMeH5XT0TEr2WbYPXu3ZvRo0fz4IMP+roGmqaJYRh88803l6yCIiIikncsexMJXPgnpgGm3QKG4f33PSmDwIV/ktbkGiVZIiIXINsE69lnnwXg22+/vWSVERERkYvINLF/swfTAOzWf9cbBtitmBlu7N/sIe2pauouKCKSS9kmWEOGDOHvv//m1ltvpXbt2tx1112Eh+sXLREREX9liUvxdgu0W7LewWbBcjIDS1wKnitCLm3lREQKiWwTrDlz5pCRkcHvv//OL7/8wscffwzALbfcQteuXS9ZBUVERC4W04TEBEhPB4cDwiMKd8ONkeL0tl5ld5GG4X0nK8V5SeslIlKYnHUUQbvdTtWqVUlISCA5OZnY2Fj++OOPS1U3ERGRi+boEdi0wSAtFTAAEwKD4IabTIoVz+/aXRxmsA3D9L5TnWWSZZre7cG2S185EZFCItsEa8aMGXz//fecPHmSmjVrUrduXV566SVsNn3pioiIfzt6BH752cBigQCbN9cwTW9L1q8/G9x6R+FMsjwlgvGE2TGSMjK/g3WK04MnzO4dsl1ERHIlm07Y8M477xAVFUXPnj158cUXueOOO5RciYiI3zNNb8uVxQIBAf825BjGP8sW2LzRwDTzt54XhWGQcW9ZDBPIcOO7SNOEDDeGCRn3li3c/SRFRC6ybFuw1qxZw7p161i5ciVjxoyhePHi3H333dSpU4dSpUqdtVCPx8PAgQPZtm0bdrudIUOGULZsWd/2GTNmsGDBAqKiogAYNGgQV199NX379uXAgQNkZGTQpUsX7r333jy6TBEREa/EBEhL9bZcZcVqhdQU734RkZe0apeEp0w4aU2uyXIerHTNgyUicsGyTbBsNhs1a9akZs2aAKxcuZIpU6YwePDgc76HtWLFCjIyMpg3bx4xMTEMHz6cSZMm+bbHxsYyYsQIqlWr5lv3ySefEBkZyciRIzlx4gSPPvqoEiwREclz6emAcdZxHsD4Z79CylMmnLSnqmGJS/EOfBFs83YLVMuViMgFyzbB2rRpE+vXr2fdunXs3LmTKlWq0KRJE0aOHHnOQtevX0/t2rUBqF69Ops3b860PTY2lqlTp3LkyBHq1q1Lp06dePDBB3nggQd8+1itWfQN/w+r1SAyMvg/6yxnrJOCSbHyL4qX/1Cszs7j9mCxZGCxgJFFQmGaJhYLFCvmIDIy2570eSZf41VEQ7HnhJ4t/6FY+ZfCFq9sE6xRo0ZRq1YtunTpwvXXX5/lP0LZSUpKIjQ01LdstVpxuVwEBHhP16BBA1q1akVoaCjR0dF899131KtXz3fsCy+8wIsvvnjO87jdJvHxKZnWRUYGn7FOCibFyr8oXv5DsTo7wwJ2u0F6OgQEnPmilcsFgYFgWNKIj7/49VG8/Idi5T8UK//ir/EqXjwsy/XZJlizZs3K9clCQ0NJTk72LXs8Hl9yZZom7dq1IyzMW6E6deqwZcsW6tWrx6FDh+jatSutWrWiUaNGuT6/iIhIdgzDOxT7Lz8buFzed65OjSLodoPpgWo3muotJyIiuXJR+j7UqFGDlStXAhATE0PlypV925KSkmjYsCHJycmYpsnatWupVq0aR48epX379vTq1YvHH3/8YlRLREQEgGLF4bY7TAIDweUEp9P7v4GBFNoh2kVE5NI460TDuVW/fn1WrVpFixYtME2ToUOHsmTJElJSUmjevDndu3enbdu22O12atasSZ06dRgyZAiJiYlMnDiRiRMnAjBt2jQCAwMvRhVFROQyV6w41LnHJDHBO6CFwwHhERrnQURELoxhmv4704fT6dY7WH5MsfIvipf/UKz8i+LlPxQr/6FY+Rd/jVd272Bd/OGRRERERERELhNKsERERERERPKIEiwREREREZE8clEGuRDJc6aJ8bcHI9nEDDEwS1r0JrqIiIiIFDhKsKTAs+x2YV+WjpHoAQPwgBlhIeNBB55yuoVFREREpOBQF0Ep0Cy7XTjmpcJJD6YdTLuB6QBOenDMS8Wy25XfVRQRERER8VGCJQWXaWL/Mh3TAtiNf7sEGgbYDUwL2Jelg//ONCAiIiIihYwSLCmwjL89GCc9YMtmBxsYCR6Mw55LWi8RERERkewowZICy0g2ve9cZTeYhWGABYwktWCJiIiISMGgBEsKLDPEAA/ZdwE0Te+AF6EaTVBERERECgYlWFJgmSUtmOEWcGazg9M7mqB5hW5jERERESkY9JepFFyGQcZDDgwPkGH+25JlmpBhYngg40GH5sMSERERkQJDCZYUaJ5yAaQ3D4IwC0Y6GBkmRjoQZiG9eZDmwRIRERGRAkV/nUqB5ykXQFonK8ZhD0aSiRlqeLsFquVKRERERAoYJVjiHwwDs6QVjRcoIiIiIgWZugiKiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHLkqC5fF4GDBgAM2bN6dNmzbs2bMn0/YZM2bQoEED2rRpQ5s2bdi5c6dv24YNG2jTps3FqJaIiIiIiMhFFXAxCl2xYgUZGRnMmzePmJgYhg8fzqRJk3zbY2NjGTFiBNWqVct03LRp01i8eDFBQUEXo1oiIiIiIiIX1UVJsNavX0/t2rUBqF69Ops3b860PTY2lqlTp3LkyBHq1q1Lp06dAChTpgzjx4/n5ZdfPq/zWK0GkZHB/1lnOWOdFEyKlX9RvPyHYuVfFC//oVj5D8XKvxS2eF2UBCspKYnQ0FDfstVqxeVyERDgPV2DBg1o1aoVoaGhREdH891331GvXj0eeOAB9u/ff97ncbtN4uNTMq2LjAw+Y50UTIqVf1G8/Idi5V8UL/+hWPkPxcq/+Gu8ihcPy3L9RXkHKzQ0lOTkZN+yx+PxJVemadKuXTuioqKw2+3UqVOHLVu2XIxqiIiIiIiIXFIXJcGqUaMGK1euBCAmJobKlSv7tiUlJdGwYUOSk5MxTZO1a9ee8S6WiIiIiIiIP7ooXQTr16/PqlWraNGiBaZpMnToUJYsWUJKSgrNmzene/futG3bFrvdTs2aNalTp87FqIaIiIiIiMglZZimaeZ3JXLL6XTrHSw/plj5F8XLfyhW/kXx8h+Klf9QrPyLv8brkr6DJSIiIiIicjlSgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkESVYIiIiIiIieUQJloiIiIiISB5RgiUiIiIiIpJHlGCJiIiIiIjkkYCLUajH42HgwIFs27YNu93OkCFDKFu2rG/7jBkzWLBgAVFRUQAMGjSIcuXKnfUYERERERGRgu6iJFgrVqwgIyODefPmERMTw/Dhw5k0aZJve2xsLCNGjKBatWq+dV9//fVZjxERERERESnoLkqCtX79emrXrg1A9erV2bx5c6btsbGxTJ06lSNHjlC3bl06dep0zmOyYrUaREYG/2ed5Yx1UjApVv5F8fIfipV/Ubz8h2LlPxQr/1LY4nVREqykpCRCQ0N9y1arFZfLRUCA93QNGjSgVatWhIaGEh0dzXfffXfOY7LidpvEx6dkWhcZGXzGOimYFCv/onj5D8XKvyhe/kOx8h+KlX/x13gVLx6W5fqLkmCFhoaSnJzsW/Z4PL5EyTRN2rVrR1iYt0J16tRhy5YtZz1GRERERETEH1yUUQRr1KjBypUrAYiJiaFy5cq+bUlJSTRs2JDk5GRM02Tt2rVUq1btrMeIiIiIiIj4g4vSRFS/fn1WrVpFixYtME2ToUOHsmTJElJSUmjevDndu3enbdu22O12atasSZ06dfB4PGccIyIiIiIi4k8M0zTN/K5Ebjmd7gLxDpZpgvMYeFLBEgS2omAYl7QKfslf+9terhQv/6FY+RfFy38oVv5DsfIv/hqvS/oO1uUk/SDErzJwJwMGYII1BCLvMnGUyu/aiYiIiIjIpXRR3sG6XKQfhGNfGbhTwLCBxeb9X3cKHPvaIP1gftdQREREREQuJSVYuWSa3pYrLP8kVv90CTQM7zKGd7v/dsAUEREREZGcUoKVS85j4E4GI5tOlkaAd7vz2KWtl4iIiIiI5B8lWLnkSQWM7AezMAzvdk/qpayViIiIiIjkJyVYuWQJAkyy7QJomt7tlqBLWSsREREREclPSrByyVbUO1qg6cp6u+nybrcVvbT1EhERERGR/KMEK5cMwzsUOx7wOP9tyTJN7zKmd7vmwxIRERERuXwowboAjlJQ9AETazCYzn8SLSdYg6Ho/ZoHS0RERETkcqOJhi+QoxSUeNzEecw7oIUlyNstUC1XIiIiIiKXHyVYecAwwF4sv2shIiIiIiL5TV0ERURERERE8ogSLBERERERkTyiBEtERERERCSPKMESERERERHJI4ZpnprBSURERERERC6EWrBERERERETyiBIsERERERGRPKIES0REREREJI8owRIREREREckjSrBERERERETyiBIsERERERGRPKIES0REREREJI8E5HcFzpfH42HgwIFs27YNu93OkCFDKFu2LABHjhyhR48evn3/+OMPXnrpJVq2bEmTJk0ICwsD4KqrrmLYsGH5Uv/LzdniBbB48WJmzJiBxWKhadOmtGrV6pzHyMWRm1gBerbyybnitXDhQqZPn05YWBiPPvoozZo107OVT3ITK9CzlZ82bNjAqFGjmDNnTqb13377Le+88w4BAQE0bdqUJ554Qs9VAZCTeIGerfyUXawAUlNTefrpp3njjTeoWLFi4Xi2TD/x1Vdfmb179zZN0zR///13s3Pnzlnu99tvv5lt2rQxXS6XmZaWZjZu3PgS1lJOOVe87rrrLvPEiRNmenq6ed9995nx8fHnHWPJW7mJlZ6t/HO2eB07dsysW7eueeLECdPtdptt2rQx9+3bp2crn+QmVnq28s/UqVPNhg0bms2aNcu0PiMjw/fdl56ebj722GNmXFycnqt8ltN46dnKP9nFyjRNc+PGjeajjz5q3nnnneaOHTtM0zz/v/kLMr/pIrh+/Xpq164NQPXq1dm8efMZ+5imyeuvv87AgQOxWq1s3bqV1NRU2rdvT9u2bYmJibnEtb58nSte1157LSdPniQjIwPTNDEM47xiLHkvN7HSs5V/zhav/fv3U6VKFSIjI7FYLNxwww1s2LBBz1Y+yU2s9GzlnzJlyjB+/Pgz1v/111+UKVOGiIgI7HY7N998M+vWrdNzlc9yGi89W/knu1gBZGRk8M4771ChQgXfusLwbPlNF8GkpCRCQ0N9y1arFZfLRUDAv5fw7bffcs011/iCFBgYSIcOHWjWrBm7d++mY8eOLFu2LNMxcnGcK17XXHMNTZs2JSgoiPr16xMeHn5eMZa8l5tY6dnKP2eLV9myZdmxYwdHjx4lJCSENWvWUK5cOT1b+SQ3sdKzlX8eeOAB9u/ff8b6pKQkX7cygJCQEJKSkvRc5bOcxkvPVv7JLlYAN9988xnrCsOz5Tc1DQ0NJTk52bfs8XjO+KAXL15M27Ztfcvly5enbNmyGIZB+fLliYyM5MiRI1x55ZWXrN6Xq7PFa+vWrXz//fd88803BAcH06tXL7788svzirHkvdzE6t5779WzlU/OFq+IiAj69OnD888/T8mSJalatSpFihTRs5VPchMr/btV8Pw3jsnJyYSFhem5KqCyi5eeLf9RGJ4tv+kiWKNGDVauXAlATEwMlStXPmOf2NhYatSo4VtesGABw4cPB+Dw4cMkJSVRvHjxS1Phy9zZ4hUWFkZgYCAOhwOr1UpUVBSJiYnnFWPJe7mJlZ6t/HO2eLlcLjZs2MD777/PiBEj2LlzJzVq1NCzlU9yEys9WwVPxYoV2bNnD/Hx8WRkZLBu3Tr+97//6bkqoLKLl54t/1EYni2/SQfr16/PqlWraNGiBaZpMnToUJYsWUJKSgrNmzfn+PHjhISEYBiG75jHH3+cPn360LJlSwzDYOjQoX6XAfurc8WrefPmtGrVCpvNRpkyZXj00UcJCAg44xi5+HITK0DPVj45V7xsNhuPPfYYDoeDp59+mqioqCyPkYsvN7HSv1sFx+mxeuWVV+jQoQOmadK0aVOuuOIKPVcFzLnipWer4Dg9VlkpDM+WYZqmmd+VEBERERERKQz8pougiIiIiIhIQacES0REREREJI8owRIREREREckjSrBERERERETyiBIsERERERGRPKIES0RELpm1a9fSvXt33/KyZcto2LAhBw8e5I033uDgwYOMHz+eDz/88Ix9Txk/fjwPPPAAbdq0oVWrVrRv354tW7bkuC7p6enMnz/fV+aHH36Y7b779++nRo0atGnThjZt2vDEE0/w1FNPkZCQkO0x8fHxLFmyBICpU6eycePGHNdRRET8jyYAEBGRfPHFF18wffp0Zs6cSbFixXj11VfP+9innnqKli1bAvDXX3/RtWtXFi1ahMPhOO8yjhw5wvz582nWrNl57V+pUiXmzJnjWx49ejQLFiygQ4cOWe6/bds2vv32Wxo1asSzzz573vUSERH/pgRLREQuuYULFzJ37lxmzJhBREQEAG3atGHgwIE5LqtixYpUrVqV9evXc8MNN/Dqq69y4sQJAPr168e1117L3Llz+frrr3G5XISFhTF+/HgmT57Mjh07mDBhAgDffPMNy5YtIz4+nm7dunHPPfdke07TNDl06BBlypQBvMnW5s2bSU5OpmLFigwbNozJkyezdetW5s2bx++//87DDz9MzZo16du3L/v27cPtdvP000/z8MMP5/iaRUSk4FKCJSIil9S6des4fPgwCQkJuN3uPCmzaNGinDhxgsmTJ3PHHXfQqlUrdu/eTZ8+fXj//feJj49n5syZWCwWOnTowKZNm+jcuTPbt28nOjqa8ePHc8UVV/DGG2+wdu1a3n333TMSrB07dtCmTRvi4+NJT0+nUaNGPProoyQlJREeHs6MGTPweDw0aNCAw4cP07lzZz766COaN2/O77//DsC8efMoUqQII0eOJCkpiccee4w77riDqKioPPkcREQk/ynBEhGRS6p48eLMmDGD+fPn06tXL6ZNm4bFkv0rwevWrWPcuHEA2XbHO3jwIPfffz8LFy7k559/5ssvvwQgMTERi8WCzWajR48eBAcH8/fff+Nyuc4oo2rVqgAUK1aMtLS0M7af6iKYlpZG586dKVq0KAEBATgcDo4fP+4rPyUlBafTmWU9//rrL+68804AQkNDqVixIvv27VOCJSJSiCjBEhGRS6ps2bI4HA5at27NTz/9xKRJk+jatWu2+99yyy2Z3n3atGlTpu3bt29nx44dVK9enQoVKvDII4/QqFEjjh07xvz589m6dSsrVqxg/vz5pKam8thjj2GaJhaLBY/H4yvHMIzzqn9gYCCjRo2iSZMm1KhRgwMHDnDo0CHGjh3L8ePHWb58eZblg7c747p166hfvz5JSUls376dq6666rzOKyIi/kEJloiI5JuhQ4fSpEkTbr755hwdN3PmTJYuXYrFYiEgIIC3336bgIAAOnfuzKuvvsrHH39MUlIS0dHRlC1blqCgIB577DHsdjvFixcnLi6O//3vfzidTkaOHElgYGCOzl+sWDFefvllBgwYwPjx45k4cSJPPPEEdrudq6++mri4OMqUKcP27duZOXOm77gnnniC/v3707JlS9LT04mOjqZo0aI5OreIiBRshmmaZn5XQkREREREpDDQPFgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5RAmWiIiIiIhIHlGCJSIiIiIikkeUYImIiIiIiOQRJVgiIiIiIiJ5JCC/KyAiueN0Otm3bx+pqWn5XRUREbnMBAUFcvXVV2Oz2fK7KiIFjmGappnflRCRnNu5cycBAQ5CQyMwDCO/qyMiIpcJ0zQ5eTIBtzudChUq5Hd1RAocdREU8VOpqWlKrkRE5JIzDIOwsAj1oBDJhroIivixHCVXpokRdxwjNRUzKAizRBQoOSt0TNPkQMpOTjoTCLNFUDq4gpJwyROmabLnpElihkm43aBsmKF76zKm2ItkTwmWyGXAsu8QAd/9jHEy2ZtUeUzM8BBc9e7Ac/WV+V09ySN/Jmzk0z3TiE8/imFY8JgeijiK8VjZjlwTcWN+V0/82JbjHuZsc3I8zTz1FULRQIM219q4PkqdYURETqd3sET8VGzsFkqVKnvO/Sz7DmFb9A1YDLAFeBMs0wSnCzwmzsb35jrJWr9+Hf369aZcuQqAicvl4tVXX6NcufJ8881y5s//CMOw4Ha7adLkMR5+uCEATZo0oGTJklgsVkzTQ3h4BAMGDGbr1j9OK8+rSJEiDB365hnn3rnzLyZMGEdaWhqpqSnceeddPPNMZ9+vqsuXf8Ubbwxi/vxFFC9enM6dO/DMM5245ZbbfGWMGTOSihUrsWFDDNu2/UF4eIRv20MPNeCRR5rQpEkD5s37lOXLv2LatMmUKlUagKSkk9x440306tXHd8zs2TOZN+8DPv10CQ6HI1efaW79mbCR6duHYjGs2C2BGIaBaZpkeNLwmB46VO6T6yTrwIH9TJgwjri4wwQGBuJwBBId3Y1SpUrz5pvDOHr0CIYBoaFhvPxyHyIiInG5XMycOZ3Vq1fhcNgBeOCBh2jSpCng/ew3bowhKCiYrl1foFq1G7I9/+DBr1G//v3UrHkXkP3nvHz5VyxYMA8Ai8VK5cqViY5+EZvN5rvnDOPfZKBbt+4kJ6f47jnDMEhOTqJ06asYNOgNvbz/jy3HPYzd4MRqmDis+O6tdDe4TYMXb8p9kjV79gx++WUtFosFw4AuXaKpUuV6ANq0aeF7xnbs+JPRo73fA7Gxm7j++qoYhoXWrdty1121feV16dKR9PQ0HI5A37pT+xw+/Ddvv/0Wx48fJz09nSpVrqN7957YbDYefrg+S5cu9x2zZs0qli//mgEDBuFyOZk1awa//PIzFouVgIAAOnV67qz37OXi4ME9VK16fX5XQ6TAUQuWSGFmmgR8+7M3ubKf9sei8c9yhpOA734mo02TXHcXvPnmWxkyZDgAa9euYfz4t3jiiZZ89tkCRo0aS2hoGGlpafTt+zIOh4N7760PwLhxE31/HE+YMI7PP19MpUrXZCovOydPnqR//z4MGzaKMmXK4Ha76dv3ZT777BMee+xxABYvXkizZi1YuPATOnbsTOPGj7F06Re+BMvpdPLTTyvp0iWaDRtiiI7u5vsDPjv33/8gXbu+AIDH46FTpw788ccWrrvO+wfGV18tpX79+1m+/CsaNnwkV59nbpimySe7p2IxrDisQb71hmHgsAaR7k7l0z3TePmGt3PcrSctLZVevbrTp08/brjhJgBiYzczcuRw7r23PkWLFmXAgEEAfPTR+0yfPo0ePXoxefI7mKaHadNmYLVaSUlJ4aWXXqB69Rrs37+PvXt38957c0hMTODFF6OZOfP9865TVp/z6tU/sWjRp4waNY6wsDBM02TcuNF88cUSmjR5DMh8z52yfv26M+65AQP68uOPP3DPPffl6LMqjEzTZPY2b3IVGPDvvWMYBoEBkOYymbPNydA77Dm+t3bt2smPP/7A1KkzMAyD7du3MXjwAObOnceGDTFUrFiJdet+JTk5mUqVrmHSpGmA9wearGJ5yoABgylXrnymdW63m169evDyy318idGYMSOZOnWS75nOztSpk3G73Uya9C4Wi4VDhw7y0kvdGDVqrO8HFxGR06ldX6QQM+KOYyQle1uusmILwDiZjHHkeJ6cLzExkSuvLMXHH39I167dCA0NAyAwMJDnn3/R17pwOo/HQ1LSSYKDg87Ylp2VK7/n5ptvpUyZMgBYrVZee+11GjVqDMDBgwdITEygXbunWbZsKS6Xk3vuuY/ffvuVtLRUXxm3334HQUHnf97TpaQkk5R0kpCQUMD7h/pVV13No48+zieffJyrMnPrQMpOEjKOYbcEZrndbgnkRPpRDqTsynHZP/64kptvvtWXXAFUrVqNiROncuWVV7J27c/8+OMPJCcn0axZC154oTsul4sVK76mS5fnsVqtAAQHBzNx4jTKlSvPrl07uf32mlgsFiIji2CxWDl27Oh51Se7z/njjz8iOvpFwsK895xhGHTr9pIvuTpfTqeTo0eP+sq53O05aXIizdtylRWHFY6nmexNynlnmCJFivD333+zZMki4uLiqFz5Wt57bw4AixZ9xj333EfduvVYunTJhVwCABs2xHDFFVdkanXq2vUFOnToeM5jly1bSpcu0Vgs3j+ZrryyFE2bPsEXXyy+4HqJSOGkFiyRQsxITfW2TGX3y/I/24yUVHLbV3j9+l/p0qUjTmfGP9143mbkyGGULn1Vpv1Kl76KQ4cO+Za7dXsOi8WKYcD111fjoYcasmFDjK+8U+66qxatW7fLVNbRo0coXTrzL8fBwcG+/7948UIaNWpMaGgY1ardwHfffUv9+g9Qu3Zdvv/+Ox588GE+/3wxnTo95ztmwoRxzJ4907f80ksvU6nSNZnO8fXXy9i8eSNHjx4lODiYp57q4EvyFi/+jEceaULZsuWw2exs3rzpknUhOulMwDAs2bYgGIaBxbBw0hmf47IPHTrIVVdd7Vvu1as7SUlJHDt2lAkTJvP00x1YsmQRr78+kIoVK/HSSy9TpEgRwsMjCAjw/hPzySfzWbHia1JSknnooQZUrnwtH3wwh2bNmnP48GF27fqL1NTU86pPdp/zoUMHufpqbz03bdrAxIkTcLlcXHHFFb7WqW7dnvN1EbRaLUyYMAX49x4+ceI4FouFxo0f49Zbb8/xZ1UYJWaY/3xNZH9vGYZJQroJOcxJIyOLMHLkWyxYMI/p06cSGBhIp05duf3229m48Xf69u1P+fIVePnlHjRr1uK8yx08eECmLoJDh76Z5XfG6S1giYkJmb53EhMTuPba6zh+/Djh4eG+e/mU0qVLExu7OWcXLCKXDSVYIoWYGRTkfRvdNLNOskzvNjMHrUf/dXr3qj17dtOx41Nce+11HDp0kPDwcN9++/btpWTJkr7l7Lr4ZNVF0Nu9rBsAt912O6VKlWbbtq2Z9jl48ACHD//NjTdWZ9mypZQqVZqfflpJYmIiCxbMo379B2jc+FEmTBjLzTffysmTiVSpcp3v+Jx0ETx48AAvvhhNmTLed+ASExNZs2YVJ06cYP78j0hOTmLBgnmXLMEKs0XgMT2YppnlH8KmaeIxPYTZInNcdokSV7B16xbf8siRbwHQoUNbDh/+m1tvvY169e7F7Xbz5Zdf8PrrrzF9+iwSE+Nxu91YrVaaNm1G06bN+PTTBRw7dpTbb6/Jli2xdO3aiWuuqUyVKtcRERHJSy+9QEpKKhUrViI6upu3i+M/94hhGGf9nEuUuIKDBw9yzTWVueGGm5g0aRq7d+9ixIihvrqf655LSIjn+eefo1SpUjn+nAqrcLvxz1dI9veWaUKEI+ddjPft20tISAj9+g0E4I8/ttCjx/M88URLPB6Tnj29z/yxY0f59de1WSa98+d/xLfffgPAoEFvAFl3ESxZ8kq+++6bTOsSEuLZtGkjtWrdTXh4hK8LIvz7DlZYWBiJiYm4XK5MSdZ/v89ERE6nBEukEDNLRGGGhWAkp2R+B+sUpwszLASzeFSenC8qqigATzzRgvHjxzJixChCQkJJSUlh/PixNG36RK7KDQ4OzvTHT3JyErNmvcdjjz3OVVddjcvlZNy4Mdx66+0kJSVx/fVVMw2M0axZE/78czvXXFOZ5OQU5s37gIYNG+f6OkuVKk3Pnq/Qt+/LfPjhfJYtW0qjRo15/vnugPe9pUcfbcSJEycoUqRIrs9zvkoHVyDSXpRE54lM72CdkuFJo4ijGKWDy2dx9NndfXdd5syZyebNG6lWzTtIxr59e4mLi2Phwk8pXrwEnTt3xWq1UqnSNdjtdgICbNStey9Tpkykc+euWCwW0tPT2bx5E6VKlWLv3j0UKRLFlCnvcfjw3wwaNICwsDBGj37bd95x48ZQsWJFGjZszNGjRyhSJOqsn3OzZt57bujQEb6uqb/9tj5H7wVFREQyaNAQnnvuWebMuZ5ixYrn+PMqbMqGGUQFGiSkmwRm8RdDuhuiAg3KhOY8wdqx408+/dT7rqbD4aBMmTKEhITy3XffMGrUWCpUqAh4u+gtWPBxlglWs2Ytzqt1q1q1Gzh48ACxsZupWrUapmkybdoUHA4HtWrdne1xNpuNe++tz+TJ7/Dcc89jsVg4cGA/n3wyn9Gjx+X4mkXk8qAES6QwMwxc99zhHUUww5nlKIKuendc0HxYp7pXWa0WUlJSeOGFHtSuXYfk5GRefDHaO1y4x80jjzxK/foPnHd5p3vrrfEEBv7b5SckJJQBAwYxbNjreDwmKSnJ1Kp1N02bNqNXr+488sijmY5/5JEmLFgwjz59+tOoUWMmTBjLwoVLM+3z3y6CNWrUoGPHLtnW87bbbufWW29n2rTJrF37M6+99rpvW2BgEPXq3cuiRZ/y1FMdznnNF8owDJqWe5bp24eS7k7NchTBx8p2zNW8NcHBwYwcOZaJE9/m2LG3cbncBARY6dOnHzfd9D9Gjx5BmzYtCAwMIigoiL59BwDeFsG5c2fRufMzWK1WkpOTqVu3Hi1btsZisfDzz6tZsmQhdruDXr1eOeO8jz7alMGDB/D554u59toqVK58La+//tpZP2eXy8XLL78EeJPwypWv9Q3AAZm7CAI0b96SsLB/W1kBypevwBNPtGDMmJFZjl55uTEMg7bX2hi7wUmaK+tRBNtca8vVvVWv3r3s3r2LDh3aEhQUjGl6aNq0GV9+udSXXJ3ab9y40Rw+/DdXXHHuVqP/dhG87777adq0GUOHvsmoUSNIS0slNTWVatVuoHPnrucsr2vXF3j33Sk880w7AgJs2O02+vYdcEY3aBGRUzRMu4ifOt9h2iGLebBMEzNM82AVNqfmwTqRfhSL5sGSPPTfebBM09typXmwLm8apl0ka0qwRPxUThIsAEwT48hx74AWwUHeboEX0HIlBZNpmhxI2cVJZzxhtkhKB5fPVeuCyH+Zpne0wIR0kwiHt1ug7q3LmxIskaypi6DI5cIwMEsUzfVogeIfDMPgqpAK595RJIcMw6BsmJHj0QJFRC43atcXERERERHJI0qwRERERERE8oi6CIpcLkwTju7ESE3ADIqAYhX0DlYhZJomu1OOkOBMIcIWTLng4npPRvKEaUJCPKSng8MBEZH6ChERyYoSLJHLgHFgE5Yfp2EkHQXDAqYHM7QYntodMUtfmslw5eLbnLCPGbt/4Fj6Sd9Q2kUdYTxdrg7VIq7O7+qJHzsSBxt+h9TUf2d6CAqCm/4HxUvkd+1ERAoWjSIo4qfOdxRB48AmrF8OxTSsYAs8bR6sNAzTjfuhvheUZB04sJ8JE8YRF3eYwMBAHI5AoqO7ZZrHpkuXjqSnp2Wam6Z167bcdVftTGX9+ed23nnnbdLT03G5nNSocQvPPNMJmy2LSZL/MW3aZGbMeJdFi76keHHvxLDHjx+nUaMH6dOnHw0bPsLDD9dn6dLlTJs2ma+/XuabQDYxMYH77rufp59+xlfeiBFDiY3dxOzZH+b6M8kPmxP2MXLb51gMg0CLzZdgpXmceEyTXtc2vOAkq0uXjkRERJCQkMChQwex2WwUK1acihUr0apVG1q3bs6111bx7X/LLbfSocOzjB//Fhs2xOB2u2nc+DGaNHnsjLJr1bqNG264ybdcvnwFXn65T6b1LpcLj8fN668Po1Sp0rhcTmbNmsEvv/yMxWIlICCATp2eo1o17/38xRdLWLp0CRaLFdM0adOmHbffXpPo6E643R727NlNkSJFCA+P4Lbbbs90H8i/jsTBz6vBYoDV+u9XiNsNHhPuuDP3Sdbs2TP45Ze1WCwWDAO6dInm44/nsW3bH4SHR2AYBm63i969X6VChYpn3CcAgwa9wYQJ4zhyJO6M+7Jnz95ZnqNKlesZPPg1kpOTGDFitK+sU98Vp2T1ffD6669x8OBB3/LGjRsYOfIt7rzzLt+69evX8dlnCxgyZDgA3367gnffncKYMW9TsqR3aow2bVpw44030atXH99xp67PMAxcLhflypXn5Zf7sGzZUvbs2U3Xri8AMG/eB6xY8TVjxownLCyM9PR0Hn20Ia1ataZ163YAHDx4kGbNmjB9+kyqVPGO9Pfppws4duwoHTt2zl3A/kOjCIpkTS1YIoWZaWL5cao3ubIH/bveMMAehJmRiuXHabibj8tVX5+0tFR69epOnz79fH/0xMZuZuTI4UyaNC3TvgMGDKZcufLZlnXs2DH69+/Dm2+OoUyZspimyXvvTWPs2FGZ/gDJytVXl+Gbb76mRYsnAVix4mtKlsx6QtKWLVvz2GOPA5CRkUHLlo/TuPFjREVFkZaWysaNMVSsWIn169dx8823nPdnkZ9M0+S93d9jMQyCrHbfeuOf5VR3BjN2/8CoG5+84O6CnTt3pVy58kybNpmiRYv5PsuDBw9Svnz5M+K+fv2v7N+/n3ffnUVGRgatWj3OPffcR3h45gl+w8Mjzjg2q/WffbaADz6YQ8+erzB16mTcbjeTJr2LxWLh0KGDvPRSN0aNGkt4eDgzZkzjww8/wWazceTIEdq3b8OiRUuZMGEKAIMHv0b9+vdTs+ZdZ5xXvEzT23JlMSDgtL8YjH+WXS7YGAP31M/5V8iuXTv58ccfmDp1BoZhsH37NgYPHkDlylWIju7mi8vq1auYMmUiI0aMzvY+GTz4DYAz7svszjF37jwANm6M4csvP+ehhxqeUWZ23wf9+/87efWsWe8RHBxMzZp3Znudy5d/xdy5sxg/fjJFixYFYMMGb7nr1v1KcnIyISEhwJn3+6uv9mb16lWZyps7dxY//7yGt9+eRFCQ93v9u+++oX79+/niiyW0atUGi8X7in1ISAivvz6QGTPmYrfbEZFLQ4NciBRmR3diJB3ztlxlxRbo7TZ4dFeuiv/xx5XcfPOtmX5Rrlq1GhMnTs1xWcuWfUGjRo0pU8bbKmcYBu3bd2T16lWkpaWd9dj77rufb75Z4Vv+6aeV1KpV+yxHeCUkJOByuXA4HACsWLGcW2+9jQYNGrFgwbwcX0N+2Z1yhOPpSQRasm7pC7TYOJZ+kt0pRy5xzaBatRt59dXXAP5pjfAQEJD73/b+/vsQYWHe5GzZsqV06RLt+2PyyitL0bTpE3zxxWKCgoJxudx8+ul89u/fR/Hixfnkk8W+feX8JMR7uwVarVlvt1ohJcW7X04VKVKEv//+myVLFhEXF0flytfy3ntzztgvMTGB4ODgnJ/gPM7x3HPPM23aFOLiDp9x7Lm+D1at+pFly5YyePAb2f5w8eWXn/Phh3MzJVcAixZ9xj333EfduvVYunRJlse6XE5SU1MJDv73x7GZM6ezbt2vjBnzti+5Ali8eCENGzbmmmsqs3r1T771V199NXfccSeTJ79zlk9JRPKaWrBECjEjNcH7zlV2Py0bBhgWjNT4XM2PdejQQa666t9uZ716dScpKYljx44yYcJkSpS4wrdt8OABmboIDh36JkWKFPEtHzhwgNtuu/0/1TMoWrQox48fp1SpUtnWo2jRYgQGBnLgwH48Hg9XXHEFdrsjy30//HAuy5cv4/DhwxQvXpy+ffv7fj1evHghr7zyKuXKlefNN4cRFxdHiRIF/wWTBGcKhpH9pK+ntiU4Uy5qPXbt2kWXLh19y4MGvUGJEiVwOBy4XE4GDx5AkyaPZfnHcmJiQqZju3XrTpUq1/vWJycnk5gYT9269/Lss104fvw44eHhZyRrpUuXJjZ2M1arlfHjJzFv3ge8+GI0LpeTNm2epmnTZhfvAyiE0tP/+Zo421eI4d0vpyIjizBy5FssWDCP6dOnEhgYSKdOXQGYMGEcs2fPxGq1UKxYcaKjuwFn3ifFi5fwtV7l5Bz33HMvAMWKFefZZ7vwxhuDGDduYqZjz/Z9sHfvXkaOHMa4cRMJCQnN8twbNvzOkSNxJCYm4Ha7feuTk5PYuPF3+vbt/09X2B40a9Yi0/V5n1moWfMubrnlNj7/fDFfffUlV199NSdPnuT0tzv27t1LWloq11xTmYYNG/PBB3OoVetu3/ZOnZ6jffs2xMT8du6giEieUIIlUoiZQRFgerz9fLL6C8k0vQNeBEXmqvwSJa5g69YtvuWRI98CoEOHtrhc7kz7nquLYPHixTl06GCmdW63m6NHjxAVFeVbFxPzO1OmeP8Qat26rW/9/fc/wPLlX+FyuXjggYdYu/bnLM9zqovg1q1b6NevD1df7W0x27VrJzt37uDtt8cA3o/rs88W0KnTc+f8HPJbhC0Y0zQxTTPLJOvUtghbzlsBUlJSsNttBAR4W8fO1sUwqy6CAImJifTt24saNW6hXbv2ALz00gukpKT63pM5VxdBt9vN66+/hs1mIzg4GJvNRmJiIi6XK1OStW/fXkqWLMmRI0dIT0+nZ89XANi7dw8vvhjNTTdVp1Kla3L8OVyuHI5/vibO9hVievfLqX379hISEkK/fgMB+OOPLfTo8TxVq96QqYvg6bK7T3J6jtO7/z744MP88MN3fPLJfN+6s30fpKSk0KdPT3r2fIWyZcsB3ufkpZe8SeBtt93OjTdWp2jRYrz99iQWL/6MgQNf5a23JmCxWFi27Es8HpOePb37Hzt2lF9/Xcutt95+1uurXPla3nxzDBMmjGPUqBG8+uoAABYv/ozU1FRefLErpmmyadNG9u3bi9XqfS7sdjv9+g3ktdde5ZFHHj3vz05Eck99JUQKs2IVMEOLgjObLnbONMzQYlAs+8TnbO6+uy6//voLmzdv9K3bt28vcXFxOX4fo0GDRixc+Cl79+4FvEnB9OlTqVmzFoGB/7Z8Va/+PyZNmsakSdMyDZJRr969rFz5AzExv1OjxrnfnapS5Xratn2a/v1fwePxsHjxQjp37srYse8wduw7TJgwhc8/X4TT6czZheSDcsHFiXKEkubJuq5pHidFHWGUCy6e47IHDx7Ahg0xeDweTpw4TmRkkXMfdPq509KIju5Mw4aNad/+35aH0aPfZtKkafTs2fu8yrFarbzySj9++OE7Vq36EZvNxr331mfy5HfweDyAd8CVTz6ZT4MGjTh27CivvfYqiYmJAJQseSWRkZFnHTBFzhQR6R0t0O3OervbDcHB3v1yaseOP3nzzWGk/9P8VaZMGUJCQrFm1x8xF7I/R+Y/f3r37ssHH8whJSUZ4KzfB6+//hr16z+QqZUoODjY9710arCUq666GofDQbNmLQgIsDFz5vR/yv6MUaPG+sru0eNlFiz4+JzXUr58BSwWC126RLN9+1a+/PJzXC4XK1Z8xeTJ0xk79h3GjZtImzZPZUoWAapUuY7773+QuXNn5vqzFJHzpxYskcLMMPDUftY7imBGatajCNbumOvJbIKDgxk5ciwTJ77NsWNv43K5CQiw0qdPPw4c2M/SpZ/TocOzwJldBO+7736aNm3GCy88x+jR4yhR4goGDhzCyJHDfKMI/u9/N9O9e08AvvrqS1JTU2jSpGmWdQkNDaNEiRKULn3Veb9n88gjTVix4mvmz/+IFSu+Ys6cj3zbSpa8kkqVKvPttyt44IGHcvX5XCqGYdC+XF1GbvucVHdGlqMIPl2uTq4GuGjVqg1jxryJ3e6gQYNGRERE5Oj4zz5bwMGD+1m06DMWLfoMgP79B1KqVOkc1yUwMJC+ffszePBr1KhxC127vsC7707hmWfaERBgw2630bfvAEqXvgqAJ55oSXR0JxyOQNxuN4880sTX4iDnxzC8Q7H/vNo7oEVWowjeWD13XyH16t3L7t276NChLUFBwZimh+eff5Effvg+22P+20UQ4Lnnos8YWfBc5wgNDcu0X2RkEbp168HLL/fA6XRm+30wd+4sVq78nvj4+Eyt5M2aNeeee+7Ltt79+g2kbduWBAUFYZpkGmW1Xr17GTduNIcP/53t8aez2WwMHjyUzp2f4fDhw1x77XWZnsuGDRvTpk1zGjfO3FrVrl17fvpp5XmdQ0QujIZpF/FT5ztMO2gerMuF5sGSi+VInHe0wJSUfxOs4GBvcqV5sC5fGqZdJGtKsET8VE4SLMD7F9HRXd4BLYIivd0CL3DIbil4TNNkd8oREpwpRNiCKRdc/IKHZhcB71dIQrx3QAuHw9stULfW5U0JlkjW1EVQ5HJhGFC8Qq5GCxT/YRgG5UPUpCB5zzAgh6/giYhcljTIhYiIiIiISB5RgiUiIiIiIpJH1EVQ5DJhmiYZ8TtxpydgdURgj6ygd3MKIdM02Z0cT4IznQibg3IhkYqz5AnTBOdRcKeCNQhsxfQOlohIVpRgiVwGUuM2cSxmGq7UoxhYMPEQEFSMotU7ElRCowgWFrEJcczYuYHjGakYgAcoag/i6Qo3UTVC72VJ7qUdgOM/gisJMAATAkIhqjYE5nzEfRGRQk2jCIr4qfMdRTA1bhOH1wzFMKwY1kDf8N2mOw1MNyVq9r2gJOvAgf1MmDCOuLjDBAYG4nAEEh3djS1bYtmzZzddu76Ay+Vi5szprF69CofDDsADDzxEkyZNOXjwIM2aNWH69JlUqeIdjerTTxdw7NhROnbsnOlc06ZN5uuvl1GsWHE8HjcORyBdu77AtddWAeDPP7fzzjtv++bRqlHjFp55ppNvctmFCz9h2bKlGIYFl8tF585dufnmc09K7A9iE+IY9ccaLIaFQIv1tHmw3HhMk57X3ZHrJGv9+nX07t2D99//mCuuKAnAO++8Tdmy5WjY8BE2b95E584dmDp1BtdfX5VBg/pTo8bNNGrUxFfGhx/OJS4ujh9//J7+/Qdz003VAdi2bSsDBvRlxoy57Nq1kylTJnrvT9NDzZq1ePLJNqxfv45+/XpTrlwFX3lFihRh6NA3fctt2rTgxhtvolevPr51Dz9cn6VLl2d7XdnVMyEhgc6duzJ79kzmzfuATz9dgsPhAGDw4NeoX/9+ata8y3fMwYMH6d//FaZPnw1ATMzvDBkykGHDRtKnT89sr7dVq2aULFkSw7Dg8bhJTU2lT5/+XHfd9b7zlC1bntatm/vu8VMmTJicpxPynk3aAYj7EjDAsP07TLvpBEwo8VDuk6zZs2fwyy9rsVgsGAZ06RLNxx/PY9u2PwgPj8AwDNxuF717v0qFChWpVeu2M+a8GjToDSZMGMeRI3EcOnQQm81GsWLFqVixEj179s7yHFWqeD/j5OQkRowY7Svrv/fMiBFDiY3dxOzZH/rWvf76axw8eNC3vHHjBkaOfIs77/z3nhg3bgxbt/7B8ePHSEtLo1Sp0r579siRIzz+eGMGDBjEvffW9x2zc+dfTJgwjrS0NFJTU7jzzrt45pnOHDp0KNP9BWd+R/73GQT4/PPFvPvuFN5/fx4hIaEA9Ov3Co8++jhXXlkq23v2mmsqA9Cz54uYpsno0eOyjZ9GERTJmlqwRAox0zQ5FjMVw7BiCQjyrTcMAyMgCI8rlWMx0yhdf1yuupGlpaXSq1d3+vTp5/ujJzZ2MyNHDqdBg0a+/SZPfgfT9DBt2gysVispKSm89NILVK9eA7vdQUhICK+/PpAZM+Zit9vPes6WLVvz2GOPA7B79y56936J2bM/JCkpif79+/Dmm2MoU6Yspmny3nvTGDt2FL169WH58q/45Ze1TJgwmYAAGwcPHqBz52eYPfsDIv18aDTTNJmxcwMWw0KQ9d+vdcMwCLIGkOp2MXPnBt6sfl+uuwvabDZef30g48dPOqOMxYsX0qpVGxYs+JgBAwbRuPFjTJkyMVPisnTp54wYMYbateswdOhgZs36AKvVytChg+nffxDBwcGMHj2CAQMGU65ceVwuJx07Ps0tt9wKwM0338qQIcOzrNuGDTFUrFiJdet+JTk5mZCQkPO6prPVE+Crr5ZSv/79LF/+FQ0bPnJeZa5fv46RI4cxZszblClTlr59X8v2egHGjZvoS95+/nk17747mdGj385UZvny5Zk0adp5nT+vmaa35QoDLKc9moYBhh08Gd7tVzbPeXfBXbt28uOPPzB16gwMw2D79m0MHjyAypWrEB3dzZfErl69iilTJjJixGjCwyOy/CwGD34D8P4IU7RoMd93RHbnmDt3HgAbN8bw5Zef89BDDc8oMy0tlY0bvffW+vXrfD/G9O8/yLfPrFnvERwcTM2ad2Y6tlu3HoA3yTn1Q9Mpn3++iObNW7Jgwce+BOvkyZP079+HYcNGUaZMGdxuN337vsxnn33CHXdkLjsr/30GT0lPT2Ps2NG8+upr2R7733sW4PDhv0lNTcHpdHHgwH7f5N0icn40yIVIIZYRvxNX6jEMa2CW2w1rIO7Uo2Qk7MpV+T/+uJKbb7410y/KVatWY+LEqb5ll8vFihVf06XL875f3IODg5k4cRrlypUH4Oqrr+aOO+5k8uR3cnT+cuXKc+21VdiwIYZly76gUaPGvj8QDMOgffuOrF69irS0ND777BPatWtPQIC3NatUqdLMmfOh3ydXALuT4zmekUqgJesWjUCLlaMZqexJTsj1OW6++VYiIiJYsGBepvUpKSmsX/8rHTo8y8aNMcTHn6B69f8RH3+CQ4e8v/Jv2RJL0aJFKVWqFDVq3Mydd9bivfemMWfOTOrUqUe1at4W1JIlr2TBgnls3boFw7AwdeqMM1pusrJo0Wfcc8991K1bj6VLl5z3NZ2tnuvXr+Oqq67m0Ucf55NPPj6v8n75ZS0jRw5j7NgJvvvwbNf7X3//fYiwsPDzrv+l4Dzq7RZo2LLebti8251Hc152kSJF+Pvvv1myZBFxcXFUrnwt770354z9EhMTfAlpXp/jueeeZ9q0KcTFHT7j2BUrlnPrrbfRoEGjM+57gFWrfmTZsqUMHvzGef9wYZomy5YtpWXL1rhcTv76awcAK1d+z80330qZMmUAsFqtvPba6zRq1PicZWb1DJ7y8MMN2b17Fz/9tDLLY7O6Z8GbsN19d10efrgBn3wy/7yuTUT+pQRLpBBzpydgYMn2H3/DMMCw4E6Lz1X5hw4d5KqrrvYt9+rVnS5dOtK8+WPExcUBkJAQT3h4BAEB3paVTz6ZT5cuHXnqqSf56KP3fcd26vQcv/zyMzExv+WoDlFRUSQkxHPgwIEzfmU1DIOiRYty/Phxjh49QunSmfsxRURE5uhcBVWCMx0DzhpnCxDvTLug87z8ch8++ugD9u7d61u3fPlX1K1bD4fDwX333c/ixYsAaNSoCcuWLQW8v+I3adLUd0znzl35+efV/Pbbetq1a+9b/+qrA4iKKsqbbw7joYfuY9y4MWRkZACwfv2vdOnS0fff3LmzAEhOTmLjxt+5885aNGzYmE8/XZCja8qunosXf8YjjzShbNly2Gx2Nm/edNZyDhzYz+TJ75CRkUF6enqmbdldL0C3bs/Rvn0bGjV6kNjYWJ5/vvsZZe/atSvTtY8bNyZH13gh3Kl4uwZmkz8Yhne7OzXnZUdGFmHkyLfYuDGGjh2fonnzx/jppx8BmDBhHF26dCQ6uhM//7za1wKUmJiQ6bMYMODVXJ8DoFix4jz7bBfeeGPQGccuXryQRx55lFtvvZ3t27f5vtMA9u7dy8iRwxg+fJSv+935+PXXX6hYsRJFihShYcPGLFjgTd6z+n4KDg72dW/+7z3w4Ydzfftl9wwCWCxW+vcfzNixo0hIiM9Ufnb3rMfj4euvl/Hggw2oX/8BVqz4mrS0C/vuELncqIugSCFmdURg4sE0zSz/+DZNE0wP1sDIXJVfosQVbN26xbc8cuRbAHTo0JYSJUqwZ89uIiIiSEyMx+12Y7Vaadq0GU2bNvO9Q3CK3W6nX7+BvPbaqzzyyKOA972AKVMmAtC6ddss63Do0CHq1r2X4sWL+1oiTnG73Rw9eoSoqChKlrySw4cPExoa5tu+du0aKlW6hqJFi+Xq+guKCJsDD5w1zh4g0pZ1S+Z5nycikhdffInXX3+NG2/0tlouXvwZVmsAL77YlbS0NOLi4mjdui0PP9yA6OjOtGrVht9+W0ePHr185TgcDmrXrkPRosV8rZrp6els3bqV9u070r59RxIS4hkyZBALF35KxYqVsu0iuGzZl3g8Jj17dgPg2LGj/PrrWm699fYsr+Gll14gJSXV935OVvVMTExkzZpVnDhxgvnzPyI5OYkFC+Zl2/LkvaZA3nprPJs2beDVV3vz7ruzCAwMzPZ6TznVRXDSpPEcPHiQqKioM8rOzy6C1iDA9HYVzCrJMk3vdmvQmdvOZd++vYSEhNCv30AA/vhjCz16PE/Vqjdk6iJ4uuy6COb0HKe/e/nggw/zww/fZWqp2bVrJzt37uDtt73JrGHAZ58toFOn50hJSaFPn5707PkKZcuWA/in27P3Hrztttt5+ulnsqzPokWfcvDgAV58sStOp5M//9xO167PU7LklWzbtjXTvgcPHuDw4b+54oorz7gHTv/+zO4ZPKVMmTI88UQr3nxzWKbvh+zu2Z9/XkNKSgqvvdYXANP0JlyPPNLkvD93kcudWrBECjF7ZAUCgop6B7TIgulOwxpUDHtE+VyVf/fddfn111/YvHmjb92+fXuJi4vj778PARAQYKNu3XuZMmUiHo8H8P4xvXnzpjOSgSpVruP++x9k7tyZgLcL16RJ05g0aRp33VX7jPP/9dcOdu/eRbVqN9CgQSMWLvzU17pimibTp0+lZs1aBAYG0qhRY957bxoulwuAvXv38MYbgzEM//8aLBcSSVF7EGked5bb0zxuitmDKBsSccHnql27DmXLlmXp0iWkpKTg8XiYOvU9xo59h8mTp1O69FX89NOPREYWoVy58rz33jTq1Knna8HMjsViYeDAfr4uUxERkZQseSV2ezZ90/6xePFnjBo1lrFj32Hs2Hfo0eNlX6tAVkaPfptJk6bRs2dvgCzruWzZUho1aszbb09k7Nh3mD59FmvX/syJEyeyLbdYsWJERERQq9bdVK/+P0aPHnHWev9Xp05dOXr0yHl3R7xUbMW8owWazqy3m07vdlsufqPYseNP3nxzmK/1pEyZMoSEhObp4B3ZnyPzc9+7d18++GAOKSnJgLf1qnPnrr77asKEKXz++SKcTievv/4a9es/QK1ad/uODw4O9n1XZZdcxcefIDZ2E9Onz2bs2Hd4552p1K17L1988Tm1atXm559Xs3//PgBcLifjxo3hr7/+Ouf1ZfcMnq5Zs+YkJCSwfv2vvnXZ3bOLF39G3779fdc+ZMiIsz5TInImtWCJFGKGYVC0+rMcXjMUjys1y1EEi1bvmOuBD4KDgxk5ciwTJ77NsWNv43K5CQiw0qdPP44fP86ePbsBiI7uxty5s+jc+RmsVivJycnUrVuPli1bn/FHa7t27bN9XwC8o7wtX/4VVquFgIAAhg59k4CAAEqUuIKBA4cwcuQw3yiC//vfzXTv3hOA+vUf4OjRI3Tq1B6bzYbb7WHgwCFZthj4G8MweLrCTYz6Yw2pbleWowg+VeGmPJsPq3v3nqxb9wuTJ79Dp07PZdrWuPGjLFgwj7vvrkPjxo/So8cLzJv32TnLtNlsvPHGCIYPfwO3241hwHXXVaVhw8Zs2BDj6yJ4uueei8Y0oUKFir519erdy7hxozl8+G8SEhJ46qknfdtatWrD/fc/eMa5/1vPxYs/47XXXvdtDwwMol69e1m06FMAxowZSUiIt2W1TJlydO7cNVN5zz/fnfbt2/DFF0syDfZyNhaLhb59B9C58zPUqXNPpm2nuoedrn//gZQqdfHHRzcM71DscV96B7TIahTBqNq5mw+rXr172b17Fx06tCUoKBjT9PD88y/yww/fZ3vMqS6Cp3vuuegzRhY81zlOb8kGb6LdrVsPXn65B06nkxUrvmLOnI9820uWvJJKlSozd+4sVq78nvj4eNau/dm3vVmz5txzz31nvd6lS7+gbt17MyWQjRs/yuDBA3jiiRYMGDCIYcNex+MxSUlJplatu2natBmHDh3KtsxFiz7jwQcbZFp36hk8/V43DIN+/V7jySebZ1nO6fdsbOzmTK3FN91UnYyMdDZu3OBruRaRs9Mw7SJ+6nyHaYd/58Fypx4Fw+LtFqh5sAqd2IQ4Zu7cwNGMVCx458EqZg/iKc2DJRdI82BJVjRMu0jWlGCJ+KmcJFjg7TKXkbALd1o81sBI7BHl86xFQwoO0zTZk5xAvDONSFsgZUMiFGfJE6bpHS3Qnep958pWLHctV1J4KMESyZq6CIpcJgzDwBFZ4dw7il8zDINyoZH5XQ0phAwD7MXzuxYiIgWf/7/dLSIiIiIiUkAowRIREREREckj6iIocpkwTZPkxJ04MxKw2SMICa+gd3MKIdM02Z2cRKLTSbjNRrmQUMVZ8oZpYhzyQLIHQiyYV1r0EpaISBaUYIlcBhKObWRn7DQy0v4dRdAeWIwKVTsSUfTG/K6e5JHY+BPM3LmDY+npvlEEizocPFWhElUji+R39cSPGbtcBCxNhQTTN4ogEQauh4Mwy+tPCRGR02kUQRE/db6jCCYc28gf64diGFYsp82D5XGnYZoerru5zwUlWQcO7GfChHHExR0mMDAQhyOQ6OhubNkSy549u+na9QVcLhczZ05n9epVOBx2AB544CGaNGnKwYMHadasCdOnz6RKFe9oVJ9+uoBjx47SsWPnTOeaNm0yX3+9jGLFvG/aJyYmcN999/sm9vzzz+28887bvnmwatS4hWee6YTN5p2s9ptvljN//kcYhgW3202TJo/x8MMNAWjSpAHz5n2Kw+Hg2LGjvPDCc7Ru3Y6HHso8x0xBFRt/gtF/bMZiGFnMgwUvXVc110nW7Nkz+OWXtVgsFgwDunSJ9sWqTZsW3HjjTfTq1ce3/+rVq/jgg9kYhgWPx02jRk148MGHzyj3jjtq8Oijj9O7d1/futGj3+THH39g4cIvGDz4NerXv5+aNe/ybX/44fosXbqc6OhOuN0e9uzZTZEiRQgPj+C2226nePESTJs2OdMcURUrVvJNLCw5Z+xyEfBhijexsvPvRFgZgAmulsG5TrL+e2+1a9eBkSOH8eGHCwD4+utlDBrUnyVLviIqKoqDBw/yyisvMXv2hwD07PkipmkyevQ4Tpw4Qd++LwPw55/bKFOmLA5HIA891IBHHmmS6bwLF37CsmVLMQwLLpeLzp27cvPNt/i2n17uKbGxm5kyZaJ3HkHTQ82atXjyyTaMGzeGrVv/4PjxY6SlpVGqVGmKFClCdPSL9O//CtOnz8507sGDX2Pbtj8ID/934u+s6ugPNIqgSNb0s5NIIWaaJjtjp2IYVqwBQb71hmFgDQjC7UplZ+w0qtd+O1fdyNLSUunVqzt9+vTzTfQZG7uZkSOHZ5pgdfLkdzBND9OmzcBqtZKSksJLL71A9eo1sNsdhISE8PrrA5kxYy52u/2s52zZsjWPPfY4ABkZGbRs+TiNGz+GaZr079+HN98cQ5kyZTFNk/fem8bYsaPo1asPa9eu4bPPFjBq1FhCQ8NIS0ujb9+XcTgc3HtvfV/5cXFx9OjxPM8+24U6derl+DPJD6ZpMnPnDiyGQZD1369145/lVLeLmTt38Ob/bslxnHft2smPP/7A1KkzMAyD7du3MXjwAObOnceGDTFUrFiJdet+JTk5mZCQEADefPMN5syZR1hYGMnJybRp04LbbrvjjEmdIyIi+f339bhcLgICAnC73WzduuW86jVhwhSAM5Kwzz9fzP33P0jXri/k6DolG6ZJwBep3uTKcdq9YxjgANJNApam4nwuNMfdBbO7tzweD/HxJ4iMLMLq1T9Rt+49rFmzigYNGvHbb79yxx13AnD48N+kpqbgdLo4cGA/pUtfxaRJ0wDo0qUjvXv3pVy58mecd/nyr/jll7VMmDCZgAAbBw8eoHPnZ5g9+wMiI4tkWS7A6NEjGDBgMOXKlcflctKx49PccsutdOvWA/Dee6d+VAI4ePBgttceHd0t0w8HIlK4aJALkUIsOXEnGWnHsFgDs9xusQaSkXaU5JO7clX+jz+u5Oabb/UlVwBVq1Zj4sSpvmWXy8WKFV/TpcvzWK1WAIKDg5k4cZrvj5+rr76aO+64k8mT38nR+RMSEnC5XDgcDpYt+4JGjRpTpoy3Vc8wDNq378jq1atIS0vj448/pGvXboSGhgEQGBjI88+/yIIF83zlHT78Ny+80IUXXujhN8kVwO7kJI6lpxNosWa5PdBi5Vh6OnuSk3JcdpEiRfj7779ZsmQRcXFxVK58Le+9NweARYs+45577qNu3XosXbrEd0xUVFHmzfuAnTv/Ijg4mI8++uSM5ArAarVSo8Yt/PLLWgDWrv2ZW2+9Pcd1lIvHOOSBRNPbcpUVO5BgYvztyXHZ2d1bt956Oxs2xODxePjrrx08+WRbVq/+CYDfflvvS7AWL17I3XfX5eGHG/DJJ/PP+7yfffYJ7dq1JyDA27JdqlRp5sz5kMh/WnizK7dkyStZsGAeW7duwTAsTJ06g2uvrZLj6xaRwk8Jlkgh5sxIAMOSbauFYRhgWHCmx+eq/EOHDnLVVVf7lnv16k6XLh1p3vwx4uLiAEhIiCc8PIKAAG/LyiefzKdLl4489dSTfPTR+75jO3V6jl9++ZmYmN/Oes4PP5xLly7P8NhjjejXrzd9+/YnJCSEAwcO+H5pPv36ihYtyvHjx7PcXrr0VRw6dMi33KdPLxwOBydOHM/V55FfEp1OLHDWOFsMSHA6c1x2ZGQRRo58i40bY+jY8SmaN3+Mn376keTkJDZu/J0776xFw4aN+fTTBb5jRo58i7S0NPr370ODBvcza9Z7ZNcb/f77H2TFiq8A+PrrL3nggYfOWp/zaYH7+utldOnS0fffl19+noMrlkySPd7Wq+w+d8Pwbk/KeYKV3b112213EBPzO3/8sYUqVa7juuuuZ/v2bXg8HrZt28qNN96Ix+Ph66+X8eCDDahf/wFWrPiatLS08zrv0aNHKF26dKZ1ERGRAGct99VXBxAVVZQ33xzGQw/dx7hxY8jIyMjxdQNMmDAu0z26Y8efuSpHRAomdREUKcRs9ggwPZimmeUfpqZpgunB5ojMVfklSlyRqUvXyJFvAdChQ1tKlCjBnj27iYiIIDExHrfbjdVqpWnTZjRt2sz3ntUpdrudfv0G8tprr/LII48CEBPzO1OmTASgdeu2wL9dBLdu3UK/fn24+mpvi1Xx4sU5dChzlxy3283Ro0eIioqiePESHDp0kPDwcN/2ffv2UrJkSd9yv34DiYqK4tln23PttVWy7F5UEIXbbHjgrHH2mBDxz7toObFv315CQkLo128gAH/8sYUePZ7niSda4vGY9OzZDYBjx47y669rufba6zh06BDR0d2Iju5GXFwcffr0pEqV61i58gf2799HkSJFGDr0TQBuuqk6I0cOIyEhnoSEBEqWvNJ3bofDgfM/SaHb7TpnndVFMA+FWLwDWphm1kmWaXq3h+b899rs7q333pvDnDkzCQkJoWbNWhiGQbVqN/DTTz9y1VVXExBgY/XqVaSkpPDaa33/qYY3McrqPaZvv13B/Pnelupu3bpTsuSVHD582NeaDbB27RoqVbqGbdu2ZVnuAw88xNatW2nfviPt23ckISGeIUMGsXDhpzzxRIscX7u6CIoUbmrBEinEQsIrYA8sised9S+7Hnca9sBihITlLpG4++66/PrrL2zevNG3bt++vcTFxfH3396WoYAAG3Xr3suUKRPxeLy/cqenp7N586YzkoEqVa7j/vsfZO7cmQBUr/4/Jk2axqRJ07jrrtr/2fd62rZ9mv79X8Hj8dCgQSMWLvyUvXv3At6kYvr0qdSsWYvAwECeeKIF48ePJfmfbnIpKSmMHz+Wpk2f8JVZoUJFrriiJN269eDVV3uf9y/i+a1cSChFHQ7SPO4st6d53BR1OCgbEprjsnfs+JM33xxGeno6AGXKlCEkJJTvvvuGUaPGMnbsO4wd+w49erzMggUf43Rm0K9fbw4f/huAYsWKUbRoMex2O6++OoBJk6b5kivwtkjdeWct3nxzGHXq1M107muvrcL333/rW46J+Y3y5Svk+Bok98wrLRBueAe0yEoGEGFglsz5nxPZ3VthYWHY7TZ++WUtt912GwA1a97F3LmzuOOOmgAsXvwZffv2991/Q4aMYMGCj7M8zz333Of7HqlS5XoaNWrMe+9Nw+XyJut79+7hjTcGYxiWbMu1WCwMHNiPv/7aAXhbvEqWvBK7Pec/WohI4acWLJFCzDAMKlR9lj/WD8XtSs1yFMEKVTvmep6k4OBgRo4cy8SJb3Ps2Nu4XG4CAqz06dOP48ePs2fPbsD7a+3cubPo3PkZrFYrycnJ1K1bj5YtW3PixIlMZbZr156fflp5Xud/5JEmrFjxNZ9+Op/HH2/OwIFDGDlymG8Uwf/972a6d+8JQO3adUhOTubFF6N9o9s98sij1K//wBnl3nPPfaxZs5qRI4fRv/+gXH02l5JhGDxVoRKj/9hMqtuV5SiCT1WolKs416t3L7t376JDh7YEBQVjmh6aNm3Gl18upUKFipn2GzduNC6Xix49evPKKz2xWq14PB7uuqs2t99eM9tzPPDAQzz9dGt693410/qGDRvx55/baNOmBcHBwdhsNl55pd856/z118vYvHmTbzk0NNTXuio5ZBi4GgR5RxFMN7MeRfDhoFzNh5XVvfX88y8SGhrG//53M+vXr/O1Mt1++x0MHNiPQYOGcPz4cWJjNzNkyHBfWTfdVJ2MjHQ2btzAjTfelN0pAahf/wGOHj1Cp07tsdlsuN0eBg4cApBtuX/8sYU33hjB8OFv4Ha7MQy47rqqNGzY+Kzn2rnzL5566knf8gsveAfEmDBhHLNnz/Str1GjBh07djm/D05ECjwN0y7ip853mHbQPFiXi0zzYBngMTUPluQNzYMlWdEw7SJZU4Il4qdykmCBt8tc8sldONPjsTkiCQkrn+uWKym4TNNkT3ISCU4nETYbZUNCFWfJG+Y/owUmeSDU4u0WqHvrsqYESyRr+tlJ5DJhGAah4Xp/pbAzDINyp728L5JnDAPzSiuQ9XQAIiLipUEuRERERERE8ogSLBERERERkTyiLoIilwnTNIlP2klaRgKB9ggiQyvo3ZxCyDRNdielkeh0E26zUi40UHGWvGGaGIdTMFKcmME2zCuC9Q6WiEgWlGCJXAYOn9jIb9unkZJ+FAMLJh6CHcWoUbkjVxTRKIKFRWx8MrN3HOJYmhOLYeAxTYoG2mhb6UqqRobkd/XEjxl7EghYvgfjZLo3qfKYmOEOXPXLYpaNyO/qiYgUKBpFUMRPne8ogodPbOSnTUMxsBJw2jxYLncaJh5q3dDngpKsAwf2M2HCOOLiDhMYGIjDEUh0dDdKlSrNm28O4+jRIxgGhIaG8fLLfYiIiMTlcjFz5nRWr16Fw2EHvHMhNWnSFIAxY0aycWMMQUHBdO36AtWq3ZDpnAcPHqR16+Zce20VwCQtLY0XX+zJTTdV9+3Ts+eLmKbJ6NHjfOuaNGlAy5atad68JQC7d+9ixIihTJo0jcGDX2Pbtj8ID4/A7XYTGRnJiy++RKlSpfn888Xs2bObW265jZkzpwOwadMGbrjBO99Ot27dqVIlf0fSio1PZszmvVgNA4fF8MU53WPiMU26VyuT6yRr9uwZ/PLLWiwWC4YBXbpE+663TZsW3HjjTfTq1ce3/+rVq/jgg9m++cYaNWrCgw8+fEa52e33+eeLGTJkIO++O8sXe5fLSYMGD/D440/QsWNnUlJSmDz5HbZv34phWAgODqZbtx6UKVOW9evX8dlnC3zzGX377QrefXcKY8a8zdSpk31xPuWhhxrwyCNNcvXZXA6MPQnYPtkOFgNsln/nwXJ6wGPibFo510nWf++tdu06MHLkMD78cAHgndNs0KD+LFnyFVFRURw8eJBXXnmJ2bM/BDI/5ydOnKBv35cB+PPPbZQpUxaHI/CM+J7+rBuGgdvtonfvV6lQoSIPP1yfpUuX+/Zds2YVy5d/zYABg2jSpAHz5n2Kw+Hwbfd4PLz99lv89dcOLBaDgAAbPXr0onTpq+jSpSPp6Wk4HIG+/Vu3bnvGpOn+TKMIimRNLVgihZhpmvy2fSoGVmwBQb71hmFgCwjC6Urlt+3TePC2t3PVjSwtLZVevbrTp08/X7IRG7uZkSOHc++99SlatCgDBngn6v3oo/eZPn0aPXr0YvLkdzBND9OmzcBqtZKSksJLL71A9eo12L9/H3v37ua99+aQmJjAiy9GM3Pm+2ecu3z58kyaNA2AvXv38MorPfngg/kAHD78N6mpKTidLg4c2E/p0lf5jvvww7nccUdNypYtd0aZ0dHdqFnzLgBiYn7j1Vd7M2PGXN/222+/g9tvvwOAhx+u7zt/fjNNk1l/HsJqGARa/3211jAMAq0GaW4Ps3ccYvjNFXMc5127dvLjjz8wdeoMDMNg+/ZtDB48gLlz57FhQwwVK1Zi3bpfSU5OJiTEm8C9+eYbzJkzj7CwMJKTk2nTpgW33XYHUVFRmcrObj+AsmXLsXz5Ml+CtWbNakJDQ33HDhv2OjfccBM9evQC4M8/t/Pyyz14992Zmc6xfPlXzJ07i/HjJ1O0aFEgc5zlHEyTgOW7vcmV/bTRA41/ljPcBCzfg7PDDTnuLpjdveXxeIiPP0FkZBFWr/6JunXvYc2aVTRo0IjffvuVO+64E8j6OT/1THbp0pHevftSrlz5LM99+j2wevUqpkyZyIgRo3P88fz882qOHj3C+PGTAPjhh/+3d+dBUVx5AMe/PTMMzAwoiMGiPOIFajRqMIIkroUH6sZspRIKjxhZjYsJJeqG6HrhEU2ZUuOKR6RcK1aM7kYjGo9szBrXJMoSxBgv4hEPiMQFKVFABwbm6P1jwiTEGXAo1kT4far4Y7pfv+npfl30r9/r3/uctLRVromtFy5c4nEfhBBNlyS5EKIJK717lYqqEnRaP7frdVo/KqtuUno3r0H1Hz16hH79+ruCK4CePXuxYcPfCA0N5dixbI4e/RKz+S7x8WOZPv01bDYbhw4dJClpGlqt84bNaDSyYcMmOnbsRF7eVaKiotFoNAQGBqHRaCkpuVnnfpSXlxMaGur6vG/fHgYNiuGZZ0axa9fOWmVnzEhhyZKF2O32Ouvs2zcCnU5HQcE1bw/LA5d/18KtKiu+Gvc3uL4ahRKLle/NFq/rDgoKoqioiP3791JcXEx4eDc2b94KwN69HzFkyDBiYgbzySf7Xdu0ahXMjh3/4OrVKxiNRrZv33VPcFVfuejop8nJOYbD4QCcgVJs7AgASktvc+XKZUaPHuuqKywsnIEDB/HFF4ddyw4c+JgPPthWK7gS3lFuVKDcqXb2XLnjo0G5U4VSXOF13Z7aVv/+UZw+fQqHw8GVK5cZPz6BrKxMAL755oQrwKrrOvdGeXkZRqOxQduGhLThwoVzfPbZvygtvc2gQTEsW7aiwfsihGgaJMASogmzVJehoPHYa6EoCigaLNWlDaq/sPC/tGvX3vV51qzXSEpKZMyYFwgLC2fSpMns37+X55//A8nJr5Kfn0dZWSktWrREp3N2oO/atZOkpEQmThzP9u1/Jzy8G9nZWdhsVq5f/4G8vCtUVlbe8915eXkkJSUyZcrLJCe/QmzsSMA5ZOfgwU8ZOXIUsbEjOHToIBbLT4HFU08NpEuXMLZufa/e39eqVTBlZQ07Ng9SudWORlHqPM+KolBWXXdQ6U5gYBArV67mzJlTJCZOZMyYF8jMPIrZfJczZ07y1FMDefbZ59i9O8O1zcqVq7FYLCxYMJdRo4azZctm3I1Gr6ucj48PvXr15uTJE5jNZszmu4SEtAHg+vXrtXola7Rt25bCwkIATp8+yZ49uykvL7snmF6/fg1JSYmuv8uXL3l9XJoLpcLq7Jny1Dv14zrFbPW6bk9tKzJyAKdOneT8+XN0796DHj0e47vvLuJwOLh48QK9e/eu9zqvT00bSE5+hezsLKZOne6xbF0dc127hjF37gKOHPmCcePimThxPGfPnnGtX7JkYa22dvv27fveRyHEw0uGCArRhPnpW6LiQFVVtzffqqqC6sBPH9ig+mue3taoGRYzeXICN24U0b9/JIMHD8Vut3PgwD9ZunQR7767hfLyUux2O1qtlri4eOLi4tm9O4OSkptERUVz7ty3TJ36CmFh4XTv3oOWLQN5/fXpVFRU0qVLV158cUKtIYIlJTdJSHiRPn36kpeXR0VFBYsWzfvxNzpvxH7+DsaMGSlMmvSS25v0nyssLCQkpA35+fkNOj4PSgsfLQ5VrfM8q6pKS733E8QWFFzDZDKRmroYgPPnz5GSMo3Ro8fhcKjMnDkDcJ6D48eP0a1bDwoLC0lOnkFy8gyKi4uZO3cm3bv34MiRL/nhhwKCgoKYMyfVY7kaI0aM5ODBTykqKiImZghWqw2ARx55hKKiQjf7WkCnTs7hWMHBrVm7Np19+z5i8eL5rF69Ho3G+UxRhgjeP9XoAw7V+c6Vu0hDda5TTT5e1+2pbW3evJWtW9/DZDIRHT0QRVHo1etxMjOP0q5de3Q6H7Ky/lPvdV7j8OFD7Ny5A3C+Lwme24DmF73AlZWVtd6h+qVLl76jQ4eOLF36FqqqkpOTzfz5s13vcckQQSGaJ+nBEqIJC/TvjNE3GJvd/ZNdm92Cwbc1gf4NuwEYNCiG48dzyM396YltQcE1iouL2bNnN9u2vQ+AVqula9cw9Ho9Op0PMTFD2bhxg2v4V1VVFbm5Z1EUhWvXvicoqBUbN25mwoSJKIqGgIAAVq1aS3r6JmbOnH3PfrRo0RJfX1/sdjv79n3EvHkLSEt7h7S0d3jzzeVkZHxYq7zJZGL27PmsXv22x9927Fg2fn5+rl6T37KO/n608vWhyuE+Z1GVw5lN8FGT5xtFTy5fvsSKFW9RVVUFQIcOHTCZ/Pn883/z9ttpruOckvIXMjI+xGqtJjV1NjduFAHQunVrgoNbo9frmT9/Ienpm1i2bEWd5WpERDxJbu5ZDh/+jCFDhrmWh4S0oW3bdmRk7HAtu3DhPJmZR4iJGQpAu3bt8fX1JT5+LDqdjys5ifCO2saI2kLvTGjhjtWBGuCLGuL9EDtPbSsgIAC93oecnGNERkYCziGj27ZtYcCAaID7us5rDBkyjPT0TaSnb6o3GU1oaFu+/jrH9fmrr7J47DHP2xw/nkN6+jrsdjuKotCpUxcMBoNMjSBEMyc9WEI0YYqiEBE+hcyzy7DaKt1mEYwIT2zwzYDRaGTlyjQ2bFhLSclabDY7Op2WuXNT6dPnCVatWs6ECWPx8zNgMBiYN28h4Hx6vG3bFl599U9otVrMZjMxMYMZN+4lNBoN2dlZ7N+/B73el1mz5rj97pohghqNQmWlheeeex6j0cS33+a6sscB9OnTl+rqKs6cOV1r+379nmT48BFcvHjRtWz9+jW8//57aLUajEZTrXp+yxRF4Y9hofw19xoWu8NtFsGErqENOs+DBw8lPz+PyZMTMBiMqKqDuLh4Dhz4hM6du9Qqt2bNKmw2Gykps5kzZyZarRaHw8HTT/+OqKjoWvUGB7f2WO7jj/cBoNFoiIwcQHHxDUwm/1rbL1q0lHXr0nj55QS0Wg0BAS1YvnwVAQEB9/yG1NTFJCSMc2WZrDnPNSIiIkhMTPL62DQLioIttqMzi2C13W0WQVvsow2aD8td25o27c/4+wfwxBP9OHHia/z9neczKmoAixen8sYbb3Lr1q06r/Pevft4+sp6zZu3gBUr3iI9fT2qqtKz5+OMHDnKtX7KlEmu62j48N8zevRY1q1bTULCOEwmfzQahUWLlrrKL1mysFYP2LBhw4mLi2/w/gkhHg6Spl2Ih9T9pmmHn+bBqqy6CYoGVAcGmQeryfn5PFg1AZbMgyUawz3zYKkqaoDMg9XcSZp2IdyTAEuIh5Q3ARY438MpvZuHpboUP30ggf6dZBhLE6SqKt+bLZRV22mp1/KoyU/Os2gcqopSXIFitqKafJzDAqVtNWsSYAnhngwRFKKZUBSFoIDOv/ZuiP8zRVHo6G+ov6AQ3lIU1DYm5KmsEELUTZJcCPEQkw5oIYQQvwb5/yOEZxJgCfGQMhj8uHOnTP7JCSGEeKBUVeXOnTIMBu8zkwrRHMg7WEI8pKxWKwUFBVRW3v/kmkIIIURjMBj8aN++PT4+3s+BJkRTJwGWEEIIIYQQQjQSGSIohBBCCCGEEI1EAiwhhBBCCCGEaCQSYAkhhBBCCCFEI5EASwghhBBCCCEaiQRYQgghhBBCCNFI/gemuM0dG1DAvAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ops_roles = operator_groups.groupby('role')\n",
    "\n",
    "for role, tbl in ops_roles:\n",
    "    sns.lmplot(x=\"k/d_ratio\", y=\"win_ratio\", data=tbl, fit_reg=False, hue='operator',\n",
    "               legend=False, size=6, aspect=2, scatter_kws={\"s\": 75})\n",
    "    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2), ncol=3)\n",
    "    plt.title(role + ' Operators')\n",
    "    plt.xlabel('Kill-Death Ratio')\n",
    "    plt.ylabel('Win Ratio')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"plots/{role}-operator-win-kill-ratio.pdf\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "        role             operator  nbwins  nbkills  nbdeaths  nbpicks  \\\n0   Attacker         BOPE-CAPITAO   65205    89647    101216   138254   \n1   Attacker        G.E.O.-JACKAL  193793   304650    291541   405461   \n2   Attacker        GIGN-MONTAGNE   98517    81475    155090   216753   \n3   Attacker         GIGN-RESERVE    2558     2941      4469     5954   \n4   Attacker          GIGN-TWITCH  227325   328186    317090   469867   \n5   Attacker           GSG9-BLITZ   45829    52784     76280    99824   \n6   Attacker              GSG9-IQ   64406    89407    101472   138740   \n7   Attacker         GSG9-RESERVE    8662    11528     15829    20175   \n8   Attacker            JTF2-BUCK  120327   169379    187398   255529   \n9   Attacker  NAVYSEAL-BLACKBEARD  134465   216468    189879   278612   \n10  Attacker          SAS-RESERVE    7077     8902     12483    16336   \n11  Attacker           SAS-SLEDGE  182531   269781    282424   384025   \n12  Attacker         SAS-THATCHER  190594   252021    276596   395212   \n13  Attacker           SAT-HIBANA  259165   385322    368381   533111   \n14  Attacker        SPETSNAZ-FUZE  233038   363138    354549   493859   \n15  Attacker        SPETSNAZ-GLAZ  216694   366726    314764   453586   \n16  Attacker     SPETSNAZ-RESERVE    3337     4131      5813     7774   \n17  Attacker             SWAT-ASH  316987   537066    469432   654668   \n18  Attacker         SWAT-RESERVE    3392     4258      5866     7697   \n19  Attacker        SWAT-THERMITE  234739   312823    347763   491555   \n20  Defender         BOPE-CAVEIRA  224757   314489    296823   414296   \n21  Defender          G.E.O.-MIRA  226284   293029    282491   423166   \n22  Defender             GIGN-DOC  159420   206250    207479   301116   \n23  Defender         GIGN-RESERVE    4116     4608      6623     8736   \n24  Defender            GIGN-ROOK  312685   384297    386406   568988   \n25  Defender          GSG9-BANDIT  289819   408088    386628   539873   \n26  Defender           GSG9-JAGER  325485   477720    432621   604988   \n27  Defender         GSG9-RESERVE   11828    15684     18535    24530   \n28  Defender           JTF2-FROST  197539   246423    245028   356571   \n29  Defender    NAVYSEAL-VALKYRIE  242487   331203    310124   446115   \n30  Defender             SAS-MUTE  235142   268555    312492   445153   \n31  Defender          SAS-RESERVE   10318    12224     16493    21576   \n32  Defender            SAS-SMOKE  216367   274319    290591   411145   \n33  Defender             SAT-ECHO   74435    91846     88730   139139   \n34  Defender      SPETSNAZ-KAPKAN  120771   155032    165644   232114   \n35  Defender     SPETSNAZ-RESERVE    3262     3746      5370     7019   \n36  Defender    SPETSNAZ-TACHANKA   25913    28358     37080    52099   \n37  Defender          SWAT-CASTLE   99671   110238    136760   192941   \n38  Defender           SWAT-PULSE  166553   224512    232647   319275   \n39  Defender         SWAT-RESERVE    2632     3055      4541     5809   \n\n    k/d_ratio  win_ratio  \n0    0.885700   0.471632  \n1    1.044965   0.477957  \n2    0.525340   0.454513  \n3    0.658089   0.429627  \n4    1.034993   0.483807  \n5    0.691977   0.459098  \n6    0.881100   0.464221  \n7    0.728284   0.429343  \n8    0.903846   0.470894  \n9    1.140031   0.482625  \n10   0.713130   0.433215  \n11   0.955234   0.475310  \n12   0.911152   0.482258  \n13   1.045988   0.486137  \n14   1.024225   0.471872  \n15   1.165082   0.477735  \n16   0.710649   0.429251  \n17   1.144076   0.484195  \n18   0.725878   0.440691  \n19   0.899529   0.477544  \n20   1.059517   0.542503  \n21   1.037304   0.534741  \n22   0.994077   0.529431  \n23   0.695757   0.471154  \n24   0.994542   0.549546  \n25   1.055506   0.536828  \n26   1.104246   0.538002  \n27   0.846183   0.482185  \n28   1.005693   0.553996  \n29   1.067970   0.543553  \n30   0.859398   0.528227  \n31   0.741163   0.478217  \n32   0.944004   0.526255  \n33   1.035118   0.534969  \n34   0.935935   0.520309  \n35   0.697579   0.464739  \n36   0.764779   0.497380  \n37   0.806069   0.516588  \n38   0.965033   0.521660  \n39   0.672759   0.453090  ",
      "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>role</th>\n      <th>operator</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n      <th>k/d_ratio</th>\n      <th>win_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>65205</td>\n      <td>89647</td>\n      <td>101216</td>\n      <td>138254</td>\n      <td>0.885700</td>\n      <td>0.471632</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>193793</td>\n      <td>304650</td>\n      <td>291541</td>\n      <td>405461</td>\n      <td>1.044965</td>\n      <td>0.477957</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Attacker</td>\n      <td>GIGN-MONTAGNE</td>\n      <td>98517</td>\n      <td>81475</td>\n      <td>155090</td>\n      <td>216753</td>\n      <td>0.525340</td>\n      <td>0.454513</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Attacker</td>\n      <td>GIGN-RESERVE</td>\n      <td>2558</td>\n      <td>2941</td>\n      <td>4469</td>\n      <td>5954</td>\n      <td>0.658089</td>\n      <td>0.429627</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Attacker</td>\n      <td>GIGN-TWITCH</td>\n      <td>227325</td>\n      <td>328186</td>\n      <td>317090</td>\n      <td>469867</td>\n      <td>1.034993</td>\n      <td>0.483807</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Attacker</td>\n      <td>GSG9-BLITZ</td>\n      <td>45829</td>\n      <td>52784</td>\n      <td>76280</td>\n      <td>99824</td>\n      <td>0.691977</td>\n      <td>0.459098</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Attacker</td>\n      <td>GSG9-IQ</td>\n      <td>64406</td>\n      <td>89407</td>\n      <td>101472</td>\n      <td>138740</td>\n      <td>0.881100</td>\n      <td>0.464221</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Attacker</td>\n      <td>GSG9-RESERVE</td>\n      <td>8662</td>\n      <td>11528</td>\n      <td>15829</td>\n      <td>20175</td>\n      <td>0.728284</td>\n      <td>0.429343</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Attacker</td>\n      <td>JTF2-BUCK</td>\n      <td>120327</td>\n      <td>169379</td>\n      <td>187398</td>\n      <td>255529</td>\n      <td>0.903846</td>\n      <td>0.470894</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Attacker</td>\n      <td>NAVYSEAL-BLACKBEARD</td>\n      <td>134465</td>\n      <td>216468</td>\n      <td>189879</td>\n      <td>278612</td>\n      <td>1.140031</td>\n      <td>0.482625</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Attacker</td>\n      <td>SAS-RESERVE</td>\n      <td>7077</td>\n      <td>8902</td>\n      <td>12483</td>\n      <td>16336</td>\n      <td>0.713130</td>\n      <td>0.433215</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Attacker</td>\n      <td>SAS-SLEDGE</td>\n      <td>182531</td>\n      <td>269781</td>\n      <td>282424</td>\n      <td>384025</td>\n      <td>0.955234</td>\n      <td>0.475310</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Attacker</td>\n      <td>SAS-THATCHER</td>\n      <td>190594</td>\n      <td>252021</td>\n      <td>276596</td>\n      <td>395212</td>\n      <td>0.911152</td>\n      <td>0.482258</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Attacker</td>\n      <td>SAT-HIBANA</td>\n      <td>259165</td>\n      <td>385322</td>\n      <td>368381</td>\n      <td>533111</td>\n      <td>1.045988</td>\n      <td>0.486137</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-FUZE</td>\n      <td>233038</td>\n      <td>363138</td>\n      <td>354549</td>\n      <td>493859</td>\n      <td>1.024225</td>\n      <td>0.471872</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-GLAZ</td>\n      <td>216694</td>\n      <td>366726</td>\n      <td>314764</td>\n      <td>453586</td>\n      <td>1.165082</td>\n      <td>0.477735</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-RESERVE</td>\n      <td>3337</td>\n      <td>4131</td>\n      <td>5813</td>\n      <td>7774</td>\n      <td>0.710649</td>\n      <td>0.429251</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Attacker</td>\n      <td>SWAT-ASH</td>\n      <td>316987</td>\n      <td>537066</td>\n      <td>469432</td>\n      <td>654668</td>\n      <td>1.144076</td>\n      <td>0.484195</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Attacker</td>\n      <td>SWAT-RESERVE</td>\n      <td>3392</td>\n      <td>4258</td>\n      <td>5866</td>\n      <td>7697</td>\n      <td>0.725878</td>\n      <td>0.440691</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Attacker</td>\n      <td>SWAT-THERMITE</td>\n      <td>234739</td>\n      <td>312823</td>\n      <td>347763</td>\n      <td>491555</td>\n      <td>0.899529</td>\n      <td>0.477544</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Defender</td>\n      <td>BOPE-CAVEIRA</td>\n      <td>224757</td>\n      <td>314489</td>\n      <td>296823</td>\n      <td>414296</td>\n      <td>1.059517</td>\n      <td>0.542503</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Defender</td>\n      <td>G.E.O.-MIRA</td>\n      <td>226284</td>\n      <td>293029</td>\n      <td>282491</td>\n      <td>423166</td>\n      <td>1.037304</td>\n      <td>0.534741</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Defender</td>\n      <td>GIGN-DOC</td>\n      <td>159420</td>\n      <td>206250</td>\n      <td>207479</td>\n      <td>301116</td>\n      <td>0.994077</td>\n      <td>0.529431</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Defender</td>\n      <td>GIGN-RESERVE</td>\n      <td>4116</td>\n      <td>4608</td>\n      <td>6623</td>\n      <td>8736</td>\n      <td>0.695757</td>\n      <td>0.471154</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Defender</td>\n      <td>GIGN-ROOK</td>\n      <td>312685</td>\n      <td>384297</td>\n      <td>386406</td>\n      <td>568988</td>\n      <td>0.994542</td>\n      <td>0.549546</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Defender</td>\n      <td>GSG9-BANDIT</td>\n      <td>289819</td>\n      <td>408088</td>\n      <td>386628</td>\n      <td>539873</td>\n      <td>1.055506</td>\n      <td>0.536828</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Defender</td>\n      <td>GSG9-JAGER</td>\n      <td>325485</td>\n      <td>477720</td>\n      <td>432621</td>\n      <td>604988</td>\n      <td>1.104246</td>\n      <td>0.538002</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Defender</td>\n      <td>GSG9-RESERVE</td>\n      <td>11828</td>\n      <td>15684</td>\n      <td>18535</td>\n      <td>24530</td>\n      <td>0.846183</td>\n      <td>0.482185</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Defender</td>\n      <td>JTF2-FROST</td>\n      <td>197539</td>\n      <td>246423</td>\n      <td>245028</td>\n      <td>356571</td>\n      <td>1.005693</td>\n      <td>0.553996</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Defender</td>\n      <td>NAVYSEAL-VALKYRIE</td>\n      <td>242487</td>\n      <td>331203</td>\n      <td>310124</td>\n      <td>446115</td>\n      <td>1.067970</td>\n      <td>0.543553</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Defender</td>\n      <td>SAS-MUTE</td>\n      <td>235142</td>\n      <td>268555</td>\n      <td>312492</td>\n      <td>445153</td>\n      <td>0.859398</td>\n      <td>0.528227</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Defender</td>\n      <td>SAS-RESERVE</td>\n      <td>10318</td>\n      <td>12224</td>\n      <td>16493</td>\n      <td>21576</td>\n      <td>0.741163</td>\n      <td>0.478217</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Defender</td>\n      <td>SAS-SMOKE</td>\n      <td>216367</td>\n      <td>274319</td>\n      <td>290591</td>\n      <td>411145</td>\n      <td>0.944004</td>\n      <td>0.526255</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Defender</td>\n      <td>SAT-ECHO</td>\n      <td>74435</td>\n      <td>91846</td>\n      <td>88730</td>\n      <td>139139</td>\n      <td>1.035118</td>\n      <td>0.534969</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-KAPKAN</td>\n      <td>120771</td>\n      <td>155032</td>\n      <td>165644</td>\n      <td>232114</td>\n      <td>0.935935</td>\n      <td>0.520309</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-RESERVE</td>\n      <td>3262</td>\n      <td>3746</td>\n      <td>5370</td>\n      <td>7019</td>\n      <td>0.697579</td>\n      <td>0.464739</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-TACHANKA</td>\n      <td>25913</td>\n      <td>28358</td>\n      <td>37080</td>\n      <td>52099</td>\n      <td>0.764779</td>\n      <td>0.497380</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Defender</td>\n      <td>SWAT-CASTLE</td>\n      <td>99671</td>\n      <td>110238</td>\n      <td>136760</td>\n      <td>192941</td>\n      <td>0.806069</td>\n      <td>0.516588</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Defender</td>\n      <td>SWAT-PULSE</td>\n      <td>166553</td>\n      <td>224512</td>\n      <td>232647</td>\n      <td>319275</td>\n      <td>0.965033</td>\n      <td>0.521660</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Defender</td>\n      <td>SWAT-RESERVE</td>\n      <td>2632</td>\n      <td>3055</td>\n      <td>4541</td>\n      <td>5809</td>\n      <td>0.672759</td>\n      <td>0.453090</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator_groups"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "       role  nbpickstotal\n0  Attacker       5409056\n1  Defender       5446979",
      "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>role</th>\n      <th>nbpickstotal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Attacker</td>\n      <td>5409056</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Defender</td>\n      <td>5446979</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_picks_total = operator_groups.groupby(\"role\")[\"nbpicks\"].sum().reset_index(name=\"nbpickstotal\")\n",
    "nb_picks_total"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "        role             operator  nbwins  nbkills  nbdeaths  nbpicks  \\\n0   Attacker         BOPE-CAPITAO   65205    89647    101216   138254   \n1   Attacker        G.E.O.-JACKAL  193793   304650    291541   405461   \n2   Attacker        GIGN-MONTAGNE   98517    81475    155090   216753   \n3   Attacker          GIGN-TWITCH  227325   328186    317090   469867   \n4   Attacker           GSG9-BLITZ   45829    52784     76280    99824   \n5   Attacker              GSG9-IQ   64406    89407    101472   138740   \n6   Attacker            JTF2-BUCK  120327   169379    187398   255529   \n7   Attacker  NAVYSEAL-BLACKBEARD  134465   216468    189879   278612   \n8   Attacker           SAS-SLEDGE  182531   269781    282424   384025   \n9   Attacker         SAS-THATCHER  190594   252021    276596   395212   \n10  Attacker           SAT-HIBANA  259165   385322    368381   533111   \n11  Attacker        SPETSNAZ-FUZE  233038   363138    354549   493859   \n12  Attacker        SPETSNAZ-GLAZ  216694   366726    314764   453586   \n13  Attacker             SWAT-ASH  316987   537066    469432   654668   \n14  Attacker        SWAT-THERMITE  234739   312823    347763   491555   \n15  Defender         BOPE-CAVEIRA  224757   314489    296823   414296   \n16  Defender          G.E.O.-MIRA  226284   293029    282491   423166   \n17  Defender             GIGN-DOC  159420   206250    207479   301116   \n18  Defender            GIGN-ROOK  312685   384297    386406   568988   \n19  Defender          GSG9-BANDIT  289819   408088    386628   539873   \n20  Defender           GSG9-JAGER  325485   477720    432621   604988   \n21  Defender           JTF2-FROST  197539   246423    245028   356571   \n22  Defender    NAVYSEAL-VALKYRIE  242487   331203    310124   446115   \n23  Defender             SAS-MUTE  235142   268555    312492   445153   \n24  Defender            SAS-SMOKE  216367   274319    290591   411145   \n25  Defender             SAT-ECHO   74435    91846     88730   139139   \n26  Defender      SPETSNAZ-KAPKAN  120771   155032    165644   232114   \n27  Defender    SPETSNAZ-TACHANKA   25913    28358     37080    52099   \n28  Defender          SWAT-CASTLE   99671   110238    136760   192941   \n29  Defender           SWAT-PULSE  166553   224512    232647   319275   \n\n    k/d_ratio  win_ratio  nbpickstotal  pick_ratio  \n0    0.885700   0.471632       5409056    0.025560  \n1    1.044965   0.477957       5409056    0.074960  \n2    0.525340   0.454513       5409056    0.040072  \n3    1.034993   0.483807       5409056    0.086867  \n4    0.691977   0.459098       5409056    0.018455  \n5    0.881100   0.464221       5409056    0.025650  \n6    0.903846   0.470894       5409056    0.047241  \n7    1.140031   0.482625       5409056    0.051508  \n8    0.955234   0.475310       5409056    0.070997  \n9    0.911152   0.482258       5409056    0.073065  \n10   1.045988   0.486137       5409056    0.098559  \n11   1.024225   0.471872       5409056    0.091302  \n12   1.165082   0.477735       5409056    0.083857  \n13   1.144076   0.484195       5409056    0.121032  \n14   0.899529   0.477544       5409056    0.090876  \n15   1.059517   0.542503       5446979    0.076060  \n16   1.037304   0.534741       5446979    0.077688  \n17   0.994077   0.529431       5446979    0.055281  \n18   0.994542   0.549546       5446979    0.104459  \n19   1.055506   0.536828       5446979    0.099114  \n20   1.104246   0.538002       5446979    0.111069  \n21   1.005693   0.553996       5446979    0.065462  \n22   1.067970   0.543553       5446979    0.081901  \n23   0.859398   0.528227       5446979    0.081725  \n24   0.944004   0.526255       5446979    0.075481  \n25   1.035118   0.534969       5446979    0.025544  \n26   0.935935   0.520309       5446979    0.042613  \n27   0.764779   0.497380       5446979    0.009565  \n28   0.806069   0.516588       5446979    0.035422  \n29   0.965033   0.521660       5446979    0.058615  ",
      "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>role</th>\n      <th>operator</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n      <th>k/d_ratio</th>\n      <th>win_ratio</th>\n      <th>nbpickstotal</th>\n      <th>pick_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>65205</td>\n      <td>89647</td>\n      <td>101216</td>\n      <td>138254</td>\n      <td>0.885700</td>\n      <td>0.471632</td>\n      <td>5409056</td>\n      <td>0.025560</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>193793</td>\n      <td>304650</td>\n      <td>291541</td>\n      <td>405461</td>\n      <td>1.044965</td>\n      <td>0.477957</td>\n      <td>5409056</td>\n      <td>0.074960</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Attacker</td>\n      <td>GIGN-MONTAGNE</td>\n      <td>98517</td>\n      <td>81475</td>\n      <td>155090</td>\n      <td>216753</td>\n      <td>0.525340</td>\n      <td>0.454513</td>\n      <td>5409056</td>\n      <td>0.040072</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Attacker</td>\n      <td>GIGN-TWITCH</td>\n      <td>227325</td>\n      <td>328186</td>\n      <td>317090</td>\n      <td>469867</td>\n      <td>1.034993</td>\n      <td>0.483807</td>\n      <td>5409056</td>\n      <td>0.086867</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Attacker</td>\n      <td>GSG9-BLITZ</td>\n      <td>45829</td>\n      <td>52784</td>\n      <td>76280</td>\n      <td>99824</td>\n      <td>0.691977</td>\n      <td>0.459098</td>\n      <td>5409056</td>\n      <td>0.018455</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Attacker</td>\n      <td>GSG9-IQ</td>\n      <td>64406</td>\n      <td>89407</td>\n      <td>101472</td>\n      <td>138740</td>\n      <td>0.881100</td>\n      <td>0.464221</td>\n      <td>5409056</td>\n      <td>0.025650</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Attacker</td>\n      <td>JTF2-BUCK</td>\n      <td>120327</td>\n      <td>169379</td>\n      <td>187398</td>\n      <td>255529</td>\n      <td>0.903846</td>\n      <td>0.470894</td>\n      <td>5409056</td>\n      <td>0.047241</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Attacker</td>\n      <td>NAVYSEAL-BLACKBEARD</td>\n      <td>134465</td>\n      <td>216468</td>\n      <td>189879</td>\n      <td>278612</td>\n      <td>1.140031</td>\n      <td>0.482625</td>\n      <td>5409056</td>\n      <td>0.051508</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Attacker</td>\n      <td>SAS-SLEDGE</td>\n      <td>182531</td>\n      <td>269781</td>\n      <td>282424</td>\n      <td>384025</td>\n      <td>0.955234</td>\n      <td>0.475310</td>\n      <td>5409056</td>\n      <td>0.070997</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Attacker</td>\n      <td>SAS-THATCHER</td>\n      <td>190594</td>\n      <td>252021</td>\n      <td>276596</td>\n      <td>395212</td>\n      <td>0.911152</td>\n      <td>0.482258</td>\n      <td>5409056</td>\n      <td>0.073065</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Attacker</td>\n      <td>SAT-HIBANA</td>\n      <td>259165</td>\n      <td>385322</td>\n      <td>368381</td>\n      <td>533111</td>\n      <td>1.045988</td>\n      <td>0.486137</td>\n      <td>5409056</td>\n      <td>0.098559</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-FUZE</td>\n      <td>233038</td>\n      <td>363138</td>\n      <td>354549</td>\n      <td>493859</td>\n      <td>1.024225</td>\n      <td>0.471872</td>\n      <td>5409056</td>\n      <td>0.091302</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-GLAZ</td>\n      <td>216694</td>\n      <td>366726</td>\n      <td>314764</td>\n      <td>453586</td>\n      <td>1.165082</td>\n      <td>0.477735</td>\n      <td>5409056</td>\n      <td>0.083857</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Attacker</td>\n      <td>SWAT-ASH</td>\n      <td>316987</td>\n      <td>537066</td>\n      <td>469432</td>\n      <td>654668</td>\n      <td>1.144076</td>\n      <td>0.484195</td>\n      <td>5409056</td>\n      <td>0.121032</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Attacker</td>\n      <td>SWAT-THERMITE</td>\n      <td>234739</td>\n      <td>312823</td>\n      <td>347763</td>\n      <td>491555</td>\n      <td>0.899529</td>\n      <td>0.477544</td>\n      <td>5409056</td>\n      <td>0.090876</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Defender</td>\n      <td>BOPE-CAVEIRA</td>\n      <td>224757</td>\n      <td>314489</td>\n      <td>296823</td>\n      <td>414296</td>\n      <td>1.059517</td>\n      <td>0.542503</td>\n      <td>5446979</td>\n      <td>0.076060</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Defender</td>\n      <td>G.E.O.-MIRA</td>\n      <td>226284</td>\n      <td>293029</td>\n      <td>282491</td>\n      <td>423166</td>\n      <td>1.037304</td>\n      <td>0.534741</td>\n      <td>5446979</td>\n      <td>0.077688</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Defender</td>\n      <td>GIGN-DOC</td>\n      <td>159420</td>\n      <td>206250</td>\n      <td>207479</td>\n      <td>301116</td>\n      <td>0.994077</td>\n      <td>0.529431</td>\n      <td>5446979</td>\n      <td>0.055281</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Defender</td>\n      <td>GIGN-ROOK</td>\n      <td>312685</td>\n      <td>384297</td>\n      <td>386406</td>\n      <td>568988</td>\n      <td>0.994542</td>\n      <td>0.549546</td>\n      <td>5446979</td>\n      <td>0.104459</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Defender</td>\n      <td>GSG9-BANDIT</td>\n      <td>289819</td>\n      <td>408088</td>\n      <td>386628</td>\n      <td>539873</td>\n      <td>1.055506</td>\n      <td>0.536828</td>\n      <td>5446979</td>\n      <td>0.099114</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Defender</td>\n      <td>GSG9-JAGER</td>\n      <td>325485</td>\n      <td>477720</td>\n      <td>432621</td>\n      <td>604988</td>\n      <td>1.104246</td>\n      <td>0.538002</td>\n      <td>5446979</td>\n      <td>0.111069</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Defender</td>\n      <td>JTF2-FROST</td>\n      <td>197539</td>\n      <td>246423</td>\n      <td>245028</td>\n      <td>356571</td>\n      <td>1.005693</td>\n      <td>0.553996</td>\n      <td>5446979</td>\n      <td>0.065462</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Defender</td>\n      <td>NAVYSEAL-VALKYRIE</td>\n      <td>242487</td>\n      <td>331203</td>\n      <td>310124</td>\n      <td>446115</td>\n      <td>1.067970</td>\n      <td>0.543553</td>\n      <td>5446979</td>\n      <td>0.081901</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Defender</td>\n      <td>SAS-MUTE</td>\n      <td>235142</td>\n      <td>268555</td>\n      <td>312492</td>\n      <td>445153</td>\n      <td>0.859398</td>\n      <td>0.528227</td>\n      <td>5446979</td>\n      <td>0.081725</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Defender</td>\n      <td>SAS-SMOKE</td>\n      <td>216367</td>\n      <td>274319</td>\n      <td>290591</td>\n      <td>411145</td>\n      <td>0.944004</td>\n      <td>0.526255</td>\n      <td>5446979</td>\n      <td>0.075481</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Defender</td>\n      <td>SAT-ECHO</td>\n      <td>74435</td>\n      <td>91846</td>\n      <td>88730</td>\n      <td>139139</td>\n      <td>1.035118</td>\n      <td>0.534969</td>\n      <td>5446979</td>\n      <td>0.025544</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-KAPKAN</td>\n      <td>120771</td>\n      <td>155032</td>\n      <td>165644</td>\n      <td>232114</td>\n      <td>0.935935</td>\n      <td>0.520309</td>\n      <td>5446979</td>\n      <td>0.042613</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-TACHANKA</td>\n      <td>25913</td>\n      <td>28358</td>\n      <td>37080</td>\n      <td>52099</td>\n      <td>0.764779</td>\n      <td>0.497380</td>\n      <td>5446979</td>\n      <td>0.009565</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Defender</td>\n      <td>SWAT-CASTLE</td>\n      <td>99671</td>\n      <td>110238</td>\n      <td>136760</td>\n      <td>192941</td>\n      <td>0.806069</td>\n      <td>0.516588</td>\n      <td>5446979</td>\n      <td>0.035422</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Defender</td>\n      <td>SWAT-PULSE</td>\n      <td>166553</td>\n      <td>224512</td>\n      <td>232647</td>\n      <td>319275</td>\n      <td>0.965033</td>\n      <td>0.521660</td>\n      <td>5446979</td>\n      <td>0.058615</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator_groups = pd.merge(left=operator_groups, right=nb_picks_total, on=\"role\", how=\"left\")\n",
    "operator_groups[\"pick_ratio\"] = operator_groups[\"nbpicks\"] / operator_groups[\"nbpickstotal\"]\n",
    "operator_groups"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\marti\\anaconda3\\lib\\site-packages\\seaborn\\regression.py:581: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 864x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ops_roles = operator_groups.groupby('role')\n",
    "\n",
    "for role, tbl in ops_roles:\n",
    "    sns.lmplot(x=\"pick_ratio\", y=\"win_ratio\", data=tbl, fit_reg=False, hue='operator',\n",
    "               legend=False, size=6, aspect=2, scatter_kws={\"s\": 75})\n",
    "    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2), ncol=3)\n",
    "    plt.title(role + ' Operators')\n",
    "    plt.xlabel('Pick Rate')\n",
    "    plt.ylabel('Win Ratio')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"plots/{role}-operator-win-pick-ratio.pdf\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "        role             operator  nbwins  nbkills  nbdeaths  nbpicks  \\\n0   Attacker         BOPE-CAPITAO   65205    89647    101216   138254   \n1   Attacker        G.E.O.-JACKAL  193793   304650    291541   405461   \n2   Attacker        GIGN-MONTAGNE   98517    81475    155090   216753   \n3   Attacker         GIGN-RESERVE    2558     2941      4469     5954   \n4   Attacker          GIGN-TWITCH  227325   328186    317090   469867   \n5   Attacker           GSG9-BLITZ   45829    52784     76280    99824   \n6   Attacker              GSG9-IQ   64406    89407    101472   138740   \n7   Attacker         GSG9-RESERVE    8662    11528     15829    20175   \n8   Attacker            JTF2-BUCK  120327   169379    187398   255529   \n9   Attacker  NAVYSEAL-BLACKBEARD  134465   216468    189879   278612   \n10  Attacker          SAS-RESERVE    7077     8902     12483    16336   \n11  Attacker           SAS-SLEDGE  182531   269781    282424   384025   \n12  Attacker         SAS-THATCHER  190594   252021    276596   395212   \n13  Attacker           SAT-HIBANA  259165   385322    368381   533111   \n14  Attacker        SPETSNAZ-FUZE  233038   363138    354549   493859   \n15  Attacker        SPETSNAZ-GLAZ  216694   366726    314764   453586   \n16  Attacker     SPETSNAZ-RESERVE    3337     4131      5813     7774   \n17  Attacker             SWAT-ASH  316987   537066    469432   654668   \n18  Attacker         SWAT-RESERVE    3392     4258      5866     7697   \n19  Attacker        SWAT-THERMITE  234739   312823    347763   491555   \n20  Defender         BOPE-CAVEIRA  224757   314489    296823   414296   \n21  Defender          G.E.O.-MIRA  226284   293029    282491   423166   \n22  Defender             GIGN-DOC  159420   206250    207479   301116   \n23  Defender         GIGN-RESERVE    4116     4608      6623     8736   \n24  Defender            GIGN-ROOK  312685   384297    386406   568988   \n25  Defender          GSG9-BANDIT  289819   408088    386628   539873   \n26  Defender           GSG9-JAGER  325485   477720    432621   604988   \n27  Defender         GSG9-RESERVE   11828    15684     18535    24530   \n28  Defender           JTF2-FROST  197539   246423    245028   356571   \n29  Defender    NAVYSEAL-VALKYRIE  242487   331203    310124   446115   \n30  Defender             SAS-MUTE  235142   268555    312492   445153   \n31  Defender          SAS-RESERVE   10318    12224     16493    21576   \n32  Defender            SAS-SMOKE  216367   274319    290591   411145   \n33  Defender             SAT-ECHO   74435    91846     88730   139139   \n34  Defender      SPETSNAZ-KAPKAN  120771   155032    165644   232114   \n35  Defender     SPETSNAZ-RESERVE    3262     3746      5370     7019   \n36  Defender    SPETSNAZ-TACHANKA   25913    28358     37080    52099   \n37  Defender          SWAT-CASTLE   99671   110238    136760   192941   \n38  Defender           SWAT-PULSE  166553   224512    232647   319275   \n39  Defender         SWAT-RESERVE    2632     3055      4541     5809   \n\n    k/d_ratio  win_ratio  lose_ratio  \n0    0.885700   0.471632    0.528368  \n1    1.044965   0.477957    0.522043  \n2    0.525340   0.454513    0.545487  \n3    0.658089   0.429627    0.570373  \n4    1.034993   0.483807    0.516193  \n5    0.691977   0.459098    0.540902  \n6    0.881100   0.464221    0.535779  \n7    0.728284   0.429343    0.570657  \n8    0.903846   0.470894    0.529106  \n9    1.140031   0.482625    0.517375  \n10   0.713130   0.433215    0.566785  \n11   0.955234   0.475310    0.524690  \n12   0.911152   0.482258    0.517742  \n13   1.045988   0.486137    0.513863  \n14   1.024225   0.471872    0.528128  \n15   1.165082   0.477735    0.522265  \n16   0.710649   0.429251    0.570749  \n17   1.144076   0.484195    0.515805  \n18   0.725878   0.440691    0.559309  \n19   0.899529   0.477544    0.522456  \n20   1.059517   0.542503    0.457497  \n21   1.037304   0.534741    0.465259  \n22   0.994077   0.529431    0.470569  \n23   0.695757   0.471154    0.528846  \n24   0.994542   0.549546    0.450454  \n25   1.055506   0.536828    0.463172  \n26   1.104246   0.538002    0.461998  \n27   0.846183   0.482185    0.517815  \n28   1.005693   0.553996    0.446004  \n29   1.067970   0.543553    0.456447  \n30   0.859398   0.528227    0.471773  \n31   0.741163   0.478217    0.521783  \n32   0.944004   0.526255    0.473745  \n33   1.035118   0.534969    0.465031  \n34   0.935935   0.520309    0.479691  \n35   0.697579   0.464739    0.535261  \n36   0.764779   0.497380    0.502620  \n37   0.806069   0.516588    0.483412  \n38   0.965033   0.521660    0.478340  \n39   0.672759   0.453090    0.546910  ",
      "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>role</th>\n      <th>operator</th>\n      <th>nbwins</th>\n      <th>nbkills</th>\n      <th>nbdeaths</th>\n      <th>nbpicks</th>\n      <th>k/d_ratio</th>\n      <th>win_ratio</th>\n      <th>lose_ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Attacker</td>\n      <td>BOPE-CAPITAO</td>\n      <td>65205</td>\n      <td>89647</td>\n      <td>101216</td>\n      <td>138254</td>\n      <td>0.885700</td>\n      <td>0.471632</td>\n      <td>0.528368</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Attacker</td>\n      <td>G.E.O.-JACKAL</td>\n      <td>193793</td>\n      <td>304650</td>\n      <td>291541</td>\n      <td>405461</td>\n      <td>1.044965</td>\n      <td>0.477957</td>\n      <td>0.522043</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Attacker</td>\n      <td>GIGN-MONTAGNE</td>\n      <td>98517</td>\n      <td>81475</td>\n      <td>155090</td>\n      <td>216753</td>\n      <td>0.525340</td>\n      <td>0.454513</td>\n      <td>0.545487</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Attacker</td>\n      <td>GIGN-RESERVE</td>\n      <td>2558</td>\n      <td>2941</td>\n      <td>4469</td>\n      <td>5954</td>\n      <td>0.658089</td>\n      <td>0.429627</td>\n      <td>0.570373</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Attacker</td>\n      <td>GIGN-TWITCH</td>\n      <td>227325</td>\n      <td>328186</td>\n      <td>317090</td>\n      <td>469867</td>\n      <td>1.034993</td>\n      <td>0.483807</td>\n      <td>0.516193</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Attacker</td>\n      <td>GSG9-BLITZ</td>\n      <td>45829</td>\n      <td>52784</td>\n      <td>76280</td>\n      <td>99824</td>\n      <td>0.691977</td>\n      <td>0.459098</td>\n      <td>0.540902</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Attacker</td>\n      <td>GSG9-IQ</td>\n      <td>64406</td>\n      <td>89407</td>\n      <td>101472</td>\n      <td>138740</td>\n      <td>0.881100</td>\n      <td>0.464221</td>\n      <td>0.535779</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Attacker</td>\n      <td>GSG9-RESERVE</td>\n      <td>8662</td>\n      <td>11528</td>\n      <td>15829</td>\n      <td>20175</td>\n      <td>0.728284</td>\n      <td>0.429343</td>\n      <td>0.570657</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Attacker</td>\n      <td>JTF2-BUCK</td>\n      <td>120327</td>\n      <td>169379</td>\n      <td>187398</td>\n      <td>255529</td>\n      <td>0.903846</td>\n      <td>0.470894</td>\n      <td>0.529106</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Attacker</td>\n      <td>NAVYSEAL-BLACKBEARD</td>\n      <td>134465</td>\n      <td>216468</td>\n      <td>189879</td>\n      <td>278612</td>\n      <td>1.140031</td>\n      <td>0.482625</td>\n      <td>0.517375</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Attacker</td>\n      <td>SAS-RESERVE</td>\n      <td>7077</td>\n      <td>8902</td>\n      <td>12483</td>\n      <td>16336</td>\n      <td>0.713130</td>\n      <td>0.433215</td>\n      <td>0.566785</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Attacker</td>\n      <td>SAS-SLEDGE</td>\n      <td>182531</td>\n      <td>269781</td>\n      <td>282424</td>\n      <td>384025</td>\n      <td>0.955234</td>\n      <td>0.475310</td>\n      <td>0.524690</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Attacker</td>\n      <td>SAS-THATCHER</td>\n      <td>190594</td>\n      <td>252021</td>\n      <td>276596</td>\n      <td>395212</td>\n      <td>0.911152</td>\n      <td>0.482258</td>\n      <td>0.517742</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Attacker</td>\n      <td>SAT-HIBANA</td>\n      <td>259165</td>\n      <td>385322</td>\n      <td>368381</td>\n      <td>533111</td>\n      <td>1.045988</td>\n      <td>0.486137</td>\n      <td>0.513863</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-FUZE</td>\n      <td>233038</td>\n      <td>363138</td>\n      <td>354549</td>\n      <td>493859</td>\n      <td>1.024225</td>\n      <td>0.471872</td>\n      <td>0.528128</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-GLAZ</td>\n      <td>216694</td>\n      <td>366726</td>\n      <td>314764</td>\n      <td>453586</td>\n      <td>1.165082</td>\n      <td>0.477735</td>\n      <td>0.522265</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Attacker</td>\n      <td>SPETSNAZ-RESERVE</td>\n      <td>3337</td>\n      <td>4131</td>\n      <td>5813</td>\n      <td>7774</td>\n      <td>0.710649</td>\n      <td>0.429251</td>\n      <td>0.570749</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Attacker</td>\n      <td>SWAT-ASH</td>\n      <td>316987</td>\n      <td>537066</td>\n      <td>469432</td>\n      <td>654668</td>\n      <td>1.144076</td>\n      <td>0.484195</td>\n      <td>0.515805</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Attacker</td>\n      <td>SWAT-RESERVE</td>\n      <td>3392</td>\n      <td>4258</td>\n      <td>5866</td>\n      <td>7697</td>\n      <td>0.725878</td>\n      <td>0.440691</td>\n      <td>0.559309</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Attacker</td>\n      <td>SWAT-THERMITE</td>\n      <td>234739</td>\n      <td>312823</td>\n      <td>347763</td>\n      <td>491555</td>\n      <td>0.899529</td>\n      <td>0.477544</td>\n      <td>0.522456</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Defender</td>\n      <td>BOPE-CAVEIRA</td>\n      <td>224757</td>\n      <td>314489</td>\n      <td>296823</td>\n      <td>414296</td>\n      <td>1.059517</td>\n      <td>0.542503</td>\n      <td>0.457497</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Defender</td>\n      <td>G.E.O.-MIRA</td>\n      <td>226284</td>\n      <td>293029</td>\n      <td>282491</td>\n      <td>423166</td>\n      <td>1.037304</td>\n      <td>0.534741</td>\n      <td>0.465259</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Defender</td>\n      <td>GIGN-DOC</td>\n      <td>159420</td>\n      <td>206250</td>\n      <td>207479</td>\n      <td>301116</td>\n      <td>0.994077</td>\n      <td>0.529431</td>\n      <td>0.470569</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Defender</td>\n      <td>GIGN-RESERVE</td>\n      <td>4116</td>\n      <td>4608</td>\n      <td>6623</td>\n      <td>8736</td>\n      <td>0.695757</td>\n      <td>0.471154</td>\n      <td>0.528846</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Defender</td>\n      <td>GIGN-ROOK</td>\n      <td>312685</td>\n      <td>384297</td>\n      <td>386406</td>\n      <td>568988</td>\n      <td>0.994542</td>\n      <td>0.549546</td>\n      <td>0.450454</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Defender</td>\n      <td>GSG9-BANDIT</td>\n      <td>289819</td>\n      <td>408088</td>\n      <td>386628</td>\n      <td>539873</td>\n      <td>1.055506</td>\n      <td>0.536828</td>\n      <td>0.463172</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Defender</td>\n      <td>GSG9-JAGER</td>\n      <td>325485</td>\n      <td>477720</td>\n      <td>432621</td>\n      <td>604988</td>\n      <td>1.104246</td>\n      <td>0.538002</td>\n      <td>0.461998</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Defender</td>\n      <td>GSG9-RESERVE</td>\n      <td>11828</td>\n      <td>15684</td>\n      <td>18535</td>\n      <td>24530</td>\n      <td>0.846183</td>\n      <td>0.482185</td>\n      <td>0.517815</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Defender</td>\n      <td>JTF2-FROST</td>\n      <td>197539</td>\n      <td>246423</td>\n      <td>245028</td>\n      <td>356571</td>\n      <td>1.005693</td>\n      <td>0.553996</td>\n      <td>0.446004</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Defender</td>\n      <td>NAVYSEAL-VALKYRIE</td>\n      <td>242487</td>\n      <td>331203</td>\n      <td>310124</td>\n      <td>446115</td>\n      <td>1.067970</td>\n      <td>0.543553</td>\n      <td>0.456447</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Defender</td>\n      <td>SAS-MUTE</td>\n      <td>235142</td>\n      <td>268555</td>\n      <td>312492</td>\n      <td>445153</td>\n      <td>0.859398</td>\n      <td>0.528227</td>\n      <td>0.471773</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Defender</td>\n      <td>SAS-RESERVE</td>\n      <td>10318</td>\n      <td>12224</td>\n      <td>16493</td>\n      <td>21576</td>\n      <td>0.741163</td>\n      <td>0.478217</td>\n      <td>0.521783</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Defender</td>\n      <td>SAS-SMOKE</td>\n      <td>216367</td>\n      <td>274319</td>\n      <td>290591</td>\n      <td>411145</td>\n      <td>0.944004</td>\n      <td>0.526255</td>\n      <td>0.473745</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Defender</td>\n      <td>SAT-ECHO</td>\n      <td>74435</td>\n      <td>91846</td>\n      <td>88730</td>\n      <td>139139</td>\n      <td>1.035118</td>\n      <td>0.534969</td>\n      <td>0.465031</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-KAPKAN</td>\n      <td>120771</td>\n      <td>155032</td>\n      <td>165644</td>\n      <td>232114</td>\n      <td>0.935935</td>\n      <td>0.520309</td>\n      <td>0.479691</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-RESERVE</td>\n      <td>3262</td>\n      <td>3746</td>\n      <td>5370</td>\n      <td>7019</td>\n      <td>0.697579</td>\n      <td>0.464739</td>\n      <td>0.535261</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>Defender</td>\n      <td>SPETSNAZ-TACHANKA</td>\n      <td>25913</td>\n      <td>28358</td>\n      <td>37080</td>\n      <td>52099</td>\n      <td>0.764779</td>\n      <td>0.497380</td>\n      <td>0.502620</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>Defender</td>\n      <td>SWAT-CASTLE</td>\n      <td>99671</td>\n      <td>110238</td>\n      <td>136760</td>\n      <td>192941</td>\n      <td>0.806069</td>\n      <td>0.516588</td>\n      <td>0.483412</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Defender</td>\n      <td>SWAT-PULSE</td>\n      <td>166553</td>\n      <td>224512</td>\n      <td>232647</td>\n      <td>319275</td>\n      <td>0.965033</td>\n      <td>0.521660</td>\n      <td>0.478340</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Defender</td>\n      <td>SWAT-RESERVE</td>\n      <td>2632</td>\n      <td>3055</td>\n      <td>4541</td>\n      <td>5809</td>\n      <td>0.672759</td>\n      <td>0.453090</td>\n      <td>0.546910</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "operator_groups[\"lose_ratio\"] = 1 - operator_groups[\"win_ratio\"]\n",
    "operator_groups"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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