data-transform-checking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from csgo_wp import data_transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('G:/datasets/csgo/example_frames.csv', nrows=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 24)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 'de_inferno']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['MatchId', 'MapName']].drop_duplicates().values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\git\\csgo-win-probability\\csgo_wp\\data_transform.py:52: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['pos'] = df[['X', 'Y', 'Z']].values.tolist()\n"
     ]
    }
   ],
   "source": [
    "transformed_df = data_transform.transform_data(df, 'de_inferno')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "shape '[10, 10, 12]' is invalid for input of size 60000",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-502b2a143b14>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtransformed_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mstacked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtransformed_df\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m: shape '[10, 10, 12]' is invalid for input of size 60000"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "n_samples = transformed_df[0].shape[0]\n",
    "stacked = torch.cat([x.reshape(n_samples, -1) for x in transformed_df], dim=1).view(n_samples, 10, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked[:, :, :10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([500, 10, 12])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MatchId', 'MapName', 'RoundNum', 'Tick', 'Seconds', 'BombPlanted',\n",
       "       'BombSite', 'PlayerId', 'SteamId', 'TeamId', 'Side', 'X', 'Y', 'Z',\n",
       "       'XViz', 'YViz', 'AreaId', 'EqVal', 'Hp', 'Armor', 'HasDefuse',\n",
       "       'HasHelmet', 'Created', 'Updated'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>MatchId</th>\n",
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       "      <th>Seconds</th>\n",
       "      <th>BombPlanted</th>\n",
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       "      <th>PlayerId</th>\n",
       "      <th>SteamId</th>\n",
       "      <th>TeamId</th>\n",
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       "      <th>X</th>\n",
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       "      <th>Z</th>\n",
       "      <th>XViz</th>\n",
       "      <th>YViz</th>\n",
       "      <th>AreaId</th>\n",
       "      <th>EqVal</th>\n",
       "      <th>Hp</th>\n",
       "      <th>Armor</th>\n",
       "      <th>HasDefuse</th>\n",
       "      <th>HasHelmet</th>\n",
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       "      <td>3</td>\n",
       "      <td>de_inferno</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>76561197961610974</td>\n",
       "      <td>5</td>\n",
       "      <td>CT</td>\n",
       "      <td>2315.194092</td>\n",
       "      <td>2039.947144</td>\n",
       "      <td>128.03125</td>\n",
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       "      <td>2020-07-24 23:43:26</td>\n",
       "      <td>2020-07-24 23:43:26</td>\n",
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       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>de_inferno</td>\n",
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       "      <td>15926</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>76561198017671061</td>\n",
       "      <td>5</td>\n",
       "      <td>CT</td>\n",
       "      <td>2381.296387</td>\n",
       "      <td>2050.294434</td>\n",
       "      <td>128.03125</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>de_inferno</td>\n",
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       "      <td>15926</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>76561197962710908</td>\n",
       "      <td>5</td>\n",
       "      <td>CT</td>\n",
       "      <td>2460.788818</td>\n",
       "      <td>2027.771484</td>\n",
       "      <td>132.03125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>900</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
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       "      <td>2020-07-24 23:43:26</td>\n",
       "      <td>2020-07-24 23:43:26</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>de_inferno</td>\n",
       "      <td>1</td>\n",
       "      <td>15926</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>76561198027839825</td>\n",
       "      <td>5</td>\n",
       "      <td>CT</td>\n",
       "      <td>2426.017090</td>\n",
       "      <td>1946.936890</td>\n",
       "      <td>128.03125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91</td>\n",
       "      <td>850</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
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       "      <td>2020-07-24 23:43:26</td>\n",
       "      <td>2020-07-24 23:43:26</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>de_inferno</td>\n",
       "      <td>1</td>\n",
       "      <td>15926</td>\n",
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       "      <td>25</td>\n",
       "      <td>76561198013172503</td>\n",
       "      <td>5</td>\n",
       "      <td>CT</td>\n",
       "      <td>2296.003174</td>\n",
       "      <td>1958.576782</td>\n",
       "      <td>128.03125</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2020-07-24 23:43:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MatchId     MapName  RoundNum   Tick  Seconds  BombPlanted  BombSite  \\\n",
       "0        3  de_inferno         1  15926       20            1       NaN   \n",
       "1        3  de_inferno         1  15926       20            1       NaN   \n",
       "2        3  de_inferno         1  15926       20            1       NaN   \n",
       "3        3  de_inferno         1  15926       20            1       NaN   \n",
       "4        3  de_inferno         1  15926       20            1       NaN   \n",
       "\n",
       "   PlayerId            SteamId  TeamId Side            X            Y  \\\n",
       "0        16  76561197961610974       5   CT  2315.194092  2039.947144   \n",
       "1        18  76561198017671061       5   CT  2381.296387  2050.294434   \n",
       "2        21  76561197962710908       5   CT  2460.788818  2027.771484   \n",
       "3        23  76561198027839825       5   CT  2426.017090  1946.936890   \n",
       "4        25  76561198013172503       5   CT  2296.003174  1958.576782   \n",
       "\n",
       "           Z  XViz  YViz  AreaId  EqVal   Hp  Armor  HasDefuse  HasHelmet  \\\n",
       "0  128.03125   NaN   NaN      67    850  100    100          1          1   \n",
       "1  128.03125   NaN   NaN      68    900  100      0          1          1   \n",
       "2  132.03125   NaN   NaN      68    900  100      0          1          1   \n",
       "3  128.03125   NaN   NaN      91    850  100    100          1          1   \n",
       "4  128.03125   NaN   NaN      67    850  100    100          1          1   \n",
       "\n",
       "               Created              Updated  \n",
       "0  2020-07-24 23:43:26  2020-07-24 23:43:26  \n",
       "1  2020-07-24 23:43:26  2020-07-24 23:43:26  \n",
       "2  2020-07-24 23:43:26  2020-07-24 23:43:26  \n",
       "3  2020-07-24 23:43:26  2020-07-24 23:43:26  \n",
       "4  2020-07-24 23:43:26  2020-07-24 23:43:26  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.max_columns = 999\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Tick'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "player_id_check = pd.read_csv('G:/datasets/csgo/example_frames.csv', usecols=['PlayerId', 'SteamId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "   PlayerId            SteamId\n",
       "0        16  76561197961610974\n",
       "1        18  76561198017671061\n",
       "2        21  76561197962710908\n",
       "3        23  76561198027839825\n",
       "4        25  76561198013172503"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_id_check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7840516, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_id_check.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped = player_id_check.groupby('PlayerId').agg({'SteamId': 'nunique'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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      ],
      "text/plain": [
       "          SteamId\n",
       "PlayerId         \n",
       "1               1\n",
       "3               2\n",
       "4               2\n",
       "16              1\n",
       "17              1"
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([76561198113666193, 76561198017671061], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_id_check[player_id_check['PlayerId'] == 3]['SteamId'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8759033679962158"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.rand(1).item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "match_map_check = pd.read_csv('G:/datasets/csgo/example_frames.csv', usecols=['MatchId', 'MapName', 'RoundNum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1295, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_map_check.drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'de_inferno'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MapName'].unique()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from csgo_wp.data_transform import CSGODataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating sample index offsets...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "dataset = CSGODataset(transform=data_transform.transform_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "508444"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_count_check = pd.read_csv('G:/datasets/csgo/example_frames.csv', usecols=['MatchId', 'MapName', 'RoundNum', 'Tick'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(844207, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_count_check.drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6022740868057241"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "508444/844207"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating sample index offsets...\n",
      "Done!\n",
      "Calculating sample index offsets...\n",
      "Done!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(168060.0, 167703.0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CSGODataset(transform=data_transform.transform_data, dataset_split='val')) / 1, len(CSGODataset(transform=data_transform.transform_data, dataset_split='test')) / 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MatchId'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\git\\csgo-win-probability\\csgo_wp\\data_transform.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['pos'] = df[['X', 'Y', 'Z']].values.tolist()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 de_inferno 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(tensor([[ 1., 10.,  7., 11., 12., 43., 38., 12., 34., 10.],\n",
       "         [10.,  1., 18.,  4.,  3., 34., 35.,  3., 30.,  3.],\n",
       "         [ 7., 18.,  1., 19., 20., 44., 37., 20., 33., 18.],\n",
       "         [11.,  4., 19.,  1.,  6., 37., 38.,  6., 33.,  2.],\n",
       "         [12.,  3., 20.,  6.,  1., 32., 33.,  1., 28.,  5.],\n",
       "         [43., 34., 44., 37., 32.,  1., 10., 32., 12., 36.],\n",
       "         [38., 35., 37., 38., 33., 10.,  1., 33.,  6., 37.],\n",
       "         [12.,  3., 20.,  6.,  1., 32., 33.,  1., 28.,  5.],\n",
       "         [34., 30., 33., 33., 28., 12.,  6., 28.,  1., 32.],\n",
       "         [10.,  3., 18.,  2.,  5., 36., 37.,  5., 32.,  1.],\n",
       "         [ 1.,  1.,  1.,  0.,  1.,  1.,  1.,  0.,  1.,  0.],\n",
       "         [ 1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  0.]]), tensor([[1.]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\git\\csgo-win-probability\\csgo_wp\\data_transform.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['pos'] = df[['X', 'Y', 'Z']].values.tolist()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 de_inferno 6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 12])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[10000][0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\git\\csgo-win-probability\\csgo_wp\\data_transform.py:53: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['pos'] = df[['X', 'Y', 'Z']].values.tolist()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 1., 52.,  2., 55., 52.,  3.,  2., 49.,  1., 53., 52.,  1.],\n",
       "        [53.,  4.,  1., 50., 51.,  2., 52.,  2.,  2., 53.,  1., 56.],\n",
       "        [53.,  4.,  3., 50.,  2., 54., 55.,  4., 56.,  1.,  4., 53.],\n",
       "        [54.,  5., 55.,  3., 52.,  1., 53.,  4.,  1., 50., 51.,  2.],\n",
       "        [52.,  2.,  3., 50.,  4., 53., 50.,  1.,  2., 47.,  3., 51.],\n",
       "        [ 2., 51.,  3., 54., 51.,  2.,  1., 48.,  2., 52., 49.,  2.],\n",
       "        [50.,  5.,  2., 47., 48.,  1., 49.,  3.,  1., 52.,  2., 55.],\n",
       "        [52.,  3.,  2., 49.,  1., 53., 53.,  2., 54.,  3.,  2., 51.],\n",
       "        [52.,  3., 53.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  0.],\n",
       "        [ 1.,  0.,  1.,  1.,  1.,  1.,  1.,  0.,  1.,  1.,  1.,  0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_transform.transform_data(pd.read_csv('G:/datasets/csgo/match-map-unique/train/match-113-de_inferno-6-676.csv'), 'de_inferno')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating sample index offsets...\n",
      "Done!\n",
      "(4800, 9) (38400, 19) 600\n",
      "(4800, 8)\n",
      "(6192, 9) (55728, 19) 688\n",
      "(6192, 9)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "stack expects each tensor to be equal size, but got [10, 8] at entry 0 and [11, 9] at entry 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-ce51e6d38880>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# check if dataset acts as expected\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    362\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 363\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    364\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_kind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_DatasetKind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIterable\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    401\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    402\u001b[0m         \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 403\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    404\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[1;34m(batch)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'each element in list of batch should be of equal size'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'each element in list of batch should be of equal size'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[0mtransposed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdefault_collate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtransposed\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m     \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefault_collate_err_msg_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melem_type\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\python37\\lib\\site-packages\\torch\\utils\\data\\_utils\\collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[1;34m(batch)\u001b[0m\n\u001b[0;32m     53\u001b[0m             \u001b[0mstorage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0melem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0melem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'numpy'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'string_'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: stack expects each tensor to be equal size, but got [10, 8] at entry 0 and [11, 9] at entry 1"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import time\n",
    "from csgo_wp.data_transform import CSGODataset, transform_data\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "dataset = CSGODataset(transform=transform_data)\n",
    "\n",
    "loader = torch.utils.data.DataLoader(dataset,\n",
    "                                         batch_size=2,\n",
    "                                         shuffle=True,\n",
    "                                         num_workers=0,\n",
    "                                         )\n",
    "start = time.time()\n",
    "# check if dataset acts as expected\n",
    "for index, (data, target) in enumerate(loader):\n",
    "    print(index, data.shape, target.shape)\n",
    "    break\n",
    "print(time.time() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating sample index offsets...\n",
      "Done!\n",
      "5.33 s ± 191 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit dataset[10000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "row_count_check = pd.read_csv('G:/datasets/csgo/match-map-unique/train/match-58-de_inferno-17-687.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2020-07-25 00:54:52</td>\n",
       "      <td>2020-07-25 00:54:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>1300312</td>\n",
       "      <td>58</td>\n",
       "      <td>de_inferno</td>\n",
       "      <td>17</td>\n",
       "      <td>259975</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>316</td>\n",
       "      <td>76561198202581684</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>287</td>\n",
       "      <td>3350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-25 00:54:52</td>\n",
       "      <td>2020-07-25 00:54:52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5496 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0  MatchId     MapName  RoundNum    Tick  Seconds  BombPlanted  \\\n",
       "0        1294817       58  de_inferno        17  243484       15            1   \n",
       "1        1294818       58  de_inferno        17  243484       15            1   \n",
       "2        1294819       58  de_inferno        17  243484       15            1   \n",
       "3        1294820       58  de_inferno        17  243484       15            1   \n",
       "4        1294821       58  de_inferno        17  243484       15            1   \n",
       "...          ...      ...         ...       ...     ...      ...          ...   \n",
       "5491     1300308       58  de_inferno        17  259975      144            1   \n",
       "5492     1300309       58  de_inferno        17  259975      144            1   \n",
       "5493     1300310       58  de_inferno        17  259975      144            1   \n",
       "5494     1300311       58  de_inferno        17  259975      144            1   \n",
       "5495     1300312       58  de_inferno        17  259975      144            1   \n",
       "\n",
       "     BombSite  PlayerId            SteamId  ...  XViz YViz  AreaId  EqVal  \\\n",
       "0         NaN       317  76561198068168375  ...   NaN  NaN      68   2000   \n",
       "1         NaN       318  76561198040378969  ...   NaN  NaN     227   1850   \n",
       "2         NaN       321  76561197982495276  ...   NaN  NaN      68   2300   \n",
       "3         NaN       322  76561198142085482  ...   NaN  NaN      67   2050   \n",
       "4         NaN       312  76561198042147080  ...   NaN  NaN    1060   3600   \n",
       "...       ...       ...                ...  ...   ...  ...     ...    ...   \n",
       "5491        B       322  76561198142085482  ...   NaN  NaN     219   2050   \n",
       "5492        B       312  76561198042147080  ...   NaN  NaN     287   3600   \n",
       "5493        B       314  76561197990650065  ...   NaN  NaN     287   3900   \n",
       "5494        B       315  76561198056070039  ...   NaN  NaN     287   4600   \n",
       "5495        B       316  76561198202581684  ...   NaN  NaN     287   3350   \n",
       "\n",
       "       Hp  Armor  HasDefuse  HasHelmet              Created  \\\n",
       "0     100    100          1          1  2020-07-25 00:54:52   \n",
       "1     100    100          1          1  2020-07-25 00:54:52   \n",
       "2     100    100          1          1  2020-07-25 00:54:52   \n",
       "3     100    100          1          1  2020-07-25 00:54:52   \n",
       "4     100    100          1          1  2020-07-25 00:54:52   \n",
       "...   ...    ...        ...        ...                  ...   \n",
       "5491    0      0          1          1  2020-07-25 00:54:52   \n",
       "5492    0      0          1          1  2020-07-25 00:54:52   \n",
       "5493    0      0          1          1  2020-07-25 00:54:52   \n",
       "5494    0      0          1          1  2020-07-25 00:54:52   \n",
       "5495    0      0          1          1  2020-07-25 00:54:52   \n",
       "\n",
       "                  Updated  \n",
       "0     2020-07-25 00:54:52  \n",
       "1     2020-07-25 00:54:52  \n",
       "2     2020-07-25 00:54:52  \n",
       "3     2020-07-25 00:54:52  \n",
       "4     2020-07-25 00:54:52  \n",
       "...                   ...  \n",
       "5491  2020-07-25 00:54:52  \n",
       "5492  2020-07-25 00:54:52  \n",
       "5493  2020-07-25 00:54:52  \n",
       "5494  2020-07-25 00:54:52  \n",
       "5495  2020-07-25 00:54:52  \n",
       "\n",
       "[5496 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_count_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "243484 259975\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MatchId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tick</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>243484</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243488</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243524</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243560</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243564</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259830</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259878</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259918</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259962</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259975</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>687 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MatchId\n",
       "Tick           \n",
       "243484        8\n",
       "243488        8\n",
       "243524        8\n",
       "243560        8\n",
       "243564        8\n",
       "...         ...\n",
       "259830        8\n",
       "259878        8\n",
       "259918        8\n",
       "259962        8\n",
       "259975        8\n",
       "\n",
       "[687 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(row_count_check['Tick'].min(), row_count_check['Tick'].max())\n",
    "grouped = row_count_check.groupby('Tick').agg({'MatchId': 'count'})\n",
    "grouped[grouped['MatchId'] != 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_count_check['SteamId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8 ** 2"
   ]
  }
 ],
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