Z-Exp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all_fouls_advanced pickle file\n",
    "\n",
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "with open('all_fouls_advanced.pkl', 'rb') as handle:\n",
    "    all_fouls_advanced = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1684"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_fouls_advanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yellow Card      574\n",
       "Red Card          13\n",
       "Second Yellow     10\n",
       "Name: foul_committed_card, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value counts of foul_committed_card column in all_fouls_advanced\n",
    "all_fouls_advanced['foul_committed_card'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all_the_features pickle file\n",
    "with open('all_the_features.pkl', 'rb') as handle:\n",
    "    all_the_features = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14,\n",
       " 0,\n",
       " 37.96840792026972,\n",
       " 1.2823667682765016,\n",
       " 1,\n",
       " 1,\n",
       " '9221da02-9c87-4f98-a8ea-de9994ad562b']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_the_features[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foul_committed_advantage</th>\n",
       "      <th>foul_committed_offensive</th>\n",
       "      <th>foul_committed_penalty</th>\n",
       "      <th>foul_committed_card</th>\n",
       "      <th>foul_committed_counterpress</th>\n",
       "      <th>foul_committed_type</th>\n",
       "      <th>foul_won_advantage</th>\n",
       "      <th>foul_won_defensive</th>\n",
       "      <th>foul_won_penalty</th>\n",
       "      <th>id</th>\n",
       "      <th>...</th>\n",
       "      <th>team</th>\n",
       "      <th>team_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>type</th>\n",
       "      <th>seconds_till_now</th>\n",
       "      <th>scoreline_till_now</th>\n",
       "      <th>distance_to_goal</th>\n",
       "      <th>angle_to_goal</th>\n",
       "      <th>foul_count_player_till_now</th>\n",
       "      <th>foul_count_team_till_now</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yellow Card</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9221da02-9c87-4f98-a8ea-de9994ad562b</td>\n",
       "      <td>...</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>773</td>\n",
       "      <td>00:14:27.221</td>\n",
       "      <td>Foul Committed</td>\n",
       "      <td>867</td>\n",
       "      <td>{'Serbia': 0, 'Switzerland': 0, 'Argentina': 0...</td>\n",
       "      <td>37.968408</td>\n",
       "      <td>1.282367</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  foul_committed_advantage foul_committed_offensive foul_committed_penalty  \\\n",
       "0                      NaN                      NaN                    NaN   \n",
       "\n",
       "  foul_committed_card  foul_committed_counterpress foul_committed_type  \\\n",
       "0         Yellow Card                          NaN                 NaN   \n",
       "\n",
       "  foul_won_advantage foul_won_defensive foul_won_penalty  \\\n",
       "0                NaN                NaN              NaN   \n",
       "\n",
       "                                     id  ...         team team_id  \\\n",
       "0  9221da02-9c87-4f98-a8ea-de9994ad562b  ...  Switzerland     773   \n",
       "\n",
       "      timestamp            type  seconds_till_now  \\\n",
       "0  00:14:27.221  Foul Committed               867   \n",
       "\n",
       "                                  scoreline_till_now distance_to_goal  \\\n",
       "0  {'Serbia': 0, 'Switzerland': 0, 'Argentina': 0...        37.968408   \n",
       "\n",
       "   angle_to_goal foul_count_player_till_now  foul_count_team_till_now  \n",
       "0       1.282367                          1                         1  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_fouls_advanced.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minutes</th>\n",
       "      <th>score_difference</th>\n",
       "      <th>distance_to_goal</th>\n",
       "      <th>angle_to_goal</th>\n",
       "      <th>foul_count_player</th>\n",
       "      <th>foul_count_team</th>\n",
       "      <th>vaep_offensive</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9221da02-9c87-4f98-a8ea-de9994ad562b</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>37.968408</td>\n",
       "      <td>1.282367</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.000196</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f2bd21f6-87c3-44c0-bb37-04e996db413e</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>65.011153</td>\n",
       "      <td>0.058485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001102</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8e51baec-6948-4285-86a1-73197126e265</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>69.641654</td>\n",
       "      <td>-0.306311</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.001255</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b711f529-182e-4ae3-8c9a-189b7697b2ea</th>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>52.106142</td>\n",
       "      <td>-0.264064</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ec46c71c-af4c-4d99-a854-e4ffc30460d6</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>42.569942</td>\n",
       "      <td>0.554117</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.016189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      minutes  score_difference  \\\n",
       "id                                                                \n",
       "9221da02-9c87-4f98-a8ea-de9994ad562b       14                 0   \n",
       "f2bd21f6-87c3-44c0-bb37-04e996db413e       23                 1   \n",
       "8e51baec-6948-4285-86a1-73197126e265       33                 0   \n",
       "b711f529-182e-4ae3-8c9a-189b7697b2ea       46                 0   \n",
       "ec46c71c-af4c-4d99-a854-e4ffc30460d6       55                 1   \n",
       "\n",
       "                                      distance_to_goal  angle_to_goal  \\\n",
       "id                                                                      \n",
       "9221da02-9c87-4f98-a8ea-de9994ad562b         37.968408       1.282367   \n",
       "f2bd21f6-87c3-44c0-bb37-04e996db413e         65.011153       0.058485   \n",
       "8e51baec-6948-4285-86a1-73197126e265         69.641654      -0.306311   \n",
       "b711f529-182e-4ae3-8c9a-189b7697b2ea         52.106142      -0.264064   \n",
       "ec46c71c-af4c-4d99-a854-e4ffc30460d6         42.569942       0.554117   \n",
       "\n",
       "                                      foul_count_player  foul_count_team  \\\n",
       "id                                                                         \n",
       "9221da02-9c87-4f98-a8ea-de9994ad562b                  1                1   \n",
       "f2bd21f6-87c3-44c0-bb37-04e996db413e                  1                1   \n",
       "8e51baec-6948-4285-86a1-73197126e265                  1                2   \n",
       "b711f529-182e-4ae3-8c9a-189b7697b2ea                  1                2   \n",
       "ec46c71c-af4c-4d99-a854-e4ffc30460d6                  1                3   \n",
       "\n",
       "                                      vaep_offensive  label  \n",
       "id                                                           \n",
       "9221da02-9c87-4f98-a8ea-de9994ad562b       -0.000196      1  \n",
       "f2bd21f6-87c3-44c0-bb37-04e996db413e       -0.001102      0  \n",
       "8e51baec-6948-4285-86a1-73197126e265       -0.001255      1  \n",
       "b711f529-182e-4ae3-8c9a-189b7697b2ea       -0.000879      1  \n",
       "ec46c71c-af4c-4d99-a854-e4ffc30460d6       -0.016189      1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "\n",
    "with open('features_with_label_df.pkl', 'rb') as handle:\n",
    "    features_with_labels = pickle.load(handle)\n",
    "\n",
    "features_with_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot minutes and label 1 in bar chart with interval of 10 minutes\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "minutes = features_with_labels['minutes']\n",
    "labels = features_with_labels['label']\n",
    "\n",
    "plt.hist(minutes[labels == 1], bins = np.arange(0, 100, 10))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distance_to_goal and label 1 in bar chart\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "distance_to_goal = features_with_labels['distance_to_goal']\n",
    "labels = features_with_labels['label']\n",
    "\n",
    "plt.hist(distance_to_goal[labels == 1], bins = np.arange(0, 100, 10))\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distance to goal and label 1 value_counts in bins\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "distance_to_goal = features_with_labels['distance_to_goal']\n",
    "labels = features_with_labels['label']\n",
    "\n",
    "plt.hist(distance_to_goal[labels == 0], bins = np.arange(0, 100, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vq1erqqpKqamp3fa3tLRIktLS0kK2n//6/P5IBINBHTx4MOLb/5zGxsaYnBeRGejr4fF4VFBQYD0GAITlyJEjMXlCmJKScsFjwg6SmpoaXXzxxbr55psjGqo33G638vPzo3rOQCCgxsZGZWdn84y2H4iX9ejJswEA6G9ycnKi/grJ4cOHe3RcWEFy7NgxvfTSS6qurpbf75ekrrdQ2tradObMGaWnp0uS/H6/Ro4c2XXb1tZWSeraHwmXy6WhQ4dGfPuf4/F4YnZuhI/1AIC+F4sngj19ghZWkDQ3NysYDGrevHnd9lVUVOjKK6/UM888I+m7a0lyc3O79jc0NMjtdisrKyucuwQAAAkgrCAZP368XnnllZBtBw8e1JNPPqkVK1aoqKhIWVlZys7O1vbt2zVt2rSu4+rq6lRSUtKj95EAAEBiCStI0tLSVFxc/KP7CgsLVVhYKElauHChlixZojFjxqi4uFh1dXXav3+/Nm3a1PuJAQBA3Ino134v5IYbblAgEFBtba02bNignJwcrVu3ThMmTIjF3QEAgAGu10FSXFys//znP922l5eXq7y8vLenBwAACYB/7RcAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0gAAIA5ggQAAJgjSAAAgDmCBAAAmCNIAACAOYIEAACYI0jQ77jdbrlcLusxAAB9KNl6AOD7XC6XCgoKlZw8yHoUAEAfIkjQ7yQnD9LTm/eq+bjfepSITRx3iSrKCqzHAIABgyBBv9R83K/6Yy3WY0Qs85JU6xEAYEDhGhIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5sIKkvfee08LFixQaWmpvF6vZs+erTfffFOO44Qct2XLFk2fPl1FRUWaNWuWdu7cGdWhAQBAfAkrSF5++WV5PB4tXbpUNTU1Ki0t1bJly1RdXd11zLZt27Rs2TL5fD7V1tbK6/WqsrJS+/bti/bsAAAgTiSHc3BNTY1GjBjR9XVJSYlOnTqlv/zlL/r973+vpKQkrVmzRjNnztSiRYskSZMnT9ahQ4dUXV2t2traqA4PAADiQ1ivkHw/Rs4bP368Tp8+rba2NjU1NamxsVE+ny/kmLKyMu3atUsdHR29mxYAAMSlsF4h+TF79+7VqFGjlJqaqr1790qScnJyQo7Jy8tTMBhUU1OT8vLyIr4vx3HU1tbWq3l/KBAIhPwJWx0dHfJ4PNZjAEBCCgQC3a4L7S3HceRyuS54XK+CZM+ePaqrq9Mf//hHSVJLS4skKS0tLeS481+f3x+pYDCogwcP9uocP6WxsTEm50V4PB6PMjIyrMcAgIR05MiRmDxBT0lJueAxEQfJ119/raqqKhUXF6uioiLS04TF7XYrPz8/qucMBAJqbGxUdnY2z8z7Ad7WAwA7OTk5UX+F5PDhwz06LqIgaW1t1T333KOMjAytXbtWSUnfXYqSnp4uSfL7/Ro5cmTI8d/fHymXy6WhQ4f26hw/xePxxOzc6LmevKwHAIiNWDwx7+nP9bA/GK29vV3z58+X3+/Xxo0bNXz48K59ubm5kqSGhoaQ2zQ0NMjtdisrKyvcuwMAAAkgrCA5e/asFi1apIaGBm3cuFGjRo0K2Z+VlaXs7Gxt3749ZHtdXZ1KSkp69B4SAABIPGG9ZbNixQrt3LlTS5cu1enTp0M+7KygoEApKSlauHChlixZojFjxqi4uFh1dXXav3+/Nm3aFO3ZAQBAnAgrSD766CNJ0urVq7vt27FjhzIzM3XDDTcoEAiotrZWGzZsUE5OjtatW6cJEyZEZ2IAABB3wgqSDz74oEfHlZeXq7y8PKKBAABA4uFf+wUAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEvQrLpfLegQAgAGCJI50djrWI/TakCFDrEcAABhIth4A0ZOU5NLTm/eq+bjfepSITRx3iSrKCqzHAAD0MYIkzjQf96v+WIv1GBHLvCTVegQAgAHesgEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYI4gAQAA5ggSAABgjiABAADmCBIAAGCOIAEAAOYIEgAAYC5mQVJfX6+77rpLXq9XU6ZM0VNPPaWOjo5Y3V2vuN1uuVwu6zEAAEhYybE4aUtLi+68805lZ2dr7dq1On78uFavXq329nY9+uijsbjLiLlcLhUUFCo5eZD1KAAAJKyYBMkbb7yhM2fOaN26dcrIyJAknTt3TitWrND8+fM1atSoWNxtxJKTB+npzXvVfNxvPUrEJo67RBVlBdZjAAAQkZgEyYcffqiSkpKuGJEkn8+n5cuX66OPPtJNN90Ui7vtlebjftUfa7EeI2KZl6RajwAAQMRcjuM40T5pSUmJbr75Zi1ZsiRk+69+9SvNnj272/ae+OSTT+Q4jtxud7TGlCQ5jqOkpCS1nO7Q2XOdUT13XxrsHqTUoW4eRz8RL49Dip/HwuPoX3gc/UvyoCSlp6YoBkmgYDAol8uliRMn/vwMUb9nSa2trUpLS+u2PT09XS0tkb0Kcf6i02hffHr+fOmpKVE9rxUeR/8SL49Dip/HwuPoX3gc/UssfsHD5XL16LwxCZJYmDBhgvUIAAAgRmLya79paWny+7tfINrS0qL09PRY3CUAABjAYhIkubm5amhoCNnm9/t14sQJ5ebmxuIuAQDAABaTICktLdXHH3+s1tbWrm3bt29XUlKSpkyZEou7BAAAA1hMfsumpaVFM2fOVE5OjubPn9/1wWi/+c1v+t0HowEAAHsxCRLpu4+Of/zxx/Xpp59q2LBhmj17tqqqqpSSEh9XIgMAgOiJWZAAAAD0FP/aLwAAMEeQAAAAcwQJAAAwR5AAAABzBAkAADBHkAAAAHMJGyT19fW666675PV6NWXKFD311FPq6OiwHishvPfee1qwYIFKS0vl9Xo1e/Zsvfnmm93+2estW7Zo+vTpKioq0qxZs7Rz506jiRPLmTNnVFpaqssvv1yff/55yD7WpO+88847uvHGG1VUVKTi4mLdfffdam9v79r/wQcfaNasWSoqKtL06dP11ltvGU4b33bs2KHy8nJNmDBBV199tR544AE1NTV1O47vj95JyCBpaWnRnXfeqWAwqLVr16qqqkp/+9vftHr1auvREsLLL78sj8ejpUuXqqamRqWlpVq2bJmqq6u7jtm2bZuWLVsmn8+n2tpaeb1eVVZWat++fXaDJ4j169fr3Llz3bazJn2npqZGjz/+uMrKyvTnP/9ZK1euVGZmZte67NmzR5WVlfJ6vaqtrZXP59Mjjzyi7du3G08ef3bv3q3Kykrl5+erurpaDz/8sL766ivNnTs3JBD5/ogCJwG9+OKLjtfrdU6ePNm17Y033nDGjx/vfP3113aDJYhvvvmm27Y//elPzsSJE51z5845juM4v/71r53FixeHHHPbbbc5d999d5/MmKgOHz7seL1e5/XXX3fGjh3r7N+/v2sfa9I36uvrnYKCAucf//jHTx4zd+5c57bbbgvZtnjxYsfn88V6vISzbNkyZ+rUqU5nZ2fXtl27djljx451/v3vf3dt4/uj9xLyFZIPP/xQJSUlysjI6Nrm8/nU2dmpjz76yG6wBDFixIhu28aPH6/Tp0+rra1NTU1NamxslM/nCzmmrKxMu3bt4q21GFq1apXmzJmjnJyckO2sSd95++23lZmZqWuuueZH93d0dGj37t2aMWNGyPaysjLV19erubm5L8ZMGGfPntWwYcPkcrm6tg0fPlySut5m5vsjOhIySBoaGpSbmxuyLS0tTSNHjlRDQ4PRVIlt7969GjVqlFJTU7vW4Id/Kebl5SkYDP7oe7fove3bt+vQoUO67777uu1jTfrOZ599prFjx2r9+vUqKSnRFVdcoTlz5uizzz6TJB09elTBYLDbz7C8vDxJ4mdYlN10002qr6/X5s2b5ff71dTUpGeffVYFBQWaOHGiJL4/oiUhg6S1tVVpaWndtqenp6ulpcVgosS2Z88e1dXVae7cuZLUtQY/XKPzX7NG0RcIBLR69WpVVVUpNTW1237WpO+cOHFC//znP7V161YtX75c1dXVcrlcmjt3rr755hvWoo9NmjRJ69at0zPPPKNJkyZp2rRp+uabb1RbW6tBgwZJ4vsjWhIySNB/fP3116qqqlJxcbEqKiqsx0lYNTU1uvjii3XzzTdbj5LwHMdRW1ubXnjhBc2YMUPXXHONampq5DiONm3aZD1ewvnkk0/04IMP6tZbb9Vf//pXvfDCC+rs7NS8efNCLmpF7yVkkKSlpcnv93fb3tLSovT0dIOJElNra6vuueceZWRkaO3atUpK+u4/x/Nr8MM1am1tDdmP6Dh27Jheeukl3X///fL7/WptbVVbW5skqa2tTWfOnGFN+lBaWpoyMjI0bty4rm0ZGRkqKCjQ4cOHWYs+tmrVKk2ePFlLly7V5MmTNWPGDG3YsEEHDhzQ1q1bJfEzK1oSMkhyc3O7vc/q9/t14sSJbu/LIjba29s1f/58+f1+bdy4sesiMUlda/DDNWpoaJDb7VZWVlafzhrvmpubFQwGNW/ePF111VW66qqrdO+990qSKioqdNddd7EmfSg/P/8n93377bcaM2aM3G73j66FJH6GRVl9fX1IHErSpZdeqosuukhHjx6VxM+saEnIICktLdXHH3/cVa/Sdxf0JSUlacqUKYaTJYazZ89q0aJFamho0MaNGzVq1KiQ/VlZWcrOzu72mQp1dXUqKSlRSkpKX44b98aPH69XXnkl5H8PPfSQJGnFihVavnw5a9KHrrvuOp06dUoHDx7s2nby5El9+eWXKiwsVEpKioqLi/X++++H3K6urk55eXnKzMzs65Hj2ujRo3XgwIGQbceOHdPJkyd12WWXSeJnVrQkWw9gYc6cOXr11Vd13333af78+Tp+/LieeuopzZkzp9tfjoi+FStWaOfOnVq6dKlOnz4d8sFBBQUFSklJ0cKFC7VkyRKNGTNGxcXFqqur0/79+3kPPQbS0tJUXFz8o/sKCwtVWFgoSaxJH5k2bZqKiop0//33q6qqSoMHD9aGDRuUkpKiO+64Q5K0YMECVVRU6LHHHpPP59Pu3bv17rvv6rnnnjOePv7MmTNHTzzxhFatWqWpU6fq1KlTXddcff/XfPn+6D2X4/zg87oTRH19vR5//HF9+umnGjZsmGbPnq2qqipKtg9MnTpVx44d+9F9O3bs6HqGt2XLFtXW1uq///2vcnJytHjxYl133XV9OWrC2r17tyoqKvTmm2+qqKioaztr0jf+97//6cknn9TOnTsVDAY1adIkPfTQQyFv5+zYsUPPP/+8jhw5otGjR2vevHm65ZZbDKeOT47j6I033tDrr7+upqYmDRs2TF6vV1VVVV2/an0e3x+9k7BBAgAA+o+EvIYEAAD0LwQJAAAwR5AAAABzBAkAADBHkAAAAHMECQAAMEeQAAAAcwQJAAAwR5AAAABzBAkAADBHkAAAAHP/D0D3LPg4mVvuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distance to goal and label 1 value_counts in bins\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "distance_to_goal = features_with_labels['distance_to_goal']\n",
    "labels = features_with_labels['label']\n",
    "\n",
    "plt.hist(distance_to_goal[labels == 1], bins = np.arange(0, 100, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1322.000000\n",
       "mean       63.619892\n",
       "std        23.386890\n",
       "min         7.683749\n",
       "25%        46.493518\n",
       "50%        63.844926\n",
       "75%        79.734873\n",
       "max       121.233700\n",
       "Name: distance_to_goal, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe column distance_to_goal where label is 1\n",
    "features_with_labels['distance_to_goal'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    559.000000\n",
       "mean      64.407139\n",
       "std       24.175936\n",
       "min        8.814193\n",
       "25%       44.504326\n",
       "50%       65.645487\n",
       "75%       81.792672\n",
       "max      121.233700\n",
       "Name: distance_to_goal, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe column distance_to_goal where label is 1\n",
    "features_with_labels[features_with_labels['label'] == 1]['distance_to_goal'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    763.000000\n",
       "mean      63.043127\n",
       "std       22.790231\n",
       "min        7.683749\n",
       "25%       48.358450\n",
       "50%       62.832476\n",
       "75%       78.262829\n",
       "max      118.481940\n",
       "Name: distance_to_goal, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe column distance_to_goal where label is 1\n",
    "features_with_labels[features_with_labels['label'] == 0]['distance_to_goal'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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