DataFest_2022_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ae217bfd",
   "metadata": {},
   "source": [
    "# DataFest 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99887bbf",
   "metadata": {},
   "source": [
    "## Import the modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b19fbd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4692fd8a",
   "metadata": {},
   "source": [
    "## Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b92b2fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/70/q89cls2j7835xgqtfjz6hw240000gn/T/ipykernel_76374/2533296147.py:1: DtypeWarning: Columns (34,38,39,40,47,50,53,54,57,58,59,60,62,68,70,72,73,74,76,77,78,81,85,88,89,90,91,92,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,118,120,121,122,123,129,131) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df=pd.read_csv(\"logs.csv\")\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"logs.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604dc112",
   "metadata": {},
   "source": [
    "## Central Question"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870ed360",
   "metadata": {},
   "source": [
    "**Q: How do player demographics such as age influence game engagement and gameplay patterns?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63dd6e0d",
   "metadata": {},
   "source": [
    "We thought that demographic variables such as age, sex, and race could be associated with player patterns in the game. \n",
    "We chose to measure success in the game by the total number of points scored in each skill category (i.e priority sense, people sense, know sense, refusal power, me power). Since the amount of time played varies between the players, we thought total points would be a good metric to use since it shows the progress of the player during their play time. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560c8bf4",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c94926ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['skill_level_know','skill_level_priority',\"skill_level_refusal\",\"skill_level_me\",\n",
    "      \"skill_level_people\",\"event_time_dbl\",\"event_id\",\"player_id\",\"avatar_age\",\"avatar_id\",\"avatar_gender\",\"school\",\"event_category\",\"stack_id\"]\n",
    "df=df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "981138aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=[\"Know_Sense\",'Priority_Sense',\"Refusal_Power\",\"Me_Power\",\n",
    "      \"People_Sense\",\"Cum_Time_Spent_inS\",\"Event_Id\",\"Player_Id\",\"Age\",\"Race\",\"Gender\",\"Interaction\",\"Category\",\"Stack\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6e61eaf",
   "metadata": {},
   "outputs": [
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       "         Know_Sense  Priority_Sense  Refusal_Power  Me_Power  People_Sense  \\\n",
       "0               NaN             NaN            NaN       NaN           NaN   \n",
       "1               NaN             NaN            NaN       NaN           NaN   \n",
       "2               NaN             NaN            NaN       NaN           NaN   \n",
       "3               NaN             NaN            NaN       NaN           NaN   \n",
       "4               NaN             NaN            NaN       NaN           NaN   \n",
       "...             ...             ...            ...       ...           ...   \n",
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       "2106594         NaN             NaN            NaN       NaN           NaN   \n",
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       "2106596         NaN             NaN            NaN       NaN           NaN   \n",
       "\n",
       "         Cum_Time_Spent_inS  Event_Id  Player_Id   Age      Race Gender  \\\n",
       "0                       198       600    6427001  13.0       NaN    NaN   \n",
       "1                       199       601    6427001   NaN       NaN   Male   \n",
       "2                       205       602    6427001  13.0       NaN   Male   \n",
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       "...                     ...       ...        ...   ...       ...    ...   \n",
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       "\n",
       "         Interaction         Category  Stack  \n",
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       "2               3561  Avatar Creation    NaN  \n",
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       "...              ...              ...    ...  \n",
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       "\n",
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4549035c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    df=df.groupby(\"Player_Id\").apply(lambda x: x.ffill().bfill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "253358f7",
   "metadata": {
    "scrolled": true
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       "    <tr>\n",
       "      <th>7540835</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>535</td>\n",
       "      <td>708</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Lifeline</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65670031</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11100</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65670032</th>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5767</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Know_Sense  Priority_Sense  Refusal_Power  Me_Power  People_Sense  \\\n",
       "Player_Id                                                                      \n",
       "6427001          30.0            30.0           26.0      30.0          29.0   \n",
       "6427002           3.0             3.0            0.0       6.0           3.0   \n",
       "6427004          29.0            29.0           28.0      30.0          29.0   \n",
       "6427005           0.0             0.0            0.0       6.0           3.0   \n",
       "6427006          29.0            30.0           30.0      30.0          27.0   \n",
       "...               ...             ...            ...       ...           ...   \n",
       "6626006           6.0             6.0            7.0      12.0           6.0   \n",
       "6626007          15.0            13.0           18.0      27.0          15.0   \n",
       "7540835           0.0             0.0            0.0       6.0           0.0   \n",
       "65670031         26.0            30.0           30.0      30.0          27.0   \n",
       "65670032         12.0            15.0           21.0      27.0          17.0   \n",
       "\n",
       "           Cum_Time_Spent_inS  Event_Id   Age              Race  Gender  \\\n",
       "Player_Id                                                                 \n",
       "6427001                 37718      1005  13.0          Hispanic    Male   \n",
       "6427002                  3735      1005  13.0  African American  Female   \n",
       "6427004                 30307      1005  13.0  African American  Female   \n",
       "6427005                  3729      1005  13.0          Hispanic    Male   \n",
       "6427006                 31927      1005  13.0  African American  Female   \n",
       "...                       ...       ...   ...               ...     ...   \n",
       "6626006                 40352      1005  13.0         Caucasian    Male   \n",
       "6626007                467294      1005  11.0         Caucasian    Male   \n",
       "7540835                   535       708  14.0          Hispanic  Female   \n",
       "65670031                11100      1005  12.0  African American  Female   \n",
       "65670032                 5767      1005  12.0          Hispanic    Male   \n",
       "\n",
       "           Interaction               Category  Stack  \n",
       "Player_Id                                             \n",
       "6427001           3561  Refuse Power Minigame   12.0  \n",
       "6427002           3561         Priority Sense   12.0  \n",
       "6427004           3561  Refuse Power Minigame   12.0  \n",
       "6427005           3561         Priority Sense   12.0  \n",
       "6427006           3561  Refuse Power Minigame   12.0  \n",
       "...                ...                    ...    ...  \n",
       "6626006           3205  Refuse Power Minigame   12.0  \n",
       "6626007           3205  Refuse Power Minigame   12.0  \n",
       "7540835           3561               Lifeline   12.0  \n",
       "65670031          1531  Refuse Power Minigame   12.0  \n",
       "65670032          1531  Refuse Power Minigame   12.0  \n",
       "\n",
       "[166 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Player_Id\").max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb8c2a7c",
   "metadata": {},
   "source": [
    "## Basic Seaborn Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ed595ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "palette = sns.color_palette([\"#C37963\", \"#BB9E95\", \"#9FCDCA\", \"#5E7D7F\",\"#203234\"])\n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad04ba55",
   "metadata": {},
   "source": [
    "## Age v Total Scores by Skill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ed01783",
   "metadata": {},
   "outputs": [],
   "source": [
    "players=df.groupby(\"Player_Id\").max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "337930b6",
   "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>Know_Sense</th>\n",
       "      <th>Priority_Sense</th>\n",
       "      <th>Refusal_Power</th>\n",
       "      <th>Me_Power</th>\n",
       "      <th>People_Sense</th>\n",
       "      <th>Cum_Time_Spent_inS</th>\n",
       "      <th>Event_Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Interaction</th>\n",
       "      <th>Category</th>\n",
       "      <th>Stack</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Player_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6427001</th>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37718</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427002</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3735</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Priority Sense</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427004</th>\n",
       "      <td>29.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30307</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427005</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3729</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Priority Sense</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6427006</th>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31927</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626006</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>40352</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>3205</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626007</th>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>467294</td>\n",
       "      <td>1005</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>3205</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7540835</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>535</td>\n",
       "      <td>708</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Lifeline</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65670031</th>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>11100</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65670032</th>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>5767</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Know_Sense  Priority_Sense  Refusal_Power  Me_Power  People_Sense  \\\n",
       "Player_Id                                                                      \n",
       "6427001          30.0            30.0           26.0      30.0          29.0   \n",
       "6427002           3.0             3.0            0.0       6.0           3.0   \n",
       "6427004          29.0            29.0           28.0      30.0          29.0   \n",
       "6427005           0.0             0.0            0.0       6.0           3.0   \n",
       "6427006          29.0            30.0           30.0      30.0          27.0   \n",
       "...               ...             ...            ...       ...           ...   \n",
       "6626006           6.0             6.0            7.0      12.0           6.0   \n",
       "6626007          15.0            13.0           18.0      27.0          15.0   \n",
       "7540835           0.0             0.0            0.0       6.0           0.0   \n",
       "65670031         26.0            30.0           30.0      30.0          27.0   \n",
       "65670032         12.0            15.0           21.0      27.0          17.0   \n",
       "\n",
       "           Cum_Time_Spent_inS  Event_Id   Age              Race  Gender  \\\n",
       "Player_Id                                                                 \n",
       "6427001                 37718      1005  13.0          Hispanic    Male   \n",
       "6427002                  3735      1005  13.0  African American  Female   \n",
       "6427004                 30307      1005  13.0  African American  Female   \n",
       "6427005                  3729      1005  13.0          Hispanic    Male   \n",
       "6427006                 31927      1005  13.0  African American  Female   \n",
       "...                       ...       ...   ...               ...     ...   \n",
       "6626006                 40352      1005  13.0         Caucasian    Male   \n",
       "6626007                467294      1005  11.0         Caucasian    Male   \n",
       "7540835                   535       708  14.0          Hispanic  Female   \n",
       "65670031                11100      1005  12.0  African American  Female   \n",
       "65670032                 5767      1005  12.0          Hispanic    Male   \n",
       "\n",
       "           Interaction               Category  Stack  \n",
       "Player_Id                                             \n",
       "6427001           3561  Refuse Power Minigame   12.0  \n",
       "6427002           3561         Priority Sense   12.0  \n",
       "6427004           3561  Refuse Power Minigame   12.0  \n",
       "6427005           3561         Priority Sense   12.0  \n",
       "6427006           3561  Refuse Power Minigame   12.0  \n",
       "...                ...                    ...    ...  \n",
       "6626006           3205  Refuse Power Minigame   12.0  \n",
       "6626007           3205  Refuse Power Minigame   12.0  \n",
       "7540835           3561               Lifeline   12.0  \n",
       "65670031          1531  Refuse Power Minigame   12.0  \n",
       "65670032          1531  Refuse Power Minigame   12.0  \n",
       "\n",
       "[166 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18390f60",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_v_total=players.melt(id_vars=['Age'],\n",
    "                    value_vars=[\"Know_Sense\",'Priority_Sense',\n",
    "                    \"Refusal_Power\",\"Me_Power\",\"People_Sense\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5ece97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "ax=sns.barplot(\n",
    "    data=age_v_total,\n",
    "    x=\"Age\", y=\"value\",hue=\"variable\",\n",
    "    ci=None, palette=palette, alpha=.9\n",
    ").set(title='Age v Total Scores by Skill',ylabel=\"Total Scores\")\n",
    "\n",
    "plt.legend(title='Skills', loc='upper left', labels=['Know', 'Priority','Refusal','Me','People']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45567c58",
   "metadata": {},
   "source": [
    "## Age v Total Time Spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f12e6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_v_total_time=players[[\"Age\",\"Cum_Time_Spent_inS\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e46c8189",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "age_v_total_time=age_v_total_time.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f571833f",
   "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>Player_Id</th>\n",
       "      <th>Cum_Time_Spent_inS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>6.520560e+06</td>\n",
       "      <td>181478.303030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.0</th>\n",
       "      <td>9.202264e+06</td>\n",
       "      <td>246768.159091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13.0</th>\n",
       "      <td>6.501618e+06</td>\n",
       "      <td>186147.837209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14.0</th>\n",
       "      <td>6.516119e+06</td>\n",
       "      <td>185617.521739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Player_Id  Cum_Time_Spent_inS\n",
       "Age                                   \n",
       "11.0  6.520560e+06       181478.303030\n",
       "12.0  9.202264e+06       246768.159091\n",
       "13.0  6.501618e+06       186147.837209\n",
       "14.0  6.516119e+06       185617.521739"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_v_total_time.groupby(\"Age\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0c572554",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "ax=sns.boxplot(\n",
    "    data=age_v_total_time,\n",
    "    x=\"Age\", y=\"Cum_Time_Spent_inS\", palette=palette, showfliers=False\n",
    ").set(title='Age v Total Time Spent',ylabel=\"Total Time Spent\")\n",
    "\n",
    "plt.legend(title='Skills', loc='upper left', labels=['Know', 'Priority','Refusal','Me','People']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79e00a5",
   "metadata": {},
   "source": [
    "## Age vs Star/Sec "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54d45e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "players_copy=players.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "69d78e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "stars_per_s=players_copy.melt(id_vars=['Age',\"Cum_Time_Spent_inS\"],\n",
    "                           value_vars=[\"Know_Sense\",'Priority_Sense',\n",
    "                         \"Refusal_Power\",\"Me_Power\",\"People_Sense\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8fad0053",
   "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>Age</th>\n",
       "      <th>Cum_Time_Spent_inS</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.0</td>\n",
       "      <td>37718</td>\n",
       "      <td>Know_Sense</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3735</td>\n",
       "      <td>Know_Sense</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.0</td>\n",
       "      <td>30307</td>\n",
       "      <td>Know_Sense</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3729</td>\n",
       "      <td>Know_Sense</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.0</td>\n",
       "      <td>31927</td>\n",
       "      <td>Know_Sense</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>13.0</td>\n",
       "      <td>40352</td>\n",
       "      <td>People_Sense</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826</th>\n",
       "      <td>11.0</td>\n",
       "      <td>467294</td>\n",
       "      <td>People_Sense</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>14.0</td>\n",
       "      <td>535</td>\n",
       "      <td>People_Sense</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11100</td>\n",
       "      <td>People_Sense</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5767</td>\n",
       "      <td>People_Sense</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Cum_Time_Spent_inS      variable  value\n",
       "0    13.0               37718    Know_Sense   30.0\n",
       "1    13.0                3735    Know_Sense    3.0\n",
       "2    13.0               30307    Know_Sense   29.0\n",
       "3    13.0                3729    Know_Sense    0.0\n",
       "4    13.0               31927    Know_Sense   29.0\n",
       "..    ...                 ...           ...    ...\n",
       "825  13.0               40352  People_Sense    6.0\n",
       "826  11.0              467294  People_Sense   15.0\n",
       "827  14.0                 535  People_Sense    0.0\n",
       "828  12.0               11100  People_Sense   27.0\n",
       "829  12.0                5767  People_Sense   17.0\n",
       "\n",
       "[830 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_per_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4449e31c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stars_per_s[\"stars/sec\"]=(stars_per_s[\"value\"]/stars_per_s[\"Cum_Time_Spent_inS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d5f4b2ac",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "p=sns.barplot(\n",
    "    data=stars_per_s,\n",
    "    x=\"Age\", y=\"stars/sec\",hue=\"variable\",\n",
    "    ci=None, palette=palette, alpha=.9,\n",
    ")\n",
    "\n",
    "p.tick_params(axis='x', labelsize=15)\n",
    "p.tick_params(axis='y', labelsize=15)\n",
    "plt.xlabel('Age', fontsize=20);\n",
    "plt.ylabel('Stars/Sec', fontsize=20);\n",
    "plt.title('Age v Stars/Sec', fontsize=20)\n",
    "plt.legend(title='Skills', loc='upper left', labels=['Know', 'Priority','Refusal','Me','People'],title_fontsize=15,fontsize=\"large\");\n",
    "plt.savefig(\"age_v_starpersec\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36d82ac",
   "metadata": {},
   "source": [
    "## Age v Interactions at 4500s\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "456389ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_45=df[df[\"Cum_Time_Spent_inS\"]<4500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "36030998",
   "metadata": {
    "scrolled": true
   },
   "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>Know_Sense</th>\n",
       "      <th>Priority_Sense</th>\n",
       "      <th>Refusal_Power</th>\n",
       "      <th>Me_Power</th>\n",
       "      <th>People_Sense</th>\n",
       "      <th>Cum_Time_Spent_inS</th>\n",
       "      <th>Event_Id</th>\n",
       "      <th>Player_Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Interaction</th>\n",
       "      <th>Category</th>\n",
       "      <th>Stack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198</td>\n",
       "      <td>600</td>\n",
       "      <td>6427001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Avatar Creation</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199</td>\n",
       "      <td>601</td>\n",
       "      <td>6427001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Avatar Creation</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>205</td>\n",
       "      <td>602</td>\n",
       "      <td>6427001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Avatar Creation</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>210</td>\n",
       "      <td>603</td>\n",
       "      <td>6427001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Avatar Creation</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>252</td>\n",
       "      <td>604</td>\n",
       "      <td>6427001</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Avatar Creation</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105646</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4480</td>\n",
       "      <td>505</td>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105647</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4481</td>\n",
       "      <td>506</td>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105648</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4488</td>\n",
       "      <td>507</td>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105649</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4490</td>\n",
       "      <td>508</td>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2105650</th>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4493</td>\n",
       "      <td>509</td>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303783 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Know_Sense  Priority_Sense  Refusal_Power  Me_Power  People_Sense  \\\n",
       "0               0.0             0.0            0.0       6.0           0.0   \n",
       "1               0.0             0.0            0.0       6.0           0.0   \n",
       "2               0.0             0.0            0.0       6.0           0.0   \n",
       "3               0.0             0.0            0.0       6.0           0.0   \n",
       "4               0.0             0.0            0.0       6.0           0.0   \n",
       "...             ...             ...            ...       ...           ...   \n",
       "2105646         6.0             9.0           12.0      18.0          17.0   \n",
       "2105647         6.0             9.0           12.0      18.0          17.0   \n",
       "2105648         6.0             9.0           12.0      18.0          17.0   \n",
       "2105649         6.0             9.0           12.0      18.0          17.0   \n",
       "2105650         6.0             9.0           12.0      18.0          17.0   \n",
       "\n",
       "         Cum_Time_Spent_inS  Event_Id  Player_Id   Age      Race Gender  \\\n",
       "0                       198       600    6427001  13.0  Hispanic   Male   \n",
       "1                       199       601    6427001  13.0  Hispanic   Male   \n",
       "2                       205       602    6427001  13.0  Hispanic   Male   \n",
       "3                       210       603    6427001  13.0  Hispanic   Male   \n",
       "4                       252       604    6427001  13.0  Hispanic   Male   \n",
       "...                     ...       ...        ...   ...       ...    ...   \n",
       "2105646                4480       505   65670032  12.0  Hispanic   Male   \n",
       "2105647                4481       506   65670032  12.0  Hispanic   Male   \n",
       "2105648                4488       507   65670032  12.0  Hispanic   Male   \n",
       "2105649                4490       508   65670032  12.0  Hispanic   Male   \n",
       "2105650                4493       509   65670032  12.0  Hispanic   Male   \n",
       "\n",
       "         Interaction               Category  Stack  \n",
       "0               3561        Avatar Creation   12.0  \n",
       "1               3561        Avatar Creation   12.0  \n",
       "2               3561        Avatar Creation   12.0  \n",
       "3               3561        Avatar Creation   12.0  \n",
       "4               3561        Avatar Creation   12.0  \n",
       "...              ...                    ...    ...  \n",
       "2105646         1531  Refuse Power Minigame    5.0  \n",
       "2105647         1531  Refuse Power Minigame    5.0  \n",
       "2105648         1531  Refuse Power Minigame    5.0  \n",
       "2105649         1531  Refuse Power Minigame    5.0  \n",
       "2105650         1531  Refuse Power Minigame    5.0  \n",
       "\n",
       "[303783 rows x 14 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b73d296b",
   "metadata": {},
   "outputs": [],
   "source": [
    "interactions=df_45.groupby(\"Player_Id\").agg({\"Interaction\":\"count\",'Know_Sense':\"max\",'Priority_Sense':\"max\",\n",
    "                                   \"Refusal_Power\":\"max\",\"Me_Power\":\"max\",\n",
    "                                   \"People_Sense\":\"max\",\"Age\":\"max\"}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dae51486",
   "metadata": {},
   "outputs": [],
   "source": [
    "players_45=interactions.groupby(\"Player_Id\").max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "961fd7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interaction</th>\n",
       "      <th>Know_Sense</th>\n",
       "      <th>Priority_Sense</th>\n",
       "      <th>Refusal_Power</th>\n",
       "      <th>Me_Power</th>\n",
       "      <th>People_Sense</th>\n",
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       "      <th>Player_Id</th>\n",
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       "      <th>6427001</th>\n",
       "      <td>1767</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>6427002</th>\n",
       "      <td>1807</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>6427004</th>\n",
       "      <td>2113</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>6427005</th>\n",
       "      <td>1573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>6427006</th>\n",
       "      <td>2084</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <td>1017</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
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       "    <tr>\n",
       "      <th>6626007</th>\n",
       "      <td>1578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7540835</th>\n",
       "      <td>264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>65670031</th>\n",
       "      <td>3408</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <th>65670032</th>\n",
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       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>166 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Interaction  Know_Sense  Priority_Sense  Refusal_Power  Me_Power  \\\n",
       "Player_Id                                                                     \n",
       "6427001           1767         1.0             3.0            0.0       6.0   \n",
       "6427002           1807         3.0             3.0            0.0       6.0   \n",
       "6427004           2113         3.0             3.0            0.0       6.0   \n",
       "6427005           1573         0.0             0.0            0.0       6.0   \n",
       "6427006           2084         3.0             3.0            3.0       9.0   \n",
       "...                ...         ...             ...            ...       ...   \n",
       "6626006           1017         0.0             3.0            0.0       6.0   \n",
       "6626007           1578         0.0             3.0            0.0       6.0   \n",
       "7540835            264         0.0             0.0            0.0       6.0   \n",
       "65670031          3408         9.0             9.0           12.0      18.0   \n",
       "65670032          3772         6.0             9.0           12.0      18.0   \n",
       "\n",
       "           People_Sense   Age  \n",
       "Player_Id                      \n",
       "6427001             2.0  13.0  \n",
       "6427002             3.0  13.0  \n",
       "6427004             5.0  13.0  \n",
       "6427005             3.0  13.0  \n",
       "6427006             6.0  13.0  \n",
       "...                 ...   ...  \n",
       "6626006             2.0  13.0  \n",
       "6626007             3.0  11.0  \n",
       "7540835             0.0  14.0  \n",
       "65670031           12.0  12.0  \n",
       "65670032           17.0  12.0  \n",
       "\n",
       "[166 rows x 7 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "22a74005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(10,8))\n",
    "p=sns.boxplot(x=\"Age\",y=\"Interaction\",\n",
    "            data=players_45,showfliers = False,palette=palette)\n",
    "\n",
    "p.tick_params(axis='x', labelsize=15)\n",
    "p.tick_params(axis='y', labelsize=15)\n",
    "plt.xlabel('Age', fontsize=20);\n",
    "plt.title('Age v Interactions at 4500s', fontsize=20);\n",
    "plt.ylabel('Interactions', fontsize=20)\n",
    "\n",
    "\n",
    "plt.savefig(\"age_v_interactions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79324055",
   "metadata": {},
   "source": [
    "From the above graphs, we see that the median interaction of the 11 y/o are less than the other age groups. This prokmpted us to question what were the groups doing at that time. Thus, we began to dig deeper as to what each group was doing at t=4500s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b24b2e9",
   "metadata": {},
   "source": [
    "## Category of Interaction by age at t=4500s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29713b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "inter=df[(df[\"Cum_Time_Spent_inS\"]>4000) & (df['Cum_Time_Spent_inS']<5000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a1cf8621",
   "metadata": {},
   "outputs": [],
   "source": [
    "inter=inter.groupby(\"Player_Id\").max().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "65bd7e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Player_Id</th>\n",
       "      <th>Know_Sense</th>\n",
       "      <th>Priority_Sense</th>\n",
       "      <th>Refusal_Power</th>\n",
       "      <th>Me_Power</th>\n",
       "      <th>People_Sense</th>\n",
       "      <th>Cum_Time_Spent_inS</th>\n",
       "      <th>Event_Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Race</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Interaction</th>\n",
       "      <th>Category</th>\n",
       "      <th>Stack</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6427001</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4987</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6427004</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4999</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6427006</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4996</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3561</td>\n",
       "      <td>Priority Sense</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6427008</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4879</td>\n",
       "      <td>1005</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6427010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4380</td>\n",
       "      <td>1004</td>\n",
       "      <td>13.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>3561</td>\n",
       "      <td>People Sense</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6626005</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4998</td>\n",
       "      <td>1004</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>3205</td>\n",
       "      <td>Priority Sense</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>6626006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4915</td>\n",
       "      <td>1004</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>3205</td>\n",
       "      <td>Minigame General</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>6626007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4999</td>\n",
       "      <td>1005</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>Male</td>\n",
       "      <td>3205</td>\n",
       "      <td>Minigame General</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>65670031</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4999</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>65670032</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4999</td>\n",
       "      <td>1005</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "      <td>1531</td>\n",
       "      <td>Refuse Power Minigame</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_Id  Know_Sense  Priority_Sense  Refusal_Power  Me_Power  \\\n",
       "0      6427001         3.0             3.0            0.0       6.0   \n",
       "1      6427004         3.0             3.0            3.0       9.0   \n",
       "2      6427006         3.0             3.0            3.0       9.0   \n",
       "3      6427008         3.0             3.0            0.0       6.0   \n",
       "4      6427010         0.0             0.0            0.0       6.0   \n",
       "..         ...         ...             ...            ...       ...   \n",
       "149    6626005         2.0             2.0            0.0       6.0   \n",
       "150    6626006         0.0             3.0            0.0       6.0   \n",
       "151    6626007         0.0             3.0            0.0       6.0   \n",
       "152   65670031         9.0             9.0           12.0      18.0   \n",
       "153   65670032        12.0            15.0           21.0      27.0   \n",
       "\n",
       "     People_Sense  Cum_Time_Spent_inS  Event_Id   Age              Race  \\\n",
       "0             3.0                4987      1005  13.0          Hispanic   \n",
       "1             5.0                4999      1005  13.0  African American   \n",
       "2             6.0                4996      1005  13.0  African American   \n",
       "3             4.0                4879      1005  13.0  African American   \n",
       "4             1.0                4380      1004  13.0  African American   \n",
       "..            ...                 ...       ...   ...               ...   \n",
       "149           3.0                4998      1004  11.0          Hispanic   \n",
       "150           2.0                4915      1004  13.0         Caucasian   \n",
       "151           3.0                4999      1005  11.0         Caucasian   \n",
       "152          12.0                4999      1005  12.0  African American   \n",
       "153          17.0                4999      1005  12.0          Hispanic   \n",
       "\n",
       "     Gender  Interaction               Category  Stack  \n",
       "0      Male         3561  Refuse Power Minigame    1.0  \n",
       "1    Female         3561  Refuse Power Minigame    2.0  \n",
       "2    Female         3561         Priority Sense    2.0  \n",
       "3      Male         3561  Refuse Power Minigame    1.0  \n",
       "4      Male         3561           People Sense    0.0  \n",
       "..      ...          ...                    ...    ...  \n",
       "149    Male         3205         Priority Sense    0.0  \n",
       "150    Male         3205       Minigame General    0.0  \n",
       "151    Male         3205       Minigame General    0.0  \n",
       "152  Female         1531  Refuse Power Minigame    5.0  \n",
       "153    Male         1531  Refuse Power Minigame    6.0  \n",
       "\n",
       "[154 rows x 14 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7f65462b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minigame General         11\n",
      "Priority Sense           11\n",
      "People Sense              4\n",
      "Refuse Power Minigame     4\n",
      "Lifeline                  1\n",
      "Name: Category, dtype: int64\n",
      "0.0    27\n",
      "1.0     3\n",
      "4.0     1\n",
      "Name: Stack, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "age_11=inter[inter[\"Age\"]==11]\n",
    "print(age_11[\"Category\"].value_counts())\n",
    "print(age_11[\"Stack\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f9be237",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minigame General         15\n",
      "Refuse Power Minigame    13\n",
      "People Sense             11\n",
      "Priority Sense            4\n",
      "Challenge Stack           2\n",
      "Lifeline                  1\n",
      "Name: Category, dtype: int64\n",
      "0.0    26\n",
      "1.0    13\n",
      "2.0     3\n",
      "5.0     2\n",
      "4.0     1\n",
      "6.0     1\n",
      "Name: Stack, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "age_12=inter[inter[\"Age\"]==12]\n",
    "print(age_12[\"Category\"].value_counts())\n",
    "print(age_12[\"Stack\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "53258121",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minigame General         13\n",
      "Refuse Power Minigame    11\n",
      "Priority Sense            8\n",
      "People Sense              7\n",
      "Name: Category, dtype: int64\n",
      "0.0    23\n",
      "1.0    10\n",
      "2.0     6\n",
      "Name: Stack, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "age_13=inter[inter[\"Age\"]==13]\n",
    "print(age_13[\"Category\"].value_counts())\n",
    "print(age_13[\"Stack\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "38b98a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minigame General         16\n",
      "People Sense             11\n",
      "Refuse Power Minigame     6\n",
      "Priority Sense            5\n",
      "Name: Category, dtype: int64\n",
      "0.0    20\n",
      "1.0    16\n",
      "2.0     2\n",
      "Name: Stack, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "age_14=inter[inter[\"Age\"]==14]\n",
    "print(age_14[\"Category\"].value_counts())\n",
    "print(age_14[\"Stack\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6e5ea76e",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_dict={\"Age 11\":[27,3,0,0,1,0,0],\"Age 12\":[26,13,3,0,1,2,1],\"Age 13\":[23,10,6,0,0,0,0],\"Age 14\":[20,16,2,0,0,0,0]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "073e568c",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_inter=pd.DataFrame(age_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fc728967",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age 11</th>\n",
       "      <th>Age 12</th>\n",
       "      <th>Age 13</th>\n",
       "      <th>Age 14</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age 11  Age 12  Age 13  Age 14\n",
       "0      27      26      23      20\n",
       "1       3      13      10      16\n",
       "2       0       3       6       2\n",
       "3       0       0       0       0\n",
       "4       1       1       0       0\n",
       "5       0       2       0       0\n",
       "6       0       1       0       0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_inter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2dacbdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_inter[\"Proportion_Age_11\"]=(age_inter[\"Age 11\"])/(age_inter[\"Age 11\"].sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f833b3f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age 11</th>\n",
       "      <th>Age 12</th>\n",
       "      <th>Age 13</th>\n",
       "      <th>Age 14</th>\n",
       "      <th>Proportion_Age_11</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>0.870968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>0.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age 11  Age 12  Age 13  Age 14  Proportion_Age_11\n",
       "0      27      26      23      20           0.870968\n",
       "1       3      13      10      16           0.096774\n",
       "2       0       3       6       2           0.000000\n",
       "3       0       0       0       0           0.000000\n",
       "4       1       1       0       0           0.032258\n",
       "5       0       2       0       0           0.000000\n",
       "6       0       1       0       0           0.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age_inter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45379548",
   "metadata": {},
   "source": [
    "From the above table, we see the proportion of 11 y/o who were stuck at stack 0 (first level) by the 4500s mark was a overwhelming 87%. Thus this led us to believe the game has a steep learning curve, as after the 10000s mark, all age groups progressed at a similar pace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c7fecd1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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