jrtechs-RandomScripts/notebooks/steam/steam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last week I scrapped a bunch of data from the Steam API using my [Steam Graph Project](https://github.com/jrtechs/SteamFriendsGraph).\n",
    "This project captures steam users, their friends, and the games that they own.\n",
    "Using the Janus-Graph traversal object, we are able to use the Gremlin graph query language to pull this data.\n",
    "Since I am storing the hours played in a game as a property on the relationship between a player and a game node, I had to do a join statement to get the hours property with the game information in a single query.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```java\n",
    "Object o = graph.con.getTraversal()\n",
    "    .V()\n",
    "    .hasLabel(Game.KEY_DB)\n",
    "    .match(\n",
    "            __.as(\"c\").values(Game.KEY_STEAM_GAME_ID).as(\"gameID\"),\n",
    "            __.as(\"c\").values(Game.KEY_GAME_NAME).as(\"gameName\"),\n",
    "            __.as(\"c\").inE(Game.KEY_RELATIONSHIP).values(Game.KEY_PLAY_TIME).as(\"time\")\n",
    "    ).select(\"gameID\", \"time\", \"gameName\").toList();\n",
    "WrappedFileWriter.writeToFile(new Gson().toJson(o).toLowerCase(), \"games.json\");\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the game indexing property on the players, I noted that I only ended up fully indexing the games of 481 games. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```java\n",
    "graph.con.getTraversal()\n",
    "    .V()\n",
    "    .hasLabel(SteamGraph.KEY_PLAYER)\n",
    "    .has(SteamGraph.KEY_CRAWLED_GAME_STATUS, 1)\n",
    "    .count().next()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now transition to python and Matlptlib to visualize the data exported from our JanusGraph Query as a json object.\n",
    "The dependencies for this [notebook](https://github.com/jrtechs/RandomScripts/tree/master/notebooks) can be installed using pip."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading pandas-1.0.5-cp38-cp38-manylinux1_x86_64.whl (10.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 10.0 MB 4.3 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting pytz>=2017.2\n",
      "  Downloading pytz-2020.1-py2.py3-none-any.whl (510 kB)\n",
      "\u001b[K     |████████████████████████████████| 510 kB 2.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /home/jeff/Documents/python/ml/lib/python3.8/site-packages (from pandas) (1.18.5)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /home/jeff/Documents/python/ml/lib/python3.8/site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/jeff/Documents/python/ml/lib/python3.8/site-packages (from python-dateutil>=2.6.1->pandas) (1.15.0)\n",
      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-1.0.5 pytz-2020.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas\n",
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step we are doing is importing our JSON data as a pandas dataframe.\n",
    "Pandas is a popular open-source data analysis and manipulation tool.\n",
    "I enjoy pandas because it has native integration with matplotlib and supports operations like aggregations and groupings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>gameid</th>\n",
       "      <th>time</th>\n",
       "      <th>gamename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210770</td>\n",
       "      <td>243</td>\n",
       "      <td>sanctum 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>210770</td>\n",
       "      <td>31</td>\n",
       "      <td>sanctum 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>210770</td>\n",
       "      <td>276</td>\n",
       "      <td>sanctum 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210770</td>\n",
       "      <td>147</td>\n",
       "      <td>sanctum 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>210770</td>\n",
       "      <td>52</td>\n",
       "      <td>sanctum 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36212</th>\n",
       "      <td>9800</td>\n",
       "      <td>9</td>\n",
       "      <td>death to spies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36213</th>\n",
       "      <td>445220</td>\n",
       "      <td>0</td>\n",
       "      <td>avorion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36214</th>\n",
       "      <td>445220</td>\n",
       "      <td>25509</td>\n",
       "      <td>avorion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36215</th>\n",
       "      <td>445220</td>\n",
       "      <td>763</td>\n",
       "      <td>avorion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36216</th>\n",
       "      <td>445220</td>\n",
       "      <td>3175</td>\n",
       "      <td>avorion</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36217 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameid   time        gamename\n",
       "0      210770    243       sanctum 2\n",
       "1      210770     31       sanctum 2\n",
       "2      210770    276       sanctum 2\n",
       "3      210770    147       sanctum 2\n",
       "4      210770     52       sanctum 2\n",
       "...       ...    ...             ...\n",
       "36212    9800      9  death to spies\n",
       "36213  445220      0         avorion\n",
       "36214  445220  25509         avorion\n",
       "36215  445220    763         avorion\n",
       "36216  445220   3175         avorion\n",
       "\n",
       "[36217 rows x 3 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "games_df = pd.read_json('games.json')\n",
    "games_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the built-in matplotlib wrapper function, we can graph a historgram of the number of hours played in a game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = games_df.hist(column='time', bins=20, range=(0, 4000))\n",
    "ax=ax[0][0]\n",
    "ax.set_title(\"Game Play Distribution\")\n",
    "ax.set_xlabel(\"Minutes Played\")\n",
    "ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the vast majority of the games are rarely ever played, however, it is skewek to the right with a lot of outliers.\n",
    "We can change the scale to make it easier to view using the range parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = games_df.hist(column='time', bins=20, range=(0, 100))\n",
    "ax=ax[0][0]\n",
    "ax.set_title(\"Game Play Distribution\")\n",
    "ax.set_xlabel(\"Minutes Played\")\n",
    "ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we remove games that have never been played, the distribution looks more normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = games_df.hist(column='time', bins=20, range=(2, 100))\n",
    "ax=ax[0][0]\n",
    "ax.set_title(\"Game Play Distribution\")\n",
    "ax.set_xlabel(\"Minutes Played\")\n",
    "ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although historgrams are useful, viewing the CDF is often more useful since it is easier to extract numerical information from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = games_df.hist(column='time',density=True, range=(0, 2000),  histtype='step',cumulative=True)\n",
    "ax=ax[0][0]\n",
    "ax.set_title(\"Game Play Distribution\")\n",
    "ax.set_xlabel(\"Minutes Played\")\n",
    "ax.set_ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "80% of people who owns a game plays it under 4 hours. In fact, nearly half of all downloaded or purchased steam games go un-played. This is a cool example of famous 80/20 principle -- aka Pareto principle. The Pareto principle states that for most events, roughly 80% of the effects from 20% of the causes.\n",
    "\n",
    "As mentioned earlier, the time in owned game distribution is heavily skewed to the right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Game Play Distribution')\n",
    "ax.boxplot(games_df['time'], vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Game Play in Minutes\")\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zooming in on the distribution we see that nearly half of all the purchased games go un-opened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Game Play Distribution')\n",
    "ax.boxplot(games_df['time']/60, vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Game Play in Hours\")\n",
    "ax.set_yticks([])\n",
    "ax.set_xlim([0, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the aggregate pool of hours in particular game data is insightful, however, comparing different games against each other is more interesting.\n",
    "In pandas, after we create a grouping on a column, we can aggregate it into metrics such as max, min, mean, etc.\n",
    "I am also sorting the data I get by count since we are more interested in \"popular\" games."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>龙魂时刻</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gryphon knight epic</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>growing pains</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shoppy mart: steam edition</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ground pounders</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</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>payday 2</th>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>84023</td>\n",
       "      <td>5115.813725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team fortress 2</th>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>304090</td>\n",
       "      <td>25291.180952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unturned</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>16974</td>\n",
       "      <td>1339.757009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>garry's mod</th>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>311103</td>\n",
       "      <td>20890.314050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>counter-strike: global offensive</th>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>506638</td>\n",
       "      <td>46356.209302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9235 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  time                          \n",
       "                                 count min     max          mean\n",
       "gamename                                                        \n",
       "龙魂时刻                                 1  14      14     14.000000\n",
       "gryphon knight epic                  1   0       0      0.000000\n",
       "growing pains                        1   0       0      0.000000\n",
       "shoppy mart: steam edition           1   0       0      0.000000\n",
       "ground pounders                      1   0       0      0.000000\n",
       "...                                ...  ..     ...           ...\n",
       "payday 2                           102   0   84023   5115.813725\n",
       "team fortress 2                    105   7  304090  25291.180952\n",
       "unturned                           107   0   16974   1339.757009\n",
       "garry's mod                        121   0  311103  20890.314050\n",
       "counter-strike: global offensive   129   0  506638  46356.209302\n",
       "\n",
       "[9235 rows x 4 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df = (games_df.groupby(\"gamename\")\n",
    "                    .agg({'time': ['count', \"min\", 'max', 'mean']})\n",
    "                    .sort_values(by=('time', 'count')))\n",
    "stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prevent one off essoteric games that I don't have a lot of data for, throwing off my metrics, I am disregarding any games that I have less than 10 values for. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamename</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>serious sam hd: the second encounter</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>329</td>\n",
       "      <td>57.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grim fandango remastered</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>248</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>evga precision x1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>21766</td>\n",
       "      <td>2498.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f.e.a.r. 2: project origin</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>292</td>\n",
       "      <td>43.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transistor</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>298.727273</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>payday 2</th>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>84023</td>\n",
       "      <td>5115.813725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>team fortress 2</th>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>304090</td>\n",
       "      <td>25291.180952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unturned</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>16974</td>\n",
       "      <td>1339.757009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>garry's mod</th>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>311103</td>\n",
       "      <td>20890.314050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>counter-strike: global offensive</th>\n",
       "      <td>129</td>\n",
       "      <td>0</td>\n",
       "      <td>506638</td>\n",
       "      <td>46356.209302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>701 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      time                          \n",
       "                                     count min     max          mean\n",
       "gamename                                                            \n",
       "serious sam hd: the second encounter    11   0     329     57.909091\n",
       "grim fandango remastered                11   0     248     35.000000\n",
       "evga precision x1                       11   0   21766   2498.181818\n",
       "f.e.a.r. 2: project origin              11   0     292     43.272727\n",
       "transistor                              11   0     972    298.727273\n",
       "...                                    ...  ..     ...           ...\n",
       "payday 2                               102   0   84023   5115.813725\n",
       "team fortress 2                        105   7  304090  25291.180952\n",
       "unturned                               107   0   16974   1339.757009\n",
       "garry's mod                            121   0  311103  20890.314050\n",
       "counter-strike: global offensive       129   0  506638  46356.209302\n",
       "\n",
       "[701 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df = stats_df[stats_df[('time', 'count')] > 10]\n",
    "stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that games on average have a play time of 5-hours in a box-plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Game Play Distribution')\n",
    "ax.boxplot(stats_df[('time', 'mean')]/60, vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Mean Game Play in Hours\")\n",
    "ax.set_xlim([0, 40])\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I had a hunch that more popular games got played more, however, I feel like this data-set is still too small the verify this hunch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd600786550>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_df.plot.scatter(x=('time', 'count'), y=('time', 'mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "We can create a new filtered dataframe that only contains the result of a single game. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>gameid</th>\n",
       "      <th>time</th>\n",
       "      <th>gamename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13196</th>\n",
       "      <td>730</td>\n",
       "      <td>742</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13197</th>\n",
       "      <td>730</td>\n",
       "      <td>16019</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13198</th>\n",
       "      <td>730</td>\n",
       "      <td>1781</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13199</th>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13200</th>\n",
       "      <td>730</td>\n",
       "      <td>0</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13320</th>\n",
       "      <td>730</td>\n",
       "      <td>3867</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13321</th>\n",
       "      <td>730</td>\n",
       "      <td>174176</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13322</th>\n",
       "      <td>730</td>\n",
       "      <td>186988</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13323</th>\n",
       "      <td>730</td>\n",
       "      <td>103341</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13324</th>\n",
       "      <td>730</td>\n",
       "      <td>10483</td>\n",
       "      <td>counter-strike: global offensive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameid    time                          gamename\n",
       "13196     730     742  counter-strike: global offensive\n",
       "13197     730   16019  counter-strike: global offensive\n",
       "13198     730    1781  counter-strike: global offensive\n",
       "13199     730       0  counter-strike: global offensive\n",
       "13200     730       0  counter-strike: global offensive\n",
       "...       ...     ...                               ...\n",
       "13320     730    3867  counter-strike: global offensive\n",
       "13321     730  174176  counter-strike: global offensive\n",
       "13322     730  186988  counter-strike: global offensive\n",
       "13323     730  103341  counter-strike: global offensive\n",
       "13324     730   10483  counter-strike: global offensive\n",
       "\n",
       "[129 rows x 3 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_df = games_df[games_df['gamename'] == \"counter-strike: global offensive\"]\n",
    "cc_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is supprizing how many hours certain people play in Counter-Strike. The highest number in the dataset was 8,444 hours or 352 days! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEWCAYAAABYGk2QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAbrklEQVR4nO3de5xdVX338c93ciGQezIzyH1CAUkyVJRIKVpFUUEEwZfah4oKiubxUvFahIdWqdR6aeO9FSpWoBrUggXE9qnYQEsUkHARCJcaIFwsMDOECSQBJpn8+sdaJ5xM5p6Zs2bmfN+v13nNPnvvs/baa/b5zjpr77NHEYGZmdVeQ+kKmJnVKwewmVkhDmAzs0IcwGZmhTiAzcwKcQCbmRXiAB6DJIWkAwpte7Wko0aorFMk/bzq+Yjul6QNkvYfqfKqyn2xpNslPSPpjJEu30DS+ZL+Ik8fJenR0nUqYUIHsKSTJd0kaaOktjz9IUkqXK+WHEYb8mOtpLNqvM0nJF0t6fXV60XE4oi4bpBlTe5vvYj4QUS8YQSqj6TrJL2vR/kzIuKBkSi/hzOBayNiZkR8YyQKlHSQpH+W1CFpvaQ7JH1C0qSRKL+f7V4k6a9GuMxXSvpV3o91kn4p6eV52WmSVg5URkR8ICLOG8l6jUcTNoAlfRL4OvA3wIuA3YEPAK8AphasWrU5ETED+BPgM5KOreE2XwJcA/yLpNNGeiMDhfMYtx+wejgv7G2/Jf0ecBPwCHBIRMwG3g4sAWbuRD1HXc/9kTQLuBr4JjAP2Av4S+D5IZQ5qn90xpWImHAPYDawEXjrAOu9CbgNeJr05ji3alkLEMB78rKnSAH+cuAOoBP4Vo/y3gvck9f9d2C/PrZbKXty1bybgU/l6QAOGEQdfwZ8pEfZdwBvGcw28/xPAU8ADfn5WuB1efpwYFXe9hPAV/L8h3NZG/LjD4HTgF8CXwWeBP4qz1tZta0AzgAeADpIfxwr2z0X+H5v9QU+D3QDz+XtfauXdpoNXAK0Aw8Bf15V9mnASuBv8+/mQeCNffxuVvTY1kGDKHu7/e6lzO8DPxvgWHwzKfQ7geuAhT3a7YCq5xdVtgMcBTwKfBJoAx4D3pOXLQU2A115X36a5+8JXJ7350HgjKqyzwUuy3V+Gnhfj3ouATr72IeFud268/Y6q+r7beBfSe/L1/W2D1XlnAHcDewN7JJ/bw+TjsHzgV1LZ8xIPYpXYFR2Co4FttAjbHpZ7yjgENIngd/Pv+CT8rKWfOCfD0wD3pAPriuAZtJf/jbg1Xn9E4E1+SCcnN+kv+pju5WyJwMi9co3AUfn5dXB0l8d/xi4qarcl5BCYGp/2+wxf/88f2F+vpYXAvgG4F15egZwRF9lkYJoC/CRvF+70nsAX0vqOe0L/Df5DU4/AZyfX8eOYVDdTpcAV5J6lC257NOr6rYZeD8wCfgg8D+A+vj9bLetQZS93X73Ut7j5FDsY3sHkYLp9cAU0hDImsrvkYEDeAvwufza40jH0tye6+bnDcAtwGdInwT3J/1BPKbq97AZOCmvu2uPus4iHWMXA2+sbKfHcbCyx7yLgPWk47yB9H7quQ+P5unPALcCTfn5V4GrSMfMTOCnwBdKZ8xIPSbqEEQj0BERWyoz8phVp6RnJb0KICKui4g7I2JrRNwBXAq8ukdZ50XEcxHxc9Kb5NKIaIuI3wHXAy/N632AdGDck7f718Chkvbrp54dwDrgQuCsiPiPnisMUMergIMkHZifvwv4UUR0Da6ZgBREkA7wnjYDB0hqjIgNEXHjQGVFxDcjYktEPNvHOl+KiHUR8TDwNdLwy07JH2lPBs6OiGciYi2wjNQeFQ9FxHciopsUHnuQhqVGouyB9ns+qWfal/9D6iFfExGbST2+XYEjB6pfthn4XERsjoh/JfU+X9zHui8nhdvnIqIr0hj6d/I+VtwQEVfkY267/YmIp4FXkv4ofAdol3SVpIHa8sqI+GUu87lelkvSV0gdnddERHs+V7MU+Hg+Zp4hva9O7uX149JEDeAngcbq8auIODIi5uRlDQCS/kDStZLaJa0nhWhjj7KeqJp+tpfnM/L0fsDXc8h3koJVpJ5yXxojYm5ELIw+Tvb0V8d8IP8IeKekBlKY/VM/2+tNpX7rell2Oql3dq+kmyUdP0BZjwxie9XrPET6OLyzGkm9v4d6lF3d9o9XJiJiU56cwcAGU/ZA+/0kKfD7smd1+RGxNZfZ37GzXfnVnQ1SD7ivfdsP2LNynOZj9f+x/R+jbfsj6d+qTtyekut3T0ScFhF7A625/l8boI4DtdEcUth+ISLW53lNwG7ALVV1/f95/oQwUQP4BtJJgRMHWG85qRe5T6QTI+eTQnM4HgH+b0TMqXrsGhG/GmZ5g63jxcApwNHApoi4YYjlv4U0lHJfzwUR8duI+BPSkMuXgMskTSf1fnozmFvr7VM1vS8v9MA3kt5sFS8aQtkdpF5g9aeNfYHfDaI+AxlM2QPt9y+At/az/H+qy889v32qtrGJ/tumPz3r9gjwYI/jdGZEHNfbayLijZGuNpkRET/YofCIe0nDCa19bK+vevT0FHA88D1Jr8jzOkidnMVVdZ0d6STyhDAhAzgiOklnZv9e0tskzZTUIOlQYHrVqjOBdRHxnKTDgXfsxGbPB86WtBhA0mxJb9+J8gZVxxy4W0kfiwfd+5W0u6Q/BT5L+ni9tZd13impKS/rzLO3kk7ebCWNHw7Vn0maK2kf4KOkHjzA7cCrJO0raTZwdo/XPdHX9vKwwo+Bz+ff9X7AJ0gnknbKCJX9WeBISX8j6UUAkg6Q9H1Jc3L5b5J0tKQppBNqzwOVP963A++QNClfKdNzmKw/Pdvt18Azkj4taddcZmvlMrKBSDpY0icl7Z2f70P65FUZnnoC2FvSkK80inT54ynATyQdno+77wBfldSct7eXpGOGWvZYNSEDGCAivkx6o5xJOiieAC4APs0LB/aHgM9JeoY0+P/jndjev5B6iT+U9DRwF+kkxc4aTB0vIZ2oG0wodEraCNxJOmHz9oj4xz7WPRZYLWkD6ZK+kyPi2fwR/vPAL/NHwyOGsD9Xkk4C3U66iuO7ABFxDSmM78jLr+7xuq8Db5P0lKTehms+QupFP0C64mE50Nd+DdVOlR0R95OuFGkhted60lUIq4BnIuI+4J2kS7s6gBOAE6rG8j+a53WSAuqKIdT9u8Ci/Hu6Iv9BOR44lHQFRAfpHMTsQZb3DPAHwE35OLqRdKx/Mi9fQbqa43FJHUOoJ7DtOHgv8FNJLyO9X9cAN+b31S/oe3x73FGEb8g+3kl6N7A0Il5Zui5mNngTtgdcLyTtRuol/0PpupjZ0DiAx7E8FtZOGl5ZXrg6ZjZEHoIwMyvEPWAzs0KGdMOUxsbGaGlpGaWqmJlNTLfccktHROzwBZIhBXBLSwurVq0auVqZmdUBSQ/1Nt9DEGZmhTiAzcwKcQCbmRXiADYzK8QBbGZWiAPYzKwQB7CZWSE1+8+1t956K52d6Zaye+yxBwsXLuxz3fvvv5+HHur1srlhOfzww5kxY8Lcw9nMJoiaBfCRRx7JEUccQVdXF21tbaxZs6bPdU855RS6u7uZOXPn/2P3vffey3nnncfpp5++02WZmY2kmgVwd3c311xzDc8++yx77dX/v7p6/PHHWbFiBfvvP5x/uLC9pUuX0t3dvdPlmJmNtJqPAc+cOZPNmzfz7LN9/dNcaGtro7m5uYa1MjOrvZoHsCSamppob2/vdfnGjRuJCKZPn97rcjOziaLIVRDNzc20tbX1uqzS+03/GNbMbOIaswFsZjbROYDNzAopEsD9jQG3t7fT1LTDfYvNzCYc94DNzApxAJuZFeIANjMrxGPAZmaFuAdsZlZIsR5wW1sbEbHDMgewmdWLIgG82267MWXKFDZs2LDd/IjwEISZ1Y1iN2Sv9IKrrV+/nmnTpjFt2rRCtTIzq51iAdzbOLCHH8ysnjiAzcwKKRrAPS9Fa29vdwCbWd0YU2PAbW1tPgFnZnXDQxBmZoU4gM3
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Game Play Distribution for Counter-Strike')\n",
    "ax.boxplot(cc_df['time']/60, vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Game Play in Hours\")\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the distribution for a different game like Unturned, yields a vastly different distribution than Counter-Strike. I believe the key difference is that Counter-Strike is played competitively where Unturned is a more leasurely game. It is likely that competative gamers skew the distribution of Counter-Strike to be very high. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>gameid</th>\n",
       "      <th>time</th>\n",
       "      <th>gamename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>304930</td>\n",
       "      <td>140</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>304930</td>\n",
       "      <td>723</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>304930</td>\n",
       "      <td>1002</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>304930</td>\n",
       "      <td>1002</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>304930</td>\n",
       "      <td>0</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>304930</td>\n",
       "      <td>97</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>304930</td>\n",
       "      <td>768</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>304930</td>\n",
       "      <td>1570</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>304930</td>\n",
       "      <td>23</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>304930</td>\n",
       "      <td>115</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     gameid  time  gamename\n",
       "167  304930   140  unturned\n",
       "168  304930   723  unturned\n",
       "169  304930  1002  unturned\n",
       "170  304930  1002  unturned\n",
       "171  304930     0  unturned\n",
       "..      ...   ...       ...\n",
       "269  304930    97  unturned\n",
       "270  304930   768  unturned\n",
       "271  304930  1570  unturned\n",
       "272  304930    23  unturned\n",
       "273  304930   115  unturned\n",
       "\n",
       "[107 rows x 3 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_df = games_df[games_df['gamename'] == \"unturned\"]\n",
    "u_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Game Play Distribution for Unturned')\n",
    "ax.boxplot(u_df['time']/60, vert=False,manage_ticks=False, notch=True)\n",
    "plt.xlabel(\"Game Play in Hours\")\n",
    "ax.set_yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I made a dataframe just containing the raw data points of games that had a aggregate count of over 80. For the sample size of the crawl that I did, having a count of 80, would make the game \"popular\". Since we only have 485 players indexed, having over 80 entries means that over 17% of people scrapped had the game. Glancing at the results, it is easy to verify that the games returned were very popular. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-142-49f39d385a2a>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['time'] = df1['time']/60\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gameid</th>\n",
       "      <th>time</th>\n",
       "      <th>gamename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>304930</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>304930</td>\n",
       "      <td>12.050000</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>304930</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>304930</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>304930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>unturned</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22682</th>\n",
       "      <td>578080</td>\n",
       "      <td>51.883333</td>\n",
       "      <td>playerunknown's battlegrounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22683</th>\n",
       "      <td>578080</td>\n",
       "      <td>47.616667</td>\n",
       "      <td>playerunknown's battlegrounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22684</th>\n",
       "      <td>578080</td>\n",
       "      <td>30.650000</td>\n",
       "      <td>playerunknown's battlegrounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22685</th>\n",
       "      <td>578080</td>\n",
       "      <td>170.083333</td>\n",
       "      <td>playerunknown's battlegrounds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22686</th>\n",
       "      <td>578080</td>\n",
       "      <td>399.950000</td>\n",
       "      <td>playerunknown's battlegrounds</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1099 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gameid        time                       gamename\n",
       "167    304930    2.333333                       unturned\n",
       "168    304930   12.050000                       unturned\n",
       "169    304930   16.700000                       unturned\n",
       "170    304930   16.700000                       unturned\n",
       "171    304930    0.000000                       unturned\n",
       "...       ...         ...                            ...\n",
       "22682  578080   51.883333  playerunknown's battlegrounds\n",
       "22683  578080   47.616667  playerunknown's battlegrounds\n",
       "22684  578080   30.650000  playerunknown's battlegrounds\n",
       "22685  578080  170.083333  playerunknown's battlegrounds\n",
       "22686  578080  399.950000  playerunknown's battlegrounds\n",
       "\n",
       "[1099 rows x 3 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = games_df[games_df['gamename'].map(games_df['gamename'].value_counts()) > 80]\n",
    "df1['time'] = df1['time']/60\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.set_title('Steps Distribution for July\\n')\n",
    "df1.boxplot(column=[\"time\"], by='gamename',ax=ax, notch=True, vert=False)\n",
    "plt.xlabel(\"Hours Played\")\n",
    "plt.ylabel(\"Games\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAEVCAYAAAA7CkXmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3gU5dn48e+dJWw4GjkZNIFNVTTsqqhoPRu0tVarpda+1kNbLRVrhWqt1fqLr6WtaK1CbbGtpa8asSZtbdV6RNDNej4ACiIs4ClgPQGiaBAS2Ny/P+bZuAm72U3YTTbk/lzXXJl55jncMwnZm2cmM6KqGGOMMcbki4LuDsAYY4wxJpElJ8YYY4zJK5acGGOMMSavWHJijDHGmLxiyYkxxhhj8oolJ8YYY4zJK5acGGN6FBGJiMgPumisZSJS2RVjpSMiR4vIyiz294iIfM+tnysiT2ex77NFZF62+jO9jyUnxpi8IyL1IrJZRBpE5AMRqRaRgTkes1pErkksU9WgqkZyOa4be5qIbBWRT92ySkRuFpGRCbE8par7ZNjX39LVU9WvquodWYg9ICIqIn0S+r5LVU/Y0b5N72XJiTEmX52iqgOBg4DxwFXdHE+u/UNVBwFDgG8AJcCixAQlG8Rjv/tNXrMfUGNMXlPVd4BHgFDbfSKyp4iEReRDEVkvIneJSLHb9zMR+Xeb+n8Qkd8n6WcycDZwuZutecCV14vIl9z6NBG5W0T+5mY3lorIGBG5UkTWisjbInJCQp+7iMitIvKeiLwjIteIiC+D492qqsuAM4B1wE9df5Ui8t+E/q9w/X4qIitF5HgRORH4f8AZ7jiWuLoREZkuIs8AnwFfSHJ5TNxszUYRWSEixyfsaDkPCeciPjvzpPv6sRvz8LaXiUTkCBFZ4PpeICJHJOyLiMivReQZdyzzRGRYuvNkdm6WnBhj8pqIlAEnAS8n2w1cB+wOVABlwDS372/AiQnJSh/g28Cctp2o6mzgLuC3qjpQVU9JEc4pwJ3Ari6eR/F+j+4B/Ar4S0LdamAbsBdwIHACkPG9MqoaA/4DHN12n4jsA0wBDnGzLV8B6lV1LnAt3izMQFU9IKHZd4DJwCBgdZIhvwi8AQwDfgHcIyJDMgj1GPe12I35XJtYhwAPAX8AhgIzgYdEZGhCtbOA84ARQF/gsgzGNTsxS06MMfnqPhH5GHgaeALvQ7cVVX1dVeeraqOqrsP74DvW7XsP73/133LVTwTWq+qiHYjpKVV9VFW3AXcDw4HfqOpW4O9AQESKRWQ3vITqElXdpKprgd/hJUcd8S7eZZ62YoAfGCsihapar6pvpOmrWlWXqeo2F29ba4Gb3MzNP4CVwMkdjDeZk4HXVPVON3YtsAIv0Yu7XVVXqepm4J/AuCyMa3owS06MMflqoqoWq+poVf2R++BqRUR2E5G/u8sbn+DNliReErgDOMetn4M364GI/D93CaJBRG7pQEwfJKxvxkt2YgnbAAOB0UAh8J6IfOySrL/gzQx0xB7AhraFqvo6cAneLNFadw52T9PX22n2v6Ot3wS7Gm9GakftzvYzNavxji3u/YT1z/DOoenFLDkxxvRk1wIK7Keqg/ESEEnYfx+wv4iEgK/hXbpBVa91lyAGquoPXd1svqL9baARGOYSrGJVHayqwUw7cDetngI8lWy/qtao6lF4iZAC18d3pegy3fHtISKJ524U3swNwCagf8K+kg70+66LMdEo4J007UwvZsmJMaYnGwQ0ABtFZA/gZ4k7VXUL8C+gBnhRVde009cHwBeyEZS7pDQPmCEig0WkwN28e2y6tiLSR0QqgFq8JGBmkjr7iMhxIuIHtuDN2jQnHEegE3+RMwL4sYgUisi38O7hedjtWwx82+0bD5ye0G6dGzvVuXsYGCMiZ7ljOwMYCzzYwfhML2LJiTGmJ/sl3p8ab8S76fKeJHXuAPbDXdJpx61493B8LCL3ZSG27+Ld3Lkc+AgvSWrvz4LPEJEGvGO5H/gQOFhV301S1w/8BliPd0lkBHCl23e3+/qhiLzUgXhfAPZ2fU4HTlfVD92+/wX2dMfxS7xkDwBV/czVf8adu8MSO3V9fA3vr44+BC4Hvqaq6zsQm+llpPUlRmOM2bmIyCi8GzBLVPWT7o7HGJOezZwYY3Za7tLGpcDfLTExpufok76KMcb0PCIyAO/+i9V4f0ZsjOkh7LKOMcYYY/KKXdYxxhhjTF6x5MQYY4wxecXuOTG9QnFxse61117dHUZGNm3axIABA7o7jIz0pFihZ8VrseaGxZo7ixYtWq+qw7PRlyUnplfYbbfdWLhwYXeHkZFIJEJlZWV3h5GRnhQr9Kx4LdbcsFhzR0SSvVCyU+yyjjHGGGPyiiUnxhhjjMkrlpyYXmHNp818/ean+frNT/PSmo+6OxxjjDHtsOTE9ArNChdW7klx/77Ur9/U3eEYY4xphyUnJitEZJyInNRNYzdkUu9LFbsxZEDfXIdjjDFmB1lyYrJlHNCh5EQ8XfIzWFD0X/r4th9q6tSpiAgiQmFhIQMHDmzZTrYMHTqU2tratONNnTqVoqIiRISioiKmTp3asq+2tpZQKERBQQFFRUUUFBQQCoVS9huv7/P52q1njDE7C0tOTFIiEhCRVxO2LxORaSISEZHrReRFEVklIkeLSF/gV3ivfF8sIme4upcltH/V9RkQkZUiMgd4FThaRKIi8lcRWSYi80Skn2uzp4jMFZFFIvKUiOzrystF5DkRWSoi13T2GKdOncott9wCwO23347f72fTpk0UFhZSXFyM3+9vqTt48GDOPPNMNm7cyAUXXNBughDv99prr2XTpk1ce+213HLLLUydOpXa2lqqqqqYOHEio0eP5tprryUQCDBx4kSqqqq26zdef9asWWzZsoVZs2YlrWeMMTsVVbXFlu0WIAC8mrB9GTANiAAzXNlJwGNu/Vzg5oT604DLErZfdX0GgGbgsIRxtgHj3PY/gXPc+uPA3m79i0DYrd8PfNetXwQ0pDueokCRqqpe8veX9d+L3lZVVb/frzNmzFBAg8GgBgIB3XXXXRXQcDishYWFWlBQoCUlJRoIBDQYDOqMGTO0sLBQg8GgphLvN9GMGTPU7/drMBjUcDjc8lVVW20Hg0Gtq6traZdYLy5eLx8kxtoT9KR4LdbcsFhzB1ioWfoMsoewmc64x31dhJdcdNRqVX0+YfstVV2c2KeIDASOAO4WkXi9+FTGkcA33fqdwPXJBhGRycBkgKJAEZFIhPc/2MKlL7/Dpf9cQmNjIzf9dzQA0WgUIJ5IEYvF2Lp1KwBr165t6XPs2LFs3bqVaDRKJBJJenCNjY2MHTu21f6xY8fS2NhINBolFou1fI1EIq22o9EoDQ0NLW0T68XF66UavyslxtoT9KR4LdbcsFh7BktOTCrbaH3ZryhhvdF9jZH6Z6i99m3/XKYxYT0G9HNtP1bVcSn6T/s6bVWdDcwG6FfeTysrK/nPB4uZedQwTjuolKKb/FxSupqfAhUVFWzatImNGzfy0Ucf4fP5KCwsJBaLMWLECIqKihgwYADLly+nsLCQMWPGpHxyo9/vZ/ny5Vx66aUtZTNnzsTv97PXXnvh8/moqKjA5/NRWVlJXV1dy3ZFRQUDBw5s6TuxXly8fj48ObKnPcGyJ8VrseaGxdoz2D0nJpUPgBEiMlRE/MDX0tT/FBiUsF0PHAQgIgcB5R0ZXFU/Ad4SkW+5PkREDnC7nwG+7dbP7kCfrbbPP/98rrjiCgAuu+wy1q1bx0cffURhYSGnnXYaBQUFNDc38/7777Nhwwb2339/Lr/8coqKiqiqqko5TrzfmTNn8tlnnzFz5kyuuOIKzj//fKqqqpg0aRITJ07k+9//PjNnzmzZnjRp0nb9xuvX1dWxdetW6urqktYzxpidSrauD9my8y3Aj4E3gCeBaj6/52S82z8MqHfrQ4AFwGLgDLzZj3nAMuA2IMrn95wk3svSdvsyYJpbLwfmAkuA5cDVCeXPAUuBa8jgnpO+JXvpOx991uqeE1XVKVOmqN/
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df1.boxplot(column=[\"time\"], by='gamename', notch=True, vert=False)\n",
    "fig = ax.get_figure()\n",
    "fig.suptitle('')\n",
    "ax.set_title('Play-time Distribution')\n",
    "plt.xlabel(\"Hours Played\")\n",
    "ax.set_xlim([0, 2000])\n",
    "plt.ylabel(\"Game\")\n",
    "plt.savefig(\"playTimes.png\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall this is really interesting to see how the distributions for different games vary. In the future I am going to re-run some of these analytics with even more data and possibly put them on my website as a interactive graph."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ml",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}