{
 "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": {
      "image/png": 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\n",
      "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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\n",
      "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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\n",
      "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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\n",
      "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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\n",
      "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": {
      "image/png": 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\n",
      "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "</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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\n",
      "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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\n",
      "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": "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\n",
      "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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\n",
      "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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\n",
      "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": 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\n",
      "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
}