Adam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "import pandas as pd\n",
    "import steamspypi\n",
    "import steamreviews\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "\n",
    "load_dotenv()\n",
    "API_KEY = os.getenv(\"API_KEY\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def VisualizeNetwork(G, title):\n",
    "    plt.figure(figsize=(12,12))\n",
    "    nx.draw(G, node_size=20, with_labels=False)\n",
    "    plt.title(title)\n",
    "    plt.savefig(title + \".pdf\")\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Networks = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir(\"Networks\")\n",
    "\n",
    "for file in files:\n",
    "    id = file.split(\".\")[0]\n",
    "    G = nx.read_pajek(f\"Networks\\{file}\")\n",
    "    G = nx.Graph(G)\n",
    "    if G.number_of_nodes() == 0:\n",
    "        continue\n",
    "    Networks.append(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "com = nx.community.fast_label_propagation_communities(Networks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_level_communities = next(com)\n",
    "# next_level_communities = next(com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Generate the communities\n",
    "communities = nx.community.fast_label_propagation_communities(Networks[0])\n",
    "\n",
    "# Create a color map\n",
    "color_map = []\n",
    "for community in communities:\n",
    "    for node in community:\n",
    "        color_map.append(len(community))  # or choose your own way to color them\n",
    "\n",
    "# Draw the graph\n",
    "nx.draw(Networks[0], node_color=color_map, with_labels=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 1,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 16,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 17,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 55,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 64,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 1,\n",
       " 1,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 78,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 26,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 9,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 27,\n",
       " 1,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 36,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 48,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 205,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 47,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " ...]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = nx.community.fast_label_propagation_communities(Networks[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "print(list(communities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "G = nx.read_pajek(f\"Networks/730.net\")\n",
    "G = nx.Graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44397\n",
      "383756\n"
     ]
    }
   ],
   "source": [
    "print(G.number_of_nodes())\n",
    "print(G.number_of_edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "542\n"
     ]
    }
   ],
   "source": [
    "communities = nx.community.fast_label_propagation_communities(G)\n",
    "print(len(list(communities)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "G1 = nx.read_pajek(f\"Networks/570.net\")\n",
    "G1 = nx.Graph(G1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "263\n"
     ]
    }
   ],
   "source": [
    "communities2 = nx.community.fast_label_propagation_communities(G1)\n",
    "print(len(list(communities2)))\n",
    "communities2 = list(communities2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "communities2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "for com in list(communities2):\n",
    "    print(com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: to be able to use all crisp methods, you need to install some additional packages:  {'bayanpy', 'wurlitzer', 'graph_tool'}\n",
      "Note: to be able to use all crisp methods, you need to install some additional packages:  {'ASLPAw', 'pyclustering'}\n",
      "Note: to be able to use all crisp methods, you need to install some additional packages:  {'wurlitzer'}\n"
     ]
    }
   ],
   "source": [
    "from cdlib import algorithms, viz, evaluation\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[127], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m coms \u001b[38;5;241m=\u001b[39m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlouvain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mweight\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m mod \u001b[38;5;241m=\u001b[39m coms\u001b[38;5;241m.\u001b[39mnewman_girvan_modularity()\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\cdlib\\algorithms\\crisp_partition.py:541\u001b[0m, in \u001b[0;36mlouvain\u001b[1;34m(g_original, weight, resolution, randomize)\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    501\u001b[0m \u001b[38;5;124;03mLouvain  maximizes a modularity score for each community.\u001b[39;00m\n\u001b[0;32m    502\u001b[0m \u001b[38;5;124;03mThe algorithm optimises the modularity in two elementary phases:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    536\u001b[0m \u001b[38;5;124;03m.. note:: Reference implementation: https://github.com/taynaud/python-louvain\u001b[39;00m\n\u001b[0;32m    537\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    539\u001b[0m g \u001b[38;5;241m=\u001b[39m convert_graph_formats(g_original, nx\u001b[38;5;241m.\u001b[39mGraph)\n\u001b[1;32m--> 541\u001b[0m coms \u001b[38;5;241m=\u001b[39m \u001b[43mcommunity_louvain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_partition\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    542\u001b[0m \u001b[43m    \u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandomize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrandomize\u001b[49m\n\u001b[0;32m    543\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    545\u001b[0m \u001b[38;5;66;03m# Reshaping the results\u001b[39;00m\n\u001b[0;32m    546\u001b[0m coms_to_node \u001b[38;5;241m=\u001b[39m defaultdict(\u001b[38;5;28mlist\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\community\\community_louvain.py:249\u001b[0m, in \u001b[0;36mbest_partition\u001b[1;34m(graph, partition, weight, resolution, randomize, random_state)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbest_partition\u001b[39m(graph,\n\u001b[0;32m    164\u001b[0m                    partition\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    165\u001b[0m                    weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m    166\u001b[0m                    resolution\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.\u001b[39m,\n\u001b[0;32m    167\u001b[0m                    randomize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m    168\u001b[0m                    random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    169\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute the partition of the graph nodes which maximises the modularity\u001b[39;00m\n\u001b[0;32m    170\u001b[0m \u001b[38;5;124;03m    (or try..) using the Louvain heuristices\u001b[39;00m\n\u001b[0;32m    171\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    247\u001b[0m \u001b[38;5;124;03m    >>> plt.show()\u001b[39;00m\n\u001b[0;32m    248\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 249\u001b[0m     dendo \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_dendrogram\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    250\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mpartition\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    251\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mrandomize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    255\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m partition_at_level(dendo, \u001b[38;5;28mlen\u001b[39m(dendo) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\community\\community_louvain.py:352\u001b[0m, in \u001b[0;36mgenerate_dendrogram\u001b[1;34m(graph, part_init, weight, resolution, randomize, random_state)\u001b[0m\n\u001b[0;32m    350\u001b[0m status\u001b[38;5;241m.\u001b[39minit(current_graph, weight, part_init)\n\u001b[0;32m    351\u001b[0m status_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m()\n\u001b[1;32m--> 352\u001b[0m \u001b[43m__one_level\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresolution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrandom_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    353\u001b[0m new_mod \u001b[38;5;241m=\u001b[39m __modularity(status, resolution)\n\u001b[0;32m    354\u001b[0m partition \u001b[38;5;241m=\u001b[39m __renumber(status\u001b[38;5;241m.\u001b[39mnode2com)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\community\\community_louvain.py:486\u001b[0m, in \u001b[0;36m__one_level\u001b[1;34m(graph, status, weight_key, resolution, random_state)\u001b[0m\n\u001b[0;32m    484\u001b[0m com_node \u001b[38;5;241m=\u001b[39m status\u001b[38;5;241m.\u001b[39mnode2com[node]\n\u001b[0;32m    485\u001b[0m degc_totw \u001b[38;5;241m=\u001b[39m status\u001b[38;5;241m.\u001b[39mgdegrees\u001b[38;5;241m.\u001b[39mget(node, \u001b[38;5;241m0.\u001b[39m) \u001b[38;5;241m/\u001b[39m (status\u001b[38;5;241m.\u001b[39mtotal_weight \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2.\u001b[39m)  \u001b[38;5;66;03m# NOQA\u001b[39;00m\n\u001b[1;32m--> 486\u001b[0m neigh_communities \u001b[38;5;241m=\u001b[39m \u001b[43m__neighcom\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatus\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    487\u001b[0m remove_cost \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m neigh_communities\u001b[38;5;241m.\u001b[39mget(com_node,\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m+\u001b[39m \\\n\u001b[0;32m    488\u001b[0m     resolution \u001b[38;5;241m*\u001b[39m (status\u001b[38;5;241m.\u001b[39mdegrees\u001b[38;5;241m.\u001b[39mget(com_node, \u001b[38;5;241m0.\u001b[39m) \u001b[38;5;241m-\u001b[39m status\u001b[38;5;241m.\u001b[39mgdegrees\u001b[38;5;241m.\u001b[39mget(node, \u001b[38;5;241m0.\u001b[39m)) \u001b[38;5;241m*\u001b[39m degc_totw\n\u001b[0;32m    489\u001b[0m __remove(node, com_node,\n\u001b[0;32m    490\u001b[0m          neigh_communities\u001b[38;5;241m.\u001b[39mget(com_node, \u001b[38;5;241m0.\u001b[39m), status)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\community\\community_louvain.py:516\u001b[0m, in \u001b[0;36m__neighcom\u001b[1;34m(node, graph, status, weight_key)\u001b[0m\n\u001b[0;32m    514\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m neighbor, datas \u001b[38;5;129;01min\u001b[39;00m graph[node]\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m    515\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m neighbor \u001b[38;5;241m!=\u001b[39m node:\n\u001b[1;32m--> 516\u001b[0m         edge_weight \u001b[38;5;241m=\u001b[39m \u001b[43mdatas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mweight_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    517\u001b[0m         neighborcom \u001b[38;5;241m=\u001b[39m status\u001b[38;5;241m.\u001b[39mnode2com[neighbor]\n\u001b[0;32m    518\u001b[0m         weights[neighborcom] \u001b[38;5;241m=\u001b[39m weights\u001b[38;5;241m.\u001b[39mget(neighborcom, \u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m+\u001b[39m edge_weight\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "coms = algorithms.louvain(G, weight='weight', resolution=1.)\n",
    "mod = coms.newman_girvan_modularity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "coms = algorithms.louvain(G1, weight='weight', resolution=1.)\n",
    "mod = coms.newman_girvan_modularity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7971\n",
      "76561199006305510\n",
      "4263\n",
      "76561199005185332\n",
      "3405\n",
      "76561198033464225\n",
      "3181\n",
      "76561198397533646\n",
      "2558\n",
      "76561198254786432\n",
      "2395\n",
      "76561198352765825\n",
      "1869\n",
      "76561197989759929\n",
      "1597\n",
      "76561198072982056\n",
      "1415\n",
      "76561198038233734\n",
      "1372\n",
      "76561199007108737\n",
      "1293\n",
      "76561197982426635\n",
      "1143\n",
      "76561198289408350\n",
      "1106\n",
      "76561199106709787\n",
      "1074\n",
      "76561198137265435\n",
      "1069\n",
      "76561197966121094\n",
      "839\n",
      "76561198006228831\n",
      "629\n",
      "76561197961387596\n",
      "594\n",
      "76561198062732658\n",
      "547\n",
      "76561198073401735\n",
      "514\n",
      "76561198371948066\n",
      "493\n",
      "76561198304490377\n",
      "465\n",
      "76561198099392543\n",
      "427\n",
      "76561198119638471\n",
      "406\n",
      "76561199188067378\n",
      "392\n",
      "76561198054750823\n",
      "357\n",
      "76561198111623827\n",
      "300\n",
      "76561198886680688\n",
      "298\n",
      "76561198176830214\n",
      "296\n",
      "76561198149344078\n",
      "277\n",
      "76561198859695485\n",
      "260\n",
      "76561198213271873\n",
      "250\n",
      "76561198260206688\n",
      "247\n",
      "76561199076544785\n",
      "204\n",
      "76561198349125835\n",
      "199\n",
      "76561198036941569\n",
      "153\n",
      "76561198320147117\n",
      "139\n",
      "76561198910167305\n",
      "135\n",
      "76561198034778503\n",
      "63\n",
      "76561198346958197\n",
      "54\n",
      "76561199116533675\n",
      "28\n",
      "76561198155287031\n",
      "19\n",
      "76561199045962933\n",
      "15\n",
      "76561198067178214\n",
      "14\n",
      "76561199012853330\n",
      "12\n",
      "76561198326740478\n",
      "11\n",
      "76561198050975030\n",
      "9\n",
      "76561198186811683\n",
      "9\n",
      "76561197982468494\n",
      "9\n",
      "76561199053581619\n",
      "8\n",
      "76561198388676951\n",
      "4\n",
      "76561198045773137\n",
      "4\n",
      "76561198355454790\n",
      "4\n",
      "76561198286475741\n",
      "3\n",
      "76561198426681011\n",
      "1\n",
      "76561199133219656\n"
     ]
    }
   ],
   "source": [
    "for com in coms.communities:\n",
    "    print(len(com))\n",
    "    print(com[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "ename": "NetworkXError",
     "evalue": "Node '76561199006305510' has no position.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:442\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins, hide_ticks)\u001b[0m\n\u001b[0;32m    441\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 442\u001b[0m     xy \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray([pos[v] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m nodelist])\n\u001b[0;32m    443\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:442\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    441\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 442\u001b[0m     xy \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray([\u001b[43mpos\u001b[49m\u001b[43m[\u001b[49m\u001b[43mv\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m nodelist])\n\u001b[0;32m    443\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "\u001b[1;31mKeyError\u001b[0m: '76561199006305510'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mNetworkXError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[119], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m pos \u001b[38;5;241m=\u001b[39m nx\u001b[38;5;241m.\u001b[39mspring_layout(G1)\n\u001b[1;32m----> 2\u001b[0m \u001b[43mviz\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot_network_clusters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m viz\u001b[38;5;241m.\u001b[39mplot_community_graph(G1, coms)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\cdlib\\viz\\networks.py:115\u001b[0m, in \u001b[0;36mplot_network_clusters\u001b[1;34m(graph, partition, position, figsize, node_size, plot_overlaps, plot_labels, cmap, top_k, min_size)\u001b[0m\n\u001b[0;32m    108\u001b[0m filtered_nodelist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(np\u001b[38;5;241m.\u001b[39mconcatenate(partition))\n\u001b[0;32m    109\u001b[0m filtered_edgelist \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\n\u001b[0;32m    110\u001b[0m     \u001b[38;5;28mfilter\u001b[39m(\n\u001b[0;32m    111\u001b[0m         \u001b[38;5;28;01mlambda\u001b[39;00m edge: \u001b[38;5;28mlen\u001b[39m(np\u001b[38;5;241m.\u001b[39mintersect1d(edge, filtered_nodelist)) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m    112\u001b[0m         graph\u001b[38;5;241m.\u001b[39medges(),\n\u001b[0;32m    113\u001b[0m     )\n\u001b[0;32m    114\u001b[0m )\n\u001b[1;32m--> 115\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mnx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_networkx_nodes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    116\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgraph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnode_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_color\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnodelist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfiltered_nodelist\u001b[49m\n\u001b[0;32m    117\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    118\u001b[0m fig\u001b[38;5;241m.\u001b[39mset_edgecolor(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    119\u001b[0m nx\u001b[38;5;241m.\u001b[39mdraw_networkx_edges(graph, position, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, edgelist\u001b[38;5;241m=\u001b[39mfiltered_edgelist)\n",
      "File \u001b[1;32mc:\\Users\\adamr\\anaconda3\\envs\\NA_env\\lib\\site-packages\\networkx\\drawing\\nx_pylab.py:444\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins, hide_ticks)\u001b[0m\n\u001b[0;32m    442\u001b[0m     xy \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray([pos[v] \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m nodelist])\n\u001b[0;32m    443\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m--> 444\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m nx\u001b[38;5;241m.\u001b[39mNetworkXError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNode \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m has no position.\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m    446\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(alpha, Iterable):\n\u001b[0;32m    447\u001b[0m     node_color \u001b[38;5;241m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
      "\u001b[1;31mNetworkXError\u001b[0m: Node '76561199006305510' has no position."
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAJ8CAYAAABunRBBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAALm0lEQVR4nO3WMQEAIAzAMMC/5+ECjiYKenbPzCwAADLO7wAAAN4ygAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAgxgACAMQYQACAGAMIABBjAAEAYgwgAECMAQQAiDGAAAAxBhAAIMYAAgDEGEAAgBgDCAAQYwABAGIMIABAjAEEAIgxgAAAMQYQACDGAAIAxBhAAIAYAwgAEGMAAQBiDCAAQIwBBACIMYAAADEGEAAg5gKQLwj0bKgFgwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.spring_layout(G1)\n",
    "viz.plot_network_clusters(G1, coms, pos)\n",
    "viz.plot_community_graph(G1, coms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2099b4bdff0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.spring_layout(G)\n",
    "viz.plot_network_clusters(G, coms, pos)\n",
    "viz.plot_community_graph(G, coms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_communities = []\n",
    "\n",
    "for network in Networks:\n",
    "    communities = nx.community.fast_label_propagation_communities(network)\n",
    "    communities = list(communities)\n",
    "    all_communities.append(communities)\n",
    "\n",
    "# Now all_communities[i] is a list of communities for Networks[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>name</th>\n",
       "      <th>appid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dota 2</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PUBG: BATTLEGROUNDS</td>\n",
       "      <td>578080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New World</td>\n",
       "      <td>1063730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Palworld</td>\n",
       "      <td>1623730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               name    appid\n",
       "0                            Dota 2      570\n",
       "1  Counter-Strike: Global Offensive      730\n",
       "2               PUBG: BATTLEGROUNDS   578080\n",
       "3                         New World  1063730\n",
       "4                          Palworld  1623730"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_request = {\"request\": \"top100owned\"}\n",
    "data = steamspypi.download(data_request)\n",
    "df = pd.DataFrame(data.values())\n",
    "df = df[['name', 'appid']]\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = 'Networks_100_1000'\n",
    "\n",
    "# Function to check if a network file exists for a given appid\n",
    "def network_exists(appid):\n",
    "    filename = os.path.join(directory, str(appid) + '.net')\n",
    "    return os.path.exists(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      True\n",
       "1      True\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "      ...  \n",
       "95    False\n",
       "96    False\n",
       "97    False\n",
       "98     True\n",
       "99    False\n",
       "Name: appid, Length: 100, dtype: bool"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df['appid'].apply(network_exists)\n",
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>name</th>\n",
       "      <th>appid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dota 2</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PUBG: BATTLEGROUNDS</td>\n",
       "      <td>578080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New World</td>\n",
       "      <td>1063730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Palworld</td>\n",
       "      <td>1623730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>DayZ</td>\n",
       "      <td>221100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Dota Underlords</td>\n",
       "      <td>1046930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Risk of Rain 2</td>\n",
       "      <td>632360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Robocraft</td>\n",
       "      <td>301520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Sons Of The Forest</td>\n",
       "      <td>1326470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name    appid\n",
       "0                             Dota 2      570\n",
       "1   Counter-Strike: Global Offensive      730\n",
       "2                PUBG: BATTLEGROUNDS   578080\n",
       "3                          New World  1063730\n",
       "4                           Palworld  1623730\n",
       "..                               ...      ...\n",
       "95                              DayZ   221100\n",
       "96                   Dota Underlords  1046930\n",
       "97                    Risk of Rain 2   632360\n",
       "98                         Robocraft   301520\n",
       "99                Sons Of The Forest  1326470\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>name</th>\n",
       "      <th>appid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dota 2</td>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Counter-Strike: Global Offensive</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PUBG: BATTLEGROUNDS</td>\n",
       "      <td>578080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New World</td>\n",
       "      <td>1063730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Palworld</td>\n",
       "      <td>1623730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Sid Meier's Civilization V</td>\n",
       "      <td>8930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Life is Strange 2</td>\n",
       "      <td>532210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>EA SPORTS FIFA 23</td>\n",
       "      <td>1811260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Human Fall Flat</td>\n",
       "      <td>477160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Robocraft</td>\n",
       "      <td>301520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                name    appid\n",
       "0                             Dota 2      570\n",
       "1   Counter-Strike: Global Offensive      730\n",
       "2                PUBG: BATTLEGROUNDS   578080\n",
       "3                          New World  1063730\n",
       "4                           Palworld  1623730\n",
       "..                               ...      ...\n",
       "80        Sid Meier's Civilization V     8930\n",
       "81                 Life is Strange 2   532210\n",
       "82                 EA SPORTS FIFA 23  1811260\n",
       "84                   Human Fall Flat   477160\n",
       "98                         Robocraft   301520\n",
       "\n",
       "[83 rows x 2 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the mask to filter the DataFrame\n",
    "df = df[mask]\n",
    "\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                name    appid  \\\n",
      "0                             Dota 2      570   \n",
      "1   Counter-Strike: Global Offensive      730   \n",
      "2                PUBG: BATTLEGROUNDS   578080   \n",
      "3                          New World  1063730   \n",
      "4                           Palworld  1623730   \n",
      "..                               ...      ...   \n",
      "80        Sid Meier's Civilization V     8930   \n",
      "81                 Life is Strange 2   532210   \n",
      "82                 EA SPORTS FIFA 23  1811260   \n",
      "84                   Human Fall Flat   477160   \n",
      "98                         Robocraft   301520   \n",
      "\n",
      "                                                 tags  \n",
      "0   {'Free to Play': 59570, 'MOBA': 19969, 'Multip...  \n",
      "1   {'FPS': 90094, 'Shooter': 64803, 'Multiplayer'...  \n",
      "2   {'Survival': 14610, 'Shooter': 12478, 'Battle ...  \n",
      "3   {'Open World': 775, 'Massively Multiplayer': 7...  \n",
      "4   {'Open World': 1002, 'Multiplayer': 999, 'Crea...  \n",
      "..                                                ...  \n",
      "80  {'Turn-Based Strategy': 4945, 'Strategy': 4348...  \n",
      "81  {'Choices Matter': 682, 'Story Rich': 636, 'Ad...  \n",
      "82  {'Football (Soccer)': 959, 'Sports': 813, 'Con...  \n",
      "84  {'Co-op': 2044, 'Funny': 1949, 'Puzzle': 1913,...  \n",
      "98  {'Free to Play': 3980, 'Robots': 2518, 'Buildi...  \n",
      "\n",
      "[83 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\tmp\\ipykernel_9352\\1263462170.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['tags'] = df['appid'].apply(get_tags)\n"
     ]
    }
   ],
   "source": [
    "def get_tags(appid):\n",
    "    data_request = {\"request\": \"appdetails\", \"appid\": appid}\n",
    "    data = steamspypi.download(data_request)\n",
    "    return data['tags']\n",
    "\n",
    "df['tags'] = df['appid'].apply(get_tags)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sor the df by appid\n",
    "df = df.sort_values(by='appid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the tags for each game\n",
    "df['tags'] = df['appid'].apply(get_tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows with nan values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a Counter to keep track of community frequencies\n",
    "community_counter = Counter()\n",
    "\n",
    "# Initialize dictionaries to keep track of community sizes and percentages\n",
    "community_sizes = {}\n",
    "community_percentages = {}\n",
    "\n",
    "# Iterate through each network and its corresponding row in the DataFrame\n",
    "for network, (_, row) in zip(Networks, df.iterrows()):\n",
    "    # Compute the communities for this network\n",
    "    communities = nx.community.fast_label_propagation_communities(network)\n",
    "    communities = list(communities)\n",
    "\n",
    "    # Update the community counter with the number of communities\n",
    "    community_counter[len(communities)] += 1\n",
    "\n",
    "    # Add the number of communities to the DataFrame\n",
    "    df.loc[row.name, 'num_communities'] = len(communities)\n",
    "\n",
    "    # Compute the size of each community (number of nodes)\n",
    "    community_sizes[row.name] = [len(c) for c in communities]\n",
    "\n",
    "    # Compute the percentage of the total size for each community\n",
    "    total_size = len(network.nodes())\n",
    "    community_percentages[row.name] = [(len(c) / total_size) * 100 for c in communities]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get all unique tags\n",
    "all_tags = set()\n",
    "for tags in df['tags']:\n",
    "    all_tags.update(tags.keys())\n",
    "\n",
    "# Now, for each tag, compute the average number of communities, average community size, and average % of total size for games with that tag\n",
    "tag_community_counts = {}\n",
    "tag_community_sizes = {}\n",
    "tag_community_percentages = {}\n",
    "\n",
    "for tag in all_tags:\n",
    "    # Get the rows that contain this tag\n",
    "    rows_with_tag = df[df['tags'].apply(lambda tags: tag in tags.keys())]\n",
    "\n",
    "    # Compute the average number of communities for these rows\n",
    "    avg_communities = rows_with_tag['num_communities'].mean()\n",
    "\n",
    "    # Compute the average community size for these rows\n",
    "    avg_community_size = np.mean([size for row in rows_with_tag.index for size in community_sizes[row]])\n",
    "\n",
    "    # Compute the average % of total size for these rows\n",
    "    avg_percentage = np.mean([percentage for row in rows_with_tag.index for percentage in community_percentages[row]])\n",
    "\n",
    "    # Store the results\n",
    "    tag_community_counts[tag] = avg_communities\n",
    "    tag_community_sizes[tag] = avg_community_size\n",
    "    tag_community_percentages[tag] = avg_percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([99, 120, 125, 118, 119, 114, 36, 117, 65, 124, 127, 66, 63, 131, 24, 116, 95, 115, 132, 123, 121, 113, 39, 50, 44, 71, 128, 45, 106, 31, 37, 53, 138, 33, 130, 122, 111])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_counter.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Games with the tag 'Controller' have an average of 106.67 communities, average community size of 9.38 and 0.94% of the total size.\n",
      "Games with the tag 'Supernatural' have an average of 130.0 communities, average community size of 7.69 and 0.77% of the total size.\n",
      "Games with the tag '4 Player Local' have an average of 125.5 communities, average community size of 7.97 and 0.8% of the total size.\n",
      "Games with the tag 'Dungeon Crawler' have an average of 103.0 communities, average community size of 9.71 and 0.97% of the total size.\n",
      "Games with the tag 'Sandbox' have an average of 87.35 communities, average community size of 11.45 and 1.14% of the total size.\n",
      "Games with the tag 'Choose Your Own Adventure' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Space' have an average of 130.5 communities, average community size of 7.66 and 0.77% of the total size.\n",
      "Games with the tag 'Silent Protagonist' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Post-apocalyptic' have an average of 106.0 communities, average community size of 9.43 and 0.94% of the total size.\n",
      "Games with the tag 'Horses' have an average of 123.0 communities, average community size of 8.13 and 0.81% of the total size.\n",
      "Games with the tag 'CRPG' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'FPS' have an average of 103.69 communities, average community size of 9.64 and 0.96% of the total size.\n",
      "Games with the tag 'Indie' have an average of 96.92 communities, average community size of 10.32 and 1.03% of the total size.\n",
      "Games with the tag 'Economy' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Grand Strategy' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'PvP' have an average of 108.0 communities, average community size of 9.26 and 0.93% of the total size.\n",
      "Games with the tag 'Tower Defense' have an average of 104.5 communities, average community size of 9.57 and 0.96% of the total size.\n",
      "Games with the tag 'Ninja' have an average of 126.0 communities, average community size of 7.94 and 0.79% of the total size.\n",
      "Games with the tag 'Trading' have an average of 118.0 communities, average community size of 8.47 and 0.85% of the total size.\n",
      "Games with the tag 'Strategy' have an average of 102.5 communities, average community size of 9.76 and 0.98% of the total size.\n",
      "Games with the tag 'Puzzle' have an average of 91.17 communities, average community size of 10.97 and 1.1% of the total size.\n",
      "Games with the tag 'Voxel' have an average of 72.5 communities, average community size of 13.79 and 1.38% of the total size.\n",
      "Games with the tag 'Driving' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Minigames' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Heist' have an average of 63.0 communities, average community size of 15.87 and 1.59% of the total size.\n",
      "Games with the tag 'Detective' have an average of 130.0 communities, average community size of 7.69 and 0.77% of the total size.\n",
      "Games with the tag 'VR' have an average of 126.0 communities, average community size of 7.94 and 0.79% of the total size.\n",
      "Games with the tag 'Hand-drawn' have an average of 128.0 communities, average community size of 7.81 and 0.78% of the total size.\n",
      "Games with the tag 'Agriculture' have an average of 45.0 communities, average community size of 22.22 and 2.22% of the total size.\n",
      "Games with the tag 'Demons' have an average of 130.0 communities, average community size of 7.69 and 0.77% of the total size.\n",
      "Games with the tag 'Old School' have an average of 112.0 communities, average community size of 8.93 and 0.89% of the total size.\n",
      "Games with the tag 'World War II' have an average of 104.5 communities, average community size of 9.56 and 0.96% of the total size.\n",
      "Games with the tag 'Base-Building' have an average of 81.0 communities, average community size of 12.35 and 1.23% of the total size.\n",
      "Games with the tag 'Side Scroller' have an average of 128.0 communities, average community size of 7.81 and 0.78% of the total size.\n",
      "Games with the tag 'Relaxing' have an average of 84.5 communities, average community size of 11.83 and 1.18% of the total size.\n",
      "Games with the tag 'Vehicular Combat' have an average of 122.5 communities, average community size of 8.16 and 0.82% of the total size.\n",
      "Games with the tag 'Sci-fi' have an average of 114.4 communities, average community size of 8.74 and 0.87% of the total size.\n",
      "Games with the tag 'Gore' have an average of 114.85 communities, average community size of 8.71 and 0.87% of the total size.\n",
      "Games with the tag 'Medieval' have an average of 81.0 communities, average community size of 12.35 and 1.23% of the total size.\n",
      "Games with the tag 'Mythology' have an average of 118.5 communities, average community size of 8.44 and 0.84% of the total size.\n",
      "Games with the tag 'Episodic' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Dark Humor' have an average of 99.75 communities, average community size of 10.03 and 1.0% of the total size.\n",
      "Games with the tag 'Looter Shooter' have an average of 99.4 communities, average community size of 10.06 and 1.01% of the total size.\n",
      "Games with the tag 'Local Multiplayer' have an average of 102.25 communities, average community size of 9.78 and 0.98% of the total size.\n",
      "Games with the tag 'Destruction' have an average of 115.0 communities, average community size of 8.7 and 0.87% of the total size.\n",
      "Games with the tag 'Hero Shooter' have an average of 97.25 communities, average community size of 10.28 and 1.03% of the total size.\n",
      "Games with the tag 'LGBTQ+' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag '2D Fighter' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Turn-Based' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Replay Value' have an average of 102.43 communities, average community size of 9.76 and 0.98% of the total size.\n",
      "Games with the tag 'Turn-Based Combat' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'MOBA' have an average of 95.75 communities, average community size of 10.44 and 1.04% of the total size.\n",
      "Games with the tag 'Strategy RPG' have an average of 118.0 communities, average community size of 8.47 and 0.85% of the total size.\n",
      "Games with the tag 'Difficult' have an average of 114.29 communities, average community size of 8.75 and 0.88% of the total size.\n",
      "Games with the tag 'Action-Adventure' have an average of 91.33 communities, average community size of 10.95 and 1.09% of the total size.\n",
      "Games with the tag '3D Platformer' have an average of 84.4 communities, average community size of 11.84 and 1.18% of the total size.\n",
      "Games with the tag 'Racing' have an average of 126.5 communities, average community size of 7.91 and 0.79% of the total size.\n",
      "Games with the tag 'Nostalgia' have an average of 89.0 communities, average community size of 11.24 and 1.12% of the total size.\n",
      "Games with the tag 'Puzzle-Platformer' have an average of 63.67 communities, average community size of 15.71 and 1.57% of the total size.\n",
      "Games with the tag 'Great Soundtrack' have an average of 110.83 communities, average community size of 9.02 and 0.9% of the total size.\n",
      "Games with the tag 'Souls-like' have an average of 125.33 communities, average community size of 7.98 and 0.8% of the total size.\n",
      "Games with the tag 'First-Person' have an average of 101.97 communities, average community size of 9.81 and 0.98% of the total size.\n",
      "Games with the tag 'Hidden Object' have an average of 113.0 communities, average community size of 8.85 and 0.88% of the total size.\n",
      "Games with the tag 'Dinosaurs' have an average of 71.0 communities, average community size of 14.08 and 1.41% of the total size.\n",
      "Games with the tag 'Gaming' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Top-Down' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Dragons' have an average of 71.0 communities, average community size of 14.08 and 1.41% of the total size.\n",
      "Games with the tag 'Zombies' have an average of 93.0 communities, average community size of 10.75 and 1.08% of the total size.\n",
      "Games with the tag 'Animation & Modeling' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Multiple Endings' have an average of 83.5 communities, average community size of 11.98 and 1.2% of the total size.\n",
      "Games with the tag 'Sports' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Cute' have an average of 100.25 communities, average community size of 9.98 and 1.0% of the total size.\n",
      "Games with the tag 'Underwater' have an average of 39.0 communities, average community size of 25.64 and 2.56% of the total size.\n",
      "Games with the tag 'Colorful' have an average of 78.25 communities, average community size of 12.78 and 1.28% of the total size.\n",
      "Games with the tag 'Casual' have an average of 94.45 communities, average community size of 10.59 and 1.06% of the total size.\n",
      "Games with the tag 'Pixel Graphics' have an average of 69.25 communities, average community size of 14.44 and 1.44% of the total size.\n",
      "Games with the tag 'Retro' have an average of 120.0 communities, average community size of 8.33 and 0.83% of the total size.\n",
      "Games with the tag 'Walking Simulator' have an average of 121.75 communities, average community size of 8.21 and 0.82% of the total size.\n",
      "Games with the tag 'Action RPG' have an average of 114.36 communities, average community size of 8.74 and 0.87% of the total size.\n",
      "Games with the tag 'Realistic' have an average of 100.64 communities, average community size of 9.93 and 0.99% of the total size.\n",
      "Games with the tag 'Co-op Campaign' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'Sexual Content' have an average of 124.67 communities, average community size of 8.02 and 0.8% of the total size.\n",
      "Games with the tag 'Comedy' have an average of 103.33 communities, average community size of 9.68 and 0.97% of the total size.\n",
      "Games with the tag 'Dark Fantasy' have an average of 108.0 communities, average community size of 9.26 and 0.93% of the total size.\n",
      "Games with the tag 'JRPG' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'Photo Editing' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Rogue-lite' have an average of 116.0 communities, average community size of 8.62 and 0.86% of the total size.\n",
      "Games with the tag 'Romance' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'Game Development' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Satire' have an average of 118.0 communities, average community size of 8.47 and 0.85% of the total size.\n",
      "Games with the tag 'Loot' have an average of 125.8 communities, average community size of 7.95 and 0.79% of the total size.\n",
      "Games with the tag 'Investigation' have an average of 130.0 communities, average community size of 7.69 and 0.77% of the total size.\n",
      "Games with the tag 'Hex Grid' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Aliens' have an average of 119.67 communities, average community size of 8.36 and 0.84% of the total size.\n",
      "Games with the tag 'Hack and Slash' have an average of 124.83 communities, average community size of 8.01 and 0.8% of the total size.\n",
      "Games with the tag 'Third Person' have an average of 113.17 communities, average community size of 8.84 and 0.88% of the total size.\n",
      "Games with the tag 'Psychological' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Dystopian ' have an average of 118.0 communities, average community size of 8.47 and 0.85% of the total size.\n",
      "Games with the tag 'Action Roguelike' have an average of 116.0 communities, average community size of 8.62 and 0.86% of the total size.\n",
      "Games with the tag 'Turn-Based Strategy' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Capitalism' have an average of 138.0 communities, average community size of 7.25 and 0.72% of the total size.\n",
      "Games with the tag 'Cinematic' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Stealth' have an average of 92.2 communities, average community size of 10.85 and 1.08% of the total size.\n",
      "Games with the tag 'Moddable' have an average of 116.45 communities, average community size of 8.59 and 0.86% of the total size.\n",
      "Games with the tag 'Cold War' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag '1980s' have an average of 99.0 communities, average community size of 10.1 and 1.01% of the total size.\n",
      "Games with the tag 'Nudity' have an average of 99.0 communities, average community size of 10.1 and 1.01% of the total size.\n",
      "Games with the tag 'Early Access' have an average of 102.47 communities, average community size of 9.76 and 0.98% of the total size.\n",
      "Games with the tag 'Flight' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Fishing' have an average of 45.0 communities, average community size of 22.22 and 2.22% of the total size.\n",
      "Games with the tag 'Futuristic' have an average of 117.5 communities, average community size of 8.51 and 0.85% of the total size.\n",
      "Games with the tag 'Family Friendly' have an average of 104.4 communities, average community size of 9.58 and 0.96% of the total size.\n",
      "Games with the tag 'Real Time Tactics' have an average of 120.0 communities, average community size of 8.32 and 0.83% of the total size.\n",
      "Games with the tag 'Management' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Exploration' have an average of 100.92 communities, average community size of 9.91 and 0.99% of the total size.\n",
      "Games with the tag 'Football (American)' have an average of 132.0 communities, average community size of 7.58 and 0.76% of the total size.\n",
      "Games with the tag 'Open World' have an average of 102.22 communities, average community size of 9.78 and 0.98% of the total size.\n",
      "Games with the tag 'Robots' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Fighting' have an average of 112.5 communities, average community size of 8.89 and 0.89% of the total size.\n",
      "Games with the tag 'RPG' have an average of 105.57 communities, average community size of 9.47 and 0.95% of the total size.\n",
      "Games with the tag 'Assassin' have an average of 110.0 communities, average community size of 9.09 and 0.91% of the total size.\n",
      "Games with the tag '2D Platformer' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Software' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Funny' have an average of 103.0 communities, average community size of 9.71 and 0.97% of the total size.\n",
      "Games with the tag 'Classic' have an average of 117.43 communities, average community size of 8.52 and 0.85% of the total size.\n",
      "Games with the tag 'Fast-Paced' have an average of 124.5 communities, average community size of 8.03 and 0.8% of the total size.\n",
      "Games with the tag 'Transportation' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Cartoon' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Team-Based' have an average of 101.53 communities, average community size of 9.85 and 0.98% of the total size.\n",
      "Games with the tag 'Modern' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Extraction Shooter' have an average of 138.0 communities, average community size of 7.25 and 0.72% of the total size.\n",
      "Games with the tag 'Remake' have an average of 116.0 communities, average community size of 8.62 and 0.86% of the total size.\n",
      "Games with the tag 'Multiplayer' have an average of 106.17 communities, average community size of 9.42 and 0.94% of the total size.\n",
      "Games with the tag 'e-sports' have an average of 113.67 communities, average community size of 8.8 and 0.88% of the total size.\n",
      "Games with the tag 'Point & Click' have an average of 119.0 communities, average community size of 8.4 and 0.84% of the total size.\n",
      "Games with the tag 'Military' have an average of 109.62 communities, average community size of 9.12 and 0.91% of the total size.\n",
      "Games with the tag '3D' have an average of 121.67 communities, average community size of 8.22 and 0.82% of the total size.\n",
      "Games with the tag 'Psychological Horror' have an average of 125.25 communities, average community size of 7.98 and 0.8% of the total size.\n",
      "Games with the tag 'Class-Based' have an average of 123.33 communities, average community size of 8.11 and 0.81% of the total size.\n",
      "Games with the tag 'Survival' have an average of 95.7 communities, average community size of 10.45 and 1.04% of the total size.\n",
      "Games with the tag 'Tanks' have an average of 122.5 communities, average community size of 8.16 and 0.82% of the total size.\n",
      "Games with the tag 'PvE' have an average of 110.25 communities, average community size of 9.07 and 0.91% of the total size.\n",
      "Games with the tag 'Dating Sim' have an average of 84.0 communities, average community size of 11.9 and 1.19% of the total size.\n",
      "Games with the tag 'Diplomacy' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Magic' have an average of 92.67 communities, average community size of 10.79 and 1.08% of the total size.\n",
      "Games with the tag 'Lore-Rich' have an average of 129.0 communities, average community size of 7.75 and 0.78% of the total size.\n",
      "Games with the tag 'Design & Illustration' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Physics' have an average of 87.14 communities, average community size of 11.48 and 1.15% of the total size.\n",
      "Games with the tag 'Online Co-Op' have an average of 105.81 communities, average community size of 9.45 and 0.95% of the total size.\n",
      "Games with the tag 'RTS' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Mystery' have an average of 122.67 communities, average community size of 8.15 and 0.82% of the total size.\n",
      "Games with the tag 'Utilities' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Platformer' have an average of 96.33 communities, average community size of 10.38 and 1.04% of the total size.\n",
      "Games with the tag 'Arena Shooter' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Party Game' have an average of 116.0 communities, average community size of 8.62 and 0.86% of the total size.\n",
      "Games with the tag 'Parkour' have an average of 97.33 communities, average community size of 10.27 and 1.03% of the total size.\n",
      "Games with the tag 'Battle Royale' have an average of 103.0 communities, average community size of 9.71 and 0.97% of the total size.\n",
      "Games with the tag 'Thriller' have an average of 130.0 communities, average community size of 7.69 and 0.77% of the total size.\n",
      "Games with the tag 'Simulation' have an average of 88.4 communities, average community size of 11.31 and 1.13% of the total size.\n",
      "Games with the tag 'Rogue-like' have an average of 118.5 communities, average community size of 8.44 and 0.84% of the total size.\n",
      "Games with the tag 'Horror' have an average of 111.0 communities, average community size of 9.01 and 0.9% of the total size.\n",
      "Games with the tag 'Naval Combat' have an average of 89.0 communities, average community size of 11.24 and 1.12% of the total size.\n",
      "Games with the tag 'Mature' have an average of 102.8 communities, average community size of 9.73 and 0.97% of the total size.\n",
      "Games with the tag 'Dungeons & Dragons' have an average of 127.0 communities, average community size of 7.87 and 0.79% of the total size.\n",
      "Games with the tag 'Open World Survival Craft' have an average of 92.09 communities, average community size of 10.86 and 1.09% of the total size.\n",
      "Games with the tag 'Science' have an average of 77.0 communities, average community size of 12.99 and 1.3% of the total size.\n",
      "Games with the tag 'Inventory Management' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Drama' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag 'Shooter' have an average of 107.29 communities, average community size of 9.32 and 0.93% of the total size.\n",
      "Games with the tag 'Short' have an average of 116.33 communities, average community size of 8.6 and 0.86% of the total size.\n",
      "Games with the tag 'Story Rich' have an average of 108.5 communities, average community size of 9.22 and 0.92% of the total size.\n",
      "Games with the tag 'Rhythm' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'NSFW' have an average of 117.0 communities, average community size of 8.55 and 0.85% of the total size.\n",
      "Games with the tag 'Combat' have an average of 124.25 communities, average community size of 8.05 and 0.8% of the total size.\n",
      "Games with the tag 'Tactical' have an average of 105.67 communities, average community size of 9.46 and 0.95% of the total size.\n",
      "Games with the tag 'Addictive' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Choices Matter' have an average of 102.25 communities, average community size of 9.78 and 0.98% of the total size.\n",
      "Games with the tag 'Social Deduction' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Automobile Sim' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Cyberpunk' have an average of 120.5 communities, average community size of 8.3 and 0.83% of the total size.\n",
      "Games with the tag 'Co-op' have an average of 104.38 communities, average community size of 9.58 and 0.96% of the total size.\n",
      "Games with the tag 'Metroidvania' have an average of 128.0 communities, average community size of 7.81 and 0.78% of the total size.\n",
      "Games with the tag 'Procedural Generation' have an average of 87.0 communities, average community size of 11.49 and 1.15% of the total size.\n",
      "Games with the tag 'Memes' have an average of 122.33 communities, average community size of 8.17 and 0.82% of the total size.\n",
      "Games with the tag 'Third-Person Shooter' have an average of 109.45 communities, average community size of 9.14 and 0.91% of the total size.\n",
      "Games with the tag 'Immersive Sim' have an average of 120.5 communities, average community size of 8.3 and 0.83% of the total size.\n",
      "Games with the tag 'Competitive' have an average of 111.27 communities, average community size of 8.99 and 0.9% of the total size.\n",
      "Games with the tag 'Local Co-Op' have an average of 104.8 communities, average community size of 9.54 and 0.95% of the total size.\n",
      "Games with the tag '1990's' have an average of 109.5 communities, average community size of 9.13 and 0.91% of the total size.\n",
      "Games with the tag 'Benchmark' have an average of 118.0 communities, average community size of 8.47 and 0.85% of the total size.\n",
      "Games with the tag 'Survival Horror' have an average of 107.0 communities, average community size of 9.35 and 0.93% of the total size.\n",
      "Games with the tag 'Massively Multiplayer' have an average of 101.77 communities, average community size of 9.82 and 0.98% of the total size.\n",
      "Games with the tag 'Life Sim' have an average of 45.0 communities, average community size of 22.22 and 2.22% of the total size.\n",
      "Games with the tag 'Level Editor' have an average of 36.0 communities, average community size of 27.78 and 2.78% of the total size.\n",
      "Games with the tag 'Free to Play' have an average of 108.46 communities, average community size of 9.22 and 0.92% of the total size.\n",
      "Games with the tag 'Blood' have an average of 120.0 communities, average community size of 8.33 and 0.83% of the total size.\n",
      "Games with the tag 'Action' have an average of 104.52 communities, average community size of 9.57 and 0.96% of the total size.\n",
      "Games with the tag 'Farming Sim' have an average of 45.0 communities, average community size of 22.22 and 2.22% of the total size.\n",
      "Games with the tag 'Cartoony' have an average of 119.67 communities, average community size of 8.36 and 0.84% of the total size.\n",
      "Games with the tag 'Singleplayer' have an average of 106.0 communities, average community size of 9.43 and 0.94% of the total size.\n",
      "Games with the tag 'Touch-Friendly' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'Football (Soccer)' have an average of 132.0 communities, average community size of 7.58 and 0.76% of the total size.\n",
      "Games with the tag 'Character Customization' have an average of 113.75 communities, average community size of 8.79 and 0.88% of the total size.\n",
      "Games with the tag 'Historical' have an average of 105.0 communities, average community size of 9.52 and 0.95% of the total size.\n",
      "Games with the tag 'Female Protagonist' have an average of 77.0 communities, average community size of 12.99 and 1.3% of the total size.\n",
      "Games with the tag 'MMORPG' have an average of 114.75 communities, average community size of 8.71 and 0.87% of the total size.\n",
      "Games with the tag 'Adventure' have an average of 103.93 communities, average community size of 9.62 and 0.96% of the total size.\n",
      "Games with the tag 'Score Attack' have an average of 99.0 communities, average community size of 10.1 and 1.01% of the total size.\n",
      "Games with the tag 'Isometric' have an average of 121.0 communities, average community size of 8.26 and 0.83% of the total size.\n",
      "Games with the tag 'Atmospheric' have an average of 105.73 communities, average community size of 9.46 and 0.95% of the total size.\n",
      "Games with the tag '4X' have an average of 124.0 communities, average community size of 8.06 and 0.81% of the total size.\n",
      "Games with the tag 'War' have an average of 112.12 communities, average community size of 8.92 and 0.89% of the total size.\n",
      "Games with the tag 'Naval' have an average of 53.0 communities, average community size of 18.87 and 1.89% of the total size.\n",
      "Games with the tag 'Building' have an average of 77.42 communities, average community size of 12.92 and 1.29% of the total size.\n",
      "Games with the tag 'Fantasy' have an average of 99.36 communities, average community size of 10.06 and 1.01% of the total size.\n",
      "Games with the tag 'Mod' have an average of 125.0 communities, average community size of 8.0 and 0.8% of the total size.\n",
      "Games with the tag '2D' have an average of 102.0 communities, average community size of 9.8 and 0.98% of the total size.\n",
      "Games with the tag 'Dark' have an average of 106.17 communities, average community size of 9.42 and 0.94% of the total size.\n",
      "Games with the tag 'Split Screen' have an average of 84.5 communities, average community size of 11.83 and 1.18% of the total size.\n",
      "Games with the tag 'Anime' have an average of 117.75 communities, average community size of 8.49 and 0.85% of the total size.\n",
      "Games with the tag 'Gun Customization' have an average of 93.5 communities, average community size of 10.7 and 1.07% of the total size.\n",
      "Games with the tag 'Crime' have an average of 100.67 communities, average community size of 9.93 and 0.99% of the total size.\n",
      "Games with the tag 'Crafting' have an average of 83.93 communities, average community size of 11.91 and 1.19% of the total size.\n",
      "Games with the tag 'Violent' have an average of 121.6 communities, average community size of 8.22 and 0.82% of the total size.\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "for tag in tag_community_counts.keys():\n",
    "    avg_communities = tag_community_counts[tag]\n",
    "    avg_community_size = tag_community_sizes[tag]\n",
    "    avg_percentage = tag_community_percentages[tag]\n",
    "    print(f\"Games with the tag '{tag}' have an average of {round(avg_communities, 2)} communities, \"\n",
    "          f\"average community size of {round(avg_community_size, 2)} and \"\n",
    "          f\"{round(avg_percentage, 2)}% of the total size.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 tags with the highest average number of communities:\n",
      "Games with the tag 'Relaxing' have an average of 1.18 communities, with an average community size of 11.83 and making up an average of 1.18% of the total size.\n",
      "Games with the tag 'Split Screen' have an average of 1.18 communities, with an average community size of 11.83 and making up an average of 1.18% of the total size.\n",
      "Games with the tag '3D Platformer' have an average of 1.18 communities, with an average community size of 11.84 and making up an average of 1.18% of the total size.\n",
      "Games with the tag 'Dating Sim' have an average of 1.19 communities, with an average community size of 11.9 and making up an average of 1.19% of the total size.\n",
      "Games with the tag 'Crafting' have an average of 1.19 communities, with an average community size of 11.91 and making up an average of 1.19% of the total size.\n",
      "Games with the tag 'Multiple Endings' have an average of 1.2 communities, with an average community size of 11.98 and making up an average of 1.2% of the total size.\n",
      "Games with the tag 'Base-Building' have an average of 1.23 communities, with an average community size of 12.35 and making up an average of 1.23% of the total size.\n",
      "Games with the tag 'Medieval' have an average of 1.23 communities, with an average community size of 12.35 and making up an average of 1.23% of the total size.\n",
      "Games with the tag 'Colorful' have an average of 1.28 communities, with an average community size of 12.78 and making up an average of 1.28% of the total size.\n",
      "Games with the tag 'Building' have an average of 1.29 communities, with an average community size of 12.92 and making up an average of 1.29% of the total size.\n",
      "Games with the tag 'Science' have an average of 1.3 communities, with an average community size of 12.99 and making up an average of 1.3% of the total size.\n",
      "Games with the tag 'Female Protagonist' have an average of 1.3 communities, with an average community size of 12.99 and making up an average of 1.3% of the total size.\n",
      "Games with the tag 'Voxel' have an average of 1.38 communities, with an average community size of 13.79 and making up an average of 1.38% of the total size.\n",
      "Games with the tag 'Dinosaurs' have an average of 1.41 communities, with an average community size of 14.08 and making up an average of 1.41% of the total size.\n",
      "Games with the tag 'Dragons' have an average of 1.41 communities, with an average community size of 14.08 and making up an average of 1.41% of the total size.\n",
      "Games with the tag 'Pixel Graphics' have an average of 1.44 communities, with an average community size of 14.44 and making up an average of 1.44% of the total size.\n",
      "Games with the tag 'Puzzle-Platformer' have an average of 1.57 communities, with an average community size of 15.71 and making up an average of 1.57% of the total size.\n",
      "Games with the tag 'Heist' have an average of 1.59 communities, with an average community size of 15.87 and making up an average of 1.59% of the total size.\n",
      "Games with the tag 'Naval' have an average of 1.89 communities, with an average community size of 18.87 and making up an average of 1.89% of the total size.\n",
      "Games with the tag 'Agriculture' have an average of 2.22 communities, with an average community size of 22.22 and making up an average of 2.22% of the total size.\n",
      "Games with the tag 'Fishing' have an average of 2.22 communities, with an average community size of 22.22 and making up an average of 2.22% of the total size.\n",
      "Games with the tag 'Life Sim' have an average of 2.22 communities, with an average community size of 22.22 and making up an average of 2.22% of the total size.\n",
      "Games with the tag 'Farming Sim' have an average of 2.22 communities, with an average community size of 22.22 and making up an average of 2.22% of the total size.\n",
      "Games with the tag 'Underwater' have an average of 2.56 communities, with an average community size of 25.64 and making up an average of 2.56% of the total size.\n",
      "Games with the tag 'Level Editor' have an average of 2.78 communities, with an average community size of 27.78 and making up an average of 2.78% of the total size.\n",
      "\n",
      "Top 5 tags with the lowest average number of communities:\n",
      "Games with the tag 'Capitalism' have an average of 0.72 communities, with an average community size of 7.25 and making up an average of 0.72% of the total size.\n",
      "Games with the tag 'Extraction Shooter' have an average of 0.72 communities, with an average community size of 7.25 and making up an average of 0.72% of the total size.\n",
      "Games with the tag 'Football (American)' have an average of 0.76 communities, with an average community size of 7.58 and making up an average of 0.76% of the total size.\n",
      "Games with the tag 'Football (Soccer)' have an average of 0.76 communities, with an average community size of 7.58 and making up an average of 0.76% of the total size.\n",
      "Games with the tag 'Space' have an average of 0.77 communities, with an average community size of 7.66 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Supernatural' have an average of 0.77 communities, with an average community size of 7.69 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Detective' have an average of 0.77 communities, with an average community size of 7.69 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Demons' have an average of 0.77 communities, with an average community size of 7.69 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Investigation' have an average of 0.77 communities, with an average community size of 7.69 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Thriller' have an average of 0.77 communities, with an average community size of 7.69 and making up an average of 0.77% of the total size.\n",
      "Games with the tag 'Lore-Rich' have an average of 0.78 communities, with an average community size of 7.75 and making up an average of 0.78% of the total size.\n",
      "Games with the tag 'Hand-drawn' have an average of 0.78 communities, with an average community size of 7.81 and making up an average of 0.78% of the total size.\n",
      "Games with the tag 'Side Scroller' have an average of 0.78 communities, with an average community size of 7.81 and making up an average of 0.78% of the total size.\n",
      "Games with the tag 'Metroidvania' have an average of 0.78 communities, with an average community size of 7.81 and making up an average of 0.78% of the total size.\n",
      "Games with the tag 'CRPG' have an average of 0.79 communities, with an average community size of 7.87 and making up an average of 0.79% of the total size.\n"
     ]
    }
   ],
   "source": [
    "#get me the top 5 tags with the highest average number of communities and the top 5 tags with the lowest average number of communities\n",
    "# sorted_tags = sorted(tag_community_counts.items(), key=lambda x: x[1])\n",
    "sorted_tags = sorted(tag_community_percentages.items(), key=lambda x: x[1])\n",
    "top_5 = sorted_tags[-25:]\n",
    "bottom_5 = sorted_tags[:15]\n",
    "\n",
    "print(\"Top 5 tags with the highest average number of communities:\")\n",
    "for tag, avg_communities in top_5:\n",
    "    avg_size = tag_community_sizes[tag]\n",
    "    avg_percent = tag_community_percentages[tag]\n",
    "    print(f\"Games with the tag '{tag}' have an average of {round(avg_communities, 2)} communities, \"\n",
    "          f\"with an average community size of {round(avg_size, 2)} and \"\n",
    "          f\"making up an average of {round(avg_percent, 2)}% of the total size.\")\n",
    "\n",
    "print(\"\\nTop 5 tags with the lowest average number of communities:\")\n",
    "for tag, avg_communities in bottom_5:\n",
    "    avg_size = tag_community_sizes[tag]\n",
    "    avg_percent = tag_community_percentages[tag]\n",
    "    print(f\"Games with the tag '{tag}' have an average of {round(avg_communities, 2)} communities, \"\n",
    "          f\"with an average community size of {round(avg_size, 2)} and \"\n",
    "          f\"making up an average of {round(avg_percent, 2)}% of the total size.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 tags with the highest average % of total size:\n",
      "4 Player Local 125.5 7.97 125.5\n",
      "Loot 125.8 7.95 125.8\n",
      "Ninja 126.0 7.94 126.0\n",
      "VR 126.0 7.94 126.0\n",
      "Racing 126.5 7.91 126.5\n",
      "CRPG 127.0 7.87 127.0\n",
      "Turn-Based Combat 127.0 7.87 127.0\n",
      "Co-op Campaign 127.0 7.87 127.0\n",
      "JRPG 127.0 7.87 127.0\n",
      "Romance 127.0 7.87 127.0\n",
      "Dungeons & Dragons 127.0 7.87 127.0\n",
      "Hand-drawn 128.0 7.81 128.0\n",
      "Side Scroller 128.0 7.81 128.0\n",
      "Metroidvania 128.0 7.81 128.0\n",
      "Lore-Rich 129.0 7.75 129.0\n",
      "Supernatural 130.0 7.69 130.0\n",
      "Detective 130.0 7.69 130.0\n",
      "Demons 130.0 7.69 130.0\n",
      "Investigation 130.0 7.69 130.0\n",
      "Thriller 130.0 7.69 130.0\n",
      "Space 130.5 7.66 130.5\n",
      "Football (American) 132.0 7.58 132.0\n",
      "Football (Soccer) 132.0 7.58 132.0\n",
      "Capitalism 138.0 7.25 138.0\n",
      "Extraction Shooter 138.0 7.25 138.0\n",
      "\n",
      "Top 5 tags with the lowest average percentage of total size:\n",
      "Level Editor 36.0 27.78 36.0\n",
      "Underwater 39.0 25.64 39.0\n",
      "Agriculture 45.0 22.22 45.0\n",
      "Fishing 45.0 22.22 45.0\n",
      "Life Sim 45.0 22.22 45.0\n",
      "Farming Sim 45.0 22.22 45.0\n",
      "Naval 53.0 18.87 53.0\n",
      "Heist 63.0 15.87 63.0\n",
      "Puzzle-Platformer 63.67 15.71 63.67\n",
      "Pixel Graphics 69.25 14.44 69.25\n",
      "Dinosaurs 71.0 14.08 71.0\n",
      "Dragons 71.0 14.08 71.0\n",
      "Voxel 72.5 13.79 72.5\n",
      "Science 77.0 12.99 77.0\n",
      "Female Protagonist 77.0 12.99 77.0\n",
      "Building 77.42 12.92 77.42\n",
      "Colorful 78.25 12.78 78.25\n",
      "Base-Building 81.0 12.35 81.0\n",
      "Medieval 81.0 12.35 81.0\n",
      "Multiple Endings 83.5 11.98 83.5\n",
      "Crafting 83.93 11.91 83.93\n",
      "Dating Sim 84.0 11.9 84.0\n",
      "3D Platformer 84.4 11.84 84.4\n",
      "Relaxing 84.5 11.83 84.5\n",
      "Split Screen 84.5 11.83 84.5\n"
     ]
    }
   ],
   "source": [
    "sorted_tags = sorted(tag_community_counts.items(), key=lambda x: x[1])\n",
    "top_5 = sorted_tags[-25:]\n",
    "bottom_5 = sorted_tags[:25]\n",
    "\n",
    "print(\"Top 5 tags with the highest average % of total size:\")\n",
    "for tag, avg_percent in top_5:\n",
    "    avg_communities = tag_community_counts[tag]\n",
    "    avg_size = tag_community_sizes[tag]\n",
    "    print(tag, round(avg_communities, 2), round(avg_size, 2), round(avg_percent, 2))\n",
    "\n",
    "print(\"\\nTop 5 tags with the lowest average percentage of total size:\")\n",
    "for tag, avg_percent in bottom_5:\n",
    "    avg_communities = tag_community_counts[tag]\n",
    "    avg_size = tag_community_sizes[tag]\n",
    "    print(tag, round(avg_communities, 2), round(avg_size, 2), round(avg_percent, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MOBA 95.75 10.44 1.04\n"
     ]
    }
   ],
   "source": [
    "tag = 'MOBA'\n",
    "avg_communities = tag_community_counts.get(tag, 0)\n",
    "avg_size = tag_community_sizes.get(tag, 0)\n",
    "avg_percent = tag_community_percentages.get(tag, 0)\n",
    "\n",
    "print(tag, round(avg_communities, 2), round(avg_size, 2), round(avg_percent, 2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "NA_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}