face_meteor_pilot_ratings_Chloe_v8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "# from pymer4.models import Lmer\n",
    "from natsort import natsorted, ns\n",
    "import glob\n",
    "import networkx as nx\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "fpath = 'C:/Users/becha/Desktop/Face_meteor_analysis'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 51 groups for v8.\n"
     ]
    }
   ],
   "source": [
    "datalist = natsorted(glob.glob(os.path.join(fpath,'Data', 'preprocessed_data', 'preprocessed_data', \"v8\",\"*\")))\n",
    "len(datalist)\n",
    "print('There are',len(datalist),'groups for v8.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "\n",
    "def loop_directory(directory: str):\n",
    "    '''Loop files in the directory'''\n",
    "\n",
    "    for filename in os.listdir(directory):\n",
    "            if filename.endswith(\".csv\"):\n",
    "                file_directory = os.path.join(directory, filename)\n",
    "                lst.append(file_directory)\n",
    "                pd.read_csv(file_directory)\n",
    "                \n",
    "                \n",
    "            else:\n",
    "                continue\n",
    "#     print(lst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data of games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_136_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_137_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_138_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_139_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_140_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_141_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_142_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_143_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_144_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_145_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_146_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_147_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_148_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_149_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_150_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_151_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_152_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_153_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_154_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_155_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_156_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_157_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_158_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_166_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_167_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_168_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_169_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_170_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_171_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_172_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_173_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_174_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_175_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_176_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_177_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_178_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_180_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_181_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_182_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_183_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_184_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_185_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_186_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_187_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_188_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_208_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_209_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_211_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_212_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_213_v8.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data/preprocessed_data/v8\\\\group_214_v8.csv']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst\n",
    "# print(len(lst))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df0 = pd.read_csv(lst[0])\n",
    "# df1 = pd.read_csv(lst[1])\n",
    "# df2 = pd.read_csv(lst[2])\n",
    "# df3 = pd.read_csv(lst[3])\n",
    "# df4 = pd.read_csv(lst[4])\n",
    "# df5 = pd.read_csv(lst[5])\n",
    "\n",
    "datlst = []\n",
    "for i in range(0, len(lst)):\n",
    "    datlst.append(\"df\" + str(i))\n",
    "    globals()[\"df\" + str(i)] = pd.read_csv(lst[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df0',\n",
       " 'df1',\n",
       " 'df2',\n",
       " 'df3',\n",
       " 'df4',\n",
       " 'df5',\n",
       " 'df6',\n",
       " 'df7',\n",
       " 'df8',\n",
       " 'df9',\n",
       " 'df10',\n",
       " 'df11',\n",
       " 'df12',\n",
       " 'df13',\n",
       " 'df14',\n",
       " 'df15',\n",
       " 'df16',\n",
       " 'df17',\n",
       " 'df18',\n",
       " 'df19',\n",
       " 'df20',\n",
       " 'df21',\n",
       " 'df22',\n",
       " 'df23',\n",
       " 'df24',\n",
       " 'df25',\n",
       " 'df26',\n",
       " 'df27',\n",
       " 'df28',\n",
       " 'df29',\n",
       " 'df30',\n",
       " 'df31',\n",
       " 'df32',\n",
       " 'df33',\n",
       " 'df34',\n",
       " 'df35',\n",
       " 'df36',\n",
       " 'df37',\n",
       " 'df38',\n",
       " 'df39',\n",
       " 'df40',\n",
       " 'df41',\n",
       " 'df42',\n",
       " 'df43',\n",
       " 'df44',\n",
       " 'df45',\n",
       " 'df46',\n",
       " 'df47',\n",
       " 'df48',\n",
       " 'df49',\n",
       " 'df50']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datlst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data of ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_dislike = []\n",
    "\n",
    "def loop_directory(directory: str):\n",
    "    '''Loop files in the directory'''\n",
    "\n",
    "    for filename in os.listdir(directory):\n",
    "            if filename.endswith(\".csv\"):\n",
    "                file_directory = os.path.join(directory, filename)\n",
    "                lst_dislike.append(file_directory)\n",
    "                pd.read_csv(file_directory)\n",
    "                \n",
    "                \n",
    "            else:\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "loop_directory('C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID136.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID137.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID138.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID139.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID140.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID141.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID142.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID143.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID144.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID145.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID146.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID147.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID148.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID149.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID150.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID151.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID152.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID153.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID154.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID155.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID156.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID157.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID158.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID166.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID167.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID168.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID169.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID170.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID171.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID172.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID173.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID174.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID175.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID176.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID177.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID178.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID180.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID181.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID182.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID183.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID184.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID185.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID186.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID187.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID188.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID208.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID209.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID211.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID212.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID213.csv',\n",
       " 'C:/Users/becha/Desktop/Face_meteor_analysis/Data/preprocessed_data_rating/preprocessed_data_rating/v8\\\\rating_GroupID214.csv']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_dislike\n",
    "# print(len(lst_dislike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['df_dislike0',\n",
       " 'df_dislike1',\n",
       " 'df_dislike2',\n",
       " 'df_dislike3',\n",
       " 'df_dislike4',\n",
       " 'df_dislike5',\n",
       " 'df_dislike6',\n",
       " 'df_dislike7',\n",
       " 'df_dislike8',\n",
       " 'df_dislike9',\n",
       " 'df_dislike10',\n",
       " 'df_dislike11',\n",
       " 'df_dislike12',\n",
       " 'df_dislike13',\n",
       " 'df_dislike14',\n",
       " 'df_dislike15',\n",
       " 'df_dislike16',\n",
       " 'df_dislike17',\n",
       " 'df_dislike18',\n",
       " 'df_dislike19',\n",
       " 'df_dislike20',\n",
       " 'df_dislike21',\n",
       " 'df_dislike22',\n",
       " 'df_dislike23',\n",
       " 'df_dislike24',\n",
       " 'df_dislike25',\n",
       " 'df_dislike26',\n",
       " 'df_dislike27',\n",
       " 'df_dislike28',\n",
       " 'df_dislike29',\n",
       " 'df_dislike30',\n",
       " 'df_dislike31',\n",
       " 'df_dislike32',\n",
       " 'df_dislike33',\n",
       " 'df_dislike34',\n",
       " 'df_dislike35',\n",
       " 'df_dislike36',\n",
       " 'df_dislike37',\n",
       " 'df_dislike38',\n",
       " 'df_dislike39',\n",
       " 'df_dislike40',\n",
       " 'df_dislike41',\n",
       " 'df_dislike42',\n",
       " 'df_dislike43',\n",
       " 'df_dislike44',\n",
       " 'df_dislike45',\n",
       " 'df_dislike46',\n",
       " 'df_dislike47',\n",
       " 'df_dislike48',\n",
       " 'df_dislike49',\n",
       " 'df_dislike50']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datlst_dislike = []\n",
    "for i in range(0, len(lst)):\n",
    "    datlst_dislike.append(\"df_dislike\" + str(i))\n",
    "    globals()[\"df_dislike\" + str(i)] = pd.read_csv(lst_dislike[i])\n",
    "datlst_dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 10 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 10的島主的pgg_receive\n",
    "dictator_pgg = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[9]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg)\n",
    "# print(len(dictator_pgg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 10的所有人的pgg_receive\n",
    "all_pgg = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 10]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg)\n",
    "# print(len(all_pgg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：v5中島主最後一round pgg表現的顯著程度\n",
    "ratio_10 = [x/y for x, y in zip(dictator_pgg, all_pgg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio = pd.DataFrame({\"group\": datlst, \"dictator's pgg receive\": dictator_pgg, \"all player's pgg receive\": all_pgg, \"ratio\": ratio_10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>132</td>\n",
       "      <td>518</td>\n",
       "      <td>0.254826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>197</td>\n",
       "      <td>325</td>\n",
       "      <td>0.606154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>153</td>\n",
       "      <td>593</td>\n",
       "      <td>0.258010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>530</td>\n",
       "      <td>750</td>\n",
       "      <td>0.706667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>263</td>\n",
       "      <td>813</td>\n",
       "      <td>0.323493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>697</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.697000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>45</td>\n",
       "      <td>363</td>\n",
       "      <td>0.123967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>235</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>330</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>728</td>\n",
       "      <td>728</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>250</td>\n",
       "      <td>918</td>\n",
       "      <td>0.272331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>555</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.555000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>301</td>\n",
       "      <td>610</td>\n",
       "      <td>0.493443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>228</td>\n",
       "      <td>905</td>\n",
       "      <td>0.251934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>815</td>\n",
       "      <td>815</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>575</td>\n",
       "      <td>818</td>\n",
       "      <td>0.702934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>152</td>\n",
       "      <td>500</td>\n",
       "      <td>0.304000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>250</td>\n",
       "      <td>875</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>270</td>\n",
       "      <td>750</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>278</td>\n",
       "      <td>675</td>\n",
       "      <td>0.411852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>0.527273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>251</td>\n",
       "      <td>825</td>\n",
       "      <td>0.304242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>238</td>\n",
       "      <td>500</td>\n",
       "      <td>0.476000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>250</td>\n",
       "      <td>875</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>222</td>\n",
       "      <td>685</td>\n",
       "      <td>0.324088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>100</td>\n",
       "      <td>283</td>\n",
       "      <td>0.353357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>290</td>\n",
       "      <td>630</td>\n",
       "      <td>0.460317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>220</td>\n",
       "      <td>850</td>\n",
       "      <td>0.258824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>288</td>\n",
       "      <td>800</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>298</td>\n",
       "      <td>500</td>\n",
       "      <td>0.596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>334</td>\n",
       "      <td>675</td>\n",
       "      <td>0.494815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>250</td>\n",
       "      <td>750</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>90</td>\n",
       "      <td>400</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>250</td>\n",
       "      <td>800</td>\n",
       "      <td>0.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>115</td>\n",
       "      <td>265</td>\n",
       "      <td>0.433962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>251</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.251000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>875</td>\n",
       "      <td>875</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>898</td>\n",
       "      <td>898</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>650</td>\n",
       "      <td>950</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>df50</td>\n",
       "      <td>257</td>\n",
       "      <td>875</td>\n",
       "      <td>0.293714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0    df0                    1000                      1000  1.000000\n",
       "1    df1                     132                       518  0.254826\n",
       "2    df2                     197                       325  0.606154\n",
       "3    df3                     250                       250  1.000000\n",
       "4    df4                     153                       593  0.258010\n",
       "5    df5                     530                       750  0.706667\n",
       "6    df6                     263                       813  0.323493\n",
       "7    df7                     697                      1000  0.697000\n",
       "8    df8                     250                      1000  0.250000\n",
       "9    df9                     250                      1000  0.250000\n",
       "10  df10                      45                       363  0.123967\n",
       "11  df11                     235                      1000  0.235000\n",
       "12  df12                     330                      1000  0.330000\n",
       "13  df13                     728                       728  1.000000\n",
       "14  df14                     250                       918  0.272331\n",
       "15  df15                     555                      1000  0.555000\n",
       "16  df16                     301                       610  0.493443\n",
       "17  df17                     228                       905  0.251934\n",
       "18  df18                     815                       815  1.000000\n",
       "19  df19                     575                       818  0.702934\n",
       "20  df20                     152                       500  0.304000\n",
       "21  df21                     250                       875  0.285714\n",
       "22  df22                     270                       750  0.360000\n",
       "23  df23                     278                       675  0.411852\n",
       "24  df24                       0                      1000  0.000000\n",
       "25  df25                      29                        55  0.527273\n",
       "26  df26                     251                       825  0.304242\n",
       "27  df27                     238                       500  0.476000\n",
       "28  df28                     250                       875  0.285714\n",
       "29  df29                     222                       685  0.324088\n",
       "30  df30                     100                       283  0.353357\n",
       "31  df31                     413                       413  1.000000\n",
       "32  df32                     290                       630  0.460317\n",
       "33  df33                     220                       850  0.258824\n",
       "34  df34                     288                       800  0.360000\n",
       "35  df35                     298                       500  0.596000\n",
       "36  df36                     334                       675  0.494815\n",
       "37  df37                     250                       750  0.333333\n",
       "38  df38                      90                       400  0.225000\n",
       "39  df39                     250                      1000  0.250000\n",
       "40  df40                     250                       800  0.312500\n",
       "41  df41                     115                       265  0.433962\n",
       "42  df42                     251                      1000  0.251000\n",
       "43  df43                     250                      1000  0.250000\n",
       "44  df44                     250                      1000  0.250000\n",
       "45  df45                     250                      1000  0.250000\n",
       "46  df46                     875                       875  1.000000\n",
       "47  df47                     898                       898  1.000000\n",
       "48  df48                     650                       950  0.684211\n",
       "49  df49                     300                       500  0.600000\n",
       "50  df50                     257                       875  0.293714"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The degree of dislike of the dictator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 先以df_dislike0為例\n",
    "dislike = []\n",
    "for i in range(0,len(lst)):\n",
    "    dislike.append(np.mean(globals()[\"df_dislike\" + str(i)]['liking_p1'][1:]))\n",
    "\n",
    "# print(dislike)\n",
    "# len(dislike)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio['dislike'] = dislike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "      <th>dislike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>132</td>\n",
       "      <td>518</td>\n",
       "      <td>0.254826</td>\n",
       "      <td>44.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>197</td>\n",
       "      <td>325</td>\n",
       "      <td>0.606154</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>153</td>\n",
       "      <td>593</td>\n",
       "      <td>0.258010</td>\n",
       "      <td>50.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>530</td>\n",
       "      <td>750</td>\n",
       "      <td>0.706667</td>\n",
       "      <td>5.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>263</td>\n",
       "      <td>813</td>\n",
       "      <td>0.323493</td>\n",
       "      <td>60.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>697</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.697000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>97.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>82.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>45</td>\n",
       "      <td>363</td>\n",
       "      <td>0.123967</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>235</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.235000</td>\n",
       "      <td>65.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>330</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>39.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>728</td>\n",
       "      <td>728</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>250</td>\n",
       "      <td>918</td>\n",
       "      <td>0.272331</td>\n",
       "      <td>67.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>555</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>301</td>\n",
       "      <td>610</td>\n",
       "      <td>0.493443</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>228</td>\n",
       "      <td>905</td>\n",
       "      <td>0.251934</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>815</td>\n",
       "      <td>815</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>575</td>\n",
       "      <td>818</td>\n",
       "      <td>0.702934</td>\n",
       "      <td>40.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>152</td>\n",
       "      <td>500</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>250</td>\n",
       "      <td>875</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>78.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>270</td>\n",
       "      <td>750</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>66.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>278</td>\n",
       "      <td>675</td>\n",
       "      <td>0.411852</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>92.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>29</td>\n",
       "      <td>55</td>\n",
       "      <td>0.527273</td>\n",
       "      <td>34.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>251</td>\n",
       "      <td>825</td>\n",
       "      <td>0.304242</td>\n",
       "      <td>64.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>238</td>\n",
       "      <td>500</td>\n",
       "      <td>0.476000</td>\n",
       "      <td>26.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>250</td>\n",
       "      <td>875</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>97.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>222</td>\n",
       "      <td>685</td>\n",
       "      <td>0.324088</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>100</td>\n",
       "      <td>283</td>\n",
       "      <td>0.353357</td>\n",
       "      <td>27.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>413</td>\n",
       "      <td>413</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>290</td>\n",
       "      <td>630</td>\n",
       "      <td>0.460317</td>\n",
       "      <td>67.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>220</td>\n",
       "      <td>850</td>\n",
       "      <td>0.258824</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>288</td>\n",
       "      <td>800</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>63.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>298</td>\n",
       "      <td>500</td>\n",
       "      <td>0.596000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>334</td>\n",
       "      <td>675</td>\n",
       "      <td>0.494815</td>\n",
       "      <td>57.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>250</td>\n",
       "      <td>750</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>90</td>\n",
       "      <td>400</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>54.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>94.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>250</td>\n",
       "      <td>800</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>115</td>\n",
       "      <td>265</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>251</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.251000</td>\n",
       "      <td>84.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>77.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>74.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>98.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>875</td>\n",
       "      <td>875</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>898</td>\n",
       "      <td>898</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>650</td>\n",
       "      <td>950</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>26.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>df50</td>\n",
       "      <td>257</td>\n",
       "      <td>875</td>\n",
       "      <td>0.293714</td>\n",
       "      <td>66.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio  \\\n",
       "0    df0                    1000                      1000  1.000000   \n",
       "1    df1                     132                       518  0.254826   \n",
       "2    df2                     197                       325  0.606154   \n",
       "3    df3                     250                       250  1.000000   \n",
       "4    df4                     153                       593  0.258010   \n",
       "5    df5                     530                       750  0.706667   \n",
       "6    df6                     263                       813  0.323493   \n",
       "7    df7                     697                      1000  0.697000   \n",
       "8    df8                     250                      1000  0.250000   \n",
       "9    df9                     250                      1000  0.250000   \n",
       "10  df10                      45                       363  0.123967   \n",
       "11  df11                     235                      1000  0.235000   \n",
       "12  df12                     330                      1000  0.330000   \n",
       "13  df13                     728                       728  1.000000   \n",
       "14  df14                     250                       918  0.272331   \n",
       "15  df15                     555                      1000  0.555000   \n",
       "16  df16                     301                       610  0.493443   \n",
       "17  df17                     228                       905  0.251934   \n",
       "18  df18                     815                       815  1.000000   \n",
       "19  df19                     575                       818  0.702934   \n",
       "20  df20                     152                       500  0.304000   \n",
       "21  df21                     250                       875  0.285714   \n",
       "22  df22                     270                       750  0.360000   \n",
       "23  df23                     278                       675  0.411852   \n",
       "24  df24                       0                      1000  0.000000   \n",
       "25  df25                      29                        55  0.527273   \n",
       "26  df26                     251                       825  0.304242   \n",
       "27  df27                     238                       500  0.476000   \n",
       "28  df28                     250                       875  0.285714   \n",
       "29  df29                     222                       685  0.324088   \n",
       "30  df30                     100                       283  0.353357   \n",
       "31  df31                     413                       413  1.000000   \n",
       "32  df32                     290                       630  0.460317   \n",
       "33  df33                     220                       850  0.258824   \n",
       "34  df34                     288                       800  0.360000   \n",
       "35  df35                     298                       500  0.596000   \n",
       "36  df36                     334                       675  0.494815   \n",
       "37  df37                     250                       750  0.333333   \n",
       "38  df38                      90                       400  0.225000   \n",
       "39  df39                     250                      1000  0.250000   \n",
       "40  df40                     250                       800  0.312500   \n",
       "41  df41                     115                       265  0.433962   \n",
       "42  df42                     251                      1000  0.251000   \n",
       "43  df43                     250                      1000  0.250000   \n",
       "44  df44                     250                      1000  0.250000   \n",
       "45  df45                     250                      1000  0.250000   \n",
       "46  df46                     875                       875  1.000000   \n",
       "47  df47                     898                       898  1.000000   \n",
       "48  df48                     650                       950  0.684211   \n",
       "49  df49                     300                       500  0.600000   \n",
       "50  df50                     257                       875  0.293714   \n",
       "\n",
       "      dislike  \n",
       "0    0.000000  \n",
       "1   44.333333  \n",
       "2   29.000000  \n",
       "3   57.333333  \n",
       "4   50.333333  \n",
       "5    5.333333  \n",
       "6   60.333333  \n",
       "7   17.000000  \n",
       "8   97.333333  \n",
       "9   82.666667  \n",
       "10  58.000000  \n",
       "11  65.333333  \n",
       "12  39.666667  \n",
       "13  43.333333  \n",
       "14  67.333333  \n",
       "15  33.333333  \n",
       "16  14.000000  \n",
       "17  45.000000  \n",
       "18  14.666667  \n",
       "19  40.666667  \n",
       "20  64.000000  \n",
       "21  78.666667  \n",
       "22  66.333333  \n",
       "23  33.333333  \n",
       "24  92.666667  \n",
       "25  34.333333  \n",
       "26  64.333333  \n",
       "27  26.333333  \n",
       "28  97.333333  \n",
       "29  17.000000  \n",
       "30  27.666667  \n",
       "31  18.000000  \n",
       "32  67.666667  \n",
       "33  48.000000  \n",
       "34  63.666667  \n",
       "35   6.666667  \n",
       "36  57.000000  \n",
       "37  66.666667  \n",
       "38  54.666667  \n",
       "39  94.666667  \n",
       "40  68.000000  \n",
       "41  60.000000  \n",
       "42  84.333333  \n",
       "43  77.666667  \n",
       "44  74.333333  \n",
       "45  98.666667  \n",
       "46  26.666667  \n",
       "47   4.333333  \n",
       "48  16.666667  \n",
       "49  26.333333  \n",
       "50  66.333333  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.683887909213658 .\n"
     ]
    }
   ],
   "source": [
    "my_rho = np.corrcoef(ratio_10, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 442
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.regplot(x= ratio_10, y=dislike, data=df_ratio, ci=None)\n",
    "ax.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 10\")\n",
    "ax.set_xlabel(\"The ratio of pgg_receive of the dictator in round 10\")\n",
    "ax.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 9 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 9的島主的pgg_receive\n",
    "dictator_pgg_9 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_9.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[8]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg_9)\n",
    "# print(len(dictator_pgg_9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 取round 9的所有人的pgg_receive\n",
    "all_pgg_9 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_9.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 9]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_9)\n",
    "# print(len(all_pgg_9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：v5中島主round 9 pgg表現的顯著程度\n",
    "ratio_9 = [x/y for x, y in zip(dictator_pgg_9, all_pgg_9)]\n",
    "# type(ratio)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratio_9 = pd.DataFrame({\"group\": datlst, \"dictator's pgg receive\": dictator_pgg_9, \"all player's pgg receive\": all_pgg_9, \"ratio\": ratio_9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>group</th>\n",
       "      <th>dictator's pgg receive</th>\n",
       "      <th>all player's pgg receive</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>df0</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>df1</td>\n",
       "      <td>123</td>\n",
       "      <td>495</td>\n",
       "      <td>0.248485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>df2</td>\n",
       "      <td>270</td>\n",
       "      <td>538</td>\n",
       "      <td>0.501859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>df3</td>\n",
       "      <td>0</td>\n",
       "      <td>475</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df4</td>\n",
       "      <td>154</td>\n",
       "      <td>613</td>\n",
       "      <td>0.251223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>df5</td>\n",
       "      <td>422</td>\n",
       "      <td>875</td>\n",
       "      <td>0.482286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>df6</td>\n",
       "      <td>260</td>\n",
       "      <td>850</td>\n",
       "      <td>0.305882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>df7</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>df8</td>\n",
       "      <td>250</td>\n",
       "      <td>850</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>df9</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>df10</td>\n",
       "      <td>131</td>\n",
       "      <td>463</td>\n",
       "      <td>0.282937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>df11</td>\n",
       "      <td>249</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.249000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>df12</td>\n",
       "      <td>251</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.251000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>df13</td>\n",
       "      <td>333</td>\n",
       "      <td>633</td>\n",
       "      <td>0.526066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>df14</td>\n",
       "      <td>220</td>\n",
       "      <td>888</td>\n",
       "      <td>0.247748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>df15</td>\n",
       "      <td>219</td>\n",
       "      <td>950</td>\n",
       "      <td>0.230526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>df16</td>\n",
       "      <td>209</td>\n",
       "      <td>613</td>\n",
       "      <td>0.340946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>df17</td>\n",
       "      <td>224</td>\n",
       "      <td>893</td>\n",
       "      <td>0.250840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>df18</td>\n",
       "      <td>250</td>\n",
       "      <td>870</td>\n",
       "      <td>0.287356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>df19</td>\n",
       "      <td>266</td>\n",
       "      <td>723</td>\n",
       "      <td>0.367911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>df20</td>\n",
       "      <td>113</td>\n",
       "      <td>475</td>\n",
       "      <td>0.237895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>df21</td>\n",
       "      <td>249</td>\n",
       "      <td>998</td>\n",
       "      <td>0.249499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>df22</td>\n",
       "      <td>270</td>\n",
       "      <td>850</td>\n",
       "      <td>0.317647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>df23</td>\n",
       "      <td>332</td>\n",
       "      <td>713</td>\n",
       "      <td>0.465638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>df24</td>\n",
       "      <td>103</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.103000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>df25</td>\n",
       "      <td>91</td>\n",
       "      <td>153</td>\n",
       "      <td>0.594771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>df26</td>\n",
       "      <td>210</td>\n",
       "      <td>733</td>\n",
       "      <td>0.286494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>df27</td>\n",
       "      <td>384</td>\n",
       "      <td>675</td>\n",
       "      <td>0.568889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>df28</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>df29</td>\n",
       "      <td>254</td>\n",
       "      <td>775</td>\n",
       "      <td>0.327742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>df30</td>\n",
       "      <td>140</td>\n",
       "      <td>555</td>\n",
       "      <td>0.252252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>df31</td>\n",
       "      <td>50</td>\n",
       "      <td>213</td>\n",
       "      <td>0.234742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>df32</td>\n",
       "      <td>301</td>\n",
       "      <td>723</td>\n",
       "      <td>0.416321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>df33</td>\n",
       "      <td>234</td>\n",
       "      <td>850</td>\n",
       "      <td>0.275294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>df34</td>\n",
       "      <td>325</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>df35</td>\n",
       "      <td>338</td>\n",
       "      <td>675</td>\n",
       "      <td>0.500741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>df36</td>\n",
       "      <td>364</td>\n",
       "      <td>813</td>\n",
       "      <td>0.447724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>df37</td>\n",
       "      <td>250</td>\n",
       "      <td>875</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>df38</td>\n",
       "      <td>90</td>\n",
       "      <td>425</td>\n",
       "      <td>0.211765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>df39</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>df40</td>\n",
       "      <td>250</td>\n",
       "      <td>688</td>\n",
       "      <td>0.363372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>df41</td>\n",
       "      <td>67</td>\n",
       "      <td>133</td>\n",
       "      <td>0.503759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>df42</td>\n",
       "      <td>250</td>\n",
       "      <td>988</td>\n",
       "      <td>0.253036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>df43</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>df44</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>df45</td>\n",
       "      <td>250</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>df46</td>\n",
       "      <td>320</td>\n",
       "      <td>875</td>\n",
       "      <td>0.365714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>df47</td>\n",
       "      <td>500</td>\n",
       "      <td>875</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>df48</td>\n",
       "      <td>388</td>\n",
       "      <td>913</td>\n",
       "      <td>0.424973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>df49</td>\n",
       "      <td>195</td>\n",
       "      <td>588</td>\n",
       "      <td>0.331633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>df50</td>\n",
       "      <td>262</td>\n",
       "      <td>975</td>\n",
       "      <td>0.268718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group  dictator's pgg receive  all player's pgg receive     ratio\n",
       "0    df0                     250                      1000  0.250000\n",
       "1    df1                     123                       495  0.248485\n",
       "2    df2                     270                       538  0.501859\n",
       "3    df3                       0                       475  0.000000\n",
       "4    df4                     154                       613  0.251223\n",
       "5    df5                     422                       875  0.482286\n",
       "6    df6                     260                       850  0.305882\n",
       "7    df7                     250                      1000  0.250000\n",
       "8    df8                     250                       850  0.294118\n",
       "9    df9                     250                      1000  0.250000\n",
       "10  df10                     131                       463  0.282937\n",
       "11  df11                     249                      1000  0.249000\n",
       "12  df12                     251                      1000  0.251000\n",
       "13  df13                     333                       633  0.526066\n",
       "14  df14                     220                       888  0.247748\n",
       "15  df15                     219                       950  0.230526\n",
       "16  df16                     209                       613  0.340946\n",
       "17  df17                     224                       893  0.250840\n",
       "18  df18                     250                       870  0.287356\n",
       "19  df19                     266                       723  0.367911\n",
       "20  df20                     113                       475  0.237895\n",
       "21  df21                     249                       998  0.249499\n",
       "22  df22                     270                       850  0.317647\n",
       "23  df23                     332                       713  0.465638\n",
       "24  df24                     103                      1000  0.103000\n",
       "25  df25                      91                       153  0.594771\n",
       "26  df26                     210                       733  0.286494\n",
       "27  df27                     384                       675  0.568889\n",
       "28  df28                     250                      1000  0.250000\n",
       "29  df29                     254                       775  0.327742\n",
       "30  df30                     140                       555  0.252252\n",
       "31  df31                      50                       213  0.234742\n",
       "32  df32                     301                       723  0.416321\n",
       "33  df33                     234                       850  0.275294\n",
       "34  df34                     325                      1000  0.325000\n",
       "35  df35                     338                       675  0.500741\n",
       "36  df36                     364                       813  0.447724\n",
       "37  df37                     250                       875  0.285714\n",
       "38  df38                      90                       425  0.211765\n",
       "39  df39                     250                      1000  0.250000\n",
       "40  df40                     250                       688  0.363372\n",
       "41  df41                      67                       133  0.503759\n",
       "42  df42                     250                       988  0.253036\n",
       "43  df43                     250                      1000  0.250000\n",
       "44  df44                     250                      1000  0.250000\n",
       "45  df45                     250                      1000  0.250000\n",
       "46  df46                     320                       875  0.365714\n",
       "47  df47                     500                       875  0.571429\n",
       "48  df48                     388                       913  0.424973\n",
       "49  df49                     195                       588  0.331633\n",
       "50  df50                     262                       975  0.268718"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratio_9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.43423398109432326 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_9 = np.corrcoef(ratio_9, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_9[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 435
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_9 = sns.regplot(x= ratio_9, y=dislike, data=df_ratio_9, ci=None)\n",
    "ax_9.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 9\")\n",
    "ax_9.set_xlabel(\"The ratio of pgg_receive of the dictator in round 9\")\n",
    "ax_9.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 9 & 10 and dislike "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 9 & 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_910 = [(x+y)/2 for x, y in zip(ratio_9, ratio_10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.7221444396907534 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_910 = np.corrcoef(ratio_910, dislike)\n",
    "\n",
    "print(\"The value of correlation is\",my_rho_910[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 1-8 and dislike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 1的島主的pgg_receive\n",
    "dictator_pgg_1 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_1.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[0]['pgg_receive'])\n",
    "\n",
    "# print(dictator_pgg_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[308, 350, 475, 600, 638, 475, 338, 638, 438, 385, 675, 413, 450, 450, 433, 688, 650, 445, 568, 525, 425, 550, 575, 613, 488, 275, 535, 625, 525, 500, 325, 375, 605, 425, 400, 650, 488, 225, 488, 600, 488, 463, 625, 513, 413, 438, 675, 450, 395, 330, 550]\n"
     ]
    }
   ],
   "source": [
    "# 取round 1的所有人的pgg_receive\n",
    "all_pgg_1 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_1.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 1]['pgg_receive']))\n",
    "\n",
    "print(all_pgg_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 1中pgg表現的顯著程度\n",
    "ratio_1 = [x/y for x, y in zip(dictator_pgg_1, all_pgg_1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 2的島主的pgg_receive\n",
    "dictator_pgg_2 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_2.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[1]['pgg_receive'])\n",
    "# print(dictator_pgg_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 2的所有人的pgg_receive\n",
    "all_pgg_2 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_2.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 2]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 2中pgg表現的顯著程度\n",
    "ratio_2 = [x/y for x, y in zip(dictator_pgg_2, all_pgg_2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 3的島主的pgg_receive\n",
    "dictator_pgg_3 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_3.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[2]['pgg_receive'])\n",
    "# print(dictator_pgg_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 3的所有人的pgg_receive\n",
    "all_pgg_3 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_3.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 3]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 3中pgg表現的顯著程度\n",
    "ratio_3 = [x/y for x, y in zip(dictator_pgg_3, all_pgg_3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 4的島主的pgg_receive\n",
    "dictator_pgg_4 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_4.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[3]['pgg_receive'])\n",
    "# print(dictator_pgg_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 4的所有人的pgg_receive\n",
    "all_pgg_4 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_4.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 4]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 4中pgg表現的顯著程度\n",
    "ratio_4 = [x/y for x, y in zip(dictator_pgg_4, all_pgg_4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 5的島主的pgg_receive\n",
    "dictator_pgg_5 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_5.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[4]['pgg_receive'])\n",
    "# print(dictator_pgg_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 5的所有人的pgg_receive\n",
    "all_pgg_5 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_5.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 5]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 5中pgg表現的顯著程度\n",
    "ratio_5 = [x/y for x, y in zip(dictator_pgg_5, all_pgg_5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 6的島主的pgg_receive\n",
    "dictator_pgg_6 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_6.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[5]['pgg_receive'])\n",
    "# print(dictator_pgg_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 6的所有人的pgg_receive\n",
    "all_pgg_6 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_6.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 6]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 6中pgg表現的顯著程度\n",
    "ratio_6 = [x/y for x, y in zip(dictator_pgg_6, all_pgg_6)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 7的島主的pgg_receive\n",
    "dictator_pgg_7 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_7.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[6]['pgg_receive'])\n",
    "# print(dictator_pgg_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 7的所有人的pgg_receive\n",
    "all_pgg_7 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_7.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 7]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 7中pgg表現的顯著程度\n",
    "ratio_7 = [x/y for x, y in zip(dictator_pgg_7, all_pgg_7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 8的島主的pgg_receive\n",
    "dictator_pgg_8 = []\n",
    "for i in range(0, len(lst)):\n",
    "    dictator_pgg_8.append(globals()['df'+str(i)][globals()['df'+str(i)]['dictator'] == 1].iloc[7]['pgg_receive'])\n",
    "# print(dictator_pgg_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取round 8的所有人的pgg_receive\n",
    "all_pgg_8 = []\n",
    "for i in range(0,len(lst)):\n",
    "    all_pgg_8.append(sum(globals()[\"df\" + str(i)][globals()[\"df\" + str(i)]['round'] == 8]['pgg_receive']))\n",
    "\n",
    "# print(all_pgg_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 計算：島主在round 8中pgg表現的顯著程度\n",
    "ratio_8 = [x/y for x, y in zip(dictator_pgg_8, all_pgg_8)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The performance of the dictator in round 1-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_RD = pd.DataFrame({\"group\": datlst, \"round 1\": ratio_1, \"round 2\": ratio_2, \"round 3\": ratio_3, \"round 4\": ratio_4, \"round 5\": ratio_5, \"round 6\": ratio_6, \"round 7\": ratio_7, \"round 8\": ratio_8})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_RD = []\n",
    "mean_RD = df_RD[['round 1', 'round 2', 'round 3', 'round 4', 'round 5', 'round 6', 'round 7', 'round 8']].mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.273554\n",
       "1     0.273747\n",
       "2     0.535530\n",
       "3     0.249893\n",
       "4     0.297714\n",
       "5     0.424706\n",
       "6     0.326361\n",
       "7     0.290167\n",
       "8     0.301059\n",
       "9     0.327952\n",
       "10    0.299373\n",
       "11    0.238994\n",
       "12    0.260570\n",
       "13    0.484052\n",
       "14    0.242234\n",
       "15    0.257995\n",
       "16    0.487850\n",
       "17    0.276857\n",
       "18    0.377418\n",
       "19    0.311614\n",
       "20    0.282883\n",
       "21    0.360205\n",
       "22    0.348262\n",
       "23    0.410281\n",
       "24    0.165537\n",
       "25    0.472274\n",
       "26    0.295689\n",
       "27    0.459183\n",
       "28    0.271665\n",
       "29    0.255052\n",
       "30    0.341881\n",
       "31    0.397508\n",
       "32    0.480841\n",
       "33    0.285698\n",
       "34    0.355111\n",
       "35    0.477385\n",
       "36    0.542955\n",
       "37    0.307181\n",
       "38    0.226095\n",
       "39    0.276770\n",
       "40    0.333341\n",
       "41    0.353419\n",
       "42    0.291924\n",
       "43    0.341137\n",
       "44    0.211987\n",
       "45    0.336069\n",
       "46    0.313817\n",
       "47    0.628600\n",
       "48    0.485766\n",
       "49    0.313797\n",
       "50    0.264115\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_RD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cor = pd.DataFrame({\"ratio of round 1-8\":mean_RD,\"dislike\":dislike})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.44734131141158645 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_1to8 = np.corrcoef(mean_RD, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_1to8[0][1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'The score of being liked of the dictator')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 447
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax_1to8 = sns.regplot(x= mean_RD, y=dislike, data=df_cor, ci=None)\n",
    "ax_1to8.set_title(\"The score of being liked of the dictator vs. The pgg_receive in round 1-8\")\n",
    "ax_1to8.set_xlabel(\"The ratio of pgg_receive of the dictator in round 1-8\")\n",
    "ax_1to8.set_ylabel(\"The score of being liked of the dictator\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The correlation between round 1-10 and dislike "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_1to10 = [(x*8+y*2)/10 for x, y in zip(mean_RD, ratio_910)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.34384281743635203,\n",
       " 0.2693285593613868,\n",
       " 0.5392254180387359,\n",
       " 0.2999142857142857,\n",
       " 0.2890941936977981,\n",
       " 0.45866039887344234,\n",
       " 0.32402641751658245,\n",
       " 0.32683393184452547,\n",
       " 0.295259222912843,\n",
       " 0.31236188511033813,\n",
       " 0.2801888964314858,\n",
       " 0.23959513170496693,\n",
       " 0.2665558617204045,\n",
       " 0.539848544684839,\n",
       " 0.24579500638438337,\n",
       " 0.2849486808844067,\n",
       " 0.4737189035694233,\n",
       " 0.2717626998021042,\n",
       " 0.430669892924694,\n",
       " 0.35637604705848,\n",
       " 0.28049566926906533,\n",
       " 0.3416851484200694,\n",
       " 0.34637466671748623,\n",
       " 0.4159741505999577,\n",
       " 0.14272967152622346,\n",
       " 0.49002368040076305,\n",
       " 0.2956250173215081,\n",
       " 0.47183497225448445,\n",
       " 0.27090311022605945,\n",
       " 0.2692249068367823,\n",
       " 0.3340658718475278,\n",
       " 0.4414809163073037,\n",
       " 0.47233629468159,\n",
       " 0.28197043319902887,\n",
       " 0.35258847877611965,\n",
       " 0.4915817182776564,\n",
       " 0.5286182083750401,\n",
       " 0.30764929030015237,\n",
       " 0.22455262915869256,\n",
       " 0.2714161057092994,\n",
       " 0.33426022455376475,\n",
       " 0.3765071941812194,\n",
       " 0.28394310055633504,\n",
       " 0.32290986536223587,\n",
       " 0.21958963718851426,\n",
       " 0.31885517550622094,\n",
       " 0.3876252594412965,\n",
       " 0.6600232078006772,\n",
       " 0.49953115836064443,\n",
       " 0.34420050395455926,\n",
       " 0.26753542514637274]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_1to10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of correlation is -0.5666854839428497 .\n"
     ]
    }
   ],
   "source": [
    "my_rho_1to10 = np.corrcoef(ratio_1to10, dislike)\n",
    "\n",
    "# print(my_rho)\n",
    "print(\"The value of correlation is\",my_rho_1to10[0][1],'.')"
   ]
  }
 ],
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