Project 1.ipynb

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     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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   "source": [
    "# DATS 6101 Group Project 1\n",
 
    "# descriptive and exploratory statistics using 'deadly quarrels' data\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Changes the width displayable on the screen, so we can see all the columns of the tables.\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "\n",
    "# load dataset\n",
    "url = 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Quarrels.csv'\n",
    "df_quarrels = pd.read_csv(url)\n",
    "\n",
    "# only select relevant columns\n",
    "df_Q = df_quarrels[['revolution','months','deaths','logDeaths','prevConflict','prevConflictYrs','chronicFighting','difWealth','difLanguage','intermingled','sameGov','sameGovYrs']]\n",
    "df_Quarrels = df_Q[df_Q.revolution == 1]\n",
    "\n",
    "# to export to Excel (optional)\n",
    "df_Quarrels.to_excel('/Users/jason/Desktop/Quarrels.xlsx', \n",
    "                    sheet_name='Sheet 1', na_rep=\"\", header=['revolution','months','deaths','logDeaths','prevConflict','prevConflictYrs','chronicFighting','difWealth','difLanguage','intermingled','sameGov','sameGovYrs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "For the presentation, I took a look at the data set on Deadly Quarrels. I wanted to look at the relationships associated with civil wars. We started off looking at eight different variables. Those variables we looked at are described here: \n",
    "* prevConflict, looking at if there a previous conflict, \n",
    "* prevConflictYrs, how long hte conflict lasted if there was one, \n",
    "* chronicFighting, whether or not there was chronic fighting between the sides, \n",
    "* difWealth, whether or not there was a difference in wealth between the sides, \n",
    "* difLanguage, whether or not there were different languages on the opposing sides, \n",
    "* intermingled, which looks at whether the two sides were intermingled in the same territory, \n",
    "* sameGov, whether or not the sides shared the same government, and \n",
    "* sameGovYrs, the number of years since the common government was created.\n",
    "\n",
    "There were also 4 variables that gave us a bit more contextual information on the civil wars: \n",
    "* revolution, which describes whether or not it is a civil war, \n",
    "* months, giving us the length of time the conflict lasted, \n",
    "* deaths, the number of recorded deaths, and \n",
    "* logDeaths, the number of recorded deaths on a log scale.\n",
    "\n",
    "Much of this data is categorical, returning either a 0 for no or a yes for 1.\n",
    "Now, for the descriptive statistics:"
   ]
  },
  {
   "cell_type": "code",
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revolution</th>\n",
       "      <th>months</th>\n",
       "      <th>deaths</th>\n",
       "      <th>logDeaths</th>\n",
       "      <th>prevConflict</th>\n",
       "      <th>prevConflictYrs</th>\n",
       "      <th>chronicFighting</th>\n",
       "      <th>difWealth</th>\n",
       "      <th>difLanguage</th>\n",
       "      <th>intermingled</th>\n",
       "      <th>sameGov</th>\n",
       "      <th>sameGovYrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>109.0</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>1.090000e+02</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.339450</td>\n",
       "      <td>7.481770e+05</td>\n",
       "      <td>3.850092</td>\n",
       "      <td>0.321101</td>\n",
       "      <td>4.972477</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>0.174312</td>\n",
       "      <td>0.724771</td>\n",
       "      <td>19.330275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.257298</td>\n",
       "      <td>3.409773e+06</td>\n",
       "      <td>1.132336</td>\n",
       "      <td>0.469056</td>\n",
       "      <td>12.516995</td>\n",
       "      <td>0.188886</td>\n",
       "      <td>0.336125</td>\n",
       "      <td>0.416284</td>\n",
       "      <td>0.381130</td>\n",
       "      <td>0.448693</td>\n",
       "      <td>51.041388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2.000000e+04</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>2.000000e+07</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>266.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       revolution      months        deaths   logDeaths  prevConflict  \\\n",
       "count       109.0  109.000000  1.090000e+02  109.000000    109.000000   \n",
       "mean          1.0   20.339450  7.481770e+05    3.850092      0.321101   \n",
       "std           0.0   30.257298  3.409773e+06    1.132336      0.469056   \n",
       "min           1.0    1.000000  0.000000e+00    2.600000      0.000000   \n",
       "25%           1.0    3.000000  1.000000e+03    3.000000      0.000000   \n",
       "50%           1.0    8.000000  2.000000e+03    3.300000      0.000000   \n",
       "75%           1.0   28.000000  2.000000e+04    4.300000      1.000000   \n",
       "max           1.0  156.000000  2.000000e+07    7.300000      1.000000   \n",
       "\n",
       "       prevConflictYrs  chronicFighting   difWealth  difLanguage  \\\n",
       "count       109.000000       109.000000  109.000000   109.000000   \n",
       "mean          4.972477         0.036697    0.128440     0.220183   \n",
       "std          12.516995         0.188886    0.336125     0.416284   \n",
       "min           0.000000         0.000000    0.000000     0.000000   \n",
       "25%           0.000000         0.000000    0.000000     0.000000   \n",
       "50%           0.000000         0.000000    0.000000     0.000000   \n",
       "75%           3.000000         0.000000    0.000000     0.000000   \n",
       "max          63.000000         1.000000    1.000000     1.000000   \n",
       "\n",
       "       intermingled     sameGov  sameGovYrs  \n",
       "count    109.000000  109.000000  109.000000  \n",
       "mean       0.174312    0.724771   19.330275  \n",
       "std        0.381130    0.448693   51.041388  \n",
       "min        0.000000    0.000000    0.000000  \n",
       "25%        0.000000    0.000000    0.000000  \n",
       "50%        0.000000    1.000000    0.000000  \n",
       "75%        0.000000    1.000000   11.000000  \n",
       "max        1.000000    1.000000  266.000000  "
      ]
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     "execution_count": 2,
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   "source": [
    "# explore variables\n",
    "df_Quarrels.shape  # number of observations and variables\n",
    "df_Quarrels.size\n",
    "df_Quarrels.head(5)\n",
    "df_Quarrels.dtypes\n",
    "# summary statistics\n",
    "df_Quarrels.describe()  \n",
    "# Data cleaning was required in a few sections, needing to replace 999 with 0.\n",
    "df_Quarrels['prevConflictYrs'].replace(999, 0, inplace=True)\n",
    "df_Quarrels['sameGovYrs'].replace(999, 0, inplace=True)\n",
    "# to see cleaned prevConflictYrs\n",
    "df_Quarrels.describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistics\n",
    "Due to the nature of the categorical nature of the data, the mode can be extrapolated from looking at the median value. For some of the data, such as prevConflict Yrs and sameGov years\n",
    "\n",
    "For prevConflict we have that the \n",
    "- mean is 0.32, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For prevConflictYrs we have that the \n",
    "- mean is 4.97, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For chronicFighting we have that the \n",
    "- mean is 0.04, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For difWealth we have that the \n",
    "- mean is 0.13, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For difLanguage we have that the \n",
    "- mean is 0.22, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For intermingled we have that the \n",
    "- mean is 0.17, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "For sameGov we have that the \n",
    "- mean is 0.72, \n",
    "- median is 1, and \n",
    "- mode is 1.\n",
    "\n",
    "For sameGovYrs we have that the \n",
    "- mean is 19.33, \n",
    "- median is 0, and \n",
    "- mode is 0.\n",
    "\n",
    "From here we move on to look at the standard deviation, variance, correlation, and covariance. Unfortunately, due to the categorical nature of the data the information gained from this isn't very helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "revolution         0.000000e+00\n",
       "months             3.025730e+01\n",
       "deaths             3.409773e+06\n",
       "logDeaths          1.132336e+00\n",
       "prevConflict       4.690561e-01\n",
       "prevConflictYrs    1.251699e+01\n",
       "chronicFighting    1.888859e-01\n",
       "difWealth          3.361249e-01\n",
       "difLanguage        4.162842e-01\n",
       "intermingled       3.811298e-01\n",
       "sameGov            4.486927e-01\n",
       "sameGovYrs         5.104139e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Quarrels.std()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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     "data": {
      "text/plain": [
       "revolution         0.000000e+00\n",
       "months             9.155041e+02\n",
       "deaths             1.162655e+13\n",
       "logDeaths          1.282184e+00\n",
       "prevConflict       2.200136e-01\n",
       "prevConflictYrs    1.566752e+02\n",
       "chronicFighting    3.567788e-02\n",
       "difWealth          1.129800e-01\n",
       "difLanguage        1.732926e-01\n",
       "intermingled       1.452599e-01\n",
       "sameGov            2.013252e-01\n",
       "sameGovYrs         2.605223e+03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Quarrels.var()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revolution</th>\n",
       "      <th>months</th>\n",
       "      <th>deaths</th>\n",
       "      <th>logDeaths</th>\n",
       "      <th>prevConflict</th>\n",
       "      <th>prevConflictYrs</th>\n",
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       "      <th>difWealth</th>\n",
       "      <th>difLanguage</th>\n",
       "      <th>intermingled</th>\n",
       "      <th>sameGov</th>\n",
       "      <th>sameGovYrs</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>revolution</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>months</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.360776</td>\n",
       "      <td>0.022260</td>\n",
       "      <td>0.065081</td>\n",
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       "      <td>0.148147</td>\n",
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       "    <tr>\n",
       "      <th>deaths</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>-0.050197</td>\n",
       "      <td>-0.062660</td>\n",
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       "      <td>-0.045414</td>\n",
       "      <td>0.099862</td>\n",
       "      <td>0.331854</td>\n",
       "      <td>0.103139</td>\n",
       "      <td>0.301933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logDeaths</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.360776</td>\n",
       "      <td>0.633344</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107332</td>\n",
       "      <td>0.082987</td>\n",
       "      <td>0.136352</td>\n",
       "      <td>0.110660</td>\n",
       "      <td>0.229389</td>\n",
       "      <td>0.374352</td>\n",
       "      <td>-0.162693</td>\n",
       "      <td>0.299527</td>\n",
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       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022260</td>\n",
       "      <td>-0.050197</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.179295</td>\n",
       "      <td>0.029634</td>\n",
       "      <td>0.345861</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>-0.141380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflictYrs</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065081</td>\n",
       "      <td>-0.062660</td>\n",
       "      <td>0.082987</td>\n",
       "      <td>0.580304</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.145334</td>\n",
       "      <td>-0.040967</td>\n",
       "      <td>0.329918</td>\n",
       "      <td>-0.006749</td>\n",
       "      <td>0.008531</td>\n",
       "      <td>0.019609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronicFighting</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044784</td>\n",
       "      <td>-0.013314</td>\n",
       "      <td>0.136352</td>\n",
       "      <td>0.179295</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.216753</td>\n",
       "      <td>-0.103713</td>\n",
       "      <td>0.038940</td>\n",
       "      <td>-0.207478</td>\n",
       "      <td>-0.074260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017524</td>\n",
       "      <td>-0.045414</td>\n",
       "      <td>0.110660</td>\n",
       "      <td>0.029634</td>\n",
       "      <td>-0.040967</td>\n",
       "      <td>0.216753</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.137811</td>\n",
       "      <td>0.401836</td>\n",
       "      <td>0.052382</td>\n",
       "      <td>-0.001416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.099868</td>\n",
       "      <td>0.099862</td>\n",
       "      <td>0.229389</td>\n",
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       "      <td>0.329918</td>\n",
       "      <td>-0.103713</td>\n",
       "      <td>-0.137811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.185787</td>\n",
       "      <td>-0.069128</td>\n",
       "      <td>0.102004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.193946</td>\n",
       "      <td>0.331854</td>\n",
       "      <td>0.374352</td>\n",
       "      <td>0.098361</td>\n",
       "      <td>-0.006749</td>\n",
       "      <td>0.038940</td>\n",
       "      <td>0.401836</td>\n",
       "      <td>-0.185787</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174852</td>\n",
       "      <td>0.175978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038068</td>\n",
       "      <td>0.103139</td>\n",
       "      <td>-0.162693</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.008531</td>\n",
       "      <td>-0.207478</td>\n",
       "      <td>0.052382</td>\n",
       "      <td>-0.069128</td>\n",
       "      <td>0.174852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.234457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGovYrs</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.148147</td>\n",
       "      <td>0.301933</td>\n",
       "      <td>0.299527</td>\n",
       "      <td>-0.141380</td>\n",
       "      <td>0.019609</td>\n",
       "      <td>-0.074260</td>\n",
       "      <td>-0.001416</td>\n",
       "      <td>0.102004</td>\n",
       "      <td>0.175978</td>\n",
       "      <td>0.234457</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 revolution    months    deaths  logDeaths  prevConflict  \\\n",
       "revolution              NaN       NaN       NaN        NaN           NaN   \n",
       "months                  NaN  1.000000  0.241536   0.360776      0.022260   \n",
       "deaths                  NaN  0.241536  1.000000   0.633344     -0.050197   \n",
       "logDeaths               NaN  0.360776  0.633344   1.000000      0.107332   \n",
       "prevConflict            NaN  0.022260 -0.050197   0.107332      1.000000   \n",
       "prevConflictYrs         NaN  0.065081 -0.062660   0.082987      0.580304   \n",
       "chronicFighting         NaN  0.044784 -0.013314   0.136352      0.179295   \n",
       "difWealth               NaN  0.017524 -0.045414   0.110660      0.029634   \n",
       "difLanguage             NaN  0.099868  0.099862   0.229389      0.345861   \n",
       "intermingled            NaN  0.193946  0.331854   0.374352      0.098361   \n",
       "sameGov                 NaN -0.038068  0.103139  -0.162693      0.027850   \n",
       "sameGovYrs              NaN  0.148147  0.301933   0.299527     -0.141380   \n",
       "\n",
       "                 prevConflictYrs  chronicFighting  difWealth  difLanguage  \\\n",
       "revolution                   NaN              NaN        NaN          NaN   \n",
       "months                  0.065081         0.044784   0.017524     0.099868   \n",
       "deaths                 -0.062660        -0.013314  -0.045414     0.099862   \n",
       "logDeaths               0.082987         0.136352   0.110660     0.229389   \n",
       "prevConflict            0.580304         0.179295   0.029634     0.345861   \n",
       "prevConflictYrs         1.000000         0.145334  -0.040967     0.329918   \n",
       "chronicFighting         0.145334         1.000000   0.216753    -0.103713   \n",
       "difWealth              -0.040967         0.216753   1.000000    -0.137811   \n",
       "difLanguage             0.329918        -0.103713  -0.137811     1.000000   \n",
       "intermingled           -0.006749         0.038940   0.401836    -0.185787   \n",
       "sameGov                 0.008531        -0.207478   0.052382    -0.069128   \n",
       "sameGovYrs              0.019609        -0.074260  -0.001416     0.102004   \n",
       "\n",
       "                 intermingled   sameGov  sameGovYrs  \n",
       "revolution                NaN       NaN         NaN  \n",
       "months               0.193946 -0.038068    0.148147  \n",
       "deaths               0.331854  0.103139    0.301933  \n",
       "logDeaths            0.374352 -0.162693    0.299527  \n",
       "prevConflict         0.098361  0.027850   -0.141380  \n",
       "prevConflictYrs     -0.006749  0.008531    0.019609  \n",
       "chronicFighting      0.038940 -0.207478   -0.074260  \n",
       "difWealth            0.401836  0.052382   -0.001416  \n",
       "difLanguage         -0.185787 -0.069128    0.102004  \n",
       "intermingled         1.000000  0.174852    0.175978  \n",
       "sameGov              0.174852  1.000000    0.234457  \n",
       "sameGovYrs           0.175978  0.234457    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Quarrels.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see above, there isn't much of a strong correlation going on here.  There are a few that have notable R values higher than 0.5, but much those are **deaths**/**logDeath** and **prevConflict**/**prevConflictYrs** which trivial and don't tell use much about the conflicts.  The highest correlation value of 0.40 is between **difWealth** and **intermingled**, but it is a moderately weak positive linear correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>revolution</th>\n",
       "      <th>months</th>\n",
       "      <th>deaths</th>\n",
       "      <th>logDeaths</th>\n",
       "      <th>prevConflict</th>\n",
       "      <th>prevConflictYrs</th>\n",
       "      <th>chronicFighting</th>\n",
       "      <th>difWealth</th>\n",
       "      <th>difLanguage</th>\n",
       "      <th>intermingled</th>\n",
       "      <th>sameGov</th>\n",
       "      <th>sameGovYrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>revolution</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>months</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.155041e+02</td>\n",
       "      <td>2.491939e+07</td>\n",
       "      <td>1.236071e+01</td>\n",
       "      <td>0.315919</td>\n",
       "      <td>2.464832e+01</td>\n",
       "      <td>0.255946</td>\n",
       "      <td>0.178220</td>\n",
       "      <td>1.257900</td>\n",
       "      <td>2.236578</td>\n",
       "      <td>-0.516820</td>\n",
       "      <td>2.287943e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deaths</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.491939e+07</td>\n",
       "      <td>1.162655e+13</td>\n",
       "      <td>2.445346e+06</td>\n",
       "      <td>-80284.110771</td>\n",
       "      <td>-2.674318e+06</td>\n",
       "      <td>-8575.075093</td>\n",
       "      <td>-52049.938753</td>\n",
       "      <td>141748.262402</td>\n",
       "      <td>431266.143306</td>\n",
       "      <td>157796.942830</td>\n",
       "      <td>5.254832e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>logDeaths</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.236071e+01</td>\n",
       "      <td>2.445346e+06</td>\n",
       "      <td>1.282184e+00</td>\n",
       "      <td>0.057007</td>\n",
       "      <td>1.176206e+00</td>\n",
       "      <td>0.029163</td>\n",
       "      <td>0.042118</td>\n",
       "      <td>0.108128</td>\n",
       "      <td>0.161558</td>\n",
       "      <td>-0.082660</td>\n",
       "      <td>1.731145e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.159191e-01</td>\n",
       "      <td>-8.028411e+04</td>\n",
       "      <td>5.700731e-02</td>\n",
       "      <td>0.220014</td>\n",
       "      <td>3.407068e+00</td>\n",
       "      <td>0.015885</td>\n",
       "      <td>0.004672</td>\n",
       "      <td>0.067533</td>\n",
       "      <td>0.017584</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>-3.384811e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflictYrs</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.464832e+01</td>\n",
       "      <td>-2.674318e+06</td>\n",
       "      <td>1.176206e+00</td>\n",
       "      <td>3.407068</td>\n",
       "      <td>1.566752e+02</td>\n",
       "      <td>0.343612</td>\n",
       "      <td>-0.172358</td>\n",
       "      <td>1.719079</td>\n",
       "      <td>-0.032195</td>\n",
       "      <td>0.047910</td>\n",
       "      <td>1.252769e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronicFighting</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.559463e-01</td>\n",
       "      <td>-8.575075e+03</td>\n",
       "      <td>2.916327e-02</td>\n",
       "      <td>0.015885</td>\n",
       "      <td>3.436120e-01</td>\n",
       "      <td>0.035678</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>-0.008155</td>\n",
       "      <td>0.002803</td>\n",
       "      <td>-0.017584</td>\n",
       "      <td>-7.159361e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.782195e-01</td>\n",
       "      <td>-5.204994e+04</td>\n",
       "      <td>4.211774e-02</td>\n",
       "      <td>0.004672</td>\n",
       "      <td>-1.723581e-01</td>\n",
       "      <td>0.013761</td>\n",
       "      <td>0.112980</td>\n",
       "      <td>-0.019283</td>\n",
       "      <td>0.051478</td>\n",
       "      <td>0.007900</td>\n",
       "      <td>-2.429494e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.257900e+00</td>\n",
       "      <td>1.417483e+05</td>\n",
       "      <td>1.081278e-01</td>\n",
       "      <td>0.067533</td>\n",
       "      <td>1.719079e+00</td>\n",
       "      <td>-0.008155</td>\n",
       "      <td>-0.019283</td>\n",
       "      <td>0.173293</td>\n",
       "      <td>-0.029477</td>\n",
       "      <td>-0.012912</td>\n",
       "      <td>2.167346e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.236578e+00</td>\n",
       "      <td>4.312661e+05</td>\n",
       "      <td>1.615579e-01</td>\n",
       "      <td>0.017584</td>\n",
       "      <td>-3.219504e-02</td>\n",
       "      <td>0.002803</td>\n",
       "      <td>0.051478</td>\n",
       "      <td>-0.029477</td>\n",
       "      <td>0.145260</td>\n",
       "      <td>0.029901</td>\n",
       "      <td>3.423378e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.168196e-01</td>\n",
       "      <td>1.577969e+05</td>\n",
       "      <td>-8.265970e-02</td>\n",
       "      <td>0.005861</td>\n",
       "      <td>4.791030e-02</td>\n",
       "      <td>-0.017584</td>\n",
       "      <td>0.007900</td>\n",
       "      <td>-0.012912</td>\n",
       "      <td>0.029901</td>\n",
       "      <td>0.201325</td>\n",
       "      <td>5.369521e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGovYrs</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.287943e+02</td>\n",
       "      <td>5.254832e+07</td>\n",
       "      <td>1.731145e+01</td>\n",
       "      <td>-3.384811</td>\n",
       "      <td>1.252769e+01</td>\n",
       "      <td>-0.715936</td>\n",
       "      <td>-0.024295</td>\n",
       "      <td>2.167346</td>\n",
       "      <td>3.423378</td>\n",
       "      <td>5.369521</td>\n",
       "      <td>2.605223e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 revolution        months        deaths     logDeaths  \\\n",
       "revolution              0.0  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "months                  0.0  9.155041e+02  2.491939e+07  1.236071e+01   \n",
       "deaths                  0.0  2.491939e+07  1.162655e+13  2.445346e+06   \n",
       "logDeaths               0.0  1.236071e+01  2.445346e+06  1.282184e+00   \n",
       "prevConflict            0.0  3.159191e-01 -8.028411e+04  5.700731e-02   \n",
       "prevConflictYrs         0.0  2.464832e+01 -2.674318e+06  1.176206e+00   \n",
       "chronicFighting         0.0  2.559463e-01 -8.575075e+03  2.916327e-02   \n",
       "difWealth               0.0  1.782195e-01 -5.204994e+04  4.211774e-02   \n",
       "difLanguage             0.0  1.257900e+00  1.417483e+05  1.081278e-01   \n",
       "intermingled            0.0  2.236578e+00  4.312661e+05  1.615579e-01   \n",
       "sameGov                 0.0 -5.168196e-01  1.577969e+05 -8.265970e-02   \n",
       "sameGovYrs              0.0  2.287943e+02  5.254832e+07  1.731145e+01   \n",
       "\n",
       "                 prevConflict  prevConflictYrs  chronicFighting     difWealth  \\\n",
       "revolution           0.000000     0.000000e+00         0.000000      0.000000   \n",
       "months               0.315919     2.464832e+01         0.255946      0.178220   \n",
       "deaths          -80284.110771    -2.674318e+06     -8575.075093 -52049.938753   \n",
       "logDeaths            0.057007     1.176206e+00         0.029163      0.042118   \n",
       "prevConflict         0.220014     3.407068e+00         0.015885      0.004672   \n",
       "prevConflictYrs      3.407068     1.566752e+02         0.343612     -0.172358   \n",
       "chronicFighting      0.015885     3.436120e-01         0.035678      0.013761   \n",
       "difWealth            0.004672    -1.723581e-01         0.013761      0.112980   \n",
       "difLanguage          0.067533     1.719079e+00        -0.008155     -0.019283   \n",
       "intermingled         0.017584    -3.219504e-02         0.002803      0.051478   \n",
       "sameGov              0.005861     4.791030e-02        -0.017584      0.007900   \n",
       "sameGovYrs          -3.384811     1.252769e+01        -0.715936     -0.024295   \n",
       "\n",
       "                   difLanguage   intermingled        sameGov    sameGovYrs  \n",
       "revolution            0.000000       0.000000       0.000000  0.000000e+00  \n",
       "months                1.257900       2.236578      -0.516820  2.287943e+02  \n",
       "deaths           141748.262402  431266.143306  157796.942830  5.254832e+07  \n",
       "logDeaths             0.108128       0.161558      -0.082660  1.731145e+01  \n",
       "prevConflict          0.067533       0.017584       0.005861 -3.384811e+00  \n",
       "prevConflictYrs       1.719079      -0.032195       0.047910  1.252769e+01  \n",
       "chronicFighting      -0.008155       0.002803      -0.017584 -7.159361e-01  \n",
       "difWealth            -0.019283       0.051478       0.007900 -2.429494e-02  \n",
       "difLanguage           0.173293      -0.029477      -0.012912  2.167346e+00  \n",
       "intermingled         -0.029477       0.145260       0.029901  3.423378e+00  \n",
       "sameGov              -0.012912       0.029901       0.201325  5.369521e+00  \n",
       "sameGovYrs            2.167346       3.423378       5.369521  2.605223e+03  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Quarrels.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Plots\n",
    "\n",
    "From here, we plotted the data to get a better visualization of what was happening.  Remember, 0 stands for no and 1 stands for yes. We start with have there been prior conflicts?\n",
    "\n",
    "## Prior Conflicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.678899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.321101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0          percent\n",
       "prevConflict          \n",
       "0             0.678899\n",
       "1             0.321101"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['prevConflict'], columns='percent'))/(pd.crosstab(index=df_Quarrels['prevConflict'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3e42e5f8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lzE3czCxjbuJmZhlzEzczy5ibuJlZxv4/PSaBrlEZMfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3a905780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['prevConflict'], columns='percent'))/(pd.crosstab(index=df_Quarrels['prevConflict'], columns='percent').sum())).plot.bar(title='Had Previous Conflict (Percent)', legend=None, grid=True).set_xlabel('prevConflict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chronic Fighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chronicFighting</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.963303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.036697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0             percent\n",
       "chronicFighting          \n",
       "0                0.963303\n",
       "1                0.036697"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['chronicFighting'], columns='percent'))/(pd.crosstab(index=df_Quarrels['chronicFighting'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3e4189b0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u4q1WNF2AWZYz8dFxEzczazFn4hnOxOvmTNxsGM7EzczGlJt4qxVNF2CW5Ux8dNzEzcxa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\nbuJmZi3mJm5m1mJu4mZmLeYmbmbWYv8fEjcngzWEZEAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3e45b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['chronicFighting'], columns='percent'))/(pd.crosstab(index=df_Quarrels['chronicFighting'], columns='percent').sum())).plot.bar(title='Had Chronic Fighting (Percent)', legend=None, grid=True).set_xlabel('chronicFighting')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Differeing Levels of Wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.87156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.12844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0      percent\n",
       "difWealth         \n",
       "0          0.87156\n",
       "1          0.12844"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['difWealth'], columns='percent'))/(pd.crosstab(index=df_Quarrels['difWealth'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3e9c60b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3e9919e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['difWealth'], columns='percent'))/(pd.crosstab(index=df_Quarrels['difWealth'], columns='percent').sum())).plot.bar(title='Differences in Wealth (Percent)', legend=None, grid=True).set_xlabel('difWealth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are different languages spoken?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.779817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.220183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0         percent\n",
       "difLanguage          \n",
       "0            0.779817\n",
       "1            0.220183"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['difLanguage'], columns='percent'))/(pd.crosstab(index=df_Quarrels['difLanguage'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3e9e0128>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MCjkT7x83cTOzGnMmXsCZeNmciZv1wpm4mdmQchOvtaTqAswKORPvHzdxM7MacyZewJl4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\nbmZWY27iZmY19v8B4bv++OJPHV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3ea07ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['difLanguage'], columns='percent'))/(pd.crosstab(index=df_Quarrels['difLanguage'], columns='percent').sum())).plot.bar(title='Have Different Language (Percent)', legend=None, grid=True).set_xlabel('difLanguage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Is the territory of the two intermingled?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.825688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.174312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0          percent\n",
       "intermingled          \n",
       "0             0.825688\n",
       "1             0.174312"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['intermingled'], columns='percent'))/(pd.crosstab(index=df_Quarrels['intermingled'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3ea746a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sy6cYViqjGSUVn2I402RZ5pJKhSY7xbCTJL4bcD1wLHA7cAWwJCLWtozbF7gReGJE/KbN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3e9f1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['intermingled'], columns='percent'))/(pd.crosstab(index=df_Quarrels['intermingled'], columns='percent').sum())).plot.bar(title='Have Intermingled Territory (Percent)', legend=None, grid=True).set_xlabel('intermingled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Did the belligerents share the same government?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col_0</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.275229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0     percent\n",
       "sameGov          \n",
       "0        0.275229\n",
       "1        0.724771"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.crosstab(index=df_Quarrels['sameGov'], columns='percent'))/(pd.crosstab(index=df_Quarrels['sameGov'], columns='percent').sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ae3eb29400>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/Kv6XwFUR8czkn7JZd/7uFDOzhDlOMTNLmJu4mVnC3MTNzBLmJm5mljA3cTOz\nhLmJm5klzE3czCxh/x+70VQ+te0IQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3eaebb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((pd.crosstab(index=df_Quarrels['sameGov'], columns='percent'))/(pd.crosstab(index=df_Quarrels['sameGov'], columns='percent').sum())).plot.bar(title='Have Same Government (Percent)', legend=None, grid=True).set_xlabel('sameGov')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now compare some variables against each other by count, percentage, and plotting them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences of Wealth vs. Differences of Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>85</td>\n",
       "      <td>24</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difLanguage   0   1  All\n",
       "difWealth               \n",
       "0            72  23   95\n",
       "1            13   1   14\n",
       "All          85  24  109"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['difLanguage'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.660550</td>\n",
       "      <td>0.211009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.119266</td>\n",
       "      <td>0.009174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difLanguage         0         1\n",
       "difWealth                      \n",
       "0            0.660550  0.211009\n",
       "1            0.119266  0.009174"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['difLanguage']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3eb59470>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tOImbmWWYk7iZVZxr4rXjJG6WIUOGNCGp4f+GDGmq96bqNfrWOwAzK9+6dauB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\n/w/5f50UAqtFPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3eb5ffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['difLanguage']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Wealth and Language (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences of Wealth vs Intermingled Territory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>intermingled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>84</td>\n",
       "      <td>11</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled   0   1  All\n",
       "difWealth                \n",
       "0             84  11   95\n",
       "1              6   8   14\n",
       "All           90  19  109"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['intermingled'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>intermingled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.770642</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.055046</td>\n",
       "      <td>0.073394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled         0         1\n",
       "difWealth                       \n",
       "0             0.770642  0.100917\n",
       "1             0.055046  0.073394"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['intermingled']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fbc6908>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zCqh1e72edmedtpOYa7293Ha7sN3Z2VlX8TRau7u7m66uLoDefNkf1dTojwCm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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fb89198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['intermingled']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Wealth and Intermingled Territory (Percent)', grid=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previous Conflicts vs Differences of Wealth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difWealth</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>95</td>\n",
       "      <td>14</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth      0   1  All\n",
       "prevConflict             \n",
       "0             65   9   74\n",
       "1             30   5   35\n",
       "All           95  14  109"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difWealth'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difWealth</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.596330</td>\n",
       "      <td>0.082569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.275229</td>\n",
       "      <td>0.045872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth            0         1\n",
       "prevConflict                    \n",
       "0             0.596330  0.082569\n",
       "1             0.275229  0.045872"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difWealth']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fc43198>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ukTQYeAwYALwr6QvAYRHxZkc23sysp2s1nVLThTmdUmM+Ze1pvG/2TDubTjEz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fc8fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difWealth']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Previous Conflicts and Wealth (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previous Conflicts vs Differences in Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>9</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>85</td>\n",
       "      <td>24</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difLanguage    0   1  All\n",
       "prevConflict             \n",
       "0             65   9   74\n",
       "1             20  15   35\n",
       "All           85  24  109"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difLanguage'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.596330</td>\n",
       "      <td>0.082569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.183486</td>\n",
       "      <td>0.137615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difLanguage          0         1\n",
       "prevConflict                    \n",
       "0             0.596330  0.082569\n",
       "1             0.183486  0.137615"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difLanguage']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fd19b38>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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om5nliIO6mVmOOKibmeWIg7qZWY44qJuZ5YiDuplZjjiom5nliIO6mVmO+Ecy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fc824e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['difLanguage']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Previous Conflicts and Language (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Previous Conflicts vs Intermingled Territory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>intermingled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>90</td>\n",
       "      <td>19</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled   0   1  All\n",
       "prevConflict             \n",
       "0             63  11   74\n",
       "1             27   8   35\n",
       "All           90  19  109"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['intermingled'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>intermingled</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.577982</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.247706</td>\n",
       "      <td>0.073394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled         0         1\n",
       "prevConflict                    \n",
       "0             0.577982  0.100917\n",
       "1             0.247706  0.073394"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['intermingled']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fd11828>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vDwe65wcuEek8tu/FkM4slW5Dd28wswuAxwgB9BZ3n2dm08Jivwk4w8zOBf4K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fd42710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['prevConflict'], df_Quarrels['intermingled']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Previous Conflicts and Intermingled Territory (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences in Wealth vs Same Government"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>68</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>30</td>\n",
       "      <td>79</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov     0   1  All\n",
       "difWealth             \n",
       "0          27  68   95\n",
       "1           3  11   14\n",
       "All        30  79  109"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['sameGov'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.247706</td>\n",
       "      <td>0.623853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.100917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov           0         1\n",
       "difWealth                    \n",
       "0          0.247706  0.623853\n",
       "1          0.027523  0.100917"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fd3be48>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vb8u7H9ZqM4kumqYq6Yi3jrW0tNDS0lLrYpi9i+tmz1L13q3ZpUoe7psHbC9peLpzYBLZ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\nGspWpSNSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fda89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['difWealth'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Wealth and Sharing Same Government (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different Languages vs having the Same Government"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>63</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>30</td>\n",
       "      <td>79</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov       0   1  All\n",
       "difLanguage             \n",
       "0            22  63   85\n",
       "1             8  16   24\n",
       "All          30  79  109"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difLanguage'], df_Quarrels['sameGov'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.201835</td>\n",
       "      <td>0.577982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.146789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov             0         1\n",
       "difLanguage                    \n",
       "0            0.201835  0.577982\n",
       "1            0.073394  0.146789"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['difLanguage'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fe20b38>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmZVPwwOPpA7gfGBfYAxwuKQPVmV7EtgzIsYC04EfN7ocZtazuI+nfTTjjmcC\n8FhELIqI5cDlwOR8hoi4NyJeScl7gWFNKIeZmZXQBk2Y5zBgcS79DFkwquXzwA1NKEdhhg4dyZIl\ni1pdDLMezX087aMZgadukvYCjgH26CrflClTGDlyJAADBw5k3Lhxq3fSyu15K9NZ0IlU2lnp/8QS\npgVPpeRW6X9Z05XSl2D7Ou102dOVzwsXLqQnUER0n2tdZijtBkyLiEkpfRoQEXFuVb6dgN8AkyLi\niS7mF40uY6NJYk3gKTPBtFaXoQ7ToOzb3Bpv1qxZvutpEElEhFpdjlqa0cczBxgtaYSkPsBhwDX5\nDJKGkwWdI7sKOmZm1vs0vKktIlZKOgm4mSywXRIR8yQdl42Oi4EzgEHAhcpuF5ZHRFf9QGbWy/lu\np300vKmt0dzU1khuajNrB+3Y1GZmts78PZ724cBjZmaFcuAxs1JwH0/7cOAxM7NCOfCYWSm4j6d9\nOPCYmVmhHHjMrBTcx9M+HHjMzKxQDjxmVgru42kfDjxmZlYoBx4zKwX38bQPBx4zMyuUA4+ZlYL7\neNqHA4+ZmRXKgcfMSsF9PO3DgcfMSmHolkORVPq/oVsObXVV9XgN/wVSM7P1seTZJT3ihwqXTFvS\n6iL0eL7jMTOzQjnwmJlZoRx4zMysUA48ZmZWKAceMzMrlAOPmZkVyoHHzMwK5cBjZmaFcuAxM7NC\nOfCYmVmhHHjMzKxQTQk8kiZJmi9pgaRTOxm/raS7JS2T9KVmlMHMzMqp4S8JldQBnA/sDfwJmCPp\ndxExP5ftReBk4MBGL9/MzMqtGXc8E4DHImJRRCwHLgcm5zNExAsR8QCwognLNzOzEmtG4BkGLM6l\nn0nDzMzMesbv8UyZMoWRI0cCMHDgQMaNG7f61worv9Pe6vQalfTEcqafSsmtKHe6UvqSbF+ni0mX\nZv/rYftn5fPChQvpCRQRjZ2htBswLSImpfRpQETEuZ3kPQt4LSK+08X8otFlbDRJQLnLmFGP+KEt\npkHZt7k1nuT9s1EkERFqdTlqaUZT2xxgtKQRkvoAhwHXdJG/tJVjZmaN1/CmtohYKekk4GaywHZJ\nRMyTdFw2Oi6WNAS4HxgArJL0RWD7iHi90eUxM7NyaUofT0TcCGxbNexHuc9LgPc3Y9lmZlZufnOB\nmZkVyoHHzMwK5cBjZmaFcuAxM7NCOfCYmVmhesSbC8xs/Q0dOpIlSxa1uhhmqznwmPVyWdAp9zft\nM/4uebsMDllaAAAFW0lEQVRwU5uZmRXKgcfMzArlwGNmZoVy4DEzs0I58JiZWaEceMzMrFAOPGZm\nVigHHjMzK5QDj5mZFcqBx8zMCuXAY2ZmhXLgMTOzQjnwmJlZoRx4zMysUA48ZmZWKAceMzMrlAOP\nmZkVyoHHzMwK5cBjZmaFcuAxM7NCOfCYmVmhmhJ4JE2SNF/SAkmn1sjzA0mPSfovSeOaUQ4zMyuf\nhgceSR3A+cC+wBjgcEkfrMqzHzAqIrYBjgN+2OhymJlZOTXjjmcC8FhELIqI5cDlwOSqPJOBnwNE\nxO+BTSUNaUJZzMysZJoReIYBi3PpZ9KwrvI820keMzPrhTZodQHqIanVRahDTygjMK3VBahPz9jm\nPUkPqc9prS5Afbx/vjPNCDzPAsNz6S3TsOo87+8mDwAR4S1sZtaLNKOpbQ4wWtIISX2Aw4BrqvJc\nAxwFIGk34K8RsaQJZTEzs5Jp+B1PRKyUdBJwM1lguyQi5kk6LhsdF0fE9ZL2l/Q48AZwTKPLYWZm\n5aSIaHUZzMysjfjNBWZmVigHHjMzK1SPeJza1k96Y8Rk1nxH6lngmoiY17pSmVm78x1PL5XekXc5\n2Rc47kt/Ai6TdFory2bWFUl+2KiX88MFvZSkBcCY9Nqi/PA+wKPpPXlmpSPp6YgY3n1O66nc1NZ7\nrQLeByyqGr5FGmfWMpIerjUK8HsbezkHnt7rfwO3SnqMNe/FGw6MBk5qWanMMkPI3mD/ctVwAXcX\nXxwrkgNPLxURN0r6ANnbwvMPF8yJiJWtK5kZAP8O9I+I/6oeIWlW8cWxIrmPx8zMCuWn2szMrFAO\nPGZmVigHHjMzK5QfLrBeRdJZwOvAAOCOiLhN0h7AD4G/AUcAV0TEji0spllbc+Cx3igiYlou/Rng\nnIj4laQRgJ+oMWshN7VZjyfpq5L+W9IdwLbZIM2QdLCkzwGHAt+QdGkX8/i8pPskPSjpCkkbpeEz\nJH1f0l2SHpd0cBouSRdK+qOkmyRdlxv3lKRB6fOHJN2ePo+XdLekByTNlrRNGt5P0kxJf5B0laR7\nJe2Sxv1dmub+lGfj5tWkWTEceKxHSyfoQ4GdgE8A48nuaAIgIi4h+8XbUyLiyC5m9ZuImBAROwPz\ngc/lxg2NiN2BvwfOTcMOAYZHxPZkv6b7kVz+6juqSnoesEdEfAg4C/i/afiJwEsRsQNwBlAJOu8B\nvgbsHRG7Ag8A/9x1jZiVn5varKf7KPDbiHgLeEvS78i+/a51nM+OkqYDA4F3Azflxl0NkH5Jd3Aa\ntjtwRRq+pHJXk9Ra9kDg5+lOJ1hz/O0BfC/N69Hc62R2A7YH7pIkYEPgnnVcL7PSceCx3qZy0l/X\nfpyfAQdExB8kHQ18LDfurU7m35UVrGlN2Cg3/BvAbRFxcOpruv1tU669DAE3R8Rn6limWY/hpjbr\n6e4ADpTUV9IAsuawoOsA0dm4/sCfJW1I9jBCd9PeBRyS+nqGABNzeZ4CPpQ+H5IbvinZa4sA8q/+\nvwv4NICk7YEd0vB7gd0ljUrjNq70C5n1ZA481qNFxIPATOBh4Dqy3x2Cte94qu9+PiDpaUmL0/9D\nyPpW7gPuJOuLqTVtJf0b4BngUeDnZP0vr6RxXwd+IOk+srufin8BviXpAdY+9i4ENpf0hzTto8Ar\nEfECMIXsN5QeInt55rZdVIdZj+B3tZmtJ0nvjog30hNsvwd2j4i/rMd8OoANI+ItSVsDtwDbRsSK\nbiY165Hcx2O2/v5d0kCyTv+vr0/QSTYGbk/NfAAnOOhYb+Y7HjMzK5T7eMzMrFAOPGZmVigHHjMz\nK5QDj5mZFcqBx8zMCvX/AcfIV1uNJFk6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fdad7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['difLanguage'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Language and Sharing Same Government (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intermingled Territory vs having the Same Government"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>62</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>30</td>\n",
       "      <td>79</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov        0   1  All\n",
       "intermingled             \n",
       "0             28  62   90\n",
       "1              2  17   19\n",
       "All           30  79  109"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['intermingled'], df_Quarrels['sameGov'], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sameGov</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.256881</td>\n",
       "      <td>0.568807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.018349</td>\n",
       "      <td>0.155963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sameGov              0         1\n",
       "intermingled                    \n",
       "0             0.256881  0.568807\n",
       "1             0.018349  0.155963"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['intermingled'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fe24ba8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tGWDviBgBnAf8aHUCNbPez20gzaXsFcgo4MmImBsRi4HrgLHFChHxUES8kYsP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fea46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['intermingled'], df_Quarrels['sameGov']).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Intermingled Territory and Sharing Same Government (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now let's take a look at three variables at the same time to see if we can get any extra information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How common are previous conflicts when compared with both language differences and wealth differences? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>67</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled   0       1     All\n",
       "difLanguage    0   1   0  1     \n",
       "prevConflict                    \n",
       "0             54   9  11  0   74\n",
       "1             13  14   7  1   35\n",
       "All           67  23  18  1  109"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.495413</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.100917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.119266</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>0.064220</td>\n",
       "      <td>0.009174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled         0                   1          \n",
       "difLanguage          0         1         0         1\n",
       "prevConflict                                        \n",
       "0             0.495413  0.082569  0.100917  0.000000\n",
       "1             0.119266  0.128440  0.064220  0.009174"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3ff21d30>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7BbnWrVuzbdu2OvNK5b35+fm4e7WfkJWmRYFdspbG2CUXtGvXrlrgbdWqVew3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3ff69860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Previous Conflicts, Intermingled Territory, and Language (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How common are previous conflicts when compared with both language differences and wealth differences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth      0       1     All\n",
       "difLanguage    0   1   0  1     \n",
       "prevConflict                    \n",
       "0             56   9   9  0   74\n",
       "1             16  14   4  1   35\n",
       "All           72  23  13  1  109"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prevConflict</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.513761</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.082569</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.146789</td>\n",
       "      <td>0.128440</td>\n",
       "      <td>0.036697</td>\n",
       "      <td>0.009174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth            0                   1          \n",
       "difLanguage          0         1         0         1\n",
       "prevConflict                                        \n",
       "0             0.513761  0.082569  0.082569  0.000000\n",
       "1             0.146789  0.128440  0.036697  0.009174"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3fffdf28>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJB8o+IiISM6p2S1D1Owm+UTNboVDzW4iIiJpUvApMOrzEZF8oOAjIu2OhtHO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3ffdf668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['prevConflict'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Differences in Previous Conflicts, Language, and Wealth  (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How common is the same government when there are both language differences and wealth differences?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth     0       1     All\n",
       "difLanguage   0   1   0  1     \n",
       "sameGov                        \n",
       "0            19   8   3  0   30\n",
       "1            53  15  10  1   79\n",
       "All          72  23  13  1  109"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>difWealth</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.174312</td>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.486239</td>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.091743</td>\n",
       "      <td>0.009174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "difWealth           0                   1          \n",
       "difLanguage         0         1         0         1\n",
       "sameGov                                            \n",
       "0            0.174312  0.073394  0.027523  0.000000\n",
       "1            0.486239  0.137615  0.091743  0.009174"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae40092978>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GUA5qolZph5+BRcB94XE41swuCBen8pnbApxkZsfHzR8Ybq9K1XX1lxPcjfqSmZUT3FDx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\na9euSuvs3Lmz0sV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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae400a6358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['difWealth'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Sharing Same Government Despite Differences in Wealth and Language (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How common is the same government when there is a language differences in intermingled territory?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>67</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled   0       1     All\n",
       "difLanguage    0   1   0  1     \n",
       "sameGov                         \n",
       "0             20   8   2  0   30\n",
       "1             47  15  16  1   79\n",
       "All           67  23  18  1  109"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']], margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>intermingled</th>\n",
       "      <th colspan=\"2\" halign=\"left\">0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difLanguage</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sameGov</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.183486</td>\n",
       "      <td>0.073394</td>\n",
       "      <td>0.018349</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.431193</td>\n",
       "      <td>0.137615</td>\n",
       "      <td>0.146789</td>\n",
       "      <td>0.009174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "intermingled         0                   1          \n",
       "difLanguage          0         1         0         1\n",
       "sameGov                                             \n",
       "0             0.183486  0.073394  0.018349  0.000000\n",
       "1             0.431193  0.137615  0.146789  0.009174"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae40113ef0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rv3v3Ytavr8pZ+d269eKDD97JKO+GDRsYNGgQ//jHP5LP0n/qqae4+uqrWbt2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae40140748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(pd.crosstab(df_Quarrels['sameGov'], columns=[df_Quarrels['intermingled'], df_Quarrels['difLanguage']]).apply(lambda r: r/len(df_Quarrels))).plot.bar(title='Sharing Same Government Despite Differences in Wealth and Language (Percent)', grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's take a couple different looks at the number of deaths, lengths of some of these quarrels, number of years in the same government, and years since previous conflict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Months fighting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001AE400AE390>]], dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3fe2d278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.hist(column='months', color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae401d3f28>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0b1913vbtoaysKnkceWRh/ks6lYb+Ns3Cde3YEb7znar927aFvpr33gu/iSef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\nIiKRUmIREZFIKbGIiEiklFhERCRS/x9+9/FjfJahHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae401df860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels['months'].plot(kind='density', xlim=(0,200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of years in the Same Government"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001AE40247A90>]], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae400f3550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.hist(column='sameGovYrs', color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae419bddd8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RXE129tkwYwb86Z8mDyczs+riMZY2OLFUn7Vr4eab4cc/Tt5/6ENw1llw6qme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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae419c8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels['sameGovYrs'].plot(kind='density', xlim=(0,300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create logMonths and logGovYrs, to deal with long tails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_Quarrels['logMonths'] = np.log10(df_Quarrels['months'])\n",
    "df_Quarrels['logGovYrs'] = np.log10(df_Quarrels['sameGovYrs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare length of Civil Wars (in months) against the number of deaths, both in regular and in log form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae3ffeb978>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae3ff0ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='months', y='deaths', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41aadbe0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MUgIQEckoJQCRGpnZKjP7zxV/v8zMvrTQe0TSTAlApHbnAW+ftUw30kjbUgKQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41af0550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='months', y='logDeaths', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41b15908>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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M0/xlesDdZ8zs7c0v+0fc/Q4zu9rMvgf8FHhzmjF3IszxAb9qZv8ZOAnMAdem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\n3n0XcR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41b54470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='logMonths', y='logDeaths', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare length of years in the Same Governement against the number of deaths, both in regular and log form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41b6eda0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41b89240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='sameGovYrs', y='logDeaths', kind='scatter', xlim=(1, 300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41bcd438>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41be8710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='logGovYrs', y='logDeaths', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between years of government and months of fighting once war occurs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41c3ab38>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41c6c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='sameGovYrs', y='months', kind='scatter', xlim=(1, 300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41ca1128>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PA/9Istqq0i6sQYCIOEzSlXWlpCfDkUHu3rrjvVrSU9Ik80fpfQBfJFkS/7Wk\nrZuGg01WnT6cdnFdQZJMzNrKYyBmiQCIiK+keyHcQdICuCwipiV9GXgpcBfJtp+3krQyINkh878D\nP5D0S5KlvHcDP03HK/6KZC+VAHZFxB3pc+1Pxy7un2ffhaVmhb0AuELSY8CjwNuzv3SzbLycu1mD\nJB0bEYeU7DJ3C8l2yfPuhGnWDdwCMWvc1yX9K5JzPD7o5GHdzi0QMzPLxIPoZmaWiROImZll4gRi\nZmaZOIGYmVkmTiBmZpaJE4iZmWXy/wH2LLuwNnV0JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41cdcac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='logGovYrs', y='logMonths', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between years since the last conflict and deaths\n",
    "#### Range starts at 1 to ignore situations where there were no previous conflicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41cf1198>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41d2edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='prevConflictYrs', y='logDeaths', kind='scatter', xlim=(1,65))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between years since a civil war and years of having the same government\n",
    "As you would imagine this should be almost perfectly correlated, and it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ae41d5e470>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sJ5zwzcxywgnfzCwn/j9c7+ULmEJn+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ae41d9f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Quarrels.plot(x='prevConflictYrs', y='sameGovYrs', kind='scatter', xlim=(1,65), ylim=(0,80))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}