kenya-2017-MPs-data-local.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This is a brief analysis of the structure of the data contained herein."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis\n",
    "To begin this exploratory analysis, first import libraries and define functions for plotting the data using `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing some libraries to facilitate this exercise**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt # plotting\n",
    "import numpy as np # linear algebra\n",
    "import os # accessing directory structure\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 2 csv files in the current version of the dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./kaggle/input/MPs.csv\n",
      "./kaggle/input/Senators.csv\n"
     ]
    }
   ],
   "source": [
    "for dirname, _, filenames in os.walk('./kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        if filename.endswith(\".csv\"):\n",
    "            print(os.path.join(dirname, filename))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define functions for plotting data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "# Distribution graphs (histogram/bar graph) of column data\n",
    "def plotPerColumnDistribution(df, nGraphShown, nGraphPerRow):\n",
    "    nunique = df.nunique()\n",
    "    df = df[[col for col in df if nunique[col] > 1 and nunique[col] < 50]] # For displaying purposes, pick columns that have between 1 and 50 unique values\n",
    "    nRow, nCol = df.shape\n",
    "    columnNames = list(df)\n",
    "    nGraphRow = (nCol + nGraphPerRow - 1) / nGraphPerRow\n",
    "    plt.figure(num = None, figsize = (6 * nGraphPerRow, 8 * nGraphRow), dpi = 80, facecolor = 'w', edgecolor = 'k')\n",
    "    for i in range(min(nCol, nGraphShown)):\n",
    "        plt.subplot(nGraphRow, nGraphPerRow, i + 1)\n",
    "        columnDf = df.iloc[:, i]\n",
    "        if (not np.issubdtype(type(columnDf.iloc[0]), np.number)):\n",
    "            valueCounts = columnDf.value_counts()\n",
    "            valueCounts.plot.bar()\n",
    "        else:\n",
    "            columnDf.hist()\n",
    "        plt.ylabel('counts')\n",
    "        plt.xticks(rotation = 90)\n",
    "        plt.title(f'{columnNames[i]} (column {i})')\n",
    "    plt.tight_layout(pad = 1.0, w_pad = 1.0, h_pad = 1.0)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "# Correlation matrix\n",
    "def plotCorrelationMatrix(df, graphWidth):\n",
    "    filename = df.dataframeName\n",
    "    df = df.dropna('columns') # drop columns with NaN\n",
    "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    if df.shape[1] < 2:\n",
    "        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n",
    "        return\n",
    "    corr = df.corr()\n",
    "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
    "    corrMat = plt.matshow(corr, fignum = 1)\n",
    "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
    "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
    "    plt.gca().xaxis.tick_bottom()\n",
    "    plt.colorbar(corrMat)\n",
    "    plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_kg_hide-input": true
   },
   "outputs": [],
   "source": [
    "# Scatter and density plots\n",
    "def plotScatterMatrix(df, plotSize, textSize):\n",
    "    df = df.select_dtypes(include =[np.number]) # keep only numerical columns\n",
    "    # Remove rows and columns that would lead to df being singular\n",
    "    df = df.dropna('columns')\n",
    "    df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values\n",
    "    columnNames = list(df)\n",
    "    if len(columnNames) > 10: # reduce the number of columns for matrix inversion of kernel density plots\n",
    "        columnNames = columnNames[:10]\n",
    "    df = df[columnNames]\n",
    "    ax = pd.plotting.scatter_matrix(df, alpha=0.75, figsize=[plotSize, plotSize], diagonal='kde')\n",
    "    corrs = df.corr().values\n",
    "    for i, j in zip(*plt.np.triu_indices_from(ax, k = 1)):\n",
    "        ax[i, j].annotate('Corr. coef = %.3f' % corrs[i, j], (0.8, 0.2), xycoords='axes fraction', ha='center', va='center', size=textSize)\n",
    "    plt.suptitle('Scatter and Density Plot')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're ready to read in the data and use the plotting functions to visualize the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check 1st file: /kaggle/input/MPs.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 351 rows and 6 columns\n"
     ]
    }
   ],
   "source": [
    "nRowsRead = None # specify 'None' if want to read whole file\n",
    "# MPs.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows\n",
    "df1 = pd.read_csv('./kaggle/input/MPs.csv', delimiter=',', nrows = nRowsRead)\n",
    "df1.dataframeName = 'MPs.csv'\n",
    "nRow, nCol = df1.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look at what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Member of Parliament</th>\n",
       "      <th>Photo</th>\n",
       "      <th>County</th>\n",
       "      <th>Constituency</th>\n",
       "      <th>Party</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hon. (Dr.) Keynan, Wehliye Adan, CBS, MP</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Wajir</td>\n",
       "      <td>Eldas</td>\n",
       "      <td>JP</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hon. Abdi, Yusuf Hassan</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Nairobi</td>\n",
       "      <td>Kamukunji</td>\n",
       "      <td>JP</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hon. Abdullah, Bashir Sheikh</td>\n",
       "      <td>http://www.parliament.go.ke/index.php/sites/de...</td>\n",
       "      <td>Mandera</td>\n",
       "      <td>Mandera North</td>\n",
       "      <td>JP</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hon. Abuor, Paul</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Migori</td>\n",
       "      <td>Rongo</td>\n",
       "      <td>ODM</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hon. Adagala, Beatrice Kahai</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Vihiga</td>\n",
       "      <td>Vihiga</td>\n",
       "      <td>ANC</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Member of Parliament  \\\n",
       "0  Hon. (Dr.) Keynan, Wehliye Adan, CBS, MP   \n",
       "1                   Hon. Abdi, Yusuf Hassan   \n",
       "2              Hon. Abdullah, Bashir Sheikh   \n",
       "3                          Hon. Abuor, Paul   \n",
       "4              Hon. Adagala, Beatrice Kahai   \n",
       "\n",
       "                                               Photo   County   Constituency  \\\n",
       "0  http://www.parliament.go.ke/sites/default/file...    Wajir          Eldas   \n",
       "1  http://www.parliament.go.ke/sites/default/file...  Nairobi      Kamukunji   \n",
       "2  http://www.parliament.go.ke/index.php/sites/de...  Mandera  Mandera North   \n",
       "3  http://www.parliament.go.ke/sites/default/file...   Migori          Rongo   \n",
       "4  http://www.parliament.go.ke/sites/default/file...   Vihiga         Vihiga   \n",
       "\n",
       "  Party   Status  \n",
       "0    JP  Elected  \n",
       "1    JP  Elected  \n",
       "2    JP  Elected  \n",
       "3   ODM  Elected  \n",
       "4   ANC  Elected  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution graphs (histogram/bar graph) of sampled columns:\n",
    "\n",
    "**Political Party and Election Status Distributions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8EAAAL5CAYAAACQFiZ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3X2U1nW97//XhQMqDjcKykDDMBo3pWgI0WZbam7zvp0UcFwlyLQz0BVqG9vBqWP7zkj3NrLyrJzKMOUsjp6gm+NdKAclT+yCFC2OCqPiMAoSKuC4URmY3x8ur18EKCYXo/N9PNa61nK+d/O+Zq368Jzre11Tam9vbw8AAAAUQJeOHgAAAAD2FxEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDB0Utu2bcuxxx6b3/72t/vkev/0T/+Uj3zkI/vkWvvTjh07MmLEiNx7770dPQoABfL444/nyCOPTGtr6z653kc/+tH8t//23/bJtfanlpaW1NbW5rnnnuvoUaBMBMNf6KMf/Wi6deuW6urq9OzZM8OHD88NN9zwtq9bX1+fH/7wh2/7Ot/73vfy3ve+Nx/60Ife9rXe6e69996MHDky3bt3z5FHHpnvfe975X1dunTJP//zP+fSSy/twAkBeKuefPLJfPrTn86AAQNSXV2dAQMG5Oyzz866deuSvPb//aVSKW1tbW/pug0NDZk4cWIlRt7JP/zDP+Syyy5LdXV1xb9XR1q0aFFOPfXU9OnTJ6VSKU1NTTvtr62tzYQJE/JP//RPHTMg7IYIhrfhy1/+clpbW/PCCy9k5syZufDCC//iVxxfffXVfTbXjh078q1vfStTpkzZZ9d8p3rqqadyzjnn5HOf+1w2bdqUG2+8MTNnzsxPf/rT8jEf//jHs3Hjxtx9990dOCkAb8XZZ5+dHj165A9/+ENaW1vz4IMP5rzzzkupVOro0d7U448/nrvuuiuTJ0/u6FEq7pBDDskFF1yQm266aY/HfP7zn8+cOXOyadOm/TgZ7JkIhn3ggAMOyMSJE9OnT5/87ne/S5L89//+3zN8+PD07NkzNTU1mTRpUjZu3Fg+5/Xbi6+44ooMGDAgI0aMyFlnnZXm5uZMmzYt1dXVOeaYY9LU1JSqqqqsWbNmp+/58Y9/PF/84hd3O88DDzyQZ555JqeccspO2x955JF84hOfSE1NTXr16pUxY8Zk7dq1SZJNmzZlypQpqa2tTd++fXPWWWflscce2+Nz3t0r1qVSKffcc0+S//839LfcckuGDh2a7t2752//9m+zadOmXHHFFenfv3/69u2bf/zHfyyfv2bNmpRKpfz4xz/OBz7wgfTo0SNjxozJ//t//2+Pc9x4440ZOnRovvCFL6Rbt245+eST83d/93e57rrryscccMAB+djHPpYFCxbs8ToAvHM899xzefTRR3PRRRflsMMOS5L069cvkydPTk1NTZqbm3PWWWclSXr37p3q6urMmjUrSfK1r30tQ4cOTY8ePTJw4MBccskl+c///M8kyaxZs/I//sf/yC233JLq6upUV1enubk5N954Y2pra3ea4c/fBnTdddflve99b3r06JF+/fqloaFhj/P/9Kc/zahRo3LooYfutP3222/PmDFjcuihh6ZPnz4ZP358ed8jjzySs846K3379k1tbW2mTp2azZs37/b6r6+Xf/qq65+/Mv76/F/72tfSv3//9OzZM1/+8pfzwgsv5LzzzkuvXr1SX1+fn//85+VrvP5zaGxsTH19fXr16pUJEyZky5Yte3yuY8aMyeTJk3PMMcfs8Zijjz46hx12WH75y1/u8RjYn0Qw7ANtbW25+eab8/zzz2f06NFJkpqamixYsCCbNm3Kb37zm6xatSqXXHLJTuf9x3/8R7p27Zonnngiy5cvz5133pm6urpcd911aW1tzcqVKzN48OD8zd/8TX7wgx+Uz2tubs5dd92Viy66aLfzLF++PEcddVQOPvjg8rZnn302J554YoYPH55Vq1bl+eefz3e/+93yMZMmTcrq1auzfPnyNDc3Z9iwYfnYxz72tt/LdPvtt+d3v/td1qxZk8ceeyx/9Vd/lX79+mXt2rW57bbb8vWvfz1Lly7d6Zybb745d911V/74xz+mtrY2F1988R6vv2LFil1u+R49enQefPDBnbYdd9xxWbZs2dt6LgDsH3369Mmxxx6bqVOnZs6cOXn44YezY8eO8v66urrceeedSV77JW5ra2u+8pWvJEmGDBmSe+65J1u2bMldd92VO++8M//6r/+aJPnKV76S888/P+edd15aW1vT2tqaurq6N51n9erV+fKXv5yf//znefHFF/P444/n7/7u7/Z4/PLlyzN8+PCdtt19990ZP358Lr/88jz77LN5+umny+vbiy++mI997GM5+uij09zcnGXLluXRRx99268k/+Y3v0mfPn3S3NycRYsW5Vvf+lZOO+20TJs2LS+88EIuvfTSfPazny3/kiBJ1q9fn0cffTSPPPJIHn300axYsSLf/OY339YciXWYdxYRDG/DNddck969e6empibXXntt5syZk5NOOilJMm7cuAwdOjRdunTJoEGDMnPmzCxcuHCn8/v165crrrgiBx10ULp3777H73PxxRdnzpw55d/u/vCHP8xHPvKRvO9979vt8c8//3x69eq107abb745NTU1mTVrVnr27JkDDjggo0ePTt++fbNu3brcdtttufbaa1NTU5Pu3bvn3//937N169bcdtttb+dHlK9//evp0aNHjjjiiJxzzjlJkmnTpqWqqipjxozJ+9///l0+vOv131ofdNBB+exnP/uGi+aWLVvSu3fvnbYdeuihu/zWulevXnn++eff1nMBYP9ZvHhxzjrrrHzve9/Lhz70ofTt2zdf+tKX8sorr7zheZMmTUpdXV1KpVKOOeaYfOELX9hl/X2rqqqq0t7enpUrV2bLli2prq4ur/e7s7t1+Nvf/nY+97nPZcKECenWrVsOOuignHrqqUmS2267La+++mquvvrqdO/ePf3798+1116bn//851m/fv1fPHddXV0uu+yydO3aNaNHj87w4cMzatSonHjiienSpUsmT56cF154IatXr97puf7bv/1bDj744PTv3z9jx47dJx+yaR3mnUQEw9vwpS99KZs2bcrGjRvzu9/9bqff2C5YsCAnnHBCjjjiiPTs2TOTJk3K888/n+3bt5ePGTRo0F69t+kTn/hEDjjggPziF79IW1tbbrjhhkydOnWPxx922GG73EL15JNPZtiwYbs9/vVbot/73veWt3Xt2jWDBg1Kc3Pzm873Rvr371/+70MOOWSnr1/f9uKLL+60bcCAATvt37p16x4/+KRnz567vMfohRdeSM+ePXfatnnz5vItdQC88/Xp0yf/8i//kt/+9rfZvHlzfvSjH+UHP/hBvvGNb7zheY2NjRk5cmT69OmTXr165atf/Wo2bNjwtmY58sgj8z//5//MnDlzUldXl9GjR2fevHl7PP4vWYcHDRqUqqqq8rbBgwcnydtah3e35v75upxkp3W4b9++6dq1607H/Pk6/ZewDvNOIoKhAlpaWjJhwoRccsklaW5uzpYtW3LzzTcnSdrb28vHdemy6/8Ed7ftgAMOyOc///l8//vfz2233ZZt27Zl3Lhxe/z+o0aNyhNPPJGXX365vK2+vn6n3/T+qYEDByZ57YM8XtfW1pbm5uY93ibWo0ePvPTSS+Wvn3nmmT3OU0kjRozY5ZXi5cuX5/jjj99p2+9///t88IMf3J+jAbCPHHjggRk7dmw+9rGP5YEHHkiy+/Vy6dKlmTZtWr75zW9m/fr12bx5c77+9a+/6dr752tasuu6du655+auu+7Kxo0b8w//8A85//zzs2rVqt3OO2rUqKxcuXKnbfX19Xs8fuDAgWlubt7pF76vr8m7W4d79OiRJO+IdXhvWYd5JxHBUAGtra3ZsWNH+vbtm4MOOiirV69+099cv66mpma3H0j1+c9/PosXL84///M/p6GhId26ddvjNUaNGpX+/ftn8eLF5W0XXHBBWlpacsUVV+TFF1/M9u3bs3z58mzcuDH9+/fP2WefXX6f0tatWzNjxox069atfAvzn/vgBz+YefPmZdOmTdmyZUtmzpy5V89vX2toaMijjz6a733ve3n11Vfzq1/9Kj/60Y/yhS98oXzM9u3bc8899+STn/xkh8wIwFvz+l9dePjhh/PKK69k+/btWbRoURYvXly+DbmmpiZJdlozN2/enAMOOCCHH354unbtmgceeGCnD0p8/bzHH398pzuzjj/++Lz44ou55ZZbsmPHjtx77735X//rf5X3P/bYY7njjjvS2tqaqqqq8q3OBxxwwG7nHzt2bJYvX77TnUqXXXZZbrjhhsyfPz+vvvpqXn755SxatChJcs4556Sqqipf+cpXsnXr1qxfvz5///d/n7/9278tP88/1adPnxx55JH54Q9/mLa2tjzxxBO55ppr3tLPeF/ZsWNHXn755fJt6q8/tz/9+T7yyCPZuHFjzjjjjA6ZEf6cCIYKeN/73pdvfOMbueCCC9KjR49Mnjx5r/8m4de+9rX8/Oc/T+/evXPccceVt/fv3z+f+MQn8tBDD73hrdDJa7/l/uIXv5jGxsbytn79+mXJkiX53e9+lyOPPDJ9+vTJJZdcUn61+Oabb059fX1GjhyZ2trarFy5Mvfcc0/5t81/7sorr0zPnj0zcODAjBo1qsMCc9CgQbnjjjvy/e9/P7169cqkSZMya9asfOpTnyofc/vtt+ewww7L6aef3iEzAvDWdOvWLRs3bsyECRPSt2/f9OnTJ5dddllmzJiRyy+/PEkydOjQXHLJJTnllFPSu3fvXHXVVTn99NNz0UUX5aMf/Wh69eqVr3zlK7t8uNTrfz6wb9++6d27d5qbm3PUUUfluuuuy5e+9KX07t07jY2N+exnP1s+59VXX83Xv/71vOc970nPnj1z+eWX56abbtrpbUR/asiQITn99NNz4403lredfvrpmTdvXq666qocfvjh5U9hTl57a8/dd9+dhx56KLW1tRk1alQGDx6cH//4x3v8Gd100025995707t370ycODEXXnjhX/SzfruWLFmSgw8+uPw5Jcccc0wOPvjg8h1wSfKDH/wgn/3sZ3f5tGzoKKX2P70/BHhHmzFjRh544IG9+nu327Zty/HHH58f/ehHu3x6cpHs2LEjo0aNyje/+c38zd/8TUePA0BBPP744zn11FPzhz/8IdXV1R09Tod5+umnM2bMmDz44IPp27dvR48DSUQwvGs0Nzdn5MiRufnmm8t/GxEAAHhr3A4N7wLnn39+jj766HzmM58RwAAA8DZ4JRgAAIDC8EowAAAAhSGCAQAAKIyqjh5gXzrwwANz+OGHd/QYAJA//vGP5b+b2ZlZewF4J3gr626niuDDDz88LS0tHT0GAKS2trajR9gvrL0AvBO8lXXX7dAAAAAUhggGAACgMEQwAHQyp59+eo477riMGDEiJ554Yh588MEkSX19fYYNG5YRI0ZkxIgRueWWW8rnrF69OieccEKGDh2a0aNHZ+XKlR01PgBUVKd6TzAAkNx6663p3bt3kuSnP/1pGhoa8tBDDyVJbrnllowYMWKXc6ZOnZopU6akoaEhP/nJT9LQ0JBly5bt17kBYH/wSjAAdDKvB3CSbN68OaVS6Q2P37BhQ5YvX56JEycmScaNG5e1a9emqamponMCQEfwSjAAdEIXXHBBFi9enCS54447dtre3t6eD33oQ7nqqqty+OGHZ+3atenfv3+qql77Z0GpVEpdXV2am5szePDgna47e/bszJ49u/x1a2vrfng2ALDveCUYADqhm266KWvXrs2VV16ZGTNmJEmWLFmShx9+OA888ED69u2byZMnv+XrTp8+PS0tLeVHdXX1vh4dACpKBANAJzZ58uQsXrw4zz33XOrq6pIkXbt2zRe/+MX86le/SpIMHDgw69atS1tbW5Kkvb09zc3N5eMBoDMRwQDQiWzatCnPPPNM+euf/exn6dOnTw466KBs2rSpvH3evHk5/vjjkyRHHHFERo4cmblz5yZJ5s+fn9ra2l1uhQaAzsB7ggGgE9m8eXMmTJiQrVu3pkuXLjn88MNz22235dlnn824ceOyffv2tLe356ijjspNN91UPq+xsTENDQ2ZNWtWevbsmTlz5nTgswCAyim1t7e3d/QQ+0ptbW1aWlo6egwAKMyaVJTnCcA721tZj9wODQAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAAqjqqMHqLT6mbe/4f41V52znyYBAN7p3uzfDbwz+Pcb8HZ4JRgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKIyKR/Cll16a+vr6lEqlrFixorz9lVdeybRp0zJkyJAce+yxmThxYnnf6tWrc8IJJ2To0KEZPXp0Vq5cWekxAQAAKICKR/D48eNz//33Z9CgQTttnzlzZkqlUlatWpXf//73ueaaa8r7pk6dmilTpmTVqlWZMWNGGhoaKj0mAAAABVBV6W9w0kkn7bLtpZdeyg033JCWlpaUSqUkSU1NTZJkw4YNWb58eRYuXJgkGTduXKZNm5ampqYMHjy40uMCAADQiXXIe4Iff/zxHHbYYZk1a1Y++MEP5sQTT8yiRYuSJGvXrk3//v1TVfVan5dKpdTV1aW5uXmX68yePTu1tbXlR2tr6359HgAAALy7dEgEt7W15amnnsrRRx+d5cuX5zvf+U7OO++8PPvss2/pOtOnT09LS0v5UV1dXaGJAQAA6Aw6JILr6urSpUuXnH/++UmS448/PkceeWR+//vfZ+DAgVm3bl3a2tqSJO3t7Wlubk5dXV1HjAoAAEAn0iER3Ldv35x66qn55S9/mSR58skn8+STT+b9739/jjjiiIwcOTJz585NksyfPz+1tbXeDwwAAMDbVvEInjp1ampra9PS0pIzzjijHLPXX399/v3f/z3HHntsxo4dm8bGxrznPe9JkjQ2NqaxsTFDhw7NVVddlTlz5lR6TAAAAAqg4p8O3djYuNvtRx11VBYvXrzbfcOGDcvSpUsrORYAAAAF1CG3QwMAAEBHEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGgE7m9NNPz3HHHZcRI0bkxBNPzIMPPpgkWb16dU444YQMHTo0o0ePzsqVK8vnvNE+AOhMRDAAdDK33nprHn744axYsSLTp09PQ0NDkmTq1KmZMmVKVq1alRkzZpS3v9k+AOhMRDAAdDK9e/cu//fmzZtTKpWyYcOGLF++PBMnTkySjBs3LmvXrk1TU9Mb7gOAzqaqowcAAPa9Cy64IIsXL06S3HHHHVm7dm369++fqqrXlv5SqZS6uro0NzenV69ee9w3ePDgna47e/bszJ49u/x1a2vrfnpGALBveCUYADqhm266KWvXrs2VV16ZGTNm7LPrTp8+PS0tLeVHdXX1Prs2AOwPIhgAOrHJkydn8eLFqa2tzbp169LW1pYkaW9vT3Nzc+rq6jJw4MA97gOAzkYEA0AnsmnTpjzzzDPlr3/2s5+lT58+OeKIIzJy5MjMnTs3STJ//vzU1tZm8ODBb7gPADob7wkGgE5k8+bNmTBhQrZu3ZouXbrk8MMPz2233ZZSqZTGxsY0NDRk1qxZ6dmzZ+bMmVM+7432AUBnIoIBoBMZNGhQfvvb3+5237Bhw7J06dK3vA8AOhO3QwMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhVHxCL700ktTX1+fUqmUFStW7LJ/zpw5KZVK+dnPflbetmHDhpx55pkZMmRIhg8fniVLllR6TAAAAAqg4hE8fvz43H///Rk0aNAu+9asWZMf/OAHGTNmzE7bZ86cmTFjxmT16tWZM2dOPvOZz2Tbtm2VHhUAAIBOruIRfNJJJ6W2tnaX7Tt27MiFF16Y7373uznwwAN32nfrrbfmoosuSpKMHj06AwYMyH333VfpUQEAAOjkOuw9wbNnz86HP/zhjBo1aqftzz33XLZt25aamprytvr6+jQ3N+/2GrW1teVHa2trxecGAADg3auqI77pH/7wh8yfP/9tv9d3+vTpmT59evnr3b3iDAAAAK/rkAj+1a9+lTVr1mTIkCFJkvXr12fKlClZt25dLr744lRVVWX9+vXlV4PXrFmTurq6jhgVAACATqRDboe++OKLs27duqxZsyZr1qzJmDFj8v3vfz8XX3xxkmTChAm5/vrrkyTLli3L008/nZNPPrkjRgUAAKATqXgET506NbW1tWlpackZZ5yRwYMHv+k5V199dX79619nyJAhaWhoyNy5c9O1a9dKjwoAAEAnV/HboRsbG9/0mHvvvXenr/v165eFCxdWaCIAAACKqsM+HRoAAAD2NxEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQDQibz88ssZO3Zshg4dmg984AM57bTT0tTUlCT56Ec/miOPPDIjRozIiBEj8q1vfat83oYNG3LmmWdmyJAhGT58eJYsWdJRTwEAKqqqowcAAPatKVOm5KyzzkqpVMp1112XCy+8MPfee2+S5Fvf+lbGjh27yzkzZ87MmDFjctddd2XZsmX55Cc/mSeffDJdu3bdz9MDQGV5JRgAOpGDDjooZ599dkqlUpJkzJgxWbNmzZued+utt+aiiy5KkowePToDBgzIfffdV8lRAaBDiGAA6MS+/e1v59xzzy1/PXPmzBx77LE577zz8sQTTyRJnnvuuWzbti01NTXl4+rr69Pc3LzL9WbPnp3a2tryo7W1tfJPAgD2IREMAJ3UrFmz0tTUlG984xtJkptvvjmPPvpoHn744Zx44on5+Mc//pavOX369LS0tJQf1dXV+3psAKgoEQwAndA111yTBQsW5M4770z37t2TJAMHDkySlEqlTJs2LU888USee+659OnTJ1VVVVm/fn35/DVr1qSurq5DZgeAShLBANDJzJ49O/Pmzcvdd9+d3r17J0na2try7LPPlo+ZP39++vXrlz59+iRJJkyYkOuvvz5JsmzZsjz99NM5+eST9//wAFBhPh0aADqRlpaWXH755TnqqKNyyimnJEkOPPDA/J//839yzjnn5JVXXkmXLl3St2/f/OIXvyifd/XVV2fSpEkZMmRIunXrlrlz5/pkaAA6JREMAJ1IbW1t2tvbd7tv+fLlezyvX79+WbhwYaXGAoB3DLdDAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMKoeARfeumlqa+vT6lUyooVK5IkL7/8csaOHZuhQ4fmAx/4QE477bQ0NTWVz9mwYUPOPPPMDBkyJMOHD8+SJUsqPSYAAAAFUPEIHj9+fO6///4MGjRop+1TpkzJY489loceeijnnntuLrzwwvK+mTNnZsyYMVm9enXmzJmTz3zmM9m2bVulRwUAAKCTq3gEn3TSSamtrd1p20EHHZSzzz47pVIpSTJmzJisWbOmvP/WW2/NRRddlCQZPXp0BgwYkPvuu6/SowIAANDJvSPeE/ztb3875557bpLkueeey7Zt21JTU1PeX19fn+bm5o4aDwAAgE6iqqMHmDVrVpqamrJo0aK3fO7s2bMze/bs8tetra37cjQAAAA6mQ59Jfiaa67JggULcuedd6Z79+5Jkj59+qSqqirr168vH7dmzZrU1dXtcv706dPT0tJSflRXV++32QEAAHj36bAInj17dubNm5e77747vXv33mnfhAkTcv311ydJli1blqeffjonn3xyR4wJAABAJ1Lx26GnTp2a22+/PevXr88ZZ5yRHj165N57783ll1+eo446KqecckqS5MADD8xvfvObJMnVV1+dSZMmZciQIenWrVvmzp2brl27VnpUAAAAOrmKR3BjY+Nut7e3t+/xnH79+mXhwoWVGgkAAICCekd8OjQAAAAlvZxiAAAgAElEQVTsDyIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDQCfy8ssvZ+zYsRk6dGg+8IEP5LTTTktTU1OSZMOGDTnzzDMzZMiQDB8+PEuWLCmf90b7AKAzqXgEX3rppamvr0+pVMqKFSvK21evXp0TTjghQ4cOzejRo7Ny5cq92gcAvLEpU6bksccey0MPPZRzzz03F154YZJk5syZGTNmTFavXp05c+bkM5/5TLZt2/am+wCgM6l4BI8fPz73339/Bg0atNP2qVOnZsqUKVm1alVmzJiRhoaGvdoHAOzZQQcdlLPPPjulUilJMmbMmKxZsyZJcuutt+aiiy5KkowePToDBgzIfffd96b7AKAzqXgEn3TSSamtrd1p24YNG7J8+fJMnDgxSTJu3LisXbs2TU1Nb7gPAHhrvv3tb+fcc8/Nc889l23btqWmpqa8r76+Ps3NzW+478/Nnj07tbW15Udra+t+eR4AsK90yHuC165dm/79+6eqqipJUiqVUldXl+bm5jfc9+csxACwZ7NmzUpTU1O+8Y1v7LNrTp8+PS0tLeVHdXX1Prs2AOwP7+oPxrIQA8DuXXPNNVmwYEHuvPPOdO/ePX369ElVVVXWr19fPmbNmjWpq6t7w30A0Nl0SAQPHDgw69atS1tbW5Kkvb09zc3Nqaure8N9AMCbmz17dubNm5e77747vXv3Lm+fMGFCrr/++iTJsmXL8vTTT+fkk09+030A0Jl0SAQfccQRGTlyZObOnZskmT9/fmprazN48OA33AcAvLGWlpZcfvnl2bRpU0455ZSMGDEif/VXf5Ukufrqq/PrX/86Q4YMSUNDQ+bOnZuuXbu+6T4A6EyqKv0Npk6dmttvvz3r16/PGWeckR49eqSpqSmNjY1paGjIrFmz0rNnz8yZM6d8zhvtAwD2rLa2Nu3t7bvd169fvyxcuPAt7wOAzqTiEdzY2Ljb7cOGDcvSpUvf8j4AAAD4S72rPxgLAAAA3goRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYex3B//t//+9s2bIlSXLNNddk/Pjx+cMf/lCxwQCgyKy7AFAZex3BX/3qV9OzZ8889NBDmTt3bk477bRcfPHFlZwNAArLugsAlbHXEVxVVZUkWbhwYaZMmZKpU6fmpZdeqthgAFBk1l0AqIy9juDt27fnN7/5TebPn59TTjklSbJt27aKDQYARWbdBYDK2OsIvvLKKzN16tR85CMfyfvf//489thjGTp0aCVnA4DCsu4CQGVU7e2BgwYNyooVK8pfDxs2LP/4j/9YkaEAoOisuwBQGXv9SnBDQ8NebQMA3j7rLgBUxpu+Erxhw4asX78+W7duze9///u0t7cnSTZv3uwDOgBgH7PuAkBlvWkEz5s3L9dee22eeeaZfOITnyhv79WrV7785S9XdDgAKBrrLgBU1ptG8GWXXZbLLrss//qv/5orrrhif8wEAIVl3QWAytrrD8a64oorsmPHjqxfvz5tbW3l7XV1dRUZDACKzLoLAJWx1xH84x//OJdcckm6du2aLl1e+zytUqmUDRs2VGw4ACgq6y4AVMZeR/C//Mu/ZNmyZRk2bFgl5wEAYt0FgErZ6z+R1LdvXwsxAOwn1l0AqIy9juCxY8fm2muvzYYNG7Jly5byAwDY96y7AFAZe3079Fe/+tUkyfTp01MqldLe3p5SqZTt27dXbDgAKCrrLgBUxl5H8I4dOyo5BwDwJ6y7AFAZe307NAAAALzb7fUrwV26dEmpVNplu9uyAGDfs+4CQGXsdQS/+OKL5f/eunVrbrrpJgsxAFSIdRcAKmOvb4c+5JBDyo++fftm+vTp+clPflLJ2QCgsKy7AFAZf/F7gh999NFs3LhxX84CAOyBdRcA9o29vh360EMPLb83qa2tLUny3e9+tzJTAUDBWXcBoDL2OoJXrFjx/59UVZWampoccMABFRkKAIrOugsAlbHXETxo0KD853/+Z3lRPvTQQ9O9e/eKDQYARWbdBYDK2OsI/vWvf51x48alpqYmSfLss89m/vz5+eu//uuKDQcARWXdBYDK2OsIfv1TKT/84Q8neW1x/vu///v8x3/8R8WGA4Cisu4CQGXs9adDb926tbwQJ8kJJ5yQl19+uSJDAUDRWXcBoDL2OoKrq6tzzz33lL9etGhRDjnkkIoMBQBFZ90FgMrY69uhv/Od7+RTn/pU+ZMpd+zYkQULFlRsMAAoMusuAFTGXkfwM888k+XLl+fZZ59NkvTr1y+//e1vKzYYABSZdRcAKmOvb4e+4oorcvjhh2f48OEZPnx4+vbtmyuuuKKSswFAYVl3AaAy9jqC/1ypVMr27dv35SwAwB5YdwFg39jrCO7Ro0d+/etfl7/+v//3/6ZHjx4VGQoAis66CwCVsdfvCf63f/u3fPKTn8z73ve+JMnq1avz05/+tGKDAUCRWXcBoDL2OoL/+q//Oo888kiWLl2a5LW/V9i7d++KDQYARWbdBYDK2OsITpJDDz00Z599dqVmAQD+hHUXAPa9v/iDsQAAAODdRgQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACiMDo3gO+64IyNHjsyIESMyfPjw/PjHP06SbNiwIWeeeWaGDBmS4cOHZ8mSJR05JgAAAJ1EVUd94/b29kycODH33ntvjjvuuKxZsybve9/78qlPfSozZ87MmDFjctddd2XZsmX55Cc/mSeffDJdu3btqHEBAADoBDr0leBSqZRNmzYlSbZs2ZI+ffrkwAMPzK233pqLLrooSTJ69OgMGDAg9913X0eOCgAAQCfQYa8El0ql3HLLLfnUpz6VQw45JC+88EIWLFiQF198Mdu2bUtNTU352Pr6+jQ3N3fUqAAAAHQSHfZKcFtbW6688sosWLAgTz31VBYtWpRJkyalra1tr68xe/bs1NbWlh+tra0VnBgAAIB3uw6L4BUrVuSZZ57JSSedlOS1255ra2vz8MMPp6qqKuvXry8fu2bNmtTV1e1yjenTp6elpaX8qK6u3m/zAwAA8O7TYRE8cODArFu3Lo888kiSpKmpKY8//niGDRuWCRMm5Prrr0+SLFu2LE8//XROPvnkjhoVAACATqLD3hPcr1+/fP/7389/+S//JV26dMmOHTty3XXXpa6uLldffXUmTZqUIUOGpFu3bpk7d65PhgYAAOBt67AITpJPf/rT+fSnP73L9n79+mXhwoUdMBEAAACdWYf+iSQAAADYn0QwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMKo6eoB3g/qZt7/pMWuuOmc/TAIAb+zSSy/NL37xizz11FN58MEHM2LEiCRJfX19DjzwwBx88MFJkv/6X/9rzjvvvCTJ6tWrM3ny5GzcuDG9evXKjTfemGOOOabDngMAVJJXggGgExk/fnzuv//+DBo0aJd9t9xyS1asWJEVK1aUAzhJpk6dmilTpmTVqlWZMWNGGhoa9uPEALB/iWAA6EROOumk1NbW7vXxGzZsyPLlyzNx4sQkybhx47J27do0NTVVakQA6FAiGAAK4oILLsixxx6bz33uc/njH/+YJFm7dm369++fqqrX3iFVKpVSV1eX5ubm3V5j9uzZqa2tLT9aW1v32/wAsC+IYAAogCVLluThhx/OAw88kL59+2by5Ml/0XWmT5+elpaW8qO6unofTwoAleWDsQCgAOrq6pIkXbt2zRe/+MUMHTo0STJw4MCsW7cubW1tqaqqSnt7e5qbm8vHA0Bn45VgAOjkXnrppWzatKn89bx583L88ccnSY444oiMHDkyc+fOTZLMnz8/tbW1GTx4cIfMCgCV5pVgAOhEpk6dmttvvz3r16/PGWeckR49emThwoUZN25ctm/fnvb29hx11FG56aabyuc0NjamoaEhs2bNSs+ePTNnzpwOfAYAUFkiGAA6kcbGxt1uf/DBB/d4zrBhw7J06dJKjQQA7yhuhwYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMIQwQAAABSGCAYAAKAwRDAAAACFIYIBAAAoDBEMAABAYYhgAAAACkMEAwAAUBgiGAAAgMLo0Ah+5ZVXMm3atAwZMiTHHntsJk6cmCRZvXp1TjjhhAwdOjSjR4/OypUrO3JMAAAAOomqjvzmM2fOTKlUyqpVq1IqlbJ+/fokydSpUzNlypQ0NDTkJz/5SRoaGrJs2bKOHBUAAIBOoMMi+KWXXsoNN9yQlpaWlEqlJElNTU02bNiQ5cuXZ+HChUmScePGZdq0aWlqasrgwYM7alwAAAA6gQ67Hfrxxx/PYYcdllmzZuWDH/xgTjzxxCxatChr165N//79U1X1Wp+XSqXU1dWlubl5l2vMnj07tbW15Udra+v+fhoAAAC8i3RYBLe1teWpp57K0UcfneXLl+c73/lOzjvvvLS1te31NaZPn56Wlpbyo7q6uoITAwAA8G7XYRFcV1eXLl265Pzzz0+SHH/88TnyyCPz1FNPZd26deUYbm9vT3Nzc+rq6jpqVAAAADqJDovgvn375tRTT80vf/nLJMmTTz6ZJ598Mh/+8IczcuTIzJ07N0kyf/781NbWej8wAAAAb1uHfjr09ddfn8997nOZMWNGunTpksbGxrznPe9JY2NjGhoaMmvWrPTs2TNz5szpyDEBAADoJDo0go866qgsXrx4l+3Dhg3L0qVLO2AiAAAAOrMOux0aAAAA9jcRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBAMAAFAYIhgAAIDCEMEAAAAUhggGAACgMEQwAAAAhSGCAQAAKAwRDAAAQGGIYAAAAApDBANAJ3LppZemvr4+pVIpK1asKG9fvXp1TjjhhAwdOjSjR4/OypUr92ofAHQ2IhgAOpHx48fn/vvvz6BBg3baPnXq1EyZMiWrVq3KjBkz0tDQsFf7AKCzEcEA0ImcdNJJqa2t3Wnbhg0bsnz58kycODFJMm7cuKxduzZNTU1vuA8AOiMRDACd3Nq1a9O/f/9UVVUlSUqlUurq6tLc3PyG+3Zn9uzZqa2tLT9aW1v32/MAgH1BBAMAe2369OlpaWkpP6qrqzt6JAB4S6o6egAAoLIGDhyYdevWpa2tLVVVVWlvb09zc3Pq6urSs2fPPe4DgM7IK8EA0MkdccQRGTlyZObOnZskmT9/fmprazN48OA33AcAnZFXggGgE5k6dWpuv/32rF+/PmeccUZ69OiRpqamNDY2pqGhIbNmzUrPnj0zZ86c8jlvtA8AOhsRDACdSGNj4263Dxs2LEuXLn3L+wCgs3E7NAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQwAAEBhiGAAAAAKQwQDAABQGCIYAACAwhDBAAAAFIYIBgAAoDBEMAAAAIUhggEAACgMEQzA/9fefcdHVeX/H38PhqYUQWkSYiItQOiEUBQQFkRQAVmkCIiAFHdFQJYiTZriIiC4IighNM3CghRFLCBlVXpJRGkBQg1LDwSSkHJ/f/DLfNNI7iRDbjLzej4eeTzm3pt78kkyd2Y+95zzOQAAAG6DJBgAAAAA4DZIggEAAAAAboMkGAAAAADgNkiCAQAAAABugyQYAAAAAOA2SIIBAAAAAG6DJBgAAAAA4DZIggEAAAAAboMkGAAAAADgNnJFEhwUFCSbzaa1a9dKki5duqS2bduqcuXK8vPz0/bt2y2OEAAAAADgCixPgsPDw/XFF1+oUaNG9n2jR49Wo0aNdPz4cQUFBalHjx6Ki4uzMEoAAAAAgCuwNAlOTExU//799cknn6hgwYL2/StXrtSgQYMkSf7+/nriiSe0bds2q8IEAAAAALgIS5PgWbNmqWnTpqpfv75939WrVxUXF6eyZcva93l7e+vMmTPpnu/p6Wn/ioqKypG4AQAAAAB5k4dVP/jQoUNavXp1tub7Dh8+XMOHD7dve3p6OiM0AAAAAICLsqwn+L///a/Cw8NVuXJleXt7a+fOnRowYIBWrlwpDw8PXbx40f694eHh8vLysipUAAAAAICLsCwJHjx4sCIiIhQeHq7w8HA1atRIn3/+uQYPHqwuXbpo/vz5kqQ9e/bo/Pnzat68uVWhAgAAAABchGXDoTPy4YcfqlevXqpcubIKFCig5cuXK3/+/FaHBQAAAADI43JNErx161b74zJlyujHH3+0LhgAAAAAgEuyfJ1gAAAAAAByCkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBkkwAAAAAMBtkAQDAAAAANwGSTAAAAAAwG2QBAMA4Ea8vb1VtWpV1alTR3Xq1NGKFSskScePH1eTJk1UpUoV+fv7648//rA4UgAAHgwPqwMAAAA5a8WKFapTp06KfQMHDtSAAQPUp08frVq1Sn369NGePXssihAAgAeHnmAAANzcpUuXtHfvXvXs2VOS1LlzZ509e1ZhYWEWRwYAgPORBAMA4GZ69+6tmjVrql+/frp8+bLOnj2rcuXKycPj3gAxm80mLy8vnTlzJs25s2bNkqenp/0rKioqp8MHACBbSIIBAHAj27dvV2hoqPbv36/HH39cr732mkPnDx8+XOfOnbN/FSlS5AFFCgDAg8GcYAAA3IiXl5ckKX/+/Bo6dKiqVKmiChUqKCIiQvHx8fLw8JBhGDpz5oz9ewEAcCX0BAMA4CZu376tGzdu2LeDg4NVt25dlS5dWvXq1dPy5cslSatXr5anp6cqVapkVagAADww9AQDAOAm/ve//6lz585KSEiQYRh66qmntHTpUknSggUL1KdPH73//vsqVqyYgoKCLI4WAIAHgyQYAAA38dRTT+nAgQPpHqtatap27NiRwxEBAJDzGA4NAAAAAHAbJMEAAAAAALfBcOgc5D16Q6bfEz69fQ5EAgAAAADuiZ5gAAAAAIDbIAkGAAAAALgNkmAAAAAAgNsgCQYAAAAAuA2SYAAAAACA2yAJBgAAAAC4DcuS4JiYGHXs2FFVqlRR7dq11bp1a4WFhUmSLl26pLZt26py5cry8/PT9u3brQoTAAAAAOBCLO0JHjBggI4ePaqQkBB16NBB/fv3lySNHj1ajRo10vHjxxUUFKQePXooLi7OylABAAAAAC7AsiS4UKFCateunWw2mySpUaNGCg8PlyStXLlSgwYNkiT5+/vriSee0LZt26wKFQAAAADgInLNnOA5c+aoQ4cOunr1quLi4lS2bFn7MW9vb505c8bC6AAAAAAArsDD6gAk6f3331dYWJg2b96s6Oho0+fNmjVLs2bNsm9HRUU9iPAAAAAAAC7C8p7gjz76SF9//bU2btyohx9+WI899pg8PDx08eJF+/eEh4fLy8srzbnDhw/XuXPn7F9FihTJydABAAAAAHmMpUnwrFmzFBwcrJ9++kmPPvqofX+XLl00f/58SdKePXt0/vx5NW/e3KowAQAAAAAuwrLh0OfOndM777yjp556Ss8++6wkqWDBgtq1a5c+/PBD9erVS5UrV1aBAgW0fPly5c+f36pQAQAAAAAuwrIk2NPTU4ZhpHusTJky+vHHH3M4IgAAAACAq7N8TjAAAAAAADmFJBgAAAAA4DZIggEAAAAAboMkGAAAAADgNkiCAQAAAABugyQYAAAAAOA2SIIBAAAAAG6DJBgAAAAA4DZIggEAAAAAboMkGAAAAADgNkiCAQAAAABugyQYAAAAAOA2SIIBAAAAAG6DJBgAAAAA4DZIggEAAAAAboMkGAAAAADgNkiCAQAAAABuw8PqAOAY79EbMv2e8Ontc6wdAAAAAMhL6AkGAAAAALgNkmAAAAAAgNsgCQYAAAAAuA2SYAAAAACA2yAJBgAAAAC4DZJgAAAAAIDbIAkGAAAAALgNkmAAAAAAgNsgCQYAAAAAuA2SYAAAAACA2/CwOgDkXd6jN2T6PeHT2+dAJAAAAABgDj3BAAAAAAC3QRIMAAAAAHAbDIeG5RhWDQAAACCn0BMMAAAAAHAbJMEAAAAAALdBEgwAAAAAcBskwQAAAAAAt0ESDAAAAABwGyTBAAAAAAC3QRIMAAAAAHAbJMEAAAAAALdBEgwAAAAAcBskwQAAAAAAt0ESDAAAAABwGyTBAAAAAAC3QRIMAAAAAHAbJMEAAAAAALfhYXUAAAAAAFyL9+gNVoeATIRPb291CJahJxgAAAAA4DZIggEAAAAAboMkGAAAAADgNpgTDJdgZt6JO897AAAAAHAPPcEAAAAAALdBEgwAAAAAcBsMhwaSccawaoZmAwAAALkXPcEAAAAAALdBEgwAAAAAcBskwQAAAAAAt8GcYCCXYm4xAAAA4Hz0BAMAAAAA3AZJMAAAAADAbZAEAwAAAADcBnOCARfmrHnFOdVOTsYCAAAA90RPMAAAAADAbZAEAwAAAADcBsOhAbil3DQ0m1jcIxYAAJA75Nqe4OPHj6tJkyaqUqWK/P399ccff1gdEgAALo33XgCAO8i1SfDAgQM1YMAAHTt2TKNGjVKfPn2sDgkAAJfGey8AwB3kyiT40qVL2rt3r3r27ClJ6ty5s86ePauwsDCLIwMAwDXx3gsAcBc2wzAMq4NIbd++ferRo4eOHj1q39ewYUNNnz5dLVu2tO+bNWuWZs2aZd++ePGiypYtm2HbUVFRKlKkSLZjdEY7xEIsxEIs7hCLs9rJa7FcvnxZsbGx2fo5OelBvvfCWs66dgB3xnWU+znyvpunC2MNHz5cw4cPd+gcT09PnTt3Lts/2xntEAuxEAuxuEMszmrHFWPJi7Ly3gtrufPzFXAWriPXkiuHQ1eoUEERERGKj4+XJBmGoTNnzsjLy8viyAAAcE289wIA3EWuTIJLly6tevXqafny5ZKk1atXy9PTU5UqVbI4MgAAXBPvvQAAd/HQe++9957VQaSncePGGj9+vP75z39qz549CgoKUpkyZZzWdm5ph1geXBvOaodYHlwbzmqHWB5cG7mtHVeMJTd5kO+9sJYrPl+BnMZ15DpyZWEsAAAAAAAehFw5HBoAAAAAgAeBJBgAAAAA4DZIggEAAAAAboMkGIBb+fPPP+97bNGiRTkYCQAAAKzgsklwQkKCPv30Uw0ZMkRLliyxOhzdunXrvscOHDiQY3HcvHlT4eHhafaHh4fr5s2bORYHYJX27dvrzJkzafYvXrxYubRYviljxozRuXPnrA4DAAAg13PZJPhvf/ubli1bpsKFC2vWrFmaNm2apfG8+OKLio2NTbP/4MGDateuXY7FMXLkSO3bty/N/v3792vUqFEOtUWPmjUOHjzo0PcfOnRIX3/9tU6cOPGAIso5N2/ezPDLjMmTJ6tNmza6fPmyfd+yZcs0fvx4bdq0yeGYjhw5ovXr16d7cykn2VBb1ukAACAASURBVGw2NWzYUJ06ddLmzZuz1EanTp308ssv2786d+6swYMHa82aNU6ONne4e/eu1SEAAAALuOwSSdWrV9fBgwdVoEABXb9+Xa1bt9bevXsdbqdevXoZHt+/f7+pdgYPHqwLFy5ozZo1ypfv3r2HkJAQPf/88/r000/VqVMn0zEZhqHvv/9ef/zxhyTJz89Pzz33nGw2W6bn1qtX774x16hRw96mGT4+Ptq2bZu8vLxS7F+8eLEmTJiQbm9banPnzk2xbbPZVLp0aT3zzDN64oknTMcSGRmpqVOn6ujRo6pfv75Gjx6tggULmj4/I7GxsZo3b56GDRtm+pwNGzbYY2nevHm2Y4iMjNSXX36phQsXKiIiQhEREabOmzdvnt59911VrVpVR48eVVBQkEPPNeneDY34+HgNGDAgxf7PP/9cBQoUUJ8+fUy1k5CQoPnz59v/Lq+99ppDcUhSvnz57M/z1C9dNptNCQkJptqZO3euFi9erK1bt+qbb77RqFGjtHnzZlWtWtWheBYsWKCRI0eqUqVKOnHihJYuXaqXXnrJoTaS3Lp1S0ePHpUk+fr6qkiRIg63cffuXa1cuVLz5s1TZGSk/va3v+m1117TI488Yur89EbNXLlyRV9++aVeffVVvfPOO6Zj2bx5syZPnmx/valXr57Gjx+vv/zlL6bOP3funIYOHapjx46pfv36mjlzpkqWLGn65yf5+OOPNXTo0DT7Y2Nj1bFjR23cuDHTNtavX59iO+l1qlatWipcuLDDMQEP0uTJkzM8PmHChByKBMi7uI7cgOGi6tatm+G2WY888ojRsGFDY/78+cbWrVvTfJmVmJhodOvWzejVq5dhGIYRGhpqlC1b1li1apVD8Vy/ft2oV6+e4eXlZXTo0MF46aWXDC8vL6N+/frGjRs3Mj2/Zs2a9z3m5+fnUCxLly41qlataly6dCnFPk9PT+Po0aOm2ujTp0+arxdeeMEoVaqU8cMPP5iOpVu3bsbLL79szJs3z2jTpo0xbNgwh34XwzCM6OhoY8aMGcbf//53+/92/vz5Rrly5YyWLVuabmfcuHGGt7e38corrxhPPPGEsWDBAodjSfLzzz8bPXr0MB599FGjSJEixsaNG434+HjT59eoUcM4ffq0YRj3nnNNmzZ1OIaAgIB0n1s3btwwGjRoYLqdgQMHGgEBAcbIkSONWrVqGVOnTnU4lmbNmtmvx8jISIfPT27SpElG9erVDU9PT+PIkSNZaqNGjRrGqVOnDMMwjIMHDxpPP/20w20kJCQYQ4YMMfLnz28UL17cKFasmFGgQAFj6NChRmJiosPt3b1711i6dKlRoUIFw8/Pz3jiiSeMZcuWOdxOcpGRkUbt2rVNf/93331nlC9f3pg3b55x4MAB48CBA8ann35qeHp6Ghs2bDDVRrt27Yy3337b+O6774zXX3/d6NevX5Zi9/X1NZYsWZJiX2xsrPH8888bXbt2NdVGixYt0nz5+fkZ5cuXN/bs2ZOluIAHZcSIEcaIESOMbt26GaVKlTL69etn9O/f3yhdurTRvXt3q8MD8gSuI9fnsklw+fLljTlz5ti/Um+bdevWLWPhwoVG06ZNjaZNmxoLFy40oqKishRTXFyc0b59e6Nnz57GE088YfznP/9xuI3hw4cbb7zxhhEXF2ffd/fuXWPgwIHG8OHDMz3f19c33eThxo0bRtWqVR2OZ86cOUbdunWNyMhIY/ny5Ub58uWznFAkd/jwYSMgIMD091evXt2eMNy5c8eoV6+ewz+zV69eRtOmTY1Ro0YZAQEBxl//+lfjySefNNavX+9QO76+vsb169cNwzCMs2fPGg0bNnQ4lqlTpxoVK1Y06tSpY3zyySfGlStXDG9vb4fbqVOnTortrNwMyijRdSQxqlatmhEbG2sYhmFcu3bNqF+/vsOxGIZhhIWFGe+++65RuXJlo2fPnsaWLVscOn/YsGHGsGHDjKFDhxqPP/648fzzz9v3OXrzJPXfN/W2GXPmzDGaNGmS4ro5cuSI8fTTTxtz58413c65c+eMd9991/D09DR69Ohh7Ny5076/QoUKDseVmiPPnYYNGxoHDx5Ms//AgQOGv7+/qTZq1KhhfxwfH5/lG5mnT582fHx87Nfx3bt3jRdeeMHo0qWLkZCQkKU2k2zdutVo3rx5ttoAHpTWrVsb58+ft29fuHDBaNOmjYURAXkP15Hr8rC6J/pBad26dYqCU8m3zQwbTlKkSBH169dP/fr109GjRxUYGKjq1avro48+UpcuXUy3kzScrmfPnnrrrbfUtm1bFShQwL7f7BDKH3/8UTt37pSHx//96/Lnz6+ZM2cqICBAM2fOzPD8bt26qVevXlq8eLFKlCghSbp+/br69eunbt26mf59kgwZMkQ3btxQ48aNdfPmzSwNKU2Pr6+vQ/P1ChYsaP+/ZnV44t69exUSEqL8+fMrMjJSZcuW1fHjx+Xp6elQO4ULF9ajjz4qSfL09FRcXJzDsUyYMEGtWrXS3Llz5evrK8mx522SmJgY/f777/ahw9HR0Sm2a9WqlWkb165du++xjAq+pVaoUCEVKFBAklSiRAklJiaaPje5ihUratq0aZoyZYrWrFmjrl27atSoURo+fLip84sXL25//Pe//z1LMSSJjY3V4cOH7X/P1NvVq1fPtI0vv/xSK1askLe3t31f1apVtWzZMnXt2lVvvfWWqVgaNGig/v37a9euXSmmEpQvX16vv/66A79VWmfPntVDDz1k+vtv3bql2rVrp9lfp04d08+ZpOeKJId+dmpeXl767rvv1KZNGxUqVEj/+te/VLBgQQUHB9unpmRV8+bNKSiIXOvChQspXgvKlSun8+fPWxgRkPdwHbkul02CM5q/WahQIcXExKhQoUKm24uLi1NoaKhCQkJUrFgxPf744w7FM3v2bPtjPz8/nTt3zr7PZrOZToINw0h3fp/ZOX/jxo1T3759VaFCBVWuXFmSdPz4cXXu3Fnjx4831UaSpKTDMAxdunRJ/v7+WrBggf34rFmzHGovuZiYGNPzO6V78weTJ0Fnz55NsW0mlkKFCil//vyS7iVKVapUcTgBlqQbN26kmEMYGRmZYtvM/zosLEyLFi1S69atVaFCBfXt29ehv0eS6OjoND8vadtms+nkyZOZtlGvXj0tWrRIffv2TbF/yZIlqlu3rulYLl26lGIOeOrtIUOGmG5r3759CgwM1MaNG9W5c2eH5uFOnDjR9Pdm5ubNm2rTpk2KfUnbNpvN1Lz4GzdupEiAk3h7eysyMtJ0LGvXrlVAQECKfaGhoapVq5YmTZpkqo30biRcvXpVmzZt0ieffGI6ltjYWCUmJqZJMhMSEtItEJiekydP6uWXX5Z07zUm+bYkff3116bj8fX11dq1a9WqVSu1atVKK1asyFZinSQhISFL1yWQEzw9PTVx4kT1799fkhQYGJil9zTAnXEduS6XLYzl4+Mjm82WbvGcuLg43bp1SzNnzlS/fv0ybCckJESLFi3St99+q9atW6tv375q2LDhgww9Q/Xr10+3urOUcdGr1MLCwuw94/Xq1VPFihUdjiWzD9Zmko3UBWekex+6ly1bphYtWpguPOCMWDw9PTVy5Ej79owZM/SPf/zDvm02SWvRosV9e21tNpt+/vlnU+1IUmJion744QcFBgZqw4YN6tSpk1599VW1b9/edBvZdfLkST399NNq1qyZGjduLEn67bfftH37dv3yyy+mnzsZ9UbabDZTFcXnzJmjJUuWqFSpUnr99dfVqVMnpxVAs0pG13RGx1JL7/p35DVBSnsd2Ww2lSpVSi1btnRohMegQYNUsGBBzZw50z5qJSEhQcOGDVNMTIw+//zzTNtIXqQrvddys4XVSpQoYb8eb9++rUKFCumhhx6SYRiy2WwZjnRIEhoammbf1atXFRgYqJIlS6Yp8AfkBhcvXtSQIUO0efNm2Ww2/eUvf9HHH3+ssmXLWh0akGdwHbkul02CMxMREaGWLVvq8OHDGX5fvnz51KBBA3Xq1Cnd3lZHeq9S++CDDzRmzBiHznn44Yftw2OTMwxDx44d0+3bt7McjxWeffbZFNtJH7qfe+45vf7661kaApxVzkjSHqQrV65oyZIlCgoK0qFDh0ydk7wic4MGDdS7d+8s/eyLFy9q3rx59oSsfv36evPNN3P8TSBfvnyqW7euvLy80n1uONI7+KAY/796e2BgoFatWpXp95cuXVo9e/ZMt52vvvpK//vf/zI8/9KlS7p48aK6du2qlStX2pPFyMhI9e/f315xOifdvHlT7dq105kzZ1S/fn1J93rvK1SooI0bN6pYsWKm2tmzZ49mzJhhX47Nz89P77zzjvz9/U3Hcvr06QyPP/nkk5m24ePjk2I7+evUu+++69CoIgAAYD23TYKle0OUM1v2pk+fPhn26mUnMXK0l0aStm3bluHxzJbjSeohT4/NZsv2WrKdO3fW6tWrs9VGVgwYMMDeu7Ru3Tp16NAhx2NI8uuvv+p///tfiqGb0r0ErWzZsmrSpEmmbbRr106dOnVShw4dVLp06SzHMmjQIB08eFDNmzfX999/r1deeUVjx47NcnvZdevWLS1fvty+FFfNmjXVo0cPFS1a1NT56S3hk1xWll1yllOnTikwMFBLly5VmTJl1KNHD1PLamV3FMOcOXP08ccfp5m3VLx4cb311luZjnZJbvr06XrkkUfSzEP+5JNPFB0dnWKkhBmbNm1KMeKkZcuWpm9s7dixQ+3atdOgQYMUEBAgwzC0Z88ezZ8/Xxs3bkwz9Dsjhw4d0rFjx1S7du0sjXoB8qLIyEiNHTtWp0+f1jfffKM///xTISEh6t69u9WhAXkG15Hrcusk2Gp169ZNUbwrJ6S3DvCBAwc0YcIEFShQQEeOHMlW+44m9pnNmUy9BrGZn5uVmwtJspukSfeKsM2fPz/Nh+2TJ09q4MCB+umnnzJtY9++fVqzZo3Wr1+v4sWLq2PHjurYsaPDH+CdsV52Rmvl2Ww203PJz58/ryZNmqh8+fJq2LChPak5f/68fvvtN5UvX96huJzpp59+UuvWrR0+LzY2VqtXr9bChQv1559/qnv37lq1apXOnj1ruo3t27dneLxZs2aZtpGYmKipU6dme93CBg0a6JdffknTsxkdHa3GjRvr4MGD2WrfEZ06dVLv3r3TrGu9bt06BQUFae3atabaccZa2UBe1K1bN/n5+enf//63Dh06ZMl1DOR1XEeuiyTYBGckRunZvHmzWrVq5dA5zkpIpHvDBMeOHavt27dr/Pjx6tu3b7aLxTia2JcqVSrNfD+bzabY2FhFRUWZLjqTPPHN6s0FZyVpDRs21O7du9M9Vrt2bYWEhDgUV1hYmNasWaO1a9fq9u3beumll9SxY0fVq1cv03NT3xDIyg2C5POikxiGoa+//lqnT582/T8aOHCgypYtm6bnc9KkSTp//rypeaLO7qlMktUbJyVKlJCfn5+GDh2ql156Sfnz59dTTz1lquBYEn9//3SvgQsXLigiIsLU39cwDNWsWdP0MPn7yWgOsiPP3WeffTbDESebN2/OtI0qVaro2LFjDh9Lzc/PT9999528vLz0+++/a/Dgwfrll19MnZskX758KX6fpPnE0r3fJz4+3qH2gJyQ9LqW/D0xK+9BgDvjOnJdLlsd2lnSS4yWLFmi999/36HEKL1lNPz9/e37zc6RS295keQJiZkk+Nq1a5o8ebKCg4M1dOhQffHFF1leVii1rVu3OvT9ly9fTrEdFxenzz77TB988IFDS1AlX/on9bJAkrmlgCZPnqw+ffqkm6RNmjTJVJIm3VtyKqM4HVWpUiX94x//0D/+8Q9dvHhR69at08iRI7Vp06ZMz3VGReYZM2ak2N60aZNGjx6tcuXKaenSpaZ/j//+97/pFhh69913Tf1/JGnVqlXpJjD9+/dX48aNs5wEZ/VeYLt27fT9999rzZo1KlmyZJo57mbs2bMnxfa1a9c0depULV++3HRVZ5vNJk9PT125csXhyvXJ3bhxI0WClyQxMdGh5bBGjBiRZl9ERITef/990+08/PDD9z1mthq+dG8JuaQRJTVr1tSdO3dMn5skvZjXrVuncePG6amnnnK4PSAnJF9mTLr3/kO/B+AYriPXRRKcCWclRo8++qj9g2XSxZPU+2Oz2Uz3pmU3IZk2bZo++eQT9erVS0ePHrWvZ5tVp0+f1rx58+y95H5+fho8eLCpYjOpBQcHa/z48apVq5Z+/vlnVatWzfS5qZcCSv7Y7FJAzkjSpHtryO3atSvNnMXdu3erTJkypttJ7fbt2/rll1+0bds203O3mzdvft/1sh118OBBjRw5UhcuXNC0adMcnnft4eGRYn3rJPnz5093f3oMw0i3CFHhwoWz9aaUlTWypXtr/EZGRurLL7/UiBEjdO3aNUVGRurUqVNpiillJiYmRrNnz9acOXPUo0cPHT58WI899pjp84sUKaI6deqoXbt2KlKkiH2/I0uVNW/eXFOnTk1zM+2DDz7ItN5Acsmrl0dFRWn69On6/PPP9cYbb2jUqFGm2oiNjU1zMytJTEyM6VicsVZ28qR7x44dGjVqlG7fvq0FCxZkaRg9kBOeffZZTZs2TTExMdq0aZNmz57NVADAQVxHrovh0JmoXr26QkND03xIj4uLU61atTKtLp2kefPmiomJUd++fdW9e3fTPb/3k9WEJF++fCpbtqzKlCmT7vA+R4aEHj58WE2bNtVzzz2XonDNDz/8oF9//TXdKtbp+fHHHzV69GgVLVpU06dPty/D44hDhw7Jz89PktKsAb1161a1aNEi0zZq1aqVbhIs3etB+v33303FsmnTJvXu3VsTJ05Uo0aNJN374Dx16lQFBQU59KH5ypUrWrdundasWaOjR4+qbdu26tChg1q0aGEqcezatatWrFiRZv+ZM2fUokULUzcHwsPDNXbsWP3666+aOHGiXnvttTTrv5qR0ZBjs8ORK1asqLCwsHR7KitVquTQMOQH4cCBAwoMDFRwcLCqVKmiHTt2ZHpOYmKiFi5cqClTpqhly5aaPHlylm4i3a/X2JF1kS9fvqzmzZurWLFi9utw586dunHjhrZt2+ZQkbb4+Hj961//0ocffqgOHTpo4sSJKleunOnzvb29MxxSbfZ/7ax2jhw5otGjR+vQoUOaMmUKRVGQ68XHx2vGjBlau3atDMNQx44dNXr06Cy9fgPuiuvIdZEEZ8JZiZEknThxQosWLdJ//vMfBQQEqF+/fqaSs+Sym5Dcr7p0XFycoqKi1LFjR9Ntvfrqq2rWrJkGDhyYYv8XX3yhLVu26Kuvvsq0jTZt2ujEiROaPHmyXnzxxTTHzd4syKgwltkEyxlJWpJNmzZp8uTJ9vmVDRo00Lhx40wnwDNnztS6det0/fp1+xxgR5aFSdKxY0eVK1dOn332mX3fuXPn1KJFCw0dOlR///vfM22jYMGC8vT01IABA9IdNm92mbCHHnpIxYsXT7PfMAxFRUUpLi4u0zb69u0rHx+fND2V06ZNU1hYmIKCgkzF4iz3q0oeExOjNWvWmEqUqlevrtjYWL333nuqXbt2muOOjELIrps3b+rcuXPauXNniuWwGjdurPLly5u+Hr/88ktNmDBB9erV0/vvv6/KlSs/yLAfuDfeeEMbN27UmDFjNGjQoGzXTgByQmhoaJrXj/T2Abg/riPXRRKcCWcmRkkSExO1Zs0avfnmmxo1apSGDx9u+lxnJSRJjh49al/WxdPT06HKwb6+vulWkzYMQ9WqVTNVaTp5Ap9ecSCzw8STFyxIXRjLbKEsZyRpzvLhhx+qRIkS9+1NN1MxWLo3pPT5559X48aNNW3aNHsC/NZbb+ntt9821carr76aZk5McmYTz9OnT+vmzZv68ccf1bFjxzQ92WZ6P53ZU+kMzqhK7qyeSunekPuDBw+mGC7syGvCoEGD1Lp1a3Xu3DnF/q+//lo//fRTipspGcmXL5+efPJJ1alTJ1eu55yYmKgNGzYoMDDQVJXpfPny6eGHH05zHSSNoLl27dqDChXIsvRek7KzegLgjriOXBdzgjMRGhqqkiVLptmflBg5at++fQoMDNTGjRvVuXPnFHNXzejevbtsNlu6CabZ9Tfv3LmjFStWKDAwUCdPnlR0dLR27NhhevhykoIFC6a732az3fdYaomJiQ79zPtJ/run/juY/bucPHkywyTNrEWLFik+Pl4DBgxIsf/zzz9XgQIF1KdPn0zbWLVqVZp9jlYMlu79j9atW2evQr5q1Sr97W9/M50AS/eGAn355Zdp9icNqTZrw4YNGjt2rKpUqaIpU6ZkaamaggULatWqVSl6Kt944w01btw43bnCOSmr9xPDw8Od8vPff/99rVq1SmfOnFHz5s31008/qVWrVg4lwbt379b8+fPT7H/55Zcdqjyf0z3yZh0/flyBgYFatmyZvLy81KNHD1PnnTp1yimvDUBOuHTpki5evJhm/ntkZKRu375tcXRA3sB15AYMZCg8PNz+tWfPHmPv3r0p9pn18ccfG3Xr1jXatGljBAcHGzExMVmKJyQkJMOvzPTv398oWbKk0bFjR2P9+vVGXFyc4e3tnaVYatWqZdy8edOIjIxM81WrVq0stZkkNDTUePvtt01/v6+vrxEaGmqEhISkeJy0bcann35qPProo0bDhg2N4sWLG19//XWWYg8ICDBu3LiRZv+NGzeMBg0aZKnNq1evGsOGDTNKlSplTJkyxfR5SX+DzZs3G48//rjRp08fh54vhmEYHTp0MAYNGpRi39mzZ42KFSsan3zyielYatSoYZw+fdowjHv/36ZNm5o+N8nAgQONVatWpdm/evXqNDFmJDw83Bg5cqTRvn17o3379saoUaMcup6TZPS8M/v3dZYaNWoY0dHRRu3atQ3DMIwjR44YL7/8skNt1KxZ877H/Pz8shVfkgMHDjilHbPu3LljLFmyxHjmmWeMcuXKGSNGjDA8PT0dasNZrw1ATvj4448Nb29vo0CBAoa3t7f9q3bt2sbChQutDg/IE7iOXB/DoU0ICgrSpEmTdPbsWUlShQoVNGHCBPXt29d0G/ny5VPdunXl5eWVreGBPj4+6Q4bNv7/sLzMhk4WLVpUDRo00KhRo/Tcc8/JZrM5vK5pkqS1M+8Xi9neyiS3bt3SV199pcDAQJ0/f17du3fXRx99ZOpcZwwpdcZ6otK9pa9SL3uTpE6dOg4tsJ66YvDYsWMdqhicUYVis38XZwyplrI+TD25jIYg1ahRw16lPCPOKugmOXcoc3Y1aNBAe/fuVa1atRQSEiKbzebw861atWratWtXmrm/kZGRCggIMDXFIT1JFbQXLlyoiIgIRUREZKmdrChevLjq1Kmj4cOHq3379vLw8HD4Nc9Zrw1ATpoyZYpDIzgApMV15LoY05WJpUuXasaMGfrss8/scxB/++03jRgxQg899JBee+01U+04a3jgqVOnsnV+RESEVqxYocmTJ2vAgAHq3bt3lue6Omso8/bt27Vw4UJ9++23atWqlc6ePasLFy6YHsYsOWdIaer1RLM63CWj+YFm10hNXTF4165dWaoYnN3ni+ScIdVS2qVqUm+bKTIRHx9/32NmC8RNnTpVH3zwQboF3SZPnmyqoFsSZw1ldobChQsrLi5OderU0YgRI+Tp6enwjahu3bqpV69eWrx4sUqUKCHp3rrX/fr1y9IyUlu2bNHChQv13XffKT4+Xv/5z39yfEmhjh076ttvv1VwcLAefvjhLP18Z702ADmpTp06unHjhn0pxOvXr+u3335LsYQZgIwlJcCxsbGKjY2178/uKi+wHj3Bmahdu7bWr1+fJgEJDw/XSy+9dN/K0XnBn3/+qUWLFmnZsmWqWLGievbsqTfffNMpba9Zs8bUfM8qVaqoYMGC6t+/v3r27KnHHntMPj4+TkneHFWtWjWtXLnSnpR17do1xbbZSoBdunTR888/n2akwJIlS/TNN9+kO983tdxUMTjpOX7lyhV17dpVL7zwgoYNG+ZwLM7oNXVGT6UzCrrlRocOHZKPj4/u3Lmjd999V9evX9e4ceNUp04d020kJCSob9++Wr16tb2i8/Hjx9W5c2ctWrTIdFXkadOmKSgoSEWLFlW/fv3UvXt3NWjQwJLrWrp38yk4OFiBgYGKiIhQVFSUdu3aZbpqtbNeG4CclHokiGEYql+/PgV9AAfs2rVLffr00bFjx1Lsd/QmM3IfkuBMZDTE0uzwyyR79uzRRx99ZD/Hz89PI0aMUIMGDZwSa1bFx8dr3bp1WrRokTZs2ODQuatWrdLp06f1wgsvqGrVqvrhhx80ZswY3blzx1Qy0bx5c4WFhalXr17q16+fKleunOXh2dnlrKGtJ06c0DPPPKNmzZqlGD2wfft2/fLLL6pYsWKOxeIMzhhS7SyTJk3S/v370+2prFWrlt57771M26hdu7ZCQkIcPpabXbt2TeHh4apcubKKFi2a7fZOnDiRouq1medscg899JBatWqluXPn2oeXW3Vdp3bo0CEFBgZq+fLl8vb2vu/UheRy0/UImJXedIi8+hoHWCUgIEBz587VoEGDtH37ds2dO1eFChXSO++8Y3VoyCaGQ2fi7t27iomJSVN5Njo6OsWwiMzs2LFD7dq106BBg9S9e3cZhqHdu3erTZs22rhxowICApwdumkeHh7q3LlzmmVRMjN8+HCtW7dO/v7+CgwM1PPPP6/AwEBNmTJFgwcPNtXGtm3bFBYWpkWLFqlFixby8fFRVFSUbt265ZQP845w1tDWihUr6sCBA/r000/1448/ymazqX79+pozZ47Kli2bo7E4g1W9d+kZN26c+vbtqwoVKqTpqXRkzs6tW7eyXM051fpV3wAAESdJREFUt1m1apVee+01FSlSRHfv3tX69ev1zDPPZKvNihUrOpz4Jpd0Tbdu3VoVKlRQ3759Lbtrvn379jT7OnXqpHbt2ikyMtJUG7npegTMKlq0qH777Tc1adJEkvTrr7/m+PsqkNfFxcUpICBA8fHxKlq0qMaOHSt/f3+SYBdAT3AmRo0apZMnT+qLL75IMa9mwIAB8vHx0T//+U9T7XTq1Em9e/dOM0R43bp1CgoKMrVWZW5TrVo17d69W0WLFlVERIS8vb21d+9e1axZ03Qb8fHx9uVGEhMT9d133ykwMFCbN29W27ZttXLlygcV/gOTfN3npMsreS/SrFmzcjwmV5OdnkpnF3SzWu3atbVo0SLVr19fmzZt0gcffKDNmzdbHZake9f0Dz/8oMDAQG3YsEGdOnXSq6++mqNzEv39/dPsy8pyY0Bes2PHDnXq1Mk+GuP48eNas2aNGjZsaHFkQN4REBCgXbt2qUWLFpo1a5YqVKggf39/bo66AJLgTMTFxem1117T+vXrU/Q8vfjii1q6dKny589vqp0qVaqkmU9g5lhulrpSb82aNfX777871EbXrl21YsWKNPvPnDmjatWq5ckCNPny5ZO/v7/atm2bbrGmiRMnWhAVXFXqIY9ZqbqdE65cuaIlS5YoKChIhw4dsiyOa9euaerUqVq+fLmGDBmicePGWRYL8KBdv35dO3bskCQ1adLEfjMfgDmzZ89W7969tW/fPnXu3FlxcXGaOnWqRowYYXVoyCaGQ2cif/78+uqrrxQWFmb/YFm3bl1VqlTJoXYefvjh+x575JFHshWjVW7cuKH169fbt+/cuZNi+6WXXsq0jdjYWA0ePFifffaZfd+5c+fUsmVLffjhh84NOIds3rxZixYt0ldffaVXXnlFffv2zdbQUuQcswXdchPDMFIM705MTEyxnVsqWD7++ON65513LBtClnq5scOHDzu03BiQF508eVJXr15Vr169dOPGDUVERKhcuXJWhwXkGUmFQNu0aaNr164pJiaGaQUugp7gHJK6umhyXbt21eHDhy2IKntatGiRYbGYn3/+OdM2nLUGbW508+ZNBQcHKygoSIULF9b06dMtnfuN/5Pdgm65SW4c3r1mzRqdPHnSnvD6+vrq0qVLkqR58+ZlabmlrEq93NjkyZOztNwYkNfMmzdPCxYsUFRUlE6cOKETJ06of//+2rJli9WhAXlKRESETp06lWKZxmbNmlkYEZyBJDiH+Pj4pPmgmsTdq4veunVLrVq1UuvWrbVq1SoNGjQoxRI8ednFixcVFBSkuXPnavr06abXlcaDk7ygW2hoaJqCbklz1JF1zZs318KFC+1TSGrVqqVvvvlGUVFRevvtt7Vp06YciyU3LTcG5KQ6depox44datKkiX0km5+fn6XTEYC8Ztq0aZoxY4aeeuop+zKBNptNu3fvtjgyZBef9nJIr1691KpVKzVu3FgFChSwOhyn2rp1q/bt2ydJql+/vlq0aGH63KQ1aKdPn25fg7ZVq1b2/XnxA2pCQoLWr1+vwMBAnT59Wr169dL+/fsZgpZLbNy4UQcPHsxWQTdkLDo6OsUavI8//ri99zUmJiZHY7lz545sNpsmTJiQ5pi734CEaytYsKAKFy6cYh83+QDHLFq0SCdOnGD6jAvi1TCHXL58WQMHDtTZs2fVuHFjPfvss2rVqpX8/f3td5bymlu3bumFF17QyZMnVb9+fUnSxx9/LB8fH23YsMHUnIkOHTrYHxcpUkRbt27V1q1bJeXdD6jly5eXl5eXXn/9dTVt2lTSvf//5cuXJeXNxN6VFC5c2P7cLFeunKpUqUIC7GSplx5KPjUi6TrIKVTwhLsqVaqUjh07Zp+2tHjxYnl5eVkcFZC3lClThgTYRTEcOodFRETYE70tW7bo4sWLeuaZZ7RhwwarQ3PYm2++KQ8PD82cOdNeJfvu3bt65513FBcXp/nz51scoTW8vb3tHzrSm6uZFxN7V/LUU0/p448/tm8PGzZMs2fPtm+bKeiGjDVs2FDLli1T1apVU+w/evSoevbsqT179lgUGeA+wsLC1L17d/3xxx967LHHVKxYMX377bfy8fGxOjQgz5g4caIiIyPVo0cPFSpUyL6fDo28jyTYAnfv3tVvv/2mLVu2KDg4WLdv39b58+etDsthlSpV0rFjx9IsAxQfHy9fX1+FhYVZFBlwf84o6IaM/fvf/9bkyZM1Z84c+5qku3fv1rBhwzR27Fh1797d4ggB95CYmKijR4/KMAxVrVo1z448A6yS3k0jOjRcA8Ohc8j27dvtvb/nz59Xo0aN1KxZM23YsCHF3Lm8JH/+/Omug+vh4WF6/WQgpyUNt3c1kZGRGjt2rMLDw/Xtt9/qzz//VEhIiCUJZ7du3RQVFaW+ffvqwoULku5NExg/fjwJMPCA3bx5M8V2+fLlJUm3b9+WlHuWTQPyglOnTlkdAh4QeoJzSL58+dSoUSNNmDBBbdu2tTocp2jYsKEWLlyYZkhISEiI+vXrp71791oUGZC57BR0y426desmPz8//fvf/9ahQ4cUHR2txo0b6+DBg5bGlTQHuFSpUpbGAbiL3LhsGpDX3L59W4888kiam0pJuJmU95EE55BffvnFPhc4PDxcDRs2VIsWLdSiRQtVqVLF6vCy5Ntvv9XgwYM1btw4NWrUSJK0Y8cOTZ06VZ999plefPFFiyME0kqvoNu+ffscKuiWG9WrV0/79+9X3bp17cuh1K5dWyEhITkey5kzZzI8TnEe4ME5dOiQ/Pz8JN2rxp58HuPWrVvz/A0/ICckvafe76YSN5PyPpJgC9y9e1e7du3Sli1b9NVXXykqKkrnzp2zOqws+f777zVt2jTt379f0r0etTFjxuj555+3ODIgfa5a0K1Ro0bauXOnPQmOjo5WQECAfbmxnFSqVKl0PzTExsYqKiqKDw/AA5T04T314/S2AcBdMSc4h124cEFbtmzR1q1b9fPPP+vSpUt6+umnrQ4rSwYMGKDPP/9cbdu21dq1a9WxY0erQwIy9eOPP6Yp6FagQAHNnj1bvr6+FkaWPc8++6ymTZummJgYbdq0SbNnz9bLL79sSSypl0GKi4vTZ599pg8++EBdunSxJCbAXSS/+ZS6n4N+DyBrbt68qfj4ePt2yZIlLYwGzpC2qhEeiDfeeENVqlRRlSpVtHjxYvn4+Gj58uW6fv26Nm7caHV4WZJ8zu/kyZMtjAQwz1ULuk2ZMkX58uVTsWLF9O6776pp06YaP3681WEpODhY1apVs9/4W7hwodUhAS4tefX71JXw71cZH0D6VqxYodKlS6tkyZIqU6aMypQpo7Jly1odFpyAnuAcUqFCBQUGBqpRo0Z5+oP2/XB3GXlF0aJFFRoamm5Bt0ceecSiqLLPw8NDY8aM0ZgxY6wORdK9HvfRo0eraNGiWrZsmRo3bmx1SIBbiI6O1u+//y7DMFI8TjoGwLwxY8bou+++U4MGDawOBU5GEpxDJkyYYHUITpf8zTUmJibFG63EQuLInSZMmKD27dvft6BbXnX27FkNHjxY586d08GDB3Xw4EFt2bJFw4YNy/FY2rRpoxMnTmjy5Mn2AnnJK2xSVRN4cKKjo/XSSy/Zt5M/picYcEzZsmVJgF0UhbGQZd7e3vd9Q2UhceRmrljQrV27durRo4dmzJihkJAQxcfHq27duvr9999zPJbkw82pqgkAyKsWL16sCxcu6K9//WuKSuuscpD3kQQDgAto0KCB9u7dm2KJpOSPAQCAYxYsWKBhw4apUKFCeuihhyTdu5l76dIliyNDdlEYC4BbGTBggP3xunXrLIzEuTw8PFL0uF6/fp25+gAAZMP777+v33//XdeuXdPly5d1+fJlEmAXQRIMwK0kr2o+adIkCyNxri5dumjgwIG6efOmFi5cqNatW6t///6WxFKiRAmVLFnS/vXYY4+pWrVqevvttxUVFWVJTAAAOMrT01MVK1a0Ogw8AAyHBuBW6tWrZ58L7GrDhYODg7V27VoZhqGOHTuqR48elsRx+vTpNPuuXLmiBQsWyGazacGCBRZEBQCAY8aPH687d+6oa9euKeYEU/w17yMJBuBWqlWrppUrV8owDHXt2tX+OAlvbA9OYmKi6tWrp4MHD1odCgAAmfLx8Umzj+KvroEkGIBbcbWq5sOHD8/w+KxZs3IoEnOS98QDAABYgXWCAbiV8PBwq0NwquLFi1sdgmnff/+9SpYsaXUYAACYtnv3bm3atEmS1KZNG9YNdhH0BAMAnKpu3bppetuvXr2q/Pnza82aNapZs6ZFkQEAYN7nn3+uqVOn6uWXX5bNZtPXX3+t8ePHW1Z4Es5DEgwAeVhwcLC6d++uuXPnpnt8yJAhORyRtG3bthTbNptNpUqVUuXKleXhwQAkAEDeUKtWLW3evFmlSpWSJF2+fFmtWrVSaGioxZEhu/g0AgB52JEjRyQp3SrX95v7/KA1b97ckp8LAICzJSXAqR8jb6MnGADgVKmHQ9tsNpUuXVpt2rTRkCFD9NBDD1kYHQAA5nTu3Fm+vr4aOHCgpHvDow8fPqzVq1dbHBmyiyQYAFxAbGysZs+erU2bNslms6l169Z6++23VbBgwRyPJfVwaOneOsGBgYGqXbu2PvjggxyPCQAAR12+fFlvvfWW/b31L3/5i+bOnUuPsAsgCQYAF/D666/r6tWr6tevnyQpKChIJUqUUFBQkMWR/Z/Y2FgFBASwTjAAIFfbvn17iu2kdClplFOzZs1yPCY4F0kwALgAX19fHT582P4GnZCQoBo1atjnDOcWrBMMAMjt/P390+yz2Wy6cOGCIiIilJCQYEFUcCYKYwGAC3jssccUHR2thx9+WNK9XtfHH3/c4qhSCg0NVeHCha0OAwCADO3ZsyfF9rVr1zR16lQtX75ckyZNsigqOBNJMADkYUlLI/n6+iogIECvvPKKJGnVqlXp3snOCZ06dUpTmfrKlSs6duyYVq5caUlMAAA4KiYmRrNnz9acOXPUo0cPHT58WI899pjVYcEJSIIBIA9LvjRSgwYNdPLkSUn3hh0nJiZaElPHjh1TbCetExwQEKCSJUtaEhMAAGYlJiZq4cKFmjJlilq2bKldu3bpySeftDosOBFzggEATjV8+PAMj8+aNSuHIgEAwHHVq1dXbGys3nvvPdWuXTvN8Vq1alkQFZyJJBgAXMDSpUvT3d+7d+8cjkTKly+f/P391bZtW+XLly/N8YkTJ+Z4TAAAmOXt7Z1mWk8Sm81mH3WFvIskGABcQJcuXeyPY2Ji9Msvv6hRo0bauHFjjseyZcsWLVq0SDt37tQrr7yivn37qmLFijkeBwAAQHpIggHABZ06dUpjx47VV199ZVkMN2/eVHBwsIKCglS4cGFNnz5dAQEBlsUDAAAgSWnHqQEA8jwfHx/98ccflsZQrFgxdejQQR06dNCRI0dy3ZrFAADAPVEdGgBcwPr16+2PExIStHPnThUsWNCSWBISErR+/XoFBgbq9OnT6tWrl/bv369y5cpZEg8AAEByDIcGABfw7LPP2h97eHioUqVKGjlypHx8fHI8lrJly8rLy0uvv/66mjZtmuY4VTUBAICVSIIBIA8LDQ2977FChQrJy8tLhQoVysGIUlbVtNlsSv42Q1VNAABgNZJgAMjDknp6Uy/lYBiG4uPjdevWLc2cOVP9+vWzIjwAAIBchyQYAFxYRESEWrZsqcOHD1sdCgAAQK5AdWgAcGHlypXTgAEDrA4DAAAg16AnGAAAAADgNugJBgAAAAC4DZJgAAAAAIDbIAkGAAAAALgNkmAAAAAAgNsgCQYAAAAAuI3/B4xNupfE0+UvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2400x768 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df1, 10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check 2nd file: /kaggle/input/Senators.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 67 rows and 5 columns\n"
     ]
    }
   ],
   "source": [
    "nRowsRead = None # specify 'None' if want to read whole file\n",
    "# Senators.csv may have more rows in reality, but we are only loading/previewing the first 1000 rows\n",
    "df2 = pd.read_csv('./kaggle/input/Senators.csv', delimiter=',', nrows = nRowsRead)\n",
    "df2.dataframeName = 'Senators.csv'\n",
    "nRow, nCol = df2.shape\n",
    "print(f'There are {nRow} rows and {nCol} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a quick look at what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Senator</th>\n",
       "      <th>Photo</th>\n",
       "      <th>County</th>\n",
       "      <th>Party</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sen. (Dr.) Ali Abdullahi Ibrahim</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Wajir</td>\n",
       "      <td>JP</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sen. (Dr.) Inimah Getrude Musuruve</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>N\\/A</td>\n",
       "      <td>ODM</td>\n",
       "      <td>Nominated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sen. (Dr.) Langat Christopher Andrew</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>Bomet</td>\n",
       "      <td>JP</td>\n",
       "      <td>Elected</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sen. (Dr.) Milgo Alice Chepkorir</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>N\\/A</td>\n",
       "      <td>JP</td>\n",
       "      <td>Nominated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sen. (Dr.) Zani Agnes Philomena</td>\n",
       "      <td>http://www.parliament.go.ke/sites/default/file...</td>\n",
       "      <td>N\\/A</td>\n",
       "      <td>N\\/A</td>\n",
       "      <td>Nominated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Senator  \\\n",
       "0      Sen. (Dr.) Ali Abdullahi Ibrahim   \n",
       "1    Sen. (Dr.) Inimah Getrude Musuruve   \n",
       "2  Sen. (Dr.) Langat Christopher Andrew   \n",
       "3      Sen. (Dr.) Milgo Alice Chepkorir   \n",
       "4       Sen. (Dr.) Zani Agnes Philomena   \n",
       "\n",
       "                                               Photo County Party     Status  \n",
       "0  http://www.parliament.go.ke/sites/default/file...  Wajir    JP    Elected  \n",
       "1  http://www.parliament.go.ke/sites/default/file...   N\\/A   ODM  Nominated  \n",
       "2  http://www.parliament.go.ke/sites/default/file...  Bomet    JP    Elected  \n",
       "3  http://www.parliament.go.ke/sites/default/file...   N\\/A    JP  Nominated  \n",
       "4  http://www.parliament.go.ke/sites/default/file...   N\\/A  N\\/A  Nominated  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution graphs (histogram/bar graph) of sampled columns:\n",
    "**Political Party and Election Status Distributions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_kg_hide-input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2400x768 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPerColumnDistribution(df2[[\"Party\",\"Status\"]], 10, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "* Jubilee has the tyranny of numbers in the current parliament\n",
    "* ODM comes second\n",
    "* Wiper third\n",
    "*Some political party data is missing or the candindate was independent*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}