PE_DATA_ADV_PYTHON.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>naam</th>\n",
       "      <th>naam.1</th>\n",
       "      <th>positie</th>\n",
       "      <th>aantal gemaakte goalen</th>\n",
       "      <th>geboortedatum</th>\n",
       "      <th>inzet</th>\n",
       "      <th>gewicht</th>\n",
       "      <th>lengte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>speler1</td>\n",
       "      <td>speler1</td>\n",
       "      <td>staart</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.3</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speler2</td>\n",
       "      <td>speler2</td>\n",
       "      <td>staart</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.2</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speler3</td>\n",
       "      <td>speler3</td>\n",
       "      <td>linkervleugel</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.8</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speler4</td>\n",
       "      <td>speler4</td>\n",
       "      <td>rechtervleugel</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.2</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speler5</td>\n",
       "      <td>speler5</td>\n",
       "      <td>piloot</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.5</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      naam   naam.1         positie  aantal gemaakte goalen  geboortedatum  \\\n",
       "0  speler1  speler1          staart                       1            NaN   \n",
       "1  speler2  speler2          staart                       0            NaN   \n",
       "2  speler3  speler3   linkervleugel                       1            NaN   \n",
       "3  speler4  speler4  rechtervleugel                       0            NaN   \n",
       "4  speler5  speler5          piloot                       6            NaN   \n",
       "\n",
       "   inzet  gewicht  lengte  \n",
       "0    NaN     25.3     123  \n",
       "1    NaN     29.2     134  \n",
       "2    NaN     22.8     116  \n",
       "3    NaN     23.2     121  \n",
       "4    NaN     24.5     121  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime \n",
    "from matplotlib import pyplot as plt\n",
    "from statistics import mode\n",
    "data = pd.read_excel('voetbal.xlsx')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = pd.to_datetime('2011-01-01')\n",
    "end = pd.to_datetime('2011-12-31')\n",
    "start_u = start.value//10**9\n",
    "end_u = end.value//10**9\n",
    "cat1_date = pd.to_datetime('2011-04-01')\n",
    "cat2_date = pd.to_datetime('2011-10-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>naam</th>\n",
       "      <th>naam.1</th>\n",
       "      <th>positie</th>\n",
       "      <th>aantal gemaakte goalen</th>\n",
       "      <th>geboortedatum</th>\n",
       "      <th>inzet</th>\n",
       "      <th>gewicht</th>\n",
       "      <th>lengte</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>speler1</td>\n",
       "      <td>speler1</td>\n",
       "      <td>staart</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-03-08</td>\n",
       "      <td>zeer goed</td>\n",
       "      <td>25.3</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>speler2</td>\n",
       "      <td>speler2</td>\n",
       "      <td>staart</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-05-18</td>\n",
       "      <td>goed</td>\n",
       "      <td>29.2</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>speler3</td>\n",
       "      <td>speler3</td>\n",
       "      <td>linkervleugel</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-09-19</td>\n",
       "      <td>goed</td>\n",
       "      <td>22.8</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>speler4</td>\n",
       "      <td>speler4</td>\n",
       "      <td>rechtervleugel</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-11-30</td>\n",
       "      <td>matig</td>\n",
       "      <td>23.2</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>speler5</td>\n",
       "      <td>speler5</td>\n",
       "      <td>piloot</td>\n",
       "      <td>6</td>\n",
       "      <td>2011-06-11</td>\n",
       "      <td>goed</td>\n",
       "      <td>24.5</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      naam   naam.1         positie  aantal gemaakte goalen geboortedatum  \\\n",
       "0  speler1  speler1          staart                       1    2011-03-08   \n",
       "1  speler2  speler2          staart                       0    2011-05-18   \n",
       "2  speler3  speler3   linkervleugel                       1    2011-09-19   \n",
       "3  speler4  speler4  rechtervleugel                       0    2011-11-30   \n",
       "4  speler5  speler5          piloot                       6    2011-06-11   \n",
       "\n",
       "       inzet  gewicht  lengte  \n",
       "0  zeer goed     25.3     123  \n",
       "1       goed     29.2     134  \n",
       "2       goed     22.8     116  \n",
       "3      matig     23.2     121  \n",
       "4       goed     24.5     121  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['inzet'] = ''\n",
    "for x, row in data.iterrows():\n",
    "    y = (pd.to_datetime(np.random.randint(start_u, end_u), unit='s'))\n",
    "    data.at[x,'geboortedatum'] = datetime.date(y.year,y.month,y.day)\n",
    "    \n",
    "    if y < pd.to_datetime('2011-04-01'):\n",
    "        data.at[x, 'inzet'] = 'zeer goed'\n",
    "    elif y < pd.to_datetime('2011-10-01'):\n",
    "        data.at[x, 'inzet'] = 'goed'\n",
    "    else:\n",
    "        data.at[x, 'inzet'] = 'matig'\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gewicht_gesorteerd = sorted(set(data.gewicht))\n",
    "for x in gewicht_gesorteerd:\n",
    "    y=data[data.gewicht == x]\n",
    "    plt.scatter(y.gewicht, y.lengte)\n",
    "plt.xlim(19,31)\n",
    "plt.ylim(110,140)\n",
    "plt.xlabel('gewicht')\n",
    "plt.ylabel('lengte')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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woOLlIiKiU5XGGrJ9EzDF9mLbJwGvHesYSXsD99q+fPDq4U4/wjVn255he8bUqVOrhBkREV2oMgz1o5KWpZic5gvAXTx7xrKR7AC8UdI/Usx7vCrFE8LqkpYunwo2AO4c5RwREdFjVZ4I3lnu90HgEWBD4C1jHWT7CNsb2J4G7Af80vYBwEXAzHK3WWSeg4iIRlXpUHZr+USwEcUAdDfYfnIc1zwMOFXSZ4ErKQayi4iIhoyZCCTtTNG6Zz5FGf+GkmbZvqTqRWxfDFxcfr6ZYtrLiIh4HqhSR/Bl4HW2bwCQ9GLgh8ArexlYRETUo0odwTIDSQDA9p/J6KMREX2jyhPBXEknAN8vlw8ALh9l/4iImESqJIL3AR+gGIFUwCXAN3sZVERE1KdKq6HHga+Ur4iI6DNVWg0NNxz1QxQjin7W9v29CCwiIupRpWjoZ8BilkxYvx9FEdFDwPeAN/QksoiIqEWVRLCD7R0GLc+T9FvbO0g6sFeBRUREPao0H11Z0rYDC5K2AVYuF58a/pCIiJgsqjwRHAKcKGngy38RcLCklYCjexZZRETUokqroT8Cfy9pNUC2Hxy0+bSeRRYREbWo8kQAgO2HehlIREQ0o9LENBER0b+SCCIiWm7EoiFJbx7tQNtnTHw4ERFRt9HqCEbrKGYgieB5btrh5zYdwoSZf8xeHR/T9vuPqGrERGD7XXUGEhERzajUakjSXsDLKCahB8D2UWMcszzFSKXLldc53fanJW0CnAqsCVwBvNP2E92FHxER4zVmZbGk44G3Ax+iGGPorcDGFc79OLCL7ZcDWwF7SHoVcCzwVdvTgQeAg7uMPSIiJkCVVkPb2/4n4AHbRwLbARuOdZALD5eLy5QvA7sAp5fr5wD7dhx1RERMmCqJ4LHy/VFJ6wFPAptUObmkKZKuAu4FLgD+F3jQ9sAYRbcD649w7KGS5kqau2DBgiqXi4iILlRJBOdIWh34IkWZ/nyKMv4x2V5seytgA2Ab4KXD7TbCsbNtz7A9Y+rUqVUuFxERXahSWfyFcpayn0g6h6LC+G+dXMT2g5IuBl4FrC5p6fKpYAPgzg5jjoiICVTlieD3Ax9sP16OOfT7UfYHQNLU8kkCSSsAuwHXARcBM8vdZgFndRp0RERMnNF6Fr+Qovx+BUmvoGgxBLAqsGKFc68LzJE0hSLhnGb7HEnXAqdK+ixwJXDCeG4gIiLGZ7SiodcDB1EU3wyeuH4R8MmxTmz7auAVw6y/maK+ICIingdG61k8h+IX/Vts/6TGmCIiokZVKovPkfQOYNrg/cfqWRwREZNDlURwFvAQcDlFb+GIiOgjVRLBBrb36HkkERHRiCrNR38n6e97HklERDSiyhPBjsBBkm6hKBoSxVBCW/Y0soiIqEWVRLBnz6OIiIjGjJkIbN8KIGltBs1HEBER/aHKfARvlHQjcAvwK4pB537W47giIqImVSqLP0MxWNyfbW8C7Ar8tqdRRUREbaokgidt3w8sJWkp2xdRzDgWERF9oEpl8YOSVqaYf/gUSfcCT41xTERETBJVngj2AR4FPgacRzHL2N69DCoiIuozZiKw/Yjtp20/VQ5EdwVFvUFERPSBKkVDSNoKeAfwNorWQ2f0MqiIiKjPaBPTvBjYD9gfuB/4ESDbr60ptoiIqMFoTwTXA78G3mD7JgBJH6slqoiIqM1odQRvAe4GLpL0HUm7smS6yjFJ2lDSRZKuk/QnSR8p168p6QJJN5bva4zvFiIiYjxGTAS2z7T9duAlwMUUrYbWkfQtSa+rcO6ngH+x/VKKDmkfkLQFcDhwoe3pwIXlckRENKRqq6FTbO9NMX/xVVT48rZ9l+0rys+LgOuA9Smao84pd5sD7Ntl7BERMQGq9CN4hu2/2v627V06OU7SNIqJ7C8D1rF9V3m+u4C1RzjmUElzJc1dsGBBJ5eLiIgOdJQIulH2Sv4J8FHbC6seZ3u27Rm2Z0ydOrV3AUZEtFxPE4GkZSiSwCm2B/oe3CNp3XL7usC9vYwhIiJG17NEIEnACcB1tr8yaNPZwKzy8yzgrF7FEBERY6vUs7hLOwDvBOZJuqpc90ngGOA0SQcDtwFv7WEMERExhp4lAtu/YeR+B7v26roREdGZnlcWR0TE81sSQUREyyURRES0XBJBRETLJRFERLRcL5uPPi9MO/zcpkOYMPOP2avpECKiD+WJICKi5ZIIIiJaLokgIqLlkggiIlouiSAiouWSCCIiWi6JICKi5ZIIIiJaLokgIqLlkggiIlouiSAiouV6OWfxiZLulXTNoHVrSrpA0o3l+xq9un5ERFTTyyeC7wF7DFl3OHCh7enAheVyREQ0qGeJwPYlwF+HrN4HmFN+ngPs26vrR0RENXXXEaxj+y6A8n3tkXaUdKikuZLmLliwoLYAIyLa5nlbWWx7tu0ZtmdMnTq16XAiIvpW3YngHknrApTv99Z8/YiIGKLuRHA2MKv8PAs4q+brR0TEEL1sPvpD4PfA5pJul3QwcAywu6Qbgd3L5YiIaFDP5iy2vf8Im3bt1TUjIqJzz9vK4oiIqEcSQUREyyURRES0XBJBRETLJRFERLRcEkFERMslEUREtFwSQUREyyURRES0XBJBRETLJRFERLRcEkFERMslEUREtFwSQUREyyURRES0XBJBRETLJRFERLRcI4lA0h6SbpB0k6TDm4ghIiIKtScCSVOA/wT2BLYA9pe0Rd1xREREoYkngm2Am2zfbPsJ4FRgnwbiiIgIQLbrvaA0E9jD9iHl8juBbW1/cMh+hwKHloubAzfUGmhn1gLuazqIBrX5/tt879Du+58M976x7alj7bR0HZEMoWHWPScb2Z4NzO59OOMnaa7tGU3H0ZQ233+b7x3aff/9dO9NFA3dDmw4aHkD4M4G4oiICJpJBH8EpkvaRNKywH7A2Q3EERERNFA0ZPspSR8Efg5MAU60/ae645hgk6IIq4fafP9tvndo9/33zb3XXlkcERHPL+lZHBHRckkEEREtl0QQEdFySQRdkrRJlXUR0R8kTZH0sabj6IUkgu79ZJh1p9ceRc0kzZN09TCveZKubjq+6D1Jx1ZZ129sL6ZPh8NpomfxpCbpJcDLgNUkvXnQplWB5ZuJqlZ7Nx1AUyQtYkkv+IEe8i4/2/aqjQRWv92Bw4as23OYdf3ot5K+AfwIeGRgpe0rmgtp/JIIOrc5xZfh6sAbBq1fBLynkYhqZPvWgc+SNgam2/6FpBXo878n26s0HUOTJL0PeD+w6ZCnv1WA3zYTVe22L9+PGrTOwC4NxDJh0o+gC+VQ2ofZ/nzTsTRF0nsoBgVc0/ZmkqYDx9veteHQaiFpR4okeJKktYBVbN/SdFy9JGk1YA3gaGDwPCKLbP+1mahiIqSOoAtlWeHuTcfRsA8AOwALAWzfCKzdaEQ1kfRpimKQI8pVywInNxdRPWw/ZHu+7f1Z8kT8Bp49dlhfk7SOpBMk/axc3kLSwU3HNV5JBN37naRvSNpJ0tYDr6aDqtHj5XwSAEhammFGke1TbwLeSFlGbPtOiuKRVpD0YeAUisS/NnCypA81G1VtvkcxPM565fKfgY82Fs0E6esy3R7ry7LCDvxK0ieBFSTtTlF2/N8Nx1SXJ2xbkgEkrdR0QDU7hGIOkUfgmRZDvwe+3mhU9VjL9mmSjoBnxk5b3HRQ45VE0CXbr206hoYdDhwMzAP+Gfgp8N1GI6rPaZK+Daxe1pW8G/hOwzHVScDgL7/FDD/PSD96RNILKJ9+Jb0KeKjZkMYvlcXjIGkviqakzzQbtX3UyEdEvyifgl5H8QX4c9sXNBxSbSR9HJgFnElx//sA37P9tUYDq0FZ/Pt14O+Aa4CpwEzbk7oPTRJBlyQdD6wIvJbil/BM4A+2J33FURWS5vHcOoGHgLnAZ23fX39UUZfyC3HHcvHXtq9sMp46lfVhm1MkwRtsP9lwSOOWoqHubW97S0lX2z5S0peBM5oOqkY/oygS+EG5vF/5vpCiQu0NwxzTF4Z0LBswkAT/xfbN9UdVu8UU/wYGnm44ltpIWp6iPmxHinv/taTjbf+t2cjGJ4mge4+V749KWg+4H2jTWEM72N5h0PI8Sb+1vYOkAxuLqh5foZhe9QcUvwr3A14I3ACcCOzcWGQ1kPQRis6TP6G4/5Mlzbbdhsri/6LoPDpwr/sD3wfe2lhEEyCJoHvnSFod+CJwBcWvg7ZUlgKsLGlb25cBSNoGWLnc9lRzYdViD9vbDlqeLelS20eVLan63cG0t9XQ5rZfPmj5Ikn/01g0EySJoEu2P1N+/Imkc4DlbU/61gMdOAQ4UdLKFL8KFwKHlE0pj240st57WtLbWDLI4MxB29pQ6dbmVkNXSnqV7UsBJG1LHwyvkcricZD0d8AWPLvV0H81F1H9ymEHZPvBpmOpi6RNgeOA7Si++C8FPgbcAbzS9m8aDK/nhrQaAtiX9rQauo6iovi2ctVGwHUU9SS2vWVTsY1HEkGXymEGdqZIBD+lGH3xN7ZnjnZcv5D078OtT/PZdhjUakjAJW1pNVQOtDiiwYMyTiYpGureTODlwJW23yVpHdpVR/DIoM/LU4zIel1DsdRK0kkMUwRk+90NhFMbSWsOWpxfvp7Z1oaB52zf2o8DDiYRdO8x209LekrSqsC9wKZNB1UX218evCzpS8DZDYVTt3MGfV6eYuyhOxuKpU6Xs2T+BXj23AymBX//ZUnADIrioZNYMuDgDqMd93yXRNC9uWWroe9Q/Ad5GPhDsyE1akVa8EUAYPtZs9NJ+iHwi4bCqY3tNjWPHsmbgFdQtBTE9p2SJv2Ag0kEXbL9/vLj8ZLOA1ad7N3MOzGkZ/EUiq72ba0fmE5RadjXJL3E9vUjjbI72WfpqqgvBxxMIuiSpAsHJmGxPX/ouhYYPGXlU8A9tvu9/wDwrJ7FA0Uid9OOaRo/TjEZ0Zd5dh3JwL9DG0be7csBB9NqqENlF/MVgYsoWg0NlJeuCvzM9ksbCq12/VhpFmMrpyV91jALwLcm+zALVfXjgINJBB0qu9d/lGJiijtY8mtoETDb9n82GF5tBlea2X5xOczGj4cMO9GXJAk4ANjE9mckbQS80HYr6ogknUbRgfCUctX+wOq239ZcVPUZMlf3isAU24uajms8MkNZh2wfV1aafQ7Yqvx8EnAzRTf7tmjzLF3fpOhM9o5yeRHQih8Apc1tH2L7ovJ1KEUrmr5XFgedDny7XLU+8P+ai2hiJBF0b6bthWXxyO4UI25+q9mQavWEi8fJvqo0q2hb2x8A/gZg+wGKZoRtcWU5IQvQP8MsVNSXc3UnEXRvYKyVvYDjbZ9Fu74Mhlaa/YI+qDSr6ElJU1iSBKfSoqGYgW0p5uyeL2k+xZPwayTNk9TvLef6cq7utBrq3h3lF+FuwLGSlqNFidX2l8pKs4UUxQL/3g+VZhX9B8U4O2tL+hxFL/N/azakWu3RdAANGm6u7knfkTKVxV0qK4n2AObZvlHSusDf2z6/4dCiBpJeAuxK0VjgQtutGF6j7ST9A7AVg1oNAXfb/u9GAxunJILoyDBt6J/ZRDH64qqNBFaDIWPtPEcbxtppO0lXAAcNdB6VtD/w0SHzU0w6SQQRFUm6hVHG2rHdiiE22qwcgvx0ihZjOwH/BOw92eciSSKIrkg6DjjVdpuazEYg6cUUTUb/Auxr+7ExDnneSyKIrkiaBbwdeDFFxemPbM9tNqp6SDoLOBU4y/ajTccTvTdkbC0omow+BDwOMFknpBmQRBDjUpabv4ViAveNbE9vOKSek/QaiiS4F8WIsz8CzmnLEAtt1K8T0gxI89EYrxcBLwGmAdc2G0o9bP+KohnhFIqB1t4DnEgx3lT0ocn+RT+WJILoiqRjgTcD/0vxi/gzLZu3eAXgDRRPBlsDc5qNKKJ7SQTRsXLQtYeB7Wzf13Q8dZP0I4retedRjDF0se029SyOPpNEEB0rJ+bY1/Znmo6lbpKWAuYB77C9eKz9IyaD1gyJEBPu0rKXZauUv/z3ShKIfpJWQ9EVSddSjDE0n2Io6oFOVZO6GV0Vko4ErgbOcP4DRR9IIoiujNScrt9bV8Azw2ysRDEC7WO0YHiN6G8pGopHhbCLAAAFBUlEQVSulF/4GwK7lJ8fpSV/T7ZXsb2U7WVsr1ouJwnEpNWK/7gx8cqpKg8DjihXLQOc3FxE9VHhQEmfKpc3lLRN03FFdCuJILqVqSqXTFX5MO2aqjL6TJqPRreeKJuRtnWqyq0lXQnFVJWS2jQ7XfSZPBFEt4abqvK7DcdUl7ZPVRl9Jq2GomvlVH3PzNTUlqkqJR3As4eWmAl8yvZpjQYW0aUkguiKpD1t/2zIuvfaPr6pmOqUqSqjn6RoKLr1KUm7DCxIOgzYp8F4aiPp+7avt/2ftr9h+zpJ3286rohupbI4uvVG4BxJnwD2oBiK+o3NhlSblw1eKOsLXtlQLBHjlieC6Eo56ugbKZpNrgfMtP1ks1H1lqQjyl7FW0paKGlRuXwvcFbD4UV0LXUE0ZHyi2/wH82ywFPlulYMsyDpaNtHjL1nxOSQRBDRBUnrAxszqHjV9iXNRRTRvdQRRNfa+mUo6RiKOZqvpRh4Doonor6/9+hPeSKIrpRTVb6dIV+Gtvu+wljSDcCWth9vOpaIiZAngujWvsDmLf0yvJlikL023nv0oSSC6FbrvgwlfZ2iCOhR4CpJFzLo/m1/uKnYIsYjiSC61cYvw7nl++XA2U0GEjGRUkcQXZE0a7j1tufUHUvdypFW/zYwb3HZoWw52482G1lEd5IIIjok6VJgN9sPl8srA+fb3r7ZyCK6k6Kh6Iik02y/TdI8nt2xDIA2TF4PLD+QBABsPyxpxSYDihiPJILo1EfK970bjaJZj0ja2vYVAJJeSTGJfcSklKKhiA5J+gfgVODOctW6wNttX95cVBHdSyKIjgwz1tAzm2jJWEMAkpYBNqe47+v7fcC96G9JBBEdKusDPg5sbPs9kqZTdK47p+HQIrqSYagjOncS8ASwXbl8O/DZ5sKJGJ8kgojObWb7C8CTALYfoygiipiUkggiOveEpBUo60okbUaLhtqI/pPmoxEdkCTgeOA8YENJpwA7AAc1GVfEeKSyOKJDki4HXge8iqJI6NJy6s6ISSlPBBGduxTY1Pa5TQcSMRHyRBDRIUnXAi8GbgUeYUkfijYMrxF9KIkgokOSNh5uve1b644lYiIkEUREtFyaj0ZEtFwSQUREyyURRCtJWizpKknXSPpxN/MJSPqupC3Kz58csu13ExVrRK+ljiBaSdLDtlcuP58CXG77KxNxvojJJk8EEfBr4EUAkj5ePiVcI+mj5bqVJJ0r6X/K9W8v118saYakY4AVyieMU8ptz8xgJukTkv4o6WpJR452zogmpENZtJqkpYE9gfPKmcbeBWxL0TfgMkm/AjYF7rS9V3nMaoPPYftwSR+0vdUw538dMB3Ypjzn2ZJeDUwd7ZwRdcoTQbTVCpKuAuYCtwEnADsCZ9p+pJyT+AxgJ2AesJukYyXtZPuhDq7zuvJ1JXAF8BKKxDCec0ZMqDwRRFs9NvQXfDmg3HPY/nP5tPCPwNGSzrd9VMXrCDja9refs6H7c0ZMqDwRRCxxCbCvpBUlrQS8Cfi1pPWAR22fDHwJ2HqYY58sp68c6ufAuyUNVEyvL2ntiueMqEWeCCJKtq+Q9D3gD+Wq79q+UtLrgS9KeppiMpr3DXP4bOBqSVfYPmDQOc+X9FLg9+UDx8PAgRSV02OdM6IWaT4aEdFyKRqKiGi5JIKIiJZLIoiIaLkkgoiIlksiiIhouSSCiIiWSyKIiGi5/w9dSdlWWTm7XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_array = {}\n",
    "per_categorie ={}\n",
    "player_per_position={}\n",
    "for position in data['positie']:\n",
    "    sum_array[position]=0\n",
    "    player_per_position[position]=0\n",
    "for x, row in data.iterrows():\n",
    "    position=data.at[x, 'positie']\n",
    "    sum_array[position]=sum_array[position]+ data.at[x, 'aantal gemaakte goalen']\n",
    "    player_per_position[position]=player_per_position[position] + 1\n",
    "x = list(sum_array.keys())\n",
    "y = list(sum_array.values())\n",
    "plt.bar(x,y)\n",
    "plt.xlabel(\"Posities\")\n",
    "plt.ylabel(\"Aantal goals gescoord\")\n",
    "plt.title(\"Goals per positie\")\n",
    "plt.xticks(list(sum_array.keys()),list(sum_array.keys()), rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8\n",
      "1.6\n",
      "1.6\n",
      "4.0\n",
      "0.0\n",
      "Mode for staart: 0\n",
      "Mode for linkervleugel: 1\n",
      "Mode for rechtervleugel: 1\n",
      "Mode for piloot: 4\n",
      "Mode for keeper: 0\n"
     ]
    }
   ],
   "source": [
    "modus_array={}\n",
    "average_goals_position={}\n",
    "for position in sum_array.keys():\n",
    "    average_goals_position[position]=sum_array[position] / player_per_position[position]\n",
    "    modus_array[position] = []\n",
    "    print(average_goals_position[position])\n",
    "for x, row in data.iterrows():\n",
    "    position = data.at[x, 'positie']\n",
    "    modus_array[position].append(data.at[x,'aantal gemaakte goalen'])\n",
    "for key in modus_array.keys():\n",
    "    print ('Mode for ' + key + ': ' + str((mode(modus_array[key]))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}