onderzoeksrapport/data-analyse/topdesk_incidenten.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import topdesk\n",
    "\n",
    "FIGURES_DIRECTORY = \"../figures\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ophalen gegevens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = [\n",
    "    \"operatorGroup.name=='Expertteam - Networking'\",\n",
    "    \"status=='secondLine'\",\n",
    "    \"creationDate=ge=2020-01-01T00:00:00Z\",\n",
    "    \"creationDate=le=2022-01-01T00:00:00Z\",\n",
    "    # \"archived==false\"\n",
    "]\n",
    "\n",
    "fields = [\n",
    "    \"timeSpent\",\n",
    "    \"category.name\",\n",
    "    \"subcategory.name\",\n",
    "    \"creationDate\",\n",
    "    \"briefDescription\",\n",
    "    \"request\"\n",
    "]\n",
    "\n",
    "results = topdesk.get_incidenten(query, fields)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.json_normalize(results).dropna().convert_dtypes()\n",
    "\n",
    "df = df.loc[df['category.name'] == 'Netwerk']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Totale tijd besteed aan incidenten per categorie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mcRLs2njgekm3RMTzwBjgYknTgDuBzbo5v2+QrgFOk/Rg7ge4BdjcN8aYmTWX3xjTh2y66aYxc+bM6gVbrN2agWk0x1lffSHOvhAj1LUpJb8xxszMrNmcBM3MrG05CZqZWdtyEjQzs7blJGhmZm3LSdDMzNqWk6CZmbUtJ0EzM2tbToJmZta2nATNzKxtuRWJPmTBwsWMOOXaVodR1UlbLWKM46ybvhLnhA8OanUIZt3mM0EzM2tbToKdkLSepKsl/V3So5LOkrRSHea7jqTL6hGjmZktPyfBMrmtvyuAqyLiXcAmwGDgjOWc74oR8UxEHFSHMM3MrA6cBJe1F/BaRFwIEBGLgf8GjpE0SdIWpYKSOiSNyg3s/iKPv0/SAXn8GEnXSLoZ+LOkEZIeyONWkXSppBmSrpR0t6SWNCViZtaunASXtQUwpTggIuYA/wCuBQ4BkLQ2sHZETAa+AtwcEdsDewLfl1S6S2Bb4KCI2KNsOf8FvBQRmwP/C7y3s2AkjZU0WdLkeXPm1GUFzcwscRLsng6gVJ15CFC6vrcPcIqkqbnMAGCDPO6miJjdybx2BS4BiIgHgGmdLTAixkfEqIgYNXjIkDqsgpmZlfgRiWXNYEmiA0DSEFJSuwd4UdLWwKHAcaUiwIERMbNsuh2A+Q2P2MzMesRngsv6M7CKpE8CSOoH/BCYEBGvAhOBLwKrRUTp7O0G4PP5phokbVPDcm5nSdXq5sBWdV0LMzOrykmwTEQE8DHgYEl/B/4GvAacmotcBhwGXFqY7BtAf2CapAdzfzXnAsMlzQC+CTwIvFKXlTAzs5q4OrQTEfEksH+Fcf+ibLtFxALg2E7KTgAmFPpnAVvm3teAIyPiNUnvBP4EPLH80ZuZWa2cBFtnFeAWSf1J1xT/KyL+3dUEA/v3Y+Z3PtSU4JZHR0cHs44Y3eowqnKc9dXR0dHqEMy6zUmwRSJiLuDnAs3MWsjXBM3MrG05CZqZWdtyEjQzs7blJGhmZm3LSdDMzNqWk6CZmbUtJ0EzM2tbToJmZta2/LB8H7Jg4WJGnHJtq8Oo6qStFjHGcdZNT+Kc1QfeLGTWG/hM0MzM2paTYJ1IOlPSiYX+GyT9rND/Q0lfk3RK7h8u6W5J90naTdIfJQ1tfuRmZu3LSbB+bgd2BpC0ArAmsEVh/M7AjRHxndz/PmB6RGwTEbdFxH4R8XIzAzYza3dOgvVzB7BT7t4CeACYK2mYpJWBdwNbSzpH0kjge8ABkqZKGihplqQ1WxK5mVmb8o0xdRIRz0haJGkD0lnfncC6pMT4CjAd+HcuO1XS14BREfE5gNwo/TIkjQXGAgxbYzhDGr0iZmZtxGeC9XUHKQGWkuCdhf7bezLDiBgfEaMiYtTgIU6BZmb15CRYX6XrgluRqkPvIp0J7kxKkGZm1os4CdbXHcCHgdkRsTgiZgNDSYnQSdDMrJdxEqyv6aS7Qu8qG/ZKRLzQmpDMzKwS3xhTRxGxGJa+dyUixhS6JwATyrtz/4iGB2hmZktxEuxDBvbvx8w+8Dqsjo4OZh0xutVhVOU4zczVoWZm1racBM3MrG05CZqZWdtyEjQzs7blJGhmZm3LSdDMzNqWk6CZmbUtJ0EzM2tbToJmZta2/MaYPmTBwsWMOOXaVodR1UlbLWKM46zJrD7wBiCztzKfCZqZWdtyEuyEpK9IelDSNElTJe1QwzSnS9o7d58oaZUq5cdJOrleMZuZWfe5OrSMpJ1IbQJuGxGvS1oTWKnadBHxtULvicCvgVcbEqSZmdVFzWeC1c5s3kLWBl6IiNcBcjuA60q6AkDSAZIWSFpJ0gBJj+XhEyQdJOl4YB3gFkm35HEflHSvpPsl/bmwrM0ldUh6LE9nZmZNVDUJStpZ0gzg4dz/HknnNjyy1rkRWF/S3ySdK2kP4D5gZB6/G/AAsB2wA3B3ceKIOBt4BtgzIvaUNBz4KXBgRLwHOLhQfDPgA8D2wNcl9W/capmZWblazgTPJH1RvwgQEfcDuzcyqFaKiHnAe4GxwPPAROBI4FFJ7yYlrP8jbYPdgNuqzHJH4NaIeDzPf3Zh3LUR8Xo+23wOWKt8YkljJU2WNHnenDnLt3JmZraUmq4JRsSTkoqDFjcmnN4htxDfAXRImg4cDdwK7AssBP5EahW+H/CF5VjU64XuxXSyPyJiPDAeYIONNo7lWJaZmZWp5UzwSUk7AyGpf76j8aEGx9UykjaV9K7CoJHAE6QzvhOBOyPieWANYFNS1Wi5ucCqufsuYHdJ78jzX70xkZuZWXfVciZ4HHAWsC7wNOma2WcbGVSLDQZ+LGkosAh4hFQ1Op9UXXlrLjcNeHtEdHZ2Nh64XtIz+brgWOAKSSuQqj3f3+B1MDOzGlRNgvl61RFNiKVXiIgpwM4VRq9cKDe2bLoxhe4fAz8u9F8HXFdWflxZ/5Y9jdnMzHqmYhKU9MWI+J6kHwPLnO1EhG/pb7KB/fsxsw+8Zqujo4NZR4xudRhV9ZU4zaxxujoTLF33m9yMQMzMzJqtYhKMiN9L6gdsFRF+vZeZmb3ldHl3aH5UYJcmxWJmZtZUtdwdOlXSNcDvSHdIAhARVzQsKjMzsyaoJQkOIL0tZq/CsACcBM3MrE+r5RGJTzUjEDMzs2ar5QXa60m6UtJz+e9ySes1IzgzM7NGquW1aRcC15CaB1oH+H0eZmZm1qfVkgSHR8SFEbEo/00Ahjc4LjMzs4arJQm+KOlISf3y35HkZpXMzMz6slruDj2G9B7MM0l3hd4B+GaZFliwcDEjTrm21WFUddJWixhTJc5ZfeD1b2b21lfL3aFPAB9pQixmZmZNVTUJSjq7k8GvAJMj4ur6h9RckhYD0wuDPgr8NiIqtSRR7+XPAkZFxAuS5kXE4GYs18zMan9YfjPSG2MADgQeB94jac+IOLFBsTXLgogYWTZsmQQoacWIWNSckMzMrBlquTFma2DPiPhxbidvb1JS/BiwTyODaxVJ8/L/0ZJuy6+Nm5FvDPq+pHskTZN0bC73E0kfyd1XSvpF7j5G0hm5+ypJUyQ9mBvZNTOzFqvlTHAYqbX1V3L/IGD1iFgs6fWGRdY8AyVNzd2PR8THysZvC2wZEY/n5PVKRGwnaWXgdkk3ArcBu5Gep1wXWDtPuxtwSe4+JiJmSxoI3CPp8oioepdtXuZYgGFrDGdIz9fTzMzK1JIEv0d6iXYHIGB34FuSBgF/amBszdJZdWjRpIh4PHfvA2wt6aDcvxrwLlISPFHS5sAMYJiktYGdgFLjw8dLKiXY9fN0VZNgRIwHxgNssNHGyzRubGZmPVfL3aE/l/RHYPs86NSIeCZ3f6FhkfUe8wvdAj4fETeUF5I0FPggcCuwOnAIMC8i5koaTapG3ikiXs0/KAY0NmwzM6umlneHCngf8J58N+iKkravMtlb1Q3AZyT1B5C0ST4jBrgLOJGUBG8DTs7/IZ0xvpQT4GbAjk2N2szMOlXLjTHnkqr1Ds/9c4GfNCyi3u1npOrOeyU9AFzAkrPp24AVI+IR4F7S2WApCV5P+vHwEPAdUsI0M7MWq+Wa4A4Rsa2k+wAi4iVJKzU4rqbp7Lm80rCI6AA6CsPfAE7Nf+XT/Bz4ee5eSLqBqDTudWDfCssf0VUsRQP792NmH3jTSkdHB7OOGN3qMMzMqqrlTHChpH6kV6YhaTjwRkOjMjMza4JakuDZwJXA2/Izb38Fvt3QqMzMzJqglrtDfyNpCunmGAEfjYiHGh6ZmZlZg9Xy7tBfRcRRwMOdDDMzM+uzaqkO3aLYk68Pvrcx4ZiZmTVPxSQo6cuS5pLekDJH0tzc/xzQ51uPMDMzq5gEI+LbEbEq8P2IGBIRq+a/NSLiy02M0czMrCFquTHmy5KGkd51OaAw/NZGBmZmZtZotdwY82ngBGA9YCrplV93Ans1NDIzM7MGq+XGmBOA7YAnImJPYBvg5UYGZWZm1gy1vDbttYh4TRKSVo6IhyVt2vDIbBkLFi5mxCnX1nWes/rAa9jMzBqlliT4VG4m6CrgJkkvAU80MigzM7NmqFodGhEfi4iXI2Ic8L+kl0R/tMFx9RmSbpH0gbJhJ0q6TtICSVMlzZB0vqQVJI2QFJI+Xyh/jqQxTQ/ezKzN1dKe4I6SVgWIiL+QWlXYpsFx9SUXA4eVDTuM9H7VR3Or9VsDm7Pkx8NzwAlvpdY4zMz6olpujDkPmFfon5eHWXIZ8KFSQpM0AlgHeLJUICIWAXcAG+dBzwN/Bo5uaqRmZraUWpKgIiJKPblNvVquJbaFiJgNTGJJe4GHAZeSm54CkLQK6QXk0wuTfhc4Ob+GriJJYyVNljR53pw5dY3dzKzd1ZIEH5N0vKT++e8E4LFGB9bHFKtED8v9AO+UNBW4Hbg2Iq4rTRARjwF3A5/oasYRMT4iRkXEqMFDhtQ9cDOzdlZLEjwO2Bl4GngK2AEY28ig+qCrgfdJ2hZYJSKm5OGPRsTIiNgm31hU7lvAl0hNVJmZWZPV8tq051j2xg8riIh5km4BfsGSs8BapntY0gxgf+CeRsVnZmadq+VM0GpzMfAeupEEszNIr6QzM7Mm8w0udRIRV1Go1oyIWcCWnZRbanhE3E+NP0YG9u/HTL/hxcysbmp5TvAdtQwzMzPra2o5A7m8k2GX1TsQMzOzZqtYHSppM2ALYDVJHy+MGkKhXUEzM7O+qqtrgpsCHwaGku5eLJkL/GcDYzIzM2uKikkwIq4Grpa0U0Tc2cSYzMzMmqKWu0MfkXQqMKJYPiKOaVRQZmZmzVBLErwauA34E7C4seGYmZk1Ty1JcJWI+FLDIzEzM2uyWh6R+IOk/RoeiZmZWZPVkgRPICXC1yTNkTRXktv0MTOzPq+WF2iv2oxArLoFCxcz4pRrezTtLL9uzcxsGbW8Nk2SjpT0v7l/fUnbNz40MzOzxqqlOvRcYCeWNP46D/hJwyKqA0kflRT5rTc9nccESQfl7p9J2rx+EZqZWW9QSxLcISI+C7wGEBEvASs1NKrldzjw1/x/uUXEpyNiRj3mZWZmvUctSXChpH5AAEgaDrzR0KiWg6TBwK7Af5AbA5Y0WtKtkq6VNFPS+ZJWyOPmSTpT0oOS/pzXr3yeHZJG5e7zJE3O5U8rlJkl6TRJ90qaXjoLlTRY0oV52DRJB+bh+0i6M5f/XY7bzMyaqJYkeDZwJfA2SWeQzrC+1dCols8BwPUR8TfgRUnvzcO3Bz4PbA68Eyi9FHwQMDkitgD+Any9yvy/EhGjgK2BPSRtXRj3QkRsC5wHnJyH/S/wSkRsFRFbAzdLWhP4KrB3Lj8Z+J/OFiZpbE66k+fN8U25Zmb1VDUJRsRvgC8C3waeBT4aEb9rdGDL4XDgktx9CUuqRCdFxGMRsZjU+vuuefgbwMTc/evC8EoOkXQvcB+plY3itcIr8v8ppNfMAexN4Rpqrk7eMU93u6SpwNHAhp0tLCLGR8SoiBg1eMiQKqGZmVl3dNWU0uqF3udIiePNcRExu5GB9USOeS9gK0kBlKpxr83/i8r7qw0vNSZ8MrBdRLwkaQJLNyv1ev6/mK4fPxFwU0TU5ZqlmZn1TFdnglNI1XRTgOeBvwF/z91TGh9ajxwE/CoiNoyIERGxPvA4sBuwvaR35GuBh5KqdSFtg4Ny9ycKwzszBJgPvCJpLWDfGmK6CfhsqUfSMOAuYBdJG+dhgyRtUutKmplZfVRMghHxjojYiPTi7P0jYs2IWIPUxuCNzQqwmw4nXb8sujwPvwc4B3iIlBhL5eaTEuQDpLPI0yvNPCLuJ1WDPgz8Fri9hpi+CQyT9ICk+4E9I+J5YAxwsaRpwJ1Ajx/nMDOznqnlBdo7RsSbjehGxHWSvtfAmHosIvbsZNjZOdGcHBEfrjDdMjelRMSYQvfozoaXlR9R6J4MjM7d80jX/MrL3wxsV2FVzMysCWpJgs9I+irpphGAI4BnGheSVTKwfz9m+vVnZmZ1U8sjEocDw0nVh1cCb6NOD6E3S0R0dHEW6OfzzMzaVC0v0J5NaknCzMzsLaWrRyR+FBEnSvo9nTw2EBEfaWhkZmZmDdbVmeCv8v8fNCMQMzOzZquYBCOi9CzgZGBBRLwBkN8junITYjMzM2uoWm6M+TOwSqF/IOnZQTMzsz6tliQ4ID/rBrz53NsqXZQ3MzPrE2pJgvMlbVvqya0yLGhcSGZmZs1Ry8PyJwK/k/QM6cXPbye9e9PMzKxPq+U5wXtyA7Gb5kEzI2JhY8OyzixYuJgRp1zbrWlm+Q0zZmYV1XImCOkdlyNy+W0lERG/bFhUZmZmTVD1mqCkX5GeFdyVlAy3A0Y1OK6qJC2WNFXS/ZLulbRzlfIjcksRtc5/jKRzuhnTrNxqPJLu6M60ZmbWfLWcCY4CNo+Iio3NtsiCiBgJIOkDpJbv92hpRAUR0WVSNjOz1qvl7tAHSDfD9GZDgJdKPZK+IOkeSdMknVZeWNJGku6TtJ2k7SXdmfvvkLRpoej6kjok/V3S1wvTHylpUj4TvSC/QKB8GfPy/9F5HpdJeljSbyQpj9svD5si6WxJf6jnRjEzs67Vcia4JjBD0iTg9dLAXvDu0IGSpgIDgLVJDeIiaR/gXcD2pLtZr5G0O/CPPH5T4BJgTETcL2kIsFtELJK0N/At4MC8jO2BLYFXgXskXUtqhPdQYJeIWCjpXFLzUl1dI90G2ILUBNXtpFblJwMXALtHxOOSLq7HRjEzs9rVkgTHNTqIHipWh+4E/FLSlsA++e++XG4wKSn+g9Qk1NXAxyNiRh6/GnCRpHeRXhTev7CMmyLixbyMK0jXRRcB7yUlRUhv0HmuSqyTIuKpPJ+ppJuM5gGPRcTjuczFwNjyCSWNLQ0ftsZwhlRZkJmZ1a6WRyT+0oxAlkdE3JlvSBlOOvv7dkRcUCwjaQTwCikZ7gqUkuA3gFsi4mO5TEdx1uWLyvO/KCK+3I0QXy90L6b2u3KJiPHAeIANNtq4t12XNTPr0ypeE5Q0V9KcTv7mSprTzCCryc8x9gNeBG4AjpE0OI9bV9LbctF/Ax8DPinpE3nYasDTuXtM2azfL2l1SQOBj5KqMv8MHFSaZx6/YQ/CnglslBMv+AUEZmZN11UrEqs2M5AeKF0ThHR2dnRELAZulPRu4M5cXTkPOJJ0BkZEzJf0YeCmfPPK90jVoV8Fyp9EnwRcDqwH/DoiJgPksjdKWgFYCHwWeKI7wUfEAkn/BVwvaT5wT7fW3szMllvN1XK9TUQsc0dmYdxZwFmdjNoyj3+Z9LxjySaF7q/mMhOACRXmPxGY2MnwEYXuwfl/B4Uq1oj4XGGSWyJis3y36E9IzVaZmVmT9Nkk+Bbxn5KOBlYi3chzQVeFB/bvx0y/Bs3MrG6cBFsoIs4Ezmx1HGZm7aqWh+XNzMzekpwEzcysbTkJmplZ23ISNDOztuUkaGZmbctJ0MzM2paToJmZtS0nQTMza1t+WL4PWbBwMSNOKX+9aedm+c0yZmZV+UzQzMzalpOgmZm1LSfBOpE0QtIDZcPGSTpZ0gRJj0uaKuleSTvl8TtKujsPf0jSuJYEb2bWpnxNsHm+EBGXSdqH1FrE1sBFwCERcb+kfsCmLY3QzKzNOAk2363Axrn7bcCzALlB4BmtCsrMrB25OrT59gem5+4zgZmSrpR0rKQB5YUljZU0WdLkeXPmNDVQM7O3OifB+okqw78vaSowFvgPgIg4HRgF3Ah8Arh+mYkjxkfEqIgYNXjIkLoHbWbWzlwdWj8vAsPKhq0OPJ67vxARl5VPFBGPAudJ+inwvKQ1IuLFxoZqZmbgM8G6iYh5wLOS9gKQtDrwQeCvlaaR9CFJyr3vAhYDLzc4VDMzy3wmWF+fBH4i6f9y/2kR8eiSPLeMo4AzJb0KLAKOyDfImJlZEzgJ1lFEzAD27GT4mArlD+vO/Af278dMvw7NzKxuXB1qZmZty0nQzMzalpOgmZm1LSdBMzNrW06CZmbWtpwEzcysbTkJmplZ23ISNDOztuUkaGZmbctJ0MzM2pZfm9aHLFi4mBGnXFu13Cy/Ws3MrCY+EzQzs7bVsCQoabGkqZIekPR7SUN7OJ+RkvarU0znS9qlbNg4SSFp48KwE/OwUd2Y9xhJ59QjTjMza45GngkuiIiREbElMBv4bA/nMxLoVhKUVKmad0fgrk6GTweKLTocDDzYnWWamVnf06zq0DuBdeHNM7u7JE2TdKWkYXl4R+nMS9KakmZJWgk4HTg0n1UeKmmQpF9ImiTpPkkH5GnGSLpG0s3An8sDkPRu4G8V2uu7CijN553AK8ALhWkPlzQ9n9V+tzD8U5L+JmkSsEth+HBJl0u6J//tkoePk3SRpNskPSHp45K+l+d9vaT+y7GNzcysmxqeBCX1A94HXJMH/RL4UkRsTToD+3qlaSPi38DXgIn5rHIi8BXg5ojYntR23/clDcqTbAscFBF7dDK7fYHrKyxqDvCkpC1JZ4QTC/GvA3wX2It0VrqdpI9KWhs4jZT8dgU2L8zvLODMiNgOOBD4WWHcO/O8PgL8GrglIrYCFgDL3NEiaaykyZImz5szp0L4ZmbWE428O3SgpKmkM8CHgJskrQYMjYi/5DIXAb/r5nz3AT4i6eTcPwDYIHffFBGzK0z3AeBTXcz3ElIC/AApaZfKbgd0RMTzAJJ+A+yexxWHTwQ2ycP3BjYvtCg/RNLg3H1dRCyUNB3ox5LEPB0YUR5URIwHxgNssNHG0UX8ZmbWTY1MggsiYqSkVYAbSNcEL+qi/CKWnJkO6KKcgAMjYuZSA6UdgPmdTpBiGBoRz3Qx3z8A3wcmR8ScQgLriRWAHSPitbI4AF4HiIg3JC2MiFJiewM/smJm1lQNrw6NiFeB44GTSEnqJUm75dFHAaWzwlnAe3P3QYVZzAVWLfTfAHxeOaNI2qaGMPYEbqkhzi8BZ5SNmgTska9T9gMOzzHfnYevka/lHVyY5kbg86UeSSNriNHMzJqsKTfGRMR9wDRSAjmadB1vGuka2+m52A+Az0i6D1izMPktpKrFqZIOBb4B9AemSXow91fT1fXAYpyXRMS9ZcOeBU7JcdwPTImIq/PwcaSbfm4nVfmWHA+Myjf/zACOqyFGMzNrsoZVv0XE4LL+/Qu9O3ZS/mFg68Kgr+bhs0nX5YqO7WT6CcCECuHsDPx3hTjHVRg+utB9MXBxJ2UuBC7sZPgLwKHVllXcRpXiKBrYvx8z/TYYM7O6aYtrUBGxbatjMDOz3sevTTMzs7blJGhmZm3LSdDMzNqWk6CZmbUtJ0EzM2tbToJmZta2nATNzKxtOQmamVnbchI0M7O25STYhyxY2Fl7wGZm1lNOgmZm1racBGsg6e2SLpH0qKQpkv4oaRNJD9Rp/mMknVOPeZmZWe3a4gXayyO3W3glcFFEHJaHvQdYq6WBmZnZcvOZYHV7Agsj4vzSgIi4H3iy1C9pgKQLJU2XdJ+kPfPwpc7wJP1B0ujc/SlJf5M0CdilSetiZmYFPhOsbktgSpUynwUiIraStBlwo6RNKhWWtDZwGvBe4BVSg733VSg7FhgLMGyN4d2P3szMKvKZYH3sCvwa3mwc+AmgYhIEdgA6IuL5iPg3MLFSwYgYHxGjImLU4CFD6hmzmVnbcxKs7kHSGVtPLGLpbTxg+cMxM7N6cRKs7mZg5VwtCYCkrYH1C2VuA47I4zYBNgBmArOAkZJWkLQ+sH0ufzewh6Q1JPUHDm74WpiZ2TKcBKuIiAA+BuydH5F4EPg28M9CsXOBFSRNJ1VtjomI14HbgceBGcDZwL15ns8C44A7c5mHmrM2ZmZW5BtjahARzwCHdDJqyzz+NeBTnUwX5DPETsZdCFzYnTgG9u/XneJmZlaFzwTNzKxtOQmamVnbchI0M7O25SRoZmZty0nQzMzaltINjNYXSJpLev6wt1sTeKHVQdTAcdaX46yfvhAj1C/ODSOiJe+F9CMSfcvMiBjV6iCqkTTZcdaP46yvvhBnX4gR+k6cXXF1qJmZtS0nQTMza1tOgn3L+FYHUCPHWV+Os776Qpx9IUboO3FW5BtjzMysbflM0MzM2paToJmZtS0nwT5C0gclzZT0iKRTWh1PJZJmSZouaaqkya2Op0TSLyQ9J+mBwrDVJd0k6e/5/7BWxphj6izOcZKeztt0qqT9Whzj+pJukTRD0oOSTsjDe9X27CLO3rY9B0iaJOn+HOdpefg7JN2dP/MTJa3US+OcIOnxwvYc2co4u8vXBPsASf2AvwHvB54C7gEOj4gZLQ2sE5JmAaMiolc96Ctpd2Ae8MuI2DIP+x4wOyK+k39YDIuIL/XCOMcB8yLiB62MrUTS2sDaEXGvpFWBKcBHgTH0ou3ZRZyH0Lu2p4BBETEvN7L9V+AE4H+AKyLiEknnA/dHxHm9MM7jgD9ExGWtim15+Eywb9geeCQiHouIfwOXAAe0OKY+JSJuBWaXDT4AuCh3X0T6gmypCnH2KhHxbESUGoieS2oUel162fbsIs5eJZJ5ubd//gtgL6CUWHrD9qwUZ5/mJNg3rAs8Weh/il74Yc4CuFHSFEljWx1MFWtFxLO5+5/AWq0MporPSZqWq0tbXm1bImkEsA1wN714e5bFCb1se0rqJ2kq8BxwE/Ao8HJELMpFesVnvjzOiChtzzPy9jxT0sqti7D7nASt3naNiG2BfYHP5uq9Xi/SdYHe+qv2POCdwEjgWeCHLY0mkzQYuBw4MSLmFMf1pu3ZSZy9bntGxOKIGAmsR6r52ay1EXWuPE5JWwJfJsW7HbA60NJLCt3lJNg3PA2sX+hfLw/rdSLi6fz/OeBK0ge6t/pXvm5Uun70XIvj6VRE/Ct/+bwB/JResE3zNaHLgd9ExBV5cK/bnp3F2Ru3Z0lEvAzcAuwEDJVUer9zr/rMF+L8YK52joh4HbiQXrQ9a+Ek2DfcA7wr3y22EnAYcE2LY1qGpEH5BgQkDQL2AR7oeqqWugY4OncfDVzdwlgqKiWW7GO0eJvmGyR+DjwUEf9XGNWrtmelOHvh9hwuaWjuHki6Ae4hUpI5KBfrDduzszgfLvzwEem6ZW/+zC/Dd4f2Efk27h8B/YBfRMQZrY1oWZI2Ip39QWqh5Le9JU5JFwOjSU2//Av4OnAVcCmwAfAEcEhEtPSmlApxjiZV3QUwCzi2cO2t6STtCtwGTAfeyINPJV1v6zXbs4s4D6d3bc+tSTe+9COdmFwaEafnz9MlpCrG+4Aj89lWb4vzZmA4IGAqcFzhBppez0nQzMzalqtDzcysbTkJmplZ23ISNDOztuUkaGZmbctJ0MzM2paToJmZtS0nQTMza1v/D0ddj+kv0mNWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def normalize(s: pd.Series) -> pd.Series:\n",
    "    return (s / s.sum()) * 100\n",
    "\n",
    "plot = (\n",
    "    df[['subcategory.name', 'timeSpent']]\n",
    "    .groupby('subcategory.name')\n",
    "    .sum()\n",
    "    .apply(normalize)\n",
    "    .sort_values(by='timeSpent', ascending=True)\n",
    "    .plot(kind='barh', title='Tijd besteed aan incidenten per categorie (genormaliseerd)', xlabel='Incident categorie', legend=False)\n",
    ")\n",
    "\n",
    "plot.grid(axis='x')\n",
    "fig = plot.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'{FIGURES_DIRECTORY}/incidenten_tijd_categorie.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gemiddelde tijd besteed per categorie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    df[['subcategory.name', 'timeSpent']]\n",
    "    .groupby('subcategory.name')\n",
    "    .mean()\n",
    "    .transform(normalize)\n",
    "    .sort_values(by='timeSpent', ascending=True)\n",
    "    .plot(kind='barh', legend=False, xlabel='')\n",
    ")\n",
    "\n",
    "plot.grid(axis='x')\n",
    "fig = plot.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'{FIGURES_DIRECTORY}/incidenten_tijd_gemiddeld_categorie.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aantal incidenten per categorie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (\n",
    "    df['subcategory.name']\n",
    "    .value_counts(normalize=True)\n",
    "    .mul(100)\n",
    "    .sort_values()\n",
    "    .plot(kind='barh')\n",
    ")\n",
    "\n",
    "plot.grid(axis='x')\n",
    "fig = plot.get_figure()\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'{FIGURES_DIRECTORY}/incidenten_aantal_categorie.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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