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onderzoeksrapport/data-analyse/topdesk_incidenten.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"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": 15,
"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": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"data": {
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",
"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.min()) / (s.max() - s.min())\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')"
]
}
],
"metadata": {
"interpreter": {
"hash": "575fec65d45321c352903a66d850f258e7db7eb07d7e800b3ab1ae68e5593d3c"
},
"kernelspec": {
"display_name": "Python 3.10.0 ('venv': venv)",
"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": "3.10.0"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}