2024-05-08 17:45:29 +02:00
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{
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"cells": [
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{
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"cell_type": "code",
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2024-05-29 09:25:12 +02:00
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"execution_count": 1,
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2024-05-08 17:45:29 +02:00
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt\n",
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"import pickle\n",
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"import wfdb"
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]
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},
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{
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"cell_type": "code",
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2024-05-29 09:25:12 +02:00
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"execution_count": 2,
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2024-05-08 17:45:29 +02:00
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reading SB\n",
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2024-05-29 09:25:12 +02:00
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"Length of SB: 50\n",
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2024-05-08 17:45:29 +02:00
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"Reading AFIB\n",
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2024-05-29 09:25:12 +02:00
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"Length of AFIB: 27\n",
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2024-05-08 17:45:29 +02:00
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"Reading GSVT\n",
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2024-05-29 09:25:12 +02:00
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"Length of GSVT: 0\n",
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2024-05-08 17:45:29 +02:00
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"Reading SR\n",
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2024-05-29 09:25:12 +02:00
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"Length of SR: 13\n"
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2024-05-08 17:45:29 +02:00
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]
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}
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],
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"source": [
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"\n",
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2024-05-29 09:25:12 +02:00
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"#path = \"C:/Studium/dsa/data\"\n",
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2024-05-08 17:45:29 +02:00
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"#path = \"C:/Users/Nils/Documents/HS-Mannheim/0000_MASTER/DSA/EKG_Prog/data\"\n",
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2024-05-29 09:25:12 +02:00
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"path = \"C:/Users/klara/projects/DSA/data\"\n",
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2024-05-08 17:45:29 +02:00
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"\n",
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"categories_dict = {\n",
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"'SB': [426177001],\n",
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"'AFIB': [164889003, 164890007],\n",
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"'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],\n",
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"'SR': [426783006, 427393009]\n",
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"}\n",
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"\n",
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"\n",
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"data = {}\n",
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"for cat_name in categories_dict.keys():\n",
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" print(f\"Reading {cat_name}\")\n",
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" with open(f'{path}/{cat_name}.pkl', 'rb') as f:\n",
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" records = pickle.load(f)\n",
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" data[cat_name] = records\n",
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" print(f\"Length of {cat_name}: {len(records)}\")\n",
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"\n",
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"data_demographic = {'age':[], 'diag':[], 'gender':[]}\n",
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"for cat_name, records in data.items():\n",
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" for record in records:\n",
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" age = record.comments[0].split(' ')[1]\n",
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" sex = record.comments[1].split(' ')[1]\n",
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" if age == 'NaN' or sex == 'NaN':\n",
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" continue\n",
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" # cut Age: from alter string \n",
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" data_demographic['age'].append(int(age))\n",
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" data_demographic['diag'].append(cat_name)\n",
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" data_demographic['gender'].append(sex)\n",
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"\n",
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"df_dgc = pd.DataFrame(data_demographic)\n",
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"\n",
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"# Change from group to category\n",
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"age_categories = [0, 20, 40, 60, 80, 100]\n",
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"df_dgc['age_group'] = pd.cut(df_dgc['age'], bins=age_categories)"
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]
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},
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{
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"cell_type": "code",
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2024-05-29 09:25:12 +02:00
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"execution_count": 3,
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2024-05-08 17:45:29 +02:00
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"metadata": {},
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"outputs": [
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{
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"data": {
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2024-05-29 09:25:12 +02:00
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"image/png": "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2024-05-08 17:45:29 +02:00
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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Correlation matrix\n",
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"corr_matrix_age_diag= pd.crosstab(df_dgc['age_group'], df_dgc['diag'])\n",
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"\n",
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"# Plot the correlation matrix\n",
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"sns.heatmap(corr_matrix_age_diag, annot=True, cmap='coolwarm', fmt='d')\n",
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"plt.title('Korrelationsmatrix von Altersgruppen und Diagnosen')\n",
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"plt.xlabel('Diagnose')\n",
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"plt.ylabel('Altersgruppe')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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|
|
"<Figure size 640x480 with 2 Axes>"
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|
]
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|
},
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"metadata": {},
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|
"output_type": "display_data"
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}
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],
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"source": [
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|
|
|
"# cut out sex 'unknown' (because only one occurence)\n",
|
|
|
|
|
"df_dgc_bineary = df_dgc[df_dgc['gender'] != 'Unknown']\n",
|
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|
"# Correlation matrix\n",
|
|
|
|
|
"corr_matrix_sex_diag = pd.crosstab(df_dgc_bineary['gender'], df_dgc_bineary['diag'])\n",
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|
"\n",
|
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|
|
|
"# Plot the correlation matrix\n",
|
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|
"sns.heatmap(corr_matrix_sex_diag, annot=True, cmap='coolwarm', fmt='d')\n",
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|
"plt.title('Korrelationsmatrix von Geschlecht und Diagnosen')\n",
|
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|
"plt.xlabel('Diagnose')\n",
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|
"plt.ylabel('Geschlecht')\n",
|
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|
"plt.show()"
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]
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},
|
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{
|
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|
"cell_type": "code",
|
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|
|
|
"execution_count": 6,
|
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|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
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|
"image/png": "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
|
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|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 640x480 with 4 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# 4 subplots for each diagnosis a histrgramm for the age\n",
|
|
|
|
|
"fig, axs = plt.subplots(2, 2)\n",
|
|
|
|
|
"fig.suptitle('Histogramm der Altersverteilung')\n",
|
|
|
|
|
"for i, cat_name in enumerate(categories_dict.keys()):\n",
|
|
|
|
|
" ax = axs[i // 2, i % 2]\n",
|
|
|
|
|
" df_dgc[df_dgc['diag'] == cat_name]['age'].hist(ax=ax)\n",
|
|
|
|
|
" ax.set_title(cat_name)\n",
|
|
|
|
|
" ax.set_xlabel('Alter')\n",
|
|
|
|
|
" ax.set_ylabel('Anzahl')\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# a barplot for each category with the age grpuoped besides each other\n",
|
|
|
|
|
"fig, ax = plt.subplots()\n",
|
|
|
|
|
"sns.countplot(data=df_dgc_bineary, x='diag', hue='gender', ax=ax)\n",
|
|
|
|
|
"plt.title('Anzahl der Diagnosen nach Geschlecht')\n",
|
|
|
|
|
"plt.xlabel('Diagnose')\n",
|
|
|
|
|
"plt.ylabel('Anzahl')\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 8,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# barplot how many diagnosis are in each age group\n",
|
|
|
|
|
"fig, ax = plt.subplots()\n",
|
|
|
|
|
"sns.countplot(data=df_dgc, x='age_group', hue='diag', ax=ax)\n",
|
|
|
|
|
"plt.title('Anzahl der Diagnosen nach Altersgruppen')\n",
|
|
|
|
|
"plt.xlabel('Altersgruppe')\n",
|
|
|
|
|
"plt.ylabel('Anzahl')\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"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",
|
2024-05-29 09:25:12 +02:00
|
|
|
"version": "3.11.9"
|
2024-05-08 17:45:29 +02:00
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 2
|
|
|
|
|
}
|
