223 lines
4.8 KiB
Plaintext
223 lines
4.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**nicht als Notebook verteilen**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Feature Engineering\n",
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"\n",
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"## Feature Engineering - Einführung\n",
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"\n",
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"### Abgrenzungen\n",
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"\n",
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"### CRISP - und die Gliederung des Kurses\n",
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"\n",
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"### Strukturierte Daten\n",
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"\n",
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"#### Aufbau und Organisation eines Data Frame"
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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": 1,
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"metadata": {
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"scrolled": true
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},
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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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" age job marital duration campaign y\n",
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"0 30.0 self-employed single 245.0 3 yes\n",
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"1 32.0 technician married 370.0 1 no\n",
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"2 27.0 blue collar single 623.0 1 yes\n",
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"3 NaN blue collar single 9.0 6 no\n",
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"4 27.0 admin. single 126.0 2 yes\n",
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"5 34.0 admin. single 548.0 2 yes\n",
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"6 46.0 None married 86.0 2 no\n",
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"7 NaN retired married 707.0 3 yes\n",
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"8 46.0 admin. married 96.0 6 no\n",
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"9 48.0 blue collar married 241.0 2 no\n",
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"10 29.0 technician married 154.0 3 no\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"data = pd.read_csv('../3_data/bank_data.csv', sep=';')\n",
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"#print(data.iloc[0:11, 0:6])\n",
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"\n",
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"## arbitrarily input\n",
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"data.iloc[3, 0] = None\n",
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"data.iloc[6, 1] = None\n",
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"#print(data.iloc[0:11, 0:6])\n",
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"print(data.iloc[0:11, [0,1,2,10,11,20]])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Begriffe"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Beispieldaten"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"import pandas as pd\n",
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"demo_data_class = pd.read_csv('../3_data/demo_data_class.csv')\n",
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"demo_data_regr = pd.read_csv('../3_data/demo_data_regr.csv') "
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]
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},
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{
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"cell_type": "raw",
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"metadata": {},
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"source": [
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"import seaborn as sns\n",
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"iris_data = sns.load_dataset('iris')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"jp-MarkdownHeadingCollapsed": true
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},
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"source": [
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"### Anforderungen an die Daten für Machine Learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Eine typische ML Sequenz"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Python Libraries"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Feature Engineering"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Die Python Libraries und CRISP-DM"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Begleitende Literatur"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.0"
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},
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"toc": {
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"base_numbering": "1.1",
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "1.1 Feature Engineering - Einführung",
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"title_sidebar": "Contents",
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"toc_cell": true,
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"toc_position": {
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"height": "calc(100% - 180px)",
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"left": "10px",
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"top": "150px",
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"width": "251px"
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},
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"toc_section_display": true,
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"toc_window_display": true
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},
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"toc-autonumbering": true,
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"toc-showcode": false,
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"toc-showmarkdowntxt": false,
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"toc-showtags": false,
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"position": {
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"height": "326.85px",
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"left": "910px",
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"right": "20px",
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"top": "120px",
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"width": "350px"
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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