mirror of
https://github.com/fhswf/aki_prj23_transparenzregister.git
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Merge pull request #6 from fhswf/feature/docs-on-timeseries
Feature/docs on timeseries
This commit is contained in:
Tristan Nolde
GitHub
commit
bd9f2369d2
3
.gitignore
vendored
3
.gitignore
vendored
@ -1,3 +1,6 @@
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# Temporary hosting of Deutschland/Bundesanzeiger library
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**/deutschland
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# Snyk
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# Snyk
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.dccache
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.dccache
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@ -33,7 +33,7 @@ repos:
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- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
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- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
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rev: v2.4.0
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rev: v2.8.0
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hooks:
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hooks:
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- id: pretty-format-ini
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- id: pretty-format-ini
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args: [--autofix]
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args: [--autofix]
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771
Jupyter/API-tests/Bundesanzeiger/notebook.ipynb
Normal file
771
Jupyter/API-tests/Bundesanzeiger/notebook.ipynb
Normal file
@ -0,0 +1,771 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Daten Extraktion aus dem Bundesanzeiger"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"In order to run this notebooks, download the `deutschland` library source code from: [TrisNol/deutschland](https://github.com/TrisNol/deutschland/tree/feat/bundesanzeiger-raw-report) and place it in the `Jupyter/API-tests/Bundesanzeiger/deutschland` directory. Since the PR adding the required features to the main repo has not been completet as of of yet (see: [PR](https://github.com/bundesAPI/deutschland/pull/88)) we have to include it in another way..."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Vorbereitung"
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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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\trist\\AppData\\Roaming\\Python\\Python310\\site-packages\\requests\\__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (5.1.0)/charset_normalizer (2.0.12) doesn't match a supported version!\n",
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" warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\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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"from deutschland.bundesanzeiger import Bundesanzeiger"
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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": {},
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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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"dict_keys(['040860c00ef9020cfb6db2f58a163256', '9eb401c5af2f0289b8207233bf852b81', 'b14d979ea9f42367d413589050bd04e5', 'ecb8c1011456ea0d40f87e850fc216bf', '3a7c6c1f1d1b89bf5ceb165f0ee88053', '03b2e6aac2f2da2c0c5e8f23de9caec4', 'a5f8dc87fa797e7d2f8fb88c49a23c36', '9a3a8a3e84290ee650cbccf32323b3d7', '6c0fcc20a58aaa18a9d13f35a51e3996', 'bf276d441c339e787e22385d2b69b277', '90a79d28f3c11a2122d2827d2bf6acda', '88c785ce3b3c580dcc285661c7790cca', 'd3064baa8246c3ed02e30b5038200edc', '5bf92eed2808b484c005409764b825b7', 'fece6303c991a280850be1900ff78f8f', '26b0624c60cdbf647f3d45f4917ec6ea', '9f98bee55f598908cca60b6a47e5d49d', '99267bb7474e6d1d5d9e091ba5ef3ee8', '102738ef4b91408ed043d84fe785b50b', '94711f3e509518d073e1760d97550347'])\n"
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]
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}
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],
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"source": [
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"ba = Bundesanzeiger()\n",
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"reports = ba.get_reports(\"Atos IT-Dienstleistung und Beratung GmbH\")\n",
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"print(reports.keys())"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"report_contents = []\n",
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"for key in reports.keys():\n",
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" report_contents.append(reports[key])"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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||||||
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"</style>\n",
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||||||
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"<table border=\"1\" class=\"dataframe\">\n",
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||||||
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" <thead>\n",
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||||||
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" <tr style=\"text-align: right;\">\n",
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||||||
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" <th></th>\n",
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||||||
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" <th>date</th>\n",
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" <th>name</th>\n",
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" <th>company</th>\n",
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" <th>report</th>\n",
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" <th>raw_report</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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||||||
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" <tr>\n",
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" <th>0</th>\n",
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||||||
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" <td>2023-03-17</td>\n",
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||||||
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" <td>Aufsichtsrat</td>\n",
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
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||||||
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstl...</td>\n",
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||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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" </tr>\n",
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||||||
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" <tr>\n",
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||||||
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" <th>1</th>\n",
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" <td>2022-03-25</td>\n",
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||||||
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
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||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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" </tr>\n",
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" <tr>\n",
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||||||
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" <th>2</th>\n",
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" <td>2021-03-11</td>\n",
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
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||||||
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
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||||||
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
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||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" </tr>\n",
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||||||
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2020-03-24</td>\n",
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
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||||||
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtosIT-Dienstleistung...</td>\n",
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||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2018-12-11</td>\n",
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
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||||||
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" </tr>\n",
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||||||
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" </tbody>\n",
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||||||
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"</table>\n",
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||||||
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"</div>"
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||||||
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],
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||||||
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"text/plain": [
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||||||
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" date name \\\n",
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||||||
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"0 2023-03-17 Aufsichtsrat \n",
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"1 2022-03-25 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
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"2 2021-03-11 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
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||||||
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"3 2020-03-24 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
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"4 2018-12-11 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
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"\n",
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" company \\\n",
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"0 Atos IT-Dienstleistung und Beratung GmbH \n",
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"1 Atos IT-Dienstleistung und Beratung GmbH \n",
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"2 Atos IT-Dienstleistung und Beratung GmbH \n",
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"3 Atos IT-Dienstleistung und Beratung GmbH \n",
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"4 Atos IT-Dienstleistung und Beratung GmbH \n",
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"\n",
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" report \\\n",
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"0 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstl... \n",
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"1 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
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"2 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
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"3 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtosIT-Dienstleistung... \n",
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"4 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
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"\n",
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" raw_report \n",
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"0 <div class=\"publication_container\">\\n <div cla... \n",
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"1 <div class=\"publication_container\">\\n <div cla... \n",
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"2 <div class=\"publication_container\">\\n <div cla... \n",
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"3 <div class=\"publication_container\">\\n <div cla... \n",
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"4 <div class=\"publication_container\">\\n <div cla... "
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]
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},
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"execution_count": 8,
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"metadata": {},
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||||||
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_reports = pd.DataFrame(report_contents)\n",
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"df_reports.head()"
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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": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
|
||||||
|
"<div>\n",
|
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|
"<style scoped>\n",
|
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|
" .dataframe tbody tr th:only-of-type {\n",
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||||||
|
" vertical-align: middle;\n",
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||||||
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" }\n",
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"\n",
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||||||
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" .dataframe tbody tr th {\n",
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||||||
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" vertical-align: top;\n",
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||||||
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" }\n",
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"\n",
|
||||||
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" .dataframe thead th {\n",
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||||||
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" text-align: right;\n",
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||||||
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" }\n",
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||||||
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"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
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||||||
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" <th>date</th>\n",
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" <th>name</th>\n",
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" <th>company</th>\n",
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" <th>report</th>\n",
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" <th>raw_report</th>\n",
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||||||
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" <th>type</th>\n",
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||||||
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" </tr>\n",
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||||||
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" </thead>\n",
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||||||
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" <tbody>\n",
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||||||
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" <tr>\n",
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||||||
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" <th>0</th>\n",
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||||||
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" <td>2023-03-17</td>\n",
|
||||||
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" <td>Aufsichtsrat</td>\n",
|
||||||
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" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstl...</td>\n",
|
||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" <td>Aufsichtsrat</td>\n",
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||||||
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" </tr>\n",
|
||||||
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" <tr>\n",
|
||||||
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" <th>1</th>\n",
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||||||
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" <td>2022-03-25</td>\n",
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||||||
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
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" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
|
||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" <td>Jahresabschluss</td>\n",
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||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
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" <th>2</th>\n",
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||||||
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" <td>2021-03-11</td>\n",
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||||||
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" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
|
||||||
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" <td>Jahresabschluss</td>\n",
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||||||
|
" </tr>\n",
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||||||
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" <tr>\n",
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||||||
|
" <th>3</th>\n",
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||||||
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" <td>2020-03-24</td>\n",
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||||||
|
" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtosIT-Dienstleistung...</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
|
" <td>Jahresabschluss</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>2018-12-11</td>\n",
|
||||||
|
" <td>Jahresabschluss zum Geschäftsjahr vom 01.01.20...</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td>\\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun...</td>\n",
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" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
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||||||
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" <td>Jahresabschluss</td>\n",
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||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" date name \\\n",
|
||||||
|
"0 2023-03-17 Aufsichtsrat \n",
|
||||||
|
"1 2022-03-25 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
|
||||||
|
"2 2021-03-11 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
|
||||||
|
"3 2020-03-24 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
|
||||||
|
"4 2018-12-11 Jahresabschluss zum Geschäftsjahr vom 01.01.20... \n",
|
||||||
|
"\n",
|
||||||
|
" company \\\n",
|
||||||
|
"0 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"1 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"2 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"3 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"4 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"\n",
|
||||||
|
" report \\\n",
|
||||||
|
"0 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstl... \n",
|
||||||
|
"1 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
|
||||||
|
"2 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
|
||||||
|
"3 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtosIT-Dienstleistung... \n",
|
||||||
|
"4 \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\nAtos IT-Dienstleistun... \n",
|
||||||
|
"\n",
|
||||||
|
" raw_report type \n",
|
||||||
|
"0 <div class=\"publication_container\">\\n <div cla... Aufsichtsrat \n",
|
||||||
|
"1 <div class=\"publication_container\">\\n <div cla... Jahresabschluss \n",
|
||||||
|
"2 <div class=\"publication_container\">\\n <div cla... Jahresabschluss \n",
|
||||||
|
"3 <div class=\"publication_container\">\\n <div cla... Jahresabschluss \n",
|
||||||
|
"4 <div class=\"publication_container\">\\n <div cla... Jahresabschluss "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"df_reports[\"type\"] = df_reports.name.apply(lambda name: name.split(\" \")[0])\n",
|
||||||
|
"df_reports.head()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stderr",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"C:\\Users\\trist\\AppData\\Local\\Temp\\ipykernel_6460\\963182859.py:2: SettingWithCopyWarning: \n",
|
||||||
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||||||
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||||||
|
"\n",
|
||||||
|
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||||||
|
" df_jahresabschluss['jahr'] = df_jahresabschluss.name.apply(\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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>date</th>\n",
|
||||||
|
" <th>company</th>\n",
|
||||||
|
" <th>raw_report</th>\n",
|
||||||
|
" <th>jahr</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>2022-03-25</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
|
||||||
|
" <td>2020</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>2021-03-11</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
|
||||||
|
" <td>2019</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>2020-03-24</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
|
||||||
|
" <td>2018</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>2018-12-11</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
|
||||||
|
" <td>2017</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>6</th>\n",
|
||||||
|
" <td>2018-01-03</td>\n",
|
||||||
|
" <td>Atos IT-Dienstleistung und Beratung GmbH</td>\n",
|
||||||
|
" <td><div class=\"publication_container\">\\n <div cla...</td>\n",
|
||||||
|
" <td>2016</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" date company \\\n",
|
||||||
|
"1 2022-03-25 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"2 2021-03-11 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"3 2020-03-24 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"4 2018-12-11 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"6 2018-01-03 Atos IT-Dienstleistung und Beratung GmbH \n",
|
||||||
|
"\n",
|
||||||
|
" raw_report jahr \n",
|
||||||
|
"1 <div class=\"publication_container\">\\n <div cla... 2020 \n",
|
||||||
|
"2 <div class=\"publication_container\">\\n <div cla... 2019 \n",
|
||||||
|
"3 <div class=\"publication_container\">\\n <div cla... 2018 \n",
|
||||||
|
"4 <div class=\"publication_container\">\\n <div cla... 2017 \n",
|
||||||
|
"6 <div class=\"publication_container\">\\n <div cla... 2016 "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"df_jahresabschluss = df_reports.loc[df_reports.type == \"Jahresabschluss\"]\n",
|
||||||
|
"df_jahresabschluss[\"jahr\"] = df_jahresabschluss.name.apply(\n",
|
||||||
|
" lambda name: name.split(\" \")[-1].split(\".\")[-1]\n",
|
||||||
|
")\n",
|
||||||
|
"df_jahresabschluss = df_jahresabschluss.drop([\"name\", \"report\", \"type\"], axis=1)\n",
|
||||||
|
"df_jahresabschluss.head()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"attachments": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Daten Extraktion"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 11,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from bs4 import BeautifulSoup\n",
|
||||||
|
"from io import StringIO"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"sample_report = df_jahresabschluss.iloc[0].raw_report"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"attachments": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Wirtschaftsprüfer"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 14,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import re\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def extract_auditors(report: str) -> list:\n",
|
||||||
|
" auditor_regex = r\"[a-z A-Z,.'-]+, Wirtschaftsprüfer\"\n",
|
||||||
|
" hits = re.findall(auditor_regex, report)\n",
|
||||||
|
" return [hit.replace(\", Wirtschaftsprüfer\", \"\").lstrip() for hit in hits]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"['Eckhard Lewe', 'Renate Hermsdorf']"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"extract_auditors(sample_report)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def extract_auditor_company(report: str) -> str:\n",
|
||||||
|
" soup = BeautifulSoup(report, features=\"html.parser\")\n",
|
||||||
|
" temp = soup.find_all(\"b\")\n",
|
||||||
|
" for elem in temp:\n",
|
||||||
|
" br = elem.findChildren(\"br\")\n",
|
||||||
|
" if len(br) > 0:\n",
|
||||||
|
" return elem.text.split(\"\\n\")[1].strip()\n",
|
||||||
|
" return None"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"'Warth & Klein Grant Thornton AG'"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"extract_auditor_company(sample_report)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"attachments": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Aufsichtsrat"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"attachments": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"**TODO**"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"attachments": {},
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Bilanz bzw. GuV"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"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>Unnamed: 0</th>\n",
|
||||||
|
" <th>Anhang</th>\n",
|
||||||
|
" <th>2020 TEUR</th>\n",
|
||||||
|
" <th>Vorjahr TEUR</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>1. Umsatzerlöse</td>\n",
|
||||||
|
" <td>(1)</td>\n",
|
||||||
|
" <td>69.819</td>\n",
|
||||||
|
" <td>77.429</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>2. Veränderung des Bestandes an unfertigen Lei...</td>\n",
|
||||||
|
" <td>NaN</td>\n",
|
||||||
|
" <td>-41.000</td>\n",
|
||||||
|
" <td>-66.000</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>3. Sonstige betriebliche Erträge</td>\n",
|
||||||
|
" <td>(2)</td>\n",
|
||||||
|
" <td>489.000</td>\n",
|
||||||
|
" <td>1.816</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>4. Materialaufwand</td>\n",
|
||||||
|
" <td>NaN</td>\n",
|
||||||
|
" <td>NaN</td>\n",
|
||||||
|
" <td>NaN</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>a) Aufwendungen für bezogene Waren</td>\n",
|
||||||
|
" <td>NaN</td>\n",
|
||||||
|
" <td>-1.220</td>\n",
|
||||||
|
" <td>-3.003</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Unnamed: 0 Anhang 2020 TEUR \\\n",
|
||||||
|
"0 1. Umsatzerlöse (1) 69.819 \n",
|
||||||
|
"1 2. Veränderung des Bestandes an unfertigen Lei... NaN -41.000 \n",
|
||||||
|
"2 3. Sonstige betriebliche Erträge (2) 489.000 \n",
|
||||||
|
"3 4. Materialaufwand NaN NaN \n",
|
||||||
|
"4 a) Aufwendungen für bezogene Waren NaN -1.220 \n",
|
||||||
|
"\n",
|
||||||
|
" Vorjahr TEUR \n",
|
||||||
|
"0 77.429 \n",
|
||||||
|
"1 -66.000 \n",
|
||||||
|
"2 1.816 \n",
|
||||||
|
"3 NaN \n",
|
||||||
|
"4 -3.003 "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 18,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def get_bilanz(report: str) -> any:\n",
|
||||||
|
" result = {}\n",
|
||||||
|
" soup = BeautifulSoup(report, features=\"html.parser\")\n",
|
||||||
|
" for pos in [\"Aktiva\", \"Passiva\"]:\n",
|
||||||
|
" tag = soup.find(\"b\", string=re.compile(pos))\n",
|
||||||
|
" if tag:\n",
|
||||||
|
" pos_results = pd.read_html(\n",
|
||||||
|
" StringIO(str(tag.findNext(\"table\", {\"class\": \"std_table\"})))\n",
|
||||||
|
" )[0]\n",
|
||||||
|
" result[pos] = pos_results\n",
|
||||||
|
" return result\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"bilanz = get_bilanz(sample_report)\n",
|
||||||
|
"bilanz[\"Passiva\"].head()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 19,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"Index(['Gesellschafterbeschluss', 'Shareholder Resolution'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', 'Anhang', '31.12.2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', 'Anhang', '2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Aufgliederung nach Tätigkeitsbereichen', '2020 TEUR',\n",
|
||||||
|
" 'Vorjahr TEUR'],\n",
|
||||||
|
" dtype='object')\n",
|
||||||
|
"Index(['Aufgliederung nach Inland und Ausland', '2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', '31.12.2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', '31.12.2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', '31.12.2020'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
|
||||||
|
"Int64Index([0, 1, 2], dtype='int64')\n",
|
||||||
|
"Index(['Unnamed: 0', 'TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', '31.12.2020 TEUR', 'Vorjahr TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Unnamed: 0', '2020 Anzahl MA', 'Vorjahr Anzahl MA'], dtype='object')\n",
|
||||||
|
"MultiIndex([('Art des Geschäfts', 'Unnamed: 0_level_1'),\n",
|
||||||
|
" ('Art der Beziehung', 'Gesellschafterin TEUR'),\n",
|
||||||
|
" ('Art der Beziehung', 'Verbundene Unternehmen TEUR')],\n",
|
||||||
|
" )\n",
|
||||||
|
"Int64Index([0, 1], dtype='int64')\n",
|
||||||
|
"MultiIndex([( 'Unnamed: 0_level_0', ...),\n",
|
||||||
|
" ('Anschaffungs- oder Herstellungskosten', ...),\n",
|
||||||
|
" ('Anschaffungs- oder Herstellungskosten', ...),\n",
|
||||||
|
" ('Anschaffungs- oder Herstellungskosten', ...),\n",
|
||||||
|
" ('Anschaffungs- oder Herstellungskosten', ...)],\n",
|
||||||
|
" )\n",
|
||||||
|
"MultiIndex([('Unnamed: 0_level_0', 'Unnamed: 0_level_1'),\n",
|
||||||
|
" ( 'Abschreibungen', 'Stand 01.01.2020 EUR'),\n",
|
||||||
|
" ( 'Abschreibungen', 'Abschreibungen des Geschäftsjahres EUR'),\n",
|
||||||
|
" ( 'Abschreibungen', 'Abgänge Umbuchung EUR'),\n",
|
||||||
|
" ( 'Abschreibungen', 'Stand 31.12.2020 EUR')],\n",
|
||||||
|
" )\n",
|
||||||
|
"MultiIndex([('Unnamed: 0_level_0', 'Unnamed: 0_level_1'),\n",
|
||||||
|
" ( 'Buchwerte', 'Stand 31.12.2020 EUR'),\n",
|
||||||
|
" ( 'Buchwerte', 'Stand 31.12.2019 EUR')],\n",
|
||||||
|
" )\n",
|
||||||
|
"Index(['Nichtfinanzieller Leistungsindikator', 'Unnamed: 1', '2020', '2019',\n",
|
||||||
|
" '2018'],\n",
|
||||||
|
" dtype='object')\n",
|
||||||
|
"Index(['Gewinn- und Verlustrechnung', '2020 TEUR', 'Vorjahr TEUR',\n",
|
||||||
|
" 'Veränderung TEUR'],\n",
|
||||||
|
" dtype='object')\n",
|
||||||
|
"Index(['Bilanz', '31.12.2020 TEUR', 'Vorjahr TEUR', 'Veränderung TEUR'], dtype='object')\n",
|
||||||
|
"Index(['Bericht des Aufsichtsrats', 'Report of the Supervisory Board'], dtype='object')\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def get_tables(raw_report: str) -> list:\n",
|
||||||
|
" soup = BeautifulSoup(raw_report, features=\"html.parser\")\n",
|
||||||
|
" tables = soup.find_all(\"table\", {\"class\": \"std_table\"})\n",
|
||||||
|
" dfs = []\n",
|
||||||
|
" for table in tables:\n",
|
||||||
|
" for df in pd.read_html(StringIO(str(table))):\n",
|
||||||
|
" dfs.append(df)\n",
|
||||||
|
" return dfs\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"for df in get_tables(sample_report):\n",
|
||||||
|
" print(df.columns)\n",
|
||||||
|
"\n",
|
||||||
|
"tables = get_tables(sample_report)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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": "3.10.7"
|
||||||
|
},
|
||||||
|
"orig_nbformat": 4
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
File diff suppressed because it is too large
Load Diff
@ -1 +0,0 @@
|
|||||||
# protocoll
|
|
||||||
62
documentations/research/Timeseries/Overview.md
Normal file
62
documentations/research/Timeseries/Overview.md
Normal file
@ -0,0 +1,62 @@
|
|||||||
|
# Timeseries (deutsch: Zeitreihen) im Kontext des Transparenzregisters Kapitalgesellschaften
|
||||||
|
|
||||||
|
## Leitfragen
|
||||||
|
1. Was zeichnet Timeseries Daten aus?
|
||||||
|
2. Wie werden Timeseries effizient gespeichert?
|
||||||
|
3. Welche Daten sind für den vorstehenden Use Case relevant und wie werden diese möglichts optimal (gemäß Software Qualitätskriterien wie Skalierbarkeit u.ä.) modelliert?
|
||||||
|
4. Passt die Struktur und Handhabung von Timeseries Daten auf den Use Case?
|
||||||
|
5. Wo können die benötigten Daten abgerufen werden?
|
||||||
|
6. Wie könnte ein beispielhafter Datensatz (ggf. in Form eines Pandas DataFrames) aussehen?
|
||||||
|
|
||||||
|
## Timeseries
|
||||||
|
Hinter Timeseries (oder Zeitreihen) verbergen sich Datenpunkte meistens numerischer Natur aber auch andere Datentypen sind möglich, die Werte einer Kennzahl oder Entität entlang der Zeit-Achse darstellen. Nennenswerte Beispiele sind die Herzfrequenz eines Menschen sowie Aktienkurse börsennotierter Unternehmen. Es wird generell zwischen `Metriken` (engl.: Metrics; bei regelmäßigem Intervall) und `Ereignissen` (engl.: Events; bei unregelmäßigen Intervallen) unterschieden.
|
||||||
|
|
||||||
|
--<cite>vgl. [influxdata - What is time series data?](https://www.influxdata.com/what-is-time-series-data/)
|
||||||
|
|
||||||
|
Einige zentrale Eigenschaften von Daten, die als Zeitreihen modelliert werden, sind:
|
||||||
|
1. `Unveränderbarkeit` (engl.: Immutability):
|
||||||
|
Da Zeitreihen in der Regel in zeitlicher Reihenfolge aufgenommen werden, werden sie normalerweise lediglich an die bestehende Reihe angehangen und nachträglich nicht mehr angepasst. Der entscheidende Identifikator des Datensatzes besteht dabei aus dem Zeitstempel (engl.: Timestamp) sowie der zugeordneten Entität (z.B. einem Sensor). Andere Features, die als Primärschlüssel fungieren können, sind nicht vorgesehen.
|
||||||
|
2. `Lebensdauer` (engl.: Retention policy):
|
||||||
|
Da Zeitreihen in hoher Frequenz geschrieben aber mit höherem Alter seltener gelesen werden, ist es üblich alte Daten zu löschen bzw. aggregiert zu speichern, um langfristig Speicher zu sparen.
|
||||||
|
3. `Aggregation` (engl.: Aggregation):
|
||||||
|
In den meisten Use Cases werden nicht alle Daten gleichzeitig oder nur einzelne Datensätze sondern Werte eines definierten Zeitraumes abgefragt und häufig aggregiert (z.B. als Summe oder Durchschnitt) zurückgegeben.
|
||||||
|
4. `Hohes Schreib- zu Lese-Verhältnis` (engl.: High write/read ratio):
|
||||||
|
Timeseries Daten werden in der Regel in hoher Frequenz (kurzen Intervallen) geschrieben jedoch nur gelegentlich bzw. wie in Punkt 3 angemerkt aggregiert abgerufen, so dass schreibende Zugriffe zur Datenbank die lesenden an Volumen übertreffen.
|
||||||
|
|
||||||
|
<cite>-- vgl. [TDengine - Characteristics of Time-Series Data](https://tdengine.com/tsdb/characteristics-of-time-series-data/)
|
||||||
|
|
||||||
|
## Modellierung relevanter Kennzahlen
|
||||||
|
|
||||||
|
### Ist die Timeseries Modellierung für den Use Case geeignet?
|
||||||
|
Die im Rahmen der vorher durchgeführten Analyse von für den Use Case [relevanten Unternehmenskennzahlen](../data_and_metrics.md) zeichnen sich durch ein Merkmal aus: sie basieren auf einer jährlich bis maximal jedes Quartal aufgestellten Kennzahl wie den Gewinn oder Umsatz des Unternehmens. Daraus resultiert eine für Zeitreihen geringe Datenfrequenz - üblich sind hier minütlich bis unter sekündlich geschriebene Daten.
|
||||||
|
|
||||||
|
Des Weiteren ist jeder Datensatz nicht nur an die Zeit-Achse sondern eine weitere Entität wie einen Quartals oder Jahresabschluss gebunden. Dies spiegelt nicht nur in der Berechnung der Kennzahl für ein einzelnes Unternehmen sondern auch den Vergleich zwischen Mehreren wieder: Hier ist es möglich, dass Unternehmen ihre Ergebnisse zu unterschiedlichen Zeiten aber den selben Ereignissen veröffentlichen. Immerhin ist es realistisch, dass ein Unternehmen seinen Jahresabschluss deutlich früher als ein anderes der Öffentlichkeit zur Verfügung stellt. So eine Abfrage ließe sich zwar durchaus über die Einführung einer zeitlichen Einschränkung filtern, jedoch ist auch hier die Verknüpfung zu einem Jahres- oder Quartalsabschluss von höherem Interesse.
|
||||||
|
|
||||||
|
In Folge dessen eignet sich eine Zeitreihen basierte Modellierung nicht für die zentralen Unternehmenskennzahlen.
|
||||||
|
|
||||||
|
Für die Speicherung und Verwaltung von Aktienkursen börsennotierter Unternehmen - folglich nicht aller Kapitalgesellschaften, die im Use Case behandelt werden - bietet sich der Einsatz einer Timeseries Datenbank jedoch durchaus an, da Daten hier nicht nur in sehr hoher Frequenz geschrieben sondern auch im bekannten Muster abgefragt werden. Demzufolge mag sich eine Modellierung auf Basis der `Polyglot persistence` (Verwendung verschiedener Datenspeicherungs Technologien) auf Gesamtsicht eignen, so dass Kennzahlen sowie Stammdaten von Unternehmen in einer relationalen DB gespeichert und abhängig ihrer Unternehmensform mit Aktiekursen aus einer Timeseries DB angereichert werden.
|
||||||
|
|
||||||
|
Dieser Ansatz wäre nicht nur für diese Anforderung lohnenswert, sondern kann auch die effiziente Speicherung der Unternehmensverflechtungen vereinfachen, da für diesen Anwendungszweck eine Graphen-Datenbank ins Spiel gebracht werden könnte, die die Relationen zwischen Unternehmen und Unternehmen leicht traversierbar speichert. Es ist daher auch abzuwägen, ob diese nicht auch die Aufgaben des relationalen Modells übernehmen könnte, um die Fülle anforderungsspezifischen DB Technologien zu reduzieren, um auch so die Komplexität des Gesamtsystem gering zu halten.
|
||||||
|
|
||||||
|
Um einen geeigneten Zugang zu den Daten von extern (z.B. der Datenvisualisierungs-Schicht in Form einer Web App) zu ermöglichen, ist der Einsatz von `GraphQL` sinnvoll. Dies ermöglicht dem Client die gesamte Datenbasis zu durchforsten und den benötigten Datenbestand abzufragen. Der ``GraphQL`` Server kann dabei durch seine `Resolver` Struktur problemlos mehrere Datenquellen (siehe oben angesprochene `Polyglot Persistence`) kombinieren.
|
||||||
|
|
||||||
|
### Überblick über Datenrelationen
|
||||||
|
|
||||||
|
```mermaid
|
||||||
|
|
||||||
|
erDiagram
|
||||||
|
Kapitalgesellschaft ||--o{ Jahresabschluss : veroeffentlicht
|
||||||
|
Wirtschaftspruefer ||--o{ Jahresabschluss : prueft
|
||||||
|
Jahresabschluss ||--o{ Metriken : beinhaltet
|
||||||
|
Kapitalgesellschaft ||--o{ Branche : operiert_in
|
||||||
|
Person ||--o{ Vorstand : agiert_als
|
||||||
|
Person ||--o{ Wirtschaftspruefer : agiert_als
|
||||||
|
Vorstand ||--o{ Kapitalgesellschaft: leitet
|
||||||
|
|
||||||
|
Kapitalgesellschaft {
|
||||||
|
}
|
||||||
|
Branche {
|
||||||
|
}
|
||||||
|
Jahresabschluss {
|
||||||
|
}
|
||||||
|
```
|
||||||
Loading…
x
Reference in New Issue
Block a user