From cac8610edaada42bc9288640654b7bcf5ab62827 Mon Sep 17 00:00:00 2001 From: SeZett Date: Thu, 20 Apr 2023 14:53:53 +0200 Subject: [PATCH] Ideas regarding timeseries --- .../01_Transparenzregister_Zeitdaten.ipynb | 288 ++++++++++++++++++ Jupyter/Timeseries/BASF_Data.csv | 25 ++ Jupyter/Timeseries/EON_Data.csv | 17 ++ Jupyter/Timeseries/Telekom_Data.csv | 19 ++ 4 files changed, 349 insertions(+) create mode 100644 Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb create mode 100644 Jupyter/Timeseries/BASF_Data.csv create mode 100644 Jupyter/Timeseries/EON_Data.csv create mode 100644 Jupyter/Timeseries/Telekom_Data.csv diff --git a/Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb b/Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb new file mode 100644 index 0000000..4b656b2 --- /dev/null +++ b/Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "attachments": { + "grafik.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "8e40a4b1", + "metadata": {}, + "source": [ + "# Aufgabe: Welche Daten müssen zeitlich persistiert werden und wie können diese gespeichert werden?\n", + "Autor: Sebastian \\\n", + "Datum: 20-04-2023\n", + "\n", + "Eine Frage ist, welche Daten von Kapitalgesellschaften benötigt werden, um eine Historie zu visualisieren und auszuwerten.\n", + "Dafür werden Daten von Kapitalgesellschaften über **Statista** verwendet.\\\n", + "Für dieses Portal gibt es eine Hochschullizenz. Man kann sich über den folgenden Link anmelden: https://de.statista.com/login/campus/ und wählt die Fachhochschule Südwestfalen aus. \\\n", + "Man wird auf die Anmeldeseite der FH SWF geleitet, meldet sich dort an und willigt der Datenweitergabe zu.\n", + "\n", + "> 20.04.2023: Für API ist ein Key notwendig, den es für die FH scheinbar nicht gibt! Daten müssen manuell ermittelt werden!\n", + "\n", + "> Die Daten sind nicht homogen formatiert, d.h. Formatierung und Skalierung (Millonen, Milliarden €) müssen angepasst werden!\n", + "\n", + "### Kennzahlen\n", + "In einer ersten Betrachtung werden die vorgeschlagenen Kennzahlen betrachtet:\n", + "- Umsatz\n", + "- EBIT\n", + "- EBIT Marge\n", + "- Bilanzsumme\n", + "- Eigenkapitalanteil (Eigenkapital / Bilanzsumme)\n", + "- Fremdkapitalanteil (Fremdkapital / Bilanzsumme)\n", + "- Verschuldungsgrad (Fremdkapital / Eigenkapital)\n", + "- Eigenkapitalrentabilität (EBIT/Eigenkapital)\n", + "- Umschlaghäufigkeit des Gesamtkapitals (Umsatz / Bilanzsumme) \n", + "\n", + "Im Zuge des Projekts wäre es wünschenswert für jedes beteiligte Unternehmen eine Zeitreihe der oben genannten Kennzahlen zu erhalten, damit man sich die Historie ansehen und ggfs. mit anderen Unternehmen vergleichen kann.\n", + "\n", + "Als ersten Anhaltspunkt welche Unternehmen betrachtet werden, wurde nach den 10 größten deutschen Unternehmen gesucht:\n", + "https://www.ig.com/de/trading-strategien/die-groessten-unternehmen-deutschlands-und-der-welt-220715\n", + "\n", + "\n", + "|Platz |\tUnternehmen |\tBranche |\tMarktkapitalisierung (EUR) in Mrd.|\n", + "|---|---|---|---|\n", + "|1. |\tSAP |\tSoftware |\t101,8|\n", + "|2. |\tDeutsche Telekom |\tTelekommunikation |\t95,8|\n", + "|3. |\tVolkswagen Vz.| \tAutomobil |\t77,3|\n", + "|4. |\tSiemens |\tEisen / Stahl \t|77,2|\n", + "|5. |\tAllianz |\tVersicherung |\t74,3|\n", + "|6. |\tMercedes-Benz Group| \tAutomobil |\t58,8|\n", + "|7. |\tBayer| \tPharma |\t56,3|\n", + "|8. |\tSiemens Healthineers| \tMedical Equipment| \t54,1|\n", + "|9. |\tDeutsche Post| \tGütertransport| \t44,4|\n", + "|10. |\tBMW |\tAutomobil| \t44,4|\n", + "\n", + "Eine erste Idee:\n", + "![grafik.png](attachment:grafik.png)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "01459424", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import ipywidgets as widgets\n", + "pd.options.plotting.backend = \"plotly\"" + ] + }, + { + "cell_type": "markdown", + "id": "a2d2f79a", + "metadata": {}, + "source": [ + "**Einlesen der bestehenden Datensätze**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "4862a09e", + "metadata": {}, + "outputs": [], + "source": [ + "dfEON=pd.read_csv('Data_Transparenzregister/EON_Data.csv', index_col=0, sep=';') \n", + "dfBASF=pd.read_csv('Data_Transparenzregister/BASF_Data.csv', index_col=0, sep=';') \n", + "dfTELEKOM=pd.read_csv('Data_Transparenzregister/TELEKOM_Data.csv', index_col=0, sep=';') " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "9f84b5d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Umsatz [Mill. €] 73100.0\n", + "EBIT [Mill. €] 9160.0\n", + "EBITDA [Mill. €] NaN\n", + "Name: 2016, dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#select a specific year\n", + "dfTELEKOM.loc[2016]" + ] + }, + { + "cell_type": "markdown", + "id": "51453575", + "metadata": {}, + "source": [ + "### Mini GUI\n", + "Folgend wird eine minimale GUI implementiert, welche die drei Metriken (Umsatz, EBIT, EBITDA) von drei Unternehmen visualisiert." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c871a495", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d5fd8316c744857930548ada689c0ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(Dropdown(description='Company:', options=('BASF', 'EON', 'Telekom'), value='BASF'), Drop…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_Data(company, metric):\n", + " if company=='BASF':\n", + " dfSelect=dfBASF\n", + " print('BASF')\n", + " if company=='EON':\n", + " dfSelect=dfEON\n", + " print('EON') \n", + " if company=='Telekom':\n", + " dfSelect=dfTELEKOM\n", + " print('Telekom') \n", + " fig=dfSelect.plot()\n", + " fig.show() \n", + " return\n", + "\n", + "W_company=widgets.Dropdown(\n", + " options=['BASF', 'EON', 'Telekom'],\n", + " value='BASF',\n", + " description='Company:',\n", + " disabled=False,\n", + ")\n", + "W_metric=widgets.Dropdown(\n", + " options=['EBIT', 'EBITDA', 'Volume'],\n", + " value='Volume',\n", + " description='Metric:',\n", + " disabled=False,\n", + ")\n", + "\n", + "out=widgets.interact(get_Data, company=W_company, metric=W_metric)" + ] + }, + { + "cell_type": "markdown", + "id": "fe99f2c6", + "metadata": {}, + "source": [ + "### Fazit\n", + "Mit wieviel Datensätzen müssen wir im Projekt rechnen? \\\n", + "Wir nehmen an, dass die Datenbank 100 deutsche Unternehmen beinhaltet, welches je mit 9 Metriken bewertet wird und max. 4 Daten punkte (Quartalszahlen) vorliegen. Ein realistischer Rückblick ist über die letzten 15 Jahre möglich, da ansonsten die Datengrundlage dünn wird. \n", + "\n", + "Eine kurze Überschlagsrechnung ergibt: \n", + "\n", + "\n", + "$\n", + "n_{Unternehmen} = 100 \\newline\n", + "n_{Metriken}=9 \\newline\n", + "n_{Datenpunkte pro Jahr }=4 \\newline\n", + "n_{Jahre} = 15 \\newline \\newline\n", + "n_{Datensätze} = n_{Unternehmen} *n_{Metriken} * n_{Datenpunkte pro Jahr } *n_{Jahre} \\newline \n", + "n_{Datensätze} = 100 * 9 * 4 * 15 = 54000 Datenpunkte\n", + "$\n", + "\n", + "D.h. für jedes Unternehmen wären es 540 Datenpunkte, welche sich auf 9 Metriken aufteilen. \\\n", + "Diese Datenmenge (60 Einträge pro Metrik) könnte als **JTS json Time Series** abgespeichert werden.\n", + "\n", + "### JTS - json Time series\n", + "\n", + "\n", + "Example for a jts document(https://docs.eagle.io/en/latest/reference/historic/jts.html):\n", + "\n", + "``` json\n", + "{\n", + " \"docType\": \"jts\",\n", + " \"version\": \"1.0\",\n", + " \"header\": {\n", + " \"startTime\": \"2018-08-16T02:00:00.000Z\",\n", + " \"endTime\": \"2018-08-16T02:20:43.000Z\",\n", + " \"recordCount\": 5,\n", + " \"columns\": {\n", + " \"0\": {\n", + " \"id\": \"541a5a129bc9b4035f906d70\",\n", + " \"name\": \"Temperature\",\n", + " \"dataType\": \"NUMBER\",\n", + " \"renderType\": \"VALUE\",\n", + " \"format\": \"0.###\",\n", + " \"aggregate\": \"NONE\"\n", + " }\n", + " }\n", + " },\n", + " \"data\": [\n", + " {\n", + " \"ts\": \"2018-08-16T02:00:39.000Z\",\n", + " \"f\": { \"0\": {\"v\": 99, \"q\": 100, \"a\": \"site maintenance\"} }\n", + " },\n", + " {\n", + " \"ts\": \"2018-08-16T02:05:40.000Z\",\n", + " \"f\": { \"0\": {\"v\": 28.22 } }\n", + " },\n", + " {\n", + " \"ts\": \"2018-08-16T02:10:41.000Z\",\n", + " \"f\": { \"0\": {\"a\": \"sensor recalibrated\" } }\n", + " },\n", + " {\n", + " \"ts\": \"2018-08-16T02:15:42.000Z\",\n", + " \"f\": { \"0\": {\"v\": 29.2, \"q\": 100 } }\n", + " },\n", + " {\n", + " \"ts\": \"2018-08-16T02:20:43.000Z\",\n", + " \"f\": { \"0\": {\"v\": 29.18 } }\n", + " }\n", + " ]\n", + "}\n", + "\n", + "```\n", + "\n", + "### Mongo DB\n", + "Mongo DB bietet die Möglichkeit auch Zeitseriendaten zu persistieren. Das bietet den Vorteil nicht mehrere Datenbanksysteme für die Entitäten und Zeitserien aufzusetzen, sondern ein Datenbanksystem zu verwenden." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "37033e3e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Jupyter/Timeseries/BASF_Data.csv b/Jupyter/Timeseries/BASF_Data.csv new file mode 100644 index 0000000..047665a --- /dev/null +++ b/Jupyter/Timeseries/BASF_Data.csv @@ -0,0 +1,25 @@ +Jahr;Umsatz [Millionen €];Ebit [Millionen €];EBITDA [Millionen €] +1999;29473;; +2000;35946;; +2001;32500;; +2002;32216;; +2003;33361;; +2004;37537;; +2005;42745;5830; +2006;52610;6750; +2007;57951;7316; +2008;62304;6463;9562 +2009;50693;3677;7388 +2010;63873;7761;11131 +2011;73497;8586;11993 +2012;72129;6742;10009 +2013;73973;7160;10432 +2014;74326;7626;11043 +2015;70449;6248;10649 +2016;57550;6275;10526 +2017;61223;7587;10765 +2018;60220;5974;8970 +2019;59316;4201;8185 +2020;59149;-191;6494 +2021;78598;7677;11355 +2022;87327;6548;10748 diff --git a/Jupyter/Timeseries/EON_Data.csv b/Jupyter/Timeseries/EON_Data.csv new file mode 100644 index 0000000..95ab1e9 --- /dev/null +++ b/Jupyter/Timeseries/EON_Data.csv @@ -0,0 +1,17 @@ +Jahr;Umsatz [Mill. €];EBIT [Mill. €];EBITDA [Mill. €] +2007;66912;; +2008;84873;; +2009;79974;; +2010;92863;; +2011;112954;; +2012;132093;7010; +2013;119615;5640; +2014;113095;4700; +2015;42656;3600; +2016;38173;3100; +2017;37965;3100; +2018;30084;2990;4840 +2019;41284;3220;5558 +2020;60944;3780;6905 +2021;77358;4720;7889 +2022;115660;5200;8059 diff --git a/Jupyter/Timeseries/Telekom_Data.csv b/Jupyter/Timeseries/Telekom_Data.csv new file mode 100644 index 0000000..91488d3 --- /dev/null +++ b/Jupyter/Timeseries/Telekom_Data.csv @@ -0,0 +1,19 @@ +Jahr ;Umsatz [Mill. €];EBIT [Mill. €];EBITDA [Mill. €] +2005;59600;7600; +2006;61300;5300; +2007;62500;5300; +2008;61700;7000; +2009;64600;6000; +2010;62420;5510; +2011;58650;5560; +2012;58170;-3960; +2013;60130;4930; +2014;62660;7250; +2015;69230;7030; +2016;73100;9160; +2017;74950;9380; +2018;75660;8000;23333 +2019;80530;9460;24731 +2020;99950;12370;35017 +2021;107610;12580;37330 +2022;114200;15410;40208