diff --git a/documentations/index.rst b/documentations/index.rst index 8e83918..bfc77f1 100644 --- a/documentations/index.rst +++ b/documentations/index.rst @@ -32,6 +32,7 @@ This is the documentation for the AKI project group on the german transparenzreg :caption: Seminararbeiten seminararbeiten/DevOps/Seminarpräsentation.ipynb + seminararbeiten/Datenspeicherung/00_Datenspeicherung.md .. toctree:: :glob: diff --git a/documentations/seminararbeiten/3_Datenspeicherung.md b/documentations/seminararbeiten/3_Datenspeicherung.md deleted file mode 100644 index 1f76b94..0000000 --- a/documentations/seminararbeiten/3_Datenspeicherung.md +++ /dev/null @@ -1,68 +0,0 @@ -# Aufgabe: Inhaltliche Skizze für die Seminararbeit zur Thematik Datenspeicherung - -# 1. Allgemeine Anforderungen an Datenbank -- **Speicherung** von strukturierten Daten, wie Kennzahlen, Stammdaten -- **Skalierbarkeit:** Datenbank sollte skalierbar sein, um zukünftige Daten weiterhin zu speichern und weitere Unternehmen hinzuzufügen -- **Sicherheit:** Die Datenbank muss Funktionen unterstützen, um die Datenvor unbefugtem Zugriff zu schützen. -- **Datensicherung- und Wiederherstellung: ** Die Datenbank muss Funktionen zur Sicherung und Wiederherstellung unterstützen. -- **Leistung:** Die Performance der Datenbank ist eher zweitrangig, da die Abfrage nicht hochdynamisch sein muss. Ausserdem werden nicht viele Anfragen erwartet. -- **Integration:** Die Datenbank muss sich in ein Python Framework einbinden lassen und mit dem bevorzugten Frontend Daten austauschen können. - -# 2. Datenarten -Welche Daten erwarten wir im Projekt? \ -Cluster, wie z.B. Stammdaten, Stimmungsdaten, Social Graph, Zeitseriendaten/Historien - -> Abstimmung mit den Bereichen Textmining und Datenbeschaffung über verwendete Daten und Formulierung von Anforderungen an Daten. - -## 2.1 strukturierte Daten -Was sind strukturierte Daten? - -## 2.2 unstrukturierte Daten -Was sind unstrukturierte Daten? - -> Definiere eine Anforderung an die Struktur der Daten. - -# 3. Arten von Datenbanken -## 3.1 Relational -Was ist eine reltionale Datenbank? -Wie werden Daten gespeichert? -Beispiel für relationale Datenbank - -## 3.2 Graph -Was ist eine Graph Datenbank? -Wie werden Daten gespeichert? -Beispiel für Graph Datenbank - -## 3.3 Zeitserien -Was ist eine Zeitserien Datenbank? -Wie werden Daten gespeichert? -Beispiel für Zeitserien Datenbank - -> Kurzvorstellung von Datenbanksystemen - -# 4. DBS Transparenzregister -## 4.1 relationales Datenbankmodell - -> Modell zur Abbildung der Relationen im Projekt Transparenzregister - -## 4.2 verteilte Datenbank oder ein System -Ein DBS: Wenn nur ein Datenbanksystem verwendet wird, muss nur ein System gepflegt und integriert werden. - - Vorteil: einfache Verwaltung und schnelle Abfrage von Datenbeziehungen - -verteiltes System: spezialisierte Datenbank für jeden Datenytp, wie z.B. Zeitseriendaten oder Graph Daten - -> Definiere eine Empfehlung/Anforderung für das Projekt Transparenzregister. - -## 4.3 Analyse zur Auswahl eines Datenbanksystems -Was sollte bei der Auswahl eines Datenbanksystems beachtet werden? - -> Empfehlungen für DBS-Auswahl - -## 4.4 Anbindung an Front- und Backend -Wie kann das DBS an das Front- und Backend angebunden werden? -> Jupyter Notebook mit Beispiel - -## 4.5 Abfragen in der Datenbank -Wie können Unternehmensdaten abgefragt werden? -Wie können Verflechtungen abgefragt werden? -> Jupyter Notebook mit Beispiel \ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md b/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md new file mode 100644 index 0000000..6cddc32 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md @@ -0,0 +1,748 @@ +![Front](images/Front.PNG) + +
+ +# Datenspeicherung +## Inhaltsverzeichnis + +- [Datenspeicherung](#datenspeicherung) + - [Inhaltsverzeichnis](#inhaltsverzeichnis) + - [Motivation: Warum speichern wird Daten?](#motivation-warum-speichern-wird-daten) + - [1. Allgemeine Anforderungen an Datenbank](#1-allgemeine-anforderungen-an-datenbank) + - [2. Datenarten](#2-datenarten) + - [2.1 Welche Daten erwarten wir im Projekt?](#21-welche-daten-erwarten-wir-im-projekt) + - [2.2 strukturierte Daten](#22-strukturierte-daten) + - [2.3 unstrukturierte Daten](#23-unstrukturierte-daten) + - [3. Arten von Datenbanken](#3-arten-von-datenbanken) + - [3.1 Relationale Datenbank](#31-relationale-datenbank) + - [3.1.1 Anlegen von Tabellen](#311-anlegen-von-tabellen) + - [3.1.2 SQL - Abfrage von relationalen Datenbanken](#312-sql---abfrage-von-relationalen-datenbanken) + - [3.2 Graphdatenbank](#32-graphdatenbank) + - [3.2.1 Erstellung eines Datensatzes](#321-erstellung-eines-datensatzes) + - [3.2.2 Cypher - Abfrage von Graphdatenbanken](#322-cypher---abfrage-von-graphdatenbanken) + - [3.3 Zeitseriendatenbank](#33-zeitseriendatenbank) + - [3.3.1 Erstellung eines Datensatzes](#331-erstellung-eines-datensatzes) + - [3.3.2 FluxQuery](#332-fluxquery) + - [3.4 Dokumenten Datenbank ](#34-dokumenten-datenbank-) + - [3.4.1 Erstellen einer Collection / Ablegen von Dokumenten](#341-erstellen-einer-collection--ablegen-von-dokumenten) + - [3.5 Aufbau einer Datenbank](#35-aufbau-einer-datenbank) + - [4. Datenbanken Transparenzregister](#4-datenbanken-transparenzregister) + - [4.1 Production DB - relationales Datenbankmodell](#41-production-db---relationales-datenbankmodell) + - [4.2 Staging DB](#42-staging-db) + - [4.3 SQL Alchemy](#43-sql-alchemy) + - [5. Proof of Concept](#5-proof-of-concept) + - [5.1 Docker](#51-docker) + - [5.2 PG Admin](#52-pg-admin) + - [5.3 Erstellen von Mock Daten](#53-erstellen-von-mock-daten) + - [5.4 Anlegen der relationalen Tabellen](#54-anlegen-der-relationalen-tabellen) + - [5.5 Abfragen der Datenbank](#55-abfragen-der-datenbank) + - [6. Zusammenfassung](#6-zusammenfassung) + - [Quellen](#quellen) + +
+ + +## Motivation: Warum speichern wird Daten? +Für die Speicherung von Daten gibt es verschiedene Motivationen: +- **Sammlung:** Zur Aufbewahrung von Wissen und Informationen über Objekte, Ereignisse oder Prozesse werden Daten gespeichert. +- **Historisierung:** Durch die Speicherung von Daten in einem zeitlichen Zusammenhang, wird eine Historie erstellt, mit welcher Muster, Trends oder Zusammenhänge erkannt werden können. Historische Daten helfen ausserdem bei der Entscheidungsfindung. +- **Bewertung:** Mit gespeicherten Daten können Systeme, Produkte und Prozesse nachvollzogen, bewertet und verbessert werden. + +Im Projekt Transparenzregister ist die Datenspeicherung eine Kernkomponente, da die gesammelten Informationen die Grundlage für Analysen darstellen. \ +Mit geeigneten Pipelines werden aus diesen Daten Erkenntnisse extrahiert, um z.B. Verflechtungen zwischen Personen und Unternehmen oder den wirtschaftlichen Trend eines Unternehmens visualisieren und bewerten zu können. + +## 1. Allgemeine Anforderungen an Datenbank +- **1.1 Speicherung/Integrität**: Das verwendete System muss Daten, wie Unternehmenskennzahlen, Stammdaten und Verflechtungen speichern. Die Daten müssen korrekt und konsistent gespeichert werden. Konsistent bedeutet in einem gültigen und widerspruchsfreien Zustand und die Transaktionen sollen den ACID-Eigenschaften entsprechen. + - **Atomarity:** Eine Transaktion wird atomar betrachte, d.h. es ist die kleinste unteilbare Einheit, wodurch eine Transaktion entweder vollständig durchgeführt und übernommen wird (Commit) oder bei einem Fehler rückgängig gemacht wird (Rollback). + - **Consistency:** Konsistenz bedeutet, dass eine Transaktion den Datenbankzustand von einem gültigen in einen anderen gültihgen Zustand überführt. Sollte eine Transaktion eine Konsitenzverletzung verursachen, wird diese abgebrochen und die Änderungen zurückgesetzt. + - **Isolation:** Isolation sorgt dafür, dass alle Transaktion unabhängig voneinander ausgeführt werden, damit sich diese bei der Ausführung nicht gegenseitig beeinflussen. + - **Durability:** Dauerhaftigkeit bedeutet, dass die Ergebnisse einer Transaktion dauerhaft in der Datenbank gespeichert werden und auch nach einem Systemneustart oder Systemfehler erhalten bleiben. +- **1.2 Skalierbarkeit:** Das System soll skalierbar sein, um zukünftige Daten weiterhin zu speichern und weitere Unternehmen hinzuzufügen. Durch Hinzufügen von Ressourcen kann das System an steigende Datenmengen und Benutzeranforderungen angepasst werden. Man spricht von horizontaler Skalierung, da die Last auf mehrere Datenbankserver verteilt wird. +- **1.3 Sicherheit:** Die Datenbank muss Mechanismen bereitstellen, um die Daten vor unbefugtem Zugriff zu schützen. + - **Authentifizierung:** Überprüfung der Identität eines Benutzers, durch Benutzername und Passwort. Meist wird eine Zwei-Faktor-Authentifizierung verwendet, um das Sicherheitslevel zu erhöhen. + - **Autorisierung:** Der authentifizierte Benutzer erhält bei der Autorisierung Zugriffsrechte und Ressourcen, welche auf seiner Benutzerrolle basieren. Ein Benutzer mit Administratorrechten, erhält Zugriff auf alle Systemressourcen, wohingegen ein normaler Benutzer nur beschränkten Zugriff erhält. + - **Verschlüsselung:** Durch Verschlüsselung werden Daten in ein nicht interpretierbaren Code umgewandelt, um den Inhalt vor unbefugtem Zugriff zu schützen. Dafür wird ein Algorithmus verwendet, welcher einen Schlüssel generiert und die Daten mit diesem verschlüsselt. Um die Daten wieder lesen zu können, müssen diese mit dem Schlüssel dechiffriert werden. +- **1.4 Datensicherung- und Wiederherstellung:** Die Datenbank muss Funktionen zur Sicherung und Wiederherstellung unterstützen. Im Falle eines Ausfalls oder Fehlers muss sichergestellt sein, dass Mechanismen die Daten schützen und wiederherstellen. +Die meisten Daten in einer Datenbank ändern sich nur langsam, manche allerdings schnell. Je nach Anwendungsfall muss eine geeignete Sicherungsstrategie ausgewählt werden, um nur die Daten zu sichern, die sich tatsächlich ändern. +Jedes Datenbankmanagementsystem bietet unterschiedliche Mechanismen zur Datensicherung und Wiederherstellung, dessen Möglichkeiten nach Auswahl eines Systems + - **vollständiges Backup:** Das vollständige Backup ist eine komplette Kopie der Datenbank inkl. aller Daten, Indizes, Tabellen und Metadaten. Es benötigt viel Speicherplatz und Zeit zur Erzeugung der Sicherung und auch zur Wiederherstellung. + - **inkrementelles Backup:** Ein inkrementelles Backup sichert nur die Änderungen seit dem letzten vollständigem bzw. inkrementellen Backup. Durch den verringerten Datenbestand ist es deutlich schneller und datensparsamer, als das vollständige Backup. Zur Wiederherstellung wird das letzte vollständige und alle inkrementellen Backups benötigt. Allerdings entsteht eine Abhängigkeitskette, da jedes Backup seine Vorgänger zur Wiederherstellung benötigt. + - **differentielles Backup:** Beim differentiellen Backup werden alle Änderungen seit dem letzten vollständigem Backup gesichert. D.h. je weiter die letzte vollständige Sicherung zurückliegt, desto größer und langsamer wird das Backup. Zur Wiederherstellung werden das letzte vollständige und differentielle Backup benötigt. + + + + +**Backuphäufigkeit:** +Die Backuphäufigkeit ist eine Abwägung aus Risiken, Kosten und Arbeitsaufwand. Dieses muss individuell abgeschätzt werden aufgrund des Datenbankumfangs und der Änderungshäufigkeit der Daten, um eine geeignete Backup-Strategie zu entwerfen. \ +*Beispiel:* +- Vorgabe: Datenbank mit 500GB Größe + - Anforderungen + - min. vierfache Backupkapazität --> 2 TB + - Backupdauer vollständig: \ + USB 2.0:$\frac {500GB}{\frac{60MB/s}{1024}} = 8533 sec. \approx 142Min. \approx 2,37 Std.$ \ + USB 3.0:$\frac {500GB}{\frac{625MB/s}{1024}} = 820 sec. \approx 13,6Min. \approx 0,23 Std.$ \ + VDSL 100:$\frac {500GB}{\frac{5MB/s}{1024}} = 102400 sec. \approx 1706Min. \approx 28,4 Std.$ \ + Glasfaser:$\frac {500GB}{\frac{62,5MB/s}{1024}} = 8192 sec. \approx 136,5Min. \approx 2,3 Std.$ + +- **1.5 Leistung:** Die Performanceanforderungen an die Datenbank ergibt sich aus verschiedenen Merkmalen. Diese können kombiniert gestellt werden und sind abhängig von den Anforderungen an das System. Eine Analyse der Anwendungsfälle ist notwendig, um die Anforderungen zu spezifizieren. + - **Latenz:** Die Datenbank soll Anfragen effizient und in einer akzeptablen Antwortzeit verarbeiten. Typische Datenbankapplikationen, wie z.B. ein Webshop benötigen viele einzelne Zugriffe, wofür jedes Mal ein Kommunikationsprotokoll angewendet wird. Durch viele kleine Datenbankzugriffe wird die Applikation verlangsamt, da auf die Netzwerkkommunikation gewartet wird. Für das Benutzererlebnis eines Webshops ist die Latenz ein wichtiges Merkmal. + - **Durchsatz:** Ist eine Metrik für die Anzahl an Transaktionen pro Zeiteinheit. Der Durchsatz ist wichtig bei großen Benutzeraufkommen in einem Webshop. + - **Verfügbarkeit:** Eine hohe Verfügbarkeit, also Erreichbarkeit der Datenbank, wird durch Redundanz (mehrfaches Vorhandensein) und Wiederherstellungsmechanismen gewährleistet, damit Daten koninuierlich verfügbar sind. + - **Wartbarkeit:** Eine einfach zu wartende Datenbank muss Funktionen zur Überwachung, Diagnose, Wartung, Datensicherung und Wiederherstellung bereitstellen. Durch diese automatisierten Pipelines können andere Eigenschaften, wie z.B. die Verfügbarkeit negativ beeinflusst werden, weil Prozesse die Datenbank blockieren. +- **1.6 Integration:** Die Datenbank muss Schnittstellen bereitstellen, um die gespeicherten Daten für eine Anwendung bzw. Systeme zur Verfügung zu stellen. + - **API:** Das *Application Programming Interface* ist eine definierte Schnittstelle, welche Methoden und Funktionen bereit stellt, um auf die Datenbank zuzugreifen bzw. um diese zu verwalten. + - **REST:** REpresential State Transfer beschreibt eine Schnittstelle, die das http-Protokoll verwendet, wo mit den Methoden GET, POST, PUT, DELETE die Kommunikation realisiert wird. + - **SOAP:** Simple Object Access Protocol ist eine Schnittstelle, welche auf XML basiert. + - **ODBC:** Open Database Connectivity ist eine standardisierte Schnittstelle zum Austausch zwischen Anwendungen und Datenbanken. + - **JDBC:** Java Database Connectivity + + +## 2. Datenarten + +Zur Beschreibung von Unternehmen, werden verschiedene Datenarten verwendet. +Die folgenden Datenarten sind eine allgemeine Zusammenfassung und sollen das Brainstorming für die projektspezifischen Daten unterstützen. +- **Stammdaten:** Stammdaten beinhalten die grundsätzlichen Eigenschaften und +Informationen von realen Objekten, welche für die periodische Verarbeitung notwendig sind. Ein Stammsatz für Personal besteht z.B. aus einer Personalnummer, dem Mitarbeiternamen, Anschrift und Bankverbindung. \ +Je nach Anwendungsfall bzw. Geschäftsprozess muss der Inhalt definiert werden, wie z.B. bei Unternehmens-, Kunden-, Material- oder Patientenstammdaten. + +- **Metadaten:** Mit Metadaten werden weitere Daten beschrieben und vereinfachen das Auffinden und Arbeiten mit diesen. Metadaten beinhalten beispielsweise den Verfasser, das Erstellungs- oder Änderungsdatum, die Dateigröße oder den Ablageort. \ +Mit Metadaten können Datenbestände einfacher und effizienter verwaltet und abgefragt werden. + +- **Transaktionsdaten:** Transaktionsdaten beschreiben eine Veränderung des Zustands, wie z.B. eine Kapitalbewegung oder eine Ein-/Auslieferung aus einem Lager. + +- **Referenzdaten:** Referenzdaten sind eine Teilmenge von Stammdaten und beschreiben die zulässigen Daten. Diese werden nur selten geändert oder angepasst und gelten als konstant. Beispiele für Referenzdaten sind: Postleitzahlen, Kostenstellen, Währungen oder Finanzhierarchien. + +- **Bestandsdaten:** Bestandsdaten sind dauerhafter Veränderung ausgesetzt, da diese z.B. die Artikelmenge in einem Lager oder das Guthaben auf einem Konto beschreiben. Diese korrelieren mit den Transaktionsdaten. + +Diese Datenarten müssen im Kontext des Projektes betrachtet werden und sollen das Brainstorming unterstützen. \ +*Stammdaten:* Unternehmensname, Anschrift, Branche \ +*Metadaten:* Verfasser einer Nachricht - Veröffentlichungsdatum; Prüfungsunternehmen - Prüfdatum \ +*Transaktionsdaten:* Wer hat wann wo gearbeitet? \ +*Referenzdaten:* Einheit von Metriken (Umsatz, EBIT usw.) \ +*Bestandsdaten:* Vorstand, Geschäftsführer, Aufsichtsrat + +### 2.1 Welche Daten erwarten wir im Projekt? +Aus den vorangehenden, allgemeinen Datenarten haben wir Cluster identifiziert, welche im Projekt benötigt werden. +Die Kombination aus den folgend aufgeführten Datenclustern ermöglicht eine ganzheitliche Betrachtung und Bewertung der Unternehmen. + +- **Unternehmensstammdaten:** Die Stammdaten beinhalten grundlegende Informationen zu einem Unternehmen, wie z.B. Name, Anschrift, Gesellschaftsform und Branche. + +- **Sentimentdaten:** Die Sentiment- oder Stimmungsdaten beschreiben die Aussenwahrnehmung des Unternehmens hinsichtlich der Mitarbeiterzufriedenheit, Nachhaltigkeit und Umweltfreundlichkeit. +> Mit Sentimentdaten können folgende Fragen beantwortet werden: +>- Welchen Ruf hat das Unternehmen? +>- Wie ist die Aussenwahrnehmung? +>- Wie ist die Kundenbindung? +- **Finanzdaten:** Die Finanzdaten sind Metriken bzw, Indikatoren, um den wirtschaftlichen Erfolg des Unternehmens zu bewerten. Hierzu zählen z.B. Umsatz, EBIT, EBIT Marge, Bilanzsumme, Eigenkapitalanteil, Fremdkapitalanteil, Verschuldungsgrad, Eigenkapitalrentabilität, Umschlaghäufigkeit des Eigenkapitals. +> Mit Finanzdaten können folgende Fragen beantwortet werden: +>- Wie rentabel wirtschaftet das Unternehmen? +>- Wie ist der wirtschaftliche Trend? +>- Bewerten anhand verschiedender Metriken. + +- **Verflechtungsdaten/Social Graph:** Die Verbindungen bzw. Beziehungen zu Personen oder Unternehmen wird in den Verflechtungsdaten abgelegt. Beziehungen entstehen, wenn eine Person Geschäftsführer, Vorstand, Aufsichtsratmitglied, Prokurist oder Auditor ist und Unternehmen z.B. gemeinsam arbeiten, beliefert wird oder Anteile an einem anderen Unternehmen besitzt. +> Mit Verflechtungsdaten können folgende Fragen beantwortet werden: +>- Gibt es strategische Partnerschaften? +>- Wie sind die Lieferketten aufgebaut? +>- Wie ist die Qualität der Geschäftsbeziehungen? +>- Ist das Unternehmen widerstandsfähig aufgestellt? +>- Gibt es Zusammenhänge zu Personen? + +Die abgebildete Mind Map ist nicht vollständig und bildet nicht den finalen Datenumfang des Projekts ab. Es ist eine Momentaufnahme, bevor das relationale Schema entwickelt und die Implementierung begonnen wurde. + + +![Data_Clusters](/documentations/seminararbeiten/Datenspeicherung/images/Data_Cluster.PNG) + +### 2.2 strukturierte Daten + +Strukturierte Daten liegen in einem definierten Format. Vorab wird ein Schema definiert, um Felder, Datentypen und Reihenfolgen festzulegen und die Daten entsprechend abzulegen. +Diese Art von Daten wird z.B. in relationalen Datenbanken verwendet, wobei jede Zeile einer Tabelle einen Datensatz repräsentiert. Die Beziehungen untereinander sind über die Entitäten definiert. +Das Beispiel unten zeigt ein einfaches Beispiel, wie die Daten für die Klasse *Company* definiert sind. Mit diesem Schema kann die Datenaufbereitung umgesetzt werden. + + + +```mermaid +--- +title: Structured Data +--- +classDiagram + class Company:::styleClass { + int ID + string Name + string Street + int ZipCode + } + + +``` +|Vorteile|Nachteile| +|:-----:|:------:| +|einfach nutzbar, da organisiert |Einschränkung der Verwendungsmöglichkeit durch Schema | +| bei bekannten Schema sind Werkzeuge vorhanden|begrenze Speichermöglichkeit, da starre Schemata vorgegeben sind | +|gut automatisierbar | | + +### 2.3 unstrukturierte Daten +Unstrukturierte Daten unterliegen keinem Schema, wie z.B. E-Mails, Textdokumente, Blogs, Chats, Bilder, Videos oder Audiodateien. +- **Textanalyse:** Aus unstrukturierten Texten werden z.B. durch Analyse und Mining Informationen gewonnen, um diese zu extrahieren. Es wird das Vorkommen von bestimmten Wörtern mittels Named Entity Recognition ermittelt oder die Stimmung bzw. das Thema in einem Artikel. +- **Audio-/Videoanalyse:** Bei der Verarbeitung unstrukturierter Audio- oder Videodateien werden Objekte, Gesichter, Stimmen oder Muster erkannt, um diese für Sprachassistenten oder autonome Fahrzeuge nutzbar zu machen. + +Eine wichtige Informationsquelle sind unstrukturierte Daten für Explorations- und Analyseaufgaben. Dabei werden Datenquellen wie z.B. E-Mails, RSS-Feeds, Blogs durchsucht, um bestimmte Informationen zu finden oder Zusammenhänge zwischen verschiedenen Quellen hherzustellen. Dies ermöglicht tiefe Einsicht in die Daten zu erhalten und unterstützt die Entscheidungsfindung bei unklaren Sachverhalten und die Entdeckung neuer Erkenntnisse. + +|Vorteile|Nachteile| +|:-----:|:------:| +|großes Potenzial Erkenntnisse zu erlangen |aufwändige Bearbeitung notwendig, um Daten nutzbar zu machen| +|unbegrenzte Anwendungsmöglichkeiten, da kein Schema vorhanden ist|spezielle Tools zur Aufbereitung notwendig| +| |Expertenwissen über die Daten und Datenaufbereitung notwendig | + +## 3. Arten von Datenbanken +### 3.1 Relationale Datenbank +Eine relationale Datenbank speichert und verwaltet strukturierte Daten. Dabei werden die Daten in Tabellen organisiert, welche aus Zeilen und Spalten bestehen. \ +In den Zeilen der Tabellen sind die Datensätze gespeichert, d.h. jede Zeile repräsentiert einen Datensatz. Durch logisches Verbinden der Tabellen können die Beziehungen zwischen den Daten abgebildet werden. \ +Die wichtigsten Elemente einer relationalen Datenbank werden folgend erklärt: + +**Tabelle:** Eine Tabelle repräsentiert eine Entität bzw. Objekt , wie z.B. Unternehmen, Kunde oder Bestellung. Die Tabelle besteht aus Spalten, welche die Attribute der Entität speichern. \ +Jede Zeile ist eine Instanz des Objekts und enthält konkrete Werte. + + +**Table_Person** +|**ID**|**Name**|**Age**|**Salary**|**Height**| +|---|---|---|---|---| +|1|Tim|31|300.00|191.20| +|2|Tom|21|400.00|181.87| +|3|Tam|51|500.00|176.54| + +https://www.sqlservercentral.com/articles/creating-markdown-formatted-text-for-results-from-sql-server-tables + +**Primärschlüssel:** Der Primärschlüssel ist ein eindeutiger Bezeichner für jede einzelne Zeile einer Tabelle und wird zur Identifikation einer einzelnen Zeile benötigt. Im oberen Beispiel ist die Spalte *ID* der Primärschlüssel. + +**Fremdschlüssel:** Ein Fremdschlüssel verweist auf einen Primärschlüssel einer anderen Tabelle, um eine Beziehung zwischen den Tabellen herzustellen. \ +Im Beispiel ist bezieht sich die Spalte *customer_id* auf den Primärschlüssel der Tabelle *Table_Person*. + +**Table_Orders** +|**ID**|**Product**|**total**|**customer_id**| +|---|---|---|---| +|1|Paper|12|2| +|2|Book|3|2| +|3|Marker|5|3| + +**Beziehungen:** Wie bereits beschrieben, können mit der Verwendung von Fremdschlüsseln Beziehungen zwischen den Tabellen hergestellt werden. \ +Es gibt verschiedene Beziehungstypen: + +|**Typ**|**Beschreibung**| +|---|---| +|1:1|Jeder Primärschlüsselwert bezieht sich auf nur einen Datensatz. **Beispiel:** Jede Person hat genau eine Bestellung. | +|1:n|Der Primärschlüssel ist eindeutig, tritt in der bezogenen Tabelle 0..n mal auf. **Beispiel:** Jede Person kann keine, eine oder mehrere Bestellungen haben. | +|n:n|Jeder Datensatz von beiden Tabellen kann zu beliebig vielen Datensätzen (oder auch zu keinem Datensatz) stehen. Meist wird für diesen Typ eine dritte Tabelle verwendet, welche als Zuordnungs- bzw. Verknüpfungstabelle angelegt wird, da andernfalls keine direkte Verbindung hergestellt werden kann. | + +https://www.ibm.com/docs/de/control-desk/7.6.1.2?topic=structure-database-relationships + +#### 3.1.1 Anlegen von Tabellen +Der Umgang von relationalen Datenbanken erfolgt mittels SQL. Folgend ein Beispiel zum Anlegen einer Tabelle mit Attributen. + +``` +CREATE TABLE Bildungsstaette ( + ID INT PRIMARY KEY NOT NULL, + Name VARCHAR(255) NOT NULL, + Anschrift VARCHAR(255), + Art VARCHAR(100) +); +``` + +#### 3.1.2 SQL - Abfrage von relationalen Datenbanken + +Für die Verwaltung und Abfrage wird SQL (Structured Query Language) verwendet. +Mit dieser Syntax können Tabellen erstellt, Daten eingefügt, aktualisiert und gelöscht und Daten abgefragt werden. + +**Anzeige aller Attribute einer Tabelle:** +``` +SELECT * FROM table_name; +``` + +**Anzeige definierter Attribute einer Tabelle:** +``` +SELECT column1, column2 FROM table_name; +``` + +**Gefilterte Anzeige einer Tabelle:** +``` +SELECT * FROM table_name WHERE condition; +``` + +**Daten aus mehreren Tabellen abrufen (Join):** +``` +SELECT t1.column1, t2.column2 +FROM table1 t1 +JOIN table2 t2 ON t1.id = t2.id; +``` + +### 3.2 Graphdatenbank +Eine Graphdatenbank basiert auf dem Graphenkonzept. \ +Ein Graph besteht aus Knoten und Kanten (Beziehungen), welche die Verbindungen zwischen den Knoten darstellen. \ +Die Stärke der Graphdatenbank liegt in der Darstellung von komplexen Beziehungen. + +**Knoten:** Jeder Knoten repräsentiert eine Entität bzw. Objekt. Jeder Knoten hat eine eindeutige ID oder Bezeichner, um auf diesen zugreifen zu können. Es können auch Attribute hinterlegt werden, um zusätzliche Informationen zu speichern, wie z.B. Geburtsjahr, Wohnort einer Person. + +**Kanten:** Die Kanten verbinden die Knoten und repräsentieren damit die Beziehungen unter den Objekten. Die Kanten können gerichtet und ungerichtet sein. Bei einer gerichteten Beziehung muss die Richtung vom Quell- zum Zielknoten beachtet werden, wohingegen eine ungerichtete Kante eine symmetrische Beziehung darstellt. \ +*gerichtete Beziehung:* Ein Unternehmen ist abhängig vom Bericht des Wirtschaftsprüfers. \ +*ungerichtete Beziehung:** Unternehmen A arbeitet gemeinsam mit Unternehmen B an einem Projekt. + +**Label:** Label werden verwendet, um die Knoten zu kategorisieren/gruppieren. Ein Knoten kann auch mehrere Label besitzen, um die Zugehörigkeit an verschiedenen Kategorien darzustellen (z.B. Unternehmensbranche). + +#### 3.2.1 Erstellung eines Datensatzes +1. Knotenerstellung: Es wird zuerst ein Knoten erstellt, der die Entität repräsentiert. +2. ID: Der Knoten benötigt eine eindeutige Identifikationsnummer, welche automatisch erzeugt oder manuell festgelegt werden kann. +3. Knoten einfügen: Wenn die beiden notwendigen Elemente (Knoten und ID) festgelegt sind, kann der Knoten eingefügt werden. +4. Beziehungen/Kanten festlegen: Wenn der Knoten Beziehungen zu anderen Knoten hat, können diese hinzugefügt werden. + +**Beispiel:** +Folgender Code legt in neo4j zwei Knoten und die entsprechenden Beziehungen an. + +``` +CREATE (:University {id: 4711, name: 'FH SWF - Iserlohn'}), + (:University {id: 1234, name: 'FH SWF - Meschede'}) +WITH * +MATCH (u1:University {id: 4711}), (u2:University {id: 1234}) +CREATE (u1)-[:cooparates_with]->(u2), + (u2)-[:cooparates_with]->(u1) +``` +![Graph_example](images/Graph.png) + +#### 3.2.2 Cypher - Abfrage von Graphdatenbanken +Um Daten abzufragen wird die Abfragesprache Cypher verwendet.\ +Es werden folgend nur einige grundlegende Befehle gezeigt.\ + +**Abfrage aller Knoten** +``` +MATCH (n) +RETURN n +``` +**Abfrage aller Kanten/Beziehungen** +``` +MATCH ()-[r]-() +RETURN r +``` + +**Abfrage von Knoten mit definierten Eigenschaften** +``` +MATCH (n:Label) +WHERE n.property = value +RETURN n +``` + +**Beziehung zwischen zwei Knoten abfragen** +``` +MATCH (n1)-[r]->(n2) +WHERE n1.property = value1 AND n2.property = value2 +RETURN r +``` + +### 3.3 Zeitseriendatenbank + +Zeitserien fallen überall dort an, wo eine Metrik zeitlich betrachtet wird, wie z.B. Umsatz oder EBIT. +D.h. zu jedem Messwert gibt es einen zeitlich zugeordneten Zeitstempel, wobei die einzelnen Zeitpunkte zu einer Serie zusammengefasst werden, um den Zusammenhang zu betrachten. \ +Diese Datenbanken sind spezialisiert auf die Speicherung, Verwaltung und Abfrage von Zeitserien. \ +Die folgenden Erklärungen beziehen sich auf die InfluxDB. + +**Bucket:** Der Bucket separiert Daten in verschiedene Speicher und ist mit der Datenbank bei relationalen Datenbanken vergleichbar. + +**Datapoint:** Unter dem Bucket werden die Datenpunkte gespeichert. Ein Datapoint setzt sich aus mehreren Elementen zusammen, welche erorderlihc oder optional sind: + +|**Element**|**Eigenschaft**| +|---|---| +|Measurement |Datentyp: String
Leerzeichen sind verboten
Max. 64kB| +|Tags| Sind optional
Bestehen aus einem Key/Value-Paar
Datentyp: String
Leerzeichen sind verboten
Max. 64 kB| +|Fields| Min. 1 Field=value Paar wird benötigt
Nicht alle Felder müssen in jedem Punkt vorhanden sein
Datentypen: Float, String, Integer, Boolean| +|Timestamp| Sind optional
Influx schreibt standardmäßig die Systemzeit als Zeitstempel
Genauigkeit kann eingestellt werden (Default: Nanosekunden)| + +#### 3.3.1 Erstellung eines Datensatzes +Die Einrichtung von Zeitseriendatenbanken erfolgt mit der CLI von Influx. + +**Anlegen eines Buckets:** +``` +CREATE DATABASE finance +``` + +#### 3.3.2 FluxQuery +Zur Abfrage von Datenpunkten gibt es FluxQuery, welche sich stark an SQL orientiert. \ + +**Abrufen aller Daten aus Bucket:** +``` +from(bucket: "my-bucket") +``` + +**Festlegen des Zeitbereich:** +``` +range(start: -1h, stop: now()) +``` + +**Filtern nach Bedingungen:** +``` +filter(fn: (r) => r._measurement == "temperature") +``` + +**Transformieren von Datenpunkten:** +``` +map(fn: (r) => ({r with temperatureF: r.temperature * 2.34 + 123})) +``` +### 3.4 Dokumenten Datenbank + +Eine Dokumentendatenbank ist ein System, welches für das Speichern von Dokumenten entwicklet wurde. Es gibt verschiedene Arten von Dokumenten, wie z.B. Textdateien (JSON, HTML, XML) oder PDF. +Es muss kein Schema für die Dokumente festgelegt werden, dadurch ist es möglich Dokumente mit verschiedenen Datenfeldern zu speichern. +Gleiche oder ähnliche Dokumente werden gemeinsam in *Collections* gespeichert. +Die wichtigsten Elemente einer Dokumenten-Datenbank sind: + +**Database:** Unter Database versteht man einen Container, unter welchem Dokumente gespeichert werden. Dies dient der Isolierung bzw. logischen Trennung von Daten. + +**Collection:** Collections werden verwendet, um Dokumente mit ähnlichen Eigenschaften zusammenzufassen. Da Dokumenten-Datenbanken schemenlos sind, dienen die Collections der Organisation. + +**Document:** Das Dokument ist ein einzelnes Datenobjekt und die kleinste Einheit in einer Dokumenten-DB. Ein Dokument kann z.B. ein JSON mit einer eigenen internen Struktur. + +![Document_DB](images/Document_DB.PNG) + +#### 3.4.1 Erstellen einer Collection / Ablegen von Dokumenten +Folgend ein Code-Snippet zum Verbinden mit der Datenbank, Anlegen einer Collection und ablegen von Dokumenten. + +``` python +from pymongo import MongoClient + +# Verbindung zur MongoDB-Datenbank herstellen +client = MongoClient('mongodb://localhost:27017') + +# erstelle ein Cleint-Objekt zur Datenbank +db = client['transparenz'] + +# Collection erstellen +collection = db['Tagesschau_API'] + +# Beispiel-Dokumente einfügen +doc1 = { + 'title': 'BASF wird verkauft!', + 'content': 'BASF wird an Bayer AG verkauft', + 'date': '2023-06-22' +} + +doc2 = { + 'title': 'Bayer Aktie erreicht Rekordniveau', + 'content': 'Aufgrund des Zukaufs von BASF.....', + 'date': '2023-06-23' +} + +# Dokumente in die Collection einfügen +collection.insert_one(doc1) +collection.insert_one(doc2) + +# Verbindung zur Datenbank schließen +client.close() + +``` + + + +### 3.5 Aufbau einer Datenbank +Vor dem Aufbau einer relationalen Datenbank sollten planerische Schritte durchgeführt werden, um ein System zu entwerfen, dass den Anforderungen gerecht wird. \ +Die wichtigsten Schritte sind: + +**Anforderungsanalyse:** Identifikation und Definition von Anforderungen an die Datenbank durch Betrachtung des Anwendungsfalls. + +**Datenmodell:** Analysieren der Strukturen und Beziehungen, die sich aus der Anforderungsanalyse ergeben. Auswahl eines Datenbankmodells, welches am besten geeignet ist. + +**Tabellenentwurf:** Basierend auf den identifizierten Anforderungen wird die Tabellenstruktur der Datenbank entworfen. Für jede Tabelle werden Spaltennamen, Datentyp und mögliche Einschränkungen wie Primärschlüssel und Fremdschlüssel definiert. + +**Erstellung der Tabellen:** Wenn der Tabellenentwurf schlüssig ist und bereits diskutiert wurde, können die Tabellen erstellt werden. Es werden die zuvor festgelegten Bezeichner, Datenytpen und Constraints hinzugefügt. + +**Beziehungen festlegen:** Um die Beziehungen zwischen Tabellen festzulegen, werden Fremdschlüssel verwendet. Mit Fremdschlüsseln verknüpft man Tabellen mit den Primärschlüsseln anderer, abhängiger Tabellen. + +## 4. Datenbanken Transparenzregister +Nachdem die Datencluster identifiziert wurden, welche für das Transparenzregister notwendig sind, wurde Rechereche zu den benötigten Datenquellen betrieben. \ +Es gibt verschiedene Quellen, mit unterschiedlichen Schnittstellen bzw. Zugriff auf die Daten, z.B. mit API´s oder über Web Scrapping. + +Es wurde eine Architektur definiert, welche den Aufbau der späteren Software skizziert: +![High_level_design](images/HLD.png) + +Mittels geeigneter Techniken werden Daten aus diversen Quellen extrahiert (Data Extraction) und in der Staging DB gespeichert. +Mit unterschiedlichen Daten-Extraktionspipelines (Dazta Loader, Sentiment Analysis, Graph Analysis) werden die Daten aus der Staging DB verarbeitet und die strukturierten und aufbereiteten Daten in der Production DB abgelegt. \ +Das Frontend kann auf diese strukturierten Daten zugreifen, um diese zu visualisieren. + +### 4.1 Production DB - relationales Datenbankmodell + +Für die Production DB ist eine relationale Datenbank vorgesehen, da diese die Daten organisiert und durch Verwendung von definierten Schemata strukturiert. \ +Diese Strukturen erleichtern die Wartung und Integration zwischen Back- und Frontend. +![Relationales Modell](images/DB_Schema.png) + +Zentrales Element ist die Stammdatentabelle **company**, welche einen zusammengesetzten Primärschlüssel aus der Nummer des Handelsregister und dem zuständigen Amtsgericht bildet. \ +Die Handelsregisternummer ist nicht eindeutig und wird deutschlandweit mehrfach vergeben, allerdings nur einfach unter einem Amtsgericht. + +Es schließt sich die Tabelle **finance** an, in welcher die Finanzdaten persisitiert werden. Diese steht in einer 1:n Beziehung zur Unternehmenstabelle, da ein Unternehmen viele Finanzdaten haben kann und jeder Datensatz genau einem Unternehmen zugewiesen ist. \ +Die einzelnen Metriken wurden als Attribute definiert, wodurch es viele NULL-Werte in jeder Zeile gibt. Vorteilhaft bei dieser Notation ist allerdings, dass die Metriken durch den Spalztenbezeichner eindeutig sind. + +Die Tabelle **Sentiment** speichert die Stimmungsdaten zu einem Unternehmen. Auch hier besteht eine 1:n Beziehung zu der Unternehmenstabelle. Es gibt einen eigenen Enumeration-Typ, der die Art der Stimmungsdaten festlegt. + +Die Tabelle **district_court** speichert die Amtsgericht, unter welchen die Unternehmen registriert sind. Diese Information ist wichtig, um mit der Handelsregisternummer und dem Amtsgericht ein Unternehmen eindeutig zu identifizieren. + +Die Tabelle **person** speichert Personen, welche unterschiedliche Beziehungen zu Unternehmen haben können. Daraus ergibt sich eine n:m Beziehung (many-to-many), da jede Person mehrere Beziehungen zu einem Unternehmen haben kann bz. jedes Unternehmen mehrfach mit einer Person in Verbindung steht. \ +Um diese Relation aufzulösen, wird eine Beziehungstabelle **person_relation** benötigt, um die n:m Beziehung auf zwei 1:n Beziehungen zu reduzieren. Diese enthält die Fremdschlüssel der bezogenen Tabellen, um die Beziehung zu modellieren. + +Abschließend gibt es noch die Tabelle **company_relation**, welche die Verbindung zwischen Unternehmen modelliert. Hierfür wurde ein Enumaration-Typ erzeugt, welcher die Art der Beziehung angibt (wird_beliefert_von, arbeitet_mit, ist_beteiligt_an, hat_Anteile_an). + +### 4.2 Staging DB + +Die Staging DB ist eine dokumentbasierte Datenbank zu Speicherung von unstrukturierten und semi-strukturierten Daten. Sie dient als Zwischenspeicher oder "Rohdatenbank" für die Extraktions-Pipelines. \ +Aufgaben der Staging-DB:\ +**1. Datenvorbereitung:** Sammlung und Speicherung von Rohdaten aus verschiedenen Quellen\ +**2. Überprüfung:** Entsprechen die Daten den Anforderungen ggfs. Ermittlung von Fehlern oder Inkonsistenzen\ +**3. Testumgebung:** Die Rohdaten aus der Staging DB können mehrfach verwendet werden, um verschiedene Szenarien und Funktionalitäten der Extraktionspipelines zu erproben\ +**4. Backup:** Wenn sich im Laufe des Projekts eine Datenquelle ändert (z.B. Struktur oder Zugang zum Bundesanzeiger) sind die Daten weiterhin verfügbar oder wenn es Änderungen am Schema der Production DB gibt, kann durch eine Änderung am Data Loader das neue Tabellenschema implementiert werden + +Die Staging DB erhält Collections der unterschiedlichen Quellen, unter welchen die Dokumente gespeichert werden. + +![Staging_DB](images/Staging_DB.PNG) + + +### 4.3 SQL Alchemy + +SQL Alchemy ist eine Python Bibliothek, um mit relationalen Datenbanken zu kommunizieren. +Dieses ORM (Object-Relational-Mapping) Framework bildet die Datenbanktabellen als Pythonklassen an und vereinfacht damit das Erstellen, Lesen, Aktualsieren und Löschen von Daten aus Pythonanwendungen.\ +Wichtige Eigenschaften: +- erleichterte Entwicklung: durch die Abbildung von Datenbanktabellen als Pythonklassen wird durchgängig Pythoncode verwendet +- Flexibilität: Durch Verwendung eines Backend-Treibers für die unterschiedlichen Datenbanken, muss der Code nicht geändert werden. Wenn eine andere Datenbank zum Einsatz kommt, muss nur der Treiber ausgetauscht werden (Plattformunabhängigkeit) +- Erhöhung der Produktivität: Es werden keine Kompetenzen für SQL Programierung und Wartung benötigt. + +## 5. Proof of Concept +### 5.1 Docker + +Für die Umsetzung der bisher vorgestellten theoretischen Betrachtungen wird ein Docker Container verwendet. Dieser Container beinhaltet eine relationale und eine dokumentbasierte Datenbank. \ +Mit Jupyter Notebooks soll die Implementierung und Befüllung der Datenbank erprobt werden, um als Startpunkt für die anstehende Softwareentwicklung zu dienen. +```yaml +version: "3.8" +services: + db: + image: postgres:14.1-alpine + container_name: postgres + restart: always + ports: + - "5432:5432" + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + volumes: + - ./PostgreSQL:/var/lib/postgresql/data + pgadmin: + image: dpage/pgadmin4:7.2 + container_name: pgadmin4_container + restart: always + ports: + - "5050:80" + environment: + PGADMIN_DEFAULT_EMAIL: admin@fh-swf.de + PGADMIN_DEFAULT_PASSWORD: admin + volumes: + - ./pgadmin:/var/lib/pgadmin + + mongodb: + image: mongo:7.0.0-rc4 + ports: + - '27017:27017' + volumes: + - ./mongo:/data/db + +``` +|Eintrag|Beschreibung| +|---|---| +|version|Version von docker-compose| +|services|Definition der Services, welche gestartet werden| + +|Option|Beschreibung| +|---|---| +|image|Angabe des zu verwendenden Image| +|restart|Option, um Container erneut zu starten, falls dieser gestoppt wurde| +|environment|Umgebungsvariablen, wie z.B. Username und Passwort| +|Ports|Mapping des Containerports zum Port der Hostmaschine| +|volumes|Angabe eines Volumes zum Persistieren der Containerdaten| + +Beim Ausführen der docker-compose werden in diesem Verzeichnis Ordner für die Datenablage angelegt. Da zum Verfassungszeitpunkt noch nicht feststeht, wie im Projekt der Datenaustausch stattfindet, könnten diese Ordner bzw. die Volumes einfach untereinander ausgetauscht werden. + +Zum Starten des Containers den folgenden Befehl ausführen: +``` +docker-compose -f docker-compose.yml up +``` + +### 5.2 PG Admin +PG Admin ist ein grafisches Administartionstool für Postgres. Wenn der Container gestartet ist, kann man sich über http://localhost:5050/browser/ mit dem Web-UI verbinden. \ +Dieses Tool dient lediglich der Überprüfung von Commits der Tabellen und daten. + +Die Anmeldedaten lauten: +>User: admin@fh-swf.de \ +>Passwort: admin + +![PGAdmin_landing](images/PG_Admin_Board.PNG) + +Zuerst muss der Server angelegt werden, dafür einen Rechtsklick auf Server und den Button „Register“ auswählen. Im geöffneten Dialog muss die Konfiguration festgelegt werden. + +|Reiter|Parameter|Wert| +|---|---|---| +|General|Name|postgres| +|Connection|Host name/address|postgres (siehe docker-compose)| +|Connection|Username|postgres (siehe docker-compose)| +|Connection|Password|postgres (siehe docker-compose)| + +![PGAdmin_Conf](images/PG_Admin_Conf.PNG) + +### 5.3 Erstellen von Mock Daten +**Unternehmensstammdaten:**\ +Um das Konzept und den Umgang mit den ausgewählten Datenbanken zu überprüfen, sollen Daten in die Datenbank geschrieben werden. Hier für wurde auf Statista recherchiert, welches die größten deutschen Unternehmen sind, um einen kleinen Stamm an Unternehmensdaten zu generieren (01_Stammdaten_Unternehmen_HR.csv). / +Die Relation zu den Amtsgerichten ist frei erfunden und wurde nicht recherchiert. +![biggest_companies](images/Statista_Companies.png) + +**Amtsgerichte:** +Die Amtsgerichte sind aus https://www.gerichtsverzeichnis.de/ extrahiert, wobei lediglich 12 Amstgerichte eingefügt wurden (Amtsgerichte.csv). + +**Finanzdaten:** Es wurden für drei Unternehmen (EON, Telekom, BASF) die Finanzdaten bezüglich Umsatz, Ebit und Ebitda auf Statista ermittelt und als separate Dateien gespeichert (BASF_data.csv, Telekom_data.csv, EON_data.csv). + +**Personen:** Die Personentabelle ist frei erfunden. Mit einer Onlinebibliothek wurde 1000 Vor- und Nachnamen erzeugt und gespeichert (Person1000.csv). + +**Personen-Unternehmens-Beziehung:** Diese Tabelle ist zufällig erzeugt und dient lediglich für weitere Experimente. Hierfür wurde ein Python-Skript erstellt, welches mit der mehreren Random-Funktionen die Beziehungen zufälloig generiert. + +**Sentiment:** keine Mock-Daten vorhanden + +**Unternehmens-Unternehmens-Beziehung:** keine Mock-Daten vorhanden + + +### 5.4 Anlegen der relationalen Tabellen +Für das Verbinden zu der Postgre Datenbank und das Anlegen der Tabellen wird ein Jupyter Notebooks verwendet (11_Create_Tables_with_SQL-Alchemy.ipynb). \ +Die benötigten Bibliotheken werden importiert und das Erstellen von Tabellen als Python-Objekte beschrieben. \ +Nach dem Anlegen der Tabellen werden die Mock-Daten in die Datenbank geschrieben. \ +Eine Überprüfung, ob die Daten abgelegt wurden ist sehr einfach mit PGAdmin möglich. +![finance_data](images/finance_data.PNG) + +Das grundsätzliche Vorgehen bei der Verwendung von SQLAlchemy ist: +1. Verbindung zur Datenbank herstellen + ```python + from sqlalchemy import create_engine + # Connection URL für postgres + url = URL.create( + drivername="postgresql", + username="postgres", + password="postgres", + host="localhost", + database="postgres") + + #Verbindung zur Datenbank + engine = create_engine(database_url) + ``` +2. Erstellen einer Klasse als Repräsentation der Tabelle. + > Es ist üblich und empfehlenswert die Klassendefinitionen in einer separaten Datei vorzunehmen (model.py), damit diese auch in andere Modulen importiert und verwendet werden können + ```python + from sqlalchemy.ext.declarative import declarative_base + from sqlalchemy import Column, Integer, String + + Base = declarative_base() + + class MyClass(Base): + __tablename__ = 'company' + + id = Column(Integer, primary_key=True) + name = Column(String) + city = Column(String) + ``` +3. Starten einer Session/Verbindung, um Daten lesen und schreiben zu können + ```python + from sqlalchemy.orm import sessionmaker + + #starte die Verbindung + Session = sessionmaker(bind=engine) + session = Session() + ``` +4. Daten abfragen + ```python + # Alle Daten der Klasse/Tabelle abrufen + data = session.query(MyClass).all() + ``` +5. Daten speichern, wenn z.B. Datensätze in die Datenbank geschrieben werden, muss dies mit der **commit()**-Funktion ausgeführt werden. Das folgende Snippet iteriert durch einen Dataframe, um jede Zeile in die Datenbank zu schreiben. + ```python + for i in range(len(df)): + #get data from dataframe + myNewData=MyClass( + name = str(df['Name'].iloc[i]), + city = str(df['Surname'].iloc[i]) + ) + session.add(myNewData) + session.commit() + ``` + +### 5.5 Abfragen der Datenbank +Der folgende Code-Snippet zeigt, wie man eine Abfrage gestaltet. + +```python +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy import Column, Integer, String + +# Erstelle eine SQLite-Datenbankdatei oder gib den Pfad zur vorhandenen Datei an +url = URL.create( + drivername="postgresql", + username="postgres", + password="postgres", + host="localhost", + database="postgres" +) + +#Erstelle eine Engine zur Verbindung mit der Datenbank +engine = create_engine(url) + +#Erstelle eine Klasse, die eine Tabelle repräsentiert +Base = declarative_base() +class Company(Base): + __tablename__ = 'company' + + hr = Column(Integer(), nullable=False, primary_key=True) + court_id = Column(Integer, ForeignKey("district_court.id"), nullable=False, primary_key=True) + name = Column(String(100), nullable=False) + street = Column(String(100), nullable=False) + zip = Column(Integer(), nullable=False) + city = Column(String(100), nullable=False) + sector = Column(String(100), nullable=False) + + __table_args__ = ( + PrimaryKeyConstraint('hr', 'court_id', name='pk_company_hr_court'), + ) + +#starte die Verbindung zur Datenbank +Session = sessionmaker(bind=engine) +session = Session() + +#Abfrage aller Spalten der Tabelle/Klasse Company +Comps = session.query(Company).all() + +#Gebe die Spalten name, hr und court_id der Tabelle company aus +for comp in Comps: + print(comp.name, comp.hr, comp.court_id) +``` +
+ +## 6. Zusammenfassung + +Die vorliegende Seminararbeit behandelt das Thema der Datenspeicherung mit Fokus auf dem Projekt Transparenzregister. Es wurde erläutert, warum Daten gespeichert werden und welche Art von Daten es gibt.\ +Für das Projekt sind Daten und die Speicherung eine Kernkomponente, um die geforderten Analysen bezüglich Verflechtungen, unternehmerischen Erfolgs und Aussenwahrnehmung zu ermöglichen. + +Es wurden Datencluster definiert und entsprechende Quellen gefunden, welche über geeignete Extraktionspipelines die erforderlichen Informationen extrahieren. Zum Speichern dieser extrahierten Daten wurde ein relationales Modell erarbeitet, um ein Konzept für die folgende Implementierung zu haben. + +Um das Konzept zu überprüfen, wurde ein Proof of Concept durchgeführt, um geeignete Werkzeuge zu erproben und das Modell auf seine Tauglichkeit zu überprüfen. \ +Hierbei wurde ein Dockercontainer eingesetzt, um die Datenbankumgebung bereitzustellen. Mithilfe der SQL-Alchemy-Bibliothek, wurden die Tabellen innerhalb der Datenbank erstellt.\ +Anschließend wurden die Tabellen mit eigenen Mock-Daten befüllt, um die Funktionalität der Datenbank zu testen. + +Insgesamt bietet die Seminararbeit einen umfassenden Überblick über die Bedeutung der Datenspeicherung und die verschiedenen Arten von Datenbanken. +Es wurde ein erstes relationales Modell und ein High level design für die Softwarearchitektur erarbeitet. +Diese Arbeit hat grundsätzliche Fragen geklärt und Verständnis für die Datenspeicherung im Zusammenhang mit dem Projekt Transparenzregister geschaffen und unterstützt die weitere Entwicklung. + +
+ +## Quellen +Klug, Uwe: SQL-Der Einstieg in die deklarative Programmierung, 2. Auflage, Dortmund, Springer, 2017\ +Steiner, Rene: Grundkurs relationale Datenbanken, 10. Auflage, Wiesbaden, Springer, 2021\ +https://backupchain.de/daten-backup-tipps-3-wie-oft-daten-sichern/ \ +https://www.talend.com/de/resources/strukturierte-vs-unstrukturierte-daten/ \ +https://www.sqlservercentral.com/articles/creating-markdown-formatted-text-for-results-from-sql-server-tables \ +https://www.sqlalchemy.org/ \ +https://medium.com/@arthurapp98/using-sqlalchemy-to-create-and-populate-a-postgresql-database-with-excel-data-eb6049d93402 + diff --git a/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.pdf b/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.pdf new file mode 100644 index 0000000..b56efb9 Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.pdf differ diff --git a/documentations/seminararbeiten/Datenspeicherung/01_Actionlist.md b/documentations/seminararbeiten/Datenspeicherung/01_Actionlist.md new file mode 100644 index 0000000..4ea2db5 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/01_Actionlist.md @@ -0,0 +1,18 @@ +### Action List "Datenspeicherung + +- [x] Erstelle ein relationales Schema für Unternehmens- und Finanzdaten, bei welchem die Jahre berücksichtigt werden +- [x] Erstelle docker-compose für postgresgl, pgadmin, neo4j +- [x] Erstelle eine Kurzanleitung für die Handhabung von Docker +- [x] erstelle Jupyter Notebook zum Verbinden mit Datenbank und Anlegen von Tabellen +- [x] Recherchiere nach den 10 größten deutschen Unternehmen und ermittel Finanzdaten (Umsatz, Ebit, Ebitda) +- [x] Erstelle ein Jupyter Notebook um diese Daten in die Datenbank zu übertragen +- [x] Erstelle ein Jupyter Notebook, um die Daten abzufragen +- [x] Erstelle ein Schema für Stimmungsdaten +- [x] Erstelle ein Schema für Verflechtungen +- [ ] Erzeuge Beispieldaten für Stimmung +- [x] Erzeuge Beispieldaten für Verflechtung +- [ ] Erstelle eine Prototypen GUI in Mercury zur einfachen Abfrage von Daten +- [ ] Verwende SQLalchemy, um eine Verbindung zur Datenbank aufzubauen, Tabellen anzulegen und Daten zu schreiben --> +- [x] Ersetze den enumeration type in den Finanzdaten gegen einzelne (eindeutig bezeichnete) Spalten +- [x] Lade das DB Schema hoch, um es den anderen Teammitgliedern bereitzustellen +- [ ] \ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/2023-06-21_Datenspeicherung.pptx b/documentations/seminararbeiten/Datenspeicherung/2023-06-21_Datenspeicherung.pptx new file mode 100644 index 0000000..5487c1c Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/2023-06-21_Datenspeicherung.pptx differ diff --git a/documentations/seminararbeiten/Datenspeicherung/DB_Scheme_19-06-2023.drawio b/documentations/seminararbeiten/Datenspeicherung/DB_Scheme_19-06-2023.drawio new file mode 100644 index 0000000..5ae62f3 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/DB_Scheme_19-06-2023.drawio @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/Docker_get_started.md b/documentations/seminararbeiten/Datenspeicherung/Docker_get_started.md new file mode 100644 index 0000000..7be0986 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Docker_get_started.md @@ -0,0 +1,70 @@ +https://geshan.com.np/blog/2021/12/docker-postgres/ +https://belowthemalt.com/2021/06/09/run-postgresql-and-pgadmin-in-docker-for-local-development-using-docker-compose/ +https://thibaut-deveraux.medium.com/how-to-install-neo4j-with-docker-compose-36e3ba939af0 +https://towardsdatascience.com/how-to-run-postgresql-and-pgadmin-using-docker-3a6a8ae918b5 + +# Installation Docker Desktop +## Starten eines Containers: + +> docker run --name basic-postgres --rm -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=4y7sV96vA9wv46VR -e PGDATA=/var/lib/postgresql/data/pgdata -v /tmp:/var/lib/postgresql/data -p 5432:5432 -it postgres:14.1-alpine + +Dieser Befehl startet einen Container mit dem Postgres14.1-alpine Image, welches von Dockerhub geladen wird. Der Container läuft unter dem Namen basic-postgres + +| Syntax | Attribut | Beschreibung | +| ----------- | ----------- | ----------- | +| basic-postgres | --name | Angabe des Containernamens| +| | --rm | Bei Beendigung des Containers wird das erstellte Dateisystem entfernt| +| |-e| Verwende Umgebungsvariablen | +| POSTGRES_USER | | Umgebungsvariable für den anzulegenden Benutzer: postgres| +|POSTGRES_PASSWORD| | Umgebungsvariable für das anzulegende Passwort: 4y7sV96vA9wv46VR | +| PGDATA | | Umgebungsvariable für den Ort der Datenbank| +| | -v | Einzubindendes Volumen: /tmp:/var/lib/postgresql/data | +| |-p | Angabe des Containerports und des öffentlich zugänglichen Ports | +| | -it | Interactive: der Container bleibt aktiv, damit mit diesem interagiert werden kann | + +Mit einem zweiten Terminalfenster kann man auf die Bash des Containers öffnen und auf die Datenbank zugreifen. + +> docker exec -it basic-postgres /bin/sh + +Die folgenden Befehle starten die Postgres CLI, Ausgabe aller Datenbanken und beendet die CLI. +> Psql –username postgres \ +> \l \ +Exit + +Der Container kann durch Betätigung von STRG + C beendet werden. + +## Docker Compose +Das oben erklärte Vorgehen zum Starten eines Containers, festlegen der Umgebungsvariablen und zusätzliche verlinken zu einer Anwendung wird nun in einer yml-Datei beschrieben, um die Verwaltung und das Erstellen zu vereinfachen. +| | | Beschreibung | +| ----------- | ----------- | ----------- | +|Version | | Version von docker-compose | +|Services| |Definition der Services, wobei jeder ein eigenen docker-run Befehl ausführt.| +| | image | Angabe des zu verwendenden Images | +| | restart | Option um Container erneut zu starten, falls dieser gestoppt wird | +| | Environment | Umgebungsvariablen: Username und Passwort | +| | Ports | Mapping des Containerports zum Port der Hostmaschine | +| | Volumes | Angabe eines Volumes zum Persistieren der Containerdaten, damit nach einem Neustart die Daten wieder verfügbar sind | + + +Nun kann der Container mittels Docker-Compose gestartet werden. +> docker-compose -f /.../docker-compose-postgres.yml up + +## pgAdmin +pgAdmin ist ein grafisches Administrationswerkezug für postgreSQL und macht die oben gezeigte Administration komfortabler. \ +Erreichbar ist das Interface über: http://localhost:5050 \ +Als Login werden die Daten aus der docker-compose verwendet: +>User: admin@fh-swf.de +>Passwort: admin + +### Anlegen eines Servers +Zuerst muss der Server angelegt werden, dafür einen Rechtsklick auf Server und den Button „Register“ auswählen. \ +Im geöffneten Dialog muss die Konfiguration festgelegt werden. + +| Reiter | Parameter | Wert | +| ----------- | ----------- | ----------- | +| General| Name | postgres_docker | +| Connection | Host name/address | local_pgdb (siehe docker-compose) | +| Connection | Username | postgres (siehe docker-compose) | +| Connection | Password | postgres (siehe docker-compose) | + + diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR.csv new file mode 100644 index 0000000..957253e --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR.csv @@ -0,0 +1,13 @@ +HR;Amtsgericht;Name;Strasse;PLZ;Stadt;Branche +12334;2;Volkswagen;Berliner Ring 2;38440;Wolfsburg;Automobil +64566;2;Mercedes-Benz Group;Mercedesstraße 120;70372;Stuttgart;Automobil +5433;3;Allianz;Reinsburgstraße 19;70178;Stuttgart;Versicherung, Finanzdienstleistung +12435;4;BMW Group;Petuelring 130;80809;München;Automobil +12336;5;Deutsche Telekom;Landgrabenweg 151;53227;Bonn;Telekommunikation, Informationstechnologie +559;6;Deutsche Post DHL Group;Charles-de-Gaulle-Str. 20;53113;Bonn;Logistik +555;7;Bosch Group;Robert-Bosch-Platz 1;70839;Gerlingen-Schillerhöhe;Kraftfahrzeugtechnik, Industrietechnik, Gebrauchsgüter, Energie- und Gebäudetechnik +12384;8;BASF;Carl-Bosch-Straße 38;67056;Ludwigshafen;Chemie +64345;9;E.ON;Arnulfstraße 203;80634;München;Energie +4344;10;Munich Re Group;Königinstr. 107;80802;München;Versicherung +866;11;Siemens;Werner-von-Siemens-Straße 1;80333;München;Automatisierung, Digitalisierung +9875;12;Deutsche Bahn;Potsdamer Platz 2;10785;Berlin;Transport, Logistik diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR2.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR2.csv new file mode 100644 index 0000000..1243d08 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR2.csv @@ -0,0 +1,13 @@ +HR;Amtsgericht;Name;Strasse;PLZ;Stadt;Branche +12334;2;Volkswagen;Berliner Ring 2;38440;Wolfsburg;Automobil +64566;2;Mercedes-Benz Group;Mercedesstraße 120;70372;Stuttgart;Automobil +5433;3;Allianz;Reinsburgstraße 19;70178;Stuttgart;Versicherung, Finanzdienstleistung +12334;4;BMW Group;Petuelring 130;80809;München;Automobil +12336;5;Deutsche Telekom;Landgrabenweg 151;53227;Bonn;Telekommunikation, Informationstechnologie +555;6;Deutsche Post DHL Group;Charles-de-Gaulle-Str. 20;53113;Bonn;Logistik +555;7;Bosch Group;Robert-Bosch-Platz 1;70839;Gerlingen-Schillerhöhe;Kraftfahrzeugtechnik, Industrietechnik, Gebrauchsgüter, Energie- und Gebäudetechnik +12384;8;BASF;Carl-Bosch-Straße 38;67056;Ludwigshafen;Chemie +64345;9;E.ON;Arnulfstraße 203;80634;München;Energie +4344;1;Munich Re Group;Königinstr. 107;80802;München;Versicherung +866;1;Siemens;Werner-von-Siemens-Straße 1;80333;München;Automatisierung, Digitalisierung +9875;1;Deutsche Bahn;Potsdamer Platz 2;10785;Berlin;Transport, Logistik diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/11_Create_Tables_with_SQL-Alchemy.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/11_Create_Tables_with_SQL-Alchemy.ipynb new file mode 100644 index 0000000..16b162b --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/11_Create_Tables_with_SQL-Alchemy.ipynb @@ -0,0 +1,1130 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "a768e970", + "metadata": {}, + "outputs": [], + "source": [ + "from sqlalchemy import create_engine, PrimaryKeyConstraint, UniqueConstraint\n", + "from sqlalchemy.engine import URL\n", + "from sqlalchemy import Column, Integer, String, DateTime, Text, Float, null\n", + "from sqlalchemy.orm import declarative_base\n", + "from sqlalchemy.orm import relationship, backref\n", + "from datetime import datetime\n", + "from sqlalchemy.orm import Mapped\n", + "from sqlalchemy.orm import mapped_column\n", + "from sqlalchemy import ForeignKey, ForeignKeyConstraint\n", + "from sqlalchemy.orm import ColumnProperty\n", + "from sqlalchemy.orm import sessionmaker\n", + "import enum\n", + "from sqlalchemy import Integer, Enum\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "id": "c345bbc4", + "metadata": {}, + "source": [ + "# Verbindung zur Datenbank\n", + "Vorbedingung: Datenbank muss vorhanden sein" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "61af408e", + "metadata": {}, + "outputs": [], + "source": [ + "url = URL.create(\n", + " drivername=\"postgresql\",\n", + " username=\"postgres\",\n", + " password=\"postgres\",\n", + " host=\"localhost\",\n", + " database=\"postgres\"\n", + ")\n", + "\n", + "engine = create_engine(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e0849ec8", + "metadata": {}, + "outputs": [], + "source": [ + "#connect to database\n", + "connection = engine.connect()" + ] + }, + { + "cell_type": "markdown", + "id": "61f23c95", + "metadata": {}, + "source": [ + "### Erstelle **Company** für Stammdaten\n", + "Folgend wird ein Objekt erstellt, welches die Tabelle **company** repräsentiert" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4d5a7e71", + "metadata": {}, + "outputs": [], + "source": [ + "#create an object *district_court* which inherits attributes from Base-class\n", + "Base = declarative_base()\n", + "\n", + "class DistrictCourt(Base):\n", + " __tablename__ = 'district_court'\n", + " \n", + " id = Column(Integer(), primary_key=True)\n", + " city = Column(String(100), nullable=False)\n", + " name = Column(String(100), nullable=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "aeef9fd4", + "metadata": {}, + "outputs": [], + "source": [ + "Base.metadata.create_all(engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0f6a3866", + "metadata": {}, + "outputs": [], + "source": [ + "class Company(Base):\n", + " __tablename__ = 'company'\n", + "\n", + " hr = Column(Integer(), nullable=False, primary_key=True)\n", + " court_id = Column(Integer, ForeignKey(\"district_court.id\"), nullable=False, primary_key=True)\n", + " name = Column(String(100), nullable=False)\n", + " street = Column(String(100), nullable=False)\n", + " zip = Column(Integer(), nullable=False)\n", + " city = Column(String(100), nullable=False)\n", + " sector = Column(String(100), nullable=False)\n", + "\n", + " __table_args__ = (\n", + " PrimaryKeyConstraint('hr', 'court_id', name='pk_company_hr_court'),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3d649e2e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Table('company', MetaData(), Column('hr', Integer(), table=, primary_key=True, nullable=False), Column('court_id', Integer(), ForeignKey('district_court.id'), table=, primary_key=True, nullable=False), Column('name', String(length=100), table=, nullable=False), Column('street', String(length=100), table=, nullable=False), Column('zip', Integer(), table=, nullable=False), Column('city', String(length=100), table=, nullable=False), Column('sector', String(length=100), table=, nullable=False), schema=None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#check if table-object is created\n", + "Company.__table__" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "21ff2ec4", + "metadata": {}, + "outputs": [], + "source": [ + "class Finance(Base):\n", + " __tablename__ = 'finance'\n", + "\n", + " id = Column(Integer, primary_key=True)\n", + " company_hr = Column(Integer)\n", + " company_court = Column(Integer)\n", + " date = Column(DateTime, default=datetime.now)\n", + " total_volume = Column(Float)\n", + " ebit = Column(Float)\n", + " ebitda = Column(Float)\n", + " ebit_margin = Column(Float)\n", + " total_balance = Column(Float)\n", + " equity = Column(Float)\n", + " debt = Column(Float)\n", + " return_on_equity = Column(Float)\n", + " capital_turnover_rate = Column(Float)\n", + "\n", + " company = relationship(\"Company\")\n", + "\n", + " __table_args__ = (\n", + " ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a87ffa9", + "metadata": {}, + "outputs": [], + "source": [ + "#create own enumeration type and sentiment object\n", + "sentiment_type=Enum(\"employee_voting\",\"sustainability\",\"environmental_aspects\",\"perception\", name=\"sentiment_type\", create_type=False)\n", + "\n", + "class Sentiment(Base):\n", + " __tablename__ = 'sentiment'\n", + "\n", + " id = Column(Integer(), primary_key=True)\n", + " #company_hr = mapped_column(ForeignKey(\"company.hr\"))\n", + " #company_court = mapped_column(ForeignKey(\"company.court_id\"))\n", + " company_hr = Column(Integer)\n", + " company_court = Column(Integer)\n", + " date = Column(DateTime(), default=datetime.now)\n", + " type = Column(sentiment_type,nullable=False)\n", + " value =Column(Float(),nullable=False)\n", + " source=Column(String(100))\n", + " \n", + " sentiment = relationship('Company')\n", + " __table_args__ = (\n", + " ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8e958000", + "metadata": {}, + "outputs": [], + "source": [ + "#create person object\n", + "class Person(Base):\n", + " __tablename__ = 'person'\n", + "\n", + " id = Column(Integer(), primary_key=True)\n", + " name=Column(String(100), nullable=False)\n", + " surname=Column(String(100), nullable=False)\n", + " works_for=Column(String(100))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f8e998e9", + "metadata": {}, + "outputs": [], + "source": [ + "#create own relation type and person_relation object\n", + "rel_type=Enum(\"Executive\",\"Auditor\",\"Supervisory_Board\",\"Managing_Director\",\"Authorized_Representive\",\"Final_Auditor\", name=\"rel_type\", create_type=False)\n", + "\n", + "class Person_Relation(Base):\n", + " __tablename__ = 'person_relation'\n", + "\n", + " id = Column(Integer(), primary_key=True)\n", + " #company_hr = mapped_column(ForeignKey(\"company.hr\"))\n", + " #company_court = mapped_column(ForeignKey(\"company.court_id\"))\n", + " company_hr = Column(Integer)\n", + " company_court = Column(Integer)\n", + " person_id = mapped_column(ForeignKey(\"person.id\"))\n", + " date_from = Column(DateTime(), default=datetime.now)\n", + " date_to = Column(DateTime(), default=datetime.now) \n", + " relation=Column(rel_type, nullable=False)\n", + " \n", + " #company = relationship(\"Company\")\n", + " #person = relationship(\"Person\", foreign_keys=[person_id])\n", + " #company = relationship('Company', foreign_keys=[company_hr,company_court])\n", + " __table_args__ = (\n", + " ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6e5665fb", + "metadata": {}, + "outputs": [], + "source": [ + "#create own relation type and company_relation object\n", + "rel_type_comp=Enum(\"participates_with\",\"has_shares_of\",\"is_supplied_by\",\"works_with\", name=\"rel_type_comp\", create_type=False)\n", + "\n", + "class Company_Relation(Base):\n", + " __tablename__ = 'company_relation'\n", + "\n", + " id = Column(Integer(), primary_key=True)\n", + " company1_id = Column(Integer,nullable=False)\n", + " company2_id= Column(Integer,nullable=False)\n", + " date_from = Column(DateTime(), default=datetime.now)\n", + " date_to = Column(DateTime(), default=datetime.now) \n", + " relation=Column(rel_type_comp, nullable=False)\n", + " \n", + " #company = relationship(\"Company\")\n", + "\n", + " __table_args__ = {'extend_existing': True}" + ] + }, + { + "cell_type": "markdown", + "id": "901f33c0", + "metadata": {}, + "source": [ + "# Erstelle die Tabellen" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "2cfbe6a8", + "metadata": {}, + "outputs": [], + "source": [ + "Base.metadata.create_all(engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "f8ee6287", + "metadata": {}, + "outputs": [], + "source": [ + "Session = sessionmaker(bind=engine)\n", + "session = Session()" + ] + }, + { + "cell_type": "markdown", + "id": "c0d962f0", + "metadata": {}, + "source": [ + "# Befülle die Tabellen *district_court*" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "21c9db99", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('Amtsgerichte.csv', sep=';') \n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "09fb47ea", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " court=DistrictCourt( \n", + " city = str(df['Stadt'].iloc[i]),\n", + " name = str(df['Name'].iloc[i]))\n", + " \n", + " session.add(court)\n", + " session.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "0efda914", + "metadata": {}, + "source": [ + "# Befülle die Tabellen *company*" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ba5c15ef", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('01_Stammdaten_Unternehmen_HR2.csv', sep=';',encoding=\"ISO-8859-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fc8afa09", + "metadata": {}, + "outputs": [ + { + "ename": "IntegrityError", + "evalue": "(psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint \"pk_company_hr_court\"\nDETAIL: Key (hr, court_id)=(12334, 2) already exists.\n\n[SQL: INSERT INTO company (hr, court_id, name, street, zip, city, sector) VALUES (%(hr)s, %(court_id)s, %(name)s, %(street)s, %(zip)s, %(city)s, %(sector)s)]\n[parameters: {'hr': 12334, 'court_id': 2, 'name': 'Volkswagen', 'street': 'Berliner Ring 2', 'zip': 38440, 'city': 'Wolfsburg', 'sector': 'Automobil'}]\n(Background on this error at: https://sqlalche.me/e/20/gkpj)", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mUniqueViolation\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[1;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[0;32m 1967\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1968\u001b[1;33m self.dialect.do_execute(\n\u001b[0m\u001b[0;32m 1969\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 920\u001b[1;33m \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 921\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mUniqueViolation\u001b[0m: duplicate key value violates unique constraint \"pk_company_hr_court\"\nDETAIL: Key (hr, court_id)=(12334, 2) already exists.\n", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mIntegrityError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 10\u001b[0m sector=str(df['Branche'].iloc[i]))\n\u001b[0;32m 11\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\session.py\u001b[0m in \u001b[0;36mcommit\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1904\u001b[0m \u001b[0mtrans\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_autobegin_t\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1905\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1906\u001b[1;33m \u001b[0mtrans\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_to_root\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1907\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1908\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprepare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnested\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\session.py\u001b[0m in \u001b[0;36m_prepare_impl\u001b[1;34m(self)\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\state_changes.py\u001b[0m in \u001b[0;36m_go\u001b[1;34m(fn, self, *arg, **kw)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_next_state\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_StateChangeStates\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCHANGE_IN_PROGRESS\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0mret_value\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1196\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1197\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1198\u001b[0m raise exc.FlushError(\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\session.py\u001b[0m in \u001b[0;36mflush\u001b[1;34m(self, objects)\u001b[0m\n\u001b[0;32m 4152\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4153\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flushing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4154\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4155\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4156\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_flushing\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\session.py\u001b[0m in \u001b[0;36m_flush\u001b[1;34m(self, objects)\u001b[0m\n\u001b[0;32m 4289\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4290\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msafe_reraise\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4291\u001b[1;33m \u001b[0mtransaction\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrollback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_capture_exception\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4292\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4293\u001b[0m def bulk_save_objects(\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\util\\langhelpers.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_, value, traceback)\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mexc_value\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;31m# remove potential circular references\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc_tb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 148\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;31m# remove potential circular references\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\session.py\u001b[0m in \u001b[0;36m_flush\u001b[1;34m(self, objects)\u001b[0m\n\u001b[0;32m 4249\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_on_events\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4250\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4251\u001b[1;33m \u001b[0mflush_context\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4252\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4253\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_warn_on_events\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\unitofwork.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 465\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 466\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtopological\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdependencies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpostsort_actions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 467\u001b[1;33m \u001b[0mrec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 468\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 469\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfinalize_flush_changes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\unitofwork.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, uow)\u001b[0m\n\u001b[0;32m 642\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreload_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"sqlalchemy.orm.persistence\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 643\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 644\u001b[1;33m util.preloaded.orm_persistence.save_obj(\n\u001b[0m\u001b[0;32m 645\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 646\u001b[0m \u001b[0muow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstates_for_mapper_hierarchy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmapper\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\persistence.py\u001b[0m in \u001b[0;36msave_obj\u001b[1;34m(base_mapper, states, uowtransaction, single)\u001b[0m\n\u001b[0;32m 91\u001b[0m )\n\u001b[0;32m 92\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 93\u001b[1;33m _emit_insert_statements(\n\u001b[0m\u001b[0;32m 94\u001b[0m \u001b[0mbase_mapper\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 95\u001b[0m \u001b[0muowtransaction\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\orm\\persistence.py\u001b[0m in \u001b[0;36m_emit_insert_statements\u001b[1;34m(base_mapper, uowtransaction, mapper, table, insert, bookkeeping, use_orm_insert_stmt, execution_options)\u001b[0m\n\u001b[0;32m 1038\u001b[0m \u001b[0mmultiparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mrec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrecords\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1039\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1040\u001b[1;33m result = connection.execute(\n\u001b[0m\u001b[0;32m 1041\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultiparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexecution_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1042\u001b[0m )\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, statement, parameters, execution_options)\u001b[0m\n\u001b[0;32m 1411\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mObjectNotExecutableError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1412\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1413\u001b[1;33m return meth(\n\u001b[0m\u001b[0;32m 1414\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1415\u001b[0m \u001b[0mdistilled_parameters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\sql\\elements.py\u001b[0m in \u001b[0;36m_execute_on_connection\u001b[1;34m(self, connection, distilled_params, execution_options)\u001b[0m\n\u001b[0;32m 481\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mTYPE_CHECKING\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 482\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExecutable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 483\u001b[1;33m return connection._execute_clauseelement(\n\u001b[0m\u001b[0;32m 484\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdistilled_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexecution_options\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 485\u001b[0m )\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_clauseelement\u001b[1;34m(self, elem, distilled_parameters, execution_options)\u001b[0m\n\u001b[0;32m 1635\u001b[0m \u001b[0mlinting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdialect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompiler_linting\u001b[0m \u001b[1;33m|\u001b[0m \u001b[0mcompiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWARN_LINTING\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1636\u001b[0m )\n\u001b[1;32m-> 1637\u001b[1;33m ret = self._execute_context(\n\u001b[0m\u001b[0;32m 1638\u001b[0m \u001b[0mdialect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1639\u001b[0m \u001b[0mdialect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecution_ctx_cls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_compiled\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_execute_context\u001b[1;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[0;32m 1844\u001b[0m )\n\u001b[0;32m 1845\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1846\u001b[1;33m return self._exec_single_context(\n\u001b[0m\u001b[0;32m 1847\u001b[0m \u001b[0mdialect\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1848\u001b[0m )\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[1;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[0;32m 1985\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1986\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1987\u001b[1;33m self._handle_dbapi_exception(\n\u001b[0m\u001b[0;32m 1988\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1989\u001b[0m )\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_handle_dbapi_exception\u001b[1;34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001b[0m\n\u001b[0;32m 2342\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mshould_wrap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2343\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2344\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0msqlalchemy_exception\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2345\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2346\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\base.py\u001b[0m in \u001b[0;36m_exec_single_context\u001b[1;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[0;32m 1966\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1967\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mevt_handled\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1968\u001b[1;33m self.dialect.do_execute(\n\u001b[0m\u001b[0;32m 1969\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr_statement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meffective_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1970\u001b[0m )\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sqlalchemy\\engine\\default.py\u001b[0m in \u001b[0;36mdo_execute\u001b[1;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[0;32m 918\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 920\u001b[1;33m \u001b[0mcursor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 921\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 922\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdo_execute_no_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcursor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatement\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mIntegrityError\u001b[0m: (psycopg2.errors.UniqueViolation) duplicate key value violates unique constraint \"pk_company_hr_court\"\nDETAIL: Key (hr, court_id)=(12334, 2) already exists.\n\n[SQL: INSERT INTO company (hr, court_id, name, street, zip, city, sector) VALUES (%(hr)s, %(court_id)s, %(name)s, %(street)s, %(zip)s, %(city)s, %(sector)s)]\n[parameters: {'hr': 12334, 'court_id': 2, 'name': 'Volkswagen', 'street': 'Berliner Ring 2', 'zip': 38440, 'city': 'Wolfsburg', 'sector': 'Automobil'}]\n(Background on this error at: https://sqlalche.me/e/20/gkpj)" + ] + } + ], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " comp=Company( \n", + " hr= int(df['HR'].iloc[i]),\n", + " court_id= int(df['Amtsgericht'].iloc[i]),\n", + " name = str(df['Name'].iloc[i]),\n", + " street = str(df['Strasse'].iloc[i]),\n", + " zip = int(df['PLZ'].iloc[i]),\n", + " city = str(df['Stadt'].iloc[i]),\n", + " sector=str(df['Branche'].iloc[i]))\n", + " session.add(comp)\n", + " session.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "bc23ecab", + "metadata": {}, + "source": [ + "# Befülle die Tabellen *company*" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "d4547b1d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Company_HR', 'Company_Court', 'Jahr', 'Umsatz', 'Ebit', 'EBITDA'], dtype='object')" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv('BASF_Data.csv', sep=';', decimal=\",\",encoding=\"ISO-8859-1\") \n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d1d86e88", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " fin=Finance( \n", + " company_hr = int(df['Company_HR'].iloc[i]),\n", + " company_court = int(df['Company_Court'].iloc[i]),\n", + " date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", + " total_volume=str(df['Umsatz'].iloc[i]),\n", + " ebit=str(df['Ebit'].iloc[i]) ,\n", + " ebitda=str(df['EBITDA'].iloc[i]),\n", + " ebit_margin=null(),\n", + " total_balance=null(),\n", + " equity=null(),\n", + " debt=null(),\n", + " return_on_equity=null(),\n", + " capital_turnover_rate=null())\n", + " \n", + " session.add(fin)\n", + " session.commit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdae69a9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "0e9ebfae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Company_HR', 'Company_Court', 'Jahr', 'Umsatz', 'Ebit', 'EBITDA'], dtype='object')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv('Telekom_Data.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "01fae1da", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " fin=Finance( \n", + " company_hr = int(df['Company_HR'].iloc[i]),\n", + " company_court = int(df['Company_Court'].iloc[i]), \n", + " date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", + " total_volume=str(df['Umsatz'].iloc[i]),\n", + " ebit=str(df['Ebit'].iloc[i]) ,\n", + " ebitda=str(df['EBITDA'].iloc[i]),\n", + " ebit_margin=null(),\n", + " total_balance=null(),\n", + " equity=null(),\n", + " debt=null(),\n", + " return_on_equity=null(),\n", + " capital_turnover_rate=null())\n", + " \n", + " session.add(fin)\n", + " session.commit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc29367e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "f48deb0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Company_HR', 'Company_Court', 'Jahr', 'Umsatz', 'Ebit', 'EBITDA'], dtype='object')" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv('EON_Data.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "a01ba01b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " fin=Finance( \n", + " company_hr = int(df['Company_HR'].iloc[i]),\n", + " company_court = int(df['Company_Court'].iloc[i]),\n", + " date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", + " total_volume=str(df['Umsatz'].iloc[i]),\n", + " ebit=str(df['Ebit'].iloc[i]) ,\n", + " ebitda=str(df['EBITDA'].iloc[i]),\n", + " ebit_margin=null(),\n", + " total_balance=null(),\n", + " equity=null(),\n", + " debt=null(),\n", + " return_on_equity=null(),\n", + " capital_turnover_rate=null())\n", + " \n", + " session.add(fin)\n", + " session.commit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f47963f1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "41a3312d", + "metadata": {}, + "source": [ + "# Befülle die Tabelle Person" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "8dcd4c4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Name', 'Surname'], dtype='object')" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv('person1000.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "a2307a07", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " per=Person( \n", + " name = str(df['Name'].iloc[i]),\n", + " surname = str(df['Surname'].iloc[i])\n", + ")\n", + " \n", + " session.add(per)\n", + " session.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "933b3ec6", + "metadata": {}, + "source": [ + "# Erzeuge die Tabelle Person-Beziehungen" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "3f38817a", + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "relation=['Executive',\n", + "'Auditor',\n", + "'Supervisory_Board',\n", + "'Managing_Director',\n", + "'Authorized_Representive',\n", + "'Final_Auditor'\n", + "]\n", + "\n", + "hr_court=[\n", + "(12334,2),\n", + "(64566,2),\n", + "(5433,3),\n", + "(12334,4),\n", + "(12336,5),\n", + "(555,6),\n", + "(555,7),\n", + "(12384,8),\n", + "(64345,9),\n", + "(4344,1),\n", + "(866,1),\n", + "(9875,1)\n", + "]\n", + "\n", + "edges=[]\n", + "\n", + "#create amount of combinations\n", + "for i in range(2000):\n", + " rand_comp=random.randint(0,11)\n", + " comp_hr=hr_court[rand_comp][0]\n", + " comp_court=hr_court[rand_comp][1]\n", + " \n", + " rand_person=random.randint(1,999)\n", + " rand_relation=random.randint(0,5)\n", + " rand_year_start=random.randint(2005,2023)\n", + " if rand_year_start<2023:\n", + " year_to=rand_year_start+1\n", + " else:\n", + " pass\n", + " # year_to=None\n", + " \n", + " #edges.append((rand_company,df['Name'].iloc[rand_person],rand_year_start,year_to,relation[rand_relation])) \n", + " edges.append((comp_hr,comp_court,rand_person,int(rand_year_start),year_to,relation[rand_relation])) \n", + " \n", + "#edges.to_csv('edges.csv')\n", + "col=['Company_HR','Company_Court','Person_ID','Year_From','Year_To','Relation_Type']\n", + "dfEdges=pd.DataFrame(edges,columns=col)\n", + "dfEdges.to_csv('edges.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "7d0063c7", + "metadata": {}, + "source": [ + "# Befülle die Tabelle Person-Beziehungen" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "5ba301ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0Company_HRCompany_CourtPerson_IDYear_FromYear_ToRelation_Type
009875187720052006Managing_Director
115433320320082009Auditor
2264345933620072008Executive
3355576320122013Executive
4464345964620162017Auditor
........................
19951995555629620232012Supervisory_Board
1996199664566264320152016Auditor
1997199764345925720112012Auditor
1998199864345927720092010Authorized_Representive
19991999866136920102011Final_Auditor
\n", + "

2000 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Company_HR Company_Court Person_ID Year_From Year_To \\\n", + "0 0 9875 1 877 2005 2006 \n", + "1 1 5433 3 203 2008 2009 \n", + "2 2 64345 9 336 2007 2008 \n", + "3 3 555 7 63 2012 2013 \n", + "4 4 64345 9 646 2016 2017 \n", + "... ... ... ... ... ... ... \n", + "1995 1995 555 6 296 2023 2012 \n", + "1996 1996 64566 2 643 2015 2016 \n", + "1997 1997 64345 9 257 2011 2012 \n", + "1998 1998 64345 9 277 2009 2010 \n", + "1999 1999 866 1 369 2010 2011 \n", + "\n", + " Relation_Type \n", + "0 Managing_Director \n", + "1 Auditor \n", + "2 Executive \n", + "3 Executive \n", + "4 Auditor \n", + "... ... \n", + "1995 Supervisory_Board \n", + "1996 Auditor \n", + "1997 Auditor \n", + "1998 Authorized_Representive \n", + "1999 Final_Auditor \n", + "\n", + "[2000 rows x 7 columns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df=pd.read_csv('edges.csv', sep=',') \n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "64e79d92", + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " edges=Person_Relation( \n", + " company_hr = int(df['Company_HR'].iloc[i]),\n", + " company_court = int(df['Company_Court'].iloc[i]),\n", + " person_id = int(df['Person_ID'].iloc[i]),\n", + " date_from=datetime.strptime(str(df['Year_From'].iloc[i]), '%Y'),\n", + " date_to=datetime.strptime(str(df['Year_To'].iloc[i]), '%Y'),\n", + " relation = str(df['Relation_Type'].iloc[i])\n", + ")\n", + " \n", + " session.add(edges)\n", + " session.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "e83da6d1", + "metadata": {}, + "source": [ + "# Abfragen in SQL Alchemy" + ] + }, + { + "cell_type": "markdown", + "id": "1c7ecb06", + "metadata": {}, + "source": [ + "Der folgende Code-Snippet zeigt, wie man eine Abfrage gestaltet.\n", + "\n", + "```python\n", + "from sqlalchemy import create_engine\n", + "from sqlalchemy.orm import sessionmaker\n", + "from sqlalchemy.ext.declarative import declarative_base\n", + "from sqlalchemy import Column, Integer, String\n", + "\n", + "# Erstelle eine SQLite-Datenbankdatei oder gib den Pfad zur vorhandenen Datei an\n", + "url = URL.create(\n", + " drivername=\"postgresql\",\n", + " username=\"postgres\",\n", + " password=\"postgres\",\n", + " host=\"localhost\",\n", + " database=\"postgres\"\n", + ")\n", + "\n", + "#Erstelle eine Engine zur Verbindung mit der Datenbank\n", + "engine = create_engine(url)\n", + "\n", + "#Erstelle eine Klasse, die eine Tabelle repräsentiert\n", + "Base = declarative_base()\n", + "class Company(Base):\n", + " __tablename__ = 'company'\n", + "\n", + " hr = Column(Integer(), nullable=False, primary_key=True)\n", + " court_id = Column(Integer, ForeignKey(\"district_court.id\"), nullable=False, primary_key=True)\n", + " name = Column(String(100), nullable=False)\n", + " street = Column(String(100), nullable=False)\n", + " zip = Column(Integer(), nullable=False)\n", + " city = Column(String(100), nullable=False)\n", + " sector = Column(String(100), nullable=False)\n", + "\n", + " __table_args__ = (\n", + " PrimaryKeyConstraint('hr', 'court_id', name='pk_company_hr_court'),\n", + " )\n", + "\n", + "#starte die Verbindung zur Datenbank\n", + "Session = sessionmaker(bind=engine)\n", + "session = Session()\n", + "\n", + "#Abfrage aller Spalten der Tabelle/Klasse Company\n", + "Comps = session.query(Company).all()\n", + "\n", + "#Gebe die Spalten name, hr und court_id der Tabelle company aus\n", + "for comp in Comps:\n", + " print(comp.name, comp.hr, comp.court_id)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "daacb128", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volkswagen 12334 2\n", + "Mercedes-Benz Group 64566 2\n", + "Allianz 5433 3\n", + "BMW Group 12334 4\n", + "Deutsche Telekom 12336 5\n", + "Deutsche Post DHL Group 555 6\n", + "Bosch Group 555 7\n", + "BASF 12384 8\n", + "E.ON 64345 9\n", + "Munich Re Group 4344 1\n", + "Siemens 866 1\n", + "Deutsche Bahn 9875 1\n" + ] + } + ], + "source": [ + "Comps = session.query(Company).all()\n", + "\n", + "for comp in Comps:\n", + " print(comp.name, comp.hr, comp.court_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36b76760", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "83a869b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total_volume|ebit|ebitda\n", + "29473.0 nan nan\n", + "35946.0 nan nan\n", + "32500.0 nan nan\n", + "32216.0 nan nan\n", + "33361.0 nan nan\n", + "37537.0 nan nan\n", + "42745.0 5830.0 nan\n", + "52610.0 6750.0 nan\n", + "57951.0 7316.0 nan\n", + "62304.0 6463.0 9562.0\n", + "50693.0 3677.0 7388.0\n", + "63873.0 7761.0 11131.0\n", + "73497.0 8586.0 11993.0\n", + "72129.0 6742.0 10009.0\n", + "73973.0 7160.0 10432.0\n", + "74326.0 7626.0 11043.0\n", + "70449.0 6248.0 10649.0\n", + "57550.0 6275.0 10526.0\n", + "61223.0 7587.0 10765.0\n", + "60220.0 5974.0 8970.0\n", + "59316.0 4201.0 8185.0\n", + "59149.0 -191.0 6494.0\n", + "78598.0 7677.0 11355.0\n", + "87327.0 6548.0 10748.0\n" + ] + } + ], + "source": [ + "Fin = session.query(Finance).filter(Finance.company_hr == 12384).all()\n", + "\n", + "print(\"total_volume|ebit|ebitda\")\n", + "for n in Fin:\n", + " print(n.total_volume, n.ebit, n.ebitda)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1b807a00", + "metadata": {}, + "outputs": [], + "source": [ + "users = session.query(User).filter(User.name == 'John').all()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d31ed85f", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Amtsgerichte.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Amtsgerichte.csv new file mode 100644 index 0000000..6f74641 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Amtsgerichte.csv @@ -0,0 +1,15 @@ +Stadt;Name +Aschaffenburg;Amtsgericht Aschaffenburg +Bamberg;Amtsgericht Bamberg +Bayreuth;Amtsgericht Bayreuth +Duesseldorf;Amtsgericht Duesseldorf +Duisburg;Amtsgericht Duisburg +Duisburg;Amtsgericht Duisburg-Hamborn +Duisburg;Amtsgericht Duisburg-Ruhrort +Oberhausen;Amtsgericht Oberhausen +Wuppertal;Amtsgericht Wuppertal +Berlin;Amtsgericht Mitte +Berlin;Amtsgericht Ost +Berlin;Amtsgericht West +Berlin;Amtsgericht Nord +Berlin;Amtsgericht Sued \ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/BASF_Data.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/BASF_Data.csv new file mode 100644 index 0000000..3447e50 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/BASF_Data.csv @@ -0,0 +1,25 @@ +Company_HR;Company_Court;Jahr;Umsatz;Ebit;EBITDA +;12384;8;1999;29473;; +;12384;8;2000;35946;; +;12384;8;2001;32500;; +;12384;8;2002;32216;; +;12384;8;2003;33361;; +;12384;8;2004;37537;; +;12384;8;2005;42745;5830; +;12384;8;2006;52610;6750; +;12384;8;2007;57951;7316; +;12384;8;2008;62304;6463;9562 +;12384;8;2009;50693;3677;7388 +;12384;8;2010;63873;7761;11131 +;12384;8;2011;73497;8586;11993 +;12384;8;2012;72129;6742;10009 +;12384;8;2013;73973;7160;10432 +;12384;8;2014;74326;7626;11043 +;12384;8;2015;70449;6248;10649 +;12384;8;2016;57550;6275;10526 +;12384;8;2017;61223;7587;10765 +;12384;8;2018;60220;5974;8970 +;12384;8;2019;59316;4201;8185 +;12384;8;2020;59149;-191;6494 +;12384;8;2021;78598;7677;11355 +;12384;8;2022;87327;6548;10748 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/EON_Data.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/EON_Data.csv new file mode 100644 index 0000000..ecbe861 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/EON_Data.csv @@ -0,0 +1,17 @@ +Company_HR;Company_Court;Jahr;Umsatz;Ebit;EBITDA +;64345;9;2007;66912;; +;64345;9;2008;84873;; +;64345;9;2009;79974;; +;64345;9;2010;92863;; +;64345;9;2011;112954;; +;64345;9;2012;132093;7010; +;64345;9;2013;119615;5640; +;64345;9;2014;113095;4700; +;64345;9;2015;42656;3600; +;64345;9;2016;38173;3100; +;64345;9;2017;37965;3100; +;64345;9;2018;30084;2990;4840 +;64345;9;2019;41284;3220;5558 +;64345;9;2020;60944;3780;6905 +;64345;9;2021;77358;4720;7889 +;64345;9;2022;115660;5200;8059 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Connect_to_Database.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Connect_to_Database.ipynb new file mode 100644 index 0000000..6c14e0c --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Connect_to_Database.ipynb @@ -0,0 +1,203 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "68f82dc2", + "metadata": {}, + "outputs": [], + "source": [ + "#pip install psycopg2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "db00bbea", + "metadata": {}, + "outputs": [], + "source": [ + "#pip install --user ipython-sql" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "74ae19d0", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2\n", + "import sql" + ] + }, + { + "cell_type": "markdown", + "id": "29fdd335", + "metadata": {}, + "source": [ + "## Verbinde zur Postgre Datenbank im Docker" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "183d5c5f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database connected successfully\n" + ] + } + ], + "source": [ + "conn = psycopg2.connect(\n", + " host=\"localhost\",\n", + " database=\"transparenz\",\n", + " user=\"postgres\",\n", + " password=\"postgres\")\n", + "\n", + "print(\"Database connected successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "e299175a", + "metadata": {}, + "source": [ + "## Anlegen der Tabelle **Company**\n", + "- Erstellen eines Cursors\n", + "- für einen Primary key benötigt man den Typ \"Serial\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4ae6b45d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table Created successfully\n" + ] + } + ], + "source": [ + "conn.autocommit = True\n", + "#create a table\n", + "cur = conn.cursor() # creating a cursor\n", + " \n", + "# executing queries to create table\n", + "cur.execute(\"\"\"\n", + "CREATE TABLE company\n", + "(\n", + " ID SERIAL PRIMARY KEY NOT NULL,\n", + " NAME TEXT NOT NULL,\n", + " STREET TEXT NOT NULL,\n", + " ZIP INT NOT NULL,\n", + " CITY VARCHAR(100) NOT NULL,\n", + " SECTOR VARCHAR(200) NOT NULL)\n", + "\"\"\")\n", + " \n", + "# commit the changes\n", + "conn.commit() # <--- makes sure the change is shown in the database\n", + "#conn.close()\n", + "#cur.close()\n", + "print(\"Table Created successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "0d59c0e9", + "metadata": {}, + "source": [ + "## Anlegen der Tabelle **finance**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "32e46d88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table Created successfully\n" + ] + } + ], + "source": [ + "conn.autocommit = True\n", + "#create a table\n", + "cur = conn.cursor() # creating a cursor\n", + " \n", + "# executing queries to create table\n", + "cur.execute(\"\"\"\n", + "CREATE TABLE finance\n", + "(\n", + " FINANCE_ID SERIAL PRIMARY KEY NOT NULL,\n", + " COMPANY_ID INT NOT NULL,\n", + " KIND_OF VARCHAR(50) NOT NULL,\n", + " DATE DATE NOT NULL,\n", + " SUM FLOAT NOT NULL)\n", + "\"\"\")\n", + " \n", + "# commit the changes\n", + "conn.commit() # <--- makes sure the change is shown in the database\n", + "#conn.close()\n", + "#cur.close()\n", + "print(\"Table Created successfully\")" + ] + }, + { + "attachments": { + "grafik.png": { + "image/png": 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b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Stammdaten_Unternehmen.csv new file mode 100644 index 0000000..8136ce6 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Stammdaten_Unternehmen.csv @@ -0,0 +1,13 @@ +Name;Straße;PLZ;Stadt;Branche +Volkswagen;Berliner Ring 2;38440;Wolfsburg;Automobil +Mercedes-Benz Group;Mercedesstraße 120;70372;Stuttgart;Automobil +Allianz;Reinsburgstraße 19;70178;Stuttgart;Versicherung, Finanzdienstleistung +BMW Group;Petuelring 130;80809;München;Automobil +Deutsche Telekom;Landgrabenweg 151;53227;Bonn;Telekommunikation, Informationstechnologie +Deutsche Post DHL Group;Charles-de-Gaulle-Str. 20;53113;Bonn;Logistik +Bosch Group;Robert-Bosch-Platz 1;70839;Gerlingen-Schillerhöhe;Kraftfahrzeugtechnik, Industrietechnik, Gebrauchsgüter, Energie- und Gebäudetechnik +BASF;Carl-Bosch-Straße 38;67056;Ludwigshafen;Chemie +E.ON;Arnulfstraße 203;80634;München;Energie +Munich Re Group;Königinstr. 107;80802;München;Versicherung +Siemens;Werner-von-Siemens-Straße 1;80333;München;Automatisierung, Digitalisierung +Deutsche Bahn;Potsdamer Platz 2;10785;Berlin;Transport, Logistik diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/02_Connect_to_Database_publish_Company_Data.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/02_Connect_to_Database_publish_Company_Data.ipynb new file mode 100644 index 0000000..7a23bcb --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/02_Connect_to_Database_publish_Company_Data.ipynb @@ -0,0 +1,330 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dbd6eae9", + "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": "code", + "execution_count": 2, + "id": "8b447b09", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('01_Stammdaten_Unternehmen.csv', sep=';') " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5fc7b7d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameStraßePLZStadtBranche
0VolkswagenBerliner Ring 238440WolfsburgAutomobil
1Mercedes-Benz GroupMercedesstraße 12070372StuttgartAutomobil
2AllianzReinsburgstraße 1970178StuttgartVersicherung, Finanzdienstleistung
3BMW GroupPetuelring 13080809MünchenAutomobil
4Deutsche TelekomLandgrabenweg 15153227BonnTelekommunikation, Informationstechnologie
5Deutsche Post DHL GroupCharles-de-Gaulle-Str. 2053113BonnLogistik
6Bosch GroupRobert-Bosch-Platz 170839Gerlingen-SchillerhöheKraftfahrzeugtechnik, Industrietechnik, Gebrau...
7BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
8E.ONArnulfstraße 20380634MünchenEnergie
9Munich Re GroupKöniginstr. 10780802MünchenVersicherung
10SiemensWerner-von-Siemens-Straße 180333MünchenAutomatisierung, Digitalisierung
11Deutsche BahnPotsdamer Platz 210785BerlinTransport, Logistik
\n", + "
" + ], + "text/plain": [ + " Name Straße PLZ \\\n", + "0 Volkswagen Berliner Ring 2 38440 \n", + "1 Mercedes-Benz Group Mercedesstraße 120 70372 \n", + "2 Allianz Reinsburgstraße 19 70178 \n", + "3 BMW Group Petuelring 130 80809 \n", + "4 Deutsche Telekom Landgrabenweg 151 53227 \n", + "5 Deutsche Post DHL Group Charles-de-Gaulle-Str. 20 53113 \n", + "6 Bosch Group Robert-Bosch-Platz 1 70839 \n", + "7 BASF Carl-Bosch-Straße 38 67056 \n", + "8 E.ON Arnulfstraße 203 80634 \n", + "9 Munich Re Group Königinstr. 107 80802 \n", + "10 Siemens Werner-von-Siemens-Straße 1 80333 \n", + "11 Deutsche Bahn Potsdamer Platz 2 10785 \n", + "\n", + " Stadt Branche \n", + "0 Wolfsburg Automobil \n", + "1 Stuttgart Automobil \n", + "2 Stuttgart Versicherung, Finanzdienstleistung \n", + "3 München Automobil \n", + "4 Bonn Telekommunikation, Informationstechnologie \n", + "5 Bonn Logistik \n", + "6 Gerlingen-Schillerhöhe Kraftfahrzeugtechnik, Industrietechnik, Gebrau... \n", + "7 Ludwigshafen Chemie \n", + "8 München Energie \n", + "9 München Versicherung \n", + "10 München Automatisierung, Digitalisierung \n", + "11 Berlin Transport, Logistik " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "d5c6c68d", + "metadata": {}, + "source": [ + "---------------------------------\n", + "# Schreibe Unternehmensdaten in PostgreSQL" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6c09bdca", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2" + ] + }, + { + "cell_type": "markdown", + "id": "383fb9a9", + "metadata": {}, + "source": [ + "### Verbinde zur Datenbank" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8675c8bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database connected successfully\n" + ] + } + ], + "source": [ + "conn = psycopg2.connect(\n", + " host=\"localhost\",\n", + " database=\"transparenz\",\n", + " user=\"postgres\",\n", + " password=\"postgres\")\n", + "\n", + "print(\"Database connected successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "22b9ab1d", + "metadata": {}, + "source": [ + "## Iteriere durch Dataframe und schreibe Datensätze in Tabelle *Company*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "961ac836", + "metadata": {}, + "outputs": [], + "source": [ + "cur = conn.cursor()\n", + "\n", + "\n", + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " name=str(df['Name'].iloc[i])\n", + " street=str(df['Straße'].iloc[i])\n", + " zipcode=int(df['PLZ'].iloc[i])\n", + " city=str(df['Stadt'].iloc[i])\n", + " sector=str(df['Branche'].iloc[i])\n", + " \n", + " postgres_insert_query = \"\"\" INSERT INTO company (NAME,STREET, ZIP, CITY,SECTOR) VALUES (%s,%s,%s,%s,%s)\"\"\" \n", + " \n", + " record_to_insert = (name,street,zipcode,city,sector)\n", + " cur.execute(postgres_insert_query, record_to_insert) \n", + " \n", + "conn.commit()\n", + "conn.close()" + ] + }, + { + "attachments": { + "grafik.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "6fb1ec01", + "metadata": {}, + "source": [ + "Überprüfe mittels pgAdmin, ob die Datensätze vollständig geschrieben wurden:\n", + "![grafik.png](attachment:grafik.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b80e0fc8", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-1_Publish_Finance_Testdata_BASF.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-1_Publish_Finance_Testdata_BASF.ipynb new file mode 100644 index 0000000..57f3586 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-1_Publish_Finance_Testdata_BASF.ipynb @@ -0,0 +1,604 @@ +{ + "cells": [ + { + "attachments": { + "grafik.png": { + "image/png": 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MetrikDatumSumme [Milliarden €]
0Umsatz01.01.199929.473
1Umsatz01.01.200035.946
2Umsatz01.01.200132.500
3Umsatz01.01.200232.216
4Umsatz01.01.200333.361
5Umsatz01.01.200437.537
6Umsatz01.01.200542.745
7Umsatz01.01.200652.610
8Umsatz01.01.200757.951
9Umsatz01.01.200862.304
10Umsatz01.01.200950.693
11Umsatz01.01.201063.873
12Umsatz01.01.201173.497
13Umsatz01.01.201272.129
14Umsatz01.01.201373.973
15Umsatz01.01.201474.326
16Umsatz01.01.201570.449
17Umsatz01.01.201657.550
18Umsatz01.01.201761.223
19Umsatz01.01.201860.220
20Umsatz01.01.201959.316
21Umsatz01.01.202059.149
22Umsatz01.01.202178.598
23Umsatz01.01.202287.327
24EBIT01.01.20055.830
25EBIT01.01.20066.750
26EBIT01.01.20077.316
27EBIT01.01.20086.463
28EBIT01.01.20093.677
29EBIT01.01.20107.761
30EBIT01.01.20118.586
31EBIT01.01.20126.742
32EBIT01.01.20137.160
33EBIT01.01.20147.626
34EBIT01.01.20156.248
35EBIT01.01.20166.275
36EBIT01.01.20177.587
37EBIT01.01.20185.974
38EBIT01.01.20194.201
39EBIT01.01.2020-0.191
40EBIT01.01.20217.677
41EBIT01.01.20226.548
42EBITDA01.01.20089.562
43EBITDA01.01.20097.388
44EBITDA01.01.201011.131
45EBITDA01.01.201111.993
46EBITDA01.01.201210.009
47EBITDA01.01.201310.432
48EBITDA01.01.201411.043
49EBITDA01.01.201510.649
50EBITDA01.01.201610.526
51EBITDA01.01.201710.765
52EBITDA01.01.20188.970
53EBITDA01.01.20198.185
54EBITDA01.01.20206.494
55EBITDA01.01.202111.355
56EBITDA01.01.202210.748
\n", + "
" + ], + "text/plain": [ + " Metrik Datum Summe [Milliarden €]\n", + "0 Umsatz 01.01.1999 29.473\n", + "1 Umsatz 01.01.2000 35.946\n", + "2 Umsatz 01.01.2001 32.500\n", + "3 Umsatz 01.01.2002 32.216\n", + "4 Umsatz 01.01.2003 33.361\n", + "5 Umsatz 01.01.2004 37.537\n", + "6 Umsatz 01.01.2005 42.745\n", + "7 Umsatz 01.01.2006 52.610\n", + "8 Umsatz 01.01.2007 57.951\n", + "9 Umsatz 01.01.2008 62.304\n", + "10 Umsatz 01.01.2009 50.693\n", + "11 Umsatz 01.01.2010 63.873\n", + "12 Umsatz 01.01.2011 73.497\n", + "13 Umsatz 01.01.2012 72.129\n", + "14 Umsatz 01.01.2013 73.973\n", + "15 Umsatz 01.01.2014 74.326\n", + "16 Umsatz 01.01.2015 70.449\n", + "17 Umsatz 01.01.2016 57.550\n", + "18 Umsatz 01.01.2017 61.223\n", + "19 Umsatz 01.01.2018 60.220\n", + "20 Umsatz 01.01.2019 59.316\n", + "21 Umsatz 01.01.2020 59.149\n", + "22 Umsatz 01.01.2021 78.598\n", + "23 Umsatz 01.01.2022 87.327\n", + "24 EBIT 01.01.2005 5.830\n", + "25 EBIT 01.01.2006 6.750\n", + "26 EBIT 01.01.2007 7.316\n", + "27 EBIT 01.01.2008 6.463\n", + "28 EBIT 01.01.2009 3.677\n", + "29 EBIT 01.01.2010 7.761\n", + "30 EBIT 01.01.2011 8.586\n", + "31 EBIT 01.01.2012 6.742\n", + "32 EBIT 01.01.2013 7.160\n", + "33 EBIT 01.01.2014 7.626\n", + "34 EBIT 01.01.2015 6.248\n", + "35 EBIT 01.01.2016 6.275\n", + "36 EBIT 01.01.2017 7.587\n", + "37 EBIT 01.01.2018 5.974\n", + "38 EBIT 01.01.2019 4.201\n", + "39 EBIT 01.01.2020 -0.191\n", + "40 EBIT 01.01.2021 7.677\n", + "41 EBIT 01.01.2022 6.548\n", + "42 EBITDA 01.01.2008 9.562\n", + "43 EBITDA 01.01.2009 7.388\n", + "44 EBITDA 01.01.2010 11.131\n", + "45 EBITDA 01.01.2011 11.993\n", + "46 EBITDA 01.01.2012 10.009\n", + "47 EBITDA 01.01.2013 10.432\n", + "48 EBITDA 01.01.2014 11.043\n", + "49 EBITDA 01.01.2015 10.649\n", + "50 EBITDA 01.01.2016 10.526\n", + "51 EBITDA 01.01.2017 10.765\n", + "52 EBITDA 01.01.2018 8.970\n", + "53 EBITDA 01.01.2019 8.185\n", + "54 EBITDA 01.01.2020 6.494\n", + "55 EBITDA 01.01.2021 11.355\n", + "56 EBITDA 01.01.2022 10.748" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "d5c6c68d", + "metadata": {}, + "source": [ + "---------------------------------\n", + "# Schreibe Unternehmensdaten in PostgreSQL" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6c09bdca", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2" + ] + }, + { + "cell_type": "markdown", + "id": "383fb9a9", + "metadata": {}, + "source": [ + "### Verbinde zur Datenbank" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3e1ea224", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database connected successfully\n" + ] + } + ], + "source": [ + "conn = psycopg2.connect(\n", + " host=\"localhost\",\n", + " database=\"transparenz\",\n", + " user=\"postgres\",\n", + " password=\"postgres\")\n", + "\n", + "print(\"Database connected successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "cba7013a", + "metadata": {}, + "source": [ + "## Iteriere durch Dataframe und schreibe Datensätze in Tabelle *Company*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "961ac836", + "metadata": {}, + "outputs": [], + "source": [ + "cur = conn.cursor()\n", + "\n", + "PK_ID=8 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", + "\n", + "\n", + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " kind_of=str(df['Metrik'].iloc[i])\n", + " date=str(df['Datum'].iloc[i])\n", + " amount=float(df['Summe [Milliarden €]'].iloc[i])\n", + " \n", + " postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", + " record_to_insert = (PK_ID,kind_of,date,amount)\n", + " cur.execute(postgres_insert_query, record_to_insert) \n", + " #print(postgres_insert_query, record_to_insert)\n", + " \n", + "conn.commit()\n", + "conn.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46b5be7c", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish-Finance_Testdata_Telekom.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish-Finance_Testdata_Telekom.ipynb new file mode 100644 index 0000000..27330e1 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish-Finance_Testdata_Telekom.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dbd6eae9", + "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": "code", + "execution_count": 2, + "id": "8b447b09", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('Telekom_Data_NewOrder.csv', sep=';',decimal=',') " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5fc7b7d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MetrikDatumSumme [Milliarden €]
0Umsatz01.01.200559.600
1Umsatz01.01.200661.300
2Umsatz01.01.200762.500
3Umsatz01.01.200861.700
4Umsatz01.01.200964.600
5Umsatz01.01.201062.420
6Umsatz01.01.201158.650
7Umsatz01.01.201258.170
8Umsatz01.01.201360.130
9Umsatz01.01.201462.660
10Umsatz01.01.201569.230
11Umsatz01.01.201673.100
12Umsatz01.01.201774.950
13Umsatz01.01.201875.660
14Umsatz01.01.201980.530
15Umsatz01.01.202099.950
16Umsatz01.01.2021107.610
17Umsatz01.01.2022114.200
18EBIT01.01.20057.600
19EBIT01.01.20065.300
20EBIT01.01.20075.300
21EBIT01.01.20087.000
22EBIT01.01.20096.000
23EBIT01.01.20105.510
24EBIT01.01.20115.560
25EBIT01.01.2012-3.960
26EBIT01.01.20134.930
27EBIT01.01.20147.250
28EBIT01.01.20157.030
29EBIT01.01.20169.160
30EBIT01.01.20179.380
31EBIT01.01.20188.000
32EBIT01.01.20199.460
33EBIT01.01.202012.370
34EBIT01.01.202112.580
35EBIT01.01.202215.410
36EBITDA01.01.201823.333
37EBITDA01.01.201924.731
38EBITDA01.01.202035.017
39EBITDA01.01.202137.330
40EBITDA01.01.202240.208
\n", + "
" + ], + "text/plain": [ + " Metrik Datum Summe [Milliarden €]\n", + "0 Umsatz 01.01.2005 59.600\n", + "1 Umsatz 01.01.2006 61.300\n", + "2 Umsatz 01.01.2007 62.500\n", + "3 Umsatz 01.01.2008 61.700\n", + "4 Umsatz 01.01.2009 64.600\n", + "5 Umsatz 01.01.2010 62.420\n", + "6 Umsatz 01.01.2011 58.650\n", + "7 Umsatz 01.01.2012 58.170\n", + "8 Umsatz 01.01.2013 60.130\n", + "9 Umsatz 01.01.2014 62.660\n", + "10 Umsatz 01.01.2015 69.230\n", + "11 Umsatz 01.01.2016 73.100\n", + "12 Umsatz 01.01.2017 74.950\n", + "13 Umsatz 01.01.2018 75.660\n", + "14 Umsatz 01.01.2019 80.530\n", + "15 Umsatz 01.01.2020 99.950\n", + "16 Umsatz 01.01.2021 107.610\n", + "17 Umsatz 01.01.2022 114.200\n", + "18 EBIT 01.01.2005 7.600\n", + "19 EBIT 01.01.2006 5.300\n", + "20 EBIT 01.01.2007 5.300\n", + "21 EBIT 01.01.2008 7.000\n", + "22 EBIT 01.01.2009 6.000\n", + "23 EBIT 01.01.2010 5.510\n", + "24 EBIT 01.01.2011 5.560\n", + "25 EBIT 01.01.2012 -3.960\n", + "26 EBIT 01.01.2013 4.930\n", + "27 EBIT 01.01.2014 7.250\n", + "28 EBIT 01.01.2015 7.030\n", + "29 EBIT 01.01.2016 9.160\n", + "30 EBIT 01.01.2017 9.380\n", + "31 EBIT 01.01.2018 8.000\n", + "32 EBIT 01.01.2019 9.460\n", + "33 EBIT 01.01.2020 12.370\n", + "34 EBIT 01.01.2021 12.580\n", + "35 EBIT 01.01.2022 15.410\n", + "36 EBITDA 01.01.2018 23.333\n", + "37 EBITDA 01.01.2019 24.731\n", + "38 EBITDA 01.01.2020 35.017\n", + "39 EBITDA 01.01.2021 37.330\n", + "40 EBITDA 01.01.2022 40.208" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "d5c6c68d", + "metadata": {}, + "source": [ + "---------------------------------\n", + "# Schreibe Unternehmensdaten in PostgreSQL" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6c09bdca", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2" + ] + }, + { + "cell_type": "markdown", + "id": "383fb9a9", + "metadata": {}, + "source": [ + "### Verbinde zur Datenbank" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3e1ea224", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database connected successfully\n" + ] + } + ], + "source": [ + "conn = psycopg2.connect(\n", + " host=\"localhost\",\n", + " database=\"transparenz\",\n", + " user=\"postgres\",\n", + " password=\"postgres\")\n", + "\n", + "print(\"Database connected successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "22b9ab1d", + "metadata": {}, + "source": [ + "## Iteriere durch Dataframe und schreibe Datensätze in Tabelle *Company*" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "961ac836", + "metadata": {}, + "outputs": [], + "source": [ + "cur = conn.cursor()\n", + "\n", + "PK_ID=5 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", + "\n", + "\n", + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " kind_of=str(df['Metrik'].iloc[i])\n", + " date=str(df['Datum'].iloc[i])\n", + " amount=float(df['Summe [Milliarden €]'].iloc[i])\n", + " \n", + " postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", + " record_to_insert = (PK_ID,kind_of,date,amount)\n", + " cur.execute(postgres_insert_query, record_to_insert) \n", + " #print(postgres_insert_query, record_to_insert)\n", + " \n", + "conn.commit()\n", + "conn.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46b5be7c", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish_Finance_Testdata_EON.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish_Finance_Testdata_EON.ipynb new file mode 100644 index 0000000..96d3223 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish_Finance_Testdata_EON.ipynb @@ -0,0 +1,416 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dbd6eae9", + "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": "code", + "execution_count": 2, + "id": "8b447b09", + "metadata": {}, + "outputs": [], + "source": [ + "df=pd.read_csv('EON_Data_NewOrder.csv', sep=';',decimal=',') " + ] + }, + { + "cell_type": "code", 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MetrikDatumSumme [Milliarden €]
0Umsatz01.01.200766.912
1Umsatz01.01.200884.873
2Umsatz01.01.200979.974
3Umsatz01.01.201092.863
4Umsatz01.01.2011112.954
5Umsatz01.01.2012132.093
6Umsatz01.01.2013119.615
7Umsatz01.01.2014113.095
8Umsatz01.01.201542.656
9Umsatz01.01.201638.173
10Umsatz01.01.201737.965
11Umsatz01.01.201830.084
12Umsatz01.01.201941.284
13Umsatz01.01.202060.944
14Umsatz01.01.202177.358
15Umsatz01.01.2022115.660
16EBIT01.01.20127.010
17EBIT01.01.20135.640
18EBIT01.01.20144.700
19EBIT01.01.20153.600
20EBIT01.01.20163.100
21EBIT01.01.20173.100
22EBIT01.01.20182.990
23EBIT01.01.20193.220
24EBIT01.01.20203.780
25EBIT01.01.20214.720
26EBIT01.01.20225.200
27EBITDA01.01.20184.840
28EBITDA01.01.20195.558
29EBITDA01.01.20206.905
30EBITDA01.01.20217.889
31EBITDA01.01.20228.059
\n", + "
" + ], + "text/plain": [ + " Metrik Datum Summe [Milliarden €]\n", + "0 Umsatz 01.01.2007 66.912\n", + "1 Umsatz 01.01.2008 84.873\n", + "2 Umsatz 01.01.2009 79.974\n", + "3 Umsatz 01.01.2010 92.863\n", + "4 Umsatz 01.01.2011 112.954\n", + "5 Umsatz 01.01.2012 132.093\n", + "6 Umsatz 01.01.2013 119.615\n", + "7 Umsatz 01.01.2014 113.095\n", + "8 Umsatz 01.01.2015 42.656\n", + "9 Umsatz 01.01.2016 38.173\n", + "10 Umsatz 01.01.2017 37.965\n", + "11 Umsatz 01.01.2018 30.084\n", + "12 Umsatz 01.01.2019 41.284\n", + "13 Umsatz 01.01.2020 60.944\n", + "14 Umsatz 01.01.2021 77.358\n", + "15 Umsatz 01.01.2022 115.660\n", + "16 EBIT 01.01.2012 7.010\n", + "17 EBIT 01.01.2013 5.640\n", + "18 EBIT 01.01.2014 4.700\n", + "19 EBIT 01.01.2015 3.600\n", + "20 EBIT 01.01.2016 3.100\n", + "21 EBIT 01.01.2017 3.100\n", + "22 EBIT 01.01.2018 2.990\n", + "23 EBIT 01.01.2019 3.220\n", + "24 EBIT 01.01.2020 3.780\n", + "25 EBIT 01.01.2021 4.720\n", + "26 EBIT 01.01.2022 5.200\n", + "27 EBITDA 01.01.2018 4.840\n", + "28 EBITDA 01.01.2019 5.558\n", + "29 EBITDA 01.01.2020 6.905\n", + "30 EBITDA 01.01.2021 7.889\n", + "31 EBITDA 01.01.2022 8.059" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "markdown", + "id": "d5c6c68d", + "metadata": {}, + "source": [ + "---------------------------------\n", + "# Schreibe Unternehmensdaten in PostgreSQL" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6c09bdca", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2" + ] + }, + { + "cell_type": "markdown", + "id": "383fb9a9", + "metadata": {}, + "source": [ + "### Verbinde zur Datenbank" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3e1ea224", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Database connected successfully\n" + ] + } + ], + "source": [ + "conn = psycopg2.connect(\n", + " host=\"localhost\",\n", + " database=\"transparenz\",\n", + " user=\"postgres\",\n", + " password=\"postgres\")\n", + "\n", + "print(\"Database connected successfully\")" + ] + }, + { + "cell_type": "markdown", + "id": "22b9ab1d", + "metadata": {}, + "source": [ + "## Iteriere durch Dataframe und schreibe Datensätze in Tabelle *Company*" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "961ac836", + "metadata": {}, + "outputs": [], + "source": [ + "cur = conn.cursor()\n", + "\n", + "PK_ID=9 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", + "\n", + "\n", + "for i in range(len(df)):\n", + " #get data from dataframe\n", + " kind_of=str(df['Metrik'].iloc[i])\n", + " date=str(df['Datum'].iloc[i])\n", + " amount=float(df['Summe [Milliarden €]'].iloc[i])\n", + " \n", + " postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", + " record_to_insert = (PK_ID,kind_of,date,amount)\n", + " cur.execute(postgres_insert_query, record_to_insert) \n", + " #print(postgres_insert_query, record_to_insert)\n", + " \n", + "conn.commit()\n", + "conn.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46b5be7c", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/04_First_Query.ipynb b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/04_First_Query.ipynb new file mode 100644 index 0000000..0901098 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/04_First_Query.ipynb @@ -0,0 +1,3329 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "30df80ed", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "dbd6eae9", + "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": "code", + "execution_count": 2, + "id": "6c09bdca", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2\n", + "import sqlalchemy" + ] + }, + { + "cell_type": "markdown", + "id": "cba7013a", + "metadata": {}, + "source": [ + "## Verbinden zur Postgre Datenbank\n", + "https://magnimindacademy.com/how-to-use-postgresql-in-a-jupyter-notebook/\n", + "https://medium.com/analytics-vidhya/postgresql-integration-with-jupyter-notebook-deb97579a38d" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "46b5be7c", + "metadata": {}, + "outputs": [], + "source": [ + "#load sql extension\n", + "%load_ext sql" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f24a52b9", + "metadata": {}, + "outputs": [], + "source": [ + "%sql postgresql://pi:password@192.168.178.130:5432/transparenz" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "12b5deb5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * postgresql://pi:***@192.168.178.130:5432/transparenz\n", + "12 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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idnamestreetzipcitysector
1VolkswagenBerliner Ring 238440WolfsburgAutomobil
2Mercedes-Benz GroupMercedesstraße 12070372StuttgartAutomobil
3AllianzReinsburgstraße 1970178StuttgartVersicherung, Finanzdienstleistung
4BMW GroupPetuelring 13080809MünchenAutomobil
5Deutsche TelekomLandgrabenweg 15153227BonnTelekommunikation, Informationstechnologie
6Deutsche Post DHL GroupCharles-de-Gaulle-Str. 2053113BonnLogistik
7Bosch GroupRobert-Bosch-Platz 170839Gerlingen-SchillerhöheKraftfahrzeugtechnik, Industrietechnik, Gebrauchsgüter, Energie- und Gebäudetechnik
8BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
9E.ONArnulfstraße 20380634MünchenEnergie
10Munich Re GroupKöniginstr. 10780802MünchenVersicherung
11SiemensWerner-von-Siemens-Straße 180333MünchenAutomatisierung, Digitalisierung
12Deutsche BahnPotsdamer Platz 210785BerlinTransport, Logistik
" + ], + "text/plain": [ + "[(1, 'Volkswagen', 'Berliner Ring 2', 38440, 'Wolfsburg', 'Automobil'),\n", + " (2, 'Mercedes-Benz Group', 'Mercedesstraße 120', 70372, 'Stuttgart', 'Automobil'),\n", + " (3, 'Allianz', 'Reinsburgstraße 19', 70178, 'Stuttgart', 'Versicherung, Finanzdienstleistung'),\n", + " (4, 'BMW Group', 'Petuelring 130', 80809, 'München', 'Automobil'),\n", + " (5, 'Deutsche Telekom', 'Landgrabenweg 151', 53227, 'Bonn', 'Telekommunikation, Informationstechnologie'),\n", + " (6, 'Deutsche Post DHL Group', 'Charles-de-Gaulle-Str. 20', 53113, 'Bonn', 'Logistik'),\n", + " (7, 'Bosch Group', 'Robert-Bosch-Platz 1', 70839, 'Gerlingen-Schillerhöhe', 'Kraftfahrzeugtechnik, Industrietechnik, Gebrauchsgüter, Energie- und Gebäudetechnik'),\n", + " (8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (9, 'E.ON', 'Arnulfstraße 203', 80634, 'München', 'Energie'),\n", + " (10, 'Munich Re Group', 'Königinstr. 107', 80802, 'München', 'Versicherung'),\n", + " (11, 'Siemens', 'Werner-von-Siemens-Straße 1', 80333, 'München', 'Automatisierung, Digitalisierung'),\n", + " (12, 'Deutsche Bahn', 'Potsdamer Platz 2', 10785, 'Berlin', 'Transport, Logistik')]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%sql SELECT * from company" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "819a9af6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " * postgresql://pi:***@192.168.178.130:5432/transparenz\n", + "130 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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finance_idcompany_idkind_ofdatesum
18Umsatz1999-01-0129.473
28Umsatz2000-01-0135.946
38Umsatz2001-01-0132.5
48Umsatz2002-01-0132.216
58Umsatz2003-01-0133.361
68Umsatz2004-01-0137.537
78Umsatz2005-01-0142.745
88Umsatz2006-01-0152.61
98Umsatz2007-01-0157.951
108Umsatz2008-01-0162.304
118Umsatz2009-01-0150.693
128Umsatz2010-01-0163.873
138Umsatz2011-01-0173.497
148Umsatz2012-01-0172.129
158Umsatz2013-01-0173.973
168Umsatz2014-01-0174.326
178Umsatz2015-01-0170.449
188Umsatz2016-01-0157.55
198Umsatz2017-01-0161.223
208Umsatz2018-01-0160.22
218Umsatz2019-01-0159.316
228Umsatz2020-01-0159.149
238Umsatz2021-01-0178.598
248Umsatz2022-01-0187.327
258EBIT2005-01-015.83
268EBIT2006-01-016.75
278EBIT2007-01-017.316
288EBIT2008-01-016.463
298EBIT2009-01-013.677
308EBIT2010-01-017.761
318EBIT2011-01-018.586
328EBIT2012-01-016.742
338EBIT2013-01-017.16
348EBIT2014-01-017.626
358EBIT2015-01-016.248
368EBIT2016-01-016.275
378EBIT2017-01-017.587
388EBIT2018-01-015.974
398EBIT2019-01-014.201
408EBIT2020-01-01-0.191
418EBIT2021-01-017.677
428EBIT2022-01-016.548
438EBITDA2008-01-019.562
448EBITDA2009-01-017.388
458EBITDA2010-01-0111.131
468EBITDA2011-01-0111.993
478EBITDA2012-01-0110.009
488EBITDA2013-01-0110.432
498EBITDA2014-01-0111.043
508EBITDA2015-01-0110.649
518EBITDA2016-01-0110.526
528EBITDA2017-01-0110.765
538EBITDA2018-01-018.97
548EBITDA2019-01-018.185
558EBITDA2020-01-016.494
568EBITDA2021-01-0111.355
578EBITDA2022-01-0110.748
589Umsatz2007-01-0166.912
599Umsatz2008-01-0184.873
609Umsatz2009-01-0179.974
619Umsatz2010-01-0192.863
629Umsatz2011-01-01112.954
639Umsatz2012-01-01132.093
649Umsatz2013-01-01119.615
659Umsatz2014-01-01113.095
669Umsatz2015-01-0142.656
679Umsatz2016-01-0138.173
689Umsatz2017-01-0137.965
699Umsatz2018-01-0130.084
709Umsatz2019-01-0141.284
719Umsatz2020-01-0160.944
729Umsatz2021-01-0177.358
739Umsatz2022-01-01115.66
749EBIT2012-01-017.01
759EBIT2013-01-015.64
769EBIT2014-01-014.7
779EBIT2015-01-013.6
789EBIT2016-01-013.1
799EBIT2017-01-013.1
809EBIT2018-01-012.99
819EBIT2019-01-013.22
829EBIT2020-01-013.78
839EBIT2021-01-014.72
849EBIT2022-01-015.2
859EBITDA2018-01-014.84
869EBITDA2019-01-015.558
879EBITDA2020-01-016.905
889EBITDA2021-01-017.889
899EBITDA2022-01-018.059
905Umsatz2005-01-0159.6
915Umsatz2006-01-0161.3
925Umsatz2007-01-0162.5
935Umsatz2008-01-0161.7
945Umsatz2009-01-0164.6
955Umsatz2010-01-0162.42
965Umsatz2011-01-0158.65
975Umsatz2012-01-0158.17
985Umsatz2013-01-0160.13
995Umsatz2014-01-0162.66
1005Umsatz2015-01-0169.23
1015Umsatz2016-01-0173.1
1025Umsatz2017-01-0174.95
1035Umsatz2018-01-0175.66
1045Umsatz2019-01-0180.53
1055Umsatz2020-01-0199.95
1065Umsatz2021-01-01107.61
1075Umsatz2022-01-01114.2
1085EBIT2005-01-017.6
1095EBIT2006-01-015.3
1105EBIT2007-01-015.3
1115EBIT2008-01-017.0
1125EBIT2009-01-016.0
1135EBIT2010-01-015.51
1145EBIT2011-01-015.56
1155EBIT2012-01-01-3.96
1165EBIT2013-01-014.93
1175EBIT2014-01-017.25
1185EBIT2015-01-017.03
1195EBIT2016-01-019.16
1205EBIT2017-01-019.38
1215EBIT2018-01-018.0
1225EBIT2019-01-019.46
1235EBIT2020-01-0112.37
1245EBIT2021-01-0112.58
1255EBIT2022-01-0115.41
1265EBITDA2018-01-0123.333
1275EBITDA2019-01-0124.731
1285EBITDA2020-01-0135.017
1295EBITDA2021-01-0137.33
1305EBITDA2022-01-0140.208
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datetime.date(2013, 1, 1), 7.16),\n", + " (34, 8, 'EBIT', datetime.date(2014, 1, 1), 7.626),\n", + " (35, 8, 'EBIT', datetime.date(2015, 1, 1), 6.248),\n", + " (36, 8, 'EBIT', datetime.date(2016, 1, 1), 6.275),\n", + " (37, 8, 'EBIT', datetime.date(2017, 1, 1), 7.587),\n", + " (38, 8, 'EBIT', datetime.date(2018, 1, 1), 5.974),\n", + " (39, 8, 'EBIT', datetime.date(2019, 1, 1), 4.201),\n", + " (40, 8, 'EBIT', datetime.date(2020, 1, 1), -0.191),\n", + " (41, 8, 'EBIT', datetime.date(2021, 1, 1), 7.677),\n", + " (42, 8, 'EBIT', datetime.date(2022, 1, 1), 6.548),\n", + " (43, 8, 'EBITDA', datetime.date(2008, 1, 1), 9.562),\n", + " (44, 8, 'EBITDA', datetime.date(2009, 1, 1), 7.388),\n", + " (45, 8, 'EBITDA', datetime.date(2010, 1, 1), 11.131),\n", + " (46, 8, 'EBITDA', datetime.date(2011, 1, 1), 11.993),\n", + " (47, 8, 'EBITDA', datetime.date(2012, 1, 1), 10.009),\n", + " (48, 8, 'EBITDA', datetime.date(2013, 1, 1), 10.432),\n", + " (49, 8, 'EBITDA', datetime.date(2014, 1, 1), 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113.095),\n", + " (66, 9, 'Umsatz', datetime.date(2015, 1, 1), 42.656),\n", + " (67, 9, 'Umsatz', datetime.date(2016, 1, 1), 38.173),\n", + " (68, 9, 'Umsatz', datetime.date(2017, 1, 1), 37.965),\n", + " (69, 9, 'Umsatz', datetime.date(2018, 1, 1), 30.084),\n", + " (70, 9, 'Umsatz', datetime.date(2019, 1, 1), 41.284),\n", + " (71, 9, 'Umsatz', datetime.date(2020, 1, 1), 60.944),\n", + " (72, 9, 'Umsatz', datetime.date(2021, 1, 1), 77.358),\n", + " (73, 9, 'Umsatz', datetime.date(2022, 1, 1), 115.66),\n", + " (74, 9, 'EBIT', datetime.date(2012, 1, 1), 7.01),\n", + " (75, 9, 'EBIT', datetime.date(2013, 1, 1), 5.64),\n", + " (76, 9, 'EBIT', datetime.date(2014, 1, 1), 4.7),\n", + " (77, 9, 'EBIT', datetime.date(2015, 1, 1), 3.6),\n", + " (78, 9, 'EBIT', datetime.date(2016, 1, 1), 3.1),\n", + " (79, 9, 'EBIT', datetime.date(2017, 1, 1), 3.1),\n", + " (80, 9, 'EBIT', datetime.date(2018, 1, 1), 2.99),\n", + " (81, 9, 'EBIT', datetime.date(2019, 1, 1), 3.22),\n", + " (82, 9, 'EBIT', 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28Umsatz2000-01-0135.9468BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
38Umsatz2001-01-0132.58BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
48Umsatz2002-01-0132.2168BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
58Umsatz2003-01-0133.3618BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
68Umsatz2004-01-0137.5378BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
78Umsatz2005-01-0142.7458BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
88Umsatz2006-01-0152.618BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
98Umsatz2007-01-0157.9518BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
108Umsatz2008-01-0162.3048BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
118Umsatz2009-01-0150.6938BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
128Umsatz2010-01-0163.8738BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
138Umsatz2011-01-0173.4978BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
148Umsatz2012-01-0172.1298BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
158Umsatz2013-01-0173.9738BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
168Umsatz2014-01-0174.3268BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
178Umsatz2015-01-0170.4498BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
188Umsatz2016-01-0157.558BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
198Umsatz2017-01-0161.2238BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
208Umsatz2018-01-0160.228BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
218Umsatz2019-01-0159.3168BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
228Umsatz2020-01-0159.1498BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
238Umsatz2021-01-0178.5988BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
248Umsatz2022-01-0187.3278BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
258EBIT2005-01-015.838BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
268EBIT2006-01-016.758BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
278EBIT2007-01-017.3168BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
288EBIT2008-01-016.4638BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
298EBIT2009-01-013.6778BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
308EBIT2010-01-017.7618BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
318EBIT2011-01-018.5868BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
328EBIT2012-01-016.7428BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
338EBIT2013-01-017.168BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
348EBIT2014-01-017.6268BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
358EBIT2015-01-016.2488BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
368EBIT2016-01-016.2758BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
378EBIT2017-01-017.5878BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
388EBIT2018-01-015.9748BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
398EBIT2019-01-014.2018BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
408EBIT2020-01-01-0.1918BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
418EBIT2021-01-017.6778BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
428EBIT2022-01-016.5488BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
438EBITDA2008-01-019.5628BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
448EBITDA2009-01-017.3888BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
458EBITDA2010-01-0111.1318BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
468EBITDA2011-01-0111.9938BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
478EBITDA2012-01-0110.0098BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
488EBITDA2013-01-0110.4328BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
498EBITDA2014-01-0111.0438BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
508EBITDA2015-01-0110.6498BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
518EBITDA2016-01-0110.5268BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
528EBITDA2017-01-0110.7658BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
538EBITDA2018-01-018.978BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
548EBITDA2019-01-018.1858BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
558EBITDA2020-01-016.4948BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
568EBITDA2021-01-0111.3558BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
578EBITDA2022-01-0110.7488BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
589Umsatz2007-01-0166.9128BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
599Umsatz2008-01-0184.8738BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
609Umsatz2009-01-0179.9748BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
619Umsatz2010-01-0192.8638BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
629Umsatz2011-01-01112.9548BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
639Umsatz2012-01-01132.0938BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
649Umsatz2013-01-01119.6158BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
659Umsatz2014-01-01113.0958BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
669Umsatz2015-01-0142.6568BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
679Umsatz2016-01-0138.1738BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
689Umsatz2017-01-0137.9658BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
699Umsatz2018-01-0130.0848BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
709Umsatz2019-01-0141.2848BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
719Umsatz2020-01-0160.9448BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
729Umsatz2021-01-0177.3588BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
739Umsatz2022-01-01115.668BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
749EBIT2012-01-017.018BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
759EBIT2013-01-015.648BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
769EBIT2014-01-014.78BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
779EBIT2015-01-013.68BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
789EBIT2016-01-013.18BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
799EBIT2017-01-013.18BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
809EBIT2018-01-012.998BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
819EBIT2019-01-013.228BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
829EBIT2020-01-013.788BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
839EBIT2021-01-014.728BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
849EBIT2022-01-015.28BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
859EBITDA2018-01-014.848BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
869EBITDA2019-01-015.5588BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
879EBITDA2020-01-016.9058BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
889EBITDA2021-01-017.8898BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
899EBITDA2022-01-018.0598BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
905Umsatz2005-01-0159.68BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
915Umsatz2006-01-0161.38BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
925Umsatz2007-01-0162.58BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
935Umsatz2008-01-0161.78BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
945Umsatz2009-01-0164.68BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
955Umsatz2010-01-0162.428BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
965Umsatz2011-01-0158.658BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
975Umsatz2012-01-0158.178BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
985Umsatz2013-01-0160.138BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
995Umsatz2014-01-0162.668BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1005Umsatz2015-01-0169.238BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1015Umsatz2016-01-0173.18BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1025Umsatz2017-01-0174.958BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1035Umsatz2018-01-0175.668BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1045Umsatz2019-01-0180.538BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1055Umsatz2020-01-0199.958BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1065Umsatz2021-01-01107.618BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1075Umsatz2022-01-01114.28BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1085EBIT2005-01-017.68BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1095EBIT2006-01-015.38BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1105EBIT2007-01-015.38BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1115EBIT2008-01-017.08BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1125EBIT2009-01-016.08BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1135EBIT2010-01-015.518BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1145EBIT2011-01-015.568BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1155EBIT2012-01-01-3.968BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1165EBIT2013-01-014.938BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1175EBIT2014-01-017.258BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1185EBIT2015-01-017.038BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1195EBIT2016-01-019.168BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1205EBIT2017-01-019.388BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1215EBIT2018-01-018.08BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1225EBIT2019-01-019.468BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1235EBIT2020-01-0112.378BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1245EBIT2021-01-0112.588BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1255EBIT2022-01-0115.418BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1265EBITDA2018-01-0123.3338BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1275EBITDA2019-01-0124.7318BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1285EBITDA2020-01-0135.0178BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1295EBITDA2021-01-0137.338BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
1305EBITDA2022-01-0140.2088BASFCarl-Bosch-Straße 3867056LudwigshafenChemie
" + ], + "text/plain": [ + "[(1, 8, 'Umsatz', datetime.date(1999, 1, 1), 29.473, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (2, 8, 'Umsatz', datetime.date(2000, 1, 1), 35.946, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (3, 8, 'Umsatz', datetime.date(2001, 1, 1), 32.5, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (4, 8, 'Umsatz', datetime.date(2002, 1, 1), 32.216, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (5, 8, 'Umsatz', datetime.date(2003, 1, 1), 33.361, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (6, 8, 'Umsatz', datetime.date(2004, 1, 1), 37.537, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (7, 8, 'Umsatz', datetime.date(2005, 1, 1), 42.745, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (8, 8, 'Umsatz', datetime.date(2006, 1, 1), 52.61, 8, 'BASF', 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73.1, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (102, 5, 'Umsatz', datetime.date(2017, 1, 1), 74.95, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (103, 5, 'Umsatz', datetime.date(2018, 1, 1), 75.66, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (104, 5, 'Umsatz', datetime.date(2019, 1, 1), 80.53, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (105, 5, 'Umsatz', datetime.date(2020, 1, 1), 99.95, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (106, 5, 'Umsatz', datetime.date(2021, 1, 1), 107.61, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (107, 5, 'Umsatz', datetime.date(2022, 1, 1), 114.2, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (108, 5, 'EBIT', datetime.date(2005, 1, 1), 7.6, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (109, 5, 'EBIT', datetime.date(2006, 1, 1), 5.3, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (110, 5, 'EBIT', datetime.date(2007, 1, 1), 5.3, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (111, 5, 'EBIT', datetime.date(2008, 1, 1), 7.0, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (112, 5, 'EBIT', datetime.date(2009, 1, 1), 6.0, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (113, 5, 'EBIT', datetime.date(2010, 1, 1), 5.51, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (114, 5, 'EBIT', datetime.date(2011, 1, 1), 5.56, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (115, 5, 'EBIT', datetime.date(2012, 1, 1), -3.96, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (116, 5, 'EBIT', datetime.date(2013, 1, 1), 4.93, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (117, 5, 'EBIT', datetime.date(2014, 1, 1), 7.25, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (118, 5, 'EBIT', datetime.date(2015, 1, 1), 7.03, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (119, 5, 'EBIT', datetime.date(2016, 1, 1), 9.16, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (120, 5, 'EBIT', datetime.date(2017, 1, 1), 9.38, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (121, 5, 'EBIT', datetime.date(2018, 1, 1), 8.0, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (122, 5, 'EBIT', datetime.date(2019, 1, 1), 9.46, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (123, 5, 'EBIT', datetime.date(2020, 1, 1), 12.37, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (124, 5, 'EBIT', datetime.date(2021, 1, 1), 12.58, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (125, 5, 'EBIT', datetime.date(2022, 1, 1), 15.41, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (126, 5, 'EBITDA', datetime.date(2018, 1, 1), 23.333, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (127, 5, 'EBITDA', datetime.date(2019, 1, 1), 24.731, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (128, 5, 'EBITDA', datetime.date(2020, 1, 1), 35.017, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (129, 5, 'EBITDA', datetime.date(2021, 1, 1), 37.33, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie'),\n", + " (130, 5, 'EBITDA', datetime.date(2022, 1, 1), 40.208, 8, 'BASF', 'Carl-Bosch-Straße 38', 67056, 'Ludwigshafen', 'Chemie')]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%sql SELECT * FROM finance INNER JOIN company ON company.name='BASF';" + ] + 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sumdate
8.5862011-01-01
7.7612010-01-01
7.6772021-01-01
7.6262014-01-01
7.5872017-01-01
7.3162007-01-01
7.162013-01-01
6.752006-01-01
6.7422012-01-01
6.5482022-01-01
6.4632008-01-01
6.2752016-01-01
6.2482015-01-01
5.9742018-01-01
5.832005-01-01
4.2012019-01-01
3.6772009-01-01
-0.1912020-01-01
" + ], + "text/plain": [ + "[(8.586, datetime.date(2011, 1, 1)),\n", + " (7.761, datetime.date(2010, 1, 1)),\n", + " (7.677, datetime.date(2021, 1, 1)),\n", + " (7.626, datetime.date(2014, 1, 1)),\n", + " (7.587, datetime.date(2017, 1, 1)),\n", + " (7.316, datetime.date(2007, 1, 1)),\n", + " (7.16, datetime.date(2013, 1, 1)),\n", + " (6.75, datetime.date(2006, 1, 1)),\n", + " (6.742, datetime.date(2012, 1, 1)),\n", + " (6.548, datetime.date(2022, 1, 1)),\n", + " (6.463, datetime.date(2008, 1, 1)),\n", + " (6.275, datetime.date(2016, 1, 1)),\n", + " (6.248, datetime.date(2015, 1, 1)),\n", + " (5.974, datetime.date(2018, 1, 1)),\n", + " (5.83, datetime.date(2005, 1, 1)),\n", + " (4.201, datetime.date(2019, 1, 1)),\n", + " (3.677, datetime.date(2009, 1, 1)),\n", + " (-0.191, datetime.date(2020, 1, 1))]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%sql Select sum, date from finance f, company c where f.company_id = c.id and c.name='BASF' and f.kind_of='EBIT' order by sum desc;" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d673a9c", + "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/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Amtsgerichte.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Amtsgerichte.csv new file mode 100644 index 0000000..0a87306 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Amtsgerichte.csv @@ -0,0 +1,10 @@ +Stadt;Name +Aschaffenburg;Amtsgericht Aschaffenburg +Bamberg;Amtsgericht Bamberg +Bayreuth;Amtsgericht Bayreuth +Duesseldorf;Amtsgericht Duesseldorf +Duisburg;Amtsgericht Duisburg +Duisburg;Amtsgericht Duisburg-Hamborn +Duisburg;Amtsgericht Duisburg-Ruhrort +Oberhausen;Amtsgericht Oberhausen +Wuppertal;Amtsgericht Wuppertal diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/BASF_Data_NewOrder.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/BASF_Data_NewOrder.csv new file mode 100644 index 0000000..191917d --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/BASF_Data_NewOrder.csv @@ -0,0 +1,58 @@ +Metrik;Datum;Summe [Milliarden €] +Umsatz;01.01.1999;29,473 +Umsatz;01.01.2000;35,946 +Umsatz;01.01.2001;32,5 +Umsatz;01.01.2002;32,216 +Umsatz;01.01.2003;33,361 +Umsatz;01.01.2004;37,537 +Umsatz;01.01.2005;42,745 +Umsatz;01.01.2006;52,61 +Umsatz;01.01.2007;57,951 +Umsatz;01.01.2008;62,304 +Umsatz;01.01.2009;50,693 +Umsatz;01.01.2010;63,873 +Umsatz;01.01.2011;73,497 +Umsatz;01.01.2012;72,129 +Umsatz;01.01.2013;73,973 +Umsatz;01.01.2014;74,326 +Umsatz;01.01.2015;70,449 +Umsatz;01.01.2016;57,55 +Umsatz;01.01.2017;61,223 +Umsatz;01.01.2018;60,22 +Umsatz;01.01.2019;59,316 +Umsatz;01.01.2020;59,149 +Umsatz;01.01.2021;78,598 +Umsatz;01.01.2022;87,327 +EBIT;01.01.2005;5,83 +EBIT;01.01.2006;6,75 +EBIT;01.01.2007;7,316 +EBIT;01.01.2008;6,463 +EBIT;01.01.2009;3,677 +EBIT;01.01.2010;7,761 +EBIT;01.01.2011;8,586 +EBIT;01.01.2012;6,742 +EBIT;01.01.2013;7,16 +EBIT;01.01.2014;7,626 +EBIT;01.01.2015;6,248 +EBIT;01.01.2016;6,275 +EBIT;01.01.2017;7,587 +EBIT;01.01.2018;5,974 +EBIT;01.01.2019;4,201 +EBIT;01.01.2020;-0,191 +EBIT;01.01.2021;7,677 +EBIT;01.01.2022;6,548 +EBITDA;01.01.2008;9,562 +EBITDA;01.01.2009;7,388 +EBITDA;01.01.2010;11,131 +EBITDA;01.01.2011;11,993 +EBITDA;01.01.2012;10,009 +EBITDA;01.01.2013;10,432 +EBITDA;01.01.2014;11,043 +EBITDA;01.01.2015;10,649 +EBITDA;01.01.2016;10,526 +EBITDA;01.01.2017;10,765 +EBITDA;01.01.2018;8,97 +EBITDA;01.01.2019;8,185 +EBITDA;01.01.2020;6,494 +EBITDA;01.01.2021;11,355 +EBITDA;01.01.2022;10,748 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/EON_Data_NewOrder.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/EON_Data_NewOrder.csv new file mode 100644 index 0000000..5629748 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/EON_Data_NewOrder.csv @@ -0,0 +1,33 @@ +Metrik;Datum;Summe [Milliarden €] +Umsatz;01.01.2007;66,912 +Umsatz;01.01.2008;84,873 +Umsatz;01.01.2009;79,974 +Umsatz;01.01.2010;92,863 +Umsatz;01.01.2011;112,954 +Umsatz;01.01.2012;132,093 +Umsatz;01.01.2013;119,615 +Umsatz;01.01.2014;113,095 +Umsatz;01.01.2015;42,656 +Umsatz;01.01.2016;38,173 +Umsatz;01.01.2017;37,965 +Umsatz;01.01.2018;30,084 +Umsatz;01.01.2019;41,284 +Umsatz;01.01.2020;60,944 +Umsatz;01.01.2021;77,358 +Umsatz;01.01.2022;115,66 +EBIT;01.01.2012;7,01 +EBIT;01.01.2013;5,64 +EBIT;01.01.2014;4,7 +EBIT;01.01.2015;3,6 +EBIT;01.01.2016;3,1 +EBIT;01.01.2017;3,1 +EBIT;01.01.2018;2,99 +EBIT;01.01.2019;3,22 +EBIT;01.01.2020;3,78 +EBIT;01.01.2021;4,72 +EBIT;01.01.2022;5,2 +EBITDA;01.01.2018;4,84 +EBITDA;01.01.2019;5,558 +EBITDA;01.01.2020;6,905 +EBITDA;01.01.2021;7,889 +EBITDA;01.01.2022;8,059 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Telekom_Data_NewOrder.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Telekom_Data_NewOrder.csv new file mode 100644 index 0000000..8ff4d2e --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/Telekom_Data_NewOrder.csv @@ -0,0 +1,42 @@ +Metrik;Datum;Summe [Milliarden €] +Umsatz;01.01.2005;59,6 +Umsatz;01.01.2006;61,3 +Umsatz;01.01.2007;62,5 +Umsatz;01.01.2008;61,7 +Umsatz;01.01.2009;64,6 +Umsatz;01.01.2010;62,42 +Umsatz;01.01.2011;58,65 +Umsatz;01.01.2012;58,17 +Umsatz;01.01.2013;60,13 +Umsatz;01.01.2014;62,66 +Umsatz;01.01.2015;69,23 +Umsatz;01.01.2016;73,1 +Umsatz;01.01.2017;74,95 +Umsatz;01.01.2018;75,66 +Umsatz;01.01.2019;80,53 +Umsatz;01.01.2020;99,95 +Umsatz;01.01.2021;107,61 +Umsatz;01.01.2022;114,2 +EBIT;01.01.2005;7,6 +EBIT;01.01.2006;5,3 +EBIT;01.01.2007;5,3 +EBIT;01.01.2008;7 +EBIT;01.01.2009;6 +EBIT;01.01.2010;5,51 +EBIT;01.01.2011;5,56 +EBIT;01.01.2012;-3,96 +EBIT;01.01.2013;4,93 +EBIT;01.01.2014;7,25 +EBIT;01.01.2015;7,03 +EBIT;01.01.2016;9,16 +EBIT;01.01.2017;9,38 +EBIT;01.01.2018;8 +EBIT;01.01.2019;9,46 +EBIT;01.01.2020;12,37 +EBIT;01.01.2021;12,58 +EBIT;01.01.2022;15,41 +EBITDA;01.01.2018;23,333 +EBITDA;01.01.2019;24,731 +EBITDA;01.01.2020;35,017 +EBITDA;01.01.2021;37,33 +EBITDA;01.01.2022;40,208 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person.csv new file mode 100644 index 0000000..8cdfd2f --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person.csv @@ -0,0 +1,1000 @@ +"Hans-Hermann;Colifu" +"Carolina;Jevugradeweiner" +"Heinz-Peter;Fowelatestein" +"Gerlind;Nasefo" +"Steven;Gutusedemüller" +"Cristina;Databison" +"Gisbert;Revebistein" +"Lilo;Gelesademüller" +"Hilmar;Virisegehein" +"Margaret;Koch" +"Fritz;Mitimode" +"Alexandre;Schmid" +"Brian;Cewamauhein" +"Sinaida;Viwagau" +"Nicole;Hotagrode" +"Rosalia;Suwofu" +"Rocco;Kotesedemüller" +"Kira;Jowifoson" +"Branko;Schulz" +"George;Schmidt" +"Walentina;Poradumeiner" +"Cemal;Pavobedemüller" +"Eberhardt;Dedugradeweiner" +"Reinhard;Welodustein" +"Friedrich;Fischer" +"Abbas;Bauer" +"Albina;Furafo" +"Patrick;Köhler" +"Karl-Heinz;Duwafiweiner" +"Birgitt;Vorewede" +"Gerolf;Potigradestein" +"Franz;Tölevade" +"Leonore;Hötubi" +"Dominik;Cuvidi" +"Friedrich-Wilhelm;Lotubi" +"Henri;König" +"Lea;Richter" +"Karla;König" +"Suse;Schulz" +"Hannes;Wötulatemüller" +"Ekkehard;Mevabede" +"Meinhard;Rötebahein" +"Sylvester;Hävado" +"Aurelia;Herrmann" +"Friedo;Sotofumüller" +"Gilbert;Piwosede" +"Meinhard;Ritedumüller" +"Ahmad;Lange" +"Adeline;Meyer" +"Edit;Sasivoremeiner" +"Josef;Fevigrade" +"Marlies;Nalofledemüller" +"Gerhart;Muwidihein" +"Susan;Redischatthein" +"Carolina;Fasifo" +"Henriette;Lisubedeweiner" +"Elvira;Neumann" +"Violetta;Folilode" +"Heike;Krüger" +"Hartmuth;Föwomodestein" +"Diethelm;Hitasedestein" +"Carin;Povebastein" +"Thekla;Schmitt" +"Albertine;Riwuwattemüller" +"Tülay;Schneider" +"Kerstin;Nolasadeson" +"Andreas;Liladumeiner" +"Nikolaos;Jevubedemeiner" +"Simona;Veruschattmüller" +"Diethard;Hüwawede" +"Dusan;Navusegeweiner" +"Hella;Zotefomüller" +"Ronald;Fasimattmüller" +"Insa;Hudemau" +"Niklas;Colibamüller" +"Evamaria;Huber" +"Loni;Viwifihein" +"Verena;Woruwattemüller" +"Ingelore;Titibedestein" +"Mehdi;Rutigradeson" +"Alida;Nasiplau" +"Annett;Gutefison" +"Leonie;Neumann" +"Eduard;Schmid" +"Joanna;Gürogau" +"Bernadette;Zeresedemeiner" +"Klaus;Schulze" +"Moritz;Bädagedestein" +"Ingelore;Setofareson" +"Nikolaj;Jetiplauson" +"Sylvie;Vitedumeiner" +"Till;Motidamüller" +"Heidelinde;Gilisege" +"Christof;Lutavademeiner" +"Torsten;Kowobimeiner" +"Nina;Nowugrodemüller" +"Jens-Uwe;Jewosede" +"Jörg;Sivuhedehein" +"Götz;Felegaumeiner" +"Danica;Hoffmann" +"Karl-Heinrich;Fewovademeiner" +"Aneta;Lürevore" +"Ignaz;König" +"Sylvia;Berulodemeiner" +"Svetlana;Zatimatthein" +"Felicia;Schulze" +"Marie-Louise;Hadawede" +"Antonietta;Neumann" +"Dragica;Zaribumeiner" +"Abram;Butoduson" +"Dina;Müsabedestein" +"Hilde;Cülusede" +"Verena;Tawugrodehein" +"Anatolij;Mulawedemeiner" +"Ira;Wulawadestein" +"Hildegard;Wotegatemeiner" +"Alwine;Koroschatt" +"Patric;Towedu" +"Nadeschda;Relolode" +"Hedi;Tadegrade" +"Dietlinde;Lelavore" +"Pascale;Tüdawedeweiner" +"Sevim;Vetisademüller" +"Erdal;Melogedeweiner" +"Rotraut;Fiduvorestein" +"Jane;Wovimode" +"Guenter;Turabimüller" +"Rosalia;Püwigrademüller" +"Leokadia;Hatavodeson" +"Magnus;Rürovadehein" +"Sieglinde;Däriplauson" +"Vincenzo;Schmidt" +"Änne;Välolaustein" +"Jobst;Rüwawatteweiner" +"Sibille;Betebustein" +"Friedericke;Märelauhein" +"Tom;Lutimau" +"Annelie;Jöwudumüller" +"Gabriela;Valafareweiner" +"Leila;Höramattson" +"Bruno;Lange" +"Mary;Ruwagradehein" +"Meral;Golovore" +"Svea;Götebahein" +"Cordula;Tawefu" +"Ralf-Peter;Gerigrodehein" +"Jörg-Peter;Wilobison" +"John;Püviwattemüller" +"Viviane;Fedabede" +"Friedbert;Jitubamüller" +"Marco;Schröder" +"Ahmet;Fuchs" +"Anita;Zesemau" +"Ekkehard;Mulobodo" +"Eberhardt;Schmitz" +"Donata;Hoffmann" +"Hilma;Hoffmann" +"Götz;Gesihedehein" +"Adriane;Cewosadestein" +"Bruno;Söwolatemeiner" +"Liliana;Cerufomeiner" +"Gilda;Cewebede" +"Kamil;Maseflodeson" +"Gotthard;Gasusegehein" +"Susann;Neumann" +"Nada;Zoruvaremüller" +"Manuel;Patofostein" +"Roland;Fitafo" +"Ronald;Voweda" +"Heike;Patedohein" +"Heiderose;Neumann" +"Birgid;Pivudistein" +"Mariola;Niwosedemüller" +"Darko;Hidamau" +"Gilbert;Bodavorehein" +"Adele;Müller" +"Annette;Wolf" +"Hulda;Cavusadeweiner" +"Benjamin;Vörivodemüller" +"Antonietta;Jörufere" +"Ernst-August;Wivugrodehein" +"Malgorzata;Düdisade" +"Eckhard;Cudumodeson" +"Rebekka;Bavuduweiner" +"Traude;König" +"Ramon;Wöridihein" +"Uwe;Taruwade" +"Ulf;Fadischattmüller" +"Wolf-Dieter;Voveba" +"Friedhelm;Giwumau" +"Ercan;Lehmann" +"Britt;Palesede" +"Eleonore;Vörugatehein" +"Drago;Lehmann" +"Sandor;Müller" +"Elisa;Nevamattstein" +"Hans;Masigate" +"Marika;Jasefareson" +"Angelique;Geloplau" +"Rosel;Folugrodestein" +"Ricarda;Schäfer" +"Wolfhard;Madigrade" +"Cemal;Sadohedemüller" +"Maria;Bölugedemeiner" +"Gesche;Noviwedehein" +"Mira;Fuwabuweiner" +"Miriam;Kutegate" +"Hilmar;Jäviwede" +"Klaus-Dieter;Fewomatt" +"Ursel;Sutomauson" +"Karl-Friedrich;Dötasegeweiner" +"Emmi;Tetaplaumeiner" +"Hans-Jochen;Wolf" +"Reinhard;Buwefere" +"Corina;Vovogatestein" +"Erdmute;Joligate" +"Edit;Hoffmann" +"Antonino;Schmitt" +"Betti;Görudo" +"Annamaria;Walter" +"Alexandra;Wagner" +"Hans-J.;Lehmann" +"Rosmarie;Covebison" +"Ottilie;Schäfer" +"Natalia;Meier" +"Edeltraut;Becker" +"Ferdi;Werner" +"Reimar;Schmitt" +"Erna;Vivesademeiner" +"Sylvia;Masumodehein" +"Thomas;Riwifledeweiner" +"Hans;Baragaumüller" +"Hüseyin;Zarebe" +"Erika;Suruvodeweiner" +"Nikolai;Jivubahein" +"Hans-J.;Hoffmann" +"Dorit;Sodemattson" +"Klaudia;Lutimodemüller" +"Mariana;Gatebeson" +"Jonas;Rawafimüller" +"Tony;Mätegrodestein" +"Sevim;Galilauhein" +"Irma;Hawogradeweiner" +"Carla;Sisuhede" +"Vinko;Bauer" +"Mirella;Siwaschatt" +"Eckart;Metelau" +"Ildiko;Mowuvore" +"Ivo;Päwofo" +"Friedhold;Covagedemeiner" +"Nancy;Jülelauhein" +"Gretel;Tödosadeson" +"Wigbert;Rewoschatt" +"Isabelle;Näligrodehein" +"Hanny;Richter" +"Rouven;Fuchs" +"Maya;Mowawedeweiner" +"Gordana;Wagner" +"Inna;Patogrode" +"Jobst;Vosavode" +"Milena;Botogrodemeiner" +"Stilla;Rutavademeiner" +"Stephanie;Schmid" +"Pirmin;Tuwelauhein" +"Fatima;Schmitt" +"Luise;Cevelaumeiner" +"Friedhilde;Zölowadestein" +"Ester;Pädibistein" +"Carmine;Schwarz" +"Damian;Hartmann" +"Serge;Kidasede" +"Andreas;Rätobiweiner" +"Walther;Liwogrademüller" +"Franziska;Jodosedestein" +"Amelie;Däwadu" +"Heinz-Gerd;Loleduson" +"Leyla;Divifustein" +"Catrin;Herrmann" +"Anna-Marie;Borewatte" +"Philomena;Rotolodehein" +"Erdogan;Lätemaustein" +"Klaus;Schäfer" +"Heidrun;Braun" +"Anna-Lena;Cudifimeiner" +"Gerhart;Jasamattson" +"Susann;Ditodiweiner" +"Simon;Pilemaumeiner" +"Kristin;Hofmann" +"Nuran;Bauer" +"Gudula;Culalatemeiner" +"Babett;Pasasademüller" +"Ludmila;Tetobumüller" +"Annelene;Jasevore" +"Maria-Luise;Meyer" +"Käthe;Herrmann" +"Jovan;Mawodu" +"Brigitte;Häsubede" +"Annegret;Silufareson" +"Heinz-Peter;Huber" +"Tibor;Lurimauweiner" +"Roland;Wuravode" +"Rafael;Meier" +"Hans-J.;Schmidt" +"Julia;Jesesegehein" +"Franz;Lange" +"Anne-Rose;Newivare" +"Antonina;Klein" +"Gino;Jilifimüller" +"Birte;Vösugedeweiner" +"Zlatko;Cäwilate" +"Luzia;Hatagateweiner" +"Hans;Wölesege" +"Hans-Heinrich;Garobodo" +"Beatrice;Norewade" +"Wenzel;Vidilatemeiner" +"Anton;Lüdomodeweiner" +"Heini;Mayer" +"George;Schmitz" +"Maya;Lange" +"Dina;Meweplauson" +"Wilhelmine;Havafledeweiner" +"Daniele;Lange" +"Christa-Maria;Tötevore" +"Anatol;Hoffmann" +"Eleni;Gilifostein" +"Roy;Cudilauson" +"Regina;Gatabuson" +"Wolfgang;Sulidason" +"Gisela;Pavusademüller" +"Heinz-Otto;Köhler" +"Massimo;Katugateweiner" +"Stephanie;Schmitt" +"Gotthard;Dätamattstein" +"Eric;Näwebison" +"Renata;Bisavorehein" +"Yasmin;Fischer" +"Andres;Futagatehein" +"Sinaida;Gorasegemüller" +"Klaus-Günter;Metafistein" +"Marko;Daladi" +"Gitte;Pösidohein" +"Frank;Zimmermann" +"Wendelin;Wolf" +"Otmar;Lövafistein" +"Janet;Rösifledehein" +"Kenan;Musobu" +"Gero;Musuvode" +"Norbert;Wolf" +"Gisa;Ludawade" +"Fredo;Lotevodehein" +"Sara;Golifledeson" +"Erica;Helavade" +"Hildegunde;Lavawatteweiner" +"Darko;Fölabodomeiner" +"Helge;Givuplaumüller" +"Julian;Zewegrademeiner" +"Aurelia;Vuwafostein" +"Lina;Wolf" +"Anne-Rose;Jorasademüller" +"Thilo;Krause" +"Margaret;Wudiflodemeiner" +"Gordon;Hesasadeson" +"Katharina;Zusadoweiner" +"Caren;Havigedestein" +"Grit;Pivafuweiner" +"Gina;Disagrade" +"Hildburg;Födobason" +"Gunther;Nuvolaustein" +"Esther;Richter" +"Luise;Kesosedemeiner" +"Antonietta;Hitalate" +"Edelbert;Maier" +"Sylke;Kaiser" +"Petra;Ralowede" +"Türkan;Zatugede" +"Corina;Dulemaumeiner" +"Kai-Uwe;Talawedemeiner" +"Ingolf;Düselodeson" +"Judith;Duleda" +"Volkhard;Meier" +"Hedi;Rädomodeweiner" +"Corina;Solibedemüller" +"Alban;Gowifo" +"Luciano;Schröder" +"Sergej;Favuflede" +"Sandor;Hotogrademeiner" +"Fredo;Raralateson" +"Daniele;Levisedeweiner" +"Magdalena;Nutewadestein" +"Edwin;Weduschatthein" +"Canan;Powomattstein" +"Tilly;Jarawedehein" +"Ulla;Neumann" +"Berthold;Pedogau" +"Thilo;Fotavade" +"Tanja;Rowomode" +"Adolfine;Madelaustein" +"Rebecca;Zodavoreweiner" +"Rigo;Netaferestein" +"Sigurd;Mowebi" +"Erwin;Läsosadeweiner" +"Tilman;Bitilateweiner" +"Angelique;Citafo" +"Trudel;Hasalau" +"Raissa;Jülodoweiner" +"Alma;Botomode" +"Stephanie;Zimmermann" +"Bernard;Mateschattmüller" +"Emma;Müller" +"Freia;Volifomüller" +"Hans-Herbert;Kurilatestein" +"Jacqueline;Schröder" +"Fredy;Sisagradeson" +"Elly;Casumodeson" +"Ellen;Pitasege" +"Petar;Schneider" +"Rosa;Palaschattweiner" +"Joana;Wavufo" +"Heide-Marie;Wiwevareweiner" +"Dittmar;Fusifare" +"Reinhart;Krause" +"Hilma;Lange" +"August;Goviwattemüller" +"Bianca;Wodimode" +"Kreszentia;Nuwiwede" +"Erdogan;Rulabodomüller" +"Sylke;Fusefomeiner" +"Hassan;Fischer" +"Ayse;Mödalateweiner" +"Hatice;Werner" +"Zlatko;Müregate" +"Tania;Werner" +"Detlef;Cewuvoremüller" +"Dariusz;Meyer" +"Oxana;Cösilodeweiner" +"Hans-Henning;Dovifledehein" +"Luka;Braun" +"Kristine;Kotewatte" +"Annedore;Mörufuhein" +"Mareen;Marehede" +"Pascal;Suresegeweiner" +"Almut;Meier" +"Reinald;Wolf" +"Sabina;Wagner" +"Katharina;Movobi" +"Athanasios;Cütefledemüller" +"Stefano;Beweda" +"Walfried;Müwawadehein" +"Damaris;Maier" +"Paulina;Detofistein" +"Artur;Folasademeiner" +"Erica;Worudustein" +"Konstantinos;Kirevademeiner" +"Natalja;Citigrademeiner" +"William;Fuchs" +"Patric;Fosagrode" +"Kilian;Kaiser" +"Heidemarie;Notuplaustein" +"Mariusz;Zowebede" +"Ulrike;Todeduson" +"Jovan;Fuvowademüller" +"Katerina;Nutevadehein" +"Elisabeth;Lewubameiner" +"Ernestine;Wötischattson" +"Aleksandar;Watehedestein" +"Halina;Jötumodehein" +"Annamaria;Mivovade" +"Irina;Dudawede" +"Heinfried;Fütefoson" +"Tassilo;Luvofare" +"Mariele;Ritigatemeiner" +"Lea;Madelaumeiner" +"Sibel;Givuwedehein" +"Leila;Kitubihein" +"Mohammed;Meyer" +"Janett;Givegrodestein" +"Vassilios;Vurigrade" +"Hans;Kutugate" +"Ralph;Budifu" +"Gotthilf;Curagradeweiner" +"Melitta;Nawifo" +"Dirk;Bitoba" +"Sandro;Vusemattweiner" +"Falko;Vesuplauweiner" +"Gesine;Hofmann" +"Fanny;Füwegedehein" +"Margaretha;Sodigrademeiner" +"Marcella;Läsilate" +"Anne;Hoffmann" +"Annaliese;Wüsuvade" +"Rocco;Hoffmann" +"Pedro;Daduhedemeiner" +"Frieda;Wolf" +"Luzia;Vewevaremüller" +"Marianne;Viribodomüller" +"Jose;Ratalodeweiner" +"Dan;Vötiwedemeiner" +"Leyla;Ruregateson" +"Orhan;Südagede" +"Reinhilde;Dudufu" +"Gerta;Koch" +"Marika;Bosefarestein" +"Chantal;Lehmann" +"James;Fedesademeiner" +"Ante;Nosegrode" +"Gottfried;Krüger" +"Theda;Sawigatehein" +"Jan;Nisischatt" +"Ullrich;Lehmann" +"Nikolaos;Tavuvaremeiner" +"Deborah;Vesimauson" +"Türkan;Jösovaremeiner" +"Ignatz;Gudafuweiner" +"Aleksandr;Säwafo" +"Hannah;Krause" +"Benedikt;Schröder" +"Erik;Leteferemeiner" +"Marga;Pirafistein" +"Arnd;Wütehedeson" +"Wiebke;Rulida" +"Isabella;Jodumodemüller" +"Marika;Küvafo" +"Björn;Sirafi" +"Branko;Bileplaumeiner" +"Dierk;Daregateson" +"Max;Fätifostein" +"Mareile;Zisaflede" +"Mahmoud;Vötivademeiner" +"Erol;Lehmann" +"Alexa;Nivusedeweiner" +"Ingmar;Hartmann" +"Victor;Fesiwattestein" +"Myriam;Hartmann" +"Emilie;Hörulauweiner" +"Rosi;Lorobimeiner" +"Vinko;Nowibedehein" +"Urban;Gewomode" +"Linus;Suvelatestein" +"Heinz-Walter;Jelimaumüller" +"Friedhilde;Wüwubastein" +"Auguste;Mayer" +"Fatih;Rädugradestein" +"Christof;Zurifu" +"Katharina;Fuwudason" +"Viktoria;Ludavoreson" +"Hans;Schmidt" +"Friedbert;Nütilaumüller" +"Geraldine;Zimmermann" +"Meta;Richter" +"Beata;Telobu" +"Jakob;Jovedi" +"Elena;Zimmermann" +"Anthony;Bötugedemeiner" +"Achim;Fädemodeson" +"Benita;Nedosade" +"Alwine;Köhler" +"Achim;Lowedihein" +"Kreszenz;Kadihede" +"Nikolai;Schneider" +"Eberhardt;Bauer" +"Mathilde;Duwefimüller" +"Linus;Hitisadeson" +"Nikolas;Schröder" +"Kordula;Mosimodehein" +"Jeanette;Walter" +"Gregor;Paviwademüller" +"Bettina;Mevewedeweiner" +"Beate;Havawattemüller" +"Walter;Votofostein" +"Hulda;Dälifumüller" +"Mary;Wütodoweiner" +"Patrick;Korofoweiner" +"Jörg;Küribahein" +"Meta;Naduflede" +"Magda;Malabodo" +"Paul-Heinz;Duwawatte" +"Stjepan;Fuchs" +"Babette;Havisedeweiner" +"Alexandre;Bivoflede" +"Michael;Reloplaumüller" +"Nicolas;Roleflode" +"Simon;Koch" +"Anna-Marie;Zulofuson" +"Giorgio;Vovoflode" +"Urs;Jelebaweiner" +"Gerdi;Kuvosade" +"Harm;Relusede" +"Fred;Pavuferehein" +"Ann;Zäraschattson" +"Gislinde;Givudohein" +"Valeria;Giteplaustein" +"Ortrud;Rutomattmeiner" +"Carmen;Zäromauhein" +"Leyla;Müller" +"Raissa;Lusugradestein" +"Ömer;Wawimattstein" +"Geza;König" +"Vittorio;Bevamaumüller" +"Patrick;Schmitt" +"Günther;Würafumüller" +"Hans-Uwe;Klein" +"Regine;Levafoweiner" +"Ekkehard;Gediduson" +"Susi;Lange" +"Heinz-Werner;Kewoflede" +"Timm;Werner" +"Gusti;Hutugatehein" +"Marie-Theres;Braun" +"Lukas;Nowematthein" +"Filippo;Dudoflode" +"Hüseyin;Votesegeson" +"Sandy;Ladagate" +"Bodo;Posibodo" +"Hellmuth;Zowemauhein" +"Sergej;Lange" +"Meinolf;Jiwobodostein" +"Heinz-Peter;Kesuduweiner" +"Hannes;Favifledestein" +"Doris;Redubimeiner" +"Reni;Jowasege" +"Liane;Braun" +"Holger;Förebihein" +"Mirko;Geresege" +"Ellinor;Pövischatthein" +"Christl;Schröder" +"Waltraud;Padufu" +"Leopoldine;Piwigrodeweiner" +"Gislinde;Tusudison" +"Leonardo;Mäveflede" +"Selim;Fotogatemeiner" +"Patricia;Bädihedemüller" +"Edelgard;Cusofoson" +"Juliana;Putebaweiner" +"Zehra;Käsolau" +"Sükrü;Dädumattmeiner" +"Hilmar;Schmid" +"Käte;Görivodemeiner" +"Ute;Getamaumüller" +"Friedhold;Silavade" +"Kristin;Saroschattson" +"Ida;Dutabison" +"Rafael;Fivalatemeiner" +"Wilma;Widavodemeiner" +"Claudio;Pätomodemüller" +"Friedo;Krüger" +"Marga;Herrmann" +"Gesa;Meier" +"Wanda;Zörefaremüller" +"Frauke;Kaiser" +"Hansjoachim;Wawodoson" +"Christel;Viwofison" +"Angelika;Becker" +"Suse;Situfledehein" +"Romuald;Puwewade" +"Erol;Midisege" +"Heini;Weber" +"Horst-Günter;Culiwedestein" +"Thea;Buludohein" +"Steve;Getusedehein" +"Heimo;Fetagrade" +"Zdenka;Vevuvareson" +"Stanislaw;Schwarz" +"Metin;Wudelateson" +"Walter;Didegatemüller" +"Ilka;Klein" +"Henning;Kotolode" +"Änne;Becker" +"Ines;Hüdofu" +"Emma;Noduwademüller" +"Wencke;Mowolau" +"Reza;Toredimeiner" +"Murat;Meliferehein" +"Hanne;Rosewade" +"Antonina;Budufiweiner" +"Hans-Wilhelm;Zisefaremüller" +"Thilo;Cöwefison" +"Marianna;Jütadu" +"Agata;Hoffmann" +"Stavros;Fitiplauhein" +"Margarita;Sutaflodehein" +"Walburga;Zosuhede" +"Sandor;Wusuferemüller" +"Ortrud;Welumatt" +"Luise;Cotuwattestein" +"Marlene;Lovomatt" +"Nicolas;Buteschattmeiner" +"James;Pevewattemüller" +"Mirco;Nodolauweiner" +"Egon;Fuchs" +"Burckhard;Schäfer" +"Dunja;Sevedi" +"Nikolas;Vovesademüller" +"Sergej;Jotosademeiner" +"Edelgard;Fuchs" +"Hermine;Newusedeweiner" +"Rico;Mayer" +"Berta;Schmidt" +"Dorothea;Fischer" +"Catherine;Klein" +"Sigfried;Lorelau" +"Karl;Jatufare" +"Galina;Becker" +"Wilhelmine;Josimode" +"Maria;Bavisedemeiner" +"Paul-Gerhard;Zuvidameiner" +"Ortrun;Wetuvodehein" +"Ingolf;Schmitz" +"Ottilie;Müller" +"Traugott;Dotofihein" +"Luciano;Werifihein" +"Siegward;Schmitz" +"Zlatko;Sisifumeiner" +"Henni;Schmid" +"Gabriella;Schwarz" +"Marcella;Tirilatehein" +"Lilian;Madusade" +"Kreszenz;Fürifumüller" +"Hans-Werner;Küsolateson" +"Emmi;Biwoferemeiner" +"Heidemarie;Wutudamüller" +"Melanie;Hofmann" +"Käthi;Cesemodestein" +"Etta;Koch" +"Heike;Sesegradestein" +"Eleonora;Tusabason" +"Gönül;Sutegede" +"Ariane;Murebodo" +"Henrik;Rewubamüller" +"Klaus-Jürgen;Schulze" +"Mike;Titifostein" +"Wenzel;Vivuvade" +"Türkan;Rasaduweiner" +"Zenta;Dedufimeiner" +"Laurenz;Butube" +"Marietta;Krause" +"Ingetraud;Dosubodohein" +"Hanni;Wowodistein" +"Ralf-Peter;Nosilodemeiner" +"Miguel;Hodifledemeiner" +"Mario;Jösugrodeweiner" +"Osman;Mitado" +"Bruno;Müller" +"Hans-Hinrich;Hotadu" +"Patrick;Schmidt" +"Birgid;Leselauweiner" +"Monica;Lüdofu" +"Georgine;Hiwematt" +"Mehmet;Fäwebason" +"Lioba;Meier" +"Hagen;Süvegatemüller" +"Kornelius;Fevefuweiner" +"Aleksej;Tadiplauhein" +"Rosmarie;Wätufohein" +"Fritz;Mayer" +"Hans;Düsuvadestein" +"Magdalene;Cusavarestein" +"Marliese;Fovuplaustein" +"Jakob;Relagauson" +"Leila;Koch" +"Stefanie;Revimattmeiner" +"Ingmar;Jevihedeweiner" +"Christine;Wusebimeiner" +"Jacob;Metufohein" +"Hans-Adolf;Neumann" +"Gisbert;Schulz" +"Meinrad;Wüwadomüller" +"Sieglinde;Tatoplau" +"Egbert;Pätofohein" +"Katy;Kölofareson" +"Sylvie;Lutigaustein" +"Knut;Puwoferestein" +"Gerhard;Hiwodomüller" +"Trudel;Wolf" +"Chantal;Krause" +"Esther;Wesewatte" +"Dragica;Fasuwade" +"Reginald;Madegauweiner" +"Fatima;Kusibu" +"Karla;Balifledestein" +"Mary;Netasedeson" +"Igor;Schäfer" +"Giovanna;Maier" +"Cornelia;Wütegrode" +"Mehmet;Bauer" +"Metin;Krüger" +"Herbert;Gelosadehein" +"Hans-Gerd;Zurulatehein" +"Mariusz;Tovemattmeiner" +"Hans-Helmut;Rasuwattemeiner" +"Baptist;Midafi" +"Pedro;Hilasedehein" +"Sarah;Tütibodomüller" +"Maximilian;Sawiwedemeiner" +"Ekrem;Muvagate" +"Kurt;Hofmann" +"Norbert;Dutematt" +"Patric;Siwubameiner" +"Gretel;Sutugedehein" +"Christl;Toreba" +"Pauline;Bitibeson" +"Franco;Fasasademeiner" +"Mariele;Laravodemeiner" +"Lili;Diribumüller" +"Kenneth;Sosemaumüller" +"Leo;Botomattmeiner" +"Henriette;Ralufere" +"Friedlinde;Lirimaustein" +"Gerold;Herrmann" +"Oliver;Zädebastein" +"Anatol;Vawavadeweiner" +"Tabea;Dutavore" +"Paulina;Dawodoweiner" +"Lilo;Nöduvarehein" +"Anthony;Rusabodostein" +"Rolf-Dieter;Dürogrodeweiner" +"Elise;Körebuweiner" +"Mona;Ketedumeiner" +"Patric;Sorisadeweiner" +"Karolina;Zutihedeson" +"Julie;Hartmann" +"Janos;Vavemauhein" +"Cathrin;Davobe" +"Katrin;Hutodahein" +"Canan;Klein" +"Heidelore;Fotubemüller" +"Toralf;Silahedemeiner" +"Adolf;Telolodemeiner" +"Katja;Lange" +"Veronika;Visebodo" +"Karlheinz;Givafoson" +"Leonie;Garaplau" +"Tassilo;Walter" +"Mirja;Becker" +"Antonios;Schneider" +"Tilman;Zusegrode" +"Bianka;Getafumüller" +"Stefani;Pawuhedehein" +"Dina;Dorafarestein" +"Silvester;Fasihedeweiner" +"Carolina;Jutilodemüller" +"Hans-Günter;Fitelauweiner" +"Eleni;Schäfer" +"Cemal;Ritigrodemüller" +"Hagen;Wutusegemüller" +"Bernt;Gorudason" +"Loretta;Becker" +"Sabrina;Katomattstein" +"Gottlieb;Zuvagaumüller" +"Rita;Witifumeiner" +"Cengiz;Detuflodestein" +"Oscar;Filafledestein" +"Pero;Müller" +"Ruthild;Muremau" +"Magret;Richter" +"Sigrid;Zimmermann" +"Nils;Lange" +"Cecilia;Zuwibodoweiner" +"Gisa;Maier" +"Dennis;Duvofo" +"Georgios;Weber" +"Kevin;Duregedestein" +"Albin;Metafaremeiner" +"Reinhard;Lowafoson" +"Christine;Duraflede" +"Guenter;Puwobuhein" +"Sylvia;Torumode" +"Marija;Mavelodemeiner" +"Tatiana;Vatawademüller" +"Türkan;Volubedemeiner" +"Pius;Wädeba" +"Rene;Fischer" +"Sebastiano;Rotavare" +"Sieghard;Fusudo" +"Imke;Züsoplaumüller" +"Egbert;Biduvade" +"Leonardo;Kevafoweiner" +"Bernd-Dieter;Mituwattemeiner" +"Leokadia;Füsuvode" +"Amelie;Mireplaumüller" +"Ingolf;Braun" +"Karolina;Pasegauson" +"Christian;Lange" +"Mara;Dotubodo" +"Helma;Häsibehein" +"Karsten;Vuvidostein" +"Andrey;Mudubodostein" +"Theres;Cevovoreweiner" +"Mareike;Braun" +"Kemal;Füribison" +"Andy;Pulusege" +"Anneliese;Nödugrodemeiner" +"Linda;Maier" +"Muharrem;Tasovode" +"Wibke;Meyer" +"Hans-Christian;Tävobiweiner" +"Walli;Walibodo" +"Gerti;Rotido" +"Giesela;Bevagede" +"Gerhart;Jarolodemeiner" +"Erhardt;Schröder" +"Mirjam;Nisabaweiner" +"Amalie;Lesolateweiner" +"Bertram;Novebedemüller" +"Eveline;Vavogedemüller" +"Walburga;Rowovoreweiner" +"Silke;Krause" +"Randolf;Huvadistein" +"Immo;Gulilatehein" +"Isa;Meyer" +"Olivia;Cativorestein" +"Arnulf;Meier" +"Elke;Vavegedeson" +"Meinrad;Basolate" +"Franz-Xaver;Kaiser" +"Irmtrud;Zativadeweiner" +"Mattias;Hedifoweiner" +"Petar;Virofuhein" +"Heino;Wurabimeiner" +"Lotte;Hötesadeson" +"Henner;Niwigate" +"Cemal;Bavugedeweiner" +"Hanny;Pologau" +"Heinz-Gerd;Vätisade" +"Kuno;Mayer" +"Philipp;Maier" +"Dietmar;Klein" +"Etta;Covibu" +"Hans;Parogedehein" +"Klaus;Cadimodestein" +"Dragan;Dovobimüller" +"Bernhardine;Caludiweiner" +"Bianka;Horiwadeweiner" +"Franz;Hatulate" +"Kuno;Wutofohein" +"Monja;Barosedestein" +"Monique;Batuschattmüller" +"Robin;Dedifereson" +"Kaspar;Husubamüller" +"Magnus;Dütomattmüller" +"Rosel;Luvewatteson" +"Tino;Hesisedemüller" +"Carlo;Buvosademüller" +"Petar;Rerovodeson" +"Murat;Bewisedeson" +"Sonja;Vutawedeson" +"Birgitta;Wavugedehein" +"Maritta;Vedosademüller" +"Clarissa;Zuvibustein" +"Urszula;Köhler" +"Hans-Werner;Zimmermann" +"Iris;Viwimattweiner" +"Stefan;Däsidason" +"Cäcilia;Schäfer" +"Gabriel;Kadufareson" +"Leopold;Zetogau" +"Adelgunde;Motivadeson" +"Kathleen;Bötifistein" +"Sybille;Süravareson" +"Andy;Becker" +"Ewald;Masegradehein" +"Christa;Modischatt" +"Ansgar;Sörasadeson" +"Wulf;Gerabu" +"Adelgunde;Motufaremeiner" +"Marian;Cesosedeson" +"Rupert;Motugrodehein" +"Friedhelm;Wowebe" +"Anne-Kathrin;Hevusegeweiner" +"Luise;Meier" +"Violetta;Bilalatemeiner" +"Edda;Rowemau" +"Viktor;Tutewatteweiner" +"Elwira;Herrmann" +"Johanna;Ridivare" +"Meinolf;Rusegedeson" +"Klaus-Ulrich;Hartmann" +"Kai-Uwe;Puvagateson" +"Korinna;Hotiwatte" +"Tatiana;Rirobihein" +"Silvana;Tilifoweiner" +"Georgia;Vodigate" +"Baptist;Gäligrodeweiner" +"Darius;Rätisademeiner" +"Magda;Köhler" +"Anne-Kathrin;Holubi" +"Theres;Nevovarehein" +"Ignatz;Walter" +"Frederike;Richter" +"Gerard;Davidustein" +"Karl-Hermann;Patevade" +"Judith;Köhler" +"Randolf;Zedufledeson" +"Henning;Vutoda" +"Maik;Ralabe" +"Ercan;Cilumattstein" +"Alicia;Sarolodestein" +"Ingmar;Vitisedehein" +"Augustin;Schmitz" +"Michaela;Werner" +"Raymund;Sewesedemüller" diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person2.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person2.csv new file mode 100644 index 0000000..1f08db7 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/person2.csv @@ -0,0 +1,20 @@ +Mohammed;Klein +Myriam;Koch +Dorothe;Zerusedemeiner +Emine;Puviplau +Galina;Tosewede +Hans-Walter;Mädidostein +Ludmilla;Krause +Jessica;Lesibedemeiner +Franz;Lowufohein +Krzysztof;Gaselatemüller +Gerolf;Navusedeson +Sibylla;Sutedihein +Nina;Golebede +Alicja;Revibodomeiner +Meryem;Kadeduhein +Janina;Zimmermann +Hendrik;Krüger +Oskar;Podadi +Maria-Luise;Nelaflodeson +Nadine;Niwogatemeiner \ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/Telekom_Data.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Telekom_Data.csv new file mode 100644 index 0000000..c0fc0d4 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/Telekom_Data.csv @@ -0,0 +1,19 @@ +Company_HR;Company_Court;Jahr;Umsatz;Ebit;EBITDA +;12336;5;2005;59600;7600; +;12336;5;2006;61300;5300; +;12336;5;2007;62500;5300; +;12336;5;2008;61700;7000; +;12336;5;2009;64600;6000; +;12336;5;2010;62420;5510; +;12336;5;2011;58650;5560; +;12336;5;2012;58170;-3960; +;12336;5;2013;60130;4930; +;12336;5;2014;62660;7250; +;12336;5;2015;69230;7030; +;12336;5;2016;73100;9160; +;12336;5;2017;74950;9380; +;12336;5;2018;75660;8000;23333 +;12336;5;2019;80530;9460;24731 +;12336;5;2020;99950;12370;35017 +;12336;5;2021;107610;12580;37330 +;12336;5;2022;114200;15410;40208 diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/edges.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/edges.csv new file mode 100644 index 0000000..719314d --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/edges.csv @@ -0,0 +1,2001 @@ +,Company_HR,Company_Court,Person_ID,Year_From,Year_To,Relation_Type +0,9875,1,877,2005,2006,Managing_Director +1,5433,3,203,2008,2009,Auditor +2,64345,9,336,2007,2008,Executive +3,555,7,63,2012,2013,Executive +4,64345,9,646,2016,2017,Auditor +5,12334,2,576,2016,2017,Authorized_Representive +6,12334,4,760,2023,2017,Authorized_Representive +7,12334,2,486,2023,2017,Authorized_Representive +8,866,1,186,2018,2019,Executive +9,64566,2,552,2023,2019,Managing_Director +10,12384,8,285,2012,2013,Authorized_Representive +11,555,7,301,2012,2013,Auditor +12,9875,1,567,2009,2010,Executive +13,12334,2,726,2019,2020,Supervisory_Board +14,12336,5,575,2017,2018,Managing_Director +15,12336,5,842,2005,2006,Final_Auditor 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+1999,866,1,369,2010,2011,Final_Auditor diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/person1000.csv b/documentations/seminararbeiten/Datenspeicherung/Jupyter/person1000.csv new file mode 100644 index 0000000..5ac40c7 --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/Jupyter/person1000.csv @@ -0,0 +1,1001 @@ +Name;Surname +Mareile;Vadedimeiner +Ehrhard;Butugau +Ruthild;Werner +Robby;Herrmann +Claus-Peter;Koch +Natali;Zosamaustein +Evi;Rüwigau +Uta;Puwufumeiner +Elfi;Hawewedehein +Dimitrios;Zimmermann +Pamela;Pötawedemüller +Helfried;Hätefi +Frieda;Favigrode +Sylke;Dosuvorehein +Waltraut;Divufostein +Jochen;Saroda +Paul-Heinz;Cedoflede +Peggy;Rüdabiweiner +Friedrich-Karl;Jivugate +Celal;Kadifu +Heinz-Jürgen;Gidebede +Meinhard;Cätodameiner +Guenther;Huber +Andree;Gedamodeson +Tillmann;Gatesegeson +Katerina;Giwifoson +Muharrem;König +Mirjana;Sisabeweiner +Heinz-Walter;Satawademüller +Wolf-Dieter;Gotuferemüller +Josefa;Hartmann +Arnfried;Lidamau +Sylvester;Bütabodostein +Ildiko;Mäwebodostein +Antonius;Pulube +Hans;Fuchs +Antonina;Müller +Raphaela;Pätagaumeiner +Walli;Ruremattmüller +Henrike;Meier +Emmerich;Wodegau +Frederike;Surogateson +Trudel;Zoruhedehein +Nadeschda;Schulz +Else;Jawalau +Yasmin;Nitusade +Rebekka;Vuvabehein +Martina;Zadosede +Klaus-Ulrich;Söravadehein +Emilia;Walter +Bogdan;Mewobodostein +Babette;Wüsasadehein +Marcel;Ladagrode +Ana;Hevuwademüller +Eckard;Meyer +Elzbieta;Jerudustein +Johann;Küwadu +Amalia;Küsevodeweiner +Romuald;Wetiwedestein +Hans-Heinrich;Titugrode +Kornelius;Jedoschattmüller +Beata;Hoffmann +Ljubica;Geledihein +Vinzenz;Ciwilodehein +Ana;Kisubodomeiner +Lars;Metuflede +Filippo;Schäfer +Jutta;Leduhedeweiner +Hubert;Lutefo +Tilmann;Gürugedemüller +Nadeshda;Hofmann +Ehrhard;Vovefledestein +Friedbert;Guwasadestein +Ellinor;Dötafledeweiner +Annita;Hivedu +Raisa;Fuchs +Steffen;Zawudason +Dorothe;Mötodohein +Klaas;Peledu +Lili;Muwewede +Yusuf;Mayer +Pirmin;Zorobedeson +Franz-Peter;Zuwilauson +Ludger;Zarohede +Hans-Martin;Jerebi +Corinna;Helomattstein +Saban;Röwamattmüller +Recep;Güdumattson +Max;Säduda +Ditmar;Kävabehein +Katharine;Hiwebodo +Alfonso;Zaruba +Rabea;Kotivaremeiner +Jochen;Datuvodemeiner +Abraham;Litusedeson +Dagmar;Girogatehein +Astrid;Hätavoremüller +Katarzyna;Tevifere +Arnold;Horovadestein +Petra;Döwagauhein +Karl-Wilhelm;Cedovade +Friederike;Zimmermann +Hans-Albert;Ludubehein +Elfi;Hatuda +Hartmut;Cuvuvoremeiner +Augusta;Müsavademeiner +Enver;Näsalodemüller +Mirco;Vidobemeiner +Ahmad;Wusefo +Artur;Puledohein +Donata;König +Änne;Neresedehein +Ilse;Wedabodoson +Marija;Schäfer +Annegrete;Fodogede +Eckhart;Pitosedemüller +Juliane;Hoffmann +Rouven;Javisegemüller +Sybilla;Tewugede +Othmar;Nolilate +Hubert;Zotagrade +Darius;Govuferestein +Gero;Büsolodemüller +Jens-Uwe;Kaiser +Klaus-Ulrich;Zärevodeweiner +Natalja;Koch +Hanife;Jitaferehein +Ingeborg;Favihedestein +Anka;Södomatt +Ludger;Pulibu +Gottfried;Krüger +Gebhard;Neumann +Magrit;Richter +Antonios;Neumann +Urte;Köwafi +Ivanka;Wevosege +Nermin;Hoduwedemüller +Sylvio;Suwufledehein +Friedemann;Rovafledeweiner +Thilo;Becker +Detlef;Zirebehein +Mira;Vivischatt +Iwan;Musosede +Willi;Neumann +Christian;Müller +Jessika;Woraplau +Slawomir;Jovesegemeiner +Annie;Schmitz +Reinhold;Sevowede +Ralf-Dieter;Söveda +Heide-Marie;Hutifere +Silva;Cerivare +Sina;Mütuflede +Hans;Zotegedeweiner +Theodora;Wolf +Dusan;Dütihedemüller +Augusta;Hulamattmeiner +Martin;Fuwesademüller +Recep;Mütolatemüller +Gotthard;Werner +Henning;Fischer +Heinz;Jisoferestein +Blanka;Notadumeiner +Marijan;Dasawattehein +Martine;Hartmann +Friedhold;Hartmann +Uwe;Pöruplau +Jaroslaw;Gusavodehein +Hans-Erich;Müller +Rosemarie;Fetefu +Volker;Letelatemeiner +Zita;Voduwedehein +Kenneth;Bewagrademüller +Nikolas;Zisalode +Kira;Tirewadehein +Elias;Powosadehein +Christiane;Häregrodeson +Romuald;Javuschattson +Priska;Pisiwademeiner +Rosita;Bewefumeiner +Henriette;Ködudu +Gernot;Fivamatthein +Felicitas;Cüravoreson +Friedrich-Karl;Schulze +Yilmaz;Notobede +Eileen;Mudobahein +Ruth;Loligrodestein +Ludmila;Malimodestein +Stanislaus;Ferufereson +Celal;Nudomau +Gisa;Duluflodemüller +Elenore;Fuchs +Heini;Bosavodehein +Mariusz;Cusolodeweiner +Monique;Weber +Hedda;Radifistein +Irma;Vetudu +Paul-Gerhard;Wewovorestein +Luise;Cadabahein +Frida;Lovudu +Sabrina;Desubedehein +Curt;Bödowattemüller +Jana;Huber +Jaroslav;Rirusedemüller +Tobias;Botiflodestein +Pascale;Hüsewedestein +Karl-Peter;Püvohedemeiner +Friedrich-Karl;Moweschattmeiner +Virginia;Giwemauweiner +Lambert;Pesesedestein +Ellen;Litedu +Ingeburg;Zatawadestein +Filippo;Wetodison +Edeltraut;Hadilode +Moritz;Schmitz +Roderich;Garovade +Isabel;Jirusedeson +Bernt;Wutivoremeiner +Eugenia;Rorodahein +Walli;Krüger +Giesela;Schwarz +Gundolf;Jitoflodeweiner +Hedda;Fuchs +Manja;Siwosegemeiner +Christopher;Tavugatemüller +Heiko;Tiwefu +Fredy;Powoplauweiner +Erwin;Schäfer +Paolo;Mutofomüller +Selim;Miwabu +Dimitri;Wagner +Mustafa;Lalobedemeiner +Käthi;Zudifareson +Friedhold;Süvusedeweiner +Wilfriede;Lötafere +Franziska;Jirewattehein +Marianna;Cosusedestein +Giuseppina;Fädusege +Erik;Zötufo +Margrit;Melufledemüller +Christa;Wodosademeiner +Tilmann;Newifereson +Götz;Wuvugrademeiner +Friedhilde;Metasegemüller +Annelise;Favumattmüller +Jaqueline;Kiwifostein +Rosalia;Meier +Erdmute;Halogate +Siegmar;Kutadoson +Aloys;Hartmann +Jeanette;Wewumode +Adelbert;Döragateson +Saskia;Savematt +Anja;Zetumode +Maurice;Zosamatt +Antonietta;Väwusege +Raphael;Meier +Walther;Zitomodehein +Nurettin;Lehmann +Isolde;Ludogauson +Julia;Tudibiweiner +Harry;Ciravade +Ellen;Cöruba +Dominic;Rorivoremeiner +Massimo;Cavihede +Bianka;Jiwavadehein +Lucia;Nusumattson +Meike;Kiregaumeiner +Susanne;Hovebodohein +Juliane;Titufu +Joanna;Sotoferestein +Loni;Sarebeson +Alma;Becker +Vladimir;Mayer +Klaus-Günter;Krause +Anatol;Vutoplauson +Romuald;Milegrodemeiner +Thorsten;Zimmermann +Slavica;Midobodo +Annamaria;Wawosadeweiner +Karl-Wilhelm;Desagatemüller +Theodor;Jewudistein +Hanni;Rewofomeiner +Sylvana;Neliba +Diethelm;Motilate +Janus;Zusuhedehein +Annelore;Vutifomeiner +Geza;Lange +Susan;Gitaflodemüller +Elly;Näwelateson +Carmela;Reriwatte +Uli;Schröder +Danuta;Meyer +Kazim;Disifistein +Gottlob;Ciwagatemeiner +Uli;Wutamatthein +Ingmar;Wusevodeweiner +Peter;Rulafomüller +Türkan;Haduhedeson +Dieter;Fedabi +Hilmar;Hisofistein +Suzanne;Zotasedestein +Aleksej;Nöreflodestein +Herrmann;Zisubumeiner +Sarina;Zutevarestein +Gernot;Lehmann +Björn;Huwevadestein +Esther;Tilaschattstein +Claudius;Godavorestein +Dorota;Dudusadeweiner +Anatoli;Vödolauson +Arnfried;Zimmermann +Cathrin;Gotagedehein +Annelene;Tarebamüller +Luzie;Ruluvaremeiner +Faruk;Keseferestein +Babett;Wiremodehein +Aribert;Mesufere +Annette;Golovademüller +Carmela;Rävimode +Adeline;Näramode +Helma;Radusadestein +Vera;Palofledehein +Luzie;Dalogatemüller +Volkmar;Töseba +Emil;Bauer +Heiko;Wudoschatt +Ljudmila;Niwogede +Lioba;Lirobedemüller +Leonardo;Radubimeiner +Hedy;Hitowademüller +Gundi;Koch +Mathias;Lehmann +Steve;Wowowade +Paulina;Möwefledestein +Anne-Kathrin;Witubi +Tanja;Belosedestein +Hans-Detlef;Nadedu +Janine;Mivifu +Dorota;Berabaweiner +Rudi;Werner +Eckhart;König +Annekatrin;Worewadeweiner +Milica;Patefledehein +Arnfried;Borafledeson +Arif;Gilobodo +Elfi;Dilubedehein +Beatrice;Beresedemüller +Sabine;Motubihein +Ignatz;Gadagrade +Aloisia;Votulate +Sören;Lehmann +Margret;Hodobodo +Theodora;Silogatemeiner +Rosmarie;Cowegrademüller +Zoran;Pevuvodeweiner +Aleksandar;Zotoflode +Mirja;Fischer +Hans-Friedrich;Perumatt +Nikolai;Cesavaremeiner +Evelyn;Schäfer +Eva-Maria;Braun +Folker;Lödobedehein +Daria;Tewesade +Hildegund;Piresadehein +Nikola;Duleflode +Claudio;Braun +Damaris;Cidesede +Boris;Hüwedo +Lisa;Dedigedeson +Zita;Vöwigedemüller +Jaroslav;Badawatteweiner +Zenta;Dadofere +Willy;Detafledemeiner +Valentin;Meier +Gloria;Kaseplaumüller +Osman;Wisavademeiner +Rosa-Maria;Lange +Edeltraud;Sitovoreson +Rolf-Dieter;Müleplaumeiner +Kay-Uwe;Bosafoson +Paul-Gerhard;Fulawedestein +Lothar;Zawoschatthein +Carmelo;Klein +Erdal;Säduflodeweiner +Bernard;Pätuba +Nelli;Kilufoson +Nikolaus;Ratubodostein +Marcel;Klein +Wulf;Meyer +Anny;Fisagede +Petra;Zisegrodeson +Ottokar;Herrmann +Sükrü;Fuveflodeson +Emmy;Gerivorehein +Darius;Krüger +Birgitt;Faridu +Constanze;Siribastein +Tabea;Pulasade +Janna;Kutofistein +Kathi;Pewiflodestein +Josef;Maier +Francesco;Jüviflode +William;Fulolaumüller +Karl-Jürgen;Kulolode +Muzaffer;Fivodomüller +Catherine;Schneider +Hanny;Lehmann +Reinhart;Maier +Silvio;Fitofarestein +Mona;Wäsemodestein +Wojciech;König +Diana;Zusivare +Beatrice;Raradamüller +Slobodan;Fischer +Hildegard;Korudihein +Ludwina;Klein +Mathilde;Maier +Hans-Jörg;Jewahedehein +Stephanie;Lüsoda +Jeannette;Hoffmann +Klaus-Dieter;Dörugaumeiner +Hans-Friedrich;Selovademeiner +Klaus-Günter;Kövohedemüller +Sylvio;Hitavade +Goran;Lulasedehein +Frieda;Zutubedeson +Camilla;Tüdischatt +Felizitas;Wäwuflede +Hermann;Varebodohein +Nils;Vasahede +Frank-Peter;Jadufuhein +Alex;Liriwadehein 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+Selma;Vavubahein +Bogdan;Vatelodeweiner +Maya;Gateferehein +Rene;Tedobumeiner +Lucie;Piludimeiner +Karsten;Köwuwade +Harriet;Vusolodemeiner +Bert;Ritobedemüller +Leonie;Zütofledemüller +Lisa;Pövivodestein +Franz-Josef;Sötowadeson +Antonina;Lovuflodeson +Mahmoud;Mötihedemeiner +Tamara;Lätosede +Janette;Mutagedemüller +Annegrete;Nalofere +Hertha;Bävagrademüller +Athanasios;Fodogateweiner +Ercan;Wuliflodeweiner +Karina;Futifare +Damaris;Sutedaweiner +Lidwina;Mayer +Detlev;Koch +Gitte;Schulz +Lars;Cösesademeiner +Klothilde;Zodubedemüller +Viktor;Fevibiweiner +Lambert;Gotawadehein +Ryszard;Nidavore +Gernot;Badawedemüller +Achim;Lange +Bruno;Krause +Milica;Novowade +Mareike;Zarodimüller +Ljudmila;Dövuwade +Gesa;Cütodo +Gilda;Weber +Rotraut;Teribodoweiner +Carmela;Buviflodeweiner +Rose;Wetoschattmüller +Gerda;Gusuvareweiner +Baptist;Ridivadestein +Clemens;Hoffmann +Maritta;Töwesade +Elli;Sösasade +Nicolas;Kedabu +Sükrü;Vilaschatthein +Heinz-Otto;Meyer +Franz-Josef;Schwarz +Wolf;Kirilode +Maria;Satagauweiner +Irena;Bisivore +Stjepan;Sevuduweiner +Maurice;Krause +Marisa;Schneider +Harri;Hartmann +Christine;Hovesademeiner +Mareike;Wolf +Sibylla;Viwomattweiner +Anatolij;Litisadeweiner +Imelda;Pusegrode +Ruthild;Schröder +Luka;Fevuwadestein +Telse;Vilidahein +Zbigniew;Witoschatthein +Hans-Michael;Schwarz +Bernd;Haruwatteson +Antoinette;Guwohede +Lia;Lurilode +Reinhard;Virilaustein +David;Dasebedestein +Suzanne;Pürowatteson +Dana;Lawumauhein +Hanspeter;Notewade +Anica;Jörovare +Rosemarie;Gatuvode +Bernfried;Wivifledestein +Franka;Hesilauweiner +Dorothea;Betavoremüller +Erna;Klein +Adriana;Datibedehein +Bernd;Schulz +Ilse;Schmidt +Emma;Culisadestein +Josip;Säwafoson +Myriam;Gawifohein +Heinz-Werner;Satosade +Anna;Krause +Johannes;Zitiwedeson +Roselinde;Lesuwatteweiner +Valentine;Zötida +Valentin;Färivodehein +Trudi;Mosuferemeiner +Karen;Kaiser +Erik;Fiwugatemeiner +Thoralf;Süsabodoweiner +Waltraut;Pödiflede +Gerfried;Nitasadeson +Birthe;König +Heinrich;Walter +Gustav;Pödodomeiner +Bert;Klein +Sepp;Jotibahein +Hilma;Fesahede +Cornelius;Wolf +Karl-Heinz;Luresadehein +Heinz;Fesagrodemüller +Carina;Wusoflode +Giuseppe;Lotefoson +Wulf;Celulatestein +Danilo;Hewibedemeiner +Isabella;Wuraplauson +Daniela;Cuwawademeiner +Hans-Joachim;Küvigedeweiner +Karl-Josef;Wötewattestein +Fatima;Zutehedeson +Senta;Litagedemüller +Sylvia;Lisisedemeiner +Rouven;Nitoplau +Erica;Vatodimüller +Tadeusz;Schulze +Henri;Schneider +Ekaterina;Schäfer +Mareile;Juvubimüller +Leopoldine;Fäsudi +Ioannis;Schröder +Nikolas;Maier +Heinz-Joachim;Hofmann +Egon;Kövobistein +Hedy;Juduplau +Rene;Duvufo +Mariola;Lutaduhein +Cäcilie;Putagauhein +Dino;Newumattson +Birger;Gitosademüller +Karlheinz;Kulafereweiner +Dirk;Krause +Carmen;Letudumeiner +Hans-Theo;Richter +Aline;Detefareson +Ilias;Cutofison +Kamil;Toremaumeiner +Mara;Lalefareson +Jose;Hartmann +Isa;Tolelodeweiner +Linus;Godavodeson +Sarah;Mayer +Kata;Töwovadeson +German;Fotograde +Augusta;Zevischattson +Randolf;Nödibodoson +Nuran;Huber +Ingeborg;Räsalatemeiner +Darko;Klein +Gunar;Tutefohein +Meinolf;Zusugatemüller +Otfried;Jüsehedehein +Arthur;Fetovaremüller +Hans-Uwe;Latesedestein +Andrzej;Köhler +Calogero;Telufere +Mirjana;Sotubestein +Mechthild;Meier +Friedbert;Wüvabi +Rosemarie;Titomodeweiner +Nikolai;Juwolodeweiner +Kornelius;Pevodaweiner +Hans-Joachim;Bavivore +Caterina;Betodiweiner +Hans-Josef;Schulze +Torben;Förugauhein +Matteo;König +Inna;Lange +Reinald;Krüger +Mattias;Siwulatemeiner +Hajo;Cirabedeweiner +Swantje;Rutobamüller +Matthias;Sosimodemeiner +Antonius;Falofaremüller +Tatiana;Vodegradeson +Birgitta;Paramode +Rahel;Föralode +Paul-Gerhard;Füsugrademüller +Silva;Jüwofu +Tillmann;Letafihein +Eugenie;Buteschatt +Eleonore;Zetimaumüller +August;Hälogradeweiner +Emil;Piwamode +Emil;Towevoremüller +Helen;Meier +Alena;Wuvaschatthein +Anika;Helodimüller +Gusti;Schulze +Carsten;Gorabustein +Uta;Futisademüller +Hans-Bernd;Litufare +Marvin;Datosadeweiner +Oswald;Jidevare +Annelies;Hodabu +Agnes;Nevufi +Artur;Rosogrodeson +Heinz-Walter;Huber +Elfriede;Hoffmann +Mehmet;Schäfer +Maxim;Putogate +Svea;Mayer +Lambert;Fischer +Klemens;Jasefareweiner +Ekrem;Diwugradeweiner +Karina;Tutifomüller +Sarina;Gütegrademüller +Aenne;Göwevodeweiner +Korbinian;Schneider +Ulla;Vitufareson +Malgorzata;Wolowedehein +Edelgard;Muribameiner +Ioannis;Mitafostein +Waltraut;Cedovodestein +Johan;Gövadi +Lidija;Schmitz +Beatrice;Zärobe +Reiner;Walter +Fred;Howafomeiner +Marjan;Gidofuweiner +Burckhard;Schmidt +Nikola;Ritolodeweiner +Marius;Nötohedeson +Alina;Purumauweiner +Mijo;Ferogate +Ana;Lesoflede +Lissy;Lehmann +Hiltrud;Corewadeweiner +Arnim;Situvade +Cetin;Wotaschattmeiner +Patrick;Badabodo +Patrizia;Wulamatt +Roger;Naweflode +Christa;Fedebuweiner +Eckhardt;Huwischatt +Janine;Bavevare +Tamara;Potudostein +Mehdi;Schulze +Raphaela;Dawegedemeiner +Ewald;Natuvaremeiner +Karolina;Hoffmann +Nuray;Mürafo +Alfonso;Wewemode +Ernst-Otto;Biwusegehein +Lina;Mütasedemüller +Leonhard;Hoffmann +Sönke;Schwarz +Doreen;Hartmann +Sergej;Lutuhede +Eugen;Kiduwede +Siegrid;Polifo +Sibilla;Kölefimeiner +Hans-H.;Rötiflode +Urszula;Cäreschattmüller +Michaela;Zetolodehein +Galina;Fitosegehein +Jaroslav;Budivoreson +Reinhardt;Juwegaumüller +Mona;Caladu +Kristine;Nuwesedehein +Luzia;Lörabuson +Carmelo;Perefere +Marcus;Huber +Frederike;Cululauson +Sigfried;Zodifaremüller +Niko;Dutomau +Abdul;Raviflede +Jan-Peter;Poriduweiner +Sybille;Nivalatemüller +Severin;Rululatestein +Sergei;Rövugaustein +Mina;Dötofledemeiner +Aloisia;Susuvode +Nikolaos;Situda +Anika;Hüdadohein +Jadwiga;Lälabe +Melissa;Wirumattson +Hulda;Müller +Ida;Füresadeweiner +Oda;Coregau +Hans-Joachim;Rolawattehein +Pius;Miwugatemeiner +Justus;Horuflodehein +Andrew;Kasasade +Cläre;Krüger +Hans-Eberhard;Guraduweiner +Dietrich;Nutigrodehein +Silja;Wevifledemüller +Ester;Hoffmann +Theda;Vosawedeson +Lydia;Werudison +Darko;Gosifison +Henri;Züsovodemeiner +Calogero;Radavarehein +Heini;Falisadeson +Magnus;Mirugaumüller +Gert;Zuriwattehein +Uwe;Krause +Adam;Dalawedehein +Wladimir;Tiwawatte +Ljudmila;Catedo +Verena;Lange +Giovanni;Schmidt +Ryszard;Patidomüller +Lore;Ruvubiweiner +Herma;Garaflede +Galina;Pawedu +Marlen;Vötelateson +Diane;Weber +Henriette;Vadahedeson +Siegmar;Mesosedestein +Birgitta;Puvobemüller +Gottlob;Wüsodomeiner +H.-Dieter;Juraschattstein +Wojciech;Wutabodo +Olivia;Kesaplau +Melita;Risasede +Hendrik;Casodustein +Anja;Nududu +Wieslaw;Paligradehein +Ottilie;Ledodomüller +Erdogan;Fövumatthein +Theodor;Metadistein +Natalija;Salofostein +Katarzyna;Ketogrodeweiner +Karl-Ernst;Losafarehein +Hedi;Nätugedehein +Valeria;Resugrade +Albrecht;Vudelatestein +Walburga;Metagrodeson +Margaretha;Nedobi +Salih;Fisegede +Helmtrud;Fischer +Tom;Fedusede +Lina;Särabiweiner +Albrecht;Böwigatehein +Gabor;Vavugradeweiner +Gerhard;Giwuvode +Corinne;Guwesede +Ben;Mirosedehein +Mara;Lehmann +Tatiana;Mölegrademeiner +Nadin;Sodelate +Margarita;Wuvomaustein +Franz;Hewasede +Hajo;Patofereson +Lisbeth;Jululauson +Insa;Nowedamüller +Isabell;Cesadostein +Freya;Mayer +Selma;Muluplau +Horst-Peter;Nalemodemeiner +Hajo;Köhler +Dorina;Polowedeweiner +Ingelore;Murodostein +Aysel;Gerischatt +Angelica;Cütiflodeweiner +Hans-H.;Krause +Lothar;Katalateson +Luigi;Tösebimüller +Anna-Maria;Podogaumüller +Fabienne;Nuvafo +Sibel;Cäriflodemeiner +Bernhardine;Gälavodemeiner +Hermann;Kelasademüller +Anneli;Hödebodomüller +Reinhold;Tedavareweiner +Natalja;Vädobemüller +Gülten;Püdigauson +Kunibert;Gasiflodemüller +Elfie;Kaiser +Marijan;Järawede +Harald;Vadufomeiner +Helge;Fischer +Ortwin;Livavodeweiner +Emmi;Lovosedemeiner +Werner;Felobodoweiner +Ercan;Ritebodomeiner +Bernt;Huber +Anna-Maria;Kosovodeweiner +Mathias;Letofu +Valerie;Haviba +Cosimo;Posimaustein +Annaliese;Katada +Wilhelm;Schäfer +Hartmuth;Hofmann +Insa;Schröder +Irmhild;Nalofledehein +Remo;Hoffmann +Hans-Friedrich;Hodivade +Ottilie;Gülifu +Karl-Ernst;Matogate +Wera;Bowivadestein +Ottokar;Fitogrode +Adeline;Rewabestein +Randolf;Covusedeweiner +Alla;Pasusade +Fredy;Jütusede +Janine;Kotifledemüller +Kirsten;Hiwuvoremeiner +Kläre;Gawawadestein +Jonathan;Fududamüller +Dragica;Jölomattweiner +Nadia;Meier +Irmingard;Cewisadeweiner +Friedrich;Huber +Claudia;Disibimüller +Klaus;Rowefu +Isabella;Liruplauweiner +Margarethe;Cötivadestein +Hans-Hinrich;Siwulaumeiner +Gunter;Madodomeiner +Silke;Schwarz +Janett;Schwarz +Theodoros;Zimmermann +Heidelinde;Meier +Semra;Vudimodeson +Anastasia;Pitomodestein +Sylke;Zadivadehein +Edgar;Fesilau +Raissa;Tetoda +Karola;Välufo +Marianna;Schulze +Nuri;Veribi +Hanne-Lore;Wovufoson +Karsten;Jüteda +Jens-Uwe;Böleba +Heini;Kodevoremüller +Abram;Ravisade +German;Böwabede +Maike;Schwarz +Esther;Latowattehein +Edelgard;Schmitt +Edmund;Tisivodemüller +Cemil;König +Francis;Vavisadeweiner +Lidwina;Nötowedemeiner +Juliane;Neumann +Almut;Titidomüller +Gustav;Keweda +Andrej;Gatevademüller +Angelina;Wagner +James;Bidimodestein +Mathias;Jatuwedemeiner +Waltraut;Husubiweiner +Edith;Tutulatemüller +Marcel;Tatawede +Wigbert;Tiwegede +Damaris;Hosibeweiner +Adriane;Ciriwedeson +Ella;Zatuduson +Lorenzo;Ciwawadestein +Giorgio;Wagner +Annamaria;Ratihedeson +Augustin;Resagedeson +Nuran;Fetadumeiner +Mira;Beloplau +Zdravko;Katafereweiner +Brit;Richter +Marlene;Cirefi +Volker;Fotumatt +Henryk;Nölevade +Remo;Peloduson +Birgitta;Tivematt +Helma;Satewademeiner +Wibke;Wulodo +Kathi;Nisesade +Anni;Käduwatteson +Telse;Wudeplauhein +Arne;Balebumüller +Bartholomäus;Neumann +Freia;Werner +Henryk;Foruwedemeiner +Cord;Rudovode +Georgine;Gütube +Antonius;Kewohedeson +Raymond;Wetavade +Mira;Luwufareweiner +Mina;Gälamau +Karl-Jürgen;Pidedumüller +Mesut;Cetemauweiner +Susi;Jötofihein +Eva;Krüger +Filippo;Gataplauson +Belinda;Besisadehein +Friedhold;Botischatt +Magdalena;Dovomauhein +Theodor;Hütusegestein +Albertine;Braun +Reinald;Dowubedestein +Ernst-Dieter;Dusegradestein +Dino;Herrmann +Vincent;Putudo +Käthi;Hiresadestein +Alwine;Pedifledeweiner +Dittmar;Schmitz +Babette;Fösagede +Anneke;Meier +Hubertine;Kusevorestein +Ingbert;Latesede +Norbert;Buravodeson +Ruben;Zörefuson +Athanasios;Päsusadestein +Flora;Schmidt +Lidija;Wövischattstein +Balthasar;Julesege +Eva;Vutivore +Carolina;Jösubihein +Felicitas;König +Mary;Citawattestein \ No newline at end of file diff --git a/documentations/seminararbeiten/Datenspeicherung/docker-compose.yml b/documentations/seminararbeiten/Datenspeicherung/docker-compose.yml new file mode 100644 index 0000000..e1781ee --- /dev/null +++ b/documentations/seminararbeiten/Datenspeicherung/docker-compose.yml @@ -0,0 +1,34 @@ +version: "3.8" +services: + db: + image: postgres:14.1-alpine + container_name: postgres + restart: always + ports: + - "5432:5432" + environment: + POSTGRES_USER: postgres + POSTGRES_PASSWORD: postgres + volumes: + #- db:/var/lib/postgresql/data + - ./PostgreSQL:/var/lib/postgresql/data + pgadmin: + image: dpage/pgadmin4:7.2 + container_name: pgadmin4_container + restart: always + ports: + - "5050:80" + environment: + PGADMIN_DEFAULT_EMAIL: admin@fh-swf.de + PGADMIN_DEFAULT_PASSWORD: admin + volumes: + # - pgadmin:/var/lib/pgadmin + - ./pgadmin:/var/lib/pgadmin + + mongodb: + image: mongo:7.0.0-rc4 + ports: + - '27017:27017' + volumes: + # - dbdata6:/data/db + - ./mongo:/data/db diff --git a/documentations/seminararbeiten/Datenspeicherung/images/DB_Schema.PNG b/documentations/seminararbeiten/Datenspeicherung/images/DB_Schema.PNG new file mode 100644 index 0000000..e797cc3 Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/images/DB_Schema.PNG differ diff --git a/documentations/seminararbeiten/Datenspeicherung/images/Data_Cluster.PNG b/documentations/seminararbeiten/Datenspeicherung/images/Data_Cluster.PNG new file mode 100644 index 0000000..e36657a Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/images/Data_Cluster.PNG differ diff --git a/documentations/seminararbeiten/Datenspeicherung/images/Document_DB.PNG b/documentations/seminararbeiten/Datenspeicherung/images/Document_DB.PNG new 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differ diff --git a/documentations/seminararbeiten/Datenspeicherung/images/Statista_Companies.png b/documentations/seminararbeiten/Datenspeicherung/images/Statista_Companies.png new file mode 100644 index 0000000..24719a1 Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/images/Statista_Companies.png differ diff --git a/documentations/seminararbeiten/Datenspeicherung/images/company.PNG b/documentations/seminararbeiten/Datenspeicherung/images/company.PNG new file mode 100644 index 0000000..ce293c8 Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/images/company.PNG differ diff --git a/documentations/seminararbeiten/Datenspeicherung/images/finance_data.PNG b/documentations/seminararbeiten/Datenspeicherung/images/finance_data.PNG new file mode 100644 index 0000000..43e939a Binary files /dev/null and b/documentations/seminararbeiten/Datenspeicherung/images/finance_data.PNG differ