From e2dd16b7931b2f5d213432e421a6f95209e21957 Mon Sep 17 00:00:00 2001 From: schaermicha1 Date: Sun, 19 Apr 2026 14:50:00 +0200 Subject: [PATCH] initial commit --- .$Bank-Datenbank ERD.drawio.bkp | 965 + .idea/.gitignore | 10 + ...-104 Datenbanken und Datenverarbeitung.iml | 8 + .idea/dataSources.xml | 12 + .idea/data_source_mapping.xml | 9 + .idea/inspectionProfiles/Project_Default.xml | 17 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 7 + .idea/modules.xml | 8 + .idea/sqldialects.xml | 10 + .../Hinten_am_Fenster-checkpoint.ipynb | 178 + .../PostgreSQL-Python-checkpoint.ipynb | 265 + ...hon Wiederholung mit Code-checkpoint.ipynb | 386 + .../Python Wiederholung-checkpoint.ipynb | 174 + .../SQL-Injectionn mit Code-checkpoint.ipynb | 274 + ...hauspieler Zusammenarbeit-checkpoint.ipynb | 179 + .../Zoo-Aufgabe-checkpoint.ipynb | 361 + Aufgabe 2.sql | 91 + Aufgabe 3 | 27 + Aufgabe 3.docx | Bin 0 -> 19900 bytes Aufgabe 3: Recherche zu SQL-Features.pdf | Bin 0 -> 66742 bytes Bank-Datenbank ERD.drawio | 965 + Bank-Datenbank ERD.pdf | Bin 0 -> 213517 bytes Bank-Datenbank ERD.png | Bin 0 -> 292879 bytes ERD Bank-Datenbank | 1 + PostgreSQL-Python.ipynb | 265 + Python Wiederholung mit Code.ipynb | 386 + Python Wiederholung.ipynb | 174 + SQL-Injectionn mit Code.ipynb | 287 + Schauspieler Zusammenarbeit.ipynb | 179 + ViewsundTrigger.sql | 82 + Zoo-Aufgabe.ipynb | 361 + create_movies.sql | 25 + movies_data.sql | 19040 ++++++++++++++++ postgreSQL_Vertiefung_CDS104_FS26.sql | 180 + Übungen aus den Folien | 45 + 36 files changed, 24977 insertions(+) create mode 100644 .$Bank-Datenbank ERD.drawio.bkp create mode 100644 .idea/.gitignore create mode 100644 .idea/cds-104 Datenbanken und Datenverarbeitung.iml create mode 100644 .idea/dataSources.xml create mode 100644 .idea/data_source_mapping.xml create mode 100644 .idea/inspectionProfiles/Project_Default.xml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/sqldialects.xml create mode 100644 .ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb create mode 100644 Aufgabe 2.sql create mode 100644 Aufgabe 3 create mode 100644 Aufgabe 3.docx create mode 100644 Aufgabe 3: Recherche zu SQL-Features.pdf create mode 100644 Bank-Datenbank ERD.drawio create mode 100644 Bank-Datenbank ERD.pdf create mode 100644 Bank-Datenbank ERD.png create mode 100644 ERD Bank-Datenbank create mode 100644 PostgreSQL-Python.ipynb create mode 100644 Python Wiederholung mit Code.ipynb create mode 100644 Python Wiederholung.ipynb create mode 100644 SQL-Injectionn mit Code.ipynb create mode 100644 Schauspieler Zusammenarbeit.ipynb create mode 100644 ViewsundTrigger.sql create mode 100644 Zoo-Aufgabe.ipynb create mode 100644 create_movies.sql create mode 100644 movies_data.sql create mode 100644 postgreSQL_Vertiefung_CDS104_FS26.sql create mode 100644 Übungen aus den Folien diff --git a/.$Bank-Datenbank ERD.drawio.bkp b/.$Bank-Datenbank ERD.drawio.bkp new file mode 100644 index 0000000..3767682 --- /dev/null +++ b/.$Bank-Datenbank ERD.drawio.bkp @@ -0,0 +1,965 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..ab1f416 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,10 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Ignored default folder with query files +/queries/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml +# Editor-based HTTP Client requests +/httpRequests/ diff --git a/.idea/cds-104 Datenbanken und Datenverarbeitung.iml b/.idea/cds-104 Datenbanken und Datenverarbeitung.iml new file mode 100644 index 0000000..c69f1c9 --- /dev/null +++ b/.idea/cds-104 Datenbanken und Datenverarbeitung.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/dataSources.xml b/.idea/dataSources.xml new file mode 100644 index 0000000..14f8e1f --- /dev/null +++ b/.idea/dataSources.xml @@ -0,0 +1,12 @@ + + + + + postgresql + true + org.postgresql.Driver + jdbc:postgresql://localhost:5432/postgres + $ProjectFileDir$ + + + \ No newline at end of file diff --git a/.idea/data_source_mapping.xml b/.idea/data_source_mapping.xml new file mode 100644 index 0000000..c8be428 --- /dev/null +++ b/.idea/data_source_mapping.xml @@ -0,0 +1,9 @@ + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..1524f19 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,17 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..5f4a9fa --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..265804b --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/sqldialects.xml b/.idea/sqldialects.xml new file mode 100644 index 0000000..0254c70 --- /dev/null +++ b/.idea/sqldialects.xml @@ -0,0 +1,10 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb b/.ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb new file mode 100644 index 0000000..785c88a --- /dev/null +++ b/.ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 46, + "id": "e7cbec5d-cb72-4ab0-a1c1-ce4cd2609934", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "915f7f56-fd86-459c-ac63-91d331544348", + "metadata": {}, + "outputs": [], + "source": [ + "# Verbindung zur Datenbank aufbauen\n", + "conn = psycopg2.connect (\"dbname=movies_database host=/var/run/postgresql user=postgres password=sml12345\")\n", + "cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "e25bb2be-926f-460d-b9f9-b4b9cfaf4277", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": 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Linz'}), RealDictRow({'actor_id': 89, 'name': 'Alex Daniels'}), RealDictRow({'actor_id': 90, 'name': 'Alex Haw'}), RealDictRow({'actor_id': 91, 'name': 'Alex Hyde-White'}), RealDictRow({'actor_id': 92, 'name': 'Alex McArthur'}), RealDictRow({'actor_id': 93, 'name': 'Alex Scott'}), RealDictRow({'actor_id': 94, 'name': 'Alex Vincent'}), RealDictRow({'actor_id': 95, 'name': 'Alexa Davalos'}), RealDictRow({'actor_id': 96, 'name': 'Alexa Vega'}), RealDictRow({'actor_id': 97, 'name': 'Alexander Fehling'}), RealDictRow({'actor_id': 98, 'name': 'Alexander Godunov'}), RealDictRow({'actor_id': 99, 'name': 'Alexander Goodwin'}), RealDictRow({'actor_id': 100, 'name': 'Alexander Knox'}), RealDictRow({'actor_id': 101, 'name': 'Alexander Morton'}), RealDictRow({'actor_id': 102, 'name': 'Alexandra Holden'}), RealDictRow({'actor_id': 103, 'name': 'Alexia Keogh'}), RealDictRow({'actor_id': 104, 'name': 'Alexis Arquette'}), RealDictRow({'actor_id': 105, 'name': 'Alexis Cruz'}), RealDictRow({'actor_id': 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Selford'}), RealDictRow({'actor_id': 124, 'name': 'Alison Whelan'}), RealDictRow({'actor_id': 125, 'name': 'Allan Jones'}), RealDictRow({'actor_id': 126, 'name': 'Allan Melvin'}), RealDictRow({'actor_id': 127, 'name': 'Allen Covert'}), RealDictRow({'actor_id': 128, 'name': 'Allen Danziger'}), RealDictRow({'actor_id': 129, 'name': 'Allen Garfield'}), RealDictRow({'actor_id': 130, 'name': 'Allen Jenkins'}), RealDictRow({'actor_id': 131, 'name': 'Allen Payne'}), RealDictRow({'actor_id': 132, 'name': 'Allison Balson'}), RealDictRow({'actor_id': 133, 'name': 'Allison Janney'}), RealDictRow({'actor_id': 134, 'name': 'Ally Sheedy'}), RealDictRow({'actor_id': 135, 'name': 'Ally Walker'}), RealDictRow({'actor_id': 136, 'name': 'Alphonsia Emmanuel'}), RealDictRow({'actor_id': 137, 'name': 'Alun Armstrong'}), RealDictRow({'actor_id': 138, 'name': 'Alyson Hannigan'}), RealDictRow({'actor_id': 139, 'name': 'Alyson Reed'}), RealDictRow({'actor_id': 140, 'name': 'Amanda Barrie'}), RealDictRow({'actor_id': 141, 'name': 'Amanda Bearse'}), RealDictRow({'actor_id': 142, 'name': 'Amanda Bynes'}), RealDictRow({'actor_id': 143, 'name': 'Amanda Plummer'}), RealDictRow({'actor_id': 144, 'name': 'Amanda Wyss'}), RealDictRow({'actor_id': 145, 'name': 'Amber Heard'}), RealDictRow({'actor_id': 146, 'name': 'Amber Smith'}), RealDictRow({'actor_id': 147, 'name': 'Amber Tamblyn'}), RealDictRow({'actor_id': 148, 'name': 'Amrish Puri'}), RealDictRow({'actor_id': 149, 'name': 'Amy Adams'}), RealDictRow({'actor_id': 150, 'name': 'Amy Brenneman'}), RealDictRow({'actor_id': 151, 'name': 'Amy Ingersoll'}), RealDictRow({'actor_id': 152, 'name': 'Amy Irving'}), RealDictRow({'actor_id': 153, 'name': 'Amy Locane'}), RealDictRow({'actor_id': 154, 'name': 'Amy Madigan'}), RealDictRow({'actor_id': 155, 'name': 'Amy Robinson'}), RealDictRow({'actor_id': 156, 'name': 'Amy Steel'}), RealDictRow({'actor_id': 157, 'name': 'Amy Veness'}), RealDictRow({'actor_id': 158, 'name': 'Amy Yasbeck'}), 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Williams'}), RealDictRow({'actor_id': 4769, 'name': 'Vanessa Lee Chester'}), RealDictRow({'actor_id': 4770, 'name': 'Vanessa Redgrave'}), RealDictRow({'actor_id': 4771, 'name': 'Vanity'}), RealDictRow({'actor_id': 4772, 'name': 'Veerendra Saxena'}), RealDictRow({'actor_id': 4773, 'name': 'Vera Ellen'}), RealDictRow({'actor_id': 4774, 'name': 'Vera Farmiga'}), RealDictRow({'actor_id': 4775, 'name': 'Vera Miles'}), RealDictRow({'actor_id': 4776, 'name': 'Verna Bloom'}), RealDictRow({'actor_id': 4777, 'name': 'Verna Felton'}), RealDictRow({'actor_id': 4778, 'name': 'Vernon Downing'}), RealDictRow({'actor_id': 4779, 'name': 'Vernon Wells'}), RealDictRow({'actor_id': 4780, 'name': 'Veronica Carlson'}), RealDictRow({'actor_id': 4781, 'name': 'Veronica Cartwright'}), RealDictRow({'actor_id': 4782, 'name': 'Veronica Hart'}), RealDictRow({'actor_id': 4783, 'name': 'Veronica Lake'}), RealDictRow({'actor_id': 4784, 'name': 'Vic Damone'}), RealDictRow({'actor_id': 4785, 'name': 'Vicki Frederick'}), RealDictRow({'actor_id': 4786, 'name': 'Vicki Lewis'}), RealDictRow({'actor_id': 4787, 'name': 'Vicky Tiu'}), RealDictRow({'actor_id': 4788, 'name': 'Victor Argo'}), RealDictRow({'actor_id': 4789, 'name': 'Victor Buono'}), RealDictRow({'actor_id': 4790, 'name': 'Victor Jory'}), RealDictRow({'actor_id': 4791, 'name': 'Victor Lundin'}), RealDictRow({'actor_id': 4792, 'name': 'Victor Mature'}), RealDictRow({'actor_id': 4793, 'name': 'Victor McLaglen'}), RealDictRow({'actor_id': 4794, 'name': 'Victor Slezak'}), RealDictRow({'actor_id': 4795, 'name': 'Victor Spinetti'}), RealDictRow({'actor_id': 4796, 'name': 'Victor Wong'}), RealDictRow({'actor_id': 4797, 'name': 'Victoria Davis'}), RealDictRow({'actor_id': 4798, 'name': 'Victoria Jackson'}), RealDictRow({'actor_id': 4799, 'name': 'Victoria Longley'}), RealDictRow({'actor_id': 4800, 'name': 'Victoria Medlin'}), RealDictRow({'actor_id': 4801, 'name': 'Victoria Tennant'}), RealDictRow({'actor_id': 4802, 'name': 'Vidal Peterson'}), RealDictRow({'actor_id': 4803, 'name': 'Viggo Mortensen'}), RealDictRow({'actor_id': 4804, 'name': 'Vin Diesel'}), RealDictRow({'actor_id': 4805, 'name': 'Vince Edwards'}), RealDictRow({'actor_id': 4806, 'name': 'Vince Vaughn'}), RealDictRow({'actor_id': 4807, 'name': \"Vincent D'Onofrio\"}), RealDictRow({'actor_id': 4808, 'name': 'Vincent Gardenia'}), RealDictRow({'actor_id': 4809, 'name': 'Vincent Klyn'}), RealDictRow({'actor_id': 4810, 'name': 'Vincent Perez'}), RealDictRow({'actor_id': 4811, 'name': 'Vincent Price'}), RealDictRow({'actor_id': 4812, 'name': 'Vincent Spano'}), RealDictRow({'actor_id': 4813, 'name': 'Vincent Van Patten'}), RealDictRow({'actor_id': 4814, 'name': 'Vinessa Shaw'}), RealDictRow({'actor_id': 4815, 'name': 'Ving Rhames'}), RealDictRow({'actor_id': 4816, 'name': 'Viola Davis'}), RealDictRow({'actor_id': 4817, 'name': 'Vira Montes'}), RealDictRow({'actor_id': 4818, 'name': 'Virginia Bruce'}), RealDictRow({'actor_id': 4819, 'name': 'Virginia Grey'}), RealDictRow({'actor_id': 4820, 'name': 'Virginia Madsen'}), RealDictRow({'actor_id': 4821, 'name': 'Virginia Mayo'}), RealDictRow({'actor_id': 4822, 'name': 'Virginia Walker'}), RealDictRow({'actor_id': 4823, 'name': 'Virna Lisi'}), RealDictRow({'actor_id': 4824, 'name': 'Vittorio De Sica'}), RealDictRow({'actor_id': 4825, 'name': 'Vittorio Gassman'}), RealDictRow({'actor_id': 4826, 'name': 'Vivean Gray'}), RealDictRow({'actor_id': 4827, 'name': 'Vivian Pickles'}), RealDictRow({'actor_id': 4828, 'name': 'Vivica A. Fox'}), RealDictRow({'actor_id': 4829, 'name': 'Vivien Leigh'}), RealDictRow({'actor_id': 4830, 'name': 'Vivien Merchant'}), RealDictRow({'actor_id': 4831, 'name': 'Vladimir Kulich'}), RealDictRow({'actor_id': 4832, 'name': 'Vladimir Radian'}), RealDictRow({'actor_id': 4833, 'name': 'Vladimir Sokoloff'}), RealDictRow({'actor_id': 4834, 'name': 'Vladimir Vdovichenkov'}), RealDictRow({'actor_id': 4835, 'name': 'Vonetta McGee'}), RealDictRow({'actor_id': 4836, 'name': 'Véra Clouzot'}), RealDictRow({'actor_id': 4837, 'name': 'W.C. Fields'}), RealDictRow({'actor_id': 4838, 'name': 'Wade Dominguez'}), RealDictRow({'actor_id': 4839, 'name': 'Walker Jones'}), RealDictRow({'actor_id': 4840, 'name': 'Wallace Beery'}), RealDictRow({'actor_id': 4841, 'name': 'Wallace Ford'}), RealDictRow({'actor_id': 4842, 'name': 'Wallace Shawn'}), RealDictRow({'actor_id': 4843, 'name': 'Walter Barnes'}), RealDictRow({'actor_id': 4844, 'name': 'Walter Brennan'}), RealDictRow({'actor_id': 4845, 'name': 'Walter Connolly'}), RealDictRow({'actor_id': 4846, 'name': 'Walter Fitzgerald'}), RealDictRow({'actor_id': 4847, 'name': 'Walter Hampden'}), RealDictRow({'actor_id': 4848, 'name': 'Walter Huston'}), RealDictRow({'actor_id': 4849, 'name': 'Walter Matthau'}), RealDictRow({'actor_id': 4850, 'name': 'Walter Pidgeon'}), RealDictRow({'actor_id': 4851, 'name': 'Ward Bond'}), RealDictRow({'actor_id': 4852, 'name': 'Ward Costello'}), RealDictRow({'actor_id': 4853, 'name': 'Warner Baxter'}), RealDictRow({'actor_id': 4854, 'name': 'Warner Oland'}), RealDictRow({'actor_id': 4855, 'name': 'Warren Ball'}), RealDictRow({'actor_id': 4856, 'name': 'Warren Beatty'}), RealDictRow({'actor_id': 4857, 'name': 'Warren Clarke'}), RealDictRow({'actor_id': 4858, 'name': 'Warren Oates'}), RealDictRow({'actor_id': 4859, 'name': 'Warren Stevens'}), RealDictRow({'actor_id': 4860, 'name': 'Warwick Davis'}), RealDictRow({'actor_id': 4861, 'name': 'Weird Al Yankovic'}), RealDictRow({'actor_id': 4862, 'name': 'Wendel Meldrum'}), RealDictRow({'actor_id': 4863, 'name': 'Wendell Corey'}), RealDictRow({'actor_id': 4864, 'name': 'Wendell Pierce'}), RealDictRow({'actor_id': 4865, 'name': 'Wendy Allnutt'}), RealDictRow({'actor_id': 4866, 'name': 'Wendy Crewson'}), RealDictRow({'actor_id': 4867, 'name': 'Wendy Gazelle'}), RealDictRow({'actor_id': 4868, 'name': 'Wendy Hiller'}), RealDictRow({'actor_id': 4869, 'name': 'Wendy Makkena'}), RealDictRow({'actor_id': 4870, 'name': 'Wes Bentley'}), RealDictRow({'actor_id': 4871, 'name': 'Wesley Addy'}), RealDictRow({'actor_id': 4872, 'name': 'Wesley Snipes'}), RealDictRow({'actor_id': 4873, 'name': 'Whitney Houston'}), RealDictRow({'actor_id': 4874, 'name': 'Whoopi Goldberg'}), RealDictRow({'actor_id': 4875, 'name': 'Wil Wheaton'}), RealDictRow({'actor_id': 4876, 'name': 'Wiley Wiggins'}), RealDictRow({'actor_id': 4877, 'name': 'Wilford Brimley'}), RealDictRow({'actor_id': 4878, 'name': 'Wilfred Lucas'}), RealDictRow({'actor_id': 4879, 'name': 'Wilfred Pickles'}), RealDictRow({'actor_id': 4880, 'name': 'Wilfrid Hyde-White'}), RealDictRow({'actor_id': 4881, 'name': 'Will Arnett'}), RealDictRow({'actor_id': 4882, 'name': 'Will Ferrell'}), RealDictRow({'actor_id': 4883, 'name': 'Will Geer'}), RealDictRow({'actor_id': 4884, 'name': 'Will Hutchins'}), RealDictRow({'actor_id': 4885, 'name': 'Will Patton'}), RealDictRow({'actor_id': 4886, 'name': 'Will Sampson'}), RealDictRow({'actor_id': 4887, 'name': 'Will Smith'}), RealDictRow({'actor_id': 4888, 'name': 'Willem Dafoe'}), RealDictRow({'actor_id': 4889, 'name': 'William Atherton'}), RealDictRow({'actor_id': 4890, 'name': 'William Baldwin'}), RealDictRow({'actor_id': 4891, 'name': 'William Bendix'}), RealDictRow({'actor_id': 4892, 'name': 'William Carroll'}), RealDictRow({'actor_id': 4893, 'name': 'William Conrad'}), RealDictRow({'actor_id': 4894, 'name': 'William Daniels'}), RealDictRow({'actor_id': 4895, 'name': 'William Demarest'}), RealDictRow({'actor_id': 4896, 'name': 'William Devane'}), RealDictRow({'actor_id': 4897, 'name': 'William Dulaney'}), RealDictRow({'actor_id': 4898, 'name': 'William E. Arnold Jr.'}), RealDictRow({'actor_id': 4899, 'name': 'William Elliott'}), RealDictRow({'actor_id': 4900, 'name': 'William Fichtner'}), RealDictRow({'actor_id': 4901, 'name': 'William Finley'}), RealDictRow({'actor_id': 4902, 'name': 'William Forsythe'}), RealDictRow({'actor_id': 4903, 'name': 'William H. Macy'}), RealDictRow({'actor_id': 4904, 'name': 'William Harrigan'}), RealDictRow({'actor_id': 4905, 'name': 'William Hartnell'}), RealDictRow({'actor_id': 4906, 'name': 'William Hickey'}), RealDictRow({'actor_id': 4907, 'name': 'William Holden'}), RealDictRow({'actor_id': 4908, 'name': 'William Hootkins'}), RealDictRow({'actor_id': 4909, 'name': 'William Hopper'}), RealDictRow({'actor_id': 4910, 'name': 'William Hurt'}), RealDictRow({'actor_id': 4911, 'name': 'William Katt'}), RealDictRow({'actor_id': 4912, 'name': 'William Kerwin'}), RealDictRow({'actor_id': 4913, 'name': 'William Lundigan'}), RealDictRow({'actor_id': 4914, 'name': 'William McNamara'}), RealDictRow({'actor_id': 4915, 'name': 'William Mervyn'}), RealDictRow({'actor_id': 4916, 'name': 'William Morgan Sheppard'}), RealDictRow({'actor_id': 4917, 'name': \"William O'Leary\"}), RealDictRow({'actor_id': 4918, 'name': 'William Petersen'}), RealDictRow({'actor_id': 4919, 'name': 'William Powell'}), RealDictRow({'actor_id': 4920, 'name': 'William Prince'}), RealDictRow({'actor_id': 4921, 'name': 'William Ragsdale'}), RealDictRow({'actor_id': 4922, 'name': 'William Redfield'}), RealDictRow({'actor_id': 4923, 'name': 'William Reynolds'}), RealDictRow({'actor_id': 4924, 'name': 'William Richert'}), RealDictRow({'actor_id': 4925, 'name': 'William Roerick'}), RealDictRow({'actor_id': 4926, 'name': 'William Russ'}), RealDictRow({'actor_id': 4927, 'name': 'William Sadler'}), RealDictRow({'actor_id': 4928, 'name': 'William Sanderson'}), RealDictRow({'actor_id': 4929, 'name': 'William Shatner'}), RealDictRow({'actor_id': 4930, 'name': 'William Smith'}), RealDictRow({'actor_id': 4931, 'name': 'William Snape'}), RealDictRow({'actor_id': 4932, 'name': 'William Swan'}), RealDictRow({'actor_id': 4933, 'name': 'William Takaku'}), RealDictRow({'actor_id': 4934, 'name': 'William Vail'}), RealDictRow({'actor_id': 4935, 'name': 'William Windom'}), RealDictRow({'actor_id': 4936, 'name': 'Willie Nelson'}), RealDictRow({'actor_id': 4937, 'name': 'Willow Smith'}), RealDictRow({'actor_id': 4938, 'name': 'Wilson Cruz'}), RealDictRow({'actor_id': 4939, 'name': 'Wilt Chamberlain'}), RealDictRow({'actor_id': 4940, 'name': 'Wings Hauser'}), RealDictRow({'actor_id': 4941, 'name': 'Winona Ryder'}), RealDictRow({'actor_id': 4942, 'name': 'Wojciech Pszoniak'}), RealDictRow({'actor_id': 4943, 'name': 'Wolfgang Bodison'}), RealDictRow({'actor_id': 4944, 'name': 'Wolfgang Preiss'}), RealDictRow({'actor_id': 4945, 'name': 'Wolfman Jack'}), RealDictRow({'actor_id': 4946, 'name': 'Woodrow Parfrey'}), RealDictRow({'actor_id': 4947, 'name': 'Woody Allen'}), RealDictRow({'actor_id': 4948, 'name': 'Woody Harrelson'}), RealDictRow({'actor_id': 4949, 'name': 'Woody Strode'}), RealDictRow({'actor_id': 4950, 'name': 'Wyatt Knight'}), RealDictRow({'actor_id': 4951, 'name': 'Xander Berkeley'}), RealDictRow({'actor_id': 4952, 'name': 'Yale Wexler'}), RealDictRow({'actor_id': 4953, 'name': 'Yamil Borges'}), RealDictRow({'actor_id': 4954, 'name': 'Yancy Butler'}), RealDictRow({'actor_id': 4955, 'name': 'Yaphet Kotto'}), RealDictRow({'actor_id': 4956, 'name': 'Yasmine Belmadi'}), RealDictRow({'actor_id': 4957, 'name': 'Ye Liu'}), RealDictRow({'actor_id': 4958, 'name': 'Yoko Tani'}), RealDictRow({'actor_id': 4959, 'name': 'Yorgo Voyagis'}), RealDictRow({'actor_id': 4960, 'name': 'Youki Kudoh'}), RealDictRow({'actor_id': 4961, 'name': 'Yul Brunner'}), RealDictRow({'actor_id': 4962, 'name': 'Yul Brynner'}), RealDictRow({'actor_id': 4963, 'name': 'Yun-ah Song'}), RealDictRow({'actor_id': 4964, 'name': 'Yves Afonso'}), RealDictRow({'actor_id': 4965, 'name': 'Yves Beneyton'}), RealDictRow({'actor_id': 4966, 'name': 'Yves Lavigne'}), RealDictRow({'actor_id': 4967, 'name': 'Yvette Mimieux'}), RealDictRow({'actor_id': 4968, 'name': 'Yvette Nipar'}), RealDictRow({'actor_id': 4969, 'name': 'Yvonne Craig'}), RealDictRow({'actor_id': 4970, 'name': 'Yvonne De Carlo'}), RealDictRow({'actor_id': 4971, 'name': 'Yvonne Elliman'}), RealDictRow({'actor_id': 4972, 'name': 'Yvonne Furneaux'}), RealDictRow({'actor_id': 4973, 'name': 'Yvonne Zima'}), RealDictRow({'actor_id': 4974, 'name': 'Zach Galifianakis'}), RealDictRow({'actor_id': 4975, 'name': 'Zach Galligan'}), RealDictRow({'actor_id': 4976, 'name': 'Zachary David Cope'}), RealDictRow({'actor_id': 4977, 'name': 'Zachary Ittimangnaq'}), RealDictRow({'actor_id': 4978, 'name': 'Zachary Knighton'}), RealDictRow({'actor_id': 4979, 'name': 'Zachary Mabry'}), RealDictRow({'actor_id': 4980, 'name': 'Zachary Scott'}), RealDictRow({'actor_id': 4981, 'name': 'Zack Norman'}), RealDictRow({'actor_id': 4982, 'name': 'Zakes Mokae'}), RealDictRow({'actor_id': 4983, 'name': 'Zeppo Marx'}), RealDictRow({'actor_id': 4984, 'name': 'Zoe Saldana'}), RealDictRow({'actor_id': 4985, 'name': 'Zohra Lampert'}), RealDictRow({'actor_id': 4986, 'name': 'Zooey Deschanel'})]\n" + ] + } + ], + "source": [ + "# Hole alle Schauspieler\n", + "cursor.execute(\"SELECT * FROM actors;\")\n", + "actors = cursor.fetchall()\n", + "print(actors)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a4b3619b-9a5d-404a-85e9-0e04636f7666", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arthur Lake [('Penny Singleton', 11), ('Larry Simms', 11), ('Daisy the Dog', 8)]\n", + "Burt Young [('Talia Shire', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Carl Weathers [('Talia Shire', 4), ('Burt Young', 4), ('Sylvester Stallone', 4)]\n", + "Clint Eastwood [('Sondra Locke', 5)]\n", + "Daisy the Dog [('Arthur Lake', 8), ('Larry Simms', 8), ('Penny Singleton', 7)]\n", + "Danny Glover [('Mel Gibson', 4)]\n", + "DeForest Kelly [('Leonard Nimoy', 5), ('James Doohan', 5), ('William Shatner', 5)]\n", + "Diane Keaton [('Woody Allen', 6)]\n", + "Herbert Lom [('Peter Sellers', 4)]\n", + "Jack Lemmon [('Walter Matthau', 4)]\n", + "James Doohan [('Leonard Nimoy', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Jim Dale [('Kenneth Williams', 5), ('Sidney James', 4)]\n", + "Kenneth Williams [('Sidney James', 5), ('Jim Dale', 5)]\n", + "Larry Simms [('Arthur Lake', 11), ('Penny Singleton', 10), ('Daisy the Dog', 8)]\n", + "Leonard Nimoy [('James Doohan', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Mel Gibson [('Danny Glover', 4)]\n", + "Oliver Hardy [('Stan Laurel', 8)]\n", + "Penny Singleton [('Arthur Lake', 11), ('Larry Simms', 10), ('Daisy the Dog', 7)]\n", + "Peter Sellers [('Herbert Lom', 4)]\n", + "Sidney James [('Kenneth Williams', 5), ('Jim Dale', 4)]\n", + "Sondra Locke [('Clint Eastwood', 5)]\n", + "Stan Laurel [('Oliver Hardy', 8)]\n", + "Sylvester Stallone [('Talia Shire', 5), ('Burt Young', 5), ('Carl Weathers', 4)]\n", + "Talia Shire [('Burt Young', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Walter Matthau [('Jack Lemmon', 4)]\n", + "William Shatner [('Leonard Nimoy', 6), ('James Doohan', 6), ('DeForest Kelly', 5)]\n", + "Woody Allen [('Diane Keaton', 6)]\n" + ] + } + ], + "source": [ + "# Iteriere über alle Schauspieler\n", + "collab = {}\n", + "\n", + "for actor in actors:\n", + " # Hole alle Filme, in welchen dieser Schauspieler dabei war\n", + " cursor.execute(f\"SELECT * FROM movies_actors where actor_id = {actor['actor_id']};\")\n", + " movies = cursor.fetchall()\n", + " movie_ids = ', '.join([str(movie['movie_id']) for movie in movies])\n", + "\n", + " # \n", + " cursor.execute(f\"SELECT actor_id, count(*) FROM movies_actors where movie_id in ({movie_ids}) and actor_id <> {actor['actor_id']} group by actor_id having count(*) > 3 order by count(*) desc;\")\n", + " result = cursor.fetchall()\n", + " \n", + " if (result):\n", + " collabs = []\n", + " for row in result:\n", + " cursor.execute(f\"SELECT name from actors where actor_id = {row['actor_id']};\")\n", + " collabs.append((cursor.fetchall()[0]['name'], row['count']))\n", + " collab[actor['name']] = collabs\n", + " \n", + "for key in collab:\n", + " print(key, collab[key])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "e5570c42-fbf0-4634-ba49-227f7fbd6ad3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "\n", + "# Baue Graphen\n", + "G = nx.Graph()\n", + "\n", + "for person, connections in collab.items():\n", + " for friend in connections:\n", + " G.add_edge(person, friend[0], width=friend[1]*5)\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Zeige Graphen an\n", + "plt.figure(figsize=(20,20))\n", + "nx.draw(G, with_labels=True, node_color='lightblue', edge_color='black')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72e58005-9800-42c3-870c-6ecc4abf8de5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb b/.ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb new file mode 100644 index 0000000..3444101 --- /dev/null +++ b/.ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53d472a-14d4-42bb-a0ee-38eb8c9c24a8", + "metadata": {}, + "source": [ + "Zunächst muss ein Paket zur Anbindung installiert werden. Wir verwenden psycopg 2 (psycopg.org).\n", + "Dies kann über den Reiter \"Environment\" im Anaconda Navicator installiert werden.\n", + "Ist dies geschehen können wir das Paket importieren." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0d3fea87-d2a6-4da8-9327-4c08bb70ef9e", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2, psycopg2.extras" + ] + }, + { + "cell_type": "markdown", + "id": "bedf4588-60dc-4169-9295-21f6b3317e9f", + "metadata": {}, + "source": [ + "Nun sind wir bereit, um eine Verbindung zur Datenbank aufzubauen. Nehmen wir zum Beispiel die \"bank\" Datenbank. Da wir die Installation local haben, muss keine URL angegeben werden. Dafür legen wir eine Variable mit dem connect Befehl an." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "62b5a0b5-bafd-4125-8b62-4a2aa493a061", + "metadata": {}, + "outputs": [], + "source": [ + "conn = psycopg2.connect(\"dbname=bank host=/var/run/postgresql user=postgres password=sml12345\")" + ] + }, + { + "cell_type": "markdown", + "id": "8861806c-5786-42fb-81d3-1ba5283b1d2b", + "metadata": {}, + "source": [ + "Als nächstes führen wir eine einfache SELECT Abfrage aus.\n", + "Zunächst werden wir verbunden, dann stellen wir die Abfrage, dann rufen wir das Ergebnis der Abfrage ab und lassen es uns anzeigen.\n", + "Dazu benötigen wir ein cursor Objekt, das uns die Abfrage aber auch die Rücklieferung der Daten liefert. Dieses kommt in die Variable cursor.\n", + "Die Variable result speichert uns die Ergebnisse. fetchall wartet bis alle Ergebniszeilen geliefert sind und ermöglicht dann die Anzeige." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "09dbc41c-0ed7-449a-853d-3a18fbb1d97e", + "metadata": {}, + "outputs": [], + "source": [ + "cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n", + "cursor.execute(\"SELECT * FROM account;\")\n", + "result = cursor.fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "81c22552-4d3c-4824-82c7-5b8a0c74402c", + "metadata": {}, + "source": [ + "Pro Klammer bekommen wir nun ein Tupel angezeigt. Das ist noch nicht so schön. Mit einer kleinen Schleife können wir uns die Tupel zeilenweise anzeigen lassen." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "dba24620-fe85-4feb-8109-59a3d7c5e3b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'CHK', 1, datetime.date(2000, 1, 15), None, datetime.date(2005, 1, 4), 'ACTIVE', 2, 10, 1057.75, 1057.75]\n", + "[2, 'SAV', 1, datetime.date(2000, 1, 15), None, datetime.date(2004, 12, 19), 'ACTIVE', 2, 10, 500.0, 500.0]\n", + "[3, 'CD', 1, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 2, 10, 3000.0, 3000.0]\n", + "[4, 'CHK', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 27), 'ACTIVE', 2, 10, 2258.02, 2258.02]\n", + "[5, 'SAV', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 11), 'ACTIVE', 2, 10, 200.0, 200.0]\n", + "[7, 'CHK', 3, datetime.date(2002, 11, 23), None, datetime.date(2004, 11, 30), 'ACTIVE', 3, 13, 1057.75, 1057.75]\n", + "[8, 'MM', 3, datetime.date(2002, 12, 15), None, datetime.date(2004, 12, 5), 'ACTIVE', 3, 13, 2212.5, 2212.5]\n", + "[10, 'CHK', 4, datetime.date(2003, 9, 12), None, datetime.date(2005, 1, 3), 'ACTIVE', 1, 1, 534.12, 534.12]\n", + "[11, 'SAV', 4, datetime.date(2000, 1, 15), None, datetime.date(2004, 10, 24), 'ACTIVE', 1, 1, 767.77, 767.77]\n", + "[12, 'MM', 4, datetime.date(2004, 9, 30), None, datetime.date(2004, 11, 11), 'ACTIVE', 1, 1, 5487.09, 5487.09]\n", + "[13, 'CHK', 5, datetime.date(2004, 1, 27), None, datetime.date(2005, 1, 5), 'ACTIVE', 4, 16, 2237.97, 2897.97]\n", + "[14, 'CHK', 6, datetime.date(2002, 8, 24), None, datetime.date(2004, 11, 29), 'ACTIVE', 1, 1, 122.37, 122.37]\n", + "[15, 'CD', 6, datetime.date(2004, 12, 28), None, datetime.date(2004, 12, 28), 'ACTIVE', 1, 1, 10000.0, 10000.0]\n", + "[17, 'CD', 7, datetime.date(2004, 1, 12), None, datetime.date(2004, 1, 12), 'ACTIVE', 2, 10, 5000.0, 5000.0]\n", + "[18, 'CHK', 8, datetime.date(2001, 5, 23), None, datetime.date(2005, 1, 3), 'ACTIVE', 4, 16, 3487.19, 3487.19]\n", + "[19, 'SAV', 8, datetime.date(2001, 5, 23), None, datetime.date(2004, 10, 12), 'ACTIVE', 4, 16, 387.99, 387.99]\n", + "[21, 'CHK', 9, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 1, 1, 125.67, 125.67]\n", + "[22, 'MM', 9, datetime.date(2004, 10, 28), None, datetime.date(2004, 10, 28), 'ACTIVE', 1, 1, 9345.55, 9845.55]\n", + "[23, 'CD', 9, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 1, 1, 1500.0, 1500.0]\n", + "[24, 'CHK', 10, datetime.date(2002, 9, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 23575.12, 23575.12]\n", + "[25, 'BUS', 10, datetime.date(2002, 10, 1), None, datetime.date(2004, 8, 28), 'ACTIVE', 4, 16, 0.0, 0.0]\n", + "[27, 'BUS', 11, datetime.date(2004, 3, 22), None, datetime.date(2004, 11, 14), 'ACTIVE', 2, 10, 9345.55, 9345.55]\n", + "[28, 'CHK', 12, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 38552.05, 38552.05]\n", + "[29, 'SBL', 13, datetime.date(2004, 2, 22), None, datetime.date(2004, 12, 17), 'ACTIVE', 3, 13, 50000.0, 50000.0]\n" + ] + } + ], + "source": [ + "for entry in result:\n", + " print(entry)" + ] + }, + { + "cell_type": "markdown", + "id": "b975ba44-de7e-4337-a2f9-a4fa06fc59b1", + "metadata": {}, + "source": [ + "Wollen wir nur eine bestimmte Spalte geht dies über die Angabe der Spaltennummer." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6159012d-ea9c-44da-bec0-5d073d210bd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1057.75\n", + "500.0\n", + "3000.0\n", + "2258.02\n", + "200.0\n", + "1057.75\n", + "2212.5\n", + "534.12\n", + "767.77\n", + "5487.09\n", + "2237.97\n", + "122.37\n", + "10000.0\n", + "5000.0\n", + "3487.19\n", + "387.99\n", + "125.67\n", + "9345.55\n", + "1500.0\n", + "23575.12\n", + "0.0\n", + "9345.55\n", + "38552.05\n", + "50000.0\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b3180855-e952-4d4a-86b5-a27c9d326f69", + "metadata": {}, + "source": [ + "Spaltennummern zählen ist nun etwas aufwendig und unschön. Mit einer Erweituerung des Pakets können wir den cursor anpassen und dann auch Spaltennamen angeben. Wir wählen psycopg2.extras und dann einen DictCursor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75b7a5c8-4677-4546-a461-e7b36b97ff5e", + "metadata": {}, + "outputs": [], + "source": [ + "for entry in result:\n", + " print(entry['avail_balance'])" + ] + }, + { + "cell_type": "markdown", + "id": "3b90228d-bbc6-40ca-bf18-abb698318603", + "metadata": {}, + "source": [ + "Problembehandlung Transaktion: Da wir Abfragen durchführen sollten wir diese auch korrekt starten und abschliessen. Vor allem wenn es zu einem Fehler (z.B. einem Tippfehler) kommt. Anosnten laufen wir auf eine Fehlermeldung und eine offen Transaktion.\n", + "Die Lösung ist unseren SQL Befehl in einen try, except bzw finally Block zu setzen.\n", + "MIt conn.commit() wird die Durchführung der Transaktion bestätigt." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2e24b6bb-4773-4652-b027-7ee4faf6db92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1057.75\n", + "3 3000.0\n", + "4 2258.02\n", + "7 1057.75\n", + "8 2212.5\n", + "12 5487.09\n", + "13 2237.97\n", + "15 10000.0\n", + "17 5000.0\n", + "18 3487.19\n", + "22 9345.55\n", + "23 1500.0\n", + "24 23575.12\n", + "27 9345.55\n", + "28 38552.05\n", + "29 50000.0\n" + ] + } + ], + "source": [ + "try:\n", + " cursor.execute(\n", + " 'select * from account where avail_balance > 1000;'\n", + " )\n", + " result = cursor.fetchall()\n", + " result\n", + "\n", + " for row in result:\n", + " print(row[0], row['avail_balance'])\n", + " \n", + " conn.commit()\n", + "except:\n", + " conn.rollback()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2f6948-9a18-421c-b64c-85ed9743b28f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb b/.ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb new file mode 100644 index 0000000..3052b14 --- /dev/null +++ b/.ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - Kurzwiederholung\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elementare Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# eine Variable mit einem int-Wert\n", + "i = 10\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python kann sher grosse Zahlen verarbeiten:\n", + "j = 123456395823842193412376429384\n", + "j+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# int-Zahlen sind richtige Objekte - sie brauchen mehr Speicher, \n", + "# haben aber auch mehr Fähigkeiten als in anderen Sprachen\n", + "import sys\n", + "\n", + "print(sys.getsizeof(10)) # Specherverbrauch einer einzelnen Zahl\n", + "print(i.bit_length()) # Methoden, hier Bitlänge der Zahl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ein Float \n", + "f = 5.0\n", + "\n", + "# auch Floats haben Methoden\n", + "print(f.is_integer())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Floats haben so ihre Probleme...\n", + "0.1 + 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Strings, Konkatenation\n", + "s = \"Hello\" + \" \" + \"World\"\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Boolsche Werte\n", + "a = True\n", + "b = False\n", + "\n", + "print(a and b)\n", + "print(a or b)\n", + "type(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Umwandlung von Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# zahl in string umwandeln\n", + "i = 5\n", + "str(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# string in zahl umwandeln\n", + "s = \"55\"\n", + "float(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kontrollstrukturen: if, while, for, range-Funktion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 10\n", + "\n", + "if i > 5:\n", + " print(\"i ist grösser 5\")\n", + " \n", + "while i > 0:\n", + " print(i)\n", + " i = i - 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(5):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rückwärts zählen\n", + "for i in range(10, 4, -1):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beispiele für Funktionen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add(i, j):\n", + " return i+j" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(add(2, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Listen, Indizierung, Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Platznummern\n", + "# 0 1 2 3 4 5 6\n", + "l = [1, 2, 3, 4, 5, 6, 7]\n", + "\n", + "# slicing\n", + "l[1::2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enumerate: Aufzählen einer Liste (Tupelbildung mit Index)\n", + "list(enumerate([5,9,42]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = [\"test\", 1, 5.5, True] # Listen können gemischte Werte enthalten" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tupel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = (4, 5) # Tupel einpacken\n", + "a, b = t # Tupel auspacken\n", + "\n", + "print(a)\n", + "print(b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "builtin-Funktionen, Standardbibliothek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# builtin-Funktion\n", + "# range, list, int, float, len, sum, bool, tuple " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ein Import - hier math\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "math.exp(2.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = { 'eins': 'one', 'zwei': 'two' }\n", + "\n", + "d.keys(), d.values()\n", + "\n", + "for key, value in d.items():\n", + " print(key, '->', value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List-Comprehensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quadratzahlen mit gerader Basis\n", + "[ x*x for x in range(0,21) if x % 2 == 0 ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = [3,4,5,6,7]\n", + "mean(a), variance(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = [0, 2, 4, 6, 8, 10]\n", + "mean(b), variance(b)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "a6b707a736c5fbba452b904aff207ddd250a7524df1f8c74db5bc52ff4a2560b" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb b/.ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb new file mode 100644 index 0000000..bfa591a --- /dev/null +++ b/.ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - Kurzwiederholung\n", + "\n", + "Hier werden wir einige Beispiele betrachten, um noch einmal einige Python-Konzepte zu wiederholen. Sie können mitschreiben, oder am Ende mein fertiges Notebook herunterladen." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elementare Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Umwandlung von Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kontrollstrukturen: if, while, for, range-Funktion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beispiele für Funktionen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Listen, Indizierung, Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tupel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "builtin-Funktionen, Standardbibliothek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List-Comprehensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb b/.ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb new file mode 100644 index 0000000..db881a6 --- /dev/null +++ b/.ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SQL - Injection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vorbereitung" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Falls noch nicht geschehen können die Pakte importiert werden\n", + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# als nächstes bauen wir unsere Verbindung auf, legen eine Tabelle an und füllen diese mit zwei Usern (bitte eigene Datenbank auswählen):\n", + "conn = psycopg2.connect (\"dbname=7Wochen user=postgres password=postgres\")\n", + "\n", + "cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n", + "\n", + "cursor.execute(\"\"\"\n", + " DROP TABLE IF EXISTS users;\n", + " CREATE TABLE IF NOT EXISTS users (\n", + " id SERIAL PRIMARY KEY,\n", + " username VARCHAR(255) NOT NULL,\n", + " password VARCHAR(255) NOT NULL\n", + " )\n", + "\"\"\")\n", + "\n", + "cursor.execute(\"INSERT INTO users (username, password) VALUES (%s, %s)\", (\"user1\", \"password1\"))\n", + "cursor.execute(\"INSERT INTO users (username, password) VALUES (%s, %s)\", (\"user2\", \"password2\"))\n", + "conn.commit();" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[RealDictRow([('id', 1), ('username', 'user1'), ('password', 'password1')]),\n", + " RealDictRow([('id', 2), ('username', 'user2'), ('password', 'password2')])]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Kurz zum überprüfen, ob die Tabelle angelegt wurde\n", + "\n", + "cursor.execute(\"SELECT * FROM users;\")\n", + "result = cursor.fetchall()\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffsszenario 1 - direkt Übermittlung von Zugangsdaten" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login erfolgreich\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass sich ein User, zum Beispiel über ein Formularfeld anmelden möchte.\n", + "# Und zwar mit den folgenden Zugangsdaten:\n", + "username = \"user1\"\n", + "password = \"password1\"\n", + "\n", + "# Das würde dann so ablaufen:\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + "# Nun holen wir uns eine Ergebniszeile. Gibt es die waren wir mit unserer Anmeldung erfolgreich. Kommt keine Zeile waren unsere Zugangsdaten falsch.\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()\n", + "\n", + "# Wir übergeben also die Zeichenkette aus der Python-Variable direkt an unsere SQL-Datenbank.\n", + "# Ändern wir das Passwort sind wir nicht erfolgreich.\n", + "# Um es schöner zu machen fügen wir noch ein Rollback ein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffszenario 2 - Übergabe von SQL-Befehlen, um Fehler zu hervorzurufen. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FEHLER: Zeichenkette in Anführungszeichen nicht abgeschlossen bei »'''«\n", + "LINE 1: SELECT * FROM users WHERE username='user1' AND password='''\n", + " ^\n", + "\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass ein User SQL-Befehle eingibt. Zunächst nur ein einfaches Anführungszeichen als Passwort.\n", + "# Das Ergebnis ist ein erzeugter SQL-Fehler.\n", + "username = \"user1\"\n", + "password = \"'\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffszenario 3 - Übergabe von SQL-Befehlen, um einen erfolgreichen Login zu generieren." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login erfolgreich\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass ein User SQL-Befehle eingibt. Diesmal die Übergabe eines echten Befehls.\n", + "# Dieser erzeugt eine wahre Aussage wodurch die Passwortüberprüfung erfolgreich wird.\n", + "username = \"user1\"\n", + "password = \"' OR 1=1 --\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abschliessend noch die Variante mit Platzhaltern, die sicherer wäre." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login fehlgeschlagen\n" + ] + } + ], + "source": [ + "# hier würde nun die Zeichenkette aus der Passworteingabe übergeben und nicht als String direkt zu SQL-Code werden.\n", + "username = \"user1\"\n", + "password = \"' OR 1=1 --\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username=%s AND password=%s\", (username, password))\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a6b707a736c5fbba452b904aff207ddd250a7524df1f8c74db5bc52ff4a2560b" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/.ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb b/.ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb new file mode 100644 index 0000000..7d5e22c --- /dev/null +++ b/.ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "e7cbec5d-cb72-4ab0-a1c1-ce4cd2609934", + "metadata": {}, + "outputs": [], + "source": [ + "# Autoren: Samuel, Robin, Michael\n", + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "915f7f56-fd86-459c-ac63-91d331544348", + "metadata": {}, + "outputs": [], + "source": [ + "# Verbindung zur Datenbank aufbauen\n", + "conn = psycopg2.connect (\"dbname=movies_database host=/var/run/postgresql user=postgres password=sml12345\")\n", + "cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e25bb2be-926f-460d-b9f9-b4b9cfaf4277", + "metadata": {}, + "outputs": [], + "source": [ + "# Hole alle Schauspieler\n", + "cursor.execute(\"SELECT * FROM actors;\")\n", + "actors = cursor.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a4b3619b-9a5d-404a-85e9-0e04636f7666", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arthur Lake [('Penny Singleton', 11), ('Larry Simms', 11), ('Daisy the Dog', 8)]\n", + "Burt Young [('Talia Shire', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Carl Weathers [('Talia Shire', 4), ('Burt Young', 4), ('Sylvester Stallone', 4)]\n", + "Clint Eastwood [('Sondra Locke', 5)]\n", + "Daisy the Dog [('Arthur Lake', 8), ('Larry Simms', 8), ('Penny Singleton', 7)]\n", + "Danny Glover [('Mel Gibson', 4)]\n", + "DeForest Kelly [('Leonard Nimoy', 5), ('James Doohan', 5), ('William Shatner', 5)]\n", + "Diane Keaton [('Woody Allen', 6)]\n", + "Herbert Lom [('Peter Sellers', 4)]\n", + "Jack Lemmon [('Walter Matthau', 4)]\n", + "James Doohan [('Leonard Nimoy', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Jim Dale [('Kenneth Williams', 5), ('Sidney James', 4)]\n", + "Kenneth Williams [('Sidney James', 5), ('Jim Dale', 5)]\n", + "Larry Simms [('Arthur Lake', 11), ('Penny Singleton', 10), ('Daisy the Dog', 8)]\n", + "Leonard Nimoy [('James Doohan', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Mel Gibson [('Danny Glover', 4)]\n", + "Oliver Hardy [('Stan Laurel', 8)]\n", + "Penny Singleton [('Arthur Lake', 11), ('Larry Simms', 10), ('Daisy the Dog', 7)]\n", + "Peter Sellers [('Herbert Lom', 4)]\n", + "Sidney James [('Kenneth Williams', 5), ('Jim Dale', 4)]\n", + "Sondra Locke [('Clint Eastwood', 5)]\n", + "Stan Laurel [('Oliver Hardy', 8)]\n", + "Sylvester Stallone [('Talia Shire', 5), ('Burt Young', 5), ('Carl Weathers', 4)]\n", + "Talia Shire [('Burt Young', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Walter Matthau [('Jack Lemmon', 4)]\n", + "William Shatner [('Leonard Nimoy', 6), ('James Doohan', 6), ('DeForest Kelly', 5)]\n", + "Woody Allen [('Diane Keaton', 6)]\n" + ] + } + ], + "source": [ + "# Iteriere über alle Schauspieler\n", + "collab = {}\n", + "\n", + "for actor in actors:\n", + " # Hole alle Filme, in welchen dieser Schauspieler dabei war\n", + " cursor.execute(f\"SELECT * FROM movies_actors where actor_id = {actor['actor_id']};\")\n", + " movies = cursor.fetchall()\n", + " movie_ids = ', '.join([str(movie['movie_id']) for movie in movies])\n", + "\n", + " # \n", + " cursor.execute(f\"SELECT actor_id, count(*) FROM movies_actors where movie_id in ({movie_ids}) and actor_id <> {actor['actor_id']} group by actor_id having count(*) > 3 order by count(*) desc;\")\n", + " result = cursor.fetchall()\n", + " \n", + " if (result):\n", + " collabs = []\n", + " for row in result:\n", + " cursor.execute(f\"SELECT name from actors where actor_id = {row['actor_id']};\")\n", + " collabs.append((cursor.fetchall()[0]['name'], row['count']))\n", + " collab[actor['name']] = collabs\n", + " \n", + "for key in collab:\n", + " print(key, collab[key])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e5570c42-fbf0-4634-ba49-227f7fbd6ad3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Baue Graphen\n", + "G = nx.Graph()\n", + "\n", + "for person, connections in collab.items():\n", + " for friend in connections:\n", + " G.add_edge(person, friend[0], weight=friend[1])\n", + "\n", + "\n", + "# Zeige Graphen an\n", + "plt.figure(figsize=(40, 40))\n", + "\n", + "pos = nx.spring_layout(G, k=2.0, iterations=100)\n", + "\n", + "nx.draw(G, pos, with_labels=True, node_size=3000, font_size=20)\n", + "\n", + "edge_labels = dict([((n1, n2), d['weight'])\n", + " for n1, n2, d in G.edges(data=True)])\n", + "\n", + "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, label_pos=0.5,\n", + " font_color='red', font_size=20, font_weight='bold')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72e58005-9800-42c3-870c-6ecc4abf8de5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb b/.ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb new file mode 100644 index 0000000..b377324 --- /dev/null +++ b/.ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zooverwaltung\n", + "\n", + "In diesem Beispiel wird ein System zur Verwaltung von Zoos implementiert. Hier gibt es eine Tabelle für Personen (TierplegerInnen) und eine Tabelle für Tiere. Eine Person kann mehrere Tiere betreuen, und ein Tier gehört einer Person.\n", + "\n", + "Im ersten Teil sollen Sie 5 Funktionen schreiben, die es Ihnen erlaubt, Personen anzulegen, zu lesen (einzeln und als Liste), zu ändern und zu löschen. Die Tabellen sind dabei vorgegeben und Sie finden im Notebook einige Testaufufe." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "# Verbindung aufbauen\n", + "# TODO: hier müssen Ihre Verbindungsdaten eingetragen werden\n", + "# TODO: ggf. müssen Sie auch den Namen der Datenbank anpassen oder eine leere Datenbank 'zoo' anlegen\n", + "conn = psycopg2.connect(\"dbname=zoo host=/var/run/postgresql user=postgres password=sml12345\")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "# Diese Zelle löscht die Tabellen, falls sie bereits existieren, und legt sie neu an\n", + "sql = \"\"\"\n", + " DROP TABLE IF EXISTS animals;\n", + " DROP TABLE IF EXISTS zookeepers;\n", + "\n", + " CREATE TABLE zookeepers (\n", + " id SERIAL PRIMARY KEY,\n", + " name VARCHAR(255) NOT NULL,\n", + " email VARCHAR(255),\n", + " specialty VARCHAR(255)\n", + " );\n", + "\n", + " CREATE TABLE animals (\n", + " id SERIAL PRIMARY KEY,\n", + " name VARCHAR(255) NOT NULL,\n", + " species VARCHAR(255) NOT NULL,\n", + " zookeeper_id INTEGER,\n", + " FOREIGN KEY (zookeeper_id) REFERENCES zookeepers(id)\n", + " );\n", + "\"\"\"\n", + "\n", + "# SQL ausführen\n", + "cur = conn.cursor()\n", + "cur.execute(sql)\n", + "conn.commit()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Implementieren Sie hier die CRUD-Operationen für Zookeeper\n", + "\n", + "# TODO: Tierpleger anlegen, liefert die ID des neuen Tierpflegers zurück\n", + "def create_zookeeper(name, email, specialty):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " INSERT INTO zookeepers (name, email, specialty)\n", + " VALUES (%s, %s, %s)\n", + " RETURNING id;\n", + " \"\"\", (name, email, specialty))\n", + " conn.commit()\n", + " return cur.fetchone()[0]\n", + " except:\n", + " conn.rollback()\n", + "\n", + "# TODO: Tierpfleger nach ID lesen, Tupel zurückgeben\n", + "def read_zookeeper(id):\n", + " cur.execute(\"SELECT * FROM zookeepers where id=%s\", (id,))\n", + " zookeeper = cur.fetchone()\n", + " return tuple(zookeeper) if zookeeper else None\n", + "\n", + "# TODO: Alle Tierpfleger lesen, liefert eine Liste von Tupeln zurück (nach ID sortiert)\n", + "def read_all_zookeepers():\n", + " cur.execute(\"SELECT * FROM zookeepers order by id\")\n", + " return [(zookeeper) for zookeeper in cur.fetchall()]\n", + "\n", + "# TODO: Tierpfleger aktualisieren\n", + "def update_zookeeper(id, name, email, specialty):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " UPDATE zookeepers\n", + " SET name=%s, email=%s, specialty=%s\n", + " WHERE id=%s\n", + " \"\"\", (name, email, specialty, id))\n", + " conn.commit()\n", + " except:\n", + " conn.rollback()\n", + "\n", + "# TODO: Tierpfleger per ID löschen\n", + "def delete_zookeeper(id):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " DELETE FROM zookeepers\n", + " WHERE id=%s\n", + " \"\"\", (id,))\n", + " conn.commit()\n", + " except:\n", + " conn.rollback()\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "# Tests - diese sollten alle erfolgreich durchlaufen werden und nicht verändert werden\n", + "\n", + "john = (\"John Doe\", \"john@example.com\", \"Elephants\")\n", + "jane = (\"Jane Doe\", \"jane@example.com\", \"Giraffes\")\n", + "\n", + "id = create_zookeeper(*john)\n", + "id2 = create_zookeeper(*jane)\n", + "\n", + "assert read_zookeeper(id) == (id, *john)\n", + "assert read_zookeeper(id2) == (id2, *jane)\n", + "\n", + "john = (\"John Smith\", \"john2@example.com\", \"Zebras\")\n", + "update_zookeeper(id, *john)\n", + "assert read_zookeeper(id) == (id, *john)\n", + "\n", + "all_zookeepers = read_all_zookeepers()\n", + "assert len(all_zookeepers) == 2\n", + "assert all_zookeepers[0] == (id, *john)\n", + "assert all_zookeepers[1] == (id2, *jane)\n", + "\n", + "delete_zookeeper(id)\n", + "delete_zookeeper(id2)\n", + "assert read_zookeeper(id) == None\n", + "assert read_zookeeper(id2) == None\n", + "\n", + "all_zookeepers = read_all_zookeepers()\n", + "assert len(all_zookeepers) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nutzung eines ORMs\n", + "\n", + "Wie wir sehen, ist das manuelle Erstellen von Zugriffsfunktionen auf die Datenbank sehr aufwändig. Daher gibt es sogenannte Object-Relational-Mapper (ORMs), die uns diese Arbeit abnehmen. Ein bekanntes ORM für Python ist `SQLAlchemy`. Im zweiten Teil des Notebooks werden wir sehen, wie mit `SQLAlchemy` Daten eingefügt und gelesen werden können.\n", + "\n", + "Da Sie Anaconda nutzen, sollte `SQLAlchemy` bereits installiert sein. Falls nicht, können Sie es mit `conda` installieren.\n", + "\n", + "Wenn man `SQLAlchemy` verwendet, definiert man zunächst eine Klasse, die die Tabelle repräsentiert. Hier sind diese Klassen bereits für Personen und Tiere definiert. Sie können sich die Klassen ansehen, um zu verstehen, wie Tabellen in `SQLAlchemy` definiert werden." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "## Nutzung von SQLAlchemy\n", + "from sqlalchemy import create_engine, Column, Integer, String, ForeignKey, select\n", + "from sqlalchemy.orm import Session, relationship, declarative_base, join, aliased\n", + "\n", + "Base = declarative_base()\n", + "\n", + "class Zookeeper(Base):\n", + " __tablename__ = 'zookeepers'\n", + " id = Column(Integer, primary_key=True)\n", + " name = Column(String, nullable=False)\n", + " email = Column(String)\n", + " specialty = Column(String)\n", + " animals = relationship(\"Animal\", back_populates=\"zookeeper\")\n", + "\n", + "class Animal(Base):\n", + " __tablename__ = 'animals'\n", + " id = Column(Integer, primary_key=True)\n", + " name = Column(String, nullable=False)\n", + " species = Column(String, nullable=False)\n", + " zookeeper_id = Column(Integer, ForeignKey('zookeepers.id'))\n", + " zookeeper = relationship(\"Zookeeper\", back_populates=\"animals\")\n", + "\n", + "# TODO: Verbingung zur Datenbank herstellen\n", + "engine = create_engine('postgresql://postgres:sml12345@localhost/zoo') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anlegen und Abfragen von Objekten in SQLAlchemy\n", + "\n", + "In diesem Teil sollen Sie 2 Personen und 5 Tiere anlegen und dann alle Personen und Tiere abfragen. Sie können sich an den Beispielen orientieren, die im Notebook gegeben sind.\n", + "\n", + "Ein kurzes und übersichtliches Tutorial zur Nutzung von `SQLAlchemy` finden Sie hier:\n", + "\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html\n", + "\n", + "Relevant sind vor allem folgende Abschnitte:\n", + "\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#opening-and-closing-a-session\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#adding-new-or-existing-items\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#querying" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: zwei Zookeeper anlegen\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " john = Zookeeper(name=\"John Doe\", email=\"john@example.com\", specialty=\"Elephants\")\n", + " jane = Zookeeper(name=\"Jane Doe\", email=\"jane@example.com\", specialty=\"Giraffes\")\n", + " \n", + " session.add_all([john, jane])" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['John Doe', 'Jane Doe']\n" + ] + } + ], + "source": [ + "# TODO: Alle Zookeeper ausgeben\n", + "with Session(engine) as session:\n", + " statement = select(Zookeeper.name)\n", + " print(session.scalars(statement).all())" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: 3 Elefanten anlegen, die John betreut\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " john_clause = select(Zookeeper.id).where(Zookeeper.name.like(\"%John%\"))\n", + " john_id = session.scalars(john_clause).first()\n", + "\n", + " session.add_all([Animal(name=animal, species=\"Elephant\", zookeeper_id=john_id) for animal in [\"Babar\", \"Dumbo\", \"Hathi\"]])\n", + " \n", + "# TODO: 2 Giraffen anlegen, die Jane betreut\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " jane_clause = select(Zookeeper.id).where(Zookeeper.name.like(\"%Jane%\"))\n", + " jane_id = session.scalars(jane_clause).first()\n", + " \n", + " session.add_all([Animal(name=animal, species=\"Giraffe\", zookeeper_id=jane_id) for animal in [\"Melman\", \"Gloria\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------\n", + "| Babar | Elephant | John Doe |\n", + "| Dumbo | Elephant | John Doe |\n", + "| Hathi | Elephant | John Doe |\n", + "| Melman | Giraffe | Jane Doe |\n", + "| Gloria | Giraffe | Jane Doe |\n", + "--------------------------------\n" + ] + } + ], + "source": [ + "# Liste aller Tiere mit ihren Pflegern ausgeben:\n", + "with Session(engine) as session:\n", + " statement = select(Animal.name, Animal.species, Zookeeper.name).select_from(join(Animal, Zookeeper, Animal.zookeeper))\n", + " animals = session.execute(statement).all()\n", + "\n", + " col_widths = (\n", + " max([len(animal[0]) for animal in animals]),\n", + " max([len(animal[1]) for animal in animals]),\n", + " max([len(animal[2]) for animal in animals]),\n", + " )\n", + "\n", + " print(\"-\" * (sum(col_widths) + 10))\n", + " for animal in animals:\n", + " print(f\"| {animal[0]:{col_widths[0]}} | {animal[1]:{col_widths[1]}} | {animal[2]:{col_widths[2]}} |\")\n", + " print(\"-\" * (sum(col_widths) + 10))\n", + " \n", + "\n", + "# Beispielausgabe:\n", + "# \n", + "# ----------------------------------------\n", + "# | Babar | Elephant | John Doe |\n", + "# | Dumbo | Elephant | John Doe |\n", + "# | Hathi | Elephant | John Doe |\n", + "# | Melman | Giraffe | Jane Doe |\n", + "# | Gloria | Giraffe | Jane Doe |\n", + "# ----------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Aufgabe 2.sql b/Aufgabe 2.sql new file mode 100644 index 0000000..54a76fc --- /dev/null +++ b/Aufgabe 2.sql @@ -0,0 +1,91 @@ +-- 1) Selektieren Sie die Employee ID, Vornamen und Nachname aller Angestellten - sortieren Sie absteigend nach Nachname und dann nach Vorname. +select emp_id "Employee ID", fname Vorname, lname Nachname +from employee +order by lname desc, fname; + +-- 2) Holen Sie Kontonummer, Kundennummer und Kontostand für alle aktiven Konten mit mehr als 2500 Dollar (nutzen Sie als Kontostand avail_balance). +select account_id Kontonummer, cust_id Kundennummer, avail_balance Kontostand +from account +where status = 'ACTIVE' +and avail_balance > 2500; + +-- 3) Holen Sie aus der Tabelle account alle IDs der Angestellten, die ein Konto eröffnet haben - geben Sie dabei jede ID nur einmal aus (Tipp: DISTINCT). +select distinct open_emp_id "ID des Angestellten" +from account; + +-- 4) Zählen Sie die Zeilen in der account-Tabelle. +select count(*) +from account; + +-- 5) Geben Sie eine Tabelle aus, in der in der ersten Spalte der Name des Kunden und in einer zweiten Spalte die Anzahl Konten dieses Kunden steht - Hinweis: hier reicht es aus, die Namen der Individualkunden zu verwenden (individual), Geschäftskunden dürfen ignoriert werden. +select individual.fname || ' ' || individual.lname "Name", count(*) "Anzahl Konten" +from customer +join individual on customer.cust_id = individual.cust_id +join account on customer.cust_id = account.cust_id +group by "Name"; + +-- 6) Wie 5), aber zeigen Sie nur Kunden an, die 2 oder mehr Konten haben. +select individual.fname || ' ' || individual.lname "Name", count(*) "Anzahl Konten" +from customer +join individual on customer.cust_id = individual.cust_id +join account on customer.cust_id = account.cust_id +group by "Name" +having count(*) >= 2; + +-- 7) Geben Sie eine Query an, die alle Accounts findet, die im Jahr 2002 eröffnet wurden, ohne die Symbole > oder < zu verwenden. +select * +from account +where extract('year' from open_date) = '2002'; + +-- 8) Geben Sie eine Query an, die alle Kunden ("individual") findet, deren Nachname an der zweiten Stelle ein 'a' danach an beliebiger Stelle ein 'e' enthält. +select * +from individual +where individual.lname like '_a%e%'; + +-- 9) Schreiben Sie eine Query, die alle Account-IDs für jeden Nicht-Geschäftskunden holt, dazu die fed_id des Kunden und den Namen des Produkts, auf dem der Account basiert. +select account.account_id "Account-ID", customer.fed_id "fed_id des Kunden", product.name "Name des Produkts" +from account +join customer on customer.cust_id = account.cust_id +join product on product.product_cd = account.product_cd +where customer.cust_type_cd = 'I'; + +-- 10) Schreiben Sie eine Query, die alle Angestellten findet, deren Supervisor in einer anderen Abteilung (department) arbeitet. Selektieren Sie ID, Vor- und Nachname. +select emp.emp_id "ID", emp.fname "Vorname", emp.lname "Nachname" +from employee emp +join employee sup on sup.emp_id = emp.superior_emp_id +where emp.dept_id != sup.dept_id; + +-- 11) (2 Punkte) Selektieren Sie alle Vornamen und Nachnamen in einer Tabelle (sowohl die der Individual-Kunden, als auch die der Angestellten). Tipp: Machen Sie sich mit der UNION-Anweisung vertraut. +select fname "Vorname", lname "Nachname" +from individual +union all +select fname "Vorname", lname "Nachname" +from employee; + +-- 12) (2 Punkte) Selektieren Sie folgende Tabelle: Vorgesetzter (Name), komma-getrennte Liste der Mitarbeiter, die zu einem Vorgesetzten gehören (Tipp 1: superior_emp_id, Tipp 2: Recherchieren Sie per Internetsuche oder ChatGPT, wie man in einer Query zweimal dieselbe Tabelle nutzen kann.) +select sup.fname || ' ' || sup.lname "Vorgesetzter", array_agg(emp.fname || ' ' || emp.lname) "Mitarbeiter" +from employee sup +join employee emp on emp.superior_emp_id = sup.emp_id +group by "Vorgesetzter"; + +-- 13) (2 Punkte) Selektieren Sie alle Account-IDs und die dazugehörige Customer-ID. Wenn es ein Geschäftskunde ist, dann soll noch der Firmenname in der dritten Spalte stehen, sonst soll in der dritten Spalte der Vor- und Nachname des Privatkunden stehen (Tipp: COALESCE). +select a.account_id "Account-ID", c.cust_id "Customer-ID", coalesce(b.name, i.fname || ' ' || i.lname) "Name" +from account a +join customer c on c.cust_id = a.cust_id +left join individual i on i.cust_id = c.cust_id +left join business b on b.cust_id = c.cust_id; + +-- 14) (2 Punkte) Selektieren Sie den Namen des Kunden mit dem höchsten Gesamtvermögen (nur eine Gesamt-Query - Subqueries dürfen genutzt werden) +select coalesce(b.name, i.fname || ' ' || i.lname) "Name" +from account a +join customer c on c.cust_id = a.cust_id +left join individual i on i.cust_id = c.cust_id +left join business b on b.cust_id = c.cust_id +order by a.avail_balance desc +limit 1; + +-- 15) (2 Punkte) Selektieren Sie alle Namen der Bank-Produkte (Tabelle product, Verbindung product_cd) mit den Accounts (account_id), die auf diesem Produkt basieren. Dabei sollen alle Produkte auftauchen, auch die ohne Account. +select p.name "Name des Bank-Produkts", array_agg(a.account_id) "Accounts" +from product p +left join account a on a.product_cd = p.product_cd +group by "Name des Bank-Produkts"; diff --git a/Aufgabe 3 b/Aufgabe 3 new file mode 100644 index 0000000..6a7a9d5 --- /dev/null +++ b/Aufgabe 3 @@ -0,0 +1,27 @@ +-- UNION +select emp_id as person_id +from employee +union +select cust_id as person_id +from customer; + +-- UNION ALL +select emp_id as person_id +from employee +union all +select cust_id as person_id +from customer; + +-- INTERSECT +select emp_id as person_id +from employee +intersect +select cust_id as person_id +from customer; + +-- EXCEPT +select emp_id as person_id +from employee +except +select cust_id as person_id +from customer; \ No newline at end of file diff --git a/Aufgabe 3.docx b/Aufgabe 3.docx new file mode 100644 index 0000000000000000000000000000000000000000..6673c78f62f2ae7ca08f5150d03ca3f2b149532b GIT binary patch literal 19900 zcmeIaWprIVuP_*Hn5kiAY?zsunVFe64Kp({GgHH9!_3T_hB=wEFZFrmyYI~3S#y1s zea`W=Y*~`My(MkQN&tf(1Av2r1DJ~$YXba{pgw-<*c(|o(9--WE2n$Jf$8DFHa)WS zQ$1XolmrdyJ3}VZMZJ7uM$f_0B}I$WH`Zk#+hmn{a2OsB4hRd@uwJ{|ixaRvftI)V zh5759^yX|j-8XauOEsJ7m{u1GULERJ4NHHA6pnrHO&Jr_K1d@Bw4ZUfQXi&=L2q=M z0p4meJEwa+^|hW>v#RuOXrwH0&W?Rsh|ChypvHh9RUcGrif0`z)GYQB^fS8F9DGbD zkzm*~ntZyzJNGa-j2-Jyh>@UShXj>y&MGj-Kw8sOFoacGe7_Pb9>qGkODizjUb*i1 zk;fH1ktP%#pS|P-C@b*^e9!()@bF{$lRy9fvi~E25I$NM+8D^%+t@nL8rawy(YRV! 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import networkx as nx\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Baue Graphen\n", + "G = nx.Graph()\n", + "\n", + "for person, connections in collab.items():\n", + " for friend in connections:\n", + " G.add_edge(person, friend[0], weight=friend[1])\n", + "\n", + "\n", + "# Zeige Graphen an\n", + "plt.figure(figsize=(40, 40))\n", + "\n", + "pos = nx.spring_layout(G, k=2.0, iterations=100)\n", + "\n", + "nx.draw(G, pos, with_labels=True, node_size=3000, font_size=20)\n", + "\n", + "edge_labels = dict([((n1, n2), d['weight'])\n", + " for n1, n2, d in G.edges(data=True)])\n", + "\n", + "nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, label_pos=0.5,\n", + " font_color='red', font_size=20, font_weight='bold')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "72e58005-9800-42c3-870c-6ecc4abf8de5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ViewsundTrigger.sql b/ViewsundTrigger.sql new file mode 100644 index 0000000..204d84e --- /dev/null +++ b/ViewsundTrigger.sql @@ -0,0 +1,82 @@ +-- Views und Trigger +-- SQL Tabellen + +-- Tabelle Pilot + +CREATE TABLE pilot ( +personalnr INTEGER PRIMARY KEY, +name VARCHAR NOT NULL, +alter INTEGER, +flugstunden NUMERIC +); + +-- Tabelle Flugszeugtype + +CREATE TABLE flugzeugtyp ( +typbezeichnung VARCHAR PRIMARY KEY, +reisegeschwindigkeit NUMERIC, +typflugstunden NUMERIC +); + +-- Tabelle Fliegt + +CREATE TABLE fliegt ( +personalnr INTEGER REFERENCES pilot(personalnr), +typbezeichnung VARCHAR REFERENCES flugzeugtyp(typbezeichnung), +fliegtflugstunden NUMERIC, +PRIMARY KEY(personalnr, typbezeichnung) +); + +-- Befüllen der Tabellen pilot und flugzeugtyp + +INSERT INTO pilot (personalnr, name, alter, flugstunden) VALUES (1007, 'James', 44, 20012); +INSERT INTO pilot (personalnr, name, alter, flugstunden) VALUES (1008, 'Zoe', 33, 10020); +INSERT INTO pilot (personalnr, name, alter, flugstunden) VALUES (1009, 'Walther', 27, 5020); + +INSERT INTO flugzeugtyp (typbezeichnung, reisegeschwindigkeit, typflugstunden) VALUES ('Airbus A320', 800, 50821); +INSERT INTO flugzeugtyp (typbezeichnung, reisegeschwindigkeit, typflugstunden) VALUES ('Airbus A370', 800, 20312); +INSERT INTO flugzeugtyp (typbezeichnung, reisegeschwindigkeit, typflugstunden) VALUES ('Boing B737', 800, 30021); + +-- View + +CREATE VIEW pflugstunden AS +SELECT personalnr, name , SUM (flugstunden) +FROM pilot +GROUP BY personalnr, name; + +CREATE VIEW typflugstunden AS +SELECT typbezeichnung, SUM (typflugstunden) +FROM flugzeugtyp +GROUP BY typbezeichnung; + +SELECT * +FROM pflugstunden; + +-- Trigger + +CREATE OR REPLACE FUNCTION AktualisiereFlugstunden ( ) RETURNS TRIGGER AS +' +BEGIN +-- aktualisiere die Flugstunden des Piloten + +UPDATE pilot SET flugstunden = flugstunden + NEW.fliegtflugstunden WHERE personalnr = NEW.personalnr; + +-- aktualisiere die Flugstunden des Flugzeugtyps + +UPDATE flugzeugtyp SET typflugstunden = typflugstunden + NEW.fliegtflugstunden WHERE typbezeichnung = NEW.typbezeichnung; + +RETURN NEW; + +END; +' LANGUAGE 'plpgsql'; + +CREATE TRIGGER pflugstunden +AFTER INSERT ON fliegt +FOR EACH ROW EXECUTE PROCEDURE AktualisiereFlugstunden ( ); + + +INSERT INTO fliegt (personalnr, typbezeichnung, fliegtflugstunden) VALUES (1007, 'Airbus A320', 10000); +INSERT INTO fliegt (personalnr, typbezeichnung, fliegtflugstunden) VALUES (1008, 'Airbus A320', 1000); + +SELECT * +FROM pflugstunden; \ No newline at end of file diff --git a/Zoo-Aufgabe.ipynb b/Zoo-Aufgabe.ipynb new file mode 100644 index 0000000..b377324 --- /dev/null +++ b/Zoo-Aufgabe.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zooverwaltung\n", + "\n", + "In diesem Beispiel wird ein System zur Verwaltung von Zoos implementiert. Hier gibt es eine Tabelle für Personen (TierplegerInnen) und eine Tabelle für Tiere. Eine Person kann mehrere Tiere betreuen, und ein Tier gehört einer Person.\n", + "\n", + "Im ersten Teil sollen Sie 5 Funktionen schreiben, die es Ihnen erlaubt, Personen anzulegen, zu lesen (einzeln und als Liste), zu ändern und zu löschen. Die Tabellen sind dabei vorgegeben und Sie finden im Notebook einige Testaufufe." + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [], + "source": [ + "# Verbindung aufbauen\n", + "# TODO: hier müssen Ihre Verbindungsdaten eingetragen werden\n", + "# TODO: ggf. müssen Sie auch den Namen der Datenbank anpassen oder eine leere Datenbank 'zoo' anlegen\n", + "conn = psycopg2.connect(\"dbname=zoo host=/var/run/postgresql user=postgres password=sml12345\")" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [], + "source": [ + "# Diese Zelle löscht die Tabellen, falls sie bereits existieren, und legt sie neu an\n", + "sql = \"\"\"\n", + " DROP TABLE IF EXISTS animals;\n", + " DROP TABLE IF EXISTS zookeepers;\n", + "\n", + " CREATE TABLE zookeepers (\n", + " id SERIAL PRIMARY KEY,\n", + " name VARCHAR(255) NOT NULL,\n", + " email VARCHAR(255),\n", + " specialty VARCHAR(255)\n", + " );\n", + "\n", + " CREATE TABLE animals (\n", + " id SERIAL PRIMARY KEY,\n", + " name VARCHAR(255) NOT NULL,\n", + " species VARCHAR(255) NOT NULL,\n", + " zookeeper_id INTEGER,\n", + " FOREIGN KEY (zookeeper_id) REFERENCES zookeepers(id)\n", + " );\n", + "\"\"\"\n", + "\n", + "# SQL ausführen\n", + "cur = conn.cursor()\n", + "cur.execute(sql)\n", + "conn.commit()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Implementieren Sie hier die CRUD-Operationen für Zookeeper\n", + "\n", + "# TODO: Tierpleger anlegen, liefert die ID des neuen Tierpflegers zurück\n", + "def create_zookeeper(name, email, specialty):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " INSERT INTO zookeepers (name, email, specialty)\n", + " VALUES (%s, %s, %s)\n", + " RETURNING id;\n", + " \"\"\", (name, email, specialty))\n", + " conn.commit()\n", + " return cur.fetchone()[0]\n", + " except:\n", + " conn.rollback()\n", + "\n", + "# TODO: Tierpfleger nach ID lesen, Tupel zurückgeben\n", + "def read_zookeeper(id):\n", + " cur.execute(\"SELECT * FROM zookeepers where id=%s\", (id,))\n", + " zookeeper = cur.fetchone()\n", + " return tuple(zookeeper) if zookeeper else None\n", + "\n", + "# TODO: Alle Tierpfleger lesen, liefert eine Liste von Tupeln zurück (nach ID sortiert)\n", + "def read_all_zookeepers():\n", + " cur.execute(\"SELECT * FROM zookeepers order by id\")\n", + " return [(zookeeper) for zookeeper in cur.fetchall()]\n", + "\n", + "# TODO: Tierpfleger aktualisieren\n", + "def update_zookeeper(id, name, email, specialty):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " UPDATE zookeepers\n", + " SET name=%s, email=%s, specialty=%s\n", + " WHERE id=%s\n", + " \"\"\", (name, email, specialty, id))\n", + " conn.commit()\n", + " except:\n", + " conn.rollback()\n", + "\n", + "# TODO: Tierpfleger per ID löschen\n", + "def delete_zookeeper(id):\n", + " try:\n", + " cur.execute(\"\"\"\n", + " DELETE FROM zookeepers\n", + " WHERE id=%s\n", + " \"\"\", (id,))\n", + " conn.commit()\n", + " except:\n", + " conn.rollback()\n", + " raise" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "# Tests - diese sollten alle erfolgreich durchlaufen werden und nicht verändert werden\n", + "\n", + "john = (\"John Doe\", \"john@example.com\", \"Elephants\")\n", + "jane = (\"Jane Doe\", \"jane@example.com\", \"Giraffes\")\n", + "\n", + "id = create_zookeeper(*john)\n", + "id2 = create_zookeeper(*jane)\n", + "\n", + "assert read_zookeeper(id) == (id, *john)\n", + "assert read_zookeeper(id2) == (id2, *jane)\n", + "\n", + "john = (\"John Smith\", \"john2@example.com\", \"Zebras\")\n", + "update_zookeeper(id, *john)\n", + "assert read_zookeeper(id) == (id, *john)\n", + "\n", + "all_zookeepers = read_all_zookeepers()\n", + "assert len(all_zookeepers) == 2\n", + "assert all_zookeepers[0] == (id, *john)\n", + "assert all_zookeepers[1] == (id2, *jane)\n", + "\n", + "delete_zookeeper(id)\n", + "delete_zookeeper(id2)\n", + "assert read_zookeeper(id) == None\n", + "assert read_zookeeper(id2) == None\n", + "\n", + "all_zookeepers = read_all_zookeepers()\n", + "assert len(all_zookeepers) == 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Nutzung eines ORMs\n", + "\n", + "Wie wir sehen, ist das manuelle Erstellen von Zugriffsfunktionen auf die Datenbank sehr aufwändig. Daher gibt es sogenannte Object-Relational-Mapper (ORMs), die uns diese Arbeit abnehmen. Ein bekanntes ORM für Python ist `SQLAlchemy`. Im zweiten Teil des Notebooks werden wir sehen, wie mit `SQLAlchemy` Daten eingefügt und gelesen werden können.\n", + "\n", + "Da Sie Anaconda nutzen, sollte `SQLAlchemy` bereits installiert sein. Falls nicht, können Sie es mit `conda` installieren.\n", + "\n", + "Wenn man `SQLAlchemy` verwendet, definiert man zunächst eine Klasse, die die Tabelle repräsentiert. Hier sind diese Klassen bereits für Personen und Tiere definiert. Sie können sich die Klassen ansehen, um zu verstehen, wie Tabellen in `SQLAlchemy` definiert werden." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "## Nutzung von SQLAlchemy\n", + "from sqlalchemy import create_engine, Column, Integer, String, ForeignKey, select\n", + "from sqlalchemy.orm import Session, relationship, declarative_base, join, aliased\n", + "\n", + "Base = declarative_base()\n", + "\n", + "class Zookeeper(Base):\n", + " __tablename__ = 'zookeepers'\n", + " id = Column(Integer, primary_key=True)\n", + " name = Column(String, nullable=False)\n", + " email = Column(String)\n", + " specialty = Column(String)\n", + " animals = relationship(\"Animal\", back_populates=\"zookeeper\")\n", + "\n", + "class Animal(Base):\n", + " __tablename__ = 'animals'\n", + " id = Column(Integer, primary_key=True)\n", + " name = Column(String, nullable=False)\n", + " species = Column(String, nullable=False)\n", + " zookeeper_id = Column(Integer, ForeignKey('zookeepers.id'))\n", + " zookeeper = relationship(\"Zookeeper\", back_populates=\"animals\")\n", + "\n", + "# TODO: Verbingung zur Datenbank herstellen\n", + "engine = create_engine('postgresql://postgres:sml12345@localhost/zoo') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Anlegen und Abfragen von Objekten in SQLAlchemy\n", + "\n", + "In diesem Teil sollen Sie 2 Personen und 5 Tiere anlegen und dann alle Personen und Tiere abfragen. Sie können sich an den Beispielen orientieren, die im Notebook gegeben sind.\n", + "\n", + "Ein kurzes und übersichtliches Tutorial zur Nutzung von `SQLAlchemy` finden Sie hier:\n", + "\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html\n", + "\n", + "Relevant sind vor allem folgende Abschnitte:\n", + "\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#opening-and-closing-a-session\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#adding-new-or-existing-items\n", + "https://docs.sqlalchemy.org/en/20/orm/session_basics.html#querying" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: zwei Zookeeper anlegen\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " john = Zookeeper(name=\"John Doe\", email=\"john@example.com\", specialty=\"Elephants\")\n", + " jane = Zookeeper(name=\"Jane Doe\", email=\"jane@example.com\", specialty=\"Giraffes\")\n", + " \n", + " session.add_all([john, jane])" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['John Doe', 'Jane Doe']\n" + ] + } + ], + "source": [ + "# TODO: Alle Zookeeper ausgeben\n", + "with Session(engine) as session:\n", + " statement = select(Zookeeper.name)\n", + " print(session.scalars(statement).all())" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: 3 Elefanten anlegen, die John betreut\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " john_clause = select(Zookeeper.id).where(Zookeeper.name.like(\"%John%\"))\n", + " john_id = session.scalars(john_clause).first()\n", + "\n", + " session.add_all([Animal(name=animal, species=\"Elephant\", zookeeper_id=john_id) for animal in [\"Babar\", \"Dumbo\", \"Hathi\"]])\n", + " \n", + "# TODO: 2 Giraffen anlegen, die Jane betreut\n", + "with Session(engine) as session:\n", + " with session.begin():\n", + " jane_clause = select(Zookeeper.id).where(Zookeeper.name.like(\"%Jane%\"))\n", + " jane_id = session.scalars(jane_clause).first()\n", + " \n", + " session.add_all([Animal(name=animal, species=\"Giraffe\", zookeeper_id=jane_id) for animal in [\"Melman\", \"Gloria\"]])" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------\n", + "| Babar | Elephant | John Doe |\n", + "| Dumbo | Elephant | John Doe |\n", + "| Hathi | Elephant | John Doe |\n", + "| Melman | Giraffe | Jane Doe |\n", + "| Gloria | Giraffe | Jane Doe |\n", + "--------------------------------\n" + ] + } + ], + "source": [ + "# Liste aller Tiere mit ihren Pflegern ausgeben:\n", + "with Session(engine) as session:\n", + " statement = select(Animal.name, Animal.species, Zookeeper.name).select_from(join(Animal, Zookeeper, Animal.zookeeper))\n", + " animals = session.execute(statement).all()\n", + "\n", + " col_widths = (\n", + " max([len(animal[0]) for animal in animals]),\n", + " max([len(animal[1]) for animal in animals]),\n", + " max([len(animal[2]) for animal in animals]),\n", + " )\n", + "\n", + " print(\"-\" * (sum(col_widths) + 10))\n", + " for animal in animals:\n", + " print(f\"| {animal[0]:{col_widths[0]}} | {animal[1]:{col_widths[1]}} | {animal[2]:{col_widths[2]}} |\")\n", + " print(\"-\" * (sum(col_widths) + 10))\n", + " \n", + "\n", + "# Beispielausgabe:\n", + "# \n", + "# ----------------------------------------\n", + "# | Babar | Elephant | John Doe |\n", + "# | Dumbo | Elephant | John Doe |\n", + "# | Hathi | Elephant | John Doe |\n", + "# | Melman | Giraffe | Jane Doe |\n", + "# | Gloria | Giraffe | Jane Doe |\n", + "# ----------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/create_movies.sql b/create_movies.sql new file mode 100644 index 0000000..3de520a --- /dev/null +++ b/create_movies.sql @@ -0,0 +1,25 @@ +CREATE TABLE genres ( + name text UNIQUE, + position integer +); + +CREATE TABLE movies ( + movie_id SERIAL PRIMARY KEY, + title text, + genre cube +); + +CREATE TABLE actors ( + actor_id SERIAL PRIMARY KEY, + name text +); + +CREATE TABLE movies_actors ( + movie_id integer REFERENCES movies NOT NULL, + actor_id integer REFERENCES actors NOT NULL, + UNIQUE (movie_id, actor_id) +); + +CREATE INDEX movies_actors_movie_id ON movies_actors (movie_id); +CREATE INDEX movies_actors_actor_id ON movies_actors (actor_id); +CREATE INDEX movies_genres_cube ON movies USING gist (genre); diff --git a/movies_data.sql b/movies_data.sql new file mode 100644 index 0000000..4a6200a --- /dev/null +++ b/movies_data.sql @@ -0,0 +1,19040 @@ +INSERT INTO genres (name,position) VALUES +('Action',1), +('Adventure',2), +('Animation',3), +('Comedy',4), +('Crime',5), +('Disaster',6), +('Documentary',7), +('Drama',8), +('Eastern',9), +('Fantasy',10), +('History',11), +('Horror',12), +('Musical',13), +('Romance',14), +('SciFi',15), +('Sport',16), +('Thriller',17), +('Western',18); + +INSERT INTO movies (movie_id,title,genre) VALUES +(1,'Star Wars','(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)'), +(2,'Forrest Gump','(0,0,0,5,0,0,0,7,0,0,0,0,0,0,0,0,0,0)'), +(3,'American Beauty','(0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0)'), +(4,'Citizen Kane','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(5,'The Dark','(0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,5,0)'), +(6,'The Fifth Element','(0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,0,0,0)'), +(7,'Apocalypse Now','(5,0,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(8,'Unforgiven','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,5)'), +(9,'Twelve Monkeys','(0,0,0,0,0,0,0,7,0,0,0,0,0,0,7,0,7,0)'), +(10,'Absolute Power','(0,0,0,0,5,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(11,'Brazil','(0,0,0,10,0,0,0,0,0,0,0,0,0,0,10,0,0,0)'), +(12,'American History X','(0,0,0,0,5,0,0,10,0,0,0,0,0,0,0,0,0,0)'), +(13,'Mars Attacks!','(0,0,0,10,0,0,0,0,0,0,0,0,0,0,12,0,0,0)'), +(14,'Before Sunrise','(0,0,0,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(15,'Blade Runner','(5,0,0,0,0,0,0,0,0,0,0,0,0,0,10,0,0,0)'), +(16,'Raiders of the Lost Ark','(7,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(17,'Indiana Jones and the Temple of Doom','(7,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(18,'Dirty Dancing','(0,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0)'), +(19,'Indiana Jones and the Last Crusade','(10,10,0,10,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(20,'Beverly Hills Cop','(7,0,0,7,7,0,0,7,0,0,0,0,0,0,0,0,0,0)'), +(21,'Anatomy of a Murder','(0,0,0,5,5,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(22,'Armageddon','(5,0,0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0)'), +(23,'Beverly Hills Cop II','(5,0,0,5,5,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(24,'Tron','(5,0,0,5,0,0,0,0,0,0,0,0,0,0,5,0,0,0)'), +(25,'Gladiator','(5,0,0,0,0,0,0,10,0,0,10,0,0,0,0,0,0,0)'), +(26,'Taxi Driver','(5,0,0,0,5,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(27,'Back to the Future','(0,5,0,7,0,0,0,0,0,0,0,0,0,0,7,0,0,0)'), +(28,'Predator','(5,0,0,0,0,0,0,0,0,0,7,7,0,0,7,0,0,0)'), +(29,'Scarface','(5,0,0,0,7,0,0,7,0,0,0,0,0,0,0,0,5,0)'), +(30,'Pretty Woman','(0,0,0,10,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(31,'The Big Lebowski','(0,0,0,10,7,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(32,'The Untouchables','(0,0,0,0,5,0,0,0,0,0,5,0,0,0,0,0,0,0)'), +(33,'Freaks','(0,0,0,0,0,0,0,5,0,0,0,5,0,0,0,0,0,0)'), +(34,'Groundhog Day','(0,0,0,7,0,0,0,5,0,10,0,0,0,0,5,0,0,0)'), 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Kill! 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Back','(0,7,0,0,0,0,0,0,0,7,0,0,0,2,10,0,0,0)'), +(533,'Star Wars: Episode I - The Phantom Menace','(7,5,0,0,0,0,0,5,0,5,0,0,0,0,10,0,0,0)'), +(534,'Salvador','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(535,'Inherit The Wind','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(536,'Don Juan DeMarco','(0,0,0,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(537,'The 13th Warrior','(5,5,0,0,0,0,0,0,0,5,5,0,0,0,0,0,0,0)'), +(538,'Crime of Passion','(0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,5,0)'), +(539,'Sweet November','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(540,'Twin Peaks: Fire Walk With Me','(0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,5,0)'), +(541,'Shakespeare in Love','(0,0,0,5,0,0,0,0,0,0,5,0,0,0,0,0,0,0)'), +(542,'River of No Return','(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5)'), +(543,'Bonjour tristesse','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(544,'Angel Face','(0,0,0,0,5,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(545,'Laura','(0,0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,5,0)'), +(546,'Porgy and 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Tomatoes!','(0,0,0,5,0,0,0,0,0,0,0,5,0,0,0,0,0,0)'), +(603,'Big Trouble','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(604,'Lassie Come Home','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(605,'Earth, Girls are Easy','(0,0,0,5,0,0,0,5,0,0,0,0,5,0,5,0,0,0)'), +(606,'Nightwatch','(0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,5,0)'), +(607,'Christine','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(608,'Malice','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(609,'Chasing Amy','(0,0,0,5,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(610,'Music Box','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(611,'Stardust','(0,7,0,0,0,0,0,0,0,7,0,0,0,0,0,0,0,0)'), +(612,'The General''s Daughter','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(613,'Bicentennial Man','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,5,0,0,0)'), +(614,'Big','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(615,'Jakob the Liar','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(616,'Jacob''s Ladder','(0,0,0,0,0,0,0,0,0,0,5,5,0,0,0,0,5,0)'), +(617,'Clerks','(0,0,0,7,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), 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Game','(0,0,0,0,5,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(1751,'Lord Jim','(0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(1752,'The Belles of St. Trinian''s','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(1753,'Superman IV – The Quest for Peace','(5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(1754,'The Long Kiss Goodnight','(5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0)'), +(1755,'The Defiant Ones','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(1756,'The Mission','(0,0,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(1757,'European Vacation','(0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(1758,'Vegas Vacation','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(1759,'Midway','(5,0,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(1760,'High Society','(0,0,0,5,0,0,0,5,0,0,0,0,5,0,0,0,0,0)'), +(1761,'From Here to Eternity','(0,0,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(1762,'Dead End','(0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,5,0)'), +(1763,'Dead Presidents','(5,0,0,0,5,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(1764,'Welcome to the 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Warshawski','(5,0,0,5,5,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(2542,'The Sons of Katie Elder','(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5)'), +(2543,'Waterloo','(5,0,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(2544,'The Molly Maguires','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(2545,'Washington Square','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(2546,'Water','(0,5,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(2547,'Impromptu','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(2548,'The Magnificent Seven Ride!','(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5)'), +(2549,'For Whom the Bell Tolls','(0,5,0,0,0,0,0,5,0,0,5,0,0,0,0,0,0,0)'), +(2550,'Waterhole #3','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,5)'), +(2551,'Never Cry Wolf','(0,5,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(2552,'Paint Your Wagon','(0,0,0,5,0,0,0,0,0,0,0,0,5,0,0,0,0,5)'), +(2553,'Betrayed','(5,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,5,0)'), +(2554,'The Parallax View','(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5,0)'), +(2555,'In Country','(0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,0,0)'), +(2556,'Who''s That Girl','(0,0,0,5,0,0,0,0,0,0,0,0,5,0,0,0,0,0)'), +(2557,'The Dark Tower','(0,0,0,0,0,0,0,0,0,5,0,0,0,0,5,0,0,0)'), +(2558,'Kissing a Fool','(0,0,0,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0)'), +(2559,'Gunfight at the O.K. 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Michael Baldwin'), +(4,'Aaron Eckhart'), +(5,'Aaron Paul'), +(6,'Aaron Stanford'), +(7,'Abbie Cornish'), +(8,'Abby Dalton'), +(9,'Abhay Deol'), +(10,'Abraham Sofaer'), +(11,'Adam Baldwin'), +(12,'Adam Beach'), +(13,'Adam Hann-Byrd'), +(14,'Adam Lavorgna'), +(15,'Adam Roarke'), +(16,'Adam Sandler'), +(17,'Adam Storke'), +(18,'Adam Trese'), +(19,'Adam West'), +(20,'Addison Richards'), +(21,'Adele Mara'), +(22,'Aden Young'), +(23,'Adewale Akinnuoye-Agbaje'), +(24,'Adolfo Celi'), +(25,'Adolphe Menjou'), +(26,'Adrian Dunbar'), +(27,'Adrian Pasdar'), +(28,'Adrian Zmed'), +(29,'Adrien Brody'), +(30,'Adrienne Barbeau'), +(31,'Adrienne Corri'), +(32,'Adrienne King'), +(33,'Adrienne Shelly'), +(34,'Agatha Hurle'), +(35,'Agathe Natanson'), +(36,'Agga Olsen'), +(37,'Agnes Bruckner'), +(38,'Agnes Moorehead'), +(39,'Aidan Gould'), +(40,'Aidan Quinn'), +(41,'Aitana Sánchez-Gijón'), +(42,'Akbar Kurtha'), +(43,'Ake Nyman'), +(44,'Akiko Wakabayashi'), +(45,'Akim Tamiroff'), +(46,'Akosua Busia'), +(47,'Aksel Hennie'), +(48,'Al Freeman Jr.'), +(49,'Al Jolson'), +(50,'Al Pacino'), +(51,'Alain Delon'), +(52,'Alan Alda'), +(53,'Alan Arkin'), +(54,'Alan Badel'), +(55,'Alan Bates'), +(56,'Alan Baxter'), +(57,'Alan Cox'), +(58,'Alan Cumming'), +(59,'Alan Curtis'), +(60,'Alan David'), +(61,'Alan Fisler'), +(62,'Alan Hale'), +(63,'Alan King'), +(64,'Alan Ladd'), +(65,'Alan Marshall'), +(66,'Alan Mowbray'), +(67,'Alan Napier'), +(68,'Alan Randolph Scott'), +(69,'Alan Rickman'), +(70,'Alan Ruck'), +(71,'Alan Webb'), +(72,'Alan Young'), +(73,'Alastair Sim'), +(74,'Albert Brooks'), +(75,'Albert Dekker'), +(76,'Albert Finney'), +(77,'Albert Hall'), +(78,'Alberta Watson'), +(79,'Alberto De Mendoza'), +(80,'Alberto Morin'), +(81,'Aldo Giuffrè'), +(82,'Aldo Ray'), +(83,'Alec Baldwin'), +(84,'Alec Cawthorne'), +(85,'Alec Clunes'), +(86,'Alec Guinness'), +(87,'Alec McCowen'), +(88,'Alex D. Linz'), +(89,'Alex Daniels'), +(90,'Alex Haw'), +(91,'Alex Hyde-White'), +(92,'Alex McArthur'), +(93,'Alex Scott'), +(94,'Alex Vincent'), +(95,'Alexa Davalos'), +(96,'Alexa Vega'), +(97,'Alexander Fehling'), +(98,'Alexander Godunov'), +(99,'Alexander Goodwin'), +(100,'Alexander Knox'), +(101,'Alexander Morton'), +(102,'Alexandra Holden'), +(103,'Alexia Keogh'), +(104,'Alexis Arquette'), +(105,'Alexis Cruz'), +(106,'Alexis Smith'), +(107,'Alexis Zegerman'), +(108,'Alfred Molina'), +(109,'Alfred Ryder'), +(110,'Ali MacGraw'), +(111,'Alice Cooper'), +(112,'Alice Krige'), +(113,'Alicia Silverstone'), +(114,'Alicia Witt'), +(115,'Alida Valli'), +(116,'Aline MacMahon'), +(117,'Alisan Porter'), +(118,'Alison Doody'), +(119,'Alison Eastwood'), +(120,'Alison Folland'), +(121,'Alison Leggatt'), +(122,'Alison Routledge'), +(123,'Alison Selford'), +(124,'Alison Whelan'), +(125,'Allan Jones'), +(126,'Allan Melvin'), +(127,'Allen Covert'), +(128,'Allen Danziger'), +(129,'Allen Garfield'), +(130,'Allen Jenkins'), +(131,'Allen Payne'), +(132,'Allison Balson'), +(133,'Allison Janney'), +(134,'Ally Sheedy'), +(135,'Ally Walker'), +(136,'Alphonsia Emmanuel'), +(137,'Alun Armstrong'), +(138,'Alyson Hannigan'), +(139,'Alyson Reed'), +(140,'Amanda Barrie'), +(141,'Amanda Bearse'), +(142,'Amanda Bynes'), +(143,'Amanda Plummer'), +(144,'Amanda Wyss'), +(145,'Amber Heard'), +(146,'Amber Smith'), +(147,'Amber Tamblyn'), +(148,'Amrish Puri'), +(149,'Amy Adams'), +(150,'Amy Brenneman'), +(151,'Amy Ingersoll'), +(152,'Amy Irving'), +(153,'Amy Locane'), +(154,'Amy Madigan'), +(155,'Amy Robinson'), +(156,'Amy Steel'), +(157,'Amy Veness'), +(158,'Amy Yasbeck'), +(159,'Anamaria Marinca'), +(160,'Anatoli Davydov'), +(161,'Anders Baasmo Christiansen'), +(162,'Andersen Gabrych'), +(163,'Andie MacDowell'), +(164,'Andre Braugher'), +(165,'Andre Gregory'), +(166,'Andrea Eckert'), +(167,'Andrea Marcovicci'), +(168,'Andrea Occhipinti'), +(169,'Andrea Riseborough'), +(170,'Andrea Roth'), +(171,'Andrew Cruickshank'), +(172,'Andrew Divoff'), +(173,'Andrew Duggan'), +(174,'Andrew Garfield'), +(175,'Andrew Knott'), +(176,'Andrew Lauer'), +(177,'Andrew McCarthy'), +(178,'Andrew Prine'), +(179,'Andrew Robinson'), +(180,'Andrew Sachs'), +(181,'Andrew Stevens'), +(182,'Andrzej Seweryn'), +(183,'André Morell'), +(184,'Andy Dick'), +(185,'Andy Garcia'), +(186,'Andy Griffith'), +(187,'Andy J. Forest'), +(188,'Andy Romano'), +(189,'Aneta Corsaut'), +(190,'Angela Bassett'), +(191,'Angela Douglas'), +(192,'Angela Featherstone'), +(193,'Angela Goethals'), +(194,'Angela Lansbury'), +(195,'Angelina Jolie'), +(196,'Angeline Ball'), +(197,'Angelo Rossito'), +(198,'Angie Brown'), +(199,'Angie Dickinson'), +(200,'Angie Everhart'), +(201,'Anicée Alvina'), +(202,'Anita Briem'), +(203,'Anita Ekberg'), +(204,'Anjelica Huston'), +(205,'Ann Blyth'), +(206,'Ann Carter'), +(207,'Ann Doran'), +(208,'Ann Dvorak'), +(209,'Ann Hearn'), +(210,'Ann Miller'), +(211,'Ann Prentiss'), +(212,'Ann Richards'), +(213,'Ann Savage'), +(214,'Ann Shirley'), +(215,'Ann Todd'), +(216,'Ann Wedgeworth'), +(217,'Ann-Margret'), +(218,'Anna Anissimova'), +(219,'Anna Chlumsky'), +(220,'Anna Friel'), +(221,'Anna Karen'), +(222,'Anna Karina'), +(223,'Anna Massey'), +(224,'Anna May Wong'), +(225,'Anna Palk'), +(226,'Anna Paquin'), +(227,'Anna Proclemer'), +(228,'Anna-Maria Monticelli'), +(229,'Annabella Sciorra'), +(230,'Annabeth Gish'), +(231,'Anne Archer'), +(232,'Anne Bancroft'), +(233,'Anne Baxter'), +(234,'Anne Brochet'), +(235,'Anne Christianson'), +(236,'Anne Francis'), +(237,'Anne Grey'), +(238,'Anne Gwynne'), +(239,'Anne Heche'), +(240,'Anne Meara'), +(241,'Anne Ramsey'), +(242,'Anne Revere'), +(243,'Anne Suzuki'), +(244,'Anne Tenney'), +(245,'Anne-Louise Lambert'), +(246,'Anne-Marie Kennedy'), +(247,'Annette Bening'), +(248,'Annette O''Toole'), +(249,'Annette O`Toole'), +(250,'Annette Woska'), +(251,'Annie Corley'), +(252,'Annie Golden'), +(253,'Annie McEnroe'), +(254,'Annie Packert'), +(255,'Annie Potts'), +(256,'Annie Ross'), +(257,'Anny Duperey'), +(258,'Anny Ondra'), +(259,'Anson Mount'), +(260,'Anthony Barrile'), +(261,'Anthony Edwards'), +(262,'Anthony Fauci'), +(263,'Anthony Franciosa'), +(264,'Anthony Geary'), +(265,'Anthony Heald'), +(266,'Anthony Higgins'), +(267,'Anthony Hopkins'), +(268,'Anthony LaPaglia'), +(269,'Anthony Michael Hall'), +(270,'Anthony Perkins'), +(271,'Anthony Quayle'), +(272,'Anthony Quinn'), +(273,'Anthony Ross'), +(274,'Anthony Simcoe'), +(275,'Anthony Zerbe'), +(276,'Anton Rodgers'), +(277,'Antonio Banderas'), +(278,'Antonio Mendoza'), +(279,'Antonio Moreno'), +(280,'Anya Ormsby'), +(281,'Anzac Wallace'), +(282,'Apollonia Kotero'), +(283,'April Grace'), +(284,'Aran Bell'), +(285,'Ariane'), +(286,'Arielle Kebbel'), +(287,'Arleen Whelan'), +(288,'Arlene Dahl'), +(289,'Arlene Francis'), +(290,'Arlo Guthrie'), +(291,'Armand Assante'), +(292,'Armin Mueller-Stahl'), +(293,'Arnold Lucy'), +(294,'Arnold Schwarzenegger'), +(295,'Arnold Vosloo'), +(296,'Arsenio Hall'), +(297,'Arsinée Khanjian'), +(298,'Art Evans'), +(299,'Art Garfunkel (as Arthur Garfunkel)'), +(300,'Art LaFleur'), +(301,'Art Metrano'), +(302,'Arte Johnson'), +(303,'Arthur Askey'), +(304,'Arthur Chesney'), +(305,'Arthur Hill'), +(306,'Arthur Hunnicutt'), +(307,'Arthur Kennedy'), +(308,'Arthur Lake'), +(309,'Arthur O''Connell'), +(310,'Artie Lange'), +(311,'Arturo de Córdova'), +(312,'Arye Gross'), +(313,'Ash Adams'), +(314,'Ashley Greene'), +(315,'Ashley Judd'), +(316,'Ashley Laurence'), +(317,'Ashley Olsen'), +(318,'Ashley Peldon'), +(319,'Aude Landry'), +(320,'Audie Murphy'), +(321,'Audra Lindley'), +(322,'Audra McDonald'), +(323,'Audrey Dalton'), +(324,'Audrey Hepburn'), +(325,'Audrey Meadows'), +(326,'Audrey Totter'), +(327,'August Diehl'), +(328,'August Schellenberg'), +(329,'Augustus Philips'), +(330,'Aure Atika'), +(331,'Austin Nagler'), +(332,'Austin O''Brien'), +(333,'Austin Pendleton'), +(334,'Ava Gardner'), +(335,'Avery Brooks'), +(336,'Aya Takanashi'), +(337,'B.D. Wong'), +(338,'Barbara Bach'), +(339,'Barbara Baxley'), +(340,'Barbara Bel Geddes'), +(341,'Barbara Bosson'), +(342,'Barbara Carrera'), +(343,'Barbara Crampton'), +(344,'Barbara Eden'), +(345,'Barbara Feldon'), +(346,'Barbara Gordon'), +(347,'Barbara Hale'), +(348,'Barbara Harris'), +(349,'Barbara Hershey'), +(350,'Barbara Lindsay'), +(351,'Barbara O''Neil'), +(352,'Barbara Parkins'), +(353,'Barbara Payton'), +(354,'Barbara Rush'), +(355,'Barbara Shelley'), +(356,'Barbara Stanwyck'), +(357,'Barbara Sukowa'), +(358,'Barbara Tyson'), +(359,'Barbara Windsor'), +(360,'Barbra Streisand'), +(361,'Barnard Hughes'), +(362,'Barney'), +(363,'Barney Clark'), +(364,'Barret Oliver'), +(365,'Barry Bostwick'), +(366,'Barry Brown'), +(367,'Barry Corbin'), +(368,'Barry Dennen'), +(369,'Barry Fitzgerald'), +(370,'Barry Foster'), +(371,'Barry Gibb'), +(372,'Barry Miller'), +(373,'Barry Newman'), +(374,'Barry Pepper'), +(375,'Barry Primus'), +(376,'Bart Burns'), +(377,'Barton Heyman'), +(378,'Basil Rathbone'), +(379,'Basil Sydney'), +(380,'Basil Wallace'), +(381,'Beatrice Kay'), +(382,'Beatrice Pearson'), +(383,'Beatrice Straight'), +(384,'Beau Bridges'), +(385,'Bebe Daniels'), +(386,'Bebe Neuwirth'), +(387,'Bee Duffell'), +(388,'Bel Deliá'), +(389,'Bela Lugosi'), +(390,'Belinda Bauer'), +(391,'Bella Randles'), +(392,'Ben Affleck'), +(393,'Ben Chaplin'), +(394,'Ben Cross'), +(395,'Ben Duncan'), +(396,'Ben Foster'), +(397,'Ben Gazzara'), +(398,'Ben Johnson'), +(399,'Ben Kingsley'), +(400,'Ben Mendelsohn'), +(401,'Ben Stiller'), +(402,'Benicio Del Toro'), +(403,'Benjamin Bratt'), +(404,'Benjamin Green'), +(405,'Benjamin Hendrickson'), +(406,'Bennett Ohta'), +(407,'Benno Fürmann'), +(408,'Berj Fazalian'), +(409,'Bernadette Peters'), +(410,'Bernard Cribbins'), +(411,'Bernard Fresson'), +(412,'Bernard Hill'), +(413,'Bernard Lee'), +(414,'Bernard Miles'), +(415,'Bernie Casey'), +(416,'Bernie Coulson'), +(417,'Berry Berenson'), +(418,'Berry Kroeger'), +(419,'Bert Freed'), +(420,'Bert Lahr'), +(421,'Bert Palmer'), +(422,'Bert Remsen'), +(423,'Bertrand Bonvoisin'), +(424,'Bess Armstrong'), +(425,'Bessie Love'), +(426,'Bethel Leslie'), +(427,'Betsy Baker'), +(428,'Betsy Blair'), +(429,'Betsy Brantley'), +(430,'Betsy Palmer'), +(431,'Bette Davis'), +(432,'Bette Midler'), +(433,'Betty Anne Rees'), +(434,'Betty Buckley'), +(435,'Betty Grable'), +(436,'Betty White'), +(437,'Beulah Bondi'), +(438,'Beverley Hope Atkinson'), +(439,'Beverly Bonner'), +(440,'Beverly D''Angelo'), +(441,'Beverly Garland'), +(442,'Beverly Lunsford'), +(443,'Bibi Andersson'), +(444,'Biff Manard'), +(445,'Biff McGuire'), +(446,'Bill Barretta'), +(447,'Bill Brochtrup'), +(448,'Bill Byrge'), +(449,'Bill Campbell'), +(450,'Bill Cobbs'), +(451,'Bill Coyne'), +(452,'Bill Duck'), +(453,'Bill Duke'), +(454,'Bill Hayes'), +(455,'Bill Hunter'), +(456,'Bill Kerr'), +(457,'Bill Maher'), +(458,'Bill McKinney'), +(459,'Bill Mumy'), +(460,'Bill Murray'), +(461,'Bill Nighy'), +(462,'Bill Nunn'), +(463,'Bill Paxton'), +(464,'Bill Pullman'), +(465,'Bill Pulmann'), +(466,'Bill Thornbury'), +(467,'Billie Dove'), +(468,'Billie Whitelaw'), +(469,'Billy Bob Thornton'), +(470,'Billy Chapin'), +(471,'Billy Connolly'), +(472,'Billy Crudup'), +(473,'Billy Crystal'), +(474,'Billy Dee Williams'), +(475,'Billy Green Bush'), +(476,'Billy Jayne'), +(477,'Billy Wirth'), +(478,'Billy Zane'), +(479,'Bin Li'), +(480,'Bin Wu'), +(481,'Bing Crosby'), +(482,'Blair Brown'), +(483,'Blythe Danner'), +(484,'Bo Derek'), +(485,'Bo Hopkins'), +(486,'Bob Balaban'), +(487,'Bob Gunton'), +(488,'Bob Hastings'), +(489,'Bob Holt'), +(490,'Bob Hope'), +(491,'Bob Hoskins'), +(492,'Bob Newhart'), +(493,'Bob Zmuda'), +(494,'Bobby Darin'), +(495,'Bobby Di Cicco'), +(496,'Bobby Driscoll'), +(497,'Bobby Fite'), +(498,'Bolo Yeung'), +(499,'Bonnie Bartlett'), +(500,'Bonnie Bedelia'), +(501,'Bonnie Hunt'), +(502,'Booker Bradshaw'), +(503,'Boris Karloff'), +(504,'Boyd Gaines'), +(505,'Bożena Dobierzewska'), +(506,'Brad Davis'), +(507,'Brad Dourif'), +(508,'Brad Greenquist'), +(509,'Brad Pitt'), +(510,'Brad Renfro'), +(511,'Bradford Dillman'), +(512,'Bradley Gregg'), +(513,'Bradley Whitford'), +(514,'Brandon De Wilde'), +(515,'Brandon Lee'), +(516,'Breckin Meyer'), +(517,'Brenda Blethyn'), +(518,'Brenda De Banzie'), +(519,'Brenda Fricker'), +(520,'Brenda Marshall'), +(521,'Brenda Vaccaro'), +(522,'Brendan Fletcher'), +(523,'Brendan Fraser'), +(524,'Brendan Gleeson'), +(525,'Brendan Sexton III'), +(526,'Brent Briscoe'), +(527,'Brent Hinkley'), +(528,'Brent Spiner'), +(529,'Brett Halsey'), +(530,'Brian Aherne'), +(531,'Brian Bennett'), +(532,'Brian Blessed'), +(533,'Brian Cox'), +(534,'Brian Dennehy'), +(535,'Brian Donlevy'), +(536,'Brian Geraghty'), +(537,'Brian Haley'), +(538,'Brian Keith'), +(539,'Brian Kerwin'), +(540,'Brian Narelle'), +(541,'Brian O''Halloran'), +(542,'Brian Tyler'), +(543,'Brian Van Holt'), +(544,'Brian Wimmer'), +(545,'Brid Brennan'), +(546,'Bridget Fonda'), +(547,'Bridgette Wilson'), +(548,'Brigitte Auber'), +(549,'Brigitte Fossey'), +(550,'Brigitte Nielsen'), +(551,'Brion James'), +(552,'Britney Spears'), +(553,'Britt Ekland'), +(554,'Brittany Murphy'), +(555,'Brittany Snow'), +(556,'Bronson Pinchot'), +(557,'Brooke Adams'), +(558,'Brooke Elliott'), +(559,'Brooke Shields'), +(560,'Bruce Abbott'), +(561,'Bruce Bennett'), +(562,'Bruce Boxleitner'), +(563,'Bruce Cabot'), +(564,'Bruce Campbell'), +(565,'Bruce Davison'), +(566,'Bruce Dern'), +(567,'Bruce Gray'), +(568,'Bruce Greenwood'), +(569,'Bruce Jones'), +(570,'Bruce McGill'), +(571,'Bruce Spence'), +(572,'Bruce Welch'), +(573,'Bruce Willis'), +(574,'Bruno Kirby'), +(575,'Bruno Lawrence'), +(576,'Bryan Brown'), +(577,'Bryan Cranston'), +(578,'Bryan Forbes'), +(579,'Bryant Haliday'), +(580,'Bubba Smith'), +(581,'Buck Henry'), +(582,'Bud Cort'), +(583,'Bud Tingwell'), +(584,'Buddy Ebsen'), +(585,'Buddy Hackett'), +(586,'Burgess Meredith'), +(587,'Burl Ives'), +(588,'Burt Kwouk'), +(589,'Burt Lancaster'), +(590,'Burt Reynolds'), +(591,'Burt Young'), +(592,'Buster Crabbe'), +(593,'Buster Keaton'), +(594,'Buzz Kilman'), +(595,'Byron Mann'), +(596,'C. 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Kim'), +(1433,'Eve Arden'), +(1434,'Eve Gordon'), +(1435,'Evelyn Del Rio'), +(1436,'Evelyn Keyes'), +(1437,'Everett McGill'), +(1438,'Everett Sloane'), +(1439,'Ewan McGregor'), +(1440,'Ewen Bremner'), +(1441,'F. 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Carrol Naish'), +(1924,'J. Trevor Edmond'), +(1925,'J.D. Cannon'), +(1926,'J.E. Freeman'), +(1927,'J.L. Reate'), +(1928,'J.T. Walsh'), +(1929,'Jacinda Barrett'), +(1930,'Jack Benny'), +(1931,'Jack Black'), +(1932,'Jack Carson'), +(1933,'Jack Cassidy'), +(1934,'Jack Elam'), +(1935,'Jack Hawkins'), +(1936,'Jack Kehoe'), +(1937,'Jack Klugman'), +(1938,'Jack Kruschen'), +(1939,'Jack Lambert'), +(1940,'Jack Lemmon'), +(1941,'Jack Lord'), +(1942,'Jack MacGowran'), +(1943,'Jack McClelland'), +(1944,'Jack Mullaney'), +(1945,'Jack Nance'), +(1946,'Jack Nicholson'), +(1947,'Jack Oakie'), +(1948,'Jack Palance'), +(1949,'Jack Thibeau'), +(1950,'Jack Thompson'), +(1951,'Jack Warden'), +(1952,'Jack Warner'), +(1953,'Jack Webb'), +(1954,'Jack Weston'), +(1955,'Jackie Chan'), +(1956,'Jackie Coogan'), +(1957,'Jackie Cooper'), +(1958,'Jackie Gleason'), +(1959,'Jackie Guerra'), +(1960,'Jackie Lyn Dufton'), +(1961,'Jackie Sawiris'), +(1962,'Jackson Hurst'), +(1963,'Jacqueline Bisset'), +(1964,'Jacqueline McKenzie'), +(1965,'Jacqueline Obradors'), +(1966,'Jacqueline Pearce'), +(1967,'Jacqueline Sassard'), +(1968,'Jacqueline Tong'), +(1969,'Jacquelyn Hyde'), +(1970,'Jacques Roux'), +(1971,'Jacques Weber'), +(1972,'Jada Pinkett Smith'), +(1973,'Jaime King'), +(1974,'Jake Blundell'), +(1975,'Jake Busey'), +(1976,'Jake Gyllenhaal'), +(1977,'Jakob Cedergren'), +(1978,'James Arness'), +(1979,'James Aubrey'), +(1980,'James Badge Dale'), +(1981,'James Belushi'), +(1982,'James Bolam'), +(1983,'James Booth'), +(1984,'James Broderick'), +(1985,'James Brolin'), +(1986,'James Brown'), +(1987,'James Caan'), +(1988,'James Cagney'), +(1989,'James Caitlin'), +(1990,'James Carpenter'), +(1991,'James Coburn'), +(1992,'James Coco'), +(1993,'James Craig'), +(1994,'James Cromwell'), +(1995,'James Dean'), +(1996,'James Donald'), +(1997,'James Doohan'), +(1998,'James Duval'), +(1999,'James Earl Jones'), +(2000,'James Edwards'), +(2001,'James Fillmore'), +(2002,'James Fleet'), +(2003,'James Fox'), +(2004,'James Franciscus'), +(2005,'James Gammon'), +(2006,'James Gandolfini'), +(2007,'James Garner'), +(2008,'James Gleason'), +(2009,'James Hampton'), +(2010,'James Hong'), +(2011,'James Keach'), +(2012,'James Larkin'), +(2013,'James LeGros'), +(2014,'James Madio'), +(2015,'James Mason'), +(2016,'James McAvoy'), +(2017,'James McIntyre'), +(2018,'James Olson'), +(2019,'James Purefoy'), +(2020,'James Rebhorn'), +(2021,'James Remar'), +(2022,'James Robertson Justice'), +(2023,'James Russo'), +(2024,'James Sikking'), +(2025,'James Simmons'), +(2026,'James Spader'), +(2027,'James Stacy'), +(2028,'James Stewart'), +(2029,'James Villiers'), +(2030,'James Whitmore'), +(2031,'James Woods'), +(2032,'Jameson Parker'), +(2033,'Jamey Sheridan'), +(2034,'Jami Gertz'), +(2035,'Jamie Bell'), +(2036,'Jamie Foreman'), +(2037,'Jamie Foxx'), +(2038,'Jamie Gillis'), +(2039,'Jamie Lee Curtis'), +(2040,'Jamie Renée Smith'), +(2041,'Jamie Smith'), +(2042,'Jamyang Jamtsho Wangchuk'), +(2043,'Jan Rubes'), +(2044,'Jan Stuart Schwartz'), +(2045,'Jan-Michael Vincent'), +(2046,'Jana Taylor'), +(2047,'Jane Adams'), +(2048,'Jane Alexander'), +(2049,'Jane Asher'), +(2050,'Jane Birkin'), +(2051,'Jane Curtin'), +(2052,'Jane Darwell'), +(2053,'Jane Fonda'), +(2054,'Jane Galloway Heitz'), +(2055,'Jane Greer'), +(2056,'Jane Horrocks'), +(2057,'Jane Krakowski'), +(2058,'Jane Merrow'), +(2059,'Jane Mortifee'), +(2060,'Jane Randolph'), +(2061,'Jane Rose'), +(2062,'Jane Russell'), +(2063,'Jane Seymour'), +(2064,'Jane Wyman'), +(2065,'Janeane Garofalo'), +(2066,'Janet Bartley'), +(2067,'Janet Jones'), +(2068,'Janet Landgard'), +(2069,'Janet Leigh'), +(2070,'Janet Margolin'), +(2071,'Janet Munro'), +(2072,'Janet Suzman'), +(2073,'Janette Scott'), +(2074,'Janice Logan'), +(2075,'Janina Sachau'), +(2076,'Janine Turner'), +(2077,'Janusz Pawłak'), +(2078,'Jared Harris'), +(2079,'Jared Leto'), +(2080,'Jared Padalecki'), +(2081,'Jarlath Conroy'), +(2082,'Jarmila Novotna'), +(2083,'Jasen Fisher'), +(2084,'Jason Alexander'), +(2085,'Jason Beghe'), +(2086,'Jason Biggs'), +(2087,'Jason Durr'), +(2088,'Jason Gedrick'), +(2089,'Jason Isaacs'), +(2090,'Jason James Richter'), +(2091,'Jason Lee'), +(2092,'Jason Lively'), +(2093,'Jason London'), +(2094,'Jason Mewes'), +(2095,'Jason Miller'), +(2096,'Jason Patric'), +(2097,'Jason Priestley'), +(2098,'Jason Robards'), +(2099,'Jason Schwartzman'), +(2100,'Jason Scott Lee'), +(2101,'Jason Statham'), +(2102,'Jay C. 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Sanders'), +(2105,'Jay Patterson'), +(2106,'Jay Thomas'), +(2107,'Jayne Mansfield'), +(2108,'Jean Adair'), +(2109,'Jean Anderson'), +(2110,'Jean Arthur'), +(2111,'Jean Brooks'), +(2112,'Jean Carson'), +(2113,'Jean Hagen'), +(2114,'Jean Lenauer'), +(2115,'Jean Louisa Kelly'), +(2116,'Jean Marsh'), +(2117,'Jean Peters'), +(2118,'Jean Reno'), +(2119,'Jean Rogers'), +(2120,'Jean Seberg'), +(2121,'Jean Simmons'), +(2122,'Jean Taylor Smith'), +(2123,'Jean Wallace'), +(2124,'Jean Yanne'), +(2125,'Jean-Claude Van Damme'), +(2126,'Jean-François Balmer'), +(2127,'Jean-Hugues Anglade'), +(2128,'Jean-Marc Barr'), +(2129,'Jean-Pierre Cassel'), +(2130,'Jean-Pierre Léaud'), +(2131,'Jeanne Mauborgne'), +(2132,'Jeanne Moreau'), +(2133,'Jeanne Tripplehorn'), +(2134,'Jeannie Elias'), +(2135,'Jeff Anderson'), +(2136,'Jeff Bridges'), +(2137,'Jeff Cadiente'), +(2138,'Jeff Chandler'), +(2139,'Jeff Chase'), +(2140,'Jeff Cohen'), +(2141,'Jeff Conaway'), +(2142,'Jeff Daniels'), +(2143,'Jeff Fahey'), +(2144,'Jeff Goldblum'), +(2145,'Jeff Morrow'), +(2146,'Jeffrey Allen'), +(2147,'Jeffrey Combs'), +(2148,'Jeffrey Falcon'), +(2149,'Jeffrey Force'), +(2150,'Jeffrey Hunter'), +(2151,'Jeffrey Jones'), +(2152,'Jeffrey Tambor'), +(2153,'Jeffrey Wright'), +(2154,'Jena Malone'), +(2155,'Jenna Elfman'), +(2156,'Jennie Linden'), +(2157,'Jennifer Aniston'), +(2158,'Jennifer Baxter'), +(2159,'Jennifer Beals'), +(2160,'Jennifer Billingsley'), +(2161,'Jennifer Clay'), +(2162,'Jennifer Connelly'), +(2163,'Jennifer Coolidge'), +(2164,'Jennifer Dale'), +(2165,'Jennifer Daniel'), +(2166,'Jennifer Delora'), +(2167,'Jennifer Ehle'), +(2168,'Jennifer Esposito'), +(2169,'Jennifer Garner'), +(2170,'Jennifer Grey'), +(2171,'Jennifer Jason Leigh'), +(2172,'Jennifer Jones'), +(2173,'Jennifer Lopez'), +(2174,'Jennifer Love Hewitt'), +(2175,'Jennifer O''Neill'), +(2176,'Jennifer Rubin'), +(2177,'Jennifer Salt'), +(2178,'Jennifer Saunders'), +(2179,'Jennifer Tilly'), +(2180,'Jennifer Warren'), +(2181,'Jenny Agutter'), +(2182,'Jenny Levine'), +(2183,'Jenny Lewis'), +(2184,'Jenny Runacre'), +(2185,'Jenny Wright'), +(2186,'Jensen Ackles'), +(2187,'Jeremy Davies'), +(2188,'Jeremy Irons'), +(2189,'Jeremy Kemp'), +(2190,'Jeremy Lloyd'), +(2191,'Jeremy London'), +(2192,'Jeremy Northam'), +(2193,'Jeremy Piven'), +(2194,'Jeremy Sisto'), +(2195,'Jeremy Slate'), +(2196,'Jeremy Theobald'), +(2197,'Jeroen Krabbé'), +(2198,'Jerome Eden'), +(2199,'Jerry Adler'), +(2200,'Jerry Butler'), +(2201,'Jerry Daugirda'), +(2202,'Jerry Kemp'), +(2203,'Jerry Lacy'), +(2204,'Jerry Levine'), +(2205,'Jerry Lewis'), +(2206,'Jerry Mayer'), +(2207,'Jerry Nelson'), +(2208,'Jerry O''Connell'), +(2209,'Jerry Orbach'), +(2210,'Jerry Reed'), +(2211,'Jerry Stiller'), +(2212,'Jesse Borrego'), +(2213,'Jesse Bradford'), +(2214,'Jesse James'), +(2215,'Jesse Metcalfe'), +(2216,'Jesse Vint'), +(2217,'Jesse White'), +(2218,'Jessica Bowman'), +(2219,'Jessica Campbell'), +(2220,'Jessica Harper'), +(2221,'Jessica Lange'), +(2222,'Jessica Stroup'), +(2223,'Jessica Tandy'), +(2224,'Jessie Matthews'), +(2225,'Jessie Royce Landis'), +(2226,'Jharana Das'), +(2227,'Jill Clayburgh'), +(2228,'Jill Haworth'), +(2229,'Jill Ireland'), +(2230,'Jill St. John'), +(2231,'Jim Armenti'), +(2232,'Jim Backus'), +(2233,'Jim Breuer'), +(2234,'Jim Broadbent'), +(2235,'Jim Brown'), +(2236,'Jim Carrey'), +(2237,'Jim Carter'), +(2238,'Jim Dale'), +(2239,'Jim Farley'), +(2240,'Jim Henson'), +(2241,'Jim Hutton'), +(2242,'Jim McKrell'), +(2243,'Jim R. 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Grant'), +(4105,'Rodney Dangerfield'), +(4106,'Rodney Eastman'), +(4107,'Rodney Mullen'), +(4108,'Rodolfo De Alexandre'), +(4109,'Roger Daltrey'), +(4110,'Roger E. 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Z. Sakall'), +(4205,'Sabrina Scharf'), +(4206,'Sabu'), +(4207,'Saeed Jaffrey'), +(4208,'Saffron Burrows'), +(4209,'Sagamore Stévenin'), +(4210,'Sage Stallone'), +(4211,'Sal Lopez'), +(4212,'Sal Mineo'), +(4213,'Sally Ann Howes'), +(4214,'Sally Field'), +(4215,'Sally Forest'), +(4216,'Sally Forrest'), +(4217,'Sally Fraser'), +(4218,'Sally Hawkins'), +(4219,'Sally Kellerman'), +(4220,'Sally Kirkland'), +(4221,'Salma Hayek'), +(4222,'Sam Elliott'), +(4223,'Sam J. Jones'), +(4224,'Sam Jaffe'), +(4225,'Sam Neill'), +(4226,'Sam Riley'), +(4227,'Sam Robards'), +(4228,'Sam Rockwell'), +(4229,'Sam Shepard'), +(4230,'Sam Wanamaker'), +(4231,'Sam Waterston'), +(4232,'Sam Worthington'), +(4233,'Samantha Eggar'), +(4234,'Samantha Lavigne'), +(4235,'Samantha Mathis'), +(4236,'Sami Frey'), +(4237,'Sammi Kraft'), +(4238,'Sammy Davis Jr.'), +(4239,'Samson Jorah'), +(4240,'Samuel E. Wright'), +(4241,'Samuel L. Jackson'), +(4242,'Samuel Page'), +(4243,'Samuel Roukin'), +(4244,'Samuel S. 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Carter'), +(4531,'T.P. 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Fields'), +(4838,'Wade Dominguez'), +(4839,'Walker Jones'), +(4840,'Wallace Beery'), +(4841,'Wallace Ford'), +(4842,'Wallace Shawn'), +(4843,'Walter Barnes'), +(4844,'Walter Brennan'), +(4845,'Walter Connolly'), +(4846,'Walter Fitzgerald'), +(4847,'Walter Hampden'), +(4848,'Walter Huston'), +(4849,'Walter Matthau'), +(4850,'Walter Pidgeon'), +(4851,'Ward Bond'), +(4852,'Ward Costello'), +(4853,'Warner Baxter'), +(4854,'Warner Oland'), +(4855,'Warren Ball'), +(4856,'Warren Beatty'), +(4857,'Warren Clarke'), +(4858,'Warren Oates'), +(4859,'Warren Stevens'), +(4860,'Warwick Davis'), +(4861,'Weird Al Yankovic'), +(4862,'Wendel Meldrum'), +(4863,'Wendell Corey'), +(4864,'Wendell Pierce'), +(4865,'Wendy Allnutt'), +(4866,'Wendy Crewson'), +(4867,'Wendy Gazelle'), +(4868,'Wendy Hiller'), +(4869,'Wendy Makkena'), +(4870,'Wes Bentley'), +(4871,'Wesley Addy'), +(4872,'Wesley Snipes'), +(4873,'Whitney Houston'), +(4874,'Whoopi Goldberg'), +(4875,'Wil Wheaton'), +(4876,'Wiley Wiggins'), +(4877,'Wilford Brimley'), +(4878,'Wilfred Lucas'), +(4879,'Wilfred Pickles'), +(4880,'Wilfrid Hyde-White'), +(4881,'Will Arnett'), +(4882,'Will Ferrell'), +(4883,'Will Geer'), +(4884,'Will Hutchins'), +(4885,'Will Patton'), +(4886,'Will Sampson'), +(4887,'Will Smith'), +(4888,'Willem Dafoe'), +(4889,'William Atherton'), +(4890,'William Baldwin'), +(4891,'William Bendix'), +(4892,'William Carroll'), +(4893,'William Conrad'), +(4894,'William Daniels'), +(4895,'William Demarest'), +(4896,'William Devane'), +(4897,'William Dulaney'), +(4898,'William E. 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Macy'), +(4904,'William Harrigan'), +(4905,'William Hartnell'), +(4906,'William Hickey'), +(4907,'William Holden'), +(4908,'William Hootkins'), +(4909,'William Hopper'), +(4910,'William Hurt'), +(4911,'William Katt'), +(4912,'William Kerwin'), +(4913,'William Lundigan'), +(4914,'William McNamara'), +(4915,'William Mervyn'), +(4916,'William Morgan Sheppard'), +(4917,'William O''Leary'), +(4918,'William Petersen'), +(4919,'William Powell'), +(4920,'William Prince'), +(4921,'William Ragsdale'), +(4922,'William Redfield'), +(4923,'William Reynolds'), +(4924,'William Richert'), +(4925,'William Roerick'), +(4926,'William Russ'), +(4927,'William Sadler'), +(4928,'William Sanderson'), +(4929,'William Shatner'), +(4930,'William Smith'), +(4931,'William Snape'), +(4932,'William Swan'), +(4933,'William Takaku'), +(4934,'William Vail'), +(4935,'William Windom'), +(4936,'Willie Nelson'), +(4937,'Willow Smith'), +(4938,'Wilson Cruz'), +(4939,'Wilt Chamberlain'), +(4940,'Wings Hauser'), 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Scott'), +(4981,'Zack Norman'), +(4982,'Zakes Mokae'), +(4983,'Zeppo Marx'), +(4984,'Zoe Saldana'), +(4985,'Zohra Lampert'), +(4986,'Zooey Deschanel'); + +INSERT INTO movies_actors (movie_id,actor_id) VALUES +(1,3165), +(1,644), +(1,1753), +(1,3768), +(2,4666), +(2,4094), +(2,1560), +(2,3489), +(3,2739), +(3,247), +(3,4606), +(3,4870), +(4,2493), +(4,3623), +(4,38), +(4,4194), +(5,4292), +(5,3120), +(5,3955), +(5,3271), +(6,573), +(6,3449), +(6,1559), +(6,1880), +(7,3205), +(7,3185), +(7,4035), +(7,2835), +(8,854), +(8,3477), +(8,1572), +(8,3961), +(9,509), +(9,573), +(9,3061), +(9,802), +(10,854), +(10,1572), +(10,1258), +(10,4285), +(11,2474), +(11,4029), +(11,2635), +(11,1880), +(12,1300), +(12,1294), +(12,440), +(12,335), +(13,1946), +(13,1658), +(13,247), +(13,3823), +(14,1417), +(14,2567), +(14,166), +(14,1735), +(15,1753), +(15,4186), +(15,4301), +(15,1298), +(16,1753), +(16,2599), +(16,3698), +(16,4133), +(17,1753), +(17,2621), +(17,2472), +(17,148), +(18,2170), +(18,3677), 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+(2853,1361), +(2853,2243), +(2853,1603), +(2854,4742), +(2854,334), +(2854,3292), +(2854,1403), +(2855,1407), +(2855,2224), +(2855,1280), +(2855,1452), +(2856,691), +(2856,2856), +(2856,1199), +(2856,4739), +(2857,1693), +(2857,413), +(2858,2049), +(2858,2408), +(2858,2612), +(2858,4291), +(2859,1414), +(2859,4765), +(2859,4705), +(2859,2330), +(2860,1674), +(2860,4261), +(2860,271), +(2860,4296), +(2861,1061), +(2861,51), +(2861,4156), +(2861,4637), +(2862,4232), +(2862,4984), +(2862,4363), +(2862,4441); diff --git a/postgreSQL_Vertiefung_CDS104_FS26.sql b/postgreSQL_Vertiefung_CDS104_FS26.sql new file mode 100644 index 0000000..65e473b --- /dev/null +++ b/postgreSQL_Vertiefung_CDS104_FS26.sql @@ -0,0 +1,180 @@ +-- postgreSQL Volltext und Mehrdimensionales +-- Volltext --------Intro ergänzen --------- +-- Als eine Übung zum Spielen mit Multidimensionalität sehen wir uns die cube extension (https://www.postgresql.org/docs/current/cube.html) in postgreSQL an. +-- Als Grundlage dient uns das Buch "7 Wochen, 7 Datenbanken" auf den Seiten 39 bis 41 (Vorbereitung), 41-51 (Volltext) und 51-53 (Mehrdimensionales). + +------------------------------------------------- +-- Zunächst in PostgeSQL eine neue Datenbank mit dem Namen "7Wochen" anlegen. +-- dort zunächst die notwendigen Extensions einbinden. +-- Sollten bestimmte extensions in der Installation nicht vorhanden sein müssen diese zunächst in folgendes Verzeichnis kopiert werden: ...\PostgreSQL\17\share\extension +-- Der Befehle zum Hinterlegen der extensions lauten (Query Tool benutzen): +CREATE EXTENSION +IF NOT EXISTS +tablefunc; +CREATE EXTENSION +IF NOT EXISTS +dict_xsyn; +CREATE EXTENSION +IF NOT EXISTS +fuzzystrmatch; +CREATE EXTENSION +IF NOT EXISTS +pg_trgm; +ALTER EXTENSION pg_trgm SET SCHEMA pg_catalog; +CREATE EXTENSION +IF NOT EXISTS +cube; + +------------------------------------------------- +-- Anlegen der nötigen Tabellen: +-- Hierzu verwenden wir die Datei create_movies.sql, die in Moodle zu finden ist. + +------------------------------------------------- +-- Als nächstes müssen die Tabellen befüllt werden. +-- Hierzu verwenden wir die Datei movies_data.sql, die in Moodle zu finden ist. +-- Nun ist alles bereit und wir können die Aufgaben durchgehen. + +------------------------------------------------- +-- Volltext +-- Genauere Beschreibungen im Buch Seiten 41-51 + +-- UNSCHARFE SUCHE + +-- LIKE und ILIKE +SELECT title FROM movies WHERE title ILIKE 'stardust%'; + +SELECT title FROM movies WHERE title ILIKE 'stardust_%'; + +-- Reguläre Ausdrücke +-- ~ Regulärer Ausdruck, hier 'the', ^ steht für am Anfang und .* entspricht dem % aus LIKE, also eine beliebige Kette von Zeichen. +-- ! steht für "nicht" und * für "schreibungsunabhängig, also nicht case sensitive. +SELECT COUNT(*) FROM movies WHERE title !~* '^the.*'; +-- oder +SELECT title FROM movies WHERE title !~* '^the.*'; + +-- Levenshtein (extension: fuzzystrmatch) +SELECT levenshtein('bat', 'fads'); + +SELECT levenshtein('bat', 'fad') fad, +levenshtein('bat', 'fat') fat, +levenshtein('bat', 'bat') bat; + +SELECT movie_id, title +FROM movies +WHERE levenshtein(lower(title), lower('a hard day nght')) <= 3; + +-- Trigramm (extension: pg_trgm) + +SELECT show_trgm('Avatar'); -- a hard day nght + +CREATE INDEX movies_title_trigram ON movies +USING gist (title gist_trgm_ops); + +SELECT title +FROM movies +WHERE title % 'Avatre'; + +-- VOLLTEXTSUCHE + +-- TSVector, TSQuery +SELECT title +FROM movies +WHERE title @@ 'night & day'; +-- Beispiel für Aufteilung in Vectoren und Queries: +SELECT to_tsvector('A Hard Day''s Night'), to_tsquery('english', 'night & day'); + +-- Beispiel für Anpassungen der Wörterbücher +SELECT to_tsvector('english','A Hard Day''s Night'); +SELECT to_tsvector('simple','A Hard Day''s Night'); + +-- METAPHONE +-- Erster Versuch +SELECT * +FROM actors +WHERE name = 'Broos Wils'; + +-- Mit Hilfe von Triagramm +SELECT * +FROM actors +WHERE name % 'Broos Wils'; + +SELECT * +FROM actors +WHERE metaphone(name, 6) = metaphone('jaunie tep', 6); + +select metaphone('Luc Julian Peyer', 6); + +-- Mit Metaphone, 6 ist hier die Länge des Ausgabestrings in Lautsprache +SELECT title +FROM movies NATURAL JOIN movies_actors NATURAL JOIN actors +WHERE metaphone(name, 6) = metaphone('Broos Wils', 6); + +-- Beispiel für verschiedene Umwandlungen +SELECT name, dmetaphone(name), dmetaphone_alt(name), metaphone (name, 8), soundex(name) +FROM actors; + +-- STRING-MATCHES KOMBINIEREN +-- Beispiel: +SELECT * +FROM actors +WHERE metaphone(name, 8) % metaphone('Robin Williams', 8) +ORDER BY levenshtein(lower('Robin Williams'), lower(name)); + + +------------------------------------------------- +-- Mehrdimensionales +-- Genauere Beschreibungen im Buch Seiten 51-53 + +-- cube_ur_coord ist ein Befehl aus der cube-extension (https://www.postgresql.org/docs/current/cube.html) + +-- Jeder movie hat also einen Wert bei insgesamt 18 genres + +SELECT name, cube_ur_coord('(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)', position) as score +FROM genres g +WHERE cube_ur_coord('(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)', position) >0; + +-- Hier werden Distanzen berechnet und danach sortiert +SELECT *, cube_distance(genre, '(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)') dist +FROM movies +ORDER BY dist; + +-- Beispiel cube_enlarge: +-- Ausgangspunkt ist (1,1), es wird um 1 erweitert und das für 2 Dimensionen +SELECT cube_enlarge('(1,1)', 1, 2); + +-- Nun wird das ganze um die 18 genres aufgebaut mit einer Erweiterung um 5 in 18 dimensionen +SELECT title, cube_distance(genre, '(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)') dist +FROM movies +WHERE cube_enlarge('(0,7,0,0,0,0,0,0,0,7,0,0,0,0,10,0,0,0)'::cube, 6,18) @> genre +ORDER BY dist; + +-- Hier wird nun noch eine Unterabfrage integriert, die es erlaubt über den Filmnamen zu suchen +SELECT m.movie_id, m.title +FROM movies m, +(SELECT genre, title +FROM movies +WHERE title = 'Mad Max') s +WHERE cube_enlarge(s.genre, 5, 18) @> m.genre AND s.title <> m.title +ORDER BY cube_distance(m.genre, s.genre) +LIMIT 10; + +-- The end + + +select * from movies where title % 'Rocky' + +select * from actors where name = 'Leonard Nimoy' + +select actor_id, count(*) from movies_actors where movie_id in ( + '426', + '427', + '430', + '431', + '432' +) group by actor_id; + + + +SELECT actor_id, count(*) +FROM movies_actors +where movie_id in (select movie_id from movies_actors where actor_id = 2887) and actor_id <> 2887 group by actor_id having count(*) > 3 order by count(*) desc; diff --git a/Übungen aus den Folien b/Übungen aus den Folien new file mode 100644 index 0000000..6168532 --- /dev/null +++ b/Übungen aus den Folien @@ -0,0 +1,45 @@ +-- Übungen zu Outer joins + +/* +Geben Sie eine Query für folgende Tabelle an: +erste Spalte: Name eines Kunden +zweite Spalte: Anzahl Bestellungen (bei Kunden, die keine +Bestellungen haben, soll hier 0 stehen) +*/ +select kunde.name, count(bestellung.id) +from kunde +left join bestellung on bestellung.kunde_id = kunde.id +group by kunde.id; + +/* +Geben Sie eine Query für folgende Tabelle an: +erste Spalte: Name des Kunden +zweite Spalte: Gesamtbetrag, den der Kunde bezahlen muss +*/ +select k.name, sum(p.preis * bp.anzahl) +from kunde k +left join bestellung b on b.kunde_id = k.id +left join bestellung_produkt bp on bp.bestellung_id = b.id +left join produkt p on p.id = bp.produkt_id +group by k.id; + +/* +Geben Sie eine Query für folgende Tabelle an: +erste Spalte: Produktname +zweite Spalte: Anzahl – wieviele Produkte müssen geliefert werden? +dritte Spalte: wie viele Produkte sind auf Lager? +*/ +select p.name, sum(bp.anzahl) bestellt, p.anzahl auf_lager +from produkt p +join bestellung_produkt bp on bp.produkt_id = p.id +group by p.id; + +/* +Wie müsste man die Query verändern, damit man nur die Produkte +sieht, die ausverkauft sind? +*/ +select p.name, sum(bp.anzahl) bestellt, p.anzahl auf_lager +from produkt p +join bestellung_produkt bp on bp.produkt_id = p.id +group by p.id +having sum(bp.anzahl) >= p.anzahl; \ No newline at end of file

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\ No newline at end of file diff --git a/PostgreSQL-Python.ipynb b/PostgreSQL-Python.ipynb new file mode 100644 index 0000000..3444101 --- /dev/null +++ b/PostgreSQL-Python.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b53d472a-14d4-42bb-a0ee-38eb8c9c24a8", + "metadata": {}, + "source": [ + "Zunächst muss ein Paket zur Anbindung installiert werden. Wir verwenden psycopg 2 (psycopg.org).\n", + "Dies kann über den Reiter \"Environment\" im Anaconda Navicator installiert werden.\n", + "Ist dies geschehen können wir das Paket importieren." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0d3fea87-d2a6-4da8-9327-4c08bb70ef9e", + "metadata": {}, + "outputs": [], + "source": [ + "import psycopg2, psycopg2.extras" + ] + }, + { + "cell_type": "markdown", + "id": "bedf4588-60dc-4169-9295-21f6b3317e9f", + "metadata": {}, + "source": [ + "Nun sind wir bereit, um eine Verbindung zur Datenbank aufzubauen. Nehmen wir zum Beispiel die \"bank\" Datenbank. Da wir die Installation local haben, muss keine URL angegeben werden. Dafür legen wir eine Variable mit dem connect Befehl an." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "62b5a0b5-bafd-4125-8b62-4a2aa493a061", + "metadata": {}, + "outputs": [], + "source": [ + "conn = psycopg2.connect(\"dbname=bank host=/var/run/postgresql user=postgres password=sml12345\")" + ] + }, + { + "cell_type": "markdown", + "id": "8861806c-5786-42fb-81d3-1ba5283b1d2b", + "metadata": {}, + "source": [ + "Als nächstes führen wir eine einfache SELECT Abfrage aus.\n", + "Zunächst werden wir verbunden, dann stellen wir die Abfrage, dann rufen wir das Ergebnis der Abfrage ab und lassen es uns anzeigen.\n", + "Dazu benötigen wir ein cursor Objekt, das uns die Abfrage aber auch die Rücklieferung der Daten liefert. Dieses kommt in die Variable cursor.\n", + "Die Variable result speichert uns die Ergebnisse. fetchall wartet bis alle Ergebniszeilen geliefert sind und ermöglicht dann die Anzeige." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "09dbc41c-0ed7-449a-853d-3a18fbb1d97e", + "metadata": {}, + "outputs": [], + "source": [ + "cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n", + "cursor.execute(\"SELECT * FROM account;\")\n", + "result = cursor.fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "81c22552-4d3c-4824-82c7-5b8a0c74402c", + "metadata": {}, + "source": [ + "Pro Klammer bekommen wir nun ein Tupel angezeigt. Das ist noch nicht so schön. Mit einer kleinen Schleife können wir uns die Tupel zeilenweise anzeigen lassen." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "dba24620-fe85-4feb-8109-59a3d7c5e3b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 'CHK', 1, datetime.date(2000, 1, 15), None, datetime.date(2005, 1, 4), 'ACTIVE', 2, 10, 1057.75, 1057.75]\n", + "[2, 'SAV', 1, datetime.date(2000, 1, 15), None, datetime.date(2004, 12, 19), 'ACTIVE', 2, 10, 500.0, 500.0]\n", + "[3, 'CD', 1, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 2, 10, 3000.0, 3000.0]\n", + "[4, 'CHK', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 27), 'ACTIVE', 2, 10, 2258.02, 2258.02]\n", + "[5, 'SAV', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 11), 'ACTIVE', 2, 10, 200.0, 200.0]\n", + "[7, 'CHK', 3, datetime.date(2002, 11, 23), None, datetime.date(2004, 11, 30), 'ACTIVE', 3, 13, 1057.75, 1057.75]\n", + "[8, 'MM', 3, datetime.date(2002, 12, 15), None, datetime.date(2004, 12, 5), 'ACTIVE', 3, 13, 2212.5, 2212.5]\n", + "[10, 'CHK', 4, datetime.date(2003, 9, 12), None, datetime.date(2005, 1, 3), 'ACTIVE', 1, 1, 534.12, 534.12]\n", + "[11, 'SAV', 4, datetime.date(2000, 1, 15), None, datetime.date(2004, 10, 24), 'ACTIVE', 1, 1, 767.77, 767.77]\n", + "[12, 'MM', 4, datetime.date(2004, 9, 30), None, datetime.date(2004, 11, 11), 'ACTIVE', 1, 1, 5487.09, 5487.09]\n", + "[13, 'CHK', 5, datetime.date(2004, 1, 27), None, datetime.date(2005, 1, 5), 'ACTIVE', 4, 16, 2237.97, 2897.97]\n", + "[14, 'CHK', 6, datetime.date(2002, 8, 24), None, datetime.date(2004, 11, 29), 'ACTIVE', 1, 1, 122.37, 122.37]\n", + "[15, 'CD', 6, datetime.date(2004, 12, 28), None, datetime.date(2004, 12, 28), 'ACTIVE', 1, 1, 10000.0, 10000.0]\n", + "[17, 'CD', 7, datetime.date(2004, 1, 12), None, datetime.date(2004, 1, 12), 'ACTIVE', 2, 10, 5000.0, 5000.0]\n", + "[18, 'CHK', 8, datetime.date(2001, 5, 23), None, datetime.date(2005, 1, 3), 'ACTIVE', 4, 16, 3487.19, 3487.19]\n", + "[19, 'SAV', 8, datetime.date(2001, 5, 23), None, datetime.date(2004, 10, 12), 'ACTIVE', 4, 16, 387.99, 387.99]\n", + "[21, 'CHK', 9, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 1, 1, 125.67, 125.67]\n", + "[22, 'MM', 9, datetime.date(2004, 10, 28), None, datetime.date(2004, 10, 28), 'ACTIVE', 1, 1, 9345.55, 9845.55]\n", + "[23, 'CD', 9, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 1, 1, 1500.0, 1500.0]\n", + "[24, 'CHK', 10, datetime.date(2002, 9, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 23575.12, 23575.12]\n", + "[25, 'BUS', 10, datetime.date(2002, 10, 1), None, datetime.date(2004, 8, 28), 'ACTIVE', 4, 16, 0.0, 0.0]\n", + "[27, 'BUS', 11, datetime.date(2004, 3, 22), None, datetime.date(2004, 11, 14), 'ACTIVE', 2, 10, 9345.55, 9345.55]\n", + "[28, 'CHK', 12, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 38552.05, 38552.05]\n", + "[29, 'SBL', 13, datetime.date(2004, 2, 22), None, datetime.date(2004, 12, 17), 'ACTIVE', 3, 13, 50000.0, 50000.0]\n" + ] + } + ], + "source": [ + "for entry in result:\n", + " print(entry)" + ] + }, + { + "cell_type": "markdown", + "id": "b975ba44-de7e-4337-a2f9-a4fa06fc59b1", + "metadata": {}, + "source": [ + "Wollen wir nur eine bestimmte Spalte geht dies über die Angabe der Spaltennummer." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6159012d-ea9c-44da-bec0-5d073d210bd3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1057.75\n", + "500.0\n", + "3000.0\n", + "2258.02\n", + "200.0\n", + "1057.75\n", + "2212.5\n", + "534.12\n", + "767.77\n", + "5487.09\n", + "2237.97\n", + "122.37\n", + "10000.0\n", + "5000.0\n", + "3487.19\n", + "387.99\n", + "125.67\n", + "9345.55\n", + "1500.0\n", + "23575.12\n", + "0.0\n", + "9345.55\n", + "38552.05\n", + "50000.0\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b3180855-e952-4d4a-86b5-a27c9d326f69", + "metadata": {}, + "source": [ + "Spaltennummern zählen ist nun etwas aufwendig und unschön. Mit einer Erweituerung des Pakets können wir den cursor anpassen und dann auch Spaltennamen angeben. Wir wählen psycopg2.extras und dann einen DictCursor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75b7a5c8-4677-4546-a461-e7b36b97ff5e", + "metadata": {}, + "outputs": [], + "source": [ + "for entry in result:\n", + " print(entry['avail_balance'])" + ] + }, + { + "cell_type": "markdown", + "id": "3b90228d-bbc6-40ca-bf18-abb698318603", + "metadata": {}, + "source": [ + "Problembehandlung Transaktion: Da wir Abfragen durchführen sollten wir diese auch korrekt starten und abschliessen. Vor allem wenn es zu einem Fehler (z.B. einem Tippfehler) kommt. Anosnten laufen wir auf eine Fehlermeldung und eine offen Transaktion.\n", + "Die Lösung ist unseren SQL Befehl in einen try, except bzw finally Block zu setzen.\n", + "MIt conn.commit() wird die Durchführung der Transaktion bestätigt." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "2e24b6bb-4773-4652-b027-7ee4faf6db92", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1057.75\n", + "3 3000.0\n", + "4 2258.02\n", + "7 1057.75\n", + "8 2212.5\n", + "12 5487.09\n", + "13 2237.97\n", + "15 10000.0\n", + "17 5000.0\n", + "18 3487.19\n", + "22 9345.55\n", + "23 1500.0\n", + "24 23575.12\n", + "27 9345.55\n", + "28 38552.05\n", + "29 50000.0\n" + ] + } + ], + "source": [ + "try:\n", + " cursor.execute(\n", + " 'select * from account where avail_balance > 1000;'\n", + " )\n", + " result = cursor.fetchall()\n", + " result\n", + "\n", + " for row in result:\n", + " print(row[0], row['avail_balance'])\n", + " \n", + " conn.commit()\n", + "except:\n", + " conn.rollback()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e2f6948-9a18-421c-b64c-85ed9743b28f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Python Wiederholung mit Code.ipynb b/Python Wiederholung mit Code.ipynb new file mode 100644 index 0000000..af82969 --- /dev/null +++ b/Python Wiederholung mit Code.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - Kurzwiederholung\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elementare Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# eine Variable mit einem int-Wert\n", + "i = 10\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Python kann sher grosse Zahlen verarbeiten:\n", + "j = 123456395823842193412376429384\n", + "j+1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# int-Zahlen sind richtige Objekte - sie brauchen mehr Speicher, \n", + "# haben aber auch mehr Fähigkeiten als in anderen Sprachen\n", + "import sys\n", + "\n", + "print(sys.getsizeof(10)) # Specherverbrauch einer einzelnen Zahl\n", + "print(i.bit_length()) # Methoden, hier Bitlänge der Zahl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ein Float \n", + "f = 5.0\n", + "\n", + "# auch Floats haben Methoden\n", + "print(f.is_integer())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Floats haben so ihre Probleme...\n", + "0.1 + 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Strings, Konkatenation\n", + "s = \"Hello\" + \" \" + \"World\"\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Boolsche Werte\n", + "a = True\n", + "b = False\n", + "\n", + "print(a and b)\n", + "print(a or b)\n", + "type(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Umwandlung von Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# zahl in string umwandeln\n", + "i = 5\n", + "str(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# string in zahl umwandeln\n", + "s = \"55\"\n", + "float(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kontrollstrukturen: if, while, for, range-Funktion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 10\n", + "\n", + "if i > 5:\n", + " print(\"i ist grösser 5\")\n", + " \n", + "while i > 0:\n", + " print(i)\n", + " i = i - 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(5):\n", + " print(i)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rückwärts zählen\n", + "for i in range(10, 4, -1):\n", + " print(i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beispiele für Funktionen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def add(i, j):\n", + " return i+j" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(add(2, 3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Listen, Indizierung, Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Platznummern\n", + "# 0 1 2 3 4 5 6\n", + "l = [1, 2, 3, 4, 5, 6, 7]\n", + "\n", + "# slicing\n", + "l[1::2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# enumerate: Aufzählen einer Liste (Tupelbildung mit Index)\n", + "list(enumerate([5,9,42]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "l = [\"test\", 1, 5.5, True] # Listen können gemischte Werte enthalten" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tupel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "t = (4, 5) # Tupel einpacken\n", + "a, b = t # Tupel auspacken\n", + "\n", + "print(a)\n", + "print(b)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "builtin-Funktionen, Standardbibliothek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# builtin-Funktion\n", + "# range, list, int, float, len, sum, bool, tuple " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ein Import - hier math\n", + "import math" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "math.exp(2.0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = { 'eins': 'one', 'zwei': 'two' }\n", + "\n", + "d.keys(), d.values()\n", + "\n", + "for key, value in d.items():\n", + " print(key, '->', value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List-Comprehensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quadratzahlen mit gerader Basis\n", + "[ x*x for x in range(0,21) if x % 2 == 0 ]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "a = [3,4,5,6,7]\n", + "mean(a), variance(a)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "b = [0, 2, 4, 6, 8, 10]\n", + "mean(b), variance(b)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "a6b707a736c5fbba452b904aff207ddd250a7524df1f8c74db5bc52ff4a2560b" + }, + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Python Wiederholung.ipynb b/Python Wiederholung.ipynb new file mode 100644 index 0000000..c628249 --- /dev/null +++ b/Python Wiederholung.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Python - Kurzwiederholung\n", + "\n", + "Hier werden wir einige Beispiele betrachten, um noch einmal einige Python-Konzepte zu wiederholen. Sie können mitschreiben, oder am Ende mein fertiges Notebook herunterladen." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Elementare Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Umwandlung von Datentypen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Kontrollstrukturen: if, while, for, range-Funktion" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Beispiele für Funktionen" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Listen, Indizierung, Slicing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tupel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "builtin-Funktionen, Standardbibliothek" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dictionaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List-Comprehensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/SQL-Injectionn mit Code.ipynb b/SQL-Injectionn mit Code.ipynb new file mode 100644 index 0000000..a3b6a17 --- /dev/null +++ b/SQL-Injectionn mit Code.ipynb @@ -0,0 +1,287 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SQL - Injection\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vorbereitung" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Falls noch nicht geschehen können die Pakte importiert werden\n", + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "OperationalError", + "evalue": "connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory\n\tIs the server running locally and accepting connections on that socket?\n", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mOperationalError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# als nächstes bauen wir unsere Verbindung auf, legen eine Tabelle an und füllen diese mit zwei Usern (bitte eigene Datenbank auswählen):\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m conn = \u001b[43mpsycopg2\u001b[49m\u001b[43m.\u001b[49m\u001b[43mconnect\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mdbname=7Wochen user=postgres password=postgres\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n\u001b[32m 6\u001b[39m cursor.execute(\u001b[33m\"\"\"\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[33m DROP TABLE IF EXISTS users;\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[33m CREATE TABLE IF NOT EXISTS users (\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 12\u001b[39m \u001b[33m )\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[33m\"\"\"\u001b[39m)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/lib/python3.13/site-packages/psycopg2/__init__.py:122\u001b[39m, in \u001b[36mconnect\u001b[39m\u001b[34m(dsn, connection_factory, cursor_factory, **kwargs)\u001b[39m\n\u001b[32m 119\u001b[39m kwasync[\u001b[33m'\u001b[39m\u001b[33masync_\u001b[39m\u001b[33m'\u001b[39m] = kwargs.pop(\u001b[33m'\u001b[39m\u001b[33masync_\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m 121\u001b[39m dsn = _ext.make_dsn(dsn, **kwargs)\n\u001b[32m--> \u001b[39m\u001b[32m122\u001b[39m conn = \u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[43m=\u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwasync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 123\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cursor_factory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 124\u001b[39m conn.cursor_factory = cursor_factory\n", + "\u001b[31mOperationalError\u001b[39m: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory\n\tIs the server running locally and accepting connections on that socket?\n" + ] + } + ], + "source": [ + "# als nächstes bauen wir unsere Verbindung auf, legen eine Tabelle an und füllen diese mit zwei Usern (bitte eigene Datenbank auswählen):\n", + "conn = psycopg2.connect (\"dbname=7Wochen user=postgres password=postgres\")\n", + "\n", + "cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)\n", + "\n", + "cursor.execute(\"\"\"\n", + " DROP TABLE IF EXISTS users;\n", + " CREATE TABLE IF NOT EXISTS users (\n", + " id SERIAL PRIMARY KEY,\n", + " username VARCHAR(255) NOT NULL,\n", + " password VARCHAR(255) NOT NULL\n", + " )\n", + "\"\"\")\n", + "\n", + "cursor.execute(\"INSERT INTO users (username, password) VALUES (%s, %s)\", (\"user1\", \"password1\"))\n", + "cursor.execute(\"INSERT INTO users (username, password) VALUES (%s, %s)\", (\"user2\", \"password2\"))\n", + "conn.commit();" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[RealDictRow([('id', 1), ('username', 'user1'), ('password', 'password1')]),\n", + " RealDictRow([('id', 2), ('username', 'user2'), ('password', 'password2')])]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Kurz zum überprüfen, ob die Tabelle angelegt wurde\n", + "\n", + "cursor.execute(\"SELECT * FROM users;\")\n", + "result = cursor.fetchall()\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffsszenario 1 - direkt Übermittlung von Zugangsdaten" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login erfolgreich\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass sich ein User, zum Beispiel über ein Formularfeld anmelden möchte.\n", + "# Und zwar mit den folgenden Zugangsdaten:\n", + "username = \"user1\"\n", + "password = \"password1\"\n", + "\n", + "# Das würde dann so ablaufen:\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + "# Nun holen wir uns eine Ergebniszeile. Gibt es die waren wir mit unserer Anmeldung erfolgreich. Kommt keine Zeile waren unsere Zugangsdaten falsch.\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()\n", + "\n", + "# Wir übergeben also die Zeichenkette aus der Python-Variable direkt an unsere SQL-Datenbank.\n", + "# Ändern wir das Passwort sind wir nicht erfolgreich.\n", + "# Um es schöner zu machen fügen wir noch ein Rollback ein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffszenario 2 - Übergabe von SQL-Befehlen, um Fehler zu hervorzurufen. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "FEHLER: Zeichenkette in Anführungszeichen nicht abgeschlossen bei »'''«\n", + "LINE 1: SELECT * FROM users WHERE username='user1' AND password='''\n", + " ^\n", + "\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass ein User SQL-Befehle eingibt. Zunächst nur ein einfaches Anführungszeichen als Passwort.\n", + "# Das Ergebnis ist ein erzeugter SQL-Fehler.\n", + "username = \"user1\"\n", + "password = \"'\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Angriffszenario 3 - Übergabe von SQL-Befehlen, um einen erfolgreichen Login zu generieren." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login erfolgreich\n" + ] + } + ], + "source": [ + "# gehen wir nun davon aus, dass ein User SQL-Befehle eingibt. Diesmal die Übergabe eines echten Befehls.\n", + "# Dieser erzeugt eine wahre Aussage wodurch die Passwortüberprüfung erfolgreich wird.\n", + "username = \"user1\"\n", + "password = \"' OR 1=1 --\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username='{username}' AND password='{password}'\")\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Abschliessend noch die Variante mit Platzhaltern, die sicherer wäre." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Login fehlgeschlagen\n" + ] + } + ], + "source": [ + "# hier würde nun die Zeichenkette aus der Passworteingabe übergeben und nicht als String direkt zu SQL-Code werden.\n", + "username = \"user1\"\n", + "password = \"' OR 1=1 --\"\n", + "\n", + "try:\n", + " cursor.execute(f\"SELECT * FROM users WHERE username=%s AND password=%s\", (username, password))\n", + " user = cursor.fetchone()\n", + "\n", + " if user:\n", + " print(\"Login erfolgreich\")\n", + " else:\n", + " print(\"Login fehlgeschlagen\")\n", + "except Exception as e:\n", + " print(e)\n", + " conn.rollback()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a6b707a736c5fbba452b904aff207ddd250a7524df1f8c74db5bc52ff4a2560b" + }, + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Schauspieler Zusammenarbeit.ipynb b/Schauspieler Zusammenarbeit.ipynb new file mode 100644 index 0000000..7d5e22c --- /dev/null +++ b/Schauspieler Zusammenarbeit.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "e7cbec5d-cb72-4ab0-a1c1-ce4cd2609934", + "metadata": {}, + "outputs": [], + "source": [ + "# Autoren: Samuel, Robin, Michael\n", + "import psycopg2\n", + "import psycopg2.extras" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "915f7f56-fd86-459c-ac63-91d331544348", + "metadata": {}, + "outputs": [], + "source": [ + "# Verbindung zur Datenbank aufbauen\n", + "conn = psycopg2.connect (\"dbname=movies_database host=/var/run/postgresql user=postgres password=sml12345\")\n", + "cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e25bb2be-926f-460d-b9f9-b4b9cfaf4277", + "metadata": {}, + "outputs": [], + "source": [ + "# Hole alle Schauspieler\n", + "cursor.execute(\"SELECT * FROM actors;\")\n", + "actors = cursor.fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a4b3619b-9a5d-404a-85e9-0e04636f7666", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arthur Lake [('Penny Singleton', 11), ('Larry Simms', 11), ('Daisy the Dog', 8)]\n", + "Burt Young [('Talia Shire', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Carl Weathers [('Talia Shire', 4), ('Burt Young', 4), ('Sylvester Stallone', 4)]\n", + "Clint Eastwood [('Sondra Locke', 5)]\n", + "Daisy the Dog [('Arthur Lake', 8), ('Larry Simms', 8), ('Penny Singleton', 7)]\n", + "Danny Glover [('Mel Gibson', 4)]\n", + "DeForest Kelly [('Leonard Nimoy', 5), ('James Doohan', 5), ('William Shatner', 5)]\n", + "Diane Keaton [('Woody Allen', 6)]\n", + "Herbert Lom [('Peter Sellers', 4)]\n", + "Jack Lemmon [('Walter Matthau', 4)]\n", + "James Doohan [('Leonard Nimoy', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Jim Dale [('Kenneth Williams', 5), ('Sidney James', 4)]\n", + "Kenneth Williams [('Sidney James', 5), ('Jim Dale', 5)]\n", + "Larry Simms [('Arthur Lake', 11), ('Penny Singleton', 10), ('Daisy the Dog', 8)]\n", + "Leonard Nimoy [('James Doohan', 6), ('William Shatner', 6), ('DeForest Kelly', 5)]\n", + "Mel Gibson [('Danny Glover', 4)]\n", + "Oliver Hardy [('Stan Laurel', 8)]\n", + "Penny Singleton [('Arthur Lake', 11), ('Larry Simms', 10), ('Daisy the Dog', 7)]\n", + "Peter Sellers [('Herbert Lom', 4)]\n", + "Sidney James [('Kenneth Williams', 5), ('Jim Dale', 4)]\n", + "Sondra Locke [('Clint Eastwood', 5)]\n", + "Stan Laurel [('Oliver Hardy', 8)]\n", + "Sylvester Stallone [('Talia Shire', 5), ('Burt Young', 5), ('Carl Weathers', 4)]\n", + "Talia Shire [('Burt Young', 5), ('Sylvester Stallone', 5), ('Carl Weathers', 4)]\n", + "Walter Matthau [('Jack Lemmon', 4)]\n", + "William Shatner [('Leonard Nimoy', 6), ('James Doohan', 6), ('DeForest Kelly', 5)]\n", + "Woody Allen [('Diane Keaton', 6)]\n" + ] + } + ], + "source": [ + "# Iteriere über alle Schauspieler\n", + "collab = {}\n", + "\n", + "for actor in actors:\n", + " # Hole alle Filme, in welchen dieser Schauspieler dabei war\n", + " cursor.execute(f\"SELECT * FROM movies_actors where actor_id = {actor['actor_id']};\")\n", + " movies = cursor.fetchall()\n", + " movie_ids = ', '.join([str(movie['movie_id']) for movie in movies])\n", + "\n", + " # \n", + " cursor.execute(f\"SELECT actor_id, count(*) FROM movies_actors where movie_id in ({movie_ids}) and actor_id <> {actor['actor_id']} group by actor_id having count(*) > 3 order by count(*) desc;\")\n", + " result = cursor.fetchall()\n", + " \n", + " if (result):\n", + " collabs = []\n", + " for row in result:\n", + " cursor.execute(f\"SELECT name from actors where actor_id = {row['actor_id']};\")\n", + " collabs.append((cursor.fetchall()[0]['name'], row['count']))\n", + " collab[actor['name']] = collabs\n", + " \n", + "for key in collab:\n", + " print(key, collab[key])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "e5570c42-fbf0-4634-ba49-227f7fbd6ad3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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