179 lines
420 KiB
Plaintext
179 lines
420 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "e7cbec5d-cb72-4ab0-a1c1-ce4cd2609934",
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"metadata": {},
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"outputs": [],
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"source": [
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"import psycopg2\n",
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"import psycopg2.extras"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "915f7f56-fd86-459c-ac63-91d331544348",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Verbindung zur Datenbank aufbauen\n",
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"conn = psycopg2.connect (\"dbname=movies_database host=/var/run/postgresql user=postgres password=sml12345\")\n",
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"cursor = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"id": "e25bb2be-926f-460d-b9f9-b4b9cfaf4277",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[RealDictRow({'actor_id': 1, 'name': '50 Cent'}), RealDictRow({'actor_id': 2, 'name': 'A Martinez'}), RealDictRow({'actor_id': 3, 'name': 'A. Michael Baldwin'}), RealDictRow({'actor_id': 4, 'name': 'Aaron Eckhart'}), RealDictRow({'actor_id': 5, 'name': 'Aaron Paul'}), RealDictRow({'actor_id': 6, 'name': 'Aaron Stanford'}), RealDictRow({'actor_id': 7, 'name': 'Abbie Cornish'}), RealDictRow({'actor_id': 8, 'name': 'Abby Dalton'}), RealDictRow({'actor_id': 9, 'name': 'Abhay Deol'}), RealDictRow({'actor_id': 10, 'name': 'Abraham Sofaer'}), RealDictRow({'actor_id': 11, 'name': 'Adam Baldwin'}), RealDictRow({'actor_id': 12, 'name': 'Adam Beach'}), RealDictRow({'actor_id': 13, 'name': 'Adam Hann-Byrd'}), RealDictRow({'actor_id': 14, 'name': 'Adam Lavorgna'}), RealDictRow({'actor_id': 15, 'name': 'Adam Roarke'}), RealDictRow({'actor_id': 16, 'name': 'Adam Sandler'}), RealDictRow({'actor_id': 17, 'name': 'Adam Storke'}), RealDictRow({'actor_id': 18, 'name': 'Adam Trese'}), RealDictRow({'actor_id': 19, 'name': 'Adam West'}), RealDictRow({'actor_id': 20, 'name': 'Addison Richards'}), RealDictRow({'actor_id': 21, 'name': 'Adele Mara'}), RealDictRow({'actor_id': 22, 'name': 'Aden Young'}), RealDictRow({'actor_id': 23, 'name': 'Adewale Akinnuoye-Agbaje'}), RealDictRow({'actor_id': 24, 'name': 'Adolfo Celi'}), RealDictRow({'actor_id': 25, 'name': 'Adolphe Menjou'}), RealDictRow({'actor_id': 26, 'name': 'Adrian Dunbar'}), RealDictRow({'actor_id': 27, 'name': 'Adrian Pasdar'}), RealDictRow({'actor_id': 28, 'name': 'Adrian Zmed'}), RealDictRow({'actor_id': 29, 'name': 'Adrien Brody'}), RealDictRow({'actor_id': 30, 'name': 'Adrienne Barbeau'}), RealDictRow({'actor_id': 31, 'name': 'Adrienne Corri'}), RealDictRow({'actor_id': 32, 'name': 'Adrienne King'}), RealDictRow({'actor_id': 33, 'name': 'Adrienne Shelly'}), RealDictRow({'actor_id': 34, 'name': 'Agatha Hurle'}), RealDictRow({'actor_id': 35, 'name': 'Agathe Natanson'}), RealDictRow({'actor_id': 36, 'name': 'Agga Olsen'}), RealDictRow({'actor_id': 37, 'name': 'Agnes Bruckner'}), RealDictRow({'actor_id': 38, 'name': 'Agnes Moorehead'}), RealDictRow({'actor_id': 39, 'name': 'Aidan Gould'}), RealDictRow({'actor_id': 40, 'name': 'Aidan Quinn'}), RealDictRow({'actor_id': 41, 'name': 'Aitana Sánchez-Gijón'}), RealDictRow({'actor_id': 42, 'name': 'Akbar Kurtha'}), RealDictRow({'actor_id': 43, 'name': 'Ake Nyman'}), RealDictRow({'actor_id': 44, 'name': 'Akiko Wakabayashi'}), RealDictRow({'actor_id': 45, 'name': 'Akim Tamiroff'}), RealDictRow({'actor_id': 46, 'name': 'Akosua Busia'}), RealDictRow({'actor_id': 47, 'name': 'Aksel Hennie'}), RealDictRow({'actor_id': 48, 'name': 'Al Freeman Jr.'}), RealDictRow({'actor_id': 49, 'name': 'Al Jolson'}), RealDictRow({'actor_id': 50, 'name': 'Al Pacino'}), RealDictRow({'actor_id': 51, 'name': 'Alain Delon'}), RealDictRow({'actor_id': 52, 'name': 'Alan Alda'}), RealDictRow({'actor_id': 53, 'name': 'Alan Arkin'}), RealDictRow({'actor_id': 54, 'name': 'Alan Badel'}), RealDictRow({'actor_id': 55, 'name': 'Alan Bates'}), RealDictRow({'actor_id': 56, 'name': 'Alan Baxter'}), RealDictRow({'actor_id': 57, 'name': 'Alan Cox'}), RealDictRow({'actor_id': 58, 'name': 'Alan Cumming'}), RealDictRow({'actor_id': 59, 'name': 'Alan Curtis'}), RealDictRow({'actor_id': 60, 'name': 'Alan David'}), RealDictRow({'actor_id': 61, 'name': 'Alan Fisler'}), RealDictRow({'actor_id': 62, 'name': 'Alan Hale'}), RealDictRow({'actor_id': 63, 'name': 'Alan King'}), RealDictRow({'actor_id': 64, 'name': 'Alan Ladd'}), RealDictRow({'actor_id': 65, 'name': 'Alan Marshall'}), RealDictRow({'actor_id': 66, 'name': 'Alan Mowbray'}), RealDictRow({'actor_id': 67, 'name': 'Alan Napier'}), RealDictRow({'actor_id': 68, 'name': 'Alan Randolph Scott'}), RealDictRow({'actor_id': 69, 'name': 'Alan Rickman'}), RealDictRow({'actor_id': 70, 'name': 'Alan Ruck'}), RealDictRow({'actor_id': 71, 'name': 'Alan Webb'}), RealDictRow({'actor_id': 72, 'name': 'Alan Young'}), RealDictRow({'actor_id': 73, 'name': 'Alastair Sim'}), RealDictRow({'actor_id': 74, 'name': 'Albert Brooks'}), RealDictRow({'actor_id': 75, 'name': 'Albert Dekker'}), RealDictRow({'actor_id': 76, 'name': 'Albert Finney'}), RealDictRow({'actor_id': 77, 'name': 'Albert Hall'}), RealDictRow({'actor_id': 78, 'name': 'Alberta Watson'}), RealDictRow({'actor_id': 79, 'name': 'Alberto De Mendoza'}), RealDictRow({'actor_id': 80, 'name': 'Alberto Morin'}), RealDictRow({'actor_id': 81, 'name': 'Aldo Giuffrè'}), RealDictRow({'actor_id': 82, 'name': 'Aldo Ray'}), RealDictRow({'actor_id': 83, 'name': 'Alec Baldwin'}), RealDictRow({'actor_id': 84, 'name': 'Alec Cawthorne'}), RealDictRow({'actor_id': 85, 'name': 'Alec Clunes'}), RealDictRow({'actor_id': 86, 'name': 'Alec Guinness'}), RealDictRow({'actor_id': 87, 'name': 'Alec McCowen'}), RealDictRow({'actor_id': 88, 'name': 'Alex D. 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': 106, 'name': 'Alexis Smith'}), RealDictRow({'actor_id': 107, 'name': 'Alexis Zegerman'}), RealDictRow({'actor_id': 108, 'name': 'Alfred Molina'}), RealDictRow({'actor_id': 109, 'name': 'Alfred Ryder'}), RealDictRow({'actor_id': 110, 'name': 'Ali MacGraw'}), RealDictRow({'actor_id': 111, 'name': 'Alice Cooper'}), RealDictRow({'actor_id': 112, 'name': 'Alice Krige'}), RealDictRow({'actor_id': 113, 'name': 'Alicia Silverstone'}), RealDictRow({'actor_id': 114, 'name': 'Alicia Witt'}), RealDictRow({'actor_id': 115, 'name': 'Alida Valli'}), RealDictRow({'actor_id': 116, 'name': 'Aline MacMahon'}), RealDictRow({'actor_id': 117, 'name': 'Alisan Porter'}), RealDictRow({'actor_id': 118, 'name': 'Alison Doody'}), RealDictRow({'actor_id': 119, 'name': 'Alison Eastwood'}), RealDictRow({'actor_id': 120, 'name': 'Alison Folland'}), RealDictRow({'actor_id': 121, 'name': 'Alison Leggatt'}), RealDictRow({'actor_id': 122, 'name': 'Alison Routledge'}), RealDictRow({'actor_id': 123, 'name': 'Alison 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'}), RealDictRow({'actor_id': 159, 'name': 'Anamaria Marinca'}), RealDictRow({'actor_id': 160, 'name': 'Anatoli Davydov'}), RealDictRow({'actor_id': 161, 'name': 'Anders Baasmo Christiansen'}), RealDictRow({'actor_id': 162, 'name': 'Andersen Gabrych'}), RealDictRow({'actor_id': 163, 'name': 'Andie MacDowell'}), RealDictRow({'actor_id': 164, 'name': 'Andre Braugher'}), RealDictRow({'actor_id': 165, 'name': 'Andre Gregory'}), RealDictRow({'actor_id': 166, 'name': 'Andrea Eckert'}), RealDictRow({'actor_id': 167, 'name': 'Andrea Marcovicci'}), RealDictRow({'actor_id': 168, 'name': 'Andrea Occhipinti'}), RealDictRow({'actor_id': 169, 'name': 'Andrea Riseborough'}), RealDictRow({'actor_id': 170, 'name': 'Andrea Roth'}), RealDictRow({'actor_id': 171, 'name': 'Andrew Cruickshank'}), RealDictRow({'actor_id': 172, 'name': 'Andrew Divoff'}), RealDictRow({'actor_id': 173, 'name': 'Andrew Duggan'}), RealDictRow({'actor_id': 174, 'name': 'Andrew Garfield'}), RealDictRow({'actor_id': 175, 'name': 'Andrew Knott'}), RealDictRow({'actor_id': 176, 'name': 'Andrew Lauer'}), RealDictRow({'actor_id': 177, 'name': 'Andrew McCarthy'}), RealDictRow({'actor_id': 178, 'name': 'Andrew Prine'}), RealDictRow({'actor_id': 179, 'name': 'Andrew Robinson'}), RealDictRow({'actor_id': 180, 'name': 'Andrew Sachs'}), RealDictRow({'actor_id': 181, 'name': 'Andrew Stevens'}), RealDictRow({'actor_id': 182, 'name': 'Andrzej Seweryn'}), RealDictRow({'actor_id': 183, 'name': 'André Morell'}), RealDictRow({'actor_id': 184, 'name': 'Andy Dick'}), RealDictRow({'actor_id': 185, 'name': 'Andy Garcia'}), RealDictRow({'actor_id': 186, 'name': 'Andy Griffith'}), RealDictRow({'actor_id': 187, 'name': 'Andy J. Forest'}), RealDictRow({'actor_id': 188, 'name': 'Andy Romano'}), RealDictRow({'actor_id': 189, 'name': 'Aneta Corsaut'}), RealDictRow({'actor_id': 190, 'name': 'Angela Bassett'}), RealDictRow({'actor_id': 191, 'name': 'Angela Douglas'}), RealDictRow({'actor_id': 192, 'name': 'Angela Featherstone'}), RealDictRow({'actor_id': 193, 'name': 'Angela Goethals'}), RealDictRow({'actor_id': 194, 'name': 'Angela Lansbury'}), RealDictRow({'actor_id': 195, 'name': 'Angelina Jolie'}), RealDictRow({'actor_id': 196, 'name': 'Angeline Ball'}), RealDictRow({'actor_id': 197, 'name': 'Angelo Rossito'}), RealDictRow({'actor_id': 198, 'name': 'Angie Brown'}), RealDictRow({'actor_id': 199, 'name': 'Angie Dickinson'}), RealDictRow({'actor_id': 200, 'name': 'Angie Everhart'}), RealDictRow({'actor_id': 201, 'name': 'Anicée Alvina'}), RealDictRow({'actor_id': 202, 'name': 'Anita Briem'}), RealDictRow({'actor_id': 203, 'name': 'Anita Ekberg'}), RealDictRow({'actor_id': 204, 'name': 'Anjelica Huston'}), RealDictRow({'actor_id': 205, 'name': 'Ann Blyth'}), RealDictRow({'actor_id': 206, 'name': 'Ann Carter'}), RealDictRow({'actor_id': 207, 'name': 'Ann Doran'}), RealDictRow({'actor_id': 208, 'name': 'Ann Dvorak'}), RealDictRow({'actor_id': 209, 'name': 'Ann Hearn'}), RealDictRow({'actor_id': 210, 'name': 'Ann Miller'}), RealDictRow({'actor_id': 211, 'name': 'Ann Prentiss'}), RealDictRow({'actor_id': 212, 'name': 'Ann Richards'}), RealDictRow({'actor_id': 213, 'name': 'Ann Savage'}), RealDictRow({'actor_id': 214, 'name': 'Ann Shirley'}), RealDictRow({'actor_id': 215, 'name': 'Ann Todd'}), RealDictRow({'actor_id': 216, 'name': 'Ann Wedgeworth'}), RealDictRow({'actor_id': 217, 'name': 'Ann-Margret'}), RealDictRow({'actor_id': 218, 'name': 'Anna Anissimova'}), RealDictRow({'actor_id': 219, 'name': 'Anna Chlumsky'}), RealDictRow({'actor_id': 220, 'name': 'Anna Friel'}), RealDictRow({'actor_id': 221, 'name': 'Anna Karen'}), RealDictRow({'actor_id': 222, 'name': 'Anna Karina'}), RealDictRow({'actor_id': 223, 'name': 'Anna Massey'}), RealDictRow({'actor_id': 224, 'name': 'Anna May Wong'}), RealDictRow({'actor_id': 225, 'name': 'Anna Palk'}), RealDictRow({'actor_id': 226, 'name': 'Anna Paquin'}), RealDictRow({'actor_id': 227, 'name': 'Anna Proclemer'}), RealDictRow({'actor_id': 228, 'name': 'Anna-Maria Monticelli'}), RealDictRow({'actor_id': 229, 'name': 'Annabella Sciorra'}), RealDictRow({'actor_id': 230, 'name': 'Annabeth Gish'}), RealDictRow({'actor_id': 231, 'name': 'Anne Archer'}), RealDictRow({'actor_id': 232, 'name': 'Anne Bancroft'}), RealDictRow({'actor_id': 233, 'name': 'Anne Baxter'}), RealDictRow({'actor_id': 234, 'name': 'Anne Brochet'}), RealDictRow({'actor_id': 235, 'name': 'Anne Christianson'}), RealDictRow({'actor_id': 236, 'name': 'Anne Francis'}), RealDictRow({'actor_id': 237, 'name': 'Anne Grey'}), RealDictRow({'actor_id': 238, 'name': 'Anne Gwynne'}), RealDictRow({'actor_id': 239, 'name': 'Anne Heche'}), RealDictRow({'actor_id': 240, 'name': 'Anne Meara'}), RealDictRow({'actor_id': 241, 'name': 'Anne Ramsey'}), RealDictRow({'actor_id': 242, 'name': 'Anne Revere'}), RealDictRow({'actor_id': 243, 'name': 'Anne Suzuki'}), RealDictRow({'actor_id': 244, 'name': 'Anne Tenney'}), RealDictRow({'actor_id': 245, 'name': 'Anne-Louise Lambert'}), RealDictRow({'actor_id': 246, 'name': 'Anne-Marie Kennedy'}), RealDictRow({'actor_id': 247, 'name': 'Annette Bening'}), RealDictRow({'actor_id': 248, 'name': \"Annette O'Toole\"}), RealDictRow({'actor_id': 249, 'name': 'Annette O`Toole'}), RealDictRow({'actor_id': 250, 'name': 'Annette Woska'}), RealDictRow({'actor_id': 251, 'name': 'Annie Corley'}), RealDictRow({'actor_id': 252, 'name': 'Annie Golden'}), RealDictRow({'actor_id': 253, 'name': 'Annie McEnroe'}), RealDictRow({'actor_id': 254, 'name': 'Annie Packert'}), RealDictRow({'actor_id': 255, 'name': 'Annie Potts'}), RealDictRow({'actor_id': 256, 'name': 'Annie Ross'}), RealDictRow({'actor_id': 257, 'name': 'Anny Duperey'}), RealDictRow({'actor_id': 258, 'name': 'Anny Ondra'}), RealDictRow({'actor_id': 259, 'name': 'Anson Mount'}), RealDictRow({'actor_id': 260, 'name': 'Anthony Barrile'}), RealDictRow({'actor_id': 261, 'name': 'Anthony Edwards'}), RealDictRow({'actor_id': 262, 'name': 'Anthony Fauci'}), RealDictRow({'actor_id': 263, 'name': 'Anthony Franciosa'}), RealDictRow({'actor_id': 264, 'name': 'Anthony Geary'}), RealDictRow({'actor_id': 265, 'name': 'Anthony Heald'}), RealDictRow({'actor_id': 266, 'name': 'Anthony Higgins'}), RealDictRow({'actor_id': 267, 'name': 'Anthony Hopkins'}), RealDictRow({'actor_id': 268, 'name': 'Anthony LaPaglia'}), RealDictRow({'actor_id': 269, 'name': 'Anthony Michael Hall'}), RealDictRow({'actor_id': 270, 'name': 'Anthony Perkins'}), RealDictRow({'actor_id': 271, 'name': 'Anthony Quayle'}), RealDictRow({'actor_id': 272, 'name': 'Anthony Quinn'}), RealDictRow({'actor_id': 273, 'name': 'Anthony Ross'}), RealDictRow({'actor_id': 274, 'name': 'Anthony Simcoe'}), RealDictRow({'actor_id': 275, 'name': 'Anthony Zerbe'}), RealDictRow({'actor_id': 276, 'name': 'Anton Rodgers'}), RealDictRow({'actor_id': 277, 'name': 'Antonio Banderas'}), RealDictRow({'actor_id': 278, 'name': 'Antonio Mendoza'}), RealDictRow({'actor_id': 279, 'name': 'Antonio Moreno'}), RealDictRow({'actor_id': 280, 'name': 'Anya Ormsby'}), RealDictRow({'actor_id': 281, 'name': 'Anzac Wallace'}), RealDictRow({'actor_id': 282, 'name': 'Apollonia Kotero'}), RealDictRow({'actor_id': 283, 'name': 'April Grace'}), RealDictRow({'actor_id': 284, 'name': 'Aran Bell'}), RealDictRow({'actor_id': 285, 'name': 'Ariane'}), RealDictRow({'actor_id': 286, 'name': 'Arielle Kebbel'}), RealDictRow({'actor_id': 287, 'name': 'Arleen Whelan'}), RealDictRow({'actor_id': 288, 'name': 'Arlene Dahl'}), RealDictRow({'actor_id': 289, 'name': 'Arlene Francis'}), RealDictRow({'actor_id': 290, 'name': 'Arlo Guthrie'}), RealDictRow({'actor_id': 291, 'name': 'Armand Assante'}), RealDictRow({'actor_id': 292, 'name': 'Armin Mueller-Stahl'}), RealDictRow({'actor_id': 293, 'name': 'Arnold Lucy'}), RealDictRow({'actor_id': 294, 'name': 'Arnold Schwarzenegger'}), RealDictRow({'actor_id': 295, 'name': 'Arnold Vosloo'}), RealDictRow({'actor_id': 296, 'name': 'Arsenio Hall'}), RealDictRow({'actor_id': 297, 'name': 'Arsinée Khanjian'}), RealDictRow({'actor_id': 298, 'name': 'Art Evans'}), RealDictRow({'actor_id': 299, 'name': 'Art Garfunkel (as Arthur Garfunkel)'}), RealDictRow({'actor_id': 300, 'name': 'Art LaFleur'}), RealDictRow({'actor_id': 301, 'name': 'Art Metrano'}), RealDictRow({'actor_id': 302, 'name': 'Arte Johnson'}), RealDictRow({'actor_id': 303, 'name': 'Arthur Askey'}), RealDictRow({'actor_id': 304, 'name': 'Arthur Chesney'}), RealDictRow({'actor_id': 305, 'name': 'Arthur Hill'}), RealDictRow({'actor_id': 306, 'name': 'Arthur Hunnicutt'}), RealDictRow({'actor_id': 307, 'name': 'Arthur Kennedy'}), RealDictRow({'actor_id': 308, 'name': 'Arthur Lake'}), RealDictRow({'actor_id': 309, 'name': \"Arthur O'Connell\"}), RealDictRow({'actor_id': 310, 'name': 'Artie Lange'}), RealDictRow({'actor_id': 311, 'name': 'Arturo de Córdova'}), RealDictRow({'actor_id': 312, 'name': 'Arye Gross'}), RealDictRow({'actor_id': 313, 'name': 'Ash Adams'}), RealDictRow({'actor_id': 314, 'name': 'Ashley Greene'}), RealDictRow({'actor_id': 315, 'name': 'Ashley Judd'}), RealDictRow({'actor_id': 316, 'name': 'Ashley Laurence'}), RealDictRow({'actor_id': 317, 'name': 'Ashley Olsen'}), RealDictRow({'actor_id': 318, 'name': 'Ashley Peldon'}), RealDictRow({'actor_id': 319, 'name': 'Aude Landry'}), RealDictRow({'actor_id': 320, 'name': 'Audie Murphy'}), RealDictRow({'actor_id': 321, 'name': 'Audra Lindley'}), RealDictRow({'actor_id': 322, 'name': 'Audra McDonald'}), RealDictRow({'actor_id': 323, 'name': 'Audrey Dalton'}), RealDictRow({'actor_id': 324, 'name': 'Audrey Hepburn'}), RealDictRow({'actor_id': 325, 'name': 'Audrey Meadows'}), RealDictRow({'actor_id': 326, 'name': 'Audrey Totter'}), 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|
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]
|
||
}
|
||
],
|
||
"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": {
|
||
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kAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAg5AEAAAAAAABgIOQBAAAAAAAAYCDkAQAAAAAAAGAQhkfBXm6BN+sAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 2000x2000 with 1 Axes>"
|
||
]
|
||
},
|
||
"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
|
||
}
|