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965
.$Bank-Datenbank ERD.drawio.bkp
Normal file
965
.$Bank-Datenbank ERD.drawio.bkp
Normal file
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<mxCell id="_Gp69ajdkHd3nP33if5o-15" value="1" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];" vertex="1" connectable="0" parent="_Gp69ajdkHd3nP33if5o-14">
|
||||
<mxGeometry x="0.8118" y="-2" relative="1" as="geometry">
|
||||
<mxPoint x="4" y="-8" as="offset" />
|
||||
</mxGeometry>
|
||||
</mxCell>
|
||||
<mxCell id="_Gp69ajdkHd3nP33if5o-11" value="carries out" style="rhombus;whiteSpace=wrap;html=1;" vertex="1" parent="1">
|
||||
<mxGeometry x="700" y="1000" width="80" height="40" as="geometry" />
|
||||
</mxCell>
|
||||
</root>
|
||||
</mxGraphModel>
|
||||
</diagram>
|
||||
</mxfile>
|
||||
10
.idea/.gitignore
generated
vendored
Normal file
10
.idea/.gitignore
generated
vendored
Normal file
@ -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/
|
||||
8
.idea/cds-104 Datenbanken und Datenverarbeitung.iml
generated
Normal file
8
.idea/cds-104 Datenbanken und Datenverarbeitung.iml
generated
Normal file
@ -0,0 +1,8 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<module type="PYTHON_MODULE" version="4">
|
||||
<component name="NewModuleRootManager">
|
||||
<content url="file://$MODULE_DIR$" />
|
||||
<orderEntry type="jdk" jdkName="$USER_HOME$/anaconda3" jdkType="Python SDK" />
|
||||
<orderEntry type="sourceFolder" forTests="false" />
|
||||
</component>
|
||||
</module>
|
||||
12
.idea/dataSources.xml
generated
Normal file
12
.idea/dataSources.xml
generated
Normal file
@ -0,0 +1,12 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
|
||||
<data-source source="LOCAL" name="postgres@localhost" uuid="e1dddddf-a4bd-4327-84c2-f4c31c6f0bd2">
|
||||
<driver-ref>postgresql</driver-ref>
|
||||
<synchronize>true</synchronize>
|
||||
<jdbc-driver>org.postgresql.Driver</jdbc-driver>
|
||||
<jdbc-url>jdbc:postgresql://localhost:5432/postgres</jdbc-url>
|
||||
<working-dir>$ProjectFileDir$</working-dir>
|
||||
</data-source>
|
||||
</component>
|
||||
</project>
|
||||
9
.idea/data_source_mapping.xml
generated
Normal file
9
.idea/data_source_mapping.xml
generated
Normal file
@ -0,0 +1,9 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="DataSourcePerFileMappings">
|
||||
<file url="file://$PROJECT_DIR$/ViewsundTrigger.sql" value="e1dddddf-a4bd-4327-84c2-f4c31c6f0bd2" />
|
||||
<file url="file://$PROJECT_DIR$/create_movies.sql" value="e1dddddf-a4bd-4327-84c2-f4c31c6f0bd2" />
|
||||
<file url="file://$PROJECT_DIR$/movies_data.sql" value="e1dddddf-a4bd-4327-84c2-f4c31c6f0bd2" />
|
||||
<file url="file://$PROJECT_DIR$/postgreSQL_Vertiefung_CDS104_FS26.sql" value="e1dddddf-a4bd-4327-84c2-f4c31c6f0bd2" />
|
||||
</component>
|
||||
</project>
|
||||
17
.idea/inspectionProfiles/Project_Default.xml
generated
Normal file
17
.idea/inspectionProfiles/Project_Default.xml
generated
Normal file
@ -0,0 +1,17 @@
|
||||
<component name="InspectionProjectProfileManager">
|
||||
<profile version="1.0">
|
||||
<option name="myName" value="Project Default" />
|
||||
<inspection_tool class="PyPackageRequirementsInspection" enabled="false" level="WARNING" enabled_by_default="false">
|
||||
<option name="ignoredPackages">
|
||||
<list>
|
||||
<option value="torch" />
|
||||
<option value="transformers" />
|
||||
<option value="sentence-transformers" />
|
||||
<option value="scikit-learn" />
|
||||
<option value="matplotlib" />
|
||||
<option value="numpy" />
|
||||
</list>
|
||||
</option>
|
||||
</inspection_tool>
|
||||
</profile>
|
||||
</component>
|
||||
6
.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
6
.idea/inspectionProfiles/profiles_settings.xml
generated
Normal file
@ -0,0 +1,6 @@
|
||||
<component name="InspectionProjectProfileManager">
|
||||
<settings>
|
||||
<option name="USE_PROJECT_PROFILE" value="false" />
|
||||
<version value="1.0" />
|
||||
</settings>
|
||||
</component>
|
||||
7
.idea/misc.xml
generated
Normal file
7
.idea/misc.xml
generated
Normal file
@ -0,0 +1,7 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="Black">
|
||||
<option name="sdkName" value="Python 3.12" />
|
||||
</component>
|
||||
<component name="ProjectRootManager" version="2" project-jdk-name="$USER_HOME$/anaconda3" project-jdk-type="Python SDK" />
|
||||
</project>
|
||||
8
.idea/modules.xml
generated
Normal file
8
.idea/modules.xml
generated
Normal file
@ -0,0 +1,8 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="ProjectModuleManager">
|
||||
<modules>
|
||||
<module fileurl="file://$PROJECT_DIR$/.idea/cds-104 Datenbanken und Datenverarbeitung.iml" filepath="$PROJECT_DIR$/.idea/cds-104 Datenbanken und Datenverarbeitung.iml" />
|
||||
</modules>
|
||||
</component>
|
||||
</project>
|
||||
10
.idea/sqldialects.xml
generated
Normal file
10
.idea/sqldialects.xml
generated
Normal file
@ -0,0 +1,10 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="SqlDialectMappings">
|
||||
<file url="file://$PROJECT_DIR$/ViewsundTrigger.sql" dialect="PostgreSQL" />
|
||||
<file url="file://$PROJECT_DIR$/create_movies.sql" dialect="PostgreSQL" />
|
||||
<file url="file://$PROJECT_DIR$/movies_data.sql" dialect="PostgreSQL" />
|
||||
<file url="file://$PROJECT_DIR$/postgreSQL_Vertiefung_CDS104_FS26.sql" dialect="PostgreSQL" />
|
||||
<file url="PROJECT" dialect="PostgreSQL" />
|
||||
</component>
|
||||
</project>
|
||||
178
.ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb
Normal file
178
.ipynb_checkpoints/Hinten_am_Fenster-checkpoint.ipynb
Normal file
File diff suppressed because one or more lines are too long
265
.ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb
Normal file
265
.ipynb_checkpoints/PostgreSQL-Python-checkpoint.ipynb
Normal file
@ -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
|
||||
}
|
||||
386
.ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb
Normal file
386
.ipynb_checkpoints/Python Wiederholung mit Code-checkpoint.ipynb
Normal file
@ -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
|
||||
}
|
||||
174
.ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb
Normal file
174
.ipynb_checkpoints/Python Wiederholung-checkpoint.ipynb
Normal file
@ -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
|
||||
}
|
||||
274
.ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb
Normal file
274
.ipynb_checkpoints/SQL-Injectionn mit Code-checkpoint.ipynb
Normal file
@ -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
|
||||
}
|
||||
179
.ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb
Normal file
179
.ipynb_checkpoints/Schauspieler Zusammenarbeit-checkpoint.ipynb
Normal file
File diff suppressed because one or more lines are too long
361
.ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb
Normal file
361
.ipynb_checkpoints/Zoo-Aufgabe-checkpoint.ipynb
Normal file
@ -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
|
||||
}
|
||||
91
Aufgabe 2.sql
Normal file
91
Aufgabe 2.sql
Normal file
@ -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";
|
||||
27
Aufgabe 3
Normal file
27
Aufgabe 3
Normal file
@ -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;
|
||||
BIN
Aufgabe 3.docx
Normal file
BIN
Aufgabe 3.docx
Normal file
Binary file not shown.
BIN
Aufgabe 3: Recherche zu SQL-Features.pdf
Normal file
BIN
Aufgabe 3: Recherche zu SQL-Features.pdf
Normal file
Binary file not shown.
965
Bank-Datenbank ERD.drawio
Normal file
965
Bank-Datenbank ERD.drawio
Normal file
@ -0,0 +1,965 @@
|
||||
<mxfile host="Electron" agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/28.2.5 Chrome/138.0.7204.251 Electron/37.6.1 Safari/537.36" version="28.2.5">
|
||||
<diagram name="Page-1" id="LcIpgzZ0pPKfpMyWdTyy">
|
||||
<mxGraphModel dx="1522" dy="1943" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
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||||
<root>
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||||
<mxCell id="0" />
|
||||
<mxCell id="1" parent="0" />
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||||
<mxCell id="fajgYWBjn6P_XPNBhK-K-5" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;strokeColor=default;endArrow=none;endFill=0;" parent="1" source="fajgYWBjn6P_XPNBhK-K-1" target="fajgYWBjn6P_XPNBhK-K-9" edge="1">
|
||||
<mxGeometry relative="1" as="geometry">
|
||||
<mxPoint x="60" y="670" as="targetPoint" />
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||||
<Array as="points">
|
||||
<mxPoint x="290" y="760" />
|
||||
<mxPoint x="290" y="715" />
|
||||
</Array>
|
||||
</mxGeometry>
|
||||
</mxCell>
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||||
<mxCell id="aLI5nJ7Lq4Vnoyky47xt-3" value="" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=none;endFill=0;" parent="1" source="aLI5nJ7Lq4Vnoyky47xt-5" target="aLI5nJ7Lq4Vnoyky47xt-2" edge="1">
|
||||
<mxGeometry relative="1" as="geometry" />
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||||
</mxCell>
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||||
<mxCell id="aLI5nJ7Lq4Vnoyky47xt-10" value="1" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];" parent="aLI5nJ7Lq4Vnoyky47xt-3" vertex="1" connectable="0">
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<mxGeometry x="0.65" y="2" relative="1" as="geometry">
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<mxPoint x="8" y="-3" as="offset" />
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BIN
Bank-Datenbank ERD.pdf
Normal file
BIN
Bank-Datenbank ERD.pdf
Normal file
Binary file not shown.
BIN
Bank-Datenbank ERD.png
Normal file
BIN
Bank-Datenbank ERD.png
Normal file
Binary file not shown.
|
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1
ERD Bank-Datenbank
Normal file
1
ERD Bank-Datenbank
Normal file
File diff suppressed because one or more lines are too long
265
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265
PostgreSQL-Python.ipynb
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"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
|
||||
}
|
||||
386
Python Wiederholung mit Code.ipynb
Normal file
386
Python Wiederholung mit Code.ipynb
Normal file
@ -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
|
||||
}
|
||||
174
Python Wiederholung.ipynb
Normal file
174
Python Wiederholung.ipynb
Normal file
@ -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
|
||||
}
|
||||
287
SQL-Injectionn mit Code.ipynb
Normal file
287
SQL-Injectionn mit Code.ipynb
Normal file
@ -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
|
||||
}
|
||||
179
Schauspieler Zusammenarbeit.ipynb
Normal file
179
Schauspieler Zusammenarbeit.ipynb
Normal file
File diff suppressed because one or more lines are too long
82
ViewsundTrigger.sql
Normal file
82
ViewsundTrigger.sql
Normal file
@ -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;
|
||||
361
Zoo-Aufgabe.ipynb
Normal file
361
Zoo-Aufgabe.ipynb
Normal file
@ -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
|
||||
}
|
||||
25
create_movies.sql
Normal file
25
create_movies.sql
Normal file
@ -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);
|
||||
19040
movies_data.sql
Normal file
19040
movies_data.sql
Normal file
File diff suppressed because it is too large
Load Diff
180
postgreSQL_Vertiefung_CDS104_FS26.sql
Normal file
180
postgreSQL_Vertiefung_CDS104_FS26.sql
Normal file
@ -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;
|
||||
45
Übungen aus den Folien
Normal file
45
Übungen aus den Folien
Normal file
@ -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;
|
||||
Loading…
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Reference in New Issue
Block a user