266 lines
9.1 KiB
Plaintext
266 lines
9.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b53d472a-14d4-42bb-a0ee-38eb8c9c24a8",
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"metadata": {},
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"source": [
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"Zunächst muss ein Paket zur Anbindung installiert werden. Wir verwenden psycopg 2 (psycopg.org).\n",
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"Dies kann über den Reiter \"Environment\" im Anaconda Navicator installiert werden.\n",
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"Ist dies geschehen können wir das Paket importieren."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "0d3fea87-d2a6-4da8-9327-4c08bb70ef9e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import psycopg2, psycopg2.extras"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bedf4588-60dc-4169-9295-21f6b3317e9f",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "62b5a0b5-bafd-4125-8b62-4a2aa493a061",
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"metadata": {},
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"outputs": [],
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"source": [
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"conn = psycopg2.connect(\"dbname=bank host=/var/run/postgresql user=postgres password=sml12345\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8861806c-5786-42fb-81d3-1ba5283b1d2b",
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"metadata": {},
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"source": [
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"Als nächstes führen wir eine einfache SELECT Abfrage aus.\n",
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"Zunächst werden wir verbunden, dann stellen wir die Abfrage, dann rufen wir das Ergebnis der Abfrage ab und lassen es uns anzeigen.\n",
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"Dazu benötigen wir ein cursor Objekt, das uns die Abfrage aber auch die Rücklieferung der Daten liefert. Dieses kommt in die Variable cursor.\n",
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"Die Variable result speichert uns die Ergebnisse. fetchall wartet bis alle Ergebniszeilen geliefert sind und ermöglicht dann die Anzeige."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "09dbc41c-0ed7-449a-853d-3a18fbb1d97e",
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"metadata": {},
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"outputs": [],
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"source": [
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"cursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n",
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"cursor.execute(\"SELECT * FROM account;\")\n",
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"result = cursor.fetchall()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "81c22552-4d3c-4824-82c7-5b8a0c74402c",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "dba24620-fe85-4feb-8109-59a3d7c5e3b3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1, 'CHK', 1, datetime.date(2000, 1, 15), None, datetime.date(2005, 1, 4), 'ACTIVE', 2, 10, 1057.75, 1057.75]\n",
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"[2, 'SAV', 1, datetime.date(2000, 1, 15), None, datetime.date(2004, 12, 19), 'ACTIVE', 2, 10, 500.0, 500.0]\n",
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"[3, 'CD', 1, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 2, 10, 3000.0, 3000.0]\n",
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"[4, 'CHK', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 27), 'ACTIVE', 2, 10, 2258.02, 2258.02]\n",
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"[5, 'SAV', 2, datetime.date(2001, 3, 12), None, datetime.date(2004, 12, 11), 'ACTIVE', 2, 10, 200.0, 200.0]\n",
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"[7, 'CHK', 3, datetime.date(2002, 11, 23), None, datetime.date(2004, 11, 30), 'ACTIVE', 3, 13, 1057.75, 1057.75]\n",
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"[8, 'MM', 3, datetime.date(2002, 12, 15), None, datetime.date(2004, 12, 5), 'ACTIVE', 3, 13, 2212.5, 2212.5]\n",
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"[10, 'CHK', 4, datetime.date(2003, 9, 12), None, datetime.date(2005, 1, 3), 'ACTIVE', 1, 1, 534.12, 534.12]\n",
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"[11, 'SAV', 4, datetime.date(2000, 1, 15), None, datetime.date(2004, 10, 24), 'ACTIVE', 1, 1, 767.77, 767.77]\n",
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"[12, 'MM', 4, datetime.date(2004, 9, 30), None, datetime.date(2004, 11, 11), 'ACTIVE', 1, 1, 5487.09, 5487.09]\n",
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"[13, 'CHK', 5, datetime.date(2004, 1, 27), None, datetime.date(2005, 1, 5), 'ACTIVE', 4, 16, 2237.97, 2897.97]\n",
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"[14, 'CHK', 6, datetime.date(2002, 8, 24), None, datetime.date(2004, 11, 29), 'ACTIVE', 1, 1, 122.37, 122.37]\n",
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"[15, 'CD', 6, datetime.date(2004, 12, 28), None, datetime.date(2004, 12, 28), 'ACTIVE', 1, 1, 10000.0, 10000.0]\n",
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"[17, 'CD', 7, datetime.date(2004, 1, 12), None, datetime.date(2004, 1, 12), 'ACTIVE', 2, 10, 5000.0, 5000.0]\n",
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"[18, 'CHK', 8, datetime.date(2001, 5, 23), None, datetime.date(2005, 1, 3), 'ACTIVE', 4, 16, 3487.19, 3487.19]\n",
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"[19, 'SAV', 8, datetime.date(2001, 5, 23), None, datetime.date(2004, 10, 12), 'ACTIVE', 4, 16, 387.99, 387.99]\n",
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"[21, 'CHK', 9, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 1, 1, 125.67, 125.67]\n",
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"[22, 'MM', 9, datetime.date(2004, 10, 28), None, datetime.date(2004, 10, 28), 'ACTIVE', 1, 1, 9345.55, 9845.55]\n",
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"[23, 'CD', 9, datetime.date(2004, 6, 30), None, datetime.date(2004, 6, 30), 'ACTIVE', 1, 1, 1500.0, 1500.0]\n",
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"[24, 'CHK', 10, datetime.date(2002, 9, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 23575.12, 23575.12]\n",
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"[25, 'BUS', 10, datetime.date(2002, 10, 1), None, datetime.date(2004, 8, 28), 'ACTIVE', 4, 16, 0.0, 0.0]\n",
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"[27, 'BUS', 11, datetime.date(2004, 3, 22), None, datetime.date(2004, 11, 14), 'ACTIVE', 2, 10, 9345.55, 9345.55]\n",
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"[28, 'CHK', 12, datetime.date(2003, 7, 30), None, datetime.date(2004, 12, 15), 'ACTIVE', 4, 16, 38552.05, 38552.05]\n",
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"[29, 'SBL', 13, datetime.date(2004, 2, 22), None, datetime.date(2004, 12, 17), 'ACTIVE', 3, 13, 50000.0, 50000.0]\n"
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]
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}
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],
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"source": [
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"for entry in result:\n",
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" print(entry)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b975ba44-de7e-4337-a2f9-a4fa06fc59b1",
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"metadata": {},
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"source": [
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"Wollen wir nur eine bestimmte Spalte geht dies über die Angabe der Spaltennummer."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"id": "6159012d-ea9c-44da-bec0-5d073d210bd3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1057.75\n",
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"500.0\n",
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"3000.0\n",
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"2258.02\n",
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"200.0\n",
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"1057.75\n",
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"2212.5\n",
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"534.12\n",
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"767.77\n",
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"5487.09\n",
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"2237.97\n",
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"122.37\n",
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"10000.0\n",
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"5000.0\n",
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"3487.19\n",
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"387.99\n",
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"125.67\n",
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"9345.55\n",
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"1500.0\n",
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"23575.12\n",
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"0.0\n",
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"9345.55\n",
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"38552.05\n",
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"50000.0\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "b3180855-e952-4d4a-86b5-a27c9d326f69",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "75b7a5c8-4677-4546-a461-e7b36b97ff5e",
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"metadata": {},
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"outputs": [],
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"source": [
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"for entry in result:\n",
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" print(entry['avail_balance'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3b90228d-bbc6-40ca-bf18-abb698318603",
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"metadata": {},
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"source": [
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"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",
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"Die Lösung ist unseren SQL Befehl in einen try, except bzw finally Block zu setzen.\n",
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"MIt conn.commit() wird die Durchführung der Transaktion bestätigt."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "2e24b6bb-4773-4652-b027-7ee4faf6db92",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1 1057.75\n",
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"3 3000.0\n",
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"4 2258.02\n",
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"7 1057.75\n",
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"8 2212.5\n",
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"12 5487.09\n",
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"13 2237.97\n",
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"15 10000.0\n",
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"17 5000.0\n",
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"18 3487.19\n",
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"22 9345.55\n",
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"23 1500.0\n",
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"24 23575.12\n",
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"27 9345.55\n",
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"28 38552.05\n",
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"29 50000.0\n"
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]
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}
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],
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"source": [
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"try:\n",
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" cursor.execute(\n",
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" 'select * from account where avail_balance > 1000;'\n",
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" )\n",
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" result = cursor.fetchall()\n",
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" result\n",
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"\n",
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" for row in result:\n",
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" print(row[0], row['avail_balance'])\n",
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" \n",
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" conn.commit()\n",
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"except:\n",
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" conn.rollback()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4e2f6948-9a18-421c-b64c-85ed9743b28f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:base] *",
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"language": "python",
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"name": "conda-base-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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