{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "92fb5068-0669-43e3-9799-9dca9ffe6638", "metadata": { "jupyter": { "source_hidden": true } }, "outputs": [], "source": [ "%%html\n", "" ] }, { "cell_type": "markdown", "id": "11174c4d-dcb5-49d8-96fc-b83ad35a6193", "metadata": {}, "source": [ "
\n", "\n", "
\n", "Chair of Cyber-Physical-Systems, Austria" ] }, { "cell_type": "markdown", "id": "a04ead7b-4fce-4ea2-9501-d60a112aadcb", "metadata": {}, "source": [ "\n", "\n", "| Credentials | |\n", "|----|---|\n", "|Host | Montanuniversitaet Leoben |\n", "|Web | https://cps.unileoben.ac.at |\n", "|Mail | cps@unileoben.ac.at |\n", "|Authors | Vedant Dave, Fotis Lygerakis, Linus Nwankwo, Melanie Neubauer, Nikolaus Feith and Elmar Rueckert|\n", "|Corresponding Authors | melanie.neubauer@unileoben.ac.at, rueckert@unileoben.ac.at |\n", "|Last edited | 28.09.2023 |\n" ] }, { "cell_type": "markdown", "id": "35a0d7a0", "metadata": {}, "source": [ "# Python Database Basics" ] }, { "cell_type": "markdown", "id": "4b05e025-9843-410e-80cd-f2db24148da8", "metadata": {}, "source": [ "
\n", "This is a tutorial on the basics of using databases in Python." ] }, { "cell_type": "markdown", "id": "b9edce42-35e4-4dcd-886c-8d19a30bfdf0", "metadata": { "jp-MarkdownHeadingCollapsed": true }, "source": [ "## Content\n", "***\n", "[1. Motivation for learning Python?](#sec:whypython) \n", "\n", "[2. Database Overview](#sec:overview)\n", "\n", "[3. Database Example in Python](#sec:example)" ] }, { "cell_type": "markdown", "id": "c4e0ceef-663a-46c3-9e52-6cd6c942422f", "metadata": {}, "source": [ "# 1. Motivation for Learning Python?\n", "....\n", " \n", "
\n", "\n", "
\n", "\n" ] }, { "attachments": {}, "cell_type": "markdown", "id": "ba97999c", "metadata": {}, "source": [ "# 2. Database Overview? \n", "\n", "| | MariaDB | InfluxDB | PostgreSQL | Graphite | TimescaleDB |\n", "|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|\n", "| Type | Relational| Time Series | Relational | Time Series | Time Series |\n", "| Language | SQL | InfluxQL | SQL | Graphite Query | --- |\n", "| Scalable | Good | Good | Good | Lose Performance when saving a big amound of data | Very Good |\n", "| Replication | Yes | Yes | Yes | No | Yes |\n", "| High-Availability | Yes | Yes | Yes | No | Yes |\n", "| Data-Transformation | Limited | Yes | Yes | Yes | Yes |\n", "| Community-Support Python | Good | OK | --- | --- | --- |\n", "| Compression | Yes | Yes | Yes | Yes | Yes |\n", "| Partitioning | Yes | Yes | Yes | No | Yes |\n", "| Image Saving | BLOB or BYTEA | No | BLOB or BYTEA | No | BLOB or BYTEA |\n", "| Python | Mysql-connector-python | Influxdb-python | psycopg2 | No | psycopg2, timescaledb |\n", "| Integrated Graphics | No (connect with Grafana) | Yes | No | Yes | Yes |\n" ] }, { "cell_type": "markdown", "id": "66f200c3", "metadata": {}, "source": [ "# 3. Database Example in Python \n", "## Create new Database (open existing Database)" ] }, { "cell_type": "code", "execution_count": 15, "id": "974c33a4", "metadata": {}, "outputs": [], "source": [ "# Import\n", "import sqlite3" ] }, { "cell_type": "code", "execution_count": 47, "id": "06024e10-c790-4e7f-b4d2-717f2f8c1826", "metadata": {}, "outputs": [], "source": [ "# Set the Path of the Database\n", "database_path = 'example_database.db'" ] }, { "cell_type": "code", "execution_count": 48, "id": "3e2e0d5c-86c3-4f58-8ec5-28888c1fef49", "metadata": {}, "outputs": [], "source": [ "# Create connection to the SQLite-Database\n", "db_conn = sqlite3.connect(database_path)\n", "db_cursor = db_conn.cursor()" ] }, { "cell_type": "markdown", "id": "fd3a2b81-9207-4a5e-8807-a1427c2f87b1", "metadata": {}, "source": [ "## Create Table in Database" ] }, { "cell_type": "code", "execution_count": 49, "id": "437ca01a-89ea-4e99-af04-c2b19ebf0f3a", "metadata": {}, "outputs": [], "source": [ "# Create a Table in Database\n", "db_cursor.execute('''\n", " CREATE TABLE IF NOT EXISTS students (\n", " m_number INTEGER PRIMARY KEY,\n", " name TEXT,\n", " age INTEGER\n", " )\n", " ''')\n", "db_conn.commit()" ] }, { "cell_type": "markdown", "id": "88e7e045-8ab1-498d-b928-3e0fe51ca624", "metadata": {}, "source": [ "## Create Entry in Database" ] }, { "cell_type": "code", "execution_count": 50, "id": "9f74ae84-7411-42e9-89ab-b7c4e12559bf", "metadata": {}, "outputs": [], "source": [ "# Create two entries for students\n", "student_1 = (123456789, 'Josef', 20)\n", "student_2 = (987654321, 'Sissi', 33)\n", "\n", "db_cursor.execute(f'''INSERT INTO students VALUES ({student_1[0]}, '{student_1[1]}', {student_1[2]})''')\n", "db_cursor.execute(f'''INSERT INTO students VALUES ({student_2[0]}, '{student_2[1]}', {student_2[2]})''')\n", "db_conn.commit()" ] }, { "cell_type": "markdown", "id": "d5b87f79-6285-432d-8f4d-7ad2730e7b11", "metadata": {}, "source": [ "## Show Table from Database" ] }, { "cell_type": "code", "execution_count": 43, "id": "ec07f074-f508-4a5c-b65f-392d28c73ce1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(987654321, 'Sissi', 33)\n" ] } ], "source": [ "# Print each row of the table students in the database\n", "for row in db_cursor.execute('''SELECT * FROM students'''):\n", " print(row)" ] }, { "cell_type": "markdown", "id": "d7e72081-859f-4c19-9393-92c2f6e72b57", "metadata": {}, "source": [ "## Search for Entry in Table" ] }, { "cell_type": "code", "execution_count": 39, "id": "b29460b0-d7e4-47d5-b65e-eb3aac352648", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# Search for Student with the m_number 123456789\n", "db_cursor.execute(f'''SELECT name, age FROM students WHERE m_number = {123456789}''')\n", "result = db_cursor.fetchall()\n", "print(result)" ] }, { "cell_type": "markdown", "id": "53a5f8d1-3444-48f1-80a4-87db8cb3858c", "metadata": {}, "source": [ "## Delete Entry from Table" ] }, { "cell_type": "code", "execution_count": 38, "id": "6248e45a-6cd9-42bb-9f30-ee979a8865a3", "metadata": {}, "outputs": [], "source": [ "# Delete Student with the m_number 123456789 from the Database\n", "db_cursor.execute(f'''DELETE FROM students WHERE m_number = {123456789}''')\n", "db_conn.commit()" ] }, { "cell_type": "code", "execution_count": 44, "id": "5c675bc4-6528-424a-88df-0023cc79eb29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(987654321, 'Sissi', 33)\n" ] } ], "source": [ "# Now you can show the table again\n", "# Print each row of the table students in the database\n", "for row in db_cursor.execute('''SELECT * FROM students'''):\n", " print(row)" ] }, { "cell_type": "markdown", "id": "119cc9ce-b361-4ccc-892b-98cb635ad6d5", "metadata": {}, "source": [ "## Close Database Connection" ] }, { "cell_type": "code", "execution_count": 51, "id": "24cacc8f-ba78-4768-a4cb-425c9c9ca7f3", "metadata": {}, "outputs": [], "source": [ "# After using, always close the connection to the database\n", "db_conn.close()" ] }, { "cell_type": "code", "execution_count": null, "id": "5946f811-d132-48cb-9409-e0bbf12c9b52", "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": "3.11.5" } }, "nbformat": 4, "nbformat_minor": 5 }