diff --git a/notebooks/data-science-learning-paths-concept.png b/notebooks/data-science-learning-paths-concept.png index 1dbe53b..f50c031 100644 Binary files a/notebooks/data-science-learning-paths-concept.png and b/notebooks/data-science-learning-paths-concept.png differ diff --git a/notebooks/data-science-learning-paths.ipynb b/notebooks/data-science-learning-paths.ipynb index d5030dc..4913a2c 100644 --- a/notebooks/data-science-learning-paths.ipynb +++ b/notebooks/data-science-learning-paths.ipynb @@ -80,6 +80,20 @@ "- **Index notebook**: [📓Machine Learning on Time Series](index/mlts-machine-learning-time-series.ipynb)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Python Data Science Ecosystem [APE]\n", + "\n", + "A 2-day advanced course on independent topics, including developement of python packages with poetry, object-oriented programming, introduction in data bases, dashboards with streamlit, and introduction of the polars library. \n", + "\n", + "- **Level**: Advanced\n", + "- **Duration**: 2 days\n", + "- **Prerequisites**: DAP+MLP\n", + "- **Index notebook**: [📓Advanced Python Data Science Ecosystem](index/ape-advanced-python-ds-ecosystem-2day.ipynb)" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/notebooks/db/RDB_example.png b/notebooks/db/RDB_example.png new file mode 100644 index 0000000..e678554 Binary files /dev/null and b/notebooks/db/RDB_example.png differ diff --git a/notebooks/db/data/firmenlauf_demo.db b/notebooks/db/data/firmenlauf_demo.db new file mode 100644 index 0000000..9d52f87 Binary files /dev/null and b/notebooks/db/data/firmenlauf_demo.db differ diff --git a/notebooks/db/data/participants.csv b/notebooks/db/data/participants.csv new file mode 100644 index 0000000..663cd74 --- /dev/null +++ b/notebooks/db/data/participants.csv @@ -0,0 +1,5 @@ +First Name;Last Name;Shoe Size;Shirt Size;Distance;Team; +Anna;Einstein;38;38;5;3; +Marius;Fermi;44;60;2;5; +James;Pauli;44;42;10;8; +Selma;Meitner;41;40;10;3; diff --git a/notebooks/db/data/teams.csv b/notebooks/db/data/teams.csv new file mode 100644 index 0000000..4ead234 --- /dev/null +++ b/notebooks/db/data/teams.csv @@ -0,0 +1,4 @@ +ID;Size;Shoe Color;;;; +3;16;Red;;;; +5;15;Green;;;; +8;11;Purple;;;; diff --git a/notebooks/db/data/training.csv b/notebooks/db/data/training.csv new file mode 100644 index 0000000..9d92315 --- /dev/null +++ b/notebooks/db/data/training.csv @@ -0,0 +1,10 @@ +ID;Date(YYYY-MM-DD);Time(mm:ss);Distance(km);Runner;; +1;2023-07-15;39:00;4.5;Anna;; +2;2023-08-05;58:00;3;Marius;; +3;2023-08-07;34:45;1.6;James;; +4;2023-07-08;32:00;4.05;Selma;; +5;2023-07-18;35:00;4.5;Anna;; +6;2023-07-25;30:00;4.5;Anna;; +7;2023-09-07;37:00;5.456;Selma;; +8;2023-07-19;41:51;2.24;James;; +9;2023-07-28;32:06;1.6;James;; \ No newline at end of file diff --git a/notebooks/db/db-pandas-sql.ipynb b/notebooks/db/db-pandas-sql.ipynb new file mode 100644 index 0000000..bcc04d5 --- /dev/null +++ b/notebooks/db/db-pandas-sql.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f37e58c-94cf-4930-8ad9-1f724740083f", + "metadata": {}, + "source": [ + "# Pandas + SQL(Alchemy)\n", + "\n", + "Pandas is a very powerful tool to work with data frames.\n", + "But it can also be used with databases!\n", + "We can load single tables from an existing database into dataframes or create new tables from dataframes, without specifying any schema!\n", + "SQLAlchemy is doing it under the hood.\n", + "\n", + "Working with Pandas and SQL will always load tables into a dataframe, we do *not* get any Python objects as we did with the ORM when using SQLAlchemy.\n", + "\n", + "Be aware that dataframes do *not* know about any relations you might have established with SQLAlchemy!\n", + "\n", + "**Be aware**: Working with SQL+Pandas is usually only a comfortable workaround for simple use cases and \"quick-and-dirty\" approaches, e.g. when you need a simple lookup of some data. Also, if your amout of data is feasible for a dataframe and you plan to load the data once from a DB and do everything else in Pandas anyway, then this workflow will do.\n", + "For more complex tasks involving joining/aggregating/grouping/selecting data on a large data volume, you might want to rely on the features of your (relational) DB itself and do all these tasks using SQLAlchemy." + ] + }, + { + "cell_type": "markdown", + "id": "92f25dbc-afab-4413-881e-0a8c9c6787c2", + "metadata": {}, + "source": [ + "# Read from a DB with Pandas" + ] + }, + { + "cell_type": "markdown", + "id": "0112bdf6-91e2-444a-b344-aefe7f185b7f", + "metadata": {}, + "source": [ + "## Open Connection\n", + "\n", + "First, we have to establish a connection to the DB we have already filled.\n", + "In this example, we use the Sqlite DB-API for this task.\n", + "You can do the same with other systems, e.g. PostgreSQL, using the respective DB-API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "890c3e23-b4e8-4a5c-a8b1-1d9b36b2a688", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sqlite3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ab98ccf-b753-4b3a-a24c-2093aacc3b04", + "metadata": {}, + "outputs": [], + "source": [ + "connection = sqlite3.connect(\"data/firmenlauf_demo.db\")" + ] + }, + { + "cell_type": "markdown", + "id": "070ba9db-9c33-4380-8e85-a9dcc43c3583", + "metadata": {}, + "source": [ + "## Run an SQL queries with Pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "766d3ccf-b612-48a7-96f3-2866b98e7dbb", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the whole table \"teams\" into a dataframe\n", + "df_teams = pd.read_sql(\"SELECT * FROM teams\", connection)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "927e0dad-675f-4b05-af81-30be0cd7c544", + "metadata": {}, + "outputs": [], + "source": [ + "df_teams" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4100ce2e-11e1-476d-9375-192ae74c5de0", + "metadata": {}, + "outputs": [], + "source": [ + "# For better readability, we define the query string separately\n", + "# Note, that we have to JOIN two tables explicitly in SQL if we want to combine data from two tables\n", + "sql_query_runner_shoe_color = \"\"\"\n", + " SELECT runners.first_name, runners.shoe_size, teams.shoe_color \n", + " FROM runners\n", + " JOIN teams\n", + " ON runners.team_id = teams.id\n", + "\"\"\"\n", + "\n", + "df_shoes = pd.read_sql(sql_query_runner_shoe_color, connection)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "30c95984-57ff-447d-adaf-f18f8c4a1f19", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_shoes" + ] + }, + { + "cell_type": "markdown", + "id": "3a03cd89-70ca-4a38-919f-9c966db4548b", + "metadata": {}, + "source": [ + "# Add a Table to a DB with Pandas\n", + "\n", + "Let's say we want to add a new table containing the ranking from the actual Firmenlauf and the money each team gets.\n", + "We first create a dataframe and add this dataframe as new table to the DB.\n", + "Note, that we can not add any relationships as we did when using SQLAlchemy, since we do not use an ORM here." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ae82b51-6ab6-4552-bac1-937ceb11a8dc", + "metadata": {}, + "outputs": [], + "source": [ + "df_ranking = pd.DataFrame({\"rank\": [1, 2, 3, 4], \"team_id\": [4, 3, 2, 1], \"prize\": [5000, 2000, 1000, 500]})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eaa43902-2fd7-4ceb-a1cb-1804b39a0dcc", + "metadata": {}, + "outputs": [], + "source": [ + "df_ranking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d95e6c3f-cd0b-4bba-b674-a443d83e93c7", + "metadata": {}, + "outputs": [], + "source": [ + "# Import the dataframe as table to the DB and replace it, if it already exists (this might cause data loss in real world!).\n", + "df_ranking.to_sql(\"rankings\", connection, if_exists=\"replace\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eedfced1-3a67-4f97-a253-f3996658f988", + "metadata": {}, + "outputs": [], + "source": [ + "# Read out the newly added table as dataframe again\n", + "pd.read_sql(\"SELECT * FROM rankings\", connection)" + ] + }, + { + "cell_type": "markdown", + "id": "1cc2e5d0-c7da-4ae0-a00e-d2c2a247348b", + "metadata": {}, + "source": [ + "# Show DB schema information" + ] + }, + { + "cell_type": "markdown", + "id": "650c92e5-d1af-4168-9ff4-15e7be36dea1", + "metadata": {}, + "source": [ + "The `sqlite_master` element contains all information of the DB schema. \n", + "\n", + "We want to have a more structured output of the available tables and their columns therefore we define the following function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b51d21e5-daa5-4060-8043-f20d19a0151e", + "metadata": {}, + "outputs": [], + "source": [ + "def table_info(c, conn):\n", + " '''\n", + " prints out all of the columns of every table in db\n", + " c : cursor object\n", + " conn : database connection object\n", + " '''\n", + " tables = c.execute(\"SELECT name FROM sqlite_master WHERE type='table';\").fetchall()\n", + " for table_name in tables:\n", + " table_name = table_name[0] # tables is a list of single item tuples\n", + " table = pd.read_sql_query(\"SELECT * from {} LIMIT 0\".format(table_name), conn)\n", + " print(table_name)\n", + " for col in table.columns:\n", + " print('\\t' + col)\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0de9d90-3eff-47be-8556-98cd03f9fde4", + "metadata": {}, + "outputs": [], + "source": [ + "cur = connection.cursor()\n", + "table_info(cur, connection)" + ] + }, + { + "cell_type": "markdown", + "id": "d3eb6810-a3f2-47e3-ad55-5ecf82c8f100", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/db/db-pymongo-mongodb.ipynb b/notebooks/db/db-pymongo-mongodb.ipynb new file mode 100644 index 0000000..3f1f281 --- /dev/null +++ b/notebooks/db/db-pymongo-mongodb.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# NoSQL Databases: MongoDB and Python\n", + "\n", + "So, far we organised data in a **fixed schema** using **relationships** (a collection of structured data) in a relational SQL database. Bit we could also store **schema-less** and using **no relationships**, like this ...\n", + "\n", + "```\n", + "{\n", + " “Runner_1”: {\n", + " “Full Name”: “Anna Einstein”,\n", + " “Shoe Size: 38,\n", + " “Shirt Size”: 38,\n", + " “Distance”: 5,\n", + " “Team”: 3,\n", + " “Training”: {\n", + " 1: {“Date”: “2023-07-15”, “Time”: “39 min”}\n", + " 2: {“Distance”: 4.5, “Time”: “35 min”}\n", + " }\n", + " },\n", + " “Runner_2”: {\n", + " “First Name”: “James”,\n", + " “Last Name”: “Pauli”,\n", + " “Shoe Size: 38,\n", + " “Shirt Size”: 38,\n", + " “Team”: 8,\n", + " }\n", + "}\n", + "```\n", + "\n", + "NoSQL does not mean that we do not use anymore SQL, but it stands for \"Not Only SQL\", which is the concept of **NOT** using a relational data model, e.g. JSON style." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## NoSQL vs. Relational\n", + "\n", + "- NoSQL does not care about relations\n", + " - Faster read and write rates, but no easy way to join data\n", + "- NoSQL scales horizontally, RDBMS scale vertically\n", + " - NoSQL DBs can be extended to much larger data volumes\n", + "- NoSQL is schema-less\n", + " - Higher flexibility, but less efficient queries\n", + "- NoSQL does not support (Atomarity, Consistency, Isolation, Durability) transactions\n", + " - Queries might be inconsistent, transactions may be carried out only half, data integrity and consistency might be corrupted\n", + "\n", + "**Use relational database**, if you have/need:\n", + "- Well structured data, clear relations\n", + "- Complex queries\n", + "- Strong data consistency and integrity (“ACID”, e.g. banking)\n", + "- Scalability for read-heavy workflows\n", + "- Mature and stable ecosystem\n", + "\n", + "**Use NoSQL database**, if you have/need:\n", + "- Semi- or unstructured data with flexible and evolving attributes\n", + "- Scalability for write-heavy workloads\n", + "- High velocity, real-time analysis\n", + "- Changing data requirements\n", + "- Distributed systems" + ] + }, + { + "cell_type": "markdown", + "id": "e44029b1-d9a0-4075-a9e9-42e327747ed4", + "metadata": {}, + "source": [ + "## Python + MongoDB\n", + "\n", + "MongoDB is a widely used NoSQL document-based database system.\n", + "With `pymongo` we can interact with a MongoDB.\n", + "\n", + "Unlike sqlite, MongoDB is a server-based DB, so we first have to install MongoDB start a MongoDB instance, before we can start to interact with it via Python!\n", + "\n", + "See here for more information: https://www.mongodb.com/docs/manual/administration/install-community/" + ] + }, + { + "cell_type": "markdown", + "id": "d8f6e15e-8fbe-4f9d-ba6e-4545a54ff809", + "metadata": {}, + "source": [ + "## Create a DB Client\n", + "\n", + "Assuming, the MongoDB server is running, we can connect to it by running the following command.\n", + "\n", + "If no port or DB URL is specified, the client bound to a default port." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c51da0f1-6eda-4af0-8639-c01c2902b3ca", + "metadata": {}, + "outputs": [], + "source": [ + "import pymongo\n", + "import pprint\n", + "from bson.objectid import ObjectId" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6af7b6bf-7738-4d00-8f12-64480da5d7a0", + "metadata": {}, + "outputs": [], + "source": [ + "DATABASE_USERNAME = \"...\"\n", + "DATABASE_PASSWORD = \"...\"\n", + "PORT = 27017\n", + "\n", + "# Use URI string format\n", + "client = pymongo.MongoClient(f\"mongodb://{DATABASE_USERNAME}:{DATABASE_PASSWORD}@localhost:{PORT}/\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae4561d6-4045-4c3d-bb47-2bfade17c9e8", + "metadata": {}, + "source": [ + "## Create a New Database\n", + "Having the client, we create a new database. A MongoDB instance can host multiple databases!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d71248f6-7096-4093-bbe5-312f1db7a103", + "metadata": {}, + "outputs": [], + "source": [ + "db = client.test_database" + ] + }, + { + "cell_type": "markdown", + "id": "1c804f41-7cd5-46df-8b26-1724778f5f4d", + "metadata": {}, + "source": [ + "## Create a New Collection\n", + "\n", + "MongoDB is a document-based database.\n", + "Data is organized in collections, which can roughly(!) be compared to the concept of tables in a relational database.\n", + "Like tables, collections contain data of logical units.\n", + "One elemnt of a collection is called \"document\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "284c4c15-a9de-49f9-9e4a-4b2dd9b94c2b", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new collection named \"runners\" in our database\n", + "runners = db.runners" + ] + }, + { + "cell_type": "markdown", + "id": "0348a54d-c104-4364-a8b3-9b9c97296ab1", + "metadata": {}, + "source": [ + "## Add a Single Document\n", + "\n", + "Un- or semi-structured data is organized in key-value pairs, like Python dicts or JSON objects.\n", + "A value can be a key-value pair itself (\"nested structures\").\n", + "In our example, we have a collection of runners, which shall contain \"documents\" of key-value pairs for every runner.\n", + "Since we are dealing with a schema-less DB, we do not have to specify up front, which key-value pairs are needed for a document.\n", + "Furthermore, each document can have different keys!\n", + "\n", + "Each document gets a unique `_id` automatically, if it is not explicitly defined in the document dict." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05443984-8e15-443b-84fc-2cc2071c59d2", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's specify a document in a dict-like structre\n", + "runner = {\"Full Name\": \"Anna Einstein\", \"Shoe Size\": 38, \"Shirt Size\": 38, \"Team\": 1}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea25ba51-1adc-43dc-9abc-df835876ca4a", + "metadata": {}, + "outputs": [], + "source": [ + "#Insert the single document into the \"runners\" collection\n", + "runners.insert_one(runner)" + ] + }, + { + "cell_type": "markdown", + "id": "62c98951-1aa3-4b50-8a16-440b805dcc23", + "metadata": {}, + "source": [ + "## SELECT Data from MongoDB\n", + "\n", + "We can select data from the a collection by using `find_one()` (which finds the first or none object) and `find()`.\n", + "Within the `()` we can specify some filters.\n", + "Note, that there is no possibility to join tables together and query data on combined informations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6f5bfa2-c669-4c1b-a6cb-dddc6da952a6", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "pprint.pprint(runners.find_one())" + ] + }, + { + "cell_type": "markdown", + "id": "936558e9-c7d5-4dd8-8c73-7041b1209875", + "metadata": {}, + "source": [ + "## Add Many Documents\n", + "\n", + "Now we want to add a bulk of documents into the runner collection:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49b29a42-84d6-4d16-9521-f60f1a7516ec", + "metadata": {}, + "outputs": [], + "source": [ + "list_runners = [\n", + " {\"Full Name\": \"Marius Fermi\", \"Shoe Size\": 44, \"Shirt Size\": 60, \"Distance\": 2, \"Team\": 5, \"Age\": 54},\n", + " {\"Full Name\": \"James Pauli\", \"Shoe Size\": 44, \"Shirt Size\": 42, \"Team\": 8, \"Country\": \"USA\"},\n", + " {\"Full Name\": \"Selma Meitner\", \"Shoe Size\": 41, \"Shirt Size\": 40, \"Team\": 3, \"Teamleader\": True},\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7185f047-9c84-4085-ad85-34b1d402d6d0", + "metadata": {}, + "outputs": [], + "source": [ + "runners.insert_many(list_runners)" + ] + }, + { + "cell_type": "markdown", + "id": "d2dd03ef-d8a0-4295-8743-2c3d856cbf10", + "metadata": {}, + "source": [ + "... and select them all by using `find`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acccc024-6f16-40a3-83b9-a6d593faf40f", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "for runner in runners.find():\n", + " pprint.pprint(runner)" + ] + }, + { + "cell_type": "markdown", + "id": "012d6fe3-fe4e-4ba1-9279-826c498ed134", + "metadata": {}, + "source": [ + "## Filter Data\n", + "\n", + "We can filter data by passing a dict to the `find()` function.\n", + "For example: find all runner in team 1!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50653660-180d-4b66-95cb-5898f48dade9", + "metadata": {}, + "outputs": [], + "source": [ + "for runner in runners.find({\"Team\":1}):\n", + " pprint.pprint(runner)" + ] + }, + { + "cell_type": "markdown", + "id": "13d5911a-a4de-471b-b5ce-dbba1d52c64f", + "metadata": {}, + "source": [ + "## List of Collections" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f4c353e-e7b0-4088-92eb-5f491a5d617b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "db.list_collection_names()" + ] + }, + { + "cell_type": "markdown", + "id": "213748b8-45eb-4cae-a081-5d02446367cf", + "metadata": {}, + "source": [ + "## Add Another Collection" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6863f03f-cb2f-488d-a71e-3b1c580f730c", + "metadata": {}, + "outputs": [], + "source": [ + "list_trainings = [\n", + " {\"runner\": \"Anna\", \"time\": \"38:45\", \"date\": \"2023-07-06\", \"distance\": 6.4},\n", + " {\"runner\": \"Anna\", \"time\": \"35:22\", \"date\": \"2023-07-07\", \"distance\": 6.4},\n", + " {\"runner\": \"Marius\", \"time\": \"1:04:22\", \"date\": \"2023-08-07\", \"distance\": 7, \"mean_heartrate\": 121},\n", + " {\"runner\": \"James\", \"time\": \"45:22\", \"distance\": \"6 miles\", \"mean_velocity\": \"7.6 mph\"}\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70ada480-0b77-4fa7-a5b2-8db5f4536221", + "metadata": {}, + "outputs": [], + "source": [ + "trainings = db.trainings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dfb5eba-adf6-4bbe-a979-99315d5bc0c8", + "metadata": {}, + "outputs": [], + "source": [ + "trainings.insert_many(list_trainings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7196bc8c-2f38-4d86-8284-d8318fc46f74", + "metadata": {}, + "outputs": [], + "source": [ + "for training in trainings.find():\n", + " pprint.pprint(training)" + ] + }, + { + "cell_type": "markdown", + "id": "1dbbe220-b19a-49b9-8f9d-462fa7536097", + "metadata": {}, + "source": [ + "## Update a Document\n", + "\n", + "You can change a document by using the `update_one()` or `update_many()` function.\n", + "You need to specify the key-value pair you want to update and the operation. E.g. `$set` just sets a new value.\n", + "\n", + "Other operators can be found here: https://www.mongodb.com/docs/manual/reference/operator/update/#std-label-update-operators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7dd247a0-467d-481c-9aaf-0bb7429de373", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Use avalid ID from the above print out\n", + "trainings.update_one({\"_id\": ObjectId(\"65141cbbc429eac58ebf20fb\")}, {\"$set\": {\"distance\": 8.3}})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33a37da2-fb1c-4e06-9077-02050994918b", + "metadata": {}, + "outputs": [], + "source": [ + "pprint.pprint(trainings.find_one({\"_id\": ObjectId(\"65141cbbc429eac58ebf20fb\")}))" + ] + }, + { + "cell_type": "markdown", + "id": "6b6ec6a7-abc6-45c6-9fb9-8a5328618f06", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/db/db-sqalchemy.ipynb b/notebooks/db/db-sqalchemy.ipynb new file mode 100644 index 0000000..7a39bb6 --- /dev/null +++ b/notebooks/db/db-sqalchemy.ipynb @@ -0,0 +1,657 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Relational Databases: ORM - Using `SQLAlchemy` with Python\n", + "\n", + "`SQLAlchemy` is an example for a ORM (Object Relational Mapper). This is a high-level interface to interact with DBs of all common types. Another concept/interface is a DB-API. An example is shown in the [db-sqlite3-notebook](db-sqlite3.ipynb).\n", + "Tabels and queries are mapped to Python objects and functions. An ORM uses a DB-API under the hood, but the user does not need to know and write SQL queries. It is a more \"pythonic\" way to interact with a DB.\n", + "\n", + "**Use**, if you have:\n", + "- Complex database shemas and larg projects\n", + "- Evolving DB including version control and migration\n", + "- The need to plug the same code to different RDBMS (Relational Database Management Systems)\n", + "\n", + "**Don't use**, if you have:\n", + "- Only one static DB that has to populated and queried once\n", + "- (You want to avoid the overhead of defining the table classes)\n", + "- The need for fine-grained control over all DB connections (usually not the case!)\n", + "- Extremely good SQL skills and no understanding of Python at all" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dce6ba8c-3cd8-4d2d-a16b-7ee81e578249", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine, UniqueConstraint, select\n", + "from sqlalchemy.orm import DeclarativeBase\n", + "from typing import List, Set\n", + "from typing import Optional\n", + "from sqlalchemy.orm import Mapped, mapped_column, relationship, sessionmaker\n", + "from sqlalchemy import String, ForeignKey\n", + "from datetime import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1143333-efc8-47fd-9b83-e302ba68b186", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sqlalchemy\n", + "sqlalchemy.__version__ " + ] + }, + { + "cell_type": "markdown", + "id": "98f77331-8dd0-4792-a6b2-7c36af09b233", + "metadata": {}, + "source": [ + "# Load test data\n", + "\n", + "For this demonstration, we load data from .csv-files into Pandas dataframes and put them into our sqlite database afterwards.\n", + "We do this mostly to use the nice dataframe visualization.\n", + "In general, it is not necessary to create dataframes before inserting data into a DB!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfb10b27-d122-4207-bde2-eef8af327666", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Load Teams table\n", + "df_teams = pd.read_csv(\n", + " \"data/teams.csv\",\n", + " sep=\";\",\n", + " index_col=\"ID\",\n", + " usecols=[\"ID\", \"Size\", \"Shoe Color\"]\n", + ")\n", + "# Load Runner table\n", + "df_runners = pd.read_csv(\n", + " \"data/participants.csv\",\n", + " sep=\";\",\n", + " usecols=[\"First Name\", \"Last Name\", \"Shoe Size\", \"Shirt Size\", \"Distance\", \"Team\"]\n", + ")\n", + "# Load Trainings table\n", + "df_training = pd.read_csv(\n", + " \"data/training.csv\",\n", + " sep=\";\",\n", + " usecols=[\"Date(YYYY-MM-DD)\", \"Time(mm:ss)\", \"Distance(km)\", \"Runner\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "756a4b9b-be91-4e71-be2f-176e63de52ca", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for df in [df_runners, df_teams, df_training]:\n", + " display(df)" + ] + }, + { + "cell_type": "markdown", + "id": "55f555a8-bb1c-4cdd-a4d2-06ae5142c232", + "metadata": {}, + "source": [ + "# Defining the DB Scheme\n", + "\n", + "We want to create a DB containing the tables `teams`, `runners` and `trainings` like:\n", + "\n", + "![RDB_example](RDB_example.png)\n", + "\n", + "A DB has much more functionalty than a simple .csv or Excel table.\n", + "This comes with some downsides.\n", + "We cannot simply insert data into an empty table, because there is no table at first.\n", + "First, we have to specify how our table is designed.\n", + "This means we have to define all columns, the data types and the relationships between different tables up front.\n", + "\n", + "(*Yes*, you can avoid this for simple and qick-and-dirty cases by using some `pandas`/`sqlalchemy` magic, but for more complex projects and real use cases, you probably can't overcome it 🤷‍♀️)\n", + "\n", + "In `sqlalchemy`, each table is a Python `class`, which inherits from `sqlalchemy`'s `DeclarativeBase`.\n", + "Within each table class, the columns and their characteristics (data types, relationships, primary keys, nullable, ...) are defined.\n", + "\n", + "Some specialities up front:\n", + "* Python classes have CamelCase names by convention. Therefore, the table class is named like `Team` (singular!). The SQL name of the table is defined in the `__tablename__` attribute and is usually a lowercase string and plural, like `\"teams\"`\n", + "* Following the new (as of 2023) `sqlalchemy 2.0` guidelines, a column is defined like:\n", + " * `: Mapped[] = mapped_column()`\n", + "* `Mapped`\n", + " * The Mapped annotation takes care of the correct translation between Python and RDBMS data types\n", + " * If a column is defined like `Mapped[Optional[float]]`, the fiel is allowed to be empty\n", + "* `UniqueConstraint` \n", + " * A UniqueConstraint is used to tell the DB that for specific columns or combinations of columns the values must be unique and can not be inserted a second time. For example, we want every runner only one time in our table of runners.\n", + " * If a UniqueConstraint failed as we try to insert duplicates into our table, we will see an `IntegrityError`\n", + "* `relationship` \n", + " * Relationships are introduced by the relationship() function and a mapped_column to the primary key column of the related table\n", + "* `__repr__` \n", + " * This function defines what is printed if we just print table row (which is an instance of a table class). Defining it is nice to have but not crucially necessary.\n", + " \n", + "\n", + "More information can be found here:\n", + "* Sqlalchemy Quick start tutorial: https://docs.sqlalchemy.org/en/20/orm/quickstart.html\n", + "* Declarative table configuration: https://docs.sqlalchemy.org/en/20/orm/declarative_tables.html#orm-declarative-table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "790a3d16-6d03-4868-bd1a-da2f3ef34160", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Base(DeclarativeBase):\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5c0e25b-4fa8-4835-9cd1-ada9ae1a8ca6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# class name CamelCase, __tablename__ lowercase!\n", + "class Team(Base):\n", + " __tablename__ = \"teams\"\n", + " __table_args__ = (\n", + " # We want each team to have a different shoe color, so we use a UniqueConstraint.\n", + " UniqueConstraint(\"shoe_color\"),\n", + " )\n", + " # The id column is always our primary key\n", + " id: Mapped[int] = mapped_column(primary_key=True)\n", + " size: Mapped[int]\n", + " # We define a maximum length or 20 for the string containing the shoe color \n", + " shoe_color: Mapped[str] = mapped_column(String(20))\n", + " # A team can have many members, so we establish a one-to-many relationship to the 'team' column in the 'Runner' table.\n", + " members: Mapped[List[\"Runner\"]] = relationship(back_populates=\"team\")\n", + " def __repr__(self) -> str:\n", + " return f\"Team {self.id!r} wears {self.shoe_color!r} shoes and has {self.size!r} members.\"\n", + "\n", + "\n", + "class Runner(Base):\n", + " __tablename__ = \"runners\"\n", + " __table_args__ = (\n", + " UniqueConstraint(\"first_name\", \"last_name\", \"team_id\"),\n", + " )\n", + " id: Mapped[int] = mapped_column(primary_key=True)\n", + " first_name: Mapped[str] = mapped_column(String(30), nullable=False)\n", + " last_name: Mapped[str] = mapped_column(String(30), nullable=False)\n", + " shoe_size: Mapped[int]\n", + " shirt_size: Mapped[int]\n", + " # We want to allow runners to leave the 'distance' column empty, so we specify the data type as 'Optional[float]'\n", + " distance: Mapped[Optional[float]]\n", + " # The team_id should be a foreign key pointing to the 'id' column of the 'Team' table\n", + " team_id = mapped_column(ForeignKey(\"teams.id\"))\n", + " # Here, we establish the many-to-one relationship to the 'members' column of the 'Team' table \n", + " team: Mapped[Team] = relationship(back_populates=\"members\")\n", + " # A runner can have many training runs, so we create a one-to-many relationship to the 'runner' column of the 'Training' table\n", + " trainings: Mapped[List[\"Training\"]] = relationship(back_populates=\"runner\")\n", + " def __repr__(self) -> str:\n", + " return f\"Runner(id={self.id!r}, first_name={self.first_name!r} last_name={self.last_name!r})\"\n", + "\n", + "\n", + "class Training(Base):\n", + " __tablename__ = \"trainings\"\n", + " __table_args__ = (\n", + " UniqueConstraint(\"runner_id\", \"date\"),\n", + " )\n", + " id: Mapped[int] = mapped_column(primary_key=True)\n", + " date: Mapped[datetime]\n", + " time: Mapped[datetime]\n", + " distance: Mapped[float]\n", + " runner_id = mapped_column(ForeignKey(\"runners.id\"))\n", + " runner: Mapped[Runner] = relationship(back_populates=\"trainings\")\n", + " def __repr__(self) -> str:\n", + " return f\"Training(id={self.id!r}, runner={self.runner.first_name!r} {self.runner.last_name!r} time={self.time!r} dictance={self.distance!r})\"" + ] + }, + { + "cell_type": "markdown", + "id": "bf3df57f-4375-4d37-8a96-eb00ce1d41aa", + "metadata": {}, + "source": [ + "# Building a DB\n", + "\n", + "## Creating an engine\n", + "\n", + "Since a DB is not necessarily a single file on your disk, we have to define, where our DB lives and how we can connect to it.\n", + "Therefore, we build an `Engine` which can handle connections.\n", + "\n", + "The engine gets a connetion string, which contains the type of the RDBMS (e.g. `sqlite` or `postgresql`), a dialect (depends on the RDBMS we use), a location, and maybe user credentials.\n", + "For our small example, we work with an sqlite DB here, that lives in memory an is not persisted on disk.\n", + "The `echo=True` only switches on some logging, which we can use to understand how the engine works. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a0fc09e-8786-4ad8-b774-4e818dd39fb4", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "engine = create_engine(\"sqlite+pysqlite:///firmenlauf_demo_sqlalchemy.db\", echo=True)" + ] + }, + { + "cell_type": "markdown", + "id": "315a45a7-68c3-4baf-8bd4-b84238e3dcb8", + "metadata": {}, + "source": [ + "## Creating all tables\n", + "\n", + "After we have carefully designed our tables in Python, we now want them to be actually created via SQL statements in our sqlite DB we connected our engine to." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5863e972-33f6-4e55-a6d3-c8b685bbbc34", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# open a connection and create all tables that inherit from Base\n", + "with engine.begin() as conn:\n", + " Base.metadata.create_all(conn)\n", + "\n", + "# Since we have 'echo=True' specified in our engine, sqlalchemy will tell us what it does" + ] + }, + { + "cell_type": "markdown", + "id": "70c60942-6831-405b-b77a-ff8483f84a1f", + "metadata": {}, + "source": [ + "## Fill the tables\n", + "\n", + "For every interaction with a database, we have to create a session. The overall workflow is:\n", + "* `begin` a session\n", + "* do something, for example `add` a row\n", + "* `commit` the changes to the DB\n", + "* `rollback` the session, if anything went wrong\n", + "* `close` the session\n", + "\n", + "If something went wrong, it can happen that we blow up the connection to the DB.\n", + "No problem in our small test case, but in real production this can cause severe loss of data!\n", + "Therefore you always have to take care of correctly handled sessions!\n", + "\n", + "We will now loop over our Pandas dataframes and add every row to the corresponding DB table.\n", + "\n", + "**If you run the following \"Fill\" cells twice, you will receive an `IntegrityError`!**\n", + "\n", + "This is caused to the `UniqueConstraint` we set for all tables. If we try to insert duplicates into the DB, we try to corrupt the intergity of our DB and an error is raised. In real world applications, you always have to deal with this kind of errors via context manager or try/except blocks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fed7377f-f470-458a-93d2-063b06556a4f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# We use sqlqlachmy's sessionmaker to handle the sessions\n", + "Session = sessionmaker(engine)" + ] + }, + { + "cell_type": "markdown", + "id": "08b15535-4bda-483f-8e39-1acc98936aae", + "metadata": {}, + "source": [ + "### Fill Team table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f586e80a-d6af-4d85-918a-5fda8fa90ce8", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "# This context manager opens a session and runs a session.commit() as we leave it and session.rollback() in case of exceptions\n", + "with Session.begin() as session:\n", + " for id, (size, shoe_color) in df_teams.iterrows():\n", + " team = Team(\n", + " id=id,\n", + " size=size,\n", + " shoe_color=shoe_color\n", + " )\n", + " session.add(team)\n", + " #context manager runs session.commit() and session.close() here\n" + ] + }, + { + "cell_type": "markdown", + "id": "cea627ac-6b95-44ba-a2f5-60018d49e646", + "metadata": {}, + "source": [ + "### Fill Runner table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd7a3942-2e4f-4c22-9787-e7c508f15661", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " for id, (first_name, last_name, shoe_size, shirt_size, distance, team_id) in df_runners.iterrows():\n", + " print(first_name, last_name)\n", + " runner = Runner(\n", + " first_name=first_name,\n", + " last_name=last_name,\n", + " shoe_size=shoe_size,\n", + " shirt_size=shirt_size,\n", + " distance=distance,\n", + " team_id=team_id \n", + " )\n", + " session.add(runner)" + ] + }, + { + "cell_type": "markdown", + "id": "73543c56-4f77-461e-88e3-7e0928116fe8", + "metadata": {}, + "source": [ + "### Fill Training table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b87886e-8202-4cf7-ae4e-4bcff5534049", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " for id, (date, time, distance, runner_id) in df_training.iterrows():\n", + " training = Training(\n", + " date=datetime.strptime(date, \"%Y-%m-%d\"),\n", + " time=datetime.strptime(time, \"%M:%S\"),\n", + " distance=distance,\n", + " runner_id=runner_id,\n", + " )\n", + " session.add(training)" + ] + }, + { + "cell_type": "markdown", + "id": "62e624e0-bd3a-4117-9bd6-001c095904e2", + "metadata": {}, + "source": [ + "# Selecting data from the DB\n", + "\n", + "sqlalchemy makes the DB accessible for us, even if we don't speak SQL.\n", + "Instead, we speak Python functions and sqlalchemy translates it into SQL queries which are sent to the DB.\n", + "The result is then again translated by sqlalchemy into Python objects for us to use.\n", + "\n", + "Again, for every interaction with the DB, we have to open a session, respectively work in a context manager." + ] + }, + { + "cell_type": "markdown", + "id": "7ae31c29-efed-498b-a70e-4815896d8752", + "metadata": {}, + "source": [ + "## `SELECT * FROM teams`\n", + "\"Give me a list of all teams!\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4d3da50-3135-4004-8af6-018c2c7df562", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " teams = session.execute(\n", + " select(Team)\n", + " ).all()\n", + " print(\"----- Query Result -----\")\n", + " print(teams)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "markdown", + "id": "086e584c-dd46-4633-a80a-f454892eb604", + "metadata": {}, + "source": [ + "## `SELECT * FROM teams WHERE size > 15;`\n", + "\"Give me a list of all teams with more than 15 members!\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c4feb3f-95aa-4215-9008-3ce9341e5cef", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " teams = session.execute(\n", + " select(Team)\n", + " .where(Team.size > 15)\n", + " ).all()\n", + " print(\"----- Query Result -----\")\n", + " print(teams)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "markdown", + "id": "b1e3d8ac-3c2a-4b16-b36f-7f0e2c8985e7", + "metadata": {}, + "source": [ + "## `SELECT shoe_color FROM teams WHERE size > 15;`\n", + "\"Give me a list of the shoe colors of all teams with more than 15 members!\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc61dd8a-0324-4b3f-ab23-d2458fb00009", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " shoe_colors = session.execute(\n", + " select(Team.shoe_color)\n", + " .where(Team.size > 15)\n", + " ).all()\n", + " print(\"----- Query Result -----\")\n", + " print(shoe_colors)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "markdown", + "id": "3df50fd8-69f2-457b-af0a-6f66cee923af", + "metadata": {}, + "source": [ + "## `JOIN`\n", + "\n", + "`SELECT first_name, shoe_size, shoe_color 
\n", + "FROM runners
\n", + " JOIN teams ON runners.team = teams.id;`\n", + "\n", + "\"Give me a list of participants including their shoe size and team shoe color!\"\n", + "\n", + "We have two options here to get our results:\n", + "* run the table `JOIN` explicitly\n", + " * we use the `.join()` function to join the tables as we query it\n", + " * more code, but more controll over queries\n", + "* use the `relationship` we have established by defining our table classes!\n", + " * less code, more pythonic\n", + " * maybe less performant or more (sub-)queries under the hood" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69bc2b4a-015d-459e-ad1f-c0f9a5c1fe9e", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "# Use JOIN\n", + "# We end up with only one query including a JOIN statement!\n", + "with Session.begin() as session:\n", + " runners = session.execute(\n", + " select(Runner.first_name, Runner.shoe_size, Team.shoe_color)\n", + " .select_from(Runner)\n", + " .join(Team)\n", + " ).all()\n", + " print(\"----- Query Result -----\")\n", + " print(runners)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2fd0bea-4a4a-4ad1-8934-cfccd99d7dca", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Use the relationship\n", + "with Session.begin() as session:\n", + " runners = session.execute(\n", + " select(Runner)\n", + " ).scalars().all()\n", + " for runner in runners:\n", + " print(\"----- Query Result -----\")\n", + " print(runner.first_name, runner.shoe_size, runner.team.shoe_color)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "markdown", + "id": "17cd35cf-4a67-4314-a1b3-d6ad262430eb", + "metadata": {}, + "source": [ + "## Use relations\n", + "Another example of the `sqlalchemy` magic would be the following query...\n", + "\n", + "Note, that sqlalchemy sends a new query to the database in the moment we call `team.members`! This is why the session is still needed. We can specify this behavior in the relationship definition and load the depending object directly (\"Eager Loading\").\n", + "\n", + "See https://docs.sqlalchemy.org/en/20/orm/queryguide/relationships.html for more details!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71003d14-918d-4027-aeb3-0cfb1d6a2c7f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "with Session.begin() as session:\n", + " teams = session.execute(\n", + " select(Team)\n", + " ).scalars().all()\n", + " \n", + " for team in teams:\n", + " print(\"----- Query Result -----\")\n", + " print(team.members)\n", + " print(\"------------------------\")" + ] + }, + { + "cell_type": "markdown", + "id": "534cb556-1da6-4939-aaed-fad3abcbf31d", + "metadata": {}, + "source": [ + "# Further Reading\n", + "\n", + "* Relationship patterns: https://docs.sqlalchemy.org/en/20/orm/basic_relationships.html\n", + "* engine definition for different RDBMSs: https://docs.sqlalchemy.org/en/20/core/engines.html#database-urls\n", + "* Working with ORM related objects: https://docs.sqlalchemy.org/en/20/tutorial/orm_related_objects.html\n", + "* All you ever want to know: https://docs.sqlalchemy.org/en/20/tutorial/index.html#unified-tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "feaa6b59-e58f-4207-9a41-d8abf8d2070e", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/db/db-sqlite3.ipynb b/notebooks/db/db-sqlite3.ipynb new file mode 100644 index 0000000..f780be4 --- /dev/null +++ b/notebooks/db/db-sqlite3.ipynb @@ -0,0 +1,527 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "eefece34-ae84-4cd5-bb7f-0db34f91e9af", + "metadata": {}, + "source": [ + "# Relational Databases: DB-API - Using `sqlite3` with SQL and Python\n", + "\n", + "`sqlite3` is an example for a DB-API. This is a low-level interface to interact with a DB. Another concept/interface is ORM (Object Relational Mapper). An example is shown in the [db-sqalchemy-notebook](db-sqalchemy.ipynb).\n", + "`sqlite3` executes plain SQL queries directly. Connections and results need to be handled and managed manually. With such a low-level DB-API there is no mapping between the DB tables and the Python objects.\n", + "\n", + "**Use**, if you have:\n", + "- Small use cases or demo/tutorials\n", + "- Static use cases, i.e. if you create a table once and never touch the schema again\n", + "- Profound SQL skills\n", + "- (Need for absolute control over DB connections)\n", + "\n", + "**Don't use**, if you have:\n", + "- Complex DB schema\n", + "- Evolving DB\n", + "- Need for migration and version control\n", + "- Need for Python objects" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What do we want to do now?\n", + "\n", + "We organise a company running event (\"Firmenlauf\") and want to know the participants including their shoe size and team shoe color, thus that we can order the shoes in the correct color and size. We started with the following very \"inefficient\" single table, which got larger with every new information and idea what to keep track of. \n", + "\n", + "![start_table](start_table.png)\n", + "\n", + "The table includes a lot of dublicated information. We know that a **relational database** is a collection of structured data. Thus, we found logical units in our single table, that we now want to insert into a database.\n", + "\n", + "We want to create a DB containing the tables `teams`, `runners` and `trainings` like:\n", + "\n", + "![RDB_example](RDB_example.png)\n", + "\n", + "Note, this code will only work with sqlite DBs, since we using the Python API for sqlite: https://docs.python.org/3/library/sqlite3.html#\n", + "\n", + "For PostgreSQL, use `psycopg2`: https://www.psycopg.org/docs/index.html" + ] + }, + { + "cell_type": "markdown", + "id": "5ffeb6dd-7e1f-4030-970a-0d08b691a8ec", + "metadata": {}, + "source": [ + "# Set up a DB connection\n", + "First, we create a simple `sqlite` DB and open a connection to it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sqlite3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da1000ef-eb87-46d8-a238-430df1a38566", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!rm firmenlauf_demo.db" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02dd0822-3f59-4ef3-b00a-1a190fdc7e3a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "database = \"firmenlauf_demo.db\"\n", + "connection = sqlite3.connect(database)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "998d90c1-3803-4674-897a-2deae38e818b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create a DB cursor to be able to execute SQL statements\n", + "cur = connection.cursor()" + ] + }, + { + "cell_type": "markdown", + "id": "c48fd7fe-66da-44f7-8f7c-1cf304fcf521", + "metadata": {}, + "source": [ + "# Create tables\n", + "\n", + "To define our tables, we have to write plain SQL statements.\n", + "Each table has a `PRIMARY KEY`, that identifies every row and a `UNIQUE` constraint which defines which combinations of values have to be unique in this table. This prevents us from inserting duplicates into the DB.\n", + "\n", + "We also define relationships via the `FOREIGN KEY ... REFERENCES` statement.\n", + "\n", + "Learn more about SQL: https://www.w3schools.com/sql/default.asp" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "928a6f57-a32f-4e1d-91e6-e975593c4b6e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define queries to create new tables in plain SQL\n", + "\n", + "sql_create_table_teams = \"\"\"\n", + " CREATE TABLE IF NOT EXISTS teams (\n", + " \tid INTEGER NOT NULL, \n", + " \tsize INTEGER NOT NULL, \n", + " \tshoe_color VARCHAR(20) NOT NULL, \n", + " \tPRIMARY KEY (id), \n", + " \tUNIQUE (shoe_color)\n", + " )\n", + "\"\"\"\n", + "\n", + "sql_create_table_runners = \"\"\"\n", + " CREATE TABLE IF NOT EXISTS runners (\n", + " \tid INTEGER NOT NULL, \n", + " \tfirst_name VARCHAR(30) NOT NULL, \n", + " \tlast_name VARCHAR(30) NOT NULL, \n", + " \tshoe_size INTEGER NOT NULL, \n", + " \tshirt_size INTEGER NOT NULL, \n", + " \tdistance FLOAT, \n", + " \tteam_id INTEGER, \n", + " \tPRIMARY KEY (id), \n", + " \tUNIQUE (first_name, last_name, team_id), \n", + " \tFOREIGN KEY(team_id) REFERENCES teams (id)\n", + " )\n", + "\"\"\"\n", + "\n", + "sql_create_table_trainings = \"\"\"\n", + " CREATE TABLE IF NOT EXISTS trainings (\n", + " \tid INTEGER NOT NULL, \n", + " \tdate DATETIME NOT NULL, \n", + " \ttime DATETIME NOT NULL, \n", + " \tdistance FLOAT NOT NULL, \n", + " \trunner_id INTEGER, \n", + " \tPRIMARY KEY (id), \n", + " \tUNIQUE (runner_id, date), \n", + " \tFOREIGN KEY(runner_id) REFERENCES runners (id)\n", + " )\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "id": "5ea77fb9-1cb3-4c19-8346-3b93cc8ca06b", + "metadata": {}, + "source": [ + "### Now, we execute these SQL statements and commit it to the DB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7156a043-22a7-4751-846f-960c4008b28f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(sql_create_table_teams)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "115395e2-6cf8-4365-a872-a188bbc06bac", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(sql_create_table_runners)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "761fc2d5-25f0-479f-b209-e71ebe47d6ca", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(sql_create_table_trainings)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccfe56e1-e373-4488-baa6-c4e1063ca618", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "connection.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "17f7e16d-da0f-4ad7-9efb-8915da5a43ea", + "metadata": {}, + "source": [ + "### We can now query the tables but they will be empty" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ece19ae1-8c5c-4e75-b822-ffed619d2160", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(\"SELECT * FROM teams\").fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "bc241c34-0eef-4f65-8993-99ba34528085", + "metadata": {}, + "source": [ + "# Insert data into the DB\n", + "\n", + "Again, we first have to write our `INSERT` statements in plain SQL and the execute them and commit the changes.\n", + "\n", + "Note, if we execute the same `INSERT` twice (meaning running the cell twice), we will get an `IntegrityError` for trying to insert duplicates into the DB. This is caused by the `UNIQUE` constraint we set in our `CREATE TABLE` statement." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ecf1ffe-4302-4946-9f13-3e94431ad1eb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sql_insert_teams = [\n", + " \"INSERT INTO teams (id, size, shoe_color) VALUES (3, 16, 'Red')\",\n", + " \"INSERT INTO teams (id, size, shoe_color) VALUES (5, 15, 'Green')\",\n", + " \"INSERT INTO teams (id, size, shoe_color) VALUES (8, 11, 'Purple')\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a05bde35-80fd-4c26-8270-805d9a20f856", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "for insert_team in sql_insert_teams:\n", + " cur.execute(insert_team)\n", + "connection.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "3f02d945-a581-45cd-abb7-4d7ca85622f8", + "metadata": {}, + "source": [ + "### Now, we can query the inserted data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3028dafe-11fb-4891-8871-8d151fd29950", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(\"SELECT * FROM teams\").fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "08a6aefa-ef12-4935-87d7-67af9b92a8cd", + "metadata": {}, + "source": [ + "### We will insert some more data into the DB" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a333c654-a60e-4a87-a76c-570f837a9d65", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sql_insert_runners = \"\"\"\n", + " INSERT INTO runners (\n", + " first_name, last_name, shoe_size, shirt_size, distance, team_id\n", + " )\n", + " VALUES (?, ?, ?, ?, ?, ?)\n", + "\"\"\"\n", + "\n", + "runners = [\n", + " ('Anna', 'Einstein', 38, 38, 5.0, 3),\n", + " ('Marius', 'Fermi', 44, 60, 2.0, 5),\n", + " ('James', 'Pauli', 44, 42, 10.0, 8),\n", + " ('Selma', 'Meitner', 41, 40, 10.0, 3),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21e0a879-9747-492e-8220-9796d4cf40e6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for runner in runners:\n", + " cur.execute(sql_insert_runners, runner)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12ec7f15-650d-4540-9a9c-b30d15d76a11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "cur.execute(\"SELECT * FROM runners\").fetchall()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ae97e12-68f4-4ac8-bb0f-099dbed3fce5", + "metadata": {}, + "outputs": [], + "source": [ + "sql_insert_trainings = \"\"\"\n", + " INSERT INTO trainings (\n", + " date, time, distance, runner_id\n", + " )\n", + " VALUES (?, ?, ?, ?)\n", + "\"\"\"\n", + "\n", + "trainings = [\n", + " (\"2023-07-15\", \"39:00\", 4.500, 1),\n", + " (\"2023-08-05\", \"58:00\", 3.000, 2),\n", + " (\"2023-08-07\", \"34:45\", 1.600, 3),\n", + " (\"2023-07-08\", \"32:00\", 4.050, 4),\n", + " (\"2023-07-18\", \"35:00\", 4.500, 1),\n", + " (\"2023-07-25\", \"30:00\", 4.500, 1),\n", + " (\"2023-09-07\", \"37:00\", 5.456, 4),\n", + " (\"2023-07-19\", \"41:51\", 2.240, 3),\n", + " (\"2023-07-28\", \"32:06\", 1.600, 3),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db1dee62-b56d-4ab1-8186-39fef5c8caec", + "metadata": {}, + "outputs": [], + "source": [ + "for training in trainings:\n", + " cur.execute(sql_insert_trainings, training)\n", + "connection.commit()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8227158-b4bb-48b5-838d-30e9e932f343", + "metadata": {}, + "outputs": [], + "source": [ + "cur.execute(\"SELECT * FROM trainings\").fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "02f48a8f-6d46-4735-9c66-c04747b0e8c1", + "metadata": {}, + "source": [ + "# `JOIN` tables\n", + "\n", + "We can now join tables and retrieve a list of shoes we have to order for the runners. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "694c4f37-803b-429f-8287-33900ced1d16", + "metadata": {}, + "outputs": [], + "source": [ + "sql_query_runner_shoe_color = \"\"\"\n", + " SELECT runners.first_name, runners.shoe_size, teams.shoe_color \n", + " FROM runners\n", + " JOIN teams\n", + " ON runners.team_id = teams.id\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa45a240-901b-4476-ad12-6cd5d7d2ff89", + "metadata": {}, + "outputs": [], + "source": [ + "cur.execute(sql_query_runner_shoe_color).fetchall()" + ] + }, + { + "cell_type": "markdown", + "id": "b90693e6-71f8-4c1a-a690-dc018be05a7e", + "metadata": {}, + "source": [ + "# Show DB schema information" + ] + }, + { + "cell_type": "markdown", + "id": "42fe622f-6494-4f1a-82d9-58e5bda0c0ed", + "metadata": {}, + "source": [ + "The `sqlite_master` element contains all information of the DB schema." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f2035a25-1fe4-41cf-a35d-720595452618", + "metadata": {}, + "outputs": [], + "source": [ + "cur.execute(\"SELECT * FROM sqlite_master ;\") \n", + "print(cur.fetchall())" + ] + }, + { + "cell_type": "markdown", + "id": "19d2241b-c5da-4a9e-9585-9e8ea8d4485a", + "metadata": {}, + "source": [ + "Filter available tables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39ac48b0-c2cc-4008-8606-58322da16daa", + "metadata": {}, + "outputs": [], + "source": [ + "cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", + "print(cur.fetchall())" + ] + }, + { + "cell_type": "markdown", + "id": "f98496ed-80d1-41b2-99b6-dc3b2d4f4f2f", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "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": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/db/start_table.png b/notebooks/db/start_table.png new file mode 100644 index 0000000..d382a48 Binary files /dev/null and b/notebooks/db/start_table.png differ diff --git a/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb b/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb new file mode 100644 index 0000000..4aacdf2 --- /dev/null +++ b/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced Python Data Science Ecosystem" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this course we will cover several advanced topics in the broader Python data science ecosystem. From object-oriented programming, to working wit databases, or the development of Python packages.\n", + "\n", + "While we recapitulate some of the foundations, they are covered in much more detail in the following modules of Data Science 101:\n", + "\n", + "- [📓 Data Analysis with Python](dap2-data-analysis-python.ipynb)\n", + "- [📓 Machine Learning with Python](mlp2-machine-learning-python-2day.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "### Curriculum\n", + "\n", + "1. **Development of Python Packages with Poetry**\n", + "\n", + "1. **Object-Oriented Programing with Python**
\n", + " - [Hands-On](../python/python-oop.ipynb)\n", + "\n", + "1. **Working with Databases**\n", + " - Relational Databases and two concepts to integrate a DB into a Python framework\n", + " - [DB-API: sqlite3](../db/db-sqlite3.ipynb)\n", + " - [ORM: SQLAlchemy](../db/db-sqalchemy.ipynb)\n", + " - NoSQL Databases \n", + " - [NoSQL Databases with PyMongo](../db/db-pymongo-mongodb.ipynb)\n", + " - SQL and `pandas` \n", + " - [SQL with Pandas](../db/db-pandas-sql.ipynb)\n", + " \n", + "1. **Data Visualizations and Dashboards**\n", + " - /training/notebooks/streamlit\n", + "\n", + "1. **Introduction to the Polars Library** \n", + " - [Translate Pandas Code to Polars](../polars/polars-translate-pandas-to-polars.ipynb)\n", + " - [Solution: Translate Pandas Code to Polars](../polars/polars-translate-pandas-to-polars-solution.ipynb)\n", + " - [Handling of larger than Memory Data](../polars/polars-pipeline-larger-than-memory.ipynb)\n", + " - [Solution: Handling of larger than Memory Data](../polars/polars-pipeline-larger-than-memory-solution.ipynb)\n", + " - [A little (not complete) Benchmark](../polars/polars-benchmark.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/polars/polars-benchmark.ipynb b/notebooks/polars/polars-benchmark.ipynb new file mode 100644 index 0000000..71bfc03 --- /dev/null +++ b/notebooks/polars/polars-benchmark.ipynb @@ -0,0 +1,237 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Measure the execution time of the different implementations in `pandas` and `polars`\n", + "## A little (not complete) benchmark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from datetime import datetime, timedelta\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate larger data set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of rows in the CSV file\n", + "n = 4_000_000 # Replace with the desired number of rows\n", + "\n", + "timestamps = [datetime.now() - timedelta(minutes=i) for i in range(n)]\n", + "substation_ids = [random.randint(1, 10) for _ in range(n)]\n", + "voltages = np.random.normal(\n", + " 230, 10, n\n", + ") # Normal distribution with mean 230 and standard deviation 10\n", + "currents = np.random.normal(\n", + " 5, 2, n\n", + ") # Normal distribution with mean 5 and standard deviation 2\n", + "\n", + "\n", + "df = pd.DataFrame(\n", + " {\n", + " \"timestamp\": timestamps,\n", + " \"substation_id\": substation_ids,\n", + " \"voltage\": voltages,\n", + " \"current\": currents,\n", + " }\n", + ")\n", + "\n", + "# Write the DataFrame to a CSV file\n", + "df.to_csv(\"electricity_usage.csv\", index=False)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df = df.sort(\"timestamp\") # data has to be sorted for group_by_dynamic!\n", + "df_grouped = df.group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + ").agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polars version with eager mode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Convert 'timestamp' from string to datetime\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + "\n", + "# calculate power outcome\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Group by 'substation_id' and calculate daily average power\n", + "df = df.sort(\"timestamp\") # data has to be sorted for group_by_dynamic!\n", + "df_grouped = df.group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + ").agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Filter data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"timestamp\"])\n", + "\n", + "# Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Polars version with lazy mode and query" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "threshold = 1000\n", + "\n", + "# Define lazy query/pipeline\n", + "q = (\n", + " pl.scan_csv(\"electricity_usage.csv\")\n", + " .with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + " .with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + " .sort(\"timestamp\")\n", + " .group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + " ).agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + " .filter(pl.col(\"daily_avg_power\") > threshold)\n", + " .sort([\"substation_id\", \"timestamp\"])\n", + ")\n", + "\n", + "# possibility to test the pipeline with reduced amount data\n", + "# df_test = q.fetch(n_rows=int(100))\n", + "\n", + "# Collect the data\n", + "df = q.collect(streaming=True) # set streaming = True if the data might not fit into memory\n", + "\n", + "# Write the transformed data to a new CSV file\n", + "df.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars-pipeline-larger-than-memory-solution.ipynb b/notebooks/polars/polars-pipeline-larger-than-memory-solution.ipynb new file mode 100644 index 0000000..01f57f5 --- /dev/null +++ b/notebooks/polars/polars-pipeline-larger-than-memory-solution.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solution: Write query for larger than memory data sets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from datetime import datetime, timedelta\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Data\n", + "This simulated dataset contains a timeseries of electrical voltage and current values for different substations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the random seeds to have the same outcome when the cell is executed again\n", + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of generated data points\n", + "n = 4_000_000 \n", + "\n", + "# Generate timestamps by subtraction from the current time the desired number of generated data points in minutes\n", + "timestamps = [datetime.now() - timedelta(minutes=i) for i in range(n)]\n", + "# Generate substation ids in the range of 1 to 10 \n", + "substation_ids = [random.randint(1, 10) for _ in range(n)]\n", + "# Generate voltages as normal distribution with mean 230 and standard deviation 10\n", + "voltages = np.random.normal(230, 10, n) \n", + "# Generate currents as normal distribution with mean 5 and standard deviation 2\n", + "currents = np.random.normal(5, 2, n)\n", + "\n", + "# Collect all lists and create a DataFrame\n", + "df = pd.DataFrame({\n", + " 'timestamp': timestamps,\n", + " 'substation_id': substation_ids,\n", + " 'voltage': voltages,\n", + " 'current': currents\n", + "})\n", + "\n", + "# Write the DataFrame to a CSV file\n", + "df.to_csv(\"electricity_usage.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: \"How can we make the code even faster?\" - polars version with lazy mode\n", + " \n", + "Now, we imagine that there is a larger than memory data set. Think about updating the number of generated data points above to e.g. 4_000_000 (not more than that, please).\n", + "Write the previous `polars` code as a `polars` query/pipeline (either use your code or the provided solution below as base).\n", + "\n", + "Execute the query in lazy mode and measure the run time with `%%timeit`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df = df.sort(\"timestamp\") # data has to be sorted for group_by_dynamic!\n", + "df_grouped = df.group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + ").agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution: \"How can we make the code even faster?\" - polars version with lazy mode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "threshold = 1000\n", + "\n", + "# Define lazy query/pipeline\n", + "q = (\n", + " pl.scan_csv(\"electricity_usage.csv\")\n", + " .with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + " .with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + " .sort(\"timestamp\")\n", + " .group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + " ).agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + " .filter(pl.col(\"daily_avg_power\") > threshold)\n", + " .sort([\"substation_id\", \"timestamp\"])\n", + ")\n", + "\n", + "# possibility to test the pipeline with reduced amount data\n", + "# df_test = q.fetch(n_rows=int(100))\n", + "\n", + "# Collect the data\n", + "df = q.collect(streaming=True) # set streaming = True if the data might not fit into memory\n", + "\n", + "# Write the transformed data to a new CSV file\n", + "df.write_csv(\"transformed_electricity_usage.csv\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### For plotting and further data exploration change to `pandas.DataFrame`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# in case you want a pandas dataframe for plotting etc.\n", + "df_pandas = df.to_pandas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars-pipeline-larger-than-memory.ipynb b/notebooks/polars/polars-pipeline-larger-than-memory.ipynb new file mode 100644 index 0000000..708a4d4 --- /dev/null +++ b/notebooks/polars/polars-pipeline-larger-than-memory.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `polars`: Write query for larger than memory data sets\n", + "\n", + "[`polars`](https://www.pola.rs) loads in *lazy mode* only the necessary data into memory. This is done via `scan` instead of `read`, which creates a lazy `polars` query. By calling `collect` in the end the selected data is loaded into memory. When setting `collect(streaming=True)` the data is collected in batches (not available for all lazy operations). This can be used for larger than memory datasets. Eager DataFrames can be converted with `.lazy()`.\n", + "\n", + "Eager mode:\n", + "```\n", + "# Full dataset is loaded to memory\n", + "df = pl.read_csv(„docs/data/iris.csv\")\n", + "\n", + "df_small = df.filter(pl.col(\"sepal_length\") > 5)\n", + "df_agg = (\n", + " df_small.group_by(„species“)\n", + " .agg(pl.col(„sepal_width\")\n", + " .mean()\n", + ")\n", + "```\n", + "\n", + "Lazy mode:\n", + "```\n", + "# Define lazy data frame (no computation will happen here)\n", + "df = pl.scan_csv(\"iris.csv\")\n", + "df = df.filter(pl.col(\"sepal length (cm)\") > 5)\n", + "df = df.group_by(\"target\").agg(pl.col(\"sepal width (cm)\").mean())\n", + "\n", + "# Data is collected (now the computation happens)\n", + "df = df.collect(streaming=True)\n", + "```\n", + "\n", + "**When not to use lazy mode?** During exploration, when intermediate results are needed and the design of the query is not completely known yet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from datetime import datetime, timedelta\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Data\n", + "This simulated dataset contains a timeseries of electrical voltage and current values for different substations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the random seeds to have the same outcome when the cell is executed again\n", + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of generated data points\n", + "n = 1_000_000\n", + "\n", + "# Generate timestamps by subtraction from the current time the desired number of generated data points in minutes\n", + "timestamps = [datetime.now() - timedelta(minutes=i) for i in range(n)]\n", + "# Generate substation ids in the range of 1 to 10\n", + "substation_ids = [random.randint(1, 10) for _ in range(n)]\n", + "# Generate voltages as normal distribution with mean 230 and standard deviation 10\n", + "voltages = np.random.normal(230, 10, n)\n", + "# Generate currents as normal distribution with mean 5 and standard deviation 2\n", + "currents = np.random.normal(5, 2, n)\n", + "\n", + "# Collect all lists and create a DataFrame\n", + "df = pd.DataFrame(\n", + " {\n", + " \"timestamp\": timestamps,\n", + " \"substation_id\": substation_ids,\n", + " \"voltage\": voltages,\n", + " \"current\": currents,\n", + " }\n", + ")\n", + "\n", + "# Write the DataFrame to a CSV file\n", + "df.to_csv(\"electricity_usage.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise: \"How can we make the code even faster?\" - polars version with lazy mode\n", + " \n", + "Now, we imagine that there is a larger than memory data set. Think about updating the number of generated data points above to e.g. 4_000_000 (not more than that, please).\n", + "Write the previous `polars` code as a `polars` query/pipeline (either use your code or the provided solution below as base).\n", + "\n", + "Execute the query in lazy mode and measure the run time with `%%timeit`\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df = df.sort(\"timestamp\") # data has to be sorted for group_by_dynamic!\n", + "df_grouped = df.group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + ").agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code goes here!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars-translate-pandas-to-polars-solution.ipynb b/notebooks/polars/polars-translate-pandas-to-polars-solution.ipynb new file mode 100644 index 0000000..8920c86 --- /dev/null +++ b/notebooks/polars/polars-translate-pandas-to-polars-solution.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solution: Translate `pandas` to `polars` code" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from datetime import datetime, timedelta\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Data\n", + "This simulated dataset contains a timeseries of electrical voltage and current values for different substations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the random seeds to have the same outcome when the cell is executed again\n", + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of generated data points\n", + "n = 1_000_000\n", + "\n", + "# Generate timestamps by subtraction from the current time the desired number of generated data points in minutes\n", + "timestamps = [datetime.now() - timedelta(minutes=i) for i in range(n)]\n", + "# Generate substation ids in the range of 1 to 10\n", + "substation_ids = [random.randint(1, 10) for _ in range(n)]\n", + "# Generate voltages as normal distribution with mean 230 and standard deviation 10\n", + "voltages = np.random.normal(230, 10, n)\n", + "# Generate currents as normal distribution with mean 5 and standard deviation 2\n", + "currents = np.random.normal(5, 2, n)\n", + "\n", + "# Collect all lists and create a DataFrame\n", + "df = pd.DataFrame(\n", + " {\n", + " \"timestamp\": timestamps,\n", + " \"substation_id\": substation_ids,\n", + " \"voltage\": voltages,\n", + " \"current\": currents,\n", + " }\n", + ")\n", + "\n", + "# Write the DataFrame to a CSV file\n", + "df.to_csv(\"electricity_usage.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"What is the daily average power?\" - `pandas` version\n", + "Now, we want to analyse the data and answer the question \"What is the daily average power for each substation when looking at powers greater than 1000 V?\", i.e. read data, data preparation, calculating power, gropuby according to substation, resampling to daily timestamps, calculating the mean, and in the end filter for powers greater than the threshold." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pd.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format and use the timestamp as index\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "df = df.set_index(df['timestamp'])\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df['power'] = df['voltage'] * df['current']\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df_grouped = df.groupby('substation_id').resample(\"D\")[\"power\"].mean()\n", + "df_grouped = df_grouped.reset_index()\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped[df_grouped['power'] > threshold]\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort_values(by=[\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.to_csv(\"transformed_electricity_usage_pandas.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise:\"What is the daily average power?\" - `polars` version\n", + "\n", + "Translate the above steps/pipeline from `pandas` to `polars` and compare run times with `%%timeit`. [`%%timeit`](https://ipython.org/ipython-doc/dev/interactive/magics.html#magic-timeit) is known as *cell magic* in IPython (for more information read [here](https://ipython.org/ipython-doc/dev/interactive/magics.html#cell-magics)). It measures the execution time of the cell by executing it more than once. Feel free to use the [`polars`](https://www.pola.rs) documentation.\n", + "\n", + "The pipeline should include the following steps:\n", + "\n", + "- Read data csv\n", + "- Convert the date string to a datetime format\n", + "- Calculate the power\n", + "- Get the power for each substation per day\n", + "- Remove all entries with a power value less than 1000\n", + "- Sort the result by substation and date\n", + "- Write the result in a csv file\n", + "\n", + "**Hint**: Polars has a `group_by_dynamic` method \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution 1: \"What is the daily average power?\" - `polars` version, with *group_by_dynamic*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Datetime, \"%Y-%m-%d %H:%M:%S%.f\"))\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df = df.sort(\"timestamp\") # data has to be sorted for group_by_dynamic!\n", + "df_grouped = df.group_by_dynamic(\n", + " index_column=\"timestamp\",\n", + " every=\"1d\",\n", + " closed=\"right\",\n", + " by=\"substation_id\",\n", + " include_boundaries=False,\n", + ").agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution 2: \"What is the daily average power?\" - `polars` version, with *group_by*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format\n", + "# Use pl.Date instead of pl.Datetime for group_by on dates directly\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Date, \"%Y-%m-%d %H:%M:%S%.f\").alias(\"date\"))\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "# Step 4: Group by 'substation_id' and date, and calculate daily average power\n", + "df_grouped = df.group_by([\"date\", \"substation_id\"]).agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped.filter(pl.col(\"daily_avg_power\") > threshold)\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"date\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.write_csv(\"transformed_electricity_usage.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars-translate-pandas-to-polars.ipynb b/notebooks/polars/polars-translate-pandas-to-polars.ipynb new file mode 100644 index 0000000..b04028f --- /dev/null +++ b/notebooks/polars/polars-translate-pandas-to-polars.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Intoduction to `polars`: Translate `pandas` to `polars` code\n", + "\n", + "[`polars`](https://www.pola.rs) as a lightning-fast DataFrame library for Rust and Python. It is an alternative to `pandas` when developing data processing pipelines, because as of late 2023 there is no real support for plotting libraries such as matplotlib or plotly.\n", + "\n", + "It is written in Rust and uses the Apache Arrow data format in the background compared to numpy as the basis of pandas. Although, as of [`pandas 2.0`](https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html) it possible to use the Arrow data format as well in pandas, but is not the default.\n", + "\n", + "`polars` is upto 5 to 10 times faster than pandas and provides \"lazy execution\" when reading data and executing queries. When to use `polars`, when you have larger than memory data sets or when you develop a pipeline for data processing and filtering." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "from datetime import datetime, timedelta\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate Data\n", + "This simulated dataset contains a timeseries of electrical voltage and current values for different substations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the random seeds to have the same outcome when the cell is executed again\n", + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of generated data points\n", + "n = 1_000_000\n", + "\n", + "# Generate timestamps by subtraction from the current time the desired number of generated data points in minutes\n", + "timestamps = [datetime.now() - timedelta(minutes=i) for i in range(n)]\n", + "# Generate substation ids in the range of 1 to 10\n", + "substation_ids = [random.randint(1, 10) for _ in range(n)]\n", + "# Generate voltages as normal distribution with mean 230 and standard deviation 10\n", + "voltages = np.random.normal(230, 10, n)\n", + "# Generate currents as normal distribution with mean 5 and standard deviation 2\n", + "currents = np.random.normal(5, 2, n)\n", + "\n", + "# Collect all lists and create a DataFrame\n", + "df = pd.DataFrame(\n", + " {\n", + " \"timestamp\": timestamps,\n", + " \"substation_id\": substation_ids,\n", + " \"voltage\": voltages,\n", + " \"current\": currents,\n", + " }\n", + ")\n", + "\n", + "# Write the DataFrame to a CSV file\n", + "df.to_csv(\"electricity_usage.csv\", index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## \"What is the daily average power?\" - `pandas` version\n", + "Now, we want to analyse the data and answer the question \"What is the daily average power for each substation when looking at powers greater than 1000 V?\", i.e. read data, data preparation, calculating power, gropuby according to substation, resampling to daily timestamps, calculating the mean, and in the end filter for powers greater than the threshold." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "# Step 1: Read data from csv\n", + "df = pd.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Set the correct timestamp format and use the timestamp as index\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "df = df.set_index(df['timestamp'])\n", + "\n", + "# Step 3: Calculate power (P=VI) and add it as a new column\n", + "df['power'] = df['voltage'] * df['current']\n", + "\n", + "# Step 4: Group by 'substation_id', resample timestamp and calculate daily average power\n", + "df_grouped = df.groupby('substation_id').resample(\"D\")[\"power\"].mean()\n", + "df_grouped = df_grouped.reset_index()\n", + "\n", + "# Step 5: Filter out data where daily average power is less than a certain threshold\n", + "threshold = 1000\n", + "df_grouped = df_grouped[df_grouped['power'] > threshold]\n", + "\n", + "# Step 6: Sort result by substation and timestamp\n", + "df_grouped = df_grouped.sort_values(by=[\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 7: Write the transformed data to a new CSV file\n", + "df_grouped.to_csv(\"transformed_electricity_usage_pandas.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exercise:\"What is the daily average power?\" - `polars` version\n", + "\n", + "Translate the above steps/pipeline from `pandas` to `polars` and compare run times with `%%timeit`. [`%%timeit`](https://ipython.org/ipython-doc/dev/interactive/magics.html#magic-timeit) is known as *cell magic* in IPython (for more information read [here](https://ipython.org/ipython-doc/dev/interactive/magics.html#cell-magics)). It measures the execution time of the cell by executing it more than once. Feel free to use the [`polars`](https://www.pola.rs) documentation.\n", + "\n", + "The pipeline should include the following steps:\n", + "\n", + "- Read data csv\n", + "- Convert the date string to a datetime format\n", + "- Calculate the power\n", + "- Get the power for each substation per day\n", + "- Remove all entries with a power value less than 1000\n", + "- Sort the result by substation and date\n", + "- Write the result in a csv file\n", + "\n", + "**Hint**: Polars has a `group_by_dynamic` method \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code goes here!\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © [Point 8 GmbH](https://point-8.de)_" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/python/python-oop.ipynb b/notebooks/python/python-oop.ipynb new file mode 100644 index 0000000..55fd46b --- /dev/null +++ b/notebooks/python/python-oop.ipynb @@ -0,0 +1,621 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5493614f-840a-403b-987a-dbd5df907468", + "metadata": { + "tags": [] + }, + "source": [ + "# Object Oriented Programming (OOP)\n", + "\n", + "Object Oriented Programming (OOP) is a powerful paradigm that allows us to organize code around real-world entities. It promotes modularity, reusability, and maintainability by defining objects with **attributes** (data) and **methods** (functions).\n", + "\n", + "In Python, everything is an object, making it an ideal language for implementing OOP principles. In this notebook will cover essential OOP concepts, including **classes**, **objects**, **inheritance**, **encapsulation**, **polymorphism** and **abstraction**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd4ebd86-88d6-4301-9c19-b6b2873e2219", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# a string\n", + "print(f\"{type('a string')=}\")\n", + "\n", + "# an integer\n", + "print(f\"{type(42)=}\")\n", + "\n", + "# the 'len()' function\n", + "print(f\"{type(len)=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e29a3aa7-86a7-43d3-9040-d44280194169", + "metadata": { + "tags": [] + }, + "source": [ + "## Classes and Objects" + ] + }, + { + "cell_type": "markdown", + "id": "4f2a0829-0e03-4ca2-a078-ac508ea21f59", + "metadata": {}, + "source": [ + "A class is a blueprint for objects. It defines the **attributes** (data) and **methods** (functions) that its objects will have.\n", + "You can use a class to create multiple objects with different values but with the same basic structure." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d9a1551-34df-474d-a0e2-2ae793953401", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Book:\n", + " title: str\n", + " author: str\n", + " year_published: int\n", + " \n", + " def __init__(self, title: str, author: str, year_published: int):\n", + " self.title = title\n", + " self.author = author\n", + " self.year_published = year_published\n", + " \n", + " def display_info(self):\n", + " return f\"Book: '{self.title}' by {self.author} and published in {self.year_published}'\"" + ] + }, + { + "cell_type": "markdown", + "id": "35b01a0d-660b-4351-b75a-a3f7819a3ee0", + "metadata": {}, + "source": [ + "Above, we have defined a **class** called `Book` - a blueprint for a new type of object, here for example to represent an entry in an library.\n", + "We have added one function to the class - it is called **methods** because it is attached to an object now. Every method has `self` as the first argument, a variable that points to the object itself. \n", + "\n", + "The `__init__` method is a special method called the **constructor** that defines how an object of type `Book` is created and initialized." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "583998d9-0d92-4401-b803-238087b85eac", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "book = Book(title=\"Lord of the Rings\", author=\"J. R. R. Tolkien\", year_published=1954)" + ] + }, + { + "cell_type": "markdown", + "id": "9d3da587-b1cc-46d0-a0fb-4d4a27b64ac3", + "metadata": {}, + "source": [ + "We have now created a new object which is an **instance** of the class `Book`, and set its attributes to the given values. We can now read these attributes as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e4bdfe4-07c7-4f82-890d-d112659f52b5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "book.title" + ] + }, + { + "cell_type": "markdown", + "id": "f2673ff6-bb73-4613-b1a0-2f0c06f9cb91", + "metadata": {}, + "source": [ + "We have also defined one method. Methods can be almost any kind function and contain any code, but a proper method does something with the object and its attributes. Here, it combines all the attributes of a `Book` to give us a predefined display message:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "045bd538-1004-467c-a728-4cb6bc503c10", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(book.display_info())" + ] + }, + { + "cell_type": "markdown", + "id": "886de0e9-745e-4533-8be6-f27a9e872d3b", + "metadata": { + "tags": [] + }, + "source": [ + "### Python Exercise:\n", + "\n", + "Create a class called **Contact** with the following attributes: **first_name**, **last_name**, and **email**. Add a method called display_info that prints out all the information about the **Contact**." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0573e8d3-5c43-493f-902c-48d07f57b252", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Your code here\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "79248a5a-0918-4db1-a145-dffb2daf24f3", + "metadata": { + "tags": [] + }, + "source": [ + "## Inheritance" + ] + }, + { + "cell_type": "markdown", + "id": "acbad3ff-d40a-4c26-b1f1-86e2ef320694", + "metadata": {}, + "source": [ + "Inheritance allows one class (**child class**) to inherit attributes and methods from another class (**parent class**). It facilitates code reuse and the creation of specialized classes. In fact, every python class inherit from the `object` class.\n", + "\n", + "Leveraging our previous `Book` class, we can create a new `EBook` class that inherits its attributes and methods while also adding new attributes specific to ebooks like file_size and file_extension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "204f5d2e-84c2-4b5c-8257-8560acfee71e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class EBook(Book):\n", + " file_size: float\n", + " file_extension: str\n", + " \n", + " def __init__(self, title: str, author: str, year_published: int, file_size: float, file_extension: str):\n", + " super().__init__(title, author, year_published)\n", + " self.file_size = file_size\n", + " self.file_extension = file_extension" + ] + }, + { + "cell_type": "markdown", + "id": "a51bb472-764d-43ec-b27e-77e26fb34701", + "metadata": {}, + "source": [ + "Since `EBook` is inheriting the `Book` class we need to satisfy the creation requiriments of `Book` parent class.\n", + "We can do this by:\n", + "\n", + "1. calling the `super()` method to reference the parent class\n", + "2. calling its `__init__` method along with the required parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e60d8871-6894-407b-b895-a6765158bd95", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ebook = EBook(title=\"The Martian\", author=\"Andy Weir\", year_published=2015, file_size=1000, file_extension=\".epub\")" + ] + }, + { + "cell_type": "markdown", + "id": "8a68a1ac-cae1-4bc5-94d4-5efcb70c3c10", + "metadata": {}, + "source": [ + "We can create an `EBook` object just like before, but we need to add the additional attributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9604ade-1087-4cca-b04d-c11537c0d81e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(ebook.display_info())" + ] + }, + { + "cell_type": "markdown", + "id": "44db8415-90b1-4c69-ae46-7e992a3669c0", + "metadata": {}, + "source": [ + "Even the `display_info()` method works but, since it was only define at the parent `Book` class, it does not know about the additional `EBook` attributes.\n", + "\n", + "To handle this it would be necessary to define `display_info` method so that `EBook` class can define how to display its attributes. The act of redefining a parent method to a more specific implementation is called **method overriding**." + ] + }, + { + "cell_type": "markdown", + "id": "e06904f5-740d-4259-94ee-2727c207250c", + "metadata": {}, + "source": [ + "### Python Exercise:\n", + "\n", + "Try to **override** the `def display_info(self):` method at our `EBook` class to account for the additional attributes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ced610d-cd7c-4907-93fd-bf1db6a363c2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Your code here\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bee97768-0a5e-4843-b3c1-fa9cb4b96195", + "metadata": { + "tags": [] + }, + "source": [ + "## Encapsulation and Access Modifiers" + ] + }, + { + "cell_type": "markdown", + "id": "94d008fe-19bc-4fdc-8aed-77035fcf7a8f", + "metadata": { + "tags": [] + }, + "source": [ + "Encapsulation involves controlling access to certain parts of an object. Python uses **access modifiers** to define the visibility of attributes and methods, helping to **encapsulate** data and ensure that unwanted changes cannot be made from outside the class.\n", + "\n", + "Access modifiers are usually:\n", + "\n", + "- **Public**: Can be accessed from any part of the program (default in Python).\n", + "- **Private**: Can only be accessed within the defining class, denoted by a prefix of double underscore (`__`).\n", + "- **Protected**: Intended for use within the defining class and subclasses, denoted by a prefix of single underscore (`_`).\n", + "\n", + "Such **encapsulation** of attributes and methods can reduce code complexity by hiding internal implementation details, especially when a class is used in other parts of the code or project.\n", + "\n", + "But it is important to remember that access modifiers in Python are not strict, they represent just an intent on how an attribute or method should be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24d58813-91a8-4df8-bc8f-e9f4f6885199", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Person:\n", + " def __init__(self, name: str, age: int):\n", + " self.name = name # Public attribute\n", + " self._age = age # Protected attribute\n", + " self.__email = None # Private attribute\n", + "\n", + " def set_email(self, email: str):\n", + " if '@' not in email:\n", + " raise Exception('Invalid email')\n", + " self.__email = email\n", + "\n", + " def get_email(self):\n", + " return self.__email\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a91302c-f671-41cf-a4ae-bf644c312579", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "person = Person(\"Bob\", age=29)\n", + "person.set_email('bob@email.com')" + ] + }, + { + "cell_type": "markdown", + "id": "a50f2bd1-6b2e-48db-b636-ba7f8139a162", + "metadata": { + "tags": [] + }, + "source": [ + "We can use the `set_email` method to assign an email to our `Person` object without knowing that in fact there is some validation code in its implementation." + ] + }, + { + "cell_type": "markdown", + "id": "554326b6-d774-40fe-af4e-3c59434eed23", + "metadata": { + "tags": [] + }, + "source": [ + "## Polymorphism" + ] + }, + { + "cell_type": "markdown", + "id": "43a3c261-193a-4a35-876c-0824d6540212", + "metadata": {}, + "source": [ + "Polymorphism is the ability of interacting with different objects, from different classes, through a common **interface** (methods)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c3cfffd-d8c3-4bd9-86ae-0f01db60ed85", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "name = \"Just a string\"\n", + "data = [22, 12, 31, 12]\n", + "\n", + "print(len(name))\n", + "print(len(data))" + ] + }, + { + "cell_type": "markdown", + "id": "50449721-5f87-44ca-b88a-86469dcb8620", + "metadata": {}, + "source": [ + "With Python it is easy to encounter some polymorphic behaviour. In the example above, we can observe that the same `len(...)` method can be used to retrieve the size of different types of objects.\n", + "\n", + "This only works because both `str` and `list` classes implements the `__len__(...)` method, which in turn is used by the `len(...)` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec07607d-6d4f-4e13-bd68-4153c29594e5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Rectangle:\n", + " def __init__(self, x: float, y:float):\n", + " self.x = x\n", + " self.y = y\n", + " def calculate_area(self):\n", + " return self.x * self.y\n", + " \n", + " \n", + "class Circle: \n", + " def __init__(self, r: float):\n", + " self.r = r\n", + " \n", + " def calculate_area(self):\n", + " return 3.14 * self.r**2" + ] + }, + { + "cell_type": "markdown", + "id": "2804f58b-f567-4aef-84fa-4c7a96dfca5d", + "metadata": {}, + "source": [ + "The concept of **polymorphism** can also be applied to custom classes we define. In the example above we created two classes `Rectangle` and `Circle` where both implement the `calculate_area` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "267529a3-e284-4b5d-8841-c628da06550d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "list_of_shapes = [Circle(3), Rectangle(3, 4), Circle(20)]" + ] + }, + { + "cell_type": "markdown", + "id": "39dd3cfb-6e6e-402c-b2d3-b0c12c994821", + "metadata": {}, + "source": [ + "We can then calculate the area of objects from these two distinct classes without having to specify or even knowing their classs types." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35dd468b-6f0b-4a09-90ca-0f10d68f4c0e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "for shape in list_of_shapes:\n", + " print(shape.calculate_area())" + ] + }, + { + "cell_type": "markdown", + "id": "412f66ad-7cab-4d2a-917c-386e06443341", + "metadata": {}, + "source": [ + "That works because Python cares more about what an object can do, rather than its type. That means that code like above will work for any class as long as it implements a `calculate_area(self)` method." + ] + }, + { + "cell_type": "markdown", + "id": "f83348ca-5a74-4e36-8e7a-cbb665c401fd", + "metadata": {}, + "source": [ + "### Python Exercise:\n", + "\n", + "Try building on the inheritance and polymorphism concepts by:\n", + "\n", + " - Making the other `Rectangle` and `Circle` classes inherit from the `Shape` class.\n", + " - Add a `Triangle` class with a **base** and **height** attributes.\n", + " - To calculate the area we could use the formula: `0.5 * base * height`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cdae6c50-2990-459f-82d0-644ae73911d5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Shape:\n", + " def calculate_area(self):\n", + " pass\n", + "\n", + "# Your code here\n" + ] + }, + { + "cell_type": "markdown", + "id": "6dd68bbd-bc77-478b-a5b6-4814f7d597fd", + "metadata": {}, + "source": [ + "## Abstraction" + ] + }, + { + "cell_type": "markdown", + "id": "436de011-ae7a-46cc-a934-919e0b477cfb", + "metadata": {}, + "source": [ + "Abstraction builds on encapsulation by emphasizing the idea of hiding complex implementation details.\n", + "This concept is crucial for managing complex systems as it allows developers to focus on the functionality provided by an object rather than its implementation details.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1b5312c-e2a8-4523-8707-61cc54ce7f92", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from abc import ABC, abstractmethod\n", + "\n", + "class Shape(ABC):\n", + " @abstractmethod\n", + " def calculate_area(self):\n", + " ...\n" + ] + }, + { + "cell_type": "markdown", + "id": "b2a25059-dfaa-4880-bc84-1fccb7ae2f5b", + "metadata": {}, + "source": [ + "The code above defines a Shape class that inherits from the Abstract Base Class (`ABC`).\n", + "It also defines `calculate_area` method, that is annotated as `@abstractmethod`.\n", + "\n", + "The `ABC` class and the `@abstractmethod` are python mechanisms to demonstrate the intention that the class and method cannot be instantiated directly as they represent just an abstraction (interface)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae46accd-c128-445e-aef8-6327181881b5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Circle(Shape):\n", + " def __init__(self, radius: float):\n", + " self.radius = radius\n", + "\n", + " def calculate_area(self):\n", + " return 3.14 * self.radius ** 2" + ] + }, + { + "cell_type": "markdown", + "id": "7be9d6a8-49ff-4b7d-9f43-f8146815a2d6", + "metadata": {}, + "source": [ + "This allows developers to focus on the data and the interactions of objects before having to implement it." + ] + }, + { + "cell_type": "markdown", + "id": "15e6581d-4565-4eaa-a476-ec3aaa3204d8", + "metadata": {}, + "source": [ + "---\n", + "_This notebook is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright © 2018-2022 [Point 8 GmbH](https://point-8.de)_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9832ff9c-807d-47cc-96f9-1fa7ddbdcde5", + "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": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/streamlit/data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx b/notebooks/streamlit/data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx new file mode 100644 index 0000000..46a0aef Binary files /dev/null and b/notebooks/streamlit/data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx differ diff --git a/notebooks/streamlit/data/Kapitel_3_Daten_NEP_2037_V2023_2_Entwurf.xlsx b/notebooks/streamlit/data/Kapitel_3_Daten_NEP_2037_V2023_2_Entwurf.xlsx new file mode 100644 index 0000000..13d5e28 Binary files /dev/null and b/notebooks/streamlit/data/Kapitel_3_Daten_NEP_2037_V2023_2_Entwurf.xlsx differ diff --git a/notebooks/streamlit/data/NEP_2037_2045_V2023_2_Entwurf_Teil1_1.pdf b/notebooks/streamlit/data/NEP_2037_2045_V2023_2_Entwurf_Teil1_1.pdf new file mode 100644 index 0000000..c4dc8ff Binary files /dev/null and b/notebooks/streamlit/data/NEP_2037_2045_V2023_2_Entwurf_Teil1_1.pdf differ diff --git a/notebooks/streamlit/example_stromnetz/.streamlit/config.toml b/notebooks/streamlit/example_stromnetz/.streamlit/config.toml new file mode 100644 index 0000000..acd1d9e --- /dev/null +++ b/notebooks/streamlit/example_stromnetz/.streamlit/config.toml @@ -0,0 +1,4 @@ +# .streamlit/config.toml + +[server] +enableStaticServing = true \ No newline at end of file diff --git a/notebooks/streamlit/example_stromnetz/Overview.py b/notebooks/streamlit/example_stromnetz/Overview.py new file mode 100644 index 0000000..6538466 --- /dev/null +++ b/notebooks/streamlit/example_stromnetz/Overview.py @@ -0,0 +1,40 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) + +import streamlit as st +import io +from PIL import Image +import base64 +import pathlib + +def add_logo(): + file = open(pathlib.Path(__file__).parent / "static/neps.png", "rb") + contents = file.read() + img_str = base64.b64encode(contents).decode("utf-8") + buffer = io.BytesIO() + file.close() + img_data = base64.b64decode(img_str) + img = Image.open(io.BytesIO(img_data)) + resized_img = img.resize((180, 80)) # x, y #450,200 + resized_img.save(buffer, format="PNG") + img_b64 = base64.b64encode(buffer.getvalue()).decode("utf-8") + + st.markdown( + f""" + + """, + unsafe_allow_html=True, + ) + + +add_logo() + +image = Image.open(pathlib.Path(__file__).parent / "static/energy.png") +st.image(image) \ No newline at end of file diff --git "a/notebooks/streamlit/example_stromnetz/pages/1_\360\237\226\245\357\270\217_Szenariorahmen.py" "b/notebooks/streamlit/example_stromnetz/pages/1_\360\237\226\245\357\270\217_Szenariorahmen.py" new file mode 100644 index 0000000..104eb31 --- /dev/null +++ "b/notebooks/streamlit/example_stromnetz/pages/1_\360\237\226\245\357\270\217_Szenariorahmen.py" @@ -0,0 +1,135 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) +import pathlib +from turtle import color +import streamlit as st +import pandas as pd +import plotly.express as px +from plotly.colors import n_colors +from PIL import Image +import matplotlib.pyplot as plt + +from Overview import add_logo + +add_logo() + +st.markdown("# Szenariorahmen") +tab_a4, tab_a11, tab_a14, = st.tabs(["Abbildung 4", "Abbildung 11", "Abbildung 14"]) + +with tab_a4: + st.markdown("### Übersicht über die Verteilung der installierten Leistungen je Energieträger") + df_c2_a4 = pd.read_excel( + pathlib.Path(__file__).parent.parent.parent + / "data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx", + sheet_name="Abbildung 4", + header=2, + index_col=0, + ).dropna() + st.dataframe(df_c2_a4) + + + fig = px.bar( + df_c2_a4, + barmode='group', + labels={ + "value": "Installierte Leistung in GW", + "variable": "Szenarien" + } + ) + st.plotly_chart(fig, use_container_width=True) + + + red_blue = n_colors('rgb(0, 0, 255)', 'rgb(255, 0, 0)', 7, colortype = 'rgb') + + fig2 = px.bar( + df_c2_a4.transpose(), + labels={ + "value": "Installierte Leistung in GW", + "index": "Szenarien" + } + ) + st.plotly_chart(fig2, use_container_width=True) + + +with tab_a11: + st.markdown("### Mittlerer, minimaler und maximaler Flexibilitätseinsatz je Tagesstunde in Szenario B 2037") + df_c2_a11 = pd.read_excel( + pathlib.Path(__file__).parent.parent.parent + / "data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx", + sheet_name="Abbildung 11", + skiprows=4, + index_col=0, + ) + col_names = ["Einheit", 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24] + df_c2_a11.columns = col_names + + st.dataframe(df_c2_a11) + + df_c2_a11_plot = df_c2_a11.drop("Einheit", axis=1).transpose() + + figX, ax = plt.subplots() + ax.plot(df_c2_a11_plot.index, df_c2_a11_plot['Flexibilitätseinsatz - Median'], ".", label="median") + ax.vlines(df_c2_a11_plot.index,df_c2_a11_plot['Flexibilitätseinsatz - Minimum'],df_c2_a11_plot['Flexibilitätseinsatz - Maximum']) + + for i in range(len(df_c2_a11_plot['Flexibilitätseinsatz - 25%-Quantil'])): + ax.fill_between([df_c2_a11_plot.index[i]-0.2,df_c2_a11_plot.index[i]+0.2] + ,df_c2_a11_plot['Flexibilitätseinsatz - 25%-Quantil'].values[i],df_c2_a11_plot['Flexibilitätseinsatz - 75%-Quantil'].values[i], + alpha=0.5, + color="C1") + + ax.set_xlabel("Stunde des Tages") + ax.set_ylabel("Flexibilitätseinsatz in GW") + ax.set_xticks([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24],['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24']) + + ax2 = ax.twinx() + ax2.plot(df_c2_a11_plot.index, df_c2_a11_plot['Marktpreise - Mittelwert'], ".--C3", label="Marktpreise") + ax2.set_ylabel("Marktpreis in Euro/MWh", color="C3") + ax2.tick_params(axis="y", labelcolor="C3") + st.pyplot(figX) + +with tab_a14: + st.markdown("### Installierte Leistungen je Bundesland im Szenario A 2037") + + df_c2_a14 = pd.read_excel( + pathlib.Path(__file__).parent.parent.parent + / "data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx", + sheet_name="Abbildung 14", + skiprows=2, + index_col=0, + ).dropna() + st.dataframe(df_c2_a14) + + df_c2_a14_w = df_c2_a14.copy().drop(['Band der Nachfrage***', 'Unnamed: 11', 'PtH','PtG ****', 'DSM'], axis=1).drop(["Summe**"]) + + + bl = ["BW","BY","BE","BB","HB","HH","HE","MV","NI","NW","RP","SL","SN","ST","SH","TH","LU","AT"] + logos = [f"app/static/logos/{x}.png" for x in bl] + + + data_df = pd.DataFrame( + { + "bl": logos + }) + + column_config = { + "bl": st.column_config.ImageColumn("Bundesländer") + } + + for key in list(df_c2_a14_w.keys()): + column_config[key] = st.column_config.ProgressColumn( + key, + #help="help", + format="%.4fGW", + min_value=0, + max_value=df_c2_a14_w[key].sum() + ) + + df_t = df_c2_a14_w.copy() + df_t["bl"] = logos + df_t = df_t.set_index("bl") + + st.data_editor( + df_t, + column_config=column_config, + ) + \ No newline at end of file diff --git "a/notebooks/streamlit/example_stromnetz/pages/2_\360\237\224\256_Marktsimulation.py" "b/notebooks/streamlit/example_stromnetz/pages/2_\360\237\224\256_Marktsimulation.py" new file mode 100644 index 0000000..3df240c --- /dev/null +++ "b/notebooks/streamlit/example_stromnetz/pages/2_\360\237\224\256_Marktsimulation.py" @@ -0,0 +1,12 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) + +import streamlit as st + +from Overview import add_logo + +add_logo() + +st.markdown("# Marktsimulation") + +st.markdown("### Hier könnte ihre Werbung stehen!") \ No newline at end of file diff --git a/notebooks/streamlit/example_stromnetz/static/energy.png b/notebooks/streamlit/example_stromnetz/static/energy.png new file mode 100644 index 0000000..5bdc370 Binary files /dev/null and b/notebooks/streamlit/example_stromnetz/static/energy.png differ diff --git a/notebooks/streamlit/example_stromnetz/static/logos/AT.png b/notebooks/streamlit/example_stromnetz/static/logos/AT.png new file mode 100644 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differ diff --git a/notebooks/streamlit/example_stromnetz/static/neps.png b/notebooks/streamlit/example_stromnetz/static/neps.png new file mode 100644 index 0000000..26744e4 Binary files /dev/null and b/notebooks/streamlit/example_stromnetz/static/neps.png differ diff --git a/notebooks/streamlit/training-app/Main.py b/notebooks/streamlit/training-app/Main.py new file mode 100644 index 0000000..11a1334 --- /dev/null +++ b/notebooks/streamlit/training-app/Main.py @@ -0,0 +1,35 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) + +import streamlit as st + +st.markdown("# Streamlit Hands-On") + +st.markdown("You can find a streamlit cheat sheet [here](https://docs.streamlit.io/library/cheatsheet).") + +st.markdown("### Exercise 1 - Playground") +st.markdown( + """ + This exercise is intended to give you a feeling for creating a streamlit app. + + - Use the *1_Playground.py* file in *./pages* for this exercise. + - A person is described by his/her *first name*, *last name*, *gender*, *date of birth* and *height*. Use useful widgets for each parameter and print out the result. + - Move the widgets to the sidebar and show only the result on the main window. + - Try to store all generated people and show them in a table. + - Feel free to pimp your app! + + """ + ) + +st.markdown("### Exercise 2 - Stromnetz") +st.markdown( + """ + Let's have a look on some real data. You can find the **Netzentwicklungsplan Strom** in *../data* together with the data of the figures from chapters 2 and 3. + + - Use the *2_Stromnetz.py* file in *./pages* for this exercise. + - Start with exploring the excel files. It is useful to do this in a notebook. + - Visualize at least **2** of the data sets inside this streamlit app in different tabs. You can use either plotly or matplotlib (or both). + - Feel free to pimp your app! + + """ + ) \ No newline at end of file diff --git a/notebooks/streamlit/training-app/pages/1_Playground.py b/notebooks/streamlit/training-app/pages/1_Playground.py new file mode 100644 index 0000000..d3223a6 --- /dev/null +++ b/notebooks/streamlit/training-app/pages/1_Playground.py @@ -0,0 +1,4 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) + +import streamlit as st diff --git a/notebooks/streamlit/training-app/pages/2_Stromnetz.py b/notebooks/streamlit/training-app/pages/2_Stromnetz.py new file mode 100644 index 0000000..087a8df --- /dev/null +++ b/notebooks/streamlit/training-app/pages/2_Stromnetz.py @@ -0,0 +1,8 @@ +# This code is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). +# Copyright © [Point 8 GmbH](https://point-8.de) + +import streamlit as st +import pandas as pd + +# read in example +# df_c2_a4 = pd.read_excel("../data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx", sheet_name="Abbildung 4", header=2,index_col=0).dropna() \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 466b837..70c981e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,132 +1,247 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + [[package]] name = "absl-py" -version = "1.3.0" +version = "2.0.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." -category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" +files = [ + {file = "absl-py-2.0.0.tar.gz", hash = "sha256:d9690211c5fcfefcdd1a45470ac2b5c5acd45241c3af71eed96bc5441746c0d5"}, + {file = "absl_py-2.0.0-py3-none-any.whl", 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+description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.9,<3.9.7 || >3.9.7,<3.11" +content-hash = "51c6dc362b920f1e116aef900852dbc495eca16e8ec47bc8c2fff85e9eeaff4d" diff --git a/pyproject.toml b/pyproject.toml index a9951fd..388f172 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,40 +11,49 @@ packages = [ ] [tool.poetry.dependencies] -python = ">=3.8,<3.11" +python = ">=3.9,<3.9.7 || >3.9.7,<3.11" bs4 = "^0.0.1" findspark = "^2.0.1" -holidays = "^0.16" -jupyter = "^1.0.0" -jupyterlab = "^3.5.0" -jupyterthemes = "^0.20.0" -LunarCalendar = "^0.0.9" -matplotlib = "^3.6.2" -msgpack = "^1.0.4" -nbstripout = "^0.6.1" -nltk = "^3.7" -opencv-python = "^4.6.0" -openpyxl = "^3.0.10" -pandas = "^1.5.1" +holidays = "^0.35" +lunarcalendar = "^0.0.9" +matplotlib = "^3.8.0" +nltk = "^3.8.1" +opencv-python = "^4.8.1.78" +openpyxl = "^3.1.2" pandas-datareader = "^0.10.0" -Pillow = "^9.3.0" -plotly = "^5.11.0" +pillow = "^10.1.0" +plotly = "^5.17.0" python-dateutil = "^2.8.2" -scikit-learn = "^1.1.3" -scikit-surprise = "^1.1.3" -seaborn = "^0.12.1" -shap = "^0.41.0" -statsmodels = "^0.13.5" -tqdm = "^4.64.1" -tsfresh = "^0.19.0" +scikit-learn = "^1.3.1" +seaborn = "^0.13.0" +shap = "^0.43.0" +statsmodels = "^0.14.0" +tqdm = "^4.66.1" +tsfresh = "^0.20.1" yellowbrick = "^1.5" -tweepy = "^4.12.1" -networkx = "^2.8.8" -sktime = "^0.14.0" -tensorflow = ">2" +tweepy = "^4.14.0" +networkx = "^3.2" +tensorflow = "^2.14.0" +pandas = "^2.1.1" +polars = "^0.19.11" +pymongo = "^4.5.0" +sqlalchemy = "^2.0.22" +numpy = "^1.26.1" +scipy = "^1.11.3" +streamlit = "^1.27.2" +watchdog = "^3.0.0" +matplotlib-inline = "^0.1.6" [tool.poetry.dev-dependencies] +[tool.poetry.group.dev.dependencies] +jupyter = "^1.0.0" +jupyterlab = "^4.0.7" +jupyterthemes = "^0.20.0" +nbstripout = "^0.6.1" +isort = "^5.12.0" +black = "^23.10.0" + [build-system] requires = ["poetry-core>=1.0.0"] build-backend = "poetry.core.masonry.api" diff --git a/requirements.txt b/requirements.txt index bacc2a0..b8bb320 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,23 +1,17 @@ bs4 findspark holidays -jupyter -jupyterlab -jupyterthemes -LunarCalendar +lunarcalendar matplotlib -msgpack -nbstripout +matplotlib-inline nltk opencv-python openpyxl -pandas pandas-datareader -Pillow +pillow plotly python-dateutil scikit-learn -scikit-surprise seaborn shap statsmodels @@ -26,6 +20,12 @@ tsfresh yellowbrick tweepy networkx -sktime tensorflow -./library/ \ No newline at end of file +pandas +polars +pymongo +sqlalchemy +numpy +scipy +streamlit +watchdog diff --git a/services/compose.yaml b/services/compose.yaml new file mode 100644 index 0000000..e473e80 --- /dev/null +++ b/services/compose.yaml @@ -0,0 +1,13 @@ +services: + mongodb: + container_name: jhub-mongodb + image: mongo:latest + environment: + - MONGO_INITDB_ROOT_USERNAME=mongo + - MONGO_INITDB_ROOT_PASSWORD=hu43fh + volumes: + - mongodb-data:/data/db + ports: + - "27017:27017" +volumes: + mongodb-data: