From 50e8272b1a9a6d6c729ab08c1b7758eaf9609c14 Mon Sep 17 00:00:00 2001 From: Christophe Cauet Date: Mon, 9 Oct 2023 12:22:20 +0200 Subject: [PATCH 01/47] Add course overview --- ...pe-advanced-python-ds-ecosystem-2day.ipynb | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb 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..43d3744 --- /dev/null +++ b/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb @@ -0,0 +1,71 @@ +{ + "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", + "- [đź““ Data Analysis with Python](dap2-data-analysis-python.ipynb)\n", + "- [đź““ Machine Learning with Python](mlp2-machine-learning-python-2day.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "\n", + "### Curriculum\n", + "\n", + "1. **Object-Oriented Programing with Python**
\n", + " \n", + "1. **Data Visualizations and Dashboards**\n", + "\n", + "1. **Working with Databases**\n", + " \n", + "1. **Development of Python Packages with Poetry**\n", + "\n", + "1. **Introduction to the Polars Library** \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": "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 +} From e1dae8574a38a3008e6506705d3c78b76bb5a654 Mon Sep 17 00:00:00 2001 From: Christophe Cauet Date: Mon, 9 Oct 2023 20:23:27 +0200 Subject: [PATCH 02/47] Add DB notebooks --- notebooks/db/data/firmenlauf_demo.db | Bin 0 -> 36864 bytes notebooks/db/data/participants.csv | 5 + notebooks/db/data/teams.csv | 4 + notebooks/db/data/training.csv | 10 + notebooks/db/db-pandas-sql.ipynb | 209 ++ notebooks/db/db-pymongo-mongodb.ipynb | 374 +++ notebooks/db/db-sqalchemy.ipynb | 633 ++++ notebooks/db/db-sqlite3.ipynb | 454 +++ poetry.lock | 4415 ++++++++++++++++++------- 9 files changed, 4871 insertions(+), 1233 deletions(-) create mode 100644 notebooks/db/data/firmenlauf_demo.db create mode 100644 notebooks/db/data/participants.csv create mode 100644 notebooks/db/data/teams.csv create mode 100644 notebooks/db/data/training.csv create mode 100644 notebooks/db/db-pandas-sql.ipynb create mode 100644 notebooks/db/db-pymongo-mongodb.ipynb create mode 100644 notebooks/db/db-sqalchemy.ipynb create mode 100644 notebooks/db/db-sqlite3.ipynb diff --git a/notebooks/db/data/firmenlauf_demo.db b/notebooks/db/data/firmenlauf_demo.db new file mode 100644 index 0000000000000000000000000000000000000000..9d52f87bfba7dfc1bdf01164a3e8d4f32d1c5e78 GIT binary patch literal 36864 zcmeI)?Qh#e90zcp9}?T8xv^5bswy*qO$|y%(yVN)K+J4z2()RJcEF@b!7LuLNYde? zVuDv2n#5~(1^Wwl4`XkUc)^=V2<;VU;>9FD6p)4x4}uq*FOHiuP4i-7V(5EHn#-O2 zySvX>mF`@3<@kcvbj>qse$8o`IboMTh;YO-1wqilH~Zj8VL#s zo6aj$w|zwWQw_teTF)%lT>aji}h1)HSctZqF<(t(wIX3kzve*D4NMnH*yxtA$0o ztETDI`qWgsM$@UEcFp;PC5wgX2#olR(rzqhW4TmVv`Qz<=k1f`q*qA=WhaV- z<0ou$GCCn`23_HG^GhYWkS_+uZ`P;Gl09#i?BY>-#cZ9`;L-F{HWoANy+q(s)p)(i z7F%U!y}8D}mm`I8Hp<2?o|59m_&B*lThqzA-3Y_VU5Zg#eGSieexq5gJIw0^t90~P zt2CL*q`F+H&Y&Y2XV=_vgUxqauIv29S`^j zfB*y_009U<00Izz00bZa0SMe{0z;}Skg=iVb^lz|)fM)|pn2bQ>#D@^BfCm&MQ3A@ z=uLtCOmDK=^hgkZ00bZa0SG_<0uX=z1Rwwb2;7?jx->>wTL7vi8H8^DC~;|ou;srL z-2a#9Wr2Q6uhEZL1_=TXfB*y_009U<00Izz00bZafx9WNM^*)6oUpXwR%_0p>owW) zE2&9cdQf6DMo{w{d$6Oi?5tP4^kceeh^jEg%NHHrTW`#}e$7iiDJpx2DvVA9Cs_5m zV|#V>2!mJOzh9Ap`~NciNT5H_&*@hzg9HHxKmY;|fB*y_009U<00Izzz<)wuL6}AbWb#4Lh`~TYTd4XP~>olFXnYf%-N$A6$ zvLYl1KmY;|fB*y_009U<00I!W0|I$1CRkS7n9fY+rZR`7rVpE$%p89m$ncb%&19xB zS@sHQa?^8}C&HSi|9n44AMn*)UhNK)o!vRm!R*{jHVkxxY+QQi>hT|2f#RKkW?83p z4wO5@y4wyk`sJ+;|N5@R*QE0?9%wWg+RT7J!MNLjcDHPUHR`<59Va`8XlDC3dAW07 zho&-v;xOu{Kf7`~@qy)~&OnC;3mu$0IJ4cHSz-`~jJ1SVpaDWNhc|m9g)4fp1!q8r z-0WtEBOS=W{eLZSMW8omf$mFuow!2ZrJv9my#v=cLO=il5P$##AOHafKmY;|fB*z; zPvBA6uv#~}qfLgMMZMO0Q19M|$`9hJ`&Ojw_C4Z{$fFkj%lW9ap4E1&=a}B@3T10! z+wDcjTf4p8K7+JR9<#!G`~5cbuCzun*NcebbBt>^!*2mk+ngMLG=-`>37YzROA z0uX=z1Rwwb2tWV=5P$##?v{WiD}-pGB$HotiDii>N}}|=CMp`w$G=dCqH08xN8V{Y z|Gy#7zv=Jv7Tvg8eMCDDfB*y_009U<00Izz00bZa0SNpn0*XY4BnLm))_}y8|B|F? F{{WBRkW>Hw literal 0 HcmV?d00001 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..4b1e57d --- /dev/null +++ b/notebooks/db/db-pandas-sql.ipynb @@ -0,0 +1,209 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "890c3e23-b4e8-4a5c-a8b1-1d9b36b2a688", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import sqlite3" + ] + }, + { + "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": "1ab98ccf-b753-4b3a-a24c-2093aacc3b04", + "metadata": {}, + "outputs": [], + "source": [ + "connection = sqlite3.connect(\"TestData/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": "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..a9701a3 --- /dev/null +++ b/notebooks/db/db-pymongo-mongodb.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "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": "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": "6af7b6bf-7738-4d00-8f12-64480da5d7a0", + "metadata": {}, + "outputs": [], + "source": [ + "client = pymongo.MongoClient()" + ] + }, + { + "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. `$set` just sets a new value.\n", + "\n", + "Other operators can be found here: https://www.mongodb.com/docs/manual/reference/operator/update/#std-label-update-operators" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7dd247a0-467d-481c-9aaf-0bb7429de373", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "trainings.update_one({\"_id\": ObjectId(\"65141cbbc429eac58ebf20fb\")}, {\"$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..a2edf2d --- /dev/null +++ b/notebooks/db/db-sqalchemy.ipynb @@ -0,0 +1,633 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "dce6ba8c-3cd8-4d2d-a16b-7ee81e578249", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\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", + "A DB has much more functionalty than a simple .csv or Excel table.\n", + "This comes with some downsides.\n", + "We can not simply insert data into an empty table, because there is no table at the first place.\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 
", + "FROM runners
", + " 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..72e03eb --- /dev/null +++ b/notebooks/db/db-sqlite3.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "172c05ce-2124-451a-91e0-c8a6d33b020f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sqlite3" + ] + }, + { + "cell_type": "markdown", + "id": "eefece34-ae84-4cd5-bb7f-0db34f91e9af", + "metadata": {}, + "source": [ + "# Simple `sqlite` example using SQL and Python\n", + "\n", + "We want to create a DB containing the tables `teams` and `runners` and perform a simple `JOIN`.\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, + "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": "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/poetry.lock b/poetry.lock index 466b837..ddbeac1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,132 +1,238 @@ +# 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", 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4b1e57d..ba63704 100644 --- a/notebooks/db/db-pandas-sql.ipynb +++ b/notebooks/db/db-pandas-sql.ipynb @@ -58,7 +58,7 @@ "metadata": {}, "outputs": [], "source": [ - "connection = sqlite3.connect(\"TestData/firmenlauf_demo.db\")" + "connection = sqlite3.connect(\"data/firmenlauf_demo.db\")" ] }, { From bd76b0df490754edc28c6a6799f4a270ea2bc0d8 Mon Sep 17 00:00:00 2001 From: jkuehlem Date: Wed, 18 Oct 2023 12:41:34 +0200 Subject: [PATCH 04/47] add polars notebooks --- notebooks/polars/polars_hands_on_part_1.ipynb | 175 ++++++++++ ...ars_hands_on_part_2_or_use_your_code.ipynb | 321 ++++++++++++++++++ .../polars/polars_pipeline_solution.ipynb | 265 +++++++++++++++ 3 files changed, 761 insertions(+) create mode 100644 notebooks/polars/polars_hands_on_part_1.ipynb create mode 100644 notebooks/polars/polars_hands_on_part_2_or_use_your_code.ipynb create mode 100644 notebooks/polars/polars_pipeline_solution.ipynb diff --git a/notebooks/polars/polars_hands_on_part_1.ipynb b/notebooks/polars/polars_hands_on_part_1.ipynb new file mode 100644 index 0000000..33d2907 --- /dev/null +++ b/notebooks/polars/polars_hands_on_part_1.ipynb @@ -0,0 +1,175 @@ +{ + "cells": [ + { + "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": [ + "# The Data\n", + "This dataset contains the measured data of multiple substations for voltage and current." + ] + }, + { + "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 = 1_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(230, 10, n) # Normal distribution with mean 230 and standard deviation 10\n", + "currents = np.random.normal(5, 2, n) # Normal distribution with mean 5 and standard deviation 2\n", + "\n", + "\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)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas version\n", + "Execution time: ~780 ms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Perform some transformations\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "df = df.set_index(df['timestamp'])\n", + "\n", + "# Calculate power (P=VI) and add it as a new column\n", + "df['power'] = df['voltage'] * df['current']\n", + "\n", + "# Group by 'substation_id' and calculate daily average power\n", + "df.set_index('timestamp', inplace=True)\n", + "df_grouped = df.groupby('substation_id').resample(\"D\")[\"power\"].mean()\n", + "df_grouped = df_grouped.reset_index()\n", + "\n", + "# 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", + "df_grouped = df_grouped.sort_values(by=[\"substation_id\", \"timestamp\"])\n", + "\n", + "# 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": [ + "# Hands on exercise:\n", + "Create the \"data pipeline\" using the polars library.\n", + "The pipeline should include the following steps:\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", + "Compare run times with `%%timeit` \n", + "Hint: Polars has a `group_by_dynamic` method \n", + "Of course the online polars documentation can be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars_hands_on_part_2_or_use_your_code.ipynb b/notebooks/polars/polars_hands_on_part_2_or_use_your_code.ipynb new file mode 100644 index 0000000..ff46b9b --- /dev/null +++ b/notebooks/polars/polars_hands_on_part_2_or_use_your_code.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timedelta\n", + "import random\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import polars as pl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# The Data\n", + "This dataset contains the measured data of multiple substations for voltage and current." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " timestamp substation_id voltage current\n", + "0 2023-10-12 09:36:10.317117 2 234.967142 4.631988\n", + "1 2023-10-12 09:35:10.317127 1 228.617357 5.087587\n", + "2 2023-10-12 09:34:10.317129 5 236.476885 7.365035\n", + "3 2023-10-12 09:33:10.317130 4 245.230299 4.431361\n", + "4 2023-10-12 09:32:10.317131 4 227.658466 4.349693" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.seed(42)\n", + "random.seed(42)\n", + "\n", + "# Number of rows in the CSV file\n", + "n = 1_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(230, 10, n) # Normal distribution with mean 230 and standard deviation 10\n", + "currents = np.random.normal(5, 2, n) # Normal distribution with mean 5 and standard deviation 2\n", + "\n", + "\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)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas version" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.28 s ± 117 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "df = pd.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Perform some transformations\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "df = df.set_index(df['timestamp'])\n", + "\n", + "# Calculate power (P=VI) and add it as a new column\n", + "df['power'] = df['voltage'] * df['current']\n", + "\n", + "# Group by 'substation_id' and calculate daily average power\n", + "df.set_index('timestamp', inplace=True)\n", + "df_grouped = df.groupby('substation_id').resample(\"D\")[\"power\"].mean()\n", + "df_grouped = df_grouped.reset_index()\n", + "\n", + "# 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", + "df_grouped = df_grouped.sort_values(by=[\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 3: 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": [ + "# One possible solution with polars for **part 1**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.46 s ± 149 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "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": [ + "## Solution part 1 with group_by instead of dynamic_group_by\n", + "Instead of the group_by_dynamic a regular group_by can also be used. \n", + "Because of missing resample functionality a column with dates for grouping has to be introduced." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Use pl.Date instead of pl.Datetime for group_by on dates\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Date, \"%Y-%m-%d %H:%M:%S%.f\").alias(\"date\"))\n", + "\n", + "# calculate power outcome\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "df_grouped = df.group_by([\"date\", \"substation_id\"]).agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"date\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hands on Part 2 - Polars version as query with lazy mode\n", + "We now imagine that there is a larger than memory data set. \n", + "Write the previous pipeline as a polars query (either use your code or the provided solution as base).\n", + "\n", + "Execute the query in lazy mode and measure the run time with `%%timeit`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/polars/polars_pipeline_solution.ipynb b/notebooks/polars/polars_pipeline_solution.ipynb new file mode 100644 index 0000000..0e21b35 --- /dev/null +++ b/notebooks/polars/polars_pipeline_solution.ipynb @@ -0,0 +1,265 @@ +{ + "cells": [ + { + "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": [ + "# The Data\n", + "This dataset contains the measured data of multiple substations for voltage and current." + ] + }, + { + "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(230, 10, n) # Normal distribution with mean 230 and standard deviation 10\n", + "currents = np.random.normal(5, 2, n) # Normal distribution with mean 5 and standard deviation 2\n", + "\n", + "\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)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pandas version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "df = pd.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Step 2: Perform some transformations\n", + "df['timestamp'] = pd.to_datetime(df['timestamp'])\n", + "df = df.set_index(df['timestamp'])\n", + "\n", + "# Calculate power (P=VI) and add it as a new column\n", + "df['power'] = df['voltage'] * df['current']\n", + "\n", + "# Group by 'substation_id' and calculate daily average power\n", + "df.set_index('timestamp', inplace=True)\n", + "df_grouped = df.groupby('substation_id').resample(\"D\")[\"power\"].mean()\n", + "df_grouped = df_grouped.reset_index()\n", + "\n", + "# 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", + "df_grouped = df_grouped.sort_values(by=[\"substation_id\", \"timestamp\"])\n", + "\n", + "# Step 3: 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": [ + "# One possible solution with polars for **part 1**" + ] + }, + { + "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": [ + "### Solution part 1 with group_by instead of dynamic_group_by\n", + "Instead of the group_by_dynamic a regular group_by can also be used. \n", + "Because of missing resample functionality a column with dates for grouping has to be introduced." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pl.read_csv(\"electricity_usage.csv\")\n", + "\n", + "# Use pl.Date instead of pl.Datetime for group_by on dates\n", + "df = df.with_columns(pl.col(\"timestamp\").str.strptime(pl.Date, \"%Y-%m-%d %H:%M:%S%.f\").alias(\"date\"))\n", + "\n", + "# calculate power outcome\n", + "df = df.with_columns((pl.col(\"voltage\") * pl.col(\"current\")).alias(\"power\"))\n", + "\n", + "df_grouped = df.group_by([\"date\", \"substation_id\"]).agg(pl.col(\"power\").mean().alias(\"daily_avg_power\"))\n", + "df_grouped = df_grouped.sort([\"substation_id\", \"date\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Solution Part 2 - Polars version as query with lazy mode" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "threshold = 1000\n", + "# Convert 'timestamp' from string to datetime\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", + "# Step 3: Write the transformed data to a new CSV file\n", + "df = q.collect(streaming=True) # set streaming = True if the data might not fit into memory\n", + "df.write_csv(\"transformed_electricity_usage.csv\")\n", + "\n", + "# in case you want a pandas dataframe for plotting etc.\n", + "df_pandas = df.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": 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packages = [ ] [tool.poetry.dependencies] -python = ">=3.8,<3.11" +python = ">=3.9,<3.11" bs4 = "^0.0.1" findspark = "^2.0.1" holidays = "^0.16" @@ -25,13 +25,11 @@ nbstripout = "^0.6.1" nltk = "^3.7" opencv-python = "^4.6.0" openpyxl = "^3.0.10" -pandas = "^1.5.1" pandas-datareader = "^0.10.0" Pillow = "^9.3.0" plotly = "^5.11.0" 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" @@ -40,8 +38,12 @@ tsfresh = "^0.19.0" yellowbrick = "^1.5" tweepy = "^4.12.1" networkx = "^2.8.8" -sktime = "^0.14.0" tensorflow = ">2" +pandas = "^2.1.1" +polars = "^0.19.9" +pymongo = "^4.5.0" +sqlalchemy = "^2.0.22" +numpy = "^1.26.1" [tool.poetry.dev-dependencies] From af812b05a50c1934d73567cecca6e9f6e7f4f1b2 Mon Sep 17 00:00:00 2001 From: Henrique Pinheiro Date: Sat, 21 Oct 2023 20:16:03 +0100 Subject: [PATCH 06/47] Add object-oriented programming notebook --- notebooks/python/python-oop.ipynb | 671 ++++++++++++++++++++++++++++++ 1 file changed, 671 insertions(+) create mode 100644 notebooks/python/python-oop.ipynb diff --git a/notebooks/python/python-oop.ipynb b/notebooks/python/python-oop.ipynb new file mode 100644 index 0000000..38bf3c1 --- /dev/null +++ b/notebooks/python/python-oop.ipynb @@ -0,0 +1,671 @@ +{ + "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": 21, + "id": "fd4ebd86-88d6-4301-9c19-b6b2873e2219", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type('a string')=\n", + "type(42)=\n", + "type(len)=\n" + ] + } + ], + "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": 86, + "id": "8d9a1551-34df-474d-a0e2-2ae793953401", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Book:\n", + " \n", + " def __init__(self, title, author, year_published):\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": 87, + "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": 24, + "id": "0e4bdfe4-07c7-4f82-890d-d112659f52b5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'Lord of the Rings'" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 25, + "id": "045bd538-1004-467c-a728-4cb6bc503c10", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Book: 'Lord of the Rings' by J. R. R. Tolkien and published in 1954'\n" + ] + } + ], + "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": 70, + "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": 71, + "id": "204f5d2e-84c2-4b5c-8257-8560acfee71e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class EBook(Book):\n", + " def __init__(self, title, author, year_published, file_size, file_extension):\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": 72, + "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": 73, + "id": "e9604ade-1087-4cca-b04d-c11537c0d81e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Book: 'The Martian' by Andy Weir and published in 2015'\n" + ] + } + ], + "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": 74, + "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": 75, + "id": "24d58813-91a8-4df8-bc8f-e9f4f6885199", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Person:\n", + " def __init__(self, name, age):\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):\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": 76, + "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": 77, + "id": "5c3cfffd-d8c3-4bd9-86ae-0f01db60ed85", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13\n", + "4\n" + ] + } + ], + "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": 78, + "id": "ec07607d-6d4f-4e13-bd68-4153c29594e5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Rectangle:\n", + " def __init__(self, x, y):\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):\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": 79, + "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": 80, + "id": "35dd468b-6f0b-4a09-90ca-0f10d68f4c0e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28.26\n", + "12\n", + "1256.0\n" + ] + } + ], + "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": 81, + "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": 83, + "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": 84, + "id": "ae46accd-c128-445e-aef8-6327181881b5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "class Circle(Shape):\n", + " def __init__(self, radius):\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": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From da2a63adcd04ba06f0915b4fcf07d578f8ef099a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Vanessa=20M=C3=BCller?= Date: Mon, 23 Oct 2023 14:36:18 +0200 Subject: [PATCH 07/47] Fix some typos and add links to db notebooks in index. --- notebooks/db/db-sqalchemy.ipynb | 6 +++--- notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb | 4 ++++ 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/notebooks/db/db-sqalchemy.ipynb b/notebooks/db/db-sqalchemy.ipynb index a2edf2d..b59f56f 100644 --- a/notebooks/db/db-sqalchemy.ipynb +++ b/notebooks/db/db-sqalchemy.ipynb @@ -97,7 +97,7 @@ "\n", "A DB has much more functionalty than a simple .csv or Excel table.\n", "This comes with some downsides.\n", - "We can not simply insert data into an empty table, because there is no table at the first place.\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", @@ -495,8 +495,8 @@ "source": [ "## `JOIN`\n", "\n", - "`SELECT first_name, shoe_size, shoe_color 
", - "FROM runners
", + "`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", diff --git a/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb b/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb index 43d3744..aab3b7d 100644 --- a/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb +++ b/notebooks/index/ape-advanced-python-ds-ecosystem-2day.ipynb @@ -32,6 +32,10 @@ "1. **Data Visualizations and Dashboards**\n", "\n", "1. **Working with Databases**\n", + " - [DB-API: sqlite3](../db/db-sqlite3.ipynb)\n", + " - [ORM: SQLAlchemy](../db/db-sqalchemy.ipynb)\n", + " - [NoSQL Databases with PyMongo](../db/db-pymongo-mongodb.ipynb)\n", + " - [SQL with Pandas](../db/db-pandas-sql.ipynb)\n", " \n", "1. **Development of Python Packages with Poetry**\n", "\n", From 426e9d3685ba4e138072159a179ec8a0f61686a7 Mon Sep 17 00:00:00 2001 From: sdungs Date: Mon, 23 Oct 2023 14:44:02 +0200 Subject: [PATCH 08/47] streamlit example of stromnetz --- ...itel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx | Bin 0 -> 73054 bytes ...itel_3_Daten_NEP_2037_V2023_2_Entwurf.xlsx | Bin 0 -> 105393 bytes .../NEP_2037_2045_V2023_2_Entwurf_Teil1_1.pdf | Bin 0 -> 23086027 bytes .../streamlit/example_stromnetz/Overview.py | 42 +++++++++ ...237\226\245\357\270\217_Szenariorahmen.py" | 82 ++++++++++++++++++ .../2_\360\237\224\256_Marktsimulation.py" | 13 +++ .../example_stromnetz/static/logo.png | Bin 0 -> 23105 bytes .../example_stromnetz/static/regelzonen.png | Bin 0 -> 300992 bytes 8 files changed, 137 insertions(+) create mode 100644 notebooks/streamlit/data/Kapitel_2_Daten_NEP_2037_V2023_2_Entwurf.xlsx create mode 100644 notebooks/streamlit/data/Kapitel_3_Daten_NEP_2037_V2023_2_Entwurf.xlsx create mode 100644 notebooks/streamlit/data/NEP_2037_2045_V2023_2_Entwurf_Teil1_1.pdf create mode 100644 notebooks/streamlit/example_stromnetz/Overview.py create mode 100644 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