diff --git a/constraints.txt b/constraints.txt index 7e35e89..5039dd6 100644 --- a/constraints.txt +++ b/constraints.txt @@ -1,3 +1,6 @@ +adjustText==1.1.1 +alabaster==0.7.16 +annotated-types==0.7.0 anyio==4.4.0 appnope==0.1.4 argon2-cffi==23.1.0 @@ -7,33 +10,55 @@ asttokens==2.4.1 async-lru==2.0.4 attrs==23.2.0 Babel==2.15.0 +beartype==0.18.5 beautifulsoup4==4.12.3 bleach==6.1.0 +build==1.2.1 certifi==2024.6.2 cffi==1.16.0 cfgv==3.4.0 charset-normalizer==3.3.2 +click==8.1.7 +click-plugins==1.1.1 +cligj==0.7.2 +colorama==0.4.6 comm==0.2.2 +contourpy==1.2.1 +coverage==7.5.3 +cycler==0.12.1 debugpy==1.8.1 decorator==5.1.1 defusedxml==0.7.1 distlib==0.3.8 +docutils==0.21.2 executing==2.0.1 fastjsonschema==2.19.1 filelock==3.14.0 +fiona==1.9.6 +fonttools==4.53.0 fqdn==1.5.1 +geopandas==0.14.4 +griffe==0.45.2 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 identify==2.5.36 idna==3.7 +imagesize==1.4.1 +importlib_metadata==7.1.0 +importlib_resources==6.4.0 +iniconfig==2.0.0 ipykernel==6.29.4 ipython==8.25.0 ipython-genutils==0.2.0 ipywidgets==8.1.3 isoduration==20.11.0 +jaraco.classes==3.4.0 +jaraco.context==5.3.0 +jaraco.functools==4.0.1 jedi==0.19.1 Jinja2==3.1.4 +joblib==1.4.2 json5==0.9.25 jsonpointer==2.4 jsonschema==4.22.0 @@ -54,23 +79,42 @@ jupyterlab==4.2.1 jupyterlab_pygments==0.3.0 jupyterlab_server==2.27.2 jupyterlab_widgets==3.0.11 +keyring==25.2.1 +kiwisolver==1.4.5 lxml==5.2.2 +markdown-it-py==3.0.0 MarkupSafe==2.1.5 +matplotlib==3.9.0 matplotlib-inline==0.1.7 +mdurl==0.1.2 mistune==3.0.2 +mizani==0.11.4 +more-itertools==10.2.0 nbclient==0.10.0 nbconvert==7.16.4 nbformat==5.10.4 +nbsphinx==0.9.4 nest-asyncio==1.6.0 +nh3==0.2.17 nodeenv==1.9.1 -notebook==7.2.0 +notebook==6.4.12 notebook_shim==0.2.4 +numpy==1.26.4 +numpydoc==1.7.0 overrides==7.7.0 packaging==24.0 +pandas==2.2.2 +pandas-stubs==2.2.2.240603 pandocfilters==1.5.1 parso==0.8.4 +patsy==0.5.6 pexpect==4.9.0 +pillow==10.3.0 +pkginfo==1.11.0 platformdirs==4.2.2 +plotnine==0.13.6 +pluggy==1.5.0 +plum-dispatch==2.4.1 pre-commit==3.7.1 prometheus_client==0.20.0 prompt_toolkit==3.0.46 @@ -78,34 +122,72 @@ psutil==5.9.8 ptyprocess==0.7.0 pure-eval==0.2.2 pycparser==2.22 +pydantic==2.7.3 +pydantic_core==2.18.4 Pygments==2.18.0 +pyparsing==3.1.2 +pyproj==3.6.1 +pyproject_hooks==1.1.0 +pyright==1.1.362 +pytest==8.2.2 +pytest-cov==5.0.0 python-dateutil==2.9.0.post0 python-json-logger==2.0.7 +pytz==2024.1 PyYAML==6.0.1 pyzmq==26.0.3 qtconsole==5.5.2 QtPy==2.4.1 +quartodoc==0.7.3 +readme_renderer==43.0 referencing==0.35.1 requests==2.32.3 +requests-toolbelt==1.0.0 rfc3339-validator==0.1.4 +rfc3986==2.0.0 rfc3986-validator==0.1.1 +rich==13.7.1 rpds-py==0.18.1 +ruff==0.4.8 +scikit-learn==1.5.0 +scikit-misc==0.3.1 +scipy==1.13.1 Send2Trash==1.8.3 setuptools==70.0.0 +shapely==2.0.4 six==1.16.0 sniffio==1.3.1 +snowballstemmer==2.2.0 soupsieve==2.5 +Sphinx==7.3.7 +sphinxcontrib-applehelp==1.0.8 +sphinxcontrib-devhelp==1.0.6 +sphinxcontrib-htmlhelp==2.0.5 +sphinxcontrib-jsmath==1.0.1 +sphinxcontrib-qthelp==1.0.7 +sphinxcontrib-serializinghtml==1.1.10 +sphobjinv==2.3.1.1 stack-data==0.6.3 +statsmodels==0.14.2 +tabulate==0.9.0 terminado==0.18.1 +threadpoolctl==3.5.0 tinycss2==1.3.0 tornado==6.4.1 traitlets==5.14.3 +twine==5.1.0 types-python-dateutil==2.9.0.20240316 +types-pytz==2024.1.0.20240417 +typing_extensions==4.12.2 +tzdata==2024.1 uri-template==1.3.0 urllib3==2.2.1 virtualenv==20.26.2 +watchdog==4.0.1 wcwidth==0.2.13 webcolors==24.6.0 webencodings==0.5.1 websocket-client==1.8.0 +wheel==0.43.0 widgetsnbextension==4.0.11 +zipp==3.19.2 diff --git a/docs/data-visualisation/data-visualisation.ipynb b/docs/data-visualisation/data-visualisation.ipynb index 91f8ce6..cbc8535 100644 --- a/docs/data-visualisation/data-visualisation.ipynb +++ b/docs/data-visualisation/data-visualisation.ipynb @@ -5453,7 +5453,9 @@ "tags": [] }, "source": [ - "[![Cover des Buches The Grammar of Graphics von Leland Wilkinson](cover-grammar-of-graphics.jpg)](cover-grammar-of-graphics.jpg)" + "### 6.1 Grundlagen\n", + "\n", + "[![Cover des Buches The Grammar of Graphics von Leland Wilkinson](grammar-of-graphics_cover.jpg)](grammar-of-graphics_cover.jpg)" ] }, { @@ -5483,6 +5485,8 @@ "source": [ "## 6. The Grammar of Graphics\n", "\n", + "### 6.1 Grundlagen\n", + "\n", "> „Ich war entschlossen, ein Paket zu entwickeln, mit dem ich jede statistische Grafik, die ich je gesehen hatte, zeichnen konnte.“\n", "\n", "– Leland Wilkinson, im Vorwort von *The Grammar of Graphics*" @@ -5506,7 +5510,7 @@ }, { "cell_type": "markdown", - "id": "bbee26b8-a1fc-4ba5-87ae-a39f2dac2fb1", + "id": "681528c0-d8fb-498c-b632-f6d9ae7870f1", "metadata": { "editable": true, "slideshow": { @@ -5517,17 +5521,168 @@ "source": [ "## 6. The Grammar of Graphics\n", "\n", + "### 6.1 Grundlagen\n", + "\n", "> „In diesem Buch geht es um grammatikalische Regeln für die Erstellung von wahrnehmbaren Diagrammen, oder wie ich es nenne: Grafiken. Diese Regeln sind manchmal mathematisch und manchmal ästhetisch. Die Mathematik liefert symbolische Werkzeuge zur Darstellung von Abstraktionen. Die Ästhetik, im ursprünglichen griechischen Sinne, bietet Prinzipien, um sensorische Attribute (Farbe, Form, Klang usw.) mit Abstraktionen in Beziehung zu setzen. Im modernen Sprachgebrauch kann Ästhetik auch Geschmack bedeuten. In diesem Buch geht es jedoch nicht um guten Geschmack, Praxis oder Grafikdesign. Im Mittelpunkt dieses Buches stehen vielmehr Regeln für die mathematische Konstruktion von Graphen und ihre anschließende ästhetische Darstellung als Grafik.“\n", "\n", "– Leland Wilkinson, im Vorwort von *The Grammar of Graphics*" ] + }, + { + "cell_type": "markdown", + "id": "9a4a5055-17d1-427f-aa50-8da46daa4d26", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "notes" + }, + "tags": [] + }, + "source": [ + "„The Grammar of Graphics“ führt viele abstrakte Konzepte ein und enthält abstrakten Pseudocode, um zu erklären, wie Datenvisualisierungen aufgebaut sind." + ] + }, + { + "cell_type": "markdown", + "id": "8f246a53-a0ae-40da-944c-cf27e9cc1cfe", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## 6. The Grammar of Graphics\n", + "\n", + "### 6.1 Grundlagen\n", + "\n", + "[![Einige Pseudocodes und eine schematische Zeichnung, die erklärt, wie ein Tortendiagramm aus Daten erstellt wird](grammar-of-graphics_pie-chart-specification.png)](grammar-of-graphics_pie-chart-specification.png)" + ] + }, + { + "cell_type": "markdown", + "id": "6f9412a4-2260-45ef-834e-66c5b6c81e5d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## 6. The Grammar of Graphics\n", + "\n", + "### 6.2 Implementierungen" + ] + }, + { + "cell_type": "markdown", + "id": "fd81ffac-341c-4b1b-b292-4cc8219b866e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "notes" + }, + "tags": [] + }, + "source": [ + "*The Grammar of Graphics* war nicht dazu gedacht, ein echtes Softwareprogramm zu sein, mit dem jede erdenkliche Visualisierung erstellt werden kann. Die im Buch vorgestellten abstrakten Konzepte erwiesen sich jedoch als gute Grundlage für die Implementierung von *The Grammar of Graphics* und der Konzepte." + ] + }, + { + "cell_type": "markdown", + "id": "cae6b076-a50c-4c2d-8b6d-2f24d8fc6ef3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## 6. The Grammar of Graphics\n", + "\n", + "### 6.2 Implementierungen\n", + "\n", + "#### plotnine" + ] + }, + { + "cell_type": "markdown", + "id": "da4d44b0-b0a8-4c68-850b-321024ce5a5c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "notes" + }, + "tags": [] + }, + "source": [ + "[plotnine](https://pyviz-tutorial.readthedocs.io/de/latest/matplotlib/plotnine/index.html) implementiert *The Grammar of Graphics* in Python basierend auf [ggplot2](https://ggplot2.tidyverse.org/)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "74049d1b-fd58-4b1c-9527-c65ba5144878", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from plotnine import *\n", + "from plotnine.data import mtcars" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f35de2fe-579d-4ab6-94e8-3013f1ba4b78", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": { + "image/png": { + "height": 480, + "width": 640 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "(\n", + " ggplot(mtcars, aes(\"disp\", \"hp\"))\n", + " + geom_point(size = 3)\n", + " + scale_color_discrete(name = \"Cylinders\")\n", + " + ylab(\"Horsepower\")\n", + " + xlab(\"Engine displacement (cubic inch)\")\n", + " + ggtitle(\"Bigger engines have more power\")\n", + " + theme_minimal()\n", + " + theme(legend_position=\"top\")\n", + ")" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.11 Kernel", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "python311" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -5539,7 +5694,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.3" }, "toc": { "base_numbering": 0 diff --git a/docs/data-visualisation/index.html b/docs/data-visualisation/index.html index e179941..5d50d6f 100644 --- a/docs/data-visualisation/index.html +++ b/docs/data-visualisation/index.html @@ -11747,7 +11747,7 @@

6. The Grammar of Graphics
-

Cover des Buches The Grammar of Graphics von Leland Wilkinson

+

6.1 Grundlagen

Cover des Buches The Grammar of Graphics von Leland Wilkinson

@@ -11770,7 +11770,7 @@

6. The Grammar of Graphics
-

6. The Grammar of Graphics

+

6. The Grammar of Graphics

6.1 Grundlagen

„Ich war entschlossen, ein Paket zu entwickeln, mit dem ich jede statistische Grafik, die ich je gesehen hatte, zeichnen konnte.“

– Leland Wilkinson, im Vorwort von The Grammar of Graphics

@@ -11791,20 +11791,124 @@

6. The Grammar of Graphics + +

+
+
+
diff --git a/requirements.txt b/requirements.txt index afa1220..68cfc7a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,4 @@ jupyter jupyter-contrib-nbextensions nbconvert +plotnine[all]