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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"*Na naucse.python.cz bohužel nefunguje zobrazování interaktivních plotly grafů. Pro zobrazení včetně grafů můžeš použít nbviewer: https://nbviewer.jupyter.org/github/coobas/pydataladies-dashboard/blob/main/notebooks/dashboardy-1.ipynb nebo si notebook pustit lokálně. Všechny soubory pohromadě najdeš v repozitáři https://github.com/coobas/pydataladies-dashboard.*\n",
"\n",
"\n",
"# Interaktivní vizualizace a aplikace\n",
"\n",
"Při práci s daty je mnoho příležitostí, kdy se hodí interaktivita. Při vizualici se hodí zvětšování / zmenšování měřítka, výběr podoblasti, ukázání vykreslených hodnot apod. Nebo při datové analýze obecně se může hodit interaktivně v notebooku měnit nějaký parametr (třeba hyperparametr pro strojové učení). Anebo chceme dát výsledky naší skvělé analýzy k dispozici \"netechnickým\" kolegům nebo kamarádům, kteří (zatím) nedokáží Jupyter notebook spustit.\n",
"\n",
"Tady si ukážeme, jak si s takovými úkoly poradit pomocí dvou nástrojů: [plotly](https://plotly.com/python/), resp. především [plotly express](https://plotly.com/python/plotly-express/), a [streamlit](https://www.streamlit.io/).\n",
"\n",
"Existují i další nástroje, které poskytují podobné možnosti. Podrobný přehled najdete na https://pyviz.org/tools.html. Na interaktivní vizualizace jsou to především [holoviews](http://github.com/pyviz/holoviews) nebo [altair](http://github.com/altair-viz/altair). Na \"dashboarding\" pak [dash](http://github.com/plotly/dash), [panel](https://panel.holoviz.org/), [voila](http://github.com/QuantStack/voila), [vizro](https://github.com/mckinsey/vizro) nebo [justpy](https://justpy.io).\n",
"\n",
"Velmi atraktivní novinkami jsou [JupyterLite](https://github.com/jupyterlite/jupyterlite) a [PyScript](https://pyscript.net). Oba projekty těží z možnosti spustit Python kód přímo ve webovém prohlížeči, konkrétně v jeho virtuálním stroji, díky technologie Web Assembly. \n",
"* JupyteLite umožňuje spustit Jupyter notebooky v prohlížeči, bez nutnosti instalace Pythonu, a to včetně knihoven na zpracováni dat jako jsou NumPy, Pandas apod. Více se můžeš dozvědět také z přednášky na pražském PyData meetup: [Jeremy Tuloup - JupyterLite: Jupyter ❤️ WebAssembly ❤️ Python (slides, video)](https://pydata.cz/#jeremy-tuloup---jupyterlite-jupyter-%EF%B8%8F-webassembly-%EF%B8%8F-python-slides-video).\n",
"* PyScript umožňuje vložit Python kód přímo do HTML kódu a tímto způsobem vytvářet interaktivní webové aplikace přímo V Pythonu. PyScript tak vlastně \"nahrazuje\" JavaScript.\n",
"\n",
"Každý z těchto nástrojů má, jako obvykle, své výhody a nevýhody. Nejrozšířenějším nástrojem je [Dash](http://github.com/plotly/dash) ze stejné dílny jako plotly, který poskytuje i enterprise řešení pro provoz aplikací. Dash je určitě dobrou volbou, jak se můžete dozvědět i na [přednášce z pražského PyData Meetupu](https://www.youtube.com/watch?v=dewrzMPPLDU). Panel (a také Voila) se od Dash liší tím, že je lze použít i v Jupyter notebooku a pak notebook použít přímo jako aplikaci. Největší přednost `voila` je jednoduchý způsob, jak udělat dashboard přímo z notebooku: viz [dokumentace](https://voila.readthedocs.io/en/stable/using.html).\n",
"\n",
"Dvě největší výhody Streamlitu jsou rychlost (jednoduchost) vývoje aplikace a atraktivní výchozí vzhled. Streamlit také (nedávno) přidal možnost vzájemné interakce mezi widgety pomocí callback funkcí. Pro Streamlit lze také poměrně snadno vytvářet nové komponenty, důkazem toho budiž [galerie komponent](https://streamlit.io/components).\n",
"\n",
"Pár článků či přednášek, které se tématu týkají:\n",
"* [Dash, Voila, Panel, & Streamlit—Our Thoughts on the Big Four Dashboarding Tools](https://quansight.com/post/dash-voila-panel-streamlit-our-thoughts-on-the-big-four-dashboarding-tools)\n",
"* [Pure Python Web Development](https://metaperl.github.io/pure-python-web-development/intro.html) - velký přehled knihoven pro webový vývoj v Pythonu.\n",
"* [Streamlit - The fastest way to build and share data science apps](https://www.youtube.com/watch?v=sdgTYy3BJiM)\n",
"* [How to Build a Reporting Dashboard using Dash and Plotly](https://towardsdatascience.com/how-to-build-a-complex-reporting-dashboard-using-dash-and-plotl-4f4257c18a7f)\n",
"* [Turn any Notebook into a Deployable Dashboard | SciPy 2019 | James Bednar](https://www.youtube.com/watch?v=L91rd1D6XTA)\n"
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pro a proti\n",
"\n",
"Je potřeba ale říct, že všechny zmíněné přístupy mají své výrazné nevýhody a limity a nehodí se pro velké a složité aplikace. Možnosti interakcí v aplikaci jsou omezené a také mohou být pomalé. Robustní škálování pro mnoho uživatelů (velký provoz) je obecně složitější. Kdy tedy především použít, co si tady ukážeme?\n",
"* Na malou aplikaci pro omezený počet uživatelů (dashboard pro kolegy).\n",
"* Na rychlý vývoj prototypu.\n",
"\n",
"A co když chceme budovat velkou (webovou) aplikaci?\n",
"* Zadáme vývojářskému týmu, aby v moderních JavaScript nástrojích typu React nebo Vue.js pro nás vytvořil krásný a rychlý \"front-end\", zatímco my vytvoříme v Pythonu \"back-end\", který s front-endem bude komunikovat např. pomocí JSON API. To uvidíme i v naší lekci o API.\n",
"* Když takový tým nemáme, naučíme se programovat v JavaScriptu ... Ne, raději v TypeScriptu ... Zkusíme PyScript ...\n",
"* ... nakonec najmeme front-end vývojářský tým pokud je to možné :-)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instalace a import grafických knihoven\n",
"\n",
"Pokud nemáte nainstalovanou knihovnu plotly, odkomentujte a spusťte příslušné řádky."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# instalace plotly\n",
"# %pip install plotly\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pro plotly express se vžila zkratka `px`, kterou použijeme i my. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import plotly.express as px\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import plotly.io as pio\n",
"\n",
"# from https://github.com/microsoft/vscode-jupyter/issues/6999\n",
"# This ensures Plotly output works in multiple places:\n",
"# plotly_mimetype: VS Code notebook UI\n",
"# notebook: \"Jupyter: Export to HTML\" command in VS Code\n",
"# See https://plotly.com/python/renderers/#multiple-renderers\n",
"pio.renderers.default = \"plotly_mimetype+notebook\"\n",
"# notebook-only does not work in Jupyter Lab\n",
"# pio.renderers.default = \"notebook\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interaktivní vizualizace dat\n",
"\n",
"Pojďme si zkusit trochu více prohlédnout data, se kterými jsme pracovali v předchozích lekcích na strojové učení."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rybí míry\n",
"\n",
"Začněme rozměry ryb, na kterých jsme ukazovali regresi a klasifikaci. Určitě stojí za to si data nejprve trochu prohlédnout. (Jen si asi nenakreslíme přímo vzhled ryb, na to nám data nestačí :)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"fish_data = pd.read_csv(\"fish_data.csv\", index_col=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"species_cs = {\n",
" \"Bream\": \"Cejn\",\n",
" \"Roach\": \"Plotice\",\n",
" \"Whitefish\": \"Bílá ryba\",\n",
" \"Parkki\": \"Karas\",\n",
" \"Perch\": \"Okoun\",\n",
" \"Pike\": \"Štika\",\n",
" \"Smelt\": \"Koruška\",\n",
"}\n",
"fish_data = fish_data.assign(Species=fish_data[\"Species\"].map(species_cs))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A místo klasického zobrazování čísel si zkusíme rovnou data vykreslit do grafu. Víme (tušíme), že v datech je spousta sloupců. Můžeme si je nechat vykreslit všechny pomocí `scatter_matrix`."
]
},
{
"cell_type": "code",
"execution_count": 7,
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"Začít můžeme poměrně častým box plotem. Bonus plotly je hlavně v interaktivním zobrazení číselných hodnot: mediánu a kvantilů, a také identifikaci (pravděpodobně) odlehlých hodnot."
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"text/html": [
""
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"px.density_contour(\n",
" fish_data,\n",
" color=\"Species\",\n",
" x=\"Height\",\n",
" y=\"Length3\",\n",
" marginal_x=\"histogram\",\n",
" marginal_y=\"box\",\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Úkol:** Zkuste si zobrazit v grafech jiné veličiny (jiné sloupce) než Height a Width. Zkuste změnit typ marginálních grafů."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Co s tím dál?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Report pro šéfa a šéfovou\n",
"\n",
"Máme i kolegy, kteří (ještě) nepoužívají Python a přesto by ocenili, kdyby mohli místo statického reportu dostat report s takto krásnými a interaktivními vizualizacemi. Pro tento účel se hodí export notebooku do html pomocí `nbconvert`.\n",
"\n",
"V příkazovém řádku spustíme `nbconvert` pomocí příkazu `jupyter nbconvert`. Pro export do html pak přidáme `--to html`, nesmíme zapomenout zadat který notebook (tj. soubor) chceme vlastně konvertovat."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Odkomentováním se spustí příkaz v příkazové řádce (díky vykřičníku)\n",
"# Možná se soubor u tebe jmenuje jinak než dashboardy-1, v takovém použij aktuální jméno souboru\n",
"# !jupyter nbconvert dashboardy-1.ipynb --to html\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Můžeme také exportovat jen výstupy a \"utajit\" zdrojový kód pomocí `--no-input`:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# !jupyter nbconvert dashboardy-1.ipynb --to html --no-input\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analýza nových dat\n",
"\n",
"Všem se naše vizualizace líbily, a jelikož je potřeba analyzovat další data, dostali jsme to za úkol my. Tentokrát se nejedná o ryby, ale o tučňáky."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Úkol:** Vyber si z grafů ten, který se ti nejvíc líbí, a místo rybích dat použij tučňáky."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"penguins = pd.read_csv(\"penguins_size_nona.csv\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vytváříme aplikaci\n",
"\n",
"Z naší práce v notebooku vykrystalizoval velice častý vzor: Podobné vizualizace a analýzy, v nichž se mění data a několik klíčových parametrů. Příležitost vytvořit aplikaci, která toto umožní nám a okruhu poučených uživatelů."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pojďme si nadefinovat naší jednoduchou aplikaci:\n",
"1. Načíst data z csv souboru.\n",
"2. Vykreslit scatter matrix, kde budu moct zvolit dimenze, sloupec pro barvu a průhlednost.\n",
"3. Pro vybraný sloupec zobrazit distribuci vybraného sloupce pomocí histogramu, box plotu nebo violin plotu."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Příprava v notebooku\n",
"\n",
"Pojďme to nejprve načrtnout tady v notebooku. Jako první si připravíme uživatelské vstupy."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# vstup 1: výběr datové sady\n",
"data_file_path = \"penguins_size_nona.csv\"\n",
"# vstup 2: výběr parametrů scatter matrix\n",
"dimensions = ['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g']\n",
"color = \"sex\"\n",
"opacity = 0.5\n",
"# výběr sloupce pro zobrazení rozdělení dat\n",
"interesting_column = \"body_mass_g\"\n",
"# výběr funkce pro zobrazení rozdělovací funkce\n",
"dist_plot = px.violin\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A tohle už je pak naše aplikace: Použili jsme stejné funkce a parametry jako na začátku práce s plotly, jen jsme je parametrizovali pomocí vstupů z předchozího bloku."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# načtení dat\n",
"data = pd.read_csv(data_file_path)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
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176,
186,
191,
191,
190,
193,
187,
191,
185,
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188,
192,
184,
195,
187,
192,
188,
198,
178,
195,
193,
185,
190,
181,
190,
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187,
195,
200,
191,
187,
187,
199,
195,
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187,
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196,
190,
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199,
193,
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191,
194,
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202,
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211,
210,
210,
211,
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214,
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210,
210,
209,
215,
213,
215,
210,
209,
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220,
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210,
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210,
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214,
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220,
216,
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216,
209,
215,
212,
212,
212,
218,
212,
218,
212,
214,
222,
203,
219,
215,
210,
208,
216,
213,
217,
214,
215,
212
]
},
{
"axis": {
"matches": true
},
"label": "body_mass_g",
"values": [
3800,
3250,
3450,
3625,
3200,
3700,
3450,
3325,
3400,
3800,
3800,
3200,
3150,
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3450,
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3550,
2850,
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3600,
3550,
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3700,
3550,
3200,
3800,
3350,
3500,
3600,
3550,
3400,
3300,
3700,
2900,
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3075,
2925,
3750,
3175,
3825,
3200,
3900,
2900,
3350,
3150,
3450,
3050,
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3050,
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3400,
3400,
3050,
3000,
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3450,
3700,
3500,
3525,
3950,
3250,
4150,
3800,
3700,
3575,
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3450,
3600,
2900,
3300,
3400,
3700,
3200,
3350,
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3850,
2700,
3650,
3500,
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3400,
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3325,
3600,
3350,
3250,
3525,
3650,
3650,
3400,
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4500,
4450,
4550,
4800,
4400,
4650,
4650,
4200,
4150,
4800,
5000,
4400,
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4600,
4700,
5050,
5150,
4950,
4350,
3950,
4300,
4900,
4200,
5100,
4850,
4400,
4900,
4300,
4450,
4200,
4400,
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4750,
5200,
4700,
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4625,
4725,
4750,
4600,
4875,
4950,
4750,
4850,
4875,
4625,
4850,
4975,
4700,
4575,
5000,
4650,
4375,
4925,
4850,
5200
]
}
],
"hovertemplate": "sex=FEMALE
%{xaxis.title.text}=%{x}
%{yaxis.title.text}=%{y}",
"legendgroup": "FEMALE",
"marker": {
"color": "#EF553B",
"opacity": 0.5,
"symbol": "circle"
},
"name": "FEMALE",
"showlegend": true,
"type": "splom"
}
],
"layout": {
"dragmode": "select",
"legend": {
"title": {
"text": "sex"
},
"tracegroupgap": 0
},
"margin": {
"t": 60
},
"template": {
"data": {
"bar": [
{
"error_x": {
"color": "#2a3f5f"
},
"error_y": {
"color": "#2a3f5f"
},
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "bar"
}
],
"barpolar": [
{
"marker": {
"line": {
"color": "#E5ECF6",
"width": 0.5
},
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "barpolar"
}
],
"carpet": [
{
"aaxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"baxis": {
"endlinecolor": "#2a3f5f",
"gridcolor": "white",
"linecolor": "white",
"minorgridcolor": "white",
"startlinecolor": "#2a3f5f"
},
"type": "carpet"
}
],
"choropleth": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "choropleth"
}
],
"contour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
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],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "contour"
}
],
"contourcarpet": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "contourcarpet"
}
],
"heatmap": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmap"
}
],
"heatmapgl": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "heatmapgl"
}
],
"histogram": [
{
"marker": {
"pattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
}
},
"type": "histogram"
}
],
"histogram2d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2d"
}
],
"histogram2dcontour": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "histogram2dcontour"
}
],
"mesh3d": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"type": "mesh3d"
}
],
"parcoords": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "parcoords"
}
],
"pie": [
{
"automargin": true,
"type": "pie"
}
],
"scatter": [
{
"fillpattern": {
"fillmode": "overlay",
"size": 10,
"solidity": 0.2
},
"type": "scatter"
}
],
"scatter3d": [
{
"line": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatter3d"
}
],
"scattercarpet": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattercarpet"
}
],
"scattergeo": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergeo"
}
],
"scattergl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattergl"
}
],
"scattermapbox": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scattermapbox"
}
],
"scatterpolar": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolar"
}
],
"scatterpolargl": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterpolargl"
}
],
"scatterternary": [
{
"marker": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"type": "scatterternary"
}
],
"surface": [
{
"colorbar": {
"outlinewidth": 0,
"ticks": ""
},
"colorscale": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"type": "surface"
}
],
"table": [
{
"cells": {
"fill": {
"color": "#EBF0F8"
},
"line": {
"color": "white"
}
},
"header": {
"fill": {
"color": "#C8D4E3"
},
"line": {
"color": "white"
}
},
"type": "table"
}
]
},
"layout": {
"annotationdefaults": {
"arrowcolor": "#2a3f5f",
"arrowhead": 0,
"arrowwidth": 1
},
"autotypenumbers": "strict",
"coloraxis": {
"colorbar": {
"outlinewidth": 0,
"ticks": ""
}
},
"colorscale": {
"diverging": [
[
0,
"#8e0152"
],
[
0.1,
"#c51b7d"
],
[
0.2,
"#de77ae"
],
[
0.3,
"#f1b6da"
],
[
0.4,
"#fde0ef"
],
[
0.5,
"#f7f7f7"
],
[
0.6,
"#e6f5d0"
],
[
0.7,
"#b8e186"
],
[
0.8,
"#7fbc41"
],
[
0.9,
"#4d9221"
],
[
1,
"#276419"
]
],
"sequential": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
],
"sequentialminus": [
[
0,
"#0d0887"
],
[
0.1111111111111111,
"#46039f"
],
[
0.2222222222222222,
"#7201a8"
],
[
0.3333333333333333,
"#9c179e"
],
[
0.4444444444444444,
"#bd3786"
],
[
0.5555555555555556,
"#d8576b"
],
[
0.6666666666666666,
"#ed7953"
],
[
0.7777777777777778,
"#fb9f3a"
],
[
0.8888888888888888,
"#fdca26"
],
[
1,
"#f0f921"
]
]
},
"colorway": [
"#636efa",
"#EF553B",
"#00cc96",
"#ab63fa",
"#FFA15A",
"#19d3f3",
"#FF6692",
"#B6E880",
"#FF97FF",
"#FECB52"
],
"font": {
"color": "#2a3f5f"
},
"geo": {
"bgcolor": "white",
"lakecolor": "white",
"landcolor": "#E5ECF6",
"showlakes": true,
"showland": true,
"subunitcolor": "white"
},
"hoverlabel": {
"align": "left"
},
"hovermode": "closest",
"mapbox": {
"style": "light"
},
"paper_bgcolor": "white",
"plot_bgcolor": "#E5ECF6",
"polar": {
"angularaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"radialaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"scene": {
"xaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"yaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
},
"zaxis": {
"backgroundcolor": "#E5ECF6",
"gridcolor": "white",
"gridwidth": 2,
"linecolor": "white",
"showbackground": true,
"ticks": "",
"zerolinecolor": "white"
}
},
"shapedefaults": {
"line": {
"color": "#2a3f5f"
}
},
"ternary": {
"aaxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"baxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
},
"bgcolor": "#E5ECF6",
"caxis": {
"gridcolor": "white",
"linecolor": "white",
"ticks": ""
}
},
"title": {
"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
}
}
},
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# scatter matric plat\n",
"px.scatter_matrix(data, dimensions=dimensions, color=color, opacity=opacity)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"alignmentgroup": "True",
"box": {
"visible": false
},
"hovertemplate": "sex=MALE
body_mass_g=%{x}",
"legendgroup": "MALE",
"marker": {
"color": "#636efa"
},
"name": "MALE",
"offsetgroup": "MALE",
"orientation": "h",
"scalegroup": "True",
"showlegend": true,
"type": "violin",
"x": [
3750,
3650,
4675,
3800,
4400,
4500,
4200,
3600,
3950,
3800,
3550,
3950,
3900,
3900,
4150,
3950,
4650,
3900,
4400,
4600,
3425,
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4100,
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4150,
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4000,
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5000,
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5650,
5700,
5800,
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5000,
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5600,
5500,
5500,
5500,
5500,
5950,
5500,
5850,
6000,
5750,
5400
],
"x0": " ",
"xaxis": "x",
"y0": " ",
"yaxis": "y"
},
{
"alignmentgroup": "True",
"box": {
"visible": false
},
"hovertemplate": "sex=FEMALE
body_mass_g=%{x}",
"legendgroup": "FEMALE",
"marker": {
"color": "#EF553B"
},
"name": "FEMALE",
"offsetgroup": "FEMALE",
"orientation": "h",
"scalegroup": "True",
"showlegend": true,
"type": "violin",
"x": [
3800,
3250,
3450,
3625,
3200,
3700,
3450,
3325,
3400,
3800,
3800,
3200,
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3600,
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3400,
3300,
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3200,
3900,
2900,
3350,
3150,
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3050,
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3325,
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3175,
3400,
3400,
3050,
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"source": [
"# zobrazení rozdělovací funkce\n",
"dist_plot(data, x=interesting_column, color=color)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A teď z toho pojďme udělat interaktivní webovou aplikaci! To nebudeme dělat přímo tady v notebooku, ale v \"obyčejném\" .py souboru s Python kódem.\n",
"\n",
"Aplikaci máme připravenou v souboru `app.py`, tady v notebooku si soubor můžeme prohlédnout:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"import pandas as pd\n",
"import plotly.express as px\n",
"import streamlit as st\n",
"\n",
"\n",
"def app():\n",
" st.title(\"PyDataLadies Dashboard\")\n",
" # vstup 1: výběr datové sady\n",
" data_file_path = st.file_uploader(\"Data file\")\n",
"\n",
" if data_file_path is None:\n",
" st.warning(\"No data file uploaded\")\n",
" return\n",
"\n",
" # read data if user uploads a file\n",
" data = pd.read_csv(data_file_path)\n",
" # seek back to position 0 after reading\n",
" data_file_path.seek(0)\n",
"\n",
" # vstup 2: výběr parametrů scatter matrix\n",
" dimensions = st.multiselect(\"Scatter matrix dimensions\", list(data.columns), default=list(data.columns))\n",
" color = st.selectbox(\"Color\", data.columns)\n",
" opacity = st.slider(\"Opacity\", 0.0, 1.0, 0.5)\n",
"\n",
" # scatter matrix plat\n",
" st.write(px.scatter_matrix(data, dimensions=dimensions, color=color, opacity=opacity))\n",
"\n",
" # výběr sloupce pro zobrazení rozdělení dat\n",
" interesting_column = st.selectbox(\"Interesting column\", data.columns)\n",
" # výběr funkce pro zobrazení rozdělovací funkce\n",
" dist_plot = st.selectbox(\"Plot type\", [px.box, px.histogram, px.violin])\n",
"\n",
" st.write(dist_plot(data, x=interesting_column, color=color))\n",
"\n",
"\n",
"if __name__ == \"__main__\":\n",
" app()\n"
]
}
],
"source": [
"%cat ../app.py\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Základem je, že uživatelské vstupy jsme předělali z podoby `proměnná = hodnota` do podoby `proměnná = st.vhodný_widget(...)`. Toto je způsob vytváření streamlit aplikace: \n",
"* Aplikaci píšeme v podstatě jako lineární skript (zdrojový kód samozřejmě můžeme strukturovat do funkcí / modulů / tříd dle vlastního uvážení, streamlit ale bude aplikaci vždy spouštět krok po kroku jako ten skript).\n",
"* Uživatelské vstupy načítáme z návratové hodnoty funkcí `st.nějaký_widget`, Streamlit se postará o to, aby widget správně fungoval a návratová hodnota byla vždy ta aktuální.\n",
"* Prvky aplikace (výstupy) zobrazíme uživateli pomocí `st.write`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Widgets - pomocné \"věcičky\":* V uživatelských grafických rozhraních (GUI) se používají *widgety*: nástroje na vybrání možností, hodnoty proměnné, zadání textu nebo datumu apod.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Spuštění\n",
"\n",
"Streamlit ještě nejspíš nemáš nainstalovaný. Instaluje se běžným způsobem přes pip:\n",
"\n",
" pip install streamlit==1.28.2\n",
" \n",
"případně pokud používáš conda\n",
"\n",
" conda install -c conda-forge streamlit=1.28.2\n",
"\n",
"Na svém počítači si pak aplikaci spustíš příkazem `streamlit run` s názvem souboru s aplikací. V našem případě tedy\n",
"\n",
" streamlit run app.py\n",
" \n",
"Pokud je vše v pořádku, zobrazí se něco takovéhoto:\n",
"\n",
"```\n",
" You can now view your Streamlit app in your browser.\n",
"\n",
" Network URL: http://192.168.2.103:8800\n",
" External URL: http://85.207.123.46:8800\n",
"```\n",
"\n",
"Dle instrukcí otevři odkaz (ten první) v prohlížeči. Je velká pravděpodobnout, že se objeví naše právě vytvořená aplikace na vizualizaci dat."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Publikujeme na internet\n",
"\n",
"V principu bychom mohli spustit aplikaci u sebe na počítači tak, aby ji mohli používat i další uživatelé. Na vnitřní síti (domácí, pracovní) by to bylo snadné (i když na pracovní síti a pracovním počítači by tomu mohla bránit bezpečnostní nastavení), přístup z vnějšího internetu by už byl komplikovanější.\n",
"\n",
"Naštěstí nejsme v podobné situaci sami :) Takže existují více či méně složité a sofistikované způsoby, jak aplikaci spustit na nějakém serveru (v cloudu) a zpřístupnit z internetu. My si ukážeme, jak to funguje na [Streamlit Cloud](https://streamlit.io/cloud), což je možná nejjednodušší způsob publikace Streamlit aplikace z GitHub repozitáře. Oproti obecnějším službám má pochopitelně méně možností a funguje pouze pro Streamlit.\n",
"\n",
"Pro sofistikovanější aplikace je pak možné použít některou z PaaS (Platform as a Service) služeb typu Render, Heroku, AWS Elastic Beanstalk, Google App Engine, Digital Ocean Apps. Některé z těchto služeb mají i zajímavé bezplatné plány nebo kredit pro nové uživatele."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Registrace na Streamlit Cloud\n",
"\n",
"Než začneme, je potřeba:\n",
"1. Github účet a Git klient: viz [README - Příprava](../README.md#příprava).\n",
"2. Použij svůj GitHub účet pro registraci na Streamlit Cloud: https://share.streamlit.io/signup\n",
"\n",
"![](\"streamlit-github.png\")
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specifikace prostředí\n",
"\n",
"Aby bylo jasné, jaké Python balíčky jsou potřeba pro naši aplikaci a tím si ji kdokoli mohl spustit, vytvoříme soubor [`requirements.txt`](https://pip.pypa.io/en/stable/reference/requirements-file-format/). Ten může použít buď lidský uživatel nebo platforma typu Streamlit Cloud.\n",
"\n",
"V souboru `requirements.txt` potřebujeme mít (alespoň) základní balíčky pro spuštění aplikace:\n",
"```\n",
"pandas==2.1.3\n",
"streamlit==1.28.2\n",
"plotly==5.18.0\n",
"```\n",
"* Je lepší specifikovat konkrétní verzi, protože novější nebo starší verze nemusí být kompatibilní.\n",
"* Ideální je specifikovat všechny závislosti včetně \"tranzitivních\", tj. závislosti závislostí (... závislostí ...). Pro zjednodušení tohoto úkolu existují pomocné nástroje, např. [Poetry](https://python-poetry.org), [pip-tools](https://pypi.org/project/pip-tools) a další. My si zatím vystačíme s jednoduchým `requirements.txt` souborem.\n",
"\n",
"Tento soubor umí použít `pip`: Pro instalaci balíčků stačí spustit\n",
"```\n",
"python -m pip install -r requirements.txt\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Publikace na GitHub\n",
"\n",
"#### 1. Vytvoř nový GitHub projekt:\n",
"\n",
"* Vytvoř nový projekt na [github.com](https://github.com)\n",
"\n",
"![](\"gh-new-repo.png\")
\n",
"\n",
"* Vyber vhodné jméno.\n",
"* Můžeš použít soukromý i veřejný projekt.\n",
"* Přidej Python `.gitignore` soubor z nabídky.\n",
"* Můžeš přidat i README soubor.\n",
"\n",
"#### 2. Naklonuj projekt k sobě na počítač\n",
"\n",
"Po vytvoření projektu se objeví instrukce na naklonování (nahrání) projektu na tvůj počítač. Použij například `gh` v příkazové řádce.\n",
"\n",
"#### 3. Přidej soubory s aplikací\n",
"\n",
"Do projektu přidej soubory s aplikací, tedy `app.py` a `requirements.txt`. Poté je přidej do Gitu a udělej commit:\n",
"\n",
"```\n",
"git add app.py requirements.txt\n",
"git commit -m \"První verze aplikace\"\n",
"```\n",
"\n",
"#### 4. Nahraj na GitHub\n",
"\n",
"Nahraj změny na GitHub pomocí `git push`.\n",
"```\n",
"git push\n",
"```"
]
},
{
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"source": [
"### Go-live\n",
"\n",
"Jakmile máme projekt na GitHubu, je snadné ho publikovat na Streamlit Cloud. Protože jsme pro registraci na Streamlit Cloud použili náš GitHub účet, má Streamlit přístup k našim repozitářům. (Pokud ne, můžeme přístup udělit dodatečně.)\n",
"\n",
"Pro vytvoření nové aplikace klikni na „New app“, poté vyplň jaký repozitář se má použít a jméno souboru s aplikací - pro nás to je `app.py`.\n",
"\n",
"Vyber také v sekci Advanced settings verzi Pythonu 3.11.\n",
"\n",
"![](\"advanced-py-version.png\")
\n",
"\n",
"Podrobný návod najdeš na https://docs.streamlit.io/streamlit-cloud/get-started/deploy-an-app.\n",
"\n",
"Za chvilku by tvoje aplikace měla být dostupná na internetu 🎉\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Úkol 1:** Pošli odkaz na tvou běžící aplikaci :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Úkol 2:** Pomocí `st.title` přidej titulek (název) aplikace. Vyzkoušej u sebe lokálně, pak změnu \"commituj\" do gitu a nahraj (`git push`) na GitHub. Tvoje změna by se měla (s drobným zpožděním) projevit i v online verzi."
]
}
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