{ "cells": [ { "cell_type": "markdown", "id": "f62220b8-2190-452e-9fd5-66c1641580f7", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "**NOTE: GitHub doesn't render all of the images in this notebook. Use nbviewer to render without full interactivity. View this locally for best results.**\n", "\n", "[![Nbviewer](https://img.shields.io/badge/render-nbviewer-lightgrey?logo=jupyter)](https://nbviewer.jupyter.org/github/stefmolin/python-data-viz-workshop/blob/main/slides/3-interactivity.ipynb) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/stefmolin/python-data-viz-workshop/main?urlpath=lab/tree/slides/3-interactivity.ipynb) [![View slides in browser](https://img.shields.io/badge/view-slides-orange?logo=reveal.js&logoColor=white)](https://stefaniemolin.com/python-data-viz-workshop/#/section-3)\n", "\n", "---" ] }, { "cell_type": "markdown", "id": "d4395d25-c867-4bda-a311-06db3c6b11d7", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_section-3" ] }, "source": [ "# Section 3: Building Interactive Visualizations for Data Exploration\n", "\n", "When exploring our data, interactive visualizations can provide the most value. Without having to create multiple iterations of the same plot, we can use mouse actions (e.g., click, hover, zoom, etc.) to explore different aspects and subsets of the data. In this section, we will learn how to use a few of the libraries in the HoloViz ecosystem to create interactive visualizations for exploring our data utilizing the Bokeh backend.\n", "\n", "
\n", "\"Baby\n", "
Listen up.
\n", "
" ] }, { "cell_type": "markdown", "id": "76caa09b-9492-4782-9f45-82d4dbc6660e", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Learning Path\n", "\n", "1. **Adding tooltips and sliders**\n", "2. Linking plots\n", "3. Additional plot types" ] }, { "cell_type": "markdown", "id": "d8dda187-d8f7-4af3-a403-57955785a902", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Adding tooltips and sliders\n", "\n", "For our first foray into interactive visualizations, we will recreate the earthquake map from the previous section. However, this time, we will make it possible to select the month using a slider, zoom in on the map, and view additional information on each earthquake with tooltips." ] }, { "cell_type": "markdown", "id": "d72539ad-e2df-486a-9ad3-19d14bbf7c3c", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "To make this visualization, we will work through the following steps:\n", "\n", "1. Read in and prepare the data.\n", "2. Import the required libraries and set up the Bokeh backend.\n", "3. Create an overlay with tooltips and a slider.\n", "4. Render the visualization." ] }, { "cell_type": "markdown", "id": "b038f85c-bd76-4754-b4a5-63f8af0c77be", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 1. Read in and prepare the data.\n", "\n", "As we did in the previous section, we will use GeoPandas to read in our dataset. We are once again creating a new column for the month, but this time, we are also dropping any rows with missing information:" ] }, { "cell_type": "code", "execution_count": 1, "id": "dfcecd50-e4d1-4342-8bd9-f7c84ba10a34", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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magplacetimetsunamimagTypegeometrymonth
02.7580 km N of Isabela, Puerto Rico2020-01-01 00:01:56.5900mdPOINT Z (-67.1275 19.2175 12)1
12.5564 km N of Isabela, Puerto Rico2020-01-01 00:03:38.2100mdPOINT Z (-67.0901 19.0766 6)1
21.8112 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:09.4400mdPOINT Z (-66.8541 17.8705 6)1
31.849 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:36.9300mdPOINT Z (-66.8636 17.8993 8)1
41.648 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:09:20.0600mdPOINT Z (-66.8685 17.9066 8)1
\n", "
" ], "text/plain": [ " mag place time \\\n", "0 2.75 80 km N of Isabela, Puerto Rico 2020-01-01 00:01:56.590 \n", "1 2.55 64 km N of Isabela, Puerto Rico 2020-01-01 00:03:38.210 \n", "2 1.81 12 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:09.440 \n", "3 1.84 9 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:36.930 \n", "4 1.64 8 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:09:20.060 \n", "\n", " tsunami magType geometry month \n", "0 0 md POINT Z (-67.1275 19.2175 12) 1 \n", "1 0 md POINT Z (-67.0901 19.0766 6) 1 \n", "2 0 md POINT Z (-66.8541 17.8705 6) 1 \n", "3 0 md POINT Z (-66.8636 17.8993 8) 1 \n", "4 0 md POINT Z (-66.8685 17.9066 8) 1 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "\n", "earthquakes = gpd.read_file('../data/earthquakes.geojson').assign(\n", " time=lambda x: pd.to_datetime(x.time, unit='ms'),\n", " month=lambda x: x.time.dt.month\n", ").dropna()\n", "\n", "earthquakes.head()" ] }, { "cell_type": "markdown", "id": "70eb29cc-2a21-4c65-b7fd-e64ca207ea5a", "metadata": {}, "source": [ "*Source: [USGS API](https://earthquake.usgs.gov/fdsnws/event/1/)*" ] }, { "cell_type": "markdown", "id": "86f62f99-42b1-420e-808b-49f254cc3e8f", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 2. Import the required libraries and set up the Bokeh backend.\n", "\n", "We will be working with GeoViews once again. However, this time, we are going to use the Bokeh backend. Bokeh maps use the [Mercator](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#mercator) projection, so we will also need to import the `crs` module from Cartopy in order to project back into the coordinate system used by our data ([Plate Carree](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#platecarree) projection):" ] }, { "cell_type": "code", "execution_count": 2, "id": "f5527998-f957-43df-a5e7-847836f34cd0", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof 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version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = 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css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < 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\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from cartopy import crs\n", "import geoviews as gv\n", "import geoviews.feature as gf\n", "\n", "gv.extension('bokeh')" ] }, { "cell_type": "markdown", "id": "c473f55b-c302-4d24-8703-b0b33c4e4a1a", "metadata": { "tags": [] }, "source": [ "*Tip: You can read more about projections in the GeoViews documentation [here](https://geoviews.org/user_guide/Projections.html) and in the Cartopy documentation [here](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html).*" ] }, { "cell_type": "markdown", "id": "4b778c0b-532f-401c-aba7-20952161e194", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 3. Create an overlay with tooltips and a slider.\n", "\n", "We will start by creating our points and specifying their ranges:" ] }, { "cell_type": "code", "execution_count": 3, "id": "d8721738-d5cc-4c77-9c9e-ebc55c3c0c71", "metadata": { "tags": [] }, "outputs": [], "source": [ "points = gv.Points(\n", " earthquakes,\n", " kdims=['longitude', 'latitude'],\n", " vdims=['month', 'place', 'tsunami', 'mag', 'magType']\n", ")\n", "\n", "# set colorbar limits for magnitude and axis limits\n", "points = points.redim.range(\n", " mag=(-2, 10), longitude=(-180, 180), latitude=(-90, 90)\n", ")" ] }, { "cell_type": "markdown", "id": "1744522c-5c93-4410-b60d-179415816ef1", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Next, we will create an overlay with a slider for the month:" ] }, { "cell_type": "code", "execution_count": 4, "id": "02a1e487-e3ed-45d9-b24f-6076a21ad2d8", "metadata": { "tags": [] }, "outputs": [], "source": [ "overlay = gf.land * gf.coastline * gf.borders * points.groupby('month')" ] }, { "cell_type": "markdown", "id": "7bbe20cf-b52f-4ece-b6c6-6ca00d80e220", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Finally, we customize each of the components of our plot, adding the option to hover over the points to trigger a tooltip:" ] }, { "cell_type": "code", "execution_count": 5, "id": "9a76f712-0b9c-4eda-ab06-bf36684bc2f4", "metadata": { "tags": [] }, "outputs": [], "source": [ "interactive_map = overlay.opts(\n", " gv.opts.Feature(projection=crs.PlateCarree()),\n", " gv.opts.Overlay(width=700, height=450),\n", " gv.opts.Points(color='mag', cmap='fire_r', colorbar=True, tools=['hover'])\n", ")" ] }, { "cell_type": "markdown", "id": "f2b18232-93ee-4490-ae4f-ec723f5f54c4", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 4. Render the visualization.\n", "\n", "While we could use the `hv.output()` function to render our visualization, we will use [Panel](https://panel.holoviz.org/) for this example. Panel, which is also part of HoloViz, provides additional functionality and flexibility when it comes to the layout. As you create more complex visualizations, Panel will become a necessity:" ] }, { "cell_type": "code", "execution_count": 6, "id": "18d06129-e26a-4d99-895f-c1088fab08f0", "metadata": { "tags": [] }, "outputs": [], "source": [ "import panel as pn\n", "\n", "earthquake_viz = pn.panel(interactive_map, widget_location='bottom')" ] }, { "cell_type": "markdown", "id": "377b9684-2639-48e1-8570-ea3bb6a2acc8", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "Try using the slider, tooltips, and zoom/pan functionality:" ] }, { "cell_type": "code", "execution_count": 7, "id": "a3d89c72-91f7-4ca9-a330-5bb4dff7ddb4", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1011" } }, "output_type": "execute_result" } ], "source": [ "earthquake_viz.embed()" ] }, { "cell_type": "markdown", "id": "f50b8972-0855-48c3-ac43-76145318a33a", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "*Tip: Whenever we interact with the visualization, it has to determine what to update, which can be slow if the JavaScript visualization has to work with the Python backend. To get around this, you can use Panel to embed the visualization like we are doing above. Be careful though – this can make your notebook file size much larger. Another option is to look into the [Datashader](https://datashader.org/) library from HoloViz.*" ] }, { "cell_type": "markdown", "id": "9d89e35b-cbd5-4c83-9739-c83d571f1664", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "

The interactivity works best in a notebook environment – here's an example of the slider, tooltips, and zoom/pan functionality in action:

\n", "\n", "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "e6896774-62b6-4ead-b99e-0ecc7dd0060e", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Learning Path\n", "\n", "1. Adding tooltips and sliders\n", "2. **Linking plots**\n", "3. Additional plot types" ] }, { "cell_type": "markdown", "id": "1ac95d88-bec0-4b26-aac7-a6d8777e20ee", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Linking plots\n", "In the previous example, we saw that we could link together a slider and a plot. We can also link together plots, which makes using interactivity to explore our data even more powerful. For this example, we will create a link between a map of the earthquakes in January 2020 and a table of those same earthquakes that provides some additional information; we will be able to select earthquakes on the map and use that to filter our dataset. To further explore HoloViz, we will use the [hvPlot](https://hvplot.holoviz.org/index.html) library here; hvPlot makes it easy to build interactive visualizations with syntax similar to plotting in pandas." ] }, { "cell_type": "markdown", "id": "02c603d0-e9cb-4924-bbd4-d5a38e81cfca", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "We will work through the following steps to build this visualization:\n", "1. Isolate the January earthquake data and prepare it for plotting.\n", "2. Enable the use of hvPlot for interactive plotting with pandas.\n", "3. Build a layout composed of an interactive map and a table with hvPlot.\n", "4. Link selections across the visualizations in the layout." ] }, { "cell_type": "markdown", "id": "80faa6f3-af7e-4d17-b9c4-eae54e3204f6", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 1. Isolate the January earthquake data and prepare it for plotting.\n", "Let's filter our dataset down to just January and then pull out the latitude and longitude information for our plot:" ] }, { "cell_type": "code", "execution_count": 8, "id": "e4aa4b63-aa0b-4263-ad6c-6f51b9ccab04", "metadata": { "tags": [] }, "outputs": [], "source": [ "january_earthquakes = earthquakes.query('month == 1').assign(\n", " longitude=lambda x: x.geometry.x,\n", " latitude=lambda x: x.geometry.y\n", ").drop(columns=['month', 'geometry'])" ] }, { "cell_type": "markdown", "id": "3423f993-d898-4c14-b306-7b767dc6be29", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 2. Enable the use of hvPlot for interactive plotting with pandas. \n", "To enable interactive plotting with pandas, we have to import the following:" ] }, { "cell_type": "code", "execution_count": 9, "id": "955cc599-498d-4f3b-a3a2-8b2e0445692a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " 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\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "f1df75d1-0489-4a2c-980a-a5052f4bcaf9" } }, "output_type": "display_data" } ], "source": [ "import hvplot.pandas" ] }, { "cell_type": "markdown", "id": "24138290-ea86-4ca3-8f89-4657aece14b1", "metadata": { "tags": [] }, "source": [ "**Important**: While hvPlot is using HoloViews and GeoViews for the plotting logic, there is currently a [bug](https://github.com/holoviz/geoviews/issues/497) with this feature in GeoViews; however, we can still put together a working example using hvPlot since the projections are handled differently." ] }, { "cell_type": "markdown", "id": "0a7df6d5-3f58-4fdc-89b0-eea4c4a82d0c", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 3. Build a layout composed of an interactive map and a table with hvPlot.\n", "\n", "Plotting with hvPlot works just like plotting with pandas – instead of calling the `plot()` method, we now call `hvplot()` to switch from static plots to interactive ones with the Bokeh backend. In doing so, hvPlot will take care of the HoloViews and GeoViews code for us. Here, we make the interactive map using tiles, which makes it possible to zoom in on the map and see more detail:" ] }, { "cell_type": "code", "execution_count": 10, "id": "4a4baa90-0dcc-4c73-a2d5-c7204cdbadf5", "metadata": { "tags": [] }, "outputs": [], "source": [ "geo = january_earthquakes.hvplot(\n", " x='longitude', y='latitude', kind='points',\n", " color='mag', cmap='fire_r', clim=(-2, 10),\n", " tiles='CartoLight', geo=True, global_extent=True,\n", " xlabel='Longitude', ylabel='Latitude', title='January 2020 Earthquakes',\n", " frame_height=450\n", ")" ] }, { "cell_type": "markdown", "id": "378f076a-3ff2-499e-bf64-ab9bff4e7d5c", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Next, we create the table by once again calling the `hvplot()` method:" ] }, { "cell_type": "code", "execution_count": 11, "id": "6a85022e-f185-4683-b916-f270c6c5d260", "metadata": { "tags": [] }, "outputs": [], "source": [ "table = january_earthquakes.sort_values(['longitude', 'latitude']).hvplot(\n", " kind='table', width=650, height=450, title='Raw Data'\n", ")" ] }, { "cell_type": "markdown", "id": "f3fdcf89-2a35-49fa-980c-b079198cc0c0", "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "Now, we create a layout with the map and table:" ] }, { "cell_type": "code", "execution_count": 12, "id": "4a59e87a-417b-448a-af6e-fdc464c9fc7b", "metadata": { "tags": [] }, "outputs": [], "source": [ "layout = geo + table" ] }, { "cell_type": "markdown", "id": "7a5d8b10-0496-46ba-aafa-3e4796c46063", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "### 4. Link selections across the visualizations in the layout.\n", "With our layout, we have everything we need to compose our visualization – we just need to link the components together. Here, we are creating an instance, so that we can use it to filter our data after interacting with the visualization:" ] }, { "cell_type": "code", "execution_count": 13, "id": "f783bae2-2116-42b5-8efb-6bdd7ad25ede", "metadata": { "tags": [] }, "outputs": [], "source": [ "import holoviews as hv\n", "\n", "# temporary bug fix for this example from\n", "# https://github.com/holoviz/holoviews/issues/4645#issuecomment-1907032005\n", "from geoviews.element.geo import WMTS\n", "WMTS._transforms = []\n", "\n", "selection = hv.link_selections.instance()\n", "map_and_table = selection(layout)" ] }, { "cell_type": "markdown", "id": "57de4e3b-f347-4409-8a8f-7576d9da0fc0", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "Let's take a look at our visualization now – note that this only works in the JupyterLab setup, not in Visual Studio Code. Try using the **Box Select** tool to select some earthquakes on the map and then take a look at the **Raw Data**:" ] }, { "cell_type": "code", "execution_count": 14, "id": "78c652e4-cbc5-48fa-8dc7-692fab7342b2", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ ":Layout\n", " .Overlay.I :DynamicMap []\n", " :Overlay\n", " .WMTS.I :WMTS [Longitude,Latitude]\n", " .Points.I :Points [longitude,latitude] (mag)\n", " .Points.II :Points [longitude,latitude] (mag)\n", " .Rectangles.I :Rectangles [x0,y0,x1,y1]\n", " .Path.I :Path [x,y]\n", " .Table.I :DynamicMap []\n", " :Table [mag,place,time,tsunami,magType,longitude,latitude]" ] }, "execution_count": 14, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "b9b97896-23d2-4551-8acb-288995038b0a" } }, "output_type": "execute_result" } ], "source": [ "map_and_table" ] }, { "cell_type": "markdown", "id": "3bcb8711-e683-4dd0-a482-1d7cb177b03a", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "

The result can be interacted with after displaying it, but this kind of interactivity only works in the notebook. Here's an example:

\n", "\n", "
\n", " \"Linked\n", "
\n", " This example uses tabs to display the layout rather than side by side like we did. Tabs are a little buggy in this version, so we didn't use them.\n", "
" ] }, { "cell_type": "markdown", "id": "25eedd0e-e592-4665-a719-25716ec363c8", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Using the selection from the visualization, we can filter our dataset as follows:" ] }, { "cell_type": "code", "execution_count": 15, "id": "ff7fdeb4-f68d-4f9f-a734-18c98c1aaf90", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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magplacetimetsunamimagTypelongitudelatitude
163625.1270 km SE of Chiniak, Alaska2020-01-31 11:25:37.2621mww-149.329555.7981
9115.0217 km SSE of Old Harbor, Alaska2020-01-02 08:54:33.0831mww-151.427455.5493
78314.3258 km SE of Chiniak, Alaska2020-01-13 09:00:21.0440mb-149.326155.9471
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" ], "text/plain": [ " mag place time tsunami \\\n", "16362 5.1 270 km SE of Chiniak, Alaska 2020-01-31 11:25:37.262 1 \n", "911 5.0 217 km SSE of Old Harbor, Alaska 2020-01-02 08:54:33.083 1 \n", "7831 4.3 258 km SE of Chiniak, Alaska 2020-01-13 09:00:21.044 0 \n", "\n", " magType longitude latitude \n", "16362 mww -149.3295 55.7981 \n", "911 mww -151.4274 55.5493 \n", "7831 mb -149.3261 55.9471 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection.filter(january_earthquakes).nlargest(3, 'mag')" ] }, { "cell_type": "markdown", "id": "5e4eb4ba-ab4d-4e08-a5e7-89557f4f2cb5", "metadata": {}, "source": [ "*Note: Selecting something other than what is shown in the screen recording will yield different results.*" ] }, { "cell_type": "markdown", "id": "a894a728-e083-4673-a677-26354594787a", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_exercise-3-1" ] }, "source": [ "### Exercise 3.1\n", "\n", "##### Using hvPlot, make histograms of earthquake magnitude (`mag`) for each magnitude type (`magType`) with a dropdown to select the magnitude type." ] }, { "cell_type": "markdown", "id": "c448d82b-d4f4-4cb2-a04e-b9e4de671613", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [ "id_solution-3-1" ] }, "source": [ "### Solution" ] }, { "cell_type": "markdown", "id": "b85ee793-4c80-423e-8742-184faf208a62", "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "We start by reading in the dataset:" ] }, { "cell_type": "code", "execution_count": 16, "id": "d0bbe397-4ed1-4251-885d-838228f1ca13", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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magplacetimetsunamimagTypegeometrymonth
02.7580 km N of Isabela, Puerto Rico2020-01-01 00:01:56.5900mdPOINT Z (-67.1275 19.2175 12)1
12.5564 km N of Isabela, Puerto Rico2020-01-01 00:03:38.2100mdPOINT Z (-67.0901 19.0766 6)1
21.8112 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:09.4400mdPOINT Z (-66.8541 17.8705 6)1
31.849 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:36.9300mdPOINT Z (-66.8636 17.8993 8)1
41.648 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:09:20.0600mdPOINT Z (-66.8685 17.9066 8)1
\n", "
" ], "text/plain": [ " mag place time \\\n", "0 2.75 80 km N of Isabela, Puerto Rico 2020-01-01 00:01:56.590 \n", "1 2.55 64 km N of Isabela, Puerto Rico 2020-01-01 00:03:38.210 \n", "2 1.81 12 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:09.440 \n", "3 1.84 9 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:36.930 \n", "4 1.64 8 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:09:20.060 \n", "\n", " tsunami magType geometry month \n", "0 0 md POINT Z (-67.1275 19.2175 12) 1 \n", "1 0 md POINT Z (-67.0901 19.0766 6) 1 \n", "2 0 md POINT Z (-66.8541 17.8705 6) 1 \n", "3 0 md POINT Z (-66.8636 17.8993 8) 1 \n", "4 0 md POINT Z (-66.8685 17.9066 8) 1 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "\n", "earthquakes = gpd.read_file('../data/earthquakes.geojson').assign(\n", " time=lambda x: pd.to_datetime(x.time, unit='ms'),\n", " month=lambda x: x.time.dt.month\n", ").dropna()\n", "\n", "earthquakes.head()" ] }, { "cell_type": "markdown", "id": "2e47a1ce-b577-4146-9b1e-ab58c12437b7", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Next, we use hvPlot to create the visualization and Panel to embed it:" ] }, { "cell_type": "code", "execution_count": 17, "id": "a1f53c03-116d-490a-8bbd-a538078221a9", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " 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OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "02664283-4c96-4186-a377-19ccc35c4848" } }, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "42a71dbd-5da7-49b9-a11e-7c1f6e5f1460" } }, "output_type": "execute_result" } ], "source": [ "import hvplot.pandas\n", "import panel as pn\n", "\n", "pn.panel(earthquakes[['mag', 'magType']].hvplot(\n", " kind='hist', x='mag', groupby='magType', ylabel='frequency',\n", " frame_height=200, responsive=True, widget_location='left'\n", ")).embed()" ] }, { "cell_type": "markdown", "id": "72fdce35-9e61-4fac-bf30-0d83e5c585cf", "metadata": {}, "source": [ "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "8dae24e4-d2bd-4a7e-80d9-c00fc61dc9ae", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Learning Path\n", "\n", "1. Adding tooltips and sliders\n", "2. Linking plots\n", "3. **Additional plot types**" ] }, { "cell_type": "markdown", "id": "d34a38b2-2c49-4f76-b360-1a156da61228", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Additional plot types\n", "\n", "So far, we've seen how easy it is to make interactive visualizations with the Bokeh backend, but another benefit of using HoloViz is the ability to easily make a variety of plots that may require significant effort to create from scratch (e.g., [network/graph diagrams](http://holoviews.org/user_guide/Network_Graphs.html), [heatmaps](http://holoviews.org/reference/elements/bokeh/HeatMap.html#elements-bokeh-gallery-heatmap), [chord diagrams](http://holoviews.org/reference/elements/bokeh/Chord.html#elements-bokeh-gallery-chord), and [Sankey plots](http://holoviews.org/reference/elements/bokeh/Sankey.html#elements-bokeh-gallery-sankey)). In this section, we will see how to create a chord diagram and a Sankey plot in just a few lines of code using the HoloViews library directly." ] }, { "cell_type": "markdown", "id": "aefc9cb3-1ea4-4cc4-9ea2-280bfa75815e", "metadata": { "tags": [] }, "source": [ "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "10bb751d-a94d-4a1f-ac56-f9a552743e19", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "We will be working with a new dataset for these examples: 2019 flight statistics from the United States Department of Transportation’s Bureau of Transportation Statistics. The [dataset](https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FMF&QO_fu146_anzr=Nv4%20Pn44vr45) contains 321,409 rows and 41 columns. Here, we read it in and perform some initial processing on it for our visualizations:" ] }, { "cell_type": "code", "execution_count": 18, "id": "0e2e4f7e-654e-4f33-aabd-2999f3d84cd7", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "\n", "flight_stats = pd.read_csv(\n", " '../data/T100_MARKET_ALL_CARRIER.zip',\n", " usecols=[\n", " 'CLASS', 'REGION', 'UNIQUE_CARRIER_NAME', 'ORIGIN_CITY_NAME', 'ORIGIN',\n", " 'DEST_CITY_NAME', 'DEST', 'PASSENGERS', 'FREIGHT', 'MAIL'\n", " ]\n", ").rename(lambda x: x.lower(), axis=1).assign(\n", " region=lambda x: x.region.replace({\n", " 'D': 'Domestic', 'I': 'International', 'A': 'Atlantic',\n", " 'L': 'Latin America', 'P': 'Pacific', 'S': 'System'\n", " }),\n", " route=lambda x: np.where(\n", " x.origin < x.dest,\n", " x.origin + '-' + x.dest,\n", " x.dest + '-' + x.origin\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "28de4156-45cd-4d95-a161-0704ed6a04a2", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Our dataset looks like this:" ] }, { "cell_type": "code", "execution_count": 19, "id": "4fee6b4b-7e21-414f-a4a8-7802d64c428d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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passengersfreightmailunique_carrier_nameregionoriginorigin_city_namedestdest_city_nameclassroute
00.053185.00.0EmiratesInternationalDXBDubai, United Arab EmiratesIAHHouston, TXGDXB-IAH
10.09002.00.0EmiratesInternationalDXBDubai, United Arab EmiratesJFKNew York, NYGDXB-JFK
20.02220750.00.0EmiratesInternationalDXBDubai, United Arab EmiratesORDChicago, ILGDXB-ORD
30.01201490.00.0EmiratesInternationalIAHHouston, TXDXBDubai, United Arab EmiratesGDXB-IAH
40.0248642.00.0EmiratesInternationalJFKNew York, NYDXBDubai, United Arab EmiratesGDXB-JFK
\n", "
" ], "text/plain": [ " passengers freight mail unique_carrier_name region origin \\\n", "0 0.0 53185.0 0.0 Emirates International DXB \n", "1 0.0 9002.0 0.0 Emirates International DXB \n", "2 0.0 2220750.0 0.0 Emirates International DXB \n", "3 0.0 1201490.0 0.0 Emirates International IAH \n", "4 0.0 248642.0 0.0 Emirates International JFK \n", "\n", " origin_city_name dest dest_city_name class \\\n", "0 Dubai, United Arab Emirates IAH Houston, TX G \n", "1 Dubai, United Arab Emirates JFK New York, NY G \n", "2 Dubai, United Arab Emirates ORD Chicago, IL G \n", "3 Houston, TX DXB Dubai, United Arab Emirates G \n", "4 New York, NY DXB Dubai, United Arab Emirates G \n", "\n", " route \n", "0 DXB-IAH \n", "1 DXB-JFK \n", "2 DXB-ORD \n", "3 DXB-IAH \n", "4 DXB-JFK " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flight_stats.head()" ] }, { "cell_type": "markdown", "id": "7a0324c0-ae90-4018-ab9c-5a2d209f19c2", "metadata": {}, "source": [ "*Source: [T-100 Market (All Carriers)](https://www.transtats.bts.gov/DL_SelectFields.asp?gnoyr_VQ=FMF&QO_fu146_anzr=Nv4%20Pn44vr450) dataset provided by the United States Bureau of Transportation Statistics.*" ] }, { "cell_type": "markdown", "id": "f90b309c-b967-4d51-931d-5bca42bac40b", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "This dataset only includes travel to/from the US, so as a starting point for our analysis, we will only consider travel to/from the top 10 cities by passenger counts and – for the Sankey plot only – the top 5 airlines in the US as found in [this article](https://stefaniemolin.com/articles/data-science/how-to-pivot-and-plot-data-with-pandas/), which analyzes this dataset:" ] }, { "cell_type": "code", "execution_count": 20, "id": "041e90e3-9c89-4681-94e9-bad8049ebe13", "metadata": { "tags": [] }, "outputs": [], "source": [ "cities = [\n", " 'Atlanta, GA', 'Chicago, IL', 'New York, NY', 'Los Angeles, CA',\n", " 'Dallas/Fort Worth, TX', 'Denver, CO', 'Houston, TX',\n", " 'San Francisco, CA', 'Seattle, WA', 'Orlando, FL'\n", "]\n", "\n", "top_airlines = [\n", " 'American Airlines Inc.', 'Delta Air Lines Inc.', 'JetBlue Airways',\n", " 'Southwest Airlines Co.', 'United Air Lines Inc.'\n", "]" ] }, { "cell_type": "markdown", "id": "1e4a6ebe-586a-4d4f-a224-844b8cf2b5d2", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_chord-diagram" ] }, "source": [ "### Chord diagram\n", "\n", "A **chord diagram** is a way of showing many-to-many relationships between a set of entities called **nodes**: the nodes are arranged in a circle, and chords (which can be thought of as **edges**) are drawn between those that are connected, with the width of the chord encoding the strength of the connection. In this section, we will be making a chord diagram for total passenger service travel between the top 10 cities in 2019:\n", "\n", "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "2fb335a7-09ad-4df2-be73-0d2c3355715a", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Let's work through these steps to create the chord diagram:\n", "\n", "1. Aggregate the dataset to get total flight statistics between the top cities.\n", "2. Create a chord diagram with HoloViews.\n", "3. Customize the tooltips using Bokeh." ] }, { "cell_type": "markdown", "id": "cebd3f7c-c468-4e12-931f-bb02b798639f", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 1. Aggregate the dataset to get total flight statistics between the top cities.\n", "\n", "Our dataset contains flights that aren't considered passenger service, so we will need to filter to just passenger service between the cities in our list. After that, we are grouping by both the city and airport for each point of the trip (origin and destination) because some cities have multiple airports. Finally, we calculate the total number of passengers and pounds of mail/freight transported in 2019. Note that we are limiting the result to rows with total passengers greater than zero since our chord diagram will use this column to draw the chords:" ] }, { "cell_type": "code", "execution_count": 21, "id": "9536a203-3d37-45a8-8fb4-d0b97aaf94a3", "metadata": { "tags": [] }, "outputs": [], "source": [ "total_flight_stats = flight_stats.query(\n", " '`class` == \"F\" and origin_city_name != dest_city_name'\n", " f' and origin_city_name.isin({cities}) and dest_city_name.isin({cities})'\n", ").groupby([\n", " 'origin', 'origin_city_name', 'dest', 'dest_city_name'\n", "])[['passengers', 'freight', 'mail']].sum().reset_index().query('passengers > 0')" ] }, { "cell_type": "markdown", "id": "ad3cea04-d382-43ed-b3c9-141a3577124a", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Our aggregated dataset looks like this:" ] }, { "cell_type": "code", "execution_count": 22, "id": "e1b5d343-a651-4b79-8e97-273b376d39cd", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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originorigin_city_namedestdest_city_namepassengersfreightmail
78LGANew York, NYDENDenver, CO589190.0506023.0293108.0
117ORDChicago, ILSEASeattle, WA810594.01063463.02627325.0
31DFWDallas/Fort Worth, TXMCOOrlando, FL683700.0187672.095570.0
5ATLAtlanta, GALAXLos Angeles, CA1121378.08707125.03267077.0
126SEASeattle, WALGANew York, NY24.00.00.0
45IAHHouston, TXATLAtlanta, GA566369.0367543.0726670.0
14DENDenver, COHOUHouston, TX305193.0363119.00.0
44HOUHouston, TXSFOSan Francisco, CA1843.05523.00.0
73LAXLos Angeles, CAMDWChicago, IL277226.02022416.00.0
89MCOOrlando, FLDENDenver, CO594878.0368516.0138811.0
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" ], "text/plain": [ " origin origin_city_name dest dest_city_name passengers \\\n", "78 LGA New York, NY DEN Denver, CO 589190.0 \n", "117 ORD Chicago, IL SEA Seattle, WA 810594.0 \n", "31 DFW Dallas/Fort Worth, TX MCO Orlando, FL 683700.0 \n", "5 ATL Atlanta, GA LAX Los Angeles, CA 1121378.0 \n", "126 SEA Seattle, WA LGA New York, NY 24.0 \n", "45 IAH Houston, TX ATL Atlanta, GA 566369.0 \n", "14 DEN Denver, CO HOU Houston, TX 305193.0 \n", "44 HOU Houston, TX SFO San Francisco, CA 1843.0 \n", "73 LAX Los Angeles, CA MDW Chicago, IL 277226.0 \n", "89 MCO Orlando, FL DEN Denver, CO 594878.0 \n", "\n", " freight mail \n", "78 506023.0 293108.0 \n", "117 1063463.0 2627325.0 \n", "31 187672.0 95570.0 \n", "5 8707125.0 3267077.0 \n", "126 0.0 0.0 \n", "45 367543.0 726670.0 \n", "14 363119.0 0.0 \n", "44 5523.0 0.0 \n", "73 2022416.0 0.0 \n", "89 368516.0 138811.0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total_flight_stats.sample(10, random_state=1)" ] }, { "cell_type": "markdown", "id": "73e2be9e-a865-4c47-b84f-4c42a3e80928", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 2. Create a chord diagram with HoloViews.\n", "\n", "Next, we create an instance of `hv.Chord` by specifying that the paths are between the `origin` and `dest` columns (which are not the city names, but rather the airport codes) and that the remaining values associated with each origin-destination pair should be used as value dimensions. Note that only the first value dimension will be used to size the chords, but the rest will be accessible in the tooltip:" ] }, { "cell_type": "code", "execution_count": 23, "id": "bffc3a42-e4e3-4b1c-a91b-6335cb6e55e0", "metadata": { "tags": [] }, "outputs": [], "source": [ "chord = hv.Chord(\n", " total_flight_stats,\n", " kdims=['origin', 'dest'],\n", " vdims=['passengers', 'origin_city_name', 'dest_city_name', 'mail', 'freight']\n", ")" ] }, { "cell_type": "markdown", "id": "f9ea6ece-2598-40d0-90c8-7b7d5e8ef9f2", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 3. Customize the tooltips using Bokeh.\n", "\n", "Our dataset contains large numbers, which can be hard to read in tooltips without formatting. In addition, the default tooltip is rather long since it lists all of the columns we provided as `kdims` and `vdims`. To improve usability of the tooltips, we should combine the city and airport information into a single line for each source/destination since those fields are related (e.g., `Chicago, IL (ORD)`). While this functionality is possible, we will have to use Bokeh directly to achieve it. Here, we instantiate an instance of Bokeh's `HoverTool` with our desired tooltip format:" ] }, { "cell_type": "code", "execution_count": 24, "id": "6c31c827-be58-4fd2-b306-8987515d2bd7", "metadata": { "tags": [] }, "outputs": [], "source": [ "from bokeh.models import HoverTool\n", "\n", "tooltips = {\n", " 'Source': '@origin_city_name (@origin)',\n", " 'Target': '@dest_city_name (@dest)',\n", " 'Passengers': '@passengers{0,.}',\n", " 'Mail': '@mail{0,.} lbs.',\n", " 'Freight': '@freight{0,.} lbs.',\n", "}\n", "hover = HoverTool(tooltips=tooltips)" ] }, { "cell_type": "markdown", "id": "0d44b71d-3585-43c0-b569-bee607daa6fd", "metadata": {}, "source": [ "*Tip: Check out the Bokeh `HoverTool` documentation [here](https://docs.bokeh.org/en/latest/docs/reference/models/tools.html#bokeh.models.HoverTool) for more information on the hover tool itself and [here](https://docs.bokeh.org/en/latest/docs/reference/models/tools.html#bokeh.models.HoverTool.tooltips) for more information on creating and formatting tooltips.*" ] }, { "cell_type": "markdown", "id": "b15f5a78-ad85-46de-9dcd-4aaa214d096f", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Now, we will set up the display options on our chord diagram and enable the hover tool. In the previous section, we passed in `tools=['hover']` to enable tooltips, but here, we pass in the `HoverTool` object that we just created along with `'tap'` to be able to select nodes and highlight their inbound and outbound chords:" ] }, { "cell_type": "code", "execution_count": 25, "id": "b04c7c84-9281-43e4-b45d-7ae9cf1a017e", "metadata": { "tags": [] }, "outputs": [], "source": [ "chord = chord.opts(\n", " labels='index', node_color='index', cmap='Category20', # node config\n", " edge_color='origin', edge_cmap='Category20', directed=True, # edge config\n", " inspection_policy='edges', tools=[hover, 'tap'], # tooltip config\n", " frame_width=500, aspect=1, # plot size config\n", " title='Total Passenger Service Travel Between Top 10 Cities in 2019'\n", ")" ] }, { "cell_type": "markdown", "id": "0735168c-763e-4e2d-bb44-1c92d55fa034", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "Try exploring the chord diagram by clicking on the nodes and using the tooltips on the chords:" ] }, { "cell_type": "code", "execution_count": 26, "id": "7a5264bc-1026-4843-a6b7-392fabfc6b16", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ ":Chord [origin,dest] (passengers,origin_city_name,dest_city_name,mail,freight)" ] }, "execution_count": 26, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "c9d187cd-f1c7-4338-a6ea-11fe9123638d" } }, "output_type": "execute_result" } ], "source": [ "chord" ] }, { "cell_type": "markdown", "id": "b1a2a951-e902-4ac8-be73-054f37091128", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "

The result can be interacted with after displaying it, but it works best in the notebook – the GIF below shows some example interactions. Note that for this visualization the interactivity is what makes it useful:

\n", "\n", "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "1a8ab42a-2639-43ea-ada3-56dd8b37cb87", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_sankey-plot" ] }, "source": [ "### Sankey plot\n", "\n", "For our final visualization, we will create a **Sankey plot**, which is a way to visualize flow as edges between nodes. Here, we will use it to analyze airline market share for passenger service flights between the top 5 US cities:\n", "\n", "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "2df15c6f-711d-4bce-a588-29d558952844", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "To build this visualization, we will work through the following steps:\n", "\n", "1. Isolate flight statistics for top routes.\n", "2. Convert the data into a set of edges.\n", "3. Create the Sankey plot with HoloViews." ] }, { "cell_type": "markdown", "id": "3954dbc9-051b-4748-880f-220809f9b438", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 1. Isolate flight statistics for top routes.\n", "\n", "We need to filter our data to just domestic passenger service between the top 5 cities. Since we want to look at market share, we need to keep information for all airlines (i.e., we can't filter to the top airlines yet):" ] }, { "cell_type": "code", "execution_count": 27, "id": "46dce1fb-3c62-4722-b69a-6a38ce5bb2eb", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
regionunique_carrier_namerouteorigin_city_namedest_city_namepassengers
0DomesticAir Wisconsin Airlines CorpATL-ORDAtlanta, GAChicago, IL915.0
1DomesticAir Wisconsin Airlines CorpATL-ORDChicago, ILAtlanta, GA556.0
2DomesticAlaska Airlines Inc.JFK-LAXLos Angeles, CANew York, NY265307.0
3DomesticAlaska Airlines Inc.JFK-LAXNew York, NYLos Angeles, CA257685.0
4DomesticAlaska Airlines Inc.LAX-ORDChicago, ILLos Angeles, CA48269.0
\n", "
" ], "text/plain": [ " region unique_carrier_name route origin_city_name \\\n", "0 Domestic Air Wisconsin Airlines Corp ATL-ORD Atlanta, GA \n", "1 Domestic Air Wisconsin Airlines Corp ATL-ORD Chicago, IL \n", "2 Domestic Alaska Airlines Inc. JFK-LAX Los Angeles, CA \n", "3 Domestic Alaska Airlines Inc. JFK-LAX New York, NY \n", "4 Domestic Alaska Airlines Inc. LAX-ORD Chicago, IL \n", "\n", " dest_city_name passengers \n", "0 Chicago, IL 915.0 \n", "1 Atlanta, GA 556.0 \n", "2 New York, NY 265307.0 \n", "3 Los Angeles, CA 257685.0 \n", "4 Los Angeles, CA 48269.0 " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_cities = cities[:5]\n", "\n", "domestic_passenger_travel = flight_stats.query(\n", " 'region == \"Domestic\" and `class` == \"F\" and origin_city_name != dest_city_name '\n", " f'and origin_city_name.isin({top_cities}) and dest_city_name.isin({top_cities})'\n", ").groupby([\n", " 'region', 'unique_carrier_name', 'route', 'origin_city_name', 'dest_city_name'\n", "]).passengers.sum().reset_index()\n", "\n", "domestic_passenger_travel.head()" ] }, { "cell_type": "markdown", "id": "b9acdab8-3daa-41e8-a94e-3020bc8b860b", "metadata": {}, "source": [ "*Note: In reality, all the routes we are considering are domestic, but we are keeping the `region` column because it will serve as the basis for a root node in our Sankey plot, which allows us to easily see the total across airlines.*" ] }, { "cell_type": "markdown", "id": "c8d32172-1972-4e9f-b76f-2decfb8d4afd", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 2. Convert the data into a set of edges.\n", "\n", "The trickiest part of building this visualization is unraveling our dataset into a set of edges: a Sankey plot can be used to represent a [directed, acyclic graph (DAG)](https://en.wikipedia.org/wiki/Directed_acyclic_graph), meaning that we have to be careful there are no cycles (loops) when compiling our edge list." ] }, { "cell_type": "markdown", "id": "7f741783-a262-4e98-b8e8-b22cb4ad59d8", "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "We will be making two sets of edges for our Sankey plot: one set from region to airline and another from airline to route. Note that there is more data than we can display in the plot, so we have to group together any airlines that aren't in the top 5 and restrict to only routes between the top 5 cities." ] }, { "cell_type": "markdown", "id": "a9b09fbb-a33e-43e8-94a5-5a6e70da62d4", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Let's start by grouping all airlines outside the top 5 into a new airline called \"Other Airlines\" – this is necessary to keep our Sankey plot a manageable size:" ] }, { "cell_type": "code", "execution_count": 28, "id": "15c59652-aa28-4114-b37a-ca6ae8ec5809", "metadata": { "tags": [] }, "outputs": [], "source": [ "domestic_passenger_travel.unique_carrier_name = (\n", " domestic_passenger_travel.unique_carrier_name.replace(\n", " '^(?!' + '|'.join(top_airlines) + ').*$',\n", " 'Other Airlines',\n", " regex=True\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "62eda0ac-4718-40be-846b-c0369892dbaf", "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "*Tip: Find more information on regular expressions (regex) in Python [here](https://docs.python.org/3/library/re.html).*" ] }, { "cell_type": "markdown", "id": "4e2c7eec-a19c-4793-8ae6-f78fbda1fac5", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Our top 5 airlines combined have close to 88% market share of travel between the top 5 cities:" ] }, { "cell_type": "code", "execution_count": 29, "id": "9119e894-b5e0-487e-92b4-c5bab1cdb21b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "unique_carrier_name\n", "American Airlines Inc. 0.337186\n", "Delta Air Lines Inc. 0.312187\n", "JetBlue Airways 0.049500\n", "Other Airlines 0.120544\n", "Southwest Airlines Co. 0.079074\n", "United Air Lines Inc. 0.101509\n", "Name: passengers, dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "domestic_passenger_travel.groupby('unique_carrier_name').passengers.sum().div(\n", " domestic_passenger_travel.passengers.sum()\n", ")" ] }, { "cell_type": "markdown", "id": "ec2eff2a-d589-4d02-8309-726e777ec938", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Next, we will define a function for converting a DataFrame into edges:" ] }, { "cell_type": "code", "execution_count": 30, "id": "06637467-c0b5-432e-a716-16609098fe92", "metadata": { "tags": [] }, "outputs": [], "source": [ "def get_edges(data, *, source_col, target_col):\n", " aggregated = data.groupby([source_col, target_col]).passengers.sum()\n", " return aggregated.reset_index().rename(\n", " columns={source_col: 'source', target_col: 'target'}\n", " ).query('passengers > 0')" ] }, { "cell_type": "markdown", "id": "14629d9c-0060-47a9-9fd9-931248da9f9a", "metadata": {}, "source": [ "*Recall: The asterisk in the function signature requires both `source_col` and `target_col` to be passed in by name. This makes sure that we explicitly define the direction for the edges when calling the function. Read more on this syntax [here](https://www.python.org/dev/peps/pep-3102/).*" ] }, { "cell_type": "markdown", "id": "220e9cfd-a44b-4ec4-afc7-3798d53d69b0", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Let's use our function to get our first set of edges going from region to airline. Here, we will also rename the node \"Domestic\" to \"Top Routes\" for a more descriptive name for the root node of our Sankey plot:" ] }, { "cell_type": "code", "execution_count": 31, "id": "fc4155dd-6b68-4c8f-aeb5-7333b70504b0", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
sourcetargetpassengers
0Top RoutesAmerican Airlines Inc.9426060.0
1Top RoutesDelta Air Lines Inc.8727210.0
2Top RoutesJetBlue Airways1383776.0
3Top RoutesOther Airlines3369815.0
4Top RoutesSouthwest Airlines Co.2210533.0
5Top RoutesUnited Air Lines Inc.2837682.0
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" ], "text/plain": [ " source target passengers\n", "0 Top Routes American Airlines Inc. 9426060.0\n", "1 Top Routes Delta Air Lines Inc. 8727210.0\n", "2 Top Routes JetBlue Airways 1383776.0\n", "3 Top Routes Other Airlines 3369815.0\n", "4 Top Routes Southwest Airlines Co. 2210533.0\n", "5 Top Routes United Air Lines Inc. 2837682.0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "carrier_edges = get_edges(\n", " domestic_passenger_travel,\n", " source_col='region',\n", " target_col='unique_carrier_name'\n", ").replace('^Domestic$', 'Top Routes', regex=True)\n", "\n", "carrier_edges" ] }, { "cell_type": "markdown", "id": "55b1774c-89d0-4cd0-9d58-ede99daec54f", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "The other set of edges that we need is from airline to route for routes between the top cities:" ] }, { "cell_type": "code", "execution_count": 32, "id": "ab0e5bbb-74b8-4332-b53e-974fbb7f3fe8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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sourcetargetpassengers
39Other AirlinesDFW-LGA157366.0
41Other AirlinesJFK-LAX523222.0
2American Airlines Inc.ATL-LAX294304.0
48Southwest Airlines Co.ATL-MDW498481.0
50Southwest Airlines Co.LAX-MDW558574.0
44Other AirlinesLAX-ORD378552.0
33Other AirlinesATL-LAX146882.0
35Other AirlinesATL-MDW1201.0
40Other AirlinesDFW-ORD241147.0
27JetBlue AirwaysDFW-JFK140.0
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" ], "text/plain": [ " source target passengers\n", "39 Other Airlines DFW-LGA 157366.0\n", "41 Other Airlines JFK-LAX 523222.0\n", "2 American Airlines Inc. ATL-LAX 294304.0\n", "48 Southwest Airlines Co. ATL-MDW 498481.0\n", "50 Southwest Airlines Co. LAX-MDW 558574.0\n", "44 Other Airlines LAX-ORD 378552.0\n", "33 Other Airlines ATL-LAX 146882.0\n", "35 Other Airlines ATL-MDW 1201.0\n", "40 Other Airlines DFW-ORD 241147.0\n", "27 JetBlue Airways DFW-JFK 140.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "carrier_to_route_edges = get_edges(\n", " domestic_passenger_travel,\n", " source_col='unique_carrier_name',\n", " target_col='route'\n", ")\n", "\n", "carrier_to_route_edges.sample(10, random_state=1)" ] }, { "cell_type": "markdown", "id": "b2ac7601-2ddf-4fbc-a4a2-2b91023b4b29", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Let's combine our edges into a single DataFrame now; we will also convert the total passengers number to millions for display purposes:" ] }, { "cell_type": "code", "execution_count": 33, "id": "56d3b4e0-718a-4967-9820-c5d31d36e541", "metadata": { "tags": [] }, "outputs": [], "source": [ "all_edges = pd.concat([carrier_edges, carrier_to_route_edges]).assign(\n", " passengers=lambda x: x.passengers / 1e6\n", ")" ] }, { "cell_type": "markdown", "id": "be9061d1-4ac4-4dac-b6f0-d7f472500acf", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "#### 4. Create the Sankey plot with HoloViews.\n", "\n", "As with the chord diagram, our key dimensions are the source and target of the edges. However, this time, we will only provide the passenger total as the value dimension – note that we are able to specify that the values are in millions by using `hv.Dimension`:" ] }, { "cell_type": "code", "execution_count": 34, "id": "3f3592da-6e72-4e22-914d-b3fed0374d05", "metadata": { "tags": [] }, "outputs": [], "source": [ "sankey = hv.Sankey(\n", " all_edges,\n", " kdims=['source', 'target'],\n", " vdims=hv.Dimension('passengers', unit='M')\n", ").opts(\n", " labels='index', label_position='right', cmap='Set1', # node config\n", " edge_color='lightgray', # edge config\n", " width=750, height=600, # plot size config\n", " title='Travel Between the Top 5 Cities in 2019'\n", ")" ] }, { "cell_type": "markdown", "id": "b6317aff-7c2b-444c-a4f6-884d7de74b48", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "Hover over the edges to explore the data in this Sankey plot:" ] }, { "cell_type": "code", "execution_count": 35, "id": "3ae9946a-f655-49c5-b28b-309835d7e1fd", "metadata": { "slideshow": { "slide_type": "skip" }, "tags": [] }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":Sankey [source,target] (passengers)" ] }, "execution_count": 35, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "25046f37-ca65-4b0d-a1e9-6004a4f2d83d" } }, "output_type": "execute_result" } ], "source": [ "sankey" ] }, { "cell_type": "markdown", "id": "4ee9b151-01ff-4367-8f2f-652d67c0b0c1", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "

The resulting visualization can be interacted with after displaying it, but it works best in the notebook. Here's an example:

\n", "\n", "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "454e67a5-274d-47a9-9c32-7c0b76c65b2e", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_exercise-3-2" ] }, "source": [ "### Exercise 3.2\n", "\n", "##### Use hvPlot to create a line plot of total earthquakes per day. Add custom tooltips using Bokeh's `HoverTool`." ] }, { "cell_type": "markdown", "id": "82c60841-8d65-4515-aaab-d47f8bd524e7", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [ "id_solution-3-2" ] }, "source": [ "### Solution" ] }, { "cell_type": "markdown", "id": "c837de18-0a95-4393-862c-2c2e09e6bdb7", "metadata": { "slideshow": { "slide_type": "fragment" }, "tags": [] }, "source": [ "We start by reading in the dataset:" ] }, { "cell_type": "code", "execution_count": 36, "id": "5178eafa-94d6-43ca-8b05-f386366b9eb3", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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magplacetimetsunamimagTypegeometrymonth
02.7580 km N of Isabela, Puerto Rico2020-01-01 00:01:56.5900mdPOINT Z (-67.1275 19.2175 12)1
12.5564 km N of Isabela, Puerto Rico2020-01-01 00:03:38.2100mdPOINT Z (-67.0901 19.0766 6)1
21.8112 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:09.4400mdPOINT Z (-66.8541 17.8705 6)1
31.849 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:05:36.9300mdPOINT Z (-66.8636 17.8993 8)1
41.648 km SSE of Maria Antonia, Puerto Rico2020-01-01 00:09:20.0600mdPOINT Z (-66.8685 17.9066 8)1
\n", "
" ], "text/plain": [ " mag place time \\\n", "0 2.75 80 km N of Isabela, Puerto Rico 2020-01-01 00:01:56.590 \n", "1 2.55 64 km N of Isabela, Puerto Rico 2020-01-01 00:03:38.210 \n", "2 1.81 12 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:09.440 \n", "3 1.84 9 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:05:36.930 \n", "4 1.64 8 km SSE of Maria Antonia, Puerto Rico 2020-01-01 00:09:20.060 \n", "\n", " tsunami magType geometry month \n", "0 0 md POINT Z (-67.1275 19.2175 12) 1 \n", "1 0 md POINT Z (-67.0901 19.0766 6) 1 \n", "2 0 md POINT Z (-66.8541 17.8705 6) 1 \n", "3 0 md POINT Z (-66.8636 17.8993 8) 1 \n", "4 0 md POINT Z (-66.8685 17.9066 8) 1 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import geopandas as gpd\n", "import pandas as pd\n", "\n", "earthquakes = gpd.read_file('../data/earthquakes.geojson').assign(\n", " time=lambda x: pd.to_datetime(x.time, unit='ms'),\n", " month=lambda x: x.time.dt.month\n", ").dropna()\n", "\n", "earthquakes.head()" ] }, { "cell_type": "markdown", "id": "9203a419-a537-47fa-b79c-6ba4089555b1", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Next, we will handle our imports. Note that `bokeh.models` provides the `HoverTool` and also classes for formatting the ticks, such as `DatetimeTickFormatter`, which we will use here to format the x-axis tick labels as month names:" ] }, { "cell_type": "code", "execution_count": 37, "id": "677c4751-7037-4880-9fc2-29469ffe6fcc", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.7.3'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " // Don't load bokeh if it is still initializing\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " // There is nothing to load\n", " 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script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " const url = js_exports[name];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " 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" }\n", " if (comm.active) {\n", " comm.open();\n", " } else {\n", " setTimeout(open, 500)\n", " }\n", " if (msg_handler) {\n", " comm.onMsg = msg_handler;\n", " }\n", " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", " comm_promise.then((comm) => {\n", " window.PyViz.comms[comm_id] = comm;\n", " if (msg_handler) {\n", " var messages = comm.messages[Symbol.asyncIterator]();\n", " function processIteratorResult(result) {\n", " var message = result.value;\n", " var content = {data: message.data};\n", " var metadata = message.metadata || {comm_id};\n", " var msg = {content, metadata}\n", " msg_handler(msg);\n", " return messages.next().then(processIteratorResult);\n", " }\n", " return messages.next().then(processIteratorResult);\n", " }\n", " })\n", " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", " return comm_promise.then((comm) => {\n", " comm.send(data, metadata, 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return\n", " }\n", " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", " if (id !== undefined) {\n", " var nchildren = toinsert.length;\n", " var html_node = toinsert[nchildren-1].children[0];\n", " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", " var scripts = [];\n", " var nodelist = html_node.querySelectorAll(\"script\");\n", " for (var i in nodelist) {\n", " if (nodelist.hasOwnProperty(i)) {\n", " scripts.push(nodelist[i])\n", " }\n", " }\n", "\n", " scripts.forEach( function (oldScript) {\n", " var newScript = document.createElement(\"script\");\n", " var attrs = [];\n", " var nodemap = oldScript.attributes;\n", " for (var j in nodemap) {\n", " if (nodemap.hasOwnProperty(j)) {\n", " attrs.push(nodemap[j])\n", " }\n", " }\n", " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", " oldScript.parentNode.replaceChild(newScript, oldScript);\n", " });\n", " if (JS_MIME_TYPE in output.data) {\n", " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", " }\n", " output_area._hv_plot_id = id;\n", " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", " window.PyViz.plot_index[id] = Bokeh.index[id];\n", " } else {\n", " window.PyViz.plot_index[id] = null;\n", " }\n", " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", "}\n", "\n", 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activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n })\n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ] }, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "7dbe5827-59f7-497f-a891-e825e2f119af" } }, "output_type": "display_data" } ], "source": [ "from bokeh.models import HoverTool, DatetimeTickFormatter\n", "import hvplot.pandas\n", "import panel as pn" ] }, { "cell_type": "markdown", "id": "df0ed49b-a7e8-4a4d-b8c4-b0f9bc194a1b", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Finally, we define our custom tooltip using `HoverTool` and use hvPlot to create the visualization, displaying it with Panel:" ] }, { "cell_type": "code", "execution_count": 38, "id": "dc74aaf0-1976-4dbf-a7d4-8821cd8738a9", "metadata": { "tags": [] }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
\n", "
\n", "
\n", "" ], "text/plain": [ "HoloViews(Curve, sizing_mode='stretch_width')" ] }, "execution_count": 38, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "ee156845-2f49-4a71-b283-8ddf94d9835a" } }, "output_type": "execute_result" } ], "source": [ "hover = HoverTool(\n", " tooltips=dict(date='@time{%b %d}', earthquakes='@0{0,.}'),\n", " formatters={'@time': 'datetime'}\n", ")\n", "line_plot = earthquakes.resample('1D', on='time').size().hvplot(\n", " title='Earthquakes per Day in 2020', ylabel='total earthquakes',\n", " tools=[hover], responsive=True, frame_height=200,\n", " xformatter=DatetimeTickFormatter(months='%B')\n", ")\n", "pn.panel(line_plot)" ] }, { "cell_type": "markdown", "id": "351a1acd-3c45-468c-840f-656ed8c620e4", "metadata": {}, "source": [ "
\n", " \n", "
" ] }, { "cell_type": "markdown", "id": "1430accd-6d77-44e8-8937-3fbe3b3d1478", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Additional resources\n", "\n", "This section was designed to give you a quick overview of interactive plotting with HoloViz. As such, we haven't discussed anywhere near all of the functionality or [available plot types](http://holoviews.org/reference/index.html). Here are some additional resources to learn more:\n", "\n", "- [(HoloViews) Customizing plots](http://holoviews.org/user_guide/Customizing_Plots.html)\n", "- [(HoloViews) Plotting with Bokeh](http://holoviews.org/user_guide/Plotting_with_Bokeh.html)\n", "- [(HoloViews) Annotators](http://holoviews.org/user_guide/Annotators.html)\n", "- [(Bokeh) First Steps](https://docs.bokeh.org/en/latest/docs/first_steps.html#first-steps)\n", "- [HoloViz Tutorial](https://holoviz.org/tutorial/index.html)" ] }, { "cell_type": "markdown", "id": "6a4415b2-ad00-4cfe-83ab-a65529561d38", "metadata": { "slideshow": { "slide_type": "subslide" }, "tags": [] }, "source": [ "Be sure to also check out the documentation for HoloViz and its component libraries:\n", "\n", "- [HoloViz](https://holoviz.org/)\n", "- [HoloViews](https://holoviews.org/)\n", "- [GeoViews](http://geoviews.org/)\n", "- [hvPlot](https://hvplot.holoviz.org/)\n", "- [Panel](https://panel.holoviz.org/)\n", "- [Datashader](http://datashader.org/)\n", "- [Param](https://param.pyviz.org/)\n", "- [Colorcet](https://colorcet.pyviz.org/)\n", "- [Lumen](https://lumen.holoviz.org)" ] }, { "cell_type": "markdown", "id": "915a990b-e10f-4fd4-8bc3-9659e5bbadb5", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [] }, "source": [ "## Section 3 Complete 🎉\n", "\n", "\"Fox" ] }, { "cell_type": "markdown", "id": "d36bb821-1fb8-457e-a885-367734d6cf95", "metadata": { "slideshow": { "slide_type": "slide" }, "tags": [ "id_related-content" ] }, "source": [ "# Related content\n", "\n", "*All examples herein were developed exclusively for this workshop – be sure to check out my book, [Hands-On Data Analysis with Pandas](https://www.amazon.com/Hands-Data-Analysis-Pandas-visualization-dp-1800563450/dp/1800563450/), and my [pandas workshop](https://stefaniemolin.com/workshops/pandas-workshop/) for more Python data science content.*\n", "\n", "
\n", " \n", "
\n", " stefaniemolin.com\n", "
\n", "
" ] }, { "cell_type": "markdown", "id": "c6fae3a2-4280-43db-815f-42f9b2ebff29", "metadata": { "editable": true, "slideshow": { "slide_type": "skip" }, "tags": [] }, "source": [ "# Thank you!\n", "\n", "*I hope you enjoyed the session. You can follow my work on the following platforms:*\n", "\n", "
\n", "
\n", " \n", " \n", " stefaniemolin.com\n", " \n", "
\n", "
\n", " \n", " \n", " github.com/stefmolin\n", " \n", "
\n", "
\n", " \n", " \n", " twitter.com/StefanieMolin\n", " \n", "
\n", "
\n", " \n", " \n", " linkedin.com/in/stefanie-molin\n", " \n", "
\n", "
" ] } ], "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": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 }