From 96825532d9591a9841839a7b713d9f16a8aac58c Mon Sep 17 00:00:00 2001 From: Daniel Kapitan Date: Tue, 8 Aug 2023 23:19:12 +0200 Subject: [PATCH] Add more blog posts --- images/altair_marks_encoding.out.ipynb | 3538 ----------------- images/fractal.png | Bin 0 -> 45446 bytes images/lartpy.jpg | Bin 0 -> 349919 bytes images/podcast-aitoday.jpg | Bin 0 -> 325146 bytes images/podcast-aitoday.png | Bin 0 -> 329375 bytes images/podcast-de-dataloog.jpg | Bin 0 -> 348084 bytes images/tree2.gif | Bin 0 -> 3374644 bytes .../lartpy-checkpoint.ipynb | 574 ++- src/blog/posts/eerlijke-algoritmen.qmd | 2 +- src/blog/posts/lartpy.ipynb | 563 ++- src/blog/posts/podcast-aitoday.qmd | 17 + .../podcast-dataloog-datasolidariteit.qmd | 17 + src/temp | 0 13 files changed, 1170 insertions(+), 3541 deletions(-) delete mode 100644 images/altair_marks_encoding.out.ipynb create mode 100644 images/fractal.png create mode 100644 images/lartpy.jpg create mode 100644 images/podcast-aitoday.jpg create mode 100644 images/podcast-aitoday.png create mode 100644 images/podcast-de-dataloog.jpg create mode 100644 images/tree2.gif rename lartpy-blog-post.ipynb => src/blog/posts/.ipynb_checkpoints/lartpy-checkpoint.ipynb (71%) create mode 100644 src/blog/posts/podcast-aitoday.qmd create mode 100644 src/blog/posts/podcast-dataloog-datasolidariteit.qmd create mode 100644 src/temp diff --git a/images/altair_marks_encoding.out.ipynb b/images/altair_marks_encoding.out.ipynb deleted file mode 100644 index 10ca36b..0000000 --- a/images/altair_marks_encoding.out.ipynb +++ /dev/null @@ -1,3538 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Types, Graphical Marks, and Visual Encoding Channels\n", - "\n", - "A visualization represents data using a collection of *graphical marks*\n", - "(bars, lines, points, etc.). The attributes of a mark — such as its\n", - "position, shape, size, or color — serve as *channels* through which we\n", - "can encode underlying data values.\n", - "\n", - "With a basic framework of *data types*, *marks*, and *encoding\n", - "channels*, we can concisely create a wide variety of visualizations. In\n", - "this notebook, we explore each of these elements and show how to use\n", - "them to create custom statistical graphics.\n", - "\n", - "*This notebook is part of the [data visualization\n", - "curriculum](https://github.com/uwdata/visualization-curriculum).*" - ], - "id": "e963f1b6-4697-4855-86db-dfb698d8ec13" - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import altair as alt" - ], - "id": "2720a589-c3e0-43c9-ac72-70ad51a461c4" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Global Development Data\n", - "\n", - "We will be visualizing global health and population data for a number of\n", - "countries, over the time period of 1955 to 2005. The data was collected\n", - "by the [Gapminder Foundation](https://www.gapminder.org/) and shared in\n", - "[Hans Rosling’s popular TED\n", - "talk](https://www.youtube.com/watch?v=hVimVzgtD6w). If you haven’t seen\n", - "the talk, we encourage you to watch it first!\n", - "\n", - "Let’s first load the dataset from the\n", - "[vega-datasets](https://github.com/vega/vega-datasets) collection into a\n", - "Pandas data frame." - ], - "id": "eeb59a95-9b35-4c7b-a368-22190fe3798d" - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from vega_datasets import data as vega_data\n", - "data = vega_data.gapminder()" - ], - "id": "2f466eb3-7183-4eec-b2ba-5f07e0a97e31" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How big is the data?" - ], - "id": "f78977b6-ac44-4e4d-9071-92fa2767d8d7" - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "outputId": "134a2f4a-c15d-4fcd-fd93-49b26a4f5e30" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/plain": [ - "(693, 6)" - ] - } - } - ], - "source": [ - "data.shape" - ], - "id": "fd87f460-366b-4c23-bd7d-bda99b849f73" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "693 rows and 6 columns! Let’s take a peek at the data content:" - ], - "id": "b0e7b812-6323-43d8-8ddf-8eee5317eda2" - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "outputId": "ec103505-4d8f-4310-a6e1-7eea3d789c28" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "" - ] - } - } - ], - "source": [ - "data.head(5)" - ], - "id": "2c66aa69-0f67-4309-be86-ba210645688e" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For each `country` and `year` (in 5-year intervals), we have measures of\n", - "fertility in terms of the number of children per woman (`fertility`),\n", - "life expectancy in years (`life_expect`), and total population (`pop`).\n", - "\n", - "We also see a `cluster` field with an integer code. What might this\n", - "represent? We’ll try and solve this mystery as we visualize the data!\n", - "\n", - "Let’s also create a smaller data frame, filtered down to values for the\n", - "year 2000 only:" - ], - "id": "a9d43206-56d0-405b-8381-03e3ba789857" - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "data2000 = data.loc[data['year'] == 2000]" - ], - "id": "fa793dfa-ccdb-43f3-b80d-e1ad5d78a3b6" - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "outputId": "8629f998-e723-4b92-9a95-f0df3c9843a8" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "" - ] - } - } - ], - "source": [ - "data2000.head(5)" - ], - "id": "d8b2057d-889c-4bdb-bd23-3d8f1fadae9e" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Types\n", - "\n", - "The first ingredient in effective visualization is the input data. Data\n", - "values can represent different forms of measurement. What kinds of\n", - "comparisons do those measurements support? And what kinds of visual\n", - "encodings then support those comparisons?\n", - "\n", - "We will start by looking at the basic data types that Altair uses to\n", - "inform visual encoding choices. These data types determine the kinds of\n", - "comparisons we can make, and thereby guide our visualization design\n", - "decisions.\n", - "\n", - "### Nominal (N)\n", - "\n", - "*Nominal* data (also called *categorical* data) consist of category\n", - "names.\n", - "\n", - "With nominal data we can compare the equality of values: *is value A the\n", - "same or different than value B? (A = B)*, supporting statements like “A\n", - "is equal to B” or “A is not equal to B”. In the dataset above, the\n", - "`country` field is nominal.\n", - "\n", - "When visualizing nominal data we should readily be able to see if values\n", - "are the same or different: position, color hue (blue, red, green,\n", - "*etc.*), and shape can help. However, using a size channel to encode\n", - "nominal data might mislead us, suggesting rank-order or magnitude\n", - "differences among values that do not exist!\n", - "\n", - "### Ordinal (O)\n", - "\n", - "*Ordinal* data consist of values that have a specific ordering.\n", - "\n", - "With ordinal data we can compare the rank-ordering of values: *does\n", - "value A come before or after value B? (A \\< B)*, supporting statements\n", - "like “A is less than B” or “A is greater than B”. In the dataset above,\n", - "we can treat the `year` field as ordinal.\n", - "\n", - "When visualizing ordinal data, we should perceive a sense of rank-order.\n", - "Position, size, or color value (brightness) might be appropriate, where\n", - "as color hue (which is not perceptually ordered) would be less\n", - "appropriate.\n", - "\n", - "### Quantitative (Q)\n", - "\n", - "With *quantitative* data we can measure numerical differences among\n", - "values. There are multiple sub-types of quantitative data:\n", - "\n", - "For *interval* data we can measure the distance (interval) between\n", - "points: *what is the distance to value A from value B? (A - B)*,\n", - "supporting statements such as “A is 12 units away from B”.\n", - "\n", - "For *ratio* data the zero-point is meaningful and so we can also measure\n", - "proportions or scale factors: *value A is what proportion of value B? (A\n", - "/ B)*, supporting statements such as “A is 10% of B” or “B is 7 times\n", - "larger than A”.\n", - "\n", - "In the dataset above, `year` is a quantitative interval field (the value\n", - "of year “zero” is subjective), whereas `fertility` and `life_expect` are\n", - "quantitative ratio fields (zero is meaningful for calculating\n", - "proportions). Vega-Lite represents quantitative data, but does not make\n", - "a distinction between interval and ratio types.\n", - "\n", - "Quantitative values can be visualized using position, size, or color\n", - "value, among other channels. An axis with a zero baseline is essential\n", - "for proportional comparisons of ratio values, but can be safely omitted\n", - "for interval comparisons.\n", - "\n", - "### Temporal (T)\n", - "\n", - "*Temporal* values measure time points or intervals. This type is a\n", - "special case of quantitative values (timestamps) with rich semantics and\n", - "conventions (i.e., the [Gregorian\n", - "calendar](https://en.wikipedia.org/wiki/Gregorian_calendar)). The\n", - "temporal type in Vega-Lite supports reasoning about time units (year,\n", - "month, day, hour, etc.), and provides methods for requesting specific\n", - "time intervals.\n", - "\n", - "Example temporal values include date strings such as `“2019-01-04”` and\n", - "`“Jan 04 2019”`, as well as standardized date-times such as the [ISO\n", - "date-time format](https://en.wikipedia.org/wiki/ISO_8601):\n", - "`“2019-01-04T17:50:35.643Z”`.\n", - "\n", - "There are no temporal values in our global development dataset above, as\n", - "the `year` field is simply encoded as an integer. For more details about\n", - "using temporal data in Altair, see the [Times and Dates\n", - "documentation](https://altair-viz.github.io/user_guide/times_and_dates.html).\n", - "\n", - "### Summary\n", - "\n", - "These data types are not mutually exclusive, but rather form a\n", - "hierarchy: ordinal data support nominal (equality) comparisons, while\n", - "quantitative data support ordinal (rank-order) comparisons.\n", - "\n", - "Moreover, these data types do *not* provide a fixed categorization. Just\n", - "because a data field is represented using a number doesn’t mean we have\n", - "to treat it as a quantitative type! For example, we might interpret a\n", - "set of ages (10 years old, 20 years old, etc) as nominal (underage or\n", - "overage), ordinal (grouped by year), or quantitative (calculate average\n", - "age).\n", - "\n", - "Now let’s examine how to visually encode these data types!\n", - "\n", - "## Encoding Channels\n", - "\n", - "At the heart of Altair is the use of *encodings* that bind data fields\n", - "(with a given data type) to available encoding *channels* of a chosen\n", - "*mark* type. In this notebook we’ll examine the following encoding\n", - "channels:\n", - "\n", - "- `x`: Horizontal (x-axis) position of the mark.\n", - "- `y`: Vertical (y-axis) position of the mark.\n", - "- `size`: Size of the mark. May correspond to area or length,\n", - " depending on the mark type.\n", - "- `color`: Mark color, specified as a [legal CSS\n", - " color](https://developer.mozilla.org/en-US/docs/Web/CSS/color_value).\n", - "- `opacity`: Mark opacity, ranging from 0 (fully transparent) to 1\n", - " (fully opaque).\n", - "- `shape`: Plotting symbol shape for `point` marks.\n", - "- `tooltip`: Tooltip text to display upon mouse hover over the mark.\n", - "- `order`: Mark ordering, determines line/area point order and drawing\n", - " order.\n", - "- `column`: Facet the data into horizontally-aligned subplots.\n", - "- `row`: Facet the data into vertically-aligned subplots.\n", - "\n", - "For a complete list of available channels, see the [Altair encoding\n", - "documentation](https://altair-viz.github.io/altair-viz-v4/user_guide/encoding.html).\n", - "\n", - "### X\n", - "\n", - "The `x` encoding channel sets a mark’s horizontal position\n", - "(x-coordinate). In addition, default choices of axis and title are made\n", - "automatically. In the chart below, the choice of a quantitative data\n", - "type results in a continuous linear axis scale:" - ], - "id": "6866dbca-e4fc-42e3-bf8e-eeb0b0ea574b" - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "outputId": "2a958755-7f70-4a86-ab6f-2cfb24ad7231" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q')\n", - ")" - ], - "id": "3d277984-d18b-44fe-8f77-c48e799af283" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Y\n", - "\n", - "The `y` encoding channel sets a mark’s vertical position (y-coordinate).\n", - "Here we’ve added the `cluster` field using an ordinal (`O`) data type.\n", - "The result is a discrete axis that includes a sized band, with a default\n", - "step size, for each unique value:" - ], - "id": "2339d5ba-7f49-453a-a585-62d6a05fb8e1" - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "outputId": "9ff5b4a8-143c-4595-845f-026ed2e747a8" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:O')\n", - ")" - ], - "id": "8fe17170-314e-4f8c-991c-aecf823f0533" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*What happens to the chart above if you swap the `O` and `Q` field\n", - "types?*\n", - "\n", - "If we instead add the `life_expect` field as a quantitative (`Q`)\n", - "variable, the result is a scatter plot with linear scales for both axes:" - ], - "id": "f27f9ca2-c0a6-4cd3-89d5-f9b76b1a4755" - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "outputId": "ac41eaeb-4872-48db-ddf1-ab6b5c84f387" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q')\n", - ")" - ], - "id": "0ee72e9e-7325-468e-aa2a-4a1c0b31fc98" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, axes for linear quantitative scales include zero to ensure a\n", - "proper baseline for comparing ratio-valued data. In some cases, however,\n", - "a zero baseline may be meaningless or you may want to focus on interval\n", - "comparisons. To disable automatic inclusion of zero, configure the scale\n", - "mapping using the encoding `scale` attribute:" - ], - "id": "80dacfdf-85dc-492a-9288-4cc90ba5302d" - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "outputId": "8cb0ca55-176b-4d2f-8891-2c124276e429" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q', scale=alt.Scale(zero=False)),\n", - " alt.Y('life_expect:Q', scale=alt.Scale(zero=False))\n", - ")" - ], - "id": "c8f7d634-8fbd-4a33-88d7-3628deef3fb5" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now the axis scales no longer include zero by default. Some padding\n", - "still remains, as the axis domain end points are automatically snapped\n", - "to *nice* numbers like multiples of 5 or 10.\n", - "\n", - "*What happens if you also add `nice=False` to the scale attribute\n", - "above?*\n", - "\n", - "### Size\n", - "\n", - "The `size` encoding channel sets a mark’s size or extent. The meaning of\n", - "the channel can vary based on the mark type. For `point` marks, the\n", - "`size` channel maps to the pixel area of the plotting symbol, such that\n", - "the diameter of the point matches the square root of the size value.\n", - "\n", - "Let’s augment our scatter plot by encoding population (`pop`) on the\n", - "`size` channel. As a result, the chart now also includes a legend for\n", - "interpreting the size values." - ], - "id": "c8d1d83c-3e90-4fab-9915-2757f22585e8" - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "outputId": "4f76557c-4e35-41fc-9e06-513be34969ae" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q')\n", - ")" - ], - "id": "b111834d-6988-4708-a57c-f3904f112b8e" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In some cases we might be unsatisfied with the default size range. To\n", - "provide a customized span of sizes, set the `range` parameter of the\n", - "`scale` attribute to an array indicating the smallest and largest sizes.\n", - "Here we update the size encoding to range from 0 pixels (for zero\n", - "values) to 1,000 pixels (for the maximum value in the scale domain):" - ], - "id": "5decfd95-f45a-46ab-a3f7-7caca88e3149" - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "outputId": "0d88f561-9737-43fa-a84a-7ad6dcdfbf0c" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000]))\n", - ")" - ], - "id": "4ce26f0f-2975-4982-9a00-49a86ee7b9ae" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Color and Opacity\n", - "\n", - "The `color` encoding channel sets a mark’s color. The style of color\n", - "encoding is highly dependent on the data type: nominal data will default\n", - "to a multi-hued qualitative color scheme, whereas ordinal and\n", - "quantitative data will use perceptually ordered color gradients.\n", - "\n", - "Here, we encode the `cluster` field using the `color` channel and a\n", - "nominal (`N`) data type, resulting in a distinct hue for each cluster\n", - "value. Can you start to guess what the `cluster` field might indicate?" - ], - "id": "f6c42427-a8f5-4bc9-9f45-4e979cce2088" - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "outputId": "f12aeb57-07c5-422e-8cb1-62253b545af8" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N')\n", - ")" - ], - "id": "933c2e97-0b39-4920-86af-3e180d6b28d2" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we prefer filled shapes, we can can pass a `filled=True` parameter to\n", - "the `mark_point` method:" - ], - "id": "c6dffa59-b80b-4b15-9093-c5af0abf2cd3" - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "outputId": "8aba4f13-bfe3-43b1-e3ea-554a8d20b7f5" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N')\n", - ")" - ], - "id": "06e539a6-6fd7-4438-a976-d91ad4b11f1b" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, Altair uses a bit of transparency to help combat\n", - "over-plotting. We are free to further adjust the opacity, either by\n", - "passing a default value to the `mark_*` method, or using a dedicated\n", - "encoding channel.\n", - "\n", - "Here we demonstrate how to provide a constant value to an encoding\n", - "channel instead of binding a data field:" - ], - "id": "7fa6c924-8908-42a0-9488-390080374298" - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "outputId": "2dd14842-8fd5-4997-ea47-91cd489e0193" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5)\n", - ")" - ], - "id": "a61246d0-09e2-46c4-b6b5-93620dbb8a99" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Shape\n", - "\n", - "The `shape` encoding channel sets the geometric shape used by `point`\n", - "marks. Unlike the other channels we have seen so far, the `shape`\n", - "channel can not be used by other mark types. The shape encoding channel\n", - "should only be used with nominal data, as perceptual rank-order and\n", - "magnitude comparisons are not supported.\n", - "\n", - "Let’s encode the `cluster` field using `shape` as well as `color`. Using\n", - "multiple channels for the same underlying data field is known as a\n", - "*redundant encoding*. The resulting chart combines both color and shape\n", - "information into a single symbol legend:" - ], - "id": "e73b515b-f27a-4725-8d9d-2553f2850345" - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "outputId": "e418f006-988c-42a9-9a81-664d1d52d93c" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "b072fe6e-8173-4726-adc7-067d7892c75d" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tooltips & Ordering\n", - "\n", - "By this point, you might feel a bit frustrated: we’ve built up a chart,\n", - "but we still don’t know what countries the visualized points correspond\n", - "to! Let’s add interactive tooltips to enable exploration.\n", - "\n", - "The `tooltip` encoding channel determines tooltip text to show when a\n", - "user moves the mouse cursor over a mark. Let’s add a tooltip encoding\n", - "for the `country` field, then investigate which countries are being\n", - "represented." - ], - "id": "1352d37d-467e-469f-b799-92f7a9678642" - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "outputId": "f13ddd14-3b6f-4f96-ca08-8d3b2ba94a34" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5),\n", - " alt.Tooltip('country')\n", - ")" - ], - "id": "86266886-d7c0-4cae-94d6-152eba5546a7" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you mouse around you may notice that you can not select some of the\n", - "points. For example, the largest dark blue circle corresponds to India,\n", - "which is drawn on top of a country with a smaller population, preventing\n", - "the mouse from hovering over that country. To fix this problem, we can\n", - "use the `order` encoding channel.\n", - "\n", - "The `order` encoding channel determines the order of data points,\n", - "affecting both the order in which they are drawn and, for `line` and\n", - "`area` marks, the order in which they are connected to one another.\n", - "\n", - "Let’s order the values in descending rank order by the population\n", - "(`pop`), ensuring that smaller circles are drawn later than larger\n", - "circles:" - ], - "id": "011f3a86-1583-41e2-83a0-562e796b6bc0" - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "outputId": "3b7254de-09f3-4850-f066-ea8318ec5479" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5),\n", - " alt.Tooltip('country:N'),\n", - " alt.Order('pop:Q', sort='descending')\n", - ")" - ], - "id": "94edd2bd-7313-489c-9be4-b942e72052b7" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can identify the smaller country being obscured by India: it’s\n", - "Bangladesh!\n", - "\n", - "We can also now figure out what the `cluster` field represents. Mouse\n", - "over the various colored points to formulate your own explanation.\n", - "\n", - "At this point we’ve added tooltips that show only a single property of\n", - "the underlying data record. To show multiple values, we can provide the\n", - "`tooltip` channel an array of encodings, one for each field we want to\n", - "include:" - ], - "id": "46461ab1-ce28-4643-8280-1b74653e4721" - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "outputId": "ee47eb3b-146d-4c6b-dfa7-94ddeb0fc5d3" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5),\n", - " alt.Order('pop:Q', sort='descending'),\n", - " tooltip = [\n", - " alt.Tooltip('country:N'),\n", - " alt.Tooltip('fertility:Q'),\n", - " alt.Tooltip('life_expect:Q')\n", - " ] \n", - ")" - ], - "id": "faac490c-d220-4e21-95ea-40a87a026de0" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can see multiple data fields upon mouse over!\n", - "\n", - "### Column and Row Facets\n", - "\n", - "Spatial position is one of the most powerful and flexible channels for\n", - "visual encoding, but what can we do if we already have assigned fields\n", - "to the `x` and `y` channels? One valuable technique is to create a\n", - "*trellis plot*, consisting of sub-plots that show a subset of the data.\n", - "A trellis plot is one example of the more general technique of\n", - "presenting data using [small\n", - "multiples](https://en.wikipedia.org/wiki/Small_multiple) of views.\n", - "\n", - "The `column` and `row` encoding channels generate either a horizontal\n", - "(columns) or vertical (rows) set of sub-plots, in which the data is\n", - "partitioned according to the provided data field.\n", - "\n", - "Here is a trellis plot that divides the data into one column per\n", - "\\`cluster\\` value:" - ], - "id": "46e12687-413c-4702-9db6-48f7ce19dd5e" - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "outputId": "b62aff8e-a2aa-409a-be1d-4cf3708f9ab3" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000])),\n", - " alt.Color('cluster:N'),\n", - " alt.OpacityValue(0.5),\n", - " alt.Tooltip('country:N'),\n", - " alt.Order('pop:Q', sort='descending'),\n", - " alt.Column('cluster:N')\n", - ")" - ], - "id": "ab597f3a-0799-45cf-97bb-0078dd34812a" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above does not fit on screen, making it difficult to compare\n", - "all the sub-plots to each other! We can set the default `width` and\n", - "`height` properties to create a smaller set of multiples. Also, as the\n", - "column headers already label the `cluster` values, let’s remove our\n", - "`color` legend by setting it to `None`. To make better use of space we\n", - "can also orient our `size` legend to the `'bottom'` of the chart." - ], - "id": "48691787-3943-49f8-b522-7ebae2e9ba14" - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "outputId": "3eb84b63-0c69-4985-ee9b-d0d69e5ffb33" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('life_expect:Q'),\n", - " alt.Size('pop:Q', scale=alt.Scale(range=[0,1000]),\n", - " legend=alt.Legend(orient='bottom', titleOrient='left')),\n", - " alt.Color('cluster:N', legend=None),\n", - " alt.OpacityValue(0.5),\n", - " alt.Tooltip('country:N'),\n", - " alt.Order('pop:Q', sort='descending'),\n", - " alt.Column('cluster:N')\n", - ").properties(width=135, height=135)" - ], - "id": "29514eee-aa04-452e-af24-1f31af0f5763" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Underneath the hood, the `column` and `row` encodings are translated\n", - "into a new specification that uses the `facet` view composition\n", - "operator. We will re-visit faceting in greater depth later on!\n", - "\n", - "In the meantime, *can you rewrite the chart above to facet into rows\n", - "instead of columns?*\n", - "\n", - "### A Peek Ahead: Interactive Filtering\n", - "\n", - "In later modules, we’ll dive into interaction techniques for data\n", - "exploration. Here is a sneak peak: binding a range slider to the `year`\n", - "field to enable interactive scrubbing through each year of data. Don’t\n", - "worry if the code below is a bit confusing at this point, as we will\n", - "cover interaction in detail later.\n", - "\n", - "*Drag the slider back and forth to see how the data values change over\n", - "time!*" - ], - "id": "86833853-8326-4952-826b-09ca69316390" - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "outputId": "3c934853-9167-4ad1-e560-b366250eb40e" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "select_year = alt.selection_single(\n", - " name='select', fields=['year'], init={'year': 1955},\n", - " bind=alt.binding_range(min=1955, max=2005, step=5)\n", - ")\n", - "\n", - "alt.Chart(data).mark_point(filled=True).encode(\n", - " alt.X('fertility:Q', scale=alt.Scale(domain=[0,9])),\n", - " alt.Y('life_expect:Q', scale=alt.Scale(domain=[0,90])),\n", - " alt.Size('pop:Q', scale=alt.Scale(domain=[0, 1200000000], range=[0,1000])),\n", - " alt.Color('cluster:N', legend=None),\n", - " alt.OpacityValue(0.5),\n", - " alt.Tooltip('country:N'),\n", - " alt.Order('pop:Q', sort='descending')\n", - ").add_selection(select_year).transform_filter(select_year)" - ], - "id": "04fb17df-77f5-4994-8398-bffc8bce9667" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Graphical Marks\n", - "\n", - "Our exploration of encoding channels above exclusively uses `point`\n", - "marks to visualize the data. However, the `point` mark type is only one\n", - "of the many geometric shapes that can be used to visually represent\n", - "data. Altair includes a number of built-in mark types, including:\n", - "\n", - "- `mark_area()` - Filled areas defined by a top-line and a baseline.\n", - "- `mark_bar()` - Rectangular bars.\n", - "- `mark_circle()` - Scatter plot points as filled circles.\n", - "- `mark_line()` - Connected line segments.\n", - "- `mark_point()` - Scatter plot points with configurable shapes.\n", - "- `mark_rect()` - Filled rectangles, useful for heatmaps.\n", - "- `mark_rule()` - Vertical or horizontal lines spanning the axis.\n", - "- `mark_square()` - Scatter plot points as filled squares.\n", - "- `mark_text()` - Scatter plot points represented by text.\n", - "- `mark_tick()` - Vertical or horizontal tick marks.\n", - "\n", - "For a complete list, and links to examples, see the [Altair marks\n", - "documentation](https://altair-viz.github.io/altair-viz-v4/user_guide/marks.html).\n", - "Next, we will step through a number of the most commonly used mark types\n", - "for statistical graphics.\n", - "\n", - "### Point Marks\n", - "\n", - "The `point` mark type conveys specific points, as in *scatter plots* and\n", - "*dot plots*. In addition to `x` and `y` encoding channels (to specify 2D\n", - "point positions), point marks can use `color`, `size`, and `shape`\n", - "encodings to convey additional data fields.\n", - "\n", - "Below is a dot plot of `fertility`, with the `cluster` field redundantly\n", - "encoded using both the `y` and `shape` channels." - ], - "id": "a66c29a1-16bf-487f-8107-84dca728bd34" - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "outputId": "6332a348-f8f0-4dd8-9c4a-63687e286d4a" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:N'),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "cfdda52d-1ccc-42ed-9bbb-00ca8080fa9f" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to encoding channels, marks can be stylized by providing\n", - "values to the `mark_*()` methods.\n", - "\n", - "For example: point marks are drawn with stroked outlines by default, but\n", - "can be specified to use `filled` shapes instead. Similarly, you can set\n", - "a default `size` to set the total pixel area of the point mark." - ], - "id": "aede0401-03de-4d9e-a898-352f41e809bf" - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "outputId": "dcf26fa9-281b-4297-da50-84c7f5ccd318" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_point(filled=True, size=100).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:N'),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "f85d0a35-df76-4e24-a85c-ffcee4c71333" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Circle Marks\n", - "\n", - "The `circle` mark type is a convenient shorthand for `point` marks drawn\n", - "as filled circles." - ], - "id": "28e37b38-fc4f-45ff-b45b-fe2958c9a0c8" - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "outputId": "80cd9d0f-c126-4673-bfcf-eaf5b34e0058" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_circle(size=100).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:N'),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "61cc7bb8-ab1f-4ddb-9cd1-3b0080525b10" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Square Marks\n", - "\n", - "The `square` mark type is a convenient shorthand for `point` marks drawn\n", - "as filled squares." - ], - "id": "0f257da8-8eac-47f2-a2f8-733e7515ef14" - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "outputId": "8ff4b0d4-f3ce-45e5-f597-1a6fd296ba47" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_square(size=100).encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:N'),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "82b432ee-4700-49fb-ac6b-cb8bc9c16b3c" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tick Marks\n", - "\n", - "The `tick` mark type conveys a data point using a short line segment or\n", - "“tick”. These are particularly useful for comparing values along a\n", - "single dimension with minimal overlap. A *dot plot* drawn with tick\n", - "marks is sometimes referred to as a *strip plot*." - ], - "id": "16e8c6f9-cffd-4725-a093-4c46afd3dd98" - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "outputId": "c6133250-333f-4c29-e217-221634dd8337" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_tick().encode(\n", - " alt.X('fertility:Q'),\n", - " alt.Y('cluster:N'),\n", - " alt.Shape('cluster:N')\n", - ")" - ], - "id": "9cfa9190-43e1-4e0f-8132-c2e5c3a78fc4" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Bar Marks\n", - "\n", - "The \\`bar\\` mark type draws a rectangle with a position, width, and\n", - "height.\n", - "\n", - "The plot below is a simple bar chart of the population (\\`pop\\`) of each\n", - "country." - ], - "id": "52fdec4c-17bc-49dd-a4db-6644a40aa8a5" - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "outputId": "8da431d3-9ba7-4ecf-de55-e478c4527a39" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_bar().encode(\n", - " alt.X('country:N'),\n", - " alt.Y('pop:Q')\n", - ")" - ], - "id": "a57e822a-2289-4d18-bf4f-2ff9aeab7ac2" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bar width is set to a default size. We will discuss how to adjust\n", - "the bar width later in this notebook. (A subsequent notebook will take a\n", - "closer look at configuring axes, scales, and legends.)\n", - "\n", - "Bars can also be stacked. Let’s change the `x` encoding to use the\n", - "`cluster` field, and encode `country` using the `color` channel. We’ll\n", - "also disable the legend (which would be very long with colors for all\n", - "countries!) and use tooltips for the country name." - ], - "id": "4eaa25d8-ba68-40fa-beb4-69074df8d5eb" - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "outputId": "7960be03-eefd-4c3f-a114-8405395c772c" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_bar().encode(\n", - " alt.X('cluster:N'),\n", - " alt.Y('pop:Q'),\n", - " alt.Color('country:N', legend=None),\n", - " alt.Tooltip('country:N')\n", - ")" - ], - "id": "df09d315-04d8-47ef-bd20-737ff0601953" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the chart above, the use of the `color` encoding channel causes\n", - "Altair / Vega-Lite to automatically stack the bar marks. Otherwise, bars\n", - "would be drawn on top of each other! Try adding the parameter\n", - "`stack=None` to the `y` encoding channel to see what happens if we don’t\n", - "apply stacking…\n", - "\n", - "The examples above create bar charts from a zero-baseline, and the `y`\n", - "channel only encodes the non-zero value (or height) of the bar. However,\n", - "the bar mark also allows you to specify starting and ending points to\n", - "convey ranges.\n", - "\n", - "The chart below uses the `x` (starting point) and `x2` (ending point)\n", - "channels to show the range of life expectancies within each regional\n", - "cluster. Below we use the `min` and `max` aggregation functions to\n", - "determine the end points of the range; we will discuss aggregation in\n", - "greater detail in the next notebook!\n", - "\n", - "Alternatively, you can use `x` and `width` to provide a starting point\n", - "plus offset, such that `x2 = x + width`." - ], - "id": "e84e49e9-1d25-4fcb-9516-843f40ab0cef" - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "outputId": "2fa67514-b66c-4449-a1e3-c0b1b377ed3a" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data2000).mark_bar().encode(\n", - " alt.X('min(life_expect):Q'),\n", - " alt.X2('max(life_expect):Q'),\n", - " alt.Y('cluster:N')\n", - ")" - ], - "id": "9249052a-d7bb-4505-8565-9d1a2979549c" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Line Marks\n", - "\n", - "The `line` mark type connects plotted points with line segments, for\n", - "example so that a line’s slope conveys information about the rate of\n", - "change.\n", - "\n", - "Let’s plot a line chart of fertility per country over the years, using\n", - "the full, unfiltered global development data frame. We’ll again hide the\n", - "legend and use tooltips instead." - ], - "id": "68fe8e75-e4d4-4254-9a67-2c6e4c504ede" - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "outputId": "7bbd15a9-4b8f-4f66-fe7e-6a55f2c38449" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data).mark_line().encode(\n", - " alt.X('year:O'),\n", - " alt.Y('fertility:Q'),\n", - " alt.Color('country:N', legend=None),\n", - " alt.Tooltip('country:N')\n", - ").properties(\n", - " width=400\n", - ")" - ], - "id": "e777bacc-6f85-4887-a26a-7ca8324c1267" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see interesting variations per country, but overall trends for\n", - "lower numbers of children per family over time. Also note that we set a\n", - "custom width of 400 pixels. *Try changing (or removing) the widths and\n", - "see what happens!*\n", - "\n", - "Let’s change some of the default mark parameters to customize the plot.\n", - "We can set the `strokeWidth` to determine the thickness of the lines and\n", - "the `opacity` to add some transparency. By default, the `line` mark uses\n", - "straight line segments to connect data points. In some cases we might\n", - "want to smooth the lines. We can adjust the interpolation used to\n", - "connect data points by setting the `interpolate` mark parameter. Let’s\n", - "use `'monotone'` interpolation to provide smooth lines that are also\n", - "guaranteed not to inadvertently generate “false” minimum or maximum\n", - "values as a result of the interpolation." - ], - "id": "03d335a8-f70f-4b5e-b39c-021a258100f9" - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "outputId": "07007338-a2c7-4dc2-9f36-bb344e4103ec" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(data).mark_line(\n", - " strokeWidth=3,\n", - " opacity=0.5,\n", - " interpolate='monotone'\n", - ").encode(\n", - " alt.X('year:O'),\n", - " alt.Y('fertility:Q'),\n", - " alt.Color('country:N', legend=None),\n", - " alt.Tooltip('country:N')\n", - ").properties(\n", - " width=400\n", - ")" - ], - "id": "1802cb26-10d6-4684-896d-46ed2aa97587" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `line` mark can also be used to create *slope graphs*, charts that\n", - "highlight the change in value between two comparison points using line\n", - "slopes.\n", - "\n", - "Below let’s create a slope graph comparing the populations of each\n", - "country at minimum and maximum years in our full dataset: 1955 and 2005.\n", - "We first create a new Pandas data frame filtered to those years, then\n", - "use Altair to create the slope graph.\n", - "\n", - "By default, Altair places the years close together. To better space out\n", - "the years along the x-axis, we can indicate the size (in pixels) of\n", - "discrete steps along the width of our chart as indicated by the comment\n", - "below. Try adjusting the width `step` value below and see how the chart\n", - "changes in response." - ], - "id": "3a45ab6f-b340-405d-a135-c77bcb40513d" - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "outputId": "edd1292a-97a7-44d1-b2ec-c5b8d755aa86" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "dataTime = data.loc[(data['year'] == 1955) | (data['year'] == 2005)]\n", - "\n", - "alt.Chart(dataTime).mark_line(opacity=0.5).encode(\n", - " alt.X('year:O'),\n", - " alt.Y('pop:Q'),\n", - " alt.Color('country:N', legend=None),\n", - " alt.Tooltip('country:N')\n", - ").properties(\n", - " width={\"step\": 50} # adjust the step parameter\n", - ")" - ], - "id": "0fe3a789-11fc-45cc-b510-d1fff51000fe" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Area Marks\n", - "\n", - "The `area` mark type combines aspects of `line` and `bar` marks: it\n", - "visualizes connections (slopes) among data points, but also shows a\n", - "filled region, with one edge defaulting to a zero-valued baseline.\n", - "\n", - "The chart below is an area chart of population over time for just the\n", - "United States:" - ], - "id": "e98f6b7e-4d34-41bc-b1f0-6c1846bb1feb" - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "outputId": "37cd899d-2ad1-42ea-fbe0-12e591a28326" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "dataUS = data.loc[data['country'] == 'United States']\n", - "\n", - "alt.Chart(dataUS).mark_area().encode(\n", - " alt.X('year:O'),\n", - " alt.Y('fertility:Q')\n", - ")" - ], - "id": "80cd4a15-08f4-4f0c-9e9c-822203843861" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to `line` marks, `area` marks support an `interpolate`\n", - "parameter." - ], - "id": "973753f5-fe0d-41c2-b278-6fbee571a233" - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "outputId": "eba5baab-44be-46ac-c9ae-0ad3b094573a" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(dataUS).mark_area(interpolate='monotone').encode(\n", - " alt.X('year:O'),\n", - " alt.Y('fertility:Q')\n", - ")" - ], - "id": "6e674da5-5188-40f3-bad0-1a8305861ebb" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similar to `bar` marks, `area` marks also support stacking. Here we\n", - "create a new data frame with data for the three North American\n", - "countries, then plot them using an `area` mark and a `color` encoding\n", - "channel to stack by country." - ], - "id": "c4af871e-3f17-4094-89a0-650ce683def0" - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "outputId": "70e210aa-4771-4ed9-e5a3-0af587d03c60" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "dataNA = data.loc[\n", - " (data['country'] == 'United States') |\n", - " (data['country'] == 'Canada') |\n", - " (data['country'] == 'Mexico')\n", - "]\n", - "\n", - "alt.Chart(dataNA).mark_area().encode(\n", - " alt.X('year:O'),\n", - " alt.Y('pop:Q'),\n", - " alt.Color('country:N')\n", - ")" - ], - "id": "d0711c43-22a8-4fcb-8074-f3f7993dd021" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By default, stacking is performed relative to a zero baseline. However,\n", - "other `stack` options are available:\n", - "\n", - "- `center` - to stack relative to a baseline in the center of the\n", - " chart, creating a *streamgraph* visualization, and\n", - "- `normalize` - to normalize the summed data at each stacking point to\n", - " 100%, enabling percentage comparisons.\n", - "\n", - "Below we adapt the chart by setting the `y` encoding `stack` attribute\n", - "to `center`. What happens if you instead set it `normalize`?" - ], - "id": "882f1e16-c660-44cf-bf22-d8534b1818e3" - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "outputId": "3a7cb75a-3acf-4b0e-d0e8-cdc7c85fd5ee" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(dataNA).mark_area().encode(\n", - " alt.X('year:O'),\n", - " alt.Y('pop:Q', stack='center'),\n", - " alt.Color('country:N')\n", - ")" - ], - "id": "18b7aa73-e5e4-4e7a-b45b-3ea00771c47a" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To disable stacking altogether, set the `stack` attribute to `None`. We\n", - "can also add `opacity` as a default mark parameter to ensure we see the\n", - "overlapping areas!" - ], - "id": "42a46d4c-add1-4992-8a82-8d88032d1e4a" - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "outputId": "41345e53-eeaa-4c69-969d-738c1479ba26" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(dataNA).mark_area(opacity=0.5).encode(\n", - " alt.X('year:O'),\n", - " alt.Y('pop:Q', stack=None),\n", - " alt.Color('country:N')\n", - ")" - ], - "id": "81f3ad39-4ca5-4d6c-8c5a-6dbe2cf6039a" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `area` mark type also supports data-driven baselines, with both the\n", - "upper and lower series determined by data fields. As with `bar` marks,\n", - "we can use the `x` and `x2` (or `y` and `y2`) channels to provide end\n", - "points for the area mark.\n", - "\n", - "The chart below visualizes the range of minimum and maximum fertility,\n", - "per year, for North American countries:" - ], - "id": "62768583-aa86-4ce8-b348-87a6fea12100" - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "outputId": "fb6d0868-79f4-4f96-8f5d-aff6d504b3fe" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(dataNA).mark_area().encode(\n", - " alt.X('year:O'),\n", - " alt.Y('min(fertility):Q'),\n", - " alt.Y2('max(fertility):Q')\n", - ").properties(\n", - " width={\"step\": 40}\n", - ")" - ], - "id": "89219037-98e9-4276-ab79-8aa617e35658" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see a larger range of values in 1995, from just under 4 to just\n", - "under 7. By 2005, both the overall fertility values and the variability\n", - "have declined, centered around 2 children per familty.\n", - "\n", - "All the `area` mark examples above use a vertically oriented area.\n", - "However, Altair and Vega-Lite support horizontal areas as well. Let’s\n", - "transpose the chart above, simply by swapping the `x` and `y` channels." - ], - "id": "6ea2faba-3462-4acb-9783-97d8dd99bc66" - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "outputId": "e7bf35e5-c94b-4316-9f8d-0dc68f242763" - }, - "outputs": [ - { - "output_type": "display_data", - "metadata": {}, - "data": { - "text/html": [ - "\n", - "
\n", - "" - ] - } - } - ], - "source": [ - "alt.Chart(dataNA).mark_area().encode(\n", - " alt.Y('year:O'),\n", - " alt.X('min(fertility):Q'),\n", - " alt.X2('max(fertility):Q')\n", - ").properties(\n", - " width={\"step\": 40}\n", - ")" - ], - "id": "be15d7ba-5fb8-48d5-a80a-96ade3cfbd6a" - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "We’ve completed our tour of data types, encoding channels, and graphical\n", - "marks! You should now be well-equipped to further explore the space of\n", - "encodings, mark types, and mark parameters. For a comprehensive\n", - "reference – including features we’ve skipped over here! – see the Altair\n", - "[marks](https://altair-viz.github.io/altair-viz-v4/user_guide/marks.html)\n", - "and\n", - "[encoding](https://altair-viz.github.io/altair-viz-v4/user_guide/encoding.html)\n", - "documentation.\n", - "\n", - "In the next module, we will look at the use of data transformations to\n", - "create charts that summarize data or visualize new derived fields. In a\n", - "later module, we’ll examine how to further customize your charts by\n", - "modifying scales, axes, and legends.\n", - "\n", - "Interested in learning more about visual encoding?\n", - "\n", - "
\n", - "\n", - "Bertin’s taxonomy of visual encodings from\n", - "Sémiologie\n", - "Graphique, as adapted by\n", - "Mike Bostock.\n", - "\n", - "- The systematic study of marks, visual encodings, and backing data\n", - " types was initiated by [Jacques\n", - " Bertin](https://en.wikipedia.org/wiki/Jacques_Bertin) in his\n", - " pioneering 1967 work [*Sémiologie Graphique (The Semiology of\n", - " Graphics)*](https://books.google.com/books/about/Semiology_of_Graphics.html?id=X5caQwAACAAJ).\n", - " The image above illustrates position, size, value (brightness),\n", - " texture, color (hue), orientation, and shape channels, alongside\n", - " Bertin’s recommendations for the data types they support.\n", - "- The framework of data types, marks, and channels also guides\n", - " *automated* visualization design tools, starting with [Mackinlay’s\n", - " APT (A Presentation\n", - " Tool)](https://scholar.google.com/scholar?cluster=10191273548472217907)\n", - " in 1986 and continuing in more recent systems such as\n", - " [Voyager](http://idl.cs.washington.edu/papers/voyager/) and\n", - " [Draco](http://idl.cs.washington.edu/papers/draco/).\n", - "- The identification of nominal, ordinal, interval, and ratio types\n", - " dates at least as far back as S. S. Steven’s 1947 article [*On the\n", - " theory of scales of\n", - " measurement*](https://scholar.google.com/scholar?cluster=14356809180080326415)." - ], - "id": "c7a1b05c-55d2-4563-9bf7-6ec4d95e7c12" - } - ], - "nbformat": 4, - "nbformat_minor": 5, - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3 (ipykernel)", - "language": "python" - }, - "language_info": { - "name": "python", - "codemirror_mode": { - "name": "ipython", - "version": "3" - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.1" - } - } -} \ No newline at end of file diff --git a/images/fractal.png b/images/fractal.png new file mode 100644 index 0000000000000000000000000000000000000000..84d7b687ba1c1ac3cc5a2e9e3a4bd7a913e1aba1 GIT binary patch literal 45446 zcmeFY^;gu}`v(e$fTYOKC7^($N-8C-A|;`8cXxw`)F2=tAPvKjp^+Sil#-H?W*A`T z?il)RK6l-};r{Yn>$uc|GxMIk_wzij+R++n%4Eb0#5g!OWUpQ-yv4!6J-qovL;(Iz 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