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  "cells": [
    {
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      "source": [
        "# Altair in `JupyterLite`\n",
        "\n",
        "**Altair** is a declarative statistical visualization library for Python.\n",
        "\n",
        "Most of the examples below are from: https://altair-viz.github.io/gallery"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import the dependencies:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "%pip install -q altair"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simple Bar Chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import altair as alt\n",
        "import pandas as pd\n",
        "\n",
        "source = pd.DataFrame({\n",
        "    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],\n",
        "    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]\n",
        "})\n",
        "\n",
        "alt.Chart(source).mark_bar().encode(\n",
        "    x='a',\n",
        "    y='b'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simple Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import altair as alt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Compute x^2 + y^2 across a 2D grid\n",
        "x, y = np.meshgrid(range(-5, 5), range(-5, 5))\n",
        "z = x ** 2 + y ** 2\n",
        "\n",
        "# Convert this grid to columnar data expected by Altair\n",
        "source = pd.DataFrame({'x': x.ravel(),\n",
        "                     'y': y.ravel(),\n",
        "                     'z': z.ravel()})\n",
        "\n",
        "alt.Chart(source).mark_rect().encode(\n",
        "    x='x:O',\n",
        "    y='y:O',\n",
        "    color='z:Q'\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Install the Vega Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "%pip install -q vega_datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Interactive Average"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import altair as alt\n",
        "from vega_datasets import data\n",
        "\n",
        "source = data.seattle_weather()\n",
        "brush = alt.selection(type='interval', encodings=['x'])\n",
        "\n",
        "bars = alt.Chart().mark_bar().encode(\n",
        "    x='month(date):O',\n",
        "    y='mean(precipitation):Q',\n",
        "    opacity=alt.condition(brush, alt.OpacityValue(1), alt.OpacityValue(0.7)),\n",
        ").add_selection(\n",
        "    brush\n",
        ")\n",
        "\n",
        "line = alt.Chart().mark_rule(color='firebrick').encode(\n",
        "    y='mean(precipitation):Q',\n",
        "    size=alt.SizeValue(3)\n",
        ").transform_filter(\n",
        "    brush\n",
        ")\n",
        "\n",
        "alt.layer(bars, line, data=source)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Locations of US Airports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import altair as alt\n",
        "from vega_datasets import data\n",
        "\n",
        "airports = data.airports.url\n",
        "states = alt.topo_feature(data.us_10m.url, feature='states')\n",
        "\n",
        "# US states background\n",
        "background = alt.Chart(states).mark_geoshape(\n",
        "    fill='lightgray',\n",
        "    stroke='white'\n",
        ").properties(\n",
        "    width=500,\n",
        "    height=300\n",
        ").project('albersUsa')\n",
        "\n",
        "# airport positions on background\n",
        "points = alt.Chart(airports).transform_aggregate(\n",
        "    latitude='mean(latitude)',\n",
        "    longitude='mean(longitude)',\n",
        "    count='count()',\n",
        "    groupby=['state']\n",
        ").mark_circle().encode(\n",
        "    longitude='longitude:Q',\n",
        "    latitude='latitude:Q',\n",
        "    size=alt.Size('count:Q', title='Number of Airports'),\n",
        "    color=alt.value('steelblue'),\n",
        "    tooltip=['state:N','count:Q']\n",
        ").properties(\n",
        "    title='Number of airports in US'\n",
        ")\n",
        "\n",
        "background + points\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
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