{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "flex_title = \"Altair plots - Scroll Layout\"\n",
    "flex_vertical_layout = \"scroll\"\n",
    "flex_author = \"built using jupyter-flex\"\n",
    "flex_source_link = \"https://github.com/danielfrg/jupyter-flex/blob/master/examples/plots/altair-scroll.ipynb\"\n",
    "flex_include_source = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Page 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Scatter Plot with Tooltips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "from vega_datasets import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "alt.renderers.set_embed_options(actions=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "source"
    ]
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.cars()\n",
    "\n",
    "plot = alt.Chart(source).mark_circle(size=60).encode(\n",
    "    x='Horsepower',\n",
    "    y='Miles_per_Gallon',\n",
    "    color='Origin',\n",
    "    tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']\n",
    ")\n",
    "plot.interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "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",
    "plot = alt.Chart(source).mark_bar().encode(\n",
    "    x='a',\n",
    "    y='b'\n",
    ")\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart with Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.us_employment()\n",
    "\n",
    "alt.Chart(source).mark_bar().encode(\n",
    "    x=\"month:T\",\n",
    "    y=\"nonfarm_change:Q\",\n",
    "    color=alt.condition(\n",
    "        alt.datum.nonfarm_change > 0,\n",
    "        alt.value(\"steelblue\"),  # The positive color\n",
    "        alt.value(\"orange\")  # The negative color\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "# 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",
    "plot = alt.Chart(source).mark_rect().encode(\n",
    "    x='x:O',\n",
    "    y='y:O',\n",
    "    color='z:Q'\n",
    ")\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.barley()\n",
    "\n",
    "alt.Chart(source).mark_bar().encode(\n",
    "    x='variety',\n",
    "    y='sum(yield)',\n",
    "    color='site'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filled Step Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.stocks()\n",
    "\n",
    "alt.Chart(source).mark_area(\n",
    "    color=\"lightblue\",\n",
    "    interpolate='step-after',\n",
    "    line=True\n",
    ").encode(\n",
    "    x='date',\n",
    "    y='price'\n",
    ").transform_filter(alt.datum.symbol == 'GOOG').properties(\n",
    "    width='container'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Page 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi Series Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.stocks()\n",
    "\n",
    "alt.Chart(source).mark_line().encode(\n",
    "    x='date',\n",
    "    y='price',\n",
    "    color='symbol'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cumulative Count Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.movies.url\n",
    "\n",
    "alt.Chart(source).transform_window(\n",
    "    cumulative_count=\"count()\",\n",
    "    sort=[{\"field\": \"IMDB_Rating\"}],\n",
    ").mark_area().encode(\n",
    "    x=\"IMDB_Rating:Q\",\n",
    "    y=\"cumulative_count:Q\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacked Density Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.iris()\n",
    "\n",
    "alt.Chart(source).transform_fold(\n",
    "    ['petalWidth',\n",
    "     'petalLength',\n",
    "     'sepalWidth',\n",
    "     'sepalLength'],\n",
    "    as_ = ['Measurement_type', 'value']\n",
    ").transform_density(\n",
    "    density='value',\n",
    "    bandwidth=0.3,\n",
    "    groupby=['Measurement_type'],\n",
    "    extent= [0, 8],\n",
    "    counts = True,\n",
    "    steps=200\n",
    ").mark_area().encode(\n",
    "    alt.X('value:Q'),\n",
    "    alt.Y('density:Q', stack='zero'),\n",
    "    alt.Color('Measurement_type:N')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binned Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.movies.url\n",
    "\n",
    "alt.Chart(source).mark_circle().encode(\n",
    "    alt.X('IMDB_Rating:Q', bin=True),\n",
    "    alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),\n",
    "    size='count()'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Row 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multifeature Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "source = data.iris()\n",
    "\n",
    "alt.Chart(source).mark_circle().encode(\n",
    "    alt.X('sepalLength', scale=alt.Scale(zero=False)),\n",
    "    alt.Y('sepalWidth', scale=alt.Scale(zero=False, padding=1)),\n",
    "    color='species',\n",
    "    size='petalWidth'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choropleth Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "from vega_datasets import data\n",
    "\n",
    "counties = alt.topo_feature(data.us_10m.url, 'counties')\n",
    "source = data.unemployment.url\n",
    "\n",
    "alt.Chart(counties).mark_geoshape().encode(\n",
    "    color='rate:Q'\n",
    ").transform_lookup(\n",
    "    lookup='id',\n",
    "    from_=alt.LookupData(source, 'id', ['rate'])\n",
    ").project(\n",
    "    type='albersUsa'\n",
    ").properties(\n",
    "    width=\"container\",\n",
    "    height=\"container\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}