{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "flex_subtitle = \"built using jupyter-flex\"\n",
    "flex_external_link = \"https://github.com/danielfrg/jupyter-flex/blob/master/examples/getting-started/3-two-columns.ipynb\""
   ]
  },
  {
   "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": [
     "source"
    ]
   },
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "plot.properties(\n",
    "    width='container',\n",
    "    height='container'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "source = data.stocks()\n",
    "\n",
    "plot = 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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "plot.properties(\n",
    "    width='container',\n",
    "    height='container'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = data.iris()\n",
    "\n",
    "plot = 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": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "body"
    ]
   },
   "outputs": [],
   "source": [
    "plot.properties(\n",
    "    width=\"container\",\n",
    "    height=\"container\"\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}