{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geoviews as gv\n",
    "\n",
    "from geoviews import dim\n",
    "\n",
    "gv.extension('matplotlib')\n",
    "gv.output(fig='svg', size=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = pd.read_csv('../../assets/cities.csv', encoding=\"ISO-8859-1\")\n",
    "population = gv.Dataset(cities, kdims=['City', 'Country', 'Year'])\n",
    "points = population.to(gv.Points, ['Longitude', 'Latitude'], ['Population', 'City', 'Country'])\n",
    "\n",
    "tiles = gv.tile_sources.OSM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiles.opts(zoom=0) * points.opts(\n",
    "    s=dim('Population')*0.00001, color='Population', cmap='viridis', global_extent=True)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}