{
 "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, tile_sources as gvts\n",
    "\n",
    "gv.extension('matplotlib')\n",
    "\n",
    "gv.output(dpi=120)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = pd.read_csv('../../assets/cities.csv', encoding=\"ISO-8859-1\")\n",
    "\n",
    "points = gv.Points(cities, ['Longitude', 'Latitude'], ['City', 'Population', 'Country', 'Year'],\n",
    "                   group='Top 20 Cities by population in 2050').select(Year=2050).sort('Population').iloc[-20:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(gvts.CartoMidnight().opts(zoom=1) *\n",
    " points.opts(\n",
    "    s=dim('Population')*0.00002, color='Population', cmap='viridis',\n",
    "    fig_size=300, global_extent=True) *\n",
    " gv.Labels(points).opts(color='white'))"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}