{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Airports of the World\n",
"\n",
"This notebook visualizes the world's airports, using data made available at http://ourairports.com/data/"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Download data and extract the columns we'll use\n",
"\n",
"# full_data = pd.read_csv('http://ourairports.com/data/airports.csv')\n",
"# data = full_data[['type', 'latitude_deg', 'longitude_deg']]\n",
"# data.to_csv('../data/airports.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" type | \n",
" latitude_deg | \n",
" longitude_deg | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" heliport | \n",
" 40.070801 | \n",
" -74.933601 | \n",
"
\n",
" \n",
" | 1 | \n",
" small_airport | \n",
" 59.949200 | \n",
" -151.695999 | \n",
"
\n",
" \n",
" | 2 | \n",
" small_airport | \n",
" 34.864799 | \n",
" -86.770302 | \n",
"
\n",
" \n",
" | 3 | \n",
" heliport | \n",
" 35.608700 | \n",
" -91.254898 | \n",
"
\n",
" \n",
" | 4 | \n",
" small_airport | \n",
" 34.305599 | \n",
" -112.165001 | \n",
"
\n",
" \n",
"
\n",
"