flights-practical.ipynb

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   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# Load the data in `citiesToCities.csv` into a table (you can write your own code or use the pandas library for\n",
    "# data processing in python; see http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html for details)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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    "# Construct a directed, weighted graph of flights between airports (the library networkx provides convenient methods, \n",
    "# e.g. see https://networkx.github.io/documentation/latest/tutorial/tutorial.html)"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Visualise the network you have constructed. Is it consistent with your intuition?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# Use the graph to query the airports you can fly to from London"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
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   "outputs": [],
   "source": [
    "# Which airport has the smallest/largest number of destination airports?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Which airport has the smallest/largest number of routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Are the in-degree (number of incoming flights from distinct airports) and the out-degree (number of outgoing \n",
    "# flights from distinct airports) correlated?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Develop a function that takes the location of a walker as input and returns the location of the walker at the next\n",
    "# time step by randomly choosing a neighbour of the input location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Develop a function that can take multiple steps at a time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Simulate a trajectory starting in London and visualise it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run a simulation of 10,000 walkers starting in London and visualise the number of walkers at each airport after\n",
    "# two time steps and 200 time steps"
   ]
  }
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