# Import the pandas library.
import pandas
# Read in the airports data.
airports = pandas.read_csv("airports.csv", header=None, dtype=str)
airports.columns = ["id", "name", "city", "country", "code", "icao", "latitude", "longitude", "altitude", "offset", "dst", "timezone"]
# Read in the airlines data.
airlines = pandas.read_csv("airlines.csv", header=None, dtype=str)
airlines.columns = ["id", "name", "alias", "iata", "icao", "callsign", "country", "active"]
# Read in the routes data.
routes = pandas.read_csv("routes.csv", header=None, dtype=str)
routes.columns = ["airline", "airline_id", "source", "source_id", "dest", "dest_id", "codeshare", "stops", "equipment"]

# Import the pandas library.
import pandas
# Read in the airports data.
airports = pandas.read_csv("airports.csv", header=None, dtype=str)
airports.columns = ["id", "name", "city", "country", "code", "icao", "latitude", "longitude", "altitude", "offset", "dst", "timezone"]
# Read in the airlines data.
airlines = pandas.read_csv("airlines.csv", header=None, dtype=str)
airlines.columns = ["id", "name", "alias", "iata", "icao", "callsign", "country", "active"]
# Read in the routes data.
routes = pandas.read_csv("routes.csv", header=None, dtype=str)
routes.columns = ["airline", "airline_id", "source", "source_id", "dest", "dest_id", "codeshare", "stops", "equipment"]

airports.head()

airports.head()

airlines.head()

airlines.head()

routes.head()

routes.head()

routes = routes[routes["airline_id"] != "\\N"]

routes = routes[routes["airline_id"] != "\\N"]

import math
def haversine(lon1, lat1, lon2, lat2):
    # Convert coordinates to floats.
    lon1, lat1, lon2, lat2 = [float(lon1), float(lat1), float(lon2), float(lat2)]
    # Convert to radians from degrees.
    lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])
    # Compute distance.
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
    c = 2 * math.asin(math.sqrt(a)) 
    km = 6367 * c
    return km

import math
def haversine(lon1, lat1, lon2, lat2):
    # Convert coordinates to floats.
    lon1, lat1, lon2, lat2 = [float(lon1), float(lat1), float(lon2), float(lat2)]
    # Convert to radians from degrees.
    lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])
    # Compute distance.
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2
    c = 2 * math.asin(math.sqrt(a)) 
    km = 6367 * c
    return km

def calc_dist(row):
    dist = 0
    try:
        # Match source and destination to get coordinates.
        source = airports[airports["id"] == row["source_id"]].iloc[0]
        dest = airports[airports["id"] == row["dest_id"]].iloc[0]
        # Use coordinates to compute distance.
        dist = haversine(dest["longitude"], dest["latitude"], source["longitude"], source["latitude"])
    except (ValueError, IndexError):
        pass
    return dist

def calc_dist(row):
    dist = 0
    try:
        # Match source and destination to get coordinates.
        source = airports[airports["id"] == row["source_id"]].iloc[0]
        dest = airports[airports["id"] == row["dest_id"]].iloc[0]
        # Use coordinates to compute distance.
        dist = haversine(dest["longitude"], dest["latitude"], source["longitude"], source["latitude"])
    except (ValueError, IndexError):
        pass
    return dist

route_lengths = routes.apply(calc_dist, axis=1)

route_lengths = routes.apply(calc_dist, axis=1)

import matplotlib.pyplot as plt 
%matplotlib inline 

plt.hist(route_lengths, bins=20)

import matplotlib.pyplot as plt 
%matplotlib inline 
 
 
plt.hist(route_lengths, bins=20)

import seaborn 
seaborn.distplot(route_lengths, bins=20)

import seaborn 
seaborn.distplot(route_lengths, bins=20)

import numpy
# Put relevant columns into a dataframe.
route_length_df = pandas.DataFrame({"length": route_lengths, "id": routes["airline_id"]})
# Compute the mean route length per airline.
airline_route_lengths = route_length_df.groupby("id").aggregate(numpy.mean)
# Sort by length so we can make a better chart.
airline_route_lengths = airline_route_lengths.sort("length", ascending=False)

import numpy
# Put relevant columns into a dataframe.
route_length_df = pandas.DataFrame({"length": route_lengths, "id": routes["airline_id"]})
# Compute the mean route length per airline.
airline_route_lengths = route_length_df.groupby("id").aggregate(numpy.mean)
# Sort by length so we can make a better chart.
airline_route_lengths = airline_route_lengths.sort("length", ascending=False)

plt.bar(range(airline_route_lengths.shape[0]), airline_route_lengths["length"])

plt.bar(range(airline_route_lengths.shape[0]), airline_route_lengths["length"])

def lookup_name(row):
    try:
        # Match the row id to the id in the airlines dataframe so we can get the name.
        name = airlines["name"][airlines["id"] == row["id"]].iloc[0]
    except (ValueError, IndexError):
        name = ""
    return name
# Add the index (the airline ids) as a column.
airline_route_lengths["id"] = airline_route_lengths.index.copy()
# Find all the airline names.
airline_route_lengths["name"] = airline_route_lengths.apply(lookup_name, axis=1)
# Remove duplicate values in the index.
airline_route_lengths.index = range(airline_route_lengths.shape[0])

def lookup_name(row):
    try:
        # Match the row id to the id in the airlines dataframe so we can get the name.
        name = airlines["name"][airlines["id"] == row["id"]].iloc[0]
    except (ValueError, IndexError):
        name = ""
    return name
# Add the index (the airline ids) as a column.
airline_route_lengths["id"] = airline_route_lengths.index.copy()
# Find all the airline names.
airline_route_lengths["name"] = airline_route_lengths.apply(lookup_name, axis=1)
# Remove duplicate values in the index.
airline_route_lengths.index = range(airline_route_lengths.shape[0])

import numpy as np
from bokeh.io import output_notebook
from bokeh.charts import Bar, show
output_notebook()
p = Bar(airline_route_lengths, 'name', values='length', title="Average airline route lengths")
show(p)

import numpy as np
from bokeh.io import output_notebook
from bokeh.charts import Bar, show
output_notebook()
p = Bar(airline_route_lengths, 'name', values='length', title="Average airline route lengths")
show(p)

long_routes = len([k for k in route_lengths if k > 10000]) / len(route_lengths)
medium_routes = len([k for k in route_lengths if k < 10000 and k > 2000]) / len(route_lengths)
short_routes = len([k for k in route_lengths if k < 2000]) / len(route_lengths)

long_routes = len([k for k in route_lengthsif k > 10000]) / len(route_lengths)
medium_routes = len([k for k in route_lengthsif k < 10000 and k > 2000]) / len(route_lengths)
short_routes = len([k for k in route_lengthsif k < 2000]) / len(route_lengths)

name_lengths = airlines["name"].apply(lambda x: len(str(x)))
plt.scatter(airlines["id"].astype(int), name_lengths)

name_lengths = airlines["name"].apply(lambda x: len(str(x)))
plt.scatter(airlines["id"].astype(int), name_lengths)

data = pandas.DataFrame({"lengths": name_lengths, "ids": airlines["id"].astype(int)})
seaborn.jointplot(x="ids", y="lengths", data=data)

data = pandas.DataFrame({"lengths": name_lengths, "ids": airlines["id"].astype(int)})
seaborn.jointplot(x="ids", y="lengths", data=data)

# Import the basemap package
from mpl_toolkits.basemap import Basemap
# Create a map on which to draw.  We're using a mercator projection, and showing the whole world.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
# Draw coastlines, and the edges of the map.
m.drawcoastlines()
m.drawmapboundary()
# Convert latitude and longitude to x and y coordinates
x, y = m(list(airports["longitude"].astype(float)), list(airports["latitude"].astype(float)))
# Use matplotlib to draw the points onto the map.
m.scatter(x,y,1,marker='o',color='red')
# Show the plot.
plt.show()

# Import the basemap package
from mpl_toolkits.basemapimport Basemap
# Create a map on which to draw.  We're using a mercator projection, and showing the whole world.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
# Draw coastlines, and the edges of the map.
m.drawcoastlines()
m.drawmapboundary()
# Convert latitude and longitude to x and y coordinates
x, y = m(list(airports["longitude"].astype(float)), list(airports["latitude"].astype(float)))
# Use matplotlib to draw the points onto the map.
m.scatter(x,y,1,marker='o',color='red')
# Show the plot.
plt.show()

import folium
# Get a basic world map.
airports_map = folium.Map(location=[30, 0], zoom_start=2)
# Draw markers on the map.
for name, row in airports.iterrows():
    # For some reason, this one airport causes issues with the map.
    if row["name"] != "South Pole Station":
        airports_map.circle_marker(location=[row["latitude"], row["longitude"]], popup=row["name"])
# Create and show the map.
airports_map.create_map('airports.html')
airports_map

import folium
# Get a basic world map.
airports_map = folium.Map(location=[30, 0], zoom_start=2)
# Draw markers on the map.
for name, rowin airports.iterrows():
    # For some reason, this one airport causes issues with the map.
    if row["name"] != "South Pole Station":
        airports_map.circle_marker(location=[row["latitude"], row["longitude"]], popup=row["name"])
# Create and show the map.
airports_map.create_map('airports.html')
airports_map

# Make a base map with a mercator projection.  Draw the coastlines.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.drawcoastlines()
# Iterate through the first 3000 rows.
for name, row in routes[:3000].iterrows():
    try:
        # Get the source and dest airports.
        source = airports[airports["id"] == row["source_id"]].iloc[0]
        dest = airports[airports["id"] == row["dest_id"]].iloc[0]
        # Don't draw overly long routes.
        if abs(float(source["longitude"]) - float(dest["longitude"])) < 90:
            # Draw a great circle between source and dest airports.
            m.drawgreatcircle(float(source["longitude"]), float(source["latitude"]), float(dest["longitude"]), float(dest["latitude"]),linewidth=1,color='b')
    except (ValueError, IndexError):
        pass

# Show the map.
plt.show()

# Make a base map with a mercator projection.  Draw the coastlines.
m = Basemap(projection='merc',llcrnrlat=-80,urcrnrlat=80,llcrnrlon=-180,urcrnrlon=180,lat_ts=20,resolution='c')
m.drawcoastlines()
# Iterate through the first 3000 rows.
for name, rowin routes[:3000].iterrows():
    try:
        # Get the source and dest airports.
        source = airports[airports["id"] == row["source_id"]].iloc[0]
        dest = airports[airports["id"] == row["dest_id"]].iloc[0]
        # Don't draw overly long routes.
        if abs(float(source["longitude"]) - float(dest["longitude"])) < 90:
            # Draw a great circle between source and dest airports.
            m.drawgreatcircle(float(source["longitude"]), float(source["latitude"]), float(dest["longitude"]), float(dest["latitude"]),linewidth=1,color='b')
    except (ValueError, IndexError):
        pass
 
# Show the map.
plt.show()

# Initialize the weights dictionary.
weights = {}
# Keep track of keys that have been added once -- we only want edges with a weight of more than 1 to keep our network size manageable.
added_keys = []
# Iterate through each route.
for name, row in routes.iterrows():
    # Extract the source and dest airport ids.
    source = row["source_id"]
    dest = row["dest_id"]

    # Create a key for the weights dictionary.
    # This corresponds to one edge, and has the start and end of the route.
    key = "{0}_{1}".format(source, dest)
    # If the key is already in weights, increment the weight.
    if key in weights:
        weights[key] += 1
    # If the key is in added keys, initialize the key in the weights dictionary, with a weight of 2.
    elif key in added_keys:
        weights[key] = 2
    # If the key isn't in added_keys yet, append it.
    # This ensures that we aren't adding edges with a weight of 1.
    else:
        added_keys.append(key)

# Initialize the weights dictionary.
weights = {}
# Keep track of keys that have been added once -- we only want edges with a weight of more than 1 to keep our network size manageable.
added_keys = []
# Iterate through each route.
for name, rowin routes.iterrows():
    # Extract the source and dest airport ids.
    source = row["source_id"]
    dest = row["dest_id"]
 
    # Create a key for the weights dictionary.
    # This corresponds to one edge, and has the start and end of the route.
    key = "{0}_{1}".format(source, dest)
    # If the key is already in weights, increment the weight.
    if keyin weights:
        weights[key] += 1
    # If the key is in added keys, initialize the key in the weights dictionary, with a weight of 2.
    elif keyin added_keys:
        weights[key] = 2
    # If the key isn't in added_keys yet, append it.
    # This ensures that we aren't adding edges with a weight of 1.
    else:
        added_keys.append(key)

# Import networkx and initialize the graph.
import networkx as nx
graph = nx.Graph()
# Keep track of added nodes in this set so we don't add twice.
nodes = set()
# Iterate through each edge.
for k, weight in weights.items():
    try:
        # Split the source and dest ids and convert to integers.
        source, dest = k.split("_")
        source, dest = [int(source), int(dest)]
        # Add the source if it isn't in the nodes.
        if source not in nodes:
            graph.add_node(source)
        # Add the dest if it isn't in the nodes.
        if dest not in nodes:
            graph.add_node(dest)
        # Add both source and dest to the nodes set.
        # Sets don't allow duplicates.
        nodes.add(source)
        nodes.add(dest)

        # Add the edge to the graph.
        graph.add_edge(source, dest, weight=weight)
    except (ValueError, IndexError):
        pass
pos=nx.spring_layout(graph)
# Draw the nodes and edges.
nx.draw_networkx_nodes(graph,pos, node_color='red', node_size=10, alpha=0.8)
nx.draw_networkx_edges(graph,pos,width=1.0,alpha=1)
# Show the plot.
plt.show()

# Import networkx and initialize the graph.
import networkxas nx
graph = nx.Graph()
# Keep track of added nodes in this set so we don't add twice.
nodes = set()
# Iterate through each edge.
for k, weightin weights.items():
    try:
        # Split the source and dest ids and convert to integers.
        source, dest = k.split("_")
        source, dest = [int(source), int(dest)]
        # Add the source if it isn't in the nodes.
        if sourcenot in nodes:
            graph.add_node(source)
        # Add the dest if it isn't in the nodes.
        if destnot in nodes:
            graph.add_node(dest)
        # Add both source and dest to the nodes set.
        # Sets don't allow duplicates.
        nodes.add(source)
        nodes.add(dest)
 
        # Add the edge to the graph.
        graph.add_edge(source, dest, weight=weight)
    except (ValueError, IndexError):
        pass
pos=nx.spring_layout(graph)
# Draw the nodes and edges.
nx.draw_networkx_nodes(graph,pos, node_color='red', node_size=10, alpha=0.8)
nx.draw_networkx_edges(graph,pos,width=1.0,alpha=1)
# Show the plot.
plt.show()