Lists and Maps Lab

Introduction

Ok, so now that we have gotten a sense of how to read from a list and alter a list in Python, let's put see how our knowledge of lists can fit into data visualizations by plotting maps.

Working with lists and maps

As we know, lists are used to store a collection of data. In describing a city, we can use lists to organize our data in all sorts of ways. For example, here are a list of neighborhoods about the city of Buenos Aires.

neighborhoods = ['Palermo', 'Ricoleta', 'Santelmo', 'Puerto Madero', 'Belgrano', 'La Boca']

Select the first element of the list, and assign it to the variable palermo.

palermo = neighborhoods[0]

Select the last element of the list, and assign it to the variable caballito.

la_boca = neighborhoods[]

Beyond the neighborhoods, another thing that we can think of representing as a collection are the coordinates of a city, latitude and longitude. The first attribute is latitude and the second is longitude.

coordinates = [-34.6037, -58.3816]

Set the latitude of Buenos Aires equal to ba_latitude and set the longitude to be Buenos Aires to be ba_longitude.

ba_latitude = None

ba_longitude = None

Now let's see if we can display this as a map.

import folium
buenos_map = folium.Map([-34.6037, -58.3816])
buenos_map

All of that from a couple of lines of code. Let's understand it:

import folium
buenos_map = folium.Map([-34.6037, -58.3816])
buenos_map

Folium is a mapping library built on Python. We created a representation of a map, by referencing the folium.Map function and passing through a list. That list represents the latitude and longitude, just like we saw previously. The map object is stored as the variable buenos. If we have buenos be the last line of a cell, the map is displayed.

Now we can also add a marker to this map.

buenos_marker = folium.Marker([-34.6037, -58.3816])
buenos_marker.add_to(buenos_map)

<folium.map.Marker at 0x1137d1d68>

So we used the folium library to create a marker. We specified the coordinates of the marker as list. Then we added the marker to our map with the add_to function.

Let's see our updated map! We see our map simply by referencing our buenos_map variable.

buenos_map

Great. So notice that the map object is just stored as a variable.

buenos_marker

<folium.map.Marker at 0x1137d1d68>

And just like anything else in Python, we can place this marker in a list, and then retrieve it from the list.

buenos_markers = [buenos_marker]

buenos_markers[0]

<folium.map.Marker at 0x1137d1d68>

So here are some other markers to add to our map.

neighborhood_markers = ['Palermo', 'Ricoleta', 'Santelmo', 'Puerto Madero', 'Belgrano', 'La Boca']
marker_one = folium.Marker([-34.5711, -58.4233])
marker_two = folium.Marker([-34.5895, -58.3974])
marker_three = folium.Marker([-34.6212, -58.3731])
marker_four = folium.Marker([-34.6177, -58.3621])
marker_five = folium.Marker([-34.43,  -58.32])
marker_six = folium.Marker([-34.6345, -58.3631])
neighborhood_markers = [marker_one, marker_two, marker_three, marker_four, marker_five, marker_six]

Select the last marker and assign it equal to la_boca_marker.

buenos_map = folium.Map([-34.6037, -58.3816], zoom_start = 13)
la_boca_marker = neighborhood_markers[-1]

la_boca_marker.add_to(buenos_map)
buenos_map

Now that we plotted la_boca we don't need the marker anymore. So remove this last element from our neighborhood_markers list.

neighborhood_markers.pop()

<folium.map.Marker at 0x1137ea9b0>

len(neighborhood_markers) # 6
neighborhood_markers[-1] == marker_five # True

True

Now select the second marker in the list and set that equal to recoleta.

buenos_map = folium.Map([-34.6037, -58.3816], zoom_start = 13)

recoleta = neighborhood_markers[1]

san_pelmo.add_to(buenos_map)
buenos_map

Of course, when we get up to loops in Python, we will see how to easily plot all of the markers in our list.

for marker in neighborhood_markers:
    marker.add_to(buenos_map)
buenos_map

But that's a lesson for another day.

Summary

In this lesson we saw how to select information from a list and then plot that information with maps. We saw that lists can make good arguments to methods, as they represent an ordered collection of information, like latitude followed by longitude. In just a few lessons we saw how to use Python to make visualizations with our data going forward.

KhaledaNYC/py-lists-with-maps-data-science-alpha

Lists and Maps Lab

Introduction

Working with lists and maps

Summary