Advanced Visualization Tools

Seaborn and Regression Plots

Seaborn

• Seaborn is a a Python visualization library based on Matplotlib.
• Visuals that need ~20 lines of code using Matplotlib to be created, with seaborn, the number of lines of code is reduced by 5-fold.

Regression Plots

• Example:

import seaborn as sns
ax = sns.regplot(x='year', y='total', data=df_total, color='green', marker='+')

Advanced Visualization Tools

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Question 1

Seaborn is a Python visualization library that provides a high-level interface for drawing attractive statistical graphics, such as regression plots and box plots.

• True
• False

Correct.

Question 2

The following code

import seaborn as sns
ax = sns.regplot(x="year", y="total", data=data_df, color="green", marker="+")

creates the following regression plot.

• True
• False

Correct.

Question 3

In Python, creating a waffle chart is straightforward since we can easily create one using the scripting layer of Matplotlib.

• True
• False

Correct.

Visualizing Geospatial Data

Introduction to Folium

What is Folium?

• Folium is a powerful Python library that helps you create several types of Leaflet maps.
• It enables both the binding of data to a map for choropleth visualizations as well as passing visualizations as markers on the map.
• The library has a number of bulit-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox API keys.

Creating a World Map

# define the world map
world_map = folium.Map()

# display world map
world_map

Creating a Map of Canada

# define the world map centered around
# Canada with a low zoom level
world_map = folium.Map(
		location=[56.130, -106.35],
		zoom_start=4
		)

# dispaly world map
world_map

Map Styles - Stamen Toner

# create a Stamen Toner map of
# the world centered around Canada
world_map = folium.Map(
		location=[56.130, -106.35],
		zoom_start=4,
		titles='Stamen Toner'
		)

# dispaly map
world_map

Map Styles - Stamen Terrain

# create a Stamen Toner map of
# the world centered around Canada
world_map = folium.Map(
		location=[56.130, -106.35],
		zoom_start=4,
		titles='Stamen Terrain'
		)

# dispaly map
world_map

Maps with Markers

Label the Marker

# generate map of Canada
canada_map = folium.Map(
		location=[56.130, -106.35],
		zoom_start=4
		)

## add a red marker to Ontario
# create a feature group
ontario = folium.map.FeatureGroup()

# style the feature group
ontario.add_child(
		folium.features.CircleMarker(
			[51.25, -85.32], radium=5,
			color='red', fill_color='Red'
			)
		)

# add the feature group to the map
canada_map.add_child(ontario)

# label the marker
folium.Marker([51.25, -85.32],
		popup='Ontario').add_to(canada_map)

# display map
canada_map

Choropleth Maps

Choropleth Maps

Geojson File

{
	"type": "FeatureCollection",
	"features": [{
		"type": "Feature",
		"properties": {
			"name": "Brunei"
		},
		"geometry": {
			"type": "Polygon",
			"coordinates": [
				[
					[114.204017, 4.525874], [114.599961, 4.900011], [115.45071, 5.44773],
					[115.4057, 4.955228], [115.347461, 4.316636], [114.869557, 4.348314],
					[114.659596, 4.007637], [114.204017, 4.525874]
				]
			]
		},
		"id": "BRN"
	},

Creating the Map

# create a plain world map
world_map = folium.Map(
	zoom_start=2,
	title="Mapbox Bright'
	)

## geojson file
world_geo = r'world_cointries.json'

# generate choropleth map using the total
# population of each country to Canada from
# 1980 to 2013
world_map.choropleth(
	geo_path=world_geo,
	data=df_canada,
	columns=['Country', 'Total'],
	key_on='feature.properties.name',
	fill_color='YlOrRd',
	legend_name='Immigration to Canada'
	)

# display map
world_map

Visualizing Geospatial Data

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Question 1

You cluster markers, superimposed onto a map in Folium, using a marker cluster object.

• True
• False

Correct.

Question 2

The following code will generate a map of Spain, displaying its hill shading and natural vegetation.

folium.Map(location=[40.4637, -3.7492], zoom_start=6, tiles='Stamen Terrain')

• True
• False

Correct.

Question 3

A choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map.

• True
• False

Correct.