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DATA Visualization Using Python

IBM Course Overview

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Tools: matplotlib, seaborn, and folium

Week 1 - Introduction to Data Visualization Tools
    Introduction to Data Visualization
    Introduction to Matplotlib
    Basic Plotting with Matplotlib
    Dataset on Immigration to Canada
    Line Plots
    Lab: Introduction to Matplotlib and Line Plots
    Quiz: Introduction to Data Visualization Tools
Week 2 - Basic and Specialized Visualization Tools
    Area Plots
    Bar Charts
    Pie Charts
    Box Plots
    Scatter Plots
    Bubble Plots
    Lab: Basic Visualization Tools
    Lab: Specialized Visualization Tools
    Quiz: Basic Visualization Tools
    Quiz: Specialized Visualization Tools
Week 3 - Advanced Visualizations and Geospatial Data
    Waffle Charts
    Word Clouds
    Seaborn and Regression Plots
    Introduction to Folium and Map Styles
    Maps with Markers
    Choropleth Maps
    Lab: Advanced Visualization Tools
    Lab: Creating Maps and Visualizing Geospatial Data
    Quiz: Advanced Visualization Tools
    Quiz: Visualizing Geospatial Data
    Peer-review Assignment
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Week 1 - Introduction to Data Visualization Tools

1.1. Introduction to Data Visualization

Why Build Visuals?

  1. For exploratory data analysis
  2. Communicate data clearly
  3. Share unbiased representation of data
  4. Use them to support recommendations to different stakeholders

Best Practices: When creating a visual, always remember:

  1. Less is more effective
  2. Less is more attractive
  3. Less is more impactive

Reference: Dark Horse Analytics Co.


An ugly pie chart figure:


Now apply the recommendations of Dark Horse Analytics: Remove background, borders, redundant legends, 3D, text bolding, and reduce color


Another way to illustrate the result:


1.2. Introduction to Matplotlib


  • Created by John Hunter, who was a neurobiologist and was part of a research team that was working on analyzing Electrocorticography (ECoG) signals

Matplotlib's architecture is composed of three main layers:

  1. Back-end layer: has three built-in abstract interface classes: FigureCanvas, Renderer, Event

    • FigureCanvas (matplotlib.backend_based.FigureCanvas): defines and encompasses the area on which the figure is drawn
    • Renderer (matplotlib.backend_based.Renderer): an instance of the renderer class knows how to draw on the figure canvas
    • Event (matplotlib.backend_based.Event): handles user inputs such as keyboard strokes and mouse clicks
  2. Artist layer:

    • Comprised of one main object---the Artist. The Artist is the object that knows how to take the Renderer and use it to put ink on the canvas
    • Everything you see on a Matplotlib figure is an Artist instance
    • Titles, lines, tick labels, images, and so on, all correspond to individual Artist instances
    • Where much of the heavy lifting happens and is usually the appropriate programming paradigm when writing a web application server, or a UI application, or perhaps a script to be shared with other developers
    • Two types of Artist objects:
      • Primitive type: Line2D, Rectangle, Circle, or Text
      • Composite type: Axis, Tick, Axes, and Figure
    • The top-level Matplotlib object that contains and manages all of the elements in a given graphic is the Figure artist, and the most important composite artist is the Axes because it is where most of the Matplotlib API plotting methods are defined, including methods to create and manipulate the ticks, the axis lines, the grid or the plot background
    • Each composite artist may contain other composite artists as well as primitive artists. So a figure artist for example would contain an axis artist as well as a rectangle or text artists.
  3. Scripting layer:

    • Comprised mainly of pylot, a scripting interface that is lighter than the Artist layer
    • Appropriate layer for everyday purposes and is considered a lighter scripting interface to simplify common tasks and for a quick and easy generation of graphics and plots.


from matplotlib.backend_based_agg import FigureCanvasAgg as FigureCanvas # import FigureCanvas
from matplotlib.figure import Figure    # import Figure artist
import matplotlib.pylot as plt          # scripting layer

fig = Figure()
canvas = FigureCanvas(fig)

# Create 10000 random numbers using numpy
import numpy as np 

x = np.random.randn(10000)
ax = fig.add_subplot(111) # create an axes artist

ax.hist(x, 100) # generate a histogram of the 10000 numbers
ax.set_title('Normal distribution with $\mu=0, \sigma=1$')

# Using scripting layer (pylot - plt):
plt.hist(x, 100) # generate a histogram of the 10000 numbers
plt.title(r'Normal distribution with $\mu=0, \sigma=1$')

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1.3. Basic Plotting with Matplotlib

  • Using Jupyter notebook: Magic function: %matplotlib, e.g., %matplotlib inline to enforce plots to be rendered within the browser (Jupyter notebook), and pass in inline as the backend.
  • Matplotlib has a number of different backends available. One limitation of this backend is that you cannot modify a figure once it is rendered. So after rendering the above figure, there is no way for us to add, for example, a figure title or label its axes. You will need to generate a new plot and add a title and the axes labels before calling the show function.
  • A backend that overcomes this limitation is the notebook backend
    • With the notebook backend in place, if a plt function is called, it checks if an active figure exists, and any functions you call will be applied to this active figure
    • If a figure does not exist, it renders a new figure. So when we call the plt.plot function to plot a circular mark at position (5, 5), the backend checks if an active figure exists since there isn't an active figure it generates a figure and adds a circular mark to position (5, 5)
    • And what is beautiful about this back end is that now we can easily add a title for example or labels to the axes after the plot was rendered, without the need to regenerate the figure
  • pandas also has a built-in implementation of plotting

Example of plotting using pandas:

# Given pandas dataframe df
df.plot(kind = "line")  # line plot
df.plot(kind = "hist")  # histogram plot
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1.4. Dataset on Immigration to Canada

import numpy as np
import pandas as pd 
from __future__ import print_function # adds compatibility to python2

# install xlrd
!pip install xlrd

print('xlrd installed!')

df_can = pd.read_excel(
    sheetname = "Canada by Citizenship",
    skiprows = range(20),   # skip first 20 rows
    skip_footer = 2)

# Display the dataframe
df_can.head()   # 5 first rows

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1.5. Line Plots

import matplotlib as mpl 
import matplotlib.pylot as plt 

years = list(map(str, range(1980, 2014)))
df_can.loc['Haiti', years].plot(kind = 'line') # plot using pandas function
plt.title('Immigration from Haiti')
plt.ylabel('Number of immigrants')
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LAB 1: Introduction to Matplotlib and Line Plots

QUIZ 1: Introduction to Data Visualization Tools

Week 2 - Basic and Specialized Visualization Tools

2.1. Area Plots

  • Also known as area chart or area graph
  • Commonly used to represent cumulated totals using numbers or percentages over time
  • Is based on the line plot

Generating Area Plots

df_canada.sort_values(['Total'], ascending=False, axis=0, inplace=True)
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NOTE: Matplotlib plots the indices of a dataframe on the horizontal axis, and with the dataframe as shown, the countries will be plotted on the horizontal axis. So to fix this, we need to take the transpose of the dataframe

years = list(map(str, range(1980, 2014)))
df_canada.sort_values(['Total'], ascending=False, axis=0, inplace=True)
df_top5 = df_canada.head()
df_top5 = df_top5[years].transpose()

# Now plot
plt.title('Immigration trend of top 5 countries')
plt.ylabel('Number of immigrants')
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2.2. Histograms

  • Is a way of representing the frequency distribution of a variable

2.3. Bar Charts

2.4. Pie Charts

  • A pie chart is a circular statistical graphic divided into slices to illustrate numerical proportion
  • Most argue that pie charts fail to accurately display data with any consistency. Bar charts are much better when it comes to representing the data in a consistent way and getting the message across

2.5. Box Plots

2.6. Scatter Plots

2.7. Bubble Plots

LAB 1: Basic Visualization Tools

LAB 2: Specialized Visualization Tools

QUIZ 1: Basic Visualization Tools

QUIZ 2 Specialized Visualization Tools

Week 3 - Advanced Visualizations and Geospatial Data

3.1. Waffle Charts

  • A waffle chart is a great way to visualize data in relation to a whole or to highlight progress against a given threshold
  • For example, say immigration from Scandinavia to Canada is comprised only of immigration from Denmark, Norway, and Sweden, and we're interested in visualizing the contribution of each of these countries to the Scandinavian immigration to Canada. The main idea here is for a given waffle chart whose desired height and width are defined, the contribution of each country is transformed into a number of tiles that is proportional to the country's contribution to the total, so that more the contribution the more the tiles, resulting in what resembles a waffle when combined
  • Unfortunately Matplotlib does not have a built-in function to create waffle charts.

3.2. Word Clouds

3.3. Seaborn and Regression Plots

3.4. Introduction to Folium and Map Styles


  • A powerful data visualization library in Python that was built primarily to help people visualize geospatial data.
  • With Folium, you can create a map of any location in the world as long as you know its latitude and longitude values
  • Folium enables both the binding of data to a map for choropleth (i.e., thematic map) visualizations as well as passing visualization as markers on the map
  • The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and support custom tilesets with Mapbox API keys
  • The map is interactive, i.e., one can zoom in and zoom out after the map is rendered
  • The default map style is the Open Street Map, which shows a street view of an area when you're zoomed in and shows the borders of the world countries when you're zoomed all the way out
# Define the world map
world_map = folium.Map()

# Display world map

# Map of Canada
canada_map = folium.Maps(
    location=[56.130, -106.35], # center
    zoom_start=4,               # zoom level
    tiles='Stamen Toner'        # map style

# Styles: Stamen Toner, Stamen Terrain
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  • Stamen Toner: great for visualizing and exploring river meanders and coastal zones
  • Stamen Terrain: great for visualizing hill shading and natural vegetation colors

3.5. Maps with Markers

Adding a marker:

# Map of Canada
canada_map = folium.Maps(
    location=[56.130, -106.35], # center
    zoom_start=4                # zoom level

# Add a red marker to Ontario
# Create a feature group
ontario =

# Style the feature group
        [51.25, -85.32], radius=5,
        color="red", fill_color="Red"

# Add the feature group to the map

# Label the marker
folium.Marker([51.25, -85.32], popup='Ontario').add_to(canada_map)
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cluster markers superimposed onto a map in Folium using a marker cluster object.w

3.6. Choropleth Maps

Definition: 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, such as population density or per capita income. The higher the measurement the darker the color

LAB 1: Advanced Visualization Tools

LAB 2: Creating Maps and Visualizing Geospatial Data

QUIZ 1: Advanced Visualization Tools

QUIZ 2: Quiz: Visualizing Geospatial Data

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