In [1]:
from lec_utils import *

Lecture 8 Supplement¶

More Visualization Examples¶

EECS 398-003: Practical Data Science, Fall 2024¶

practicaldsc.org • github.com/practicaldsc/fa24

This notebook just serves to show you even more examples of visualizations you can create using plotly.

Historical examples¶

William Playfair is known as the "father of data visualization", and is the creator of line charts, bar charts, and pie charts, among other things.

In this first section, we'll create some of his historical charts using plotly!

Imports and exports from Scotland¶

First, we'll recreate the very first known example of a bar chart, which depicts the imports and exports of Scotland to various countries in 1781.

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In [2]:
scotland = pd.read_csv('data/playfair-scotland.csv')
scotland
Out[2]:
country imports exports
0 Ireland 195685 305167
1 America 49826 183620
2 West Indies 169375 141220
... ... ... ...
13 Greenland 8291 0
14 Isle of Man & Jersey 802 1818
15 Denmark and Norway 28118 35011

16 rows × 3 columns

Let's see how we can make an interactive version of this plot. The library plotly will come in handy here.

In [3]:
fig = px.bar(scotland.sort_values('imports', ascending=False), 
             x=['exports', 'imports'], 
             y='country', 
             barmode='group', 
             orientation='h',
             color_discrete_map={
                 'exports': '#151EA6',
                 'imports': '#FCB305',
              },      
             title='Exports and Imports of <b>Scotland</b> to and from different parts for one Year'
            )

fig.update_layout(
    font_family="Arial",
    title_font_family="Arial",
    paper_bgcolor='#FFFFFF',
    plot_bgcolor='#FFFFFF',
    legend = {
        'title': '',
        'orientation': 'h'
    }
)

fig.add_annotation( # add a text callout with arrow
    text="no exports to Greenland!", x=10000, y="Greenland", ax=125,
    arrowhead=2, showarrow=True
)

fig.update_xaxes(title_text='',
                 side='top',
                 showline=True, 
                 linewidth=2, 
                 linecolor='black',
                 mirror=True,
                 showgrid=True, 
                 gridwidth=1, 
                 gridcolor='#EEEEEE',
                 tick0=0, 
                 dtick=25000,
                 tickangle=0)

fig.update_yaxes(title_text='',
                 side='right',
                 showline=True, 
                 linewidth=2, 
                 linecolor='black',
                 mirror=True,
                 showgrid=True, 
                 gridwidth=1, 
                 gridcolor='#EEEEEE',
                 tickson='boundaries')

As an aside – what if we want to export this chart to HTML, to put on a website? (Say, for making a data science portfolio?)

The .to_html() method will come in handy.

In [4]:
with open('scotland.html', 'w') as f:
    f.write(fig.to_html())
    f.close()

Wheat and wages¶

This next plot shows the relationship between weekly labor wages and the cost of a “quarter” of wheat, along with a timeline of English monarchs, from 1565 to 1821.

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In [5]:
wheat = pd.read_csv('data/Wheat.csv').drop(columns=['Unnamed: 0']).iloc[:-1]
wheat.head()
Out[5]:
Year Wheat Wages
0 1565 41.0 5.00
1 1570 45.0 5.05
2 1575 42.0 5.08
3 1580 49.0 5.12
4 1585 41.5 5.15

This task is a bit different, since it involves two different types of visualizations – a line chart and a bar chart.

In [6]:
px.line(wheat, x='Year', y='Wages')
In [7]:
px.bar(wheat, x='Year', y='Wages')

Instead of using plotly.express, which is a "lite" version of plotly, we will use plotly's graph_objects module.

In [8]:
import plotly.graph_objects as go
In [9]:
wheat_fig = go.Figure()

# Add bar chart
wheat_fig.add_trace(
    go.Bar(
        x=wheat['Year'],
        y=wheat['Wheat'],
        name='Wheat',
        marker={'color': '#AAAAAA'},
        width=5
    )
)

# Add line chart
wheat_fig.add_trace(
    go.Scatter(
        x=wheat['Year'],
        y=wheat['Wages'],
        name='Wages',
        marker={'color': 'red'},
        fill='tozeroy',
        fillcolor='rgba(135,206,235,0.65)'
    )
)

# Adjust overall attributes
wheat_fig.update_layout(
    font_family="Arial",
    title_font_family="Arial",
    paper_bgcolor='#FFFFFF',
    plot_bgcolor='#FFFFFF',
    showlegend=False
)

# Adjust x-axis
wheat_fig.update_xaxes(title_text='<i>5 Years each division</i>', 
                       tickmode='array',
                       tickvals=[1565, 1600, 1650, 1700, 1750, 1800, 1820], 
                       tickangle=0,
                       showgrid=False,
                       showline=True, 
                       linewidth=2, 
                       linecolor='black',
                       mirror=True)

# Adjust y-axis
wheat_fig.update_yaxes(title_text='<i>Price of the Quarter of Wheat in Shillings</i>',
                       side='right',
                       tick0=0, 
                       dtick=5, 
                       gridcolor='#EEEEEE',
                       gridwidth=1,
                       showline=True, 
                       linewidth=2, 
                       linecolor='black',
                       mirror=True)

# Add annotations
wheat_fig.add_annotation( # add a text callout with arrow
    text="<i>Weekly Wages of a Good Mechanic</i>", 
    x=1640, 
    y=9, 
    showarrow=False, 
    font = {
        'size': 10,
        'color': 'white'
    }
    
)

# Add annotations
title_text = 'CHART,<br><i>Showing at One View</i><br><i>The Price of The Quarter of Wheat</i><br>& Wages of Labour by the Week,<br>-- from --<br><i>The Year 1565 to 1821</i><br>-- by --<br><i>William Playfair</i>'

wheat_fig.add_annotation(
    text=title_text, 
    x=1640, 
    y=70, 
    font = {
        'size': 10,
        'color': 'black'
    },
    bordercolor="black",
    borderwidth=2,
    borderpad=4,
    bgcolor="#FFFFFF",
    opacity=1
    
)

wheat_fig.add_annotation(
    text="<i>Weekly Wages of a Good Mechanic</i>", 
    x=1640, 
    y=9, 
    showarrow=False, 
    font = {
        'size': 10,
        'color': 'black'
    }
    
)

Distribution of the Turkish Empire¶

Finally, we'll look at Playfair's first pie chart, describing the land distribution of the Turkish Empire.

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In [10]:
dist = pd.DataFrame().assign(
    continent=['African', 'European', 'Asiatic'],
    proportion=[0.2, 0.25, 0.55]
)

dist
Out[10]:
continent proportion
0 African 0.20
1 European 0.25
2 Asiatic 0.55
In [11]:
px.pie(dist,
       values='proportion',
       names='continent',
       width=400,
       height=300)

Other examples¶

Gantt charts (i.e. timelines)¶

In [12]:
phases = [
 ['Newborn', '1998-11-26', '1999-11-26', 'Canada'],
 ['Toddler, Preschooler', '1999-11-26', '2005-09-03', 'US'],
 ['Elementary School Student', '2005-09-03', '2009-06-30', 'Canada'],
 ['Middle School Student', '2009-09-15', '2012-06-15', 'Canada'],
 ['High School Student', '2012-09-05', '2016-05-30', 'Canada'],
 ['Undergrad @ UC Berkeley', '2016-08-22','2020-05-15', 'US'],
 ['Masters @ UC Berkeley', '2020-08-25', '2021-05-14', 'Canada'],
 ['Lecturer @ UCSD', '2021-09-01', '2024-06-30', 'US'],
 ['Lecturer @ UMich', '2024-08-26', '2024-09-19', 'US']]

phases_df = pd.DataFrame(phases, columns=['Phase', 'Start', 'End', 'Location'])
phases_df
Out[12]:
Phase Start End Location
0 Newborn 1998-11-26 1999-11-26 Canada
1 Toddler, Preschooler 1999-11-26 2005-09-03 US
2 Elementary School Student 2005-09-03 2009-06-30 Canada
... ... ... ... ...
6 Masters @ UC Berkeley 2020-08-25 2021-05-14 Canada
7 Lecturer @ UCSD 2021-09-01 2024-06-30 US
8 Lecturer @ UMich 2024-08-26 2024-09-19 US

9 rows × 4 columns

In [13]:
tim = px.timeline(phases_df,
                  x_start = 'Start',
                  x_end = 'End',
                  y = 'Phase',
                  text = 'Location',
                  title = 'My Life Trajectory',
                  width=700,
                  height=400)

tim.update_yaxes(autorange='reversed')

Animated scatter plots¶

In [14]:
world = px.data.gapminder()
world
Out[14]:
country continent year lifeExp pop gdpPercap iso_alpha iso_num
0 Afghanistan Asia 1952 28.80 8425333 779.45 AFG 4
1 Afghanistan Asia 1957 30.33 9240934 820.85 AFG 4
2 Afghanistan Asia 1962 32.00 10267083 853.10 AFG 4
... ... ... ... ... ... ... ... ...
1701 Zimbabwe Africa 1997 46.81 11404948 792.45 ZWE 716
1702 Zimbabwe Africa 2002 39.99 11926563 672.04 ZWE 716
1703 Zimbabwe Africa 2007 43.49 12311143 469.71 ZWE 716

1704 rows × 8 columns

In [15]:
px.scatter(world,
           x = 'gdpPercap',
           y = 'lifeExp', 
           hover_name = 'country',
           color = 'continent',
           size = 'pop',
           size_max = 60,
           log_x = True,
           range_y = [30, 90],
           animation_frame = 'year',
           title = 'Life Expectancy, GDP Per Capita, and Population over Time'
          )

Animated histograms¶

In [16]:
px.histogram(world,
            x = 'lifeExp',
            animation_frame = 'year',
            range_x = [20, 90],
            range_y = [0, 50],
            title = 'Distribution of Life Expectancy over Time')

3D scatter plots¶

In [17]:
penguins = sns.load_dataset('penguins')
penguins
Out[17]:
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex
0 Adelie Torgersen 39.1 18.7 181.0 3750.0 Male
1 Adelie Torgersen 39.5 17.4 186.0 3800.0 Female
2 Adelie Torgersen 40.3 18.0 195.0 3250.0 Female
... ... ... ... ... ... ... ...
341 Gentoo Biscoe 50.4 15.7 222.0 5750.0 Male
342 Gentoo Biscoe 45.2 14.8 212.0 5200.0 Female
343 Gentoo Biscoe 49.9 16.1 213.0 5400.0 Male

344 rows × 7 columns

In [18]:
px.scatter_3d(penguins,
             x = 'bill_length_mm',
             y = 'bill_depth_mm',
             z = 'flipper_length_mm',
             color = 'species',
             hover_name = 'island',
             title = 'Flipper Length vs. Bill Depth vs. Bill Length')