Lecture 24 – Visualizing Categorical Variables

Data 6, Summer 2021

from datascience import * # datascience has plotting features built in
import numpy as np

Table.interactive_plots() 

Bar charts

schools = Table.read_table('data/r1_with_students.csv')

schools

University	Number_students	Score_Result	Control	City	State
Auburn University	26641	33.4	Public	Auburn	AL
Boston College	12904	45.9	Private (non-profit)	Chestnut Hill	MA
Boston University	25662	68.4	Private (non-profit)	Boston	MA
Brandeis University	5375	50.3	Private (non-profit)	Waltham	MA
Brown University	9391	70	Private (non-profit)	Providence	RI
California Institute of Technology	2240	94.5	Private (non-profit)	Pasadena	CA
Carnegie Mellon University	13430	81.3	Private (non-profit)	Pittsburgh	PA
Case Western Reserve University	10654	60	Private (non-profit)	Cleveland	OH
Clemson University	21436	30.7	Public	Clemson	SC
Columbia University	26586	87	Private (non-profit)	New York	NY

... (86 rows omitted)

schools.group('Control')

Control	count
Private (non-profit)	36
Public	60

schools.group('Control').barh('Control')

Example 1: Top 10 songs on Spotify

You can download an up-to-date copy of this data here.

streams = Table.read_table('data/regional-global-daily-latest.csv', header = 1)
top_10 = streams.select('Track Name', 'Streams').take(np.arange(10))

top_10

Track Name	Streams
Peaches (feat. Daniel Caesar & Giveon)	7167120
Astronaut In The Ocean	4525731
drivers license	4156025
telepatía	4105827
Save Your Tears	3963053
Hold On	3636991
Leave The Door Open	3562490
Heartbreak Anniversary	3253686
Blinding Lights	3246141
DÁKITI	3141723

top_10.barh('Track Name')

Example 2: Artists with the most songs in the Spotify Top 200 right now

streams

Position	Track Name	Artist	Streams	URL
1	Peaches (feat. Daniel Caesar & Giveon)	Justin Bieber	7167120	https://open.spotify.com/track/4iJyoBOLtHqaGxP12qzhQI
2	Astronaut In The Ocean	Masked Wolf	4525731	https://open.spotify.com/track/3VT8hOC5vuDXBsHrR53WFh
3	drivers license	Olivia Rodrigo	4156025	https://open.spotify.com/track/7lPN2DXiMsVn7XUKtOW1CS
4	telepatía	Kali Uchis	4105827	https://open.spotify.com/track/6tDDoYIxWvMLTdKpjFkc1B
5	Save Your Tears	The Weeknd	3963053	https://open.spotify.com/track/5QO79kh1waicV47BqGRL3g
6	Hold On	Justin Bieber	3636991	https://open.spotify.com/track/1nahzW3kfMuwReTka28tH5
7	Leave The Door Open	Bruno Mars	3562490	https://open.spotify.com/track/7MAibcTli4IisCtbHKrGMh
8	Heartbreak Anniversary	Giveon	3253686	https://open.spotify.com/track/3FAJ6O0NOHQV8Mc5Ri6ENp
9	Blinding Lights	The Weeknd	3246141	https://open.spotify.com/track/0VjIjW4GlUZAMYd2vXMi3b
10	DÁKITI	Bad Bunny	3141723	https://open.spotify.com/track/4MzXwWMhyBbmu6hOcLVD49

... (190 rows omitted)

streams.group('Artist') \
       .sort('count', descending = True) \
       .where('count', are.above(2))

Artist	count
Justin Bieber	16
Juice WRLD	5
The Weeknd	5
Billie Eilish	4
Drake	4
Dua Lipa	4
Harry Styles	4
KAROL G	4
Pop Smoke	4
Travis Scott	4

... (5 rows omitted)

streams.group('Artist') \
       .sort('count', descending = True) \
       .where('count', are.above(2)) \
       .barh('Artist')

Example 3: Number of students at the 15 largest universities (in our dataset)

schools

University	Number_students	Score_Result	Control	City	State
Auburn University	26641	33.4	Public	Auburn	AL
Boston College	12904	45.9	Private (non-profit)	Chestnut Hill	MA
Boston University	25662	68.4	Private (non-profit)	Boston	MA
Brandeis University	5375	50.3	Private (non-profit)	Waltham	MA
Brown University	9391	70	Private (non-profit)	Providence	RI
California Institute of Technology	2240	94.5	Private (non-profit)	Pasadena	CA
Carnegie Mellon University	13430	81.3	Private (non-profit)	Pittsburgh	PA
Case Western Reserve University	10654	60	Private (non-profit)	Cleveland	OH
Clemson University	21436	30.7	Public	Clemson	SC
Columbia University	26586	87	Private (non-profit)	New York	NY

... (86 rows omitted)

schools.select('University', 'Number_students') \
       .sort('Number_students', descending = True) \
       .take(np.arange(15))

University	Number_students
University of Minnesota	61120
Texas A&M University	60818
University of Central Florida	56228
University of Texas at Austin	49165
University of Florida	46642
University of Washington	45692
Michigan State University	44789
New York University	44466
University of Houston	44128
University of California, Berkeley	41081

... (5 rows omitted)

schools.select('University', 'Number_students') \
       .sort('Number_students', descending = True) \
       .take(np.arange(15)) \
       .barh('University')

Quick Check 1

schools

University	Number_students	Score_Result	Control	City	State
Auburn University	26641	33.4	Public	Auburn	AL
Boston College	12904	45.9	Private (non-profit)	Chestnut Hill	MA
Boston University	25662	68.4	Private (non-profit)	Boston	MA
Brandeis University	5375	50.3	Private (non-profit)	Waltham	MA
Brown University	9391	70	Private (non-profit)	Providence	RI
California Institute of Technology	2240	94.5	Private (non-profit)	Pasadena	CA
Carnegie Mellon University	13430	81.3	Private (non-profit)	Pittsburgh	PA
Case Western Reserve University	10654	60	Private (non-profit)	Cleveland	OH
Clemson University	21436	30.7	Public	Clemson	SC
Columbia University	26586	87	Private (non-profit)	New York	NY

... (86 rows omitted)

schools.where('State', are.contained_in(['CA', 'TX', 'FL', 'NY', 'PA'])) \
       .group('State', np.mean) \
       .select('State', 'Number_students mean') \
       .barh('State')

Disclaimer

schools.take(np.arange(5)).barh('Control')

Grouped bar charts

# Run this cell.
def remove_comma(s):
    return int(s.replace(',', ''))

nominal = Table.read_table('data/gdp-nominal.csv')
ppp = Table.read_table('data/gdp-ppp.csv').drop(3)
gdp = nominal.join('Country/Territory', ppp) \
       .drop(1, 3) \
       .relabeled(['GDP(US$million)', 'GDP(millions of current Int$)'], ['GDP Nominal', 'GDP PPP'])
gdp = gdp.with_columns(
    'GDP Nominal', gdp.apply(remove_comma, 'GDP Nominal'),
    'GDP PPP', gdp.apply(remove_comma, 'GDP PPP')
)
gdp = gdp.sort('GDP Nominal', descending = True)

gdp

Country/Territory	GDP Nominal	GDP PPP
United States	20807269	20807269
China	14860775	24162435
Japan	4910580	5236138
Germany	3780553	4454498
United Kingdom	2638296	2978564
India	2592583	8681303
France	2551451	2954196
Italy	1848222	2415410
Canada	1600264	1808995
South Korea	1586786	2293475

... (171 rows omitted)

gdp.select('Country/Territory', 'GDP Nominal') \
   .take(np.arange(15)) \
   .barh('Country/Territory')

gdp.select('Country/Territory', 'GDP PPP') \
   .take(np.arange(15)) \
   .barh('Country/Territory')

gdp

Country/Territory	GDP Nominal	GDP PPP
United States	20807269	20807269
China	14860775	24162435
Japan	4910580	5236138
Germany	3780553	4454498
United Kingdom	2638296	2978564
India	2592583	8681303
France	2551451	2954196
Italy	1848222	2415410
Canada	1600264	1808995
South Korea	1586786	2293475

... (171 rows omitted)

gdp.take(np.arange(15)).barh('Country/Territory')

We can sort by GDP PPP, too:

gdp.sort('GDP PPP', descending = True).take(np.arange(15)).barh('Country/Territory')

Another example:

schools

University	Number_students	Score_Result	Control	City	State
Auburn University	26641	33.4	Public	Auburn	AL
Boston College	12904	45.9	Private (non-profit)	Chestnut Hill	MA
Boston University	25662	68.4	Private (non-profit)	Boston	MA
Brandeis University	5375	50.3	Private (non-profit)	Waltham	MA
Brown University	9391	70	Private (non-profit)	Providence	RI
California Institute of Technology	2240	94.5	Private (non-profit)	Pasadena	CA
Carnegie Mellon University	13430	81.3	Private (non-profit)	Pittsburgh	PA
Case Western Reserve University	10654	60	Private (non-profit)	Cleveland	OH
Clemson University	21436	30.7	Public	Clemson	SC
Columbia University	26586	87	Private (non-profit)	New York	NY

... (86 rows omitted)

schools.pivot('Control', 'State')

/opt/conda/lib/python3.8/site-packages/datascience/tables.py:920: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

State	Private (non-profit)	Public
AL	0	3
AR	0	1
AZ	0	1
CA	3	8
CO	0	1
CT	1	1
DC	2	0
DE	0	1
FL	1	4
GA	1	3

... (29 rows omitted)

schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0))

State	Private (non-profit)	Public
CA	3	8
CT	1	1
FL	1	4
GA	1	3
IL	2	1
MD	1	1
NC	1	2
NJ	1	1
NY	6	2
OH	1	1

... (2 rows omitted)

schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0)) \
       .barh('State')

Customization

schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0)) \
       .barh('State')

schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0)) \
       .barh('State', xaxis_title = 'Number of Universities',
                      title = 'Number of Private and Public R1 Universities in Each State',
                      width = 700,
                      height = 700)

Quick Check 2

top_gdp = gdp.take(np.arange(7))
top_gdp

Country/Territory	GDP Nominal	GDP PPP
United States	20807269	20807269
China	14860775	24162435
Japan	4910580	5236138
Germany	3780553	4454498
United Kingdom	2638296	2978564
India	2592583	8681303
France	2551451	2954196

top_gdp.barh('Country/Territory', width = 800, height = 400,
             xaxis_title = 'GDP (Millions of USD)', yaxis_title = 'Country',
             title = 'The top 7 ranked countries by GDP (Nominal) in 2020')