Lecture 25 – Visualizing Categorical Variables

Data 94, Spring 2021

In [1]:
from datascience import *
import numpy as np

Table.interactive_plots()

Bar charts

In [2]:
schools = Table.read_table('data/r1_with_students.csv')
In [3]:
schools
Out[3]:
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)

In [4]:
schools.group('Control')
Out[4]:
Control count
Private (non-profit) 36
Public 60
In [5]:
schools.group('Control').barh('Control')

Example 1: Top 10 songs on Spotify

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

In [6]:
streams = Table.read_table('data/regional-global-daily-latest.csv', header = 1)
top_10 = streams.select('Track Name', 'Streams').take(np.arange(10))
In [7]:
top_10
Out[7]:
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
In [8]:
top_10.barh('Track Name')

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

In [9]:
streams
Out[9]:
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)

In [10]:
streams.group('Artist') \
       .sort('count', descending = True) \
       .where('count', are.above(2))
Out[10]:
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)

In [11]:
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)

In [12]:
schools
Out[12]:
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)

In [13]:
schools.select('University', 'Number_students') \
       .sort('Number_students', descending = True) \
       .take(np.arange(15))
Out[13]:
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)

In [14]:
schools.select('University', 'Number_students') \
       .sort('Number_students', descending = True) \
       .take(np.arange(15)) \
       .barh('University')

Quick Check 1

In [15]:
schools
Out[15]:
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)

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

Note: bar order

In [ ]:

Disclaimer

In [17]:
schools.take(np.arange(5)).barh('Control')

Grouped bar charts

In [18]:
# 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)
In [19]:
gdp
Out[19]:
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)

In [20]:
gdp.select('Country/Territory', 'GDP Nominal') \
   .take(np.arange(15)) \
   .barh('Country/Territory')
In [21]:
gdp.select('Country/Territory', 'GDP PPP') \
   .take(np.arange(15)) \
   .barh('Country/Territory')
In [22]:
gdp
Out[22]:
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)

In [23]:
gdp.take(np.arange(15)).barh('Country/Territory')

We can sort by GDP PPP, too:

In [24]:
gdp.sort('GDP PPP', descending = True).take(np.arange(15)).barh('Country/Territory')

Another example:

In [25]:
schools
Out[25]:
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)

In [26]:
schools.pivot('Control', 'State')

/opt/miniconda3/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
Out[26]:
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)

In [27]:
schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0))
Out[27]:
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)

In [28]:
schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0)) \
       .barh('State')

Customization

In [29]:
schools.pivot('Control', 'State') \
       .where('Private (non-profit)', are.above(0)) \
       .where('Public', are.above(0)) \
       .barh('State')
In [30]:
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

In [31]:
top_gdp = gdp.take(np.arange(7))
top_gdp
Out[31]:
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
In [ ]: