In [1]:

# Set up packages for lecture. Don't worry about understanding this code, but
# make sure to run it if you're following along.
import numpy as np
import babypandas as bpd
import pandas as pd

import matplotlib.pyplot as plt
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats("svg")
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (10, 5)

np.set_printoptions(threshold=20, precision=2, suppress=True)
pd.set_option("display.max_rows", 7)
pd.set_option("display.max_columns", 8)
pd.set_option("display.precision", 2)

from IPython.display import HTML, display, IFrame

Lecture 7 – Data Visualization 📈¶

DSC 10, Fall 2022¶

Announcements¶

Lab 2 is due on Saturday 10/8 at 11:59pm.
Homework 2 is due on Tuesday 10/11 at 11:59pm.
Take a look at the solutions notebook and supplemental video for Lecture 6.

Agenda¶

Why visualize?
Terminology.
Scatter plots.
Line plots.
Bar charts.

Don't forget about the DSC 10 Reference Sheet and the Resources tab of the course website!

Aside: keyboard shortcuts¶

There are several keyboard shortcuts built into Jupyter Notebooks designed to help you save time. To see them, either click the keyboard button in the toolbar above or hit the H key on your keyboard (as long as you're not actively editing a cell).

Particularly useful shortcuts:

Action	Keyboard shortcut
Run cell + jump to next cell	SHIFT + ENTER
Save the notebook	CTRL/CMD + S
Create new cell above/below	A/B
Delete cell	DD
Convert cell to Markdown	M
Convert cell to code	Y

Note: the latter three only work if you're not actively editing a cell (to exit "edit mode", click somewhere outside of a cell).

Run these cells to load the Little Women data from Lecture 1.

In [3]:

# Counts of names in the chapters of Little Women

counts = bpd.DataFrame().assign(
    Amy=np.char.count(chapters, 'Amy'),
    Beth=np.char.count(chapters, 'Beth'),
    Jo=np.char.count(chapters, 'Jo'),
    Meg=np.char.count(chapters, 'Meg'),
    Laurie=np.char.count(chapters, 'Laurie'),
)

# cumulative number of times each name appears

lw_counts = bpd.DataFrame().assign(
    Amy=np.cumsum(counts.get('Amy')),
    Beth=np.cumsum(counts.get('Beth')),
    Jo=np.cumsum(counts.get('Jo')),
    Meg=np.cumsum(counts.get('Meg')),
    Laurie=np.cumsum(counts.get('Laurie')),
    Chapter=np.arange(1, 48, 1)
)

lw_counts

Out[3]:

	Amy	Beth	Jo	Meg	Laurie	Chapter
0	23	26	44	26	0	1
1	36	38	65	46	0	2
2	38	40	127	82	16	3
...	...	...	...	...	...	...
44	633	461	1450	675	581	45
45	635	462	1506	679	583	46
46	645	465	1543	685	596	47

47 rows × 6 columns

Little Women¶

In Lecture 1, we were able to answer questions about the plot of Little Women without having to read the novel. Some of those questions included:

Who is the main character?
Which pair of characters gets married in Chapter 35?

Computers are better than humans at crunching numbers, but humans are better at identifying visual patterns.

Visualizations allow us to understand lots of data quickly – they make it easier to spot trends and communicate our results with others.

There are many types of visualizations; in this class, we'll look at scatter plots, line plots, bar charts, and histograms, but there are many others.
- The right choice depends on the type of data.

Individuals and variables¶

Individual (row): Person/place/thing for which data is recorded. Also called an observation.

Variable (column): Something that is recorded for each individual. Also called a feature.

Types of variables¶

There are two main types of variables:

Numerical: It makes sense to do arithmetic with the values.
Categorical: Values fall into categories, that may or may not have some order to them.

Examples of numerical variables¶

Salaries of NBA players 🏀.
- Individual: an NBA player.
- Variable: their salary.

Movie gross earnings 💰.
- Individual: a movie.
- Variable: its gross earnings.

Booster doses administered per day 💉.
- Individual: date.
- Variable: number of booster doses administered on that date.

Examples of categorical variables¶

Movie genres 🎬.
- Individual: a movie.
- Variable: its genre.

Zip codes 🏠.
- Individual: US resident.
- Variable: zip code.
  - Even though they look like numbers, zip codes are categorical (arithmetic doesn't make sense).

Level of prior programming experience for students in DSC 10 🧑‍🎓.
- Individual: student in DSC 10.
- Variable: their level of prior programming experience, e.g. none, low, medium, or high.
  - There is an order to these categories!

Concept Check ✅ – Answer at cc.dsc10.com ¶

Which of these is not a numerical variable?

A. Fuel economy in miles per gallon.

B. Number of quarters at UCSD.

C. College at UCSD (Sixth, Seventh, etc).

D. Bank account number.

E. More than one of these are not numerical variables.

Types of visualizations¶

The type of visualization we create depends on the kinds of variables we're visualizing.

Scatter plot: numerical vs. numerical.
Line plot: sequential numerical (time) vs. numerical.
Bar chart: categorical vs. numerical.
Histogram: numerical.
- Will cover next time.

Note: We may interchange the words "plot", "chart", and "graph"; they all mean the same thing.

Dataset of 50 top-grossing actors¶

Column	Contents

'Actor'|Name of actor 'Total Gross'| Total gross domestic box office receipt, in millions of dollars, of all of the actor’s movies 'Number of Movies'| The number of movies the actor has been in 'Average per Movie'| Total gross divided by number of movies '#1 Movie'| The highest grossing movie the actor has been in 'Gross'| Gross domestic box office receipt, in millions of dollars, of the actor’s #1 Movie

In [5]:

actors = bpd.read_csv('data/actors.csv').set_index('Actor')
actors

Out[5]:

	Total Gross	Number of Movies	Average per Movie	#1 Movie	Gross
Actor
Harrison Ford	4871.7	41	118.8	Star Wars: The Force Awakens	936.7
Samuel L. Jackson	4772.8	69	69.2	The Avengers	623.4
Morgan Freeman	4468.3	61	73.3	The Dark Knight	534.9
...	...	...	...	...	...
Sandra Bullock	2462.6	35	70.4	Minions	336.0
Chris Evans	2457.8	23	106.9	The Avengers	623.4
Anne Hathaway	2416.5	25	96.7	The Dark Knight Rises	448.1

50 rows × 5 columns

Scatter plots¶

What is the relationship between 'Number of Movies' and 'Total Gross'?

Scatter plots¶

Scatter plots visualize the relationship between two numerical variables.

To create one from a DataFrame df, use

df.plot(
  kind='scatter', 
  x=x_column_for_horizontal, 
  y=y_column_for_vertical
)

The resulting scatter plot has one point per row of df.
If you put a semicolon after a call to .plot, it will hide the weird text output that displays.

Scatter plots¶

What is the relationship between 'Number of Movies' and 'Average per Movie'?

Note that in the above plot, there's a negative association and an outlier.

Who was in 60 or more movies?¶

In [8]:

actors[actors.get('Number of Movies') >= 60]

Out[8]:

	Total Gross	Number of Movies	Average per Movie	#1 Movie	Gross
Actor
Samuel L. Jackson	4772.8	69	69.2	The Avengers	623.4
Morgan Freeman	4468.3	61	73.3	The Dark Knight	534.9
Bruce Willis	3189.4	60	53.2	Sixth Sense	293.5
Robert DeNiro	3081.3	79	39.0	Meet the Fockers	279.3
Liam Neeson	2942.7	63	46.7	The Phantom Menace	474.5

Who is the outlier?¶

Whoever they are, they made very few, high grossing movies.

In [9]:

actors[actors.get('Number of Movies') < 10]

Out[9]:

	Total Gross	Number of Movies	Average per Movie	#1 Movie	Gross
Actor
Anthony Daniels	3162.9	7	451.8	Star Wars: The Force Awakens	936.7

Dataset aggregating movies by year¶

Column	Content

'Year'| Year 'Total Gross in Billions'| Total domestic box office gross, in billions of dollars, of all movies released 'Number of Movies'| Number of movies released '#1 Movie'| Highest grossing movie

In [10]:

movies_by_year = bpd.read_csv('data/movies_by_year.csv').set_index('Year')
movies_by_year

Out[10]:

	Total Gross in Billions	Number of Movies	#1 Movie
Year
2022	5.64	380	Top Gun: Maverick
2021	4.48	439	Spider-Man: No Way Home
2020	2.11	456	Bad Boys for Life
...	...	...	...
1979	1.23	40	Superman
1978	0.83	13	Grease
1977	0.44	9	Star Wars: Episode IV - A New Hope

46 rows × 3 columns

Line plots¶

How has the number of movies changed over time? 🤔

Line plots¶

Line plots show trends in numerical variables over time.

To create one from a DataFrame df, use

df.plot(
  kind='line', 
  x=x_column_for_horizontal, 
  y=y_column_for_vertical
)

Plotting tip¶

Tip: if you want the x-axis to be the index, omit the x= argument!
Doesn't work for scatter plots, but works for most other plot types.

Since the year 2000¶

We can create a line plot of just 2000 onwards by querying movies_by_year before calling .plot.

What do you think explains the declines around 2008 and 2020?

How did this affect total gross?¶

What was the top grossing movie of 2016? 🐟¶

In [15]:

...

Out[15]:

Ellipsis

Dataset of the global top 200 songs on Spotify as of Tuesday (10/4/22)¶

Downloaded from here – check it out!

In [16]:

charts = (bpd.read_csv('data/regional-global-daily-2022-10-04.csv')
          .set_index('rank')
          .get(['track_name', 'artist_names', 'streams', 'uri'])
         )
charts

Out[16]:

	track_name	artist_names	streams	uri
rank
1	Unholy (feat. Kim Petras)	Sam Smith, Kim Petras	7921301	spotify:track:3nqQXoyQOWXiESFLlDF1hG
2	I'm Good (Blue)	David Guetta, Bebe Rexha	4708193	spotify:track:4uUG5RXrOk84mYEfFvj3cK
3	As It Was	Harry Styles	4608998	spotify:track:4Dvkj6JhhA12EX05fT7y2e
...	...	...	...	...
198	There's Nothing Holdin' Me Back	Shawn Mendes	848871	spotify:track:7JJmb5XwzOO8jgpou264Ml
199	Heartless	Kanye West	841645	spotify:track:4EWCNWgDS8707fNSZ1oaA5
200	Mr. Brightside	The Killers	838350	spotify:track:003vvx7Niy0yvhvHt4a68B

200 rows × 4 columns

Bar charts¶

How many streams do the top 10 songs have?

In [17]:

charts

Out[17]:

	track_name	artist_names	streams	uri
rank
1	Unholy (feat. Kim Petras)	Sam Smith, Kim Petras	7921301	spotify:track:3nqQXoyQOWXiESFLlDF1hG
2	I'm Good (Blue)	David Guetta, Bebe Rexha	4708193	spotify:track:4uUG5RXrOk84mYEfFvj3cK
3	As It Was	Harry Styles	4608998	spotify:track:4Dvkj6JhhA12EX05fT7y2e
...	...	...	...	...
198	There's Nothing Holdin' Me Back	Shawn Mendes	848871	spotify:track:7JJmb5XwzOO8jgpou264Ml
199	Heartless	Kanye West	841645	spotify:track:4EWCNWgDS8707fNSZ1oaA5
200	Mr. Brightside	The Killers	838350	spotify:track:003vvx7Niy0yvhvHt4a68B

200 rows × 4 columns

In [18]:

charts.take(np.arange(10))

Out[18]:

	track_name	artist_names	streams	uri
rank
1	Unholy (feat. Kim Petras)	Sam Smith, Kim Petras	7921301	spotify:track:3nqQXoyQOWXiESFLlDF1hG
2	I'm Good (Blue)	David Guetta, Bebe Rexha	4708193	spotify:track:4uUG5RXrOk84mYEfFvj3cK
3	As It Was	Harry Styles	4608998	spotify:track:4Dvkj6JhhA12EX05fT7y2e
...	...	...	...	...
8	Me Porto Bonito	Bad Bunny, Chencho Corleone	3605144	spotify:track:6Sq7ltF9Qa7SNFBsV5Cogx
9	I Ain't Worried	OneRepublic	3521848	spotify:track:4h9wh7iOZ0GGn8QVp4RAOB
10	Bad Habit	Steve Lacy	3178653	spotify:track:4k6Uh1HXdhtusDW5y8Gbvy

10 rows × 4 columns

Bar charts¶

Bar charts visualize the relationship between a categorical variable and a numerical variable.
In a bar chart...
- The thickness and spacing of bars is arbitrary.
- The order of the categorical labels doesn't matter.

To create one from a DataFrame df, use

df.plot(
  kind='barh', 
  x=categorical_column_name, 
  y=numerical_column_name
)

The "h" in 'barh' stands for "horizontal".
- It's easier to read labels this way.
In the previous chart, we set y='Streams' even though streams are measured by x-axis length.

In [20]:

# The bars appear in the opposite order relative to the DataFrame
(charts
 .take(np.arange(10))
 .sort_values(by='streams')
 .plot(kind='barh', x='track_name', y='streams')
);

How many songs do the top 15 artists have in the top 200?¶

First, let's create a DataFrame with a single column that describes the number of songs in the top 200 per artist. This involves using .groupby with .count(). Since we want one row per artist, we will group by 'artist_names'.

In [21]:

charts

Out[21]:

	track_name	artist_names	streams	uri
rank
1	Unholy (feat. Kim Petras)	Sam Smith, Kim Petras	7921301	spotify:track:3nqQXoyQOWXiESFLlDF1hG
2	I'm Good (Blue)	David Guetta, Bebe Rexha	4708193	spotify:track:4uUG5RXrOk84mYEfFvj3cK
3	As It Was	Harry Styles	4608998	spotify:track:4Dvkj6JhhA12EX05fT7y2e
...	...	...	...	...
198	There's Nothing Holdin' Me Back	Shawn Mendes	848871	spotify:track:7JJmb5XwzOO8jgpou264Ml
199	Heartless	Kanye West	841645	spotify:track:4EWCNWgDS8707fNSZ1oaA5
200	Mr. Brightside	The Killers	838350	spotify:track:003vvx7Niy0yvhvHt4a68B

200 rows × 4 columns

In [22]:

songs_per_artist = charts.groupby('artist_names').count()
songs_per_artist

Out[22]:

	track_name	streams	uri
artist_names
Adele	1	1	1
Arctic Monkeys	4	4	4
Armani White	1	1	1
...	...	...	...
Zach Bryan	1	1	1
d4vd	1	1	1
girl in red	1	1	1

151 rows × 3 columns

Using .sort_values and .take, we'll keep just the top 15 artists. Note that all columns in songs_per_artist contain the same information (this is a consequence of using .count()).

In [23]:

top_15_artists = (songs_per_artist
                  .sort_values('streams', ascending=False)
                  .take(np.arange(15)))
top_15_artists

Out[23]:

	track_name	streams	uri
artist_names
Bad Bunny	9	9	9
The Weeknd	5	5	5
Ed Sheeran	5	5	5
...	...	...	...
BLACKPINK	3	3	3
Imagine Dragons	3	3	3
Bruno Mars	2	2	2

15 rows × 3 columns

Using .assign and .drop, we'll create a column named 'count' that contains the same information that the other 3 columns contain, and then .get only that column (or equivalently, drop the other 3 columns).

In [24]:

# If we give .get a list, it will return a DataFrame instead of a Series!
top_15_artists = (top_15_artists
                  .assign(count=top_15_artists.get('streams'))
                  .get(['count']))
top_15_artists

Out[24]:

	count
artist_names
Bad Bunny	9
The Weeknd	5
Ed Sheeran	5
...	...
BLACKPINK	3
Imagine Dragons	3
Bruno Mars	2

15 rows × 1 columns

Before calling .plot(kind='barh', y='count'), we'll sort top_15_artists by 'count' in increasing order. This is because, weirdly, Python reverses the order of rows when creating bars in horizontal bar charts.

Vertical bar charts¶

To create a vertical bar chart, use kind='bar' instead of kind='barh'. These are typically harder to read, though.

Aside: How many streams did Justin Bieber's songs on the chart receive?¶

In [27]:

(charts
 [charts.get('artist_names') == 'Justin Bieber']
 .sort_values('streams')
 .plot(kind='barh', x='track_name', y='streams')
);

It seems like we're missing a popular song...

How do we include featured songs, as well?¶

Answer: Using .str.contains.

In [28]:

bieber = charts[charts.get('artist_names').str.contains('Justin Bieber')]
bieber

Out[28]:

	track_name	artist_names	streams	uri
rank
29	STAY (with Justin Bieber)	The Kid LAROI, Justin Bieber	2069641	spotify:track:5PjdY0CKGZdEuoNab3yDmX
62	Ghost	Justin Bieber	1418876	spotify:track:6I3mqTwhRpn34SLVafSH7G

In [30]:

# Run this cell, don't worry about what it does.
def show_spotify(uri):
    code = uri[uri.rfind(':')+1:]
    src = f"https://open.spotify.com/embed/track/{code}"
    width = 400
    height = 75
    display(IFrame(src, width, height))

Let's find the URI of a song we care about.¶

In [31]:

charts

Out[31]:

	track_name	artist_names	streams	uri
rank
1	Unholy (feat. Kim Petras)	Sam Smith, Kim Petras	7921301	spotify:track:3nqQXoyQOWXiESFLlDF1hG
2	I'm Good (Blue)	David Guetta, Bebe Rexha	4708193	spotify:track:4uUG5RXrOk84mYEfFvj3cK
3	As It Was	Harry Styles	4608998	spotify:track:4Dvkj6JhhA12EX05fT7y2e
...	...	...	...	...
198	There's Nothing Holdin' Me Back	Shawn Mendes	848871	spotify:track:7JJmb5XwzOO8jgpou264Ml
199	Heartless	Kanye West	841645	spotify:track:4EWCNWgDS8707fNSZ1oaA5
200	Mr. Brightside	The Killers	838350	spotify:track:003vvx7Niy0yvhvHt4a68B

200 rows × 4 columns

In [33]:

song_uri = (charts
            [charts.get('track_name') == favorite_song]
            .get('uri')
            .iloc[0])
song_uri

Out[33]:

'spotify:track:003vvx7Niy0yvhvHt4a68B'

Watch what happens! 🎶

Try it out yourself!

Summary¶

Visualizations make it easy to extract patterns from datasets.
There are two main types of variables: categorical and numerical.
The types of the variables we're visualizing inform our choice of which type of visualization to use.
Today, we looked at scatter plots, line plots, and bar charts.
Next time: Histograms and overlaid plots.

Let's discuss!¶

As mentioned earlier, visualizations allow us to easily spot trends and communicate our results with others.
Some visualizations make it more difficult to see the trend in data, by
- Adding "chart junk."
- Using misleading axes and sizes.

In this thread on EdStem, post some examples of particularly misleading or interesting visualizations! We'll share our favorites in class on Monday.

Lecture 7 – Data Visualization 📈¶

DSC 10, Fall 2022¶

Announcements¶

Agenda¶

Aside: keyboard shortcuts¶

Why visualize?¶

Little Women¶

Napoleon's March¶

John Snow¶

Why visualize?¶

Terminology¶

Individuals and variables¶

Types of variables¶

Examples of numerical variables¶

Examples of categorical variables¶

Concept Check ✅ – Answer at cc.dsc10.com¶

Types of visualizations¶

Scatter plots¶

Dataset of 50 top-grossing actors¶

Scatter plots¶

Scatter plots¶

Scatter plots¶

Who was in 60 or more movies?¶

Who is the outlier?¶

Anthony Daniels¶

Line plots 📉¶

Dataset aggregating movies by year¶

Line plots¶

Line plots¶

Plotting tip¶

Since the year 2000¶

How did this affect total gross?¶

What was the top grossing movie of 2016? 🐟¶

Bar charts 📊¶

Dataset of the global top 200 songs on Spotify as of Tuesday (10/4/22)¶

Bar charts¶

Bar charts¶

How many songs do the top 15 artists have in the top 200?¶

Vertical bar charts¶

Aside: How many streams did Justin Bieber's songs on the chart receive?¶

How do we include featured songs, as well?¶

Fun demo 🎵¶

Let's find the URI of a song we care about.¶

Summary¶

Summary¶

Let's discuss!¶

Concept Check ✅ – Answer at cc.dsc10.com ¶