Introduction to siuba

Introduction to siuba

Hi, I’m Michael–the creator of siuba, a library for data analysis in Python.

In this course, you’ll use Python to explore and visualize data.

The course is designed for people who have never programmed before, and anyone interested in siuba!

Data Analysis

Data analysis is the act of using data to produce effective communication. Communication that leads to meaningful action.

In this course, we’ll focus on one area of data analysis where siuba shines: transforming data.

We’ll also combine siuba with a library called plotnine to visualize data. Together, these libraries make a powerful combo.

Meet the data: Spotify top 200

We’ll use data that Spotify publishes every week on the 200 most streamed songs, in 62 different countries.

You can check it out on spotifycharts.com

Meet the data: Spotify top 200

music_top200

	country	position	track_name	artist	streams	duration	continent
0	Argentina	1	Tusa	KAROL G	1858666	200.960	Americas
1	Argentina	2	Tattoo	Rauw Alejandro	1344382	202.887	Americas
2	Argentina	3	Hola - Remix	Dalex	1330011	249.520	Americas
...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11771	193.090	Africa
12398	South Africa	199	When I See U	Fantasia	11752	217.347	Africa
12399	South Africa	200	Psycho!	MASN	11743	197.217	Africa

12400 rows × 7 columns

The data we’ll use is held in a DataFrame. A DataFrame is a big table of data, made up of rows and columns. In the example below, the variable called music_top200 lets us refer to and work on the data.

Notice that in the bottom-left of the table, it shows the number of rows and columns. This data has 12,000 rows and 6 columns.

Meet the data: Spotify top 200

music_top200

	country	position	track_name	artist	streams	duration	continent
0	Argentina	1	Tusa	KAROL G	1858666	200.960	Americas
1	Argentina	2	Tattoo	Rauw Alejandro	1344382	202.887	Americas
2	Argentina	3	Hola - Remix	Dalex	1330011	249.520	Americas
...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11771	193.090	Africa
12398	South Africa	199	When I See U	Fantasia	11752	217.347	Africa
12399	South Africa	200	Psycho!	MASN	11743	197.217	Africa

Every observation–or row–in the DataFrame is a track in the top 200 for a country.

For example, in the highlighted code is the track in position 2 (second most streamed) in Argentina. The track name is Tattoo, and has been streamed 1,344,382 times, and is 202 seconds long!

Meet the data: Spotify song features

Later in the course, we’ll look at measures Spotify calculates for each song.

Their measures for a song include:

• energy
• valence (how happy or positive)
• danceability
• speechiness
• acousticness

Data Analysis

The skills you’ll build doing transformation and visualization in this course will allow you to start analyzing your own data.

The course is interactive: between short lessons you’ll complete interactive exercises by typing your own code.

How code is structured

(track_features
  >> filter(_.artist == "The Weeknd")
  >> ggplot(aes("energy", "valence"))
   + geom_point()
)

Here’s an example of plotting the energy and valence of tracks by The Weeknd. Each point is a single track (for example, the song “Blinding Lights”).

The top line of the code is the data. Every additional line is an action applied to the data.

Click down to see what each line of code does.

How code is structured

(track_features
  >> filter(_.artist == "The Weeknd")

 
)

	artist	album	track_name	energy	valence	danceability	speechiness	acousticness	popularity	duration
568	The Weeknd	My Dear Melancholy,	Call Out My Name	0.593	0.175	0.461	0.0356	0.17000	82	228.373
2753	The Weeknd	Blinding Lights	Blinding Lights	0.796	0.345	0.513	0.0629	0.00147	75	201.573
3004	The Weeknd	In Your Eyes (Remix)	In Your Eyes (with Doja Cat) - Remix	0.731	0.727	0.679	0.0319	0.00518	81	237.912
...	...	...	...	...	...	...	...	...	...	...
23966	The Weeknd	Beauty Behind The Madness	The Hills	0.564	0.137	0.585	0.0515	0.06710	83	242.253
24688	The Weeknd	Starboy	Starboy	0.587	0.486	0.679	0.2760	0.14100	84	230.453
24982	The Weeknd	After Hours	In Your Eyes	0.719	0.717	0.667	0.0346	0.00285	91	237.520

23 rows × 10 columns

The first action is filtering the data, to keep only observations (rows) where artist is The Weeknd.

Don’t worry to much about the details for now. Filter is the first thing you’ll learn, once you start the first chapter!

How code is structured

(track_features
  >> filter(_.artist == "The Weeknd")
  >> ggplot(aes("energy", "valence"))
 
)

The next action, ggplot(...), gets ready to make a plot, based on the data in the previous step.

How code is structured

(track_features
  >> filter(_.artist == "The Weeknd")
  >> ggplot(aes("energy", "valence"))
   + geom_point()
)

Finally, geom_point() adds points to the plot. Each point comes from a row of data.

So in this case each point is a track by The Weeknd.

Let’s practice!

• Workshop setup instructions

For the practice below, you’ll get to explore the two datasets by testing out different options.

This is to make sure you get the big picture, before we dive into the specifics of how each piece of code works!