The mutate verb

The mutate verb

You’ve already learned two verbs for manipulating data: filter for extracting a subset of the observations, and arrange for sorting them.

Suppose you want to change one of the variables in your dataset, based on the other ones. Or suppose you want to add a new variable.

For that, you would use the mutate verb.

The mutate verb

Using mutate to change a variable

(music_top200
  >> mutate(streams = _.streams / 1000)
)

First you’ll learn how to change an existing variable.

You use mutate like you would filter or arrange, after a pipe operator.

Inside this mutate statement, what’s on the right of the equals sign is what’s being calculated, and what’s on the left is what’s being replaced. Here, you’re calculating streams slash one thousand.

(Click the down arrow for a breakdown)

Using mutate to change a variable

(music_top200
  >> mutate(streams = _.streams / 1000)
)

The slash in Python performs division, so “streams divided by one thousand”.

Using mutate to change a variable

(music_top200
  >> mutate(streams = _.streams / 1000)
)

On the left, you’re saying you want to replace the existing streams column, by writing streams equals.

Using mutate to change a variable (result)

(music_top200
  >> mutate(streams = _.streams / 1000)
)

	country	position	track_name	artist	streams	duration	continent
0	Argentina	1	Tusa	KAROL G	1858.666	200.960	Americas
1	Argentina	2	Tattoo	Rauw Alejandro	1344.382	202.887	Americas
2	Argentina	3	Hola - Remix	Dalex	1330.011	249.520	Americas
...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11.771	193.090	Africa
12398	South Africa	199	When I See U	Fantasia	11.752	217.347	Africa
12399	South Africa	200	Psycho!	MASN	11.743	197.217	Africa

12400 rows × 7 columns

The result is the same table, but with streams replaced with a new value, one that’s much smaller than it was before.

This is how you manipulate existing variables in the table. That’s often necessary during data processing and cleaning.

Just like filter and arrange, you’re not altering the original music_top200 data, you’re just changing the value in this new data frame that’s being returned.

Using mutate to add a new variable

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
0	Argentina	1	Tusa	KAROL G	1858666	200.960	Americas	3.735175e+08
1	Argentina	2	Tattoo	Rauw Alejandro	1344382	202.887	Americas	2.727576e+08
2	Argentina	3	Hola - Remix	Dalex	1330011	249.520	Americas	3.318643e+08
...	...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11771	193.090	Africa	2.272862e+06
12398	South Africa	199	When I See U	Fantasia	11752	217.347	Africa	2.554262e+06
12399	South Africa	200	Psycho!	MASN	11743	197.217	Africa	2.315919e+06

12400 rows × 8 columns

Alternatively, you may want to add a new variable.

For instance, suppose you want to know the total seconds people have listened to a track.

In the data you have a tracks duration in seconds, and its number of streams. This means total time listened is duration multiplied by streams.

You would use mutate in almost the same way. You pipe your music data to the mutate verb.

The asterisk in Python represents multiplication, so you write streams asterisk duration to multiply the two columns.

Using mutate to add a new variable (result)

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
0	Argentina	1	Tusa	KAROL G	1858666	200.960	Americas	3.735175e+08
1	Argentina	2	Tattoo	Rauw Alejandro	1344382	202.887	Americas	2.727576e+08
2	Argentina	3	Hola - Remix	Dalex	1330011	249.520	Americas	3.318643e+08
...	...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11771	193.090	Africa	2.272862e+06
12398	South Africa	199	When I See U	Fantasia	11752	217.347	Africa	2.554262e+06
12399	South Africa	200	Psycho!	MASN	11743	197.217	Africa	2.315919e+06

12400 rows × 8 columns

Notice that to be informative, we have named the column ttl_stream_time, that’s what’s to the left of the equals sign in our code.

Column names need to be one word like this: you can’t use spaces.

Notice that in the results, there’s a brand new ttl_stream_time column, which is much larger than streams.

Answering a question

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
  >> filter(_.country == "Costa Rica")
  >> arrange(-_.ttl_stream_time)
)

Let’s put together the three verbs you learned in this chapter to answer a question about our data.

Suppose we wanted to know for a country, like Costa Rica, what songs were listened to longest.

We would do this in three steps:

• create the column
• filter for country
• then sort

(Click the down arrow for a breakdown)

Answering a question

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
 
 
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
0	Argentina	1	Tusa	KAROL G	1858666	200.960	Americas	3.735175e+08
1	Argentina	2	Tattoo	Rauw Alejandro	1344382	202.887	Americas	2.727576e+08
2	Argentina	3	Hola - Remix	Dalex	1330011	249.520	Americas	3.318643e+08
...	...	...	...	...	...	...	...	...
12397	South Africa	198	Black And White	Niall Horan	11771	193.090	Africa	2.272862e+06
12398	South Africa	199	When I See U	Fantasia	11752	217.347	Africa	2.554262e+06
12399	South Africa	200	Psycho!	MASN	11743	197.217	Africa	2.315919e+06

12400 rows × 8 columns

We use mutate to create the ttl_stream_time column.

Answering a question

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
  >> filter(_.country == "Costa Rica")
 
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
2200	Costa Rica	1	Safaera	Bad Bunny	338078	295.177	Americas	9.979285e+07
2201	Costa Rica	2	Si Veo a Tu Mamá	Bad Bunny	244932	170.972	Americas	4.187651e+07
2202	Costa Rica	3	Ignorantes	Bad Bunny	233113	210.607	Americas	4.909523e+07
...	...	...	...	...	...	...	...	...
2397	Costa Rica	198	Ride It	Regard	21731	157.606	Americas	3.424936e+06
2398	Costa Rica	199	Sola	Manuel Turizo	21704	195.044	Americas	4.233235e+06
2399	Costa Rica	200	Nena Maldición (feat. Lenny Tavárez)	Paulo Londra	21684	228.875	Americas	4.962926e+06

200 rows × 8 columns

Then we’d use filter to restrict our analysis to only Costa Rica.

Answering a question

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
  >> filter(_.country == "Costa Rica") 
  >> arrange(-_.ttl_stream_time) 
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
2200	Costa Rica	1	Safaera	Bad Bunny	338078	295.177	Americas	9.979285e+07
2202	Costa Rica	3	Ignorantes	Bad Bunny	233113	210.607	Americas	4.909523e+07
2222	Costa Rica	23	René	Residente	101872	457.592	Americas	4.661581e+07
...	...	...	...	...	...	...	...	...
2377	Costa Rica	178	Dónde Estás	KHEA	23177	153.560	Americas	3.559060e+06
2394	Costa Rica	195	Blueberry Faygo	Lil Mosey	21771	162.547	Americas	3.538811e+06
2397	Costa Rica	198	Ride It	Regard	21731	157.606	Americas	3.424936e+06

200 rows × 8 columns

Then we’d use arrange to sort the data in descending order of our new ttl_stream_time variable.

Answering a question (result)

(music_top200
  >> mutate(ttl_stream_time = _.streams * _.duration)
  >> filter(_.country == "Costa Rica") 
  >> arrange(-_.ttl_stream_time) 
)

	country	position	track_name	artist	streams	duration	continent	ttl_stream_time
2200	Costa Rica	1	Safaera	Bad Bunny	338078	295.177	Americas	9.979285e+07
2202	Costa Rica	3	Ignorantes	Bad Bunny	233113	210.607	Americas	4.909523e+07
2222	Costa Rica	23	René	Residente	101872	457.592	Americas	4.661581e+07
...	...	...	...	...	...	...	...	...
2377	Costa Rica	178	Dónde Estás	KHEA	23177	153.560	Americas	3.559060e+06
2394	Costa Rica	195	Blueberry Faygo	Lil Mosey	21771	162.547	Americas	3.538811e+06
2397	Costa Rica	198	Ride It	Regard	21731	157.606	Americas	3.424936e+06

200 rows × 8 columns

This gives us the answer we’re looking for.

The most streamed track in Costa Rica is still the first in our result.

However, it looks like the track normally in the 23rd position–René by Residente–is third, since it’s over 7 minutes long!

Let’s practice!

• siuba guide - mutate
• siuba api - mutate