Creating subplots with col and row
We've seen in prior exercises that students with more absences ("absences") tend to have lower final grades ("G3"). Does this relationship hold regardless of how much time students study each week?
To answer this, we'll look at the relationship between the number of absences that a student has in school and their final grade in the course, creating separate subplots based on each student's weekly study time ("study_time").
Seaborn has been imported as sns and matplotlib.pyplot has been imported as plt.
# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
url = 'https://assets.datacamp.com/production/repositories/3996/datasets/61e08004fef1a1b02b62620e3cd2533834239c90/student-alcohol-consumption.csv'
student_data = pd.read_csv(url)
print(student_data) Unnamed: 0 school sex age famsize Pstatus Medu Fedu traveltime \
0 0 GP F 18 GT3 A 4 4 2
1 1 GP F 17 GT3 T 1 1 1
2 2 GP F 15 LE3 T 1 1 1
3 3 GP F 15 GT3 T 4 2 1
4 4 GP F 16 GT3 T 3 3 1
5 5 GP M 16 LE3 T 4 3 1
6 6 GP M 16 LE3 T 2 2 1
7 7 GP F 17 GT3 A 4 4 2
8 8 GP M 15 LE3 A 3 2 1
9 9 GP M 15 GT3 T 3 4 1
10 10 GP F 15 GT3 T 4 4 1
11 11 GP F 15 GT3 T 2 1 3
12 12 GP M 15 LE3 T 4 4 1
13 13 GP M 15 GT3 T 4 3 2
14 14 GP M 15 GT3 A 2 2 1
15 15 GP F 16 GT3 T 4 4 1
16 16 GP F 16 GT3 T 4 4 1
17 17 GP F 16 GT3 T 3 3 3
18 18 GP M 17 GT3 T 3 2 1
19 19 GP M 16 LE3 T 4 3 1
20 20 GP M 15 GT3 T 4 3 1
21 21 GP M 15 GT3 T 4 4 1
22 22 GP M 16 LE3 T 4 2 1
23 23 GP M 16 LE3 T 2 2 2
24 24 GP F 15 GT3 T 2 4 1
25 25 GP F 16 GT3 T 2 2 1
26 26 GP M 15 GT3 T 2 2 1
27 27 GP M 15 GT3 T 4 2 1
28 28 GP M 16 LE3 A 3 4 1
29 29 GP M 16 GT3 T 4 4 1
.. ... ... .. ... ... ... ... ... ...
365 365 MS M 18 GT3 T 1 3 2
366 366 MS M 18 LE3 T 4 4 2
367 367 MS F 17 GT3 T 1 1 3
368 368 MS F 18 GT3 T 2 3 2
369 369 MS F 18 GT3 T 4 4 3
370 370 MS F 19 LE3 T 3 2 2
371 371 MS M 18 LE3 T 1 2 3
372 372 MS F 17 GT3 T 2 2 1
373 373 MS F 17 GT3 T 1 2 1
374 374 MS F 18 LE3 T 4 4 2
375 375 MS F 18 GT3 T 1 1 4
376 376 MS F 20 GT3 T 4 2 2
377 377 MS F 18 LE3 T 4 4 1
378 378 MS F 18 GT3 T 3 3 1
379 379 MS F 17 GT3 T 3 1 1
380 380 MS M 18 GT3 T 4 4 1
381 381 MS M 18 GT3 T 2 1 2
382 382 MS M 17 GT3 T 2 3 2
383 383 MS M 19 GT3 T 1 1 2
384 384 MS M 18 GT3 T 4 2 2
385 385 MS F 18 GT3 T 2 2 2
386 386 MS F 18 GT3 T 4 4 3
387 387 MS F 19 GT3 T 2 3 1
388 388 MS F 18 LE3 T 3 1 1
389 389 MS F 18 GT3 T 1 1 2
390 390 MS M 20 LE3 A 2 2 1
391 391 MS M 17 LE3 T 3 1 2
392 392 MS M 21 GT3 T 1 1 1
393 393 MS M 18 LE3 T 3 2 3
394 394 MS M 19 LE3 T 1 1 1
failures ... goout Dalc Walc health absences G1 G2 G3 location \
0 0 ... 4 1 1 3 6 5 6 6 Urban
1 0 ... 3 1 1 3 4 5 5 6 Urban
2 3 ... 2 2 3 3 10 7 8 10 Urban
3 0 ... 2 1 1 5 2 15 14 15 Urban
4 0 ... 2 1 2 5 4 6 10 10 Urban
5 0 ... 2 1 2 5 10 15 15 15 Urban
6 0 ... 4 1 1 3 0 12 12 11 Urban
7 0 ... 4 1 1 1 6 6 5 6 Urban
8 0 ... 2 1 1 1 0 16 18 19 Urban
9 0 ... 1 1 1 5 0 14 15 15 Urban
10 0 ... 3 1 2 2 0 10 8 9 Urban
11 0 ... 2 1 1 4 4 10 12 12 Urban
12 0 ... 3 1 3 5 2 14 14 14 Urban
13 0 ... 3 1 2 3 2 10 10 11 Urban
14 0 ... 2 1 1 3 0 14 16 16 Urban
15 0 ... 4 1 2 2 4 14 14 14 Urban
16 0 ... 3 1 2 2 6 13 14 14 Urban
17 0 ... 2 1 1 4 4 8 10 10 Urban
18 3 ... 5 2 4 5 16 6 5 5 Urban
19 0 ... 3 1 3 5 4 8 10 10 Urban
20 0 ... 1 1 1 1 0 13 14 15 Urban
21 0 ... 2 1 1 5 0 12 15 15 Urban
22 0 ... 1 1 3 5 2 15 15 16 Urban
23 0 ... 4 2 4 5 0 13 13 12 Urban
24 0 ... 2 1 1 5 2 10 9 8 Rural
25 2 ... 2 1 3 5 14 6 9 8 Urban
26 0 ... 2 1 2 5 2 12 12 11 Urban
27 0 ... 4 2 4 1 4 15 16 15 Urban
28 0 ... 3 1 1 5 4 11 11 11 Urban
29 0 ... 5 5 5 5 16 10 12 11 Urban
.. ... ... ... ... ... ... ... .. .. .. ...
365 0 ... 4 2 4 3 4 10 10 10 Rural
366 0 ... 2 2 2 5 0 13 13 13 Urban
367 1 ... 1 1 2 1 0 7 6 0 Rural
368 0 ... 3 1 2 4 0 11 10 10 Urban
369 0 ... 2 4 2 5 10 14 12 11 Rural
370 2 ... 2 1 1 3 4 7 7 9 Urban
371 0 ... 3 2 3 3 3 14 12 12 Rural
372 0 ... 3 1 1 3 8 13 11 11 Urban
373 0 ... 5 1 3 1 14 6 5 5 Rural
374 0 ... 4 1 1 1 0 19 18 19 Rural
375 0 ... 2 1 2 4 2 8 8 10 Rural
376 2 ... 3 1 1 3 4 15 14 15 Urban
377 0 ... 3 3 4 2 4 8 9 10 Rural
378 0 ... 3 1 2 1 0 15 15 15 Urban
379 0 ... 4 2 3 1 17 10 10 10 Rural
380 0 ... 4 1 4 2 4 15 14 14 Urban
381 0 ... 3 1 3 5 5 7 6 7 Rural
382 0 ... 3 1 1 3 2 11 11 10 Urban
383 1 ... 2 1 3 5 0 6 5 0 Rural
384 1 ... 3 4 3 3 14 6 5 5 Rural
385 0 ... 3 1 3 4 2 10 9 10 Rural
386 0 ... 3 2 2 5 7 6 5 6 Rural
387 1 ... 2 1 2 5 0 7 5 0 Rural
388 0 ... 4 1 1 1 0 7 9 8 Urban
389 1 ... 1 1 1 5 0 6 5 0 Urban
390 2 ... 4 4 5 4 11 9 9 9 Urban
391 0 ... 5 3 4 2 3 14 16 16 Urban
392 3 ... 3 3 3 3 3 10 8 7 Rural
393 0 ... 1 3 4 5 0 11 12 10 Rural
394 0 ... 3 3 3 5 5 8 9 9 Urban
study_time
0 2 to 5 hours
1 2 to 5 hours
2 2 to 5 hours
3 5 to 10 hours
4 2 to 5 hours
5 2 to 5 hours
6 2 to 5 hours
7 2 to 5 hours
8 2 to 5 hours
9 2 to 5 hours
10 2 to 5 hours
11 5 to 10 hours
12 <2 hours
13 2 to 5 hours
14 5 to 10 hours
15 <2 hours
16 5 to 10 hours
17 2 to 5 hours
18 <2 hours
19 <2 hours
20 2 to 5 hours
21 <2 hours
22 2 to 5 hours
23 2 to 5 hours
24 5 to 10 hours
25 <2 hours
26 <2 hours
27 <2 hours
28 2 to 5 hours
29 2 to 5 hours
.. ...
365 2 to 5 hours
366 5 to 10 hours
367 <2 hours
368 <2 hours
369 2 to 5 hours
370 2 to 5 hours
371 <2 hours
372 5 to 10 hours
373 <2 hours
374 5 to 10 hours
375 5 to 10 hours
376 5 to 10 hours
377 2 to 5 hours
378 2 to 5 hours
379 2 to 5 hours
380 2 to 5 hours
381 <2 hours
382 2 to 5 hours
383 <2 hours
384 <2 hours
385 5 to 10 hours
386 <2 hours
387 5 to 10 hours
388 2 to 5 hours
389 2 to 5 hours
390 2 to 5 hours
391 <2 hours
392 <2 hours
393 <2 hours
394 <2 hours
[395 rows x 30 columns]# Change to make subplots based on study time
sns.relplot(x="absences", y="G3",
data=student_data,
kind="scatter")
# Show plot
plt.show()
# Modify the code to create one scatter plot for each level of the variable "study_time", arranged in columns.
sns.relplot(x="absences", y="G3",
data=student_data,
kind="scatter", col = "study_time")
# Show plot
plt.show()
# Adapt your code to create one scatter plot for each level of a student's weekly study time, this time arranged in rows.
# Change this scatter plot to arrange the plots in rows instead of columns
sns.relplot(x="absences", y="G3",
data=student_data,
kind="scatter",
row="study_time")
# Show plot
plt.show()
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