cozyDS

Changing the style of scatter plot points

Let's continue exploring Seaborn's mpg dataset by looking at the relationship between how fast a car can accelerate ("acceleration") and its fuel efficiency ("mpg"). Do these properties vary by country of origin ("origin")?

Note that the "acceleration" variable is the time to accelerate from 0 to 60 miles per hour, in seconds. Higher values indicate slower acceleration.

# 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/e0b285b89bdbfbbe8d81123e64727ff150d544e0/mpg.csv'
mpg = pd.read_csv(url)
print(mpg)

      mpg  cylinders  displacement  horsepower  weight  acceleration  \
0    18.0          8         307.0       130.0    3504          12.0   
1    15.0          8         350.0       165.0    3693          11.5   
2    18.0          8         318.0       150.0    3436          11.0   
3    16.0          8         304.0       150.0    3433          12.0   
4    17.0          8         302.0       140.0    3449          10.5   
5    15.0          8         429.0       198.0    4341          10.0   
6    14.0          8         454.0       220.0    4354           9.0   
7    14.0          8         440.0       215.0    4312           8.5   
8    14.0          8         455.0       225.0    4425          10.0   
9    15.0          8         390.0       190.0    3850           8.5   
10   15.0          8         383.0       170.0    3563          10.0   
11   14.0          8         340.0       160.0    3609           8.0   
12   15.0          8         400.0       150.0    3761           9.5   
13   14.0          8         455.0       225.0    3086          10.0   
14   24.0          4         113.0        95.0    2372          15.0   
15   22.0          6         198.0        95.0    2833          15.5   
16   18.0          6         199.0        97.0    2774          15.5   
17   21.0          6         200.0        85.0    2587          16.0   
18   27.0          4          97.0        88.0    2130          14.5   
19   26.0          4          97.0        46.0    1835          20.5   
20   25.0          4         110.0        87.0    2672          17.5   
21   24.0          4         107.0        90.0    2430          14.5   
22   25.0          4         104.0        95.0    2375          17.5   
23   26.0          4         121.0       113.0    2234          12.5   
24   21.0          6         199.0        90.0    2648          15.0   
25   10.0          8         360.0       215.0    4615          14.0   
26   10.0          8         307.0       200.0    4376          15.0   
27   11.0          8         318.0       210.0    4382          13.5   
28    9.0          8         304.0       193.0    4732          18.5   
29   27.0          4          97.0        88.0    2130          14.5   
..    ...        ...           ...         ...     ...           ...   
368  27.0          4         112.0        88.0    2640          18.6   
369  34.0          4         112.0        88.0    2395          18.0   
370  31.0          4         112.0        85.0    2575          16.2   
371  29.0          4         135.0        84.0    2525          16.0   
372  27.0          4         151.0        90.0    2735          18.0   
373  24.0          4         140.0        92.0    2865          16.4   
374  23.0          4         151.0         NaN    3035          20.5   
375  36.0          4         105.0        74.0    1980          15.3   
376  37.0          4          91.0        68.0    2025          18.2   
377  31.0          4          91.0        68.0    1970          17.6   
378  38.0          4         105.0        63.0    2125          14.7   
379  36.0          4          98.0        70.0    2125          17.3   
380  36.0          4         120.0        88.0    2160          14.5   
381  36.0          4         107.0        75.0    2205          14.5   
382  34.0          4         108.0        70.0    2245          16.9   
383  38.0          4          91.0        67.0    1965          15.0   
384  32.0          4          91.0        67.0    1965          15.7   
385  38.0          4          91.0        67.0    1995          16.2   
386  25.0          6         181.0       110.0    2945          16.4   
387  38.0          6         262.0        85.0    3015          17.0   
388  26.0          4         156.0        92.0    2585          14.5   
389  22.0          6         232.0       112.0    2835          14.7   
390  32.0          4         144.0        96.0    2665          13.9   
391  36.0          4         135.0        84.0    2370          13.0   
392  27.0          4         151.0        90.0    2950          17.3   
393  27.0          4         140.0        86.0    2790          15.6   
394  44.0          4          97.0        52.0    2130          24.6   
395  32.0          4         135.0        84.0    2295          11.6   
396  28.0          4         120.0        79.0    2625          18.6   
397  31.0          4         119.0        82.0    2720          19.4   
 
     model_year  origin                               name  
0            70     usa          chevrolet chevelle malibu  
1            70     usa                  buick skylark 320  
2            70     usa                 plymouth satellite  
3            70     usa                      amc rebel sst  
4            70     usa                        ford torino  
5            70     usa                   ford galaxie 500  
6            70     usa                   chevrolet impala  
7            70     usa                  plymouth fury iii  
8            70     usa                   pontiac catalina  
9            70     usa                 amc ambassador dpl  
10           70     usa                dodge challenger se  
11           70     usa                 plymouth 'cuda 340  
12           70     usa              chevrolet monte carlo  
13           70     usa            buick estate wagon (sw)  
14           70   japan              toyota corona mark ii  
15           70     usa                    plymouth duster  
16           70     usa                         amc hornet  
17           70     usa                      ford maverick  
18           70   japan                       datsun pl510  
19           70  europe       volkswagen 1131 deluxe sedan  
20           70  europe                        peugeot 504  
21           70  europe                        audi 100 ls  
22           70  europe                           saab 99e  
23           70  europe                           bmw 2002  
24           70     usa                        amc gremlin  
25           70     usa                          ford f250  
26           70     usa                          chevy c20  
27           70     usa                         dodge d200  
28           70     usa                           hi 1200d  
29           71   japan                       datsun pl510  
..          ...     ...                                ...  
368          82     usa           chevrolet cavalier wagon  
369          82     usa          chevrolet cavalier 2-door  
370          82     usa         pontiac j2000 se hatchback  
371          82     usa                     dodge aries se  
372          82     usa                    pontiac phoenix  
373          82     usa               ford fairmont futura  
374          82     usa                     amc concord dl  
375          82  europe                volkswagen rabbit l  
376          82   japan                 mazda glc custom l  
377          82   japan                   mazda glc custom  
378          82     usa             plymouth horizon miser  
379          82     usa                     mercury lynx l  
380          82   japan                   nissan stanza xe  
381          82   japan                       honda accord  
382          82   japan                     toyota corolla  
383          82   japan                        honda civic  
384          82   japan                 honda civic (auto)  
385          82   japan                      datsun 310 gx  
386          82     usa              buick century limited  
387          82     usa  oldsmobile cutlass ciera (diesel)  
388          82     usa         chrysler lebaron medallion  
389          82     usa                     ford granada l  
390          82   japan                   toyota celica gt  
391          82     usa                  dodge charger 2.2  
392          82     usa                   chevrolet camaro  
393          82     usa                    ford mustang gl  
394          82  europe                          vw pickup  
395          82     usa                      dodge rampage  
396          82     usa                        ford ranger  
397          82     usa                         chevy s-10  
 
[398 rows x 9 columns]

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
 
# Create a scatter plot of acceleration vs. mpg
sns.relplot(x='acceleration', y='mpg', 
            data=mpg, 
            kind='scatter',
            style='origin', 
            hue='origin')
 
# Show plot
plt.show()

All the contents are from DataCamp

Changing the size of scatter plot points

In this exercise, we'll explore Seaborn's mpg dataset, which contains one row per car model and includes information such as the year the car was made, the number of miles per gallon ("M.P.G.") it achieves, the power of its engine (measured in "horsepower"), and its country of origin.

What is the relationship between the power of a car's engine ("horsepower") and its fuel efficiency ("mpg")? And how does this relationship vary by the number of cylinders ("cylinders") the car has? Let's find out.

Let's continue to use relplot() instead of scatterplot() since it offers more flexibility.

# 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/e0b285b89bdbfbbe8d81123e64727ff150d544e0/mpg.csv'
mpg = pd.read_csv(url)
print(mpg)

      mpg  cylinders  displacement  horsepower  weight  acceleration  \
0    18.0          8         307.0       130.0    3504          12.0   
1    15.0          8         350.0       165.0    3693          11.5   
2    18.0          8         318.0       150.0    3436          11.0   
3    16.0          8         304.0       150.0    3433          12.0   
4    17.0          8         302.0       140.0    3449          10.5   
5    15.0          8         429.0       198.0    4341          10.0   
6    14.0          8         454.0       220.0    4354           9.0   
7    14.0          8         440.0       215.0    4312           8.5   
8    14.0          8         455.0       225.0    4425          10.0   
9    15.0          8         390.0       190.0    3850           8.5   
10   15.0          8         383.0       170.0    3563          10.0   
11   14.0          8         340.0       160.0    3609           8.0   
12   15.0          8         400.0       150.0    3761           9.5   
13   14.0          8         455.0       225.0    3086          10.0   
14   24.0          4         113.0        95.0    2372          15.0   
15   22.0          6         198.0        95.0    2833          15.5   
16   18.0          6         199.0        97.0    2774          15.5   
17   21.0          6         200.0        85.0    2587          16.0   
18   27.0          4          97.0        88.0    2130          14.5   
19   26.0          4          97.0        46.0    1835          20.5   
20   25.0          4         110.0        87.0    2672          17.5   
21   24.0          4         107.0        90.0    2430          14.5   
22   25.0          4         104.0        95.0    2375          17.5   
23   26.0          4         121.0       113.0    2234          12.5   
24   21.0          6         199.0        90.0    2648          15.0   
25   10.0          8         360.0       215.0    4615          14.0   
26   10.0          8         307.0       200.0    4376          15.0   
27   11.0          8         318.0       210.0    4382          13.5   
28    9.0          8         304.0       193.0    4732          18.5   
29   27.0          4          97.0        88.0    2130          14.5   
..    ...        ...           ...         ...     ...           ...   
368  27.0          4         112.0        88.0    2640          18.6   
369  34.0          4         112.0        88.0    2395          18.0   
370  31.0          4         112.0        85.0    2575          16.2   
371  29.0          4         135.0        84.0    2525          16.0   
372  27.0          4         151.0        90.0    2735          18.0   
373  24.0          4         140.0        92.0    2865          16.4   
374  23.0          4         151.0         NaN    3035          20.5   
375  36.0          4         105.0        74.0    1980          15.3   
376  37.0          4          91.0        68.0    2025          18.2   
377  31.0          4          91.0        68.0    1970          17.6   
378  38.0          4         105.0        63.0    2125          14.7   
379  36.0          4          98.0        70.0    2125          17.3   
380  36.0          4         120.0        88.0    2160          14.5   
381  36.0          4         107.0        75.0    2205          14.5   
382  34.0          4         108.0        70.0    2245          16.9   
383  38.0          4          91.0        67.0    1965          15.0   
384  32.0          4          91.0        67.0    1965          15.7   
385  38.0          4          91.0        67.0    1995          16.2   
386  25.0          6         181.0       110.0    2945          16.4   
387  38.0          6         262.0        85.0    3015          17.0   
388  26.0          4         156.0        92.0    2585          14.5   
389  22.0          6         232.0       112.0    2835          14.7   
390  32.0          4         144.0        96.0    2665          13.9   
391  36.0          4         135.0        84.0    2370          13.0   
392  27.0          4         151.0        90.0    2950          17.3   
393  27.0          4         140.0        86.0    2790          15.6   
394  44.0          4          97.0        52.0    2130          24.6   
395  32.0          4         135.0        84.0    2295          11.6   
396  28.0          4         120.0        79.0    2625          18.6   
397  31.0          4         119.0        82.0    2720          19.4   
 
     model_year  origin                               name  
0            70     usa          chevrolet chevelle malibu  
1            70     usa                  buick skylark 320  
2            70     usa                 plymouth satellite  
3            70     usa                      amc rebel sst  
4            70     usa                        ford torino  
5            70     usa                   ford galaxie 500  
6            70     usa                   chevrolet impala  
7            70     usa                  plymouth fury iii  
8            70     usa                   pontiac catalina  
9            70     usa                 amc ambassador dpl  
10           70     usa                dodge challenger se  
11           70     usa                 plymouth 'cuda 340  
12           70     usa              chevrolet monte carlo  
13           70     usa            buick estate wagon (sw)  
14           70   japan              toyota corona mark ii  
15           70     usa                    plymouth duster  
16           70     usa                         amc hornet  
17           70     usa                      ford maverick  
18           70   japan                       datsun pl510  
19           70  europe       volkswagen 1131 deluxe sedan  
20           70  europe                        peugeot 504  
21           70  europe                        audi 100 ls  
22           70  europe                           saab 99e  
23           70  europe                           bmw 2002  
24           70     usa                        amc gremlin  
25           70     usa                          ford f250  
26           70     usa                          chevy c20  
27           70     usa                         dodge d200  
28           70     usa                           hi 1200d  
29           71   japan                       datsun pl510  
..          ...     ...                                ...  
368          82     usa           chevrolet cavalier wagon  
369          82     usa          chevrolet cavalier 2-door  
370          82     usa         pontiac j2000 se hatchback  
371          82     usa                     dodge aries se  
372          82     usa                    pontiac phoenix  
373          82     usa               ford fairmont futura  
374          82     usa                     amc concord dl  
375          82  europe                volkswagen rabbit l  
376          82   japan                 mazda glc custom l  
377          82   japan                   mazda glc custom  
378          82     usa             plymouth horizon miser  
379          82     usa                     mercury lynx l  
380          82   japan                   nissan stanza xe  
381          82   japan                       honda accord  
382          82   japan                     toyota corolla  
383          82   japan                        honda civic  
384          82   japan                 honda civic (auto)  
385          82   japan                      datsun 310 gx  
386          82     usa              buick century limited  
387          82     usa  oldsmobile cutlass ciera (diesel)  
388          82     usa         chrysler lebaron medallion  
389          82     usa                     ford granada l  
390          82   japan                   toyota celica gt  
391          82     usa                  dodge charger 2.2  
392          82     usa                   chevrolet camaro  
393          82     usa                    ford mustang gl  
394          82  europe                          vw pickup  
395          82     usa                      dodge rampage  
396          82     usa                        ford ranger  
397          82     usa                         chevy s-10  
 
[398 rows x 9 columns]

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
 
# Create scatter plot of horsepower vs. mpg
sns.relplot(x="horsepower", y="mpg", 
            data=mpg, 
            kind="scatter", 
            size="cylinders", # size
            hue='cylinders')  # color
 
# Show plot
plt.show()

All the contents are from DataCamp

Creating two-factor subplots

Let's continue looking at the student_data dataset of students in secondary school. Here, we want to answer the following question: does a student's first semester grade ("G1") tend to correlate with their final grade ("G3")?

There are many aspects of a student's life that could result in a higher or lower final grade in the class. For example, some students receive extra educational support from their school ("schoolsup") or from their family ("famsup"), which could result in higher grades. Let's try to control for these two factors by creating subplots based on whether the student received extra educational support from their school or family.

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]

# Adjust to add subplots based on school support
sns.relplot(x="G1", y="G3", 
            data=student_data,
            kind="scatter", 
            col = "schoolsup", 
            col_order=["yes", "no"])
 
# Show plot
plt.show()

# Adjust further to add subplots based on family support
sns.relplot(x="G1", y="G3", 
            data=student_data,
            kind="scatter", 
            col="schoolsup",
            col_order=["yes", "no"],
            row="famsup",
            row_order=["yes", "no"])
 
# Show plot
plt.show()

All the Contents are from DataCamp

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()

All the Contents Are from DataCamp