cozyDS

Count plots

In this exercise, we'll return to exploring our dataset that contains the responses to a survey sent out to young people. We might suspect that young people spend a lot of time on the internet, but how much do they report using the internet each day? Let's use a count plot to break down the number of survey responses in each category and then explore whether it changes based on age.

이번 예제는 설문 조사에 대한 젋은층 응답을 포함하는 데이터 세트를 살펴 봅니다. 젊은 사람들이 인터넷에서 많은 시간을 보냈다고 의심 할 수도 있지만, 실제로 젋은 사람들이 매일 인터넷 사용에 관한 보고는 실제로 확인할 필요가 있습니다. 이번에 카운트 플롯(Count plots)을 사용하여 각 카테고리의 설문 응답 수를 분석 한 다음 연령에 따라 설문 응답이 어떻게 변하는 지 여부를 알아 봅니다.

As a reminder, to create a count plot, we'll use the catplot() function and specify the name of the categorical variable to count (x=____), the Pandas DataFrame to use (data=____), and the type of plot (kind="count").

다시 말해 카운트 플롯을 만들려면 catplot() 함수를 사용하고 계산할 범주 변수의 이름 (x = ____), 사용할 팬더 데이터 프레임 (data = ____) 및 유형을 지정합니다. plot (kind = "count").

Seaborn has been imported as sns and matplotlib.pyplot has been imported as plt.

Seaborn은 sn으로 가져오고 matplotlib.pyplot은 plt로 가져 왔습니다.

# 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/ab13162732ae9ca1a9a27e2efd3da923ed6a4e7b/young-people-survey-responses.csv'
survey_data = pd.read_csv(url)
print(survey_data)

      Unnamed: 0  Music  Techno  Movies  History  Mathematics  Pets  Spiders  \
0              0    5.0     1.0     5.0      1.0          3.0   4.0      1.0   
1              1    4.0     1.0     5.0      1.0          5.0   5.0      1.0   
2              2    5.0     1.0     5.0      1.0          5.0   5.0      1.0   
3              3    5.0     2.0     5.0      4.0          4.0   1.0      5.0   
4              4    5.0     2.0     5.0      3.0          2.0   1.0      1.0   
5              5    5.0     1.0     5.0      5.0          2.0   2.0      1.0   
6              6    5.0     5.0     4.0      3.0          1.0   5.0      1.0   
7              7    5.0     3.0     5.0      5.0          1.0   5.0      1.0   
8              8    5.0     1.0     5.0      3.0          1.0   1.0      5.0   
9              9    5.0     1.0     5.0      3.0          3.0   2.0      3.0   
10            10    5.0     4.0     5.0      3.0          2.0   5.0      2.0   
11            11    5.0     1.0     5.0      2.0          1.0   1.0      5.0   
12            12    5.0     1.0     5.0      4.0          1.0   2.0      1.0   
13            13    5.0     1.0     5.0      2.0          1.0   5.0      3.0   
14            14    5.0     1.0     4.0      2.0          1.0   5.0      2.0   
15            15    1.0     1.0     5.0      5.0          3.0   1.0      1.0   
16            16    5.0     4.0     5.0      1.0          1.0   5.0      5.0   
17            17    5.0     1.0     5.0      3.0          1.0   5.0      4.0   
18            18    5.0     4.0     5.0      3.0          5.0   4.0      1.0   
19            19    5.0     3.0     3.0      2.0          3.0   1.0      4.0   
20            20    5.0     3.0     5.0      5.0          2.0   3.0      2.0   
21            21    5.0     1.0     5.0      4.0          3.0   5.0      1.0   
22            22    5.0     3.0     5.0      3.0          1.0   5.0      5.0   
23            23    5.0     1.0     5.0      2.0          2.0   5.0      3.0   
24            24    5.0     3.0     5.0      4.0          2.0   3.0      5.0   
25            25    5.0     2.0     5.0      2.0          3.0   3.0      5.0   
26            26    5.0     2.0     5.0      3.0          2.0   5.0      1.0   
27            27    4.0     4.0     5.0      2.0          1.0   5.0      5.0   
28            28    5.0     3.0     5.0      5.0          1.0   3.0      4.0   
29            29    5.0     3.0     5.0      5.0          2.0   2.0      5.0   
...          ...    ...     ...     ...      ...          ...   ...      ...   
980          980    5.0     1.0     5.0      5.0          1.0   3.0      5.0   
981          981    5.0     1.0     5.0      5.0          1.0   1.0      5.0   
982          982    5.0     1.0     4.0      1.0          3.0   4.0      1.0   
983          983    5.0     2.0     5.0      2.0          1.0   4.0      5.0   
984          984    5.0     1.0     4.0      1.0          1.0   4.0      5.0   
985          985    5.0     1.0     5.0      4.0          1.0   2.0      4.0   
986          986    4.0     4.0     4.0      5.0          5.0   1.0      1.0   
987          987    4.0     1.0     4.0      5.0          2.0   2.0      2.0   
988          988    5.0     NaN     5.0      5.0          4.0   1.0      1.0   
989          989    5.0     5.0     3.0      5.0          1.0   1.0      1.0   
990          990    5.0     1.0     5.0      3.0          1.0   1.0      1.0   
991          991    5.0     2.0     5.0      4.0          1.0   5.0      3.0   
992          992    4.0     5.0     4.0      5.0          5.0   1.0      5.0   
993          993    5.0     2.0     5.0      1.0          1.0   2.0      3.0   
994          994    5.0     1.0     5.0      5.0          1.0   5.0      4.0   
995          995    5.0     5.0     5.0      3.0          4.0   5.0      1.0   
996          996    5.0     1.0     3.0      4.0          1.0   2.0      2.0   
997          997    5.0     1.0     4.0      1.0          2.0   5.0      4.0   
998          998    5.0     5.0     5.0      3.0          5.0   1.0      1.0   
999          999    5.0     5.0     4.0      4.0          5.0   1.0      2.0   
1000        1000    5.0     3.0     5.0      3.0          1.0   5.0      1.0   
1001        1001    5.0     3.0     3.0      3.0          1.0   4.0      1.0   
1002        1002    5.0     3.0     3.0      2.0          1.0   2.0      1.0   
1003        1003    4.0     4.0     5.0      3.0          1.0   5.0      4.0   
1004        1004    5.0     5.0     5.0      3.0          2.0   4.0      3.0   
1005        1005    5.0     3.0     5.0      4.0          1.0   4.0      2.0   
1006        1006    4.0     4.0     5.0      4.0          5.0   5.0      1.0   
1007        1007    4.0     1.0     4.0      2.0          3.0   5.0      2.0   
1008        1008    5.0     2.0     5.0      3.0          1.0   4.0      3.0   
1009        1009    5.0     3.0     5.0      2.0          2.0   5.0      1.0   
 
      Loneliness  Parents' advice           Internet usage  Finances   Age  \
0            3.0              4.0          few hours a day       3.0  20.0   
1            2.0              2.0          few hours a day       3.0  19.0   
2            5.0              3.0          few hours a day       2.0  20.0   
3            5.0              2.0          most of the day       2.0  22.0   
4            3.0              3.0          few hours a day       4.0  20.0   
5            2.0              3.0          few hours a day       2.0  20.0   
6            3.0              4.0  less than an hour a day       4.0  20.0   
7            2.0              3.0          few hours a day       3.0  19.0   
8            4.0              4.0          few hours a day       2.0  18.0   
9            2.0              3.0          few hours a day       4.0  19.0   
10           2.0              4.0  less than an hour a day       2.0  19.0   
11           4.0              4.0          few hours a day       2.0  17.0   
12           5.0              4.0          few hours a day       4.0  24.0   
13           2.0              3.0          few hours a day       3.0  19.0   
14           2.0              4.0          most of the day       5.0  22.0   
15           4.0              4.0          few hours a day       3.0  18.0   
16           2.0              3.0          few hours a day       3.0  19.0   
17           4.0              1.0          few hours a day       1.0  20.0   
18           4.0              3.0          few hours a day       4.0  18.0   
19           2.0              2.0          few hours a day       2.0  18.0   
20           2.0              3.0          few hours a day       2.0  20.0   
21           4.0              3.0          few hours a day       2.0  24.0   
22           3.0              3.0          few hours a day       5.0  22.0   
23           3.0              2.0          few hours a day       2.0  20.0   
24           4.0              4.0          few hours a day       3.0  19.0   
25           2.0              4.0          few hours a day       2.0  20.0   
26           4.0              5.0          few hours a day       5.0  22.0   
27           3.0              4.0          few hours a day       3.0  19.0   
28           3.0              3.0          few hours a day       3.0  20.0   
29           3.0              3.0          few hours a day       4.0  19.0   
...          ...              ...                      ...       ...   ...   
980          2.0              4.0  less than an hour a day       1.0  18.0   
981          4.0              2.0          most of the day       5.0  19.0   
982          3.0              4.0          few hours a day       2.0  18.0   
983          2.0              3.0          few hours a day       2.0  22.0   
984          3.0              1.0          most of the day       4.0  21.0   
985          3.0              1.0          few hours a day       5.0  20.0   
986          3.0              3.0          few hours a day       1.0  19.0   
987          4.0              2.0          few hours a day       3.0  20.0   
988          5.0              3.0          few hours a day       1.0  19.0   
989          5.0              3.0          most of the day       3.0  30.0   
990          2.0              2.0          few hours a day       1.0  29.0   
991          2.0              1.0          most of the day       3.0  21.0   
992          3.0              3.0          most of the day       1.0  30.0   
993          4.0              3.0          few hours a day       4.0  21.0   
994          3.0              4.0          few hours a day       3.0  20.0   
995          4.0              4.0          few hours a day       4.0  18.0   
996          2.0              4.0          few hours a day       3.0  20.0   
997          2.0              NaN  less than an hour a day       3.0  19.0   
998          2.0              3.0          few hours a day       4.0  28.0   
999          3.0              3.0          few hours a day       3.0  19.0   
1000         3.0              2.0          few hours a day       1.0  16.0   
1001         2.0              2.0          few hours a day       2.0  18.0   
1002         3.0              2.0          few hours a day       4.0  22.0   
1003         3.0              4.0          few hours a day       3.0  20.0   
1004         3.0              2.0          few hours a day       4.0  22.0   
1005         4.0              4.0          few hours a day       3.0  20.0   
1006         1.0              4.0  less than an hour a day       3.0  27.0   
1007         4.0              4.0          most of the day       1.0  18.0   
1008         3.0              3.0          most of the day       3.0  25.0   
1009         3.0              3.0          few hours a day       5.0  21.0   
 
      Siblings  Gender Village - town  
0          1.0  female        village  
1          2.0  female           city  
2          2.0  female           city  
3          1.0  female           city  
4          1.0  female        village  
5          1.0    male           city  
6          1.0  female        village  
7          1.0    male           city  
8          1.0  female           city  
9          3.0  female           city  
10         2.0  female           city  
11         1.0  female           city  
12        10.0  female           city  
13         1.0  female           city  
14         1.0  female           city  
15         0.0    male           city  
16         2.0  female           city  
17         1.0  female        village  
18         2.0    male           city  
19         1.0    male           city  
20         1.0    male           city  
21         1.0    male           city  
22         1.0  female           city  
23         3.0  female           city  
24         1.0  female           city  
25         1.0  female           city  
26         1.0  female           city  
27         1.0  female           city  
28         2.0    male        village  
29         2.0  female        village  
...        ...     ...            ...  
980        2.0  female           city  
981        1.0  female        village  
982        2.0    male           city  
983        2.0  female        village  
984        1.0  female           city  
985        1.0  female           city  
986        3.0  female           city  
987        1.0    male           city  
988        0.0    male           city  
989        2.0  female        village  
990        1.0    male           city  
991        0.0    male           city  
992        1.0    male           city  
993        0.0  female           city  
994        0.0  female           city  
995        0.0  female           city  
996        0.0    male            NaN  
997        1.0  female            NaN  
998        1.0    male           city  
999        1.0    male           city  
1000       1.0  female           city  
1001       2.0  female           city  
1002       1.0    male           city  
1003       1.0  female           city  
1004       1.0    male           city  
1005       1.0  female           city  
1006       5.0    male        village  
1007       0.0  female           city  
1008       1.0  female           city  
1009       1.0    male        village  
 
[1010 rows x 16 columns]

Quesiton

• Use sns.catplot() to create a count plot using the survey_data DataFrame with "Internet usage" on the x-axis.

# Change the orientation of the plot
sns.catplot(x="Internet usage", data=survey_data,
            kind="count")
 
# Show plot
plt.show()

Question

• Make the bars horizontal instead of vertical.

# Change the orientation of the plot
sns.catplot(y="Internet usage", data=survey_data,
            kind="count")
 
# Show plot
plt.show()

Question

• Create column subplots based on "Age Category", which separates respondents into those that are younger than 21 vs. 21 and older.

# Create Age Category column by condition
 
import numpy as np
survey_data['Age Category'] = np.where(survey_data['Age'] >= 21, "21+", "Less than 21")
print(survey_data[['Age', 'Age Category']].head())

    Age  Age Category
0  20.0  Less than 21
1  19.0  Less than 21
2  20.0  Less than 21
3  22.0           21+
4  20.0  Less than 21

# Create column subplots based on age category
sns.catplot(y="Internet usage", data=survey_data,
            kind="count", col='Age Category')
 
# Show plot
plt.show()

All the Contents are from DataCamp

파이썬 Seaborn을 이용한 선 그래프에 하위 그룹을 적용해보겠습니다. 

Let's continue to look at the mpg dataset. We've seen that the average miles per gallon for cars has increased over time, but how has the average horsepower for cars changed over time? And does this trend differ by country of origin?

mpg 데이터 세트를 계속 살펴 보겠습니다. 자동차에 대한 갤런 당 평균 마일이 시간이 지남에 따라 증가했다는 것을 알았지만, 차의 평균 마력은 시간이 지남에 따라 어떻게 변했는가? 그리고 이러한 데이터의 흐름은 차량 생산지에 따라 다른지 확인하는 작업을 하려고 합니다. 

# 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]

Step 1. Turn off Confidence Intervals on the Plot

Use relplot() and the mpg DataFrame to create a line plot with "model_year" on the x-axis and "horsepower" on the y-axis. Turn off the confidence intervals on the plot.

relplot ()과 mpg DataFrame을 사용하여 x 축에 "model_year", y 축에 "horsepower"을 가진 선 그림을 만듭니다. 플롯에서 신뢰 구간 기능은 Off 하도록 합니다.

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
 
# Create line plot of model year vs. horsepower
sns.relplot(x='model_year', 
            y='horsepower', 
            data=mpg, 
            kind='line', 
            ci=None)
 
# Show plot
plt.show()

Add Style and Color

Create different lines for each country of origin ("origin") that vary in both line style and color.

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
 
# Change to create subgroups for country of origin
sns.relplot(x="model_year", 
y="horsepower", 
            data=mpg, kind="line", 
            ci=None, style='origin', hue='origin')
 
# Show plot
plt.show()

Add Markers

Add markers for each data point to the lines.

각 데이터 포인트에 대한 마커를 선에 추가하십시오.

# Import Matplotlib and Seaborn
import matplotlib.pyplot as plt
import seaborn as sns
 
# Add markers and make each line have the same style
sns.relplot(x="model_year", y="horsepower", 
            data=mpg, kind="line", 
            ci=None, style="origin", 
            hue="origin", 
            markers=True)
 
# Show plot
plt.show()

Now that we've added subgroups, we can see that this downward trend in horsepower was more pronounced among cars from the USA.

이제 하위 그룹을 추가 했으므로 'horsepower'의 하락 추세 중 미국의 자동차 가운데 더 두드러 졌다는 것을 알 수 있습니다.

All the contents are from DataCamp

Visualizing standard deviation with line plots

In the last exercise, we looked at how the average miles per gallon achieved by cars has changed over time. Now let's use a line plot to visualize how the distribution of miles per gallon has changed over 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/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]

# Change the plot so the shaded area shows the standard deviation instead of the confidence interval for the mean.
# Make the shaded area show the standard deviation
sns.relplot(x="model_year", y="mpg",
            data=mpg, 
            kind="line",
            ci="sd")
 
 
# Show plot
plt.show()

All the contents are from DataCamp

Interpreting line plots

In this exercise, we'll continue to explore Seaborn's mpg dataset, which contains one row per car model and includes information such as the year the car was made, its fuel efficiency (measured in "miles per gallon" or "M.P.G"), and its country of origin (USA, Europe, or Japan).

How has the average miles per gallon achieved by these cars changed over time? Let's use line plots to find out!

# 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]

# Create line plot
sns.relplot(x='model_year', y='mpg', 
            data=mpg, 
            kind='line')
 
# Show plot
plt.show()

Question

• Which of the following is NOT a correct interpretation of this line plot?

Possible Answers

1. The average miles per gallon has increased over time.

2. The distribution of miles per gallon is smaller in 1973 compared to 1977.

3. We can be 95% confident that the average miles per gallon for all cars in 1970 is between 16 and 20 miles per gallon.

4. This plot assumes that our data is a random sample of all cars in the US, Europe, and Japan.

The answer is 2. It's that the shaded region represents a confidence interval for the mean, not the distribution of the observations.

All the contents are from DataCamp