데이터 분석을 공부 하려면? 데이터가 있어야 한다!

데이터 분석을 공부하려고 구글링을 해보면 Pandas를 잘 다루어야 한다는 이야기가 많다. Pandas 라이브러리는 데이터를 DataFrame의 형태로 가공하기 쉽게 해주는 라이브러리이다. Pandas를 잘 다루기 위해서는 다양한 데이터를 가공해 보는것이 좋다. pydataset 라이브러리는 다양한 데이터를 제공해 준다. pip를 통해 pydataset 라이브러리를 설치하고 데이터를 불러와 보자.

pydataset 라이브러리 설치하기

  • jupyter notebook을 열고 하기 명령어를 통해 pydataset을 설치하자.
!pip install pydataset

Collecting pydataset
  Downloading pydataset-0.2.0.tar.gz (15.9 MB)
  Preparing metadata (setup.py): started
  Preparing metadata (setup.py): finished with status 'done'
Requirement already satisfied: pandas in c:\users\sjhsj\appdata\local\programs\python\python38\lib\site-packages (from pydataset) (1.2.1)
Requirement already satisfied: numpy>=1.16.5 in c:\users\sjhsj\appdata\local\programs\python\python38\lib\site-packages (from pandas->pydataset) (1.19.5)
Requirement already satisfied: pytz>=2017.3 in c:\users\sjhsj\appdata\local\programs\python\python38\lib\site-packages (from pandas->pydataset) (2020.5)
Requirement already satisfied: python-dateutil>=2.7.3 in c:\users\sjhsj\appdata\local\programs\python\python38\lib\site-packages (from pandas->pydataset) (2.8.1)
Requirement already satisfied: six>=1.5 in c:\users\sjhsj\appdata\roaming\python\python38\site-packages (from python-dateutil>=2.7.3->pandas->pydataset) (1.15.0)
Building wheels for collected packages: pydataset
  Building wheel for pydataset (setup.py): started
  Building wheel for pydataset (setup.py): finished with status 'done'
  Created wheel for pydataset: filename=pydataset-0.2.0-py3-none-any.whl size=15939431 sha256=4d0313a83c7772e1ae2e97cbb576bc03e137971b2a78cd873c2464504c2cb668
  Stored in directory: c:\users\sjhsj\appdata\local\pip\cache\wheels\d7\e5\36\85d319586b4a405d001029d489102f526ce5546248c295932a
Successfully built pydataset
Installing collected packages: pydataset
Successfully installed pydataset-0.2.0

pydataset 불러오기

  • import 명령을 통해 pydataset을 불러오자
import pydataset

initiated datasets repo at: C:\Users\sjhsj\.pydataset/
  • pydataset의 data()명령어를 통해 Data의 목록을 살펴보자
pydataset.data()
dataset_id title
0 AirPassengers Monthly Airline Passenger Numbers 1949-1960
1 BJsales Sales Data with Leading Indicator
2 BOD Biochemical Oxygen Demand
3 Formaldehyde Determination of Formaldehyde
4 HairEyeColor Hair and Eye Color of Statistics Students
... ... ...
752 VerbAgg Verbal Aggression item responses
753 cake Breakage Angle of Chocolate Cakes
754 cbpp Contagious bovine pleuropneumonia
755 grouseticks Data on red grouse ticks from Elston et al. 2001
756 sleepstudy Reaction times in a sleep deprivation study

757 rows × 2 columns

데이터 목록 개수를 확인해 보면 총 757개의 데이터 셋 목록이 있는 것을 확인 할 수 있다

Iris Dataset 불러오기

  • data 명령어 괄호 사이에 Dataset 이름을 넣어주면 바로 데이터 셋을 불러 올 수 있다 pydataset.data(‘iris’)
pydataset.data('iris')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica
  • show_doc option을 통해 Iris Dataset을 정보를 확인해 보자
pydataset.data('iris', show_doc=True)

iris

PyDataset Documentation (adopted from R Documentation. The displayed examples are in R)

## Edgar Anderson's Iris Data

### Description

This famous (Fisher's or Anderson's) iris data set gives the measurements in
centimeters of the variables sepal length and width and petal length and
width, respectively, for 50 flowers from each of 3 species of iris. The
species are _Iris setosa_, _versicolor_, and _virginica_.

### Usage

    iris
    iris3

### Format

`iris` is a data frame with 150 cases (rows) and 5 variables (columns) named
`Sepal.Length`, `Sepal.Width`, `Petal.Length`, `Petal.Width`, and `Species`.

`iris3` gives the same data arranged as a 3-dimensional array of size 50 by 4
by 3, as represented by S-PLUS. The first dimension gives the case number
within the species subsample, the second the measurements with names `Sepal
L.`, `Sepal W.`, `Petal L.`, and `Petal W.`, and the third the species.

### Source

Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems.
_Annals of Eugenics_, **7**, Part II, 179–188.

The data were collected by Anderson, Edgar (1935). The irises of the Gaspe
Peninsula, _Bulletin of the American Iris Society_, **59**, 2–5.

### References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) _The New S Language_.
Wadsworth & Brooks/Cole. (has `iris3` as `iris`.)

### See Also

`matplot` some examples of which use `iris`.

### Examples

    dni3 <- dimnames(iris3)
    ii <- data.frame(matrix(aperm(iris3, c(1,3,2)), ncol = 4,
                            dimnames = list(NULL, sub(" L.",".Length",
                                            sub(" W.",".Width", dni3[[2]])))),
        Species = gl(3, 50, labels = sub("S", "s", sub("V", "v", dni3[[3]]))))
    all.equal(ii, iris) # TRUE
  • iris를 변수에 저장해 보자
iris = pydataset.data('iris')

iris
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica

다른 데이터셋도 불러와 보자

  • AirPassengers Dataset
airpassengers = pydataset.data('AirPassengers')
airpassengers
time AirPassengers
1 1949.000000 112
2 1949.083333 118
3 1949.166667 132
4 1949.250000 129
5 1949.333333 121
6 1949.416667 135
7 1949.500000 148
8 1949.583333 148
9 1949.666667 136
10 1949.750000 119
11 1949.833333 104
12 1949.916667 118
13 1950.000000 115
14 1950.083333 126
15 1950.166667 141
16 1950.250000 135
17 1950.333333 125
18 1950.416667 149
19 1950.500000 170
20 1950.583333 170
21 1950.666667 158
22 1950.750000 133
23 1950.833333 114
24 1950.916667 140
25 1951.000000 145
26 1951.083333 150
27 1951.166667 178
28 1951.250000 163
29 1951.333333 172
30 1951.416667 178
31 1951.500000 199
32 1951.583333 199
33 1951.666667 184
34 1951.750000 162
35 1951.833333 146
36 1951.916667 166
37 1952.000000 171
38 1952.083333 180
39 1952.166667 193
40 1952.250000 181
41 1952.333333 183
42 1952.416667 218
43 1952.500000 230
44 1952.583333 242
45 1952.666667 209
46 1952.750000 191
47 1952.833333 172
48 1952.916667 194
49 1953.000000 196
50 1953.083333 196
51 1953.166667 236
52 1953.250000 235
53 1953.333333 229
54 1953.416667 243
55 1953.500000 264
56 1953.583333 272
57 1953.666667 237
58 1953.750000 211
59 1953.833333 180
60 1953.916667 201
61 1954.000000 204
62 1954.083333 188
63 1954.166667 235
64 1954.250000 227
65 1954.333333 234
66 1954.416667 264
67 1954.500000 302
68 1954.583333 293
69 1954.666667 259
70 1954.750000 229
71 1954.833333 203
72 1954.916667 229
73 1955.000000 242
74 1955.083333 233
75 1955.166667 267
76 1955.250000 269
77 1955.333333 270
78 1955.416667 315
79 1955.500000 364
80 1955.583333 347
81 1955.666667 312
82 1955.750000 274
83 1955.833333 237
84 1955.916667 278
85 1956.000000 284
86 1956.083333 277
87 1956.166667 317
88 1956.250000 313
89 1956.333333 318
90 1956.416667 374
91 1956.500000 413
92 1956.583333 405
93 1956.666667 355
94 1956.750000 306
95 1956.833333 271
96 1956.916667 306
97 1957.000000 315
98 1957.083333 301
99 1957.166667 356
100 1957.250000 348
101 1957.333333 355
102 1957.416667 422
103 1957.500000 465
104 1957.583333 467
105 1957.666667 404
106 1957.750000 347
107 1957.833333 305
108 1957.916667 336
109 1958.000000 340
110 1958.083333 318
111 1958.166667 362
112 1958.250000 348
113 1958.333333 363
114 1958.416667 435
115 1958.500000 491
116 1958.583333 505
117 1958.666667 404
118 1958.750000 359
119 1958.833333 310
120 1958.916667 337
121 1959.000000 360
122 1959.083333 342
123 1959.166667 406
124 1959.250000 396
125 1959.333333 420
126 1959.416667 472
127 1959.500000 548
128 1959.583333 559
129 1959.666667 463
130 1959.750000 407
131 1959.833333 362
132 1959.916667 405
133 1960.000000 417
134 1960.083333 391
135 1960.166667 419
136 1960.250000 461
137 1960.333333 472
138 1960.416667 535
139 1960.500000 622
140 1960.583333 606
141 1960.666667 508
142 1960.750000 461
143 1960.833333 390
144 1960.916667 432
  • Titanic Dataset
Titanic = pydataset.data('Titanic')
Titanic
Class Sex Age Survived Freq
1 1st Male Child No 0
2 2nd Male Child No 0
3 3rd Male Child No 35
4 Crew Male Child No 0
5 1st Female Child No 0
6 2nd Female Child No 0
7 3rd Female Child No 17
8 Crew Female Child No 0
9 1st Male Adult No 118
10 2nd Male Adult No 154
11 3rd Male Adult No 387
12 Crew Male Adult No 670
13 1st Female Adult No 4
14 2nd Female Adult No 13
15 3rd Female Adult No 89
16 Crew Female Adult No 3
17 1st Male Child Yes 5
18 2nd Male Child Yes 11
19 3rd Male Child Yes 13
20 Crew Male Child Yes 0
21 1st Female Child Yes 1
22 2nd Female Child Yes 13
23 3rd Female Child Yes 14
24 Crew Female Child Yes 0
25 1st Male Adult Yes 57
26 2nd Male Adult Yes 14
27 3rd Male Adult Yes 75
28 Crew Male Adult Yes 192
29 1st Female Adult Yes 140
30 2nd Female Adult Yes 80
31 3rd Female Adult Yes 76
32 Crew Female Adult Yes 20

pydataset에 등록된 다양한 Dataset을 통해 Pandas를 열심히 공부 하자

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