Overview

Dataset statistics

Number of variables8
Number of observations365
Missing cells717
Missing cells (%)24.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 KiB
Average record size in memory64.4 B

Variable types

DateTime3
Text3
Categorical2

Dataset

Description2018년 제부도 이용 관광객 현황을 나타낸 데이터입니다.
Author화성도시공사
URLhttps://www.data.go.kr/data/15073431/fileData.do

Alerts

누적 관광객수 has constant value ""Constant
관리기관 has constant value ""Constant
관리자 연락처 has constant value ""Constant
월별 관광객수 has 353 (96.7%) missing valuesMissing
누적 관광객수 has 364 (99.7%) missing valuesMissing
일자 has unique valuesUnique
데이터 기준일자 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:59:54.514074
Analysis finished2023-12-11 23:59:55.255935
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

UNIQUE 

Distinct365
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2018-01-01 00:00:00
Maximum2018-12-31 00:00:00
2023-12-12T08:59:55.321484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:59:55.485610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입차대수
Text

Distinct346
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T08:59:55.945752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8575342
Min length3

Characters and Unicode

Total characters1773
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)90.1%

Sample

1st row3,770
2nd row1,033
3rd row1,024
4th row1,153
5th row1,263
ValueCountFrequency (%)
1,100 3
 
0.8%
1,180 3
 
0.8%
4,494 2
 
0.5%
1,358 2
 
0.5%
2,478 2
 
0.5%
1,574 2
 
0.5%
1,079 2
 
0.5%
1,063 2
 
0.5%
1,581 2
 
0.5%
1,824 2
 
0.5%
Other values (336) 343
94.0%
2023-12-12T08:59:56.637808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 339
19.1%
1 290
16.4%
4 159
9.0%
3 157
8.9%
2 150
8.5%
0 133
 
7.5%
5 119
 
6.7%
9 115
 
6.5%
6 109
 
6.1%
7 108
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1434
80.9%
Other Punctuation 339
 
19.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 290
20.2%
4 159
11.1%
3 157
10.9%
2 150
10.5%
0 133
9.3%
5 119
8.3%
9 115
 
8.0%
6 109
 
7.6%
7 108
 
7.5%
8 94
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 339
19.1%
1 290
16.4%
4 159
9.0%
3 157
8.9%
2 150
8.5%
0 133
 
7.5%
5 119
 
6.7%
9 115
 
6.5%
6 109
 
6.1%
7 108
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 339
19.1%
1 290
16.4%
4 159
9.0%
3 157
8.9%
2 150
8.5%
0 133
 
7.5%
5 119
 
6.7%
9 115
 
6.5%
6 109
 
6.1%
7 108
 
6.1%

이용객
Text

Distinct346
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T08:59:57.100345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1534247
Min length5

Characters and Unicode

Total characters1881
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique329 ?
Unique (%)90.1%

Sample

1st row9,425
2nd row2,583
3rd row2,560
4th row2,883
5th row3,158
ValueCountFrequency (%)
2,750 3
 
0.8%
2,950 3
 
0.8%
11,235 2
 
0.5%
3,395 2
 
0.5%
6,195 2
 
0.5%
3,935 2
 
0.5%
2,698 2
 
0.5%
2,658 2
 
0.5%
3,953 2
 
0.5%
4,560 2
 
0.5%
Other values (336) 343
94.0%
2023-12-12T08:59:57.626346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 365
19.4%
3 278
14.8%
0 198
10.5%
5 184
9.8%
8 175
9.3%
2 154
8.2%
4 138
 
7.3%
1 136
 
7.2%
6 98
 
5.2%
9 86
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1516
80.6%
Other Punctuation 365
 
19.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 278
18.3%
0 198
13.1%
5 184
12.1%
8 175
11.5%
2 154
10.2%
4 138
9.1%
1 136
9.0%
6 98
 
6.5%
9 86
 
5.7%
7 69
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1881
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 365
19.4%
3 278
14.8%
0 198
10.5%
5 184
9.8%
8 175
9.3%
2 154
8.2%
4 138
 
7.3%
1 136
 
7.2%
6 98
 
5.2%
9 86
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 365
19.4%
3 278
14.8%
0 198
10.5%
5 184
9.8%
8 175
9.3%
2 154
8.2%
4 138
 
7.3%
1 136
 
7.2%
6 98
 
5.2%
9 86
 
4.6%

월별 관광객수
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing353
Missing (%)96.7%
Memory size3.0 KiB
2023-12-12T08:59:57.807122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters84
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st row118,104
2nd row112,860
3rd row156,494
4th row154,971
5th row204,170
ValueCountFrequency (%)
118,104 1
8.3%
112,860 1
8.3%
156,494 1
8.3%
154,971 1
8.3%
204,170 1
8.3%
216,911 1
8.3%
211,508 1
8.3%
228,303 1
8.3%
205,293 1
8.3%
177,407 1
8.3%
Other values (2) 2
16.7%
2023-12-12T08:59:58.135716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
20.2%
, 12
14.3%
0 9
10.7%
4 9
10.7%
2 8
9.5%
7 6
 
7.1%
8 5
 
6.0%
5 5
 
6.0%
3 5
 
6.0%
6 4
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
85.7%
Other Punctuation 12
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
23.6%
0 9
12.5%
4 9
12.5%
2 8
11.1%
7 6
 
8.3%
8 5
 
6.9%
5 5
 
6.9%
3 5
 
6.9%
6 4
 
5.6%
9 4
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
20.2%
, 12
14.3%
0 9
10.7%
4 9
10.7%
2 8
9.5%
7 6
 
7.1%
8 5
 
6.0%
5 5
 
6.0%
3 5
 
6.0%
6 4
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
20.2%
, 12
14.3%
0 9
10.7%
4 9
10.7%
2 8
9.5%
7 6
 
7.1%
8 5
 
6.0%
5 5
 
6.0%
3 5
 
6.0%
6 4
 
4.8%

누적 관광객수
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing364
Missing (%)99.7%
Memory size3.0 KiB
Minimum2069-02-01 00:00:00
Maximum2069-02-01 00:00:00
2023-12-12T08:59:58.247847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:59:58.367725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
제부도관리팀
365 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제부도관리팀
2nd row제부도관리팀
3rd row제부도관리팀
4th row제부도관리팀
5th row제부도관리팀

Common Values

ValueCountFrequency (%)
제부도관리팀 365
100.0%

Length

2023-12-12T08:59:58.515243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:59:58.624195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제부도관리팀 365
100.0%

관리자 연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
031-355-3624
365 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row031-355-3624
2nd row031-355-3624
3rd row031-355-3624
4th row031-355-3624
5th row031-355-3624

Common Values

ValueCountFrequency (%)
031-355-3624 365
100.0%

Length

2023-12-12T08:59:58.727482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:59:58.817663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
031-355-3624 365
100.0%

데이터 기준일자
Date

UNIQUE 

Distinct365
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2018-01-01 00:00:00
Maximum2018-12-31 00:00:00
2023-12-12T08:59:58.914808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:59:59.045797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-12T08:59:54.950631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:59:55.079618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T08:59:55.206230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

일자입차대수이용객월별 관광객수누적 관광객수관리기관관리자 연락처데이터 기준일자
02018-01-013,7709,425<NA><NA>제부도관리팀031-355-36242018-01-01
12018-01-021,0332,583<NA><NA>제부도관리팀031-355-36242018-01-02
22018-01-031,0242,560<NA><NA>제부도관리팀031-355-36242018-01-03
32018-01-041,1532,883<NA><NA>제부도관리팀031-355-36242018-01-04
42018-01-051,2633,158<NA><NA>제부도관리팀031-355-36242018-01-05
52018-01-062,9777,443<NA><NA>제부도관리팀031-355-36242018-01-06
62018-01-072,5476,368<NA><NA>제부도관리팀031-355-36242018-01-07
72018-01-081,0642,660<NA><NA>제부도관리팀031-355-36242018-01-08
82018-01-099602,400<NA><NA>제부도관리팀031-355-36242018-01-09
92018-01-108512,128<NA><NA>제부도관리팀031-355-36242018-01-10
일자입차대수이용객월별 관광객수누적 관광객수관리기관관리자 연락처데이터 기준일자
3552018-12-221,9634,908<NA><NA>제부도관리팀031-355-36242018-12-22
3562018-12-232,8767,190<NA><NA>제부도관리팀031-355-36242018-12-23
3572018-12-241,3583,395<NA><NA>제부도관리팀031-355-36242018-12-24
3582018-12-251,2093,023<NA><NA>제부도관리팀031-355-36242018-12-25
3592018-12-261,2803,200<NA><NA>제부도관리팀031-355-36242018-12-26
3602018-12-272,9647,410<NA><NA>제부도관리팀031-355-36242018-12-27
3612018-12-283,3388,345<NA><NA>제부도관리팀031-355-36242018-12-28
3622018-12-294,34510,863<NA>2,069,513제부도관리팀031-355-36242018-12-29
3632018-12-305,95414,885147,454<NA>제부도관리팀031-355-36242018-12-30
3642018-12-311,2073,018<NA><NA>제부도관리팀031-355-36242018-12-31