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

Description2019년 제부도 이용관광객 현황을 나타낸 데이터입니다.
Author화성도시공사
URLhttps://www.data.go.kr/data/15073432/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 started2023-12-12 14:20:37.729581
Analysis finished2023-12-12 14:20:38.191195
Duration0.46 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
Minimum2019-01-01 00:00:00
Maximum2019-12-31 00:00:00
2023-12-12T23:20:38.256218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:38.391357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입차대수
Text

Distinct344
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T23:20:38.787487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9671233
Min length3

Characters and Unicode

Total characters1813
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

Unique323 ?
Unique (%)88.5%

Sample

1st row990
2nd row1,211
3rd row2,466
4th row2,381
5th row1,115
ValueCountFrequency (%)
1,255 2
 
0.5%
992 2
 
0.5%
1,577 2
 
0.5%
1,225 2
 
0.5%
3,394 2
 
0.5%
1,177 2
 
0.5%
1,212 2
 
0.5%
1,459 2
 
0.5%
1,516 2
 
0.5%
1,435 2
 
0.5%
Other values (334) 345
94.5%
2023-12-12T23:20:39.368708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 359
19.8%
1 337
18.6%
2 169
9.3%
3 160
8.8%
4 145
8.0%
5 141
 
7.8%
7 117
 
6.5%
0 107
 
5.9%
6 106
 
5.8%
9 105
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1454
80.2%
Other Punctuation 359
 
19.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 337
23.2%
2 169
11.6%
3 160
11.0%
4 145
10.0%
5 141
9.7%
7 117
 
8.0%
0 107
 
7.4%
6 106
 
7.3%
9 105
 
7.2%
8 67
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 359
19.8%
1 337
18.6%
2 169
9.3%
3 160
8.8%
4 145
8.0%
5 141
 
7.8%
7 117
 
6.5%
0 107
 
5.9%
6 106
 
5.8%
9 105
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 359
19.8%
1 337
18.6%
2 169
9.3%
3 160
8.8%
4 145
8.0%
5 141
 
7.8%
7 117
 
6.5%
0 107
 
5.9%
6 106
 
5.8%
9 105
 
5.8%

이용객
Text

Distinct344
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T23:20:39.821651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1342466
Min length5

Characters and Unicode

Total characters1874
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

Unique323 ?
Unique (%)88.5%

Sample

1st row2,475
2nd row3,028
3rd row6,165
4th row5,953
5th row2,788
ValueCountFrequency (%)
3,138 2
 
0.5%
2,480 2
 
0.5%
3,943 2
 
0.5%
3,063 2
 
0.5%
8,485 2
 
0.5%
2,943 2
 
0.5%
3,030 2
 
0.5%
3,648 2
 
0.5%
3,790 2
 
0.5%
3,588 2
 
0.5%
Other values (334) 345
94.5%
2023-12-12T23:20:40.457209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 365
19.5%
3 319
17.0%
8 194
10.4%
5 179
9.6%
0 176
9.4%
4 154
8.2%
1 131
 
7.0%
2 109
 
5.8%
6 85
 
4.5%
7 83
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1509
80.5%
Other Punctuation 365
 
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 319
21.1%
8 194
12.9%
5 179
11.9%
0 176
11.7%
4 154
10.2%
1 131
8.7%
2 109
 
7.2%
6 85
 
5.6%
7 83
 
5.5%
9 79
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 365
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1874
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 365
19.5%
3 319
17.0%
8 194
10.4%
5 179
9.6%
0 176
9.4%
4 154
8.2%
1 131
 
7.0%
2 109
 
5.8%
6 85
 
4.5%
7 83
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 365
19.5%
3 319
17.0%
8 194
10.4%
5 179
9.6%
0 176
9.4%
4 154
8.2%
1 131
 
7.0%
2 109
 
5.8%
6 85
 
4.5%
7 83
 
4.4%

월별 관광객수
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing353
Missing (%)96.7%
Memory size3.0 KiB
2023-12-12T23:20:40.708984image/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 row137,423
2nd row131,578
3rd row171,792
4th row161,535
5th row201,814
ValueCountFrequency (%)
137,423 1
8.3%
131,578 1
8.3%
171,792 1
8.3%
161,535 1
8.3%
201,814 1
8.3%
210,414 1
8.3%
184,158 1
8.3%
242,062 1
8.3%
169,647 1
8.3%
193,203 1
8.3%
Other values (2) 2
16.7%
2023-12-12T23:20:41.035454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
20.2%
, 12
14.3%
4 10
11.9%
2 9
10.7%
3 7
8.3%
7 7
8.3%
9 5
 
6.0%
0 5
 
6.0%
5 4
 
4.8%
8 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%
4 10
13.9%
2 9
12.5%
3 7
9.7%
7 7
9.7%
9 5
 
6.9%
0 5
 
6.9%
5 4
 
5.6%
8 4
 
5.6%
6 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%
4 10
11.9%
2 9
10.7%
3 7
8.3%
7 7
8.3%
9 5
 
6.0%
0 5
 
6.0%
5 4
 
4.8%
8 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%
4 10
11.9%
2 9
10.7%
3 7
8.3%
7 7
8.3%
9 5
 
6.0%
0 5
 
6.0%
5 4
 
4.8%
8 4
 
4.8%

누적 관광객수
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing364
Missing (%)99.7%
Memory size3.0 KiB
Minimum1989-02-01 00:00:00
Maximum1989-02-01 00:00:00
2023-12-12T23:20:41.155568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:41.268753image/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

2023-12-12T23:20:41.392430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:20:41.503714image/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

2023-12-12T23:20:41.638592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:20:41.767558image/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
Minimum2019-01-01 00:00:00
Maximum2019-12-31 00:00:00
2023-12-12T23:20:41.914741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:42.076295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-12T23:20:37.901608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:20:38.059455image/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.
2023-12-12T23:20:38.145766image/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
02019-01-019902,475<NA><NA>제부도관리팀031-355-36242019-01-01
12019-01-021,2113,028<NA><NA>제부도관리팀031-355-36242019-01-02
22019-01-032,4666,165<NA><NA>제부도관리팀031-355-36242019-01-03
32019-01-042,3815,953<NA><NA>제부도관리팀031-355-36242019-01-04
42019-01-051,1152,788<NA><NA>제부도관리팀031-355-36242019-01-05
52019-01-061,0572,643<NA><NA>제부도관리팀031-355-36242019-01-06
62019-01-079922,480<NA><NA>제부도관리팀031-355-36242019-01-07
72019-01-081,1562,890<NA><NA>제부도관리팀031-355-36242019-01-08
82019-01-091,4603,650<NA><NA>제부도관리팀031-355-36242019-01-09
92019-01-103,3968,490<NA><NA>제부도관리팀031-355-36242019-01-10
일자입차대수이용객월별 관광객수누적 관광객수관리기관관리자 연락처데이터 기준일자
3552019-12-221,6334,083<NA><NA>제부도관리팀031-355-36242019-12-22
3562019-12-232,4536,133<NA><NA>제부도관리팀031-355-36242019-12-23
3572019-12-241,1692,923<NA><NA>제부도관리팀031-355-36242019-12-24
3582019-12-251,5173,793<NA><NA>제부도관리팀031-355-36242019-12-25
3592019-12-262,9897,473<NA><NA>제부도관리팀031-355-36242019-12-26
3602019-12-272,4036,008<NA><NA>제부도관리팀031-355-36242019-12-27
3612019-12-281,3833,458<NA><NA>제부도관리팀031-355-36242019-12-28
3622019-12-292,9117,278143,0492,089,472제부도관리팀031-355-36242019-12-29
3632019-12-304,42911,073<NA><NA>제부도관리팀031-355-36242019-12-30
3642019-12-311,4763,690<NA><NA>제부도관리팀031-355-36242019-12-31