Overview

Dataset statistics

Number of variables8
Number of observations251
Missing cells492
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory64.5 B

Variable types

DateTime2
Text4
Categorical2

Dataset

Description2020년 제부도 이용관광객 현황을 나타낸 데이터입니다.
Author화성도시공사
URLhttps://www.data.go.kr/data/15073433/fileData.do

Alerts

누적 관광객수 has constant value ""Constant
관리기관 has constant value ""Constant
관리자 연락처 has constant value ""Constant
월별 관광객수 has 242 (96.4%) missing valuesMissing
누적 관광객수 has 250 (99.6%) missing valuesMissing
일자 has unique valuesUnique
데이터 기준일자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:14:55.564316
Analysis finished2023-12-12 07:14:56.016641
Duration0.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2020-01-01 00:00:00
Maximum2020-09-07 00:00:00
2023-12-12T16:14:56.086470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:56.254157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입차대수
Text

Distinct241
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T16:14:56.768603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9282869
Min length3

Characters and Unicode

Total characters1237
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

Unique232 ?
Unique (%)92.4%

Sample

1st row1,661
2nd row3,321
3rd row3,025
4th row1,217
5th row795
ValueCountFrequency (%)
1,252 3
 
1.2%
1,324 2
 
0.8%
1,195 2
 
0.8%
2,044 2
 
0.8%
1,844 2
 
0.8%
1,213 2
 
0.8%
1,859 2
 
0.8%
2,151 2
 
0.8%
1,766 2
 
0.8%
1,795 1
 
0.4%
Other values (231) 231
92.0%
2023-12-12T16:14:57.375280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 242
19.6%
1 203
16.4%
2 149
12.0%
5 90
 
7.3%
3 86
 
7.0%
6 84
 
6.8%
8 84
 
6.8%
7 82
 
6.6%
4 79
 
6.4%
9 74
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 995
80.4%
Other Punctuation 242
 
19.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 203
20.4%
2 149
15.0%
5 90
9.0%
3 86
8.6%
6 84
8.4%
8 84
8.4%
7 82
8.2%
4 79
 
7.9%
9 74
 
7.4%
0 64
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1237
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 242
19.6%
1 203
16.4%
2 149
12.0%
5 90
 
7.3%
3 86
 
7.0%
6 84
 
6.8%
8 84
 
6.8%
7 82
 
6.6%
4 79
 
6.4%
9 74
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 242
19.6%
1 203
16.4%
2 149
12.0%
5 90
 
7.3%
3 86
 
7.0%
6 84
 
6.8%
8 84
 
6.8%
7 82
 
6.6%
4 79
 
6.4%
9 74
 
6.0%

이용객
Text

Distinct241
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-12T16:14:57.881119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1474104
Min length5

Characters and Unicode

Total characters1292
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

Unique232 ?
Unique (%)92.4%

Sample

1st row4,153
2nd row8,303
3rd row7,563
4th row3,043
5th row1,988
ValueCountFrequency (%)
3,130 3
 
1.2%
3,310 2
 
0.8%
2,988 2
 
0.8%
5,110 2
 
0.8%
4,610 2
 
0.8%
3,033 2
 
0.8%
4,648 2
 
0.8%
5,378 2
 
0.8%
4,415 2
 
0.8%
4,488 1
 
0.4%
Other values (231) 231
92.0%
2023-12-12T16:14:58.400120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 251
19.4%
3 166
12.8%
5 147
11.4%
0 117
9.1%
4 116
9.0%
8 115
8.9%
1 108
8.4%
2 86
 
6.7%
9 63
 
4.9%
6 62
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1041
80.6%
Other Punctuation 251
 
19.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 166
15.9%
5 147
14.1%
0 117
11.2%
4 116
11.1%
8 115
11.0%
1 108
10.4%
2 86
8.3%
9 63
 
6.1%
6 62
 
6.0%
7 61
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 251
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 251
19.4%
3 166
12.8%
5 147
11.4%
0 117
9.1%
4 116
9.0%
8 115
8.9%
1 108
8.4%
2 86
 
6.7%
9 63
 
4.9%
6 62
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 251
19.4%
3 166
12.8%
5 147
11.4%
0 117
9.1%
4 116
9.0%
8 115
8.9%
1 108
8.4%
2 86
 
6.7%
9 63
 
4.9%
6 62
 
4.8%

월별 관광객수
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing242
Missing (%)96.4%
Memory size2.1 KiB
2023-12-12T16:14:58.574234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8888889
Min length6

Characters and Unicode

Total characters62
Distinct characters10
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

Unique9 ?
Unique (%)100.0%

Sample

1st row138,776
2nd row132,659
3rd row179,683
4th row182,761
5th row243,949
ValueCountFrequency (%)
138,776 1
11.1%
132,659 1
11.1%
179,683 1
11.1%
182,761 1
11.1%
243,949 1
11.1%
212,466 1
11.1%
217,836 1
11.1%
194,414 1
11.1%
49,767 1
11.1%
2023-12-12T16:14:58.821971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9
14.5%
, 9
14.5%
6 8
12.9%
7 7
11.3%
4 7
11.3%
2 6
9.7%
9 6
9.7%
3 5
8.1%
8 4
6.5%
5 1
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53
85.5%
Other Punctuation 9
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
17.0%
6 8
15.1%
7 7
13.2%
4 7
13.2%
2 6
11.3%
9 6
11.3%
3 5
9.4%
8 4
7.5%
5 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
14.5%
, 9
14.5%
6 8
12.9%
7 7
11.3%
4 7
11.3%
2 6
9.7%
9 6
9.7%
3 5
8.1%
8 4
6.5%
5 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
14.5%
, 9
14.5%
6 8
12.9%
7 7
11.3%
4 7
11.3%
2 6
9.7%
9 6
9.7%
3 5
8.1%
8 4
6.5%
5 1
 
1.6%

누적 관광객수
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing250
Missing (%)99.6%
Memory size2.1 KiB
2023-12-12T16:14:58.941526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters5
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

Unique1 ?
Unique (%)100.0%

Sample

1st row1,552,311
ValueCountFrequency (%)
1,552,311 1
100.0%
2023-12-12T16:14:59.174804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3
33.3%
, 2
22.2%
5 2
22.2%
2 1
 
11.1%
3 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7
77.8%
Other Punctuation 2
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
42.9%
5 2
28.6%
2 1
 
14.3%
3 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3
33.3%
, 2
22.2%
5 2
22.2%
2 1
 
11.1%
3 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3
33.3%
, 2
22.2%
5 2
22.2%
2 1
 
11.1%
3 1
 
11.1%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
제부도관리팀
251 

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 251
100.0%

Length

2023-12-12T16:14:59.293540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:14:59.370357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제부도관리팀 251
100.0%

관리자 연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
031-355-3624
251 

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 251
100.0%

Length

2023-12-12T16:14:59.456020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:14:59.563291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
031-355-3624 251
100.0%

데이터 기준일자
Date

UNIQUE 

Distinct251
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2020-01-01 00:00:00
Maximum2020-09-07 00:00:00
2023-12-12T16:14:59.696042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:14:59.859462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Missing values

2023-12-12T16:14:55.747983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:14:55.868753image/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.
2023-12-12T16:14:55.965918image/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
02020-01-011,6614,153<NA><NA>제부도관리팀031-355-36242020-01-01
12020-01-023,3218,303<NA><NA>제부도관리팀031-355-36242020-01-02
22020-01-033,0257,563<NA><NA>제부도관리팀031-355-36242020-01-03
32020-01-041,2173,043<NA><NA>제부도관리팀031-355-36242020-01-04
42020-01-057951,988<NA><NA>제부도관리팀031-355-36242020-01-05
52020-01-069672,418<NA><NA>제부도관리팀031-355-36242020-01-06
62020-01-079262,315<NA><NA>제부도관리팀031-355-36242020-01-07
72020-01-081,1762,940<NA><NA>제부도관리팀031-355-36242020-01-08
82020-01-092,4676,168<NA><NA>제부도관리팀031-355-36242020-01-09
92020-01-102,4466,115<NA><NA>제부도관리팀031-355-36242020-01-10
일자입차대수이용객월별 관광객수누적 관광객수관리기관관리자 연락처데이터 기준일자
2412020-08-291,9374,843194,414<NA>제부도관리팀031-355-36242020-08-29
2422020-08-301,8574,643<NA><NA>제부도관리팀031-355-36242020-08-30
2432020-08-311,3353,338<NA><NA>제부도관리팀031-355-36242020-08-31
2442020-09-011,5153,788<NA><NA>제부도관리팀031-355-36242020-09-01
2452020-09-022,7266,815<NA><NA>제부도관리팀031-355-36242020-09-02
2462020-09-035,52013,800<NA><NA>제부도관리팀031-355-36242020-09-03
2472020-09-042,1445,360<NA><NA>제부도관리팀031-355-36242020-09-04
2482020-09-057531,883<NA><NA>제부도관리팀031-355-36242020-09-05
2492020-09-062,2905,725<NA><NA>제부도관리팀031-355-36242020-09-06
2502020-09-071,7664,41549,7671,552,311제부도관리팀031-355-36242020-09-07