Overview

Dataset statistics

Number of variables6
Number of observations89
Missing cells14
Missing cells (%)2.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory49.5 B

Variable types

Categorical2
Text4

Dataset

Description인천광역시 중구 관내에 위치한 경로당에 대한 데이터 입니다.파일명 인천광역시_중구_경로당 현황파일내용 소재지, 경로당 이름, 주소 등
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3043897&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일 has constant value ""Constant
연락처 has 14 (15.7%) missing valuesMissing
경로당 has unique valuesUnique
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:36:27.865973
Analysis finished2024-01-28 16:36:28.582463
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지동명
Categorical

Distinct11
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
운서동
18 
영종동
14 
영종1동
14 
용유동
12 
신흥동
9 
Other values (6)
22 

Length

Max length4
Median length3
Mean length3.1910112
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
운서동 18
20.2%
영종동 14
15.7%
영종1동 14
15.7%
용유동 12
13.5%
신흥동 9
10.1%
개항동 8
9.0%
신포동 5
 
5.6%
도원동 3
 
3.4%
동인천동 3
 
3.4%
연안동 2
 
2.2%

Length

2024-01-29T01:36:28.674187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
운서동 18
20.2%
영종동 14
15.7%
영종1동 14
15.7%
용유동 12
13.5%
신흥동 9
10.1%
개항동 8
9.0%
신포동 5
 
5.6%
도원동 3
 
3.4%
동인천동 3
 
3.4%
연안동 2
 
2.2%

경로당
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-01-29T01:36:28.987900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.2359551
Min length2

Characters and Unicode

Total characters466
Distinct characters146
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row중앙동
2nd row신포동
3rd row신생삼성A
4th row신포시장
5th row답동맨션
ValueCountFrequency (%)
중앙동 1
 
1.1%
영종7단지a 1
 
1.1%
영종풍림2차 1
 
1.1%
금호베스트빌2단지 1
 
1.1%
영종풍림6-1 1
 
1.1%
영종풍림아이원1차8단지 1
 
1.1%
영종풍림아이원(2-1블럭 1
 
1.1%
주공7단지a 1
 
1.1%
금호a 1
 
1.1%
영종오션하임a 1
 
1.1%
Other values (79) 79
88.8%
2024-01-29T01:36:29.480078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 29
 
6.2%
영 25
 
5.4%
종 25
 
5.4%
동 16
 
3.4%
지 12
 
2.6%
이 12
 
2.6%
단 11
 
2.4%
11
 
2.4%
신 11
 
2.4%
1 11
 
2.4%
Other values (136) 303
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 382
82.0%
Uppercase Letter 35
 
7.5%
Decimal Number 31
 
6.7%
Space Separator 11
 
2.4%
Dash Punctuation 4
 
0.9%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 25
 
6.5%
종 25
 
6.5%
동 16
 
4.2%
지 12
 
3.1%
이 12
 
3.1%
단 11
 
2.9%
신 11
 
2.9%
스 10
 
2.6%
림 10
 
2.6%
풍 6
 
1.6%
Other values (118) 244
63.9%
Decimal Number
ValueCountFrequency (%)
1 11
35.5%
2 8
25.8%
7 3
 
9.7%
4 2
 
6.5%
9 2
 
6.5%
8 1
 
3.2%
6 1
 
3.2%
0 1
 
3.2%
3 1
 
3.2%
5 1
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
A 29
82.9%
H 3
 
8.6%
L 3
 
8.6%
Space Separator
ValueCountFrequency (%)
11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 382
82.0%
Common 48
 
10.3%
Latin 36
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 25
 
6.5%
종 25
 
6.5%
동 16
 
4.2%
지 12
 
3.1%
이 12
 
3.1%
단 11
 
2.9%
신 11
 
2.9%
스 10
 
2.6%
림 10
 
2.6%
풍 6
 
1.6%
Other values (118) 244
63.9%
Common
ValueCountFrequency (%)
11
22.9%
1 11
22.9%
2 8
16.7%
- 4
 
8.3%
7 3
 
6.2%
4 2
 
4.2%
9 2
 
4.2%
8 1
 
2.1%
( 1
 
2.1%
) 1
 
2.1%
Other values (4) 4
 
8.3%
Latin
ValueCountFrequency (%)
A 29
80.6%
H 3
 
8.3%
L 3
 
8.3%
e 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 382
82.0%
ASCII 84
 
18.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 29
34.5%
11
 
13.1%
1 11
 
13.1%
2 8
 
9.5%
- 4
 
4.8%
7 3
 
3.6%
H 3
 
3.6%
L 3
 
3.6%
4 2
 
2.4%
9 2
 
2.4%
Other values (8) 8
 
9.5%
Hangul
ValueCountFrequency (%)
영 25
 
6.5%
종 25
 
6.5%
동 16
 
4.2%
지 12
 
3.1%
이 12
 
3.1%
단 11
 
2.9%
신 11
 
2.9%
스 10
 
2.6%
림 10
 
2.6%
풍 6
 
1.6%
Other values (118) 244
63.9%

도로명주소
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-01-29T01:36:29.895828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length18.617978
Min length15

Characters and Unicode

Total characters1657
Distinct characters107
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row인천광역시 중구 제물량로 232번안길 19
2nd row인천광역시 중구 우현로20번길 43
3rd row인천광역시 중구 인중로 111
4th row인천광역시 중구 우현로49번길 11-5
5th row인천광역시 중구 제물량로 132-3
ValueCountFrequency (%)
인천광역시 89
25.0%
중구 89
25.0%
은하수로 5
 
1.4%
하늘별빛로 4
 
1.1%
인중로 4
 
1.1%
흰바위로 4
 
1.1%
제물량로 3
 
0.8%
12 3
 
0.8%
하늘달빛로 3
 
0.8%
15 3
 
0.8%
Other values (133) 149
41.9%
2024-01-29T01:36:30.914076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
268
16.2%
중 99
 
6.0%
시 99
 
6.0%
인 96
 
5.8%
구 89
 
5.4%
천 89
 
5.4%
역 89
 
5.4%
광 89
 
5.4%
로 88
 
5.3%
1 68
 
4.1%
Other values (97) 583
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1058
63.9%
Decimal Number 304
 
18.3%
Space Separator 268
 
16.2%
Dash Punctuation 18
 
1.1%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Other Punctuation 2
 
0.1%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 99
 
9.4%
시 99
 
9.4%
인 96
 
9.1%
구 89
 
8.4%
천 89
 
8.4%
역 89
 
8.4%
광 89
 
8.4%
로 88
 
8.3%
길 35
 
3.3%
번 32
 
3.0%
Other values (81) 253
23.9%
Decimal Number
ValueCountFrequency (%)
1 68
22.4%
2 47
15.5%
3 36
11.8%
4 31
10.2%
7 27
 
8.9%
6 21
 
6.9%
5 21
 
6.9%
9 18
 
5.9%
8 18
 
5.9%
0 17
 
5.6%
Space Separator
ValueCountFrequency (%)
268
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1058
63.9%
Common 598
36.1%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 99
 
9.4%
시 99
 
9.4%
인 96
 
9.1%
구 89
 
8.4%
천 89
 
8.4%
역 89
 
8.4%
광 89
 
8.4%
로 88
 
8.3%
길 35
 
3.3%
번 32
 
3.0%
Other values (81) 253
23.9%
Common
ValueCountFrequency (%)
268
44.8%
1 68
 
11.4%
2 47
 
7.9%
3 36
 
6.0%
4 31
 
5.2%
7 27
 
4.5%
6 21
 
3.5%
5 21
 
3.5%
9 18
 
3.0%
- 18
 
3.0%
Other values (5) 43
 
7.2%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1058
63.9%
ASCII 599
36.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
268
44.7%
1 68
 
11.4%
2 47
 
7.8%
3 36
 
6.0%
4 31
 
5.2%
7 27
 
4.5%
6 21
 
3.5%
5 21
 
3.5%
9 18
 
3.0%
- 18
 
3.0%
Other values (6) 44
 
7.3%
Hangul
ValueCountFrequency (%)
중 99
 
9.4%
시 99
 
9.4%
인 96
 
9.1%
구 89
 
8.4%
천 89
 
8.4%
역 89
 
8.4%
광 89
 
8.4%
로 88
 
8.3%
길 35
 
3.3%
번 32
 
3.0%
Other values (81) 253
23.9%

지번주소
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-01-29T01:36:31.267289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length21
Mean length18.52809
Min length14

Characters and Unicode

Total characters1649
Distinct characters55
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row인천광역시 중구 중앙동 1가 3-2
2nd row인천광역시 중구 답동 63
3rd row인천광역시 중구 신생동 38-5
4th row인천광역시 중구 신포동 3-2
5th row인천광역시 중구 답동 8-1
ValueCountFrequency (%)
인천광역시 89
24.9%
중구 89
24.9%
운서동 18
 
5.0%
중산동 16
 
4.5%
운남동 8
 
2.2%
을왕동 5
 
1.4%
신흥동1가 4
 
1.1%
무의동 4
 
1.1%
운북동 4
 
1.1%
북성동1가 3
 
0.8%
Other values (109) 118
33.0%
2024-01-29T01:36:31.896315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
269
16.3%
중 106
 
6.4%
1 101
 
6.1%
인 90
 
5.5%
광 89
 
5.4%
역 89
 
5.4%
시 89
 
5.4%
구 89
 
5.4%
천 89
 
5.4%
동 89
 
5.4%
Other values (45) 549
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 905
54.9%
Decimal Number 394
23.9%
Space Separator 269
 
16.3%
Dash Punctuation 78
 
4.7%
Close Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 106
11.7%
인 90
9.9%
광 89
9.8%
역 89
9.8%
시 89
9.8%
구 89
9.8%
천 89
9.8%
동 89
9.8%
운 30
 
3.3%
가 19
 
2.1%
Other values (30) 126
13.9%
Decimal Number
ValueCountFrequency (%)
1 101
25.6%
2 56
14.2%
7 51
12.9%
8 43
10.9%
3 33
 
8.4%
4 30
 
7.6%
5 27
 
6.9%
9 21
 
5.3%
0 18
 
4.6%
6 14
 
3.6%
Space Separator
ValueCountFrequency (%)
269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 905
54.9%
Common 744
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 106
11.7%
인 90
9.9%
광 89
9.8%
역 89
9.8%
시 89
9.8%
구 89
9.8%
천 89
9.8%
동 89
9.8%
운 30
 
3.3%
가 19
 
2.1%
Other values (30) 126
13.9%
Common
ValueCountFrequency (%)
269
36.2%
1 101
 
13.6%
- 78
 
10.5%
2 56
 
7.5%
7 51
 
6.9%
8 43
 
5.8%
3 33
 
4.4%
4 30
 
4.0%
5 27
 
3.6%
9 21
 
2.8%
Other values (5) 35
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 905
54.9%
ASCII 744
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269
36.2%
1 101
 
13.6%
- 78
 
10.5%
2 56
 
7.5%
7 51
 
6.9%
8 43
 
5.8%
3 33
 
4.4%
4 30
 
4.0%
5 27
 
3.6%
9 21
 
2.8%
Other values (5) 35
 
4.7%
Hangul
ValueCountFrequency (%)
중 106
11.7%
인 90
9.9%
광 89
9.8%
역 89
9.8%
시 89
9.8%
구 89
9.8%
천 89
9.8%
동 89
9.8%
운 30
 
3.3%
가 19
 
2.1%
Other values (30) 126
13.9%

연락처
Text

MISSING 

Distinct75
Distinct (%)100.0%
Missing14
Missing (%)15.7%
Memory size844.0 B
2024-01-29T01:36:32.262949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters900
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

Unique75 ?
Unique (%)100.0%

Sample

1st row032-762-0026
2nd row032-763-1159
3rd row032-891-0686
4th row032-772-5812
5th row032-764-7153
ValueCountFrequency (%)
032-889-2333 1
 
1.3%
032-751-2746 1
 
1.3%
032-746-5201 1
 
1.3%
032-752-4012 1
 
1.3%
032-752-6661 1
 
1.3%
032-751-4498 1
 
1.3%
032-746-5755 1
 
1.3%
032-751-3096 1
 
1.3%
032-751-1364 1
 
1.3%
032-746-8601 1
 
1.3%
Other values (65) 65
86.7%
2024-01-29T01:36:32.777892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 150
16.7%
2 127
14.1%
3 115
12.8%
7 110
12.2%
0 109
12.1%
6 61
6.8%
5 54
 
6.0%
4 51
 
5.7%
8 50
 
5.6%
1 45
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
83.3%
Dash Punctuation 150
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 127
16.9%
3 115
15.3%
7 110
14.7%
0 109
14.5%
6 61
8.1%
5 54
7.2%
4 51
6.8%
8 50
 
6.7%
1 45
 
6.0%
9 28
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 150
16.7%
2 127
14.1%
3 115
12.8%
7 110
12.2%
0 109
12.1%
6 61
6.8%
5 54
 
6.0%
4 51
 
5.7%
8 50
 
5.6%
1 45
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 150
16.7%
2 127
14.1%
3 115
12.8%
7 110
12.2%
0 109
12.1%
6 61
6.8%
5 54
 
6.0%
4 51
 
5.7%
8 50
 
5.6%
1 45
 
5.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-09-22
89 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-22
2nd row2023-09-22
3rd row2023-09-22
4th row2023-09-22
5th row2023-09-22

Common Values

ValueCountFrequency (%)
2023-09-22 89
100.0%

Length

2024-01-29T01:36:32.971671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:36:33.096563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-22 89
100.0%

Correlations

2024-01-29T01:36:33.169167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지동명경로당도로명주소지번주소연락처
소재지동명1.0001.0001.0001.0001.000
경로당1.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.000

Missing values

2024-01-29T01:36:28.400671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:36:28.535283image/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.

Sample

소재지동명경로당도로명주소지번주소연락처데이터기준일
0신포동중앙동인천광역시 중구 제물량로 232번안길 19인천광역시 중구 중앙동 1가 3-2032-762-00262023-09-22
1신포동신포동인천광역시 중구 우현로20번길 43인천광역시 중구 답동 63032-763-11592023-09-22
2신포동신생삼성A인천광역시 중구 인중로 111인천광역시 중구 신생동 38-5032-891-06862023-09-22
3신포동신포시장인천광역시 중구 우현로49번길 11-5인천광역시 중구 신포동 3-2032-772-58122023-09-22
4신포동답동맨션인천광역시 중구 제물량로 132-3인천광역시 중구 답동 8-1032-764-71532023-09-22
5연안동연안동인천광역시 중구 축항대로86번길 47(라이프아파트)인천광역시 중구 항동7가 27-107032-884-52012023-09-22
6연안동연안A인천광역시 중구 축항대로 234(연안아파트)인천광역시 중구 항동7가 91-2032-882-19562023-09-22
7신흥동신흥동인천광역시 중구 답동로 4-2인천광역시 중구 신흥동1가 20-13032-772-96752023-09-22
8신흥동신선동인천광역시 중구 인중로50번길 28-1인천광역시 중구 선화동 14-11032-884-51712023-09-22
9신흥동신흥아이파크A인천광역시 중구 인항로 30인천광역시 중구 신흥동3가 7-235032-889-23332023-09-22
소재지동명경로당도로명주소지번주소연락처데이터기준일
79용유동왕산인천광역시 중구 왕산로38번길 15인천광역시 중구 을왕동 959-1032-746-33322023-09-22
80용유동용마인천광역시 중구 마시란로160인천광역시 중구 덕교동 615-11032-746-92222023-09-22
81용유동늘목마을인천광역시 중구 늘목로59번길 14인천광역시 중구 을왕동 532032-746-58032023-09-22
82용유동을왕2통인천광역시 중구 을왕로13번길17, 2층인천광역시 중구 을왕동 717-11032-751-77982023-09-22
83용유동신설마을인천광역시 중구 마시란로 376-31인천광역시 중구 을왕동 179-110032-752-23992023-09-22
84용유동을왕4통인천광역시 중구 선녀바위로 70인천광역시 중구 을왕동 678-179032-751-67712023-09-22
85용유동큰무리인천광역시 중구 큰무리로7번길 32인천광역시 중구 무의동 631032-747-02282023-09-22
86용유동소무의인천광역시 중구 소무의로 23-1인천광역시 중구 무의동 988032-752-88442023-09-22
87용유동광명인천광역시 중구 대무의로 491인천광역시 중구 무의동 6-1032-751-06442023-09-22
88용유동포내인천광역시 중구 하나개로 7-10인천광역시 중구 무의동 219032-752-99322023-09-22