Overview

Dataset statistics

Number of variables7
Number of observations55
Missing cells8
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory59.4 B

Variable types

Numeric1
Categorical3
Text3

Dataset

Description인천광역시 남동구 베스트업소현황에 대한 데이터로 연번, 업종구분, 업소명, 소재지주소, 전화번호, 자료기준일자, 데이터기준일자를 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15067443&srcSe=7661IVAWM27C61E190

Alerts

자료기준일자 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업종구분High correlation
업종구분 is highly overall correlated with 연번High correlation
전화번호 has 8 (14.5%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique
소재지주소 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:43:48.278606
Analysis finished2024-03-18 04:43:48.770588
Duration0.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-03-18T13:43:48.862337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2024-03-18T13:43:49.045173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

업종구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
미용업
21 
세탁업
15 
숙박업
7 
목욕장업
6 
이용업
6 

Length

Max length4
Median length3
Mean length3.1090909
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 21
38.2%
세탁업 15
27.3%
숙박업 7
 
12.7%
목욕장업 6
 
10.9%
이용업 6
 
10.9%

Length

2024-03-18T13:43:49.164789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:43:49.272032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미용업 21
38.2%
세탁업 15
27.3%
숙박업 7
 
12.7%
목욕장업 6
 
10.9%
이용업 6
 
10.9%

업소명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-03-18T13:43:49.491200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.5818182
Min length3

Characters and Unicode

Total characters307
Distinct characters130
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row폴로관광호텔
2nd row파크마린관광호텔
3rd row라마다인천호텔
4th row소래호텔카카오
5th row알로하 호텔
ValueCountFrequency (%)
폴로관광호텔 1
 
1.6%
풍림세탁 1
 
1.6%
라스파연 1
 
1.6%
성현헤어 1
 
1.6%
위드헤어 1
 
1.6%
워니네일 1
 
1.6%
비바헤어 1
 
1.6%
리안헤어 1
 
1.6%
휴이엠헤어 1
 
1.6%
김남순헤어플레이스 1
 
1.6%
Other values (51) 51
83.6%
2024-03-18T13:43:49.869723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탁 13
 
4.2%
세 13
 
4.2%
어 12
 
3.9%
헤 12
 
3.9%
이 11
 
3.6%
호 8
 
2.6%
라 7
 
2.3%
우 7
 
2.3%
스 7
 
2.3%
소 7
 
2.3%
Other values (120) 210
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
97.1%
Space Separator 6
 
2.0%
Decimal Number 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탁 13
 
4.4%
세 13
 
4.4%
어 12
 
4.0%
헤 12
 
4.0%
이 11
 
3.7%
호 8
 
2.7%
라 7
 
2.3%
우 7
 
2.3%
스 7
 
2.3%
소 7
 
2.3%
Other values (117) 201
67.4%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
97.1%
Common 9
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탁 13
 
4.4%
세 13
 
4.4%
어 12
 
4.0%
헤 12
 
4.0%
이 11
 
3.7%
호 8
 
2.7%
라 7
 
2.3%
우 7
 
2.3%
스 7
 
2.3%
소 7
 
2.3%
Other values (117) 201
67.4%
Common
ValueCountFrequency (%)
6
66.7%
1 2
 
22.2%
9 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
97.1%
ASCII 9
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탁 13
 
4.4%
세 13
 
4.4%
어 12
 
4.0%
헤 12
 
4.0%
이 11
 
3.7%
호 8
 
2.7%
라 7
 
2.3%
우 7
 
2.3%
스 7
 
2.3%
소 7
 
2.3%
Other values (117) 201
67.4%
ASCII
ValueCountFrequency (%)
6
66.7%
1 2
 
22.2%
9 1
 
11.1%

소재지주소
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-03-18T13:43:50.067930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length35.309091
Min length23

Characters and Unicode

Total characters1942
Distinct characters152
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row인천광역시 남동구 앵고개로934번길 30 (논현동)
2nd row인천광역시 남동구 소래역로 44 (논현동)
3rd row인천광역시 남동구 소래역로 36, 6~16층 (논현동)
4th row인천광역시 남동구 앵고개로948번길 48 (논현동)
5th row인천광역시 남동구 남동대로777번길 32 (구월동)
ValueCountFrequency (%)
인천광역시 48
 
14.9%
남동구 48
 
14.9%
1층 12
 
3.7%
논현동 7
 
2.2%
만수동 7
 
2.2%
구월동 6
 
1.9%
간석동 6
 
1.9%
구월로 4
 
1.2%
상가동 4
 
1.2%
서창동 3
 
0.9%
Other values (159) 177
55.0%
2024-03-18T13:43:50.403201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
274
 
14.1%
동 126
 
6.5%
1 96
 
4.9%
구 74
 
3.8%
, 63
 
3.2%
남 61
 
3.1%
인 59
 
3.0%
역 57
 
2.9%
시 57
 
2.9%
천 57
 
2.9%
Other values (142) 1018
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1157
59.6%
Decimal Number 331
 
17.0%
Space Separator 274
 
14.1%
Other Punctuation 63
 
3.2%
Open Punctuation 55
 
2.8%
Close Punctuation 55
 
2.8%
Uppercase Letter 4
 
0.2%
Math Symbol 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 126
 
10.9%
구 74
 
6.4%
남 61
 
5.3%
인 59
 
5.1%
역 57
 
4.9%
시 57
 
4.9%
천 57
 
4.9%
광 55
 
4.8%
로 55
 
4.8%
호 38
 
3.3%
Other values (124) 518
44.8%
Decimal Number
ValueCountFrequency (%)
1 96
29.0%
2 45
13.6%
0 44
13.3%
3 33
 
10.0%
4 24
 
7.3%
5 22
 
6.6%
7 20
 
6.0%
6 19
 
5.7%
9 16
 
4.8%
8 12
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Space Separator
ValueCountFrequency (%)
274
100.0%
Other Punctuation
ValueCountFrequency (%)
, 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1156
59.5%
Common 781
40.2%
Latin 4
 
0.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 126
 
10.9%
구 74
 
6.4%
남 61
 
5.3%
인 59
 
5.1%
역 57
 
4.9%
시 57
 
4.9%
천 57
 
4.9%
광 55
 
4.8%
로 55
 
4.8%
호 38
 
3.3%
Other values (123) 517
44.7%
Common
ValueCountFrequency (%)
274
35.1%
1 96
 
12.3%
, 63
 
8.1%
( 55
 
7.0%
) 55
 
7.0%
2 45
 
5.8%
0 44
 
5.6%
3 33
 
4.2%
4 24
 
3.1%
5 22
 
2.8%
Other values (6) 70
 
9.0%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1156
59.5%
ASCII 785
40.4%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
274
34.9%
1 96
 
12.2%
, 63
 
8.0%
( 55
 
7.0%
) 55
 
7.0%
2 45
 
5.7%
0 44
 
5.6%
3 33
 
4.2%
4 24
 
3.1%
5 22
 
2.8%
Other values (8) 74
 
9.4%
Hangul
ValueCountFrequency (%)
동 126
 
10.9%
구 74
 
6.4%
남 61
 
5.3%
인 59
 
5.1%
역 57
 
4.9%
시 57
 
4.9%
천 57
 
4.9%
광 55
 
4.8%
로 55
 
4.8%
호 38
 
3.3%
Other values (123) 517
44.7%
CJK
ValueCountFrequency (%)
內 1
100.0%

전화번호
Text

MISSING 

Distinct47
Distinct (%)100.0%
Missing8
Missing (%)14.5%
Memory size572.0 B
2024-03-18T13:43:50.610906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters564
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

Unique47 ?
Unique (%)100.0%

Sample

1st row032-426-9250
2nd row032-425-2700
3rd row032-460-1100
4th row032-424-9993
5th row032-421-8673
ValueCountFrequency (%)
032-426-9250 1
 
2.1%
032-424-9320 1
 
2.1%
032-471-3481 1
 
2.1%
032-426-8810 1
 
2.1%
032-423-4545 1
 
2.1%
032-466-1121 1
 
2.1%
032-435-5004 1
 
2.1%
032-461-3072 1
 
2.1%
032-429-4054 1
 
2.1%
032-466-3839 1
 
2.1%
Other values (37) 37
78.7%
2024-03-18T13:43:50.908260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 94
16.7%
2 87
15.4%
0 86
15.2%
3 81
14.4%
4 70
12.4%
6 33
 
5.9%
5 32
 
5.7%
1 24
 
4.3%
7 23
 
4.1%
9 19
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 470
83.3%
Dash Punctuation 94
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 87
18.5%
0 86
18.3%
3 81
17.2%
4 70
14.9%
6 33
 
7.0%
5 32
 
6.8%
1 24
 
5.1%
7 23
 
4.9%
9 19
 
4.0%
8 15
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 94
16.7%
2 87
15.4%
0 86
15.2%
3 81
14.4%
4 70
12.4%
6 33
 
5.9%
5 32
 
5.7%
1 24
 
4.3%
7 23
 
4.1%
9 19
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 94
16.7%
2 87
15.4%
0 86
15.2%
3 81
14.4%
4 70
12.4%
6 33
 
5.9%
5 32
 
5.7%
1 24
 
4.3%
7 23
 
4.1%
9 19
 
3.4%

자료기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2022-12-31
55 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 55
100.0%

Length

2024-03-18T13:43:51.025830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:43:51.118618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 55
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-05-12
55 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-12
2nd row2023-05-12
3rd row2023-05-12
4th row2023-05-12
5th row2023-05-12

Common Values

ValueCountFrequency (%)
2023-05-12 55
100.0%

Length

2024-03-18T13:43:51.224863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:43:51.301209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-12 55
100.0%

Interactions

2024-03-18T13:43:48.531696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:43:51.357884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종구분업소명소재지주소전화번호
연번1.0000.9951.0001.0001.000
업종구분0.9951.0001.0001.0001.000
업소명1.0001.0001.0001.0001.000
소재지주소1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000
2024-03-18T13:43:51.435417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종구분
연번1.0000.866
업종구분0.8661.000

Missing values

2024-03-18T13:43:48.619857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:43:48.726228image/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

연번업종구분업소명소재지주소전화번호자료기준일자데이터기준일자
01숙박업폴로관광호텔인천광역시 남동구 앵고개로934번길 30 (논현동)032-426-92502022-12-312023-05-12
12숙박업파크마린관광호텔인천광역시 남동구 소래역로 44 (논현동)032-425-27002022-12-312023-05-12
23숙박업라마다인천호텔인천광역시 남동구 소래역로 36, 6~16층 (논현동)032-460-11002022-12-312023-05-12
34숙박업소래호텔카카오인천광역시 남동구 앵고개로948번길 48 (논현동)032-424-99932022-12-312023-05-12
45숙박업알로하 호텔인천광역시 남동구 남동대로777번길 32 (구월동)032-421-86732022-12-312023-05-12
56숙박업라르시티인천광역시 남동구 호구포로 209 (논현동, 라르시티호텔 1층 일부, 6층~15층)032-456-00002022-12-312023-05-12
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4950이용업금호이발관인천광역시남동구구월로73,6동1층105호(간석동,금호아파트內상가)<NA>2022-12-312023-05-12
5051이용업아트헤어샾인천광역시남동구논현로158,1층일부호(논현동)032-421-04012022-12-312023-05-12
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