Overview

Dataset statistics

Number of variables5
Number of observations58
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory44.3 B

Variable types

Numeric2
Categorical1
Text2

Dataset

Description인천광역시 부평구 우수 공중위생업소 명단 데이터는 업소명, 소재지, 지정연도, 지정여부에 대한 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15102604&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 지정연도High correlation
지정연도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:08:48.359470
Analysis finished2024-03-18 05:08:50.815687
Duration2.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.5
Minimum1
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2024-03-18T14:08:50.883989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.85
Q115.25
median29.5
Q343.75
95-th percentile55.15
Maximum58
Range57
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation16.886879
Coefficient of variation (CV)0.57243656
Kurtosis-1.2
Mean29.5
Median Absolute Deviation (MAD)14.5
Skewness0
Sum1711
Variance285.16667
MonotonicityStrictly increasing
2024-03-18T14:08:51.020319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.7%
45 1
 
1.7%
33 1
 
1.7%
34 1
 
1.7%
35 1
 
1.7%
36 1
 
1.7%
37 1
 
1.7%
38 1
 
1.7%
39 1
 
1.7%
40 1
 
1.7%
Other values (48) 48
82.8%
ValueCountFrequency (%)
1 1
1.7%
2 1
1.7%
3 1
1.7%
4 1
1.7%
5 1
1.7%
6 1
1.7%
7 1
1.7%
8 1
1.7%
9 1
1.7%
10 1
1.7%
ValueCountFrequency (%)
58 1
1.7%
57 1
1.7%
56 1
1.7%
55 1
1.7%
54 1
1.7%
53 1
1.7%
52 1
1.7%
51 1
1.7%
50 1
1.7%
49 1
1.7%

업종명
Categorical

Distinct3
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size596.0 B
숙박업
30 
세탁업
23 
목욕장업
5 

Length

Max length4
Median length3
Mean length3.0862069
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업 30
51.7%
세탁업 23
39.7%
목욕장업 5
 
8.6%

Length

2024-03-18T14:08:51.137041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:08:51.224787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업 30
51.7%
세탁업 23
39.7%
목욕장업 5
 
8.6%

업소명
Text

Distinct57
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-03-18T14:08:51.434639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.4827586
Min length2

Characters and Unicode

Total characters318
Distinct characters130
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

Unique56 ?
Unique (%)96.6%

Sample

1st row갤러리아호텔
2nd row고추잠자리호텔
3rd row샴푸모텔
4th row이오스호텔
5th row호텔러브스업
ValueCountFrequency (%)
주공세탁소 2
 
3.4%
갈산24시불가마사우나 1
 
1.7%
뉴서울세탁소 1
 
1.7%
부르심빨리세탁소 1
 
1.7%
호텔두루와 1
 
1.7%
호텔주노 1
 
1.7%
신성세탁소 1
 
1.7%
은아세탁소 1
 
1.7%
하얀세탁 1
 
1.7%
현대그린세탁 1
 
1.7%
Other values (48) 48
81.4%
2024-03-18T14:08:51.768393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 21
 
6.6%
텔 21
 
6.6%
탁 20
 
6.3%
호 17
 
5.3%
소 13
 
4.1%
스 7
 
2.2%
이 7
 
2.2%
아 6
 
1.9%
) 6
 
1.9%
( 6
 
1.9%
Other values (120) 194
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 283
89.0%
Uppercase Letter 15
 
4.7%
Close Punctuation 6
 
1.9%
Open Punctuation 6
 
1.9%
Decimal Number 4
 
1.3%
Other Punctuation 2
 
0.6%
Space Separator 1
 
0.3%
Lowercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 21
 
7.4%
텔 21
 
7.4%
탁 20
 
7.1%
호 17
 
6.0%
소 13
 
4.6%
스 7
 
2.5%
이 7
 
2.5%
아 6
 
2.1%
리 5
 
1.8%
사 5
 
1.8%
Other values (100) 161
56.9%
Uppercase Letter
ValueCountFrequency (%)
O 2
13.3%
I 2
13.3%
Y 2
13.3%
W 1
6.7%
V 1
6.7%
L 1
6.7%
U 1
6.7%
E 1
6.7%
R 1
6.7%
D 1
6.7%
Other values (2) 2
13.3%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
4 1
25.0%
7 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 283
89.0%
Common 19
 
6.0%
Latin 16
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 21
 
7.4%
텔 21
 
7.4%
탁 20
 
7.1%
호 17
 
6.0%
소 13
 
4.6%
스 7
 
2.5%
이 7
 
2.5%
아 6
 
2.1%
리 5
 
1.8%
사 5
 
1.8%
Other values (100) 161
56.9%
Latin
ValueCountFrequency (%)
O 2
12.5%
I 2
12.5%
Y 2
12.5%
W 1
 
6.2%
V 1
 
6.2%
L 1
 
6.2%
U 1
 
6.2%
E 1
 
6.2%
g 1
 
6.2%
R 1
 
6.2%
Other values (3) 3
18.8%
Common
ValueCountFrequency (%)
) 6
31.6%
( 6
31.6%
. 2
 
10.5%
2 2
 
10.5%
1
 
5.3%
4 1
 
5.3%
7 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 283
89.0%
ASCII 35
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 21
 
7.4%
텔 21
 
7.4%
탁 20
 
7.1%
호 17
 
6.0%
소 13
 
4.6%
스 7
 
2.5%
이 7
 
2.5%
아 6
 
2.1%
리 5
 
1.8%
사 5
 
1.8%
Other values (100) 161
56.9%
ASCII
ValueCountFrequency (%)
) 6
17.1%
( 6
17.1%
. 2
 
5.7%
O 2
 
5.7%
I 2
 
5.7%
Y 2
 
5.7%
2 2
 
5.7%
W 1
 
2.9%
1
 
2.9%
V 1
 
2.9%
Other values (10) 10
28.6%

소재지
Text

UNIQUE 

Distinct58
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size596.0 B
2024-03-18T14:08:52.032997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length39.5
Mean length32.12069
Min length22

Characters and Unicode

Total characters1863
Distinct characters119
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

Unique58 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 대정로90번길 24 (부평동)
2nd row인천광역시 부평구 동암광장로4번길 12 (십정동)
3rd row인천광역시 부평구 광장로4번길 23 (부평동)
4th row인천광역시 부평구 대정로82번길 25 (부평동)
5th row인천광역시 부평구 부평문화로116번길 17 (부평동)
ValueCountFrequency (%)
부평구 59
 
18.2%
인천광역시 58
 
17.9%
부평동 17
 
5.2%
경원대로 3
 
0.9%
101호 3
 
0.9%
체육관로 3
 
0.9%
27 3
 
0.9%
삼산동 3
 
0.9%
십정동 3
 
0.9%
1층 3
 
0.9%
Other values (143) 169
52.2%
2024-03-18T14:08:52.426965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
267
 
14.3%
부 113
 
6.1%
평 94
 
5.0%
1 91
 
4.9%
동 77
 
4.1%
광 65
 
3.5%
시 61
 
3.3%
구 60
 
3.2%
) 60
 
3.2%
천 60
 
3.2%
Other values (109) 915
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1113
59.7%
Decimal Number 313
 
16.8%
Space Separator 267
 
14.3%
Close Punctuation 60
 
3.2%
Open Punctuation 60
 
3.2%
Other Punctuation 36
 
1.9%
Dash Punctuation 11
 
0.6%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 113
 
10.2%
평 94
 
8.4%
동 77
 
6.9%
광 65
 
5.8%
시 61
 
5.5%
구 60
 
5.4%
천 60
 
5.4%
인 60
 
5.4%
역 59
 
5.3%
로 58
 
5.2%
Other values (91) 406
36.5%
Decimal Number
ValueCountFrequency (%)
1 91
29.1%
2 41
13.1%
0 38
12.1%
3 31
 
9.9%
7 25
 
8.0%
4 24
 
7.7%
6 21
 
6.7%
8 16
 
5.1%
9 14
 
4.5%
5 12
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 32
88.9%
@ 4
 
11.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
A 1
33.3%
Space Separator
ValueCountFrequency (%)
267
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1113
59.7%
Common 747
40.1%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 113
 
10.2%
평 94
 
8.4%
동 77
 
6.9%
광 65
 
5.8%
시 61
 
5.5%
구 60
 
5.4%
천 60
 
5.4%
인 60
 
5.4%
역 59
 
5.3%
로 58
 
5.2%
Other values (91) 406
36.5%
Common
ValueCountFrequency (%)
267
35.7%
1 91
 
12.2%
) 60
 
8.0%
( 60
 
8.0%
2 41
 
5.5%
0 38
 
5.1%
, 32
 
4.3%
3 31
 
4.1%
7 25
 
3.3%
4 24
 
3.2%
Other values (6) 78
 
10.4%
Latin
ValueCountFrequency (%)
B 2
66.7%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1113
59.7%
ASCII 750
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
267
35.6%
1 91
 
12.1%
) 60
 
8.0%
( 60
 
8.0%
2 41
 
5.5%
0 38
 
5.1%
, 32
 
4.3%
3 31
 
4.1%
7 25
 
3.3%
4 24
 
3.2%
Other values (8) 81
 
10.8%
Hangul
ValueCountFrequency (%)
부 113
 
10.2%
평 94
 
8.4%
동 77
 
6.9%
광 65
 
5.8%
시 61
 
5.5%
구 60
 
5.4%
천 60
 
5.4%
인 60
 
5.4%
역 59
 
5.3%
로 58
 
5.2%
Other values (91) 406
36.5%

지정연도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.7069
Minimum2012
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size654.0 B
2024-03-18T14:08:52.551063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12015
median2017
Q32021
95-th percentile2023
Maximum2023
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7929233
Coefficient of variation (CV)0.0018798188
Kurtosis-1.361338
Mean2017.7069
Median Absolute Deviation (MAD)4
Skewness-0.050778981
Sum117027
Variance14.386267
MonotonicityIncreasing
2024-03-18T14:08:52.684660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2021 10
17.2%
2023 10
17.2%
2013 9
15.5%
2017 9
15.5%
2019 8
13.8%
2015 7
12.1%
2012 5
8.6%
ValueCountFrequency (%)
2012 5
8.6%
2013 9
15.5%
2015 7
12.1%
2017 9
15.5%
2019 8
13.8%
2021 10
17.2%
2023 10
17.2%
ValueCountFrequency (%)
2023 10
17.2%
2021 10
17.2%
2019 8
13.8%
2017 9
15.5%
2015 7
12.1%
2013 9
15.5%
2012 5
8.6%

Interactions

2024-03-18T14:08:50.462483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:08:50.223707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:08:50.543301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T14:08:50.374778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:08:52.770390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명업소명소재지지정연도
연번1.0000.2800.9251.0000.928
업종명0.2801.0001.0001.0000.359
업소명0.9251.0001.0001.0000.879
소재지1.0001.0001.0001.0001.000
지정연도0.9280.3590.8791.0001.000
2024-03-18T14:08:52.897760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지정연도업종명
연번1.0000.9890.154
지정연도0.9891.0000.050
업종명0.1540.0501.000

Missing values

2024-03-18T14:08:50.660536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:08:50.778895image/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

연번업종명업소명소재지지정연도
01숙박업갤러리아호텔인천광역시 부평구 대정로90번길 24 (부평동)2012
12숙박업고추잠자리호텔인천광역시 부평구 동암광장로4번길 12 (십정동)2012
23숙박업샴푸모텔인천광역시 부평구 광장로4번길 23 (부평동)2012
34숙박업이오스호텔인천광역시 부평구 대정로82번길 25 (부평동)2012
45숙박업호텔러브스업인천광역시 부평구 부평문화로116번길 17 (부평동)2012
56숙박업라임호텔인천광역시 부평구 부평문화로79번길 40 (부평동)2013
67숙박업버니인천광역시 부평구 경원대로1417번길 17-6 (부평동)2013
78숙박업호텔민트인천광역시 부평구 동암광장로4번길 7 (십정동)2013
89목욕장업스파세븐인천광역시 부평구 길주로 659, 미라쥬타워 904호, 1001호 (삼산동)2013
910목욕장업오남사우나인천광역시 부평구 부영로189번길 51, 지하층 101호 (산곡동, 오남프라자)2013
연번업종명업소명소재지지정연도
4849숙박업호텔코고라인천광역시 부평구 경원대로 1417번길 4(부평동)2023
4950숙박업리치모텔인천광역시 부평구 동암광장로 8번길 33(십정동)2023
5051숙박업굿타임호텔인천광역시 부평구 경원대로 1417번길 18-9(부평동)2023
5152숙박업모텔린인천광역시 부평구 동암광장로12번길 13(십정동)2023
5253숙박업큐모텔인천광역시 부평구 세월천로 44-2(청천동)2023
5354목욕장업대박사우나인천광역시 부평구 경인로 1038, 지하201호, 301호(부개동)2023
5455세탁업빨다인천광역시 부평구 장제로36, 부일빌딩 1층(부평동)2023
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