Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells1
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory47.7 B

Variable types

Numeric2
Text3

Dataset

Description인천광역시 부평구 목욕장업 현황 데이터는 업소명, 영업소 주소, 우편번호, 소재지 전화번호에 대한 데이터를 제공합니다
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15103784&srcSe=7661IVAWM27C61E190

Alerts

소재지전화 has 1 (4.3%) missing valuesMissing
순번 has unique valuesUnique
업소명 has unique valuesUnique
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:12:20.636736
Analysis finished2024-01-28 16:12:21.453044
Duration0.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T01:12:21.510426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-01-29T01:12:21.599611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

업소명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-29T01:12:21.763565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.5217391
Min length4

Characters and Unicode

Total characters150
Distinct characters69
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row동수목욕탕
2nd row청화목욕탕
3rd row태평목욕탕
4th row도형목욕탕
5th row세현사우나
ValueCountFrequency (%)
동수목욕탕 1
 
4.3%
뉴서울불가마사우나 1
 
4.3%
오남사우나 1
 
4.3%
동아목욕탕 1
 
4.3%
전방자수정비취사우나 1
 
4.3%
우일여성사우나 1
 
4.3%
건영사우나 1
 
4.3%
수랜드사우나 1
 
4.3%
스파세븐 1
 
4.3%
리조트휘트니스부평점 1
 
4.3%
Other values (13) 13
56.5%
2024-01-29T01:12:22.028135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 13
 
8.7%
사 12
 
8.0%
나 12
 
8.0%
목 9
 
6.0%
욕 9
 
6.0%
탕 9
 
6.0%
대 4
 
2.7%
가 3
 
2.0%
화 3
 
2.0%
수 3
 
2.0%
Other values (59) 73
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 148
98.7%
Decimal Number 2
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 13
 
8.8%
사 12
 
8.1%
나 12
 
8.1%
목 9
 
6.1%
욕 9
 
6.1%
탕 9
 
6.1%
대 4
 
2.7%
가 3
 
2.0%
화 3
 
2.0%
수 3
 
2.0%
Other values (57) 71
48.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 148
98.7%
Common 2
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 13
 
8.8%
사 12
 
8.1%
나 12
 
8.1%
목 9
 
6.1%
욕 9
 
6.1%
탕 9
 
6.1%
대 4
 
2.7%
가 3
 
2.0%
화 3
 
2.0%
수 3
 
2.0%
Other values (57) 71
48.0%
Common
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 148
98.7%
ASCII 2
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 13
 
8.8%
사 12
 
8.1%
나 12
 
8.1%
목 9
 
6.1%
욕 9
 
6.1%
탕 9
 
6.1%
대 4
 
2.7%
가 3
 
2.0%
화 3
 
2.0%
수 3
 
2.0%
Other values (57) 71
48.0%
ASCII
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

영업소 주소(도로명)
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-29T01:12:22.231200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length40
Mean length32.434783
Min length22

Characters and Unicode

Total characters746
Distinct characters93
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

Unique23 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 육동로 11, 지하1,지상2층 (부평동)
2nd row인천광역시 부평구 배곶남로21번길 9 (십정동)
3rd row인천광역시 부평구 백범로422번길 63 (십정동,,27,28)
4th row인천광역시 부평구 길주남로 35 (부평동)
5th row인천광역시 부평구 백범로 520 (십정동)
ValueCountFrequency (%)
인천광역시 23
 
16.9%
부평구 23
 
16.9%
부평동 6
 
4.4%
부개동 4
 
2.9%
상가동 3
 
2.2%
십정동 2
 
1.5%
삼산동 2
 
1.5%
부흥로 2
 
1.5%
5 2
 
1.5%
갈산동 2
 
1.5%
Other values (66) 67
49.3%
2024-01-29T01:12:22.535064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
15.3%
부 43
 
5.8%
평 32
 
4.3%
동 30
 
4.0%
1 29
 
3.9%
, 28
 
3.8%
인 24
 
3.2%
광 24
 
3.2%
시 23
 
3.1%
( 23
 
3.1%
Other values (83) 376
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 428
57.4%
Decimal Number 125
 
16.8%
Space Separator 114
 
15.3%
Other Punctuation 30
 
4.0%
Open Punctuation 23
 
3.1%
Close Punctuation 23
 
3.1%
Uppercase Letter 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 43
 
10.0%
평 32
 
7.5%
동 30
 
7.0%
인 24
 
5.6%
광 24
 
5.6%
시 23
 
5.4%
역 23
 
5.4%
로 23
 
5.4%
천 23
 
5.4%
구 23
 
5.4%
Other values (66) 160
37.4%
Decimal Number
ValueCountFrequency (%)
1 29
23.2%
2 22
17.6%
0 15
12.0%
4 13
10.4%
3 10
 
8.0%
8 9
 
7.2%
5 8
 
6.4%
6 7
 
5.6%
9 6
 
4.8%
7 6
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 28
93.3%
. 2
 
6.7%
Space Separator
ValueCountFrequency (%)
114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 428
57.4%
Common 316
42.4%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 43
 
10.0%
평 32
 
7.5%
동 30
 
7.0%
인 24
 
5.6%
광 24
 
5.6%
시 23
 
5.4%
역 23
 
5.4%
로 23
 
5.4%
천 23
 
5.4%
구 23
 
5.4%
Other values (66) 160
37.4%
Common
ValueCountFrequency (%)
114
36.1%
1 29
 
9.2%
, 28
 
8.9%
( 23
 
7.3%
) 23
 
7.3%
2 22
 
7.0%
0 15
 
4.7%
4 13
 
4.1%
3 10
 
3.2%
8 9
 
2.8%
Other values (6) 30
 
9.5%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 428
57.4%
ASCII 318
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114
35.8%
1 29
 
9.1%
, 28
 
8.8%
( 23
 
7.2%
) 23
 
7.2%
2 22
 
6.9%
0 15
 
4.7%
4 13
 
4.1%
3 10
 
3.1%
8 9
 
2.8%
Other values (7) 32
 
10.1%
Hangul
ValueCountFrequency (%)
부 43
 
10.0%
평 32
 
7.5%
동 30
 
7.0%
인 24
 
5.6%
광 24
 
5.6%
시 23
 
5.4%
역 23
 
5.4%
로 23
 
5.4%
천 23
 
5.4%
구 23
 
5.4%
Other values (66) 160
37.4%

우편번호(도로명)
Real number (ℝ)

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21391.696
Minimum21319
Maximum21458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T01:12:22.629314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21319
5-th percentile21329.7
Q121358.5
median21396
Q321422.5
95-th percentile21450
Maximum21458
Range139
Interquartile range (IQR)64

Descriptive statistics

Standard deviation40.54003
Coefficient of variation (CV)0.0018951294
Kurtosis-0.92347897
Mean21391.696
Median Absolute Deviation (MAD)31
Skewness-0.11639165
Sum492009
Variance1643.4941
MonotonicityNot monotonic
2024-01-29T01:12:22.715621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21450 2
 
8.7%
21377 2
 
8.7%
21424 1
 
4.3%
21458 1
 
4.3%
21336 1
 
4.3%
21362 1
 
4.3%
21408 1
 
4.3%
21391 1
 
4.3%
21344 1
 
4.3%
21388 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
21319 1
4.3%
21329 1
4.3%
21336 1
4.3%
21344 1
4.3%
21351 1
4.3%
21355 1
4.3%
21362 1
4.3%
21377 2
8.7%
21388 1
4.3%
21391 1
4.3%
ValueCountFrequency (%)
21458 1
4.3%
21450 2
8.7%
21439 1
4.3%
21427 1
4.3%
21424 1
4.3%
21421 1
4.3%
21408 1
4.3%
21405 1
4.3%
21404 1
4.3%
21398 1
4.3%

소재지전화
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Memory size316.0 B
2024-01-29T01:12:22.871114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters264
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

Unique22 ?
Unique (%)100.0%

Sample

1st row032-527-8630
2nd row032-425-3703
3rd row032-435-4722
4th row032-502-6178
5th row032-442-2687
ValueCountFrequency (%)
032-527-8630 1
 
4.5%
032-425-3703 1
 
4.5%
032-330-0773 1
 
4.5%
032-528-9090 1
 
4.5%
032-508-0001 1
 
4.5%
032-516-4787 1
 
4.5%
032-518-8277 1
 
4.5%
032-330-0700 1
 
4.5%
032-508-0888 1
 
4.5%
032-504-4434 1
 
4.5%
Other values (12) 12
54.5%
2024-01-29T01:12:23.126321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 51
19.3%
- 44
16.7%
2 39
14.8%
3 38
14.4%
8 22
8.3%
5 21
8.0%
7 14
 
5.3%
6 12
 
4.5%
4 10
 
3.8%
1 7
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 220
83.3%
Dash Punctuation 44
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51
23.2%
2 39
17.7%
3 38
17.3%
8 22
10.0%
5 21
9.5%
7 14
 
6.4%
6 12
 
5.5%
4 10
 
4.5%
1 7
 
3.2%
9 6
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51
19.3%
- 44
16.7%
2 39
14.8%
3 38
14.4%
8 22
8.3%
5 21
8.0%
7 14
 
5.3%
6 12
 
4.5%
4 10
 
3.8%
1 7
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51
19.3%
- 44
16.7%
2 39
14.8%
3 38
14.4%
8 22
8.3%
5 21
8.0%
7 14
 
5.3%
6 12
 
4.5%
4 10
 
3.8%
1 7
 
2.7%

Interactions

2024-01-29T01:12:20.946622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:20.809717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:21.014255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:20.881258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:12:23.203979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업소명영업소 주소(도로명)우편번호(도로명)소재지전화
순번1.0001.0001.0000.5221.000
업소명1.0001.0001.0001.0001.000
영업소 주소(도로명)1.0001.0001.0001.0001.000
우편번호(도로명)0.5221.0001.0001.0001.000
소재지전화1.0001.0001.0001.0001.000
2024-01-29T01:12:23.301935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호(도로명)
순번1.000-0.388
우편번호(도로명)-0.3881.000

Missing values

2024-01-29T01:12:21.333301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:12:21.414898image/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

순번업소명영업소 주소(도로명)우편번호(도로명)소재지전화
01동수목욕탕인천광역시 부평구 육동로 11, 지하1,지상2층 (부평동)21424032-527-8630
12청화목욕탕인천광역시 부평구 배곶남로21번길 9 (십정동)21450032-425-3703
23태평목욕탕인천광역시 부평구 백범로422번길 63 (십정동,,27,28)21439032-435-4722
34도형목욕탕인천광역시 부평구 길주남로 35 (부평동)21355032-502-6178
45세현사우나인천광역시 부평구 백범로 520 (십정동)21450032-442-2687
56금강사우나목욕탕인천광역시 부평구 경원대로1256번길 10 (부평동,,23,24,25,38,42. 794-7.8)21405032-508-6989
67신화목욕탕인천광역시 부평구 장제로83번길 5 (부평동)21396032-529-6850
78주화대중목욕탕인천광역시 부평구 부흥로 420 (부개동)21398032-361-0628
89맥대중목욕탕인천광역시 부평구 광장로 16 (부평동)21404032-275-2003
910현대사우나인천광역시 부평구 마분로20번길 5 (부개동)21421032-505-6861
순번업소명영업소 주소(도로명)우편번호(도로명)소재지전화
1314갈산24시불가마사우나인천광역시 부평구 주부토로 231 (갈산동)21329032-504-4434
1415리조트휘트니스부평점인천광역시 부평구 부흥로 264, 동아웰빙타운 8층 (부평동)21388032-508-0888
1516스파세븐인천광역시 부평구 길주로 659, 미라쥬타워 904호, 1001호 (삼산동)21344032-330-0700
1617수랜드사우나인천광역시 부평구 원적로 434 (산곡동,헤성프라자6층,7층)21377032-518-8277
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