Overview

Dataset statistics

Number of variables15
Number of observations133
Missing cells159
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory127.0 B

Variable types

Categorical4
Text3
Numeric6
Boolean2

Dataset

Description목욕장업(한증막업) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=W9T2JP3TC9WT64P2BU2414396282&infSeq=1

Alerts

영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
위생업태명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
다중이용업소여부 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
발한실여부 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
위생업종명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
인허가일자 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
폐업일자 is highly overall correlated with 영업상태명 and 2 other fieldsHigh correlation
욕실수(개) is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84경도 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
위생업종명 is highly imbalanced (73.5%)Imbalance
위생업태명 is highly imbalanced (73.5%)Imbalance
폐업일자 has 45 (33.8%) missing valuesMissing
다중이용업소여부 has 6 (4.5%) missing valuesMissing
발한실여부 has 9 (6.8%) missing valuesMissing
욕실수(개) has 58 (43.6%) missing valuesMissing
소재지도로명주소 has 19 (14.3%) missing valuesMissing
WGS84위도 has 11 (8.3%) missing valuesMissing
WGS84경도 has 11 (8.3%) missing valuesMissing
욕실수(개) has 36 (27.1%) zerosZeros

Reproduction

Analysis started2023-12-10 21:21:01.394399
Analysis finished2023-12-10 21:21:06.333091
Duration4.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
수원시
15 
고양시
14 
성남시
14 
파주시
9 
부천시
8 
Other values (19)
73 

Length

Max length4
Median length3
Mean length3.0676692
Min length3

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
수원시 15
 
11.3%
고양시 14
 
10.5%
성남시 14
 
10.5%
파주시 9
 
6.8%
부천시 8
 
6.0%
시흥시 8
 
6.0%
평택시 7
 
5.3%
안산시 6
 
4.5%
용인시 6
 
4.5%
안양시 5
 
3.8%
Other values (14) 41
30.8%

Length

2023-12-11T06:21:06.422156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 15
 
11.3%
성남시 14
 
10.5%
고양시 14
 
10.5%
파주시 9
 
6.8%
부천시 8
 
6.0%
시흥시 8
 
6.0%
평택시 7
 
5.3%
안산시 6
 
4.5%
용인시 6
 
4.5%
안양시 5
 
3.8%
Other values (14) 41
30.8%

사업장명
Text

Distinct130
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T06:21:06.715542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length6.6315789
Min length2

Characters and Unicode

Total characters882
Distinct characters192
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)95.5%

Sample

1st row천월약한증막
2nd row솟틀불한증막
3rd row모내불한증막
4th row고봉산재래한증막
5th row숲속의한증막
ValueCountFrequency (%)
한증막 5
 
3.4%
숲속의한증막 2
 
1.3%
샘내한증막 2
 
1.3%
사우나 2
 
1.3%
황토불한증막 2
 
1.3%
용문첨성대불한증막 1
 
0.7%
참 1
 
0.7%
숯 1
 
0.7%
익는 1
 
0.7%
마을 1
 
0.7%
Other values (131) 131
87.9%
2023-12-11T06:21:07.185560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
한 85
 
9.6%
증 85
 
9.6%
막 76
 
8.6%
불 26
 
2.9%
우 21
 
2.4%
사 19
 
2.2%
나 18
 
2.0%
16
 
1.8%
마 16
 
1.8%
성 15
 
1.7%
Other values (182) 505
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 843
95.6%
Space Separator 16
 
1.8%
Close Punctuation 9
 
1.0%
Open Punctuation 8
 
0.9%
Decimal Number 3
 
0.3%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 85
 
10.1%
증 85
 
10.1%
막 76
 
9.0%
불 26
 
3.1%
우 21
 
2.5%
사 19
 
2.3%
나 18
 
2.1%
마 16
 
1.9%
성 15
 
1.8%
광 12
 
1.4%
Other values (174) 470
55.8%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
4 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
T 1
50.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 843
95.6%
Common 37
 
4.2%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 85
 
10.1%
증 85
 
10.1%
막 76
 
9.0%
불 26
 
3.1%
우 21
 
2.5%
사 19
 
2.3%
나 18
 
2.1%
마 16
 
1.9%
성 15
 
1.8%
광 12
 
1.4%
Other values (174) 470
55.8%
Common
ValueCountFrequency (%)
16
43.2%
) 9
24.3%
( 8
21.6%
2 2
 
5.4%
. 1
 
2.7%
4 1
 
2.7%
Latin
ValueCountFrequency (%)
S 1
50.0%
T 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 843
95.6%
ASCII 39
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
한 85
 
10.1%
증 85
 
10.1%
막 76
 
9.0%
불 26
 
3.1%
우 21
 
2.5%
사 19
 
2.3%
나 18
 
2.1%
마 16
 
1.9%
성 15
 
1.8%
광 12
 
1.4%
Other values (174) 470
55.8%
ASCII
ValueCountFrequency (%)
16
41.0%
) 9
23.1%
( 8
20.5%
2 2
 
5.1%
S 1
 
2.6%
. 1
 
2.6%
T 1
 
2.6%
4 1
 
2.6%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19981344
Minimum19610309
Maximum20170328
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:07.369927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19610309
5-th percentile19787160
Q119930805
median19990312
Q320060112
95-th percentile20140589
Maximum20170328
Range560019
Interquartile range (IQR)129307

Descriptive statistics

Standard deviation104504.93
Coefficient of variation (CV)0.0052301254
Kurtosis1.487085
Mean19981344
Median Absolute Deviation (MAD)60087
Skewness-0.89011464
Sum2.6575187 × 109
Variance1.0921281 × 1010
MonotonicityNot monotonic
2023-12-11T06:21:07.513386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000202 2
 
1.5%
19901020 2
 
1.5%
19980224 2
 
1.5%
19911231 2
 
1.5%
19930225 1
 
0.8%
19801002 1
 
0.8%
19961212 1
 
0.8%
20071121 1
 
0.8%
19980514 1
 
0.8%
19910321 1
 
0.8%
Other values (119) 119
89.5%
ValueCountFrequency (%)
19610309 1
0.8%
19640716 1
0.8%
19680424 1
0.8%
19740423 1
0.8%
19740921 1
0.8%
19750731 1
0.8%
19781228 1
0.8%
19791114 1
0.8%
19801002 1
0.8%
19811107 1
0.8%
ValueCountFrequency (%)
20170328 1
0.8%
20161010 1
0.8%
20160913 1
0.8%
20160113 1
0.8%
20150805 1
0.8%
20141204 1
0.8%
20141014 1
0.8%
20140306 1
0.8%
20130403 1
0.8%
20120903 1
0.8%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
폐업 등
88 
운영중
45 

Length

Max length4
Median length4
Mean length3.6616541
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row운영중
3rd row폐업 등
4th row운영중
5th row폐업 등

Common Values

ValueCountFrequency (%)
폐업 등 88
66.2%
운영중 45
33.8%

Length

2023-12-11T06:21:07.687763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:21:07.782151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 88
39.8%
등 88
39.8%
운영중 45
20.4%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)95.5%
Missing45
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean20076538
Minimum19900801
Maximum20180824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:07.883700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19900801
5-th percentile19990930
Q120031216
median20060862
Q320130817
95-th percentile20161095
Maximum20180824
Range280023
Interquartile range (IQR)99600.75

Descriptive statistics

Standard deviation57055.777
Coefficient of variation (CV)0.0028419132
Kurtosis-0.31094484
Mean20076538
Median Absolute Deviation (MAD)30636
Skewness0.010115049
Sum1.7667353 × 109
Variance3.2553617 × 109
MonotonicityNot monotonic
2023-12-11T06:21:08.061836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20030227 3
 
2.3%
20060308 2
 
1.5%
20030226 2
 
1.5%
20040908 1
 
0.8%
20031218 1
 
0.8%
20060512 1
 
0.8%
20150304 1
 
0.8%
20040214 1
 
0.8%
20041201 1
 
0.8%
20090416 1
 
0.8%
Other values (74) 74
55.6%
(Missing) 45
33.8%
ValueCountFrequency (%)
19900801 1
0.8%
19980203 1
0.8%
19990317 1
0.8%
19990330 1
0.8%
19990830 1
0.8%
19991116 1
0.8%
20000324 1
0.8%
20010427 1
0.8%
20020514 1
0.8%
20020923 1
0.8%
ValueCountFrequency (%)
20180824 1
0.8%
20180402 1
0.8%
20180119 1
0.8%
20171011 1
0.8%
20161129 1
0.8%
20161031 1
0.8%
20161007 1
0.8%
20160318 1
0.8%
20160212 1
0.8%
20151221 1
0.8%

다중이용업소여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.6%
Missing6
Missing (%)4.5%
Memory size398.0 B
False
112 
True
15 
(Missing)
 
6
ValueCountFrequency (%)
False 112
84.2%
True 15
 
11.3%
(Missing) 6
 
4.5%
2023-12-11T06:21:08.204129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

발한실여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.6%
Missing9
Missing (%)6.8%
Memory size398.0 B
False
81 
True
43 
(Missing)
9 
ValueCountFrequency (%)
False 81
60.9%
True 43
32.3%
(Missing) 9
 
6.8%
2023-12-11T06:21:08.282483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

욕실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)10.7%
Missing58
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean1.1733333
Minimum0
Maximum10
Zeros36
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:08.392282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.3
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8843117
Coefficient of variation (CV)1.6059475
Kurtosis11.006409
Mean1.1733333
Median Absolute Deviation (MAD)1
Skewness3.0545743
Sum88
Variance3.5506306
MonotonicityNot monotonic
2023-12-11T06:21:08.501916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 36
27.1%
2 18
 
13.5%
1 15
 
11.3%
3 2
 
1.5%
8 1
 
0.8%
9 1
 
0.8%
4 1
 
0.8%
10 1
 
0.8%
(Missing) 58
43.6%
ValueCountFrequency (%)
0 36
27.1%
1 15
11.3%
2 18
13.5%
3 2
 
1.5%
4 1
 
0.8%
8 1
 
0.8%
9 1
 
0.8%
10 1
 
0.8%
ValueCountFrequency (%)
10 1
 
0.8%
9 1
 
0.8%
8 1
 
0.8%
4 1
 
0.8%
3 2
 
1.5%
2 18
13.5%
1 15
11.3%
0 36
27.1%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
목욕장업
127 
<NA>
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목욕장업 127
95.5%
<NA> 6
 
4.5%

Length

2023-12-11T06:21:08.640240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:21:08.743929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목욕장업 127
95.5%
na 6
 
4.5%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
한증막업
127 
<NA>
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한증막업 127
95.5%
<NA> 6
 
4.5%

Length

2023-12-11T06:21:08.851347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:21:08.959008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한증막업 127
95.5%
na 6
 
4.5%

소재지도로명주소
Text

MISSING 

Distinct109
Distinct (%)95.6%
Missing19
Missing (%)14.3%
Memory size1.2 KiB
2023-12-11T06:21:09.211881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length32
Mean length23.912281
Min length14

Characters and Unicode

Total characters2726
Distinct characters196
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

Unique105 ?
Unique (%)92.1%

Sample

1st row경기도 가평군 상면 수목원로 30-44
2nd row경기도 가평군 청평면 솥틀로 54-23
3rd row경기도 가평군 청평면 머내길 176
4th row경기도 고양시 일산동구 성석로 67 (중산동)
5th row경기도 고양시 일산동구 애니골길 74
ValueCountFrequency (%)
경기도 114
 
18.9%
성남시 14
 
2.3%
수원시 13
 
2.2%
고양시 11
 
1.8%
팔달구 10
 
1.7%
부천시 8
 
1.3%
평택시 7
 
1.2%
일산동구 7
 
1.2%
시흥시 6
 
1.0%
분당구 6
 
1.0%
Other values (303) 408
67.5%
2023-12-11T06:21:09.652311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
490
 
18.0%
경 117
 
4.3%
시 117
 
4.3%
도 115
 
4.2%
기 115
 
4.2%
1 107
 
3.9%
로 96
 
3.5%
동 61
 
2.2%
2 60
 
2.2%
길 59
 
2.2%
Other values (186) 1389
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1622
59.5%
Space Separator 490
 
18.0%
Decimal Number 461
 
16.9%
Close Punctuation 48
 
1.8%
Open Punctuation 48
 
1.8%
Dash Punctuation 32
 
1.2%
Other Punctuation 25
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 117
 
7.2%
시 117
 
7.2%
도 115
 
7.1%
기 115
 
7.1%
로 96
 
5.9%
동 61
 
3.8%
길 59
 
3.6%
구 53
 
3.3%
번 40
 
2.5%
산 31
 
1.9%
Other values (171) 818
50.4%
Decimal Number
ValueCountFrequency (%)
1 107
23.2%
2 60
13.0%
3 51
11.1%
4 48
10.4%
7 41
 
8.9%
5 37
 
8.0%
8 34
 
7.4%
0 32
 
6.9%
6 30
 
6.5%
9 21
 
4.6%
Space Separator
ValueCountFrequency (%)
490
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1622
59.5%
Common 1104
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 117
 
7.2%
시 117
 
7.2%
도 115
 
7.1%
기 115
 
7.1%
로 96
 
5.9%
동 61
 
3.8%
길 59
 
3.6%
구 53
 
3.3%
번 40
 
2.5%
산 31
 
1.9%
Other values (171) 818
50.4%
Common
ValueCountFrequency (%)
490
44.4%
1 107
 
9.7%
2 60
 
5.4%
3 51
 
4.6%
4 48
 
4.3%
) 48
 
4.3%
( 48
 
4.3%
7 41
 
3.7%
5 37
 
3.4%
8 34
 
3.1%
Other values (5) 140
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1622
59.5%
ASCII 1104
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
490
44.4%
1 107
 
9.7%
2 60
 
5.4%
3 51
 
4.6%
4 48
 
4.3%
) 48
 
4.3%
( 48
 
4.3%
7 41
 
3.7%
5 37
 
3.4%
8 34
 
3.1%
Other values (5) 140
 
12.7%
Hangul
ValueCountFrequency (%)
경 117
 
7.2%
시 117
 
7.2%
도 115
 
7.1%
기 115
 
7.1%
로 96
 
5.9%
동 61
 
3.8%
길 59
 
3.6%
구 53
 
3.3%
번 40
 
2.5%
산 31
 
1.9%
Other values (171) 818
50.4%

소재지지번주소
Text

Distinct127
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T06:21:09.956485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length35
Mean length23.796992
Min length17

Characters and Unicode

Total characters3165
Distinct characters176
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

Unique121 ?
Unique (%)91.0%

Sample

1st row경기도 가평군 상면 임초리 432번지
2nd row경기도 가평군 청평면 하천리 426번지
3rd row경기도 가평군 청평면 대성리 243-4번지 외1필지
4th row경기도 고양시 일산동구 중산동 3-1번지
5th row경기도 고양시 일산동구 풍동 614-1번지 번지
ValueCountFrequency (%)
경기도 133
 
19.5%
수원시 15
 
2.2%
성남시 14
 
2.0%
고양시 14
 
2.0%
팔달구 10
 
1.5%
파주시 9
 
1.3%
일산동구 9
 
1.3%
시흥시 8
 
1.2%
부천시 8
 
1.2%
평택시 7
 
1.0%
Other values (325) 456
66.8%
2023-12-11T06:21:10.402005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
550
 
17.4%
지 157
 
5.0%
번 139
 
4.4%
1 138
 
4.4%
시 137
 
4.3%
기 135
 
4.3%
도 134
 
4.2%
경 133
 
4.2%
동 122
 
3.9%
- 101
 
3.2%
Other values (166) 1419
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1888
59.7%
Decimal Number 603
 
19.1%
Space Separator 550
 
17.4%
Dash Punctuation 101
 
3.2%
Other Punctuation 13
 
0.4%
Open Punctuation 4
 
0.1%
Close Punctuation 4
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 157
 
8.3%
번 139
 
7.4%
시 137
 
7.3%
기 135
 
7.2%
도 134
 
7.1%
경 133
 
7.0%
동 122
 
6.5%
구 60
 
3.2%
양 38
 
2.0%
산 36
 
1.9%
Other values (149) 797
42.2%
Decimal Number
ValueCountFrequency (%)
1 138
22.9%
2 84
13.9%
3 69
11.4%
4 59
9.8%
5 57
9.5%
6 49
 
8.1%
8 47
 
7.8%
0 38
 
6.3%
7 32
 
5.3%
9 30
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 10
76.9%
. 3
 
23.1%
Space Separator
ValueCountFrequency (%)
550
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1888
59.7%
Common 1275
40.3%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 157
 
8.3%
번 139
 
7.4%
시 137
 
7.3%
기 135
 
7.2%
도 134
 
7.1%
경 133
 
7.0%
동 122
 
6.5%
구 60
 
3.2%
양 38
 
2.0%
산 36
 
1.9%
Other values (149) 797
42.2%
Common
ValueCountFrequency (%)
550
43.1%
1 138
 
10.8%
- 101
 
7.9%
2 84
 
6.6%
3 69
 
5.4%
4 59
 
4.6%
5 57
 
4.5%
6 49
 
3.8%
8 47
 
3.7%
0 38
 
3.0%
Other values (6) 83
 
6.5%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1888
59.7%
ASCII 1277
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
550
43.1%
1 138
 
10.8%
- 101
 
7.9%
2 84
 
6.6%
3 69
 
5.4%
4 59
 
4.6%
5 57
 
4.5%
6 49
 
3.8%
8 47
 
3.7%
0 38
 
3.0%
Other values (7) 85
 
6.7%
Hangul
ValueCountFrequency (%)
지 157
 
8.3%
번 139
 
7.4%
시 137
 
7.3%
기 135
 
7.2%
도 134
 
7.1%
경 133
 
7.0%
동 122
 
6.5%
구 60
 
3.2%
양 38
 
2.0%
산 36
 
1.9%
Other values (149) 797
42.2%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416039.71
Minimum14538
Maximum487896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:10.538291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14538
5-th percentile14759.8
Q1415833
median442834
Q3462827
95-th percentile483034
Maximum487896
Range473358
Interquartile range (IQR)46994

Descriptive statistics

Standard deviation110895.32
Coefficient of variation (CV)0.26654984
Kurtosis9.1512693
Mean416039.71
Median Absolute Deviation (MAD)21014
Skewness-3.2178533
Sum55333281
Variance1.2297772 × 1010
MonotonicityNot monotonic
2023-12-11T06:21:10.922397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413861 3
 
2.3%
429858 3
 
2.3%
482812 3
 
2.3%
410842 3
 
2.3%
430843 3
 
2.3%
482100 2
 
1.5%
462827 2
 
1.5%
467050 2
 
1.5%
413851 2
 
1.5%
426823 2
 
1.5%
Other values (105) 108
81.2%
ValueCountFrequency (%)
14538 1
0.8%
14637 1
0.8%
14643 1
0.8%
14669 1
0.8%
14725 1
0.8%
14729 1
0.8%
14731 1
0.8%
14779 1
0.8%
18511 1
0.8%
410330 1
0.8%
ValueCountFrequency (%)
487896 1
 
0.8%
487855 1
 
0.8%
487826 2
1.5%
483120 1
 
0.8%
483050 1
 
0.8%
483040 1
 
0.8%
483030 1
 
0.8%
482812 3
2.3%
482100 2
1.5%
480867 1
 
0.8%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)93.4%
Missing11
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean37.455578
Minimum36.991468
Maximum38.06278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:11.080440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.991468
5-th percentile37.033691
Q137.274199
median37.411057
Q337.684667
95-th percentile37.815272
Maximum38.06278
Range1.0713119
Interquartile range (IQR)0.41046748

Descriptive statistics

Standard deviation0.24477511
Coefficient of variation (CV)0.0065350776
Kurtosis-0.7114781
Mean37.455578
Median Absolute Deviation (MAD)0.15994344
Skewness0.19103743
Sum4569.5805
Variance0.059914854
MonotonicityNot monotonic
2023-12-11T06:21:11.218889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3377771272 3
 
2.3%
37.294445422 2
 
1.5%
37.815824844 2
 
1.5%
37.6739968967 2
 
1.5%
37.2766342646 2
 
1.5%
37.800446322 2
 
1.5%
37.8047730113 2
 
1.5%
37.4192772246 1
 
0.8%
37.7359885424 1
 
0.8%
37.7074324227 1
 
0.8%
Other values (104) 104
78.2%
(Missing) 11
 
8.3%
ValueCountFrequency (%)
36.9914676212 1
0.8%
36.9927401797 1
0.8%
36.9928132283 1
0.8%
36.9932095204 1
0.8%
37.0177027729 1
0.8%
37.0212145354 1
0.8%
37.0311036171 1
0.8%
37.0828523735 1
0.8%
37.0896570394 1
0.8%
37.1427251864 1
0.8%
ValueCountFrequency (%)
38.0627795249 1
0.8%
37.9968592623 1
0.8%
37.9092504659 1
0.8%
37.903062666 1
0.8%
37.8958314177 1
0.8%
37.815824844 2
1.5%
37.8047730113 2
1.5%
37.800446322 2
1.5%
37.7736349167 1
0.8%
37.7735476455 1
0.8%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct114
Distinct (%)93.4%
Missing11
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean127.00289
Minimum126.5349
Maximum127.62638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T06:21:11.366819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5349
5-th percentile126.72997
Q1126.81753
median127.01786
Q3127.12961
95-th percentile127.43235
Maximum127.62638
Range1.0914831
Interquartile range (IQR)0.31207621

Descriptive statistics

Standard deviation0.21866963
Coefficient of variation (CV)0.0017217689
Kurtosis0.081254197
Mean127.00289
Median Absolute Deviation (MAD)0.13984828
Skewness0.51233752
Sum15494.353
Variance0.047816409
MonotonicityNot monotonic
2023-12-11T06:21:11.537120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7498217877 3
 
2.3%
126.8434111141 2
 
1.5%
126.7992035884 2
 
1.5%
126.792080301 2
 
1.5%
127.4525172301 2
 
1.5%
127.1298511981 2
 
1.5%
127.0403829779 2
 
1.5%
126.9174980815 1
 
0.8%
126.9765266995 1
 
0.8%
126.9356342863 1
 
0.8%
Other values (104) 104
78.2%
(Missing) 11
 
8.3%
ValueCountFrequency (%)
126.5348971212 1
 
0.8%
126.573112799 1
 
0.8%
126.5767166197 1
 
0.8%
126.6931599279 1
 
0.8%
126.6964651409 1
 
0.8%
126.7019685483 1
 
0.8%
126.7289270346 1
 
0.8%
126.7498217877 3
2.3%
126.7536812956 1
 
0.8%
126.7596716299 1
 
0.8%
ValueCountFrequency (%)
127.626380271 1
0.8%
127.6000235762 1
0.8%
127.4578602178 1
0.8%
127.4525172301 2
1.5%
127.4374389808 1
0.8%
127.432409358 1
0.8%
127.4312109048 1
0.8%
127.3805086543 1
0.8%
127.3707002165 1
0.8%
127.3576278919 1
0.8%

Interactions

2023-12-11T06:21:05.191760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.189557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.702016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.246769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.047572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.591578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.271524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.285667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.787298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.353281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.146252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.679779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.387457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.364172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.871129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.447550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.243624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.822578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.488382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.440295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.948693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.531744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.323718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.921764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.564025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.526651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.026032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.626074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.420067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.026009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.651573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:02.601804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.135676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:03.703433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:04.514475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:21:05.117487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:21:11.626990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)소재지우편번호WGS84위도WGS84경도
시군명1.0000.4160.6360.2360.4700.6040.0811.0000.9670.949
인허가일자0.4161.0000.5470.0000.2600.5020.1700.0000.0000.427
영업상태명0.6360.5471.000NaN0.5430.4450.1800.0000.3380.441
폐업일자0.2360.000NaN1.0000.4760.2280.0000.0000.3990.000
다중이용업소여부0.4700.2600.5430.4761.0000.2280.0090.1720.3590.000
발한실여부0.6040.5020.4450.2280.2281.0000.4110.0000.4860.405
욕실수(개)0.0810.1700.1800.0000.0090.4111.0000.0000.0000.552
소재지우편번호1.0000.0000.0000.0000.1720.0000.0001.0000.7130.774
WGS84위도0.9670.0000.3380.3990.3590.4860.0000.7131.0000.738
WGS84경도0.9490.4270.4410.0000.0000.4050.5520.7740.7381.000
2023-12-11T06:21:11.763139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명위생업태명다중이용업소여부발한실여부위생업종명시군명
영업상태명1.0001.0000.3650.2931.0000.465
위생업태명1.0001.0001.0001.0001.0001.000
다중이용업소여부0.3651.0001.0000.1461.0000.338
발한실여부0.2931.0000.1461.0001.0000.437
위생업종명1.0001.0001.0001.0001.0001.000
시군명0.4651.0000.3380.4371.0001.000
2023-12-11T06:21:11.892984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자욕실수(개)소재지우편번호WGS84위도WGS84경도시군명영업상태명다중이용업소여부발한실여부위생업종명위생업태명
인허가일자1.0000.1520.4540.0020.127-0.0480.1430.4120.1950.3781.0001.000
폐업일자0.1521.0000.1410.0340.0850.0580.0001.0000.3270.1611.0001.000
욕실수(개)0.4540.1411.0000.2400.0740.2360.2880.0000.0000.0001.0001.000
소재지우편번호0.0020.0340.2401.0000.0100.8030.8980.0000.2820.0001.0001.000
WGS84위도0.1270.0850.0740.0101.000-0.2410.7700.2490.2640.3601.0001.000
WGS84경도-0.0480.0580.2360.803-0.2411.0000.7040.3270.0000.2991.0001.000
시군명0.1430.0000.2880.8980.7700.7041.0000.4650.3380.4371.0001.000
영업상태명0.4121.0000.0000.0000.2490.3270.4651.0000.3650.2931.0001.000
다중이용업소여부0.1950.3270.0000.2820.2640.0000.3380.3651.0000.1461.0001.000
발한실여부0.3780.1610.0000.0000.3600.2990.4370.2930.1461.0001.0001.000
위생업종명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위생업태명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T06:21:05.791422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:21:06.029793image/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.
2023-12-11T06:21:06.214974image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군천월약한증막19930225운영중<NA>NN<NA>목욕장업한증막업경기도 가평군 상면 수목원로 30-44경기도 가평군 상면 임초리 432번지47782437.773548127.3707
1가평군솟틀불한증막19980629운영중<NA>NN<NA>목욕장업한증막업경기도 가평군 청평면 솥틀로 54-23경기도 가평군 청평면 하천리 426번지47781637.761611127.431211
2가평군모내불한증막20080114폐업 등20100129NY2목욕장업한증막업경기도 가평군 청평면 머내길 176경기도 가평군 청평면 대성리 243-4번지 외1필지47781237.718505127.380509
3고양시고봉산재래한증막20060421운영중<NA>YY1목욕장업한증막업경기도 고양시 일산동구 성석로 67 (중산동)경기도 고양시 일산동구 중산동 3-1번지41083137.686013126.792284
4고양시숲속의한증막19950822폐업 등20010427NN0목욕장업한증막업경기도 고양시 일산동구 애니골길 74경기도 고양시 일산동구 풍동 614-1번지 번지41084237.673997126.79208
5고양시광장불한증막사우나20110428폐업 등20140925NN1목욕장업한증막업경기도 고양시 일산동구 중앙로1261번길 19 (장항동,호수광장빌딩 지하101호)경기도 고양시 일산동구 장항동 857번지 호수광장빌딩 지하101호41083737.658259126.772662
6고양시해룡파크19950519폐업 등19990830NN<NA>목욕장업한증막업경기도 고양시 일산동구 문원길 53경기도 고양시 일산동구 설문동 153-10번지 번지41081837.712488126.818819
7고양시덕수불한증막19990309폐업 등20060719NN0목욕장업한증막업경기도 고양시 일산서구 원일로 85-13경기도 고양시 일산서구 일산동 591-29번지 번지41185237.686582126.771829
8고양시석정한증막19960207폐업 등19980203NN0목욕장업한증막업경기도 고양시 덕양구 행주로15번길 11-38경기도 고양시 덕양구 행주내동 85번지41223037.599949126.828512
9고양시행주관광호텔19880116폐업 등20030417NN<NA>목욕장업한증막업<NA>경기도 고양시 덕양구 토당동 331-12 2번지412818<NA><NA>
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
123평택시이화한증막19750731폐업 등20070410NN<NA>목욕장업한증막업경기도 평택시 통복로26번길 28경기도 평택시 평택동 78-1번지45082736.99321127.083814
124평택시송탄한증막19680424폐업 등20130718NN0목욕장업한증막업경기도 평택시 신장로82번길 77-11 (신장동)경기도 평택시 신장동 338-34번지 324-6945982237.082852127.052879
125평택시오목한증막업19910306폐업 등20030908NN<NA>목욕장업한증막업경기도 평택시 무지개공원2길 29-7경기도 평택시 비전동 143-18번지45080436.99274127.110192
126평택시평택관광호텔사우나19980522폐업 등20050319NN<NA>목욕장업한증막업경기도 평택시 평택1로 7경기도 평택시 평택동 62-10번지45082636.992813127.086491
127포천시부인터 여성전용 한증막20130403운영중<NA>YY2목욕장업한증막업경기도 포천시 소흘읍 호국로 302경기도 포천시 소흘읍 이동교리 371-5번지48782637.800446127.129851
128포천시일동사이판 참숯가마20080314운영중<NA>N<NA><NA>목욕장업한증막업경기도 포천시 일동면 수입로 352경기도 포천시 일동면 수입리 705번지48785537.996859127.313157
129포천시우둠지찜질방20160113운영중<NA>YN2목욕장업한증막업경기도 포천시 영북면 산정호수로 446, 제13동경기도 포천시 영북면 산정리 456-7번지48789638.06278127.313366
130포천시송우한증막19960318폐업 등20060804NN2목욕장업한증막업경기도 포천시 소흘읍 호국로 302경기도 포천시 소흘읍 이동교리 371-5번지48782637.800446127.129851
131화성시황토불한증막19980224운영중<NA>NN<NA>목욕장업한증막업경기도 화성시 세자로442번길 34 (안녕동)경기도 화성시 안녕동 180-141번지44538037.204671126.986507
132화성시동탄한증막19930401운영중<NA>NN0목욕장업한증막업경기도 화성시 금곡로163번길 12경기도 화성시 금곡동 473-1번지1851137.174982127.078955