Overview

Dataset statistics

Number of variables15
Number of observations172
Missing cells145
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.3 KiB
Average record size in memory126.8 B

Variable types

Categorical4
Text3
Numeric6
Boolean2

Dataset

Description목욕장업(찜질시설서비스영업) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=VK10PZJWS5VFXTHQYTKE14363877&infSeq=1

Alerts

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
위생업태명 is highly overall correlated with 인허가일자 and 10 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
다중이용업소여부 is highly overall correlated with 위생업종명 and 1 other fieldsHigh correlation
인허가일자 is highly overall correlated with 폐업일자 and 2 other fieldsHigh correlation
폐업일자 is highly overall correlated with 인허가일자 and 3 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 (70.4%)Imbalance
위생업태명 is highly imbalanced (70.4%)Imbalance
폐업일자 has 89 (51.7%) missing valuesMissing
다중이용업소여부 has 9 (5.2%) missing valuesMissing
발한실여부 has 10 (5.8%) missing valuesMissing
욕실수(개) has 25 (14.5%) missing valuesMissing
소재지도로명주소 has 8 (4.7%) missing valuesMissing
WGS84위도 has 2 (1.2%) missing valuesMissing
WGS84경도 has 2 (1.2%) missing valuesMissing
욕실수(개) has 77 (44.8%) zerosZeros

Reproduction

Analysis started2023-12-10 22:32:28.248560
Analysis finished2023-12-10 22:32:32.988828
Duration4.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
고양시
29 
남양주시
21 
부천시
13 
성남시
11 
수원시
9 
Other values (23)
89 

Length

Max length4
Median length3
Mean length3.1569767
Min length3

Unique

Unique5 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 29
16.9%
남양주시 21
12.2%
부천시 13
 
7.6%
성남시 11
 
6.4%
수원시 9
 
5.2%
안양시 9
 
5.2%
광주시 9
 
5.2%
양주시 7
 
4.1%
양평군 7
 
4.1%
김포시 6
 
3.5%
Other values (18) 51
29.7%

Length

2023-12-11T07:32:33.051607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 29
16.9%
남양주시 21
12.2%
부천시 13
 
7.6%
성남시 11
 
6.4%
수원시 9
 
5.2%
안양시 9
 
5.2%
광주시 9
 
5.2%
양주시 7
 
4.1%
양평군 7
 
4.1%
김포시 6
 
3.5%
Other values (18) 51
29.7%

사업장명
Text

Distinct168
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T07:32:33.268162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12.5
Mean length7.3255814
Min length3

Characters and Unicode

Total characters1260
Distinct characters245
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)95.3%

Sample

1st row힐링찜질방
2nd row(주)유명산 숯고을
3rd row설악황토불가마
4th row신기한토르마린(찜질방)
5th row통일로불가마사우나
ValueCountFrequency (%)
찜질방 4
 
2.0%
금강산 3
 
1.5%
참숯가마 3
 
1.5%
신기한토르마린(찜질방 2
 
1.0%
월드사우나 2
 
1.0%
스파 2
 
1.0%
제주마그마 2
 
1.0%
숯가마 2
 
1.0%
찜가마 2
 
1.0%
일명타운 2
 
1.0%
Other values (178) 179
88.2%
2023-12-11T07:32:33.659044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 51
 
4.0%
마 48
 
3.8%
우 48
 
3.8%
사 46
 
3.7%
방 34
 
2.7%
가 33
 
2.6%
찜 31
 
2.5%
31
 
2.5%
질 29
 
2.3%
한 29
 
2.3%
Other values (235) 880
69.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1180
93.7%
Space Separator 31
 
2.5%
Decimal Number 25
 
2.0%
Close Punctuation 12
 
1.0%
Open Punctuation 11
 
0.9%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 51
 
4.3%
마 48
 
4.1%
우 48
 
4.1%
사 46
 
3.9%
방 34
 
2.9%
가 33
 
2.8%
찜 31
 
2.6%
질 29
 
2.5%
한 29
 
2.5%
스 27
 
2.3%
Other values (225) 804
68.1%
Decimal Number
ValueCountFrequency (%)
4 10
40.0%
2 10
40.0%
9 2
 
8.0%
1 1
 
4.0%
8 1
 
4.0%
3 1
 
4.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1180
93.7%
Common 79
 
6.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 51
 
4.3%
마 48
 
4.1%
우 48
 
4.1%
사 46
 
3.9%
방 34
 
2.9%
가 33
 
2.8%
찜 31
 
2.6%
질 29
 
2.5%
한 29
 
2.5%
스 27
 
2.3%
Other values (225) 804
68.1%
Common
ValueCountFrequency (%)
31
39.2%
) 12
 
15.2%
( 11
 
13.9%
4 10
 
12.7%
2 10
 
12.7%
9 2
 
2.5%
1 1
 
1.3%
8 1
 
1.3%
3 1
 
1.3%
Latin
ValueCountFrequency (%)
P 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1180
93.7%
ASCII 80
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 51
 
4.3%
마 48
 
4.1%
우 48
 
4.1%
사 46
 
3.9%
방 34
 
2.9%
가 33
 
2.8%
찜 31
 
2.6%
질 29
 
2.5%
한 29
 
2.5%
스 27
 
2.3%
Other values (225) 804
68.1%
ASCII
ValueCountFrequency (%)
31
38.8%
) 12
 
15.0%
( 11
 
13.8%
4 10
 
12.5%
2 10
 
12.5%
9 2
 
2.5%
1 1
 
1.2%
8 1
 
1.2%
3 1
 
1.2%
P 1
 
1.2%

인허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct170
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20077703
Minimum19820625
Maximum20180629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:33.814789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19820625
5-th percentile20000684
Q120040129
median20070166
Q320121048
95-th percentile20170757
Maximum20180629
Range360004
Interquartile range (IQR)80918.75

Descriptive statistics

Standard deviation61094.809
Coefficient of variation (CV)0.0030429183
Kurtosis1.9199305
Mean20077703
Median Absolute Deviation (MAD)39858.5
Skewness-0.63138071
Sum3.4533648 × 109
Variance3.7325757 × 109
MonotonicityNot monotonic
2023-12-11T07:32:33.974426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20131104 2
 
1.2%
20140312 2
 
1.2%
20140912 1
 
0.6%
20031129 1
 
0.6%
20040203 1
 
0.6%
20051027 1
 
0.6%
20110104 1
 
0.6%
20010321 1
 
0.6%
20021220 1
 
0.6%
20040402 1
 
0.6%
Other values (160) 160
93.0%
ValueCountFrequency (%)
19820625 1
0.6%
19850924 1
0.6%
19880204 1
0.6%
19970123 1
0.6%
19980624 1
0.6%
19980725 1
0.6%
19980730 1
0.6%
19980908 1
0.6%
20000524 1
0.6%
20000814 1
0.6%
ValueCountFrequency (%)
20180629 1
0.6%
20180521 1
0.6%
20180425 1
0.6%
20180208 1
0.6%
20180205 1
0.6%
20180130 1
0.6%
20171020 1
0.6%
20170830 1
0.6%
20170814 1
0.6%
20170710 1
0.6%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
운영중
89 
폐업 등
83 

Length

Max length4
Median length3
Mean length3.4825581
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 89
51.7%
폐업 등 83
48.3%

Length

2023-12-11T07:32:34.112938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:32:34.206347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 89
34.9%
폐업 83
32.5%
등 83
32.5%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)98.8%
Missing89
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean20133787
Minimum20060705
Maximum20180727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:34.312009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060705
5-th percentile20070312
Q120100507
median20140721
Q320170373
95-th percentile20180205
Maximum20180727
Range120022
Interquartile range (IQR)69866

Descriptive statistics

Standard deviation37954.855
Coefficient of variation (CV)0.0018851325
Kurtosis-1.2456585
Mean20133787
Median Absolute Deviation (MAD)30208
Skewness-0.39191454
Sum1.6711043 × 109
Variance1.440571 × 109
MonotonicityNot monotonic
2023-12-11T07:32:34.749414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20171130 2
 
1.2%
20161004 1
 
0.6%
20180718 1
 
0.6%
20080710 1
 
0.6%
20141222 1
 
0.6%
20170302 1
 
0.6%
20170322 1
 
0.6%
20160331 1
 
0.6%
20151111 1
 
0.6%
20180103 1
 
0.6%
Other values (72) 72
41.9%
(Missing) 89
51.7%
ValueCountFrequency (%)
20060705 1
0.6%
20060720 1
0.6%
20060824 1
0.6%
20061027 1
0.6%
20070213 1
0.6%
20071206 1
0.6%
20080318 1
0.6%
20080425 1
0.6%
20080710 1
0.6%
20080922 1
0.6%
ValueCountFrequency (%)
20180727 1
0.6%
20180718 1
0.6%
20180611 1
0.6%
20180308 1
0.6%
20180213 1
0.6%
20180130 1
0.6%
20180103 1
0.6%
20171227 1
0.6%
20171221 1
0.6%
20171130 2
1.2%

다중이용업소여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.2%
Missing9
Missing (%)5.2%
Memory size476.0 B
False
125 
True
38 
(Missing)
 
9
ValueCountFrequency (%)
False 125
72.7%
True 38
 
22.1%
(Missing) 9
 
5.2%
2023-12-11T07:32:34.885235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

발한실여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)1.2%
Missing10
Missing (%)5.8%
Memory size476.0 B
True
95 
False
67 
(Missing)
10 
ValueCountFrequency (%)
True 95
55.2%
False 67
39.0%
(Missing) 10
 
5.8%
2023-12-11T07:32:34.973239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

욕실수(개)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)6.8%
Missing25
Missing (%)14.5%
Infinite0
Infinite (%)0.0%
Mean1.6938776
Minimum0
Maximum16
Zeros77
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:35.067370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8177817
Coefficient of variation (CV)1.6635097
Kurtosis7.2411272
Mean1.6938776
Median Absolute Deviation (MAD)0
Skewness2.5505309
Sum249
Variance7.9398938
MonotonicityNot monotonic
2023-12-11T07:32:35.183426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 77
44.8%
2 44
25.6%
1 6
 
3.5%
6 5
 
2.9%
4 4
 
2.3%
8 4
 
2.3%
12 3
 
1.7%
9 2
 
1.2%
7 1
 
0.6%
16 1
 
0.6%
(Missing) 25
 
14.5%
ValueCountFrequency (%)
0 77
44.8%
1 6
 
3.5%
2 44
25.6%
4 4
 
2.3%
6 5
 
2.9%
7 1
 
0.6%
8 4
 
2.3%
9 2
 
1.2%
12 3
 
1.7%
16 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
12 3
 
1.7%
9 2
 
1.2%
8 4
 
2.3%
7 1
 
0.6%
6 5
 
2.9%
4 4
 
2.3%
2 44
25.6%
1 6
 
3.5%
0 77
44.8%

위생업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
목욕장업
163 
<NA>
 
9

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 163
94.8%
<NA> 9
 
5.2%

Length

2023-12-11T07:32:35.322641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:32:35.429625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목욕장업 163
94.8%
na 9
 
5.2%

위생업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
찜질시설서비스영업
163 
<NA>
 
9

Length

Max length9
Median length9
Mean length8.7383721
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
찜질시설서비스영업 163
94.8%
<NA> 9
 
5.2%

Length

2023-12-11T07:32:35.533892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:32:35.645915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
찜질시설서비스영업 163
94.8%
na 9
 
5.2%

소재지도로명주소
Text

MISSING 

Distinct164
Distinct (%)100.0%
Missing8
Missing (%)4.7%
Memory size1.5 KiB
2023-12-11T07:32:35.910055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length42
Mean length28.817073
Min length14

Characters and Unicode

Total characters4726
Distinct characters263
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique164 ?
Unique (%)100.0%

Sample

1st row경기도 가평군 가평읍 문화로 152
2nd row경기도 가평군 설악면 어비산길 184-103
3rd row경기도 가평군 설악면 유명로 1808-20
4th row경기도 고양시 덕양구 보광로 56, 지하1(일부)층 (벽제동)
5th row경기도 고양시 덕양구 대양로 15 (대자동, 지하1층, 1~2층)
ValueCountFrequency (%)
경기도 164
 
16.5%
고양시 29
 
2.9%
남양주시 19
 
1.9%
덕양구 18
 
1.8%
부천시 13
 
1.3%
일산동구 11
 
1.1%
성남시 11
 
1.1%
광주시 9
 
0.9%
안양시 9
 
0.9%
수원시 8
 
0.8%
Other values (511) 701
70.7%
2023-12-11T07:32:36.368373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
829
 
17.5%
1 223
 
4.7%
경 184
 
3.9%
도 171
 
3.6%
기 168
 
3.6%
시 154
 
3.3%
로 150
 
3.2%
동 125
 
2.6%
, 117
 
2.5%
2 108
 
2.3%
Other values (253) 2497
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2689
56.9%
Space Separator 829
 
17.5%
Decimal Number 826
 
17.5%
Other Punctuation 117
 
2.5%
Close Punctuation 99
 
2.1%
Open Punctuation 99
 
2.1%
Dash Punctuation 43
 
0.9%
Uppercase Letter 15
 
0.3%
Math Symbol 9
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 184
 
6.8%
도 171
 
6.4%
기 168
 
6.2%
시 154
 
5.7%
로 150
 
5.6%
동 125
 
4.6%
양 100
 
3.7%
구 66
 
2.5%
층 66
 
2.5%
길 65
 
2.4%
Other values (232) 1440
53.6%
Decimal Number
ValueCountFrequency (%)
1 223
27.0%
2 108
13.1%
0 84
 
10.2%
4 77
 
9.3%
3 69
 
8.4%
5 66
 
8.0%
7 56
 
6.8%
8 52
 
6.3%
6 52
 
6.3%
9 39
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
B 9
60.0%
A 4
26.7%
D 1
 
6.7%
C 1
 
6.7%
Math Symbol
ValueCountFrequency (%)
~ 8
88.9%
+ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
829
100.0%
Other Punctuation
ValueCountFrequency (%)
, 117
100.0%
Close Punctuation
ValueCountFrequency (%)
) 99
100.0%
Open Punctuation
ValueCountFrequency (%)
( 99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2689
56.9%
Common 2022
42.8%
Latin 15
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 184
 
6.8%
도 171
 
6.4%
기 168
 
6.2%
시 154
 
5.7%
로 150
 
5.6%
동 125
 
4.6%
양 100
 
3.7%
구 66
 
2.5%
층 66
 
2.5%
길 65
 
2.4%
Other values (232) 1440
53.6%
Common
ValueCountFrequency (%)
829
41.0%
1 223
 
11.0%
, 117
 
5.8%
2 108
 
5.3%
) 99
 
4.9%
( 99
 
4.9%
0 84
 
4.2%
4 77
 
3.8%
3 69
 
3.4%
5 66
 
3.3%
Other values (7) 251
 
12.4%
Latin
ValueCountFrequency (%)
B 9
60.0%
A 4
26.7%
D 1
 
6.7%
C 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2689
56.9%
ASCII 2037
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
829
40.7%
1 223
 
10.9%
, 117
 
5.7%
2 108
 
5.3%
) 99
 
4.9%
( 99
 
4.9%
0 84
 
4.1%
4 77
 
3.8%
3 69
 
3.4%
5 66
 
3.2%
Other values (11) 266
 
13.1%
Hangul
ValueCountFrequency (%)
경 184
 
6.8%
도 171
 
6.4%
기 168
 
6.2%
시 154
 
5.7%
로 150
 
5.6%
동 125
 
4.6%
양 100
 
3.7%
구 66
 
2.5%
층 66
 
2.5%
길 65
 
2.4%
Other values (232) 1440
53.6%

소재지지번주소
Text

Distinct171
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T07:32:36.683305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length42
Mean length28.139535
Min length17

Characters and Unicode

Total characters4840
Distinct characters240
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)98.8%

Sample

1st row경기도 가평군 가평읍 대곡리 318-7번지 외 1필지
2nd row경기도 가평군 설악면 가일리 118번지
3rd row경기도 가평군 설악면 선촌리 534-2번지
4th row경기도 고양시 덕양구 벽제동 394-1번지 지하1(일부)층
5th row경기도 고양시 덕양구 대자동 1150-22번지 지하1층, 1~2층
ValueCountFrequency (%)
경기도 172
 
17.2%
고양시 29
 
2.9%
남양주시 21
 
2.1%
덕양구 18
 
1.8%
부천시 13
 
1.3%
성남시 11
 
1.1%
일산동구 11
 
1.1%
외 10
 
1.0%
수원시 9
 
0.9%
지하1층 9
 
0.9%
Other values (496) 697
69.7%
2023-12-11T07:32:37.147163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
831
 
17.2%
지 230
 
4.8%
1 209
 
4.3%
도 178
 
3.7%
기 177
 
3.7%
번 174
 
3.6%
경 172
 
3.6%
시 161
 
3.3%
동 151
 
3.1%
2 139
 
2.9%
Other values (230) 2418
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2828
58.4%
Decimal Number 940
 
19.4%
Space Separator 831
 
17.2%
Dash Punctuation 134
 
2.8%
Other Punctuation 54
 
1.1%
Uppercase Letter 18
 
0.4%
Open Punctuation 12
 
0.2%
Close Punctuation 12
 
0.2%
Math Symbol 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 230
 
8.1%
도 178
 
6.3%
기 177
 
6.3%
번 174
 
6.2%
경 172
 
6.1%
시 161
 
5.7%
동 151
 
5.3%
양 98
 
3.5%
층 71
 
2.5%
구 67
 
2.4%
Other values (209) 1349
47.7%
Decimal Number
ValueCountFrequency (%)
1 209
22.2%
2 139
14.8%
3 101
10.7%
0 95
10.1%
4 95
10.1%
5 79
 
8.4%
7 60
 
6.4%
6 59
 
6.3%
9 57
 
6.1%
8 46
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 12
66.7%
A 4
 
22.2%
D 1
 
5.6%
C 1
 
5.6%
Math Symbol
ValueCountFrequency (%)
~ 10
90.9%
+ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
831
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Other Punctuation
ValueCountFrequency (%)
, 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2828
58.4%
Common 1994
41.2%
Latin 18
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 230
 
8.1%
도 178
 
6.3%
기 177
 
6.3%
번 174
 
6.2%
경 172
 
6.1%
시 161
 
5.7%
동 151
 
5.3%
양 98
 
3.5%
층 71
 
2.5%
구 67
 
2.4%
Other values (209) 1349
47.7%
Common
ValueCountFrequency (%)
831
41.7%
1 209
 
10.5%
2 139
 
7.0%
- 134
 
6.7%
3 101
 
5.1%
0 95
 
4.8%
4 95
 
4.8%
5 79
 
4.0%
7 60
 
3.0%
6 59
 
3.0%
Other values (7) 192
 
9.6%
Latin
ValueCountFrequency (%)
B 12
66.7%
A 4
 
22.2%
D 1
 
5.6%
C 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2828
58.4%
ASCII 2012
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
831
41.3%
1 209
 
10.4%
2 139
 
6.9%
- 134
 
6.7%
3 101
 
5.0%
0 95
 
4.7%
4 95
 
4.7%
5 79
 
3.9%
7 60
 
3.0%
6 59
 
2.9%
Other values (11) 210
 
10.4%
Hangul
ValueCountFrequency (%)
지 230
 
8.1%
도 178
 
6.3%
기 177
 
6.3%
번 174
 
6.2%
경 172
 
6.1%
시 161
 
5.7%
동 151
 
5.3%
양 98
 
3.5%
층 71
 
2.5%
구 67
 
2.4%
Other values (209) 1349
47.7%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct148
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416900.23
Minimum14676
Maximum487913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:37.311342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14676
5-th percentile14729.55
Q1413834.5
median450832
Q3472841.75
95-th percentile482821
Maximum487913
Range473237
Interquartile range (IQR)59007.25

Descriptive statistics

Standard deviation118133.14
Coefficient of variation (CV)0.2833607
Kurtosis7.5534538
Mean416900.23
Median Absolute Deviation (MAD)24970
Skewness-2.9781456
Sum71706839
Variance1.3955439 × 1010
MonotonicityNot monotonic
2023-12-11T07:32:37.497622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
482812 4
 
2.3%
462830 2
 
1.2%
462831 2
 
1.2%
472883 2
 
1.2%
472841 2
 
1.2%
14709 2
 
1.2%
14711 2
 
1.2%
480030 2
 
1.2%
464130 2
 
1.2%
410835 2
 
1.2%
Other values (138) 150
87.2%
ValueCountFrequency (%)
14676 1
0.6%
14679 1
0.6%
14689 1
0.6%
14709 2
1.2%
14711 2
1.2%
14724 1
0.6%
14729 1
0.6%
14730 1
0.6%
14731 2
1.2%
14782 1
0.6%
ValueCountFrequency (%)
487913 1
 
0.6%
487873 2
1.2%
487829 2
1.2%
487821 1
 
0.6%
486881 1
 
0.6%
486870 1
 
0.6%
482832 1
 
0.6%
482812 4
2.3%
482160 1
 
0.6%
482130 1
 
0.6%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct167
Distinct (%)98.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean37.529404
Minimum36.956721
Maximum38.088707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:37.637270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.956721
5-th percentile37.173643
Q137.392883
median37.581524
Q337.659721
95-th percentile37.820439
Maximum38.088707
Range1.1319854
Interquartile range (IQR)0.26683858

Descriptive statistics

Standard deviation0.21187514
Coefficient of variation (CV)0.005645577
Kurtosis0.20037982
Mean37.529404
Median Absolute Deviation (MAD)0.13768441
Skewness-0.43566801
Sum6379.9988
Variance0.044891076
MonotonicityNot monotonic
2023-12-11T07:32:37.778770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4859670817 2
 
1.2%
37.6275746135 2
 
1.2%
37.4608291183 2
 
1.2%
37.4302733563 1
 
0.6%
37.384443451 1
 
0.6%
37.0653747712 1
 
0.6%
37.3871654461 1
 
0.6%
37.3994603987 1
 
0.6%
37.3674460465 1
 
0.6%
37.393317648 1
 
0.6%
Other values (157) 157
91.3%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
36.9567213545 1
0.6%
36.9824411848 1
0.6%
36.9833396186 1
0.6%
36.9894850109 1
0.6%
37.0083604555 1
0.6%
37.0150053119 1
0.6%
37.0653747712 1
0.6%
37.093558097 1
0.6%
37.1469359271 1
0.6%
37.2062857936 1
0.6%
ValueCountFrequency (%)
38.0887067588 1
0.6%
37.99394137 1
0.6%
37.9650695726 1
0.6%
37.8883968612 1
0.6%
37.8882823495 1
0.6%
37.8809875395 1
0.6%
37.8707122382 1
0.6%
37.8568741361 1
0.6%
37.8265979042 1
0.6%
37.8129110814 1
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct167
Distinct (%)98.2%
Missing2
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean127.04083
Minimum126.6005
Maximum127.6726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T07:32:37.942544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.6005
5-th percentile126.7559
Q1126.83217
median127.00543
Q3127.20755
95-th percentile127.49305
Maximum127.6726
Range1.0721038
Interquartile range (IQR)0.37538488

Descriptive statistics

Standard deviation0.23686831
Coefficient of variation (CV)0.0018645054
Kurtosis-0.22752424
Mean127.04083
Median Absolute Deviation (MAD)0.18441809
Skewness0.59320005
Sum21596.941
Variance0.056106595
MonotonicityNot monotonic
2023-12-11T07:32:38.115866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7536812956 2
 
1.2%
126.8950469208 2
 
1.2%
127.1691519314 2
 
1.2%
127.129873139 1
 
0.6%
126.9601613949 1
 
0.6%
127.3584474992 1
 
0.6%
126.9480939931 1
 
0.6%
126.9446090277 1
 
0.6%
126.9548171878 1
 
0.6%
126.9631706254 1
 
0.6%
Other values (157) 157
91.3%
(Missing) 2
 
1.2%
ValueCountFrequency (%)
126.6004972397 1
0.6%
126.6661434291 1
0.6%
126.6679636456 1
0.6%
126.6869607949 1
0.6%
126.6964651409 1
0.6%
126.7045871508 1
0.6%
126.7109579245 1
0.6%
126.7536812956 2
1.2%
126.758622168 1
0.6%
126.7608616601 1
0.6%
ValueCountFrequency (%)
127.6726010411 1
0.6%
127.6592185663 1
0.6%
127.6497639185 1
0.6%
127.64189926 1
0.6%
127.6212283428 1
0.6%
127.5518357936 1
0.6%
127.5085337678 1
0.6%
127.5073290538 1
0.6%
127.4943666332 1
0.6%
127.4914306122 1
0.6%

Interactions

2023-12-11T07:32:31.909511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.132574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.932194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.440170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.928052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.410579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.997293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.236986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.016507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.520411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.000443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.502563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:32.103499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.321673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.126902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.605684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.078852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.581764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:32.215347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.677459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.207565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.694751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.158790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.669798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:32.319861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.760945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.284944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.773233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.234520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.740623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:32.406025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:29.852152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.359642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:30.847608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.331546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:32:31.819544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:32:38.226250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)소재지우편번호WGS84위도WGS84경도
시군명1.0000.4710.2640.6090.4730.5030.3941.0000.9620.942
인허가일자0.4711.0000.0370.5220.2270.2070.1070.3990.0640.249
영업상태명0.2640.0371.000NaN0.4970.0470.0000.0000.1250.252
폐업일자0.6090.522NaN1.0000.2100.1330.3460.0970.4520.367
다중이용업소여부0.4730.2270.4970.2101.0000.0000.0000.0000.1850.278
발한실여부0.5030.2070.0470.1330.0001.0000.3740.1660.2980.438
욕실수(개)0.3940.1070.0000.3460.0000.3741.0000.0000.3870.304
소재지우편번호1.0000.3990.0000.0970.0000.1660.0001.0000.6330.813
WGS84위도0.9620.0640.1250.4520.1850.2980.3870.6331.0000.611
WGS84경도0.9420.2490.2520.3670.2780.4380.3040.8130.6111.000
2023-12-11T07:32:38.394793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
발한실여부위생업종명시군명위생업태명영업상태명다중이용업소여부
발한실여부1.0001.0000.3661.0000.0290.000
위생업종명1.0001.0001.0001.0001.0001.000
시군명0.3661.0001.0001.0000.1900.344
위생업태명1.0001.0001.0001.0001.0001.000
영업상태명0.0291.0000.1901.0001.0000.331
다중이용업소여부0.0001.0000.3441.0000.3311.000
2023-12-11T07:32:38.506629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가일자폐업일자욕실수(개)소재지우편번호WGS84위도WGS84경도시군명영업상태명다중이용업소여부발한실여부위생업종명위생업태명
인허가일자1.0000.560-0.1750.203-0.1840.1780.2130.0820.2380.2181.0001.000
폐업일자0.5601.000-0.2190.047-0.3220.1890.2591.0000.1560.0001.0001.000
욕실수(개)-0.175-0.2191.0000.2000.0500.2320.0620.0000.0000.2761.0001.000
소재지우편번호0.2030.0470.2001.0000.1990.7910.9230.0000.0820.2521.0001.000
WGS84위도-0.184-0.3220.0500.1991.000-0.1520.7430.0920.1370.2221.0001.000
WGS84경도0.1780.1890.2320.791-0.1521.0000.6770.1880.2070.3271.0001.000
시군명0.2130.2590.0620.9230.7430.6771.0000.1900.3440.3661.0001.000
영업상태명0.0821.0000.0000.0000.0920.1880.1901.0000.3310.0291.0001.000
다중이용업소여부0.2380.1560.0000.0820.1370.2070.3440.3311.0000.0001.0001.000
발한실여부0.2180.0000.2760.2520.2220.3270.3660.0290.0001.0001.0001.000
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

2023-12-11T07:32:32.542135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:32:32.732404image/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.
2023-12-11T07:32:32.888226image/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
0가평군힐링찜질방20140912운영중<NA>YY0목욕장업찜질시설서비스영업경기도 가평군 가평읍 문화로 152경기도 가평군 가평읍 대곡리 318-7번지 외 1필지47780437.826598127.508534
1가평군(주)유명산 숯고을20100812폐업 등20150720NY0목욕장업찜질시설서비스영업경기도 가평군 설악면 어비산길 184-103경기도 가평군 설악면 가일리 118번지47785137.593991127.507329
2가평군설악황토불가마20060407폐업 등20090115NY2목욕장업찜질시설서비스영업경기도 가평군 설악면 유명로 1808-20경기도 가평군 설악면 선촌리 534-2번지47785437.678219127.473872
3고양시신기한토르마린(찜질방)20170608운영중<NA>NN0목욕장업찜질시설서비스영업경기도 고양시 덕양구 보광로 56, 지하1(일부)층 (벽제동)경기도 고양시 덕양구 벽제동 394-1번지 지하1(일부)층41251037.720753126.905455
4고양시통일로불가마사우나20021101운영중<NA>NN0목욕장업찜질시설서비스영업경기도 고양시 덕양구 대양로 15 (대자동, 지하1층, 1~2층)경기도 고양시 덕양구 대자동 1150-22번지 지하1층, 1~2층41248037.689072126.874348
5고양시조은여성불가마사우나19980725운영중<NA>NN0목욕장업찜질시설서비스영업경기도 고양시 덕양구 중앙로 548 (행신동, 현대프라자 B01~B06호)경기도 고양시 덕양구 행신동 950-2번지 현대프라자 B01~B06호41283637.623303126.836529
6고양시굿모닝건강랜드20030304운영중<NA>YY2목욕장업찜질시설서비스영업경기도 고양시 일산동구 고양대로 1030 (식사동, 일산훼미리굿모닝건강랜드)경기도 고양시 일산동구 식사동 861-3번지 일산훼미리굿모닝건강랜드41082037.671747126.806378
7고양시덕온사우나20040217운영중<NA>NN0목욕장업찜질시설서비스영업경기도 고양시 덕양구 원당로 316 (원당동, 지하1층, 1~2층)경기도 고양시 덕양구 원당동 434-5번지 지하1층, 1~2층41203037.676475126.849868
8고양시휴스파앤헬스20080908운영중<NA>NY2목욕장업찜질시설서비스영업경기도 고양시 덕양구 호국로 822 (성사동, 명지캐럿86, 301호)경기도 고양시 덕양구 성사동 501-2번지 명지캐럿86, 301호41280637.658531126.838846
9고양시세원조은스파20010312운영중<NA>NN0목욕장업찜질시설서비스영업경기도 고양시 덕양구 토당로 115, 금호프라자 지하1층 비101(일부)호 (토당동)경기도 고양시 덕양구 토당동 282-23번지 금호프라자 지하1층 비101(일부)호41281737.625499126.816962
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부발한실여부욕실수(개)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
162평택시머슬짐20180130운영중<NA>NY0목욕장업찜질시설서비스영업경기도 평택시 안중읍 안현로서6길 7, 대건빌딩 4층경기도 평택시 안중읍 현화리 854-1번지 대건빌딩45188536.98334126.928063
163포천시일명타운 찜가마20140312운영중<NA>YN16목욕장업찜질시설서비스영업경기도 포천시 군내면 반월산성로371번길 86-33, 4,5동경기도 포천시 군내면 상성북리 350-42번지 4,5동48787337.888397127.237474
164포천시일명타운 찜가마20140312운영중<NA>YN8목욕장업찜질시설서비스영업경기도 포천시 군내면 반월산성로371번길 86-66경기도 포천시 군내면 상성북리 350-38번지 1동48787337.888282127.238668
165포천시광릉참숯20090320운영중<NA>YY6목욕장업찜질시설서비스영업경기도 포천시 소흘읍 이곡길 134경기도 포천시 소흘읍 이곡리 410-8번지48782937.7877127.150096
166포천시광릉숲 황토 숯가마20090519폐업 등20171025NY8목욕장업찜질시설서비스영업경기도 포천시 소흘읍 광릉수목원로 700-10경기도 포천시 소흘읍 직동리 368번지48782937.773221127.160816
167포천시대우구들장찜질방20080703폐업 등20180130NY2목욕장업찜질시설서비스영업경기도 포천시 소흘읍 죽엽산로 539-6경기도 포천시 소흘읍 고모리 144-6번지48782137.786115127.165776
168포천시황토방20101006폐업 등20140721NY0목욕장업찜질시설서비스영업경기도 포천시 신북면 포천로 2419경기도 포천시 신북면 고일리 10-6번지48791337.96507127.192506
169하남시미사리참숯가마사우나20060913폐업 등20120511NY2목욕장업찜질시설서비스영업<NA>경기도 하남시 창우동 138-3번지46512037.545401127.224895
170화성시수빈소나무톱밥찜질방20140203폐업 등20180727NY1목욕장업찜질시설서비스영업경기도 화성시 봉담읍 와우안길 57경기도 화성시 봉담읍 와우리 82-6번지44589737.216449126.975528
171화성시따솜이쉼터20170814폐업 등20170814NN0목욕장업찜질시설서비스영업경기도 화성시 병점중앙로155번길 17-4, 1층 전부호 (진안동)경기도 화성시 진안동 524-27번지 1층전부호44539037.210693127.037012