Overview

Dataset statistics

Number of variables35
Number of observations116
Missing cells191
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory302.1 B

Variable types

Text5
DateTime2
Categorical23
Numeric4
Boolean1

Dataset

Description이 데이터는 서울특별시 동작구의 대중목욕탕, 찜질방, 사우나 등 물로 목욕 할 수 있거나 맥반석ㆍ황토 등에게 발생되는 열기 또는 원적외선 등을 이용하여 땀을 낼 수 있는 시설 및 설비 등의 서비스를 제공하는 업소정보를 포함하고 있습니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15094591/fileData.do

Alerts

좌석수 has constant value ""Constant
세탁기수 has constant value ""Constant
여성종사자수 has constant value ""Constant
남성종사자수 has constant value ""Constant
회수건조수 has constant value ""Constant
침대수 has constant value ""Constant
업태구분명 is highly imbalanced (58.1%)Imbalance
위생업태명 is highly imbalanced (77.1%)Imbalance
건물지상층수 is highly imbalanced (81.9%)Imbalance
건물지하층수 is highly imbalanced (84.2%)Imbalance
사용시작지상층 is highly imbalanced (85.4%)Imbalance
사용끝지상층 is highly imbalanced (87.7%)Imbalance
사용시작지하층 is highly imbalanced (74.8%)Imbalance
사용끝지하층 is highly imbalanced (76.9%)Imbalance
한실수 is highly imbalanced (87.4%)Imbalance
양실수 is highly imbalanced (87.4%)Imbalance
욕실수 is highly imbalanced (72.7%)Imbalance
발한실여부 is highly imbalanced (72.7%)Imbalance
건물소유구분명 is highly imbalanced (87.4%)Imbalance
다중이용업소여부 is highly imbalanced (87.4%)Imbalance
폐업일자 has 18 (15.5%) missing valuesMissing
도로명주소 has 71 (61.2%) missing valuesMissing
도로명우편번호 has 72 (62.1%) missing valuesMissing
X좌표정보 has 15 (12.9%) missing valuesMissing
Y좌표정보 has 15 (12.9%) missing valuesMissing
관리번호 has unique valuesUnique
소재지면적 has 6 (5.2%) zerosZeros

Reproduction

Analysis started2024-04-13 11:23:11.066343
Analysis finished2024-04-13 11:23:12.614939
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct116
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-04-13T20:23:13.134938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2552
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

Unique116 ?
Unique (%)100.0%

Sample

1st row3190000-202-1963-00134
2nd row3190000-202-1963-00140
3rd row3190000-202-1965-00141
4th row3190000-202-1967-00195
5th row3190000-202-1968-00197
ValueCountFrequency (%)
3190000-202-1963-00134 1
 
0.9%
3190000-202-1994-00156 1
 
0.9%
3190000-202-1998-00149 1
 
0.9%
3190000-202-1997-00151 1
 
0.9%
3190000-202-1997-00148 1
 
0.9%
3190000-202-1996-00147 1
 
0.9%
3190000-202-1995-00222 1
 
0.9%
3190000-202-1995-00221 1
 
0.9%
3190000-202-1995-00161 1
 
0.9%
3190000-202-1995-00160 1
 
0.9%
Other values (106) 106
91.4%
2024-04-13T20:23:14.245087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 925
36.2%
- 348
 
13.6%
2 322
 
12.6%
1 316
 
12.4%
9 268
 
10.5%
3 147
 
5.8%
8 71
 
2.8%
7 46
 
1.8%
4 38
 
1.5%
5 37
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2204
86.4%
Dash Punctuation 348
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 925
42.0%
2 322
 
14.6%
1 316
 
14.3%
9 268
 
12.2%
3 147
 
6.7%
8 71
 
3.2%
7 46
 
2.1%
4 38
 
1.7%
5 37
 
1.7%
6 34
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 925
36.2%
- 348
 
13.6%
2 322
 
12.6%
1 316
 
12.4%
9 268
 
10.5%
3 147
 
5.8%
8 71
 
2.8%
7 46
 
1.8%
4 38
 
1.5%
5 37
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 925
36.2%
- 348
 
13.6%
2 322
 
12.6%
1 316
 
12.4%
9 268
 
10.5%
3 147
 
5.8%
8 71
 
2.8%
7 46
 
1.8%
4 38
 
1.5%
5 37
 
1.4%

인허가일자
Date

Distinct113
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum1963-06-12 00:00:00
Maximum2020-08-26 00:00:00
2024-04-13T20:23:14.655265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:23:15.089976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태코드
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
3
98 
1
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 98
84.5%
1 18
 
15.5%

Length

2024-04-13T20:23:15.511420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:15.822104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 98
84.5%
1 18
 
15.5%

영업상태명
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
폐업
98 
영업/정상
18 

Length

Max length5
Median length2
Mean length2.4655172
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 98
84.5%
영업/정상 18
 
15.5%

Length

2024-04-13T20:23:16.163561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:16.491136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 98
84.5%
영업/정상 18
 
15.5%

상세영업상태코드
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2
98 
1
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 98
84.5%
1 18
 
15.5%

Length

2024-04-13T20:23:16.678901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:16.969557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 98
84.5%
1 18
 
15.5%

상세영업상태명
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
폐업
98 
영업
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 98
84.5%
영업 18
 
15.5%

Length

2024-04-13T20:23:17.294827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:17.637928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 98
84.5%
영업 18
 
15.5%

폐업일자
Date

MISSING 

Distinct94
Distinct (%)95.9%
Missing18
Missing (%)15.5%
Memory size1.0 KiB
Minimum1994-07-27 00:00:00
Maximum2024-01-03 00:00:00
2024-04-13T20:23:17.971397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:23:18.384854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소재지면적
Real number (ℝ)

ZEROS 

Distinct110
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.19621
Minimum0
Maximum981.74
Zeros6
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-13T20:23:18.803726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.915
Q1191.91
median280.815
Q3404.025
95-th percentile630.2875
Maximum981.74
Range981.74
Interquartile range (IQR)212.115

Descriptive statistics

Standard deviation189.77338
Coefficient of variation (CV)0.60399642
Kurtosis2.3312627
Mean314.19621
Median Absolute Deviation (MAD)99.715
Skewness1.1491751
Sum36446.76
Variance36013.937
MonotonicityNot monotonic
2024-04-13T20:23:19.235179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
5.2%
625.09 2
 
1.7%
301.06 1
 
0.9%
512.29 1
 
0.9%
975.02 1
 
0.9%
229.82 1
 
0.9%
427.2 1
 
0.9%
427.83 1
 
0.9%
686.07 1
 
0.9%
253.29 1
 
0.9%
Other values (100) 100
86.2%
ValueCountFrequency (%)
0.0 6
5.2%
13.22 1
 
0.9%
33.0 1
 
0.9%
118.68 1
 
0.9%
118.79 1
 
0.9%
123.39 1
 
0.9%
129.84 1
 
0.9%
135.96 1
 
0.9%
140.2 1
 
0.9%
141.96 1
 
0.9%
ValueCountFrequency (%)
981.74 1
0.9%
975.02 1
0.9%
966.22 1
0.9%
686.07 1
0.9%
667.5 1
0.9%
645.88 1
0.9%
625.09 2
1.7%
610.28 1
0.9%
593.52 1
0.9%
587.92 1
0.9%

소재지우편번호
Real number (ℝ)

Distinct56
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156740.8
Minimum156010
Maximum156878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-13T20:23:19.780699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156010
5-th percentile156030
Q1156809
median156822
Q3156841.5
95-th percentile156862
Maximum156878
Range868
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation250.15884
Coefficient of variation (CV)0.0015960033
Kurtosis4.2330733
Mean156740.8
Median Absolute Deviation (MAD)16.5
Skewness-2.466698
Sum18181933
Variance62579.447
MonotonicityNot monotonic
2024-04-13T20:23:20.201722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
156030 7
 
6.0%
156831 6
 
5.2%
156816 5
 
4.3%
156811 4
 
3.4%
156813 4
 
3.4%
156810 4
 
3.4%
156844 4
 
3.4%
156818 3
 
2.6%
156859 3
 
2.6%
156852 3
 
2.6%
Other values (46) 73
62.9%
ValueCountFrequency (%)
156010 1
 
0.9%
156020 1
 
0.9%
156030 7
6.0%
156060 1
 
0.9%
156080 2
 
1.7%
156090 1
 
0.9%
156759 1
 
0.9%
156800 3
2.6%
156801 2
 
1.7%
156803 1
 
0.9%
ValueCountFrequency (%)
156878 1
 
0.9%
156877 1
 
0.9%
156871 1
 
0.9%
156868 1
 
0.9%
156863 1
 
0.9%
156862 2
1.7%
156861 1
 
0.9%
156860 1
 
0.9%
156859 3
2.6%
156858 1
 
0.9%

지번주소
Text

Distinct110
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-04-13T20:23:21.308879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length22.991379
Min length18

Characters and Unicode

Total characters2667
Distinct characters72
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

Unique105 ?
Unique (%)90.5%

Sample

1st row서울특별시 동작구 노량진동 119-68번지
2nd row서울특별시 동작구 본동 48-11번지
3rd row서울특별시 동작구 흑석동 98-1번지
4th row서울특별시 동작구 신대방동 584-3번지
5th row서울특별시 동작구 대방동 416-1번지
ValueCountFrequency (%)
서울특별시 116
23.8%
동작구 116
23.8%
상도동 30
 
6.2%
사당동 28
 
5.7%
대방동 15
 
3.1%
노량진동 12
 
2.5%
신대방동 12
 
2.5%
흑석동 11
 
2.3%
376-16번지 3
 
0.6%
본동 3
 
0.6%
Other values (129) 141
29.0%
2024-04-13T20:23:22.873931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
467
17.5%
동 240
 
9.0%
1 124
 
4.6%
작 120
 
4.5%
서 116
 
4.3%
특 116
 
4.3%
별 116
 
4.3%
시 116
 
4.3%
구 116
 
4.3%
울 116
 
4.3%
Other values (62) 1020
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1553
58.2%
Decimal Number 533
 
20.0%
Space Separator 467
 
17.5%
Dash Punctuation 108
 
4.0%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 240
15.5%
작 120
 
7.7%
서 116
 
7.5%
특 116
 
7.5%
별 116
 
7.5%
시 116
 
7.5%
구 116
 
7.5%
울 116
 
7.5%
지 95
 
6.1%
번 87
 
5.6%
Other values (46) 315
20.3%
Decimal Number
ValueCountFrequency (%)
1 124
23.3%
2 75
14.1%
3 60
11.3%
6 49
 
9.2%
4 45
 
8.4%
8 37
 
6.9%
0 37
 
6.9%
7 37
 
6.9%
5 36
 
6.8%
9 33
 
6.2%
Space Separator
ValueCountFrequency (%)
467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1553
58.2%
Common 1113
41.7%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 240
15.5%
작 120
 
7.7%
서 116
 
7.5%
특 116
 
7.5%
별 116
 
7.5%
시 116
 
7.5%
구 116
 
7.5%
울 116
 
7.5%
지 95
 
6.1%
번 87
 
5.6%
Other values (46) 315
20.3%
Common
ValueCountFrequency (%)
467
42.0%
1 124
 
11.1%
- 108
 
9.7%
2 75
 
6.7%
3 60
 
5.4%
6 49
 
4.4%
4 45
 
4.0%
8 37
 
3.3%
0 37
 
3.3%
7 37
 
3.3%
Other values (5) 74
 
6.6%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1553
58.2%
ASCII 1114
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
467
41.9%
1 124
 
11.1%
- 108
 
9.7%
2 75
 
6.7%
3 60
 
5.4%
6 49
 
4.4%
4 45
 
4.0%
8 37
 
3.3%
0 37
 
3.3%
7 37
 
3.3%
Other values (6) 75
 
6.7%
Hangul
ValueCountFrequency (%)
동 240
15.5%
작 120
 
7.7%
서 116
 
7.5%
특 116
 
7.5%
별 116
 
7.5%
시 116
 
7.5%
구 116
 
7.5%
울 116
 
7.5%
지 95
 
6.1%
번 87
 
5.6%
Other values (46) 315
20.3%

도로명주소
Text

MISSING 

Distinct44
Distinct (%)97.8%
Missing71
Missing (%)61.2%
Memory size1.0 KiB
2024-04-13T20:23:23.818086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length28
Mean length24.733333
Min length17

Characters and Unicode

Total characters1113
Distinct characters76
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

Unique43 ?
Unique (%)95.6%

Sample

1st row서울특별시 동작구 사당로25길 8 (사당동)
2nd row서울특별시 동작구 상도로22길 37 (상도동
3rd row서울특별시 동작구 흑석로11길 4 (흑석동)
4th row서울특별시 동작구 장승배기로 114 (노량진동)
5th row서울특별시 동작구 노량진로6길 53 (노량진동)
ValueCountFrequency (%)
서울특별시 45
20.4%
동작구 45
20.4%
사당동 13
 
5.9%
상도동 7
 
3.2%
신대방동 6
 
2.7%
노량진동 6
 
2.7%
흑석동 5
 
2.3%
3 3
 
1.4%
16 3
 
1.4%
대방동 2
 
0.9%
Other values (77) 86
38.9%
2024-04-13T20:23:25.076620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
 
16.1%
동 94
 
8.4%
작 50
 
4.5%
서 47
 
4.2%
특 45
 
4.0%
별 45
 
4.0%
시 45
 
4.0%
구 45
 
4.0%
울 45
 
4.0%
1 37
 
3.3%
Other values (66) 481
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 697
62.6%
Space Separator 179
 
16.1%
Decimal Number 166
 
14.9%
Open Punctuation 34
 
3.1%
Close Punctuation 30
 
2.7%
Dash Punctuation 6
 
0.5%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 94
13.5%
작 50
 
7.2%
서 47
 
6.7%
특 45
 
6.5%
별 45
 
6.5%
시 45
 
6.5%
구 45
 
6.5%
울 45
 
6.5%
로 35
 
5.0%
길 28
 
4.0%
Other values (51) 218
31.3%
Decimal Number
ValueCountFrequency (%)
1 37
22.3%
2 32
19.3%
4 17
10.2%
3 16
9.6%
6 14
 
8.4%
7 12
 
7.2%
9 11
 
6.6%
0 11
 
6.6%
8 8
 
4.8%
5 8
 
4.8%
Space Separator
ValueCountFrequency (%)
179
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 697
62.6%
Common 415
37.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 94
13.5%
작 50
 
7.2%
서 47
 
6.7%
특 45
 
6.5%
별 45
 
6.5%
시 45
 
6.5%
구 45
 
6.5%
울 45
 
6.5%
로 35
 
5.0%
길 28
 
4.0%
Other values (51) 218
31.3%
Common
ValueCountFrequency (%)
179
43.1%
1 37
 
8.9%
( 34
 
8.2%
2 32
 
7.7%
) 30
 
7.2%
4 17
 
4.1%
3 16
 
3.9%
6 14
 
3.4%
7 12
 
2.9%
9 11
 
2.7%
Other values (4) 33
 
8.0%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 697
62.6%
ASCII 416
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
43.0%
1 37
 
8.9%
( 34
 
8.2%
2 32
 
7.7%
) 30
 
7.2%
4 17
 
4.1%
3 16
 
3.8%
6 14
 
3.4%
7 12
 
2.9%
9 11
 
2.6%
Other values (5) 34
 
8.2%
Hangul
ValueCountFrequency (%)
동 94
13.5%
작 50
 
7.2%
서 47
 
6.7%
특 45
 
6.5%
별 45
 
6.5%
시 45
 
6.5%
구 45
 
6.5%
울 45
 
6.5%
로 35
 
5.0%
길 28
 
4.0%
Other values (51) 218
31.3%

도로명우편번호
Text

MISSING 

Distinct36
Distinct (%)81.8%
Missing72
Missing (%)62.1%
Memory size1.0 KiB
2024-04-13T20:23:25.743778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0227273
Min length4

Characters and Unicode

Total characters177
Distinct characters14
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

Unique28 ?
Unique (%)63.6%

Sample

1st row7006
2nd row6961
3rd row6910
4th row6925
5th row6930
ValueCountFrequency (%)
7072 2
 
4.5%
7071 2
 
4.5%
7037 2
 
4.5%
7011 2
 
4.5%
6904 2
 
4.5%
6970 2
 
4.5%
7016 2
 
4.5%
6964 2
 
4.5%
6936 1
 
2.3%
7007 1
 
2.3%
Other values (26) 26
59.1%
2024-04-13T20:23:26.601884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 33
18.6%
7 31
17.5%
0 31
17.5%
9 28
15.8%
1 17
9.6%
4 9
 
5.1%
3 9
 
5.1%
2 6
 
3.4%
5 6
 
3.4%
8 3
 
1.7%
Other values (4) 4
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 173
97.7%
Other Letter 3
 
1.7%
Close Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 33
19.1%
7 31
17.9%
0 31
17.9%
9 28
16.2%
1 17
9.8%
4 9
 
5.2%
3 9
 
5.2%
2 6
 
3.5%
5 6
 
3.5%
8 3
 
1.7%
Other Letter
ValueCountFrequency (%)
외 1
33.3%
필 1
33.3%
지 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174
98.3%
Hangul 3
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
6 33
19.0%
7 31
17.8%
0 31
17.8%
9 28
16.1%
1 17
9.8%
4 9
 
5.2%
3 9
 
5.2%
2 6
 
3.4%
5 6
 
3.4%
8 3
 
1.7%
Hangul
ValueCountFrequency (%)
외 1
33.3%
필 1
33.3%
지 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
98.3%
Hangul 3
 
1.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 33
19.0%
7 31
17.8%
0 31
17.8%
9 28
16.1%
1 17
9.8%
4 9
 
5.2%
3 9
 
5.2%
2 6
 
3.4%
5 6
 
3.4%
8 3
 
1.7%
Hangul
ValueCountFrequency (%)
외 1
33.3%
필 1
33.3%
지 1
33.3%

사업장명
Text

Distinct108
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-04-13T20:23:27.727447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length3
Mean length4.137931
Min length2

Characters and Unicode

Total characters480
Distinct characters132
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)86.2%

Sample

1st row백노탕
2nd row용봉탕
3rd row명천탕
4th row대림탕
5th row강남탕
ValueCountFrequency (%)
대방한증원 2
 
1.7%
대호탕 2
 
1.7%
대성탕 2
 
1.7%
상도한증원 2
 
1.7%
영화대중탕 2
 
1.7%
수정탕 2
 
1.7%
대림탕 2
 
1.7%
여성사우나 2
 
1.7%
우석탕 1
 
0.8%
경산사우나 1
 
0.8%
Other values (101) 101
84.9%
2024-04-13T20:23:29.296052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 82
 
17.1%
성 23
 
4.8%
대 21
 
4.4%
우 19
 
4.0%
사 16
 
3.3%
나 15
 
3.1%
수 13
 
2.7%
목 11
 
2.3%
욕 11
 
2.3%
스 11
 
2.3%
Other values (122) 258
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 471
98.1%
Space Separator 3
 
0.6%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 82
 
17.4%
성 23
 
4.9%
대 21
 
4.5%
우 19
 
4.0%
사 16
 
3.4%
나 15
 
3.2%
수 13
 
2.8%
목 11
 
2.3%
욕 11
 
2.3%
스 11
 
2.3%
Other values (117) 249
52.9%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 471
98.1%
Common 9
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 82
 
17.4%
성 23
 
4.9%
대 21
 
4.5%
우 19
 
4.0%
사 16
 
3.4%
나 15
 
3.2%
수 13
 
2.8%
목 11
 
2.3%
욕 11
 
2.3%
스 11
 
2.3%
Other values (117) 249
52.9%
Common
ValueCountFrequency (%)
3
33.3%
) 2
22.2%
( 2
22.2%
2 1
 
11.1%
4 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 471
98.1%
ASCII 9
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 82
 
17.4%
성 23
 
4.9%
대 21
 
4.5%
우 19
 
4.0%
사 16
 
3.4%
나 15
 
3.2%
수 13
 
2.8%
목 11
 
2.3%
욕 11
 
2.3%
스 11
 
2.3%
Other values (117) 249
52.9%
ASCII
ValueCountFrequency (%)
3
33.3%
) 2
22.2%
( 2
22.2%
2 1
 
11.1%
4 1
 
11.1%

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
공동탕업
97 
공동탕업+찜질시설서비스영업
13 
한증막업
 
4
목욕장업 기타
 
2

Length

Max length14
Median length4
Mean length5.1724138
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 97
83.6%
공동탕업+찜질시설서비스영업 13
 
11.2%
한증막업 4
 
3.4%
목욕장업 기타 2
 
1.7%

Length

2024-04-13T20:23:29.708984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:30.024261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 97
82.2%
공동탕업+찜질시설서비스영업 13
 
11.0%
한증막업 4
 
3.4%
목욕장업 2
 
1.7%
기타 2
 
1.7%

X좌표정보
Real number (ℝ)

MISSING 

Distinct90
Distinct (%)89.1%
Missing15
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean195342.39
Minimum191691.68
Maximum198410.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-13T20:23:30.376577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191691.68
5-th percentile191963.16
Q1193962.13
median195032.8
Q3197306.18
95-th percentile198229.01
Maximum198410.18
Range6718.4997
Interquartile range (IQR)3344.0539

Descriptive statistics

Standard deviation1951.9613
Coefficient of variation (CV)0.0099925125
Kurtosis-1.1447761
Mean195342.39
Median Absolute Deviation (MAD)1699.4732
Skewness0.036215634
Sum19729582
Variance3810153
MonotonicityNot monotonic
2024-04-13T20:23:30.809732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193324.6232 3
 
2.6%
193333.3275 2
 
1.7%
197306.1822 2
 
1.7%
194987.3796 2
 
1.7%
193992.2534 2
 
1.7%
193103.4092 2
 
1.7%
198145.311 2
 
1.7%
197753.4542 2
 
1.7%
191691.6784 2
 
1.7%
196619.5929 2
 
1.7%
Other values (80) 80
69.0%
(Missing) 15
 
12.9%
ValueCountFrequency (%)
191691.6784 2
1.7%
191827.1171 1
0.9%
191930.5637 1
0.9%
191938.4076 1
0.9%
191963.1624 1
0.9%
192109.8356 1
0.9%
192967.2953 1
0.9%
193050.8692 1
0.9%
193103.4092 2
1.7%
193157.4516 1
0.9%
ValueCountFrequency (%)
198410.1781 1
0.9%
198310.4685 1
0.9%
198278.3024 1
0.9%
198277.1823 1
0.9%
198243.2088 1
0.9%
198229.0109 1
0.9%
198225.7982 1
0.9%
198145.311 2
1.7%
198098.1677 1
0.9%
198086.299 1
0.9%

Y좌표정보
Real number (ℝ)

MISSING 

Distinct90
Distinct (%)89.1%
Missing15
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean443968.71
Minimum441595.66
Maximum445640.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-04-13T20:23:31.221906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441595.66
5-th percentile442143.84
Q1442918.66
median444185.41
Q3444860.35
95-th percentile445553.82
Maximum445640.1
Range4044.4349
Interquartile range (IQR)1941.6869

Descriptive statistics

Standard deviation1112.2762
Coefficient of variation (CV)0.002505303
Kurtosis-1.0832638
Mean443968.71
Median Absolute Deviation (MAD)887.3149
Skewness-0.36853989
Sum44840840
Variance1237158.3
MonotonicityNot monotonic
2024-04-13T20:23:31.670280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444662.4618 3
 
2.6%
443949.7263 2
 
1.7%
443234.2335 2
 
1.7%
443961.4176 2
 
1.7%
445331.9542 2
 
1.7%
444641.4058 2
 
1.7%
442773.8348 2
 
1.7%
442143.8434 2
 
1.7%
442818.1137 2
 
1.7%
444995.2829 2
 
1.7%
Other values (80) 80
69.0%
(Missing) 15
 
12.9%
ValueCountFrequency (%)
441595.6605 1
0.9%
441785.8293 1
0.9%
441856.128 1
0.9%
442015.8013 1
0.9%
442057.3422 1
0.9%
442143.8434 2
1.7%
442189.3797 1
0.9%
442293.2456 1
0.9%
442302.9854 1
0.9%
442398.9665 1
0.9%
ValueCountFrequency (%)
445640.0954 1
0.9%
445588.0116 1
0.9%
445569.678 1
0.9%
445567.079 1
0.9%
445561.6981 1
0.9%
445553.8238 1
0.9%
445509.9798 1
0.9%
445413.2884 1
0.9%
445382.0475 1
0.9%
445331.9542 2
1.7%

위생업태명
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
공동탕업
108 
한증막업
 
4
공동탕업+찜질시설서비스영업
 
3
목욕장업 기타
 
1

Length

Max length14
Median length4
Mean length4.2844828
Min length4

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 108
93.1%
한증막업 4
 
3.4%
공동탕업+찜질시설서비스영업 3
 
2.6%
목욕장업 기타 1
 
0.9%

Length

2024-04-13T20:23:32.092701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:32.408994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 108
92.3%
한증막업 4
 
3.4%
공동탕업+찜질시설서비스영업 3
 
2.6%
목욕장업 1
 
0.9%
기타 1
 
0.9%

건물지상층수
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
111 
4
 
4
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
95.7%
4 4
 
3.4%
5 1
 
0.9%

Length

2024-04-13T20:23:32.754655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:33.062218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
95.7%
4 4
 
3.4%
5 1
 
0.9%

건물지하층수
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
111 
1
 
3
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
95.7%
1 3
 
2.6%
3 1
 
0.9%
4 1
 
0.9%

Length

2024-04-13T20:23:33.387410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:33.697302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
95.7%
1 3
 
2.6%
3 1
 
0.9%
4 1
 
0.9%

사용시작지상층
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
111 
1
 
2
4
 
1
3
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0258621
Min length1

Unique

Unique3 ?
Unique (%)2.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111
95.7%
1 2
 
1.7%
4 1
 
0.9%
3 1
 
0.9%
<NA> 1
 
0.9%

Length

2024-04-13T20:23:34.057273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:34.391988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 111
95.7%
1 2
 
1.7%
4 1
 
0.9%
3 1
 
0.9%
na 1
 
0.9%

사용끝지상층
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
112 
2
 
1
5
 
1
1
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique4 ?
Unique (%)3.4%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112
96.6%
2 1
 
0.9%
5 1
 
0.9%
1 1
 
0.9%
3 1
 
0.9%

Length

2024-04-13T20:23:34.742268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:35.059302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 112
96.6%
2 1
 
0.9%
5 1
 
0.9%
1 1
 
0.9%
3 1
 
0.9%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
107 
1
 
5
<NA>
 
2
4
 
2

Length

Max length4
Median length1
Mean length1.0517241
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107
92.2%
1 5
 
4.3%
<NA> 2
 
1.7%
4 2
 
1.7%

Length

2024-04-13T20:23:35.424894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:35.750614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107
92.2%
1 5
 
4.3%
na 2
 
1.7%
4 2
 
1.7%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
107 
1
 
4
2
 
2
4
 
2
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0258621
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107
92.2%
1 4
 
3.4%
2 2
 
1.7%
4 2
 
1.7%
<NA> 1
 
0.9%

Length

2024-04-13T20:23:36.108887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:36.443310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107
92.2%
1 4
 
3.4%
2 2
 
1.7%
4 2
 
1.7%
na 1
 
0.9%

한실수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
114 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0517241
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 114
98.3%
<NA> 2
 
1.7%

Length

2024-04-13T20:23:36.808412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:37.298247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 114
98.3%
na 2
 
1.7%

양실수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
114 
<NA>
 
2

Length

Max length4
Median length1
Mean length1.0517241
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 114
98.3%
<NA> 2
 
1.7%

Length

2024-04-13T20:23:37.628399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:37.939027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 114
98.3%
na 2
 
1.7%

욕실수
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
107 
2
 
8
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107
92.2%
2 8
 
6.9%
1 1
 
0.9%

Length

2024-04-13T20:23:38.254887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:38.561568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 107
92.2%
2 8
 
6.9%
1 1
 
0.9%

발한실여부
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
N
107 
Y
 
8
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
N 107
92.2%
Y 8
 
6.9%
0 1
 
0.9%

Length

2024-04-13T20:23:38.884665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:39.191779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 107
92.2%
y 8
 
6.9%
0 1
 
0.9%

좌석수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:39.517888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:39.810442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

건물소유구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
임대
114 
자가
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대 114
98.3%
자가 2
 
1.7%

Length

2024-04-13T20:23:40.115471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:40.416677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 114
98.3%
자가 2
 
1.7%

세탁기수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:40.732326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:41.021075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

여성종사자수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:41.328392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:41.618665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

남성종사자수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:41.923892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:42.214887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

회수건조수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:42.521978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:42.811430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

침대수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116
100.0%

Length

2024-04-13T20:23:43.115894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:23:43.406575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 116
100.0%

다중이용업소여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size244.0 B
False
114 
True
 
2
ValueCountFrequency (%)
False 114
98.3%
True 2
 
1.7%
2024-04-13T20:23:43.647298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

관리번호인허가일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명업태구분명X좌표정보Y좌표정보위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
03190000-202-1963-001341963-08-033폐업2폐업2006-10-170.0156801서울특별시 동작구 노량진동 119-68번지<NA><NA>백노탕공동탕업194984.4173445509.9798공동탕업000000000N0임대00000N
13190000-202-1963-001401963-06-123폐업2폐업2007-12-12140.2156060서울특별시 동작구 본동 48-11번지<NA><NA>용봉탕공동탕업195947.6294445569.678공동탕업000000000N0임대00000N
23190000-202-1965-001411965-09-093폐업2폐업2005-06-08152.51156859서울특별시 동작구 흑석동 98-1번지<NA><NA>명천탕공동탕업196619.5929444995.2829공동탕업000000000N0임대00000N
33190000-202-1967-001951967-01-233폐업2폐업1995-09-13129.84156852서울특별시 동작구 신대방동 584-3번지<NA><NA>대림탕공동탕업191930.5637443313.3224공동탕업000000000N0임대00000N
43190000-202-1968-001971968-09-093폐업2폐업1997-05-19141.96156811서울특별시 동작구 대방동 416-1번지<NA><NA>강남탕공동탕업193050.8692444112.0299공동탕업000000000N0임대00000N
53190000-202-1968-002091968-09-283폐업2폐업1995-12-18142.37156030서울특별시 동작구 상도동 산 291-0번지<NA><NA>우정탕공동탕업<NA><NA>공동탕업000000000N0임대00000N
63190000-202-1969-001431969-11-143폐업2폐업2005-04-150.0156863서울특별시 동작구 흑석동 271-11번지<NA><NA>동작탕공동탕업197213.5768444807.1331공동탕업000000000N0임대00000N
73190000-202-1969-002111969-06-133폐업2폐업2001-02-02153.98156030서울특별시 동작구 상도동 산 181-2번지<NA><NA>장성탕공동탕업<NA><NA>공동탕업000000000N0임대00000N
83190000-202-1969-002131969-12-123폐업2폐업1997-04-08317.9156839서울특별시 동작구 상도동 357-6번지<NA><NA>수복탕공동탕업194151.7836444487.9621공동탕업000000000N0임대00000N
93190000-202-1969-002141969-11-133폐업2폐업2000-05-04177.78156839서울특별시 동작구 상도동 346-11번지<NA><NA>옥천탕공동탕업193969.3975444252.4433공동탕업000000000N0임대00000N
관리번호인허가일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명업태구분명X좌표정보Y좌표정보위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
1063190000-202-2005-000032005-09-153폐업2폐업2023-07-18424.44156807서울특별시 동작구 대방동 44-5서울특별시 동작구 등용로12길 14 (대방동)6932영화대중탕공동탕업193992.2534445331.9542공동탕업000000000N0임대00000N
1073190000-202-2005-000042005-11-151영업/정상1영업<NA>408.51156849서울특별시 동작구 신대방동 395-68 B1서울특별시 동작구 신대방동 395-68 B17071보라매 스파공동탕업+찜질시설서비스영업193204.7822443168.5136공동탕업000044002Y0임대00000N
1083190000-202-2007-000012007-11-071영업/정상1영업<NA>273.0156857서울특별시 동작구 흑석동 26-2서울특별시 동작구 흑석동 26-2번지6904리얼엔젤플러스(주)공동탕업197089.736444960.9078공동탕업440044002Y0임대00000Y
1093190000-202-2008-000012008-04-023폐업2폐업2020-08-25625.09156759서울특별시 동작구 신대방동 719 동작상떼빌 105동 지층101호서울특별시 동작구 신대방동 719 동작상떼빌 105동 지층101호7072성원상떼빌사우나공동탕업+찜질시설서비스영업191691.6784442818.1137공동탕업000000000N0임대00000N
1103190000-202-2010-000012010-10-013폐업2폐업2021-10-19456.21156816서울특별시 동작구 사당동 147-81서울특별시 동작구 동작대로27가길 44 (사당동)7008영스파공동탕업+찜질시설서비스영업198243.2088442610.758공동탕업000000000N0임대00000N
1113190000-202-2010-000022010-11-101영업/정상1영업<NA>318.78156859서울특별시 동작구 흑석동 98-1서울특별시 동작구 서달로14가길 20 (흑석동)6979드봉여성전용사우나공동탕업+찜질시설서비스영업196619.5929444995.2829공동탕업000000000N0임대00000N
1123190000-202-2015-000012015-01-163폐업2폐업2020-10-27165.0156823서울특별시 동작구 사당동 316-240서울특별시 동작구 사당로20나길 3 (사당동)7011여성사우나공동탕업197753.4542442143.8434공동탕업000000000N0임대00000N
1133190000-202-2016-000012016-01-261영업/정상1영업<NA>33.0156831서울특별시 동작구 상도동 22-56번지 지하1증서울특별시 동작구 상도로 2416921모어짐목욕장업 기타195126.5853444789.9502목욕장업 기타000000002Y0임대00000N
1143190000-202-2017-000012017-06-133폐업2폐업2021-05-1713.22156030서울특별시 동작구 상도동 413 5층서울특별시 동작구 양녕로26길 277037상도휘트니스클럽목욕장업 기타195032.8007443883.1349공동탕업000000000N0임대00000N
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