Overview

Dataset statistics

Number of variables47
Number of observations3065
Missing cells30597
Missing cells (%)21.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory403.0 B

Variable types

Categorical19
Text8
DateTime4
Unsupported4
Numeric10
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-17921/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (60.2%)Imbalance
사용시작지하층 is highly imbalanced (51.9%)Imbalance
사용끝지하층 is highly imbalanced (54.9%)Imbalance
조건부허가시작일자 is highly imbalanced (99.4%)Imbalance
조건부허가종료일자 is highly imbalanced (99.2%)Imbalance
건물소유구분명 is highly imbalanced (71.9%)Imbalance
남성종사자수 is highly imbalanced (69.3%)Imbalance
인허가취소일자 has 3065 (100.0%) missing valuesMissing
폐업일자 has 1016 (33.1%) missing valuesMissing
휴업시작일자 has 3065 (100.0%) missing valuesMissing
휴업종료일자 has 3065 (100.0%) missing valuesMissing
재개업일자 has 3065 (100.0%) missing valuesMissing
전화번호 has 849 (27.7%) missing valuesMissing
도로명주소 has 1121 (36.6%) missing valuesMissing
도로명우편번호 has 1137 (37.1%) missing valuesMissing
좌표정보(X) has 141 (4.6%) missing valuesMissing
좌표정보(Y) has 141 (4.6%) missing valuesMissing
건물지상층수 has 965 (31.5%) missing valuesMissing
건물지하층수 has 1026 (33.5%) missing valuesMissing
사용시작지상층 has 1337 (43.6%) missing valuesMissing
사용끝지상층 has 1718 (56.1%) missing valuesMissing
발한실여부 has 475 (15.5%) missing valuesMissing
좌석수 has 510 (16.6%) missing valuesMissing
조건부허가신고사유 has 3062 (99.9%) missing valuesMissing
여성종사자수 has 2428 (79.2%) missing valuesMissing
침대수 has 1964 (64.1%) missing valuesMissing
다중이용업소여부 has 442 (14.4%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = -26.14577034)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 1441 (47.0%) zerosZeros
건물지하층수 has 1733 (56.5%) zerosZeros
사용시작지상층 has 702 (22.9%) zerosZeros
사용끝지상층 has 737 (24.0%) zerosZeros
좌석수 has 510 (16.6%) zerosZeros
여성종사자수 has 495 (16.2%) zerosZeros
침대수 has 807 (26.3%) zerosZeros

Reproduction

Analysis started2024-04-21 01:45:07.944484
Analysis finished2024-04-21 01:45:10.119608
Duration2.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
3190000
3065 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3190000
2nd row3190000
3rd row3190000
4th row3190000
5th row3190000

Common Values

ValueCountFrequency (%)
3190000 3065
100.0%

Length

2024-04-21T10:45:10.238596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:10.401313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3190000 3065
100.0%

관리번호
Text

UNIQUE 

Distinct3065
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-21T10:45:10.979269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters67430
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

Unique3065 ?
Unique (%)100.0%

Sample

1st row3190000-204-1968-00965
2nd row3190000-204-1969-00969
3rd row3190000-204-1969-01045
4th row3190000-204-1970-00806
5th row3190000-204-1970-00848
ValueCountFrequency (%)
3190000-204-1968-00965 1
 
< 0.1%
3190000-211-2018-00026 1
 
< 0.1%
3190000-211-2018-00028 1
 
< 0.1%
3190000-211-2018-00018 1
 
< 0.1%
3190000-211-2018-00019 1
 
< 0.1%
3190000-211-2018-00020 1
 
< 0.1%
3190000-211-2018-00021 1
 
< 0.1%
3190000-211-2018-00022 1
 
< 0.1%
3190000-211-2018-00023 1
 
< 0.1%
3190000-211-2018-00024 1
 
< 0.1%
Other values (3055) 3055
99.7%
2024-04-21T10:45:11.808367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25814
38.3%
1 9716
 
14.4%
- 9195
 
13.6%
2 7086
 
10.5%
9 5474
 
8.1%
3 4353
 
6.5%
4 2099
 
3.1%
8 1066
 
1.6%
5 989
 
1.5%
7 823
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58235
86.4%
Dash Punctuation 9195
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25814
44.3%
1 9716
 
16.7%
2 7086
 
12.2%
9 5474
 
9.4%
3 4353
 
7.5%
4 2099
 
3.6%
8 1066
 
1.8%
5 989
 
1.7%
7 823
 
1.4%
6 815
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 9195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25814
38.3%
1 9716
 
14.4%
- 9195
 
13.6%
2 7086
 
10.5%
9 5474
 
8.1%
3 4353
 
6.5%
4 2099
 
3.1%
8 1066
 
1.6%
5 989
 
1.5%
7 823
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25814
38.3%
1 9716
 
14.4%
- 9195
 
13.6%
2 7086
 
10.5%
9 5474
 
8.1%
3 4353
 
6.5%
4 2099
 
3.1%
8 1066
 
1.6%
5 989
 
1.5%
7 823
 
1.2%
Distinct2451
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
Minimum1965-06-24 00:00:00
Maximum2024-04-15 00:00:00
2024-04-21T10:45:12.051868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:12.308900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3065
Missing (%)100.0%
Memory size27.1 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
3
2049 
1
1016 

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 2049
66.9%
1 1016
33.1%

Length

2024-04-21T10:45:12.546575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:12.721649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2049
66.9%
1 1016
33.1%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
폐업
2049 
영업/정상
1016 

Length

Max length5
Median length2
Mean length2.9944535
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2049
66.9%
영업/정상 1016
33.1%

Length

2024-04-21T10:45:12.910775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:13.084239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2049
66.9%
영업/정상 1016
33.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2
2049 
1
1016 

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 2049
66.9%
1 1016
33.1%

Length

2024-04-21T10:45:13.259433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:13.430062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2049
66.9%
1 1016
33.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
폐업
2049 
영업
1016 

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 2049
66.9%
영업 1016
33.1%

Length

2024-04-21T10:45:13.603698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:13.774735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2049
66.9%
영업 1016
33.1%

폐업일자
Date

MISSING 

Distinct1531
Distinct (%)74.7%
Missing1016
Missing (%)33.1%
Memory size24.1 KiB
Minimum1991-12-02 00:00:00
Maximum2024-04-16 00:00:00
2024-04-21T10:45:13.970290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:14.211195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3065
Missing (%)100.0%
Memory size27.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3065
Missing (%)100.0%
Memory size27.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3065
Missing (%)100.0%
Memory size27.1 KiB

전화번호
Text

MISSING 

Distinct2022
Distinct (%)91.2%
Missing849
Missing (%)27.7%
Memory size24.1 KiB
2024-04-21T10:45:15.160261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.124549
Min length2

Characters and Unicode

Total characters22436
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

Unique1882 ?
Unique (%)84.9%

Sample

1st row02 8135701
2nd row0208164697
3rd row0208122610
4th row0208159665
5th row02 8151970
ValueCountFrequency (%)
02 1511
38.1%
0200000000 24
 
0.6%
822 19
 
0.5%
070 15
 
0.4%
812 14
 
0.4%
824 11
 
0.3%
00000 10
 
0.3%
825 9
 
0.2%
817 8
 
0.2%
821 7
 
0.2%
Other values (2052) 2342
59.0%
2024-04-21T10:45:16.343529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4024
17.9%
0 3846
17.1%
8 2583
11.5%
2130
9.5%
5 1820
8.1%
1 1634
7.3%
3 1611
7.2%
4 1298
 
5.8%
7 1276
 
5.7%
6 1124
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20306
90.5%
Space Separator 2130
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4024
19.8%
0 3846
18.9%
8 2583
12.7%
5 1820
9.0%
1 1634
8.0%
3 1611
7.9%
4 1298
 
6.4%
7 1276
 
6.3%
6 1124
 
5.5%
9 1090
 
5.4%
Space Separator
ValueCountFrequency (%)
2130
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4024
17.9%
0 3846
17.1%
8 2583
11.5%
2130
9.5%
5 1820
8.1%
1 1634
7.3%
3 1611
7.2%
4 1298
 
5.8%
7 1276
 
5.7%
6 1124
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4024
17.9%
0 3846
17.1%
8 2583
11.5%
2130
9.5%
5 1820
8.1%
1 1634
7.3%
3 1611
7.2%
4 1298
 
5.8%
7 1276
 
5.7%
6 1124
 
5.0%
Distinct1449
Distinct (%)47.3%
Missing1
Missing (%)< 0.1%
Memory size24.1 KiB
2024-04-21T10:45:17.622111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.8609661
Min length3

Characters and Unicode

Total characters14894
Distinct characters12
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

Unique1032 ?
Unique (%)33.7%

Sample

1st row20.80
2nd row.00
3rd row.00
4th row1,619.00
5th row13.20
ValueCountFrequency (%)
00 380
 
12.4%
33.00 85
 
2.8%
20.00 41
 
1.3%
30.00 39
 
1.3%
21.00 30
 
1.0%
26.40 27
 
0.9%
24.00 25
 
0.8%
23.10 24
 
0.8%
17.00 24
 
0.8%
25.00 22
 
0.7%
Other values (1439) 2367
77.3%
2024-04-21T10:45:19.068017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3306
22.2%
. 3064
20.6%
1 1414
9.5%
2 1409
9.5%
3 1164
 
7.8%
4 905
 
6.1%
5 901
 
6.0%
6 796
 
5.3%
8 680
 
4.6%
9 611
 
4.1%
Other values (2) 644
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11765
79.0%
Other Punctuation 3129
 
21.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3306
28.1%
1 1414
12.0%
2 1409
12.0%
3 1164
 
9.9%
4 905
 
7.7%
5 901
 
7.7%
6 796
 
6.8%
8 680
 
5.8%
9 611
 
5.2%
7 579
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 3064
97.9%
, 65
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14894
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3306
22.2%
. 3064
20.6%
1 1414
9.5%
2 1409
9.5%
3 1164
 
7.8%
4 905
 
6.1%
5 901
 
6.0%
6 796
 
5.3%
8 680
 
4.6%
9 611
 
4.1%
Other values (2) 644
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3306
22.2%
. 3064
20.6%
1 1414
9.5%
2 1409
9.5%
3 1164
 
7.8%
4 905
 
6.1%
5 901
 
6.0%
6 796
 
5.3%
8 680
 
4.6%
9 611
 
4.1%
Other values (2) 644
 
4.3%
Distinct160
Distinct (%)5.2%
Missing2
Missing (%)0.1%
Memory size24.1 KiB
2024-04-21T10:45:20.220458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0904342
Min length6

Characters and Unicode

Total characters18655
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

Unique27 ?
Unique (%)0.9%

Sample

1st row156813
2nd row156861
3rd row156802
4th row156800
5th row156831
ValueCountFrequency (%)
156030 190
 
6.2%
156816 129
 
4.2%
156815 92
 
3.0%
156821 83
 
2.7%
156831 76
 
2.5%
156824 76
 
2.5%
156826 71
 
2.3%
156847 66
 
2.2%
156853 65
 
2.1%
156839 64
 
2.1%
Other values (150) 2151
70.2%
2024-04-21T10:45:21.551942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4090
21.9%
5 3606
19.3%
6 3518
18.9%
8 2851
15.3%
0 1322
 
7.1%
3 874
 
4.7%
2 670
 
3.6%
4 655
 
3.5%
7 522
 
2.8%
- 277
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18378
98.5%
Dash Punctuation 277
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4090
22.3%
5 3606
19.6%
6 3518
19.1%
8 2851
15.5%
0 1322
 
7.2%
3 874
 
4.8%
2 670
 
3.6%
4 655
 
3.6%
7 522
 
2.8%
9 270
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18655
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4090
21.9%
5 3606
19.3%
6 3518
18.9%
8 2851
15.3%
0 1322
 
7.1%
3 874
 
4.7%
2 670
 
3.6%
4 655
 
3.5%
7 522
 
2.8%
- 277
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4090
21.9%
5 3606
19.3%
6 3518
18.9%
8 2851
15.3%
0 1322
 
7.1%
3 874
 
4.7%
2 670
 
3.6%
4 655
 
3.5%
7 522
 
2.8%
- 277
 
1.5%
Distinct2526
Distinct (%)82.5%
Missing2
Missing (%)0.1%
Memory size24.1 KiB
2024-04-21T10:45:22.572097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length46
Mean length24.197192
Min length16

Characters and Unicode

Total characters74116
Distinct characters305
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2147 ?
Unique (%)70.1%

Sample

1st row서울특별시 동작구 본동 426번지
2nd row서울특별시 동작구 흑석동 232-87번지
3rd row서울특별시 동작구 노량진동 206-22번지
4th row서울특별시 동작구 노량진동 28-2번지
5th row서울특별시 동작구 상도동 22-107번지
ValueCountFrequency (%)
서울특별시 3063
22.4%
동작구 3061
22.4%
사당동 1045
 
7.6%
상도동 738
 
5.4%
신대방동 434
 
3.2%
노량진동 264
 
1.9%
흑석동 228
 
1.7%
1층 195
 
1.4%
대방동 174
 
1.3%
상도1동 109
 
0.8%
Other values (2588) 4377
32.0%
2024-04-21T10:45:23.975158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13111
17.7%
6327
 
8.5%
1 3281
 
4.4%
3114
 
4.2%
3078
 
4.2%
3076
 
4.2%
3066
 
4.1%
3065
 
4.1%
3063
 
4.1%
3063
 
4.1%
Other values (295) 29872
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42591
57.5%
Decimal Number 15466
 
20.9%
Space Separator 13111
 
17.7%
Dash Punctuation 2746
 
3.7%
Uppercase Letter 64
 
0.1%
Open Punctuation 44
 
0.1%
Close Punctuation 44
 
0.1%
Other Punctuation 44
 
0.1%
Lowercase Letter 5
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6327
14.9%
3114
 
7.3%
3078
 
7.2%
3076
 
7.2%
3066
 
7.2%
3065
 
7.2%
3063
 
7.2%
3063
 
7.2%
2103
 
4.9%
2045
 
4.8%
Other values (260) 10591
24.9%
Uppercase Letter
ValueCountFrequency (%)
B 26
40.6%
A 17
26.6%
R 5
 
7.8%
G 2
 
3.1%
D 2
 
3.1%
S 2
 
3.1%
I 2
 
3.1%
E 2
 
3.1%
L 2
 
3.1%
V 1
 
1.6%
Other values (3) 3
 
4.7%
Decimal Number
ValueCountFrequency (%)
1 3281
21.2%
2 2347
15.2%
3 1957
12.7%
0 1552
10.0%
4 1351
8.7%
5 1146
 
7.4%
6 1098
 
7.1%
7 1023
 
6.6%
9 937
 
6.1%
8 774
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 41
93.2%
& 1
 
2.3%
. 1
 
2.3%
/ 1
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
e 3
60.0%
s 1
 
20.0%
c 1
 
20.0%
Space Separator
ValueCountFrequency (%)
13111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2746
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42590
57.5%
Common 31455
42.4%
Latin 70
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6327
14.9%
3114
 
7.3%
3078
 
7.2%
3076
 
7.2%
3066
 
7.2%
3065
 
7.2%
3063
 
7.2%
3063
 
7.2%
2103
 
4.9%
2045
 
4.8%
Other values (259) 10590
24.9%
Common
ValueCountFrequency (%)
13111
41.7%
1 3281
 
10.4%
- 2746
 
8.7%
2 2347
 
7.5%
3 1957
 
6.2%
0 1552
 
4.9%
4 1351
 
4.3%
5 1146
 
3.6%
6 1098
 
3.5%
7 1023
 
3.3%
Other values (8) 1843
 
5.9%
Latin
ValueCountFrequency (%)
B 26
37.1%
A 17
24.3%
R 5
 
7.1%
e 3
 
4.3%
G 2
 
2.9%
D 2
 
2.9%
S 2
 
2.9%
I 2
 
2.9%
E 2
 
2.9%
L 2
 
2.9%
Other values (7) 7
 
10.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42590
57.5%
ASCII 31524
42.5%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13111
41.6%
1 3281
 
10.4%
- 2746
 
8.7%
2 2347
 
7.4%
3 1957
 
6.2%
0 1552
 
4.9%
4 1351
 
4.3%
5 1146
 
3.6%
6 1098
 
3.5%
7 1023
 
3.2%
Other values (24) 1912
 
6.1%
Hangul
ValueCountFrequency (%)
6327
14.9%
3114
 
7.3%
3078
 
7.2%
3076
 
7.2%
3066
 
7.2%
3065
 
7.2%
3063
 
7.2%
3063
 
7.2%
2103
 
4.9%
2045
 
4.8%
Other values (259) 10590
24.9%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1750
Distinct (%)90.0%
Missing1121
Missing (%)36.6%
Memory size24.1 KiB
2024-04-21T10:45:24.999446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length55
Mean length31.328189
Min length21

Characters and Unicode

Total characters60902
Distinct characters325
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1585 ?
Unique (%)81.5%

Sample

1st row서울특별시 동작구 서달로14사길 3-1 (흑석동)
2nd row서울특별시 동작구 양녕로23길 110, 101호 (상도동, 이레빌)
3rd row서울특별시 동작구 만양로 95 (노량진동)
4th row서울특별시 동작구 장승배기로11가길 5 (노량진동)
5th row서울특별시 동작구 동작대로33길 109 (사당동)
ValueCountFrequency (%)
서울특별시 1944
 
16.0%
동작구 1942
 
16.0%
사당동 642
 
5.3%
1층 616
 
5.1%
상도동 473
 
3.9%
신대방동 287
 
2.4%
2층 258
 
2.1%
흑석동 130
 
1.1%
노량진동 127
 
1.0%
상도로 119
 
1.0%
Other values (1164) 5594
46.1%
2024-04-21T10:45:26.536099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10191
 
16.7%
4486
 
7.4%
1 2756
 
4.5%
2307
 
3.8%
2020
 
3.3%
1982
 
3.3%
) 1969
 
3.2%
( 1969
 
3.2%
1946
 
3.2%
1945
 
3.2%
Other values (315) 29331
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 35365
58.1%
Space Separator 10191
 
16.7%
Decimal Number 9470
 
15.5%
Close Punctuation 1969
 
3.2%
Open Punctuation 1969
 
3.2%
Other Punctuation 1660
 
2.7%
Dash Punctuation 172
 
0.3%
Uppercase Letter 97
 
0.2%
Lowercase Letter 8
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4486
 
12.7%
2307
 
6.5%
2020
 
5.7%
1982
 
5.6%
1946
 
5.5%
1945
 
5.5%
1944
 
5.5%
1944
 
5.5%
1736
 
4.9%
1270
 
3.6%
Other values (275) 13785
39.0%
Uppercase Letter
ValueCountFrequency (%)
B 50
51.5%
A 21
21.6%
R 5
 
5.2%
S 4
 
4.1%
G 3
 
3.1%
E 2
 
2.1%
L 2
 
2.1%
I 2
 
2.1%
T 2
 
2.1%
C 2
 
2.1%
Other values (4) 4
 
4.1%
Decimal Number
ValueCountFrequency (%)
1 2756
29.1%
2 1775
18.7%
3 940
 
9.9%
0 882
 
9.3%
4 622
 
6.6%
5 590
 
6.2%
6 568
 
6.0%
9 469
 
5.0%
7 451
 
4.8%
8 417
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
s 2
25.0%
a 1
12.5%
l 1
12.5%
i 1
12.5%
n 1
12.5%
c 1
12.5%
e 1
12.5%
Other Punctuation
ValueCountFrequency (%)
, 1656
99.8%
. 2
 
0.1%
# 1
 
0.1%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
10191
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1969
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1969
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 35364
58.1%
Common 25431
41.8%
Latin 106
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4486
 
12.7%
2307
 
6.5%
2020
 
5.7%
1982
 
5.6%
1946
 
5.5%
1945
 
5.5%
1944
 
5.5%
1944
 
5.5%
1736
 
4.9%
1270
 
3.6%
Other values (274) 13784
39.0%
Latin
ValueCountFrequency (%)
B 50
47.2%
A 21
19.8%
R 5
 
4.7%
S 4
 
3.8%
G 3
 
2.8%
s 2
 
1.9%
E 2
 
1.9%
L 2
 
1.9%
I 2
 
1.9%
T 2
 
1.9%
Other values (12) 13
 
12.3%
Common
ValueCountFrequency (%)
10191
40.1%
1 2756
 
10.8%
) 1969
 
7.7%
( 1969
 
7.7%
2 1775
 
7.0%
, 1656
 
6.5%
3 940
 
3.7%
0 882
 
3.5%
4 622
 
2.4%
5 590
 
2.3%
Other values (8) 2081
 
8.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 35364
58.1%
ASCII 25536
41.9%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10191
39.9%
1 2756
 
10.8%
) 1969
 
7.7%
( 1969
 
7.7%
2 1775
 
7.0%
, 1656
 
6.5%
3 940
 
3.7%
0 882
 
3.5%
4 622
 
2.4%
5 590
 
2.3%
Other values (29) 2186
 
8.6%
Hangul
ValueCountFrequency (%)
4486
 
12.7%
2307
 
6.5%
2020
 
5.7%
1982
 
5.6%
1946
 
5.5%
1945
 
5.5%
1944
 
5.5%
1944
 
5.5%
1736
 
4.9%
1270
 
3.6%
Other values (274) 13784
39.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct154
Distinct (%)8.0%
Missing1137
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean6997.9523
Minimum3779
Maximum7357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:27.124894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3779
5-th percentile6917.35
Q16964
median7008
Q37039
95-th percentile7070
Maximum7357
Range3578
Interquartile range (IQR)75

Descriptive statistics

Standard deviation87.170745
Coefficient of variation (CV)0.012456608
Kurtosis965.24631
Mean6997.9523
Median Absolute Deviation (MAD)36
Skewness-26.14577
Sum13492052
Variance7598.7388
MonotonicityNot monotonic
2024-04-21T10:45:27.563216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7008 103
 
3.4%
7015 61
 
2.0%
7004 44
 
1.4%
7010 44
 
1.4%
7009 42
 
1.4%
7071 42
 
1.4%
7013 36
 
1.2%
7042 35
 
1.1%
6979 34
 
1.1%
6980 33
 
1.1%
Other values (144) 1454
47.4%
(Missing) 1137
37.1%
ValueCountFrequency (%)
3779 1
 
< 0.1%
6900 1
 
< 0.1%
6902 3
 
0.1%
6904 7
0.2%
6906 4
 
0.1%
6908 4
 
0.1%
6909 8
0.3%
6910 17
0.6%
6912 6
 
0.2%
6913 9
0.3%
ValueCountFrequency (%)
7357 1
 
< 0.1%
7074 9
 
0.3%
7073 3
 
0.1%
7072 31
1.0%
7071 42
1.4%
7070 23
0.8%
7069 25
0.8%
7068 15
 
0.5%
7067 20
0.7%
7066 1
 
< 0.1%
Distinct2622
Distinct (%)85.5%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-21T10:45:28.826570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length5.924633
Min length1

Characters and Unicode

Total characters18159
Distinct characters690
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2339 ?
Unique (%)76.3%

Sample

1st row도원
2nd row성은미용실
3rd row
4th row차밍미용실
5th row제이미용실
ValueCountFrequency (%)
헤어 75
 
2.0%
hair 43
 
1.1%
미용실 34
 
0.9%
네일 26
 
0.7%
에스테틱 23
 
0.6%
nail 18
 
0.5%
머리하는날 14
 
0.4%
리안헤어 14
 
0.4%
뷰티 13
 
0.3%
헤어샵 13
 
0.3%
Other values (2795) 3569
92.9%
2024-04-21T10:45:30.541246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1133
 
6.2%
1101
 
6.1%
816
 
4.5%
779
 
4.3%
641
 
3.5%
631
 
3.5%
430
 
2.4%
369
 
2.0%
363
 
2.0%
206
 
1.1%
Other values (680) 11690
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15170
83.5%
Lowercase Letter 982
 
5.4%
Space Separator 779
 
4.3%
Uppercase Letter 601
 
3.3%
Other Punctuation 165
 
0.9%
Close Punctuation 152
 
0.8%
Open Punctuation 152
 
0.8%
Decimal Number 150
 
0.8%
Dash Punctuation 3
 
< 0.1%
Connector Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1133
 
7.5%
1101
 
7.3%
816
 
5.4%
641
 
4.2%
631
 
4.2%
430
 
2.8%
369
 
2.4%
363
 
2.4%
206
 
1.4%
193
 
1.3%
Other values (605) 9287
61.2%
Lowercase Letter
ValueCountFrequency (%)
a 150
15.3%
i 123
12.5%
e 88
9.0%
r 74
 
7.5%
o 73
 
7.4%
l 69
 
7.0%
n 66
 
6.7%
h 56
 
5.7%
u 46
 
4.7%
y 41
 
4.2%
Other values (15) 196
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 55
 
9.2%
H 52
 
8.7%
A 52
 
8.7%
N 43
 
7.2%
O 34
 
5.7%
M 34
 
5.7%
T 33
 
5.5%
J 32
 
5.3%
I 31
 
5.2%
B 30
 
5.0%
Other values (15) 205
34.1%
Decimal Number
ValueCountFrequency (%)
0 61
40.7%
2 30
20.0%
7 11
 
7.3%
1 11
 
7.3%
9 9
 
6.0%
5 8
 
5.3%
3 8
 
5.3%
8 6
 
4.0%
6 3
 
2.0%
4 3
 
2.0%
Other Punctuation
ValueCountFrequency (%)
? 46
27.9%
. 37
22.4%
& 34
20.6%
# 17
 
10.3%
' 14
 
8.5%
, 13
 
7.9%
/ 2
 
1.2%
: 1
 
0.6%
1
 
0.6%
Space Separator
ValueCountFrequency (%)
779
100.0%
Close Punctuation
ValueCountFrequency (%)
) 152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15158
83.5%
Latin 1585
 
8.7%
Common 1404
 
7.7%
Han 12
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1133
 
7.5%
1101
 
7.3%
816
 
5.4%
641
 
4.2%
631
 
4.2%
430
 
2.8%
369
 
2.4%
363
 
2.4%
206
 
1.4%
193
 
1.3%
Other values (600) 9275
61.2%
Latin
ValueCountFrequency (%)
a 150
 
9.5%
i 123
 
7.8%
e 88
 
5.6%
r 74
 
4.7%
o 73
 
4.6%
l 69
 
4.4%
n 66
 
4.2%
h 56
 
3.5%
S 55
 
3.5%
H 52
 
3.3%
Other values (41) 779
49.1%
Common
ValueCountFrequency (%)
779
55.5%
) 152
 
10.8%
( 152
 
10.8%
0 61
 
4.3%
? 46
 
3.3%
. 37
 
2.6%
& 34
 
2.4%
2 30
 
2.1%
# 17
 
1.2%
' 14
 
1.0%
Other values (14) 82
 
5.8%
Han
ValueCountFrequency (%)
7
58.3%
2
 
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15158
83.5%
ASCII 2986
 
16.4%
CJK 12
 
0.1%
Number Forms 2
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1133
 
7.5%
1101
 
7.3%
816
 
5.4%
641
 
4.2%
631
 
4.2%
430
 
2.8%
369
 
2.4%
363
 
2.4%
206
 
1.4%
193
 
1.3%
Other values (600) 9275
61.2%
ASCII
ValueCountFrequency (%)
779
26.1%
) 152
 
5.1%
( 152
 
5.1%
a 150
 
5.0%
i 123
 
4.1%
e 88
 
2.9%
r 74
 
2.5%
o 73
 
2.4%
l 69
 
2.3%
n 66
 
2.2%
Other values (63) 1260
42.2%
CJK
ValueCountFrequency (%)
7
58.3%
2
 
16.7%
1
 
8.3%
1
 
8.3%
1
 
8.3%
Number Forms
ValueCountFrequency (%)
2
100.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct2343
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
Minimum1998-12-23 00:00:00
Maximum2024-04-16 13:31:20
2024-04-21T10:45:30.937041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:31.382109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
I
2058 
U
994 
D
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 2058
67.1%
U 994
32.4%
D 13
 
0.4%

Length

2024-04-21T10:45:31.801710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:32.114613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2058
67.1%
u 994
32.4%
d 13
 
0.4%
Distinct735
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-04-21T10:45:32.444514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:32.873540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
일반미용업
2411 
피부미용업
378 
네일아트업
 
226
메이크업업
 
44
기타
 
5

Length

Max length6
Median length5
Mean length4.9954323
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2411
78.7%
피부미용업 378
 
12.3%
네일아트업 226
 
7.4%
메이크업업 44
 
1.4%
기타 5
 
0.2%
미용업 기타 1
 
< 0.1%

Length

2024-04-21T10:45:33.321645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:33.654104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2411
78.6%
피부미용업 378
 
12.3%
네일아트업 226
 
7.4%
메이크업업 44
 
1.4%
기타 6
 
0.2%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1681
Distinct (%)57.5%
Missing141
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean195607.12
Minimum191548.54
Maximum198443.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:33.875593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191548.54
5-th percentile191980.86
Q1193977.34
median195489.42
Q3197545.99
95-th percentile198249.93
Maximum198443.84
Range6895.3054
Interquartile range (IQR)3568.6471

Descriptive statistics

Standard deviation2003.6401
Coefficient of variation (CV)0.010243186
Kurtosis-1.1796467
Mean195607.12
Median Absolute Deviation (MAD)1829.9991
Skewness-0.18244334
Sum5.7195523 × 108
Variance4014573.7
MonotonicityNot monotonic
2024-04-21T10:45:34.136600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191691.678396263 36
 
1.2%
197802.054854046 21
 
0.7%
192170.155815215 21
 
0.7%
193218.36400997 20
 
0.7%
196587.030630143 20
 
0.7%
197545.985381694 15
 
0.5%
193273.484014052 11
 
0.4%
193441.389289517 11
 
0.4%
197996.381794257 10
 
0.3%
198161.292479758 9
 
0.3%
Other values (1671) 2750
89.7%
(Missing) 141
 
4.6%
ValueCountFrequency (%)
191548.535775618 3
 
0.1%
191555.354381759 1
 
< 0.1%
191647.259479634 1
 
< 0.1%
191659.935227825 1
 
< 0.1%
191688.979039976 1
 
< 0.1%
191691.678396263 36
1.2%
191693.443270494 1
 
< 0.1%
191697.950111661 1
 
< 0.1%
191705.34451018 1
 
< 0.1%
191705.505630595 1
 
< 0.1%
ValueCountFrequency (%)
198443.841171167 1
 
< 0.1%
198428.611275 1
 
< 0.1%
198409.210165239 1
 
< 0.1%
198372.084206789 1
 
< 0.1%
198369.365881805 1
 
< 0.1%
198362.996258131 1
 
< 0.1%
198362.860072 1
 
< 0.1%
198336.0155892 1
 
< 0.1%
198332.497687081 4
0.1%
198328.849512449 2
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1680
Distinct (%)57.5%
Missing141
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean443694.78
Minimum441569.06
Maximum450422.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:34.403296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441569.06
5-th percentile441940.88
Q1442712.18
median443833.93
Q3444647.6
95-th percentile445484.6
Maximum450422.37
Range8853.3159
Interquartile range (IQR)1935.4256

Descriptive statistics

Standard deviation1122.2603
Coefficient of variation (CV)0.002529352
Kurtosis-0.77330047
Mean443694.78
Median Absolute Deviation (MAD)972.53221
Skewness0.058641942
Sum1.2973635 × 109
Variance1259468.2
MonotonicityNot monotonic
2024-04-21T10:45:34.643909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442818.113681285 36
 
1.2%
443599.41901313 21
 
0.7%
443049.47147487 21
 
0.7%
444939.455414947 20
 
0.7%
443239.581054145 20
 
0.7%
442685.886949892 15
 
0.5%
445011.250115029 11
 
0.4%
444008.600480435 11
 
0.4%
442918.664628817 10
 
0.3%
444995.675592494 9
 
0.3%
Other values (1670) 2750
89.7%
(Missing) 141
 
4.6%
ValueCountFrequency (%)
441569.058208929 3
0.1%
441581.838470649 1
 
< 0.1%
441589.480102134 1
 
< 0.1%
441606.177364885 2
0.1%
441616.77006268 2
0.1%
441621.408468674 1
 
< 0.1%
441622.478200576 2
0.1%
441632.166495668 1
 
< 0.1%
441637.16783987 1
 
< 0.1%
441638.132325662 1
 
< 0.1%
ValueCountFrequency (%)
450422.374151297 1
< 0.1%
445868.076456773 1
< 0.1%
445764.45259219 2
0.1%
445752.440199471 1
< 0.1%
445740.716082081 1
< 0.1%
445718.683478288 1
< 0.1%
445693.456564059 1
< 0.1%
445692.196103762 1
< 0.1%
445691.30421674 1
< 0.1%
445688.821452573 2
0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
미용업
1125 
일반미용업
944 
<NA>
442 
피부미용업
256 
종합미용업
126 
Other values (12)
172 

Length

Max length23
Median length19
Mean length4.4463295
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1125
36.7%
일반미용업 944
30.8%
<NA> 442
 
14.4%
피부미용업 256
 
8.4%
종합미용업 126
 
4.1%
네일미용업 76
 
2.5%
피부미용업, 네일미용업 22
 
0.7%
네일미용업, 화장ㆍ분장 미용업 20
 
0.7%
일반미용업, 화장ㆍ분장 미용업 15
 
0.5%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 14
 
0.5%
Other values (7) 25
 
0.8%

Length

2024-04-21T10:45:34.900410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1190
36.8%
일반미용업 972
30.0%
na 442
 
13.7%
피부미용업 301
 
9.3%
네일미용업 139
 
4.3%
종합미용업 126
 
3.9%
화장ㆍ분장 65
 
2.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)1.0%
Missing965
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean1.0261905
Minimum0
Maximum42
Zeros1441
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:35.121647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum42
Range42
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2864807
Coefficient of variation (CV)2.2281251
Kurtosis76.532209
Mean1.0261905
Median Absolute Deviation (MAD)0
Skewness6.4632851
Sum2155
Variance5.2279941
MonotonicityNot monotonic
2024-04-21T10:45:35.318802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1441
47.0%
3 164
 
5.4%
2 159
 
5.2%
4 123
 
4.0%
1 117
 
3.8%
5 51
 
1.7%
6 17
 
0.6%
7 12
 
0.4%
11 2
 
0.1%
23 2
 
0.1%
Other values (10) 12
 
0.4%
(Missing) 965
31.5%
ValueCountFrequency (%)
0 1441
47.0%
1 117
 
3.8%
2 159
 
5.2%
3 164
 
5.4%
4 123
 
4.0%
5 51
 
1.7%
6 17
 
0.6%
7 12
 
0.4%
8 1
 
< 0.1%
10 2
 
0.1%
ValueCountFrequency (%)
42 1
< 0.1%
25 1
< 0.1%
24 1
< 0.1%
23 2
0.1%
22 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
17 1
< 0.1%
15 2
0.1%
11 2
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.4%
Missing1026
Missing (%)33.5%
Infinite0
Infinite (%)0.0%
Mean0.17165277
Minimum0
Maximum8
Zeros1733
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:35.506153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48627963
Coefficient of variation (CV)2.8329262
Kurtosis56.420917
Mean0.17165277
Median Absolute Deviation (MAD)0
Skewness5.5421355
Sum350
Variance0.23646788
MonotonicityNot monotonic
2024-04-21T10:45:35.702387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1733
56.5%
1 286
 
9.3%
2 10
 
0.3%
4 4
 
0.1%
3 3
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1026
33.5%
ValueCountFrequency (%)
0 1733
56.5%
1 286
 
9.3%
2 10
 
0.3%
3 3
 
0.1%
4 4
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 4
 
0.1%
3 3
 
0.1%
2 10
 
0.3%
1 286
 
9.3%
0 1733
56.5%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.5%
Missing1337
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean0.88599537
Minimum0
Maximum10
Zeros702
Zeros (%)22.9%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:35.906910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0044894
Coefficient of variation (CV)1.1337411
Kurtosis7.6132245
Mean0.88599537
Median Absolute Deviation (MAD)1
Skewness1.9215583
Sum1531
Variance1.0089989
MonotonicityNot monotonic
2024-04-21T10:45:36.099556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 702
22.9%
1 685
22.3%
2 240
 
7.8%
3 65
 
2.1%
4 22
 
0.7%
5 7
 
0.2%
6 4
 
0.1%
7 2
 
0.1%
10 1
 
< 0.1%
(Missing) 1337
43.6%
ValueCountFrequency (%)
0 702
22.9%
1 685
22.3%
2 240
 
7.8%
3 65
 
2.1%
4 22
 
0.7%
5 7
 
0.2%
6 4
 
0.1%
7 2
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
7 2
 
0.1%
6 4
 
0.1%
5 7
 
0.2%
4 22
 
0.7%
3 65
 
2.1%
2 240
 
7.8%
1 685
22.3%
0 702
22.9%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.5%
Missing1718
Missing (%)56.1%
Infinite0
Infinite (%)0.0%
Mean0.63400148
Minimum0
Maximum7
Zeros737
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:36.277875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86045823
Coefficient of variation (CV)1.3571865
Kurtosis6.0303147
Mean0.63400148
Median Absolute Deviation (MAD)0
Skewness1.8654638
Sum854
Variance0.74038836
MonotonicityNot monotonic
2024-04-21T10:45:36.467676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 737
24.0%
1 429
 
14.0%
2 138
 
4.5%
3 32
 
1.0%
4 7
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
(Missing) 1718
56.1%
ValueCountFrequency (%)
0 737
24.0%
1 429
14.0%
2 138
 
4.5%
3 32
 
1.0%
4 7
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 3
 
0.1%
4 7
 
0.2%
3 32
 
1.0%
2 138
 
4.5%
1 429
14.0%
0 737
24.0%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
2184 
0
833 
1
 
42
2
 
6

Length

Max length4
Median length4
Mean length3.1376835
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 2184
71.3%
0 833
 
27.2%
1 42
 
1.4%
2 6
 
0.2%

Length

2024-04-21T10:45:36.688390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:36.872457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2184
71.3%
0 833
 
27.2%
1 42
 
1.4%
2 6
 
0.2%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
2211 
0
834 
1
 
17
2
 
3

Length

Max length4
Median length4
Mean length3.1641109
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 2211
72.1%
0 834
 
27.2%
1 17
 
0.6%
2 3
 
0.1%

Length

2024-04-21T10:45:37.071121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:37.255203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2211
72.1%
0 834
 
27.2%
1 17
 
0.6%
2 3
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
1830 
<NA>
1235 

Length

Max length4
Median length1
Mean length2.2088091
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1830
59.7%
<NA> 1235
40.3%

Length

2024-04-21T10:45:37.552763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:37.880908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1830
59.7%
na 1235
40.3%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
1830 
<NA>
1235 

Length

Max length4
Median length1
Mean length2.2088091
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1830
59.7%
<NA> 1235
40.3%

Length

2024-04-21T10:45:38.253134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:38.583592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1830
59.7%
na 1235
40.3%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
1830 
<NA>
1235 

Length

Max length4
Median length1
Mean length2.2088091
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1830
59.7%
<NA> 1235
40.3%

Length

2024-04-21T10:45:39.161211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:39.488328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1830
59.7%
na 1235
40.3%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing475
Missing (%)15.5%
Memory size6.1 KiB
False
2590 
(Missing)
475 
ValueCountFrequency (%)
False 2590
84.5%
(Missing) 475
 
15.5%
2024-04-21T10:45:39.745978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)0.9%
Missing510
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean3.0594912
Minimum0
Maximum33
Zeros510
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:40.026416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4608888
Coefficient of variation (CV)0.80434578
Kurtosis14.182395
Mean3.0594912
Median Absolute Deviation (MAD)1
Skewness2.1707922
Sum7817
Variance6.0559739
MonotonicityNot monotonic
2024-04-21T10:45:40.406118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 861
28.1%
0 510
16.6%
4 390
12.7%
2 332
 
10.8%
5 158
 
5.2%
6 104
 
3.4%
8 51
 
1.7%
7 40
 
1.3%
1 33
 
1.1%
10 31
 
1.0%
Other values (12) 45
 
1.5%
(Missing) 510
16.6%
ValueCountFrequency (%)
0 510
16.6%
1 33
 
1.1%
2 332
 
10.8%
3 861
28.1%
4 390
12.7%
5 158
 
5.2%
6 104
 
3.4%
7 40
 
1.3%
8 51
 
1.7%
9 15
 
0.5%
ValueCountFrequency (%)
33 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
19 2
 
0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
0.1%
14 3
 
0.1%
13 2
 
0.1%
12 9
0.3%
Distinct3
Distinct (%)100.0%
Missing3062
Missing (%)99.9%
Memory size24.1 KiB
2024-04-21T10:45:41.070987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length8
Mean length25
Min length4

Characters and Unicode

Total characters75
Distinct characters48
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

Unique3 ?
Unique (%)100.0%

Sample

1st row임시사용승인기간
2nd row무단폐업 직권말소 처리기간중 여하한 문제 발생으로 직권말소가 불가능할 경우, 즉시 해당 조건부 신규 영업신고 철회
3rd row임시사용
ValueCountFrequency (%)
임시사용승인기간 1
 
5.9%
경우 1
 
5.9%
철회 1
 
5.9%
영업신고 1
 
5.9%
신규 1
 
5.9%
조건부 1
 
5.9%
해당 1
 
5.9%
즉시 1
 
5.9%
불가능할 1
 
5.9%
무단폐업 1
 
5.9%
Other values (7) 7
41.2%
2024-04-21T10:45:42.108962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14
 
18.7%
3
 
4.0%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
2
 
2.7%
Other values (38) 42
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60
80.0%
Space Separator 14
 
18.7%
Other Punctuation 1
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
5.0%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
Other values (36) 39
65.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60
80.0%
Common 15
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
5.0%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
Other values (36) 39
65.0%
Common
ValueCountFrequency (%)
14
93.3%
, 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60
80.0%
ASCII 15
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14
93.3%
, 1
 
6.7%
Hangul
ValueCountFrequency (%)
3
 
5.0%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
2
 
3.3%
Other values (36) 39
65.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
3062 
20110708
 
1
20130228
 
1
20110704
 
1

Length

Max length8
Median length4
Mean length4.0039152
Min length4

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3062
99.9%
20110708 1
 
< 0.1%
20130228 1
 
< 0.1%
20110704 1
 
< 0.1%

Length

2024-04-21T10:45:42.543725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:42.891544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3062
99.9%
20110708 1
 
< 0.1%
20130228 1
 
< 0.1%
20110704 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
3062 
20120325
 
2
20130827
 
1

Length

Max length8
Median length4
Mean length4.0039152
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 3062
99.9%
20120325 2
 
0.1%
20130827 1
 
< 0.1%

Length

2024-04-21T10:45:43.281763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:43.625319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3062
99.9%
20120325 2
 
0.1%
20130827 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
2788 
임대
 
274
자가
 
3

Length

Max length4
Median length4
Mean length3.8192496
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 2788
91.0%
임대 274
 
8.9%
자가 3
 
0.1%

Length

2024-04-21T10:45:43.999868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:44.344073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2788
91.0%
임대 274
 
8.9%
자가 3
 
0.1%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
1862 
0
1203 

Length

Max length4
Median length4
Mean length2.8225122
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1862
60.8%
0 1203
39.2%

Length

2024-04-21T10:45:44.711104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:45.043058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1862
60.8%
0 1203
39.2%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)1.1%
Missing2428
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean0.30926217
Minimum0
Maximum8
Zeros495
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:45.331393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.2
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.73338553
Coefficient of variation (CV)2.371404
Kurtosis27.996857
Mean0.30926217
Median Absolute Deviation (MAD)0
Skewness4.2177002
Sum197
Variance0.53785433
MonotonicityNot monotonic
2024-04-21T10:45:45.699235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 495
 
16.2%
1 110
 
3.6%
2 21
 
0.7%
3 6
 
0.2%
5 3
 
0.1%
8 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 2428
79.2%
ValueCountFrequency (%)
0 495
16.2%
1 110
 
3.6%
2 21
 
0.7%
3 6
 
0.2%
4 1
 
< 0.1%
5 3
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
5 3
 
0.1%
4 1
 
< 0.1%
3 6
 
0.2%
2 21
 
0.7%
1 110
 
3.6%
0 495
16.2%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
2429 
0
611 
1
 
20
3
 
2
2
 
2

Length

Max length4
Median length4
Mean length3.3774878
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 2429
79.2%
0 611
 
19.9%
1 20
 
0.7%
3 2
 
0.1%
2 2
 
0.1%
5 1
 
< 0.1%

Length

2024-04-21T10:45:46.115994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:46.459116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2429
79.2%
0 611
 
19.9%
1 20
 
0.7%
3 2
 
0.1%
2 2
 
0.1%
5 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
<NA>
1929 
0
1136 

Length

Max length4
Median length4
Mean length2.8880914
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1929
62.9%
0 1136
37.1%

Length

2024-04-21T10:45:46.839963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:47.160088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1929
62.9%
0 1136
37.1%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)1.0%
Missing1964
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean0.74386921
Minimum0
Maximum10
Zeros807
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-04-21T10:45:47.461280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5666325
Coefficient of variation (CV)2.1060591
Kurtosis8.9778313
Mean0.74386921
Median Absolute Deviation (MAD)0
Skewness2.7781515
Sum819
Variance2.4543374
MonotonicityNot monotonic
2024-04-21T10:45:47.815963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 807
26.3%
2 114
 
3.7%
1 63
 
2.1%
3 53
 
1.7%
5 21
 
0.7%
4 16
 
0.5%
7 10
 
0.3%
6 8
 
0.3%
10 4
 
0.1%
8 3
 
0.1%
(Missing) 1964
64.1%
ValueCountFrequency (%)
0 807
26.3%
1 63
 
2.1%
2 114
 
3.7%
3 53
 
1.7%
4 16
 
0.5%
5 21
 
0.7%
6 8
 
0.3%
7 10
 
0.3%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
10 4
 
0.1%
9 2
 
0.1%
8 3
 
0.1%
7 10
 
0.3%
6 8
 
0.3%
5 21
 
0.7%
4 16
 
0.5%
3 53
1.7%
2 114
3.7%
1 63
2.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing442
Missing (%)14.4%
Memory size6.1 KiB
False
2623 
(Missing)
442 
ValueCountFrequency (%)
False 2623
85.6%
(Missing) 442
 
14.4%
2024-04-21T10:45:48.120706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031900003190000-204-1968-0096519680828<NA>3폐업2폐업20070910<NA><NA><NA>02 813570120.80156813서울특별시 동작구 본동 426번지<NA><NA>도원2004-09-03 00:00:00I2018-08-31 23:59:59.0일반미용업195531.62765445554.779222미용업<NA><NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131900003190000-204-1969-0096919691013<NA>3폐업2폐업19930623<NA><NA><NA>0208164697.00156861서울특별시 동작구 흑석동 232-87번지<NA><NA>성은미용실2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업196334.666993444725.827994미용업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231900003190000-204-1969-0104519690729<NA>3폐업2폐업19930320<NA><NA><NA>0208122610.00156802서울특별시 동작구 노량진동 206-22번지<NA><NA>2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331900003190000-204-1970-0080619700705<NA>3폐업2폐업20030226<NA><NA><NA>02081596651,619.00156800서울특별시 동작구 노량진동 28-2번지<NA><NA>차밍미용실2003-02-26 00:00:00I2018-08-31 23:59:59.0일반미용업194250.146098445658.555936미용업000000000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431900003190000-204-1970-0084819700727<NA>3폐업2폐업19990928<NA><NA><NA>02 815197013.20156831서울특별시 동작구 상도동 22-107번지<NA><NA>제이미용실1999-09-28 00:00:00I2018-08-31 23:59:59.0일반미용업195208.298148444814.102361미용업000000000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531900003190000-204-1970-0096819700316<NA>3폐업2폐업20130118<NA><NA><NA>020814697025.38156857서울특별시 동작구 흑석동 43-133번지서울특별시 동작구 서달로14사길 3-1 (흑석동)6979예쁘다2006-03-24 00:00:00I2018-08-31 23:59:59.0일반미용업196853.867896444985.410899미용업<NA><NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631900003190000-204-1971-0076019710216<NA>3폐업2폐업20010620<NA><NA><NA>02 822042311.20156844서울특별시 동작구 상도동 298-31번지<NA><NA>나나미용실2001-06-20 00:00:00I2018-08-31 23:59:59.0일반미용업193831.119174443811.737475미용업000000000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731900003190000-204-1972-0075819720918<NA>3폐업2폐업19990518<NA><NA><NA>02 813915113.50156030서울특별시 동작구 상도동 산 126-95번지<NA><NA>유성1999-06-02 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831900003190000-204-1973-0095819730206<NA>3폐업2폐업19960719<NA><NA><NA>02 5322827.00156090서울특별시 동작구 사당동 39-13번지<NA><NA>가나미용실2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업198256.233661443221.324057미용업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931900003190000-204-1973-0101319731112<NA>3폐업2폐업20030226<NA><NA><NA>02000000001,102.00156815서울특별시 동작구 사당동 128-24번지<NA><NA>에덴2003-02-26 00:00:00I2018-08-31 23:59:59.0일반미용업198120.883205442878.730392미용업000000000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
305531900003190000-226-2019-0000120190516<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.06156825서울특별시 동작구 사당동 1027-3서울특별시 동작구 사당로26길 89, 1층 (사당동)7015네일리2020-11-06 20:35:09U2020-11-08 02:40:00.0네일아트업197998.24258441961.1473피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>00001N
305631900003190000-226-2019-0000220191210<NA>1영업/정상1영업<NA><NA><NA><NA><NA>22.77156844서울특별시 동작구 상도동 256-1번지서울특별시 동작구 성대로 52, B04호 (상도동)7044까망네일2019-12-10 14:22:25I2019-12-12 00:23:28.0네일아트업194059.15378444047.302097피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N5<NA><NA><NA><NA>00001N
305731900003190000-226-2019-0000320190711<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.00156816서울특별시 동작구 사당동 141-51서울특별시 동작구 사당로29가길 9, 1층 (사당동)7007네일은봄2021-12-28 11:24:37U2021-12-30 02:40:00.0네일아트업198082.665329442594.976188피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N0<NA><NA><NA><NA>00001N
305831900003190000-226-2020-0000120200427<NA>1영업/정상1영업<NA><NA><NA><NA><NA>19.80156823서울특별시 동작구 사당동 317-6서울특별시 동작구 사당로20가길 39, 1층 (사당동)7011네일이즈유2020-11-02 12:24:18U2020-11-04 02:40:00.0네일아트업197577.738457442238.896091피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N3<NA><NA><NA><NA>00001N
305931900003190000-226-2021-0000120210326<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.24156030서울특별시 동작구 상도동 529 상도두산위브아파트서울특별시 동작구 상도로30길 39, 상도두산위브 120동 1층 105-1호 (상도동, 상도두산위브아파트)6964빈벨네일2021-03-26 14:25:04I2021-03-28 00:22:59.0네일아트업194919.059416444714.631338피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N4<NA><NA><NA><NA>00001N
306031900003190000-226-2021-0000220210412<NA>1영업/정상1영업<NA><NA><NA><NA><NA>38.35156815서울특별시 동작구 사당동 89-27서울특별시 동작구 동작대로33길 7, 1층 (사당동)6997뷰리플리2021-04-12 10:49:35I2021-04-14 00:22:57.0피부미용업198321.192662443023.890187피부미용업, 네일미용업, 화장ㆍ분장 미용업501<NA><NA><NA>000N0<NA><NA><NA><NA>00000N
306131900003190000-226-2021-000032021-10-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>21.47156-820서울특별시 동작구 사당동 260-14서울특별시 동작구 사당로17라길 12, 101호 (사당동)7004시온네일2024-01-17 00:00:00D2023-11-30 23:09:00.0네일아트업197475.552575442592.111267<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
306231900003190000-226-2021-0000420211108<NA>1영업/정상1영업<NA><NA><NA><NA><NA>82.92156831서울특별시 동작구 상도동 22-108 가동서울특별시 동작구 만양로 5, 가동 가동 301호 (상도동)6921크리미뷰티2021-12-13 10:53:18I2021-12-15 00:22:43.0네일아트업195200.897171444798.79487피부미용업, 네일미용업, 화장ㆍ분장 미용업003300000N0<NA><NA><NA><NA>01002N
306331900003190000-226-2023-000012023-03-17<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.90156-090서울특별시 동작구 사당동 314-8 , 1층서울특별시 동작구 사당로20나길 52, 1층 (사당동)7011네일봐, 쑴2023-03-17 13:30:05I2022-12-02 22:00:00.0네일아트업197565.240124442120.529509<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
306431900003190000-226-2023-000022023-04-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.00156-090서울특별시 동작구 사당동 1028-27 , 1층 101호서울특별시 동작구 동작대로9가길 16, 1층 101호 (사당동)7015누 아이(Nu i)2023-05-15 15:19:47I2022-12-04 23:07:00.0메이크업업198143.002261441946.398202<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>