Overview

Dataset statistics

Number of variables47
Number of observations3601
Missing cells40006
Missing cells (%)23.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory403.0 B

Variable types

Categorical18
Text7
DateTime4
Unsupported4
Numeric12
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
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-17918/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (52.5%)Imbalance
사용시작지하층 is highly imbalanced (58.0%)Imbalance
조건부허가신고사유 is highly imbalanced (97.3%)Imbalance
건물소유구분명 is highly imbalanced (56.0%)Imbalance
여성종사자수 is highly imbalanced (68.8%)Imbalance
남성종사자수 is highly imbalanced (53.8%)Imbalance
다중이용업소여부 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 3601 (100.0%) missing valuesMissing
폐업일자 has 1178 (32.7%) missing valuesMissing
휴업시작일자 has 3601 (100.0%) missing valuesMissing
휴업종료일자 has 3601 (100.0%) missing valuesMissing
재개업일자 has 3601 (100.0%) missing valuesMissing
전화번호 has 1274 (35.4%) missing valuesMissing
도로명주소 has 1502 (41.7%) missing valuesMissing
도로명우편번호 has 1514 (42.0%) missing valuesMissing
좌표정보(X) has 120 (3.3%) missing valuesMissing
좌표정보(Y) has 120 (3.3%) missing valuesMissing
건물지상층수 has 835 (23.2%) missing valuesMissing
건물지하층수 has 1410 (39.2%) missing valuesMissing
사용시작지상층 has 1092 (30.3%) missing valuesMissing
사용끝지상층 has 1938 (53.8%) missing valuesMissing
사용끝지하층 has 3184 (88.4%) missing valuesMissing
발한실여부 has 638 (17.7%) missing valuesMissing
좌석수 has 667 (18.5%) missing valuesMissing
조건부허가시작일자 has 3560 (98.9%) missing valuesMissing
조건부허가종료일자 has 3561 (98.9%) missing valuesMissing
침대수 has 2373 (65.9%) missing valuesMissing
다중이용업소여부 has 596 (16.6%) missing valuesMissing
사용끝지상층 is highly skewed (γ1 = 24.07883069)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 1877 (52.1%) zerosZeros
건물지하층수 has 2011 (55.8%) zerosZeros
사용시작지상층 has 884 (24.5%) zerosZeros
사용끝지상층 has 125 (3.5%) zerosZeros
사용끝지하층 has 318 (8.8%) zerosZeros
좌석수 has 338 (9.4%) zerosZeros
침대수 has 844 (23.4%) zerosZeros

Reproduction

Analysis started2024-04-29 19:30:50.197989
Analysis finished2024-04-29 19:30:51.683507
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
3160000
3601 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3160000
2nd row3160000
3rd row3160000
4th row3160000
5th row3160000

Common Values

ValueCountFrequency (%)
3160000 3601
100.0%

Length

2024-04-30T04:30:51.747825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:51.830466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3160000 3601
100.0%

관리번호
Text

UNIQUE 

Distinct3601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2024-04-30T04:30:51.989341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters79222
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

Unique3601 ?
Unique (%)100.0%

Sample

1st row3160000-204-1965-01207
2nd row3160000-204-1969-00632
3rd row3160000-204-1971-00646
4th row3160000-204-1971-00673
5th row3160000-204-1971-00704
ValueCountFrequency (%)
3160000-204-1965-01207 1
 
< 0.1%
3160000-211-2017-00005 1
 
< 0.1%
3160000-211-2017-00047 1
 
< 0.1%
3160000-211-2017-00036 1
 
< 0.1%
3160000-211-2017-00037 1
 
< 0.1%
3160000-211-2017-00038 1
 
< 0.1%
3160000-211-2017-00039 1
 
< 0.1%
3160000-211-2017-00040 1
 
< 0.1%
3160000-211-2017-00041 1
 
< 0.1%
3160000-211-2017-00042 1
 
< 0.1%
Other values (3591) 3591
99.7%
2024-04-30T04:30:52.280590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30346
38.3%
1 11213
 
14.2%
- 10803
 
13.6%
2 8726
 
11.0%
3 5053
 
6.4%
6 4616
 
5.8%
4 2578
 
3.3%
9 2510
 
3.2%
8 1273
 
1.6%
5 1133
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68419
86.4%
Dash Punctuation 10803
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30346
44.4%
1 11213
 
16.4%
2 8726
 
12.8%
3 5053
 
7.4%
6 4616
 
6.7%
4 2578
 
3.8%
9 2510
 
3.7%
8 1273
 
1.9%
5 1133
 
1.7%
7 971
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 10803
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30346
38.3%
1 11213
 
14.2%
- 10803
 
13.6%
2 8726
 
11.0%
3 5053
 
6.4%
6 4616
 
5.8%
4 2578
 
3.3%
9 2510
 
3.2%
8 1273
 
1.6%
5 1133
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30346
38.3%
1 11213
 
14.2%
- 10803
 
13.6%
2 8726
 
11.0%
3 5053
 
6.4%
6 4616
 
5.8%
4 2578
 
3.3%
9 2510
 
3.2%
8 1273
 
1.6%
5 1133
 
1.4%
Distinct2591
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
Minimum1965-04-12 00:00:00
Maximum2024-04-25 00:00:00
2024-04-30T04:30:52.407900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:52.526784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3601
Missing (%)100.0%
Memory size31.8 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
3
2423 
1
1178 

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 2423
67.3%
1 1178
32.7%

Length

2024-04-30T04:30:52.653833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:52.736938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2423
67.3%
1 1178
32.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
폐업
2423 
영업/정상
1178 

Length

Max length5
Median length2
Mean length2.9813941
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2423
67.3%
영업/정상 1178
32.7%

Length

2024-04-30T04:30:52.822394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:52.903197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2423
67.3%
영업/정상 1178
32.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2
2423 
1
1178 

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 2423
67.3%
1 1178
32.7%

Length

2024-04-30T04:30:52.991886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:53.087616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2423
67.3%
1 1178
32.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
폐업
2423 
영업
1178 

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 2423
67.3%
영업 1178
32.7%

Length

2024-04-30T04:30:53.184175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:53.262260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2423
67.3%
영업 1178
32.7%

폐업일자
Date

MISSING 

Distinct1638
Distinct (%)67.6%
Missing1178
Missing (%)32.7%
Memory size28.3 KiB
Minimum1991-01-10 00:00:00
Maximum2024-04-22 00:00:00
2024-04-30T04:30:53.370828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:53.485763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3601
Missing (%)100.0%
Memory size31.8 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3601
Missing (%)100.0%
Memory size31.8 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3601
Missing (%)100.0%
Memory size31.8 KiB

전화번호
Text

MISSING 

Distinct1943
Distinct (%)83.5%
Missing1274
Missing (%)35.4%
Memory size28.3 KiB
2024-04-30T04:30:53.701411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.076064
Min length6

Characters and Unicode

Total characters23447
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

Unique1764 ?
Unique (%)75.8%

Sample

1st row02 00000
2nd row0226135569
3rd row02 8571420
4th row0200000000
5th row0208554548
ValueCountFrequency (%)
02 1074
30.8%
0200000000 78
 
2.2%
0 57
 
1.6%
00000 35
 
1.0%
070 7
 
0.2%
8520791 6
 
0.2%
032 5
 
0.1%
855 5
 
0.1%
851 4
 
0.1%
0232814187 4
 
0.1%
Other values (1984) 2212
63.4%
2024-04-30T04:30:54.072414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4728
20.2%
2 4291
18.3%
6 2669
11.4%
8 2410
10.3%
5 1529
 
6.5%
1 1510
 
6.4%
1448
 
6.2%
7 1355
 
5.8%
3 1332
 
5.7%
4 1117
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21999
93.8%
Space Separator 1448
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4728
21.5%
2 4291
19.5%
6 2669
12.1%
8 2410
11.0%
5 1529
 
7.0%
1 1510
 
6.9%
7 1355
 
6.2%
3 1332
 
6.1%
4 1117
 
5.1%
9 1058
 
4.8%
Space Separator
ValueCountFrequency (%)
1448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23447
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4728
20.2%
2 4291
18.3%
6 2669
11.4%
8 2410
10.3%
5 1529
 
6.5%
1 1510
 
6.4%
1448
 
6.2%
7 1355
 
5.8%
3 1332
 
5.7%
4 1117
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23447
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4728
20.2%
2 4291
18.3%
6 2669
11.4%
8 2410
10.3%
5 1529
 
6.5%
1 1510
 
6.4%
1448
 
6.2%
7 1355
 
5.8%
3 1332
 
5.7%
4 1117
 
4.8%
Distinct1489
Distinct (%)41.4%
Missing1
Missing (%)< 0.1%
Memory size28.3 KiB
2024-04-30T04:30:54.359664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.9283333
Min length3

Characters and Unicode

Total characters17742
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

Unique972 ?
Unique (%)27.0%

Sample

1st row12.00
2nd row26.40
3rd row10.69
4th row12.00
5th row13.59
ValueCountFrequency (%)
00 199
 
5.5%
33.00 125
 
3.5%
26.40 71
 
2.0%
30.00 69
 
1.9%
23.00 57
 
1.6%
15.00 52
 
1.4%
24.00 51
 
1.4%
16.50 47
 
1.3%
20.00 46
 
1.3%
19.80 44
 
1.2%
Other values (1479) 2839
78.9%
2024-04-30T04:30:54.792206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3862
21.8%
. 3600
20.3%
1 1785
10.1%
2 1777
10.0%
3 1362
 
7.7%
6 1053
 
5.9%
5 1046
 
5.9%
4 1020
 
5.7%
8 815
 
4.6%
9 716
 
4.0%
Other values (2) 706
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14141
79.7%
Other Punctuation 3601
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3862
27.3%
1 1785
12.6%
2 1777
12.6%
3 1362
 
9.6%
6 1053
 
7.4%
5 1046
 
7.4%
4 1020
 
7.2%
8 815
 
5.8%
9 716
 
5.1%
7 705
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 3600
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 17742
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3862
21.8%
. 3600
20.3%
1 1785
10.1%
2 1777
10.0%
3 1362
 
7.7%
6 1053
 
5.9%
5 1046
 
5.9%
4 1020
 
5.7%
8 815
 
4.6%
9 716
 
4.0%
Other values (2) 706
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3862
21.8%
. 3600
20.3%
1 1785
10.1%
2 1777
10.0%
3 1362
 
7.7%
6 1053
 
5.9%
5 1046
 
5.9%
4 1020
 
5.7%
8 815
 
4.6%
9 716
 
4.0%
Other values (2) 706
 
4.0%
Distinct219
Distinct (%)6.1%
Missing21
Missing (%)0.6%
Memory size28.3 KiB
2024-04-30T04:30:55.076143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1108939
Min length6

Characters and Unicode

Total characters21877
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

Unique40 ?
Unique (%)1.1%

Sample

1st row152050
2nd row152824
3rd row152846
4th row152848
5th row152801
ValueCountFrequency (%)
152815 160
 
4.5%
152800 96
 
2.7%
152871 95
 
2.7%
152090 92
 
2.6%
152872 87
 
2.4%
152888 85
 
2.4%
152887 83
 
2.3%
152814 77
 
2.2%
152880 64
 
1.8%
152896 63
 
1.8%
Other values (209) 2678
74.8%
2024-04-30T04:30:55.495413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4379
20.0%
5 4373
20.0%
2 4165
19.0%
8 3887
17.8%
0 1350
 
6.2%
7 837
 
3.8%
4 703
 
3.2%
3 667
 
3.0%
9 579
 
2.6%
6 540
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21480
98.2%
Dash Punctuation 397
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4379
20.4%
5 4373
20.4%
2 4165
19.4%
8 3887
18.1%
0 1350
 
6.3%
7 837
 
3.9%
4 703
 
3.3%
3 667
 
3.1%
9 579
 
2.7%
6 540
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21877
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4379
20.0%
5 4373
20.0%
2 4165
19.0%
8 3887
17.8%
0 1350
 
6.2%
7 837
 
3.8%
4 703
 
3.2%
3 667
 
3.0%
9 579
 
2.6%
6 540
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4379
20.0%
5 4373
20.0%
2 4165
19.0%
8 3887
17.8%
0 1350
 
6.2%
7 837
 
3.8%
4 703
 
3.2%
3 667
 
3.0%
9 579
 
2.6%
6 540
 
2.5%
Distinct2983
Distinct (%)83.3%
Missing18
Missing (%)0.5%
Memory size28.3 KiB
2024-04-30T04:30:55.732671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length56
Mean length26.134245
Min length16

Characters and Unicode

Total characters93639
Distinct characters358
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2584 ?
Unique (%)72.1%

Sample

1st row서울특별시 구로구 구로동 949-0번지
2nd row서울특별시 구로구 고척동 52-58번지
3rd row서울특별시 구로구 구로동 142-65번지
4th row서울특별시 구로구 구로동 202-0번지
5th row서울특별시 구로구 가리봉동 132-97번지
ValueCountFrequency (%)
구로구 3605
20.8%
서울특별시 3583
20.7%
구로동 1386
 
8.0%
개봉동 819
 
4.7%
고척동 479
 
2.8%
오류동 312
 
1.8%
신도림동 246
 
1.4%
1층 193
 
1.1%
가리봉동 170
 
1.0%
2층 97
 
0.6%
Other values (3312) 6433
37.1%
2024-04-30T04:30:56.101439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16305
17.4%
8700
 
9.3%
5080
 
5.4%
1 4477
 
4.8%
3960
 
4.2%
3602
 
3.8%
3597
 
3.8%
3596
 
3.8%
3584
 
3.8%
3583
 
3.8%
Other values (348) 37155
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53756
57.4%
Decimal Number 19748
 
21.1%
Space Separator 16305
 
17.4%
Dash Punctuation 3222
 
3.4%
Close Punctuation 183
 
0.2%
Open Punctuation 183
 
0.2%
Uppercase Letter 157
 
0.2%
Other Punctuation 67
 
0.1%
Lowercase Letter 15
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8700
16.2%
5080
 
9.5%
3960
 
7.4%
3602
 
6.7%
3597
 
6.7%
3596
 
6.7%
3584
 
6.7%
3583
 
6.7%
2607
 
4.8%
2401
 
4.5%
Other values (298) 13046
24.3%
Uppercase Letter
ValueCountFrequency (%)
B 35
22.3%
A 30
19.1%
S 21
13.4%
K 20
12.7%
C 7
 
4.5%
D 6
 
3.8%
T 5
 
3.2%
I 5
 
3.2%
L 4
 
2.5%
G 4
 
2.5%
Other values (10) 20
12.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
b 2
13.3%
a 2
13.3%
l 1
 
6.7%
v 1
 
6.7%
s 1
 
6.7%
u 1
 
6.7%
p 1
 
6.7%
o 1
 
6.7%
i 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 4477
22.7%
2 3028
15.3%
3 2355
11.9%
0 1860
9.4%
4 1745
 
8.8%
5 1568
 
7.9%
7 1538
 
7.8%
6 1301
 
6.6%
8 1018
 
5.2%
9 858
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 62
92.5%
. 3
 
4.5%
& 1
 
1.5%
@ 1
 
1.5%
Space Separator
ValueCountFrequency (%)
16305
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3222
100.0%
Close Punctuation
ValueCountFrequency (%)
) 183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 183
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53752
57.4%
Common 39711
42.4%
Latin 172
 
0.2%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8700
16.2%
5080
 
9.5%
3960
 
7.4%
3602
 
6.7%
3597
 
6.7%
3596
 
6.7%
3584
 
6.7%
3583
 
6.7%
2607
 
4.9%
2401
 
4.5%
Other values (296) 13042
24.3%
Latin
ValueCountFrequency (%)
B 35
20.3%
A 30
17.4%
S 21
12.2%
K 20
11.6%
C 7
 
4.1%
D 6
 
3.5%
T 5
 
2.9%
I 5
 
2.9%
L 4
 
2.3%
G 4
 
2.3%
Other values (21) 35
20.3%
Common
ValueCountFrequency (%)
16305
41.1%
1 4477
 
11.3%
- 3222
 
8.1%
2 3028
 
7.6%
3 2355
 
5.9%
0 1860
 
4.7%
4 1745
 
4.4%
5 1568
 
3.9%
7 1538
 
3.9%
6 1301
 
3.3%
Other values (9) 2312
 
5.8%
Han
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53752
57.4%
ASCII 39883
42.6%
CJK 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16305
40.9%
1 4477
 
11.2%
- 3222
 
8.1%
2 3028
 
7.6%
3 2355
 
5.9%
0 1860
 
4.7%
4 1745
 
4.4%
5 1568
 
3.9%
7 1538
 
3.9%
6 1301
 
3.3%
Other values (40) 2484
 
6.2%
Hangul
ValueCountFrequency (%)
8700
16.2%
5080
 
9.5%
3960
 
7.4%
3602
 
6.7%
3597
 
6.7%
3596
 
6.7%
3584
 
6.7%
3583
 
6.7%
2607
 
4.9%
2401
 
4.5%
Other values (296) 13042
24.3%
CJK
ValueCountFrequency (%)
2
50.0%
2
50.0%

도로명주소
Text

MISSING 

Distinct1921
Distinct (%)91.5%
Missing1502
Missing (%)41.7%
Memory size28.3 KiB
2024-04-30T04:30:56.501768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length86
Median length53
Mean length34.649833
Min length21

Characters and Unicode

Total characters72730
Distinct characters354
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1763 ?
Unique (%)84.0%

Sample

1st row서울특별시 구로구 구로중앙로27가길 23 (구로동,(반디길 8))
2nd row서울특별시 구로구 우마길 20 (가리봉동)
3rd row서울특별시 구로구 구로동로28길 78-2 (구로동)
4th row서울특별시 구로구 구로동로28길 122 (구로동, 1층)
5th row서울특별시 구로구 경서로 15 (고척동)
ValueCountFrequency (%)
서울특별시 2099
 
15.3%
구로구 2099
 
15.3%
구로동 698
 
5.1%
1층 429
 
3.1%
개봉동 401
 
2.9%
고척동 222
 
1.6%
2층 208
 
1.5%
경인로 172
 
1.3%
오류동 153
 
1.1%
신도림동 152
 
1.1%
Other values (1721) 7093
51.7%
2024-04-30T04:30:56.895406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11631
 
16.0%
5509
 
7.6%
5420
 
7.5%
1 3444
 
4.7%
2729
 
3.8%
) 2219
 
3.1%
( 2219
 
3.1%
, 2208
 
3.0%
2160
 
3.0%
2133
 
2.9%
Other values (344) 33058
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41552
57.1%
Decimal Number 12376
 
17.0%
Space Separator 11631
 
16.0%
Close Punctuation 2219
 
3.1%
Open Punctuation 2219
 
3.1%
Other Punctuation 2210
 
3.0%
Dash Punctuation 347
 
0.5%
Uppercase Letter 153
 
0.2%
Lowercase Letter 15
 
< 0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5509
 
13.3%
5420
 
13.0%
2729
 
6.6%
2160
 
5.2%
2133
 
5.1%
2109
 
5.1%
2099
 
5.1%
2099
 
5.1%
1372
 
3.3%
1131
 
2.7%
Other values (296) 14791
35.6%
Uppercase Letter
ValueCountFrequency (%)
B 52
34.0%
A 22
14.4%
K 18
 
11.8%
S 18
 
11.8%
C 5
 
3.3%
I 5
 
3.3%
T 5
 
3.3%
G 4
 
2.6%
L 4
 
2.6%
F 4
 
2.6%
Other values (11) 16
 
10.5%
Decimal Number
ValueCountFrequency (%)
1 3444
27.8%
2 2068
16.7%
0 1444
11.7%
3 1330
 
10.7%
5 778
 
6.3%
4 751
 
6.1%
6 740
 
6.0%
7 697
 
5.6%
8 619
 
5.0%
9 505
 
4.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
33.3%
b 2
 
13.3%
a 1
 
6.7%
v 1
 
6.7%
i 1
 
6.7%
r 1
 
6.7%
t 1
 
6.7%
n 1
 
6.7%
c 1
 
6.7%
w 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 2208
99.9%
. 2
 
0.1%
Space Separator
ValueCountFrequency (%)
11631
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2219
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 347
100.0%
Math Symbol
ValueCountFrequency (%)
~ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41548
57.1%
Common 31010
42.6%
Latin 168
 
0.2%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5509
 
13.3%
5420
 
13.0%
2729
 
6.6%
2160
 
5.2%
2133
 
5.1%
2109
 
5.1%
2099
 
5.1%
2099
 
5.1%
1372
 
3.3%
1131
 
2.7%
Other values (294) 14787
35.6%
Latin
ValueCountFrequency (%)
B 52
31.0%
A 22
13.1%
K 18
 
10.7%
S 18
 
10.7%
C 5
 
3.0%
e 5
 
3.0%
I 5
 
3.0%
T 5
 
3.0%
G 4
 
2.4%
L 4
 
2.4%
Other values (21) 30
17.9%
Common
ValueCountFrequency (%)
11631
37.5%
1 3444
 
11.1%
) 2219
 
7.2%
( 2219
 
7.2%
, 2208
 
7.1%
2 2068
 
6.7%
0 1444
 
4.7%
3 1330
 
4.3%
5 778
 
2.5%
4 751
 
2.4%
Other values (7) 2918
 
9.4%
Han
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41548
57.1%
ASCII 31178
42.9%
CJK 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11631
37.3%
1 3444
 
11.0%
) 2219
 
7.1%
( 2219
 
7.1%
, 2208
 
7.1%
2 2068
 
6.6%
0 1444
 
4.6%
3 1330
 
4.3%
5 778
 
2.5%
4 751
 
2.4%
Other values (38) 3086
 
9.9%
Hangul
ValueCountFrequency (%)
5509
 
13.3%
5420
 
13.0%
2729
 
6.6%
2160
 
5.2%
2133
 
5.1%
2109
 
5.1%
2099
 
5.1%
2099
 
5.1%
1372
 
3.3%
1131
 
2.7%
Other values (294) 14787
35.6%
CJK
ValueCountFrequency (%)
2
50.0%
2
50.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct174
Distinct (%)8.3%
Missing1514
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean8294.3958
Minimum8201
Maximum8395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:57.041417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8201
5-th percentile8208
Q18246
median8294
Q38337
95-th percentile8386
Maximum8395
Range194
Interquartile range (IQR)91

Descriptive statistics

Standard deviation54.996971
Coefficient of variation (CV)0.0066306182
Kurtosis-1.0941861
Mean8294.3958
Median Absolute Deviation (MAD)46
Skewness0.01737781
Sum17310404
Variance3024.6669
MonotonicityNot monotonic
2024-04-30T04:30:57.182640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8288 78
 
2.2%
8208 63
 
1.7%
8353 39
 
1.1%
8235 38
 
1.1%
8312 38
 
1.1%
8227 36
 
1.0%
8332 35
 
1.0%
8318 31
 
0.9%
8392 29
 
0.8%
8210 29
 
0.8%
Other values (164) 1671
46.4%
(Missing) 1514
42.0%
ValueCountFrequency (%)
8201 7
 
0.2%
8202 7
 
0.2%
8203 4
 
0.1%
8204 1
 
< 0.1%
8205 4
 
0.1%
8206 12
 
0.3%
8207 9
 
0.2%
8208 63
1.7%
8209 24
 
0.7%
8210 29
0.8%
ValueCountFrequency (%)
8395 5
 
0.1%
8393 14
0.4%
8392 29
0.8%
8391 25
0.7%
8390 12
0.3%
8389 2
 
0.1%
8388 7
 
0.2%
8387 10
 
0.3%
8386 5
 
0.1%
8385 3
 
0.1%
Distinct3053
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
2024-04-30T04:30:57.389106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length5.5859483
Min length1

Characters and Unicode

Total characters20115
Distinct characters717
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2713 ?
Unique (%)75.3%

Sample

1st row진원
2nd row미미
3rd row나포리
4th row오캐
5th row가리봉
ValueCountFrequency (%)
헤어 173
 
3.7%
미용실 107
 
2.3%
네일 47
 
1.0%
헤어샵 45
 
1.0%
hair 29
 
0.6%
에스테틱 27
 
0.6%
리안헤어 21
 
0.4%
블루클럽 20
 
0.4%
스킨케어 18
 
0.4%
헤어살롱 16
 
0.3%
Other values (3156) 4222
89.4%
2024-04-30T04:30:57.734936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1328
 
6.6%
1264
 
6.3%
1127
 
5.6%
734
 
3.6%
499
 
2.5%
472
 
2.3%
466
 
2.3%
379
 
1.9%
376
 
1.9%
298
 
1.5%
Other values (707) 13172
65.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16981
84.4%
Space Separator 1127
 
5.6%
Lowercase Letter 709
 
3.5%
Uppercase Letter 678
 
3.4%
Other Punctuation 175
 
0.9%
Decimal Number 154
 
0.8%
Open Punctuation 140
 
0.7%
Close Punctuation 140
 
0.7%
Dash Punctuation 7
 
< 0.1%
Other Number 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1328
 
7.8%
1264
 
7.4%
734
 
4.3%
499
 
2.9%
472
 
2.8%
466
 
2.7%
379
 
2.2%
376
 
2.2%
298
 
1.8%
277
 
1.6%
Other values (630) 10888
64.1%
Uppercase Letter
ValueCountFrequency (%)
A 73
 
10.8%
I 57
 
8.4%
J 54
 
8.0%
N 49
 
7.2%
S 47
 
6.9%
H 42
 
6.2%
L 42
 
6.2%
O 37
 
5.5%
R 33
 
4.9%
B 31
 
4.6%
Other values (16) 213
31.4%
Lowercase Letter
ValueCountFrequency (%)
a 94
13.3%
i 83
11.7%
e 77
10.9%
o 63
8.9%
r 58
8.2%
h 49
 
6.9%
n 47
 
6.6%
l 45
 
6.3%
s 35
 
4.9%
t 31
 
4.4%
Other values (15) 127
17.9%
Decimal Number
ValueCountFrequency (%)
0 40
26.0%
2 30
19.5%
1 24
15.6%
3 20
13.0%
5 13
 
8.4%
4 13
 
8.4%
9 6
 
3.9%
6 3
 
1.9%
8 3
 
1.9%
7 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
? 58
33.1%
& 43
24.6%
. 33
18.9%
, 14
 
8.0%
# 14
 
8.0%
' 6
 
3.4%
! 3
 
1.7%
2
 
1.1%
: 2
 
1.1%
Space Separator
ValueCountFrequency (%)
1127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 140
100.0%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Number
ValueCountFrequency (%)
2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16956
84.3%
Common 1746
 
8.7%
Latin 1388
 
6.9%
Han 25
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1328
 
7.8%
1264
 
7.5%
734
 
4.3%
499
 
2.9%
472
 
2.8%
466
 
2.7%
379
 
2.2%
376
 
2.2%
298
 
1.8%
277
 
1.6%
Other values (617) 10863
64.1%
Latin
ValueCountFrequency (%)
a 94
 
6.8%
i 83
 
6.0%
e 77
 
5.5%
A 73
 
5.3%
o 63
 
4.5%
r 58
 
4.2%
I 57
 
4.1%
J 54
 
3.9%
N 49
 
3.5%
h 49
 
3.5%
Other values (42) 731
52.7%
Common
ValueCountFrequency (%)
1127
64.5%
( 140
 
8.0%
) 140
 
8.0%
? 58
 
3.3%
& 43
 
2.5%
0 40
 
2.3%
. 33
 
1.9%
2 30
 
1.7%
1 24
 
1.4%
3 20
 
1.1%
Other values (15) 91
 
5.2%
Han
ValueCountFrequency (%)
10
40.0%
3
 
12.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (3) 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16956
84.3%
ASCII 3129
 
15.6%
CJK 24
 
0.1%
None 4
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1328
 
7.8%
1264
 
7.5%
734
 
4.3%
499
 
2.9%
472
 
2.8%
466
 
2.7%
379
 
2.2%
376
 
2.2%
298
 
1.8%
277
 
1.6%
Other values (617) 10863
64.1%
ASCII
ValueCountFrequency (%)
1127
36.0%
( 140
 
4.5%
) 140
 
4.5%
a 94
 
3.0%
i 83
 
2.7%
e 77
 
2.5%
A 73
 
2.3%
o 63
 
2.0%
r 58
 
1.9%
? 58
 
1.9%
Other values (64) 1216
38.9%
CJK
ValueCountFrequency (%)
10
41.7%
3
 
12.5%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other values (2) 2
 
8.3%
None
ValueCountFrequency (%)
2
50.0%
2
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct2517
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
Minimum1999-01-06 00:00:00
Maximum2024-04-25 11:33:24
2024-04-30T04:30:57.870215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:57.989824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
I
2524 
U
1060 
D
 
17

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 2524
70.1%
U 1060
29.4%
D 17
 
0.5%

Length

2024-04-30T04:30:58.103083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:58.190942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2524
70.1%
u 1060
29.4%
d 17
 
0.5%
Distinct767
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:07:00
2024-04-30T04:30:58.283420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:58.396575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
일반미용업
2789 
피부미용업
421 
네일아트업
302 
메이크업업
 
60
기타
 
29

Length

Max length5
Median length5
Mean length4.97584
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2789
77.5%
피부미용업 421
 
11.7%
네일아트업 302
 
8.4%
메이크업업 60
 
1.7%
기타 29
 
0.8%

Length

2024-04-30T04:30:58.523620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:58.632159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2789
77.5%
피부미용업 421
 
11.7%
네일아트업 302
 
8.4%
메이크업업 60
 
1.7%
기타 29
 
0.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1722
Distinct (%)49.5%
Missing120
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean188331.2
Minimum183701.98
Maximum191280.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:58.746964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183701.98
5-th percentile185598.72
Q1186885.9
median188757.87
Q3190005.13
95-th percentile190541.56
Maximum191280.66
Range7578.6764
Interquartile range (IQR)3119.2303

Descriptive statistics

Standard deviation1815.8098
Coefficient of variation (CV)0.0096415771
Kurtosis-1.1180761
Mean188331.2
Median Absolute Deviation (MAD)1475.3551
Skewness-0.32559749
Sum6.555809 × 108
Variance3297165.1
MonotonicityNot monotonic
2024-04-30T04:30:58.866434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190232.524534335 73
 
2.0%
190005.132500398 42
 
1.2%
189372.1910653 22
 
0.6%
189894.636867358 20
 
0.6%
189841.35019681 17
 
0.5%
190349.885855531 16
 
0.4%
187826.698183821 16
 
0.4%
190272.747250041 15
 
0.4%
188674.995072188 15
 
0.4%
189682.799434964 15
 
0.4%
Other values (1712) 3230
89.7%
(Missing) 120
 
3.3%
ValueCountFrequency (%)
183701.981267554 1
 
< 0.1%
183746.686550444 1
 
< 0.1%
183856.646621367 4
0.1%
183856.953680121 1
 
< 0.1%
183916.821164485 1
 
< 0.1%
183987.376421538 1
 
< 0.1%
183995.144929261 1
 
< 0.1%
184007.005868999 2
0.1%
184018.014359305 1
 
< 0.1%
184018.256080314 4
0.1%
ValueCountFrequency (%)
191280.657696914 1
 
< 0.1%
191250.050385034 3
0.1%
191239.693382941 5
0.1%
191229.896362573 3
0.1%
191225.591920564 1
 
< 0.1%
191205.791543965 3
0.1%
191200.722936426 2
 
0.1%
191191.935645894 1
 
< 0.1%
191191.131566069 4
0.1%
191173.834097241 5
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1722
Distinct (%)49.5%
Missing120
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean443660.08
Minimum441556.05
Maximum445650.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:59.004174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441556.05
5-th percentile442291.1
Q1442927.23
median443623.31
Q3444419.28
95-th percentile445005.93
Maximum445650.18
Range4094.1364
Interquartile range (IQR)1492.0482

Descriptive statistics

Standard deviation874.59837
Coefficient of variation (CV)0.0019713254
Kurtosis-0.98081498
Mean443660.08
Median Absolute Deviation (MAD)737.22582
Skewness0.071351983
Sum1.5443807 × 109
Variance764922.32
MonotonicityNot monotonic
2024-04-30T04:30:59.127888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444978.682746138 73
 
2.0%
445250.538868558 42
 
1.2%
444854.451693268 22
 
0.6%
444982.548165651 20
 
0.6%
445153.61016346 17
 
0.5%
444836.673561831 16
 
0.4%
443925.52468616 16
 
0.4%
442929.283068723 15
 
0.4%
443749.503020388 15
 
0.4%
444193.860003095 15
 
0.4%
Other values (1712) 3230
89.7%
(Missing) 120
 
3.3%
ValueCountFrequency (%)
441556.047600555 1
< 0.1%
441596.481083253 2
0.1%
441744.40616131 1
< 0.1%
441773.484823975 1
< 0.1%
441811.482181569 2
0.1%
441862.85496884 1
< 0.1%
441868.707510887 1
< 0.1%
441915.448590069 2
0.1%
441920.235817252 1
< 0.1%
441929.798809285 1
< 0.1%
ValueCountFrequency (%)
445650.183972882 4
 
0.1%
445556.641493041 5
0.1%
445532.056886722 2
 
0.1%
445458.335742134 6
0.2%
445430.97917589 5
0.1%
445426.184267564 1
 
< 0.1%
445369.517787461 11
0.3%
445315.229707712 1
 
< 0.1%
445312.235627537 1
 
< 0.1%
445273.42270345 2
 
0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
미용업
1479 
일반미용업
956 
<NA>
596 
피부미용업
269 
네일미용업
 
106
Other values (11)
195 

Length

Max length23
Median length19
Mean length4.3612885
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1479
41.1%
일반미용업 956
26.5%
<NA> 596
16.6%
피부미용업 269
 
7.5%
네일미용업 106
 
2.9%
종합미용업 71
 
2.0%
피부미용업, 네일미용업 31
 
0.9%
네일미용업, 화장ㆍ분장 미용업 29
 
0.8%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 15
 
0.4%
화장ㆍ분장 미용업 11
 
0.3%
Other values (6) 38
 
1.1%

Length

2024-04-30T04:30:59.250446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1558
40.8%
일반미용업 985
25.8%
na 596
 
15.6%
피부미용업 329
 
8.6%
네일미용업 196
 
5.1%
화장ㆍ분장 79
 
2.1%
종합미용업 71
 
1.9%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)0.9%
Missing835
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean1.1344902
Minimum0
Maximum38
Zeros1877
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:59.348137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum38
Range38
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5828737
Coefficient of variation (CV)2.2766822
Kurtosis70.314067
Mean1.1344902
Median Absolute Deviation (MAD)0
Skewness6.6586526
Sum3138
Variance6.6712368
MonotonicityNot monotonic
2024-04-30T04:30:59.446407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1877
52.1%
3 272
 
7.6%
2 206
 
5.7%
4 205
 
5.7%
1 97
 
2.7%
5 61
 
1.7%
6 12
 
0.3%
15 5
 
0.1%
7 4
 
0.1%
14 3
 
0.1%
Other values (14) 24
 
0.7%
(Missing) 835
23.2%
ValueCountFrequency (%)
0 1877
52.1%
1 97
 
2.7%
2 206
 
5.7%
3 272
 
7.6%
4 205
 
5.7%
5 61
 
1.7%
6 12
 
0.3%
7 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 3
0.1%
30 1
 
< 0.1%
25 2
 
0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
20 2
 
0.1%
19 3
0.1%
17 1
 
< 0.1%
15 5
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.4%
Missing1410
Missing (%)39.2%
Infinite0
Infinite (%)0.0%
Mean0.11410315
Minimum0
Maximum15
Zeros2011
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:59.544574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.55207132
Coefficient of variation (CV)4.8383531
Kurtosis272.01192
Mean0.11410315
Median Absolute Deviation (MAD)0
Skewness12.859357
Sum250
Variance0.30478275
MonotonicityNot monotonic
2024-04-30T04:30:59.632619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 2011
55.8%
1 155
 
4.3%
2 10
 
0.3%
5 6
 
0.2%
3 5
 
0.1%
4 2
 
0.1%
7 1
 
< 0.1%
15 1
 
< 0.1%
(Missing) 1410
39.2%
ValueCountFrequency (%)
0 2011
55.8%
1 155
 
4.3%
2 10
 
0.3%
3 5
 
0.1%
4 2
 
0.1%
5 6
 
0.2%
7 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
7 1
 
< 0.1%
5 6
 
0.2%
4 2
 
0.1%
3 5
 
0.1%
2 10
 
0.3%
1 155
 
4.3%
0 2011
55.8%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.4%
Missing1092
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean0.8844161
Minimum0
Maximum12
Zeros884
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:59.728141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92037036
Coefficient of variation (CV)1.0406531
Kurtosis14.25168
Mean0.8844161
Median Absolute Deviation (MAD)1
Skewness2.3373208
Sum2219
Variance0.84708161
MonotonicityNot monotonic
2024-04-30T04:30:59.816547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1213
33.7%
0 884
24.5%
2 309
 
8.6%
3 57
 
1.6%
4 32
 
0.9%
5 6
 
0.2%
7 3
 
0.1%
6 3
 
0.1%
12 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1092
30.3%
ValueCountFrequency (%)
0 884
24.5%
1 1213
33.7%
2 309
 
8.6%
3 57
 
1.6%
4 32
 
0.9%
5 6
 
0.2%
6 3
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6 3
 
0.1%
5 6
 
0.2%
4 32
 
0.9%
3 57
 
1.6%
2 309
 
8.6%
1 1213
33.7%
0 884
24.5%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)0.8%
Missing1938
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean1.6067348
Minimum0
Maximum235
Zeros125
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:30:59.904678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum235
Range235
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.6207016
Coefficient of variation (CV)4.7429741
Kurtosis641.03907
Mean1.6067348
Median Absolute Deviation (MAD)0
Skewness24.078831
Sum2672
Variance58.075093
MonotonicityNot monotonic
2024-04-30T04:30:59.990365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1154
32.0%
2 284
 
7.9%
0 125
 
3.5%
3 52
 
1.4%
4 29
 
0.8%
5 6
 
0.2%
6 4
 
0.1%
7 3
 
0.1%
101 2
 
0.1%
146 1
 
< 0.1%
Other values (3) 3
 
0.1%
(Missing) 1938
53.8%
ValueCountFrequency (%)
0 125
 
3.5%
1 1154
32.0%
2 284
 
7.9%
3 52
 
1.4%
4 29
 
0.8%
5 6
 
0.2%
6 4
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
235 1
 
< 0.1%
146 1
 
< 0.1%
101 2
 
0.1%
12 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6 4
 
0.1%
5 6
 
0.2%
4 29
0.8%
3 52
1.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2406 
0
1088 
1
 
89
2
 
11
3
 
6

Length

Max length4
Median length4
Mean length3.0049986
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2406
66.8%
0 1088
30.2%
1 89
 
2.5%
2 11
 
0.3%
3 6
 
0.2%
175 1
 
< 0.1%

Length

2024-04-30T04:31:00.102870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:00.202803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2406
66.8%
0 1088
30.2%
1 89
 
2.5%
2 11
 
0.3%
3 6
 
0.2%
175 1
 
< 0.1%

사용끝지하층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.4%
Missing3184
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean1.0551559
Minimum0
Maximum176
Zeros318
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:31:00.283258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum176
Range176
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.273664
Coefficient of variation (CV)10.684359
Kurtosis210.7281
Mean1.0551559
Median Absolute Deviation (MAD)0
Skewness14.484409
Sum440
Variance127.09551
MonotonicityNot monotonic
2024-04-30T04:31:00.379097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 318
 
8.8%
1 83
 
2.3%
2 10
 
0.3%
3 4
 
0.1%
149 1
 
< 0.1%
176 1
 
< 0.1%
(Missing) 3184
88.4%
ValueCountFrequency (%)
0 318
8.8%
1 83
 
2.3%
2 10
 
0.3%
3 4
 
0.1%
149 1
 
< 0.1%
176 1
 
< 0.1%
ValueCountFrequency (%)
176 1
 
< 0.1%
149 1
 
< 0.1%
3 4
 
0.1%
2 10
 
0.3%
1 83
 
2.3%
0 318
8.8%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0
2108 
<NA>
1493 

Length

Max length4
Median length1
Mean length2.2438212
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2108
58.5%
<NA> 1493
41.5%

Length

2024-04-30T04:31:00.476186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:00.552947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2108
58.5%
na 1493
41.5%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0
2108 
<NA>
1493 

Length

Max length4
Median length1
Mean length2.2438212
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2108
58.5%
<NA> 1493
41.5%

Length

2024-04-30T04:31:00.634658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:00.920082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2108
58.5%
na 1493
41.5%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
0
2108 
<NA>
1493 

Length

Max length4
Median length1
Mean length2.2438212
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2108
58.5%
<NA> 1493
41.5%

Length

2024-04-30T04:31:01.019625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:01.116437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2108
58.5%
na 1493
41.5%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing638
Missing (%)17.7%
Memory size7.2 KiB
False
2963 
(Missing)
638 
ValueCountFrequency (%)
False 2963
82.3%
(Missing) 638
 
17.7%
2024-04-30T04:31:01.190640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)0.7%
Missing667
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.2549421
Minimum0
Maximum88
Zeros338
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:31:01.287474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum88
Range88
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6547761
Coefficient of variation (CV)0.81561393
Kurtosis356.50485
Mean3.2549421
Median Absolute Deviation (MAD)1
Skewness12.059083
Sum9550
Variance7.0478361
MonotonicityNot monotonic
2024-04-30T04:31:01.389463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 1154
32.0%
2 483
13.4%
4 476
13.2%
0 338
 
9.4%
5 160
 
4.4%
6 119
 
3.3%
8 57
 
1.6%
7 35
 
1.0%
1 32
 
0.9%
9 23
 
0.6%
Other values (12) 57
 
1.6%
(Missing) 667
18.5%
ValueCountFrequency (%)
0 338
 
9.4%
1 32
 
0.9%
2 483
13.4%
3 1154
32.0%
4 476
13.2%
5 160
 
4.4%
6 119
 
3.3%
7 35
 
1.0%
8 57
 
1.6%
9 23
 
0.6%
ValueCountFrequency (%)
88 1
 
< 0.1%
20 2
 
0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 2
 
0.1%
14 3
 
0.1%
13 1
 
< 0.1%
12 19
0.5%

조건부허가신고사유
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
3565 
체류기간 연장시 영업신고증 재교부 받으시기 바랍니다.
 
16
체류기간 연장시 영업신고증을 재교부 받으시기 바랍니다.
 
4
체류기간연장
 
3
가설건축물 존치기간까지
 
1
Other values (12)
 
12

Length

Max length64
Median length4
Mean length4.2274368
Min length4

Unique

Unique13 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3565
99.0%
체류기간 연장시 영업신고증 재교부 받으시기 바랍니다. 16
 
0.4%
체류기간 연장시 영업신고증을 재교부 받으시기 바랍니다. 4
 
0.1%
체류기간연장 3
 
0.1%
가설건축물 존치기간까지 1
 
< 0.1%
횡성생고기 직권폐업관련 1
 
< 0.1%
무단폐업 직권말소 처리기간중 여하한 문제 발생으로 직권말소가 불가능할 경우, 즉시 해당 조건부 신규 영업신고 철회 1
 
< 0.1%
전 영업소 직권말소관련 조건부영업신고 1
 
< 0.1%
전 영업소(음식점:세렌디피티) 직권폐업 관련 1
 
< 0.1%
전 영업소(야다이) 직권폐업 관련 1
 
< 0.1%
Other values (7) 7
 
0.2%

Length

2024-04-30T04:31:01.486284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3565
94.7%
재교부 23
 
0.6%
바랍니다 23
 
0.6%
받으시기 23
 
0.6%
체류기간 22
 
0.6%
연장시 20
 
0.5%
영업신고증 17
 
0.5%
영업신고증을 6
 
0.2%
5
 
0.1%
관련 4
 
0.1%
Other values (35) 57
 
1.5%

조건부허가시작일자
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)97.6%
Missing3560
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean20158730
Minimum20031001
Maximum20210622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:31:01.589904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20031001
5-th percentile20130228
Q120160119
median20160819
Q320171011
95-th percentile20190821
Maximum20210622
Range179621
Interquartile range (IQR)10892

Descriptive statistics

Standard deviation33440.413
Coefficient of variation (CV)0.0016588551
Kurtosis9.4614776
Mean20158730
Median Absolute Deviation (MAD)10191
Skewness-2.7372891
Sum8.2650794 × 108
Variance1.1182612 × 109
MonotonicityNot monotonic
2024-04-30T04:31:01.700327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20031001 2
 
0.1%
20130228 1
 
< 0.1%
20150218 1
 
< 0.1%
20160523 1
 
< 0.1%
20161128 1
 
< 0.1%
20171010 1
 
< 0.1%
20171101 1
 
< 0.1%
20180209 1
 
< 0.1%
20210622 1
 
< 0.1%
20131219 1
 
< 0.1%
Other values (30) 30
 
0.8%
(Missing) 3560
98.9%
ValueCountFrequency (%)
20031001 2
0.1%
20130228 1
< 0.1%
20131219 1
< 0.1%
20140331 1
< 0.1%
20141205 1
< 0.1%
20150106 1
< 0.1%
20150218 1
< 0.1%
20150519 1
< 0.1%
20160113 1
< 0.1%
20160119 1
< 0.1%
ValueCountFrequency (%)
20210622 1
< 0.1%
20191115 1
< 0.1%
20190821 1
< 0.1%
20190718 1
< 0.1%
20190710 1
< 0.1%
20190531 1
< 0.1%
20180209 1
< 0.1%
20171114 1
< 0.1%
20171101 1
< 0.1%
20171026 1
< 0.1%

조건부허가종료일자
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)97.5%
Missing3561
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean20180933
Minimum20081231
Maximum20240718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:31:01.806674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20081231
5-th percentile20139843
Q120160921
median20190114
Q320200321
95-th percentile20220654
Maximum20240718
Range159487
Interquartile range (IQR)39400.25

Descriptive statistics

Standard deviation28198.754
Coefficient of variation (CV)0.0013972969
Kurtosis2.9801724
Mean20180933
Median Absolute Deviation (MAD)10913.5
Skewness-1.0382028
Sum8.072373 × 108
Variance7.9516972 × 108
MonotonicityNot monotonic
2024-04-30T04:31:01.912781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
20201027 2
 
0.1%
20171124 1
 
< 0.1%
20190925 1
 
< 0.1%
20181001 1
 
< 0.1%
20170728 1
 
< 0.1%
20181126 1
 
< 0.1%
20180312 1
 
< 0.1%
20190906 1
 
< 0.1%
20240718 1
 
< 0.1%
20140318 1
 
< 0.1%
Other values (29) 29
 
0.8%
(Missing) 3561
98.9%
ValueCountFrequency (%)
20081231 1
< 0.1%
20130827 1
< 0.1%
20140318 1
< 0.1%
20150531 1
< 0.1%
20150630 1
< 0.1%
20151214 1
< 0.1%
20160427 1
< 0.1%
20160503 1
< 0.1%
20160608 1
< 0.1%
20160630 1
< 0.1%
ValueCountFrequency (%)
20240718 1
< 0.1%
20221227 1
< 0.1%
20220624 1
< 0.1%
20210821 1
< 0.1%
20210121 1
< 0.1%
20201027 2
0.1%
20200728 1
< 0.1%
20200522 1
< 0.1%
20200328 1
< 0.1%
20200319 1
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2987 
임대
592 
자가
 
22

Length

Max length4
Median length4
Mean length3.6589836
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> 2987
82.9%
임대 592
 
16.4%
자가 22
 
0.6%

Length

2024-04-30T04:31:02.024622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:02.112555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2987
82.9%
임대 592
 
16.4%
자가 22
 
0.6%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2266 
0
1335 

Length

Max length4
Median length4
Mean length2.8878089
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> 2266
62.9%
0 1335
37.1%

Length

2024-04-30T04:31:02.219393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:02.301702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2266
62.9%
0 1335
37.1%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2884 
0
665 
1
 
45
2
 
5
3
 
1

Length

Max length4
Median length4
Mean length3.4026659
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2884
80.1%
0 665
 
18.5%
1 45
 
1.2%
2 5
 
0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%

Length

2024-04-30T04:31:02.396094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:02.515244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2884
80.1%
0 665
 
18.5%
1 45
 
1.2%
2 5
 
0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2884 
0
712 
1
 
5

Length

Max length4
Median length4
Mean length3.4026659
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> 2884
80.1%
0 712
 
19.8%
1 5
 
0.1%

Length

2024-04-30T04:31:02.625872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:02.711298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2884
80.1%
0 712
 
19.8%
1 5
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.3 KiB
<NA>
2359 
0
1242 

Length

Max length4
Median length4
Mean length2.9652874
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> 2359
65.5%
0 1242
34.5%

Length

2024-04-30T04:31:02.806842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:31:02.901086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2359
65.5%
0 1242
34.5%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)1.3%
Missing2373
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean0.93973941
Minimum0
Maximum22
Zeros844
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size31.8 KiB
2024-04-30T04:31:02.994612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum22
Range22
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9495555
Coefficient of variation (CV)2.0745703
Kurtosis25.351784
Mean0.93973941
Median Absolute Deviation (MAD)0
Skewness3.9324951
Sum1154
Variance3.8007667
MonotonicityNot monotonic
2024-04-30T04:31:03.091381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 844
 
23.4%
2 129
 
3.6%
1 81
 
2.2%
3 75
 
2.1%
4 40
 
1.1%
5 21
 
0.6%
6 14
 
0.4%
7 7
 
0.2%
8 6
 
0.2%
10 3
 
0.1%
Other values (6) 8
 
0.2%
(Missing) 2373
65.9%
ValueCountFrequency (%)
0 844
23.4%
1 81
 
2.2%
2 129
 
3.6%
3 75
 
2.1%
4 40
 
1.1%
5 21
 
0.6%
6 14
 
0.4%
7 7
 
0.2%
8 6
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
18 2
 
0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
0.1%
9 2
 
0.1%
8 6
0.2%
7 7
0.2%
6 14
0.4%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing596
Missing (%)16.6%
Memory size7.2 KiB
False
3004 
True
 
1
(Missing)
596 
ValueCountFrequency (%)
False 3004
83.4%
True 1
 
< 0.1%
(Missing) 596
 
16.6%
2024-04-30T04:31:03.189415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
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개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
359131600003160000-226-2019-0000320191115<NA>3폐업2폐업20211202<NA><NA><NA>02854414121.00152873서울특별시 구로구 구로동 771-10서울특별시 구로구 도림로 35-1, 1층 우측호 (구로동)8314네일 리나2021-12-02 14:11:12U2021-12-04 02:40:00.0네일아트업190049.611824442823.529335피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N5체류기간 연장시 영업신고증을 재교부 받으시기 바랍니다.2019111520221227<NA>00000N
359231600003160000-226-2019-000042019-11-25<NA>3폐업2폐업2023-02-02<NA><NA><NA><NA>17.55152-725서울특별시 구로구 구로동 3-25 신도림테크노마트서울특별시 구로구 새말로 97, 신도림테크노마트 지하1층 비에스004호 (구로동)8288이앤뷰티2023-02-03 08:57:55U2022-12-02 00:05:00.0네일아트업190232.524534444978.682746<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
359331600003160000-226-2019-000052019-02-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.39152-725서울특별시 구로구 구로동 3-25 신도림테크노마트서울특별시 구로구 새말로 97, 신도림테크노마트 지1층 029호 (구로동)8288라라뷰티2024-04-03 17:18:49I2023-12-04 00:06:00.0피부미용업190232.524534444978.682746<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
359431600003160000-226-2020-0000120200102<NA>1영업/정상1영업<NA><NA><NA><NA><NA>45.24152880서울특별시 구로구 구로동 1128-3 파트너스타워2차서울특별시 구로구 디지털로32가길 16, 파트너스타워2차 2층 205호 (구로동)8393베버리힐즈왁싱앤스웨디시2022-06-20 16:04:23U2021-12-05 22:02:00.0피부미용업190967.257865442305.216741<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
359531600003160000-226-2020-0000220200224<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.00152889서울특별시 구로구 오류동 6-140번지 1층서울특별시 구로구 고척로3길 7, 1층 (오류동)8254꼼네일2020-02-24 13:05:52I2020-02-26 00:23:23.0네일아트업185795.65998443726.495397피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N2<NA><NA><NA><NA>00001N
359631600003160000-226-2021-000012021-02-10<NA>1영업/정상1영업<NA><NA><NA><NA><NA>21.79152-887서울특별시 구로구 신도림동 400-1 109호서울특별시 구로구 신도림로 7, 109호 (신도림동)8206이뿜네일2023-03-24 12:13:27U2022-12-02 22:06:00.0네일아트업189067.276002444891.605594<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
359731600003160000-226-2022-0000120220117<NA>1영업/정상1영업<NA><NA><NA><NA><NA>50.00152847서울특별시 구로구 구로동 170-10 대륭포스트타워7차서울특별시 구로구 디지털로33길 48, 대륭포스트타워7차 206-1호 (구로동)8377에이브 네일 앤 왁싱2022-01-17 10:32:18I2022-01-19 00:22:40.0피부미용업190609.413675442782.030555피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N4<NA><NA><NA><NA>00003N
359831600003160000-226-2022-0000220221024<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.00152880서울특별시 구로구 구로동 1128-3 파트너스타워2차서울특별시 구로구 디지털로32가길 16, 파트너스타워2차 208-1호 (구로동)8393비비블루2022-10-24 14:04:35I2021-10-30 22:06:00.0메이크업업190967.257865442305.216741<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
359931600003160000-226-2023-000022023-04-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.00152-800서울특별시 구로구 가리봉동 123-49서울특별시 구로구 우마2길 24, 1층 (가리봉동)8387스마일네일2023-04-12 17:07:55I2022-12-03 23:04:00.0네일아트업190134.346813442210.41903<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
360031600003160000-226-2023-000032023-05-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>30.74152-800서울특별시 구로구 가리봉동 89-56서울특별시 구로구 구로동로 26, 1층 (가리봉동)8385별빛네일2023-12-21 13:34:34I2022-11-01 22:03:00.0네일아트업189908.59631442432.026331<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>