Overview

Dataset statistics

Number of variables47
Number of observations3821
Missing cells34922
Missing cells (%)19.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory405.0 B

Variable types

Categorical18
Text6
DateTime4
Unsupported6
Numeric11
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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-17920/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (53.3%)Imbalance
사용시작지하층 is highly imbalanced (66.9%)Imbalance
조건부허가종료일자 is highly imbalanced (98.8%)Imbalance
건물소유구분명 is highly imbalanced (53.9%)Imbalance
여성종사자수 is highly imbalanced (65.0%)Imbalance
남성종사자수 is highly imbalanced (69.7%)Imbalance
다중이용업소여부 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 3821 (100.0%) missing valuesMissing
폐업일자 has 1279 (33.5%) missing valuesMissing
휴업시작일자 has 3821 (100.0%) missing valuesMissing
휴업종료일자 has 3821 (100.0%) missing valuesMissing
재개업일자 has 3821 (100.0%) missing valuesMissing
전화번호 has 1245 (32.6%) missing valuesMissing
도로명주소 has 1453 (38.0%) missing valuesMissing
도로명우편번호 has 1489 (39.0%) missing valuesMissing
좌표정보(X) has 161 (4.2%) missing valuesMissing
좌표정보(Y) has 161 (4.2%) missing valuesMissing
건물지상층수 has 683 (17.9%) missing valuesMissing
건물지하층수 has 683 (17.9%) missing valuesMissing
사용시작지상층 has 683 (17.9%) missing valuesMissing
사용끝지상층 has 683 (17.9%) missing valuesMissing
사용끝지하층 has 683 (17.9%) missing valuesMissing
발한실여부 has 720 (18.8%) missing valuesMissing
좌석수 has 683 (17.9%) missing valuesMissing
조건부허가신고사유 has 3821 (100.0%) missing valuesMissing
조건부허가시작일자 has 3821 (100.0%) missing valuesMissing
침대수 has 683 (17.9%) missing valuesMissing
다중이용업소여부 has 683 (17.9%) missing valuesMissing
좌표정보(Y) is highly skewed (γ1 = -37.41331892)Skewed
건물지하층수 is highly skewed (γ1 = 49.82391693)Skewed
사용시작지상층 is highly skewed (γ1 = 40.13565862)Skewed
사용끝지하층 is highly skewed (γ1 = 37.95084103)Skewed
좌석수 is highly skewed (γ1 = 31.48974167)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
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적 has 315 (8.2%) zerosZeros
건물지상층수 has 2821 (73.8%) zerosZeros
건물지하층수 has 3044 (79.7%) zerosZeros
사용시작지상층 has 2111 (55.2%) zerosZeros
사용끝지상층 has 2495 (65.3%) zerosZeros
사용끝지하층 has 3084 (80.7%) zerosZeros
좌석수 has 395 (10.3%) zerosZeros
침대수 has 2664 (69.7%) zerosZeros

Reproduction

Analysis started2024-05-11 05:41:07.408921
Analysis finished2024-05-11 05:41:09.465288
Duration2.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
3180000
3821 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 3821
100.0%

Length

2024-05-11T14:41:09.558582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:09.697858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 3821
100.0%

관리번호
Text

UNIQUE 

Distinct3821
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2024-05-11T14:41:09.964847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters84062
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

Unique3821 ?
Unique (%)100.0%

Sample

1st row3180000-204-1930-01254
2nd row3180000-204-1942-00814
3rd row3180000-204-1959-01020
4th row3180000-204-1962-01028
5th row3180000-204-1964-01055
ValueCountFrequency (%)
3180000-204-1930-01254 1
 
< 0.1%
3180000-211-2023-00016 1
 
< 0.1%
3180000-211-2023-00018 1
 
< 0.1%
3180000-211-2023-00005 1
 
< 0.1%
3180000-211-2023-00006 1
 
< 0.1%
3180000-211-2023-00007 1
 
< 0.1%
3180000-211-2023-00008 1
 
< 0.1%
3180000-211-2023-00009 1
 
< 0.1%
3180000-211-2023-00010 1
 
< 0.1%
3180000-211-2023-00011 1
 
< 0.1%
Other values (3811) 3811
99.7%
2024-05-11T14:41:10.555206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32089
38.2%
1 11917
 
14.2%
- 11463
 
13.6%
2 9425
 
11.2%
3 5552
 
6.6%
8 5203
 
6.2%
9 2716
 
3.2%
4 2501
 
3.0%
5 1223
 
1.5%
7 993
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72599
86.4%
Dash Punctuation 11463
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32089
44.2%
1 11917
 
16.4%
2 9425
 
13.0%
3 5552
 
7.6%
8 5203
 
7.2%
9 2716
 
3.7%
4 2501
 
3.4%
5 1223
 
1.7%
7 993
 
1.4%
6 980
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 11463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32089
38.2%
1 11917
 
14.2%
- 11463
 
13.6%
2 9425
 
11.2%
3 5552
 
6.6%
8 5203
 
6.2%
9 2716
 
3.2%
4 2501
 
3.0%
5 1223
 
1.5%
7 993
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32089
38.2%
1 11917
 
14.2%
- 11463
 
13.6%
2 9425
 
11.2%
3 5552
 
6.6%
8 5203
 
6.2%
9 2716
 
3.2%
4 2501
 
3.0%
5 1223
 
1.5%
7 993
 
1.2%
Distinct2815
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
Minimum1930-07-20 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T14:41:10.789997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:11.009990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
3
2542 
1
1279 

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 2542
66.5%
1 1279
33.5%

Length

2024-05-11T14:41:11.216078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:11.395840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2542
66.5%
1 1279
33.5%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
폐업
2542 
영업/정상
1279 

Length

Max length5
Median length2
Mean length3.0041874
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2542
66.5%
영업/정상 1279
33.5%

Length

2024-05-11T14:41:11.586787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:11.763726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2542
66.5%
영업/정상 1279
33.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2
2542 
1
1279 

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 2542
66.5%
1 1279
33.5%

Length

2024-05-11T14:41:11.936552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:12.081459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2542
66.5%
1 1279
33.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
폐업
2542 
영업
1279 

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 2542
66.5%
영업 1279
33.5%

Length

2024-05-11T14:41:12.236022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:12.436080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2542
66.5%
영업 1279
33.5%

폐업일자
Date

MISSING 

Distinct1830
Distinct (%)72.0%
Missing1279
Missing (%)33.5%
Memory size30.0 KiB
Minimum1992-06-08 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T14:41:12.686148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:12.950999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB

전화번호
Text

MISSING 

Distinct2356
Distinct (%)91.5%
Missing1245
Missing (%)32.6%
Memory size30.0 KiB
2024-05-11T14:41:13.391777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.9697205
Min length2

Characters and Unicode

Total characters25682
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2220 ?
Unique (%)86.2%

Sample

1st row0206765902
2nd row0208340952
3rd row0208439031
4th row0208469872
5th row0208423521
ValueCountFrequency (%)
02 1175
29.4%
0200000000 42
 
1.1%
070 31
 
0.8%
831 21
 
0.5%
832 21
 
0.5%
833 20
 
0.5%
845 12
 
0.3%
780 12
 
0.3%
842 10
 
0.3%
846 10
 
0.3%
Other values (2345) 2642
66.1%
2024-05-11T14:41:14.040091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4485
17.5%
2 4225
16.5%
8 2752
10.7%
3 2316
9.0%
6 2119
8.3%
4 2010
7.8%
7 1913
7.4%
1869
7.3%
5 1416
 
5.5%
1 1412
 
5.5%
Other values (2) 1165
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23812
92.7%
Space Separator 1869
 
7.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4485
18.8%
2 4225
17.7%
8 2752
11.6%
3 2316
9.7%
6 2119
8.9%
4 2010
8.4%
7 1913
8.0%
5 1416
 
5.9%
1 1412
 
5.9%
9 1164
 
4.9%
Space Separator
ValueCountFrequency (%)
1869
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25682
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4485
17.5%
2 4225
16.5%
8 2752
10.7%
3 2316
9.0%
6 2119
8.3%
4 2010
7.8%
7 1913
7.4%
1869
7.3%
5 1416
 
5.5%
1 1412
 
5.5%
Other values (2) 1165
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4485
17.5%
2 4225
16.5%
8 2752
10.7%
3 2316
9.0%
6 2119
8.3%
4 2010
7.8%
7 1913
7.4%
1869
7.3%
5 1416
 
5.5%
1 1412
 
5.5%
Other values (2) 1165
 
4.5%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1668
Distinct (%)43.7%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean38.943543
Minimum0
Maximum356.7
Zeros315
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:14.270260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.16
median27
Q346.2
95-th percentile112.514
Maximum356.7
Range356.7
Interquartile range (IQR)29.04

Descriptive statistics

Standard deviation38.855557
Coefficient of variation (CV)0.99774069
Kurtosis12.284565
Mean38.943543
Median Absolute Deviation (MAD)12
Skewness2.8659067
Sum148725.39
Variance1509.7543
MonotonicityNot monotonic
2024-05-11T14:41:14.478796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 315
 
8.2%
33.0 106
 
2.8%
30.0 54
 
1.4%
20.0 49
 
1.3%
26.4 42
 
1.1%
26.0 39
 
1.0%
23.1 38
 
1.0%
19.8 37
 
1.0%
25.0 37
 
1.0%
16.5 36
 
0.9%
Other values (1658) 3066
80.2%
ValueCountFrequency (%)
0.0 315
8.2%
2.5 1
 
< 0.1%
3.0 1
 
< 0.1%
3.3 2
 
0.1%
4.0 1
 
< 0.1%
4.96 1
 
< 0.1%
5.0 1
 
< 0.1%
5.96 1
 
< 0.1%
6.0 6
 
0.2%
6.6 1
 
< 0.1%
ValueCountFrequency (%)
356.7 1
< 0.1%
353.06 1
< 0.1%
347.0 2
0.1%
346.85 1
< 0.1%
327.54 1
< 0.1%
310.39 1
< 0.1%
281.32 1
< 0.1%
270.6 1
< 0.1%
269.95 1
< 0.1%
266.84 1
< 0.1%
Distinct256
Distinct (%)6.7%
Missing11
Missing (%)0.3%
Memory size30.0 KiB
2024-05-11T14:41:15.037364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1165354
Min length6

Characters and Unicode

Total characters23304
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

Unique45 ?
Unique (%)1.2%

Sample

1st row150045
2nd row150815
3rd row150050
4th row150841
5th row150841
ValueCountFrequency (%)
150841 230
 
6.0%
150045 117
 
3.1%
150840 115
 
3.0%
150033 92
 
2.4%
150839 86
 
2.3%
150070 82
 
2.2%
150815 80
 
2.1%
150899 74
 
1.9%
150037 72
 
1.9%
150818 70
 
1.8%
Other values (246) 2792
73.3%
2024-05-11T14:41:15.783211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5793
24.9%
1 4756
20.4%
5 4578
19.6%
8 3080
13.2%
3 1265
 
5.4%
4 988
 
4.2%
9 811
 
3.5%
2 552
 
2.4%
6 526
 
2.3%
7 511
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22860
98.1%
Dash Punctuation 444
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5793
25.3%
1 4756
20.8%
5 4578
20.0%
8 3080
13.5%
3 1265
 
5.5%
4 988
 
4.3%
9 811
 
3.5%
2 552
 
2.4%
6 526
 
2.3%
7 511
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 444
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5793
24.9%
1 4756
20.4%
5 4578
19.6%
8 3080
13.2%
3 1265
 
5.4%
4 988
 
4.2%
9 811
 
3.5%
2 552
 
2.4%
6 526
 
2.3%
7 511
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5793
24.9%
1 4756
20.4%
5 4578
19.6%
8 3080
13.2%
3 1265
 
5.4%
4 988
 
4.2%
9 811
 
3.5%
2 552
 
2.4%
6 526
 
2.3%
7 511
 
2.2%
Distinct3271
Distinct (%)85.9%
Missing11
Missing (%)0.3%
Memory size30.0 KiB
2024-05-11T14:41:16.193864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length46
Mean length27.84042
Min length18

Characters and Unicode

Total characters106072
Distinct characters372
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2840 ?
Unique (%)74.5%

Sample

1st row서울특별시 영등포구 당산동5가 11-1번지
2nd row서울특별시 영등포구 대림동 728-2번지
3rd row서울특별시 영등포구 신길동 0-0번지
4th row서울특별시 영등포구 신길동 284-1번지
5th row서울특별시 영등포구 신길동 220번지 2층48호
ValueCountFrequency (%)
서울특별시 3809
20.1%
영등포구 3809
20.1%
신길동 975
 
5.2%
대림동 714
 
3.8%
여의도동 432
 
2.3%
1층 315
 
1.7%
2층 164
 
0.9%
도림동 160
 
0.8%
당산동3가 143
 
0.8%
당산동5가 129
 
0.7%
Other values (3408) 8271
43.7%
2024-05-11T14:41:16.804990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17666
 
16.7%
1 4566
 
4.3%
4451
 
4.2%
4420
 
4.2%
4397
 
4.1%
4145
 
3.9%
3868
 
3.6%
3831
 
3.6%
3825
 
3.6%
3813
 
3.6%
Other values (362) 51090
48.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63687
60.0%
Decimal Number 21085
 
19.9%
Space Separator 17666
 
16.7%
Dash Punctuation 3044
 
2.9%
Uppercase Letter 342
 
0.3%
Open Punctuation 67
 
0.1%
Close Punctuation 67
 
0.1%
Other Punctuation 67
 
0.1%
Lowercase Letter 29
 
< 0.1%
Math Symbol 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4451
 
7.0%
4420
 
6.9%
4397
 
6.9%
4145
 
6.5%
3868
 
6.1%
3831
 
6.0%
3825
 
6.0%
3813
 
6.0%
3811
 
6.0%
3809
 
6.0%
Other values (305) 23317
36.6%
Uppercase Letter
ValueCountFrequency (%)
B 96
28.1%
A 38
 
11.1%
S 29
 
8.5%
C 29
 
8.5%
E 26
 
7.6%
M 12
 
3.5%
U 12
 
3.5%
K 12
 
3.5%
L 11
 
3.2%
G 10
 
2.9%
Other values (15) 67
19.6%
Lowercase Letter
ValueCountFrequency (%)
e 6
20.7%
c 5
17.2%
r 3
10.3%
n 3
10.3%
t 3
10.3%
b 2
 
6.9%
l 2
 
6.9%
a 2
 
6.9%
y 1
 
3.4%
w 1
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 4566
21.7%
2 3032
14.4%
3 2650
12.6%
0 2117
10.0%
4 2110
10.0%
5 1585
 
7.5%
6 1469
 
7.0%
7 1283
 
6.1%
8 1150
 
5.5%
9 1123
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 52
77.6%
. 9
 
13.4%
/ 4
 
6.0%
? 1
 
1.5%
& 1
 
1.5%
Space Separator
ValueCountFrequency (%)
17666
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3044
100.0%
Open Punctuation
ValueCountFrequency (%)
( 67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 67
100.0%
Math Symbol
ValueCountFrequency (%)
~ 16
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63686
60.0%
Common 42014
39.6%
Latin 371
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4451
 
7.0%
4420
 
6.9%
4397
 
6.9%
4145
 
6.5%
3868
 
6.1%
3831
 
6.0%
3825
 
6.0%
3813
 
6.0%
3811
 
6.0%
3809
 
6.0%
Other values (304) 23316
36.6%
Latin
ValueCountFrequency (%)
B 96
25.9%
A 38
 
10.2%
S 29
 
7.8%
C 29
 
7.8%
E 26
 
7.0%
M 12
 
3.2%
U 12
 
3.2%
K 12
 
3.2%
L 11
 
3.0%
G 10
 
2.7%
Other values (26) 96
25.9%
Common
ValueCountFrequency (%)
17666
42.0%
1 4566
 
10.9%
- 3044
 
7.2%
2 3032
 
7.2%
3 2650
 
6.3%
0 2117
 
5.0%
4 2110
 
5.0%
5 1585
 
3.8%
6 1469
 
3.5%
7 1283
 
3.1%
Other values (11) 2492
 
5.9%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63686
60.0%
ASCII 42385
40.0%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17666
41.7%
1 4566
 
10.8%
- 3044
 
7.2%
2 3032
 
7.2%
3 2650
 
6.3%
0 2117
 
5.0%
4 2110
 
5.0%
5 1585
 
3.7%
6 1469
 
3.5%
7 1283
 
3.0%
Other values (47) 2863
 
6.8%
Hangul
ValueCountFrequency (%)
4451
 
7.0%
4420
 
6.9%
4397
 
6.9%
4145
 
6.5%
3868
 
6.1%
3831
 
6.0%
3825
 
6.0%
3813
 
6.0%
3811
 
6.0%
3809
 
6.0%
Other values (304) 23316
36.6%
CJK
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct2126
Distinct (%)89.8%
Missing1453
Missing (%)38.0%
Memory size30.0 KiB
2024-05-11T14:41:17.313549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length52
Mean length35.269426
Min length23

Characters and Unicode

Total characters83518
Distinct characters359
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1926 ?
Unique (%)81.3%

Sample

1st row서울특별시 영등포구 국제금융로 79 (여의도동)
2nd row서울특별시 영등포구 여의대방로39길 9-1 (신길동)
3rd row서울특별시 영등포구 여의대방로 386 (여의도동,진주상가404호)
4th row서울특별시 영등포구 영중로 93 (영등포동7가,(1층))
5th row서울특별시 영등포구 영등포로53길 50-4 (영등포동2가)
ValueCountFrequency (%)
서울특별시 2368
 
15.2%
영등포구 2368
 
15.2%
1층 576
 
3.7%
신길동 451
 
2.9%
대림동 390
 
2.5%
2층 295
 
1.9%
여의도동 249
 
1.6%
당산로 118
 
0.8%
3층 110
 
0.7%
당산동3가 97
 
0.6%
Other values (1908) 8511
54.8%
2024-05-11T14:41:18.120102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13175
 
15.8%
1 3536
 
4.2%
3165
 
3.8%
2974
 
3.6%
2924
 
3.5%
2694
 
3.2%
2445
 
2.9%
, 2428
 
2.9%
2414
 
2.9%
2412
 
2.9%
Other values (349) 45351
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48936
58.6%
Decimal Number 13424
 
16.1%
Space Separator 13175
 
15.8%
Other Punctuation 2437
 
2.9%
Open Punctuation 2402
 
2.9%
Close Punctuation 2402
 
2.9%
Dash Punctuation 414
 
0.5%
Uppercase Letter 286
 
0.3%
Lowercase Letter 29
 
< 0.1%
Math Symbol 13
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3165
 
6.5%
2974
 
6.1%
2924
 
6.0%
2694
 
5.5%
2445
 
5.0%
2414
 
4.9%
2412
 
4.9%
2378
 
4.9%
2370
 
4.8%
2368
 
4.8%
Other values (291) 22792
46.6%
Uppercase Letter
ValueCountFrequency (%)
B 101
35.3%
S 25
 
8.7%
A 23
 
8.0%
E 22
 
7.7%
C 20
 
7.0%
L 10
 
3.5%
U 10
 
3.5%
K 10
 
3.5%
Y 9
 
3.1%
G 8
 
2.8%
Other values (14) 48
16.8%
Lowercase Letter
ValueCountFrequency (%)
c 6
20.7%
e 6
20.7%
r 3
10.3%
t 3
10.3%
n 3
10.3%
l 2
 
6.9%
a 2
 
6.9%
w 1
 
3.4%
y 1
 
3.4%
u 1
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 3536
26.3%
2 2175
16.2%
3 1665
12.4%
0 1339
 
10.0%
4 1123
 
8.4%
5 872
 
6.5%
7 819
 
6.1%
6 809
 
6.0%
8 609
 
4.5%
9 477
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 2428
99.6%
/ 3
 
0.1%
. 3
 
0.1%
? 1
 
< 0.1%
& 1
 
< 0.1%
* 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2401
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2401
> 99.9%
] 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
13175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 414
100.0%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48936
58.6%
Common 34267
41.0%
Latin 315
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3165
 
6.5%
2974
 
6.1%
2924
 
6.0%
2694
 
5.5%
2445
 
5.0%
2414
 
4.9%
2412
 
4.9%
2378
 
4.9%
2370
 
4.8%
2368
 
4.8%
Other values (291) 22792
46.6%
Latin
ValueCountFrequency (%)
B 101
32.1%
S 25
 
7.9%
A 23
 
7.3%
E 22
 
7.0%
C 20
 
6.3%
L 10
 
3.2%
U 10
 
3.2%
K 10
 
3.2%
Y 9
 
2.9%
G 8
 
2.5%
Other values (25) 77
24.4%
Common
ValueCountFrequency (%)
13175
38.4%
1 3536
 
10.3%
, 2428
 
7.1%
( 2401
 
7.0%
) 2401
 
7.0%
2 2175
 
6.3%
3 1665
 
4.9%
0 1339
 
3.9%
4 1123
 
3.3%
5 872
 
2.5%
Other values (13) 3152
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48936
58.6%
ASCII 34582
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13175
38.1%
1 3536
 
10.2%
, 2428
 
7.0%
( 2401
 
6.9%
) 2401
 
6.9%
2 2175
 
6.3%
3 1665
 
4.8%
0 1339
 
3.9%
4 1123
 
3.2%
5 872
 
2.5%
Other values (48) 3467
 
10.0%
Hangul
ValueCountFrequency (%)
3165
 
6.5%
2974
 
6.1%
2924
 
6.0%
2694
 
5.5%
2445
 
5.0%
2414
 
4.9%
2412
 
4.9%
2378
 
4.9%
2370
 
4.8%
2368
 
4.8%
Other values (291) 22792
46.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct218
Distinct (%)9.3%
Missing1489
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean7324.9211
Minimum7203
Maximum7448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:18.341600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7203
5-th percentile7213
Q17259
median7328
Q37387
95-th percentile7436
Maximum7448
Range245
Interquartile range (IQR)128

Descriptive statistics

Standard deviation73.469078
Coefficient of variation (CV)0.010030016
Kurtosis-1.2459842
Mean7324.9211
Median Absolute Deviation (MAD)64
Skewness0.0096696483
Sum17081716
Variance5397.7055
MonotonicityNot monotonic
2024-05-11T14:41:18.567011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7333 88
 
2.3%
7246 62
 
1.6%
7213 46
 
1.2%
7214 44
 
1.2%
7305 39
 
1.0%
7223 36
 
0.9%
7294 34
 
0.9%
7257 33
 
0.9%
7362 33
 
0.9%
7206 32
 
0.8%
Other values (208) 1885
49.3%
(Missing) 1489
39.0%
ValueCountFrequency (%)
7203 2
 
0.1%
7204 15
0.4%
7205 12
 
0.3%
7206 32
0.8%
7207 3
 
0.1%
7208 8
 
0.2%
7209 1
 
< 0.1%
7210 2
 
0.1%
7211 5
 
0.1%
7212 2
 
0.1%
ValueCountFrequency (%)
7448 6
 
0.2%
7447 7
 
0.2%
7446 18
0.5%
7445 15
0.4%
7444 17
0.4%
7443 18
0.5%
7442 10
0.3%
7440 1
 
< 0.1%
7439 8
0.2%
7438 8
0.2%
Distinct3264
Distinct (%)85.4%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
2024-05-11T14:41:18.986290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length29
Mean length5.9712117
Min length1

Characters and Unicode

Total characters22816
Distinct characters779
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

Unique2882 ?
Unique (%)75.4%

Sample

1st row강남
2nd row이숙경헤어아트
3rd row
4th row
5th row카네숀
ValueCountFrequency (%)
헤어 135
 
2.6%
미용실 83
 
1.6%
hair 51
 
1.0%
네일 48
 
0.9%
헤어샵 38
 
0.7%
에스테틱 37
 
0.7%
nail 27
 
0.5%
피부관리 23
 
0.4%
헤어살롱 21
 
0.4%
당산점 21
 
0.4%
Other values (3456) 4662
90.6%
2024-05-11T14:41:19.726665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1331
 
5.8%
1269
 
5.6%
1213
 
5.3%
740
 
3.2%
527
 
2.3%
496
 
2.2%
462
 
2.0%
447
 
2.0%
440
 
1.9%
322
 
1.4%
Other values (769) 15569
68.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18815
82.5%
Space Separator 1331
 
5.8%
Lowercase Letter 1010
 
4.4%
Uppercase Letter 902
 
4.0%
Open Punctuation 216
 
0.9%
Close Punctuation 216
 
0.9%
Other Punctuation 170
 
0.7%
Decimal Number 142
 
0.6%
Dash Punctuation 8
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1269
 
6.7%
1213
 
6.4%
740
 
3.9%
527
 
2.8%
496
 
2.6%
462
 
2.5%
447
 
2.4%
440
 
2.3%
322
 
1.7%
294
 
1.6%
Other values (683) 12605
67.0%
Lowercase Letter
ValueCountFrequency (%)
a 141
14.0%
i 127
12.6%
e 87
 
8.6%
r 79
 
7.8%
n 77
 
7.6%
o 75
 
7.4%
l 64
 
6.3%
h 48
 
4.8%
u 43
 
4.3%
s 42
 
4.2%
Other values (16) 227
22.5%
Uppercase Letter
ValueCountFrequency (%)
A 90
 
10.0%
I 73
 
8.1%
S 71
 
7.9%
H 69
 
7.6%
N 61
 
6.8%
E 56
 
6.2%
L 54
 
6.0%
M 52
 
5.8%
O 47
 
5.2%
R 45
 
5.0%
Other values (16) 284
31.5%
Other Punctuation
ValueCountFrequency (%)
? 49
28.8%
& 45
26.5%
. 24
14.1%
, 18
 
10.6%
' 12
 
7.1%
# 9
 
5.3%
: 5
 
2.9%
2
 
1.2%
2
 
1.2%
/ 1
 
0.6%
Other values (3) 3
 
1.8%
Decimal Number
ValueCountFrequency (%)
0 32
22.5%
1 32
22.5%
2 30
21.1%
9 12
 
8.5%
3 11
 
7.7%
5 6
 
4.2%
6 6
 
4.2%
8 6
 
4.2%
4 4
 
2.8%
7 3
 
2.1%
Math Symbol
ValueCountFrequency (%)
< 1
25.0%
> 1
25.0%
+ 1
25.0%
= 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 215
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 215
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1331
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18773
82.3%
Common 2089
 
9.2%
Latin 1912
 
8.4%
Han 42
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1269
 
6.8%
1213
 
6.5%
740
 
3.9%
527
 
2.8%
496
 
2.6%
462
 
2.5%
447
 
2.4%
440
 
2.3%
322
 
1.7%
294
 
1.6%
Other values (655) 12563
66.9%
Latin
ValueCountFrequency (%)
a 141
 
7.4%
i 127
 
6.6%
A 90
 
4.7%
e 87
 
4.6%
r 79
 
4.1%
n 77
 
4.0%
o 75
 
3.9%
I 73
 
3.8%
S 71
 
3.7%
H 69
 
3.6%
Other values (42) 1023
53.5%
Common
ValueCountFrequency (%)
1331
63.7%
( 215
 
10.3%
) 215
 
10.3%
? 49
 
2.3%
& 45
 
2.2%
0 32
 
1.5%
1 32
 
1.5%
2 30
 
1.4%
. 24
 
1.1%
, 18
 
0.9%
Other values (24) 98
 
4.7%
Han
ValueCountFrequency (%)
13
31.0%
2
 
4.8%
2
 
4.8%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (18) 18
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18772
82.3%
ASCII 3996
 
17.5%
CJK 42
 
0.2%
None 5
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1331
33.3%
( 215
 
5.4%
) 215
 
5.4%
a 141
 
3.5%
i 127
 
3.2%
A 90
 
2.3%
e 87
 
2.2%
r 79
 
2.0%
n 77
 
1.9%
o 75
 
1.9%
Other values (73) 1559
39.0%
Hangul
ValueCountFrequency (%)
1269
 
6.8%
1213
 
6.5%
740
 
3.9%
527
 
2.8%
496
 
2.6%
462
 
2.5%
447
 
2.4%
440
 
2.3%
322
 
1.7%
294
 
1.6%
Other values (654) 12562
66.9%
CJK
ValueCountFrequency (%)
13
31.0%
2
 
4.8%
2
 
4.8%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
1
 
2.4%
Other values (18) 18
42.9%
None
ValueCountFrequency (%)
2
40.0%
2
40.0%
1
20.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Distinct2757
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
Minimum1999-01-27 00:00:00
Maximum2024-05-09 13:26:40
2024-05-11T14:41:19.941751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:20.167093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
I
2901 
U
884 
D
 
36

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 2901
75.9%
U 884
 
23.1%
D 36
 
0.9%

Length

2024-05-11T14:41:20.382754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:20.837043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2901
75.9%
u 884
 
23.1%
d 36
 
0.9%
Distinct862
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T14:41:21.010630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:41:21.258965image/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 size30.0 KiB
일반미용업
2819 
피부미용업
551 
네일아트업
338 
메이크업업
 
92
기타
 
20

Length

Max length6
Median length5
Mean length4.984559
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2819
73.8%
피부미용업 551
 
14.4%
네일아트업 338
 
8.8%
메이크업업 92
 
2.4%
기타 20
 
0.5%
미용업 기타 1
 
< 0.1%

Length

2024-05-11T14:41:21.496718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:21.720829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2819
73.8%
피부미용업 551
 
14.4%
네일아트업 338
 
8.8%
메이크업업 92
 
2.4%
기타 21
 
0.5%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1841
Distinct (%)50.3%
Missing161
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean191673.62
Minimum189549.85
Maximum207735.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:21.912304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189549.85
5-th percentile190176.23
Q1190950.13
median191514.91
Q3192221.33
95-th percentile193798.95
Maximum207735.68
Range18185.834
Interquartile range (IQR)1271.2052

Descriptive statistics

Standard deviation1047.8863
Coefficient of variation (CV)0.0054670347
Kurtosis14.668452
Mean191673.62
Median Absolute Deviation (MAD)614.65196
Skewness1.4941006
Sum7.0152544 × 108
Variance1098065.7
MonotonicityNot monotonic
2024-05-11T14:41:22.155833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193844.169062846 30
 
0.8%
193469.554731741 23
 
0.6%
191586.660635006 21
 
0.5%
191768.875865987 21
 
0.5%
193798.948251805 20
 
0.5%
193209.283508484 19
 
0.5%
190394.571819262 19
 
0.5%
191362.376780507 19
 
0.5%
191186.037189337 19
 
0.5%
191025.411208067 18
 
0.5%
Other values (1831) 3451
90.3%
(Missing) 161
 
4.2%
ValueCountFrequency (%)
189549.847307536 2
 
0.1%
189570.401236233 1
 
< 0.1%
189574.962072527 9
0.2%
189586.236800721 2
 
0.1%
189600.906705305 2
 
0.1%
189617.235259629 1
 
< 0.1%
189635.487139725 1
 
< 0.1%
189653.829218246 1
 
< 0.1%
189659.894354143 1
 
< 0.1%
189669.393790716 2
 
0.1%
ValueCountFrequency (%)
207735.681798 1
 
< 0.1%
194632.526367463 1
 
< 0.1%
194592.276750438 2
 
0.1%
194561.746032498 5
0.1%
194504.656267957 4
 
0.1%
194370.32715363 10
0.3%
194124.497455922 12
0.3%
194084.264051974 6
0.2%
194056.681860495 2
 
0.1%
194037.750337785 2
 
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING  SKEWED 

Distinct1841
Distinct (%)50.3%
Missing161
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean445618.31
Minimum300566.97
Maximum448975.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:22.369834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300566.97
5-th percentile443175.68
Q1444397.28
median445789.47
Q3446747.1
95-th percentile447937.94
Maximum448975.88
Range148408.91
Interquartile range (IQR)2349.8168

Descriptive statistics

Standard deviation2815.1544
Coefficient of variation (CV)0.0063174119
Kurtosis1926.5763
Mean445618.31
Median Absolute Deviation (MAD)1089.9176
Skewness-37.413319
Sum1.630963 × 109
Variance7925094.4
MonotonicityNot monotonic
2024-05-11T14:41:22.590816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446511.783285705 30
 
0.8%
446508.068667777 23
 
0.6%
446798.452967557 21
 
0.5%
446921.869148871 21
 
0.5%
446579.652565611 20
 
0.5%
446474.956573488 19
 
0.5%
448656.726986041 19
 
0.5%
447750.192654125 19
 
0.5%
447739.896956998 19
 
0.5%
448084.396254976 18
 
0.5%
Other values (1831) 3451
90.3%
(Missing) 161
 
4.2%
ValueCountFrequency (%)
300566.974467 1
 
< 0.1%
442621.787911877 1
 
< 0.1%
442649.873938101 1
 
< 0.1%
442663.748373343 2
 
0.1%
442710.662421803 6
0.2%
442715.143228546 1
 
< 0.1%
442715.677609564 1
 
< 0.1%
442717.639571997 1
 
< 0.1%
442756.531513655 1
 
< 0.1%
442829.231926853 1
 
< 0.1%
ValueCountFrequency (%)
448975.881003491 1
 
< 0.1%
448937.07552692 1
 
< 0.1%
448896.727242794 1
 
< 0.1%
448848.126659455 1
 
< 0.1%
448838.238448329 1
 
< 0.1%
448681.552913108 2
 
0.1%
448656.726986041 19
0.5%
448598.231636157 1
 
< 0.1%
448566.018530834 4
 
0.1%
448549.637439388 1
 
< 0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
미용업
1361 
일반미용업
966 
<NA>
683 
피부미용업
356 
종합미용업
211 
Other values (11)
244 

Length

Max length23
Median length16
Mean length4.5056268
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1361
35.6%
일반미용업 966
25.3%
<NA> 683
17.9%
피부미용업 356
 
9.3%
종합미용업 211
 
5.5%
네일미용업 99
 
2.6%
피부미용업, 네일미용업 34
 
0.9%
네일미용업, 화장ㆍ분장 미용업 27
 
0.7%
일반미용업, 네일미용업 15
 
0.4%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 15
 
0.4%
Other values (6) 54
 
1.4%

Length

2024-05-11T14:41:22.811824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1453
35.6%
일반미용업 1013
24.8%
na 683
16.8%
피부미용업 421
 
10.3%
종합미용업 211
 
5.2%
네일미용업 204
 
5.0%
화장ㆍ분장 92
 
2.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)0.6%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.33046526
Minimum0
Maximum41
Zeros2821
Zeros (%)73.8%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:22.999809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6168056
Coefficient of variation (CV)4.8925131
Kurtosis251.4999
Mean0.33046526
Median Absolute Deviation (MAD)0
Skewness12.615536
Sum1037
Variance2.6140604
MonotonicityNot monotonic
2024-05-11T14:41:23.178666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 2821
73.8%
1 115
 
3.0%
3 60
 
1.6%
2 49
 
1.3%
4 45
 
1.2%
5 16
 
0.4%
6 5
 
0.1%
11 4
 
0.1%
7 4
 
0.1%
12 3
 
0.1%
Other values (9) 16
 
0.4%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 2821
73.8%
1 115
 
3.0%
2 49
 
1.3%
3 60
 
1.6%
4 45
 
1.2%
5 16
 
0.4%
6 5
 
0.1%
7 4
 
0.1%
8 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
39 1
 
< 0.1%
16 1
 
< 0.1%
15 2
0.1%
14 2
0.1%
13 3
0.1%
12 3
0.1%
11 4
0.1%
10 2
0.1%
9 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)0.2%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.078075207
Minimum0
Maximum103
Zeros3044
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:23.333163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum103
Range103
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9254409
Coefficient of variation (CV)24.661362
Kurtosis2620.8255
Mean0.078075207
Median Absolute Deviation (MAD)0
Skewness49.823917
Sum245
Variance3.7073228
MonotonicityNot monotonic
2024-05-11T14:41:23.485623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3044
79.7%
1 83
 
2.2%
4 4
 
0.1%
2 4
 
0.1%
6 1
 
< 0.1%
103 1
 
< 0.1%
29 1
 
< 0.1%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 3044
79.7%
1 83
 
2.2%
2 4
 
0.1%
4 4
 
0.1%
6 1
 
< 0.1%
29 1
 
< 0.1%
103 1
 
< 0.1%
ValueCountFrequency (%)
103 1
 
< 0.1%
29 1
 
< 0.1%
6 1
 
< 0.1%
4 4
 
0.1%
2 4
 
0.1%
1 83
 
2.2%
0 3044
79.7%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)0.4%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.60261313
Minimum0
Maximum121
Zeros2111
Zeros (%)55.2%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:23.626565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum121
Range121
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4083757
Coefficient of variation (CV)3.9965537
Kurtosis1992.4927
Mean0.60261313
Median Absolute Deviation (MAD)0
Skewness40.135659
Sum1891
Variance5.8002737
MonotonicityNot monotonic
2024-05-11T14:41:23.776782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 2111
55.2%
1 618
 
16.2%
2 251
 
6.6%
3 73
 
1.9%
4 39
 
1.0%
5 22
 
0.6%
6 11
 
0.3%
7 6
 
0.2%
9 3
 
0.1%
10 1
 
< 0.1%
Other values (3) 3
 
0.1%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 2111
55.2%
1 618
 
16.2%
2 251
 
6.6%
3 73
 
1.9%
4 39
 
1.0%
5 22
 
0.6%
6 11
 
0.3%
7 6
 
0.2%
8 1
 
< 0.1%
9 3
 
0.1%
ValueCountFrequency (%)
121 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 3
 
0.1%
8 1
 
< 0.1%
7 6
 
0.2%
6 11
 
0.3%
5 22
 
0.6%
4 39
1.0%
3 73
1.9%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct32
Distinct (%)1.0%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean1.945188
Minimum0
Maximum601
Zeros2495
Zeros (%)65.3%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:23.943008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum601
Range601
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.351653
Coefficient of variation (CV)10.976652
Kurtosis356.29571
Mean1.945188
Median Absolute Deviation (MAD)0
Skewness17.284284
Sum6104
Variance455.89307
MonotonicityNot monotonic
2024-05-11T14:41:24.116432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 2495
65.3%
1 389
 
10.2%
2 148
 
3.9%
3 34
 
0.9%
4 20
 
0.5%
5 9
 
0.2%
6 6
 
0.2%
101 5
 
0.1%
7 4
 
0.1%
8 3
 
0.1%
Other values (22) 25
 
0.7%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 2495
65.3%
1 389
 
10.2%
2 148
 
3.9%
3 34
 
0.9%
4 20
 
0.5%
5 9
 
0.2%
6 6
 
0.2%
7 4
 
0.1%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
601 1
< 0.1%
410 1
< 0.1%
407 1
< 0.1%
402 1
< 0.1%
303 1
< 0.1%
210 1
< 0.1%
209 1
< 0.1%
208 1
< 0.1%
207 1
< 0.1%
206 1
< 0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3045 
<NA>
683 
1
 
77
2
 
14
108
 
1

Length

Max length4
Median length1
Mean length1.5367705
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3045
79.7%
<NA> 683
 
17.9%
1 77
 
2.0%
2 14
 
0.4%
108 1
 
< 0.1%
3 1
 
< 0.1%

Length

2024-05-11T14:41:24.278528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:24.482430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3045
79.7%
na 683
 
17.9%
1 77
 
2.0%
2 14
 
0.4%
108 1
 
< 0.1%
3 1
 
< 0.1%

사용끝지하층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct6
Distinct (%)0.2%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.10006373
Minimum0
Maximum117
Zeros3084
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:24.629453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum117
Range117
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9277007
Coefficient of variation (CV)29.258359
Kurtosis1469.4216
Mean0.10006373
Median Absolute Deviation (MAD)0
Skewness37.950841
Sum314
Variance8.5714313
MonotonicityNot monotonic
2024-05-11T14:41:24.763411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 3084
80.7%
1 45
 
1.2%
2 6
 
0.2%
29 1
 
< 0.1%
117 1
 
< 0.1%
111 1
 
< 0.1%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 3084
80.7%
1 45
 
1.2%
2 6
 
0.2%
29 1
 
< 0.1%
111 1
 
< 0.1%
117 1
 
< 0.1%
ValueCountFrequency (%)
117 1
 
< 0.1%
111 1
 
< 0.1%
29 1
 
< 0.1%
2 6
 
0.2%
1 45
 
1.2%
0 3084
80.7%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3138 
<NA>
683 

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3138
82.1%
<NA> 683
 
17.9%

Length

2024-05-11T14:41:24.927118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:25.061223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
82.1%
na 683
 
17.9%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3138 
<NA>
683 

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3138
82.1%
<NA> 683
 
17.9%

Length

2024-05-11T14:41:25.187592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:25.312362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
82.1%
na 683
 
17.9%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3138 
<NA>
683 

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3138
82.1%
<NA> 683
 
17.9%

Length

2024-05-11T14:41:25.451621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:25.565073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
82.1%
na 683
 
17.9%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing720
Missing (%)18.8%
Memory size7.6 KiB
False
3101 
(Missing)
720 
ValueCountFrequency (%)
False 3101
81.2%
(Missing) 720
 
18.8%
2024-05-11T14:41:25.653867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct24
Distinct (%)0.8%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean3.3362014
Minimum0
Maximum194
Zeros395
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:25.742290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum194
Range194
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.1494363
Coefficient of variation (CV)1.2437607
Kurtosis1423.9477
Mean3.3362014
Median Absolute Deviation (MAD)1
Skewness31.489742
Sum10469
Variance17.217822
MonotonicityNot monotonic
2024-05-11T14:41:25.889019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 1203
31.5%
4 517
13.5%
2 448
 
11.7%
0 395
 
10.3%
5 200
 
5.2%
6 123
 
3.2%
8 61
 
1.6%
1 53
 
1.4%
7 40
 
1.0%
9 25
 
0.7%
Other values (14) 73
 
1.9%
(Missing) 683
17.9%
ValueCountFrequency (%)
0 395
 
10.3%
1 53
 
1.4%
2 448
 
11.7%
3 1203
31.5%
4 517
13.5%
5 200
 
5.2%
6 123
 
3.2%
7 40
 
1.0%
8 61
 
1.6%
9 25
 
0.7%
ValueCountFrequency (%)
194 1
 
< 0.1%
41 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 3
0.1%
16 2
 
0.1%
15 2
 
0.1%
14 5
0.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3821
Missing (%)100.0%
Memory size33.7 KiB

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
<NA>
3817 
2
 
4

Length

Max length4
Median length4
Mean length3.9968595
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> 3817
99.9%
2 4
 
0.1%

Length

2024-05-11T14:41:26.056945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:26.228780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3817
99.9%
2 4
 
0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
<NA>
3068 
임대
746 
자가
 
7

Length

Max length4
Median length4
Mean length3.6058623
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> 3068
80.3%
임대 746
 
19.5%
자가 7
 
0.2%

Length

2024-05-11T14:41:26.371999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:26.556301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3068
80.3%
임대 746
 
19.5%
자가 7
 
0.2%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3138 
<NA>
683 

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3138
82.1%
<NA> 683
 
17.9%

Length

2024-05-11T14:41:26.752622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:26.892581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
82.1%
na 683
 
17.9%

여성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3068 
<NA>
683 
1
 
65
3
 
3
2
 
2

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3068
80.3%
<NA> 683
 
17.9%
1 65
 
1.7%
3 3
 
0.1%
2 2
 
0.1%

Length

2024-05-11T14:41:27.059750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:27.334859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3068
80.3%
na 683
 
17.9%
1 65
 
1.7%
3 3
 
0.1%
2 2
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3129 
<NA>
683 
1
 
7
3
 
1
2
 
1

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 3129
81.9%
<NA> 683
 
17.9%
1 7
 
0.2%
3 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-11T14:41:27.560707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:27.696493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3129
81.9%
na 683
 
17.9%
1 7
 
0.2%
3 1
 
< 0.1%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
0
3138 
<NA>
683 

Length

Max length4
Median length1
Mean length1.5362471
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 3138
82.1%
<NA> 683
 
17.9%

Length

2024-05-11T14:41:27.843580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:41:27.956874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3138
82.1%
na 683
 
17.9%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)0.4%
Missing683
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.50924156
Minimum0
Maximum15
Zeros2664
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size33.7 KiB
2024-05-11T14:41:28.056350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5497521
Coefficient of variation (CV)3.0432554
Kurtosis18.608115
Mean0.50924156
Median Absolute Deviation (MAD)0
Skewness4.0229984
Sum1598
Variance2.4017316
MonotonicityNot monotonic
2024-05-11T14:41:28.245006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 2664
69.7%
2 123
 
3.2%
1 108
 
2.8%
3 81
 
2.1%
4 51
 
1.3%
5 30
 
0.8%
6 25
 
0.7%
8 18
 
0.5%
7 11
 
0.3%
10 9
 
0.2%
Other values (3) 18
 
0.5%
(Missing) 683
 
17.9%
ValueCountFrequency (%)
0 2664
69.7%
1 108
 
2.8%
2 123
 
3.2%
3 81
 
2.1%
4 51
 
1.3%
5 30
 
0.8%
6 25
 
0.7%
7 11
 
0.3%
8 18
 
0.5%
9 8
 
0.2%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 9
 
0.2%
10 9
 
0.2%
9 8
 
0.2%
8 18
 
0.5%
7 11
 
0.3%
6 25
 
0.7%
5 30
 
0.8%
4 51
1.3%
3 81
2.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing683
Missing (%)17.9%
Memory size7.6 KiB
False
3137 
True
 
1
(Missing)
683 
ValueCountFrequency (%)
False 3137
82.1%
True 1
 
< 0.1%
(Missing) 683
 
17.9%
2024-05-11T14:41:28.375067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031800003180000-204-1930-0125419300720<NA>3폐업2폐업20000630<NA><NA><NA>020676590227.62150045서울특별시 영등포구 당산동5가 11-1번지<NA><NA>강남2003-02-11 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N4<NA><NA><NA><NA>00000N
131800003180000-204-1942-0081419420411<NA>3폐업2폐업20030225<NA><NA><NA>020834095216.79150815서울특별시 영등포구 대림동 728-2번지<NA><NA>이숙경헤어아트2003-03-24 00:00:00I2018-08-31 23:59:59.0일반미용업190783.529277443667.336772미용업000000000N3<NA><NA><NA><NA>00000N
231800003180000-204-1959-0102019590809<NA>3폐업2폐업19961228<NA><NA><NA>020843903117.16150050서울특별시 영등포구 신길동 0-0번지<NA><NA>2001-08-02 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N3<NA><NA><NA><NA>00000N
331800003180000-204-1962-0102819620208<NA>3폐업2폐업20010109<NA><NA><NA>020846987212.48150841서울특별시 영등포구 신길동 284-1번지<NA><NA>2002-01-14 00:00:00I2018-08-31 23:59:59.0일반미용업191834.607339445122.633063미용업000000000N2<NA><NA><NA><NA>00000N
431800003180000-204-1964-0105519641130<NA>3폐업2폐업20111201<NA><NA><NA>020842352125.8150841서울특별시 영등포구 신길동 220번지 2층48호<NA><NA>카네숀2011-05-30 14:58:38I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N2<NA><NA><NA><NA>00000N
531800003180000-204-1965-0130319650326<NA>3폐업2폐업20000113<NA><NA><NA>020678938712.0150034서울특별시 영등포구 영등포동4가 151-0번지<NA><NA>선화2003-02-06 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N2<NA><NA><NA><NA>00000N
631800003180000-204-1968-0121319680415<NA>3폐업2폐업20110208<NA><NA><NA>020849482318.0150829서울특별시 영등포구 도림동 81-4번지 2층<NA><NA>태양2005-06-30 00:00:00I2018-08-31 23:59:59.0일반미용업191716.327815445272.035758미용업000000000N2<NA><NA><NA><NA>00000N
731800003180000-204-1968-0127319681010<NA>3폐업2폐업20110708<NA><NA><NA>022677651315.42150806서울특별시 영등포구 당산동4가 39-2번지<NA><NA>중앙2005-06-28 00:00:00I2018-08-31 23:59:59.0일반미용업190891.90132447421.456334미용업000000000N2<NA><NA><NA><NA>00000N
831800003180000-204-1968-0135219680921<NA>3폐업2폐업20100811<NA><NA><NA>022637104033.0150033서울특별시 영등포구 영등포동3가 8번지 208호<NA><NA>Leega 이가 미용실2009-04-01 10:51:41I2018-08-31 23:59:59.0일반미용업191616.285409446238.776341미용업000000000N4<NA><NA><NA><NA>00000N
931800003180000-204-1970-0097919700924<NA>3폐업2폐업19990710<NA><NA><NA>0211.65150839서울특별시 영등포구 신길동 131-0번지<NA><NA>최윤진머리방1999-07-10 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N3<NA><NA><NA><NA>00000N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
381131800003180000-226-2020-000022020-02-04<NA>1영업/정상1영업<NA><NA><NA><NA>026264535127.28150-860서울특별시 영등포구 신길동 4780 보라매 경남아너스빌 상가동 108호서울특별시 영등포구 여의대방로 25, 상가동 108호 (신길동, 보라매 경남아너스빌)7437율네일2024-02-26 15:03:14I2023-12-01 22:08:00.0네일아트업191982.734798443643.389854<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
381231800003180000-226-2021-0000120210520<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.0150887서울특별시 영등포구 여의도동 37 아일렉스 비13호서울특별시 영등포구 의사당대로 108, 아일렉스 B1층 비13호 (여의도동)7322늘, 솜네일2021-05-20 11:33:36I2021-05-22 00:22:55.0네일아트업193209.283508446474.956573피부미용업, 네일미용업, 화장ㆍ분장 미용업000010000N5<NA><NA><NA>임대00002N
381331800003180000-226-2021-0000220210604<NA>1영업/정상1영업<NA><NA><NA><NA><NA>30.51150101서울특별시 영등포구 양평동1가 247 영등포 중흥 S-CLASS 111호서울특별시 영등포구 선유서로24길 6, 111호 (양평동1가, 영등포 중흥 S-CLASS)7273아뜰리에 제이2021-06-04 16:06:29I2021-06-06 00:23:02.0네일아트업189877.032334446801.782257피부미용업, 네일미용업, 화장ㆍ분장 미용업001000000N5<NA><NA><NA><NA>00002N
381431800003180000-226-2021-0000320210924<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.1150815서울특별시 영등포구 대림동 718-4 207호서울특별시 영등포구 대림로27가길 3, 207호 (대림동)7413네일고와요2021-09-24 09:46:07I2021-09-26 00:22:48.0네일아트업190975.540887443689.545422피부미용업, 네일미용업, 화장ㆍ분장 미용업002000000N5<NA><NA><NA>임대00001N
381531800003180000-226-2022-0000120220302<NA>1영업/정상1영업<NA><NA><NA><NA><NA>13.17150860서울특별시 영등포구 신길동 4668 KT대방지점서울특별시 영등포구 신길로 28, KT대방지점 4층 (신길동)7437미니네일2022-03-02 11:42:07I2022-03-04 00:22:37.0네일아트업191892.606363443679.272217피부미용업, 네일미용업, 화장ㆍ분장 미용업004000000N4<NA><NA><NA>임대00000N
381631800003180000-226-2022-0000220220321<NA>1영업/정상1영업<NA><NA><NA><NA><NA>39.87150751서울특별시 영등포구 양평동5가 76 양평동한신아파트 109동 19,20호서울특별시 영등포구 양평로24길 9, 109동 상가1층 19,20호 (양평동5가, 양평동한신아파트)7204살롱 드 디에스2022-03-21 14:19:48I2022-03-23 00:22:45.0피부미용업190394.571819448656.726986피부미용업, 네일미용업, 화장ㆍ분장 미용업001000000N2<NA><NA><NA>임대00002N
381731800003180000-226-2022-0000320220720<NA>1영업/정상1영업<NA><NA><NA><NA><NA>54.0150034서울특별시 영등포구 영등포동4가 425-41서울특별시 영등포구 경인로 831, 2층 (영등포동4가)7305바른왁싱 영등포점2022-07-20 14:00:55I2021-12-06 22:02:00.0피부미용업191551.723487445992.231392<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
381831800003180000-226-2022-000042022-08-31<NA>1영업/정상1영업<NA><NA><NA><NA><NA>19.5150-750서울특별시 영등포구 당산동1가 16-1 진로아파트 상가동 114호서울특별시 영등포구 당산로 68, 상가동 114호 (당산동1가, 진로아파트)7292더예쁨2023-02-03 16:10:19I2022-12-02 00:05:00.0네일아트업190766.238732446496.183037<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
381931800003180000-226-2023-000012023-02-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>21.0150-887서울특별시 영등포구 여의도동 37 아일렉스 111호서울특별시 영등포구 의사당대로 108, 아일렉스 1층 111호 (여의도동)7322르블랑네일2023-05-17 16:19:20I2022-12-04 23:09:00.0네일아트업193209.283508446474.956573<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
382031800003180000-226-2023-000022023-05-11<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.53150-858서울특별시 영등포구 신길동 3842서울특별시 영등포구 신풍로 48-1, 1층 (신길동)7430브리드네일2023-08-23 17:17:55I2022-12-07 22:05:00.0네일아트업192095.334136444196.003652<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>