Overview

Dataset statistics

Number of variables47
Number of observations3751
Missing cells42153
Missing cells (%)23.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 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
Author중랑구
URLhttps://data.seoul.go.kr/dataList/OA-17908/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 is highly imbalanced (55.5%)Imbalance
사용시작지하층 is highly imbalanced (50.1%)Imbalance
사용끝지하층 is highly imbalanced (78.4%)Imbalance
발한실여부 is highly imbalanced (99.6%)Imbalance
조건부허가종료일자 is highly imbalanced (98.8%)Imbalance
건물소유구분명 is highly imbalanced (67.1%)Imbalance
남성종사자수 is highly imbalanced (68.7%)Imbalance
다중이용업소여부 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 3751 (100.0%) missing valuesMissing
폐업일자 has 1240 (33.1%) missing valuesMissing
휴업시작일자 has 3751 (100.0%) missing valuesMissing
휴업종료일자 has 3751 (100.0%) missing valuesMissing
재개업일자 has 3751 (100.0%) missing valuesMissing
전화번호 has 1567 (41.8%) missing valuesMissing
도로명주소 has 1612 (43.0%) missing valuesMissing
도로명우편번호 has 1624 (43.3%) missing valuesMissing
좌표정보(X) has 80 (2.1%) missing valuesMissing
좌표정보(Y) has 80 (2.1%) missing valuesMissing
건물지상층수 has 1304 (34.8%) missing valuesMissing
건물지하층수 has 1341 (35.8%) missing valuesMissing
사용시작지상층 has 1202 (32.0%) missing valuesMissing
사용끝지상층 has 2446 (65.2%) missing valuesMissing
발한실여부 has 630 (16.8%) missing valuesMissing
좌석수 has 568 (15.1%) missing valuesMissing
조건부허가신고사유 has 3751 (100.0%) missing valuesMissing
조건부허가시작일자 has 3751 (100.0%) missing valuesMissing
여성종사자수 has 2993 (79.8%) missing valuesMissing
침대수 has 2421 (64.5%) missing valuesMissing
다중이용업소여부 has 538 (14.3%) missing valuesMissing
사용시작지상층 is highly skewed (γ1 = 23.90891603)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 45 (1.2%) zerosZeros
건물지상층수 has 2179 (58.1%) zerosZeros
건물지하층수 has 2351 (62.7%) zerosZeros
사용시작지상층 has 1039 (27.7%) zerosZeros
사용끝지상층 has 145 (3.9%) zerosZeros
좌석수 has 99 (2.6%) zerosZeros
여성종사자수 has 445 (11.9%) zerosZeros
침대수 has 1049 (28.0%) zerosZeros

Reproduction

Analysis started2024-05-11 08:11:40.052750
Analysis finished2024-05-11 08:11:41.474233
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
3060000
3751 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3060000
2nd row3060000
3rd row3060000
4th row3060000
5th row3060000

Common Values

ValueCountFrequency (%)
3060000 3751
100.0%

Length

2024-05-11T17:11:41.532708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:41.623960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3060000 3751
100.0%

관리번호
Text

UNIQUE 

Distinct3751
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
2024-05-11T17:11:41.787789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters82522
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

Unique3751 ?
Unique (%)100.0%

Sample

1st row3060000-204-1900-01190
2nd row3060000-204-1919-01344
3rd row3060000-204-1969-00713
4th row3060000-204-1970-00817
5th row3060000-204-1970-00982
ValueCountFrequency (%)
3060000-204-1900-01190 1
 
< 0.1%
3060000-211-2017-00001 1
 
< 0.1%
3060000-211-2016-00055 1
 
< 0.1%
3060000-211-2017-00016 1
 
< 0.1%
3060000-211-2016-00056 1
 
< 0.1%
3060000-211-2016-00057 1
 
< 0.1%
3060000-211-2016-00058 1
 
< 0.1%
3060000-211-2016-00059 1
 
< 0.1%
3060000-211-2016-00060 1
 
< 0.1%
3060000-211-2016-00061 1
 
< 0.1%
Other values (3741) 3741
99.7%
2024-05-11T17:11:42.103917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35140
42.6%
- 11253
 
13.6%
2 8717
 
10.6%
1 7366
 
8.9%
3 5227
 
6.3%
6 4866
 
5.9%
4 3127
 
3.8%
9 3052
 
3.7%
8 1388
 
1.7%
5 1227
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71269
86.4%
Dash Punctuation 11253
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35140
49.3%
2 8717
 
12.2%
1 7366
 
10.3%
3 5227
 
7.3%
6 4866
 
6.8%
4 3127
 
4.4%
9 3052
 
4.3%
8 1388
 
1.9%
5 1227
 
1.7%
7 1159
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 11253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82522
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35140
42.6%
- 11253
 
13.6%
2 8717
 
10.6%
1 7366
 
8.9%
3 5227
 
6.3%
6 4866
 
5.9%
4 3127
 
3.8%
9 3052
 
3.7%
8 1388
 
1.7%
5 1227
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82522
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35140
42.6%
- 11253
 
13.6%
2 8717
 
10.6%
1 7366
 
8.9%
3 5227
 
6.3%
6 4866
 
5.9%
4 3127
 
3.8%
9 3052
 
3.7%
8 1388
 
1.7%
5 1227
 
1.5%
Distinct2817
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
Minimum1900-07-13 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T17:11:42.277139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:11:42.449805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
3
2511 
1
1240 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 2511
66.9%
1 1240
33.1%

Length

2024-05-11T17:11:42.596791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:42.712188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2511
66.9%
1 1240
33.1%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
폐업
2511 
영업/정상
1240 

Length

Max length5
Median length2
Mean length2.9917355
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row폐업
3rd row영업/정상
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 2511
66.9%
영업/정상 1240
33.1%

Length

2024-05-11T17:11:42.821243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:42.929453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2511
66.9%
영업/정상 1240
33.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
2
2511 
1
1240 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 2511
66.9%
1 1240
33.1%

Length

2024-05-11T17:11:43.027658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:43.126727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2511
66.9%
1 1240
33.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
폐업
2511 
영업
1240 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2511
66.9%
영업 1240
33.1%

Length

2024-05-11T17:11:43.227447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:43.330300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2511
66.9%
영업 1240
33.1%

폐업일자
Date

MISSING 

Distinct1865
Distinct (%)74.3%
Missing1240
Missing (%)33.1%
Memory size29.4 KiB
Minimum1992-01-27 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T17:11:43.436816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:11:43.570738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB

전화번호
Text

MISSING 

Distinct2010
Distinct (%)92.0%
Missing1567
Missing (%)41.8%
Memory size29.4 KiB
2024-05-11T17:11:43.813670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.9565018
Min length2

Characters and Unicode

Total characters21745
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

Unique1887 ?
Unique (%)86.4%

Sample

1st row0204947535
2nd row0204374126
3rd row0204957337
4th row02 4968022
5th row02 4340647
ValueCountFrequency (%)
02 1180
32.9%
0200000000 16
 
0.4%
02432 12
 
0.3%
02439 11
 
0.3%
0 10
 
0.3%
02433 10
 
0.3%
02491 10
 
0.3%
493 9
 
0.3%
02435 9
 
0.3%
02493 9
 
0.3%
Other values (2051) 2316
64.5%
2024-05-11T17:11:44.224535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3902
17.9%
2 3796
17.5%
4 2584
11.9%
3 2098
9.6%
9 1870
8.6%
1571
7.2%
7 1412
 
6.5%
5 1213
 
5.6%
8 1136
 
5.2%
6 1110
 
5.1%
Other values (2) 1053
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20173
92.8%
Space Separator 1571
 
7.2%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3902
19.3%
2 3796
18.8%
4 2584
12.8%
3 2098
10.4%
9 1870
9.3%
7 1412
 
7.0%
5 1213
 
6.0%
8 1136
 
5.6%
6 1110
 
5.5%
1 1052
 
5.2%
Space Separator
ValueCountFrequency (%)
1571
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3902
17.9%
2 3796
17.5%
4 2584
11.9%
3 2098
9.6%
9 1870
8.6%
1571
7.2%
7 1412
 
6.5%
5 1213
 
5.6%
8 1136
 
5.2%
6 1110
 
5.1%
Other values (2) 1053
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3902
17.9%
2 3796
17.5%
4 2584
11.9%
3 2098
9.6%
9 1870
8.6%
1571
7.2%
7 1412
 
6.5%
5 1213
 
5.6%
8 1136
 
5.2%
6 1110
 
5.1%
Other values (2) 1053
 
4.8%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1602
Distinct (%)42.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean33.817957
Minimum0
Maximum388.88
Zeros45
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:44.400758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.2545
Q119.2
median26.4
Q336.3
95-th percentile82.5
Maximum388.88
Range388.88
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation27.384495
Coefficient of variation (CV)0.80976196
Kurtosis19.657492
Mean33.817957
Median Absolute Deviation (MAD)8.1
Skewness3.4836236
Sum126817.34
Variance749.91059
MonotonicityNot monotonic
2024-05-11T17:11:44.565200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 180
 
4.8%
26.4 142
 
3.8%
23.1 72
 
1.9%
30.0 71
 
1.9%
19.8 61
 
1.6%
16.5 59
 
1.6%
25.0 49
 
1.3%
0.0 45
 
1.2%
20.0 43
 
1.1%
24.0 37
 
1.0%
Other values (1592) 2991
79.7%
ValueCountFrequency (%)
0.0 45
1.2%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.61 1
 
< 0.1%
4.37 1
 
< 0.1%
4.6 1
 
< 0.1%
4.9 1
 
< 0.1%
5.0 1
 
< 0.1%
5.33 1
 
< 0.1%
6.0 1
 
< 0.1%
ValueCountFrequency (%)
388.88 1
 
< 0.1%
243.62 1
 
< 0.1%
240.0 1
 
< 0.1%
233.91 1
 
< 0.1%
231.0 4
0.1%
223.08 2
0.1%
217.65 1
 
< 0.1%
208.8 1
 
< 0.1%
198.0 1
 
< 0.1%
183.39 1
 
< 0.1%
Distinct166
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
2024-05-11T17:11:44.863392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0941082
Min length6

Characters and Unicode

Total characters22859
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

Unique23 ?
Unique (%)0.6%

Sample

1st row131827
2nd row131859
3rd row131827
4th row131865
5th row131814
ValueCountFrequency (%)
131848 125
 
3.3%
131831 110
 
2.9%
131828 110
 
2.9%
131809 100
 
2.7%
131816 90
 
2.4%
131813 89
 
2.4%
131881 89
 
2.4%
131859 88
 
2.3%
131821 84
 
2.2%
131802 83
 
2.2%
Other values (156) 2783
74.2%
2024-05-11T17:11:45.363347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8745
38.3%
3 4384
19.2%
8 4291
18.8%
2 1082
 
4.7%
0 955
 
4.2%
7 787
 
3.4%
6 765
 
3.3%
5 681
 
3.0%
4 413
 
1.8%
9 403
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22506
98.5%
Dash Punctuation 353
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8745
38.9%
3 4384
19.5%
8 4291
19.1%
2 1082
 
4.8%
0 955
 
4.2%
7 787
 
3.5%
6 765
 
3.4%
5 681
 
3.0%
4 413
 
1.8%
9 403
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22859
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8745
38.3%
3 4384
19.2%
8 4291
18.8%
2 1082
 
4.7%
0 955
 
4.2%
7 787
 
3.4%
6 765
 
3.3%
5 681
 
3.0%
4 413
 
1.8%
9 403
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22859
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8745
38.3%
3 4384
19.2%
8 4291
18.8%
2 1082
 
4.7%
0 955
 
4.2%
7 787
 
3.4%
6 765
 
3.3%
5 681
 
3.0%
4 413
 
1.8%
9 403
 
1.8%
Distinct2846
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
2024-05-11T17:11:45.669497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length23.609971
Min length15

Characters and Unicode

Total characters88561
Distinct characters341
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2271 ?
Unique (%)60.5%

Sample

1st row서울특별시 중랑구 면목동 653-2번지
2nd row서울특별시 중랑구 상봉동 105-0번지
3rd row서울특별시 중랑구 면목동 353-8
4th row서울특별시 중랑구 신내동 330-6번지
5th row서울특별시 중랑구 면목동 413-6번지 1층
ValueCountFrequency (%)
서울특별시 3751
22.9%
중랑구 3751
22.9%
면목동 1513
 
9.2%
망우동 542
 
3.3%
중화동 480
 
2.9%
묵동 479
 
2.9%
상봉동 436
 
2.7%
신내동 304
 
1.9%
지상1층 79
 
0.5%
1층 61
 
0.4%
Other values (3020) 4967
30.4%
2024-05-11T17:11:46.332494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15686
17.7%
4252
 
4.8%
3922
 
4.4%
3798
 
4.3%
3760
 
4.2%
3759
 
4.2%
1 3759
 
4.2%
3756
 
4.2%
3755
 
4.2%
3753
 
4.2%
Other values (331) 38361
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51176
57.8%
Decimal Number 18033
 
20.4%
Space Separator 15686
 
17.7%
Dash Punctuation 3409
 
3.8%
Uppercase Letter 100
 
0.1%
Other Punctuation 67
 
0.1%
Close Punctuation 33
 
< 0.1%
Open Punctuation 31
 
< 0.1%
Lowercase Letter 25
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4252
 
8.3%
3922
 
7.7%
3798
 
7.4%
3760
 
7.3%
3759
 
7.3%
3756
 
7.3%
3755
 
7.3%
3753
 
7.3%
3751
 
7.3%
2900
 
5.7%
Other values (280) 13770
26.9%
Uppercase Letter
ValueCountFrequency (%)
S 14
14.0%
B 12
12.0%
A 10
10.0%
E 9
9.0%
Y 8
 
8.0%
M 6
 
6.0%
O 6
 
6.0%
H 5
 
5.0%
R 4
 
4.0%
T 4
 
4.0%
Other values (9) 22
22.0%
Lowercase Letter
ValueCountFrequency (%)
a 7
28.0%
e 4
16.0%
s 4
16.0%
t 2
 
8.0%
c 1
 
4.0%
f 1
 
4.0%
k 1
 
4.0%
p 1
 
4.0%
n 1
 
4.0%
i 1
 
4.0%
Other values (2) 2
 
8.0%
Decimal Number
ValueCountFrequency (%)
1 3759
20.8%
2 2261
12.5%
3 2012
11.2%
4 1972
10.9%
5 1584
8.8%
0 1529
8.5%
6 1528
8.5%
7 1225
 
6.8%
8 1096
 
6.1%
9 1067
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 45
67.2%
& 16
 
23.9%
' 2
 
3.0%
. 2
 
3.0%
/ 2
 
3.0%
Space Separator
ValueCountFrequency (%)
15686
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3409
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51175
57.8%
Common 37260
42.1%
Latin 125
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4252
 
8.3%
3922
 
7.7%
3798
 
7.4%
3760
 
7.3%
3759
 
7.3%
3756
 
7.3%
3755
 
7.3%
3753
 
7.3%
3751
 
7.3%
2900
 
5.7%
Other values (279) 13769
26.9%
Latin
ValueCountFrequency (%)
S 14
 
11.2%
B 12
 
9.6%
A 10
 
8.0%
E 9
 
7.2%
Y 8
 
6.4%
a 7
 
5.6%
M 6
 
4.8%
O 6
 
4.8%
H 5
 
4.0%
e 4
 
3.2%
Other values (21) 44
35.2%
Common
ValueCountFrequency (%)
15686
42.1%
1 3759
 
10.1%
- 3409
 
9.1%
2 2261
 
6.1%
3 2012
 
5.4%
4 1972
 
5.3%
5 1584
 
4.3%
0 1529
 
4.1%
6 1528
 
4.1%
7 1225
 
3.3%
Other values (10) 2295
 
6.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51174
57.8%
ASCII 37385
42.2%
Compat Jamo 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15686
42.0%
1 3759
 
10.1%
- 3409
 
9.1%
2 2261
 
6.0%
3 2012
 
5.4%
4 1972
 
5.3%
5 1584
 
4.2%
0 1529
 
4.1%
6 1528
 
4.1%
7 1225
 
3.3%
Other values (41) 2420
 
6.5%
Hangul
ValueCountFrequency (%)
4252
 
8.3%
3922
 
7.7%
3798
 
7.4%
3760
 
7.3%
3759
 
7.3%
3756
 
7.3%
3755
 
7.3%
3753
 
7.3%
3751
 
7.3%
2900
 
5.7%
Other values (278) 13768
26.9%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct1953
Distinct (%)91.3%
Missing1612
Missing (%)43.0%
Memory size29.4 KiB
2024-05-11T17:11:46.630619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length56
Mean length31.375877
Min length21

Characters and Unicode

Total characters67113
Distinct characters350
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

Unique1788 ?
Unique (%)83.6%

Sample

1st row서울특별시 중랑구 사가정로46길 21-22 (면목동)
2nd row서울특별시 중랑구 사가정로50길 67 (면목동)
3rd row서울특별시 중랑구 면목로56가길 5 (면목동,1층)
4th row서울특별시 중랑구 망우로42길 10 (상봉동)
5th row서울특별시 중랑구 면목로39길 5 (면목동)
ValueCountFrequency (%)
서울특별시 2139
 
16.1%
중랑구 2139
 
16.1%
면목동 766
 
5.8%
1층 688
 
5.2%
상봉동 276
 
2.1%
묵동 274
 
2.1%
망우동 259
 
2.0%
중화동 232
 
1.7%
2층 203
 
1.5%
신내동 185
 
1.4%
Other values (1469) 6108
46.0%
2024-05-11T17:11:47.086248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11131
 
16.6%
1 2964
 
4.4%
2750
 
4.1%
2580
 
3.8%
2261
 
3.4%
2193
 
3.3%
( 2161
 
3.2%
) 2161
 
3.2%
2147
 
3.2%
2146
 
3.2%
Other values (340) 34619
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38401
57.2%
Space Separator 11131
 
16.6%
Decimal Number 10991
 
16.4%
Open Punctuation 2161
 
3.2%
Close Punctuation 2161
 
3.2%
Other Punctuation 1898
 
2.8%
Dash Punctuation 193
 
0.3%
Uppercase Letter 154
 
0.2%
Lowercase Letter 20
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2750
 
7.2%
2580
 
6.7%
2261
 
5.9%
2193
 
5.7%
2147
 
5.6%
2146
 
5.6%
2144
 
5.6%
2141
 
5.6%
2140
 
5.6%
2140
 
5.6%
Other values (291) 15759
41.0%
Uppercase Letter
ValueCountFrequency (%)
B 28
18.2%
A 24
15.6%
S 21
13.6%
C 14
9.1%
E 11
 
7.1%
Y 8
 
5.2%
O 7
 
4.5%
M 6
 
3.9%
D 5
 
3.2%
H 5
 
3.2%
Other values (9) 25
16.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
30.0%
s 4
20.0%
a 2
 
10.0%
n 1
 
5.0%
i 1
 
5.0%
y 1
 
5.0%
l 1
 
5.0%
k 1
 
5.0%
t 1
 
5.0%
f 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 2964
27.0%
2 1751
15.9%
3 1146
 
10.4%
0 1016
 
9.2%
4 921
 
8.4%
5 892
 
8.1%
9 629
 
5.7%
6 629
 
5.7%
7 548
 
5.0%
8 495
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 1874
98.7%
& 16
 
0.8%
. 7
 
0.4%
' 1
 
0.1%
Space Separator
ValueCountFrequency (%)
11131
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2161
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 193
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38400
57.2%
Common 28538
42.5%
Latin 174
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2750
 
7.2%
2580
 
6.7%
2261
 
5.9%
2193
 
5.7%
2147
 
5.6%
2146
 
5.6%
2144
 
5.6%
2141
 
5.6%
2140
 
5.6%
2140
 
5.6%
Other values (290) 15758
41.0%
Latin
ValueCountFrequency (%)
B 28
16.1%
A 24
13.8%
S 21
12.1%
C 14
 
8.0%
E 11
 
6.3%
Y 8
 
4.6%
O 7
 
4.0%
e 6
 
3.4%
M 6
 
3.4%
D 5
 
2.9%
Other values (20) 44
25.3%
Common
ValueCountFrequency (%)
11131
39.0%
1 2964
 
10.4%
( 2161
 
7.6%
) 2161
 
7.6%
, 1874
 
6.6%
2 1751
 
6.1%
3 1146
 
4.0%
0 1016
 
3.6%
4 921
 
3.2%
5 892
 
3.1%
Other values (9) 2521
 
8.8%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38400
57.2%
ASCII 28712
42.8%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11131
38.8%
1 2964
 
10.3%
( 2161
 
7.5%
) 2161
 
7.5%
, 1874
 
6.5%
2 1751
 
6.1%
3 1146
 
4.0%
0 1016
 
3.5%
4 921
 
3.2%
5 892
 
3.1%
Other values (39) 2695
 
9.4%
Hangul
ValueCountFrequency (%)
2750
 
7.2%
2580
 
6.7%
2261
 
5.9%
2193
 
5.7%
2147
 
5.6%
2146
 
5.6%
2144
 
5.6%
2141
 
5.6%
2140
 
5.6%
2140
 
5.6%
Other values (290) 15758
41.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct229
Distinct (%)10.8%
Missing1624
Missing (%)43.3%
Infinite0
Infinite (%)0.0%
Mean2129.5595
Minimum2002
Maximum2260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:47.259843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2012
Q12066
median2135
Q32199
95-th percentile2245
Maximum2260
Range258
Interquartile range (IQR)133

Descriptive statistics

Standard deviation75.87977
Coefficient of variation (CV)0.035631675
Kurtosis-1.2349151
Mean2129.5595
Median Absolute Deviation (MAD)67
Skewness-0.0028725693
Sum4529573
Variance5757.7395
MonotonicityNot monotonic
2024-05-11T17:11:47.405147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2228 44
 
1.2%
2076 33
 
0.9%
2237 32
 
0.9%
2169 30
 
0.8%
2249 28
 
0.7%
2148 28
 
0.7%
2244 28
 
0.7%
2163 27
 
0.7%
2034 26
 
0.7%
2024 26
 
0.7%
Other values (219) 1825
48.7%
(Missing) 1624
43.3%
ValueCountFrequency (%)
2002 5
 
0.1%
2003 10
0.3%
2004 10
0.3%
2005 11
0.3%
2006 8
0.2%
2007 19
0.5%
2008 7
 
0.2%
2009 12
0.3%
2010 14
0.4%
2011 6
 
0.2%
ValueCountFrequency (%)
2260 2
 
0.1%
2259 7
 
0.2%
2258 2
 
0.1%
2257 4
 
0.1%
2256 1
 
< 0.1%
2255 7
 
0.2%
2253 12
0.3%
2252 17
0.5%
2250 12
0.3%
2249 28
0.7%
Distinct3106
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
2024-05-11T17:11:47.747138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length5.4620101
Min length1

Characters and Unicode

Total characters20488
Distinct characters721
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2702 ?
Unique (%)72.0%

Sample

1st row수진미용실
2nd row3정희미용실
3rd row아폴로
4th row형제
5th row선희
ValueCountFrequency (%)
헤어 73
 
1.7%
hair 41
 
0.9%
네일 34
 
0.8%
미용실 27
 
0.6%
nail 18
 
0.4%
뷰티 16
 
0.4%
에이바헤어 13
 
0.3%
머리사랑 13
 
0.3%
12
 
0.3%
헤어살롱 10
 
0.2%
Other values (3215) 4136
94.1%
2024-05-11T17:11:48.214515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1507
 
7.4%
1447
 
7.1%
731
 
3.6%
643
 
3.1%
507
 
2.5%
484
 
2.4%
481
 
2.3%
407
 
2.0%
394
 
1.9%
245
 
1.2%
Other values (711) 13642
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17483
85.3%
Lowercase Letter 852
 
4.2%
Uppercase Letter 727
 
3.5%
Space Separator 643
 
3.1%
Close Punctuation 221
 
1.1%
Open Punctuation 221
 
1.1%
Other Punctuation 210
 
1.0%
Decimal Number 120
 
0.6%
Dash Punctuation 8
 
< 0.1%
Modifier Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1507
 
8.6%
1447
 
8.3%
731
 
4.2%
507
 
2.9%
484
 
2.8%
481
 
2.8%
407
 
2.3%
394
 
2.3%
245
 
1.4%
235
 
1.3%
Other values (632) 11045
63.2%
Uppercase Letter
ValueCountFrequency (%)
S 65
 
8.9%
A 63
 
8.7%
N 56
 
7.7%
H 55
 
7.6%
I 47
 
6.5%
J 46
 
6.3%
O 43
 
5.9%
B 40
 
5.5%
M 39
 
5.4%
L 37
 
5.1%
Other values (16) 236
32.5%
Lowercase Letter
ValueCountFrequency (%)
a 113
13.3%
i 100
11.7%
e 94
11.0%
o 67
 
7.9%
r 64
 
7.5%
n 59
 
6.9%
h 49
 
5.8%
l 48
 
5.6%
y 39
 
4.6%
s 38
 
4.5%
Other values (14) 181
21.2%
Other Punctuation
ValueCountFrequency (%)
? 56
26.7%
. 41
19.5%
& 40
19.0%
, 21
 
10.0%
# 21
 
10.0%
' 18
 
8.6%
: 8
 
3.8%
% 2
 
1.0%
; 1
 
0.5%
1
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 29
24.2%
0 29
24.2%
2 25
20.8%
9 9
 
7.5%
3 9
 
7.5%
7 6
 
5.0%
5 6
 
5.0%
8 3
 
2.5%
4 3
 
2.5%
6 1
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 220
99.5%
] 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 220
99.5%
[ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
643
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17458
85.2%
Latin 1579
 
7.7%
Common 1426
 
7.0%
Han 25
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1507
 
8.6%
1447
 
8.3%
731
 
4.2%
507
 
2.9%
484
 
2.8%
481
 
2.8%
407
 
2.3%
394
 
2.3%
245
 
1.4%
235
 
1.3%
Other values (621) 11020
63.1%
Latin
ValueCountFrequency (%)
a 113
 
7.2%
i 100
 
6.3%
e 94
 
6.0%
o 67
 
4.2%
S 65
 
4.1%
r 64
 
4.1%
A 63
 
4.0%
n 59
 
3.7%
N 56
 
3.5%
H 55
 
3.5%
Other values (40) 843
53.4%
Common
ValueCountFrequency (%)
643
45.1%
) 220
 
15.4%
( 220
 
15.4%
? 56
 
3.9%
. 41
 
2.9%
& 40
 
2.8%
1 29
 
2.0%
0 29
 
2.0%
2 25
 
1.8%
, 21
 
1.5%
Other values (19) 102
 
7.2%
Han
ValueCountFrequency (%)
14
56.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17455
85.2%
ASCII 3004
 
14.7%
CJK 24
 
0.1%
Compat Jamo 3
 
< 0.1%
None 1
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1507
 
8.6%
1447
 
8.3%
731
 
4.2%
507
 
2.9%
484
 
2.8%
481
 
2.8%
407
 
2.3%
394
 
2.3%
245
 
1.4%
235
 
1.3%
Other values (619) 11017
63.1%
ASCII
ValueCountFrequency (%)
643
21.4%
) 220
 
7.3%
( 220
 
7.3%
a 113
 
3.8%
i 100
 
3.3%
e 94
 
3.1%
o 67
 
2.2%
S 65
 
2.2%
r 64
 
2.1%
A 63
 
2.1%
Other values (68) 1355
45.1%
CJK
ValueCountFrequency (%)
14
58.3%
2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Compat Jamo
ValueCountFrequency (%)
2
66.7%
1
33.3%
None
ValueCountFrequency (%)
1
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Distinct2658
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
Minimum1998-12-28 00:00:00
Maximum2024-05-09 14:32:24
2024-05-11T17:11:48.395913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:11:48.591848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
I
2849 
U
888 
D
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 2849
76.0%
U 888
 
23.7%
D 14
 
0.4%

Length

2024-05-11T17:11:48.775325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:48.895755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2849
76.0%
u 888
 
23.7%
d 14
 
0.4%
Distinct787
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:05:00
2024-05-11T17:11:49.008826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:11:49.146915image/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 size29.4 KiB
일반미용업
2992 
피부미용업
393 
네일아트업
 
266
메이크업업
 
76
기타
 
24

Length

Max length5
Median length5
Mean length4.9808051
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2992
79.8%
피부미용업 393
 
10.5%
네일아트업 266
 
7.1%
메이크업업 76
 
2.0%
기타 24
 
0.6%

Length

2024-05-11T17:11:49.302397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:49.414418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2992
79.8%
피부미용업 393
 
10.5%
네일아트업 266
 
7.1%
메이크업업 76
 
2.0%
기타 24
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2033
Distinct (%)55.4%
Missing80
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean207618.77
Minimum206261.05
Maximum209931.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:49.546010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum206261.05
5-th percentile206595.57
Q1206968.87
median207577.09
Q3208205.15
95-th percentile208867.46
Maximum209931.17
Range3670.1203
Interquartile range (IQR)1236.2777

Descriptive statistics

Standard deviation735.09353
Coefficient of variation (CV)0.0035405928
Kurtosis-0.67247047
Mean207618.77
Median Absolute Deviation (MAD)611.41146
Skewness0.3666655
Sum7.6216852 × 108
Variance540362.5
MonotonicityNot monotonic
2024-05-11T17:11:49.686242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208520.092335397 23
 
0.6%
208295.099818379 21
 
0.6%
208263.170512627 19
 
0.5%
207163.791145804 18
 
0.5%
207734.217753337 16
 
0.4%
208267.592391698 16
 
0.4%
208294.31154469 15
 
0.4%
207923.745922676 14
 
0.4%
206808.914669794 14
 
0.4%
208185.587866215 12
 
0.3%
Other values (2023) 3503
93.4%
(Missing) 80
 
2.1%
ValueCountFrequency (%)
206261.052582648 1
 
< 0.1%
206281.995703937 2
0.1%
206286.597871589 1
 
< 0.1%
206296.725620359 4
0.1%
206299.997077967 1
 
< 0.1%
206306.20378659 1
 
< 0.1%
206316.270834781 1
 
< 0.1%
206322.49602413 1
 
< 0.1%
206326.197574635 1
 
< 0.1%
206331.265061613 2
0.1%
ValueCountFrequency (%)
209931.172836 2
0.1%
209892.628462066 4
0.1%
209797.760188391 3
0.1%
209505.319471833 1
 
< 0.1%
209499.121728645 1
 
< 0.1%
209497.551646711 1
 
< 0.1%
209493.910124005 1
 
< 0.1%
209479.161719823 1
 
< 0.1%
209478.477207992 2
0.1%
209474.190548079 4
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2033
Distinct (%)55.4%
Missing80
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean454749.74
Minimum452077.78
Maximum457702.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:49.819247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum452077.78
5-th percentile452824.06
Q1453826.35
median454745.84
Q3455545.69
95-th percentile456958.7
Maximum457702.63
Range5624.8483
Interquartile range (IQR)1719.3411

Descriptive statistics

Standard deviation1225.1478
Coefficient of variation (CV)0.0026941143
Kurtosis-0.69860701
Mean454749.74
Median Absolute Deviation (MAD)843.2316
Skewness0.10767454
Sum1.6693863 × 109
Variance1500987.1
MonotonicityNot monotonic
2024-05-11T17:11:49.955143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
456195.877460936 23
 
0.6%
456014.999180519 21
 
0.6%
454982.610877472 19
 
0.5%
454984.412728653 18
 
0.5%
454934.006784906 16
 
0.4%
455042.983909137 16
 
0.4%
455843.287574845 15
 
0.4%
455090.718335439 14
 
0.4%
456949.35802282 14
 
0.4%
455005.951408018 12
 
0.3%
Other values (2023) 3503
93.4%
(Missing) 80
 
2.1%
ValueCountFrequency (%)
452077.782812274 2
0.1%
452098.766989799 1
 
< 0.1%
452102.965513448 1
 
< 0.1%
452112.148887923 1
 
< 0.1%
452113.435570628 1
 
< 0.1%
452127.833751152 3
0.1%
452163.883585036 2
0.1%
452168.856484877 1
 
< 0.1%
452180.759882096 1
 
< 0.1%
452185.72663944 1
 
< 0.1%
ValueCountFrequency (%)
457702.631123 3
0.1%
457462.766652935 1
 
< 0.1%
457446.479605 2
0.1%
457438.909754748 1
 
< 0.1%
457380.933864584 2
0.1%
457308.946603553 1
 
< 0.1%
457291.314216792 2
0.1%
457288.079083435 1
 
< 0.1%
457274.92173469 2
0.1%
457265.507431525 1
 
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
미용업
1969 
일반미용업
663 
<NA>
538 
피부미용업
220 
종합미용업
 
146
Other values (12)
215 

Length

Max length23
Median length3
Mean length4.1303652
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1969
52.5%
일반미용업 663
 
17.7%
<NA> 538
 
14.3%
피부미용업 220
 
5.9%
종합미용업 146
 
3.9%
네일미용업 96
 
2.6%
화장ㆍ분장 미용업 20
 
0.5%
일반미용업, 화장ㆍ분장 미용업 19
 
0.5%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 19
 
0.5%
피부미용업, 네일미용업 18
 
0.5%
Other values (7) 43
 
1.1%

Length

2024-05-11T17:11:50.106295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 2052
51.8%
일반미용업 720
 
18.2%
na 538
 
13.6%
피부미용업 256
 
6.5%
네일미용업 165
 
4.2%
종합미용업 146
 
3.7%
화장ㆍ분장 83
 
2.1%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.4%
Missing1304
Missing (%)34.8%
Infinite0
Infinite (%)0.0%
Mean0.29791582
Minimum0
Maximum28
Zeros2179
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:50.219163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.7
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2285476
Coefficient of variation (CV)4.123808
Kurtosis202.25062
Mean0.29791582
Median Absolute Deviation (MAD)0
Skewness10.797917
Sum729
Variance1.5093293
MonotonicityNot monotonic
2024-05-11T17:11:50.320556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2179
58.1%
1 100
 
2.7%
3 56
 
1.5%
2 45
 
1.2%
4 30
 
0.8%
5 24
 
0.6%
7 4
 
0.1%
6 4
 
0.1%
8 3
 
0.1%
28 1
 
< 0.1%
(Missing) 1304
34.8%
ValueCountFrequency (%)
0 2179
58.1%
1 100
 
2.7%
2 45
 
1.2%
3 56
 
1.5%
4 30
 
0.8%
5 24
 
0.6%
6 4
 
0.1%
7 4
 
0.1%
8 3
 
0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
28 1
 
< 0.1%
27 1
 
< 0.1%
8 3
 
0.1%
7 4
 
0.1%
6 4
 
0.1%
5 24
 
0.6%
4 30
 
0.8%
3 56
1.5%
2 45
1.2%
1 100
2.7%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.2%
Missing1341
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.03153527
Minimum0
Maximum5
Zeros2351
Zeros (%)62.7%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:50.422137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.24072959
Coefficient of variation (CV)7.6336619
Kurtosis201.37771
Mean0.03153527
Median Absolute Deviation (MAD)0
Skewness12.231456
Sum76
Variance0.057950735
MonotonicityNot monotonic
2024-05-11T17:11:50.525530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2351
62.7%
1 51
 
1.4%
2 4
 
0.1%
5 2
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 1341
35.8%
ValueCountFrequency (%)
0 2351
62.7%
1 51
 
1.4%
2 4
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 2
 
0.1%
ValueCountFrequency (%)
5 2
 
0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 4
 
0.1%
1 51
 
1.4%
0 2351
62.7%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16
Distinct (%)0.6%
Missing1202
Missing (%)32.0%
Infinite0
Infinite (%)0.0%
Mean1.2228325
Minimum0
Maximum304
Zeros1039
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:50.630733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum304
Range304
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.4351341
Coefficient of variation (CV)7.7158026
Kurtosis635.24451
Mean1.2228325
Median Absolute Deviation (MAD)1
Skewness23.908916
Sum3117
Variance89.021755
MonotonicityNot monotonic
2024-05-11T17:11:50.766762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 1170
31.2%
0 1039
27.7%
2 266
 
7.1%
3 38
 
1.0%
4 14
 
0.4%
5 9
 
0.2%
6 3
 
0.1%
12 2
 
0.1%
109 1
 
< 0.1%
101 1
 
< 0.1%
Other values (6) 6
 
0.2%
(Missing) 1202
32.0%
ValueCountFrequency (%)
0 1039
27.7%
1 1170
31.2%
2 266
 
7.1%
3 38
 
1.0%
4 14
 
0.4%
5 9
 
0.2%
6 3
 
0.1%
7 1
 
< 0.1%
12 2
 
0.1%
101 1
 
< 0.1%
ValueCountFrequency (%)
304 1
 
< 0.1%
219 1
 
< 0.1%
205 1
 
< 0.1%
109 1
 
< 0.1%
107 1
 
< 0.1%
106 1
 
< 0.1%
101 1
 
< 0.1%
12 2
0.1%
7 1
 
< 0.1%
6 3
0.1%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct42
Distinct (%)3.2%
Missing2446
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean8.5831418
Minimum0
Maximum402
Zeros145
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:50.895824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile6
Maximum402
Range402
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.192865
Coefficient of variation (CV)4.3332461
Kurtosis39.937609
Mean8.5831418
Median Absolute Deviation (MAD)0
Skewness5.9418061
Sum11201
Variance1383.3092
MonotonicityNot monotonic
2024-05-11T17:11:51.027363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 873
 
23.3%
2 177
 
4.7%
0 145
 
3.9%
3 29
 
0.8%
4 9
 
0.2%
102 9
 
0.2%
101 7
 
0.2%
5 5
 
0.1%
202 4
 
0.1%
205 3
 
0.1%
Other values (32) 44
 
1.2%
(Missing) 2446
65.2%
ValueCountFrequency (%)
0 145
 
3.9%
1 873
23.3%
2 177
 
4.7%
3 29
 
0.8%
4 9
 
0.2%
5 5
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
12 1
 
< 0.1%
67 1
 
< 0.1%
ValueCountFrequency (%)
402 1
< 0.1%
371 1
< 0.1%
319 1
< 0.1%
304 2
0.1%
301 1
< 0.1%
251 1
< 0.1%
226 2
0.1%
211 1
< 0.1%
209 1
< 0.1%
206 1
< 0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
2451 
0
1262 
1
 
37
2
 
1

Length

Max length4
Median length4
Mean length2.9602773
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2451
65.3%
0 1262
33.6%
1 37
 
1.0%
2 1
 
< 0.1%

Length

2024-05-11T17:11:51.160055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:51.289713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2451
65.3%
0 1262
33.6%
1 37
 
1.0%
2 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
3386 
0
 
337
1
 
26
101
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.708611
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> 3386
90.3%
0 337
 
9.0%
1 26
 
0.7%
101 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-11T17:11:51.469035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:51.623894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3386
90.3%
0 337
 
9.0%
1 26
 
0.7%
101 1
 
< 0.1%
2 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
0
2384 
<NA>
1367 

Length

Max length4
Median length1
Mean length2.0933085
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2384
63.6%
<NA> 1367
36.4%

Length

2024-05-11T17:11:51.760652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:51.864568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2384
63.6%
na 1367
36.4%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
0
2384 
<NA>
1367 

Length

Max length4
Median length1
Mean length2.0933085
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2384
63.6%
<NA> 1367
36.4%

Length

2024-05-11T17:11:51.973023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:52.068153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2384
63.6%
na 1367
36.4%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
0
2384 
<NA>
1367 

Length

Max length4
Median length1
Mean length2.0933085
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2384
63.6%
<NA> 1367
36.4%

Length

2024-05-11T17:11:52.175461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:52.276123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2384
63.6%
na 1367
36.4%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing630
Missing (%)16.8%
Memory size7.5 KiB
False
3120 
True
 
1
(Missing)
630 
ValueCountFrequency (%)
False 3120
83.2%
True 1
 
< 0.1%
(Missing) 630
 
16.8%
2024-05-11T17:11:52.360879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)0.6%
Missing568
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean3.4159598
Minimum0
Maximum85
Zeros99
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:52.449575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile6
Maximum85
Range85
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2442936
Coefficient of variation (CV)0.65700236
Kurtosis551.97249
Mean3.4159598
Median Absolute Deviation (MAD)1
Skewness16.14616
Sum10873
Variance5.0368539
MonotonicityNot monotonic
2024-05-11T17:11:52.548138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 1488
39.7%
4 556
 
14.8%
2 541
 
14.4%
5 202
 
5.4%
6 103
 
2.7%
0 99
 
2.6%
8 59
 
1.6%
7 42
 
1.1%
1 40
 
1.1%
10 15
 
0.4%
Other values (8) 38
 
1.0%
(Missing) 568
 
15.1%
ValueCountFrequency (%)
0 99
 
2.6%
1 40
 
1.1%
2 541
 
14.4%
3 1488
39.7%
4 556
 
14.8%
5 202
 
5.4%
6 103
 
2.7%
7 42
 
1.1%
8 59
 
1.6%
9 12
 
0.3%
ValueCountFrequency (%)
85 1
 
< 0.1%
20 2
 
0.1%
15 1
 
< 0.1%
14 4
 
0.1%
13 5
 
0.1%
12 7
 
0.2%
11 6
 
0.2%
10 15
 
0.4%
9 12
 
0.3%
8 59
1.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3751
Missing (%)100.0%
Memory size33.1 KiB

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
3747 
2
 
4

Length

Max length4
Median length4
Mean length3.9968009
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> 3747
99.9%
2 4
 
0.1%

Length

2024-05-11T17:11:52.976944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:53.083720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3747
99.9%
2 4
 
0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
3335 
임대
406 
자가
 
10

Length

Max length4
Median length4
Mean length3.7781925
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> 3335
88.9%
임대 406
 
10.8%
자가 10
 
0.3%

Length

2024-05-11T17:11:53.204067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:53.320003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3335
88.9%
임대 406
 
10.8%
자가 10
 
0.3%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
2290 
0
1461 

Length

Max length4
Median length4
Mean length2.8315116
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> 2290
61.1%
0 1461
38.9%

Length

2024-05-11T17:11:53.438507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:53.542700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2290
61.1%
0 1461
38.9%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)1.1%
Missing2993
Missing (%)79.8%
Infinite0
Infinite (%)0.0%
Mean0.49604222
Minimum0
Maximum7
Zeros445
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:53.643475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7426328
Coefficient of variation (CV)1.4971161
Kurtosis16.038048
Mean0.49604222
Median Absolute Deviation (MAD)0
Skewness2.9047289
Sum376
Variance0.55150347
MonotonicityNot monotonic
2024-05-11T17:11:53.751644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 445
 
11.9%
1 276
 
7.4%
2 25
 
0.7%
3 5
 
0.1%
4 3
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 2993
79.8%
ValueCountFrequency (%)
0 445
11.9%
1 276
7.4%
2 25
 
0.7%
3 5
 
0.1%
4 3
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
0.1%
4 3
 
0.1%
3 5
 
0.1%
2 25
 
0.7%
1 276
7.4%
0 445
11.9%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
2995 
0
703 
1
 
45
2
 
5
3
 
2

Length

Max length4
Median length4
Mean length3.3953612
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2995
79.8%
0 703
 
18.7%
1 45
 
1.2%
2 5
 
0.1%
3 2
 
0.1%
5 1
 
< 0.1%

Length

2024-05-11T17:11:53.876158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:53.992648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2995
79.8%
0 703
 
18.7%
1 45
 
1.2%
2 5
 
0.1%
3 2
 
0.1%
5 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.4 KiB
<NA>
2394 
0
1357 

Length

Max length4
Median length4
Mean length2.9146894
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> 2394
63.8%
0 1357
36.2%

Length

2024-05-11T17:11:54.117629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:11:54.222831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2394
63.8%
0 1357
36.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.7%
Missing2421
Missing (%)64.5%
Infinite0
Infinite (%)0.0%
Mean0.4593985
Minimum0
Maximum9
Zeros1049
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size33.1 KiB
2024-05-11T17:11:54.320062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1000744
Coefficient of variation (CV)2.3945973
Kurtosis14.794991
Mean0.4593985
Median Absolute Deviation (MAD)0
Skewness3.3655026
Sum611
Variance1.2101637
MonotonicityNot monotonic
2024-05-11T17:11:54.419940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1049
28.0%
2 121
 
3.2%
1 94
 
2.5%
3 33
 
0.9%
4 13
 
0.3%
5 8
 
0.2%
6 7
 
0.2%
8 3
 
0.1%
9 2
 
0.1%
(Missing) 2421
64.5%
ValueCountFrequency (%)
0 1049
28.0%
1 94
 
2.5%
2 121
 
3.2%
3 33
 
0.9%
4 13
 
0.3%
5 8
 
0.2%
6 7
 
0.2%
8 3
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 3
 
0.1%
6 7
 
0.2%
5 8
 
0.2%
4 13
 
0.3%
3 33
 
0.9%
2 121
 
3.2%
1 94
 
2.5%
0 1049
28.0%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing538
Missing (%)14.3%
Memory size7.5 KiB
False
3212 
True
 
1
(Missing)
538 
ValueCountFrequency (%)
False 3212
85.6%
True 1
 
< 0.1%
(Missing) 538
 
14.3%
2024-05-11T17:11:54.530113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030600003060000-204-1900-0119019000713<NA>1영업/정상1영업<NA><NA><NA><NA>020494753517.5131827서울특별시 중랑구 면목동 653-2번지서울특별시 중랑구 사가정로46길 21-22 (면목동)2237수진미용실2003-01-29 00:00:00I2018-08-31 23:59:59.0일반미용업207481.970944453027.400419미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130600003060000-204-1919-0134419190412<NA>3폐업2폐업19941230<NA><NA><NA>020437412614.48131859서울특별시 중랑구 상봉동 105-0번지<NA><NA>3정희미용실2001-10-04 00:00:00I2018-08-31 23:59:59.0일반미용업207758.132259454697.528771미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230600003060000-204-1969-0071319691106<NA>1영업/정상1영업<NA><NA><NA><NA>020495733715.3131827서울특별시 중랑구 면목동 353-8서울특별시 중랑구 사가정로50길 67 (면목동)2240아폴로2021-01-07 14:38:34U2021-01-09 02:40:00.0일반미용업207445.638823452843.965869미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330600003060000-204-1970-0081719700220<NA>3폐업2폐업20031107<NA><NA><NA>02 496802212.71131865서울특별시 중랑구 신내동 330-6번지<NA><NA>형제2003-11-07 00:00:00I2018-08-31 23:59:59.0일반미용업208835.809279457010.282896미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430600003060000-204-1970-0098219700613<NA>3폐업2폐업20161101<NA><NA><NA>02 434064733.0131814서울특별시 중랑구 면목동 413-6번지 1층서울특별시 중랑구 면목로56가길 5 (면목동,1층)2205선희2011-02-15 10:03:28I2018-08-31 23:59:59.0일반미용업207765.177128453572.699584미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530600003060000-204-1970-0123819701104<NA>3폐업2폐업20181224<NA><NA><NA>020432417615.05131860서울특별시 중랑구 상봉동 118-1번지서울특별시 중랑구 망우로42길 10 (상봉동)2136봉황미용실2018-12-24 16:19:37U2018-12-26 02:40:00.0일반미용업207235.533859454752.984582미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630600003060000-204-1971-0071619710430<NA>3폐업2폐업20131008<NA><NA><NA>020492821012.0131828서울특별시 중랑구 면목동 633-2번지서울특별시 중랑구 면목로39길 5 (면목동)2239진오네2003-02-05 00:00:00I2018-08-31 23:59:59.0일반미용업207610.554916453033.841616미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730600003060000-204-1971-0083119710727<NA>1영업/정상1영업<NA><NA><NA><NA>02 436926228.2131862서울특별시 중랑구 상봉동 190-103번지서울특별시 중랑구 상봉중앙로1나길 17 (상봉동)2091김선희미용실2005-06-29 00:00:00I2018-08-31 23:59:59.0일반미용업207588.307891455121.193346미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830600003060000-204-1973-0067919730619<NA>3폐업2폐업20020331<NA><NA><NA>020495121212.31131830서울특별시 중랑구 면목동 373-18번지<NA><NA>부산2002-04-25 00:00:00I2018-08-31 23:59:59.0일반미용업207422.332798452606.740698미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930600003060000-204-1973-0100019730723<NA>3폐업2폐업19990807<NA><NA><NA>020433561712.12131818서울특별시 중랑구 면목동 549-1번지<NA><NA>혜원2001-12-11 00:00:00I2018-08-31 23:59:59.0일반미용업207404.857629453722.471374미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
374130600003060000-226-2017-0000120170717<NA>3폐업2폐업20201203<NA><NA><NA><NA>25.0131860서울특별시 중랑구 상봉동 112-51서울특별시 중랑구 면목로91길 46, 1층 (상봉동)2137에스(S)네일샵2020-12-03 09:33:49U2020-12-05 02:40:00.0네일아트업207297.398053454648.366597피부미용업, 네일미용업, 화장ㆍ분장 미용업001100000N3<NA><NA><NA><NA>00001N
374230600003060000-226-2018-0000220180807<NA>3폐업2폐업20190625<NA><NA><NA><NA>26.0131831서울특별시 중랑구 면목동 597-7번지 세일약국서울특별시 중랑구 면목로45길 36, 세일약국 1층 (면목동)2228무루이 뷰티(Murui Beauty)2019-06-26 09:44:47U2019-06-28 02:40:00.0피부미용업207573.307039453298.72226피부미용업, 네일미용업, 화장ㆍ분장 미용업002200000N0<NA><NA><NA><NA>01<NA>02N
374330600003060000-226-2018-000032018-03-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>57.28131-827서울특별시 중랑구 면목동 673-59서울특별시 중랑구 사가정로42길 69, 1층 (면목동)2240네일하자2023-03-02 16:19:52I2022-12-03 00:04:00.0네일아트업207202.639048452895.142938<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
374430600003060000-226-2019-0000120190311<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.5131818서울특별시 중랑구 면목동 118-1번지서울특별시 중랑구 겸재로 164, 1층 (면목동)2218멜팅모드(melting mode)2019-03-11 14:33:37I2019-03-13 02:21:48.0네일아트업207529.02437453948.919233피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N1<NA><NA><NA><NA>01001N
374530600003060000-226-2020-0000120200103<NA>1영업/정상1영업<NA><NA><NA><NA><NA>38.5131819서울특별시 중랑구 면목동 132-27번지서울특별시 중랑구 봉우재로26길 7, 1층 (면목동)2143블링블링2020-01-03 09:27:26I2020-01-05 00:23:25.0네일아트업207367.145614454389.508691피부미용업, 네일미용업, 화장ㆍ분장 미용업001100000N3<NA><NA><NA><NA>00001N
374630600003060000-226-2020-0000220200604<NA>3폐업2폐업20210609<NA><NA><NA><NA>21.0131856서울특별시 중랑구 상봉동 279-7서울특별시 중랑구 상봉중앙로8나길 38-11, 1층 (상봉동)2081더니네일2021-06-14 09:06:18U2021-06-16 02:40:00.0네일아트업207949.442475455367.337938피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N2<NA><NA><NA><NA>01001N
374730600003060000-226-2020-0000320200716<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.0131811서울특별시 중랑구 면목동 17-1서울특별시 중랑구 용마산로 440, 1층 (면목동)2189플로라2022-06-15 13:36:04I2021-12-05 23:07:00.0네일아트업208555.355763454237.017538<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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374930600003060000-226-2021-000012021-08-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.0131-838서울특별시 중랑구 면목동 1342-7서울특별시 중랑구 용마산로 198, 103호 (면목동)2256요온네일2023-11-28 13:32:11I2022-10-31 21:00:00.0네일아트업207563.994624452098.76699<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
375030600003060000-226-2022-0000120220422<NA>1영업/정상1영업<NA><NA><NA><NA><NA>82.08131816서울특별시 중랑구 면목동 126-2서울특별시 중랑구 면목로 427, 2층 (면목동)2146에이린뷰티 면목2022-09-01 15:22:52I2021-12-09 00:03:00.0피부미용업207609.268788454190.574208<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>