Overview

Dataset statistics

Number of variables47
Number of observations5057
Missing cells59654
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 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
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-17917/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
사용시작지하층 is highly imbalanced (55.3%)Imbalance
사용끝지하층 is highly imbalanced (73.8%)Imbalance
조건부허가종료일자 is highly imbalanced (99.5%)Imbalance
남성종사자수 is highly imbalanced (58.6%)Imbalance
다중이용업소여부 is highly imbalanced (99.7%)Imbalance
인허가취소일자 has 5057 (100.0%) missing valuesMissing
폐업일자 has 1972 (39.0%) missing valuesMissing
휴업시작일자 has 5057 (100.0%) missing valuesMissing
휴업종료일자 has 5057 (100.0%) missing valuesMissing
재개업일자 has 5057 (100.0%) missing valuesMissing
전화번호 has 1660 (32.8%) missing valuesMissing
도로명주소 has 1481 (29.3%) missing valuesMissing
도로명우편번호 has 1492 (29.5%) missing valuesMissing
좌표정보(X) has 460 (9.1%) missing valuesMissing
좌표정보(Y) has 460 (9.1%) missing valuesMissing
건물지상층수 has 2139 (42.3%) missing valuesMissing
건물지하층수 has 2324 (46.0%) missing valuesMissing
사용시작지상층 has 2451 (48.5%) missing valuesMissing
사용끝지상층 has 4047 (80.0%) missing valuesMissing
발한실여부 has 1280 (25.3%) missing valuesMissing
좌석수 has 1350 (26.7%) missing valuesMissing
조건부허가신고사유 has 5057 (100.0%) missing valuesMissing
조건부허가시작일자 has 5057 (100.0%) missing valuesMissing
여성종사자수 has 3800 (75.1%) missing valuesMissing
침대수 has 3157 (62.4%) missing valuesMissing
다중이용업소여부 has 1232 (24.4%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = 28.14658005)Skewed
좌석수 is highly skewed (γ1 = 32.96671293)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 147 (2.9%) zerosZeros
건물지상층수 has 1885 (37.3%) zerosZeros
건물지하층수 has 2210 (43.7%) zerosZeros
사용시작지상층 has 871 (17.2%) zerosZeros
사용끝지상층 has 331 (6.5%) zerosZeros
좌석수 has 416 (8.2%) zerosZeros
여성종사자수 has 1135 (22.4%) zerosZeros
침대수 has 1224 (24.2%) zerosZeros

Reproduction

Analysis started2024-04-06 11:18:23.468844
Analysis finished2024-04-06 11:18:26.658380
Duration3.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
3150000
5057 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3150000
2nd row3150000
3rd row3150000
4th row3150000
5th row3150000

Common Values

ValueCountFrequency (%)
3150000 5057
100.0%

Length

2024-04-06T20:18:26.805081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:27.111275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3150000 5057
100.0%

관리번호
Text

UNIQUE 

Distinct5057
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
2024-04-06T20:18:27.424670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters111254
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

Unique5057 ?
Unique (%)100.0%

Sample

1st row3150000-204-1967-00963
2nd row3150000-204-1970-00972
3rd row3150000-204-1972-00962
4th row3150000-204-1972-00971
5th row3150000-204-1974-00983
ValueCountFrequency (%)
3150000-204-1967-00963 1
 
< 0.1%
3150000-212-2010-00001 1
 
< 0.1%
3150000-212-2010-00008 1
 
< 0.1%
3150000-212-2010-00007 1
 
< 0.1%
3150000-212-2010-00006 1
 
< 0.1%
3150000-212-2010-00005 1
 
< 0.1%
3150000-212-2010-00004 1
 
< 0.1%
3150000-212-2010-00003 1
 
< 0.1%
3150000-212-2010-00014 1
 
< 0.1%
3150000-212-2009-00058 1
 
< 0.1%
Other values (5047) 5047
99.8%
2024-04-06T20:18:27.966300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42726
38.4%
1 15777
 
14.2%
- 15171
 
13.6%
2 12931
 
11.6%
3 7163
 
6.4%
5 6750
 
6.1%
9 3324
 
3.0%
4 2942
 
2.6%
8 1664
 
1.5%
6 1498
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96083
86.4%
Dash Punctuation 15171
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42726
44.5%
1 15777
 
16.4%
2 12931
 
13.5%
3 7163
 
7.5%
5 6750
 
7.0%
9 3324
 
3.5%
4 2942
 
3.1%
8 1664
 
1.7%
6 1498
 
1.6%
7 1308
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 15171
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 111254
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42726
38.4%
1 15777
 
14.2%
- 15171
 
13.6%
2 12931
 
11.6%
3 7163
 
6.4%
5 6750
 
6.1%
9 3324
 
3.0%
4 2942
 
2.6%
8 1664
 
1.5%
6 1498
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42726
38.4%
1 15777
 
14.2%
- 15171
 
13.6%
2 12931
 
11.6%
3 7163
 
6.4%
5 6750
 
6.1%
9 3324
 
3.0%
4 2942
 
2.6%
8 1664
 
1.5%
6 1498
 
1.3%
Distinct3418
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
Minimum1967-09-06 00:00:00
Maximum2024-04-02 00:00:00
2024-04-06T20:18:28.210852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:18:28.443468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
3
3085 
1
1972 

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 3085
61.0%
1 1972
39.0%

Length

2024-04-06T20:18:28.742302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:28.905906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3085
61.0%
1 1972
39.0%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
폐업
3085 
영업/정상
1972 

Length

Max length5
Median length2
Mean length3.1698636
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3085
61.0%
영업/정상 1972
39.0%

Length

2024-04-06T20:18:29.082753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:29.254642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3085
61.0%
영업/정상 1972
39.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
2
3085 
1
1972 

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 3085
61.0%
1 1972
39.0%

Length

2024-04-06T20:18:29.418939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:29.580476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3085
61.0%
1 1972
39.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
폐업
3085 
영업
1972 

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 3085
61.0%
영업 1972
39.0%

Length

2024-04-06T20:18:29.762187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:29.923744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3085
61.0%
영업 1972
39.0%

폐업일자
Date

MISSING 

Distinct2184
Distinct (%)70.8%
Missing1972
Missing (%)39.0%
Memory size39.6 KiB
Minimum1991-07-19 00:00:00
Maximum2024-04-02 00:00:00
2024-04-06T20:18:30.096346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:18:30.333656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB

전화번호
Text

MISSING 

Distinct2925
Distinct (%)86.1%
Missing1660
Missing (%)32.8%
Memory size39.6 KiB
2024-04-06T20:18:30.716782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.133059
Min length2

Characters and Unicode

Total characters34422
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

Unique2644 ?
Unique (%)77.8%

Sample

1st row02 6631333
2nd row02 00000
3rd row0206622586
4th row02 6645319
5th row02 6939313
ValueCountFrequency (%)
02 1107
 
24.6%
0200000000 34
 
0.8%
00000 33
 
0.7%
070 21
 
0.5%
0 15
 
0.3%
032 6
 
0.1%
0226918882 4
 
0.1%
0226471284 4
 
0.1%
0236636603 4
 
0.1%
0236616941 4
 
0.1%
Other values (2951) 3268
72.6%
2024-04-06T20:18:31.251703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6948
20.2%
0 6430
18.7%
6 5824
16.9%
3 2323
 
6.7%
5 2084
 
6.1%
9 2062
 
6.0%
8 1850
 
5.4%
4 1755
 
5.1%
1 1741
 
5.1%
7 1737
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32754
95.2%
Space Separator 1668
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 6948
21.2%
0 6430
19.6%
6 5824
17.8%
3 2323
 
7.1%
5 2084
 
6.4%
9 2062
 
6.3%
8 1850
 
5.6%
4 1755
 
5.4%
1 1741
 
5.3%
7 1737
 
5.3%
Space Separator
ValueCountFrequency (%)
1668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 6948
20.2%
0 6430
18.7%
6 5824
16.9%
3 2323
 
6.7%
5 2084
 
6.1%
9 2062
 
6.0%
8 1850
 
5.4%
4 1755
 
5.1%
1 1741
 
5.1%
7 1737
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6948
20.2%
0 6430
18.7%
6 5824
16.9%
3 2323
 
6.7%
5 2084
 
6.1%
9 2062
 
6.0%
8 1850
 
5.4%
4 1755
 
5.1%
1 1741
 
5.1%
7 1737
 
5.0%

소재지면적
Real number (ℝ)

ZEROS 

Distinct2028
Distinct (%)40.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.340026
Minimum0
Maximum589.48
Zeros147
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:31.488526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.545
Q121.3575
median31.02
Q346.3
95-th percentile110.89
Maximum589.48
Range589.48
Interquartile range (IQR)24.9425

Descriptive statistics

Standard deviation36.861409
Coefficient of variation (CV)0.8916639
Kurtosis20.148964
Mean41.340026
Median Absolute Deviation (MAD)11.23
Skewness3.3199207
Sum209015.17
Variance1358.7634
MonotonicityNot monotonic
2024-04-06T20:18:31.725607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 235
 
4.6%
0.0 147
 
2.9%
30.0 101
 
2.0%
24.0 62
 
1.2%
26.4 59
 
1.2%
20.0 56
 
1.1%
25.0 51
 
1.0%
23.1 35
 
0.7%
66.0 35
 
0.7%
27.0 33
 
0.7%
Other values (2018) 4242
83.9%
ValueCountFrequency (%)
0.0 147
2.9%
1.5 1
 
< 0.1%
1.72 3
 
0.1%
3.3 4
 
0.1%
3.38 1
 
< 0.1%
4.9 1
 
< 0.1%
5.0 3
 
0.1%
5.13 1
 
< 0.1%
5.3 1
 
< 0.1%
5.6 1
 
< 0.1%
ValueCountFrequency (%)
589.48 1
< 0.1%
402.53 1
< 0.1%
310.14 1
< 0.1%
285.24 1
< 0.1%
279.95 1
< 0.1%
279.0 1
< 0.1%
275.51 1
< 0.1%
271.2 1
< 0.1%
270.77 1
< 0.1%
270.0 1
< 0.1%
Distinct216
Distinct (%)4.3%
Missing4
Missing (%)0.1%
Memory size39.6 KiB
2024-04-06T20:18:32.305243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1278448
Min length6

Characters and Unicode

Total characters30964
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

Unique24 ?
Unique (%)0.5%

Sample

1st row157812
2nd row157860
3rd row157852
4th row157846
5th row157871
ValueCountFrequency (%)
157210 573
 
11.3%
157-210 307
 
6.1%
157280 188
 
3.7%
157930 166
 
3.3%
157918 111
 
2.2%
157846 98
 
1.9%
157884 97
 
1.9%
157847 93
 
1.8%
157879 87
 
1.7%
157925 83
 
1.6%
Other values (206) 3250
64.3%
2024-04-06T20:18:33.197731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6924
22.4%
7 5761
18.6%
5 5680
18.3%
8 3571
11.5%
0 2407
 
7.8%
2 1945
 
6.3%
9 1702
 
5.5%
3 898
 
2.9%
6 737
 
2.4%
4 693
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30318
97.9%
Dash Punctuation 646
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6924
22.8%
7 5761
19.0%
5 5680
18.7%
8 3571
11.8%
0 2407
 
7.9%
2 1945
 
6.4%
9 1702
 
5.6%
3 898
 
3.0%
6 737
 
2.4%
4 693
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6924
22.4%
7 5761
18.6%
5 5680
18.3%
8 3571
11.5%
0 2407
 
7.8%
2 1945
 
6.3%
9 1702
 
5.5%
3 898
 
2.9%
6 737
 
2.4%
4 693
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6924
22.4%
7 5761
18.6%
5 5680
18.3%
8 3571
11.5%
0 2407
 
7.8%
2 1945
 
6.3%
9 1702
 
5.5%
3 898
 
2.9%
6 737
 
2.4%
4 693
 
2.2%
Distinct4262
Distinct (%)84.3%
Missing2
Missing (%)< 0.1%
Memory size39.6 KiB
2024-04-06T20:18:33.696988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length49
Mean length27.414441
Min length17

Characters and Unicode

Total characters138580
Distinct characters418
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3721 ?
Unique (%)73.6%

Sample

1st row서울특별시 강서구 공항동 52-5번지
2nd row서울특별시 강서구 염창동 108-0번지
3rd row서울특별시 강서구 방화동 608-49번지
4th row서울특별시 강서구 방화동 249-153번지
5th row서울특별시 강서구 화곡동 98-59번지
ValueCountFrequency (%)
서울특별시 5053
19.5%
강서구 5053
19.5%
화곡동 1999
 
7.7%
마곡동 880
 
3.4%
방화동 597
 
2.3%
등촌동 560
 
2.2%
1층 501
 
1.9%
내발산동 401
 
1.5%
염창동 241
 
0.9%
지상 233
 
0.9%
Other values (4231) 10364
40.0%
2024-04-06T20:18:34.581491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23786
 
17.2%
10180
 
7.3%
1 6097
 
4.4%
5509
 
4.0%
5149
 
3.7%
5137
 
3.7%
5079
 
3.7%
5070
 
3.7%
5056
 
3.6%
5053
 
3.6%
Other values (408) 62464
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79689
57.5%
Decimal Number 29493
 
21.3%
Space Separator 23786
 
17.2%
Dash Punctuation 4527
 
3.3%
Open Punctuation 319
 
0.2%
Close Punctuation 319
 
0.2%
Uppercase Letter 261
 
0.2%
Letter Number 92
 
0.1%
Other Punctuation 68
 
< 0.1%
Math Symbol 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10180
 
12.8%
5509
 
6.9%
5149
 
6.5%
5137
 
6.4%
5079
 
6.4%
5070
 
6.4%
5056
 
6.3%
5053
 
6.3%
3681
 
4.6%
3294
 
4.1%
Other values (358) 26481
33.2%
Uppercase Letter
ValueCountFrequency (%)
B 93
35.6%
A 72
27.6%
W 20
 
7.7%
C 14
 
5.4%
M 12
 
4.6%
N 11
 
4.2%
I 6
 
2.3%
T 5
 
1.9%
E 4
 
1.5%
R 4
 
1.5%
Other values (12) 20
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 6097
20.7%
2 3573
12.1%
0 3470
11.8%
7 2885
9.8%
3 2746
9.3%
9 2244
 
7.6%
6 2237
 
7.6%
4 2234
 
7.6%
5 2070
 
7.0%
8 1937
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 52
76.5%
@ 10
 
14.7%
. 4
 
5.9%
/ 1
 
1.5%
? 1
 
1.5%
Letter Number
ValueCountFrequency (%)
53
57.6%
35
38.0%
2
 
2.2%
2
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
e 3
37.5%
c 3
37.5%
b 1
 
12.5%
n 1
 
12.5%
Space Separator
ValueCountFrequency (%)
23786
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4527
100.0%
Open Punctuation
ValueCountFrequency (%)
( 319
100.0%
Close Punctuation
ValueCountFrequency (%)
) 319
100.0%
Math Symbol
ValueCountFrequency (%)
~ 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79689
57.5%
Common 58530
42.2%
Latin 361
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10180
 
12.8%
5509
 
6.9%
5149
 
6.5%
5137
 
6.4%
5079
 
6.4%
5070
 
6.4%
5056
 
6.3%
5053
 
6.3%
3681
 
4.6%
3294
 
4.1%
Other values (358) 26481
33.2%
Latin
ValueCountFrequency (%)
B 93
25.8%
A 72
19.9%
53
14.7%
35
 
9.7%
W 20
 
5.5%
C 14
 
3.9%
M 12
 
3.3%
N 11
 
3.0%
I 6
 
1.7%
T 5
 
1.4%
Other values (20) 40
11.1%
Common
ValueCountFrequency (%)
23786
40.6%
1 6097
 
10.4%
- 4527
 
7.7%
2 3573
 
6.1%
0 3470
 
5.9%
7 2885
 
4.9%
3 2746
 
4.7%
9 2244
 
3.8%
6 2237
 
3.8%
4 2234
 
3.8%
Other values (10) 4731
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79688
57.5%
ASCII 58799
42.4%
Number Forms 92
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23786
40.5%
1 6097
 
10.4%
- 4527
 
7.7%
2 3573
 
6.1%
0 3470
 
5.9%
7 2885
 
4.9%
3 2746
 
4.7%
9 2244
 
3.8%
6 2237
 
3.8%
4 2234
 
3.8%
Other values (36) 5000
 
8.5%
Hangul
ValueCountFrequency (%)
10180
 
12.8%
5509
 
6.9%
5149
 
6.5%
5137
 
6.4%
5079
 
6.4%
5070
 
6.4%
5056
 
6.3%
5053
 
6.3%
3681
 
4.6%
3294
 
4.1%
Other values (357) 26480
33.2%
Number Forms
ValueCountFrequency (%)
53
57.6%
35
38.0%
2
 
2.2%
2
 
2.2%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct3256
Distinct (%)91.1%
Missing1481
Missing (%)29.3%
Memory size39.6 KiB
2024-04-06T20:18:35.160785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length51
Mean length35.280761
Min length17

Characters and Unicode

Total characters126164
Distinct characters416
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2986 ?
Unique (%)83.5%

Sample

1st row서울특별시 강서구 곰달래로 227 (화곡동)
2nd row서울특별시 강서구 허준로 234 (가양동,가양9단지상가206)
3rd row서울특별시 강서구 화곡로66길 90 (등촌동,코오롱아파트 상가106호)
4th row서울특별시 강서구 양천로75길 13 (염창동)
5th row서울특별시 강서구 초록마을로 167 (화곡동)
ValueCountFrequency (%)
서울특별시 3574
 
14.8%
강서구 3574
 
14.8%
화곡동 1140
 
4.7%
1층 907
 
3.8%
마곡동 871
 
3.6%
2층 498
 
2.1%
방화동 325
 
1.3%
등촌동 309
 
1.3%
공항대로 290
 
1.2%
내발산동 248
 
1.0%
Other values (2339) 12447
51.5%
2024-04-06T20:18:35.959049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20618
 
16.3%
7940
 
6.3%
1 5675
 
4.5%
4288
 
3.4%
4190
 
3.3%
( 3682
 
2.9%
) 3682
 
2.9%
, 3673
 
2.9%
3663
 
2.9%
3652
 
2.9%
Other values (406) 65101
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72227
57.2%
Decimal Number 21303
 
16.9%
Space Separator 20618
 
16.3%
Open Punctuation 3682
 
2.9%
Close Punctuation 3682
 
2.9%
Other Punctuation 3677
 
2.9%
Dash Punctuation 509
 
0.4%
Uppercase Letter 326
 
0.3%
Letter Number 92
 
0.1%
Math Symbol 39
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7940
 
11.0%
4288
 
5.9%
4190
 
5.8%
3663
 
5.1%
3652
 
5.1%
3598
 
5.0%
3578
 
5.0%
3577
 
5.0%
3574
 
4.9%
3514
 
4.9%
Other values (357) 30653
42.4%
Uppercase Letter
ValueCountFrequency (%)
B 121
37.1%
A 96
29.4%
W 20
 
6.1%
C 18
 
5.5%
M 14
 
4.3%
N 11
 
3.4%
I 6
 
1.8%
G 5
 
1.5%
T 5
 
1.5%
O 4
 
1.2%
Other values (11) 26
 
8.0%
Decimal Number
ValueCountFrequency (%)
1 5675
26.6%
2 3413
16.0%
0 2263
 
10.6%
3 2159
 
10.1%
4 1909
 
9.0%
5 1650
 
7.7%
6 1362
 
6.4%
7 1037
 
4.9%
8 1031
 
4.8%
9 804
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
33.3%
c 2
22.2%
b 1
 
11.1%
n 1
 
11.1%
a 1
 
11.1%
g 1
 
11.1%
Letter Number
ValueCountFrequency (%)
53
57.6%
35
38.0%
2
 
2.2%
2
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 3673
99.9%
. 2
 
0.1%
@ 2
 
0.1%
Space Separator
ValueCountFrequency (%)
20618
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3682
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3682
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 509
100.0%
Math Symbol
ValueCountFrequency (%)
~ 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72227
57.2%
Common 53510
42.4%
Latin 427
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7940
 
11.0%
4288
 
5.9%
4190
 
5.8%
3663
 
5.1%
3652
 
5.1%
3598
 
5.0%
3578
 
5.0%
3577
 
5.0%
3574
 
4.9%
3514
 
4.9%
Other values (357) 30653
42.4%
Latin
ValueCountFrequency (%)
B 121
28.3%
A 96
22.5%
53
12.4%
35
 
8.2%
W 20
 
4.7%
C 18
 
4.2%
M 14
 
3.3%
N 11
 
2.6%
I 6
 
1.4%
G 5
 
1.2%
Other values (21) 48
 
11.2%
Common
ValueCountFrequency (%)
20618
38.5%
1 5675
 
10.6%
( 3682
 
6.9%
) 3682
 
6.9%
, 3673
 
6.9%
2 3413
 
6.4%
0 2263
 
4.2%
3 2159
 
4.0%
4 1909
 
3.6%
5 1650
 
3.1%
Other values (8) 4786
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72227
57.2%
ASCII 53845
42.7%
Number Forms 92
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20618
38.3%
1 5675
 
10.5%
( 3682
 
6.8%
) 3682
 
6.8%
, 3673
 
6.8%
2 3413
 
6.3%
0 2263
 
4.2%
3 2159
 
4.0%
4 1909
 
3.5%
5 1650
 
3.1%
Other values (35) 5121
 
9.5%
Hangul
ValueCountFrequency (%)
7940
 
11.0%
4288
 
5.9%
4190
 
5.8%
3663
 
5.1%
3652
 
5.1%
3598
 
5.0%
3578
 
5.0%
3577
 
5.0%
3574
 
4.9%
3514
 
4.9%
Other values (357) 30653
42.4%
Number Forms
ValueCountFrequency (%)
53
57.6%
35
38.0%
2
 
2.2%
2
 
2.2%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct268
Distinct (%)7.5%
Missing1492
Missing (%)29.5%
Infinite0
Infinite (%)0.0%
Mean7682.4581
Minimum7504
Maximum14545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:36.370082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7504
5-th percentile7527
Q17604
median7681
Q37764
95-th percentile7803
Maximum14545
Range7041
Interquartile range (IQR)160

Descriptive statistics

Standard deviation186.03528
Coefficient of variation (CV)0.024215594
Kurtosis1037.6316
Mean7682.4581
Median Absolute Deviation (MAD)78
Skewness28.14658
Sum27387963
Variance34609.127
MonotonicityNot monotonic
2024-04-06T20:18:36.639303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7631 157
 
3.1%
7788 153
 
3.0%
7803 115
 
2.3%
7801 99
 
2.0%
7806 73
 
1.4%
7807 70
 
1.4%
7604 68
 
1.3%
7639 58
 
1.1%
7802 49
 
1.0%
7651 47
 
0.9%
Other values (258) 2676
52.9%
(Missing) 1492
29.5%
ValueCountFrequency (%)
7504 2
 
< 0.1%
7505 7
 
0.1%
7506 2
 
< 0.1%
7509 3
 
0.1%
7510 30
0.6%
7511 9
 
0.2%
7512 4
 
0.1%
7513 3
 
0.1%
7514 2
 
< 0.1%
7515 5
 
0.1%
ValueCountFrequency (%)
14545 1
 
< 0.1%
14544 1
 
< 0.1%
7811 4
 
0.1%
7809 4
 
0.1%
7808 11
 
0.2%
7807 70
1.4%
7806 73
1.4%
7805 1
 
< 0.1%
7803 115
2.3%
7802 49
1.0%
Distinct4244
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
2024-04-06T20:18:37.177603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length31
Mean length6.0913585
Min length1

Characters and Unicode

Total characters30804
Distinct characters779
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3751 ?
Unique (%)74.2%

Sample

1st row월계
2nd row유일
3rd row소망
4th row성민주미용타운
5th row
ValueCountFrequency (%)
헤어 143
 
2.2%
네일 73
 
1.1%
미용실 62
 
0.9%
hair 53
 
0.8%
에스테틱 52
 
0.8%
마곡점 38
 
0.6%
살롱 28
 
0.4%
헤어살롱 27
 
0.4%
nail 25
 
0.4%
리안헤어 24
 
0.4%
Other values (4427) 6065
92.0%
2024-04-06T20:18:38.353666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1757
 
5.7%
1693
 
5.5%
1535
 
5.0%
1009
 
3.3%
748
 
2.4%
679
 
2.2%
679
 
2.2%
677
 
2.2%
591
 
1.9%
497
 
1.6%
Other values (769) 20939
68.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25459
82.6%
Space Separator 1535
 
5.0%
Lowercase Letter 1461
 
4.7%
Uppercase Letter 1195
 
3.9%
Open Punctuation 285
 
0.9%
Close Punctuation 285
 
0.9%
Decimal Number 284
 
0.9%
Other Punctuation 281
 
0.9%
Dash Punctuation 8
 
< 0.1%
Connector Punctuation 5
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1757
 
6.9%
1693
 
6.6%
1009
 
4.0%
748
 
2.9%
679
 
2.7%
679
 
2.7%
677
 
2.7%
591
 
2.3%
497
 
2.0%
462
 
1.8%
Other values (686) 16667
65.5%
Uppercase Letter
ValueCountFrequency (%)
A 125
 
10.5%
N 90
 
7.5%
S 80
 
6.7%
I 77
 
6.4%
H 75
 
6.3%
O 75
 
6.3%
L 75
 
6.3%
E 69
 
5.8%
R 69
 
5.8%
B 64
 
5.4%
Other values (16) 396
33.1%
Lowercase Letter
ValueCountFrequency (%)
a 206
14.1%
i 163
11.2%
e 154
10.5%
l 125
8.6%
o 113
 
7.7%
n 108
 
7.4%
r 91
 
6.2%
h 73
 
5.0%
s 64
 
4.4%
y 57
 
3.9%
Other values (15) 307
21.0%
Other Punctuation
ValueCountFrequency (%)
& 69
24.6%
? 63
22.4%
. 44
15.7%
# 39
13.9%
, 36
12.8%
' 19
 
6.8%
: 4
 
1.4%
; 3
 
1.1%
/ 3
 
1.1%
! 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 60
21.1%
0 55
19.4%
2 49
17.3%
3 25
8.8%
9 24
 
8.5%
8 15
 
5.3%
7 15
 
5.3%
5 15
 
5.3%
4 14
 
4.9%
6 12
 
4.2%
Open Punctuation
ValueCountFrequency (%)
( 284
99.6%
[ 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 284
99.6%
] 1
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 2
66.7%
= 1
33.3%
Other Symbol
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
1535
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25449
82.6%
Common 2689
 
8.7%
Latin 2656
 
8.6%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1757
 
6.9%
1693
 
6.7%
1009
 
4.0%
748
 
2.9%
679
 
2.7%
679
 
2.7%
677
 
2.7%
591
 
2.3%
497
 
2.0%
462
 
1.8%
Other values (681) 16657
65.5%
Latin
ValueCountFrequency (%)
a 206
 
7.8%
i 163
 
6.1%
e 154
 
5.8%
A 125
 
4.7%
l 125
 
4.7%
o 113
 
4.3%
n 108
 
4.1%
r 91
 
3.4%
N 90
 
3.4%
S 80
 
3.0%
Other values (41) 1401
52.7%
Common
ValueCountFrequency (%)
1535
57.1%
( 284
 
10.6%
) 284
 
10.6%
& 69
 
2.6%
? 63
 
2.3%
1 60
 
2.2%
0 55
 
2.0%
2 49
 
1.8%
. 44
 
1.6%
# 39
 
1.5%
Other values (22) 207
 
7.7%
Han
ValueCountFrequency (%)
6
60.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25444
82.6%
ASCII 5343
 
17.3%
CJK 10
 
< 0.1%
Compat Jamo 5
 
< 0.1%
Misc Symbols 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1757
 
6.9%
1693
 
6.7%
1009
 
4.0%
748
 
2.9%
679
 
2.7%
679
 
2.7%
677
 
2.7%
591
 
2.3%
497
 
2.0%
462
 
1.8%
Other values (680) 16652
65.4%
ASCII
ValueCountFrequency (%)
1535
28.7%
( 284
 
5.3%
) 284
 
5.3%
a 206
 
3.9%
i 163
 
3.1%
e 154
 
2.9%
A 125
 
2.3%
l 125
 
2.3%
o 113
 
2.1%
n 108
 
2.0%
Other values (71) 2246
42.0%
CJK
ValueCountFrequency (%)
6
60.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Compat Jamo
ValueCountFrequency (%)
5
100.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Distinct4186
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
Minimum1998-12-31 00:00:00
Maximum2024-04-02 15:57:04
2024-04-06T20:18:38.593377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:18:38.858046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
I
3441 
U
1591 
D
 
25

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 3441
68.0%
U 1591
31.5%
D 25
 
0.5%

Length

2024-04-06T20:18:39.100055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:39.276650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 3441
68.0%
u 1591
31.5%
d 25
 
0.5%
Distinct1117
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:04:00
2024-04-06T20:18:39.450773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:18:39.742047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
일반미용업
3527 
피부미용업
789 
네일아트업
510 
메이크업업
 
205
기타
 
25

Length

Max length6
Median length5
Mean length4.9853668
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 3527
69.7%
피부미용업 789
 
15.6%
네일아트업 510
 
10.1%
메이크업업 205
 
4.1%
기타 25
 
0.5%
미용업 기타 1
 
< 0.1%

Length

2024-04-06T20:18:40.031959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:40.304571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 3527
69.7%
피부미용업 789
 
15.6%
네일아트업 510
 
10.1%
메이크업업 205
 
4.1%
기타 26
 
0.5%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2171
Distinct (%)47.2%
Missing460
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean185785.31
Minimum178029.09
Maximum189200.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:40.606360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum178029.09
5-th percentile183209.16
Q1184876.79
median185830
Q3186748.17
95-th percentile188029.31
Maximum189200.15
Range11171.054
Interquartile range (IQR)1871.3792

Descriptive statistics

Standard deviation1472.271
Coefficient of variation (CV)0.0079245823
Kurtosis-0.29182775
Mean185785.31
Median Absolute Deviation (MAD)928.9896
Skewness-0.26734503
Sum8.5405507 × 108
Variance2167581.8
MonotonicityNot monotonic
2024-04-06T20:18:40.824699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185813.15048215 34
 
0.7%
185509.165465662 21
 
0.4%
184526.084367975 20
 
0.4%
184786.585 19
 
0.4%
184971.522700042 19
 
0.4%
186336.003796738 17
 
0.3%
184650.0 17
 
0.3%
185365.0 17
 
0.3%
185798.0 17
 
0.3%
184876.792845641 16
 
0.3%
Other values (2161) 4400
87.0%
(Missing) 460
 
9.1%
ValueCountFrequency (%)
178029.093258404 1
 
< 0.1%
178411.442079953 1
 
< 0.1%
182141.205465089 3
0.1%
182285.447416016 2
 
< 0.1%
182325.929915034 1
 
< 0.1%
182338.465992546 1
 
< 0.1%
182343.628553333 1
 
< 0.1%
182524.823835629 6
0.1%
182735.786874703 1
 
< 0.1%
182742.537963411 3
0.1%
ValueCountFrequency (%)
189200.147733153 2
 
< 0.1%
189172.250232972 1
 
< 0.1%
189124.441211963 2
 
< 0.1%
189098.806779959 7
0.1%
189085.29181102 1
 
< 0.1%
189037.964421932 1
 
< 0.1%
189027.554806104 1
 
< 0.1%
189010.481823545 1
 
< 0.1%
188998.678607376 1
 
< 0.1%
188983.896916895 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2167
Distinct (%)47.1%
Missing460
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean449938.88
Minimum444675.57
Maximum453595.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:41.093880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum444675.57
5-th percentile447677.94
Q1448744.34
median450057.82
Q3450969.02
95-th percentile452244.34
Maximum453595.39
Range8919.8166
Interquartile range (IQR)2224.6752

Descriptive statistics

Standard deviation1479.2381
Coefficient of variation (CV)0.0032876424
Kurtosis-1.0102368
Mean449938.88
Median Absolute Deviation (MAD)1144.5851
Skewness-0.034956334
Sum2.068369 × 109
Variance2188145.5
MonotonicityNot monotonic
2024-04-06T20:18:41.343572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450434.088151226 34
 
0.7%
451786.0 22
 
0.4%
449450.069694741 21
 
0.4%
451722.650631223 20
 
0.4%
450643.056387457 19
 
0.4%
450708.875 19
 
0.4%
451778.0 17
 
0.3%
450960.614576167 17
 
0.3%
450789.0 17
 
0.3%
451667.0 17
 
0.3%
Other values (2157) 4394
86.9%
(Missing) 460
 
9.1%
ValueCountFrequency (%)
444675.571322009 1
 
< 0.1%
444684.405331659 1
 
< 0.1%
447266.978172789 1
 
< 0.1%
447297.791614934 4
0.1%
447299.338292737 1
 
< 0.1%
447316.214981355 1
 
< 0.1%
447330.069531356 1
 
< 0.1%
447347.779817416 1
 
< 0.1%
447382.849201871 1
 
< 0.1%
447397.916160011 1
 
< 0.1%
ValueCountFrequency (%)
453595.387945696 4
0.1%
453178.305694113 4
0.1%
453050.733807442 3
0.1%
453018.018573883 1
 
< 0.1%
453016.85493941 1
 
< 0.1%
453015.644256685 1
 
< 0.1%
453006.779350668 1
 
< 0.1%
453004.645243977 2
< 0.1%
452928.777524775 1
 
< 0.1%
452922.98039021 2
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
미용업
1434 
일반미용업
1320 
<NA>
1232 
피부미용업
454 
종합미용업
209 
Other values (12)
408 

Length

Max length23
Median length19
Mean length4.6476172
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1434
28.4%
일반미용업 1320
26.1%
<NA> 1232
24.4%
피부미용업 454
 
9.0%
종합미용업 209
 
4.1%
네일미용업 176
 
3.5%
네일미용업, 화장ㆍ분장 미용업 50
 
1.0%
화장ㆍ분장 미용업 40
 
0.8%
피부미용업, 화장ㆍ분장 미용업 38
 
0.8%
피부미용업, 네일미용업 35
 
0.7%
Other values (7) 69
 
1.4%

Length

2024-04-06T20:18:41.694643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1611
29.5%
일반미용업 1362
24.9%
na 1232
22.5%
피부미용업 563
 
10.3%
네일미용업 310
 
5.7%
종합미용업 209
 
3.8%
화장ㆍ분장 177
 
3.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)0.7%
Missing2139
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean1.517135
Minimum0
Maximum42
Zeros1885
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:41.925618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum42
Range42
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8200907
Coefficient of variation (CV)1.8588265
Kurtosis22.926233
Mean1.517135
Median Absolute Deviation (MAD)0
Skewness3.4329115
Sum4427
Variance7.9529116
MonotonicityNot monotonic
2024-04-06T20:18:42.106660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1885
37.3%
3 255
 
5.0%
2 214
 
4.2%
4 200
 
4.0%
5 108
 
2.1%
1 66
 
1.3%
6 43
 
0.9%
10 38
 
0.8%
7 26
 
0.5%
8 22
 
0.4%
Other values (11) 61
 
1.2%
(Missing) 2139
42.3%
ValueCountFrequency (%)
0 1885
37.3%
1 66
 
1.3%
2 214
 
4.2%
3 255
 
5.0%
4 200
 
4.0%
5 108
 
2.1%
6 43
 
0.9%
7 26
 
0.5%
8 22
 
0.4%
9 10
 
0.2%
ValueCountFrequency (%)
42 1
 
< 0.1%
25 2
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
15 10
0.2%
14 7
0.1%
13 15
0.3%
12 6
 
0.1%
11 6
 
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.3%
Missing2324
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean0.28430296
Minimum0
Maximum9
Zeros2210
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:42.275151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76242672
Coefficient of variation (CV)2.6817403
Kurtosis26.78143
Mean0.28430296
Median Absolute Deviation (MAD)0
Skewness4.4198702
Sum777
Variance0.58129451
MonotonicityNot monotonic
2024-04-06T20:18:42.436001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2210
43.7%
1 406
 
8.0%
2 49
 
1.0%
3 30
 
0.6%
4 22
 
0.4%
5 8
 
0.2%
7 4
 
0.1%
6 3
 
0.1%
9 1
 
< 0.1%
(Missing) 2324
46.0%
ValueCountFrequency (%)
0 2210
43.7%
1 406
 
8.0%
2 49
 
1.0%
3 30
 
0.6%
4 22
 
0.4%
5 8
 
0.2%
6 3
 
0.1%
7 4
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 4
 
0.1%
6 3
 
0.1%
5 8
 
0.2%
4 22
 
0.4%
3 30
 
0.6%
2 49
 
1.0%
1 406
 
8.0%
0 2210
43.7%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.5%
Missing2451
Missing (%)48.5%
Infinite0
Infinite (%)0.0%
Mean1.1162701
Minimum0
Maximum44
Zeros871
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:42.616187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum44
Range44
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4967361
Coefficient of variation (CV)1.3408368
Kurtosis263.02459
Mean1.1162701
Median Absolute Deviation (MAD)1
Skewness10.430376
Sum2909
Variance2.2402189
MonotonicityNot monotonic
2024-04-06T20:18:42.837888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1088
21.5%
0 871
 
17.2%
2 383
 
7.6%
3 164
 
3.2%
4 54
 
1.1%
5 15
 
0.3%
7 9
 
0.2%
6 9
 
0.2%
8 5
 
0.1%
10 3
 
0.1%
Other values (4) 5
 
0.1%
(Missing) 2451
48.5%
ValueCountFrequency (%)
0 871
17.2%
1 1088
21.5%
2 383
 
7.6%
3 164
 
3.2%
4 54
 
1.1%
5 15
 
0.3%
6 9
 
0.2%
7 9
 
0.2%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
44 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
0.1%
9 2
 
< 0.1%
8 5
 
0.1%
7 9
 
0.2%
6 9
 
0.2%
5 15
 
0.3%
4 54
1.1%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)2.3%
Missing4047
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean4.5861386
Minimum0
Maximum315
Zeros331
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:43.047253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum315
Range315
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.153375
Coefficient of variation (CV)5.7027005
Kurtosis71.34541
Mean4.5861386
Median Absolute Deviation (MAD)1
Skewness8.227348
Sum4632
Variance683.99901
MonotonicityNot monotonic
2024-04-06T20:18:43.277093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 389
 
7.7%
0 331
 
6.5%
2 143
 
2.8%
3 77
 
1.5%
4 33
 
0.7%
5 7
 
0.1%
201 5
 
0.1%
8 3
 
0.1%
10 3
 
0.1%
7 2
 
< 0.1%
Other values (13) 17
 
0.3%
(Missing) 4047
80.0%
ValueCountFrequency (%)
0 331
6.5%
1 389
7.7%
2 143
 
2.8%
3 77
 
1.5%
4 33
 
0.7%
5 7
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
315 1
 
< 0.1%
304 1
 
< 0.1%
206 2
 
< 0.1%
205 1
 
< 0.1%
204 1
 
< 0.1%
203 1
 
< 0.1%
202 1
 
< 0.1%
201 5
0.1%
110 1
 
< 0.1%
104 1
 
< 0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
3831 
0
1141 
1
 
78
2
 
7

Length

Max length4
Median length4
Mean length3.2726913
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3831
75.8%
0 1141
 
22.6%
1 78
 
1.5%
2 7
 
0.1%

Length

2024-04-06T20:18:43.510535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:43.708106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3831
75.8%
0 1141
 
22.6%
1 78
 
1.5%
2 7
 
0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
4525 
0
499 
1
 
29
2
 
4

Length

Max length4
Median length4
Mean length3.6843979
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> 4525
89.5%
0 499
 
9.9%
1 29
 
0.6%
2 4
 
0.1%

Length

2024-04-06T20:18:43.914589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:44.084335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4525
89.5%
0 499
 
9.9%
1 29
 
0.6%
2 4
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
2711 
<NA>
2346 

Length

Max length4
Median length1
Mean length2.3917342
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2711
53.6%
<NA> 2346
46.4%

Length

2024-04-06T20:18:44.271944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:44.436145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2711
53.6%
na 2346
46.4%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
2711 
<NA>
2346 

Length

Max length4
Median length1
Mean length2.3917342
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2711
53.6%
<NA> 2346
46.4%

Length

2024-04-06T20:18:44.603774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:44.758610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2711
53.6%
na 2346
46.4%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
0
2711 
<NA>
2346 

Length

Max length4
Median length1
Mean length2.3917342
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2711
53.6%
<NA> 2346
46.4%

Length

2024-04-06T20:18:44.930593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:45.114232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2711
53.6%
na 2346
46.4%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing1280
Missing (%)25.3%
Memory size10.0 KiB
False
3777 
(Missing)
1280 
ValueCountFrequency (%)
False 3777
74.7%
(Missing) 1280
 
25.3%
2024-04-06T20:18:45.285867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct21
Distinct (%)0.6%
Missing1350
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean3.6485028
Minimum0
Maximum325
Zeros416
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:45.477402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum325
Range325
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.3359863
Coefficient of variation (CV)2.284769
Kurtosis1201.7524
Mean3.6485028
Median Absolute Deviation (MAD)1
Skewness32.966713
Sum13525
Variance69.488667
MonotonicityNot monotonic
2024-04-06T20:18:45.696444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1362
26.9%
4 702
13.9%
2 459
 
9.1%
0 416
 
8.2%
5 228
 
4.5%
6 179
 
3.5%
8 84
 
1.7%
1 80
 
1.6%
7 71
 
1.4%
10 42
 
0.8%
Other values (11) 84
 
1.7%
(Missing) 1350
26.7%
ValueCountFrequency (%)
0 416
 
8.2%
1 80
 
1.6%
2 459
 
9.1%
3 1362
26.9%
4 702
13.9%
5 228
 
4.5%
6 179
 
3.5%
7 71
 
1.4%
8 84
 
1.7%
9 19
 
0.4%
ValueCountFrequency (%)
325 1
 
< 0.1%
316 1
 
< 0.1%
195 1
 
< 0.1%
20 3
 
0.1%
16 7
 
0.1%
15 5
 
0.1%
14 8
 
0.2%
13 6
 
0.1%
12 23
0.5%
11 10
0.2%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5057
Missing (%)100.0%
Memory size44.6 KiB

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
5055 
2
 
2

Length

Max length4
Median length4
Mean length3.9988135
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> 5055
> 99.9%
2 2
 
< 0.1%

Length

2024-04-06T20:18:45.918740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:46.074498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5055
> 99.9%
2 2
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
3498 
임대
1541 
자가
 
18

Length

Max length4
Median length4
Mean length3.3834289
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> 3498
69.2%
임대 1541
30.5%
자가 18
 
0.4%

Length

2024-04-06T20:18:46.272851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:46.448234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3498
69.2%
임대 1541
30.5%
자가 18
 
0.4%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
2998 
0
2059 

Length

Max length4
Median length4
Mean length2.7785248
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> 2998
59.3%
0 2059
40.7%

Length

2024-04-06T20:18:46.634242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:46.827086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2998
59.3%
0 2059
40.7%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.6%
Missing3800
Missing (%)75.1%
Infinite0
Infinite (%)0.0%
Mean0.12490056
Minimum0
Maximum7
Zeros1135
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:46.941757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.48166264
Coefficient of variation (CV)3.856369
Kurtosis68.561086
Mean0.12490056
Median Absolute Deviation (MAD)0
Skewness6.9176872
Sum157
Variance0.2319989
MonotonicityNot monotonic
2024-04-06T20:18:47.110498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1135
 
22.4%
1 108
 
2.1%
2 5
 
0.1%
4 5
 
0.1%
3 2
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 3800
75.1%
ValueCountFrequency (%)
0 1135
22.4%
1 108
 
2.1%
2 5
 
0.1%
3 2
 
< 0.1%
4 5
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
4 5
 
0.1%
3 2
 
< 0.1%
2 5
 
0.1%
1 108
 
2.1%
0 1135
22.4%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
3801 
0
1245 
1
 
10
2
 
1

Length

Max length4
Median length4
Mean length3.2548942
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> 3801
75.2%
0 1245
 
24.6%
1 10
 
0.2%
2 1
 
< 0.1%

Length

2024-04-06T20:18:47.476923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:47.645945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3801
75.2%
0 1245
 
24.6%
1 10
 
0.2%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
<NA>
3116 
0
1941 

Length

Max length4
Median length4
Mean length2.8485268
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> 3116
61.6%
0 1941
38.4%

Length

2024-04-06T20:18:47.869819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:18:48.053573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3116
61.6%
0 1941
38.4%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)0.8%
Missing3157
Missing (%)62.4%
Infinite0
Infinite (%)0.0%
Mean1.0073684
Minimum0
Maximum14
Zeros1224
Zeros (%)24.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2024-04-06T20:18:48.190124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7919569
Coefficient of variation (CV)1.7788496
Kurtosis8.6188559
Mean1.0073684
Median Absolute Deviation (MAD)0
Skewness2.5366481
Sum1914
Variance3.2111094
MonotonicityNot monotonic
2024-04-06T20:18:48.765475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 1224
 
24.2%
2 221
 
4.4%
1 156
 
3.1%
3 143
 
2.8%
4 51
 
1.0%
5 46
 
0.9%
6 26
 
0.5%
8 12
 
0.2%
7 7
 
0.1%
9 4
 
0.1%
Other values (5) 10
 
0.2%
(Missing) 3157
62.4%
ValueCountFrequency (%)
0 1224
24.2%
1 156
 
3.1%
2 221
 
4.4%
3 143
 
2.8%
4 51
 
1.0%
5 46
 
0.9%
6 26
 
0.5%
7 7
 
0.1%
8 12
 
0.2%
9 4
 
0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 1
 
< 0.1%
12 4
 
0.1%
11 1
 
< 0.1%
10 3
 
0.1%
9 4
 
0.1%
8 12
 
0.2%
7 7
 
0.1%
6 26
0.5%
5 46
0.9%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing1232
Missing (%)24.4%
Memory size10.0 KiB
False
3824 
True
 
1
(Missing)
1232 
ValueCountFrequency (%)
False 3824
75.6%
True 1
 
< 0.1%
(Missing) 1232
 
24.4%
2024-04-06T20:18:48.982606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031500003150000-204-1967-0096319670906<NA>3폐업2폐업20030225<NA><NA><NA>02 663133313.56157812서울특별시 강서구 공항동 52-5번지<NA><NA>월계2003-04-08 00:00:00I2018-08-31 23:59:59.0일반미용업183118.598958450884.384415미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131500003150000-204-1970-0097219700828<NA>3폐업2폐업19970113<NA><NA><NA>02 0000013.6157860서울특별시 강서구 염창동 108-0번지<NA><NA>유일2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231500003150000-204-1972-0096219720309<NA>3폐업2폐업19930317<NA><NA><NA>020662258616.65157852서울특별시 강서구 방화동 608-49번지<NA><NA>소망2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업183674.059422451387.536361미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331500003150000-204-1972-0097119721206<NA>3폐업2폐업19980821<NA><NA><NA>02 664531915.2157846서울특별시 강서구 방화동 249-153번지<NA><NA>성민주미용타운2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업183411.500294451829.209569미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431500003150000-204-1974-0098319740130<NA>3폐업2폐업19970822<NA><NA><NA>02 693931312.39157871서울특별시 강서구 화곡동 98-59번지<NA><NA>2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업186060.178827448951.886728미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531500003150000-204-1975-0094519751201<NA>3폐업2폐업20030225<NA><NA><NA>020698719018.48157884서울특별시 강서구 화곡동 362-124번지<NA><NA>희영헤어아카데미2003-04-03 00:00:00I2018-08-31 23:59:59.0일반미용업185795.015806447924.839232미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631500003150000-204-1975-0096419751117<NA>3폐업2폐업19971117<NA><NA><NA>02 662456314.7157812서울특별시 강서구 공항동 53-41번지<NA><NA>박진숙2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업183166.343309450992.871859미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731500003150000-204-1975-0096619750320<NA>3폐업2폐업20071004<NA><NA><NA>022651091917.6157836서울특별시 강서구 등촌동 506-26번지<NA><NA>2003-04-28 00:00:00I2018-08-31 23:59:59.0일반미용업187796.411929449826.543882미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831500003150000-204-1975-0096819750319<NA>3폐업2폐업20030225<NA><NA><NA>02 694885014.08157861서울특별시 강서구 염창동 241-15번지<NA><NA>한강2003-04-16 00:00:00I2018-08-31 23:59:59.0일반미용업188063.975815450389.846832미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931500003150000-204-1975-0098519750424<NA>3폐업2폐업19940624<NA><NA><NA>020602423233.48157010서울특별시 강서구 화곡동 산 976-7번지<NA><NA>2002-07-26 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
504731500003150000-226-2021-0000520211223<NA>1영업/정상1영업<NA><NA><NA><NA><NA>22.6157918서울특별시 강서구 화곡동 1017-14서울특별시 강서구 화곡로13길 98-12, B102호 (화곡동)7709더영뷰티스튜디오2021-12-23 15:59:08I2021-12-25 00:22:42.0메이크업업185223.388288449126.148458피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N3<NA><NA><NA><NA>00001N
504831500003150000-226-2021-000062021-09-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.57157-210서울특별시 강서구 마곡동 759-1 두산더랜드타워 B동 117호서울특별시 강서구 마곡서로 152, 두산더랜드타워 B동 117호 (마곡동)7788무유네일2024-03-12 17:10:46I2023-12-02 23:04:00.0네일아트업184526.084368451722.650631<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
504931500003150000-226-2022-0000120220415<NA>1영업/정상1영업<NA><NA><NA><NA><NA>64.25157807서울특별시 강서구 가양동 1479-4 시드프라자 301호서울특별시 강서구 양천로57길 13, 시드프라자 3층 301호 (가양동)7527BEAUTY HEAL(뷰티힐)2022-05-17 14:45:17U2021-12-04 23:09:00.0피부미용업186856.937643451268.389267<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505031500003150000-226-2022-000022022-08-16<NA>3폐업2폐업2023-04-20<NA><NA><NA><NA>40.56157-210서울특별시 강서구 마곡동 794 문영비즈웍스 2층 203호서울특별시 강서구 강서로 391, 문영비즈웍스 2층 203호 (마곡동)7803모블링 맨즈 살롱2023-04-20 14:24:40U2022-12-03 22:03:00.0피부미용업185655.7716450931.675671<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505131500003150000-226-2022-000032022-06-22<NA>1영업/정상1영업<NA><NA><NA><NA>021899307310.0157-010서울특별시 강서구 화곡동 1165-1 강서힐스테이트 상가동 507호서울특별시 강서구 강서로 242, 상가동 5층 507호 (화곡동, 강서힐스테이트)7694윤뷰티2024-02-16 11:33:40I2023-12-01 23:08:00.0메이크업업185509.165466449450.069695<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505231500003150000-226-2023-000012023-02-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>86.22157-839서울특별시 강서구 등촌동 631 등촌동두산위브센티움서울특별시 강서구 양천로 564, 등촌동두산위브센티움 2층 203호 (등촌동)7551릴리스 뷰티샵2023-02-20 13:39:42I2022-12-01 22:02:00.0네일아트업187682.690357450578.892821<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505331500003150000-226-2023-000022023-06-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>36.63157-210서울특별시 강서구 마곡동 798-2 류마타워서울특별시 강서구 공항대로 164, 류마타워 지하1층 111호 (마곡동)7807디네일2023-06-28 17:43:20I2022-12-05 21:00:00.0네일아트업184514.388815450797.722244<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505431500003150000-226-2023-000032023-08-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.92157-925서울특별시 강서구 화곡동 1082-5 웰스파크빌서울특별시 강서구 화곡로26가길 49, 101호 (화곡동, 웰스파크빌)7717아이예쁘다 브로우&속눈썹2024-02-21 09:59:55U2023-12-01 22:03:00.0메이크업업185559.65402448658.478975<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505531500003150000-226-2023-000042023-11-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.13157-880서울특별시 강서구 화곡동 342-98서울특별시 강서구 곰달래로 143, 1층 (화곡동)7760리리뷰티2024-02-15 11:12:19I2023-12-01 23:07:00.0네일아트업186563.648555447633.464413<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
505631500003150000-226-2024-000012024-02-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>48.74157-280서울특별시 강서구 내발산동 657 우장산힐스테이트 상가1동 208호서울특별시 강서구 강서로 348, 상가1동 2층 208호 (내발산동, 우장산힐스테이트)7651벨르뷰티2024-02-02 11:49:00I2023-12-02 00:04:00.0피부미용업185813.150482450434.088151<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>