Overview

Dataset statistics

Number of variables47
Number of observations10000
Missing cells114855
Missing cells (%)24.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory411.0 B

Variable types

Numeric12
Text8
DateTime4
Unsupported4
Categorical17
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
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-16065/S/1/datasetView.do

Alerts

업태구분명 is highly imbalanced (86.5%)Imbalance
위생업태명 is highly imbalanced (72.1%)Imbalance
발한실여부 is highly imbalanced (97.6%)Imbalance
좌석수 is highly imbalanced (61.0%)Imbalance
조건부허가시작일자 is highly imbalanced (99.8%)Imbalance
조건부허가종료일자 is highly imbalanced (99.8%)Imbalance
건물소유구분명 is highly imbalanced (54.8%)Imbalance
여성종사자수 is highly imbalanced (65.3%)Imbalance
남성종사자수 is highly imbalanced (75.7%)Imbalance
다중이용업소여부 is highly imbalanced (99.5%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 2375 (23.8%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
전화번호 has 912 (9.1%) missing valuesMissing
도로명주소 has 5005 (50.0%) missing valuesMissing
도로명우편번호 has 5129 (51.3%) missing valuesMissing
좌표정보(X) has 955 (9.6%) missing valuesMissing
좌표정보(Y) has 955 (9.6%) missing valuesMissing
건물지상층수 has 3816 (38.2%) missing valuesMissing
건물지하층수 has 4630 (46.3%) missing valuesMissing
사용시작지상층 has 4232 (42.3%) missing valuesMissing
사용끝지상층 has 6181 (61.8%) missing valuesMissing
사용시작지하층 has 6215 (62.2%) missing valuesMissing
사용끝지하층 has 8172 (81.7%) missing valuesMissing
발한실여부 has 937 (9.4%) missing valuesMissing
조건부허가신고사유 has 9996 (> 99.9%) missing valuesMissing
세탁기수 has 7117 (71.2%) missing valuesMissing
회수건조수 has 7368 (73.7%) missing valuesMissing
다중이용업소여부 has 828 (8.3%) missing valuesMissing
사용끝지상층 is highly skewed (γ1 = 38.64869562)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 4093 (40.9%) zerosZeros
건물지하층수 has 4461 (44.6%) zerosZeros
사용시작지상층 has 3030 (30.3%) zerosZeros
사용끝지상층 has 1138 (11.4%) zerosZeros
사용시작지하층 has 3576 (35.8%) zerosZeros
사용끝지하층 has 1625 (16.2%) zerosZeros
세탁기수 has 1178 (11.8%) zerosZeros
회수건조수 has 1310 (13.1%) zerosZeros

Reproduction

Analysis started2024-05-11 08:03:36.747768
Analysis finished2024-05-11 08:03:39.181221
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3133696
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:39.244079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3020000
Q13070000
median3140000
Q33200000
95-th percentile3240000
Maximum3240000
Range240000
Interquartile range (IQR)130000

Descriptive statistics

Standard deviation71753.707
Coefficient of variation (CV)0.022897469
Kurtosis-1.2356631
Mean3133696
Median Absolute Deviation (MAD)70000
Skewness-0.1307755
Sum3.133696 × 1010
Variance5.1485944 × 109
MonotonicityNot monotonic
2024-05-11T17:03:39.393609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3230000 734
 
7.3%
3220000 605
 
6.0%
3070000 550
 
5.5%
3200000 542
 
5.4%
3240000 528
 
5.3%
3110000 473
 
4.7%
3140000 446
 
4.5%
3040000 437
 
4.4%
3060000 433
 
4.3%
3160000 420
 
4.2%
Other values (15) 4832
48.3%
ValueCountFrequency (%)
3000000 203
 
2.0%
3010000 194
 
1.9%
3020000 291
2.9%
3030000 346
3.5%
3040000 437
4.4%
3050000 306
3.1%
3060000 433
4.3%
3070000 550
5.5%
3080000 335
3.4%
3090000 309
3.1%
ValueCountFrequency (%)
3240000 528
5.3%
3230000 734
7.3%
3220000 605
6.0%
3210000 388
3.9%
3200000 542
5.4%
3190000 354
3.5%
3180000 372
3.7%
3170000 287
 
2.9%
3160000 420
4.2%
3150000 385
3.9%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T17:03:39.618185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters220000
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row3100000-205-2003-00034
2nd row3040000-205-2011-00001
3rd row3200000-205-1988-02649
4th row3220000-205-1989-02837
5th row3140000-205-1987-01722
ValueCountFrequency (%)
3100000-205-2003-00034 1
 
< 0.1%
3150000-205-1993-00010 1
 
< 0.1%
3230000-205-1991-03049 1
 
< 0.1%
3200000-205-1994-02488 1
 
< 0.1%
3020000-205-1993-01477 1
 
< 0.1%
3120000-205-1989-00818 1
 
< 0.1%
3020000-205-1987-01641 1
 
< 0.1%
3240000-205-1987-02287 1
 
< 0.1%
3060000-205-2008-00013 1
 
< 0.1%
3200000-205-1998-02892 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T17:03:39.968174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84241
38.3%
- 30000
 
13.6%
2 24004
 
10.9%
1 18657
 
8.5%
3 15108
 
6.9%
5 14005
 
6.4%
9 12873
 
5.9%
8 6965
 
3.2%
7 5698
 
2.6%
4 4422
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190000
86.4%
Dash Punctuation 30000
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84241
44.3%
2 24004
 
12.6%
1 18657
 
9.8%
3 15108
 
8.0%
5 14005
 
7.4%
9 12873
 
6.8%
8 6965
 
3.7%
7 5698
 
3.0%
4 4422
 
2.3%
6 4027
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84241
38.3%
- 30000
 
13.6%
2 24004
 
10.9%
1 18657
 
8.5%
3 15108
 
6.9%
5 14005
 
6.4%
9 12873
 
5.9%
8 6965
 
3.2%
7 5698
 
2.6%
4 4422
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84241
38.3%
- 30000
 
13.6%
2 24004
 
10.9%
1 18657
 
8.5%
3 15108
 
6.9%
5 14005
 
6.4%
9 12873
 
5.9%
8 6965
 
3.2%
7 5698
 
2.6%
4 4422
 
2.0%
Distinct4777
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1957-05-28 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T17:03:40.122012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:03:40.254717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
7625 
1
2375 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 7625
76.2%
1 2375
 
23.8%

Length

2024-05-11T17:03:40.377647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:40.466831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 7625
76.2%
1 2375
 
23.8%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
7625 
영업/정상
2375 

Length

Max length5
Median length2
Mean length2.7125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 7625
76.2%
영업/정상 2375
 
23.8%

Length

2024-05-11T17:03:40.567450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:40.666744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 7625
76.2%
영업/정상 2375
 
23.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
7625 
1
2375 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 7625
76.2%
1 2375
 
23.8%

Length

2024-05-11T17:03:40.770680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:40.884070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 7625
76.2%
1 2375
 
23.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
7625 
영업
2375 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 7625
76.2%
영업 2375
 
23.8%

Length

2024-05-11T17:03:40.994712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:41.144403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 7625
76.2%
영업 2375
 
23.8%

폐업일자
Date

MISSING 

Distinct4292
Distinct (%)56.3%
Missing2375
Missing (%)23.8%
Memory size156.2 KiB
Minimum1988-01-01 00:00:00
Maximum2024-05-07 00:00:00
2024-05-11T17:03:41.260844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:03:41.411234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

전화번호
Text

MISSING 

Distinct8257
Distinct (%)90.9%
Missing912
Missing (%)9.1%
Memory size156.2 KiB
2024-05-11T17:03:41.764669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.962368
Min length2

Characters and Unicode

Total characters90538
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

Unique7888 ?
Unique (%)86.8%

Sample

1st row02 9381850
2nd row02 8885340
3rd row0222268340
4th row0226956541
5th row02 4273451
ValueCountFrequency (%)
02 5445
36.0%
0200000000 165
 
1.1%
0 126
 
0.8%
00000 67
 
0.4%
070 10
 
0.1%
8285 10
 
0.1%
442 8
 
0.1%
8284 7
 
< 0.1%
454 7
 
< 0.1%
532 6
 
< 0.1%
Other values (8553) 9289
61.4%
2024-05-11T17:03:42.229754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17344
19.2%
2 15810
17.5%
4 7434
8.2%
7185
7.9%
3 7046
7.8%
8 6644
 
7.3%
6 6290
 
6.9%
9 6159
 
6.8%
5 6051
 
6.7%
7 5672
 
6.3%
Other values (2) 4903
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83352
92.1%
Space Separator 7185
 
7.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17344
20.8%
2 15810
19.0%
4 7434
8.9%
3 7046
8.5%
8 6644
 
8.0%
6 6290
 
7.5%
9 6159
 
7.4%
5 6051
 
7.3%
7 5672
 
6.8%
1 4902
 
5.9%
Space Separator
ValueCountFrequency (%)
7185
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 90538
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17344
19.2%
2 15810
17.5%
4 7434
8.2%
7185
7.9%
3 7046
7.8%
8 6644
 
7.3%
6 6290
 
6.9%
9 6159
 
6.8%
5 6051
 
6.7%
7 5672
 
6.3%
Other values (2) 4903
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17344
19.2%
2 15810
17.5%
4 7434
8.2%
7185
7.9%
3 7046
7.8%
8 6644
 
7.3%
6 6290
 
6.9%
9 6159
 
6.8%
5 6051
 
6.7%
7 5672
 
6.3%
Other values (2) 4903
 
5.4%
Distinct2797
Distinct (%)28.0%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T17:03:42.594274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.6739413
Min length3

Characters and Unicode

Total characters46688
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1808 ?
Unique (%)18.1%

Sample

1st row29.67
2nd row29.70
3rd row39.60
4th row20.00
5th row14.80
ValueCountFrequency (%)
00 1871
 
18.7%
33.00 395
 
4.0%
26.40 252
 
2.5%
23.10 195
 
2.0%
24.00 145
 
1.5%
30.00 129
 
1.3%
19.80 120
 
1.2%
16.50 108
 
1.1%
20.00 102
 
1.0%
15.00 86
 
0.9%
Other values (2787) 6586
65.9%
2024-05-11T17:03:43.133634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11916
25.5%
. 9989
21.4%
2 4494
 
9.6%
1 4072
 
8.7%
3 3518
 
7.5%
4 2623
 
5.6%
6 2478
 
5.3%
5 2270
 
4.9%
8 1917
 
4.1%
9 1847
 
4.0%
Other values (2) 1564
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36683
78.6%
Other Punctuation 10005
 
21.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11916
32.5%
2 4494
 
12.3%
1 4072
 
11.1%
3 3518
 
9.6%
4 2623
 
7.2%
6 2478
 
6.8%
5 2270
 
6.2%
8 1917
 
5.2%
9 1847
 
5.0%
7 1548
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 9989
99.8%
, 16
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 46688
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11916
25.5%
. 9989
21.4%
2 4494
 
9.6%
1 4072
 
8.7%
3 3518
 
7.5%
4 2623
 
5.6%
6 2478
 
5.3%
5 2270
 
4.9%
8 1917
 
4.1%
9 1847
 
4.0%
Other values (2) 1564
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46688
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11916
25.5%
. 9989
21.4%
2 4494
 
9.6%
1 4072
 
8.7%
3 3518
 
7.5%
4 2623
 
5.6%
6 2478
 
5.3%
5 2270
 
4.9%
8 1917
 
4.1%
9 1847
 
4.0%
Other values (2) 1564
 
3.3%
Distinct2576
Distinct (%)25.8%
Missing12
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T17:03:43.486905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0414497
Min length6

Characters and Unicode

Total characters60342
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

Unique827 ?
Unique (%)8.3%

Sample

1st row139816
2nd row143841
3rd row151812
4th row135943
5th row158829
ValueCountFrequency (%)
138210 112
 
1.1%
100450 58
 
0.6%
150841 37
 
0.4%
122200 30
 
0.3%
139240 29
 
0.3%
137040 24
 
0.2%
153801 23
 
0.2%
158070 22
 
0.2%
150840 21
 
0.2%
134830 21
 
0.2%
Other values (2566) 9611
96.2%
2024-05-11T17:03:43.921408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14161
23.5%
8 10123
16.8%
3 7524
12.5%
0 6146
10.2%
5 5409
 
9.0%
2 5179
 
8.6%
4 3747
 
6.2%
6 2670
 
4.4%
7 2620
 
4.3%
9 2349
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59928
99.3%
Dash Punctuation 414
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14161
23.6%
8 10123
16.9%
3 7524
12.6%
0 6146
10.3%
5 5409
 
9.0%
2 5179
 
8.6%
4 3747
 
6.3%
6 2670
 
4.5%
7 2620
 
4.4%
9 2349
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60342
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14161
23.5%
8 10123
16.8%
3 7524
12.5%
0 6146
10.2%
5 5409
 
9.0%
2 5179
 
8.6%
4 3747
 
6.2%
6 2670
 
4.4%
7 2620
 
4.3%
9 2349
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14161
23.5%
8 10123
16.8%
3 7524
12.5%
0 6146
10.2%
5 5409
 
9.0%
2 5179
 
8.6%
4 3747
 
6.2%
6 2670
 
4.4%
7 2620
 
4.3%
9 2349
 
3.9%
Distinct9606
Distinct (%)96.1%
Missing9
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T17:03:44.312727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length54
Mean length25.200781
Min length8

Characters and Unicode

Total characters251781
Distinct characters487
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9249 ?
Unique (%)92.6%

Sample

1st row서울특별시 노원구 상계동 1305 상계동 동양메이저아파트 상가동 206호
2nd row서울특별시 광진구 자양동 11-4번지
3rd row서울특별시 관악구 봉천동 1690-152번지
4th row서울특별시 강남구 일원동 627-0번지 지상1층
5th row서울특별시 양천구 신월동 180-12
ValueCountFrequency (%)
서울특별시 9990
 
21.7%
송파구 733
 
1.6%
1층 649
 
1.4%
강남구 605
 
1.3%
성북구 550
 
1.2%
관악구 542
 
1.2%
강동구 528
 
1.1%
은평구 473
 
1.0%
양천구 446
 
1.0%
광진구 437
 
1.0%
Other values (10900) 31033
67.5%
2024-05-11T17:03:44.858045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
44609
 
17.7%
12280
 
4.9%
11367
 
4.5%
1 11351
 
4.5%
10717
 
4.3%
10195
 
4.0%
10006
 
4.0%
9998
 
4.0%
9990
 
4.0%
9304
 
3.7%
Other values (477) 111964
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 145874
57.9%
Decimal Number 50923
 
20.2%
Space Separator 44609
 
17.7%
Dash Punctuation 8679
 
3.4%
Close Punctuation 532
 
0.2%
Open Punctuation 530
 
0.2%
Uppercase Letter 423
 
0.2%
Other Punctuation 176
 
0.1%
Lowercase Letter 29
 
< 0.1%
Math Symbol 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12280
 
8.4%
11367
 
7.8%
10717
 
7.3%
10195
 
7.0%
10006
 
6.9%
9998
 
6.9%
9990
 
6.8%
9304
 
6.4%
8385
 
5.7%
2167
 
1.5%
Other values (420) 51465
35.3%
Uppercase Letter
ValueCountFrequency (%)
B 135
31.9%
A 122
28.8%
P 34
 
8.0%
T 28
 
6.6%
K 21
 
5.0%
S 19
 
4.5%
C 14
 
3.3%
D 8
 
1.9%
I 7
 
1.7%
M 6
 
1.4%
Other values (11) 29
 
6.9%
Decimal Number
ValueCountFrequency (%)
1 11351
22.3%
2 7009
13.8%
3 5343
10.5%
0 5010
9.8%
4 4528
 
8.9%
5 4152
 
8.2%
6 3966
 
7.8%
7 3409
 
6.7%
8 3164
 
6.2%
9 2991
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 7
24.1%
a 5
17.2%
s 4
13.8%
k 4
13.8%
b 4
13.8%
c 2
 
6.9%
r 1
 
3.4%
p 1
 
3.4%
g 1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 124
70.5%
@ 32
 
18.2%
. 14
 
8.0%
/ 5
 
2.8%
? 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 522
98.1%
] 6
 
1.1%
4
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 521
98.3%
[ 5
 
0.9%
4
 
0.8%
Space Separator
ValueCountFrequency (%)
44609
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8679
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 145874
57.9%
Common 105454
41.9%
Latin 453
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12280
 
8.4%
11367
 
7.8%
10717
 
7.3%
10195
 
7.0%
10006
 
6.9%
9998
 
6.9%
9990
 
6.8%
9304
 
6.4%
8385
 
5.7%
2167
 
1.5%
Other values (420) 51465
35.3%
Latin
ValueCountFrequency (%)
B 135
29.8%
A 122
26.9%
P 34
 
7.5%
T 28
 
6.2%
K 21
 
4.6%
S 19
 
4.2%
C 14
 
3.1%
D 8
 
1.8%
I 7
 
1.5%
e 7
 
1.5%
Other values (21) 58
12.8%
Common
ValueCountFrequency (%)
44609
42.3%
1 11351
 
10.8%
- 8679
 
8.2%
2 7009
 
6.6%
3 5343
 
5.1%
0 5010
 
4.8%
4 4528
 
4.3%
5 4152
 
3.9%
6 3966
 
3.8%
7 3409
 
3.2%
Other values (16) 7398
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 145874
57.9%
ASCII 105896
42.1%
None 9
 
< 0.1%
Number Forms 1
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
44609
42.1%
1 11351
 
10.7%
- 8679
 
8.2%
2 7009
 
6.6%
3 5343
 
5.0%
0 5010
 
4.7%
4 4528
 
4.3%
5 4152
 
3.9%
6 3966
 
3.7%
7 3409
 
3.2%
Other values (42) 7840
 
7.4%
Hangul
ValueCountFrequency (%)
12280
 
8.4%
11367
 
7.8%
10717
 
7.3%
10195
 
7.0%
10006
 
6.9%
9998
 
6.9%
9990
 
6.8%
9304
 
6.4%
8385
 
5.7%
2167
 
1.5%
Other values (420) 51465
35.3%
None
ValueCountFrequency (%)
4
44.4%
4
44.4%
1
 
11.1%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK Compat
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct4946
Distinct (%)99.0%
Missing5005
Missing (%)50.0%
Memory size156.2 KiB
2024-05-11T17:03:45.255535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length57
Mean length31.316717
Min length20

Characters and Unicode

Total characters156427
Distinct characters508
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4899 ?
Unique (%)98.1%

Sample

1st row서울특별시 노원구 상계로5길 12 (상계동,상계동 동양메이저아파트 상가동 206호)
2nd row서울특별시 광진구 동일로18길 62 (자양동)
3rd row서울특별시 관악구 남부순환로 1951 (봉천동)
4th row서울특별시 강남구 개포로128길 35 (일원동,지상1층)
5th row서울특별시 양천구 남부순환로40길 70 (신월동)
ValueCountFrequency (%)
서울특별시 4995
 
17.0%
1층 765
 
2.6%
강남구 351
 
1.2%
송파구 328
 
1.1%
관악구 262
 
0.9%
광진구 249
 
0.8%
강서구 247
 
0.8%
서초구 247
 
0.8%
은평구 245
 
0.8%
성북구 233
 
0.8%
Other values (6461) 21444
73.0%
2024-05-11T17:03:45.857680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24381
 
15.6%
1 7050
 
4.5%
6895
 
4.4%
6027
 
3.9%
5458
 
3.5%
) 5226
 
3.3%
( 5224
 
3.3%
5198
 
3.3%
5177
 
3.3%
5034
 
3.2%
Other values (498) 80757
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91773
58.7%
Decimal Number 25355
 
16.2%
Space Separator 24381
 
15.6%
Close Punctuation 5229
 
3.3%
Open Punctuation 5227
 
3.3%
Other Punctuation 3470
 
2.2%
Dash Punctuation 647
 
0.4%
Uppercase Letter 317
 
0.2%
Lowercase Letter 21
 
< 0.1%
Math Symbol 5
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6895
 
7.5%
6027
 
6.6%
5458
 
5.9%
5198
 
5.7%
5177
 
5.6%
5034
 
5.5%
4999
 
5.4%
4995
 
5.4%
3945
 
4.3%
1726
 
1.9%
Other values (442) 42319
46.1%
Uppercase Letter
ValueCountFrequency (%)
B 142
44.8%
A 66
20.8%
K 15
 
4.7%
S 13
 
4.1%
P 13
 
4.1%
C 10
 
3.2%
T 8
 
2.5%
I 7
 
2.2%
D 7
 
2.2%
R 6
 
1.9%
Other values (12) 30
 
9.5%
Decimal Number
ValueCountFrequency (%)
1 7050
27.8%
2 3828
15.1%
3 2618
 
10.3%
0 2450
 
9.7%
4 2049
 
8.1%
5 1820
 
7.2%
6 1664
 
6.6%
7 1412
 
5.6%
8 1292
 
5.1%
9 1172
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
e 5
23.8%
b 4
19.0%
a 3
14.3%
k 3
14.3%
s 3
14.3%
r 1
 
4.8%
p 1
 
4.8%
c 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 3435
99.0%
@ 21
 
0.6%
. 8
 
0.2%
/ 4
 
0.1%
? 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 5226
99.9%
] 2
 
< 0.1%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 5224
99.9%
[ 2
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
24381
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 647
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91773
58.7%
Common 64315
41.1%
Latin 339
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6895
 
7.5%
6027
 
6.6%
5458
 
5.9%
5198
 
5.7%
5177
 
5.6%
5034
 
5.5%
4999
 
5.4%
4995
 
5.4%
3945
 
4.3%
1726
 
1.9%
Other values (442) 42319
46.1%
Latin
ValueCountFrequency (%)
B 142
41.9%
A 66
19.5%
K 15
 
4.4%
S 13
 
3.8%
P 13
 
3.8%
C 10
 
2.9%
T 8
 
2.4%
I 7
 
2.1%
D 7
 
2.1%
R 6
 
1.8%
Other values (21) 52
 
15.3%
Common
ValueCountFrequency (%)
24381
37.9%
1 7050
 
11.0%
) 5226
 
8.1%
( 5224
 
8.1%
2 3828
 
6.0%
, 3435
 
5.3%
3 2618
 
4.1%
0 2450
 
3.8%
4 2049
 
3.2%
5 1820
 
2.8%
Other values (15) 6234
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91773
58.7%
ASCII 64650
41.3%
None 3
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24381
37.7%
1 7050
 
10.9%
) 5226
 
8.1%
( 5224
 
8.1%
2 3828
 
5.9%
, 3435
 
5.3%
3 2618
 
4.0%
0 2450
 
3.8%
4 2049
 
3.2%
5 1820
 
2.8%
Other values (42) 6569
 
10.2%
Hangul
ValueCountFrequency (%)
6895
 
7.5%
6027
 
6.6%
5458
 
5.9%
5198
 
5.7%
5177
 
5.6%
5034
 
5.5%
4999
 
5.4%
4995
 
5.4%
3945
 
4.3%
1726
 
1.9%
Other values (442) 42319
46.1%
None
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct2961
Distinct (%)60.8%
Missing5129
Missing (%)51.3%
Infinite0
Infinite (%)0.0%
Mean5122.8265
Minimum1002
Maximum8865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:46.027339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1364.5
Q13164.5
median5237
Q37020.5
95-th percentile8710
Maximum8865
Range7863
Interquartile range (IQR)3856

Descriptive statistics

Standard deviation2291.0247
Coefficient of variation (CV)0.44721888
Kurtosis-1.1279424
Mean5122.8265
Median Absolute Deviation (MAD)1905
Skewness-0.074060651
Sum24953288
Variance5248794.3
MonotonicityNot monotonic
2024-05-11T17:03:46.447180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5015 7
 
0.1%
7938 7
 
0.1%
4374 7
 
0.1%
2831 7
 
0.1%
8771 6
 
0.1%
6002 6
 
0.1%
5735 6
 
0.1%
2829 6
 
0.1%
8735 6
 
0.1%
8784 6
 
0.1%
Other values (2951) 4807
48.1%
(Missing) 5129
51.3%
ValueCountFrequency (%)
1002 2
< 0.1%
1003 1
 
< 0.1%
1004 1
 
< 0.1%
1005 4
< 0.1%
1006 1
 
< 0.1%
1010 1
 
< 0.1%
1015 1
 
< 0.1%
1020 2
< 0.1%
1021 1
 
< 0.1%
1023 2
< 0.1%
ValueCountFrequency (%)
8865 2
< 0.1%
8864 2
< 0.1%
8863 3
< 0.1%
8862 2
< 0.1%
8860 1
 
< 0.1%
8859 2
< 0.1%
8858 2
< 0.1%
8857 4
< 0.1%
8856 4
< 0.1%
8854 1
 
< 0.1%
Distinct4394
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T17:03:46.785768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length23
Mean length4.4526
Min length1

Characters and Unicode

Total characters44526
Distinct characters651
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3203 ?
Unique (%)32.0%

Sample

1st row동양
2nd row럭스운동화
3rd row백영세탁
4th row강남세탁소
5th row서광사
ValueCountFrequency (%)
백양사 204
 
1.9%
세탁소 132
 
1.2%
현대사 132
 
1.2%
백조사 102
 
1.0%
현대세탁소 98
 
0.9%
현대세탁 85
 
0.8%
백양세탁소 83
 
0.8%
제일사 83
 
0.8%
세탁 71
 
0.7%
월풀빨래방 68
 
0.6%
Other values (4358) 9569
90.0%
2024-05-11T17:03:47.269602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3949
 
8.9%
3937
 
8.8%
3849
 
8.6%
2222
 
5.0%
1073
 
2.4%
1024
 
2.3%
892
 
2.0%
858
 
1.9%
830
 
1.9%
702
 
1.6%
Other values (641) 25190
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43222
97.1%
Space Separator 631
 
1.4%
Decimal Number 180
 
0.4%
Uppercase Letter 156
 
0.4%
Lowercase Letter 114
 
0.3%
Open Punctuation 90
 
0.2%
Close Punctuation 89
 
0.2%
Other Punctuation 39
 
0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3949
 
9.1%
3937
 
9.1%
3849
 
8.9%
2222
 
5.1%
1073
 
2.5%
1024
 
2.4%
892
 
2.1%
858
 
2.0%
830
 
1.9%
702
 
1.6%
Other values (575) 23886
55.3%
Uppercase Letter
ValueCountFrequency (%)
K 20
 
12.8%
S 13
 
8.3%
L 13
 
8.3%
C 12
 
7.7%
G 10
 
6.4%
O 10
 
6.4%
A 8
 
5.1%
M 7
 
4.5%
R 7
 
4.5%
I 6
 
3.8%
Other values (16) 50
32.1%
Lowercase Letter
ValueCountFrequency (%)
e 18
15.8%
n 12
10.5%
l 12
10.5%
a 11
9.6%
o 10
8.8%
s 9
7.9%
r 9
7.9%
c 7
 
6.1%
u 4
 
3.5%
i 4
 
3.5%
Other values (10) 18
15.8%
Decimal Number
ValueCountFrequency (%)
2 52
28.9%
1 42
23.3%
4 37
20.6%
8 13
 
7.2%
9 12
 
6.7%
6 7
 
3.9%
5 6
 
3.3%
7 6
 
3.3%
3 4
 
2.2%
0 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 23
59.0%
& 8
 
20.5%
? 3
 
7.7%
, 3
 
7.7%
' 1
 
2.6%
# 1
 
2.6%
Space Separator
ValueCountFrequency (%)
631
100.0%
Open Punctuation
ValueCountFrequency (%)
( 90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43220
97.1%
Common 1034
 
2.3%
Latin 270
 
0.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3949
 
9.1%
3937
 
9.1%
3849
 
8.9%
2222
 
5.1%
1073
 
2.5%
1024
 
2.4%
892
 
2.1%
858
 
2.0%
830
 
1.9%
702
 
1.6%
Other values (573) 23884
55.3%
Latin
ValueCountFrequency (%)
K 20
 
7.4%
e 18
 
6.7%
S 13
 
4.8%
L 13
 
4.8%
n 12
 
4.4%
l 12
 
4.4%
C 12
 
4.4%
a 11
 
4.1%
G 10
 
3.7%
o 10
 
3.7%
Other values (36) 139
51.5%
Common
ValueCountFrequency (%)
631
61.0%
( 90
 
8.7%
) 89
 
8.6%
2 52
 
5.0%
1 42
 
4.1%
4 37
 
3.6%
. 23
 
2.2%
8 13
 
1.3%
9 12
 
1.2%
& 8
 
0.8%
Other values (10) 37
 
3.6%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43217
97.1%
ASCII 1304
 
2.9%
Compat Jamo 3
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3949
 
9.1%
3937
 
9.1%
3849
 
8.9%
2222
 
5.1%
1073
 
2.5%
1024
 
2.4%
892
 
2.1%
858
 
2.0%
830
 
1.9%
702
 
1.6%
Other values (570) 23881
55.3%
ASCII
ValueCountFrequency (%)
631
48.4%
( 90
 
6.9%
) 89
 
6.8%
2 52
 
4.0%
1 42
 
3.2%
4 37
 
2.8%
. 23
 
1.8%
K 20
 
1.5%
e 18
 
1.4%
S 13
 
1.0%
Other values (56) 289
22.2%
Compat Jamo
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct6060
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-12-29 00:00:00
Maximum2024-05-09 14:24:10
2024-05-11T17:03:47.438262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:03:47.653241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
7701 
U
2299 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 7701
77.0%
U 2299
 
23.0%

Length

2024-05-11T17:03:47.802410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:47.895170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 7701
77.0%
u 2299
 
23.0%
Distinct1105
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T17:03:48.003901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:03:48.138234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반세탁업
9644 
빨래방업
 
185
운동화전문세탁업
 
135
세탁업 기타
 
36

Length

Max length8
Median length5
Mean length5.0256
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반세탁업
2nd row운동화전문세탁업
3rd row일반세탁업
4th row일반세탁업
5th row일반세탁업

Common Values

ValueCountFrequency (%)
일반세탁업 9644
96.4%
빨래방업 185
 
1.8%
운동화전문세탁업 135
 
1.4%
세탁업 기타 36
 
0.4%

Length

2024-05-11T17:03:48.298552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:48.415365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 9644
96.1%
빨래방업 185
 
1.8%
운동화전문세탁업 135
 
1.3%
세탁업 36
 
0.4%
기타 36
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct7727
Distinct (%)85.4%
Missing955
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean199560.67
Minimum182742.54
Maximum215888.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:48.534558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182742.54
5-th percentile186393.73
Q1192806.18
median201191.1
Q3205705.34
95-th percentile211753.41
Maximum215888.9
Range33146.361
Interquartile range (IQR)12899.155

Descriptive statistics

Standard deviation7777.1727
Coefficient of variation (CV)0.038971471
Kurtosis-0.99929178
Mean199560.67
Median Absolute Deviation (MAD)6201.1901
Skewness-0.15174568
Sum1.8050262 × 109
Variance60484416
MonotonicityNot monotonic
2024-05-11T17:03:48.668949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200841.726990037 12
 
0.1%
211047.878697259 7
 
0.1%
207373.50021477 6
 
0.1%
202245.377899369 6
 
0.1%
200750.455125653 6
 
0.1%
188467.215363052 6
 
0.1%
202398.594377612 6
 
0.1%
204056.199691995 6
 
0.1%
200575.420521471 6
 
0.1%
196350.708293561 6
 
0.1%
Other values (7717) 8978
89.8%
(Missing) 955
 
9.6%
ValueCountFrequency (%)
182742.537963411 1
< 0.1%
182852.799221009 1
< 0.1%
182859.059692916 1
< 0.1%
182876.367858149 1
< 0.1%
182933.115073744 2
< 0.1%
182952.373706395 1
< 0.1%
182962.656182429 2
< 0.1%
182999.506242492 1
< 0.1%
183001.153395361 1
< 0.1%
183014.511131124 1
< 0.1%
ValueCountFrequency (%)
215888.898816 1
 
< 0.1%
215875.024969 1
 
< 0.1%
215784.2264 1
 
< 0.1%
215527.721278 2
< 0.1%
215422.746119624 3
< 0.1%
215361.163713829 1
 
< 0.1%
215303.913311463 1
 
< 0.1%
215223.604630417 1
 
< 0.1%
215182.34482972 1
 
< 0.1%
215166.563843196 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct7727
Distinct (%)85.4%
Missing955
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean449345.52
Minimum437632.51
Maximum465103.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:48.813303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437632.51
5-th percentile441465.28
Q1444497.82
median448809.67
Q3453432.25
95-th percentile460180.55
Maximum465103.76
Range27471.246
Interquartile range (IQR)8934.4352

Descriptive statistics

Standard deviation5770.4215
Coefficient of variation (CV)0.012841836
Kurtosis-0.56498587
Mean449345.52
Median Absolute Deviation (MAD)4410.4179
Skewness0.44803366
Sum4.0643303 × 109
Variance33297765
MonotonicityNot monotonic
2024-05-11T17:03:48.980354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
454721.505180141 12
 
0.1%
450021.504957301 7
 
0.1%
445545.656024061 6
 
0.1%
442987.986999742 6
 
0.1%
449638.824308081 6
 
0.1%
446003.941216623 6
 
0.1%
447864.763737276 6
 
0.1%
443750.88453048 6
 
0.1%
457360.617372111 6
 
0.1%
442532.440501316 6
 
0.1%
Other values (7717) 8978
89.8%
(Missing) 955
 
9.6%
ValueCountFrequency (%)
437632.508826604 1
 
< 0.1%
437816.239800826 1
 
< 0.1%
437823.090862848 1
 
< 0.1%
438160.729202982 2
< 0.1%
438356.170313032 3
< 0.1%
438367.332881743 1
 
< 0.1%
438395.806833518 1
 
< 0.1%
438399.362677856 1
 
< 0.1%
438410.522111193 2
< 0.1%
438421.820423834 1
 
< 0.1%
ValueCountFrequency (%)
465103.755134816 1
 
< 0.1%
464926.884466418 1
 
< 0.1%
464905.033086911 1
 
< 0.1%
464814.717432497 3
< 0.1%
464807.990354563 1
 
< 0.1%
464768.43323261 1
 
< 0.1%
464721.115684815 1
 
< 0.1%
464715.959316893 1
 
< 0.1%
464699.323138366 1
 
< 0.1%
464621.700290709 1
 
< 0.1%

위생업태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반세탁업
8861 
<NA>
 
828
빨래방업
 
168
운동화전문세탁업
 
116
세탁업 기타
 
27

Length

Max length8
Median length5
Mean length4.9379
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반세탁업
2nd row운동화전문세탁업
3rd row일반세탁업
4th row일반세탁업
5th row일반세탁업

Common Values

ValueCountFrequency (%)
일반세탁업 8861
88.6%
<NA> 828
 
8.3%
빨래방업 168
 
1.7%
운동화전문세탁업 116
 
1.2%
세탁업 기타 27
 
0.3%

Length

2024-05-11T17:03:49.124667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:49.241376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 8861
88.4%
na 828
 
8.3%
빨래방업 168
 
1.7%
운동화전문세탁업 116
 
1.2%
세탁업 27
 
0.3%
기타 27
 
0.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)0.5%
Missing3816
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1.0648448
Minimum0
Maximum102
Zeros4093
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:49.369261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum102
Range102
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.672755
Coefficient of variation (CV)2.509995
Kurtosis396.01625
Mean1.0648448
Median Absolute Deviation (MAD)0
Skewness14.066845
Sum6585
Variance7.1436192
MonotonicityNot monotonic
2024-05-11T17:03:49.493902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4093
40.9%
3 574
 
5.7%
2 551
 
5.5%
1 391
 
3.9%
4 349
 
3.5%
5 138
 
1.4%
6 20
 
0.2%
7 14
 
0.1%
15 13
 
0.1%
17 4
 
< 0.1%
Other values (19) 37
 
0.4%
(Missing) 3816
38.2%
ValueCountFrequency (%)
0 4093
40.9%
1 391
 
3.9%
2 551
 
5.5%
3 574
 
5.7%
4 349
 
3.5%
5 138
 
1.4%
6 20
 
0.2%
7 14
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
102 1
 
< 0.1%
51 1
 
< 0.1%
46 2
< 0.1%
37 1
 
< 0.1%
32 1
 
< 0.1%
30 3
< 0.1%
28 1
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
21 2
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing4630
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean0.2018622
Minimum0
Maximum8
Zeros4461
Zeros (%)44.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:49.603240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.54916272
Coefficient of variation (CV)2.7204832
Kurtosis39.862359
Mean0.2018622
Median Absolute Deviation (MAD)0
Skewness5.0348309
Sum1084
Variance0.30157969
MonotonicityNot monotonic
2024-05-11T17:03:49.716398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4461
44.6%
1 836
 
8.4%
2 30
 
0.3%
5 13
 
0.1%
3 13
 
0.1%
4 10
 
0.1%
6 6
 
0.1%
8 1
 
< 0.1%
(Missing) 4630
46.3%
ValueCountFrequency (%)
0 4461
44.6%
1 836
 
8.4%
2 30
 
0.3%
3 13
 
0.1%
4 10
 
0.1%
5 13
 
0.1%
6 6
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 6
 
0.1%
5 13
 
0.1%
4 10
 
0.1%
3 13
 
0.1%
2 30
 
0.3%
1 836
 
8.4%
0 4461
44.6%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing4232
Missing (%)42.3%
Infinite0
Infinite (%)0.0%
Mean0.5601595
Minimum0
Maximum20
Zeros3030
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:49.841131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72090399
Coefficient of variation (CV)1.286962
Kurtosis94.176419
Mean0.5601595
Median Absolute Deviation (MAD)0
Skewness4.5310538
Sum3231
Variance0.51970256
MonotonicityNot monotonic
2024-05-11T17:03:49.944274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3030
30.3%
1 2351
23.5%
2 323
 
3.2%
3 47
 
0.5%
4 9
 
0.1%
5 5
 
0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
(Missing) 4232
42.3%
ValueCountFrequency (%)
0 3030
30.3%
1 2351
23.5%
2 323
 
3.2%
3 47
 
0.5%
4 9
 
0.1%
5 5
 
0.1%
6 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
6 2
 
< 0.1%
5 5
 
0.1%
4 9
 
0.1%
3 47
 
0.5%
2 323
 
3.2%
1 2351
23.5%
0 3030
30.3%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct15
Distinct (%)0.4%
Missing6181
Missing (%)61.8%
Infinite0
Infinite (%)0.0%
Mean1.0219953
Minimum0
Maximum302
Zeros1138
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:50.060743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum302
Range302
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.0030146
Coefficient of variation (CV)5.8738183
Kurtosis1752.6446
Mean1.0219953
Median Absolute Deviation (MAD)0
Skewness38.648696
Sum3903
Variance36.036184
MonotonicityNot monotonic
2024-05-11T17:03:50.160911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 2305
 
23.1%
0 1138
 
11.4%
2 314
 
3.1%
3 36
 
0.4%
4 11
 
0.1%
5 6
 
0.1%
110 1
 
< 0.1%
6 1
 
< 0.1%
107 1
 
< 0.1%
101 1
 
< 0.1%
Other values (5) 5
 
0.1%
(Missing) 6181
61.8%
ValueCountFrequency (%)
0 1138
11.4%
1 2305
23.1%
2 314
 
3.1%
3 36
 
0.4%
4 11
 
0.1%
5 6
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
20 1
 
< 0.1%
30 1
 
< 0.1%
ValueCountFrequency (%)
302 1
 
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
30 1
 
< 0.1%
20 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 6
0.1%

사용시작지하층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.2%
Missing6215
Missing (%)62.2%
Infinite0
Infinite (%)0.0%
Mean0.065785997
Minimum0
Maximum11
Zeros3576
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:50.260870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33734259
Coefficient of variation (CV)5.1278783
Kurtosis321.57352
Mean0.065785997
Median Absolute Deviation (MAD)0
Skewness12.85862
Sum249
Variance0.11380002
MonotonicityNot monotonic
2024-05-11T17:03:50.369378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3576
35.8%
1 187
 
1.9%
2 16
 
0.2%
3 2
 
< 0.1%
4 2
 
< 0.1%
11 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 6215
62.2%
ValueCountFrequency (%)
0 3576
35.8%
1 187
 
1.9%
2 16
 
0.2%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
2 16
 
0.2%
1 187
 
1.9%
0 3576
35.8%

사용끝지하층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.4%
Missing8172
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean0.13840263
Minimum0
Maximum20
Zeros1625
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:50.475867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61364887
Coefficient of variation (CV)4.433795
Kurtosis605.22648
Mean0.13840263
Median Absolute Deviation (MAD)0
Skewness19.758421
Sum253
Variance0.37656493
MonotonicityNot monotonic
2024-05-11T17:03:50.599194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1625
 
16.2%
1 180
 
1.8%
2 17
 
0.2%
4 2
 
< 0.1%
3 2
 
< 0.1%
20 1
 
< 0.1%
5 1
 
< 0.1%
(Missing) 8172
81.7%
ValueCountFrequency (%)
0 1625
16.2%
1 180
 
1.8%
2 17
 
0.2%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
2 17
 
0.2%
1 180
 
1.8%
0 1625
16.2%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5160 
0
4840 

Length

Max length4
Median length4
Mean length2.548
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> 5160
51.6%
0 4840
48.4%

Length

2024-05-11T17:03:50.739596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:50.837096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5160
51.6%
0 4840
48.4%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5160 
0
4840 

Length

Max length4
Median length4
Mean length2.548
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> 5160
51.6%
0 4840
48.4%

Length

2024-05-11T17:03:50.942401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:51.036254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5160
51.6%
0 4840
48.4%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5160 
0
4840 

Length

Max length4
Median length4
Mean length2.548
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> 5160
51.6%
0 4840
48.4%

Length

2024-05-11T17:03:51.142627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:51.236753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5160
51.6%
0 4840
48.4%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing937
Missing (%)9.4%
Memory size97.7 KiB
False
9042 
True
 
21
(Missing)
937 
ValueCountFrequency (%)
False 9042
90.4%
True 21
 
0.2%
(Missing) 937
 
9.4%
2024-05-11T17:03:51.315713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5157 
0
4836 
4
 
3
3
 
2
5
 
1

Length

Max length4
Median length4
Mean length2.5471
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> 5157
51.6%
0 4836
48.4%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-11T17:03:51.421664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:51.521240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5157
51.6%
0 4836
48.4%
4 3
 
< 0.1%
3 2
 
< 0.1%
5 1
 
< 0.1%
2 1
 
< 0.1%
Distinct3
Distinct (%)75.0%
Missing9996
Missing (%)> 99.9%
Memory size156.2 KiB
2024-05-11T17:03:51.681830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length43
Mean length32.5
Min length22

Characters and Unicode

Total characters130
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row준공인가전 사용허가 기한에 따라 조건부 신고이므로 조건부기간 만료시 영업신고증 재교부
2nd row보건위생과-32299(2019.12.5)
3rd row주택과-2366(2007.2.8)호[준공인가전 사용허가 기간연장 승인]
4th row보건위생과-32299(2019.12.5)
ValueCountFrequency (%)
보건위생과-32299(2019.12.5 2
12.5%
사용허가 2
12.5%
준공인가전 1
 
6.2%
기한에 1
 
6.2%
따라 1
 
6.2%
조건부 1
 
6.2%
신고이므로 1
 
6.2%
조건부기간 1
 
6.2%
만료시 1
 
6.2%
영업신고증 1
 
6.2%
Other values (4) 4
25.0%
2024-05-11T17:03:52.011788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
9.2%
2 11
 
8.5%
. 6
 
4.6%
9 6
 
4.6%
1 4
 
3.1%
4
 
3.1%
4
 
3.1%
0 4
 
3.1%
3
 
2.3%
3
 
2.3%
Other values (46) 73
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
51.5%
Decimal Number 34
26.2%
Space Separator 12
 
9.2%
Other Punctuation 6
 
4.6%
Close Punctuation 4
 
3.1%
Open Punctuation 4
 
3.1%
Dash Punctuation 3
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
6.0%
4
 
6.0%
3
 
4.5%
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (30) 39
58.2%
Decimal Number
ValueCountFrequency (%)
2 11
32.4%
9 6
17.6%
1 4
 
11.8%
0 4
 
11.8%
3 3
 
8.8%
6 2
 
5.9%
5 2
 
5.9%
8 1
 
2.9%
7 1
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 3
75.0%
] 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 3
75.0%
[ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
51.5%
Common 63
48.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
 
6.0%
4
 
6.0%
3
 
4.5%
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (30) 39
58.2%
Common
ValueCountFrequency (%)
12
19.0%
2 11
17.5%
. 6
9.5%
9 6
9.5%
1 4
 
6.3%
0 4
 
6.3%
) 3
 
4.8%
( 3
 
4.8%
3 3
 
4.8%
- 3
 
4.8%
Other values (6) 8
12.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
51.5%
ASCII 63
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
19.0%
2 11
17.5%
. 6
9.5%
9 6
9.5%
1 4
 
6.3%
0 4
 
6.3%
) 3
 
4.8%
( 3
 
4.8%
3 3
 
4.8%
- 3
 
4.8%
Other values (6) 8
12.7%
Hangul
ValueCountFrequency (%)
4
 
6.0%
4
 
6.0%
3
 
4.5%
3
 
4.5%
3
 
4.5%
3
 
4.5%
2
 
3.0%
2
 
3.0%
2
 
3.0%
2
 
3.0%
Other values (30) 39
58.2%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
20111219
 
1
20071224
 
1

Length

Max length8
Median length4
Mean length4.0008
Min length4

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> 9998
> 99.9%
20111219 1
 
< 0.1%
20071224 1
 
< 0.1%

Length

2024-05-11T17:03:52.158875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:52.268189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
20111219 1
 
< 0.1%
20071224 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
20120929
 
1
20080211
 
1

Length

Max length8
Median length4
Mean length4.0008
Min length4

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> 9998
> 99.9%
20120929 1
 
< 0.1%
20080211 1
 
< 0.1%

Length

2024-05-11T17:03:52.388333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:52.495881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
20120929 1
 
< 0.1%
20080211 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8282 
임대
1618 
자가
 
100

Length

Max length4
Median length4
Mean length3.6564
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 8282
82.8%
임대 1618
 
16.2%
자가 100
 
1.0%

Length

2024-05-11T17:03:52.609230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:52.708805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8282
82.8%
임대 1618
 
16.2%
자가 100
 
1.0%

세탁기수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.4%
Missing7117
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean1.1692681
Minimum0
Maximum31
Zeros1178
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:52.798740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3831289
Coefficient of variation (CV)1.1829014
Kurtosis76.775381
Mean1.1692681
Median Absolute Deviation (MAD)1
Skewness4.4905874
Sum3371
Variance1.9130455
MonotonicityNot monotonic
2024-05-11T17:03:52.910607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1178
 
11.8%
1 663
 
6.6%
2 639
 
6.4%
3 281
 
2.8%
4 89
 
0.9%
5 13
 
0.1%
6 9
 
0.1%
8 4
 
< 0.1%
7 4
 
< 0.1%
10 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 7117
71.2%
ValueCountFrequency (%)
0 1178
11.8%
1 663
6.6%
2 639
6.4%
3 281
 
2.8%
4 89
 
0.9%
5 13
 
0.1%
6 9
 
0.1%
7 4
 
< 0.1%
8 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
8 4
 
< 0.1%
7 4
 
< 0.1%
6 9
 
0.1%
5 13
 
0.1%
4 89
 
0.9%
3 281
2.8%
2 639
6.4%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8786 
0
1190 
1
 
24

Length

Max length4
Median length4
Mean length3.6358
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> 8786
87.9%
0 1190
 
11.9%
1 24
 
0.2%

Length

2024-05-11T17:03:53.305089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:53.404276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8786
87.9%
0 1190
 
11.9%
1 24
 
0.2%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8787 
0
1163 
1
 
47
2
 
2
3
 
1

Length

Max length4
Median length4
Mean length3.6361
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> 8787
87.9%
0 1163
 
11.6%
1 47
 
0.5%
2 2
 
< 0.1%
3 1
 
< 0.1%

Length

2024-05-11T17:03:53.509769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:53.623696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8787
87.9%
0 1163
 
11.6%
1 47
 
0.5%
2 2
 
< 0.1%
3 1
 
< 0.1%

회수건조수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.3%
Missing7368
Missing (%)73.7%
Infinite0
Infinite (%)0.0%
Mean0.59232523
Minimum0
Maximum8
Zeros1310
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T17:03:53.729698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73867092
Coefficient of variation (CV)1.2470698
Kurtosis11.118968
Mean0.59232523
Median Absolute Deviation (MAD)1
Skewness2.2848738
Sum1559
Variance0.54563473
MonotonicityNot monotonic
2024-05-11T17:03:53.845477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1310
 
13.1%
1 1186
 
11.9%
2 75
 
0.8%
3 34
 
0.3%
4 20
 
0.2%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
(Missing) 7368
73.7%
ValueCountFrequency (%)
0 1310
13.1%
1 1186
11.9%
2 75
 
0.8%
3 34
 
0.3%
4 20
 
0.2%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 3
 
< 0.1%
5 3
 
< 0.1%
4 20
 
0.2%
3 34
 
0.3%
2 75
 
0.8%
1 1186
11.9%
0 1310
13.1%

침대수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7610 
0
2390 

Length

Max length4
Median length4
Mean length3.283
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> 7610
76.1%
0 2390
 
23.9%

Length

2024-05-11T17:03:54.002612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:03:54.139388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7610
76.1%
0 2390
 
23.9%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing828
Missing (%)8.3%
Memory size97.7 KiB
False
9168 
True
 
4
(Missing)
 
828
ValueCountFrequency (%)
False 9168
91.7%
True 4
 
< 0.1%
(Missing) 828
 
8.3%
2024-05-11T17:03:54.224921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
580331000003100000-205-2003-0003420030926<NA>3폐업2폐업20210531<NA><NA><NA>02 938185029.67139816서울특별시 노원구 상계동 1305 상계동 동양메이저아파트 상가동 206호서울특별시 노원구 상계로5길 12 (상계동,상계동 동양메이저아파트 상가동 206호)1692동양2021-06-01 08:05:28U2021-06-03 02:40:00.0일반세탁업205575.736739461696.659067일반세탁업2<NA>22<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA>1<NA>N
270630400003040000-205-2011-0000120110214<NA>3폐업2폐업20140923<NA><NA><NA><NA>29.70143841서울특별시 광진구 자양동 11-4번지서울특별시 광진구 동일로18길 62 (자양동)5078럭스운동화2011-02-14 10:56:59I2018-08-31 23:59:59.0운동화전문세탁업205766.923712448521.355284운동화전문세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1127232000003200000-205-1988-0264919880810<NA>1영업/정상1영업<NA><NA><NA><NA>02 888534039.60151812서울특별시 관악구 봉천동 1690-152번지서울특별시 관악구 남부순환로 1951 (봉천동)8802백영세탁2015-06-11 09:33:02I2018-08-31 23:59:59.0일반세탁업196929.221579441579.156778일반세탁업3111<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1304432200003220000-205-1989-0283719891017<NA>3폐업2폐업20161209<NA><NA><NA>022226834020.00135943서울특별시 강남구 일원동 627-0번지 지상1층서울특별시 강남구 개포로128길 35 (일원동,지상1층)6339강남세탁소2004-11-20 00:00:00I2018-08-31 23:59:59.0일반세탁업207384.346328443415.419086일반세탁업4<NA><NA>1<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
780631400003140000-205-1987-0172219870716<NA>1영업/정상1영업<NA><NA><NA><NA>022695654114.80158829서울특별시 양천구 신월동 180-12서울특별시 양천구 남부순환로40길 70 (신월동)7911서광사2021-12-16 10:16:58U2021-12-18 02:40:00.0일반세탁업184562.582733447865.819282일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1450632400003240000-205-2003-0001120030307<NA>1영업/정상1영업<NA><NA><NA><NA>02 427345133.00134854서울특별시 강동구 암사동 421-10번지 1층서울특별시 강동구 암사길 104 (암사동,1층)5258영동세탁소2004-01-14 00:00:00I2018-08-31 23:59:59.0일반세탁업212314.137099449896.588417일반세탁업5111<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA><NA><NA>N
1458132400003240000-205-1988-0215619880819<NA>3폐업2폐업20030225<NA><NA><NA>020476727311.59134838서울특별시 강동구 상일동 301-7번지<NA><NA>백양2003-05-21 00:00:00I2018-08-31 23:59:59.0일반세탁업215182.34483449521.383143일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
353730600003060000-205-1987-0200019870709<NA>3폐업2폐업19930720<NA><NA><NA>0209731981139.64131849서울특별시 중랑구 묵동 121-77번지<NA><NA>형제사2001-10-04 00:00:00I2018-08-31 23:59:59.0일반세탁업206965.674147456628.137064일반세탁업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
928531600003160000-205-1991-0198319910920<NA>3폐업2폐업20041108<NA><NA><NA>02 852362315.73152854서울특별시 구로구 구로동 412-46번지<NA><NA>크로바2004-11-08 00:00:00I2018-08-31 23:59:59.0일반세탁업189433.833164443385.197006일반세탁업3<NA>11<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA><NA><NA>N
1286832200003220000-205-1993-0270619930401<NA>3폐업2폐업20030227<NA><NA><NA><NA>19.60135945서울특별시 강남구 일원동 642-11번지<NA><NA>크린프라자(주)일원1호점2003-06-23 00:00:00I2018-08-31 23:59:59.0일반세탁업207609.219449443445.922791일반세탁업4<NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
1037131800003180000-205-1992-0232819920811<NA>3폐업2폐업20021231<NA><NA><NA>020000000015.05150820서울특별시 영등포구 대림동 909-54번지<NA><NA>금강2003-04-01 00:00:00I2018-08-31 23:59:59.0일반세탁업191688.549623443803.838557일반세탁업000000000N0<NA><NA><NA><NA>00000N
1377932300003230000-205-1991-0307119910510<NA>3폐업2폐업20030226<NA><NA><NA>02 417968516.53138911서울특별시 송파구 잠실동 22-0번지 주공2단지223동지하1호내5호<NA><NA>성신세탁소2003-06-05 00:00:00I2018-08-31 23:59:59.0일반세탁업207750.499014445762.808121일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
716431200003120000-205-1994-0213619940309<NA>3폐업2폐업20221213<NA><NA><NA>02 720366343.00120853서울특별시 서대문구 홍제동 82 홍제한양아파트상가 106호서울특별시 서대문구 통일로25길 30, 1층 106호 (홍제동, 홍제한양아파트상가)3730한양세탁소2022-12-13 10:08:08U2021-11-01 23:05:00.0일반세탁업195259.429273453430.811542<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
184930300003030000-205-1987-0177619871024<NA>3폐업2폐업19950929<NA><NA><NA>02 465533414.00133822서울특별시 성동구 성수동1가 9-9번지<NA><NA>충북사2001-09-25 00:00:00I2018-08-31 23:59:59.0일반세탁업204127.79728449348.7131일반세탁업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1125532000003200000-205-2003-000082003-01-24<NA>3폐업2폐업2024-03-19<NA><NA><NA>02 885333333.00151-891서울특별시 관악구 신림동 1450-47서울특별시 관악구 봉천로4길 25 (신림동)8706경주사2024-03-19 15:03:36U2023-12-02 22:01:00.0일반세탁업193226.009855442951.148707<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
617931100003110000-205-1996-0167319960904<NA>3폐업2폐업19980818<NA><NA><NA>02 3525144244.48122050서울특별시 은평구 갈현동 산 499-28번지<NA><NA>금성세탁사2001-09-28 00:00:00I2018-08-31 23:59:59.0일반세탁업<NA><NA>일반세탁업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1200632100003210000-205-1989-0165119891014<NA>3폐업2폐업20001220<NA><NA><NA>02 5827936.00137849서울특별시 서초구 방배동 988번지 신동아상가<NA><NA>신동아세탁소2003-02-14 00:00:00I2018-08-31 23:59:59.0일반세탁업<NA><NA>일반세탁업<NA><NA><NA><NA><NA><NA>000N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
96630100003010000-205-1993-0156819930105<NA>1영업/정상1영업<NA><NA><NA><NA>0222303560361.80100392서울특별시 중구 장충동2가 202-0번지 (객실팀)서울특별시 중구 동호로 249 (장충동2가,(객실팀))4605신라세탁소2011-12-19 15:49:50I2018-08-31 23:59:59.0일반세탁업200632.985507450610.610876일반세탁업000000000N0<NA><NA><NA><NA>00030N
1351432300003230000-205-1987-0283019870730<NA>3폐업2폐업20030226<NA><NA><NA>02 4732185.00138874서울특별시 송파구 풍납동 221-28번지 미성A상가<NA><NA>백성사2003-06-05 00:00:00I2018-08-31 23:59:59.0일반세탁업210103.321559448039.202974일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
1218332100003210000-205-2006-0002320061205<NA>3폐업2폐업20160630<NA><NA><NA><NA>19.80137801서울특별시 서초구 반포동 30-20번지서울특별시 서초구 고무래로 79 (반포동)<NA>삼호2006-12-05 00:00:00I2018-08-31 23:59:59.0일반세탁업201525.412592444344.374375일반세탁업<NA>1<NA><NA>11<NA><NA><NA>N<NA><NA><NA><NA>임대3<NA><NA><NA><NA>N