Overview

Dataset statistics

Number of variables47
Number of observations2215
Missing cells24604
Missing cells (%)23.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory876.2 KiB
Average record size in memory405.1 B

Variable types

Categorical18
Text6
DateTime4
Unsupported5
Numeric12
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-17919/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (58.3%)Imbalance
사용끝지하층 is highly imbalanced (74.4%)Imbalance
조건부허가시작일자 is highly imbalanced (99.4%)Imbalance
조건부허가종료일자 is highly imbalanced (99.4%)Imbalance
건물소유구분명 is highly imbalanced (60.5%)Imbalance
남성종사자수 is highly imbalanced (69.6%)Imbalance
다중이용업소여부 is highly imbalanced (98.3%)Imbalance
인허가취소일자 has 2215 (100.0%) missing valuesMissing
폐업일자 has 731 (33.0%) missing valuesMissing
휴업시작일자 has 2215 (100.0%) missing valuesMissing
휴업종료일자 has 2215 (100.0%) missing valuesMissing
재개업일자 has 2215 (100.0%) missing valuesMissing
전화번호 has 624 (28.2%) missing valuesMissing
도로명주소 has 984 (44.4%) missing valuesMissing
도로명우편번호 has 990 (44.7%) missing valuesMissing
좌표정보(X) has 96 (4.3%) missing valuesMissing
좌표정보(Y) has 96 (4.3%) missing valuesMissing
건물지상층수 has 801 (36.2%) missing valuesMissing
건물지하층수 has 855 (38.6%) missing valuesMissing
사용시작지상층 has 946 (42.7%) missing valuesMissing
사용끝지상층 has 1465 (66.1%) missing valuesMissing
사용시작지하층 has 1419 (64.1%) missing valuesMissing
발한실여부 has 370 (16.7%) missing valuesMissing
좌석수 has 417 (18.8%) missing valuesMissing
조건부허가신고사유 has 2215 (100.0%) missing valuesMissing
여성종사자수 has 1849 (83.5%) missing valuesMissing
침대수 has 1535 (69.3%) missing valuesMissing
다중이용업소여부 has 350 (15.8%) missing valuesMissing
사용끝지상층 is highly skewed (γ1 = 26.64239546)Skewed
사용시작지하층 is highly skewed (γ1 = 26.32379354)Skewed
좌석수 is highly skewed (γ1 = 20.4579062)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
소재지면적 has 68 (3.1%) zerosZeros
건물지상층수 has 922 (41.6%) zerosZeros
건물지하층수 has 998 (45.1%) zerosZeros
사용시작지상층 has 546 (24.7%) zerosZeros
사용끝지상층 has 98 (4.4%) zerosZeros
사용시작지하층 has 765 (34.5%) zerosZeros
좌석수 has 110 (5.0%) zerosZeros
여성종사자수 has 244 (11.0%) zerosZeros
침대수 has 528 (23.8%) zerosZeros

Reproduction

Analysis started2024-05-11 06:16:45.059791
Analysis finished2024-05-11 06:16:46.950290
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
3170000
2215 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3170000
2nd row3170000
3rd row3170000
4th row3170000
5th row3170000

Common Values

ValueCountFrequency (%)
3170000 2215
100.0%

Length

2024-05-11T15:16:47.052824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:47.259960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3170000 2215
100.0%

관리번호
Text

UNIQUE 

Distinct2215
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2024-05-11T15:16:47.610882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters48730
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

Unique2215 ?
Unique (%)100.0%

Sample

1st row3170000-204-1968-00523
2nd row3170000-204-1975-00954
3rd row3170000-204-1976-00901
4th row3170000-204-1976-00914
5th row3170000-204-1977-00556
ValueCountFrequency (%)
3170000-204-1968-00523 1
 
< 0.1%
3170000-211-2019-00010 1
 
< 0.1%
3170000-211-2019-00001 1
 
< 0.1%
3170000-211-2019-00015 1
 
< 0.1%
3170000-211-2019-00014 1
 
< 0.1%
3170000-211-2019-00013 1
 
< 0.1%
3170000-211-2019-00012 1
 
< 0.1%
3170000-211-2019-00011 1
 
< 0.1%
3170000-211-2019-00017 1
 
< 0.1%
3170000-211-2019-00016 1
 
< 0.1%
Other values (2205) 2205
99.5%
2024-05-11T15:16:48.259046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18597
38.2%
- 6645
 
13.6%
1 6049
 
12.4%
2 5111
 
10.5%
3 3352
 
6.9%
7 2848
 
5.8%
4 1901
 
3.9%
9 1890
 
3.9%
8 1007
 
2.1%
5 725
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 42085
86.4%
Dash Punctuation 6645
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18597
44.2%
1 6049
 
14.4%
2 5111
 
12.1%
3 3352
 
8.0%
7 2848
 
6.8%
4 1901
 
4.5%
9 1890
 
4.5%
8 1007
 
2.4%
5 725
 
1.7%
6 605
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 6645
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18597
38.2%
- 6645
 
13.6%
1 6049
 
12.4%
2 5111
 
10.5%
3 3352
 
6.9%
7 2848
 
5.8%
4 1901
 
3.9%
9 1890
 
3.9%
8 1007
 
2.1%
5 725
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18597
38.2%
- 6645
 
13.6%
1 6049
 
12.4%
2 5111
 
10.5%
3 3352
 
6.9%
7 2848
 
5.8%
4 1901
 
3.9%
9 1890
 
3.9%
8 1007
 
2.1%
5 725
 
1.5%
Distinct1851
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum1968-11-12 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T15:16:48.521207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:49.080514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2215
Missing (%)100.0%
Memory size19.6 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
3
1484 
1
731 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 1484
67.0%
1 731
33.0%

Length

2024-05-11T15:16:49.350990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:49.516277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1484
67.0%
1 731
33.0%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
폐업
1484 
영업/정상
731 

Length

Max length5
Median length2
Mean length2.9900677
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1484
67.0%
영업/정상 731
33.0%

Length

2024-05-11T15:16:49.711746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:49.902807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1484
67.0%
영업/정상 731
33.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2
1484 
1
731 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 1484
67.0%
1 731
33.0%

Length

2024-05-11T15:16:50.089292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:50.259913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1484
67.0%
1 731
33.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
폐업
1484 
영업
731 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1484
67.0%
영업 731
33.0%

Length

2024-05-11T15:16:50.422824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:50.590003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1484
67.0%
영업 731
33.0%

폐업일자
Date

MISSING 

Distinct1014
Distinct (%)68.3%
Missing731
Missing (%)33.0%
Memory size17.4 KiB
Minimum1992-10-01 00:00:00
Maximum2024-05-01 00:00:00
2024-05-11T15:16:50.792014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:51.015823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2215
Missing (%)100.0%
Memory size19.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2215
Missing (%)100.0%
Memory size19.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2215
Missing (%)100.0%
Memory size19.6 KiB

전화번호
Text

MISSING 

Distinct1306
Distinct (%)82.1%
Missing624
Missing (%)28.2%
Memory size17.4 KiB
2024-05-11T15:16:51.530904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.086738
Min length2

Characters and Unicode

Total characters16048
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

Unique1216 ?
Unique (%)76.4%

Sample

1st row02 8020865
2nd row02 8551833
3rd row0200000000
4th row0200000000
5th row02 8945243
ValueCountFrequency (%)
02 1313
43.6%
00000 106
 
3.5%
0200000000 66
 
2.2%
0 19
 
0.6%
808 9
 
0.3%
070 9
 
0.3%
807 7
 
0.2%
802 6
 
0.2%
858 6
 
0.2%
891 5
 
0.2%
Other values (1338) 1466
48.7%
2024-05-11T15:16:52.403364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4068
25.3%
2 2361
14.7%
8 2056
12.8%
1788
11.1%
5 1014
 
6.3%
9 938
 
5.8%
6 927
 
5.8%
3 823
 
5.1%
7 735
 
4.6%
1 670
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14260
88.9%
Space Separator 1788
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4068
28.5%
2 2361
16.6%
8 2056
14.4%
5 1014
 
7.1%
9 938
 
6.6%
6 927
 
6.5%
3 823
 
5.8%
7 735
 
5.2%
1 670
 
4.7%
4 668
 
4.7%
Space Separator
ValueCountFrequency (%)
1788
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4068
25.3%
2 2361
14.7%
8 2056
12.8%
1788
11.1%
5 1014
 
6.3%
9 938
 
5.8%
6 927
 
5.8%
3 823
 
5.1%
7 735
 
4.6%
1 670
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4068
25.3%
2 2361
14.7%
8 2056
12.8%
1788
11.1%
5 1014
 
6.3%
9 938
 
5.8%
6 927
 
5.8%
3 823
 
5.1%
7 735
 
4.6%
1 670
 
4.2%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1132
Distinct (%)51.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean30.966188
Minimum0
Maximum498
Zeros68
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:16:52.679765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.05
Q116.09
median23.18
Q334.14
95-th percentile79.105
Maximum498
Range498
Interquartile range (IQR)18.05

Descriptive statistics

Standard deviation28.932293
Coefficient of variation (CV)0.93431884
Kurtosis55.872508
Mean30.966188
Median Absolute Deviation (MAD)8.37
Skewness5.3535528
Sum68559.14
Variance837.07757
MonotonicityNot monotonic
2024-05-11T15:16:52.906899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 68
 
3.1%
33.0 65
 
2.9%
30.0 31
 
1.4%
26.4 26
 
1.2%
18.0 25
 
1.1%
23.1 24
 
1.1%
25.0 23
 
1.0%
15.0 22
 
1.0%
23.0 20
 
0.9%
20.0 19
 
0.9%
Other values (1122) 1891
85.4%
ValueCountFrequency (%)
0.0 68
3.1%
5.0 2
 
0.1%
5.25 1
 
< 0.1%
5.5 1
 
< 0.1%
6.6 2
 
0.1%
6.97 1
 
< 0.1%
7.0 1
 
< 0.1%
7.07 1
 
< 0.1%
7.47 1
 
< 0.1%
7.8 1
 
< 0.1%
ValueCountFrequency (%)
498.0 1
< 0.1%
419.21 1
< 0.1%
300.78 1
< 0.1%
277.0 1
< 0.1%
262.5 1
< 0.1%
190.08 1
< 0.1%
182.2 1
< 0.1%
181.0 1
< 0.1%
165.84 1
< 0.1%
165.8 1
< 0.1%
Distinct123
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2024-05-11T15:16:53.366062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1069977
Min length6

Characters and Unicode

Total characters13527
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

Unique19 ?
Unique (%)0.9%

Sample

1st row153864
2nd row153805
3rd row153030
4th row153010
5th row153841
ValueCountFrequency (%)
153801 129
 
5.8%
153825 107
 
4.8%
153857 101
 
4.6%
153832 81
 
3.7%
153854 74
 
3.3%
153856 72
 
3.3%
153841 70
 
3.2%
153859 69
 
3.1%
153030 65
 
2.9%
153010 64
 
2.9%
Other values (113) 1383
62.4%
2024-05-11T15:16:54.061854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2897
21.4%
1 2814
20.8%
3 2799
20.7%
8 2154
15.9%
0 834
 
6.2%
2 476
 
3.5%
6 473
 
3.5%
4 371
 
2.7%
7 302
 
2.2%
- 237
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13290
98.2%
Dash Punctuation 237
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2897
21.8%
1 2814
21.2%
3 2799
21.1%
8 2154
16.2%
0 834
 
6.3%
2 476
 
3.6%
6 473
 
3.6%
4 371
 
2.8%
7 302
 
2.3%
9 170
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 237
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13527
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2897
21.4%
1 2814
20.8%
3 2799
20.7%
8 2154
15.9%
0 834
 
6.2%
2 476
 
3.5%
6 473
 
3.5%
4 371
 
2.7%
7 302
 
2.2%
- 237
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2897
21.4%
1 2814
20.8%
3 2799
20.7%
8 2154
15.9%
0 834
 
6.2%
2 476
 
3.5%
6 473
 
3.5%
4 371
 
2.7%
7 302
 
2.2%
- 237
 
1.8%
Distinct1937
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2024-05-11T15:16:54.520068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length49
Mean length26.509255
Min length18

Characters and Unicode

Total characters58718
Distinct characters307
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

Unique1722 ?
Unique (%)77.7%

Sample

1st row서울특별시 금천구 시흥동 998-8번지
2nd row서울특별시 금천구 독산동 60-32번지
3rd row서울특별시 금천구 시흥동 76-16번지
4th row서울특별시 금천구 독산동 205-4번지
5th row서울특별시 금천구 시흥동 830-10번지
ValueCountFrequency (%)
금천구 2221
20.5%
서울특별시 2215
20.4%
독산동 1048
 
9.7%
시흥동 916
 
8.5%
가산동 252
 
2.3%
1층 186
 
1.7%
지상1층 81
 
0.7%
2층 52
 
0.5%
101호 37
 
0.3%
102호 35
 
0.3%
Other values (2137) 3795
35.0%
2024-05-11T15:16:55.219050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10237
 
17.4%
3184
 
5.4%
1 2989
 
5.1%
2402
 
4.1%
2317
 
3.9%
2312
 
3.9%
2239
 
3.8%
2222
 
3.8%
2222
 
3.8%
2220
 
3.8%
Other values (297) 26374
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32926
56.1%
Decimal Number 12802
 
21.8%
Space Separator 10237
 
17.4%
Dash Punctuation 2057
 
3.5%
Open Punctuation 275
 
0.5%
Close Punctuation 275
 
0.5%
Uppercase Letter 123
 
0.2%
Other Punctuation 18
 
< 0.1%
Math Symbol 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3184
 
9.7%
2402
 
7.3%
2317
 
7.0%
2312
 
7.0%
2239
 
6.8%
2222
 
6.7%
2222
 
6.7%
2220
 
6.7%
2215
 
6.7%
1814
 
5.5%
Other values (252) 9779
29.7%
Uppercase Letter
ValueCountFrequency (%)
B 50
40.7%
A 24
19.5%
C 10
 
8.1%
T 7
 
5.7%
S 4
 
3.3%
I 4
 
3.3%
D 3
 
2.4%
J 3
 
2.4%
Y 3
 
2.4%
E 2
 
1.6%
Other values (9) 13
 
10.6%
Decimal Number
ValueCountFrequency (%)
1 2989
23.3%
0 1476
11.5%
2 1468
11.5%
9 1369
10.7%
8 1123
 
8.8%
3 1102
 
8.6%
4 976
 
7.6%
5 868
 
6.8%
7 749
 
5.9%
6 682
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 13
72.2%
. 2
 
11.1%
: 2
 
11.1%
/ 1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
[ 188
68.4%
( 78
28.4%
9
 
3.3%
Close Punctuation
ValueCountFrequency (%)
] 187
68.0%
) 78
28.4%
10
 
3.6%
Lowercase Letter
ValueCountFrequency (%)
w 1
50.0%
e 1
50.0%
Space Separator
ValueCountFrequency (%)
10237
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2057
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32927
56.1%
Common 25666
43.7%
Latin 125
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3184
 
9.7%
2402
 
7.3%
2317
 
7.0%
2312
 
7.0%
2239
 
6.8%
2222
 
6.7%
2222
 
6.7%
2220
 
6.7%
2215
 
6.7%
1814
 
5.5%
Other values (253) 9780
29.7%
Common
ValueCountFrequency (%)
10237
39.9%
1 2989
 
11.6%
- 2057
 
8.0%
0 1476
 
5.8%
2 1468
 
5.7%
9 1369
 
5.3%
8 1123
 
4.4%
3 1102
 
4.3%
4 976
 
3.8%
5 868
 
3.4%
Other values (13) 2001
 
7.8%
Latin
ValueCountFrequency (%)
B 50
40.0%
A 24
19.2%
C 10
 
8.0%
T 7
 
5.6%
S 4
 
3.2%
I 4
 
3.2%
D 3
 
2.4%
J 3
 
2.4%
Y 3
 
2.4%
E 2
 
1.6%
Other values (11) 15
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32926
56.1%
ASCII 25772
43.9%
None 20
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10237
39.7%
1 2989
 
11.6%
- 2057
 
8.0%
0 1476
 
5.7%
2 1468
 
5.7%
9 1369
 
5.3%
8 1123
 
4.4%
3 1102
 
4.3%
4 976
 
3.8%
5 868
 
3.4%
Other values (32) 2107
 
8.2%
Hangul
ValueCountFrequency (%)
3184
 
9.7%
2402
 
7.3%
2317
 
7.0%
2312
 
7.0%
2239
 
6.8%
2222
 
6.7%
2222
 
6.7%
2220
 
6.7%
2215
 
6.7%
1814
 
5.5%
Other values (252) 9779
29.7%
None
ValueCountFrequency (%)
10
50.0%
9
45.0%
1
 
5.0%

도로명주소
Text

MISSING 

Distinct1131
Distinct (%)91.9%
Missing984
Missing (%)44.4%
Memory size17.4 KiB
2024-05-11T15:16:55.614907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length50
Mean length32.842405
Min length21

Characters and Unicode

Total characters40429
Distinct characters262
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1040 ?
Unique (%)84.5%

Sample

1st row서울특별시 금천구 금하로 601-1 (시흥동)
2nd row서울특별시 금천구 금하로25길 16 (시흥동)
3rd row서울특별시 금천구 범안로17길 23, 102호 (독산동, 우정오피스텔)
4th row서울특별시 금천구 독산로44길 15, B01호 (시흥동, 오성타운)
5th row서울특별시 금천구 남부순환로128길 31, 1층 102호 (독산동)
ValueCountFrequency (%)
서울특별시 1231
 
15.4%
금천구 1231
 
15.4%
독산동 564
 
7.1%
시흥동 490
 
6.1%
1층 373
 
4.7%
가산동 177
 
2.2%
시흥대로 140
 
1.8%
2층 125
 
1.6%
독산로 120
 
1.5%
금하로 74
 
0.9%
Other values (948) 3445
43.2%
2024-05-11T15:16:56.348497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6740
 
16.7%
2144
 
5.3%
1 1969
 
4.9%
1452
 
3.6%
1398
 
3.5%
1304
 
3.2%
1287
 
3.2%
1245
 
3.1%
1238
 
3.1%
) 1236
 
3.1%
Other values (252) 20416
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22947
56.8%
Decimal Number 6881
 
17.0%
Space Separator 6740
 
16.7%
Close Punctuation 1237
 
3.1%
Open Punctuation 1237
 
3.1%
Other Punctuation 1118
 
2.8%
Uppercase Letter 141
 
0.3%
Dash Punctuation 123
 
0.3%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2144
 
9.3%
1452
 
6.3%
1398
 
6.1%
1304
 
5.7%
1287
 
5.6%
1245
 
5.4%
1238
 
5.4%
1232
 
5.4%
1231
 
5.4%
1231
 
5.4%
Other values (210) 9185
40.0%
Uppercase Letter
ValueCountFrequency (%)
B 71
50.4%
A 21
 
14.9%
C 8
 
5.7%
T 6
 
4.3%
Y 4
 
2.8%
S 4
 
2.8%
E 4
 
2.8%
D 4
 
2.8%
I 3
 
2.1%
X 3
 
2.1%
Other values (9) 13
 
9.2%
Decimal Number
ValueCountFrequency (%)
1 1969
28.6%
2 1052
15.3%
0 746
 
10.8%
3 724
 
10.5%
4 503
 
7.3%
6 434
 
6.3%
9 429
 
6.2%
5 368
 
5.3%
8 337
 
4.9%
7 319
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 1113
99.6%
. 3
 
0.3%
/ 2
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
b 1
33.3%
e 1
33.3%
w 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 1236
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1236
99.9%
[ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
6740
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22947
56.8%
Common 17338
42.9%
Latin 144
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2144
 
9.3%
1452
 
6.3%
1398
 
6.1%
1304
 
5.7%
1287
 
5.6%
1245
 
5.4%
1238
 
5.4%
1232
 
5.4%
1231
 
5.4%
1231
 
5.4%
Other values (210) 9185
40.0%
Latin
ValueCountFrequency (%)
B 71
49.3%
A 21
 
14.6%
C 8
 
5.6%
T 6
 
4.2%
Y 4
 
2.8%
S 4
 
2.8%
E 4
 
2.8%
D 4
 
2.8%
I 3
 
2.1%
X 3
 
2.1%
Other values (12) 16
 
11.1%
Common
ValueCountFrequency (%)
6740
38.9%
1 1969
 
11.4%
) 1236
 
7.1%
( 1236
 
7.1%
, 1113
 
6.4%
2 1052
 
6.1%
0 746
 
4.3%
3 724
 
4.2%
4 503
 
2.9%
6 434
 
2.5%
Other values (10) 1585
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22947
56.8%
ASCII 17482
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6740
38.6%
1 1969
 
11.3%
) 1236
 
7.1%
( 1236
 
7.1%
, 1113
 
6.4%
2 1052
 
6.0%
0 746
 
4.3%
3 724
 
4.1%
4 503
 
2.9%
6 434
 
2.5%
Other values (32) 1729
 
9.9%
Hangul
ValueCountFrequency (%)
2144
 
9.3%
1452
 
6.3%
1398
 
6.1%
1304
 
5.7%
1287
 
5.6%
1245
 
5.4%
1238
 
5.4%
1232
 
5.4%
1231
 
5.4%
1231
 
5.4%
Other values (210) 9185
40.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct138
Distinct (%)11.3%
Missing990
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean8584.3037
Minimum8501
Maximum8657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:16:56.593858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8501
5-th percentile8510
Q18546
median8584
Q38624
95-th percentile8650
Maximum8657
Range156
Interquartile range (IQR)78

Descriptive statistics

Standard deviation44.587241
Coefficient of variation (CV)0.0051940429
Kurtosis-1.2066966
Mean8584.3037
Median Absolute Deviation (MAD)39
Skewness-0.14375579
Sum10515772
Variance1988.0221
MonotonicityNot monotonic
2024-05-11T15:16:56.842975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8608 61
 
2.8%
8618 36
 
1.6%
8584 33
 
1.5%
8638 28
 
1.3%
8507 27
 
1.2%
8644 26
 
1.2%
8625 25
 
1.1%
8600 21
 
0.9%
8635 20
 
0.9%
8539 20
 
0.9%
Other values (128) 928
41.9%
(Missing) 990
44.7%
ValueCountFrequency (%)
8501 2
 
0.1%
8502 1
 
< 0.1%
8503 6
 
0.3%
8504 9
 
0.4%
8505 2
 
0.1%
8506 6
 
0.3%
8507 27
1.2%
8509 3
 
0.1%
8510 10
 
0.5%
8511 14
0.6%
ValueCountFrequency (%)
8657 11
0.5%
8656 13
0.6%
8655 17
0.8%
8654 1
 
< 0.1%
8653 1
 
< 0.1%
8652 9
0.4%
8651 9
0.4%
8650 2
 
0.1%
8649 8
0.4%
8648 3
 
0.1%
Distinct1840
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
2024-05-11T15:16:57.269828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length5.1200903
Min length1

Characters and Unicode

Total characters11341
Distinct characters626
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1609 ?
Unique (%)72.6%

Sample

1st row평화
2nd row현대
3rd row
4th row영미
5th row은진
ValueCountFrequency (%)
헤어 35
 
1.3%
hair 25
 
1.0%
미용실 18
 
0.7%
네일 14
 
0.5%
13
 
0.5%
nail 10
 
0.4%
에스테틱 9
 
0.3%
시흥점 9
 
0.3%
beauty 8
 
0.3%
리안헤어 8
 
0.3%
Other values (1953) 2449
94.3%
2024-05-11T15:16:57.878060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
768
 
6.8%
726
 
6.4%
383
 
3.4%
344
 
3.0%
279
 
2.5%
215
 
1.9%
200
 
1.8%
192
 
1.7%
188
 
1.7%
143
 
1.3%
Other values (616) 7903
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9587
84.5%
Lowercase Letter 546
 
4.8%
Uppercase Letter 439
 
3.9%
Space Separator 383
 
3.4%
Decimal Number 112
 
1.0%
Close Punctuation 92
 
0.8%
Open Punctuation 92
 
0.8%
Other Punctuation 84
 
0.7%
Dash Punctuation 3
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
768
 
8.0%
726
 
7.6%
344
 
3.6%
279
 
2.9%
215
 
2.2%
200
 
2.1%
192
 
2.0%
188
 
2.0%
143
 
1.5%
139
 
1.4%
Other values (541) 6393
66.7%
Lowercase Letter
ValueCountFrequency (%)
a 69
12.6%
i 62
11.4%
e 59
10.8%
r 42
 
7.7%
o 41
 
7.5%
h 37
 
6.8%
n 37
 
6.8%
l 30
 
5.5%
s 28
 
5.1%
t 25
 
4.6%
Other values (14) 116
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 55
12.5%
N 35
 
8.0%
O 34
 
7.7%
I 33
 
7.5%
M 29
 
6.6%
H 28
 
6.4%
L 27
 
6.2%
S 26
 
5.9%
T 24
 
5.5%
R 21
 
4.8%
Other values (14) 127
28.9%
Decimal Number
ValueCountFrequency (%)
0 29
25.9%
2 25
22.3%
1 22
19.6%
9 10
 
8.9%
8 6
 
5.4%
7 5
 
4.5%
3 5
 
4.5%
6 5
 
4.5%
5 4
 
3.6%
4 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
? 19
22.6%
. 19
22.6%
& 17
20.2%
# 14
16.7%
: 6
 
7.1%
, 4
 
4.8%
' 3
 
3.6%
1
 
1.2%
/ 1
 
1.2%
Close Punctuation
ValueCountFrequency (%)
) 91
98.9%
] 1
 
1.1%
Open Punctuation
ValueCountFrequency (%)
( 91
98.9%
[ 1
 
1.1%
Space Separator
ValueCountFrequency (%)
383
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9574
84.4%
Latin 986
 
8.7%
Common 768
 
6.8%
Han 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
768
 
8.0%
726
 
7.6%
344
 
3.6%
279
 
2.9%
215
 
2.2%
200
 
2.1%
192
 
2.0%
188
 
2.0%
143
 
1.5%
139
 
1.5%
Other values (534) 6380
66.6%
Latin
ValueCountFrequency (%)
a 69
 
7.0%
i 62
 
6.3%
e 59
 
6.0%
A 55
 
5.6%
r 42
 
4.3%
o 41
 
4.2%
h 37
 
3.8%
n 37
 
3.8%
N 35
 
3.5%
O 34
 
3.4%
Other values (39) 515
52.2%
Common
ValueCountFrequency (%)
383
49.9%
) 91
 
11.8%
( 91
 
11.8%
0 29
 
3.8%
2 25
 
3.3%
1 22
 
2.9%
? 19
 
2.5%
. 19
 
2.5%
& 17
 
2.2%
# 14
 
1.8%
Other values (16) 58
 
7.6%
Han
ValueCountFrequency (%)
5
38.5%
2
 
15.4%
2
 
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9574
84.4%
ASCII 1752
 
15.4%
CJK 13
 
0.1%
Number Forms 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
768
 
8.0%
726
 
7.6%
344
 
3.6%
279
 
2.9%
215
 
2.2%
200
 
2.1%
192
 
2.0%
188
 
2.0%
143
 
1.5%
139
 
1.5%
Other values (534) 6380
66.6%
ASCII
ValueCountFrequency (%)
383
21.9%
) 91
 
5.2%
( 91
 
5.2%
a 69
 
3.9%
i 62
 
3.5%
e 59
 
3.4%
A 55
 
3.1%
r 42
 
2.4%
o 41
 
2.3%
h 37
 
2.1%
Other values (63) 822
46.9%
CJK
ValueCountFrequency (%)
5
38.5%
2
 
15.4%
2
 
15.4%
1
 
7.7%
1
 
7.7%
1
 
7.7%
1
 
7.7%
Number Forms
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct1609
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum1999-01-04 00:00:00
Maximum2024-05-09 15:49:06
2024-05-11T15:16:58.071951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:58.324207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
I
1471 
U
737 
D
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 1471
66.4%
U 737
33.3%
D 7
 
0.3%

Length

2024-05-11T15:16:58.583872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:58.733969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1471
66.4%
u 737
33.3%
d 7
 
0.3%
Distinct513
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:05:00
2024-05-11T15:16:58.871096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:16:59.055996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
일반미용업
1804 
피부미용업
208 
네일아트업
 
157
메이크업업
 
31
기타
 
15

Length

Max length5
Median length5
Mean length4.979684
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1804
81.4%
피부미용업 208
 
9.4%
네일아트업 157
 
7.1%
메이크업업 31
 
1.4%
기타 15
 
0.7%

Length

2024-05-11T15:16:59.247775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:16:59.409221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 1804
81.4%
피부미용업 208
 
9.4%
네일아트업 157
 
7.1%
메이크업업 31
 
1.4%
기타 15
 
0.7%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1218
Distinct (%)57.5%
Missing96
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean191176.85
Minimum189055.14
Maximum192754.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:16:59.578805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189055.14
5-th percentile189964.95
Q1190899.32
median191281.81
Q3191546.08
95-th percentile192029.9
Maximum192754.35
Range3699.2079
Interquartile range (IQR)646.7562

Descriptive statistics

Standard deviation642.28942
Coefficient of variation (CV)0.0033596611
Kurtosis0.92007867
Mean191176.85
Median Absolute Deviation (MAD)311.01502
Skewness-0.68099396
Sum4.0510373 × 108
Variance412535.7
MonotonicityNot monotonic
2024-05-11T15:16:59.776990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190023.426907798 26
 
1.2%
190858.649482406 23
 
1.0%
192754.34619252 23
 
1.0%
189538.020935968 22
 
1.0%
190567.418315661 22
 
1.0%
191341.238749155 13
 
0.6%
190838.324818618 10
 
0.5%
189662.959175776 10
 
0.5%
190352.36451174 9
 
0.4%
191311.743052035 9
 
0.4%
Other values (1208) 1952
88.1%
(Missing) 96
 
4.3%
ValueCountFrequency (%)
189055.138252216 9
0.4%
189089.927764903 2
 
0.1%
189127.981104583 1
 
< 0.1%
189158.740846154 2
 
0.1%
189238.017238916 1
 
< 0.1%
189282.642165078 2
 
0.1%
189303.743525683 1
 
< 0.1%
189350.592434703 2
 
0.1%
189364.095969911 1
 
< 0.1%
189369.53962474 2
 
0.1%
ValueCountFrequency (%)
192754.34619252 23
1.0%
192742.326147617 8
 
0.4%
192480.368331844 1
 
< 0.1%
192434.31669691 4
 
0.2%
192434.047852853 3
 
0.1%
192400.017231384 1
 
< 0.1%
192394.502820184 5
 
0.2%
192394.491668961 1
 
< 0.1%
192385.40455499 2
 
0.1%
192371.96913206 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1218
Distinct (%)57.5%
Missing96
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean440153.93
Minimum436884.96
Maximum442569.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:00.316868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum436884.96
5-th percentile438458.21
Q1439072.25
median440130.82
Q3441289.78
95-th percentile441928.69
Maximum442569.3
Range5684.3423
Interquartile range (IQR)2217.532

Descriptive statistics

Standard deviation1192.7888
Coefficient of variation (CV)0.0027099355
Kurtosis-1.1244604
Mean440153.93
Median Absolute Deviation (MAD)1092.9358
Skewness-0.033706766
Sum9.3268619 × 108
Variance1422745.1
MonotonicityNot monotonic
2024-05-11T15:17:00.551007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
439379.272866954 26
 
1.2%
440808.541334678 23
 
1.0%
438827.143732711 23
 
1.0%
441982.427934953 22
 
1.0%
439569.401459017 22
 
1.0%
438365.613175801 13
 
0.6%
439761.362000391 10
 
0.5%
442139.514492212 10
 
0.5%
441545.208758244 9
 
0.4%
438358.367852933 9
 
0.4%
Other values (1208) 1952
88.1%
(Missing) 96
 
4.3%
ValueCountFrequency (%)
436884.958413433 1
 
< 0.1%
436893.312157815 1
 
< 0.1%
436897.466167682 2
0.1%
436909.870493711 1
 
< 0.1%
436911.774494656 1
 
< 0.1%
436946.60407049 1
 
< 0.1%
437571.204954672 3
0.1%
437601.753603288 1
 
< 0.1%
437602.625094324 1
 
< 0.1%
437632.508826604 1
 
< 0.1%
ValueCountFrequency (%)
442569.300676147 2
0.1%
442460.505542105 1
< 0.1%
442438.365006302 1
< 0.1%
442354.304734197 1
< 0.1%
442283.977342742 2
0.1%
442277.026054363 1
< 0.1%
442250.594205395 1
< 0.1%
442236.253610783 2
0.1%
442223.92565551 1
< 0.1%
442205.867457803 1
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
미용업
1175 
<NA>
350 
일반미용업
282 
종합미용업
153 
피부미용업
122 
Other values (12)
133 

Length

Max length23
Median length3
Mean length4.1467269
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1175
53.0%
<NA> 350
 
15.8%
일반미용업 282
 
12.7%
종합미용업 153
 
6.9%
피부미용업 122
 
5.5%
네일미용업 61
 
2.8%
일반미용업, 화장ㆍ분장 미용업 17
 
0.8%
피부미용업, 네일미용업 13
 
0.6%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 9
 
0.4%
피부미용업, 네일미용업, 화장ㆍ분장 미용업 8
 
0.4%
Other values (7) 25
 
1.1%

Length

2024-05-11T15:17:00.887949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1227
52.1%
na 350
 
14.9%
일반미용업 316
 
13.4%
피부미용업 154
 
6.5%
종합미용업 153
 
6.5%
네일미용업 101
 
4.3%
화장ㆍ분장 52
 
2.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)1.1%
Missing801
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean1.1838755
Minimum0
Maximum20
Zeros922
Zeros (%)41.6%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:01.096960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1602784
Coefficient of variation (CV)1.8247513
Kurtosis15.645665
Mean1.1838755
Median Absolute Deviation (MAD)0
Skewness3.2007889
Sum1674
Variance4.6668028
MonotonicityNot monotonic
2024-05-11T15:17:01.271158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 922
41.6%
3 156
 
7.0%
2 137
 
6.2%
4 88
 
4.0%
1 42
 
1.9%
5 33
 
1.5%
7 8
 
0.4%
15 8
 
0.4%
6 4
 
0.2%
9 4
 
0.2%
Other values (6) 12
 
0.5%
(Missing) 801
36.2%
ValueCountFrequency (%)
0 922
41.6%
1 42
 
1.9%
2 137
 
6.2%
3 156
 
7.0%
4 88
 
4.0%
5 33
 
1.5%
6 4
 
0.2%
7 8
 
0.4%
8 3
 
0.1%
9 4
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 8
0.4%
14 1
 
< 0.1%
13 2
 
0.1%
11 3
 
0.1%
10 2
 
0.1%
9 4
0.2%
8 3
 
0.1%
7 8
0.4%
6 4
0.2%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.4%
Missing855
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean0.31176471
Minimum0
Maximum5
Zeros998
Zeros (%)45.1%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:01.455326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.59419052
Coefficient of variation (CV)1.9058941
Kurtosis10.248018
Mean0.31176471
Median Absolute Deviation (MAD)0
Skewness2.6135393
Sum424
Variance0.35306237
MonotonicityNot monotonic
2024-05-11T15:17:01.612875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 998
45.1%
1 324
 
14.6%
2 21
 
0.9%
3 11
 
0.5%
4 5
 
0.2%
5 1
 
< 0.1%
(Missing) 855
38.6%
ValueCountFrequency (%)
0 998
45.1%
1 324
 
14.6%
2 21
 
0.9%
3 11
 
0.5%
4 5
 
0.2%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 5
 
0.2%
3 11
 
0.5%
2 21
 
0.9%
1 324
 
14.6%
0 998
45.1%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.9%
Missing946
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean0.7927502
Minimum0
Maximum19
Zeros546
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:01.760386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1457277
Coefficient of variation (CV)1.4452569
Kurtosis78.436812
Mean0.7927502
Median Absolute Deviation (MAD)1
Skewness6.4395323
Sum1006
Variance1.3126919
MonotonicityNot monotonic
2024-05-11T15:17:01.923314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 557
25.1%
0 546
24.7%
2 124
 
5.6%
3 17
 
0.8%
4 15
 
0.7%
6 3
 
0.1%
5 2
 
0.1%
8 2
 
0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
(Missing) 946
42.7%
ValueCountFrequency (%)
0 546
24.7%
1 557
25.1%
2 124
 
5.6%
3 17
 
0.8%
4 15
 
0.7%
5 2
 
0.1%
6 3
 
0.1%
8 2
 
0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
8 2
 
0.1%
6 3
 
0.1%
5 2
 
0.1%
4 15
 
0.7%
3 17
 
0.8%
2 124
 
5.6%
1 557
25.1%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct11
Distinct (%)1.5%
Missing1465
Missing (%)66.1%
Infinite0
Infinite (%)0.0%
Mean1.492
Minimum0
Maximum224
Zeros98
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:02.123668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum224
Range224
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.2124212
Coefficient of variation (CV)5.5043037
Kurtosis722.28233
Mean1.492
Median Absolute Deviation (MAD)0
Skewness26.642395
Sum1119
Variance67.443861
MonotonicityNot monotonic
2024-05-11T15:17:02.304179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 497
 
22.4%
2 116
 
5.2%
0 98
 
4.4%
3 17
 
0.8%
4 15
 
0.7%
5 2
 
0.1%
8 1
 
< 0.1%
224 1
 
< 0.1%
12 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 1465
66.1%
ValueCountFrequency (%)
0 98
 
4.4%
1 497
22.4%
2 116
 
5.2%
3 17
 
0.8%
4 15
 
0.7%
5 2
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
12 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
224 1
 
< 0.1%
19 1
 
< 0.1%
12 1
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
0.1%
4 15
 
0.7%
3 17
 
0.8%
2 116
 
5.2%
1 497
22.4%

사용시작지하층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct6
Distinct (%)0.8%
Missing1419
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean0.091708543
Minimum0
Maximum35
Zeros765
Zeros (%)34.5%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:02.455701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2690338
Coefficient of variation (CV)13.837684
Kurtosis722.82364
Mean0.091708543
Median Absolute Deviation (MAD)0
Skewness26.323794
Sum73
Variance1.6104469
MonotonicityNot monotonic
2024-05-11T15:17:02.607129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 765
34.5%
1 25
 
1.1%
2 3
 
0.1%
3 1
 
< 0.1%
35 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 1419
64.1%
ValueCountFrequency (%)
0 765
34.5%
1 25
 
1.1%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
35 1
 
< 0.1%
ValueCountFrequency (%)
35 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 3
 
0.1%
1 25
 
1.1%
0 765
34.5%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
1899 
0
290 
1
 
21
2
 
3
3
 
1

Length

Max length4
Median length4
Mean length3.572009
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> 1899
85.7%
0 290
 
13.1%
1 21
 
0.9%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-05-11T15:17:02.821080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:03.009487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1899
85.7%
0 290
 
13.1%
1 21
 
0.9%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1219 
<NA>
996 

Length

Max length4
Median length1
Mean length2.3489842
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1219
55.0%
<NA> 996
45.0%

Length

2024-05-11T15:17:03.228874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:03.424502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1219
55.0%
na 996
45.0%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1219 
<NA>
996 

Length

Max length4
Median length1
Mean length2.3489842
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1219
55.0%
<NA> 996
45.0%

Length

2024-05-11T15:17:03.604571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:03.766394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1219
55.0%
na 996
45.0%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
0
1219 
<NA>
996 

Length

Max length4
Median length1
Mean length2.3489842
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1219
55.0%
<NA> 996
45.0%

Length

2024-05-11T15:17:03.916759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:04.079883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1219
55.0%
na 996
45.0%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing370
Missing (%)16.7%
Memory size4.5 KiB
False
1845 
(Missing)
370 
ValueCountFrequency (%)
False 1845
83.3%
(Missing) 370
 
16.7%
2024-05-11T15:17:04.221127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16
Distinct (%)0.9%
Missing417
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean3.1540601
Minimum0
Maximum91
Zeros110
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:04.364117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum91
Range91
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.66329
Coefficient of variation (CV)0.84440054
Kurtosis659.77116
Mean3.1540601
Median Absolute Deviation (MAD)1
Skewness20.457906
Sum5671
Variance7.0931137
MonotonicityNot monotonic
2024-05-11T15:17:04.523357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 787
35.5%
2 389
17.6%
4 299
 
13.5%
0 110
 
5.0%
5 82
 
3.7%
6 40
 
1.8%
1 27
 
1.2%
7 19
 
0.9%
8 17
 
0.8%
10 13
 
0.6%
Other values (6) 15
 
0.7%
(Missing) 417
18.8%
ValueCountFrequency (%)
0 110
 
5.0%
1 27
 
1.2%
2 389
17.6%
3 787
35.5%
4 299
 
13.5%
5 82
 
3.7%
6 40
 
1.8%
7 19
 
0.9%
8 17
 
0.8%
9 3
 
0.1%
ValueCountFrequency (%)
91 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
0.1%
13 2
 
0.1%
12 6
 
0.3%
10 13
 
0.6%
9 3
 
0.1%
8 17
0.8%
7 19
0.9%
6 40
1.8%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2215
Missing (%)100.0%
Memory size19.6 KiB

조건부허가시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
2214 
20210614
 
1

Length

Max length8
Median length4
Mean length4.0018059
Min length4

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> 2214
> 99.9%
20210614 1
 
< 0.1%

Length

2024-05-11T15:17:04.699528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:04.843096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2214
> 99.9%
20210614 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
2214 
20240821
 
1

Length

Max length8
Median length4
Mean length4.0018059
Min length4

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> 2214
> 99.9%
20240821 1
 
< 0.1%

Length

2024-05-11T15:17:05.007394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:05.174713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2214
> 99.9%
20240821 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
1884 
임대
326 
자가
 
5

Length

Max length4
Median length4
Mean length3.7011287
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> 1884
85.1%
임대 326
 
14.7%
자가 5
 
0.2%

Length

2024-05-11T15:17:05.357325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:05.562548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1884
85.1%
임대 326
 
14.7%
자가 5
 
0.2%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
1474 
0
741 

Length

Max length4
Median length4
Mean length2.9963883
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> 1474
66.5%
0 741
33.5%

Length

2024-05-11T15:17:05.782986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:05.926289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1474
66.5%
0 741
33.5%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.6%
Missing1849
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean0.43442623
Minimum0
Maximum6
Zeros244
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:06.039139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7655712
Coefficient of variation (CV)1.7622582
Kurtosis11.483562
Mean0.43442623
Median Absolute Deviation (MAD)0
Skewness2.7639048
Sum159
Variance0.58609926
MonotonicityNot monotonic
2024-05-11T15:17:06.168722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 244
 
11.0%
1 101
 
4.6%
2 11
 
0.5%
3 6
 
0.3%
4 3
 
0.1%
6 1
 
< 0.1%
(Missing) 1849
83.5%
ValueCountFrequency (%)
0 244
11.0%
1 101
4.6%
2 11
 
0.5%
3 6
 
0.3%
4 3
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
4 3
 
0.1%
3 6
 
0.3%
2 11
 
0.5%
1 101
4.6%
0 244
11.0%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
1850 
0
343 
1
 
19
2
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.5056433
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> 1850
83.5%
0 343
 
15.5%
1 19
 
0.9%
2 2
 
0.1%
4 1
 
< 0.1%

Length

2024-05-11T15:17:06.338057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:06.499364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1850
83.5%
0 343
 
15.5%
1 19
 
0.9%
2 2
 
0.1%
4 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
<NA>
1524 
0
691 

Length

Max length4
Median length4
Mean length3.0641084
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> 1524
68.8%
0 691
31.2%

Length

2024-05-11T15:17:06.678936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:17:06.845671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1524
68.8%
0 691
31.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)1.2%
Missing1535
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean0.57794118
Minimum0
Maximum8
Zeros528
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size19.6 KiB
2024-05-11T15:17:06.940042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.259935
Coefficient of variation (CV)2.1800401
Kurtosis6.1415616
Mean0.57794118
Median Absolute Deviation (MAD)0
Skewness2.4555187
Sum393
Variance1.5874361
MonotonicityNot monotonic
2024-05-11T15:17:07.077125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 528
 
23.8%
2 57
 
2.6%
3 34
 
1.5%
1 31
 
1.4%
4 14
 
0.6%
5 8
 
0.4%
6 7
 
0.3%
8 1
 
< 0.1%
(Missing) 1535
69.3%
ValueCountFrequency (%)
0 528
23.8%
1 31
 
1.4%
2 57
 
2.6%
3 34
 
1.5%
4 14
 
0.6%
5 8
 
0.4%
6 7
 
0.3%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 7
 
0.3%
5 8
 
0.4%
4 14
 
0.6%
3 34
 
1.5%
2 57
 
2.6%
1 31
 
1.4%
0 528
23.8%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing350
Missing (%)15.8%
Memory size4.5 KiB
False
1862 
True
 
3
(Missing)
350 
ValueCountFrequency (%)
False 1862
84.1%
True 3
 
0.1%
(Missing) 350
 
15.8%
2024-05-11T15:17:07.230142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031700003170000-204-1968-0052319681112<NA>3폐업2폐업20160616<NA><NA><NA>02 802086517.56153864서울특별시 금천구 시흥동 998-8번지서울특별시 금천구 금하로 601-1 (시흥동)<NA>평화2013-04-10 10:26:00I2018-08-31 23:59:59.0일반미용업190918.882661439099.83508미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131700003170000-204-1975-0095419750823<NA>3폐업2폐업19970620<NA><NA><NA>02 855183316.29153805서울특별시 금천구 독산동 60-32번지<NA><NA>현대2002-01-13 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231700003170000-204-1976-0090119760605<NA>3폐업2폐업19951005<NA><NA><NA>020000000016.03153030서울특별시 금천구 시흥동 76-16번지<NA><NA>2002-01-13 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331700003170000-204-1976-0091419760707<NA>3폐업2폐업19951004<NA><NA><NA>020000000011.9153010서울특별시 금천구 독산동 205-4번지<NA><NA>영미2002-01-13 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
431700003170000-204-1977-0055619770829<NA>1영업/정상1영업<NA><NA><NA><NA>02 894524314.09153841서울특별시 금천구 시흥동 830-10번지서울특별시 금천구 금하로25길 16 (시흥동)8574은진2019-09-18 11:05:21U2019-09-20 02:40:00.0일반미용업191933.345442438783.811339미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531700003170000-204-1977-0063219770423<NA>3폐업2폐업19970926<NA><NA><NA>02 803416610.9153856서울특별시 금천구 시흥동 853-50번지<NA><NA>2002-01-13 00:00:00I2018-08-31 23:59:59.0일반미용업191342.797233439483.027877미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631700003170000-204-1977-0089619770216<NA>3폐업2폐업19930720<NA><NA><NA>020802170513.05153847서울특별시 금천구 시흥동 539-51번지<NA><NA>2002-01-13 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
731700003170000-204-1977-0090319770811<NA>3폐업2폐업19931021<NA><NA><NA>020802228812.88153030서울특별시 금천구 시흥동 621-12번지<NA><NA>유미2002-01-13 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
831700003170000-204-1977-0092919770920<NA>3폐업2폐업20110516<NA><NA><NA>02 857189811.9153821서울특별시 금천구 독산동 1015-1번지 [우정길 49]<NA><NA>나나미용실2007-10-19 13:37:19I2018-08-31 23:59:59.0일반미용업190984.789086440676.587377미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931700003170000-204-1978-0084419781120<NA>3폐업2폐업19951004<NA><NA><NA>020000000011.78153010서울특별시 금천구 독산동 399-57번지<NA><NA>목화2002-01-13 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
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
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221231700003170000-226-2023-000022023-02-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>17.16153-803서울특별시 금천구 가산동 549-1서울특별시 금천구 가산디지털2로 101, 한라원앤원타워 1층 112호 (가산동)8505블룸인네일2023-07-03 13:16:14I2022-12-07 00:05:00.0네일아트업189282.642165441654.484591<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
221331700003170000-226-2023-000032023-05-15<NA>1영업/정상1영업<NA><NA><NA><NA><NA>39.67153-803서울특별시 금천구 가산동 685 디지털엠파이어빌딩서울특별시 금천구 범안로 1130, 디지털엠파이어빌딩 2층 210-1호 (가산동)8595보네르 브로우2024-02-26 14:28:02I2023-12-01 22:08:00.0메이크업업189978.050926440363.954454<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
221431700003170000-226-2024-000012024-03-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>60.14153-010서울특별시 금천구 독산동 1155 금천롯데캐슬골드파크3차서울특별시 금천구 시흥대로 291, 310동 210호 (독산동, 금천롯데캐슬골드파크3차)8608호감왁싱네일속눈썹2024-03-25 11:43:34I2023-12-02 22:07:00.0네일아트업190838.324819439761.362<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>