Overview

Dataset statistics

Number of variables47
Number of observations4142
Missing cells49648
Missing cells (%)25.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory405.0 B

Variable types

Categorical18
Text6
DateTime4
Unsupported7
Numeric10
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
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-17916/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
사용시작지하층 is highly imbalanced (68.6%)Imbalance
사용끝지하층 is highly imbalanced (70.2%)Imbalance
건물소유구분명 is highly imbalanced (51.9%)Imbalance
여성종사자수 is highly imbalanced (70.2%)Imbalance
남성종사자수 is highly imbalanced (67.7%)Imbalance
인허가취소일자 has 4142 (100.0%) missing valuesMissing
폐업일자 has 1285 (31.0%) missing valuesMissing
휴업시작일자 has 4142 (100.0%) missing valuesMissing
휴업종료일자 has 4142 (100.0%) missing valuesMissing
재개업일자 has 4142 (100.0%) missing valuesMissing
전화번호 has 1346 (32.5%) missing valuesMissing
도로명주소 has 1661 (40.1%) missing valuesMissing
도로명우편번호 has 1669 (40.3%) missing valuesMissing
좌표정보(X) has 139 (3.4%) missing valuesMissing
좌표정보(Y) has 139 (3.4%) missing valuesMissing
건물지상층수 has 2166 (52.3%) missing valuesMissing
건물지하층수 has 2217 (53.5%) missing valuesMissing
사용시작지상층 has 2590 (62.5%) missing valuesMissing
사용끝지상층 has 2675 (64.6%) missing valuesMissing
발한실여부 has 732 (17.7%) missing valuesMissing
좌석수 has 952 (23.0%) missing valuesMissing
조건부허가신고사유 has 4142 (100.0%) missing valuesMissing
조건부허가시작일자 has 4142 (100.0%) missing valuesMissing
조건부허가종료일자 has 4142 (100.0%) missing valuesMissing
침대수 has 2429 (58.6%) missing valuesMissing
다중이용업소여부 has 654 (15.8%) missing valuesMissing
사용끝지상층 is highly skewed (γ1 = 29.64216461)Skewed
좌석수 is highly skewed (γ1 = 31.16334419)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적 has 140 (3.4%) zerosZeros
건물지상층수 has 1705 (41.2%) zerosZeros
건물지하층수 has 1823 (44.0%) zerosZeros
사용시작지상층 has 376 (9.1%) zerosZeros
사용끝지상층 has 341 (8.2%) zerosZeros
좌석수 has 367 (8.9%) zerosZeros
침대수 has 1181 (28.5%) zerosZeros

Reproduction

Analysis started2024-04-29 19:30:32.551600
Analysis finished2024-04-29 19:30:34.011737
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
3140000
4142 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3140000
2nd row3140000
3rd row3140000
4th row3140000
5th row3140000

Common Values

ValueCountFrequency (%)
3140000 4142
100.0%

Length

2024-04-30T04:30:34.071557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:34.161888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 4142
100.0%

관리번호
Text

UNIQUE 

Distinct4142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
2024-04-30T04:30:34.329314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters91124
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

Unique4142 ?
Unique (%)100.0%

Sample

1st row3140000-204-1968-01433
2nd row3140000-204-1971-00457
3rd row3140000-204-1972-00458
4th row3140000-204-1975-00460
5th row3140000-204-1975-00461
ValueCountFrequency (%)
3140000-204-1968-01433 1
 
< 0.1%
3140000-211-2023-00013 1
 
< 0.1%
3140000-211-2022-00022 1
 
< 0.1%
3140000-211-2022-00023 1
 
< 0.1%
3140000-211-2022-00038 1
 
< 0.1%
3140000-211-2022-00024 1
 
< 0.1%
3140000-211-2022-00025 1
 
< 0.1%
3140000-211-2022-00026 1
 
< 0.1%
3140000-211-2022-00027 1
 
< 0.1%
3140000-211-2022-00028 1
 
< 0.1%
Other values (4132) 4132
99.8%
2024-04-30T04:30:34.618919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34914
38.3%
1 13048
 
14.3%
- 12426
 
13.6%
2 9849
 
10.8%
4 6869
 
7.5%
3 5964
 
6.5%
9 2997
 
3.3%
8 1430
 
1.6%
5 1350
 
1.5%
6 1184
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 78698
86.4%
Dash Punctuation 12426
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34914
44.4%
1 13048
 
16.6%
2 9849
 
12.5%
4 6869
 
8.7%
3 5964
 
7.6%
9 2997
 
3.8%
8 1430
 
1.8%
5 1350
 
1.7%
6 1184
 
1.5%
7 1093
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 12426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 91124
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34914
38.3%
1 13048
 
14.3%
- 12426
 
13.6%
2 9849
 
10.8%
4 6869
 
7.5%
3 5964
 
6.5%
9 2997
 
3.3%
8 1430
 
1.6%
5 1350
 
1.5%
6 1184
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91124
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34914
38.3%
1 13048
 
14.3%
- 12426
 
13.6%
2 9849
 
10.8%
4 6869
 
7.5%
3 5964
 
6.5%
9 2997
 
3.3%
8 1430
 
1.6%
5 1350
 
1.5%
6 1184
 
1.3%

인허가일자
Date

Distinct2916
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
Minimum1968-04-15 00:00:00
Maximum2024-04-25 00:00:00
2024-04-30T04:30:34.751441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:34.862106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

영업상태코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
3
2857 
1
1285 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 2857
69.0%
1 1285
31.0%

Length

2024-04-30T04:30:34.970291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:35.049231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2857
69.0%
1 1285
31.0%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
폐업
2857 
영업/정상
1285 

Length

Max length5
Median length2
Mean length2.9307098
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2857
69.0%
영업/정상 1285
31.0%

Length

2024-04-30T04:30:35.139370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:35.230616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2857
69.0%
영업/정상 1285
31.0%

상세영업상태코드
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
2
2857 
1
1285 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 2857
69.0%
1 1285
31.0%

Length

2024-04-30T04:30:35.317135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:35.423347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2857
69.0%
1 1285
31.0%

상세영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
폐업
2857 
영업
1285 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2857
69.0%
영업 1285
31.0%

Length

2024-04-30T04:30:35.519499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:35.601198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2857
69.0%
영업 1285
31.0%

폐업일자
Date

MISSING 

Distinct2039
Distinct (%)71.4%
Missing1285
Missing (%)31.0%
Memory size32.5 KiB
Minimum1991-03-14 00:00:00
Maximum2024-04-23 00:00:00
2024-04-30T04:30:35.697564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:35.824855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

전화번호
Text

MISSING 

Distinct2507
Distinct (%)89.7%
Missing1346
Missing (%)32.5%
Memory size32.5 KiB
2024-04-30T04:30:36.017452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.126609
Min length2

Characters and Unicode

Total characters28314
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

Unique2307 ?
Unique (%)82.5%

Sample

1st row02 6076968
2nd row02 6488988
3rd row0200000000
4th row0206462566
5th row02 6439715
ValueCountFrequency (%)
02 933
 
24.9%
070 30
 
0.8%
00000 19
 
0.5%
0200000000 13
 
0.3%
6079881 4
 
0.1%
0220619555 4
 
0.1%
0226518978 4
 
0.1%
0226443277 4
 
0.1%
0226441955 4
 
0.1%
0226493695 3
 
0.1%
Other values (2523) 2733
72.9%
2024-04-30T04:30:36.314497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5950
21.0%
0 5228
18.5%
6 3972
14.0%
4 2146
 
7.6%
9 1923
 
6.8%
5 1854
 
6.5%
3 1561
 
5.5%
7 1493
 
5.3%
1 1463
 
5.2%
8 1434
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27024
95.4%
Space Separator 1290
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5950
22.0%
0 5228
19.3%
6 3972
14.7%
4 2146
 
7.9%
9 1923
 
7.1%
5 1854
 
6.9%
3 1561
 
5.8%
7 1493
 
5.5%
1 1463
 
5.4%
8 1434
 
5.3%
Space Separator
ValueCountFrequency (%)
1290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28314
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5950
21.0%
0 5228
18.5%
6 3972
14.0%
4 2146
 
7.6%
9 1923
 
6.8%
5 1854
 
6.5%
3 1561
 
5.5%
7 1493
 
5.3%
1 1463
 
5.2%
8 1434
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28314
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5950
21.0%
0 5228
18.5%
6 3972
14.0%
4 2146
 
7.6%
9 1923
 
6.8%
5 1854
 
6.5%
3 1561
 
5.5%
7 1493
 
5.3%
1 1463
 
5.2%
8 1434
 
5.1%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1728
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.827757
Minimum0
Maximum502.24
Zeros140
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:36.445472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.981
Q119.8
median28.07
Q345.23
95-th percentile105.997
Maximum502.24
Range502.24
Interquartile range (IQR)25.43

Descriptive statistics

Standard deviation34.770872
Coefficient of variation (CV)0.89551585
Kurtosis22.767686
Mean38.827757
Median Absolute Deviation (MAD)10.61
Skewness3.4511177
Sum160824.57
Variance1209.0135
MonotonicityNot monotonic
2024-04-30T04:30:36.578281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 174
 
4.2%
0.0 140
 
3.4%
30.0 84
 
2.0%
26.4 68
 
1.6%
24.0 67
 
1.6%
20.0 51
 
1.2%
25.0 49
 
1.2%
23.0 44
 
1.1%
23.1 41
 
1.0%
66.0 41
 
1.0%
Other values (1718) 3383
81.7%
ValueCountFrequency (%)
0.0 140
3.4%
3.3 1
 
< 0.1%
3.9 1
 
< 0.1%
4.0 2
 
< 0.1%
4.95 2
 
< 0.1%
5.5 1
 
< 0.1%
6.0 2
 
< 0.1%
6.6 2
 
< 0.1%
7.0 2
 
< 0.1%
7.18 1
 
< 0.1%
ValueCountFrequency (%)
502.24 1
< 0.1%
483.12 1
< 0.1%
335.96 1
< 0.1%
292.0 1
< 0.1%
290.0 1
< 0.1%
287.97 1
< 0.1%
277.63 1
< 0.1%
274.04 1
< 0.1%
264.0 1
< 0.1%
262.38 1
< 0.1%

소재지우편번호
Text

Distinct187
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
2024-04-30T04:30:36.858695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0866731
Min length6

Characters and Unicode

Total characters25211
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.6%

Sample

1st row158090
2nd row158808
3rd row158851
4th row158850
5th row158812
ValueCountFrequency (%)
158070 175
 
4.2%
158827 163
 
3.9%
158860 163
 
3.9%
158806 156
 
3.8%
158849 154
 
3.7%
158811 152
 
3.7%
158859 124
 
3.0%
158861 111
 
2.7%
158819 99
 
2.4%
158050 97
 
2.3%
Other values (177) 2748
66.3%
2024-04-30T04:30:37.259073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 8183
32.5%
1 5249
20.8%
5 5008
19.9%
0 1483
 
5.9%
7 1034
 
4.1%
6 932
 
3.7%
2 837
 
3.3%
4 808
 
3.2%
9 718
 
2.8%
3 600
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24852
98.6%
Dash Punctuation 359
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 8183
32.9%
1 5249
21.1%
5 5008
20.2%
0 1483
 
6.0%
7 1034
 
4.2%
6 932
 
3.8%
2 837
 
3.4%
4 808
 
3.3%
9 718
 
2.9%
3 600
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 359
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 8183
32.5%
1 5249
20.8%
5 5008
19.9%
0 1483
 
5.9%
7 1034
 
4.1%
6 932
 
3.7%
2 837
 
3.3%
4 808
 
3.2%
9 718
 
2.8%
3 600
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 8183
32.5%
1 5249
20.8%
5 5008
19.9%
0 1483
 
5.9%
7 1034
 
4.1%
6 932
 
3.7%
2 837
 
3.3%
4 808
 
3.2%
9 718
 
2.8%
3 600
 
2.4%

지번주소
Text

Distinct3528
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
2024-04-30T04:30:37.490748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length47
Mean length25.97803
Min length16

Characters and Unicode

Total characters107601
Distinct characters330
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3054 ?
Unique (%)73.7%

Sample

1st row서울특별시 양천구 신월동 산 923-4번지
2nd row서울특별시 양천구 목동 526-21번지
3rd row서울특별시 양천구 신정동 182-12번지
4th row서울특별시 양천구 신정동 174-1번지
5th row서울특별시 양천구 목동 646-11번지
ValueCountFrequency (%)
서울특별시 4142
20.1%
양천구 4142
20.1%
신정동 1494
 
7.3%
목동 1454
 
7.1%
신월동 1244
 
6.0%
1층 836
 
4.1%
2층 256
 
1.2%
101호 85
 
0.4%
102호 81
 
0.4%
상가동 76
 
0.4%
Other values (3347) 6752
32.8%
2024-04-30T04:30:38.045548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19455
18.1%
1 5915
 
5.5%
동 4747
 
4.4%
시 4251
 
4.0%
양 4166
 
3.9%
천 4151
 
3.9%
서 4150
 
3.9%
구 4148
 
3.9%
특 4142
 
3.8%
별 4142
 
3.8%
Other values (320) 48334
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60150
55.9%
Decimal Number 23436
 
21.8%
Space Separator 19455
 
18.1%
Dash Punctuation 3755
 
3.5%
Close Punctuation 270
 
0.3%
Open Punctuation 269
 
0.2%
Uppercase Letter 146
 
0.1%
Other Punctuation 68
 
0.1%
Lowercase Letter 31
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other values (2) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4747
 
7.9%
시 4251
 
7.1%
양 4166
 
6.9%
천 4151
 
6.9%
서 4150
 
6.9%
구 4148
 
6.9%
특 4142
 
6.9%
별 4142
 
6.9%
울 4142
 
6.9%
지 3166
 
5.3%
Other values (277) 18945
31.5%
Lowercase Letter
ValueCountFrequency (%)
s 4
12.9%
l 4
12.9%
e 4
12.9%
p 4
12.9%
b 3
9.7%
a 3
9.7%
o 2
6.5%
h 2
6.5%
t 2
6.5%
i 1
 
3.2%
Other values (2) 2
6.5%
Decimal Number
ValueCountFrequency (%)
1 5915
25.2%
2 3461
14.8%
0 2542
10.8%
9 2156
 
9.2%
3 1815
 
7.7%
4 1723
 
7.4%
7 1565
 
6.7%
5 1546
 
6.6%
6 1440
 
6.1%
8 1273
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 63
43.2%
A 56
38.4%
T 5
 
3.4%
S 5
 
3.4%
P 5
 
3.4%
C 5
 
3.4%
K 2
 
1.4%
G 2
 
1.4%
L 2
 
1.4%
H 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 58
85.3%
@ 6
 
8.8%
. 4
 
5.9%
Letter Number
ValueCountFrequency (%)
Ⅰ 8
88.9%
Ⅱ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
19455
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3755
100.0%
Close Punctuation
ValueCountFrequency (%)
) 270
100.0%
Open Punctuation
ValueCountFrequency (%)
( 269
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60150
55.9%
Common 47265
43.9%
Latin 186
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4747
 
7.9%
시 4251
 
7.1%
양 4166
 
6.9%
천 4151
 
6.9%
서 4150
 
6.9%
구 4148
 
6.9%
특 4142
 
6.9%
별 4142
 
6.9%
울 4142
 
6.9%
지 3166
 
5.3%
Other values (277) 18945
31.5%
Latin
ValueCountFrequency (%)
B 63
33.9%
A 56
30.1%
Ⅰ 8
 
4.3%
T 5
 
2.7%
S 5
 
2.7%
P 5
 
2.7%
C 5
 
2.7%
s 4
 
2.2%
l 4
 
2.2%
e 4
 
2.2%
Other values (14) 27
14.5%
Common
ValueCountFrequency (%)
19455
41.2%
1 5915
 
12.5%
- 3755
 
7.9%
2 3461
 
7.3%
0 2542
 
5.4%
9 2156
 
4.6%
3 1815
 
3.8%
4 1723
 
3.6%
7 1565
 
3.3%
5 1546
 
3.3%
Other values (9) 3332
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60150
55.9%
ASCII 47442
44.1%
Number Forms 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19455
41.0%
1 5915
 
12.5%
- 3755
 
7.9%
2 3461
 
7.3%
0 2542
 
5.4%
9 2156
 
4.5%
3 1815
 
3.8%
4 1723
 
3.6%
7 1565
 
3.3%
5 1546
 
3.3%
Other values (31) 3509
 
7.4%
Hangul
ValueCountFrequency (%)
동 4747
 
7.9%
시 4251
 
7.1%
양 4166
 
6.9%
천 4151
 
6.9%
서 4150
 
6.9%
구 4148
 
6.9%
특 4142
 
6.9%
별 4142
 
6.9%
울 4142
 
6.9%
지 3166
 
5.3%
Other values (277) 18945
31.5%
Number Forms
ValueCountFrequency (%)
Ⅰ 8
88.9%
Ⅱ 1
 
11.1%

도로명주소
Text

MISSING 

Distinct2151
Distinct (%)86.7%
Missing1661
Missing (%)40.1%
Memory size32.5 KiB
2024-04-30T04:30:38.293753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length59
Mean length34.012092
Min length21

Characters and Unicode

Total characters84384
Distinct characters324
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

Unique1898 ?
Unique (%)76.5%

Sample

1st row서울특별시 양천구 목동중앙남로1길 17-1 (목동)
2nd row서울특별시 양천구 신월로 175 (신월동)
3rd row서울특별시 양천구 중앙로 272, 지상2층 (신정동)
4th row서울특별시 양천구 곰달래로 39 (신월동,(1층))
5th row서울특별시 양천구 남부순환로70길 12, 1층 (신월동)
ValueCountFrequency (%)
서울특별시 2481
 
14.7%
양천구 2481
 
14.7%
1층 1249
 
7.4%
목동 947
 
5.6%
신정동 879
 
5.2%
신월동 592
 
3.5%
2층 384
 
2.3%
오목로 250
 
1.5%
목동동로 199
 
1.2%
목동서로 146
 
0.9%
Other values (1457) 7298
43.2%
2024-04-30T04:30:38.665486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14431
 
17.1%
동 4374
 
5.2%
1 4123
 
4.9%
, 2994
 
3.5%
목 2714
 
3.2%
서 2700
 
3.2%
시 2583
 
3.1%
양 2568
 
3.0%
( 2546
 
3.0%
) 2546
 
3.0%
Other values (314) 42805
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47935
56.8%
Space Separator 14431
 
17.1%
Decimal Number 13449
 
15.9%
Other Punctuation 2995
 
3.5%
Open Punctuation 2546
 
3.0%
Close Punctuation 2546
 
3.0%
Dash Punctuation 318
 
0.4%
Uppercase Letter 113
 
0.1%
Lowercase Letter 26
 
< 0.1%
Math Symbol 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4374
 
9.1%
목 2714
 
5.7%
서 2700
 
5.6%
시 2583
 
5.4%
양 2568
 
5.4%
로 2530
 
5.3%
천 2487
 
5.2%
구 2485
 
5.2%
울 2481
 
5.2%
별 2481
 
5.2%
Other values (276) 20532
42.8%
Lowercase Letter
ValueCountFrequency (%)
s 4
15.4%
l 4
15.4%
p 4
15.4%
e 3
11.5%
o 2
7.7%
t 2
7.7%
h 2
7.7%
i 1
 
3.8%
v 1
 
3.8%
y 1
 
3.8%
Other values (2) 2
7.7%
Decimal Number
ValueCountFrequency (%)
1 4123
30.7%
2 2375
17.7%
0 1633
 
12.1%
3 1451
 
10.8%
4 836
 
6.2%
5 811
 
6.0%
7 635
 
4.7%
6 627
 
4.7%
9 492
 
3.7%
8 466
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
B 52
46.0%
A 41
36.3%
C 8
 
7.1%
S 5
 
4.4%
K 2
 
1.8%
L 2
 
1.8%
G 2
 
1.8%
H 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 2994
> 99.9%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
14431
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2546
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2546
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 318
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47935
56.8%
Common 36302
43.0%
Latin 147
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4374
 
9.1%
목 2714
 
5.7%
서 2700
 
5.6%
시 2583
 
5.4%
양 2568
 
5.4%
로 2530
 
5.3%
천 2487
 
5.2%
구 2485
 
5.2%
울 2481
 
5.2%
별 2481
 
5.2%
Other values (276) 20532
42.8%
Latin
ValueCountFrequency (%)
B 52
35.4%
A 41
27.9%
Ⅰ 8
 
5.4%
C 8
 
5.4%
S 5
 
3.4%
s 4
 
2.7%
l 4
 
2.7%
p 4
 
2.7%
e 3
 
2.0%
K 2
 
1.4%
Other values (11) 16
 
10.9%
Common
ValueCountFrequency (%)
14431
39.8%
1 4123
 
11.4%
, 2994
 
8.2%
( 2546
 
7.0%
) 2546
 
7.0%
2 2375
 
6.5%
0 1633
 
4.5%
3 1451
 
4.0%
4 836
 
2.3%
5 811
 
2.2%
Other values (7) 2556
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47935
56.8%
ASCII 36441
43.2%
Number Forms 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14431
39.6%
1 4123
 
11.3%
, 2994
 
8.2%
( 2546
 
7.0%
) 2546
 
7.0%
2 2375
 
6.5%
0 1633
 
4.5%
3 1451
 
4.0%
4 836
 
2.3%
5 811
 
2.2%
Other values (27) 2695
 
7.4%
Hangul
ValueCountFrequency (%)
동 4374
 
9.1%
목 2714
 
5.7%
서 2700
 
5.6%
시 2583
 
5.4%
양 2568
 
5.4%
로 2530
 
5.3%
천 2487
 
5.2%
구 2485
 
5.2%
울 2481
 
5.2%
별 2481
 
5.2%
Other values (276) 20532
42.8%
Number Forms
ValueCountFrequency (%)
Ⅰ 8
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct191
Distinct (%)7.7%
Missing1669
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean7994.0332
Minimum7900
Maximum8111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:38.794963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7900
5-th percentile7912.6
Q17949
median7995
Q38027
95-th percentile8092
Maximum8111
Range211
Interquartile range (IQR)78

Descriptive statistics

Standard deviation53.967355
Coefficient of variation (CV)0.0067509546
Kurtosis-0.81418126
Mean7994.0332
Median Absolute Deviation (MAD)40
Skewness0.28584317
Sum19769244
Variance2912.4754
MonotonicityNot monotonic
2024-04-30T04:30:38.921796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7946 52
 
1.3%
7983 50
 
1.2%
7938 48
 
1.2%
8022 46
 
1.1%
7960 45
 
1.1%
7923 44
 
1.1%
8009 43
 
1.0%
7950 42
 
1.0%
7965 42
 
1.0%
7999 40
 
1.0%
Other values (181) 2021
48.8%
(Missing) 1669
40.3%
ValueCountFrequency (%)
7900 10
0.2%
7902 17
0.4%
7903 14
0.3%
7904 15
0.4%
7905 4
 
0.1%
7906 7
0.2%
7907 6
 
0.1%
7909 6
 
0.1%
7910 11
0.3%
7911 16
0.4%
ValueCountFrequency (%)
8111 2
 
< 0.1%
8110 4
 
0.1%
8109 2
 
< 0.1%
8108 1
 
< 0.1%
8107 2
 
< 0.1%
8106 5
 
0.1%
8105 2
 
< 0.1%
8104 25
0.6%
8102 5
 
0.1%
8101 13
0.3%

사업장명
Text

Distinct3403
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
2024-04-30T04:30:39.148292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length26
Mean length5.800338
Min length1

Characters and Unicode

Total characters24025
Distinct characters764
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2929 ?
Unique (%)70.7%

Sample

1st row머리머리미용실
2nd row민주미용실
3rd row희
4th row수
5th row희망
ValueCountFrequency (%)
헤어 53
 
1.1%
목동점 35
 
0.7%
hair 27
 
0.6%
에스테틱 26
 
0.5%
nail 22
 
0.5%
네일 22
 
0.5%
미용실 20
 
0.4%
18
 
0.4%
머리사랑 16
 
0.3%
헤어샵 15
 
0.3%
Other values (3570) 4574
94.7%
2024-04-30T04:30:39.510126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 1535
 
6.4%
헤 1476
 
6.1%
미 1090
 
4.5%
실 794
 
3.3%
용 793
 
3.3%
688
 
2.9%
스 578
 
2.4%
리 540
 
2.2%
이 501
 
2.1%
라 312
 
1.3%
Other values (754) 15718
65.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20961
87.2%
Lowercase Letter 869
 
3.6%
Space Separator 688
 
2.9%
Uppercase Letter 684
 
2.8%
Open Punctuation 214
 
0.9%
Close Punctuation 213
 
0.9%
Other Punctuation 213
 
0.9%
Decimal Number 153
 
0.6%
Connector Punctuation 14
 
0.1%
Dash Punctuation 9
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 1535
 
7.3%
헤 1476
 
7.0%
미 1090
 
5.2%
실 794
 
3.8%
용 793
 
3.8%
스 578
 
2.8%
리 540
 
2.6%
이 501
 
2.4%
라 312
 
1.5%
일 286
 
1.4%
Other values (671) 13056
62.3%
Uppercase Letter
ValueCountFrequency (%)
A 72
 
10.5%
S 61
 
8.9%
L 52
 
7.6%
N 47
 
6.9%
B 45
 
6.6%
H 44
 
6.4%
O 44
 
6.4%
E 39
 
5.7%
I 37
 
5.4%
T 29
 
4.2%
Other values (16) 214
31.3%
Lowercase Letter
ValueCountFrequency (%)
a 115
13.2%
i 96
11.0%
e 80
9.2%
n 79
9.1%
o 77
8.9%
l 67
7.7%
r 61
 
7.0%
h 50
 
5.8%
s 42
 
4.8%
y 35
 
4.0%
Other values (15) 167
19.2%
Other Punctuation
ValueCountFrequency (%)
? 68
31.9%
& 46
21.6%
. 43
20.2%
, 20
 
9.4%
' 16
 
7.5%
# 15
 
7.0%
; 2
 
0.9%
＆ 1
 
0.5%
: 1
 
0.5%
% 1
 
0.5%
Decimal Number
ValueCountFrequency (%)
0 52
34.0%
1 27
17.6%
2 19
 
12.4%
3 14
 
9.2%
8 11
 
7.2%
4 11
 
7.2%
9 7
 
4.6%
7 5
 
3.3%
5 5
 
3.3%
6 2
 
1.3%
Modifier Symbol
ValueCountFrequency (%)
` 3
60.0%
´ 1
 
20.0%
˚ 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 213
99.5%
[ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 212
99.5%
] 1
 
0.5%
Space Separator
ValueCountFrequency (%)
688
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20951
87.2%
Latin 1554
 
6.5%
Common 1510
 
6.3%
Han 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 1535
 
7.3%
헤 1476
 
7.0%
미 1090
 
5.2%
실 794
 
3.8%
용 793
 
3.8%
스 578
 
2.8%
리 540
 
2.6%
이 501
 
2.4%
라 312
 
1.5%
일 286
 
1.4%
Other values (667) 13046
62.3%
Latin
ValueCountFrequency (%)
a 115
 
7.4%
i 96
 
6.2%
e 80
 
5.1%
n 79
 
5.1%
o 77
 
5.0%
A 72
 
4.6%
l 67
 
4.3%
S 61
 
3.9%
r 61
 
3.9%
L 52
 
3.3%
Other values (42) 794
51.1%
Common
ValueCountFrequency (%)
688
45.6%
( 213
 
14.1%
) 212
 
14.0%
? 68
 
4.5%
0 52
 
3.4%
& 46
 
3.0%
. 43
 
2.8%
1 27
 
1.8%
, 20
 
1.3%
2 19
 
1.3%
Other values (21) 122
 
8.1%
Han
ValueCountFrequency (%)
美 7
70.0%
李 1
 
10.0%
年 1
 
10.0%
晧 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20946
87.2%
ASCII 3060
 
12.7%
CJK 8
 
< 0.1%
Compat Jamo 5
 
< 0.1%
None 2
 
< 0.1%
CJK Compat Ideographs 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 1535
 
7.3%
헤 1476
 
7.0%
미 1090
 
5.2%
실 794
 
3.8%
용 793
 
3.8%
스 578
 
2.8%
리 540
 
2.6%
이 501
 
2.4%
라 312
 
1.5%
일 286
 
1.4%
Other values (666) 13041
62.3%
ASCII
ValueCountFrequency (%)
688
22.5%
( 213
 
7.0%
) 212
 
6.9%
a 115
 
3.8%
i 96
 
3.1%
e 80
 
2.6%
n 79
 
2.6%
o 77
 
2.5%
A 72
 
2.4%
? 68
 
2.2%
Other values (69) 1360
44.4%
CJK
ValueCountFrequency (%)
美 7
87.5%
晧 1
 
12.5%
Compat Jamo
ValueCountFrequency (%)
ㅍ 5
100.0%
None
ValueCountFrequency (%)
´ 1
50.0%
＆ 1
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
李 1
50.0%
年 1
50.0%
Modifier Letters
ValueCountFrequency (%)
˚ 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

최종수정일자
Date

Distinct3178
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
Minimum1999-01-22 00:00:00
Maximum2024-04-25 11:23:34
2024-04-30T04:30:39.652579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:39.821892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
I
2640 
U
1485 
D
 
17

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 2640
63.7%
U 1485
35.9%
D 17
 
0.4%

Length

2024-04-30T04:30:40.030827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:40.171174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2640
63.7%
u 1485
35.9%
d 17
 
0.4%

데이터갱신일자
Date

Distinct835
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:04:00
2024-04-30T04:30:40.326011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:40.500120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
일반미용업
3165 
피부미용업
581 
네일아트업
332 
메이크업업
 
64

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 3165
76.4%
피부미용업 581
 
14.0%
네일아트업 332
 
8.0%
메이크업업 64
 
1.5%

Length

2024-04-30T04:30:40.658360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:40.802512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 3165
76.4%
피부미용업 581
 
14.0%
네일아트업 332
 
8.0%
메이크업업 64
 
1.5%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1849
Distinct (%)46.2%
Missing139
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean187273.91
Minimum184400.77
Maximum189755.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:40.962751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184400.77
5-th percentile184966.89
Q1186017.17
median187652.9
Q3188386.94
95-th percentile189021.8
Maximum189755.54
Range5354.7718
Interquartile range (IQR)2369.77

Descriptive statistics

Standard deviation1368.2287
Coefficient of variation (CV)0.0073060298
Kurtosis-1.0403909
Mean187273.91
Median Absolute Deviation (MAD)952.85875
Skewness-0.4271836
Sum7.4965745 × 108
Variance1872049.9
MonotonicityNot monotonic
2024-04-30T04:30:41.132942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188431.286329151 34
 
0.8%
189423.528553032 18
 
0.4%
188951.756197598 17
 
0.4%
187923.919592336 16
 
0.4%
188467.215363052 16
 
0.4%
188494.598982992 16
 
0.4%
187822.906666898 15
 
0.4%
188965.738829492 15
 
0.4%
187587.063452976 14
 
0.3%
187766.276177683 14
 
0.3%
Other values (1839) 3828
92.4%
(Missing) 139
 
3.4%
ValueCountFrequency (%)
184400.769531722 1
 
< 0.1%
184425.126732761 2
< 0.1%
184432.602258078 1
 
< 0.1%
184443.469766368 1
 
< 0.1%
184449.386660638 1
 
< 0.1%
184457.852171363 1
 
< 0.1%
184467.782954393 2
< 0.1%
184468.248189952 2
< 0.1%
184477.027557886 3
0.1%
184477.053491229 1
 
< 0.1%
ValueCountFrequency (%)
189755.541308355 3
 
0.1%
189749.776358917 12
0.3%
189743.464254868 1
 
< 0.1%
189709.803505321 3
 
0.1%
189519.862506193 2
 
< 0.1%
189508.199599752 8
0.2%
189498.233991849 5
0.1%
189487.566030824 1
 
< 0.1%
189473.530827695 5
0.1%
189471.306217651 2
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1849
Distinct (%)46.2%
Missing139
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean447330.81
Minimum444854.17
Maximum449843.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:41.255577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum444854.17
5-th percentile445898.88
Q1446567.9
median447026.32
Q3448157.69
95-th percentile449367.59
Maximum449843.2
Range4989.037
Interquartile range (IQR)1589.7893

Descriptive statistics

Standard deviation1073.6755
Coefficient of variation (CV)0.0024001822
Kurtosis-0.58470362
Mean447330.81
Median Absolute Deviation (MAD)739.24895
Skewness0.42319166
Sum1.7906652 × 109
Variance1152779
MonotonicityNot monotonic
2024-04-30T04:30:41.378056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446909.365903882 34
 
0.8%
448240.505845376 18
 
0.4%
446969.388649775 17
 
0.4%
447850.891096367 16
 
0.4%
446003.941216623 16
 
0.4%
447318.660520705 16
 
0.4%
447060.377699409 15
 
0.4%
448365.713974147 15
 
0.4%
446444.996171878 14
 
0.3%
446265.890841351 14
 
0.3%
Other values (1839) 3828
92.4%
(Missing) 139
 
3.4%
ValueCountFrequency (%)
444854.165988454 2
 
< 0.1%
444905.824632031 1
 
< 0.1%
444919.882944057 2
 
< 0.1%
444958.276659499 1
 
< 0.1%
445006.611450798 1
 
< 0.1%
445053.283553223 1
 
< 0.1%
445081.396522145 6
0.1%
445093.792485881 1
 
< 0.1%
445094.16222004 3
0.1%
445104.207668176 4
0.1%
ValueCountFrequency (%)
449843.203005652 1
 
< 0.1%
449833.140237863 1
 
< 0.1%
449821.719712552 1
 
< 0.1%
449818.916783 1
 
< 0.1%
449812.907673 1
 
< 0.1%
449798.833406283 2
< 0.1%
449785.227427003 1
 
< 0.1%
449783.451870726 3
0.1%
449767.441786782 1
 
< 0.1%
449753.158413664 1
 
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
미용업
1472 
일반미용업
1031 
<NA>
654 
피부미용업
403 
종합미용업
331 
Other values (12)
251 

Length

Max length23
Median length19
Mean length4.4688556
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1472
35.5%
일반미용업 1031
24.9%
<NA> 654
15.8%
피부미용업 403
 
9.7%
종합미용업 331
 
8.0%
네일미용업 109
 
2.6%
피부미용업, 네일미용업 33
 
0.8%
네일미용업, 화장ㆍ분장 미용업 20
 
0.5%
일반미용업, 네일미용업 20
 
0.5%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 17
 
0.4%
Other values (7) 52
 
1.3%

Length

2024-04-30T04:30:41.505453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1552
35.5%
일반미용업 1094
25.0%
na 654
14.9%
피부미용업 460
 
10.5%
종합미용업 331
 
7.6%
네일미용업 205
 
4.7%
화장ㆍ분장 80
 
1.8%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)0.9%
Missing2166
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean0.45850202
Minimum0
Maximum49
Zeros1705
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:41.599901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum49
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0144537
Coefficient of variation (CV)4.3935547
Kurtosis217.58035
Mean0.45850202
Median Absolute Deviation (MAD)0
Skewness11.966338
Sum906
Variance4.0580239
MonotonicityNot monotonic
2024-04-30T04:30:41.695832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 1705
41.2%
1 92
 
2.2%
2 56
 
1.4%
3 41
 
1.0%
4 35
 
0.8%
5 18
 
0.4%
6 9
 
0.2%
10 4
 
0.1%
7 4
 
0.1%
9 3
 
0.1%
Other values (7) 9
 
0.2%
(Missing) 2166
52.3%
ValueCountFrequency (%)
0 1705
41.2%
1 92
 
2.2%
2 56
 
1.4%
3 41
 
1.0%
4 35
 
0.8%
5 18
 
0.4%
6 9
 
0.2%
7 4
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
ValueCountFrequency (%)
49 1
 
< 0.1%
26 2
< 0.1%
22 2
< 0.1%
21 1
 
< 0.1%
15 1
 
< 0.1%
11 1
 
< 0.1%
10 4
0.1%
9 3
0.1%
8 1
 
< 0.1%
7 4
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.4%
Missing2217
Missing (%)53.5%
Infinite0
Infinite (%)0.0%
Mean0.069090909
Minimum0
Maximum7
Zeros1823
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:41.788416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.3813553
Coefficient of variation (CV)5.5196162
Kurtosis152.48894
Mean0.069090909
Median Absolute Deviation (MAD)0
Skewness10.4916
Sum133
Variance0.14543187
MonotonicityNot monotonic
2024-04-30T04:30:41.880307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1823
44.0%
1 91
 
2.2%
2 4
 
0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
4 1
 
< 0.1%
(Missing) 2217
53.5%
ValueCountFrequency (%)
0 1823
44.0%
1 91
 
2.2%
2 4
 
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 4
 
0.1%
1 91
 
2.2%
0 1823
44.0%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.6%
Missing2590
Missing (%)62.5%
Infinite0
Infinite (%)0.0%
Mean1.1082474
Minimum0
Maximum11
Zeros376
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:41.978170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0028375
Coefficient of variation (CV)0.90488594
Kurtosis13.176432
Mean1.1082474
Median Absolute Deviation (MAD)0
Skewness2.3437691
Sum1720
Variance1.0056831
MonotonicityNot monotonic
2024-04-30T04:30:42.071661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 808
 
19.5%
0 376
 
9.1%
2 262
 
6.3%
3 72
 
1.7%
4 18
 
0.4%
5 7
 
0.2%
6 6
 
0.1%
9 2
 
< 0.1%
11 1
 
< 0.1%
(Missing) 2590
62.5%
ValueCountFrequency (%)
0 376
9.1%
1 808
19.5%
2 262
 
6.3%
3 72
 
1.7%
4 18
 
0.4%
5 7
 
0.2%
6 6
 
0.1%
9 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
9 2
 
< 0.1%
6 6
 
0.1%
5 7
 
0.2%
4 18
 
0.4%
3 72
 
1.7%
2 262
 
6.3%
1 808
19.5%
0 376
9.1%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct11
Distinct (%)0.7%
Missing2675
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean1.3210634
Minimum0
Maximum204
Zeros341
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:42.180684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum204
Range204
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.9946417
Coefficient of variation (CV)4.5377396
Kurtosis946.50145
Mean1.3210634
Median Absolute Deviation (MAD)0
Skewness29.642165
Sum1938
Variance35.935729
MonotonicityNot monotonic
2024-04-30T04:30:42.276218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 776
 
18.7%
0 341
 
8.2%
2 248
 
6.0%
3 70
 
1.7%
4 16
 
0.4%
5 6
 
0.1%
6 6
 
0.1%
102 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 2675
64.6%
ValueCountFrequency (%)
0 341
8.2%
1 776
18.7%
2 248
 
6.0%
3 70
 
1.7%
4 16
 
0.4%
5 6
 
0.1%
6 6
 
0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
102 1
 
< 0.1%
ValueCountFrequency (%)
204 1
 
< 0.1%
102 1
 
< 0.1%
11 1
 
< 0.1%
9 1
 
< 0.1%
6 6
 
0.1%
5 6
 
0.1%
4 16
 
0.4%
3 70
 
1.7%
2 248
 
6.0%
1 776
18.7%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
3461 
0
616 
1
 
58
2
 
6
6
 
1

Length

Max length4
Median length4
Mean length3.50676
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> 3461
83.6%
0 616
 
14.9%
1 58
 
1.4%
2 6
 
0.1%
6 1
 
< 0.1%

Length

2024-04-30T04:30:42.391867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:42.511797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3461
83.6%
0 616
 
14.9%
1 58
 
1.4%
2 6
 
0.1%
6 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
3514 
0
566 
1
 
55
2
 
6
6
 
1

Length

Max length4
Median length4
Mean length3.5451473
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> 3514
84.8%
0 566
 
13.7%
1 55
 
1.3%
2 6
 
0.1%
6 1
 
< 0.1%

Length

2024-04-30T04:30:42.616775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:42.712173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3514
84.8%
0 566
 
13.7%
1 55
 
1.3%
2 6
 
0.1%
6 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
0
2349 
<NA>
1793 

Length

Max length4
Median length1
Mean length2.298648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2349
56.7%
<NA> 1793
43.3%

Length

2024-04-30T04:30:42.825853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:42.907992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
56.7%
na 1793
43.3%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
0
2349 
<NA>
1793 

Length

Max length4
Median length1
Mean length2.298648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2349
56.7%
<NA> 1793
43.3%

Length

2024-04-30T04:30:42.997611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:43.093744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
56.7%
na 1793
43.3%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
0
2349 
<NA>
1793 

Length

Max length4
Median length1
Mean length2.298648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2349
56.7%
<NA> 1793
43.3%

Length

2024-04-30T04:30:43.386205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:43.469398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
56.7%
na 1793
43.3%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing732
Missing (%)17.7%
Memory size8.2 KiB
False
3410 
(Missing)
732 
ValueCountFrequency (%)
False 3410
82.3%
(Missing) 732
 
17.7%
2024-04-30T04:30:43.543753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct22
Distinct (%)0.7%
Missing952
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean3.3482759
Minimum0
Maximum195
Zeros367
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:43.623644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile7
Maximum195
Range195
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.1877416
Coefficient of variation (CV)1.2507158
Kurtosis1385.7724
Mean3.3482759
Median Absolute Deviation (MAD)1
Skewness31.163344
Sum10681
Variance17.53718
MonotonicityNot monotonic
2024-04-30T04:30:43.734101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 1248
30.1%
4 524
12.7%
2 459
 
11.1%
0 367
 
8.9%
5 198
 
4.8%
6 134
 
3.2%
1 64
 
1.5%
8 54
 
1.3%
7 47
 
1.1%
10 34
 
0.8%
Other values (12) 61
 
1.5%
(Missing) 952
23.0%
ValueCountFrequency (%)
0 367
 
8.9%
1 64
 
1.5%
2 459
 
11.1%
3 1248
30.1%
4 524
12.7%
5 198
 
4.8%
6 134
 
3.2%
7 47
 
1.1%
8 54
 
1.3%
9 26
 
0.6%
ValueCountFrequency (%)
195 1
 
< 0.1%
56 1
 
< 0.1%
40 1
 
< 0.1%
26 1
 
< 0.1%
23 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
14 11
0.3%
12 9
0.2%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4142
Missing (%)100.0%
Memory size36.5 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
3278 
임대
851 
자가
 
13

Length

Max length4
Median length4
Mean length3.5828102
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> 3278
79.1%
임대 851
 
20.5%
자가 13
 
0.3%

Length

2024-04-30T04:30:43.863836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:43.962135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3278
79.1%
임대 851
 
20.5%
자가 13
 
0.3%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
2294 
0
1848 

Length

Max length4
Median length4
Mean length2.6615162
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> 2294
55.4%
0 1848
44.6%

Length

2024-04-30T04:30:44.061332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:44.164920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2294
55.4%
0 1848
44.6%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
3272 
0
856 
1
 
11
12
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.3701111
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3272
79.0%
0 856
 
20.7%
1 11
 
0.3%
12 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

Length

2024-04-30T04:30:44.273866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:44.395107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3272
79.0%
0 856
 
20.7%
1 11
 
0.3%
12 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
3274 
0
864 
1
 
2
6
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.3713182
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> 3274
79.0%
0 864
 
20.9%
1 2
 
< 0.1%
6 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-04-30T04:30:44.501956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:44.594235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3274
79.0%
0 864
 
20.9%
1 2
 
< 0.1%
6 1
 
< 0.1%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.5 KiB
<NA>
2399 
0
1743 

Length

Max length4
Median length4
Mean length2.7375664
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> 2399
57.9%
0 1743
42.1%

Length

2024-04-30T04:30:44.684403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:44.765737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2399
57.9%
0 1743
42.1%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.8%
Missing2429
Missing (%)58.6%
Infinite0
Infinite (%)0.0%
Mean1.0776416
Minimum0
Maximum14
Zeros1181
Zeros (%)28.5%
Negative0
Negative (%)0.0%
Memory size36.5 KiB
2024-04-30T04:30:44.835002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0627812
Coefficient of variation (CV)1.9141626
Kurtosis5.7297788
Mean1.0776416
Median Absolute Deviation (MAD)0
Skewness2.3290747
Sum1846
Variance4.2550664
MonotonicityNot monotonic
2024-04-30T04:30:44.933563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1181
28.5%
2 145
 
3.5%
3 105
 
2.5%
1 92
 
2.2%
5 45
 
1.1%
4 44
 
1.1%
6 43
 
1.0%
8 20
 
0.5%
7 18
 
0.4%
9 8
 
0.2%
Other values (4) 12
 
0.3%
(Missing) 2429
58.6%
ValueCountFrequency (%)
0 1181
28.5%
1 92
 
2.2%
2 145
 
3.5%
3 105
 
2.5%
4 44
 
1.1%
5 45
 
1.1%
6 43
 
1.0%
7 18
 
0.4%
8 20
 
0.5%
9 8
 
0.2%
ValueCountFrequency (%)
14 1
 
< 0.1%
12 3
 
0.1%
11 2
 
< 0.1%
10 6
 
0.1%
9 8
 
0.2%
8 20
0.5%
7 18
 
0.4%
6 43
1.0%
5 45
1.1%
4 44
1.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing654
Missing (%)15.8%
Memory size8.2 KiB
False
3488 
(Missing)
654 
ValueCountFrequency (%)
False 3488
84.2%
(Missing) 654
 
15.8%
2024-04-30T04:30:45.022660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031400003140000-204-1968-0143319680415<NA>3폐업2폐업19980923<NA><NA><NA>02 607696814.85158090서울특별시 양천구 신월동 산 923-4번지<NA><NA>머리머리미용실1999-01-25 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131400003140000-204-1971-0045719710930<NA>3폐업2폐업20040525<NA><NA><NA>02 648898820.52158808서울특별시 양천구 목동 526-21번지<NA><NA>민주미용실2004-05-25 00:00:00I2018-08-31 23:59:59.0일반미용업188787.821827449260.999661미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231400003140000-204-1972-0045819720308<NA>3폐업2폐업19950228<NA><NA><NA>020000000017.8158851서울특별시 양천구 신정동 182-12번지<NA><NA>희1999-02-11 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331400003140000-204-1975-0046019750319<NA>3폐업2폐업19931124<NA><NA><NA>020646256615.04158850서울특별시 양천구 신정동 174-1번지<NA><NA>수1999-02-11 00:00:00I2018-08-31 23:59:59.0일반미용업188131.296204445197.198551미용업<NA><NA><NA><NA><NA><NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431400003140000-204-1975-0046119750902<NA>3폐업2폐업19980709<NA><NA><NA>02 643971513.07158812서울특별시 양천구 목동 646-11번지<NA><NA>희망1999-01-22 00:00:00I2018-08-31 23:59:59.0일반미용업187997.129109449309.00915미용업<NA><NA><NA><NA><NA><NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531400003140000-204-1976-0045619760728<NA>3폐업2폐업20011122<NA><NA><NA>02 645430813.2158859서울특별시 양천구 신정동 944-7번지<NA><NA>샛별2001-11-23 00:00:00I2018-08-31 23:59:59.0일반미용업186860.400809446828.518281미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631400003140000-204-1977-0045219771013<NA>3폐업2폐업19971223<NA><NA><NA>02 649964513.95158861서울특별시 양천구 신정동 1029-42번지<NA><NA>고려미용실1999-01-22 00:00:00I2018-08-31 23:59:59.0일반미용업187069.905071446501.258871미용업<NA><NA><NA><NA><NA><NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731400003140000-204-1977-0045519770627<NA>3폐업2폐업20110317<NA><NA><NA>022645297011.12158851서울특별시 양천구 신정동 190-16번지<NA><NA>현희2004-08-30 00:00:00I2018-08-31 23:59:59.0일반미용업187904.405658444919.882944미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831400003140000-204-1978-0046219780404<NA>3폐업2폐업20030324<NA><NA><NA>02 691841911.52158829서울특별시 양천구 신월동 180-12번지<NA><NA>나나미용실2003-03-24 00:00:00I2018-08-31 23:59:59.0일반미용업184562.582733447865.819282미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931400003140000-204-1978-0047119780801<NA>3폐업2폐업19940927<NA><NA><NA>020643832314.82158813서울특별시 양천구 목동 720-1번지<NA><NA>샘터1999-02-11 00:00:00I2018-08-31 23:59:59.0일반미용업187965.297955448663.907854미용업<NA><NA><NA><NA><NA><NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
413231400003140000-226-2019-000012019-04-11<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.5158-823서울특별시 양천구 신월동 45-15서울특별시 양천구 남부순환로36길 14-1, 1층 (신월동)7909린네일(LYn(린)네일)2023-02-03 17:06:55U2022-12-02 00:05:00.0네일아트업184565.919101448239.393931<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413331400003140000-226-2019-000042019-11-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA>88.0158-806서울특별시 양천구 목동 405-164서울특별시 양천구 목동동로14길 3, 2층 전체호 (목동)8005다다네일2023-11-14 00:00:00D2022-10-31 23:06:00.0네일아트업188617.447543446914.092825<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413431400003140000-226-2019-000052019-09-09<NA>3폐업2폐업2023-08-04<NA><NA><NA><NA>23.64158-864서울특별시 양천구 신정동 1182-28 1층 3호서울특별시 양천구 중앙로43길 27-2, 1층 3호 (신정동)8073챠니뷰티2023-08-04 15:12:35U2022-12-08 00:06:00.0피부미용업186936.401008446236.783753<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413531400003140000-226-2020-0000120200624<NA>3폐업2폐업20200911<NA><NA><NA><NA>29.6158758서울특별시 양천구 목동 956 롯데캐슬위너아파트 상가동 103-1호서울특별시 양천구 목동중앙북로 38, 상가동 103-1호 (목동, 롯데캐슬위너아파트)7949네일콩2020-09-11 09:05:07U2020-09-13 02:40:00.0네일아트업188198.091576449411.580894피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N3<NA><NA><NA><NA>00000N
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413731400003140000-226-2021-0000220210524<NA>3폐업2폐업20220613<NA><NA><NA><NA>32.32158806서울특별시 양천구 목동 405 목동대림아파트서울특별시 양천구 목동동로12길 23, 상가동 1층 108,112~114, 120, 122호 (목동, 목동대림아파트)8006아네뷰티2022-06-13 10:41:10U2021-12-05 23:06:00.0피부미용업188815.060184446814.889014<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413831400003140000-226-2021-000032021-02-09<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.56158-832서울특별시 양천구 신월동 423-9서울특별시 양천구 월정로7길 20, 1층 102호 (신월동)7929네일스토리2024-02-20 10:03:14I2023-12-01 22:02:00.0네일아트업185754.955101446864.830917<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
413931400003140000-226-2022-000012022-02-22<NA>1영업/정상1영업<NA><NA><NA><NA><NA>55.33158-849서울특별시 양천구 신정동 89-29 유원빌딩 2층서울특별시 양천구 신목로 70-1, 유원빌딩 2층 (신정동)8015잇쁨 네일래쉬2024-02-23 15:57:21U2023-12-01 22:05:00.0네일아트업188771.320871446551.856851<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
414031400003140000-226-2022-0000220220729<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.57158851서울특별시 양천구 신정동 201-1 세양청마루2차 주상복합서울특별시 양천구 목동남로4길 2, 1층 104호 (신정동, 세양청마루2차 주상복합)8104아이모네일2022-07-29 11:07:34I2021-12-06 21:01:00.0네일아트업187954.189632445124.131589<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
414131400003140000-226-2023-000012023-03-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>16.5158-842서울특별시 양천구 신월동 575-22서울특별시 양천구 남부순환로79길 43, 1층 (신월동)8064Be REST2023-04-26 15:37:52U2022-12-03 22:08:00.0네일아트업186114.682431446282.338519<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>