Overview

Dataset statistics

Number of variables47
Number of observations4551
Missing cells47272
Missing cells (%)22.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory404.0 B

Variable types

Categorical19
Text8
DateTime3
Unsupported4
Numeric11
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-17915/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
조건부허가신고사유 has constant value ""Constant
사용시작지하층 is highly imbalanced (63.6%)Imbalance
사용끝지하층 is highly imbalanced (72.9%)Imbalance
조건부허가시작일자 is highly imbalanced (99.7%)Imbalance
조건부허가종료일자 is highly imbalanced (99.7%)Imbalance
건물소유구분명 is highly imbalanced (60.5%)Imbalance
남성종사자수 is highly imbalanced (62.2%)Imbalance
다중이용업소여부 is highly imbalanced (99.3%)Imbalance
인허가취소일자 has 4551 (100.0%) missing valuesMissing
폐업일자 has 1847 (40.6%) missing valuesMissing
휴업시작일자 has 4551 (100.0%) missing valuesMissing
휴업종료일자 has 4551 (100.0%) missing valuesMissing
재개업일자 has 4551 (100.0%) missing valuesMissing
전화번호 has 1807 (39.7%) missing valuesMissing
도로명주소 has 1093 (24.0%) missing valuesMissing
도로명우편번호 has 1104 (24.3%) missing valuesMissing
좌표정보(X) has 196 (4.3%) missing valuesMissing
좌표정보(Y) has 196 (4.3%) missing valuesMissing
건물지상층수 has 1722 (37.8%) missing valuesMissing
건물지하층수 has 1820 (40.0%) missing valuesMissing
사용시작지상층 has 2517 (55.3%) missing valuesMissing
사용끝지상층 has 3024 (66.4%) missing valuesMissing
발한실여부 has 1198 (26.3%) missing valuesMissing
좌석수 has 1251 (27.5%) missing valuesMissing
조건부허가신고사유 has 4550 (> 99.9%) missing valuesMissing
여성종사자수 has 3140 (69.0%) missing valuesMissing
침대수 has 2431 (53.4%) missing valuesMissing
다중이용업소여부 has 1157 (25.4%) missing valuesMissing
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
소재지면적 has 105 (2.3%) zerosZeros
건물지상층수 has 2288 (50.3%) zerosZeros
건물지하층수 has 2440 (53.6%) zerosZeros
사용시작지상층 has 578 (12.7%) zerosZeros
사용끝지상층 has 211 (4.6%) zerosZeros
좌석수 has 149 (3.3%) zerosZeros
여성종사자수 has 1209 (26.6%) zerosZeros
침대수 has 1847 (40.6%) zerosZeros

Reproduction

Analysis started2024-04-29 19:30:13.167982
Analysis finished2024-04-29 19:30:14.791949
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
3130000
4551 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 4551
100.0%

Length

2024-04-30T04:30:14.855317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:14.937560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 4551
100.0%

관리번호
Text

UNIQUE 

Distinct4551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
2024-04-30T04:30:15.076569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters100122
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

Unique4551 ?
Unique (%)100.0%

Sample

1st row3130000-204-1963-01061
2nd row3130000-204-1964-01065
3rd row3130000-204-1965-01062
4th row3130000-204-1965-01069
5th row3130000-204-1965-01071
ValueCountFrequency (%)
3130000-204-1963-01061 1
 
< 0.1%
3130000-212-2014-00021 1
 
< 0.1%
3130000-212-2014-00027 1
 
< 0.1%
3130000-212-2014-00026 1
 
< 0.1%
3130000-212-2014-00025 1
 
< 0.1%
3130000-212-2014-00024 1
 
< 0.1%
3130000-212-2014-00023 1
 
< 0.1%
3130000-212-2014-00033 1
 
< 0.1%
3130000-212-2014-00020 1
 
< 0.1%
3130000-212-2014-00029 1
 
< 0.1%
Other values (4541) 4541
99.8%
2024-04-30T04:30:15.387131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38703
38.7%
1 13961
 
13.9%
- 13653
 
13.6%
2 12000
 
12.0%
3 10983
 
11.0%
4 2723
 
2.7%
9 2420
 
2.4%
5 1704
 
1.7%
8 1436
 
1.4%
6 1283
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86469
86.4%
Dash Punctuation 13653
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38703
44.8%
1 13961
 
16.1%
2 12000
 
13.9%
3 10983
 
12.7%
4 2723
 
3.1%
9 2420
 
2.8%
5 1704
 
2.0%
8 1436
 
1.7%
6 1283
 
1.5%
7 1256
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 13653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38703
38.7%
1 13961
 
13.9%
- 13653
 
13.6%
2 12000
 
12.0%
3 10983
 
11.0%
4 2723
 
2.7%
9 2420
 
2.4%
5 1704
 
1.7%
8 1436
 
1.4%
6 1283
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38703
38.7%
1 13961
 
13.9%
- 13653
 
13.6%
2 12000
 
12.0%
3 10983
 
11.0%
4 2723
 
2.7%
9 2420
 
2.4%
5 1704
 
1.7%
8 1436
 
1.4%
6 1283
 
1.3%
Distinct2943
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
Minimum1963-06-22 00:00:00
Maximum2024-04-25 00:00:00
2024-04-30T04:30:15.508038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:15.633056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4551
Missing (%)100.0%
Memory size40.1 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
3
2704 
1
1847 

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 2704
59.4%
1 1847
40.6%

Length

2024-04-30T04:30:15.749026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:15.827008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2704
59.4%
1 1847
40.6%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
폐업
2704 
영업/정상
1847 

Length

Max length5
Median length2
Mean length3.2175346
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2704
59.4%
영업/정상 1847
40.6%

Length

2024-04-30T04:30:15.906993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:15.984394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2704
59.4%
영업/정상 1847
40.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
2
2704 
1
1847 

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 2704
59.4%
1 1847
40.6%

Length

2024-04-30T04:30:16.059516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:16.127891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2704
59.4%
1 1847
40.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
폐업
2704 
영업
1847 

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 2704
59.4%
영업 1847
40.6%

Length

2024-04-30T04:30:16.218375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:16.297925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2704
59.4%
영업 1847
40.6%

폐업일자
Text

MISSING 

Distinct1817
Distinct (%)67.2%
Missing1847
Missing (%)40.6%
Memory size35.7 KiB
2024-04-30T04:30:16.585867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.1671598
Min length8

Characters and Unicode

Total characters22084
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1408 ?
Unique (%)52.1%

Sample

1st row20030220
2nd row19940107
3rd row20030220
4th row20030220
5th row19930820
ValueCountFrequency (%)
20030220 219
 
8.1%
20110210 32
 
1.2%
20160926 24
 
0.9%
20050216 18
 
0.7%
20220613 13
 
0.5%
20211230 11
 
0.4%
20161017 10
 
0.4%
20111213 9
 
0.3%
20070302 9
 
0.3%
20180425 8
 
0.3%
Other values (1807) 2351
86.9%
2024-04-30T04:30:16.999978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6932
31.4%
2 5628
25.5%
1 3801
17.2%
3 1194
 
5.4%
9 882
 
4.0%
6 722
 
3.3%
8 659
 
3.0%
7 649
 
2.9%
4 593
 
2.7%
5 571
 
2.6%
Other values (2) 453
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21631
97.9%
Dash Punctuation 452
 
2.0%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6932
32.0%
2 5628
26.0%
1 3801
17.6%
3 1194
 
5.5%
9 882
 
4.1%
6 722
 
3.3%
8 659
 
3.0%
7 649
 
3.0%
4 593
 
2.7%
5 571
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 452
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22084
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6932
31.4%
2 5628
25.5%
1 3801
17.2%
3 1194
 
5.4%
9 882
 
4.0%
6 722
 
3.3%
8 659
 
3.0%
7 649
 
2.9%
4 593
 
2.7%
5 571
 
2.6%
Other values (2) 453
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6932
31.4%
2 5628
25.5%
1 3801
17.2%
3 1194
 
5.4%
9 882
 
4.0%
6 722
 
3.3%
8 659
 
3.0%
7 649
 
2.9%
4 593
 
2.7%
5 571
 
2.6%
Other values (2) 453
 
2.1%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4551
Missing (%)100.0%
Memory size40.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4551
Missing (%)100.0%
Memory size40.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4551
Missing (%)100.0%
Memory size40.1 KiB

전화번호
Text

MISSING 

Distinct2333
Distinct (%)85.0%
Missing1807
Missing (%)39.7%
Memory size35.7 KiB
2024-04-30T04:30:17.287568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.831268
Min length2

Characters and Unicode

Total characters29721
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

Unique2159 ?
Unique (%)78.7%

Sample

1st row0200000000
2nd row02 00000
3rd row0200000000
4th row0200000000
5th row0201768800
ValueCountFrequency (%)
02 2110
35.4%
332 130
 
2.2%
0200000000 122
 
2.0%
336 94
 
1.6%
070 83
 
1.4%
322 79
 
1.3%
333 58
 
1.0%
337 42
 
0.7%
323 42
 
0.7%
00000 42
 
0.7%
Other values (2334) 3153
52.9%
2024-04-30T04:30:17.715043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5949
20.0%
2 4710
15.8%
4621
15.5%
3 3884
13.1%
7 2079
 
7.0%
1 1813
 
6.1%
4 1526
 
5.1%
6 1497
 
5.0%
5 1301
 
4.4%
8 1256
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25100
84.5%
Space Separator 4621
 
15.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5949
23.7%
2 4710
18.8%
3 3884
15.5%
7 2079
 
8.3%
1 1813
 
7.2%
4 1526
 
6.1%
6 1497
 
6.0%
5 1301
 
5.2%
8 1256
 
5.0%
9 1085
 
4.3%
Space Separator
ValueCountFrequency (%)
4621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29721
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5949
20.0%
2 4710
15.8%
4621
15.5%
3 3884
13.1%
7 2079
 
7.0%
1 1813
 
6.1%
4 1526
 
5.1%
6 1497
 
5.0%
5 1301
 
4.4%
8 1256
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29721
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5949
20.0%
2 4710
15.8%
4621
15.5%
3 3884
13.1%
7 2079
 
7.0%
1 1813
 
6.1%
4 1526
 
5.1%
6 1497
 
5.0%
5 1301
 
4.4%
8 1256
 
4.2%

소재지면적
Real number (ℝ)

ZEROS 

Distinct2378
Distinct (%)52.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean52.363429
Minimum0
Maximum728.7
Zeros105
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:17.854703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q121.785
median35
Q366
95-th percentile150.534
Maximum728.7
Range728.7
Interquartile range (IQR)44.215

Descriptive statistics

Standard deviation49.128041
Coefficient of variation (CV)0.93821284
Kurtosis14.772881
Mean52.363429
Median Absolute Deviation (MAD)17.5
Skewness2.7590997
Sum238253.6
Variance2413.5644
MonotonicityNot monotonic
2024-04-30T04:30:18.134471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 119
 
2.6%
0.0 105
 
2.3%
30.0 66
 
1.5%
20.0 45
 
1.0%
26.4 39
 
0.9%
23.1 38
 
0.8%
40.0 31
 
0.7%
66.0 29
 
0.6%
16.5 27
 
0.6%
24.0 25
 
0.5%
Other values (2368) 4026
88.5%
ValueCountFrequency (%)
0.0 105
2.3%
3.0 1
 
< 0.1%
3.2 2
 
< 0.1%
3.3 4
 
0.1%
5.0 1
 
< 0.1%
5.5 3
 
0.1%
5.97 1
 
< 0.1%
6.0 2
 
< 0.1%
6.3 1
 
< 0.1%
6.5 1
 
< 0.1%
ValueCountFrequency (%)
728.7 1
< 0.1%
422.19 1
< 0.1%
407.74 1
< 0.1%
387.26 1
< 0.1%
363.0 1
< 0.1%
352.4 1
< 0.1%
351.7 1
< 0.1%
350.45 1
< 0.1%
344.41 1
< 0.1%
342.48 1
< 0.1%
Distinct248
Distinct (%)5.5%
Missing7
Missing (%)0.2%
Memory size35.7 KiB
2024-04-30T04:30:18.400695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1635123
Min length6

Characters and Unicode

Total characters28007
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

Unique43 ?
Unique (%)0.9%

Sample

1st row121807
2nd row121853
3rd row121870
4th row121807
5th row121857
ValueCountFrequency (%)
121837 181
 
4.0%
121836 124
 
2.7%
121838 107
 
2.4%
121895 104
 
2.3%
121829 98
 
2.2%
121865 92
 
2.0%
121818 92
 
2.0%
121812 84
 
1.8%
121820 78
 
1.7%
121893 75
 
1.7%
Other values (238) 3509
77.2%
2024-04-30T04:30:18.779575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10074
36.0%
2 5422
19.4%
8 5046
18.0%
0 1227
 
4.4%
3 1054
 
3.8%
9 1028
 
3.7%
7 1021
 
3.6%
5 920
 
3.3%
6 877
 
3.1%
- 743
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27264
97.3%
Dash Punctuation 743
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10074
36.9%
2 5422
19.9%
8 5046
18.5%
0 1227
 
4.5%
3 1054
 
3.9%
9 1028
 
3.8%
7 1021
 
3.7%
5 920
 
3.4%
6 877
 
3.2%
4 595
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10074
36.0%
2 5422
19.4%
8 5046
18.0%
0 1227
 
4.4%
3 1054
 
3.8%
9 1028
 
3.7%
7 1021
 
3.6%
5 920
 
3.3%
6 877
 
3.1%
- 743
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10074
36.0%
2 5422
19.4%
8 5046
18.0%
0 1227
 
4.4%
3 1054
 
3.8%
9 1028
 
3.7%
7 1021
 
3.6%
5 920
 
3.3%
6 877
 
3.1%
- 743
 
2.7%
Distinct4137
Distinct (%)91.0%
Missing7
Missing (%)0.2%
Memory size35.7 KiB
2024-04-30T04:30:19.047140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length52
Mean length26.15493
Min length17

Characters and Unicode

Total characters118848
Distinct characters426
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3798 ?
Unique (%)83.6%

Sample

1st row서울특별시 마포구 노고산동 123-0번지
2nd row서울특별시 마포구 신공덕동 128-21번지
3rd row서울특별시 마포구 염리동 3-0번지
4th row서울특별시 마포구 노고산동 57-15번지
5th row서울특별시 마포구 아현동 8-27번지
ValueCountFrequency (%)
서울특별시 4544
19.7%
마포구 4543
19.7%
서교동 1004
 
4.4%
1층 661
 
2.9%
2층 506
 
2.2%
망원동 431
 
1.9%
동교동 416
 
1.8%
연남동 283
 
1.2%
3층 278
 
1.2%
합정동 274
 
1.2%
Other values (4180) 10091
43.8%
2024-04-30T04:30:19.408454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21584
18.2%
5616
 
4.7%
5225
 
4.4%
1 5175
 
4.4%
4696
 
4.0%
4686
 
3.9%
4613
 
3.9%
4569
 
3.8%
4550
 
3.8%
4548
 
3.8%
Other values (416) 53586
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67320
56.6%
Decimal Number 24692
 
20.8%
Space Separator 21584
 
18.2%
Dash Punctuation 3934
 
3.3%
Uppercase Letter 432
 
0.4%
Close Punctuation 332
 
0.3%
Open Punctuation 331
 
0.3%
Other Punctuation 114
 
0.1%
Lowercase Letter 65
 
0.1%
Math Symbol 22
 
< 0.1%
Other values (2) 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5616
 
8.3%
5225
 
7.8%
4696
 
7.0%
4686
 
7.0%
4613
 
6.9%
4569
 
6.8%
4550
 
6.8%
4548
 
6.8%
4544
 
6.7%
3093
 
4.6%
Other values (352) 21180
31.5%
Uppercase Letter
ValueCountFrequency (%)
B 116
26.9%
C 36
 
8.3%
K 35
 
8.1%
G 35
 
8.1%
L 34
 
7.9%
S 32
 
7.4%
A 28
 
6.5%
M 19
 
4.4%
D 17
 
3.9%
I 14
 
3.2%
Other values (12) 66
15.3%
Lowercase Letter
ValueCountFrequency (%)
i 8
12.3%
e 7
10.8%
a 6
9.2%
s 5
7.7%
l 5
7.7%
t 5
7.7%
y 5
7.7%
h 5
7.7%
o 4
 
6.2%
n 3
 
4.6%
Other values (7) 12
18.5%
Decimal Number
ValueCountFrequency (%)
1 5175
21.0%
2 3610
14.6%
3 3524
14.3%
4 2691
10.9%
0 2141
8.7%
5 1919
 
7.8%
6 1661
 
6.7%
7 1509
 
6.1%
8 1287
 
5.2%
9 1175
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 99
86.8%
. 9
 
7.9%
& 3
 
2.6%
: 2
 
1.8%
? 1
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 331
99.7%
] 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 330
99.7%
[ 1
 
0.3%
Letter Number
ValueCountFrequency (%)
20
95.2%
1
 
4.8%
Space Separator
ValueCountFrequency (%)
21584
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3934
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67312
56.6%
Common 51010
42.9%
Latin 518
 
0.4%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5616
 
8.3%
5225
 
7.8%
4696
 
7.0%
4686
 
7.0%
4613
 
6.9%
4569
 
6.8%
4550
 
6.8%
4548
 
6.8%
4544
 
6.8%
3093
 
4.6%
Other values (346) 21172
31.5%
Latin
ValueCountFrequency (%)
B 116
22.4%
C 36
 
6.9%
K 35
 
6.8%
G 35
 
6.8%
L 34
 
6.6%
S 32
 
6.2%
A 28
 
5.4%
20
 
3.9%
M 19
 
3.7%
D 17
 
3.3%
Other values (31) 146
28.2%
Common
ValueCountFrequency (%)
21584
42.3%
1 5175
 
10.1%
- 3934
 
7.7%
2 3610
 
7.1%
3 3524
 
6.9%
4 2691
 
5.3%
0 2141
 
4.2%
5 1919
 
3.8%
6 1661
 
3.3%
7 1509
 
3.0%
Other values (13) 3262
 
6.4%
Han
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67312
56.6%
ASCII 51507
43.3%
Number Forms 21
 
< 0.1%
CJK 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21584
41.9%
1 5175
 
10.0%
- 3934
 
7.6%
2 3610
 
7.0%
3 3524
 
6.8%
4 2691
 
5.2%
0 2141
 
4.2%
5 1919
 
3.7%
6 1661
 
3.2%
7 1509
 
2.9%
Other values (52) 3759
 
7.3%
Hangul
ValueCountFrequency (%)
5616
 
8.3%
5225
 
7.8%
4696
 
7.0%
4686
 
7.0%
4613
 
6.9%
4569
 
6.8%
4550
 
6.8%
4548
 
6.8%
4544
 
6.8%
3093
 
4.6%
Other values (346) 21172
31.5%
Number Forms
ValueCountFrequency (%)
20
95.2%
1
 
4.8%
CJK
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

도로명주소
Text

MISSING 

Distinct3102
Distinct (%)89.7%
Missing1093
Missing (%)24.0%
Memory size35.7 KiB
2024-04-30T04:30:19.651093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length57
Mean length33.056969
Min length21

Characters and Unicode

Total characters114311
Distinct characters408
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2815 ?
Unique (%)81.4%

Sample

1st row서울특별시 마포구 마포대로4나길 4, 1층 (마포동)
2nd row서울특별시 마포구 신촌로34길 6 (아현동)
3rd row서울특별시 마포구 새창로 52, 상가12동 2층 206호 (도화동, 도화동현대아파트)
4th row서울특별시 마포구 숭문길 217 (대흥동)
5th row서울특별시 마포구 월드컵북로16길 54 (연남동)
ValueCountFrequency (%)
서울특별시 3458
 
15.2%
마포구 3457
 
15.2%
1층 826
 
3.6%
서교동 811
 
3.6%
2층 705
 
3.1%
3층 362
 
1.6%
동교동 361
 
1.6%
망원동 277
 
1.2%
연남동 245
 
1.1%
합정동 201
 
0.9%
Other values (2238) 12023
52.9%
2024-04-30T04:30:20.065794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19274
 
16.9%
1 4975
 
4.4%
4431
 
3.9%
4421
 
3.9%
4000
 
3.5%
3885
 
3.4%
, 3755
 
3.3%
3639
 
3.2%
( 3578
 
3.1%
) 3577
 
3.1%
Other values (398) 58776
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64611
56.5%
Space Separator 19274
 
16.9%
Decimal Number 18221
 
15.9%
Other Punctuation 3768
 
3.3%
Open Punctuation 3579
 
3.1%
Close Punctuation 3578
 
3.1%
Dash Punctuation 714
 
0.6%
Uppercase Letter 458
 
0.4%
Lowercase Letter 64
 
0.1%
Math Symbol 23
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4431
 
6.9%
4421
 
6.8%
4000
 
6.2%
3885
 
6.0%
3639
 
5.6%
3528
 
5.5%
3482
 
5.4%
3465
 
5.4%
3458
 
5.4%
3329
 
5.2%
Other values (334) 26973
41.7%
Uppercase Letter
ValueCountFrequency (%)
B 161
35.2%
C 38
 
8.3%
L 34
 
7.4%
G 31
 
6.8%
K 29
 
6.3%
S 27
 
5.9%
A 24
 
5.2%
M 20
 
4.4%
D 18
 
3.9%
H 13
 
2.8%
Other values (13) 63
 
13.8%
Lowercase Letter
ValueCountFrequency (%)
i 7
10.9%
y 6
9.4%
e 6
9.4%
a 6
9.4%
l 5
7.8%
t 5
7.8%
h 5
7.8%
s 5
7.8%
o 4
 
6.2%
r 3
 
4.7%
Other values (8) 12
18.8%
Decimal Number
ValueCountFrequency (%)
1 4975
27.3%
2 3536
19.4%
3 1997
11.0%
0 1785
 
9.8%
4 1387
 
7.6%
5 1178
 
6.5%
6 999
 
5.5%
7 851
 
4.7%
9 834
 
4.6%
8 679
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 3755
99.7%
. 8
 
0.2%
& 4
 
0.1%
? 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 3578
> 99.9%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3577
> 99.9%
] 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
20
95.2%
1
 
4.8%
Space Separator
ValueCountFrequency (%)
19274
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 714
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64603
56.5%
Common 49157
43.0%
Latin 543
 
0.5%
Han 8
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4431
 
6.9%
4421
 
6.8%
4000
 
6.2%
3885
 
6.0%
3639
 
5.6%
3528
 
5.5%
3482
 
5.4%
3465
 
5.4%
3458
 
5.4%
3329
 
5.2%
Other values (328) 26965
41.7%
Latin
ValueCountFrequency (%)
B 161
29.7%
C 38
 
7.0%
L 34
 
6.3%
G 31
 
5.7%
K 29
 
5.3%
S 27
 
5.0%
A 24
 
4.4%
M 20
 
3.7%
20
 
3.7%
D 18
 
3.3%
Other values (33) 141
26.0%
Common
ValueCountFrequency (%)
19274
39.2%
1 4975
 
10.1%
, 3755
 
7.6%
( 3578
 
7.3%
) 3577
 
7.3%
2 3536
 
7.2%
3 1997
 
4.1%
0 1785
 
3.6%
4 1387
 
2.8%
5 1178
 
2.4%
Other values (11) 4115
 
8.4%
Han
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64603
56.5%
ASCII 49679
43.5%
Number Forms 21
 
< 0.1%
CJK 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19274
38.8%
1 4975
 
10.0%
, 3755
 
7.6%
( 3578
 
7.2%
) 3577
 
7.2%
2 3536
 
7.1%
3 1997
 
4.0%
0 1785
 
3.6%
4 1387
 
2.8%
5 1178
 
2.4%
Other values (52) 4637
 
9.3%
Hangul
ValueCountFrequency (%)
4431
 
6.9%
4421
 
6.8%
4000
 
6.2%
3885
 
6.0%
3639
 
5.6%
3528
 
5.5%
3482
 
5.4%
3465
 
5.4%
3458
 
5.4%
3329
 
5.2%
Other values (328) 26965
41.7%
Number Forms
ValueCountFrequency (%)
20
95.2%
1
 
4.8%
CJK
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct269
Distinct (%)7.8%
Missing1104
Missing (%)24.3%
Infinite0
Infinite (%)0.0%
Mean4049.8576
Minimum3766
Maximum4214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:20.192522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3766
5-th percentile3936
Q13995
median4049
Q34086
95-th percentile4176
Maximum4214
Range448
Interquartile range (IQR)91

Descriptive statistics

Standard deviation69.922615
Coefficient of variation (CV)0.01726545
Kurtosis-0.28299619
Mean4049.8576
Median Absolute Deviation (MAD)47
Skewness0.30789247
Sum13959859
Variance4889.1721
MonotonicityNot monotonic
2024-04-30T04:30:20.304211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4049 125
 
2.7%
4053 118
 
2.6%
4055 87
 
1.9%
4050 87
 
1.9%
4047 67
 
1.5%
4048 64
 
1.4%
3982 64
 
1.4%
4051 60
 
1.3%
3964 57
 
1.3%
4040 56
 
1.2%
Other values (259) 2662
58.5%
(Missing) 1104
24.3%
ValueCountFrequency (%)
3766 1
 
< 0.1%
3902 10
0.2%
3903 2
 
< 0.1%
3905 2
 
< 0.1%
3906 1
 
< 0.1%
3907 4
 
0.1%
3908 7
0.2%
3911 2
 
< 0.1%
3912 3
 
0.1%
3913 1
 
< 0.1%
ValueCountFrequency (%)
4214 3
 
0.1%
4213 1
 
< 0.1%
4211 8
0.2%
4210 3
 
0.1%
4209 11
0.2%
4208 7
0.2%
4207 1
 
< 0.1%
4206 13
0.3%
4205 9
0.2%
4204 2
 
< 0.1%
Distinct4034
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
2024-04-30T04:30:20.553737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length30
Mean length6.756537
Min length1

Characters and Unicode

Total characters30749
Distinct characters800
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3647 ?
Unique (%)80.1%

Sample

1st row미형
2nd row남일
3rd row
4th row
5th row영광
ValueCountFrequency (%)
헤어 107
 
1.7%
네일 78
 
1.3%
hair 69
 
1.1%
nail 53
 
0.9%
홍대점 48
 
0.8%
에스테틱 37
 
0.6%
미용실 30
 
0.5%
28
 
0.5%
뷰티 27
 
0.4%
살롱 23
 
0.4%
Other values (4453) 5712
92.0%
2024-04-30T04:30:20.909022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1664
 
5.4%
1371
 
4.5%
1287
 
4.2%
778
 
2.5%
744
 
2.4%
678
 
2.2%
( 593
 
1.9%
) 593
 
1.9%
518
 
1.7%
498
 
1.6%
Other values (790) 22025
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22281
72.5%
Lowercase Letter 3096
 
10.1%
Uppercase Letter 2052
 
6.7%
Space Separator 1664
 
5.4%
Open Punctuation 593
 
1.9%
Close Punctuation 593
 
1.9%
Other Punctuation 240
 
0.8%
Decimal Number 205
 
0.7%
Dash Punctuation 14
 
< 0.1%
Connector Punctuation 8
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1371
 
6.2%
1287
 
5.8%
778
 
3.5%
744
 
3.3%
678
 
3.0%
518
 
2.3%
498
 
2.2%
465
 
2.1%
445
 
2.0%
427
 
1.9%
Other values (712) 15070
67.6%
Lowercase Letter
ValueCountFrequency (%)
a 400
12.9%
e 324
10.5%
i 303
9.8%
o 264
 
8.5%
n 258
 
8.3%
l 246
 
7.9%
r 204
 
6.6%
u 139
 
4.5%
h 132
 
4.3%
t 131
 
4.2%
Other values (16) 695
22.4%
Uppercase Letter
ValueCountFrequency (%)
A 213
 
10.4%
N 174
 
8.5%
I 153
 
7.5%
O 149
 
7.3%
H 140
 
6.8%
E 129
 
6.3%
S 125
 
6.1%
M 119
 
5.8%
L 109
 
5.3%
T 97
 
4.7%
Other values (16) 644
31.4%
Decimal Number
ValueCountFrequency (%)
1 48
23.4%
2 48
23.4%
3 24
11.7%
4 20
9.8%
0 17
 
8.3%
9 13
 
6.3%
5 12
 
5.9%
7 10
 
4.9%
6 8
 
3.9%
8 5
 
2.4%
Other Punctuation
ValueCountFrequency (%)
. 58
24.2%
& 49
20.4%
? 46
19.2%
, 24
10.0%
# 23
 
9.6%
' 19
 
7.9%
: 16
 
6.7%
; 4
 
1.7%
! 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1664
100.0%
Open Punctuation
ValueCountFrequency (%)
( 593
100.0%
Close Punctuation
ValueCountFrequency (%)
) 593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22276
72.4%
Latin 5150
 
16.7%
Common 3318
 
10.8%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1371
 
6.2%
1287
 
5.8%
778
 
3.5%
744
 
3.3%
678
 
3.0%
518
 
2.3%
498
 
2.2%
465
 
2.1%
445
 
2.0%
427
 
1.9%
Other values (709) 15065
67.6%
Latin
ValueCountFrequency (%)
a 400
 
7.8%
e 324
 
6.3%
i 303
 
5.9%
o 264
 
5.1%
n 258
 
5.0%
l 246
 
4.8%
A 213
 
4.1%
r 204
 
4.0%
N 174
 
3.4%
I 153
 
3.0%
Other values (43) 2611
50.7%
Common
ValueCountFrequency (%)
1664
50.2%
( 593
 
17.9%
) 593
 
17.9%
. 58
 
1.7%
& 49
 
1.5%
1 48
 
1.4%
2 48
 
1.4%
? 46
 
1.4%
3 24
 
0.7%
, 24
 
0.7%
Other values (15) 171
 
5.2%
Han
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22267
72.4%
ASCII 8465
 
27.5%
Compat Jamo 9
 
< 0.1%
CJK 5
 
< 0.1%
Number Forms 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1664
19.7%
( 593
 
7.0%
) 593
 
7.0%
a 400
 
4.7%
e 324
 
3.8%
i 303
 
3.6%
o 264
 
3.1%
n 258
 
3.0%
l 246
 
2.9%
A 213
 
2.5%
Other values (66) 3607
42.6%
Hangul
ValueCountFrequency (%)
1371
 
6.2%
1287
 
5.8%
778
 
3.5%
744
 
3.3%
678
 
3.0%
518
 
2.3%
498
 
2.2%
465
 
2.1%
445
 
2.0%
427
 
1.9%
Other values (705) 15056
67.6%
Compat Jamo
ValueCountFrequency (%)
5
55.6%
2
 
22.2%
1
 
11.1%
1
 
11.1%
CJK
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
1
 
20.0%
Number Forms
ValueCountFrequency (%)
2
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct4002
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
Minimum1999-01-13 00:00:00
Maximum2024-04-25 13:27:37
2024-04-30T04:30:21.036778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:21.194926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
I
2892 
U
1629 
D
 
30

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 2892
63.5%
U 1629
35.8%
D 30
 
0.7%

Length

2024-04-30T04:30:21.316582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:21.399766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2892
63.5%
u 1629
35.8%
d 30
 
0.7%
Distinct1143
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:07:00
2024-04-30T04:30:21.506959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:30:21.627096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
일반미용업
2963 
피부미용업
708 
네일아트업
569 
메이크업업
 
242
기타
 
69

Length

Max length5
Median length5
Mean length4.9545155
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2963
65.1%
피부미용업 708
 
15.6%
네일아트업 569
 
12.5%
메이크업업 242
 
5.3%
기타 69
 
1.5%

Length

2024-04-30T04:30:21.738018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:21.821762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2963
65.1%
피부미용업 708
 
15.6%
네일아트업 569
 
12.5%
메이크업업 242
 
5.3%
기타 69
 
1.5%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2392
Distinct (%)54.9%
Missing196
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean193350.8
Minimum189212.71
Maximum196721.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:21.922222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189212.71
5-th percentile191251.09
Q1192393.91
median193166.43
Q3194442.94
95-th percentile195891.31
Maximum196721.09
Range7508.3839
Interquartile range (IQR)2049.026

Descriptive statistics

Standard deviation1498.6132
Coefficient of variation (CV)0.0077507473
Kurtosis-0.43964084
Mean193350.8
Median Absolute Deviation (MAD)995.19475
Skewness0.14818229
Sum8.4204274 × 108
Variance2245841.5
MonotonicityNot monotonic
2024-04-30T04:30:22.040385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193166.430144679 36
 
0.8%
193048.463050526 33
 
0.7%
193187.802952358 32
 
0.7%
195782.167947617 20
 
0.4%
193241.196948821 18
 
0.4%
192171.23539752 17
 
0.4%
191435.876990887 16
 
0.4%
190125.564768858 16
 
0.4%
191263.451931121 16
 
0.4%
192311.642580307 15
 
0.3%
Other values (2382) 4136
90.9%
(Missing) 196
 
4.3%
ValueCountFrequency (%)
189212.708916184 2
 
< 0.1%
189212.737535822 7
0.2%
189286.651086068 1
 
< 0.1%
189315.310470024 2
 
< 0.1%
189315.370584751 9
0.2%
189392.975995366 2
 
< 0.1%
189461.587933606 3
 
0.1%
189520.410979113 3
 
0.1%
189586.493821292 1
 
< 0.1%
189857.566447564 3
 
0.1%
ValueCountFrequency (%)
196721.09277056 1
< 0.1%
196717.946323293 1
< 0.1%
196717.811595364 1
< 0.1%
196717.583485115 2
< 0.1%
196701.597935248 1
< 0.1%
196700.29270525 1
< 0.1%
196699.188309549 1
< 0.1%
196697.874709656 1
< 0.1%
196693.578355085 2
< 0.1%
196692.102667417 2
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2392
Distinct (%)54.9%
Missing196
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean450230.01
Minimum448229.06
Maximum453685.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:22.179057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum448229.06
5-th percentile448831.82
Q1449641.77
median450235.83
Q3450569.67
95-th percentile451951.97
Maximum453685.55
Range5456.482
Interquartile range (IQR)927.90159

Descriptive statistics

Standard deviation899.2961
Coefficient of variation (CV)0.0019974148
Kurtosis2.0453002
Mean450230.01
Median Absolute Deviation (MAD)484.34269
Skewness0.96553451
Sum1.9607517 × 109
Variance808733.48
MonotonicityNot monotonic
2024-04-30T04:30:22.312178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450425.852790862 36
 
0.8%
450243.570663618 33
 
0.7%
450172.178899424 32
 
0.7%
449467.125357845 20
 
0.4%
450137.711902742 18
 
0.4%
449751.486972783 17
 
0.4%
451990.707795053 16
 
0.4%
453090.149821248 16
 
0.4%
452207.500653521 16
 
0.4%
449855.643910445 15
 
0.3%
Other values (2382) 4136
90.9%
(Missing) 196
 
4.3%
ValueCountFrequency (%)
448229.063825491 1
 
< 0.1%
448236.655548283 8
0.2%
448276.965250949 2
 
< 0.1%
448281.659430993 1
 
< 0.1%
448318.358910522 1
 
< 0.1%
448373.28570163 1
 
< 0.1%
448386.773359662 1
 
< 0.1%
448390.399678267 2
 
< 0.1%
448390.835655217 1
 
< 0.1%
448393.658663222 4
0.1%
ValueCountFrequency (%)
453685.545865753 3
 
0.1%
453647.349314742 9
0.2%
453647.190988422 2
 
< 0.1%
453577.133041615 1
 
< 0.1%
453468.379147545 7
0.2%
453468.335655795 2
 
< 0.1%
453342.222208599 1
 
< 0.1%
453302.04990022 6
0.1%
453279.956171678 3
 
0.1%
453271.198852486 3
 
0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
미용업
1254 
<NA>
1157 
일반미용업
1046 
피부미용업
464 
네일미용업
198 
Other values (12)
432 

Length

Max length23
Median length19
Mean length4.8668425
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1254
27.6%
<NA> 1157
25.4%
일반미용업 1046
23.0%
피부미용업 464
 
10.2%
네일미용업 198
 
4.4%
종합미용업 110
 
2.4%
화장ㆍ분장 미용업 62
 
1.4%
피부미용업, 네일미용업 55
 
1.2%
네일미용업, 화장ㆍ분장 미용업 46
 
1.0%
일반미용업, 화장ㆍ분장 미용업 45
 
1.0%
Other values (7) 114
 
2.5%

Length

2024-04-30T04:30:22.447215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1478
29.1%
일반미용업 1160
22.8%
na 1157
22.8%
피부미용업 583
 
11.5%
네일미용업 372
 
7.3%
화장ㆍ분장 224
 
4.4%
종합미용업 110
 
2.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)0.9%
Missing1722
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean0.82043125
Minimum0
Maximum36
Zeros2288
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:22.556098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4743935
Coefficient of variation (CV)3.015967
Kurtosis58.02008
Mean0.82043125
Median Absolute Deviation (MAD)0
Skewness6.2855241
Sum2321
Variance6.1226234
MonotonicityNot monotonic
2024-04-30T04:30:22.663164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 2288
50.3%
4 110
 
2.4%
3 105
 
2.3%
2 98
 
2.2%
5 84
 
1.8%
1 64
 
1.4%
6 32
 
0.7%
7 14
 
0.3%
8 5
 
0.1%
18 5
 
0.1%
Other values (15) 24
 
0.5%
(Missing) 1722
37.8%
ValueCountFrequency (%)
0 2288
50.3%
1 64
 
1.4%
2 98
 
2.2%
3 105
 
2.3%
4 110
 
2.4%
5 84
 
1.8%
6 32
 
0.7%
7 14
 
0.3%
8 5
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
36 2
 
< 0.1%
28 1
 
< 0.1%
24 3
0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 2
 
< 0.1%
19 2
 
< 0.1%
18 5
0.1%
17 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.3%
Missing1820
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean0.15452215
Minimum0
Maximum8
Zeros2440
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:22.753941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.60121722
Coefficient of variation (CV)3.8908157
Kurtosis63.585289
Mean0.15452215
Median Absolute Deviation (MAD)0
Skewness6.9368951
Sum422
Variance0.36146214
MonotonicityNot monotonic
2024-04-30T04:30:22.844320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2440
53.6%
1 239
 
5.3%
2 24
 
0.5%
5 7
 
0.2%
3 7
 
0.2%
4 6
 
0.1%
7 3
 
0.1%
6 3
 
0.1%
8 2
 
< 0.1%
(Missing) 1820
40.0%
ValueCountFrequency (%)
0 2440
53.6%
1 239
 
5.3%
2 24
 
0.5%
3 7
 
0.2%
4 6
 
0.1%
5 7
 
0.2%
6 3
 
0.1%
7 3
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 3
 
0.1%
6 3
 
0.1%
5 7
 
0.2%
4 6
 
0.1%
3 7
 
0.2%
2 24
 
0.5%
1 239
 
5.3%
0 2440
53.6%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.6%
Missing2517
Missing (%)55.3%
Infinite0
Infinite (%)0.0%
Mean1.3579154
Minimum0
Maximum15
Zeros578
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:22.942307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3243041
Coefficient of variation (CV)0.97524785
Kurtosis8.6353002
Mean1.3579154
Median Absolute Deviation (MAD)1
Skewness1.8058553
Sum2762
Variance1.7537814
MonotonicityNot monotonic
2024-04-30T04:30:23.036701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 700
 
15.4%
0 578
 
12.7%
2 420
 
9.2%
3 209
 
4.6%
4 81
 
1.8%
5 29
 
0.6%
6 10
 
0.2%
7 3
 
0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 2517
55.3%
ValueCountFrequency (%)
0 578
12.7%
1 700
15.4%
2 420
9.2%
3 209
 
4.6%
4 81
 
1.8%
5 29
 
0.6%
6 10
 
0.2%
7 3
 
0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 3
 
0.1%
6 10
 
0.2%
5 29
 
0.6%
4 81
 
1.8%
3 209
4.6%
2 420
9.2%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.8%
Missing3024
Missing (%)66.4%
Infinite0
Infinite (%)0.0%
Mean1.6450557
Minimum0
Maximum15
Zeros211
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:23.138598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3026891
Coefficient of variation (CV)0.79188145
Kurtosis10.815266
Mean1.6450557
Median Absolute Deviation (MAD)1
Skewness1.9868492
Sum2512
Variance1.6969988
MonotonicityNot monotonic
2024-04-30T04:30:23.237812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 630
 
13.8%
2 377
 
8.3%
0 211
 
4.6%
3 189
 
4.2%
4 78
 
1.7%
5 26
 
0.6%
6 10
 
0.2%
7 2
 
< 0.1%
10 1
 
< 0.1%
15 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 3024
66.4%
ValueCountFrequency (%)
0 211
 
4.6%
1 630
13.8%
2 377
8.3%
3 189
 
4.2%
4 78
 
1.7%
5 26
 
0.6%
6 10
 
0.2%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 2
 
< 0.1%
6 10
 
0.2%
5 26
 
0.6%
4 78
 
1.7%
3 189
4.2%
2 377
8.3%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
3532 
0
848 
1
 
149
2
 
14
3
 
6

Length

Max length4
Median length4
Mean length3.3282795
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3532
77.6%
0 848
 
18.6%
1 149
 
3.3%
2 14
 
0.3%
3 6
 
0.1%
5 2
 
< 0.1%

Length

2024-04-30T04:30:23.344424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:23.451670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3532
77.6%
0 848
 
18.6%
1 149
 
3.3%
2 14
 
0.3%
3 6
 
0.1%
5 2
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
3940 
0
460 
1
 
132
2
 
9
3
 
6

Length

Max length4
Median length4
Mean length3.5972314
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> 3940
86.6%
0 460
 
10.1%
1 132
 
2.9%
2 9
 
0.2%
3 6
 
0.1%
5 4
 
0.1%

Length

2024-04-30T04:30:23.560596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:23.656665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3940
86.6%
0 460
 
10.1%
1 132
 
2.9%
2 9
 
0.2%
3 6
 
0.1%
5 4
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
0
2651 
<NA>
1900 

Length

Max length4
Median length1
Mean length2.252472
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2651
58.3%
<NA> 1900
41.7%

Length

2024-04-30T04:30:23.766201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:23.862306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2651
58.3%
na 1900
41.7%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
0
2651 
<NA>
1900 

Length

Max length4
Median length1
Mean length2.252472
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2651
58.3%
<NA> 1900
41.7%

Length

2024-04-30T04:30:24.156697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:24.232751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2651
58.3%
na 1900
41.7%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
0
2651 
<NA>
1900 

Length

Max length4
Median length1
Mean length2.252472
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2651
58.3%
<NA> 1900
41.7%

Length

2024-04-30T04:30:24.324850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:24.407547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2651
58.3%
na 1900
41.7%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing1198
Missing (%)26.3%
Memory size9.0 KiB
False
3353 
(Missing)
1198 
ValueCountFrequency (%)
False 3353
73.7%
(Missing) 1198
 
26.3%
2024-04-30T04:30:24.473581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)0.7%
Missing1251
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean3.9878788
Minimum0
Maximum26
Zeros149
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:24.546800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.790422
Coefficient of variation (CV)0.69972589
Kurtosis7.5503003
Mean3.9878788
Median Absolute Deviation (MAD)1
Skewness2.2193179
Sum13160
Variance7.786455
MonotonicityNot monotonic
2024-04-30T04:30:24.647309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 1046
23.0%
2 604
13.3%
4 549
12.1%
5 258
 
5.7%
6 220
 
4.8%
0 149
 
3.3%
8 108
 
2.4%
1 81
 
1.8%
7 71
 
1.6%
10 60
 
1.3%
Other values (14) 154
 
3.4%
(Missing) 1251
27.5%
ValueCountFrequency (%)
0 149
 
3.3%
1 81
 
1.8%
2 604
13.3%
3 1046
23.0%
4 549
12.1%
5 258
 
5.7%
6 220
 
4.8%
7 71
 
1.6%
8 108
 
2.4%
9 34
 
0.7%
ValueCountFrequency (%)
26 1
 
< 0.1%
24 1
 
< 0.1%
21 1
 
< 0.1%
20 3
 
0.1%
19 1
 
< 0.1%
18 8
0.2%
17 5
 
0.1%
16 9
0.2%
15 9
0.2%
14 15
0.3%

조건부허가신고사유
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing4550
Missing (%)> 99.9%
Memory size35.7 KiB
2024-04-30T04:30:24.775072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row임시사용승인기간까지
ValueCountFrequency (%)
임시사용승인기간까지 1
100.0%
2024-04-30T04:30:24.998729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
4550 
20030926
 
1

Length

Max length8
Median length4
Mean length4.0008789
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> 4550
> 99.9%
20030926 1
 
< 0.1%

Length

2024-04-30T04:30:25.107979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:25.191882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4550
> 99.9%
20030926 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
4550 
20040630
 
1

Length

Max length8
Median length4
Mean length4.0008789
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> 4550
> 99.9%
20040630 1
 
< 0.1%

Length

2024-04-30T04:30:25.292230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:25.391282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4550
> 99.9%
20040630 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
3877 
임대
661 
자가
 
13

Length

Max length4
Median length4
Mean length3.7038014
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> 3877
85.2%
임대 661
 
14.5%
자가 13
 
0.3%

Length

2024-04-30T04:30:25.488791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:25.572490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3877
85.2%
임대 661
 
14.5%
자가 13
 
0.3%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
2292 
0
2259 

Length

Max length4
Median length4
Mean length2.5108767
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> 2292
50.4%
0 2259
49.6%

Length

2024-04-30T04:30:25.667608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:25.744005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2292
50.4%
0 2259
49.6%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.5%
Missing3140
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean0.21686747
Minimum0
Maximum6
Zeros1209
Zeros (%)26.6%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:25.815511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64886073
Coefficient of variation (CV)2.9919689
Kurtosis24.262276
Mean0.21686747
Median Absolute Deviation (MAD)0
Skewness4.3244949
Sum306
Variance0.42102025
MonotonicityNot monotonic
2024-04-30T04:30:25.907015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1209
 
26.6%
1 142
 
3.1%
2 34
 
0.7%
3 16
 
0.4%
4 5
 
0.1%
6 3
 
0.1%
5 2
 
< 0.1%
(Missing) 3140
69.0%
ValueCountFrequency (%)
0 1209
26.6%
1 142
 
3.1%
2 34
 
0.7%
3 16
 
0.4%
4 5
 
0.1%
5 2
 
< 0.1%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 2
 
< 0.1%
4 5
 
0.1%
3 16
 
0.4%
2 34
 
0.7%
1 142
 
3.1%
0 1209
26.6%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
3147 
0
1350 
1
 
41
2
 
9
3
 
3

Length

Max length4
Median length4
Mean length3.0744891
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> 3147
69.1%
0 1350
29.7%
1 41
 
0.9%
2 9
 
0.2%
3 3
 
0.1%
4 1
 
< 0.1%

Length

2024-04-30T04:30:26.029773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:26.118615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3147
69.1%
0 1350
29.7%
1 41
 
0.9%
2 9
 
0.2%
3 3
 
0.1%
4 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.7 KiB
<NA>
2394 
0
2157 

Length

Max length4
Median length4
Mean length2.5781147
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2394
52.6%
0 2157
47.4%

Length

2024-04-30T04:30:26.211983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:30:26.292438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2394
52.6%
0 2157
47.4%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)0.6%
Missing2431
Missing (%)53.4%
Infinite0
Infinite (%)0.0%
Mean0.3745283
Minimum0
Maximum12
Zeros1847
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size40.1 KiB
2024-04-30T04:30:26.365014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.05
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2694219
Coefficient of variation (CV)3.3893885
Kurtosis27.901058
Mean0.3745283
Median Absolute Deviation (MAD)0
Skewness4.7985119
Sum794
Variance1.6114321
MonotonicityNot monotonic
2024-04-30T04:30:26.460067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 1847
40.6%
2 90
 
2.0%
1 77
 
1.7%
3 30
 
0.7%
4 29
 
0.6%
5 14
 
0.3%
6 11
 
0.2%
7 7
 
0.2%
8 5
 
0.1%
10 4
 
0.1%
Other values (3) 6
 
0.1%
(Missing) 2431
53.4%
ValueCountFrequency (%)
0 1847
40.6%
1 77
 
1.7%
2 90
 
2.0%
3 30
 
0.7%
4 29
 
0.6%
5 14
 
0.3%
6 11
 
0.2%
7 7
 
0.2%
8 5
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
11 3
 
0.1%
10 4
 
0.1%
9 1
 
< 0.1%
8 5
 
0.1%
7 7
 
0.2%
6 11
 
0.2%
5 14
0.3%
4 29
0.6%
3 30
0.7%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing1157
Missing (%)25.4%
Memory size9.0 KiB
False
3392 
True
 
2
(Missing)
1157 
ValueCountFrequency (%)
False 3392
74.5%
True 2
 
< 0.1%
(Missing) 1157
 
25.4%
2024-04-30T04:30:26.557996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
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831300003130000-204-1969-0106819690410<NA>3폐업2폐업20190207<NA><NA><NA>02 712 307922.24121050서울특별시 마포구 마포동 189번지 1층서울특별시 마포구 마포대로4나길 4, 1층 (마포동)4176삼아2019-02-07 15:25:59U2019-02-09 02:40:00.0일반미용업195176.033235448495.56596미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
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개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
454131300003130000-226-2021-000022021-05-31<NA>3폐업2폐업2023-02-10<NA><NA><NA>02 336 410335.33121-895서울특별시 마포구 서교동 400-23 지하1층 좌측서울특별시 마포구 어울마당로5길 31, 지하1층 (서교동)4047아르테2023-12-28 13:21:32U2022-11-01 21:00:00.0네일아트업192757.397103449688.838797<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454231300003130000-226-2021-000032021-08-04<NA>3폐업2폐업2023-11-28<NA><NA><NA><NA>39.0121-822서울특별시 마포구 망원동 403-21 4층 일부(402호)서울특별시 마포구 월드컵로13길 70, 4층 402호 (망원동)4010소담네일2023-11-28 13:27:26U2022-10-31 21:00:00.0네일아트업191653.739302450339.960143<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454331300003130000-226-2021-000042021-08-05<NA>3폐업2폐업2024-03-11<NA><NA><NA><NA>20.59121-869서울특별시 마포구 연남동 515-12 미스바(2층 202호 전체)서울특별시 마포구 연남로3길 10, 2층 202호 (연남동, 미스바)3988네일 미드나잇2024-04-01 09:54:27U2023-12-04 00:03:00.0네일아트업193028.591601451018.894331<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454431300003130000-226-2021-0000520210823<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.94121820서울특별시 마포구 망원동 57-57서울특별시 마포구 월드컵로19길 31-1, 1층 (망원동)4012네일루화2021-08-23 12:05:25I2021-08-25 00:22:50.0네일아트업191797.095373450422.16144피부미용업, 네일미용업, 화장ㆍ분장 미용업211000000N3<NA><NA><NA>임대00001N
454531300003130000-226-2022-0000120220111<NA>3폐업2폐업20220228<NA><NA><NA><NA>13.2121808서울특별시 마포구 대흥동 2-10 3층 일부(반디인하우스 내)서울특별시 마포구 대흥로 194, 3층 (대흥동)4112반디인하우스 이대점2022-02-28 09:05:48U2022-03-02 02:40:00.0메이크업업195169.659457450442.360354피부미용업, 네일미용업, 화장ㆍ분장 미용업513300000N0<NA><NA><NA>임대00002N
454631300003130000-226-2022-0000220220104<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.4121829서울특별시 마포구 상수동 331-8 1층 2호서울특별시 마포구 독막로18길 27, 1층 2호 (상수동)4075별빛네일2022-04-07 10:40:12I2021-12-04 00:09:00.0네일아트업193155.2513449366.19873<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454731300003130000-226-2022-0000320220622<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.84121861서울특별시 마포구 아현동 437-3 고려아카데미텔 지하1층 45호서울특별시 마포구 마포대로 196, 고려아카데미텔 지하1층 45호 (아현동)4206넘버원 네일(NO.1 NAIL)2022-06-22 17:42:27I2021-12-05 22:05:00.0네일아트업196057.949966449897.213071<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454831300003130000-226-2022-000042022-02-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>97.19121-807서울특별시 마포구 노고산동 57-38 영화빌딩서울특별시 마포구 서강로 135, 영화빌딩 3층 (노고산동)4058신촌24시마벨브라질리언왁싱2023-10-17 17:13:44U2022-10-30 23:09:00.0피부미용업194224.210759450250.907145<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
454931300003130000-226-2023-000012023-02-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>34.03121-835서울특별시 마포구 상암동 1452 바라라서울특별시 마포구 월드컵북로48길 55, 바라라 2층 (상암동)3927미나링뷰티2023-03-03 16:12:39I2022-12-03 00:05:00.0네일아트업190775.971006452855.777195<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
455031300003130000-226-2023-000022023-08-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>84.61121-818서울특별시 마포구 동교동 166-14 동교동 스타피카소 7층 A32,A37,A38서울특별시 마포구 양화로 176, 동교동 스타피카소 7층 A32,A37,A38호 (동교동)4051네일의 날씨2023-08-30 15:24:36I2022-12-09 00:01:00.0네일아트업193282.1308450549.280167<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>