Overview

Dataset statistics

Number of variables47
Number of observations4471
Missing cells44773
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 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
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-17922/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
조건부허가신고사유 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (51.5%)Imbalance
사용시작지하층 is highly imbalanced (59.3%)Imbalance
사용끝지하층 is highly imbalanced (75.6%)Imbalance
발한실여부 is highly imbalanced (97.9%)Imbalance
조건부허가시작일자 is highly imbalanced (99.7%)Imbalance
조건부허가종료일자 is highly imbalanced (99.7%)Imbalance
남성종사자수 is highly imbalanced (68.8%)Imbalance
인허가취소일자 has 4471 (100.0%) missing valuesMissing
폐업일자 has 1455 (32.5%) missing valuesMissing
휴업시작일자 has 4471 (100.0%) missing valuesMissing
휴업종료일자 has 4471 (100.0%) missing valuesMissing
재개업일자 has 4471 (100.0%) missing valuesMissing
전화번호 has 1817 (40.6%) missing valuesMissing
도로명주소 has 1756 (39.3%) missing valuesMissing
도로명우편번호 has 1768 (39.5%) missing valuesMissing
좌표정보(X) has 97 (2.2%) missing valuesMissing
좌표정보(Y) has 97 (2.2%) missing valuesMissing
건물지상층수 has 1253 (28.0%) missing valuesMissing
건물지하층수 has 1537 (34.4%) missing valuesMissing
사용시작지상층 has 1378 (30.8%) missing valuesMissing
사용끝지상층 has 2050 (45.9%) missing valuesMissing
발한실여부 has 938 (21.0%) missing valuesMissing
좌석수 has 942 (21.1%) missing valuesMissing
조건부허가신고사유 has 4470 (> 99.9%) missing valuesMissing
여성종사자수 has 3607 (80.7%) missing valuesMissing
침대수 has 2851 (63.8%) missing valuesMissing
다중이용업소여부 has 871 (19.5%) missing valuesMissing
건물지하층수 is highly skewed (γ1 = 51.06148473)Skewed
사용시작지상층 is highly skewed (γ1 = 37.47870614)Skewed
사용끝지상층 is highly skewed (γ1 = 47.37694721)Skewed
좌석수 is highly skewed (γ1 = 30.32857856)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
소재지면적 has 119 (2.7%) zerosZeros
건물지상층수 has 1515 (33.9%) zerosZeros
건물지하층수 has 1785 (39.9%) zerosZeros
사용시작지상층 has 644 (14.4%) zerosZeros
사용끝지상층 has 110 (2.5%) zerosZeros
좌석수 has 91 (2.0%) zerosZeros
여성종사자수 has 833 (18.6%) zerosZeros
침대수 has 1394 (31.2%) zerosZeros

Reproduction

Analysis started2024-05-11 06:34:29.749594
Analysis finished2024-05-11 06:34:32.265979
Duration2.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
3200000
4471 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 4471
100.0%

Length

2024-05-11T15:34:32.371069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:32.547264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 4471
100.0%

관리번호
Text

UNIQUE 

Distinct4471
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
2024-05-11T15:34:32.926336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters98362
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

Unique4471 ?
Unique (%)100.0%

Sample

1st row3200000-204-1969-01713
2nd row3200000-204-1969-01727
3rd row3200000-204-1970-01690
4th row3200000-204-1970-01705
5th row3200000-204-1971-01709
ValueCountFrequency (%)
3200000-204-1969-01713 1
 
< 0.1%
3200000-211-2018-00013 1
 
< 0.1%
3200000-211-2018-00027 1
 
< 0.1%
3200000-211-2018-00026 1
 
< 0.1%
3200000-211-2018-00025 1
 
< 0.1%
3200000-211-2018-00024 1
 
< 0.1%
3200000-211-2018-00023 1
 
< 0.1%
3200000-211-2018-00022 1
 
< 0.1%
3200000-211-2018-00020 1
 
< 0.1%
3200000-211-2018-00047 1
 
< 0.1%
Other values (4461) 4461
99.8%
2024-05-11T15:34:33.466564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41779
42.5%
2 15455
 
15.7%
- 13413
 
13.6%
1 9394
 
9.6%
3 6294
 
6.4%
9 3215
 
3.3%
4 3188
 
3.2%
8 1503
 
1.5%
5 1502
 
1.5%
6 1344
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84949
86.4%
Dash Punctuation 13413
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41779
49.2%
2 15455
 
18.2%
1 9394
 
11.1%
3 6294
 
7.4%
9 3215
 
3.8%
4 3188
 
3.8%
8 1503
 
1.8%
5 1502
 
1.8%
6 1344
 
1.6%
7 1275
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 13413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98362
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41779
42.5%
2 15455
 
15.7%
- 13413
 
13.6%
1 9394
 
9.6%
3 6294
 
6.4%
9 3215
 
3.3%
4 3188
 
3.2%
8 1503
 
1.5%
5 1502
 
1.5%
6 1344
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41779
42.5%
2 15455
 
15.7%
- 13413
 
13.6%
1 9394
 
9.6%
3 6294
 
6.4%
9 3215
 
3.3%
4 3188
 
3.2%
8 1503
 
1.5%
5 1502
 
1.5%
6 1344
 
1.4%
Distinct3285
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
Minimum1968-10-14 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T15:34:33.742664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:34:33.989183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4471
Missing (%)100.0%
Memory size39.4 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
3
3016 
1
1455 

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 3016
67.5%
1 1455
32.5%

Length

2024-05-11T15:34:34.299102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:34.438431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3016
67.5%
1 1455
32.5%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
폐업
3016 
영업/정상
1455 

Length

Max length5
Median length2
Mean length2.9762917
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 3016
67.5%
영업/정상 1455
32.5%

Length

2024-05-11T15:34:34.633807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:34.795631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3016
67.5%
영업/정상 1455
32.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
2
3016 
1
1455 

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 3016
67.5%
1 1455
32.5%

Length

2024-05-11T15:34:34.991752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:35.195638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3016
67.5%
1 1455
32.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
폐업
3016 
영업
1455 

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 3016
67.5%
영업 1455
32.5%

Length

2024-05-11T15:34:35.372388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:35.567692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 3016
67.5%
영업 1455
32.5%

폐업일자
Text

MISSING 

Distinct2144
Distinct (%)71.1%
Missing1455
Missing (%)32.5%
Memory size35.1 KiB
2024-05-11T15:34:36.066230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.1067639
Min length8

Characters and Unicode

Total characters24450
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

Unique1699 ?
Unique (%)56.3%

Sample

1st row20030326
2nd row20030407
3rd row19981001
4th row19971030
5th row20030326
ValueCountFrequency (%)
20030407 92
 
3.1%
20030326 85
 
2.8%
20030430 66
 
2.2%
20030411 31
 
1.0%
20180205 16
 
0.5%
20180323 14
 
0.5%
20170102 11
 
0.4%
19991013 8
 
0.3%
2023-04-17 7
 
0.2%
20180413 7
 
0.2%
Other values (2134) 2679
88.8%
2024-05-11T15:34:36.909490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7602
31.1%
2 5139
21.0%
1 4108
16.8%
9 1604
 
6.6%
3 1461
 
6.0%
4 1073
 
4.4%
7 840
 
3.4%
6 803
 
3.3%
8 769
 
3.1%
5 727
 
3.0%
Other values (2) 324
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24126
98.7%
Dash Punctuation 322
 
1.3%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7602
31.5%
2 5139
21.3%
1 4108
17.0%
9 1604
 
6.6%
3 1461
 
6.1%
4 1073
 
4.4%
7 840
 
3.5%
6 803
 
3.3%
8 769
 
3.2%
5 727
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 322
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7602
31.1%
2 5139
21.0%
1 4108
16.8%
9 1604
 
6.6%
3 1461
 
6.0%
4 1073
 
4.4%
7 840
 
3.4%
6 803
 
3.3%
8 769
 
3.1%
5 727
 
3.0%
Other values (2) 324
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7602
31.1%
2 5139
21.0%
1 4108
16.8%
9 1604
 
6.6%
3 1461
 
6.0%
4 1073
 
4.4%
7 840
 
3.4%
6 803
 
3.3%
8 769
 
3.1%
5 727
 
3.0%
Other values (2) 324
 
1.3%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4471
Missing (%)100.0%
Memory size39.4 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4471
Missing (%)100.0%
Memory size39.4 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4471
Missing (%)100.0%
Memory size39.4 KiB

전화번호
Text

MISSING 

Distinct2369
Distinct (%)89.3%
Missing1817
Missing (%)40.6%
Memory size35.1 KiB
2024-05-11T15:34:37.537935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.201583
Min length2

Characters and Unicode

Total characters27075
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

Unique2158 ?
Unique (%)81.3%

Sample

1st row02 8786256
2nd row02 8787806
3rd row02 8827674
4th row02 8788732
5th row0208892581
ValueCountFrequency (%)
02 2189
42.5%
877 33
 
0.6%
070 22
 
0.4%
876 20
 
0.4%
00000 19
 
0.4%
0 18
 
0.3%
883 16
 
0.3%
0200000000 15
 
0.3%
882 15
 
0.3%
871 9
 
0.2%
Other values (2425) 2796
54.3%
2024-05-11T15:34:38.639409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 4633
17.1%
0 4504
16.6%
2 4082
15.1%
2864
10.6%
7 2313
8.5%
5 1721
 
6.4%
6 1629
 
6.0%
3 1628
 
6.0%
1 1289
 
4.8%
4 1266
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24211
89.4%
Space Separator 2864
 
10.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 4633
19.1%
0 4504
18.6%
2 4082
16.9%
7 2313
9.6%
5 1721
 
7.1%
6 1629
 
6.7%
3 1628
 
6.7%
1 1289
 
5.3%
4 1266
 
5.2%
9 1146
 
4.7%
Space Separator
ValueCountFrequency (%)
2864
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27075
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 4633
17.1%
0 4504
16.6%
2 4082
15.1%
2864
10.6%
7 2313
8.5%
5 1721
 
6.4%
6 1629
 
6.0%
3 1628
 
6.0%
1 1289
 
4.8%
4 1266
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 4633
17.1%
0 4504
16.6%
2 4082
15.1%
2864
10.6%
7 2313
8.5%
5 1721
 
6.4%
6 1629
 
6.0%
3 1628
 
6.0%
1 1289
 
4.8%
4 1266
 
4.7%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1724
Distinct (%)38.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38.895596
Minimum0
Maximum495.98
Zeros119
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:38.943507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.554
Q118.5
median26.44
Q345
95-th percentile112.586
Maximum495.98
Range495.98
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation36.521971
Coefficient of variation (CV)0.93897445
Kurtosis17.507489
Mean38.895596
Median Absolute Deviation (MAD)10.2
Skewness3.2191687
Sum173824.42
Variance1333.8544
MonotonicityNot monotonic
2024-05-11T15:34:39.289727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 203
 
4.5%
0.0 119
 
2.7%
26.4 118
 
2.6%
23.1 94
 
2.1%
30.0 84
 
1.9%
19.8 66
 
1.5%
20.0 58
 
1.3%
24.0 55
 
1.2%
16.5 51
 
1.1%
26.0 51
 
1.1%
Other values (1714) 3570
79.8%
ValueCountFrequency (%)
0.0 119
2.7%
1.36 1
 
< 0.1%
2.0 1
 
< 0.1%
3.0 2
 
< 0.1%
3.3 1
 
< 0.1%
4.31 1
 
< 0.1%
4.95 1
 
< 0.1%
5.0 3
 
0.1%
5.81 1
 
< 0.1%
6.0 2
 
< 0.1%
ValueCountFrequency (%)
495.98 1
< 0.1%
382.8 1
< 0.1%
374.0 1
< 0.1%
348.96 1
< 0.1%
335.12 1
< 0.1%
323.4 1
< 0.1%
308.93 1
< 0.1%
290.0 1
< 0.1%
287.43 1
< 0.1%
257.85 1
< 0.1%
Distinct213
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
2024-05-11T15:34:39.961005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1359875
Min length6

Characters and Unicode

Total characters27434
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

Unique26 ?
Unique (%)0.6%

Sample

1st row151050
2nd row151050
3rd row151859
4th row151050
5th row151853
ValueCountFrequency (%)
151050 114
 
2.5%
151832 113
 
2.5%
151890 105
 
2.3%
151843 94
 
2.1%
151872 90
 
2.0%
151930 88
 
2.0%
151892 88
 
2.0%
151015 87
 
1.9%
151895 78
 
1.7%
151844 75
 
1.7%
Other values (203) 3539
79.2%
2024-05-11T15:34:41.089783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9835
35.8%
5 5297
19.3%
8 4370
15.9%
0 1639
 
6.0%
9 1444
 
5.3%
3 1182
 
4.3%
4 941
 
3.4%
2 876
 
3.2%
7 678
 
2.5%
- 608
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26826
97.8%
Dash Punctuation 608
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9835
36.7%
5 5297
19.7%
8 4370
16.3%
0 1639
 
6.1%
9 1444
 
5.4%
3 1182
 
4.4%
4 941
 
3.5%
2 876
 
3.3%
7 678
 
2.5%
6 564
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 608
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27434
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9835
35.8%
5 5297
19.3%
8 4370
15.9%
0 1639
 
6.0%
9 1444
 
5.3%
3 1182
 
4.3%
4 941
 
3.4%
2 876
 
3.2%
7 678
 
2.5%
- 608
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27434
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9835
35.8%
5 5297
19.3%
8 4370
15.9%
0 1639
 
6.0%
9 1444
 
5.3%
3 1182
 
4.3%
4 941
 
3.4%
2 876
 
3.2%
7 678
 
2.5%
- 608
 
2.2%
Distinct3594
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
2024-05-11T15:34:41.552984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length48
Mean length24.308208
Min length17

Characters and Unicode

Total characters108682
Distinct characters292
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2959 ?
Unique (%)66.2%

Sample

1st row서울특별시 관악구 봉천동 94-1번지
2nd row서울특별시 관악구 봉천동 89-0번지
3rd row서울특별시 관악구 신림동 246-32번지
4th row서울특별시 관악구 봉천동 81-0번지
5th row서울특별시 관악구 신림동 81-36번지
ValueCountFrequency (%)
서울특별시 4471
22.4%
관악구 4471
22.4%
신림동 2310
11.6%
봉천동 2027
 
10.1%
1층 451
 
2.3%
2층 213
 
1.1%
남현동 134
 
0.7%
3층 80
 
0.4%
101호 44
 
0.2%
지상1층 37
 
0.2%
Other values (3419) 5743
28.7%
2024-05-11T15:34:42.398196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18934
 
17.4%
1 5974
 
5.5%
4634
 
4.3%
4540
 
4.2%
4534
 
4.2%
4487
 
4.1%
4483
 
4.1%
4482
 
4.1%
4477
 
4.1%
4471
 
4.1%
Other values (282) 47666
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 60387
55.6%
Decimal Number 24579
22.6%
Space Separator 18934
 
17.4%
Dash Punctuation 4330
 
4.0%
Uppercase Letter 153
 
0.1%
Close Punctuation 114
 
0.1%
Open Punctuation 114
 
0.1%
Other Punctuation 66
 
0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4634
 
7.7%
4540
 
7.5%
4534
 
7.5%
4487
 
7.4%
4483
 
7.4%
4482
 
7.4%
4477
 
7.4%
4471
 
7.4%
4471
 
7.4%
3212
 
5.3%
Other values (241) 16596
27.5%
Uppercase Letter
ValueCountFrequency (%)
B 34
22.2%
A 24
15.7%
S 20
13.1%
K 12
 
7.8%
T 11
 
7.2%
G 10
 
6.5%
E 8
 
5.2%
M 4
 
2.6%
D 4
 
2.6%
F 3
 
2.0%
Other values (12) 23
15.0%
Decimal Number
ValueCountFrequency (%)
1 5974
24.3%
2 2838
11.5%
6 2555
10.4%
4 2348
 
9.6%
5 2255
 
9.2%
3 2141
 
8.7%
0 1795
 
7.3%
8 1620
 
6.6%
7 1532
 
6.2%
9 1521
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 58
87.9%
. 4
 
6.1%
/ 4
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
80.0%
b 1
 
20.0%
Space Separator
ValueCountFrequency (%)
18934
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4330
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 60387
55.6%
Common 48137
44.3%
Latin 158
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4634
 
7.7%
4540
 
7.5%
4534
 
7.5%
4487
 
7.4%
4483
 
7.4%
4482
 
7.4%
4477
 
7.4%
4471
 
7.4%
4471
 
7.4%
3212
 
5.3%
Other values (241) 16596
27.5%
Latin
ValueCountFrequency (%)
B 34
21.5%
A 24
15.2%
S 20
12.7%
K 12
 
7.6%
T 11
 
7.0%
G 10
 
6.3%
E 8
 
5.1%
M 4
 
2.5%
D 4
 
2.5%
e 4
 
2.5%
Other values (14) 27
17.1%
Common
ValueCountFrequency (%)
18934
39.3%
1 5974
 
12.4%
- 4330
 
9.0%
2 2838
 
5.9%
6 2555
 
5.3%
4 2348
 
4.9%
5 2255
 
4.7%
3 2141
 
4.4%
0 1795
 
3.7%
8 1620
 
3.4%
Other values (7) 3347
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 60386
55.6%
ASCII 48295
44.4%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18934
39.2%
1 5974
 
12.4%
- 4330
 
9.0%
2 2838
 
5.9%
6 2555
 
5.3%
4 2348
 
4.9%
5 2255
 
4.7%
3 2141
 
4.4%
0 1795
 
3.7%
8 1620
 
3.4%
Other values (31) 3505
 
7.3%
Hangul
ValueCountFrequency (%)
4634
 
7.7%
4540
 
7.5%
4534
 
7.5%
4487
 
7.4%
4483
 
7.4%
4482
 
7.4%
4477
 
7.4%
4471
 
7.4%
4471
 
7.4%
3212
 
5.3%
Other values (240) 16595
27.5%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct2386
Distinct (%)87.9%
Missing1756
Missing (%)39.3%
Memory size35.1 KiB
2024-05-11T15:34:42.967364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length49
Mean length30.299448
Min length21

Characters and Unicode

Total characters82263
Distinct characters309
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2127 ?
Unique (%)78.3%

Sample

1st row서울특별시 관악구 중앙길 5 (봉천동)
2nd row서울특별시 관악구 신원로 23 (신림동)
3rd row서울특별시 관악구 양지2길 24 (신림동)
4th row서울특별시 관악구 청룡1길 7 (봉천동)
5th row서울특별시 관악구 청림2길 2 (봉천동)
ValueCountFrequency (%)
서울특별시 2715
16.2%
관악구 2715
16.2%
신림동 1296
 
7.7%
봉천동 1184
 
7.1%
1층 904
 
5.4%
2층 425
 
2.5%
남부순환로 303
 
1.8%
3층 193
 
1.2%
신림로 135
 
0.8%
봉천로 132
 
0.8%
Other values (1549) 6760
40.3%
2024-05-11T15:34:43.726148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14054
 
17.1%
1 3779
 
4.6%
3037
 
3.7%
2981
 
3.6%
2909
 
3.5%
( 2766
 
3.4%
) 2766
 
3.4%
2758
 
3.4%
2739
 
3.3%
2728
 
3.3%
Other values (299) 41746
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46587
56.6%
Space Separator 14054
 
17.1%
Decimal Number 13260
 
16.1%
Open Punctuation 2766
 
3.4%
Close Punctuation 2766
 
3.4%
Other Punctuation 2353
 
2.9%
Dash Punctuation 276
 
0.3%
Uppercase Letter 194
 
0.2%
Lowercase Letter 6
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3037
 
6.5%
2981
 
6.4%
2909
 
6.2%
2758
 
5.9%
2739
 
5.9%
2728
 
5.9%
2725
 
5.8%
2715
 
5.8%
2715
 
5.8%
2200
 
4.7%
Other values (256) 19080
41.0%
Uppercase Letter
ValueCountFrequency (%)
B 56
28.9%
S 20
 
10.3%
A 14
 
7.2%
L 12
 
6.2%
E 12
 
6.2%
K 12
 
6.2%
T 11
 
5.7%
G 10
 
5.2%
R 9
 
4.6%
J 6
 
3.1%
Other values (13) 32
16.5%
Decimal Number
ValueCountFrequency (%)
1 3779
28.5%
2 2180
16.4%
3 1459
 
11.0%
0 1277
 
9.6%
4 1008
 
7.6%
6 887
 
6.7%
5 855
 
6.4%
8 617
 
4.7%
9 606
 
4.6%
7 592
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 2346
99.7%
. 5
 
0.2%
/ 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
66.7%
b 2
33.3%
Space Separator
ValueCountFrequency (%)
14054
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2766
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2766
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46587
56.6%
Common 35476
43.1%
Latin 200
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3037
 
6.5%
2981
 
6.4%
2909
 
6.2%
2758
 
5.9%
2739
 
5.9%
2728
 
5.9%
2725
 
5.8%
2715
 
5.8%
2715
 
5.8%
2200
 
4.7%
Other values (256) 19080
41.0%
Latin
ValueCountFrequency (%)
B 56
28.0%
S 20
 
10.0%
A 14
 
7.0%
L 12
 
6.0%
E 12
 
6.0%
K 12
 
6.0%
T 11
 
5.5%
G 10
 
5.0%
R 9
 
4.5%
J 6
 
3.0%
Other values (15) 38
19.0%
Common
ValueCountFrequency (%)
14054
39.6%
1 3779
 
10.7%
( 2766
 
7.8%
) 2766
 
7.8%
, 2346
 
6.6%
2 2180
 
6.1%
3 1459
 
4.1%
0 1277
 
3.6%
4 1008
 
2.8%
6 887
 
2.5%
Other values (8) 2954
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46587
56.6%
ASCII 35676
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14054
39.4%
1 3779
 
10.6%
( 2766
 
7.8%
) 2766
 
7.8%
, 2346
 
6.6%
2 2180
 
6.1%
3 1459
 
4.1%
0 1277
 
3.6%
4 1008
 
2.8%
6 887
 
2.5%
Other values (33) 3154
 
8.8%
Hangul
ValueCountFrequency (%)
3037
 
6.5%
2981
 
6.4%
2909
 
6.2%
2758
 
5.9%
2739
 
5.9%
2728
 
5.9%
2725
 
5.8%
2715
 
5.8%
2715
 
5.8%
2200
 
4.7%
Other values (256) 19080
41.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct151
Distinct (%)5.6%
Missing1768
Missing (%)39.5%
Infinite0
Infinite (%)0.0%
Mean8772.7429
Minimum8700
Maximum8866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:44.051191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8700
5-th percentile8705
Q18742
median8771
Q38793
95-th percentile8854
Maximum8866
Range166
Interquartile range (IQR)51

Descriptive statistics

Standard deviation42.904453
Coefficient of variation (CV)0.0048906544
Kurtosis-0.51426422
Mean8772.7429
Median Absolute Deviation (MAD)26
Skewness0.345394
Sum23712724
Variance1840.7921
MonotonicityNot monotonic
2024-05-11T15:34:44.394659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8793 102
 
2.3%
8754 99
 
2.2%
8774 73
 
1.6%
8788 70
 
1.6%
8776 61
 
1.4%
8762 60
 
1.3%
8737 52
 
1.2%
8702 51
 
1.1%
8787 45
 
1.0%
8701 44
 
1.0%
Other values (141) 2046
45.8%
(Missing) 1768
39.5%
ValueCountFrequency (%)
8700 8
 
0.2%
8701 44
1.0%
8702 51
1.1%
8703 8
 
0.2%
8704 6
 
0.1%
8705 30
0.7%
8706 14
 
0.3%
8707 37
0.8%
8708 34
0.8%
8709 10
 
0.2%
ValueCountFrequency (%)
8866 2
 
< 0.1%
8865 9
 
0.2%
8864 36
0.8%
8863 5
 
0.1%
8862 4
 
0.1%
8861 8
 
0.2%
8860 10
 
0.2%
8859 14
 
0.3%
8858 22
0.5%
8857 12
 
0.3%
Distinct3738
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
2024-05-11T15:34:45.168094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length5.6099307
Min length1

Characters and Unicode

Total characters25082
Distinct characters773
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

Unique3293 ?
Unique (%)73.7%

Sample

1st row봉천
2nd row호산나
3rd row태양
4th row
5th row
ValueCountFrequency (%)
헤어 180
 
3.1%
hair 73
 
1.3%
미용실 65
 
1.1%
네일 55
 
1.0%
에스테틱 39
 
0.7%
헤어살롱 21
 
0.4%
스킨케어 21
 
0.4%
헤어샵 20
 
0.3%
뷰티 19
 
0.3%
nail 18
 
0.3%
Other values (3879) 5263
91.1%
2024-05-11T15:34:45.936218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1532
 
6.1%
1471
 
5.9%
1310
 
5.2%
839
 
3.3%
596
 
2.4%
577
 
2.3%
556
 
2.2%
520
 
2.1%
509
 
2.0%
319
 
1.3%
Other values (763) 16853
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20730
82.6%
Space Separator 1310
 
5.2%
Uppercase Letter 1129
 
4.5%
Lowercase Letter 1086
 
4.3%
Other Punctuation 264
 
1.1%
Decimal Number 219
 
0.9%
Close Punctuation 163
 
0.6%
Open Punctuation 163
 
0.6%
Dash Punctuation 12
 
< 0.1%
Math Symbol 3
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1532
 
7.4%
1471
 
7.1%
839
 
4.0%
596
 
2.9%
577
 
2.8%
556
 
2.7%
520
 
2.5%
509
 
2.5%
319
 
1.5%
308
 
1.5%
Other values (683) 13503
65.1%
Lowercase Letter
ValueCountFrequency (%)
a 168
15.5%
i 135
12.4%
e 87
 
8.0%
r 86
 
7.9%
n 84
 
7.7%
o 74
 
6.8%
l 72
 
6.6%
h 52
 
4.8%
y 50
 
4.6%
t 43
 
4.0%
Other values (16) 235
21.6%
Uppercase Letter
ValueCountFrequency (%)
A 109
 
9.7%
H 98
 
8.7%
I 91
 
8.1%
S 78
 
6.9%
N 72
 
6.4%
O 65
 
5.8%
R 63
 
5.6%
J 62
 
5.5%
M 62
 
5.5%
E 59
 
5.2%
Other values (16) 370
32.8%
Decimal Number
ValueCountFrequency (%)
0 67
30.6%
2 41
18.7%
1 30
13.7%
3 27
12.3%
5 16
 
7.3%
4 12
 
5.5%
8 9
 
4.1%
9 8
 
3.7%
7 6
 
2.7%
6 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 77
29.2%
? 64
24.2%
& 51
19.3%
, 26
 
9.8%
# 23
 
8.7%
: 10
 
3.8%
' 8
 
3.0%
/ 4
 
1.5%
! 1
 
0.4%
Close Punctuation
ValueCountFrequency (%)
) 162
99.4%
] 1
 
0.6%
Open Punctuation
ValueCountFrequency (%)
( 162
99.4%
[ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
= 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20717
82.6%
Latin 2215
 
8.8%
Common 2137
 
8.5%
Han 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1532
 
7.4%
1471
 
7.1%
839
 
4.0%
596
 
2.9%
577
 
2.8%
556
 
2.7%
520
 
2.5%
509
 
2.5%
319
 
1.5%
308
 
1.5%
Other values (680) 13490
65.1%
Latin
ValueCountFrequency (%)
a 168
 
7.6%
i 135
 
6.1%
A 109
 
4.9%
H 98
 
4.4%
I 91
 
4.1%
e 87
 
3.9%
r 86
 
3.9%
n 84
 
3.8%
S 78
 
3.5%
o 74
 
3.3%
Other values (42) 1205
54.4%
Common
ValueCountFrequency (%)
1310
61.3%
) 162
 
7.6%
( 162
 
7.6%
. 77
 
3.6%
0 67
 
3.1%
? 64
 
3.0%
& 51
 
2.4%
2 41
 
1.9%
1 30
 
1.4%
3 27
 
1.3%
Other values (18) 146
 
6.8%
Han
ValueCountFrequency (%)
11
84.6%
1
 
7.7%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20708
82.6%
ASCII 4352
 
17.4%
CJK 13
 
0.1%
Compat Jamo 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1532
 
7.4%
1471
 
7.1%
839
 
4.1%
596
 
2.9%
577
 
2.8%
556
 
2.7%
520
 
2.5%
509
 
2.5%
319
 
1.5%
308
 
1.5%
Other values (679) 13481
65.1%
ASCII
ValueCountFrequency (%)
1310
30.1%
a 168
 
3.9%
) 162
 
3.7%
( 162
 
3.7%
i 135
 
3.1%
A 109
 
2.5%
H 98
 
2.3%
I 91
 
2.1%
e 87
 
2.0%
r 86
 
2.0%
Other values (70) 1944
44.7%
CJK
ValueCountFrequency (%)
11
84.6%
1
 
7.7%
1
 
7.7%
Compat Jamo
ValueCountFrequency (%)
9
100.0%
Distinct3349
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
Minimum1999-01-05 00:00:00
Maximum2024-05-08 17:05:45
2024-05-11T15:34:46.259312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:34:46.555531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
I
3120 
U
1322 
D
 
29

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 3120
69.8%
U 1322
29.6%
D 29
 
0.6%

Length

2024-05-11T15:34:46.923001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:47.159595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 3120
69.8%
u 1322
29.6%
d 29
 
0.6%
Distinct904
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:03:00
2024-05-11T15:34:47.420329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:34:47.646038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
일반미용업
3446 
피부미용업
535 
네일아트업
355 
메이크업업
 
88
기타
 
47

Length

Max length5
Median length5
Mean length4.9684634
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 3446
77.1%
피부미용업 535
 
12.0%
네일아트업 355
 
7.9%
메이크업업 88
 
2.0%
기타 47
 
1.1%

Length

2024-05-11T15:34:47.985210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:48.215431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 3446
77.1%
피부미용업 535
 
12.0%
네일아트업 355
 
7.9%
메이크업업 88
 
2.0%
기타 47
 
1.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2321
Distinct (%)53.1%
Missing97
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean194411.48
Minimum191161.85
Maximum198374.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:48.476935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191161.85
5-th percentile192158.55
Q1193286.87
median194287.02
Q3195725.43
95-th percentile196957.06
Maximum198374.47
Range7212.6238
Interquartile range (IQR)2438.5687

Descriptive statistics

Standard deviation1589.35
Coefficient of variation (CV)0.0081751856
Kurtosis-0.67317344
Mean194411.48
Median Absolute Deviation (MAD)1302.4168
Skewness0.23602838
Sum8.5035583 × 108
Variance2526033.3
MonotonicityNot monotonic
2024-05-11T15:34:48.801132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193735.829747067 28
 
0.6%
198284.078546351 27
 
0.6%
195563.161650411 21
 
0.5%
196257.833652996 20
 
0.4%
195888.192100225 19
 
0.4%
195045.515107949 16
 
0.4%
195703.043681259 13
 
0.3%
196384.544308046 13
 
0.3%
195635.738009117 12
 
0.3%
193578.742465284 12
 
0.3%
Other values (2311) 4193
93.8%
(Missing) 97
 
2.2%
ValueCountFrequency (%)
191161.849432287 1
 
< 0.1%
191204.884621244 1
 
< 0.1%
191206.518253355 1
 
< 0.1%
191210.00657717 2
 
< 0.1%
191215.879312952 3
0.1%
191217.026445816 1
 
< 0.1%
191222.075430617 1
 
< 0.1%
191224.178786196 7
0.2%
191228.411082685 1
 
< 0.1%
191237.262245059 3
0.1%
ValueCountFrequency (%)
198374.473281221 3
 
0.1%
198316.361984331 1
 
< 0.1%
198315.174254041 2
 
< 0.1%
198305.07819874 1
 
< 0.1%
198297.240031378 1
 
< 0.1%
198288.009848713 2
 
< 0.1%
198284.078546351 27
0.6%
198282.196473514 5
 
0.1%
198278.931088754 1
 
< 0.1%
198278.438025281 3
 
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2321
Distinct (%)53.1%
Missing97
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean442040.15
Minimum439023.17
Maximum443547.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:49.195767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439023.17
5-th percentile440718.8
Q1441542.57
median442183.47
Q3442563.75
95-th percentile443018.32
Maximum443547.05
Range4523.8826
Interquartile range (IQR)1021.1805

Descriptive statistics

Standard deviation718.02214
Coefficient of variation (CV)0.0016243369
Kurtosis0.12903282
Mean442040.15
Median Absolute Deviation (MAD)467.33021
Skewness-0.70027331
Sum1.9334836 × 109
Variance515555.79
MonotonicityNot monotonic
2024-05-11T15:34:49.511855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442552.922888015 28
 
0.6%
441368.909286824 27
 
0.6%
443064.437566493 21
 
0.5%
443341.379446435 20
 
0.4%
442019.999681675 19
 
0.4%
443011.455007816 16
 
0.4%
442094.085759338 13
 
0.3%
442822.980265735 13
 
0.3%
442172.892032644 12
 
0.3%
443547.049696825 12
 
0.3%
Other values (2311) 4193
93.8%
(Missing) 97
 
2.2%
ValueCountFrequency (%)
439023.167125842 2
 
< 0.1%
439787.715563055 5
0.1%
439809.669640911 1
 
< 0.1%
439816.999224208 6
0.1%
439834.740321124 1
 
< 0.1%
439843.900681742 1
 
< 0.1%
439852.868058712 3
0.1%
439853.488919065 1
 
< 0.1%
439855.187608334 1
 
< 0.1%
439885.315238411 3
0.1%
ValueCountFrequency (%)
443547.049696825 12
0.3%
443513.655954844 1
 
< 0.1%
443437.692580028 1
 
< 0.1%
443362.75555511 3
 
0.1%
443347.669199144 2
 
< 0.1%
443341.379446435 20
0.4%
443335.812378395 2
 
< 0.1%
443331.236806488 1
 
< 0.1%
443315.864044162 1
 
< 0.1%
443314.941516933 1
 
< 0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
미용업
1787 
일반미용업
1152 
<NA>
871 
피부미용업
307 
네일미용업
 
116
Other values (11)
238 

Length

Max length23
Median length16
Mean length4.3413107
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1787
40.0%
일반미용업 1152
25.8%
<NA> 871
19.5%
피부미용업 307
 
6.9%
네일미용업 116
 
2.6%
종합미용업 86
 
1.9%
피부미용업, 네일미용업 35
 
0.8%
일반미용업, 네일미용업 26
 
0.6%
네일미용업, 화장ㆍ분장 미용업 22
 
0.5%
화장ㆍ분장 미용업 16
 
0.4%
Other values (6) 53
 
1.2%

Length

2024-05-11T15:34:49.809551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1875
39.7%
일반미용업 1204
25.5%
na 871
18.4%
피부미용업 375
 
7.9%
네일미용업 224
 
4.7%
화장ㆍ분장 88
 
1.9%
종합미용업 86
 
1.8%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)0.8%
Missing1253
Missing (%)28.0%
Infinite0
Infinite (%)0.0%
Mean1.9838409
Minimum0
Maximum42
Zeros1515
Zeros (%)33.9%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:50.051580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum42
Range42
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7227703
Coefficient of variation (CV)1.3724741
Kurtosis31.663444
Mean1.9838409
Median Absolute Deviation (MAD)2
Skewness3.7370916
Sum6384
Variance7.4134783
MonotonicityNot monotonic
2024-05-11T15:34:50.362953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 1515
33.9%
3 486
 
10.9%
2 436
 
9.8%
4 386
 
8.6%
5 169
 
3.8%
1 58
 
1.3%
6 52
 
1.2%
7 34
 
0.8%
15 18
 
0.4%
8 18
 
0.4%
Other values (15) 46
 
1.0%
(Missing) 1253
28.0%
ValueCountFrequency (%)
0 1515
33.9%
1 58
 
1.3%
2 436
 
9.8%
3 486
 
10.9%
4 386
 
8.6%
5 169
 
3.8%
6 52
 
1.2%
7 34
 
0.8%
8 18
 
0.4%
9 9
 
0.2%
ValueCountFrequency (%)
42 1
 
< 0.1%
36 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
15 18
0.4%

건물지하층수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct10
Distinct (%)0.3%
Missing1537
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean0.52931152
Minimum0
Maximum202
Zeros1785
Zeros (%)39.9%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:50.575925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum202
Range202
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.7953522
Coefficient of variation (CV)7.1703562
Kurtosis2709.8251
Mean0.52931152
Median Absolute Deviation (MAD)0
Skewness51.061485
Sum1553
Variance14.404698
MonotonicityNot monotonic
2024-05-11T15:34:50.794075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 1785
39.9%
1 1058
23.7%
2 46
 
1.0%
4 14
 
0.3%
3 13
 
0.3%
5 7
 
0.2%
7 5
 
0.1%
8 3
 
0.1%
6 2
 
< 0.1%
202 1
 
< 0.1%
(Missing) 1537
34.4%
ValueCountFrequency (%)
0 1785
39.9%
1 1058
23.7%
2 46
 
1.0%
3 13
 
0.3%
4 14
 
0.3%
5 7
 
0.2%
6 2
 
< 0.1%
7 5
 
0.1%
8 3
 
0.1%
202 1
 
< 0.1%
ValueCountFrequency (%)
202 1
 
< 0.1%
8 3
 
0.1%
7 5
 
0.1%
6 2
 
< 0.1%
5 7
 
0.2%
4 14
 
0.3%
3 13
 
0.3%
2 46
 
1.0%
1 1058
23.7%
0 1785
39.9%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)0.4%
Missing1378
Missing (%)30.8%
Infinite0
Infinite (%)0.0%
Mean1.199806
Minimum0
Maximum109
Zeros644
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:51.035968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum109
Range109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.2185359
Coefficient of variation (CV)1.8490788
Kurtosis1804.756
Mean1.199806
Median Absolute Deviation (MAD)0
Skewness37.478706
Sum3711
Variance4.9219016
MonotonicityNot monotonic
2024-05-11T15:34:51.341148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1736
38.8%
0 644
 
14.4%
2 466
 
10.4%
3 162
 
3.6%
4 38
 
0.8%
5 23
 
0.5%
6 9
 
0.2%
7 5
 
0.1%
13 3
 
0.1%
8 3
 
0.1%
Other values (3) 4
 
0.1%
(Missing) 1378
30.8%
ValueCountFrequency (%)
0 644
 
14.4%
1 1736
38.8%
2 466
 
10.4%
3 162
 
3.6%
4 38
 
0.8%
5 23
 
0.5%
6 9
 
0.2%
7 5
 
0.1%
8 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
109 1
 
< 0.1%
13 3
 
0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
8 3
 
0.1%
7 5
 
0.1%
6 9
 
0.2%
5 23
 
0.5%
4 38
 
0.8%
3 162
3.6%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)0.5%
Missing2050
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean1.716233
Minimum0
Maximum706
Zeros110
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:51.614015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum706
Range706
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.518442
Coefficient of variation (CV)8.4594818
Kurtosis2292.1195
Mean1.716233
Median Absolute Deviation (MAD)0
Skewness47.376947
Sum4155
Variance210.78515
MonotonicityNot monotonic
2024-05-11T15:34:51.881417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1650
36.9%
2 439
 
9.8%
3 152
 
3.4%
0 110
 
2.5%
4 32
 
0.7%
5 20
 
0.4%
6 6
 
0.1%
7 4
 
0.1%
13 3
 
0.1%
8 2
 
< 0.1%
Other values (3) 3
 
0.1%
(Missing) 2050
45.9%
ValueCountFrequency (%)
0 110
 
2.5%
1 1650
36.9%
2 439
 
9.8%
3 152
 
3.4%
4 32
 
0.7%
5 20
 
0.4%
6 6
 
0.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
706 1
 
< 0.1%
109 1
 
< 0.1%
13 3
 
0.1%
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
0.1%
6 6
 
0.1%
5 20
 
0.4%
4 32
 
0.7%
3 152
3.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
3321 
0
1047 
1
 
93
2
 
9
3
 
1

Length

Max length4
Median length4
Mean length3.2283605
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3321
74.3%
0 1047
 
23.4%
1 93
 
2.1%
2 9
 
0.2%
3 1
 
< 0.1%

Length

2024-05-11T15:34:52.198498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:52.431426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3321
74.3%
0 1047
 
23.4%
1 93
 
2.1%
2 9
 
0.2%
3 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
3922 
0
454 
1
 
85
2
 
8
102
 
1

Length

Max length4
Median length4
Mean length3.6320734
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> 3922
87.7%
0 454
 
10.2%
1 85
 
1.9%
2 8
 
0.2%
102 1
 
< 0.1%
3 1
 
< 0.1%

Length

2024-05-11T15:34:52.656485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:52.904498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3922
87.7%
0 454
 
10.2%
1 85
 
1.9%
2 8
 
0.2%
102 1
 
< 0.1%
3 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
0
2339 
<NA>
2132 

Length

Max length4
Median length1
Mean length2.4305524
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2339
52.3%
<NA> 2132
47.7%

Length

2024-05-11T15:34:53.239928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:53.454342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2339
52.3%
na 2132
47.7%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
0
2339 
<NA>
2132 

Length

Max length4
Median length1
Mean length2.4305524
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2339
52.3%
<NA> 2132
47.7%

Length

2024-05-11T15:34:53.667667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:53.957360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2339
52.3%
na 2132
47.7%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
0
2339 
<NA>
2132 

Length

Max length4
Median length1
Mean length2.4305524
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2339
52.3%
<NA> 2132
47.7%

Length

2024-05-11T15:34:54.194157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:54.415914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2339
52.3%
na 2132
47.7%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing938
Missing (%)21.0%
Memory size8.9 KiB
False
3526 
True
 
7
(Missing)
938 
ValueCountFrequency (%)
False 3526
78.9%
True 7
 
0.2%
(Missing) 938
 
21.0%
2024-05-11T15:34:55.087920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct21
Distinct (%)0.6%
Missing942
Missing (%)21.1%
Infinite0
Infinite (%)0.0%
Mean3.7979598
Minimum0
Maximum195
Zeros91
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:55.270106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile8
Maximum195
Range195
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.0928718
Coefficient of variation (CV)1.3409494
Kurtosis1126.4619
Mean3.7979598
Median Absolute Deviation (MAD)1
Skewness30.328579
Sum13403
Variance25.937343
MonotonicityNot monotonic
2024-05-11T15:34:55.551411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 1475
33.0%
4 625
14.0%
2 584
 
13.1%
5 239
 
5.3%
6 167
 
3.7%
0 91
 
2.0%
8 83
 
1.9%
7 69
 
1.5%
1 69
 
1.5%
10 35
 
0.8%
Other values (11) 92
 
2.1%
(Missing) 942
21.1%
ValueCountFrequency (%)
0 91
 
2.0%
1 69
 
1.5%
2 584
 
13.1%
3 1475
33.0%
4 625
14.0%
5 239
 
5.3%
6 167
 
3.7%
7 69
 
1.5%
8 83
 
1.9%
9 17
 
0.4%
ValueCountFrequency (%)
195 2
 
< 0.1%
40 1
 
< 0.1%
31 1
 
< 0.1%
22 2
 
< 0.1%
16 4
 
0.1%
15 7
 
0.2%
14 14
0.3%
13 4
 
0.1%
12 32
0.7%
11 8
 
0.2%

조건부허가신고사유
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing4470
Missing (%)> 99.9%
Memory size35.1 KiB
2024-05-11T15:34:55.849759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters35
Distinct characters27
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row건축과-32754(2014.12.30)호에 의거 임시사용승인기간
ValueCountFrequency (%)
건축과-32754(2014.12.30)호에 1
33.3%
의거 1
33.3%
임시사용승인기간 1
33.3%
2024-05-11T15:34:56.438248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3
 
8.6%
4 2
 
5.7%
. 2
 
5.7%
1 2
 
5.7%
0 2
 
5.7%
2
 
5.7%
3 2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (17) 17
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
42.9%
Decimal Number 13
37.1%
Other Punctuation 2
 
5.7%
Space Separator 2
 
5.7%
Close Punctuation 1
 
2.9%
Open Punctuation 1
 
2.9%
Dash Punctuation 1
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (5) 5
33.3%
Decimal Number
ValueCountFrequency (%)
2 3
23.1%
4 2
15.4%
1 2
15.4%
0 2
15.4%
3 2
15.4%
5 1
 
7.7%
7 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
57.1%
Hangul 15
42.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (5) 5
33.3%
Common
ValueCountFrequency (%)
2 3
15.0%
4 2
10.0%
. 2
10.0%
1 2
10.0%
0 2
10.0%
2
10.0%
3 2
10.0%
) 1
 
5.0%
( 1
 
5.0%
5 1
 
5.0%
Other values (2) 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
57.1%
Hangul 15
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3
15.0%
4 2
10.0%
. 2
10.0%
1 2
10.0%
0 2
10.0%
2
10.0%
3 2
10.0%
) 1
 
5.0%
( 1
 
5.0%
5 1
 
5.0%
Other values (2) 2
10.0%
Hangul
ValueCountFrequency (%)
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (5) 5
33.3%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
4470 
20170413
 
1

Length

Max length8
Median length4
Mean length4.0008947
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> 4470
> 99.9%
20170413 1
 
< 0.1%

Length

2024-05-11T15:34:56.753133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:56.951812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4470
> 99.9%
20170413 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
4470 
20181231
 
1

Length

Max length8
Median length4
Mean length4.0008947
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> 4470
> 99.9%
20181231 1
 
< 0.1%

Length

2024-05-11T15:34:57.172535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:57.393556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4470
> 99.9%
20181231 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
3070 
임대
1375 
자가
 
26

Length

Max length4
Median length4
Mean length3.3732946
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> 3070
68.7%
임대 1375
30.8%
자가 26
 
0.6%

Length

2024-05-11T15:34:57.677001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:57.925036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3070
68.7%
임대 1375
30.8%
자가 26
 
0.6%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
2728 
0
1743 

Length

Max length4
Median length4
Mean length2.830463
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> 2728
61.0%
0 1743
39.0%

Length

2024-05-11T15:34:58.145130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:58.384958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2728
61.0%
0 1743
39.0%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.8%
Missing3607
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean0.079861111
Minimum0
Maximum13
Zeros833
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:58.554945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61601792
Coefficient of variation (CV)7.7136157
Kurtosis254.16651
Mean0.079861111
Median Absolute Deviation (MAD)0
Skewness14.140528
Sum69
Variance0.37947808
MonotonicityNot monotonic
2024-05-11T15:34:58.823537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 833
 
18.6%
1 16
 
0.4%
2 9
 
0.2%
3 3
 
0.1%
6 1
 
< 0.1%
13 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 3607
80.7%
ValueCountFrequency (%)
0 833
18.6%
1 16
 
0.4%
2 9
 
0.2%
3 3
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
3 3
 
0.1%
2 9
 
0.2%
1 16
 
0.4%
0 833
18.6%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
3609 
0
854 
1
 
6
7
 
1
2
 
1

Length

Max length4
Median length4
Mean length3.4216059
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> 3609
80.7%
0 854
 
19.1%
1 6
 
0.1%
7 1
 
< 0.1%
2 1
 
< 0.1%

Length

2024-05-11T15:34:59.096003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:59.314842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3609
80.7%
0 854
 
19.1%
1 6
 
0.1%
7 1
 
< 0.1%
2 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
<NA>
2838 
0
1633 

Length

Max length4
Median length4
Mean length2.904272
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> 2838
63.5%
0 1633
36.5%

Length

2024-05-11T15:34:59.526272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:59.783935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2838
63.5%
0 1633
36.5%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.9%
Missing2851
Missing (%)63.8%
Infinite0
Infinite (%)0.0%
Mean0.44753086
Minimum0
Maximum16
Zeros1394
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size39.4 KiB
2024-05-11T15:34:59.941095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4615813
Coefficient of variation (CV)3.2658783
Kurtosis26.414289
Mean0.44753086
Median Absolute Deviation (MAD)0
Skewness4.5929443
Sum725
Variance2.13622
MonotonicityNot monotonic
2024-05-11T15:35:00.168945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1394
31.2%
1 65
 
1.5%
2 56
 
1.3%
3 30
 
0.7%
4 24
 
0.5%
5 15
 
0.3%
7 15
 
0.3%
6 7
 
0.2%
8 5
 
0.1%
10 4
 
0.1%
Other values (4) 5
 
0.1%
(Missing) 2851
63.8%
ValueCountFrequency (%)
0 1394
31.2%
1 65
 
1.5%
2 56
 
1.3%
3 30
 
0.7%
4 24
 
0.5%
5 15
 
0.3%
6 7
 
0.2%
7 15
 
0.3%
8 5
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
10 4
 
0.1%
9 2
 
< 0.1%
8 5
 
0.1%
7 15
0.3%
6 7
 
0.2%
5 15
0.3%
4 24
0.5%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing871
Missing (%)19.5%
Memory size8.9 KiB
False
3600 
(Missing)
871 
ValueCountFrequency (%)
False 3600
80.5%
(Missing) 871
 
19.5%
2024-05-11T15:35:00.446836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
032000003200000-204-1969-0171319690813<NA>3폐업2폐업20030326<NA><NA><NA>02 878625620.24151050서울특별시 관악구 봉천동 94-1번지<NA><NA>봉천2003-03-26 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
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개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
446132000003200000-226-2021-0000220210413<NA>1영업/정상1영업<NA><NA><NA><NA><NA>41.82151832서울특별시 관악구 봉천동 1659-4 동림오피스텔서울특별시 관악구 남부순환로 1924, 동림오피스텔 b202호 (봉천동)8793원앤원 속눈썹2021-04-13 10:45:45I2021-04-15 00:22:57.0메이크업업196651.522369441647.080647피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA>1<NA>000N5<NA><NA><NA><NA>00000N
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446332000003200000-226-2021-000042021-03-05<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.0151-913서울특별시 관악구 봉천동 636-126서울특별시 관악구 국회단지길 27, 1층 (봉천동)8717오르막네일2023-04-20 10:21:36U2022-12-03 22:03:00.0네일아트업194606.507511442809.978728<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446432000003200000-226-2022-0000120220307<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.0151930서울특별시 관악구 신림동 1641-52서울특별시 관악구 신림로 294, 2층 201호 (신림동)8778뷰뷰팩토리2023-01-02 16:46:09I2022-12-01 00:04:00.0네일아트업193779.096297442059.279669<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446532000003200000-226-2022-000022022-12-01<NA>1영업/정상1영업<NA><NA><NA><NA>05071494502837.3151-843서울특별시 관악구 봉천동 951-1서울특별시 관악구 은천로 36, 2층 202호 (봉천동)8749신데렐라스킨앤바디2023-04-10 15:10:19I2022-12-03 23:02:00.0피부미용업194555.426657442649.537946<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446632000003200000-226-2022-000032022-10-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.0151-891서울특별시 관악구 신림동 1427-9 영지빌 101호서울특별시 관악구 봉천로14길 6, 영지빌 1층 101호 (신림동)8753미미아이2024-03-25 15:50:08I2023-12-02 22:07:00.0메이크업업193744.020487442858.29694<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446732000003200000-226-2023-000012023-02-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>36.42151-895서울특별시 관악구 신림동 1523-1 일성트루엘서울특별시 관악구 신림로23길 16, 일성트루엘 1층 1008호 (신림동)8812bbnail2023-02-16 13:17:49I2022-12-01 23:08:00.0네일아트업194135.772735440917.845099<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446832000003200000-226-2023-000022023-03-17<NA>3폐업2폐업2023-05-08<NA><NA><NA><NA>50.49151-844서울특별시 관악구 봉천동 927-8서울특별시 관악구 남부순환로 1728, 3층 (봉천동)8784블루문 SPA2023-05-08 11:01:52U2022-12-04 23:00:00.0피부미용업194803.029134442215.131492<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
446932000003200000-226-2023-000032023-03-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.0151-822서울특별시 관악구 봉천동 871-80서울특별시 관악구 봉천로49길 5, 1층 (봉천동)8745뷰뷰네일 서울대입구역점2023-09-07 11:59:02I2022-12-09 00:09:00.0메이크업업195668.911567442276.497742<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
447032000003200000-226-2023-000042023-11-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>53.45151-930서울특별시 관악구 신림동 1640-47서울특별시 관악구 신림로59길 6, 3층 (신림동)8776유앤왁싱2024-02-22 11:15:49I2023-12-01 22:04:00.0피부미용업193694.0105442322.804815<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>