Overview

Dataset statistics

Number of variables47
Number of observations10000
Missing cells118907
Missing cells (%)25.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 MiB
Average record size in memory411.0 B

Variable types

Numeric11
Text8
DateTime4
Unsupported4
Categorical18
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-16063/S/1/datasetView.do

Alerts

사용시작지하층 is highly imbalanced (64.7%)Imbalance
사용끝지하층 is highly imbalanced (75.0%)Imbalance
발한실여부 is highly imbalanced (99.1%)Imbalance
조건부허가시작일자 is highly imbalanced (99.7%)Imbalance
조건부허가종료일자 is highly imbalanced (99.7%)Imbalance
건물소유구분명 is highly imbalanced (68.5%)Imbalance
남성종사자수 is highly imbalanced (67.6%)Imbalance
다중이용업소여부 is highly imbalanced (98.8%)Imbalance
인허가취소일자 has 10000 (100.0%) missing valuesMissing
폐업일자 has 4872 (48.7%) missing valuesMissing
휴업시작일자 has 10000 (100.0%) missing valuesMissing
휴업종료일자 has 10000 (100.0%) missing valuesMissing
재개업일자 has 10000 (100.0%) missing valuesMissing
전화번호 has 4981 (49.8%) missing valuesMissing
도로명주소 has 2638 (26.4%) missing valuesMissing
도로명우편번호 has 2679 (26.8%) missing valuesMissing
좌표정보(X) has 577 (5.8%) missing valuesMissing
좌표정보(Y) has 577 (5.8%) missing valuesMissing
건물지상층수 has 5249 (52.5%) missing valuesMissing
건물지하층수 has 5714 (57.1%) missing valuesMissing
사용시작지상층 has 5914 (59.1%) missing valuesMissing
사용끝지상층 has 7145 (71.5%) missing valuesMissing
발한실여부 has 4429 (44.3%) missing valuesMissing
좌석수 has 4502 (45.0%) missing valuesMissing
조건부허가신고사유 has 9997 (> 99.9%) missing valuesMissing
여성종사자수 has 8019 (80.2%) missing valuesMissing
침대수 has 7239 (72.4%) missing valuesMissing
다중이용업소여부 has 4349 (43.5%) missing valuesMissing
사용시작지상층 is highly skewed (γ1 = 32.1552733)Skewed
좌석수 is highly skewed (γ1 = 30.00003857)Skewed
여성종사자수 is highly skewed (γ1 = 23.31213701)Skewed
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 3553 (35.5%) zerosZeros
건물지하층수 has 3857 (38.6%) zerosZeros
사용시작지상층 has 1755 (17.5%) zerosZeros
사용끝지상층 has 643 (6.4%) zerosZeros
좌석수 has 651 (6.5%) zerosZeros
여성종사자수 has 1683 (16.8%) zerosZeros
침대수 has 1982 (19.8%) zerosZeros

Reproduction

Analysis started2024-05-11 05:34:17.722106
Analysis finished2024-05-11 05:34:21.601290
Duration3.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3081484
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:21.726118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3000000
Q13030000
median3050000
Q33140000
95-th percentile3230000
Maximum3240000
Range240000
Interquartile range (IQR)110000

Descriptive statistics

Standard deviation73154.719
Coefficient of variation (CV)0.023740094
Kurtosis-0.58587188
Mean3081484
Median Absolute Deviation (MAD)30000
Skewness0.90596269
Sum3.081484 × 1010
Variance5.3516129 × 109
MonotonicityNot monotonic
2024-05-11T14:34:21.940539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3040000 1500
15.0%
3030000 946
 
9.5%
3050000 915
 
9.2%
3010000 801
 
8.0%
3060000 800
 
8.0%
3020000 783
 
7.8%
3000000 593
 
5.9%
3220000 460
 
4.6%
3150000 333
 
3.3%
3230000 316
 
3.2%
Other values (15) 2553
25.5%
ValueCountFrequency (%)
3000000 593
 
5.9%
3010000 801
8.0%
3020000 783
7.8%
3030000 946
9.5%
3040000 1500
15.0%
3050000 915
9.2%
3060000 800
8.0%
3070000 153
 
1.5%
3080000 142
 
1.4%
3090000 107
 
1.1%
ValueCountFrequency (%)
3240000 218
2.2%
3230000 316
3.2%
3220000 460
4.6%
3210000 226
2.3%
3200000 209
2.1%
3190000 126
 
1.3%
3180000 167
 
1.7%
3170000 94
 
0.9%
3160000 175
 
1.8%
3150000 333
3.3%

관리번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:34:22.270014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters220000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row3030000-212-2024-00005
2nd row3240000-204-1991-01139
3rd row3030000-213-2017-00010
4th row3070000-211-2015-00028
5th row3220000-212-2016-00142
ValueCountFrequency (%)
3030000-212-2024-00005 1
 
< 0.1%
3040000-211-2019-00055 1
 
< 0.1%
3010000-204-1997-00443 1
 
< 0.1%
3050000-215-2019-00007 1
 
< 0.1%
3010000-204-1978-00870 1
 
< 0.1%
3040000-204-1992-00819 1
 
< 0.1%
3060000-204-1993-01604 1
 
< 0.1%
3060000-204-2000-01805 1
 
< 0.1%
3100000-204-2005-00039 1
 
< 0.1%
3030000-211-2014-00013 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T14:34:22.732886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 92750
42.2%
2 30029
 
13.6%
- 30000
 
13.6%
1 23257
 
10.6%
3 16700
 
7.6%
4 7753
 
3.5%
9 5896
 
2.7%
5 4507
 
2.0%
6 3400
 
1.5%
8 3161
 
1.4%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92750
48.8%
2 30029
 
15.8%
1 23257
 
12.2%
3 16700
 
8.8%
4 7753
 
4.1%
9 5896
 
3.1%
5 4507
 
2.4%
6 3400
 
1.8%
8 3161
 
1.7%
7 2547
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92750
42.2%
2 30029
 
13.6%
- 30000
 
13.6%
1 23257
 
10.6%
3 16700
 
7.6%
4 7753
 
3.5%
9 5896
 
2.7%
5 4507
 
2.0%
6 3400
 
1.5%
8 3161
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92750
42.2%
2 30029
 
13.6%
- 30000
 
13.6%
1 23257
 
10.6%
3 16700
 
7.6%
4 7753
 
3.5%
9 5896
 
2.7%
5 4507
 
2.0%
6 3400
 
1.5%
8 3161
 
1.4%
Distinct5274
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1957-04-14 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T14:34:23.002520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:34:23.196190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
5128 
1
4872 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 5128
51.3%
1 4872
48.7%

Length

2024-05-11T14:34:23.381011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:23.501815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 5128
51.3%
1 4872
48.7%

영업상태명
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5128 
영업/정상
4872 

Length

Max length5
Median length2
Mean length3.4616
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5128
51.3%
영업/정상 4872
48.7%

Length

2024-05-11T14:34:23.663250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:23.818360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5128
51.3%
영업/정상 4872
48.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2
5128 
1
4872 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5128
51.3%
1 4872
48.7%

Length

2024-05-11T14:34:23.992714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:24.121188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5128
51.3%
1 4872
48.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업
5128 
영업
4872 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 5128
51.3%
영업 4872
48.7%

Length

2024-05-11T14:34:24.333393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:24.668235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 5128
51.3%
영업 4872
48.7%

폐업일자
Date

MISSING 

Distinct3116
Distinct (%)60.8%
Missing4872
Missing (%)48.7%
Memory size156.2 KiB
Minimum1991-03-18 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T14:34:24.801551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:34:24.965214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

전화번호
Text

MISSING 

Distinct4557
Distinct (%)90.8%
Missing4981
Missing (%)49.8%
Memory size156.2 KiB
2024-05-11T14:34:25.487329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.259414
Min length1

Characters and Unicode

Total characters51492
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

Unique4386 ?
Unique (%)87.4%

Sample

1st row0204746510
2nd row30152121
3rd row0226627800
4th row0222999887
5th row0222497635
ValueCountFrequency (%)
02 3154
35.1%
0200000000 110
 
1.2%
00000 87
 
1.0%
070 61
 
0.7%
0 42
 
0.5%
447 19
 
0.2%
318 16
 
0.2%
456 15
 
0.2%
454 14
 
0.2%
749 13
 
0.1%
Other values (4817) 5446
60.7%
2024-05-11T14:34:26.263515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9588
18.6%
0 9405
18.3%
5199
10.1%
4 4170
8.1%
7 3952
7.7%
3 3845
7.5%
6 3414
 
6.6%
9 3372
 
6.5%
5 3268
 
6.3%
1 2652
 
5.2%
Other values (2) 2627
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46292
89.9%
Space Separator 5199
 
10.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9588
20.7%
0 9405
20.3%
4 4170
9.0%
7 3952
8.5%
3 3845
8.3%
6 3414
 
7.4%
9 3372
 
7.3%
5 3268
 
7.1%
1 2652
 
5.7%
8 2626
 
5.7%
Space Separator
ValueCountFrequency (%)
5199
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9588
18.6%
0 9405
18.3%
5199
10.1%
4 4170
8.1%
7 3952
7.7%
3 3845
7.5%
6 3414
 
6.6%
9 3372
 
6.5%
5 3268
 
6.3%
1 2652
 
5.2%
Other values (2) 2627
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9588
18.6%
0 9405
18.3%
5199
10.1%
4 4170
8.1%
7 3952
7.7%
3 3845
7.5%
6 3414
 
6.6%
9 3372
 
6.5%
5 3268
 
6.3%
1 2652
 
5.2%
Other values (2) 2627
 
5.1%
Distinct3926
Distinct (%)39.3%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-05-11T14:34:26.800088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.0087017
Min length3

Characters and Unicode

Total characters50077
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2577 ?
Unique (%)25.8%

Sample

1st row32.01
2nd row32.10
3rd row53.17
4th row40.62
5th row272.05
ValueCountFrequency (%)
33.00 306
 
3.1%
00 263
 
2.6%
30.00 192
 
1.9%
26.40 162
 
1.6%
20.00 139
 
1.4%
16.50 113
 
1.1%
24.00 104
 
1.0%
25.00 95
 
1.0%
23.10 94
 
0.9%
19.80 94
 
0.9%
Other values (3916) 8436
84.4%
2024-05-11T14:34:27.622769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10102
20.2%
. 9998
20.0%
2 4978
9.9%
1 4656
9.3%
3 4100
8.2%
4 3176
 
6.3%
5 3132
 
6.3%
6 2953
 
5.9%
8 2487
 
5.0%
9 2370
 
4.7%
Other values (2) 2125
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40076
80.0%
Other Punctuation 10001
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10102
25.2%
2 4978
12.4%
1 4656
11.6%
3 4100
10.2%
4 3176
 
7.9%
5 3132
 
7.8%
6 2953
 
7.4%
8 2487
 
6.2%
9 2370
 
5.9%
7 2122
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 9998
> 99.9%
, 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 50077
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10102
20.2%
. 9998
20.0%
2 4978
9.9%
1 4656
9.3%
3 4100
8.2%
4 3176
 
6.3%
5 3132
 
6.3%
6 2953
 
5.9%
8 2487
 
5.0%
9 2370
 
4.7%
Other values (2) 2125
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50077
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10102
20.2%
. 9998
20.0%
2 4978
9.9%
1 4656
9.3%
3 4100
8.2%
4 3176
 
6.3%
5 3132
 
6.3%
6 2953
 
5.9%
8 2487
 
5.0%
9 2370
 
4.7%
Other values (2) 2125
 
4.2%
Distinct2665
Distinct (%)26.7%
Missing13
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T14:34:28.096601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3205167
Min length6

Characters and Unicode

Total characters63123
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

Unique1124 ?
Unique (%)11.3%

Sample

1st row133-020
2nd row134-890
3rd row133-827
4th row136-051
5th row135825
ValueCountFrequency (%)
100450 126
 
1.3%
157-210 95
 
1.0%
143914 69
 
0.7%
143915 50
 
0.5%
143900 46
 
0.5%
157210 46
 
0.5%
100011 45
 
0.5%
143841 44
 
0.4%
130840 39
 
0.4%
143888 38
 
0.4%
Other values (2655) 9389
94.0%
2024-05-11T14:34:28.782537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14779
23.4%
8 9200
14.6%
3 8923
14.1%
0 7956
12.6%
4 4673
 
7.4%
5 3787
 
6.0%
2 3732
 
5.9%
- 3201
 
5.1%
7 2459
 
3.9%
9 2353
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59922
94.9%
Dash Punctuation 3201
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14779
24.7%
8 9200
15.4%
3 8923
14.9%
0 7956
13.3%
4 4673
 
7.8%
5 3787
 
6.3%
2 3732
 
6.2%
7 2459
 
4.1%
9 2353
 
3.9%
6 2060
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 3201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63123
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14779
23.4%
8 9200
14.6%
3 8923
14.1%
0 7956
12.6%
4 4673
 
7.4%
5 3787
 
6.0%
2 3732
 
5.9%
- 3201
 
5.1%
7 2459
 
3.9%
9 2353
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14779
23.4%
8 9200
14.6%
3 8923
14.1%
0 7956
12.6%
4 4673
 
7.4%
5 3787
 
6.0%
2 3732
 
5.9%
- 3201
 
5.1%
7 2459
 
3.9%
9 2353
 
3.7%
Distinct9185
Distinct (%)92.0%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2024-05-11T14:34:29.253046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length52
Mean length24.743017
Min length15

Characters and Unicode

Total characters247158
Distinct characters583
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

Unique8575 ?
Unique (%)85.8%

Sample

1st row서울특별시 성동구 하왕십리동 1070 센트라스
2nd row서울특별시 강동구 성내동 426-24
3rd row서울특별시 성동구 성수동2가 331-59
4th row서울특별시 성북구 동선동1가 85-97 2층
5th row서울특별시 강남구 논현동 143-6
ValueCountFrequency (%)
서울특별시 9988
 
21.1%
광진구 1500
 
3.2%
성동구 946
 
2.0%
동대문구 915
 
1.9%
1층 877
 
1.9%
중구 801
 
1.7%
중랑구 800
 
1.7%
용산구 783
 
1.7%
종로구 581
 
1.2%
강남구 460
 
1.0%
Other values (10945) 29734
62.7%
2024-05-11T14:34:29.974682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42768
 
17.3%
12406
 
5.0%
10959
 
4.4%
1 10791
 
4.4%
10556
 
4.3%
10177
 
4.1%
10031
 
4.1%
9990
 
4.0%
9988
 
4.0%
- 8572
 
3.5%
Other values (573) 110920
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143992
58.3%
Decimal Number 50004
 
20.2%
Space Separator 42768
 
17.3%
Dash Punctuation 8572
 
3.5%
Uppercase Letter 606
 
0.2%
Close Punctuation 462
 
0.2%
Open Punctuation 462
 
0.2%
Other Punctuation 159
 
0.1%
Lowercase Letter 93
 
< 0.1%
Letter Number 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12406
 
8.6%
10959
 
7.6%
10556
 
7.3%
10177
 
7.1%
10031
 
7.0%
9990
 
6.9%
9988
 
6.9%
5599
 
3.9%
4885
 
3.4%
2197
 
1.5%
Other values (500) 57204
39.7%
Uppercase Letter
ValueCountFrequency (%)
B 124
20.5%
A 68
11.2%
S 56
 
9.2%
T 33
 
5.4%
K 32
 
5.3%
E 29
 
4.8%
L 28
 
4.6%
I 28
 
4.6%
G 23
 
3.8%
M 21
 
3.5%
Other values (14) 164
27.1%
Lowercase Letter
ValueCountFrequency (%)
e 16
17.2%
o 9
9.7%
w 8
 
8.6%
l 8
 
8.6%
i 7
 
7.5%
r 7
 
7.5%
a 6
 
6.5%
c 5
 
5.4%
t 4
 
4.3%
n 3
 
3.2%
Other values (13) 20
21.5%
Decimal Number
ValueCountFrequency (%)
1 10791
21.6%
2 7772
15.5%
3 5699
11.4%
4 4361
8.7%
0 4223
 
8.4%
5 4093
 
8.2%
6 3791
 
7.6%
7 3310
 
6.6%
8 3029
 
6.1%
9 2935
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 129
81.1%
. 9
 
5.7%
@ 7
 
4.4%
& 7
 
4.4%
/ 5
 
3.1%
* 1
 
0.6%
? 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 460
99.6%
] 2
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 460
99.6%
[ 2
 
0.4%
Letter Number
ValueCountFrequency (%)
20
80.0%
5
 
20.0%
Space Separator
ValueCountFrequency (%)
42768
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8572
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143985
58.3%
Common 102442
41.4%
Latin 724
 
0.3%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12406
 
8.6%
10959
 
7.6%
10556
 
7.3%
10177
 
7.1%
10031
 
7.0%
9990
 
6.9%
9988
 
6.9%
5599
 
3.9%
4885
 
3.4%
2197
 
1.5%
Other values (495) 57197
39.7%
Latin
ValueCountFrequency (%)
B 124
17.1%
A 68
 
9.4%
S 56
 
7.7%
T 33
 
4.6%
K 32
 
4.4%
E 29
 
4.0%
L 28
 
3.9%
I 28
 
3.9%
G 23
 
3.2%
M 21
 
2.9%
Other values (39) 282
39.0%
Common
ValueCountFrequency (%)
42768
41.7%
1 10791
 
10.5%
- 8572
 
8.4%
2 7772
 
7.6%
3 5699
 
5.6%
4 4361
 
4.3%
0 4223
 
4.1%
5 4093
 
4.0%
6 3791
 
3.7%
7 3310
 
3.2%
Other values (14) 7062
 
6.9%
Han
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143985
58.3%
ASCII 103141
41.7%
Number Forms 25
 
< 0.1%
CJK 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42768
41.5%
1 10791
 
10.5%
- 8572
 
8.3%
2 7772
 
7.5%
3 5699
 
5.5%
4 4361
 
4.2%
0 4223
 
4.1%
5 4093
 
4.0%
6 3791
 
3.7%
7 3310
 
3.2%
Other values (61) 7761
 
7.5%
Hangul
ValueCountFrequency (%)
12406
 
8.6%
10959
 
7.6%
10556
 
7.3%
10177
 
7.1%
10031
 
7.0%
9990
 
6.9%
9988
 
6.9%
5599
 
3.9%
4885
 
3.4%
2197
 
1.5%
Other values (495) 57197
39.7%
Number Forms
ValueCountFrequency (%)
20
80.0%
5
 
20.0%
CJK
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

도로명주소
Text

MISSING 

Distinct7111
Distinct (%)96.6%
Missing2638
Missing (%)26.4%
Memory size156.2 KiB
2024-05-11T14:34:30.578267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length57
Mean length34.319614
Min length20

Characters and Unicode

Total characters252661
Distinct characters594
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

Unique6871 ?
Unique (%)93.3%

Sample

1st row서울특별시 성동구 왕십리로 410, I동 117호 (하왕십리동, 센트라스)
2nd row서울특별시 강동구 양재대로89길 62, 1층 103호 (성내동)
3rd row서울특별시 성동구 뚝섬로9길 3-1, 2층 (성수동2가)
4th row서울특별시 성북구 동소문로22길 29-2 (동선동1가)
5th row서울특별시 강남구 강남대로 528, 지상12층 (논현동)
ValueCountFrequency (%)
서울특별시 7361
 
14.8%
1층 2450
 
4.9%
2층 1116
 
2.2%
광진구 958
 
1.9%
동대문구 662
 
1.3%
성동구 579
 
1.2%
용산구 465
 
0.9%
중구 461
 
0.9%
강남구 458
 
0.9%
3층 432
 
0.9%
Other values (7781) 34764
69.9%
2024-05-11T14:34:31.490443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42361
 
16.8%
1 12199
 
4.8%
10454
 
4.1%
8614
 
3.4%
, 8036
 
3.2%
7932
 
3.1%
7870
 
3.1%
7649
 
3.0%
) 7572
 
3.0%
( 7571
 
3.0%
Other values (584) 132403
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142246
56.3%
Decimal Number 42658
 
16.9%
Space Separator 42361
 
16.8%
Other Punctuation 8055
 
3.2%
Close Punctuation 7572
 
3.0%
Open Punctuation 7571
 
3.0%
Dash Punctuation 1182
 
0.5%
Uppercase Letter 875
 
0.3%
Lowercase Letter 91
 
< 0.1%
Math Symbol 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10454
 
7.3%
8614
 
6.1%
7932
 
5.6%
7870
 
5.5%
7649
 
5.4%
7439
 
5.2%
7366
 
5.2%
7361
 
5.2%
5675
 
4.0%
4257
 
3.0%
Other values (516) 67629
47.5%
Uppercase Letter
ValueCountFrequency (%)
B 298
34.1%
A 101
 
11.5%
S 59
 
6.7%
C 40
 
4.6%
L 37
 
4.2%
K 36
 
4.1%
E 35
 
4.0%
I 33
 
3.8%
T 29
 
3.3%
G 23
 
2.6%
Other values (15) 184
21.0%
Lowercase Letter
ValueCountFrequency (%)
e 15
16.5%
r 9
9.9%
o 8
8.8%
c 7
 
7.7%
w 7
 
7.7%
b 7
 
7.7%
t 6
 
6.6%
a 6
 
6.6%
i 4
 
4.4%
k 4
 
4.4%
Other values (10) 18
19.8%
Decimal Number
ValueCountFrequency (%)
1 12199
28.6%
2 7441
17.4%
3 4570
 
10.7%
0 4330
 
10.2%
4 3340
 
7.8%
5 2820
 
6.6%
6 2378
 
5.6%
7 1993
 
4.7%
8 1838
 
4.3%
9 1749
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 8036
99.8%
. 8
 
0.1%
& 8
 
0.1%
/ 1
 
< 0.1%
@ 1
 
< 0.1%
* 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
20
80.0%
5
 
20.0%
Space Separator
ValueCountFrequency (%)
42361
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7572
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7571
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1182
100.0%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142239
56.3%
Common 109424
43.3%
Latin 991
 
0.4%
Han 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10454
 
7.3%
8614
 
6.1%
7932
 
5.6%
7870
 
5.5%
7649
 
5.4%
7439
 
5.2%
7366
 
5.2%
7361
 
5.2%
5675
 
4.0%
4257
 
3.0%
Other values (511) 67622
47.5%
Latin
ValueCountFrequency (%)
B 298
30.1%
A 101
 
10.2%
S 59
 
6.0%
C 40
 
4.0%
L 37
 
3.7%
K 36
 
3.6%
E 35
 
3.5%
I 33
 
3.3%
T 29
 
2.9%
G 23
 
2.3%
Other values (37) 300
30.3%
Common
ValueCountFrequency (%)
42361
38.7%
1 12199
 
11.1%
, 8036
 
7.3%
) 7572
 
6.9%
( 7571
 
6.9%
2 7441
 
6.8%
3 4570
 
4.2%
0 4330
 
4.0%
4 3340
 
3.1%
5 2820
 
2.6%
Other values (11) 9184
 
8.4%
Han
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142239
56.3%
ASCII 110390
43.7%
Number Forms 25
 
< 0.1%
CJK 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42361
38.4%
1 12199
 
11.1%
, 8036
 
7.3%
) 7572
 
6.9%
( 7571
 
6.9%
2 7441
 
6.7%
3 4570
 
4.1%
0 4330
 
3.9%
4 3340
 
3.0%
5 2820
 
2.6%
Other values (56) 10150
 
9.2%
Hangul
ValueCountFrequency (%)
10454
 
7.3%
8614
 
6.1%
7932
 
5.6%
7870
 
5.5%
7649
 
5.4%
7439
 
5.2%
7366
 
5.2%
7361
 
5.2%
5675
 
4.0%
4257
 
3.0%
Other values (511) 67622
47.5%
Number Forms
ValueCountFrequency (%)
20
80.0%
5
 
20.0%
CJK
ValueCountFrequency (%)
2
28.6%
2
28.6%
1
14.3%
1
14.3%
1
14.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct2629
Distinct (%)35.9%
Missing2679
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean4810.0228
Minimum1006
Maximum14545
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:31.744185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1006
5-th percentile1831
Q13186
median4750
Q36038
95-th percentile8306
Maximum14545
Range13539
Interquartile range (IQR)2852

Descriptive statistics

Standard deviation1934.1482
Coefficient of variation (CV)0.40210791
Kurtosis-0.53360778
Mean4810.0228
Median Absolute Deviation (MAD)1364
Skewness0.23053715
Sum35214177
Variance3740929.3
MonotonicityNot monotonic
2024-05-11T14:34:32.009777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4536 56
 
0.6%
7631 36
 
0.4%
4378 31
 
0.3%
5010 27
 
0.3%
4908 26
 
0.3%
7788 24
 
0.2%
4700 24
 
0.2%
4701 24
 
0.2%
5855 23
 
0.2%
4564 21
 
0.2%
Other values (2619) 7029
70.3%
(Missing) 2679
 
26.8%
ValueCountFrequency (%)
1006 1
 
< 0.1%
1009 1
 
< 0.1%
1028 2
 
< 0.1%
1029 1
 
< 0.1%
1035 1
 
< 0.1%
1040 1
 
< 0.1%
1041 5
0.1%
1047 1
 
< 0.1%
1048 3
< 0.1%
1050 1
 
< 0.1%
ValueCountFrequency (%)
14545 1
 
< 0.1%
8865 1
 
< 0.1%
8864 1
 
< 0.1%
8860 2
< 0.1%
8858 1
 
< 0.1%
8854 2
< 0.1%
8852 3
< 0.1%
8850 2
< 0.1%
8849 1
 
< 0.1%
8846 2
< 0.1%
Distinct8748
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:34:32.559287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length34
Mean length6.3965
Min length1

Characters and Unicode

Total characters63965
Distinct characters975
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8017 ?
Unique (%)80.2%

Sample

1st row헤일리 뷰티
2nd row머리하는날
3rd row잭앤클리퍼(JackandClipper)
4th row맷블랙(MATT BLACK HAIR)
5th row아이오마 끌레르 신논현점
ValueCountFrequency (%)
헤어 227
 
1.7%
네일 146
 
1.1%
hair 140
 
1.1%
에스테틱 106
 
0.8%
미용실 104
 
0.8%
nail 76
 
0.6%
49
 
0.4%
뷰티 46
 
0.3%
39
 
0.3%
살롱 36
 
0.3%
Other values (9387) 12349
92.7%
2024-05-11T14:34:33.486092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3322
 
5.2%
3224
 
5.0%
3060
 
4.8%
1652
 
2.6%
1405
 
2.2%
1337
 
2.1%
1268
 
2.0%
1083
 
1.7%
1064
 
1.7%
1062
 
1.7%
Other values (965) 45488
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49550
77.5%
Lowercase Letter 4592
 
7.2%
Uppercase Letter 3459
 
5.4%
Space Separator 3322
 
5.2%
Close Punctuation 972
 
1.5%
Open Punctuation 972
 
1.5%
Other Punctuation 590
 
0.9%
Decimal Number 454
 
0.7%
Dash Punctuation 31
 
< 0.1%
Connector Punctuation 14
 
< 0.1%
Other values (4) 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3224
 
6.5%
3060
 
6.2%
1652
 
3.3%
1405
 
2.8%
1337
 
2.7%
1268
 
2.6%
1083
 
2.2%
1064
 
2.1%
1062
 
2.1%
1045
 
2.1%
Other values (880) 33350
67.3%
Lowercase Letter
ValueCountFrequency (%)
a 617
13.4%
i 493
10.7%
e 464
10.1%
o 376
 
8.2%
n 358
 
7.8%
l 334
 
7.3%
r 304
 
6.6%
h 231
 
5.0%
t 196
 
4.3%
s 195
 
4.2%
Other values (16) 1024
22.3%
Uppercase Letter
ValueCountFrequency (%)
A 383
 
11.1%
N 257
 
7.4%
I 246
 
7.1%
O 230
 
6.6%
H 227
 
6.6%
S 227
 
6.6%
E 207
 
6.0%
L 189
 
5.5%
R 179
 
5.2%
B 168
 
4.9%
Other values (16) 1146
33.1%
Other Punctuation
ValueCountFrequency (%)
& 142
24.1%
. 127
21.5%
? 115
19.5%
, 81
13.7%
# 49
 
8.3%
' 34
 
5.8%
: 24
 
4.1%
/ 8
 
1.4%
; 6
 
1.0%
% 3
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 101
22.2%
2 84
18.5%
0 79
17.4%
3 37
 
8.1%
5 31
 
6.8%
8 26
 
5.7%
7 25
 
5.5%
6 25
 
5.5%
9 24
 
5.3%
4 22
 
4.8%
Close Punctuation
ValueCountFrequency (%)
) 970
99.8%
] 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 970
99.8%
[ 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
= 2
50.0%
+ 2
50.0%
Space Separator
ValueCountFrequency (%)
3322
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49516
77.4%
Latin 8052
 
12.6%
Common 6363
 
9.9%
Han 34
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3224
 
6.5%
3060
 
6.2%
1652
 
3.3%
1405
 
2.8%
1337
 
2.7%
1268
 
2.6%
1083
 
2.2%
1064
 
2.1%
1062
 
2.1%
1045
 
2.1%
Other values (860) 33316
67.3%
Latin
ValueCountFrequency (%)
a 617
 
7.7%
i 493
 
6.1%
e 464
 
5.8%
A 383
 
4.8%
o 376
 
4.7%
n 358
 
4.4%
l 334
 
4.1%
r 304
 
3.8%
N 257
 
3.2%
I 246
 
3.1%
Other values (43) 4220
52.4%
Common
ValueCountFrequency (%)
3322
52.2%
) 970
 
15.2%
( 970
 
15.2%
& 142
 
2.2%
. 127
 
2.0%
? 115
 
1.8%
1 101
 
1.6%
2 84
 
1.3%
, 81
 
1.3%
0 79
 
1.2%
Other values (22) 372
 
5.8%
Han
ValueCountFrequency (%)
12
35.3%
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (10) 10
29.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49514
77.4%
ASCII 14412
 
22.5%
CJK 33
 
0.1%
Compat Jamo 2
 
< 0.1%
Punctuation 2
 
< 0.1%
CJK Compat Ideographs 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3322
23.1%
) 970
 
6.7%
( 970
 
6.7%
a 617
 
4.3%
i 493
 
3.4%
e 464
 
3.2%
A 383
 
2.7%
o 376
 
2.6%
n 358
 
2.5%
l 334
 
2.3%
Other values (73) 6125
42.5%
Hangul
ValueCountFrequency (%)
3224
 
6.5%
3060
 
6.2%
1652
 
3.3%
1405
 
2.8%
1337
 
2.7%
1268
 
2.6%
1083
 
2.2%
1064
 
2.1%
1062
 
2.1%
1045
 
2.1%
Other values (859) 33314
67.3%
CJK
ValueCountFrequency (%)
12
36.4%
2
 
6.1%
2
 
6.1%
2
 
6.1%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
Other values (9) 9
27.3%
Compat Jamo
ValueCountFrequency (%)
2
100.0%
Punctuation
ValueCountFrequency (%)
2
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct8368
Distinct (%)83.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum1998-12-28 00:00:00
Maximum2024-05-09 15:49:06
2024-05-11T14:34:33.745921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:34:33.940679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
I
6779 
U
3118 
D
 
103

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 6779
67.8%
U 3118
31.2%
D 103
 
1.0%

Length

2024-05-11T14:34:34.155517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:34.313802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 6779
67.8%
u 3118
31.2%
d 103
 
1.0%
Distinct1377
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T14:34:34.510059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:34:34.750962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반미용업
6597 
피부미용업
1677 
네일아트업
1189 
메이크업업
 
479
기타
 
57

Length

Max length5
Median length5
Mean length4.9829
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 6597
66.0%
피부미용업 1677
 
16.8%
네일아트업 1189
 
11.9%
메이크업업 479
 
4.8%
기타 57
 
0.6%
일반이용업 1
 
< 0.1%

Length

2024-05-11T14:34:34.940042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:35.107401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 6597
66.0%
피부미용업 1677
 
16.8%
네일아트업 1189
 
11.9%
메이크업업 479
 
4.8%
기타 57
 
0.6%
일반이용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct7445
Distinct (%)79.0%
Missing577
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean201555.7
Minimum178411.44
Maximum215901.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:35.344604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum178411.44
5-th percentile187836.56
Q1197757.47
median202896.51
Q3206558.58
95-th percentile209074.9
Maximum215901.59
Range37490.153
Interquartile range (IQR)8801.1046

Descriptive statistics

Standard deviation6418.9897
Coefficient of variation (CV)0.031847225
Kurtosis0.095045695
Mean201555.7
Median Absolute Deviation (MAD)4134.894
Skewness-0.81893839
Sum1.8992593 × 109
Variance41203429
MonotonicityNot monotonic
2024-05-11T14:34:35.599909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202113.869605464 24
 
0.2%
202372.912023599 16
 
0.2%
202511.142930696 14
 
0.1%
196861.350875235 14
 
0.1%
200750.455125653 14
 
0.1%
198150.300374121 13
 
0.1%
193048.463050526 13
 
0.1%
202326.503044305 12
 
0.1%
190232.524534335 12
 
0.1%
206521.405320134 10
 
0.1%
Other values (7435) 9281
92.8%
(Missing) 577
 
5.8%
ValueCountFrequency (%)
178411.442079953 1
< 0.1%
182524.823835629 1
< 0.1%
183046.848238676 1
< 0.1%
183063.084025247 1
< 0.1%
183064.578010899 1
< 0.1%
183139.24997675 1
< 0.1%
183198.183706614 1
< 0.1%
183242.500497164 1
< 0.1%
183269.526391991 1
< 0.1%
183301.512115904 1
< 0.1%
ValueCountFrequency (%)
215901.594590118 1
< 0.1%
215422.746119624 1
< 0.1%
215333.558347275 1
< 0.1%
215332.045598845 1
< 0.1%
215313.230134197 1
< 0.1%
215303.913311463 1
< 0.1%
215289.815449411 1
< 0.1%
215186.099366286 1
< 0.1%
215086.854230503 1
< 0.1%
215039.878759936 1
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct7445
Distinct (%)79.0%
Missing577
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean450208.83
Minimum436946.6
Maximum464814.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:35.849190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum436946.6
5-th percentile442692.52
Q1447891.91
median450351.53
Q3452566.13
95-th percentile457149.46
Maximum464814.72
Range27868.113
Interquartile range (IQR)4674.2221

Descriptive statistics

Standard deviation4229.676
Coefficient of variation (CV)0.0093949202
Kurtosis0.50250657
Mean450208.83
Median Absolute Deviation (MAD)2355.3668
Skewness0.18296302
Sum4.2423178 × 109
Variance17890159
MonotonicityNot monotonic
2024-05-11T14:34:36.187613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451897.581865192 24
 
0.2%
451536.680876573 16
 
0.2%
450401.303715561 14
 
0.1%
447226.720509901 14
 
0.1%
449638.824308081 14
 
0.1%
452019.212642931 13
 
0.1%
450243.570663618 13
 
0.1%
450625.58422744 12
 
0.1%
444978.682746138 12
 
0.1%
451940.119059947 10
 
0.1%
Other values (7435) 9281
92.8%
(Missing) 577
 
5.8%
ValueCountFrequency (%)
436946.60407049 1
< 0.1%
437773.047519431 1
< 0.1%
437813.081908505 1
< 0.1%
438307.022609784 2
< 0.1%
438368.191101666 1
< 0.1%
438491.712309438 1
< 0.1%
438583.251827435 1
< 0.1%
438640.589372513 1
< 0.1%
438641.653485665 1
< 0.1%
438711.564180845 1
< 0.1%
ValueCountFrequency (%)
464814.717432497 2
< 0.1%
464715.959316893 1
< 0.1%
464474.910004698 1
< 0.1%
464381.406804887 1
< 0.1%
464295.628375287 1
< 0.1%
464208.305428933 1
< 0.1%
464199.048415229 1
< 0.1%
464099.692139359 1
< 0.1%
464080.593904719 2
< 0.1%
464007.070100909 1
< 0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4349 
미용업
2714 
일반미용업
1516 
피부미용업
566 
종합미용업
 
405
Other values (12)
450 

Length

Max length23
Median length19
Mean length4.2846
Min length3

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> 4349
43.5%
미용업 2714
27.1%
일반미용업 1516
 
15.2%
피부미용업 566
 
5.7%
종합미용업 405
 
4.0%
네일미용업 195
 
1.9%
피부미용업, 네일미용업 50
 
0.5%
화장ㆍ분장 미용업 35
 
0.4%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 35
 
0.4%
네일미용업, 화장ㆍ분장 미용업 30
 
0.3%
Other values (7) 105
 
1.1%

Length

2024-05-11T14:34:36.776772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 4349
41.6%
미용업 2878
27.6%
일반미용업 1617
 
15.5%
피부미용업 670
 
6.4%
종합미용업 405
 
3.9%
네일미용업 362
 
3.5%
화장ㆍ분장 164
 
1.6%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)0.5%
Missing5249
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean0.83182488
Minimum0
Maximum40
Zeros3553
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:36.988739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9816329
Coefficient of variation (CV)2.3822718
Kurtosis72.03172
Mean0.83182488
Median Absolute Deviation (MAD)0
Skewness6.0664114
Sum3952
Variance3.9268691
MonotonicityNot monotonic
2024-05-11T14:34:37.216968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3553
35.5%
3 310
 
3.1%
4 281
 
2.8%
2 253
 
2.5%
1 195
 
1.9%
5 81
 
0.8%
6 26
 
0.3%
7 17
 
0.2%
8 8
 
0.1%
10 4
 
< 0.1%
Other values (16) 23
 
0.2%
(Missing) 5249
52.5%
ValueCountFrequency (%)
0 3553
35.5%
1 195
 
1.9%
2 253
 
2.5%
3 310
 
3.1%
4 281
 
2.8%
5 81
 
0.8%
6 26
 
0.3%
7 17
 
0.2%
8 8
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
30 1
 
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
17 3
< 0.1%
16 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.2%
Missing5714
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean0.12832478
Minimum0
Maximum13
Zeros3857
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:37.422591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4967623
Coefficient of variation (CV)3.8711331
Kurtosis155.64789
Mean0.12832478
Median Absolute Deviation (MAD)0
Skewness9.134247
Sum550
Variance0.24677278
MonotonicityNot monotonic
2024-05-11T14:34:37.602807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 3857
38.6%
1 371
 
3.7%
2 30
 
0.3%
3 13
 
0.1%
4 10
 
0.1%
13 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 5714
57.1%
ValueCountFrequency (%)
0 3857
38.6%
1 371
 
3.7%
2 30
 
0.3%
3 13
 
0.1%
4 10
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 10
 
0.1%
3 13
 
0.1%
2 30
 
0.3%
1 371
 
3.7%
0 3857
38.6%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct18
Distinct (%)0.4%
Missing5914
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean0.95031816
Minimum0
Maximum106
Zeros1755
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:37.882756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum106
Range106
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0853816
Coefficient of variation (CV)2.1944036
Kurtosis1579.6685
Mean0.95031816
Median Absolute Deviation (MAD)1
Skewness32.155273
Sum3883
Variance4.3488163
MonotonicityNot monotonic
2024-05-11T14:34:38.220195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1755
 
17.5%
1 1559
 
15.6%
2 468
 
4.7%
3 157
 
1.6%
4 71
 
0.7%
5 31
 
0.3%
6 12
 
0.1%
7 9
 
0.1%
8 8
 
0.1%
9 4
 
< 0.1%
Other values (8) 12
 
0.1%
(Missing) 5914
59.1%
ValueCountFrequency (%)
0 1755
17.5%
1 1559
15.6%
2 468
 
4.7%
3 157
 
1.6%
4 71
 
0.7%
5 31
 
0.3%
6 12
 
0.1%
7 9
 
0.1%
8 8
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
106 1
 
< 0.1%
17 1
 
< 0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 1
 
< 0.1%
11 3
 
< 0.1%
10 2
 
< 0.1%
9 4
< 0.1%
8 8
0.1%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct30
Distinct (%)1.1%
Missing7145
Missing (%)71.5%
Infinite0
Infinite (%)0.0%
Mean2.1509632
Minimum0
Maximum226
Zeros643
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:38.518481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile4
Maximum226
Range226
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.80228
Coefficient of variation (CV)5.9518822
Kurtosis222.81585
Mean2.1509632
Median Absolute Deviation (MAD)0
Skewness14.587988
Sum6141
Variance163.89837
MonotonicityNot monotonic
2024-05-11T14:34:38.779821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1512
 
15.1%
0 643
 
6.4%
2 414
 
4.1%
3 142
 
1.4%
4 59
 
0.6%
5 27
 
0.3%
6 10
 
0.1%
7 9
 
0.1%
8 6
 
0.1%
9 4
 
< 0.1%
Other values (20) 29
 
0.3%
(Missing) 7145
71.5%
ValueCountFrequency (%)
0 643
6.4%
1 1512
15.1%
2 414
 
4.1%
3 142
 
1.4%
4 59
 
0.6%
5 27
 
0.3%
6 10
 
0.1%
7 9
 
0.1%
8 6
 
0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
226 2
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
203 1
 
< 0.1%
202 3
< 0.1%
201 1
 
< 0.1%
120 1
 
< 0.1%
108 1
 
< 0.1%
106 2
< 0.1%
105 1
 
< 0.1%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7479 
0
2365 
1
 
127
2
 
21
3
 
5

Length

Max length4
Median length4
Mean length3.2437
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> 7479
74.8%
0 2365
 
23.6%
1 127
 
1.3%
2 21
 
0.2%
3 5
 
0.1%
4 3
 
< 0.1%

Length

2024-05-11T14:34:39.128880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:39.345207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7479
74.8%
0 2365
 
23.6%
1 127
 
1.3%
2 21
 
0.2%
3 5
 
< 0.1%
4 3
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8651 
0
1208 
1
 
119
2
 
15
3
 
5

Length

Max length4
Median length4
Mean length3.5953
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> 8651
86.5%
0 1208
 
12.1%
1 119
 
1.2%
2 15
 
0.1%
3 5
 
0.1%
4 2
 
< 0.1%

Length

2024-05-11T14:34:39.582219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:39.782338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8651
86.5%
0 1208
 
12.1%
1 119
 
1.2%
2 15
 
0.1%
3 5
 
< 0.1%
4 2
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5876 
0
4124 

Length

Max length4
Median length4
Mean length2.7628
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> 5876
58.8%
0 4124
41.2%

Length

2024-05-11T14:34:40.032824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:40.205585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5876
58.8%
0 4124
41.2%

양실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5876 
0
4124 

Length

Max length4
Median length4
Mean length2.7628
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> 5876
58.8%
0 4124
41.2%

Length

2024-05-11T14:34:40.393049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:40.559014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5876
58.8%
0 4124
41.2%

욕실수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5876 
0
4124 

Length

Max length4
Median length4
Mean length2.7628
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> 5876
58.8%
0 4124
41.2%

Length

2024-05-11T14:34:40.731368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:40.893761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5876
58.8%
0 4124
41.2%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4429
Missing (%)44.3%
Memory size97.7 KiB
False
5567 
True
 
4
(Missing)
4429 
ValueCountFrequency (%)
False 5567
55.7%
True 4
 
< 0.1%
(Missing) 4429
44.3%
2024-05-11T14:34:41.032471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct29
Distinct (%)0.5%
Missing4502
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean3.470171
Minimum0
Maximum214
Zeros651
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:41.206417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum214
Range214
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8910861
Coefficient of variation (CV)1.1212952
Kurtosis1566.1554
Mean3.470171
Median Absolute Deviation (MAD)1
Skewness30.000039
Sum19079
Variance15.140551
MonotonicityNot monotonic
2024-05-11T14:34:41.411148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 1928
19.3%
4 915
 
9.2%
2 879
 
8.8%
0 651
 
6.5%
5 364
 
3.6%
6 261
 
2.6%
8 133
 
1.3%
7 93
 
0.9%
1 75
 
0.8%
10 68
 
0.7%
Other values (19) 131
 
1.3%
(Missing) 4502
45.0%
ValueCountFrequency (%)
0 651
 
6.5%
1 75
 
0.8%
2 879
8.8%
3 1928
19.3%
4 915
9.2%
5 364
 
3.6%
6 261
 
2.6%
7 93
 
0.9%
8 133
 
1.3%
9 32
 
0.3%
ValueCountFrequency (%)
214 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
32 1
 
< 0.1%
30 2
 
< 0.1%
24 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
20 7
0.1%
19 3
< 0.1%
Distinct2
Distinct (%)66.7%
Missing9997
Missing (%)> 99.9%
Memory size156.2 KiB
2024-05-11T14:34:41.659918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length69
Mean length57.666667
Min length35

Characters and Unicode

Total characters173
Distinct characters55
Distinct categories4 ?
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 (%)33.3%

Sample

1st row이 영업신고의 효력은 건축물 임시사용승인기간 2012.4.30까지이며 건축물 사용승인 연장 또는 준공 완료시 재신청 해야 함
2nd row본 영업신고증은 건축물 등재 전으로 아래 조건부기간까지만 유효함
3rd row이 영업신고의 효력은 건축물 임시사용승인기간 2012.4.30까지이며 건축물 사용승인 연장 또는 준공 완료시 재신청 해야 함
ValueCountFrequency (%)
건축물 5
 
13.2%
2
 
5.3%
또는 2
 
5.3%
2
 
5.3%
해야 2
 
5.3%
재신청 2
 
5.3%
영업신고의 2
 
5.3%
준공 2
 
5.3%
완료시 2
 
5.3%
연장 2
 
5.3%
Other values (11) 15
39.5%
2024-05-11T14:34:42.168581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
 
20.2%
6
 
3.5%
5
 
2.9%
5
 
2.9%
5
 
2.9%
4
 
2.3%
4
 
2.3%
4
 
2.3%
4
 
2.3%
4
 
2.3%
Other values (45) 97
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120
69.4%
Space Separator 35
 
20.2%
Decimal Number 14
 
8.1%
Other Punctuation 4
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
5.0%
5
 
4.2%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (38) 75
62.5%
Decimal Number
ValueCountFrequency (%)
2 4
28.6%
0 4
28.6%
3 2
14.3%
4 2
14.3%
1 2
14.3%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120
69.4%
Common 53
30.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
5.0%
5
 
4.2%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (38) 75
62.5%
Common
ValueCountFrequency (%)
35
66.0%
2 4
 
7.5%
0 4
 
7.5%
. 4
 
7.5%
3 2
 
3.8%
4 2
 
3.8%
1 2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120
69.4%
ASCII 53
30.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35
66.0%
2 4
 
7.5%
0 4
 
7.5%
. 4
 
7.5%
3 2
 
3.8%
4 2
 
3.8%
1 2
 
3.8%
Hangul
ValueCountFrequency (%)
6
 
5.0%
5
 
4.2%
5
 
4.2%
5
 
4.2%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
4
 
3.3%
Other values (38) 75
62.5%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
20120301
 
2
20131029
 
1

Length

Max length8
Median length4
Mean length4.0012
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> 9997
> 99.9%
20120301 2
 
< 0.1%
20131029 1
 
< 0.1%

Length

2024-05-11T14:34:42.401526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:42.575105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
20120301 2
 
< 0.1%
20131029 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
20120430
 
2
20141028
 
1

Length

Max length8
Median length4
Mean length4.0012
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> 9997
> 99.9%
20120430 2
 
< 0.1%
20141028 1
 
< 0.1%

Length

2024-05-11T14:34:42.791723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:42.969801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
20120430 2
 
< 0.1%
20141028 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8948 
임대
1032 
자가
 
20

Length

Max length4
Median length4
Mean length3.7896
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> 8948
89.5%
임대 1032
 
10.3%
자가 20
 
0.2%

Length

2024-05-11T14:34:43.147824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:43.316016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8948
89.5%
임대 1032
 
10.3%
자가 20
 
0.2%

세탁기수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7035 
0
2965 

Length

Max length4
Median length4
Mean length3.1105
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> 7035
70.3%
0 2965
29.6%

Length

2024-05-11T14:34:43.493705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:43.651408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7035
70.3%
0 2965
29.6%

여성종사자수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)0.6%
Missing8019
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean0.23725391
Minimum0
Maximum41
Zeros1683
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:43.799002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1695831
Coefficient of variation (CV)4.9296683
Kurtosis760.40713
Mean0.23725391
Median Absolute Deviation (MAD)0
Skewness23.312137
Sum470
Variance1.3679246
MonotonicityNot monotonic
2024-05-11T14:34:43.960585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1683
 
16.8%
1 235
 
2.4%
2 39
 
0.4%
4 7
 
0.1%
3 7
 
0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
41 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 8019
80.2%
ValueCountFrequency (%)
0 1683
16.8%
1 235
 
2.4%
2 39
 
0.4%
3 7
 
0.1%
4 7
 
0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
14 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
6 2
 
< 0.1%
5 3
 
< 0.1%
4 7
 
0.1%
3 7
 
0.1%
2 39
0.4%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8021 
0
1936 
1
 
33
2
 
8
4
 
2

Length

Max length4
Median length4
Mean length3.4063
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> 8021
80.2%
0 1936
 
19.4%
1 33
 
0.3%
2 8
 
0.1%
4 2
 
< 0.1%

Length

2024-05-11T14:34:44.151986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:44.320879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 8021
80.2%
0 1936
 
19.4%
1 33
 
0.3%
2 8
 
0.1%
4 2
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
7183 
0
2817 

Length

Max length4
Median length4
Mean length3.1549
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> 7183
71.8%
0 2817
 
28.2%

Length

2024-05-11T14:34:44.534992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:34:44.725955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 7183
71.8%
0 2817
 
28.2%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)0.7%
Missing7239
Missing (%)72.4%
Infinite0
Infinite (%)0.0%
Mean0.96631655
Minimum0
Maximum20
Zeros1982
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:34:44.929038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1513282
Coefficient of variation (CV)2.2263182
Kurtosis14.536551
Mean0.96631655
Median Absolute Deviation (MAD)0
Skewness3.3533495
Sum2668
Variance4.6282128
MonotonicityNot monotonic
2024-05-11T14:34:45.141660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1982
 
19.8%
2 224
 
2.2%
1 175
 
1.8%
3 129
 
1.3%
4 67
 
0.7%
5 54
 
0.5%
6 31
 
0.3%
7 26
 
0.3%
8 24
 
0.2%
10 15
 
0.1%
Other values (10) 34
 
0.3%
(Missing) 7239
72.4%
ValueCountFrequency (%)
0 1982
19.8%
1 175
 
1.8%
2 224
 
2.2%
3 129
 
1.3%
4 67
 
0.7%
5 54
 
0.5%
6 31
 
0.3%
7 26
 
0.3%
8 24
 
0.2%
9 13
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 3
 
< 0.1%
14 3
 
< 0.1%
13 2
 
< 0.1%
12 4
 
< 0.1%
11 4
 
< 0.1%
10 15
0.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4349
Missing (%)43.5%
Memory size97.7 KiB
False
5645 
True
 
6
(Missing)
4349 
ValueCountFrequency (%)
False 5645
56.5%
True 6
 
0.1%
(Missing) 4349
43.5%
2024-05-11T14:34:45.322473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
637830300003030000-212-2024-000052024-03-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>32.01133-020서울특별시 성동구 하왕십리동 1070 센트라스서울특별시 성동구 왕십리로 410, I동 117호 (하왕십리동, 센트라스)4701헤일리 뷰티2024-03-06 11:18:04I2023-12-03 00:08:00.0피부미용업202372.912024451536.680877<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
824232400003240000-204-1991-011391991-03-30<NA>1영업/정상1영업<NA><NA><NA><NA>020474651032.10134-890서울특별시 강동구 성내동 426-24서울특별시 강동구 양재대로89길 62, 1층 103호 (성내동)5403머리하는날2024-04-08 15:46:12U2023-12-03 23:00:00.0일반미용업211616.133364447245.285999<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1618530300003030000-213-2017-000102017-11-28<NA>3폐업2폐업2024-02-05<NA><NA><NA><NA>53.17133-827서울특별시 성동구 성수동2가 331-59서울특별시 성동구 뚝섬로9길 3-1, 2층 (성수동2가)4784잭앤클리퍼(JackandClipper)2024-02-05 15:28:06U2023-12-02 00:07:00.0일반미용업204946.913757448493.266318<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
850730700003070000-211-2015-000282015-05-22<NA>3폐업2폐업2023-02-27<NA><NA><NA><NA>40.62136-051서울특별시 성북구 동선동1가 85-97 2층서울특별시 성북구 동소문로22길 29-2 (동선동1가)2845맷블랙(MATT BLACK HAIR)2023-02-27 14:43:35U2022-12-03 00:01:00.0일반미용업201536.236365454372.060297<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
897932200003220000-212-2016-0014220160122<NA>1영업/정상1영업<NA><NA><NA><NA>30152121272.05135825서울특별시 강남구 논현동 143-6서울특별시 강남구 강남대로 528, 지상12층 (논현동)6114아이오마 끌레르 신논현점2022-05-24 10:01:41I2021-12-04 22:06:00.0피부미용업201930.835054445229.332205<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
290030800003080000-222-2023-000032023-08-31<NA>1영업/정상1영업<NA><NA><NA><NA><NA>39.00142-878서울특별시 강북구 수유동 223-27 4층서울특별시 강북구 노해로8길 9-20, 4층 (수유동)1072벨라수뷰티2023-08-31 10:13:41I2022-12-09 00:02:00.0메이크업업202006.083443459592.130285<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1107331500003150000-211-2021-0006120211005<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.00157210서울특별시 강서구 마곡동 794-1 우성에스비타워서울특별시 강서구 강서로 385, 우성에스비타워 1009-1010 일부호 (마곡동)7803라봄 살롱드코라2023-01-16 11:53:08U2022-11-30 23:08:00.0일반미용업185642.667278450883.006755<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
530431500003150000-212-2024-000092024-02-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>178.12157-210서울특별시 강서구 마곡동 794-1 우성에스비타워 611~2호서울특별시 강서구 강서로 385, 우성에스비타워 6층 611,612호 (마곡동)7803보떼벨르2024-02-02 15:30:21I2023-12-02 00:04:00.0피부미용업185642.667278450883.006755<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
594332400003240000-219-2024-000012024-02-23<NA>1영업/정상1영업<NA><NA><NA><NA><NA>47.21134-890서울특별시 강동구 성내동 426-11 RUN J서울특별시 강동구 양재대로89길 56-5, RUN J 1층 (성내동)5403미마루(memaru)2024-02-23 11:17:57I2023-12-01 22:05:00.0메이크업업211646.230185447257.187843<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
82132000003200000-213-2024-000132024-04-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.10151-832서울특별시 관악구 봉천동 1656-27서울특별시 관악구 인헌길 10, 1층 일부호 (봉천동)8793윤슬네일2024-04-03 15:00:53I2023-12-04 00:05:00.0네일아트업196996.997651441448.62699<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
1131830000003000000-211-2020-0000620201105<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.00110826서울특별시 종로구 숭인동 221서울특별시 종로구 종로 368, 2층 (숭인동)3115행운2020-11-05 15:03:00I2020-11-17 00:23:09.0일반미용업201559.999855452366.842565일반미용업0022<NA><NA>000N4<NA><NA><NA><NA>00000N
1865830400003040000-204-1989-0068919891107<NA>3폐업2폐업20100303<NA><NA><NA>02 456335320.00143805서울특별시 광진구 광장동 245-11번지<NA><NA>꽃송이미용실2003-03-06 00:00:00I2018-08-31 23:59:59.0일반미용업209081.410882449307.040939미용업3<NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
867530100003010000-213-2011-0000320110324<NA>3폐업2폐업20140326<NA><NA><NA>02 310 133521.97100011서울특별시 중구 충무로1가 52-5번지 신세계백화점 4층서울특별시 중구 소공로 63, 4층 (충무로1가, 신세계백화점)4530크리스찬네일2012-03-19 11:07:48I2018-08-31 23:59:59.0네일아트업198263.908392450960.762965종합미용업004400000N4<NA><NA><NA><NA>00000N
1282030100003010000-211-2011-0000120110113<NA>1영업/정상1영업<NA><NA><NA><NA>022285215076.00100851서울특별시 중구 을지로6가 18-84번지 미진빌딩 302호서울특별시 중구 을지로43길 30 (을지로6가,미진빌딩 302호)4564벨라뮤트헤어2011-08-02 13:46:24I2018-08-31 23:59:59.0일반미용업200588.95991451679.88405일반미용업503000000N6<NA><NA><NA>임대00000N
942632100003210000-211-2022-0001120220329<NA>1영업/정상1영업<NA><NA><NA><NA>02 573 433922.27137900서울특별시 서초구 우면동 67 한라상가 106호서울특별시 서초구 바우뫼로 31, 한라아파트 상가 1층 106호 (우면동)6762나이스가이2022-03-29 11:08:33I2021-12-02 21:01:00.0일반미용업202220.497179441039.412939<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1467930200003020000-215-2016-000032016-01-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>30.04140-854서울특별시 용산구 이촌동 302-52 LG프라자서울특별시 용산구 이촌로 200, LG프라자 지하1층 107호 (이촌동)4427희네일(Hee Nail)2023-07-24 13:29:11U2022-12-06 22:06:00.0네일아트업197221.439324446524.508386<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2018630400003040000-211-2013-0006820130910<NA>3폐업2폐업20160913<NA><NA><NA>02 444 625826.00143891서울특별시 광진구 중곡동 138-29번지서울특별시 광진구 용마산로3길 102 (중곡동)4926한스헤어2013-09-10 13:35:20I2018-08-31 23:59:59.0일반미용업207249.353673450575.205065일반미용업10<NA><NA><NA><NA>000N3<NA><NA><NA><NA>0<NA><NA>00N
1000132400003240000-219-2020-0000520200601<NA>1영업/정상1영업<NA><NA><NA><NA><NA>31.53134080서울특별시 강동구 고덕동 694 고덕그라시움(제4상가)서울특별시 강동구 고덕로 385, 고덕그라시움(제4상가) 지하1층 B103호 (고덕동)5223핸드메이드2022-07-20 16:28:18I2021-12-06 22:03:00.0메이크업업214687.925331450626.949693<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
819232300003230000-204-1986-0158719860805<NA>3폐업2폐업19970129<NA><NA><NA>02 0000010.88138210서울특별시 송파구 장지동 산 88-29번지<NA><NA>진화2002-09-04 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
2230530500003050000-211-2011-0002520110627<NA>3폐업2폐업20171130<NA><NA><NA><NA>33.00130842서울특별시 동대문구 장안동 382-18번지서울특별시 동대문구 답십리로68길 71, 1층 (장안동)2624이제이헤어?2017-11-30 10:25:10I2018-08-31 23:59:59.0일반미용업205956.617847451905.713361일반미용업0011<NA><NA>000N3<NA><NA><NA><NA>0<NA><NA>00N