Overview

Dataset statistics

Number of variables47
Number of observations2359
Missing cells22276
Missing cells (%)20.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory930.8 KiB
Average record size in memory404.1 B

Variable types

Categorical20
Text7
DateTime4
Unsupported4
Numeric10
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author동대문구
URLhttps://data.seoul.go.kr/dataList/OA-17907/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (55.2%)Imbalance
사용시작지하층 is highly imbalanced (72.6%)Imbalance
사용끝지하층 is highly imbalanced (73.0%)Imbalance
조건부허가시작일자 is highly imbalanced (99.2%)Imbalance
조건부허가종료일자 is highly imbalanced (99.1%)Imbalance
건물소유구분명 is highly imbalanced (54.3%)Imbalance
여성종사자수 is highly imbalanced (68.2%)Imbalance
남성종사자수 is highly imbalanced (51.0%)Imbalance
인허가취소일자 has 2359 (100.0%) missing valuesMissing
폐업일자 has 1008 (42.7%) missing valuesMissing
휴업시작일자 has 2359 (100.0%) missing valuesMissing
휴업종료일자 has 2359 (100.0%) missing valuesMissing
재개업일자 has 2359 (100.0%) missing valuesMissing
전화번호 has 1109 (47.0%) missing valuesMissing
도로명주소 has 628 (26.6%) missing valuesMissing
도로명우편번호 has 641 (27.2%) missing valuesMissing
좌표정보(X) has 62 (2.6%) missing valuesMissing
좌표정보(Y) has 62 (2.6%) missing valuesMissing
건물지상층수 has 790 (33.5%) missing valuesMissing
건물지하층수 has 1177 (49.9%) missing valuesMissing
사용시작지상층 has 888 (37.6%) missing valuesMissing
사용끝지상층 has 921 (39.0%) missing valuesMissing
발한실여부 has 556 (23.6%) missing valuesMissing
좌석수 has 626 (26.5%) missing valuesMissing
조건부허가신고사유 has 2357 (99.9%) missing valuesMissing
침대수 has 1489 (63.1%) missing valuesMissing
다중이용업소여부 has 523 (22.2%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적 has 56 (2.4%) zerosZeros
건물지상층수 has 879 (37.3%) zerosZeros
건물지하층수 has 968 (41.0%) zerosZeros
사용시작지상층 has 55 (2.3%) zerosZeros
사용끝지상층 has 66 (2.8%) zerosZeros
좌석수 has 153 (6.5%) zerosZeros
침대수 has 597 (25.3%) zerosZeros

Reproduction

Analysis started2024-05-11 05:42:01.946028
Analysis finished2024-05-11 05:42:03.756297
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
3050000
2359 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3050000
2nd row3050000
3rd row3050000
4th row3050000
5th row3050000

Common Values

ValueCountFrequency (%)
3050000 2359
100.0%

Length

2024-05-11T14:42:03.893630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:04.018862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3050000 2359
100.0%

관리번호
Text

UNIQUE 

Distinct2359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2024-05-11T14:42:04.256989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters51898
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

Unique2359 ?
Unique (%)100.0%

Sample

1st row3050000-204-1977-01190
2nd row3050000-204-1977-01206
3rd row3050000-204-1977-01412
4th row3050000-204-1977-01504
5th row3050000-204-1977-01645
ValueCountFrequency (%)
3050000-204-1977-01190 1
 
< 0.1%
3050000-212-2011-00015 1
 
< 0.1%
3050000-212-2013-00004 1
 
< 0.1%
3050000-212-2012-00017 1
 
< 0.1%
3050000-212-2012-00018 1
 
< 0.1%
3050000-212-2012-00019 1
 
< 0.1%
3050000-212-2013-00001 1
 
< 0.1%
3050000-212-2013-00002 1
 
< 0.1%
3050000-212-2013-00003 1
 
< 0.1%
3050000-212-2013-00005 1
 
< 0.1%
Other values (2349) 2349
99.6%
2024-05-11T14:42:04.677569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 23123
44.6%
- 7077
 
13.6%
2 6094
 
11.7%
1 5198
 
10.0%
3 3499
 
6.7%
5 3046
 
5.9%
4 1172
 
2.3%
9 1028
 
2.0%
8 598
 
1.2%
6 542
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44821
86.4%
Dash Punctuation 7077
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23123
51.6%
2 6094
 
13.6%
1 5198
 
11.6%
3 3499
 
7.8%
5 3046
 
6.8%
4 1172
 
2.6%
9 1028
 
2.3%
8 598
 
1.3%
6 542
 
1.2%
7 521
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 7077
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23123
44.6%
- 7077
 
13.6%
2 6094
 
11.7%
1 5198
 
10.0%
3 3499
 
6.7%
5 3046
 
5.9%
4 1172
 
2.3%
9 1028
 
2.0%
8 598
 
1.2%
6 542
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23123
44.6%
- 7077
 
13.6%
2 6094
 
11.7%
1 5198
 
10.0%
3 3499
 
6.7%
5 3046
 
5.9%
4 1172
 
2.3%
9 1028
 
2.0%
8 598
 
1.2%
6 542
 
1.0%
Distinct1874
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
Minimum1977-06-02 00:00:00
Maximum2024-05-07 00:00:00
2024-05-11T14:42:04.832228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:04.976609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2359
Missing (%)100.0%
Memory size20.9 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
3
1351 
1
1008 

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 1351
57.3%
1 1008
42.7%

Length

2024-05-11T14:42:05.114594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:05.210989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1351
57.3%
1 1008
42.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
폐업
1351 
영업/정상
1008 

Length

Max length5
Median length2
Mean length3.2818991
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1351
57.3%
영업/정상 1008
42.7%

Length

2024-05-11T14:42:05.381475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:05.525150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1351
57.3%
영업/정상 1008
42.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2
1351 
1
1008 

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 1351
57.3%
1 1008
42.7%

Length

2024-05-11T14:42:05.656182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:05.814035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1351
57.3%
1 1008
42.7%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
폐업
1351 
영업
1008 

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 1351
57.3%
영업 1008
42.7%

Length

2024-05-11T14:42:05.958307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:06.069714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1351
57.3%
영업 1008
42.7%

폐업일자
Date

MISSING 

Distinct1117
Distinct (%)82.7%
Missing1008
Missing (%)42.7%
Memory size18.6 KiB
Minimum2003-02-25 00:00:00
Maximum2024-05-03 00:00:00
2024-05-11T14:42:06.195494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:06.364914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2359
Missing (%)100.0%
Memory size20.9 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2359
Missing (%)100.0%
Memory size20.9 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2359
Missing (%)100.0%
Memory size20.9 KiB

전화번호
Text

MISSING 

Distinct1169
Distinct (%)93.5%
Missing1109
Missing (%)47.0%
Memory size18.6 KiB
2024-05-11T14:42:06.638624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.1272
Min length6

Characters and Unicode

Total characters12659
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

Unique1099 ?
Unique (%)87.9%

Sample

1st row02 9262158
2nd row0209656526
3rd row0222475296
4th row0209636862
5th row02 9621374
ValueCountFrequency (%)
02 478
 
26.4%
02959 6
 
0.3%
02960 5
 
0.3%
02963 5
 
0.3%
02965 4
 
0.2%
0222481383 3
 
0.2%
02966 3
 
0.2%
02957 3
 
0.2%
02969 3
 
0.2%
070 3
 
0.2%
Other values (1205) 1300
71.7%
2024-05-11T14:42:07.123126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3252
25.7%
0 1938
15.3%
9 1136
 
9.0%
6 999
 
7.9%
4 958
 
7.6%
1 815
 
6.4%
5 799
 
6.3%
7 777
 
6.1%
3 764
 
6.0%
628
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12031
95.0%
Space Separator 628
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3252
27.0%
0 1938
16.1%
9 1136
 
9.4%
6 999
 
8.3%
4 958
 
8.0%
1 815
 
6.8%
5 799
 
6.6%
7 777
 
6.5%
3 764
 
6.4%
8 593
 
4.9%
Space Separator
ValueCountFrequency (%)
628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12659
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3252
25.7%
0 1938
15.3%
9 1136
 
9.0%
6 999
 
7.9%
4 958
 
7.6%
1 815
 
6.4%
5 799
 
6.3%
7 777
 
6.1%
3 764
 
6.0%
628
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3252
25.7%
0 1938
15.3%
9 1136
 
9.0%
6 999
 
7.9%
4 958
 
7.6%
1 815
 
6.4%
5 799
 
6.3%
7 777
 
6.1%
3 764
 
6.0%
628
 
5.0%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1143
Distinct (%)48.5%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40.261694
Minimum0
Maximum513
Zeros56
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:07.300468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.51
Q120
median28.61
Q344.04
95-th percentile115.55
Maximum513
Range513
Interquartile range (IQR)24.04

Descriptive statistics

Standard deviation39.147915
Coefficient of variation (CV)0.97233651
Kurtosis28.803379
Mean40.261694
Median Absolute Deviation (MAD)10.39
Skewness4.0287622
Sum94856.55
Variance1532.5592
MonotonicityNot monotonic
2024-05-11T14:42:07.483520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.0 87
 
3.7%
0.0 56
 
2.4%
26.4 43
 
1.8%
20.0 42
 
1.8%
30.0 40
 
1.7%
16.5 31
 
1.3%
21.0 29
 
1.2%
23.0 27
 
1.1%
23.1 23
 
1.0%
15.0 21
 
0.9%
Other values (1133) 1957
83.0%
ValueCountFrequency (%)
0.0 56
2.4%
3.0 1
 
< 0.1%
3.08 1
 
< 0.1%
3.3 1
 
< 0.1%
4.48 1
 
< 0.1%
4.5 1
 
< 0.1%
5.0 2
 
0.1%
5.38 1
 
< 0.1%
5.6 1
 
< 0.1%
6.16 1
 
< 0.1%
ValueCountFrequency (%)
513.0 2
0.1%
444.19 1
 
< 0.1%
287.0 1
 
< 0.1%
285.15 1
 
< 0.1%
281.75 1
 
< 0.1%
271.92 1
 
< 0.1%
271.0 1
 
< 0.1%
264.66 1
 
< 0.1%
231.4 3
0.1%
220.0 1
 
< 0.1%
Distinct156
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2024-05-11T14:42:07.924121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1216617
Min length6

Characters and Unicode

Total characters14441
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

Unique25 ?
Unique (%)1.1%

Sample

1st row130862
2nd row130860
3rd row130868
4th row130836
5th row130865
ValueCountFrequency (%)
130840 128
 
5.4%
130842 96
 
4.1%
130872 89
 
3.8%
130827 62
 
2.6%
130867 58
 
2.5%
130837 57
 
2.4%
130851 57
 
2.4%
130817 56
 
2.4%
130883 56
 
2.4%
130839 56
 
2.4%
Other values (146) 1644
69.7%
2024-05-11T14:42:08.537510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3104
21.5%
3 2906
20.1%
1 2718
18.8%
8 2526
17.5%
7 602
 
4.2%
4 600
 
4.2%
2 551
 
3.8%
5 525
 
3.6%
6 438
 
3.0%
- 287
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14154
98.0%
Dash Punctuation 287
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3104
21.9%
3 2906
20.5%
1 2718
19.2%
8 2526
17.8%
7 602
 
4.3%
4 600
 
4.2%
2 551
 
3.9%
5 525
 
3.7%
6 438
 
3.1%
9 184
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14441
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3104
21.5%
3 2906
20.1%
1 2718
18.8%
8 2526
17.5%
7 602
 
4.2%
4 600
 
4.2%
2 551
 
3.8%
5 525
 
3.6%
6 438
 
3.0%
- 287
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14441
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3104
21.5%
3 2906
20.1%
1 2718
18.8%
8 2526
17.5%
7 602
 
4.2%
4 600
 
4.2%
2 551
 
3.8%
5 525
 
3.6%
6 438
 
3.0%
- 287
 
2.0%
Distinct2066
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2024-05-11T14:42:09.034142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length53
Mean length26.104281
Min length17

Characters and Unicode

Total characters61580
Distinct characters297
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

Unique1842 ?
Unique (%)78.1%

Sample

1st row서울특별시 동대문구 제기동 576-0번지
2nd row서울특별시 동대문구 제기동 138-9번지
3rd row서울특별시 동대문구 청량리동 205-180번지
4th row서울특별시 동대문구 장안동 129-22번지
5th row서울특별시 동대문구 제기동 1142-6번지 19통1반 (회기로30)
ValueCountFrequency (%)
서울특별시 2359
21.6%
동대문구 2359
21.6%
장안동 656
 
6.0%
1층 399
 
3.6%
전농동 343
 
3.1%
답십리동 333
 
3.0%
이문동 256
 
2.3%
휘경동 178
 
1.6%
용두동 155
 
1.4%
청량리동 140
 
1.3%
Other values (2315) 3761
34.4%
2024-05-11T14:42:09.698781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10455
 
17.0%
4823
 
7.8%
2645
 
4.3%
1 2545
 
4.1%
2433
 
4.0%
2391
 
3.9%
2365
 
3.8%
2363
 
3.8%
2362
 
3.8%
2360
 
3.8%
Other values (287) 26838
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36029
58.5%
Decimal Number 12033
 
19.5%
Space Separator 10455
 
17.0%
Dash Punctuation 2170
 
3.5%
Close Punctuation 408
 
0.7%
Open Punctuation 408
 
0.7%
Uppercase Letter 51
 
0.1%
Other Punctuation 20
 
< 0.1%
Lowercase Letter 4
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4823
13.4%
2645
 
7.3%
2433
 
6.8%
2391
 
6.6%
2365
 
6.6%
2363
 
6.6%
2362
 
6.6%
2360
 
6.6%
2359
 
6.5%
1627
 
4.5%
Other values (250) 10301
28.6%
Uppercase Letter
ValueCountFrequency (%)
S 11
21.6%
K 11
21.6%
A 6
11.8%
Y 5
9.8%
L 3
 
5.9%
E 2
 
3.9%
W 2
 
3.9%
T 2
 
3.9%
G 1
 
2.0%
F 1
 
2.0%
Other values (7) 7
13.7%
Decimal Number
ValueCountFrequency (%)
1 2545
21.2%
2 1736
14.4%
3 1635
13.6%
4 1048
8.7%
5 995
 
8.3%
0 920
 
7.6%
6 881
 
7.3%
9 785
 
6.5%
8 765
 
6.4%
7 723
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 14
70.0%
@ 3
 
15.0%
. 2
 
10.0%
* 1
 
5.0%
Space Separator
ValueCountFrequency (%)
10455
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 408
100.0%
Open Punctuation
ValueCountFrequency (%)
( 408
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36029
58.5%
Common 25496
41.4%
Latin 55
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4823
13.4%
2645
 
7.3%
2433
 
6.8%
2391
 
6.6%
2365
 
6.6%
2363
 
6.6%
2362
 
6.6%
2360
 
6.6%
2359
 
6.5%
1627
 
4.5%
Other values (250) 10301
28.6%
Common
ValueCountFrequency (%)
10455
41.0%
1 2545
 
10.0%
- 2170
 
8.5%
2 1736
 
6.8%
3 1635
 
6.4%
4 1048
 
4.1%
5 995
 
3.9%
0 920
 
3.6%
6 881
 
3.5%
9 785
 
3.1%
Other values (9) 2326
 
9.1%
Latin
ValueCountFrequency (%)
S 11
20.0%
K 11
20.0%
A 6
10.9%
Y 5
9.1%
e 4
 
7.3%
L 3
 
5.5%
E 2
 
3.6%
W 2
 
3.6%
T 2
 
3.6%
G 1
 
1.8%
Other values (8) 8
14.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36029
58.5%
ASCII 25551
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10455
40.9%
1 2545
 
10.0%
- 2170
 
8.5%
2 1736
 
6.8%
3 1635
 
6.4%
4 1048
 
4.1%
5 995
 
3.9%
0 920
 
3.6%
6 881
 
3.4%
9 785
 
3.1%
Other values (27) 2381
 
9.3%
Hangul
ValueCountFrequency (%)
4823
13.4%
2645
 
7.3%
2433
 
6.8%
2391
 
6.6%
2365
 
6.6%
2363
 
6.6%
2362
 
6.6%
2360
 
6.6%
2359
 
6.5%
1627
 
4.5%
Other values (250) 10301
28.6%

도로명주소
Text

MISSING 

Distinct1490
Distinct (%)86.1%
Missing628
Missing (%)26.6%
Memory size18.6 KiB
2024-05-11T14:42:10.067738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length54
Mean length32.652224
Min length23

Characters and Unicode

Total characters56521
Distinct characters279
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

Unique1302 ?
Unique (%)75.2%

Sample

1st row서울특별시 동대문구 답십리로63길 47 (장안동)
2nd row서울특별시 동대문구 전농로29길 68, 1층 (전농동)
3rd row서울특별시 동대문구 사가정로20길 49 (전농동)
4th row서울특별시 동대문구 천호대로77나길 3 (장안동)
5th row서울특별시 동대문구 정릉천동로 90, 110호 (제기동, 현대아파트상가)
ValueCountFrequency (%)
서울특별시 1731
 
15.6%
동대문구 1731
 
15.6%
1층 1033
 
9.3%
장안동 476
 
4.3%
전농동 253
 
2.3%
답십리동 242
 
2.2%
2층 234
 
2.1%
이문동 172
 
1.6%
휘경동 135
 
1.2%
용두동 109
 
1.0%
Other values (1155) 4967
44.8%
2024-05-11T14:42:10.874427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9362
 
16.6%
3656
 
6.5%
1 2789
 
4.9%
2025
 
3.6%
1995
 
3.5%
, 1863
 
3.3%
1820
 
3.2%
1779
 
3.1%
1773
 
3.1%
) 1759
 
3.1%
Other values (269) 27700
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32794
58.0%
Space Separator 9362
 
16.6%
Decimal Number 8672
 
15.3%
Other Punctuation 1866
 
3.3%
Close Punctuation 1759
 
3.1%
Open Punctuation 1759
 
3.1%
Dash Punctuation 230
 
0.4%
Uppercase Letter 74
 
0.1%
Math Symbol 3
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3656
 
11.1%
2025
 
6.2%
1995
 
6.1%
1820
 
5.5%
1779
 
5.4%
1773
 
5.4%
1735
 
5.3%
1731
 
5.3%
1731
 
5.3%
1731
 
5.3%
Other values (234) 12818
39.1%
Uppercase Letter
ValueCountFrequency (%)
S 18
24.3%
K 17
23.0%
A 10
13.5%
B 8
10.8%
Y 5
 
6.8%
L 3
 
4.1%
W 2
 
2.7%
E 2
 
2.7%
T 2
 
2.7%
O 1
 
1.4%
Other values (6) 6
 
8.1%
Decimal Number
ValueCountFrequency (%)
1 2789
32.2%
2 1365
15.7%
3 918
 
10.6%
0 727
 
8.4%
4 617
 
7.1%
6 560
 
6.5%
5 494
 
5.7%
8 469
 
5.4%
7 432
 
5.0%
9 301
 
3.5%
Other Punctuation
ValueCountFrequency (%)
, 1863
99.8%
@ 2
 
0.1%
. 1
 
0.1%
Space Separator
ValueCountFrequency (%)
9362
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1759
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1759
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 230
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32794
58.0%
Common 23651
41.8%
Latin 76
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3656
 
11.1%
2025
 
6.2%
1995
 
6.1%
1820
 
5.5%
1779
 
5.4%
1773
 
5.4%
1735
 
5.3%
1731
 
5.3%
1731
 
5.3%
1731
 
5.3%
Other values (234) 12818
39.1%
Common
ValueCountFrequency (%)
9362
39.6%
1 2789
 
11.8%
, 1863
 
7.9%
) 1759
 
7.4%
( 1759
 
7.4%
2 1365
 
5.8%
3 918
 
3.9%
0 727
 
3.1%
4 617
 
2.6%
6 560
 
2.4%
Other values (8) 1932
 
8.2%
Latin
ValueCountFrequency (%)
S 18
23.7%
K 17
22.4%
A 10
13.2%
B 8
10.5%
Y 5
 
6.6%
L 3
 
3.9%
W 2
 
2.6%
E 2
 
2.6%
T 2
 
2.6%
e 2
 
2.6%
Other values (7) 7
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32794
58.0%
ASCII 23727
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9362
39.5%
1 2789
 
11.8%
, 1863
 
7.9%
) 1759
 
7.4%
( 1759
 
7.4%
2 1365
 
5.8%
3 918
 
3.9%
0 727
 
3.1%
4 617
 
2.6%
6 560
 
2.4%
Other values (25) 2008
 
8.5%
Hangul
ValueCountFrequency (%)
3656
 
11.1%
2025
 
6.2%
1995
 
6.1%
1820
 
5.5%
1779
 
5.4%
1773
 
5.4%
1735
 
5.3%
1731
 
5.3%
1731
 
5.3%
1731
 
5.3%
Other values (234) 12818
39.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct215
Distinct (%)12.5%
Missing641
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean2539.9488
Minimum2400
Maximum2646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:11.082103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2400
5-th percentile2423
Q12484
median2541
Q32604
95-th percentile2638
Maximum2646
Range246
Interquartile range (IQR)120

Descriptive statistics

Standard deviation70.116724
Coefficient of variation (CV)0.027605566
Kurtosis-1.1318104
Mean2539.9488
Median Absolute Deviation (MAD)61
Skewness-0.20296173
Sum4363632
Variance4916.355
MonotonicityNot monotonic
2024-05-11T14:42:11.261531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2624 58
 
2.5%
2637 33
 
1.4%
2453 28
 
1.2%
2452 28
 
1.2%
2507 27
 
1.1%
2615 27
 
1.1%
2524 24
 
1.0%
2639 24
 
1.0%
2636 23
 
1.0%
2638 21
 
0.9%
Other values (205) 1425
60.4%
(Missing) 641
27.2%
ValueCountFrequency (%)
2400 3
 
0.1%
2401 1
 
< 0.1%
2403 3
 
0.1%
2404 3
 
0.1%
2405 3
 
0.1%
2406 8
0.3%
2407 1
 
< 0.1%
2409 8
0.3%
2410 9
0.4%
2411 1
 
< 0.1%
ValueCountFrequency (%)
2646 2
 
0.1%
2645 4
 
0.2%
2644 16
0.7%
2643 15
0.6%
2642 8
 
0.3%
2641 6
 
0.3%
2640 8
 
0.3%
2639 24
1.0%
2638 21
0.9%
2637 33
1.4%
Distinct2053
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
2024-05-11T14:42:11.561236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length5.7846545
Min length1

Characters and Unicode

Total characters13646
Distinct characters666
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1832 ?
Unique (%)77.7%

Sample

1st row동화
2nd row
3rd row화신미장원
4th row라인헤어
5th row태양
ValueCountFrequency (%)
헤어 42
 
1.5%
hair 30
 
1.1%
네일 26
 
0.9%
미용실 20
 
0.7%
태후사랑 14
 
0.5%
nail 14
 
0.5%
청량리점 10
 
0.4%
살롱 9
 
0.3%
salon 9
 
0.3%
에스테틱 8
 
0.3%
Other values (2189) 2642
93.6%
2024-05-11T14:42:12.091552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
910
 
6.7%
866
 
6.3%
466
 
3.4%
405
 
3.0%
363
 
2.7%
283
 
2.1%
276
 
2.0%
265
 
1.9%
258
 
1.9%
220
 
1.6%
Other values (656) 9334
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11263
82.5%
Lowercase Letter 852
 
6.2%
Uppercase Letter 480
 
3.5%
Space Separator 466
 
3.4%
Open Punctuation 179
 
1.3%
Close Punctuation 179
 
1.3%
Other Punctuation 137
 
1.0%
Decimal Number 81
 
0.6%
Dash Punctuation 7
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
910
 
8.1%
866
 
7.7%
405
 
3.6%
363
 
3.2%
283
 
2.5%
276
 
2.5%
265
 
2.4%
258
 
2.3%
220
 
2.0%
215
 
1.9%
Other values (584) 7202
63.9%
Uppercase Letter
ValueCountFrequency (%)
H 49
 
10.2%
A 49
 
10.2%
S 41
 
8.5%
N 38
 
7.9%
I 36
 
7.5%
J 26
 
5.4%
O 26
 
5.4%
M 25
 
5.2%
T 24
 
5.0%
E 23
 
4.8%
Other values (15) 143
29.8%
Lowercase Letter
ValueCountFrequency (%)
a 113
13.3%
i 102
12.0%
e 79
9.3%
o 75
8.8%
n 68
8.0%
l 67
7.9%
r 64
 
7.5%
h 49
 
5.8%
y 39
 
4.6%
t 35
 
4.1%
Other values (14) 161
18.9%
Decimal Number
ValueCountFrequency (%)
0 29
35.8%
2 15
18.5%
1 12
14.8%
3 7
 
8.6%
4 5
 
6.2%
7 3
 
3.7%
6 3
 
3.7%
9 3
 
3.7%
5 2
 
2.5%
8 2
 
2.5%
Other Punctuation
ValueCountFrequency (%)
? 47
34.3%
& 35
25.5%
. 20
14.6%
, 15
 
10.9%
# 11
 
8.0%
' 4
 
2.9%
: 3
 
2.2%
% 2
 
1.5%
Space Separator
ValueCountFrequency (%)
466
100.0%
Open Punctuation
ValueCountFrequency (%)
( 179
100.0%
Close Punctuation
ValueCountFrequency (%)
) 179
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11252
82.5%
Latin 1332
 
9.8%
Common 1051
 
7.7%
Han 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
910
 
8.1%
866
 
7.7%
405
 
3.6%
363
 
3.2%
283
 
2.5%
276
 
2.5%
265
 
2.4%
258
 
2.3%
220
 
2.0%
215
 
1.9%
Other values (577) 7191
63.9%
Latin
ValueCountFrequency (%)
a 113
 
8.5%
i 102
 
7.7%
e 79
 
5.9%
o 75
 
5.6%
n 68
 
5.1%
l 67
 
5.0%
r 64
 
4.8%
h 49
 
3.7%
H 49
 
3.7%
A 49
 
3.7%
Other values (39) 617
46.3%
Common
ValueCountFrequency (%)
466
44.3%
( 179
 
17.0%
) 179
 
17.0%
? 47
 
4.5%
& 35
 
3.3%
0 29
 
2.8%
. 20
 
1.9%
2 15
 
1.4%
, 15
 
1.4%
1 12
 
1.1%
Other values (13) 54
 
5.1%
Han
ValueCountFrequency (%)
5
45.5%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%
1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11252
82.5%
ASCII 2383
 
17.5%
CJK 10
 
0.1%
CJK Compat Ideographs 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
910
 
8.1%
866
 
7.7%
405
 
3.6%
363
 
3.2%
283
 
2.5%
276
 
2.5%
265
 
2.4%
258
 
2.3%
220
 
2.0%
215
 
1.9%
Other values (577) 7191
63.9%
ASCII
ValueCountFrequency (%)
466
19.6%
( 179
 
7.5%
) 179
 
7.5%
a 113
 
4.7%
i 102
 
4.3%
e 79
 
3.3%
o 75
 
3.1%
n 68
 
2.9%
l 67
 
2.8%
r 64
 
2.7%
Other values (62) 991
41.6%
CJK
ValueCountFrequency (%)
5
50.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
CJK Compat Ideographs
ValueCountFrequency (%)
1
100.0%
Distinct2064
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
Minimum1999-04-06 00:00:00
Maximum2024-05-08 12:31:04
2024-05-11T14:42:12.299849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:12.503067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
I
1657 
U
688 
D
 
14

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 1657
70.2%
U 688
29.2%
D 14
 
0.6%

Length

2024-05-11T14:42:12.706082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:12.860651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1657
70.2%
u 688
29.2%
d 14
 
0.6%
Distinct702
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T14:42:13.022281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:42:13.196229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
일반미용업
1697 
피부미용업
344 
네일아트업
258 
메이크업업
 
43
기타
 
14
Other values (2)
 
3

Length

Max length6
Median length5
Mean length4.9826198
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1697
71.9%
피부미용업 344
 
14.6%
네일아트업 258
 
10.9%
메이크업업 43
 
1.8%
기타 14
 
0.6%
일반이용업 2
 
0.1%
미용업 기타 1
 
< 0.1%

Length

2024-05-11T14:42:13.375461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:13.536214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 1697
71.9%
피부미용업 344
 
14.6%
네일아트업 258
 
10.9%
메이크업업 43
 
1.8%
기타 15
 
0.6%
일반이용업 2
 
0.1%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1427
Distinct (%)62.1%
Missing62
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean204984.49
Minimum202045.98
Maximum206693.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:13.720309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202045.98
5-th percentile202832.37
Q1204385.5
median205104.45
Q3205839.72
95-th percentile206419.5
Maximum206693.09
Range4647.1033
Interquartile range (IQR)1454.22

Descriptive statistics

Standard deviation1066.5486
Coefficient of variation (CV)0.0052030696
Kurtosis-0.12223551
Mean204984.49
Median Absolute Deviation (MAD)731.25744
Skewness-0.70800885
Sum4.7084938 × 108
Variance1137525.9
MonotonicityNot monotonic
2024-05-11T14:42:13.890327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206521.405320134 30
 
1.3%
206101.9138443 21
 
0.9%
205271.704936121 16
 
0.7%
204089.817117361 14
 
0.6%
206590.046202018 11
 
0.5%
205980.772169045 11
 
0.5%
206484.233644024 10
 
0.4%
205208.167449959 9
 
0.4%
204399.083398765 8
 
0.3%
203085.866786086 8
 
0.3%
Other values (1417) 2159
91.5%
(Missing) 62
 
2.6%
ValueCountFrequency (%)
202045.98498517 2
 
0.1%
202072.50235181 1
 
< 0.1%
202076.135036947 2
 
0.1%
202079.097098443 1
 
< 0.1%
202104.76690462 1
 
< 0.1%
202106.009984293 6
0.3%
202126.110138211 1
 
< 0.1%
202137.335523647 1
 
< 0.1%
202158.360161691 1
 
< 0.1%
202165.405300438 3
0.1%
ValueCountFrequency (%)
206693.088296004 2
 
0.1%
206618.082278385 2
 
0.1%
206603.229942876 1
 
< 0.1%
206590.046202018 11
0.5%
206586.572428103 2
 
0.1%
206562.649224959 7
0.3%
206543.156690365 1
 
< 0.1%
206538.84863498 2
 
0.1%
206524.120752695 2
 
0.1%
206522.763968502 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1427
Distinct (%)62.1%
Missing62
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean453018.08
Minimum451031.57
Maximum455899.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:14.048574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum451031.57
5-th percentile451484.88
Q1452155.69
median452794.83
Q3453884.04
95-th percentile455101.01
Maximum455899.98
Range4868.4126
Interquartile range (IQR)1728.3488

Descriptive statistics

Standard deviation1119.2248
Coefficient of variation (CV)0.0024705963
Kurtosis-0.59264047
Mean453018.08
Median Absolute Deviation (MAD)790.87661
Skewness0.52704211
Sum1.0405825 × 109
Variance1252664.2
MonotonicityNot monotonic
2024-05-11T14:42:14.254941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451940.119059947 30
 
1.3%
452155.691148899 21
 
0.9%
452706.897879436 16
 
0.7%
453314.135663101 14
 
0.6%
452410.619464697 11
 
0.5%
455275.835949587 11
 
0.5%
452169.871290467 10
 
0.4%
452466.868525878 9
 
0.4%
453656.481200804 8
 
0.3%
452766.014195644 8
 
0.3%
Other values (1417) 2159
91.5%
(Missing) 62
 
2.6%
ValueCountFrequency (%)
451031.569748553 1
 
< 0.1%
451038.454241239 2
0.1%
451059.346901649 1
 
< 0.1%
451061.072414994 1
 
< 0.1%
451130.882150272 1
 
< 0.1%
451134.504571525 1
 
< 0.1%
451139.60744767 3
0.1%
451141.444359504 4
0.2%
451143.974833143 2
0.1%
451160.978337791 2
0.1%
ValueCountFrequency (%)
455899.982370316 4
0.2%
455813.713540339 3
0.1%
455803.028832488 1
 
< 0.1%
455797.417581881 4
0.2%
455771.62005247 1
 
< 0.1%
455765.205744964 1
 
< 0.1%
455731.978184886 1
 
< 0.1%
455715.581 1
 
< 0.1%
455704.09186061 1
 
< 0.1%
455696.676662392 3
0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
일반미용업
733 
미용업
555 
<NA>
523 
피부미용업
207 
종합미용업
158 
Other values (12)
183 

Length

Max length23
Median length19
Mean length4.8024587
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 733
31.1%
미용업 555
23.5%
<NA> 523
22.2%
피부미용업 207
 
8.8%
종합미용업 158
 
6.7%
네일미용업 79
 
3.3%
피부미용업, 네일미용업 22
 
0.9%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 21
 
0.9%
네일미용업, 화장ㆍ분장 미용업 14
 
0.6%
일반미용업, 네일미용업 10
 
0.4%
Other values (7) 37
 
1.6%

Length

2024-05-11T14:42:14.488972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반미용업 785
30.8%
미용업 614
24.1%
na 523
20.5%
피부미용업 255
 
10.0%
종합미용업 158
 
6.2%
네일미용업 157
 
6.2%
화장ㆍ분장 59
 
2.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)1.0%
Missing790
Missing (%)33.5%
Infinite0
Infinite (%)0.0%
Mean1.3224984
Minimum0
Maximum30
Zeros879
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:14.664247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile4
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.018855
Coefficient of variation (CV)1.5265462
Kurtosis37.228886
Mean1.3224984
Median Absolute Deviation (MAD)0
Skewness3.8922489
Sum2075
Variance4.0757754
MonotonicityNot monotonic
2024-05-11T14:42:14.824909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 879
37.3%
3 225
 
9.5%
4 137
 
5.8%
2 133
 
5.6%
1 125
 
5.3%
5 48
 
2.0%
6 8
 
0.3%
7 6
 
0.3%
8 1
 
< 0.1%
11 1
 
< 0.1%
Other values (6) 6
 
0.3%
(Missing) 790
33.5%
ValueCountFrequency (%)
0 879
37.3%
1 125
 
5.3%
2 133
 
5.6%
3 225
 
9.5%
4 137
 
5.8%
5 48
 
2.0%
6 8
 
0.3%
7 6
 
0.3%
8 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
21 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
8 1
 
< 0.1%
7 6
0.3%
6 8
0.3%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.7%
Missing1177
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean0.21235195
Minimum0
Maximum7
Zeros968
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:14.957297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.54880338
Coefficient of variation (CV)2.5844048
Kurtosis40.365735
Mean0.21235195
Median Absolute Deviation (MAD)0
Skewness4.9008957
Sum251
Variance0.30118515
MonotonicityNot monotonic
2024-05-11T14:42:15.098814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 968
41.0%
1 198
 
8.4%
2 6
 
0.3%
3 5
 
0.2%
4 2
 
0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 1177
49.9%
ValueCountFrequency (%)
0 968
41.0%
1 198
 
8.4%
2 6
 
0.3%
3 5
 
0.2%
4 2
 
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 2
 
0.1%
3 5
 
0.2%
2 6
 
0.3%
1 198
 
8.4%
0 968
41.0%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.7%
Missing888
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean1.3467029
Minimum0
Maximum14
Zeros55
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:15.270707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92698821
Coefficient of variation (CV)0.68833905
Kurtosis35.087086
Mean1.3467029
Median Absolute Deviation (MAD)0
Skewness4.2372242
Sum1981
Variance0.85930715
MonotonicityNot monotonic
2024-05-11T14:42:15.395420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 1050
44.5%
2 254
 
10.8%
3 68
 
2.9%
0 55
 
2.3%
4 29
 
1.2%
5 6
 
0.3%
8 3
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
9 1
 
< 0.1%
(Missing) 888
37.6%
ValueCountFrequency (%)
0 55
 
2.3%
1 1050
44.5%
2 254
 
10.8%
3 68
 
2.9%
4 29
 
1.2%
5 6
 
0.3%
6 2
 
0.1%
7 2
 
0.1%
8 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
9 1
 
< 0.1%
8 3
 
0.1%
7 2
 
0.1%
6 2
 
0.1%
5 6
 
0.3%
4 29
 
1.2%
3 68
 
2.9%
2 254
 
10.8%
1 1050
44.5%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.7%
Missing921
Missing (%)39.0%
Infinite0
Infinite (%)0.0%
Mean1.2858136
Minimum0
Maximum9
Zeros66
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:15.524309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.80600158
Coefficient of variation (CV)0.62684169
Kurtosis19.767462
Mean1.2858136
Median Absolute Deviation (MAD)0
Skewness3.2619663
Sum1849
Variance0.64963855
MonotonicityNot monotonic
2024-05-11T14:42:15.637486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1038
44.0%
2 244
 
10.3%
0 66
 
2.8%
3 62
 
2.6%
4 20
 
0.8%
8 3
 
0.1%
6 2
 
0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 921
39.0%
ValueCountFrequency (%)
0 66
 
2.8%
1 1038
44.0%
2 244
 
10.3%
3 62
 
2.6%
4 20
 
0.8%
5 1
 
< 0.1%
6 2
 
0.1%
7 1
 
< 0.1%
8 3
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 3
 
0.1%
7 1
 
< 0.1%
6 2
 
0.1%
5 1
 
< 0.1%
4 20
 
0.8%
3 62
 
2.6%
2 244
 
10.3%
1 1038
44.0%
0 66
 
2.8%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
2113 
0
216 
1
 
22
2
 
8

Length

Max length4
Median length4
Mean length3.6871556
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> 2113
89.6%
0 216
 
9.2%
1 22
 
0.9%
2 8
 
0.3%

Length

2024-05-11T14:42:15.790016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:15.900926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2113
89.6%
0 216
 
9.2%
1 22
 
0.9%
2 8
 
0.3%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
2117 
0
214 
1
 
20
2
 
8

Length

Max length4
Median length4
Mean length3.6922425
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> 2117
89.7%
0 214
 
9.1%
1 20
 
0.8%
2 8
 
0.3%

Length

2024-05-11T14:42:16.057735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:16.196420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2117
89.7%
0 214
 
9.1%
1 20
 
0.8%
2 8
 
0.3%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1329 
0
1030 

Length

Max length4
Median length4
Mean length2.6901229
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> 1329
56.3%
0 1030
43.7%

Length

2024-05-11T14:42:16.379881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:16.496232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1329
56.3%
0 1030
43.7%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1329 
0
1030 

Length

Max length4
Median length4
Mean length2.6901229
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> 1329
56.3%
0 1030
43.7%

Length

2024-05-11T14:42:16.635968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:16.811180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1329
56.3%
0 1030
43.7%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1329 
0
1030 

Length

Max length4
Median length4
Mean length2.6901229
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> 1329
56.3%
0 1030
43.7%

Length

2024-05-11T14:42:16.955776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:17.070043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1329
56.3%
0 1030
43.7%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing556
Missing (%)23.6%
Memory size4.7 KiB
False
1803 
(Missing)
556 
ValueCountFrequency (%)
False 1803
76.4%
(Missing) 556
 
23.6%
2024-05-11T14:42:17.153255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)1.0%
Missing626
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean3.4766301
Minimum0
Maximum26
Zeros153
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:17.257710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1957212
Coefficient of variation (CV)0.63156594
Kurtosis10.2137
Mean3.4766301
Median Absolute Deviation (MAD)1
Skewness1.8783854
Sum6025
Variance4.8211914
MonotonicityNot monotonic
2024-05-11T14:42:17.400466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 618
26.2%
4 324
13.7%
2 258
10.9%
0 153
 
6.5%
5 117
 
5.0%
6 98
 
4.2%
8 40
 
1.7%
1 36
 
1.5%
7 34
 
1.4%
10 26
 
1.1%
Other values (7) 29
 
1.2%
(Missing) 626
26.5%
ValueCountFrequency (%)
0 153
 
6.5%
1 36
 
1.5%
2 258
10.9%
3 618
26.2%
4 324
13.7%
5 117
 
5.0%
6 98
 
4.2%
7 34
 
1.4%
8 40
 
1.7%
9 14
 
0.6%
ValueCountFrequency (%)
26 1
 
< 0.1%
18 2
 
0.1%
15 1
 
< 0.1%
14 1
 
< 0.1%
12 5
 
0.2%
11 5
 
0.2%
10 26
1.1%
9 14
 
0.6%
8 40
1.7%
7 34
1.4%
Distinct2
Distinct (%)100.0%
Missing2357
Missing (%)99.9%
Memory size18.6 KiB
2024-05-11T14:42:17.638849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length25.5
Mean length25.5
Min length16

Characters and Unicode

Total characters51
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row건축물생성시 영업신고증 재교부
2nd row본 영업신고증은 건축물 등재 전으로 아래 조건부기간까지만 유효함
ValueCountFrequency (%)
건축물생성시 1
9.1%
영업신고증 1
9.1%
재교부 1
9.1%
1
9.1%
영업신고증은 1
9.1%
건축물 1
9.1%
등재 1
9.1%
전으로 1
9.1%
아래 1
9.1%
조건부기간까지만 1
9.1%
2024-05-11T14:42:18.018660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
 
17.6%
3
 
5.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
Other values (22) 23
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
82.4%
Space Separator 9
 
17.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (21) 21
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
82.4%
Common 9
 
17.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (21) 21
50.0%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
82.4%
ASCII 9
 
17.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9
100.0%
Hangul
ValueCountFrequency (%)
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (21) 21
50.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
2356 
20150629
 
1
20140515
 
1
20131029
 
1

Length

Max length8
Median length4
Mean length4.0050869
Min length4

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2356
99.9%
20150629 1
 
< 0.1%
20140515 1
 
< 0.1%
20131029 1
 
< 0.1%

Length

2024-05-11T14:42:18.216434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:18.345653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2356
99.9%
20150629 1
 
< 0.1%
20140515 1
 
< 0.1%
20131029 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
2355 
2
 
1
20160628
 
1
20150514
 
1
20141028
 
1

Length

Max length8
Median length4
Mean length4.0038152
Min length1

Unique

Unique4 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2355
99.8%
2 1
 
< 0.1%
20160628 1
 
< 0.1%
20150514 1
 
< 0.1%
20141028 1
 
< 0.1%

Length

2024-05-11T14:42:18.488644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:18.613516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2355
99.8%
2 1
 
< 0.1%
20160628 1
 
< 0.1%
20150514 1
 
< 0.1%
20141028 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1913 
임대
438 
자가
 
8

Length

Max length4
Median length4
Mean length3.6218737
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> 1913
81.1%
임대 438
 
18.6%
자가 8
 
0.3%

Length

2024-05-11T14:42:18.770518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:18.899664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1913
81.1%
임대 438
 
18.6%
자가 8
 
0.3%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1332 
0
1027 

Length

Max length4
Median length4
Mean length2.6939381
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> 1332
56.5%
0 1027
43.5%

Length

2024-05-11T14:42:19.067427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:19.212131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1332
56.5%
0 1027
43.5%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1827 
0
516 
1
 
11
2
 
3
3
 
1

Length

Max length4
Median length4
Mean length3.3234421
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> 1827
77.4%
0 516
 
21.9%
1 11
 
0.5%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-05-11T14:42:19.329478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:19.821334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1827
77.4%
0 516
 
21.9%
1 11
 
0.5%
2 3
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1828 
0
529 
1
 
2

Length

Max length4
Median length4
Mean length3.3247139
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> 1828
77.5%
0 529
 
22.4%
1 2
 
0.1%

Length

2024-05-11T14:42:20.031737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:20.195299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1828
77.5%
0 529
 
22.4%
1 2
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
<NA>
1425 
0
934 

Length

Max length4
Median length4
Mean length2.8122086
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> 1425
60.4%
0 934
39.6%

Length

2024-05-11T14:42:20.346418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:42:20.466135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1425
60.4%
0 934
39.6%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)1.6%
Missing1489
Missing (%)63.1%
Infinite0
Infinite (%)0.0%
Mean0.93908046
Minimum0
Maximum15
Zeros597
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size20.9 KiB
2024-05-11T14:42:20.578768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8862263
Coefficient of variation (CV)2.0085886
Kurtosis12.404533
Mean0.93908046
Median Absolute Deviation (MAD)0
Skewness3.0300983
Sum817
Variance3.5578496
MonotonicityNot monotonic
2024-05-11T14:42:20.721286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 597
25.3%
2 79
 
3.3%
1 68
 
2.9%
3 55
 
2.3%
4 22
 
0.9%
5 19
 
0.8%
6 10
 
0.4%
7 7
 
0.3%
8 3
 
0.1%
10 3
 
0.1%
Other values (4) 7
 
0.3%
(Missing) 1489
63.1%
ValueCountFrequency (%)
0 597
25.3%
1 68
 
2.9%
2 79
 
3.3%
3 55
 
2.3%
4 22
 
0.9%
5 19
 
0.8%
6 10
 
0.4%
7 7
 
0.3%
8 3
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
15 2
 
0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
0.1%
9 3
 
0.1%
8 3
 
0.1%
7 7
 
0.3%
6 10
0.4%
5 19
0.8%
4 22
0.9%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing523
Missing (%)22.2%
Memory size4.7 KiB
False
1836 
(Missing)
523 
ValueCountFrequency (%)
False 1836
77.8%
(Missing) 523
 
22.2%
2024-05-11T14:42:20.834305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030500003050000-204-1977-0119019771015<NA>3폐업2폐업20140519<NA><NA><NA><NA>32.2130862서울특별시 동대문구 제기동 576-0번지<NA><NA>동화2011-03-15 17:39:24I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130500003050000-204-1977-0120619771015<NA>3폐업2폐업20030415<NA><NA><NA>02 926215811.97130860서울특별시 동대문구 제기동 138-9번지<NA><NA>2003-04-16 00:00:00I2018-08-31 23:59:59.0일반미용업202984.990349453714.01478미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230500003050000-204-1977-0141219771015<NA>3폐업2폐업20030225<NA><NA><NA>020965652613.2130868서울특별시 동대문구 청량리동 205-180번지<NA><NA>화신미장원2003-04-17 00:00:00I2018-08-31 23:59:59.0일반미용업203600.65865454101.073875미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330500003050000-204-1977-0150419771223<NA>3폐업2폐업20120919<NA><NA><NA>022247529612.16130836서울특별시 동대문구 장안동 129-22번지서울특별시 동대문구 답십리로63길 47 (장안동)2529라인헤어2012-03-13 10:44:15I2018-08-31 23:59:59.0일반미용업206002.519175452496.929301미용업2<NA>11<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430500003050000-204-1977-0164519771015<NA>3폐업2폐업20091026<NA><NA><NA>020963686220.5130865서울특별시 동대문구 제기동 1142-6번지 19통1반 (회기로30)<NA><NA>태양2007-05-18 00:00:00I2018-08-31 23:59:59.0일반미용업203429.509972454305.388965미용업2<NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530500003050000-204-1977-0217119771015<NA>3폐업2폐업20110113<NA><NA><NA>02 962137482.49130872서울특별시 동대문구 회기동 60-44번지 (경희대1길5)<NA><NA>비둘기미용실2007-05-18 00:00:00I2018-08-31 23:59:59.0일반미용업204505.628469454365.799421미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630500003050000-204-1977-0231719770602<NA>3폐업2폐업20080424<NA><NA><NA>02967 817119.2130817서울특별시 동대문구 용두동 39-419번지 (목련2길30)<NA><NA>기쁨헤어2008-04-24 21:41:49I2018-08-31 23:59:59.0일반미용업203589.797341452591.266044미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
730500003050000-204-1978-0133219780524<NA>3폐업2폐업20040223<NA><NA><NA>022212170323.52130883서울특별시 동대문구 답십리동 36-20번지<NA><NA>우종2003-04-08 00:00:00I2018-08-31 23:59:59.0일반미용업205029.59466452066.160888미용업2<NA>11<NA><NA><NA><NA><NA>N3<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
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