Overview

Dataset statistics

Number of variables47
Number of observations3696
Missing cells37406
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory403.0 B

Variable types

Categorical19
Text8
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
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-17906/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
조건부허가신고사유 has constant value ""Constant
사용시작지하층 is highly imbalanced (60.7%)Imbalance
사용끝지하층 is highly imbalanced (74.5%)Imbalance
조건부허가시작일자 is highly imbalanced (99.6%)Imbalance
조건부허가종료일자 is highly imbalanced (99.6%)Imbalance
건물소유구분명 is highly imbalanced (62.1%)Imbalance
남성종사자수 is highly imbalanced (64.9%)Imbalance
다중이용업소여부 is highly imbalanced (99.6%)Imbalance
인허가취소일자 has 3696 (100.0%) missing valuesMissing
폐업일자 has 1296 (35.1%) missing valuesMissing
휴업시작일자 has 3696 (100.0%) missing valuesMissing
휴업종료일자 has 3696 (100.0%) missing valuesMissing
재개업일자 has 3696 (100.0%) missing valuesMissing
전화번호 has 1398 (37.8%) missing valuesMissing
도로명주소 has 1285 (34.8%) missing valuesMissing
도로명우편번호 has 1295 (35.0%) missing valuesMissing
좌표정보(X) has 77 (2.1%) missing valuesMissing
좌표정보(Y) has 77 (2.1%) missing valuesMissing
건물지상층수 has 1041 (28.2%) missing valuesMissing
건물지하층수 has 1629 (44.1%) missing valuesMissing
사용시작지상층 has 1716 (46.4%) missing valuesMissing
사용끝지상층 has 2088 (56.5%) missing valuesMissing
발한실여부 has 643 (17.4%) missing valuesMissing
좌석수 has 701 (19.0%) missing valuesMissing
조건부허가신고사유 has 3695 (> 99.9%) missing valuesMissing
여성종사자수 has 2811 (76.1%) missing valuesMissing
침대수 has 2253 (61.0%) missing valuesMissing
다중이용업소여부 has 610 (16.5%) 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
건물지상층수 has 1387 (37.5%) zerosZeros
건물지하층수 has 1674 (45.3%) zerosZeros
사용시작지상층 has 540 (14.6%) zerosZeros
사용끝지상층 has 91 (2.5%) zerosZeros
좌석수 has 309 (8.4%) zerosZeros
여성종사자수 has 439 (11.9%) zerosZeros
침대수 has 1004 (27.2%) zerosZeros

Reproduction

Analysis started2024-04-17 18:23:09.769645
Analysis finished2024-04-17 18:23:10.987450
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
3040000
3696 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3040000
2nd row3040000
3rd row3040000
4th row3040000
5th row3040000

Common Values

ValueCountFrequency (%)
3040000 3696
100.0%

Length

2024-04-18T03:23:11.035854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:11.104974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3040000 3696
100.0%

관리번호
Text

UNIQUE 

Distinct3696
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
2024-04-18T03:23:11.239027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters81312
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

Unique3696 ?
Unique (%)100.0%

Sample

1st row3040000-204-1969-00649
2nd row3040000-204-1971-00443
3rd row3040000-204-1971-00818
4th row3040000-204-1971-01001
5th row3040000-204-1972-01303
ValueCountFrequency (%)
3040000-204-1969-00649 1
 
< 0.1%
3040000-212-2010-00010 1
 
< 0.1%
3040000-212-2010-00012 1
 
< 0.1%
3040000-212-2010-00013 1
 
< 0.1%
3040000-212-2010-00014 1
 
< 0.1%
3040000-212-2010-00015 1
 
< 0.1%
3040000-212-2010-00016 1
 
< 0.1%
3040000-212-2010-00017 1
 
< 0.1%
3040000-212-2010-00018 1
 
< 0.1%
3040000-212-2010-00019 1
 
< 0.1%
Other values (3686) 3686
99.7%
2024-04-18T03:23:11.491616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34866
42.9%
- 11088
 
13.6%
2 8908
 
11.0%
1 7214
 
8.9%
4 6518
 
8.0%
3 5386
 
6.6%
9 2664
 
3.3%
5 1314
 
1.6%
8 1275
 
1.6%
7 1060
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70224
86.4%
Dash Punctuation 11088
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34866
49.6%
2 8908
 
12.7%
1 7214
 
10.3%
4 6518
 
9.3%
3 5386
 
7.7%
9 2664
 
3.8%
5 1314
 
1.9%
8 1275
 
1.8%
7 1060
 
1.5%
6 1019
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 11088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34866
42.9%
- 11088
 
13.6%
2 8908
 
11.0%
1 7214
 
8.9%
4 6518
 
8.0%
3 5386
 
6.6%
9 2664
 
3.3%
5 1314
 
1.6%
8 1275
 
1.6%
7 1060
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34866
42.9%
- 11088
 
13.6%
2 8908
 
11.0%
1 7214
 
8.9%
4 6518
 
8.0%
3 5386
 
6.6%
9 2664
 
3.3%
5 1314
 
1.6%
8 1275
 
1.6%
7 1060
 
1.3%
Distinct2669
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
Minimum1969-12-18 00:00:00
Maximum2024-04-11 00:00:00
2024-04-18T03:23:11.598506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:23:11.703675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3696
Missing (%)100.0%
Memory size32.6 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
3
2400 
1
1296 

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 2400
64.9%
1 1296
35.1%

Length

2024-04-18T03:23:12.016954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:12.089668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2400
64.9%
1 1296
35.1%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
폐업
2400 
영업/정상
1296 

Length

Max length5
Median length2
Mean length3.0519481
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 2400
64.9%
영업/정상 1296
35.1%

Length

2024-04-18T03:23:12.176771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:12.258157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2400
64.9%
영업/정상 1296
35.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
2
2400 
1
1296 

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 2400
64.9%
1 1296
35.1%

Length

2024-04-18T03:23:12.338157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:12.414369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 2400
64.9%
1 1296
35.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
폐업
2400 
영업
1296 

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 2400
64.9%
영업 1296
35.1%

Length

2024-04-18T03:23:12.490734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:12.564876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 2400
64.9%
영업 1296
35.1%

폐업일자
Date

MISSING 

Distinct1672
Distinct (%)69.7%
Missing1296
Missing (%)35.1%
Memory size29.0 KiB
Minimum1993-10-28 00:00:00
Maximum2024-04-09 00:00:00
2024-04-18T03:23:12.661024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:23:12.785110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3696
Missing (%)100.0%
Memory size32.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3696
Missing (%)100.0%
Memory size32.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3696
Missing (%)100.0%
Memory size32.6 KiB

전화번호
Text

MISSING 

Distinct1975
Distinct (%)85.9%
Missing1398
Missing (%)37.8%
Memory size29.0 KiB
2024-04-18T03:23:13.018255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.385988
Min length2

Characters and Unicode

Total characters23867
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

Unique1800 ?
Unique (%)78.3%

Sample

1st row02 0
2nd row02 4656591
3rd row02 00000
4th row02 4661405
5th row0200000000
ValueCountFrequency (%)
02 1912
40.4%
0200000000 50
 
1.1%
455 40
 
0.8%
070 36
 
0.8%
447 36
 
0.8%
0 34
 
0.7%
457 31
 
0.7%
00000 30
 
0.6%
456 28
 
0.6%
454 27
 
0.6%
Other values (2020) 2512
53.0%
2024-04-18T03:23:13.357929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4052
17.0%
2 3486
14.6%
4 3425
14.4%
3156
13.2%
5 1858
7.8%
6 1790
7.5%
3 1416
 
5.9%
7 1369
 
5.7%
9 1158
 
4.9%
8 1130
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20711
86.8%
Space Separator 3156
 
13.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4052
19.6%
2 3486
16.8%
4 3425
16.5%
5 1858
9.0%
6 1790
8.6%
3 1416
 
6.8%
7 1369
 
6.6%
9 1158
 
5.6%
8 1130
 
5.5%
1 1027
 
5.0%
Space Separator
ValueCountFrequency (%)
3156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23867
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4052
17.0%
2 3486
14.6%
4 3425
14.4%
3156
13.2%
5 1858
7.8%
6 1790
7.5%
3 1416
 
5.9%
7 1369
 
5.7%
9 1158
 
4.9%
8 1130
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23867
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4052
17.0%
2 3486
14.6%
4 3425
14.4%
3156
13.2%
5 1858
7.8%
6 1790
7.5%
3 1416
 
5.9%
7 1369
 
5.7%
9 1158
 
4.9%
8 1130
 
4.7%
Distinct1355
Distinct (%)36.7%
Missing2
Missing (%)0.1%
Memory size29.0 KiB
2024-04-18T03:23:13.696476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9805089
Min length3

Characters and Unicode

Total characters18398
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

Unique926 ?
Unique (%)25.1%

Sample

1st row11.88
2nd row15.90
3rd row15.90
4th row12.20
5th row15.29
ValueCountFrequency (%)
30.00 158
 
4.3%
33.00 135
 
3.7%
00 129
 
3.5%
24.00 127
 
3.4%
20.00 104
 
2.8%
21.00 74
 
2.0%
26.40 70
 
1.9%
25.00 53
 
1.4%
18.00 53
 
1.4%
23.10 45
 
1.2%
Other values (1345) 2746
74.3%
2024-04-18T03:23:14.102970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4720
25.7%
. 3694
20.1%
2 1793
 
9.7%
1 1570
 
8.5%
3 1406
 
7.6%
4 1109
 
6.0%
5 1007
 
5.5%
6 981
 
5.3%
8 762
 
4.1%
9 678
 
3.7%
Other values (2) 678
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14703
79.9%
Other Punctuation 3695
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4720
32.1%
2 1793
 
12.2%
1 1570
 
10.7%
3 1406
 
9.6%
4 1109
 
7.5%
5 1007
 
6.8%
6 981
 
6.7%
8 762
 
5.2%
9 678
 
4.6%
7 677
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 3694
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 18398
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4720
25.7%
. 3694
20.1%
2 1793
 
9.7%
1 1570
 
8.5%
3 1406
 
7.6%
4 1109
 
6.0%
5 1007
 
5.5%
6 981
 
5.3%
8 762
 
4.1%
9 678
 
3.7%
Other values (2) 678
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4720
25.7%
. 3694
20.1%
2 1793
 
9.7%
1 1570
 
8.5%
3 1406
 
7.6%
4 1109
 
6.0%
5 1007
 
5.5%
6 981
 
5.3%
8 762
 
4.1%
9 678
 
3.7%
Other values (2) 678
 
3.7%
Distinct195
Distinct (%)5.3%
Missing3
Missing (%)0.1%
Memory size29.0 KiB
2024-04-18T03:23:14.379764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1042513
Min length6

Characters and Unicode

Total characters22543
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

Unique28 ?
Unique (%)0.8%

Sample

1st row143825
2nd row143914
3rd row143914
4th row143843
5th row143862
ValueCountFrequency (%)
143914 160
 
4.3%
143841 116
 
3.1%
143915 114
 
3.1%
143866 102
 
2.8%
143888 98
 
2.7%
143900 81
 
2.2%
143916 76
 
2.1%
143873 72
 
1.9%
143840 72
 
1.9%
143867 71
 
1.9%
Other values (185) 2731
74.0%
2024-04-18T03:23:14.747646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4912
21.8%
4 4484
19.9%
3 4228
18.8%
8 3264
14.5%
9 1450
 
6.4%
2 874
 
3.9%
0 837
 
3.7%
6 814
 
3.6%
7 666
 
3.0%
5 629
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22158
98.3%
Dash Punctuation 385
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4912
22.2%
4 4484
20.2%
3 4228
19.1%
8 3264
14.7%
9 1450
 
6.5%
2 874
 
3.9%
0 837
 
3.8%
6 814
 
3.7%
7 666
 
3.0%
5 629
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4912
21.8%
4 4484
19.9%
3 4228
18.8%
8 3264
14.5%
9 1450
 
6.4%
2 874
 
3.9%
0 837
 
3.7%
6 814
 
3.6%
7 666
 
3.0%
5 629
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4912
21.8%
4 4484
19.9%
3 4228
18.8%
8 3264
14.5%
9 1450
 
6.4%
2 874
 
3.9%
0 837
 
3.7%
6 814
 
3.6%
7 666
 
3.0%
5 629
 
2.8%
Distinct3024
Distinct (%)81.9%
Missing2
Missing (%)0.1%
Memory size29.0 KiB
2024-04-18T03:23:15.017585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length52
Mean length23.796697
Min length17

Characters and Unicode

Total characters87905
Distinct characters304
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

Unique2542 ?
Unique (%)68.8%

Sample

1st row서울특별시 광진구 구의동 242-0번지
2nd row서울특별시 광진구 화양동 9-49번지
3rd row서울특별시 광진구 화양동 2-82번지
4th row서울특별시 광진구 자양동 49-117번지
5th row서울특별시 광진구 자양동 553-69번지
ValueCountFrequency (%)
서울특별시 3694
22.1%
광진구 3694
22.1%
자양동 1017
 
6.1%
중곡동 933
 
5.6%
구의동 734
 
4.4%
1층 541
 
3.2%
화양동 490
 
2.9%
군자동 240
 
1.4%
광장동 186
 
1.1%
2층 150
 
0.9%
Other values (2951) 5054
30.2%
2024-04-18T03:23:15.403883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15915
18.1%
4460
 
5.1%
3923
 
4.5%
1 3870
 
4.4%
3792
 
4.3%
3735
 
4.2%
3718
 
4.2%
3699
 
4.2%
3698
 
4.2%
3694
 
4.2%
Other values (294) 37401
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50208
57.1%
Decimal Number 17768
 
20.2%
Space Separator 15915
 
18.1%
Dash Punctuation 3499
 
4.0%
Open Punctuation 176
 
0.2%
Close Punctuation 176
 
0.2%
Uppercase Letter 90
 
0.1%
Other Punctuation 48
 
0.1%
Lowercase Letter 21
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4460
 
8.9%
3923
 
7.8%
3792
 
7.6%
3735
 
7.4%
3718
 
7.4%
3699
 
7.4%
3698
 
7.4%
3694
 
7.4%
3694
 
7.4%
2625
 
5.2%
Other values (242) 13170
26.2%
Uppercase Letter
ValueCountFrequency (%)
F 21
23.3%
B 21
23.3%
A 13
14.4%
W 4
 
4.4%
S 4
 
4.4%
P 3
 
3.3%
I 3
 
3.3%
C 3
 
3.3%
H 2
 
2.2%
Y 2
 
2.2%
Other values (11) 14
15.6%
Lowercase Letter
ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
c 2
9.5%
l 2
9.5%
s 2
9.5%
k 1
 
4.8%
t 1
 
4.8%
n 1
 
4.8%
h 1
 
4.8%
r 1
 
4.8%
Other values (3) 3
14.3%
Decimal Number
ValueCountFrequency (%)
1 3870
21.8%
2 2984
16.8%
3 1830
10.3%
5 1659
9.3%
4 1640
9.2%
6 1615
9.1%
0 1160
 
6.5%
7 1027
 
5.8%
9 993
 
5.6%
8 990
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 42
87.5%
. 6
 
12.5%
Space Separator
ValueCountFrequency (%)
15915
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3499
100.0%
Open Punctuation
ValueCountFrequency (%)
( 176
100.0%
Close Punctuation
ValueCountFrequency (%)
) 176
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50207
57.1%
Common 37585
42.8%
Latin 112
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4460
 
8.9%
3923
 
7.8%
3792
 
7.6%
3735
 
7.4%
3718
 
7.4%
3699
 
7.4%
3698
 
7.4%
3694
 
7.4%
3694
 
7.4%
2625
 
5.2%
Other values (241) 13169
26.2%
Latin
ValueCountFrequency (%)
F 21
18.8%
B 21
18.8%
A 13
 
11.6%
W 4
 
3.6%
e 4
 
3.6%
S 4
 
3.6%
P 3
 
2.7%
I 3
 
2.7%
C 3
 
2.7%
i 3
 
2.7%
Other values (25) 33
29.5%
Common
ValueCountFrequency (%)
15915
42.3%
1 3870
 
10.3%
- 3499
 
9.3%
2 2984
 
7.9%
3 1830
 
4.9%
5 1659
 
4.4%
4 1640
 
4.4%
6 1615
 
4.3%
0 1160
 
3.1%
7 1027
 
2.7%
Other values (7) 2386
 
6.3%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50207
57.1%
ASCII 37696
42.9%
CJK 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15915
42.2%
1 3870
 
10.3%
- 3499
 
9.3%
2 2984
 
7.9%
3 1830
 
4.9%
5 1659
 
4.4%
4 1640
 
4.4%
6 1615
 
4.3%
0 1160
 
3.1%
7 1027
 
2.7%
Other values (41) 2497
 
6.6%
Hangul
ValueCountFrequency (%)
4460
 
8.9%
3923
 
7.8%
3792
 
7.6%
3735
 
7.4%
3718
 
7.4%
3699
 
7.4%
3698
 
7.4%
3694
 
7.4%
3694
 
7.4%
2625
 
5.2%
Other values (241) 13169
26.2%
CJK
ValueCountFrequency (%)
1
100.0%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct2119
Distinct (%)87.9%
Missing1285
Missing (%)34.8%
Memory size29.0 KiB
2024-04-18T03:23:15.636153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length53
Mean length30.829117
Min length21

Characters and Unicode

Total characters74329
Distinct characters314
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

Unique1878 ?
Unique (%)77.9%

Sample

1st row서울특별시 광진구 아차산로33길 90 (화양동)
2nd row서울특별시 광진구 아차산로65길 68 (구의동)
3rd row서울특별시 광진구 아차산로57길 9 (구의동)
4th row서울특별시 광진구 아차산로40길 23 (자양동)
5th row서울특별시 광진구 자양번영로11길 15-4 (자양동)
ValueCountFrequency (%)
서울특별시 2411
 
16.2%
광진구 2411
 
16.2%
1층 1006
 
6.7%
자양동 603
 
4.0%
중곡동 572
 
3.8%
구의동 459
 
3.1%
2층 293
 
2.0%
화양동 292
 
2.0%
능동로 189
 
1.3%
군자동 150
 
1.0%
Other values (1371) 6536
43.8%
2024-04-18T03:23:15.970006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12515
 
16.8%
1 3477
 
4.7%
3142
 
4.2%
3020
 
4.1%
2813
 
3.8%
) 2535
 
3.4%
( 2535
 
3.4%
2450
 
3.3%
2434
 
3.3%
2421
 
3.3%
Other values (304) 36987
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41996
56.5%
Space Separator 12515
 
16.8%
Decimal Number 12227
 
16.4%
Close Punctuation 2535
 
3.4%
Open Punctuation 2535
 
3.4%
Other Punctuation 2133
 
2.9%
Dash Punctuation 271
 
0.4%
Uppercase Letter 93
 
0.1%
Lowercase Letter 21
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3142
 
7.5%
3020
 
7.2%
2813
 
6.7%
2450
 
5.8%
2434
 
5.8%
2421
 
5.8%
2414
 
5.7%
2413
 
5.7%
2411
 
5.7%
2411
 
5.7%
Other values (251) 16067
38.3%
Uppercase Letter
ValueCountFrequency (%)
B 35
37.6%
F 15
16.1%
A 9
 
9.7%
C 4
 
4.3%
S 3
 
3.2%
P 3
 
3.2%
I 3
 
3.2%
H 2
 
2.2%
E 2
 
2.2%
D 2
 
2.2%
Other values (11) 15
16.1%
Lowercase Letter
ValueCountFrequency (%)
e 4
19.0%
i 3
14.3%
s 2
9.5%
c 2
9.5%
l 2
9.5%
h 1
 
4.8%
r 1
 
4.8%
a 1
 
4.8%
o 1
 
4.8%
t 1
 
4.8%
Other values (3) 3
14.3%
Decimal Number
ValueCountFrequency (%)
1 3477
28.4%
2 1793
14.7%
3 1370
 
11.2%
0 1124
 
9.2%
5 1039
 
8.5%
4 889
 
7.3%
6 851
 
7.0%
7 598
 
4.9%
8 550
 
4.5%
9 536
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 2128
99.8%
. 3
 
0.1%
? 2
 
0.1%
Space Separator
ValueCountFrequency (%)
12515
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2535
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 271
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41995
56.5%
Common 32218
43.3%
Latin 115
 
0.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3142
 
7.5%
3020
 
7.2%
2813
 
6.7%
2450
 
5.8%
2434
 
5.8%
2421
 
5.8%
2414
 
5.7%
2413
 
5.7%
2411
 
5.7%
2411
 
5.7%
Other values (250) 16066
38.3%
Latin
ValueCountFrequency (%)
B 35
30.4%
F 15
13.0%
A 9
 
7.8%
C 4
 
3.5%
e 4
 
3.5%
S 3
 
2.6%
P 3
 
2.6%
I 3
 
2.6%
i 3
 
2.6%
H 2
 
1.7%
Other values (25) 34
29.6%
Common
ValueCountFrequency (%)
12515
38.8%
1 3477
 
10.8%
) 2535
 
7.9%
( 2535
 
7.9%
, 2128
 
6.6%
2 1793
 
5.6%
3 1370
 
4.3%
0 1124
 
3.5%
5 1039
 
3.2%
4 889
 
2.8%
Other values (8) 2813
 
8.7%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41995
56.5%
ASCII 32332
43.5%
Number Forms 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12515
38.7%
1 3477
 
10.8%
) 2535
 
7.8%
( 2535
 
7.8%
, 2128
 
6.6%
2 1793
 
5.5%
3 1370
 
4.2%
0 1124
 
3.5%
5 1039
 
3.2%
4 889
 
2.7%
Other values (42) 2927
 
9.1%
Hangul
ValueCountFrequency (%)
3142
 
7.5%
3020
 
7.2%
2813
 
6.7%
2450
 
5.8%
2434
 
5.8%
2421
 
5.8%
2414
 
5.7%
2413
 
5.7%
2411
 
5.7%
2411
 
5.7%
Other values (250) 16066
38.3%
Number Forms
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct192
Distinct (%)8.0%
Missing1295
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean5009.0067
Minimum4901
Maximum5119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:16.077035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4901
5-th percentile4908
Q14954
median5011
Q35058
95-th percentile5105
Maximum5119
Range218
Interquartile range (IQR)104

Descriptive statistics

Standard deviation62.096853
Coefficient of variation (CV)0.012397039
Kurtosis-1.0687306
Mean5009.0067
Median Absolute Deviation (MAD)51
Skewness-0.080774642
Sum12026625
Variance3856.0191
MonotonicityNot monotonic
2024-04-18T03:23:16.195037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4908 55
 
1.5%
5010 53
 
1.4%
5073 51
 
1.4%
5017 40
 
1.1%
5018 38
 
1.0%
5009 38
 
1.0%
5116 38
 
1.0%
5039 34
 
0.9%
5041 34
 
0.9%
5026 33
 
0.9%
Other values (182) 1987
53.8%
(Missing) 1295
35.0%
ValueCountFrequency (%)
4901 4
 
0.1%
4902 14
 
0.4%
4903 19
 
0.5%
4904 23
0.6%
4905 5
 
0.1%
4906 7
 
0.2%
4907 5
 
0.1%
4908 55
1.5%
4909 17
 
0.5%
4910 7
 
0.2%
ValueCountFrequency (%)
5119 23
0.6%
5118 10
 
0.3%
5117 19
0.5%
5116 38
1.0%
5115 2
 
0.1%
5113 3
 
0.1%
5112 14
 
0.4%
5110 2
 
0.1%
5108 2
 
0.1%
5105 16
0.4%
Distinct3217
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
2024-04-18T03:23:16.451622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length6.3714827
Min length1

Characters and Unicode

Total characters23549
Distinct characters749
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2891 ?
Unique (%)78.2%

Sample

1st row정미용실
2nd row치치미용실
3rd row치치
4th row마샬
5th row연화
ValueCountFrequency (%)
헤어 130
 
2.7%
hair 49
 
1.0%
미용실 48
 
1.0%
네일 32
 
0.7%
nail 31
 
0.7%
에스테틱 29
 
0.6%
건대점 25
 
0.5%
19
 
0.4%
헤어샵 15
 
0.3%
뷰티 13
 
0.3%
Other values (3458) 4365
91.8%
2024-04-18T03:23:16.829781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1399
 
5.9%
1341
 
5.7%
1063
 
4.5%
946
 
4.0%
708
 
3.0%
702
 
3.0%
532
 
2.3%
459
 
1.9%
433
 
1.8%
( 392
 
1.7%
Other values (739) 15574
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18733
79.5%
Lowercase Letter 1407
 
6.0%
Uppercase Letter 1153
 
4.9%
Space Separator 1063
 
4.5%
Open Punctuation 392
 
1.7%
Close Punctuation 392
 
1.7%
Other Punctuation 216
 
0.9%
Decimal Number 174
 
0.7%
Connector Punctuation 7
 
< 0.1%
Dash Punctuation 6
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1399
 
7.5%
1341
 
7.2%
946
 
5.0%
708
 
3.8%
702
 
3.7%
532
 
2.8%
459
 
2.5%
433
 
2.3%
314
 
1.7%
294
 
1.6%
Other values (659) 11605
61.9%
Lowercase Letter
ValueCountFrequency (%)
a 201
14.3%
i 144
10.2%
e 137
9.7%
o 118
 
8.4%
l 106
 
7.5%
n 103
 
7.3%
r 97
 
6.9%
h 70
 
5.0%
u 65
 
4.6%
s 58
 
4.1%
Other values (16) 308
21.9%
Uppercase Letter
ValueCountFrequency (%)
A 125
 
10.8%
I 90
 
7.8%
N 86
 
7.5%
H 84
 
7.3%
L 76
 
6.6%
O 74
 
6.4%
B 74
 
6.4%
S 65
 
5.6%
R 64
 
5.6%
E 61
 
5.3%
Other values (16) 354
30.7%
Decimal Number
ValueCountFrequency (%)
1 41
23.6%
0 30
17.2%
2 27
15.5%
9 19
10.9%
3 19
10.9%
5 9
 
5.2%
7 8
 
4.6%
6 8
 
4.6%
8 7
 
4.0%
4 6
 
3.4%
Other Punctuation
ValueCountFrequency (%)
? 63
29.2%
& 41
19.0%
. 40
18.5%
# 26
12.0%
, 22
 
10.2%
' 14
 
6.5%
: 8
 
3.7%
! 1
 
0.5%
1
 
0.5%
Space Separator
ValueCountFrequency (%)
1063
100.0%
Open Punctuation
ValueCountFrequency (%)
( 392
100.0%
Close Punctuation
ValueCountFrequency (%)
) 392
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18718
79.5%
Latin 2562
 
10.9%
Common 2254
 
9.6%
Han 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1399
 
7.5%
1341
 
7.2%
946
 
5.1%
708
 
3.8%
702
 
3.8%
532
 
2.8%
459
 
2.5%
433
 
2.3%
314
 
1.7%
294
 
1.6%
Other values (648) 11590
61.9%
Latin
ValueCountFrequency (%)
a 201
 
7.8%
i 144
 
5.6%
e 137
 
5.3%
A 125
 
4.9%
o 118
 
4.6%
l 106
 
4.1%
n 103
 
4.0%
r 97
 
3.8%
I 90
 
3.5%
N 86
 
3.4%
Other values (43) 1355
52.9%
Common
ValueCountFrequency (%)
1063
47.2%
( 392
 
17.4%
) 392
 
17.4%
? 63
 
2.8%
1 41
 
1.8%
& 41
 
1.8%
. 40
 
1.8%
0 30
 
1.3%
2 27
 
1.2%
# 26
 
1.2%
Other values (17) 139
 
6.2%
Han
ValueCountFrequency (%)
5
33.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18717
79.5%
ASCII 4811
 
20.4%
CJK 15
 
0.1%
Number Forms 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%
Punctuation 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1399
 
7.5%
1341
 
7.2%
946
 
5.1%
708
 
3.8%
702
 
3.8%
532
 
2.8%
459
 
2.5%
433
 
2.3%
314
 
1.7%
294
 
1.6%
Other values (647) 11589
61.9%
ASCII
ValueCountFrequency (%)
1063
22.1%
( 392
 
8.1%
) 392
 
8.1%
a 201
 
4.2%
i 144
 
3.0%
e 137
 
2.8%
A 125
 
2.6%
o 118
 
2.5%
l 106
 
2.2%
n 103
 
2.1%
Other values (66) 2030
42.2%
CJK
ValueCountFrequency (%)
5
33.3%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Number Forms
ValueCountFrequency (%)
2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Compat Jamo
ValueCountFrequency (%)
1
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
None
ValueCountFrequency (%)
1
100.0%
Distinct2815
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
Minimum1999-01-14 00:00:00
Maximum2024-04-15 16:02:15
2024-04-18T03:23:16.935820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:23:17.060894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
I
2563 
U
1118 
D
 
15

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 2563
69.3%
U 1118
30.2%
D 15
 
0.4%

Length

2024-04-18T03:23:17.161020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:17.235954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 2563
69.3%
u 1118
30.2%
d 15
 
0.4%
Distinct910
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-04-18T03:23:17.316460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:23:17.418504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
일반미용업
2767 
피부미용업
489 
네일아트업
324 
메이크업업
 
94
기타
 
22

Length

Max length5
Median length5
Mean length4.9821429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2767
74.9%
피부미용업 489
 
13.2%
네일아트업 324
 
8.8%
메이크업업 94
 
2.5%
기타 22
 
0.6%

Length

2024-04-18T03:23:17.517851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:17.612200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2767
74.9%
피부미용업 489
 
13.2%
네일아트업 324
 
8.8%
메이크업업 94
 
2.5%
기타 22
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct2038
Distinct (%)56.3%
Missing77
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean207115.39
Minimum205360.7
Maximum209775.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:17.707395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205360.7
5-th percentile205838.02
Q1206363.85
median207162.87
Q3207699.86
95-th percentile208558.29
Maximum209775.27
Range4414.5633
Interquartile range (IQR)1336.0117

Descriptive statistics

Standard deviation849.3734
Coefficient of variation (CV)0.004100967
Kurtosis-0.22070818
Mean207115.39
Median Absolute Deviation (MAD)639.44705
Skewness0.29605174
Sum7.4955061 × 108
Variance721435.17
MonotonicityNot monotonic
2024-04-18T03:23:17.799410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208558.286631653 26
 
0.7%
208394.416382167 20
 
0.5%
208644.819209521 19
 
0.5%
207135.958384677 16
 
0.4%
208403.746065593 12
 
0.3%
206217.434726027 12
 
0.3%
206212.244268239 11
 
0.3%
208738.952014366 11
 
0.3%
205945.98820137 11
 
0.3%
206088.852238736 10
 
0.3%
Other values (2028) 3471
93.9%
(Missing) 77
 
2.1%
ValueCountFrequency (%)
205360.704520746 1
< 0.1%
205366.118854171 1
< 0.1%
205367.268135886 1
< 0.1%
205383.765820741 1
< 0.1%
205390.463339147 1
< 0.1%
205419.524601632 1
< 0.1%
205438.159106242 1
< 0.1%
205450.621773006 1
< 0.1%
205458.029352736 1
< 0.1%
205467.039489419 1
< 0.1%
ValueCountFrequency (%)
209775.267831522 1
 
< 0.1%
209766.973555533 3
0.1%
209734.218553133 1
 
< 0.1%
209637.078215509 4
0.1%
209602.113978282 3
0.1%
209551.051963422 2
0.1%
209523.395129627 3
0.1%
209512.089430461 1
 
< 0.1%
209506.54940258 2
0.1%
209505.045903118 1
 
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct2038
Distinct (%)56.3%
Missing77
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean449368.21
Minimum447331.9
Maximum452140.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:17.897466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum447331.9
5-th percentile447727.03
Q1448428.75
median449097.49
Q3450385.29
95-th percentile451534.12
Maximum452140.33
Range4808.4262
Interquartile range (IQR)1956.5321

Descriptive statistics

Standard deviation1196.2508
Coefficient of variation (CV)0.0026620726
Kurtosis-0.88874487
Mean449368.21
Median Absolute Deviation (MAD)937.07468
Skewness0.41543705
Sum1.6262635 × 109
Variance1431016
MonotonicityNot monotonic
2024-04-18T03:23:18.012164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448353.905156369 26
 
0.7%
448165.279999905 20
 
0.5%
448711.725750126 19
 
0.5%
448386.668907366 16
 
0.4%
448490.239179557 12
 
0.3%
448506.218743834 12
 
0.3%
449577.174890763 11
 
0.3%
448858.223522999 11
 
0.3%
447862.351973505 11
 
0.3%
448297.963860543 10
 
0.3%
Other values (2028) 3471
93.9%
(Missing) 77
 
2.1%
ValueCountFrequency (%)
447331.904249819 1
 
< 0.1%
447382.310354354 1
 
< 0.1%
447399.697266999 1
 
< 0.1%
447411.712462396 2
0.1%
447414.538114925 2
0.1%
447421.91647025 2
0.1%
447434.777242906 3
0.1%
447456.019020609 1
 
< 0.1%
447461.07290075 1
 
< 0.1%
447461.92922345 1
 
< 0.1%
ValueCountFrequency (%)
452140.330430016 1
 
< 0.1%
452136.717870103 3
 
0.1%
452134.45276901 1
 
< 0.1%
452124.901251429 2
 
0.1%
452120.341711469 1
 
< 0.1%
452108.545091879 1
 
< 0.1%
452106.544784594 1
 
< 0.1%
452097.000128126 8
0.2%
452093.521893286 1
 
< 0.1%
452089.198171901 3
 
0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
미용업
1588 
일반미용업
662 
<NA>
610 
종합미용업
294 
피부미용업
282 
Other values (11)
260 

Length

Max length23
Median length16
Mean length4.338474
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1588
43.0%
일반미용업 662
17.9%
<NA> 610
 
16.5%
종합미용업 294
 
8.0%
피부미용업 282
 
7.6%
네일미용업 105
 
2.8%
피부미용업, 네일미용업 44
 
1.2%
화장ㆍ분장 미용업 25
 
0.7%
일반미용업, 네일미용업 23
 
0.6%
네일미용업, 화장ㆍ분장 미용업 15
 
0.4%
Other values (6) 48
 
1.3%

Length

2024-04-18T03:23:18.120186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1666
42.5%
일반미용업 718
18.3%
na 610
 
15.6%
피부미용업 353
 
9.0%
종합미용업 294
 
7.5%
네일미용업 202
 
5.2%
화장ㆍ분장 78
 
2.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)1.0%
Missing1041
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean1.7506591
Minimum0
Maximum102
Zeros1387
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:18.223017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile5
Maximum102
Range102
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5741123
Coefficient of variation (CV)2.0415809
Kurtosis264.89479
Mean1.7506591
Median Absolute Deviation (MAD)0
Skewness11.987957
Sum4648
Variance12.774279
MonotonicityNot monotonic
2024-04-18T03:23:18.316844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 1387
37.5%
4 368
 
10.0%
3 337
 
9.1%
2 233
 
6.3%
1 158
 
4.3%
5 99
 
2.7%
6 26
 
0.7%
7 8
 
0.2%
11 4
 
0.1%
8 4
 
0.1%
Other values (16) 31
 
0.8%
(Missing) 1041
28.2%
ValueCountFrequency (%)
0 1387
37.5%
1 158
 
4.3%
2 233
 
6.3%
3 337
 
9.1%
4 368
 
10.0%
5 99
 
2.7%
6 26
 
0.7%
7 8
 
0.2%
8 4
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
102 1
 
< 0.1%
50 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
26 2
0.1%
25 4
0.1%
24 4
0.1%
23 1
 
< 0.1%
20 3
0.1%
19 1
 
< 0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.3%
Missing1629
Missing (%)44.1%
Infinite0
Infinite (%)0.0%
Mean0.22786647
Minimum0
Maximum6
Zeros1674
Zeros (%)45.3%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:18.398138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.55990374
Coefficient of variation (CV)2.4571572
Kurtosis24.76621
Mean0.22786647
Median Absolute Deviation (MAD)0
Skewness4.0018084
Sum471
Variance0.3134922
MonotonicityNot monotonic
2024-04-18T03:23:18.493760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1674
45.3%
1 354
 
9.6%
2 17
 
0.5%
3 11
 
0.3%
4 7
 
0.2%
5 2
 
0.1%
6 2
 
0.1%
(Missing) 1629
44.1%
ValueCountFrequency (%)
0 1674
45.3%
1 354
 
9.6%
2 17
 
0.5%
3 11
 
0.3%
4 7
 
0.2%
5 2
 
0.1%
6 2
 
0.1%
ValueCountFrequency (%)
6 2
 
0.1%
5 2
 
0.1%
4 7
 
0.2%
3 11
 
0.3%
2 17
 
0.5%
1 354
 
9.6%
0 1674
45.3%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)0.5%
Missing1716
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean0.98434343
Minimum0
Maximum9
Zeros540
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:18.576566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91102801
Coefficient of variation (CV)0.92551845
Kurtosis11.272032
Mean0.98434343
Median Absolute Deviation (MAD)0
Skewness2.2103369
Sum1949
Variance0.82997203
MonotonicityNot monotonic
2024-04-18T03:23:18.648920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1102
29.8%
0 540
 
14.6%
2 231
 
6.2%
3 72
 
1.9%
4 23
 
0.6%
5 4
 
0.1%
6 4
 
0.1%
9 2
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 1716
46.4%
ValueCountFrequency (%)
0 540
14.6%
1 1102
29.8%
2 231
 
6.2%
3 72
 
1.9%
4 23
 
0.6%
5 4
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 4
 
0.1%
5 4
 
0.1%
4 23
 
0.6%
3 72
 
1.9%
2 231
 
6.2%
1 1102
29.8%
0 540
14.6%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.9%
Missing2088
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean1.7966418
Minimum0
Maximum202
Zeros91
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:18.723048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum202
Range202
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.6851654
Coefficient of variation (CV)5.3907047
Kurtosis355.50379
Mean1.7966418
Median Absolute Deviation (MAD)0
Skewness18.375425
Sum2889
Variance93.802428
MonotonicityNot monotonic
2024-04-18T03:23:18.809433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1200
32.5%
2 215
 
5.8%
0 91
 
2.5%
3 67
 
1.8%
4 16
 
0.4%
5 4
 
0.1%
6 4
 
0.1%
9 3
 
0.1%
101 2
 
0.1%
201 2
 
0.1%
Other values (4) 4
 
0.1%
(Missing) 2088
56.5%
ValueCountFrequency (%)
0 91
 
2.5%
1 1200
32.5%
2 215
 
5.8%
3 67
 
1.8%
4 16
 
0.4%
5 4
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 3
 
0.1%
ValueCountFrequency (%)
202 1
 
< 0.1%
201 2
 
0.1%
102 1
 
< 0.1%
101 2
 
0.1%
9 3
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 4
 
0.1%
5 4
 
0.1%
4 16
0.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
2800 
0
822 
1
 
64
2
 
9
3
 
1

Length

Max length4
Median length4
Mean length3.2727273
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2800
75.8%
0 822
 
22.2%
1 64
 
1.7%
2 9
 
0.2%
3 1
 
< 0.1%

Length

2024-04-18T03:23:18.912331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:19.227154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2800
75.8%
0 822
 
22.2%
1 64
 
1.7%
2 9
 
0.2%
3 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
3272 
0
362 
1
 
56
2
 
5
3
 
1

Length

Max length4
Median length4
Mean length3.6558442
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3272
88.5%
0 362
 
9.8%
1 56
 
1.5%
2 5
 
0.1%
3 1
 
< 0.1%

Length

2024-04-18T03:23:19.321681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:19.405685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3272
88.5%
0 362
 
9.8%
1 56
 
1.5%
2 5
 
0.1%
3 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
0
2025 
<NA>
1671 

Length

Max length4
Median length1
Mean length2.3563312
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2025
54.8%
<NA> 1671
45.2%

Length

2024-04-18T03:23:19.489957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:19.564699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2025
54.8%
na 1671
45.2%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
0
2025 
<NA>
1671 

Length

Max length4
Median length1
Mean length2.3563312
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2025
54.8%
<NA> 1671
45.2%

Length

2024-04-18T03:23:19.641148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:19.724212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2025
54.8%
na 1671
45.2%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
0
2025 
<NA>
1671 

Length

Max length4
Median length1
Mean length2.3563312
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 2025
54.8%
<NA> 1671
45.2%

Length

2024-04-18T03:23:19.816926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:19.891122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2025
54.8%
na 1671
45.2%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing643
Missing (%)17.4%
Memory size7.3 KiB
False
3053 
(Missing)
643 
ValueCountFrequency (%)
False 3053
82.6%
(Missing) 643
 
17.4%
2024-04-18T03:23:19.952393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct24
Distinct (%)0.8%
Missing701
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean3.5308848
Minimum0
Maximum34
Zeros309
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:20.017390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum34
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6559504
Coefficient of variation (CV)0.75220534
Kurtosis19.938146
Mean3.5308848
Median Absolute Deviation (MAD)1
Skewness3.0835129
Sum10575
Variance7.0540725
MonotonicityNot monotonic
2024-04-18T03:23:20.104717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 1173
31.7%
4 487
13.2%
2 428
 
11.6%
0 309
 
8.4%
5 186
 
5.0%
6 122
 
3.3%
8 70
 
1.9%
7 59
 
1.6%
10 45
 
1.2%
1 33
 
0.9%
Other values (14) 83
 
2.2%
(Missing) 701
19.0%
ValueCountFrequency (%)
0 309
 
8.4%
1 33
 
0.9%
2 428
 
11.6%
3 1173
31.7%
4 487
13.2%
5 186
 
5.0%
6 122
 
3.3%
7 59
 
1.6%
8 70
 
1.9%
9 18
 
0.5%
ValueCountFrequency (%)
34 1
 
< 0.1%
30 2
 
0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
20 3
 
0.1%
18 2
 
0.1%
17 2
 
0.1%
16 6
0.2%
15 6
0.2%
14 12
0.3%

조건부허가신고사유
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing3695
Missing (%)> 99.9%
Memory size29.0 KiB
2024-04-18T03:23:20.220804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters12
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

Unique1 ?
Unique (%)100.0%

Sample

1st row미용면허유효기간내 영업
ValueCountFrequency (%)
미용면허유효기간내 1
50.0%
영업 1
50.0%
2024-04-18T03:23:20.429149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2) 2
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11
91.7%
Space Separator 1
 
8.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11
91.7%
Common 1
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11
91.7%
ASCII 1
 
8.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
1
9.1%
ASCII
ValueCountFrequency (%)
1
100.0%

조건부허가시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
3695 
20041006
 
1

Length

Max length8
Median length4
Mean length4.0010823
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> 3695
> 99.9%
20041006 1
 
< 0.1%

Length

2024-04-18T03:23:20.536308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:20.617588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3695
> 99.9%
20041006 1
 
< 0.1%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
3695 
20041105
 
1

Length

Max length8
Median length4
Mean length4.0010823
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> 3695
> 99.9%
20041105 1
 
< 0.1%

Length

2024-04-18T03:23:20.700969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:20.794694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3695
> 99.9%
20041105 1
 
< 0.1%

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
3177 
임대
511 
자가
 
8

Length

Max length4
Median length4
Mean length3.7191558
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> 3177
86.0%
임대 511
 
13.8%
자가 8
 
0.2%

Length

2024-04-18T03:23:20.882306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:20.974922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3177
86.0%
임대 511
 
13.8%
자가 8
 
0.2%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
2132 
0
1564 

Length

Max length4
Median length4
Mean length2.7305195
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> 2132
57.7%
0 1564
42.3%

Length

2024-04-18T03:23:21.074745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:21.149429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2132
57.7%
0 1564
42.3%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)1.5%
Missing2811
Missing (%)76.1%
Infinite0
Infinite (%)0.0%
Mean0.76497175
Minimum0
Maximum41
Zeros439
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:21.217432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum41
Range41
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7576961
Coefficient of variation (CV)2.2977268
Kurtosis313.93511
Mean0.76497175
Median Absolute Deviation (MAD)1
Skewness14.576763
Sum677
Variance3.0894956
MonotonicityNot monotonic
2024-04-18T03:23:21.290745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 439
 
11.9%
1 358
 
9.7%
2 48
 
1.3%
3 16
 
0.4%
4 8
 
0.2%
5 5
 
0.1%
7 3
 
0.1%
6 3
 
0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
Other values (3) 3
 
0.1%
(Missing) 2811
76.1%
ValueCountFrequency (%)
0 439
11.9%
1 358
9.7%
2 48
 
1.3%
3 16
 
0.4%
4 8
 
0.2%
5 5
 
0.1%
6 3
 
0.1%
7 3
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
41 1
 
< 0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 3
 
0.1%
6 3
 
0.1%
5 5
 
0.1%
4 8
0.2%
3 16
0.4%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
2813 
0
810 
1
 
60
2
 
11
3
 
1

Length

Max length4
Median length4
Mean length3.2832792
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> 2813
76.1%
0 810
 
21.9%
1 60
 
1.6%
2 11
 
0.3%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-04-18T03:23:21.390281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:21.480417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2813
76.1%
0 810
 
21.9%
1 60
 
1.6%
2 11
 
0.3%
3 1
 
< 0.1%
4 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size29.0 KiB
<NA>
2224 
0
1472 

Length

Max length4
Median length4
Mean length2.8051948
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> 2224
60.2%
0 1472
39.8%

Length

2024-04-18T03:23:21.568975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:23:21.643359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2224
60.2%
0 1472
39.8%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)1.0%
Missing2253
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean0.86139986
Minimum0
Maximum15
Zeros1004
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size32.6 KiB
2024-04-18T03:23:21.712976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7428833
Coefficient of variation (CV)2.023315
Kurtosis10.655754
Mean0.86139986
Median Absolute Deviation (MAD)0
Skewness2.8858771
Sum1243
Variance3.0376421
MonotonicityNot monotonic
2024-04-18T03:23:21.800125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1004
27.2%
2 142
 
3.8%
1 118
 
3.2%
3 72
 
1.9%
4 36
 
1.0%
5 25
 
0.7%
7 13
 
0.4%
8 12
 
0.3%
6 12
 
0.3%
9 3
 
0.1%
Other values (4) 6
 
0.2%
(Missing) 2253
61.0%
ValueCountFrequency (%)
0 1004
27.2%
1 118
 
3.2%
2 142
 
3.8%
3 72
 
1.9%
4 36
 
1.0%
5 25
 
0.7%
6 12
 
0.3%
7 13
 
0.4%
8 12
 
0.3%
9 3
 
0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
13 1
 
< 0.1%
11 2
 
0.1%
10 2
 
0.1%
9 3
 
0.1%
8 12
 
0.3%
7 13
 
0.4%
6 12
 
0.3%
5 25
0.7%
4 36
1.0%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing610
Missing (%)16.5%
Memory size7.3 KiB
False
3085 
True
 
1
(Missing)
610 
ValueCountFrequency (%)
False 3085
83.5%
True 1
 
< 0.1%
(Missing) 610
 
16.5%
2024-04-18T03:23:21.883999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030400003040000-204-1969-0064919691218<NA>3폐업2폐업20030819<NA><NA><NA>02 011.88143825서울특별시 광진구 구의동 242-0번지<NA><NA>정미용실2003-03-07 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130400003040000-204-1971-0044319710904<NA>3폐업2폐업19980225<NA><NA><NA>02 465659115.90143914서울특별시 광진구 화양동 9-49번지<NA><NA>치치미용실2001-11-29 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230400003040000-204-1971-0081819710904<NA>3폐업2폐업19940820<NA><NA><NA>02 0000015.90143914서울특별시 광진구 화양동 2-82번지<NA><NA>치치2001-11-29 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330400003040000-204-1971-0100119710209<NA>3폐업2폐업19960325<NA><NA><NA>02 466140512.20143843서울특별시 광진구 자양동 49-117번지<NA><NA>마샬2001-11-29 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
430400003040000-204-1972-0130319720722<NA>3폐업2폐업20030226<NA><NA><NA>020000000015.29143862서울특별시 광진구 자양동 553-69번지<NA><NA>연화2003-03-05 00:00:00I2018-08-31 23:59:59.0일반미용업205829.388021448134.099068미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
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개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
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368930400003040000-226-2017-0000720170906<NA>3폐업2폐업20221014<NA><NA><NA><NA>51.75143841서울특별시 광진구 자양동 1-17 2층서울특별시 광진구 동일로20길 117, 2층 (자양동)5073Zoom's 뷰티2022-10-14 17:10:18U2021-10-30 23:06:00.0피부미용업206111.505079448595.987894<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
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