Overview

Dataset statistics

Number of variables44
Number of observations202
Missing cells1991
Missing cells (%)22.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.5 KiB
Average record size in memory377.7 B

Variable types

Categorical22
Text5
DateTime3
Unsupported8
Numeric5
Boolean1

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,남성종사자수,여성종사자수,영업장주변구분명,등급구분명,급수시설구분명,총인원,본사종업원수,공장사무직종업원수,공장판매직종업원수,공장생산직종업원수,건물소유구분명,보증액,월세액,다중이용업소여부,시설총규모,전통업소지정번호,전통업소주된음식,홈페이지
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-18546/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
데이터갱신일자 is highly imbalanced (68.6%)Imbalance
위생업태명 is highly imbalanced (65.5%)Imbalance
등급구분명 is highly imbalanced (56.2%)Imbalance
급수시설구분명 is highly imbalanced (51.1%)Imbalance
총인원 is highly imbalanced (86.0%)Imbalance
본사종업원수 is highly imbalanced (86.0%)Imbalance
공장사무직종업원수 is highly imbalanced (86.0%)Imbalance
공장판매직종업원수 is highly imbalanced (86.0%)Imbalance
공장생산직종업원수 is highly imbalanced (86.0%)Imbalance
보증액 is highly imbalanced (86.0%)Imbalance
월세액 is highly imbalanced (86.0%)Imbalance
다중이용업소여부 is highly imbalanced (58.2%)Imbalance
인허가취소일자 has 202 (100.0%) missing valuesMissing
폐업일자 has 31 (15.3%) missing valuesMissing
휴업시작일자 has 202 (100.0%) missing valuesMissing
휴업종료일자 has 202 (100.0%) missing valuesMissing
재개업일자 has 202 (100.0%) missing valuesMissing
전화번호 has 11 (5.4%) missing valuesMissing
도로명주소 has 141 (69.8%) missing valuesMissing
도로명우편번호 has 142 (70.3%) missing valuesMissing
좌표정보(X) has 12 (5.9%) missing valuesMissing
좌표정보(Y) has 12 (5.9%) missing valuesMissing
건물소유구분명 has 202 (100.0%) missing valuesMissing
다중이용업소여부 has 13 (6.4%) missing valuesMissing
시설총규모 has 13 (6.4%) missing valuesMissing
전통업소지정번호 has 202 (100.0%) missing valuesMissing
전통업소주된음식 has 202 (100.0%) missing valuesMissing
홈페이지 has 202 (100.0%) 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
건물소유구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소지정번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전통업소주된음식 is an unsupported type, check if it needs cleaning or further analysisUnsupported
홈페이지 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-29 19:44:58.321179
Analysis finished2024-04-29 19:44:59.075776
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
3190000
202 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3190000
2nd row3190000
3rd row3190000
4th row3190000
5th row3190000

Common Values

ValueCountFrequency (%)
3190000 202
100.0%

Length

2024-04-30T04:44:59.135851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:44:59.207726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3190000 202
100.0%

관리번호
Text

UNIQUE 

Distinct202
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-30T04:44:59.359723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4444
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

Unique202 ?
Unique (%)100.0%

Sample

1st row3190000-103-1993-00765
2nd row3190000-103-1993-00792
3rd row3190000-103-1993-00793
4th row3190000-103-1993-01801
5th row3190000-103-1993-01802
ValueCountFrequency (%)
3190000-103-1993-00765 1
 
0.5%
3190000-103-1996-01750 1
 
0.5%
3190000-103-1996-01722 1
 
0.5%
3190000-103-1997-01705 1
 
0.5%
3190000-103-1996-01724 1
 
0.5%
3190000-103-1996-01725 1
 
0.5%
3190000-103-1996-01726 1
 
0.5%
3190000-103-1996-01727 1
 
0.5%
3190000-103-1996-01729 1
 
0.5%
3190000-103-1996-01730 1
 
0.5%
Other values (192) 192
95.0%
2024-04-30T04:44:59.626341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1389
31.3%
1 796
17.9%
- 606
13.6%
9 591
13.3%
3 495
 
11.1%
7 178
 
4.0%
8 85
 
1.9%
4 82
 
1.8%
5 80
 
1.8%
2 76
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3838
86.4%
Dash Punctuation 606
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1389
36.2%
1 796
20.7%
9 591
15.4%
3 495
 
12.9%
7 178
 
4.6%
8 85
 
2.2%
4 82
 
2.1%
5 80
 
2.1%
2 76
 
2.0%
6 66
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 606
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4444
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1389
31.3%
1 796
17.9%
- 606
13.6%
9 591
13.3%
3 495
 
11.1%
7 178
 
4.0%
8 85
 
1.9%
4 82
 
1.8%
5 80
 
1.8%
2 76
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1389
31.3%
1 796
17.9%
- 606
13.6%
9 591
13.3%
3 495
 
11.1%
7 178
 
4.0%
8 85
 
1.9%
4 82
 
1.8%
5 80
 
1.8%
2 76
 
1.7%

인허가일자
Date

Distinct182
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1993-09-15 00:00:00
Maximum2022-05-18 00:00:00
2024-04-30T04:44:59.753995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:44:59.868354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

영업상태코드
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
3
171 
1
31 

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 (%)
3 171
84.7%
1 31
 
15.3%

Length

2024-04-30T04:44:59.963624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:00.052032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 171
84.7%
1 31
 
15.3%

영업상태명
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
폐업
171 
영업/정상
31 

Length

Max length5
Median length2
Mean length2.460396
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 171
84.7%
영업/정상 31
 
15.3%

Length

2024-04-30T04:45:00.152447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:00.240421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 171
84.7%
영업/정상 31
 
15.3%

상세영업상태코드
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2
171 
1
31 

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 (%)
2 171
84.7%
1 31
 
15.3%

Length

2024-04-30T04:45:00.323585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:00.407642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 171
84.7%
1 31
 
15.3%

상세영업상태명
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
폐업
171 
영업
31 

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 171
84.7%
영업 31
 
15.3%

Length

2024-04-30T04:45:00.512547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:00.610162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 171
84.7%
영업 31
 
15.3%

폐업일자
Date

MISSING 

Distinct158
Distinct (%)92.4%
Missing31
Missing (%)15.3%
Memory size1.7 KiB
Minimum1994-01-24 00:00:00
Maximum2024-02-05 00:00:00
2024-04-30T04:45:00.708164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:45:00.816343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

전화번호
Text

MISSING 

Distinct185
Distinct (%)96.9%
Missing11
Missing (%)5.4%
Memory size1.7 KiB
2024-04-30T04:45:01.059329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.9319372
Min length2

Characters and Unicode

Total characters1897
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

Unique179 ?
Unique (%)93.7%

Sample

1st row02 5833001
2nd row0208495083
3rd row02 8211330
4th row0208160771
5th row02 8253501
ValueCountFrequency (%)
02 135
40.7%
522 3
 
0.9%
5335761 2
 
0.6%
5851457 2
 
0.6%
8222990 2
 
0.6%
8176262 2
 
0.6%
8136373 2
 
0.6%
8127677 1
 
0.3%
8263164 1
 
0.3%
5923330 1
 
0.3%
Other values (181) 181
54.5%
2024-04-30T04:45:01.380486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 350
18.5%
0 318
16.8%
8 209
11.0%
5 170
9.0%
3 154
8.1%
148
7.8%
1 144
7.6%
6 114
 
6.0%
9 108
 
5.7%
4 99
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1749
92.2%
Space Separator 148
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 350
20.0%
0 318
18.2%
8 209
11.9%
5 170
9.7%
3 154
8.8%
1 144
8.2%
6 114
 
6.5%
9 108
 
6.2%
4 99
 
5.7%
7 83
 
4.7%
Space Separator
ValueCountFrequency (%)
148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1897
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 350
18.5%
0 318
16.8%
8 209
11.0%
5 170
9.0%
3 154
8.1%
148
7.8%
1 144
7.6%
6 114
 
6.0%
9 108
 
5.7%
4 99
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 350
18.5%
0 318
16.8%
8 209
11.0%
5 170
9.0%
3 154
8.1%
148
7.8%
1 144
7.6%
6 114
 
6.0%
9 108
 
5.7%
4 99
 
5.2%

소재지면적
Real number (ℝ)

Distinct198
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.645297
Minimum31.18
Maximum152.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-30T04:45:01.512878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.18
5-th percentile49.5275
Q167.8325
median92.61
Q3118
95-th percentile143.218
Maximum152.93
Range121.75
Interquartile range (IQR)50.1675

Descriptive statistics

Standard deviation30.127176
Coefficient of variation (CV)0.32518841
Kurtosis-0.96920401
Mean92.645297
Median Absolute Deviation (MAD)25.21
Skewness0.082137509
Sum18714.35
Variance907.64676
MonotonicityNot monotonic
2024-04-30T04:45:01.656037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.13 2
 
1.0%
96.86 2
 
1.0%
123.62 2
 
1.0%
116.77 2
 
1.0%
109.08 1
 
0.5%
94.11 1
 
0.5%
134.16 1
 
0.5%
68.84 1
 
0.5%
76.41 1
 
0.5%
96.5 1
 
0.5%
Other values (188) 188
93.1%
ValueCountFrequency (%)
31.18 1
0.5%
31.86 1
0.5%
33.6 1
0.5%
37.56 1
0.5%
40.19 1
0.5%
41.18 1
0.5%
42.0 1
0.5%
45.54 1
0.5%
47.1 1
0.5%
47.28 1
0.5%
ValueCountFrequency (%)
152.93 1
0.5%
148.62 1
0.5%
148.22 1
0.5%
147.98 1
0.5%
145.9 1
0.5%
145.84 1
0.5%
144.71 1
0.5%
144.7 1
0.5%
144.13 1
0.5%
143.75 1
0.5%

소재지우편번호
Categorical

Distinct47
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
156816
27 
156800
14 
156832
13 
156824
13 
156811
13 
Other values (42)
122 

Length

Max length7
Median length6
Mean length6.039604
Min length6

Unique

Unique19 ?
Unique (%)9.4%

Sample

1st row156826
2nd row156854
3rd row156848
4th row156860
5th row156030

Common Values

ValueCountFrequency (%)
156816 27
 
13.4%
156800 14
 
6.9%
156832 13
 
6.4%
156824 13
 
6.4%
156811 13
 
6.4%
156848 9
 
4.5%
156804 9
 
4.5%
156030 9
 
4.5%
156826 7
 
3.5%
156827 6
 
3.0%
Other values (37) 82
40.6%

Length

2024-04-30T04:45:01.762794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
156816 27
 
13.4%
156800 14
 
6.9%
156832 13
 
6.4%
156824 13
 
6.4%
156811 13
 
6.4%
156848 9
 
4.5%
156804 9
 
4.5%
156030 9
 
4.5%
156826 7
 
3.5%
156860 6
 
3.0%
Other values (37) 82
40.6%

지번주소
Text

Distinct189
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-30T04:45:01.999978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length35
Mean length23.074257
Min length19

Characters and Unicode

Total characters4661
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)87.6%

Sample

1st row서울특별시 동작구 사당동 1030-13번지
2nd row서울특별시 동작구 신대방동 686-3번지
3rd row서울특별시 동작구 신대방동 362-23번지
4th row서울특별시 동작구 흑석동 181-1번지
5th row서울특별시 동작구 상도동 316-11번지
ValueCountFrequency (%)
서울특별시 202
24.5%
동작구 202
24.5%
사당동 74
 
9.0%
상도동 35
 
4.2%
노량진동 34
 
4.1%
신대방동 22
 
2.7%
대방동 19
 
2.3%
흑석동 10
 
1.2%
상도1동 3
 
0.4%
지하1층 3
 
0.4%
Other values (204) 221
26.8%
2024-04-30T04:45:02.374241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
811
17.4%
동 408
 
8.8%
1 212
 
4.5%
작 205
 
4.4%
서 202
 
4.3%
특 202
 
4.3%
별 202
 
4.3%
시 202
 
4.3%
구 202
 
4.3%
울 202
 
4.3%
Other values (48) 1813
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2679
57.5%
Decimal Number 962
 
20.6%
Space Separator 811
 
17.4%
Dash Punctuation 201
 
4.3%
Other Punctuation 4
 
0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 408
15.2%
작 205
7.7%
서 202
7.5%
특 202
7.5%
별 202
7.5%
시 202
7.5%
구 202
7.5%
울 202
7.5%
지 184
 
6.9%
번 178
 
6.6%
Other values (33) 492
18.4%
Decimal Number
ValueCountFrequency (%)
1 212
22.0%
3 118
12.3%
2 108
11.2%
0 98
10.2%
4 94
9.8%
6 81
 
8.4%
7 72
 
7.5%
8 72
 
7.5%
5 71
 
7.4%
9 36
 
3.7%
Space Separator
ValueCountFrequency (%)
811
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 201
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2679
57.5%
Common 1982
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 408
15.2%
작 205
7.7%
서 202
7.5%
특 202
7.5%
별 202
7.5%
시 202
7.5%
구 202
7.5%
울 202
7.5%
지 184
 
6.9%
번 178
 
6.6%
Other values (33) 492
18.4%
Common
ValueCountFrequency (%)
811
40.9%
1 212
 
10.7%
- 201
 
10.1%
3 118
 
6.0%
2 108
 
5.4%
0 98
 
4.9%
4 94
 
4.7%
6 81
 
4.1%
7 72
 
3.6%
8 72
 
3.6%
Other values (5) 115
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2679
57.5%
ASCII 1982
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
811
40.9%
1 212
 
10.7%
- 201
 
10.1%
3 118
 
6.0%
2 108
 
5.4%
0 98
 
4.9%
4 94
 
4.7%
6 81
 
4.1%
7 72
 
3.6%
8 72
 
3.6%
Other values (5) 115
 
5.8%
Hangul
ValueCountFrequency (%)
동 408
15.2%
작 205
7.7%
서 202
7.5%
특 202
7.5%
별 202
7.5%
시 202
7.5%
구 202
7.5%
울 202
7.5%
지 184
 
6.9%
번 178
 
6.6%
Other values (33) 492
18.4%

도로명주소
Text

MISSING 

Distinct61
Distinct (%)100.0%
Missing141
Missing (%)69.8%
Memory size1.7 KiB
2024-04-30T04:45:02.614899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length38
Mean length26.606557
Min length22

Characters and Unicode

Total characters1623
Distinct characters80
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)100.0%

Sample

1st row서울특별시 동작구 서달로15길 23 (흑석동)
2nd row서울특별시 동작구 서달로14길 38 (흑석동)
3rd row서울특별시 동작구 상도로 206, 지하1층 (상도동)
4th row서울특별시 동작구 서달로14길 34 (흑석동)
5th row서울특별시 동작구 노량진로12길 17 (노량진동)
ValueCountFrequency (%)
서울특별시 61
19.1%
동작구 61
19.1%
사당동 16
 
5.0%
상도동 14
 
4.4%
상도로 13
 
4.1%
대방동 9
 
2.8%
흑석동 6
 
1.9%
지하1층 5
 
1.6%
신대방동 5
 
1.6%
노량진동 5
 
1.6%
Other values (97) 125
39.1%
2024-04-30T04:45:03.006653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
259
 
16.0%
동 136
 
8.4%
작 73
 
4.5%
서 67
 
4.1%
( 62
 
3.8%
) 62
 
3.8%
시 62
 
3.8%
구 61
 
3.8%
울 61
 
3.8%
특 61
 
3.8%
Other values (70) 719
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1005
61.9%
Space Separator 259
 
16.0%
Decimal Number 219
 
13.5%
Open Punctuation 62
 
3.8%
Close Punctuation 62
 
3.8%
Other Punctuation 12
 
0.7%
Dash Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 136
13.5%
작 73
 
7.3%
서 67
 
6.7%
시 62
 
6.2%
구 61
 
6.1%
울 61
 
6.1%
특 61
 
6.1%
별 61
 
6.1%
로 60
 
6.0%
대 39
 
3.9%
Other values (53) 324
32.2%
Decimal Number
ValueCountFrequency (%)
1 55
25.1%
2 45
20.5%
3 25
11.4%
4 21
 
9.6%
6 17
 
7.8%
5 14
 
6.4%
7 14
 
6.4%
8 13
 
5.9%
0 8
 
3.7%
9 7
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
259
100.0%
Open Punctuation
ValueCountFrequency (%)
( 62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 62
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1005
61.9%
Common 616
38.0%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 136
13.5%
작 73
 
7.3%
서 67
 
6.7%
시 62
 
6.2%
구 61
 
6.1%
울 61
 
6.1%
특 61
 
6.1%
별 61
 
6.1%
로 60
 
6.0%
대 39
 
3.9%
Other values (53) 324
32.2%
Common
ValueCountFrequency (%)
259
42.0%
( 62
 
10.1%
) 62
 
10.1%
1 55
 
8.9%
2 45
 
7.3%
3 25
 
4.1%
4 21
 
3.4%
6 17
 
2.8%
5 14
 
2.3%
7 14
 
2.3%
Other values (5) 42
 
6.8%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1005
61.9%
ASCII 618
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
259
41.9%
( 62
 
10.0%
) 62
 
10.0%
1 55
 
8.9%
2 45
 
7.3%
3 25
 
4.0%
4 21
 
3.4%
6 17
 
2.8%
5 14
 
2.3%
7 14
 
2.3%
Other values (7) 44
 
7.1%
Hangul
ValueCountFrequency (%)
동 136
13.5%
작 73
 
7.3%
서 67
 
6.7%
시 62
 
6.2%
구 61
 
6.1%
울 61
 
6.1%
특 61
 
6.1%
별 61
 
6.1%
로 60
 
6.0%
대 39
 
3.9%
Other values (53) 324
32.2%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)50.0%
Missing142
Missing (%)70.3%
Infinite0
Infinite (%)0.0%
Mean7000.7
Minimum6900
Maximum7071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-30T04:45:03.129302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6900
5-th percentile6927.85
Q16964
median7008
Q37041
95-th percentile7056.05
Maximum7071
Range171
Interquartile range (IQR)77

Descriptive statistics

Standard deviation45.170975
Coefficient of variation (CV)0.0064523511
Kurtosis-0.94081108
Mean7000.7
Median Absolute Deviation (MAD)38.5
Skewness-0.31342158
Sum420042
Variance2040.4169
MonotonicityNot monotonic
2024-04-30T04:45:03.234536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7055 7
 
3.5%
7008 7
 
3.5%
6979 4
 
2.0%
7025 3
 
1.5%
6956 3
 
1.5%
7056 3
 
1.5%
7015 2
 
1.0%
7040 2
 
1.0%
6959 2
 
1.0%
7041 2
 
1.0%
Other values (20) 25
 
12.4%
(Missing) 142
70.3%
ValueCountFrequency (%)
6900 1
 
0.5%
6916 1
 
0.5%
6925 1
 
0.5%
6928 2
1.0%
6930 2
1.0%
6945 1
 
0.5%
6952 1
 
0.5%
6956 3
1.5%
6959 2
1.0%
6964 2
1.0%
ValueCountFrequency (%)
7071 1
 
0.5%
7068 1
 
0.5%
7057 1
 
0.5%
7056 3
1.5%
7055 7
3.5%
7042 1
 
0.5%
7041 2
 
1.0%
7040 2
 
1.0%
7025 3
1.5%
7015 2
 
1.0%

사업장명
Text

Distinct197
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-04-30T04:45:03.470037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length4.6287129
Min length1

Characters and Unicode

Total characters935
Distinct characters264
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)95.0%

Sample

1st row발리Ⅱ
2nd row서울단란주점
3rd row목화단란주점
4th row밴소주호프 노래타운
5th row주유소단란주점
ValueCountFrequency (%)
파티파티 3
 
1.4%
단란주점 3
 
1.4%
로얄 2
 
0.9%
팡팡 2
 
0.9%
노래하는 2
 
0.9%
굿타임 2
 
0.9%
노래타운 2
 
0.9%
7080 2
 
0.9%
스타단란주점 2
 
0.9%
오페라 2
 
0.9%
Other values (195) 195
89.9%
2024-04-30T04:45:03.814094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 81
 
8.7%
점 76
 
8.1%
란 66
 
7.1%
단 65
 
7.0%
노 19
 
2.0%
래 19
 
2.0%
15
 
1.6%
스 15
 
1.6%
리 14
 
1.5%
이 13
 
1.4%
Other values (254) 552
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 881
94.2%
Decimal Number 18
 
1.9%
Space Separator 15
 
1.6%
Uppercase Letter 6
 
0.6%
Close Punctuation 4
 
0.4%
Open Punctuation 4
 
0.4%
Lowercase Letter 3
 
0.3%
Other Punctuation 2
 
0.2%
Letter Number 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 81
 
9.2%
점 76
 
8.6%
란 66
 
7.5%
단 65
 
7.4%
노 19
 
2.2%
래 19
 
2.2%
스 15
 
1.7%
리 14
 
1.6%
이 13
 
1.5%
라 11
 
1.2%
Other values (235) 502
57.0%
Decimal Number
ValueCountFrequency (%)
0 9
50.0%
8 3
 
16.7%
7 3
 
16.7%
2 2
 
11.1%
1 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
S 2
33.3%
M 2
33.3%
B 1
16.7%
L 1
16.7%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
v 1
33.3%
i 1
33.3%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
? 1
50.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 881
94.2%
Common 44
 
4.7%
Latin 10
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 81
 
9.2%
점 76
 
8.6%
란 66
 
7.5%
단 65
 
7.4%
노 19
 
2.2%
래 19
 
2.2%
스 15
 
1.7%
리 14
 
1.6%
이 13
 
1.5%
라 11
 
1.2%
Other values (235) 502
57.0%
Common
ValueCountFrequency (%)
15
34.1%
0 9
20.5%
) 4
 
9.1%
( 4
 
9.1%
8 3
 
6.8%
7 3
 
6.8%
2 2
 
4.5%
# 1
 
2.3%
? 1
 
2.3%
1 1
 
2.3%
Latin
ValueCountFrequency (%)
S 2
20.0%
M 2
20.0%
Ⅱ 1
10.0%
B 1
10.0%
e 1
10.0%
v 1
10.0%
i 1
10.0%
L 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 881
94.2%
ASCII 53
 
5.7%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 81
 
9.2%
점 76
 
8.6%
란 66
 
7.5%
단 65
 
7.4%
노 19
 
2.2%
래 19
 
2.2%
스 15
 
1.7%
리 14
 
1.6%
이 13
 
1.5%
라 11
 
1.2%
Other values (235) 502
57.0%
ASCII
ValueCountFrequency (%)
15
28.3%
0 9
17.0%
) 4
 
7.5%
( 4
 
7.5%
8 3
 
5.7%
7 3
 
5.7%
2 2
 
3.8%
S 2
 
3.8%
M 2
 
3.8%
B 1
 
1.9%
Other values (8) 8
15.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

최종수정일자
Date

Distinct144
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1999-01-07 00:00:00
Maximum2024-03-27 11:33:44
2024-04-30T04:45:03.937316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T04:45:04.237356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
I
165 
U
37 

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 165
81.7%
U 37
 
18.3%

Length

2024-04-30T04:45:04.352062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:04.426381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 165
81.7%
u 37
 
18.3%

데이터갱신일자
Categorical

IMBALANCE 

Distinct34
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2018-08-31 23:59:59.0
165 
2020-06-13 02:40:00.0
 
3
2020-04-19 02:40:00.0
 
2
2021-12-07 22:07:00.0
 
2
2022-11-02 00:02:00.0
 
1
Other values (29)
29 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique30 ?
Unique (%)14.9%

Sample

1st row2018-08-31 23:59:59.0
2nd row2018-08-31 23:59:59.0
3rd row2018-08-31 23:59:59.0
4th row2018-08-31 23:59:59.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 165
81.7%
2020-06-13 02:40:00.0 3
 
1.5%
2020-04-19 02:40:00.0 2
 
1.0%
2021-12-07 22:07:00.0 2
 
1.0%
2022-11-02 00:02:00.0 1
 
0.5%
2019-07-10 02:40:00.0 1
 
0.5%
2019-08-24 02:40:00.0 1
 
0.5%
2022-12-03 22:09:00.0 1
 
0.5%
2021-05-26 02:40:00.0 1
 
0.5%
2021-12-04 23:09:00.0 1
 
0.5%
Other values (24) 24
 
11.9%

Length

2024-04-30T04:45:04.516478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 165
40.8%
23:59:59.0 165
40.8%
02:40:00.0 24
 
5.9%
2022-12-03 3
 
0.7%
2023-12-02 3
 
0.7%
2020-06-13 3
 
0.7%
2021-12-07 2
 
0.5%
22:07:00.0 2
 
0.5%
2020-04-19 2
 
0.5%
22:09:00.0 2
 
0.5%
Other values (33) 33
 
8.2%

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
단란주점
202 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단란주점 202
100.0%

Length

2024-04-30T04:45:04.617596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:04.718544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단란주점 202
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct173
Distinct (%)91.1%
Missing12
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean195601.85
Minimum191704.48
Maximum198335.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-30T04:45:04.825784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191704.48
5-th percentile192886.43
Q1194009.1
median194783.52
Q3198147.43
95-th percentile198294.1
Maximum198335.95
Range6631.4714
Interquartile range (IQR)4138.3277

Descriptive statistics

Standard deviation2122.1753
Coefficient of variation (CV)0.010849464
Kurtosis-1.4159883
Mean195601.85
Median Absolute Deviation (MAD)1779.2717
Skewness0.038737152
Sum37164352
Variance4503628.1
MonotonicityNot monotonic
2024-04-30T04:45:04.950464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194244.121127019 3
 
1.5%
197574.13835954 2
 
1.0%
193517.010058767 2
 
1.0%
191843.518015126 2
 
1.0%
196571.708365477 2
 
1.0%
194295.883109793 2
 
1.0%
194575.543984777 2
 
1.0%
198294.096785509 2
 
1.0%
198264.499574198 2
 
1.0%
193554.744988075 2
 
1.0%
Other values (163) 169
83.7%
(Missing) 12
 
5.9%
ValueCountFrequency (%)
191704.477106486 1
0.5%
191753.03973638 1
0.5%
191765.918928731 1
0.5%
191787.677056618 1
0.5%
191831.932633571 1
0.5%
191843.518015126 2
1.0%
191861.809766455 1
0.5%
191878.73237632 1
0.5%
192863.847951935 1
0.5%
192914.034335248 1
0.5%
ValueCountFrequency (%)
198335.94846779 1
0.5%
198325.779257298 1
0.5%
198320.791404486 1
0.5%
198313.263455469 1
0.5%
198308.848894783 1
0.5%
198302.882425901 1
0.5%
198302.154149509 1
0.5%
198296.909027397 1
0.5%
198294.141170686 1
0.5%
198294.096785509 2
1.0%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct173
Distinct (%)91.1%
Missing12
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean443837.06
Minimum441634.27
Maximum445790.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-30T04:45:05.060390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441634.27
5-th percentile441825.05
Q1442584.6
median444098.2
Q3444860.98
95-th percentile445642.04
Maximum445790.55
Range4156.2839
Interquartile range (IQR)2276.3795

Descriptive statistics

Standard deviation1292.4762
Coefficient of variation (CV)0.0029120511
Kurtosis-1.3933251
Mean443837.06
Median Absolute Deviation (MAD)1354.7335
Skewness-0.069454757
Sum84329042
Variance1670494.7
MonotonicityNot monotonic
2024-04-30T04:45:05.174636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
445650.221084825 3
 
1.5%
442391.300903019 2
 
1.0%
444097.092900063 2
 
1.0%
443195.828157727 2
 
1.0%
445183.067231695 2
 
1.0%
444526.672268745 2
 
1.0%
445507.77024306 2
 
1.0%
442062.207077499 2
 
1.0%
442629.962323045 2
 
1.0%
444142.040717888 2
 
1.0%
Other values (163) 169
83.7%
(Missing) 12
 
5.9%
ValueCountFrequency (%)
441634.265956603 1
0.5%
441661.732003124 1
0.5%
441671.438697802 1
0.5%
441672.226561514 1
0.5%
441690.743818873 1
0.5%
441690.921865632 1
0.5%
441696.145605791 1
0.5%
441807.784204037 1
0.5%
441808.553923544 1
0.5%
441812.908240047 1
0.5%
ValueCountFrequency (%)
445790.549860682 1
 
0.5%
445700.034625184 1
 
0.5%
445692.196103762 1
 
0.5%
445688.994115429 1
 
0.5%
445665.826651026 1
 
0.5%
445650.221084825 3
1.5%
445650.173146813 1
 
0.5%
445649.406187751 1
 
0.5%
445633.047209129 1
 
0.5%
445632.107878468 1
 
0.5%

위생업태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
단란주점
189 
<NA>
 
13

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
단란주점 189
93.6%
<NA> 13
 
6.4%

Length

2024-04-30T04:45:05.296231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:05.383931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단란주점 189
93.6%
na 13
 
6.4%

남성종사자수
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
103 
<NA>
56 
1
35 
2
 
7
3
 
1

Length

Max length4
Median length1
Mean length1.8316832
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row3
2nd row1
3rd row0
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 103
51.0%
<NA> 56
27.7%
1 35
 
17.3%
2 7
 
3.5%
3 1
 
0.5%

Length

2024-04-30T04:45:05.469992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:05.559347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 103
51.0%
na 56
27.7%
1 35
 
17.3%
2 7
 
3.5%
3 1
 
0.5%

여성종사자수
Categorical

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
98 
<NA>
56 
1
38 
2
10 

Length

Max length4
Median length1
Mean length1.8316832
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
0 98
48.5%
<NA> 56
27.7%
1 38
 
18.8%
2 10
 
5.0%

Length

2024-04-30T04:45:05.654535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:05.749104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 98
48.5%
na 56
27.7%
1 38
 
18.8%
2 10
 
5.0%

영업장주변구분명
Categorical

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
주택가주변
109 
<NA>
37 
기타
28 
유흥업소밀집지역
24 
학교정화(상대)
 
2

Length

Max length8
Median length5
Mean length4.7871287
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주택가주변 109
54.0%
<NA> 37
 
18.3%
기타 28
 
13.9%
유흥업소밀집지역 24
 
11.9%
학교정화(상대) 2
 
1.0%
아파트지역 2
 
1.0%

Length

2024-04-30T04:45:05.840687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:05.947844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택가주변 109
54.0%
na 37
 
18.3%
기타 28
 
13.9%
유흥업소밀집지역 24
 
11.9%
학교정화(상대 2
 
1.0%
아파트지역 2
 
1.0%

등급구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
기타
155 
<NA>
44 
자율
 
2
관리
 
1

Length

Max length4
Median length2
Mean length2.4356436
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
기타 155
76.7%
<NA> 44
 
21.8%
자율 2
 
1.0%
관리 1
 
0.5%

Length

2024-04-30T04:45:06.055856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:06.154238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 155
76.7%
na 44
 
21.8%
자율 2
 
1.0%
관리 1
 
0.5%

급수시설구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상수도전용
162 
<NA>
38 
상수도(음용)지하수(주방용)겸용
 
2

Length

Max length17
Median length5
Mean length4.9306931
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 162
80.2%
<NA> 38
 
18.8%
상수도(음용)지하수(주방용)겸용 2
 
1.0%

Length

2024-04-30T04:45:06.245159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:06.334675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 162
80.2%
na 38
 
18.8%
상수도(음용)지하수(주방용)겸용 2
 
1.0%

총인원
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:06.434173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:06.520588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

본사종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:06.624618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:06.711592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

공장사무직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:06.810841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:06.910308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

공장판매직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:07.013523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:07.095525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

공장생산직종업원수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:07.180429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:07.263047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

건물소유구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

보증액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:07.363325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:07.455245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

월세액
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
198 
0
 
4

Length

Max length4
Median length4
Mean length3.9405941
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> 198
98.0%
0 4
 
2.0%

Length

2024-04-30T04:45:07.559426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T04:45:07.649955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 198
98.0%
0 4
 
2.0%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.1%
Missing13
Missing (%)6.4%
Memory size536.0 B
False
173 
True
 
16
(Missing)
 
13
ValueCountFrequency (%)
False 173
85.6%
True 16
 
7.9%
(Missing) 13
 
6.4%
2024-04-30T04:45:07.727068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시설총규모
Real number (ℝ)

MISSING 

Distinct187
Distinct (%)98.9%
Missing13
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean92.678466
Minimum31.18
Maximum152.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-04-30T04:45:07.822078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.18
5-th percentile48.148
Q166.49
median90.61
Q3118.75
95-th percentile143.546
Maximum152.93
Range121.75
Interquartile range (IQR)52.26

Descriptive statistics

Standard deviation30.811263
Coefficient of variation (CV)0.33245331
Kurtosis-1.0339513
Mean92.678466
Median Absolute Deviation (MAD)26.17
Skewness0.083622763
Sum17516.23
Variance949.33391
MonotonicityNot monotonic
2024-04-30T04:45:07.944890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.77 2
 
1.0%
65.13 2
 
1.0%
101.04 1
 
0.5%
59.53 1
 
0.5%
78.72 1
 
0.5%
31.86 1
 
0.5%
70.93 1
 
0.5%
134.16 1
 
0.5%
68.84 1
 
0.5%
76.41 1
 
0.5%
Other values (177) 177
87.6%
(Missing) 13
 
6.4%
ValueCountFrequency (%)
31.18 1
0.5%
31.86 1
0.5%
33.6 1
0.5%
37.56 1
0.5%
40.19 1
0.5%
41.18 1
0.5%
42.0 1
0.5%
45.54 1
0.5%
47.1 1
0.5%
47.28 1
0.5%
ValueCountFrequency (%)
152.93 1
0.5%
148.62 1
0.5%
148.22 1
0.5%
147.98 1
0.5%
145.9 1
0.5%
145.84 1
0.5%
144.71 1
0.5%
144.7 1
0.5%
144.13 1
0.5%
143.75 1
0.5%

전통업소지정번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

전통업소주된음식
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

홈페이지
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing202
Missing (%)100.0%
Memory size1.9 KiB

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
031900003190000-103-1993-0076519931122<NA>3폐업2폐업20071122<NA><NA><NA>02 583300152.8156826서울특별시 동작구 사당동 1030-13번지<NA><NA>발리Ⅱ2007-02-07 00:00:00I2018-08-31 23:59:59.0단란주점198276.099219441987.393695단란주점30유흥업소밀집지역기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N52.8<NA><NA><NA>
131900003190000-103-1993-0079219931011<NA>3폐업2폐업20110125<NA><NA><NA>020849508353.9156854서울특별시 동작구 신대방동 686-3번지<NA><NA>서울단란주점2010-02-10 17:42:38I2018-08-31 23:59:59.0단란주점191753.039736442872.617058단란주점10주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N53.9<NA><NA><NA>
231900003190000-103-1993-0079319931025<NA>3폐업2폐업20090616<NA><NA><NA>02 8211330100.47156848서울특별시 동작구 신대방동 362-23번지<NA><NA>목화단란주점2001-03-19 00:00:00I2018-08-31 23:59:59.0단란주점193263.623858444030.62372단란주점01주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N100.47<NA><NA><NA>
331900003190000-103-1993-0180119931220<NA>3폐업2폐업20161027<NA><NA><NA>020816077197.32156860서울특별시 동작구 흑석동 181-1번지서울특별시 동작구 서달로15길 23 (흑석동)6972밴소주호프 노래타운2010-12-23 16:12:42I2018-08-31 23:59:59.0단란주점196547.688324445149.179769단란주점<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N97.32<NA><NA><NA>
431900003190000-103-1993-0180219931222<NA>3폐업2폐업19960716<NA><NA><NA>02 825350168.81156030서울특별시 동작구 상도동 316-11번지<NA><NA>주유소단란주점2001-09-29 00:00:00I2018-08-31 23:59:59.0단란주점193919.79455444155.585642단란주점00주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N68.81<NA><NA><NA>
531900003190000-103-1993-0180319931223<NA>3폐업2폐업20100714<NA><NA><NA>0205226359109.57156824서울특별시 동작구 사당동 1006-10번지<NA><NA>은하수단란주점2010-02-10 17:39:37I2018-08-31 23:59:59.0단란주점198226.015297442433.221335단란주점02기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N109.57<NA><NA><NA>
631900003190000-103-1993-0180419931227<NA>3폐업2폐업20061229<NA><NA><NA>020523966040.19156824서울특별시 동작구 사당동 708-287번지<NA><NA>포인트2005-06-10 00:00:00I2018-08-31 23:59:59.0단란주점<NA><NA>단란주점11기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N40.19<NA><NA><NA>
731900003190000-103-1993-0180519931228<NA>3폐업2폐업20101228<NA><NA><NA>0208497396137.3156852서울특별시 동작구 신대방동 587-13번지<NA><NA>대야2010-02-10 17:41:17I2018-08-31 23:59:59.0단란주점191861.809766443201.449292단란주점01주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N137.3<NA><NA><NA>
831900003190000-103-1993-0180619931229<NA>3폐업2폐업19950510<NA><NA><NA>02 599638173.1156816서울특별시 동작구 사당동 130-14번지<NA><NA>홍천단란주점2001-09-29 00:00:00I2018-08-31 23:59:59.0단란주점198241.889765442863.677106단란주점20기타기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N73.1<NA><NA><NA>
931900003190000-103-1993-0180719931229<NA>3폐업2폐업20201005<NA><NA><NA>02 826747862.65156857서울특별시 동작구 흑석동 43-125서울특별시 동작구 서달로14길 38 (흑석동)6979엠(M)노래빠2020-10-05 15:29:52U2020-10-07 02:40:00.0단란주점196824.619237445010.68388단란주점12주택가주변기타상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>Y62.65<NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명남성종사자수여성종사자수영업장주변구분명등급구분명급수시설구분명총인원본사종업원수공장사무직종업원수공장판매직종업원수공장생산직종업원수건물소유구분명보증액월세액다중이용업소여부시설총규모전통업소지정번호전통업소주된음식홈페이지
19231900003190000-103-2010-000012010-06-17<NA>1영업/정상1영업<NA><NA><NA><NA><NA>58.0156-827서울특별시 동작구 사당동 1044-28 (지하1층)서울특별시 동작구 동작대로1길 33 (사당동,(지하1층))7025파파코2023-02-23 14:34:00U2022-12-01 22:05:00.0단란주점198117.655838441672.226562<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19331900003190000-103-2010-0000220100909<NA>1영업/정상1영업<NA><NA><NA><NA>02 863885387.48156816서울특별시 동작구 사당동 145-11번지서울특별시 동작구 동작대로27가길 36 (사당동)7008SBS노래주점2020-04-17 15:29:56U2020-04-19 02:40:00.0단란주점198248.251718442647.130287단란주점<NA><NA>기타<NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N87.48<NA><NA><NA>
19431900003190000-103-2010-0000320101124<NA>3폐업2폐업20211119<NA><NA><NA>02 522 8688117.7156827서울특별시 동작구 사당동 1044-1 지하 1층서울특별시 동작구 남부순환로269길 8 (사당동,지하 1층)7025스타킹 라이브주점2021-11-19 14:54:57U2021-11-21 02:40:00.0단란주점198032.516809441661.732003단란주점00기타기타상수도전용00000<NA>00Y117.7<NA><NA><NA>
19531900003190000-103-2010-0000420101206<NA>3폐업2폐업20140113<NA><NA><NA>02 582191197.15156827서울특별시 동작구 사당동 1042-8번지 지하1층서울특별시 동작구 동작대로1길 8 (사당동)7025팡팡노래주점2014-01-08 18:05:12I2018-08-31 23:59:59.0단란주점198246.23418441690.743819단란주점11유흥업소밀집지역관리상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N97.15<NA><NA><NA>
19631900003190000-103-2011-0000120110509<NA>1영업/정상1영업<NA><NA><NA><NA>02 522 522986.58156826서울특별시 동작구 사당동 1031-24번지서울특별시 동작구 동작대로3길 10 (사당동)7015해성2019-11-12 15:01:59U2019-11-14 02:40:00.0단란주점198233.484972441808.553924단란주점<NA><NA>유흥업소밀집지역<NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N86.58<NA><NA><NA>
19731900003190000-103-2012-0000120120726<NA>1영업/정상1영업<NA><NA><NA><NA>02 533 3809119.8156816서울특별시 동작구 사당동 145-26 (지하1층)서울특별시 동작구 동작대로25길 11 (사당동)7008스타 70802021-10-29 10:21:33U2021-10-31 02:40:00.0단란주점198283.149216442678.864321단란주점00<NA><NA><NA>00000<NA>00Y119.8<NA><NA><NA>
19831900003190000-103-2013-0000120131010<NA>3폐업2폐업20150707<NA><NA><NA><NA>138.77156826서울특별시 동작구 사당동 1031-28번지서울특별시 동작구 동작대로 25 (사당동)701510번출구라이브2013-10-11 09:59:20I2018-08-31 23:59:59.0단란주점198275.378712441839.878978단란주점<NA><NA>유흥업소밀집지역<NA>상수도전용<NA><NA><NA><NA><NA><NA><NA><NA>N138.77<NA><NA><NA>
19931900003190000-103-2016-0000120160311<NA>3폐업2폐업20220126<NA><NA><NA><NA>101.04156811서울특별시 동작구 대방동 405-11서울특별시 동작구 보라매로 113, 지상2층 203호 (대방동)7055칠공팔공삼거리2022-01-26 15:43:03U2022-01-28 02:40:00.0단란주점193554.744988444142.040718단란주점00<NA><NA><NA>00000<NA>00Y101.04<NA><NA><NA>
20031900003190000-103-2017-0000120171030<NA>1영업/정상1영업<NA><NA><NA><NA>02501 126594.15156816서울특별시 동작구 사당동 145-14번지서울특별시 동작구 동작대로25길 25, 지하1층 (사당동)7008모아2017-12-04 15:00:17I2018-08-31 23:59:59.0단란주점198234.761506442633.156688단란주점<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>Y94.15<NA><NA><NA>
20131900003190000-103-2022-000022022-05-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>68.17156-800서울특별시 동작구 노량진동 16-1 노량진 드림스퀘어 복합빌딩서울특별시 동작구 노들로2길 7, 노량진 드림스퀘어 복합빌딩 지하1층 A동 B01호 (노량진동)6900별밤2023-04-12 16:41:52U2022-12-03 23:04:00.0단란주점194571.152376445790.549861<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>