Overview

Dataset statistics

Number of variables13
Number of observations359
Missing cells828
Missing cells (%)17.7%
Duplicate rows1
Duplicate rows (%)0.3%
Total size in memory38.3 KiB
Average record size in memory109.4 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-11510/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
Dataset has 1 (0.3%) duplicate rowsDuplicates
업종명 is highly overall correlated with 지도점검일자 and 1 other fieldsHigh correlation
업종코드 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
인허가번호 is highly overall correlated with 업종코드High correlation
지도점검일자 is highly overall correlated with 업종명High correlation
업종코드 is highly imbalanced (71.8%)Imbalance
처분대상여부 is highly imbalanced (81.7%)Imbalance
처분대상여부 has 179 (49.9%) missing valuesMissing
점검사항 has 52 (14.5%) missing valuesMissing
점검결과 has 359 (100.0%) missing valuesMissing
소재지도로명주소 has 236 (65.7%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 05:47:03.896954
Analysis finished2024-05-11 05:47:06.652805
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct115
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2024-05-11T14:47:07.031105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length8.0167131
Min length4

Characters and Unicode

Total characters2878
Distinct characters206
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)10.0%

Sample

1st rowSK스피드메이트
2nd row코웨이 주식회사
3rd row서울대학교세차장
4th row관악세차장
5th row대보실업(주)
ValueCountFrequency (%)
한독운수(주 13
 
3.0%
서울대학교 13
 
3.0%
신림점 12
 
2.8%
한남여객운수(주 11
 
2.5%
애니카랜드 11
 
2.5%
주식회사 9
 
2.1%
주)관악정비센터 8
 
1.9%
크로바셀프세차장 8
 
1.9%
관악세차장 7
 
1.6%
수광셀프세차장 7
 
1.6%
Other values (116) 333
77.1%
2024-05-11T14:47:07.695557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 203
 
7.1%
( 127
 
4.4%
) 127
 
4.4%
차 106
 
3.7%
유 91
 
3.2%
세 89
 
3.1%
소 88
 
3.1%
장 76
 
2.6%
73
 
2.5%
스 60
 
2.1%
Other values (196) 1838
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2500
86.9%
Open Punctuation 127
 
4.4%
Close Punctuation 127
 
4.4%
Space Separator 73
 
2.5%
Uppercase Letter 28
 
1.0%
Decimal Number 16
 
0.6%
Other Punctuation 7
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 203
 
8.1%
차 106
 
4.2%
유 91
 
3.6%
세 89
 
3.6%
소 88
 
3.5%
장 76
 
3.0%
스 60
 
2.4%
한 54
 
2.2%
관 53
 
2.1%
서 50
 
2.0%
Other values (181) 1630
65.2%
Decimal Number
ValueCountFrequency (%)
9 4
25.0%
3 2
12.5%
6 2
12.5%
5 2
12.5%
1 2
12.5%
0 2
12.5%
7 2
12.5%
Uppercase Letter
ValueCountFrequency (%)
S 14
50.0%
K 7
25.0%
G 7
25.0%
Other Punctuation
ValueCountFrequency (%)
, 5
71.4%
. 2
 
28.6%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 127
100.0%
Space Separator
ValueCountFrequency (%)
73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2500
86.9%
Common 350
 
12.2%
Latin 28
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 203
 
8.1%
차 106
 
4.2%
유 91
 
3.6%
세 89
 
3.6%
소 88
 
3.5%
장 76
 
3.0%
스 60
 
2.4%
한 54
 
2.2%
관 53
 
2.1%
서 50
 
2.0%
Other values (181) 1630
65.2%
Common
ValueCountFrequency (%)
( 127
36.3%
) 127
36.3%
73
20.9%
, 5
 
1.4%
9 4
 
1.1%
. 2
 
0.6%
3 2
 
0.6%
6 2
 
0.6%
5 2
 
0.6%
1 2
 
0.6%
Other values (2) 4
 
1.1%
Latin
ValueCountFrequency (%)
S 14
50.0%
K 7
25.0%
G 7
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2500
86.9%
ASCII 378
 
13.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 203
 
8.1%
차 106
 
4.2%
유 91
 
3.6%
세 89
 
3.6%
소 88
 
3.5%
장 76
 
3.0%
스 60
 
2.4%
한 54
 
2.2%
관 53
 
2.1%
서 50
 
2.0%
Other values (181) 1630
65.2%
ASCII
ValueCountFrequency (%)
( 127
33.6%
) 127
33.6%
73
19.3%
S 14
 
3.7%
K 7
 
1.9%
G 7
 
1.9%
, 5
 
1.3%
9 4
 
1.1%
. 2
 
0.5%
3 2
 
0.5%
Other values (5) 10
 
2.6%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct124
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2000002 × 1017
Minimum3.2000002 × 1017
Maximum3.2000006 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-11T14:47:07.925757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.2000002 × 1017
5-th percentile3.2000002 × 1017
Q13.2000002 × 1017
median3.2000002 × 1017
Q33.2000002 × 1017
95-th percentile3.2000002 × 1017
Maximum3.2000006 × 1017
Range4.10015 × 1010
Interquartile range (IQR)1100032

Descriptive statistics

Standard deviation2.2703132 × 109
Coefficient of variation (CV)7.0947283 × 10-9
Kurtosis270.14188
Mean3.2000002 × 1017
Median Absolute Deviation (MAD)299968
Skewness15.69521
Sum4.1995436 × 1018
Variance5.1543222 × 1018
MonotonicityNot monotonic
2024-05-11T14:47:08.165086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320000022197900001 9
 
2.5%
320000021200400008 8
 
2.2%
320000022199400001 7
 
1.9%
320000022200900001 7
 
1.9%
320000022200600004 6
 
1.7%
320000022200500043 6
 
1.7%
320000022200600011 6
 
1.7%
320000022200800003 6
 
1.7%
320000022199600008 6
 
1.7%
320000022200500059 6
 
1.7%
Other values (114) 292
81.3%
ValueCountFrequency (%)
320000021199500001 1
 
0.3%
320000021199500002 6
1.7%
320000021199800001 3
 
0.8%
320000021199900004 2
 
0.6%
320000021200400003 4
1.1%
320000021200400004 4
1.1%
320000021200400006 4
1.1%
320000021200400008 8
2.2%
320000021201500017 1
 
0.3%
320000021201500018 1
 
0.3%
ValueCountFrequency (%)
320000062201000001 1
0.3%
320000034200000005 1
0.3%
320000027200700002 1
0.3%
320000025201600018 1
0.3%
320000025200900010 1
0.3%
320000025200300020 1
0.3%
320000025199500001 1
0.3%
320000025199400014 1
0.3%
320000022201600001 1
0.3%
320000022201500003 2
0.6%

업종코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
22
321 
21
 
32
25
 
5
34
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 321
89.4%
21 32
 
8.9%
25 5
 
1.4%
34 1
 
0.3%

Length

2024-05-11T14:47:08.402027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:47:08.622469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 321
89.4%
21 32
 
8.9%
25 5
 
1.4%
34 1
 
0.3%

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
<NA>
180 
폐수배출업소관리
160 
대기배출업소관리
19 

Length

Max length8
Median length4
Mean length5.994429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 180
50.1%
폐수배출업소관리 160
44.6%
대기배출업소관리 19
 
5.3%

Length

2024-05-11T14:47:08.837568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:47:09.353729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 180
50.1%
폐수배출업소관리 160
44.6%
대기배출업소관리 19
 
5.3%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct160
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20128675
Minimum20100112
Maximum20170608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2024-05-11T14:47:09.547853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100112
5-th percentile20100610
Q120110768
median20130111
Q320150468
95-th percentile20161103
Maximum20170608
Range70496
Interquartile range (IQR)39700

Descriptive statistics

Standard deviation21599.029
Coefficient of variation (CV)0.0010730478
Kurtosis-1.1675831
Mean20128675
Median Absolute Deviation (MAD)19591
Skewness0.29813936
Sum7.2261942 × 109
Variance4.6651808 × 108
MonotonicityDecreasing
2024-05-11T14:47:09.799479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161021 6
 
1.7%
20160520 6
 
1.7%
20161020 6
 
1.7%
20161103 5
 
1.4%
20151015 5
 
1.4%
20131031 5
 
1.4%
20170526 5
 
1.4%
20150515 5
 
1.4%
20170525 5
 
1.4%
20131007 5
 
1.4%
Other values (150) 306
85.2%
ValueCountFrequency (%)
20100112 1
 
0.3%
20100122 1
 
0.3%
20100222 2
0.6%
20100223 1
 
0.3%
20100428 3
0.8%
20100429 2
0.6%
20100430 3
0.8%
20100503 3
0.8%
20100525 1
 
0.3%
20100610 2
0.6%
ValueCountFrequency (%)
20170608 1
 
0.3%
20170526 5
1.4%
20170525 5
1.4%
20170519 1
 
0.3%
20170213 1
 
0.3%
20161110 3
0.8%
20161103 5
1.4%
20161102 3
0.8%
20161021 6
1.7%
20161020 6
1.7%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
3200000
359 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3200000 359
100.0%

Length

2024-05-11T14:47:10.085450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:47:10.299586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 359
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
서울특별시 관악구
359 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 관악구
2nd row서울특별시 관악구
3rd row서울특별시 관악구
4th row서울특별시 관악구
5th row서울특별시 관악구

Common Values

ValueCountFrequency (%)
서울특별시 관악구 359
100.0%

Length

2024-05-11T14:47:10.471005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:47:10.641203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 359
50.0%
관악구 359
50.0%

지도점검구분
Categorical

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
정기
175 
합동
107 
수시
44 
기타
24 
일제
 
6

Length

Max length4
Median length2
Mean length2.0167131
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 175
48.7%
합동 107
29.8%
수시 44
 
12.3%
기타 24
 
6.7%
일제 6
 
1.7%
<NA> 3
 
0.8%

Length

2024-05-11T14:47:10.837645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:47:11.045063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 175
48.7%
합동 107
29.8%
수시 44
 
12.3%
기타 24
 
6.7%
일제 6
 
1.7%
na 3
 
0.8%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.1%
Missing179
Missing (%)49.9%
Memory size850.0 B
False
175 
True
 
5
(Missing)
179 
ValueCountFrequency (%)
False 175
48.7%
True 5
 
1.4%
(Missing) 179
49.9%
2024-05-11T14:47:11.210234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

MISSING 

Distinct91
Distinct (%)29.6%
Missing52
Missing (%)14.5%
Memory size2.9 KiB
2024-05-11T14:47:11.584655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length54
Mean length26.153094
Min length5

Characters and Unicode

Total characters8029
Distinct characters121
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)15.6%

Sample

1st row폐수배출시설 적정가동여부 등
2nd row폐수배출업소 지도점검
3rd row환경오염물질 배출사업장 적정 운영 여부
4th row안양천유역 배출사업장 합동점검 - 배출시설 및 방지시설 적정운영 여부
5th row신고이행 미가동(가동개시전 미설치 상태)
ValueCountFrequency (%)
적정 186
 
9.5%
여부 178
 
9.1%
방지시설 159
 
8.2%
및 143
 
7.3%
운영 141
 
7.2%
138
 
7.1%
배출사업장 113
 
5.8%
등 111
 
5.7%
환경오염물질 106
 
5.4%
배출시설 102
 
5.2%
Other values (96) 573
29.4%
2024-05-11T14:47:12.236974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1682
20.9%
설 389
 
4.8%
시 379
 
4.7%
배 322
 
4.0%
출 322
 
4.0%
적 245
 
3.1%
정 240
 
3.0%
지 223
 
2.8%
운 215
 
2.7%
영 215
 
2.7%
Other values (111) 3797
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6132
76.4%
Space Separator 1682
 
20.9%
Dash Punctuation 85
 
1.1%
Other Punctuation 61
 
0.8%
Open Punctuation 31
 
0.4%
Close Punctuation 31
 
0.4%
Other Symbol 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 389
 
6.3%
시 379
 
6.2%
배 322
 
5.3%
출 322
 
5.3%
적 245
 
4.0%
정 240
 
3.9%
지 223
 
3.6%
운 215
 
3.5%
영 215
 
3.5%
여 192
 
3.1%
Other values (103) 3390
55.3%
Other Punctuation
ValueCountFrequency (%)
: 57
93.4%
. 4
 
6.6%
Other Symbol
ValueCountFrequency (%)
▶ 4
57.1%
○ 3
42.9%
Space Separator
ValueCountFrequency (%)
1682
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6132
76.4%
Common 1897
 
23.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 389
 
6.3%
시 379
 
6.2%
배 322
 
5.3%
출 322
 
5.3%
적 245
 
4.0%
정 240
 
3.9%
지 223
 
3.6%
운 215
 
3.5%
영 215
 
3.5%
여 192
 
3.1%
Other values (103) 3390
55.3%
Common
ValueCountFrequency (%)
1682
88.7%
- 85
 
4.5%
: 57
 
3.0%
( 31
 
1.6%
) 31
 
1.6%
. 4
 
0.2%
▶ 4
 
0.2%
○ 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6132
76.4%
ASCII 1890
 
23.5%
Geometric Shapes 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1682
89.0%
- 85
 
4.5%
: 57
 
3.0%
( 31
 
1.6%
) 31
 
1.6%
. 4
 
0.2%
Hangul
ValueCountFrequency (%)
설 389
 
6.3%
시 379
 
6.2%
배 322
 
5.3%
출 322
 
5.3%
적 245
 
4.0%
정 240
 
3.9%
지 223
 
3.6%
운 215
 
3.5%
영 215
 
3.5%
여 192
 
3.1%
Other values (103) 3390
55.3%
Geometric Shapes
ValueCountFrequency (%)
▶ 4
57.1%
○ 3
42.9%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing359
Missing (%)100.0%
Memory size3.3 KiB

소재지도로명주소
Text

MISSING 

Distinct83
Distinct (%)67.5%
Missing236
Missing (%)65.7%
Memory size2.9 KiB
2024-05-11T14:47:12.690115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length33
Mean length25.130081
Min length21

Characters and Unicode

Total characters3091
Distinct characters92
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

Unique60 ?
Unique (%)48.8%

Sample

1st row서울특별시 관악구 남부순환로151길 73 (신림동)
2nd row서울특별시 관악구 관악로 1 (신림동, 서울대학교 연구공원내(코웨이알앤디센타))
3rd row서울특별시 관악구 관악로 1 (신림동,서울대학교 자연과학대학 기초과학연구동 203동)
4th row서울특별시 관악구 남부순환로 1920 (봉천동)
5th row서울특별시 관악구 봉천로 521 (봉천동)
ValueCountFrequency (%)
서울특별시 123
19.6%
관악구 123
19.6%
봉천동 55
 
8.8%
신림동 51
 
8.1%
남부순환로 19
 
3.0%
봉천로 17
 
2.7%
신림로 14
 
2.2%
관악로 14
 
2.2%
남현동 8
 
1.3%
1 8
 
1.3%
Other values (83) 195
31.1%
2024-05-11T14:47:13.390497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
539
 
17.4%
관 137
 
4.4%
악 137
 
4.4%
구 127
 
4.1%
서 126
 
4.1%
울 126
 
4.1%
) 125
 
4.0%
동 125
 
4.0%
( 125
 
4.0%
특 123
 
4.0%
Other values (82) 1401
45.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1907
61.7%
Space Separator 539
 
17.4%
Decimal Number 369
 
11.9%
Close Punctuation 125
 
4.0%
Open Punctuation 125
 
4.0%
Other Punctuation 15
 
0.5%
Dash Punctuation 9
 
0.3%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 137
 
7.2%
악 137
 
7.2%
구 127
 
6.7%
서 126
 
6.6%
울 126
 
6.6%
동 125
 
6.6%
특 123
 
6.4%
별 123
 
6.4%
시 123
 
6.4%
로 117
 
6.1%
Other values (65) 643
33.7%
Decimal Number
ValueCountFrequency (%)
1 88
23.8%
2 58
15.7%
8 41
11.1%
9 35
 
9.5%
4 28
 
7.6%
3 26
 
7.0%
0 24
 
6.5%
6 24
 
6.5%
7 23
 
6.2%
5 22
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
539
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1907
61.7%
Common 1182
38.2%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 137
 
7.2%
악 137
 
7.2%
구 127
 
6.7%
서 126
 
6.6%
울 126
 
6.6%
동 125
 
6.6%
특 123
 
6.4%
별 123
 
6.4%
시 123
 
6.4%
로 117
 
6.1%
Other values (65) 643
33.7%
Common
ValueCountFrequency (%)
539
45.6%
) 125
 
10.6%
( 125
 
10.6%
1 88
 
7.4%
2 58
 
4.9%
8 41
 
3.5%
9 35
 
3.0%
4 28
 
2.4%
3 26
 
2.2%
0 24
 
2.0%
Other values (5) 93
 
7.9%
Latin
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1907
61.7%
ASCII 1184
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
539
45.5%
) 125
 
10.6%
( 125
 
10.6%
1 88
 
7.4%
2 58
 
4.9%
8 41
 
3.5%
9 35
 
3.0%
4 28
 
2.4%
3 26
 
2.2%
0 24
 
2.0%
Other values (7) 95
 
8.0%
Hangul
ValueCountFrequency (%)
관 137
 
7.2%
악 137
 
7.2%
구 127
 
6.7%
서 126
 
6.6%
울 126
 
6.6%
동 125
 
6.6%
특 123
 
6.4%
별 123
 
6.4%
시 123
 
6.4%
로 117
 
6.1%
Other values (65) 643
33.7%

소재지주소
Text

Distinct90
Distinct (%)25.2%
Missing2
Missing (%)0.6%
Memory size2.9 KiB
2024-05-11T14:47:13.786743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length23
Mean length23.843137
Min length14

Characters and Unicode

Total characters8512
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)7.0%

Sample

1st row서울특별시 관악구 신림동 523-1번지
2nd row서울특별시 관악구 봉천동 산 4-1번지 서울대학교 연구공원내 웅진알앤디센타
3rd row서울특별시 관악구 신림동 산 56-1번지
4th row서울특별시 관악구 신림동 725-29번지
5th row서울특별시 관악구 신림동 211번지
ValueCountFrequency (%)
서울특별시 357
23.3%
관악구 357
23.3%
신림동 170
11.1%
봉천동 167
10.9%
산 40
 
2.6%
남현동 20
 
1.3%
56-1번지 18
 
1.2%
941-22번지 13
 
0.8%
241-42번지 13
 
0.8%
1664-2번지 9
 
0.6%
Other values (105) 371
24.2%
2024-05-11T14:47:14.470707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1548
18.2%
1 399
 
4.7%
서 367
 
4.3%
구 367
 
4.3%
울 367
 
4.3%
- 360
 
4.2%
동 359
 
4.2%
관 357
 
4.2%
특 357
 
4.2%
시 357
 
4.2%
Other values (50) 3674
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4847
56.9%
Decimal Number 1749
 
20.5%
Space Separator 1548
 
18.2%
Dash Punctuation 360
 
4.2%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 367
 
7.6%
구 367
 
7.6%
울 367
 
7.6%
동 359
 
7.4%
관 357
 
7.4%
특 357
 
7.4%
시 357
 
7.4%
지 357
 
7.4%
악 357
 
7.4%
별 357
 
7.4%
Other values (37) 1245
25.7%
Decimal Number
ValueCountFrequency (%)
1 399
22.8%
2 245
14.0%
6 220
12.6%
4 218
12.5%
5 141
 
8.1%
9 122
 
7.0%
3 113
 
6.5%
7 111
 
6.3%
8 103
 
5.9%
0 77
 
4.4%
Space Separator
ValueCountFrequency (%)
1548
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 360
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4847
56.9%
Common 3665
43.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 367
 
7.6%
구 367
 
7.6%
울 367
 
7.6%
동 359
 
7.4%
관 357
 
7.4%
특 357
 
7.4%
시 357
 
7.4%
지 357
 
7.4%
악 357
 
7.4%
별 357
 
7.4%
Other values (37) 1245
25.7%
Common
ValueCountFrequency (%)
1548
42.2%
1 399
 
10.9%
- 360
 
9.8%
2 245
 
6.7%
6 220
 
6.0%
4 218
 
5.9%
5 141
 
3.8%
9 122
 
3.3%
3 113
 
3.1%
7 111
 
3.0%
Other values (3) 188
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4847
56.9%
ASCII 3665
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1548
42.2%
1 399
 
10.9%
- 360
 
9.8%
2 245
 
6.7%
6 220
 
6.0%
4 218
 
5.9%
5 141
 
3.8%
9 122
 
3.3%
3 113
 
3.1%
7 111
 
3.0%
Other values (3) 188
 
5.1%
Hangul
ValueCountFrequency (%)
서 367
 
7.6%
구 367
 
7.6%
울 367
 
7.6%
동 359
 
7.4%
관 357
 
7.4%
특 357
 
7.4%
시 357
 
7.4%
지 357
 
7.4%
악 357
 
7.4%
별 357
 
7.4%
Other values (37) 1245
25.7%

Interactions

2024-05-11T14:47:05.630176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:47:05.328954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:47:05.766978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:47:05.471412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:47:14.633244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부점검사항소재지도로명주소소재지주소
인허가번호1.0000.8930.0000.0000.0730.3111.0000.0000.957
업종코드0.8931.0000.9990.2490.1110.0000.9680.9180.980
업종명0.0000.9991.0000.6980.4410.0000.9321.0000.903
지도점검일자0.0000.2490.6981.0000.4280.5470.8740.0000.000
지도점검구분0.0730.1110.4410.4281.0000.2370.8900.8670.531
처분대상여부0.3110.0000.0000.5470.2371.0000.663NaN0.243
점검사항1.0000.9680.9320.8740.8900.6631.0000.9770.976
소재지도로명주소0.0000.9181.0000.0000.867NaN0.9771.0001.000
소재지주소0.9570.9800.9030.0000.5310.2430.9761.0001.000
2024-05-11T14:47:14.846890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지도점검구분처분대상여부업종명업종코드
지도점검구분1.0000.1570.2950.090
처분대상여부0.1571.0000.0000.000
업종명0.2950.0001.0000.970
업종코드0.0900.0000.9701.000
2024-05-11T14:47:15.002355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.0870.5730.0000.0610.202
지도점검일자-0.0871.0000.1130.5230.2810.406
업종코드0.5730.1131.0000.9700.0900.000
업종명0.0000.5230.9701.0000.2950.000
지도점검구분0.0610.2810.0900.2951.0000.157
처분대상여부0.2020.4060.0000.0000.1571.000

Missing values

2024-05-11T14:47:05.992041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:47:06.305687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-11T14:47:06.523067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
0SK스피드메이트32000002220110000122<NA>201706083200000서울특별시 관악구정기<NA>폐수배출시설 적정가동여부 등<NA>서울특별시 관악구 남부순환로151길 73 (신림동)서울특별시 관악구 신림동 523-1번지
1코웨이 주식회사32000002220080000122폐수배출업소관리201705263200000서울특별시 관악구정기N폐수배출업소 지도점검<NA>서울특별시 관악구 관악로 1 (신림동, 서울대학교 연구공원내(코웨이알앤디센타))서울특별시 관악구 봉천동 산 4-1번지 서울대학교 연구공원내 웅진알앤디센타
2서울대학교세차장32000002219930000422<NA>201705263200000서울특별시 관악구정기<NA>환경오염물질 배출사업장 적정 운영 여부<NA><NA>서울특별시 관악구 신림동 산 56-1번지
3관악세차장32000002219980006622<NA>201705263200000서울특별시 관악구정기<NA>안양천유역 배출사업장 합동점검 - 배출시설 및 방지시설 적정운영 여부<NA><NA>서울특별시 관악구 신림동 725-29번지
4대보실업(주)32000002220160000122<NA>201705263200000서울특별시 관악구<NA><NA>신고이행 미가동(가동개시전 미설치 상태)<NA><NA>서울특별시 관악구 신림동 211번지
5(주)바이로메드32000002220020002922<NA>201705263200000서울특별시 관악구합동<NA>폐수배출업소 지도점검<NA>서울특별시 관악구 관악로 1 (신림동,서울대학교 자연과학대학 기초과학연구동 203동)서울특별시 관악구 신림동 산 56-1번지 서울대학교 자연과학대학 기초과학연구동 203동
6서일석유(주) 락성주유소32000002219850000222<NA>201705253200000서울특별시 관악구정기<NA>환경오염물질 배출사업장 적정 운영 여부<NA>서울특별시 관악구 남부순환로 1920 (봉천동)서울특별시 관악구 봉천동 1659-1번지
7관악세차장32000002219790000122<NA>201705253200000서울특별시 관악구정기<NA>환경오염물질 배출사업장 적정 운영 여부<NA>서울특별시 관악구 봉천로 521 (봉천동)서울특별시 관악구 봉천동 1664-2번지
8수광셀프세차장32000002220090000122<NA>201705253200000서울특별시 관악구정기<NA>배출시설 및 방지시설 적정 운영 여부 등<NA>서울특별시 관악구 은천로 141 (봉천동)서울특별시 관악구 봉천동 457-312번지
9크로바셀프세차장32000002220040001722<NA>201705253200000서울특별시 관악구수시<NA>환경오염물질 배출사업장 적정 운영 여부 변경신고 가동개시 확인<NA><NA>서울특별시 관악구 봉천동 1662-1번지
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
349(주)한진 관악주유소32000002220060002922폐수배출업소관리201004293200000서울특별시 관악구합동N환경오염물질 배출사업장 민관합동 통합지도점검<NA><NA>서울특별시 관악구 봉천동 912-15번지
350벧엘세차장32000002219930000322폐수배출업소관리201004293200000서울특별시 관악구합동N환경오염물질 배출사업장 민관합동 통합지도점검<NA><NA>서울특별시 관악구 신림동 1477-3번지
351크로바셀프세차장32000002220050006022폐수배출업소관리201004283200000서울특별시 관악구합동N환경오염물질 배출사업장 민관합동 통합지도점검<NA><NA>서울특별시 관악구 봉천동 1662-1번지
352SK네트워크(주) 신봉천주유소32000002220040005022폐수배출업소관리201004283200000서울특별시 관악구합동N환경오염물질 배출사업장 민관합동 통합지도점검<NA>서울특별시 관악구 남부순환로 1880 (봉천동)서울특별시 관악구 봉천동 1663-11번지
353다원에너지(주)32000002220060001722폐수배출업소관리201004283200000서울특별시 관악구합동N환경오염물질 배출사업장 민관합동 통합지도점검<NA><NA>서울특별시 관악구 봉천동 883-12번지
354덕원스팀세차32000006220100000122폐수배출업소관리201002233200000서울특별시 관악구기타Y미신고폐수배출시설관련 민원사항 점검<NA><NA>서울특별시 관악구 신림동 산 74-3번지
355신림동충전소32000002220060000522폐수배출업소관리201002223200000서울특별시 관악구기타N폐수배출시설 및 방지시설 적정운영 여부 등<NA><NA>서울특별시 관악구 신림동 112-46번지
356관악윤활유급유소32000002219790000122폐수배출업소관리201002223200000서울특별시 관악구기타N민원에 따른 폐수배출시설 지도점검차<NA><NA>서울특별시 관악구 봉천동 1664-2번지
357관악윤활유급유소32000002219790000122폐수배출업소관리201001223200000서울특별시 관악구기타N폐수배출시설 설치사업장 지도점검<NA><NA>서울특별시 관악구 봉천동 1664-2번지
358낙원교통(주)세차장32000002219960000822폐수배출업소관리201001123200000서울특별시 관악구기타N폐수배출시설 설치사업장 지도점검<NA><NA>서울특별시 관악구 신림동 112-36번지

Duplicate rows

Most frequently occurring

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항소재지도로명주소소재지주소# duplicates
0관악세차장32000002219790000122폐수배출업소관리201505143200000서울특별시 관악구정기N환경오염물질 배출사업장 적정 운영 여부서울특별시 관악구 봉천로 521 (봉천동)서울특별시 관악구 봉천동 1664-2번지2