Overview

Dataset statistics

Number of variables13
Number of observations1702
Missing cells2632
Missing cells (%)11.9%
Duplicate rows6
Duplicate rows (%)0.4%
Total size in memory181.3 KiB
Average record size in memory109.1 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author강남구
URLhttps://data.seoul.go.kr/dataList/OA-11279/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
Dataset has 6 (0.4%) duplicate rowsDuplicates
업종코드 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
인허가번호 is highly overall correlated with 업종코드 and 1 other fieldsHigh correlation
지도점검구분 is highly imbalanced (65.3%)Imbalance
처분대상여부 is highly imbalanced (94.7%)Imbalance
처분대상여부 has 23 (1.4%) missing valuesMissing
점검결과 has 1702 (100.0%) missing valuesMissing
소재지도로명주소 has 765 (44.9%) missing valuesMissing
소재지주소 has 140 (8.2%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 08:02:27.986232
Analysis finished2024-05-11 08:02:29.926180
Duration1.94 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct680
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
2024-05-11T17:02:30.100072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length8.5346651
Min length2

Characters and Unicode

Total characters14526
Distinct characters457
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)15.9%

Sample

1st row한국자산관리공사(캠코양재타워)
2nd row능인선원
3rd row한국지역난방공사 강남지사(수서열원)
4th row강남자원회수시설
5th row서울시탄천물재생센터((주)탄천환경)
ValueCountFrequency (%)
덴트닥터칼라 21
 
1.1%
삼성서울병원 18
 
0.9%
논현점 15
 
0.8%
논현서비스프라자 14
 
0.7%
쌍용자동차 14
 
0.7%
서울자동차서비스 13
 
0.7%
코션코리아역삼 13
 
0.7%
강남자원회수시설 13
 
0.7%
강남제일세차장 13
 
0.7%
애니카랜드 13
 
0.7%
Other values (751) 1839
92.6%
2024-05-11T17:02:30.578282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 802
 
5.5%
) 693
 
4.8%
( 690
 
4.8%
스 429
 
3.0%
동 346
 
2.4%
자 302
 
2.1%
차 297
 
2.0%
284
 
2.0%
사 233
 
1.6%
성 220
 
1.5%
Other values (447) 10230
70.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12571
86.5%
Close Punctuation 693
 
4.8%
Open Punctuation 690
 
4.8%
Space Separator 284
 
2.0%
Uppercase Letter 186
 
1.3%
Decimal Number 62
 
0.4%
Lowercase Letter 23
 
0.2%
Other Punctuation 11
 
0.1%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 802
 
6.4%
스 429
 
3.4%
동 346
 
2.8%
자 302
 
2.4%
차 297
 
2.4%
사 233
 
1.9%
성 220
 
1.8%
서 220
 
1.8%
터 213
 
1.7%
삼 204
 
1.6%
Other values (405) 9305
74.0%
Uppercase Letter
ValueCountFrequency (%)
S 59
31.7%
K 39
21.0%
G 24
12.9%
L 8
 
4.3%
O 6
 
3.2%
P 6
 
3.2%
B 6
 
3.2%
C 5
 
2.7%
R 5
 
2.7%
M 4
 
2.2%
Other values (10) 24
12.9%
Lowercase Letter
ValueCountFrequency (%)
e 10
43.5%
y 4
 
17.4%
f 2
 
8.7%
h 2
 
8.7%
u 2
 
8.7%
l 1
 
4.3%
s 1
 
4.3%
i 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
1 33
53.2%
9 11
 
17.7%
2 6
 
9.7%
6 6
 
9.7%
3 3
 
4.8%
4 2
 
3.2%
8 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
& 6
54.5%
. 4
36.4%
, 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 693
100.0%
Open Punctuation
ValueCountFrequency (%)
( 690
100.0%
Space Separator
ValueCountFrequency (%)
284
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12571
86.5%
Common 1746
 
12.0%
Latin 209
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 802
 
6.4%
스 429
 
3.4%
동 346
 
2.8%
자 302
 
2.4%
차 297
 
2.4%
사 233
 
1.9%
성 220
 
1.8%
서 220
 
1.8%
터 213
 
1.7%
삼 204
 
1.6%
Other values (405) 9305
74.0%
Latin
ValueCountFrequency (%)
S 59
28.2%
K 39
18.7%
G 24
11.5%
e 10
 
4.8%
L 8
 
3.8%
O 6
 
2.9%
P 6
 
2.9%
B 6
 
2.9%
C 5
 
2.4%
R 5
 
2.4%
Other values (18) 41
19.6%
Common
ValueCountFrequency (%)
) 693
39.7%
( 690
39.5%
284
16.3%
1 33
 
1.9%
9 11
 
0.6%
2 6
 
0.3%
6 6
 
0.3%
& 6
 
0.3%
- 6
 
0.3%
. 4
 
0.2%
Other values (4) 7
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12571
86.5%
ASCII 1955
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 802
 
6.4%
스 429
 
3.4%
동 346
 
2.8%
자 302
 
2.4%
차 297
 
2.4%
사 233
 
1.9%
성 220
 
1.8%
서 220
 
1.8%
터 213
 
1.7%
삼 204
 
1.6%
Other values (405) 9305
74.0%
ASCII
ValueCountFrequency (%)
) 693
35.4%
( 690
35.3%
284
14.5%
S 59
 
3.0%
K 39
 
2.0%
1 33
 
1.7%
G 24
 
1.2%
9 11
 
0.6%
e 10
 
0.5%
L 8
 
0.4%
Other values (32) 104
 
5.3%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct650
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2200002 × 1017
Minimum3.2200002 × 1017
Maximum3.2200006 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-05-11T17:02:30.743455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.2200002 × 1017
5-th percentile3.2200002 × 1017
Q13.2200002 × 1017
median3.2200002 × 1017
Q33.2200002 × 1017
95-th percentile3.2200003 × 1017
Maximum3.2200006 × 1017
Range4.00026 × 1010
Interquartile range (IQR)9.9939994 × 108

Descriptive statistics

Standard deviation1.421301 × 109
Coefficient of variation (CV)4.4139779 × 10-9
Kurtosis334.77662
Mean3.2200002 × 1017
Median Absolute Deviation (MAD)999552
Skewness12.96589
Sum-5.3582845 × 1018
Variance2.0200964 × 1018
MonotonicityNot monotonic
2024-05-11T17:02:30.931181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
322000022200900001 21
 
1.2%
322000022200600001 16
 
0.9%
322000022199700185 14
 
0.8%
322000022200900015 14
 
0.8%
322000022200100145 13
 
0.8%
322000022200700007 13
 
0.8%
322000022199600034 12
 
0.7%
322000021199800143 11
 
0.6%
322000022200800018 11
 
0.6%
322000021199300161 11
 
0.6%
Other values (640) 1566
92.0%
ValueCountFrequency (%)
322000021199100145 1
 
0.1%
322000021199100147 6
0.4%
322000021199100152 7
0.4%
322000021199200155 10
0.6%
322000021199300161 11
0.6%
322000021199400163 9
0.5%
322000021199400164 11
0.6%
322000021199400168 10
0.6%
322000021199500167 3
 
0.2%
322000021199500169 1
 
0.1%
ValueCountFrequency (%)
322000061201700001 1
0.1%
322000025201600006 1
0.1%
322000025201600005 1
0.1%
322000025201600004 1
0.1%
322000025201600003 1
0.1%
322000025201500007 2
0.1%
322000025201500006 1
0.1%
322000025201500005 2
0.1%
322000025201500004 2
0.1%
322000025201500003 2
0.1%

업종코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
22
991 
21
560 
25
151 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row21
3rd row21
4th row21
5th row21

Common Values

ValueCountFrequency (%)
22 991
58.2%
21 560
32.9%
25 151
 
8.9%

Length

2024-05-11T17:02:31.080532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:02:31.194934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 991
58.2%
21 560
32.9%
25 151
 
8.9%

업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
폐수배출업소관리
976 
대기배출업소관리
553 
기타수질오염원관리
151 
<NA>
 
22

Length

Max length9
Median length8
Mean length8.0370153
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐수배출업소관리 976
57.3%
대기배출업소관리 553
32.5%
기타수질오염원관리 151
 
8.9%
<NA> 22
 
1.3%

Length

2024-05-11T17:02:31.327467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:02:31.450353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐수배출업소관리 976
57.3%
대기배출업소관리 553
32.5%
기타수질오염원관리 151
 
8.9%
na 22
 
1.3%

지도점검일자
Real number (ℝ)

Distinct420
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20140527
Minimum20100204
Maximum20171130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.1 KiB
2024-05-11T17:02:31.613732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100204
5-th percentile20100812
Q120111224
median20150706
Q320161110
95-th percentile20171115
Maximum20171130
Range70926
Interquartile range (IQR)49886.25

Descriptive statistics

Standard deviation25687.091
Coefficient of variation (CV)0.0012753932
Kurtosis-1.4732864
Mean20140527
Median Absolute Deviation (MAD)20079
Skewness-0.28945091
Sum3.4279177 × 1010
Variance6.5982667 × 108
MonotonicityDecreasing
2024-05-11T17:02:31.795527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20161020 28
 
1.6%
20100914 26
 
1.5%
20161026 22
 
1.3%
20100913 19
 
1.1%
20100915 18
 
1.1%
20171116 16
 
0.9%
20171114 16
 
0.9%
20161018 15
 
0.9%
20171115 15
 
0.9%
20161021 14
 
0.8%
Other values (410) 1513
88.9%
ValueCountFrequency (%)
20100204 2
0.1%
20100205 4
0.2%
20100212 1
 
0.1%
20100217 1
 
0.1%
20100222 1
 
0.1%
20100428 1
 
0.1%
20100513 1
 
0.1%
20100609 1
 
0.1%
20100610 1
 
0.1%
20100621 3
0.2%
ValueCountFrequency (%)
20171130 7
0.4%
20171129 13
0.8%
20171127 7
0.4%
20171124 11
0.6%
20171122 7
0.4%
20171121 13
0.8%
20171117 11
0.6%
20171116 16
0.9%
20171115 15
0.9%
20171114 16
0.9%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
3220000
1702 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3220000
2nd row3220000
3rd row3220000
4th row3220000
5th row3220000

Common Values

ValueCountFrequency (%)
3220000 1702
100.0%

Length

2024-05-11T17:02:31.970709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:02:32.111624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3220000 1702
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
서울특별시 강남구
1702 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 강남구 1702
100.0%

Length

2024-05-11T17:02:32.245414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:02:32.360204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 1702
50.0%
강남구 1702
50.0%

지도점검구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.4 KiB
정기
1441 
수시
183 
기타
 
56
일제
 
12
합동
 
10

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 1441
84.7%
수시 183
 
10.8%
기타 56
 
3.3%
일제 12
 
0.7%
합동 10
 
0.6%

Length

2024-05-11T17:02:32.476921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:02:32.600546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 1441
84.7%
수시 183
 
10.8%
기타 56
 
3.3%
일제 12
 
0.7%
합동 10
 
0.6%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing23
Missing (%)1.4%
Memory size3.5 KiB
False
1669 
True
 
10
(Missing)
 
23
ValueCountFrequency (%)
False 1669
98.1%
True 10
 
0.6%
(Missing) 23
 
1.4%
2024-05-11T17:02:32.734978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

Distinct137
Distinct (%)8.1%
Missing2
Missing (%)0.1%
Memory size13.4 KiB
2024-05-11T17:02:32.960937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length54
Mean length17.264118
Min length6

Characters and Unicode

Total characters29349
Distinct characters132
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)4.3%

Sample

1st row대기배출시설 및 방지시설 적정 운영 여부
2nd row대기배출시설 및 방지시설 적정 운영 여부
3rd row대기배출시설 및 방지시설 적정 운영 여부
4th row대기배출시설 및 방지시설 적정 운영 여부
5th row대기배출시설 및 방지시설 적정 운영 여부
ValueCountFrequency (%)
및 1091
15.8%
방지시설 1082
15.7%
폐수배출시설 693
10.0%
대기배출시설 473
 
6.9%
가동상태 453
 
6.6%
배출시설 403
 
5.8%
점검 400
 
5.8%
운영상태 396
 
5.7%
여부 382
 
5.5%
적정 257
 
3.7%
Other values (134) 1272
18.4%
2024-05-11T17:02:33.418009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5205
17.7%
시 2738
 
9.3%
설 2698
 
9.2%
배 1624
 
5.5%
출 1623
 
5.5%
지 1204
 
4.1%
및 1096
 
3.7%
방 1089
 
3.7%
운 1035
 
3.5%
영 1029
 
3.5%
Other values (122) 10008
34.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23892
81.4%
Space Separator 5205
 
17.7%
Other Punctuation 95
 
0.3%
Decimal Number 52
 
0.2%
Dash Punctuation 45
 
0.2%
Open Punctuation 30
 
0.1%
Close Punctuation 30
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2738
 
11.5%
설 2698
 
11.3%
배 1624
 
6.8%
출 1623
 
6.8%
지 1204
 
5.0%
및 1096
 
4.6%
방 1089
 
4.6%
운 1035
 
4.3%
영 1029
 
4.3%
상 901
 
3.8%
Other values (105) 8855
37.1%
Decimal Number
ValueCountFrequency (%)
1 21
40.4%
0 20
38.5%
4 3
 
5.8%
3 2
 
3.8%
6 2
 
3.8%
7 1
 
1.9%
8 1
 
1.9%
9 1
 
1.9%
2 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 50
52.6%
, 25
26.3%
% 19
 
20.0%
？ 1
 
1.1%
Space Separator
ValueCountFrequency (%)
5205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23892
81.4%
Common 5457
 
18.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2738
 
11.5%
설 2698
 
11.3%
배 1624
 
6.8%
출 1623
 
6.8%
지 1204
 
5.0%
및 1096
 
4.6%
방 1089
 
4.6%
운 1035
 
4.3%
영 1029
 
4.3%
상 901
 
3.8%
Other values (105) 8855
37.1%
Common
ValueCountFrequency (%)
5205
95.4%
. 50
 
0.9%
- 45
 
0.8%
( 30
 
0.5%
) 30
 
0.5%
, 25
 
0.5%
1 21
 
0.4%
0 20
 
0.4%
% 19
 
0.3%
4 3
 
0.1%
Other values (7) 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23891
81.4%
ASCII 5456
 
18.6%
None 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5205
95.4%
. 50
 
0.9%
- 45
 
0.8%
( 30
 
0.5%
) 30
 
0.5%
, 25
 
0.5%
1 21
 
0.4%
0 20
 
0.4%
% 19
 
0.3%
4 3
 
0.1%
Other values (6) 8
 
0.1%
Hangul
ValueCountFrequency (%)
시 2738
 
11.5%
설 2698
 
11.3%
배 1624
 
6.8%
출 1623
 
6.8%
지 1204
 
5.0%
및 1096
 
4.6%
방 1089
 
4.6%
운 1035
 
4.3%
영 1029
 
4.3%
상 901
 
3.8%
Other values (104) 8854
37.1%
None
ValueCountFrequency (%)
？ 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅌ 1
100.0%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1702
Missing (%)100.0%
Memory size15.1 KiB

소재지도로명주소
Text

MISSING 

Distinct518
Distinct (%)55.3%
Missing765
Missing (%)44.9%
Memory size13.4 KiB
2024-05-11T17:02:33.755303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length27.665955
Min length22

Characters and Unicode

Total characters25923
Distinct characters262
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)27.1%

Sample

1st row서울특별시 강남구 강남대로 262 (도곡동)
2nd row서울특별시 강남구 양재대로 340 (개포동, 능인선원)
3rd row서울특별시 강남구 광평로39길 105 (수서동)
4th row서울특별시 강남구 남부순환로 3318 (일원동)
5th row서울특별시 강남구 개포로 625 (일원동, 탄천물재생센터)
ValueCountFrequency (%)
서울특별시 937
 
18.4%
강남구 937
 
18.4%
역삼동 207
 
4.1%
대치동 114
 
2.2%
논현동 110
 
2.2%
테헤란로 108
 
2.1%
신사동 103
 
2.0%
삼성동 89
 
1.7%
개포동 59
 
1.2%
언주로 57
 
1.1%
Other values (681) 2367
46.5%
2024-05-11T17:02:34.266863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4304
 
16.6%
남 1070
 
4.1%
동 1030
 
4.0%
강 1026
 
4.0%
구 1002
 
3.9%
서 982
 
3.8%
로 951
 
3.7%
) 948
 
3.7%
( 948
 
3.7%
울 948
 
3.7%
Other values (252) 12714
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15690
60.5%
Space Separator 4304
 
16.6%
Decimal Number 3560
 
13.7%
Close Punctuation 948
 
3.7%
Open Punctuation 948
 
3.7%
Other Punctuation 377
 
1.5%
Uppercase Letter 57
 
0.2%
Dash Punctuation 38
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1070
 
6.8%
동 1030
 
6.6%
강 1026
 
6.5%
구 1002
 
6.4%
서 982
 
6.3%
로 951
 
6.1%
울 948
 
6.0%
시 946
 
6.0%
특 937
 
6.0%
별 937
 
6.0%
Other values (221) 5861
37.4%
Uppercase Letter
ValueCountFrequency (%)
B 13
22.8%
A 12
21.1%
S 9
15.8%
K 3
 
5.3%
H 3
 
5.3%
C 3
 
5.3%
P 3
 
5.3%
D 2
 
3.5%
G 2
 
3.5%
Y 2
 
3.5%
Other values (3) 5
 
8.8%
Decimal Number
ValueCountFrequency (%)
1 718
20.2%
2 563
15.8%
3 406
11.4%
5 379
10.6%
4 323
9.1%
6 304
8.5%
0 274
 
7.7%
7 253
 
7.1%
8 218
 
6.1%
9 122
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 374
99.2%
& 2
 
0.5%
/ 1
 
0.3%
Space Separator
ValueCountFrequency (%)
4304
100.0%
Close Punctuation
ValueCountFrequency (%)
) 948
100.0%
Open Punctuation
ValueCountFrequency (%)
( 948
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15690
60.5%
Common 10175
39.3%
Latin 58
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1070
 
6.8%
동 1030
 
6.6%
강 1026
 
6.5%
구 1002
 
6.4%
서 982
 
6.3%
로 951
 
6.1%
울 948
 
6.0%
시 946
 
6.0%
특 937
 
6.0%
별 937
 
6.0%
Other values (221) 5861
37.4%
Common
ValueCountFrequency (%)
4304
42.3%
) 948
 
9.3%
( 948
 
9.3%
1 718
 
7.1%
2 563
 
5.5%
3 406
 
4.0%
5 379
 
3.7%
, 374
 
3.7%
4 323
 
3.2%
6 304
 
3.0%
Other values (7) 908
 
8.9%
Latin
ValueCountFrequency (%)
B 13
22.4%
A 12
20.7%
S 9
15.5%
K 3
 
5.2%
H 3
 
5.2%
C 3
 
5.2%
P 3
 
5.2%
D 2
 
3.4%
G 2
 
3.4%
Y 2
 
3.4%
Other values (4) 6
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15690
60.5%
ASCII 10233
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4304
42.1%
) 948
 
9.3%
( 948
 
9.3%
1 718
 
7.0%
2 563
 
5.5%
3 406
 
4.0%
5 379
 
3.7%
, 374
 
3.7%
4 323
 
3.2%
6 304
 
3.0%
Other values (21) 966
 
9.4%
Hangul
ValueCountFrequency (%)
남 1070
 
6.8%
동 1030
 
6.6%
강 1026
 
6.5%
구 1002
 
6.4%
서 982
 
6.3%
로 951
 
6.1%
울 948
 
6.0%
시 946
 
6.0%
특 937
 
6.0%
별 937
 
6.0%
Other values (221) 5861
37.4%

소재지주소
Text

MISSING 

Distinct546
Distinct (%)35.0%
Missing140
Missing (%)8.2%
Memory size13.4 KiB
2024-05-11T17:02:34.596463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length38
Mean length23.537772
Min length14

Characters and Unicode

Total characters36766
Distinct characters178
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)14.0%

Sample

1st row서울특별시 강남구 도곡동 949-3번지
2nd row서울특별시 강남구 개포동 1055번지
3rd row서울특별시 강남구 일원동 4-1번지
4th row서울특별시 강남구 일원동 580번지
5th row서울특별시 강남구 도곡동 448-2번지
ValueCountFrequency (%)
서울특별시 1562
22.9%
강남구 1562
22.9%
역삼동 313
 
4.6%
신사동 308
 
4.5%
논현동 184
 
2.7%
대치동 157
 
2.3%
삼성동 153
 
2.2%
개포동 110
 
1.6%
도곡동 85
 
1.2%
청담동 77
 
1.1%
Other values (653) 2313
33.9%
2024-05-11T17:02:35.153955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6831
18.6%
1 1638
 
4.5%
서 1609
 
4.4%
동 1591
 
4.3%
지 1579
 
4.3%
구 1575
 
4.3%
시 1572
 
4.3%
남 1568
 
4.3%
강 1565
 
4.3%
별 1562
 
4.2%
Other values (168) 15676
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21525
58.5%
Decimal Number 6909
 
18.8%
Space Separator 6831
 
18.6%
Dash Punctuation 1343
 
3.7%
Other Punctuation 51
 
0.1%
Uppercase Letter 36
 
0.1%
Open Punctuation 33
 
0.1%
Close Punctuation 33
 
0.1%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1609
 
7.5%
동 1591
 
7.4%
지 1579
 
7.3%
구 1575
 
7.3%
시 1572
 
7.3%
남 1568
 
7.3%
강 1565
 
7.3%
별 1562
 
7.3%
특 1562
 
7.3%
울 1562
 
7.3%
Other values (146) 5780
26.9%
Decimal Number
ValueCountFrequency (%)
1 1638
23.7%
2 994
14.4%
5 743
10.8%
3 639
 
9.2%
6 578
 
8.4%
7 541
 
7.8%
4 468
 
6.8%
8 451
 
6.5%
9 434
 
6.3%
0 423
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 48
94.1%
: 2
 
3.9%
/ 1
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
B 17
47.2%
A 16
44.4%
C 3
 
8.3%
Space Separator
ValueCountFrequency (%)
6831
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1343
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21525
58.5%
Common 15202
41.3%
Latin 39
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1609
 
7.5%
동 1591
 
7.4%
지 1579
 
7.3%
구 1575
 
7.3%
시 1572
 
7.3%
남 1568
 
7.3%
강 1565
 
7.3%
별 1562
 
7.3%
특 1562
 
7.3%
울 1562
 
7.3%
Other values (146) 5780
26.9%
Common
ValueCountFrequency (%)
6831
44.9%
1 1638
 
10.8%
- 1343
 
8.8%
2 994
 
6.5%
5 743
 
4.9%
3 639
 
4.2%
6 578
 
3.8%
7 541
 
3.6%
4 468
 
3.1%
8 451
 
3.0%
Other values (8) 976
 
6.4%
Latin
ValueCountFrequency (%)
B 17
43.6%
A 16
41.0%
C 3
 
7.7%
a 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21525
58.5%
ASCII 15241
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6831
44.8%
1 1638
 
10.7%
- 1343
 
8.8%
2 994
 
6.5%
5 743
 
4.9%
3 639
 
4.2%
6 578
 
3.8%
7 541
 
3.5%
4 468
 
3.1%
8 451
 
3.0%
Other values (12) 1015
 
6.7%
Hangul
ValueCountFrequency (%)
서 1609
 
7.5%
동 1591
 
7.4%
지 1579
 
7.3%
구 1575
 
7.3%
시 1572
 
7.3%
남 1568
 
7.3%
강 1565
 
7.3%
별 1562
 
7.3%
특 1562
 
7.3%
울 1562
 
7.3%
Other values (146) 5780
26.9%

Interactions

2024-05-11T17:02:29.150287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:02:28.903680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:02:29.275880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:02:29.025290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T17:02:35.269374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부
인허가번호1.0000.9430.9430.3740.1250.202
업종코드0.9431.0001.0000.5590.1760.042
업종명0.9431.0001.0000.5580.1770.042
지도점검일자0.3740.5590.5581.0000.3000.097
지도점검구분0.1250.1760.1770.3001.0000.000
처분대상여부0.2020.0420.0420.0970.0001.000
2024-05-11T17:02:35.408674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분대상여부업종코드지도점검구분업종명
처분대상여부1.0000.0700.0000.070
업종코드0.0701.0000.1341.000
지도점검구분0.0000.1341.0000.135
업종명0.0701.0000.1351.000
2024-05-11T17:02:35.518670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.1310.7070.7070.0940.314
지도점검일자-0.1311.0000.4250.4240.1880.072
업종코드0.7070.4251.0001.0000.1340.070
업종명0.7070.4241.0001.0000.1350.070
지도점검구분0.0940.1880.1340.1351.0000.000
처분대상여부0.3140.0720.0700.0700.0001.000

Missing values

2024-05-11T17:02:29.463059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T17:02:29.689008image/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.
2024-05-11T17:02:29.842986image/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
0한국자산관리공사(캠코양재타워)32200002120150011021대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 강남대로 262 (도곡동)서울특별시 강남구 도곡동 949-3번지
1능인선원32200002120150003221대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 양재대로 340 (개포동, 능인선원)서울특별시 강남구 개포동 1055번지
2한국지역난방공사 강남지사(수서열원)32200002120150015321대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 광평로39길 105 (수서동)<NA>
3강남자원회수시설32200002119990017621대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 남부순환로 3318 (일원동)서울특별시 강남구 일원동 4-1번지
4서울시탄천물재생센터((주)탄천환경)32200002120060000121대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 개포로 625 (일원동, 탄천물재생센터)서울특별시 강남구 일원동 580번지
5골든사우나32200002120160001221대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 삼성로135길 28 (청담동, 골든아트빌)<NA>
6(주)퍼시픽제8호기업구조조정부동산투자회사32200002120160000821대기배출업소관리201711303220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 양재천로 163 (도곡동, 바디프랜드도곡타워)서울특별시 강남구 도곡동 448-2번지
7르노삼성자동차역삼센타32200002520030022925기타수질오염원관리201711293220000서울특별시 강남구정기N배출시설 운영상태 점검<NA>서울특별시 강남구 테헤란로 114 (역삼동,5,6)서울특별시 강남구 역삼동 824번지 5,6
8공우이엔씨주식회사(군인공제회관)32200002120150012521대기배출업소관리201711293220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 남부순환로 2806 (도곡동, 군인공제회관)서울특별시 강남구 도곡동 467-12번지
9대림아크로텔관리단32200002120150013221대기배출업소관리201711293220000서울특별시 강남구정기N대기배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 강남구 언주로30길 13 (도곡동,(선릉로서81길 56))서울특별시 강남구 도곡동 467-6번지 (선릉로서81길 56)
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
1692현대오토피아 삼성점32200002220100000122폐수배출업소관리201004283220000서울특별시 강남구수시<NA>폐수배출시설 및 방지시설 적정 운영 여부(가동개시)<NA><NA>서울특별시 강남구 삼성동 44-13번지
1693GS칼덱스(주)직영 삼성로점32200002220080001822폐수배출업소관리201002223220000서울특별시 강남구수시N조업정지 이행보고 확인<NA><NA>서울특별시 강남구 삼성동 119-18번지
1694GS칼덱스(주)직영 삼성로점32200002220080001822폐수배출업소관리201002173220000서울특별시 강남구수시N행정처분 이행여부 확인<NA><NA>서울특별시 강남구 삼성동 119-18번지
1695GS칼덱스(주)직영 삼성로점32200002220080001822<NA>201002123220000서울특별시 강남구수시<NA>행정처분 철저히 이행하도록 독려<NA><NA>서울특별시 강남구 삼성동 119-18번지
1696주식회사 맥바겐32200002220090001122폐수배출업소관리201002053220000서울특별시 강남구수시N가동개시신고확인<NA>서울특별시 강남구 역삼로 425 (대치동)서울특별시 강남구 대치동 906-19번지
1697GS칼덱스(주)직영 삼성로점32200002220080001822폐수배출업소관리201002053220000서울특별시 강남구수시N설연휴전 중점대상 사업장 점검<NA><NA>서울특별시 강남구 삼성동 119-18번지
1698코션코리아역삼32200002219970018522폐수배출업소관리201002053220000서울특별시 강남구수시N설연휴전 중점대상 사업장 점검<NA><NA>서울특별시 강남구 역삼동 834-68번지
1699강남제일세차장32200002220070000722폐수배출업소관리201002053220000서울특별시 강남구수시N설연휴전 중점대상 사업장 점검<NA><NA>서울특별시 강남구 율현동 113-1번지 외 1필지 A동
1700덴트닥터칼라32200002220090000122폐수배출업소관리201002043220000서울특별시 강남구수시N설연휴전 중점대상 사업장 점검<NA><NA>서울특별시 강남구 신사동 628-6번지
1701현대오일뱅크(주)직영 한양주유소32200002220070001522폐수배출업소관리201002043220000서울특별시 강남구수시N설연휴전 중점대상 사업장 점검<NA><NA>서울특별시 강남구 신사동 638번지

Duplicate rows

Most frequently occurring

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항소재지도로명주소소재지주소# duplicates
0광신32200002220010008622폐수배출업소관리201009163220000서울특별시 강남구정기N폐수배출시설 운영사항<NA>서울특별시 강남구 신사동 615-1번지 코끼리상가 303호2
1기성모터스32200002219940001122폐수배출업소관리201206213220000서울특별시 강남구정기N폐수배출시설 및 방지시설 가동상태<NA>서울특별시 강남구 신사동 564-16번지2
2기성모터스32200002219940001122폐수배출업소관리201307313220000서울특별시 강남구정기N폐수배출시설 및 방지시설 가동상태<NA>서울특별시 강남구 신사동 564-16번지2
3대치손세차장32200002220110000222폐수배출업소관리201206133220000서울특별시 강남구정기N폐수배출시설 및 방지시설 가동상태서울특별시 강남구 도곡로 435 (대치동)서울특별시 강남구 대치동 940-3번지 12, 132
4덴트닥터칼라32200002220090000122폐수배출업소관리201206213220000서울특별시 강남구정기N폐수배출시설 및 방지시설 가동상태<NA>서울특별시 강남구 신사동 628-6번지2
5아성카랜드32200002220040000722폐수배출업소관리201307313220000서울특별시 강남구정기N폐수배출시설 및 방지시설 가동상태<NA>서울특별시 강남구 신사동 628-22번지2