Overview

Dataset statistics

Number of variables13
Number of observations438
Missing cells677
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.8 KiB
Average record size in memory109.3 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-9893/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
업종명 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
업종코드 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
인허가번호 is highly overall correlated with 업종코드 and 1 other fieldsHigh correlation
처분대상여부 is highly imbalanced (76.4%)Imbalance
처분대상여부 has 49 (11.2%) missing valuesMissing
점검결과 has 438 (100.0%) missing valuesMissing
소재지도로명주소 has 173 (39.5%) missing valuesMissing
소재지주소 has 14 (3.2%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:35:13.483261
Analysis finished2024-05-11 06:35:16.213958
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct108
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
2024-05-11T15:35:16.510349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length7.260274
Min length2

Characters and Unicode

Total characters3180
Distinct characters188
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)8.4%

Sample

1st row태진운수(주)
2nd row메트로셀프세차장화양점
3rd row(주)김家네
4th row한강자동차공업사
5th row광진구 행정차고
ValueCountFrequency (%)
우정염색공업사 13
 
2.7%
건국대학교병원 12
 
2.5%
한강모터스 12
 
2.5%
현대자동차세차장 11
 
2.3%
월성운수(주 11
 
2.3%
한미자동차공업사 11
 
2.3%
뉴서울택시주식회사 11
 
2.3%
삼성유리 10
 
2.1%
태광상사 9
 
1.9%
건국대학교 9
 
1.9%
Other values (116) 376
77.5%
2024-05-11T15:35:17.150819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 213
 
6.7%
) 125
 
3.9%
( 125
 
3.9%
동 114
 
3.6%
대 96
 
3.0%
사 94
 
3.0%
차 92
 
2.9%
업 92
 
2.9%
유 83
 
2.6%
소 81
 
2.5%
Other values (178) 2065
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2829
89.0%
Close Punctuation 125
 
3.9%
Open Punctuation 125
 
3.9%
Space Separator 47
 
1.5%
Uppercase Letter 25
 
0.8%
Lowercase Letter 14
 
0.4%
Other Punctuation 7
 
0.2%
Dash Punctuation 4
 
0.1%
Decimal Number 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 213
 
7.5%
동 114
 
4.0%
대 96
 
3.4%
사 94
 
3.3%
차 92
 
3.3%
업 92
 
3.3%
유 83
 
2.9%
소 81
 
2.9%
자 60
 
2.1%
공 58
 
2.1%
Other values (154) 1846
65.3%
Lowercase Letter
ValueCountFrequency (%)
s 5
35.7%
k 3
21.4%
i 1
 
7.1%
h 1
 
7.1%
e 1
 
7.1%
l 1
 
7.1%
a 1
 
7.1%
c 1
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
T 6
24.0%
K 6
24.0%
S 5
20.0%
M 3
12.0%
C 2
 
8.0%
P 2
 
8.0%
A 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
3 1
25.0%
5 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 5
71.4%
/ 2
 
28.6%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Space Separator
ValueCountFrequency (%)
47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2823
88.8%
Common 312
 
9.8%
Latin 39
 
1.2%
Han 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 213
 
7.5%
동 114
 
4.0%
대 96
 
3.4%
사 94
 
3.3%
차 92
 
3.3%
업 92
 
3.3%
유 83
 
2.9%
소 81
 
2.9%
자 60
 
2.1%
공 58
 
2.1%
Other values (153) 1840
65.2%
Latin
ValueCountFrequency (%)
T 6
15.4%
K 6
15.4%
S 5
12.8%
s 5
12.8%
M 3
7.7%
k 3
7.7%
C 2
 
5.1%
P 2
 
5.1%
i 1
 
2.6%
A 1
 
2.6%
Other values (5) 5
12.8%
Common
ValueCountFrequency (%)
) 125
40.1%
( 125
40.1%
47
 
15.1%
. 5
 
1.6%
- 4
 
1.3%
0 2
 
0.6%
/ 2
 
0.6%
3 1
 
0.3%
5 1
 
0.3%
Han
ValueCountFrequency (%)
家 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2823
88.8%
ASCII 351
 
11.0%
CJK 6
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 213
 
7.5%
동 114
 
4.0%
대 96
 
3.4%
사 94
 
3.3%
차 92
 
3.3%
업 92
 
3.3%
유 83
 
2.9%
소 81
 
2.9%
자 60
 
2.1%
공 58
 
2.1%
Other values (153) 1840
65.2%
ASCII
ValueCountFrequency (%)
) 125
35.6%
( 125
35.6%
47
 
13.4%
T 6
 
1.7%
K 6
 
1.7%
. 5
 
1.4%
S 5
 
1.4%
s 5
 
1.4%
- 4
 
1.1%
M 3
 
0.9%
Other values (14) 20
 
5.7%
CJK
ValueCountFrequency (%)
家 6
100.0%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0400002 × 1017
Minimum3.0400002 × 1017
Maximum3.0400003 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-05-11T15:35:17.394087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0400002 × 1017
5-th percentile3.0400002 × 1017
Q13.0400002 × 1017
median3.0400002 × 1017
Q33.0400002 × 1017
95-th percentile3.0400002 × 1017
Maximum3.0400003 × 1017
Range4.0012 × 109
Interquartile range (IQR)1900288

Descriptive statistics

Standard deviation7.457836 × 108
Coefficient of variation (CV)2.4532353 × 10-9
Kurtosis9.3884803
Mean3.0400002 × 1017
Median Absolute Deviation (MAD)900288
Skewness2.5029618
Sum4.0248012 × 1018
Variance5.5619317 × 1017
MonotonicityNot monotonic
2024-05-11T15:35:17.947749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304000022199700010 13
 
3.0%
304000022199400020 12
 
2.7%
304000021199800006 11
 
2.5%
304000022200400305 11
 
2.5%
304000022200500013 10
 
2.3%
304000022200200301 10
 
2.3%
304000022201000001 9
 
2.1%
304000022199800095 9
 
2.1%
304000022199600037 9
 
2.1%
304000022198200002 9
 
2.1%
Other values (98) 335
76.5%
ValueCountFrequency (%)
304000021199800006 11
2.5%
304000021199800007 7
1.6%
304000021199800008 4
 
0.9%
304000021199800009 8
1.8%
304000021199900012 3
 
0.7%
304000021199900013 5
1.1%
304000021199900014 1
 
0.2%
304000021199900015 6
1.4%
304000021199900016 5
1.1%
304000021199900017 5
1.1%
ValueCountFrequency (%)
304000025201000001 1
0.2%
304000025200900003 1
0.2%
304000025200800028 1
0.2%
304000025200700010 1
0.2%
304000025200600010 1
0.2%
304000025200300073 2
0.5%
304000025200300051 1
0.2%
304000025200200142 1
0.2%
304000025200100135 1
0.2%
304000025200000109 1
0.2%

업종코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
22
336 
21
84 
25
 
18

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
22 336
76.7%
21 84
 
19.2%
25 18
 
4.1%

Length

2024-05-11T15:35:18.156575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:18.312344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 336
76.7%
21 84
 
19.2%
25 18
 
4.1%

업종명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
폐수배출업소관리
311 
대기배출업소관리
55 
<NA>
54 
기타수질오염원관리
 
18

Length

Max length9
Median length8
Mean length7.5479452
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐수배출업소관리 311
71.0%
대기배출업소관리 55
 
12.6%
<NA> 54
 
12.3%
기타수질오염원관리 18
 
4.1%

Length

2024-05-11T15:35:18.482383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:18.739353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐수배출업소관리 311
71.0%
대기배출업소관리 55
 
12.6%
na 54
 
12.3%
기타수질오염원관리 18
 
4.1%

지도점검일자
Real number (ℝ)

Distinct155
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20135277
Minimum20100122
Maximum20170921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-05-11T15:35:18.972896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100122
5-th percentile20100820
Q120120426
median20130903
Q320150923
95-th percentile20170609
Maximum20170921
Range70799
Interquartile range (IQR)30497

Descriptive statistics

Standard deviation21929.286
Coefficient of variation (CV)0.0010890978
Kurtosis-1.1420959
Mean20135277
Median Absolute Deviation (MAD)19609
Skewness0.1275651
Sum8.8192513 × 109
Variance4.8089357 × 108
MonotonicityDecreasing
2024-05-11T15:35:19.225132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120718 8
 
1.8%
20131031 7
 
1.6%
20130903 7
 
1.6%
20120906 7
 
1.6%
20120305 7
 
1.6%
20141031 7
 
1.6%
20121019 6
 
1.4%
20120120 6
 
1.4%
20120229 6
 
1.4%
20130905 6
 
1.4%
Other values (145) 371
84.7%
ValueCountFrequency (%)
20100122 3
0.7%
20100210 2
0.5%
20100220 1
 
0.2%
20100405 1
 
0.2%
20100430 4
0.9%
20100511 2
0.5%
20100520 1
 
0.2%
20100609 1
 
0.2%
20100610 2
0.5%
20100722 4
0.9%
ValueCountFrequency (%)
20170921 2
 
0.5%
20170726 3
0.7%
20170719 1
 
0.2%
20170718 3
0.7%
20170630 1
 
0.2%
20170629 1
 
0.2%
20170613 5
1.1%
20170612 4
0.9%
20170609 4
0.9%
20170525 4
0.9%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
3040000
438 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3040000 438
100.0%

Length

2024-05-11T15:35:19.449658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:19.633271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3040000 438
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
서울특별시 광진구
438 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 광진구 438
100.0%

Length

2024-05-11T15:35:19.825329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:20.083390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 438
50.0%
광진구 438
50.0%

지도점검구분
Categorical

Distinct5
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
정기
261 
수시
120 
합동
41 
기타
 
15
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0045662
Min length2

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
정기 261
59.6%
수시 120
27.4%
합동 41
 
9.4%
기타 15
 
3.4%
<NA> 1
 
0.2%

Length

2024-05-11T15:35:20.294212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:20.513595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 261
59.6%
수시 120
27.4%
합동 41
 
9.4%
기타 15
 
3.4%
na 1
 
0.2%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.5%
Missing49
Missing (%)11.2%
Memory size1008.0 B
False
374 
True
 
15
(Missing)
49 
ValueCountFrequency (%)
False 374
85.4%
True 15
 
3.4%
(Missing) 49
 
11.2%
2024-05-11T15:35:20.669523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

Distinct142
Distinct (%)32.6%
Missing3
Missing (%)0.7%
Memory size3.6 KiB
2024-05-11T15:35:21.059072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length42
Mean length22.586207
Min length6

Characters and Unicode

Total characters9825
Distinct characters123
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

Unique83 ?
Unique (%)19.1%

Sample

1st row배출시설 및 방지시설 적정 운영 여부
2nd row배출시설 및 방지시설 적정 운영 여부
3rd row배출시설 및 방지시설 적정 운영 여부
4th row배출시설 및 방지시설 적정 운영 여부
5th row배출시설 및 방지시설 적정 운영 여부
ValueCountFrequency (%)
및 364
15.0%
여부 275
11.3%
방지시설 264
 
10.9%
등 159
 
6.5%
적정 140
 
5.8%
적정운영 139
 
5.7%
배출시설 134
 
5.5%
폐수배출시설 129
 
5.3%
운영 92
 
3.8%
운영일지 72
 
3.0%
Other values (123) 665
27.3%
2024-05-11T15:35:21.679223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2000
20.4%
설 713
 
7.3%
시 693
 
7.1%
및 414
 
4.2%
지 409
 
4.2%
영 398
 
4.1%
운 398
 
4.1%
정 390
 
4.0%
여 372
 
3.8%
적 369
 
3.8%
Other values (113) 3669
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7710
78.5%
Space Separator 2000
 
20.4%
Other Punctuation 73
 
0.7%
Close Punctuation 21
 
0.2%
Open Punctuation 21
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 713
 
9.2%
시 693
 
9.0%
및 414
 
5.4%
지 409
 
5.3%
영 398
 
5.2%
운 398
 
5.2%
정 390
 
5.1%
여 372
 
4.8%
적 369
 
4.8%
부 369
 
4.8%
Other values (107) 3185
41.3%
Other Punctuation
ValueCountFrequency (%)
, 56
76.7%
? 16
 
21.9%
. 1
 
1.4%
Space Separator
ValueCountFrequency (%)
2000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7710
78.5%
Common 2115
 
21.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 713
 
9.2%
시 693
 
9.0%
및 414
 
5.4%
지 409
 
5.3%
영 398
 
5.2%
운 398
 
5.2%
정 390
 
5.1%
여 372
 
4.8%
적 369
 
4.8%
부 369
 
4.8%
Other values (107) 3185
41.3%
Common
ValueCountFrequency (%)
2000
94.6%
, 56
 
2.6%
) 21
 
1.0%
( 21
 
1.0%
? 16
 
0.8%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7710
78.5%
ASCII 2115
 
21.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2000
94.6%
, 56
 
2.6%
) 21
 
1.0%
( 21
 
1.0%
? 16
 
0.8%
. 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
설 713
 
9.2%
시 693
 
9.0%
및 414
 
5.4%
지 409
 
5.3%
영 398
 
5.2%
운 398
 
5.2%
정 390
 
5.1%
여 372
 
4.8%
적 369
 
4.8%
부 369
 
4.8%
Other values (107) 3185
41.3%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing438
Missing (%)100.0%
Memory size4.0 KiB

소재지도로명주소
Text

MISSING 

Distinct98
Distinct (%)37.0%
Missing173
Missing (%)39.5%
Memory size3.6 KiB
2024-05-11T15:35:22.120989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length24.569811
Min length21

Characters and Unicode

Total characters6511
Distinct characters118
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

Unique38 ?
Unique (%)14.3%

Sample

1st row서울특별시 광진구 능동로 5 (자양동)
2nd row서울특별시 광진구 동일로 126 (화양동)
3rd row서울특별시 광진구 아차산로 439 (구의동)
4th row서울특별시 광진구 아차산로76길 10 (광장동)
5th row서울특별시 광진구 천호대로 788 (광장동)
ValueCountFrequency (%)
서울특별시 265
19.6%
광진구 265
19.6%
중곡동 77
 
5.7%
구의동 55
 
4.1%
자양동 53
 
3.9%
동일로 47
 
3.5%
능동로 31
 
2.3%
화양동 26
 
1.9%
군자동 26
 
1.9%
광나루로 23
 
1.7%
Other values (126) 482
35.7%
2024-05-11T15:35:22.759453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1107
 
17.0%
동 355
 
5.5%
구 325
 
5.0%
광 316
 
4.9%
진 267
 
4.1%
울 266
 
4.1%
서 266
 
4.1%
시 266
 
4.1%
별 265
 
4.1%
특 265
 
4.1%
Other values (108) 2813
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4041
62.1%
Space Separator 1107
 
17.0%
Decimal Number 788
 
12.1%
Close Punctuation 265
 
4.1%
Open Punctuation 265
 
4.1%
Other Punctuation 25
 
0.4%
Dash Punctuation 20
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 355
 
8.8%
구 325
 
8.0%
광 316
 
7.8%
진 267
 
6.6%
울 266
 
6.6%
서 266
 
6.6%
시 266
 
6.6%
별 265
 
6.6%
특 265
 
6.6%
로 264
 
6.5%
Other values (93) 1186
29.3%
Decimal Number
ValueCountFrequency (%)
1 108
13.7%
3 94
11.9%
4 92
11.7%
0 86
10.9%
2 85
10.8%
5 77
9.8%
6 67
8.5%
7 64
8.1%
9 59
7.5%
8 56
7.1%
Space Separator
ValueCountFrequency (%)
1107
100.0%
Close Punctuation
ValueCountFrequency (%)
) 265
100.0%
Open Punctuation
ValueCountFrequency (%)
( 265
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4041
62.1%
Common 2470
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 355
 
8.8%
구 325
 
8.0%
광 316
 
7.8%
진 267
 
6.6%
울 266
 
6.6%
서 266
 
6.6%
시 266
 
6.6%
별 265
 
6.6%
특 265
 
6.6%
로 264
 
6.5%
Other values (93) 1186
29.3%
Common
ValueCountFrequency (%)
1107
44.8%
) 265
 
10.7%
( 265
 
10.7%
1 108
 
4.4%
3 94
 
3.8%
4 92
 
3.7%
0 86
 
3.5%
2 85
 
3.4%
5 77
 
3.1%
6 67
 
2.7%
Other values (5) 224
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4041
62.1%
ASCII 2470
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1107
44.8%
) 265
 
10.7%
( 265
 
10.7%
1 108
 
4.4%
3 94
 
3.8%
4 92
 
3.7%
0 86
 
3.5%
2 85
 
3.4%
5 77
 
3.1%
6 67
 
2.7%
Other values (5) 224
 
9.1%
Hangul
ValueCountFrequency (%)
동 355
 
8.8%
구 325
 
8.0%
광 316
 
7.8%
진 267
 
6.6%
울 266
 
6.6%
서 266
 
6.6%
시 266
 
6.6%
별 265
 
6.6%
특 265
 
6.6%
로 264
 
6.5%
Other values (93) 1186
29.3%

소재지주소
Text

MISSING 

Distinct97
Distinct (%)22.9%
Missing14
Missing (%)3.2%
Memory size3.6 KiB
2024-05-11T15:35:23.100303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length22.313679
Min length14

Characters and Unicode

Total characters9461
Distinct characters54
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

Unique22 ?
Unique (%)5.2%

Sample

1st row서울특별시 광진구 자양동 72-1번지
2nd row서울특별시 광진구 화양동 42-4번지
3rd row서울특별시 광진구 구의동 219-25번지
4th row서울특별시 광진구 광장동 325-6번지
5th row서울특별시 광진구 광장동 401-24번지
ValueCountFrequency (%)
서울특별시 424
24.4%
광진구 424
24.4%
중곡동 129
 
7.4%
구의동 95
 
5.5%
자양동 91
 
5.2%
화양동 35
 
2.0%
군자동 35
 
2.0%
광장동 31
 
1.8%
612-7번지 13
 
0.7%
번지 12
 
0.7%
Other values (104) 451
25.9%
2024-05-11T15:35:23.593189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1759
18.6%
구 519
 
5.5%
광 455
 
4.8%
지 427
 
4.5%
서 424
 
4.5%
진 424
 
4.5%
동 424
 
4.5%
울 424
 
4.5%
시 424
 
4.5%
별 424
 
4.5%
Other values (44) 3757
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5561
58.8%
Space Separator 1759
 
18.6%
Decimal Number 1743
 
18.4%
Dash Punctuation 396
 
4.2%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 519
9.3%
광 455
 
8.2%
지 427
 
7.7%
서 424
 
7.6%
진 424
 
7.6%
동 424
 
7.6%
울 424
 
7.6%
시 424
 
7.6%
별 424
 
7.6%
특 424
 
7.6%
Other values (31) 1192
21.4%
Decimal Number
ValueCountFrequency (%)
1 352
20.2%
2 304
17.4%
6 212
12.2%
3 172
9.9%
5 158
9.1%
0 140
 
8.0%
4 137
 
7.9%
7 112
 
6.4%
8 93
 
5.3%
9 63
 
3.6%
Space Separator
ValueCountFrequency (%)
1759
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 396
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5561
58.8%
Common 3900
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 519
9.3%
광 455
 
8.2%
지 427
 
7.7%
서 424
 
7.6%
진 424
 
7.6%
동 424
 
7.6%
울 424
 
7.6%
시 424
 
7.6%
별 424
 
7.6%
특 424
 
7.6%
Other values (31) 1192
21.4%
Common
ValueCountFrequency (%)
1759
45.1%
- 396
 
10.2%
1 352
 
9.0%
2 304
 
7.8%
6 212
 
5.4%
3 172
 
4.4%
5 158
 
4.1%
0 140
 
3.6%
4 137
 
3.5%
7 112
 
2.9%
Other values (3) 158
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5561
58.8%
ASCII 3900
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1759
45.1%
- 396
 
10.2%
1 352
 
9.0%
2 304
 
7.8%
6 212
 
5.4%
3 172
 
4.4%
5 158
 
4.1%
0 140
 
3.6%
4 137
 
3.5%
7 112
 
2.9%
Other values (3) 158
 
4.1%
Hangul
ValueCountFrequency (%)
구 519
9.3%
광 455
 
8.2%
지 427
 
7.7%
서 424
 
7.6%
진 424
 
7.6%
동 424
 
7.6%
울 424
 
7.6%
시 424
 
7.6%
별 424
 
7.6%
특 424
 
7.6%
Other values (31) 1192
21.4%

Interactions

2024-05-11T15:35:15.100760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:35:14.718947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:35:15.355989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:35:14.902257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:35:23.725637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부소재지도로명주소소재지주소
인허가번호1.0001.0000.9990.4770.1020.0080.9970.972
업종코드1.0001.0001.0000.4500.1150.0000.9950.982
업종명0.9991.0001.0000.4590.1310.0000.9980.976
지도점검일자0.4770.4500.4591.0000.6370.0670.0000.000
지도점검구분0.1020.1150.1310.6371.0000.0000.0000.121
처분대상여부0.0080.0000.0000.0670.0001.0000.2520.501
소재지도로명주소0.9970.9950.9980.0000.0000.2521.0001.000
소재지주소0.9720.9820.9760.0000.1210.5011.0001.000
2024-05-11T15:35:23.974927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지도점검구분처분대상여부업종명업종코드
지도점검구분1.0000.0000.1240.108
처분대상여부0.0001.0000.0000.000
업종명0.1240.0001.0001.000
업종코드0.1080.0001.0001.000
2024-05-11T15:35:24.165246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.1000.9810.9730.1030.000
지도점검일자-0.1001.0000.3210.3280.3200.044
업종코드0.9810.3211.0001.0000.1080.000
업종명0.9730.3281.0001.0000.1240.000
지도점검구분0.1030.3200.1080.1241.0000.000
처분대상여부0.0000.0440.0000.0000.0001.000

Missing values

2024-05-11T15:35:15.572243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:35:15.863287image/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.
2024-05-11T15:35:16.093219image/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
0태진운수(주)30400002219940001122폐수배출업소관리201709213040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 능동로 5 (자양동)서울특별시 광진구 자양동 72-1번지
1메트로셀프세차장화양점30400002219980009522폐수배출업소관리201709213040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 동일로 126 (화양동)서울특별시 광진구 화양동 42-4번지
2(주)김家네30400002220050030622폐수배출업소관리201707263040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 아차산로 439 (구의동)서울특별시 광진구 구의동 219-25번지
3한강자동차공업사30400002219940000822폐수배출업소관리201707263040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 아차산로76길 10 (광장동)서울특별시 광진구 광장동 325-6번지
4광진구 행정차고30400002220050030822폐수배출업소관리201707263040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 천호대로 788 (광장동)서울특별시 광진구 광장동 401-24번지
5우정염색공업사30400002219970001022폐수배출업소관리201707193040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 자양로37길 55 (구의동)서울특별시 광진구 구의동 612-7번지
6동호카센타30400002219940001822폐수배출업소관리201707183040000서울특별시 광진구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 광진구 뚝섬로 480 (자양동)서울특별시 광진구 자양동 52-201번지
7한강모터스30400002219940002022폐수배출업소관리201707183040000서울특별시 광진구정기N배출시설 및 방지시설 적정 운영 여부<NA>서울특별시 광진구 뚝섬로 486-1 (자양동)서울특별시 광진구 자양동 52-5번지
8신성기업사30400002219970007122폐수배출업소관리201707183040000서울특별시 광진구정기N폐수 적정 보관 및 처리 여부<NA>서울특별시 광진구 자양강변길 19 (자양동)서울특별시 광진구 자양동 191-1번지
9한미자동차공업사30400002119980000621대기배출업소관리201706303040000서울특별시 광진구정기N배출시설 및 방지시설 적정운영<NA>서울특별시 광진구 자양번영로 24 (자양동)서울특별시 광진구 자양동 601-10번지
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
428성일주유소30400002220040030322폐수배출업소관리201004303040000서울특별시 광진구정기N폐수배출시설 및 방지시설 적정운영 여부 등<NA><NA>서울특별시 광진구 자양동 236-147번지
429(주)태우 동서울주유소30400002220060030922폐수배출업소관리201004303040000서울특별시 광진구정기N폐수배출시설 및 방지시설 적정운영 여부 등<NA><NA>서울특별시 광진구 구의동 587-10번지
430SK네트웍스(주)장호주유소30400002219950004922폐수배출업소관리201004303040000서울특별시 광진구정기N폐수배출시설 및 방지시설 적정운영 여부 등<NA><NA>서울특별시 광진구 자양동 677-5번지
431현대자동차세차장30400002220040030522폐수배출업소관리201004053040000서울특별시 광진구수시N폐수배출시설 및 방지시설 적정 운영 여부<NA><NA>서울특별시 광진구 구의동 201-10번지
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