Overview

Dataset statistics

Number of variables13
Number of observations730
Missing cells1092
Missing cells (%)11.5%
Duplicate rows2
Duplicate rows (%)0.3%
Total size in memory77.8 KiB
Average record size in memory109.2 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author도봉구
URLhttps://data.seoul.go.kr/dataList/OA-10047/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
Dataset has 2 (0.3%) duplicate rowsDuplicates
업종코드 is highly overall correlated with 업종명High correlation
업종명 is highly overall correlated with 업종코드High correlation
지도점검구분 is highly imbalanced (77.1%)Imbalance
처분대상여부 is highly imbalanced (93.0%)Imbalance
처분대상여부 has 15 (2.1%) missing valuesMissing
점검결과 has 730 (100.0%) missing valuesMissing
소재지도로명주소 has 343 (47.0%) missing valuesMissing
인허가번호 is highly skewed (γ1 = 23.50289216)Skewed
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-10 23:43:08.655350
Analysis finished2024-05-10 23:43:12.100428
Duration3.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct181
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
2024-05-10T23:43:12.396218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length18
Mean length8.7575342
Min length2

Characters and Unicode

Total characters6393
Distinct characters238
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

Unique39 ?
Unique (%)5.3%

Sample

1st row기아자동차(주) 도봉서비스센터
2nd row한라자동차정비(주)
3rd row대성자동차공업사
4th row한국수력원자력(주)방사선보건연구원
5th row의료법인 한전의료재단 한일병원
ValueCountFrequency (%)
의료법인 18
 
2.1%
영흥트림 15
 
1.8%
대성자동차공업사 14
 
1.7%
쌍문점 13
 
1.5%
용광자동차공업사 13
 
1.5%
상원의료재단 13
 
1.5%
강북힘찬병원 13
 
1.5%
쌍문주유소 13
 
1.5%
한양자동차정비(주 12
 
1.4%
한라자동차정비(주 11
 
1.3%
Other values (191) 707
84.0%
2024-05-10T23:43:13.314884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 493
 
7.7%
) 385
 
6.0%
( 379
 
5.9%
동 240
 
3.8%
차 198
 
3.1%
자 161
 
2.5%
업 133
 
2.1%
사 125
 
2.0%
소 120
 
1.9%
유 118
 
1.8%
Other values (228) 4041
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5403
84.5%
Close Punctuation 385
 
6.0%
Open Punctuation 379
 
5.9%
Space Separator 112
 
1.8%
Uppercase Letter 63
 
1.0%
Decimal Number 34
 
0.5%
Lowercase Letter 8
 
0.1%
Other Punctuation 5
 
0.1%
Other Symbol 3
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 493
 
9.1%
동 240
 
4.4%
차 198
 
3.7%
자 161
 
3.0%
업 133
 
2.5%
사 125
 
2.3%
소 120
 
2.2%
유 118
 
2.2%
스 113
 
2.1%
운 105
 
1.9%
Other values (199) 3597
66.6%
Uppercase Letter
ValueCountFrequency (%)
C 11
17.5%
S 9
14.3%
K 6
9.5%
G 6
9.5%
O 6
9.5%
V 5
7.9%
I 5
7.9%
A 5
7.9%
P 4
 
6.3%
M 4
 
6.3%
Other values (2) 2
 
3.2%
Decimal Number
ValueCountFrequency (%)
2 11
32.4%
1 10
29.4%
4 4
 
11.8%
7 3
 
8.8%
6 3
 
8.8%
8 3
 
8.8%
Lowercase Letter
ValueCountFrequency (%)
f 2
25.0%
l 2
25.0%
s 2
25.0%
e 2
25.0%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 385
100.0%
Open Punctuation
ValueCountFrequency (%)
( 379
100.0%
Space Separator
ValueCountFrequency (%)
112
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5406
84.6%
Common 916
 
14.3%
Latin 71
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 493
 
9.1%
동 240
 
4.4%
차 198
 
3.7%
자 161
 
3.0%
업 133
 
2.5%
사 125
 
2.3%
소 120
 
2.2%
유 118
 
2.2%
스 113
 
2.1%
운 105
 
1.9%
Other values (200) 3600
66.6%
Latin
ValueCountFrequency (%)
C 11
15.5%
S 9
12.7%
K 6
8.5%
G 6
8.5%
O 6
8.5%
V 5
7.0%
I 5
7.0%
A 5
7.0%
P 4
 
5.6%
M 4
 
5.6%
Other values (6) 10
14.1%
Common
ValueCountFrequency (%)
) 385
42.0%
( 379
41.4%
112
 
12.2%
2 11
 
1.2%
1 10
 
1.1%
4 4
 
0.4%
, 4
 
0.4%
7 3
 
0.3%
6 3
 
0.3%
8 3
 
0.3%
Other values (2) 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5403
84.5%
ASCII 987
 
15.4%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 493
 
9.1%
동 240
 
4.4%
차 198
 
3.7%
자 161
 
3.0%
업 133
 
2.5%
사 125
 
2.3%
소 120
 
2.2%
유 118
 
2.2%
스 113
 
2.1%
운 105
 
1.9%
Other values (199) 3597
66.6%
ASCII
ValueCountFrequency (%)
) 385
39.0%
( 379
38.4%
112
 
11.3%
C 11
 
1.1%
2 11
 
1.1%
1 10
 
1.0%
S 9
 
0.9%
K 6
 
0.6%
G 6
 
0.6%
O 6
 
0.6%
Other values (18) 52
 
5.3%
None
ValueCountFrequency (%)
㈜ 3
100.0%

인허가번호
Real number (ℝ)

SKEWED 

Distinct168
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0900002 × 1017
Minimum3.0900002 × 1017
Maximum3.0900006 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-05-10T23:43:13.873402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0900002 × 1017
5-th percentile3.0900002 × 1017
Q13.0900002 × 1017
median3.0900002 × 1017
Q33.0900002 × 1017
95-th percentile3.0900002 × 1017
Maximum3.0900006 × 1017
Range4.00015 × 1010
Interquartile range (IQR)9.994 × 108

Descriptive statistics

Standard deviation1.5224875 × 109
Coefficient of variation (CV)4.9271435 × 10-9
Kurtosis606.86992
Mean3.0900002 × 1017
Median Absolute Deviation (MAD)1001024
Skewness23.502892
Sum4.2090872 × 1018
Variance2.317968 × 1018
MonotonicityNot monotonic
2024-05-10T23:43:14.362770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
309000021200700007 14
 
1.9%
309000021200400026 14
 
1.9%
309000022199700091 14
 
1.9%
309000021200600008 11
 
1.5%
309000022200900004 11
 
1.5%
309000022200300015 10
 
1.4%
309000021200600007 10
 
1.4%
309000021200000013 9
 
1.2%
309000022198800061 9
 
1.2%
309000022200300010 9
 
1.2%
Other values (158) 619
84.8%
ValueCountFrequency (%)
309000021200000001 6
0.8%
309000021200000006 1
 
0.1%
309000021200000010 1
 
0.1%
309000021200000013 9
1.2%
309000021200000015 2
 
0.3%
309000021200000025 6
0.8%
309000021200000028 3
 
0.4%
309000021200000038 4
0.5%
309000021200000046 4
0.5%
309000021200000140 6
0.8%
ValueCountFrequency (%)
309000061201500001 1
 
0.1%
309000022201700001 1
 
0.1%
309000022201600003 1
 
0.1%
309000022201600002 1
 
0.1%
309000022201600001 2
 
0.3%
309000022201400001 2
 
0.3%
309000022201300003 2
 
0.3%
309000022201300002 1
 
0.1%
309000022201300001 1
 
0.1%
309000022201200003 6
0.8%

업종코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
22
520 
21
210 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row21
3rd row21
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 520
71.2%
21 210
28.8%

Length

2024-05-10T23:43:14.786540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:43:15.057238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 520
71.2%
21 210
28.8%

업종명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
폐수배출업소관리
506 
대기배출업소관리
210 
<NA>
 
14

Length

Max length8
Median length8
Mean length7.9232877
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐수배출업소관리 506
69.3%
대기배출업소관리 210
28.8%
<NA> 14
 
1.9%

Length

2024-05-10T23:43:15.438425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:43:15.789853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐수배출업소관리 506
69.3%
대기배출업소관리 210
28.8%
na 14
 
1.9%

지도점검일자
Real number (ℝ)

Distinct267
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20129664
Minimum20100114
Maximum20170913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-05-10T23:43:16.071455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100114
5-th percentile20100611
Q120110930
median20120925
Q320150325
95-th percentile20170328
Maximum20170913
Range70799
Interquartile range (IQR)39394.75

Descriptive statistics

Standard deviation22137.202
Coefficient of variation (CV)0.0010997303
Kurtosis-1.1124905
Mean20129664
Median Absolute Deviation (MAD)19686
Skewness0.33000801
Sum1.4694655 × 1010
Variance4.9005569 × 108
MonotonicityDecreasing
2024-05-10T23:43:16.506277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120725 15
 
2.1%
20120112 12
 
1.6%
20120113 11
 
1.5%
20110930 10
 
1.4%
20100617 9
 
1.2%
20100611 9
 
1.2%
20161004 8
 
1.1%
20100903 8
 
1.1%
20100511 7
 
1.0%
20100930 7
 
1.0%
Other values (257) 634
86.8%
ValueCountFrequency (%)
20100114 1
 
0.1%
20100309 1
 
0.1%
20100406 1
 
0.1%
20100408 5
0.7%
20100415 6
0.8%
20100511 7
1.0%
20100513 6
0.8%
20100520 1
 
0.1%
20100609 3
0.4%
20100610 2
 
0.3%
ValueCountFrequency (%)
20170913 3
0.4%
20170830 4
0.5%
20170803 4
0.5%
20170706 1
 
0.1%
20170629 2
 
0.3%
20170627 3
0.4%
20170626 3
0.4%
20170609 4
0.5%
20170607 6
0.8%
20170524 2
 
0.3%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3090000
730 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3090000
2nd row3090000
3rd row3090000
4th row3090000
5th row3090000

Common Values

ValueCountFrequency (%)
3090000 730
100.0%

Length

2024-05-10T23:43:16.935036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:43:17.246657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3090000 730
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
서울특별시 도봉구
730 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 도봉구 730
100.0%

Length

2024-05-10T23:43:17.587214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:43:17.895838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 730
50.0%
도봉구 730
50.0%

지도점검구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
정기
674 
수시
 
43
기타
 
12
합동
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 674
92.3%
수시 43
 
5.9%
기타 12
 
1.6%
합동 1
 
0.1%

Length

2024-05-10T23:43:18.224457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T23:43:18.613726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 674
92.3%
수시 43
 
5.9%
기타 12
 
1.6%
합동 1
 
0.1%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing15
Missing (%)2.1%
Memory size1.6 KiB
False
709 
True
 
6
(Missing)
 
15
ValueCountFrequency (%)
False 709
97.1%
True 6
 
0.8%
(Missing) 15
 
2.1%
2024-05-10T23:43:18.900648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

Distinct114
Distinct (%)15.6%
Missing1
Missing (%)0.1%
Memory size5.8 KiB
2024-05-10T23:43:19.288681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length30
Mean length19.230453
Min length4

Characters and Unicode

Total characters14019
Distinct characters97
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)8.6%

Sample

1st row대기배출업소 정기점검
2nd row대기배출시설 지도점검
3rd row대기배출시설 지도점검
4th row폐수배출시설 및 방지시설 정상운영 여부
5th row폐수배출시설 및 방지시설 정상운영 여부
ValueCountFrequency (%)
및 611
18.6%
방지시설 555
16.9%
여부 382
11.6%
폐수배출시설 353
10.7%
정상운영 279
8.5%
배출시설 172
 
5.2%
적정 168
 
5.1%
대기배출시설 140
 
4.3%
운영여부 104
 
3.2%
적정운영여부 88
 
2.7%
Other values (91) 434
13.2%
2024-05-10T23:43:20.186030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2558
18.2%
시 1313
 
9.4%
설 1304
 
9.3%
배 704
 
5.0%
출 704
 
5.0%
지 678
 
4.8%
정 621
 
4.4%
방 619
 
4.4%
및 616
 
4.4%
부 605
 
4.3%
Other values (87) 4297
30.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11394
81.3%
Space Separator 2558
 
18.2%
Open Punctuation 30
 
0.2%
Close Punctuation 30
 
0.2%
Other Punctuation 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1313
 
11.5%
설 1304
 
11.4%
배 704
 
6.2%
출 704
 
6.2%
지 678
 
6.0%
정 621
 
5.5%
방 619
 
5.4%
및 616
 
5.4%
부 605
 
5.3%
여 604
 
5.3%
Other values (81) 3626
31.8%
Other Punctuation
ValueCountFrequency (%)
. 3
42.9%
, 3
42.9%
？ 1
 
14.3%
Space Separator
ValueCountFrequency (%)
2558
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11394
81.3%
Common 2625
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1313
 
11.5%
설 1304
 
11.4%
배 704
 
6.2%
출 704
 
6.2%
지 678
 
6.0%
정 621
 
5.5%
방 619
 
5.4%
및 616
 
5.4%
부 605
 
5.3%
여 604
 
5.3%
Other values (81) 3626
31.8%
Common
ValueCountFrequency (%)
2558
97.4%
( 30
 
1.1%
) 30
 
1.1%
. 3
 
0.1%
, 3
 
0.1%
？ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11394
81.3%
ASCII 2624
 
18.7%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2558
97.5%
( 30
 
1.1%
) 30
 
1.1%
. 3
 
0.1%
, 3
 
0.1%
Hangul
ValueCountFrequency (%)
시 1313
 
11.5%
설 1304
 
11.4%
배 704
 
6.2%
출 704
 
6.2%
지 678
 
6.0%
정 621
 
5.5%
방 619
 
5.4%
및 616
 
5.4%
부 605
 
5.3%
여 604
 
5.3%
Other values (81) 3626
31.8%
None
ValueCountFrequency (%)
？ 1
100.0%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing730
Missing (%)100.0%
Memory size6.5 KiB

소재지도로명주소
Text

MISSING 

Distinct136
Distinct (%)35.1%
Missing343
Missing (%)47.0%
Memory size5.8 KiB
2024-05-10T23:43:20.705348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length25.400517
Min length22

Characters and Unicode

Total characters9830
Distinct characters101
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 (%)9.8%

Sample

1st row서울특별시 도봉구 도봉로180나길 48 (도봉동, 기아자동차서비스도봉사업소)
2nd row서울특별시 도봉구 노해로69길 103, 108동 101호(창동,동아청솔아파트)
3rd row서울특별시 도봉구 도봉로150다길 42 (방학동)
4th row서울특별시 도봉구 우이천로 308 (쌍문동)
5th row서울특별시 도봉구 우이천로 308 (쌍문동)
ValueCountFrequency (%)
서울특별시 387
19.6%
도봉구 387
19.6%
창동 172
 
8.7%
방학동 77
 
3.9%
쌍문동 73
 
3.7%
도봉로 71
 
3.6%
도봉동 57
 
2.9%
우이천로 31
 
1.6%
방학로 27
 
1.4%
도봉로136길 22
 
1.1%
Other values (176) 668
33.9%
2024-05-10T23:43:21.624622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1602
 
16.3%
도 665
 
6.8%
봉 661
 
6.7%
동 413
 
4.2%
시 393
 
4.0%
서 391
 
4.0%
구 389
 
4.0%
별 387
 
3.9%
특 387
 
3.9%
) 387
 
3.9%
Other values (91) 4155
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5776
58.8%
Space Separator 1602
 
16.3%
Decimal Number 1589
 
16.2%
Close Punctuation 387
 
3.9%
Open Punctuation 387
 
3.9%
Dash Punctuation 47
 
0.5%
Other Punctuation 42
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 665
11.5%
봉 661
11.4%
동 413
 
7.2%
시 393
 
6.8%
서 391
 
6.8%
구 389
 
6.7%
별 387
 
6.7%
특 387
 
6.7%
울 387
 
6.7%
로 384
 
6.6%
Other values (76) 1319
22.8%
Decimal Number
ValueCountFrequency (%)
1 336
21.1%
0 181
11.4%
4 178
11.2%
3 176
11.1%
2 162
10.2%
6 157
9.9%
8 119
 
7.5%
5 110
 
6.9%
9 96
 
6.0%
7 74
 
4.7%
Space Separator
ValueCountFrequency (%)
1602
100.0%
Close Punctuation
ValueCountFrequency (%)
) 387
100.0%
Open Punctuation
ValueCountFrequency (%)
( 387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5776
58.8%
Common 4054
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 665
11.5%
봉 661
11.4%
동 413
 
7.2%
시 393
 
6.8%
서 391
 
6.8%
구 389
 
6.7%
별 387
 
6.7%
특 387
 
6.7%
울 387
 
6.7%
로 384
 
6.6%
Other values (76) 1319
22.8%
Common
ValueCountFrequency (%)
1602
39.5%
) 387
 
9.5%
( 387
 
9.5%
1 336
 
8.3%
0 181
 
4.5%
4 178
 
4.4%
3 176
 
4.3%
2 162
 
4.0%
6 157
 
3.9%
8 119
 
2.9%
Other values (5) 369
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5776
58.8%
ASCII 4054
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1602
39.5%
) 387
 
9.5%
( 387
 
9.5%
1 336
 
8.3%
0 181
 
4.5%
4 178
 
4.4%
3 176
 
4.3%
2 162
 
4.0%
6 157
 
3.9%
8 119
 
2.9%
Other values (5) 369
 
9.1%
Hangul
ValueCountFrequency (%)
도 665
11.5%
봉 661
11.4%
동 413
 
7.2%
시 393
 
6.8%
서 391
 
6.8%
구 389
 
6.7%
별 387
 
6.7%
특 387
 
6.7%
울 387
 
6.7%
로 384
 
6.6%
Other values (76) 1319
22.8%

소재지주소
Text

Distinct159
Distinct (%)21.9%
Missing3
Missing (%)0.4%
Memory size5.8 KiB
2024-05-10T23:43:22.091954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length22.013755
Min length13

Characters and Unicode

Total characters16004
Distinct characters43
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)3.3%

Sample

1st row서울특별시 도봉구 도봉동 78-12번지
2nd row서울특별시 도봉구 창동 777번지
3rd row서울특별시 도봉구 방학동 717-20번지
4th row서울특별시 도봉구 쌍문동 388-1번지
5th row서울특별시 도봉구 쌍문동 388-1번지
ValueCountFrequency (%)
서울특별시 727
24.4%
도봉구 727
24.4%
창동 322
10.8%
방학동 162
 
5.4%
쌍문동 141
 
4.7%
도봉동 102
 
3.4%
388-1번지 16
 
0.5%
749-15번지 15
 
0.5%
593-3번지 15
 
0.5%
691-3번지 15
 
0.5%
Other values (161) 734
24.7%
2024-05-10T23:43:23.068820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2978
18.6%
도 829
 
5.2%
봉 829
 
5.2%
지 740
 
4.6%
동 729
 
4.6%
구 727
 
4.5%
서 727
 
4.5%
시 727
 
4.5%
별 727
 
4.5%
특 727
 
4.5%
Other values (33) 6264
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9233
57.7%
Decimal Number 3145
 
19.7%
Space Separator 2978
 
18.6%
Dash Punctuation 608
 
3.8%
Other Punctuation 25
 
0.2%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 829
9.0%
봉 829
9.0%
지 740
8.0%
동 729
7.9%
구 727
7.9%
서 727
7.9%
시 727
7.9%
별 727
7.9%
특 727
7.9%
울 727
7.9%
Other values (17) 1744
18.9%
Decimal Number
ValueCountFrequency (%)
1 480
15.3%
7 411
13.1%
6 350
11.1%
2 343
10.9%
3 328
10.4%
5 326
10.4%
4 321
10.2%
0 229
7.3%
9 187
 
5.9%
8 170
 
5.4%
Space Separator
ValueCountFrequency (%)
2978
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 608
100.0%
Other Punctuation
ValueCountFrequency (%)
, 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9233
57.7%
Common 6771
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 829
9.0%
봉 829
9.0%
지 740
8.0%
동 729
7.9%
구 727
7.9%
서 727
7.9%
시 727
7.9%
별 727
7.9%
특 727
7.9%
울 727
7.9%
Other values (17) 1744
18.9%
Common
ValueCountFrequency (%)
2978
44.0%
- 608
 
9.0%
1 480
 
7.1%
7 411
 
6.1%
6 350
 
5.2%
2 343
 
5.1%
3 328
 
4.8%
5 326
 
4.8%
4 321
 
4.7%
0 229
 
3.4%
Other values (6) 397
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9233
57.7%
ASCII 6771
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2978
44.0%
- 608
 
9.0%
1 480
 
7.1%
7 411
 
6.1%
6 350
 
5.2%
2 343
 
5.1%
3 328
 
4.8%
5 326
 
4.8%
4 321
 
4.7%
0 229
 
3.4%
Other values (6) 397
 
5.9%
Hangul
ValueCountFrequency (%)
도 829
9.0%
봉 829
9.0%
지 740
8.0%
동 729
7.9%
구 727
7.9%
서 727
7.9%
시 727
7.9%
별 727
7.9%
특 727
7.9%
울 727
7.9%
Other values (17) 1744
18.9%

Interactions

2024-05-10T23:43:10.179524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:43:09.758374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:43:10.472053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T23:43:09.962986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T23:43:23.256335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부
인허가번호1.0000.0000.0000.1190.4160.306
업종코드0.0001.0001.0000.2130.0000.000
업종명0.0001.0001.0000.2030.0000.000
지도점검일자0.1190.2130.2031.0000.2680.127
지도점검구분0.4160.0000.0000.2681.0000.155
처분대상여부0.3060.0000.0000.1270.1551.000
2024-05-10T23:43:23.550333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종코드업종명처분대상여부지도점검구분
업종코드1.0000.9970.0000.000
업종명0.9971.0000.0000.000
처분대상여부0.0000.0001.0000.103
지도점검구분0.0000.0000.1031.000
2024-05-10T23:43:23.911132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.0730.0000.0000.2790.198
지도점검일자-0.0731.0000.1600.1520.1280.095
업종코드0.0000.1601.0000.9970.0000.000
업종명0.0000.1520.9971.0000.0000.000
지도점검구분0.2790.1280.0000.0001.0000.103
처분대상여부0.1980.0950.0000.0000.1031.000

Missing values

2024-05-10T23:43:10.885433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T23:43:11.501450image/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.
2024-05-10T23:43:11.904476image/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
0기아자동차(주) 도봉서비스센터30900002120000014021대기배출업소관리201709133090000서울특별시 도봉구수시N대기배출업소 정기점검<NA>서울특별시 도봉구 도봉로180나길 48 (도봉동, 기아자동차서비스도봉사업소)서울특별시 도봉구 도봉동 78-12번지
1한라자동차정비(주)30900002120060000821대기배출업소관리201709133090000서울특별시 도봉구수시N대기배출시설 지도점검<NA>서울특별시 도봉구 노해로69길 103, 108동 101호(창동,동아청솔아파트)서울특별시 도봉구 창동 777번지
2대성자동차공업사30900002120040002621대기배출업소관리201709133090000서울특별시 도봉구수시N대기배출시설 지도점검<NA>서울특별시 도봉구 도봉로150다길 42 (방학동)서울특별시 도봉구 방학동 717-20번지
3한국수력원자력(주)방사선보건연구원30900002220070000322폐수배출업소관리201708303090000서울특별시 도봉구정기N폐수배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 우이천로 308 (쌍문동)서울특별시 도봉구 쌍문동 388-1번지
4의료법인 한전의료재단 한일병원30900002219880006122폐수배출업소관리201708303090000서울특별시 도봉구정기N폐수배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 우이천로 308 (쌍문동)서울특별시 도봉구 쌍문동 388-1번지
5의료법인 한전의료재단 한일병원30900002120150000421대기배출업소관리201708303090000서울특별시 도봉구정기N대기배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 우이천로 308 (쌍문동, 한전병원)서울특별시 도봉구 쌍문동 388-1번지
6도봉병원30900002220050000122폐수배출업소관리201708303090000서울특별시 도봉구정기N대기폐수배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 도봉로 720 (방학동)서울특별시 도봉구 방학동 705-4번지
7의료법인 상원의료재단 강북힘찬병원30900002120150000321대기배출업소관리201708033090000서울특별시 도봉구정기N대기배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 도봉로 446 (창동, 강북힘찬병원)서울특별시 도봉구 창동 650-46번지
8의료법인 상원의료재단 강북힘찬병원30900002220090000422폐수배출업소관리201708033090000서울특별시 도봉구정기N폐수배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 도봉로 446 (창동)서울특별시 도봉구 창동 650-46번지
9에스원자동차30900002220060000622폐수배출업소관리201708033090000서울특별시 도봉구정기N폐수배출시설 및 방지시설 정상운영 여부<NA>서울특별시 도봉구 방학로 200-34 (방학동)서울특별시 도봉구 방학동 446-1번지
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
720(주)대농석유직영창동지점30900002219950005522폐수배출업소관리201004153090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA>서울특별시 도봉구 도봉로 434 (창동)서울특별시 도봉구 창동 650-65번지
721쌍문주유소30900002219970009122폐수배출업소관리201004153090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA><NA>서울특별시 도봉구 쌍문동 699번지
722SK네트웍스(주)영신주유소30900002219970008322폐수배출업소관리201004083090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA><NA>서울특별시 도봉구 방학동 671-2번지
723오복주유소30900002220080000322폐수배출업소관리201004083090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA><NA>서울특별시 도봉구 방학동 724-5번지
724지에스칼텍스(주)직영 방학동점30900002220080000222폐수배출업소관리201004083090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA><NA>서울특별시 도봉구 방학동 668-10번지
725삼미상사(주) 북부주유소30900002219940005922폐수배출업소관리201004083090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA><NA>서울특별시 도봉구 쌍문동 103-7번지
726정다운주유소30900002220010033722폐수배출업소관리201004083090000서울특별시 도봉구정기N배출시설 및 방지시설의 적정 운영여부<NA>서울특별시 도봉구 도봉로 635 (쌍문동)서울특별시 도봉구 쌍문동 707번지 9,20
727스마일자동차공업사30900002120090000521대기배출업소관리201004063090000서울특별시 도봉구기타N개선이행확인<NA><NA>서울특별시 도봉구 창동 635-8번지 4-5층
728창동 주공2단지아파트30900002120090000221대기배출업소관리201003093090000서울특별시 도봉구수시N개선명령이행확인<NA><NA>서울특별시 도봉구 창동 316번지 주공2단지아파트
729빛나리세차장30900002220050000322폐수배출업소관리201001143090000서울특별시 도봉구기타N배출시설 및 방지시설의 적정운영 여부<NA><NA>서울특별시 도봉구 방학동 677-19번지 ,21

Duplicate rows

Most frequently occurring

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항소재지도로명주소소재지주소# duplicates
0㈜흥진자동차공업사30900002120070000721대기배출업소관리201706273090000서울특별시 도봉구정기N대기배출시설 및 방지시설 정상운영 여부서울특별시 도봉구 도봉로136길 19 (창동)서울특별시 도봉구 창동 749-15번지2
1현대모터스30900002219940001022폐수배출업소관리201101073090000서울특별시 도봉구수시N폐수배출시설 및 방지시설 적정운영여부<NA>서울특별시 도봉구 창동 700-99번지2