Overview

Dataset statistics

Number of variables13
Number of observations772
Missing cells887
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory82.3 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
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-10971/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
업종코드 is highly imbalanced (60.1%)Imbalance
업종명 is highly imbalanced (60.1%)Imbalance
지도점검구분 is highly imbalanced (57.8%)Imbalance
처분대상여부 is highly imbalanced (88.4%)Imbalance
점검결과 has 772 (100.0%) missing valuesMissing
소재지도로명주소 has 78 (10.1%) missing valuesMissing
소재지주소 has 34 (4.4%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 05:53:52.530393
Analysis finished2024-05-11 05:53:54.883163
Duration2.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct172
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-11T14:53:55.211020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16
Mean length8.3277202
Min length3

Characters and Unicode

Total characters6429
Distinct characters263
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

Unique23 ?
Unique (%)3.0%

Sample

1st row대동택시
2nd row대영탕
3rd row삼육대학교
4th row하이렉스파
5th row흥안운수(주)
ValueCountFrequency (%)
노원자원회수시설 25
 
2.8%
노원열병합발전소 20
 
2.3%
서울특별시 19
 
2.2%
을지병원 16
 
1.8%
대림택시(주 15
 
1.7%
장우카클리닉세차장 15
 
1.7%
기아오토큐중계점 14
 
1.6%
태릉솔밭주유소 14
 
1.6%
한국원자력의학원 13
 
1.5%
현대공업사 12
 
1.4%
Other values (181) 718
81.5%
2024-05-11T14:53:55.946810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 293
 
4.6%
차 232
 
3.6%
) 219
 
3.4%
( 206
 
3.2%
원 183
 
2.8%
장 177
 
2.8%
세 154
 
2.4%
대 146
 
2.3%
자 129
 
2.0%
소 124
 
1.9%
Other values (253) 4566
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5780
89.9%
Close Punctuation 219
 
3.4%
Open Punctuation 206
 
3.2%
Space Separator 109
 
1.7%
Decimal Number 69
 
1.1%
Uppercase Letter 43
 
0.7%
Other Punctuation 2
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 293
 
5.1%
차 232
 
4.0%
원 183
 
3.2%
장 177
 
3.1%
세 154
 
2.7%
대 146
 
2.5%
자 129
 
2.2%
소 124
 
2.1%
동 111
 
1.9%
시 105
 
1.8%
Other values (232) 4126
71.4%
Uppercase Letter
ValueCountFrequency (%)
S 10
23.3%
G 9
20.9%
K 7
16.3%
L 5
11.6%
P 4
 
9.3%
N 3
 
7.0%
E 3
 
7.0%
I 1
 
2.3%
O 1
 
2.3%
Decimal Number
ValueCountFrequency (%)
4 19
27.5%
2 18
26.1%
1 15
21.7%
3 8
11.6%
9 5
 
7.2%
0 2
 
2.9%
6 2
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 219
100.0%
Open Punctuation
ValueCountFrequency (%)
( 206
100.0%
Space Separator
ValueCountFrequency (%)
109
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5780
89.9%
Common 606
 
9.4%
Latin 43
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 293
 
5.1%
차 232
 
4.0%
원 183
 
3.2%
장 177
 
3.1%
세 154
 
2.7%
대 146
 
2.5%
자 129
 
2.2%
소 124
 
2.1%
동 111
 
1.9%
시 105
 
1.8%
Other values (232) 4126
71.4%
Common
ValueCountFrequency (%)
) 219
36.1%
( 206
34.0%
109
18.0%
4 19
 
3.1%
2 18
 
3.0%
1 15
 
2.5%
3 8
 
1.3%
9 5
 
0.8%
0 2
 
0.3%
6 2
 
0.3%
Other values (2) 3
 
0.5%
Latin
ValueCountFrequency (%)
S 10
23.3%
G 9
20.9%
K 7
16.3%
L 5
11.6%
P 4
 
9.3%
N 3
 
7.0%
E 3
 
7.0%
I 1
 
2.3%
O 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5780
89.9%
ASCII 649
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 293
 
5.1%
차 232
 
4.0%
원 183
 
3.2%
장 177
 
3.1%
세 154
 
2.7%
대 146
 
2.5%
자 129
 
2.2%
소 124
 
2.1%
동 111
 
1.9%
시 105
 
1.8%
Other values (232) 4126
71.4%
ASCII
ValueCountFrequency (%)
) 219
33.7%
( 206
31.7%
109
16.8%
4 19
 
2.9%
2 18
 
2.8%
1 15
 
2.3%
S 10
 
1.5%
G 9
 
1.4%
3 8
 
1.2%
K 7
 
1.1%
Other values (11) 29
 
4.5%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct178
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1000002 × 1017
Minimum3.1000002 × 1017
Maximum3.1000006 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:53:56.189912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1000002 × 1017
5-th percentile3.1000002 × 1017
Q13.1000002 × 1017
median3.1000002 × 1017
Q33.1000002 × 1017
95-th percentile3.1000002 × 1017
Maximum3.1000006 × 1017
Range4.00022 × 1010
Interquartile range (IQR)1400000

Descriptive statistics

Standard deviation3.5499751 × 109
Coefficient of variation (CV)1.1451532 × 10-8
Kurtosis110.47207
Mean3.1000002 × 1017
Median Absolute Deviation (MAD)699968
Skewness10.431413
Sum-4.8765568 × 1017
Variance1.2602323 × 1019
MonotonicityNot monotonic
2024-05-11T14:53:56.514243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310000022201100001 15
 
1.9%
310000022199800076 14
 
1.8%
310000022200600006 14
 
1.8%
310000022199400008 14
 
1.8%
310000022199400009 14
 
1.8%
310000022200600003 12
 
1.6%
310000022200000011 12
 
1.6%
310000022199400007 12
 
1.6%
310000022198400001 11
 
1.4%
310000021199400002 11
 
1.4%
Other values (168) 643
83.3%
ValueCountFrequency (%)
310000021199400002 11
1.4%
310000021199500032 4
 
0.5%
310000021199600001 10
1.3%
310000021199800002 5
0.6%
310000021199900001 4
 
0.5%
310000021199900002 2
 
0.3%
310000021199900012 6
0.8%
310000021199900017 4
 
0.5%
310000021199900018 1
 
0.1%
310000021200000002 3
 
0.4%
ValueCountFrequency (%)
310000061201600005 1
0.1%
310000061201400001 1
0.1%
310000061201200002 1
0.1%
310000061201200001 1
0.1%
310000061201100001 1
0.1%
310000061201000001 1
0.1%
310000042200000001 1
0.1%
310000025200900002 1
0.1%
310000025199700011 1
0.1%
310000025199600007 1
0.1%

업종코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
22
615 
21
148 
25
 
8
42
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
22 615
79.7%
21 148
 
19.2%
25 8
 
1.0%
42 1
 
0.1%

Length

2024-05-11T14:53:56.768671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:56.976432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 615
79.7%
21 148
 
19.2%
25 8
 
1.0%
42 1
 
0.1%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
폐수배출업소관리
615 
대기배출업소관리
148 
기타수질오염원관리
 
8
유독물판매업관리
 
1

Length

Max length9
Median length8
Mean length8.0103627
Min length8

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
폐수배출업소관리 615
79.7%
대기배출업소관리 148
 
19.2%
기타수질오염원관리 8
 
1.0%
유독물판매업관리 1
 
0.1%

Length

2024-05-11T14:53:57.350851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:57.590665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐수배출업소관리 615
79.7%
대기배출업소관리 148
 
19.2%
기타수질오염원관리 8
 
1.0%
유독물판매업관리 1
 
0.1%

지도점검일자
Real number (ℝ)

Distinct231
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20132218
Minimum20100203
Maximum20171026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:53:57.795550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100203
5-th percentile20100420
Q120110608
median20130613
Q320160309
95-th percentile20170720
Maximum20171026
Range70823
Interquartile range (IQR)49701

Descriptive statistics

Standard deviation24556.215
Coefficient of variation (CV)0.0012197471
Kurtosis-1.3487961
Mean20132218
Median Absolute Deviation (MAD)20393
Skewness0.22677002
Sum1.5542072 × 1010
Variance6.030077 × 108
MonotonicityDecreasing
2024-05-11T14:53:58.080451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170518 11
 
1.4%
20100916 10
 
1.3%
20161028 9
 
1.2%
20100611 8
 
1.0%
20160712 8
 
1.0%
20110811 8
 
1.0%
20100709 8
 
1.0%
20130808 8
 
1.0%
20110128 7
 
0.9%
20110706 7
 
0.9%
Other values (221) 688
89.1%
ValueCountFrequency (%)
20100203 5
0.6%
20100204 5
0.6%
20100224 3
0.4%
20100312 4
0.5%
20100317 5
0.6%
20100325 4
0.5%
20100329 4
0.5%
20100416 3
0.4%
20100419 2
 
0.3%
20100420 6
0.8%
ValueCountFrequency (%)
20171026 1
 
0.1%
20171025 1
 
0.1%
20171024 5
0.6%
20171019 1
 
0.1%
20171013 3
0.4%
20170926 4
0.5%
20170920 1
 
0.1%
20170911 5
0.6%
20170906 1
 
0.1%
20170901 1
 
0.1%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
3100000
772 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3100000
2nd row3100000
3rd row3100000
4th row3100000
5th row3100000

Common Values

ValueCountFrequency (%)
3100000 772
100.0%

Length

2024-05-11T14:53:58.318759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:58.477583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3100000 772
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
서울특별시 노원구
772 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 노원구 772
100.0%

Length

2024-05-11T14:53:58.662156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:58.850466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 772
50.0%
노원구 772
50.0%

지도점검구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
정기
593 
수시
140 
기타
 
36
합동
 
2
일제
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
정기 593
76.8%
수시 140
 
18.1%
기타 36
 
4.7%
합동 2
 
0.3%
일제 1
 
0.1%

Length

2024-05-11T14:53:59.026041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:59.194568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 593
76.8%
수시 140
 
18.1%
기타 36
 
4.7%
합동 2
 
0.3%
일제 1
 
0.1%

처분대상여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size904.0 B
False
760 
True
 
12
ValueCountFrequency (%)
False 760
98.4%
True 12
 
1.6%
2024-05-11T14:53:59.368329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

Distinct159
Distinct (%)20.7%
Missing3
Missing (%)0.4%
Memory size6.2 KiB
2024-05-11T14:53:59.840957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length43
Mean length18.977893
Min length2

Characters and Unicode

Total characters14594
Distinct characters154
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)10.0%

Sample

1st row배출시설 및 방지시설 적정관리 여부
2nd row배출시설 및 방지시설 적정운영 여부
3rd row배출시설 및 방지시설 적정운영 여부
4th row배출시설 및 방지시설 적정운영 여부
5th row배출시설 및 방지시설 적정운영 여부
ValueCountFrequency (%)
및 505
14.6%
방지시설 499
14.5%
배출시설 437
12.7%
여부 435
12.6%
적정운영 283
 
8.2%
적정관리 109
 
3.2%
폐수배출시설 108
 
3.1%
등 91
 
2.6%
적정가동여부 56
 
1.6%
적정관리여부 44
 
1.3%
Other values (180) 886
25.7%
2024-05-11T14:54:00.731905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2685
18.4%
설 1225
 
8.4%
시 1221
 
8.4%
출 669
 
4.6%
배 666
 
4.6%
여 641
 
4.4%
부 641
 
4.4%
정 638
 
4.4%
적 595
 
4.1%
지 585
 
4.0%
Other values (144) 5028
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11782
80.7%
Space Separator 2685
 
18.4%
Other Punctuation 80
 
0.5%
Close Punctuation 21
 
0.1%
Open Punctuation 21
 
0.1%
Decimal Number 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Other Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 1225
 
10.4%
시 1221
 
10.4%
출 669
 
5.7%
배 666
 
5.7%
여 641
 
5.4%
부 641
 
5.4%
정 638
 
5.4%
적 595
 
5.1%
지 585
 
5.0%
방 577
 
4.9%
Other values (136) 4324
36.7%
Uppercase Letter
ValueCountFrequency (%)
O 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
2685
100.0%
Other Punctuation
ValueCountFrequency (%)
, 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Other Number
ValueCountFrequency (%)
₂ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11782
80.7%
Common 2810
 
19.3%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 1225
 
10.4%
시 1221
 
10.4%
출 669
 
5.7%
배 666
 
5.7%
여 641
 
5.4%
부 641
 
5.4%
정 638
 
5.4%
적 595
 
5.1%
지 585
 
5.0%
방 577
 
4.9%
Other values (136) 4324
36.7%
Common
ValueCountFrequency (%)
2685
95.6%
, 80
 
2.8%
) 21
 
0.7%
( 21
 
0.7%
2 2
 
0.1%
₂ 1
 
< 0.1%
Latin
ValueCountFrequency (%)
O 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11782
80.7%
ASCII 2811
 
19.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2685
95.5%
, 80
 
2.8%
) 21
 
0.7%
( 21
 
0.7%
2 2
 
0.1%
O 1
 
< 0.1%
C 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
설 1225
 
10.4%
시 1221
 
10.4%
출 669
 
5.7%
배 666
 
5.7%
여 641
 
5.4%
부 641
 
5.4%
정 638
 
5.4%
적 595
 
5.1%
지 585
 
5.0%
방 577
 
4.9%
Other values (136) 4324
36.7%
None
ValueCountFrequency (%)
₂ 1
100.0%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing772
Missing (%)100.0%
Memory size6.9 KiB

소재지도로명주소
Text

MISSING 

Distinct144
Distinct (%)20.7%
Missing78
Missing (%)10.1%
Memory size6.2 KiB
2024-05-11T14:54:01.273761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length38
Mean length24.710375
Min length22

Characters and Unicode

Total characters17149
Distinct characters89
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

Unique22 ?
Unique (%)3.2%

Sample

1st row서울특별시 노원구 한글비석로24마길 88 (상계동)
2nd row서울특별시 노원구 덕릉로 826 (상계동)
3rd row서울특별시 노원구 화랑로 815 (공릉동)
4th row서울특별시 노원구 노원로1길 67 (공릉동)
5th row서울특별시 노원구 덕릉로126길 24 (상계동)
ValueCountFrequency (%)
서울특별시 694
19.8%
노원구 694
19.8%
상계동 254
 
7.2%
공릉동 193
 
5.5%
월계동 135
 
3.8%
동일로 63
 
1.8%
중계동 55
 
1.6%
하계동 52
 
1.5%
화랑로 51
 
1.5%
공릉로 50
 
1.4%
Other values (183) 1267
36.1%
2024-05-11T14:54:02.002072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2828
 
16.5%
동 857
 
5.0%
노 793
 
4.6%
원 768
 
4.5%
서 712
 
4.2%
울 712
 
4.2%
) 694
 
4.0%
( 694
 
4.0%
로 694
 
4.0%
구 694
 
4.0%
Other values (79) 7703
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10395
60.6%
Space Separator 2828
 
16.5%
Decimal Number 2470
 
14.4%
Close Punctuation 694
 
4.0%
Open Punctuation 694
 
4.0%
Other Punctuation 41
 
0.2%
Dash Punctuation 18
 
0.1%
Uppercase Letter 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 857
 
8.2%
노 793
 
7.6%
원 768
 
7.4%
서 712
 
6.8%
울 712
 
6.8%
로 694
 
6.7%
구 694
 
6.7%
시 694
 
6.7%
별 694
 
6.7%
특 694
 
6.7%
Other values (63) 3083
29.7%
Decimal Number
ValueCountFrequency (%)
2 404
16.4%
1 370
15.0%
4 293
11.9%
3 244
9.9%
9 244
9.9%
5 193
7.8%
6 189
7.7%
7 187
7.6%
0 186
7.5%
8 160
 
6.5%
Space Separator
ValueCountFrequency (%)
2828
100.0%
Close Punctuation
ValueCountFrequency (%)
) 694
100.0%
Open Punctuation
ValueCountFrequency (%)
( 694
100.0%
Other Punctuation
ValueCountFrequency (%)
, 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Uppercase Letter
ValueCountFrequency (%)
F 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10395
60.6%
Common 6745
39.3%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 857
 
8.2%
노 793
 
7.6%
원 768
 
7.4%
서 712
 
6.8%
울 712
 
6.8%
로 694
 
6.7%
구 694
 
6.7%
시 694
 
6.7%
별 694
 
6.7%
특 694
 
6.7%
Other values (63) 3083
29.7%
Common
ValueCountFrequency (%)
2828
41.9%
) 694
 
10.3%
( 694
 
10.3%
2 404
 
6.0%
1 370
 
5.5%
4 293
 
4.3%
3 244
 
3.6%
9 244
 
3.6%
5 193
 
2.9%
6 189
 
2.8%
Other values (5) 592
 
8.8%
Latin
ValueCountFrequency (%)
F 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10395
60.6%
ASCII 6754
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2828
41.9%
) 694
 
10.3%
( 694
 
10.3%
2 404
 
6.0%
1 370
 
5.5%
4 293
 
4.3%
3 244
 
3.6%
9 244
 
3.6%
5 193
 
2.9%
6 189
 
2.8%
Other values (6) 601
 
8.9%
Hangul
ValueCountFrequency (%)
동 857
 
8.2%
노 793
 
7.6%
원 768
 
7.4%
서 712
 
6.8%
울 712
 
6.8%
로 694
 
6.7%
구 694
 
6.7%
시 694
 
6.7%
별 694
 
6.7%
특 694
 
6.7%
Other values (63) 3083
29.7%

소재지주소
Text

MISSING 

Distinct136
Distinct (%)18.4%
Missing34
Missing (%)4.4%
Memory size6.2 KiB
2024-05-11T14:54:02.442272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length34
Mean length23.227642
Min length14

Characters and Unicode

Total characters17142
Distinct characters86
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

Unique16 ?
Unique (%)2.2%

Sample

1st row서울특별시 노원구 상계동 138-204번지
2nd row서울특별시 노원구 공릉동 26-21번지
3rd row서울특별시 노원구 상계동 68-2번지
4th row서울특별시 노원구 상계동 68-2번지
5th row서울특별시 노원구 공릉동 26-21번지
ValueCountFrequency (%)
서울특별시 738
23.5%
노원구 738
23.5%
상계동 267
 
8.5%
공릉동 207
 
6.6%
월계동 133
 
4.2%
중계동 67
 
2.1%
하계동 64
 
2.0%
772번지 47
 
1.5%
172번지 29
 
0.9%
서울테크노파크 22
 
0.7%
Other values (154) 822
26.2%
2024-05-11T14:54:03.074548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3134
18.3%
원 783
 
4.6%
서 778
 
4.5%
울 778
 
4.5%
노 766
 
4.5%
구 746
 
4.4%
시 745
 
4.3%
지 744
 
4.3%
특 738
 
4.3%
별 738
 
4.3%
Other values (76) 7192
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10359
60.4%
Space Separator 3134
 
18.3%
Decimal Number 3061
 
17.9%
Dash Punctuation 556
 
3.2%
Other Punctuation 10
 
0.1%
Uppercase Letter 10
 
0.1%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 783
 
7.6%
서 778
 
7.5%
울 778
 
7.5%
노 766
 
7.4%
구 746
 
7.2%
시 745
 
7.2%
지 744
 
7.2%
특 738
 
7.1%
별 738
 
7.1%
동 738
 
7.1%
Other values (59) 2805
27.1%
Decimal Number
ValueCountFrequency (%)
1 601
19.6%
2 453
14.8%
7 325
10.6%
6 320
10.5%
3 308
10.1%
4 229
 
7.5%
0 224
 
7.3%
8 219
 
7.2%
5 205
 
6.7%
9 177
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
F 9
90.0%
B 1
 
10.0%
Space Separator
ValueCountFrequency (%)
3134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 556
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10359
60.4%
Common 6773
39.5%
Latin 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 783
 
7.6%
서 778
 
7.5%
울 778
 
7.5%
노 766
 
7.4%
구 746
 
7.2%
시 745
 
7.2%
지 744
 
7.2%
특 738
 
7.1%
별 738
 
7.1%
동 738
 
7.1%
Other values (59) 2805
27.1%
Common
ValueCountFrequency (%)
3134
46.3%
1 601
 
8.9%
- 556
 
8.2%
2 453
 
6.7%
7 325
 
4.8%
6 320
 
4.7%
3 308
 
4.5%
4 229
 
3.4%
0 224
 
3.3%
8 219
 
3.2%
Other values (5) 404
 
6.0%
Latin
ValueCountFrequency (%)
F 9
90.0%
B 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10359
60.4%
ASCII 6783
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3134
46.2%
1 601
 
8.9%
- 556
 
8.2%
2 453
 
6.7%
7 325
 
4.8%
6 320
 
4.7%
3 308
 
4.5%
4 229
 
3.4%
0 224
 
3.3%
8 219
 
3.2%
Other values (7) 414
 
6.1%
Hangul
ValueCountFrequency (%)
원 783
 
7.6%
서 778
 
7.5%
울 778
 
7.5%
노 766
 
7.4%
구 746
 
7.2%
시 745
 
7.2%
지 744
 
7.2%
특 738
 
7.1%
별 738
 
7.1%
동 738
 
7.1%
Other values (59) 2805
27.1%

Interactions

2024-05-11T14:53:53.850852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:53.506726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:54.020570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:53.677679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:54:03.264220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부
인허가번호1.0000.9310.9310.0000.3040.133
업종코드0.9311.0001.0000.3820.0820.000
업종명0.9311.0001.0000.3820.0820.000
지도점검일자0.0000.3820.3821.0000.3520.272
지도점검구분0.3040.0820.0820.3521.0000.000
처분대상여부0.1330.0000.0000.2720.0001.000
2024-05-11T14:54:03.456276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명지도점검구분업종코드처분대상여부
업종명1.0000.0661.0000.000
지도점검구분0.0661.0000.0660.000
업종코드1.0000.0661.0000.000
처분대상여부0.0000.0000.0001.000
2024-05-11T14:54:03.626274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.0840.6520.6520.2520.089
지도점검일자-0.0841.0000.1790.1790.2270.204
업종코드0.6520.1791.0001.0000.0660.000
업종명0.6520.1791.0001.0000.0660.000
지도점검구분0.2520.2270.0660.0661.0000.000
처분대상여부0.0890.2040.0000.0000.0001.000

Missing values

2024-05-11T14:53:54.259455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:53:54.557495image/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.
2024-05-11T14:53:54.776997image/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
0대동택시31000002219950005122폐수배출업소관리201710263100000서울특별시 노원구정기N배출시설 및 방지시설 적정관리 여부<NA>서울특별시 노원구 한글비석로24마길 88 (상계동)서울특별시 노원구 상계동 138-204번지
1대영탕31000002120150001321대기배출업소관리201710253100000서울특별시 노원구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 노원구 덕릉로 826 (상계동)<NA>
2삼육대학교31000002120150000621대기배출업소관리201710243100000서울특별시 노원구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 노원구 화랑로 815 (공릉동)서울특별시 노원구 공릉동 26-21번지
3하이렉스파31000002120150002421대기배출업소관리201710243100000서울특별시 노원구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 노원구 노원로1길 67 (공릉동)<NA>
4흥안운수(주)31000002120010000321대기배출업소관리201710243100000서울특별시 노원구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 노원구 덕릉로126길 24 (상계동)서울특별시 노원구 상계동 68-2번지
5흥안운수(주)31000002519970001125기타수질오염원관리201710243100000서울특별시 노원구정기N기타수질오염원 적정관리 여부<NA>서울특별시 노원구 덕릉로126길 24 (상계동)서울특별시 노원구 상계동 68-2번지
6삼육대학교31000002219900002322폐수배출업소관리201710243100000서울특별시 노원구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 노원구 화랑로 815 (공릉동)서울특별시 노원구 공릉동 26-21번지
7궁전보석불가마사우나31000002120150000921대기배출업소관리201710193100000서울특별시 노원구정기N배출시설 및 방지시설 적정관리 여부<NA>서울특별시 노원구 한글비석로 444 (상계동)<NA>
8월계주유소31000002219970007022폐수배출업소관리201710133100000서울특별시 노원구정기N배출시설 및 방지시설 적정관리 여부<NA>서울특별시 노원구 월계로 252 (월계동)서울특별시 노원구 월계동 935번지
9SK네트웍스(주)하계주유소31000002219950004322폐수배출업소관리201710133100000서울특별시 노원구정기N배출시설 및 방지시설 적정관리 여부<NA>서울특별시 노원구 노원로17길 29 (하계동)서울특별시 노원구 하계동 243-3번지
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
762에이치에스31000002219960001022폐수배출업소관리201002043100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 월계로42길 19 (월계동)서울특별시 노원구 월계동 874-1번지
763서울메트로창동차량사업소31000002219840001622폐수배출업소관리201002043100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 노원로 573 (상계동)서울특별시 노원구 상계동 820번지
764태릉솔밭주유소31000002220060000622폐수배출업소관리201002043100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 노원로 49 (공릉동)서울특별시 노원구 공릉동 230-17번지
765(주)서울개인택시복지조합복지공릉LPG충전소31000002220050000222폐수배출업소관리201002043100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 화랑로 826 (공릉동)서울특별시 노원구 공릉동 26-69번지
766현대성북출고사무소31000002219940003222폐수배출업소관리201002043100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 화랑로45길 49 (월계동)서울특별시 노원구 월계동 85번지
767한국원자력의학원31000002219840000122폐수배출업소관리201002033100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별 감시<NA>서울특별시 노원구 노원로 75 (공릉동)서울특별시 노원구 공릉동 251-4번지 한국원자력의학원
768학교법인인제학원상계백병원31000002219890000222폐수배출업소관리201002033100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별 감시<NA>서울특별시 노원구 동일로 1342 (상계동)서울특별시 노원구 상계동 761-1번지 상계백병원
769의료법인 을지병원31000002219940000822폐수배출업소관리201002033100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별 감시<NA>서울특별시 노원구 한글비석로 68 (하계동)서울특별시 노원구 하계동 280-1번지 을지병원
770노원자원회수시설31000002219940000922폐수배출업소관리201002033100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별감시<NA>서울특별시 노원구 덕릉로70길 99 (상계동)서울특별시 노원구 상계동 772번지
771노원열병합발전소31000002219940000722폐수배출업소관리201002033100000서울특별시 노원구수시N설 연휴기간 환경오염 예방을 위한 특별 감시<NA>서울특별시 노원구 덕릉로70길 99 (상계동)서울특별시 노원구 상계동 772번지