Overview

Dataset statistics

Number of variables14
Number of observations165
Missing cells6
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.5 KiB
Average record size in memory114.8 B

Variable types

Text5
Categorical7
Numeric2

Dataset

Description경기도_하남시_환경오염물질 배출시설 현황에 대한 데이터로 사업장명, 업종명, 대표자명, 폐수관리등급 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15029979/fileData.do

Alerts

관할기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
폐수관리등급 is highly overall correlated with 대기관리등급 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 폐수종별구분명High correlation
대기관리등급 is highly overall correlated with 폐수관리등급High correlation
폐수종별구분명 is highly overall correlated with 위도(WGS84) and 3 other fieldsHigh correlation
위도(WGS84) is highly overall correlated with 폐수종별구분명High correlation
경도(WGS84) is highly overall correlated with 폐수종별구분명High correlation
대표자명 has 2 (1.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:57:10.167414
Analysis finished2023-12-12 19:57:11.999120
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

Distinct154
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T04:57:12.186817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length8.4666667
Min length3

Characters and Unicode

Total characters1397
Distinct characters263
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)86.7%

Sample

1st row(주)1급 현대신광서비스
2nd row(주)대원산업
3rd row(주)양헌기공
4th row(주)을지전기
5th row(주)캐슬렉스서울
ValueCountFrequency (%)
주식회사 10
 
4.4%
워시존 5
 
2.2%
모터스 4
 
1.7%
하남점 3
 
1.3%
주)1급 2
 
0.9%
기아오토큐 2
 
0.9%
세차장 2
 
0.9%
에스지메디칼 2
 
0.9%
경기지역본부 2
 
0.9%
한국가스공사 2
 
0.9%
Other values (182) 195
85.2%
2023-12-13T04:57:12.607238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 66
 
4.7%
64
 
4.6%
스 60
 
4.3%
( 36
 
2.6%
) 36
 
2.6%
사 34
 
2.4%
유 30
 
2.1%
남 30
 
2.1%
하 29
 
2.1%
서 27
 
1.9%
Other values (253) 985
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1211
86.7%
Space Separator 64
 
4.6%
Open Punctuation 36
 
2.6%
Close Punctuation 36
 
2.6%
Decimal Number 15
 
1.1%
Uppercase Letter 15
 
1.1%
Other Symbol 13
 
0.9%
Lowercase Letter 6
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 66
 
5.5%
스 60
 
5.0%
사 34
 
2.8%
유 30
 
2.5%
남 30
 
2.5%
하 29
 
2.4%
서 27
 
2.2%
소 25
 
2.1%
지 24
 
2.0%
차 22
 
1.8%
Other values (226) 864
71.3%
Decimal Number
ValueCountFrequency (%)
1 6
40.0%
3 2
 
13.3%
2 2
 
13.3%
0 1
 
6.7%
4 1
 
6.7%
8 1
 
6.7%
6 1
 
6.7%
7 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
13.3%
P 2
13.3%
L 2
13.3%
V 2
13.3%
E 2
13.3%
S 2
13.3%
K 2
13.3%
O 1
6.7%
Lowercase Letter
ValueCountFrequency (%)
r 1
16.7%
e 1
16.7%
g 1
16.7%
n 1
16.7%
i 1
16.7%
t 1
16.7%
Space Separator
ValueCountFrequency (%)
64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1224
87.6%
Common 152
 
10.9%
Latin 21
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 66
 
5.4%
스 60
 
4.9%
사 34
 
2.8%
유 30
 
2.5%
남 30
 
2.5%
하 29
 
2.4%
서 27
 
2.2%
소 25
 
2.0%
지 24
 
2.0%
차 22
 
1.8%
Other values (227) 877
71.7%
Latin
ValueCountFrequency (%)
G 2
9.5%
P 2
9.5%
L 2
9.5%
V 2
9.5%
E 2
9.5%
S 2
9.5%
K 2
9.5%
O 1
 
4.8%
r 1
 
4.8%
e 1
 
4.8%
Other values (4) 4
19.0%
Common
ValueCountFrequency (%)
64
42.1%
( 36
23.7%
) 36
23.7%
1 6
 
3.9%
3 2
 
1.3%
2 2
 
1.3%
- 1
 
0.7%
0 1
 
0.7%
4 1
 
0.7%
8 1
 
0.7%
Other values (2) 2
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1211
86.7%
ASCII 173
 
12.4%
None 13
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 66
 
5.5%
스 60
 
5.0%
사 34
 
2.8%
유 30
 
2.5%
남 30
 
2.5%
하 29
 
2.4%
서 27
 
2.2%
소 25
 
2.1%
지 24
 
2.0%
차 22
 
1.8%
Other values (226) 864
71.3%
ASCII
ValueCountFrequency (%)
64
37.0%
( 36
20.8%
) 36
20.8%
1 6
 
3.5%
G 2
 
1.2%
P 2
 
1.2%
L 2
 
1.2%
3 2
 
1.2%
V 2
 
1.2%
E 2
 
1.2%
Other values (16) 19
 
11.0%
None
ValueCountFrequency (%)
㈜ 13
100.0%

업종명
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
자동차 세차업
27 
자동차 종합 수리업
23 
주유소 운영업
22 
운수장비 세차시설
15 
자동차 수리 및 세차업
12 
Other values (39)
66 

Length

Max length25
Median length20
Mean length9.1454545
Min length3

Unique

Unique25 ?
Unique (%)15.2%

Sample

1st row자동차 종합 수리업
2nd row플라스틱제품 제조업
3rd row도장 및 기타 피막처리업
4th row가공금속제품
5th row골프장 운영업

Common Values

ValueCountFrequency (%)
자동차 세차업 27
16.4%
자동차 종합 수리업 23
13.9%
주유소 운영업 22
13.3%
운수장비 세차시설 15
 
9.1%
자동차 수리 및 세차업 12
 
7.3%
판유리 및 판유리가공품 제조업 5
 
3.0%
자동차 수리업 4
 
2.4%
레미콘 제조업 4
 
2.4%
가스 충전업 4
 
2.4%
자동차 전문 수리업 3
 
1.8%
Other values (34) 46
27.9%

Length

2023-12-13T04:57:12.817018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차 69
15.9%
세차업 39
 
9.0%
수리업 31
 
7.1%
및 30
 
6.9%
운영업 27
 
6.2%
종합 26
 
6.0%
주유소 24
 
5.5%
제조업 18
 
4.1%
운수장비 15
 
3.4%
세차시설 15
 
3.4%
Other values (70) 141
32.4%

대표자명
Text

MISSING 

Distinct126
Distinct (%)77.3%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2023-12-13T04:57:13.168740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length3.8343558
Min length3

Characters and Unicode

Total characters625
Distinct characters139
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)71.8%

Sample

1st row대표이사
2nd row대표이사
3rd row대표이사
4th row백남홍
5th row대표이사
ValueCountFrequency (%)
대표이사 30
 
16.3%
외 5
 
2.7%
박준호 3
 
1.6%
1인 3
 
1.6%
김영열 2
 
1.1%
경기지역본부장 2
 
1.1%
김천웅 2
 
1.1%
김민종 2
 
1.1%
허상준 2
 
1.1%
유영묵 2
 
1.1%
Other values (127) 131
71.2%
2023-12-13T04:57:13.642089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 45
 
7.2%
대 37
 
5.9%
사 36
 
5.8%
김 33
 
5.3%
표 30
 
4.8%
22
 
3.5%
준 15
 
2.4%
영 14
 
2.2%
박 13
 
2.1%
장 11
 
1.8%
Other values (129) 369
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 591
94.6%
Space Separator 22
 
3.5%
Decimal Number 11
 
1.8%
Other Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 45
 
7.6%
대 37
 
6.3%
사 36
 
6.1%
김 33
 
5.6%
표 30
 
5.1%
준 15
 
2.5%
영 14
 
2.4%
박 13
 
2.2%
장 11
 
1.9%
외 9
 
1.5%
Other values (123) 348
58.9%
Decimal Number
ValueCountFrequency (%)
1 7
63.6%
2 2
 
18.2%
8 1
 
9.1%
3 1
 
9.1%
Space Separator
ValueCountFrequency (%)
22
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
94.7%
Common 33
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 45
 
7.6%
대 37
 
6.2%
사 36
 
6.1%
김 33
 
5.6%
표 30
 
5.1%
준 15
 
2.5%
영 14
 
2.4%
박 13
 
2.2%
장 11
 
1.9%
외 9
 
1.5%
Other values (124) 349
59.0%
Common
ValueCountFrequency (%)
22
66.7%
1 7
 
21.2%
2 2
 
6.1%
8 1
 
3.0%
3 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 591
94.6%
ASCII 33
 
5.3%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 45
 
7.6%
대 37
 
6.3%
사 36
 
6.1%
김 33
 
5.6%
표 30
 
5.1%
준 15
 
2.5%
영 14
 
2.4%
박 13
 
2.2%
장 11
 
1.9%
외 9
 
1.5%
Other values (123) 348
58.9%
ASCII
ValueCountFrequency (%)
22
66.7%
1 7
 
21.2%
2 2
 
6.1%
8 1
 
3.0%
3 1
 
3.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

관할기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
하남시
165 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
하남시 165
100.0%

Length

2023-12-13T04:57:13.806324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:13.932812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하남시 165
100.0%

폐수관리등급
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
우수
90 
일반
43 
<NA>
32 

Length

Max length4
Median length2
Mean length2.3878788
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
우수 90
54.5%
일반 43
26.1%
<NA> 32
 
19.4%

Length

2023-12-13T04:57:14.072876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:14.200758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수 90
54.5%
일반 43
26.1%
na 32
 
19.4%

대기관리등급
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
127 
일반
33 
우수
 
5

Length

Max length4
Median length4
Mean length3.5393939
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 127
77.0%
일반 33
 
20.0%
우수 5
 
3.0%

Length

2023-12-13T04:57:14.330899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:14.451530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 127
77.0%
일반 33
 
20.0%
우수 5
 
3.0%

폐수종별구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
5종
122 
<NA>
43 

Length

Max length4
Median length2
Mean length2.5212121
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
5종 122
73.9%
<NA> 43
 
26.1%

Length

2023-12-13T04:57:14.574404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:14.699334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5종 122
73.9%
na 43
 
26.1%

대기종별구분명
Categorical

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
121 
5종
31 
4종
13 

Length

Max length4
Median length4
Mean length3.4666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5종
2nd row4종
3rd row5종
4th row5종
5th row4종

Common Values

ValueCountFrequency (%)
<NA> 121
73.3%
5종 31
 
18.8%
4종 13
 
7.9%

Length

2023-12-13T04:57:14.832201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:14.940662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 121
73.3%
5종 31
 
18.8%
4종 13
 
7.9%

소재지우편번호
Text

Distinct53
Distinct (%)32.3%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2023-12-13T04:57:15.118856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9756098
Min length1

Characters and Unicode

Total characters816
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)11.6%

Sample

1st row12959
2nd row12956
3rd row12918
4th row12992
5th row13006
ValueCountFrequency (%)
12989 21
 
12.8%
12988 12
 
7.3%
12986 7
 
4.3%
12959 7
 
4.3%
12991 6
 
3.7%
12992 5
 
3.0%
12939 5
 
3.0%
13024 5
 
3.0%
12964 4
 
2.4%
12927 4
 
2.4%
Other values (43) 88
53.7%
2023-12-13T04:57:15.465799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 190
23.3%
1 182
22.3%
2 176
21.6%
8 68
 
8.3%
0 52
 
6.4%
3 50
 
6.1%
6 28
 
3.4%
5 26
 
3.2%
4 25
 
3.1%
7 18
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 815
99.9%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 190
23.3%
1 182
22.3%
2 176
21.6%
8 68
 
8.3%
0 52
 
6.4%
3 50
 
6.1%
6 28
 
3.4%
5 26
 
3.2%
4 25
 
3.1%
7 18
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 190
23.3%
1 182
22.3%
2 176
21.6%
8 68
 
8.3%
0 52
 
6.4%
3 50
 
6.1%
6 28
 
3.4%
5 26
 
3.2%
4 25
 
3.1%
7 18
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 190
23.3%
1 182
22.3%
2 176
21.6%
8 68
 
8.3%
0 52
 
6.4%
3 50
 
6.1%
6 28
 
3.4%
5 26
 
3.2%
4 25
 
3.1%
7 18
 
2.2%

소재지지번주소
Text

Distinct151
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-13T04:57:15.780591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length43
Mean length19.018182
Min length15

Characters and Unicode

Total characters3138
Distinct characters115
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)86.1%

Sample

1st row경기도 하남시 신장동 438-1
2nd row경기도 하남시 천현동 456-1
3rd row경기도 하남시 풍산동 489 미사 테스타타워 지식산업센터
4th row경기도 하남시 감일동 327
5th row경기도 하남시 감이동 260-1
ValueCountFrequency (%)
경기도 165
22.7%
하남시 165
22.7%
신장동 28
 
3.9%
광암동 22
 
3.0%
덕풍동 22
 
3.0%
초이동 21
 
2.9%
풍산동 17
 
2.3%
감북동 7
 
1.0%
감일동 7
 
1.0%
401-2 6
 
0.8%
Other values (197) 267
36.7%
2023-12-13T04:57:16.281402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
575
18.3%
하 175
 
5.6%
남 169
 
5.4%
경 167
 
5.3%
동 167
 
5.3%
시 166
 
5.3%
기 166
 
5.3%
도 165
 
5.3%
- 131
 
4.2%
1 110
 
3.5%
Other values (105) 1147
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1707
54.4%
Decimal Number 694
22.1%
Space Separator 575
 
18.3%
Dash Punctuation 131
 
4.2%
Uppercase Letter 19
 
0.6%
Other Punctuation 5
 
0.2%
Math Symbol 3
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
하 175
10.3%
남 169
 
9.9%
경 167
 
9.8%
동 167
 
9.8%
시 166
 
9.7%
기 166
 
9.7%
도 165
 
9.7%
풍 41
 
2.4%
산 33
 
1.9%
장 31
 
1.8%
Other values (79) 427
25.0%
Decimal Number
ValueCountFrequency (%)
1 110
15.9%
2 107
15.4%
4 88
12.7%
3 86
12.4%
0 60
8.6%
5 59
8.5%
9 59
8.5%
6 48
6.9%
7 40
 
5.8%
8 37
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 4
21.1%
E 4
21.1%
R 2
10.5%
T 2
10.5%
N 2
10.5%
U 2
10.5%
B 1
 
5.3%
D 1
 
5.3%
L 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
· 1
 
20.0%
Space Separator
ValueCountFrequency (%)
575
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 131
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1707
54.4%
Common 1412
45.0%
Latin 19
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
하 175
10.3%
남 169
 
9.9%
경 167
 
9.8%
동 167
 
9.8%
시 166
 
9.7%
기 166
 
9.7%
도 165
 
9.7%
풍 41
 
2.4%
산 33
 
1.9%
장 31
 
1.8%
Other values (79) 427
25.0%
Common
ValueCountFrequency (%)
575
40.7%
- 131
 
9.3%
1 110
 
7.8%
2 107
 
7.6%
4 88
 
6.2%
3 86
 
6.1%
0 60
 
4.2%
5 59
 
4.2%
9 59
 
4.2%
6 48
 
3.4%
Other values (7) 89
 
6.3%
Latin
ValueCountFrequency (%)
C 4
21.1%
E 4
21.1%
R 2
10.5%
T 2
10.5%
N 2
10.5%
U 2
10.5%
B 1
 
5.3%
D 1
 
5.3%
L 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1707
54.4%
ASCII 1430
45.6%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
575
40.2%
- 131
 
9.2%
1 110
 
7.7%
2 107
 
7.5%
4 88
 
6.2%
3 86
 
6.0%
0 60
 
4.2%
5 59
 
4.1%
9 59
 
4.1%
6 48
 
3.4%
Other values (15) 107
 
7.5%
Hangul
ValueCountFrequency (%)
하 175
10.3%
남 169
 
9.9%
경 167
 
9.8%
동 167
 
9.8%
시 166
 
9.7%
기 166
 
9.7%
도 165
 
9.7%
풍 41
 
2.4%
산 33
 
1.9%
장 31
 
1.8%
Other values (79) 427
25.0%
None
ValueCountFrequency (%)
· 1
100.0%

소재지도로명주소
Text

Distinct154
Distinct (%)93.9%
Missing1
Missing (%)0.6%
Memory size1.4 KiB
2023-12-13T04:57:16.593182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length46
Mean length24.432927
Min length1

Characters and Unicode

Total characters4007
Distinct characters115
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

Unique144 ?
Unique (%)87.8%

Sample

1st row경기도 하남시 신장1로 5 (신장동)
2nd row경기도 하남시 하남대로622번길 33 (천현동)
3rd row경기도 하남시 미사강변서로 25 미사 테스타타워 지식산업센터 FB144호 (풍산동)
4th row경기도 하남시 감일로15번길 54 (감일동)
5th row경기도 하남시 감이로 317 (감이동)
ValueCountFrequency (%)
경기도 161
18.8%
하남시 161
18.8%
신장동 26
 
3.0%
덕풍동 22
 
2.6%
광암동 20
 
2.3%
초이동 18
 
2.1%
풍산동 14
 
1.6%
하남대로 13
 
1.5%
초광산단로 13
 
1.5%
8 9
 
1.0%
Other values (235) 401
46.7%
2023-12-13T04:57:17.033294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
724
18.1%
하 207
 
5.2%
남 205
 
5.1%
동 189
 
4.7%
경 162
 
4.0%
기 162
 
4.0%
시 161
 
4.0%
도 161
 
4.0%
( 160
 
4.0%
) 160
 
4.0%
Other values (105) 1716
42.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2361
58.9%
Space Separator 724
 
18.1%
Decimal Number 565
 
14.1%
Open Punctuation 160
 
4.0%
Close Punctuation 160
 
4.0%
Other Punctuation 12
 
0.3%
Dash Punctuation 12
 
0.3%
Uppercase Letter 9
 
0.2%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
하 207
 
8.8%
남 205
 
8.7%
동 189
 
8.0%
경 162
 
6.9%
기 162
 
6.9%
시 161
 
6.8%
도 161
 
6.8%
로 160
 
6.8%
초 62
 
2.6%
산 59
 
2.5%
Other values (84) 833
35.3%
Decimal Number
ValueCountFrequency (%)
1 110
19.5%
2 70
12.4%
5 59
10.4%
3 57
10.1%
0 52
9.2%
6 49
8.7%
8 48
8.5%
7 47
8.3%
4 40
 
7.1%
9 33
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
F 3
33.3%
C 2
22.2%
B 2
22.2%
L 1
 
11.1%
D 1
 
11.1%
Space Separator
ValueCountFrequency (%)
724
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2361
58.9%
Common 1637
40.9%
Latin 9
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
하 207
 
8.8%
남 205
 
8.7%
동 189
 
8.0%
경 162
 
6.9%
기 162
 
6.9%
시 161
 
6.8%
도 161
 
6.8%
로 160
 
6.8%
초 62
 
2.6%
산 59
 
2.5%
Other values (84) 833
35.3%
Common
ValueCountFrequency (%)
724
44.2%
( 160
 
9.8%
) 160
 
9.8%
1 110
 
6.7%
2 70
 
4.3%
5 59
 
3.6%
3 57
 
3.5%
0 52
 
3.2%
6 49
 
3.0%
8 48
 
2.9%
Other values (6) 148
 
9.0%
Latin
ValueCountFrequency (%)
F 3
33.3%
C 2
22.2%
B 2
22.2%
L 1
 
11.1%
D 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2361
58.9%
ASCII 1646
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
724
44.0%
( 160
 
9.7%
) 160
 
9.7%
1 110
 
6.7%
2 70
 
4.3%
5 59
 
3.6%
3 57
 
3.5%
0 52
 
3.2%
6 49
 
3.0%
8 48
 
2.9%
Other values (11) 157
 
9.5%
Hangul
ValueCountFrequency (%)
하 207
 
8.8%
남 205
 
8.7%
동 189
 
8.0%
경 162
 
6.9%
기 162
 
6.9%
시 161
 
6.8%
도 161
 
6.8%
로 160
 
6.8%
초 62
 
2.6%
산 59
 
2.5%
Other values (84) 833
35.3%

위도(WGS84)
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)89.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean37.535521
Minimum37.496106
Maximum37.580399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T04:57:17.188382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.496106
5-th percentile37.506287
Q137.52751
median37.536582
Q337.548199
95-th percentile37.558563
Maximum37.580399
Range0.084293
Interquartile range (IQR)0.020688385

Descriptive statistics

Standard deviation0.016345846
Coefficient of variation (CV)0.00043547674
Kurtosis0.091653035
Mean37.535521
Median Absolute Deviation (MAD)0.0100605
Skewness-0.15134916
Sum6155.8255
Variance0.00026718669
MonotonicityNot monotonic
2023-12-13T04:57:17.341602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5287 5
 
3.0%
37.5536078 4
 
2.4%
37.53 3
 
1.8%
37.55273251 3
 
1.8%
37.52917 2
 
1.2%
37.52816074 2
 
1.2%
37.552476 2
 
1.2%
37.527607 2
 
1.2%
37.5359241 2
 
1.2%
37.546023 2
 
1.2%
Other values (136) 137
83.0%
ValueCountFrequency (%)
37.496106 1
0.6%
37.4979324 1
0.6%
37.498272 1
0.6%
37.4989893 1
0.6%
37.4993839 1
0.6%
37.4998791 1
0.6%
37.500741 1
0.6%
37.5045715 1
0.6%
37.506236 1
0.6%
37.506578 1
0.6%
ValueCountFrequency (%)
37.580399 1
0.6%
37.5755385 1
0.6%
37.569897 1
0.6%
37.569049 1
0.6%
37.567808 1
0.6%
37.567191 1
0.6%
37.56650833 1
0.6%
37.5662896 1
0.6%
37.558909 1
0.6%
37.5566046 1
0.6%

경도(WGS84)
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)89.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean127.19168
Minimum127.14063
Maximum127.24258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-13T04:57:17.491099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.14063
5-th percentile127.14742
Q1127.17238
median127.19396
Q3127.20951
95-th percentile127.2301
Maximum127.24258
Range0.1019475
Interquartile range (IQR)0.03713575

Descriptive statistics

Standard deviation0.024467442
Coefficient of variation (CV)0.00019236668
Kurtosis-0.86896624
Mean127.19168
Median Absolute Deviation (MAD)0.02123805
Skewness-0.13868145
Sum20859.436
Variance0.0005986557
MonotonicityNot monotonic
2023-12-13T04:57:17.657050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1726 5
 
3.0%
127.1946579 4
 
2.4%
127.183643 3
 
1.8%
127.172956 2
 
1.2%
127.172724 2
 
1.2%
127.169117 2
 
1.2%
127.165742 2
 
1.2%
127.1558049 2
 
1.2%
127.186326 2
 
1.2%
127.193745 2
 
1.2%
Other values (136) 138
83.6%
ValueCountFrequency (%)
127.140631 1
0.6%
127.1416185 1
0.6%
127.142094 1
0.6%
127.1440525 1
0.6%
127.145252 1
0.6%
127.1454568 1
0.6%
127.1456752 1
0.6%
127.1459519 1
0.6%
127.1473164 1
0.6%
127.147999 1
0.6%
ValueCountFrequency (%)
127.2425785 1
0.6%
127.2351953 1
0.6%
127.234185 1
0.6%
127.233159 1
0.6%
127.2326114 1
0.6%
127.2317036 1
0.6%
127.2314986 1
0.6%
127.230555 1
0.6%
127.2302507 1
0.6%
127.2292508 1
0.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-08-07
165 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-07
2nd row2023-08-07
3rd row2023-08-07
4th row2023-08-07
5th row2023-08-07

Common Values

ValueCountFrequency (%)
2023-08-07 165
100.0%

Length

2023-12-13T04:57:17.796075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:57:17.896392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-07 165
100.0%

Interactions

2023-12-13T04:57:11.103925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:57:10.957111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:57:11.180113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:57:11.028804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:57:17.968027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명폐수관리등급대기관리등급대기종별구분명소재지우편번호위도(WGS84)경도(WGS84)
업종명1.0000.6330.7400.4760.8460.4920.714
폐수관리등급0.6331.000NaN0.0000.4760.1480.290
대기관리등급0.740NaN1.0000.0000.7450.0000.000
대기종별구분명0.4760.0000.0001.0000.2060.2670.464
소재지우편번호0.8460.4760.7450.2061.0000.9850.974
위도(WGS84)0.4920.1480.0000.2670.9851.0000.853
경도(WGS84)0.7140.2900.0000.4640.9740.8531.000
2023-12-13T04:57:18.079034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐수관리등급대기종별구분명업종명대기관리등급폐수종별구분명
폐수관리등급1.0000.0000.4741.0001.000
대기종별구분명0.0001.0000.3140.000NaN
업종명0.4740.3141.0000.4821.000
대기관리등급1.0000.0000.4821.000NaN
폐수종별구분명1.000NaN1.000NaN1.000
2023-12-13T04:57:18.185234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도(WGS84)경도(WGS84)업종명폐수관리등급대기관리등급폐수종별구분명대기종별구분명
위도(WGS84)1.0000.2570.1640.1070.0001.0000.262
경도(WGS84)0.2571.0000.2940.2140.0001.0000.419
업종명0.1640.2941.0000.4740.4821.0000.314
폐수관리등급0.1070.2140.4741.0001.0001.0000.000
대기관리등급0.0000.0000.4821.0001.000NaN0.000
폐수종별구분명1.0001.0001.0001.000NaN1.000NaN
대기종별구분명0.2620.4190.3140.0000.000NaN1.000

Missing values

2023-12-13T04:57:11.555673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:57:11.744542image/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.
2023-12-13T04:57:11.889477image/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

사업장명업종명대표자명관할기관명폐수관리등급대기관리등급폐수종별구분명대기종별구분명소재지우편번호소재지지번주소소재지도로명주소위도(WGS84)경도(WGS84)데이터기준일자
0(주)1급 현대신광서비스자동차 종합 수리업대표이사하남시<NA>일반<NA>5종12959경기도 하남시 신장동 438-1경기도 하남시 신장1로 5 (신장동)37.53719127.2087532023-08-07
1(주)대원산업플라스틱제품 제조업대표이사하남시<NA>일반<NA>4종12956경기도 하남시 천현동 456-1경기도 하남시 하남대로622번길 33 (천현동)37.5302127.2224312023-08-07
2(주)양헌기공도장 및 기타 피막처리업대표이사하남시<NA>일반<NA>5종12918경기도 하남시 풍산동 489 미사 테스타타워 지식산업센터경기도 하남시 미사강변서로 25 미사 테스타타워 지식산업센터 FB144호 (풍산동)37.553955127.1828882023-08-07
3(주)을지전기가공금속제품백남홍하남시<NA>일반<NA>5종12992경기도 하남시 감일동 327경기도 하남시 감일로15번길 54 (감일동)37.51036127.1452522023-08-07
4(주)캐슬렉스서울골프장 운영업대표이사하남시<NA>일반<NA>4종13006경기도 하남시 감이동 260-1경기도 하남시 감이로 317 (감이동)37.498272127.1681082023-08-07
5(주)하남자동차서비스자동차 종합 수리업임상범하남시<NA>일반<NA>4종12957경기도 하남시 신장동 413-2경기도 하남시 하남대로 801 (신장동)37.540965127.2082532023-08-07
6경주사업총괄본부기타 스포츠 서비스업이사장하남시<NA>우수<NA>5종12900경기도 하남시 신장동 281 미사리경정장·조정카누경기장경기도 하남시 미사대로 505 경정장 선수동 (신장동)37.553742127.2133042023-08-07
7도이치 미사 서비스센터수리업대표이사하남시<NA>일반<NA>5종12927경기도 하남시 덕풍동 740경기도 하남시 덕풍북로 192 (덕풍동)37.552476127.2095122023-08-07
8동부자동차공업사자동차 전문 수리업윤성택하남시<NA>일반<NA>5종12958경기도 하남시 신장동 409-1경기도 하남시 하남대로787번길 6 (신장동)37.540166127.2092212023-08-07
9동진모터스자동차 수리업안진배하남시<NA>일반<NA>5종12989경기도 하남시 광암동 401-2 디엠모터스 주식회사 4층경기도 하남시 초광산단동로6번길 8 디엠모터스 주식회사 4층 (광암동)37.527607127.1729562023-08-07
사업장명업종명대표자명관할기관명폐수관리등급대기관리등급폐수종별구분명대기종별구분명소재지우편번호소재지지번주소소재지도로명주소위도(WGS84)경도(WGS84)데이터기준일자
155성내세차장운수장비 세차시설임정옥하남시일반<NA>5종<NA>13024경기도 하남시 하산곡동 159경기도 하남시 검단남로26번길 24, 1층(하산곡동)37.518064127.2247332023-08-07
156온코닉테라퓨틱스㈜운수장비 세차시설대표이사하남시일반<NA>5종<NA>12925경기도 하남시 덕풍동 831-1, 현대지식산업센터 한강미사2차(25BL) C동 1012~1013호경기도 하남시 미사대로 520 현대지식산업센터 한강미사2차(25BL) C동 1012~1013호(덕풍동)37.558909127.2041532023-08-07
157쿠샵 하남점운수장비 세차시설임양기하남시일반<NA>5종<NA>12940경기도 하남시 신장동 303-59경기도 하남시 신평로 17037.547506127.2117992023-08-07
158㈜솔리비스 미사중앙연구소연구 및 개발업대표이사하남시일반<NA>5종<NA>12939경기도 하남시 풍산동 588-5경기도 하남시 미사강변중앙로31번길 30, 미사동일 넥서스 8층(풍산동)37.550271127.1912332023-08-07
159서하남서비스 기아오토큐자동차 종합 수리업황택순외1명하남시일반<NA><NA>4종-경기도 하남시 광암동 401-137.5282127.17272023-08-07
160(주)테브코리아자동차 종합 수리업최정식 박준호하남시일반<NA><NA>5종12989경기도 하남시 광암동 399-5경기도 하남시 초광산단동로 5 지하1층 (광암동)37.5287127.17262023-08-07
161에이투지자동차 종합 수리업박정일 외 1명하남시일반<NA><NA>5종12989경기도 하남시 광암동 399-5경기도 하남시 초광산단동로 5 5층 (광암동)37.5287127.17262023-08-07
162초이 현대 모터스자동차 종합 수리업이도훈하남시일반<NA><NA>5종12989경기도 하남시 광암동 399-5경기도 하남시 초광산단동로 5 4층 (광암동)37.5287127.17262023-08-07
163럭키 카독크자동차 종합 수리업박광수하남시일반<NA><NA>5종12989경기도 하남시 광암동 399-5경기도 하남시 초광산단동로 5 2층 (광암동)37.5287127.17262023-08-07
164리드라인 모터스자동차 종합 수리업김종곤하남시일반<NA><NA>5종12989경기도 하남시 광암동 399-5경기도 하남시 초광산단동로 5 3층 (광암동)37.5287127.17262023-08-07