Overview

Dataset statistics

Number of variables5
Number of observations142
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory41.9 B

Variable types

Categorical3
Text2

Dataset

Description대전광역시 동구 관내 폐수배출시설 사업장 현황으로서,상호명, 소재지 주소 및 업종 등의 정보를 포함하고 있습니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15107061/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
종 is highly imbalanced (89.3%)Imbalance
업종 is highly imbalanced (63.6%)Imbalance
상호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:28:08.810264
Analysis finished2023-12-12 23:28:09.215207
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
5
140 
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 140
98.6%
4 2
 
1.4%

Length

2023-12-13T08:28:09.277670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:09.391288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 140
98.6%
4 2
 
1.4%

상호
Text

UNIQUE 

Distinct142
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T08:28:09.612401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length21
Mean length7.3661972
Min length2

Characters and Unicode

Total characters1046
Distinct characters244
Distinct categories11 ?
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 (%)100.0%

Sample

1st row성남세차장
2nd row호산카센타
3rd row태용카크리닉
4th row현대세차장
5th row케이앤엘에너지㈜ 용전셀프주유소
ValueCountFrequency (%)
주식회사 3
 
1.7%
lpg충전소 2
 
1.1%
8 2
 
1.1%
세차장 2
 
1.1%
성남세차장 1
 
0.6%
건영세차장 1
 
0.6%
우송대학교 1
 
0.6%
오토카프라자 1
 
0.6%
신도셀프세차타운 1
 
0.6%
유니나손세차장 1
 
0.6%
Other values (162) 162
91.5%
2023-12-13T08:28:10.051611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 38
 
3.6%
35
 
3.3%
세 33
 
3.2%
차 33
 
3.2%
㈜ 29
 
2.8%
장 27
 
2.6%
전 25
 
2.4%
카 24
 
2.3%
프 23
 
2.2%
소 22
 
2.1%
Other values (234) 757
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 876
83.7%
Uppercase Letter 42
 
4.0%
Space Separator 35
 
3.3%
Other Symbol 29
 
2.8%
Decimal Number 22
 
2.1%
Lowercase Letter 17
 
1.6%
Other Punctuation 9
 
0.9%
Close Punctuation 7
 
0.7%
Open Punctuation 7
 
0.7%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 38
 
4.3%
세 33
 
3.8%
차 33
 
3.8%
장 27
 
3.1%
전 25
 
2.9%
카 24
 
2.7%
프 23
 
2.6%
소 22
 
2.5%
스 21
 
2.4%
유 20
 
2.3%
Other values (189) 610
69.6%
Uppercase Letter
ValueCountFrequency (%)
C 7
16.7%
K 5
11.9%
P 5
11.9%
T 4
9.5%
I 3
7.1%
A 3
7.1%
N 2
 
4.8%
H 2
 
4.8%
L 2
 
4.8%
G 2
 
4.8%
Other values (6) 7
16.7%
Lowercase Letter
ValueCountFrequency (%)
a 3
17.6%
e 2
11.8%
f 2
11.8%
h 2
11.8%
s 2
11.8%
w 2
11.8%
c 1
 
5.9%
o 1
 
5.9%
y 1
 
5.9%
d 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 6
27.3%
5 3
13.6%
4 3
13.6%
1 2
 
9.1%
0 2
 
9.1%
3 2
 
9.1%
8 2
 
9.1%
9 1
 
4.5%
6 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
. 6
66.7%
; 1
 
11.1%
, 1
 
11.1%
& 1
 
11.1%
Space Separator
ValueCountFrequency (%)
35
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 905
86.5%
Common 82
 
7.8%
Latin 59
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 38
 
4.2%
세 33
 
3.6%
차 33
 
3.6%
㈜ 29
 
3.2%
장 27
 
3.0%
전 25
 
2.8%
카 24
 
2.7%
프 23
 
2.5%
소 22
 
2.4%
스 21
 
2.3%
Other values (190) 630
69.6%
Latin
ValueCountFrequency (%)
C 7
 
11.9%
K 5
 
8.5%
P 5
 
8.5%
T 4
 
6.8%
I 3
 
5.1%
A 3
 
5.1%
a 3
 
5.1%
e 2
 
3.4%
f 2
 
3.4%
N 2
 
3.4%
Other values (16) 23
39.0%
Common
ValueCountFrequency (%)
35
42.7%
) 7
 
8.5%
( 7
 
8.5%
2 6
 
7.3%
. 6
 
7.3%
5 3
 
3.7%
4 3
 
3.7%
1 2
 
2.4%
0 2
 
2.4%
3 2
 
2.4%
Other values (8) 9
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 876
83.7%
ASCII 141
 
13.5%
None 29
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 38
 
4.3%
세 33
 
3.8%
차 33
 
3.8%
장 27
 
3.1%
전 25
 
2.9%
카 24
 
2.7%
프 23
 
2.6%
소 22
 
2.5%
스 21
 
2.4%
유 20
 
2.3%
Other values (189) 610
69.6%
ASCII
ValueCountFrequency (%)
35
24.8%
C 7
 
5.0%
) 7
 
5.0%
( 7
 
5.0%
2 6
 
4.3%
. 6
 
4.3%
K 5
 
3.5%
P 5
 
3.5%
T 4
 
2.8%
I 3
 
2.1%
Other values (34) 56
39.7%
None
ValueCountFrequency (%)
㈜ 29
100.0%

소재지
Text

Distinct141
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T08:28:10.378523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28.5
Mean length22.612676
Min length16

Characters and Unicode

Total characters3211
Distinct characters102
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

Unique140 ?
Unique (%)98.6%

Sample

1st row대전광역시 동구 계족로 332(성남동)
2nd row대전광역시 동구 동대전로 258 (가양동)
3rd row대전광역시 동구 동대전로 253 (가양동)
4th row대전광역시 동구 선화로 224 (정동)
5th row대전광역시 동구 동서대로 1627 (홍도동)
ValueCountFrequency (%)
대전광역시 143
21.7%
동구 142
21.6%
대전로 18
 
2.7%
가양동 15
 
2.3%
계족로 14
 
2.1%
동대전로 13
 
2.0%
용전동 11
 
1.7%
삼성동 11
 
1.7%
판암동 8
 
1.2%
태전로 7
 
1.1%
Other values (209) 276
41.9%
2023-12-13T08:28:10.803866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
517
16.1%
동 307
 
9.6%
대 215
 
6.7%
전 199
 
6.2%
구 147
 
4.6%
광 143
 
4.5%
역 143
 
4.5%
시 143
 
4.5%
로 136
 
4.2%
) 132
 
4.1%
Other values (92) 1129
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1913
59.6%
Space Separator 517
 
16.1%
Decimal Number 485
 
15.1%
Close Punctuation 132
 
4.1%
Open Punctuation 132
 
4.1%
Dash Punctuation 22
 
0.7%
Other Punctuation 9
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 307
16.0%
대 215
11.2%
전 199
10.4%
구 147
 
7.7%
광 143
 
7.5%
역 143
 
7.5%
시 143
 
7.5%
로 136
 
7.1%
성 37
 
1.9%
길 31
 
1.6%
Other values (76) 412
21.5%
Decimal Number
ValueCountFrequency (%)
1 91
18.8%
2 71
14.6%
5 47
9.7%
6 47
9.7%
3 43
8.9%
4 42
8.7%
7 38
7.8%
0 36
 
7.4%
8 35
 
7.2%
9 35
 
7.2%
Space Separator
ValueCountFrequency (%)
517
100.0%
Close Punctuation
ValueCountFrequency (%)
) 132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1913
59.6%
Common 1297
40.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 307
16.0%
대 215
11.2%
전 199
10.4%
구 147
 
7.7%
광 143
 
7.5%
역 143
 
7.5%
시 143
 
7.5%
로 136
 
7.1%
성 37
 
1.9%
길 31
 
1.6%
Other values (76) 412
21.5%
Common
ValueCountFrequency (%)
517
39.9%
) 132
 
10.2%
( 132
 
10.2%
1 91
 
7.0%
2 71
 
5.5%
5 47
 
3.6%
6 47
 
3.6%
3 43
 
3.3%
4 42
 
3.2%
7 38
 
2.9%
Other values (5) 137
 
10.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1913
59.6%
ASCII 1298
40.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
517
39.8%
) 132
 
10.2%
( 132
 
10.2%
1 91
 
7.0%
2 71
 
5.5%
5 47
 
3.6%
6 47
 
3.6%
3 43
 
3.3%
4 42
 
3.2%
7 38
 
2.9%
Other values (6) 138
 
10.6%
Hangul
ValueCountFrequency (%)
동 307
16.0%
대 215
11.2%
전 199
10.4%
구 147
 
7.7%
광 143
 
7.5%
역 143
 
7.5%
시 143
 
7.5%
로 136
 
7.1%
성 37
 
1.9%
길 31
 
1.6%
Other values (76) 412
21.5%

업종
Categorical

IMBALANCE 

Distinct13
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
자동차 세차업
108 
기타 인쇄업
20 
물리, 화학 및 생물학 연구개발업
 
4
측정대행업
 
1
산업용 세탁업
 
1
Other values (8)
 
8

Length

Max length20
Median length7
Mean length7.2746479
Min length3

Unique

Unique10 ?
Unique (%)7.0%

Sample

1st row자동차 세차업
2nd row자동차 세차업
3rd row자동차 세차업
4th row자동차 세차업
5th row자동차 세차업

Common Values

ValueCountFrequency (%)
자동차 세차업 108
76.1%
기타 인쇄업 20
 
14.1%
물리, 화학 및 생물학 연구개발업 4
 
2.8%
측정대행업 1
 
0.7%
산업용 세탁업 1
 
0.7%
종합 병원 1
 
0.7%
목욕업 1
 
0.7%
전자집적회로 제조업 1
 
0.7%
일반 병원 1
 
0.7%
합성수지 및 기타 플라스틱물질 제조업 1
 
0.7%
Other values (3) 3
 
2.1%

Length

2023-12-13T08:28:10.942287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차 108
36.0%
세차업 108
36.0%
기타 21
 
7.0%
인쇄업 20
 
6.7%
및 6
 
2.0%
물리 4
 
1.3%
화학 4
 
1.3%
생물학 4
 
1.3%
연구개발업 4
 
1.3%
제조업 3
 
1.0%
Other values (17) 18
 
6.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-11-07
142 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-07
2nd row2023-11-07
3rd row2023-11-07
4th row2023-11-07
5th row2023-11-07

Common Values

ValueCountFrequency (%)
2023-11-07 142
100.0%

Length

2023-12-13T08:28:11.063254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:28:11.147553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-07 142
100.0%

Correlations

2023-12-13T08:28:11.196779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종업종
종1.0000.000
업종0.0001.000
2023-12-13T08:28:11.271291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종업종
종1.0000.000
업종0.0001.000
2023-12-13T08:28:11.342215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종업종
종1.0000.000
업종0.0001.000

Missing values

2023-12-13T08:28:09.087986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:28:09.179257image/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.

Sample

종상호소재지업종데이터기준일자
05성남세차장대전광역시 동구 계족로 332(성남동)자동차 세차업2023-11-07
15호산카센타대전광역시 동구 동대전로 258 (가양동)자동차 세차업2023-11-07
25태용카크리닉대전광역시 동구 동대전로 253 (가양동)자동차 세차업2023-11-07
35현대세차장대전광역시 동구 선화로 224 (정동)자동차 세차업2023-11-07
45케이앤엘에너지㈜ 용전셀프주유소대전광역시 동구 동서대로 1627 (홍도동)자동차 세차업2023-11-07
55대전케이모터스대전광역시 동구 우암로 285(가양동)자동차 세차업2023-11-07
65삐까뻔쩍세차장대전광역시 동구 동대전로 192, 1층 (자양동)자동차 세차업2023-11-07
75동광카인테리어대전광역시 동구 계족로 307 (성남동)자동차 세차업2023-11-07
85명성카센타대전광역시 동구 매봉로 5 (가양동)자동차 세차업2023-11-07
95형우세차장대전광역시 동구 계족로 307 (성남동)자동차 세차업2023-11-07
종상호소재지업종데이터기준일자
1325월드CTP대전광역시 동구 대전로839번길 62 (중동)기타 인쇄업2023-11-07
1335캠핑카빌리지대전광역시 동구 옥천로 52-3(신흥동)자동차 세차업2023-11-07
1345재단법인 한국산업보건연구재단 비엠엘의원대전광역시 동구 동대전로 332(가양동)보건업2023-11-07
1355대전세차장대전광역시 동구 삼성동 110-11자동차 세차업2023-11-07
1365현대모터스대전광역시 동구 옥천로96번길 92-32자동차 세차업2023-11-07
1375은진모터스대전광역시 동구 판암동 420-4자동차 세차업2023-11-07
1385디케이워시판암점대전광역시 동구 판암동 414-8자동차 세차업2023-11-07
1395㈜동대전현대서비스대전광역시 동구 새울로 58-15자동차 세차업2023-11-07
1405CITZA대전동구점대전광역시 동구 대전로 916자동차 세차업2023-11-07
1415가온대전광역시 동구 옥천로 219자동차 세차업2023-11-07