Overview

Dataset statistics

Number of variables6
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory52.5 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description인천시 소재한 관내 폐수처리업체에 대한 연번, 업종, 사업소명, 소재지(도로명주소), 전화번호, 팩스 등을 알 수 있는 데이터입니다.
URLhttps://www.data.go.kr/data/15021765/fileData.do

Alerts

연번 has unique valuesUnique
업소명 has unique valuesUnique
도로명주소 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:11:39.448520
Analysis finished2023-12-12 07:11:40.147468
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.5
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-12T16:11:40.241380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q110.25
median19.5
Q328.75
95-th percentile36.15
Maximum38
Range37
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation11.113055
Coefficient of variation (CV)0.56990028
Kurtosis-1.2
Mean19.5
Median Absolute Deviation (MAD)9.5
Skewness0
Sum741
Variance123.5
MonotonicityStrictly increasing
2023-12-12T16:11:40.439666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 1
 
2.6%
30 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
31 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%
29 1
2.6%

업 종
Categorical

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
재이용
21 
수탁,재이용
13 
수탁
4 

Length

Max length6
Median length3
Mean length3.9210526
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수탁
2nd row수탁,재이용
3rd row재이용
4th row수탁,재이용
5th row수탁,재이용

Common Values

ValueCountFrequency (%)
재이용 21
55.3%
수탁,재이용 13
34.2%
수탁 4
 
10.5%

Length

2023-12-12T16:11:40.611601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:11:40.763312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재이용 21
55.3%
수탁,재이용 13
34.2%
수탁 4
 
10.5%

업소명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:11:41.027894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length5.3157895
Min length3

Characters and Unicode

Total characters202
Distinct characters84
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row㈜에이치엔이
2nd row이경이앤씨㈜
3rd row삼원분석㈜
4th row㈜일성
5th row(주)천일화학
ValueCountFrequency (%)
㈜에이치엔이 1
 
2.5%
디에스그린㈜ 1
 
2.5%
세바엔텍 1
 
2.5%
엘티메탈㈜ 1
 
2.5%
㈜우광엔텍 1
 
2.5%
미래e비젼 1
 
2.5%
㈜부광에스지 1
 
2.5%
주)에닉스 1
 
2.5%
삼덕금속㈜ 1
 
2.5%
㈜골드코리아 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T16:11:41.519964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 25
 
12.4%
이 12
 
5.9%
스 7
 
3.5%
텍 6
 
3.0%
엔 6
 
3.0%
에 6
 
3.0%
금 5
 
2.5%
속 5
 
2.5%
성 4
 
2.0%
일 4
 
2.0%
Other values (74) 122
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 168
83.2%
Other Symbol 25
 
12.4%
Uppercase Letter 3
 
1.5%
Open Punctuation 2
 
1.0%
Space Separator 2
 
1.0%
Close Punctuation 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 12
 
7.1%
스 7
 
4.2%
텍 6
 
3.6%
엔 6
 
3.6%
에 6
 
3.6%
금 5
 
3.0%
속 5
 
3.0%
성 4
 
2.4%
일 4
 
2.4%
린 4
 
2.4%
Other values (67) 109
64.9%
Uppercase Letter
ValueCountFrequency (%)
E 1
33.3%
P 1
33.3%
M 1
33.3%
Other Symbol
ValueCountFrequency (%)
㈜ 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
95.5%
Common 6
 
3.0%
Latin 3
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 25
 
13.0%
이 12
 
6.2%
스 7
 
3.6%
텍 6
 
3.1%
엔 6
 
3.1%
에 6
 
3.1%
금 5
 
2.6%
속 5
 
2.6%
성 4
 
2.1%
일 4
 
2.1%
Other values (68) 113
58.5%
Common
ValueCountFrequency (%)
( 2
33.3%
2
33.3%
) 2
33.3%
Latin
ValueCountFrequency (%)
E 1
33.3%
P 1
33.3%
M 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 168
83.2%
None 25
 
12.4%
ASCII 9
 
4.5%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 25
100.0%
Hangul
ValueCountFrequency (%)
이 12
 
7.1%
스 7
 
4.2%
텍 6
 
3.6%
엔 6
 
3.6%
에 6
 
3.6%
금 5
 
3.0%
속 5
 
3.0%
성 4
 
2.4%
일 4
 
2.4%
린 4
 
2.4%
Other values (67) 109
64.9%
ASCII
ValueCountFrequency (%)
( 2
22.2%
2
22.2%
) 2
22.2%
E 1
11.1%
P 1
11.1%
M 1
11.1%

도로명주소
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:11:41.834311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length20.052632
Min length15

Characters and Unicode

Total characters762
Distinct characters61
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row서구 중봉대로376번길 14(원창동)
2nd row서구 중봉대로286번길 29(석남동)
3rd row서구 건지로109번길 44-1(석남동)
4th row남동구 청능대로409번길 19(고잔동)
5th row서구 건지로95번길 66(석남동)
ValueCountFrequency (%)
서구 19
 
16.2%
남동구 18
 
15.4%
건지로153번길 3
 
2.6%
중봉대로376번길 3
 
2.6%
건지로109번길 2
 
1.7%
건지로97번길 2
 
1.7%
건지로95번길 2
 
1.7%
앵고개로 2
 
1.7%
719번길 2
 
1.7%
승기천로 2
 
1.7%
Other values (59) 62
53.0%
2023-12-12T16:11:42.285823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
 
13.1%
동 54
 
7.1%
구 38
 
5.0%
로 38
 
5.0%
남 33
 
4.3%
) 33
 
4.3%
( 33
 
4.3%
1 31
 
4.1%
번 31
 
4.1%
길 31
 
4.1%
Other values (51) 340
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 393
51.6%
Decimal Number 188
24.7%
Space Separator 100
 
13.1%
Close Punctuation 33
 
4.3%
Open Punctuation 33
 
4.3%
Dash Punctuation 14
 
1.8%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 54
13.7%
구 38
 
9.7%
로 38
 
9.7%
남 33
 
8.4%
번 31
 
7.9%
길 31
 
7.9%
서 21
 
5.3%
고 15
 
3.8%
잔 12
 
3.1%
대 11
 
2.8%
Other values (36) 109
27.7%
Decimal Number
ValueCountFrequency (%)
1 31
16.5%
4 28
14.9%
3 27
14.4%
8 20
10.6%
9 19
10.1%
6 18
9.6%
2 18
9.6%
5 12
 
6.4%
7 8
 
4.3%
0 7
 
3.7%
Space Separator
ValueCountFrequency (%)
100
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 393
51.6%
Common 368
48.3%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 54
13.7%
구 38
 
9.7%
로 38
 
9.7%
남 33
 
8.4%
번 31
 
7.9%
길 31
 
7.9%
서 21
 
5.3%
고 15
 
3.8%
잔 12
 
3.1%
대 11
 
2.8%
Other values (36) 109
27.7%
Common
ValueCountFrequency (%)
100
27.2%
) 33
 
9.0%
( 33
 
9.0%
1 31
 
8.4%
4 28
 
7.6%
3 27
 
7.3%
8 20
 
5.4%
9 19
 
5.2%
6 18
 
4.9%
2 18
 
4.9%
Other values (4) 41
11.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 393
51.6%
ASCII 369
48.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
27.1%
) 33
 
8.9%
( 33
 
8.9%
1 31
 
8.4%
4 28
 
7.6%
3 27
 
7.3%
8 20
 
5.4%
9 19
 
5.1%
6 18
 
4.9%
2 18
 
4.9%
Other values (5) 42
11.4%
Hangul
ValueCountFrequency (%)
동 54
13.7%
구 38
 
9.7%
로 38
 
9.7%
남 33
 
8.4%
번 31
 
7.9%
길 31
 
7.9%
서 21
 
5.3%
고 15
 
3.8%
잔 12
 
3.1%
대 11
 
2.8%
Other values (36) 109
27.7%

전화번호
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:11:42.523534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.026316
Min length12

Characters and Unicode

Total characters457
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

Unique38 ?
Unique (%)100.0%

Sample

1st row032-578-8027
2nd row032-575-6541
3rd row032-581-9200
4th row032-821-2141
5th row032-571-5522
ValueCountFrequency (%)
032-578-8027 1
 
2.6%
032-446-5466 1
 
2.6%
032-422-1588 1
 
2.6%
032-576-1152 1
 
2.6%
032-576-4590 1
 
2.6%
032-571-1562 1
 
2.6%
032-579-2144 1
 
2.6%
032-574-0025 1
 
2.6%
032-816-0284 1
 
2.6%
032-433-3694 1
 
2.6%
Other values (28) 28
73.7%
2023-12-12T16:11:42.926554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 76
16.6%
0 70
15.3%
2 69
15.1%
3 57
12.5%
5 38
8.3%
1 36
7.9%
4 33
7.2%
7 28
 
6.1%
8 24
 
5.3%
6 16
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 381
83.4%
Dash Punctuation 76
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 70
18.4%
2 69
18.1%
3 57
15.0%
5 38
10.0%
1 36
9.4%
4 33
8.7%
7 28
 
7.3%
8 24
 
6.3%
6 16
 
4.2%
9 10
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 76
16.6%
0 70
15.3%
2 69
15.1%
3 57
12.5%
5 38
8.3%
1 36
7.9%
4 33
7.2%
7 28
 
6.1%
8 24
 
5.3%
6 16
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 76
16.6%
0 70
15.3%
2 69
15.1%
3 57
12.5%
5 38
8.3%
1 36
7.9%
4 33
7.2%
7 28
 
6.1%
8 24
 
5.3%
6 16
 
3.5%

팩스
Text

Distinct36
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-12T16:11:43.147096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters456
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

Unique34 ?
Unique (%)89.5%

Sample

1st row032-572-2326
2nd row032-582-7477
3rd row032-571-9300
4th row032-821-2145
5th row032-583-8333
ValueCountFrequency (%)
032-572-2326 2
 
5.3%
032-433-8476 2
 
5.3%
032-816-0285 1
 
2.6%
032-446-5467 1
 
2.6%
032-574-1152 1
 
2.6%
032-579-4593 1
 
2.6%
032-574-0318 1
 
2.6%
032-579-2175 1
 
2.6%
032-574-0026 1
 
2.6%
032-876-0711 1
 
2.6%
Other values (26) 26
68.4%
2023-12-12T16:11:43.800950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 76
16.7%
3 70
15.4%
2 66
14.5%
0 64
14.0%
4 35
7.7%
5 32
7.0%
7 28
 
6.1%
1 26
 
5.7%
6 23
 
5.0%
8 23
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380
83.3%
Dash Punctuation 76
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 70
18.4%
2 66
17.4%
0 64
16.8%
4 35
9.2%
5 32
8.4%
7 28
 
7.4%
1 26
 
6.8%
6 23
 
6.1%
8 23
 
6.1%
9 13
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 456
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 76
16.7%
3 70
15.4%
2 66
14.5%
0 64
14.0%
4 35
7.7%
5 32
7.0%
7 28
 
6.1%
1 26
 
5.7%
6 23
 
5.0%
8 23
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 76
16.7%
3 70
15.4%
2 66
14.5%
0 64
14.0%
4 35
7.7%
5 32
7.0%
7 28
 
6.1%
1 26
 
5.7%
6 23
 
5.0%
8 23
 
5.0%

Interactions

2023-12-12T16:11:39.787059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:11:43.953261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업 종업소명도로명주소전화번호팩스
연번1.0000.4761.0001.0001.0000.959
업 종0.4761.0001.0001.0001.0001.000
업소명1.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
팩스0.9591.0001.0001.0001.0001.000
2023-12-12T16:11:44.128727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업 종
연번1.0000.281
업 종0.2811.000

Missing values

2023-12-12T16:11:39.941876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:11:40.089724image/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

연번업 종업소명도로명주소전화번호팩스
01수탁㈜에이치엔이서구 중봉대로376번길 14(원창동)032-578-8027032-572-2326
12수탁,재이용이경이앤씨㈜서구 중봉대로286번길 29(석남동)032-575-6541032-582-7477
23재이용삼원분석㈜서구 건지로109번길 44-1(석남동)032-581-9200032-571-9300
34수탁,재이용㈜일성남동구 청능대로409번길 19(고잔동)032-821-2141032-821-2145
45수탁,재이용(주)천일화학서구 건지로95번길 66(석남동)032-571-5522032-583-8333
56수탁,재이용㈜그린엔텍서구 건지로109번길 54(석남동)032-571-7111032-575-3043
67수탁,재이용㈜그린워터텍서구 중봉대로376번길 21(원창동)032-574-7133032-574-7137
78수탁㈜그린스코남동구 앵고개로654번길 99(고잔동)032-718-0114032-718-0119
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910수탁,재이용㈜세화엔스텍서구 건지로153번길 46-17(석남동)032-571-4221032-571-4217
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2829재이용시온금속미추홀구 염전로165번길 38-44(도화동)032-875-0711032-876-0711
2930재이용㈜오르타남동구 앵고개로 719번길 26032-433-3694032-432-3694
3031재이용청우금속남동구 논현고잔로130번길 38(고잔동)032-822-0745032-822-0746
3132재이용제일금속남동구 고잔로51번길 46-1(고잔동)032-424-2362032-424-0362
3233재이용㈜엠알메탈로남동구 앵고개로 719번길 84032-435-9321032-435-9325
3334재이용㈜디에스씨 남동지점남동구 고잔로 89(고잔동)070-7458-9847032-466-2849
3435재이용나은산업남동구 청능대로484번길 28-26(2층)032-441-0407032-441-0408
3536재이용㈜제이엘이남동구 청능대로 484번길 28-36032-422-8476032-433-8476
3637재이용세바엔텍남동구 청능대로 484번길 28-14 B동032-422-1588032-433-8476
3738재이용알오케이골드남동구 에코중앙로 25-14032-446-0403032-439-0403