Overview

Dataset statistics

Number of variables6
Number of observations317
Missing cells57
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory49.4 B

Variable types

Numeric1
Categorical1
Text4

Dataset

Description인천광역시 서구 폐기물처리업에 관한 데이터입니다. 연번, 업종, 상호, 전화번호, 주소, 폐기물 종류 등의 데이터를 제공하고 있습니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15121153/fileData.do

Alerts

연번 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 연번High correlation
전화번호 has 57 (18.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:12:13.688649
Analysis finished2023-12-12 01:12:14.628480
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159
Minimum1
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T10:12:14.730733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.8
Q180
median159
Q3238
95-th percentile301.2
Maximum317
Range316
Interquartile range (IQR)158

Descriptive statistics

Standard deviation91.654242
Coefficient of variation (CV)0.57644177
Kurtosis-1.2
Mean159
Median Absolute Deviation (MAD)79
Skewness0
Sum50403
Variance8400.5
MonotonicityStrictly increasing
2023-12-12T10:12:14.906506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
210 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
213 1
 
0.3%
212 1
 
0.3%
211 1
 
0.3%
209 1
 
0.3%
Other values (307) 307
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%
312 1
0.3%
311 1
0.3%
310 1
0.3%
309 1
0.3%
308 1
0.3%

업종
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
폐기물수집운반업
149 
폐기물중간재활용업
78 
폐기물종합재활용업
72 
건설폐기물 중간처리업
15 
폐기물중간처분업
 
3

Length

Max length11
Median length9
Mean length8.615142
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건설폐기물 중간처리업
2nd row건설폐기물 중간처리업
3rd row건설폐기물 중간처리업
4th row건설폐기물 중간처리업
5th row건설폐기물 중간처리업

Common Values

ValueCountFrequency (%)
폐기물수집운반업 149
47.0%
폐기물중간재활용업 78
24.6%
폐기물종합재활용업 72
22.7%
건설폐기물 중간처리업 15
 
4.7%
폐기물중간처분업 3
 
0.9%

Length

2023-12-12T10:12:15.083255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:12:15.212171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐기물수집운반업 149
44.9%
폐기물중간재활용업 78
23.5%
폐기물종합재활용업 72
21.7%
건설폐기물 15
 
4.5%
중간처리업 15
 
4.5%
폐기물중간처분업 3
 
0.9%

상호
Text

Distinct264
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T10:12:15.614198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length29
Mean length6.4794953
Min length3

Characters and Unicode

Total characters2054
Distinct characters243
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

Unique222 ?
Unique (%)70.0%

Sample

1st row㈜아이케이
2nd row㈜이도(Yido co., Ltd.)
3rd row㈜순환
4th row㈜장형기업
5th row㈜한성기업
ValueCountFrequency (%)
㈜이도(yido 5
 
1.4%
ltd 5
 
1.4%
co 5
 
1.4%
㈜아이케이 4
 
1.2%
㈜원광에스앤티 4
 
1.2%
㈜대건산업개발 3
 
0.9%
인천지점 3
 
0.9%
그린에코넥서스(주 3
 
0.9%
대진상사 3
 
0.9%
충북자원 3
 
0.9%
Other values (268) 308
89.0%
2023-12-12T10:12:16.226011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 194
 
9.4%
이 85
 
4.1%
경 58
 
2.8%
업 56
 
2.7%
에 54
 
2.6%
산 49
 
2.4%
( 46
 
2.2%
) 46
 
2.2%
스 45
 
2.2%
환 44
 
2.1%
Other values (233) 1377
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1625
79.1%
Other Symbol 194
 
9.4%
Lowercase Letter 53
 
2.6%
Close Punctuation 47
 
2.3%
Open Punctuation 46
 
2.2%
Space Separator 34
 
1.7%
Uppercase Letter 27
 
1.3%
Other Punctuation 24
 
1.2%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 85
 
5.2%
경 58
 
3.6%
업 56
 
3.4%
에 54
 
3.3%
산 49
 
3.0%
스 45
 
2.8%
환 44
 
2.7%
지 43
 
2.6%
인 41
 
2.5%
원 39
 
2.4%
Other values (199) 1111
68.4%
Lowercase Letter
ValueCountFrequency (%)
o 12
22.6%
d 11
20.8%
c 6
11.3%
t 6
11.3%
n 5
9.4%
i 5
9.4%
y 2
 
3.8%
g 2
 
3.8%
e 1
 
1.9%
u 1
 
1.9%
Other values (2) 2
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
L 7
25.9%
Y 5
18.5%
S 3
11.1%
I 3
11.1%
E 2
 
7.4%
C 2
 
7.4%
D 1
 
3.7%
T 1
 
3.7%
W 1
 
3.7%
J 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 15
62.5%
, 6
 
25.0%
? 2
 
8.3%
& 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 46
97.9%
] 1
 
2.1%
Decimal Number
ValueCountFrequency (%)
2 3
75.0%
1 1
 
25.0%
Other Symbol
ValueCountFrequency (%)
㈜ 194
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1819
88.6%
Common 155
 
7.5%
Latin 80
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 194
 
10.7%
이 85
 
4.7%
경 58
 
3.2%
업 56
 
3.1%
에 54
 
3.0%
산 49
 
2.7%
스 45
 
2.5%
환 44
 
2.4%
지 43
 
2.4%
인 41
 
2.3%
Other values (200) 1150
63.2%
Latin
ValueCountFrequency (%)
o 12
15.0%
d 11
13.8%
L 7
8.8%
c 6
 
7.5%
t 6
 
7.5%
n 5
 
6.2%
i 5
 
6.2%
Y 5
 
6.2%
S 3
 
3.8%
I 3
 
3.8%
Other values (13) 17
21.2%
Common
ValueCountFrequency (%)
( 46
29.7%
) 46
29.7%
34
21.9%
. 15
 
9.7%
, 6
 
3.9%
2 3
 
1.9%
? 2
 
1.3%
& 1
 
0.6%
1 1
 
0.6%
] 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1625
79.1%
ASCII 235
 
11.4%
None 194
 
9.4%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 194
100.0%
Hangul
ValueCountFrequency (%)
이 85
 
5.2%
경 58
 
3.6%
업 56
 
3.4%
에 54
 
3.3%
산 49
 
3.0%
스 45
 
2.8%
환 44
 
2.7%
지 43
 
2.6%
인 41
 
2.5%
원 39
 
2.4%
Other values (199) 1111
68.4%
ASCII
ValueCountFrequency (%)
( 46
19.6%
) 46
19.6%
34
14.5%
. 15
 
6.4%
o 12
 
5.1%
d 11
 
4.7%
L 7
 
3.0%
c 6
 
2.6%
, 6
 
2.6%
t 6
 
2.6%
Other values (23) 46
19.6%

전화번호
Text

MISSING 

Distinct209
Distinct (%)80.4%
Missing57
Missing (%)18.0%
Memory size2.6 KiB
2023-12-12T10:12:16.630992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.042308
Min length12

Characters and Unicode

Total characters3131
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)65.4%

Sample

1st row032-563-3114
2nd row032-567-0181
3rd row032-562-3605
4th row032-562-1658
5th row032-565-9110
ValueCountFrequency (%)
032-567-0181 5
 
1.9%
032-715-5885 4
 
1.5%
032-563-3114 4
 
1.5%
032-563-5700 3
 
1.2%
032-566-1703 3
 
1.2%
032-562-1658 3
 
1.2%
032-577-2525 3
 
1.2%
032-563-5738 3
 
1.2%
032-288-5425 2
 
0.8%
032-565-9110 2
 
0.8%
Other values (199) 228
87.7%
2023-12-12T10:12:17.151080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 520
16.6%
0 420
13.4%
2 401
12.8%
5 384
12.3%
3 380
12.1%
6 276
8.8%
7 190
 
6.1%
1 175
 
5.6%
8 166
 
5.3%
4 115
 
3.7%
Other values (2) 104
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2605
83.2%
Dash Punctuation 520
 
16.6%
Space Separator 6
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 420
16.1%
2 401
15.4%
5 384
14.7%
3 380
14.6%
6 276
10.6%
7 190
7.3%
1 175
6.7%
8 166
 
6.4%
4 115
 
4.4%
9 98
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 520
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3131
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 520
16.6%
0 420
13.4%
2 401
12.8%
5 384
12.3%
3 380
12.1%
6 276
8.8%
7 190
 
6.1%
1 175
 
5.6%
8 166
 
5.3%
4 115
 
3.7%
Other values (2) 104
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 520
16.6%
0 420
13.4%
2 401
12.8%
5 384
12.3%
3 380
12.1%
6 276
8.8%
7 190
 
6.1%
1 175
 
5.6%
8 166
 
5.3%
4 115
 
3.7%
Other values (2) 104
 
3.3%

주소
Text

Distinct275
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T10:12:17.496219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length33
Mean length18.608833
Min length6

Characters and Unicode

Total characters5899
Distinct characters176
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

Unique239 ?
Unique (%)75.4%

Sample

1st row검단천로 151(오류동)
2nd row드림로 174
3rd row검단천로 201(오류동)
4th row검단천로 203(오류동)
5th row원당대로 537, 1동 2층(에이치제이프라자,왕길동)
ValueCountFrequency (%)
완정로 17
 
1.9%
두루물로 15
 
1.7%
원당대로 14
 
1.6%
자원순환특화산업단지 13
 
1.5%
검단천로 12
 
1.4%
봉수대로 11
 
1.3%
오류동 10
 
1.1%
길무로 10
 
1.1%
드림로 9
 
1.0%
석남동 8
 
0.9%
Other values (501) 760
86.5%
2023-12-12T10:12:18.022971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
573
 
9.7%
동 327
 
5.5%
로 316
 
5.4%
1 307
 
5.2%
) 301
 
5.1%
( 300
 
5.1%
2 212
 
3.6%
길 212
 
3.6%
3 199
 
3.4%
, 164
 
2.8%
Other values (166) 2988
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2884
48.9%
Decimal Number 1596
27.1%
Space Separator 573
 
9.7%
Close Punctuation 301
 
5.1%
Open Punctuation 300
 
5.1%
Other Punctuation 168
 
2.8%
Dash Punctuation 75
 
1.3%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 327
 
11.3%
로 316
 
11.0%
길 212
 
7.4%
번 159
 
5.5%
오 109
 
3.8%
류 105
 
3.6%
대 73
 
2.5%
호 70
 
2.4%
원 62
 
2.1%
단 57
 
2.0%
Other values (148) 1394
48.3%
Decimal Number
ValueCountFrequency (%)
1 307
19.2%
2 212
13.3%
3 199
12.5%
4 158
9.9%
5 150
9.4%
6 128
8.0%
0 124
7.8%
7 122
 
7.6%
8 107
 
6.7%
9 89
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 164
97.6%
: 4
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
573
100.0%
Close Punctuation
ValueCountFrequency (%)
) 301
100.0%
Open Punctuation
ValueCountFrequency (%)
( 300
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3013
51.1%
Hangul 2884
48.9%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 327
 
11.3%
로 316
 
11.0%
길 212
 
7.4%
번 159
 
5.5%
오 109
 
3.8%
류 105
 
3.6%
대 73
 
2.5%
호 70
 
2.4%
원 62
 
2.1%
단 57
 
2.0%
Other values (148) 1394
48.3%
Common
ValueCountFrequency (%)
573
19.0%
1 307
10.2%
) 301
10.0%
( 300
10.0%
2 212
 
7.0%
3 199
 
6.6%
, 164
 
5.4%
4 158
 
5.2%
5 150
 
5.0%
6 128
 
4.2%
Other values (6) 521
17.3%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3015
51.1%
Hangul 2884
48.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
573
19.0%
1 307
10.2%
) 301
10.0%
( 300
10.0%
2 212
 
7.0%
3 199
 
6.6%
, 164
 
5.4%
4 158
 
5.2%
5 150
 
5.0%
6 128
 
4.2%
Other values (8) 523
17.3%
Hangul
ValueCountFrequency (%)
동 327
 
11.3%
로 316
 
11.0%
길 212
 
7.4%
번 159
 
5.5%
오 109
 
3.8%
류 105
 
3.6%
대 73
 
2.5%
호 70
 
2.4%
원 62
 
2.1%
단 57
 
2.0%
Other values (148) 1394
48.3%

폐기물종류
Text

Distinct255
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T10:12:18.475866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length922
Median length299
Mean length89.192429
Min length3

Characters and Unicode

Total characters28274
Distinct characters338
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique236 ?
Unique (%)74.4%

Sample

1st row건설폐기물
2nd row건설폐기물
3rd row건설폐기물
4th row건설폐기물
5th row건설폐기물
ValueCountFrequency (%)
그 147
 
4.5%
밖의 143
 
4.3%
51-03-01 58
 
1.8%
말한다 53
 
1.6%
제외한다 53
 
1.6%
및 53
 
1.6%
폐합성수지류(폐염화비닐수지류는 47
 
1.4%
건설폐기물 41
 
1.2%
사용된 27
 
0.8%
폐가구류 26
 
0.8%
Other values (943) 2653
80.4%
2023-12-12T10:12:19.091689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3213
 
11.4%
- 2091
 
7.4%
0 1752
 
6.2%
1 1621
 
5.7%
, 1406
 
5.0%
폐 1350
 
4.8%
5 1033
 
3.7%
( 718
 
2.5%
) 715
 
2.5%
9 591
 
2.1%
Other values (328) 13784
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12869
45.5%
Decimal Number 6605
23.4%
Space Separator 3213
 
11.4%
Dash Punctuation 2091
 
7.4%
Other Punctuation 1461
 
5.2%
Close Punctuation 805
 
2.8%
Open Punctuation 803
 
2.8%
Uppercase Letter 289
 
1.0%
Math Symbol 116
 
0.4%
Lowercase Letter 22
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
폐 1350
 
10.5%
류 518
 
4.0%
재 441
 
3.4%
물 392
 
3.0%
성 382
 
3.0%
합 371
 
2.9%
기 331
 
2.6%
지 324
 
2.5%
수 321
 
2.5%
의 285
 
2.2%
Other values (267) 8154
63.4%
Uppercase Letter
ValueCountFrequency (%)
P 115
39.8%
S 33
 
11.4%
E 28
 
9.7%
B 28
 
9.7%
C 25
 
8.7%
A 22
 
7.6%
R 17
 
5.9%
T 8
 
2.8%
O 3
 
1.0%
N 2
 
0.7%
Other values (6) 8
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
p 6
27.3%
l 2
 
9.1%
e 2
 
9.1%
s 2
 
9.1%
t 1
 
4.5%
b 1
 
4.5%
a 1
 
4.5%
c 1
 
4.5%
g 1
 
4.5%
d 1
 
4.5%
Other values (4) 4
18.2%
Decimal Number
ValueCountFrequency (%)
0 1752
26.5%
1 1621
24.5%
5 1033
15.6%
9 591
 
8.9%
2 576
 
8.7%
3 436
 
6.6%
4 256
 
3.9%
7 124
 
1.9%
8 117
 
1.8%
6 99
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 1406
96.2%
· 23
 
1.6%
: 15
 
1.0%
? 8
 
0.5%
. 5
 
0.3%
" 2
 
0.1%
; 1
 
0.1%
/ 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 718
89.4%
[ 62
 
7.7%
「 21
 
2.6%
【 2
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 715
88.8%
] 63
 
7.8%
」 25
 
3.1%
】 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
~ 110
94.8%
< 3
 
2.6%
> 3
 
2.6%
Space Separator
ValueCountFrequency (%)
3213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2091
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15094
53.4%
Hangul 12869
45.5%
Latin 311
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
폐 1350
 
10.5%
류 518
 
4.0%
재 441
 
3.4%
물 392
 
3.0%
성 382
 
3.0%
합 371
 
2.9%
기 331
 
2.6%
지 324
 
2.5%
수 321
 
2.5%
의 285
 
2.2%
Other values (267) 8154
63.4%
Common
ValueCountFrequency (%)
3213
21.3%
- 2091
13.9%
0 1752
11.6%
1 1621
10.7%
, 1406
9.3%
5 1033
 
6.8%
( 718
 
4.8%
) 715
 
4.7%
9 591
 
3.9%
2 576
 
3.8%
Other values (21) 1378
9.1%
Latin
ValueCountFrequency (%)
P 115
37.0%
S 33
 
10.6%
E 28
 
9.0%
B 28
 
9.0%
C 25
 
8.0%
A 22
 
7.1%
R 17
 
5.5%
T 8
 
2.6%
p 6
 
1.9%
O 3
 
1.0%
Other values (20) 26
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15332
54.2%
Hangul 12867
45.5%
None 73
 
0.3%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3213
21.0%
- 2091
13.6%
0 1752
11.4%
1 1621
10.6%
, 1406
9.2%
5 1033
 
6.7%
( 718
 
4.7%
) 715
 
4.7%
9 591
 
3.9%
2 576
 
3.8%
Other values (46) 1616
10.5%
Hangul
ValueCountFrequency (%)
폐 1350
 
10.5%
류 518
 
4.0%
재 441
 
3.4%
물 392
 
3.0%
성 382
 
3.0%
합 371
 
2.9%
기 331
 
2.6%
지 324
 
2.5%
수 321
 
2.5%
의 285
 
2.2%
Other values (266) 8152
63.4%
None
ValueCountFrequency (%)
」 25
34.2%
· 23
31.5%
「 21
28.8%
】 2
 
2.7%
【 2
 
2.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%

Interactions

2023-12-12T10:12:14.109222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:12:19.244388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.968
업종0.9681.000
2023-12-12T10:12:19.353995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.745
업종0.7451.000

Missing values

2023-12-12T10:12:14.477969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:12:14.585453image/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

연번업종상호전화번호주소폐기물종류
01건설폐기물 중간처리업㈜아이케이032-563-3114검단천로 151(오류동)건설폐기물
12건설폐기물 중간처리업㈜이도(Yido co., Ltd.)032-567-0181드림로 174건설폐기물
23건설폐기물 중간처리업㈜순환032-562-3605검단천로 201(오류동)건설폐기물
34건설폐기물 중간처리업㈜장형기업032-562-1658검단천로 203(오류동)건설폐기물
45건설폐기물 중간처리업㈜한성기업032-565-9110원당대로 537, 1동 2층(에이치제이프라자,왕길동)건설폐기물
56건설폐기물 중간처리업한밭미래자원㈜032-562-9973거월로 51건설폐기물
67건설폐기물 중간처리업동아공사㈜032-565-0701드림로 176건설폐기물
78건설폐기물 중간처리업케이와이PC㈜032-565-1300가람로 24,25(오류동)건설폐기물
89건설폐기물 중간처리업삼덕유화㈜032-564-8071도담로 109(오류동)건설폐기물
910건설폐기물 중간처리업에스지이㈜석남지점032-579-1093봉수대로300번길 15(석남동)건설폐기물
연번업종상호전화번호주소폐기물종류
307308폐기물수집운반업㈜미래이앤씨<NA>길주로 79, 8379호 (석남동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-20-02 제재부산물(원목 가공과정에서 발생되는 나무 껍질, 톱밥, 대패밥 등을 말한다), 51-20-03 목재가공공장 부산물(원목상태의 깨끗한 목재부산물 및 분진을 말한다), 51-99-00 그 밖의 폐기물
308309폐기물수집운반업㈜인천기계032-858-9197거북로 119번길 20, 102호 (석남동)51-03-99 그 밖의 폐합성고분자화합물(합성수지류로 피복된 폐전선을 포함한다)
309310폐기물수집운반업태광전자032-584-0881거북로24번길 18 (석남동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-03-99 그 밖의 폐합성고분자화합물(합성수지류로 피복된 폐전선을 포함한다), 51-04-99 그 밖의 광재류, 51-10-02 폐이온교환수지 51-18-02 산업용폐전기전자제품, 51-18-99 그 밖의 폐전기전자제품류, 51-29-01 고철 51-29-02 비철금속, 51-29-99 그 밖의 폐금속류, 51-30-01 폐유리 51-41-01 1차폐전지(「자원의 절약과 재활용촉진에 관한 법률 시행령」 제18조제4호에 해당하는 것을 말한다), 51-41-02 2차폐전지, 51-41-03 2차폐축전지(지정폐기물 중 폐황산이 포함된 2차폐축전지는 제외한다), 51-41-04 폐태양전지ㆍ전자기기페이스트ㆍ태양광 폐패널, 51-41-05 전기자동차 폐배터리
310311폐기물수집운반업선우산업개발㈜032-569-5061길무로171, 2층 (오류동)51-03-01 폐합성수지(폐염화비닐수지류는 제외한다), 51-03-02 폐합성고무51-20-99 그 밖의 폐목재류, 51-27-02 폐합성섬유
311312폐기물수집운반업태산환경<NA>거북로17, 나동 318호 (석남동)51-27-99 그 밖의 폐섬유(의료기관 일회용기저귀에 한함)
312313폐기물수집운반업대진상사032-564-0454길무로177번길 25 (오류동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-03-02 폐합성고무류 51-20-99 그 밖의 폐목재류, 51-27-02 폐합성섬유
313314폐기물수집운반업올바른환경032-562-8788검단로 784, 퀸스타운길훈아파트상가 306호(불로동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-05-99 그 밖의 분진 51-20-06 폐가구류, 폐도장목, 폐목재포장재, 폐전선드럼(원목상태의 깨끗한 목재를 말한다) 51-27-02 폐합성섬유
314315폐기물수집운반업선진철재<NA>거북로 18, 1층(석남동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-03-02 폐합성고무류 51-03-03 폐폴리염화비닐수지류, 51-03-04 폐폴리우레탄폼류, 51-03-07 플라스틱폐포장재 51-03-99 그 밖의 폐합성고분자화합물(합성수지류로 피복된 폐전선을 포함한다) 51-20-06 폐가구류, 폐도장목, 폐목재포장재, 폐전선드럼(원목상태의 깨끗한 목재를 말한다) 51-20-07 폐가구류, 폐도장목, 폐목재포장재, 폐전선드럼(접착제, 페인트, 기름, 콘크리트 등의 물질이 사용된 목재를 말한다), 51-20-08 폐가구류, 폐도장목, 폐목재포장재, 폐전선 드럼 (할로겐족 유기화합물 또는 방부제가 사용된 목재를 말한다), 51-20-99 그 밖의 폐목재류, 51-29-01 고철, 51-29-02 비철금속, 51-29-03 폐금속캔류(「자원의 절약과 재활용촉진 에 관한 법률 시행령」 제18조제1호에 해당하는 것을 말한다), 51-29-04 폐금속용기류(폐금속 캔류는 제외한다), 51-29-99 그 밖의 폐금속류, 51-30-01 폐유리, 51-30-02 폐유리병류 (「자원의 절약과 재활용촉진에 관한 법률 시행령」 제18조제1호에 해당하는 것을 말한다), 51-30-03 폐스마트유리, 51-30-04 폐유리섬유, 51-30-05 수은함유폐기물 처리잔재물 [수은함유폐기물을 처리하는 과정에서 발생되는 것과 폐형광등을 재활용하는 과정에서 발생되는 것을 포함하되, 폐기물공정시험기준에 따른 용출시험 결과 용출액 1리터당 0.005 밀리그램 미만의 수은 및 그 화합물이 함유된 것으로 한정한다(이하 "수은폐기물이 아닌 수은 함유폐기물 처리잔재물"이라 한다)], 51-30-99 그 밖의 폐유리, 51-31-00 폐타일, 51-32-00 폐보드류, 51-33-00 폐판넬, 51-99-00 그 밖의 폐기물
315316폐기물수집운반업드림이앤아이(E&I)032-563-2316봉수대로 1344-20(왕길동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-03-03 폐폴리염화비닐수지류 51-03-06 폐발포합성수지, 51-18-02 산업용폐전기전자제품 51-20-09 산업현장의 실외목재구조물에서 발생되는 폐목재, 폐선박 및 차량에서 나오는 목재, 건축물 화재현장에서 발생한 폐목재, 냉각탑, 산업용 바닥재 등에 사용된 폐목재
316317폐기물수집운반업㈜드림산업 인천지점<NA>검단로188번길 19(오류동)51-03-01 폐합성수지류(폐염화비닐수지류는 제외한다), 51-03-02 폐합성고무류, 51-03-03 폐폴리염화비닐수지류, 51-03-04 폐폴리우레탄폼류, 51-03-05 양식용폐부자, 51-03-06 폐발포합성수지, 51-03-07 플라스틱폐포장재, 51-03-08 폐어망, 51-03-99 그 밖의 폐합성고분자화합물(합성수지류로 피복된 폐전선을 포함한다), 51-15-01 자동차 폐타이어, 51-15-02 그 밖의 폐타이어, 51-18-01 가정용폐전기전자제품, 51-18-02 산업용폐전기전자제품, 51-18-03 프린트토너 및 카트리지폐부속품, 51-18-99 그 밖의 폐전기전자제품류, 51-27-01 폐천연섬유, 51-27-02 폐합성섬유, 51-27-03 폐의류, 51-27-99 그 밖의 폐섬유, 51-28-01 폐종이팩(「자원의 절약과 재활용촉진에 관한 법률 시행령」제18조제1호에 해당하는 것을 말한다) 51-28-02 폐종이류, 51-28-03 폐벽지, 51-29-01 고철, 51-29-02 비철금속, 51-29-03 폐금속캔류 (「자원의 절약과 재활용촉진에 관한 법률 시행령」 제18조 제1호에 해당하는 것을 말한다) 51-29-04 폐금속용기류(폐금속캔류는 제외한다), 51-29-99 그 밖의 폐금속류