Overview

Dataset statistics

Number of variables5
Number of observations161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory41.8 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description인천광역시 부평구 대기 환경오염물질 배출사업장 현황입니다.<br/>예) 연번,사업장명,소재지,사업장,업종,종별<br/>(그리디언코리아(유),부평공장,인천광역시 부평구 백범로 584 (십정동),전분 및 당류 제조시설,허1)<br/><br/>
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15081065&srcSe=7661IVAWM27C61E190

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 09:42:07.321546
Analysis finished2024-04-06 09:42:08.001555
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81
Minimum1
Maximum161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-04-06T18:42:08.098184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q141
median81
Q3121
95-th percentile153
Maximum161
Range160
Interquartile range (IQR)80

Descriptive statistics

Standard deviation46.620811
Coefficient of variation (CV)0.57556557
Kurtosis-1.2
Mean81
Median Absolute Deviation (MAD)40
Skewness0
Sum13041
Variance2173.5
MonotonicityStrictly increasing
2024-04-06T18:42:08.258648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
122 1
 
0.6%
104 1
 
0.6%
105 1
 
0.6%
106 1
 
0.6%
107 1
 
0.6%
108 1
 
0.6%
109 1
 
0.6%
110 1
 
0.6%
111 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
161 1
0.6%
160 1
0.6%
159 1
0.6%
158 1
0.6%
157 1
0.6%
156 1
0.6%
155 1
0.6%
154 1
0.6%
153 1
0.6%
152 1
0.6%

사업장명
Text

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-06T18:42:08.533546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length7.3664596
Min length2

Characters and Unicode

Total characters1186
Distinct characters238
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)98.8%

Sample

1st row(주)진영알엔에치
2nd row강인여객㈜
3rd row근로복지공단 인천병원
4th row서부사료㈜
5th row(주)한진다이캐스팅
ValueCountFrequency (%)
주식회사 8
 
3.9%
2공장 3
 
1.5%
성일금속 2
 
1.0%
명신산업 2
 
1.0%
근로복지공단 2
 
1.0%
인영자동차공업 1
 
0.5%
코스맥스네오 1
 
0.5%
부평국민체육센터(부평구 1
 
0.5%
㈜희창 1
 
0.5%
시설관리공단 1
 
0.5%
Other values (181) 181
89.2%
2024-04-06T18:42:08.975369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
4.1%
업 43
 
3.6%
㈜ 41
 
3.5%
사 31
 
2.6%
동 29
 
2.4%
스 29
 
2.4%
공 28
 
2.4%
산 27
 
2.3%
주 26
 
2.2%
부 24
 
2.0%
Other values (228) 859
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1017
85.8%
Space Separator 49
 
4.1%
Other Symbol 41
 
3.5%
Close Punctuation 19
 
1.6%
Open Punctuation 19
 
1.6%
Uppercase Letter 18
 
1.5%
Lowercase Letter 13
 
1.1%
Decimal Number 8
 
0.7%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 43
 
4.2%
사 31
 
3.0%
동 29
 
2.9%
스 29
 
2.9%
공 28
 
2.8%
산 27
 
2.7%
주 26
 
2.6%
부 24
 
2.4%
차 23
 
2.3%
자 23
 
2.3%
Other values (199) 734
72.2%
Uppercase Letter
ValueCountFrequency (%)
T 3
16.7%
B 2
11.1%
S 2
11.1%
R 2
11.1%
U 2
11.1%
P 1
 
5.6%
V 1
 
5.6%
E 1
 
5.6%
M 1
 
5.6%
C 1
 
5.6%
Other values (2) 2
11.1%
Lowercase Letter
ValueCountFrequency (%)
r 3
23.1%
o 3
23.1%
t 2
15.4%
s 1
 
7.7%
e 1
 
7.7%
h 1
 
7.7%
g 1
 
7.7%
i 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 3
37.5%
4 1
 
12.5%
Space Separator
ValueCountFrequency (%)
49
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1058
89.2%
Common 97
 
8.2%
Latin 31
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 43
 
4.1%
㈜ 41
 
3.9%
사 31
 
2.9%
동 29
 
2.7%
스 29
 
2.7%
공 28
 
2.6%
산 27
 
2.6%
주 26
 
2.5%
부 24
 
2.3%
차 23
 
2.2%
Other values (200) 757
71.6%
Latin
ValueCountFrequency (%)
r 3
 
9.7%
o 3
 
9.7%
T 3
 
9.7%
B 2
 
6.5%
t 2
 
6.5%
S 2
 
6.5%
R 2
 
6.5%
U 2
 
6.5%
P 1
 
3.2%
V 1
 
3.2%
Other values (10) 10
32.3%
Common
ValueCountFrequency (%)
49
50.5%
) 19
 
19.6%
( 19
 
19.6%
2 4
 
4.1%
1 3
 
3.1%
4 1
 
1.0%
& 1
 
1.0%
- 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1017
85.8%
ASCII 128
 
10.8%
None 41
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
38.3%
) 19
 
14.8%
( 19
 
14.8%
2 4
 
3.1%
r 3
 
2.3%
1 3
 
2.3%
o 3
 
2.3%
T 3
 
2.3%
B 2
 
1.6%
t 2
 
1.6%
Other values (18) 21
16.4%
Hangul
ValueCountFrequency (%)
업 43
 
4.2%
사 31
 
3.0%
동 29
 
2.9%
스 29
 
2.9%
공 28
 
2.8%
산 27
 
2.7%
주 26
 
2.6%
부 24
 
2.4%
차 23
 
2.3%
자 23
 
2.3%
Other values (199) 734
72.2%
None
ValueCountFrequency (%)
㈜ 41
100.0%

소재지
Text

Distinct147
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-06T18:42:09.293575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length34
Mean length26.080745
Min length19

Characters and Unicode

Total characters4199
Distinct characters88
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 (%)89.4%

Sample

1st row인천광역시 부평구 부평북로 50(청천동)
2nd row인천광역시 부평구 백범로 570(십정동)
3rd row인천광역시 부평구 무네미로 446(구산동)
4th row인천광역시 부평구 부평북로 325(갈산동)
5th row인천광역시 부평구 평천로 17(청천동)
ValueCountFrequency (%)
부평구 162
22.9%
인천광역시 161
22.7%
백범로578번길 22
 
3.1%
청천동 21
 
3.0%
부평북로 21
 
3.0%
서달로298번길 16
 
2.3%
십정동 11
 
1.6%
청천마차로 10
 
1.4%
65(십정동 9
 
1.3%
평천로 7
 
1.0%
Other values (210) 268
37.9%
2024-04-06T18:42:09.786088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
637
 
15.2%
천 272
 
6.5%
평 216
 
5.1%
부 207
 
4.9%
구 167
 
4.0%
동 166
 
4.0%
인 163
 
3.9%
시 162
 
3.9%
광 162
 
3.9%
역 161
 
3.8%
Other values (78) 1886
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2500
59.5%
Decimal Number 683
 
16.3%
Space Separator 637
 
15.2%
Open Punctuation 160
 
3.8%
Close Punctuation 160
 
3.8%
Dash Punctuation 37
 
0.9%
Other Punctuation 18
 
0.4%
Uppercase Letter 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 272
10.9%
평 216
 
8.6%
부 207
 
8.3%
구 167
 
6.7%
동 166
 
6.6%
인 163
 
6.5%
시 162
 
6.5%
광 162
 
6.5%
역 161
 
6.4%
로 160
 
6.4%
Other values (60) 664
26.6%
Decimal Number
ValueCountFrequency (%)
1 100
14.6%
2 97
14.2%
8 75
11.0%
5 71
10.4%
4 64
9.4%
3 62
9.1%
7 61
8.9%
6 56
8.2%
0 49
7.2%
9 48
7.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
C 1
33.3%
Space Separator
ValueCountFrequency (%)
637
100.0%
Open Punctuation
ValueCountFrequency (%)
( 160
100.0%
Close Punctuation
ValueCountFrequency (%)
) 160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2500
59.5%
Common 1696
40.4%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 272
10.9%
평 216
 
8.6%
부 207
 
8.3%
구 167
 
6.7%
동 166
 
6.6%
인 163
 
6.5%
시 162
 
6.5%
광 162
 
6.5%
역 161
 
6.4%
로 160
 
6.4%
Other values (60) 664
26.6%
Common
ValueCountFrequency (%)
637
37.6%
( 160
 
9.4%
) 160
 
9.4%
1 100
 
5.9%
2 97
 
5.7%
8 75
 
4.4%
5 71
 
4.2%
4 64
 
3.8%
3 62
 
3.7%
7 61
 
3.6%
Other values (6) 209
 
12.3%
Latin
ValueCountFrequency (%)
B 2
66.7%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2500
59.5%
ASCII 1699
40.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
637
37.5%
( 160
 
9.4%
) 160
 
9.4%
1 100
 
5.9%
2 97
 
5.7%
8 75
 
4.4%
5 71
 
4.2%
4 64
 
3.8%
3 62
 
3.6%
7 61
 
3.6%
Other values (8) 212
 
12.5%
Hangul
ValueCountFrequency (%)
천 272
10.9%
평 216
 
8.6%
부 207
 
8.3%
구 167
 
6.7%
동 166
 
6.6%
인 163
 
6.5%
시 162
 
6.5%
광 162
 
6.5%
역 161
 
6.4%
로 160
 
6.4%
Other values (60) 664
26.6%

사업장 업종
Text

Distinct80
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-06T18:42:10.104162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length27
Mean length9.7391304
Min length2

Characters and Unicode

Total characters1568
Distinct characters157
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)41.6%

Sample

1st row금속가공제품 제조시설
2nd row운수장비수선및세차또는세척시설
3rd row병원시설
4th row동물용 사료 및 조제식품 제조업
5th row금속제품제조가공
ValueCountFrequency (%)
도금업 20
 
6.8%
도장및기타피막처리업 20
 
6.8%
및 18
 
6.1%
자동차종합수리업 17
 
5.8%
제조업 15
 
5.1%
기타 13
 
4.4%
자동차정비업 12
 
4.1%
그외 8
 
2.7%
금속가공업 7
 
2.4%
플라스틱 6
 
2.0%
Other values (123) 159
53.9%
2024-04-06T18:42:10.617921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 139
 
8.9%
134
 
8.5%
제 65
 
4.1%
기 60
 
3.8%
리 54
 
3.4%
차 53
 
3.4%
도 50
 
3.2%
및 48
 
3.1%
동 47
 
3.0%
조 46
 
2.9%
Other values (147) 872
55.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1412
90.1%
Space Separator 134
 
8.5%
Other Punctuation 12
 
0.8%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Decimal Number 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 139
 
9.8%
제 65
 
4.6%
기 60
 
4.2%
리 54
 
3.8%
차 53
 
3.8%
도 50
 
3.5%
및 48
 
3.4%
동 47
 
3.3%
조 46
 
3.3%
타 45
 
3.2%
Other values (139) 805
57.0%
Other Punctuation
ValueCountFrequency (%)
, 11
91.7%
· 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
V 1
50.0%
Space Separator
ValueCountFrequency (%)
134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1412
90.1%
Common 154
 
9.8%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 139
 
9.8%
제 65
 
4.6%
기 60
 
4.2%
리 54
 
3.8%
차 53
 
3.8%
도 50
 
3.5%
및 48
 
3.4%
동 47
 
3.3%
조 46
 
3.3%
타 45
 
3.2%
Other values (139) 805
57.0%
Common
ValueCountFrequency (%)
134
87.0%
, 11
 
7.1%
( 3
 
1.9%
) 3
 
1.9%
1 2
 
1.3%
· 1
 
0.6%
Latin
ValueCountFrequency (%)
U 1
50.0%
V 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1412
90.1%
ASCII 155
 
9.9%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 139
 
9.8%
제 65
 
4.6%
기 60
 
4.2%
리 54
 
3.8%
차 53
 
3.8%
도 50
 
3.5%
및 48
 
3.4%
동 47
 
3.3%
조 46
 
3.3%
타 45
 
3.2%
Other values (139) 805
57.0%
ASCII
ValueCountFrequency (%)
134
86.5%
, 11
 
7.1%
( 3
 
1.9%
) 3
 
1.9%
1 2
 
1.3%
U 1
 
0.6%
V 1
 
0.6%
None
ValueCountFrequency (%)
· 1
100.0%

종별
Categorical

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
신5
88 
신4
47 
허5
13 
허4
10 
허1
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row신4
2nd row신5
3rd row신4
4th row신4
5th row신5

Common Values

ValueCountFrequency (%)
신5 88
54.7%
신4 47
29.2%
허5 13
 
8.1%
허4 10
 
6.2%
허1 2
 
1.2%
신3 1
 
0.6%

Length

2024-04-06T18:42:10.812504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:42:10.957806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신5 88
54.7%
신4 47
29.2%
허5 13
 
8.1%
허4 10
 
6.2%
허1 2
 
1.2%
신3 1
 
0.6%

Interactions

2024-04-06T18:42:07.703285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:42:11.064522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업장 업종종별
연번1.0000.8420.468
사업장 업종0.8421.0000.928
종별0.4680.9281.000
2024-04-06T18:42:11.183077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종별
연번1.0000.292
종별0.2921.000

Missing values

2024-04-06T18:42:07.872573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:42:07.965215image/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

연번사업장명소재지사업장 업종종별
01(주)진영알엔에치인천광역시 부평구 부평북로 50(청천동)금속가공제품 제조시설신4
12강인여객㈜인천광역시 부평구 백범로 570(십정동)운수장비수선및세차또는세척시설신5
23근로복지공단 인천병원인천광역시 부평구 무네미로 446(구산동)병원시설신4
34서부사료㈜인천광역시 부평구 부평북로 325(갈산동)동물용 사료 및 조제식품 제조업신4
45(주)한진다이캐스팅인천광역시 부평구 평천로 17(청천동)금속제품제조가공신5
56㈜서울피막인천광역시 부평구 장제로 419, 421(갈산동)도금착색 및 기타표면처리 강재제조업신4
67학교법인 가톨릭학원 가톨릭대학교인천성모병원인천광역시 부평구 동수로 56(부평동)병원신3
78인천탁주제조제1공장인천광역시 부평구 안남로433번길 26(청천동)알콜음료 제조시설신4
89(합)신영자동차서비스센타인천광역시 부평구 평천로 7(청천동)자동차종합정비업신5
910㈜일신자동차인천광역시 부평구 서촌로 23(일신동)자동차정비업신5
연번사업장명소재지사업장 업종종별
151152롯데쇼핑㈜ 롯데마트 부평점인천광역시 부평구 마장로 296(산곡동)대형할인점신4
1521531급 계양스카이 자동차공업사인천광역시 부평구 부평북로 9,1~3층(청천동)자동차종합수리업, 운수장비수선및세차또는세척시설신5
153154재단법인 인천광역시 부평구 문화재단인천광역시 부평구 아트센터로 166(십정동)공연장대관신5
154155근로복지공단 인천북부지사인천광역시 부평구 무네미로 478(구산동)금융 및 보험신5
155156갑도물산(주) 부평엠에이치타워인천광역시 부평구 시장로7(부평동)부동산 임대업신5
156157썬텍인천광역시 부평구 부평북로 245(청천동)전기용 탄소제품 및 절연제품 제조업, 산업용 로봇 제조업신5
157158㈜에스앤에이이알인천광역시 부평구 가좌로84번길 67(십정동)지정 외 폐기물 처리업신4
158159KS 모터스인천광역시 부평구 부평북로 9(청천동) 4층자동차종합수리업신5
159160현대 U V인천광역시 부평구 청천마차로 184(청천동), 3층플라스틱 용기 U V 코팅신5
160161부평남부체육센터인천광역시 부평구 부평동 663-30 외 1필지공공기관신4