Overview

Dataset statistics

Number of variables4
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory35.8 B

Variable types

Numeric1
Text2
Categorical1

Dataset

Description인천광역시 부평구 특정토양오염 관리대상시설 현황에 대한 데이터 목록입니다. 부평구에 위치한 특정토양오염관리대상시설의 사업장명, 소재지(주소), 업종에 대한 정보를 포함하고 있습니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15127277&srcSe=7661IVAWM27C61E190

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2024-04-06 09:41:13.797829
Analysis finished2024-04-06 09:41:14.349976
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2024-04-06T18:41:14.769390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.35
Q112.75
median24.5
Q336.25
95-th percentile45.65
Maximum48
Range47
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14
Coefficient of variation (CV)0.57142857
Kurtosis-1.2
Mean24.5
Median Absolute Deviation (MAD)12
Skewness0
Sum1176
Variance196
MonotonicityStrictly increasing
2024-04-06T18:41:15.023444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
26 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
35 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
48 1
2.1%
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%

시설명
Text

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T18:41:15.309535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length9.6666667
Min length4

Characters and Unicode

Total characters464
Distinct characters126
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

Unique46 ?
Unique (%)95.8%

Sample

1st row굿모닝 주유소
2nd row갈산셀프주유소
3rd row제일주유소
4th row㈜정현에너지
5th row㈜캉가루
ValueCountFrequency (%)
주식회사 5
 
7.0%
주유소 3
 
4.2%
에이치디 2
 
2.8%
현대오일뱅크㈜직영 2
 
2.8%
㈜인에너지 2
 
2.8%
㈜정현에너지 1
 
1.4%
㈜소모 1
 
1.4%
현대주유소 1
 
1.4%
인그리디언코리아(유 1
 
1.4%
태리 1
 
1.4%
Other values (52) 52
73.2%
2024-04-06T18:41:15.930533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 42
 
9.1%
유 39
 
8.4%
소 35
 
7.5%
25
 
5.4%
㈜ 17
 
3.7%
에 14
 
3.0%
지 14
 
3.0%
너 11
 
2.4%
이 11
 
2.4%
프 9
 
1.9%
Other values (116) 247
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 388
83.6%
Space Separator 25
 
5.4%
Other Symbol 17
 
3.7%
Uppercase Letter 16
 
3.4%
Lowercase Letter 8
 
1.7%
Close Punctuation 4
 
0.9%
Open Punctuation 4
 
0.9%
Dash Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 42
 
10.8%
유 39
 
10.1%
소 35
 
9.0%
에 14
 
3.6%
지 14
 
3.6%
너 11
 
2.8%
이 11
 
2.8%
프 9
 
2.3%
셀 8
 
2.1%
사 8
 
2.1%
Other values (99) 197
50.8%
Uppercase Letter
ValueCountFrequency (%)
S 5
31.2%
K 4
25.0%
I 2
 
12.5%
C 2
 
12.5%
A 1
 
6.2%
M 1
 
6.2%
P 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
f 2
25.0%
s 2
25.0%
l 2
25.0%
e 2
25.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
7 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 405
87.3%
Common 35
 
7.5%
Latin 24
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 42
 
10.4%
유 39
 
9.6%
소 35
 
8.6%
㈜ 17
 
4.2%
에 14
 
3.5%
지 14
 
3.5%
너 11
 
2.7%
이 11
 
2.7%
프 9
 
2.2%
셀 8
 
2.0%
Other values (100) 205
50.6%
Latin
ValueCountFrequency (%)
S 5
20.8%
K 4
16.7%
f 2
 
8.3%
s 2
 
8.3%
l 2
 
8.3%
e 2
 
8.3%
I 2
 
8.3%
C 2
 
8.3%
A 1
 
4.2%
M 1
 
4.2%
Common
ValueCountFrequency (%)
25
71.4%
) 4
 
11.4%
( 4
 
11.4%
- 1
 
2.9%
7 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 388
83.6%
ASCII 59
 
12.7%
None 17
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 42
 
10.8%
유 39
 
10.1%
소 35
 
9.0%
에 14
 
3.6%
지 14
 
3.6%
너 11
 
2.8%
이 11
 
2.8%
프 9
 
2.3%
셀 8
 
2.1%
사 8
 
2.1%
Other values (99) 197
50.8%
ASCII
ValueCountFrequency (%)
25
42.4%
S 5
 
8.5%
K 4
 
6.8%
) 4
 
6.8%
( 4
 
6.8%
f 2
 
3.4%
s 2
 
3.4%
l 2
 
3.4%
e 2
 
3.4%
I 2
 
3.4%
Other values (6) 7
 
11.9%
None
ValueCountFrequency (%)
㈜ 17
100.0%

주소
Text

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size516.0 B
2024-04-06T18:41:16.244597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length24.5
Min length23

Characters and Unicode

Total characters1176
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)95.8%

Sample

1st row인천광역시 부평구 서달로 338 (청천동)
2nd row인천광역시 부평구 장제로 362 (갈산동)
3rd row인천광역시 부평구 장제로 151 (부평동)
4th row인천광역시 부평구 장제로 379 (삼산동)
5th row인천광역시 부평구 안남로418번길 56 (청천동)
ValueCountFrequency (%)
인천광역시 48
20.0%
부평구 48
20.0%
청천동 10
 
4.2%
십정동 9
 
3.8%
경인로 8
 
3.3%
장제로 6
 
2.5%
삼산동 6
 
2.5%
산곡동 6
 
2.5%
경원대로 5
 
2.1%
부평대로 5
 
2.1%
Other values (66) 89
37.1%
2024-04-06T18:41:16.791416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229
19.5%
부 62
 
5.3%
평 62
 
5.3%
천 61
 
5.2%
인 56
 
4.8%
구 52
 
4.4%
동 49
 
4.2%
( 48
 
4.1%
) 48
 
4.1%
로 48
 
4.1%
Other values (51) 461
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
58.9%
Space Separator 229
 
19.5%
Decimal Number 157
 
13.4%
Open Punctuation 48
 
4.1%
Close Punctuation 48
 
4.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 62
 
8.9%
평 62
 
8.9%
천 61
 
8.8%
인 56
 
8.1%
구 52
 
7.5%
동 49
 
7.1%
로 48
 
6.9%
시 48
 
6.9%
역 48
 
6.9%
광 48
 
6.9%
Other values (37) 159
22.9%
Decimal Number
ValueCountFrequency (%)
1 38
24.2%
2 20
12.7%
3 19
12.1%
4 16
10.2%
0 15
 
9.6%
9 15
 
9.6%
5 15
 
9.6%
7 8
 
5.1%
6 6
 
3.8%
8 5
 
3.2%
Space Separator
ValueCountFrequency (%)
229
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
58.9%
Common 483
41.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 62
 
8.9%
평 62
 
8.9%
천 61
 
8.8%
인 56
 
8.1%
구 52
 
7.5%
동 49
 
7.1%
로 48
 
6.9%
시 48
 
6.9%
역 48
 
6.9%
광 48
 
6.9%
Other values (37) 159
22.9%
Common
ValueCountFrequency (%)
229
47.4%
( 48
 
9.9%
) 48
 
9.9%
1 38
 
7.9%
2 20
 
4.1%
3 19
 
3.9%
4 16
 
3.3%
0 15
 
3.1%
9 15
 
3.1%
5 15
 
3.1%
Other values (4) 20
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
58.9%
ASCII 483
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
229
47.4%
( 48
 
9.9%
) 48
 
9.9%
1 38
 
7.9%
2 20
 
4.1%
3 19
 
3.9%
4 16
 
3.3%
0 15
 
3.1%
9 15
 
3.1%
5 15
 
3.1%
Other values (4) 20
 
4.1%
Hangul
ValueCountFrequency (%)
부 62
 
8.9%
평 62
 
8.9%
천 61
 
8.8%
인 56
 
8.1%
구 52
 
7.5%
동 49
 
7.1%
로 48
 
6.9%
시 48
 
6.9%
역 48
 
6.9%
광 48
 
6.9%
Other values (37) 159
22.9%

업종
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
주유소
39 
산업시설
9 

Length

Max length4
Median length3
Mean length3.1875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주유소
2nd row주유소
3rd row주유소
4th row주유소
5th row산업시설

Common Values

ValueCountFrequency (%)
주유소 39
81.2%
산업시설 9
 
18.8%

Length

2024-04-06T18:41:16.999102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:41:17.132327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주유소 39
81.2%
산업시설 9
 
18.8%

Interactions

2024-04-06T18:41:14.093755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:41:17.237139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설명주소업종
연번1.0000.9380.9380.313
시설명0.9381.0000.9960.000
주소0.9380.9961.0001.000
업종0.3130.0001.0001.000
2024-04-06T18:41:17.364607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종
연번1.0000.209
업종0.2091.000

Missing values

2024-04-06T18:41:14.220675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:41:14.309633image/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

연번시설명주소업종
01굿모닝 주유소인천광역시 부평구 서달로 338 (청천동)주유소
12갈산셀프주유소인천광역시 부평구 장제로 362 (갈산동)주유소
23제일주유소인천광역시 부평구 장제로 151 (부평동)주유소
34㈜정현에너지인천광역시 부평구 장제로 379 (삼산동)주유소
45㈜캉가루인천광역시 부평구 안남로418번길 56 (청천동)산업시설
56이안주유소인천광역시 부평구 경인로 1151 (일신동)주유소
67SK에너지㈜보보프라자주유소인천광역시 부평구 무네미로 471 (구산동)주유소
78구도일주유소인천광역시 부평구 경인로 1020 (부개동)주유소
89씨앤에스유통㈜ 청중로주유소인천광역시 부평구 청중로 109 (청천동)주유소
910공단하이웨이주유소인천광역시 부평구 부평대로 262 (갈산동)주유소
연번시설명주소업종
3839SK에너지㈜삼산주유소인천광역시 부평구 장제로 347 (삼산동)주유소
3940주식회사 진주크린텍인천광역시 부평구 항동로125번길 14 (일신동)산업시설
4041SK에너지(주)대양self주유소인천광역시 부평구 평천로 545 (삼산동)주유소
4142삼미상사㈜부평마장셀프주유소인천광역시 부평구 마장로 173 (산곡동)주유소
4243SK에너지㈜삼성self주유소인천광역시 부평구 평천로 546 (삼산동)주유소
4344십정주유소인천광역시 부평구 경원대로 1037 (십정동)주유소
4445㈜태보에너지 (지점) - 송내IC주유소인천광역시 부평구 경인로 1182-1 (일신동)주유소
4546지엠테크니컬센터코리아㈜인천광역시 부평구 부평대로 233 (청천동)산업시설
4647주식회사 SIMPAC인천광역시 부평구 부평북로 141 (청천동)산업시설
4748주식회사 동남합성인천광역시 부평구 부평북로 99 (청천동)산업시설