Overview

Dataset statistics

Number of variables5
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 KiB
Average record size in memory47.8 B

Variable types

Text2
Categorical2
Numeric1

Dataset

Description보건환경연구원 먹는 물 검사항목 및 수수료 정보(검사 종목, 검사 항목, 검체량, 처리 기간, 수수료)를 제공하고 있습니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15045395/fileData.do

Alerts

처리기간 has constant value ""Constant
수수료 has unique valuesUnique

Reproduction

Analysis started2024-03-14 10:23:23.409660
Analysis finished2024-03-14 10:23:24.411484
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검사종목
Text

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-14T19:23:24.969861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length9.3636364
Min length4

Characters and Unicode

Total characters206
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)90.9%

Sample

1st row지하수(먹는물)
2nd row지하수(먹는물)
3rd row지하수(생활용수)
4th row지하수(공업·농업·어업용수)
5th row지하수(검사정수)
ValueCountFrequency (%)
지하수(먹는물 2
 
9.1%
상수도(옥내급수관 1
 
4.5%
먹는물공동시설(약수 1
 
4.5%
수영장수 1
 
4.5%
목욕물(순환여과식욕조수 1
 
4.5%
목욕물(욕조수 1
 
4.5%
목욕물(원수 1
 
4.5%
마을상수도(호소원수 1
 
4.5%
마을상수도(하천원수 1
 
4.5%
마을상수도(지하원수 1
 
4.5%
Other values (11) 11
50.0%
2024-03-14T19:23:25.892830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 35
17.0%
( 20
 
9.7%
) 20
 
9.7%
도 10
 
4.9%
상 9
 
4.4%
물 9
 
4.4%
하 8
 
3.9%
원 7
 
3.4%
지 7
 
3.4%
욕 5
 
2.4%
Other values (50) 76
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 163
79.1%
Open Punctuation 20
 
9.7%
Close Punctuation 20
 
9.7%
Other Punctuation 2
 
1.0%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 35
21.5%
도 10
 
6.1%
상 9
 
5.5%
물 9
 
5.5%
하 8
 
4.9%
원 7
 
4.3%
지 7
 
4.3%
욕 5
 
3.1%
는 4
 
2.5%
먹 4
 
2.5%
Other values (46) 65
39.9%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 163
79.1%
Common 43
 
20.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 35
21.5%
도 10
 
6.1%
상 9
 
5.5%
물 9
 
5.5%
하 8
 
4.9%
원 7
 
4.3%
지 7
 
4.3%
욕 5
 
3.1%
는 4
 
2.5%
먹 4
 
2.5%
Other values (46) 65
39.9%
Common
ValueCountFrequency (%)
( 20
46.5%
) 20
46.5%
· 2
 
4.7%
- 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 163
79.1%
ASCII 41
 
19.9%
None 2
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 35
21.5%
도 10
 
6.1%
상 9
 
5.5%
물 9
 
5.5%
하 8
 
4.9%
원 7
 
4.3%
지 7
 
4.3%
욕 5
 
3.1%
는 4
 
2.5%
먹 4
 
2.5%
Other values (46) 65
39.9%
ASCII
ValueCountFrequency (%)
( 20
48.8%
) 20
48.8%
- 1
 
2.4%
None
ValueCountFrequency (%)
· 2
100.0%

검사항목
Text

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-14T19:23:26.545213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6363636
Min length3

Characters and Unicode

Total characters80
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)59.1%

Sample

1st row46항목
2nd row12항목
3rd row20항목
4th row15항목
5th row24항목
ValueCountFrequency (%)
15항목 3
13.6%
4항목 2
 
9.1%
5항목 2
 
9.1%
31항목 2
 
9.1%
11항목 1
 
4.5%
46항목 1
 
4.5%
6항목 1
 
4.5%
9항목 1
 
4.5%
3항목 1
 
4.5%
7항목 1
 
4.5%
Other values (7) 7
31.8%
2024-03-14T19:23:27.326252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 22
27.5%
목 22
27.5%
1 8
 
10.0%
5 7
 
8.8%
4 6
 
7.5%
2 4
 
5.0%
3 3
 
3.8%
9 2
 
2.5%
7 2
 
2.5%
6 2
 
2.5%
Other values (2) 2
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
55.0%
Decimal Number 36
45.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
22.2%
5 7
19.4%
4 6
16.7%
2 4
11.1%
3 3
 
8.3%
9 2
 
5.6%
7 2
 
5.6%
6 2
 
5.6%
0 1
 
2.8%
8 1
 
2.8%
Other Letter
ValueCountFrequency (%)
항 22
50.0%
목 22
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
55.0%
Common 36
45.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
22.2%
5 7
19.4%
4 6
16.7%
2 4
11.1%
3 3
 
8.3%
9 2
 
5.6%
7 2
 
5.6%
6 2
 
5.6%
0 1
 
2.8%
8 1
 
2.8%
Hangul
ValueCountFrequency (%)
항 22
50.0%
목 22
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
55.0%
ASCII 36
45.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 22
50.0%
목 22
50.0%
ASCII
ValueCountFrequency (%)
1 8
22.2%
5 7
19.4%
4 6
16.7%
2 4
11.1%
3 3
 
8.3%
9 2
 
5.6%
7 2
 
5.6%
6 2
 
5.6%
0 1
 
2.8%
8 1
 
2.8%

검체량
Categorical

Distinct4
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size304.0 B
4
9 
2
6 
1
4 
6
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 9
40.9%
2 6
27.3%
1 4
18.2%
6 3
 
13.6%

Length

2024-03-14T19:23:27.617577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:23:27.966228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 9
40.9%
2 6
27.3%
1 4
18.2%
6 3
 
13.6%

처리기간
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
14일
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14일
2nd row14일
3rd row14일
4th row14일
5th row14일

Common Values

ValueCountFrequency (%)
14일 22
100.0%

Length

2024-03-14T19:23:28.342102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:23:28.653204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14일 22
100.0%

수수료
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162481.82
Minimum9400
Maximum363300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T19:23:28.954874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9400
5-th percentile15005
Q133975
median123600
Q3296500
95-th percentile360990
Maximum363300
Range353900
Interquartile range (IQR)262525

Descriptive statistics

Standard deviation134383.88
Coefficient of variation (CV)0.82707029
Kurtosis-1.6597238
Mean162481.82
Median Absolute Deviation (MAD)107650
Skewness0.30693935
Sum3574600
Variance1.8059028 × 1010
MonotonicityNot monotonic
2024-03-14T19:23:29.346802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
267700 1
 
4.5%
27700 1
 
4.5%
17000 1
 
4.5%
306100 1
 
4.5%
75300 1
 
4.5%
54500 1
 
4.5%
9400 1
 
4.5%
14900 1
 
4.5%
249200 1
 
4.5%
247700 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
9400 1
4.5%
14900 1
4.5%
17000 1
4.5%
21600 1
4.5%
22300 1
4.5%
27700 1
4.5%
52800 1
4.5%
54500 1
4.5%
75300 1
4.5%
75800 1
4.5%
ValueCountFrequency (%)
363300 1
4.5%
361800 1
4.5%
345600 1
4.5%
326400 1
4.5%
323800 1
4.5%
306100 1
4.5%
267700 1
4.5%
249200 1
4.5%
247700 1
4.5%
164500 1
4.5%

Interactions

2024-03-14T19:23:23.617654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:23:29.605762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검사종목검사항목검체량수수료
검사종목1.0000.9080.8810.757
검사항목0.9081.0000.9500.906
검체량0.8810.9501.0000.627
수수료0.7570.9060.6271.000
2024-03-14T19:23:29.757667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수수료검체량
수수료1.0000.357
검체량0.3571.000

Missing values

2024-03-14T19:23:23.959354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:23:24.287729image/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

검사종목검사항목검체량처리기간수수료
0지하수(먹는물)46항목414일267700
1지하수(먹는물)12항목214일52800
2지하수(생활용수)20항목414일137800
3지하수(공업·농업·어업용수)15항목414일109400
4지하수(검사정수)24항목414일164500
5먹는샘물(제품수)52항목614일345600
6샘물(원수)48항목614일323800
7상수도(하천원수)31항목414일361800
8상수도(정수)59항목414일326400
9상수도(호소원수)31항목414일363300
검사종목검사항목검체량처리기간수수료
12상수도(옥내급수관)7항목114일27700
13마을상수도(지하원수)11항목214일75800
14마을상수도(하천원수)15항목414일247700
15마을상수도(호소원수)15항목414일249200
16목욕물(원수)5항목214일14900
17목욕물(욕조수)3항목214일9400
18목욕물(순환여과식욕조수)5항목214일54500
19수영장수9항목114일75300
20먹는물공동시설(약수)47항목614일306100
21물놀이수경시설4항목114일17000