Overview

Dataset statistics

Number of variables14
Number of observations1576
Missing cells1381
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory186.4 KiB
Average record size in memory121.1 B

Variable types

Categorical1
Text4
Numeric9

Dataset

Description경남도 내 어장예찰 해황조사 결과에 대한 데이터로, 해황조사 지점, 수온, 염분, 용존산소, 수소이온농도 등의 정보를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3083900

Alerts

표층 수온 is highly overall correlated with 10미터 수온 and 3 other fieldsHigh correlation
10미터 수온 is highly overall correlated with 표층 수온 and 3 other fieldsHigh correlation
표층 염분 is highly overall correlated with 표층 수온 and 2 other fieldsHigh correlation
표층 용존산소 is highly overall correlated with 표층 수온 and 2 other fieldsHigh correlation
10미터 용존산소 is highly overall correlated with 표층 수온 and 3 other fieldsHigh correlation
표층 수소이온농도_피에이치 is highly overall correlated with 10미터 수소이온농도_피에이치High correlation
10미터 수소이온농도_피에이치 is highly overall correlated with 표층 수소이온농도_피에이치High correlation
표층 수온 has 29 (1.8%) missing valuesMissing
10미터 수온 has 225 (14.3%) missing valuesMissing
표층 염분 has 29 (1.8%) missing valuesMissing
10미터 염분 has 224 (14.2%) missing valuesMissing
표층 용존산소 has 29 (1.8%) missing valuesMissing
10미터 용존산소 has 224 (14.2%) missing valuesMissing
표층 수소이온농도_피에이치 has 168 (10.7%) missing valuesMissing
10미터 수소이온농도_피에이치 has 358 (22.7%) missing valuesMissing
투명도 has 95 (6.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:49:11.008660
Analysis finished2023-12-10 23:49:21.050186
Duration10.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
통영
500 
남해
352 
거제
236 
마산
200 
고성
180 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통영 500
31.7%
남해 352
22.3%
거제 236
15.0%
마산 200
 
12.7%
고성 180
 
11.4%
사천 108
 
6.9%

Length

2023-12-11T08:49:21.131123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:49:21.267791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통영 500
31.7%
남해 352
22.3%
거제 236
15.0%
마산 200
 
12.7%
고성 180
 
11.4%
사천 108
 
6.9%

일자
Text

Distinct116
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T08:49:21.572628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.840736
Min length3

Characters and Unicode

Total characters15509
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

Unique18 ?
Unique (%)1.1%

Sample

1st row2021-12-13
2nd row2021-12-13
3rd row2021-12-13
4th row2021-12-13
5th row2021-12-13
ValueCountFrequency (%)
2023-02-15 55
 
3.5%
2021-12-16 41
 
2.6%
2023-05-15 40
 
2.5%
2023-06-15 37
 
2.3%
2022-03-16 37
 
2.3%
2023-07-20 32
 
2.0%
2022-08-25 31
 
2.0%
2022-12-20 30
 
1.9%
2022-01-18 29
 
1.8%
2023-01-17 28
 
1.8%
Other values (106) 1216
77.2%
2023-12-11T08:49:22.290859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4905
31.6%
0 3108
20.0%
- 3066
19.8%
1 1695
 
10.9%
3 913
 
5.9%
5 526
 
3.4%
6 368
 
2.4%
4 309
 
2.0%
7 293
 
1.9%
8 169
 
1.1%
Other values (2) 157
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12375
79.8%
Dash Punctuation 3066
 
19.8%
Other Punctuation 68
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4905
39.6%
0 3108
25.1%
1 1695
 
13.7%
3 913
 
7.4%
5 526
 
4.3%
6 368
 
3.0%
4 309
 
2.5%
7 293
 
2.4%
8 169
 
1.4%
9 89
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 3066
100.0%
Other Punctuation
ValueCountFrequency (%)
. 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15509
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4905
31.6%
0 3108
20.0%
- 3066
19.8%
1 1695
 
10.9%
3 913
 
5.9%
5 526
 
3.4%
6 368
 
2.4%
4 309
 
2.0%
7 293
 
1.9%
8 169
 
1.1%
Other values (2) 157
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15509
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4905
31.6%
0 3108
20.0%
- 3066
19.8%
1 1695
 
10.9%
3 913
 
5.9%
5 526
 
3.4%
6 368
 
2.4%
4 309
 
2.0%
7 293
 
1.9%
8 169
 
1.1%
Other values (2) 157
 
1.0%
Distinct94
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T08:49:22.578317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.5837563
Min length2

Characters and Unicode

Total characters10376
Distinct characters110
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.5%

Sample

1st row방화도(통1)
2nd row비산(통2)
3rd row창좌(통3)
4th row추원(통4)
5th row곡용포(통5)
ValueCountFrequency (%)
방화도(통1 20
 
1.3%
율도(남16 20
 
1.3%
욱곡(마4 20
 
1.3%
용호(마5 20
 
1.3%
실리도(마6 20
 
1.3%
난포(마7 20
 
1.3%
수정(마8 20
 
1.3%
우도(마9 20
 
1.3%
초리도(마10 20
 
1.3%
선소(남18 20
 
1.3%
Other values (84) 1376
87.3%
2023-12-11T08:49:23.087950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 1568
 
15.1%
) 1568
 
15.1%
1 654
 
6.3%
500
 
4.8%
372
 
3.6%
351
 
3.4%
2 317
 
3.1%
248
 
2.4%
219
 
2.1%
211
 
2.0%
Other values (100) 4368
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5095
49.1%
Decimal Number 2145
20.7%
Open Punctuation 1568
 
15.1%
Close Punctuation 1568
 
15.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
500
 
9.8%
372
 
7.3%
351
 
6.9%
248
 
4.9%
219
 
4.3%
211
 
4.1%
200
 
3.9%
122
 
2.4%
99
 
1.9%
97
 
1.9%
Other values (88) 2676
52.5%
Decimal Number
ValueCountFrequency (%)
1 654
30.5%
2 317
14.8%
4 178
 
8.3%
3 178
 
8.3%
5 172
 
8.0%
6 152
 
7.1%
7 138
 
6.4%
8 138
 
6.4%
9 119
 
5.5%
0 99
 
4.6%
Open Punctuation
ValueCountFrequency (%)
( 1568
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1568
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5281
50.9%
Hangul 5095
49.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
500
 
9.8%
372
 
7.3%
351
 
6.9%
248
 
4.9%
219
 
4.3%
211
 
4.1%
200
 
3.9%
122
 
2.4%
99
 
1.9%
97
 
1.9%
Other values (88) 2676
52.5%
Common
ValueCountFrequency (%)
( 1568
29.7%
) 1568
29.7%
1 654
12.4%
2 317
 
6.0%
4 178
 
3.4%
3 178
 
3.4%
5 172
 
3.3%
6 152
 
2.9%
7 138
 
2.6%
8 138
 
2.6%
Other values (2) 218
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5281
50.9%
Hangul 5095
49.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 1568
29.7%
) 1568
29.7%
1 654
12.4%
2 317
 
6.0%
4 178
 
3.4%
3 178
 
3.4%
5 172
 
3.3%
6 152
 
2.9%
7 138
 
2.6%
8 138
 
2.6%
Other values (2) 218
 
4.1%
Hangul
ValueCountFrequency (%)
500
 
9.8%
372
 
7.3%
351
 
6.9%
248
 
4.9%
219
 
4.3%
211
 
4.1%
200
 
3.9%
122
 
2.4%
99
 
1.9%
97
 
1.9%
Other values (88) 2676
52.5%

수심_미터
Real number (ℝ)

Distinct39
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.970876
Minimum3
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:23.262194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q110
median10
Q315
95-th percentile25
Maximum33
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.8124974
Coefficient of variation (CV)0.44811912
Kurtosis1.3997958
Mean12.970876
Median Absolute Deviation (MAD)3
Skewness1.0682861
Sum20442.1
Variance33.785126
MonotonicityNot monotonic
2023-12-11T08:49:23.392210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
10.0 524
33.2%
15.0 261
16.6%
20.0 130
 
8.2%
5.0 62
 
3.9%
13.0 59
 
3.7%
7.0 58
 
3.7%
8.0 53
 
3.4%
19.0 46
 
2.9%
12.0 40
 
2.5%
18.0 33
 
2.1%
Other values (29) 310
19.7%
ValueCountFrequency (%)
3.0 20
 
1.3%
4.0 21
 
1.3%
4.3 1
 
0.1%
4.5 5
 
0.3%
4.7 12
 
0.8%
5.0 62
3.9%
5.2 12
 
0.8%
5.3 1
 
0.1%
5.6 1
 
0.1%
6.0 6
 
0.4%
ValueCountFrequency (%)
33.0 20
 
1.3%
30.0 13
 
0.8%
29.0 20
 
1.3%
26.0 13
 
0.8%
25.0 18
 
1.1%
23.0 14
 
0.9%
21.1 12
 
0.8%
20.6 1
 
0.1%
20.0 130
8.2%
19.0 46
 
2.9%

표층 수온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct296
Distinct (%)19.1%
Missing29
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean16.10501
Minimum4
Maximum28.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:23.555851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.6
Q110.75
median15.8
Q321.4
95-th percentile25.3
Maximum28.1
Range24.1
Interquartile range (IQR)10.65

Descriptive statistics

Standard deviation6.2233377
Coefficient of variation (CV)0.38642248
Kurtosis-1.2405415
Mean16.10501
Median Absolute Deviation (MAD)5.4
Skewness0.021840585
Sum24914.45
Variance38.729933
MonotonicityNot monotonic
2023-12-11T08:49:23.724856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.7 20
 
1.3%
23.8 16
 
1.0%
7.1 14
 
0.9%
17.3 14
 
0.9%
6.4 14
 
0.9%
20.3 14
 
0.9%
20.2 13
 
0.8%
24.7 13
 
0.8%
25.0 13
 
0.8%
13.8 13
 
0.8%
Other values (286) 1403
89.0%
(Missing) 29
 
1.8%
ValueCountFrequency (%)
4.0 1
0.1%
4.2 1
0.1%
4.3 1
0.1%
4.6 1
0.1%
4.9 2
0.1%
4.98 1
0.1%
5.1 1
0.1%
5.2 1
0.1%
5.3 2
0.1%
5.32 1
0.1%
ValueCountFrequency (%)
28.1 1
 
0.1%
27.8 2
0.1%
27.6 1
 
0.1%
27.5 2
0.1%
27.4 2
0.1%
27.3 2
0.1%
27.2 3
0.2%
27.1 2
0.1%
27.0 2
0.1%
26.9 2
0.1%

10미터 수온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct247
Distinct (%)18.3%
Missing225
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean15.017654
Minimum5.2
Maximum25.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:23.939729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.2
5-th percentile6.9
Q110.925
median15.2
Q319.4
95-th percentile23.6
Maximum25.2
Range20
Interquartile range (IQR)8.475

Descriptive statistics

Standard deviation5.1241386
Coefficient of variation (CV)0.34120767
Kurtosis-1.0665439
Mean15.017654
Median Absolute Deviation (MAD)4.2
Skewness-0.016186444
Sum20288.85
Variance26.256797
MonotonicityNot monotonic
2023-12-11T08:49:24.122720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 16
 
1.0%
16.5 15
 
1.0%
19.8 15
 
1.0%
12.4 15
 
1.0%
20.4 14
 
0.9%
16.3 14
 
0.9%
17.8 14
 
0.9%
20.1 14
 
0.9%
17.2 14
 
0.9%
17.4 13
 
0.8%
Other values (237) 1207
76.6%
(Missing) 225
 
14.3%
ValueCountFrequency (%)
5.2 1
 
0.1%
5.6 1
 
0.1%
5.7 2
 
0.1%
5.8 7
0.4%
5.9 6
0.4%
6.0 2
 
0.1%
6.1 2
 
0.1%
6.2 3
 
0.2%
6.3 7
0.4%
6.4 11
0.7%
ValueCountFrequency (%)
25.2 1
 
0.1%
24.9 1
 
0.1%
24.6 5
0.3%
24.5 5
0.3%
24.4 3
 
0.2%
24.3 12
0.8%
24.2 7
0.4%
24.1 6
0.4%
24.0 3
 
0.2%
23.9 7
0.4%

표층 염분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct490
Distinct (%)31.7%
Missing29
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.025275
Minimum5.8
Maximum35.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:24.300412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile29.853
Q132.61
median33.43
Q334.14
95-th percentile34.927
Maximum35.78
Range29.98
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation2.1836749
Coefficient of variation (CV)0.066121323
Kurtosis45.183562
Mean33.025275
Median Absolute Deviation (MAD)0.75
Skewness-5.2280898
Sum51090.1
Variance4.7684359
MonotonicityNot monotonic
2023-12-11T08:49:24.429121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3 15
 
1.0%
33.76 13
 
0.8%
33.09 12
 
0.8%
33.19 10
 
0.6%
33.7 10
 
0.6%
33.78 10
 
0.6%
33.73 9
 
0.6%
33.53 9
 
0.6%
34.19 9
 
0.6%
33.36 9
 
0.6%
Other values (480) 1441
91.4%
(Missing) 29
 
1.8%
ValueCountFrequency (%)
5.8 1
0.1%
7.29 1
0.1%
11.9 1
0.1%
13.07 1
0.1%
15.14 1
0.1%
18.27 1
0.1%
20.89 1
0.1%
22.02 1
0.1%
22.43 1
0.1%
22.46 1
0.1%
ValueCountFrequency (%)
35.78 1
 
0.1%
35.39 1
 
0.1%
35.32 1
 
0.1%
35.24 4
0.3%
35.2 1
 
0.1%
35.19 1
 
0.1%
35.18 5
0.3%
35.17 3
0.2%
35.16 1
 
0.1%
35.15 2
 
0.1%

10미터 염분
Text

MISSING 

Distinct360
Distinct (%)26.6%
Missing224
Missing (%)14.2%
Memory size12.4 KiB
2023-12-11T08:49:24.853401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.885355
Min length2

Characters and Unicode

Total characters6605
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

Unique93 ?
Unique (%)6.9%

Sample

1st row33.76
2nd row33.99
3rd row34.01
4th row34.22
5th row34.34
ValueCountFrequency (%)
33.41 12
 
0.9%
33.5 12
 
0.9%
33.1 11
 
0.8%
33.43 11
 
0.8%
33.38 11
 
0.8%
33.63 11
 
0.8%
34.15 11
 
0.8%
33.81 10
 
0.7%
33.31 10
 
0.7%
33.75 10
 
0.7%
Other values (350) 1243
91.9%
2023-12-11T08:49:25.427392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 2195
33.2%
. 1343
20.3%
4 691
 
10.5%
2 485
 
7.3%
5 329
 
5.0%
1 319
 
4.8%
9 303
 
4.6%
7 271
 
4.1%
6 259
 
3.9%
8 258
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5262
79.7%
Other Punctuation 1343
 
20.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2195
41.7%
4 691
 
13.1%
2 485
 
9.2%
5 329
 
6.3%
1 319
 
6.1%
9 303
 
5.8%
7 271
 
5.2%
6 259
 
4.9%
8 258
 
4.9%
0 152
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 1343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6605
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2195
33.2%
. 1343
20.3%
4 691
 
10.5%
2 485
 
7.3%
5 329
 
5.0%
1 319
 
4.8%
9 303
 
4.6%
7 271
 
4.1%
6 259
 
3.9%
8 258
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2195
33.2%
. 1343
20.3%
4 691
 
10.5%
2 485
 
7.3%
5 329
 
5.0%
1 319
 
4.8%
9 303
 
4.6%
7 271
 
4.1%
6 259
 
3.9%
8 258
 
3.9%

표층 용존산소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct498
Distinct (%)32.2%
Missing29
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean8.4672527
Minimum5.02
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:25.595530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.02
5-th percentile6.406
Q17.52
median8.42
Q39.52
95-th percentile10.49
Maximum12
Range6.98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2928368
Coefficient of variation (CV)0.15268669
Kurtosis-0.61629737
Mean8.4672527
Median Absolute Deviation (MAD)0.97
Skewness0.0014917362
Sum13098.84
Variance1.6714271
MonotonicityNot monotonic
2023-12-11T08:49:25.756501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.84 10
 
0.6%
8.85 9
 
0.6%
7.49 9
 
0.6%
7.52 9
 
0.6%
8.19 9
 
0.6%
7.76 8
 
0.5%
7.74 8
 
0.5%
8.81 8
 
0.5%
9.94 8
 
0.5%
8.64 8
 
0.5%
Other values (488) 1461
92.7%
(Missing) 29
 
1.8%
ValueCountFrequency (%)
5.02 1
0.1%
5.04 1
0.1%
5.17 2
0.1%
5.22 1
0.1%
5.25 1
0.1%
5.35 2
0.1%
5.37 1
0.1%
5.38 1
0.1%
5.41 1
0.1%
5.43 1
0.1%
ValueCountFrequency (%)
12.0 1
0.1%
11.66 1
0.1%
11.62 1
0.1%
11.57 1
0.1%
11.56 1
0.1%
11.47 1
0.1%
11.42 2
0.1%
11.33 2
0.1%
11.3 1
0.1%
11.29 1
0.1%

10미터 용존산소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct613
Distinct (%)45.3%
Missing224
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean7.773247
Minimum0.89
Maximum11.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:25.922712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.89
5-th percentile4.205
Q16.5775
median8.03
Q39.2
95-th percentile10.39
Maximum11.47
Range10.58
Interquartile range (IQR)2.6225

Descriptive statistics

Standard deviation1.933376
Coefficient of variation (CV)0.2487218
Kurtosis0.15248254
Mean7.773247
Median Absolute Deviation (MAD)1.31
Skewness-0.68888621
Sum10509.43
Variance3.7379429
MonotonicityNot monotonic
2023-12-11T08:49:26.060465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.02 8
 
0.5%
8.89 8
 
0.5%
8.38 8
 
0.5%
7.77 7
 
0.4%
8.64 7
 
0.4%
8.92 7
 
0.4%
9.22 7
 
0.4%
9.14 7
 
0.4%
7.96 6
 
0.4%
8.58 6
 
0.4%
Other values (603) 1281
81.3%
(Missing) 224
 
14.2%
ValueCountFrequency (%)
0.89 1
0.1%
1.18 1
0.1%
1.27 2
0.1%
1.37 1
0.1%
1.87 1
0.1%
1.9 1
0.1%
1.98 1
0.1%
2.07 1
0.1%
2.13 1
0.1%
2.16 1
0.1%
ValueCountFrequency (%)
11.47 1
0.1%
11.43 1
0.1%
11.28 2
0.1%
11.15 1
0.1%
11.09 1
0.1%
11.07 1
0.1%
11.01 1
0.1%
11.0 1
0.1%
10.99 2
0.1%
10.94 1
0.1%

표층 수소이온농도_피에이치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct172
Distinct (%)12.2%
Missing168
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean8.0453835
Minimum6.51
Maximum10.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:26.203375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.51
5-th percentile7.65
Q17.89
median8.03
Q38.16
95-th percentile8.56
Maximum10.13
Range3.62
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.31285306
Coefficient of variation (CV)0.038886035
Kurtosis5.6990642
Mean8.0453835
Median Absolute Deviation (MAD)0.13
Skewness0.85872233
Sum11327.9
Variance0.097877038
MonotonicityNot monotonic
2023-12-11T08:49:26.335001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.06 42
 
2.7%
8.07 39
 
2.5%
8.04 33
 
2.1%
8.03 32
 
2.0%
7.95 32
 
2.0%
8.05 31
 
2.0%
8.01 29
 
1.8%
8.08 28
 
1.8%
8.0 28
 
1.8%
7.94 27
 
1.7%
Other values (162) 1087
69.0%
(Missing) 168
 
10.7%
ValueCountFrequency (%)
6.51 1
0.1%
6.64 1
0.1%
6.76 1
0.1%
6.87 2
0.1%
6.91 1
0.1%
6.93 1
0.1%
6.97 1
0.1%
6.99 1
0.1%
7.02 1
0.1%
7.12 1
0.1%
ValueCountFrequency (%)
10.13 1
0.1%
9.59 1
0.1%
9.45 1
0.1%
9.4 1
0.1%
9.38 2
0.1%
9.36 1
0.1%
9.33 2
0.1%
9.3 1
0.1%
9.29 1
0.1%
9.26 1
0.1%

10미터 수소이온농도_피에이치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct163
Distinct (%)13.4%
Missing358
Missing (%)22.7%
Infinite0
Infinite (%)0.0%
Mean8.041092
Minimum6.05
Maximum10.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:26.498847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.05
5-th percentile7.65
Q17.89
median8.01
Q38.12
95-th percentile8.603
Maximum10.28
Range4.23
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.31066214
Coefficient of variation (CV)0.038634323
Kurtosis8.0275536
Mean8.041092
Median Absolute Deviation (MAD)0.12
Skewness1.408277
Sum9794.05
Variance0.096510968
MonotonicityNot monotonic
2023-12-11T08:49:26.640147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.03 43
 
2.7%
7.99 38
 
2.4%
8.02 37
 
2.3%
8.05 35
 
2.2%
8.0 35
 
2.2%
7.97 34
 
2.2%
8.04 34
 
2.2%
8.01 31
 
2.0%
8.06 26
 
1.6%
7.96 26
 
1.6%
Other values (153) 879
55.8%
(Missing) 358
22.7%
ValueCountFrequency (%)
6.05 1
0.1%
6.88 1
0.1%
6.91 1
0.1%
6.93 1
0.1%
6.98 1
0.1%
7.29 2
0.1%
7.35 1
0.1%
7.38 2
0.1%
7.39 1
0.1%
7.4 1
0.1%
ValueCountFrequency (%)
10.28 1
0.1%
9.65 1
0.1%
9.59 1
0.1%
9.56 1
0.1%
9.54 1
0.1%
9.43 1
0.1%
9.42 2
0.1%
9.41 1
0.1%
9.37 1
0.1%
9.33 1
0.1%

투명도
Real number (ℝ)

MISSING 

Distinct66
Distinct (%)4.5%
Missing95
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean3.3792708
Minimum0.4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.0 KiB
2023-12-11T08:49:26.799316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.1
Q12
median3
Q34.1
95-th percentile6
Maximum15
Range14.6
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.6097703
Coefficient of variation (CV)0.47636619
Kurtosis2.9168015
Mean3.3792708
Median Absolute Deviation (MAD)1
Skewness1.1299211
Sum5004.7
Variance2.5913605
MonotonicityNot monotonic
2023-12-11T08:49:26.952981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 246
15.6%
2.0 238
15.1%
5.0 186
11.8%
4.0 174
11.0%
2.5 102
 
6.5%
3.5 68
 
4.3%
1.0 62
 
3.9%
6.0 56
 
3.6%
1.5 37
 
2.3%
4.5 28
 
1.8%
Other values (56) 284
18.0%
(Missing) 95
 
6.0%
ValueCountFrequency (%)
0.4 2
 
0.1%
0.5 4
 
0.3%
0.7 2
 
0.1%
0.8 3
 
0.2%
1.0 62
3.9%
1.1 4
 
0.3%
1.2 8
 
0.5%
1.3 11
 
0.7%
1.4 8
 
0.5%
1.5 37
2.3%
ValueCountFrequency (%)
15.0 1
 
0.1%
12.0 1
 
0.1%
11.0 1
 
0.1%
10.0 2
 
0.1%
9.0 4
 
0.3%
8.9 1
 
0.1%
8.0 15
1.0%
7.5 3
 
0.2%
7.3 1
 
0.1%
7.0 27
1.7%
Distinct105
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2023-12-11T08:49:27.339470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.26967
Min length1

Characters and Unicode

Total characters28793
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

Unique19 ?
Unique (%)1.2%

Sample

1st row34.840357 128.469015
2nd row34.812095 128.500752
3rd row34.777615 128.512548
4th row34.7653316 128.535929
5th row34.7497689 128.5625623
ValueCountFrequency (%)
34.98167 24
 
0.8%
34.91778 24
 
0.8%
128.03778 24
 
0.8%
34.96333 24
 
0.8%
127.99667 24
 
0.8%
128.04417 24
 
0.8%
128.02167 24
 
0.8%
34.98056 23
 
0.7%
128.55876 20
 
0.6%
128.63296 20
 
0.6%
Other values (192) 2861
92.5%
2023-12-11T08:49:27.831891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 3463
12.0%
8 3206
11.1%
. 3092
10.7%
2 3055
10.6%
1 2896
10.1%
4 2676
9.3%
5 1951
6.8%
7 1942
6.7%
9 1939
6.7%
1576
5.5%
Other values (2) 2997
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24125
83.8%
Other Punctuation 3092
 
10.7%
Space Separator 1576
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 3463
14.4%
8 3206
13.3%
2 3055
12.7%
1 2896
12.0%
4 2676
11.1%
5 1951
8.1%
7 1942
8.0%
9 1939
8.0%
6 1571
6.5%
0 1426
5.9%
Other Punctuation
ValueCountFrequency (%)
. 3092
100.0%
Space Separator
ValueCountFrequency (%)
1576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28793
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 3463
12.0%
8 3206
11.1%
. 3092
10.7%
2 3055
10.6%
1 2896
10.1%
4 2676
9.3%
5 1951
6.8%
7 1942
6.7%
9 1939
6.7%
1576
5.5%
Other values (2) 2997
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28793
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 3463
12.0%
8 3206
11.1%
. 3092
10.7%
2 3055
10.6%
1 2896
10.1%
4 2676
9.3%
5 1951
6.8%
7 1942
6.7%
9 1939
6.7%
1576
5.5%
Other values (2) 2997
10.4%

Interactions

2023-12-11T08:49:19.406776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:11.933928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.889906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.837975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.811403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.011910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.961229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.788472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.601029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.520943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.026947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.999723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.928626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.923379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.098975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.045214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.881132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.692252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.616477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.120241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.122229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.023813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.027892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.205002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.128139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.961648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.774396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.712731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.250633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.257427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.152412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.153316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.308943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.231396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.054993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.874403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.800444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.353410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.367407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.272064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.248292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.411646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.329427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.141568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.953806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.897534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.476871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.455072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.373502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.357419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.507985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.426662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.225892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.035643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.993986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.565852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.535698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.465477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.449549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.615372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.530136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.319821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.119665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:20.195682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.678463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.626030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.583038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.556610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.723208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.614872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.419233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.208037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:20.354967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:12.774374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:13.731240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:14.693730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:15.921116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:16.841301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:17.691588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:18.510752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:49:19.301498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:49:27.969832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군해황조사 지점명칭수심_미터표층 수온10미터 수온표층 염분표층 용존산소10미터 용존산소표층 수소이온농도_피에이치10미터 수소이온농도_피에이치투명도
시군1.0001.0000.6500.2340.2600.3780.3570.2500.4590.3900.424
해황조사 지점명칭1.0001.0000.9920.0000.0000.3360.3430.1880.4670.3650.539
수심_미터0.6500.9921.0000.2050.1590.1870.2350.1270.3140.0000.305
표층 수온0.2340.0000.2051.0000.9550.4320.8170.8610.4200.3530.269
10미터 수온0.2600.0000.1590.9551.0000.4180.8130.8400.4100.3720.270
표층 염분0.3780.3360.1870.4320.4181.0000.2230.4600.1530.0890.260
표층 용존산소0.3570.3430.2350.8170.8130.2231.0000.8620.3120.3980.264
10미터 용존산소0.2500.1880.1270.8610.8400.4600.8621.0000.2340.4830.246
표층 수소이온농도_피에이치0.4590.4670.3140.4200.4100.1530.3120.2341.0000.9580.000
10미터 수소이온농도_피에이치0.3900.3650.0000.3530.3720.0890.3980.4830.9581.0000.000
투명도0.4240.5390.3050.2690.2700.2600.2640.2460.0000.0001.000
2023-12-11T08:49:28.139678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수심_미터표층 수온10미터 수온표층 염분표층 용존산소10미터 용존산소표층 수소이온농도_피에이치10미터 수소이온농도_피에이치투명도시군
수심_미터1.0000.002-0.005-0.0220.0400.0640.0410.0830.2510.394
표층 수온0.0021.0000.965-0.570-0.787-0.9180.151-0.091-0.2510.125
10미터 수온-0.0050.9651.000-0.521-0.822-0.8790.130-0.053-0.2830.139
표층 염분-0.022-0.570-0.5211.0000.3120.514-0.1820.1070.0920.198
표층 용존산소0.040-0.787-0.8220.3121.0000.8170.0820.1310.2730.196
10미터 용존산소0.064-0.918-0.8790.5140.8171.000-0.0550.2410.2970.133
표층 수소이온농도_피에이치0.0410.1510.130-0.1820.082-0.0551.0000.7690.0630.262
10미터 수소이온농도_피에이치0.083-0.091-0.0530.1070.1310.2410.7691.0000.1160.217
투명도0.251-0.251-0.2830.0920.2730.2970.0630.1161.0000.226
시군0.3940.1250.1390.1980.1960.1330.2620.2170.2261.000

Missing values

2023-12-11T08:49:20.568898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:49:20.772699image/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.
2023-12-11T08:49:20.926025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군일자해황조사 지점명칭수심_미터표층 수온10미터 수온표층 염분10미터 염분표층 용존산소10미터 용존산소표층 수소이온농도_피에이치10미터 수소이온농도_피에이치투명도조사지점 좌표
0통영2021-12-13방화도(통1)15.012.512.433.7633.769.559.247.887.892.834.840357 128.469015
1통영2021-12-13비산(통2)8.013.113.133.9933.998.598.657.697.853.334.812095 128.500752
2통영2021-12-13창좌(통3)10.012.712.734.0134.019.149.347.847.883.834.777615 128.512548
3통영2021-12-13추원(통4)18.013.913.834.2334.227.947.827.677.793.834.7653316 128.535929
4통영2021-12-13곡용포(통5)25.014.314.334.3234.347.767.627.77.794.734.7497689 128.5625623
5통영2021-12-13용초(통6)19.014.114.134.2634.37.847.767.667.84.134.74808 128.48568
6통영2021-12-13학림(통7)20.014.014.034.1534.267.967.787.667.83.434.754641 128.4152946
7통영2021-12-13곤리(통8)26.013.913.934.1834.227.987.997.77.812.534.78548 128.37402
8통영2021-12-13오비(통9)15.013.713.734.1134.228.288.447.77.843.434.8068848 128.3623538
9통영2021-12-13풍서(통10)10.013.813.934.1534.228.268.417.697.843.034.8164487 128.3401229
시군일자해황조사 지점명칭수심_미터표층 수온10미터 수온표층 염분10미터 염분표층 용존산소10미터 용존산소표층 수소이온농도_피에이치10미터 수소이온농도_피에이치투명도조사지점 좌표
1566남해2023-07-17평산(남9)5.023.420.222.8932.887.775.297.957.862.534.76766 127.83769
1567남해2023-07-17장항(남10)10.022.518.723.8533.846.734.327.817.812.034.78877 127.83319
1568남해2023-07-17서상(남11)10.022.119.025.2933.646.174.517.767.791.834.80388 127.82911
1569남해2023-07-17갈화(남12)13.023.820.05.832.687.554.447.487.791.034.90781 127.83812
1570남해2023-07-17대도(남13)15.023.620.011.932.767.154.287.587.761.234.93074 127.84253
1571남해2023-07-17중평(남14)10.024.9<NA>15.14<NA>8.97<NA>8.1<NA>0.834.96829 127.9082
1572남해2023-07-17문항(남15)7.024.1<NA>23.1<NA>8.87<NA>8.13<NA>1.334.90995 127.93478
1573남해2023-07-17율도(남16)7.024.2<NA>24.42<NA>9.13<NA>8.17<NA>1.334.90063 127.98418
1574남해2023-07-17광천(남17)4.023.7<NA>26.51<NA>7.56<NA>7.82<NA>1.534.86342 127.94687
1575남해2023-07-17선소(남18)3.023.1<NA>28.5<NA>6.15<NA>7.74<NA>2.034.84453 127.93373