Overview

Dataset statistics

Number of variables14
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory129.3 B

Variable types

Text1
Numeric11
Categorical2

Dataset

Description부산해양환경현황(2015년)
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3077374

Alerts

DIN(ug/L) is highly overall correlated with DIP(ug/L) and 6 other fieldsHigh correlation
DIP(ug/L) is highly overall correlated with DIN(ug/L) and 5 other fieldsHigh correlation
Chl-a(ug/L) is highly overall correlated with COD(㎎/L) and 1 other fieldsHigh correlation
투명도(m) is highly overall correlated with DO(㎎/L)High correlation
총대장균군(MPN/100㎎/L) is highly overall correlated with DIN(ug/L) and 4 other fieldsHigh correlation
COD(㎎/L) is highly overall correlated with DIN(ug/L) and 6 other fieldsHigh correlation
DO(㎎/L) is highly overall correlated with DIP(ug/L) and 1 other fieldsHigh correlation
수온(℃) is highly overall correlated with pH and 1 other fieldsHigh correlation
전기전도도(mS/㎝) is highly overall correlated with DIN(ug/L) and 5 other fieldsHigh correlation
염분(‰) is highly overall correlated with DIN(ug/L) and 6 other fieldsHigh correlation
pH is highly overall correlated with DIN(ug/L) and 6 other fieldsHigh correlation
Cd(㎎/L) is highly overall correlated with DIN(ug/L) and 5 other fieldsHigh correlation
Cd(㎎/L) is highly imbalanced (65.5%)Imbalance
조사지점 has unique valuesUnique
DIN(ug/L) has unique valuesUnique
총대장균군(MPN/100㎎/L) has unique valuesUnique
Pb(㎎/L) has 14 (45.2%) zerosZeros

Reproduction

Analysis started2023-12-10 16:02:03.598520
Analysis finished2023-12-10 16:02:20.653121
Duration17.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조사지점
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T01:02:20.919410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.9677419
Min length2

Characters and Unicode

Total characters92
Distinct characters55
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

Unique31 ?
Unique (%)100.0%

Sample

1st row5부두
2nd row가덕도
3rd row감천항
4th row고리
5th row남외항
ValueCountFrequency (%)
5부두 1
 
3.2%
북외항 1
 
3.2%
칠암 1
 
3.2%
청사포 1
 
3.2%
장림 1
 
3.2%
일광 1
 
3.2%
오륙도 1
 
3.2%
암남공원 1
 
3.2%
신호 1
 
3.2%
신항 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T01:02:21.654147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
8.7%
6
 
6.5%
4
 
4.3%
4
 
4.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
Other values (45) 53
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
98.9%
Decimal Number 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
 
8.8%
6
 
6.6%
4
 
4.4%
4
 
4.4%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
Other values (44) 52
57.1%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
98.9%
Common 1
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
 
8.8%
6
 
6.6%
4
 
4.4%
4
 
4.4%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
Other values (44) 52
57.1%
Common
ValueCountFrequency (%)
5 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
98.9%
ASCII 1
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
 
8.8%
6
 
6.6%
4
 
4.4%
4
 
4.4%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
3
 
3.3%
2
 
2.2%
Other values (44) 52
57.1%
ASCII
ValueCountFrequency (%)
5 1
100.0%

DIN(ug/L)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.53548
Minimum94.9
Maximum1743.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:21.892225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum94.9
5-th percentile96.25
Q1109.8
median122.9
Q3192.7
95-th percentile1100.9
Maximum1743.7
Range1648.8
Interquartile range (IQR)82.9

Descriptive statistics

Standard deviation369.56722
Coefficient of variation (CV)1.3970422
Kurtosis9.3698159
Mean264.53548
Median Absolute Deviation (MAD)20.4
Skewness3.0770187
Sum8200.6
Variance136579.93
MonotonicityNot monotonic
2023-12-11T01:02:22.119386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
178.2 1
 
3.2%
164.8 1
 
3.2%
207.2 1
 
3.2%
107.1 1
 
3.2%
102.5 1
 
3.2%
1743.7 1
 
3.2%
112.4 1
 
3.2%
139.2 1
 
3.2%
110.3 1
 
3.2%
1033.1 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
94.9 1
3.2%
96.0 1
3.2%
96.5 1
3.2%
100.6 1
3.2%
102.5 1
3.2%
107.1 1
3.2%
108.4 1
3.2%
109.3 1
3.2%
110.3 1
3.2%
112.4 1
3.2%
ValueCountFrequency (%)
1743.7 1
3.2%
1168.7 1
3.2%
1033.1 1
3.2%
364.5 1
3.2%
336.3 1
3.2%
293.0 1
3.2%
261.8 1
3.2%
207.2 1
3.2%
178.2 1
3.2%
164.8 1
3.2%

DIP(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.719355
Minimum7.3
Maximum71.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:22.331915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile7.5
Q18.4
median9.3
Q314.5
95-th percentile29.3
Maximum71.5
Range64.2
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation12.418063
Coefficient of variation (CV)0.90514917
Kurtosis16.709779
Mean13.719355
Median Absolute Deviation (MAD)1.8
Skewness3.9105801
Sum425.3
Variance154.20828
MonotonicityNot monotonic
2023-12-11T01:02:22.509479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
8.5 4
 
12.9%
9.3 3
 
9.7%
8.3 3
 
9.7%
14.5 2
 
6.5%
7.5 2
 
6.5%
7.8 2
 
6.5%
9.5 1
 
3.2%
16.0 1
 
3.2%
40.8 1
 
3.2%
7.3 1
 
3.2%
Other values (11) 11
35.5%
ValueCountFrequency (%)
7.3 1
 
3.2%
7.5 2
6.5%
7.8 2
6.5%
8.3 3
9.7%
8.5 4
12.9%
8.8 1
 
3.2%
9.3 3
9.7%
9.5 1
 
3.2%
10.0 1
 
3.2%
11.5 1
 
3.2%
ValueCountFrequency (%)
71.5 1
3.2%
40.8 1
3.2%
17.8 1
3.2%
16.8 1
3.2%
16.3 1
3.2%
16.0 1
3.2%
15.5 1
3.2%
14.5 2
6.5%
13.3 1
3.2%
12.0 1
3.2%

Chl-a(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4064516
Minimum1.2
Maximum11.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:22.698315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.7
Q12.1
median2.5
Q33.95
95-th percentile7.85
Maximum11.4
Range10.2
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation2.2860352
Coefficient of variation (CV)0.67108988
Kurtosis5.1189728
Mean3.4064516
Median Absolute Deviation (MAD)0.6
Skewness2.1936824
Sum105.6
Variance5.225957
MonotonicityNot monotonic
2023-12-11T01:02:22.876442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.7 4
 
12.9%
2.4 3
 
9.7%
2.2 3
 
9.7%
2.9 2
 
6.5%
2.0 2
 
6.5%
4.4 1
 
3.2%
3.5 1
 
3.2%
2.8 1
 
3.2%
1.9 1
 
3.2%
5.2 1
 
3.2%
Other values (12) 12
38.7%
ValueCountFrequency (%)
1.2 1
 
3.2%
1.7 4
12.9%
1.9 1
 
3.2%
2.0 2
6.5%
2.2 3
9.7%
2.3 1
 
3.2%
2.4 3
9.7%
2.5 1
 
3.2%
2.7 1
 
3.2%
2.8 1
 
3.2%
ValueCountFrequency (%)
11.4 1
3.2%
9.5 1
3.2%
6.2 1
3.2%
5.9 1
3.2%
5.2 1
3.2%
5.0 1
3.2%
4.5 1
3.2%
4.4 1
3.2%
3.5 1
3.2%
3.2 1
3.2%

투명도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.009677
Minimum81.2
Maximum109.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:23.070085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81.2
5-th percentile87.45
Q190.5
median93
Q398.05
95-th percentile101.3
Maximum109.7
Range28.5
Interquartile range (IQR)7.55

Descriptive statistics

Standard deviation5.5263825
Coefficient of variation (CV)0.058785251
Kurtosis1.3071548
Mean94.009677
Median Absolute Deviation (MAD)3.8
Skewness0.45074097
Sum2914.3
Variance30.540903
MonotonicityNot monotonic
2023-12-11T01:02:23.267249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
90.5 2
 
6.5%
98.1 2
 
6.5%
89.5 1
 
3.2%
103.5 1
 
3.2%
92.9 1
 
3.2%
98.0 1
 
3.2%
89.2 1
 
3.2%
91.4 1
 
3.2%
94.4 1
 
3.2%
98.6 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
81.2 1
3.2%
86.2 1
3.2%
88.7 1
3.2%
88.8 1
3.2%
89.2 1
3.2%
89.5 1
3.2%
89.8 1
3.2%
90.5 2
6.5%
90.7 1
3.2%
90.9 1
3.2%
ValueCountFrequency (%)
109.7 1
3.2%
103.5 1
3.2%
99.1 1
3.2%
99.0 1
3.2%
98.6 1
3.2%
98.4 1
3.2%
98.1 2
6.5%
98.0 1
3.2%
97.8 1
3.2%
97.5 1
3.2%

pH
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
8.2
20 
8.1
8.3
 
2
8.0
 
1
7.8
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)6.5%

Sample

1st row8.1
2nd row8.1
3rd row8.3
4th row8.2
5th row8.2

Common Values

ValueCountFrequency (%)
8.2 20
64.5%
8.1 7
 
22.6%
8.3 2
 
6.5%
8.0 1
 
3.2%
7.8 1
 
3.2%

Length

2023-12-11T01:02:23.464577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:02:23.640907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8.2 20
64.5%
8.1 7
 
22.6%
8.3 2
 
6.5%
8.0 1
 
3.2%
7.8 1
 
3.2%

총대장균군(MPN/100㎎/L)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean702.96129
Minimum0.5
Maximum10522.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:24.201387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.75
Q111.8
median46.5
Q3321
95-th percentile3287.5
Maximum10522.5
Range10522
Interquartile range (IQR)309.2

Descriptive statistics

Standard deviation2024.269
Coefficient of variation (CV)2.8796309
Kurtosis19.722921
Mean702.96129
Median Absolute Deviation (MAD)45.5
Skewness4.2797473
Sum21791.8
Variance4097665.1
MonotonicityNot monotonic
2023-12-11T01:02:24.418478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1443.8 1
 
3.2%
23.8 1
 
3.2%
189.3 1
 
3.2%
4.8 1
 
3.2%
2.5 1
 
3.2%
10522.5 1
 
3.2%
12.8 1
 
3.2%
22.3 1
 
3.2%
99.0 1
 
3.2%
231.5 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0.5 1
3.2%
1.0 1
3.2%
2.5 1
3.2%
3.8 1
3.2%
4.0 1
3.2%
4.8 1
3.2%
6.8 1
3.2%
10.8 1
3.2%
12.8 1
3.2%
14.5 1
3.2%
ValueCountFrequency (%)
10522.5 1
3.2%
4450.0 1
3.2%
2125.0 1
3.2%
1443.8 1
3.2%
585.8 1
3.2%
457.5 1
3.2%
424.5 1
3.2%
380.0 1
3.2%
262.0 1
3.2%
241.0 1
3.2%

Cd(㎎/L)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
0.0
28 
0.1
 
2
0.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 28
90.3%
0.1 2
 
6.5%
0.5 1
 
3.2%

Length

2023-12-11T01:02:24.633391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:02:24.754511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 28
90.3%
0.1 2
 
6.5%
0.5 1
 
3.2%

Pb(㎎/L)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14193548
Minimum0
Maximum1.2
Zeros14
Zeros (%)45.2%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:24.885272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q30.1
95-th percentile0.6
Maximum1.2
Range1.2
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.25400025
Coefficient of variation (CV)1.7895472
Kurtosis9.9034463
Mean0.14193548
Median Absolute Deviation (MAD)0.1
Skewness2.9511374
Sum4.4
Variance0.064516129
MonotonicityNot monotonic
2023-12-11T01:02:25.065735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 14
45.2%
0.1 11
35.5%
0.6 2
 
6.5%
0.4 1
 
3.2%
0.2 1
 
3.2%
1.2 1
 
3.2%
0.3 1
 
3.2%
ValueCountFrequency (%)
0.0 14
45.2%
0.1 11
35.5%
0.2 1
 
3.2%
0.3 1
 
3.2%
0.4 1
 
3.2%
0.6 2
 
6.5%
1.2 1
 
3.2%
ValueCountFrequency (%)
1.2 1
 
3.2%
0.6 2
 
6.5%
0.4 1
 
3.2%
0.3 1
 
3.2%
0.2 1
 
3.2%
0.1 11
35.5%
0.0 14
45.2%

COD(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4387097
Minimum0.7
Maximum3.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:25.185747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.7
Q10.95
median1.2
Q31.5
95-th percentile3.4
Maximum3.8
Range3.1
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.81677309
Coefficient of variation (CV)0.56771224
Kurtosis2.7545664
Mean1.4387097
Median Absolute Deviation (MAD)0.3
Skewness1.8293361
Sum44.6
Variance0.66711828
MonotonicityNot monotonic
2023-12-11T01:02:25.326988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1.0 4
12.9%
1.4 4
12.9%
1.5 3
9.7%
0.7 3
9.7%
1.1 3
9.7%
0.8 3
9.7%
0.9 2
 
6.5%
1.3 2
 
6.5%
3.8 1
 
3.2%
1.7 1
 
3.2%
Other values (5) 5
16.1%
ValueCountFrequency (%)
0.7 3
9.7%
0.8 3
9.7%
0.9 2
6.5%
1.0 4
12.9%
1.1 3
9.7%
1.2 1
 
3.2%
1.3 2
6.5%
1.4 4
12.9%
1.5 3
9.7%
1.7 1
 
3.2%
ValueCountFrequency (%)
3.8 1
 
3.2%
3.6 1
 
3.2%
3.2 1
 
3.2%
2.6 1
 
3.2%
2.2 1
 
3.2%
1.7 1
 
3.2%
1.5 3
9.7%
1.4 4
12.9%
1.3 2
6.5%
1.2 1
 
3.2%

DO(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2935484
Minimum7.5
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:25.474899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile7.55
Q18
median8.3
Q38.55
95-th percentile9.2
Maximum9.3
Range1.8
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.49392002
Coefficient of variation (CV)0.059554729
Kurtosis-0.35634088
Mean8.2935484
Median Absolute Deviation (MAD)0.3
Skewness0.29147413
Sum257.1
Variance0.24395699
MonotonicityNot monotonic
2023-12-11T01:02:25.655751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8.2 4
12.9%
8.6 3
9.7%
8.5 3
9.7%
8.3 3
9.7%
7.5 2
 
6.5%
7.7 2
 
6.5%
8.4 2
 
6.5%
8.0 2
 
6.5%
7.6 2
 
6.5%
9.2 2
 
6.5%
Other values (5) 6
19.4%
ValueCountFrequency (%)
7.5 2
6.5%
7.6 2
6.5%
7.7 2
6.5%
7.9 1
 
3.2%
8.0 2
6.5%
8.1 2
6.5%
8.2 4
12.9%
8.3 3
9.7%
8.4 2
6.5%
8.5 3
9.7%
ValueCountFrequency (%)
9.3 1
 
3.2%
9.2 2
6.5%
9.0 1
 
3.2%
8.9 1
 
3.2%
8.6 3
9.7%
8.5 3
9.7%
8.4 2
6.5%
8.3 3
9.7%
8.2 4
12.9%
8.1 2
6.5%

수온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.748387
Minimum14.8
Maximum18.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:25.787820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.8
5-th percentile14.95
Q115.15
median15.7
Q316
95-th percentile17.05
Maximum18.1
Range3.3
Interquartile range (IQR)0.85

Descriptive statistics

Standard deviation0.78352663
Coefficient of variation (CV)0.049752817
Kurtosis1.3249303
Mean15.748387
Median Absolute Deviation (MAD)0.5
Skewness1.2108558
Sum488.2
Variance0.61391398
MonotonicityNot monotonic
2023-12-11T01:02:25.964462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
15.0 4
12.9%
15.7 4
12.9%
15.2 4
12.9%
15.8 3
9.7%
15.1 2
 
6.5%
15.5 2
 
6.5%
16.4 2
 
6.5%
16.8 1
 
3.2%
14.8 1
 
3.2%
17.0 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
14.8 1
 
3.2%
14.9 1
 
3.2%
15.0 4
12.9%
15.1 2
6.5%
15.2 4
12.9%
15.5 2
6.5%
15.6 1
 
3.2%
15.7 4
12.9%
15.8 3
9.7%
15.9 1
 
3.2%
ValueCountFrequency (%)
18.1 1
 
3.2%
17.1 1
 
3.2%
17.0 1
 
3.2%
16.9 1
 
3.2%
16.8 1
 
3.2%
16.4 2
6.5%
16.1 1
 
3.2%
15.9 1
 
3.2%
15.8 3
9.7%
15.7 4
12.9%

전기전도도(mS/㎝)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.477419
Minimum34.3
Maximum51.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:26.125372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.3
5-th percentile43.75
Q150.15
median50.8
Q350.9
95-th percentile51.3
Maximum51.3
Range17
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation3.4884294
Coefficient of variation (CV)0.070505484
Kurtosis12.477527
Mean49.477419
Median Absolute Deviation (MAD)0.2
Skewness-3.3716689
Sum1533.8
Variance12.16914
MonotonicityNot monotonic
2023-12-11T01:02:26.322647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
50.8 12
38.7%
51.0 5
16.1%
51.3 3
 
9.7%
50.3 2
 
6.5%
48.3 1
 
3.2%
46.0 1
 
3.2%
48.0 1
 
3.2%
50.5 1
 
3.2%
46.3 1
 
3.2%
50.0 1
 
3.2%
Other values (3) 3
 
9.7%
ValueCountFrequency (%)
34.3 1
3.2%
41.5 1
3.2%
46.0 1
3.2%
46.3 1
3.2%
48.0 1
3.2%
48.3 1
3.2%
49.8 1
3.2%
50.0 1
3.2%
50.3 2
6.5%
50.5 1
3.2%
ValueCountFrequency (%)
51.3 3
 
9.7%
51.0 5
16.1%
50.8 12
38.7%
50.5 1
 
3.2%
50.3 2
 
6.5%
50.0 1
 
3.2%
49.8 1
 
3.2%
48.3 1
 
3.2%
48.0 1
 
3.2%
46.3 1
 
3.2%

염분(‰)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.3
Minimum21.7
Maximum33.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:26.475200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile28.15
Q132.7
median33.2
Q333.3
95-th percentile33.6
Maximum33.6
Range11.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation2.4681977
Coefficient of variation (CV)0.07641479
Kurtosis11.809493
Mean32.3
Median Absolute Deviation (MAD)0.2
Skewness-3.2854551
Sum1001.3
Variance6.092
MonotonicityNot monotonic
2023-12-11T01:02:26.623475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
33.3 8
25.8%
33.2 4
12.9%
33.6 3
 
9.7%
33.4 3
 
9.7%
31.4 2
 
6.5%
32.7 2
 
6.5%
33.1 1
 
3.2%
29.7 1
 
3.2%
33.0 1
 
3.2%
33.5 1
 
3.2%
Other values (5) 5
16.1%
ValueCountFrequency (%)
21.7 1
3.2%
26.6 1
3.2%
29.7 1
3.2%
29.8 1
3.2%
31.4 2
6.5%
32.6 1
3.2%
32.7 2
6.5%
32.9 1
3.2%
33.0 1
3.2%
33.1 1
3.2%
ValueCountFrequency (%)
33.6 3
 
9.7%
33.5 1
 
3.2%
33.4 3
 
9.7%
33.3 8
25.8%
33.2 4
12.9%
33.1 1
 
3.2%
33.0 1
 
3.2%
32.9 1
 
3.2%
32.7 2
 
6.5%
32.6 1
 
3.2%

Interactions

2023-12-11T01:02:18.243084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.180542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.634263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.840036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.196740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.372131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.385597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.404376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.316947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:14.503232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.660876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.373906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.313841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.756500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.932022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.288602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.466100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.467528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.486047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.406336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.032001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.793965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.489924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.426576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.859232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.022388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.399535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.553511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.562415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.567472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.484693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.335056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.951741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.578978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.576232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.952489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.436815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.511740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.631675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.647681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.646770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.560536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.490508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.090785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.699055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.697664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.058009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.541968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.621073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.713904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.737494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.743721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.643451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.640018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.262786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.836117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.809294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.167242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.620840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.730965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.794634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.827026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.825303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.725342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.776404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.395087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:19.002104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:04.920899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.291393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.699892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.856576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.893604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.924194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.905334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:13.120151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:15.949509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.542047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:19.165847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.083759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.384028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.787780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.956060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.001819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.019632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.978732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:13.290701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.083123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.659598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:19.345065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.247191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.507315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.876569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.047483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.098947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.128629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.068634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:13.431240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.223485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.822901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:19.490133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.386513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.642596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:07.975062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.155925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.198910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.229894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.157636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:13.633837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.376446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:17.960602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:19.674246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:05.503804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:06.743973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:08.097294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:09.264271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:10.303075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:11.317877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:12.236457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:13.940579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:16.507972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:18.116953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:02:26.727789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사지점DIN(ug/L)DIP(ug/L)Chl-a(ug/L)투명도(m)pH총대장균군(MPN/100㎎/L)Cd(㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
조사지점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
DIN(ug/L)1.0001.0000.8780.4750.7100.9640.9550.8140.0000.8840.0000.6700.9270.979
DIP(ug/L)1.0000.8781.0000.2840.7901.0000.8580.4780.0000.9450.4220.7040.9430.761
Chl-a(ug/L)1.0000.4750.2841.0000.7590.5030.6390.7310.0000.7280.6320.5870.0000.235
투명도(m)1.0000.7100.7900.7591.0000.6930.8450.7490.4120.6670.6480.3880.6300.397
pH1.0000.9641.0000.5030.6931.0000.9560.5090.0000.8160.7560.7340.8060.908
총대장균군(MPN/100㎎/L)1.0000.9550.8580.6390.8450.9561.0000.4340.0000.7760.0000.6090.7620.878
Cd(㎎/L)1.0000.8140.4780.7310.7490.5090.4341.0000.0000.7930.6020.6840.9810.814
Pb(㎎/L)1.0000.0000.0000.0000.4120.0000.0000.0001.0000.0000.6600.0000.0000.000
COD(㎎/L)1.0000.8840.9450.7280.6670.8160.7760.7930.0001.0000.4430.4080.8110.769
DO(㎎/L)1.0000.0000.4220.6320.6480.7560.0000.6020.6600.4431.0000.5750.1630.000
수온(℃)1.0000.6700.7040.5870.3880.7340.6090.6840.0000.4080.5751.0000.6820.688
전기전도도(mS/㎝)1.0000.9270.9430.0000.6300.8060.7620.9810.0000.8110.1630.6821.0001.000
염분(‰)1.0000.9790.7610.2350.3970.9080.8780.8140.0000.7690.0000.6881.0001.000
2023-12-11T01:02:27.046871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Cd(㎎/L)pH
Cd(㎎/L)1.0000.425
pH0.4251.000
2023-12-11T01:02:27.295819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DIN(ug/L)DIP(ug/L)Chl-a(ug/L)투명도(m)총대장균군(MPN/100㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)pHCd(㎎/L)
DIN(ug/L)1.0000.6230.215-0.1930.754-0.1660.509-0.4990.074-0.630-0.7250.7270.808
DIP(ug/L)0.6231.0000.204-0.2450.6500.0200.441-0.6110.058-0.645-0.6790.9810.463
Chl-a(ug/L)0.2150.2041.000-0.2060.315-0.0520.5050.1750.224-0.275-0.3770.3290.604
투명도(m)-0.193-0.245-0.2061.000-0.224-0.171-0.2220.5380.382-0.050-0.0150.4450.393
총대장균군(MPN/100㎎/L)0.7540.6500.315-0.2241.000-0.2460.526-0.369-0.056-0.460-0.6240.7000.345
Pb(㎎/L)-0.1660.020-0.052-0.171-0.2461.000-0.2490.051-0.076-0.0360.0970.0000.000
COD(㎎/L)0.5090.4410.505-0.2220.526-0.2491.000-0.0080.271-0.643-0.6550.6390.644
DO(㎎/L)-0.499-0.6110.1750.538-0.3690.051-0.0081.0000.2660.1900.2140.3470.259
수온(℃)0.0740.0580.2240.382-0.056-0.0760.2710.2661.000-0.487-0.4430.5630.545
전기전도도(mS/㎝)-0.630-0.645-0.275-0.050-0.460-0.036-0.6430.190-0.4871.0000.9260.6810.786
염분(‰)-0.725-0.679-0.377-0.015-0.6240.097-0.6550.214-0.4430.9261.0000.5810.808
pH0.7270.9810.3290.4450.7000.0000.6390.3470.5630.6810.5811.0000.425
Cd(㎎/L)0.8080.4630.6040.3930.3450.0000.6440.2590.5450.7860.8080.4251.000

Missing values

2023-12-11T01:02:20.001524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:02:20.495876image/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

조사지점DIN(ug/L)DIP(ug/L)Chl-a(ug/L)투명도(m)pH총대장균군(MPN/100㎎/L)Cd(㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
05부두178.214.54.489.58.11443.80.00.11.57.515.050.833.1
1가덕도164.814.52.294.28.123.80.00.01.07.716.448.331.4
2감천항96.08.33.290.78.310.80.00.11.49.316.850.833.2
3고리108.48.52.490.58.24.00.00.40.78.215.751.333.6
4남외항122.57.52.798.18.214.50.00.01.18.615.751.033.4
5남천만136.97.82.988.88.215.50.00.01.48.414.851.033.3
6남항142.313.31.798.48.2380.00.00.00.78.015.151.033.3
7녹산336.316.82.393.08.1262.00.00.01.57.717.046.029.7
8다대포261.88.82.299.08.294.50.00.01.58.515.548.031.4
9다대포어시장364.516.311.481.28.12125.00.00.03.87.615.750.332.7
조사지점DIN(ug/L)DIP(ug/L)Chl-a(ug/L)투명도(m)pH총대장균군(MPN/100㎎/L)Cd(㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
21신외항122.99.33.5109.78.23.80.10.61.09.016.450.032.7
22신항119.89.33.094.08.217.80.00.30.98.116.149.832.6
23신호1033.115.56.299.18.1231.50.50.03.28.317.141.526.6
24암남공원110.37.32.098.68.299.00.00.00.88.615.851.033.3
25오륙도139.29.31.294.48.222.30.00.11.38.315.650.833.3
26일광112.47.52.091.48.212.80.00.00.78.215.251.333.6
27장림1743.740.85.289.27.810522.50.10.13.68.218.134.321.7
28청사포102.58.31.998.08.22.50.00.11.18.415.950.833.4
29칠암107.18.52.890.58.24.80.00.10.88.215.251.333.6
30해운대207.216.02.492.98.1189.30.00.11.08.015.050.833.2