Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells100
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.8 KiB
Average record size in memory161.3 B

Variable types

Categorical4
Numeric13
Unsupported1

Dataset

Description부산해양환경현황(2016년)
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3077374

Alerts

WQI is highly overall correlated with 저층산소포화도(DO,%) and 2 other fieldsHigh correlation
DIN(ug/L) is highly overall correlated with DIP(ug/L) and 1 other fieldsHigh correlation
DIP(ug/L) is highly overall correlated with DIN(ug/L)High correlation
저층산소포화도(DO,%) is highly overall correlated with WQIHigh correlation
투명도(m) is highly overall correlated with WQIHigh correlation
pH is highly overall correlated with DO(㎎/L)High correlation
총대장균군(MPN/100㎎/L) is highly overall correlated with DIN(ug/L)High correlation
COD(㎎/L) is highly overall correlated with 전기전도도(mS/㎝) and 1 other fieldsHigh correlation
DO(㎎/L) is highly overall correlated with pH and 3 other fieldsHigh correlation
수온(℃) is highly overall correlated with DO(㎎/L) and 3 other fieldsHigh correlation
전기전도도(mS/㎝) is highly overall correlated with COD(㎎/L) and 3 other fieldsHigh correlation
염분(‰) is highly overall correlated with COD(㎎/L) and 3 other fieldsHigh correlation
2016년 분기 is highly overall correlated with 수온(℃)High correlation
등급 is highly overall correlated with WQIHigh correlation
Cd(㎎/L) has 100 (100.0%) missing valuesMissing
Cd(㎎/L) is an unsupported type, check if it needs cleaning or further analysisUnsupported
총대장균군(MPN/100㎎/L) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 16:01:30.056543
Analysis finished2023-12-10 16:01:53.378289
Duration23.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2016년 분기
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
30 
3
30 
2
20 
4
20 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 30
30.0%
3 30
30.0%
2 20
20.0%
4 20
20.0%

Length

2023-12-11T01:01:53.478226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:01:53.611419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
30.0%
3 30
30.0%
2 20
20.0%
4 20
20.0%
Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
5부두
 
4
발전소앞
 
4
남외항
 
4
남천만
 
4
남항
 
4
Other values (25)
80 

Length

Max length7
Median length6
Mean length3.72
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5부두
2nd row가덕대교
3rd row감천항
4th row고리
5th row광안리해수욕장

Common Values

ValueCountFrequency (%)
5부두 4
 
4.0%
발전소앞 4
 
4.0%
남외항 4
 
4.0%
남천만 4
 
4.0%
남항 4
 
4.0%
다대포어시장 4
 
4.0%
동천하류 4
 
4.0%
다대포항 4
 
4.0%
광안리해수욕장 4
 
4.0%
해운대해수욕장 4
 
4.0%
Other values (20) 60
60.0%

Length

2023-12-11T01:01:53.793243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5부두 4
 
4.0%
민락동 4
 
4.0%
부산대교 4
 
4.0%
해운대 4
 
4.0%
자갈치시장 4
 
4.0%
이기대 4
 
4.0%
발전소앞 4
 
4.0%
송도해수욕장 4
 
4.0%
북외항 4
 
4.0%
북내항 4
 
4.0%
Other values (20) 60
60.0%

WQI
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.65
Minimum20
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:53.970521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median32
Q339
95-th percentile56.1
Maximum65
Range45
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.095057
Coefficient of variation (CV)0.37044585
Kurtosis-0.19023704
Mean32.65
Median Absolute Deviation (MAD)9
Skewness0.78792506
Sum3265
Variance146.2904
MonotonicityNot monotonic
2023-12-11T01:01:54.158329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20 29
29.0%
36 9
 
9.0%
32 7
 
7.0%
23 5
 
5.0%
26 4
 
4.0%
29 4
 
4.0%
33 4
 
4.0%
30 4
 
4.0%
49 3
 
3.0%
38 2
 
2.0%
Other values (20) 29
29.0%
ValueCountFrequency (%)
20 29
29.0%
23 5
 
5.0%
24 2
 
2.0%
26 4
 
4.0%
29 4
 
4.0%
30 4
 
4.0%
32 7
 
7.0%
33 4
 
4.0%
34 2
 
2.0%
36 9
 
9.0%
ValueCountFrequency (%)
65 1
1.0%
62 1
1.0%
61 1
1.0%
60 1
1.0%
58 1
1.0%
56 1
1.0%
55 1
1.0%
53 1
1.0%
52 2
2.0%
50 2
2.0%

등급
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
I
34 
II
25 
III
25 
IV
12 
V

Length

Max length3
Median length2
Mean length1.87
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowII
2nd rowII
3rd rowI
4th rowI
5th rowI

Common Values

ValueCountFrequency (%)
I 34
34.0%
II 25
25.0%
III 25
25.0%
IV 12
 
12.0%
V 4
 
4.0%

Length

2023-12-11T01:01:54.378851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:01:54.580395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 34
34.0%
ii 25
25.0%
iii 25
25.0%
iv 12
 
12.0%
v 4
 
4.0%

DIN(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.327
Minimum20.9
Maximum2293.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:54.763950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.9
5-th percentile36.055
Q1104.475
median158.3
Q3273.325
95-th percentile1431.465
Maximum2293.3
Range2272.4
Interquartile range (IQR)168.85

Descriptive statistics

Standard deviation500.50052
Coefficient of variation (CV)1.4881366
Kurtosis6.5851306
Mean336.327
Median Absolute Deviation (MAD)63.2
Skewness2.6809116
Sum33632.7
Variance250500.77
MonotonicityNot monotonic
2023-12-11T01:01:54.957614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.2 2
 
2.0%
178.9 1
 
1.0%
793.4 1
 
1.0%
409.6 1
 
1.0%
280.6 1
 
1.0%
88.7 1
 
1.0%
180.1 1
 
1.0%
1412.0 1
 
1.0%
36.1 1
 
1.0%
44.6 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
20.9 1
1.0%
28.1 1
1.0%
30.5 1
1.0%
33.8 1
1.0%
35.2 1
1.0%
36.1 1
1.0%
44.6 1
1.0%
52.6 1
1.0%
57.1 1
1.0%
60.4 1
1.0%
ValueCountFrequency (%)
2293.3 1
1.0%
2271.6 1
1.0%
2252.3 1
1.0%
1897.3 1
1.0%
1482.1 1
1.0%
1428.8 1
1.0%
1421.8 1
1.0%
1412.0 1
1.0%
1394.5 1
1.0%
1332.2 1
1.0%

DIP(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.943
Minimum0.7
Maximum275.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:55.126939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.995
Q14.975
median10.45
Q319.4
95-th percentile103.475
Maximum275.4
Range274.7
Interquartile range (IQR)14.425

Descriptive statistics

Standard deviation40.76454
Coefficient of variation (CV)1.7767746
Kurtosis16.564765
Mean22.943
Median Absolute Deviation (MAD)6.5
Skewness3.7182007
Sum2294.3
Variance1661.7477
MonotonicityNot monotonic
2023-12-11T01:01:55.327642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 3
 
3.0%
5.7 3
 
3.0%
0.7 3
 
3.0%
1.2 2
 
2.0%
12.5 2
 
2.0%
11.7 2
 
2.0%
7.2 2
 
2.0%
2.5 2
 
2.0%
11.2 2
 
2.0%
2.6 2
 
2.0%
Other values (75) 77
77.0%
ValueCountFrequency (%)
0.7 3
3.0%
0.8 1
 
1.0%
0.9 1
 
1.0%
1.0 1
 
1.0%
1.1 1
 
1.0%
1.2 2
2.0%
1.9 1
 
1.0%
2.1 1
 
1.0%
2.2 1
 
1.0%
2.5 2
2.0%
ValueCountFrequency (%)
275.4 1
1.0%
167.6 1
1.0%
135.2 1
1.0%
132.6 1
1.0%
125.8 1
1.0%
102.3 1
1.0%
101.7 1
1.0%
97.1 1
1.0%
94.1 1
1.0%
57.5 1
1.0%

Chl-a(ug/L)
Real number (ℝ)

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4697
Minimum0.19
Maximum18.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:55.552809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19
5-th percentile0.309
Q10.48
median1.145
Q32.9925
95-th percentile8.055
Maximum18.34
Range18.15
Interquartile range (IQR)2.5125

Descriptive statistics

Standard deviation3.0510695
Coefficient of variation (CV)1.2354009
Kurtosis7.3495248
Mean2.4697
Median Absolute Deviation (MAD)0.785
Skewness2.3655529
Sum246.97
Variance9.3090252
MonotonicityNot monotonic
2023-12-11T01:01:55.785183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.31 3
 
3.0%
0.36 3
 
3.0%
0.35 2
 
2.0%
0.38 2
 
2.0%
0.39 2
 
2.0%
0.81 2
 
2.0%
1.29 2
 
2.0%
0.48 2
 
2.0%
0.98 2
 
2.0%
0.44 2
 
2.0%
Other values (75) 78
78.0%
ValueCountFrequency (%)
0.19 1
 
1.0%
0.24 2
2.0%
0.25 1
 
1.0%
0.29 1
 
1.0%
0.31 3
3.0%
0.35 2
2.0%
0.36 3
3.0%
0.37 1
 
1.0%
0.38 2
2.0%
0.39 2
2.0%
ValueCountFrequency (%)
18.34 1
1.0%
11.86 1
1.0%
10.11 2
2.0%
8.15 1
1.0%
8.05 1
1.0%
7.76 1
1.0%
7.46 1
1.0%
7.44 1
1.0%
7.0 1
1.0%
6.6 1
1.0%

저층산소포화도(DO,%)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.745
Minimum55.2
Maximum109.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:56.071643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55.2
5-th percentile77.095
Q185.75
median94.2
Q398.1
95-th percentile102.915
Maximum109.1
Range53.9
Interquartile range (IQR)12.35

Descriptive statistics

Standard deviation9.9150674
Coefficient of variation (CV)0.10807202
Kurtosis3.2689079
Mean91.745
Median Absolute Deviation (MAD)5.55
Skewness-1.4665406
Sum9174.5
Variance98.308561
MonotonicityNot monotonic
2023-12-11T01:01:56.335923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.3 4
 
4.0%
85.3 3
 
3.0%
102.6 3
 
3.0%
91.2 2
 
2.0%
98.1 2
 
2.0%
84.3 2
 
2.0%
91.6 2
 
2.0%
98.4 2
 
2.0%
101.6 2
 
2.0%
94.1 2
 
2.0%
Other values (72) 76
76.0%
ValueCountFrequency (%)
55.2 1
1.0%
57.8 1
1.0%
59.4 1
1.0%
61.0 1
1.0%
77.0 1
1.0%
77.1 1
1.0%
77.6 1
1.0%
80.0 1
1.0%
80.4 1
1.0%
82.2 1
1.0%
ValueCountFrequency (%)
109.1 1
 
1.0%
108.4 1
 
1.0%
104.8 1
 
1.0%
103.9 1
 
1.0%
103.2 1
 
1.0%
102.9 1
 
1.0%
102.8 1
 
1.0%
102.6 3
3.0%
102.3 1
 
1.0%
101.6 2
2.0%

투명도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.548
Minimum0.5
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:56.574059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.9
Q11.875
median2.5
Q32.925
95-th percentile4.51
Maximum7.5
Range7
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation1.1850483
Coefficient of variation (CV)0.46508959
Kurtosis4.3534967
Mean2.548
Median Absolute Deviation (MAD)0.5
Skewness1.4953933
Sum254.8
Variance1.4043394
MonotonicityNot monotonic
2023-12-11T01:01:56.776884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2.1 11
 
11.0%
2.9 10
 
10.0%
2.5 7
 
7.0%
1.3 6
 
6.0%
2.8 5
 
5.0%
1.8 4
 
4.0%
1.5 4
 
4.0%
3.0 4
 
4.0%
1.9 3
 
3.0%
2.0 3
 
3.0%
Other values (29) 43
43.0%
ValueCountFrequency (%)
0.5 1
 
1.0%
0.6 1
 
1.0%
0.8 2
 
2.0%
0.9 2
 
2.0%
1.0 2
 
2.0%
1.3 6
6.0%
1.4 1
 
1.0%
1.5 4
4.0%
1.6 2
 
2.0%
1.8 4
4.0%
ValueCountFrequency (%)
7.5 1
1.0%
7.2 1
1.0%
5.5 1
1.0%
5.3 1
1.0%
4.7 1
1.0%
4.5 1
1.0%
4.1 1
1.0%
4.0 2
2.0%
3.9 1
1.0%
3.8 1
1.0%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.112
Minimum7.4
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:56.996557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile8
Q18.1
median8.1
Q38.2
95-th percentile8.3
Maximum8.4
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.12167186
Coefficient of variation (CV)0.014998996
Kurtosis11.023924
Mean8.112
Median Absolute Deviation (MAD)0.1
Skewness-1.7105833
Sum811.2
Variance0.01480404
MonotonicityNot monotonic
2023-12-11T01:01:57.183132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
8.1 43
43.0%
8.2 24
24.0%
8.0 21
21.0%
8.3 7
 
7.0%
8.4 2
 
2.0%
7.9 2
 
2.0%
7.4 1
 
1.0%
ValueCountFrequency (%)
7.4 1
 
1.0%
7.9 2
 
2.0%
8.0 21
21.0%
8.1 43
43.0%
8.2 24
24.0%
8.3 7
 
7.0%
8.4 2
 
2.0%
ValueCountFrequency (%)
8.4 2
 
2.0%
8.3 7
 
7.0%
8.2 24
24.0%
8.1 43
43.0%
8.0 21
21.0%
7.9 2
 
2.0%
7.4 1
 
1.0%

총대장균군(MPN/100㎎/L)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.98
Minimum0
Maximum3500
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:57.390155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.85
Q123
median130
Q3350
95-th percentile2800
Maximum3500
Range3500
Interquartile range (IQR)327

Descriptive statistics

Standard deviation909.96529
Coefficient of variation (CV)1.8019828
Kurtosis3.9385933
Mean504.98
Median Absolute Deviation (MAD)113
Skewness2.243652
Sum50498
Variance828036.83
MonotonicityNot monotonic
2023-12-11T01:01:57.614618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
33 9
 
9.0%
170 5
 
5.0%
2400 5
 
5.0%
130 5
 
5.0%
350 5
 
5.0%
8 5
 
5.0%
11 4
 
4.0%
49 4
 
4.0%
3500 4
 
4.0%
240 4
 
4.0%
Other values (28) 50
50.0%
ValueCountFrequency (%)
0 2
 
2.0%
2 3
3.0%
5 4
4.0%
7 1
 
1.0%
8 5
5.0%
11 4
4.0%
17 2
 
2.0%
20 1
 
1.0%
22 1
 
1.0%
23 3
3.0%
ValueCountFrequency (%)
3500 4
4.0%
2800 2
 
2.0%
2400 5
5.0%
1700 2
 
2.0%
1300 1
 
1.0%
1100 1
 
1.0%
950 1
 
1.0%
920 1
 
1.0%
790 1
 
1.0%
540 2
 
2.0%

Cd(㎎/L)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

Pb(㎎/L)
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.0
48 
<NA>
40 
0.1
0.2
 
4
0.3
 
2

Length

Max length4
Median length3
Mean length3.4
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
0.0 48
48.0%
<NA> 40
40.0%
0.1 5
 
5.0%
0.2 4
 
4.0%
0.3 2
 
2.0%
0.4 1
 
1.0%

Length

2023-12-11T01:01:57.835785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:01:58.335065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 48
48.0%
na 40
40.0%
0.1 5
 
5.0%
0.2 4
 
4.0%
0.3 2
 
2.0%
0.4 1
 
1.0%

COD(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.62
Minimum0.2
Maximum5.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:58.510443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.8
median1.2
Q32.025
95-th percentile4.2
Maximum5.9
Range5.7
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation1.2018504
Coefficient of variation (CV)0.74188298
Kurtosis1.823727
Mean1.62
Median Absolute Deviation (MAD)0.6
Skewness1.371768
Sum162
Variance1.4444444
MonotonicityNot monotonic
2023-12-11T01:01:58.680744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1.0 11
 
11.0%
0.8 7
 
7.0%
0.2 6
 
6.0%
0.9 6
 
6.0%
1.8 5
 
5.0%
1.1 5
 
5.0%
1.2 4
 
4.0%
1.7 4
 
4.0%
2.0 4
 
4.0%
4.2 3
 
3.0%
Other values (23) 45
45.0%
ValueCountFrequency (%)
0.2 6
6.0%
0.3 3
 
3.0%
0.4 3
 
3.0%
0.5 3
 
3.0%
0.6 2
 
2.0%
0.7 2
 
2.0%
0.8 7
7.0%
0.9 6
6.0%
1.0 11
11.0%
1.1 5
5.0%
ValueCountFrequency (%)
5.9 1
 
1.0%
5.6 1
 
1.0%
4.5 1
 
1.0%
4.2 3
3.0%
4.1 1
 
1.0%
3.5 1
 
1.0%
3.4 2
2.0%
3.3 2
2.0%
3.2 3
3.0%
2.9 2
2.0%

DO(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.762
Minimum5
Maximum11.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:58.876308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.195
Q16.7
median7.6
Q38.7
95-th percentile10.1
Maximum11.6
Range6.6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3509279
Coefficient of variation (CV)0.17404378
Kurtosis-0.43208969
Mean7.762
Median Absolute Deviation (MAD)1
Skewness0.43732163
Sum776.2
Variance1.8250061
MonotonicityNot monotonic
2023-12-11T01:01:59.127934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
6.8 12
 
12.0%
8.6 7
 
7.0%
6.7 6
 
6.0%
8.5 5
 
5.0%
8.1 4
 
4.0%
8.8 4
 
4.0%
6.3 4
 
4.0%
6.4 4
 
4.0%
6.5 4
 
4.0%
8.7 3
 
3.0%
Other values (31) 47
47.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
5.4 1
 
1.0%
5.6 1
 
1.0%
5.7 1
 
1.0%
6.1 1
 
1.0%
6.2 3
3.0%
6.3 4
4.0%
6.4 4
4.0%
6.5 4
4.0%
6.6 2
2.0%
ValueCountFrequency (%)
11.6 1
 
1.0%
11.1 1
 
1.0%
10.2 1
 
1.0%
10.1 3
3.0%
10.0 1
 
1.0%
9.9 1
 
1.0%
9.8 2
2.0%
9.5 1
 
1.0%
9.4 3
3.0%
9.3 1
 
1.0%

수온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.918
Minimum7.7
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:59.308019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile8.9
Q111.75
median17.5
Q328.425
95-th percentile29.005
Maximum30
Range22.3
Interquartile range (IQR)16.675

Descriptive statistics

Standard deviation7.2233121
Coefficient of variation (CV)0.38182219
Kurtosis-1.2910391
Mean18.918
Median Absolute Deviation (MAD)6.2
Skewness0.23622325
Sum1891.8
Variance52.176238
MonotonicityNot monotonic
2023-12-11T01:01:59.488483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.8 6
 
6.0%
10.1 5
 
5.0%
28.6 5
 
5.0%
10.8 4
 
4.0%
17.2 4
 
4.0%
16.5 3
 
3.0%
8.1 3
 
3.0%
28.5 3
 
3.0%
28.7 3
 
3.0%
17.6 2
 
2.0%
Other values (47) 62
62.0%
ValueCountFrequency (%)
7.7 1
 
1.0%
8.1 3
3.0%
8.9 2
 
2.0%
9.4 1
 
1.0%
10.1 5
5.0%
10.5 1
 
1.0%
10.6 1
 
1.0%
10.8 4
4.0%
11.1 1
 
1.0%
11.3 2
 
2.0%
ValueCountFrequency (%)
30.0 2
 
2.0%
29.8 1
 
1.0%
29.4 1
 
1.0%
29.1 1
 
1.0%
29.0 1
 
1.0%
28.9 2
 
2.0%
28.8 6
6.0%
28.7 3
3.0%
28.6 5
5.0%
28.5 3
3.0%

전기전도도(mS/㎝)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.43
Minimum28
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:01:59.683038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile39.95
Q146
median50
Q351
95-th percentile52
Maximum52
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5709999
Coefficient of variation (CV)0.094383645
Kurtosis7.8198758
Mean48.43
Median Absolute Deviation (MAD)2
Skewness-2.4993729
Sum4843
Variance20.89404
MonotonicityNot monotonic
2023-12-11T01:01:59.843310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
51 25
25.0%
52 21
21.0%
50 13
13.0%
46 11
11.0%
48 7
 
7.0%
45 6
 
6.0%
47 6
 
6.0%
49 2
 
2.0%
28 2
 
2.0%
40 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
28 2
 
2.0%
33 1
 
1.0%
37 1
 
1.0%
39 1
 
1.0%
40 1
 
1.0%
42 1
 
1.0%
43 1
 
1.0%
44 1
 
1.0%
45 6
6.0%
46 11
11.0%
ValueCountFrequency (%)
52 21
21.0%
51 25
25.0%
50 13
13.0%
49 2
 
2.0%
48 7
 
7.0%
47 6
 
6.0%
46 11
11.0%
45 6
 
6.0%
44 1
 
1.0%
43 1
 
1.0%

염분(‰)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.521
Minimum17.1
Maximum34.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-11T01:02:00.023400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile25.575
Q130
median33
Q333.6
95-th percentile34
Maximum34.2
Range17.1
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation3.251023
Coefficient of variation (CV)0.10313832
Kurtosis7.4117277
Mean31.521
Median Absolute Deviation (MAD)0.9
Skewness-2.4460431
Sum3152.1
Variance10.569151
MonotonicityNot monotonic
2023-12-11T01:02:00.226208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
33.5 8
 
8.0%
33.4 7
 
7.0%
33.9 6
 
6.0%
33.7 6
 
6.0%
30.0 5
 
5.0%
33.6 4
 
4.0%
34.0 4
 
4.0%
33.0 4
 
4.0%
32.8 4
 
4.0%
30.9 3
 
3.0%
Other values (33) 49
49.0%
ValueCountFrequency (%)
17.1 1
1.0%
17.2 1
1.0%
20.7 1
1.0%
23.1 1
1.0%
25.1 1
1.0%
25.6 1
1.0%
26.7 1
1.0%
27.6 1
1.0%
28.0 1
1.0%
28.8 1
1.0%
ValueCountFrequency (%)
34.2 1
 
1.0%
34.1 2
 
2.0%
34.0 4
4.0%
33.9 6
6.0%
33.8 3
 
3.0%
33.7 6
6.0%
33.6 4
4.0%
33.5 8
8.0%
33.4 7
7.0%
33.3 2
 
2.0%

Interactions

2023-12-11T01:01:50.391061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:31.458310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.291216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.882030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.797066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.998870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.366889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.717479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.948675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.599041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.277278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.027383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.750619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:50.596215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:31.563256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.419376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.997449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.908900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.075515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.494627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.792657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.026278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.689103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.383227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.136045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.873700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:50.738169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:31.712814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.525399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.098999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.999684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.159098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.577945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.872687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.107754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.779349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.496167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.252854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.977984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:51.256769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:31.886127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.638613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.188972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.086593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.289470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.667568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.961968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.197654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.889637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.625524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.387200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.098879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:51.399669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.051591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.759329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.291123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.172767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.412973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.780506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.069224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.292328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.014009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.760464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.522398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.252964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:51.551769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.185214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.875450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.428504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.251766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.520044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.918940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.172019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.392902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.114408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.916354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.651642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.372014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:51.701005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.325136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.006012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.570336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.349026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.628181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.030640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.255840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.562864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.257258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.050830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.790214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.489127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:51.841762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.461350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.127451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.692225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.439109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.733977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.132104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.343166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:42.926282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.391367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.180546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:47.937985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.599048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:52.007984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.616190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.272793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.822992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.530932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.859207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.248987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.452580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.016584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.527677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.331329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.099897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.740176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:52.169916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.754571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.410298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:35.969743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.613744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:38.951695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.347401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.552173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.137488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.722803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.484137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.255774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.844173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:52.303730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:32.888578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.531567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.108409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.695878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.048605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.440328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.631573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.229324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:44.897831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.633609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.391607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:49.979984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:52.440994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.003623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.635048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.222025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.786131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.151226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.537761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.758541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.324146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.018742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.757194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.506797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:50.114493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:52.607049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:33.147153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:34.751556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:36.327713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:37.877101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:39.258202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:40.614573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:41.860258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:43.426244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:45.151167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:46.884321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:48.621278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:01:50.236759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:02:00.396059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016년 분기수질조사정점WQI등급DIN(ug/L)DIP(ug/L)Chl-a(ug/L)저층산소포화도(DO,%)투명도(m)pH총대장균군(MPN/100㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
2016년 분기1.0000.0000.4470.3460.0620.0000.5000.7680.3080.4130.3020.0800.6310.7040.9880.6230.613
수질조사정점0.0001.0000.5150.5700.8150.1570.4680.5810.5150.0000.6360.0000.3920.0000.0000.8160.810
WQI0.4470.5151.0000.9990.4780.5090.5890.6610.4120.6560.5580.0000.5220.5300.3480.5730.541
등급0.3460.5700.9991.0000.4840.3930.4980.6230.3800.5770.3690.0000.5630.4660.4380.5010.435
DIN(ug/L)0.0620.8150.4780.4841.0000.8110.0000.0000.0000.6590.8390.0000.6000.4570.3540.8090.812
DIP(ug/L)0.0000.1570.5090.3930.8111.0000.0590.0000.0000.6770.6970.0000.7410.3800.3570.5800.597
Chl-a(ug/L)0.5000.4680.5890.4980.0000.0591.0000.4600.0000.0000.6190.0000.6400.2460.4960.4760.396
저층산소포화도(DO,%)0.7680.5810.6610.6230.0000.0000.4601.0000.2630.4190.0000.0000.2760.4510.7500.4930.471
투명도(m)0.3080.5150.4120.3800.0000.0000.0000.2631.0000.2330.0000.5590.5030.4870.3130.3630.376
pH0.4130.0000.6560.5770.6590.6770.0000.4190.2331.0000.3390.2470.4890.5860.3780.5230.518
총대장균군(MPN/100㎎/L)0.3020.6360.5580.3690.8390.6970.6190.0000.0000.3391.0000.0000.5290.4240.0000.6360.656
Pb(㎎/L)0.0800.0000.0000.0000.0000.0000.0000.0000.5590.2470.0001.0000.1900.3430.0000.2070.299
COD(㎎/L)0.6310.3920.5220.5630.6000.7410.6400.2760.5030.4890.5290.1901.0000.7480.6790.8250.825
DO(㎎/L)0.7040.0000.5300.4660.4570.3800.2460.4510.4870.5860.4240.3430.7481.0000.6100.7200.721
수온(℃)0.9880.0000.3480.4380.3540.3570.4960.7500.3130.3780.0000.0000.6790.6101.0000.5770.574
전기전도도(mS/㎝)0.6230.8160.5730.5010.8090.5800.4760.4930.3630.5230.6360.2070.8250.7200.5771.0000.999
염분(‰)0.6130.8100.5410.4350.8120.5970.3960.4710.3760.5180.6560.2990.8250.7210.5740.9991.000
2023-12-11T01:02:00.640342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Pb(㎎/L)2016년 분기수질조사정점등급
Pb(㎎/L)1.0000.0890.0000.000
2016년 분기0.0891.0000.0000.287
수질조사정점0.0000.0001.0000.238
등급0.0000.2870.2381.000
2023-12-11T01:02:00.778716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WQIDIN(ug/L)DIP(ug/L)Chl-a(ug/L)저층산소포화도(DO,%)투명도(m)pH총대장균군(MPN/100㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)2016년 분기수질조사정점등급Pb(㎎/L)
WQI1.0000.4090.2280.122-0.633-0.661-0.4850.4380.418-0.3940.281-0.436-0.4440.2870.1600.9190.000
DIN(ug/L)0.4091.0000.557-0.117-0.109-0.211-0.3600.6400.033-0.111-0.234-0.149-0.1300.0040.4260.3170.000
DIP(ug/L)0.2280.5571.0000.064-0.083-0.140-0.1520.4790.0440.018-0.2810.1670.1840.0000.0130.2600.000
Chl-a(ug/L)0.122-0.1170.0641.0000.005-0.2470.0750.0120.4240.2170.117-0.094-0.0820.2350.1690.3280.000
저층산소포화도(DO,%)-0.633-0.109-0.0830.0051.0000.3080.451-0.132-0.1140.496-0.2950.1580.1500.4250.2300.4380.000
투명도(m)-0.661-0.211-0.140-0.2470.3081.0000.285-0.167-0.1480.0660.0430.0310.0680.1820.2280.2270.375
pH-0.485-0.360-0.1520.0750.4510.2851.000-0.319-0.0480.591-0.1530.2250.2330.3560.0000.2470.202
총대장균군(MPN/100㎎/L)0.4380.6400.4790.012-0.132-0.167-0.3191.0000.330-0.2540.036-0.305-0.2630.1340.2660.2310.000
COD(㎎/L)0.4180.0330.0440.424-0.114-0.148-0.0480.3301.000-0.1430.432-0.683-0.6770.4480.1250.3600.095
DO(㎎/L)-0.394-0.1110.0180.2170.4960.0660.591-0.254-0.1431.000-0.6670.5220.5040.4950.0000.2810.194
수온(℃)0.281-0.234-0.2810.117-0.2950.043-0.1530.0360.432-0.6671.000-0.697-0.6660.8310.0000.2820.000
전기전도도(mS/㎝)-0.436-0.1490.167-0.0940.1580.0310.225-0.305-0.6830.522-0.6971.0000.9750.4400.4100.3100.106
염분(‰)-0.444-0.1300.184-0.0820.1500.0680.233-0.263-0.6770.504-0.6660.9751.0000.4310.4030.2620.164
2016년 분기0.2870.0040.0000.2350.4250.1820.3560.1340.4480.4950.8310.4400.4311.0000.0000.2870.089
수질조사정점0.1600.4260.0130.1690.2300.2280.0000.2660.1250.0000.0000.4100.4030.0001.0000.2380.000
등급0.9190.3170.2600.3280.4380.2270.2470.2310.3600.2810.2820.3100.2620.2870.2381.0000.000
Pb(㎎/L)0.0000.0000.0000.0000.0000.3750.2020.0000.0950.1940.0000.1060.1640.0890.0000.0001.000

Missing values

2023-12-11T01:01:52.878027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:01:53.227375image/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

2016년 분기수질조사정점WQI등급DIN(ug/L)DIP(ug/L)Chl-a(ug/L)저층산소포화도(DO,%)투명도(m)pH총대장균군(MPN/100㎎/L)Cd(㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
015부두26II178.910.62.7295.91.98.033<NA>0.00.58.610.15233.6
11가덕대교32II134.612.53.397.40.98.233<NA>0.01.49.48.15133.3
21감천항23I161.77.82.1797.42.58.17<NA>0.00.48.810.85233.9
31고리20I187.619.70.7196.32.98.223<NA>0.00.28.612.25234.1
41광안리해수욕장20I175.016.20.9196.13.48.211<NA>0.00.38.511.35234.0
51남외항23I162.68.00.8996.92.58.122<NA>0.00.38.710.85233.9
61남천만23I163.611.81.2295.12.48.217<NA>0.00.28.311.45234.0
71남항20I149.711.70.9896.53.48.1540<NA>0.00.38.610.85233.9
81녹산29II141.33.00.48103.91.38.223<NA>0.00.810.08.95233.5
91다대포어시장49IV201.625.20.6980.01.68.0460<NA>0.00.97.210.15133.6
2016년 분기수질조사정점WQI등급DIN(ug/L)DIP(ug/L)Chl-a(ug/L)저층산소포화도(DO,%)투명도(m)pH총대장균군(MPN/100㎎/L)Cd(㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)
904발전소앞33II157.19.30.3985.32.58.149<NA><NA>1.06.818.45133.4
914부산대교36III109.17.70.3682.21.98.1140<NA><NA>0.76.219.85032.8
924북내항36III139.65.70.1984.32.18.179<NA><NA>1.06.419.75032.9
934북외항39III150.95.40.7485.51.88.133<NA><NA>1.06.719.45032.8
944송도해수욕장30II161.82.70.4888.22.98.123<NA><NA>0.88.120.15133.4
954수영만52IV2252.394.10.3187.12.18.13500<NA><NA>2.46.217.64630.0
964이기대47IV345.415.10.5285.31.58.0170<NA><NA>1.06.717.15133.4
974자갈치시장33II123.64.20.3188.42.58.1460<NA><NA>0.96.720.05033.0
984해운대33II153.29.60.4787.42.58.1110<NA><NA>1.06.817.25032.4
994해운대해수욕장44III564.625.60.3688.92.28.0350<NA><NA>0.96.917.15133.6