Overview

Dataset statistics

Number of variables17
Number of observations31
Missing cells31
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory154.3 B

Variable types

Text1
Numeric13
Categorical2
Unsupported1

Dataset

Description부산해양환경현황(2014)
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3077374

Alerts

WQI is highly overall correlated with DIN(ug/L) and 6 other fieldsHigh correlation
DIN(ug/L) is highly overall correlated with WQI and 7 other fieldsHigh correlation
DIP(ug/L) is highly overall correlated with WQI and 8 other fieldsHigh correlation
Chl-a(ug/L) is highly overall correlated with DO(㎎/L)High correlation
투명도(m) is highly overall correlated with WQI and 7 other fieldsHigh correlation
pH is highly overall correlated with WQI and 7 other fieldsHigh correlation
총대장균군(MPN/100㎎/L) is highly overall correlated with DIN(ug/L) and 8 other fieldsHigh correlation
Pb(㎎/L) is highly overall correlated with DIP(ug/L) and 2 other fieldsHigh correlation
COD(㎎/L) is highly overall correlated with WQI and 4 other fieldsHigh correlation
DO(㎎/L) is highly overall correlated with Chl-a(ug/L) and 1 other fieldsHigh correlation
수온(℃) is highly overall correlated with WQI and 9 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 5 other fieldsHigh correlation
등급 is highly overall correlated with WQI and 3 other fieldsHigh correlation
Cd(㎎/L) is highly imbalanced (79.4%)Imbalance
저층산소포화도(DO,%) has 31 (100.0%) missing valuesMissing
조사지점 has unique valuesUnique
DIN(ug/L) has unique valuesUnique
전기전도도(mS/㎝) has unique valuesUnique
저층산소포화도(DO,%) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Pb(㎎/L) has 7 (22.6%) zerosZeros

Reproduction

Analysis started2023-12-10 16:02:29.466593
Analysis finished2023-12-10 16:02:48.445858
Duration18.98 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
2023-12-11T01:02:48.612380image/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%
2023-12-11T01:02:49.128625image/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%

WQI
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.451613
Minimum20
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:49.321893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median32
Q340
95-th percentile55.5
Maximum65
Range45
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.911275
Coefficient of variation (CV)0.30777936
Kurtosis0.71572239
Mean35.451613
Median Absolute Deviation (MAD)6
Skewness0.81948282
Sum1099
Variance119.05591
MonotonicityNot monotonic
2023-12-11T01:02:49.471770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
30 8
25.8%
20 4
12.9%
40 3
 
9.7%
32 3
 
9.7%
38 2
 
6.5%
43 1
 
3.2%
46 1
 
3.2%
36 1
 
3.2%
55 1
 
3.2%
50 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
20 4
12.9%
24 1
 
3.2%
30 8
25.8%
32 3
 
9.7%
33 1
 
3.2%
35 1
 
3.2%
36 1
 
3.2%
38 2
 
6.5%
40 3
 
9.7%
43 1
 
3.2%
ValueCountFrequency (%)
65 1
 
3.2%
56 1
 
3.2%
55 1
 
3.2%
50 1
 
3.2%
46 1
 
3.2%
44 1
 
3.2%
43 1
 
3.2%
40 3
9.7%
38 2
6.5%
36 1
 
3.2%

등급
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
II
13 
III
10 
I
IV
V
 
1

Length

Max length3
Median length2
Mean length2.1612903
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
II 13
41.9%
III 10
32.3%
I 4
 
12.9%
IV 3
 
9.7%
V 1
 
3.2%

Length

2023-12-11T01:02:49.674116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:02:49.852762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ii 13
41.9%
iii 10
32.3%
i 4
 
12.9%
iv 3
 
9.7%
v 1
 
3.2%

DIN(ug/L)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.77742
Minimum74.1
Maximum2292.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:50.009434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.1
5-th percentile79.45
Q1156.85
median184.6
Q3270.95
95-th percentile742.4
Maximum2292.5
Range2218.4
Interquartile range (IQR)114.1

Descriptive statistics

Standard deviation414.62283
Coefficient of variation (CV)1.2766369
Kurtosis17.501132
Mean324.77742
Median Absolute Deviation (MAD)40
Skewness3.8762163
Sum10068.1
Variance171912.09
MonotonicityNot monotonic
2023-12-11T01:02:50.141699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
195.9 1
 
3.2%
321.0 1
 
3.2%
282.7 1
 
3.2%
176.6 1
 
3.2%
181.6 1
 
3.2%
2292.5 1
 
3.2%
157.1 1
 
3.2%
188.8 1
 
3.2%
134.8 1
 
3.2%
752.7 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
74.1 1
3.2%
77.1 1
3.2%
81.8 1
3.2%
118.7 1
3.2%
132.8 1
3.2%
134.8 1
3.2%
145.5 1
3.2%
156.6 1
3.2%
157.1 1
3.2%
165.2 1
3.2%
ValueCountFrequency (%)
2292.5 1
3.2%
752.7 1
3.2%
732.1 1
3.2%
709.4 1
3.2%
678.7 1
3.2%
495.4 1
3.2%
321.0 1
3.2%
282.7 1
3.2%
259.2 1
3.2%
224.6 1
3.2%

DIP(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.935484
Minimum7
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:50.265474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11
Q117.5
median25
Q332.5
95-th percentile75.5
Maximum86
Range79
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.349307
Coefficient of variation (CV)0.66870516
Kurtosis2.852606
Mean28.935484
Median Absolute Deviation (MAD)8
Skewness1.7565784
Sum897
Variance374.3957
MonotonicityNot monotonic
2023-12-11T01:02:50.400304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
26 2
 
6.5%
12 2
 
6.5%
11 2
 
6.5%
21 2
 
6.5%
19 2
 
6.5%
25 2
 
6.5%
33 2
 
6.5%
17 1
 
3.2%
45 1
 
3.2%
16 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
7 1
3.2%
11 2
6.5%
12 2
6.5%
13 1
3.2%
16 1
3.2%
17 1
3.2%
18 1
3.2%
19 2
6.5%
20 1
3.2%
21 2
6.5%
ValueCountFrequency (%)
86 1
3.2%
79 1
3.2%
72 1
3.2%
46 1
3.2%
45 1
3.2%
41 1
3.2%
33 2
6.5%
32 1
3.2%
31 1
3.2%
30 1
3.2%

Chl-a(ug/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9190323
Minimum0.79
Maximum11.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:50.536177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.79
5-th percentile1.19
Q11.685
median2.08
Q32.765
95-th percentile8.535
Maximum11.52
Range10.73
Interquartile range (IQR)1.08

Descriptive statistics

Standard deviation2.4077879
Coefficient of variation (CV)0.82485826
Kurtosis5.906921
Mean2.9190323
Median Absolute Deviation (MAD)0.47
Skewness2.4566842
Sum90.49
Variance5.7974424
MonotonicityNot monotonic
2023-12-11T01:02:50.659047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.22 2
 
6.5%
1.82 2
 
6.5%
1.67 1
 
3.2%
4.46 1
 
3.2%
1.56 1
 
3.2%
2.08 1
 
3.2%
8.08 1
 
3.2%
2.07 1
 
3.2%
2.14 1
 
3.2%
3.34 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
0.79 1
3.2%
0.88 1
3.2%
1.5 1
3.2%
1.53 1
3.2%
1.56 1
3.2%
1.61 1
3.2%
1.65 1
3.2%
1.67 1
3.2%
1.7 1
3.2%
1.82 2
6.5%
ValueCountFrequency (%)
11.52 1
3.2%
8.99 1
3.2%
8.08 1
3.2%
4.46 1
3.2%
4.22 2
6.5%
3.34 1
3.2%
2.93 1
3.2%
2.6 1
3.2%
2.59 1
3.2%
2.55 1
3.2%

저층산소포화도(DO,%)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

투명도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5322581
Minimum0.6
Maximum6.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:50.779135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.75
Q12.35
median3.2
Q35.25
95-th percentile6.6
Maximum6.8
Range6.2
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation1.9303518
Coefficient of variation (CV)0.54649229
Kurtosis-1.0873031
Mean3.5322581
Median Absolute Deviation (MAD)1.5
Skewness0.14351371
Sum109.5
Variance3.7262581
MonotonicityNot monotonic
2023-12-11T01:02:50.905755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.5 3
 
9.7%
5.8 3
 
9.7%
0.9 2
 
6.5%
2.8 2
 
6.5%
6.1 1
 
3.2%
4.5 1
 
3.2%
6.7 1
 
3.2%
5.2 1
 
3.2%
0.8 1
 
3.2%
6.8 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
0.6 1
 
3.2%
0.7 1
 
3.2%
0.8 1
 
3.2%
0.9 2
6.5%
1.4 1
 
3.2%
1.7 1
 
3.2%
2.2 1
 
3.2%
2.5 3
9.7%
2.8 2
6.5%
3.0 1
 
3.2%
ValueCountFrequency (%)
6.8 1
 
3.2%
6.7 1
 
3.2%
6.5 1
 
3.2%
6.1 1
 
3.2%
5.8 3
9.7%
5.3 1
 
3.2%
5.2 1
 
3.2%
4.5 1
 
3.2%
4.2 1
 
3.2%
4.1 1
 
3.2%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1058065
Minimum7.86
Maximum8.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:51.031956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.86
5-th percentile7.945
Q18.065
median8.12
Q38.17
95-th percentile8.2
Maximum8.22
Range0.36
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.084725997
Coefficient of variation (CV)0.010452507
Kurtosis1.3670582
Mean8.1058065
Median Absolute Deviation (MAD)0.05
Skewness-1.2015603
Sum251.28
Variance0.0071784946
MonotonicityNot monotonic
2023-12-11T01:02:51.182288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8.17 4
12.9%
8.05 3
 
9.7%
8.14 3
 
9.7%
8.19 2
 
6.5%
8.1 2
 
6.5%
8.16 2
 
6.5%
8.12 2
 
6.5%
8.08 2
 
6.5%
8.15 1
 
3.2%
8.18 1
 
3.2%
Other values (9) 9
29.0%
ValueCountFrequency (%)
7.86 1
 
3.2%
7.92 1
 
3.2%
7.97 1
 
3.2%
7.99 1
 
3.2%
8.03 1
 
3.2%
8.05 3
9.7%
8.08 2
6.5%
8.09 1
 
3.2%
8.1 2
6.5%
8.11 1
 
3.2%
ValueCountFrequency (%)
8.22 1
 
3.2%
8.21 1
 
3.2%
8.19 2
6.5%
8.18 1
 
3.2%
8.17 4
12.9%
8.16 2
6.5%
8.15 1
 
3.2%
8.14 3
9.7%
8.12 2
6.5%
8.11 1
 
3.2%

총대장균군(MPN/100㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.63548
Minimum2
Maximum2800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:51.315909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13.25
median17
Q3185
95-th percentile665
Maximum2800
Range2798
Interquartile range (IQR)181.75

Descriptive statistics

Standard deviation519.64051
Coefficient of variation (CV)2.6292875
Kurtosis22.383307
Mean197.63548
Median Absolute Deviation (MAD)15
Skewness4.507113
Sum6126.7
Variance270026.26
MonotonicityNot monotonic
2023-12-11T01:02:51.441915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2.0 8
25.8%
7.8 3
 
9.7%
13.0 2
 
6.5%
17.0 2
 
6.5%
23.0 2
 
6.5%
49.0 2
 
6.5%
240.0 2
 
6.5%
540.0 1
 
3.2%
430.0 1
 
3.2%
790.0 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
2.0 8
25.8%
4.5 1
 
3.2%
6.8 1
 
3.2%
7.8 3
 
9.7%
13.0 2
 
6.5%
17.0 2
 
6.5%
22.0 1
 
3.2%
23.0 2
 
6.5%
49.0 2
 
6.5%
130.0 1
 
3.2%
ValueCountFrequency (%)
2800.0 1
3.2%
790.0 1
3.2%
540.0 1
3.2%
430.0 1
3.2%
350.0 1
3.2%
330.0 1
3.2%
240.0 2
6.5%
130.0 1
3.2%
49.0 2
6.5%
23.0 2
6.5%

Cd(㎎/L)
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
< 0.1
30 
0.1
 
1

Length

Max length5
Median length5
Mean length4.9354839
Min length3

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row0.1
2nd row< 0.1
3rd row< 0.1
4th row< 0.1
5th row< 0.1

Common Values

ValueCountFrequency (%)
< 0.1 30
96.8%
0.1 1
 
3.2%

Length

2023-12-11T01:02:51.638282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:02:51.793682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.1 31
50.8%
30
49.2%

Pb(㎎/L)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17932258
Minimum0
Maximum0.554
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:51.887483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.038
median0.133
Q30.305
95-th percentile0.5215
Maximum0.554
Range0.554
Interquartile range (IQR)0.267

Descriptive statistics

Standard deviation0.17464428
Coefficient of variation (CV)0.97391128
Kurtosis-0.32532312
Mean0.17932258
Median Absolute Deviation (MAD)0.133
Skewness0.88933257
Sum5.559
Variance0.030500626
MonotonicityNot monotonic
2023-12-11T01:02:52.006912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.0 7
22.6%
0.133 2
 
6.5%
0.187 1
 
3.2%
0.551 1
 
3.2%
0.019 1
 
3.2%
0.331 1
 
3.2%
0.143 1
 
3.2%
0.113 1
 
3.2%
0.402 1
 
3.2%
0.492 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0.0 7
22.6%
0.019 1
 
3.2%
0.057 1
 
3.2%
0.066 1
 
3.2%
0.069 1
 
3.2%
0.084 1
 
3.2%
0.113 1
 
3.2%
0.124 1
 
3.2%
0.133 2
 
6.5%
0.138 1
 
3.2%
ValueCountFrequency (%)
0.554 1
3.2%
0.551 1
3.2%
0.492 1
3.2%
0.466 1
3.2%
0.402 1
3.2%
0.37 1
3.2%
0.331 1
3.2%
0.322 1
3.2%
0.288 1
3.2%
0.187 1
3.2%

COD(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79677419
Minimum0.2
Maximum3.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:52.125839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.35
Q10.5
median0.6
Q30.8
95-th percentile1.5
Maximum3.9
Range3.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.66457198
Coefficient of variation (CV)0.83407819
Kurtosis16.257375
Mean0.79677419
Median Absolute Deviation (MAD)0.2
Skewness3.6552402
Sum24.7
Variance0.44165591
MonotonicityNot monotonic
2023-12-11T01:02:52.228487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.6 7
22.6%
0.4 5
16.1%
0.5 4
12.9%
0.8 4
12.9%
1.4 2
 
6.5%
0.7 2
 
6.5%
1.2 1
 
3.2%
0.9 1
 
3.2%
0.3 1
 
3.2%
1.0 1
 
3.2%
Other values (3) 3
9.7%
ValueCountFrequency (%)
0.2 1
 
3.2%
0.3 1
 
3.2%
0.4 5
16.1%
0.5 4
12.9%
0.6 7
22.6%
0.7 2
 
6.5%
0.8 4
12.9%
0.9 1
 
3.2%
1.0 1
 
3.2%
1.2 1
 
3.2%
ValueCountFrequency (%)
3.9 1
 
3.2%
1.6 1
 
3.2%
1.4 2
 
6.5%
1.2 1
 
3.2%
1.0 1
 
3.2%
0.9 1
 
3.2%
0.8 4
12.9%
0.7 2
 
6.5%
0.6 7
22.6%
0.5 4
12.9%

DO(㎎/L)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5116129
Minimum5.09
Maximum9.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:52.370177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.09
5-th percentile6.315
Q17.275
median7.56
Q37.69
95-th percentile8.68
Maximum9.32
Range4.23
Interquartile range (IQR)0.415

Descriptive statistics

Standard deviation0.7618753
Coefficient of variation (CV)0.10142633
Kurtosis3.815707
Mean7.5116129
Median Absolute Deviation (MAD)0.25
Skewness-0.8178307
Sum232.86
Variance0.58045398
MonotonicityNot monotonic
2023-12-11T01:02:52.517452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
7.59 2
 
6.5%
7.55 2
 
6.5%
6.95 1
 
3.2%
6.86 1
 
3.2%
7.38 1
 
3.2%
7.25 1
 
3.2%
9.32 1
 
3.2%
7.56 1
 
3.2%
7.24 1
 
3.2%
7.81 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
5.09 1
3.2%
5.77 1
3.2%
6.86 1
3.2%
6.95 1
3.2%
7.12 1
3.2%
7.21 1
3.2%
7.24 1
3.2%
7.25 1
3.2%
7.3 1
3.2%
7.33 1
3.2%
ValueCountFrequency (%)
9.32 1
3.2%
8.82 1
3.2%
8.54 1
3.2%
8.24 1
3.2%
8.04 1
3.2%
7.99 1
3.2%
7.81 1
3.2%
7.74 1
3.2%
7.64 1
3.2%
7.63 1
3.2%

수온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.805806
Minimum13.01
Maximum17.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:52.632366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.01
5-th percentile14.195
Q115.355
median15.98
Q316.42
95-th percentile16.98
Maximum17.13
Range4.12
Interquartile range (IQR)1.065

Descriptive statistics

Standard deviation0.9243476
Coefficient of variation (CV)0.058481521
Kurtosis1.505673
Mean15.805806
Median Absolute Deviation (MAD)0.5
Skewness-1.1037003
Sum489.98
Variance0.85441849
MonotonicityNot monotonic
2023-12-11T01:02:52.763821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
15.93 2
 
6.5%
15.51 1
 
3.2%
15.98 1
 
3.2%
16.51 1
 
3.2%
16.29 1
 
3.2%
13.01 1
 
3.2%
17.13 1
 
3.2%
16.48 1
 
3.2%
14.19 1
 
3.2%
15.19 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
13.01 1
3.2%
14.19 1
3.2%
14.2 1
3.2%
14.78 1
3.2%
14.85 1
3.2%
14.99 1
3.2%
15.19 1
3.2%
15.25 1
3.2%
15.46 1
3.2%
15.51 1
3.2%
ValueCountFrequency (%)
17.13 1
3.2%
17.08 1
3.2%
16.88 1
3.2%
16.82 1
3.2%
16.52 1
3.2%
16.51 1
3.2%
16.48 1
3.2%
16.44 1
3.2%
16.4 1
3.2%
16.37 1
3.2%

전기전도도(mS/㎝)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.463484
Minimum16.193
Maximum50.689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:52.903868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.193
5-th percentile41.966
Q149.837
median50.144
Q350.4165
95-th percentile50.605
Maximum50.689
Range34.496
Interquartile range (IQR)0.5795

Descriptive statistics

Standard deviation6.3605742
Coefficient of variation (CV)0.13124468
Kurtosis23.791517
Mean48.463484
Median Absolute Deviation (MAD)0.295
Skewness-4.7248072
Sum1502.368
Variance40.456904
MonotonicityNot monotonic
2023-12-11T01:02:53.024129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
50.419 1
 
3.2%
48.896 1
 
3.2%
49.989 1
 
3.2%
50.498 1
 
3.2%
50.386 1
 
3.2%
16.193 1
 
3.2%
50.414 1
 
3.2%
50.55 1
 
3.2%
50.144 1
 
3.2%
40.218 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
16.193 1
3.2%
40.218 1
3.2%
43.714 1
3.2%
48.325 1
3.2%
48.896 1
3.2%
48.93 1
3.2%
49.385 1
3.2%
49.825 1
3.2%
49.849 1
3.2%
49.946 1
3.2%
ValueCountFrequency (%)
50.689 1
3.2%
50.66 1
3.2%
50.55 1
3.2%
50.498 1
3.2%
50.479 1
3.2%
50.459 1
3.2%
50.439 1
3.2%
50.419 1
3.2%
50.414 1
3.2%
50.405 1
3.2%

염분(‰)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.710323
Minimum9.52
Maximum33.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T01:02:53.139920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.52
5-th percentile26.955
Q132.655
median32.9
Q333.095
95-th percentile33.23
Maximum33.28
Range23.76
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation4.4022384
Coefficient of variation (CV)0.13882667
Kurtosis23.129944
Mean31.710323
Median Absolute Deviation (MAD)0.22
Skewness-4.649584
Sum983.02
Variance19.379703
MonotonicityNot monotonic
2023-12-11T01:02:53.511475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
32.84 2
 
6.5%
33.08 1
 
3.2%
33.27 1
 
3.2%
32.77 1
 
3.2%
33.16 1
 
3.2%
33.07 1
 
3.2%
9.52 1
 
3.2%
33.1 1
 
3.2%
33.19 1
 
3.2%
32.9 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
9.52 1
3.2%
25.71 1
3.2%
28.2 1
3.2%
31.55 1
3.2%
31.96 1
3.2%
31.98 1
3.2%
32.31 1
3.2%
32.64 1
3.2%
32.67 1
3.2%
32.73 1
3.2%
ValueCountFrequency (%)
33.28 1
3.2%
33.27 1
3.2%
33.19 1
3.2%
33.16 1
3.2%
33.14 1
3.2%
33.13 1
3.2%
33.12 1
3.2%
33.1 1
3.2%
33.09 1
3.2%
33.08 1
3.2%

Interactions

2023-12-11T01:02:46.574546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.176124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.397946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.803437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.617305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.815865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.150338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.792384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.158368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.666490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.834808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.997396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.072913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.672083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.263437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.494272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.906579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.724017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.919150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.254923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.879306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.565939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.759046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.908241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.092749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.153631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.769149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.343630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.586543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.029878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.840878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.001212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.378966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.976941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.656493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.838441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.998007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.175451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.222169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.868562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.444506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.681194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.166428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.957537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.094046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.504990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.094354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.756515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.955305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.101323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.268595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.317585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.951349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.565234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.792522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.273882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.036638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.207762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.665097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.184637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.832932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.043931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.199982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.341563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.402098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.045046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.650886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.899440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.391152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.115713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.322287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.819587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.294127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.921899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.154041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.308403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.413929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.499623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.154437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.762622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.013175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.850203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.214748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.435203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.959120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.398672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.023051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.240365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.409607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.516508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.585162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.297435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.857575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.123917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:33.979252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.293237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.541863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.074653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.517111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.127430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.317383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.494833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.595887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.661983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.393722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:30.961308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.236260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.099310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.375889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.632038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.201038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.601706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.224378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.392272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.586245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.686379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.739313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.496176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.054076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.357378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.199842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.463914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.736044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.326731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.725487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.325534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.492539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.675050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.777747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.819208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.596396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.142901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.458210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.312672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.545727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.825845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.433516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.812481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.414287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.567665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.749335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.851083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.216645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.698796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.218035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.574478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.415467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.621064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:36.924930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.558952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:39.923435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.495016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.654602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.817294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:44.926582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.322371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:47.815767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:31.307178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:32.706711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:34.529041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:35.726612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:37.052418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:38.686391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:40.073703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:41.581350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:42.759019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:43.908645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:45.003851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:02:46.448866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:02:53.603057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사지점WQI등급DIN(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(‰)
조사지점1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
WQI1.0001.0000.9650.8610.5990.0000.2100.7040.9930.4410.4740.7420.9280.6720.9450.945
등급1.0000.9651.0000.8570.4840.0000.4930.7820.7470.0000.0000.8560.7750.3650.4170.417
DIN(ug/L)1.0000.8610.8571.0000.5880.5200.0000.7640.8020.0000.4620.9530.8790.8420.8700.870
DIP(ug/L)1.0000.5990.4840.5881.0000.1670.4280.7920.4360.0000.5380.6200.5300.6890.3780.378
Chl-a(ug/L)1.0000.0000.0000.5200.1671.0000.5710.0000.0000.0000.0000.5570.7870.5500.6070.607
투명도(m)1.0000.2100.4930.0000.4280.5711.0000.4000.1830.0000.0000.0000.2870.0000.0000.000
pH1.0000.7040.7820.7640.7920.0000.4001.0000.7440.2680.8060.7400.8020.7310.3960.396
총대장균군(MPN/100㎎/L)1.0000.9930.7470.8020.4360.0000.1830.7441.0000.0000.0000.5900.9930.8730.9010.901
Cd(㎎/L)1.0000.4410.0000.0000.0000.0000.0000.2680.0001.0000.2680.0000.4410.0000.0000.000
Pb(㎎/L)1.0000.4740.0000.4620.5380.0000.0000.8060.0000.2681.0000.4360.0000.4530.7050.705
COD(㎎/L)1.0000.7420.8560.9530.6200.5570.0000.7400.5900.0000.4361.0000.8220.7780.7480.748
DO(㎎/L)1.0000.9280.7750.8790.5300.7870.2870.8020.9930.4410.0000.8221.0000.8670.9760.976
수온(℃)1.0000.6720.3650.8420.6890.5500.0000.7310.8730.0000.4530.7780.8671.0000.9660.966
전기전도도(mS/㎝)1.0000.9450.4170.8700.3780.6070.0000.3960.9010.0000.7050.7480.9760.9661.0001.000
염분(‰)1.0000.9450.4170.8700.3780.6070.0000.3960.9010.0000.7050.7480.9760.9661.0001.000
2023-12-11T01:02:53.772143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Cd(㎎/L)등급
Cd(㎎/L)1.0000.000
등급0.0001.000
2023-12-11T01:02:53.891214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
WQIDIN(ug/L)DIP(ug/L)Chl-a(ug/L)투명도(m)pH총대장균군(MPN/100㎎/L)Pb(㎎/L)COD(㎎/L)DO(㎎/L)수온(℃)전기전도도(mS/㎝)염분(‰)등급Cd(㎎/L)
WQI1.0000.5010.5270.133-0.522-0.5750.4960.2890.614-0.145-0.593-0.408-0.4170.9000.284
DIN(ug/L)0.5011.0000.556-0.017-0.427-0.7860.5950.2560.553-0.235-0.817-0.643-0.6550.4920.000
DIP(ug/L)0.5270.5561.0000.224-0.733-0.6730.6630.6360.472-0.050-0.722-0.600-0.6130.3130.000
Chl-a(ug/L)0.133-0.0170.2241.000-0.3880.2140.050-0.0100.2640.701-0.071-0.352-0.3480.0000.000
투명도(m)-0.522-0.427-0.733-0.3881.0000.590-0.697-0.600-0.467-0.1610.7520.5100.5240.2190.000
pH-0.575-0.786-0.6730.2140.5901.000-0.649-0.482-0.5300.4490.9090.4450.4610.5460.214
총대장균군(MPN/100㎎/L)0.4960.5950.6630.050-0.697-0.6491.0000.4550.514-0.072-0.728-0.545-0.5530.6770.000
Pb(㎎/L)0.2890.2560.636-0.010-0.600-0.4820.4551.0000.1980.033-0.504-0.424-0.4340.0000.214
COD(㎎/L)0.6140.5530.4720.264-0.467-0.5300.5140.1981.0000.050-0.544-0.360-0.3540.4910.000
DO(㎎/L)-0.145-0.235-0.0500.701-0.1610.449-0.0720.0330.0501.0000.193-0.202-0.1920.5810.284
수온(℃)-0.593-0.817-0.722-0.0710.7520.909-0.728-0.504-0.5440.1931.0000.6430.6580.2000.000
전기전도도(mS/㎝)-0.408-0.643-0.600-0.3520.5100.445-0.545-0.424-0.360-0.2020.6431.0000.9990.3390.000
염분(‰)-0.417-0.655-0.613-0.3480.5240.461-0.553-0.434-0.354-0.1920.6580.9991.0000.3390.000
등급0.9000.4920.3130.0000.2190.5460.6770.0000.4910.5810.2000.3390.3391.0000.000
Cd(㎎/L)0.2840.0000.0000.0000.0000.2140.0000.2140.0000.2840.0000.0000.0000.0001.000

Missing values

2023-12-11T01:02:47.986287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:02:48.310112image/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

조사지점WQI등급DIN(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(‰)
05부두43III195.9261.67<NA>2.58.0513.00.10.1870.56.9515.5150.41933.08
1가덕도38III321.0332.13<NA>0.77.97330.0< 0.10.4660.87.6214.9948.89631.96
2감천항38III74.12711.52<NA>2.28.217.8< 0.10.1240.88.5416.3750.34133.04
3고리30II77.1111.84<NA>5.88.172.0< 0.10.1850.67.5816.5250.40533.09
4남외항40III156.674.22<NA>5.38.192.0< 0.10.0690.57.9916.4450.06132.84
5남천만30II206.6212.0<NA>4.28.117.0< 0.10.0570.87.3315.8250.1932.92
6남항20I208.5191.82<NA>3.08.16130.0< 0.10.1380.47.5916.1750.12332.88
7녹산44III678.7412.59<NA>0.97.9949.0< 0.10.371.47.7414.243.71428.2
8다대포33II184.6332.6<NA>2.58.146.8< 0.10.1790.67.4816.0849.84932.67
9다대포어시장65V495.4862.55<NA>1.47.922800.0< 0.10.1331.25.7714.7849.38532.31
조사지점WQI등급DIN(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(‰)
21신외항35III165.2461.87<NA>1.78.05240.0< 0.10.4920.77.615.5550.68933.28
22신항32II178.3312.21<NA>0.68.087.8< 0.10.4020.57.5515.1949.94632.73
23신호50IV752.7252.93<NA>0.98.08790.0< 0.10.1131.68.2414.1940.21825.71
24암남공원30II134.8323.34<NA>3.78.177.8< 0.10.1430.27.8116.4850.14432.9
25오륙도40III188.8162.14<NA>3.98.1213.0< 0.10.00.67.2415.9350.5533.19
26일광30II157.1192.07<NA>6.88.182.0< 0.10.00.67.5617.1350.41433.1
27장림55IV2292.5458.08<NA>0.88.05430.0< 0.10.3313.99.3213.0116.1939.52
28청사포30II181.6172.08<NA>5.28.1423.0< 0.10.00.67.2516.2950.38633.07
29칠암20I176.6121.56<NA>6.78.162.0< 0.10.0190.77.5916.5150.49833.16
30해운대36III282.7251.82<NA>4.58.1223.0< 0.10.00.67.3815.9349.98932.77