Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory159.0 B

Variable types

Categorical2
Text1
Numeric14

Dataset

Description전국 연안 및 근해 정점 수질 상시측정을 통하여 향후 해양환경 상태 측정 및 변화를 예측·예보 할 수 있는 실시간 해양수질자료, 정제한 자료는 한달에 한번씩 meis.go.kr에서 갱신되며 해수일반(수온,염분등 6개), COD,TN,TP등의 자료를 5분 단위로 측정한 자료
Author해양환경공단
URLhttps://www.data.go.kr/data/15068988/fileData.do

Alerts

측정소명 has constant value ""Constant
측정소코드 has constant value ""Constant
염분 is highly overall correlated with 전기전도도 and 2 other fieldsHigh correlation
전기전도도 is highly overall correlated with 염분 and 2 other fieldsHigh correlation
수온 is highly overall correlated with 화학적산소요구량 and 2 other fieldsHigh correlation
수소이온농도 is highly overall correlated with 염분 and 2 other fieldsHigh correlation
용존산소 is highly overall correlated with 염분 and 2 other fieldsHigh correlation
화학적산소요구량 is highly overall correlated with 수온 and 1 other fieldsHigh correlation
총질소 is highly overall correlated with 총인 and 1 other fieldsHigh correlation
총인 is highly overall correlated with 수온 and 6 other fieldsHigh correlation
암모니아질소 is highly overall correlated with 총인 and 3 other fieldsHigh correlation
질산질소 is highly overall correlated with 총인 and 3 other fieldsHigh correlation
인산인 is highly overall correlated with 수온 and 5 other fieldsHigh correlation
규산규소 is highly overall correlated with 총인 and 3 other fieldsHigh correlation
측정일시 has unique valuesUnique
염분 has 1114 (11.1%) zerosZeros
전기전도도 has 1109 (11.1%) zerosZeros
수온 has 1108 (11.1%) zerosZeros
수소이온농도 has 1142 (11.4%) zerosZeros
용존산소 has 1157 (11.6%) zerosZeros
탁도 has 1492 (14.9%) zerosZeros
클로로필 has 1354 (13.5%) zerosZeros
화학적산소요구량 has 1460 (14.6%) zerosZeros
총질소 has 2873 (28.7%) zerosZeros
총인 has 1786 (17.9%) zerosZeros
암모니아질소 has 2263 (22.6%) zerosZeros
질산질소 has 2616 (26.2%) zerosZeros
인산인 has 1773 (17.7%) zerosZeros
규산규소 has 2506 (25.1%) zerosZeros

Reproduction

Analysis started2023-12-11 23:57:13.406569
Analysis finished2023-12-11 23:57:40.769764
Duration27.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정소명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
새만금
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
새만금 10000
100.0%

Length

2023-12-12T08:57:40.824204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:57:40.903837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
새만금 10000
100.0%

측정소코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
108
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row108
2nd row108
3rd row108
4th row108
5th row108

Common Values

ValueCountFrequency (%)
108 10000
100.0%

Length

2023-12-12T08:57:40.980458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:57:41.059490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
108 10000
100.0%

측정일시
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T08:57:41.321594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row2225-09-12
2nd row2176-01-28
3rd row2192-02-04
4th row2252-07-16
5th row2103-02-05
ValueCountFrequency (%)
2225-09-12 1
 
< 0.1%
2203-05-16 1
 
< 0.1%
2088-04-21 1
 
< 0.1%
2078-08-07 1
 
< 0.1%
2211-02-15 1
 
< 0.1%
2049-06-13 1
 
< 0.1%
2127-03-18 1
 
< 0.1%
2095-11-21 1
 
< 0.1%
2247-11-29 1
 
< 0.1%
2130-04-10 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T08:57:41.759871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 21327
21.3%
- 20000
20.0%
0 16846
16.8%
1 13977
14.0%
3 4396
 
4.4%
8 4013
 
4.0%
5 3994
 
4.0%
7 3993
 
4.0%
4 3954
 
4.0%
6 3834
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80000
80.0%
Dash Punctuation 20000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21327
26.7%
0 16846
21.1%
1 13977
17.5%
3 4396
 
5.5%
8 4013
 
5.0%
5 3994
 
5.0%
7 3993
 
5.0%
4 3954
 
4.9%
6 3834
 
4.8%
9 3666
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21327
21.3%
- 20000
20.0%
0 16846
16.8%
1 13977
14.0%
3 4396
 
4.4%
8 4013
 
4.0%
5 3994
 
4.0%
7 3993
 
4.0%
4 3954
 
4.0%
6 3834
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21327
21.3%
- 20000
20.0%
0 16846
16.8%
1 13977
14.0%
3 4396
 
4.4%
8 4013
 
4.0%
5 3994
 
4.0%
7 3993
 
4.0%
4 3954
 
4.0%
6 3834
 
3.8%

염분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct524
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.516631
Minimum0
Maximum33.09
Zeros1114
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:41.892668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.86
median31.49
Q331.87
95-th percentile32.81
Maximum33.09
Range33.09
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation10.381793
Coefficient of variation (CV)0.37729156
Kurtosis3.005717
Mean27.516631
Median Absolute Deviation (MAD)0.42
Skewness-2.2095853
Sum275166.31
Variance107.78162
MonotonicityNot monotonic
2023-12-12T08:57:42.009856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1114
 
11.1%
31.91 145
 
1.5%
31.87 140
 
1.4%
31.92 140
 
1.4%
31.88 134
 
1.3%
31.89 134
 
1.3%
31.86 116
 
1.2%
31.9 114
 
1.1%
31.93 109
 
1.1%
31.5 97
 
1.0%
Other values (514) 7757
77.6%
ValueCountFrequency (%)
0.0 1114
11.1%
0.07 3
 
< 0.1%
0.08 9
 
0.1%
0.09 10
 
0.1%
0.1 9
 
0.1%
0.11 11
 
0.1%
0.12 14
 
0.1%
0.13 9
 
0.1%
0.14 12
 
0.1%
0.15 3
 
< 0.1%
ValueCountFrequency (%)
33.09 1
 
< 0.1%
33.08 4
 
< 0.1%
33.07 1
 
< 0.1%
33.06 8
 
0.1%
33.05 28
0.3%
33.04 32
0.3%
33.03 32
0.3%
33.02 23
0.2%
33.01 20
0.2%
33.0 16
0.2%

전기전도도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct664
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.266997
Minimum0
Maximum50.33
Zeros1109
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:42.131023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147.44
median48.32
Q348.95
95-th percentile50.01
Maximum50.33
Range50.33
Interquartile range (IQR)1.51

Descriptive statistics

Standard deviation15.906328
Coefficient of variation (CV)0.37632973
Kurtosis3.0513084
Mean42.266997
Median Absolute Deviation (MAD)0.7
Skewness-2.2216346
Sum422669.97
Variance253.01126
MonotonicityNot monotonic
2023-12-12T08:57:42.251707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1109
 
11.1%
48.56 70
 
0.7%
47.95 69
 
0.7%
48.57 64
 
0.6%
49.88 63
 
0.6%
47.97 63
 
0.6%
48.76 62
 
0.6%
48.79 60
 
0.6%
48.82 59
 
0.6%
48.75 57
 
0.6%
Other values (654) 8324
83.2%
ValueCountFrequency (%)
0.0 1109
11.1%
0.15 1
 
< 0.1%
0.16 3
 
< 0.1%
0.17 5
 
0.1%
0.18 3
 
< 0.1%
0.19 5
 
0.1%
0.2 5
 
0.1%
0.21 6
 
0.1%
0.22 4
 
< 0.1%
0.23 7
 
0.1%
ValueCountFrequency (%)
50.33 2
 
< 0.1%
50.32 4
 
< 0.1%
50.31 2
 
< 0.1%
50.29 2
 
< 0.1%
50.28 1
 
< 0.1%
50.27 9
0.1%
50.26 7
 
0.1%
50.25 21
0.2%
50.24 19
0.2%
50.23 14
0.1%

수온
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2299
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.755464
Minimum0
Maximum31.25
Zeros1108
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:42.369552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.9
median13.56
Q321.77
95-th percentile27.27
Maximum31.25
Range31.25
Interquartile range (IQR)16.87

Descriptive statistics

Standard deviation8.8519417
Coefficient of variation (CV)0.64352185
Kurtosis-1.3067097
Mean13.755464
Median Absolute Deviation (MAD)8.42
Skewness-0.02587906
Sum137554.64
Variance78.356872
MonotonicityNot monotonic
2023-12-12T08:57:42.487495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1108
 
11.1%
4.34 26
 
0.3%
4.27 26
 
0.3%
4.39 25
 
0.2%
4.07 24
 
0.2%
4.05 23
 
0.2%
4.36 23
 
0.2%
4.47 23
 
0.2%
4.33 22
 
0.2%
4.35 22
 
0.2%
Other values (2289) 8678
86.8%
ValueCountFrequency (%)
0.0 1108
11.1%
3.42 1
 
< 0.1%
3.58 1
 
< 0.1%
3.6 1
 
< 0.1%
3.61 3
 
< 0.1%
3.62 5
 
0.1%
3.63 3
 
< 0.1%
3.64 1
 
< 0.1%
3.65 3
 
< 0.1%
3.66 3
 
< 0.1%
ValueCountFrequency (%)
31.25 1
< 0.1%
31.21 1
< 0.1%
31.17 1
< 0.1%
31.16 1
< 0.1%
31.01 1
< 0.1%
30.99 1
< 0.1%
30.94 1
< 0.1%
30.82 1
< 0.1%
30.35 1
< 0.1%
29.95 1
< 0.1%

수소이온농도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.257504
Minimum0
Maximum8.45
Zeros1142
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:42.632021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.03
median8.22
Q38.29
95-th percentile8.34
Maximum8.45
Range8.45
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation2.6090649
Coefficient of variation (CV)0.35949893
Kurtosis3.8573657
Mean7.257504
Median Absolute Deviation (MAD)0.09
Skewness-2.4154195
Sum72575.04
Variance6.8072196
MonotonicityNot monotonic
2023-12-12T08:57:42.765107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1142
 
11.4%
8.31 532
 
5.3%
8.29 518
 
5.2%
8.3 505
 
5.1%
8.28 468
 
4.7%
8.32 456
 
4.6%
8.26 386
 
3.9%
8.27 382
 
3.8%
8.25 290
 
2.9%
8.33 278
 
2.8%
Other values (76) 5043
50.4%
ValueCountFrequency (%)
0.0 1142
11.4%
7.55 1
 
< 0.1%
7.56 1
 
< 0.1%
7.57 2
 
< 0.1%
7.59 1
 
< 0.1%
7.61 1
 
< 0.1%
7.62 2
 
< 0.1%
7.65 1
 
< 0.1%
7.66 1
 
< 0.1%
7.67 2
 
< 0.1%
ValueCountFrequency (%)
8.45 1
 
< 0.1%
8.43 1
 
< 0.1%
8.42 4
 
< 0.1%
8.41 7
 
0.1%
8.4 15
 
0.1%
8.39 9
 
0.1%
8.38 14
 
0.1%
8.37 42
 
0.4%
8.36 82
0.8%
8.35 127
1.3%

용존산소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1034
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.587076
Minimum0
Maximum13.21
Zeros1157
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:42.979671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.92
median7.98
Q310.28
95-th percentile12.3
Maximum13.21
Range13.21
Interquartile range (IQR)4.36

Descriptive statistics

Standard deviation3.5903714
Coefficient of variation (CV)0.47322202
Kurtosis-0.10457738
Mean7.587076
Median Absolute Deviation (MAD)2.18
Skewness-0.75274594
Sum75870.76
Variance12.890767
MonotonicityNot monotonic
2023-12-12T08:57:43.113792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1157
 
11.6%
7.85 26
 
0.3%
11.92 26
 
0.3%
9.72 25
 
0.2%
7.17 25
 
0.2%
10.02 25
 
0.2%
8.72 25
 
0.2%
10.05 24
 
0.2%
7.05 22
 
0.2%
6.01 22
 
0.2%
Other values (1024) 8623
86.2%
ValueCountFrequency (%)
0.0 1157
11.6%
1.43 1
 
< 0.1%
1.51 1
 
< 0.1%
1.55 1
 
< 0.1%
1.6 1
 
< 0.1%
1.65 1
 
< 0.1%
1.84 2
 
< 0.1%
1.97 1
 
< 0.1%
2.11 1
 
< 0.1%
2.18 1
 
< 0.1%
ValueCountFrequency (%)
13.21 1
< 0.1%
13.2 1
< 0.1%
13.16 1
< 0.1%
13.14 1
< 0.1%
13.13 1
< 0.1%
13.12 1
< 0.1%
13.11 1
< 0.1%
13.06 1
< 0.1%
13.05 2
< 0.1%
13.04 1
< 0.1%

탁도
Real number (ℝ)

ZEROS 

Distinct3028
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.590053
Minimum-0.14
Maximum132.95
Zeros1492
Zeros (%)14.9%
Negative18
Negative (%)0.2%
Memory size166.0 KiB
2023-12-12T08:57:43.575868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.14
5-th percentile0
Q11.2875
median3.8
Q311.72
95-th percentile46.6625
Maximum132.95
Range133.09
Interquartile range (IQR)10.4325

Descriptive statistics

Standard deviation16.020651
Coefficient of variation (CV)1.5128018
Kurtosis6.2844823
Mean10.590053
Median Absolute Deviation (MAD)3.36
Skewness2.387533
Sum105900.53
Variance256.66126
MonotonicityNot monotonic
2023-12-12T08:57:43.713370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1492
 
14.9%
3.07 23
 
0.2%
1.01 22
 
0.2%
0.97 21
 
0.2%
3.0 20
 
0.2%
2.7 19
 
0.2%
1.05 19
 
0.2%
0.96 19
 
0.2%
0.95 18
 
0.2%
2.97 18
 
0.2%
Other values (3018) 8329
83.3%
ValueCountFrequency (%)
-0.14 1
 
< 0.1%
-0.11 2
 
< 0.1%
-0.08 1
 
< 0.1%
-0.07 1
 
< 0.1%
-0.06 3
 
< 0.1%
-0.04 2
 
< 0.1%
-0.03 4
 
< 0.1%
-0.02 1
 
< 0.1%
-0.01 3
 
< 0.1%
0.0 1492
14.9%
ValueCountFrequency (%)
132.95 1
< 0.1%
117.56 1
< 0.1%
116.79 1
< 0.1%
110.19 1
< 0.1%
110.18 1
< 0.1%
106.87 1
< 0.1%
105.67 1
< 0.1%
103.63 1
< 0.1%
102.66 1
< 0.1%
100.55 1
< 0.1%

클로로필
Real number (ℝ)

ZEROS 

Distinct1478
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.932355
Minimum-0.49
Maximum37.22
Zeros1354
Zeros (%)13.5%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-12T08:57:43.871097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.49
5-th percentile0
Q11.36
median2.72
Q35.29
95-th percentile11.4105
Maximum37.22
Range37.71
Interquartile range (IQR)3.93

Descriptive statistics

Standard deviation4.2602037
Coefficient of variation (CV)1.0833721
Kurtosis9.185113
Mean3.932355
Median Absolute Deviation (MAD)1.77
Skewness2.5724202
Sum39323.55
Variance18.149336
MonotonicityNot monotonic
2023-12-12T08:57:44.011723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1354
 
13.5%
1.68 38
 
0.4%
1.57 33
 
0.3%
1.81 32
 
0.3%
2.32 29
 
0.3%
1.6 28
 
0.3%
1.91 28
 
0.3%
1.74 27
 
0.3%
1.82 27
 
0.3%
1.55 26
 
0.3%
Other values (1468) 8378
83.8%
ValueCountFrequency (%)
-0.49 1
 
< 0.1%
0.0 1354
13.5%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
37.22 1
< 0.1%
31.94 1
< 0.1%
31.09 1
< 0.1%
30.61 1
< 0.1%
30.39 1
< 0.1%
30.33 1
< 0.1%
30.04 1
< 0.1%
29.83 1
< 0.1%
29.67 1
< 0.1%
29.16 1
< 0.1%

화학적산소요구량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct465
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.824404
Minimum0
Maximum5.62
Zeros1460
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:44.159155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.21
median1.9
Q32.55
95-th percentile3.51
Maximum5.62
Range5.62
Interquartile range (IQR)1.34

Descriptive statistics

Standard deviation1.0875874
Coefficient of variation (CV)0.596133
Kurtosis-0.088650801
Mean1.824404
Median Absolute Deviation (MAD)0.67
Skewness0.023411399
Sum18244.04
Variance1.1828464
MonotonicityNot monotonic
2023-12-12T08:57:44.324078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1460
 
14.6%
1.75 70
 
0.7%
1.99 62
 
0.6%
2.14 61
 
0.6%
1.83 59
 
0.6%
2.09 55
 
0.5%
2.11 55
 
0.5%
2.39 55
 
0.5%
1.96 54
 
0.5%
1.43 54
 
0.5%
Other values (455) 8015
80.2%
ValueCountFrequency (%)
0.0 1460
14.6%
0.12 2
 
< 0.1%
0.13 1
 
< 0.1%
0.14 2
 
< 0.1%
0.17 2
 
< 0.1%
0.18 4
 
< 0.1%
0.19 3
 
< 0.1%
0.2 1
 
< 0.1%
0.22 6
 
0.1%
0.24 2
 
< 0.1%
ValueCountFrequency (%)
5.62 4
< 0.1%
5.59 2
< 0.1%
5.52 2
< 0.1%
5.49 2
< 0.1%
5.38 1
 
< 0.1%
5.37 1
 
< 0.1%
5.36 2
< 0.1%
5.32 1
 
< 0.1%
5.31 3
< 0.1%
5.3 4
< 0.1%

총질소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct690
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1911901
Minimum0
Maximum1.164
Zeros2873
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:44.484746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.142
Q30.30525
95-th percentile0.55
Maximum1.164
Range1.164
Interquartile range (IQR)0.30525

Descriptive statistics

Standard deviation0.19271197
Coefficient of variation (CV)1.00796
Kurtosis1.8895703
Mean0.1911901
Median Absolute Deviation (MAD)0.142
Skewness1.2824898
Sum1911.901
Variance0.037137902
MonotonicityNot monotonic
2023-12-12T08:57:44.681960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2873
28.7%
0.132 62
 
0.6%
0.141 59
 
0.6%
0.118 53
 
0.5%
0.128 51
 
0.5%
0.134 50
 
0.5%
0.12 50
 
0.5%
0.116 50
 
0.5%
0.121 50
 
0.5%
0.13 49
 
0.5%
Other values (680) 6653
66.5%
ValueCountFrequency (%)
0.0 2873
28.7%
0.038 1
 
< 0.1%
0.041 4
 
< 0.1%
0.046 2
 
< 0.1%
0.048 1
 
< 0.1%
0.051 1
 
< 0.1%
0.052 1
 
< 0.1%
0.053 1
 
< 0.1%
0.056 2
 
< 0.1%
0.057 1
 
< 0.1%
ValueCountFrequency (%)
1.164 1
 
< 0.1%
1.13 1
 
< 0.1%
1.119 2
< 0.1%
1.115 1
 
< 0.1%
1.107 2
< 0.1%
1.101 4
< 0.1%
1.098 2
< 0.1%
1.063 1
 
< 0.1%
1.053 2
< 0.1%
1.051 1
 
< 0.1%

총인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0293034
Minimum0
Maximum0.111
Zeros1786
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:44.870787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.014
median0.023
Q30.048
95-th percentile0.071
Maximum0.111
Range0.111
Interquartile range (IQR)0.034

Descriptive statistics

Standard deviation0.02284579
Coefficient of variation (CV)0.77962934
Kurtosis-0.69519845
Mean0.0293034
Median Absolute Deviation (MAD)0.019
Skewness0.52708246
Sum293.034
Variance0.00052193014
MonotonicityNot monotonic
2023-12-12T08:57:45.060984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1786
 
17.9%
0.017 438
 
4.4%
0.016 379
 
3.8%
0.015 360
 
3.6%
0.018 335
 
3.4%
0.014 322
 
3.2%
0.019 306
 
3.1%
0.013 225
 
2.2%
0.02 206
 
2.1%
0.021 169
 
1.7%
Other values (85) 5474
54.7%
ValueCountFrequency (%)
0.0 1786
17.9%
0.006 1
 
< 0.1%
0.007 2
 
< 0.1%
0.008 9
 
0.1%
0.009 23
 
0.2%
0.01 43
 
0.4%
0.011 90
 
0.9%
0.012 148
 
1.5%
0.013 225
 
2.2%
0.014 322
 
3.2%
ValueCountFrequency (%)
0.111 3
< 0.1%
0.107 1
 
< 0.1%
0.098 2
 
< 0.1%
0.097 2
 
< 0.1%
0.096 2
 
< 0.1%
0.095 1
 
< 0.1%
0.093 5
0.1%
0.092 7
0.1%
0.091 1
 
< 0.1%
0.09 3
< 0.1%

암모니아질소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct352
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0889374
Minimum0
Maximum0.4
Zeros2263
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:45.231447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.031
median0.068
Q30.134
95-th percentile0.27
Maximum0.4
Range0.4
Interquartile range (IQR)0.103

Descriptive statistics

Standard deviation0.083217264
Coefficient of variation (CV)0.93568357
Kurtosis0.49259517
Mean0.0889374
Median Absolute Deviation (MAD)0.055
Skewness1.0570099
Sum889.374
Variance0.006925113
MonotonicityNot monotonic
2023-12-12T08:57:45.400412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2263
 
22.6%
0.068 107
 
1.1%
0.055 98
 
1.0%
0.065 89
 
0.9%
0.057 87
 
0.9%
0.042 85
 
0.9%
0.04 84
 
0.8%
0.038 82
 
0.8%
0.05 82
 
0.8%
0.058 81
 
0.8%
Other values (342) 6942
69.4%
ValueCountFrequency (%)
0.0 2263
22.6%
0.005 4
 
< 0.1%
0.006 2
 
< 0.1%
0.007 2
 
< 0.1%
0.008 3
 
< 0.1%
0.009 3
 
< 0.1%
0.01 4
 
< 0.1%
0.011 3
 
< 0.1%
0.012 2
 
< 0.1%
0.013 5
 
0.1%
ValueCountFrequency (%)
0.4 1
 
< 0.1%
0.386 2
 
< 0.1%
0.377 1
 
< 0.1%
0.376 2
 
< 0.1%
0.375 1
 
< 0.1%
0.371 2
 
< 0.1%
0.359 1
 
< 0.1%
0.357 2
 
< 0.1%
0.354 7
0.1%
0.353 4
< 0.1%

질산질소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0315332
Minimum0
Maximum0.265
Zeros2616
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:45.585019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.007
Q30.051
95-th percentile0.134
Maximum0.265
Range0.265
Interquartile range (IQR)0.051

Descriptive statistics

Standard deviation0.045049933
Coefficient of variation (CV)1.4286508
Kurtosis2.411366
Mean0.0315332
Median Absolute Deviation (MAD)0.007
Skewness1.7140419
Sum315.332
Variance0.0020294964
MonotonicityNot monotonic
2023-12-12T08:57:45.717052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2616
26.2%
0.002 585
 
5.9%
0.003 521
 
5.2%
0.001 427
 
4.3%
0.004 385
 
3.9%
0.005 257
 
2.6%
0.006 175
 
1.8%
0.007 121
 
1.2%
0.012 104
 
1.0%
0.008 101
 
1.0%
Other values (204) 4708
47.1%
ValueCountFrequency (%)
0.0 2616
26.2%
0.001 427
 
4.3%
0.002 585
 
5.9%
0.003 521
 
5.2%
0.004 385
 
3.9%
0.005 257
 
2.6%
0.006 175
 
1.8%
0.007 121
 
1.2%
0.008 101
 
1.0%
0.009 69
 
0.7%
ValueCountFrequency (%)
0.265 1
 
< 0.1%
0.248 1
 
< 0.1%
0.245 2
< 0.1%
0.24 1
 
< 0.1%
0.231 1
 
< 0.1%
0.23 4
< 0.1%
0.228 1
 
< 0.1%
0.227 1
 
< 0.1%
0.222 2
< 0.1%
0.216 1
 
< 0.1%

인산인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020475
Minimum0
Maximum0.086
Zeros1773
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:45.859418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.005
median0.013
Q30.03825
95-th percentile0.053
Maximum0.086
Range0.086
Interquartile range (IQR)0.03325

Descriptive statistics

Standard deviation0.01864569
Coefficient of variation (CV)0.91065639
Kurtosis-0.58664283
Mean0.020475
Median Absolute Deviation (MAD)0.013
Skewness0.70392815
Sum204.75
Variance0.00034766174
MonotonicityNot monotonic
2023-12-12T08:57:46.006554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1773
 
17.7%
0.005 423
 
4.2%
0.007 341
 
3.4%
0.008 321
 
3.2%
0.006 318
 
3.2%
0.01 289
 
2.9%
0.012 284
 
2.8%
0.009 276
 
2.8%
0.013 274
 
2.7%
0.011 273
 
2.7%
Other values (75) 5428
54.3%
ValueCountFrequency (%)
0.0 1773
17.7%
0.001 14
 
0.1%
0.002 78
 
0.8%
0.003 93
 
0.9%
0.004 270
 
2.7%
0.005 423
 
4.2%
0.006 318
 
3.2%
0.007 341
 
3.4%
0.008 321
 
3.2%
0.009 276
 
2.8%
ValueCountFrequency (%)
0.086 3
 
< 0.1%
0.084 2
 
< 0.1%
0.083 4
< 0.1%
0.082 2
 
< 0.1%
0.081 2
 
< 0.1%
0.079 1
 
< 0.1%
0.078 1
 
< 0.1%
0.077 1
 
< 0.1%
0.076 6
0.1%
0.075 9
0.1%

규산규소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1267
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3638521
Minimum0
Maximum2.137
Zeros2506
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T08:57:46.154542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.177
Q30.608
95-th percentile1.34505
Maximum2.137
Range2.137
Interquartile range (IQR)0.608

Descriptive statistics

Standard deviation0.43396778
Coefficient of variation (CV)1.1927038
Kurtosis1.6564055
Mean0.3638521
Median Absolute Deviation (MAD)0.177
Skewness1.460449
Sum3638.521
Variance0.18832803
MonotonicityNot monotonic
2023-12-12T08:57:46.295000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2506
 
25.1%
0.18 39
 
0.4%
0.17 37
 
0.4%
0.185 37
 
0.4%
0.745 35
 
0.4%
0.093 34
 
0.3%
0.165 33
 
0.3%
0.05 30
 
0.3%
0.098 29
 
0.3%
0.068 29
 
0.3%
Other values (1257) 7191
71.9%
ValueCountFrequency (%)
0.0 2506
25.1%
0.001 1
 
< 0.1%
0.005 2
 
< 0.1%
0.007 1
 
< 0.1%
0.008 4
 
< 0.1%
0.009 4
 
< 0.1%
0.01 1
 
< 0.1%
0.011 2
 
< 0.1%
0.012 8
 
0.1%
0.014 3
 
< 0.1%
ValueCountFrequency (%)
2.137 3
< 0.1%
2.079 1
 
< 0.1%
2.075 1
 
< 0.1%
2.074 2
< 0.1%
2.072 3
< 0.1%
2.056 1
 
< 0.1%
2.049 1
 
< 0.1%
2.048 1
 
< 0.1%
2.038 1
 
< 0.1%
2.007 2
< 0.1%

Interactions

2023-12-12T08:57:39.028990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:18.802235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.348726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.094512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.000644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.299724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.747460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.075613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.164460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.465895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.871721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.700031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.561004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:37.490023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.120537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:18.912909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.467999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.204743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.090389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.407039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.847489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.156242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.261897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.541816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.990321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.843835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.685349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:37.615484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.208115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.004545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.582324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.295351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.192704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.493955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.959972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.231581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.354351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.620321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.090365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.000502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.821981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:37.750655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.334319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.105474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.688934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.414172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.311415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.584324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.059368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.307215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.431416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.696325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.204384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.144939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.934239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:37.882396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.444216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.205941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.808581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.513218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.405959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.692666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.154954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.385788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.497642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.778170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.326645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.262066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.029294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.016367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.546186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.335371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.961457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.637326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.508055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.808825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.271452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.463891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.573931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.855531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.446443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.382165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.142462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.150724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.636955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.460333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.091677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.768760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.599524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.907760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.387308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.548528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.648209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.943309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.555154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.518463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.271178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.249866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.739110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.584181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.246012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:22.896031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.688145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.008337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.485430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.629944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.725771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.044163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.670369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.645996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.425439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.341116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.833192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.701153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.389205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.019854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.774333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.145297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.567774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.708596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.004545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.146225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.815982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.776016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.541149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.443624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:39.929211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.823679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.516510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.141977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.874077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.283842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.649709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.786388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.079797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.287259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:32.967496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:34.915178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.655154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.564326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:40.016973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:19.939159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.634798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.265199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:24.962718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.409781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.726571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.861586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.151215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.416470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.114492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.042019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.756810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.674964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:40.142956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.039011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.759334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.417330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.055380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.510922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.812138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:28.943594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.233502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.519284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.265067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.182980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.859290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.769983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:40.252703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.135500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.871276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.540221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.136069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.594981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.893706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.019885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.316317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.655041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.427847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.314157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:36.948808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.861931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:40.342730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:20.226816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:21.982786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:23.640336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:25.216466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:26.668726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:27.970921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:29.093972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:30.394045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:31.761463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:33.544110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:35.431356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:37.367213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:57:38.942179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:57:46.404793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인규산규소
염분1.0000.9810.6420.9160.6790.1770.2250.5190.4190.4880.3950.1820.3410.253
전기전도도0.9811.0000.6400.9150.6780.1800.2250.5240.4920.4880.3890.1770.3330.242
수온0.6420.6401.0000.8000.9160.4600.5660.8070.6610.8450.7270.6440.7920.725
수소이온농도0.9160.9150.8001.0000.8360.2190.3400.6600.4140.6060.5140.2740.4560.361
용존산소0.6790.6780.9160.8361.0000.3350.4570.7380.7010.8280.7130.6530.7850.716
탁도0.1770.1800.4600.2190.3351.0000.3680.2180.2790.3090.3470.1590.2810.331
클로로필0.2250.2250.5660.3400.4570.3681.0000.3360.3260.4650.3580.2420.3850.398
화학적산소요구량0.5190.5240.8070.6600.7380.2180.3361.0000.5700.7470.5820.4090.6010.551
총질소0.4190.4920.6610.4140.7010.2790.3260.5701.0000.7260.6970.5770.6580.587
총인0.4880.4880.8450.6060.8280.3090.4650.7470.7261.0000.7490.6730.8800.749
암모니아질소0.3950.3890.7270.5140.7130.3470.3580.5820.6970.7491.0000.5960.7100.629
질산질소0.1820.1770.6440.2740.6530.1590.2420.4090.5770.6730.5961.0000.7070.723
인산인0.3410.3330.7920.4560.7850.2810.3850.6010.6580.8800.7100.7071.0000.801
규산규소0.2530.2420.7250.3610.7160.3310.3980.5510.5870.7490.6290.7230.8011.000
2023-12-12T08:57:46.553648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인규산규소
염분1.0000.899-0.1980.7490.6490.2110.205-0.0750.1090.0410.2000.3010.1560.273
전기전도도0.8991.000-0.3250.7480.8280.2340.300-0.134-0.024-0.0980.0760.109-0.0510.148
수온-0.198-0.3251.000-0.206-0.3210.2550.2280.6440.4900.6820.4480.4530.6560.468
수소이온농도0.7490.748-0.2061.0000.7950.2110.311-0.0330.115-0.0020.1920.1470.0270.124
용존산소0.6490.828-0.3210.7951.0000.3110.468-0.096-0.123-0.225-0.035-0.092-0.205-0.051
탁도0.2110.2340.2550.2110.3111.0000.4640.2080.1180.0920.1700.1200.1470.143
클로로필0.2050.3000.2280.3110.4680.4641.0000.2680.0900.0990.1590.0510.1020.110
화학적산소요구량-0.075-0.1340.644-0.033-0.0960.2080.2681.0000.4730.6010.3650.3390.4800.283
총질소0.109-0.0240.4900.115-0.1230.1180.0900.4731.0000.5680.4680.4370.5080.394
총인0.041-0.0980.682-0.002-0.2250.0920.0990.6010.5681.0000.6020.5630.7080.566
암모니아질소0.2000.0760.4480.192-0.0350.1700.1590.3650.4680.6021.0000.5290.6490.515
질산질소0.3010.1090.4530.147-0.0920.1200.0510.3390.4370.5630.5291.0000.7060.612
인산인0.156-0.0510.6560.027-0.2050.1470.1020.4800.5080.7080.6490.7061.0000.691
규산규소0.2730.1480.4680.124-0.0510.1430.1100.2830.3940.5660.5150.6120.6911.000

Missing values

2023-12-12T08:57:40.493209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:57:40.690024image/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

측정소명측정소코드측정일시염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인규산규소
74398새만금1082225-09-120.00.00.00.00.00.00.00.00.00.00.00.00.00.0
56274새만금1082176-01-2831.3148.0424.747.985.677.252.62.290.00.0590.1140.0970.0391.314
62125새만금1082192-02-0431.0247.726.698.126.461.54.074.430.4480.0670.1430.040.0450.539
84202새만금1082252-07-1631.1247.720.378.287.020.03.613.140.00.0390.1730.0490.030.098
29619새만금1082103-02-0531.7848.8611.078.239.620.972.281.550.00.0170.0740.0020.0160.218
18881새만금1082073-09-1131.9749.596.758.3112.238.9713.451.90.1660.0140.0680.0160.0060.869
96843새만금1082287-02-240.00.00.00.00.00.00.00.00.00.00.00.00.00.0
59739새만금1082185-07-240.00.00.00.00.00.00.00.00.00.00.00.00.00.0
11033새만금1082052-03-170.00.00.00.00.00.00.00.00.00.00.00.00.00.0
98984새만금1082293-01-0432.248.199.48.338.763.031.21.480.3120.00.1630.1070.0370.674
측정소명측정소코드측정일시염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인규산규소
47362새만금1082151-09-0431.3247.9720.028.056.4311.834.941.760.2480.0250.1110.0010.0220.356
21795새만금1082081-09-0331.9249.367.948.2710.715.651.081.930.1070.0140.0440.0020.0050.063
41148새만금1082134-08-3031.2247.8318.798.268.720.05.082.380.1380.0240.0320.0030.0020.12
96081새만금1082285-01-2332.7848.811.858.318.094.541.870.820.3270.050.1360.1550.0360.762
12988새만금1082057-07-2431.9550.044.058.3111.951.644.712.290.1070.0190.0590.0040.0080.104
5987새만금1082038-05-2431.8849.944.088.3411.922.923.141.660.10.0150.0330.0010.0050.019
61200새만금1082189-07-240.00.00.00.00.00.00.00.00.00.00.00.00.00.0
24239새만금1082088-05-1331.6248.6910.328.229.838.230.02.540.1010.00.0510.0340.010.0
18189새만금1082071-10-2031.3648.766.538.312.1839.522.80.990.00.0120.00.0020.0060.084
466새만금1082023-04-1231.9449.884.88.2812.010.00.01.610.1870.0160.0850.0050.0140.034