Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells16831
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory159.0 B

Variable types

Categorical2
Text1
Numeric13
Unsupported1

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/15068983/fileData.do

Alerts

정점명 has constant value ""Constant
정점코드 has constant value ""Constant
염분 is highly overall correlated with 수온 and 2 other fieldsHigh correlation
전기전도도 is highly overall correlated with 수온 and 1 other fieldsHigh correlation
수온 is highly overall correlated with 염분 and 2 other fieldsHigh correlation
용존산소 is highly overall correlated with 염분 and 4 other fieldsHigh correlation
클로로필 is highly overall correlated with 용존산소High correlation
총질소 is highly overall correlated with 인산인High correlation
총인 is highly overall correlated with 암모니아질소 and 1 other fieldsHigh correlation
암모니아질소 is highly overall correlated with 총인 and 1 other fieldsHigh correlation
인산인 is highly overall correlated with 염분 and 4 other fieldsHigh correlation
탁도 has 247 (2.5%) missing valuesMissing
클로로필 has 105 (1.1%) missing valuesMissing
남조류 has 10000 (100.0%) missing valuesMissing
화학적산소요구량 has 1046 (10.5%) missing valuesMissing
총질소 has 1283 (12.8%) missing valuesMissing
총인 has 594 (5.9%) missing valuesMissing
암모니아질소 has 1183 (11.8%) missing valuesMissing
질산질소 has 1504 (15.0%) missing valuesMissing
인산인 has 628 (6.3%) missing valuesMissing
측정일시 has unique valuesUnique
남조류 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 16:27:17.971839
Analysis finished2023-12-12 16:27:43.366677
Duration25.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정점명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
천수만 10000
100.0%

Length

2023-12-13T01:27:43.450342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:27:43.550569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
천수만 10000
100.0%

정점코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
111
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row111
2nd row111
3rd row111
4th row111
5th row111

Common Values

ValueCountFrequency (%)
111 10000
100.0%

Length

2023-12-13T01:27:43.651566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:27:43.742296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
111 10000
100.0%

측정일시
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T01:27:44.069357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters160000
Distinct characters13
Distinct categories4 ?
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 row2022-02-23 01:55
2nd row2022-10-05 18:00
3rd row2022-11-20 11:30
4th row2022-11-08 19:05
5th row2022-07-09 04:00
ValueCountFrequency (%)
22:20 55
 
0.3%
2022-04-24 52
 
0.3%
04:05 51
 
0.3%
16:50 51
 
0.3%
2022-05-11 50
 
0.2%
2022-11-29 49
 
0.2%
20:40 49
 
0.2%
2022-12-15 49
 
0.2%
2022-03-08 49
 
0.2%
2022-10-02 48
 
0.2%
Other values (552) 19497
97.5%
2023-12-13T01:27:44.505117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 40882
25.6%
0 33571
21.0%
- 20000
12.5%
1 15749
 
9.8%
10000
 
6.2%
: 10000
 
6.2%
5 9725
 
6.1%
3 5462
 
3.4%
4 4619
 
2.9%
7 2963
 
1.9%
Other values (3) 7029
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120000
75.0%
Dash Punctuation 20000
 
12.5%
Space Separator 10000
 
6.2%
Other Punctuation 10000
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40882
34.1%
0 33571
28.0%
1 15749
 
13.1%
5 9725
 
8.1%
3 5462
 
4.6%
4 4619
 
3.8%
7 2963
 
2.5%
6 2869
 
2.4%
9 2119
 
1.8%
8 2041
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 160000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40882
25.6%
0 33571
21.0%
- 20000
12.5%
1 15749
 
9.8%
10000
 
6.2%
: 10000
 
6.2%
5 9725
 
6.1%
3 5462
 
3.4%
4 4619
 
2.9%
7 2963
 
1.9%
Other values (3) 7029
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40882
25.6%
0 33571
21.0%
- 20000
12.5%
1 15749
 
9.8%
10000
 
6.2%
: 10000
 
6.2%
5 9725
 
6.1%
3 5462
 
3.4%
4 4619
 
2.9%
7 2963
 
1.9%
Other values (3) 7029
 
4.4%

염분
Real number (ℝ)

HIGH CORRELATION 

Distinct746
Distinct (%)7.5%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.351744
Minimum15.84
Maximum31.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:44.663807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.84
5-th percentile26.887
Q128.955
median29.86
Q330.26
95-th percentile30.62
Maximum31.15
Range15.31
Interquartile range (IQR)1.305

Descriptive statistics

Standard deviation1.6587489
Coefficient of variation (CV)0.056512789
Kurtosis19.661087
Mean29.351744
Median Absolute Deviation (MAD)0.51
Skewness-3.7974227
Sum292783.65
Variance2.751448
MonotonicityNot monotonic
2023-12-13T01:27:44.828263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.27 106
 
1.1%
29.9 96
 
1.0%
30.43 94
 
0.9%
29.97 89
 
0.9%
30.28 87
 
0.9%
30.29 86
 
0.9%
30.02 85
 
0.9%
30.3 85
 
0.9%
30.01 82
 
0.8%
29.96 82
 
0.8%
Other values (736) 9083
90.8%
ValueCountFrequency (%)
15.84 1
< 0.1%
16.32 1
< 0.1%
16.78 1
< 0.1%
17.08 1
< 0.1%
17.13 1
< 0.1%
17.17 2
< 0.1%
17.19 2
< 0.1%
17.21 1
< 0.1%
17.22 1
< 0.1%
17.23 1
< 0.1%
ValueCountFrequency (%)
31.15 1
 
< 0.1%
31.1 1
 
< 0.1%
31.09 1
 
< 0.1%
31.06 3
< 0.1%
31.05 5
0.1%
31.04 4
< 0.1%
31.03 3
< 0.1%
31.02 2
 
< 0.1%
31.01 4
< 0.1%
31.0 6
0.1%

전기전도도
Real number (ℝ)

HIGH CORRELATION 

Distinct1611
Distinct (%)16.2%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.357753
Minimum27.94
Maximum50.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:44.980558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.94
5-th percentile34.5
Q137.495
median42.81
Q344.89
95-th percentile47.17
Maximum50.86
Range22.92
Interquartile range (IQR)7.395

Descriptive statistics

Standard deviation4.3698046
Coefficient of variation (CV)0.10565866
Kurtosis-1.0712282
Mean41.357753
Median Absolute Deviation (MAD)3.53
Skewness-0.37772249
Sum412543.59
Variance19.095192
MonotonicityNot monotonic
2023-12-13T01:27:45.138042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.72 24
 
0.2%
44.23 22
 
0.2%
43.18 20
 
0.2%
44.67 20
 
0.2%
43.06 20
 
0.2%
42.83 20
 
0.2%
45.85 19
 
0.2%
43.65 19
 
0.2%
42.71 19
 
0.2%
46.08 19
 
0.2%
Other values (1601) 9773
97.7%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
27.94 1
 
< 0.1%
28.58 1
 
< 0.1%
29.55 1
 
< 0.1%
29.71 1
 
< 0.1%
29.72 3
< 0.1%
29.73 1
 
< 0.1%
29.74 2
< 0.1%
29.75 2
< 0.1%
29.76 2
< 0.1%
29.79 2
< 0.1%
ValueCountFrequency (%)
50.86 1
< 0.1%
49.82 1
< 0.1%
49.19 1
< 0.1%
48.71 1
< 0.1%
48.68 1
< 0.1%
48.57 1
< 0.1%
48.49 1
< 0.1%
48.48 1
< 0.1%
48.44 1
< 0.1%
48.43 1
< 0.1%

수온
Real number (ℝ)

HIGH CORRELATION 

Distinct1915
Distinct (%)19.2%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean14.543266
Minimum1.66
Maximum26.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:45.601377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.66
5-th percentile6.85
Q19.75
median15.22
Q319.51
95-th percentile21.84
Maximum26.43
Range24.77
Interquartile range (IQR)9.76

Descriptive statistics

Standard deviation5.3384748
Coefficient of variation (CV)0.36707537
Kurtosis-1.2751848
Mean14.543266
Median Absolute Deviation (MAD)4.92
Skewness-0.082706587
Sum145069.08
Variance28.499313
MonotonicityNot monotonic
2023-12-13T01:27:45.750547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.14 27
 
0.3%
7.37 24
 
0.2%
20.82 23
 
0.2%
7.33 22
 
0.2%
7.18 21
 
0.2%
7.41 21
 
0.2%
21.21 20
 
0.2%
7.36 20
 
0.2%
7.42 20
 
0.2%
7.17 20
 
0.2%
Other values (1905) 9757
97.6%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
1.66 1
< 0.1%
1.76 1
< 0.1%
1.77 2
< 0.1%
1.78 1
< 0.1%
1.86 1
< 0.1%
1.89 1
< 0.1%
1.95 1
< 0.1%
1.98 2
< 0.1%
2.1 1
< 0.1%
2.2 1
< 0.1%
ValueCountFrequency (%)
26.43 1
< 0.1%
26.41 1
< 0.1%
26.39 1
< 0.1%
26.37 1
< 0.1%
26.35 1
< 0.1%
26.34 1
< 0.1%
26.32 1
< 0.1%
26.26 1
< 0.1%
26.17 1
< 0.1%
26.05 1
< 0.1%

수소이온농도
Real number (ℝ)

Distinct91
Distinct (%)0.9%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean8.3935202
Minimum7.76
Maximum8.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:45.883040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.76
5-th percentile8.13
Q18.29
median8.41
Q38.52
95-th percentile8.57
Maximum8.68
Range0.92
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.14291807
Coefficient of variation (CV)0.017027191
Kurtosis0.19321939
Mean8.3935202
Median Absolute Deviation (MAD)0.11
Skewness-0.70588086
Sum83263.72
Variance0.020425575
MonotonicityNot monotonic
2023-12-13T01:27:46.003747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.54 523
 
5.2%
8.55 507
 
5.1%
8.56 405
 
4.0%
8.52 378
 
3.8%
8.53 354
 
3.5%
8.51 339
 
3.4%
8.27 303
 
3.0%
8.5 262
 
2.6%
8.31 259
 
2.6%
8.29 257
 
2.6%
Other values (81) 6333
63.3%
ValueCountFrequency (%)
7.76 1
 
< 0.1%
7.77 1
 
< 0.1%
7.79 3
< 0.1%
7.8 1
 
< 0.1%
7.81 2
 
< 0.1%
7.82 1
 
< 0.1%
7.83 5
0.1%
7.84 2
 
< 0.1%
7.85 3
< 0.1%
7.86 4
< 0.1%
ValueCountFrequency (%)
8.68 1
 
< 0.1%
8.67 3
 
< 0.1%
8.66 2
 
< 0.1%
8.65 6
 
0.1%
8.64 8
 
0.1%
8.63 8
 
0.1%
8.62 26
0.3%
8.61 27
0.3%
8.6 50
0.5%
8.59 59
0.6%

용존산소
Real number (ℝ)

HIGH CORRELATION 

Distinct987
Distinct (%)10.0%
Missing86
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean10.320907
Minimum0.89
Maximum15.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:46.121059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.89
5-th percentile6.78
Q18.72
median10.04
Q312.36
95-th percentile14
Maximum15.1
Range14.21
Interquartile range (IQR)3.64

Descriptive statistics

Standard deviation2.296271
Coefficient of variation (CV)0.22248733
Kurtosis-0.43204251
Mean10.320907
Median Absolute Deviation (MAD)1.76
Skewness-0.067933029
Sum102321.47
Variance5.2728604
MonotonicityNot monotonic
2023-12-13T01:27:46.247291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.06 31
 
0.3%
9.75 31
 
0.3%
9.76 31
 
0.3%
9.7 30
 
0.3%
9.99 30
 
0.3%
8.73 30
 
0.3%
9.94 29
 
0.3%
12.42 29
 
0.3%
10.03 28
 
0.3%
8.98 27
 
0.3%
Other values (977) 9618
96.2%
(Missing) 86
 
0.9%
ValueCountFrequency (%)
0.89 1
< 0.1%
0.91 2
< 0.1%
0.92 1
< 0.1%
0.95 1
< 0.1%
0.96 1
< 0.1%
1.07 1
< 0.1%
1.12 1
< 0.1%
1.16 1
< 0.1%
1.17 1
< 0.1%
1.3 1
< 0.1%
ValueCountFrequency (%)
15.1 1
 
< 0.1%
15.05 1
 
< 0.1%
15.02 1
 
< 0.1%
15.01 1
 
< 0.1%
14.93 1
 
< 0.1%
14.92 3
< 0.1%
14.9 2
< 0.1%
14.89 1
 
< 0.1%
14.88 1
 
< 0.1%
14.87 2
< 0.1%

탁도
Real number (ℝ)

MISSING 

Distinct2906
Distinct (%)29.8%
Missing247
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean13.093753
Minimum1.73
Maximum198.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:46.378848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.86
Q15.54
median8.29
Q316.05
95-th percentile36.258
Maximum198.79
Range197.06
Interquartile range (IQR)10.51

Descriptive statistics

Standard deviation13.215771
Coefficient of variation (CV)1.0093188
Kurtosis25.977008
Mean13.093753
Median Absolute Deviation (MAD)3.5
Skewness3.8796131
Sum127703.37
Variance174.65659
MonotonicityNot monotonic
2023-12-13T01:27:46.510164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.52 22
 
0.2%
4.64 21
 
0.2%
5.47 20
 
0.2%
4.8 19
 
0.2%
4.76 19
 
0.2%
5.27 19
 
0.2%
4.83 19
 
0.2%
4.58 19
 
0.2%
6.15 19
 
0.2%
5.12 18
 
0.2%
Other values (2896) 9558
95.6%
(Missing) 247
 
2.5%
ValueCountFrequency (%)
1.73 1
< 0.1%
1.9 1
< 0.1%
2.12 1
< 0.1%
2.14 1
< 0.1%
2.22 1
< 0.1%
2.25 1
< 0.1%
2.26 1
< 0.1%
2.3 1
< 0.1%
2.31 1
< 0.1%
2.36 1
< 0.1%
ValueCountFrequency (%)
198.79 1
< 0.1%
179.39 1
< 0.1%
176.83 1
< 0.1%
175.5 1
< 0.1%
146.91 1
< 0.1%
145.02 1
< 0.1%
141.15 1
< 0.1%
134.8 1
< 0.1%
133.5 1
< 0.1%
132.97 1
< 0.1%

클로로필
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1459
Distinct (%)14.7%
Missing105
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean6.1881455
Minimum1.66
Maximum49.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:46.668612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.66
5-th percentile2.37
Q13.52
median5.17
Q37.64
95-th percentile13.2
Maximum49.57
Range47.91
Interquartile range (IQR)4.12

Descriptive statistics

Standard deviation3.6983081
Coefficient of variation (CV)0.597644
Kurtosis7.0851658
Mean6.1881455
Median Absolute Deviation (MAD)1.87
Skewness1.9212901
Sum61231.7
Variance13.677482
MonotonicityNot monotonic
2023-12-13T01:27:46.799513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.01 29
 
0.3%
3.1 28
 
0.3%
3.31 27
 
0.3%
2.99 27
 
0.3%
3.49 27
 
0.3%
3.2 27
 
0.3%
2.67 26
 
0.3%
3.32 26
 
0.3%
3.46 25
 
0.2%
4.2 25
 
0.2%
Other values (1449) 9628
96.3%
(Missing) 105
 
1.1%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.79 1
 
< 0.1%
1.8 1
 
< 0.1%
1.82 4
< 0.1%
1.84 1
 
< 0.1%
1.85 1
 
< 0.1%
1.86 2
< 0.1%
1.87 2
< 0.1%
1.88 1
 
< 0.1%
1.9 3
< 0.1%
ValueCountFrequency (%)
49.57 1
< 0.1%
41.74 1
< 0.1%
38.09 1
< 0.1%
34.81 1
< 0.1%
33.96 1
< 0.1%
32.05 1
< 0.1%
31.05 1
< 0.1%
27.95 1
< 0.1%
27.42 1
< 0.1%
27.15 1
< 0.1%

남조류
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

화학적산소요구량
Real number (ℝ)

MISSING 

Distinct805
Distinct (%)9.0%
Missing1046
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean4.0297766
Minimum0.01
Maximum14.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:46.947720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.93
Q12.91
median3.77
Q34.57
95-th percentile7.4
Maximum14.84
Range14.83
Interquartile range (IQR)1.66

Descriptive statistics

Standard deviation1.7636048
Coefficient of variation (CV)0.43764332
Kurtosis4.4602707
Mean4.0297766
Median Absolute Deviation (MAD)0.84
Skewness1.6069418
Sum36082.62
Variance3.1103019
MonotonicityNot monotonic
2023-12-13T01:27:47.072282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 76
 
0.8%
4.02 55
 
0.5%
3.41 53
 
0.5%
3.99 52
 
0.5%
4.18 52
 
0.5%
3.93 50
 
0.5%
3.92 47
 
0.5%
2.9 46
 
0.5%
3.94 46
 
0.5%
3.97 43
 
0.4%
Other values (795) 8434
84.3%
(Missing) 1046
 
10.5%
ValueCountFrequency (%)
0.01 2
 
< 0.1%
0.04 6
0.1%
0.05 2
 
< 0.1%
0.08 2
 
< 0.1%
0.14 2
 
< 0.1%
0.21 3
< 0.1%
0.22 1
 
< 0.1%
0.28 1
 
< 0.1%
0.34 1
 
< 0.1%
0.39 3
< 0.1%
ValueCountFrequency (%)
14.84 3
< 0.1%
14.8 3
< 0.1%
14.67 2
< 0.1%
13.99 2
< 0.1%
13.94 2
< 0.1%
13.65 1
 
< 0.1%
13.58 2
< 0.1%
13.26 2
< 0.1%
13.08 2
< 0.1%
12.8 1
 
< 0.1%

총질소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct811
Distinct (%)9.3%
Missing1283
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean0.25405151
Minimum0.003
Maximum3.194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:47.196612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.047
Q10.109
median0.176
Q30.305
95-th percentile0.722
Maximum3.194
Range3.191
Interquartile range (IQR)0.196

Descriptive statistics

Standard deviation0.24942947
Coefficient of variation (CV)0.98180668
Kurtosis18.84128
Mean0.25405151
Median Absolute Deviation (MAD)0.08
Skewness3.3515744
Sum2214.567
Variance0.06221506
MonotonicityNot monotonic
2023-12-13T01:27:47.315657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 67
 
0.7%
0.109 64
 
0.6%
0.152 54
 
0.5%
0.099 53
 
0.5%
0.129 52
 
0.5%
0.095 50
 
0.5%
0.102 50
 
0.5%
0.216 48
 
0.5%
0.105 48
 
0.5%
0.107 48
 
0.5%
Other values (801) 8183
81.8%
(Missing) 1283
 
12.8%
ValueCountFrequency (%)
0.003 1
 
< 0.1%
0.005 2
 
< 0.1%
0.007 1
 
< 0.1%
0.009 2
 
< 0.1%
0.01 4
< 0.1%
0.011 2
 
< 0.1%
0.012 5
0.1%
0.013 4
< 0.1%
0.014 5
0.1%
0.015 2
 
< 0.1%
ValueCountFrequency (%)
3.194 1
 
< 0.1%
2.931 2
< 0.1%
2.711 2
< 0.1%
2.525 2
< 0.1%
2.459 4
< 0.1%
2.221 1
 
< 0.1%
2.123 1
 
< 0.1%
1.992 1
 
< 0.1%
1.983 2
< 0.1%
1.909 1
 
< 0.1%

총인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct168
Distinct (%)1.8%
Missing594
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean0.027850734
Minimum0.001
Maximum0.431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:47.444371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.009
Q10.013
median0.021
Q30.03
95-th percentile0.072
Maximum0.431
Range0.43
Interquartile range (IQR)0.017

Descriptive statistics

Standard deviation0.028038764
Coefficient of variation (CV)1.0067514
Kurtosis44.08614
Mean0.027850734
Median Absolute Deviation (MAD)0.008
Skewness5.2340667
Sum261.964
Variance0.00078617229
MonotonicityNot monotonic
2023-12-13T01:27:47.575320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.012 532
 
5.3%
0.011 477
 
4.8%
0.013 445
 
4.5%
0.021 424
 
4.2%
0.01 383
 
3.8%
0.014 373
 
3.7%
0.015 360
 
3.6%
0.023 334
 
3.3%
0.02 328
 
3.3%
0.018 308
 
3.1%
Other values (158) 5442
54.4%
(Missing) 594
 
5.9%
ValueCountFrequency (%)
0.001 27
 
0.3%
0.002 16
 
0.2%
0.003 9
 
0.1%
0.004 30
 
0.3%
0.005 22
 
0.2%
0.006 33
 
0.3%
0.007 69
 
0.7%
0.008 140
 
1.4%
0.009 208
2.1%
0.01 383
3.8%
ValueCountFrequency (%)
0.431 2
< 0.1%
0.384 3
< 0.1%
0.362 2
< 0.1%
0.353 1
 
< 0.1%
0.346 1
 
< 0.1%
0.312 3
< 0.1%
0.303 2
< 0.1%
0.299 1
 
< 0.1%
0.293 1
 
< 0.1%
0.278 3
< 0.1%

암모니아질소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct310
Distinct (%)3.5%
Missing1183
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean0.054026993
Minimum0
Maximum2.129
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:47.699212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006
Q10.017
median0.031
Q30.053
95-th percentile0.165
Maximum2.129
Range2.129
Interquartile range (IQR)0.036

Descriptive statistics

Standard deviation0.10079203
Coefficient of variation (CV)1.8655865
Kurtosis107.23322
Mean0.054026993
Median Absolute Deviation (MAD)0.016
Skewness8.5952794
Sum476.356
Variance0.010159034
MonotonicityNot monotonic
2023-12-13T01:27:47.852157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.017 206
 
2.1%
0.015 202
 
2.0%
0.018 196
 
2.0%
0.02 188
 
1.9%
0.019 187
 
1.9%
0.028 187
 
1.9%
0.013 182
 
1.8%
0.011 177
 
1.8%
0.025 177
 
1.8%
0.014 173
 
1.7%
Other values (300) 6942
69.4%
(Missing) 1183
 
11.8%
ValueCountFrequency (%)
0.0 13
 
0.1%
0.001 52
 
0.5%
0.002 63
0.6%
0.003 63
0.6%
0.004 97
1.0%
0.005 102
1.0%
0.006 95
0.9%
0.007 105
1.1%
0.008 150
1.5%
0.009 146
1.5%
ValueCountFrequency (%)
2.129 1
< 0.1%
1.954 2
< 0.1%
1.683 2
< 0.1%
1.415 1
< 0.1%
1.328 1
< 0.1%
1.308 2
< 0.1%
1.174 1
< 0.1%
1.166 2
< 0.1%
1.141 1
< 0.1%
1.062 1
< 0.1%

질산질소
Real number (ℝ)

MISSING 

Distinct141
Distinct (%)1.7%
Missing1504
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean0.016315913
Minimum0
Maximum0.273
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:47.973548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00175
Q10.005
median0.01
Q30.018
95-th percentile0.055
Maximum0.273
Range0.273
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.02258428
Coefficient of variation (CV)1.3841873
Kurtosis33.330957
Mean0.016315913
Median Absolute Deviation (MAD)0.006
Skewness4.7551352
Sum138.62
Variance0.00051004969
MonotonicityNot monotonic
2023-12-13T01:27:48.096310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006 524
 
5.2%
0.003 492
 
4.9%
0.002 470
 
4.7%
0.011 447
 
4.5%
0.005 445
 
4.5%
0.004 435
 
4.3%
0.01 427
 
4.3%
0.012 412
 
4.1%
0.001 400
 
4.0%
0.007 396
 
4.0%
Other values (131) 4048
40.5%
(Missing) 1504
 
15.0%
ValueCountFrequency (%)
0.0 25
 
0.2%
0.001 400
4.0%
0.002 470
4.7%
0.003 492
4.9%
0.004 435
4.3%
0.005 445
4.5%
0.006 524
5.2%
0.007 396
4.0%
0.008 349
3.5%
0.009 364
3.6%
ValueCountFrequency (%)
0.273 3
< 0.1%
0.272 2
< 0.1%
0.267 3
< 0.1%
0.236 1
 
< 0.1%
0.226 2
< 0.1%
0.214 3
< 0.1%
0.205 1
 
< 0.1%
0.204 3
< 0.1%
0.201 1
 
< 0.1%
0.195 2
< 0.1%

인산인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct140
Distinct (%)1.5%
Missing628
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean0.019150448
Minimum0.001
Maximum0.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T01:27:48.221163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.003
Q10.009
median0.015
Q30.022
95-th percentile0.048
Maximum0.336
Range0.335
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.021395698
Coefficient of variation (CV)1.1172427
Kurtosis58.406322
Mean0.019150448
Median Absolute Deviation (MAD)0.007
Skewness6.0134029
Sum179.478
Variance0.00045777589
MonotonicityNot monotonic
2023-12-13T01:27:48.389589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011 463
 
4.6%
0.01 446
 
4.5%
0.014 407
 
4.1%
0.012 394
 
3.9%
0.007 384
 
3.8%
0.015 372
 
3.7%
0.006 369
 
3.7%
0.009 365
 
3.6%
0.013 338
 
3.4%
0.019 338
 
3.4%
Other values (130) 5496
55.0%
(Missing) 628
 
6.3%
ValueCountFrequency (%)
0.001 146
 
1.5%
0.002 141
 
1.4%
0.003 263
2.6%
0.004 305
3.0%
0.005 306
3.1%
0.006 369
3.7%
0.007 384
3.8%
0.008 317
3.2%
0.009 365
3.6%
0.01 446
4.5%
ValueCountFrequency (%)
0.336 3
< 0.1%
0.326 2
< 0.1%
0.31 1
 
< 0.1%
0.287 2
< 0.1%
0.282 1
 
< 0.1%
0.28 1
 
< 0.1%
0.273 1
 
< 0.1%
0.258 1
 
< 0.1%
0.249 2
< 0.1%
0.228 2
< 0.1%

Interactions

2023-12-13T01:27:41.435277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.107896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.993025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:25.455056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.546566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.973336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.449778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.435847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.885565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.293299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.853263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.396937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.208752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.541525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.240593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.093434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:25.725106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.640903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.078451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.589131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.562918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.032125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.408926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.990319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.514066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.347783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.625158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.360279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.176142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:25.922813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.730901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.190701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.705832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.664227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.119989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.504235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.085096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.624530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.434820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.710744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.466629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.255748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.150080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.832148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.309032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.814779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.774477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.219179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.621700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.177303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.711141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.526294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.818228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.595018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.346228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.344900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.945071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.428297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.918876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.893826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.308257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.737616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.279141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.797804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.617505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.906706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.725594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.445347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.495362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.062959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.536629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:31.046427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.028546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.416985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.840028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.404608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.237462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.703000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.002108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:22.865205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.535370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.669959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.182153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.666504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:31.169540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.147161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.511953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.957965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.542509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.356878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.806695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.103266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.015396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.620257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.776417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.306791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.773403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:31.356347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.247931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.603182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.081886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.657301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.463260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.889994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.199297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.160832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.709972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:26.894228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.432683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:29.894819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:31.492635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.370053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.751556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.219754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.761721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.579078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.991578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.290275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.578414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.795221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.037489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.536664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.002190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:31.966367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.480536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.844676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.335892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.884405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.701274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.074024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.383592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.685956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:24.880290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.155126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.624792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.117197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.082943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.575322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:34.965390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.464494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:37.993275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.826717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.165229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.471855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.797778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:25.031265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.287430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.745209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.216472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.186413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.670032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.066512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.574944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.119212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:39.949389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.254047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:42.574911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:23.886911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:25.242037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:27.422230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:28.866827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:30.312860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:32.306173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:33.777269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:35.180039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:36.693732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:38.234361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:40.071294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:27:41.336996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:27:48.529349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인
염분1.0000.7420.7500.4780.6540.1040.3480.3320.5840.3980.5170.5320.241
전기전도도0.7421.0000.9430.6300.7880.2040.5050.4850.3800.3280.1680.4260.307
수온0.7500.9431.0000.6840.8060.2850.4730.5540.5290.4500.4320.4550.320
수소이온농도0.4780.6300.6841.0000.6520.2620.2730.5190.2900.3510.1330.2580.229
용존산소0.6540.7880.8060.6521.0000.2700.5460.3680.3240.2990.2410.2600.224
탁도0.1040.2040.2850.2620.2701.0000.5360.3330.0550.4950.1320.6690.515
클로로필0.3480.5050.4730.2730.5460.5361.0000.3220.1830.6540.0820.5000.446
화학적산소요구량0.3320.4850.5540.5190.3680.3330.3221.0000.1560.4310.1640.3850.446
총질소0.5840.3800.5290.2900.3240.0550.1830.1561.0000.3830.8780.3820.348
총인0.3980.3280.4500.3510.2990.4950.6540.4310.3831.0000.4430.5720.900
암모니아질소0.5170.1680.4320.1330.2410.1320.0820.1640.8780.4431.0000.3960.237
질산질소0.5320.4260.4550.2580.2600.6690.5000.3850.3820.5720.3961.0000.444
인산인0.2410.3070.3200.2290.2240.5150.4460.4460.3480.9000.2370.4441.000
2023-12-13T01:27:48.695127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
염분전기전도도수온수소이온농도용존산소탁도클로로필화학적산소요구량총질소총인암모니아질소질산질소인산인
염분1.000-0.490-0.7180.1950.687-0.2050.266-0.153-0.321-0.463-0.457-0.093-0.527
전기전도도-0.4901.0000.882-0.105-0.7560.173-0.3580.2840.0760.2060.334-0.1160.371
수온-0.7180.8821.000-0.159-0.8510.225-0.3240.3030.1500.3210.465-0.0130.473
수소이온농도0.195-0.105-0.1591.0000.065-0.224-0.338-0.1640.073-0.209-0.297-0.116-0.102
용존산소0.687-0.756-0.8510.0651.000-0.2550.513-0.180-0.346-0.355-0.4020.034-0.582
탁도-0.2050.1730.225-0.224-0.2551.0000.2960.2890.0800.2720.3600.2080.291
클로로필0.266-0.358-0.324-0.3380.5130.2961.0000.130-0.371-0.0260.0760.276-0.301
화학적산소요구량-0.1530.2840.303-0.164-0.1800.2890.1301.000-0.157-0.1260.1030.060-0.110
총질소-0.3210.0760.1500.073-0.3460.080-0.371-0.1571.0000.4170.2910.0430.505
총인-0.4630.2060.321-0.209-0.3550.272-0.026-0.1260.4171.0000.5880.2930.838
암모니아질소-0.4570.3340.465-0.297-0.4020.3600.0760.1030.2910.5881.0000.2730.598
질산질소-0.093-0.116-0.013-0.1160.0340.2080.2760.0600.0430.2930.2731.0000.133
인산인-0.5270.3710.473-0.102-0.5820.291-0.301-0.1100.5050.8380.5980.1331.000

Missing values

2023-12-13T01:27:42.716419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:27:42.946410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T01:27:43.209685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

정점명정점코드측정일시염분전기전도도수온수소이온농도용존산소탁도클로로필남조류화학적산소요구량총질소총인암모니아질소질산질소인산인
3187천수만1112022-02-23 01:5530.0934.516.528.2813.6818.98.23<NA>2.810.160.016<NA><NA>0.009
53034천수만1112022-10-05 18:0028.8246.2920.58.426.8533.643.87<NA>3.950.2550.030.0340.0080.03
66180천수만1112022-11-20 11:3029.4942.6413.58.559.0511.782.79<NA>2.90.5110.0180.029<NA>0.018
62815천수만1112022-11-08 19:0529.9743.215.188.539.469.012.69<NA>3.890.30.029<NA>0.0060.028
41528천수만1112022-07-09 04:0028.6746.5320.968.318.9112.775.21<NA>3.860.1310.0250.0780.0030.023
61807천수만1112022-11-05 07:0528.7341.5514.588.529.186.642.4<NA>3.990.2440.020.0110.0010.018
52252천수만1112022-10-03 00:5029.347.6821.188.447.3325.313.6<NA>5.850.1510.0120.0160.0060.012
39219천수만1112022-07-01 03:3528.6345.9220.48.088.3621.03.88<NA><NA>0.3460.043<NA>0.0050.033
69334천수만1112022-12-01 10:2029.7937.4710.058.5811.368.589.11<NA>0.790.1370.053<NA>0.0230.051
77969천수만1112022-12-31 09:5528.3830.944.478.3213.0571.448.33<NA>1.170.2970.160.1090.0340.127
정점명정점코드측정일시염분전기전도도수온수소이온농도용존산소탁도클로로필남조류화학적산소요구량총질소총인암모니아질소질산질소인산인
51362천수만1112022-09-29 22:4026.7743.9121.158.426.8319.273.68<NA>6.280.090.020.0240.0120.017
73036천수만1112022-12-14 06:5030.6934.656.08.4810.8131.195.03<NA>3.030.4180.0350.0290.0180.034
27958천수만1112022-05-19 03:4530.1643.6415.958.2810.218.215.1<NA>3.680.140.0120.050.0140.012
46964천수만1112022-07-28 04:0527.8745.7521.378.347.21<NA><NA><NA><NA>0.2370.0430.1910.0140.02
10166천수만1112022-03-18 07:5030.1435.867.918.3213.437.9112.05<NA>3.410.0760.0110.0180.0050.004
22469천수만1112022-04-30 01:2530.5141.012.848.4911.374.085.74<NA>6.710.0830.008<NA>0.0040.007
50974천수만1112022-09-28 14:2029.448.0621.428.487.5119.873.88<NA>6.880.1250.0070.0060.0110.006
60285천수만1112022-10-31 00:1529.6543.4616.448.558.7313.883.78<NA>2.70.1140.0220.0240.0250.017
64279천수만1112022-11-13 21:0528.8441.6815.098.479.34.632.74<NA>4.040.5320.0160.0210.0020.016
17179천수만1112022-04-11 16:3530.4438.3210.168.5813.0710.259.29<NA>3.560.1040.0140.0040.0030.005