Overview

Dataset statistics

Number of variables11
Number of observations77
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory98.7 B

Variable types

Numeric9
Text1
DateTime1

Dataset

Description수질측정망자료2019년11월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=203237

Alerts

Water_Date has constant value ""Constant
Water_DO is highly overall correlated with Water_BOD and 2 other fieldsHigh correlation
Water_BOD is highly overall correlated with Water_DO and 4 other fieldsHigh correlation
Water_COD is highly overall correlated with Water_DO and 4 other fieldsHigh correlation
Water_SS is highly overall correlated with Water_DO and 4 other fieldsHigh correlation
Water_TN is highly overall correlated with Water_BOD and 3 other fieldsHigh correlation
Water_TP is highly overall correlated with Water_BOD and 3 other fieldsHigh correlation
Water_Site has unique valuesUnique

Reproduction

Analysis started2024-03-14 03:09:32.512132
Analysis finished2024-03-14 03:09:39.316118
Duration6.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Water_ID
Real number (ℝ)

Distinct76
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.558442
Minimum1
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:39.385366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.8
Q124
median83
Q3105
95-th percentile119.2
Maximum123
Range122
Interquartile range (IQR)81

Descriptive statistics

Standard deviation41.666752
Coefficient of variation (CV)0.62601755
Kurtosis-1.6188172
Mean66.558442
Median Absolute Deviation (MAD)32
Skewness-0.20647346
Sum5125
Variance1736.1183
MonotonicityNot monotonic
2024-03-14T12:09:39.535784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 2
 
2.6%
116 1
 
1.3%
90 1
 
1.3%
110 1
 
1.3%
109 1
 
1.3%
98 1
 
1.3%
8 1
 
1.3%
115 1
 
1.3%
114 1
 
1.3%
113 1
 
1.3%
Other values (66) 66
85.7%
ValueCountFrequency (%)
1 1
1.3%
2 1
1.3%
3 1
1.3%
6 1
1.3%
7 1
1.3%
8 1
1.3%
10 1
1.3%
11 1
1.3%
12 1
1.3%
13 1
1.3%
ValueCountFrequency (%)
123 1
1.3%
122 1
1.3%
121 1
1.3%
120 1
1.3%
119 1
1.3%
118 1
1.3%
117 1
1.3%
116 1
1.3%
115 1
1.3%
114 1
1.3%

Water_Site
Text

UNIQUE 

Distinct77
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size748.0 B
2024-03-14T12:09:39.757309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.5714286
Min length2

Characters and Unicode

Total characters275
Distinct characters81
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)100.0%

Sample

1st row산북천
2nd row고부천2
3rd row고부천3
4th row고부천1
5th row동진강3
ValueCountFrequency (%)
산북천 1
 
1.3%
가막 1
 
1.3%
율천 1
 
1.3%
오수천-1 1
 
1.3%
대강 1
 
1.3%
적성 1
 
1.3%
옥택천 1
 
1.3%
수홍천 1
 
1.3%
치천 1
 
1.3%
임실 1
 
1.3%
Other values (67) 67
87.0%
2024-03-14T12:09:40.109434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 60
21.8%
1 18
 
6.5%
주 13
 
4.7%
- 9
 
3.3%
2 9
 
3.3%
전 8
 
2.9%
C 7
 
2.5%
동 7
 
2.5%
진 6
 
2.2%
정 6
 
2.2%
Other values (71) 132
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
80.4%
Decimal Number 36
 
13.1%
Dash Punctuation 9
 
3.3%
Uppercase Letter 8
 
2.9%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 60
27.1%
주 13
 
5.9%
전 8
 
3.6%
동 7
 
3.2%
진 6
 
2.7%
정 6
 
2.7%
산 6
 
2.7%
수 6
 
2.7%
평 5
 
2.3%
읍 5
 
2.3%
Other values (61) 99
44.8%
Decimal Number
ValueCountFrequency (%)
1 18
50.0%
2 9
25.0%
3 5
 
13.9%
4 2
 
5.6%
6 1
 
2.8%
5 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
C 7
87.5%
A 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
80.4%
Common 46
 
16.7%
Latin 8
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 60
27.1%
주 13
 
5.9%
전 8
 
3.6%
동 7
 
3.2%
진 6
 
2.7%
정 6
 
2.7%
산 6
 
2.7%
수 6
 
2.7%
평 5
 
2.3%
읍 5
 
2.3%
Other values (61) 99
44.8%
Common
ValueCountFrequency (%)
1 18
39.1%
- 9
19.6%
2 9
19.6%
3 5
 
10.9%
4 2
 
4.3%
. 1
 
2.2%
6 1
 
2.2%
5 1
 
2.2%
Latin
ValueCountFrequency (%)
C 7
87.5%
A 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
80.4%
ASCII 54
 
19.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 60
27.1%
주 13
 
5.9%
전 8
 
3.6%
동 7
 
3.2%
진 6
 
2.7%
정 6
 
2.7%
산 6
 
2.7%
수 6
 
2.7%
평 5
 
2.3%
읍 5
 
2.3%
Other values (61) 99
44.8%
ASCII
ValueCountFrequency (%)
1 18
33.3%
- 9
16.7%
2 9
16.7%
C 7
 
13.0%
3 5
 
9.3%
4 2
 
3.7%
A 1
 
1.9%
. 1
 
1.9%
6 1
 
1.9%
5 1
 
1.9%

Water_Date
Date

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size748.0 B
Minimum2019-11-01 00:00:00
Maximum2019-11-01 00:00:00
2024-03-14T12:09:40.203701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:40.271155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Water_Temp
Real number (ℝ)

Distinct58
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.390909
Minimum7.1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:40.421925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.1
5-th percentile7.88
Q110.1
median12.3
Q314.4
95-th percentile17.32
Maximum18
Range10.9
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation2.9186867
Coefficient of variation (CV)0.23555065
Kurtosis-0.84014291
Mean12.390909
Median Absolute Deviation (MAD)2.2
Skewness0.12955728
Sum954.1
Variance8.5187321
MonotonicityNot monotonic
2024-03-14T12:09:40.563294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.3 4
 
5.2%
9.9 3
 
3.9%
14.4 3
 
3.9%
10.6 2
 
2.6%
13.4 2
 
2.6%
10.8 2
 
2.6%
9.5 2
 
2.6%
7.9 2
 
2.6%
13.0 2
 
2.6%
12.9 2
 
2.6%
Other values (48) 53
68.8%
ValueCountFrequency (%)
7.1 1
1.3%
7.3 1
1.3%
7.4 1
1.3%
7.8 1
1.3%
7.9 2
2.6%
8.0 1
1.3%
8.1 1
1.3%
8.6 1
1.3%
8.7 1
1.3%
9.1 1
1.3%
ValueCountFrequency (%)
18.0 1
1.3%
17.6 1
1.3%
17.5 1
1.3%
17.4 1
1.3%
17.3 1
1.3%
17.2 1
1.3%
17.0 1
1.3%
16.9 1
1.3%
16.8 1
1.3%
16.4 1
1.3%

Water_pH
Real number (ℝ)

Distinct22
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7311688
Minimum6.7
Maximum8.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:40.659437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile6.78
Q17.4
median7.8
Q38.2
95-th percentile8.5
Maximum8.9
Range2.2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.51993414
Coefficient of variation (CV)0.067251686
Kurtosis-0.55285578
Mean7.7311688
Median Absolute Deviation (MAD)0.4
Skewness-0.19870277
Sum595.3
Variance0.27033151
MonotonicityNot monotonic
2024-03-14T12:09:40.746804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7.9 12
15.6%
8.2 10
13.0%
7.4 7
 
9.1%
7.8 6
 
7.8%
7.5 5
 
6.5%
6.7 4
 
5.2%
8.4 4
 
5.2%
7.2 4
 
5.2%
7.3 4
 
5.2%
8.1 3
 
3.9%
Other values (12) 18
23.4%
ValueCountFrequency (%)
6.7 4
5.2%
6.8 1
 
1.3%
6.9 3
3.9%
7.0 1
 
1.3%
7.1 1
 
1.3%
7.2 4
5.2%
7.3 4
5.2%
7.4 7
9.1%
7.5 5
6.5%
7.6 2
 
2.6%
ValueCountFrequency (%)
8.9 1
 
1.3%
8.7 1
 
1.3%
8.6 1
 
1.3%
8.5 2
 
2.6%
8.4 4
 
5.2%
8.3 1
 
1.3%
8.2 10
13.0%
8.1 3
 
3.9%
8.0 2
 
2.6%
7.9 12
15.6%

Water_DO
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.264935
Minimum6
Maximum16.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:40.874181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8.52
Q110.2
median11.4
Q312.5
95-th percentile14
Maximum16.1
Range10.1
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.8788831
Coefficient of variation (CV)0.16679041
Kurtosis0.24192648
Mean11.264935
Median Absolute Deviation (MAD)1.2
Skewness-0.22493263
Sum867.4
Variance3.5302016
MonotonicityNot monotonic
2024-03-14T12:09:41.011091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9.3 4
 
5.2%
10.2 4
 
5.2%
14.0 3
 
3.9%
13.1 3
 
3.9%
10.4 3
 
3.9%
11.9 3
 
3.9%
9.1 2
 
2.6%
13.2 2
 
2.6%
12.2 2
 
2.6%
13.5 2
 
2.6%
Other values (36) 49
63.6%
ValueCountFrequency (%)
6.0 1
 
1.3%
6.9 1
 
1.3%
7.3 1
 
1.3%
7.8 1
 
1.3%
8.7 1
 
1.3%
8.9 2
2.6%
9.1 2
2.6%
9.2 1
 
1.3%
9.3 4
5.2%
9.4 1
 
1.3%
ValueCountFrequency (%)
16.1 1
 
1.3%
14.9 1
 
1.3%
14.0 3
3.9%
13.8 1
 
1.3%
13.7 1
 
1.3%
13.5 2
2.6%
13.4 2
2.6%
13.3 1
 
1.3%
13.2 2
2.6%
13.1 3
3.9%

Water_BOD
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3714286
Minimum0.3
Maximum12.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:41.147048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.38
Q10.7
median1.2
Q33.5
95-th percentile8.16
Maximum12.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.6103467
Coefficient of variation (CV)1.1007486
Kurtosis4.1518193
Mean2.3714286
Median Absolute Deviation (MAD)0.8
Skewness2.0201864
Sum182.6
Variance6.8139098
MonotonicityNot monotonic
2024-03-14T12:09:41.284027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.9 8
 
10.4%
0.7 6
 
7.8%
0.4 5
 
6.5%
0.6 4
 
5.2%
0.5 4
 
5.2%
0.3 4
 
5.2%
1.6 3
 
3.9%
3.7 3
 
3.9%
1.1 3
 
3.9%
3.0 3
 
3.9%
Other values (25) 34
44.2%
ValueCountFrequency (%)
0.3 4
5.2%
0.4 5
6.5%
0.5 4
5.2%
0.6 4
5.2%
0.7 6
7.8%
0.8 3
 
3.9%
0.9 8
10.4%
1.0 1
 
1.3%
1.1 3
 
3.9%
1.2 3
 
3.9%
ValueCountFrequency (%)
12.3 1
1.3%
10.9 1
1.3%
10.8 1
1.3%
8.4 1
1.3%
8.1 1
1.3%
6.9 1
1.3%
6.1 1
1.3%
5.5 2
2.6%
4.7 1
1.3%
4.6 1
1.3%

Water_COD
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2350649
Minimum1.5
Maximum25.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:41.402323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.28
Q13.1
median4.5
Q38
95-th percentile15.46
Maximum25.7
Range24.2
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation4.6026813
Coefficient of variation (CV)0.738193
Kurtosis3.7972445
Mean6.2350649
Median Absolute Deviation (MAD)1.9
Skewness1.8079121
Sum480.1
Variance21.184675
MonotonicityNot monotonic
2024-03-14T12:09:41.548783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2.6 5
 
6.5%
3.7 3
 
3.9%
4.5 3
 
3.9%
3.1 3
 
3.9%
3.5 3
 
3.9%
3.2 3
 
3.9%
3.8 2
 
2.6%
2.4 2
 
2.6%
2.9 2
 
2.6%
2.2 2
 
2.6%
Other values (38) 49
63.6%
ValueCountFrequency (%)
1.5 2
 
2.6%
2.2 2
 
2.6%
2.3 2
 
2.6%
2.4 2
 
2.6%
2.5 2
 
2.6%
2.6 5
6.5%
2.9 2
 
2.6%
3.0 1
 
1.3%
3.1 3
3.9%
3.2 3
3.9%
ValueCountFrequency (%)
25.7 1
1.3%
18.8 1
1.3%
16.9 1
1.3%
16.5 1
1.3%
15.2 1
1.3%
14.9 1
1.3%
13.3 1
1.3%
12.5 1
1.3%
12.4 1
1.3%
11.7 1
1.3%

Water_SS
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.811688
Minimum0.4
Maximum73.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:41.670212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.88
Q11.6
median4.2
Q312
95-th percentile60.8
Maximum73.3
Range72.9
Interquartile range (IQR)10.4

Descriptive statistics

Standard deviation17.367332
Coefficient of variation (CV)1.4703513
Kurtosis4.8802993
Mean11.811688
Median Absolute Deviation (MAD)3.1
Skewness2.325027
Sum909.5
Variance301.6242
MonotonicityNot monotonic
2024-03-14T12:09:41.798495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 3
 
3.9%
1.3 3
 
3.9%
2.8 3
 
3.9%
1.2 3
 
3.9%
1.6 3
 
3.9%
6.8 2
 
2.6%
2.3 2
 
2.6%
2.2 2
 
2.6%
3.5 2
 
2.6%
0.9 2
 
2.6%
Other values (49) 52
67.5%
ValueCountFrequency (%)
0.4 1
 
1.3%
0.6 1
 
1.3%
0.7 1
 
1.3%
0.8 1
 
1.3%
0.9 2
2.6%
1.0 1
 
1.3%
1.1 1
 
1.3%
1.2 3
3.9%
1.3 3
3.9%
1.4 2
2.6%
ValueCountFrequency (%)
73.3 1
1.3%
69.0 1
1.3%
68.4 1
1.3%
64.0 1
1.3%
60.0 1
1.3%
49.3 1
1.3%
37.7 1
1.3%
28.5 1
1.3%
26.9 1
1.3%
25.6 1
1.3%

Water_TN
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8063896
Minimum0.736
Maximum17.115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:42.155042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.736
5-th percentile1.41
Q12.141
median2.958
Q34.015
95-th percentile8.8648
Maximum17.115
Range16.379
Interquartile range (IQR)1.874

Descriptive statistics

Standard deviation2.8884892
Coefficient of variation (CV)0.75885275
Kurtosis7.2802755
Mean3.8063896
Median Absolute Deviation (MAD)0.972
Skewness2.4711382
Sum293.092
Variance8.34337
MonotonicityNot monotonic
2024-03-14T12:09:42.259715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.168 2
 
2.6%
6.931 1
 
1.3%
1.673 1
 
1.3%
2.056 1
 
1.3%
2.141 1
 
1.3%
1.762 1
 
1.3%
1.823 1
 
1.3%
1.804 1
 
1.3%
2.757 1
 
1.3%
1.843 1
 
1.3%
Other values (66) 66
85.7%
ValueCountFrequency (%)
0.736 1
1.3%
1.27 1
1.3%
1.352 1
1.3%
1.402 1
1.3%
1.412 1
1.3%
1.66 1
1.3%
1.672 1
1.3%
1.673 1
1.3%
1.679 1
1.3%
1.708 1
1.3%
ValueCountFrequency (%)
17.115 1
1.3%
13.413 1
1.3%
13.161 1
1.3%
9.508 1
1.3%
8.704 1
1.3%
8.188 1
1.3%
7.988 1
1.3%
7.58 1
1.3%
6.931 1
1.3%
6.669 1
1.3%

Water_TP
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09712987
Minimum0.003
Maximum0.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size825.0 B
2024-03-14T12:09:42.393307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.008
Q10.026
median0.054
Q30.106
95-th percentile0.2746
Maximum0.77
Range0.767
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.134342
Coefficient of variation (CV)1.3831172
Kurtosis11.348649
Mean0.09712987
Median Absolute Deviation (MAD)0.031
Skewness3.117754
Sum7.479
Variance0.018047772
MonotonicityNot monotonic
2024-03-14T12:09:42.514176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026 5
 
6.5%
0.008 4
 
5.2%
0.029 3
 
3.9%
0.025 3
 
3.9%
0.062 2
 
2.6%
0.006 2
 
2.6%
0.035 2
 
2.6%
0.14 2
 
2.6%
0.05 2
 
2.6%
0.082 2
 
2.6%
Other values (45) 50
64.9%
ValueCountFrequency (%)
0.003 1
 
1.3%
0.006 2
2.6%
0.008 4
5.2%
0.009 2
2.6%
0.01 1
 
1.3%
0.012 1
 
1.3%
0.015 1
 
1.3%
0.021 1
 
1.3%
0.022 1
 
1.3%
0.023 2
2.6%
ValueCountFrequency (%)
0.77 1
1.3%
0.64 1
1.3%
0.506 1
1.3%
0.393 1
1.3%
0.245 1
1.3%
0.24 1
1.3%
0.238 1
1.3%
0.236 1
1.3%
0.219 1
1.3%
0.211 1
1.3%

Interactions

2024-03-14T12:09:38.384685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:32.769988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.411832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.066584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.736157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.381220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.072778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.717118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.666860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.467498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:32.864384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.474678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.126881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.798970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.450197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.150621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.822854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.735911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.535476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:32.930520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.535623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.189989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.861435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.520062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.215961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.906327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.808321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.599041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:32.990446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.604474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.250509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.931863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.596589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.279127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.219838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.899109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.681667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.056489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.673448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.316423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.019215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.683161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.341152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.295724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.968534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.772958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.121290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.740429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.387727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.099655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.766561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.409587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.376786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.044314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.844734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.184263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.830951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.473459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.161162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.825561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.467377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.444132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.112985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.918781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.258053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.923975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.581840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.233520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.894705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.533102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.517624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.200341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:39.002449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.335442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:33.997049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:34.658403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.304006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:35.970533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:36.609343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:37.592370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T12:09:38.299562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T12:09:42.626320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Water_IDWater_SiteWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
Water_ID1.0001.0000.3790.4340.2360.0000.1910.3230.0000.161
Water_Site1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Water_Temp0.3791.0001.0000.2590.7790.4070.3930.4450.2830.374
Water_pH0.4341.0000.2591.0000.0850.0000.0000.3450.0000.000
Water_DO0.2361.0000.7790.0851.0000.6600.6930.5170.7120.384
Water_BOD0.0001.0000.4070.0000.6601.0000.9420.7140.7480.830
Water_COD0.1911.0000.3930.0000.6930.9421.0000.7030.7800.865
Water_SS0.3231.0000.4450.3450.5170.7140.7031.0000.7910.824
Water_TN0.0001.0000.2830.0000.7120.7480.7800.7911.0000.867
Water_TP0.1611.0000.3740.0000.3840.8300.8650.8240.8671.000
2024-03-14T12:09:42.770945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Water_IDWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
Water_ID1.000-0.225-0.179-0.0240.1520.0820.1040.0930.130
Water_Temp-0.2251.0000.085-0.3320.4980.2930.2300.1930.335
Water_pH-0.1790.0851.000-0.0380.0030.2020.1380.090-0.006
Water_DO-0.024-0.332-0.0381.000-0.546-0.599-0.593-0.375-0.362
Water_BOD0.1520.4980.003-0.5461.0000.8330.7350.6980.750
Water_COD0.0820.2930.202-0.5990.8331.0000.8260.7530.740
Water_SS0.1040.2300.138-0.5930.7350.8261.0000.7140.733
Water_TN0.0930.1930.090-0.3750.6980.7530.7141.0000.685
Water_TP0.1300.335-0.006-0.3620.7500.7400.7330.6851.000

Missing values

2024-03-14T12:09:39.117344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:09:39.250323image/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

Water_IDWater_SiteWater_DateWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
0116산북천2019-11-0110.67.99.110.916.564.06.9310.506
131고부천22019-11-0114.07.410.23.58.073.33.8790.198
295고부천32019-11-0112.87.610.03.411.460.04.8390.236
330고부천12019-11-0114.57.510.03.88.668.44.7170.148
425동진강32019-11-0112.67.511.01.36.310.13.9450.089
524동진강22019-11-019.97.911.60.55.07.53.0180.05
623동진강12019-11-0111.68.011.10.33.75.12.4410.023
765용호천2019-11-0112.37.310.22.27.19.04.0150.106
8105신평천2019-11-0111.37.99.46.914.969.07.9880.245
994원평천32019-11-0112.07.810.93.710.949.34.4310.187
Water_IDWater_SiteWater_DateWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
67120C목천포천2019-11-0117.27.47.38.112.418.69.5080.238
68119C석암천2019-11-0114.47.96.04.19.820.93.6120.14
69122C비응도동수로2019-11-0115.08.29.312.325.720.24.5040.77
70121C소룡동수로2019-11-0113.08.49.34.016.922.43.8820.24
71117C팔복동수로2019-11-0114.48.26.93.98.015.01.4120.048
7254전주천32019-11-0116.48.214.00.92.51.52.3210.081
7355전주천42019-11-0118.08.313.81.12.92.82.1540.082
7456전주천52019-11-0117.47.911.11.73.23.01.9410.061
7587전주천2-12019-11-0117.08.113.10.92.51.42.2390.026
7653전주천22019-11-0115.17.411.40.71.51.12.3760.006