Overview

Dataset statistics

Number of variables10
Number of observations73
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory89.8 B

Variable types

Text1
Categorical1
Numeric8

Dataset

Description수질측정망자료2018년07월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=203057

Alerts

Water_Date has constant value ""Constant
Water_Temp is highly overall correlated with Water_BODHigh correlation
Water_BOD is highly overall correlated with Water_Temp and 3 other fieldsHigh correlation
Water_COD is highly overall correlated with Water_BOD and 2 other fieldsHigh correlation
Water_SS is highly overall correlated with Water_BOD and 2 other fieldsHigh correlation
Water_TP is highly overall correlated with Water_BOD and 2 other fieldsHigh correlation
Water_Site has unique valuesUnique
Water_TP has 1 (1.4%) zerosZeros

Reproduction

Analysis started2024-03-14 02:53:21.162854
Analysis finished2024-03-14 02:53:27.004903
Duration5.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Water_Site
Text

UNIQUE 

Distinct73
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size716.0 B
2024-03-14T11:53:27.173920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.6849315
Min length2

Characters and Unicode

Total characters269
Distinct characters75
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)100.0%

Sample

1st row가막
2nd row계수천
3rd row고부천1
4th row고부천2
5th row고부천3
ValueCountFrequency (%)
가막 1
 
1.4%
운암 1
 
1.4%
정자천 1
 
1.4%
정읍천4 1
 
1.4%
정읍천3 1
 
1.4%
정읍천2 1
 
1.4%
정읍천1 1
 
1.4%
전주천6 1
 
1.4%
전주천1 1
 
1.4%
전주 1
 
1.4%
Other values (63) 63
86.3%
2024-03-14T11:53:27.503034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 58
21.6%
1 17
 
6.3%
주 13
 
4.8%
2 10
 
3.7%
- 8
 
3.0%
전 8
 
3.0%
동 7
 
2.6%
산 7
 
2.6%
C 7
 
2.6%
수 6
 
2.2%
Other values (65) 128
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 215
79.9%
Decimal Number 36
 
13.4%
Dash Punctuation 8
 
3.0%
Uppercase Letter 7
 
2.6%
Other Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 58
27.0%
주 13
 
6.0%
전 8
 
3.7%
동 7
 
3.3%
산 7
 
3.3%
수 6
 
2.8%
정 6
 
2.8%
진 6
 
2.8%
읍 5
 
2.3%
원 5
 
2.3%
Other values (54) 94
43.7%
Decimal Number
ValueCountFrequency (%)
1 17
47.2%
2 10
27.8%
3 5
 
13.9%
4 2
 
5.6%
6 1
 
2.8%
5 1
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 215
79.9%
Common 47
 
17.5%
Latin 7
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 58
27.0%
주 13
 
6.0%
전 8
 
3.7%
동 7
 
3.3%
산 7
 
3.3%
수 6
 
2.8%
정 6
 
2.8%
진 6
 
2.8%
읍 5
 
2.3%
원 5
 
2.3%
Other values (54) 94
43.7%
Common
ValueCountFrequency (%)
1 17
36.2%
2 10
21.3%
- 8
17.0%
3 5
 
10.6%
4 2
 
4.3%
6 1
 
2.1%
. 1
 
2.1%
( 1
 
2.1%
) 1
 
2.1%
5 1
 
2.1%
Latin
ValueCountFrequency (%)
C 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 215
79.9%
ASCII 54
 
20.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 58
27.0%
주 13
 
6.0%
전 8
 
3.7%
동 7
 
3.3%
산 7
 
3.3%
수 6
 
2.8%
정 6
 
2.8%
진 6
 
2.8%
읍 5
 
2.3%
원 5
 
2.3%
Other values (54) 94
43.7%
ASCII
ValueCountFrequency (%)
1 17
31.5%
2 10
18.5%
- 8
14.8%
C 7
13.0%
3 5
 
9.3%
4 2
 
3.7%
6 1
 
1.9%
. 1
 
1.9%
( 1
 
1.9%
) 1
 
1.9%

Water_Date
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size716.0 B
2018-07
73 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-07
2nd row2018-07
3rd row2018-07
4th row2018-07
5th row2018-07

Common Values

ValueCountFrequency (%)
2018-07 73
100.0%

Length

2024-03-14T11:53:27.610206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:53:27.692908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-07 73
100.0%

Water_Temp
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.226027
Minimum16.5
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:27.789814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.5
5-th percentile20.28
Q122.8
median25.2
Q327.8
95-th percentile29.94
Maximum31.5
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2937663
Coefficient of variation (CV)0.13057016
Kurtosis-0.61397812
Mean25.226027
Median Absolute Deviation (MAD)2.6
Skewness-0.27299482
Sum1841.5
Variance10.848896
MonotonicityNot monotonic
2024-03-14T11:53:27.896685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.8 4
 
5.5%
26.2 3
 
4.1%
28.0 3
 
4.1%
20.9 3
 
4.1%
22.4 3
 
4.1%
27.4 3
 
4.1%
24.9 2
 
2.7%
28.8 2
 
2.7%
27.8 2
 
2.7%
23.6 2
 
2.7%
Other values (41) 46
63.0%
ValueCountFrequency (%)
16.5 1
 
1.4%
18.5 1
 
1.4%
18.9 1
 
1.4%
20.1 1
 
1.4%
20.4 1
 
1.4%
20.9 3
4.1%
21.0 1
 
1.4%
21.5 1
 
1.4%
21.6 1
 
1.4%
21.9 1
 
1.4%
ValueCountFrequency (%)
31.5 1
1.4%
30.3 1
1.4%
30.2 1
1.4%
30.0 1
1.4%
29.9 1
1.4%
29.7 1
1.4%
29.6 2
2.7%
29.5 1
1.4%
28.8 2
2.7%
28.6 1
1.4%

Water_pH
Real number (ℝ)

Distinct22
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.790411
Minimum6.4
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:27.996492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile7.1
Q17.4
median7.7
Q38.1
95-th percentile8.74
Maximum9.3
Range2.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.53649179
Coefficient of variation (CV)0.06886566
Kurtosis0.19444383
Mean7.790411
Median Absolute Deviation (MAD)0.4
Skewness0.41904178
Sum568.7
Variance0.28782344
MonotonicityNot monotonic
2024-03-14T11:53:28.094704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7.7 9
12.3%
7.5 8
11.0%
7.4 7
 
9.6%
8.1 7
 
9.6%
7.3 6
 
8.2%
7.9 4
 
5.5%
8.6 4
 
5.5%
7.1 3
 
4.1%
7.6 3
 
4.1%
8.2 3
 
4.1%
Other values (12) 19
26.0%
ValueCountFrequency (%)
6.4 1
 
1.4%
6.9 1
 
1.4%
7.0 1
 
1.4%
7.1 3
 
4.1%
7.2 2
 
2.7%
7.3 6
8.2%
7.4 7
9.6%
7.5 8
11.0%
7.6 3
 
4.1%
7.7 9
12.3%
ValueCountFrequency (%)
9.3 1
 
1.4%
8.9 1
 
1.4%
8.8 2
 
2.7%
8.7 1
 
1.4%
8.6 4
5.5%
8.5 2
 
2.7%
8.3 2
 
2.7%
8.2 3
4.1%
8.1 7
9.6%
8.0 3
4.1%

Water_DO
Real number (ℝ)

Distinct39
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5041096
Minimum3
Maximum12.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:28.247949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.62
Q17.8
median8.6
Q39.3
95-th percentile10.44
Maximum12.6
Range9.6
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.3722891
Coefficient of variation (CV)0.16136776
Kurtosis3.5926064
Mean8.5041096
Median Absolute Deviation (MAD)0.7
Skewness-0.64436716
Sum620.8
Variance1.8831773
MonotonicityNot monotonic
2024-03-14T11:53:28.451385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
8.9 7
 
9.6%
9.3 7
 
9.6%
8.3 4
 
5.5%
7.7 4
 
5.5%
8.5 4
 
5.5%
6.7 3
 
4.1%
9.1 3
 
4.1%
8.6 3
 
4.1%
9.4 2
 
2.7%
8.0 2
 
2.7%
Other values (29) 34
46.6%
ValueCountFrequency (%)
3.0 1
 
1.4%
5.5 1
 
1.4%
5.6 1
 
1.4%
6.5 1
 
1.4%
6.7 3
4.1%
6.9 1
 
1.4%
7.0 1
 
1.4%
7.2 1
 
1.4%
7.4 1
 
1.4%
7.5 1
 
1.4%
ValueCountFrequency (%)
12.6 1
1.4%
11.2 1
1.4%
11.1 1
1.4%
10.8 1
1.4%
10.2 1
1.4%
10.0 1
1.4%
9.9 2
2.7%
9.8 1
1.4%
9.7 1
1.4%
9.6 1
1.4%

Water_BOD
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4287671
Minimum0.2
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:28.575722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.46
Q10.8
median1.5
Q33.6
95-th percentile5.98
Maximum7.6
Range7.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation1.9359522
Coefficient of variation (CV)0.79709256
Kurtosis0.035313131
Mean2.4287671
Median Absolute Deviation (MAD)0.9
Skewness0.9737607
Sum177.3
Variance3.747911
MonotonicityNot monotonic
2024-03-14T11:53:28.711062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.8 7
 
9.6%
0.9 4
 
5.5%
1.5 4
 
5.5%
0.7 4
 
5.5%
5.0 3
 
4.1%
3.2 3
 
4.1%
1.3 3
 
4.1%
1.0 3
 
4.1%
7.6 2
 
2.7%
3.6 2
 
2.7%
Other values (27) 38
52.1%
ValueCountFrequency (%)
0.2 2
 
2.7%
0.4 2
 
2.7%
0.5 2
 
2.7%
0.6 2
 
2.7%
0.7 4
5.5%
0.8 7
9.6%
0.9 4
5.5%
1.0 3
4.1%
1.1 1
 
1.4%
1.2 2
 
2.7%
ValueCountFrequency (%)
7.6 2
2.7%
6.9 1
 
1.4%
6.1 1
 
1.4%
5.9 1
 
1.4%
5.8 1
 
1.4%
5.6 1
 
1.4%
5.2 1
 
1.4%
5.0 3
4.1%
4.8 1
 
1.4%
4.7 1
 
1.4%

Water_COD
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9561644
Minimum2.4
Maximum24.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:28.859138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile2.6
Q14
median4.9
Q310
95-th percentile13.86
Maximum24.2
Range21.8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1241493
Coefficient of variation (CV)0.59287692
Kurtosis2.9418622
Mean6.9561644
Median Absolute Deviation (MAD)2.2
Skewness1.4103112
Sum507.8
Variance17.008607
MonotonicityNot monotonic
2024-03-14T11:53:29.024476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 4
 
5.5%
4.8 4
 
5.5%
10.0 3
 
4.1%
4.9 2
 
2.7%
3.9 2
 
2.7%
3.3 2
 
2.7%
5.9 2
 
2.7%
4.2 2
 
2.7%
9.5 2
 
2.7%
10.9 2
 
2.7%
Other values (42) 48
65.8%
ValueCountFrequency (%)
2.4 2
2.7%
2.5 1
1.4%
2.6 2
2.7%
2.7 1
1.4%
2.8 1
1.4%
2.9 2
2.7%
3.0 1
1.4%
3.3 2
2.7%
3.5 1
1.4%
3.6 1
1.4%
ValueCountFrequency (%)
24.2 1
1.4%
16.4 1
1.4%
15.4 1
1.4%
14.1 1
1.4%
13.7 1
1.4%
12.9 1
1.4%
12.4 1
1.4%
11.8 2
2.7%
11.3 1
1.4%
10.9 2
2.7%

Water_SS
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.849315
Minimum1.1
Maximum174.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:29.205364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.88
Q13.7
median9.2
Q319.2
95-th percentile64.74
Maximum174.4
Range173.3
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation26.711208
Coefficient of variation (CV)1.5852994
Kurtosis18.693047
Mean16.849315
Median Absolute Deviation (MAD)6.4
Skewness3.9812363
Sum1230
Variance713.48865
MonotonicityNot monotonic
2024-03-14T11:53:29.398698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 3
 
4.1%
4.3 3
 
4.1%
11.1 3
 
4.1%
2.5 2
 
2.7%
2.6 2
 
2.7%
9.3 2
 
2.7%
9.2 2
 
2.7%
2.2 2
 
2.7%
5.4 2
 
2.7%
1.4 2
 
2.7%
Other values (50) 50
68.5%
ValueCountFrequency (%)
1.1 1
 
1.4%
1.2 1
 
1.4%
1.4 2
2.7%
2.2 2
2.7%
2.5 2
2.7%
2.6 2
2.7%
2.7 1
 
1.4%
2.8 3
4.1%
3.2 1
 
1.4%
3.3 1
 
1.4%
ValueCountFrequency (%)
174.4 1
1.4%
107.7 1
1.4%
96.2 1
1.4%
66.0 1
1.4%
63.9 1
1.4%
32.9 1
1.4%
29.8 1
1.4%
27.9 1
1.4%
27.4 1
1.4%
26.9 1
1.4%

Water_TN
Real number (ℝ)

Distinct71
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9731233
Minimum1.044
Maximum7.597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:29.555686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.044
5-th percentile1.1764
Q12.039
median2.827
Q33.52
95-th percentile5.3928
Maximum7.597
Range6.553
Interquartile range (IQR)1.481

Descriptive statistics

Standard deviation1.3611315
Coefficient of variation (CV)0.45781198
Kurtosis1.0530184
Mean2.9731233
Median Absolute Deviation (MAD)0.788
Skewness1.0145238
Sum217.038
Variance1.8526789
MonotonicityNot monotonic
2024-03-14T11:53:29.691280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.908 2
 
2.7%
2.039 2
 
2.7%
2.812 1
 
1.4%
5.176 1
 
1.4%
3.385 1
 
1.4%
3.677 1
 
1.4%
3.462 1
 
1.4%
2.361 1
 
1.4%
7.597 1
 
1.4%
2.189 1
 
1.4%
Other values (61) 61
83.6%
ValueCountFrequency (%)
1.044 1
1.4%
1.074 1
1.4%
1.091 1
1.4%
1.114 1
1.4%
1.218 1
1.4%
1.284 1
1.4%
1.34 1
1.4%
1.437 1
1.4%
1.489 1
1.4%
1.637 1
1.4%
ValueCountFrequency (%)
7.597 1
1.4%
6.264 1
1.4%
6.059 1
1.4%
5.718 1
1.4%
5.176 1
1.4%
5.089 1
1.4%
5.066 1
1.4%
4.984 1
1.4%
4.909 1
1.4%
4.8 1
1.4%

Water_TP
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.099684932
Minimum0
Maximum0.39
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size789.0 B
2024-03-14T11:53:30.052261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0262
Q10.044
median0.067
Q30.117
95-th percentile0.3026
Maximum0.39
Range0.39
Interquartile range (IQR)0.073

Descriptive statistics

Standard deviation0.084647615
Coefficient of variation (CV)0.84915156
Kurtosis2.7402762
Mean0.099684932
Median Absolute Deviation (MAD)0.031
Skewness1.7575414
Sum7.277
Variance0.0071652188
MonotonicityNot monotonic
2024-03-14T11:53:30.181844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.067 4
 
5.5%
0.029 2
 
2.7%
0.046 2
 
2.7%
0.059 2
 
2.7%
0.04 2
 
2.7%
0.129 2
 
2.7%
0.044 2
 
2.7%
0.036 2
 
2.7%
0.136 2
 
2.7%
0.053 2
 
2.7%
Other values (51) 51
69.9%
ValueCountFrequency (%)
0.0 1
1.4%
0.02 1
1.4%
0.021 1
1.4%
0.025 1
1.4%
0.027 1
1.4%
0.028 1
1.4%
0.029 2
2.7%
0.032 1
1.4%
0.035 1
1.4%
0.036 2
2.7%
ValueCountFrequency (%)
0.39 1
1.4%
0.354 1
1.4%
0.336 1
1.4%
0.32 1
1.4%
0.291 1
1.4%
0.242 1
1.4%
0.24 1
1.4%
0.226 1
1.4%
0.199 1
1.4%
0.195 1
1.4%

Interactions

2024-03-14T11:53:26.170602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.457172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.112765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.775220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.292197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.044568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.596474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.414122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.258063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.602479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.187750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.844194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.365999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.113561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.717304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.488390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.329747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.679420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.261181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.904267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.435107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.174901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.784720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.556101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.395176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.743804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.335945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.964417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.509431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.232173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.848750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.633896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.478709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.817631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.436897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.030082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.593144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.300138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.140893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.707169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.544912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.883435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.556903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.091569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.726544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.373053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.201824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.768157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.620063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:21.959142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.632242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.155784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.882009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.452381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.268853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.848264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.689881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.032866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:22.701975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.219953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:23.965833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:24.522833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:25.341228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:26.010119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:53:30.271902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Water_SiteWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
Water_Site1.0001.0001.0001.0001.0001.0001.0001.0001.000
Water_Temp1.0001.0000.6420.3480.2590.4800.0000.3380.000
Water_pH1.0000.6421.0000.3400.3180.0000.0000.0000.000
Water_DO1.0000.3480.3401.0000.7140.4670.0000.2550.575
Water_BOD1.0000.2590.3180.7141.0000.6950.5860.2890.595
Water_COD1.0000.4800.0000.4670.6951.0000.4800.2300.759
Water_SS1.0000.0000.0000.0000.5860.4801.0000.0000.711
Water_TN1.0000.3380.0000.2550.2890.2300.0001.0000.147
Water_TP1.0000.0000.0000.5750.5950.7590.7110.1471.000
2024-03-14T11:53:30.373896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Water_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
Water_Temp1.0000.347-0.0340.5340.4970.221-0.2830.285
Water_pH0.3471.0000.230-0.1590.147-0.040-0.324-0.108
Water_DO-0.0340.2301.000-0.085-0.286-0.253-0.083-0.371
Water_BOD0.534-0.159-0.0851.0000.7670.6210.0280.709
Water_COD0.4970.147-0.2860.7671.0000.7220.0200.793
Water_SS0.221-0.040-0.2530.6210.7221.0000.3370.731
Water_TN-0.283-0.324-0.0830.0280.0200.3371.0000.176
Water_TP0.285-0.108-0.3710.7090.7930.7310.1761.000

Missing values

2024-03-14T11:53:26.804088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:53:26.951828image/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_SiteWater_DateWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
0가막2018-0727.18.59.50.94.04.12.8120.05
1계수천2018-0723.46.98.03.04.15.72.1220.087
2고부천12018-0727.57.78.95.210.021.82.1330.078
3고부천22018-0727.07.910.05.09.715.92.5030.077
4고부천32018-0728.48.09.85.610.314.61.9310.101
5고산2018-0718.58.19.10.44.04.53.3030.053
6구량천2018-0722.87.79.00.84.35.41.7050.035
7김제2018-0728.08.59.35.811.896.23.6620.136
8남원2018-0724.38.67.70.24.89.42.0390.046
9동계2018-0730.28.67.20.84.72.51.2180.028
Water_SiteWater_DateWater_TempWater_pHWater_DOWater_BODWater_CODWater_SSWater_TNWater_TP
63C석암천2018-0727.87.23.03.010.015.83.520.199
64C석탑천2018-0727.47.56.71.28.629.82.9050.105
65C소룡동수로2018-0726.28.37.02.615.410.71.9080.24
66C정읍천2018-0723.88.06.52.214.18.53.4470.336
67C팔복동수로2018-0727.67.65.57.611.324.32.9230.164
68전주천22018-0720.17.79.30.62.65.44.9090.036
69전주천2-12018-0720.97.39.30.82.63.35.0890.04
70전주천32018-0720.97.49.40.82.94.44.9840.044
71전주천42018-0721.97.49.31.02.98.65.0660.059
72전주천52018-0723.67.78.81.45.220.93.230.07