Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric7

Alerts

저수위(m) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 저수위(m)High correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 54 (54.0%) zerosZeros
유입량(ms) has 31 (31.0%) zerosZeros
방류량(ms) has 31 (31.0%) zerosZeros
저수량(백만m3) has 31 (31.0%) zerosZeros
저수율 has 31 (31.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:53:01.526054
Analysis finished2023-12-10 10:53:08.931160
Duration7.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강천보
31 
공주보
31 
구담보
31 
구미보
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강천보 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

Length

2023-12-10T19:53:09.038944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:53:09.197326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강천보 31
31.0%
공주보 31
31.0%
구담보 31
31.0%
구미보 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191215
Minimum20191201
Maximum20191231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:09.353116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191201
5-th percentile20191202
Q120191207
median20191215
Q320191223
95-th percentile20191230
Maximum20191231
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5635222 × 10-7
Kurtosis-1.2602055
Mean20191215
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0191215 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:53:09.526980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20191201 4
 
4.0%
20191203 4
 
4.0%
20191204 4
 
4.0%
20191205 4
 
4.0%
20191206 4
 
4.0%
20191207 4
 
4.0%
20191202 4
 
4.0%
20191226 3
 
3.0%
20191222 3
 
3.0%
20191223 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20191201 4
4.0%
20191202 4
4.0%
20191203 4
4.0%
20191204 4
4.0%
20191205 4
4.0%
20191206 4
4.0%
20191207 4
4.0%
20191208 3
3.0%
20191209 3
3.0%
20191210 3
3.0%
ValueCountFrequency (%)
20191231 3
3.0%
20191230 3
3.0%
20191229 3
3.0%
20191228 3
3.0%
20191227 3
3.0%
20191226 3
3.0%
20191225 3
3.0%
20191224 3
3.0%
20191223 3
3.0%
20191222 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.8024
Minimum4.3
Maximum62.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:09.723100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3
5-th percentile4.32
Q14.34
median38.11
Q362.45
95-th percentile62.46
Maximum62.5
Range58.2
Interquartile range (IQR)58.11

Descriptive statistics

Standard deviation23.108177
Coefficient of variation (CV)0.66398227
Kurtosis-1.4045945
Mean34.8024
Median Absolute Deviation (MAD)24.345
Skewness-0.18398659
Sum3480.24
Variance533.98783
MonotonicityNot monotonic
2023-12-10T19:53:09.868720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
38.11 16
16.0%
62.46 15
15.0%
62.45 11
11.0%
4.33 11
11.0%
4.34 8
 
8.0%
38.12 4
 
4.0%
38.1 4
 
4.0%
4.32 4
 
4.0%
4.35 3
 
3.0%
62.47 3
 
3.0%
Other values (13) 21
21.0%
ValueCountFrequency (%)
4.3 1
 
1.0%
4.31 3
 
3.0%
4.32 4
 
4.0%
4.33 11
11.0%
4.34 8
8.0%
4.35 3
 
3.0%
4.37 1
 
1.0%
32.6 2
 
2.0%
32.61 2
 
2.0%
32.64 1
 
1.0%
ValueCountFrequency (%)
62.5 1
 
1.0%
62.47 3
 
3.0%
62.46 15
15.0%
62.45 11
11.0%
62.44 1
 
1.0%
38.15 1
 
1.0%
38.13 2
 
2.0%
38.12 4
 
4.0%
38.11 16
16.0%
38.1 4
 
4.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.463698
Minimum0
Maximum9.3032
Zeros54
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:10.068328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.116575
95-th percentile2.368315
Maximum9.3032
Range9.3032
Interquartile range (IQR)0.116575

Descriptive statistics

Standard deviation1.46492
Coefficient of variation (CV)3.1592115
Kurtosis19.649625
Mean0.463698
Median Absolute Deviation (MAD)0
Skewness4.3047729
Sum46.3698
Variance2.1459907
MonotonicityNot monotonic
2023-12-10T19:53:10.249749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 54
54.0%
0.0442 2
 
2.0%
0.0192 2
 
2.0%
0.0795 1
 
1.0%
0.171 1
 
1.0%
0.2769 1
 
1.0%
1.8271 1
 
1.0%
0.131 1
 
1.0%
0.0785 1
 
1.0%
0.083 1
 
1.0%
Other values (35) 35
35.0%
ValueCountFrequency (%)
0.0 54
54.0%
0.0055 1
 
1.0%
0.0115 1
 
1.0%
0.0192 2
 
2.0%
0.0221 1
 
1.0%
0.0241 1
 
1.0%
0.0274 1
 
1.0%
0.0298 1
 
1.0%
0.0316 1
 
1.0%
0.0401 1
 
1.0%
ValueCountFrequency (%)
9.3032 1
1.0%
7.2569 1
1.0%
5.9587 1
1.0%
5.177 1
1.0%
3.5333 1
1.0%
2.307 1
1.0%
2.2032 1
1.0%
1.8271 1
1.0%
1.7407 1
1.0%
0.9556 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.62599
Minimum0
Maximum95.921
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:10.430828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39.5655
Q384.92275
95-th percentile92.3679
Maximum95.921
Range95.921
Interquartile range (IQR)84.92275

Descriptive statistics

Standard deviation35.426794
Coefficient of variation (CV)0.81205709
Kurtosis-1.4316736
Mean43.62599
Median Absolute Deviation (MAD)39.5655
Skewness0.071903841
Sum4362.599
Variance1255.0578
MonotonicityNot monotonic
2023-12-10T19:53:10.653142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
88.65 2
 
2.0%
39.041 1
 
1.0%
37.125 1
 
1.0%
38.109 1
 
1.0%
39.236 1
 
1.0%
40.138 1
 
1.0%
40.965 1
 
1.0%
39.903 1
 
1.0%
92.689 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 31
31.0%
34.312 1
 
1.0%
34.815 1
 
1.0%
35.347 1
 
1.0%
36.643 1
 
1.0%
36.799 1
 
1.0%
37.008 1
 
1.0%
37.125 1
 
1.0%
37.808 1
 
1.0%
37.818 1
 
1.0%
ValueCountFrequency (%)
95.921 1
1.0%
94.248 1
1.0%
93.919 1
1.0%
93.116 1
1.0%
92.689 1
1.0%
92.351 1
1.0%
91.865 1
1.0%
90.549 1
1.0%
89.89 1
1.0%
89.813 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.55206
Minimum0
Maximum95.82
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:10.931843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median39.484
Q384.52975
95-th percentile92.59685
Maximum95.82
Range95.82
Interquartile range (IQR)84.52975

Descriptive statistics

Standard deviation35.431623
Coefficient of variation (CV)0.81354643
Kurtosis-1.4258138
Mean43.55206
Median Absolute Deviation (MAD)39.484
Skewness0.080958207
Sum4355.206
Variance1255.3999
MonotonicityNot monotonic
2023-12-10T19:53:11.124099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
88.65 2
 
2.0%
38.962 1
 
1.0%
37.046 1
 
1.0%
38.03 1
 
1.0%
39.157 1
 
1.0%
40.296 1
 
1.0%
41.044 1
 
1.0%
39.824 1
 
1.0%
92.689 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 31
31.0%
34.233 1
 
1.0%
35.13 1
 
1.0%
35.505 1
 
1.0%
36.722 1
 
1.0%
36.85 1
 
1.0%
36.957 1
 
1.0%
37.046 1
 
1.0%
37.582 1
 
1.0%
37.682 1
 
1.0%
ValueCountFrequency (%)
95.82 1
1.0%
94.873 1
1.0%
93.399 1
1.0%
92.871 1
1.0%
92.689 1
1.0%
92.592 1
1.0%
92.389 1
1.0%
92.121 1
1.0%
90.938 1
1.0%
89.813 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3667
Minimum0
Maximum53.71
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:11.298966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.4535
Q39.226
95-th percentile53.38725
Maximum53.71
Range53.71
Interquartile range (IQR)9.226

Descriptive statistics

Standard deviation13.279409
Coefficient of variation (CV)1.8026266
Kurtosis7.9195453
Mean7.3667
Median Absolute Deviation (MAD)2.4535
Skewness2.9618294
Sum736.67
Variance176.34271
MonotonicityNot monotonic
2023-12-10T19:53:11.460550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 31
31.0%
9.226 16
16.0%
2.45 11
 
11.0%
2.457 8
 
8.0%
9.271 4
 
4.0%
9.181 4
 
4.0%
2.443 4
 
4.0%
2.463 3
 
3.0%
9.135 3
 
3.0%
2.436 3
 
3.0%
Other values (9) 13
13.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.429 1
 
1.0%
2.436 3
 
3.0%
2.443 4
 
4.0%
2.45 11
 
11.0%
2.457 8
 
8.0%
2.463 3
 
3.0%
2.477 1
 
1.0%
9.09 1
 
1.0%
9.135 3
 
3.0%
ValueCountFrequency (%)
53.71 2
 
2.0%
53.645 1
 
1.0%
53.449 2
 
2.0%
53.384 2
 
2.0%
9.407 1
 
1.0%
9.316 2
 
2.0%
9.271 4
 
4.0%
9.226 16
16.0%
9.181 4
 
4.0%
9.135 3
 
3.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.763
Minimum0
Maximum107.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:53:11.614954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.8
Q3105.2
95-th percentile106.2
Maximum107.8
Range107.8
Interquartile range (IQR)105.2

Descriptive statistics

Standard deviation47.75609
Coefficient of variation (CV)1.0668653
Kurtosis-1.7593183
Mean44.763
Median Absolute Deviation (MAD)15.8
Skewness0.45030029
Sum4476.3
Variance2280.6442
MonotonicityNot monotonic
2023-12-10T19:53:11.821633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 31
31.0%
15.8 19
19.0%
105.7 16
16.0%
15.7 7
 
7.0%
106.2 4
 
4.0%
105.2 4
 
4.0%
15.9 4
 
4.0%
104.7 3
 
3.0%
101.9 2
 
2.0%
101.2 2
 
2.0%
Other values (6) 8
 
8.0%
ValueCountFrequency (%)
0.0 31
31.0%
15.6 1
 
1.0%
15.7 7
 
7.0%
15.8 19
19.0%
15.9 4
 
4.0%
101.2 2
 
2.0%
101.4 2
 
2.0%
101.7 1
 
1.0%
101.9 2
 
2.0%
104.2 1
 
1.0%
ValueCountFrequency (%)
107.8 1
 
1.0%
106.7 2
 
2.0%
106.2 4
 
4.0%
105.7 16
16.0%
105.2 4
 
4.0%
104.7 3
 
3.0%
104.2 1
 
1.0%
101.9 2
 
2.0%
101.7 1
 
1.0%
101.4 2
 
2.0%

Interactions

2023-12-10T19:53:07.530951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.081229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.904438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.889653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.815110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.712698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.588884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:07.901208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.209012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.031762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.056611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.941268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.852270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.734157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:08.013810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.318317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.222693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.201782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.051649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.980725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.864672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:08.118125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.426713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.372820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.320921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.160930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.085596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.990453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:08.245130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.549958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.484322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.446955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.280990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.212540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:07.151240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:08.368122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.662504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.615813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.573063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.422574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.332171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:07.286298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:08.486443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:02.774006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:03.751334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:04.708055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:05.591753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:06.462063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:53:07.407130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:53:11.935076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.3000.9670.9491.0001.000
일자/시간(t)0.0001.0000.0000.1380.3110.2710.1390.000
저수위(m)1.0000.0001.0000.3000.9670.9491.0001.000
강우량(mm)0.3000.1380.3001.0000.2930.0000.2660.000
유입량(ms)0.9670.3110.9670.2931.0001.0000.9440.964
방류량(ms)0.9490.2710.9490.0001.0001.0000.9150.950
저수량(백만m3)1.0000.1391.0000.2660.9440.9151.0000.940
저수율1.0000.0001.0000.0000.9640.9500.9401.000
2023-12-10T19:53:12.351110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.022-0.113-0.140-0.144-0.223-0.1230.000
저수위(m)-0.0221.000-0.522-0.369-0.371-0.378-0.3341.000
강우량(mm)-0.113-0.5221.0000.4940.4880.4520.4240.132
유입량(ms)-0.140-0.3690.4941.0000.9990.9010.9550.959
방류량(ms)-0.144-0.3710.4880.9991.0000.8990.9540.931
저수량(백만m3)-0.223-0.3780.4520.9010.8991.0000.9460.995
저수율-0.123-0.3340.4240.9550.9540.9461.0000.995
댐이름0.0001.0000.1320.9590.9310.9950.9951.000

Missing values

2023-12-10T19:53:08.658238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:53:08.854638image/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

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
0강천보2019120138.135.17792.68992.6899.316106.7
1강천보2019120238.150.24495.92194.8739.407107.8
2강천보2019120338.120.172694.24895.829.271106.2
3강천보2019120438.130.497993.11692.5929.316106.7
4강천보2019120538.10.027490.54992.1219.181105.2
5강천보2019120638.120.093.91992.8719.271106.2
6강천보2019120738.110.088.96289.4869.226105.7
7강천보2019120838.110.088.66888.6689.226105.7
8강천보2019120938.110.088.66388.6639.226105.7
9강천보2019121038.110.059588.64288.6429.226105.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2019122962.460.00.00.00.00.0
91구담보2019123062.450.00.00.00.00.0
92구담보2019123162.460.00.00.00.00.0
93구미보2019120132.619.303252.98246.19953.449101.4
94구미보2019120232.60.17952.86353.61753.384101.2
95구미보2019120332.650.051854.14150.37253.71101.9
96구미보2019120432.60.057958.80762.57653.384101.2
97구미보2019120532.610.09666.58365.82953.449101.4
98구미보2019120632.650.005562.47859.46353.71101.9
99구미보2019120732.640.049.04249.79653.645101.7