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 댐이름High 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 39 (39.0%) zerosZeros
유입량(ms) has 10 (10.0%) zerosZeros
방류량(ms) has 10 (10.0%) zerosZeros
저수량(백만m3) has 10 (10.0%) zerosZeros
저수율 has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:48:48.271490
Analysis finished2023-12-10 10:48:55.978930
Duration7.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
공주보
19 
구미보
16 
낙단보
14 
귤현보
13 
강천보
12 
Other values (3)
26 

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 (%)
공주보 19
19.0%
구미보 16
16.0%
낙단보 14
14.0%
귤현보 13
13.0%
강천보 12
12.0%
구담보 10
10.0%
달성보 9
9.0%
백제보 7
 
7.0%

Length

2023-12-10T19:48:56.094621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:48:56.266165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공주보 19
19.0%
구미보 16
16.0%
낙단보 14
14.0%
귤현보 13
13.0%
강천보 12
12.0%
구담보 10
10.0%
달성보 9
9.0%
백제보 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210616
Minimum20210601
Maximum20210630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:56.463790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210601
5-th percentile20210602
Q120210609
median20210618
Q320210624
95-th percentile20210629
Maximum20210630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6181392
Coefficient of variation (CV)4.2641644 × 10-7
Kurtosis-1.1650985
Mean20210616
Median Absolute Deviation (MAD)7.5
Skewness-0.16555685
Sum2.0210616 × 109
Variance74.272323
MonotonicityNot monotonic
2023-12-10T19:48:56.681871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20210619 6
 
6.0%
20210628 6
 
6.0%
20210625 6
 
6.0%
20210612 5
 
5.0%
20210605 4
 
4.0%
20210602 4
 
4.0%
20210618 4
 
4.0%
20210603 4
 
4.0%
20210621 4
 
4.0%
20210630 4
 
4.0%
Other values (19) 53
53.0%
ValueCountFrequency (%)
20210601 2
2.0%
20210602 4
4.0%
20210603 4
4.0%
20210605 4
4.0%
20210606 3
3.0%
20210607 3
3.0%
20210608 3
3.0%
20210609 4
4.0%
20210610 2
2.0%
20210611 2
2.0%
ValueCountFrequency (%)
20210630 4
4.0%
20210629 2
 
2.0%
20210628 6
6.0%
20210627 3
3.0%
20210626 3
3.0%
20210625 6
6.0%
20210624 3
3.0%
20210623 4
4.0%
20210622 2
 
2.0%
20210621 4
4.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.0189
Minimum1.64
Maximum60.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:56.888912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.64
5-th percentile1.937
Q13.7775
median32.54
Q338.325
95-th percentile60.4
Maximum60.4
Range58.76
Interquartile range (IQR)34.5475

Descriptive statistics

Standard deviation19.537458
Coefficient of variation (CV)0.81342017
Kurtosis-1.1694017
Mean24.0189
Median Absolute Deviation (MAD)18.66
Skewness0.31950282
Sum2401.89
Variance381.71226
MonotonicityNot monotonic
2023-12-10T19:48:57.355049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.4 10
 
10.0%
3.75 4
 
4.0%
3.79 4
 
4.0%
3.84 3
 
3.0%
32.56 3
 
3.0%
3.74 3
 
3.0%
32.59 2
 
2.0%
3.76 2
 
2.0%
3.77 2
 
2.0%
3.72 2
 
2.0%
Other values (61) 65
65.0%
ValueCountFrequency (%)
1.64 1
1.0%
1.74 1
1.0%
1.77 1
1.0%
1.84 1
1.0%
1.88 1
1.0%
1.94 1
1.0%
1.97 1
1.0%
3.61 1
1.0%
3.65 1
1.0%
3.69 1
1.0%
ValueCountFrequency (%)
60.4 10
10.0%
39.95 1
 
1.0%
39.92 1
 
1.0%
39.91 1
 
1.0%
39.9 1
 
1.0%
39.89 1
 
1.0%
39.88 1
 
1.0%
39.85 1
 
1.0%
39.83 1
 
1.0%
39.82 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.159315
Minimum0
Maximum23.8461
Zeros39
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:57.574590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0437
Q31.899425
95-th percentile12.504635
Maximum23.8461
Range23.8461
Interquartile range (IQR)1.899425

Descriptive statistics

Standard deviation4.6443355
Coefficient of variation (CV)2.1508374
Kurtosis9.186283
Mean2.159315
Median Absolute Deviation (MAD)0.0437
Skewness3.0081719
Sum215.9315
Variance21.569853
MonotonicityNot monotonic
2023-12-10T19:48:57.804535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 39
39.0%
2.0 2
 
2.0%
4.0 2
 
2.0%
15.9853 1
 
1.0%
1.2111 1
 
1.0%
1.2219 1
 
1.0%
3.2544 1
 
1.0%
1.0 1
 
1.0%
15.0 1
 
1.0%
23.8461 1
 
1.0%
Other values (50) 50
50.0%
ValueCountFrequency (%)
0.0 39
39.0%
0.0146 1
 
1.0%
0.0205 1
 
1.0%
0.0211 1
 
1.0%
0.0232 1
 
1.0%
0.0245 1
 
1.0%
0.0271 1
 
1.0%
0.0278 1
 
1.0%
0.0331 1
 
1.0%
0.0344 1
 
1.0%
ValueCountFrequency (%)
23.8461 1
1.0%
21.6097 1
1.0%
18.8779 1
1.0%
15.9853 1
1.0%
15.0 1
1.0%
12.3733 1
1.0%
11.8201 1
1.0%
9.1414 1
1.0%
8.7879 1
1.0%
6.8025 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.24738
Minimum0
Maximum313.538
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:58.020292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.33775
median96.143
Q3131.01525
95-th percentile205.27075
Maximum313.538
Range313.538
Interquartile range (IQR)80.6775

Descriptive statistics

Standard deviation66.341879
Coefficient of variation (CV)0.71146105
Kurtosis0.075462298
Mean93.24738
Median Absolute Deviation (MAD)43.9875
Skewness0.49989094
Sum9324.738
Variance4401.2449
MonotonicityNot monotonic
2023-12-10T19:48:58.251160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
196.33 1
 
1.0%
8.145 1
 
1.0%
115.135 1
 
1.0%
109.521 1
 
1.0%
129.327 1
 
1.0%
107.106 1
 
1.0%
124.771 1
 
1.0%
124.733 1
 
1.0%
128.586 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
8.145 1
 
1.0%
11.276 1
 
1.0%
12.594 1
 
1.0%
13.257 1
 
1.0%
13.338 1
 
1.0%
13.603 1
 
1.0%
13.61 1
 
1.0%
13.63 1
 
1.0%
13.871 1
 
1.0%
ValueCountFrequency (%)
313.538 1
1.0%
237.981 1
1.0%
219.83 1
1.0%
216.288 1
1.0%
208.116 1
1.0%
205.121 1
1.0%
203.711 1
1.0%
196.33 1
1.0%
189.639 1
1.0%
187.467 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.66429
Minimum0
Maximum315.994
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:58.469345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.324
median90.3765
Q3131.51175
95-th percentile208.2828
Maximum315.994
Range315.994
Interquartile range (IQR)81.18775

Descriptive statistics

Standard deviation66.793564
Coefficient of variation (CV)0.72081234
Kurtosis0.12016648
Mean92.66429
Median Absolute Deviation (MAD)40.3265
Skewness0.54897895
Sum9266.429
Variance4461.3802
MonotonicityNot monotonic
2023-12-10T19:48:58.686102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
195.806 1
 
1.0%
8.701 1
 
1.0%
123.963 1
 
1.0%
93.903 1
 
1.0%
127.969 1
 
1.0%
89.451 1
 
1.0%
128.845 1
 
1.0%
124.054 1
 
1.0%
138.093 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 10
10.0%
8.701 1
 
1.0%
11.39 1
 
1.0%
11.542 1
 
1.0%
13.037 1
 
1.0%
13.182 1
 
1.0%
13.489 1
 
1.0%
13.552 1
 
1.0%
13.908 1
 
1.0%
14.392 1
 
1.0%
ValueCountFrequency (%)
315.994 1
1.0%
234.314 1
1.0%
227.367 1
1.0%
213.832 1
1.0%
208.64 1
1.0%
208.264 1
1.0%
203.711 1
1.0%
202.039 1
1.0%
195.806 1
1.0%
193.323 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.87709
Minimum0
Maximum58.85
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:58.893769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.09875
median9.86
Q338.9875
95-th percentile57.94165
Maximum58.85
Range58.85
Interquartile range (IQR)36.88875

Descriptive statistics

Standard deviation22.479437
Coefficient of variation (CV)1.0767514
Kurtosis-1.3352633
Mean20.87709
Median Absolute Deviation (MAD)8.9205
Skewness0.6297179
Sum2087.709
Variance505.32507
MonotonicityNot monotonic
2023-12-10T19:48:59.038865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
2.105 4
 
4.0%
2.124 3
 
3.0%
53.124 3
 
3.0%
53.059 2
 
2.0%
53.319 2
 
2.0%
2.11 2
 
2.0%
0.982 2
 
2.0%
2.114 2
 
2.0%
53.189 2
 
2.0%
Other values (63) 68
68.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.93 1
 
1.0%
0.949 1
 
1.0%
0.968 1
 
1.0%
0.982 2
 
2.0%
0.991 2
 
2.0%
1.015 1
 
1.0%
1.039 2
 
2.0%
1.088 1
 
1.0%
1.199 1
 
1.0%
ValueCountFrequency (%)
58.85 1
1.0%
58.662 1
1.0%
58.356 1
1.0%
58.25 1
1.0%
58.144 1
1.0%
57.931 1
1.0%
57.613 1
1.0%
57.401 1
1.0%
57.189 1
1.0%
54.101 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.731
Minimum0
Maximum119.2
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:48:59.229713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.5
median94.55
Q3100.6
95-th percentile114.5
Maximum119.2
Range119.2
Interquartile range (IQR)87.1

Descriptive statistics

Standard deviation47.128474
Coefficient of variation (CV)0.81634605
Kurtosis-1.8742419
Mean57.731
Median Absolute Deviation (MAD)23.1
Skewness-0.11808246
Sum5773.1
Variance2221.0931
MonotonicityNot monotonic
2023-12-10T19:48:59.453725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.1 10
 
10.0%
13.5 6
 
6.0%
13.6 5
 
5.0%
13.7 4
 
4.0%
100.7 3
 
3.0%
0.2 3
 
3.0%
101.1 2
 
2.0%
100.9 2
 
2.0%
100.6 2
 
2.0%
Other values (49) 53
53.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.1 10
10.0%
0.2 3
 
3.0%
13.4 1
 
1.0%
13.5 6
6.0%
13.6 5
5.0%
13.7 4
 
4.0%
13.8 1
 
1.0%
13.9 2
 
2.0%
36.8 1
 
1.0%
ValueCountFrequency (%)
119.2 1
1.0%
116.1 1
1.0%
115.6 1
1.0%
115.0 1
1.0%
114.5 2
2.0%
113.0 2
2.0%
111.9 1
1.0%
110.4 1
1.0%
109.3 1
1.0%
106.7 1
1.0%

Interactions

2023-12-10T19:48:54.695774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:48.646312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.616430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.610430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.734207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.689422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.659662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.828163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:48.778878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.773711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.734964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.867501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.830963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.801219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.934005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:48.908936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.920869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.844333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.987469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.971587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.952266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:55.032377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.052091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.056034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.240424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.111595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.098330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.089069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:55.159802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.206394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.191765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.352160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.266143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.236714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.242693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:55.343148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.344047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.334927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.465550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.396240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.370697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.388583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:55.478608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:49.483313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:50.476561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:51.603854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:52.533149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:53.518011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:48:54.532346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:48:59.633115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.7840.7960.9180.956
일자/시간(t)0.0001.0000.0000.5200.0000.0000.0000.000
저수위(m)1.0000.0001.0000.0000.6790.6990.9390.746
강우량(mm)0.0000.5200.0001.0000.3270.3280.0000.109
유입량(ms)0.7840.0000.6790.3271.0000.9970.6630.854
방류량(ms)0.7960.0000.6990.3280.9971.0000.6730.869
저수량(백만m3)0.9180.0000.9390.0000.6630.6731.0000.810
저수율0.9560.0000.7460.1090.8540.8690.8101.000
2023-12-10T19:48:59.802020image/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)1.0000.1030.275-0.039-0.005-0.0660.0550.000
저수위(m)0.1031.0000.0540.1650.1680.1390.2380.984
강우량(mm)0.2750.0541.0000.3530.3690.2420.3910.000
유입량(ms)-0.0390.1650.3531.0000.9910.7720.8680.534
방류량(ms)-0.0050.1680.3690.9911.0000.7720.8630.548
저수량(백만m3)-0.0660.1390.2420.7720.7721.0000.8090.849
저수율0.0550.2380.3910.8680.8630.8091.0000.881
댐이름0.0000.9840.0000.5340.5480.8490.8811.000

Missing values

2023-12-10T19:48:55.663381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:48:55.891729image/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
0강천보2021062238.315.9853196.33195.80610.086115.6
1강천보2021062938.136.8025151.355155.0229.316106.7
2강천보2021061938.310.0208.116208.6410.131116.1
3강천보2021061138.239.1414169.382163.629.769111.9
4강천보2021062638.2512.3733179.092180.6649.86113.0
5강천보2021062838.20.8197159.232161.3279.633110.4
6강천보2021060338.2518.8779172.401171.8779.86113.0
7강천보2021062138.290.1571203.711203.71110.041115.0
8강천보2021062538.283.0819205.121208.2649.995114.5
9강천보2021063038.185.5522150.263147.6449.543109.3
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달성보2021061713.940.0331189.639193.32357.93198.9
91달성보2021062513.912.736999.478114.87757.61398.4
92달성보2021061813.981.7433178.461173.54958.35699.6
93백제보202106141.840.333695.9691.3029.89540.9
94백제보202106241.770.087.1482.4829.54339.5
95백제보202106191.880.0100.87693.88810.09741.8
96백제보202106121.640.0136.08125.0168.88936.8
97백제보202106271.940.0211100.8591.53310.39943.0
98백제보202106201.970.064110.048104.80710.54943.7
99백제보202106081.740.085.13381.0579.39238.9