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 65 (65.0%) 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 10:55:08.580753
Analysis finished2023-12-10 10:55:17.874456
Duration9.29 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

2023-12-10T19:55:17.958874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:55:18.121385image/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%
Mean20190315
Minimum20190301
Maximum20190331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:18.303241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190301
5-th percentile20190302
Q120190307
median20190315
Q320190323
95-th percentile20190330
Maximum20190331
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5637257 × 10-7
Kurtosis-1.2602055
Mean20190315
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190315 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T19:55:18.496430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190301 4
 
4.0%
20190303 4
 
4.0%
20190304 4
 
4.0%
20190305 4
 
4.0%
20190306 4
 
4.0%
20190307 4
 
4.0%
20190302 4
 
4.0%
20190326 3
 
3.0%
20190322 3
 
3.0%
20190323 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190301 4
4.0%
20190302 4
4.0%
20190303 4
4.0%
20190304 4
4.0%
20190305 4
4.0%
20190306 4
4.0%
20190307 4
4.0%
20190308 3
3.0%
20190309 3
3.0%
20190310 3
3.0%
ValueCountFrequency (%)
20190331 3
3.0%
20190330 3
3.0%
20190329 3
3.0%
20190328 3
3.0%
20190327 3
3.0%
20190326 3
3.0%
20190325 3
3.0%
20190324 3
3.0%
20190323 3
3.0%
20190322 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.7535
Minimum4.23
Maximum62.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:18.701876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.23
5-th percentile4.3
Q14.33
median38.03
Q362.48
95-th percentile62.49
Maximum62.65
Range58.42
Interquartile range (IQR)58.15

Descriptive statistics

Standard deviation23.128895
Coefficient of variation (CV)0.66551268
Kurtosis-1.406538
Mean34.7535
Median Absolute Deviation (MAD)24.46
Skewness-0.17654623
Sum3475.35
Variance534.94578
MonotonicityNot monotonic
2023-12-10T19:55:18.899929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
62.49 22
22.0%
38.03 8
 
8.0%
4.32 7
 
7.0%
38.02 7
 
7.0%
4.33 6
 
6.0%
4.3 6
 
6.0%
62.48 6
 
6.0%
38.04 5
 
5.0%
4.31 4
 
4.0%
38.01 3
 
3.0%
Other values (22) 26
26.0%
ValueCountFrequency (%)
4.23 1
 
1.0%
4.26 1
 
1.0%
4.27 1
 
1.0%
4.29 1
 
1.0%
4.3 6
6.0%
4.31 4
4.0%
4.32 7
7.0%
4.33 6
6.0%
4.34 2
 
2.0%
4.35 1
 
1.0%
ValueCountFrequency (%)
62.65 1
 
1.0%
62.5 1
 
1.0%
62.49 22
22.0%
62.48 6
 
6.0%
62.4 1
 
1.0%
38.15 1
 
1.0%
38.14 1
 
1.0%
38.12 1
 
1.0%
38.1 1
 
1.0%
38.06 2
 
2.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.683986
Minimum0
Maximum15.5453
Zeros65
Zeros (%)65.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:19.162921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0619
95-th percentile3.794205
Maximum15.5453
Range15.5453
Interquartile range (IQR)0.0619

Descriptive statistics

Standard deviation2.3309862
Coefficient of variation (CV)3.4079444
Kurtosis27.517269
Mean0.683986
Median Absolute Deviation (MAD)0
Skewness4.9909585
Sum68.3986
Variance5.4334969
MonotonicityNot monotonic
2023-12-10T19:55:19.416249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 65
65.0%
0.0119 2
 
2.0%
0.1506 1
 
1.0%
4.0603 1
 
1.0%
6.3676 1
 
1.0%
0.051 1
 
1.0%
3.4126 1
 
1.0%
1.346 1
 
1.0%
15.5453 1
 
1.0%
0.0655 1
 
1.0%
Other values (25) 25
 
25.0%
ValueCountFrequency (%)
0.0 65
65.0%
0.0017 1
 
1.0%
0.0035 1
 
1.0%
0.0104 1
 
1.0%
0.0119 2
 
2.0%
0.0192 1
 
1.0%
0.0238 1
 
1.0%
0.051 1
 
1.0%
0.0559 1
 
1.0%
0.0607 1
 
1.0%
ValueCountFrequency (%)
15.5453 1
1.0%
14.2299 1
1.0%
6.3676 1
1.0%
4.7537 1
1.0%
4.0603 1
1.0%
3.7802 1
1.0%
3.6627 1
1.0%
3.5326 1
1.0%
3.4126 1
1.0%
1.4892 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.60698
Minimum0
Maximum134.657
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:19.922188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34.4485
Q3121.76925
95-th percentile129.32485
Maximum134.657
Range134.657
Interquartile range (IQR)121.76925

Descriptive statistics

Standard deviation51.616016
Coefficient of variation (CV)1.0001751
Kurtosis-1.3741896
Mean51.60698
Median Absolute Deviation (MAD)34.4485
Skewness0.55816797
Sum5160.698
Variance2664.2132
MonotonicityNot monotonic
2023-12-10T19:55:20.181293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
41.217 1
 
1.0%
45.524 1
 
1.0%
35.048 1
 
1.0%
34.17 1
 
1.0%
33.66 1
 
1.0%
34.314 1
 
1.0%
32.874 1
 
1.0%
34.372 1
 
1.0%
122.906 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
3.052 1
 
1.0%
4.243 1
 
1.0%
9.489 1
 
1.0%
31.539 1
 
1.0%
31.891 1
 
1.0%
32.297 1
 
1.0%
32.442 1
 
1.0%
32.478 1
 
1.0%
32.611 1
 
1.0%
ValueCountFrequency (%)
134.657 1
1.0%
134.233 1
1.0%
134.146 1
1.0%
132.089 1
1.0%
131.013 1
1.0%
129.236 1
1.0%
128.211 1
1.0%
126.258 1
1.0%
126.041 1
1.0%
125.846 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.20247
Minimum0
Maximum135.194
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:20.477193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34.0355
Q3120.551
95-th percentile129.35245
Maximum135.194
Range135.194
Interquartile range (IQR)120.551

Descriptive statistics

Standard deviation51.997407
Coefficient of variation (CV)1.035754
Kurtosis-1.3482048
Mean50.20247
Median Absolute Deviation (MAD)34.0355
Skewness0.61130045
Sum5020.247
Variance2703.7303
MonotonicityNot monotonic
2023-12-10T19:55:20.751576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
31.0%
41.453 1
 
1.0%
45.445 1
 
1.0%
34.654 1
 
1.0%
34.249 1
 
1.0%
33.66 1
 
1.0%
34.472 1
 
1.0%
32.953 1
 
1.0%
34.057 1
 
1.0%
122.906 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 31
31.0%
0.5 1
 
1.0%
3.098 1
 
1.0%
6.795 1
 
1.0%
12.296 1
 
1.0%
13.867 1
 
1.0%
16.396 1
 
1.0%
26.79 1
 
1.0%
31.854 1
 
1.0%
31.97 1
 
1.0%
ValueCountFrequency (%)
135.194 1
1.0%
134.657 1
1.0%
134.233 1
1.0%
133.108 1
1.0%
131.565 1
1.0%
129.236 1
1.0%
126.639 1
1.0%
126.565 1
1.0%
126.258 1
1.0%
125.888 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.03126
Minimum0
Maximum52.571
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:20.985784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.443
Q38.864
95-th percentile46.4349
Maximum52.571
Range52.571
Interquartile range (IQR)8.864

Descriptive statistics

Standard deviation12.46604
Coefficient of variation (CV)1.7729454
Kurtosis8.0734712
Mean7.03126
Median Absolute Deviation (MAD)2.443
Skewness2.9655095
Sum703.126
Variance155.40215
MonotonicityNot monotonic
2023-12-10T19:55:21.336988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 31
31.0%
8.864 8
 
8.0%
2.443 7
 
7.0%
8.819 7
 
7.0%
2.45 6
 
6.0%
2.429 6
 
6.0%
8.909 5
 
5.0%
2.436 4
 
4.0%
8.773 3
 
3.0%
52.571 2
 
2.0%
Other values (18) 21
21.0%
ValueCountFrequency (%)
0.0 31
31.0%
2.382 1
 
1.0%
2.402 1
 
1.0%
2.409 1
 
1.0%
2.422 1
 
1.0%
2.429 6
 
6.0%
2.436 4
 
4.0%
2.443 7
 
7.0%
2.45 6
 
6.0%
2.457 2
 
2.0%
ValueCountFrequency (%)
52.571 2
2.0%
52.329 1
1.0%
52.248 1
1.0%
51.601 1
1.0%
46.163 1
1.0%
43.424 1
1.0%
9.407 1
1.0%
9.362 1
1.0%
9.271 1
1.0%
9.181 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.216
Minimum0
Maximum107.8
Zeros31
Zeros (%)31.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:55:21.931383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.7
Q3101
95-th percentile103.1
Maximum107.8
Range107.8
Interquartile range (IQR)101

Descriptive statistics

Standard deviation45.907753
Coefficient of variation (CV)1.062286
Kurtosis-1.7405463
Mean43.216
Median Absolute Deviation (MAD)15.7
Skewness0.45475361
Sum4321.6
Variance2107.5218
MonotonicityNot monotonic
2023-12-10T19:55:22.468106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 31
31.0%
15.7 11
 
11.0%
101.6 8
 
8.0%
15.8 8
 
8.0%
15.6 7
 
7.0%
101.0 7
 
7.0%
102.1 5
 
5.0%
100.5 3
 
3.0%
15.5 2
 
2.0%
103.1 2
 
2.0%
Other values (13) 16
16.0%
ValueCountFrequency (%)
0.0 31
31.0%
15.3 1
 
1.0%
15.5 2
 
2.0%
15.6 7
 
7.0%
15.7 11
 
11.0%
15.8 8
 
8.0%
15.9 2
 
2.0%
82.4 1
 
1.0%
87.5 1
 
1.0%
97.9 1
 
1.0%
ValueCountFrequency (%)
107.8 1
 
1.0%
107.3 1
 
1.0%
106.2 1
 
1.0%
105.2 1
 
1.0%
103.1 2
 
2.0%
102.6 2
 
2.0%
102.1 5
5.0%
101.6 8
8.0%
101.0 7
7.0%
100.5 3
 
3.0%

Interactions

2023-12-10T19:55:16.462312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.013368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.214273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:11.454473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.745566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.236375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.358691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.620799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.161303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.412210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:11.639969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.919332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.393661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.529920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.754979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.314001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.565616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:11.812553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:13.091282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.553959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.679376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.915685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.482098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.778705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.026824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:13.263315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.728333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.845435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:17.093643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.650894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.953649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.216893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:13.727368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.895101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.011056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:17.250138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:09.829300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:11.123355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.389940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:13.908524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.042401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.166706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:17.393640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:10.014820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:11.298879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:12.583105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:14.080328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:15.202529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:55:16.321717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:55:22.936190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.8760.9690.9810.871
일자/시간(t)0.0001.0000.0000.5030.1300.1760.0000.000
저수위(m)1.0000.0001.0000.0000.8760.9690.9810.871
강우량(mm)0.0000.5030.0001.0000.6420.1720.0000.000
유입량(ms)0.8760.1300.8760.6421.0000.9230.7540.803
방류량(ms)0.9690.1760.9690.1720.9231.0000.8190.820
저수량(백만m3)0.9810.0000.9810.0000.7540.8191.0000.835
저수율0.8710.0000.8710.0000.8030.8200.8351.000
2023-12-10T19:55:23.561179image/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)1.0000.0490.2190.0150.070-0.133-0.0270.000
저수위(m)0.0491.000-0.295-0.400-0.431-0.373-0.3241.000
강우량(mm)0.219-0.2951.0000.4570.4840.4080.4740.000
유입량(ms)0.015-0.4000.4571.0000.9770.8270.9200.804
방류량(ms)0.070-0.4310.4840.9771.0000.7550.8890.875
저수량(백만m3)-0.133-0.3730.4080.8270.7551.0000.9480.810
저수율-0.027-0.3240.4740.9200.8890.9481.0000.855
댐이름0.0001.0000.0000.8040.8750.8100.8551.000

Missing values

2023-12-10T19:55:17.584807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:55:17.799901image/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
0강천보2019030138.030.0122.906122.9068.864101.6
1강천보2019030238.030.0124.563124.5638.864101.6
2강천보2019030338.030.0125.669125.6698.864101.6
3강천보2019030438.030.0125.661125.6618.864101.6
4강천보2019030538.030.0124.149124.1498.864101.6
5강천보2019030638.010.0017122.698123.7468.773100.5
6강천보2019030738.020.0559121.754121.238.819101.0
7강천보2019030838.020.0123.583123.5838.819101.0
8강천보2019030938.030.0123.155122.6318.864101.6
9강천보2019031038.010.0104123.226124.2748.773100.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구담보2019032962.480.00.00.00.00.0
91구담보2019033062.480.00.00.00.00.0
92구담보2019033162.480.00.00.00.00.0
93구미보2019030131.250.050.64813.86743.42482.4
94구미보2019030231.640.034.7993.09846.16387.5
95구미보2019030332.360.079.34216.39651.60197.9
96구미보2019030432.480.038.01826.7952.57199.7
97구미보2019030532.440.03.0526.79552.24899.1
98구미보2019030632.480.04.2430.552.57199.7
99구미보2019030732.450.15069.48912.29652.32999.2