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

Numeric6
Categorical2

Alerts

저수위(m) has constant value ""Constant
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
강우량(mm) has 19 (19.0%) zerosZeros
유입량(ms) has 19 (19.0%) zerosZeros
방류량(ms) has 19 (19.0%) zerosZeros
저수량(백만m3) has 19 (19.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:03.665753
Analysis finished2023-12-10 12:53:07.850461
Duration4.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190701 × 109
Minimum2.0190701 × 109
Maximum2.0190701 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:07.896160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190701 × 109
5-th percentile2.0190701 × 109
Q12.0190701 × 109
median2.0190701 × 109
Q32.0190701 × 109
95-th percentile2.0190701 × 109
Maximum2.0190701 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3488445
Coefficient of variation (CV)1.1633299 × 10-9
Kurtosis-1.2186953
Mean2.0190701 × 109
Median Absolute Deviation (MAD)2
Skewness0.0013568749
Sum2.0190701 × 1011
Variance5.5170707
MonotonicityIncreasing
2023-12-10T21:53:08.084704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019070107 16
16.0%
2019070103 14
14.0%
2019070105 13
13.0%
2019070101 12
12.0%
2019070102 12
12.0%
2019070106 11
11.0%
2019070104 10
10.0%
2019070108 10
10.0%
2019070109 2
 
2.0%
ValueCountFrequency (%)
2019070101 12
12.0%
2019070102 12
12.0%
2019070103 14
14.0%
2019070104 10
10.0%
2019070105 13
13.0%
2019070106 11
11.0%
2019070107 16
16.0%
2019070108 10
10.0%
2019070109 2
 
2.0%
ValueCountFrequency (%)
2019070109 2
 
2.0%
2019070108 10
10.0%
2019070107 16
16.0%
2019070106 11
11.0%
2019070105 13
13.0%
2019070104 10
10.0%
2019070103 14
14.0%
2019070102 12
12.0%
2019070101 12
12.0%

저수위(m)
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:53:08.230624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:08.354502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.77521
Minimum0
Maximum99.914
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:08.477395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.45525
median16.178
Q353.1565
95-th percentile80.565
Maximum99.914
Range99.914
Interquartile range (IQR)50.70125

Descriptive statistics

Standard deviation29.157961
Coefficient of variation (CV)1.0133014
Kurtosis-0.4122223
Mean28.77521
Median Absolute Deviation (MAD)16.178
Skewness0.85121448
Sum2877.521
Variance850.18669
MonotonicityNot monotonic
2023-12-10T21:53:08.619492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
8.773 5
 
5.0%
11.398 5
 
5.0%
2.457 3
 
3.0%
58.356 3
 
3.0%
37.574 2
 
2.0%
37.675 2
 
2.0%
53.319 2
 
2.0%
24.258 2
 
2.0%
27.696 2
 
2.0%
Other values (44) 55
55.0%
ValueCountFrequency (%)
0.0 19
19.0%
0.971 1
 
1.0%
0.972 2
 
2.0%
0.973 1
 
1.0%
2.443 1
 
1.0%
2.45 1
 
1.0%
2.457 3
 
3.0%
6.147 1
 
1.0%
6.192 1
 
1.0%
6.207 2
 
2.0%
ValueCountFrequency (%)
99.914 1
1.0%
98.896 1
1.0%
98.285 1
1.0%
97.878 1
1.0%
80.983 1
1.0%
80.543 1
1.0%
80.25 1
1.0%
79.957 1
1.0%
76.033 2
2.0%
75.93 2
2.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.41
Minimum0
Maximum104.6
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:08.776066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.2
median98.1
Q3100.7
95-th percentile101.355
Maximum104.6
Range104.6
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation42.118638
Coefficient of variation (CV)0.59819114
Kurtosis-1.0711761
Mean70.41
Median Absolute Deviation (MAD)3.1
Skewness-0.88458661
Sum7041
Variance1773.9797
MonotonicityNot monotonic
2023-12-10T21:53:08.956789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 19
19.0%
101.2 7
 
7.0%
100.5 6
 
6.0%
101.0 4
 
4.0%
15.8 4
 
4.0%
100.6 4
 
4.0%
17.2 4
 
4.0%
101.1 4
 
4.0%
99.6 3
 
3.0%
69.2 2
 
2.0%
Other values (33) 43
43.0%
ValueCountFrequency (%)
0.0 19
19.0%
15.7 1
 
1.0%
15.8 4
 
4.0%
17.2 4
 
4.0%
62.2 1
 
1.0%
62.5 1
 
1.0%
62.8 1
 
1.0%
63.2 1
 
1.0%
68.5 1
 
1.0%
69.0 1
 
1.0%
ValueCountFrequency (%)
104.6 2
 
2.0%
104.5 1
 
1.0%
104.3 2
 
2.0%
101.2 7
7.0%
101.1 4
4.0%
101.0 4
4.0%
100.9 2
 
2.0%
100.8 2
 
2.0%
100.7 2
 
2.0%
100.6 4
4.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.03323
Minimum0
Maximum692.301
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:09.125658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.2065
median78.0925
Q3151.45125
95-th percentile445.8663
Maximum692.301
Range692.301
Interquartile range (IQR)101.24475

Descriptive statistics

Standard deviation149.38064
Coefficient of variation (CV)1.1667334
Kurtosis4.9050209
Mean128.03323
Median Absolute Deviation (MAD)39.2035
Skewness2.1838136
Sum12803.323
Variance22314.576
MonotonicityNot monotonic
2023-12-10T21:53:09.292433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
179.415 1
 
1.0%
60.411 1
 
1.0%
55.008 1
 
1.0%
77.065 1
 
1.0%
156.834 1
 
1.0%
50.097 1
 
1.0%
637.402 1
 
1.0%
168.7 1
 
1.0%
60.566 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 19
19.0%
35.971 1
 
1.0%
44.2 1
 
1.0%
44.372 1
 
1.0%
47.901 1
 
1.0%
49.217 1
 
1.0%
50.097 1
 
1.0%
50.243 1
 
1.0%
50.841 1
 
1.0%
54.693 1
 
1.0%
ValueCountFrequency (%)
692.301 1
1.0%
664.317 1
1.0%
637.402 1
1.0%
618.584 1
1.0%
559.492 1
1.0%
439.886 1
1.0%
352.6 1
1.0%
352.583 1
1.0%
333.4 1
1.0%
326.586 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.93411
Minimum0
Maximum748.857
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:09.466953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150.342
median77.9415
Q3135.88275
95-th percentile442.1998
Maximum748.857
Range748.857
Interquartile range (IQR)85.54075

Descriptive statistics

Standard deviation161.39545
Coefficient of variation (CV)1.242133
Kurtosis5.6692904
Mean129.93411
Median Absolute Deviation (MAD)35.2585
Skewness2.3568196
Sum12993.411
Variance26048.492
MonotonicityNot monotonic
2023-12-10T21:53:09.910243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
195.721 1
 
1.0%
60.411 1
 
1.0%
55.008 1
 
1.0%
77.065 1
 
1.0%
138.751 1
 
1.0%
50.375 1
 
1.0%
693.958 1
 
1.0%
168.7 1
 
1.0%
60.566 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 19
19.0%
43.79 1
 
1.0%
44.193 1
 
1.0%
44.2 1
 
1.0%
44.372 1
 
1.0%
49.467 1
 
1.0%
50.243 1
 
1.0%
50.375 1
 
1.0%
50.563 1
 
1.0%
54.693 1
 
1.0%
ValueCountFrequency (%)
748.857 1
1.0%
731.695 1
1.0%
720.873 1
1.0%
693.958 1
1.0%
453.539 1
1.0%
441.603 1
1.0%
399.83 1
1.0%
390.212 1
1.0%
386.166 1
1.0%
372.315 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.8478
Minimum1.44
Maximum132.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:10.108035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.44
5-th percentile4.2
Q19.8825
median32.585
Q347.0725
95-th percentile86.8905
Maximum132.33
Range130.89
Interquartile range (IQR)37.19

Descriptive statistics

Standard deviation32.757847
Coefficient of variation (CV)0.88900414
Kurtosis1.5431602
Mean36.8478
Median Absolute Deviation (MAD)18.62
Skewness1.3201305
Sum3684.78
Variance1073.0765
MonotonicityNot monotonic
2023-12-10T21:53:10.260763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.01 5
 
5.0%
33.03 5
 
5.0%
13.98 3
 
3.0%
4.34 3
 
3.0%
132.33 2
 
2.0%
4.2 2
 
2.0%
132.31 2
 
2.0%
5.56 2
 
2.0%
25.57 2
 
2.0%
47.06 2
 
2.0%
Other values (59) 72
72.0%
ValueCountFrequency (%)
1.44 1
 
1.0%
1.46 1
 
1.0%
1.48 1
 
1.0%
1.5 1
 
1.0%
4.2 2
2.0%
4.21 2
2.0%
4.32 1
 
1.0%
4.33 1
 
1.0%
4.34 3
3.0%
4.85 1
 
1.0%
ValueCountFrequency (%)
132.33 2
2.0%
132.32 1
1.0%
132.31 2
2.0%
84.5 1
1.0%
84.48 1
1.0%
84.46 1
1.0%
84.42 1
1.0%
84.39 1
1.0%
84.33 1
1.0%
80.74 1
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
구미보
7 
상주보
7 
강천보
 
6
안동보
 
6
수하보
 
5
Other values (16)
69 

Length

Max length5
Median length3
Mean length3.34
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수하보
2nd row구미보
3rd row강천보
4th row칠곡보
5th row이포보

Common Values

ValueCountFrequency (%)
구미보 7
 
7.0%
상주보 7
 
7.0%
강천보 6
 
6.0%
안동보 6
 
6.0%
수하보 5
 
5.0%
단양수중보 5
 
5.0%
달성보 5
 
5.0%
여주보 5
 
5.0%
낙단보 5
 
5.0%
공주보 5
 
5.0%
Other values (11) 44
44.0%

Length

2023-12-10T21:53:10.426011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구미보 7
 
7.0%
상주보 7
 
7.0%
강천보 6
 
6.0%
안동보 6
 
6.0%
수하보 5
 
5.0%
단양수중보 5
 
5.0%
달성보 5
 
5.0%
여주보 5
 
5.0%
낙단보 5
 
5.0%
공주보 5
 
5.0%
Other values (11) 44
44.0%

Interactions

2023-12-10T21:53:07.115112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:03.895958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.752737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.336572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.902774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.556176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.210119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.004325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.874634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.430430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.006444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.653886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.289150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.091621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.953866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.532097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.103797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.740973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.372722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.179747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.047221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.632303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.203258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.842641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.459886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.292761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.151392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.730205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.327601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.933121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.539325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:04.649834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.238274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:05.821468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:06.453284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:07.022267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:10.513708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.0000.000
강우량(mm)0.0001.0000.8120.9100.8060.7921.000
유입량(ms)0.0000.8121.0000.6900.5250.7210.985
방류량(ms)0.0000.9100.6901.0000.8640.5460.882
저수량(백만m3)0.0000.8060.5250.8641.0000.5110.886
저수율0.0000.7920.7210.5460.5111.0001.000
댐이름0.0001.0000.9850.8820.8861.0001.000
2023-12-10T21:53:10.618461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0500.0660.1020.1010.0040.000
강우량(mm)0.0501.0000.5910.8860.897-0.3390.932
유입량(ms)0.0660.5911.0000.4850.4930.0390.837
방류량(ms)0.1020.8860.4851.0000.982-0.3880.555
저수량(백만m3)0.1010.8970.4930.9821.000-0.4000.585
저수율0.004-0.3390.039-0.388-0.4001.0000.922
댐이름0.0000.9320.8370.5550.5850.9221.000

Missing values

2023-12-10T21:53:07.668491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:07.804821image/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

일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
0201907010100.00.00.00.080.74수하보
12019070101052.994100.5107.708107.70832.54구미보
2201907010108.773100.577.88377.88338.01강천보
32019070101075.93100.868.5196.98225.56칠곡보
42019070101014.387100.387.08787.08728.01이포보
5201907010100.00.00.00.062.65구담보
6201907010100.00.00.00.084.5안동보
72019070101067.50596.5145.86386.16610.33합천창녕보
8201907010102.44315.761.38361.3834.32공주보
92019070101079.95786.6211.707171.01318.73강정고령보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902019070108058.46299.8559.492441.60313.99달성보
91201907010802.45715.854.69354.6934.34공주보
92201907010800.00.00.00.047.82쾌쾌보
93201907010800.97117.249.21749.4678.52세종보
942019070108053.384101.2257.178239.09532.6구미보
95201907010808.819101.079.12579.12538.02강천보
962019070108027.738101.2149.651137.92947.08상주보
972019070108037.675104.676.10876.108132.33단양수중보
98201907010900.00.00.00.084.33안동보
992019070109033.91497.8291.081307.38739.87낙단보