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 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 3 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 12:53:18.875908
Analysis finished2023-12-10 12:53:22.923960
Duration4.05 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%
Mean2.0190501 × 109
Minimum2.0190501 × 109
Maximum2.0190501 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:22.970179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 109
5-th percentile2.0190501 × 109
Q12.0190501 × 109
median2.0190501 × 109
Q32.0190501 × 109
95-th percentile2.0190501 × 109
Maximum2.0190501 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4234544
Coefficient of variation (CV)1.2002943 × 10-9
Kurtosis-1.2221364
Mean2.0190501 × 109
Median Absolute Deviation (MAD)2
Skewness0.025708346
Sum2.0190501 × 1011
Variance5.8731313
MonotonicityIncreasing
2023-12-10T21:53:23.090326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019050106 18
18.0%
2019050102 16
16.0%
2019050104 16
16.0%
2019050108 14
14.0%
2019050103 9
9.0%
2019050107 9
9.0%
2019050101 8
8.0%
2019050105 5
 
5.0%
2019050109 5
 
5.0%
ValueCountFrequency (%)
2019050101 8
8.0%
2019050102 16
16.0%
2019050103 9
9.0%
2019050104 16
16.0%
2019050105 5
 
5.0%
2019050106 18
18.0%
2019050107 9
9.0%
2019050108 14
14.0%
2019050109 5
 
5.0%
ValueCountFrequency (%)
2019050109 5
 
5.0%
2019050108 14
14.0%
2019050107 9
9.0%
2019050106 18
18.0%
2019050105 5
 
5.0%
2019050104 16
16.0%
2019050103 9
9.0%
2019050102 16
16.0%
2019050101 8
8.0%

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

2023-12-10T21:53:23.209660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:23.294626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.78541
Minimum0
Maximum99.507
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:23.394449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.457
median18.0805
Q353.0915
95-th percentile75.6281
Maximum99.507
Range99.507
Interquartile range (IQR)50.6345

Descriptive statistics

Standard deviation27.976692
Coefficient of variation (CV)1.0068843
Kurtosis-0.14163744
Mean27.78541
Median Absolute Deviation (MAD)18.0805
Skewness0.89548932
Sum2778.541
Variance782.69531
MonotonicityNot monotonic
2023-12-10T21:53:23.550691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
24.347 5
 
5.0%
12.392 4
 
4.0%
55.237 3
 
3.0%
55.15 3
 
3.0%
2.457 3
 
3.0%
73.069 3
 
3.0%
9.724 3
 
3.0%
37.422 3
 
3.0%
15.547 3
 
3.0%
Other values (45) 51
51.0%
ValueCountFrequency (%)
0.0 19
19.0%
0.962 1
 
1.0%
0.963 1
 
1.0%
0.965 2
 
2.0%
2.457 3
 
3.0%
2.463 1
 
1.0%
4.643 1
 
1.0%
4.657 1
 
1.0%
4.67 1
 
1.0%
4.683 1
 
1.0%
ValueCountFrequency (%)
99.507 2
2.0%
99.303 1
 
1.0%
99.1 1
 
1.0%
75.725 1
 
1.0%
75.623 1
 
1.0%
75.52 2
2.0%
73.069 3
3.0%
72.923 1
 
1.0%
55.237 3
3.0%
55.15 3
3.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.47
Minimum0
Maximum111.4
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:23.717634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.8
median79.1
Q3100.6
95-th percentile110.02
Maximum111.4
Range111.4
Interquartile range (IQR)84.8

Descriptive statistics

Standard deviation43.15144
Coefficient of variation (CV)0.65910249
Kurtosis-1.4127683
Mean65.47
Median Absolute Deviation (MAD)26.95
Skewness-0.57482673
Sum6547
Variance1862.0468
MonotonicityNot monotonic
2023-12-10T21:53:23.866065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
100.7 5
 
5.0%
7.2 4
 
4.0%
110.0 4
 
4.0%
79.0 4
 
4.0%
15.8 3
 
3.0%
108.4 3
 
3.0%
111.4 3
 
3.0%
79.1 3
 
3.0%
78.8 3
 
3.0%
Other values (40) 49
49.0%
ValueCountFrequency (%)
0.0 19
19.0%
7.2 4
 
4.0%
15.8 3
 
3.0%
15.9 1
 
1.0%
17.0 2
 
2.0%
17.1 2
 
2.0%
51.8 1
 
1.0%
51.9 1
 
1.0%
52.1 1
 
1.0%
52.2 1
 
1.0%
ValueCountFrequency (%)
111.4 3
3.0%
110.9 1
 
1.0%
110.4 1
 
1.0%
110.0 4
4.0%
108.4 3
3.0%
108.1 2
2.0%
107.8 1
 
1.0%
106.5 1
 
1.0%
101.2 1
 
1.0%
101.1 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.09669
Minimum0
Maximum623.968
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:24.002636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.19625
median104.123
Q3187.79225
95-th percentile388.51625
Maximum623.968
Range623.968
Interquartile range (IQR)154.596

Descriptive statistics

Standard deviation142.58243
Coefficient of variation (CV)1.0250598
Kurtosis1.9651253
Mean139.09669
Median Absolute Deviation (MAD)83.5395
Skewness1.4276429
Sum13909.669
Variance20329.748
MonotonicityNot monotonic
2023-12-10T21:53:24.152718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
170.97 2
 
2.0%
229.2 2
 
2.0%
15.757 1
 
1.0%
64.562 1
 
1.0%
193.797 1
 
1.0%
384.33 1
 
1.0%
179.442 1
 
1.0%
63.503 1
 
1.0%
48.894 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 19
19.0%
15.757 1
 
1.0%
17.563 1
 
1.0%
17.621 1
 
1.0%
17.691 1
 
1.0%
20.45 1
 
1.0%
21.2 1
 
1.0%
37.195 1
 
1.0%
38.314 1
 
1.0%
39.695 1
 
1.0%
ValueCountFrequency (%)
623.968 1
1.0%
611.276 1
1.0%
548.701 1
1.0%
518.68 1
1.0%
419.985 1
1.0%
386.86 1
1.0%
384.33 1
1.0%
383.197 1
1.0%
382.89 1
1.0%
379.408 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.0261
Minimum0
Maximum611.276
Zeros19
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:24.297847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.21125
median115.61
Q3187.64775
95-th percentile401.02585
Maximum611.276
Range611.276
Interquartile range (IQR)154.4365

Descriptive statistics

Standard deviation146.14849
Coefficient of variation (CV)1.0147362
Kurtosis1.4398628
Mean144.0261
Median Absolute Deviation (MAD)75.8835
Skewness1.3105822
Sum14402.61
Variance21359.382
MonotonicityNot monotonic
2023-12-10T21:53:24.423321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 19
 
19.0%
170.97 2
 
2.0%
229.2 2
 
2.0%
15.757 1
 
1.0%
64.562 1
 
1.0%
193.797 1
 
1.0%
384.33 1
 
1.0%
163.136 1
 
1.0%
63.503 1
 
1.0%
49.033 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 19
19.0%
2.2 1
 
1.0%
10.413 1
 
1.0%
15.757 1
 
1.0%
21.2 1
 
1.0%
21.202 1
 
1.0%
21.26 1
 
1.0%
37.195 1
 
1.0%
38.592 1
 
1.0%
39.695 1
 
1.0%
ValueCountFrequency (%)
611.276 1
1.0%
605.257 1
1.0%
567.412 1
1.0%
518.68 1
1.0%
419.985 1
1.0%
400.028 1
1.0%
389.801 1
1.0%
386.86 1
1.0%
384.33 1
1.0%
383.197 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.8575
Minimum1.95
Maximum132.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:24.592337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile4.22
Q18.455
median28.27
Q344.5
95-th percentile83.3705
Maximum132.29
Range130.34
Interquartile range (IQR)36.045

Descriptive statistics

Standard deviation31.312526
Coefficient of variation (CV)0.92483279
Kurtosis1.9865677
Mean33.8575
Median Absolute Deviation (MAD)19.02
Skewness1.4203569
Sum3385.75
Variance980.47429
MonotonicityNot monotonic
2023-12-10T21:53:24.754853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.56 5
 
5.0%
4.22 5
 
5.0%
33.26 4
 
4.0%
80.7 4
 
4.0%
44.5 4
 
4.0%
4.34 3
 
3.0%
9.24 3
 
3.0%
28.27 3
 
3.0%
9.25 3
 
3.0%
132.28 3
 
3.0%
Other values (50) 63
63.0%
ValueCountFrequency (%)
1.95 1
 
1.0%
1.96 1
 
1.0%
1.98 1
 
1.0%
1.99 1
 
1.0%
4.22 5
5.0%
4.34 3
3.0%
4.35 1
 
1.0%
4.44 1
 
1.0%
4.45 1
 
1.0%
4.46 1
 
1.0%
ValueCountFrequency (%)
132.29 1
 
1.0%
132.28 3
3.0%
83.38 1
 
1.0%
83.37 3
3.0%
80.71 2
 
2.0%
80.7 4
4.0%
62.56 5
5.0%
46.88 1
 
1.0%
46.86 1
 
1.0%
46.84 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
구미보
7 
이포보
7 
승촌보
 
6
수하보
 
6
합천창녕보
 
6
Other values (16)
68 

Length

Max length5
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승촌보
2nd row쾌쾌보
3rd row낙단보
4th row백제보
5th row단양수중보

Common Values

ValueCountFrequency (%)
구미보 7
 
7.0%
이포보 7
 
7.0%
승촌보 6
 
6.0%
수하보 6
 
6.0%
합천창녕보 6
 
6.0%
백제보 5
 
5.0%
낙단보 5
 
5.0%
강천보 5
 
5.0%
구담보 5
 
5.0%
강정고령보 4
 
4.0%
Other values (11) 44
44.0%

Length

2023-12-10T21:53:24.895258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구미보 7
 
7.0%
이포보 7
 
7.0%
승촌보 6
 
6.0%
수하보 6
 
6.0%
합천창녕보 6
 
6.0%
백제보 5
 
5.0%
낙단보 5
 
5.0%
강천보 5
 
5.0%
구담보 5
 
5.0%
안동보 4
 
4.0%
Other values (11) 44
44.0%

Interactions

2023-12-10T21:53:22.145632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.132402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.763273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.284118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.828477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.353661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.236282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.253296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.868336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.362358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.913665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.468478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.312823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.369755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.952246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.436783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.988683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.813969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.383550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.466965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.025885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.517641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.080312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.894516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.462565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.557353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.102307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.610598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.161638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.982533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.544823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:19.659962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.192530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:20.701522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:21.265114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:22.064552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:24.981489image/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)0.0001.0000.8590.8630.8660.9121.000
유입량(ms)0.0000.8591.0000.6950.6990.8641.000
방류량(ms)0.0000.8630.6951.0000.9870.6690.945
저수량(백만m3)0.0000.8660.6990.9871.0000.6890.939
저수율0.0000.9120.8640.6690.6891.0001.000
댐이름0.0001.0001.0000.9450.9391.0001.000
2023-12-10T21:53:25.104953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.002-0.064-0.036-0.034-0.0200.000
강우량(mm)-0.0021.0000.5060.7920.799-0.3200.922
유입량(ms)-0.0640.5061.0000.7010.701-0.2560.922
방류량(ms)-0.0360.7920.7011.0000.989-0.2770.701
저수량(백만m3)-0.0340.7990.7010.9891.000-0.2850.683
저수율-0.020-0.320-0.256-0.277-0.2851.0000.922
댐이름0.0000.9220.9220.7010.6830.9221.000

Missing values

2023-12-10T21:53:22.669363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:22.877385image/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
0201905010104.68352.221.221.24.47승촌보
1201905010100.00.00.00.044.5쾌쾌보
22019050101033.79797.5163.124179.40239.85낙단보
32019050101024.347100.763.49163.4914.22백제보
42019050101037.4737.278.81964.791132.29단양수중보
52019050101012.392110.0188.093188.09333.26여주보
62019050101053.384101.2170.93170.9332.6구미보
7201905010100.00.00.00.080.7수하보
82019050102054.4392.9419.985419.98513.61달성보
9201905010200.96517.139.86739.8678.46세종보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90201905010800.96217.037.19537.1958.43세종보
912019050108053.26390.9312.095341.56713.5달성보
922019050108075.52100.3106.3106.325.52칠곡보
932019050108072.92379.0150.403231.8218.25강정고령보
94201905010802.45715.847.44447.5834.34공주보
95201905010904.78853.417.69110.4134.55승촌보
962019050109015.279106.5304.261341.48328.21이포보
972019050109052.928100.4139.023157.10632.53구미보
98201905010900.00.00.00.080.71수하보
99201905010900.00.00.00.062.56구담보