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 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 1 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
강우량(mm) has 22 (22.0%) zerosZeros
유입량(ms) has 26 (26.0%) zerosZeros
방류량(ms) has 25 (25.0%) zerosZeros
저수량(백만m3) has 22 (22.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:51:57.702683
Analysis finished2023-12-10 12:52:01.868458
Duration4.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0200401 × 109
Minimum2.0200401 × 109
Maximum2.0200401 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:01.911611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0200401 × 109
5-th percentile2.0200401 × 109
Q12.0200401 × 109
median2.0200401 × 109
Q32.0200401 × 109
95-th percentile2.0200401 × 109
Maximum2.0200401 × 109
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7487658
Coefficient of variation (CV)8.6570846 × 10-10
Kurtosis-1.1268266
Mean2.0200401 × 109
Median Absolute Deviation (MAD)2
Skewness0.038687472
Sum2.0200401 × 1011
Variance3.0581818
MonotonicityIncreasing
2023-12-10T21:52:02.011255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2020040102 21
21.0%
2020040105 18
18.0%
2020040104 17
17.0%
2020040106 16
16.0%
2020040103 14
14.0%
2020040101 9
9.0%
2020040107 5
 
5.0%
ValueCountFrequency (%)
2020040101 9
9.0%
2020040102 21
21.0%
2020040103 14
14.0%
2020040104 17
17.0%
2020040105 18
18.0%
2020040106 16
16.0%
2020040107 5
 
5.0%
ValueCountFrequency (%)
2020040107 5
 
5.0%
2020040106 16
16.0%
2020040105 18
18.0%
2020040104 17
17.0%
2020040103 14
14.0%
2020040102 21
21.0%
2020040101 9
9.0%

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

2023-12-10T21:52:02.120387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:52:02.213543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.38226
Minimum0
Maximum98.489
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:02.373883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.737
median16.178
Q352.798
95-th percentile82.3093
Maximum98.489
Range98.489
Interquartile range (IQR)52.061

Descriptive statistics

Standard deviation29.950265
Coefficient of variation (CV)1.0937835
Kurtosis-0.50152408
Mean27.38226
Median Absolute Deviation (MAD)16.178
Skewness0.87616589
Sum2738.226
Variance897.01834
MonotonicityNot monotonic
2023-12-10T21:52:02.508511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 22
22.0%
75.623 4
 
4.0%
6.931 4
 
4.0%
50.711 4
 
4.0%
24.789 3
 
3.0%
0.737 3
 
3.0%
14.655 3
 
3.0%
16.178 3
 
3.0%
75.52 3
 
3.0%
52.798 3
 
3.0%
Other values (38) 48
48.0%
ValueCountFrequency (%)
0.0 22
22.0%
0.733 1
 
1.0%
0.737 3
 
3.0%
0.995 1
 
1.0%
0.997 2
 
2.0%
0.998 1
 
1.0%
0.999 1
 
1.0%
2.531 1
 
1.0%
2.538 2
 
2.0%
6.931 4
 
4.0%
ValueCountFrequency (%)
98.489 1
 
1.0%
98.285 1
 
1.0%
98.082 2
2.0%
82.448 1
 
1.0%
82.302 1
 
1.0%
82.009 1
 
1.0%
75.623 4
4.0%
75.52 3
3.0%
55.671 1
 
1.0%
55.597 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.477
Minimum0
Maximum140.8
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:02.672863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median79.35
Q3100.325
95-th percentile107.915
Maximum140.8
Range140.8
Interquartile range (IQR)100.325

Descriptive statistics

Standard deviation45.952634
Coefficient of variation (CV)0.72392574
Kurtosis-1.373738
Mean63.477
Median Absolute Deviation (MAD)23.05
Skewness-0.32510281
Sum6347.7
Variance2111.6446
MonotonicityNot monotonic
2023-12-10T21:52:02.929362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 26
26.0%
100.4 5
 
5.0%
102.2 4
 
4.0%
77.3 4
 
4.0%
140.8 4
 
4.0%
102.6 3
 
3.0%
63.0 3
 
3.0%
17.6 3
 
3.0%
100.1 3
 
3.0%
100.3 3
 
3.0%
Other values (31) 42
42.0%
ValueCountFrequency (%)
0.0 26
26.0%
16.3 3
 
3.0%
17.6 3
 
3.0%
17.7 2
 
2.0%
63.0 3
 
3.0%
63.2 2
 
2.0%
63.3 1
 
1.0%
63.6 1
 
1.0%
63.7 1
 
1.0%
63.8 2
 
2.0%
ValueCountFrequency (%)
140.8 4
4.0%
140.5 1
 
1.0%
106.2 1
 
1.0%
105.7 1
 
1.0%
104.7 2
2.0%
104.2 1
 
1.0%
103.8 1
 
1.0%
103.5 2
2.0%
102.6 3
3.0%
102.2 4
4.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.52745
Minimum0
Maximum131.618
Zeros25
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:03.070851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2475
median33.6765
Q385.4255
95-th percentile114.65065
Maximum131.618
Range131.618
Interquartile range (IQR)85.178

Descriptive statistics

Standard deviation43.228671
Coefficient of variation (CV)0.94950785
Kurtosis-1.41046
Mean45.52745
Median Absolute Deviation (MAD)33.6765
Skewness0.3786098
Sum4552.745
Variance1868.718
MonotonicityNot monotonic
2023-12-10T21:52:03.219233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
25.0%
13.2 3
 
3.0%
32.691 2
 
2.0%
0.33 2
 
2.0%
9.339 2
 
2.0%
86.9 1
 
1.0%
7.966 1
 
1.0%
131.618 1
 
1.0%
105.593 1
 
1.0%
81.228 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 25
25.0%
0.33 2
 
2.0%
0.59 1
 
1.0%
1.89 1
 
1.0%
3.136 1
 
1.0%
7.966 1
 
1.0%
9.339 2
 
2.0%
9.948 1
 
1.0%
10.197 1
 
1.0%
10.22 1
 
1.0%
ValueCountFrequency (%)
131.618 1
1.0%
123.816 1
1.0%
117.525 1
1.0%
114.88 1
1.0%
114.663 1
1.0%
114.65 1
1.0%
113.808 1
1.0%
112.837 1
1.0%
110.236 1
1.0%
107.242 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.03037
Minimum0
Maximum136.157
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:03.373345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.33
median43.6065
Q383.08725
95-th percentile113.8501
Maximum136.157
Range136.157
Interquartile range (IQR)82.75725

Descriptive statistics

Standard deviation41.058005
Coefficient of variation (CV)0.87301047
Kurtosis-1.3794212
Mean47.03037
Median Absolute Deviation (MAD)39.9435
Skewness0.24658926
Sum4703.037
Variance1685.7598
MonotonicityNot monotonic
2023-12-10T21:52:03.558762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 22
 
22.0%
0.33 5
 
5.0%
13.2 4
 
4.0%
20.7 3
 
3.0%
83.735 2
 
2.0%
81.228 1
 
1.0%
90.865 1
 
1.0%
83.088 1
 
1.0%
10.188 1
 
1.0%
75.062 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 22
22.0%
0.33 5
 
5.0%
8.3 1
 
1.0%
9.948 1
 
1.0%
10.188 1
 
1.0%
10.197 1
 
1.0%
10.215 1
 
1.0%
10.22 1
 
1.0%
13.2 4
 
4.0%
18.87 1
 
1.0%
ValueCountFrequency (%)
136.157 1
1.0%
117.525 1
1.0%
114.88 1
1.0%
114.663 1
1.0%
114.65 1
1.0%
113.808 1
1.0%
112.18 1
1.0%
111.622 1
1.0%
105.748 1
1.0%
105.742 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.372
Minimum1.49
Maximum134.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:03.738695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.49
5-th percentile1.5095
Q18.09
median28.07
Q344.9125
95-th percentile88.4255
Maximum134.72
Range133.23
Interquartile range (IQR)36.8225

Descriptive statistics

Standard deviation33.86475
Coefficient of variation (CV)0.9573886
Kurtosis1.611123
Mean35.372
Median Absolute Deviation (MAD)19.265
Skewness1.3716973
Sum3537.2
Variance1146.8213
MonotonicityNot monotonic
2023-12-10T21:52:03.929742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.8 7
 
7.0%
44.43 6
 
6.0%
85.99 4
 
4.0%
6.05 4
 
4.0%
62.46 4
 
4.0%
134.72 4
 
4.0%
25.53 4
 
4.0%
3.17 3
 
3.0%
28.07 3
 
3.0%
25.52 3
 
3.0%
Other values (42) 58
58.0%
ValueCountFrequency (%)
1.49 3
3.0%
1.5 2
2.0%
1.51 1
 
1.0%
3.16 1
 
1.0%
3.17 3
3.0%
4.26 1
 
1.0%
4.27 3
3.0%
4.45 1
 
1.0%
4.46 2
2.0%
4.86 2
2.0%
ValueCountFrequency (%)
134.72 4
4.0%
134.7 1
 
1.0%
85.99 4
4.0%
80.8 7
7.0%
62.46 4
4.0%
62.45 1
 
1.0%
44.94 1
 
1.0%
44.93 2
 
2.0%
44.92 1
 
1.0%
44.91 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
수하보
7 
칠곡보
7 
죽산보
 
6
구미보
 
6
쾌쾌보
 
6
Other values (17)
68 

Length

Max length5
Median length3
Mean length3.32
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안동보
2nd row쾌쾌보
3rd row수하보
4th row합천창녕보
5th row칠곡보

Common Values

ValueCountFrequency (%)
수하보 7
 
7.0%
칠곡보 7
 
7.0%
죽산보 6
 
6.0%
구미보 6
 
6.0%
쾌쾌보 6
 
6.0%
구담보 5
 
5.0%
세종보 5
 
5.0%
단양수중보 5
 
5.0%
상주보 5
 
5.0%
달성보 4
 
4.0%
Other values (12) 44
44.0%

Length

2023-12-10T21:52:04.121463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수하보 7
 
7.0%
칠곡보 7
 
7.0%
죽산보 6
 
6.0%
구미보 6
 
6.0%
쾌쾌보 6
 
6.0%
구담보 5
 
5.0%
세종보 5
 
5.0%
단양수중보 5
 
5.0%
상주보 5
 
5.0%
승촌보 4
 
4.0%
Other values (12) 44
44.0%

Interactions

2023-12-10T21:52:01.066558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:57.968069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.528952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.045390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.627856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.462863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:01.199952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.075940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.617994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.154986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.719656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.551575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:01.294578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.172495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.708929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.236210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.799922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.631620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:01.382905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.262786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.798330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.326851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.883622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.722201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:01.472752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.355947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.873283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.432453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.978594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.845312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:01.561201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.441555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:58.959781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:51:59.543127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.094092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:00.961283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:52:04.209200image/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)0.0001.0000.7770.5990.8170.8141.000
유입량(ms)0.0000.7771.0000.6910.7310.8001.000
방류량(ms)0.0000.5990.6911.0000.7220.5050.852
저수량(백만m3)0.0000.8170.7310.7221.0000.6720.966
저수율0.0000.8140.8000.5050.6721.0001.000
댐이름0.0001.0001.0000.8520.9661.0001.000
2023-12-10T21:52:04.321592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0550.044-0.0050.0170.0600.000
강우량(mm)0.0551.0000.6980.4340.484-0.3250.921
유입량(ms)0.0440.6981.0000.5680.577-0.0670.916
방류량(ms)-0.0050.4340.5681.0000.880-0.4440.490
저수량(백만m3)0.0170.4840.5770.8801.000-0.4820.774
저수율0.060-0.325-0.067-0.444-0.4821.0000.921
댐이름0.0000.9210.9160.4900.7740.9211.000

Missing values

2023-12-10T21:52:01.684677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:52:01.821597image/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
0202004010100.00.00.00.085.99안동보
1202004010100.00.00.00.044.43쾌쾌보
2202004010100.00.00.00.080.8수하보
32020040101055.58479.5123.81651.519.29합천창녕보
42020040101075.623100.449.46549.46525.53칠곡보
52020040101024.789102.6114.88114.884.27백제보
6202004010100.7370.010.19710.1973.17귤현보
72020040101014.655102.2105.748105.74828.07이포보
82020040101016.17863.09.33920.71.49죽산보
92020040102052.928100.439.25539.25532.53구미보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90202004010600.00.00.00.062.46구담보
912020040106075.52100.320.23248.70425.52칠곡보
92202004010600.00.00.00.044.43쾌쾌보
932020040106011.744104.2106.63394.63333.11여주보
94202004010606.93177.313.213.26.05승촌보
952020040107052.733100.017.14735.2332.5구미보
96202004010700.00.00.00.062.46구담보
972020040107075.52100.348.1648.1625.52칠곡보
982020040107098.08297.279.601136.1574.86창녕함안보
99202004010700.00.00.00.080.8수하보