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

Categorical2
Numeric6

Alerts

댐이름 has constant value ""Constant
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 1 other fieldsHigh correlation
저수율 is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
방류량(ms) has 27 (27.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:44:14.480458
Analysis finished2023-12-10 10:44:20.724906
Duration6.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

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

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강하굿둑 100
100.0%

Length

2023-12-10T19:44:20.824969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:20.977458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 100
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190502 × 1011
Minimum2.0190501 × 1011
Maximum2.0190502 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:21.131975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 1011
5-th percentile2.0190502 × 1011
Q12.0190502 × 1011
median2.0190502 × 1011
Q32.0190502 × 1011
95-th percentile2.0190502 × 1011
Maximum2.0190502 × 1011
Range10090
Interquartile range (IQR)815

Descriptive statistics

Standard deviation1042.8952
Coefficient of variation (CV)5.165276 × 10-9
Kurtosis61.544559
Mean2.0190502 × 1011
Median Absolute Deviation (MAD)410
Skewness-6.9813355
Sum2.0190502 × 1013
Variance1087630.3
MonotonicityNot monotonic
2023-12-10T19:44:21.382559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201905022120 1
 
1.0%
201905021040 1
 
1.0%
201905020900 1
 
1.0%
201905020910 1
 
1.0%
201905020920 1
 
1.0%
201905020930 1
 
1.0%
201905020940 1
 
1.0%
201905020950 1
 
1.0%
201905021000 1
 
1.0%
201905021010 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201905012040 1
1.0%
201905020510 1
1.0%
201905020520 1
1.0%
201905020530 1
1.0%
201905020540 1
1.0%
201905020550 1
1.0%
201905020600 1
1.0%
201905020610 1
1.0%
201905020620 1
1.0%
201905020630 1
1.0%
ValueCountFrequency (%)
201905022130 1
1.0%
201905022120 1
1.0%
201905022110 1
1.0%
201905022100 1
1.0%
201905022050 1
1.0%
201905022040 1
1.0%
201905022030 1
1.0%
201905022020 1
1.0%
201905022010 1
1.0%
201905022000 1
1.0%

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

2023-12-10T19:44:21.613768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:21.762117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.19246
Minimum295.46
Maximum305.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:21.917963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295.46
5-th percentile296.377
Q1298.675
median300.059
Q3301.445
95-th percentile303.78725
Maximum305.625
Range10.165
Interquartile range (IQR)2.77

Descriptive statistics

Standard deviation2.3154213
Coefficient of variation (CV)0.0077131227
Kurtosis-0.42824563
Mean300.19246
Median Absolute Deviation (MAD)1.386
Skewness0.094111392
Sum30019.246
Variance5.3611757
MonotonicityNot monotonic
2023-12-10T19:44:22.119395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
300.059 13
13.0%
300.52 11
 
11.0%
297.755 7
 
7.0%
301.445 7
 
7.0%
298.675 7
 
7.0%
300.983 6
 
6.0%
299.136 5
 
5.0%
303.764 5
 
5.0%
303.299 4
 
4.0%
296.377 4
 
4.0%
Other values (11) 31
31.0%
ValueCountFrequency (%)
295.46 2
 
2.0%
295.918 1
 
1.0%
296.377 4
4.0%
296.836 4
4.0%
297.295 2
 
2.0%
297.755 7
7.0%
298.215 3
3.0%
298.675 7
7.0%
299.136 5
5.0%
299.597 4
4.0%
ValueCountFrequency (%)
305.625 2
 
2.0%
304.229 3
 
3.0%
303.764 5
5.0%
303.299 4
 
4.0%
302.835 2
 
2.0%
302.372 4
 
4.0%
301.908 4
 
4.0%
301.445 7
7.0%
300.983 6
6.0%
300.52 11
11.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.716
Minimum96.2
Maximum99.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:22.320255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96.2
5-th percentile96.5
Q197.2
median97.7
Q398.1
95-th percentile98.905
Maximum99.5
Range3.3
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.75286926
Coefficient of variation (CV)0.0077046672
Kurtosis-0.4281989
Mean97.716
Median Absolute Deviation (MAD)0.5
Skewness0.097920677
Sum9771.6
Variance0.56681212
MonotonicityNot monotonic
2023-12-10T19:44:22.524921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
97.7 13
13.0%
97.8 11
 
11.0%
96.9 7
 
7.0%
98.1 7
 
7.0%
97.2 7
 
7.0%
98.0 6
 
6.0%
97.4 5
 
5.0%
98.9 5
 
5.0%
98.7 4
 
4.0%
96.5 4
 
4.0%
Other values (11) 31
31.0%
ValueCountFrequency (%)
96.2 2
 
2.0%
96.3 1
 
1.0%
96.5 4
4.0%
96.6 4
4.0%
96.8 2
 
2.0%
96.9 7
7.0%
97.1 3
3.0%
97.2 7
7.0%
97.4 5
5.0%
97.5 4
4.0%
ValueCountFrequency (%)
99.5 2
 
2.0%
99.0 3
 
3.0%
98.9 5
5.0%
98.7 4
 
4.0%
98.6 2
 
2.0%
98.4 4
 
4.0%
98.3 4
 
4.0%
98.1 7
7.0%
98.0 6
6.0%
97.8 11
11.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.24757
Minimum0
Maximum3932.273
Zeros27
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:22.790625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median258.5095
Q3774.02725
95-th percentile2073.3859
Maximum3932.273
Range3932.273
Interquartile range (IQR)774.02725

Descriptive statistics

Standard deviation827.05805
Coefficient of variation (CV)1.4059694
Kurtosis5.7336454
Mean588.24757
Median Absolute Deviation (MAD)258.5095
Skewness2.2791083
Sum58824.757
Variance684025.03
MonotonicityNot monotonic
2023-12-10T19:44:23.069785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 27
27.0%
1015.936 1
 
1.0%
259.384 1
 
1.0%
14.872 1
 
1.0%
3545.988 1
 
1.0%
3932.273 1
 
1.0%
3800.386 1
 
1.0%
424.423 1
 
1.0%
1149.007 1
 
1.0%
85.016 1
 
1.0%
Other values (64) 64
64.0%
ValueCountFrequency (%)
0.0 27
27.0%
1.063 1
 
1.0%
1.072 1
 
1.0%
5.899 1
 
1.0%
7.058 1
 
1.0%
14.872 1
 
1.0%
73.594 1
 
1.0%
76.154 1
 
1.0%
84.415 1
 
1.0%
85.016 1
 
1.0%
ValueCountFrequency (%)
3932.273 1
1.0%
3800.386 1
1.0%
3545.988 1
1.0%
3140.508 1
1.0%
2574.358 1
1.0%
2047.019 1
1.0%
1920.961 1
1.0%
1830.564 1
1.0%
1793.454 1
1.0%
1451.703 1
1.0%

저수량(백만m3)
Real number (ℝ)

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.84031
Minimum1
Maximum833.309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:23.330723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q134.1385
median163.491
Q3411.14475
95-th percentile738.0679
Maximum833.309
Range832.309
Interquartile range (IQR)377.00625

Descriptive statistics

Standard deviation236.74652
Coefficient of variation (CV)0.92176542
Kurtosis-0.45065529
Mean256.84031
Median Absolute Deviation (MAD)162.435
Skewness0.7651045
Sum25684.031
Variance56048.913
MonotonicityNot monotonic
2023-12-10T19:44:23.576509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 13
 
13.0%
344.049 1
 
1.0%
780.12 1
 
1.0%
561.261 1
 
1.0%
549.261 1
 
1.0%
558.485 1
 
1.0%
305.101 1
 
1.0%
180.115 1
 
1.0%
218.164 1
 
1.0%
151.548 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
1.0 13
13.0%
1.019 1
 
1.0%
1.027 1
 
1.0%
1.085 1
 
1.0%
1.105 1
 
1.0%
1.106 1
 
1.0%
1.139 1
 
1.0%
1.348 1
 
1.0%
1.472 1
 
1.0%
2.515 1
 
1.0%
ValueCountFrequency (%)
833.309 1
1.0%
781.789 1
1.0%
780.12 1
1.0%
776.39 1
1.0%
770.328 1
1.0%
736.37 1
1.0%
714.647 1
1.0%
673.112 1
1.0%
644.273 1
1.0%
635.753 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8228
Minimum0.72
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:23.819513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.72
5-th percentile0.74
Q10.79
median0.82
Q30.85
95-th percentile0.9005
Maximum0.94
Range0.22
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.050132753
Coefficient of variation (CV)0.060929452
Kurtosis-0.43189019
Mean0.8228
Median Absolute Deviation (MAD)0.03
Skewness0.084892041
Sum82.28
Variance0.0025132929
MonotonicityNot monotonic
2023-12-10T19:44:24.022423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.82 13
13.0%
0.83 11
 
11.0%
0.77 7
 
7.0%
0.85 7
 
7.0%
0.79 7
 
7.0%
0.84 6
 
6.0%
0.8 5
 
5.0%
0.9 5
 
5.0%
0.89 4
 
4.0%
0.74 4
 
4.0%
Other values (11) 31
31.0%
ValueCountFrequency (%)
0.72 2
 
2.0%
0.73 1
 
1.0%
0.74 4
4.0%
0.75 4
4.0%
0.76 2
 
2.0%
0.77 7
7.0%
0.78 3
3.0%
0.79 7
7.0%
0.8 5
5.0%
0.81 4
4.0%
ValueCountFrequency (%)
0.94 2
 
2.0%
0.91 3
 
3.0%
0.9 5
5.0%
0.89 4
 
4.0%
0.88 2
 
2.0%
0.87 4
 
4.0%
0.86 4
 
4.0%
0.85 7
7.0%
0.84 6
6.0%
0.83 11
11.0%

Interactions

2023-12-10T19:44:19.241621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:14.772457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.651670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.586100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.396423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.363779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:19.379896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:14.925785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.796723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.722354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.565418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.493335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:19.515831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.077482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.954851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.873029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.734076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.631979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:19.955011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.216178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.108455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.015931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.915848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.782000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:20.112019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.348991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.292119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.159517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.083093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.945501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:20.254442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:15.491759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:16.443036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:17.288524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:18.227019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:19.090049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:24.165284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.4960.5300.1380.6790.477
강우량(mm)0.4961.0001.0000.5280.7900.999
유입량(ms)0.5301.0001.0000.5250.7920.999
방류량(ms)0.1380.5280.5251.0000.4470.515
저수량(백만m3)0.6790.7900.7920.4471.0000.786
저수율0.4770.9990.9990.5150.7861.000
2023-12-10T19:44:24.330847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.3880.388-0.077-0.2730.388
강우량(mm)0.3881.0001.0000.313-0.2731.000
유입량(ms)0.3881.0001.0000.313-0.2731.000
방류량(ms)-0.0770.3130.3131.000-0.3060.313
저수량(백만m3)-0.273-0.273-0.273-0.3061.000-0.273
저수율0.3881.0001.0000.313-0.2731.000

Missing values

2023-12-10T19:44:20.458649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:44:20.650721image/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
0낙동강하굿둑2019050221200303.29998.785.016344.0490.89
1낙동강하굿둑2019050221100303.29998.784.415341.7970.89
2낙동강하굿둑2019050221000303.76498.973.594343.3680.9
3낙동강하굿둑2019050220500303.76498.95.899342.2460.9
4낙동강하굿둑2019050220400303.76498.9150.53309.8360.9
5낙동강하굿둑2019050220300304.22999.0563.863140.2840.91
6낙동강하굿둑2019050220200304.22999.01032.8241.4720.91
7낙동강하굿둑2019050220100303.76498.9774.4511.00.9
8낙동강하굿둑2019050220000303.29998.7773.8861.00.89
9낙동강하굿둑2019050219500302.37298.41029.4971.0190.87
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑2019050206300300.05997.71920.961130.5720.82
91낙동강하굿둑2019050206200298.67597.21302.85112.840.79
92낙동강하굿둑2019050206100298.21597.11451.703377.8750.78
93낙동강하굿둑2019050206000296.83696.6721.412452.3380.75
94낙동강하굿둑2019050205500296.83696.6726.436461.8970.75
95낙동강하굿둑2019050205400296.37796.5734.945485.170.74
96낙동강하굿둑2019050205300296.37796.5740.586467.4080.74
97낙동강하굿둑2019050205200296.37796.51000.276487.9240.74
98낙동강하굿둑2019050205100295.91896.3753.549502.0950.73
99낙동강하굿둑2019050221300302.83598.60.0343.7050.88