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
일자/시간(t) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
방류량(ms) has 9 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:30:19.096602
Analysis finished2023-12-10 10:30:26.050282
Duration6.95 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
군남
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 100
100.0%

Length

2023-12-10T19:30:26.158925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:30:26.302916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190428 × 109
Minimum2.0190419 × 109
Maximum2.019043 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:26.506063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190419 × 109
5-th percentile2.0190419 × 109
Q12.0190427 × 109
median2.0190428 × 109
Q32.0190429 × 109
95-th percentile2.019043 × 109
Maximum2.019043 × 109
Range1118
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation252.69567
Coefficient of variation (CV)1.2515617 × 10-7
Kurtosis7.3902384
Mean2.0190428 × 109
Median Absolute Deviation (MAD)101
Skewness-2.6578989
Sum2.0190428 × 1011
Variance63855.101
MonotonicityNot monotonic
2023-12-10T19:30:26.753429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019042816 1
 
1.0%
2019041908 1
 
1.0%
2019043008 1
 
1.0%
2019043011 1
 
1.0%
2019043012 1
 
1.0%
2019043014 1
 
1.0%
2019043015 1
 
1.0%
2019043019 1
 
1.0%
2019043020 1
 
1.0%
2019043022 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019041906 1
1.0%
2019041907 1
1.0%
2019041908 1
1.0%
2019041909 1
1.0%
2019041910 1
1.0%
2019041911 1
1.0%
2019042703 1
1.0%
2019042704 1
1.0%
2019042705 1
1.0%
2019042706 1
1.0%
ValueCountFrequency (%)
2019043024 1
1.0%
2019043023 1
1.0%
2019043022 1
1.0%
2019043021 1
1.0%
2019043020 1
1.0%
2019043019 1
1.0%
2019043018 1
1.0%
2019043017 1
1.0%
2019043016 1
1.0%
2019043015 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

2023-12-10T19:30:26.984045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:30:27.474578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.07113
Minimum8.797
Maximum13.965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:27.670381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.797
5-th percentile8.94385
Q19.393
median9.852
Q310.24075
95-th percentile13.935
Maximum13.965
Range5.168
Interquartile range (IQR)0.84775

Descriptive statistics

Standard deviation1.1029311
Coefficient of variation (CV)0.10951414
Kurtosis6.9450378
Mean10.07113
Median Absolute Deviation (MAD)0.433
Skewness2.5161687
Sum1007.113
Variance1.216457
MonotonicityNot monotonic
2023-12-10T19:30:27.921394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.213 10
 
10.0%
9.393 6
 
6.0%
9.829 6
 
6.0%
9.852 5
 
5.0%
10.631 5
 
5.0%
13.965 3
 
3.0%
13.935 3
 
3.0%
9.807 2
 
2.0%
8.797 2
 
2.0%
9.372 2
 
2.0%
Other values (55) 56
56.0%
ValueCountFrequency (%)
8.797 2
2.0%
8.818 1
1.0%
8.861 1
1.0%
8.903 1
1.0%
8.946 1
1.0%
8.967 1
1.0%
8.988 1
1.0%
9.053 1
1.0%
9.117 1
1.0%
9.139 2
2.0%
ValueCountFrequency (%)
13.965 3
3.0%
13.935 3
3.0%
10.631 5
5.0%
10.607 1
 
1.0%
10.584 1
 
1.0%
10.56 1
 
1.0%
10.537 1
 
1.0%
10.513 1
 
1.0%
10.49 1
 
1.0%
10.467 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.075
Minimum12.3
Maximum19.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:28.134839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.3
5-th percentile12.495
Q113.1
median13.8
Q314.3
95-th percentile19.5
Maximum19.5
Range7.2
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.5463463
Coefficient of variation (CV)0.10986475
Kurtosis6.8509657
Mean14.075
Median Absolute Deviation (MAD)0.6
Skewness2.4954439
Sum1407.5
Variance2.3911869
MonotonicityNot monotonic
2023-12-10T19:30:28.460672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
14.3 12
 
12.0%
13.7 9
 
9.0%
13.1 8
 
8.0%
13.8 7
 
7.0%
19.5 6
 
6.0%
14.9 5
 
5.0%
14.0 4
 
4.0%
14.4 3
 
3.0%
13.5 3
 
3.0%
13.2 3
 
3.0%
Other values (18) 40
40.0%
ValueCountFrequency (%)
12.3 3
 
3.0%
12.4 2
 
2.0%
12.5 2
 
2.0%
12.6 2
 
2.0%
12.7 1
 
1.0%
12.8 3
 
3.0%
12.9 2
 
2.0%
13.0 3
 
3.0%
13.1 8
8.0%
13.2 3
 
3.0%
ValueCountFrequency (%)
19.5 6
6.0%
14.9 5
5.0%
14.8 3
 
3.0%
14.7 3
 
3.0%
14.6 2
 
2.0%
14.5 2
 
2.0%
14.4 3
 
3.0%
14.3 12
12.0%
14.2 3
 
3.0%
14.1 2
 
2.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.44538
Minimum0
Maximum16.257
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:28.778735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.88125
median4.086
Q310.15
95-th percentile13.5
Maximum16.257
Range16.257
Interquartile range (IQR)6.26875

Descriptive statistics

Standard deviation4.2623419
Coefficient of variation (CV)0.66130187
Kurtosis-0.80759476
Mean6.44538
Median Absolute Deviation (MAD)3.456
Skewness0.33517512
Sum644.538
Variance18.167558
MonotonicityNot monotonic
2023-12-10T19:30:29.079840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
10.3 9
 
9.0%
13.5 6
 
6.0%
10.0 5
 
5.0%
10.6 5
 
5.0%
3.922 4
 
4.0%
7.6 4
 
4.0%
3.906 3
 
3.0%
4.011 3
 
3.0%
10.1 3
 
3.0%
Other values (43) 49
49.0%
ValueCountFrequency (%)
0.0 9
9.0%
1.32 1
 
1.0%
1.322 1
 
1.0%
1.34 1
 
1.0%
1.49 1
 
1.0%
1.57 1
 
1.0%
1.604 1
 
1.0%
1.606 1
 
1.0%
1.628 1
 
1.0%
1.656 1
 
1.0%
ValueCountFrequency (%)
16.257 1
 
1.0%
15.794 1
 
1.0%
15.767 1
 
1.0%
13.5 6
6.0%
13.2 1
 
1.0%
13.0 1
 
1.0%
10.6 5
5.0%
10.3 9
9.0%
10.1 3
 
3.0%
10.0 5
5.0%

저수량(백만m3)
Real number (ℝ)

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.11667
Minimum7.6
Maximum13.795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:29.360369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q110.1
median10.4
Q313.2
95-th percentile13.55915
Maximum13.795
Range6.195
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.6993639
Coefficient of variation (CV)0.15286627
Kurtosis-0.73113844
Mean11.11667
Median Absolute Deviation (MAD)0.399
Skewness0.15344784
Sum1111.667
Variance2.8878376
MonotonicityNot monotonic
2023-12-10T19:30:29.620353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
10.3 13
13.0%
10.0 9
 
9.0%
10.6 9
 
9.0%
13.5 8
 
8.0%
10.1 7
 
7.0%
7.6 6
 
6.0%
10.2 5
 
5.0%
10.4 4
 
4.0%
10.5 4
 
4.0%
13.2 4
 
4.0%
Other values (27) 31
31.0%
ValueCountFrequency (%)
7.6 6
6.0%
9.9 1
 
1.0%
9.963 1
 
1.0%
10.0 9
9.0%
10.002 1
 
1.0%
10.02 1
 
1.0%
10.09 1
 
1.0%
10.1 7
7.0%
10.138 1
 
1.0%
10.2 5
5.0%
ValueCountFrequency (%)
13.795 1
 
1.0%
13.7 2
 
2.0%
13.645 1
 
1.0%
13.6 1
 
1.0%
13.557 1
 
1.0%
13.5 8
8.0%
13.465 1
 
1.0%
13.4 2
 
2.0%
13.348 1
 
1.0%
13.3 2
 
2.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.3497
Minimum28.78
Maximum30.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:30:29.914863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.78
5-th percentile28.849
Q129.06
median29.27
Q329.4425
95-th percentile30.86
Maximum30.87
Range2.09
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.4454766
Coefficient of variation (CV)0.015178234
Kurtosis5.7974707
Mean29.3497
Median Absolute Deviation (MAD)0.195
Skewness2.2144172
Sum2934.97
Variance0.1984494
MonotonicityNot monotonic
2023-12-10T19:30:30.271474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.43 10
 
10.0%
29.06 6
 
6.0%
29.26 6
 
6.0%
29.27 5
 
5.0%
29.61 5
 
5.0%
30.87 3
 
3.0%
30.86 3
 
3.0%
29.25 2
 
2.0%
28.78 2
 
2.0%
29.05 2
 
2.0%
Other values (55) 56
56.0%
ValueCountFrequency (%)
28.78 2
2.0%
28.79 1
1.0%
28.81 1
1.0%
28.83 1
1.0%
28.85 1
1.0%
28.86 1
1.0%
28.87 1
1.0%
28.9 1
1.0%
28.93 1
1.0%
28.94 2
2.0%
ValueCountFrequency (%)
30.87 3
3.0%
30.86 3
3.0%
29.61 5
5.0%
29.6 1
 
1.0%
29.59 1
 
1.0%
29.58 1
 
1.0%
29.57 1
 
1.0%
29.56 1
 
1.0%
29.55 1
 
1.0%
29.54 1
 
1.0%

Interactions

2023-12-10T19:30:24.948992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:19.467278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:20.467048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:21.697540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.742760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:23.855984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:25.081332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:19.635252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:20.618529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:21.941393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.880182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:24.055448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:25.202861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:19.786606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:20.780157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.113907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:23.036772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:24.218417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:25.344402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:19.983100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:21.001002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.286117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:23.357631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:24.552210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:25.478521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:20.190140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:21.272041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.444017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:23.512907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:24.688956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:25.600366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:20.344243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:21.511474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:22.596830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:23.672833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:24.826751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:30:30.447802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8690.8700.8610.9170.891
강우량(mm)0.8691.0001.0000.6260.8480.978
유입량(ms)0.8701.0001.0000.6360.8490.976
방류량(ms)0.8610.6260.6361.0000.6980.697
저수량(백만m3)0.9170.8480.8490.6981.0000.859
저수율0.8910.9780.9760.6970.8591.000
2023-12-10T19:30:30.656032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.997-0.996-0.2150.489-0.997
강우량(mm)-0.9971.0000.9990.224-0.4871.000
유입량(ms)-0.9960.9991.0000.232-0.4890.999
방류량(ms)-0.2150.2240.2321.0000.0180.224
저수량(백만m3)0.489-0.487-0.4890.0181.000-0.487
저수율-0.9971.0000.9990.224-0.4871.000

Missing values

2023-12-10T19:30:25.758344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:30:25.971732image/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
0군남2019042816010.00814.03.92210.229.34
1군남2019042705010.63114.910.610.629.61
2군남201904292409.39313.113.513.529.06
3군남201904281709.98614.03.97810.229.33
4군남2019042721010.25814.34.01110.429.45
5군남2019042706010.63114.910.610.629.61
6군남201904301609.05312.60.013.19228.9
7군남201904300109.39313.113.513.529.06
8군남201904290909.80713.73.83310.029.25
9군남201904281809.96313.93.88810.13829.32
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남201904290109.82913.710.010.029.26
91군남201904282409.82913.710.010.029.26
92군남201904282209.85213.83.90610.129.27
93군남201904282109.87413.83.90610.129.28
94군남2019042815010.03114.03.92210.229.35
95군남2019042814010.05314.03.92210.229.36
96군남2019042812010.09914.10.010.29829.38
97군남2019042811010.14414.23.96710.329.4
98군남2019042703010.63114.910.610.629.61
99군남2019042704010.63114.910.610.629.61