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
저수량(백만m3) has unique valuesUnique
방류량(ms) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:52:57.660607
Analysis finished2023-12-10 11:53:03.276944
Duration5.62 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

2023-12-10T20:53:03.381567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:03.517759image/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%
Mean2.0190511 × 109
Minimum2.0190509 × 109
Maximum2.0190513 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:03.692054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190509 × 109
5-th percentile2.0190509 × 109
Q12.019051 × 109
median2.0190511 × 109
Q32.0190512 × 109
95-th percentile2.0190513 × 109
Maximum2.0190513 × 109
Range403
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation125.02027
Coefficient of variation (CV)6.1920312 × 10-8
Kurtosis-1.0966527
Mean2.0190511 × 109
Median Absolute Deviation (MAD)101
Skewness-0.01200353
Sum2.0190511 × 1011
Variance15630.069
MonotonicityNot monotonic
2023-12-10T20:53:03.932746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019051314 1
 
1.0%
2019051022 1
 
1.0%
2019051012 1
 
1.0%
2019051013 1
 
1.0%
2019051014 1
 
1.0%
2019051015 1
 
1.0%
2019051016 1
 
1.0%
2019051017 1
 
1.0%
2019051018 1
 
1.0%
2019051019 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019050912 1
1.0%
2019050913 1
1.0%
2019050914 1
1.0%
2019050915 1
1.0%
2019050916 1
1.0%
2019050917 1
1.0%
2019050918 1
1.0%
2019050919 1
1.0%
2019050920 1
1.0%
2019050921 1
1.0%
ValueCountFrequency (%)
2019051315 1
1.0%
2019051314 1
1.0%
2019051313 1
1.0%
2019051312 1
1.0%
2019051311 1
1.0%
2019051310 1
1.0%
2019051309 1
1.0%
2019051308 1
1.0%
2019051307 1
1.0%
2019051306 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

2023-12-10T20:53:04.248349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:04.389552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.03456
Minimum299.136
Maximum303.299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:04.538198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum299.136
5-th percentile299.136
Q1300.40475
median300.983
Q3301.908
95-th percentile302.835
Maximum303.299
Range4.163
Interquartile range (IQR)1.50325

Descriptive statistics

Standard deviation1.0758447
Coefficient of variation (CV)0.0035738244
Kurtosis-0.74024813
Mean301.03456
Median Absolute Deviation (MAD)0.924
Skewness-0.0095673889
Sum30103.456
Variance1.1574417
MonotonicityNot monotonic
2023-12-10T20:53:04.720542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
300.983 20
20.0%
301.445 15
15.0%
302.372 15
15.0%
300.52 13
13.0%
300.059 9
9.0%
299.597 8
 
8.0%
299.136 8
 
8.0%
301.908 6
 
6.0%
302.835 4
 
4.0%
303.299 2
 
2.0%
ValueCountFrequency (%)
299.136 8
 
8.0%
299.597 8
 
8.0%
300.059 9
9.0%
300.52 13
13.0%
300.983 20
20.0%
301.445 15
15.0%
301.908 6
 
6.0%
302.372 15
15.0%
302.835 4
 
4.0%
303.299 2
 
2.0%
ValueCountFrequency (%)
303.299 2
 
2.0%
302.835 4
 
4.0%
302.372 15
15.0%
301.908 6
 
6.0%
301.445 15
15.0%
300.983 20
20.0%
300.52 13
13.0%
300.059 9
9.0%
299.597 8
 
8.0%
299.136 8
 
8.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.99
Minimum97.4
Maximum98.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:04.994445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.4
5-th percentile97.4
Q197.775
median98
Q398.3
95-th percentile98.6
Maximum98.7
Range1.3
Interquartile range (IQR)0.525

Descriptive statistics

Standard deviation0.34392271
Coefficient of variation (CV)0.0035097735
Kurtosis-0.74986967
Mean97.99
Median Absolute Deviation (MAD)0.3
Skewness-0.0092733631
Sum9799
Variance0.11828283
MonotonicityNot monotonic
2023-12-10T20:53:05.195317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
98.0 20
20.0%
98.1 15
15.0%
98.4 15
15.0%
97.8 13
13.0%
97.7 9
9.0%
97.5 8
 
8.0%
97.4 8
 
8.0%
98.3 6
 
6.0%
98.6 4
 
4.0%
98.7 2
 
2.0%
ValueCountFrequency (%)
97.4 8
 
8.0%
97.5 8
 
8.0%
97.7 9
9.0%
97.8 13
13.0%
98.0 20
20.0%
98.1 15
15.0%
98.3 6
 
6.0%
98.4 15
15.0%
98.6 4
 
4.0%
98.7 2
 
2.0%
ValueCountFrequency (%)
98.7 2
 
2.0%
98.6 4
 
4.0%
98.4 15
15.0%
98.3 6
 
6.0%
98.1 15
15.0%
98.0 20
20.0%
97.8 13
13.0%
97.7 9
9.0%
97.5 8
 
8.0%
97.4 8
 
8.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.03605
Minimum0
Maximum781.868
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:05.458149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.602
median61.8655
Q3179.9585
95-th percentile399.9356
Maximum781.868
Range781.868
Interquartile range (IQR)148.3565

Descriptive statistics

Standard deviation147.88492
Coefficient of variation (CV)1.2118134
Kurtosis4.977068
Mean122.03605
Median Absolute Deviation (MAD)61.8655
Skewness1.9921118
Sum12203.605
Variance21869.95
MonotonicityNot monotonic
2023-12-10T20:53:05.708173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
188.378 1
 
1.0%
226.282 1
 
1.0%
433.852 1
 
1.0%
56.342 1
 
1.0%
53.405 1
 
1.0%
168.858 1
 
1.0%
185.195 1
 
1.0%
216.616 1
 
1.0%
100.353 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
0.0 20
20.0%
1.16 1
 
1.0%
1.318 1
 
1.0%
1.493 1
 
1.0%
30.652 1
 
1.0%
31.422 1
 
1.0%
31.662 1
 
1.0%
31.71 1
 
1.0%
31.996 1
 
1.0%
32.036 1
 
1.0%
ValueCountFrequency (%)
781.868 1
1.0%
659.536 1
1.0%
584.518 1
1.0%
433.852 1
1.0%
413.608 1
1.0%
399.216 1
1.0%
358.361 1
1.0%
330.979 1
1.0%
309.681 1
1.0%
303.795 1
1.0%

저수량(백만m3)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.40463
Minimum1.16
Maximum312.553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:05.985180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.16
5-th percentile29.20935
Q133.3775
median72.2255
Q3122.317
95-th percentile276.58395
Maximum312.553
Range311.393
Interquartile range (IQR)88.9395

Descriptive statistics

Standard deviation85.521466
Coefficient of variation (CV)0.87800206
Kurtosis0.37165873
Mean97.40463
Median Absolute Deviation (MAD)38.892
Skewness1.2457739
Sum9740.463
Variance7313.9212
MonotonicityNot monotonic
2023-12-10T20:53:06.263490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.295 1
 
1.0%
88.2 1
 
1.0%
176.158 1
 
1.0%
185.231 1
 
1.0%
182.211 1
 
1.0%
168.858 1
 
1.0%
185.195 1
 
1.0%
127.302 1
 
1.0%
87.894 1
 
1.0%
100.353 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.16 1
1.0%
1.216 1
1.0%
1.318 1
1.0%
1.493 1
1.0%
1.799 1
1.0%
30.652 1
1.0%
31.299 1
1.0%
31.422 1
1.0%
31.662 1
1.0%
31.71 1
1.0%
ValueCountFrequency (%)
312.553 1
1.0%
309.681 1
1.0%
303.795 1
1.0%
285.241 1
1.0%
284.886 1
1.0%
276.147 1
1.0%
274.647 1
1.0%
273.991 1
1.0%
273.757 1
1.0%
270.716 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8411
Minimum0.8
Maximum0.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:06.494081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.8
Q10.8275
median0.84
Q30.86
95-th percentile0.88
Maximum0.89
Range0.09
Interquartile range (IQR)0.0325

Descriptive statistics

Standard deviation0.023263749
Coefficient of variation (CV)0.02765872
Kurtosis-0.74033091
Mean0.8411
Median Absolute Deviation (MAD)0.02
Skewness-0.013052432
Sum84.11
Variance0.00054120202
MonotonicityNot monotonic
2023-12-10T20:53:06.690135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.84 20
20.0%
0.85 15
15.0%
0.87 15
15.0%
0.83 13
13.0%
0.82 9
9.0%
0.81 8
 
8.0%
0.8 8
 
8.0%
0.86 6
 
6.0%
0.88 4
 
4.0%
0.89 2
 
2.0%
ValueCountFrequency (%)
0.8 8
 
8.0%
0.81 8
 
8.0%
0.82 9
9.0%
0.83 13
13.0%
0.84 20
20.0%
0.85 15
15.0%
0.86 6
 
6.0%
0.87 15
15.0%
0.88 4
 
4.0%
0.89 2
 
2.0%
ValueCountFrequency (%)
0.89 2
 
2.0%
0.88 4
 
4.0%
0.87 15
15.0%
0.86 6
 
6.0%
0.85 15
15.0%
0.84 20
20.0%
0.83 13
13.0%
0.82 9
9.0%
0.81 8
 
8.0%
0.8 8
 
8.0%

Interactions

2023-12-10T20:53:02.258471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:57.924700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.773840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.460136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.260299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.432191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.386559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.091497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.909477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.587818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.378732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.625195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.507462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.228418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.013088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.721040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.500338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.747338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.634165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.386047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.136572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.840787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.643248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.873040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.765393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.518666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.262660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.014357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.780233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.010125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.896688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:58.647962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:52:59.360916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.147841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:00.927774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.151502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:53:06.832442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.6530.6390.3610.6420.637
강우량(mm)0.6531.0001.0000.3570.7271.000
유입량(ms)0.6391.0001.0000.2030.5751.000
방류량(ms)0.3610.3570.2031.0000.7000.276
저수량(백만m3)0.6420.7270.5750.7001.0000.603
저수율0.6371.0001.0000.2760.6031.000
2023-12-10T20:53:07.031842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.303-0.303-0.039-0.061-0.303
강우량(mm)-0.3031.0001.0000.3100.2031.000
유입량(ms)-0.3031.0001.0000.3100.2031.000
방류량(ms)-0.0390.3100.3101.0000.4170.310
저수량(백만m3)-0.0610.2030.2030.4171.0000.203
저수율-0.3031.0001.0000.3100.2031.000

Missing values

2023-12-10T20:53:03.043301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:53:03.212862image/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
0낙동강하굿둑20190513140299.59797.5188.37860.2950.81
1낙동강하굿둑20190513130299.13697.474.38774.3870.8
2낙동강하굿둑20190513120299.13697.476.29576.2950.8
3낙동강하굿둑20190513110299.13697.40.089.0580.8
4낙동강하굿둑20190513100300.98398.00.0108.8080.84
5낙동강하굿둑20190513090301.44598.1121.609121.6090.85
6낙동강하굿둑20190513080301.44598.1195.695195.6950.85
7낙동강하굿둑20190513070301.44598.1273.991273.9910.85
8낙동강하굿둑20190513060301.44598.1273.757273.7570.85
9낙동강하굿둑20190513050301.44598.1399.216270.7160.85
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑20190509200300.98398.0584.51871.5460.84
91낙동강하굿둑20190509190299.13697.496.11796.1170.8
92낙동강하굿둑20190509180299.13697.40.0103.1850.8
93낙동강하굿둑20190509170300.98398.00.0124.4410.84
94낙동강하굿둑20190509160301.44598.1272.66144.160.85
95낙동강하굿둑20190509150300.98398.0659.536274.6470.84
96낙동강하굿둑20190509140299.59797.5309.681309.6810.81
97낙동강하굿둑20190509130299.59797.5303.795303.7950.81
98낙동강하굿둑20190509120299.59797.50.0312.5530.81
99낙동강하굿둑20190513150299.59797.571.41671.4160.81