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 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:53:08.470767
Analysis finished2023-12-10 11:53:15.440460
Duration6.97 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

2023-12-10T20:53:15.547010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:15.705964image/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%
Mean2.0190413 × 109
Minimum2.0190411 × 109
Maximum2.0190415 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:15.897611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190411 × 109
5-th percentile2.0190411 × 109
Q12.0190412 × 109
median2.0190413 × 109
Q32.0190414 × 109
95-th percentile2.0190415 × 109
Maximum2.0190415 × 109
Range403
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation124.27383
Coefficient of variation (CV)6.1550909 × 10-8
Kurtosis-1.1039746
Mean2.0190413 × 109
Median Absolute Deviation (MAD)101
Skewness0.046082704
Sum2.0190413 × 1011
Variance15443.984
MonotonicityNot monotonic
2023-12-10T20:53:16.182378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019041509 1
 
1.0%
2019041217 1
 
1.0%
2019041207 1
 
1.0%
2019041208 1
 
1.0%
2019041209 1
 
1.0%
2019041210 1
 
1.0%
2019041211 1
 
1.0%
2019041212 1
 
1.0%
2019041213 1
 
1.0%
2019041214 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019041107 1
1.0%
2019041108 1
1.0%
2019041109 1
1.0%
2019041110 1
1.0%
2019041111 1
1.0%
2019041112 1
1.0%
2019041113 1
1.0%
2019041114 1
1.0%
2019041115 1
1.0%
2019041116 1
1.0%
ValueCountFrequency (%)
2019041510 1
1.0%
2019041509 1
1.0%
2019041508 1
1.0%
2019041507 1
1.0%
2019041506 1
1.0%
2019041505 1
1.0%
2019041504 1
1.0%
2019041503 1
1.0%
2019041502 1
1.0%
2019041501 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

2023-12-10T20:53:16.437211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:16.577744image/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%
Mean300.88173
Minimum299.136
Maximum303.764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:16.719145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum299.136
5-th percentile299.597
Q1300.52
median300.983
Q3301.445
95-th percentile302.372
Maximum303.764
Range4.628
Interquartile range (IQR)0.925

Descriptive statistics

Standard deviation0.97218067
Coefficient of variation (CV)0.0032311057
Kurtosis1.1397303
Mean300.88173
Median Absolute Deviation (MAD)0.463
Skewness0.74359547
Sum30088.173
Variance0.94513525
MonotonicityNot monotonic
2023-12-10T20:53:16.936564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
300.52 25
25.0%
300.983 24
24.0%
301.445 14
14.0%
299.597 11
11.0%
300.059 7
 
7.0%
302.372 7
 
7.0%
299.136 4
 
4.0%
301.908 4
 
4.0%
303.764 3
 
3.0%
303.299 1
 
1.0%
ValueCountFrequency (%)
299.136 4
 
4.0%
299.597 11
11.0%
300.059 7
 
7.0%
300.52 25
25.0%
300.983 24
24.0%
301.445 14
14.0%
301.908 4
 
4.0%
302.372 7
 
7.0%
303.299 1
 
1.0%
303.764 3
 
3.0%
ValueCountFrequency (%)
303.764 3
 
3.0%
303.299 1
 
1.0%
302.372 7
 
7.0%
301.908 4
 
4.0%
301.445 14
14.0%
300.983 24
24.0%
300.52 25
25.0%
300.059 7
 
7.0%
299.597 11
11.0%
299.136 4
 
4.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.938
Minimum97.4
Maximum98.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:17.107979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.4
5-th percentile97.5
Q197.8
median98
Q398.1
95-th percentile98.4
Maximum98.9
Range1.5
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.31743288
Coefficient of variation (CV)0.0032411616
Kurtosis1.214831
Mean97.938
Median Absolute Deviation (MAD)0.2
Skewness0.75768727
Sum9793.8
Variance0.10076364
MonotonicityNot monotonic
2023-12-10T20:53:17.307926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
97.8 25
25.0%
98.0 24
24.0%
98.1 14
14.0%
97.5 11
11.0%
97.7 7
 
7.0%
98.4 7
 
7.0%
97.4 4
 
4.0%
98.3 4
 
4.0%
98.9 3
 
3.0%
98.7 1
 
1.0%
ValueCountFrequency (%)
97.4 4
 
4.0%
97.5 11
11.0%
97.7 7
 
7.0%
97.8 25
25.0%
98.0 24
24.0%
98.1 14
14.0%
98.3 4
 
4.0%
98.4 7
 
7.0%
98.7 1
 
1.0%
98.9 3
 
3.0%
ValueCountFrequency (%)
98.9 3
 
3.0%
98.7 1
 
1.0%
98.4 7
 
7.0%
98.3 4
 
4.0%
98.1 14
14.0%
98.0 24
24.0%
97.8 25
25.0%
97.7 7
 
7.0%
97.5 11
11.0%
97.4 4
 
4.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.40456
Minimum0
Maximum490.624
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:17.560348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q191.28875
median157.0065
Q3276.5955
95-th percentile441.6747
Maximum490.624
Range490.624
Interquartile range (IQR)185.30675

Descriptive statistics

Standard deviation126.57173
Coefficient of variation (CV)0.66826127
Kurtosis-0.46626512
Mean189.40456
Median Absolute Deviation (MAD)81.6325
Skewness0.46826592
Sum18940.456
Variance16020.403
MonotonicityNot monotonic
2023-12-10T20:53:17.823536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
157.264 1
 
1.0%
100.907 1
 
1.0%
76.677 1
 
1.0%
202.315 1
 
1.0%
201.52 1
 
1.0%
331.677 1
 
1.0%
463.629 1
 
1.0%
344.942 1
 
1.0%
215.638 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 6
6.0%
16.92 1
 
1.0%
18.536 1
 
1.0%
19.16 1
 
1.0%
21.716 1
 
1.0%
24.419 1
 
1.0%
25.629 1
 
1.0%
37.937 1
 
1.0%
39.506 1
 
1.0%
47.853 1
 
1.0%
ValueCountFrequency (%)
490.624 1
1.0%
471.512 1
1.0%
467.791 1
1.0%
463.629 1
1.0%
454.038 1
1.0%
441.024 1
1.0%
407.741 1
1.0%
380.877 1
1.0%
367.304 1
1.0%
357.948 1
1.0%

저수량(백만m3)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.05511
Minimum47.364
Maximum276.657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:18.075599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.364
5-th percentile78.03415
Q1151.76975
median188.6945
Q3219.8795
95-th percentile241.28445
Maximum276.657
Range229.293
Interquartile range (IQR)68.10975

Descriptive statistics

Standard deviation50.305159
Coefficient of variation (CV)0.27784446
Kurtosis0.54977646
Mean181.05511
Median Absolute Deviation (MAD)35.413
Skewness-0.77778965
Sum18105.511
Variance2530.609
MonotonicityNot monotonic
2023-12-10T20:53:18.329875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.264 1
 
1.0%
214.003 1
 
1.0%
207.914 1
 
1.0%
204.76 1
 
1.0%
202.315 1
 
1.0%
201.52 1
 
1.0%
203.594 1
 
1.0%
207.157 1
 
1.0%
216.525 1
 
1.0%
215.638 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
47.364 1
1.0%
47.676 1
1.0%
49.036 1
1.0%
50.984 1
1.0%
51.912 1
1.0%
79.409 1
1.0%
91.295 1
1.0%
92.149 1
1.0%
107.149 1
1.0%
128.638 1
1.0%
ValueCountFrequency (%)
276.657 1
1.0%
272.064 1
1.0%
257.256 1
1.0%
252.46 1
1.0%
241.559 1
1.0%
241.27 1
1.0%
238.276 1
1.0%
237.737 1
1.0%
235.83 1
1.0%
234.088 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8378
Minimum0.8
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:18.533950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.81
Q10.83
median0.84
Q30.85
95-th percentile0.87
Maximum0.9
Range0.1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.021013704
Coefficient of variation (CV)0.025082005
Kurtosis1.1266885
Mean0.8378
Median Absolute Deviation (MAD)0.01
Skewness0.73710777
Sum83.78
Variance0.00044157576
MonotonicityNot monotonic
2023-12-10T20:53:18.728275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.83 25
25.0%
0.84 24
24.0%
0.85 14
14.0%
0.81 11
11.0%
0.82 7
 
7.0%
0.87 7
 
7.0%
0.8 4
 
4.0%
0.86 4
 
4.0%
0.9 3
 
3.0%
0.89 1
 
1.0%
ValueCountFrequency (%)
0.8 4
 
4.0%
0.81 11
11.0%
0.82 7
 
7.0%
0.83 25
25.0%
0.84 24
24.0%
0.85 14
14.0%
0.86 4
 
4.0%
0.87 7
 
7.0%
0.89 1
 
1.0%
0.9 3
 
3.0%
ValueCountFrequency (%)
0.9 3
 
3.0%
0.89 1
 
1.0%
0.87 7
 
7.0%
0.86 4
 
4.0%
0.85 14
14.0%
0.84 24
24.0%
0.83 25
25.0%
0.82 7
 
7.0%
0.81 11
11.0%
0.8 4
 
4.0%

Interactions

2023-12-10T20:53:14.100866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.868792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.953971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.846285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.204440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.171064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.295566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.088189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.132640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.052075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.405139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.329932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.444914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.266897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.259754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.214362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.555809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.467448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.615413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.460221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.406210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.403735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.723905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.641463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.766798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.622873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.539811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.899419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.878688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.783627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.918458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.771487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.679663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.045284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.023396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.929430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:53:18.874061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.6480.6230.5820.6760.660
강우량(mm)0.6481.0001.0000.3920.3811.000
유입량(ms)0.6231.0001.0000.3980.4270.973
방류량(ms)0.5820.3920.3981.0000.8020.299
저수량(백만m3)0.6760.3810.4270.8021.0000.469
저수율0.6601.0000.9730.2990.4691.000
2023-12-10T20:53:19.042253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.0810.081-0.025-0.2690.081
강우량(mm)0.0811.0001.0000.4160.4461.000
유입량(ms)0.0811.0001.0000.4160.4461.000
방류량(ms)-0.0250.4160.4161.0000.4050.416
저수량(백만m3)-0.2690.4460.4460.4051.0000.446
저수율0.0811.0001.0000.4160.4461.000

Missing values

2023-12-10T20:53:15.136114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:53:15.357404image/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
0낙동강하굿둑20190415090301.44598.1157.264157.2640.85
1낙동강하굿둑20190415080301.44598.1157.232157.2320.85
2낙동강하굿둑20190415070301.44598.1157.076157.0760.85
3낙동강하굿둑20190415060301.44598.1284.257155.7570.85
4낙동강하굿둑20190415050300.98398.0155.2155.20.84
5낙동강하굿둑20190415040300.98398.0155.288155.2880.84
6낙동강하굿둑20190415030300.98398.0155.784155.7840.84
7낙동강하굿둑20190415020300.98398.0154.716154.7160.84
8낙동강하굿둑20190415010300.98398.0281.43153.0130.84
9낙동강하굿둑20190414240300.5297.8151.802151.8020.83
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑20190411150301.44598.1187.933187.9330.85
91낙동강하굿둑20190411140301.44598.1454.038197.1210.85
92낙동강하굿둑20190411130300.5297.8104.951233.3680.83
93낙동강하굿둑20190411120300.98398.0109.237237.7370.84
94낙동강하굿둑20190411110301.44598.1319.265190.7650.85
95낙동강하굿둑20190411100300.98398.0441.024184.330.84
96낙동강하굿둑20190411090300.05997.7306.987178.7930.82
97낙동강하굿둑20190411080299.59797.547.853176.0470.81
98낙동강하굿둑20190411070300.05997.750.693178.9710.82
99낙동강하굿둑20190415100301.44598.1156.735156.7350.85