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 3 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
방류량(ms) has 23 (23.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:44:03.180284
Analysis finished2023-12-10 10:44:09.764492
Duration6.58 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

2023-12-10T19:44:09.857282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:10.006218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190602 × 1011
Minimum2.0190602 × 1011
Maximum2.0190602 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:10.159669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190602 × 1011
5-th percentile2.0190602 × 1011
Q12.0190602 × 1011
median2.0190602 × 1011
Q32.0190602 × 1011
95-th percentile2.0190602 × 1011
Maximum2.0190602 × 1011
Range1630
Interquartile range (IQR)815

Descriptive statistics

Standard deviation483.55272
Coefficient of variation (CV)2.3949396 × 10-9
Kurtosis-1.200258
Mean2.0190602 × 1011
Median Absolute Deviation (MAD)410
Skewness0
Sum2.0190602 × 1013
Variance233823.23
MonotonicityNot monotonic
2023-12-10T19:44:10.351941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201906021950 1
 
1.0%
201906020950 1
 
1.0%
201906020810 1
 
1.0%
201906020820 1
 
1.0%
201906020830 1
 
1.0%
201906020840 1
 
1.0%
201906020850 1
 
1.0%
201906020900 1
 
1.0%
201906020910 1
 
1.0%
201906020920 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201906020410 1
1.0%
201906020420 1
1.0%
201906020430 1
1.0%
201906020440 1
1.0%
201906020450 1
1.0%
201906020500 1
1.0%
201906020510 1
1.0%
201906020520 1
1.0%
201906020530 1
1.0%
201906020540 1
1.0%
ValueCountFrequency (%)
201906022040 1
1.0%
201906022030 1
1.0%
201906022020 1
1.0%
201906022010 1
1.0%
201906022000 1
1.0%
201906021950 1
1.0%
201906021940 1
1.0%
201906021930 1
1.0%
201906021920 1
1.0%
201906021910 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

2023-12-10T19:44:10.560265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:10.679500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.78461
Minimum299.136
Maximum302.372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:10.779185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum299.136
5-th percentile299.57395
Q1300.059
median300.983
Q3301.445
95-th percentile302.372
Maximum302.372
Range3.236
Interquartile range (IQR)1.386

Descriptive statistics

Standard deviation0.88694122
Coefficient of variation (CV)0.0029487586
Kurtosis-0.81564203
Mean300.78461
Median Absolute Deviation (MAD)0.463
Skewness-0.071504507
Sum30078.461
Variance0.78666473
MonotonicityNot monotonic
2023-12-10T19:44:10.920784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
300.983 29
29.0%
299.597 14
14.0%
300.059 13
13.0%
301.445 13
13.0%
301.908 10
 
10.0%
300.52 9
 
9.0%
302.372 7
 
7.0%
299.136 5
 
5.0%
ValueCountFrequency (%)
299.136 5
 
5.0%
299.597 14
14.0%
300.059 13
13.0%
300.52 9
 
9.0%
300.983 29
29.0%
301.445 13
13.0%
301.908 10
 
10.0%
302.372 7
 
7.0%
ValueCountFrequency (%)
302.372 7
 
7.0%
301.908 10
 
10.0%
301.445 13
13.0%
300.983 29
29.0%
300.52 9
 
9.0%
300.059 13
13.0%
299.597 14
14.0%
299.136 5
 
5.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.914
Minimum97.4
Maximum98.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:11.176338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.4
5-th percentile97.495
Q197.7
median98
Q398.1
95-th percentile98.4
Maximum98.4
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.28850713
Coefficient of variation (CV)0.002946536
Kurtosis-0.89545013
Mean97.914
Median Absolute Deviation (MAD)0.2
Skewness-0.1426568
Sum9791.4
Variance0.083236364
MonotonicityNot monotonic
2023-12-10T19:44:11.357546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
98.0 29
29.0%
97.5 14
14.0%
97.7 13
13.0%
98.1 13
13.0%
98.3 10
 
10.0%
97.8 9
 
9.0%
98.4 7
 
7.0%
97.4 5
 
5.0%
ValueCountFrequency (%)
97.4 5
 
5.0%
97.5 14
14.0%
97.7 13
13.0%
97.8 9
 
9.0%
98.0 29
29.0%
98.1 13
13.0%
98.3 10
 
10.0%
98.4 7
 
7.0%
ValueCountFrequency (%)
98.4 7
 
7.0%
98.3 10
 
10.0%
98.1 13
13.0%
98.0 29
29.0%
97.8 9
 
9.0%
97.7 13
13.0%
97.5 14
14.0%
97.4 5
 
5.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.53616
Minimum0
Maximum458.423
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:11.593948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.063
median179.746
Q3201.57175
95-th percentile451.40925
Maximum458.423
Range458.423
Interquartile range (IQR)200.50875

Descriptive statistics

Standard deviation152.29879
Coefficient of variation (CV)0.82980263
Kurtosis-0.73092134
Mean183.53616
Median Absolute Deviation (MAD)100.6605
Skewness0.45189713
Sum18353.616
Variance23194.921
MonotonicityNot monotonic
2023-12-10T19:44:11.802379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
23.0%
1.063 2
 
2.0%
179.746 2
 
2.0%
257.897 1
 
1.0%
179.326 1
 
1.0%
199.748 1
 
1.0%
199.37 1
 
1.0%
199.105 1
 
1.0%
197.444 1
 
1.0%
196.6 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 23
23.0%
1.006 1
 
1.0%
1.063 2
 
2.0%
1.098 1
 
1.0%
1.135 1
 
1.0%
1.408 1
 
1.0%
160.277 1
 
1.0%
160.596 1
 
1.0%
160.909 1
 
1.0%
161.795 1
 
1.0%
ValueCountFrequency (%)
458.423 1
1.0%
457.732 1
1.0%
456.528 1
1.0%
454.288 1
1.0%
453.067 1
1.0%
451.322 1
1.0%
450.914 1
1.0%
450.449 1
1.0%
450.035 1
1.0%
437.198 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.22809
Minimum1
Maximum203.352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:12.063870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.01615
Q1168.50025
median180.2345
Q3194.64875
95-th percentile201.21
Maximum203.352
Range202.352
Interquartile range (IQR)26.1485

Descriptive statistics

Standard deviation60.529849
Coefficient of variation (CV)0.37311571
Kurtosis3.0867836
Mean162.22809
Median Absolute Deviation (MAD)13.898
Skewness-2.1522626
Sum16222.809
Variance3663.8626
MonotonicityNot monotonic
2023-12-10T19:44:12.633946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 5
 
5.0%
1.19 1
 
1.0%
174.331 1
 
1.0%
199.487 1
 
1.0%
202.339 1
 
1.0%
197.567 1
 
1.0%
197.409 1
 
1.0%
197.355 1
 
1.0%
195.036 1
 
1.0%
194.56 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
1.0 5
5.0%
1.017 1
 
1.0%
1.063 1
 
1.0%
1.135 1
 
1.0%
1.19 1
 
1.0%
1.293 1
 
1.0%
1.818 1
 
1.0%
37.29 1
 
1.0%
99.957 1
 
1.0%
160.277 1
 
1.0%
ValueCountFrequency (%)
203.352 1
1.0%
202.339 1
1.0%
201.612 1
1.0%
201.547 1
1.0%
201.381 1
1.0%
201.201 1
1.0%
201.187 1
1.0%
201.167 1
1.0%
200.408 1
1.0%
200.001 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8357
Minimum0.8
Maximum0.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:12.860647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.8095
Q10.82
median0.84
Q30.85
95-th percentile0.87
Maximum0.87
Range0.07
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.019188328
Coefficient of variation (CV)0.022960785
Kurtosis-0.81646584
Mean0.8357
Median Absolute Deviation (MAD)0.01
Skewness-0.074158241
Sum83.57
Variance0.00036819192
MonotonicityNot monotonic
2023-12-10T19:44:13.045596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.84 29
29.0%
0.81 14
14.0%
0.82 13
13.0%
0.85 13
13.0%
0.86 10
 
10.0%
0.83 9
 
9.0%
0.87 7
 
7.0%
0.8 5
 
5.0%
ValueCountFrequency (%)
0.8 5
 
5.0%
0.81 14
14.0%
0.82 13
13.0%
0.83 9
 
9.0%
0.84 29
29.0%
0.85 13
13.0%
0.86 10
 
10.0%
0.87 7
 
7.0%
ValueCountFrequency (%)
0.87 7
 
7.0%
0.86 10
 
10.0%
0.85 13
13.0%
0.84 29
29.0%
0.83 9
 
9.0%
0.82 13
13.0%
0.81 14
14.0%
0.8 5
 
5.0%

Interactions

2023-12-10T19:44:08.556385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:03.494211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.509178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.534958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.463774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.641796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.715869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:03.668660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.672722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.709204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.628144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.817906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.871579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:03.839166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.836563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.874413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.801618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.992831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.021030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:03.973308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.015649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.012845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.183408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.132252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.167798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.105315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.171896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.143077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.316294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.253676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:09.314041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:04.286058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:05.357144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:06.300817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:07.481748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:08.415333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:13.198333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8740.9070.5750.8120.869
강우량(mm)0.8741.0001.0000.3610.5431.000
유입량(ms)0.9071.0001.0000.3930.7731.000
방류량(ms)0.5750.3610.3931.0000.8800.478
저수량(백만m3)0.8120.5430.7730.8801.0000.586
저수율0.8691.0001.0000.4780.5861.000
2023-12-10T19:44:13.371670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.701-0.701-0.211-0.815-0.701
강우량(mm)-0.7011.0001.0000.3550.6221.000
유입량(ms)-0.7011.0001.0000.3550.6221.000
방류량(ms)-0.2110.3550.3551.0000.2490.355
저수량(백만m3)-0.8150.6220.6220.2491.0000.622
저수율-0.7011.0001.0000.3550.6221.000

Missing values

2023-12-10T19:44:09.508198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:44:09.690604image/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
0낙동강하굿둑2019060219500300.98398.0257.8971.190.84
1낙동강하굿둑2019060220400300.98398.01.4081.8180.84
2낙동강하굿둑2019060220300300.98398.01.1351.1350.84
3낙동강하굿둑2019060220200300.98398.01.0981.2930.84
4낙동강하굿둑2019060220100300.98398.01.0631.00.84
5낙동강하굿둑2019060219400300.98398.0257.8391.00.84
6낙동강하굿둑2019060219300300.98398.01.0061.00.84
7낙동강하굿둑2019060219200300.5297.80.01.0170.83
8낙동강하굿둑2019060219100300.5297.80.01.00.83
9낙동강하굿둑2019060219000300.98398.0302.9161.00.84
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑2019060205300301.90898.3197.801196.9560.86
91낙동강하굿둑2019060205200301.90898.3454.288197.3420.86
92낙동강하굿둑2019060205100301.90898.3453.067199.1060.86
93낙동강하굿둑2019060205000301.90898.3451.322194.9150.86
94낙동강하굿둑2019060204500301.44598.1193.752193.6810.85
95낙동강하굿둑2019060204400301.44598.1193.753193.870.85
96낙동강하굿둑2019060204300301.44598.1450.914193.9140.85
97낙동강하굿둑2019060204200301.44598.1450.449193.6840.85
98낙동강하굿둑2019060204100301.44598.1450.035194.3510.85
99낙동강하굿둑2019060220000300.98398.01.0631.0630.84