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 18 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:44:25.603173
Analysis finished2023-12-10 10:44:32.255176
Duration6.65 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

2023-12-10T19:44:32.387359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:32.565573image/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%
Mean2.0190402 × 1011
Minimum2.0190401 × 1011
Maximum2.0190402 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:33.161293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190401 × 1011
5-th percentile2.0190401 × 1011
Q12.0190402 × 1011
median2.0190402 × 1011
Q32.0190402 × 1011
95-th percentile2.0190402 × 1011
Maximum2.0190402 × 1011
Range9270
Interquartile range (IQR)825

Descriptive statistics

Standard deviation3408.3076
Coefficient of variation (CV)1.6880831 × 10-8
Kurtosis0.27457489
Mean2.0190402 × 1011
Median Absolute Deviation (MAD)410
Skewness-1.4843965
Sum2.0190402 × 1013
Variance11616560
MonotonicityNot monotonic
2023-12-10T19:44:33.538093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201904021310 1
 
1.0%
201904020230 1
 
1.0%
201904020050 1
 
1.0%
201904020100 1
 
1.0%
201904020110 1
 
1.0%
201904020120 1
 
1.0%
201904020130 1
 
1.0%
201904020140 1
 
1.0%
201904020150 1
 
1.0%
201904020200 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201904012050 1
1.0%
201904012100 1
1.0%
201904012110 1
1.0%
201904012120 1
1.0%
201904012130 1
1.0%
201904012140 1
1.0%
201904012150 1
1.0%
201904012200 1
1.0%
201904012210 1
1.0%
201904012220 1
1.0%
ValueCountFrequency (%)
201904021320 1
1.0%
201904021310 1
1.0%
201904021300 1
1.0%
201904021250 1
1.0%
201904021240 1
1.0%
201904021230 1
1.0%
201904021220 1
1.0%
201904021210 1
1.0%
201904021200 1
1.0%
201904021150 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

2023-12-10T19:44:33.920083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:44:34.214775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.70388
Minimum302.372
Maximum305.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:34.383730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum302.372
5-th percentile302.372
Q1303.299
median303.764
Q3304.229
95-th percentile305.159
Maximum305.159
Range2.787
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.73736779
Coefficient of variation (CV)0.0024279169
Kurtosis-0.46635973
Mean303.70388
Median Absolute Deviation (MAD)0.465
Skewness0.15870284
Sum30370.388
Variance0.54371126
MonotonicityNot monotonic
2023-12-10T19:44:34.562148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
303.299 27
27.0%
303.764 23
23.0%
304.229 17
17.0%
304.694 9
 
9.0%
302.835 9
 
9.0%
302.372 8
 
8.0%
305.159 7
 
7.0%
ValueCountFrequency (%)
302.372 8
 
8.0%
302.835 9
 
9.0%
303.299 27
27.0%
303.764 23
23.0%
304.229 17
17.0%
304.694 9
 
9.0%
305.159 7
 
7.0%
ValueCountFrequency (%)
305.159 7
 
7.0%
304.694 9
 
9.0%
304.229 17
17.0%
303.764 23
23.0%
303.299 27
27.0%
302.835 9
 
9.0%
302.372 8
 
8.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.851
Minimum98.4
Maximum99.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:34.758858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.4
5-th percentile98.4
Q198.7
median98.9
Q399
95-th percentile99.3
Maximum99.3
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.24099751
Coefficient of variation (CV)0.0024379875
Kurtosis-0.52474378
Mean98.851
Median Absolute Deviation (MAD)0.2
Skewness0.078056265
Sum9885.1
Variance0.058079798
MonotonicityNot monotonic
2023-12-10T19:44:34.959977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
98.7 27
27.0%
98.9 23
23.0%
99.0 17
17.0%
99.2 9
 
9.0%
98.6 9
 
9.0%
98.4 8
 
8.0%
99.3 7
 
7.0%
ValueCountFrequency (%)
98.4 8
 
8.0%
98.6 9
 
9.0%
98.7 27
27.0%
98.9 23
23.0%
99.0 17
17.0%
99.2 9
 
9.0%
99.3 7
 
7.0%
ValueCountFrequency (%)
99.3 7
 
7.0%
99.2 9
 
9.0%
99.0 17
17.0%
98.9 23
23.0%
98.7 27
27.0%
98.6 9
 
9.0%
98.4 8
 
8.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.72798
Minimum0
Maximum548.196
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:35.187120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.56675
median35.088
Q3290.29375
95-th percentile293.70365
Maximum548.196
Range548.196
Interquartile range (IQR)256.727

Descriptive statistics

Standard deviation128.61393
Coefficient of variation (CV)1.2164606
Kurtosis-0.12024057
Mean105.72798
Median Absolute Deviation (MAD)2.3015
Skewness1.1046374
Sum10572.798
Variance16541.542
MonotonicityNot monotonic
2023-12-10T19:44:35.464304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
18.0%
36.3 2
 
2.0%
291.068 1
 
1.0%
33.008 1
 
1.0%
32.33 1
 
1.0%
32.161 1
 
1.0%
32.565 1
 
1.0%
290.256 1
 
1.0%
290.407 1
 
1.0%
548.196 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 18
18.0%
32.161 1
 
1.0%
32.33 1
 
1.0%
32.565 1
 
1.0%
33.008 1
 
1.0%
33.443 1
 
1.0%
33.514 1
 
1.0%
33.53 1
 
1.0%
33.579 1
 
1.0%
33.865 1
 
1.0%
ValueCountFrequency (%)
548.196 1
1.0%
295.124 1
1.0%
295.017 1
1.0%
294.972 1
1.0%
293.982 1
1.0%
293.689 1
1.0%
293.485 1
1.0%
293.148 1
1.0%
293.145 1
1.0%
293.074 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.55094
Minimum31.912
Maximum39.257
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:35.776364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31.912
5-th percentile32.37265
Q133.644
median34.2925
Q335.24825
95-th percentile36.4845
Maximum39.257
Range7.345
Interquartile range (IQR)1.60425

Descriptive statistics

Standard deviation1.3787096
Coefficient of variation (CV)0.039903679
Kurtosis0.77796594
Mean34.55094
Median Absolute Deviation (MAD)0.8205
Skewness0.52954399
Sum3455.094
Variance1.9008403
MonotonicityNot monotonic
2023-12-10T19:44:36.031192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.3 4
 
4.0%
34.804 2
 
2.0%
32.427 1
 
1.0%
32.806 1
 
1.0%
32.376 1
 
1.0%
32.96 1
 
1.0%
34.22 1
 
1.0%
32.816 1
 
1.0%
31.988 1
 
1.0%
32.187 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
31.912 1
1.0%
31.988 1
1.0%
32.032 1
1.0%
32.187 1
1.0%
32.309 1
1.0%
32.376 1
1.0%
32.427 1
1.0%
32.523 1
1.0%
32.763 1
1.0%
32.806 1
1.0%
ValueCountFrequency (%)
39.257 1
 
1.0%
38.591 1
 
1.0%
37.327 1
 
1.0%
36.895 1
 
1.0%
36.589 1
 
1.0%
36.479 1
 
1.0%
36.403 1
 
1.0%
36.349 1
 
1.0%
36.32 1
 
1.0%
36.3 4
4.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8987
Minimum0.87
Maximum0.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:44:36.245296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile0.87
Q10.89
median0.9
Q30.91
95-th percentile0.93
Maximum0.93
Range0.06
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.015869098
Coefficient of variation (CV)0.017657837
Kurtosis-0.4647323
Mean0.8987
Median Absolute Deviation (MAD)0.01
Skewness0.15591309
Sum89.87
Variance0.00025182828
MonotonicityNot monotonic
2023-12-10T19:44:36.434750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.89 27
27.0%
0.9 23
23.0%
0.91 17
17.0%
0.92 9
 
9.0%
0.88 9
 
9.0%
0.87 8
 
8.0%
0.93 7
 
7.0%
ValueCountFrequency (%)
0.87 8
 
8.0%
0.88 9
 
9.0%
0.89 27
27.0%
0.9 23
23.0%
0.91 17
17.0%
0.92 9
 
9.0%
0.93 7
 
7.0%
ValueCountFrequency (%)
0.93 7
 
7.0%
0.92 9
 
9.0%
0.91 17
17.0%
0.9 23
23.0%
0.89 27
27.0%
0.88 9
 
9.0%
0.87 8
 
8.0%

Interactions

2023-12-10T19:44:30.828137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:25.931644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.972299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.835032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:28.894184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.829907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.999223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.327271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.112815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.963733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.043069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.003903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:31.147573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.473807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.259886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:28.096158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.206843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.170322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:31.296247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.605776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.397770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:28.233047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.352646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.312245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:31.494761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.738543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.550926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:28.380217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.502843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.466697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:31.678677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:26.859428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:27.698249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:28.657112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:29.662655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:30.648715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:44:36.617567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8410.7060.3440.7620.706
강우량(mm)0.8411.0001.0000.0000.7941.000
유입량(ms)0.7061.0001.0000.1300.7281.000
방류량(ms)0.3440.0000.1301.0000.3570.130
저수량(백만m3)0.7620.7940.7280.3571.0000.728
저수율0.7061.0001.0000.1300.7281.000
2023-12-10T19:44:36.812854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.7850.7850.0250.5010.785
강우량(mm)0.7851.0001.0000.3720.7171.000
유입량(ms)0.7851.0001.0000.3720.7171.000
방류량(ms)0.0250.3720.3721.0000.1980.372
저수량(백만m3)0.5010.7170.7170.1981.0000.717
저수율0.7851.0001.0000.3720.7171.000

Missing values

2023-12-10T19:44:31.910955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:44:32.166079image/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
0낙동강하굿둑2019040213100304.22999.034.92434.9540.91
1낙동강하굿둑2019040213000304.22999.034.87434.8040.91
2낙동강하굿둑2019040212500304.22999.034.93635.0140.91
3낙동강하굿둑2019040212400304.22999.034.87934.8040.91
4낙동강하굿둑2019040212300304.22999.034.91234.9120.91
5낙동강하굿둑2019040212200304.22999.034.91634.8450.91
6낙동강하굿둑2019040212100304.22999.00.034.9020.91
7낙동강하굿둑2019040212000304.22999.035.09335.00.91
8낙동강하굿둑2019040211500304.22999.035.2835.070.91
9낙동강하굿둑2019040211400304.69499.235.44335.210.92
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑2019040122100303.76498.9292.30334.0580.9
91낙동강하굿둑2019040122000303.76498.9292.06234.0060.9
92낙동강하굿둑2019040121500303.76498.9291.84934.7910.9
93낙동강하굿둑2019040121400303.29998.7291.64433.3330.89
94낙동강하굿둑2019040121300303.29998.7291.37433.2560.89
95낙동강하굿둑2019040121200303.29998.7291.08635.0090.89
96낙동강하굿둑2019040121100302.83598.6289.88732.5230.88
97낙동강하굿둑2019040121000302.83598.6289.64332.0320.88
98낙동강하굿둑2019040120500302.83598.6289.46531.9120.88
99낙동강하굿둑2019040213200304.22999.034.85834.8170.91