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

Numeric6
Categorical2

Alerts

강우량(mm) has constant value ""Constant
저수위(m) is highly overall correlated with 방류량(ms) and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
유입량(ms) has 17 (17.0%) zerosZeros
방류량(ms) has 17 (17.0%) zerosZeros
저수량(백만m3) has 17 (17.0%) zerosZeros
저수율 has 22 (22.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:50:30.319344
Analysis finished2023-12-10 12:50:34.597488
Duration4.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0211001 × 109
Minimum2.0211001 × 109
Maximum2.0211001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:34.647836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0211001 × 109
5-th percentile2.0211001 × 109
Q12.0211001 × 109
median2.0211001 × 109
Q32.0211001 × 109
95-th percentile2.0211001 × 109
Maximum2.0211001 × 109
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3564011
Coefficient of variation (CV)1.1659003 × 10-9
Kurtosis-1.1371547
Mean2.0211001 × 109
Median Absolute Deviation (MAD)2
Skewness0.026402888
Sum2.0211001 × 1011
Variance5.5526263
MonotonicityIncreasing
2023-12-10T21:50:34.756489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021100102 13
13.0%
2021100104 13
13.0%
2021100106 13
13.0%
2021100103 12
12.0%
2021100105 12
12.0%
2021100107 12
12.0%
2021100108 12
12.0%
2021100101 9
9.0%
2021100109 4
 
4.0%
ValueCountFrequency (%)
2021100101 9
9.0%
2021100102 13
13.0%
2021100103 12
12.0%
2021100104 13
13.0%
2021100105 12
12.0%
2021100106 13
13.0%
2021100107 12
12.0%
2021100108 12
12.0%
2021100109 4
 
4.0%
ValueCountFrequency (%)
2021100109 4
 
4.0%
2021100108 12
12.0%
2021100107 12
12.0%
2021100106 13
13.0%
2021100105 12
12.0%
2021100104 13
13.0%
2021100103 12
12.0%
2021100102 13
13.0%
2021100101 9
9.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.51216
Minimum1.31
Maximum156.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:34.936012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.31
5-th percentile1.4295
Q15.8975
median26.955
Q347.75
95-th percentile135.821
Maximum156.86
Range155.55
Interquartile range (IQR)41.8525

Descriptive statistics

Standard deviation41.65169
Coefficient of variation (CV)1.1103517
Kurtosis1.7713314
Mean37.51216
Median Absolute Deviation (MAD)20.925
Skewness1.5584377
Sum3751.216
Variance1734.8633
MonotonicityNot monotonic
2023-12-10T21:50:35.114144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.4 5
 
5.0%
80.701 4
 
4.0%
156.86 3
 
3.0%
2.34 3
 
3.0%
33.27 3
 
3.0%
32.56 3
 
3.0%
25.63 2
 
2.0%
28.29 2
 
2.0%
1.44 2
 
2.0%
38.23 2
 
2.0%
Other values (66) 71
71.0%
ValueCountFrequency (%)
1.31 1
 
1.0%
1.34 1
 
1.0%
1.36 1
 
1.0%
1.38 1
 
1.0%
1.42 1
 
1.0%
1.43 1
 
1.0%
1.44 2
2.0%
2.33 2
2.0%
2.34 3
3.0%
2.36 1
 
1.0%
ValueCountFrequency (%)
156.86 3
3.0%
156.85 1
 
1.0%
135.84 1
 
1.0%
135.82 1
 
1.0%
135.81 1
 
1.0%
135.804 1
 
1.0%
135.796 1
 
1.0%
86.076 1
 
1.0%
86.065 1
 
1.0%
86.062 1
 
1.0%

강우량(mm)
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

2023-12-10T21:50:35.274206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:50:35.385036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.73375
Minimum0
Maximum531.547
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:35.501150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.11025
median122.1785
Q3189.115
95-th percentile389.0944
Maximum531.547
Range531.547
Interquartile range (IQR)174.00475

Descriptive statistics

Standard deviation123.17823
Coefficient of variation (CV)0.971945
Kurtosis0.77079754
Mean126.73375
Median Absolute Deviation (MAD)93.5325
Skewness1.0345942
Sum12673.375
Variance15172.877
MonotonicityNot monotonic
2023-12-10T21:50:35.661821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
19.023 2
 
2.0%
131.078 1
 
1.0%
15.83 1
 
1.0%
178.248 1
 
1.0%
60.963 1
 
1.0%
154.448 1
 
1.0%
172.82 1
 
1.0%
427.552 1
 
1.0%
5.629 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.33 1
 
1.0%
3.001 1
 
1.0%
3.174 1
 
1.0%
5.499 1
 
1.0%
5.629 1
 
1.0%
8.052 1
 
1.0%
10.465 1
 
1.0%
12.951 1
 
1.0%
15.83 1
 
1.0%
ValueCountFrequency (%)
531.547 1
1.0%
451.121 1
1.0%
431.397 1
1.0%
427.552 1
1.0%
414.98 1
1.0%
387.732 1
1.0%
325.963 1
1.0%
322.024 1
1.0%
320.991 1
1.0%
319.756 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.50624
Minimum0
Maximum427.552
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:35.827125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.35275
median121.878
Q3208.918
95-th percentile367.08715
Maximum427.552
Range427.552
Interquartile range (IQR)198.56525

Descriptive statistics

Standard deviation118.66584
Coefficient of variation (CV)0.94549753
Kurtosis-0.27873403
Mean125.50624
Median Absolute Deviation (MAD)101.2725
Skewness0.73873679
Sum12550.624
Variance14081.582
MonotonicityNot monotonic
2023-12-10T21:50:35.961592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.33 5
 
5.0%
19.023 2
 
2.0%
131.078 1
 
1.0%
204.844 1
 
1.0%
218.942 1
 
1.0%
49.602 1
 
1.0%
154.448 1
 
1.0%
202.292 1
 
1.0%
427.552 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.33 5
 
5.0%
9.943 1
 
1.0%
10.073 1
 
1.0%
10.223 1
 
1.0%
10.396 1
 
1.0%
10.465 1
 
1.0%
15.236 1
 
1.0%
19.023 2
 
2.0%
19.263 1
 
1.0%
ValueCountFrequency (%)
427.552 1
1.0%
414.98 1
1.0%
410.76 1
1.0%
387.732 1
1.0%
370.13 1
1.0%
366.927 1
1.0%
350.675 1
1.0%
322.491 1
1.0%
322.024 1
1.0%
320.991 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.73907
Minimum0
Maximum76.648
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:36.099776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.99325
median14.014
Q353.124
95-th percentile75.28265
Maximum76.648
Range76.648
Interquartile range (IQR)52.13075

Descriptive statistics

Standard deviation26.113362
Coefficient of variation (CV)1.0145418
Kurtosis-1.1906005
Mean25.73907
Median Absolute Deviation (MAD)14.014
Skewness0.59376853
Sum2573.907
Variance681.90766
MonotonicityNot monotonic
2023-12-10T21:50:36.263233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
54.262 3
 
3.0%
12.436 3
 
3.0%
53.124 3
 
3.0%
3.983 3
 
3.0%
76.648 2
 
2.0%
6.207 2
 
2.0%
15.637 2
 
2.0%
9.769 2
 
2.0%
51.696 2
 
2.0%
Other values (58) 61
61.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.712 1
 
1.0%
0.72 1
 
1.0%
0.728 1
 
1.0%
0.741 1
 
1.0%
0.754 1
 
1.0%
0.984 1
 
1.0%
0.987 1
 
1.0%
0.991 1
 
1.0%
0.994 1
 
1.0%
ValueCountFrequency (%)
76.648 2
2.0%
76.545 2
2.0%
75.561 1
1.0%
75.268 1
1.0%
75.121 1
1.0%
74.828 1
1.0%
57.839 1
1.0%
57.699 1
1.0%
57.629 1
1.0%
57.588 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.02
Minimum0
Maximum160.6
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:50:36.659822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.575
median73.4
Q3101.075
95-th percentile114.86
Maximum160.6
Range160.6
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation46.438274
Coefficient of variation (CV)0.71421523
Kurtosis-0.96424759
Mean65.02
Median Absolute Deviation (MAD)35.45
Skewness-0.035372965
Sum6502
Variance2156.5133
MonotonicityNot monotonic
2023-12-10T21:50:36.801052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 22
 
22.0%
53.8 3
 
3.0%
105.0 3
 
3.0%
110.4 3
 
3.0%
100.7 3
 
3.0%
101.8 2
 
2.0%
101.6 2
 
2.0%
62.2 2
 
2.0%
17.5 2
 
2.0%
69.2 2
 
2.0%
Other values (51) 56
56.0%
ValueCountFrequency (%)
0.0 22
22.0%
17.4 1
 
1.0%
17.5 2
 
2.0%
17.6 1
 
1.0%
29.5 1
 
1.0%
29.6 1
 
1.0%
29.7 1
 
1.0%
29.9 2
 
2.0%
30.5 1
 
1.0%
31.0 1
 
1.0%
ValueCountFrequency (%)
160.6 1
 
1.0%
160.2 1
 
1.0%
160.0 1
 
1.0%
159.9 1
 
1.0%
159.7 1
 
1.0%
112.5 1
 
1.0%
111.9 2
2.0%
111.4 1
 
1.0%
110.4 3
3.0%
110.0 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
창녕함안보
 
7
합천창녕보
 
6
귤현보
 
5
구담보
 
5
단양수중보
 
5
Other values (18)
72 

Length

Max length7
Median length3
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row낙단보
2nd row수하보
3rd row영주유사조절지
4th row구담보
5th row세종보

Common Values

ValueCountFrequency (%)
창녕함안보 7
 
7.0%
합천창녕보 6
 
6.0%
귤현보 5
 
5.0%
구담보 5
 
5.0%
단양수중보 5
 
5.0%
칠곡보 4
 
4.0%
영주유사조절지 4
 
4.0%
세종보 4
 
4.0%
승촌보 4
 
4.0%
이포보 4
 
4.0%
Other values (13) 52
52.0%

Length

2023-12-10T21:50:36.936784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창녕함안보 7
 
7.0%
합천창녕보 6
 
6.0%
귤현보 5
 
5.0%
구담보 5
 
5.0%
단양수중보 5
 
5.0%
낙단보 4
 
4.0%
죽산보 4
 
4.0%
안동보 4
 
4.0%
달성보 4
 
4.0%
공주보 4
 
4.0%
Other values (13) 52
52.0%

Interactions

2023-12-10T21:50:33.818828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:30.592897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.191925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.052964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.622914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.155281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.931669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:30.705755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.297835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.172600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.715904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.264108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:34.030594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:30.802445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.636439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.280883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.835158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.382754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:34.127765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:30.907943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.730515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.355719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.912059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.487277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:34.205067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:30.996889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.829647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.434334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.980671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.595968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:34.304698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.096393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:31.927948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:32.528918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.063163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:50:33.697492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:50:37.026156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.0000.000
저수위(m)0.0001.0000.4390.5360.7330.8101.000
유입량(ms)0.0000.4391.0000.9660.7020.7150.870
방류량(ms)0.0000.5360.9661.0000.7830.7940.946
저수량(백만m3)0.0000.7330.7020.7831.0000.8881.000
저수율0.0000.8100.7150.7940.8881.0000.987
댐이름0.0001.0000.8700.9461.0000.9871.000
2023-12-10T21:50:37.146569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.052-0.038-0.0170.069-0.0240.000
저수위(m)-0.0521.000-0.491-0.530-0.2140.2460.910
유입량(ms)-0.038-0.4911.0000.9760.6550.4580.515
방류량(ms)-0.017-0.5300.9761.0000.6610.4150.690
저수량(백만m3)0.069-0.2140.6550.6611.0000.6460.915
저수율-0.0240.2460.4580.4150.6461.0000.847
댐이름0.0000.9100.5150.6900.9150.8471.000

Missing values

2023-12-10T21:50:34.422674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:50:34.548569image/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

일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
0202110010139.870124.745124.74533.91497.8낙단보
1202110010180.70100.00.00.00.0수하보
22021100101156.85012.95119.6733.971104.7영주유사조절지
3202110010160.400.00.00.00.0구담보
420211001018.75054.9655.2380.99417.6세종보
520211001015.54027.56915.2366.17768.9승촌보
6202110010128.30212.999212.99915.681109.3이포보
7202110010133.270215.858215.85812.436110.4여주보
82021100101135.81031.2740.3357.629160.0단양수중보
9202110010218.430226.718226.71875.56181.8강정고령보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
90202110010832.560120.417120.41753.124100.7구미보
9120211001081.38090.56490.5647.58131.4백제보
92202110010886.05900.00.00.00.0안동보
93202110010847.060147.759124.31527.654100.9상주보
9420211001085.90108.135108.1354.60229.6공주보
9520211001081.44064.34764.34715.97362.2죽산보
96202110010960.400.00.00.00.0구담보
972021100109135.84019.7190.3357.839160.6단양수중보
9820211001098.850151.659227.85351.69673.9합천창녕보
9920211001092.360205.51366.92754.55354.1창녕함안보