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

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 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
강우량(mm) has 18 (18.0%) zerosZeros
유입량(ms) has 18 (18.0%) zerosZeros
방류량(ms) has 28 (28.0%) zerosZeros
저수량(백만m3) has 35 (35.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:25.897282
Analysis finished2023-12-10 12:53:30.468890
Duration4.57 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%
Mean2.0190401 × 109
Minimum2.0190401 × 109
Maximum2.0190401 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:30.521205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190401 × 109
5-th percentile2.0190401 × 109
Q12.0190401 × 109
median2.0190401 × 109
Q32.0190401 × 109
95-th percentile2.0190401 × 109
Maximum2.0190401 × 109
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.568083
Coefficient of variation (CV)1.2719327 × 10-9
Kurtosis-1.2191025
Mean2.0190401 × 109
Median Absolute Deviation (MAD)2
Skewness0.11198599
Sum2.0190401 × 1011
Variance6.5950505
MonotonicityIncreasing
2023-12-10T21:53:30.631352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019040101 20
20.0%
2019040107 18
18.0%
2019040105 17
17.0%
2019040103 14
14.0%
2019040104 8
 
8.0%
2019040102 7
 
7.0%
2019040108 7
 
7.0%
2019040109 6
 
6.0%
2019040106 3
 
3.0%
ValueCountFrequency (%)
2019040101 20
20.0%
2019040102 7
 
7.0%
2019040103 14
14.0%
2019040104 8
 
8.0%
2019040105 17
17.0%
2019040106 3
 
3.0%
2019040107 18
18.0%
2019040108 7
 
7.0%
2019040109 6
 
6.0%
ValueCountFrequency (%)
2019040109 6
 
6.0%
2019040108 7
 
7.0%
2019040107 18
18.0%
2019040106 3
 
3.0%
2019040105 17
17.0%
2019040104 8
 
8.0%
2019040103 14
14.0%
2019040102 7
 
7.0%
2019040101 20
20.0%

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

2023-12-10T21:53:30.787988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:30.892953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.88493
Minimum0
Maximum97.471
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:31.007539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.4275
median18.2035
Q352.80825
95-th percentile75.623
Maximum97.471
Range97.471
Interquartile range (IQR)50.38075

Descriptive statistics

Standard deviation28.929138
Coefficient of variation (CV)1.0374471
Kurtosis-0.46994999
Mean27.88493
Median Absolute Deviation (MAD)17.247
Skewness0.85109078
Sum2788.493
Variance836.89505
MonotonicityNot monotonic
2023-12-10T21:53:31.174395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.0 18
18.0%
54.63 6
 
6.0%
75.623 5
 
5.0%
27.443 4
 
4.0%
73.656 4
 
4.0%
14.833 4
 
4.0%
11.7 4
 
4.0%
2.981 4
 
4.0%
0.957 4
 
4.0%
24.17 3
 
3.0%
Other values (32) 44
44.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.956 1
 
1.0%
0.957 4
 
4.0%
2.423 2
 
2.0%
2.429 2
 
2.0%
2.436 2
 
2.0%
2.981 4
 
4.0%
8.909 1
 
1.0%
8.954 1
 
1.0%
9.045 1
 
1.0%
ValueCountFrequency (%)
97.471 2
 
2.0%
97.267 2
 
2.0%
75.623 5
5.0%
73.656 4
4.0%
73.509 2
 
2.0%
54.63 6
6.0%
53.157 1
 
1.0%
52.863 2
 
2.0%
52.839 1
 
1.0%
52.798 2
 
2.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.594
Minimum0
Maximum105.2
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:31.319668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.6
median79.8
Q3100.2
95-th percentile103.82
Maximum105.2
Range105.2
Interquartile range (IQR)84.6

Descriptive statistics

Standard deviation43.874561
Coefficient of variation (CV)0.71231875
Kurtosis-1.6689662
Mean61.594
Median Absolute Deviation (MAD)23.6
Skewness-0.44996076
Sum6159.4
Variance1924.9771
MonotonicityNot monotonic
2023-12-10T21:53:31.469550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 18
18.0%
2.7 6
 
6.0%
78.1 6
 
6.0%
100.4 5
 
5.0%
16.9 5
 
5.0%
100.2 4
 
4.0%
15.6 4
 
4.0%
33.2 4
 
4.0%
103.8 4
 
4.0%
103.4 4
 
4.0%
Other values (23) 40
40.0%
ValueCountFrequency (%)
0.0 18
18.0%
2.7 6
 
6.0%
15.6 4
 
4.0%
15.7 2
 
2.0%
16.9 5
 
5.0%
33.2 4
 
4.0%
78.1 6
 
6.0%
79.6 2
 
2.0%
79.8 4
 
4.0%
88.8 1
 
1.0%
ValueCountFrequency (%)
105.2 1
 
1.0%
104.7 1
 
1.0%
104.6 1
 
1.0%
104.2 2
 
2.0%
103.8 4
4.0%
103.6 1
 
1.0%
103.4 4
4.0%
102.6 1
 
1.0%
102.1 1
 
1.0%
100.4 5
5.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.7274
Minimum0
Maximum154.297
Zeros28
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:31.608914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.78
Q352.51575
95-th percentile146.5238
Maximum154.297
Range154.297
Interquartile range (IQR)52.51575

Descriptive statistics

Standard deviation47.752694
Coefficient of variation (CV)1.0920543
Kurtosis0.4535407
Mean43.7274
Median Absolute Deviation (MAD)28.164
Skewness1.268746
Sum4372.74
Variance2280.3197
MonotonicityNot monotonic
2023-12-10T21:53:31.763003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
28.0%
30.78 5
 
5.0%
58.944 5
 
5.0%
35.517 3
 
3.0%
29.8 3
 
3.0%
41.143 2
 
2.0%
11.417 2
 
2.0%
39.295 1
 
1.0%
30.869 1
 
1.0%
52.428 1
 
1.0%
Other values (49) 49
49.0%
ValueCountFrequency (%)
0.0 28
28.0%
0.028 1
 
1.0%
11.417 2
 
2.0%
16.672 1
 
1.0%
23.947 1
 
1.0%
24.238 1
 
1.0%
26.111 1
 
1.0%
26.366 1
 
1.0%
28.667 1
 
1.0%
28.8 1
 
1.0%
ValueCountFrequency (%)
154.297 1
1.0%
152.692 1
1.0%
151.197 1
1.0%
149.741 1
1.0%
147.641 1
1.0%
146.465 1
1.0%
146.372 1
1.0%
146.098 1
1.0%
145.725 1
1.0%
140.807 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.59786
Minimum0
Maximum146.465
Zeros35
Zeros (%)35.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:31.907220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median30.4415
Q341.771
95-th percentile140.85235
Maximum146.465
Range146.465
Interquartile range (IQR)41.771

Descriptive statistics

Standard deviation46.931389
Coefficient of variation (CV)1.1852001
Kurtosis0.6264872
Mean39.59786
Median Absolute Deviation (MAD)30.4415
Skewness1.3671693
Sum3959.786
Variance2202.5553
MonotonicityNot monotonic
2023-12-10T21:53:32.046905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 35
35.0%
30.78 5
 
5.0%
29.8 3
 
3.0%
35.6 3
 
3.0%
41.143 2
 
2.0%
41.142 1
 
1.0%
32.309 1
 
1.0%
41.13 1
 
1.0%
138.614 1
 
1.0%
134.093 1
 
1.0%
Other values (47) 47
47.0%
ValueCountFrequency (%)
0.0 35
35.0%
23.64 1
 
1.0%
23.947 1
 
1.0%
23.987 1
 
1.0%
24.238 1
 
1.0%
28.467 1
 
1.0%
28.667 1
 
1.0%
28.8 1
 
1.0%
29.483 1
 
1.0%
29.667 1
 
1.0%
ValueCountFrequency (%)
146.465 1
1.0%
146.372 1
1.0%
146.098 1
1.0%
145.725 1
1.0%
141.714 1
1.0%
140.807 1
1.0%
140.664 1
1.0%
138.614 1
1.0%
137.325 1
1.0%
137.185 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.898
Minimum1.98
Maximum83.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:32.450670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.98
5-th percentile2.049
Q17.485
median25.53
Q339.85
95-th percentile80.7115
Maximum83.36
Range81.38
Interquartile range (IQR)32.365

Descriptive statistics

Standard deviation23.779309
Coefficient of variation (CV)0.8523661
Kurtosis0.067600716
Mean27.898
Median Absolute Deviation (MAD)17.15
Skewness0.91415049
Sum2789.8
Variance565.45555
MonotonicityNot monotonic
2023-12-10T21:53:32.625741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.18 6
 
6.0%
25.53 5
 
5.0%
44.51 4
 
4.0%
8.38 4
 
4.0%
18.3 4
 
4.0%
2.7 4
 
4.0%
33.1 4
 
4.0%
28.11 4
 
4.0%
47.01 4
 
4.0%
4.2 3
 
3.0%
Other values (43) 58
58.0%
ValueCountFrequency (%)
1.98 1
 
1.0%
1.99 1
 
1.0%
2.0 1
 
1.0%
2.02 1
 
1.0%
2.03 1
 
1.0%
2.05 1
 
1.0%
2.7 4
4.0%
4.19 2
2.0%
4.2 3
3.0%
4.29 2
2.0%
ValueCountFrequency (%)
83.36 1
1.0%
83.32 1
1.0%
83.31 2
2.0%
80.74 1
1.0%
80.71 2
2.0%
80.69 2
2.0%
62.69 1
1.0%
62.68 1
1.0%
62.64 1
1.0%
62.53 1
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강정고령보
 
6
공주보
 
6
강천보
 
6
여주보
 
6
합천창녕보
 
6
Other values (15)
70 

Length

Max length5
Median length3
Mean length3.32
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상주보
2nd row구담보
3rd row구미보
4th row안동보
5th row낙단보

Common Values

ValueCountFrequency (%)
강정고령보 6
 
6.0%
공주보 6
 
6.0%
강천보 6
 
6.0%
여주보 6
 
6.0%
합천창녕보 6
 
6.0%
죽산보 6
 
6.0%
쾌쾌보 5
 
5.0%
달성보 5
 
5.0%
세종보 5
 
5.0%
백제보 5
 
5.0%
Other values (10) 44
44.0%

Length

2023-12-10T21:53:32.779008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강정고령보 6
 
6.0%
공주보 6
 
6.0%
강천보 6
 
6.0%
여주보 6
 
6.0%
합천창녕보 6
 
6.0%
죽산보 6
 
6.0%
칠곡보 5
 
5.0%
구미보 5
 
5.0%
수하보 5
 
5.0%
낙단보 5
 
5.0%
Other values (10) 44
44.0%

Interactions

2023-12-10T21:53:29.514071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.160476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.056170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.674345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.225312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.907988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.620331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.270331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.168879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.774229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.358553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.005626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.715622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.364075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.308283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.863173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.471324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.086078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.816847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.448781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.393619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.953560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.580975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.173682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.945946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.552815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.487757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.044920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.686723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.289505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:30.073977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:26.652542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:27.573945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.128432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:28.790811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:29.397373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:32.857821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.0000.000
강우량(mm)0.0001.0000.6410.8210.7850.8071.000
유입량(ms)0.0000.6411.0000.7210.6440.8151.000
방류량(ms)0.0000.8210.7211.0000.8160.7390.942
저수량(백만m3)0.0000.7850.6440.8161.0000.7010.996
저수율0.0000.8070.8150.7390.7011.0001.000
댐이름0.0001.0001.0000.9420.9961.0001.000
2023-12-10T21:53:32.964425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0230.0350.0290.005-0.0340.000
강우량(mm)0.0231.0000.5250.2130.116-0.3490.927
유입량(ms)0.0350.5251.0000.8040.811-0.0070.923
방류량(ms)0.0290.2130.8041.0000.810-0.0130.720
저수량(백만m3)0.0050.1160.8110.8101.0000.0180.836
저수율-0.034-0.349-0.007-0.0130.0181.0000.933
댐이름0.0000.9270.9230.7200.8360.9331.000

Missing values

2023-12-10T21:53:30.258994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:30.420236image/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
02019040101027.443100.241.14341.14347.01상주보
1201904010100.00.00.00.062.53구담보
22019040101052.733100.038.33338.33332.5구미보
3201904010100.00.00.00.083.36안동보
42019040101033.85697.652.5152.5139.86낙단보
52019040101024.17100.026.36650.9224.2백제보
6201904010102.43615.730.06431.9534.31공주보
7201904010100.95616.928.66728.6678.37세종보
82019040101097.47196.60.023.644.83창녕함안보
9201904010100.00.00.00.044.51쾌쾌보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902019040108075.623100.430.7830.7825.53칠곡보
91201904010802.42315.633.12533.1254.29공주보
922019040108033.79797.552.40752.40739.85낙단보
932019040108011.787104.6152.692140.66433.12여주보
942019040109052.863100.343.65543.65532.52구미보
952019040109024.17100.062.25833.5084.2백제보
96201904010900.00.00.00.044.5쾌쾌보
972019040109018.52.70.0280.02.05죽산보
98201904010900.95716.930.50830.5088.38세종보
992019040109053.15790.858.9440.013.49달성보