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 2 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 강우량(mm) and 3 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 20 (20.0%) zerosZeros
저수량(백만m3) has 18 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:53:48.259172
Analysis finished2023-12-10 12:53:52.339270
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190101 × 109
Minimum2.0190101 × 109
Maximum2.0190101 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:52.395847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190101 × 109
5-th percentile2.0190101 × 109
Q12.0190101 × 109
median2.0190101 × 109
Q32.0190101 × 109
95-th percentile2.0190101 × 109
Maximum2.0190101 × 109
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8822671
Coefficient of variation (CV)9.3227223 × 10-10
Kurtosis-1.0236161
Mean2.0190101 × 109
Median Absolute Deviation (MAD)1
Skewness-0.0098532297
Sum2.0190101 × 1011
Variance3.5429293
MonotonicityIncreasing
2023-12-10T21:53:52.491223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019010103 18
18.0%
2019010104 18
18.0%
2019010105 16
16.0%
2019010101 14
14.0%
2019010106 13
13.0%
2019010107 11
11.0%
2019010102 10
10.0%
ValueCountFrequency (%)
2019010101 14
14.0%
2019010102 10
10.0%
2019010103 18
18.0%
2019010104 18
18.0%
2019010105 16
16.0%
2019010106 13
13.0%
2019010107 11
11.0%
ValueCountFrequency (%)
2019010107 11
11.0%
2019010106 13
13.0%
2019010105 16
16.0%
2019010104 18
18.0%
2019010103 18
18.0%
2019010102 10
10.0%
2019010101 14
14.0%

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

2023-12-10T21:53:52.620071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:52.702012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.27957
Minimum0
Maximum97.471
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:52.805570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.443
median14.387
Q343.5565
95-th percentile76.60735
Maximum97.471
Range97.471
Interquartile range (IQR)41.1135

Descriptive statistics

Standard deviation29.169078
Coefficient of variation (CV)1.1099526
Kurtosis-0.0057935553
Mean26.27957
Median Absolute Deviation (MAD)14.387
Skewness1.0823056
Sum2627.957
Variance850.8351
MonotonicityNot monotonic
2023-12-10T21:53:52.945320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0.0 18
18.0%
72.923 6
 
6.0%
75.52 5
 
5.0%
6.828 5
 
5.0%
2.443 5
 
5.0%
8.864 5
 
5.0%
18.149 5
 
5.0%
24.17 5
 
5.0%
4.694 4
 
4.0%
11.441 4
 
4.0%
Other values (25) 38
38.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.953 1
 
1.0%
0.954 3
 
3.0%
0.955 1
 
1.0%
2.443 5
 
5.0%
4.694 4
 
4.0%
4.725 1
 
1.0%
6.828 5
 
5.0%
8.864 5
 
5.0%
11.398 1
 
1.0%
ValueCountFrequency (%)
97.471 4
4.0%
97.267 1
 
1.0%
75.52 5
5.0%
73.069 1
 
1.0%
72.923 6
6.0%
52.863 2
 
2.0%
52.798 2
 
2.0%
44.076 1
 
1.0%
43.895 1
 
1.0%
43.805 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.22
Minimum0
Maximum101.6
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:53.075794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.9
median77.55
Q3100.3
95-th percentile101.505
Maximum101.6
Range101.6
Interquartile range (IQR)83.4

Descriptive statistics

Standard deviation41.576516
Coefficient of variation (CV)0.67913289
Kurtosis-1.6055034
Mean61.22
Median Absolute Deviation (MAD)23.05
Skewness-0.44898949
Sum6122
Variance1728.6067
MonotonicityNot monotonic
2023-12-10T21:53:53.191864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 18
18.0%
100.3 11
 
11.0%
79.0 6
 
6.0%
15.7 5
 
5.0%
101.6 5
 
5.0%
100.0 5
 
5.0%
16.9 5
 
5.0%
76.1 5
 
5.0%
25.9 5
 
5.0%
101.5 4
 
4.0%
Other values (19) 31
31.0%
ValueCountFrequency (%)
0.0 18
18.0%
15.7 5
 
5.0%
16.9 5
 
5.0%
18.3 4
 
4.0%
18.4 1
 
1.0%
25.9 5
 
5.0%
74.0 1
 
1.0%
74.2 1
 
1.0%
74.3 1
 
1.0%
74.5 1
 
1.0%
ValueCountFrequency (%)
101.6 5
5.0%
101.5 4
 
4.0%
101.2 1
 
1.0%
100.8 1
 
1.0%
100.6 4
 
4.0%
100.3 11
11.0%
100.1 2
 
2.0%
100.0 5
5.0%
96.6 4
 
4.0%
96.4 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.43379
Minimum0
Maximum123.6
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:53.393151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.8
median33.345
Q359.907
95-th percentile105.3789
Maximum123.6
Range123.6
Interquartile range (IQR)52.107

Descriptive statistics

Standard deviation33.100029
Coefficient of variation (CV)0.86122208
Kurtosis0.061923374
Mean38.43379
Median Absolute Deviation (MAD)25.545
Skewness0.81020951
Sum3843.379
Variance1095.6119
MonotonicityNot monotonic
2023-12-10T21:53:53.534789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 20
 
20.0%
32.2 6
 
6.0%
7.8 5
 
5.0%
20.5 3
 
3.0%
32.7 2
 
2.0%
38.232 1
 
1.0%
35.707 1
 
1.0%
123.51 1
 
1.0%
35.329 1
 
1.0%
24.632 1
 
1.0%
Other values (59) 59
59.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.525 1
 
1.0%
2.874 1
 
1.0%
7.8 5
 
5.0%
12.044 1
 
1.0%
14.002 1
 
1.0%
20.5 3
 
3.0%
24.632 1
 
1.0%
26.537 1
 
1.0%
29.303 1
 
1.0%
ValueCountFrequency (%)
123.6 1
1.0%
123.51 1
1.0%
120.9 1
1.0%
118.56 1
1.0%
112.578 1
1.0%
105.0 1
1.0%
104.105 1
1.0%
84.973 1
1.0%
84.868 1
1.0%
83.345 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.01104
Minimum0
Maximum123.6
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:53.698643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120.5
median35.3285
Q357.189
95-th percentile105.3789
Maximum123.6
Range123.6
Interquartile range (IQR)36.689

Descriptive statistics

Standard deviation32.731474
Coefficient of variation (CV)0.79811372
Kurtosis0.024500788
Mean41.01104
Median Absolute Deviation (MAD)18.2255
Skewness0.73657435
Sum4101.104
Variance1071.3494
MonotonicityNot monotonic
2023-12-10T21:53:53.892494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
18.0%
32.2 6
 
6.0%
7.8 5
 
5.0%
20.5 3
 
3.0%
32.7 2
 
2.0%
33.437 1
 
1.0%
33.32 1
 
1.0%
83.345 1
 
1.0%
35.735 1
 
1.0%
123.51 1
 
1.0%
Other values (61) 61
61.0%
ValueCountFrequency (%)
0.0 18
18.0%
6.632 1
 
1.0%
7.8 5
 
5.0%
20.5 3
 
3.0%
20.6 1
 
1.0%
20.747 1
 
1.0%
26.47 1
 
1.0%
31.7 1
 
1.0%
32.133 1
 
1.0%
32.2 6
 
6.0%
ValueCountFrequency (%)
123.6 1
1.0%
123.51 1
1.0%
120.9 1
1.0%
118.56 1
1.0%
112.578 1
1.0%
105.0 1
1.0%
104.105 1
1.0%
103.114 1
1.0%
85.388 1
1.0%
84.973 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.2021
Minimum-1.1
Maximum85.8
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)5.0%
Memory size1.0 KiB
2023-12-10T21:53:54.072071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1
5-th percentile3.9355
Q15.705
median25.52
Q339.7325
95-th percentile78.15
Maximum85.8
Range86.9
Interquartile range (IQR)34.0275

Descriptive statistics

Standard deviation23.959605
Coefficient of variation (CV)0.88079982
Kurtosis0.12437846
Mean27.2021
Median Absolute Deviation (MAD)19.23
Skewness0.94657286
Sum2720.21
Variance574.06266
MonotonicityNot monotonic
2023-12-10T21:53:54.225097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
18.25 6
 
6.0%
25.52 5
 
5.0%
5.98 5
 
5.0%
4.32 5
 
5.0%
4.2 5
 
5.0%
4.88 5
 
5.0%
38.03 5
 
5.0%
62.48 4
 
4.0%
78.15 4
 
4.0%
33.04 4
 
4.0%
Other values (34) 52
52.0%
ValueCountFrequency (%)
-1.1 4
4.0%
-1.09 1
 
1.0%
4.2 5
5.0%
4.32 5
5.0%
4.82 1
 
1.0%
4.83 4
4.0%
4.88 5
5.0%
5.98 5
5.0%
8.34 1
 
1.0%
8.35 3
3.0%
ValueCountFrequency (%)
85.8 1
 
1.0%
85.79 3
3.0%
78.15 4
4.0%
78.14 1
 
1.0%
62.48 4
4.0%
47.05 1
 
1.0%
47.04 2
2.0%
47.02 1
 
1.0%
44.82 1
 
1.0%
44.81 1
 
1.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
달성보
 
7
강정고령보
 
7
합천창녕보
 
5
낙단보
 
5
여주보
 
5
Other values (15)
71 

Length

Max length5
Median length3
Mean length3.34
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row칠곡보
2nd row상주보
3rd row구미보
4th row달성보
5th row낙단보

Common Values

ValueCountFrequency (%)
달성보 7
 
7.0%
강정고령보 7
 
7.0%
합천창녕보 5
 
5.0%
낙단보 5
 
5.0%
여주보 5
 
5.0%
수하보 5
 
5.0%
세종보 5
 
5.0%
죽산보 5
 
5.0%
승촌보 5
 
5.0%
이포보 5
 
5.0%
Other values (10) 46
46.0%

Length

2023-12-10T21:53:54.402153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
달성보 7
 
7.0%
강정고령보 7
 
7.0%
이포보 5
 
5.0%
칠곡보 5
 
5.0%
쾌쾌보 5
 
5.0%
공주보 5
 
5.0%
창녕함안보 5
 
5.0%
강천보 5
 
5.0%
백제보 5
 
5.0%
승촌보 5
 
5.0%
Other values (10) 46
46.0%

Interactions

2023-12-10T21:53:51.579621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.511431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.059321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.628295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.447074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.016817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.678198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.607763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.148082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.732151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.576049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.122141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.773966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.696028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.235489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.809241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.672322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.226811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.857518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.784717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.332142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.887505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.751525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.326493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.960114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.869342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.428352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.976367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.837776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.412245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:52.046151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:48.969432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:49.529733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.360824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:50.927327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:53:51.503907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:54.490355image/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)0.0001.0000.7310.7440.8490.9151.000
유입량(ms)0.0000.7311.0000.8120.8680.7901.000
방류량(ms)0.0000.7440.8121.0000.9800.6380.903
저수량(백만m3)0.0000.8490.8680.9801.0000.7470.968
저수율0.0000.9150.7900.6380.7471.0001.000
댐이름0.0001.0001.0000.9030.9681.0001.000
2023-12-10T21:53:54.638098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.019-0.0360.044-0.005-0.0670.000
강우량(mm)-0.0191.0000.6360.4740.540-0.3000.933
유입량(ms)-0.0360.6361.0000.6800.664-0.0470.918
방류량(ms)0.0440.4740.6801.0000.853-0.2830.613
저수량(백만m3)-0.0050.5400.6640.8531.000-0.2970.798
저수율-0.067-0.300-0.047-0.283-0.2971.0000.933
댐이름0.0000.9330.9180.6130.7980.9331.000

Missing values

2023-12-10T21:53:52.160485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:52.291174image/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
02019010101075.52100.333.43733.43725.52칠곡보
12019010101027.612100.844.59744.59747.05상주보
22019010101052.798100.134.07534.07532.51구미보
32019010101044.07675.30.054.09512.57달성보
42019010101033.03495.341.04541.04539.72낙단보
52019010101011.398101.267.47367.47333.03여주보
6201901010100.00.00.00.078.15수하보
7201901010100.95316.931.731.78.34세종보
8201901010104.69418.320.520.5-1.1죽산보
9201901010106.82876.17.87.85.98승촌보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902019010107011.441101.574.42174.42133.04여주보
91201901010700.00.00.00.078.14수하보
92201901010700.00.00.00.044.82쾌쾌보
93201901010702.44315.735.30135.3284.32공주보
942019010107097.26796.446.558103.1144.82창녕함안보
952019010107024.17100.038.5238.524.2백제보
962019010107073.06979.167.16426.4718.26강정고령보
97201901010704.69418.312.04420.6-1.1죽산보
982019010107043.44474.231.7156.63212.5달성보
99201901010706.82876.17.87.85.98승촌보