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 방류량(ms) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 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 14 (14.0%) zerosZeros
유입량(ms) has 14 (14.0%) zerosZeros
방류량(ms) has 16 (16.0%) zerosZeros
저수량(백만m3) has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:52:56.403755
Analysis finished2023-12-10 12:53:00.277308
Duration3.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190801 × 109
Minimum2.0190801 × 109
Maximum2.0190801 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:00.345877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190801 × 109
5-th percentile2.0190801 × 109
Q12.0190801 × 109
median2.0190801 × 109
Q32.0190801 × 109
95-th percentile2.0190801 × 109
Maximum2.0190801 × 109
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1479377
Coefficient of variation (CV)1.06382 × 10-9
Kurtosis-1.120244
Mean2.0190801 × 109
Median Absolute Deviation (MAD)2
Skewness0.007566635
Sum2.0190801 × 1011
Variance4.6136364
MonotonicityIncreasing
2023-12-10T21:53:00.478645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019080104 15
15.0%
2019080105 15
15.0%
2019080107 15
15.0%
2019080102 13
13.0%
2019080103 13
13.0%
2019080106 11
11.0%
2019080101 10
10.0%
2019080108 8
8.0%
ValueCountFrequency (%)
2019080101 10
10.0%
2019080102 13
13.0%
2019080103 13
13.0%
2019080104 15
15.0%
2019080105 15
15.0%
2019080106 11
11.0%
2019080107 15
15.0%
2019080108 8
8.0%
ValueCountFrequency (%)
2019080108 8
8.0%
2019080107 15
15.0%
2019080106 11
11.0%
2019080105 15
15.0%
2019080104 15
15.0%
2019080103 13
13.0%
2019080102 13
13.0%
2019080101 10
10.0%

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

2023-12-10T21:53:00.607855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:53:00.718887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.98747
Minimum0
Maximum98.692
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:00.834548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.24275
median16.4435
Q353.71375
95-th percentile97.674
Maximum98.692
Range98.692
Interquartile range (IQR)48.471

Descriptive statistics

Standard deviation29.737133
Coefficient of variation (CV)0.95965024
Kurtosis-0.36818899
Mean30.98747
Median Absolute Deviation (MAD)16.4435
Skewness0.8208454
Sum3098.747
Variance884.29708
MonotonicityNot monotonic
2023-12-10T21:53:01.009978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
2.086 6
 
6.0%
27.485 4
 
4.0%
55.411 3
 
3.0%
16.382 3
 
3.0%
9.362 3
 
3.0%
97.674 3
 
3.0%
15.369 3
 
3.0%
53.687 3
 
3.0%
53.581 2
 
2.0%
Other values (44) 56
56.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.968 1
 
1.0%
0.969 1
 
1.0%
0.97 2
 
2.0%
2.081 1
 
1.0%
2.086 6
6.0%
6.295 1
 
1.0%
6.325 1
 
1.0%
9.271 2
 
2.0%
9.316 1
 
1.0%
ValueCountFrequency (%)
98.692 1
 
1.0%
98.489 2
2.0%
98.285 1
 
1.0%
97.674 3
3.0%
75.623 1
 
1.0%
75.52 1
 
1.0%
74.828 2
2.0%
74.681 2
2.0%
74.535 1
 
1.0%
55.584 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.676
Minimum0
Maximum108.7
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:01.161019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.4
median81
Q3100.3
95-th percentile107.705
Maximum108.7
Range108.7
Interquartile range (IQR)86.9

Descriptive statistics

Standard deviation43.214918
Coefficient of variation (CV)0.66817549
Kurtosis-1.4663862
Mean64.676
Median Absolute Deviation (MAD)24.8
Skewness-0.58847
Sum6467.6
Variance1867.5291
MonotonicityNot monotonic
2023-12-10T21:53:01.295382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 14
 
14.0%
2.4 8
 
8.0%
13.4 7
 
7.0%
100.3 5
 
5.0%
96.8 5
 
5.0%
79.2 3
 
3.0%
107.3 3
 
3.0%
107.2 3
 
3.0%
91.7 3
 
3.0%
100.4 3
 
3.0%
Other values (36) 46
46.0%
ValueCountFrequency (%)
0.0 14
14.0%
2.4 8
8.0%
13.4 7
7.0%
17.1 2
 
2.0%
17.2 2
 
2.0%
59.8 1
 
1.0%
60.1 1
 
1.0%
60.3 1
 
1.0%
70.2 1
 
1.0%
70.5 1
 
1.0%
ValueCountFrequency (%)
108.7 1
 
1.0%
108.1 1
 
1.0%
108.0 1
 
1.0%
107.9 1
 
1.0%
107.8 1
 
1.0%
107.7 1
 
1.0%
107.5 2
2.0%
107.3 3
3.0%
107.2 3
3.0%
106.7 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.9736
Minimum0
Maximum246.152
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:01.445534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.97875
median97.606
Q3157.93525
95-th percentile218.49955
Maximum246.152
Range246.152
Interquartile range (IQR)117.9565

Descriptive statistics

Standard deviation72.291543
Coefficient of variation (CV)0.71594499
Kurtosis-1.2112957
Mean100.9736
Median Absolute Deviation (MAD)61.2485
Skewness0.063032878
Sum10097.36
Variance5226.0672
MonotonicityNot monotonic
2023-12-10T21:53:01.666236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
20.956 2
 
2.0%
150.13 2
 
2.0%
116.52 1
 
1.0%
175.962 1
 
1.0%
67.138 1
 
1.0%
149.137 1
 
1.0%
139.853 1
 
1.0%
9.567 1
 
1.0%
16.683 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 16
16.0%
9.539 1
 
1.0%
9.567 1
 
1.0%
16.683 1
 
1.0%
16.711 1
 
1.0%
20.949 1
 
1.0%
20.956 2
 
2.0%
32.261 1
 
1.0%
32.289 1
 
1.0%
42.542 1
 
1.0%
ValueCountFrequency (%)
246.152 1
1.0%
234.87 1
1.0%
229.562 1
1.0%
221.827 1
1.0%
218.529 1
1.0%
218.498 1
1.0%
200.083 1
1.0%
198.916 1
1.0%
198.655 1
1.0%
197.408 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.14164
Minimum0
Maximum250.95
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:01.825294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143.223
median92.549
Q3149.77675
95-th percentile229.19
Maximum250.95
Range250.95
Interquartile range (IQR)106.55375

Descriptive statistics

Standard deviation72.300544
Coefficient of variation (CV)0.71484449
Kurtosis-1.0011618
Mean101.14164
Median Absolute Deviation (MAD)56.9545
Skewness0.21355164
Sum10114.164
Variance5227.3686
MonotonicityNot monotonic
2023-12-10T21:53:02.010093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
20.9 7
 
7.0%
12.6 2
 
2.0%
67.192 2
 
2.0%
150.13 2
 
2.0%
67.52 1
 
1.0%
148.82 1
 
1.0%
175.962 1
 
1.0%
67.138 1
 
1.0%
119.665 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0.0 14
14.0%
12.6 2
 
2.0%
20.893 1
 
1.0%
20.9 7
7.0%
42.542 1
 
1.0%
43.45 1
 
1.0%
44.425 1
 
1.0%
44.427 1
 
1.0%
62.405 1
 
1.0%
67.138 1
 
1.0%
ValueCountFrequency (%)
250.95 1
1.0%
241.387 1
1.0%
234.87 1
1.0%
233.425 1
1.0%
230.33 1
1.0%
229.13 1
1.0%
225.085 1
1.0%
208.958 1
1.0%
200.083 1
1.0%
198.655 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.1925
Minimum1.54
Maximum132.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:53:02.185168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.54
5-th percentile1.5595
Q15.4375
median18.375
Q339.82
95-th percentile86.01
Maximum132.58
Range131.04
Interquartile range (IQR)34.3825

Descriptive statistics

Standard deviation31.246709
Coefficient of variation (CV)1.0349163
Kurtosis1.9146801
Mean30.1925
Median Absolute Deviation (MAD)14.665
Skewness1.4875827
Sum3019.25
Variance976.35682
MonotonicityNot monotonic
2023-12-10T21:53:02.365600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.71 6
 
6.0%
86.01 5
 
5.0%
47.02 4
 
4.0%
1.54 3
 
3.0%
13.54 3
 
3.0%
28.23 3
 
3.0%
4.84 3
 
3.0%
38.14 3
 
3.0%
9.27 3
 
3.0%
86.0 2
 
2.0%
Other values (49) 65
65.0%
ValueCountFrequency (%)
1.54 3
3.0%
1.55 2
 
2.0%
1.56 1
 
1.0%
1.57 2
 
2.0%
2.71 1
 
1.0%
2.72 1
 
1.0%
2.73 1
 
1.0%
3.7 1
 
1.0%
3.71 6
6.0%
4.84 3
3.0%
ValueCountFrequency (%)
132.58 1
 
1.0%
132.57 1
 
1.0%
132.56 1
 
1.0%
86.01 5
5.0%
86.0 2
 
2.0%
80.85 2
 
2.0%
80.84 2
 
2.0%
62.57 2
 
2.0%
62.56 1
 
1.0%
47.02 4
4.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
달성보
8 
죽산보
8 
합천창녕보
8 
창녕함안보
7 
이포보
7 
Other values (15)
62 

Length

Max length5
Median length3
Mean length3.46
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row달성보
2nd row백제보
3rd row이포보
4th row합천창녕보
5th row칠곡보

Common Values

ValueCountFrequency (%)
달성보 8
 
8.0%
죽산보 8
 
8.0%
합천창녕보 8
 
8.0%
창녕함안보 7
 
7.0%
이포보 7
 
7.0%
공주보 7
 
7.0%
낙단보 7
 
7.0%
안동보 7
 
7.0%
강천보 6
 
6.0%
강정고령보 5
 
5.0%
Other values (10) 30
30.0%

Length

2023-12-10T21:53:02.533964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
달성보 8
 
8.0%
합천창녕보 8
 
8.0%
죽산보 8
 
8.0%
창녕함안보 7
 
7.0%
이포보 7
 
7.0%
공주보 7
 
7.0%
낙단보 7
 
7.0%
안동보 7
 
7.0%
강천보 6
 
6.0%
강정고령보 5
 
5.0%
Other values (10) 30
30.0%

Interactions

2023-12-10T21:52:59.521272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:56.665549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.157515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.635214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.134501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.627218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.610022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:56.757292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.243484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.731984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.238111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.713582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.689472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:56.838569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.317941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.805083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.317791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.170589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.770658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:56.918438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.390716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.878125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.399164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.250881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.852429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:56.997271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.469935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.955097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.477389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.343592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.934202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.079509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:57.543067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.052660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:58.551901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:59.428947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:53:02.646111image/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)0.0001.0000.8580.7190.7760.9501.000
유입량(ms)0.0000.8581.0000.7360.7820.8421.000
방류량(ms)0.0000.7190.7361.0000.9400.7440.952
저수량(백만m3)0.0000.7760.7820.9401.0000.7100.980
저수율0.0000.9500.8420.7440.7101.0001.000
댐이름0.0001.0001.0000.9520.9801.0001.000
2023-12-10T21:53:02.780455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.051-0.0720.0270.019-0.0160.000
강우량(mm)-0.0511.0000.4680.5270.606-0.2740.927
유입량(ms)-0.0720.4681.0000.7950.8240.2080.927
방류량(ms)0.0270.5270.7951.0000.8780.0250.619
저수량(백만m3)0.0190.6060.8240.8781.000-0.0110.719
저수율-0.016-0.2740.2080.025-0.0111.0000.927
댐이름0.0000.9270.9270.6190.7190.9271.000

Missing values

2023-12-10T21:53:00.071989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:53:00.224314image/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
02019080101053.58191.5116.52116.5213.53달성보
12019080101014.56960.370.35385.0752.73백제보
22019080101015.413107.5198.655198.65528.24이포보
32019080101055.41179.2218.49873.8879.27합천창녕보
42019080101075.52100.370.6370.6325.52칠곡보
52019080101038.952108.1135.891150.197132.58단양수중보
6201908010100.00.00.00.086.01안동보
72019080101016.5052.432.26120.91.57죽산보
82019080101027.485100.381.09492.81647.02상주보
92019080101098.69297.881.283250.954.89창녕함안보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902019080107014.51660.177.12762.4052.72백제보
912019080107074.82881.0117.0117.018.38강정고령보
922019080108054.8978.5156.824229.139.21합천창녕보
932019080108033.79797.5181.959149.34839.85낙단보
94201908010800.9717.244.42744.4278.51세종보
952019080108054.11292.4198.916139.97213.58달성보
96201908010800.00.00.00.080.85수하보
97201908010802.08113.465.85967.1923.7공주보
982019080108074.82881.0148.85148.8518.38강정고령보
992019080108016.3822.420.94920.8931.54죽산보