Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows44
Duplicate rows (%)44.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical3
Numeric5

Alerts

댐이름 has constant value ""Constant
Dataset has 44 (44.0%) duplicate rowsDuplicates
일자/시간(t) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수위(m) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
방류량(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
강우량(mm) is highly imbalanced (76.9%)Imbalance
유입량(ms) has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:59:42.561201
Analysis finished2023-12-10 11:59:47.118152
Duration4.56 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
군위
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군위
2nd row군위
3rd row군위
4th row군위
5th row군위

Common Values

ValueCountFrequency (%)
군위 100
100.0%

Length

2023-12-10T20:59:47.217051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:59:47.346861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190606 × 109
Minimum2.0190601 × 109
Maximum2.0190608 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:59:47.486672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190601 × 109
5-th percentile2.0190601 × 109
Q12.0190607 × 109
median2.0190607 × 109
Q32.0190608 × 109
95-th percentile2.0190608 × 109
Maximum2.0190608 × 109
Range717
Interquartile range (IQR)101

Descriptive statistics

Standard deviation279.19728
Coefficient of variation (CV)1.3828078 × 10-7
Kurtosis-0.39063662
Mean2.0190606 × 109
Median Absolute Deviation (MAD)87.5
Skewness-1.2204259
Sum2.0190606 × 1011
Variance77951.121
MonotonicityNot monotonic
2023-12-10T20:59:47.702260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019060723 2
 
2.0%
2019060715 2
 
2.0%
2019060102 2
 
2.0%
2019060818 2
 
2.0%
2019060813 2
 
2.0%
2019060812 2
 
2.0%
2019060810 2
 
2.0%
2019060806 2
 
2.0%
2019060802 2
 
2.0%
2019060801 2
 
2.0%
Other values (41) 80
80.0%
ValueCountFrequency (%)
2019060101 2
2.0%
2019060102 2
2.0%
2019060103 2
2.0%
2019060104 2
2.0%
2019060105 2
2.0%
2019060106 2
2.0%
2019060107 2
2.0%
2019060108 2
2.0%
2019060109 2
2.0%
2019060110 2
2.0%
ValueCountFrequency (%)
2019060818 2
2.0%
2019060817 2
2.0%
2019060816 2
2.0%
2019060815 2
2.0%
2019060814 2
2.0%
2019060813 2
2.0%
2019060812 2
2.0%
2019060811 2
2.0%
2019060810 2
2.0%
2019060809 2
2.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.6311
Minimum190.52
Maximum190.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:59:47.931228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190.52
5-th percentile190.52
Q1190.54
median190.57
Q3190.58
95-th percentile190.9
Maximum190.9
Range0.38
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.1424596
Coefficient of variation (CV)0.00074730513
Kurtosis-0.35266982
Mean190.6311
Median Absolute Deviation (MAD)0.02
Skewness1.2477922
Sum19063.11
Variance0.020294737
MonotonicityNot monotonic
2023-12-10T20:59:48.108088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
190.58 17
17.0%
190.57 12
12.0%
190.55 12
12.0%
190.56 10
10.0%
190.52 10
10.0%
190.54 10
10.0%
190.89 8
8.0%
190.88 8
8.0%
190.9 6
 
6.0%
190.53 6
 
6.0%
ValueCountFrequency (%)
190.52 10
10.0%
190.53 6
 
6.0%
190.54 10
10.0%
190.55 12
12.0%
190.56 10
10.0%
190.57 12
12.0%
190.58 17
17.0%
190.87 1
 
1.0%
190.88 8
8.0%
190.89 8
8.0%
ValueCountFrequency (%)
190.9 6
 
6.0%
190.89 8
8.0%
190.88 8
8.0%
190.87 1
 
1.0%
190.58 17
17.0%
190.57 12
12.0%
190.56 10
10.0%
190.55 12
12.0%
190.54 10
10.0%
190.53 6
 
6.0%

강우량(mm)
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
94 
1
 
5
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 94
94.0%
1 5
 
5.0%
2 1
 
1.0%

Length

2023-12-10T20:59:48.304162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:59:48.449051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 94
94.0%
1 5
 
5.0%
2 1
 
1.0%

유입량(ms)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8741
Minimum0
Maximum1.111
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:59:48.608707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.995
median1.0395
Q31.072
95-th percentile1.111
Maximum1.111
Range1.111
Interquartile range (IQR)0.077

Descriptive statistics

Standard deviation0.39970549
Coefficient of variation (CV)0.45727661
Kurtosis1.12785
Mean0.8741
Median Absolute Deviation (MAD)0.0385
Skewness-1.7420786
Sum87.41
Variance0.15976447
MonotonicityNot monotonic
2023-12-10T20:59:48.788913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.111 18
18.0%
0.0 17
17.0%
1.039 6
 
6.0%
1.043 6
 
6.0%
1.033 6
 
6.0%
1.034 5
 
5.0%
1.072 4
 
4.0%
1.055 4
 
4.0%
0.995 4
 
4.0%
1.032 4
 
4.0%
Other values (13) 26
26.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.923 2
 
2.0%
0.928 2
 
2.0%
0.99 2
 
2.0%
0.995 4
 
4.0%
1.032 4
 
4.0%
1.033 6
 
6.0%
1.034 5
 
5.0%
1.036 2
 
2.0%
1.039 6
 
6.0%
ValueCountFrequency (%)
1.111 18
18.0%
1.1 2
 
2.0%
1.098 2
 
2.0%
1.078 2
 
2.0%
1.072 4
 
4.0%
1.064 2
 
2.0%
1.06 2
 
2.0%
1.057 2
 
2.0%
1.055 4
 
4.0%
1.052 2
 
2.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.06131
Minimum1.032
Maximum1.111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:59:48.944654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.032
5-th percentile1.033
Q11.039
median1.051
Q31.075
95-th percentile1.111
Maximum1.111
Range0.079
Interquartile range (IQR)0.036

Descriptive statistics

Standard deviation0.029494631
Coefficient of variation (CV)0.027790778
Kurtosis-0.86183888
Mean1.06131
Median Absolute Deviation (MAD)0.0145
Skewness0.87058162
Sum106.131
Variance0.00086993323
MonotonicityNot monotonic
2023-12-10T20:59:49.121356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.111 23
23.0%
1.039 8
 
8.0%
1.043 8
 
8.0%
1.034 7
 
7.0%
1.051 6
 
6.0%
1.033 6
 
6.0%
1.06 4
 
4.0%
1.032 4
 
4.0%
1.042 4
 
4.0%
1.072 4
 
4.0%
Other values (11) 26
26.0%
ValueCountFrequency (%)
1.032 4
4.0%
1.033 6
6.0%
1.034 7
7.0%
1.036 2
 
2.0%
1.037 2
 
2.0%
1.039 8
8.0%
1.04 4
4.0%
1.042 4
4.0%
1.043 8
8.0%
1.044 2
 
2.0%
ValueCountFrequency (%)
1.111 23
23.0%
1.078 2
 
2.0%
1.074 2
 
2.0%
1.072 4
 
4.0%
1.064 2
 
2.0%
1.06 4
 
4.0%
1.057 2
 
2.0%
1.055 4
 
4.0%
1.052 2
 
2.0%
1.051 6
 
6.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.35151
Minimum19.166
Maximum19.801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:59:49.331860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.166
5-th percentile19.166
Q119.199
median19.249
Q319.266
95-th percentile19.801
Maximum19.801
Range0.635
Interquartile range (IQR)0.067

Descriptive statistics

Standard deviation0.23833494
Coefficient of variation (CV)0.01231609
Kurtosis-0.352329
Mean19.35151
Median Absolute Deviation (MAD)0.033
Skewness1.2484189
Sum1935.151
Variance0.056803545
MonotonicityNot monotonic
2023-12-10T20:59:49.466754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19.266 17
17.0%
19.249 12
12.0%
19.216 12
12.0%
19.232 10
10.0%
19.166 10
10.0%
19.199 10
10.0%
19.785 8
8.0%
19.768 8
8.0%
19.801 6
 
6.0%
19.183 6
 
6.0%
ValueCountFrequency (%)
19.166 10
10.0%
19.183 6
 
6.0%
19.199 10
10.0%
19.216 12
12.0%
19.232 10
10.0%
19.249 12
12.0%
19.266 17
17.0%
19.751 1
 
1.0%
19.768 8
8.0%
19.785 8
8.0%
ValueCountFrequency (%)
19.801 6
 
6.0%
19.785 8
8.0%
19.768 8
8.0%
19.751 1
 
1.0%
19.266 17
17.0%
19.249 12
12.0%
19.232 10
10.0%
19.216 12
12.0%
19.199 10
10.0%
19.183 6
 
6.0%

저수율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
39.5
34 
39.4
26 
39.6
17 
40.6
17 
40.7
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row39.5
2nd row39.6
3rd row39.4
4th row39.5
5th row39.5

Common Values

ValueCountFrequency (%)
39.5 34
34.0%
39.4 26
26.0%
39.6 17
17.0%
40.6 17
17.0%
40.7 6
 
6.0%

Length

2023-12-10T20:59:49.614852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:59:49.732556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
39.5 34
34.0%
39.4 26
26.0%
39.6 17
17.0%
40.6 17
17.0%
40.7 6
 
6.0%

Interactions

2023-12-10T20:59:45.737363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:42.897871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.636795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.340787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.012702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.874673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.038103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.783961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.482792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.150522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:46.047273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.198565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.919796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.619979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.307922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:46.187771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.336795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.052380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.733912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.436550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:46.338397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:43.502501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.208040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:44.871516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:45.584206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:59:49.827495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.9990.4240.6250.8691.0000.858
저수위(m)0.9991.0000.4220.7200.8931.0000.834
강우량(mm)0.4240.4221.0000.0000.2370.4520.221
유입량(ms)0.6250.7200.0001.0000.2310.6370.260
방류량(ms)0.8690.8930.2370.2311.0000.8660.779
저수량(백만m3)1.0001.0000.4520.6370.8661.0000.851
저수율0.8580.8340.2210.2600.7790.8511.000
2023-12-10T20:59:49.965723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수율강우량(mm)
저수율1.0000.167
강우량(mm)0.1671.000
2023-12-10T20:59:50.119418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)강우량(mm)저수율
일자/시간(t)1.000-0.994-0.356-0.526-0.9940.1620.890
저수위(m)-0.9941.0000.4100.5341.0000.1740.879
유입량(ms)-0.3560.4101.0000.4970.4100.0000.199
방류량(ms)-0.5260.5340.4971.0000.5340.1580.643
저수량(백만m3)-0.9941.0000.4100.5341.0000.1740.879
강우량(mm)0.1620.1740.0000.1580.1741.0000.167
저수율0.8900.8790.1990.6430.8790.1671.000

Missing values

2023-12-10T20:59:46.861471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:59:47.043532image/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
0군위2019060723190.5601.0521.05219.23239.5
1군위2019060705190.5801.0331.03319.26639.6
2군위2019060815190.5201.0391.03919.16639.4
3군위2019060723190.5601.0521.05219.23239.5
4군위2019060713190.5710.01.07419.24939.5
5군위2019060706190.5811.0331.03319.26639.6
6군위2019060105190.8901.1111.11119.78540.6
7군위2019060815190.5201.0391.03919.16639.4
8군위2019060807190.5401.0421.04219.19939.4
9군위2019060724190.5500.01.05119.21639.5
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군위2019060803190.5500.991.04619.21639.5
91군위2019060803190.5500.991.04619.21639.5
92군위2019060802190.5500.9951.05119.21639.5
93군위2019060801190.5500.9951.05119.21639.5
94군위2019060722190.5601.0551.05519.23239.5
95군위2019060722190.5601.0551.05519.23239.5
96군위2019060721190.5601.0551.05519.23239.5
97군위2019060720190.5601.0571.05719.23239.5
98군위2019060704190.5821.0341.03419.26639.6
99군위2019060705190.5811.0331.03319.26639.6

Duplicate rows

Most frequently occurring

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율# duplicates
0군위2019060101190.901.1111.11119.80140.72
1군위2019060102190.901.1111.11119.80140.72
2군위2019060103190.901.1111.11119.80140.72
3군위2019060104190.8900.01.11119.78540.62
4군위2019060105190.8901.1111.11119.78540.62
5군위2019060106190.8901.1111.11119.78540.62
6군위2019060107190.8901.1111.11119.78540.62
7군위2019060108190.8800.01.11119.76840.62
8군위2019060109190.8801.1111.11119.76840.62
9군위2019060110190.8801.1111.11119.76840.62