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

Categorical1
Numeric7

Alerts

댐이름 has constant value ""Constant
일자/시간(t) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수량 is highly overall correlated with 방류량(ms)High correlation
저수율(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
유입량(백만m3) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수량High correlation
일자/시간(t) has unique valuesUnique
저수위(m) has 93 (93.0%) zerosZeros
저수량 has 2 (2.0%) zerosZeros
방류량(ms) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2024-04-17 06:02:44.004866
Analysis finished2024-04-17 06:02:48.617582
Duration4.61 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
감포
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
감포 100
100.0%

Length

2024-04-17T15:02:48.669860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:02:48.748319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190409 × 109
Minimum2.0190405 × 109
Maximum2.0190413 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:48.844271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190405 × 109
5-th percentile2.0190405 × 109
Q12.0190407 × 109
median2.0190409 × 109
Q32.0190411 × 109
95-th percentile2.0190412 × 109
Maximum2.0190413 × 109
Range806
Interquartile range (IQR)403

Descriptive statistics

Standard deviation241.9365
Coefficient of variation (CV)1.1982744 × 10-7
Kurtosis-1.1923273
Mean2.0190409 × 109
Median Absolute Deviation (MAD)202
Skewness0.027309609
Sum2.0190409 × 1011
Variance58533.27
MonotonicityNot monotonic
2024-04-17T15:02:48.982822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019040803 1
 
1.0%
2019041301 1
 
1.0%
2019041111 1
 
1.0%
2019041117 1
 
1.0%
2019041119 1
 
1.0%
2019041123 1
 
1.0%
2019041201 1
 
1.0%
2019041209 1
 
1.0%
2019041211 1
 
1.0%
2019041215 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019040501 1
1.0%
2019040503 1
1.0%
2019040505 1
1.0%
2019040507 1
1.0%
2019040509 1
1.0%
2019040511 1
1.0%
2019040513 1
1.0%
2019040515 1
1.0%
2019040517 1
1.0%
2019040519 1
1.0%
ValueCountFrequency (%)
2019041307 1
1.0%
2019041305 1
1.0%
2019041303 1
1.0%
2019041301 1
1.0%
2019041223 1
1.0%
2019041221 1
1.0%
2019041219 1
1.0%
2019041217 1
1.0%
2019041215 1
1.0%
2019041213 1
1.0%

저수위(m)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13
Minimum0
Maximum5
Zeros93
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:49.093373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.525
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.62206888
Coefficient of variation (CV)4.7851453
Kurtosis42.633506
Mean0.13
Median Absolute Deviation (MAD)0
Skewness6.2016894
Sum13
Variance0.3869697
MonotonicityNot monotonic
2024-04-17T15:02:49.183664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 93
93.0%
1.5 2
 
2.0%
0.5 2
 
2.0%
1.0 1
 
1.0%
3.0 1
 
1.0%
5.0 1
 
1.0%
ValueCountFrequency (%)
0.0 93
93.0%
0.5 2
 
2.0%
1.0 1
 
1.0%
1.5 2
 
2.0%
3.0 1
 
1.0%
5.0 1
 
1.0%
ValueCountFrequency (%)
5.0 1
 
1.0%
3.0 1
 
1.0%
1.5 2
 
2.0%
1.0 1
 
1.0%
0.5 2
 
2.0%
0.0 93
93.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.02029
Minimum2.015
Maximum2.029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:49.269322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.015
5-th percentile2.015
Q12.017
median2.018
Q32.024
95-th percentile2.027
Maximum2.029
Range0.014
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.004015865
Coefficient of variation (CV)0.0019877666
Kurtosis-0.89401519
Mean2.02029
Median Absolute Deviation (MAD)0.001
Skewness0.66261747
Sum202.029
Variance1.6127172 × 10-5
MonotonicityNot monotonic
2024-04-17T15:02:49.361741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2.018 33
33.0%
2.017 18
18.0%
2.026 12
 
12.0%
2.024 11
 
11.0%
2.022 8
 
8.0%
2.015 8
 
8.0%
2.029 4
 
4.0%
2.02 3
 
3.0%
2.027 3
 
3.0%
ValueCountFrequency (%)
2.015 8
 
8.0%
2.017 18
18.0%
2.018 33
33.0%
2.02 3
 
3.0%
2.022 8
 
8.0%
2.024 11
 
11.0%
2.026 12
 
12.0%
2.027 3
 
3.0%
2.029 4
 
4.0%
ValueCountFrequency (%)
2.029 4
 
4.0%
2.027 3
 
3.0%
2.026 12
 
12.0%
2.024 11
 
11.0%
2.022 8
 
8.0%
2.02 3
 
3.0%
2.018 33
33.0%
2.017 18
18.0%
2.015 8
 
8.0%

저수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03747
Minimum0
Maximum0.062
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:49.458556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02835
Q10.04
median0.04
Q30.04
95-th percentile0.0402
Maximum0.062
Range0.062
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0086122476
Coefficient of variation (CV)0.22984381
Kurtosis8.8439415
Mean0.03747
Median Absolute Deviation (MAD)0
Skewness-2.2853531
Sum3.747
Variance7.4170808 × 10-5
MonotonicityNot monotonic
2024-04-17T15:02:49.558892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.04 72
72.0%
0.03 7
 
7.0%
0.031 4
 
4.0%
0.034 3
 
3.0%
0.0 2
 
2.0%
0.012 1
 
1.0%
0.029 1
 
1.0%
0.032 1
 
1.0%
0.006 1
 
1.0%
0.049 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
0.0 2
 
2.0%
0.006 1
 
1.0%
0.012 1
 
1.0%
0.016 1
 
1.0%
0.029 1
 
1.0%
0.03 7
7.0%
0.031 4
4.0%
0.032 1
 
1.0%
0.034 3
3.0%
0.037 1
 
1.0%
ValueCountFrequency (%)
0.062 1
 
1.0%
0.056 1
 
1.0%
0.05 1
 
1.0%
0.049 1
 
1.0%
0.044 1
 
1.0%
0.04 72
72.0%
0.038 1
 
1.0%
0.037 1
 
1.0%
0.034 3
 
3.0%
0.032 1
 
1.0%

저수율(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.0605
Minimum38.03
Maximum38.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:49.657562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.03
5-th percentile38.03
Q138.04
median38.05
Q338.08
95-th percentile38.1
Maximum38.11
Range0.08
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.022127957
Coefficient of variation (CV)0.00058138902
Kurtosis-0.7401227
Mean38.0605
Median Absolute Deviation (MAD)0.01
Skewness0.61479758
Sum3806.05
Variance0.00048964646
MonotonicityNot monotonic
2024-04-17T15:02:49.756802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
38.05 33
33.0%
38.04 18
18.0%
38.09 12
 
12.0%
38.08 11
 
11.0%
38.07 8
 
8.0%
38.03 8
 
8.0%
38.11 4
 
4.0%
38.06 3
 
3.0%
38.1 3
 
3.0%
ValueCountFrequency (%)
38.03 8
 
8.0%
38.04 18
18.0%
38.05 33
33.0%
38.06 3
 
3.0%
38.07 8
 
8.0%
38.08 11
 
11.0%
38.09 12
 
12.0%
38.1 3
 
3.0%
38.11 4
 
4.0%
ValueCountFrequency (%)
38.11 4
 
4.0%
38.1 3
 
3.0%
38.09 12
 
12.0%
38.08 11
 
11.0%
38.07 8
 
8.0%
38.06 3
 
3.0%
38.05 33
33.0%
38.04 18
18.0%
38.03 8
 
8.0%

유입량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.645
Minimum76.4
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:49.861011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.4
5-th percentile76.4
Q176.5
median76.6
Q376.8
95-th percentile76.9
Maximum77
Range0.6
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.14932508
Coefficient of variation (CV)0.001948269
Kurtosis-0.36275091
Mean76.645
Median Absolute Deviation (MAD)0.1
Skewness0.43299268
Sum7664.5
Variance0.02229798
MonotonicityNot monotonic
2024-04-17T15:02:49.956169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
76.6 36
36.0%
76.8 23
23.0%
76.5 18
18.0%
76.7 8
 
8.0%
76.4 8
 
8.0%
77.0 4
 
4.0%
76.9 3
 
3.0%
ValueCountFrequency (%)
76.4 8
 
8.0%
76.5 18
18.0%
76.6 36
36.0%
76.7 8
 
8.0%
76.8 23
23.0%
76.9 3
 
3.0%
77.0 4
 
4.0%
ValueCountFrequency (%)
77.0 4
 
4.0%
76.9 3
 
3.0%
76.8 23
23.0%
76.7 8
 
8.0%
76.6 36
36.0%
76.5 18
18.0%
76.4 8
 
8.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03548
Minimum0
Maximum0.062
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:02:50.055018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.03625
median0.04
Q30.04
95-th percentile0.04
Maximum0.062
Range0.062
Interquartile range (IQR)0.00375

Descriptive statistics

Standard deviation0.011788952
Coefficient of variation (CV)0.33227036
Kurtosis4.1608258
Mean0.03548
Median Absolute Deviation (MAD)0
Skewness-2.0313832
Sum3.548
Variance0.00013897939
MonotonicityNot monotonic
2024-04-17T15:02:50.155852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.04 69
69.0%
0.0 7
 
7.0%
0.03 6
 
6.0%
0.031 4
 
4.0%
0.034 3
 
3.0%
0.012 1
 
1.0%
0.029 1
 
1.0%
0.032 1
 
1.0%
0.006 1
 
1.0%
0.016 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.006 1
 
1.0%
0.012 1
 
1.0%
0.016 1
 
1.0%
0.029 1
 
1.0%
0.03 6
6.0%
0.031 4
4.0%
0.032 1
 
1.0%
0.034 3
3.0%
0.037 1
 
1.0%
ValueCountFrequency (%)
0.062 1
 
1.0%
0.056 1
 
1.0%
0.05 1
 
1.0%
0.044 1
 
1.0%
0.04 69
69.0%
0.038 1
 
1.0%
0.037 1
 
1.0%
0.034 3
 
3.0%
0.032 1
 
1.0%
0.031 4
 
4.0%

Interactions

2024-04-17T15:02:47.839753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.188526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.779213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.353763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.896375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.439809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.025773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.927353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.269313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.861155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.435897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.981573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.529189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.115017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.998111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.343465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.935552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.505221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.051556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.606218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.184881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:48.075815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.426584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.014234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.579437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.128861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.686797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.260068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:48.150017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.513468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.105262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.657220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.202645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.768950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.338982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:48.251749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.615809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.203243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.742003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.288312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.855546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.421861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:48.330887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:44.697288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.282747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:45.822706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.364708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:46.937602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:02:47.757396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T15:02:50.234637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.0000.1300.8620.5990.8620.8690.536
저수위(m)0.1301.0000.0000.7090.0000.0000.711
강우량(mm)0.8620.0001.0000.5721.0001.0000.523
저수량0.5990.7090.5721.0000.5720.4260.998
저수율(ms)0.8620.0001.0000.5721.0001.0000.523
유입량(백만m3)0.8690.0001.0000.4261.0001.0000.453
방류량(ms)0.5360.7110.5230.9980.5230.4531.000
2024-04-17T15:02:50.332718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.0000.118-0.8820.006-0.882-0.8660.026
저수위(m)0.1181.000-0.217-0.292-0.217-0.211-0.238
강우량(mm)-0.882-0.2171.000-0.0091.0000.9900.049
저수량0.006-0.292-0.0091.000-0.009-0.0120.827
저수율(ms)-0.882-0.2171.000-0.0091.0000.9900.049
유입량(백만m3)-0.866-0.2110.990-0.0120.9901.0000.070
방류량(ms)0.026-0.2380.0490.8270.0490.0701.000

Missing values

2024-04-17T15:02:48.447582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T15:02:48.570626image/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)저수량저수율(ms)유입량(백만m3)방류량(ms)
0감포20190408030.02.0220.0438.0776.70.04
1감포20190405050.02.0290.0338.1177.00.03
2감포20190410190.02.0180.0438.0576.60.04
3감포20190408050.02.020.04938.0676.60.0
4감포20190406130.02.0260.0438.0976.80.04
5감포20190405070.02.0290.0338.1177.00.03
6감포20190412030.02.0170.00638.0476.50.006
7감포20190410210.02.0180.02938.0576.60.029
8감포20190409130.02.0170.0438.0476.50.04
9감포20190408070.02.020.0438.0676.60.04
댐이름일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
90감포20190408210.02.0180.0438.0576.60.04
91감포20190408190.02.0180.0538.0576.60.05
92감포20190408150.02.0180.05638.0576.60.056
93감포20190408130.02.0180.0438.0576.60.04
94감포20190408010.02.0220.0438.0776.70.04
95감포20190407230.02.0220.0438.0776.70.04
96감포20190407190.02.0220.0438.0776.70.04
97감포20190407170.02.0220.0438.0776.70.04
98감포20190405010.02.0290.03138.1177.00.031
99감포20190405030.02.0290.0338.1177.00.03