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

Categorical2
Numeric6

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
저수위(m) is highly imbalanced (79.3%)Imbalance
일자/시간(t) has unique valuesUnique
방류량(ms) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2024-04-17 06:03:02.706864
Analysis finished2024-04-17 06:03:06.275303
Duration3.57 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

2024-04-17T15:03:06.329028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:06.410805image/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%
Mean2.019011 × 109
Minimum2.0190106 × 109
Maximum2.0190114 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:06.501899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190106 × 109
5-th percentile2.0190106 × 109
Q12.0190108 × 109
median2.019011 × 109
Q32.0190112 × 109
95-th percentile2.0190114 × 109
Maximum2.0190114 × 109
Range806
Interquartile range (IQR)403

Descriptive statistics

Standard deviation243.16402
Coefficient of variation (CV)1.204372 × 10-7
Kurtosis-1.1770461
Mean2.019011 × 109
Median Absolute Deviation (MAD)202
Skewness0.014645878
Sum2.019011 × 1011
Variance59128.742
MonotonicityNot monotonic
2024-04-17T15:03:06.632406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019010908 1
 
1.0%
2019011406 1
 
1.0%
2019011216 1
 
1.0%
2019011222 1
 
1.0%
2019011224 1
 
1.0%
2019011304 1
 
1.0%
2019011306 1
 
1.0%
2019011314 1
 
1.0%
2019011316 1
 
1.0%
2019011320 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2019010606 1
1.0%
2019010608 1
1.0%
2019010610 1
1.0%
2019010612 1
1.0%
2019010614 1
1.0%
2019010616 1
1.0%
2019010618 1
1.0%
2019010620 1
1.0%
2019010622 1
1.0%
2019010624 1
1.0%
ValueCountFrequency (%)
2019011412 1
1.0%
2019011410 1
1.0%
2019011408 1
1.0%
2019011406 1
1.0%
2019011404 1
1.0%
2019011402 1
1.0%
2019011324 1
1.0%
2019011322 1
1.0%
2019011320 1
1.0%
2019011318 1
1.0%

저수위(m)
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.0
94 
1.0
 
3
0.5
 
2
1.5
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 94
94.0%
1.0 3
 
3.0%
0.5 2
 
2.0%
1.5 1
 
1.0%

Length

2024-04-17T15:03:06.754358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T15:03:06.836853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 94
94.0%
1.0 3
 
3.0%
0.5 2
 
2.0%
1.5 1
 
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.23489
Minimum2.227
Maximum2.246
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:06.914226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.227
5-th percentile2.227
Q12.229
median2.234
Q32.241
95-th percentile2.24505
Maximum2.246
Range0.019
Interquartile range (IQR)0.012

Descriptive statistics

Standard deviation0.0061363217
Coefficient of variation (CV)0.002745693
Kurtosis-1.1912515
Mean2.23489
Median Absolute Deviation (MAD)0.005
Skewness0.34425476
Sum223.489
Variance3.7654444 × 10-5
MonotonicityNot monotonic
2024-04-17T15:03:07.016656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2.229 17
17.0%
2.231 15
15.0%
2.227 13
13.0%
2.237 12
12.0%
2.241 12
12.0%
2.239 7
7.0%
2.245 7
7.0%
2.246 5
 
5.0%
2.233 5
 
5.0%
2.235 5
 
5.0%
ValueCountFrequency (%)
2.227 13
13.0%
2.229 17
17.0%
2.231 15
15.0%
2.233 5
 
5.0%
2.235 5
 
5.0%
2.237 12
12.0%
2.239 7
7.0%
2.241 12
12.0%
2.243 2
 
2.0%
2.245 7
7.0%
ValueCountFrequency (%)
2.246 5
 
5.0%
2.245 7
7.0%
2.243 2
 
2.0%
2.241 12
12.0%
2.239 7
7.0%
2.237 12
12.0%
2.235 5
 
5.0%
2.233 5
 
5.0%
2.231 15
15.0%
2.229 17
17.0%

저수량
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03847
Minimum0
Maximum0.09
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:07.118941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.03975
median0.04
Q30.04
95-th percentile0.04
Maximum0.09
Range0.09
Interquartile range (IQR)0.00025

Descriptive statistics

Standard deviation0.0082736713
Coefficient of variation (CV)0.21506814
Kurtosis19.319034
Mean0.03847
Median Absolute Deviation (MAD)0
Skewness1.5965701
Sum3.847
Variance6.8453636 × 10-5
MonotonicityNot monotonic
2024-04-17T15:03:07.214960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.04 71
71.0%
0.03 16
 
16.0%
0.033 3
 
3.0%
0.039 1
 
1.0%
0.034 1
 
1.0%
0.0 1
 
1.0%
0.025 1
 
1.0%
0.09 1
 
1.0%
0.056 1
 
1.0%
0.062 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.025 1
 
1.0%
0.03 16
 
16.0%
0.033 3
 
3.0%
0.034 1
 
1.0%
0.035 1
 
1.0%
0.036 1
 
1.0%
0.039 1
 
1.0%
0.04 71
71.0%
0.051 1
 
1.0%
ValueCountFrequency (%)
0.09 1
 
1.0%
0.062 1
 
1.0%
0.056 1
 
1.0%
0.051 1
 
1.0%
0.04 71
71.0%
0.039 1
 
1.0%
0.036 1
 
1.0%
0.035 1
 
1.0%
0.034 1
 
1.0%
0.033 3
 
3.0%

저수율(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.2197
Minimum39.18
Maximum39.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:07.314170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.18
5-th percentile39.18
Q139.19
median39.215
Q339.25
95-th percentile39.2705
Maximum39.28
Range0.1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.031154713
Coefficient of variation (CV)0.00079436389
Kurtosis-1.0943726
Mean39.2197
Median Absolute Deviation (MAD)0.025
Skewness0.39044216
Sum3921.97
Variance0.00097061616
MonotonicityNot monotonic
2024-04-17T15:03:07.420337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
39.19 17
17.0%
39.2 15
15.0%
39.18 13
13.0%
39.23 12
12.0%
39.25 12
12.0%
39.24 7
7.0%
39.27 7
7.0%
39.28 5
 
5.0%
39.21 5
 
5.0%
39.22 5
 
5.0%
ValueCountFrequency (%)
39.18 13
13.0%
39.19 17
17.0%
39.2 15
15.0%
39.21 5
 
5.0%
39.22 5
 
5.0%
39.23 12
12.0%
39.24 7
7.0%
39.25 12
12.0%
39.26 2
 
2.0%
39.27 7
7.0%
ValueCountFrequency (%)
39.28 5
 
5.0%
39.27 7
7.0%
39.26 2
 
2.0%
39.25 12
12.0%
39.24 7
7.0%
39.23 12
12.0%
39.22 5
 
5.0%
39.21 5
 
5.0%
39.2 15
15.0%
39.19 17
17.0%

유입량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.789
Minimum84.5
Maximum85.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:07.508231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84.5
5-th percentile84.5
Q184.6
median84.75
Q385
95-th percentile85.2
Maximum85.2
Range0.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.23045629
Coefficient of variation (CV)0.0027179975
Kurtosis-1.1160323
Mean84.789
Median Absolute Deviation (MAD)0.15
Skewness0.419917
Sum8478.9
Variance0.053110101
MonotonicityNot monotonic
2024-04-17T15:03:07.603950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
84.6 32
32.0%
84.9 19
19.0%
84.5 13
13.0%
85.2 12
 
12.0%
85.0 12
 
12.0%
84.7 5
 
5.0%
84.8 5
 
5.0%
85.1 2
 
2.0%
ValueCountFrequency (%)
84.5 13
13.0%
84.6 32
32.0%
84.7 5
 
5.0%
84.8 5
 
5.0%
84.9 19
19.0%
85.0 12
 
12.0%
85.1 2
 
2.0%
85.2 12
 
12.0%
ValueCountFrequency (%)
85.2 12
 
12.0%
85.1 2
 
2.0%
85.0 12
 
12.0%
84.9 19
19.0%
84.8 5
 
5.0%
84.7 5
 
5.0%
84.6 32
32.0%
84.5 13
13.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03647
Minimum0
Maximum0.09
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T15:03:07.710958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.033
median0.04
Q30.04
95-th percentile0.04
Maximum0.09
Range0.09
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.011791501
Coefficient of variation (CV)0.32332056
Kurtosis7.73027
Mean0.03647
Median Absolute Deviation (MAD)0
Skewness-0.71396911
Sum3.647
Variance0.00013903949
MonotonicityNot monotonic
2024-04-17T15:03:07.800766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.04 66
66.0%
0.03 16
 
16.0%
0.0 6
 
6.0%
0.033 3
 
3.0%
0.039 1
 
1.0%
0.034 1
 
1.0%
0.025 1
 
1.0%
0.09 1
 
1.0%
0.056 1
 
1.0%
0.062 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
0.0 6
 
6.0%
0.025 1
 
1.0%
0.03 16
 
16.0%
0.033 3
 
3.0%
0.034 1
 
1.0%
0.035 1
 
1.0%
0.036 1
 
1.0%
0.039 1
 
1.0%
0.04 66
66.0%
0.051 1
 
1.0%
ValueCountFrequency (%)
0.09 1
 
1.0%
0.062 1
 
1.0%
0.056 1
 
1.0%
0.051 1
 
1.0%
0.04 66
66.0%
0.039 1
 
1.0%
0.036 1
 
1.0%
0.035 1
 
1.0%
0.034 1
 
1.0%
0.033 3
 
3.0%

Interactions

2024-04-17T15:03:05.639581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:02.906122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.444970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.947436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.403302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.152366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.721757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:02.993333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.543815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.030954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.489652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.237702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.798002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.095143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.630503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.109246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.581257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.322959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.879496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.178461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.703814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.172446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.661767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.397728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.957317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.258950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.783225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.243444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.739154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.473298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:06.033633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.351899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:03.868465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:04.324235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.085045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T15:03:05.559830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T15:03:07.872502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
일자/시간(t)1.0000.4260.9860.5370.9250.9220.528
저수위(m)0.4261.0000.2050.0000.2490.0000.000
강우량(mm)0.9860.2051.0000.5910.9801.0000.597
저수량0.5370.0000.5911.0000.4800.5500.998
저수율(ms)0.9250.2490.9800.4801.0000.9570.480
유입량(백만m3)0.9220.0001.0000.5500.9571.0000.552
방류량(ms)0.5280.0000.5970.9980.4800.5521.000
2024-04-17T15:03:07.977239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)저수위(m)
일자/시간(t)1.000-0.9930.284-0.993-0.9770.2580.259
강우량(mm)-0.9931.000-0.2781.0000.984-0.2320.116
저수량0.284-0.2781.000-0.278-0.2840.8450.000
저수율(ms)-0.9931.000-0.2781.0000.984-0.2320.000
유입량(백만m3)-0.9770.984-0.2840.9841.000-0.2390.000
방류량(ms)0.258-0.2320.845-0.232-0.2391.0000.000
저수위(m)0.2590.1160.0000.0000.0000.0001.000

Missing values

2024-04-17T15:03:06.124117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T15:03:06.231426image/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)
0감포20190109080.02.2370.0339.2384.90.03
1감포20190106100.02.2460.03339.2885.20.033
2감포20190111240.02.2310.0439.284.60.04
3감포20190109100.02.2370.0339.2384.90.03
4감포20190107180.02.2410.0439.2585.00.04
5감포20190106120.02.2460.03339.2885.20.033
6감포20190113080.02.2290.0439.1984.60.04
7감포20190112020.02.2290.0439.1984.60.0
8감포20190110180.02.2330.0339.2184.70.03
9감포20190109120.02.2370.03939.2384.90.039
댐이름일자/시간(t)저수위(m)강우량(mm)저수량저수율(ms)유입량(백만m3)방류량(ms)
90감포20190110020.02.2350.0439.2284.80.04
91감포20190109240.02.2350.0439.2284.80.0
92감포20190109200.02.2370.0439.2384.90.04
93감포20190109180.02.2370.0439.2384.90.04
94감포20190109060.02.2370.0339.2384.90.03
95감포20190109040.02.2370.0339.2384.90.03
96감포20190108240.02.2370.0439.2384.90.04
97감포20190108220.02.2390.0439.2484.90.04
98감포20190106060.02.2460.03539.2885.20.035
99감포20190106080.02.2460.03639.2885.20.036