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

Categorical6
Numeric2

Alerts

댐이름 has constant value ""Constant
저수위(m) has constant value ""Constant
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
저수량(백만m3) is highly imbalanced (81.3%)Imbalance
일자/시간(t) has unique valuesUnique
방류량(ms) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2024-04-16 18:11:21.815944
Analysis finished2024-04-16 18:11:22.547264
Duration0.73 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

2024-04-17T03:11:22.601543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:22.670856image/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%
Mean2.0190209 × 1011
Minimum2.0190208 × 1011
Maximum2.0190209 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:22.762081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190208 × 1011
5-th percentile2.0190208 × 1011
Q12.0190208 × 1011
median2.0190209 × 1011
Q32.0190209 × 1011
95-th percentile2.0190209 × 1011
Maximum2.0190209 × 1011
Range10900
Interquartile range (IQR)9240

Descriptive statistics

Standard deviation4225.8083
Coefficient of variation (CV)2.0929988 × 10-8
Kurtosis-1.0493003
Mean2.0190209 × 1011
Median Absolute Deviation (MAD)920
Skewness-0.93309791
Sum2.0190209 × 1013
Variance17857456
MonotonicityNot monotonic
2024-04-17T03:11:22.886050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201902090310 1
 
1.0%
201902092250 1
 
1.0%
201902091630 1
 
1.0%
201902091730 1
 
1.0%
201902091750 1
 
1.0%
201902091830 1
 
1.0%
201902091850 1
 
1.0%
201902092010 1
 
1.0%
201902092030 1
 
1.0%
201902092110 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201902081450 1
1.0%
201902081510 1
1.0%
201902081530 1
1.0%
201902081550 1
1.0%
201902081610 1
1.0%
201902081630 1
1.0%
201902081650 1
1.0%
201902081710 1
1.0%
201902081730 1
1.0%
201902081750 1
1.0%
ValueCountFrequency (%)
201902092350 1
1.0%
201902092330 1
1.0%
201902092310 1
1.0%
201902092250 1
1.0%
201902092230 1
1.0%
201902092210 1
1.0%
201902092150 1
1.0%
201902092130 1
1.0%
201902092110 1
1.0%
201902092050 1
1.0%

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

2024-04-17T03:11:22.990609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:23.068870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2.148
33 
2.154
26 
2.152
22 
2.15
19 

Length

Max length5
Median length5
Mean length4.81
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.152
2nd row2.154
3rd row2.148
4th row2.152
5th row2.154

Common Values

ValueCountFrequency (%)
2.148 33
33.0%
2.154 26
26.0%
2.152 22
22.0%
2.15 19
19.0%

Length

2024-04-17T03:11:23.157339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:23.239503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.148 33
33.0%
2.154 26
26.0%
2.152 22
22.0%
2.15 19
19.0%

유입량(ms)
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
81.6
41 
81.5
33 
81.7
26 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row81.6
2nd row81.7
3rd row81.5
4th row81.6
5th row81.7

Common Values

ValueCountFrequency (%)
81.6 41
41.0%
81.5 33
33.0%
81.7 26
26.0%

Length

2024-04-17T03:11:23.341765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:23.430047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
81.6 41
41.0%
81.5 33
33.0%
81.7 26
26.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03676
Minimum0
Maximum0.04
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T03:11:23.506560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.010450576
Coefficient of variation (CV)0.28429207
Kurtosis8.4840795
Mean0.03676
Median Absolute Deviation (MAD)0
Skewness-3.1621814
Sum3.676
Variance0.00010921455
MonotonicityNot monotonic
2024-04-17T03:11:23.599856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.04 89
89.0%
0.0 7
 
7.0%
0.022 1
 
1.0%
0.031 1
 
1.0%
0.024 1
 
1.0%
0.039 1
 
1.0%
ValueCountFrequency (%)
0.0 7
 
7.0%
0.022 1
 
1.0%
0.024 1
 
1.0%
0.031 1
 
1.0%
0.039 1
 
1.0%
0.04 89
89.0%
ValueCountFrequency (%)
0.04 89
89.0%
0.039 1
 
1.0%
0.031 1
 
1.0%
0.024 1
 
1.0%
0.022 1
 
1.0%
0.0 7
 
7.0%

저수량(백만m3)
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.04
94 
0.0
 
3
0.012
 
1
0.007
 
1
0.02
 
1

Length

Max length5
Median length4
Mean length3.99
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row0.04
2nd row0.04
3rd row0.012
4th row0.04
5th row0.04

Common Values

ValueCountFrequency (%)
0.04 94
94.0%
0.0 3
 
3.0%
0.012 1
 
1.0%
0.007 1
 
1.0%
0.02 1
 
1.0%

Length

2024-04-17T03:11:23.721837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:23.819086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.04 94
94.0%
0.0 3
 
3.0%
0.012 1
 
1.0%
0.007 1
 
1.0%
0.02 1
 
1.0%

저수율
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38.76
33 
38.79
26 
38.78
22 
38.77
19 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38.78
2nd row38.79
3rd row38.76
4th row38.78
5th row38.79

Common Values

ValueCountFrequency (%)
38.76 33
33.0%
38.79 26
26.0%
38.78 22
22.0%
38.77 19
19.0%

Length

2024-04-17T03:11:23.905367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T03:11:23.991131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38.76 33
33.0%
38.79 26
26.0%
38.78 22
22.0%
38.77 19
19.0%

Interactions

2024-04-17T03:11:22.202381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:22.046528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:22.290570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T03:11:22.112867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T03:11:24.055001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.9830.8970.0000.1090.983
강우량(mm)0.9831.0001.0000.0000.1231.000
유입량(ms)0.8971.0001.0000.0630.2451.000
방류량(ms)0.0000.0000.0631.0000.8220.000
저수량(백만m3)0.1090.1230.2450.8221.0000.123
저수율0.9831.0001.0000.0000.1231.000
2024-04-17T03:11:24.160881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수량(백만m3)강우량(mm)저수율유입량(ms)
저수량(백만m3)1.0000.0980.0980.187
강우량(mm)0.0981.0001.0000.995
저수율0.0981.0001.0000.995
유입량(ms)0.1870.9950.9951.000
2024-04-17T03:11:24.252120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)방류량(ms)강우량(mm)유입량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.0620.8210.9640.0820.821
방류량(ms)0.0621.0000.0000.1100.4700.000
강우량(mm)0.8210.0001.0000.9950.0981.000
유입량(ms)0.9640.1100.9951.0000.1870.995
저수량(백만m3)0.0820.4700.0980.1871.0000.098
저수율0.8210.0001.0000.9950.0981.000

Missing values

2024-04-17T03:11:22.396569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T03:11:22.494531image/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
0감포20190209031002.15281.60.040.0438.78
1감포20190208153002.15481.70.040.0438.79
2감포20190209135002.14881.50.00.01238.76
3감포20190209033002.15281.60.040.0438.78
4감포20190208205002.15481.70.0220.0438.79
5감포20190208155002.15481.70.040.0438.79
6감포20190209191002.14881.50.040.0438.76
7감포20190209141002.14881.50.0310.0438.76
8감포20190209085002.1581.60.040.0438.77
9감포20190209035002.15281.60.040.0438.78
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90감포20190209061002.15281.60.040.0438.78
91감포20190209055002.15281.60.040.0438.78
92감포20190209051002.15281.60.040.0438.78
93감포20190209045002.15281.60.040.0438.78
94감포20190209025002.15281.60.040.0438.78
95감포20190209023002.15281.60.040.0438.78
96감포20190209015002.15281.60.040.0438.78
97감포20190209013002.15281.60.040.0438.78
98감포20190208145002.15481.70.040.0438.79
99감포20190208151002.15481.70.040.0438.79