Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory44.3 B

Variable types

Categorical3
Numeric2

Alerts

댐명 has constant value ""Constant
유량(ms) has constant value ""Constant
수위(m) is highly overall correlated with 관측소명High correlation
관측소명 is highly overall correlated with 수위(m)High correlation

Reproduction

Analysis started2023-12-10 13:09:09.143798
Analysis finished2023-12-10 13:09:09.889125
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광동
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광동 100
100.0%

Length

2023-12-10T22:09:09.987269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:09:10.107239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광동 100
100.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
태백시(무사교)
25 
삼척시(광동교)
25 
삼척시(갈밭교)
25 
삼척시(번천교)
25 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row태백시(무사교)
2nd row삼척시(광동교)
3rd row삼척시(갈밭교)
4th row삼척시(광동교)
5th row삼척시(광동교)

Common Values

ValueCountFrequency (%)
태백시(무사교) 25
25.0%
삼척시(광동교) 25
25.0%
삼척시(갈밭교) 25
25.0%
삼척시(번천교) 25
25.0%

Length

2023-12-10T22:09:10.249643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:09:10.392808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태백시(무사교 25
25.0%
삼척시(광동교 25
25.0%
삼척시(갈밭교 25
25.0%
삼척시(번천교 25
25.0%

계측시각
Real number (ℝ)

Distinct35
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190102 × 109
Minimum2.0190101 × 109
Maximum2.0190103 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:10.614933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190101 × 109
5-th percentile2.0190101 × 109
Q12.0190101 × 109
median2.0190102 × 109
Q32.0190102 × 109
95-th percentile2.0190102 × 109
Maximum2.0190103 × 109
Range201
Interquartile range (IQR)101

Descriptive statistics

Standard deviation57.044378
Coefficient of variation (CV)2.8253636 × 10-8
Kurtosis-1.0486585
Mean2.0190102 × 109
Median Absolute Deviation (MAD)78
Skewness0.30362284
Sum2.0190102 × 1011
Variance3254.061
MonotonicityNot monotonic
2023-12-10T22:09:10.821673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2019010119 4
 
4.0%
2019010123 4
 
4.0%
2019010107 4
 
4.0%
2019010203 4
 
4.0%
2019010115 4
 
4.0%
2019010117 4
 
4.0%
2019010121 4
 
4.0%
2019010201 4
 
4.0%
2019010205 4
 
4.0%
2019010113 4
 
4.0%
Other values (25) 60
60.0%
ValueCountFrequency (%)
2019010101 4
4.0%
2019010103 4
4.0%
2019010105 4
4.0%
2019010107 4
4.0%
2019010109 4
4.0%
2019010111 4
4.0%
2019010113 4
4.0%
2019010115 4
4.0%
2019010117 4
4.0%
2019010119 4
4.0%
ValueCountFrequency (%)
2019010302 3
3.0%
2019010301 1
 
1.0%
2019010224 3
3.0%
2019010223 1
 
1.0%
2019010222 3
3.0%
2019010221 1
 
1.0%
2019010220 3
3.0%
2019010219 1
 
1.0%
2019010218 3
3.0%
2019010217 1
 
1.0%

수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3142
Minimum0.1
Maximum0.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:11.078631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1195
Q10.1475
median0.3
Q30.4175
95-th percentile0.6
Maximum0.61
Range0.51
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.17695049
Coefficient of variation (CV)0.56317787
Kurtosis-1.1739156
Mean0.3142
Median Absolute Deviation (MAD)0.155
Skewness0.50774088
Sum31.42
Variance0.031311475
MonotonicityNot monotonic
2023-12-10T22:09:11.279907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.34 10
 
10.0%
0.14 8
 
8.0%
0.57 7
 
7.0%
0.13 7
 
7.0%
0.3 6
 
6.0%
0.35 6
 
6.0%
0.15 5
 
5.0%
0.6 5
 
5.0%
0.12 5
 
5.0%
0.59 5
 
5.0%
Other values (16) 36
36.0%
ValueCountFrequency (%)
0.1 2
 
2.0%
0.11 3
 
3.0%
0.12 5
5.0%
0.13 7
7.0%
0.14 8
8.0%
0.15 5
5.0%
0.16 5
5.0%
0.17 4
4.0%
0.19 1
 
1.0%
0.23 1
 
1.0%
ValueCountFrequency (%)
0.61 2
 
2.0%
0.6 5
5.0%
0.59 5
5.0%
0.58 4
 
4.0%
0.57 7
7.0%
0.56 2
 
2.0%
0.37 1
 
1.0%
0.36 1
 
1.0%
0.35 6
6.0%
0.34 10
10.0%

유량(ms)
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

2023-12-10T22:09:11.497075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:09:11.625542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

Interactions

2023-12-10T22:09:09.465986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:09.274746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:09.559892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:09.377384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:09:11.694629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명계측시각수위(m)
관측소명1.0000.0000.882
계측시각0.0001.0000.000
수위(m)0.8820.0001.000
2023-12-10T22:09:11.819458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계측시각수위(m)관측소명
계측시각1.0000.0890.000
수위(m)0.0891.0000.815
관측소명0.0000.8151.000

Missing values

2023-12-10T22:09:09.712104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:09:09.839957image/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

댐명관측소명계측시각수위(m)유량(ms)
0광동태백시(무사교)20190102220.110
1광동삼척시(광동교)20190103020.350
2광동삼척시(갈밭교)20190102180.570
3광동삼척시(광동교)20190102220.350
4광동삼척시(광동교)20190102240.350
5광동삼척시(번천교)20190103010.30
6광동삼척시(갈밭교)20190102160.580
7광동태백시(무사교)20190102180.340
8광동태백시(무사교)20190102200.10
9광동삼척시(번천교)20190102210.30
댐명관측소명계측시각수위(m)유량(ms)
90광동태백시(무사교)20190101010.150
91광동삼척시(번천교)20190101090.130
92광동태백시(무사교)20190102120.370
93광동삼척시(번천교)20190101230.160
94광동태백시(무사교)20190101130.140
95광동삼척시(갈밭교)20190101130.590
96광동삼척시(번천교)20190102010.160
97광동태백시(무사교)20190101030.150
98광동삼척시(갈밭교)20190103020.560
99광동태백시(무사교)20190103020.10