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 13:08:55.099626
Analysis finished2023-12-10 13:08:55.934506
Duration0.83 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

2023-12-10T22:08:56.020806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:08:56.115203image/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(번천교)
25 
삼척시(갈밭교)
25 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T22:08:56.224064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:08:56.676096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태백시(무사교 25
25.0%
삼척시(광동교 25
25.0%
삼척시(번천교 25
25.0%
삼척시(갈밭교 25
25.0%

계측시각
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190502 × 109
Minimum2.0190501 × 109
Maximum2.0190503 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:08:56.843997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190501 × 109
5-th percentile2.0190501 × 109
Q12.0190501 × 109
median2.0190502 × 109
Q32.0190502 × 109
95-th percentile2.0190502 × 109
Maximum2.0190503 × 109
Range201
Interquartile range (IQR)100.25

Descriptive statistics

Standard deviation56.745702
Coefficient of variation (CV)2.8105147 × 10-8
Kurtosis-1.0405231
Mean2.0190502 × 109
Median Absolute Deviation (MAD)78
Skewness0.30506613
Sum2.0190502 × 1011
Variance3220.0746
MonotonicityNot monotonic
2023-12-10T22:08:57.049681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019050222 3
 
3.0%
2019050206 3
 
3.0%
2019050302 3
 
3.0%
2019050120 3
 
3.0%
2019050202 3
 
3.0%
2019050110 3
 
3.0%
2019050118 3
 
3.0%
2019050122 3
 
3.0%
2019050108 3
 
3.0%
2019050116 3
 
3.0%
Other values (40) 70
70.0%
ValueCountFrequency (%)
2019050101 1
 
1.0%
2019050102 3
3.0%
2019050103 1
 
1.0%
2019050104 3
3.0%
2019050105 1
 
1.0%
2019050106 3
3.0%
2019050107 1
 
1.0%
2019050108 3
3.0%
2019050109 1
 
1.0%
2019050110 3
3.0%
ValueCountFrequency (%)
2019050302 3
3.0%
2019050301 1
 
1.0%
2019050224 3
3.0%
2019050223 1
 
1.0%
2019050222 3
3.0%
2019050221 1
 
1.0%
2019050220 3
3.0%
2019050219 1
 
1.0%
2019050218 3
3.0%
2019050217 1
 
1.0%

수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6438
Minimum0.39
Maximum1.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:08:57.231984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.39
5-th percentile0.39
Q10.435
median0.525
Q30.765
95-th percentile1.1105
Maximum1.13
Range0.74
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.27126632
Coefficient of variation (CV)0.42135184
Kurtosis-0.87423394
Mean0.6438
Median Absolute Deviation (MAD)0.115
Skewness0.90075909
Sum64.38
Variance0.073585414
MonotonicityNot monotonic
2023-12-10T22:08:57.415246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.4 9
 
9.0%
0.45 7
 
7.0%
0.39 6
 
6.0%
1.06 6
 
6.0%
0.41 6
 
6.0%
0.47 5
 
5.0%
0.48 5
 
5.0%
1.1 5
 
5.0%
0.59 4
 
4.0%
0.46 4
 
4.0%
Other values (21) 43
43.0%
ValueCountFrequency (%)
0.39 6
6.0%
0.4 9
9.0%
0.41 6
6.0%
0.42 4
4.0%
0.44 1
 
1.0%
0.45 7
7.0%
0.46 4
4.0%
0.47 5
5.0%
0.48 5
5.0%
0.49 3
 
3.0%
ValueCountFrequency (%)
1.13 2
 
2.0%
1.12 3
3.0%
1.11 2
 
2.0%
1.1 5
5.0%
1.09 3
3.0%
1.08 2
 
2.0%
1.07 1
 
1.0%
1.06 6
6.0%
1.05 1
 
1.0%
0.67 2
 
2.0%

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

2023-12-10T22:08:57.589247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:08:57.709485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

Interactions

2023-12-10T22:08:55.460602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:08:55.228436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:08:55.594753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:08:55.338733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:08:57.781804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명계측시각수위(m)
관측소명1.0000.0000.966
계측시각0.0001.0000.659
수위(m)0.9660.6591.000
2023-12-10T22:08:57.889270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계측시각수위(m)관측소명
계측시각1.000-0.2610.000
수위(m)-0.2611.0000.870
관측소명0.0000.8701.000

Missing values

2023-12-10T22:08:55.752344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:08:55.884310image/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)20190502220.390
1광동삼척시(광동교)20190503020.560
2광동삼척시(번천교)20190502180.450
3광동삼척시(광동교)20190502220.570
4광동삼척시(갈밭교)20190502231.060
5광동삼척시(갈밭교)20190503011.050
6광동삼척시(번천교)20190502160.450
7광동태백시(무사교)20190502180.390
8광동태백시(무사교)20190502200.390
9광동삼척시(갈밭교)20190502211.060
댐명관측소명계측시각수위(m)유량(ms)
90광동삼척시(갈밭교)20190501051.120
91광동삼척시(광동교)20190502020.620
92광동태백시(무사교)20190501060.420
93광동삼척시(광동교)20190501060.660
94광동태백시(무사교)20190501020.420
95광동삼척시(광동교)20190501140.650
96광동삼척시(번천교)20190501020.490
97광동삼척시(갈밭교)20190501011.130
98광동삼척시(번천교)20190503020.440
99광동태백시(무사교)20190503020.390