Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows49
Duplicate rows (%)49.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical3
Numeric5

Alerts

댐이름 has constant value ""Constant
강우량(mm) has constant value ""Constant
Dataset has 49 (49.0%) duplicate rowsDuplicates
일자/시간(t) is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
저수위(m) is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
유입량(ms) has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:59:56.146866
Analysis finished2023-12-10 12:00:00.093215
Duration3.95 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
군위
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군위 100
100.0%

Length

2023-12-10T21:00:00.194692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:00.327016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190402 × 109
Minimum2.0190401 × 109
Maximum2.0190403 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:00.553885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190401 × 109
5-th percentile2.0190401 × 109
Q12.0190402 × 109
median2.0190402 × 109
Q32.0190403 × 109
95-th percentile2.0190403 × 109
Maximum2.0190403 × 109
Range202
Interquartile range (IQR)119.5

Descriptive statistics

Standard deviation68.696004
Coefficient of variation (CV)3.4024089 × 10-8
Kurtosis-1.0762718
Mean2.0190402 × 109
Median Absolute Deviation (MAD)88.5
Skewness-0.024521778
Sum2.0190402 × 1011
Variance4719.141
MonotonicityNot monotonic
2023-12-10T21:00:00.786159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019040115 2
 
2.0%
2019040311 2
 
2.0%
2019040215 2
 
2.0%
2019040205 2
 
2.0%
2019040207 2
 
2.0%
2019040209 2
 
2.0%
2019040206 2
 
2.0%
2019040204 2
 
2.0%
2019040203 2
 
2.0%
2019040201 2
 
2.0%
Other values (41) 80
80.0%
ValueCountFrequency (%)
2019040112 1
1.0%
2019040113 2
2.0%
2019040114 2
2.0%
2019040115 2
2.0%
2019040116 2
2.0%
2019040117 2
2.0%
2019040118 2
2.0%
2019040119 2
2.0%
2019040120 2
2.0%
2019040121 2
2.0%
ValueCountFrequency (%)
2019040314 1
1.0%
2019040313 2
2.0%
2019040312 2
2.0%
2019040311 2
2.0%
2019040310 2
2.0%
2019040309 2
2.0%
2019040308 2
2.0%
2019040307 2
2.0%
2019040306 2
2.0%
2019040305 2
2.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.6414
Minimum192.59
Maximum192.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:00.972919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192.59
5-th percentile192.6
Q1192.62
median192.64
Q3192.67
95-th percentile192.68
Maximum192.69
Range0.1
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.026856145
Coefficient of variation (CV)0.00013941004
Kurtosis-1.2247993
Mean192.6414
Median Absolute Deviation (MAD)0.025
Skewness-0.11396429
Sum19264.14
Variance0.00072125253
MonotonicityNot monotonic
2023-12-10T21:00:01.153684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
192.67 20
20.0%
192.63 16
16.0%
192.61 12
12.0%
192.62 10
10.0%
192.66 10
10.0%
192.68 8
 
8.0%
192.65 8
 
8.0%
192.64 6
 
6.0%
192.6 6
 
6.0%
192.59 3
 
3.0%
ValueCountFrequency (%)
192.59 3
 
3.0%
192.6 6
 
6.0%
192.61 12
12.0%
192.62 10
10.0%
192.63 16
16.0%
192.64 6
 
6.0%
192.65 8
 
8.0%
192.66 10
10.0%
192.67 20
20.0%
192.68 8
 
8.0%
ValueCountFrequency (%)
192.69 1
 
1.0%
192.68 8
 
8.0%
192.67 20
20.0%
192.66 10
10.0%
192.65 8
 
8.0%
192.64 6
 
6.0%
192.63 16
16.0%
192.62 10
10.0%
192.61 12
12.0%
192.6 6
 
6.0%

강우량(mm)
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

2023-12-10T21:00:01.322448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:01.461369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90487
Minimum0
Maximum1.14
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:01.601000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.125
median1.13
Q31.133
95-th percentile1.13605
Maximum1.14
Range1.14
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.45472802
Coefficient of variation (CV)0.50253408
Kurtosis0.32513483
Mean0.90487
Median Absolute Deviation (MAD)0.004
Skewness-1.52271
Sum90.487
Variance0.20677757
MonotonicityNot monotonic
2023-12-10T21:00:01.777138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 20
20.0%
1.131 10
10.0%
1.136 8
 
8.0%
1.133 8
 
8.0%
1.127 8
 
8.0%
1.125 7
 
7.0%
1.135 6
 
6.0%
1.132 6
 
6.0%
1.126 6
 
6.0%
1.128 4
 
4.0%
Other values (6) 17
17.0%
ValueCountFrequency (%)
0.0 20
20.0%
1.125 7
 
7.0%
1.126 6
 
6.0%
1.127 8
 
8.0%
1.128 4
 
4.0%
1.129 4
 
4.0%
1.13 4
 
4.0%
1.131 10
10.0%
1.132 6
 
6.0%
1.133 8
 
8.0%
ValueCountFrequency (%)
1.14 1
 
1.0%
1.139 2
 
2.0%
1.137 2
 
2.0%
1.136 8
8.0%
1.135 6
6.0%
1.134 4
 
4.0%
1.133 8
8.0%
1.132 6
6.0%
1.131 10
10.0%
1.13 4
 
4.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.13093
Minimum1.123
Maximum1.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:01.945008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.123
5-th percentile1.125
Q11.127
median1.131
Q31.134
95-th percentile1.1371
Maximum1.14
Range0.017
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.0041615241
Coefficient of variation (CV)0.0036797362
Kurtosis-0.69394351
Mean1.13093
Median Absolute Deviation (MAD)0.0035
Skewness0.13201446
Sum113.093
Variance1.7318283 × 10-5
MonotonicityNot monotonic
2023-12-10T21:00:02.141435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.131 12
12.0%
1.133 10
10.0%
1.127 10
10.0%
1.132 8
8.0%
1.135 8
8.0%
1.136 8
8.0%
1.129 8
8.0%
1.125 7
 
7.0%
1.126 6
 
6.0%
1.13 4
 
4.0%
Other values (7) 19
19.0%
ValueCountFrequency (%)
1.123 2
 
2.0%
1.124 2
 
2.0%
1.125 7
7.0%
1.126 6
6.0%
1.127 10
10.0%
1.128 4
 
4.0%
1.129 8
8.0%
1.13 4
 
4.0%
1.131 12
12.0%
1.132 8
8.0%
ValueCountFrequency (%)
1.14 3
 
3.0%
1.139 2
 
2.0%
1.137 2
 
2.0%
1.136 8
8.0%
1.135 8
8.0%
1.134 4
 
4.0%
1.133 10
10.0%
1.132 8
8.0%
1.131 12
12.0%
1.13 4
 
4.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.83952
Minimum22.747
Maximum22.927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:02.351308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.747
5-th percentile22.765
Q122.801
median22.837
Q322.891
95-th percentile22.909
Maximum22.927
Range0.18
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.048341061
Coefficient of variation (CV)0.0021165533
Kurtosis-1.2247993
Mean22.83952
Median Absolute Deviation (MAD)0.045
Skewness-0.11396429
Sum2283.952
Variance0.0023368582
MonotonicityNot monotonic
2023-12-10T21:00:02.530035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.891 20
20.0%
22.819 16
16.0%
22.783 12
12.0%
22.801 10
10.0%
22.873 10
10.0%
22.909 8
 
8.0%
22.855 8
 
8.0%
22.837 6
 
6.0%
22.765 6
 
6.0%
22.747 3
 
3.0%
ValueCountFrequency (%)
22.747 3
 
3.0%
22.765 6
 
6.0%
22.783 12
12.0%
22.801 10
10.0%
22.819 16
16.0%
22.837 6
 
6.0%
22.855 8
 
8.0%
22.873 10
10.0%
22.891 20
20.0%
22.909 8
 
8.0%
ValueCountFrequency (%)
22.927 1
 
1.0%
22.909 8
 
8.0%
22.891 20
20.0%
22.873 10
10.0%
22.855 8
 
8.0%
22.837 6
 
6.0%
22.819 16
16.0%
22.801 10
10.0%
22.783 12
12.0%
22.765 6
 
6.0%

저수율
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
47.0
38 
46.9
30 
46.8
28 
46.7
 
3
47.1
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row47.0
2nd row47.0
3rd row47.0
4th row47.0
5th row47.0

Common Values

ValueCountFrequency (%)
47.0 38
38.0%
46.9 30
30.0%
46.8 28
28.0%
46.7 3
 
3.0%
47.1 1
 
1.0%

Length

2023-12-10T21:00:02.725773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:02.869426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47.0 38
38.0%
46.9 30
30.0%
46.8 28
28.0%
46.7 3
 
3.0%
47.1 1
 
1.0%

Interactions

2023-12-10T20:59:58.899518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:56.431700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.081430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.656604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.267133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:59.088090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:56.575007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.192817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.795414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.403526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:59.204451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:56.700015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.291292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.912351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.527610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:59.435885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:56.824023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.409446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.027503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.649899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:59.605561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:56.953466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:57.527716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.140198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:59:58.770712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:00:02.982670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.9340.0000.8990.8980.939
저수위(m)0.9341.0000.0000.9281.0001.000
유입량(ms)0.0000.0001.0000.5900.0000.074
방류량(ms)0.8990.9280.5901.0000.9190.926
저수량(백만m3)0.8981.0000.0000.9191.0001.000
저수율0.9391.0000.0740.9261.0001.000
2023-12-10T21:00:03.126010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.992-0.614-0.946-0.9920.668
저수위(m)-0.9921.0000.6670.9521.0000.869
유입량(ms)-0.6140.6671.0000.6870.6670.087
방류량(ms)-0.9460.9520.6871.0000.9520.621
저수량(백만m3)-0.9921.0000.6670.9521.0000.869
저수율0.6680.8690.0870.6210.8691.000

Missing values

2023-12-10T20:59:59.800982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:00:00.001667image/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
0군위2019040115192.6801.1391.13922.90947.0
1군위2019040113192.6800.01.1422.90947.0
2군위2019040118192.6701.1351.13522.89147.0
3군위2019040118192.6701.1351.13522.89147.0
4군위2019040114192.6801.1351.13522.90947.0
5군위2019040114192.6801.1351.13522.90947.0
6군위2019040120192.6701.1361.13622.89147.0
7군위2019040120192.6701.1361.13622.89147.0
8군위2019040119192.6701.1361.13622.89147.0
9군위2019040119192.6701.1361.13622.89147.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군위2019040216192.6301.1311.13122.81946.9
91군위2019040215192.6300.01.12922.81946.9
92군위2019040214192.6401.1281.12822.83746.9
93군위2019040214192.6401.1281.12822.83746.9
94군위2019040209192.6501.1311.13122.85546.9
95군위2019040211192.6501.1321.13222.85546.9
96군위2019040210192.6501.1311.13122.85546.9
97군위2019040210192.6501.1311.13122.85546.9
98군위2019040112192.6901.141.1422.92747.1
99군위2019040113192.6800.01.1422.90947.0

Duplicate rows

Most frequently occurring

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율# duplicates
0군위2019040113192.6800.01.1422.90947.02
1군위2019040114192.6801.1351.13522.90947.02
2군위2019040115192.6801.1391.13922.90947.02
3군위2019040116192.6801.1361.13622.90947.02
4군위2019040117192.6700.01.13522.89147.02
5군위2019040118192.6701.1351.13522.89147.02
6군위2019040119192.6701.1361.13622.89147.02
7군위2019040120192.6701.1361.13622.89147.02
8군위2019040121192.6701.1331.13322.89147.02
9군위2019040122192.6701.1351.13522.89147.02