Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows50
Duplicate rows (%)50.0%
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 50 (50.0%) 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 4 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 4 other fieldsHigh correlation
유입량(ms) has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:00:21.604170
Analysis finished2023-12-10 12:00:25.294742
Duration3.69 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

2023-12-10T21:00:25.369855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:25.487750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
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
2023-12-10T21:00:25.642551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation64.135365
Coefficient of variation (CV)3.1765745 × 10-8
Kurtosis-0.89963817
Mean2.0190102 × 109
Median Absolute Deviation (MAD)82
Skewness-0.30568813
Sum2.0190102 × 1011
Variance4113.3451
MonotonicityNot monotonic
2023-12-10T21:00:25.825985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019010213 2
 
2.0%
2019010203 2
 
2.0%
2019010307 2
 
2.0%
2019010304 2
 
2.0%
2019010224 2
 
2.0%
2019010223 2
 
2.0%
2019010220 2
 
2.0%
2019010219 2
 
2.0%
2019010217 2
 
2.0%
2019010212 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
2019010119 2
2.0%
2019010120 2
2.0%
2019010121 2
2.0%
2019010122 2
2.0%
2019010123 2
2.0%
2019010124 2
2.0%
2019010201 2
2.0%
2019010202 2
2.0%
2019010203 2
2.0%
2019010204 2
2.0%
ValueCountFrequency (%)
2019010320 2
2.0%
2019010319 2
2.0%
2019010318 2
2.0%
2019010317 2
2.0%
2019010316 2
2.0%
2019010315 2
2.0%
2019010314 2
2.0%
2019010313 2
2.0%
2019010312 2
2.0%
2019010311 2
2.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.3964
Minimum195.36
Maximum195.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:25.974473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195.36
5-th percentile195.36
Q1195.38
median195.4
Q3195.42
95-th percentile195.43
Maximum195.43
Range0.07
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.020671554
Coefficient of variation (CV)0.00010579291
Kurtosis-1.1548762
Mean195.3964
Median Absolute Deviation (MAD)0.02
Skewness-0.077539358
Sum19539.64
Variance0.00042731313
MonotonicityNot monotonic
2023-12-10T21:00:26.137136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
195.42 22
22.0%
195.4 16
16.0%
195.39 16
16.0%
195.37 14
14.0%
195.38 12
12.0%
195.41 8
 
8.0%
195.43 6
 
6.0%
195.36 6
 
6.0%
ValueCountFrequency (%)
195.36 6
 
6.0%
195.37 14
14.0%
195.38 12
12.0%
195.39 16
16.0%
195.4 16
16.0%
195.41 8
 
8.0%
195.42 22
22.0%
195.43 6
 
6.0%
ValueCountFrequency (%)
195.43 6
 
6.0%
195.42 22
22.0%
195.41 8
 
8.0%
195.4 16
16.0%
195.39 16
16.0%
195.38 12
12.0%
195.37 14
14.0%
195.36 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

2023-12-10T21:00:26.345724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:26.475725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82232
Minimum0
Maximum1.112
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:26.595647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.875
median0.892
Q31.108
95-th percentile1.111
Maximum1.112
Range1.112
Interquartile range (IQR)0.233

Descriptive statistics

Standard deviation0.34746135
Coefficient of variation (CV)0.42253788
Kurtosis1.7545002
Mean0.82232
Median Absolute Deviation (MAD)0.0195
Skewness-1.7529408
Sum82.232
Variance0.12072939
MonotonicityNot monotonic
2023-12-10T21:00:26.777791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 14
14.0%
1.111 10
 
10.0%
0.897 8
 
8.0%
1.109 8
 
8.0%
0.887 6
 
6.0%
0.875 4
 
4.0%
0.893 4
 
4.0%
0.872 4
 
4.0%
0.89 4
 
4.0%
1.11 4
 
4.0%
Other values (16) 34
34.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.778 2
 
2.0%
0.872 4
 
4.0%
0.873 4
 
4.0%
0.875 4
 
4.0%
0.878 2
 
2.0%
0.88 2
 
2.0%
0.885 2
 
2.0%
0.887 6
6.0%
0.888 2
 
2.0%
ValueCountFrequency (%)
1.112 2
 
2.0%
1.111 10
10.0%
1.11 4
 
4.0%
1.109 8
8.0%
1.108 2
 
2.0%
1.004 2
 
2.0%
0.942 2
 
2.0%
0.902 2
 
2.0%
0.9 2
 
2.0%
0.899 2
 
2.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95708
Minimum0.872
Maximum1.112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:26.931484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.872
5-th percentile0.873
Q10.887
median0.897
Q31.109
95-th percentile1.111
Maximum1.112
Range0.24
Interquartile range (IQR)0.222

Descriptive statistics

Standard deviation0.10118067
Coefficient of variation (CV)0.1057181
Kurtosis-1.3033183
Mean0.95708
Median Absolute Deviation (MAD)0.018
Skewness0.80175739
Sum95.708
Variance0.010237529
MonotonicityNot monotonic
2023-12-10T21:00:27.076786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.897 10
 
10.0%
1.111 10
 
10.0%
1.109 8
 
8.0%
0.887 6
 
6.0%
0.889 6
 
6.0%
0.873 6
 
6.0%
1.11 6
 
6.0%
0.899 4
 
4.0%
0.875 4
 
4.0%
0.89 4
 
4.0%
Other values (16) 36
36.0%
ValueCountFrequency (%)
0.872 4
4.0%
0.873 6
6.0%
0.875 4
4.0%
0.878 2
 
2.0%
0.88 2
 
2.0%
0.882 2
 
2.0%
0.885 2
 
2.0%
0.887 6
6.0%
0.888 2
 
2.0%
0.889 6
6.0%
ValueCountFrequency (%)
1.112 2
 
2.0%
1.111 10
10.0%
1.11 6
6.0%
1.109 8
8.0%
1.108 2
 
2.0%
1.077 2
 
2.0%
1.004 2
 
2.0%
0.942 2
 
2.0%
0.902 2
 
2.0%
0.9 2
 
2.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.07908
Minimum28.006
Maximum28.147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:00:27.193566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.006
5-th percentile28.006
Q128.046
median28.086
Q328.127
95-th percentile28.147
Maximum28.147
Range0.141
Interquartile range (IQR)0.081

Descriptive statistics

Standard deviation0.041696276
Coefficient of variation (CV)0.0014849588
Kurtosis-1.1578807
Mean28.07908
Median Absolute Deviation (MAD)0.04
Skewness-0.063060428
Sum2807.908
Variance0.0017385794
MonotonicityNot monotonic
2023-12-10T21:00:27.345828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
28.127 22
22.0%
28.086 16
16.0%
28.066 16
16.0%
28.026 14
14.0%
28.046 12
12.0%
28.106 8
 
8.0%
28.147 6
 
6.0%
28.006 6
 
6.0%
ValueCountFrequency (%)
28.006 6
 
6.0%
28.026 14
14.0%
28.046 12
12.0%
28.066 16
16.0%
28.086 16
16.0%
28.106 8
 
8.0%
28.127 22
22.0%
28.147 6
 
6.0%
ValueCountFrequency (%)
28.147 6
 
6.0%
28.127 22
22.0%
28.106 8
 
8.0%
28.086 16
16.0%
28.066 16
16.0%
28.046 12
12.0%
28.026 14
14.0%
28.006 6
 
6.0%

저수율
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
57.6
42 
57.8
28 
57.7
24 
57.5
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row57.7
2nd row57.8
3rd row57.6
4th row57.7
5th row57.8

Common Values

ValueCountFrequency (%)
57.6 42
42.0%
57.8 28
28.0%
57.7 24
24.0%
57.5 6
 
6.0%

Length

2023-12-10T21:00:27.490790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:00:27.606197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
57.6 42
42.0%
57.8 28
28.0%
57.7 24
24.0%
57.5 6
 
6.0%

Interactions

2023-12-10T21:00:24.537576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:21.860993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.454201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.382927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.984866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.647409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:21.975653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.597746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.539809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.138829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.763238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.107832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.731965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.662395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.244475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.863768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.208615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.153258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.761194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.349585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.968348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:22.315795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.266786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:23.854624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:00:24.436826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:00:27.752682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8250.5780.6190.9610.913
저수위(m)0.8251.0000.7330.7941.0001.000
유입량(ms)0.5780.7331.0000.6970.7510.652
방류량(ms)0.6190.7940.6971.0000.8120.771
저수량(백만m3)0.9611.0000.7510.8121.0001.000
저수율0.9131.0000.6520.7711.0001.000
2023-12-10T21:00:27.892740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.988-0.666-0.885-0.9880.619
저수위(m)-0.9881.0000.7200.8901.0000.979
유입량(ms)-0.6660.7201.0000.7650.7200.580
방류량(ms)-0.8850.8900.7651.0000.8900.606
저수량(백만m3)-0.9881.0000.7200.8901.0000.979
저수율0.6190.9790.5800.6060.9791.000

Missing values

2023-12-10T21:00:25.106666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:00:25.237305image/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
0군위2019010213195.400.00.89728.08657.7
1군위2019010120195.4301.1111.11128.14757.8
2군위2019010305195.3800.00.89928.04657.6
3군위2019010214195.400.8970.89728.08657.7
4군위2019010204195.4201.1091.10928.12757.8
5군위2019010120195.4301.1111.11128.14757.8
6군위2019010313195.3700.8780.87828.02657.6
7군위2019010306195.3800.9020.90228.04657.6
8군위2019010222195.3900.8870.88728.06657.6
9군위2019010214195.400.8970.89728.08657.7
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군위2019010218195.400.890.8928.08657.7
91군위2019010217195.400.8930.89328.08657.7
92군위2019010216195.400.8930.89328.08657.7
93군위2019010216195.400.8930.89328.08657.7
94군위2019010213195.400.00.89728.08657.7
95군위2019010212195.4100.90.928.10657.7
96군위2019010211195.4100.9420.94228.10657.7
97군위2019010211195.4100.9420.94228.10657.7
98군위2019010119195.4301.1111.11128.14757.8
99군위2019010119195.4301.1111.11128.14757.8

Duplicate rows

Most frequently occurring

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율# duplicates
0군위2019010119195.4301.1111.11128.14757.82
1군위2019010120195.4301.1111.11128.14757.82
2군위2019010121195.4301.1121.11228.14757.82
3군위2019010122195.4200.01.1128.12757.82
4군위2019010123195.4201.1081.10828.12757.82
5군위2019010124195.4201.1091.10928.12757.82
6군위2019010201195.4201.1091.10928.12757.82
7군위2019010202195.4201.1111.11128.12757.82
8군위2019010203195.4201.111.1128.12757.82
9군위2019010204195.4201.1091.10928.12757.82