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

Categorical2
Numeric6

Alerts

댐이름 has constant value ""Constant
일자/시간(t) is highly overall correlated with 저수율High correlation
저수위(m) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
강우량(mm) has 80 (80.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:25:15.868190
Analysis finished2023-12-10 10:25:22.940709
Duration7.07 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
군남
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 100
100.0%

Length

2023-12-10T19:25:23.051195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:25:23.190690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0210529 × 109
Minimum2.0210527 × 109
Maximum2.0210531 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:23.367803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0210527 × 109
5-th percentile2.0210527 × 109
Q12.0210528 × 109
median2.0210529 × 109
Q32.021053 × 109
95-th percentile2.0210531 × 109
Maximum2.0210531 × 109
Range409
Interquartile range (IQR)201.5

Descriptive statistics

Standard deviation125.10177
Coefficient of variation (CV)6.1899303 × 10-8
Kurtosis-1.0757651
Mean2.0210529 × 109
Median Absolute Deviation (MAD)101
Skewness-0.067879151
Sum2.0210529 × 1011
Variance15650.452
MonotonicityNot monotonic
2023-12-10T19:25:23.640440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021052904 1
 
1.0%
2021053115 1
 
1.0%
2021053020 1
 
1.0%
2021053023 1
 
1.0%
2021053024 1
 
1.0%
2021053102 1
 
1.0%
2021053103 1
 
1.0%
2021053107 1
 
1.0%
2021053108 1
 
1.0%
2021053110 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2021052709 1
1.0%
2021052710 1
1.0%
2021052711 1
1.0%
2021052712 1
1.0%
2021052713 1
1.0%
2021052714 1
1.0%
2021052715 1
1.0%
2021052716 1
1.0%
2021052723 1
1.0%
2021052724 1
1.0%
ValueCountFrequency (%)
2021053118 1
1.0%
2021053117 1
1.0%
2021053116 1
1.0%
2021053115 1
1.0%
2021053114 1
1.0%
2021053113 1
1.0%
2021053112 1
1.0%
2021053111 1
1.0%
2021053110 1
1.0%
2021053109 1
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.6983
Minimum23.56
Maximum23.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:23.874075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.56
5-th percentile23.56
Q123.67
median23.71
Q323.76
95-th percentile23.78
Maximum23.78
Range0.22
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.068711886
Coefficient of variation (CV)0.0028994437
Kurtosis-0.40936005
Mean23.6983
Median Absolute Deviation (MAD)0.04
Skewness-0.81179458
Sum2369.83
Variance0.0047213232
MonotonicityNot monotonic
2023-12-10T19:25:24.089256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
23.77 10
 
10.0%
23.78 9
 
9.0%
23.73 8
 
8.0%
23.7 8
 
8.0%
23.69 8
 
8.0%
23.56 7
 
7.0%
23.76 7
 
7.0%
23.57 6
 
6.0%
23.68 6
 
6.0%
23.72 5
 
5.0%
Other values (12) 26
26.0%
ValueCountFrequency (%)
23.56 7
7.0%
23.57 6
6.0%
23.58 1
 
1.0%
23.59 1
 
1.0%
23.6 1
 
1.0%
23.61 1
 
1.0%
23.62 1
 
1.0%
23.63 1
 
1.0%
23.64 1
 
1.0%
23.65 1
 
1.0%
ValueCountFrequency (%)
23.78 9
9.0%
23.77 10
10.0%
23.76 7
7.0%
23.75 4
 
4.0%
23.74 5
5.0%
23.73 8
8.0%
23.72 5
5.0%
23.71 4
 
4.0%
23.7 8
8.0%
23.69 8
8.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53
Minimum0
Maximum9
Zeros80
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:24.256813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.05
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3886626
Coefficient of variation (CV)2.6201181
Kurtosis15.391344
Mean0.53
Median Absolute Deviation (MAD)0
Skewness3.5740531
Sum53
Variance1.9283838
MonotonicityNot monotonic
2023-12-10T19:25:24.417949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 80
80.0%
1 8
 
8.0%
3 4
 
4.0%
2 3
 
3.0%
5 2
 
2.0%
4 2
 
2.0%
9 1
 
1.0%
ValueCountFrequency (%)
0 80
80.0%
1 8
 
8.0%
2 3
 
3.0%
3 4
 
4.0%
4 2
 
2.0%
5 2
 
2.0%
9 1
 
1.0%
ValueCountFrequency (%)
9 1
 
1.0%
5 2
 
2.0%
4 2
 
2.0%
3 4
 
4.0%
2 3
 
3.0%
1 8
 
8.0%
0 80
80.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.05294
Minimum50.4
Maximum91.225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:24.667672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.4
5-th percentile50.4
Q170.7
median78.4
Q386.8
95-th percentile90.3
Maximum91.225
Range40.825
Interquartile range (IQR)16.1

Descriptive statistics

Standard deviation12.608533
Coefficient of variation (CV)0.16578627
Kurtosis-0.32710618
Mean76.05294
Median Absolute Deviation (MAD)7.7
Skewness-0.87803595
Sum7605.294
Variance158.9751
MonotonicityNot monotonic
2023-12-10T19:25:24.934129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
90.3 8
 
8.0%
88.9 8
 
8.0%
50.4 7
 
7.0%
77.0 6
 
6.0%
74.9 6
 
6.0%
81.9 6
 
6.0%
86.8 5
 
5.0%
51.8 5
 
5.0%
72.8 4
 
4.0%
70.7 3
 
3.0%
Other values (36) 42
42.0%
ValueCountFrequency (%)
50.4 7
7.0%
51.8 5
5.0%
53.477 1
 
1.0%
54.072 1
 
1.0%
56.37 1
 
1.0%
57.724 1
 
1.0%
59.139 1
 
1.0%
62.052 1
 
1.0%
64.494 1
 
1.0%
66.517 1
 
1.0%
ValueCountFrequency (%)
91.225 1
 
1.0%
90.723 1
 
1.0%
90.3 8
8.0%
88.915 1
 
1.0%
88.9 8
8.0%
87.929 1
 
1.0%
87.235 1
 
1.0%
86.8 5
5.0%
85.887 1
 
1.0%
85.4 2
 
2.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.81211
Minimum50.4
Maximum90.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:25.179307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50.4
5-th percentile50.4
Q171.015
median78.4
Q386.78275
95-th percentile90.3
Maximum90.3
Range39.9
Interquartile range (IQR)15.76775

Descriptive statistics

Standard deviation12.840747
Coefficient of variation (CV)0.16937594
Kurtosis-0.41545656
Mean75.81211
Median Absolute Deviation (MAD)7.7
Skewness-0.87085663
Sum7581.211
Variance164.8848
MonotonicityNot monotonic
2023-12-10T19:25:25.403157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
90.3 8
 
8.0%
88.9 8
 
8.0%
50.4 7
 
7.0%
77.0 6
 
6.0%
74.9 6
 
6.0%
81.9 6
 
6.0%
86.8 5
 
5.0%
51.8 5
 
5.0%
72.8 4
 
4.0%
70.7 3
 
3.0%
Other values (36) 42
42.0%
ValueCountFrequency (%)
50.4 7
7.0%
51.31 1
 
1.0%
51.8 5
5.0%
51.905 1
 
1.0%
54.203 1
 
1.0%
55.557 1
 
1.0%
56.945 1
 
1.0%
59.885 1
 
1.0%
62.3 1
 
1.0%
64.295 1
 
1.0%
ValueCountFrequency (%)
90.3 8
8.0%
90.207 1
 
1.0%
88.947 1
 
1.0%
88.9 8
8.0%
88.445 1
 
1.0%
88.165 1
 
1.0%
86.8 5
5.0%
86.777 1
 
1.0%
86.637 1
 
1.0%
85.4 2
 
2.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4906
Minimum1.381
Maximum1.557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:25:25.648811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.381
5-th percentile1.381
Q11.467
median1.5
Q31.54
95-th percentile1.557
Maximum1.557
Range0.176
Interquartile range (IQR)0.073

Descriptive statistics

Standard deviation0.055116039
Coefficient of variation (CV)0.036975741
Kurtosis-0.46072661
Mean1.4906
Median Absolute Deviation (MAD)0.033
Skewness-0.77612036
Sum149.06
Variance0.0030377778
MonotonicityNot monotonic
2023-12-10T19:25:25.897002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1.549 10
 
10.0%
1.557 9
 
9.0%
1.516 8
 
8.0%
1.491 8
 
8.0%
1.483 8
 
8.0%
1.381 7
 
7.0%
1.54 7
 
7.0%
1.388 6
 
6.0%
1.475 6
 
6.0%
1.508 5
 
5.0%
Other values (12) 26
26.0%
ValueCountFrequency (%)
1.381 7
7.0%
1.388 6
6.0%
1.396 1
 
1.0%
1.404 1
 
1.0%
1.412 1
 
1.0%
1.42 1
 
1.0%
1.428 1
 
1.0%
1.435 1
 
1.0%
1.443 1
 
1.0%
1.451 1
 
1.0%
ValueCountFrequency (%)
1.557 9
9.0%
1.549 10
10.0%
1.54 7
7.0%
1.532 4
 
4.0%
1.524 5
5.0%
1.516 8
8.0%
1.508 5
5.0%
1.5 4
 
4.0%
1.491 8
8.0%
1.483 8
8.0%

저수율
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2.1
53 
2.2
26 
1.9
13 
2.0
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.2
2nd row1.9
3rd row2.1
4th row2.2
5th row2.0

Common Values

ValueCountFrequency (%)
2.1 53
53.0%
2.2 26
26.0%
1.9 13
 
13.0%
2.0 8
 
8.0%

Length

2023-12-10T19:25:26.128336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:25:26.289831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.1 53
53.0%
2.2 26
26.0%
1.9 13
 
13.0%
2.0 8
 
8.0%

Interactions

2023-12-10T19:25:21.538253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:16.246705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.276587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.192415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:19.023393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:20.347625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:21.739005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:16.442469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.465449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.340963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:19.518386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:20.542226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:21.894182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:16.583437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.587620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.470696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:19.668642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:20.709571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:22.050084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:16.722288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.707526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.584898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:19.803470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:20.871032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:22.235955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:16.938314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.886156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.725295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:19.997419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:21.055566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:22.402751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:17.120100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.042694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:18.870575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:20.176460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:25:21.275903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:25:26.504654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8490.1720.8230.8440.8490.906
저수위(m)0.8491.0000.5770.9910.9840.9990.995
강우량(mm)0.1720.5771.0000.5760.3060.5620.462
유입량(ms)0.8230.9910.5761.0000.9680.9900.958
방류량(ms)0.8440.9840.3060.9681.0000.9780.968
저수량(백만m3)0.8490.9990.5620.9900.9781.0000.995
저수율0.9060.9950.4620.9580.9680.9951.000
2023-12-10T19:25:26.682802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.230-0.3250.2020.2470.2300.786
저수위(m)0.2301.000-0.2130.9940.9981.0000.912
강우량(mm)-0.325-0.2131.000-0.193-0.224-0.2130.329
유입량(ms)0.2020.994-0.1931.0000.9870.9940.861
방류량(ms)0.2470.998-0.2240.9871.0000.9980.879
저수량(백만m3)0.2301.000-0.2130.9940.9981.0000.934
저수율0.7860.9120.3290.8610.8790.9341.000

Missing values

2023-12-10T19:25:22.630694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:25:22.857079image/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
0군남202105290423.78090.390.31.5572.2
1군남202105271123.56050.450.41.3811.9
2군남202105301223.73081.981.91.5162.1
3군남202105290523.78090.390.31.5572.2
4군남202105280923.63364.49462.31.4352.0
5군남202105271223.56050.450.41.3811.9
6군남202105310423.7077.077.01.4912.1
7군남202105301323.73081.981.91.5162.1
8군남202105292123.76086.886.81.542.2
9군남202105290623.78090.390.31.5572.2
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남202105291323.77088.988.91.5492.2
91군남202105291223.77088.988.91.5492.2
92군남202105291023.78090.390.31.5572.2
93군남202105290923.78090.390.31.5572.2
94군남202105290323.78090.390.31.5572.2
95군남202105290223.78091.22588.9471.5572.2
96군남202105282423.76086.886.81.542.2
97군남202105282323.76088.91586.6371.542.2
98군남202105270923.56450.450.41.3811.9
99군남202105271023.56550.450.41.3811.9