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
저수위(m) has constant value ""Constant
일자/시간(t) is highly overall correlated with 강우량(mm) and 2 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
저수량(백만m3) has unique valuesUnique
방류량(ms) has 8 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:44:53.556438
Analysis finished2023-12-10 10:45:01.088001
Duration7.53 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
낙동강하굿둑
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강하굿둑 100
100.0%

Length

2023-12-10T19:45:01.193696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:01.344404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190102 × 1011
Minimum2.0190102 × 1011
Maximum2.0190102 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:01.620288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190102 × 1011
5-th percentile2.0190102 × 1011
Q12.0190102 × 1011
median2.0190102 × 1011
Q32.0190102 × 1011
95-th percentile2.0190102 × 1011
Maximum2.0190102 × 1011
Range1630
Interquartile range (IQR)815

Descriptive statistics

Standard deviation483.44308
Coefficient of variation (CV)2.3944558 × 10-9
Kurtosis-1.201081
Mean2.0190102 × 1011
Median Absolute Deviation (MAD)410
Skewness-0.0032814126
Sum2.0190102 × 1013
Variance233717.21
MonotonicityNot monotonic
2023-12-10T19:45:01.903107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201901021840 1
 
1.0%
201901020800 1
 
1.0%
201901020620 1
 
1.0%
201901020630 1
 
1.0%
201901020640 1
 
1.0%
201901020650 1
 
1.0%
201901020700 1
 
1.0%
201901020710 1
 
1.0%
201901020720 1
 
1.0%
201901020730 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
201901020220 1
1.0%
201901020230 1
1.0%
201901020240 1
1.0%
201901020250 1
1.0%
201901020300 1
1.0%
201901020310 1
1.0%
201901020320 1
1.0%
201901020330 1
1.0%
201901020340 1
1.0%
201901020350 1
1.0%
ValueCountFrequency (%)
201901021850 1
1.0%
201901021840 1
1.0%
201901021830 1
1.0%
201901021820 1
1.0%
201901021810 1
1.0%
201901021800 1
1.0%
201901021750 1
1.0%
201901021740 1
1.0%
201901021730 1
1.0%
201901021720 1
1.0%

저수위(m)
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

2023-12-10T19:45:02.244738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:45:02.446098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.271
Minimum302.372
Maximum305.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:02.626840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum302.372
5-th percentile302.835
Q1303.764
median304.4615
Q3304.694
95-th percentile305.159
Maximum305.159
Range2.787
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.72694246
Coefficient of variation (CV)0.0023891283
Kurtosis0.046857027
Mean304.271
Median Absolute Deviation (MAD)0.6975
Skewness-0.79679761
Sum30427.1
Variance0.52844533
MonotonicityNot monotonic
2023-12-10T19:45:02.849522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
304.694 32
32.0%
303.764 21
21.0%
305.159 18
18.0%
304.229 16
16.0%
302.835 6
 
6.0%
303.299 4
 
4.0%
302.372 3
 
3.0%
ValueCountFrequency (%)
302.372 3
 
3.0%
302.835 6
 
6.0%
303.299 4
 
4.0%
303.764 21
21.0%
304.229 16
16.0%
304.694 32
32.0%
305.159 18
18.0%
ValueCountFrequency (%)
305.159 18
18.0%
304.694 32
32.0%
304.229 16
16.0%
303.764 21
21.0%
303.299 4
 
4.0%
302.835 6
 
6.0%
302.372 3
 
3.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.043
Minimum98.4
Maximum99.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:03.145160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.4
5-th percentile98.6
Q198.9
median99.1
Q399.2
95-th percentile99.3
Maximum99.3
Range0.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.23192432
Coefficient of variation (CV)0.0023416528
Kurtosis0.26787463
Mean99.043
Median Absolute Deviation (MAD)0.2
Skewness-0.92032587
Sum9904.3
Variance0.053788889
MonotonicityNot monotonic
2023-12-10T19:45:03.370126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
99.2 32
32.0%
98.9 21
21.0%
99.3 18
18.0%
99.0 16
16.0%
98.6 6
 
6.0%
98.7 4
 
4.0%
98.4 3
 
3.0%
ValueCountFrequency (%)
98.4 3
 
3.0%
98.6 6
 
6.0%
98.7 4
 
4.0%
98.9 21
21.0%
99.0 16
16.0%
99.2 32
32.0%
99.3 18
18.0%
ValueCountFrequency (%)
99.3 18
18.0%
99.2 32
32.0%
99.0 16
16.0%
98.9 21
21.0%
98.7 4
 
4.0%
98.6 6
 
6.0%
98.4 3
 
3.0%

방류량(ms)
Real number (ℝ)

ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.64342
Minimum0
Maximum317.377
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:03.618633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.22725
median54.486
Q3295.82325
95-th percentile316.50875
Maximum317.377
Range317.377
Interquartile range (IQR)256.596

Descriptive statistics

Standard deviation117.03584
Coefficient of variation (CV)1.057775
Kurtosis-0.78689839
Mean110.64342
Median Absolute Deviation (MAD)15.2775
Skewness1.0607029
Sum11064.342
Variance13697.389
MonotonicityNot monotonic
2023-12-10T19:45:03.904729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
40.773 1
 
1.0%
51.842 1
 
1.0%
316.507 1
 
1.0%
316.717 1
 
1.0%
58.309 1
 
1.0%
56.966 1
 
1.0%
56.76 1
 
1.0%
55.732 1
 
1.0%
56.668 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 8
8.0%
37.093 1
 
1.0%
37.25 1
 
1.0%
37.696 1
 
1.0%
37.805 1
 
1.0%
37.84 1
 
1.0%
38.01 1
 
1.0%
38.175 1
 
1.0%
38.192 1
 
1.0%
38.383 1
 
1.0%
ValueCountFrequency (%)
317.377 1
1.0%
316.734 1
1.0%
316.717 1
1.0%
316.59 1
1.0%
316.542 1
1.0%
316.507 1
1.0%
316.388 1
1.0%
316.155 1
1.0%
315.724 1
1.0%
315.246 1
1.0%

저수량(백만m3)
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.99827
Minimum36.177
Maximum60.497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:04.590828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.177
5-th percentile37.57125
Q139.148
median43.9895
Q357.45225
95-th percentile58.60135
Maximum60.497
Range24.32
Interquartile range (IQR)18.30425

Descriptive statistics

Standard deviation9.1281531
Coefficient of variation (CV)0.19017671
Kurtosis-1.9283076
Mean47.99827
Median Absolute Deviation (MAD)7.277
Skewness0.059000863
Sum4799.827
Variance83.323179
MonotonicityNot monotonic
2023-12-10T19:45:04.891538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.134 1
 
1.0%
58.462 1
 
1.0%
57.44 1
 
1.0%
60.497 1
 
1.0%
57.547 1
 
1.0%
56.883 1
 
1.0%
56.468 1
 
1.0%
56.93 1
 
1.0%
56.236 1
 
1.0%
56.273 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
36.177 1
1.0%
36.256 1
1.0%
37.169 1
1.0%
37.205 1
1.0%
37.557 1
1.0%
37.572 1
1.0%
37.766 1
1.0%
37.781 1
1.0%
37.855 1
1.0%
37.888 1
1.0%
ValueCountFrequency (%)
60.497 1
1.0%
59.7 1
1.0%
59.017 1
1.0%
58.709 1
1.0%
58.665 1
1.0%
58.598 1
1.0%
58.462 1
1.0%
58.383 1
1.0%
58.29 1
1.0%
58.174 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9109
Minimum0.87
Maximum0.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:45:05.168473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile0.88
Q10.9
median0.915
Q30.92
95-th percentile0.93
Maximum0.93
Range0.06
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.015640856
Coefficient of variation (CV)0.017170771
Kurtosis0.052211817
Mean0.9109
Median Absolute Deviation (MAD)0.015
Skewness-0.7986003
Sum91.09
Variance0.00024463636
MonotonicityNot monotonic
2023-12-10T19:45:05.418928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.92 32
32.0%
0.9 21
21.0%
0.93 18
18.0%
0.91 16
16.0%
0.88 6
 
6.0%
0.89 4
 
4.0%
0.87 3
 
3.0%
ValueCountFrequency (%)
0.87 3
 
3.0%
0.88 6
 
6.0%
0.89 4
 
4.0%
0.9 21
21.0%
0.91 16
16.0%
0.92 32
32.0%
0.93 18
18.0%
ValueCountFrequency (%)
0.93 18
18.0%
0.92 32
32.0%
0.91 16
16.0%
0.9 21
21.0%
0.89 4
 
4.0%
0.88 6
 
6.0%
0.87 3
 
3.0%

Interactions

2023-12-10T19:44:59.608056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:53.906276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.106914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.047863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.284930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.440445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.796032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.103318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.269898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.241028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.499509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.696730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.966886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.271821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.409375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.394967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.676989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.950717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.149131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.439332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.540851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.530996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:57.833397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.118715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.326765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.629170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.700753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.695567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.040256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.283485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:45:00.492929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:54.787172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:55.872612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:56.846940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:58.246296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:44:59.443803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:45:05.588907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8880.8540.5550.4720.854
강우량(mm)0.8881.0001.0000.6360.3621.000
유입량(ms)0.8541.0001.0000.4390.3151.000
방류량(ms)0.5550.6360.4391.0000.2000.439
저수량(백만m3)0.4720.3620.3150.2001.0000.315
저수율0.8541.0001.0000.4390.3151.000
2023-12-10T19:45:05.785283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.9280.9280.0870.4520.928
강우량(mm)0.9281.0001.0000.2780.4881.000
유입량(ms)0.9281.0001.0000.2780.4881.000
방류량(ms)0.0870.2780.2781.0000.3860.278
저수량(백만m3)0.4520.4880.4880.3861.0000.488
저수율0.9281.0001.0000.2780.4881.000

Missing values

2023-12-10T19:45:00.736921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:45:01.006040image/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
0낙동강하굿둑2019010218400305.15999.340.77340.1340.93
1낙동강하굿둑2019010218300305.15999.340.70741.0470.93
2낙동강하굿둑2019010218200305.15999.342.55141.1390.93
3낙동강하굿둑2019010218100305.15999.348.10539.9360.93
4낙동강하굿둑2019010218000305.15999.354.11854.1180.93
5낙동강하굿둑2019010217500305.15999.357.97957.80.93
6낙동강하굿둑2019010217400305.15999.358.28257.9760.93
7낙동강하굿둑2019010217300305.15999.358.51158.1610.93
8낙동강하굿둑2019010217200305.15999.358.45658.7090.93
9낙동강하굿둑2019010217100305.15999.358.20158.6650.93
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90낙동강하굿둑2019010203400302.83598.637.2539.3170.88
91낙동강하굿둑2019010203300302.83598.637.09336.2560.88
92낙동강하굿둑2019010203200302.83598.638.17536.1770.88
93낙동강하굿둑2019010203100302.83598.6296.31638.8470.88
94낙동강하굿둑2019010203000302.83598.6296.13739.5020.88
95낙동강하굿둑2019010202500302.83598.6296.22237.7660.88
96낙동강하굿둑2019010202400302.37298.438.85838.310.87
97낙동강하굿둑2019010202300302.37298.439.08939.7560.87
98낙동강하굿둑2019010202200302.37298.437.69638.5090.87
99낙동강하굿둑2019010218500305.15999.340.39139.9920.93