Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory71.3 B

Variable types

Categorical2
Numeric6

Alerts

관측소명 is highly overall correlated with 당년 누적 강우량(mm) and 2 other fieldsHigh correlation
댐명 is highly overall correlated with 당년 누적 강우량(mm) and 2 other fieldsHigh correlation
당년 누적 강우량(mm) is highly overall correlated with 전년 누적 강우량(mm) and 3 other fieldsHigh correlation
전년 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
예년 평균 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 1 other fieldsHigh correlation
강우량(mm) has 71 (71.0%) zerosZeros
전년 강우량(mm) has 76 (76.0%) zerosZeros
전년 누적 강우량(mm) has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:59:42.833669
Analysis finished2023-12-10 13:59:51.595942
Duration8.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광동
18 
군남
18 
군위
17 
구천
15 
괴산
14 
Other values (2)
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광동 18
18.0%
군남 18
18.0%
군위 17
17.0%
구천 15
15.0%
괴산 14
14.0%
감포 13
13.0%
남강 5
 
5.0%

Length

2023-12-10T22:59:52.046835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:52.290092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광동 18
18.0%
군남 18
18.0%
군위 17
17.0%
구천 15
15.0%
괴산 14
14.0%
감포 13
13.0%
남강 5
 
5.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
거제시(구천댐)
15 
경주시(감포댐)
13 
군위군(양지리)
12 
연천군(횡산리)
11 
태백시(상사미동)
10 
Other values (10)
39 

Length

Max length9
Median length8
Mean length7.92
Min length6

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row경주시(감포댐)
2nd row경주시(감포댐)
3rd row경주시(감포댐)
4th row경주시(감포댐)
5th row경주시(감포댐)

Common Values

ValueCountFrequency (%)
거제시(구천댐) 15
15.0%
경주시(감포댐) 13
13.0%
군위군(양지리) 12
12.0%
연천군(횡산리) 11
11.0%
태백시(상사미동) 10
10.0%
내속리(전) 9
9.0%
삼척시(광동댐) 8
8.0%
연천군(군남댐) 7
7.0%
괴산댐(FTP) 5
 
5.0%
군위군(군위댐) 5
 
5.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T22:59:52.572264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
거제시(구천댐 15
15.0%
경주시(감포댐 13
13.0%
군위군(양지리 12
12.0%
연천군(횡산리 11
11.0%
태백시(상사미동 10
10.0%
내속리(전 9
9.0%
삼척시(광동댐 8
8.0%
연천군(군남댐 7
7.0%
괴산댐(ftp 5
 
5.0%
군위군(군위댐 5
 
5.0%
Other values (5) 5
 
5.0%

관측일
Real number (ℝ)

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190417
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:52.825474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190404
Q120190409
median20190418
Q320190425
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)16.25

Descriptive statistics

Standard deviation8.8647469
Coefficient of variation (CV)4.3905715 × 10-7
Kurtosis-1.3700287
Mean20190417
Median Absolute Deviation (MAD)8
Skewness-0.10643443
Sum2.0190417 × 109
Variance78.583737
MonotonicityNot monotonic
2023-12-10T22:59:53.075976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20190427 6
 
6.0%
20190418 6
 
6.0%
20190407 6
 
6.0%
20190416 6
 
6.0%
20190420 6
 
6.0%
20190422 6
 
6.0%
20190409 6
 
6.0%
20190426 5
 
5.0%
20190428 5
 
5.0%
20190414 5
 
5.0%
Other values (13) 43
43.0%
ValueCountFrequency (%)
20190401 1
 
1.0%
20190402 1
 
1.0%
20190403 3
3.0%
20190404 4
4.0%
20190405 4
4.0%
20190406 4
4.0%
20190407 6
6.0%
20190409 6
6.0%
20190410 3
3.0%
20190412 5
5.0%
ValueCountFrequency (%)
20190430 4
4.0%
20190429 5
5.0%
20190428 5
5.0%
20190427 6
6.0%
20190426 5
5.0%
20190425 3
3.0%
20190424 5
5.0%
20190422 6
6.0%
20190420 6
6.0%
20190418 6
6.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.11
Minimum0
Maximum43
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:53.342443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile20.3
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.6688337
Coefficient of variation (CV)2.4658629
Kurtosis10.048822
Mean3.11
Median Absolute Deviation (MAD)0
Skewness3.0923506
Sum311
Variance58.81101
MonotonicityNot monotonic
2023-12-10T22:59:53.537854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 71
71.0%
1.0 6
 
6.0%
3.0 3
 
3.0%
2.0 2
 
2.0%
11.0 2
 
2.0%
9.0 2
 
2.0%
7.0 2
 
2.0%
17.0 1
 
1.0%
30.0 1
 
1.0%
26.0 1
 
1.0%
Other values (9) 9
 
9.0%
ValueCountFrequency (%)
0.0 71
71.0%
0.5 1
 
1.0%
1.0 6
 
6.0%
2.0 2
 
2.0%
3.0 3
 
3.0%
6.0 1
 
1.0%
7.0 2
 
2.0%
9.0 2
 
2.0%
9.5 1
 
1.0%
11.0 2
 
2.0%
ValueCountFrequency (%)
43.0 1
1.0%
30.0 1
1.0%
28.0 1
1.0%
27.0 1
1.0%
26.0 1
1.0%
20.0 1
1.0%
18.0 1
1.0%
17.0 1
1.0%
13.0 1
1.0%
11.0 2
2.0%

전년 강우량(mm)
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.645
Minimum0
Maximum62
Zeros76
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:53.748779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile22.1
Maximum62
Range62
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.3255636
Coefficient of variation (CV)2.5584537
Kurtosis16.78598
Mean3.645
Median Absolute Deviation (MAD)0
Skewness3.6707692
Sum364.5
Variance86.966136
MonotonicityNot monotonic
2023-12-10T22:59:53.948946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 76
76.0%
8.0 3
 
3.0%
1.0 2
 
2.0%
24.0 1
 
1.0%
10.0 1
 
1.0%
27.0 1
 
1.0%
12.0 1
 
1.0%
4.0 1
 
1.0%
16.0 1
 
1.0%
15.0 1
 
1.0%
Other values (12) 12
 
12.0%
ValueCountFrequency (%)
0.0 76
76.0%
0.5 1
 
1.0%
1.0 2
 
2.0%
2.0 1
 
1.0%
4.0 1
 
1.0%
5.0 1
 
1.0%
8.0 3
 
3.0%
9.0 1
 
1.0%
10.0 1
 
1.0%
12.0 1
 
1.0%
ValueCountFrequency (%)
62.0 1
1.0%
37.0 1
1.0%
29.0 1
1.0%
27.0 1
1.0%
24.0 1
1.0%
22.0 1
1.0%
19.0 1
1.0%
18.0 1
1.0%
16.0 1
1.0%
15.0 1
1.0%

당년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.47
Minimum24
Maximum371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:54.155045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile44.85
Q152
median92
Q3139
95-th percentile339
Maximum371
Range347
Interquartile range (IQR)87

Descriptive statistics

Standard deviation93.7356
Coefficient of variation (CV)0.7470758
Kurtosis1.0148607
Mean125.47
Median Absolute Deviation (MAD)40
Skewness1.4493745
Sum12547
Variance8786.3627
MonotonicityNot monotonic
2023-12-10T22:59:54.376231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
52.0 11
 
11.0%
108.5 6
 
6.0%
50.0 5
 
5.0%
338.0 4
 
4.0%
88.0 4
 
4.0%
63.5 3
 
3.0%
56.0 3
 
3.0%
139.0 3
 
3.0%
81.0 3
 
3.0%
128.0 3
 
3.0%
Other values (39) 55
55.0%
ValueCountFrequency (%)
24.0 2
 
2.0%
30.0 1
 
1.0%
41.0 1
 
1.0%
42.0 1
 
1.0%
45.0 1
 
1.0%
49.0 2
 
2.0%
50.0 5
5.0%
51.0 2
 
2.0%
52.0 11
11.0%
56.0 3
 
3.0%
ValueCountFrequency (%)
371.0 2
2.0%
358.0 1
 
1.0%
339.0 3
3.0%
338.0 4
4.0%
321.0 1
 
1.0%
293.0 1
 
1.0%
273.0 3
3.0%
181.0 1
 
1.0%
177.0 1
 
1.0%
171.0 2
2.0%

전년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.2
Minimum0
Maximum547
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:54.623896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1132
median216
Q3307
95-th percentile422
Maximum547
Range547
Interquartile range (IQR)175

Descriptive statistics

Standard deviation135.10886
Coefficient of variation (CV)0.62204816
Kurtosis-0.30760516
Mean217.2
Median Absolute Deviation (MAD)91
Skewness0.14986923
Sum21720
Variance18254.404
MonotonicityNot monotonic
2023-12-10T22:59:54.841853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 14
 
14.0%
422 5
 
5.0%
385 5
 
5.0%
313 5
 
5.0%
229 4
 
4.0%
375 4
 
4.0%
166 4
 
4.0%
289 3
 
3.0%
547 3
 
3.0%
277 3
 
3.0%
Other values (30) 50
50.0%
ValueCountFrequency (%)
0 14
14.0%
67 1
 
1.0%
78 1
 
1.0%
82 1
 
1.0%
84 1
 
1.0%
90 2
 
2.0%
100 3
 
3.0%
103 1
 
1.0%
108 1
 
1.0%
140 1
 
1.0%
ValueCountFrequency (%)
547 3
3.0%
422 5
5.0%
385 5
5.0%
375 4
4.0%
356 2
 
2.0%
313 5
5.0%
307 3
3.0%
289 3
3.0%
285 1
 
1.0%
277 3
3.0%

예년 평균 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.88
Minimum45.5
Maximum281.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:55.075608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.5
5-th percentile57.88
Q181.975
median126.5
Q3192.775
95-th percentile265.465
Maximum281.3
Range235.8
Interquartile range (IQR)110.8

Descriptive statistics

Standard deviation65.671633
Coefficient of variation (CV)0.46286745
Kurtosis-0.90272288
Mean141.88
Median Absolute Deviation (MAD)50.65
Skewness0.46240391
Sum14188
Variance4312.7634
MonotonicityNot monotonic
2023-12-10T22:59:55.379789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.5 3
 
3.0%
78.5 2
 
2.0%
198.9 1
 
1.0%
89.8 1
 
1.0%
81.9 1
 
1.0%
92.1 1
 
1.0%
116.0 1
 
1.0%
99.6 1
 
1.0%
73.4 1
 
1.0%
125.6 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
45.5 1
1.0%
49.0 1
1.0%
50.0 1
1.0%
55.7 1
1.0%
57.5 1
1.0%
57.9 1
1.0%
60.5 1
1.0%
61.7 1
1.0%
63.9 1
1.0%
64.1 1
1.0%
ValueCountFrequency (%)
281.3 1
1.0%
279.6 1
1.0%
276.9 1
1.0%
270.3 1
1.0%
266.7 1
1.0%
265.4 1
1.0%
261.0 1
1.0%
250.5 1
1.0%
249.1 1
1.0%
240.7 1
1.0%

Interactions

2023-12-10T22:59:50.364886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:44.065515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:46.453632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:47.457850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.578306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.464451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.530150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:44.577022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:46.683236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:47.662266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.788668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.610493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.670434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:44.994741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:46.812177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:47.835632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.910102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.730213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.821682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:45.415598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:46.993234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.080718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.054168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.875476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.974946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:45.855648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:47.156623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.255773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.205962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.031307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:51.124524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:46.156991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:47.314911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:48.401982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:49.336441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:50.176385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:59:55.532940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
댐명1.0001.0000.0000.0650.2700.7520.8590.659
관측소명1.0001.0000.0000.0000.2950.8150.8610.765
관측일0.0000.0001.0000.2810.5020.2430.3300.571
강우량(mm)0.0650.0000.2811.0000.0000.4830.3190.070
전년 강우량(mm)0.2700.2950.5020.0001.0000.3380.4100.300
당년 누적 강우량(mm)0.7520.8150.2430.4830.3381.0000.7690.652
전년 누적 강우량(mm)0.8590.8610.3300.3190.4100.7691.0000.751
예년 평균 누적 강우량(mm)0.6590.7650.5710.0700.3000.6520.7511.000
2023-12-10T22:59:55.730671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명댐명
관측소명1.0000.956
댐명0.9561.000
2023-12-10T22:59:55.862098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)댐명관측소명
관측일1.0000.034-0.3070.2460.1740.3250.0000.000
강우량(mm)0.0341.0000.0860.1770.1930.1090.0000.000
전년 강우량(mm)-0.3070.0861.000-0.0120.094-0.0060.0930.126
당년 누적 강우량(mm)0.2460.177-0.0121.0000.9100.6570.5280.504
전년 누적 강우량(mm)0.1740.1930.0940.9101.0000.6550.6810.559
예년 평균 누적 강우량(mm)0.3250.109-0.0060.6570.6551.0000.4020.396
댐명0.0000.0000.0930.5280.6810.4021.0000.956
관측소명0.0000.0000.1260.5040.5590.3960.9561.000

Missing values

2023-12-10T22:59:51.302178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:59:51.519866image/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

댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
0감포경주시(감포댐)201904140.024.0108.5313198.9
1감포경주시(감포댐)201904160.00.0108.5313200.5
2감포경주시(감포댐)201904249.022.0121.0375238.1
3감포경주시(감포댐)201904120.00.0108.5289191.0
4감포경주시(감포댐)201904050.00.580.5271169.3
5감포경주시(감포댐)201904250.50.0121.5375240.7
6감포경주시(감포댐)2019042943.00.0181.0375265.4
7감포경주시(감포댐)201904220.00.0108.5313229.2
8감포경주시(감포댐)201904099.50.090.0289184.7
9감포경주시(감포댐)201904180.00.0108.5313208.6
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
90군위군위군(양지리)201904180.00.088.020468.1
91군위군위군(양지리)201904030.00.052.014049.0
92군위군위군(양지리)201904220.00.088.020478.3
93군위군위군(양지리)201904070.00.052.018455.7
94군위군위군(양지리)201904040.012.052.015250.0
95남강산청군(내원리)201904072.00.0154.0266159.9
96남강합천군(소오리)201904010.00.093.019063.9
97남강남원시(덕동리)201904050.027.0147.0257130.4
98남강산청군(실매리)201904300.00.0177.0285118.9
99남강함양군(화촌리)201904270.00.0134.0257106.6