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

Categorical3
Numeric5

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
관측일 is highly overall correlated with 예년 평균 누적 강우량(mm)High 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 4 other fieldsHigh correlation
예년 평균 누적 강우량(mm) is highly overall correlated with 관측일 and 2 other fieldsHigh correlation
전년 강우량(mm) is highly overall correlated with 전년 누적 강우량(mm)High correlation
전년 강우량(mm) is highly imbalanced (86.1%)Imbalance
강우량(mm) has 88 (88.0%) zerosZeros
당년 누적 강우량(mm) has 3 (3.0%) zerosZeros
전년 누적 강우량(mm) has 17 (17.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:00:10.070972
Analysis finished2023-12-10 14:00:15.384267
Duration5.31 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
괴산
19 
남강
19 
광동
18 
군위
12 
감포
11 
Other values (2)
21 

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 (%)
괴산 19
19.0%
남강 19
19.0%
광동 18
18.0%
군위 12
12.0%
감포 11
11.0%
군남 11
11.0%
구천 10
10.0%

Length

2023-12-10T23:00:15.504662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:15.680010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
괴산 19
19.0%
남강 19
19.0%
광동 18
18.0%
군위 12
12.0%
감포 11
11.0%
군남 11
11.0%
구천 10
10.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
내속리(전)
17 
경주시(감포댐)
11 
태백시(상사미동)
10 
거제시(구천댐)
10 
연천군(군남댐)
10 
Other values (14)
42 

Length

Max length9
Median length8
Mean length7.76
Min length6

Unique

Unique6 ?
Unique (%)6.0%

Sample

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

Common Values

ValueCountFrequency (%)
내속리(전) 17
17.0%
경주시(감포댐) 11
11.0%
태백시(상사미동) 10
10.0%
거제시(구천댐) 10
10.0%
연천군(군남댐) 10
10.0%
삼척시(광동댐) 8
8.0%
군위군(양지리) 6
 
6.0%
군위군(군위댐) 6
 
6.0%
산청군(장천리) 5
 
5.0%
함양군(의탄리) 5
 
5.0%
Other values (9) 12
12.0%

Length

2023-12-10T23:00:15.956545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
내속리(전 17
17.0%
경주시(감포댐 11
11.0%
태백시(상사미동 10
10.0%
거제시(구천댐 10
10.0%
연천군(군남댐 10
10.0%
삼척시(광동댐 8
8.0%
군위군(양지리 6
 
6.0%
군위군(군위댐 6
 
6.0%
함양군(의탄리 5
 
5.0%
산청군(장천리 5
 
5.0%
Other values (9) 12
12.0%

관측일
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190214
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:16.289875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190207
median20190213
Q320190221
95-th percentile20190227
Maximum20190228
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.0431852
Coefficient of variation (CV)3.9837048 × 10-7
Kurtosis-1.2183405
Mean20190214
Median Absolute Deviation (MAD)6.5
Skewness0.13338009
Sum2.0190214 × 109
Variance64.692828
MonotonicityNot monotonic
2023-12-10T23:00:16.542478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190217 7
 
7.0%
20190221 6
 
6.0%
20190207 6
 
6.0%
20190203 6
 
6.0%
20190212 6
 
6.0%
20190202 5
 
5.0%
20190208 5
 
5.0%
20190227 5
 
5.0%
20190225 5
 
5.0%
20190219 5
 
5.0%
Other values (18) 44
44.0%
ValueCountFrequency (%)
20190201 2
 
2.0%
20190202 5
5.0%
20190203 6
6.0%
20190204 4
4.0%
20190205 2
 
2.0%
20190206 4
4.0%
20190207 6
6.0%
20190208 5
5.0%
20190209 4
4.0%
20190210 2
 
2.0%
ValueCountFrequency (%)
20190228 2
 
2.0%
20190227 5
5.0%
20190226 1
 
1.0%
20190225 5
5.0%
20190224 2
 
2.0%
20190223 4
4.0%
20190222 1
 
1.0%
20190221 6
6.0%
20190220 1
 
1.0%
20190219 5
5.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1
Minimum0
Maximum28
Zeros88
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:16.771373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.2
Maximum28
Range28
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.958114
Coefficient of variation (CV)3.5982855
Kurtosis24.834695
Mean1.1
Median Absolute Deviation (MAD)0
Skewness4.6724422
Sum110
Variance15.666667
MonotonicityNot monotonic
2023-12-10T23:00:16.978462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 88
88.0%
3 3
 
3.0%
12 2
 
2.0%
5 2
 
2.0%
1 1
 
1.0%
8 1
 
1.0%
28 1
 
1.0%
13 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
0 88
88.0%
1 1
 
1.0%
3 3
 
3.0%
5 2
 
2.0%
8 1
 
1.0%
12 2
 
2.0%
13 1
 
1.0%
17 1
 
1.0%
28 1
 
1.0%
ValueCountFrequency (%)
28 1
 
1.0%
17 1
 
1.0%
13 1
 
1.0%
12 2
 
2.0%
8 1
 
1.0%
5 2
 
2.0%
3 3
 
3.0%
1 1
 
1.0%
0 88
88.0%

전년 강우량(mm)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
96 
43
 
1
4
 
1
1
 
1
29
 
1

Length

Max length2
Median length1
Mean length1.02
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 96
96.0%
43 1
 
1.0%
4 1
 
1.0%
1 1
 
1.0%
29 1
 
1.0%

Length

2023-12-10T23:00:17.260300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:17.509100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 96
96.0%
43 1
 
1.0%
4 1
 
1.0%
1 1
 
1.0%
29 1
 
1.0%

당년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.735
Minimum0
Maximum76
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:17.755067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111.75
median19.75
Q336
95-th percentile51.05
Maximum76
Range76
Interquartile range (IQR)24.25

Descriptive statistics

Standard deviation18.308586
Coefficient of variation (CV)0.74018944
Kurtosis0.80425023
Mean24.735
Median Absolute Deviation (MAD)9.25
Skewness1.0774716
Sum2473.5
Variance335.20432
MonotonicityNot monotonic
2023-12-10T23:00:17.987777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
5.0 13
 
13.0%
17.0 7
 
7.0%
26.0 6
 
6.0%
36.0 5
 
5.0%
11.0 5
 
5.0%
20.0 5
 
5.0%
29.0 5
 
5.0%
49.5 5
 
5.0%
13.0 4
 
4.0%
48.0 4
 
4.0%
Other values (22) 41
41.0%
ValueCountFrequency (%)
0.0 3
 
3.0%
2.0 1
 
1.0%
5.0 13
13.0%
9.0 1
 
1.0%
10.0 2
 
2.0%
11.0 5
 
5.0%
12.0 1
 
1.0%
13.0 4
 
4.0%
14.0 2
 
2.0%
15.0 4
 
4.0%
ValueCountFrequency (%)
76.0 4
4.0%
71.0 1
 
1.0%
50.0 1
 
1.0%
49.5 5
5.0%
48.0 4
4.0%
47.0 2
 
2.0%
46.0 1
 
1.0%
45.0 1
 
1.0%
44.0 2
 
2.0%
36.0 5
5.0%

전년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.12
Minimum0
Maximum74
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:18.196754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median15
Q327
95-th percentile40
Maximum74
Range74
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation15.305825
Coefficient of variation (CV)0.89403184
Kurtosis0.72554929
Mean17.12
Median Absolute Deviation (MAD)12
Skewness0.8909654
Sum1712
Variance234.26828
MonotonicityNot monotonic
2023-12-10T23:00:18.379718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 17
17.0%
7 13
13.0%
31 10
10.0%
40 10
10.0%
6 8
8.0%
3 8
8.0%
27 6
 
6.0%
19 6
 
6.0%
15 6
 
6.0%
22 5
 
5.0%
Other values (7) 11
11.0%
ValueCountFrequency (%)
0 17
17.0%
3 8
8.0%
6 8
8.0%
7 13
13.0%
15 6
 
6.0%
18 2
 
2.0%
19 6
 
6.0%
20 3
 
3.0%
22 5
 
5.0%
24 1
 
1.0%
ValueCountFrequency (%)
74 1
 
1.0%
59 1
 
1.0%
42 2
 
2.0%
40 10
10.0%
31 10
10.0%
27 6
6.0%
26 1
 
1.0%
24 1
 
1.0%
22 5
5.0%
20 3
 
3.0%

예년 평균 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.253
Minimum4.5
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:18.611838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile7
Q114.7
median25
Q335.8
95-th percentile64.505
Maximum102
Range97.5
Interquartile range (IQR)21.1

Descriptive statistics

Standard deviation19.457394
Coefficient of variation (CV)0.68868419
Kurtosis2.3140932
Mean28.253
Median Absolute Deviation (MAD)10.8
Skewness1.3957319
Sum2825.3
Variance378.59019
MonotonicityNot monotonic
2023-12-10T23:00:18.885500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0 9
 
9.0%
28.3 3
 
3.0%
22.3 2
 
2.0%
26.6 2
 
2.0%
17.7 2
 
2.0%
8.0 2
 
2.0%
11.5 2
 
2.0%
4.5 2
 
2.0%
35.8 2
 
2.0%
26.1 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
4.5 2
 
2.0%
6.0 1
 
1.0%
7.0 9
9.0%
7.3 1
 
1.0%
8.0 2
 
2.0%
10.9 1
 
1.0%
11.5 2
 
2.0%
11.8 1
 
1.0%
12.0 1
 
1.0%
13.0 1
 
1.0%
ValueCountFrequency (%)
102.0 1
1.0%
95.1 1
1.0%
77.2 1
1.0%
67.5 1
1.0%
66.5 1
1.0%
64.4 1
1.0%
63.3 1
1.0%
61.6 1
1.0%
60.0 1
1.0%
57.7 1
1.0%

Interactions

2023-12-10T23:00:14.200439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:10.488250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.233679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.952609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.338071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.386787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:10.610072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.359206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:12.096437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.480780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.543491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:10.769283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.508721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:12.843646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.629023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.734011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:10.913875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.677514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.031104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.844573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.911304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.089837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:11.831368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:13.214302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.017461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:00:19.028664image/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)
댐명1.0001.0000.0000.0000.1600.7640.9570.646
관측소명1.0001.0000.0000.0000.6920.9370.9720.825
관측일0.0000.0001.0000.5070.2420.2140.0000.255
강우량(mm)0.0000.0000.5071.0000.0000.1710.0000.000
전년 강우량(mm)0.1600.6920.2420.0001.0000.2130.8080.705
당년 누적 강우량(mm)0.7640.9370.2140.1710.2131.0000.7810.692
전년 누적 강우량(mm)0.9570.9720.0000.0000.8080.7811.0000.745
예년 평균 누적 강우량(mm)0.6460.8250.2550.0000.7050.6920.7451.000
2023-12-10T23:00:19.208404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명댐명전년 강우량(mm)
관측소명1.0000.9330.396
댐명0.9331.0000.098
전년 강우량(mm)0.3960.0981.000
2023-12-10T23:00:19.360914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)댐명관측소명전년 강우량(mm)
관측일1.000-0.1660.4810.1580.5140.0000.0000.106
강우량(mm)-0.1661.000-0.052-0.093-0.0800.0000.0000.000
당년 누적 강우량(mm)0.481-0.0521.0000.8180.6800.5410.7180.127
전년 누적 강우량(mm)0.158-0.0930.8181.0000.6320.6830.8330.686
예년 평균 누적 강우량(mm)0.514-0.0800.6800.6321.0000.4080.4760.494
댐명0.0000.0000.5410.6830.4081.0000.9330.098
관측소명0.0000.0000.7180.8330.4760.9331.0000.396
전년 강우량(mm)0.1060.0000.1270.6860.4940.0980.3961.000

Missing values

2023-12-10T23:00:15.090185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:00:15.308956image/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)201902020016.53133.2
1감포경주시(감포댐)201902240049.53164.4
2감포경주시(감포댐)201902070019.53134.2
3감포경주시(감포댐)201902080019.53135.8
4감포경주시(감포댐)201902033019.53133.4
5감포경주시(감포댐)201902120020.03146.3
6감포경주시(감포댐)201902170026.03157.7
7감포경주시(감포댐)201902260049.53167.5
8감포경주시(감포댐)201902200049.53161.6
9감포경주시(감포댐)201902220049.53163.3
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
90남강산청군(장천리)201902180026.02235.2
91남강함양군(의탄리)201902070036.02716.5
92남강산청군(실매리)201902230044.02022.3
93남강함양군(화촌리)201902100029.02413.7
94남강산청군(장천리)2019020312026.02221.3
95남강산청군(장천리)201902060026.02222.3
96남강산청군(경호교)201902240045.02017.7
97남강하동군(위태리)2019022802971.05955.8
98남강산청군(장천리)201902020014.02219.7
99남강함양군(의탄리)201902170036.02726.5