Overview

Dataset statistics

Number of variables16
Number of observations6955
Missing cells1533
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory964.6 KiB
Average record size in memory142.0 B

Variable types

DateTime1
Categorical4
Numeric11

Dataset

Description제주도 인근에서 측정한 해양기상부이 데이터입니다. 해양기상부이는 해수면에서 다양한 기상장비로 해양기상현상을 관측합니다. 출처는 기상청입니다. (데이터 미집계로 인하여 일부 데이터값에 공란이 존재할 수 있습니다.)
Author제주특별자치도
URLhttps://www.data.go.kr/data/15110785/fileData.do

Alerts

경도 is highly overall correlated with 정점명 and 2 other fieldsHigh correlation
정점 코드 is highly overall correlated with 정점명 and 2 other fieldsHigh correlation
정점명 is highly overall correlated with 정점 코드 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 정점명 and 2 other fieldsHigh correlation
평균풍속 is highly overall correlated with 평균최대파고 and 3 other fieldsHigh correlation
평균기압 is highly overall correlated with 평균상대습도 and 2 other fieldsHigh correlation
평균상대습도 is highly overall correlated with 평균기압 and 1 other fieldsHigh correlation
평균기온 is highly overall correlated with 평균기압 and 2 other fieldsHigh correlation
평균수온 is highly overall correlated with 평균기압 and 1 other fieldsHigh correlation
평균최대파고 is highly overall correlated with 평균풍속 and 3 other fieldsHigh correlation
평균유의파고 is highly overall correlated with 평균풍속 and 3 other fieldsHigh correlation
최고유의파고 is highly overall correlated with 평균풍속 and 3 other fieldsHigh correlation
최고최대파고 is highly overall correlated with 평균풍속 and 3 other fieldsHigh correlation
평균파주기 is highly overall correlated with 최고파주기High correlation
최고파주기 is highly overall correlated with 평균파주기High correlation
평균풍속 has 168 (2.4%) missing valuesMissing
평균기압 has 214 (3.1%) missing valuesMissing
평균상대습도 has 240 (3.5%) missing valuesMissing
평균기온 has 216 (3.1%) missing valuesMissing
평균수온 has 188 (2.7%) missing valuesMissing
평균최대파고 has 131 (1.9%) missing valuesMissing
평균유의파고 has 153 (2.2%) missing valuesMissing
평균파주기 has 137 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 08:11:31.616878
Analysis finished2023-12-12 08:11:51.021302
Duration19.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2396
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
Minimum2016-01-01 00:00:00
Maximum2022-07-23 00:00:00
2023-12-12T17:11:51.119442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:51.311301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

정점명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
마라도
2370 
서귀포
2293 
추자도
2292 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마라도 2370
34.1%
서귀포 2293
33.0%
추자도 2292
33.0%

Length

2023-12-12T17:11:51.457672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:11:51.588961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마라도 2370
34.1%
서귀포 2293
33.0%
추자도 2292
33.0%

정점 코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
22107
2370 
22187
2293 
22184
2292 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22107
2nd row22107
3rd row22107
4th row22107
5th row22107

Common Values

ValueCountFrequency (%)
22107 2370
34.1%
22187 2293
33.0%
22184 2292
33.0%

Length

2023-12-12T17:11:51.713028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:11:51.830514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22107 2370
34.1%
22187 2293
33.0%
22184 2292
33.0%

위도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
33.083333
2370 
33.128056
2293 
33.793611
2292 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33.083333
2nd row33.083333
3rd row33.083333
4th row33.083333
5th row33.083333

Common Values

ValueCountFrequency (%)
33.083333 2370
34.1%
33.128056 2293
33.0%
33.793611 2292
33.0%

Length

2023-12-12T17:11:51.961200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:11:52.083372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33.083333 2370
34.1%
33.128056 2293
33.0%
33.793611 2292
33.0%

경도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.5 KiB
126.033333
2370 
127.022778
2293 
126.141111
2292 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row126.033333
2nd row126.033333
3rd row126.033333
4th row126.033333
5th row126.033333

Common Values

ValueCountFrequency (%)
126.033333 2370
34.1%
127.022778 2293
33.0%
126.141111 2292
33.0%

Length

2023-12-12T17:11:52.230730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:11:52.358357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126.033333 2370
34.1%
127.022778 2293
33.0%
126.141111 2292
33.0%

평균풍속
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct159
Distinct (%)2.3%
Missing168
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean6.6720642
Minimum0.8
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:52.485469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.5
Q14.4
median6.3
Q38.7
95-th percentile11.9
Maximum19
Range18.2
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation2.8946444
Coefficient of variation (CV)0.4338454
Kurtosis-0.22504969
Mean6.6720642
Median Absolute Deviation (MAD)2.1
Skewness0.48340686
Sum45283.3
Variance8.378966
MonotonicityNot monotonic
2023-12-12T17:11:52.637381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 104
 
1.5%
5.3 103
 
1.5%
5.4 102
 
1.5%
5.7 102
 
1.5%
4.3 102
 
1.5%
4.7 97
 
1.4%
5.2 95
 
1.4%
6.3 94
 
1.4%
4.5 92
 
1.3%
5.1 92
 
1.3%
Other values (149) 5804
83.5%
(Missing) 168
 
2.4%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
0.9 2
 
< 0.1%
1.0 1
 
< 0.1%
1.1 2
 
< 0.1%
1.2 3
 
< 0.1%
1.3 10
0.1%
1.4 10
0.1%
1.5 7
 
0.1%
1.6 15
0.2%
1.7 22
0.3%
ValueCountFrequency (%)
19.0 1
< 0.1%
18.0 1
< 0.1%
17.8 2
< 0.1%
17.2 1
< 0.1%
17.1 1
< 0.1%
16.8 1
< 0.1%
16.6 1
< 0.1%
16.3 2
< 0.1%
16.1 1
< 0.1%
15.8 1
< 0.1%

평균기압
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct388
Distinct (%)5.8%
Missing214
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1016.0167
Minimum982.8
Maximum1035.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:52.813423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum982.8
5-th percentile1003.6
Q11009.9
median1016.2
Q31022.1
95-th percentile1028.2
Maximum1035.8
Range53
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation7.7844186
Coefficient of variation (CV)0.0076617033
Kurtosis-0.64660048
Mean1016.0167
Median Absolute Deviation (MAD)6.1
Skewness-0.064474628
Sum6848968.7
Variance60.597173
MonotonicityNot monotonic
2023-12-12T17:11:52.956009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1011.3 47
 
0.7%
1023.0 46
 
0.7%
1020.8 41
 
0.6%
1020.2 40
 
0.6%
1010.9 39
 
0.6%
1008.6 38
 
0.5%
1010.8 38
 
0.5%
1017.6 38
 
0.5%
1020.5 38
 
0.5%
1010.7 38
 
0.5%
Other values (378) 6338
91.1%
(Missing) 214
 
3.1%
ValueCountFrequency (%)
982.8 1
< 0.1%
984.3 1
< 0.1%
984.6 1
< 0.1%
990.2 1
< 0.1%
990.3 1
< 0.1%
992.6 1
< 0.1%
993.0 1
< 0.1%
993.4 1
< 0.1%
993.9 2
< 0.1%
994.1 1
< 0.1%
ValueCountFrequency (%)
1035.8 1
 
< 0.1%
1035.7 1
 
< 0.1%
1035.1 1
 
< 0.1%
1035.0 2
< 0.1%
1034.8 2
< 0.1%
1034.3 1
 
< 0.1%
1034.1 3
< 0.1%
1034.0 3
< 0.1%
1033.9 2
< 0.1%
1033.8 2
< 0.1%

평균상대습도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)1.0%
Missing240
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean75.567535
Minimum33
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:53.109239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q165
median76
Q387
95-th percentile94
Maximum100
Range67
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.608718
Coefficient of variation (CV)0.16685363
Kurtosis-1.0147151
Mean75.567535
Median Absolute Deviation (MAD)11
Skewness-0.14285094
Sum507436
Variance158.97976
MonotonicityNot monotonic
2023-12-12T17:11:53.250967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 208
 
3.0%
65 205
 
2.9%
90 205
 
2.9%
64 201
 
2.9%
89 199
 
2.9%
86 189
 
2.7%
83 188
 
2.7%
67 185
 
2.7%
85 180
 
2.6%
63 179
 
2.6%
Other values (57) 4776
68.7%
(Missing) 240
 
3.5%
ValueCountFrequency (%)
33 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 1
 
< 0.1%
43 3
< 0.1%
ValueCountFrequency (%)
100 1
 
< 0.1%
99 13
 
0.2%
98 33
 
0.5%
97 58
 
0.8%
96 85
1.2%
95 111
1.6%
94 130
1.9%
93 149
2.1%
92 158
2.3%
91 177
2.5%

평균기온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct339
Distinct (%)5.0%
Missing216
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean17.360365
Minimum-1.8
Maximum34.6
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)0.2%
Memory size61.3 KiB
2023-12-12T17:11:53.389805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.8
5-th percentile5.9
Q111.7
median17.3
Q323.1
95-th percentile28.7
Maximum34.6
Range36.4
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation7.2241396
Coefficient of variation (CV)0.41612832
Kurtosis-0.85786315
Mean17.360365
Median Absolute Deviation (MAD)5.7
Skewness-0.0050134492
Sum116991.5
Variance52.188193
MonotonicityNot monotonic
2023-12-12T17:11:53.513712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.9 47
 
0.7%
16.6 45
 
0.6%
18.4 41
 
0.6%
23.6 41
 
0.6%
14.5 41
 
0.6%
20.5 38
 
0.5%
20.9 38
 
0.5%
15.4 38
 
0.5%
9.5 37
 
0.5%
15.8 37
 
0.5%
Other values (329) 6336
91.1%
(Missing) 216
 
3.1%
ValueCountFrequency (%)
-1.8 1
< 0.1%
-1.7 2
< 0.1%
-1.1 1
< 0.1%
-0.9 1
< 0.1%
-0.6 2
< 0.1%
-0.3 1
< 0.1%
-0.2 1
< 0.1%
-0.1 2
< 0.1%
0.3 1
< 0.1%
0.6 1
< 0.1%
ValueCountFrequency (%)
34.6 1
 
< 0.1%
34.3 4
0.1%
34.1 3
 
< 0.1%
34.0 2
 
< 0.1%
33.9 5
0.1%
33.8 8
0.1%
33.7 2
 
< 0.1%
33.6 3
 
< 0.1%
33.5 2
 
< 0.1%
33.4 3
 
< 0.1%

평균수온
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct236
Distinct (%)3.5%
Missing188
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean19.903887
Minimum7.3
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:53.647719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.3
5-th percentile13.3
Q116.2
median19
Q323.4
95-th percentile28.4
Maximum31.5
Range24.2
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation4.7979798
Coefficient of variation (CV)0.24105743
Kurtosis-0.73028937
Mean19.903887
Median Absolute Deviation (MAD)3.4
Skewness0.34074501
Sum134689.6
Variance23.02061
MonotonicityNot monotonic
2023-12-12T17:11:53.778544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.1 86
 
1.2%
16.0 83
 
1.2%
17.0 77
 
1.1%
16.3 76
 
1.1%
16.7 75
 
1.1%
15.9 72
 
1.0%
16.4 71
 
1.0%
16.8 70
 
1.0%
17.2 70
 
1.0%
17.5 70
 
1.0%
Other values (226) 6017
86.5%
(Missing) 188
 
2.7%
ValueCountFrequency (%)
7.3 1
 
< 0.1%
7.5 2
< 0.1%
7.6 2
< 0.1%
7.7 2
< 0.1%
7.8 2
< 0.1%
7.9 1
 
< 0.1%
8.2 2
< 0.1%
8.3 1
 
< 0.1%
8.4 1
 
< 0.1%
8.5 3
< 0.1%
ValueCountFrequency (%)
31.5 3
 
< 0.1%
31.4 3
 
< 0.1%
31.3 3
 
< 0.1%
31.2 4
 
0.1%
31.1 1
 
< 0.1%
31.0 4
 
0.1%
30.9 5
0.1%
30.8 10
0.1%
30.7 10
0.1%
30.6 12
0.2%

평균최대파고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct85
Distinct (%)1.2%
Missing131
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2.0129836
Minimum0.3
Maximum12.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:53.904000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.7
Q11.2
median1.8
Q32.5
95-th percentile4.2
Maximum12.7
Range12.4
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.1555682
Coefficient of variation (CV)0.57405742
Kurtosis5.2550966
Mean2.0129836
Median Absolute Deviation (MAD)0.6
Skewness1.7392677
Sum13736.6
Variance1.3353378
MonotonicityNot monotonic
2023-12-12T17:11:54.107701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 342
 
4.9%
1.3 331
 
4.8%
1.0 305
 
4.4%
0.9 296
 
4.3%
1.4 294
 
4.2%
1.2 292
 
4.2%
1.1 290
 
4.2%
1.7 281
 
4.0%
1.6 276
 
4.0%
1.8 271
 
3.9%
Other values (75) 3846
55.3%
ValueCountFrequency (%)
0.3 5
 
0.1%
0.4 35
 
0.5%
0.5 74
 
1.1%
0.6 145
2.1%
0.7 174
2.5%
0.8 231
3.3%
0.9 296
4.3%
1.0 305
4.4%
1.1 290
4.2%
1.2 292
4.2%
ValueCountFrequency (%)
12.7 1
 
< 0.1%
10.5 1
 
< 0.1%
9.3 1
 
< 0.1%
9.2 1
 
< 0.1%
8.8 1
 
< 0.1%
8.6 2
< 0.1%
8.5 1
 
< 0.1%
8.3 1
 
< 0.1%
8.0 4
0.1%
7.9 1
 
< 0.1%

평균유의파고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)0.8%
Missing153
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1.2356072
Minimum0.2
Maximum7.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:54.600756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.4
Q10.7
median1.1
Q31.6
95-th percentile2.6
Maximum7.6
Range7.4
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.71277708
Coefficient of variation (CV)0.57686382
Kurtosis5.369019
Mean1.2356072
Median Absolute Deviation (MAD)0.4
Skewness1.752603
Sum8404.6
Variance0.50805116
MonotonicityNot monotonic
2023-12-12T17:11:54.796971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 507
 
7.3%
0.8 500
 
7.2%
0.6 488
 
7.0%
0.7 473
 
6.8%
1.0 458
 
6.6%
1.1 457
 
6.6%
0.5 400
 
5.8%
1.2 398
 
5.7%
1.4 368
 
5.3%
1.3 355
 
5.1%
Other values (46) 2398
34.5%
ValueCountFrequency (%)
0.2 22
 
0.3%
0.3 127
 
1.8%
0.4 264
3.8%
0.5 400
5.8%
0.6 488
7.0%
0.7 473
6.8%
0.8 500
7.2%
0.9 507
7.3%
1.0 458
6.6%
1.1 457
6.6%
ValueCountFrequency (%)
7.6 1
 
< 0.1%
6.7 1
 
< 0.1%
5.9 2
< 0.1%
5.4 1
 
< 0.1%
5.3 2
< 0.1%
5.2 2
< 0.1%
5.1 1
 
< 0.1%
5.0 1
 
< 0.1%
4.9 1
 
< 0.1%
4.8 4
0.1%

최고유의파고
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)1.2%
Missing28
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.7181608
Minimum0
Maximum11.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:55.015250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11
median1.5
Q32.2
95-th percentile3.6
Maximum11.7
Range11.7
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.0305593
Coefficient of variation (CV)0.59980375
Kurtosis8.4525429
Mean1.7181608
Median Absolute Deviation (MAD)0.6
Skewness2.0444156
Sum11901.7
Variance1.0620525
MonotonicityNot monotonic
2023-12-12T17:11:55.203120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 391
 
5.6%
1.2 372
 
5.3%
1.1 363
 
5.2%
1.3 355
 
5.1%
0.8 347
 
5.0%
1.6 342
 
4.9%
1.4 331
 
4.8%
0.9 330
 
4.7%
0.7 295
 
4.2%
1.8 279
 
4.0%
Other values (73) 3522
50.6%
ValueCountFrequency (%)
0.0 1
 
< 0.1%
0.2 7
 
0.1%
0.3 35
 
0.5%
0.4 93
 
1.3%
0.5 153
 
2.2%
0.6 249
3.6%
0.7 295
4.2%
0.8 347
5.0%
0.9 330
4.7%
1.0 391
5.6%
ValueCountFrequency (%)
11.7 1
< 0.1%
11.5 1
< 0.1%
10.4 1
< 0.1%
10.0 1
< 0.1%
9.7 1
< 0.1%
9.3 1
< 0.1%
8.8 1
< 0.1%
8.7 1
< 0.1%
8.6 1
< 0.1%
8.2 2
< 0.1%

최고최대파고
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)1.7%
Missing29
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.9109731
Minimum0.1
Maximum17.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:55.399858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1
Q11.7
median2.5
Q33.7
95-th percentile6.075
Maximum17.7
Range17.6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.719614
Coefficient of variation (CV)0.5907351
Kurtosis6.9930552
Mean2.9109731
Median Absolute Deviation (MAD)0.9
Skewness1.9117208
Sum20161.4
Variance2.9570724
MonotonicityNot monotonic
2023-12-12T17:11:55.594997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 230
 
3.3%
2.1 226
 
3.2%
1.7 225
 
3.2%
2.3 220
 
3.2%
1.3 214
 
3.1%
2.0 212
 
3.0%
1.6 212
 
3.0%
1.9 211
 
3.0%
2.4 210
 
3.0%
1.5 196
 
2.8%
Other values (111) 4770
68.6%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.4 2
 
< 0.1%
0.5 17
 
0.2%
0.6 32
 
0.5%
0.7 55
 
0.8%
0.8 89
1.3%
0.9 100
1.4%
1.0 147
2.1%
1.1 156
2.2%
1.2 177
2.5%
ValueCountFrequency (%)
17.7 1
 
< 0.1%
17.6 1
 
< 0.1%
16.3 1
 
< 0.1%
16.1 1
 
< 0.1%
15.7 1
 
< 0.1%
15.5 1
 
< 0.1%
14.6 1
 
< 0.1%
14.1 1
 
< 0.1%
13.7 3
< 0.1%
13.4 2
< 0.1%

평균파주기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct107
Distinct (%)1.6%
Missing137
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean5.6395571
Minimum2.3
Maximum13.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:55.784743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.6
Q14.6
median5.4
Q36.5
95-th percentile8.3
Maximum13.2
Range10.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.5113303
Coefficient of variation (CV)0.26798742
Kurtosis1.7716823
Mean5.6395571
Median Absolute Deviation (MAD)0.9
Skewness0.96418259
Sum38450.5
Variance2.2841193
MonotonicityNot monotonic
2023-12-12T17:11:55.965701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9 215
 
3.1%
5.5 206
 
3.0%
5.4 206
 
3.0%
4.4 205
 
2.9%
4.8 201
 
2.9%
5.1 198
 
2.8%
4.7 193
 
2.8%
5.6 192
 
2.8%
5.0 189
 
2.7%
5.3 188
 
2.7%
Other values (97) 4825
69.4%
ValueCountFrequency (%)
2.3 2
 
< 0.1%
2.4 5
 
0.1%
2.5 9
 
0.1%
2.6 9
 
0.1%
2.7 12
 
0.2%
2.8 13
 
0.2%
2.9 21
0.3%
3.0 28
0.4%
3.1 23
0.3%
3.2 33
0.5%
ValueCountFrequency (%)
13.2 1
 
< 0.1%
13.1 2
< 0.1%
12.9 1
 
< 0.1%
12.8 2
< 0.1%
12.7 3
< 0.1%
12.6 1
 
< 0.1%
12.5 1
 
< 0.1%
12.4 1
 
< 0.1%
12.3 2
< 0.1%
12.1 1
 
< 0.1%

최고파주기
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.8%
Missing29
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean7.3454086
Minimum2.8
Maximum21.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.3 KiB
2023-12-12T17:11:56.158083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.6
Q15.8
median7.1
Q38
95-th percentile10.7
Maximum21.3
Range18.5
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.9498207
Coefficient of variation (CV)0.26544754
Kurtosis1.5516741
Mean7.3454086
Median Absolute Deviation (MAD)1.3
Skewness0.83138969
Sum50874.3
Variance3.8018006
MonotonicityNot monotonic
2023-12-12T17:11:56.338775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.0 1240
17.8%
7.1 1229
17.7%
9.1 1036
14.9%
6.4 877
12.6%
5.8 574
8.3%
10.7 422
 
6.1%
5.3 359
 
5.2%
4.9 208
 
3.0%
12.8 182
 
2.6%
4.6 91
 
1.3%
Other values (43) 708
10.2%
ValueCountFrequency (%)
2.8 2
 
< 0.1%
3.0 1
 
< 0.1%
3.1 1
 
< 0.1%
3.3 2
 
< 0.1%
3.4 4
 
0.1%
3.5 6
 
0.1%
3.6 8
 
0.1%
3.7 16
0.2%
3.8 25
0.4%
3.9 15
0.2%
ValueCountFrequency (%)
21.3 1
 
< 0.1%
16.0 22
 
0.3%
12.8 182
 
2.6%
10.7 422
 
6.1%
9.3 1
 
< 0.1%
9.1 1036
14.9%
8.2 1
 
< 0.1%
8.0 1240
17.8%
7.7 2
 
< 0.1%
7.5 1
 
< 0.1%

Interactions

2023-12-12T17:11:48.316892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:34.714087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.821610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.240765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.668011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.200664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.533179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.995806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.333458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.780365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.928755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:48.476860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:34.823116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.936575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.395439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.810872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.339450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.971637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.144376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.450098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.897280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.066057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:48.612155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:34.924555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.033515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.508338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.936276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.443763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.073044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.256921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.552657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.999280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.153481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.129182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.029785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.141505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.625845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.078487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.582221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.183134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.387473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.677150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.114496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.258588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.297690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.147623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.252603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.736458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.210038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.718442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.280924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.509786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.788759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.216719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.369360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.439479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.244604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.368092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.873823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.367199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.847210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.372633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.619124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.917934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.308702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.474352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.602891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.345318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.477875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.023452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.512287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.951257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.463206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.736761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.053365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.417968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.614290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.752766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.440137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.607692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.155220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.646373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.071884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.566089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.836742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.175613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.523301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.734174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:49.919395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.531526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.784045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.278850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.790456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.192315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.661947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:43.948818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.331446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.651587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.873117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:50.060676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.638439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:36.941411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.416397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:39.917501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.291597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.764979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.062131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.495279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.748933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:47.986753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:50.182741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:35.727885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:37.088149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:38.536268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:40.046966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:41.389058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:42.860123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:44.200386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:45.644773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:46.833490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:11:48.165245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:11:56.466675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정점명정점 코드위도경도평균풍속평균기압평균상대습도평균기온평균수온평균최대파고평균유의파고최고유의파고최고최대파고평균파주기최고파주기
정점명1.0001.0001.0001.0000.1140.0000.1250.2820.4920.2100.2080.1870.1970.3210.287
정점 코드1.0001.0001.0001.0000.1140.0000.1250.2820.4920.2100.2080.1870.1970.3210.287
위도1.0001.0001.0001.0000.1140.0000.1250.2820.4920.2100.2080.1870.1970.3210.287
경도1.0001.0001.0001.0000.1140.0000.1250.2820.4920.2100.2080.1870.1970.3210.287
평균풍속0.1140.1140.1140.1141.0000.6160.3410.4200.2260.8960.8880.8000.8250.3590.180
평균기압0.0000.0000.0000.0000.6161.0000.6550.6960.5600.7460.7130.6200.5340.3500.208
평균상대습도0.1250.1250.1250.1250.3410.6551.0000.5820.4540.1750.1780.1920.1990.2560.139
평균기온0.2820.2820.2820.2820.4200.6960.5821.0000.8540.3050.3090.2920.2940.3240.194
평균수온0.4920.4920.4920.4920.2260.5600.4540.8541.0000.1530.1520.1730.1640.3150.225
평균최대파고0.2100.2100.2100.2100.8960.7460.1750.3050.1531.0000.9960.9400.9370.6070.326
평균유의파고0.2080.2080.2080.2080.8880.7130.1780.3090.1520.9961.0000.9400.9340.6030.323
최고유의파고0.1870.1870.1870.1870.8000.6200.1920.2920.1730.9400.9401.0000.9640.5710.360
최고최대파고0.1970.1970.1970.1970.8250.5340.1990.2940.1640.9370.9340.9641.0000.5730.360
평균파주기0.3210.3210.3210.3210.3590.3500.2560.3240.3150.6070.6030.5710.5731.0000.728
최고파주기0.2870.2870.2870.2870.1800.2080.1390.1940.2250.3260.3230.3600.3600.7281.000
2023-12-12T17:11:56.619147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도정점 코드정점명위도
경도1.0001.0001.0001.000
정점 코드1.0001.0001.0001.000
정점명1.0001.0001.0001.000
위도1.0001.0001.0001.000
2023-12-12T17:11:56.757513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균풍속평균기압평균상대습도평균기온평균수온평균최대파고평균유의파고최고유의파고최고최대파고평균파주기최고파주기정점명정점 코드위도경도
평균풍속1.0000.245-0.254-0.347-0.1650.8130.8100.7970.8010.1010.0040.0680.0680.0680.068
평균기압0.2451.000-0.714-0.754-0.5390.0580.0610.0830.079-0.230-0.1420.0000.0000.0000.000
평균상대습도-0.254-0.7141.0000.5680.316-0.074-0.072-0.100-0.0990.1720.0810.0750.0750.0750.075
평균기온-0.347-0.7540.5681.0000.852-0.148-0.156-0.201-0.1910.2240.1730.1750.1750.1750.175
평균수온-0.165-0.5390.3160.8521.000-0.004-0.015-0.075-0.0610.2350.1760.3390.3390.3390.339
평균최대파고0.8130.058-0.074-0.148-0.0041.0000.9930.9510.9580.4530.2400.1280.1280.1280.128
평균유의파고0.8100.061-0.072-0.156-0.0150.9931.0000.9530.9520.4460.2280.1270.1270.1270.127
최고유의파고0.7970.083-0.100-0.201-0.0750.9510.9531.0000.9870.4010.2320.1130.1130.1130.113
최고최대파고0.8010.079-0.099-0.191-0.0610.9580.9520.9871.0000.4060.2370.1190.1190.1190.119
평균파주기0.101-0.2300.1720.2240.2350.4530.4460.4010.4061.0000.7580.2030.2030.2030.203
최고파주기0.004-0.1420.0810.1730.1760.2400.2280.2320.2370.7581.0000.1910.1910.1910.191
정점명0.0680.0000.0750.1750.3390.1280.1270.1130.1190.2030.1911.0001.0001.0001.000
정점 코드0.0680.0000.0750.1750.3390.1280.1270.1130.1190.2030.1911.0001.0001.0001.000
위도0.0680.0000.0750.1750.3390.1280.1270.1130.1190.2030.1911.0001.0001.0001.000
경도0.0680.0000.0750.1750.3390.1280.1270.1130.1190.2030.1911.0001.0001.0001.000

Missing values

2023-12-12T17:11:50.374653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:11:50.636962image/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.
2023-12-12T17:11:50.861869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

측정 일자정점명정점 코드위도경도평균풍속평균기압평균상대습도평균기온평균수온평균최대파고평균유의파고최고유의파고최고최대파고평균파주기최고파주기
02016-01-01마라도2210733.083333126.0333333.81031.65911.917.31.30.81.42.15.46.4
12016-01-02마라도2210733.083333126.0333332.81023.96816.217.20.70.50.61.05.79.1
22016-01-03마라도2210733.083333126.0333333.91020.56616.217.10.90.60.61.14.89.1
32016-01-04마라도2210733.083333126.0333337.71021.96815.117.01.50.91.32.34.59.1
42016-01-05마라도2210733.083333126.03333310.01023.47212.216.92.51.51.92.95.36.4
52016-01-06마라도2210733.083333126.03333311.31023.87010.816.92.91.92.33.55.45.8
62016-01-07마라도2210733.083333126.03333310.01024.86410.216.72.71.72.13.35.66.4
72016-01-08마라도2210733.083333126.0333338.71023.9639.0<NA>2.51.62.03.15.56.4
82016-01-09마라도2210733.083333126.0333335.91026.0579.716.71.71.11.72.85.56.4
92016-01-10마라도2210733.083333126.0333335.11025.95910.916.71.00.60.81.64.15.3
측정 일자정점명정점 코드위도경도평균풍속평균기압평균상대습도평균기온평균수온평균최대파고평균유의파고최고유의파고최고최대파고평균파주기최고파주기
69452022-07-14서귀포2218733.128056127.0227786.31001.99228.227.42.41.51.82.94.95.6
69462022-07-15서귀포2218733.128056127.0227783.21000.09426.527.41.71.11.52.74.95.4
69472022-07-16서귀포2218733.128056127.0227784.2999.97527.927.51.71.01.12.34.65.3
69482022-07-17서귀포2218733.128056127.0227784.21002.28427.727.51.81.11.32.35.56.3
69492022-07-18서귀포2218733.128056127.0227788.4999.89428.027.03.42.12.74.95.36.1
69502022-07-19서귀포2218733.128056127.0227784.71004.18527.327.03.01.92.74.35.86.3
69512022-07-20서귀포2218733.128056127.0227783.31007.08926.827.12.21.41.83.06.36.6
69522022-07-21서귀포2218733.128056127.0227786.91005.58527.126.82.71.72.23.55.15.9
69532022-07-22서귀포2218733.128056127.0227783.01007.38026.426.61.71.11.42.45.15.6
69542022-07-23서귀포2218733.128056127.0227784.51006.18527.227.01.10.71.11.84.65.3