Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory167.0 B

Variable types

Categorical2
Numeric14
Text2

Dataset

Description자동기상관측장비(AWS) 시간별 관측현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=458YRRY04VI3BBMI6Q8326869752&infSeq=1

Alerts

지점번호 is highly overall correlated with 시군명High correlation
WGS84위도 is highly overall correlated with 시군명High correlation
WGS84경도 is highly overall correlated with 고도(m) and 1 other fieldsHigh correlation
고도(m) is highly overall correlated with WGS84경도 and 1 other fieldsHigh correlation
기온(℃) is highly overall correlated with 습도(%)High correlation
습도(%) is highly overall correlated with 기온(℃) and 1 other fieldsHigh correlation
현지기압(hPa) is highly overall correlated with 해면기압(hPa) and 1 other fieldsHigh correlation
해면기압(hPa) is highly overall correlated with 현지기압(hPa) and 1 other fieldsHigh correlation
시간누적강우량(mm) is highly overall correlated with 일누적강우량(mm) and 1 other fieldsHigh correlation
일누적강우량(mm) is highly overall correlated with 시간누적강우량(mm) and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 지점번호 and 8 other fieldsHigh correlation
강수감지(0:없음1:있음2:오류) is highly imbalanced (59.7%)Imbalance
기온(℃) is highly skewed (γ1 = -38.54531672)Skewed
관측시간 has 444 (4.4%) zerosZeros
풍향(deg) has 716 (7.2%) zerosZeros
풍속(m/s) has 920 (9.2%) zerosZeros
시간누적강우량(mm) has 8959 (89.6%) zerosZeros
일누적강우량(mm) has 7867 (78.7%) zerosZeros

Reproduction

Analysis started2024-05-10 20:14:43.645625
Analysis finished2024-05-10 20:15:56.313000
Duration1 minute and 12.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
포천시
993 
연천군
705 
여주시
 
599
화성시
 
566
양주시
 
526
Other values (26)
6611 

Length

Max length4
Median length3
Mean length3.0582
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안성시
2nd row연천군
3rd row안산시
4th row남양주시
5th row가평군

Common Values

ValueCountFrequency (%)
포천시 993
 
9.9%
연천군 705
 
7.0%
여주시 599
 
6.0%
화성시 566
 
5.7%
양주시 526
 
5.3%
안성시 525
 
5.2%
파주시 494
 
4.9%
평택시 492
 
4.9%
이천시 470
 
4.7%
용인시 450
 
4.5%
Other values (21) 4180
41.8%

Length

2024-05-10T20:15:56.528459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포천시 993
 
9.9%
연천군 705
 
7.0%
여주시 599
 
6.0%
화성시 566
 
5.7%
양주시 526
 
5.3%
안성시 525
 
5.2%
파주시 494
 
4.9%
평택시 492
 
4.9%
이천시 470
 
4.7%
용인시 450
 
4.5%
Other values (21) 4180
41.8%

지점번호
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.3655
Minimum98
Maximum967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:15:56.919633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile326
Q1435
median470
Q3532
95-th percentile590
Maximum967
Range869
Interquartile range (IQR)97

Descriptive statistics

Standard deviation115.55431
Coefficient of variation (CV)0.24777628
Kurtosis5.5982488
Mean466.3655
Median Absolute Deviation (MAD)39
Skewness0.096886132
Sum4663655
Variance13352.798
MonotonicityNot monotonic
2024-05-10T20:15:57.325263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
539 94
 
0.9%
445 91
 
0.9%
446 90
 
0.9%
534 90
 
0.9%
116 89
 
0.9%
441 89
 
0.9%
545 89
 
0.9%
464 88
 
0.9%
476 88
 
0.9%
473 87
 
0.9%
Other values (124) 9105
91.0%
ValueCountFrequency (%)
98 79
0.8%
99 71
0.7%
116 89
0.9%
119 75
0.8%
202 81
0.8%
203 82
0.8%
326 76
0.8%
351 76
0.8%
352 67
0.7%
353 66
0.7%
ValueCountFrequency (%)
967 63
0.6%
966 75
0.8%
692 78
0.8%
652 86
0.9%
599 71
0.7%
598 79
0.8%
590 65
0.7%
589 72
0.7%
576 83
0.8%
575 82
0.8%
Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T20:15:57.925146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.5143
Min length2

Characters and Unicode

Total characters25143
Distinct characters122
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안성
2nd row연천청산
3rd row고잔
4th row오남
5th row신천 *
ValueCountFrequency (%)
807
 
7.5%
포천이동 94
 
0.9%
의왕 91
 
0.8%
남촌 90
 
0.8%
장호원 90
 
0.8%
관악(레 89
 
0.8%
안산 89
 
0.8%
김포 89
 
0.8%
점동 88
 
0.8%
화현 88
 
0.8%
Other values (125) 9192
85.1%
2024-05-10T20:15:59.137977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1142
 
4.5%
859
 
3.4%
856
 
3.4%
807
 
3.2%
* 807
 
3.2%
804
 
3.2%
702
 
2.8%
595
 
2.4%
505
 
2.0%
481
 
1.9%
Other values (112) 17585
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23351
92.9%
Space Separator 807
 
3.2%
Other Punctuation 807
 
3.2%
Open Punctuation 89
 
0.4%
Close Punctuation 89
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1142
 
4.9%
859
 
3.7%
856
 
3.7%
804
 
3.4%
702
 
3.0%
595
 
2.5%
505
 
2.2%
481
 
2.1%
477
 
2.0%
435
 
1.9%
Other values (108) 16495
70.6%
Space Separator
ValueCountFrequency (%)
807
100.0%
Other Punctuation
ValueCountFrequency (%)
* 807
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23351
92.9%
Common 1792
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1142
 
4.9%
859
 
3.7%
856
 
3.7%
804
 
3.4%
702
 
3.0%
595
 
2.5%
505
 
2.2%
481
 
2.1%
477
 
2.0%
435
 
1.9%
Other values (108) 16495
70.6%
Common
ValueCountFrequency (%)
807
45.0%
* 807
45.0%
( 89
 
5.0%
) 89
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23351
92.9%
ASCII 1792
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1142
 
4.9%
859
 
3.7%
856
 
3.7%
804
 
3.4%
702
 
3.0%
595
 
2.5%
505
 
2.2%
481
 
2.1%
477
 
2.0%
435
 
1.9%
Other values (108) 16495
70.6%
ASCII
ValueCountFrequency (%)
807
45.0%
* 807
45.0%
( 89
 
5.0%
) 89
 
5.0%
Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-10T20:15:59.921062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.6648
Min length10

Characters and Unicode

Total characters136648
Distinct characters154
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기도 안성시 옥산동
2nd row경기도 연천군 청산면 초성리
3rd row경기도 안산시단원구 고잔동
4th row경기도 남양주시 오남읍 양지리
5th row경기도 가평군 설악면 신천리
ValueCountFrequency (%)
경기도 10000
27.8%
포천시 993
 
2.8%
연천군 705
 
2.0%
여주시 599
 
1.7%
화성시 566
 
1.6%
양주시 526
 
1.5%
안성시 525
 
1.5%
파주시 494
 
1.4%
평택시 492
 
1.4%
이천시 470
 
1.3%
Other values (234) 20602
57.3%
2024-05-10T20:16:01.197337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25972
19.0%
10317
 
7.6%
10218
 
7.5%
10000
 
7.3%
8637
 
6.3%
6218
 
4.6%
4955
 
3.6%
4696
 
3.4%
3427
 
2.5%
2265
 
1.7%
Other values (144) 49943
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110594
80.9%
Space Separator 25972
 
19.0%
Decimal Number 82
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10317
 
9.3%
10218
 
9.2%
10000
 
9.0%
8637
 
7.8%
6218
 
5.6%
4955
 
4.5%
4696
 
4.2%
3427
 
3.1%
2265
 
2.0%
2174
 
2.0%
Other values (142) 47687
43.1%
Space Separator
ValueCountFrequency (%)
25972
100.0%
Decimal Number
ValueCountFrequency (%)
3 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110594
80.9%
Common 26054
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10317
 
9.3%
10218
 
9.2%
10000
 
9.0%
8637
 
7.8%
6218
 
5.6%
4955
 
4.5%
4696
 
4.2%
3427
 
3.1%
2265
 
2.0%
2174
 
2.0%
Other values (142) 47687
43.1%
Common
ValueCountFrequency (%)
25972
99.7%
3 82
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110594
80.9%
ASCII 26054
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25972
99.7%
3 82
 
0.3%
Hangul
ValueCountFrequency (%)
10317
 
9.3%
10218
 
9.2%
10000
 
9.0%
8637
 
7.8%
6218
 
5.6%
4955
 
4.5%
4696
 
4.2%
3427
 
3.1%
2265
 
2.0%
2174
 
2.0%
Other values (142) 47687
43.1%

관측일자
Real number (ℝ)

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20240452
Minimum20240413
Maximum20240511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:16:01.615694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240413
5-th percentile20240414
Q120240420
median20240427
Q320240504
95-th percentile20240509
Maximum20240511
Range98
Interquartile range (IQR)84

Descriptive statistics

Standard deviation40.543416
Coefficient of variation (CV)2.0030885 × 10-6
Kurtosis-1.6586265
Mean20240452
Median Absolute Deviation (MAD)10
Skewness0.54038383
Sum2.0240452 × 1011
Variance1643.7686
MonotonicityNot monotonic
2024-05-10T20:16:02.072982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20240414 395
 
4.0%
20240422 392
 
3.9%
20240420 390
 
3.9%
20240507 384
 
3.8%
20240427 382
 
3.8%
20240425 380
 
3.8%
20240415 377
 
3.8%
20240430 376
 
3.8%
20240508 375
 
3.8%
20240429 371
 
3.7%
Other values (19) 6178
61.8%
ValueCountFrequency (%)
20240413 118
 
1.2%
20240414 395
4.0%
20240415 377
3.8%
20240416 359
3.6%
20240417 356
3.6%
20240418 350
3.5%
20240419 352
3.5%
20240420 390
3.9%
20240421 368
3.7%
20240422 392
3.9%
ValueCountFrequency (%)
20240511 62
 
0.6%
20240510 351
3.5%
20240509 330
3.3%
20240508 375
3.8%
20240507 384
3.8%
20240506 354
3.5%
20240505 352
3.5%
20240504 358
3.6%
20240503 351
3.5%
20240502 370
3.7%

관측시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.629
Minimum0
Maximum23
Zeros444
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:16:02.472578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9848864
Coefficient of variation (CV)0.60064377
Kurtosis-1.2101043
Mean11.629
Median Absolute Deviation (MAD)6
Skewness-0.02985965
Sum116290
Variance48.788638
MonotonicityNot monotonic
2024-05-10T20:16:02.996706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 455
 
4.5%
12 453
 
4.5%
21 446
 
4.5%
0 444
 
4.4%
13 442
 
4.4%
14 437
 
4.4%
22 431
 
4.3%
20 431
 
4.3%
3 426
 
4.3%
2 424
 
4.2%
Other values (14) 5611
56.1%
ValueCountFrequency (%)
0 444
4.4%
1 403
4.0%
2 424
4.2%
3 426
4.3%
4 397
4.0%
5 368
3.7%
6 423
4.2%
7 396
4.0%
8 384
3.8%
9 414
4.1%
ValueCountFrequency (%)
23 455
4.5%
22 431
4.3%
21 446
4.5%
20 431
4.3%
19 397
4.0%
18 415
4.2%
17 422
4.2%
16 424
4.2%
15 387
3.9%
14 437
4.4%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.518723
Minimum36.9431
Maximum38.1725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:16:03.414718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.9431
5-th percentile37.0331
Q137.2377
median37.4453
Q337.8312
95-th percentile38.0586
Maximum38.1725
Range1.2294
Interquartile range (IQR)0.5935

Descriptive statistics

Standard deviation0.33328756
Coefficient of variation (CV)0.0088832332
Kurtosis-1.1599389
Mean37.518723
Median Absolute Deviation (MAD)0.2787
Skewness0.18540833
Sum375187.23
Variance0.1110806
MonotonicityNot monotonic
2024-05-10T20:16:03.858478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0242 94
 
0.9%
37.3445 91
 
0.9%
37.1403 90
 
0.9%
37.121 90
 
0.9%
37.4453 89
 
0.9%
37.6076 89
 
0.9%
37.281 89
 
0.9%
37.2061 88
 
0.9%
37.9013 88
 
0.9%
37.8486 87
 
0.9%
Other values (124) 9105
91.0%
ValueCountFrequency (%)
36.9431 67
0.7%
36.9672 81
0.8%
36.9806 75
0.8%
36.9845 80
0.8%
36.9877 78
0.8%
37.0037 68
0.7%
37.0331 79
0.8%
37.0467 58
0.6%
37.0481 69
0.7%
37.0664 64
0.6%
ValueCountFrequency (%)
38.1725 78
0.8%
38.1577 78
0.8%
38.1361 73
0.7%
38.0963 65
0.7%
38.0891 77
0.8%
38.0822 85
0.9%
38.0586 81
0.8%
38.0474 78
0.8%
38.0404 78
0.8%
38.0248 73
0.7%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11894
Minimum126.3856
Maximum127.7549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:16:04.283301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.3856
5-th percentile126.7119
Q1126.9245
median127.0735
Q3127.2883
95-th percentile127.6106
Maximum127.7549
Range1.3693
Interquartile range (IQR)0.3638

Descriptive statistics

Standard deviation0.27257194
Coefficient of variation (CV)0.0021442277
Kurtosis-0.37449905
Mean127.11894
Median Absolute Deviation (MAD)0.1784
Skewness0.14129203
Sum1271189.4
Variance0.074295464
MonotonicityNot monotonic
2024-05-10T20:16:04.746458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.2592 138
 
1.4%
127.3657 94
 
0.9%
126.9688 91
 
0.9%
127.0637 90
 
0.9%
127.6106 90
 
0.9%
126.7624 89
 
0.9%
126.8385 89
 
0.9%
126.964 89
 
0.9%
127.6627 88
 
0.9%
127.2883 88
 
0.9%
Other values (123) 9054
90.5%
ValueCountFrequency (%)
126.3856 75
0.8%
126.5523 75
0.8%
126.5787 76
0.8%
126.6128 63
0.6%
126.6544 71
0.7%
126.7088 73
0.7%
126.7119 80
0.8%
126.7162 80
0.8%
126.73 68
0.7%
126.7624 89
0.9%
ValueCountFrequency (%)
127.7549 66
0.7%
127.7135 77
0.8%
127.6791 84
0.8%
127.6627 88
0.9%
127.6396 71
0.7%
127.6113 76
0.8%
127.6106 90
0.9%
127.5871 64
0.6%
127.5522 65
0.7%
127.5451 79
0.8%

고도(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.632032
Minimum5.77
Maximum624.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-10T20:16:05.182537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.77
5-th percentile12.88
Q135
median63.05
Q3105.14
95-th percentile163
Maximum624.82
Range619.05
Interquartile range (IQR)70.14

Descriptive statistics

Standard deviation68.844931
Coefficient of variation (CV)0.91026155
Kurtosis33.647747
Mean75.632032
Median Absolute Deviation (MAD)29.81
Skewness4.6554792
Sum756320.32
Variance4739.6245
MonotonicityNot monotonic
2024-05-10T20:16:05.604794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.85 150
 
1.5%
13.0 150
 
1.5%
39.0 147
 
1.5%
32.0 142
 
1.4%
163.0 139
 
1.4%
137.74 94
 
0.9%
80.4 91
 
0.9%
88.2 90
 
0.9%
17.48 90
 
0.9%
5.92 89
 
0.9%
Other values (119) 8818
88.2%
ValueCountFrequency (%)
5.77 79
0.8%
5.92 89
0.9%
8.0 62
0.6%
10.0 76
0.8%
10.47 73
0.7%
10.96 71
0.7%
12.88 70
0.7%
13.0 150
1.5%
15.28 79
0.8%
15.67 86
0.9%
ValueCountFrequency (%)
624.82 89
0.9%
241.0 63
0.6%
191.01 76
0.8%
185.81 78
0.8%
170.45 70
0.7%
163.0 139
1.4%
151.72 88
0.9%
150.0 87
0.9%
143.0 71
0.7%
142.87 77
0.8%

풍향(deg)
Real number (ℝ)

ZEROS 

Distinct3250
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.75408
Minimum-999
Maximum360
Zeros716
Zeros (%)7.2%
Negative408
Negative (%)4.1%
Memory size166.0 KiB
2024-05-10T20:16:06.063910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile0
Q158.675
median161.85
Q3248.8
95-th percentile330.405
Maximum360
Range1359
Interquartile range (IQR)190.125

Descriptive statistics

Standard deviation252.28196
Coefficient of variation (CV)2.1607978
Kurtosis12.709034
Mean116.75408
Median Absolute Deviation (MAD)94.5
Skewness-3.429988
Sum1167540.8
Variance63646.186
MonotonicityNot monotonic
2024-05-10T20:16:06.619532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 716
 
7.2%
-999.0 408
 
4.1%
264.1 10
 
0.1%
205.5 10
 
0.1%
260.3 10
 
0.1%
232.4 9
 
0.1%
226.3 9
 
0.1%
221.1 9
 
0.1%
218.9 9
 
0.1%
259.6 9
 
0.1%
Other values (3240) 8801
88.0%
ValueCountFrequency (%)
-999.0 408
4.1%
0.0 716
7.2%
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 2
 
< 0.1%
0.5 1
 
< 0.1%
0.6 6
 
0.1%
0.7 3
 
< 0.1%
1.0 1
 
< 0.1%
ValueCountFrequency (%)
360.0 1
 
< 0.1%
359.9 2
 
< 0.1%
359.8 1
 
< 0.1%
359.7 3
< 0.1%
359.6 5
0.1%
359.5 2
 
< 0.1%
359.4 1
 
< 0.1%
359.3 2
 
< 0.1%
359.2 4
< 0.1%
359.1 2
 
< 0.1%

풍속(m/s)
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.6105
Minimum-999
Maximum9.8
Zeros920
Zeros (%)9.2%
Negative330
Negative (%)3.3%
Memory size166.0 KiB
2024-05-10T20:16:07.039150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile0
Q10.4
median1.1
Q32
95-th percentile3.6
Maximum9.8
Range1008.8
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation178.72121
Coefficient of variation (CV)-5.6538558
Kurtosis25.348017
Mean-31.6105
Median Absolute Deviation (MAD)0.8
Skewness-5.2289201
Sum-316105
Variance31941.27
MonotonicityNot monotonic
2024-05-10T20:16:07.401788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 920
 
9.2%
0.1 404
 
4.0%
0.6 371
 
3.7%
0.2 357
 
3.6%
0.7 357
 
3.6%
0.3 356
 
3.6%
0.9 342
 
3.4%
1.1 333
 
3.3%
-999.0 330
 
3.3%
1.4 326
 
3.3%
Other values (72) 5904
59.0%
ValueCountFrequency (%)
-999.0 330
 
3.3%
0.0 920
9.2%
0.1 404
4.0%
0.2 357
 
3.6%
0.3 356
 
3.6%
0.4 326
 
3.3%
0.5 322
 
3.2%
0.6 371
3.7%
0.7 357
 
3.6%
0.8 315
 
3.1%
ValueCountFrequency (%)
9.8 1
< 0.1%
9.7 1
< 0.1%
8.8 1
< 0.1%
8.7 1
< 0.1%
8.4 1
< 0.1%
8.1 1
< 0.1%
7.7 1
< 0.1%
7.5 2
< 0.1%
7.3 1
< 0.1%
7.2 2
< 0.1%

기온(℃)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct257
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.41193
Minimum-996
Maximum31
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)0.1%
Memory size166.0 KiB
2024-05-10T20:16:07.828880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-996
5-th percentile8.9
Q112.5
median15.2
Q319.3
95-th percentile25
Maximum31
Range1027
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation25.254706
Coefficient of variation (CV)1.6386465
Kurtosis1541.5402
Mean15.41193
Median Absolute Deviation (MAD)3.3
Skewness-38.545317
Sum154119.3
Variance637.80017
MonotonicityNot monotonic
2024-05-10T20:16:08.276895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.8 119
 
1.2%
13.3 110
 
1.1%
14.1 101
 
1.0%
13.9 100
 
1.0%
14.0 98
 
1.0%
13.2 97
 
1.0%
13.0 96
 
1.0%
12.4 96
 
1.0%
14.8 95
 
0.9%
13.1 93
 
0.9%
Other values (247) 8995
90.0%
ValueCountFrequency (%)
-996.0 6
0.1%
4.3 1
 
< 0.1%
4.5 3
< 0.1%
4.8 2
 
< 0.1%
5.1 1
 
< 0.1%
5.3 1
 
< 0.1%
5.4 1
 
< 0.1%
5.5 2
 
< 0.1%
5.6 4
< 0.1%
5.7 5
0.1%
ValueCountFrequency (%)
31.0 1
 
< 0.1%
30.8 2
< 0.1%
30.4 2
< 0.1%
30.2 2
< 0.1%
30.0 2
< 0.1%
29.9 1
 
< 0.1%
29.8 3
< 0.1%
29.7 2
< 0.1%
29.6 1
 
< 0.1%
29.5 4
< 0.1%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct823
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.92313
Minimum-997
Maximum100
Zeros0
Zeros (%)0.0%
Negative519
Negative (%)5.2%
Memory size166.0 KiB
2024-05-10T20:16:08.693635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-997
5-th percentile-992
Q145.2
median67.4
Q388
95-th percentile98.5
Maximum100
Range1097
Interquartile range (IQR)42.8

Descriptive statistics

Standard deviation236.51945
Coefficient of variation (CV)18.302025
Kurtosis14.038951
Mean12.92313
Median Absolute Deviation (MAD)21.4
Skewness-3.9833997
Sum129231.3
Variance55941.448
MonotonicityNot monotonic
2024-05-10T20:16:09.144313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-992.0 352
 
3.5%
-997.0 166
 
1.7%
99.0 142
 
1.4%
99.9 101
 
1.0%
100.0 64
 
0.6%
96.6 35
 
0.4%
96.3 32
 
0.3%
96.5 32
 
0.3%
94.7 31
 
0.3%
94.5 31
 
0.3%
Other values (813) 9014
90.1%
ValueCountFrequency (%)
-997.0 166
1.7%
-996.0 1
 
< 0.1%
-992.0 352
3.5%
11.4 1
 
< 0.1%
11.5 1
 
< 0.1%
13.4 1
 
< 0.1%
14.3 1
 
< 0.1%
15.1 1
 
< 0.1%
15.9 1
 
< 0.1%
16.0 1
 
< 0.1%
ValueCountFrequency (%)
100.0 64
0.6%
99.9 101
1.0%
99.8 26
 
0.3%
99.7 6
 
0.1%
99.6 9
 
0.1%
99.5 22
 
0.2%
99.4 9
 
0.1%
99.3 14
 
0.1%
99.2 7
 
0.1%
99.1 6
 
0.1%

현지기압(hPa)
Real number (ℝ)

HIGH CORRELATION 

Distinct387
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-184.68609
Minimum-997
Maximum1020.1
Zeros0
Zeros (%)0.0%
Negative5635
Negative (%)56.4%
Memory size166.0 KiB
2024-05-10T20:16:09.548742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-997
5-th percentile-997
Q1-997
median-997
Q3998.3
95-th percentile1011.1
Maximum1020.1
Range2017.1
Interquartile range (IQR)1995.3

Descriptive statistics

Standard deviation934.87587
Coefficient of variation (CV)-5.0619723
Kurtosis-1.8214493
Mean-184.68609
Median Absolute Deviation (MAD)0
Skewness0.32541014
Sum-1846860.9
Variance873992.9
MonotonicityNot monotonic
2024-05-10T20:16:10.004727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-997.0 5635
56.4%
500.0 1127
 
11.3%
501.0 38
 
0.4%
1007.2 28
 
0.3%
1005.1 24
 
0.2%
1008.0 23
 
0.2%
1006.6 23
 
0.2%
1005.4 23
 
0.2%
1001.7 22
 
0.2%
1004.2 22
 
0.2%
Other values (377) 3035
30.3%
ValueCountFrequency (%)
-997.0 5635
56.4%
500.0 1127
 
11.3%
500.5 2
 
< 0.1%
500.6 4
 
< 0.1%
500.7 3
 
< 0.1%
500.8 13
 
0.1%
500.9 17
 
0.2%
501.0 38
 
0.4%
933.5 1
 
< 0.1%
933.6 1
 
< 0.1%
ValueCountFrequency (%)
1020.1 1
 
< 0.1%
1019.6 1
 
< 0.1%
1019.5 1
 
< 0.1%
1019.3 2
< 0.1%
1019.2 3
< 0.1%
1019.0 3
< 0.1%
1018.9 2
< 0.1%
1018.8 1
 
< 0.1%
1018.7 3
< 0.1%
1018.6 2
< 0.1%

해면기압(hPa)
Real number (ℝ)

HIGH CORRELATION 

Distinct179
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-377.36927
Minimum-999
Maximum1021.5
Zeros0
Zeros (%)0.0%
Negative6910
Negative (%)69.1%
Memory size166.0 KiB
2024-05-10T20:16:10.387343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q1-999
median-999
Q31009.4
95-th percentile1017.5
Maximum1021.5
Range2020.5
Interquartile range (IQR)2008.4

Descriptive statistics

Standard deviation929.6407
Coefficient of variation (CV)-2.4634775
Kurtosis-1.3165809
Mean-377.36927
Median Absolute Deviation (MAD)0
Skewness0.82683588
Sum-3773692.7
Variance864231.83
MonotonicityNot monotonic
2024-05-10T20:16:10.753898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999.0 6910
69.1%
1011.9 46
 
0.5%
1011.6 44
 
0.4%
1012.2 44
 
0.4%
1011.5 44
 
0.4%
1012.6 42
 
0.4%
1011.3 42
 
0.4%
1012.7 41
 
0.4%
1011.1 39
 
0.4%
1011.4 38
 
0.4%
Other values (169) 2710
 
27.1%
ValueCountFrequency (%)
-999.0 6910
69.1%
1003.0 1
 
< 0.1%
1003.5 1
 
< 0.1%
1003.6 1
 
< 0.1%
1003.9 1
 
< 0.1%
1004.0 1
 
< 0.1%
1004.2 1
 
< 0.1%
1004.3 2
 
< 0.1%
1004.4 1
 
< 0.1%
1004.5 3
 
< 0.1%
ValueCountFrequency (%)
1021.5 2
 
< 0.1%
1021.3 1
 
< 0.1%
1021.2 2
 
< 0.1%
1021.1 1
 
< 0.1%
1021.0 1
 
< 0.1%
1020.9 3
< 0.1%
1020.8 2
 
< 0.1%
1020.7 2
 
< 0.1%
1020.6 5
0.1%
1020.5 3
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
8823 
-992
 
674
10
 
503

Length

Max length4
Median length1
Mean length1.2525
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 8823
88.2%
-992 674
 
6.7%
10 503
 
5.0%

Length

2024-05-10T20:16:11.161456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T20:16:11.644196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 8823
88.2%
992 674
 
6.7%
10 503
 
5.0%

시간누적강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.44182
Minimum-999
Maximum9.5
Zeros8959
Zeros (%)89.6%
Negative226
Negative (%)2.3%
Memory size166.0 KiB
2024-05-10T20:16:11.974699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile0
Q10
median0
Q30
95-th percentile0.5
Maximum9.5
Range1008.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation148.50534
Coefficient of variation (CV)-6.6173484
Kurtosis39.289526
Mean-22.44182
Median Absolute Deviation (MAD)0
Skewness-6.4250126
Sum-224418.2
Variance22053.836
MonotonicityNot monotonic
2024-05-10T20:16:12.392762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 8959
89.6%
0.5 321
 
3.2%
-999.0 226
 
2.3%
1.0 131
 
1.3%
1.5 71
 
0.7%
2.0 49
 
0.5%
2.5 40
 
0.4%
3.5 36
 
0.4%
3.0 33
 
0.3%
4.0 28
 
0.3%
Other values (30) 106
 
1.1%
ValueCountFrequency (%)
-999.0 226
 
2.3%
0.0 8959
89.6%
0.1 7
 
0.1%
0.2 5
 
0.1%
0.3 3
 
< 0.1%
0.4 3
 
< 0.1%
0.5 321
 
3.2%
0.6 3
 
< 0.1%
0.7 2
 
< 0.1%
0.8 2
 
< 0.1%
ValueCountFrequency (%)
9.5 1
 
< 0.1%
9.0 1
 
< 0.1%
8.5 3
 
< 0.1%
8.0 2
 
< 0.1%
7.5 2
 
< 0.1%
7.0 5
0.1%
6.6 1
 
< 0.1%
6.5 5
0.1%
6.1 1
 
< 0.1%
6.0 9
0.1%

일누적강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct156
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-20.31491
Minimum-992
Maximum70.5
Zeros7867
Zeros (%)78.7%
Negative222
Negative (%)2.2%
Memory size166.0 KiB
2024-05-10T20:16:12.841330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-992
5-th percentile0
Q10
median0
Q30
95-th percentile11.5
Maximum70.5
Range1062.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation146.52249
Coefficient of variation (CV)-7.2125593
Kurtosis39.967709
Mean-20.31491
Median Absolute Deviation (MAD)0
Skewness-6.4723613
Sum-203149.1
Variance21468.841
MonotonicityNot monotonic
2024-05-10T20:16:13.290074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7867
78.7%
-992.0 222
 
2.2%
0.5 152
 
1.5%
1.0 123
 
1.2%
1.5 114
 
1.1%
2.0 100
 
1.0%
2.5 79
 
0.8%
5.5 76
 
0.8%
3.5 62
 
0.6%
4.0 61
 
0.6%
Other values (146) 1144
 
11.4%
ValueCountFrequency (%)
-992.0 222
 
2.2%
0.0 7867
78.7%
0.1 5
 
0.1%
0.2 2
 
< 0.1%
0.3 1
 
< 0.1%
0.5 152
 
1.5%
0.6 2
 
< 0.1%
0.7 1
 
< 0.1%
0.8 1
 
< 0.1%
1.0 123
 
1.2%
ValueCountFrequency (%)
70.5 1
< 0.1%
60.5 1
< 0.1%
58.5 1
< 0.1%
57.5 1
< 0.1%
56.5 2
< 0.1%
56.0 1
< 0.1%
54.5 2
< 0.1%
54.0 1
< 0.1%
52.0 2
< 0.1%
50.5 1
< 0.1%

Interactions

2024-05-10T20:15:51.165721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:14:59.454595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:03.210972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:06.941406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:10.772316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:14.928947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:19.700577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:23.506104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:27.922401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:31.775852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:35.273424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:39.187584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:43.188356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:47.190940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:51.445483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:14:59.759671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:03.471476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:07.176390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:11.013790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:15.214347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:19.965634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:23.761025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:28.218195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:31.984803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:35.532258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:39.444086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:43.459615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:47.426614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:51.694089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:14:59.973624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:03.730290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:07.427705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:11.283459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:15.688418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:20.242025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:24.026323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:28.501949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:32.214470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:35.800974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:39.668417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:43.735834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:47.637064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:51.997881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:00.255550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:03.985724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:07.678220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:11.548734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:15.986663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:20.559931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:24.262299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:28.775137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:32.541432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:36.179412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:39.935960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:44.009971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:47.857775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:52.309035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:00.529992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:04.265329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:07.946950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:11.831017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:16.348081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:20.828467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:24.523565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:29.096313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:32.774288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:36.454579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:40.226337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:44.312622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:48.100711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:52.708116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:00.768909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:04.545191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:08.226417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:12.083459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:16.648471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:21.120245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:24.789579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:29.394527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:33.023325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:36.739639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:40.460729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:44.612136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:48.386619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:52.990185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:00.995656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:04.819588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:08.486858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:12.488142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:16.985571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:21.397310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:25.069851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:29.673900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:33.248968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:37.010704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:40.734211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:44.889507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:48.647478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:53.345731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:01.292615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:05.052798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:08.760902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:12.797846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:17.279329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:21.670217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:25.321555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:29.967412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:33.483036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:37.287560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:41.015538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:45.191789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:48.932663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:53.609202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:01.547303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:05.387110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:09.011869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:13.181206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:17.762723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:21.936280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:25.612366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:30.241758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:33.745486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:37.550988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:41.293086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:45.473506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:49.187742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:53.855848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:01.812627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:05.688285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:09.270094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:13.479887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:18.075495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:22.202342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:26.088776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:30.496545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:33.977296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:37.824460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:41.567462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:45.721235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:49.451813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:54.114398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:02.104360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:05.949322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:09.530593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:13.763453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:18.368593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:22.539162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:26.630755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:30.735359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:34.211939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:38.102030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:42.055561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:45.926740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:49.857067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:54.412190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:02.407339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:06.186866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:09.789030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:14.055354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:18.712314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:22.776820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:26.964846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:31.020992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:34.446103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:38.386297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:42.353395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:46.330206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:50.183573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:54.680024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:02.680172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:06.468610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:10.244125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:14.314572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:19.024200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:23.033661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:27.275048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:31.278690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:34.737157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:38.674383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:42.663295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:46.634662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:50.623136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:54.905247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:02.951027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:06.727356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:10.506121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:14.581535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:19.300896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:23.280819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:27.581579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:31.532114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:35.011650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:38.941799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:42.937602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:46.915190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T20:15:50.897502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T20:16:13.549411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명지점번호관측일자관측시간WGS84위도WGS84경도고도(m)풍향(deg)풍속(m/s)기온(℃)습도(%)현지기압(hPa)해면기압(hPa)강수감지(0:없음1:있음2:오류)시간누적강우량(mm)일누적강우량(mm)
시군명1.0000.8400.0000.0000.9450.9230.8610.220NaNNaN0.5080.887NaN0.643NaNNaN
지점번호0.8401.0000.0000.0000.5440.6740.4530.102NaNNaN0.4360.789NaN0.275NaNNaN
관측일자0.0000.0001.0000.0340.0000.0100.0000.060NaNNaN0.0000.000NaN0.110NaNNaN
관측시간0.0000.0000.0341.0000.0000.0000.0000.313NaNNaN0.0000.008NaN0.081NaNNaN
WGS84위도0.9450.5440.0000.0001.0000.6340.6350.142NaNNaN0.1740.619NaN0.313NaNNaN
WGS84경도0.9230.6740.0100.0000.6341.0000.6260.094NaNNaN0.2190.556NaN0.279NaNNaN
고도(m)0.8610.4530.0000.0000.6350.6261.0000.025NaNNaN0.0480.181NaN0.278NaNNaN
풍향(deg)0.2200.1020.0600.3130.1420.0940.0251.000NaNNaN0.0120.033NaN0.200NaNNaN
풍속(m/s)NaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
기온(℃)NaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaN
습도(%)0.5080.4360.0000.0000.1740.2190.0480.012NaNNaN1.0000.000NaN0.061NaNNaN
현지기압(hPa)0.8870.7890.0000.0080.6190.5560.1810.033NaNNaN0.0001.000NaN0.032NaNNaN
해면기압(hPa)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaN
강수감지(0:없음1:있음2:오류)0.6430.2750.1100.0810.3130.2790.2780.200NaNNaN0.0610.032NaN1.000NaNNaN
시간누적강우량(mm)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
일누적강우량(mm)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000
2024-05-10T20:16:13.947650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명강수감지(0:없음1:있음2:오류)
시군명1.0000.417
강수감지(0:없음1:있음2:오류)0.4171.000
2024-05-10T20:16:14.214792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점번호관측일자관측시간WGS84위도WGS84경도고도(m)풍향(deg)풍속(m/s)기온(℃)습도(%)현지기압(hPa)해면기압(hPa)시간누적강우량(mm)일누적강우량(mm)시군명강수감지(0:없음1:있음2:오류)
지점번호1.0000.0060.0070.0420.022-0.1890.0500.124-0.0670.1200.3190.4280.0030.0100.5210.181
관측일자0.0061.000-0.0290.0070.012-0.0040.0290.0300.0110.0240.0270.0400.0860.0610.0000.105
관측시간0.007-0.0291.0000.0130.0150.0140.1840.2270.421-0.308-0.009-0.0100.0080.1290.0000.048
WGS84위도0.0420.0070.0131.000-0.0310.217-0.024-0.009-0.017-0.0360.0150.0970.0260.0030.7190.197
WGS84경도0.0220.0120.015-0.0311.0000.510-0.028-0.0870.054-0.015-0.0280.041-0.016-0.0250.6520.173
고도(m)-0.189-0.0040.0140.2170.5101.000-0.002-0.0730.018-0.112-0.0190.012-0.015-0.0250.6180.218
풍향(deg)0.0500.0290.184-0.024-0.028-0.0021.0000.3020.195-0.1410.0230.0420.0010.0420.2200.060
풍속(m/s)0.1240.0300.227-0.009-0.087-0.0730.3021.0000.379-0.2440.0950.1080.0840.1640.1610.015
기온(℃)-0.0670.0110.421-0.0170.0540.0180.1950.3791.000-0.739-0.052-0.068-0.060-0.1720.1930.000
습도(%)0.1200.024-0.308-0.036-0.015-0.112-0.141-0.244-0.7391.0000.0760.0520.2670.3560.5360.066
현지기압(hPa)0.3190.027-0.0090.015-0.028-0.0190.0230.095-0.0520.0761.0000.8820.0310.0180.5340.152
해면기압(hPa)0.4280.040-0.0100.0970.0410.0120.0420.108-0.0680.0520.8821.0000.003-0.0120.5340.159
시간누적강우량(mm)0.0030.0860.0080.026-0.016-0.0150.0010.084-0.0600.2670.0310.0031.0000.6800.5000.453
일누적강우량(mm)0.0100.0610.1290.003-0.025-0.0250.0420.164-0.1720.3560.018-0.0120.6801.0000.5050.457
시군명0.5210.0000.0000.7190.6520.6180.2200.1610.1930.5360.5340.5340.5000.5051.0000.417
강수감지(0:없음1:있음2:오류)0.1810.1050.0480.1970.1730.2180.0600.0150.0000.0660.1520.1590.4530.4570.4171.000

Missing values

2024-05-10T20:15:55.320835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T20:15:56.016743image/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

시군명지점번호지점명법정동명관측일자관측시간WGS84위도WGS84경도고도(m)풍향(deg)풍속(m/s)기온(℃)습도(%)현지기압(hPa)해면기압(hPa)강수감지(0:없음1:있음2:오류)시간누적강우량(mm)일누적강우량(mm)
58686안성시516안성경기도 안성시 옥산동202404221737.0037127.250223.92106.01.722.942.41008.71011.400.00.0
69603연천군652연천청산경기도 연천군 청산면 초성리20240419737.99127.072738.29131.61.29.077.6-997.0-999.000.00.0
40073안산시435고잔경기도 안산시단원구 고잔동202404281337.3271126.83473.0147.51.525.244.0-997.0-999.000.00.0
16323남양주시451오남경기도 남양주시 오남읍 양지리20240506037.6985127.204887.99165.80.816.593.3-997.0-999.001.038.0
531가평군485신천 *경기도 가평군 설악면 신천리20240511037.6762127.494875.61290.60.210.964.7-997.0-999.000.00.0
66361남양주시541남양주경기도 남양주시 퇴계원면 퇴계원리20240420737.634127.150625.36223.90.814.076.41008.01011.000.00.0
38295포천시452신북경기도 포천시 신북면 기지리20240429337.9341127.226495.19321.40.711.566.8-997.0-999.000.00.0
54833양평군547양동경기도 양평군 양동면 쌍학리202404232237.4159127.7549108.62358.92.214.059.9997.51010.300.00.0
14825파주시567적성경기도 파주시 적성면 구읍리202405061237.9533126.931871.25250.61.713.682.4999.01007.4101.012.0
82639가평군505가평조종경기도 가평군 조종면20240415437.8244127.3454170.4511.20.712.374.7992.91013.200.00.0
시군명지점번호지점명법정동명관측일자관측시간WGS84위도WGS84경도고도(m)풍향(deg)풍속(m/s)기온(℃)습도(%)현지기압(hPa)해면기압(hPa)강수감지(0:없음1:있음2:오류)시간누적강우량(mm)일누적강우량(mm)
52448용인시436역삼경기도 용인시 역북동202404241637.2344127.1884110.83300.91.117.460.7-997.0-999.000.015.5
9192용인시576백암경기도 용인시 백암면 근삼리20240508737.1297127.3663111.4626.81.79.983.91005.91019.400.00.0
38628양주시598양주경기도 양주시 광적면 가납리20240429037.8312126.990587.7664.20.415.976.91002.21012.600.00.0
17040의정부시431신곡경기도 의정부시 신곡동202405051937.7487127.071852.95227.00.418.295.0-997.0-999.004.048.5
6915안양시365석수동경기도 안양시만안구 석수동20240509037.4092126.895130.13-999.0-999.010.876.3-997.0-999.000.00.0
82986용인시436역삼경기도 용인시 역북동20240415237.2344127.1884110.83181.10.216.753.1-997.0-999.000.00.0
6683오산시446남촌 *경기도 오산시 오산동20240509237.1403127.063717.48161.70.010.790.0-997.0-999.000.00.0
48542안성시443보개경기도 안성시 보개면 불현리202404252137.0331127.306366.040.00.011.689.3-997.0-999.000.00.0
19812용인시576백암경기도 용인시 백암면 근삼리202405042237.1297127.3663111.46345.10.215.575.91003.01016.200.00.0
23936용인시370이동묵리경기도 용인시 이동면 묵리202405031537.1789127.25241.0259.10.828.836.2500.0-999.000.00.0