Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells507
Missing cells (%)9.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory97.3 B

Variable types

Categorical3
Numeric8

Dataset

Description샘플 데이터
Author서울시
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=40

Alerts

강수유무(S04A) is highly imbalanced (80.3%)Imbalance
습도(S05A) has 173 (34.6%) missing valuesMissing
일사(S06M) has 167 (33.4%) missing valuesMissing
일조(S07M) has 166 (33.2%) missing valuesMissing
년월일시분(SECTIME) has unique valuesUnique
풍향(S00A) has 39 (7.8%) zerosZeros
풍속(S01A) has 59 (11.8%) zerosZeros
강수(S03M) has 444 (88.8%) zerosZeros
습도(S05A) has 29 (5.8%) zerosZeros
일사(S06M) has 281 (56.2%) zerosZeros
일조(S07M) has 273 (54.6%) zerosZeros

Reproduction

Analysis started2023-12-10 14:59:03.117706
Analysis finished2023-12-10 14:59:18.898341
Duration15.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지점명(SCAPTION)
Categorical

Distinct32
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
[기] 기상청
 
25
[기] 도봉
 
23
[기] 강북
 
22
[서] 은평
 
21
[서] 강동
 
20
Other values (27)
389 

Length

Max length7
Median length6
Mean length6.176
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[기] 광진
2nd row[기] 남현
3rd row[서] 남산
4th row[기] 남현
5th row[기] 마포

Common Values

ValueCountFrequency (%)
[기] 기상청 25
 
5.0%
[기] 도봉 23
 
4.6%
[기] 강북 22
 
4.4%
[서] 은평 21
 
4.2%
[서] 강동 20
 
4.0%
[기] 서초 18
 
3.6%
[기] 동대문 18
 
3.6%
[서] 남산 18
 
3.6%
[기] 중랑 17
 
3.4%
[기] 금천 17
 
3.4%
Other values (22) 301
60.2%

Length

2023-12-10T23:59:19.041277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기 432
43.2%
서 68
 
6.8%
은평 37
 
3.7%
강동 29
 
2.9%
기상청 25
 
2.5%
노원 24
 
2.4%
도봉 23
 
2.3%
강북 22
 
2.2%
서초 18
 
1.8%
동대문 18
 
1.8%
Other values (21) 304
30.4%

년월일시분(SECTIME)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.013978 × 1013
Minimum2.0090116 × 1013
Maximum2.0191022 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:19.309547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0090116 × 1013
5-th percentile2.009073 × 1013
Q12.0120121 × 1013
median2.0140504 × 1013
Q32.0170124 × 1013
95-th percentile2.0190314 × 1013
Maximum2.0191022 × 1013
Range1.0090613 × 1011
Interquartile range (IQR)5.0003158 × 1010

Descriptive statistics

Standard deviation3.048894 × 1010
Coefficient of variation (CV)0.0015138666
Kurtosis-1.1309943
Mean2.013978 × 1013
Median Absolute Deviation (MAD)2.9295511 × 1010
Skewness0.029125683
Sum1.006989 × 1016
Variance9.2957547 × 1020
MonotonicityNot monotonic
2023-12-10T23:59:19.586601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20140504073600 1
 
0.2%
20121122211000 1
 
0.2%
20180612174800 1
 
0.2%
20170512081700 1
 
0.2%
20091104004000 1
 
0.2%
20170303132900 1
 
0.2%
20130308013500 1
 
0.2%
20110301104800 1
 
0.2%
20090208184900 1
 
0.2%
20170114011300 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20090116071100 1
0.2%
20090120074300 1
0.2%
20090208184900 1
0.2%
20090215014100 1
0.2%
20090219224000 1
0.2%
20090306094600 1
0.2%
20090409142600 1
0.2%
20090417035000 1
0.2%
20090419045600 1
0.2%
20090507211400 1
0.2%
ValueCountFrequency (%)
20191022202800 1
0.2%
20191019172900 1
0.2%
20191006132500 1
0.2%
20190930194500 1
0.2%
20190917201500 1
0.2%
20190912215300 1
0.2%
20190903025000 1
0.2%
20190826072500 1
0.2%
20190808160000 1
0.2%
20190802124900 1
0.2%

풍향(S00A)
Real number (ℝ)

ZEROS 

Distinct424
Distinct (%)85.0%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean156.31763
Minimum0
Maximum358.79999
Zeros39
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:19.832928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153.149999
median132.5
Q3275
95-th percentile338.64001
Maximum358.79999
Range358.79999
Interquartile range (IQR)221.85

Descriptive statistics

Standard deviation116.34231
Coefficient of variation (CV)0.74426862
Kurtosis-1.3497452
Mean156.31763
Median Absolute Deviation (MAD)96.399994
Skewness0.26944674
Sum78002.5
Variance13535.533
MonotonicityNot monotonic
2023-12-10T23:59:20.149774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 39
 
7.8%
120.900002 3
 
0.6%
83.400002 3
 
0.6%
50.0 3
 
0.6%
56.700001 3
 
0.6%
66.699997 2
 
0.4%
17.4 2
 
0.4%
21.700001 2
 
0.4%
105.599998 2
 
0.4%
120.699997 2
 
0.4%
Other values (414) 438
87.6%
ValueCountFrequency (%)
0.0 39
7.8%
0.1 1
 
0.2%
0.6 1
 
0.2%
1.3 1
 
0.2%
2.7 1
 
0.2%
4.2 1
 
0.2%
4.9 1
 
0.2%
5.6 1
 
0.2%
6.0 1
 
0.2%
7.8 1
 
0.2%
ValueCountFrequency (%)
358.799988 1
0.2%
358.600006 1
0.2%
357.799988 1
0.2%
355.200012 1
0.2%
354.100006 2
0.4%
353.399994 1
0.2%
352.899994 1
0.2%
352.799988 1
0.2%
352.399994 1
0.2%
351.899994 1
0.2%

풍향(S00A_S)
Categorical

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
북
65 
동북동
56 
북동
40 
북북서
37 
동
35 
Other values (11)
267 

Length

Max length3
Median length3
Mean length2.222
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남남동
2nd row북북서
3rd row남서
4th row서북서
5th row동북동

Common Values

ValueCountFrequency (%)
북 65
13.0%
동북동 56
11.2%
북동 40
 
8.0%
북북서 37
 
7.4%
동 35
 
7.0%
서북서 34
 
6.8%
북북동 32
 
6.4%
북서 29
 
5.8%
남남서 25
 
5.0%
남남동 23
 
4.6%
Other values (6) 124
24.8%

Length

2023-12-10T23:59:20.483490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
북 65
13.0%
동북동 56
11.2%
북동 40
 
8.0%
북북서 37
 
7.4%
동 35
 
7.0%
서북서 34
 
6.8%
북북동 32
 
6.4%
북서 29
 
5.8%
남남서 25
 
5.0%
남남동 23
 
4.6%
Other values (6) 124
24.8%

풍속(S01A)
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4242
Minimum0
Maximum7.2
Zeros59
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:20.777535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6
median1.2
Q32.1
95-th percentile3.6
Maximum7.2
Range7.2
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.2143903
Coefficient of variation (CV)0.85268243
Kurtosis3.110156
Mean1.4242
Median Absolute Deviation (MAD)0.75
Skewness1.3988241
Sum712.1
Variance1.4747438
MonotonicityNot monotonic
2023-12-10T23:59:21.091360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 59
 
11.8%
0.8 25
 
5.0%
0.7 22
 
4.4%
0.6 21
 
4.2%
1.2 21
 
4.2%
1.1 19
 
3.8%
1.0 19
 
3.8%
0.4 18
 
3.6%
1.3 18
 
3.6%
1.4 17
 
3.4%
Other values (41) 261
52.2%
ValueCountFrequency (%)
0.0 59
11.8%
0.1 11
 
2.2%
0.2 8
 
1.6%
0.3 12
 
2.4%
0.4 18
 
3.6%
0.5 16
 
3.2%
0.6 21
 
4.2%
0.7 22
 
4.4%
0.8 25
5.0%
0.9 15
 
3.0%
ValueCountFrequency (%)
7.2 1
0.2%
7.0 2
0.4%
6.7 1
0.2%
6.0 1
0.2%
5.8 1
0.2%
5.0 1
0.2%
4.8 1
0.2%
4.5 1
0.2%
4.4 2
0.4%
4.3 1
0.2%

기온(S02A)
Real number (ℝ)

Distinct175
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5658
Minimum1.1
Maximum27.799999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:21.427693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile9.5
Q113.275
median16.4
Q320.025
95-th percentile23.715001
Maximum27.799999
Range26.699999
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.537328
Coefficient of variation (CV)0.27389731
Kurtosis-0.28162633
Mean16.5658
Median Absolute Deviation (MAD)3.300001
Skewness-0.034045244
Sum8282.9
Variance20.587345
MonotonicityNot monotonic
2023-12-10T23:59:21.714399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.299999 9
 
1.8%
21.200001 9
 
1.8%
12.4 8
 
1.6%
15.2 7
 
1.4%
12.5 7
 
1.4%
15.3 7
 
1.4%
14.8 7
 
1.4%
13.9 7
 
1.4%
21.0 7
 
1.4%
19.6 6
 
1.2%
Other values (165) 426
85.2%
ValueCountFrequency (%)
1.1 1
0.2%
3.3 1
0.2%
5.0 2
0.4%
5.3 1
0.2%
5.7 1
0.2%
7.2 1
0.2%
7.9 1
0.2%
8.0 1
0.2%
8.1 1
0.2%
8.3 2
0.4%
ValueCountFrequency (%)
27.799999 1
0.2%
27.700001 1
0.2%
26.9 1
0.2%
26.700001 1
0.2%
26.5 1
0.2%
26.299999 1
0.2%
26.200001 1
0.2%
26.1 1
0.2%
26.0 1
0.2%
25.9 1
0.2%

강수(S03M)
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8428
Minimum0
Maximum24
Zeros444
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:21.986172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.62
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4868445
Coefficient of variation (CV)4.1372146
Kurtosis25.537624
Mean0.8428
Median Absolute Deviation (MAD)0
Skewness5.0010299
Sum421.4
Variance12.158084
MonotonicityNot monotonic
2023-12-10T23:59:22.304726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 444
88.8%
0.5 8
 
1.6%
4.5 6
 
1.2%
1.0 5
 
1.0%
7.0 5
 
1.0%
2.0 4
 
0.8%
20.0 2
 
0.4%
24.0 2
 
0.4%
5.0 2
 
0.4%
19.5 2
 
0.4%
Other values (18) 20
 
4.0%
ValueCountFrequency (%)
0.0 444
88.8%
0.1 2
 
0.4%
0.5 8
 
1.6%
1.0 5
 
1.0%
2.0 4
 
0.8%
2.5 2
 
0.4%
3.0 1
 
0.2%
3.5 1
 
0.2%
4.0 1
 
0.2%
4.5 6
 
1.2%
ValueCountFrequency (%)
24.0 2
0.4%
23.1 1
0.2%
22.0 1
0.2%
21.0 1
0.2%
20.5 1
0.2%
20.0 2
0.4%
19.5 2
0.4%
17.5 1
0.2%
17.0 1
0.2%
14.5 1
0.2%

강수유무(S04A)
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
475 
10
 
22
1
 
3

Length

Max length2
Median length1
Mean length1.044
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 475
95.0%
10 22
 
4.4%
1 3
 
0.6%

Length

2023-12-10T23:59:22.613915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:59:22.836110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 475
95.0%
10 22
 
4.4%
1 3
 
0.6%

습도(S05A)
Real number (ℝ)

MISSING  ZEROS 

Distinct226
Distinct (%)69.1%
Missing173
Missing (%)34.6%
Infinite0
Infinite (%)0.0%
Mean61.542202
Minimum0
Maximum99.900002
Zeros29
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:23.065277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.75
median62.799999
Q381.650002
95-th percentile97.400002
Maximum99.900002
Range99.900002
Interquartile range (IQR)32.900002

Descriptive statistics

Standard deviation25.981192
Coefficient of variation (CV)0.42216871
Kurtosis0.44298799
Mean61.542202
Median Absolute Deviation (MAD)16.399997
Skewness-0.85199411
Sum20124.3
Variance675.02233
MonotonicityNot monotonic
2023-12-10T23:59:23.353627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 29
 
5.8%
94.699997 5
 
1.0%
97.400002 5
 
1.0%
97.5 5
 
1.0%
62.900002 3
 
0.6%
70.0 3
 
0.6%
46.599998 3
 
0.6%
83.0 3
 
0.6%
99.900002 3
 
0.6%
55.900002 3
 
0.6%
Other values (216) 265
53.0%
(Missing) 173
34.6%
ValueCountFrequency (%)
0.0 29
5.8%
22.200001 1
 
0.2%
27.0 1
 
0.2%
28.200001 1
 
0.2%
32.099998 2
 
0.4%
32.599998 1
 
0.2%
33.799999 1
 
0.2%
36.0 1
 
0.2%
36.5 1
 
0.2%
37.099998 1
 
0.2%
ValueCountFrequency (%)
99.900002 3
0.6%
99.599998 2
 
0.4%
98.900002 1
 
0.2%
98.5 1
 
0.2%
97.900002 1
 
0.2%
97.5 5
1.0%
97.400002 5
1.0%
96.400002 1
 
0.2%
94.900002 1
 
0.2%
94.800003 2
 
0.4%

일사(S06M)
Real number (ℝ)

MISSING  ZEROS 

Distinct46
Distinct (%)13.8%
Missing167
Missing (%)33.4%
Infinite0
Infinite (%)0.0%
Mean1.2890991
Minimum0
Maximum17.370001
Zeros281
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:23.653035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.52
Maximum17.370001
Range17.370001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6950592
Coefficient of variation (CV)2.8663888
Kurtosis7.6657355
Mean1.2890991
Median Absolute Deviation (MAD)0
Skewness2.9517158
Sum429.27
Variance13.653463
MonotonicityNot monotonic
2023-12-10T23:59:23.942660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 281
56.2%
7.6 2
 
0.4%
9.7 2
 
0.4%
5.9 2
 
0.4%
10.7 2
 
0.4%
0.1 2
 
0.4%
1.1 2
 
0.4%
16.9 2
 
0.4%
5.0 1
 
0.2%
14.8 1
 
0.2%
Other values (36) 36
 
7.2%
(Missing) 167
33.4%
ValueCountFrequency (%)
0.0 281
56.2%
0.03 1
 
0.2%
0.1 2
 
0.4%
0.3 1
 
0.2%
0.5 1
 
0.2%
0.6 1
 
0.2%
1.1 2
 
0.4%
1.3 1
 
0.2%
1.38 1
 
0.2%
2.5 1
 
0.2%
ValueCountFrequency (%)
17.370001 1
0.2%
16.9 2
0.4%
16.799999 1
0.2%
16.52 1
0.2%
16.200001 1
0.2%
14.8 1
0.2%
14.0 1
0.2%
13.94 1
0.2%
13.9 1
0.2%
13.6 1
0.2%

일조(S07M)
Real number (ℝ)

MISSING  ZEROS 

Distinct56
Distinct (%)16.8%
Missing166
Missing (%)33.2%
Infinite0
Infinite (%)0.0%
Mean721.1494
Minimum0
Maximum32100
Zeros273
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:24.235509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.305
Maximum32100
Range32100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3912.7453
Coefficient of variation (CV)5.425707
Kurtosis37.475457
Mean721.1494
Median Absolute Deviation (MAD)0
Skewness6.0189965
Sum240863.9
Variance15309576
MonotonicityNot monotonic
2023-12-10T23:59:24.542803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 273
54.6%
0.7 3
 
0.6%
3.3 2
 
0.4%
6.1 2
 
0.4%
6.6 2
 
0.4%
1.0 2
 
0.4%
10.2 1
 
0.2%
2.2 1
 
0.2%
16620.0 1
 
0.2%
3.8 1
 
0.2%
Other values (46) 46
 
9.2%
(Missing) 166
33.2%
ValueCountFrequency (%)
0.0 273
54.6%
0.1 1
 
0.2%
0.6 1
 
0.2%
0.7 3
 
0.6%
1.0 2
 
0.4%
1.2 1
 
0.2%
1.3 1
 
0.2%
1.8 1
 
0.2%
1.9 1
 
0.2%
2.2 1
 
0.2%
ValueCountFrequency (%)
32100.0 1
0.2%
31068.0 1
0.2%
25848.0 1
0.2%
23580.0 1
0.2%
23520.0 1
0.2%
19320.0 1
0.2%
19008.0 1
0.2%
16620.0 1
0.2%
14340.0 1
0.2%
10188.0 1
0.2%

Interactions

2023-12-10T23:59:16.256197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.160094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:06.744668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.594337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.171267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.689441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.264484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.758365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:16.429922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.338840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.128172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.806516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.340497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.872756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.427217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.930502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:16.636865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.533910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.374920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.005599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.532544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.075912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.641648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.156154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:16.822952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.724409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.588484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.211378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.755036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.305111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.854023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.375051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:16.994372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.925741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.814120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.398979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.951118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.519919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.052047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.566226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:17.176480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.104344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.030835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.590235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.130883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.724837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.202770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.750791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:17.350633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.294243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.204451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.789265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.316719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.912105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.361661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.911281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:17.947011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.664521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.390984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.987429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.509602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.088121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.553506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:16.098922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:59:24.763641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점명(SCAPTION)년월일시분(SECTIME)풍향(S00A)풍향(S00A_S)풍속(S01A)기온(S02A)강수(S03M)강수유무(S04A)습도(S05A)일사(S06M)일조(S07M)
지점명(SCAPTION)1.0000.1480.2020.2600.0000.1540.0000.0910.1670.1430.000
년월일시분(SECTIME)0.1481.0000.0000.0000.0000.0000.0950.0000.0000.0000.000
풍향(S00A)0.2020.0001.0000.0000.0000.1600.2570.1220.0000.1090.000
풍향(S00A_S)0.2600.0000.0001.0000.0000.0000.0000.1590.0000.0110.000
풍속(S01A)0.0000.0000.0000.0001.0000.0000.0000.4200.0000.0000.448
기온(S02A)0.1540.0000.1600.0000.0001.0000.0000.4200.0000.0000.145
강수(S03M)0.0000.0950.2570.0000.0000.0001.0000.0000.0000.2490.000
강수유무(S04A)0.0910.0000.1220.1590.4200.4200.0001.0000.0000.0000.000
습도(S05A)0.1670.0000.0000.0000.0000.0000.0000.0001.0000.0000.153
일사(S06M)0.1430.0000.1090.0110.0000.0000.2490.0000.0001.0000.441
일조(S07M)0.0000.0000.0000.0000.4480.1450.0000.0000.1530.4411.000
2023-12-10T23:59:25.047256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점명(SCAPTION)강수유무(S04A)풍향(S00A_S)
지점명(SCAPTION)1.0000.0420.066
강수유무(S04A)0.0421.0000.085
풍향(S00A_S)0.0660.0851.000
2023-12-10T23:59:25.244788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월일시분(SECTIME)풍향(S00A)풍속(S01A)기온(S02A)강수(S03M)습도(S05A)일사(S06M)일조(S07M)지점명(SCAPTION)풍향(S00A_S)강수유무(S04A)
년월일시분(SECTIME)1.0000.0140.005-0.0090.0550.0500.0400.0290.0510.0000.000
풍향(S00A)0.0141.000-0.0180.092-0.0340.022-0.042-0.0310.0700.0000.072
풍속(S01A)0.005-0.0181.000-0.000-0.0360.002-0.0330.0410.0000.0000.203
기온(S02A)-0.0090.092-0.0001.000-0.098-0.028-0.0590.0330.0520.0000.277
강수(S03M)0.055-0.034-0.036-0.0981.0000.0720.1580.0110.0000.0000.000
습도(S05A)0.0500.0220.002-0.0280.0721.000-0.006-0.0090.0580.0000.000
일사(S06M)0.040-0.042-0.033-0.0590.158-0.0061.0000.0230.0460.0000.000
일조(S07M)0.029-0.0310.0410.0330.011-0.0090.0231.0000.0000.0000.000
지점명(SCAPTION)0.0510.0700.0000.0520.0000.0580.0460.0001.0000.0660.042
풍향(S00A_S)0.0000.0000.0000.0000.0000.0000.0000.0000.0661.0000.085
강수유무(S04A)0.0000.0720.2030.2770.0000.0000.0000.0000.0420.0851.000

Missing values

2023-12-10T23:59:18.193247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:59:18.519856image/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.
2023-12-10T23:59:18.769307image/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

지점명(SCAPTION)년월일시분(SECTIME)풍향(S00A)풍향(S00A_S)풍속(S01A)기온(S02A)강수(S03M)강수유무(S04A)습도(S05A)일사(S06M)일조(S07M)
0[기] 광진2014050407360010.7남남동1.413.90.010<NA>2.750.0
1[기] 남현20181210180700332.799988북북서0.422.10.000.00.00.0
2[서] 남산20160127211100292.799988남서0.818.2999990.00<NA>0.0<NA>
3[기] 남현20090622014700342.299988서북서2.99.90.0081.199997<NA>0.0
4[기] 마포20190802124900200.899994동북동2.121.04.0078.900002<NA>0.0
5[서] 강동201012140509005.6서남서0.011.30.000.0<NA>12.5
6[기] 중랑20121029152300327.700012동0.022.2999990.000.00.0<NA>
7[기] 광진201110241256004.9서북서0.615.40.000.0<NA><NA>
8[기] 서초20170209201400279.200012동북동0.615.20.00<NA><NA>0.0
9[기] 동대문20120328094700324.299988북북서2.35.70.0041.2999990.0<NA>
지점명(SCAPTION)년월일시분(SECTIME)풍향(S00A)풍향(S00A_S)풍속(S01A)기온(S02A)강수(S03M)강수유무(S04A)습도(S05A)일사(S06M)일조(S07M)
490[기] 양천20171111063600285.100006남2.910.40.0093.9000020.0<NA>
491[기] 한강20131229223700338.5북0.521.7000010.0071.8000030.0<NA>
492[기] 중구20111005121900296.5서3.221.40.0084.8000030.00.0
493[기] 용산2010112705160094.800003동0.120.7000010.0050.4000020.00.0
494[기] 노원2018010714100081.599998서북서2.018.7000010.0081.5<NA><NA>
495[서] 강동2012032521180033.200001북0.016.2999990.0052.5999980.00.0
496[기] 강북20111221131000244.800003동1.926.50.00<NA>0.00.0
497[기] 양천20110717103700345.600006동북동2.911.90.0092.800003<NA>0.0
498[기] 은평20130415043900353.399994남0.925.7999990.00<NA>0.00.0
499[서] 남산20141024102500109.900002남남동0.015.70.0062.0999980.07.8