Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells789
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.5 KiB
Average record size in memory97.3 B

Variable types

Numeric9
Categorical2

Dataset

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

Alerts

SAWS일사량(SAWS_SOLAR) has 402 (80.4%) missing valuesMissing
SAWS일조량(SAWS_SHINES) has 386 (77.2%) missing valuesMissing
측정일시(SAWS_OBS_TM) has unique valuesUnique
SAWS누적강수량(SAWS_RN_SUM) has 420 (84.0%) zerosZeros
SAWS일사량(SAWS_SOLAR) has 26 (5.2%) zerosZeros
SAWS일조량(SAWS_SHINES) has 43 (8.6%) zerosZeros

Reproduction

Analysis started2023-12-10 14:59:27.937041
Analysis finished2023-12-10 14:59:45.901551
Duration17.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시(SAWS_OBS_TM)
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0138405 × 109
Minimum2.0090115 × 109
Maximum2.0191024 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:46.155511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0090115 × 109
5-th percentile2.0090804 × 109
Q12.011072 × 109
median2.0140216 × 109
Q32.0170246 × 109
95-th percentile2.0181209 × 109
Maximum2.0191024 × 109
Range10090883
Interquartile range (IQR)5952596.5

Descriptive statistics

Standard deviation3119586.5
Coefficient of variation (CV)0.0015490733
Kurtosis-1.3167121
Mean2.0138405 × 109
Median Absolute Deviation (MAD)2984548.5
Skewness0.014373451
Sum1.0069203 × 1012
Variance9.7318202 × 1012
MonotonicityNot monotonic
2023-12-10T23:59:46.499542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014021416 1
 
0.2%
2010030123 1
 
0.2%
2010100712 1
 
0.2%
2017032919 1
 
0.2%
2013013116 1
 
0.2%
2010011714 1
 
0.2%
2018010605 1
 
0.2%
2018070609 1
 
0.2%
2017010405 1
 
0.2%
2018090412 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
2009011522 1
0.2%
2009020305 1
0.2%
2009022110 1
0.2%
2009022812 1
0.2%
2009030804 1
0.2%
2009030814 1
0.2%
2009031612 1
0.2%
2009031619 1
0.2%
2009032221 1
0.2%
2009032317 1
0.2%
ValueCountFrequency (%)
2019102405 1
0.2%
2019091320 1
0.2%
2019082903 1
0.2%
2019081722 1
0.2%
2019081416 1
0.2%
2019081022 1
0.2%
2019072801 1
0.2%
2019071920 1
0.2%
2019071716 1
0.2%
2019062514 1
0.2%

지점코드(STN_ID)
Real number (ℝ)

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1163.422
Minimum1151
Maximum1176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:46.777096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1151
5-th percentile1151.95
Q11157
median1163
Q31170
95-th percentile1176
Maximum1176
Range25
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.6982102
Coefficient of variation (CV)0.0066168684
Kurtosis-1.2020941
Mean1163.422
Median Absolute Deviation (MAD)7
Skewness0.035041485
Sum581711
Variance59.262441
MonotonicityNot monotonic
2023-12-10T23:59:47.053742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1176 26
 
5.2%
1162 26
 
5.2%
1151 25
 
5.0%
1158 22
 
4.4%
1172 22
 
4.4%
1156 22
 
4.4%
1166 22
 
4.4%
1168 21
 
4.2%
1175 21
 
4.2%
1169 21
 
4.2%
Other values (16) 272
54.4%
ValueCountFrequency (%)
1151 25
5.0%
1152 21
4.2%
1153 18
3.6%
1154 18
3.6%
1155 17
3.4%
1156 22
4.4%
1157 15
3.0%
1158 22
4.4%
1159 18
3.6%
1160 20
4.0%
ValueCountFrequency (%)
1176 26
5.2%
1175 21
4.2%
1174 19
3.8%
1173 18
3.6%
1172 22
4.4%
1171 11
2.2%
1170 13
2.6%
1169 21
4.2%
1168 21
4.2%
1167 17
3.4%

지점명(STN_NM)
Categorical

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서대문
 
27
성북
 
27
중랑
 
25
동작
 
23
도봉
 
23
Other values (21)
375 

Length

Max length3
Median length2
Mean length2.128
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동작
2nd row성동
3rd row성동
4th row중구
5th row도봉

Common Values

ValueCountFrequency (%)
서대문 27
 
5.4%
성북 27
 
5.4%
중랑 25
 
5.0%
동작 23
 
4.6%
도봉 23
 
4.6%
강남 23
 
4.6%
광진 21
 
4.2%
구로 21
 
4.2%
동대문 20
 
4.0%
강동 20
 
4.0%
Other values (16) 270
54.0%

Length

2023-12-10T23:59:47.336098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서대문 27
 
5.4%
성북 27
 
5.4%
중랑 25
 
5.0%
동작 23
 
4.6%
도봉 23
 
4.6%
강남 23
 
4.6%
광진 21
 
4.2%
구로 21
 
4.2%
동대문 20
 
4.0%
강동 20
 
4.0%
Other values (16) 270
54.0%

SAWS온도평균(SAWS_TA_AVG)
Real number (ℝ)

Distinct298
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.7856
Minimum-11
Maximum37.3
Zeros2
Zeros (%)0.4%
Negative62
Negative (%)12.4%
Memory size4.5 KiB
2023-12-10T23:59:47.598265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile-4.025
Q14.3
median15.15
Q323.325
95-th percentile28.905
Maximum37.3
Range48.3
Interquartile range (IQR)19.025

Descriptive statistics

Standard deviation10.980533
Coefficient of variation (CV)0.79652192
Kurtosis-1.0675571
Mean13.7856
Median Absolute Deviation (MAD)9.4
Skewness-0.22788705
Sum6892.8
Variance120.5721
MonotonicityNot monotonic
2023-12-10T23:59:47.942048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.1 5
 
1.0%
22.5 4
 
0.8%
17.0 4
 
0.8%
18.9 4
 
0.8%
23.6 4
 
0.8%
26.2 4
 
0.8%
2.8 4
 
0.8%
22.7 4
 
0.8%
2.6 4
 
0.8%
4.4 4
 
0.8%
Other values (288) 459
91.8%
ValueCountFrequency (%)
-11.0 1
 
0.2%
-10.5 1
 
0.2%
-9.9 1
 
0.2%
-8.7 1
 
0.2%
-8.4 3
0.6%
-8.1 2
0.4%
-8.0 1
 
0.2%
-7.7 1
 
0.2%
-7.2 1
 
0.2%
-6.9 1
 
0.2%
ValueCountFrequency (%)
37.3 1
0.2%
33.8 1
0.2%
33.5 1
0.2%
33.400002 1
0.2%
32.7 1
0.2%
32.599998 1
0.2%
32.1 1
0.2%
31.9 1
0.2%
31.2 1
0.2%
30.9 1
0.2%

SAWS습도평균(SAWS_HD)
Real number (ℝ)

Distinct385
Distinct (%)77.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean61.2
Minimum0
Maximum99.900002
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:48.287881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.09
Q146.5
median62
Q376
95-th percentile93.82
Maximum99.900002
Range99.900002
Interquartile range (IQR)29.5

Descriptive statistics

Standard deviation20.050566
Coefficient of variation (CV)0.32762362
Kurtosis-0.60120849
Mean61.2
Median Absolute Deviation (MAD)15
Skewness-0.15359013
Sum30538.8
Variance402.02518
MonotonicityNot monotonic
2023-12-10T23:59:48.617920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.8 4
 
0.8%
71.3 3
 
0.6%
57.5 3
 
0.6%
31.2 3
 
0.6%
77.8 3
 
0.6%
65.7 3
 
0.6%
51.5 3
 
0.6%
92.3 3
 
0.6%
73.4 3
 
0.6%
68.5 3
 
0.6%
Other values (375) 468
93.6%
ValueCountFrequency (%)
0.0 1
0.2%
5.6 1
0.2%
9.7 1
0.2%
14.5 1
0.2%
19.4 1
0.2%
20.0 2
0.4%
20.2 1
0.2%
20.6 1
0.2%
22.0 1
0.2%
22.3 1
0.2%
ValueCountFrequency (%)
99.900002 1
 
0.2%
99.9 3
0.6%
99.4 1
 
0.2%
98.5 1
 
0.2%
97.7 1
 
0.2%
97.1 3
0.6%
97.0 1
 
0.2%
96.9 1
 
0.2%
96.4 1
 
0.2%
96.2 1
 
0.2%

풍향1(CODE)
Real number (ℝ)

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.166
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:48.865941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median10
Q313.25
95-th percentile16
Maximum16
Range15
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation4.7467365
Coefficient of variation (CV)0.51786346
Kurtosis-1.373335
Mean9.166
Median Absolute Deviation (MAD)4
Skewness-0.20100431
Sum4583
Variance22.531507
MonotonicityNot monotonic
2023-12-10T23:59:49.102909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14 56
11.2%
13 47
 
9.4%
4 42
 
8.4%
3 38
 
7.6%
11 37
 
7.4%
15 36
 
7.2%
16 33
 
6.6%
10 30
 
6.0%
12 30
 
6.0%
6 30
 
6.0%
Other values (6) 121
24.2%
ValueCountFrequency (%)
1 21
4.2%
2 25
5.0%
3 38
7.6%
4 42
8.4%
5 24
4.8%
6 30
6.0%
7 22
4.4%
8 15
 
3.0%
9 14
 
2.8%
10 30
6.0%
ValueCountFrequency (%)
16 33
6.6%
15 36
7.2%
14 56
11.2%
13 47
9.4%
12 30
6.0%
11 37
7.4%
10 30
6.0%
9 14
 
2.8%
8 15
 
3.0%
7 22
 
4.4%

풍향2(NAME)
Categorical

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서북서
48 
북서
45 
서남서
44 
서
39 
남서
39 
Other values (11)
285 

Length

Max length3
Median length2
Mean length2.258
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서북서
2nd row서북서
3rd row서
4th row서
5th row남남동

Common Values

ValueCountFrequency (%)
서북서 48
 
9.6%
북서 45
 
9.0%
서남서 44
 
8.8%
서 39
 
7.8%
남서 39
 
7.8%
북동 36
 
7.2%
북 30
 
6.0%
북북서 30
 
6.0%
동북동 30
 
6.0%
남동 29
 
5.8%
Other values (6) 130
26.0%

Length

2023-12-10T23:59:49.379890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서북서 48
 
9.6%
북서 45
 
9.0%
서남서 44
 
8.8%
서 39
 
7.8%
남서 39
 
7.8%
북동 36
 
7.2%
북 30
 
6.0%
북북서 30
 
6.0%
동북동 30
 
6.0%
남동 29
 
5.8%
Other values (6) 130
26.0%

SAWS풍속평균(SAWS_WS_AVG)
Real number (ℝ)

Distinct50
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.669
Minimum0
Maximum7.3
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:49.656602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.9
median1.5
Q32.125
95-th percentile3.7
Maximum7.3
Range7.3
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation1.0657254
Coefficient of variation (CV)0.63854126
Kurtosis2.9299692
Mean1.669
Median Absolute Deviation (MAD)0.6
Skewness1.2861786
Sum834.5
Variance1.1357705
MonotonicityNot monotonic
2023-12-10T23:59:50.027752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 34
 
6.8%
1.2 25
 
5.0%
1.3 22
 
4.4%
1.6 21
 
4.2%
0.8 21
 
4.2%
1.4 20
 
4.0%
0.7 20
 
4.0%
1.1 20
 
4.0%
2.1 19
 
3.8%
1.5 19
 
3.8%
Other values (40) 279
55.8%
ValueCountFrequency (%)
0.0 2
 
0.4%
0.1 8
 
1.6%
0.2 5
 
1.0%
0.3 14
2.8%
0.4 11
 
2.2%
0.5 15
3.0%
0.6 13
 
2.6%
0.7 20
4.0%
0.8 21
4.2%
0.9 34
6.8%
ValueCountFrequency (%)
7.3 1
 
0.2%
7.2 1
 
0.2%
5.9 1
 
0.2%
5.4 1
 
0.2%
4.8 1
 
0.2%
4.7 1
 
0.2%
4.5 1
 
0.2%
4.2 3
0.6%
4.1 1
 
0.2%
4.0 3
0.6%

SAWS누적강수량(SAWS_RN_SUM)
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.539
Minimum0
Maximum94
Zeros420
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:50.489723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.525
Maximum94
Range94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7090238
Coefficient of variation (CV)4.3593397
Kurtosis86.256606
Mean1.539
Median Absolute Deviation (MAD)0
Skewness8.0410554
Sum769.5
Variance45.011001
MonotonicityNot monotonic
2023-12-10T23:59:50.877705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 420
84.0%
0.5 9
 
1.8%
1.0 8
 
1.6%
4.5 5
 
1.0%
2.0 5
 
1.0%
2.5 4
 
0.8%
3.5 4
 
0.8%
3.0 4
 
0.8%
1.5 3
 
0.6%
6.0 3
 
0.6%
Other values (26) 35
 
7.0%
ValueCountFrequency (%)
0.0 420
84.0%
0.5 9
 
1.8%
1.0 8
 
1.6%
1.5 3
 
0.6%
2.0 5
 
1.0%
2.5 4
 
0.8%
3.0 4
 
0.8%
3.5 4
 
0.8%
4.0 1
 
0.2%
4.5 5
 
1.0%
ValueCountFrequency (%)
94.0 1
0.2%
55.0 1
0.2%
39.0 1
0.2%
36.0 1
0.2%
35.0 1
0.2%
34.5 1
0.2%
27.5 1
0.2%
27.0 1
0.2%
26.0 2
0.4%
21.5 1
0.2%

SAWS일사량(SAWS_SOLAR)
Real number (ℝ)

MISSING  ZEROS 

Distinct64
Distinct (%)65.3%
Missing402
Missing (%)80.4%
Infinite0
Infinite (%)0.0%
Mean6.8256123
Minimum0
Maximum26.29
Zeros26
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:52.519888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.85
Q311.725
95-th percentile19.8515
Maximum26.29
Range26.29
Interquartile range (IQR)11.725

Descriptive statistics

Standard deviation6.9139905
Coefficient of variation (CV)1.012948
Kurtosis-0.34456743
Mean6.8256123
Median Absolute Deviation (MAD)4.85
Skewness0.77817452
Sum668.91
Variance47.803264
MonotonicityNot monotonic
2023-12-10T23:59:53.348818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
5.2%
4.3 2
 
0.4%
1.0 2
 
0.4%
0.1 2
 
0.4%
6.1 2
 
0.4%
1.9 2
 
0.4%
11.8 2
 
0.4%
4.9 2
 
0.4%
9.1 2
 
0.4%
16.4 2
 
0.4%
Other values (54) 54
 
10.8%
(Missing) 402
80.4%
ValueCountFrequency (%)
0.0 26
5.2%
0.1 2
 
0.4%
0.2 1
 
0.2%
0.3 1
 
0.2%
0.4 1
 
0.2%
0.8 1
 
0.2%
1.0 2
 
0.4%
1.4 1
 
0.2%
1.9 2
 
0.4%
2.2 1
 
0.2%
ValueCountFrequency (%)
26.29 1
0.2%
24.4 1
0.2%
22.2 1
0.2%
20.3 1
0.2%
20.200001 1
0.2%
19.79 1
0.2%
18.3 1
0.2%
17.9 1
0.2%
17.89 1
0.2%
17.0 1
0.2%

SAWS일조량(SAWS_SHINES)
Real number (ℝ)

MISSING  ZEROS 

Distinct60
Distinct (%)52.6%
Missing386
Missing (%)77.2%
Infinite0
Infinite (%)0.0%
Mean3.2604386
Minimum0
Maximum13.9
Zeros43
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-10T23:59:53.861989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.6
Q36.325
95-th percentile10.64
Maximum13.9
Range13.9
Interquartile range (IQR)6.325

Descriptive statistics

Standard deviation3.8045849
Coefficient of variation (CV)1.1668936
Kurtosis-0.36183186
Mean3.2604386
Median Absolute Deviation (MAD)1.6
Skewness0.90833506
Sum371.69
Variance14.474866
MonotonicityNot monotonic
2023-12-10T23:59:54.239102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 43
 
8.6%
5.9 3
 
0.6%
1.9 2
 
0.4%
0.3 2
 
0.4%
10.9 2
 
0.4%
2.2 2
 
0.4%
9.7 2
 
0.4%
1.4 2
 
0.4%
3.9 2
 
0.4%
8.0 2
 
0.4%
Other values (50) 52
 
10.4%
(Missing) 386
77.2%
ValueCountFrequency (%)
0.0 43
8.6%
0.01 1
 
0.2%
0.17 1
 
0.2%
0.3 2
 
0.4%
0.4 1
 
0.2%
0.5 1
 
0.2%
0.6 1
 
0.2%
0.8 1
 
0.2%
1.0 1
 
0.2%
1.1 1
 
0.2%
ValueCountFrequency (%)
13.9 1
0.2%
12.8 1
0.2%
12.3 1
0.2%
11.0 1
0.2%
10.9 2
0.4%
10.5 1
0.2%
9.9 1
0.2%
9.7 2
0.4%
9.2 1
0.2%
9.08 1
0.2%

Interactions

2023-12-10T23:59:43.256131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.042931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:30.265862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.812249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.575527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:35.357201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:37.034060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:39.066868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:41.503068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:43.478708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.165154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:30.405543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.984169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.755482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:35.540717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:37.237414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:39.262460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:41.681070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:43.672183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.300590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:30.543377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:32.179174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.971017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-10T23:59:32.353950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:34.170641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:35.896371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:37.712562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:39.691820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:42.075135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:44.036344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.537373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:30.839226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:32.540298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:34.348281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:36.076830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:37.919849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:39.925389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:42.252457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:44.226989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.691610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.038376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:32.770073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:34.643197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:36.254147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:38.155919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:40.123311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:42.459100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:44.425952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.867318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.294851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.008496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:34.849291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:36.459144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:38.407536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:40.814482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:42.663029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:44.597964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:29.995533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.453681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.196582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:35.005338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:36.639530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:38.608357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:41.084329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:42.864877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:44.808000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:30.113035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:31.627171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:33.379581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:35.180144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:36.832568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:38.847300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:41.319221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:43.050763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:59:54.538907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시(SAWS_OBS_TM)지점코드(STN_ID)지점명(STN_NM)SAWS온도평균(SAWS_TA_AVG)SAWS습도평균(SAWS_HD)풍향1(CODE)풍향2(NAME)SAWS풍속평균(SAWS_WS_AVG)SAWS누적강수량(SAWS_RN_SUM)SAWS일사량(SAWS_SOLAR)SAWS일조량(SAWS_SHINES)
측정일시(SAWS_OBS_TM)1.0000.2120.0000.0000.1240.0200.0560.0930.0000.1500.000
지점코드(STN_ID)0.2121.0000.2560.0820.0000.1270.1400.0000.0000.0000.000
지점명(STN_NM)0.0000.2561.0000.0750.1430.0850.2300.0000.2320.2250.000
SAWS온도평균(SAWS_TA_AVG)0.0000.0820.0751.0000.0960.0000.1140.0000.1120.6530.462
SAWS습도평균(SAWS_HD)0.1240.0000.1430.0961.0000.0950.1160.0000.0000.0000.776
풍향1(CODE)0.0200.1270.0850.0000.0951.0000.1380.0000.0000.0000.000
풍향2(NAME)0.0560.1400.2300.1140.1160.1381.0000.0000.0000.0000.281
SAWS풍속평균(SAWS_WS_AVG)0.0930.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
SAWS누적강수량(SAWS_RN_SUM)0.0000.0000.2320.1120.0000.0000.0000.0001.0000.3840.314
SAWS일사량(SAWS_SOLAR)0.1500.0000.2250.6530.0000.0000.0000.0000.3841.0000.000
SAWS일조량(SAWS_SHINES)0.0000.0000.0000.4620.7760.0000.2810.0000.3140.0001.000
2023-12-10T23:59:54.873702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점명(STN_NM)풍향2(NAME)
지점명(STN_NM)1.0000.068
풍향2(NAME)0.0681.000
2023-12-10T23:59:55.189221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일시(SAWS_OBS_TM)지점코드(STN_ID)SAWS온도평균(SAWS_TA_AVG)SAWS습도평균(SAWS_HD)풍향1(CODE)SAWS풍속평균(SAWS_WS_AVG)SAWS누적강수량(SAWS_RN_SUM)SAWS일사량(SAWS_SOLAR)SAWS일조량(SAWS_SHINES)지점명(STN_NM)풍향2(NAME)
측정일시(SAWS_OBS_TM)1.000-0.013-0.0170.0910.0100.0040.0500.0750.0740.0000.010
지점코드(STN_ID)-0.0131.000-0.050-0.040-0.022-0.0260.0130.1000.0590.0870.049
SAWS온도평균(SAWS_TA_AVG)-0.017-0.0501.000-0.0380.034-0.055-0.045-0.142-0.0260.0250.044
SAWS습도평균(SAWS_HD)0.091-0.040-0.0381.000-0.018-0.0340.0170.0300.0330.0500.045
풍향1(CODE)0.010-0.0220.034-0.0181.0000.0220.009-0.163-0.0990.0100.000
SAWS풍속평균(SAWS_WS_AVG)0.004-0.026-0.055-0.0340.0221.0000.0010.094-0.0610.0000.000
SAWS누적강수량(SAWS_RN_SUM)0.0500.013-0.0450.0170.0090.0011.000-0.052-0.0770.0980.000
SAWS일사량(SAWS_SOLAR)0.0750.100-0.1420.030-0.1630.094-0.0521.0000.1910.0520.000
SAWS일조량(SAWS_SHINES)0.0740.059-0.0260.033-0.099-0.061-0.0770.1911.0000.0000.105
지점명(STN_NM)0.0000.0870.0250.0500.0100.0000.0980.0520.0001.0000.068
풍향2(NAME)0.0100.0490.0440.0450.0000.0000.0000.0000.1050.0681.000

Missing values

2023-12-10T23:59:45.071340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:59:45.437036image/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.
2023-12-10T23:59:45.750566image/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

측정일시(SAWS_OBS_TM)지점코드(STN_ID)지점명(STN_NM)SAWS온도평균(SAWS_TA_AVG)SAWS습도평균(SAWS_HD)풍향1(CODE)풍향2(NAME)SAWS풍속평균(SAWS_WS_AVG)SAWS누적강수량(SAWS_RN_SUM)SAWS일사량(SAWS_SOLAR)SAWS일조량(SAWS_SHINES)
020140214161168동작1.737.816서북서1.60.00.0<NA>
120171209001175성동-7.793.54서북서0.90.020.35.9
220120829011161성동18.776.010서3.00.07.4<NA>
320150925091158중구17.147.63서3.10.0<NA><NA>
420120924151172도봉0.070.710남남동2.118.00.0<NA>
520130219001152양천22.934.516남동3.60.0<NA>9.7
620110324141165서초32.59999894.814서남서0.80.5<NA><NA>
720180929161161용산7.457.40000210남3.00.0<NA><NA>
820120417231164송파-5.769.92북2.627.5<NA>9.9
920110506131163구로-5.461.44북서0.30.0<NA><NA>
측정일시(SAWS_OBS_TM)지점코드(STN_ID)지점명(STN_NM)SAWS온도평균(SAWS_TA_AVG)SAWS습도평균(SAWS_HD)풍향1(CODE)풍향2(NAME)SAWS풍속평균(SAWS_WS_AVG)SAWS누적강수량(SAWS_RN_SUM)SAWS일사량(SAWS_SOLAR)SAWS일조량(SAWS_SHINES)
49020100928231151광진19.036.513서남서2.40.0<NA><NA>
49120140124091159강남-8.433.11서2.016.022.2<NA>
49220091216231175관악22.861.52북서1.50.010.86.7
49320131013091169노원11.548.710서북서1.21.0<NA><NA>
49420100815081151영등포11.097.714동남동2.40.01.0<NA>
49520140822011176서대문5.444.010남동1.60.00.01.9
49620161222081160강서19.472.06서북서2.10.0<NA><NA>
49720131230051176마포14.757.515북북서0.80.0<NA>5.63
49820100813201175송파13.567.8000033북서0.50.0<NA><NA>
49920190515001162동대문20.062.511서남서1.60.0<NA><NA>