Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory859.4 KiB
Average record size in memory88.0 B

Variable types

Numeric7
Categorical2

Dataset

Description한국지역난방공사에서 제공하는 날씨 예보 정보 현황입니다(관측소 구분, 예측일, 예측시간, 날씨아이콘, 기온, 강수량, 풍속, 습도 정보 포함)
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15124165/fileData.do

Alerts

예측일 is highly overall correlated with 기온High correlation
날씨아이콘코드 is highly overall correlated with 날씨아이콘명High correlation
기온 is highly overall correlated with 예측일High correlation
날씨아이콘명 is highly overall correlated with 날씨아이콘코드High correlation
기온 has 207 (2.1%) zerosZeros
강수량 has 9632 (96.3%) zerosZeros

Reproduction

Analysis started2023-12-12 02:02:18.219956
Analysis finished2023-12-12 02:02:27.458106
Duration9.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.7054
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:27.547512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q318
95-th percentile24
Maximum133
Range132
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.891431
Coefficient of variation (CV)1.4301622
Kurtosis18.039086
Mean16.7054
Median Absolute Deviation (MAD)6
Skewness4.2650324
Sum167054
Variance570.80049
MonotonicityNot monotonic
2023-12-12T11:02:27.708366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 471
 
4.7%
7 448
 
4.5%
5 441
 
4.4%
2 439
 
4.4%
11 438
 
4.4%
15 434
 
4.3%
18 427
 
4.3%
24 427
 
4.3%
13 424
 
4.2%
8 421
 
4.2%
Other values (14) 5630
56.3%
ValueCountFrequency (%)
1 404
4.0%
2 439
4.4%
3 394
3.9%
4 394
3.9%
5 441
4.4%
6 403
4.0%
7 448
4.5%
8 421
4.2%
9 409
4.1%
10 418
4.2%
ValueCountFrequency (%)
133 374
3.7%
24 427
4.3%
23 471
4.7%
22 408
4.1%
20 402
4.0%
19 413
4.1%
18 427
4.3%
17 408
4.1%
16 415
4.2%
15 434
4.3%

예측일
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20230402
Minimum20230115
Maximum20230707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:27.914876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230115
5-th percentile20230124
Q120230228
median20230411
Q320230525
95-th percentile20230629
Maximum20230707
Range592
Interquartile range (IQR)297

Descriptive statistics

Standard deviation167.23715
Coefficient of variation (CV)8.2666253 × 10-6
Kurtosis-1.0995044
Mean20230402
Median Absolute Deviation (MAD)114
Skewness-0.026620924
Sum2.0230402 × 1011
Variance27968.265
MonotonicityNot monotonic
2023-12-12T11:02:28.088458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20230402 79
 
0.8%
20230520 79
 
0.8%
20230303 75
 
0.8%
20230618 74
 
0.7%
20230223 70
 
0.7%
20230514 70
 
0.7%
20230117 70
 
0.7%
20230411 70
 
0.7%
20230511 69
 
0.7%
20230319 69
 
0.7%
Other values (164) 9275
92.8%
ValueCountFrequency (%)
20230115 39
0.4%
20230116 53
0.5%
20230117 70
0.7%
20230118 50
0.5%
20230119 35
0.4%
20230120 48
0.5%
20230121 59
0.6%
20230122 65
0.7%
20230123 50
0.5%
20230124 47
0.5%
ValueCountFrequency (%)
20230707 56
0.6%
20230706 53
0.5%
20230705 55
0.5%
20230704 58
0.6%
20230703 63
0.6%
20230702 52
0.5%
20230701 57
0.6%
20230630 53
0.5%
20230629 58
0.6%
20230628 55
0.5%

예측시간
Real number (ℝ)

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5088
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:28.262957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median13
Q318
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9144417
Coefficient of variation (CV)0.55276619
Kurtosis-1.2041345
Mean12.5088
Median Absolute Deviation (MAD)6
Skewness-0.010867464
Sum125088
Variance47.809504
MonotonicityNot monotonic
2023-12-12T11:02:28.411308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 460
 
4.6%
15 447
 
4.5%
18 446
 
4.5%
10 442
 
4.4%
3 436
 
4.4%
23 435
 
4.3%
17 433
 
4.3%
13 427
 
4.3%
19 423
 
4.2%
12 422
 
4.2%
Other values (14) 5629
56.3%
ValueCountFrequency (%)
1 387
3.9%
2 460
4.6%
3 436
4.4%
4 396
4.0%
5 418
4.2%
6 407
4.1%
7 413
4.1%
8 403
4.0%
9 391
3.9%
10 442
4.4%
ValueCountFrequency (%)
24 405
4.0%
23 435
4.3%
22 382
3.8%
21 419
4.2%
20 411
4.1%
19 423
4.2%
18 446
4.5%
17 433
4.3%
16 406
4.1%
15 447
4.5%

날씨아이콘코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1875
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:28.523512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.366503
Coefficient of variation (CV)0.42870683
Kurtosis2.2140581
Mean3.1875
Median Absolute Deviation (MAD)1
Skewness1.2350862
Sum31875
Variance1.8673305
MonotonicityNot monotonic
2023-12-12T11:02:28.645607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 3084
30.8%
2 3056
30.6%
4 2139
21.4%
5 823
 
8.2%
1 429
 
4.3%
7 201
 
2.0%
8 186
 
1.9%
6 74
 
0.7%
9 8
 
0.1%
ValueCountFrequency (%)
1 429
 
4.3%
2 3056
30.6%
3 3084
30.8%
4 2139
21.4%
5 823
 
8.2%
6 74
 
0.7%
7 201
 
2.0%
8 186
 
1.9%
9 8
 
0.1%
ValueCountFrequency (%)
9 8
 
0.1%
8 186
 
1.9%
7 201
 
2.0%
6 74
 
0.7%
5 823
 
8.2%
4 2139
21.4%
3 3084
30.8%
2 3056
30.6%
1 429
 
4.3%

기온
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.2149
Minimum-19
Maximum35
Zeros207
Zeros (%)2.1%
Negative972
Negative (%)9.7%
Memory size166.0 KiB
2023-12-12T11:02:28.813317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile-3
Q16
median14
Q321
95-th percentile27
Maximum35
Range54
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.7927579
Coefficient of variation (CV)0.74103912
Kurtosis-0.69929782
Mean13.2149
Median Absolute Deviation (MAD)8
Skewness-0.30188659
Sum132149
Variance95.898108
MonotonicityNot monotonic
2023-12-12T11:02:29.310838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 431
 
4.3%
19 392
 
3.9%
24 376
 
3.8%
22 373
 
3.7%
16 366
 
3.7%
18 365
 
3.6%
17 350
 
3.5%
21 331
 
3.3%
25 327
 
3.3%
14 324
 
3.2%
Other values (45) 6365
63.6%
ValueCountFrequency (%)
-19 1
 
< 0.1%
-18 1
 
< 0.1%
-17 5
 
0.1%
-16 5
 
0.1%
-15 4
 
< 0.1%
-14 10
0.1%
-13 8
0.1%
-12 16
0.2%
-11 12
0.1%
-10 14
0.1%
ValueCountFrequency (%)
35 4
 
< 0.1%
34 4
 
< 0.1%
33 18
 
0.2%
32 35
 
0.4%
31 55
 
0.5%
30 95
 
0.9%
29 87
 
0.9%
28 165
1.7%
27 193
1.9%
26 321
3.2%

강수량
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.122
Minimum0
Maximum20
Zeros9632
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:29.501598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98479083
Coefficient of variation (CV)8.072056
Kurtosis212.84737
Mean0.122
Median Absolute Deviation (MAD)0
Skewness13.136969
Sum1220
Variance0.96981298
MonotonicityNot monotonic
2023-12-12T11:02:29.681018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9632
96.3%
1 203
 
2.0%
3 68
 
0.7%
5 46
 
0.5%
10 29
 
0.3%
20 10
 
0.1%
7 6
 
0.1%
2 3
 
< 0.1%
15 3
 
< 0.1%
ValueCountFrequency (%)
0 9632
96.3%
1 203
 
2.0%
2 3
 
< 0.1%
3 68
 
0.7%
5 46
 
0.5%
7 6
 
0.1%
10 29
 
0.3%
15 3
 
< 0.1%
20 10
 
0.1%
ValueCountFrequency (%)
20 10
 
0.1%
15 3
 
< 0.1%
10 29
 
0.3%
7 6
 
0.1%
5 46
 
0.5%
3 68
 
0.7%
2 3
 
< 0.1%
1 203
 
2.0%
0 9632
96.3%

풍속
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
6141 
2
3119 
7
667 
10
 
72
15
 
1

Length

Max length2
Median length1
Mean length1.0073
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4
2nd row2
3rd row10
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 6141
61.4%
2 3119
31.2%
7 667
 
6.7%
10 72
 
0.7%
15 1
 
< 0.1%

Length

2023-12-12T11:02:29.862443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:02:30.024765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 6141
61.4%
2 3119
31.2%
7 667
 
6.7%
10 72
 
0.7%
15 1
 
< 0.1%

습도
Real number (ℝ)

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.0637
Minimum6
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:02:30.174476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile30
Q150
median65
Q380
95-th percentile90
Maximum99
Range93
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.417733
Coefficient of variation (CV)0.31286779
Kurtosis-0.76912124
Mean62.0637
Median Absolute Deviation (MAD)15
Skewness-0.1687606
Sum620637
Variance377.04835
MonotonicityNot monotonic
2023-12-12T11:02:30.307409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
60 873
8.7%
65 859
8.6%
50 848
 
8.5%
85 810
 
8.1%
75 804
 
8.0%
70 802
 
8.0%
55 799
 
8.0%
80 777
 
7.8%
45 690
 
6.9%
40 598
 
6.0%
Other values (12) 2140
21.4%
ValueCountFrequency (%)
6 2
 
< 0.1%
10 13
 
0.1%
15 45
 
0.4%
20 100
 
1.0%
25 226
 
2.3%
30 363
3.6%
35 430
4.3%
40 598
6.0%
42 1
 
< 0.1%
45 690
6.9%
ValueCountFrequency (%)
99 98
 
1.0%
95 393
3.9%
90 468
4.7%
85 810
8.1%
80 777
7.8%
75 804
8.0%
70 802
8.0%
65 859
8.6%
60 873
8.7%
55 799
8.0%

날씨아이콘명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
구름많음
3084 
구름조금
3056 
흐림
2139 
흐리고비
823 
맑음
429 
Other values (4)
469 

Length

Max length4
Median length4
Mean length3.4678
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row흐림
2nd row소나기
3rd row흐림
4th row구름조금
5th row구름조금

Common Values

ValueCountFrequency (%)
구름많음 3084
30.8%
구름조금 3056
30.6%
흐림 2139
21.4%
흐리고비 823
 
8.2%
맑음 429
 
4.3%
비온후갬 201
 
2.0%
소나기 186
 
1.9%
흐리고눈 74
 
0.7%
눈또는비 8
 
0.1%

Length

2023-12-12T11:02:30.506281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:02:30.681542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구름많음 3084
30.8%
구름조금 3056
30.6%
흐림 2139
21.4%
흐리고비 823
 
8.2%
맑음 429
 
4.3%
비온후갬 201
 
2.0%
소나기 186
 
1.9%
흐리고눈 74
 
0.7%
눈또는비 8
 
0.1%

Interactions

2023-12-12T11:02:26.205367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.229174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:21.492141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.441216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.324244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.225992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.150103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.343172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.369091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:21.629707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.561273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.441076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.380520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.314371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.491065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.511670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:21.767850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.702515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.565584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.506576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.474051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.611463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.649067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:21.876493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.814330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.715179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.646981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.612660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.751558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.794939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.020209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.950736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.854119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.800929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.770016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.861982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:20.917715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.148666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.077177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.958858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.910616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.900601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:27.016087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:21.040016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:22.306515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:23.194221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:24.089493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:25.036219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:02:26.068069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:02:30.828516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소예측일예측시간날씨아이콘코드기온강수량풍속습도날씨아이콘명
관측소1.0000.0000.0330.0340.0460.0420.0480.0450.034
예측일0.0001.0000.0000.5650.7180.1450.2050.3090.565
예측시간0.0330.0001.0000.0000.3940.0590.4850.5950.000
날씨아이콘코드0.0340.5650.0001.0000.3270.2780.1700.3261.000
기온0.0460.7180.3940.3271.0000.1250.2390.3250.327
강수량0.0420.1450.0590.2780.1251.0000.0950.1930.278
풍속0.0480.2050.4850.1700.2390.0951.0000.3440.170
습도0.0450.3090.5950.3260.3250.1930.3441.0000.326
날씨아이콘명0.0340.5650.0001.0000.3270.2780.1700.3261.000
2023-12-12T11:02:30.994667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날씨아이콘명풍속
날씨아이콘명1.0000.099
풍속0.0991.000
2023-12-12T11:02:31.112985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소예측일예측시간날씨아이콘코드기온강수량습도풍속날씨아이콘명
관측소1.0000.0040.008-0.015-0.018-0.0100.0120.0360.015
예측일0.0041.0000.0120.2520.8880.1520.3350.1200.211
예측시간0.0080.0121.0000.0090.2300.054-0.3740.2220.000
날씨아이콘코드-0.0150.2520.0091.0000.2540.3020.3580.0991.000
기온-0.0180.8880.2300.2541.0000.1460.1010.1010.155
강수량-0.0100.1520.0540.3020.1461.0000.2230.0610.151
습도0.0120.335-0.3740.3580.1010.2231.0000.1490.155
풍속0.0360.1200.2220.0990.1010.0610.1491.0000.099
날씨아이콘명0.0150.2110.0001.0000.1550.1510.1550.0991.000

Missing values

2023-12-12T11:02:27.192416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:02:27.386442image/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

관측소예측일예측시간날씨아이콘코드기온강수량풍속습도날씨아이콘명
493851820230413184180425흐림
154502020230611198250275소나기
82157162023021564101075흐림
554226202304027250255구름조금
77457122023022310210270구름조금
287513320230703203310450구름많음
983491820230118222-10455구름조금
3941311202304306580485흐리고비
7987717202302196230295구름조금
28978820230518114210480흐림
관측소예측일예측시간날씨아이콘코드기온강수량풍속습도날씨아이콘명
24321620230526103180475구름많음
163551020230609123260460구름많음
4445352023042163140480구름많음
92528162023012893-601040구름많음
217611920230531183270450구름많음
597111620230326244130440흐림
4900132023062957240495비온후갬
7727742023022322280235구름조금
273291120230521183220745구름많음
80291102023021812460465흐림