Overview

Dataset statistics

Number of variables13
Number of observations7975
Missing cells15398
Missing cells (%)14.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory895.8 KiB
Average record size in memory115.0 B

Variable types

DateTime1
Numeric10
Unsupported1
Categorical1

Dataset

Description2000-2021.10.31 제주도 4지점(제주, 고산, 성산, 서귀포) 일산 기상(기온, 풍속, 풍향, 습도, 일조, 일사, 전운량) 관측 데이터입니다. ※ 출처 : 기상자료개방포털 https://data.kma.go.kr/cmmn/main.do
Author고성빈
URLhttps://www.jejudatahub.net/data/view/data/877

Alerts

Tem_mean is highly overall correlated with Tem_max and 3 other fieldsHigh correlation
Tem_max is highly overall correlated with Tem_mean and 3 other fieldsHigh correlation
Tem_min is highly overall correlated with Tem_mean and 3 other fieldsHigh correlation
Wspeed_mean is highly overall correlated with Wspeed_max and 1 other fieldsHigh correlation
Wspeed_max is highly overall correlated with Wspeed_meanHigh correlation
Wspeed_min is highly overall correlated with Wspeed_meanHigh correlation
Humid_mean is highly overall correlated with Tem_mean and 2 other fieldsHigh correlation
Sunshine_mean is highly overall correlated with Cloud_max and 1 other fieldsHigh correlation
Cloud_max is highly overall correlated with Sunshine_mean and 1 other fieldsHigh correlation
Cloud_min is highly overall correlated with Sunshine_mean and 1 other fieldsHigh correlation
Season is highly overall correlated with Tem_mean and 2 other fieldsHigh correlation
Sunshine_mean has 81 (1.0%) missing valuesMissing
Solar_mean has 7975 (100.0%) missing valuesMissing
Cloud_max has 3662 (45.9%) missing valuesMissing
Cloud_min has 3662 (45.9%) missing valuesMissing
Date has unique valuesUnique
Solar_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
Wspeed_min has 891 (11.2%) zerosZeros
Sunshine_mean has 1000 (12.5%) zerosZeros
Cloud_min has 1615 (20.3%) zerosZeros

Reproduction

Analysis started2023-12-11 20:03:51.192926
Analysis finished2023-12-11 20:04:03.711766
Duration12.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Date
Date

UNIQUE 

Distinct7975
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
Minimum2000-01-01 00:00:00
Maximum2021-10-31 00:00:00
2023-12-12T05:04:03.833891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:04.089321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tem_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct325
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.816339
Minimum-4.7
Maximum30.2
Zeros1
Zeros (%)< 0.1%
Negative39
Negative (%)0.5%
Memory size70.2 KiB
2023-12-12T05:04:04.259938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.7
5-th percentile3.2
Q19.3
median16.4
Q322.3
95-th percentile27.5
Maximum30.2
Range34.9
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.7123286
Coefficient of variation (CV)0.48761783
Kurtosis-1.065784
Mean15.816339
Median Absolute Deviation (MAD)6.5
Skewness-0.14885504
Sum126135.3
Variance59.480013
MonotonicityNot monotonic
2023-12-12T05:04:04.437293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.5 49
 
0.6%
20.7 48
 
0.6%
27.5 47
 
0.6%
18.5 45
 
0.6%
23.6 45
 
0.6%
20.8 44
 
0.6%
8.8 43
 
0.5%
17.2 43
 
0.5%
23.1 43
 
0.5%
19.3 42
 
0.5%
Other values (315) 7526
94.4%
ValueCountFrequency (%)
-4.7 1
< 0.1%
-2.9 1
< 0.1%
-2.5 1
< 0.1%
-2.4 2
< 0.1%
-2.3 1
< 0.1%
-2.2 1
< 0.1%
-2.1 2
< 0.1%
-2.0 1
< 0.1%
-1.9 2
< 0.1%
-1.8 1
< 0.1%
ValueCountFrequency (%)
30.2 2
 
< 0.1%
30.1 1
 
< 0.1%
29.8 6
0.1%
29.7 4
 
0.1%
29.6 7
0.1%
29.5 6
0.1%
29.4 8
0.1%
29.3 4
 
0.1%
29.2 10
0.1%
29.1 9
0.1%

Tem_max
Real number (ℝ)

HIGH CORRELATION 

Distinct347
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.957705
Minimum-1.7
Maximum34.5
Zeros1
Zeros (%)< 0.1%
Negative10
Negative (%)0.1%
Memory size70.2 KiB
2023-12-12T05:04:04.612545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.7
5-th percentile5.9
Q112.9
median19.8
Q325
95-th percentile30.5
Maximum34.5
Range36.2
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation7.6249747
Coefficient of variation (CV)0.40220979
Kurtosis-0.86918467
Mean18.957705
Median Absolute Deviation (MAD)5.9
Skewness-0.25184607
Sum151187.7
Variance58.140239
MonotonicityNot monotonic
2023-12-12T05:04:04.746898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.7 72
 
0.9%
25.2 60
 
0.8%
23.2 57
 
0.7%
26.2 55
 
0.7%
21.2 53
 
0.7%
24.2 53
 
0.7%
22.7 52
 
0.7%
24.1 52
 
0.7%
24.6 50
 
0.6%
21.7 49
 
0.6%
Other values (337) 7422
93.1%
ValueCountFrequency (%)
-1.7 1
 
< 0.1%
-1.6 1
 
< 0.1%
-1.5 1
 
< 0.1%
-1.3 1
 
< 0.1%
-0.9 3
< 0.1%
-0.7 1
 
< 0.1%
-0.5 1
 
< 0.1%
-0.2 1
 
< 0.1%
0.0 1
 
< 0.1%
0.1 1
 
< 0.1%
ValueCountFrequency (%)
34.5 1
< 0.1%
34.2 1
< 0.1%
34.1 1
< 0.1%
34.0 2
< 0.1%
33.9 1
< 0.1%
33.8 1
< 0.1%
33.7 2
< 0.1%
33.6 2
< 0.1%
33.5 2
< 0.1%
33.4 1
< 0.1%

Tem_min
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.737931
Minimum-6.4
Maximum28.3
Zeros15
Zeros (%)0.2%
Negative291
Negative (%)3.6%
Memory size70.2 KiB
2023-12-12T05:04:04.909007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.4
5-th percentile0.4
Q15.5
median12.9
Q319.9
95-th percentile25.1
Maximum28.3
Range34.7
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation8.1208541
Coefficient of variation (CV)0.63753321
Kurtosis-1.2089525
Mean12.737931
Median Absolute Deviation (MAD)7.2
Skewness-0.017242807
Sum101585
Variance65.948271
MonotonicityNot monotonic
2023-12-12T05:04:05.076528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.8 54
 
0.7%
1.2 51
 
0.6%
4.5 50
 
0.6%
3.7 47
 
0.6%
8.8 46
 
0.6%
22.2 45
 
0.6%
8.3 44
 
0.6%
8.6 44
 
0.6%
16.7 44
 
0.6%
0.6 44
 
0.6%
Other values (317) 7506
94.1%
ValueCountFrequency (%)
-6.4 1
 
< 0.1%
-5.4 1
 
< 0.1%
-5.0 1
 
< 0.1%
-4.6 2
< 0.1%
-4.5 1
 
< 0.1%
-4.4 1
 
< 0.1%
-3.9 1
 
< 0.1%
-3.6 2
< 0.1%
-3.5 4
0.1%
-3.4 3
< 0.1%
ValueCountFrequency (%)
28.3 1
 
< 0.1%
28.2 2
< 0.1%
28.1 1
 
< 0.1%
28.0 1
 
< 0.1%
27.9 1
 
< 0.1%
27.8 1
 
< 0.1%
27.7 3
< 0.1%
27.6 2
< 0.1%
27.5 2
< 0.1%
27.4 3
< 0.1%

Wspeed_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)1.2%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.1305183
Minimum0.3
Maximum13.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:05.224726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1.5
Q12.2
median2.9
Q33.8
95-th percentile5.3
Maximum13.7
Range13.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.2232919
Coefficient of variation (CV)0.39076337
Kurtosis3.1206955
Mean3.1305183
Median Absolute Deviation (MAD)0.8
Skewness1.1193067
Sum24947.1
Variance1.496443
MonotonicityNot monotonic
2023-12-12T05:04:05.409960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 293
 
3.7%
2.5 291
 
3.6%
3.0 290
 
3.6%
2.7 283
 
3.5%
2.2 279
 
3.5%
2.4 277
 
3.5%
2.8 274
 
3.4%
2.3 274
 
3.4%
3.1 273
 
3.4%
2.9 273
 
3.4%
Other values (83) 5162
64.7%
ValueCountFrequency (%)
0.3 1
 
< 0.1%
0.4 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.7 7
 
0.1%
0.8 4
 
0.1%
0.9 13
 
0.2%
1.0 27
0.3%
1.1 33
0.4%
1.2 55
0.7%
ValueCountFrequency (%)
13.7 1
< 0.1%
13.3 1
< 0.1%
12.2 1
< 0.1%
11.9 1
< 0.1%
10.2 1
< 0.1%
9.8 1
< 0.1%
9.7 1
< 0.1%
9.4 1
< 0.1%
9.3 2
< 0.1%
9.1 1
< 0.1%

Wspeed_max
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)1.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.3489522
Minimum1.3
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:05.558037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile3.1
Q14.1
median5.1
Q36.3
95-th percentile8.6
Maximum22.4
Range21.1
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.8047194
Coefficient of variation (CV)0.33739682
Kurtosis5.9697191
Mean5.3489522
Median Absolute Deviation (MAD)1.1
Skewness1.5044228
Sum42625.8
Variance3.2570123
MonotonicityNot monotonic
2023-12-12T05:04:05.682931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 219
 
2.7%
5.0 216
 
2.7%
4.3 215
 
2.7%
4.7 214
 
2.7%
4.6 213
 
2.7%
4.9 212
 
2.7%
3.9 209
 
2.6%
4.4 208
 
2.6%
5.3 204
 
2.6%
4.2 195
 
2.4%
Other values (129) 5864
73.5%
ValueCountFrequency (%)
1.3 2
 
< 0.1%
1.4 1
 
< 0.1%
1.6 3
 
< 0.1%
1.7 4
 
0.1%
1.8 3
 
< 0.1%
1.9 4
 
0.1%
2.0 7
0.1%
2.1 7
0.1%
2.2 8
0.1%
2.3 16
0.2%
ValueCountFrequency (%)
22.4 1
< 0.1%
21.2 1
< 0.1%
20.8 1
< 0.1%
20.3 1
< 0.1%
18.3 1
< 0.1%
18.1 1
< 0.1%
17.7 1
< 0.1%
16.9 1
< 0.1%
16.7 1
< 0.1%
16.6 1
< 0.1%

Wspeed_min
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)0.8%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.0747271
Minimum0
Maximum9.9
Zeros891
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:05.859240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median0.8
Q31.5
95-th percentile3.1
Maximum9.9
Range9.9
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.99140065
Coefficient of variation (CV)0.92246737
Kurtosis3.662522
Mean1.0747271
Median Absolute Deviation (MAD)0.6
Skewness1.5442047
Sum8564.5
Variance0.98287525
MonotonicityNot monotonic
2023-12-12T05:04:06.028545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 891
 
11.2%
0.6 425
 
5.3%
0.4 422
 
5.3%
0.8 392
 
4.9%
0.5 390
 
4.9%
0.9 389
 
4.9%
0.2 382
 
4.8%
0.1 380
 
4.8%
0.7 379
 
4.8%
1.0 363
 
4.6%
Other values (56) 3556
44.6%
ValueCountFrequency (%)
0.0 891
11.2%
0.1 380
4.8%
0.2 382
4.8%
0.3 356
 
4.5%
0.4 422
5.3%
0.5 390
4.9%
0.6 425
5.3%
0.7 379
4.8%
0.8 392
4.9%
0.9 389
4.9%
ValueCountFrequency (%)
9.9 1
< 0.1%
8.9 1
< 0.1%
7.0 1
< 0.1%
6.8 1
< 0.1%
6.6 2
< 0.1%
6.5 1
< 0.1%
6.0 2
< 0.1%
5.8 2
< 0.1%
5.7 2
< 0.1%
5.6 2
< 0.1%

Humid_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct641
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.975273
Minimum16.7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:06.236734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.7
5-th percentile50.5
Q163.15
median73.3
Q384
95-th percentile93.6
Maximum100
Range83.3
Interquartile range (IQR)20.85

Descriptive statistics

Standard deviation13.540153
Coefficient of variation (CV)0.18554439
Kurtosis-0.44810295
Mean72.975273
Median Absolute Deviation (MAD)10.4
Skewness-0.27514891
Sum581977.8
Variance183.33574
MonotonicityNot monotonic
2023-12-12T05:04:06.481633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.8 40
 
0.5%
64.5 38
 
0.5%
71.3 35
 
0.4%
66.8 35
 
0.4%
65.0 33
 
0.4%
76.0 32
 
0.4%
64.3 32
 
0.4%
66.0 32
 
0.4%
86.5 31
 
0.4%
64.0 31
 
0.4%
Other values (631) 7636
95.7%
ValueCountFrequency (%)
16.7 1
< 0.1%
17.8 1
< 0.1%
23.3 1
< 0.1%
24.1 1
< 0.1%
25.0 1
< 0.1%
25.3 2
< 0.1%
26.7 1
< 0.1%
27.3 1
< 0.1%
28.5 1
< 0.1%
30.2 1
< 0.1%
ValueCountFrequency (%)
100.0 2
 
< 0.1%
99.9 2
 
< 0.1%
99.8 1
 
< 0.1%
99.7 1
 
< 0.1%
99.6 1
 
< 0.1%
99.1 2
 
< 0.1%
99.0 15
0.2%
98.9 1
 
< 0.1%
98.8 5
 
0.1%
98.7 3
 
< 0.1%

Sunshine_mean
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct134
Distinct (%)1.7%
Missing81
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean5.5405625
Minimum0
Maximum13.3
Zeros1000
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:06.672329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median5.9
Q39
95-th percentile11.5
Maximum13.3
Range13.3
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation3.9838056
Coefficient of variation (CV)0.71902549
Kurtosis-1.3594358
Mean5.5405625
Median Absolute Deviation (MAD)3.6
Skewness-0.024324066
Sum43737.2
Variance15.870707
MonotonicityNot monotonic
2023-12-12T05:04:07.138502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1000
 
12.5%
0.1 137
 
1.7%
0.2 117
 
1.5%
9.5 93
 
1.2%
8.9 86
 
1.1%
0.3 85
 
1.1%
8.4 84
 
1.1%
8.6 82
 
1.0%
0.5 81
 
1.0%
9.0 81
 
1.0%
Other values (124) 6048
75.8%
(Missing) 81
 
1.0%
ValueCountFrequency (%)
0.0 1000
12.5%
0.1 137
 
1.7%
0.2 117
 
1.5%
0.3 85
 
1.1%
0.4 70
 
0.9%
0.5 81
 
1.0%
0.6 49
 
0.6%
0.7 58
 
0.7%
0.8 58
 
0.7%
0.9 49
 
0.6%
ValueCountFrequency (%)
13.3 1
 
< 0.1%
13.2 2
 
< 0.1%
13.1 6
 
0.1%
13.0 4
 
0.1%
12.9 7
 
0.1%
12.8 13
0.2%
12.7 18
0.2%
12.6 15
0.2%
12.5 17
0.2%
12.4 20
0.3%

Solar_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7975
Missing (%)100.0%
Memory size70.2 KiB

Cloud_max
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.3%
Missing3662
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean8.5158822
Minimum0
Maximum10
Zeros62
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:07.273748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1953084
Coefficient of variation (CV)0.2577899
Kurtosis3.0675725
Mean8.5158822
Median Absolute Deviation (MAD)0
Skewness-1.7988425
Sum36729
Variance4.819379
MonotonicityNot monotonic
2023-12-12T05:04:07.372913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 2305
28.9%
8 614
 
7.7%
9 434
 
5.4%
7 308
 
3.9%
6 199
 
2.5%
5 149
 
1.9%
4 111
 
1.4%
3 75
 
0.9%
0 62
 
0.8%
2 41
 
0.5%
(Missing) 3662
45.9%
ValueCountFrequency (%)
0 62
 
0.8%
1 15
 
0.2%
2 41
 
0.5%
3 75
 
0.9%
4 111
 
1.4%
5 149
 
1.9%
6 199
 
2.5%
7 308
3.9%
8 614
7.7%
9 434
5.4%
ValueCountFrequency (%)
10 2305
28.9%
9 434
 
5.4%
8 614
 
7.7%
7 308
 
3.9%
6 199
 
2.5%
5 149
 
1.9%
4 111
 
1.4%
3 75
 
0.9%
2 41
 
0.5%
1 15
 
0.2%

Cloud_min
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.3%
Missing3662
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean3.0027823
Minimum0
Maximum10
Zeros1615
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:07.479443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1622031
Coefficient of variation (CV)1.053091
Kurtosis-0.52286637
Mean3.0027823
Median Absolute Deviation (MAD)2
Skewness0.7876616
Sum12951
Variance9.9995284
MonotonicityNot monotonic
2023-12-12T05:04:07.596755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1615
20.3%
3 487
 
6.1%
2 417
 
5.2%
4 357
 
4.5%
6 345
 
4.3%
10 274
 
3.4%
1 206
 
2.6%
5 196
 
2.5%
8 186
 
2.3%
7 163
 
2.0%
(Missing) 3662
45.9%
ValueCountFrequency (%)
0 1615
20.3%
1 206
 
2.6%
2 417
 
5.2%
3 487
 
6.1%
4 357
 
4.5%
5 196
 
2.5%
6 345
 
4.3%
7 163
 
2.0%
8 186
 
2.3%
9 67
 
0.8%
ValueCountFrequency (%)
10 274
3.4%
9 67
 
0.8%
8 186
 
2.3%
7 163
 
2.0%
6 345
4.3%
5 196
2.5%
4 357
4.5%
3 487
6.1%
2 417
5.2%
1 206
2.6%

Season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
Spring
2024 
Summer
2024 
Automn
1972 
Winter
1955 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Spring 2024
25.4%
Summer 2024
25.4%
Automn 1972
24.7%
Winter 1955
24.5%

Length

2023-12-12T05:04:07.717505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:04:07.829921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
spring 2024
25.4%
summer 2024
25.4%
automn 1972
24.7%
winter 1955
24.5%

Interactions

2023-12-12T05:04:01.633656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:52.800022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:53.612351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:54.595546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:55.827569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.802402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.622543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.471897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.375854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.515076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.056040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:52.884261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:59.466225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.619952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.200477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:52.970827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:53.797441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:56.016074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.992796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:58.646580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.562459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.727870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:57.068760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.912677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.735865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.655649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.845933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.446806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:04:02.538414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:53.216092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:54.104730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:55.119482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.296834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.207571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.062742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.935516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.858217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:01.068153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.640631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:55.216780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.412260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.287302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.135425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.015997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.953610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:01.165870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T05:03:58.222635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.102388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.071246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:01.271153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.859784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:53.456389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:54.385411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:55.626009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.615531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.463343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.315789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.195565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.253579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:01.413904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:02.989992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:53.535800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:54.494369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:55.724608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:56.716557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:57.546591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:58.393085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:59.284618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:00.400901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:01.515198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:04:07.917749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9630.9620.2600.2080.2880.5690.3560.2990.3790.791
Tem_max0.9631.0000.9110.2720.2240.3140.5290.3680.2860.3640.770
Tem_min0.9620.9111.0000.2400.2010.2580.5910.3140.2700.3700.777
Wspeed_mean0.2600.2720.2401.0000.9100.8890.2480.1710.1480.2440.227
Wspeed_max0.2080.2240.2010.9101.0000.6910.1620.1360.1100.1440.210
Wspeed_min0.2880.3140.2580.8890.6911.0000.2060.1730.0770.2330.241
Humid_mean0.5690.5290.5910.2480.1620.2061.0000.5070.4430.5230.538
Sunshine_mean0.3560.3680.3140.1710.1360.1730.5071.0000.6480.7180.339
Cloud_max0.2990.2860.2700.1480.1100.0770.4430.6481.0000.5440.206
Cloud_min0.3790.3640.3700.2440.1440.2330.5230.7180.5441.0000.293
Season0.7910.7700.7770.2270.2100.2410.5380.3390.2060.2931.000
2023-12-12T05:04:08.054572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9850.984-0.187-0.169-0.2260.5760.0570.0950.1270.611
Tem_max0.9851.0000.946-0.234-0.201-0.2740.5100.1580.0230.0340.584
Tem_min0.9840.9461.000-0.137-0.135-0.1650.616-0.0290.1550.2070.593
Wspeed_mean-0.187-0.234-0.1371.0000.8720.701-0.154-0.0950.0560.1300.137
Wspeed_max-0.169-0.201-0.1350.8721.0000.462-0.094-0.0990.0850.1350.127
Wspeed_min-0.226-0.274-0.1650.7010.4621.000-0.201-0.0980.0210.0840.146
Humid_mean0.5760.5100.616-0.154-0.094-0.2011.000-0.4530.4130.4980.352
Sunshine_mean0.0570.158-0.029-0.095-0.099-0.098-0.4531.000-0.657-0.7400.208
Cloud_max0.0950.0230.1550.0560.0850.0210.413-0.6571.0000.5460.130
Cloud_min0.1270.0340.2070.1300.1350.0840.498-0.7400.5461.0000.165
Season0.6110.5840.5930.1370.1270.1460.3520.2080.1300.1651.000

Missing values

2023-12-12T05:04:03.153931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:04:03.378528image/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.
2023-12-12T05:04:03.560287image/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

DateTem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanSolar_meanCloud_maxCloud_minSeason
02000-01-0112.415.46.13.95.62.070.44.3<NA>80Winter
12000-01-0212.217.46.23.16.10.372.05.4<NA>100Winter
22000-01-036.29.13.63.86.32.468.07.3<NA>100Winter
32000-01-049.313.63.02.54.61.268.53.9<NA>90Winter
42000-01-0514.415.413.13.76.52.081.20.2<NA>104Winter
52000-01-0610.714.65.94.47.11.284.80.0<NA>109Winter
62000-01-073.35.01.14.18.61.467.33.4<NA>106Winter
72000-01-084.77.21.92.94.41.671.70.0<NA>108Winter
82000-01-097.79.45.53.04.41.491.00.0<NA>1010Winter
92000-01-107.210.24.64.16.61.676.87.2<NA>93Winter
DateTem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanSolar_meanCloud_maxCloud_minSeason
79652021-10-2214.717.711.52.54.11.155.69.3<NA>70Automn
79662021-10-2314.918.911.42.23.91.259.310.1<NA>60Automn
79672021-10-2417.020.813.81.83.30.861.13.4<NA>102Automn
79682021-10-2516.319.813.12.33.41.162.610.1<NA>100Automn
79692021-10-2615.320.211.92.53.41.663.810.2<NA>80Automn
79702021-10-2716.320.813.22.93.91.264.59.9<NA>80Automn
79712021-10-2816.020.712.22.63.81.358.010.1<NA>60Automn
79722021-10-2919.020.816.93.13.90.959.88.5<NA>81Automn
79732021-10-3017.819.914.82.43.80.966.13.5<NA>100Automn
79742021-10-3116.420.613.62.33.41.168.010.1<NA>80Automn