Overview

Dataset statistics

Number of variables13
Number of observations7975
Missing cells10392
Missing cells (%)10.0%
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 2 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 3 other fieldsHigh correlation
Sunshine_mean is highly overall correlated with Cloud_max and 1 other fieldsHigh correlation
Cloud_max is highly overall correlated with Humid_mean and 2 other fieldsHigh correlation
Cloud_min is highly overall correlated with Humid_mean and 2 other fieldsHigh correlation
Season is highly overall correlated with Tem_mean and 2 other fieldsHigh correlation
Solar_mean has 7975 (100.0%) missing valuesMissing
Cloud_max has 1194 (15.0%) missing valuesMissing
Cloud_min has 1194 (15.0%) missing valuesMissing
Date has unique valuesUnique
Solar_mean is an unsupported type, check if it needs cleaning or further analysisUnsupported
Wspeed_min has 615 (7.7%) zerosZeros
Sunshine_mean has 1057 (13.3%) zerosZeros
Cloud_max has 109 (1.4%) zerosZeros
Cloud_min has 2552 (32.0%) zerosZeros

Reproduction

Analysis started2023-12-11 20:04:12.504848
Analysis finished2023-12-11 20:04:26.953106
Duration14.45 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
2023-12-12T05:04:27.079974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:27.311341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tem_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct320
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.134194
Minimum-4.4
Maximum31.1
Zeros2
Zeros (%)< 0.1%
Negative14
Negative (%)0.2%
Memory size70.2 KiB
2023-12-12T05:04:27.507091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.4
5-th percentile5.4
Q111.2
median17.6
Q323.1
95-th percentile28.1
Maximum31.1
Range35.5
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation7.2404296
Coefficient of variation (CV)0.42257193
Kurtosis-0.9936341
Mean17.134194
Median Absolute Deviation (MAD)6
Skewness-0.15959359
Sum136645.2
Variance52.423821
MonotonicityNot monotonic
2023-12-12T05:04:27.709368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.8 49
 
0.6%
22.4 48
 
0.6%
22.5 48
 
0.6%
21.2 47
 
0.6%
24.1 47
 
0.6%
21.1 46
 
0.6%
22.2 44
 
0.6%
15.4 44
 
0.6%
20.2 44
 
0.6%
23.9 43
 
0.5%
Other values (310) 7515
94.2%
ValueCountFrequency (%)
-4.4 1
 
< 0.1%
-1.8 1
 
< 0.1%
-1.4 1
 
< 0.1%
-1.3 1
 
< 0.1%
-1.2 1
 
< 0.1%
-0.9 1
 
< 0.1%
-0.8 1
 
< 0.1%
-0.6 1
 
< 0.1%
-0.4 3
< 0.1%
-0.3 1
 
< 0.1%
ValueCountFrequency (%)
31.1 1
 
< 0.1%
31.0 4
0.1%
30.9 1
 
< 0.1%
30.8 1
 
< 0.1%
30.7 2
 
< 0.1%
30.6 2
 
< 0.1%
30.4 2
 
< 0.1%
30.3 7
0.1%
30.2 3
< 0.1%
30.1 7
0.1%

Tem_max
Real number (ℝ)

HIGH CORRELATION 

Distinct331
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.059812
Minimum-2
Maximum35.5
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size70.2 KiB
2023-12-12T05:04:27.875843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile8.3
Q114.3
median20.7
Q325.8
95-th percentile30.7
Maximum35.5
Range37.5
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0635555
Coefficient of variation (CV)0.35212471
Kurtosis-0.87825876
Mean20.059812
Median Absolute Deviation (MAD)5.6
Skewness-0.21647832
Sum159977
Variance49.893816
MonotonicityNot monotonic
2023-12-12T05:04:28.046610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.2 55
 
0.7%
23.8 53
 
0.7%
27.4 53
 
0.7%
22.8 52
 
0.7%
24.7 52
 
0.7%
24.5 52
 
0.7%
25.3 50
 
0.6%
25.6 49
 
0.6%
26.4 48
 
0.6%
24.1 47
 
0.6%
Other values (321) 7464
93.6%
ValueCountFrequency (%)
-2.0 1
 
< 0.1%
0.2 1
 
< 0.1%
0.6 1
 
< 0.1%
1.0 1
 
< 0.1%
1.2 2
< 0.1%
1.5 1
 
< 0.1%
1.7 1
 
< 0.1%
1.8 2
< 0.1%
2.0 3
< 0.1%
2.1 2
< 0.1%
ValueCountFrequency (%)
35.5 1
 
< 0.1%
35.4 1
 
< 0.1%
34.7 1
 
< 0.1%
34.6 1
 
< 0.1%
34.5 1
 
< 0.1%
34.1 1
 
< 0.1%
33.9 1
 
< 0.1%
33.8 2
< 0.1%
33.7 2
< 0.1%
33.6 3
< 0.1%

Tem_min
Real number (ℝ)

HIGH CORRELATION 

Distinct317
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.484451
Minimum-6.2
Maximum28.7
Zeros12
Zeros (%)0.2%
Negative88
Negative (%)1.1%
Memory size70.2 KiB
2023-12-12T05:04:28.236459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2
5-th percentile2.4
Q18.1
median14.8
Q320.9
95-th percentile26.2
Maximum28.7
Range34.9
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation7.6450088
Coefficient of variation (CV)0.52780796
Kurtosis-1.0922795
Mean14.484451
Median Absolute Deviation (MAD)6.4
Skewness-0.08973559
Sum115513.5
Variance58.446159
MonotonicityNot monotonic
2023-12-12T05:04:28.428535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.7 45
 
0.6%
6.9 44
 
0.6%
6.5 44
 
0.6%
16.4 43
 
0.5%
9.6 43
 
0.5%
18.6 42
 
0.5%
20.5 42
 
0.5%
23.5 40
 
0.5%
20.3 40
 
0.5%
21.9 40
 
0.5%
Other values (307) 7552
94.7%
ValueCountFrequency (%)
-6.2 1
 
< 0.1%
-4.0 1
 
< 0.1%
-3.8 1
 
< 0.1%
-3.3 2
< 0.1%
-3.0 1
 
< 0.1%
-2.9 2
< 0.1%
-2.8 2
< 0.1%
-2.7 1
 
< 0.1%
-2.5 1
 
< 0.1%
-2.4 4
0.1%
ValueCountFrequency (%)
28.7 3
 
< 0.1%
28.5 2
 
< 0.1%
28.4 1
 
< 0.1%
28.3 1
 
< 0.1%
28.2 7
0.1%
28.1 4
 
0.1%
28.0 9
0.1%
27.9 6
 
0.1%
27.8 15
0.2%
27.7 14
0.2%

Wspeed_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4527273
Minimum0.2
Maximum12.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:28.624791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.1
Q11.7
median2.2
Q33
95-th percentile4.5
Maximum12.3
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.0944406
Coefficient of variation (CV)0.44621372
Kurtosis4.5484311
Mean2.4527273
Median Absolute Deviation (MAD)0.6
Skewness1.5349444
Sum19560.5
Variance1.1978001
MonotonicityNot monotonic
2023-12-12T05:04:28.834199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 385
 
4.8%
2.1 375
 
4.7%
2.2 373
 
4.7%
1.8 362
 
4.5%
1.7 356
 
4.5%
2.3 335
 
4.2%
1.6 329
 
4.1%
1.9 329
 
4.1%
1.5 325
 
4.1%
2.5 323
 
4.1%
Other values (76) 4483
56.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.4 2
 
< 0.1%
0.5 2
 
< 0.1%
0.6 5
 
0.1%
0.7 19
 
0.2%
0.8 40
 
0.5%
0.9 76
 
1.0%
1.0 123
1.5%
1.1 144
1.8%
1.2 218
2.7%
ValueCountFrequency (%)
12.3 1
 
< 0.1%
10.2 1
 
< 0.1%
10.0 1
 
< 0.1%
9.7 4
0.1%
9.6 1
 
< 0.1%
9.0 1
 
< 0.1%
8.4 2
< 0.1%
8.3 1
 
< 0.1%
8.1 1
 
< 0.1%
8.0 1
 
< 0.1%

Wspeed_max
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3195361
Minimum0.8
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:29.066452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile2.1
Q13.2
median4.1
Q35.2
95-th percentile7.3
Maximum19
Range18.2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.650054
Coefficient of variation (CV)0.38199797
Kurtosis4.7833061
Mean4.3195361
Median Absolute Deviation (MAD)1
Skewness1.3879702
Sum34448.3
Variance2.7226782
MonotonicityNot monotonic
2023-12-12T05:04:29.261307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3 240
 
3.0%
3.5 232
 
2.9%
3.9 229
 
2.9%
3.8 222
 
2.8%
3.3 221
 
2.8%
3.0 214
 
2.7%
3.2 213
 
2.7%
3.7 213
 
2.7%
3.4 211
 
2.6%
4.2 208
 
2.6%
Other values (112) 5772
72.4%
ValueCountFrequency (%)
0.8 1
 
< 0.1%
1.2 1
 
< 0.1%
1.3 6
 
0.1%
1.4 7
 
0.1%
1.5 22
 
0.3%
1.6 26
 
0.3%
1.7 27
 
0.3%
1.8 64
0.8%
1.9 81
1.0%
2.0 82
1.0%
ValueCountFrequency (%)
19.0 1
< 0.1%
18.2 1
< 0.1%
16.2 1
< 0.1%
15.7 1
< 0.1%
15.5 2
< 0.1%
15.0 1
< 0.1%
14.7 1
< 0.1%
14.5 1
< 0.1%
13.8 2
< 0.1%
13.4 1
< 0.1%

Wspeed_min
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86542947
Minimum0
Maximum7.2
Zeros615
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:29.479772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median0.6
Q31.1
95-th percentile2.6
Maximum7.2
Range7.2
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.84373782
Coefficient of variation (CV)0.9749354
Kurtosis6.3510255
Mean0.86542947
Median Absolute Deviation (MAD)0.4
Skewness2.0965477
Sum6901.8
Variance0.71189352
MonotonicityNot monotonic
2023-12-12T05:04:29.675451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 632
 
7.9%
0.0 615
 
7.7%
0.5 595
 
7.5%
0.4 595
 
7.5%
0.2 591
 
7.4%
0.6 540
 
6.8%
0.7 506
 
6.3%
0.1 469
 
5.9%
0.8 449
 
5.6%
0.9 396
 
5.0%
Other values (55) 2587
32.4%
ValueCountFrequency (%)
0.0 615
7.7%
0.1 469
5.9%
0.2 591
7.4%
0.3 632
7.9%
0.4 595
7.5%
0.5 595
7.5%
0.6 540
6.8%
0.7 506
6.3%
0.8 449
5.6%
0.9 396
5.0%
ValueCountFrequency (%)
7.2 1
< 0.1%
7.0 1
< 0.1%
6.9 1
< 0.1%
6.8 2
< 0.1%
6.5 1
< 0.1%
6.3 1
< 0.1%
6.2 1
< 0.1%
6.1 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%

Humid_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct665
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.581179
Minimum22.7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:29.841058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile46.7
Q159.2
median69.9
Q382.7
95-th percentile95
Maximum99
Range76.3
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation15.084074
Coefficient of variation (CV)0.21371242
Kurtosis-0.73291775
Mean70.581179
Median Absolute Deviation (MAD)11.6
Skewness-0.048910125
Sum562884.9
Variance227.5293
MonotonicityNot monotonic
2023-12-12T05:04:30.012328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.0 101
 
1.3%
60.5 42
 
0.5%
62.8 35
 
0.4%
65.3 35
 
0.4%
66.5 32
 
0.4%
59.0 31
 
0.4%
63.8 30
 
0.4%
58.5 30
 
0.4%
62.5 30
 
0.4%
64.0 29
 
0.4%
Other values (655) 7580
95.0%
ValueCountFrequency (%)
22.7 1
< 0.1%
23.4 1
< 0.1%
24.4 1
< 0.1%
25.3 1
< 0.1%
26.6 1
< 0.1%
27.3 1
< 0.1%
29.1 1
< 0.1%
29.5 2
< 0.1%
29.8 1
< 0.1%
30.0 2
< 0.1%
ValueCountFrequency (%)
99.0 101
1.3%
98.9 12
 
0.2%
98.8 21
 
0.3%
98.7 12
 
0.2%
98.6 12
 
0.2%
98.5 9
 
0.1%
98.4 2
 
< 0.1%
98.3 13
 
0.2%
98.2 4
 
0.1%
98.1 6
 
0.1%

Sunshine_mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)1.7%
Missing29
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean5.7294739
Minimum0
Maximum13.3
Zeros1057
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:30.474308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.6
median6.2
Q39.3
95-th percentile11.6
Maximum13.3
Range13.3
Interquartile range (IQR)7.7

Descriptive statistics

Standard deviation4.0552108
Coefficient of variation (CV)0.70778065
Kurtosis-1.3800349
Mean5.7294739
Median Absolute Deviation (MAD)3.6
Skewness-0.097184258
Sum45526.4
Variance16.444735
MonotonicityNot monotonic
2023-12-12T05:04:30.685000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1057
 
13.3%
0.1 159
 
2.0%
9.5 96
 
1.2%
9.2 95
 
1.2%
0.3 91
 
1.1%
9.6 90
 
1.1%
9.3 89
 
1.1%
10.8 87
 
1.1%
9.0 87
 
1.1%
0.2 84
 
1.1%
Other values (123) 6011
75.4%
ValueCountFrequency (%)
0.0 1057
13.3%
0.1 159
 
2.0%
0.2 84
 
1.1%
0.3 91
 
1.1%
0.4 53
 
0.7%
0.5 59
 
0.7%
0.6 46
 
0.6%
0.7 58
 
0.7%
0.8 51
 
0.6%
0.9 47
 
0.6%
ValueCountFrequency (%)
13.3 2
 
< 0.1%
13.2 2
 
< 0.1%
13.0 5
 
0.1%
12.9 14
0.2%
12.8 10
 
0.1%
12.7 8
 
0.1%
12.6 24
0.3%
12.5 31
0.4%
12.4 33
0.4%
12.3 29
0.4%

Solar_mean
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

Cloud_max
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing1194
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean8.2039522
Minimum0
Maximum10
Zeros109
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:30.847485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q310
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4217833
Coefficient of variation (CV)0.29519715
Kurtosis1.3016763
Mean8.2039522
Median Absolute Deviation (MAD)0
Skewness-1.3922913
Sum55631
Variance5.8650345
MonotonicityNot monotonic
2023-12-12T05:04:30.995997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 3468
43.5%
8 874
 
11.0%
7 527
 
6.6%
6 457
 
5.7%
9 453
 
5.7%
5 337
 
4.2%
4 245
 
3.1%
3 187
 
2.3%
0 109
 
1.4%
2 101
 
1.3%
(Missing) 1194
 
15.0%
ValueCountFrequency (%)
0 109
 
1.4%
1 23
 
0.3%
2 101
 
1.3%
3 187
 
2.3%
4 245
 
3.1%
5 337
 
4.2%
6 457
5.7%
7 527
6.6%
8 874
11.0%
9 453
5.7%
ValueCountFrequency (%)
10 3468
43.5%
9 453
 
5.7%
8 874
 
11.0%
7 527
 
6.6%
6 457
 
5.7%
5 337
 
4.2%
4 245
 
3.1%
3 187
 
2.3%
2 101
 
1.3%
1 23
 
0.3%

Cloud_min
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing1194
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean3.0126825
Minimum0
Maximum10
Zeros2552
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:04:31.140648image/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 deviation3.170404
Coefficient of variation (CV)1.0523525
Kurtosis-0.4966697
Mean3.0126825
Median Absolute Deviation (MAD)2
Skewness0.79477032
Sum20429
Variance10.051462
MonotonicityNot monotonic
2023-12-12T05:04:31.288701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2552
32.0%
3 788
 
9.9%
2 704
 
8.8%
4 499
 
6.3%
6 491
 
6.2%
10 458
 
5.7%
5 399
 
5.0%
7 274
 
3.4%
1 259
 
3.2%
8 250
 
3.1%
(Missing) 1194
15.0%
ValueCountFrequency (%)
0 2552
32.0%
1 259
 
3.2%
2 704
 
8.8%
3 788
 
9.9%
4 499
 
6.3%
5 399
 
5.0%
6 491
 
6.2%
7 274
 
3.4%
8 250
 
3.1%
9 107
 
1.3%
ValueCountFrequency (%)
10 458
5.7%
9 107
 
1.3%
8 250
 
3.1%
7 274
 
3.4%
6 491
6.2%
5 399
5.0%
4 499
6.3%
3 788
9.9%
2 704
8.8%
1 259
 
3.2%

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

2023-12-12T05:04:31.425210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:04:31.543658image/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

2023-12-12T05:04:25.403364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:14.659945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.823168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.177011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.309561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.468060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.362947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.380568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.629451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.033424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.502213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:14.772201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.973186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.299706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.428768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.552403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.486106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.484691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.775603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.144894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.596438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:14.901345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.089858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.418822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.741540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.634400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.575818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.638530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.901060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.259758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.700880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.035160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.243679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.536309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.830372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.727001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.667081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.766314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.009383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.365548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.805742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.146646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.377849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.651447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.912261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.810390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.769400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.863993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.192472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.478549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.909506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.243023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.510555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.757542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.995952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.893687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.865023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.987464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.356259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.600583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:26.001783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.348435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.642646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.851058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.081590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.989979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.973102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.131179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.484327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:24.709565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:26.110614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.495844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.783381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.961348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.198542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.102678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.086268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.253512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.609929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.076442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:26.229406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.635324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:16.915421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.073365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.307138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.193754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.194816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.385735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.758531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.195204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:26.335062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:15.729082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:17.054917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:18.190316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:19.394037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:20.277765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:21.282092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:22.505022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:23.903265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:25.309976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:04:31.635756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9760.9760.1390.1430.1770.5570.3650.3250.3840.792
Tem_max0.9761.0000.9410.1330.1350.1350.5180.3520.2940.3650.773
Tem_min0.9760.9411.0000.1500.1190.1950.5740.3530.3170.3820.787
Wspeed_mean0.1390.1330.1501.0000.9240.8920.2570.1610.1900.2410.093
Wspeed_max0.1430.1350.1190.9241.0000.6850.2230.1410.1630.2210.116
Wspeed_min0.1770.1350.1950.8920.6851.0000.1970.1510.1320.2260.117
Humid_mean0.5570.5180.5740.2570.2230.1971.0000.5310.5270.5550.511
Sunshine_mean0.3650.3520.3530.1610.1410.1510.5311.0000.6560.7150.363
Cloud_max0.3250.2940.3170.1900.1630.1320.5270.6561.0000.5710.194
Cloud_min0.3840.3650.3820.2410.2210.2260.5550.7150.5711.0000.282
Season0.7920.7730.7870.0930.1160.1170.5110.3630.1940.2821.000
2023-12-12T05:04:31.780559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9890.992-0.022-0.069-0.0190.5470.0190.1060.1430.612
Tem_max0.9891.0000.968-0.021-0.066-0.0220.4730.1050.0390.0640.588
Tem_min0.9920.9681.000-0.017-0.067-0.0130.596-0.0500.1570.2090.606
Wspeed_mean-0.022-0.021-0.0171.0000.8790.685-0.163-0.038-0.0380.1390.056
Wspeed_max-0.069-0.066-0.0670.8791.0000.473-0.156-0.036-0.0000.1390.069
Wspeed_min-0.019-0.022-0.0130.6850.4731.000-0.127-0.013-0.0740.0530.070
Humid_mean0.5470.4730.596-0.163-0.156-0.1271.000-0.4830.5120.5020.330
Sunshine_mean0.0190.105-0.050-0.038-0.036-0.013-0.4831.000-0.682-0.7310.224
Cloud_max0.1060.0390.157-0.038-0.000-0.0740.512-0.6821.0000.5540.116
Cloud_min0.1430.0640.2090.1390.1390.0530.502-0.7310.5541.0000.164
Season0.6120.5880.6060.0560.0690.0700.3300.2240.1160.1641.000

Missing values

2023-12-12T05:04:26.464920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:04:26.732470image/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.
2023-12-12T05:04:26.883132image/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
02000-01-0113.416.710.23.74.82.962.86.7<NA>80Winter
12000-01-0213.017.68.72.46.00.066.26.0<NA>70Winter
22000-01-0310.113.27.32.03.40.665.35.9<NA>70Winter
32000-01-0411.414.37.52.12.81.367.03.2<NA>80Winter
42000-01-0515.016.912.72.34.91.078.30.1<NA>104Winter
52000-01-0613.916.311.32.85.10.779.70.8<NA>105Winter
62000-01-075.210.92.83.98.01.151.19.0<NA>50Winter
72000-01-086.89.54.11.53.20.063.70.0<NA>103Winter
82000-01-098.710.07.33.75.10.982.80.0<NA>1010Winter
92000-01-1011.013.79.22.04.60.671.36.6<NA>103Winter
DateTem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanSolar_meanCloud_maxCloud_minSeason
79652021-10-2215.719.613.51.53.20.750.79.7<NA>90Automn
79662021-10-2316.220.412.12.23.30.651.69.5<NA>80Automn
79672021-10-2418.020.016.11.92.51.260.11.3<NA>105Automn
79682021-10-2517.821.214.71.62.50.764.310.0<NA>100Automn
79692021-10-2616.920.614.11.42.40.469.49.8<NA>80Automn
79702021-10-2717.522.213.31.63.00.156.59.8<NA>80Automn
79712021-10-2817.321.013.81.72.90.262.89.7<NA>80Automn
79722021-10-2918.421.216.93.04.21.968.19.5<NA>80Automn
79732021-10-3017.720.714.91.92.90.670.33.0<NA>100Automn
79742021-10-3117.322.313.31.53.10.262.49.7<NA>70Automn