Overview

Dataset statistics

Number of variables13
Number of observations7975
Missing cells5105
Missing cells (%)4.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory888.0 KiB
Average record size in memory114.0 B

Variable types

DateTime1
Numeric10
Text1
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 2 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_minHigh 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
Cloud_min has 5082 (63.7%) missing valuesMissing
Date has unique valuesUnique
Wspeed_min has 614 (7.7%) zerosZeros
Sunshine_mean has 1492 (18.7%) zerosZeros
Cloud_max has 1950 (24.5%) zerosZeros
Cloud_min has 1079 (13.5%) zerosZeros

Reproduction

Analysis started2023-12-11 20:03:13.546224
Analysis finished2023-12-11 20:03:26.232412
Duration12.69 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
2023-12-12T05:03:26.296661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:26.421568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tem_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct327
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.355661
Minimum-3
Maximum31.9
Zeros1
Zeros (%)< 0.1%
Negative17
Negative (%)0.2%
Memory size70.2 KiB
2023-12-12T05:03:26.543921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile4.2
Q19.8
median16.8
Q322.6
95-th percentile28.5
Maximum31.9
Range34.9
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation7.7089094
Coefficient of variation (CV)0.47132972
Kurtosis-1.0715227
Mean16.355661
Median Absolute Deviation (MAD)6.4
Skewness-0.059589811
Sum130436.4
Variance59.427284
MonotonicityNot monotonic
2023-12-12T05:03:26.665019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.1 50
 
0.6%
19.1 48
 
0.6%
10.7 48
 
0.6%
24.2 47
 
0.6%
8.7 46
 
0.6%
20.6 46
 
0.6%
28.8 46
 
0.6%
21.2 45
 
0.6%
19.5 42
 
0.5%
22.5 42
 
0.5%
Other values (317) 7515
94.2%
ValueCountFrequency (%)
-3.0 1
 
< 0.1%
-1.9 1
 
< 0.1%
-1.3 2
< 0.1%
-1.1 2
< 0.1%
-0.9 2
< 0.1%
-0.8 2
< 0.1%
-0.7 3
< 0.1%
-0.3 1
 
< 0.1%
-0.1 3
< 0.1%
0.0 1
 
< 0.1%
ValueCountFrequency (%)
31.9 2
< 0.1%
31.7 1
 
< 0.1%
31.5 1
 
< 0.1%
31.4 1
 
< 0.1%
31.3 1
 
< 0.1%
31.2 1
 
< 0.1%
31.1 1
 
< 0.1%
31.0 4
0.1%
30.9 4
0.1%
30.8 2
< 0.1%

Tem_max
Real number (ℝ)

HIGH CORRELATION 

Distinct357
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.182683
Minimum-1.4
Maximum36.8
Zeros1
Zeros (%)< 0.1%
Negative4
Negative (%)0.1%
Memory size70.2 KiB
2023-12-12T05:03:26.799795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.4
5-th percentile6
Q112.6
median20.1
Q325.4
95-th percentile31.5
Maximum36.8
Range38.2
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation7.9549757
Coefficient of variation (CV)0.41469567
Kurtosis-0.9372256
Mean19.182683
Median Absolute Deviation (MAD)6.2
Skewness-0.15482158
Sum152981.9
Variance63.281638
MonotonicityNot monotonic
2023-12-12T05:03:26.937354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.5 56
 
0.7%
24.8 55
 
0.7%
23.3 50
 
0.6%
26.2 47
 
0.6%
24.5 47
 
0.6%
24.7 47
 
0.6%
21.7 45
 
0.6%
22.2 45
 
0.6%
22.7 44
 
0.6%
23.7 44
 
0.6%
Other values (347) 7495
94.0%
ValueCountFrequency (%)
-1.4 1
< 0.1%
-0.8 1
< 0.1%
-0.3 1
< 0.1%
-0.2 1
< 0.1%
0.0 1
< 0.1%
0.1 2
< 0.1%
0.2 2
< 0.1%
0.3 1
< 0.1%
0.5 1
< 0.1%
0.8 2
< 0.1%
ValueCountFrequency (%)
36.8 1
 
< 0.1%
36.0 1
 
< 0.1%
35.8 1
 
< 0.1%
35.7 2
< 0.1%
35.6 2
< 0.1%
35.5 2
< 0.1%
35.4 2
< 0.1%
35.3 1
 
< 0.1%
35.2 4
0.1%
35.0 2
< 0.1%

Tem_min
Real number (ℝ)

HIGH CORRELATION 

Distinct323
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.797755
Minimum-5.3
Maximum29.8
Zeros10
Zeros (%)0.1%
Negative80
Negative (%)1.0%
Memory size70.2 KiB
2023-12-12T05:03:27.111826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.3
5-th percentile2.1
Q17
median13.9
Q320.5
95-th percentile26.1
Maximum29.8
Range35.1
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation7.7991162
Coefficient of variation (CV)0.56524528
Kurtosis-1.1643193
Mean13.797755
Median Absolute Deviation (MAD)6.8
Skewness0.021224736
Sum110037.1
Variance60.826214
MonotonicityNot monotonic
2023-12-12T05:03:27.278589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.7 48
 
0.6%
4.8 45
 
0.6%
20.8 45
 
0.6%
26.1 45
 
0.6%
5.9 44
 
0.6%
8.1 44
 
0.6%
21.2 43
 
0.5%
6.9 42
 
0.5%
7.4 42
 
0.5%
18.3 42
 
0.5%
Other values (313) 7535
94.5%
ValueCountFrequency (%)
-5.3 1
 
< 0.1%
-3.0 2
< 0.1%
-2.8 1
 
< 0.1%
-2.7 2
< 0.1%
-2.6 2
< 0.1%
-2.4 2
< 0.1%
-2.3 2
< 0.1%
-2.2 2
< 0.1%
-2.1 3
< 0.1%
-2.0 2
< 0.1%
ValueCountFrequency (%)
29.8 1
 
< 0.1%
29.4 1
 
< 0.1%
29.2 1
 
< 0.1%
29.1 1
 
< 0.1%
29.0 2
< 0.1%
28.9 1
 
< 0.1%
28.8 1
 
< 0.1%
28.7 4
0.1%
28.6 3
< 0.1%
28.5 3
< 0.1%

Wspeed_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2329781
Minimum0.6
Maximum12.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:27.396621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.6
Q12.2
median2.9
Q33.9
95-th percentile6.1
Maximum12.4
Range11.8
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.4313435
Coefficient of variation (CV)0.44273221
Kurtosis2.9165158
Mean3.2329781
Median Absolute Deviation (MAD)0.8
Skewness1.4775482
Sum25783
Variance2.0487443
MonotonicityNot monotonic
2023-12-12T05:03:27.521158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 349
 
4.4%
2.4 345
 
4.3%
2.2 341
 
4.3%
2.0 338
 
4.2%
2.3 329
 
4.1%
2.6 311
 
3.9%
1.9 308
 
3.9%
2.5 295
 
3.7%
2.9 276
 
3.5%
2.8 262
 
3.3%
Other values (91) 4821
60.5%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 3
 
< 0.1%
0.8 5
 
0.1%
0.9 5
 
0.1%
1.0 17
 
0.2%
1.1 13
 
0.2%
1.2 28
 
0.4%
1.3 42
0.5%
1.4 65
0.8%
1.5 92
1.2%
ValueCountFrequency (%)
12.4 1
< 0.1%
12.1 1
< 0.1%
11.7 1
< 0.1%
11.5 1
< 0.1%
11.3 1
< 0.1%
10.6 1
< 0.1%
10.5 2
< 0.1%
10.3 1
< 0.1%
10.0 1
< 0.1%
9.8 1
< 0.1%

Wspeed_max
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5323762
Minimum1.6
Maximum36.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:27.631127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile3.1
Q14.1
median5.2
Q36.5
95-th percentile9.3
Maximum36.3
Range34.7
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation2.0236103
Coefficient of variation (CV)0.36577598
Kurtosis11.394961
Mean5.5323762
Median Absolute Deviation (MAD)1.2
Skewness1.8526246
Sum44120.7
Variance4.0949988
MonotonicityNot monotonic
2023-12-12T05:03:27.747931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.6 211
 
2.6%
3.9 205
 
2.6%
4.3 202
 
2.5%
4.2 191
 
2.4%
4.8 189
 
2.4%
4.9 189
 
2.4%
5.1 187
 
2.3%
5.0 183
 
2.3%
3.8 182
 
2.3%
5.2 181
 
2.3%
Other values (128) 6055
75.9%
ValueCountFrequency (%)
1.6 1
 
< 0.1%
1.9 1
 
< 0.1%
2.1 5
 
0.1%
2.2 4
 
0.1%
2.3 9
 
0.1%
2.4 19
 
0.2%
2.5 26
 
0.3%
2.6 32
0.4%
2.7 49
0.6%
2.8 78
1.0%
ValueCountFrequency (%)
36.3 1
< 0.1%
26.6 1
< 0.1%
23.5 1
< 0.1%
21.9 1
< 0.1%
18.7 1
< 0.1%
17.7 1
< 0.1%
16.7 1
< 0.1%
16.6 1
< 0.1%
16.2 1
< 0.1%
16.1 2
< 0.1%

Wspeed_min
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2134796
Minimum0
Maximum9.3
Zeros614
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:27.857528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4
median0.9
Q31.6
95-th percentile3.7
Maximum9.3
Range9.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1828853
Coefficient of variation (CV)0.97478792
Kurtosis4.1417439
Mean1.2134796
Median Absolute Deviation (MAD)0.5
Skewness1.8381489
Sum9677.5
Variance1.3992176
MonotonicityNot monotonic
2023-12-12T05:03:27.959267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 614
 
7.7%
0.5 457
 
5.7%
0.7 451
 
5.7%
0.6 434
 
5.4%
0.4 423
 
5.3%
0.9 398
 
5.0%
0.3 392
 
4.9%
0.2 388
 
4.9%
0.8 386
 
4.8%
1.0 379
 
4.8%
Other values (69) 3653
45.8%
ValueCountFrequency (%)
0.0 614
7.7%
0.1 317
4.0%
0.2 388
4.9%
0.3 392
4.9%
0.4 423
5.3%
0.5 457
5.7%
0.6 434
5.4%
0.7 451
5.7%
0.8 386
4.8%
0.9 398
5.0%
ValueCountFrequency (%)
9.3 1
 
< 0.1%
8.2 1
 
< 0.1%
7.9 1
 
< 0.1%
7.8 1
 
< 0.1%
7.5 1
 
< 0.1%
7.4 2
< 0.1%
7.2 2
< 0.1%
7.1 3
< 0.1%
7.0 2
< 0.1%
6.9 4
0.1%

Humid_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct646
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.86247
Minimum18.5
Maximum99.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:28.060442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.5
5-th percentile47.2
Q158.6
median67.6
Q377.4
95-th percentile88.8
Maximum99.5
Range81
Interquartile range (IQR)18.8

Descriptive statistics

Standard deviation12.835181
Coefficient of variation (CV)0.18913519
Kurtosis-0.36557134
Mean67.86247
Median Absolute Deviation (MAD)9.4
Skewness-0.11547611
Sum541203.2
Variance164.74188
MonotonicityNot monotonic
2023-12-12T05:03:28.172839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.0 38
 
0.5%
63.3 36
 
0.5%
69.5 35
 
0.4%
63.5 35
 
0.4%
67.0 34
 
0.4%
69.3 34
 
0.4%
69.0 33
 
0.4%
59.5 33
 
0.4%
65.1 32
 
0.4%
64.5 32
 
0.4%
Other values (636) 7633
95.7%
ValueCountFrequency (%)
18.5 1
< 0.1%
18.8 1
< 0.1%
21.9 1
< 0.1%
22.8 1
< 0.1%
25.6 1
< 0.1%
26.1 2
< 0.1%
26.2 1
< 0.1%
26.8 1
< 0.1%
27.0 1
< 0.1%
27.3 1
< 0.1%
ValueCountFrequency (%)
99.5 1
< 0.1%
99.0 1
< 0.1%
98.7 1
< 0.1%
98.6 1
< 0.1%
98.3 1
< 0.1%
97.9 1
< 0.1%
97.8 1
< 0.1%
97.7 1
< 0.1%
97.5 1
< 0.1%
97.3 1
< 0.1%

Sunshine_mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct515
Distinct (%)6.5%
Missing21
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.37515345
Minimum0
Maximum0.92307692
Zeros1492
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:28.286315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.046666667
median0.37333333
Q30.69230769
95-th percentile0.81818182
Maximum0.92307692
Range0.92307692
Interquartile range (IQR)0.64564102

Descriptive statistics

Standard deviation0.30525229
Coefficient of variation (CV)0.81367315
Kurtosis-1.4948083
Mean0.37515345
Median Absolute Deviation (MAD)0.32
Skewness0.12622419
Sum2983.9705
Variance0.093178959
MonotonicityNot monotonic
2023-12-12T05:03:28.392124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1492
 
18.7%
0.8 439
 
5.5%
0.7 417
 
5.2%
0.1 313
 
3.9%
0.6 281
 
3.5%
0.4 274
 
3.4%
0.2 250
 
3.1%
0.5 226
 
2.8%
0.3 224
 
2.8%
0.9 82
 
1.0%
Other values (505) 3956
49.6%
ValueCountFrequency (%)
0.0 1492
18.7%
0.006666667 22
 
0.3%
0.007142857 8
 
0.1%
0.007692308 25
 
0.3%
0.008333333 24
 
0.3%
0.009090909 50
 
0.6%
0.013333333 20
 
0.3%
0.014285714 2
 
< 0.1%
0.015384615 12
 
0.2%
0.016666667 11
 
0.1%
ValueCountFrequency (%)
0.923076923 1
 
< 0.1%
0.907692308 2
 
< 0.1%
0.9 82
1.0%
0.893333333 1
 
< 0.1%
0.892307692 3
 
< 0.1%
0.891666667 5
 
0.1%
0.886666667 3
 
< 0.1%
0.884615385 6
 
0.1%
0.883333333 5
 
0.1%
0.881818182 3
 
< 0.1%
Distinct208
Distinct (%)2.6%
Missing1
Missing (%)< 0.1%
Memory size62.4 KiB
2023-12-12T05:03:28.691333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3066215
Min length3

Characters and Unicode

Total characters50289
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row 0.94
2nd row 0.23
3rd row 0.50
4th row 1.00
5th row 0.42
ValueCountFrequency (%)
10.00 2664
33.4%
8.00 726
 
9.1%
9.00 679
 
8.5%
7.00 298
 
3.7%
5.00 192
 
2.4%
6.00 178
 
2.2%
4.00 95
 
1.2%
3.00 85
 
1.1%
73
 
0.9%
2.00 59
 
0.7%
Other values (198) 2925
36.7%
2023-12-12T05:03:29.160886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15948
31.7%
0 14600
29.0%
. 7901
15.7%
1 4744
 
9.4%
8 1247
 
2.5%
9 1132
 
2.3%
7 875
 
1.7%
5 841
 
1.7%
6 778
 
1.5%
4 734
 
1.5%
Other values (3) 1489
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26367
52.4%
Space Separator 15948
31.7%
Other Punctuation 7901
 
15.7%
Dash Punctuation 73
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14600
55.4%
1 4744
 
18.0%
8 1247
 
4.7%
9 1132
 
4.3%
7 875
 
3.3%
5 841
 
3.2%
6 778
 
3.0%
4 734
 
2.8%
3 708
 
2.7%
2 708
 
2.7%
Space Separator
ValueCountFrequency (%)
15948
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7901
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50289
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15948
31.7%
0 14600
29.0%
. 7901
15.7%
1 4744
 
9.4%
8 1247
 
2.5%
9 1132
 
2.3%
7 875
 
1.7%
5 841
 
1.7%
6 778
 
1.5%
4 734
 
1.5%
Other values (3) 1489
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15948
31.7%
0 14600
29.0%
. 7901
15.7%
1 4744
 
9.4%
8 1247
 
2.5%
9 1132
 
2.3%
7 875
 
1.7%
5 841
 
1.7%
6 778
 
1.5%
4 734
 
1.5%
Other values (3) 1489
 
3.0%

Cloud_max
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.0032606
Minimum0
Maximum10
Zeros1950
Zeros (%)24.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:29.289128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.8359681
Coefficient of variation (CV)0.76669365
Kurtosis-1.5264876
Mean5.0032606
Median Absolute Deviation (MAD)4
Skewness-0.048060711
Sum39896
Variance14.714651
MonotonicityNot monotonic
2023-12-12T05:03:29.390819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1950
24.5%
10 1663
20.9%
8 782
9.8%
6 669
 
8.4%
3 668
 
8.4%
7 493
 
6.2%
2 410
 
5.1%
9 381
 
4.8%
4 378
 
4.7%
5 300
 
3.8%
ValueCountFrequency (%)
0 1950
24.5%
1 280
 
3.5%
2 410
 
5.1%
3 668
 
8.4%
4 378
 
4.7%
5 300
 
3.8%
6 669
 
8.4%
7 493
 
6.2%
8 782
9.8%
9 381
 
4.8%
ValueCountFrequency (%)
10 1663
20.9%
9 381
 
4.8%
8 782
9.8%
7 493
 
6.2%
6 669
8.4%
5 300
 
3.8%
4 378
 
4.7%
3 668
8.4%
2 410
 
5.1%
1 280
 
3.5%

Cloud_min
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.4%
Missing5082
Missing (%)63.7%
Infinite0
Infinite (%)0.0%
Mean3.0535776
Minimum0
Maximum10
Zeros1079
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:29.489841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1034879
Coefficient of variation (CV)1.0163449
Kurtosis-0.65685773
Mean3.0535776
Median Absolute Deviation (MAD)3
Skewness0.68896277
Sum8834
Variance9.6316374
MonotonicityNot monotonic
2023-12-12T05:03:29.627445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1079
 
13.5%
3 346
 
4.3%
2 287
 
3.6%
6 250
 
3.1%
5 203
 
2.5%
4 200
 
2.5%
8 188
 
2.4%
10 147
 
1.8%
7 86
 
1.1%
1 79
 
1.0%
(Missing) 5082
63.7%
ValueCountFrequency (%)
0 1079
13.5%
1 79
 
1.0%
2 287
 
3.6%
3 346
 
4.3%
4 200
 
2.5%
5 203
 
2.5%
6 250
 
3.1%
7 86
 
1.1%
8 188
 
2.4%
9 28
 
0.4%
ValueCountFrequency (%)
10 147
1.8%
9 28
 
0.4%
8 188
2.4%
7 86
 
1.1%
6 250
3.1%
5 203
2.5%
4 200
2.5%
3 346
4.3%
2 287
3.6%
1 79
 
1.0%

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

2023-12-12T05:03:29.740958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:03:29.839581image/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

2023-12-12T05:03:24.405016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:15.836736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.747363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.627572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.573338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.406789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.446143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.212508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.152706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.265558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.537004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:15.936470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.837639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.715528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.674287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.497618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.517828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.297071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.234090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.359520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.645957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.046135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.919198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.802461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.758843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.580247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.592927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.392700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.356454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.492235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.749316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.144775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.008636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.900564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.846361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.659348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.666429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.478908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.459544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.617017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.838988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.235703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.109927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.995747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.924964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.734180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.734020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.559015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.551980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.709571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.951762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.329072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.196935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.115951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.006894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.046928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.808043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.652089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.683632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.823624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:25.065096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.410403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.279083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.204350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.085276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.115023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.874285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.760266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.793087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.942895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:25.167615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.493954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.385586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.322329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.177751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.199192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.963619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.869896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.934302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.060232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:25.258741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.576251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.466411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.405984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.252478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.286391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.056983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.958033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.048274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.163787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:25.373441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:16.656121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:17.543937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:18.490504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:19.323715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:20.359902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:21.138490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:22.041590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:23.158199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:24.268041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:03:29.930037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9690.9720.4770.2190.5070.5360.2940.3330.3960.794
Tem_max0.9691.0000.9230.4720.2180.5280.5100.3320.3300.4190.776
Tem_min0.9720.9231.0000.4580.2170.4610.5450.2240.2930.3630.793
Wspeed_mean0.4770.4720.4581.0000.7630.8670.2500.3220.2870.3700.299
Wspeed_max0.2190.2180.2170.7631.0000.4500.0960.1900.1420.2260.173
Wspeed_min0.5070.5280.4610.8670.4501.0000.2390.2750.2670.3570.341
Humid_mean0.5360.5100.5450.2500.0960.2391.0000.4560.3180.5510.462
Sunshine_mean0.2940.3320.2240.3220.1900.2750.4561.0000.5750.7370.249
Cloud_max0.3330.3300.2930.2870.1420.2670.3180.5751.0000.5610.228
Cloud_min0.3960.4190.3630.3700.2260.3570.5510.7370.5611.0000.265
Season0.7940.7760.7930.2990.1730.3410.4620.2490.2280.2651.000
2023-12-12T05:03:30.105238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.9870.989-0.320-0.188-0.4000.4760.166-0.039-0.0300.616
Tem_max0.9871.0000.958-0.332-0.188-0.4190.4290.227-0.075-0.0980.591
Tem_min0.9890.9581.000-0.296-0.177-0.3690.5170.0990.0030.0450.613
Wspeed_mean-0.320-0.332-0.2961.0000.8610.725-0.171-0.2320.1670.2350.183
Wspeed_max-0.188-0.188-0.1770.8611.0000.482-0.050-0.2540.1540.2620.111
Wspeed_min-0.400-0.419-0.3690.7250.4821.000-0.255-0.1490.0860.1360.210
Humid_mean0.4760.4290.517-0.171-0.050-0.2551.000-0.4040.2560.4380.294
Sunshine_mean0.1660.2270.099-0.232-0.254-0.149-0.4041.000-0.585-0.7720.151
Cloud_max-0.039-0.0750.0030.1670.1540.0860.256-0.5851.0000.5610.129
Cloud_min-0.030-0.0980.0450.2350.2620.1360.438-0.7720.5611.0000.159
Season0.6160.5910.6130.1830.1110.2100.2940.1510.1290.1591.000

Missing values

2023-12-12T05:03:25.530972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:03:25.790646image/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.
2023-12-12T05:03:26.170542image/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
02000-01-0111.915.78.84.07.31.766.00.80.9440Winter
12000-01-0211.116.68.04.68.80.567.10.30.2391Winter
22000-01-036.98.74.03.86.31.557.20.70.5082Winter
32000-01-049.015.03.63.86.72.056.30.91.0030Winter
42000-01-0513.816.99.04.48.00.571.20.20.4290Winter
52000-01-0610.714.26.46.08.62.277.50.00.03107Winter
62000-01-073.85.72.87.69.54.255.20.30.3596Winter
72000-01-085.16.73.12.95.11.360.20.00.20108Winter
82000-01-097.69.16.33.96.92.083.00.00.061010Winter
92000-01-107.89.95.53.96.41.466.30.40.51103Winter
DateTem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanSolar_meanCloud_maxCloud_minSeason
79652021-10-2216.319.015.05.48.43.558.80.510.001<NA>Automn
79662021-10-2316.219.912.22.77.10.859.70.8583337.000<NA>Automn
79672021-10-2417.120.413.82.34.70.768.80.0916677.005<NA>Automn
79682021-10-2517.220.714.22.34.20.867.80.7166679.000<NA>Automn
79692021-10-2616.021.012.32.63.21.762.80.79166710.000<NA>Automn
79702021-10-2717.521.813.92.94.60.467.30.6416679.000<NA>Automn
79712021-10-2816.720.713.42.23.90.961.50.8258.000<NA>Automn
79722021-10-2918.321.116.13.37.31.370.30.6833336.000<NA>Automn
79732021-10-3017.521.414.92.24.10.873.80.0916678.000<NA>Automn
79742021-10-3117.221.813.62.33.91.268.60.8510.000<NA>Automn