Overview

Dataset statistics

Number of variables13
Number of observations7975
Missing cells2278
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory849.0 KiB
Average record size in memory109.0 B

Variable types

DateTime1
Numeric5
Text6
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 Humid_mean and 1 other fieldsHigh correlation
Humid_mean is highly overall correlated with Tem_meanHigh 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_meanHigh correlation
Cloud_max has 1119 (14.0%) missing valuesMissing
Cloud_min has 1119 (14.0%) missing valuesMissing
Date has unique valuesUnique
Sunshine_mean has 997 (12.5%) zerosZeros
Cloud_max has 91 (1.1%) zerosZeros
Cloud_min has 2421 (30.4%) zerosZeros

Reproduction

Analysis started2023-12-11 20:03:34.963920
Analysis finished2023-12-11 20:03:39.715620
Duration4.75 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
2023-12-12T05:03:39.802246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:39.957331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Tem_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct315
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.839009
Minimum-4
Maximum30.4
Zeros2
Zeros (%)< 0.1%
Negative20
Negative (%)0.3%
Memory size70.2 KiB
2023-12-12T05:03:40.121030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile4
Q19.7
median16.2
Q322
95-th percentile27.3
Maximum30.4
Range34.4
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation7.4060316
Coefficient of variation (CV)0.46758174
Kurtosis-1.0673053
Mean15.839009
Median Absolute Deviation (MAD)6.1
Skewness-0.08439537
Sum126316.1
Variance54.849305
MonotonicityNot monotonic
2023-12-12T05:03:40.275228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.6 50
 
0.6%
22.0 48
 
0.6%
20.1 44
 
0.6%
21.0 44
 
0.6%
18.9 43
 
0.5%
12.7 43
 
0.5%
10.2 43
 
0.5%
10.9 43
 
0.5%
18.7 43
 
0.5%
23.2 43
 
0.5%
Other values (305) 7531
94.4%
ValueCountFrequency (%)
-4.0 1
 
< 0.1%
-1.5 1
 
< 0.1%
-1.4 1
 
< 0.1%
-1.3 1
 
< 0.1%
-1.1 1
 
< 0.1%
-1.0 1
 
< 0.1%
-0.8 2
< 0.1%
-0.6 1
 
< 0.1%
-0.5 3
< 0.1%
-0.4 3
< 0.1%
ValueCountFrequency (%)
30.4 1
 
< 0.1%
30.3 1
 
< 0.1%
30.0 1
 
< 0.1%
29.9 3
< 0.1%
29.8 2
< 0.1%
29.7 1
 
< 0.1%
29.6 1
 
< 0.1%
29.5 2
< 0.1%
29.4 4
0.1%
29.3 3
< 0.1%

Tem_max
Text

Distinct339
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2023-12-12T05:03:40.724560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8270846
Min length3

Characters and Unicode

Total characters46471
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

Unique16 ?
Unique (%)0.2%

Sample

1st row 16.5
2nd row 14.1
3rd row 7.5
4th row 13.0
5th row 15.8
ValueCountFrequency (%)
19.6 53
 
0.7%
24.7 48
 
0.6%
24.2 47
 
0.6%
24.3 46
 
0.6%
23.2 45
 
0.6%
24.5 45
 
0.6%
23.0 44
 
0.6%
14.8 43
 
0.5%
15.4 43
 
0.5%
25.2 43
 
0.5%
Other values (327) 7518
94.3%
2023-12-12T05:03:41.393464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15946
34.3%
. 7974
17.2%
2 4827
 
10.4%
1 4462
 
9.6%
3 1949
 
4.2%
6 1657
 
3.6%
8 1644
 
3.5%
4 1618
 
3.5%
0 1613
 
3.5%
7 1608
 
3.5%
Other values (3) 3173
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22546
48.5%
Space Separator 15946
34.3%
Other Punctuation 7974
 
17.2%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4827
21.4%
1 4462
19.8%
3 1949
8.6%
6 1657
 
7.3%
8 1644
 
7.3%
4 1618
 
7.2%
0 1613
 
7.2%
7 1608
 
7.1%
5 1600
 
7.1%
9 1568
 
7.0%
Space Separator
ValueCountFrequency (%)
15946
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7974
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15946
34.3%
. 7974
17.2%
2 4827
 
10.4%
1 4462
 
9.6%
3 1949
 
4.2%
6 1657
 
3.6%
8 1644
 
3.5%
4 1618
 
3.5%
0 1613
 
3.5%
7 1608
 
3.5%
Other values (3) 3173
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15946
34.3%
. 7974
17.2%
2 4827
 
10.4%
1 4462
 
9.6%
3 1949
 
4.2%
6 1657
 
3.6%
8 1644
 
3.5%
4 1618
 
3.5%
0 1613
 
3.5%
7 1608
 
3.5%
Other values (3) 3173
 
6.8%

Tem_min
Text

Distinct314
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2023-12-12T05:03:41.871011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6327273
Min length3

Characters and Unicode

Total characters44921
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

Unique16 ?
Unique (%)0.2%

Sample

1st row 8.5
2nd row 7.4
3rd row 5.2
4th row 5.9
5th row 11.4
ValueCountFrequency (%)
5.8 50
 
0.6%
7.3 46
 
0.6%
18.9 46
 
0.6%
17.2 46
 
0.6%
8.1 45
 
0.6%
2.8 45
 
0.6%
17.3 43
 
0.5%
18.6 43
 
0.5%
19.5 43
 
0.5%
22.7 43
 
0.5%
Other values (272) 7525
94.4%
2023-12-12T05:03:42.471015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15862
35.3%
. 7967
17.7%
1 4745
 
10.6%
2 3611
 
8.0%
5 1758
 
3.9%
4 1651
 
3.7%
3 1642
 
3.7%
6 1585
 
3.5%
0 1530
 
3.4%
7 1517
 
3.4%
Other values (3) 3053
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20996
46.7%
Space Separator 15862
35.3%
Other Punctuation 7967
 
17.7%
Dash Punctuation 96
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4745
22.6%
2 3611
17.2%
5 1758
 
8.4%
4 1651
 
7.9%
3 1642
 
7.8%
6 1585
 
7.5%
0 1530
 
7.3%
7 1517
 
7.2%
8 1513
 
7.2%
9 1444
 
6.9%
Space Separator
ValueCountFrequency (%)
15862
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7967
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44921
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15862
35.3%
. 7967
17.7%
1 4745
 
10.6%
2 3611
 
8.0%
5 1758
 
3.9%
4 1651
 
3.7%
3 1642
 
3.7%
6 1585
 
3.5%
0 1530
 
3.4%
7 1517
 
3.4%
Other values (3) 3053
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15862
35.3%
. 7967
17.7%
1 4745
 
10.6%
2 3611
 
8.0%
5 1758
 
3.9%
4 1651
 
3.7%
3 1642
 
3.7%
6 1585
 
3.5%
0 1530
 
3.4%
7 1517
 
3.4%
Other values (3) 3053
 
6.8%

Wspeed_mean
Text

Distinct226
Distinct (%)2.8%
Missing11
Missing (%)0.1%
Memory size62.4 KiB
2023-12-12T05:03:42.994469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1780512
Min length3

Characters and Unicode

Total characters41238
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

Unique21 ?
Unique (%)0.3%

Sample

1st row 4.9
2nd row 8.9
3rd row 9.9
4th row 5.2
5th row 7.2
ValueCountFrequency (%)
3.9 135
 
1.7%
4.8 134
 
1.7%
4.2 132
 
1.7%
5.1 129
 
1.6%
5.2 129
 
1.6%
3.2 129
 
1.6%
4.4 128
 
1.6%
3.6 127
 
1.6%
4.6 126
 
1.6%
3.5 125
 
1.6%
Other values (216) 6670
83.8%
2023-12-12T05:03:43.582266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15928
38.6%
. 7962
19.3%
1 2516
 
6.1%
3 2114
 
5.1%
4 2111
 
5.1%
5 1955
 
4.7%
6 1726
 
4.2%
2 1708
 
4.1%
7 1529
 
3.7%
8 1362
 
3.3%
Other values (3) 2327
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17346
42.1%
Space Separator 15928
38.6%
Other Punctuation 7962
19.3%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2516
14.5%
3 2114
12.2%
4 2111
12.2%
5 1955
11.3%
6 1726
10.0%
2 1708
9.8%
7 1529
8.8%
8 1362
7.9%
9 1235
7.1%
0 1090
6.3%
Space Separator
ValueCountFrequency (%)
15928
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7962
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41238
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15928
38.6%
. 7962
19.3%
1 2516
 
6.1%
3 2114
 
5.1%
4 2111
 
5.1%
5 1955
 
4.7%
6 1726
 
4.2%
2 1708
 
4.1%
7 1529
 
3.7%
8 1362
 
3.3%
Other values (3) 2327
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41238
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15928
38.6%
. 7962
19.3%
1 2516
 
6.1%
3 2114
 
5.1%
4 2111
 
5.1%
5 1955
 
4.7%
6 1726
 
4.2%
2 1708
 
4.1%
7 1529
 
3.7%
8 1362
 
3.3%
Other values (3) 2327
 
5.6%

Wspeed_max
Text

Distinct279
Distinct (%)3.5%
Missing11
Missing (%)0.1%
Memory size62.4 KiB
2023-12-12T05:03:44.100808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.4288046
Min length3

Characters and Unicode

Total characters43235
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

Unique33 ?
Unique (%)0.4%

Sample

1st row 8.5
2nd row 17.0
3rd row 15.5
4th row 8.0
5th row 9.8
ValueCountFrequency (%)
8.6 103
 
1.3%
6.3 99
 
1.2%
7.0 99
 
1.2%
7.6 98
 
1.2%
5.9 98
 
1.2%
7.4 96
 
1.2%
6.8 96
 
1.2%
9.0 94
 
1.2%
7.1 94
 
1.2%
7.2 93
 
1.2%
Other values (269) 6994
87.8%
2023-12-12T05:03:44.832386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15928
36.8%
. 7962
18.4%
1 4259
 
9.9%
6 1959
 
4.5%
7 1903
 
4.4%
5 1828
 
4.2%
8 1770
 
4.1%
2 1690
 
3.9%
9 1630
 
3.8%
0 1489
 
3.4%
Other values (3) 2817
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19343
44.7%
Space Separator 15928
36.8%
Other Punctuation 7962
18.4%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4259
22.0%
6 1959
10.1%
7 1903
9.8%
5 1828
9.5%
8 1770
9.2%
2 1690
 
8.7%
9 1630
 
8.4%
0 1489
 
7.7%
4 1487
 
7.7%
3 1328
 
6.9%
Space Separator
ValueCountFrequency (%)
15928
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7962
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43235
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15928
36.8%
. 7962
18.4%
1 4259
 
9.9%
6 1959
 
4.5%
7 1903
 
4.4%
5 1828
 
4.2%
8 1770
 
4.1%
2 1690
 
3.9%
9 1630
 
3.8%
0 1489
 
3.4%
Other values (3) 2817
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43235
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15928
36.8%
. 7962
18.4%
1 4259
 
9.9%
6 1959
 
4.5%
7 1903
 
4.4%
5 1828
 
4.2%
8 1770
 
4.1%
2 1690
 
3.9%
9 1630
 
3.8%
0 1489
 
3.4%
Other values (3) 2817
 
6.5%

Wspeed_min
Text

Distinct184
Distinct (%)2.3%
Missing11
Missing (%)0.1%
Memory size62.4 KiB
2023-12-12T05:03:45.218145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9544199
Min length3

Characters and Unicode

Total characters39457
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

Unique21 ?
Unique (%)0.3%

Sample

1st row 1.2
2nd row 1.0
3rd row 6.0
4th row 2.5
5th row 3.3
ValueCountFrequency (%)
374
 
4.7%
1.2 200
 
2.5%
1.1 194
 
2.4%
1.0 184
 
2.3%
0.8 175
 
2.2%
1.4 172
 
2.2%
0.7 172
 
2.2%
1.5 168
 
2.1%
0.9 164
 
2.1%
1.3 161
 
2.0%
Other values (174) 6000
75.3%
2023-12-12T05:03:45.844439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15928
40.4%
. 7590
19.2%
1 2916
 
7.4%
2 2127
 
5.4%
0 1963
 
5.0%
3 1755
 
4.4%
4 1540
 
3.9%
5 1322
 
3.4%
6 1090
 
2.8%
7 1007
 
2.6%
Other values (3) 2219
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 15928
40.4%
Decimal Number 15565
39.4%
Other Punctuation 7590
19.2%
Dash Punctuation 374
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2916
18.7%
2 2127
13.7%
0 1963
12.6%
3 1755
11.3%
4 1540
9.9%
5 1322
8.5%
6 1090
 
7.0%
7 1007
 
6.5%
8 976
 
6.3%
9 869
 
5.6%
Space Separator
ValueCountFrequency (%)
15928
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7590
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39457
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15928
40.4%
. 7590
19.2%
1 2916
 
7.4%
2 2127
 
5.4%
0 1963
 
5.0%
3 1755
 
4.4%
4 1540
 
3.9%
5 1322
 
3.4%
6 1090
 
2.8%
7 1007
 
2.6%
Other values (3) 2219
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15928
40.4%
. 7590
19.2%
1 2916
 
7.4%
2 2127
 
5.4%
0 1963
 
5.0%
3 1755
 
4.4%
4 1540
 
3.9%
5 1322
 
3.4%
6 1090
 
2.8%
7 1007
 
2.6%
Other values (3) 2219
 
5.6%

Humid_mean
Real number (ℝ)

HIGH CORRELATION 

Distinct583
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.479699
Minimum27.4
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:46.048119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27.4
5-th percentile53.5
Q163.65
median74.5
Q385.5
95-th percentile95.6
Maximum100
Range72.6
Interquartile range (IQR)21.85

Descriptive statistics

Standard deviation13.403498
Coefficient of variation (CV)0.17996177
Kurtosis-0.8843036
Mean74.479699
Median Absolute Deviation (MAD)10.9
Skewness-0.063509604
Sum593975.6
Variance179.65377
MonotonicityNot monotonic
2023-12-12T05:03:46.550494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.5 35
 
0.4%
61.5 34
 
0.4%
82.5 34
 
0.4%
67.8 33
 
0.4%
62.8 33
 
0.4%
67.3 33
 
0.4%
66.8 32
 
0.4%
63.3 32
 
0.4%
62.3 32
 
0.4%
84.5 31
 
0.4%
Other values (573) 7646
95.9%
ValueCountFrequency (%)
27.4 1
< 0.1%
31.2 1
< 0.1%
35.9 1
< 0.1%
36.2 1
< 0.1%
36.3 1
< 0.1%
36.7 1
< 0.1%
37.4 1
< 0.1%
37.7 2
< 0.1%
38.3 1
< 0.1%
38.4 2
< 0.1%
ValueCountFrequency (%)
100.0 26
0.3%
99.9 4
 
0.1%
99.8 9
 
0.1%
99.7 3
 
< 0.1%
99.6 3
 
< 0.1%
99.5 6
 
0.1%
99.4 12
0.2%
99.3 11
0.1%
99.2 2
 
< 0.1%
99.1 7
 
0.1%

Sunshine_mean
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct135
Distinct (%)1.7%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.4825263
Minimum0
Maximum13.4
Zeros997
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:46.696885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2
median5.5
Q39.3
95-th percentile11.9
Maximum13.4
Range13.4
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation4.1939995
Coefficient of variation (CV)0.76497572
Kurtosis-1.4200549
Mean5.4825263
Median Absolute Deviation (MAD)4
Skewness0.088059701
Sum43706.7
Variance17.589632
MonotonicityNot monotonic
2023-12-12T05:03:46.872653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 997
 
12.5%
0.1 184
 
2.3%
0.2 121
 
1.5%
0.3 109
 
1.4%
10.3 87
 
1.1%
10.9 82
 
1.0%
0.7 79
 
1.0%
9.2 79
 
1.0%
0.4 78
 
1.0%
0.6 77
 
1.0%
Other values (125) 6079
76.2%
ValueCountFrequency (%)
0.0 997
12.5%
0.1 184
 
2.3%
0.2 121
 
1.5%
0.3 109
 
1.4%
0.4 78
 
1.0%
0.5 76
 
1.0%
0.6 77
 
1.0%
0.7 79
 
1.0%
0.8 74
 
0.9%
0.9 68
 
0.9%
ValueCountFrequency (%)
13.4 3
 
< 0.1%
13.3 7
 
0.1%
13.2 13
 
0.2%
13.1 13
 
0.2%
13.0 17
0.2%
12.9 18
0.2%
12.8 17
0.2%
12.7 31
0.4%
12.6 24
0.3%
12.5 35
0.4%

Solar_mean
Text

Distinct223
Distinct (%)2.8%
Missing4
Missing (%)0.1%
Memory size62.4 KiB
2023-12-12T05:03:47.395803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9996236
Min length3

Characters and Unicode

Total characters47823
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

Unique5 ?
Unique (%)0.1%

Sample

1st row 0.67
2nd row 0.34
3rd row 0.59
4th row 0.41
5th row 0.31
ValueCountFrequency (%)
0.23 60
 
0.8%
0.34 59
 
0.7%
0.26 59
 
0.7%
1.22 59
 
0.7%
1.32 58
 
0.7%
1.16 57
 
0.7%
0.77 57
 
0.7%
0.27 57
 
0.7%
0.43 57
 
0.7%
1.60 56
 
0.7%
Other values (213) 7392
92.7%
2023-12-12T05:03:48.036409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15942
33.3%
. 7970
16.7%
1 5511
 
11.5%
0 5456
 
11.4%
2 1958
 
4.1%
3 1728
 
3.6%
4 1703
 
3.6%
6 1659
 
3.5%
5 1604
 
3.4%
7 1556
 
3.3%
Other values (3) 2736
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23910
50.0%
Space Separator 15942
33.3%
Other Punctuation 7970
 
16.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5511
23.0%
0 5456
22.8%
2 1958
 
8.2%
3 1728
 
7.2%
4 1703
 
7.1%
6 1659
 
6.9%
5 1604
 
6.7%
7 1556
 
6.5%
8 1423
 
6.0%
9 1312
 
5.5%
Space Separator
ValueCountFrequency (%)
15942
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7970
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15942
33.3%
. 7970
16.7%
1 5511
 
11.5%
0 5456
 
11.4%
2 1958
 
4.1%
3 1728
 
3.6%
4 1703
 
3.6%
6 1659
 
3.5%
5 1604
 
3.4%
7 1556
 
3.3%
Other values (3) 2736
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15942
33.3%
. 7970
16.7%
1 5511
 
11.5%
0 5456
 
11.4%
2 1958
 
4.1%
3 1728
 
3.6%
4 1703
 
3.6%
6 1659
 
3.5%
5 1604
 
3.4%
7 1556
 
3.3%
Other values (3) 2736
 
5.7%

Cloud_max
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct12
Distinct (%)0.2%
Missing1119
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean8.4709743
Minimum0
Maximum11
Zeros91
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:48.215523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median10
Q310
95-th percentile10
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2428824
Coefficient of variation (CV)0.26477266
Kurtosis2.5873751
Mean8.4709743
Median Absolute Deviation (MAD)0
Skewness-1.7181613
Sum58077
Variance5.0305214
MonotonicityNot monotonic
2023-12-12T05:03:48.389404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 3646
45.7%
8 1005
 
12.6%
9 669
 
8.4%
7 404
 
5.1%
6 345
 
4.3%
5 246
 
3.1%
4 211
 
2.6%
3 119
 
1.5%
2 97
 
1.2%
0 91
 
1.1%
Other values (2) 23
 
0.3%
(Missing) 1119
 
14.0%
ValueCountFrequency (%)
0 91
 
1.1%
1 22
 
0.3%
2 97
 
1.2%
3 119
 
1.5%
4 211
 
2.6%
5 246
 
3.1%
6 345
 
4.3%
7 404
5.1%
8 1005
12.6%
9 669
8.4%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 3646
45.7%
9 669
 
8.4%
8 1005
 
12.6%
7 404
 
5.1%
6 345
 
4.3%
5 246
 
3.1%
4 211
 
2.6%
3 119
 
1.5%
2 97
 
1.2%

Cloud_min
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing1119
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean3.2539382
Minimum0
Maximum10
Zeros2421
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size70.2 KiB
2023-12-12T05:03:48.532520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2475518
Coefficient of variation (CV)0.99803735
Kurtosis-0.84071455
Mean3.2539382
Median Absolute Deviation (MAD)3
Skewness0.63220979
Sum22309
Variance10.546593
MonotonicityNot monotonic
2023-12-12T05:03:48.700129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2421
30.4%
2 712
 
8.9%
3 703
 
8.8%
6 534
 
6.7%
4 504
 
6.3%
10 429
 
5.4%
8 403
 
5.1%
5 379
 
4.8%
7 356
 
4.5%
1 260
 
3.3%
(Missing) 1119
14.0%
ValueCountFrequency (%)
0 2421
30.4%
1 260
 
3.3%
2 712
 
8.9%
3 703
 
8.8%
4 504
 
6.3%
5 379
 
4.8%
6 534
 
6.7%
7 356
 
4.5%
8 403
 
5.1%
9 155
 
1.9%
ValueCountFrequency (%)
10 429
5.4%
9 155
 
1.9%
8 403
5.1%
7 356
4.5%
6 534
6.7%
5 379
4.8%
4 504
6.3%
3 703
8.8%
2 712
8.9%
1 260
 
3.3%

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

2023-12-12T05:03:48.857035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:03:48.987047image/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

2023-12-12T05:03:38.233921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:35.804882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.414691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.970911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.537617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.362349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:35.889714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.529463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.088754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.632779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.585883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.023236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.644418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.187589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.755508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.804682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.140531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.744405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.324816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.939915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.938462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.300705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:36.872971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:37.434212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:03:38.082745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:03:49.073819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.5800.3690.3620.3780.790
Humid_mean0.5801.0000.3570.4170.4210.526
Sunshine_mean0.3690.3571.0000.6520.7150.344
Cloud_max0.3620.4170.6521.0000.5630.215
Cloud_min0.3780.4210.7150.5631.0000.291
Season0.7900.5260.3440.2150.2911.000
2023-12-12T05:03:49.201692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Tem_meanHumid_meanSunshine_meanCloud_maxCloud_minSeason
Tem_mean1.0000.5920.191-0.001-0.0060.610
Humid_mean0.5921.000-0.2490.3880.3310.342
Sunshine_mean0.191-0.2491.000-0.661-0.7470.212
Cloud_max-0.0010.388-0.6611.0000.5360.130
Cloud_min-0.0060.331-0.7470.5361.0000.171
Season0.6100.3420.2120.1300.1711.000

Missing values

2023-12-12T05:03:39.144070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:03:39.421303image/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.
2023-12-12T05:03:39.603092image/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
02000-01-0112.316.58.54.98.51.261.58.80.6740Winter
12000-01-0210.614.17.48.917.01.071.92.00.34102Winter
22000-01-036.57.55.29.915.56.062.06.00.59100Winter
32000-01-0410.113.05.95.28.02.567.33.10.4190Winter
42000-01-0514.215.811.47.29.83.378.71.70.31104Winter
52000-01-0610.214.75.613.820.25.082.20.00.08107Winter
62000-01-073.35.32.215.319.78.863.33.30.4597Winter
72000-01-084.96.22.76.09.73.263.90.00.25106Winter
82000-01-097.18.94.94.47.81.086.90.00.121010Winter
92000-01-107.48.65.210.216.55.766.85.90.61102Winter
DateTem_meanTem_maxTem_minWspeed_meanWspeed_maxWspeed_minHumid_meanSunshine_meanSolar_meanCloud_maxCloud_minSeason
79652021-10-2215.917.614.910.814.27.558.49.01.1790Automn
79662021-10-2315.417.712.05.58.52.157.210.21.3260Automn
79672021-10-2417.320.114.84.75.92.566.92.70.73104Automn
79682021-10-2516.617.815.15.88.24.069.610.31.3690Automn
79692021-10-2616.117.914.55.27.13.866.89.91.3280Automn
79702021-10-2717.619.916.17.810.45.365.39.71.1570Automn
79712021-10-2816.118.313.76.710.92.760.79.91.3580Automn
79722021-10-2918.122.414.13.85.11.467.19.51.1680Automn
79732021-10-3017.120.015.73.35.80.868.32.50.50101Automn
79742021-10-3117.519.516.15.07.13.669.010.41.2970Automn