Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory116.3 B

Variable types

Categorical5
Numeric8

Alerts

관측소코드 is highly overall correlated with 관측소명 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 관측소코드 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 관측소코드 and 3 other fieldsHigh correlation
주소 is highly overall correlated with 관측소코드 and 3 other fieldsHigh correlation
관측소명 is highly overall correlated with 관측소코드 and 3 other fieldsHigh correlation
평균습도(%) is highly overall correlated with 평균온도(℃) and 2 other fieldsHigh correlation
평균온도(℃) is highly overall correlated with 평균습도(%) and 2 other fieldsHigh correlation
평균이슬점온도(℃) is highly overall correlated with 최저기온(℃)High correlation
최고기온(℃) is highly overall correlated with 평균습도(%) and 2 other fieldsHigh correlation
최저기온(℃) is highly overall correlated with 평균온도(℃) and 2 other fieldsHigh correlation
일조량 is highly overall correlated with 평균습도(%)High correlation
일조량 has 11 (11.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:55.025700
Analysis finished2023-12-10 13:22:08.845413
Duration13.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
8000090
31 
8000100
31 
8000101
31 
8000105
7 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8000090
2nd row8000090
3rd row8000090
4th row8000090
5th row8000090

Common Values

ValueCountFrequency (%)
8000090 31
31.0%
8000100 31
31.0%
8000101 31
31.0%
8000105 7
 
7.0%

Length

2023-12-10T22:22:08.935907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:09.084120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8000090 31
31.0%
8000100 31
31.0%
8000101 31
31.0%
8000105 7
 
7.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
속초(기)
31 
대관령(기)
31 
춘천(기)
31 
강릉(기)
7 

Length

Max length6
Median length5
Mean length5.31
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row속초(기)
2nd row속초(기)
3rd row속초(기)
4th row속초(기)
5th row속초(기)

Common Values

ValueCountFrequency (%)
속초(기) 31
31.0%
대관령(기) 31
31.0%
춘천(기) 31
31.0%
강릉(기) 7
 
7.0%

Length

2023-12-10T22:22:09.257587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:09.426051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
속초(기 31
31.0%
대관령(기 31
31.0%
춘천(기 31
31.0%
강릉(기 7
 
7.0%

위도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
128.56472
31 
128.758698
31 
127.7357
31 
128.89098
7 

Length

Max length10
Median length9
Mean length9
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row128.56472
2nd row128.56472
3rd row128.56472
4th row128.56472
5th row128.56472

Common Values

ValueCountFrequency (%)
128.56472 31
31.0%
128.758698 31
31.0%
127.7357 31
31.0%
128.89098 7
 
7.0%

Length

2023-12-10T22:22:09.639606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:09.826470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128.56472 31
31.0%
128.758698 31
31.0%
127.7357 31
31.0%
128.89098 7
 
7.0%

경도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38.25085
31 
37.686924
31 
37.90256
31 
37.75147
7 

Length

Max length9
Median length8
Mean length8.31
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38.25085
2nd row38.25085
3rd row38.25085
4th row38.25085
5th row38.25085

Common Values

ValueCountFrequency (%)
38.25085 31
31.0%
37.686924 31
31.0%
37.90256 31
31.0%
37.75147 7
 
7.0%

Length

2023-12-10T22:22:10.037529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:10.218836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38.25085 31
31.0%
37.686924 31
31.0%
37.90256 31
31.0%
37.75147 7
 
7.0%

주소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도 고성군 토성면 봉포리
31 
(산간)강원도 평창군 대관령면 횡계리
31 
강원도 춘천시 우두동
31 
강원도 강릉시 용강동
7 

Length

Max length20
Median length15
Mean length15.03
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원도 고성군 토성면 봉포리
2nd row강원도 고성군 토성면 봉포리
3rd row강원도 고성군 토성면 봉포리
4th row강원도 고성군 토성면 봉포리
5th row강원도 고성군 토성면 봉포리

Common Values

ValueCountFrequency (%)
강원도 고성군 토성면 봉포리 31
31.0%
(산간)강원도 평창군 대관령면 횡계리 31
31.0%
강원도 춘천시 우두동 31
31.0%
강원도 강릉시 용강동 7
 
7.0%

Length

2023-12-10T22:22:10.433723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:10.603570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 69
19.1%
고성군 31
8.6%
토성면 31
8.6%
봉포리 31
8.6%
산간)강원도 31
8.6%
평창군 31
8.6%
대관령면 31
8.6%
횡계리 31
8.6%
춘천시 31
8.6%
우두동 31
8.6%
Other values (2) 14
 
3.9%

관측일자
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200516
Minimum20200501
Maximum20200531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:10.782295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200501
5-th percentile20200502
Q120200508
median20200516
Q320200523
95-th percentile20200530
Maximum20200531
Range30
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation9.0064848
Coefficient of variation (CV)4.458542 × 10-7
Kurtosis-1.2330797
Mean20200516
Median Absolute Deviation (MAD)8
Skewness-0.017872677
Sum2.0200516 × 109
Variance81.116768
MonotonicityNot monotonic
2023-12-10T22:22:11.030241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20200519 4
 
4.0%
20200503 4
 
4.0%
20200522 4
 
4.0%
20200505 4
 
4.0%
20200504 4
 
4.0%
20200523 4
 
4.0%
20200524 4
 
4.0%
20200531 3
 
3.0%
20200525 3
 
3.0%
20200518 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20200501 3
3.0%
20200502 3
3.0%
20200503 4
4.0%
20200504 4
4.0%
20200505 4
4.0%
20200506 3
3.0%
20200507 3
3.0%
20200508 3
3.0%
20200509 3
3.0%
20200510 3
3.0%
ValueCountFrequency (%)
20200531 3
3.0%
20200530 3
3.0%
20200529 3
3.0%
20200528 3
3.0%
20200527 3
3.0%
20200526 3
3.0%
20200525 3
3.0%
20200524 4
4.0%
20200523 4
4.0%
20200522 4
4.0%

평균습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.309
Minimum23.8
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:11.599664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.8
5-th percentile43.825
Q163.375
median75.2
Q382.525
95-th percentile94.91
Maximum98
Range74.2
Interquartile range (IQR)19.15

Descriptive statistics

Standard deviation15.968891
Coefficient of variation (CV)0.22084237
Kurtosis0.68634188
Mean72.309
Median Absolute Deviation (MAD)8.9
Skewness-0.84494377
Sum7230.9
Variance255.00547
MonotonicityNot monotonic
2023-12-10T22:22:11.831894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.4 3
 
3.0%
67.6 2
 
2.0%
81.1 2
 
2.0%
78.4 2
 
2.0%
88.9 2
 
2.0%
63.4 1
 
1.0%
83.5 1
 
1.0%
61.8 1
 
1.0%
43.9 1
 
1.0%
72.8 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
23.8 1
1.0%
25.1 1
1.0%
32.3 1
1.0%
34.3 1
1.0%
42.4 1
1.0%
43.9 1
1.0%
45.6 1
1.0%
47.9 1
1.0%
49.0 1
1.0%
49.1 1
1.0%
ValueCountFrequency (%)
98.0 1
1.0%
97.6 1
1.0%
97.5 1
1.0%
95.8 1
1.0%
95.1 1
1.0%
94.9 1
1.0%
94.0 1
1.0%
93.9 1
1.0%
91.6 1
1.0%
91.5 1
1.0%

평균온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.068
Minimum5.6
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:12.128890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile10.28
Q113.175
median16.2
Q318.45
95-th percentile21.71
Maximum25.5
Range19.9
Interquartile range (IQR)5.275

Descriptive statistics

Standard deviation3.8321047
Coefficient of variation (CV)0.23849295
Kurtosis-0.090747771
Mean16.068
Median Absolute Deviation (MAD)2.8
Skewness-0.062699163
Sum1606.8
Variance14.685026
MonotonicityNot monotonic
2023-12-10T22:22:12.401426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.1 5
 
5.0%
14.7 3
 
3.0%
16.5 3
 
3.0%
14.1 3
 
3.0%
10.3 2
 
2.0%
12.0 2
 
2.0%
17.3 2
 
2.0%
21.2 2
 
2.0%
17.7 2
 
2.0%
17.2 2
 
2.0%
Other values (65) 74
74.0%
ValueCountFrequency (%)
5.6 1
1.0%
7.6 1
1.0%
7.8 1
1.0%
9.7 1
1.0%
9.9 1
1.0%
10.3 2
2.0%
10.6 1
1.0%
10.7 1
1.0%
11.4 1
1.0%
11.5 1
1.0%
ValueCountFrequency (%)
25.5 1
1.0%
24.8 1
1.0%
23.1 1
1.0%
23.0 1
1.0%
21.9 1
1.0%
21.7 1
1.0%
21.6 1
1.0%
21.5 1
1.0%
21.3 1
1.0%
21.2 2
2.0%

평균풍속(m/s)
Real number (ℝ)

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.257
Minimum0.9
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:12.681542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.195
Q11.5
median1.8
Q32.4
95-th percentile5.515
Maximum8.8
Range7.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.4021092
Coefficient of variation (CV)0.62122692
Kurtosis7.203144
Mean2.257
Median Absolute Deviation (MAD)0.4
Skewness2.5632985
Sum225.7
Variance1.9659101
MonotonicityNot monotonic
2023-12-10T22:22:13.057109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1.7 10
 
10.0%
1.2 9
 
9.0%
1.5 8
 
8.0%
2.0 8
 
8.0%
1.8 7
 
7.0%
2.1 5
 
5.0%
1.9 5
 
5.0%
1.3 4
 
4.0%
1.4 4
 
4.0%
2.4 4
 
4.0%
Other values (24) 36
36.0%
ValueCountFrequency (%)
0.9 1
 
1.0%
1.0 1
 
1.0%
1.1 3
 
3.0%
1.2 9
9.0%
1.3 4
 
4.0%
1.4 4
 
4.0%
1.5 8
8.0%
1.6 4
 
4.0%
1.7 10
10.0%
1.8 7
7.0%
ValueCountFrequency (%)
8.8 1
1.0%
7.3 1
1.0%
7.2 1
1.0%
5.9 1
1.0%
5.8 1
1.0%
5.5 1
1.0%
5.4 1
1.0%
4.4 1
1.0%
4.3 1
1.0%
4.0 1
1.0%

평균이슬점온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.09
Minimum-1.5
Maximum16.2
Zeros1
Zeros (%)1.0%
Negative2
Negative (%)2.0%
Memory size1.0 KiB
2023-12-10T22:22:13.393021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.5
5-th percentile2.66
Q18
median11.1
Q312.925
95-th percentile14.905
Maximum16.2
Range17.7
Interquartile range (IQR)4.925

Descriptive statistics

Standard deviation3.9309829
Coefficient of variation (CV)0.38959196
Kurtosis0.42293478
Mean10.09
Median Absolute Deviation (MAD)2.35
Skewness-0.94398156
Sum1009
Variance15.452626
MonotonicityNot monotonic
2023-12-10T22:22:13.701891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 4
 
4.0%
13.1 3
 
3.0%
12.7 3
 
3.0%
13.5 3
 
3.0%
11.1 3
 
3.0%
8.5 3
 
3.0%
14.7 2
 
2.0%
12.5 2
 
2.0%
3.7 2
 
2.0%
11.2 2
 
2.0%
Other values (65) 73
73.0%
ValueCountFrequency (%)
-1.5 1
1.0%
-0.4 1
1.0%
0.0 1
1.0%
0.2 1
1.0%
1.9 1
1.0%
2.7 1
1.0%
3.6 1
1.0%
3.7 2
2.0%
4.5 1
1.0%
5.0 1
1.0%
ValueCountFrequency (%)
16.2 2
2.0%
15.4 1
1.0%
15.2 1
1.0%
15.0 1
1.0%
14.9 1
1.0%
14.7 2
2.0%
14.6 1
1.0%
14.5 1
1.0%
14.3 1
1.0%
14.0 1
1.0%

최고기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.353
Minimum9
Maximum32.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:14.047811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14.37
Q117.55
median21.9
Q324.725
95-th percentile28.605
Maximum32.4
Range23.4
Interquartile range (IQR)7.175

Descriptive statistics

Standard deviation4.6840036
Coefficient of variation (CV)0.21936045
Kurtosis-0.50677421
Mean21.353
Median Absolute Deviation (MAD)3.3
Skewness-0.087984532
Sum2135.3
Variance21.93989
MonotonicityNot monotonic
2023-12-10T22:22:14.288383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.4 4
 
4.0%
22.3 4
 
4.0%
21.7 3
 
3.0%
25.2 3
 
3.0%
21.1 2
 
2.0%
16.9 2
 
2.0%
16.8 2
 
2.0%
19.3 2
 
2.0%
24.6 2
 
2.0%
13.8 2
 
2.0%
Other values (68) 74
74.0%
ValueCountFrequency (%)
9.0 1
1.0%
11.1 1
1.0%
13.6 1
1.0%
13.8 2
2.0%
14.4 1
1.0%
14.5 1
1.0%
14.6 1
1.0%
14.9 1
1.0%
15.2 1
1.0%
15.6 1
1.0%
ValueCountFrequency (%)
32.4 1
1.0%
30.1 1
1.0%
29.3 1
1.0%
29.1 1
1.0%
28.7 1
1.0%
28.6 1
1.0%
28.2 1
1.0%
28.1 1
1.0%
28.0 1
1.0%
27.7 1
1.0%

최저기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.323
Minimum0.6
Maximum20.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:14.535030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile3.985
Q18.875
median12.1
Q314.1
95-th percentile16.815
Maximum20.6
Range20
Interquartile range (IQR)5.225

Descriptive statistics

Standard deviation4.1121612
Coefficient of variation (CV)0.36316888
Kurtosis-0.018309057
Mean11.323
Median Absolute Deviation (MAD)2.65
Skewness-0.46124134
Sum1132.3
Variance16.90987
MonotonicityNot monotonic
2023-12-10T22:22:14.797752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.0 3
 
3.0%
7.0 3
 
3.0%
11.7 3
 
3.0%
12.5 3
 
3.0%
14.8 3
 
3.0%
9.3 3
 
3.0%
8.3 2
 
2.0%
4.0 2
 
2.0%
8.6 2
 
2.0%
9.5 2
 
2.0%
Other values (65) 74
74.0%
ValueCountFrequency (%)
0.6 1
1.0%
1.3 1
1.0%
1.9 1
1.0%
3.0 1
1.0%
3.7 1
1.0%
4.0 2
2.0%
4.4 1
1.0%
4.5 1
1.0%
4.6 1
1.0%
5.6 1
1.0%
ValueCountFrequency (%)
20.6 1
1.0%
19.3 1
1.0%
19.1 1
1.0%
18.2 1
1.0%
17.1 1
1.0%
16.8 1
1.0%
16.7 1
1.0%
16.4 1
1.0%
16.3 1
1.0%
16.2 1
1.0%

일조량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.048
Minimum0
Maximum14
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:15.061807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.875
median7.5
Q310.625
95-th percentile13.4
Maximum14
Range14
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation4.3386094
Coefficient of variation (CV)0.61558022
Kurtosis-1.0830947
Mean7.048
Median Absolute Deviation (MAD)3.4
Skewness-0.1667306
Sum704.8
Variance18.823531
MonotonicityNot monotonic
2023-12-10T22:22:15.318737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
11.0%
7.7 3
 
3.0%
8.2 3
 
3.0%
13.4 3
 
3.0%
5.7 3
 
3.0%
10.5 2
 
2.0%
8.9 2
 
2.0%
5.9 2
 
2.0%
0.1 2
 
2.0%
3.9 2
 
2.0%
Other values (63) 67
67.0%
ValueCountFrequency (%)
0.0 11
11.0%
0.1 2
 
2.0%
0.2 1
 
1.0%
1.7 1
 
1.0%
1.8 1
 
1.0%
2.0 1
 
1.0%
2.1 1
 
1.0%
2.2 1
 
1.0%
2.7 1
 
1.0%
2.9 1
 
1.0%
ValueCountFrequency (%)
14.0 2
2.0%
13.9 1
 
1.0%
13.5 1
 
1.0%
13.4 3
3.0%
13.3 1
 
1.0%
13.1 1
 
1.0%
13.0 1
 
1.0%
12.9 1
 
1.0%
12.7 1
 
1.0%
12.5 1
 
1.0%

Interactions

2023-12-10T22:22:06.980783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.157646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.382838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.750796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.054556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.367290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:03.978219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:05.384655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:07.126291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.326398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.544428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.958944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.330201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.615697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:04.113561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:05.545546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:07.276252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.477553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.721548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.105784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.667176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.778479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:04.248093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:05.852996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:07.421821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.632330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.882519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.246114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.837026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.942803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:04.384311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:06.047428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:07.666250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.788640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.044014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.408535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.998975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:03.151567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:04.552234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:06.264616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:07.810440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.957566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.198365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.562577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.169913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:03.402482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:04.795455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:06.487740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:08.026526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.080187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.328544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.723161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.859092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:03.668176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:05.011895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:06.651897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:08.214495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.240791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.504670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.877286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.145136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:03.827879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:05.193921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:06.819923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:22:15.493348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
관측소코드1.0001.0001.0001.0001.0000.0000.1020.5160.3240.2380.2620.5180.000
관측소명1.0001.0001.0001.0001.0000.0000.1020.5160.3240.2380.2620.5180.000
위도1.0001.0001.0001.0001.0000.0000.1020.5160.3240.2380.2620.5180.000
경도1.0001.0001.0001.0001.0000.0000.1020.5160.3240.2380.2620.5180.000
주소1.0001.0001.0001.0001.0000.0000.1020.5160.3240.2380.2620.5180.000
관측일자0.0000.0000.0000.0000.0001.0000.4430.2600.2680.6320.6300.3590.665
평균습도(%)0.1020.1020.1020.1020.1020.4431.0000.6260.6290.7380.6720.0430.495
평균온도(℃)0.5160.5160.5160.5160.5160.2600.6261.0000.4170.6150.9200.8260.196
평균풍속(m/s)0.3240.3240.3240.3240.3240.2680.6290.4171.0000.5480.0000.0000.000
평균이슬점온도(℃)0.2380.2380.2380.2380.2380.6320.7380.6150.5481.0000.3690.7700.473
최고기온(℃)0.2620.2620.2620.2620.2620.6300.6720.9200.0000.3691.0000.6930.471
최저기온(℃)0.5180.5180.5180.5180.5180.3590.0430.8260.0000.7700.6931.0000.000
일조량0.0000.0000.0000.0000.0000.6650.4950.1960.0000.4730.4710.0001.000
2023-12-10T22:22:15.723533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드경도위도주소관측소명
관측소코드1.0001.0001.0001.0001.000
경도1.0001.0001.0001.0001.000
위도1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.000
관측소명1.0001.0001.0001.0001.000
2023-12-10T22:22:15.889557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량관측소코드관측소명위도경도주소
관측일자1.0000.106-0.068-0.3110.010-0.088-0.1240.0950.0000.0000.0000.0000.000
평균습도(%)0.1061.000-0.5880.0670.339-0.648-0.244-0.6290.0510.0510.0510.0510.051
평균온도(℃)-0.068-0.5881.000-0.2680.4670.8870.8360.2960.3220.3220.3220.3220.322
평균풍속(m/s)-0.3110.067-0.2681.000-0.279-0.286-0.1320.0240.1900.1900.1900.1900.190
평균이슬점온도(℃)0.0100.3390.467-0.2791.0000.2720.652-0.2910.1360.1360.1360.1360.136
최고기온(℃)-0.088-0.6480.887-0.2860.2721.0000.6010.4220.1510.1510.1510.1510.151
최저기온(℃)-0.124-0.2440.836-0.1320.6520.6011.000-0.1000.3380.3380.3380.3380.338
일조량0.095-0.6290.2960.024-0.2910.422-0.1001.0000.0000.0000.0000.0000.000
관측소코드0.0000.0510.3220.1900.1360.1510.3380.0001.0001.0001.0001.0001.000
관측소명0.0000.0510.3220.1900.1360.1510.3380.0001.0001.0001.0001.0001.000
위도0.0000.0510.3220.1900.1360.1510.3380.0001.0001.0001.0001.0001.000
경도0.0000.0510.3220.1900.1360.1510.3380.0001.0001.0001.0001.0001.000
주소0.0000.0510.3220.1900.1360.1510.3380.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T22:22:08.425989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:22:08.745225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
08000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020051063.419.02.111.324.114.08.7
18000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020052284.813.22.610.614.912.00.0
28000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020051691.015.21.613.716.414.00.0
38000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020050776.314.72.010.418.78.97.6
48000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020050678.815.62.411.819.511.412.7
58000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020051325.120.84.0-0.426.514.713.4
68000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020050995.813.71.613.015.711.70.0
78000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020050362.920.81.712.726.814.69.5
88000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020050481.915.31.812.028.012.84.0
98000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020052753.319.11.58.823.812.913.5
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
908000101춘천(기)127.735737.90256강원도 춘천시 우두동2020050277.420.51.116.223.916.80.1
918000101춘천(기)127.735737.90256강원도 춘천시 우두동2020052868.818.31.411.723.813.57.7
928000101춘천(기)127.735737.90256강원도 춘천시 우두동2020053057.121.71.511.230.113.613.9
938000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020052470.519.41.713.624.814.66.1
948000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020052381.616.51.513.120.912.78.9
958000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020050576.915.21.211.018.912.53.0
968000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020052281.114.11.810.817.212.40.1
978000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020051984.912.23.49.516.79.20.2
988000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020050360.123.11.914.726.919.14.8
998000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020050479.517.21.813.127.112.55.9