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 3 other fieldsHigh correlation
최저기온(℃) is highly overall correlated with 평균온도(℃) and 2 other fieldsHigh correlation
일조량 is highly overall correlated with 평균습도(%) and 1 other fieldsHigh correlation
일조량 has 11 (11.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:30.764403
Analysis finished2023-12-10 13:21:44.648919
Duration13.88 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
30 
8000100
30 
8000101
30 
8000105
10 

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 30
30.0%
8000100 30
30.0%
8000101 30
30.0%
8000105 10
 
10.0%

Length

2023-12-10T22:21:44.760110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:44.920475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8000090 30
30.0%
8000100 30
30.0%
8000101 30
30.0%
8000105 10
 
10.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
속초(기)
30 
대관령(기)
30 
춘천(기)
30 
강릉(기)
10 

Length

Max length6
Median length5
Mean length5.3
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) 30
30.0%
대관령(기) 30
30.0%
춘천(기) 30
30.0%
강릉(기) 10
 
10.0%

Length

2023-12-10T22:21:45.122549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:45.307320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
속초(기 30
30.0%
대관령(기 30
30.0%
춘천(기 30
30.0%
강릉(기 10
 
10.0%

위도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
128.56472
30 
128.758698
30 
127.7357
30 
128.89098
10 

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 30
30.0%
128.758698 30
30.0%
127.7357 30
30.0%
128.89098 10
 
10.0%

Length

2023-12-10T22:21:45.546421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:45.712822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
128.56472 30
30.0%
128.758698 30
30.0%
127.7357 30
30.0%
128.89098 10
 
10.0%

경도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38.25085
30 
37.686924
30 
37.90256
30 
37.75147
10 

Length

Max length9
Median length8
Mean length8.3
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 30
30.0%
37.686924 30
30.0%
37.90256 30
30.0%
37.75147 10
 
10.0%

Length

2023-12-10T22:21:45.900940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:46.078953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38.25085 30
30.0%
37.686924 30
30.0%
37.90256 30
30.0%
37.75147 10
 
10.0%

주소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원도 고성군 토성면 봉포리
30 
(산간)강원도 평창군 대관령면 횡계리
30 
강원도 춘천시 우두동
30 
강원도 강릉시 용강동
10 

Length

Max length20
Median length15
Mean length14.9
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 30
30.0%
(산간)강원도 평창군 대관령면 횡계리 30
30.0%
강원도 춘천시 우두동 30
30.0%
강원도 강릉시 용강동 10
 
10.0%

Length

2023-12-10T22:21:46.263750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:46.416042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 70
19.4%
고성군 30
8.3%
토성면 30
8.3%
봉포리 30
8.3%
산간)강원도 30
8.3%
평창군 30
8.3%
대관령면 30
8.3%
횡계리 30
8.3%
춘천시 30
8.3%
우두동 30
8.3%
Other values (2) 20
 
5.6%

관측일자
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20200615
Minimum20200601
Maximum20200630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:46.616632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200601
5-th percentile20200602
Q120200608
median20200616
Q320200623
95-th percentile20200629
Maximum20200630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6192819
Coefficient of variation (CV)4.2668412 × 10-7
Kurtosis-1.2014335
Mean20200615
Median Absolute Deviation (MAD)7
Skewness-0.03943275
Sum2.0200615 × 109
Variance74.29202
MonotonicityNot monotonic
2023-12-10T22:21:46.818160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20200622 4
 
4.0%
20200621 4
 
4.0%
20200619 4
 
4.0%
20200616 4
 
4.0%
20200615 4
 
4.0%
20200604 4
 
4.0%
20200623 4
 
4.0%
20200603 4
 
4.0%
20200624 4
 
4.0%
20200605 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20200601 3
3.0%
20200602 3
3.0%
20200603 4
4.0%
20200604 4
4.0%
20200605 4
4.0%
20200606 3
3.0%
20200607 3
3.0%
20200608 3
3.0%
20200609 3
3.0%
20200610 3
3.0%
ValueCountFrequency (%)
20200630 3
3.0%
20200629 3
3.0%
20200628 3
3.0%
20200627 3
3.0%
20200626 3
3.0%
20200625 3
3.0%
20200624 4
4.0%
20200623 4
4.0%
20200622 4
4.0%
20200621 4
4.0%

평균습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.853
Minimum35.5
Maximum96.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:47.048967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.5
5-th percentile50.875
Q165.2
median72.65
Q384.15
95-th percentile94.135
Maximum96.1
Range60.6
Interquartile range (IQR)18.95

Descriptive statistics

Standard deviation13.746827
Coefficient of variation (CV)0.18869266
Kurtosis-0.37641051
Mean72.853
Median Absolute Deviation (MAD)10.25
Skewness-0.28943171
Sum7285.3
Variance188.97524
MonotonicityNot monotonic
2023-12-10T22:21:47.439912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.6 3
 
3.0%
72.8 2
 
2.0%
83.3 2
 
2.0%
71.5 2
 
2.0%
78.4 2
 
2.0%
65.3 2
 
2.0%
70.1 2
 
2.0%
86.0 2
 
2.0%
90.5 2
 
2.0%
70.0 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
35.5 1
1.0%
40.8 1
1.0%
41.6 1
1.0%
47.5 1
1.0%
48.5 1
1.0%
51.0 1
1.0%
51.9 1
1.0%
53.0 1
1.0%
54.0 1
1.0%
54.8 1
1.0%
ValueCountFrequency (%)
96.1 1
1.0%
95.8 1
1.0%
95.5 1
1.0%
95.3 1
1.0%
94.8 1
1.0%
94.1 1
1.0%
93.9 1
1.0%
92.6 1
1.0%
91.4 1
1.0%
91.0 1
1.0%

평균온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.909
Minimum15.1
Maximum28.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:47.748659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile16.165
Q119.3
median21.8
Q324.275
95-th percentile27.11
Maximum28.4
Range13.3
Interquartile range (IQR)4.975

Descriptive statistics

Standard deviation3.297201
Coefficient of variation (CV)0.15049528
Kurtosis-0.71976318
Mean21.909
Median Absolute Deviation (MAD)2.5
Skewness-0.12522565
Sum2190.9
Variance10.871534
MonotonicityNot monotonic
2023-12-10T22:21:47.993269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 4
 
4.0%
22.6 3
 
3.0%
25.1 3
 
3.0%
19.3 3
 
3.0%
24.2 3
 
3.0%
27.6 2
 
2.0%
23.3 2
 
2.0%
23.9 2
 
2.0%
18.6 2
 
2.0%
24.8 2
 
2.0%
Other values (59) 74
74.0%
ValueCountFrequency (%)
15.1 1
1.0%
15.2 1
1.0%
15.3 1
1.0%
15.4 1
1.0%
15.5 1
1.0%
16.2 1
1.0%
16.6 1
1.0%
17.0 1
1.0%
17.2 1
1.0%
17.7 2
2.0%
ValueCountFrequency (%)
28.4 1
1.0%
27.6 2
2.0%
27.4 1
1.0%
27.3 1
1.0%
27.1 2
2.0%
26.9 1
1.0%
26.6 2
2.0%
26.5 1
1.0%
26.4 1
1.0%
25.9 1
1.0%

평균풍속(m/s)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.195
Minimum1.1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:48.310209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.295
Q11.5
median1.8
Q32.4
95-th percentile4.825
Maximum6
Range4.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.0782866
Coefficient of variation (CV)0.49124675
Kurtosis3.6937925
Mean2.195
Median Absolute Deviation (MAD)0.4
Skewness1.9851741
Sum219.5
Variance1.162702
MonotonicityNot monotonic
2023-12-10T22:21:48.523663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.8 11
 
11.0%
1.5 8
 
8.0%
1.7 8
 
8.0%
1.4 7
 
7.0%
1.3 7
 
7.0%
2.0 7
 
7.0%
1.6 7
 
7.0%
2.3 7
 
7.0%
1.9 5
 
5.0%
2.4 4
 
4.0%
Other values (20) 29
29.0%
ValueCountFrequency (%)
1.1 2
 
2.0%
1.2 3
 
3.0%
1.3 7
7.0%
1.4 7
7.0%
1.5 8
8.0%
1.6 7
7.0%
1.7 8
8.0%
1.8 11
11.0%
1.9 5
5.0%
2.0 7
7.0%
ValueCountFrequency (%)
6.0 1
1.0%
5.9 1
1.0%
5.6 1
1.0%
5.3 2
2.0%
4.8 1
1.0%
4.7 1
1.0%
4.1 1
1.0%
3.8 1
1.0%
3.7 1
1.0%
3.6 1
1.0%

평균이슬점온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.058
Minimum6.5
Maximum19.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:48.794086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile11.285
Q115
median16.7
Q318
95-th percentile19.2
Maximum19.9
Range13.4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.643925
Coefficient of variation (CV)0.16464846
Kurtosis1.4186922
Mean16.058
Median Absolute Deviation (MAD)1.45
Skewness-1.1673532
Sum1605.8
Variance6.9903394
MonotonicityNot monotonic
2023-12-10T22:21:49.189473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.0 5
 
5.0%
16.1 5
 
5.0%
17.1 4
 
4.0%
15.9 4
 
4.0%
17.5 3
 
3.0%
18.0 3
 
3.0%
16.7 3
 
3.0%
19.2 3
 
3.0%
18.7 3
 
3.0%
15.7 3
 
3.0%
Other values (46) 64
64.0%
ValueCountFrequency (%)
6.5 1
1.0%
8.8 1
1.0%
9.3 1
1.0%
9.4 1
1.0%
11.0 1
1.0%
11.3 2
2.0%
11.5 1
1.0%
11.6 2
2.0%
12.7 1
1.0%
12.8 1
1.0%
ValueCountFrequency (%)
19.9 1
 
1.0%
19.8 1
 
1.0%
19.4 1
 
1.0%
19.3 1
 
1.0%
19.2 3
3.0%
19.1 2
2.0%
19.0 2
2.0%
18.9 1
 
1.0%
18.7 3
3.0%
18.6 1
 
1.0%

최고기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.948
Minimum17.1
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:49.722698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile20.495
Q123.75
median26.95
Q330.1
95-th percentile33.3
Maximum36.4
Range19.3
Interquartile range (IQR)6.35

Descriptive statistics

Standard deviation4.2499074
Coefficient of variation (CV)0.15770771
Kurtosis-0.59154892
Mean26.948
Median Absolute Deviation (MAD)3.15
Skewness-0.10675269
Sum2694.8
Variance18.061713
MonotonicityNot monotonic
2023-12-10T22:21:50.589473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.6 3
 
3.0%
27.1 3
 
3.0%
23.6 2
 
2.0%
33.3 2
 
2.0%
25.5 2
 
2.0%
31.0 2
 
2.0%
26.0 2
 
2.0%
34.4 2
 
2.0%
26.2 2
 
2.0%
27.3 2
 
2.0%
Other values (67) 78
78.0%
ValueCountFrequency (%)
17.1 1
1.0%
17.4 1
1.0%
18.1 1
1.0%
18.9 1
1.0%
20.4 1
1.0%
20.5 1
1.0%
20.6 1
1.0%
21.0 1
1.0%
21.1 1
1.0%
21.2 1
1.0%
ValueCountFrequency (%)
36.4 1
 
1.0%
34.4 2
2.0%
34.3 1
 
1.0%
33.3 2
2.0%
33.2 2
2.0%
33.0 1
 
1.0%
32.6 3
3.0%
32.2 1
 
1.0%
32.1 1
 
1.0%
32.0 1
 
1.0%

최저기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.25
Minimum8.1
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:50.986083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.1
5-th percentile10.49
Q115.225
median18.25
Q319.625
95-th percentile21.425
Maximum23.5
Range15.4
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.3381329
Coefficient of variation (CV)0.19351495
Kurtosis-0.0058409807
Mean17.25
Median Absolute Deviation (MAD)2.1
Skewness-0.66269248
Sum1725
Variance11.143131
MonotonicityNot monotonic
2023-12-10T22:21:51.344628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.9 4
 
4.0%
20.4 4
 
4.0%
19.3 3
 
3.0%
15.5 3
 
3.0%
18.8 3
 
3.0%
19.6 3
 
3.0%
15.8 3
 
3.0%
13.9 2
 
2.0%
9.8 2
 
2.0%
14.0 2
 
2.0%
Other values (57) 71
71.0%
ValueCountFrequency (%)
8.1 1
1.0%
9.1 1
1.0%
9.8 2
2.0%
10.3 1
1.0%
10.5 1
1.0%
10.8 1
1.0%
11.3 1
1.0%
12.6 1
1.0%
12.7 1
1.0%
13.0 2
2.0%
ValueCountFrequency (%)
23.5 1
1.0%
23.4 1
1.0%
22.8 1
1.0%
22.7 1
1.0%
21.9 1
1.0%
21.4 1
1.0%
21.1 1
1.0%
21.0 1
1.0%
20.8 1
1.0%
20.6 2
2.0%

일조량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.939
Minimum0
Maximum14
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:51.600091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.85
median9
Q311.95
95-th percentile13.205
Maximum14
Range14
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation4.6962245
Coefficient of variation (CV)0.59153854
Kurtosis-1.163429
Mean7.939
Median Absolute Deviation (MAD)3.45
Skewness-0.56093001
Sum793.9
Variance22.054524
MonotonicityNot monotonic
2023-12-10T22:21:52.020801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
11.0%
12.6 4
 
4.0%
12.2 3
 
3.0%
11.6 3
 
3.0%
12.5 3
 
3.0%
6.6 3
 
3.0%
11.3 2
 
2.0%
1.7 2
 
2.0%
7.5 2
 
2.0%
7.4 2
 
2.0%
Other values (53) 65
65.0%
ValueCountFrequency (%)
0.0 11
11.0%
0.1 1
 
1.0%
0.2 1
 
1.0%
0.4 1
 
1.0%
1.0 1
 
1.0%
1.2 1
 
1.0%
1.4 1
 
1.0%
1.7 2
 
2.0%
2.0 1
 
1.0%
2.1 1
 
1.0%
ValueCountFrequency (%)
14.0 2
2.0%
13.4 2
2.0%
13.3 1
 
1.0%
13.2 1
 
1.0%
13.1 2
2.0%
13.0 1
 
1.0%
12.9 2
2.0%
12.7 1
 
1.0%
12.6 4
4.0%
12.5 3
3.0%

Interactions

2023-12-10T22:21:42.761257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.950001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.475127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.855554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:36.623504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.086677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.277535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.642821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.064044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.143954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.769409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:35.030690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:36.855804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.254200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.471533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.200824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.195432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.307265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.916505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:35.178530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.043103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.389799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.600574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.349823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.367664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.468871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.055299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:35.401459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.251227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.534738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.757067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.501128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.529230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.633957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.204059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:35.703697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.514432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.671124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.901443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:41.747772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.695516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.814810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.356889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:35.956795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.646445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.812283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.082119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:42.037135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:43.857045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:32.957021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.547942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:36.209981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.797159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:38.955943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.231625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:42.287607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:44.026743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:33.259428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:34.683395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:36.395812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:37.944042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:39.117144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:40.449572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:42.532458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:52.262026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
관측소코드1.0001.0001.0001.0001.0000.0000.5550.5520.4470.3580.3720.6540.178
관측소명1.0001.0001.0001.0001.0000.0000.5550.5520.4470.3580.3720.6540.178
위도1.0001.0001.0001.0001.0000.0000.5550.5520.4470.3580.3720.6540.178
경도1.0001.0001.0001.0001.0000.0000.5550.5520.4470.3580.3720.6540.178
주소1.0001.0001.0001.0001.0000.0000.5550.5520.4470.3580.3720.6540.178
관측일자0.0000.0000.0000.0000.0001.0000.5010.2960.5510.4080.2370.0000.500
평균습도(%)0.5550.5550.5550.5550.5550.5011.0000.7240.7450.7090.6820.6350.572
평균온도(℃)0.5520.5520.5520.5520.5520.2960.7241.0000.4580.3590.8510.8280.348
평균풍속(m/s)0.4470.4470.4470.4470.4470.5510.7450.4581.0000.7520.2260.6880.000
평균이슬점온도(℃)0.3580.3580.3580.3580.3580.4080.7090.3590.7521.0000.5260.7720.543
최고기온(℃)0.3720.3720.3720.3720.3720.2370.6820.8510.2260.5261.0000.7070.237
최저기온(℃)0.6540.6540.6540.6540.6540.0000.6350.8280.6880.7720.7071.0000.000
일조량0.1780.1780.1780.1780.1780.5000.5720.3480.0000.5430.2370.0001.000
2023-12-10T22:21:52.538444image/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
2023-12-10T22:21:52.815834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량관측소코드관측소명위도경도주소
관측일자1.0000.489-0.090-0.1550.473-0.2100.177-0.4780.0000.0000.0000.0000.000
평균습도(%)0.4891.000-0.699-0.0820.398-0.740-0.333-0.6290.3530.3530.3530.3530.353
평균온도(℃)-0.090-0.6991.000-0.2340.2900.9170.7830.3890.3500.3500.3500.3500.350
평균풍속(m/s)-0.155-0.082-0.2341.000-0.496-0.237-0.2490.1120.2720.2720.2720.2720.272
평균이슬점온도(℃)0.4730.3980.290-0.4961.0000.1250.536-0.3900.2120.2120.2120.2120.212
최고기온(℃)-0.210-0.7400.917-0.2370.1251.0000.5390.5670.2220.2220.2220.2220.222
최저기온(℃)0.177-0.3330.783-0.2490.5360.5391.000-0.0420.4690.4690.4690.4690.469
일조량-0.478-0.6290.3890.112-0.3900.567-0.0421.0000.1480.1480.1480.1480.148
관측소코드0.0000.3530.3500.2720.2120.2220.4690.1481.0001.0001.0001.0001.000
관측소명0.0000.3530.3500.2720.2120.2220.4690.1481.0001.0001.0001.0001.000
위도0.0000.3530.3500.2720.2120.2220.4690.1481.0001.0001.0001.0001.000
경도0.0000.3530.3500.2720.2120.2220.4690.1481.0001.0001.0001.0001.000
주소0.0000.3530.3500.2720.2120.2220.4690.1481.0001.0001.0001.0001.000

Missing values

2023-12-10T22:21:44.233246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:44.543293image/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 봉포리2020060871.922.81.917.029.416.713.3
18000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020062668.623.82.017.128.618.28.7
28000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020060582.518.92.815.724.417.011.5
38000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020062766.525.32.018.130.420.68.4
48000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020060686.018.41.515.921.815.57.7
58000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020061170.124.71.818.729.121.911.1
68000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020062495.320.01.719.221.818.80.0
78000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020061073.524.21.718.729.619.65.7
88000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020061373.323.51.818.129.519.211.6
98000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020062988.321.41.819.323.120.50.0
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
908000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020062488.821.21.719.222.819.90.0
918000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020062376.422.91.718.426.019.86.6
928000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020060578.020.01.815.925.717.12.1
938000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020062259.126.61.517.033.220.112.2
948000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020061990.519.12.317.420.418.50.0
958000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020060354.825.12.615.228.716.211.6
968000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020060447.527.63.815.032.622.811.9
978000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020061648.524.82.312.929.221.111.5
988000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020061541.627.43.712.832.223.411.3
998000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020062176.123.31.418.628.319.66.6