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 평균이슬점온도(℃) and 1 other fieldsHigh correlation
평균온도(℃) is highly overall correlated with 최고기온(℃) and 1 other fieldsHigh correlation
평균이슬점온도(℃) is highly overall correlated with 평균습도(%) and 1 other fieldsHigh correlation
최고기온(℃) is highly overall correlated with 평균온도(℃) and 1 other fieldsHigh correlation
최저기온(℃) is highly overall correlated with 평균온도(℃) and 1 other fieldsHigh correlation
일조량 is highly overall correlated with 평균습도(%) and 1 other fieldsHigh correlation
일조량 has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:22:18.319791
Analysis finished2023-12-10 13:22:30.988650
Duration12.67 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

2023-12-10T22:22:31.084866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:31.253439image/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

2023-12-10T22:22:31.803705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:31.985744image/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

2023-12-10T22:22:32.204187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:32.401091image/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

2023-12-10T22:22:32.617788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:32.794995image/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

2023-12-10T22:22:32.990421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:33.182729image/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%
Mean20200415
Minimum20200401
Maximum20200430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:33.362658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200401
5-th percentile20200402
Q120200408
median20200416
Q320200423
95-th percentile20200429
Maximum20200430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6192819
Coefficient of variation (CV)4.2668835 × 10-7
Kurtosis-1.2014335
Mean20200415
Median Absolute Deviation (MAD)7
Skewness-0.03943275
Sum2.0200415 × 109
Variance74.29202
MonotonicityNot monotonic
2023-12-10T22:22:33.561737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20200422 4
 
4.0%
20200421 4
 
4.0%
20200419 4
 
4.0%
20200416 4
 
4.0%
20200415 4
 
4.0%
20200404 4
 
4.0%
20200423 4
 
4.0%
20200403 4
 
4.0%
20200424 4
 
4.0%
20200405 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20200401 3
3.0%
20200402 3
3.0%
20200403 4
4.0%
20200404 4
4.0%
20200405 4
4.0%
20200406 3
3.0%
20200407 3
3.0%
20200408 3
3.0%
20200409 3
3.0%
20200410 3
3.0%
ValueCountFrequency (%)
20200430 3
3.0%
20200429 3
3.0%
20200428 3
3.0%
20200427 3
3.0%
20200426 3
3.0%
20200425 3
3.0%
20200424 4
4.0%
20200423 4
4.0%
20200422 4
4.0%
20200421 4
4.0%

평균습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.52
Minimum18.8
Maximum95.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:33.782256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile31.82
Q140.9
median49.85
Q367
95-th percentile85.805
Maximum95.4
Range76.6
Interquartile range (IQR)26.1

Descriptive statistics

Standard deviation17.471518
Coefficient of variation (CV)0.32046071
Kurtosis-0.53863491
Mean54.52
Median Absolute Deviation (MAD)11.05
Skewness0.42488514
Sum5452
Variance305.25394
MonotonicityNot monotonic
2023-12-10T22:22:33.999702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.0 3
 
3.0%
45.0 2
 
2.0%
45.3 2
 
2.0%
67.3 2
 
2.0%
48.4 2
 
2.0%
35.8 2
 
2.0%
70.9 2
 
2.0%
53.4 2
 
2.0%
60.6 2
 
2.0%
82.8 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
18.8 1
1.0%
20.5 1
1.0%
26.8 1
1.0%
28.0 1
1.0%
28.4 1
1.0%
32.0 1
1.0%
32.4 1
1.0%
32.6 1
1.0%
35.1 1
1.0%
35.3 1
1.0%
ValueCountFrequency (%)
95.4 1
1.0%
93.8 1
1.0%
90.8 1
1.0%
86.0 1
1.0%
85.9 1
1.0%
85.8 1
1.0%
84.5 1
1.0%
83.6 1
1.0%
83.1 1
1.0%
82.8 1
1.0%

평균온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.061
Minimum0.2
Maximum19.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:34.253540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.67
Q16.775
median9.4
Q311.4
95-th percentile15.725
Maximum19.2
Range19
Interquartile range (IQR)4.625

Descriptive statistics

Standard deviation4.1169739
Coefficient of variation (CV)0.45436198
Kurtosis-0.17613325
Mean9.061
Median Absolute Deviation (MAD)2.4
Skewness-0.10400622
Sum906.1
Variance16.949474
MonotonicityNot monotonic
2023-12-10T22:22:34.522158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 4
 
4.0%
10.2 3
 
3.0%
9.4 3
 
3.0%
8.7 3
 
3.0%
8.4 3
 
3.0%
9.1 2
 
2.0%
10.8 2
 
2.0%
13.9 2
 
2.0%
12.0 2
 
2.0%
9.9 2
 
2.0%
Other values (65) 74
74.0%
ValueCountFrequency (%)
0.2 1
1.0%
0.5 1
1.0%
0.6 1
1.0%
0.9 1
1.0%
1.1 1
1.0%
1.7 1
1.0%
2.3 1
1.0%
2.7 2
2.0%
2.8 1
1.0%
3.0 1
1.0%
ValueCountFrequency (%)
19.2 1
1.0%
17.6 1
1.0%
16.8 1
1.0%
16.5 1
1.0%
16.2 1
1.0%
15.7 1
1.0%
15.4 1
1.0%
15.3 1
1.0%
15.2 1
1.0%
14.7 1
1.0%

평균풍속(m/s)
Real number (ℝ)

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.698
Minimum1.1
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:34.739185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.3
Q11.8
median2.3
Q33.025
95-th percentile5.955
Maximum7.8
Range6.7
Interquartile range (IQR)1.225

Descriptive statistics

Standard deviation1.4346351
Coefficient of variation (CV)0.5317402
Kurtosis3.9196792
Mean2.698
Median Absolute Deviation (MAD)0.6
Skewness1.9322663
Sum269.8
Variance2.0581778
MonotonicityNot monotonic
2023-12-10T22:22:34.955647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
2.8 7
 
7.0%
1.5 7
 
7.0%
2.3 7
 
7.0%
2.1 6
 
6.0%
1.6 6
 
6.0%
2.0 6
 
6.0%
1.8 6
 
6.0%
2.5 5
 
5.0%
1.3 4
 
4.0%
1.9 4
 
4.0%
Other values (28) 42
42.0%
ValueCountFrequency (%)
1.1 1
 
1.0%
1.2 1
 
1.0%
1.3 4
4.0%
1.4 3
3.0%
1.5 7
7.0%
1.6 6
6.0%
1.7 1
 
1.0%
1.8 6
6.0%
1.9 4
4.0%
2.0 6
6.0%
ValueCountFrequency (%)
7.8 1
1.0%
7.7 1
1.0%
7.3 1
1.0%
7.2 1
1.0%
7.0 1
1.0%
5.9 1
1.0%
5.1 1
1.0%
4.6 1
1.0%
4.5 1
1.0%
4.4 1
1.0%

평균이슬점온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.121
Minimum-9.7
Maximum7.7
Zeros1
Zeros (%)1.0%
Negative58
Negative (%)58.0%
Memory size1.0 KiB
2023-12-10T22:22:35.197375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.7
5-th percentile-7.91
Q1-4.6
median-1.5
Q32.2
95-th percentile6.505
Maximum7.7
Range17.4
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation4.5290598
Coefficient of variation (CV)-4.0401961
Kurtosis-1.0177824
Mean-1.121
Median Absolute Deviation (MAD)3.5
Skewness0.14832378
Sum-112.1
Variance20.512383
MonotonicityNot monotonic
2023-12-10T22:22:35.389993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.6 4
 
4.0%
-3.9 3
 
3.0%
0.5 3
 
3.0%
-1.5 2
 
2.0%
3.3 2
 
2.0%
-4.1 2
 
2.0%
4.5 2
 
2.0%
1.5 2
 
2.0%
-5.8 2
 
2.0%
-4.3 2
 
2.0%
Other values (68) 76
76.0%
ValueCountFrequency (%)
-9.7 1
1.0%
-8.8 1
1.0%
-8.7 1
1.0%
-8.3 1
1.0%
-8.1 1
1.0%
-7.9 1
1.0%
-7.8 1
1.0%
-7.4 1
1.0%
-7.3 1
1.0%
-7.1 1
1.0%
ValueCountFrequency (%)
7.7 1
1.0%
7.3 1
1.0%
6.9 1
1.0%
6.8 1
1.0%
6.6 1
1.0%
6.5 1
1.0%
6.4 1
1.0%
6.3 1
1.0%
5.5 1
1.0%
5.2 1
1.0%

최고기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.93
Minimum3.4
Maximum24.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:35.648255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile8.08
Q111.8
median14.65
Q318.4
95-th percentile23.215
Maximum24.5
Range21.1
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation4.6668506
Coefficient of variation (CV)0.31258209
Kurtosis-0.37540402
Mean14.93
Median Absolute Deviation (MAD)3.25
Skewness0.081647269
Sum1493
Variance21.779495
MonotonicityNot monotonic
2023-12-10T22:22:35.983159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4 5
 
5.0%
13.6 3
 
3.0%
18.5 3
 
3.0%
13.9 3
 
3.0%
13.0 3
 
3.0%
13.8 3
 
3.0%
11.7 2
 
2.0%
22.7 2
 
2.0%
9.8 2
 
2.0%
17.0 2
 
2.0%
Other values (59) 72
72.0%
ValueCountFrequency (%)
3.4 1
1.0%
4.5 1
1.0%
6.2 1
1.0%
6.9 1
1.0%
7.7 1
1.0%
8.1 1
1.0%
8.5 2
2.0%
8.8 2
2.0%
9.0 1
1.0%
9.1 1
1.0%
ValueCountFrequency (%)
24.5 1
1.0%
24.3 1
1.0%
23.7 2
2.0%
23.5 1
1.0%
23.2 1
1.0%
22.8 1
1.0%
22.7 2
2.0%
22.1 1
1.0%
21.1 2
2.0%
20.8 1
1.0%

최저기온(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.982
Minimum-9.2
Maximum12.4
Zeros0
Zeros (%)0.0%
Negative28
Negative (%)28.0%
Memory size1.0 KiB
2023-12-10T22:22:36.226289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.2
5-th percentile-3.935
Q1-0.45
median3.4
Q36.5
95-th percentile9.1
Maximum12.4
Range21.6
Interquartile range (IQR)6.95

Descriptive statistics

Standard deviation4.4972804
Coefficient of variation (CV)1.5081423
Kurtosis-0.4647189
Mean2.982
Median Absolute Deviation (MAD)3.65
Skewness-0.28754067
Sum298.2
Variance20.225531
MonotonicityNot monotonic
2023-12-10T22:22:36.467387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4 3
 
3.0%
5.0 3
 
3.0%
2.6 3
 
3.0%
7.3 3
 
3.0%
8.0 3
 
3.0%
4.6 3
 
3.0%
3.3 3
 
3.0%
4.0 3
 
3.0%
6.4 2
 
2.0%
-3.5 2
 
2.0%
Other values (60) 72
72.0%
ValueCountFrequency (%)
-9.2 1
1.0%
-6.9 1
1.0%
-5.6 2
2.0%
-4.6 1
1.0%
-3.9 1
1.0%
-3.8 1
1.0%
-3.5 2
2.0%
-3.1 1
1.0%
-3.0 2
2.0%
-2.6 1
1.0%
ValueCountFrequency (%)
12.4 1
1.0%
11.3 1
1.0%
11.2 1
1.0%
10.6 1
1.0%
9.1 2
2.0%
9.0 1
1.0%
8.6 1
1.0%
8.5 1
1.0%
8.4 2
2.0%
8.3 1
1.0%

일조량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.712
Minimum0
Maximum12.8
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:36.717655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.285
Q17.425
median9.8
Q311.6
95-th percentile12.4
Maximum12.8
Range12.8
Interquartile range (IQR)4.175

Descriptive statistics

Standard deviation3.6994206
Coefficient of variation (CV)0.42463506
Kurtosis0.36035015
Mean8.712
Median Absolute Deviation (MAD)1.9
Skewness-1.1878476
Sum871.2
Variance13.685713
MonotonicityNot monotonic
2023-12-10T22:22:36.992286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6 5
 
5.0%
10.0 5
 
5.0%
0.0 5
 
5.0%
12.1 4
 
4.0%
11.5 3
 
3.0%
9.4 3
 
3.0%
9.6 3
 
3.0%
8.7 3
 
3.0%
12.0 3
 
3.0%
9.2 3
 
3.0%
Other values (46) 63
63.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.3 1
 
1.0%
0.7 2
 
2.0%
0.9 1
 
1.0%
1.1 1
 
1.0%
1.7 1
 
1.0%
2.7 1
 
1.0%
2.8 1
 
1.0%
3.5 1
 
1.0%
4.0 1
 
1.0%
ValueCountFrequency (%)
12.8 1
 
1.0%
12.7 1
 
1.0%
12.6 1
 
1.0%
12.5 1
 
1.0%
12.4 2
2.0%
12.3 1
 
1.0%
12.2 2
2.0%
12.1 4
4.0%
12.0 3
3.0%
11.9 2
2.0%

Interactions

2023-12-10T22:22:28.792121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.286922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.589614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.164508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.393710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.628244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.978482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.308238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.062777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.467201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.774190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.330150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.541234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.796459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.201299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.451343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.313792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.634425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.917007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.506354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.704390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.977258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.459901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.598118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.540656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.812110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:21.062676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.663379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.848741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.137422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.636785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.813241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.735932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:19.975823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:21.219316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.814297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.008232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.295704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.809595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.996130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:29.914255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.151468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:21.364409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.965048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.183983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.433109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:26.944808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.155044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.039833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.294586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:21.868671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.093685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.333857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.572118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.065942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.313797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:30.187757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:20.429998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:22.011608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:23.241201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:24.451513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:25.728819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:27.183073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:28.471983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:22:37.208632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
관측소코드1.0001.0001.0001.0001.0000.0000.4310.6360.4170.0000.5580.5950.000
관측소명1.0001.0001.0001.0001.0000.0000.4310.6360.4170.0000.5580.5950.000
위도1.0001.0001.0001.0001.0000.0000.4310.6360.4170.0000.5580.5950.000
경도1.0001.0001.0001.0001.0000.0000.4310.6360.4170.0000.5580.5950.000
주소1.0001.0001.0001.0001.0000.0000.4310.6360.4170.0000.5580.5950.000
관측일자0.0000.0000.0000.0000.0001.0000.6540.5240.2760.7050.4250.5180.667
평균습도(%)0.4310.4310.4310.4310.4310.6541.0000.6260.0000.6880.5810.6330.577
평균온도(℃)0.6360.6360.6360.6360.6360.5240.6261.0000.1790.6480.8660.7950.000
평균풍속(m/s)0.4170.4170.4170.4170.4170.2760.0000.1791.0000.1760.3140.2740.449
평균이슬점온도(℃)0.0000.0000.0000.0000.0000.7050.6880.6480.1761.0000.0000.6540.651
최고기온(℃)0.5580.5580.5580.5580.5580.4250.5810.8660.3140.0001.0000.6490.204
최저기온(℃)0.5950.5950.5950.5950.5950.5180.6330.7950.2740.6540.6491.0000.177
일조량0.0000.0000.0000.0000.0000.6670.5770.0000.4490.6510.2040.1771.000
2023-12-10T22:22:37.484297image/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
2023-12-10T22:22:37.773484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량관측소코드관측소명위도경도주소
관측일자1.0000.0390.3060.2180.3510.1810.3420.0300.0000.0000.0000.0000.000
평균습도(%)0.0391.000-0.361-0.2020.660-0.480-0.187-0.6250.2610.2610.2610.2610.261
평균온도(℃)0.306-0.3611.000-0.1680.3940.8740.8100.0700.4200.4200.4200.4200.420
평균풍속(m/s)0.218-0.202-0.1681.000-0.267-0.2230.0330.1460.2700.2700.2700.2700.270
평균이슬점온도(℃)0.3510.6600.394-0.2671.0000.1770.472-0.5910.0000.0000.0000.0000.000
최고기온(℃)0.181-0.4800.874-0.2230.1771.0000.5100.2990.3560.3560.3560.3560.356
최저기온(℃)0.342-0.1870.8100.0330.4720.5101.000-0.2160.3860.3860.3860.3860.386
일조량0.030-0.6250.0700.146-0.5910.299-0.2161.0000.0000.0000.0000.0000.000
관측소코드0.0000.2610.4200.2700.0000.3560.3860.0001.0001.0001.0001.0001.000
관측소명0.0000.2610.4200.2700.0000.3560.3860.0001.0001.0001.0001.0001.000
위도0.0000.2610.4200.2700.0000.3560.3860.0001.0001.0001.0001.0001.000
경도0.0000.2610.4200.2700.0000.3560.3860.0001.0001.0001.0001.0001.000
주소0.0000.2610.4200.2700.0000.3560.3860.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T22:22:30.518169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:22:30.861074image/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 봉포리2020040856.87.42.8-1.510.73.48.0
18000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020042667.310.52.64.514.08.36.7
28000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020040535.16.61.6-8.111.80.912.1
38000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020042766.110.21.73.913.96.57.6
48000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020040639.010.91.9-3.014.73.611.6
58000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020041156.410.11.51.615.44.011.4
68000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020042428.412.82.8-5.518.06.510.9
78000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020041060.68.41.90.713.83.210.0
88000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020041345.39.52.5-2.213.85.011.6
98000090속초(기)128.5647238.25085강원도 고성군 토성면 봉포리2020042947.316.82.64.522.111.36.9
관측소코드관측소명위도경도주소관측일자평균습도(%)평균온도(℃)평균풍속(m/s)평균이슬점온도(℃)최고기온(℃)최저기온(℃)일조량
908000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020042432.012.33.9-4.417.86.110.0
918000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020042341.09.43.0-3.615.34.910.3
928000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020040539.47.32.1-6.312.21.411.6
938000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020042237.98.03.6-5.612.05.012.0
948000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020041982.810.21.87.313.47.30.0
958000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020040320.515.33.1-7.420.410.611.4
968000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020040440.38.73.9-4.313.04.02.8
978000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020041670.610.92.15.514.67.37.9
988000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020041553.913.93.13.321.19.19.7
998000105강릉(기)128.8909837.75147강원도 강릉시 용강동2020042138.09.53.9-4.513.65.49.2