Overview

Dataset statistics

Number of variables9
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory84.1 B

Variable types

DateTime1
Numeric8

Dataset

Description한국동서발전의 경주 풍력 풍속데이터 및 경주시 풍향 정보입니다. 경주 풍력 풍속데이터 및 경주시 풍향 정보는 연월, 경주풍력1단계, 경주풍력2단계, 경쥐 평균풍속(m/s) 등의 항목으로 구성됩니다.
Author한국동서발전(주)
URLhttps://www.data.go.kr/data/15104982/fileData.do

Alerts

경주풍력1단계 is highly overall correlated with 경주풍력2단계 and 3 other fieldsHigh correlation
경주풍력2단계 is highly overall correlated with 경주풍력1단계 and 3 other fieldsHigh correlation
경주시 평균풍속(m-s) is highly overall correlated with 경주풍력1단계 and 3 other fieldsHigh correlation
경주시 최대풍속(m-s) is highly overall correlated with 경주풍력1단계 and 3 other fieldsHigh correlation
경주시 최대순간풍속(m-s) is highly overall correlated with 경주풍력1단계 and 3 other fieldsHigh correlation
경주시 최대풍속 풍향(16방위) is highly overall correlated with 경주시 최대순간풍속 풍향(16방위)High correlation
경주시 최대순간풍속 풍향(16방위) is highly overall correlated with 경주시 최대풍속 풍향(16방위)High correlation
연월 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:35:36.116636
Analysis finished2023-12-12 08:35:44.289440
Duration8.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2019-01-01 00:00:00
Maximum2021-08-01 00:00:00
2023-12-12T17:35:44.358312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:44.528608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)

경주풍력1단계
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)71.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.61875
Minimum4.9
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:44.722523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile5.165
Q16.075
median6.7
Q37.125
95-th percentile8.025
Maximum8.8
Range3.9
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation0.92123252
Coefficient of variation (CV)0.13918527
Kurtosis0.006161325
Mean6.61875
Median Absolute Deviation (MAD)0.55
Skewness0.064516392
Sum211.8
Variance0.84866935
MonotonicityNot monotonic
2023-12-12T17:35:44.906172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7.0 3
 
9.4%
5.5 2
 
6.2%
6.8 2
 
6.2%
5.3 2
 
6.2%
6.4 2
 
6.2%
6.6 2
 
6.2%
6.7 2
 
6.2%
7.3 2
 
6.2%
8.3 1
 
3.1%
5.0 1
 
3.1%
Other values (13) 13
40.6%
ValueCountFrequency (%)
4.9 1
3.1%
5.0 1
3.1%
5.3 2
6.2%
5.5 2
6.2%
5.6 1
3.1%
6.0 1
3.1%
6.1 1
3.1%
6.3 1
3.1%
6.4 2
6.2%
6.5 1
3.1%
ValueCountFrequency (%)
8.8 1
 
3.1%
8.3 1
 
3.1%
7.8 1
 
3.1%
7.7 1
 
3.1%
7.4 1
 
3.1%
7.3 2
6.2%
7.2 1
 
3.1%
7.1 1
 
3.1%
7.0 3
9.4%
6.9 1
 
3.1%

경주풍력2단계
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1590625
Minimum5.12
Maximum10.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:45.065732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.12
5-th percentile5.23
Q16.09
median7.045
Q37.77
95-th percentile9.7165
Maximum10.43
Range5.31
Interquartile range (IQR)1.68

Descriptive statistics

Standard deviation1.3762885
Coefficient of variation (CV)0.19224424
Kurtosis-0.062887703
Mean7.1590625
Median Absolute Deviation (MAD)0.815
Skewness0.57508427
Sum229.09
Variance1.8941701
MonotonicityNot monotonic
2023-12-12T17:35:45.215656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5.23 2
 
6.2%
7.77 2
 
6.2%
9.81 1
 
3.1%
7.03 1
 
3.1%
5.8 1
 
3.1%
5.33 1
 
3.1%
5.12 1
 
3.1%
6.86 1
 
3.1%
7.31 1
 
3.1%
6.89 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
5.12 1
3.1%
5.23 2
6.2%
5.33 1
3.1%
5.71 1
3.1%
5.79 1
3.1%
5.8 1
3.1%
5.94 1
3.1%
6.14 1
3.1%
6.38 1
3.1%
6.52 1
3.1%
ValueCountFrequency (%)
10.43 1
3.1%
9.81 1
3.1%
9.64 1
3.1%
9.01 1
3.1%
8.84 1
3.1%
8.53 1
3.1%
8.21 1
3.1%
7.77 2
6.2%
7.64 1
3.1%
7.33 1
3.1%

경주시 평균풍속(m-s)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.478125
Minimum1.7
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:45.348258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile1.8
Q12.1
median2.5
Q32.725
95-th percentile3.29
Maximum3.4
Range1.7
Interquartile range (IQR)0.625

Descriptive statistics

Standard deviation0.47228639
Coefficient of variation (CV)0.19058215
Kurtosis-0.65439635
Mean2.478125
Median Absolute Deviation (MAD)0.3
Skewness0.20091134
Sum79.3
Variance0.22305444
MonotonicityNot monotonic
2023-12-12T17:35:45.475811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2.7 5
15.6%
2.5 4
12.5%
2.4 3
9.4%
2.1 3
9.4%
1.8 3
9.4%
2.8 2
 
6.2%
1.9 2
 
6.2%
2.2 2
 
6.2%
3.1 2
 
6.2%
3.4 2
 
6.2%
Other values (4) 4
12.5%
ValueCountFrequency (%)
1.7 1
 
3.1%
1.8 3
9.4%
1.9 2
 
6.2%
2.1 3
9.4%
2.2 2
 
6.2%
2.3 1
 
3.1%
2.4 3
9.4%
2.5 4
12.5%
2.7 5
15.6%
2.8 2
 
6.2%
ValueCountFrequency (%)
3.4 2
 
6.2%
3.2 1
 
3.1%
3.1 2
 
6.2%
2.9 1
 
3.1%
2.8 2
 
6.2%
2.7 5
15.6%
2.5 4
12.5%
2.4 3
9.4%
2.3 1
 
3.1%
2.2 2
 
6.2%

경주시 최대풍속(m-s)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.865625
Minimum6.6
Maximum16.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:45.605749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.6
5-th percentile7.055
Q18.475
median10.1
Q310.8
95-th percentile12.57
Maximum16.7
Range10.1
Interquartile range (IQR)2.325

Descriptive statistics

Standard deviation2.0872283
Coefficient of variation (CV)0.21156575
Kurtosis2.3504822
Mean9.865625
Median Absolute Deviation (MAD)1.3
Skewness0.91081972
Sum315.7
Variance4.3565222
MonotonicityNot monotonic
2023-12-12T17:35:46.065131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9.5 2
 
6.2%
7.1 2
 
6.2%
10.8 2
 
6.2%
10.2 2
 
6.2%
10.1 2
 
6.2%
10.6 2
 
6.2%
10.3 1
 
3.1%
16.7 1
 
3.1%
7.0 1
 
3.1%
8.1 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
6.6 1
3.1%
7.0 1
3.1%
7.1 2
6.2%
7.2 1
3.1%
7.7 1
3.1%
8.0 1
3.1%
8.1 1
3.1%
8.6 1
3.1%
8.7 1
3.1%
8.8 1
3.1%
ValueCountFrequency (%)
16.7 1
3.1%
12.9 1
3.1%
12.3 1
3.1%
12.0 1
3.1%
11.8 1
3.1%
11.4 1
3.1%
11.2 1
3.1%
10.8 2
6.2%
10.6 2
6.2%
10.5 1
3.1%

경주시 최대순간풍속(m-s)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.359375
Minimum10.3
Maximum24.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:46.194260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.3
5-th percentile11.44
Q114.075
median16.35
Q317.925
95-th percentile22.495
Maximum24.8
Range14.5
Interquartile range (IQR)3.85

Descriptive statistics

Standard deviation3.370255
Coefficient of variation (CV)0.20601368
Kurtosis0.30869503
Mean16.359375
Median Absolute Deviation (MAD)2.1
Skewness0.50865954
Sum523.5
Variance11.358619
MonotonicityNot monotonic
2023-12-12T17:35:46.325587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15.6 2
 
6.2%
17.8 2
 
6.2%
13.7 2
 
6.2%
16.5 2
 
6.2%
19.3 2
 
6.2%
15.1 2
 
6.2%
10.3 1
 
3.1%
11.0 1
 
3.1%
14.8 1
 
3.1%
19.5 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
10.3 1
3.1%
11.0 1
3.1%
11.8 1
3.1%
12.3 1
3.1%
12.4 1
3.1%
13.7 2
6.2%
14.0 1
3.1%
14.1 1
3.1%
14.8 1
3.1%
14.9 1
3.1%
ValueCountFrequency (%)
24.8 1
3.1%
23.1 1
3.1%
22.0 1
3.1%
20.2 1
3.1%
19.5 1
3.1%
19.3 2
6.2%
18.3 1
3.1%
17.8 2
6.2%
17.3 1
3.1%
17.0 1
3.1%

경주시 최대풍속 풍향(16방위)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.0625
Minimum20
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:46.440846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q1180
median280
Q3290
95-th percentile320
Maximum340
Range320
Interquartile range (IQR)110

Descriptive statistics

Standard deviation86.336154
Coefficient of variation (CV)0.3611447
Kurtosis1.8473756
Mean239.0625
Median Absolute Deviation (MAD)30
Skewness-1.5395978
Sum7650
Variance7453.9315
MonotonicityNot monotonic
2023-12-12T17:35:46.557170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
290 12
37.5%
180 5
15.6%
320 3
 
9.4%
20 3
 
9.4%
250 3
 
9.4%
270 2
 
6.2%
230 2
 
6.2%
340 1
 
3.1%
160 1
 
3.1%
ValueCountFrequency (%)
20 3
 
9.4%
160 1
 
3.1%
180 5
15.6%
230 2
 
6.2%
250 3
 
9.4%
270 2
 
6.2%
290 12
37.5%
320 3
 
9.4%
340 1
 
3.1%
ValueCountFrequency (%)
340 1
 
3.1%
320 3
 
9.4%
290 12
37.5%
270 2
 
6.2%
250 3
 
9.4%
230 2
 
6.2%
180 5
15.6%
160 1
 
3.1%
20 3
 
9.4%

경주시 최대순간풍속 풍향(16방위)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.625
Minimum20
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:46.691328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile36.5
Q1200
median270
Q3320
95-th percentile340
Maximum360
Range340
Interquartile range (IQR)120

Descriptive statistics

Standard deviation98.109139
Coefficient of variation (CV)0.39942652
Kurtosis0.50664957
Mean245.625
Median Absolute Deviation (MAD)70
Skewness-1.1595963
Sum7860
Variance9625.4032
MonotonicityNot monotonic
2023-12-12T17:35:46.820143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
270 8
25.0%
340 6
18.8%
320 3
 
9.4%
290 3
 
9.4%
200 3
 
9.4%
180 2
 
6.2%
20 2
 
6.2%
50 2
 
6.2%
140 1
 
3.1%
360 1
 
3.1%
ValueCountFrequency (%)
20 2
 
6.2%
50 2
 
6.2%
140 1
 
3.1%
180 2
 
6.2%
200 3
 
9.4%
230 1
 
3.1%
270 8
25.0%
290 3
 
9.4%
320 3
 
9.4%
340 6
18.8%
ValueCountFrequency (%)
360 1
 
3.1%
340 6
18.8%
320 3
 
9.4%
290 3
 
9.4%
270 8
25.0%
230 1
 
3.1%
200 3
 
9.4%
180 2
 
6.2%
140 1
 
3.1%
50 2
 
6.2%

경주시 최다풍향(16방위)
Real number (ℝ)

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218.75
Minimum20
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:35:46.926028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile180
Q1180
median180
Q3297.5
95-th percentile320
Maximum360
Range340
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation74.433343
Coefficient of variation (CV)0.34026671
Kurtosis0.24621495
Mean218.75
Median Absolute Deviation (MAD)0
Skewness0.14903089
Sum7000
Variance5540.3226
MonotonicityNot monotonic
2023-12-12T17:35:47.044373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
180 20
62.5%
320 7
 
21.9%
290 2
 
6.2%
360 1
 
3.1%
20 1
 
3.1%
200 1
 
3.1%
ValueCountFrequency (%)
20 1
 
3.1%
180 20
62.5%
200 1
 
3.1%
290 2
 
6.2%
320 7
 
21.9%
360 1
 
3.1%
ValueCountFrequency (%)
360 1
 
3.1%
320 7
 
21.9%
290 2
 
6.2%
200 1
 
3.1%
180 20
62.5%
20 1
 
3.1%

Interactions

2023-12-12T17:35:43.035193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.375165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.259162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.067070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.317950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.229576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.172851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.156772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.145663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.472888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.359231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.188655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.452002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.339201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.307514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.271397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.275345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.571628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.445189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.289179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.562814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.439180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.443040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.373676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.391446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.693805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.533679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.391263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.675890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.540277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.581482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.494164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.483219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.802040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.617707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.474007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.761390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.644295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.689445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.615235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.602464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:36.909789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.750891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:38.578492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.895540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.770005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.795406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.727484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.723115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.012433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.862270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.049582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.010454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.908277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.904211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.833830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:43.845127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.129847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:37.965652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:39.180883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:40.119148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:41.032791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.032988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:35:42.929076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:35:47.139713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월경주풍력1단계경주풍력2단계경주시 평균풍속(m-s)경주시 최대풍속(m-s)경주시 최대순간풍속(m-s)경주시 최대풍속 풍향(16방위)경주시 최대순간풍속 풍향(16방위)경주시 최다풍향(16방위)
연월1.0001.0001.0001.0001.0001.0001.0001.0001.000
경주풍력1단계1.0001.0000.9470.6910.6840.5250.0000.0000.350
경주풍력2단계1.0000.9471.0000.8360.6340.0000.0000.1920.162
경주시 평균풍속(m-s)1.0000.6910.8361.0000.3920.6520.3550.0000.000
경주시 최대풍속(m-s)1.0000.6840.6340.3921.0000.7660.0000.6830.654
경주시 최대순간풍속(m-s)1.0000.5250.0000.6520.7661.0000.0000.2840.828
경주시 최대풍속 풍향(16방위)1.0000.0000.0000.3550.0000.0001.0000.7170.000
경주시 최대순간풍속 풍향(16방위)1.0000.0000.1920.0000.6830.2840.7171.0000.452
경주시 최다풍향(16방위)1.0000.3500.1620.0000.6540.8280.0000.4521.000
2023-12-12T17:35:47.284129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경주풍력1단계경주풍력2단계경주시 평균풍속(m-s)경주시 최대풍속(m-s)경주시 최대순간풍속(m-s)경주시 최대풍속 풍향(16방위)경주시 최대순간풍속 풍향(16방위)경주시 최다풍향(16방위)
경주풍력1단계1.0000.9570.8150.7410.7220.2830.2620.354
경주풍력2단계0.9571.0000.7830.7120.6860.3540.3300.417
경주시 평균풍속(m-s)0.8150.7831.0000.6840.6930.4710.4130.264
경주시 최대풍속(m-s)0.7410.7120.6841.0000.8750.1090.2500.162
경주시 최대순간풍속(m-s)0.7220.6860.6930.8751.0000.0890.3320.076
경주시 최대풍속 풍향(16방위)0.2830.3540.4710.1090.0891.0000.6140.356
경주시 최대순간풍속 풍향(16방위)0.2620.3300.4130.2500.3320.6141.0000.157
경주시 최다풍향(16방위)0.3540.4170.2640.1620.0760.3560.1571.000

Missing values

2023-12-12T17:35:44.012986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:35:44.214577image/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

연월경주풍력1단계경주풍력2단계경주시 평균풍속(m-s)경주시 최대풍속(m-s)경주시 최대순간풍속(m-s)경주시 최대풍속 풍향(16방위)경주시 최대순간풍속 풍향(16방위)경주시 최다풍향(16방위)
02019-01-018.39.812.79.515.6290320290
12019-02-016.77.642.48.815.1320290320
22019-03-017.07.022.810.617.8290290290
32019-04-016.36.382.57.713.7340340180
42019-05-016.87.272.410.316.5180180180
52019-06-015.35.231.97.112.4180200180
62019-07-016.46.522.18.615.6160140180
72019-08-015.65.711.77.111.82020180
82019-09-016.05.941.811.820.220360180
92019-10-016.97.32.210.217.02020180
연월경주풍력1단계경주풍력2단계경주시 평균풍속(m-s)경주시 최대풍속(m-s)경주시 최대순간풍속(m-s)경주시 최대풍속 풍향(16방위)경주시 최대순간풍속 풍향(16방위)경주시 최다풍향(16방위)
222020-11-017.48.532.910.219.3290340180
232020-12-018.810.433.411.419.3290290320
242021-01-017.09.643.212.322.0290340320
252021-02-017.79.013.412.923.1290270320
262021-03-016.66.892.710.516.2320340180
272021-04-017.07.313.110.116.5250340180
282021-05-016.66.862.79.519.5290320180
292021-06-014.95.122.16.614.823050180
302021-07-015.05.331.98.113.7250270180
312021-08-015.55.81.87.011.0290270200