Overview

Dataset statistics

Number of variables19
Number of observations200
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory168.7 B

Variable types

Categorical9
Numeric9
DateTime1

Dataset

DescriptionSample
Author(주)넥스트이지
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=NXEWETHERORGN0000000

Alerts

위치명 has constant value ""Constant
위도X좌표 has constant value ""Constant
적설값 has constant value ""Constant
경도Y좌표 has constant value ""Constant
최저운고값 is highly overall correlated with 운량비율 and 4 other fieldsHigh correlation
운형명 is highly overall correlated with 하층운운량비율 and 2 other fieldsHigh correlation
현상코드명 is highly overall correlated with 기압값 and 7 other fieldsHigh correlation
지면코드명 is highly overall correlated with 현상코드명High correlation
기압값 is highly overall correlated with 현상코드명High correlation
운량비율 is highly overall correlated with 하층운운량비율 and 2 other fieldsHigh correlation
하층운운량비율 is highly overall correlated with 운량비율 and 3 other fieldsHigh correlation
공기거리값 is highly overall correlated with 최저운고값High correlation
기온값 is highly overall correlated with 현상코드명High correlation
풍속값 is highly overall correlated with 현상코드명High correlation
습도값 is highly overall correlated with 이슬점값High correlation
이슬점값 is highly overall correlated with 습도값High correlation
현상코드명 is highly imbalanced (68.8%)Imbalance
강수량값 is highly imbalanced (87.8%)Imbalance
측정일시 has unique valuesUnique
운량비율 has 47 (23.5%) zerosZeros
하층운운량비율 has 48 (24.0%) zerosZeros
공기거리값 has 47 (23.5%) zerosZeros
이슬점값 has 27 (13.5%) zerosZeros

Reproduction

Analysis started2023-12-10 06:46:45.320159
Analysis finished2023-12-10 06:46:55.213105
Duration9.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
제주
200 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주 200
100.0%

Length

2023-12-10T15:46:55.293954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:55.401149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 200
100.0%

위도X좌표
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
33.51411
200 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33.51411
2nd row33.51411
3rd row33.51411
4th row33.51411
5th row33.51411

Common Values

ValueCountFrequency (%)
33.51411 200
100.0%

Length

2023-12-10T15:46:55.519566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:55.618953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33.51411 200
100.0%

기압값
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.46
Minimum1021
Maximum1027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:55.706028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1021
5-th percentile1022
Q11023.75
median1025
Q31026
95-th percentile1027
Maximum1027
Range6
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation1.4864209
Coefficient of variation (CV)0.0014509312
Kurtosis-0.33439722
Mean1024.46
Median Absolute Deviation (MAD)1
Skewness-0.34346332
Sum204892
Variance2.2094472
MonotonicityNot monotonic
2023-12-10T15:46:55.807370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1025 53
26.5%
1024 46
23.0%
1026 36
18.0%
1023 30
15.0%
1027 15
 
7.5%
1022 12
 
6.0%
1021 8
 
4.0%
ValueCountFrequency (%)
1021 8
 
4.0%
1022 12
 
6.0%
1023 30
15.0%
1024 46
23.0%
1025 53
26.5%
1026 36
18.0%
1027 15
 
7.5%
ValueCountFrequency (%)
1027 15
 
7.5%
1026 36
18.0%
1025 53
26.5%
1024 46
23.0%
1023 30
15.0%
1022 12
 
6.0%
1021 8
 
4.0%

적설값
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
200 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 200
100.0%

Length

2023-12-10T15:46:55.928152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:56.028418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 200
100.0%

운량비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.02
Minimum0
Maximum10
Zeros47
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:56.122605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5922306
Coefficient of variation (CV)0.59671604
Kurtosis-0.83202211
Mean6.02
Median Absolute Deviation (MAD)2
Skewness-0.83703603
Sum1204
Variance12.904121
MonotonicityNot monotonic
2023-12-10T15:46:56.228625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 58
29.0%
0 47
23.5%
10 29
14.5%
7 26
13.0%
6 18
 
9.0%
9 14
 
7.0%
4 4
 
2.0%
5 3
 
1.5%
3 1
 
0.5%
ValueCountFrequency (%)
0 47
23.5%
3 1
 
0.5%
4 4
 
2.0%
5 3
 
1.5%
6 18
 
9.0%
7 26
13.0%
8 58
29.0%
9 14
 
7.0%
10 29
14.5%
ValueCountFrequency (%)
10 29
14.5%
9 14
 
7.0%
8 58
29.0%
7 26
13.0%
6 18
 
9.0%
5 3
 
1.5%
4 4
 
2.0%
3 1
 
0.5%
0 47
23.5%

하층운운량비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.095
Minimum0
Maximum9
Zeros48
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:56.326103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q38
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.254062
Coefficient of variation (CV)0.63867753
Kurtosis-1.1840099
Mean5.095
Median Absolute Deviation (MAD)2
Skewness-0.62661871
Sum1019
Variance10.58892
MonotonicityNot monotonic
2023-12-10T15:46:56.459681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 54
27.0%
0 48
24.0%
7 29
14.5%
6 24
12.0%
4 19
 
9.5%
9 13
 
6.5%
5 7
 
3.5%
2 4
 
2.0%
3 1
 
0.5%
1 1
 
0.5%
ValueCountFrequency (%)
0 48
24.0%
1 1
 
0.5%
2 4
 
2.0%
3 1
 
0.5%
4 19
 
9.5%
5 7
 
3.5%
6 24
12.0%
7 29
14.5%
8 54
27.0%
9 13
 
6.5%
ValueCountFrequency (%)
9 13
 
6.5%
8 54
27.0%
7 29
14.5%
6 24
12.0%
5 7
 
3.5%
4 19
 
9.5%
3 1
 
0.5%
2 4
 
2.0%
1 1
 
0.5%
0 48
24.0%

운형명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Sc
91 
<NA>
47 
ScAs
28 
Cu
26 
ScCi
 
5
Other values (3)
 
3

Length

Max length4
Median length2
Mean length2.82
Min length2

Unique

Unique3 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
Sc 91
45.5%
<NA> 47
23.5%
ScAs 28
 
14.0%
Cu 26
 
13.0%
ScCi 5
 
2.5%
StNs 1
 
0.5%
ScAc 1
 
0.5%
Ci 1
 
0.5%

Length

2023-12-10T15:46:56.599845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:56.744337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sc 91
45.5%
na 47
23.5%
scas 28
 
14.0%
cu 26
 
13.0%
scci 5
 
2.5%
stns 1
 
0.5%
scac 1
 
0.5%
ci 1
 
0.5%

최저운고값
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
10
125 
0
48 
8
26 
7
 
1

Length

Max length2
Median length2
Mean length1.625
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
10 125
62.5%
0 48
 
24.0%
8 26
 
13.0%
7 1
 
0.5%

Length

2023-12-10T15:46:56.885950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:57.008672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 125
62.5%
0 48
 
24.0%
8 26
 
13.0%
7 1
 
0.5%

공기거리값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1437
Minimum0
Maximum2500
Zeros47
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:57.124372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11200
median2000
Q32000
95-th percentile2500
Maximum2500
Range2500
Interquartile range (IQR)800

Descriptive statistics

Standard deviation856.09315
Coefficient of variation (CV)0.59575028
Kurtosis-0.80885724
Mean1437
Median Absolute Deviation (MAD)500
Skewness-0.83083566
Sum287400
Variance732895.48
MonotonicityNot monotonic
2023-12-10T15:46:57.236331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2000 84
42.0%
0 47
23.5%
1500 30
 
15.0%
2500 18
 
9.0%
1200 14
 
7.0%
1800 7
 
3.5%
ValueCountFrequency (%)
0 47
23.5%
1200 14
 
7.0%
1500 30
 
15.0%
1800 7
 
3.5%
2000 84
42.0%
2500 18
 
9.0%
ValueCountFrequency (%)
2500 18
 
9.0%
2000 84
42.0%
1800 7
 
3.5%
1500 30
 
15.0%
1200 14
 
7.0%
0 47
23.5%

지면코드명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
167 
1
19 
0
 
14

Length

Max length4
Median length4
Mean length3.505
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 167
83.5%
1 19
 
9.5%
0 14
 
7.0%

Length

2023-12-10T15:46:57.402626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:57.525191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 167
83.5%
1 19
 
9.5%
0 14
 
7.0%

현상코드명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
182 
8
 
15
1
 
3

Length

Max length4
Median length4
Mean length3.73
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
<NA> 182
91.0%
8 15
 
7.5%
1 3
 
1.5%

Length

2023-12-10T15:46:57.634267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:57.742727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 182
91.0%
8 15
 
7.5%
1 3
 
1.5%

경도Y좌표
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
126.52969
200 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row126.52969
2nd row126.52969
3rd row126.52969
4th row126.52969
5th row126.52969

Common Values

ValueCountFrequency (%)
126.52969 200
100.0%

Length

2023-12-10T15:46:57.836462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:57.922943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
126.52969 200
100.0%

측정일시
Date

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2009-01-01 00:00:00
Maximum2009-01-09 07:00:00
2023-12-10T15:46:58.019803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:58.146406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기온값
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.565
Minimum2.5
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:58.288975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3
Q14.875
median5.75
Q36.325
95-th percentile7.5
Maximum8.4
Range5.9
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.2580095
Coefficient of variation (CV)0.22605742
Kurtosis-0.032676065
Mean5.565
Median Absolute Deviation (MAD)0.75
Skewness-0.43399421
Sum1113
Variance1.5825879
MonotonicityNot monotonic
2023-12-10T15:46:58.449133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.1 11
 
5.5%
6.0 11
 
5.5%
6.2 9
 
4.5%
5.9 9
 
4.5%
5.0 8
 
4.0%
6.5 7
 
3.5%
5.5 7
 
3.5%
5.7 7
 
3.5%
5.2 6
 
3.0%
6.8 6
 
3.0%
Other values (47) 119
59.5%
ValueCountFrequency (%)
2.5 1
 
0.5%
2.6 1
 
0.5%
2.7 5
2.5%
2.8 1
 
0.5%
2.9 1
 
0.5%
3.0 2
 
1.0%
3.1 1
 
0.5%
3.2 1
 
0.5%
3.3 2
 
1.0%
3.4 1
 
0.5%
ValueCountFrequency (%)
8.4 1
 
0.5%
8.3 1
 
0.5%
8.0 1
 
0.5%
7.9 2
1.0%
7.8 1
 
0.5%
7.7 2
1.0%
7.6 1
 
0.5%
7.5 4
2.0%
7.4 1
 
0.5%
7.3 2
1.0%

강수량값
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0.0
195 
0.5
 
3
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 195
97.5%
0.5 3
 
1.5%
1.0 2
 
1.0%

Length

2023-12-10T15:46:58.876383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:46:58.967309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 195
97.5%
0.5 3
 
1.5%
1.0 2
 
1.0%

풍속값
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4255
Minimum0.9
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:59.094129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.195
Q13.1
median4.1
Q35.325
95-th percentile8.305
Maximum9.8
Range8.9
Interquartile range (IQR)2.225

Descriptive statistics

Standard deviation1.8529075
Coefficient of variation (CV)0.41868884
Kurtosis0.38590525
Mean4.4255
Median Absolute Deviation (MAD)1.1
Skewness0.84637275
Sum885.1
Variance3.4332661
MonotonicityNot monotonic
2023-12-10T15:46:59.226240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 10
 
5.0%
3.1 9
 
4.5%
4.3 8
 
4.0%
3.2 7
 
3.5%
4.4 7
 
3.5%
4.0 7
 
3.5%
3.4 7
 
3.5%
2.9 6
 
3.0%
2.6 6
 
3.0%
3.7 6
 
3.0%
Other values (55) 127
63.5%
ValueCountFrequency (%)
0.9 1
 
0.5%
1.0 1
 
0.5%
1.2 2
1.0%
1.6 2
1.0%
1.7 1
 
0.5%
1.9 2
1.0%
2.1 1
 
0.5%
2.2 3
1.5%
2.3 2
1.0%
2.4 2
1.0%
ValueCountFrequency (%)
9.8 1
 
0.5%
9.7 1
 
0.5%
9.2 3
1.5%
9.1 1
 
0.5%
9.0 1
 
0.5%
8.6 2
1.0%
8.4 1
 
0.5%
8.3 2
1.0%
7.9 1
 
0.5%
7.6 3
1.5%

풍향값
Real number (ℝ)

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.8
Minimum180
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:59.329053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile269
Q1320
median340
Q3340
95-th percentile360
Maximum360
Range180
Interquartile range (IQR)20

Descriptive statistics

Standard deviation33.776683
Coefficient of variation (CV)0.10304052
Kurtosis9.5269592
Mean327.8
Median Absolute Deviation (MAD)20
Skewness-2.8983558
Sum65560
Variance1140.8643
MonotonicityNot monotonic
2023-12-10T15:46:59.437699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
340 88
44.0%
320 66
33.0%
360 30
 
15.0%
290 5
 
2.5%
200 4
 
2.0%
180 4
 
2.0%
250 1
 
0.5%
270 1
 
0.5%
230 1
 
0.5%
ValueCountFrequency (%)
180 4
 
2.0%
200 4
 
2.0%
230 1
 
0.5%
250 1
 
0.5%
270 1
 
0.5%
290 5
 
2.5%
320 66
33.0%
340 88
44.0%
360 30
 
15.0%
ValueCountFrequency (%)
360 30
 
15.0%
340 88
44.0%
320 66
33.0%
290 5
 
2.5%
270 1
 
0.5%
250 1
 
0.5%
230 1
 
0.5%
200 4
 
2.0%
180 4
 
2.0%

습도값
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.54
Minimum45
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:46:59.574546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile48
Q153
median56
Q360
95-th percentile63
Maximum80
Range35
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0751538
Coefficient of variation (CV)0.089762182
Kurtosis2.2688508
Mean56.54
Median Absolute Deviation (MAD)3
Skewness0.64693808
Sum11308
Variance25.757186
MonotonicityNot monotonic
2023-12-10T15:46:59.710109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
55 19
 
9.5%
57 18
 
9.0%
53 16
 
8.0%
58 15
 
7.5%
56 15
 
7.5%
54 14
 
7.0%
60 14
 
7.0%
59 13
 
6.5%
62 12
 
6.0%
61 11
 
5.5%
Other values (14) 53
26.5%
ValueCountFrequency (%)
45 1
 
0.5%
46 1
 
0.5%
47 7
3.5%
48 3
 
1.5%
49 4
 
2.0%
50 6
 
3.0%
51 5
 
2.5%
52 10
5.0%
53 16
8.0%
54 14
7.0%
ValueCountFrequency (%)
80 1
 
0.5%
73 2
 
1.0%
68 1
 
0.5%
67 1
 
0.5%
66 4
 
2.0%
63 7
3.5%
62 12
6.0%
61 11
5.5%
60 14
7.0%
59 13
6.5%

이슬점값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.96
Minimum-6
Maximum0
Zeros27
Zeros (%)13.5%
Negative173
Negative (%)86.5%
Memory size1.9 KiB
2023-12-10T15:46:59.816590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-4
Q1-3
median-2
Q3-1
95-th percentile0
Maximum0
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2271633
Coefficient of variation (CV)-0.6261037
Kurtosis-0.4003694
Mean-1.96
Median Absolute Deviation (MAD)1
Skewness-0.15923589
Sum-392
Variance1.5059296
MonotonicityNot monotonic
2023-12-10T15:46:59.931200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-2 58
29.0%
-3 49
24.5%
-1 46
23.0%
0 27
13.5%
-4 18
 
9.0%
-5 1
 
0.5%
-6 1
 
0.5%
ValueCountFrequency (%)
-6 1
 
0.5%
-5 1
 
0.5%
-4 18
 
9.0%
-3 49
24.5%
-2 58
29.0%
-1 46
23.0%
0 27
13.5%
ValueCountFrequency (%)
0 27
13.5%
-1 46
23.0%
-2 58
29.0%
-3 49
24.5%
-4 18
 
9.0%
-5 1
 
0.5%
-6 1
 
0.5%

Interactions

2023-12-10T15:46:53.867726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.192904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.218408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.138200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.975845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.859522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.781425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.679068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.949047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.982055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.319209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.317072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.239009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.079469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.966242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.893982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.787257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.045639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.086725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.420433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.414415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.327080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.180823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.064718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.004179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.882664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.157115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.164929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.513723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.509938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.405150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.267255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.173582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.108438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.984227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.252476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.247868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.647158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.609000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.496942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.348454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.272788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.193874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.094859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.346591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.349153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.743305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.710712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.590414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.451425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.383110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.287407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.211961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.452536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.447449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.845498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.808793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.664062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.545714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.474398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.384715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.319002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.545419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.561989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:46.978813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.932015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.764875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.660624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.582322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.480404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.428354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.666855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:54.677699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:47.128031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.033876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:48.877764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:49.757537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:50.690281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:51.575394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:52.835564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:46:53.758948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:47:00.034834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기압값운량비율하층운운량비율운형명최저운고값공기거리값지면코드명현상코드명기온값강수량값풍속값풍향값습도값이슬점값
기압값1.0000.3000.4480.3600.2690.3310.0000.9430.5010.3470.4300.1520.2920.438
운량비율0.3001.0000.9400.8110.9050.7160.0000.7240.5370.0000.5030.5800.1150.198
하층운운량비율0.4480.9401.0000.8100.8410.8810.4660.5150.7170.4260.6670.3990.3980.366
운형명0.3600.8110.8101.0001.0000.5990.2751.0000.5400.3010.5480.0000.3470.360
최저운고값0.2690.9050.8411.0001.0000.7640.2021.0000.6420.2800.6150.0000.3710.246
공기거리값0.3310.7160.8810.5990.7641.0000.1880.6170.8140.3800.7960.1400.5370.301
지면코드명0.0000.0000.4660.2750.2020.1881.000NaN0.0000.0710.0000.0000.5560.529
현상코드명0.9430.7240.5151.0001.0000.617NaN1.0001.0000.0000.6780.0000.2660.362
기온값0.5010.5370.7170.5400.6420.8140.0001.0001.0000.4350.7840.1850.5330.473
강수량값0.3470.0000.4260.3010.2800.3800.0710.0000.4351.0000.4550.0000.4940.608
풍속값0.4300.5030.6670.5480.6150.7960.0000.6780.7840.4551.0000.5690.5460.367
풍향값0.1520.5800.3990.0000.0000.1400.0000.0000.1850.0000.5691.0000.0000.000
습도값0.2920.1150.3980.3470.3710.5370.5560.2660.5330.4940.5460.0001.0000.437
이슬점값0.4380.1980.3660.3600.2460.3010.5290.3620.4730.6080.3670.0000.4371.000
2023-12-10T15:47:00.190219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최저운고값운형명강수량값현상코드명지면코드명
최저운고값1.0000.9900.2670.9680.323
운형명0.9901.0000.2090.9350.314
강수량값0.2670.2091.0000.0000.107
현상코드명0.9680.9350.0001.0001.000
지면코드명0.3230.3140.1071.0001.000
2023-12-10T15:47:00.319571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기압값운량비율하층운운량비율공기거리값기온값풍속값풍향값습도값이슬점값운형명최저운고값지면코드명현상코드명강수량값
기압값1.000-0.203-0.1520.193-0.099-0.2180.198-0.072-0.0710.2020.1970.0000.5100.168
운량비율-0.2031.0000.5960.4280.0260.196-0.0020.2260.1860.4610.6570.2860.7520.269
하층운운량비율-0.1520.5961.0000.495-0.2230.3430.0140.2400.0290.5830.6720.4530.7520.278
공기거리값0.1930.4280.4951.0000.277-0.223-0.063-0.0680.1780.4530.7110.1080.4200.309
기온값-0.0990.026-0.2230.2771.000-0.297-0.102-0.3530.4820.3060.4360.0000.9010.286
풍속값-0.2180.1960.343-0.223-0.2971.0000.2270.152-0.0800.3120.4110.0000.7270.302
풍향값0.198-0.0020.014-0.063-0.1020.2271.0000.1740.1330.0000.0000.0000.0000.000
습도값-0.0720.2260.240-0.068-0.3530.1520.1741.0000.5660.1880.2400.4380.0410.248
이슬점값-0.0710.1860.0290.1780.482-0.0800.1330.5661.0000.1310.1690.3510.2920.497
운형명0.2020.4610.5830.4530.3060.3120.0000.1880.1311.0000.9900.3140.9350.209
최저운고값0.1970.6570.6720.7110.4360.4110.0000.2400.1690.9901.0000.3230.9680.267
지면코드명0.0000.2860.4530.1080.0000.0000.0000.4380.3510.3140.3231.0001.0000.107
현상코드명0.5100.7520.7520.4200.9010.7270.0000.0410.2920.9350.9681.0001.0000.000
강수량값0.1680.2690.2780.3090.2860.3020.0000.2480.4970.2090.2670.1070.0001.000

Missing values

2023-12-10T15:46:54.842952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:46:55.114665image/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

위치명위도X좌표기압값적설값운량비율하층운운량비율운형명최저운고값공기거리값지면코드명현상코드명경도Y좌표측정일시기온값강수량값풍속값풍향값습도값이슬점값
0제주33.514111022099Cu81200<NA>8126.529692009-01-01 00:00:002.50.57.032061-4
1제주33.514111021088Cu81200<NA>8126.529692009-01-01 01:00:003.00.09.132061-3
2제주33.514111021088Cu81200<NA>8126.529692009-01-01 02:00:002.70.08.6340800
3제주33.514111022099Cu8120018126.529692009-01-01 03:00:002.71.09.832054-5
4제주33.514111022099Cu81200<NA>8126.529692009-01-01 04:00:002.60.09.232062-3
5제주33.514111021088Cu81200<NA>8126.529692009-01-01 05:00:003.10.09.232051-6
6제주33.514111021088Cu81200<NA>8126.529692009-01-01 06:00:002.90.59.232067-2
7제주33.514111022088Cu81200<NA>8126.529692009-01-01 07:00:002.70.09.034068-2
8제주33.514111023088Cu81200<NA><NA>126.529692009-01-01 08:00:002.80.08.332062-3
9제주33.514111023099Cu8120018126.529692009-01-01 09:00:003.21.08.432057-4
위치명위도X좌표기압값적설값운량비율하층운운량비율운형명최저운고값공기거리값지면코드명현상코드명경도Y좌표측정일시기온값강수량값풍속값풍향값습도값이슬점값
190제주33.514111024000<NA>00<NA><NA>126.529692009-01-08 22:00:005.00.05.236057-2
191제주33.514111024000<NA>00<NA><NA>126.529692009-01-08 23:00:004.60.04.132050-4
192제주33.514111024077Sc102000<NA><NA>126.529692009-01-09 00:00:005.00.04.434056-3
193제주33.514111023000<NA>00<NA><NA>126.529692009-01-09 01:00:005.20.04.334050-4
194제주33.514111022000<NA>00<NA><NA>126.529692009-01-09 02:00:005.50.04.432052-3
195제주33.514111022088Sc1015000<NA>126.529692009-01-09 03:00:005.90.06.032050-3
196제주33.514111021088Sc101500<NA><NA>126.529692009-01-09 04:00:005.70.04.534058-1
197제주33.514111021088Sc101500<NA>1126.529692009-01-09 05:00:005.90.07.034059-1
198제주33.514111021088Sc101500<NA><NA>126.529692009-01-09 06:00:006.10.07.232047-4
199제주33.514111021088Sc101500<NA><NA>126.529692009-01-09 07:00:005.90.06.332060-1