Overview

Dataset statistics

Number of variables13
Number of observations24
Missing cells42
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory121.5 B

Variable types

Categorical2
Numeric10
DateTime1

Dataset

Description인천광역시 미추홀구의 일기일수 통계 데이터로 연도,월,맑음,강수,눈,황사 등의 정보를 제공하고 있습니다. (단위: 일)
URLhttps://www.data.go.kr/data/15100980/fileData.do

Alerts

기준일 has constant value ""Constant
맑음 is highly overall correlated with 흐림 and 2 other fieldsHigh correlation
구름많음 is highly overall correlated with 눈High correlation
흐림 is highly overall correlated with 맑음 and 2 other fieldsHigh correlation
강수 is highly overall correlated with 맑음 and 2 other fieldsHigh correlation
서리 is highly overall correlated with 황사High correlation
안개 is highly overall correlated with 황사High correlation
눈 is highly overall correlated with 구름많음 and 1 other fieldsHigh correlation
뇌전 is highly overall correlated with 맑음 and 3 other fieldsHigh correlation
연도 is highly overall correlated with 황사High correlation
황사 is highly overall correlated with 서리 and 4 other fieldsHigh correlation
맑음 has 2 (8.3%) missing valuesMissing
서리 has 13 (54.2%) missing valuesMissing
안개 has 3 (12.5%) missing valuesMissing
눈 has 14 (58.3%) missing valuesMissing
뇌전 has 10 (41.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:12:29.662931
Analysis finished2023-12-12 17:12:41.514278
Duration11.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2020
12 
2021
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 12
50.0%
2021 12
50.0%

Length

2023-12-13T02:12:41.587370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:12:41.682553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 12
50.0%
2021 12
50.0%

월
Real number (ℝ)

Distinct12
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:41.787311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.15
Q13.75
median6.5
Q39.25
95-th percentile11.85
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5262987
Coefficient of variation (CV)0.54250749
Kurtosis-1.2156934
Mean6.5
Median Absolute Deviation (MAD)3
Skewness0
Sum156
Variance12.434783
MonotonicityNot monotonic
2023-12-13T02:12:41.913465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2
8.3%
2 2
8.3%
3 2
8.3%
4 2
8.3%
5 2
8.3%
6 2
8.3%
7 2
8.3%
8 2
8.3%
9 2
8.3%
10 2
8.3%
Other values (2) 4
16.7%
ValueCountFrequency (%)
1 2
8.3%
2 2
8.3%
3 2
8.3%
4 2
8.3%
5 2
8.3%
6 2
8.3%
7 2
8.3%
8 2
8.3%
9 2
8.3%
10 2
8.3%
ValueCountFrequency (%)
12 2
8.3%
11 2
8.3%
10 2
8.3%
9 2
8.3%
8 2
8.3%
7 2
8.3%
6 2
8.3%
5 2
8.3%
4 2
8.3%
3 2
8.3%

맑음
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)50.0%
Missing2
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean8
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:42.015017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median8.5
Q39
95-th percentile14.95
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.163332
Coefficient of variation (CV)0.5204165
Kurtosis-0.16304333
Mean8
Median Absolute Deviation (MAD)2.5
Skewness0.017420535
Sum176
Variance17.333333
MonotonicityNot monotonic
2023-12-13T02:12:42.131468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 6
25.0%
8 3
12.5%
6 3
12.5%
1 3
12.5%
12 1
 
4.2%
14 1
 
4.2%
4 1
 
4.2%
11 1
 
4.2%
15 1
 
4.2%
5 1
 
4.2%
(Missing) 2
 
8.3%
ValueCountFrequency (%)
1 3
12.5%
4 1
 
4.2%
5 1
 
4.2%
6 3
12.5%
8 3
12.5%
9 6
25.0%
11 1
 
4.2%
12 1
 
4.2%
14 1
 
4.2%
15 1
 
4.2%
ValueCountFrequency (%)
16 1
 
4.2%
15 1
 
4.2%
14 1
 
4.2%
12 1
 
4.2%
11 1
 
4.2%
9 6
25.0%
8 3
12.5%
6 3
12.5%
5 1
 
4.2%
4 1
 
4.2%

구름조금
Real number (ℝ)

Distinct11
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0416667
Minimum4
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:42.257288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.15
Q17.75
median8.5
Q311.25
95-th percentile12.85
Maximum15
Range11
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.8357833
Coefficient of variation (CV)0.31363501
Kurtosis-0.36440261
Mean9.0416667
Median Absolute Deviation (MAD)1.5
Skewness0.028865779
Sum217
Variance8.0416667
MonotonicityNot monotonic
2023-12-13T02:12:42.369352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 6
25.0%
10 4
16.7%
12 4
16.7%
4 2
 
8.3%
7 2
 
8.3%
5 1
 
4.2%
6 1
 
4.2%
11 1
 
4.2%
15 1
 
4.2%
13 1
 
4.2%
ValueCountFrequency (%)
4 2
 
8.3%
5 1
 
4.2%
6 1
 
4.2%
7 2
 
8.3%
8 6
25.0%
9 1
 
4.2%
10 4
16.7%
11 1
 
4.2%
12 4
16.7%
13 1
 
4.2%
ValueCountFrequency (%)
15 1
 
4.2%
13 1
 
4.2%
12 4
16.7%
11 1
 
4.2%
10 4
16.7%
9 1
 
4.2%
8 6
25.0%
7 2
 
8.3%
6 1
 
4.2%
5 1
 
4.2%

구름많음
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8333333
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:42.469412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14.75
median6
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.0571544
Coefficient of variation (CV)0.35265503
Kurtosis-0.081576732
Mean5.8333333
Median Absolute Deviation (MAD)1
Skewness-0.31176317
Sum140
Variance4.2318841
MonotonicityNot monotonic
2023-12-13T02:12:42.571771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 5
20.8%
5 4
16.7%
7 4
16.7%
4 3
12.5%
9 3
12.5%
8 2
 
8.3%
3 2
 
8.3%
1 1
 
4.2%
ValueCountFrequency (%)
1 1
 
4.2%
3 2
 
8.3%
4 3
12.5%
5 4
16.7%
6 5
20.8%
7 4
16.7%
8 2
 
8.3%
9 3
12.5%
ValueCountFrequency (%)
9 3
12.5%
8 2
 
8.3%
7 4
16.7%
6 5
20.8%
5 4
16.7%
4 3
12.5%
3 2
 
8.3%
1 1
 
4.2%

흐림
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.25
Minimum3
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:42.691270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13.75
median8
Q39.25
95-th percentile17.85
Maximum23
Range20
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.2606001
Coefficient of variation (CV)0.63764849
Kurtosis1.5784536
Mean8.25
Median Absolute Deviation (MAD)3.5
Skewness1.2928671
Sum198
Variance27.673913
MonotonicityNot monotonic
2023-12-13T02:12:42.826473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 6
25.0%
8 4
16.7%
9 3
12.5%
6 2
 
8.3%
17 1
 
4.2%
12 1
 
4.2%
18 1
 
4.2%
23 1
 
4.2%
5 1
 
4.2%
7 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
3 6
25.0%
4 1
 
4.2%
5 1
 
4.2%
6 2
 
8.3%
7 1
 
4.2%
8 4
16.7%
9 3
12.5%
10 1
 
4.2%
12 1
 
4.2%
13 1
 
4.2%
ValueCountFrequency (%)
23 1
 
4.2%
18 1
 
4.2%
17 1
 
4.2%
13 1
 
4.2%
12 1
 
4.2%
10 1
 
4.2%
9 3
12.5%
8 4
16.7%
7 1
 
4.2%
6 2
8.3%

강수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.875
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:42.952515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.15
Q16
median8
Q310.25
95-th percentile15.85
Maximum21
Range19
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation4.6747471
Coefficient of variation (CV)0.52673207
Kurtosis0.52661879
Mean8.875
Median Absolute Deviation (MAD)2
Skewness0.89399877
Sum213
Variance21.853261
MonotonicityNot monotonic
2023-12-13T02:12:43.063156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
8 4
16.7%
6 4
16.7%
10 3
12.5%
15 3
12.5%
4 2
8.3%
5 1
 
4.2%
3 1
 
4.2%
2 1
 
4.2%
11 1
 
4.2%
16 1
 
4.2%
Other values (3) 3
12.5%
ValueCountFrequency (%)
2 1
 
4.2%
3 1
 
4.2%
4 2
8.3%
5 1
 
4.2%
6 4
16.7%
7 1
 
4.2%
8 4
16.7%
9 1
 
4.2%
10 3
12.5%
11 1
 
4.2%
ValueCountFrequency (%)
21 1
 
4.2%
16 1
 
4.2%
15 3
12.5%
11 1
 
4.2%
10 3
12.5%
9 1
 
4.2%
8 4
16.7%
7 1
 
4.2%
6 4
16.7%
5 1
 
4.2%

서리
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)72.7%
Missing13
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean9.0909091
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:43.170596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median8
Q313.5
95-th percentile16
Maximum16
Range14
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.9285808
Coefficient of variation (CV)0.54214389
Kurtosis-1.2555624
Mean9.0909091
Median Absolute Deviation (MAD)4
Skewness0.31928112
Sum100
Variance24.290909
MonotonicityNot monotonic
2023-12-13T02:12:43.297065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
16 2
 
8.3%
8 2
 
8.3%
7 2
 
8.3%
15 1
 
4.2%
5 1
 
4.2%
2 1
 
4.2%
12 1
 
4.2%
4 1
 
4.2%
(Missing) 13
54.2%
ValueCountFrequency (%)
2 1
4.2%
4 1
4.2%
5 1
4.2%
7 2
8.3%
8 2
8.3%
12 1
4.2%
15 1
4.2%
16 2
8.3%
ValueCountFrequency (%)
16 2
8.3%
15 1
4.2%
12 1
4.2%
8 2
8.3%
7 2
8.3%
5 1
4.2%
4 1
4.2%
2 1
4.2%

안개
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)52.4%
Missing3
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:43.413054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q37
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9495762
Coefficient of variation (CV)0.58991525
Kurtosis0.041095605
Mean5
Median Absolute Deviation (MAD)2
Skewness0.8398881
Sum105
Variance8.7
MonotonicityNot monotonic
2023-12-13T02:12:43.530935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 5
20.8%
2 3
12.5%
6 2
 
8.3%
4 2
 
8.3%
7 2
 
8.3%
5 2
 
8.3%
9 1
 
4.2%
10 1
 
4.2%
8 1
 
4.2%
12 1
 
4.2%
(Missing) 3
12.5%
ValueCountFrequency (%)
1 1
 
4.2%
2 3
12.5%
3 5
20.8%
4 2
 
8.3%
5 2
 
8.3%
6 2
 
8.3%
7 2
 
8.3%
8 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
ValueCountFrequency (%)
12 1
 
4.2%
10 1
 
4.2%
9 1
 
4.2%
8 1
 
4.2%
7 2
 
8.3%
6 2
 
8.3%
5 2
 
8.3%
4 2
 
8.3%
3 5
20.8%
2 3
12.5%

눈
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)70.0%
Missing14
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean4.6
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:43.640696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.5
Q37.5
95-th percentile9.65
Maximum11
Range10
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.4705107
Coefficient of variation (CV)0.75445885
Kurtosis-0.7543446
Mean4.6
Median Absolute Deviation (MAD)2.5
Skewness0.70015324
Sum46
Variance12.044444
MonotonicityNot monotonic
2023-12-13T02:12:43.754904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 2
 
8.3%
8 2
 
8.3%
1 2
 
8.3%
11 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
6 1
 
4.2%
(Missing) 14
58.3%
ValueCountFrequency (%)
1 2
8.3%
2 2
8.3%
3 1
4.2%
4 1
4.2%
6 1
4.2%
8 2
8.3%
11 1
4.2%
ValueCountFrequency (%)
11 1
4.2%
8 2
8.3%
6 1
4.2%
4 1
4.2%
3 1
4.2%
2 2
8.3%
1 2
8.3%

뇌전
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)42.9%
Missing10
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean2.4285714
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T02:12:43.893085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5.35
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6508406
Coefficient of variation (CV)0.6797579
Kurtosis0.078821185
Mean2.4285714
Median Absolute Deviation (MAD)1
Skewness0.984347
Sum34
Variance2.7252747
MonotonicityNot monotonic
2023-12-13T02:12:44.001578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 6
25.0%
3 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
(Missing) 10
41.7%
ValueCountFrequency (%)
1 6
25.0%
2 2
 
8.3%
3 3
12.5%
4 1
 
4.2%
5 1
 
4.2%
6 1
 
4.2%
ValueCountFrequency (%)
6 1
 
4.2%
5 1
 
4.2%
4 1
 
4.2%
3 3
12.5%
2 2
 
8.3%
1 6
25.0%

황사
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
16 
1
3 
2
3 
4
2 

Length

Max length4
Median length4
Mean length3
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row2
5th row1

Common Values

ValueCountFrequency (%)
<NA> 16
66.7%
1 3
 
12.5%
2 3
 
12.5%
4 2
 
8.3%

Length

2023-12-13T02:12:44.138900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:12:44.256163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
66.7%
1 3
 
12.5%
2 3
 
12.5%
4 2
 
8.3%

기준일
Date

CONSTANT 

Distinct1
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2023-06-05 00:00:00
Maximum2023-06-05 00:00:00
2023-12-13T02:12:44.387781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:44.503769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T02:12:40.094053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.021776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.850896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.039043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.433718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.470821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.487203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.614670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.657989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.768322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.188505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.084383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.940899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.143054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.526776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.574450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.577834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.704205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.773532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.163112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.284732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.156790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.067981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.243927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.629321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.667982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.674349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.804159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.898217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.256206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.382381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.225053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.219012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.363993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.728934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.760913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.773418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.913068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.988333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.346095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.493232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.306641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.364260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.806923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.847216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.876577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.898144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.037286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.097832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.439725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.595548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.392541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.496444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:32.906687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.960992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.977139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.021092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.143686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.217438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.565643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.683281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.472022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.592862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.025913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.084019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.077794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.176630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.238343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.323197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.680006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.761558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.616482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.719289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.139047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.174918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.191590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.296203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.353957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.454882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.781534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.857085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.710563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.831810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.243211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.281824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.292214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.405838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.467135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.543074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.895258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:40.955629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:30.780472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:31.922350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:33.341433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:34.375403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:35.391711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:36.502126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:37.549706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:38.647572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:12:39.992247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:12:44.589995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월맑음구름조금구름많음흐림강수서리안개눈뇌전황사
연도1.0000.0000.0000.5470.4000.3650.0000.7140.0000.6550.0000.487
월0.0001.0000.7790.7300.4680.4800.7720.7970.2450.0000.5740.556
맑음0.0000.7791.0000.4200.6500.0000.6010.6150.6560.4860.0000.620
구름조금0.5470.7300.4201.0000.1700.0000.0000.7890.0001.0000.6640.909
구름많음0.4000.4680.6500.1701.0000.0000.5530.7520.4720.0000.5160.000
흐림0.3650.4800.0000.0000.0001.0000.8200.0000.6520.0000.6230.000
강수0.0000.7720.6010.0000.5530.8201.0000.0000.0000.0000.5330.000
서리0.7140.7970.6150.7890.7520.0000.0001.0000.6620.807NaN1.000
안개0.0000.2450.6560.0000.4720.6520.0000.6621.0000.8700.0001.000
눈0.6550.0000.4861.0000.0000.0000.0000.8070.8701.000NaN1.000
뇌전0.0000.5740.0000.6640.5160.6230.533NaN0.000NaN1.0000.000
황사0.4870.5560.6200.9090.0000.0000.0001.0001.0001.0000.0001.000
2023-12-13T02:12:44.728853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
황사연도
황사1.0000.667
연도0.6671.000
2023-12-13T02:12:44.842177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월맑음구름조금구름많음흐림강수서리안개눈뇌전연도황사
월1.0000.090-0.0880.036-0.0750.154-0.204-0.446-0.099-0.0650.0000.274
맑음0.0901.0000.139-0.463-0.825-0.6650.005-0.1010.163-0.6510.0000.365
구름조금-0.0880.1391.000-0.126-0.484-0.491-0.446-0.2690.173-0.4440.3150.298
구름많음0.036-0.463-0.1261.0000.1580.099-0.0540.107-0.6350.2110.2310.000
흐림-0.075-0.825-0.4840.1581.0000.8850.4640.2870.0510.6570.0000.000
강수0.154-0.665-0.4910.0990.8851.0000.3770.2500.2460.6080.0000.000
서리-0.2040.005-0.446-0.0540.4640.3771.000-0.3420.463NaN0.4911.000
안개-0.446-0.101-0.2690.1070.2870.250-0.3421.000-0.267-0.1750.0001.000
눈-0.0990.1630.173-0.6350.0510.2460.463-0.2671.000NaN0.4031.000
뇌전-0.065-0.651-0.4440.2110.6570.608NaN-0.175NaN1.0000.0001.000
연도0.0000.0000.3150.2310.0000.0000.4910.0000.4030.0001.0000.667
황사0.2740.3650.2980.0000.0000.0001.0001.0001.0001.0000.6671.000

Missing values

2023-12-13T02:12:41.108455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:12:41.306450image/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.
2023-12-13T02:12:41.435750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도월맑음구름조금구름많음흐림강수서리안개눈뇌전황사기준일
0202018105851632<NA><NA>2023-06-05
1202029866101568<NA>12023-06-05
220203128833561<NA><NA>2023-06-05
32020414121322<NA><NA><NA>22023-06-05
4202056441715<NA>9<NA>212023-06-05
5202064861211<NA>10<NA>1<NA>2023-06-05
6202071571816<NA>4<NA>3<NA>2023-06-05
720208<NA>442321<NA>7<NA>6<NA>2023-06-05
82020967898<NA><NA><NA>3<NA>2023-06-05
9202010912734<NA>2<NA><NA>12023-06-05
연도월맑음구름조금구름많음흐림강수서리안개눈뇌전황사기준일
1420213810766782<NA>42023-06-05
152021496699<NA>3<NA><NA>22023-06-05
162021551151015<NA>5<NA>442023-06-05
1720216<NA>129910<NA>12<NA>1<NA>2023-06-05
1820217115788<NA>2<NA>2<NA>2023-06-05
19202181891315<NA>2<NA>5<NA>2023-06-05
202021967988<NA><NA><NA>3<NA>2023-06-05
212021109104810<NA>3<NA>1<NA>2023-06-05
22202111813547433<NA><NA>2023-06-05
232021121693368161<NA>2023-06-05