Overview

Dataset statistics

Number of variables13
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory118.7 B

Variable types

Categorical4
Numeric9

Dataset

Description인천광역시 일기일수 현황(기상현황별 맑음/ 구름조금 등)관측지점별(인천, 강화, 백령도 등) 데이터 자료를 제공합니다
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15054883&srcSe=7661IVAWM27C61E190

Alerts

맑음 is highly overall correlated with 흐림 and 1 other fieldsHigh correlation
흐림 is highly overall correlated with 맑음 and 1 other fieldsHigh correlation
강수 is highly overall correlated with 맑음 and 1 other fieldsHigh correlation
서리 is highly overall correlated with 눈High correlation
안개 is highly overall correlated with 뇌전High correlation
눈 is highly overall correlated with 서리High correlation
뇌전 is highly overall correlated with 안개High correlation
맑음 has 1 (2.8%) zerosZeros
강수 has 1 (2.8%) zerosZeros
서리 has 25 (69.4%) zerosZeros
안개 has 16 (44.4%) zerosZeros
눈 has 27 (75.0%) zerosZeros
뇌전 has 22 (61.1%) zerosZeros

Reproduction

Analysis started2024-03-18 02:21:16.851920
Analysis finished2024-03-18 02:21:25.933154
Duration9.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측지점별
Categorical

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
인천
12 
강화
12 
백령도
12 

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천 12
33.3%
강화 12
33.3%
백령도 12
33.3%

Length

2024-03-18T11:21:26.033645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:21:26.215668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천 12
33.3%
강화 12
33.3%
백령도 12
33.3%

월별
Categorical

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
1월
3 
2월
3 
3월
3 
4월
3 
5월
3 
Other values (7)
21 

Length

Max length3
Median length2
Mean length2.25
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1월
2nd row2월
3rd row3월
4th row4월
5th row5월

Common Values

ValueCountFrequency (%)
1월 3
8.3%
2월 3
8.3%
3월 3
8.3%
4월 3
8.3%
5월 3
8.3%
6월 3
8.3%
7월 3
8.3%
8월 3
8.3%
9월 3
8.3%
10월 3
8.3%
Other values (2) 6
16.7%

Length

2024-03-18T11:21:26.348379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1월 3
8.3%
2월 3
8.3%
3월 3
8.3%
4월 3
8.3%
5월 3
8.3%
6월 3
8.3%
7월 3
8.3%
8월 3
8.3%
9월 3
8.3%
10월 3
8.3%
Other values (2) 6
16.7%

맑음
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3055556
Minimum0
Maximum14
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:26.485793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14.75
median7
Q310.25
95-th percentile12.25
Maximum14
Range14
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.7018871
Coefficient of variation (CV)0.50672219
Kurtosis-1.002706
Mean7.3055556
Median Absolute Deviation (MAD)3
Skewness-0.063029596
Sum263
Variance13.703968
MonotonicityNot monotonic
2024-03-18T11:21:26.619375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 5
13.9%
5 4
11.1%
12 4
11.1%
2 4
11.1%
4 3
8.3%
11 3
8.3%
9 3
8.3%
10 3
8.3%
6 2
 
5.6%
14 1
 
2.8%
Other values (4) 4
11.1%
ValueCountFrequency (%)
0 1
 
2.8%
2 4
11.1%
3 1
 
2.8%
4 3
8.3%
5 4
11.1%
6 2
 
5.6%
7 5
13.9%
8 1
 
2.8%
9 3
8.3%
10 3
8.3%
ValueCountFrequency (%)
14 1
 
2.8%
13 1
 
2.8%
12 4
11.1%
11 3
8.3%
10 3
8.3%
9 3
8.3%
8 1
 
2.8%
7 5
13.9%
6 2
 
5.6%
5 4
11.1%

구름조금
Real number (ℝ)

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1111111
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:26.737661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q17
median8
Q310.25
95-th percentile12
Maximum15
Range13
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.7854497
Coefficient of variation (CV)0.34341161
Kurtosis-0.04583248
Mean8.1111111
Median Absolute Deviation (MAD)2
Skewness0.088792983
Sum292
Variance7.7587302
MonotonicityNot monotonic
2024-03-18T11:21:26.879280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 8
22.2%
11 6
16.7%
8 6
16.7%
10 3
 
8.3%
4 3
 
8.3%
5 3
 
8.3%
9 2
 
5.6%
12 2
 
5.6%
15 1
 
2.8%
6 1
 
2.8%
ValueCountFrequency (%)
2 1
 
2.8%
4 3
 
8.3%
5 3
 
8.3%
6 1
 
2.8%
7 8
22.2%
8 6
16.7%
9 2
 
5.6%
10 3
 
8.3%
11 6
16.7%
12 2
 
5.6%
ValueCountFrequency (%)
15 1
 
2.8%
12 2
 
5.6%
11 6
16.7%
10 3
 
8.3%
9 2
 
5.6%
8 6
16.7%
7 8
22.2%
6 1
 
2.8%
5 3
 
8.3%
4 3
 
8.3%

구름많음
Real number (ℝ)

Distinct10
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8888889
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:26.991763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.75
Q15
median7
Q38.25
95-th percentile10
Maximum12
Range10
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation2.2775648
Coefficient of variation (CV)0.33061425
Kurtosis-0.37064608
Mean6.8888889
Median Absolute Deviation (MAD)2
Skewness0.068030011
Sum248
Variance5.1873016
MonotonicityNot monotonic
2024-03-18T11:21:27.081631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 8
22.2%
5 6
16.7%
10 5
13.9%
8 4
11.1%
6 4
11.1%
4 3
 
8.3%
9 3
 
8.3%
3 1
 
2.8%
2 1
 
2.8%
12 1
 
2.8%
ValueCountFrequency (%)
2 1
 
2.8%
3 1
 
2.8%
4 3
 
8.3%
5 6
16.7%
6 4
11.1%
7 8
22.2%
8 4
11.1%
9 3
 
8.3%
10 5
13.9%
12 1
 
2.8%
ValueCountFrequency (%)
12 1
 
2.8%
10 5
13.9%
9 3
 
8.3%
8 4
11.1%
7 8
22.2%
6 4
11.1%
5 6
16.7%
4 3
 
8.3%
3 1
 
2.8%
2 1
 
2.8%

흐림
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1111111
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:27.175582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.75
Q15.75
median7
Q310.25
95-th percentile16
Maximum17
Range15
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.0268937
Coefficient of variation (CV)0.49646635
Kurtosis-0.12882101
Mean8.1111111
Median Absolute Deviation (MAD)2
Skewness0.95100842
Sum292
Variance16.215873
MonotonicityNot monotonic
2024-03-18T11:21:27.283465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 7
19.4%
7 7
19.4%
5 5
13.9%
15 3
8.3%
8 2
 
5.6%
4 2
 
5.6%
11 2
 
5.6%
16 2
 
5.6%
2 1
 
2.8%
9 1
 
2.8%
Other values (4) 4
11.1%
ValueCountFrequency (%)
2 1
 
2.8%
3 1
 
2.8%
4 2
 
5.6%
5 5
13.9%
6 7
19.4%
7 7
19.4%
8 2
 
5.6%
9 1
 
2.8%
10 1
 
2.8%
11 2
 
5.6%
ValueCountFrequency (%)
17 1
 
2.8%
16 2
 
5.6%
15 3
8.3%
12 1
 
2.8%
11 2
 
5.6%
10 1
 
2.8%
9 1
 
2.8%
8 2
 
5.6%
7 7
19.4%
6 7
19.4%

강수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9722222
Minimum0
Maximum15
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:27.384413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.75
Q14
median7
Q310
95-th percentile13.25
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6291369
Coefficient of variation (CV)0.52051366
Kurtosis-0.46910856
Mean6.9722222
Median Absolute Deviation (MAD)3
Skewness0.1869727
Sum251
Variance13.170635
MonotonicityNot monotonic
2024-03-18T11:21:27.491578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 6
16.7%
3 4
11.1%
9 4
11.1%
7 4
11.1%
4 4
11.1%
5 3
8.3%
6 2
 
5.6%
8 2
 
5.6%
2 1
 
2.8%
14 1
 
2.8%
Other values (5) 5
13.9%
ValueCountFrequency (%)
0 1
 
2.8%
1 1
 
2.8%
2 1
 
2.8%
3 4
11.1%
4 4
11.1%
5 3
8.3%
6 2
5.6%
7 4
11.1%
8 2
5.6%
9 4
11.1%
ValueCountFrequency (%)
15 1
 
2.8%
14 1
 
2.8%
13 1
 
2.8%
11 1
 
2.8%
10 6
16.7%
9 4
11.1%
8 2
 
5.6%
7 4
11.1%
6 2
 
5.6%
5 3
8.3%

서리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2222222
Minimum0
Maximum20
Zeros25
Zeros (%)69.4%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:27.609857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11.25
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.5613977
Coefficient of variation (CV)2.052629
Kurtosis6.1718127
Mean2.2222222
Median Absolute Deviation (MAD)0
Skewness2.4487418
Sum80
Variance20.806349
MonotonicityNot monotonic
2024-03-18T11:21:27.710231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 25
69.4%
4 2
 
5.6%
2 2
 
5.6%
20 1
 
2.8%
10 1
 
2.8%
11 1
 
2.8%
12 1
 
2.8%
8 1
 
2.8%
1 1
 
2.8%
6 1
 
2.8%
ValueCountFrequency (%)
0 25
69.4%
1 1
 
2.8%
2 2
 
5.6%
4 2
 
5.6%
6 1
 
2.8%
8 1
 
2.8%
10 1
 
2.8%
11 1
 
2.8%
12 1
 
2.8%
20 1
 
2.8%
ValueCountFrequency (%)
20 1
 
2.8%
12 1
 
2.8%
11 1
 
2.8%
10 1
 
2.8%
8 1
 
2.8%
6 1
 
2.8%
4 2
 
5.6%
2 2
 
5.6%
1 1
 
2.8%
0 25
69.4%

안개
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7222222
Minimum0
Maximum21
Zeros16
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:27.808723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile15.5
Maximum21
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4908293
Coefficient of variation (CV)1.4751482
Kurtosis2.2857346
Mean3.7222222
Median Absolute Deviation (MAD)1
Skewness1.7105008
Sum134
Variance30.149206
MonotonicityNot monotonic
2024-03-18T11:21:27.905329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 16
44.4%
2 3
 
8.3%
1 3
 
8.3%
3 3
 
8.3%
5 2
 
5.6%
9 2
 
5.6%
11 1
 
2.8%
8 1
 
2.8%
15 1
 
2.8%
12 1
 
2.8%
Other values (3) 3
 
8.3%
ValueCountFrequency (%)
0 16
44.4%
1 3
 
8.3%
2 3
 
8.3%
3 3
 
8.3%
4 1
 
2.8%
5 2
 
5.6%
8 1
 
2.8%
9 2
 
5.6%
11 1
 
2.8%
12 1
 
2.8%
ValueCountFrequency (%)
21 1
 
2.8%
17 1
 
2.8%
15 1
 
2.8%
12 1
 
2.8%
11 1
 
2.8%
9 2
5.6%
8 1
 
2.8%
5 2
5.6%
4 1
 
2.8%
3 3
8.3%

눈
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0555556
Minimum0
Maximum9
Zeros27
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:28.310249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile5.75
Maximum9
Range9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.2417397
Coefficient of variation (CV)2.1237534
Kurtosis5.710912
Mean1.0555556
Median Absolute Deviation (MAD)0
Skewness2.4401161
Sum38
Variance5.0253968
MonotonicityNot monotonic
2024-03-18T11:21:28.462580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 27
75.0%
2 3
 
8.3%
3 2
 
5.6%
5 1
 
2.8%
8 1
 
2.8%
4 1
 
2.8%
9 1
 
2.8%
ValueCountFrequency (%)
0 27
75.0%
2 3
 
8.3%
3 2
 
5.6%
4 1
 
2.8%
5 1
 
2.8%
8 1
 
2.8%
9 1
 
2.8%
ValueCountFrequency (%)
9 1
 
2.8%
8 1
 
2.8%
5 1
 
2.8%
4 1
 
2.8%
3 2
 
5.6%
2 3
 
8.3%
0 27
75.0%

뇌전
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91666667
Minimum0
Maximum7
Zeros22
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-18T11:21:28.597835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5743479
Coefficient of variation (CV)1.7174705
Kurtosis5.6007317
Mean0.91666667
Median Absolute Deviation (MAD)0
Skewness2.2382712
Sum33
Variance2.4785714
MonotonicityNot monotonic
2024-03-18T11:21:28.706080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22
61.1%
1 6
 
16.7%
2 3
 
8.3%
3 2
 
5.6%
4 2
 
5.6%
7 1
 
2.8%
ValueCountFrequency (%)
0 22
61.1%
1 6
 
16.7%
2 3
 
8.3%
3 2
 
5.6%
4 2
 
5.6%
7 1
 
2.8%
ValueCountFrequency (%)
7 1
 
2.8%
4 2
 
5.6%
3 2
 
5.6%
2 3
 
8.3%
1 6
 
16.7%
0 22
61.1%

폭풍
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
28 
1
3 
5
3 
2
 
2

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 28
77.8%
1 3
 
8.3%
5 3
 
8.3%
2 2
 
5.6%

Length

2024-03-18T11:21:28.819031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:21:28.913961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
77.8%
1 3
 
8.3%
5 3
 
8.3%
2 2
 
5.6%

황사
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
28 
1
4 
2
3 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
0 28
77.8%
1 4
 
11.1%
2 3
 
8.3%
3 1
 
2.8%

Length

2024-03-18T11:21:29.028582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:21:29.135298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 28
77.8%
1 4
 
11.1%
2 3
 
8.3%
3 1
 
2.8%

Interactions

2024-03-18T11:21:24.800253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.485430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.303107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.055213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.870659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.930290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.777165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.703944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.516047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.905620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.559896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.383109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.132775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.977250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.012355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.867475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.842554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.600109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.015595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.638322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.477221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.206566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.308237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.078823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.970251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.928709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.027030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.120722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.711696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.552330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.293931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.383792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.147953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.057927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.004423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.136534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.208295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.817530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.649797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.437091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.475634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.224325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.151290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.083843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.231474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.301087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:17.915123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.723279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.537115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.564075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.320925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.287614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.196171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.357479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.417976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.018172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.802913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.642429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.678397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.413457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.422585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.292097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.492517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.505569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.118217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.900557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.714375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.756116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.490218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.508016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.369719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.592781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:25.580788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.198147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:18.976084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:19.775888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:20.845410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:21.617606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:22.587686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:23.437449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:21:24.689605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:21:29.219657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측지점별월별맑음구름조금구름많음흐림강수서리안개눈뇌전폭풍황사
관측지점별1.0000.0000.0000.0000.2810.0000.0000.4160.6980.1430.7370.3810.139
월별0.0001.0000.4370.4870.4640.6030.8030.2680.5570.6400.0000.5120.819
맑음0.0000.4371.0000.0000.0000.5180.6480.0000.5940.0000.4390.0000.000
구름조금0.0000.4870.0001.0000.6200.5730.0000.0000.4080.0000.5350.0000.000
구름많음0.2810.4640.0000.6201.0000.6150.7080.0000.0000.0000.0000.3000.000
흐림0.0000.6030.5180.5730.6151.0000.5790.2220.2990.0000.5750.0000.000
강수0.0000.8030.6480.0000.7080.5791.0000.7270.6730.7040.6270.0000.000
서리0.4160.2680.0000.0000.0000.2220.7271.0000.0000.9320.0000.8930.000
안개0.6980.5570.5940.4080.0000.2990.6730.0001.0000.0000.8410.4720.000
눈0.1430.6400.0000.0000.0000.0000.7040.9320.0001.0000.0000.6510.000
뇌전0.7370.0000.4390.5350.0000.5750.6270.0000.8410.0001.0000.0000.391
폭풍0.3810.5120.0000.0000.3000.0000.0000.8930.4720.6510.0001.0000.563
황사0.1390.8190.0000.0000.0000.0000.0000.0000.0000.0000.3910.5631.000
2024-03-18T11:21:29.384450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별폭풍관측지점별황사
월별1.0000.2030.0000.434
폭풍0.2031.0000.3620.243
관측지점별0.0000.3621.0000.120
황사0.4340.2430.1201.000
2024-03-18T11:21:29.505732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
맑음구름조금구름많음흐림강수서리안개눈뇌전관측지점별월별폭풍황사
맑음1.000-0.049-0.269-0.699-0.5730.091-0.107-0.010-0.1480.0000.1450.0000.000
구름조금-0.0491.000-0.248-0.372-0.2570.200-0.0300.179-0.1580.0000.1920.0000.000
구름많음-0.269-0.2481.000-0.1670.2160.070-0.0100.030-0.0540.0530.1760.0000.000
흐림-0.699-0.372-0.1671.0000.574-0.3180.123-0.1820.2960.0000.3240.0000.000
강수-0.573-0.2570.2160.5741.000-0.2910.068-0.1590.3340.0000.4400.0000.000
서리0.0910.2000.070-0.318-0.2911.0000.1360.896-0.1760.1720.0000.4090.000
안개-0.107-0.030-0.0100.1230.0680.1361.000-0.0290.5940.3590.2380.2840.000
눈-0.0100.1790.030-0.182-0.1590.896-0.0291.000-0.1430.0560.3320.4830.000
뇌전-0.148-0.158-0.0540.2960.334-0.1760.594-0.1431.0000.3920.0000.0000.245
관측지점별0.0000.0000.0530.0000.0000.1720.3590.0560.3921.0000.0000.3620.120
월별0.1450.1920.1760.3240.4400.0000.2380.3320.0000.0001.0000.2030.434
폭풍0.0000.0000.0000.0000.0000.4090.2840.4830.0000.3620.2031.0000.243
황사0.0000.0000.0000.0000.0000.0000.0000.0000.2450.1200.4340.2431.000

Missing values

2024-03-18T11:21:25.705438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:21:25.864865image/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

관측지점별월별맑음구름조금구름많음흐림강수서리안개눈뇌전폭풍황사
0인천1월14114222023000
1인천2월4115831055000
2인천3월710104611112200
3인천4월511598080101
4인천5월129463020001
5인천6월8471110010300
6인천7월08716140150400
7인천8월2121079010300
8인천9월553179030410
9인천10월127845010102
관측지점별월별맑음구름조금구름많음흐림강수서리안개눈뇌전폭풍황사
26백령도3월9710534120050
27백령도4월710674130001
28백령도5월118665090101
29백령도6월54615110170700
30백령도7월5291590210200
31백령도8월611779090100
32백령도9월477127030010
33백령도10월117764040113
34백령도11월108577202152
35백령도12월2581610609050