Overview

Dataset statistics

Number of variables13
Number of observations48
Missing cells168
Missing cells (%)26.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory118.8 B

Variable types

Text1
Numeric12

Dataset

Description전국에 흩어져 있는 송전탑에서 관측한 풍속정보
Author한국전력공사
URLhttps://www.data.go.kr/data/3076395/fileData.do

Alerts

4월 is highly overall correlated with 5월 and 1 other fieldsHigh correlation
5월 is highly overall correlated with 4월 and 3 other fieldsHigh correlation
6월 is highly overall correlated with 7월High correlation
7월 is highly overall correlated with 6월High correlation
9월 is highly overall correlated with 5월 and 3 other fieldsHigh correlation
10월 is highly overall correlated with 9월High correlation
11월 is highly overall correlated with 4월 and 3 other fieldsHigh correlation
12월 is highly overall correlated with 5월 and 2 other fieldsHigh correlation
1월 has 19 (39.6%) missing valuesMissing
2월 has 15 (31.2%) missing valuesMissing
3월 has 11 (22.9%) missing valuesMissing
4월 has 12 (25.0%) missing valuesMissing
5월 has 13 (27.1%) missing valuesMissing
6월 has 17 (35.4%) missing valuesMissing
7월 has 14 (29.2%) missing valuesMissing
8월 has 3 (6.2%) missing valuesMissing
9월 has 20 (41.7%) missing valuesMissing
10월 has 1 (2.1%) missing valuesMissing
11월 has 21 (43.8%) missing valuesMissing
12월 has 22 (45.8%) missing valuesMissing
지역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:22:54.291456
Analysis finished2023-12-12 11:23:18.538503
Duration24.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-12T20:23:18.816709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.125
Min length2

Characters and Unicode

Total characters102
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row강릉
2nd row거제
3rd row경주
4th row경주강동
5th row고양
ValueCountFrequency (%)
강릉 1
 
2.1%
거제 1
 
2.1%
임실 1
 
2.1%
양양 1
 
2.1%
여수 1
 
2.1%
영광 1
 
2.1%
영덕 1
 
2.1%
영도 1
 
2.1%
완도 1
 
2.1%
울산 1
 
2.1%
Other values (38) 38
79.2%
2023-12-12T20:23:19.448127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
5.9%
6
 
5.9%
5
 
4.9%
4
 
3.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (51) 63
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
5.9%
6
 
5.9%
5
 
4.9%
4
 
3.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (51) 63
61.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
5.9%
6
 
5.9%
5
 
4.9%
4
 
3.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (51) 63
61.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
5.9%
6
 
5.9%
5
 
4.9%
4
 
3.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (51) 63
61.8%

1월
Real number (ℝ)

MISSING 

Distinct27
Distinct (%)93.1%
Missing19
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean23.731034
Minimum3.5
Maximum63.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:19.645003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile8.86
Q118
median20.2
Q327.4
95-th percentile50.76
Maximum63.9
Range60.4
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation12.719111
Coefficient of variation (CV)0.53596952
Kurtosis3.5909095
Mean23.731034
Median Absolute Deviation (MAD)4.1
Skewness1.6657298
Sum688.2
Variance161.77579
MonotonicityNot monotonic
2023-12-12T20:23:19.847878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
18.0 2
 
4.2%
16.0 2
 
4.2%
16.9 1
 
2.1%
24.5 1
 
2.1%
27.4 1
 
2.1%
19.4 1
 
2.1%
27.5 1
 
2.1%
18.4 1
 
2.1%
28.8 1
 
2.1%
19.6 1
 
2.1%
Other values (17) 17
35.4%
(Missing) 19
39.6%
ValueCountFrequency (%)
3.5 1
2.1%
5.1 1
2.1%
14.5 1
2.1%
16.0 2
4.2%
16.9 1
2.1%
17.5 1
2.1%
18.0 2
4.2%
18.4 1
2.1%
19.1 1
2.1%
19.4 1
2.1%
ValueCountFrequency (%)
63.9 1
2.1%
55.2 1
2.1%
44.1 1
2.1%
37.5 1
2.1%
28.8 1
2.1%
27.6 1
2.1%
27.5 1
2.1%
27.4 1
2.1%
24.5 1
2.1%
24.3 1
2.1%

2월
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)84.8%
Missing15
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean22.351515
Minimum11.9
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:20.070344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.9
5-th percentile13.46
Q118.5
median20.7
Q325.2
95-th percentile29.8
Maximum56
Range44.1
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation7.6227505
Coefficient of variation (CV)0.34103954
Kurtosis11.443012
Mean22.351515
Median Absolute Deviation (MAD)3.5
Skewness2.7098724
Sum737.6
Variance58.106326
MonotonicityNot monotonic
2023-12-12T20:23:20.311452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
18.8 2
 
4.2%
24.8 2
 
4.2%
18.5 2
 
4.2%
26.3 2
 
4.2%
29.8 2
 
4.2%
27.1 1
 
2.1%
18.0 1
 
2.1%
56.0 1
 
2.1%
20.7 1
 
2.1%
21.8 1
 
2.1%
Other values (18) 18
37.5%
(Missing) 15
31.2%
ValueCountFrequency (%)
11.9 1
2.1%
12.5 1
2.1%
14.1 1
2.1%
17.1 1
2.1%
17.2 1
2.1%
18.0 1
2.1%
18.2 1
2.1%
18.5 2
4.2%
18.6 1
2.1%
18.8 2
4.2%
ValueCountFrequency (%)
56.0 1
2.1%
29.8 2
4.2%
28.5 1
2.1%
27.1 1
2.1%
27.0 1
2.1%
26.3 2
4.2%
25.2 1
2.1%
24.9 1
2.1%
24.8 2
4.2%
22.7 1
2.1%

3월
Real number (ℝ)

MISSING 

Distinct34
Distinct (%)91.9%
Missing11
Missing (%)22.9%
Infinite0
Infinite (%)0.0%
Mean28.175676
Minimum7.9
Maximum91.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:20.538404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.9
5-th percentile10.28
Q119.6
median24.2
Q327.9
95-th percentile67.84
Maximum91.3
Range83.4
Interquartile range (IQR)8.3

Descriptive statistics

Standard deviation17.908043
Coefficient of variation (CV)0.63558522
Kurtosis5.3891151
Mean28.175676
Median Absolute Deviation (MAD)4.1
Skewness2.2555026
Sum1042.5
Variance320.698
MonotonicityNot monotonic
2023-12-12T20:23:20.748365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20.1 2
 
4.2%
26.9 2
 
4.2%
19.6 2
 
4.2%
24.2 1
 
2.1%
33.4 1
 
2.1%
28.2 1
 
2.1%
24.9 1
 
2.1%
82.0 1
 
2.1%
19.2 1
 
2.1%
20.2 1
 
2.1%
Other values (24) 24
50.0%
(Missing) 11
22.9%
ValueCountFrequency (%)
7.9 1
2.1%
8.6 1
2.1%
10.7 1
2.1%
11.0 1
2.1%
14.0 1
2.1%
18.1 1
2.1%
18.6 1
2.1%
19.2 1
2.1%
19.6 2
4.2%
20.1 2
4.2%
ValueCountFrequency (%)
91.3 1
2.1%
82.0 1
2.1%
64.3 1
2.1%
55.3 1
2.1%
39.7 1
2.1%
35.5 1
2.1%
33.4 1
2.1%
28.7 1
2.1%
28.2 1
2.1%
27.9 1
2.1%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)94.4%
Missing12
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean22.947222
Minimum5
Maximum75.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:20.965839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16.325
Q119.55
median21.55
Q324.725
95-th percentile28.2
Maximum75.5
Range70.5
Interquartile range (IQR)5.175

Descriptive statistics

Standard deviation10.054721
Coefficient of variation (CV)0.43816725
Kurtosis22.444123
Mean22.947222
Median Absolute Deviation (MAD)2.95
Skewness4.1112926
Sum826.1
Variance101.09742
MonotonicityNot monotonic
2023-12-12T20:23:21.210375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
24.8 2
 
4.2%
21.5 2
 
4.2%
75.5 1
 
2.1%
27.1 1
 
2.1%
21.6 1
 
2.1%
23.4 1
 
2.1%
19.9 1
 
2.1%
20.1 1
 
2.1%
22.4 1
 
2.1%
18.2 1
 
2.1%
Other values (24) 24
50.0%
(Missing) 12
25.0%
ValueCountFrequency (%)
5.0 1
2.1%
12.8 1
2.1%
17.5 1
2.1%
17.9 1
2.1%
18.1 1
2.1%
18.2 1
2.1%
18.5 1
2.1%
18.7 1
2.1%
19.4 1
2.1%
19.6 1
2.1%
ValueCountFrequency (%)
75.5 1
2.1%
30.3 1
2.1%
27.5 1
2.1%
27.1 1
2.1%
27.0 1
2.1%
26.7 1
2.1%
24.9 1
2.1%
24.8 2
4.2%
24.7 1
2.1%
24.6 1
2.1%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)97.1%
Missing13
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean22.705714
Minimum5.6
Maximum81.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:21.437746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile6.82
Q118.1
median20.5
Q323
95-th percentile43.61
Maximum81.8
Range76.2
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation13.720636
Coefficient of variation (CV)0.60428118
Kurtosis11.034
Mean22.705714
Median Absolute Deviation (MAD)2.5
Skewness2.9644019
Sum794.7
Variance188.25585
MonotonicityNot monotonic
2023-12-12T20:23:21.667826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
17.4 2
 
4.2%
6.4 1
 
2.1%
23.5 1
 
2.1%
24.5 1
 
2.1%
20.5 1
 
2.1%
21.0 1
 
2.1%
18.0 1
 
2.1%
60.2 1
 
2.1%
26.0 1
 
2.1%
27.0 1
 
2.1%
Other values (24) 24
50.0%
(Missing) 13
27.1%
ValueCountFrequency (%)
5.6 1
2.1%
6.4 1
2.1%
7.0 1
2.1%
9.9 1
2.1%
16.3 1
2.1%
17.3 1
2.1%
17.4 2
4.2%
18.0 1
2.1%
18.2 1
2.1%
18.3 1
2.1%
ValueCountFrequency (%)
81.8 1
2.1%
60.2 1
2.1%
36.5 1
2.1%
28.6 1
2.1%
27.0 1
2.1%
26.0 1
2.1%
24.7 1
2.1%
24.5 1
2.1%
23.5 1
2.1%
22.5 1
2.1%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)90.3%
Missing17
Missing (%)35.4%
Infinite0
Infinite (%)0.0%
Mean21.041935
Minimum8.5
Maximum84.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:21.890873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile9.85
Q112.2
median15.2
Q322.15
95-th percentile49.4
Maximum84.3
Range75.8
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation15.600273
Coefficient of variation (CV)0.74138964
Kurtosis8.9116498
Mean21.041935
Median Absolute Deviation (MAD)3.8
Skewness2.7776529
Sum652.3
Variance243.36852
MonotonicityNot monotonic
2023-12-12T20:23:22.111767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8.5 2
 
4.2%
14.0 2
 
4.2%
11.5 2
 
4.2%
13.4 1
 
2.1%
17.2 1
 
2.1%
20.3 1
 
2.1%
11.4 1
 
2.1%
25.7 1
 
2.1%
55.0 1
 
2.1%
11.6 1
 
2.1%
Other values (18) 18
37.5%
(Missing) 17
35.4%
ValueCountFrequency (%)
8.5 2
4.2%
11.2 1
2.1%
11.4 1
2.1%
11.5 2
4.2%
11.6 1
2.1%
11.8 1
2.1%
12.6 1
2.1%
12.8 1
2.1%
13.4 1
2.1%
14.0 2
4.2%
ValueCountFrequency (%)
84.3 1
2.1%
55.0 1
2.1%
43.8 1
2.1%
36.9 1
2.1%
25.7 1
2.1%
25.3 1
2.1%
25.2 1
2.1%
22.7 1
2.1%
21.6 1
2.1%
21.5 1
2.1%

7월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)85.3%
Missing14
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean17.823529
Minimum6.5
Maximum55.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:22.330796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile7.415
Q110.95
median13.3
Q321.075
95-th percentile39.92
Maximum55.6
Range49.1
Interquartile range (IQR)10.125

Descriptive statistics

Standard deviation11.021936
Coefficient of variation (CV)0.61839243
Kurtosis3.5756664
Mean17.823529
Median Absolute Deviation (MAD)3.8
Skewness1.8683268
Sum606
Variance121.48307
MonotonicityNot monotonic
2023-12-12T20:23:22.523097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
12.5 3
 
6.2%
10.9 2
 
4.2%
11.8 2
 
4.2%
13.3 2
 
4.2%
12.4 1
 
2.1%
7.8 1
 
2.1%
18.0 1
 
2.1%
15.9 1
 
2.1%
15.0 1
 
2.1%
55.6 1
 
2.1%
Other values (19) 19
39.6%
(Missing) 14
29.2%
ValueCountFrequency (%)
6.5 1
2.1%
6.7 1
2.1%
7.8 1
2.1%
9.4 1
2.1%
9.6 1
2.1%
10.4 1
2.1%
10.8 1
2.1%
10.9 2
4.2%
11.1 1
2.1%
11.8 2
4.2%
ValueCountFrequency (%)
55.6 1
2.1%
42.0 1
2.1%
38.8 1
2.1%
36.9 1
2.1%
28.3 1
2.1%
23.1 1
2.1%
22.8 1
2.1%
21.5 1
2.1%
21.3 1
2.1%
20.4 1
2.1%

8월
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)84.4%
Missing3
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean31.875556
Minimum2.7
Maximum365.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:22.721921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile7.38
Q118.5
median23.4
Q325.7
95-th percentile31.48
Maximum365.4
Range362.7
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation56.003146
Coefficient of variation (CV)1.7569308
Kurtosis30.57014
Mean31.875556
Median Absolute Deviation (MAD)3.3
Skewness5.3746591
Sum1434.4
Variance3136.3523
MonotonicityNot monotonic
2023-12-12T20:23:22.934214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
23.5 3
 
6.2%
25.7 2
 
4.2%
24.5 2
 
4.2%
13.9 2
 
4.2%
23.4 2
 
4.2%
28.6 2
 
4.2%
13.7 1
 
2.1%
24.3 1
 
2.1%
20.4 1
 
2.1%
3.5 1
 
2.1%
Other values (28) 28
58.3%
(Missing) 3
 
6.2%
ValueCountFrequency (%)
2.7 1
2.1%
3.5 1
2.1%
6.7 1
2.1%
10.1 1
2.1%
12.2 1
2.1%
12.9 1
2.1%
13.7 1
2.1%
13.9 2
4.2%
14.4 1
2.1%
14.7 1
2.1%
ValueCountFrequency (%)
365.4 1
2.1%
171.6 1
2.1%
31.7 1
2.1%
30.6 1
2.1%
29.2 1
2.1%
28.6 2
4.2%
28.1 1
2.1%
27.3 1
2.1%
26.7 1
2.1%
25.7 2
4.2%

9월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)96.4%
Missing20
Missing (%)41.7%
Infinite0
Infinite (%)0.0%
Mean34.457143
Minimum5.1
Maximum392.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:23.107553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile11.8
Q115.175
median17.3
Q321.05
95-th percentile91
Maximum392.8
Range387.7
Interquartile range (IQR)5.875

Descriptive statistics

Standard deviation73.285653
Coefficient of variation (CV)2.1268639
Kurtosis23.201497
Mean34.457143
Median Absolute Deviation (MAD)2.6
Skewness4.7246403
Sum964.8
Variance5370.787
MonotonicityNot monotonic
2023-12-12T20:23:23.330886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
11.8 2
 
4.2%
17.4 1
 
2.1%
5.1 1
 
2.1%
14.4 1
 
2.1%
20.0 1
 
2.1%
19.2 1
 
2.1%
15.2 1
 
2.1%
126.0 1
 
2.1%
17.5 1
 
2.1%
13.9 1
 
2.1%
Other values (17) 17
35.4%
(Missing) 20
41.7%
ValueCountFrequency (%)
5.1 1
2.1%
11.8 2
4.2%
13.9 1
2.1%
14.4 1
2.1%
14.8 1
2.1%
15.1 1
2.1%
15.2 1
2.1%
15.5 1
2.1%
15.6 1
2.1%
16.7 1
2.1%
ValueCountFrequency (%)
392.8 1
2.1%
126.0 1
2.1%
26.0 1
2.1%
22.5 1
2.1%
22.4 1
2.1%
21.3 1
2.1%
21.2 1
2.1%
21.0 1
2.1%
20.0 1
2.1%
19.2 1
2.1%

10월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)91.5%
Missing1
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean36.053191
Minimum1.6
Maximum511.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:24.065099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.83
Q13.1
median16.8
Q327.6
95-th percentile65.14
Maximum511.1
Range509.5
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation93.022982
Coefficient of variation (CV)2.5801594
Kurtosis20.725238
Mean36.053191
Median Absolute Deviation (MAD)12.5
Skewness4.5828342
Sum1694.5
Variance8653.2752
MonotonicityNot monotonic
2023-12-12T20:23:24.321008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2.3 2
 
4.2%
1.9 2
 
4.2%
2.6 2
 
4.2%
15.0 2
 
4.2%
29.3 1
 
2.1%
37.6 1
 
2.1%
27.8 1
 
2.1%
3.7 1
 
2.1%
14.2 1
 
2.1%
75.1 1
 
2.1%
Other values (33) 33
68.8%
ValueCountFrequency (%)
1.6 1
2.1%
1.7 1
2.1%
1.8 1
2.1%
1.9 2
4.2%
2.2 1
2.1%
2.3 2
4.2%
2.6 2
4.2%
2.7 1
2.1%
2.8 1
2.1%
3.4 1
2.1%
ValueCountFrequency (%)
511.1 1
2.1%
418.5 1
2.1%
75.1 1
2.1%
41.9 1
2.1%
38.7 1
2.1%
37.6 1
2.1%
33.8 1
2.1%
30.3 1
2.1%
29.8 1
2.1%
29.3 1
2.1%

11월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)88.9%
Missing21
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean56.122222
Minimum6.5
Maximum487.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:24.573729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5
5-th percentile10.77
Q114.15
median18.3
Q324.1
95-th percentile348.46
Maximum487.9
Range481.4
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation119.64356
Coefficient of variation (CV)2.1318392
Kurtosis9.5717065
Mean56.122222
Median Absolute Deviation (MAD)5.4
Skewness3.2246887
Sum1515.3
Variance14314.58
MonotonicityNot monotonic
2023-12-12T20:23:24.824987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12.9 2
 
4.2%
24.1 2
 
4.2%
15.0 2
 
4.2%
12.1 1
 
2.1%
22.8 1
 
2.1%
14.2 1
 
2.1%
16.0 1
 
2.1%
27.4 1
 
2.1%
13.5 1
 
2.1%
14.1 1
 
2.1%
Other values (14) 14
29.2%
(Missing) 21
43.8%
ValueCountFrequency (%)
6.5 1
2.1%
10.2 1
2.1%
12.1 1
2.1%
12.9 2
4.2%
13.5 1
2.1%
14.1 1
2.1%
14.2 1
2.1%
15.0 2
4.2%
16.0 1
2.1%
16.3 1
2.1%
ValueCountFrequency (%)
487.9 1
2.1%
431.5 1
2.1%
154.7 1
2.1%
33.6 1
2.1%
27.4 1
2.1%
27.1 1
2.1%
24.1 2
4.2%
23.8 1
2.1%
22.8 1
2.1%
22.5 1
2.1%

12월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)76.9%
Missing22
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean50.173077
Minimum3.5
Maximum373.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T20:23:25.051033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile12.85
Q115.4
median20.2
Q323.8
95-th percentile299.9
Maximum373.6
Range370.1
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation95.417611
Coefficient of variation (CV)1.9017692
Kurtosis8.729694
Mean50.173077
Median Absolute Deviation (MAD)4.8
Skewness3.1034196
Sum1304.5
Variance9104.5204
MonotonicityNot monotonic
2023-12-12T20:23:25.293725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15.4 4
 
8.3%
20.2 3
 
6.2%
21.1 2
 
4.2%
19.6 1
 
2.1%
131.6 1
 
2.1%
18.3 1
 
2.1%
26.1 1
 
2.1%
21.3 1
 
2.1%
20.4 1
 
2.1%
356.0 1
 
2.1%
Other values (10) 10
20.8%
(Missing) 22
45.8%
ValueCountFrequency (%)
3.5 1
 
2.1%
12.7 1
 
2.1%
13.3 1
 
2.1%
14.9 1
 
2.1%
15.4 4
8.3%
16.6 1
 
2.1%
18.3 1
 
2.1%
19.6 1
 
2.1%
20.2 3
6.2%
20.4 1
 
2.1%
ValueCountFrequency (%)
373.6 1
2.1%
356.0 1
2.1%
131.6 1
2.1%
35.2 1
2.1%
30.4 1
2.1%
26.1 1
2.1%
24.3 1
2.1%
22.3 1
2.1%
21.3 1
2.1%
21.1 2
4.2%

Interactions

2023-12-12T20:23:15.279086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:54.944591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.878035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.653083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.367195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.068507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.879886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.118574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.812958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.709800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.713866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.242658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:15.476343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.099044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.007242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.785434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.511031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.228741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:04.479005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.252973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.965032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.838097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.858026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.368340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:15.643449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.265213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.139755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.919259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.652156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.391181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:04.615790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.356537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.095636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.969653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.978080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.487799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:15.846011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.439153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.279070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.045599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.789017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.537546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:04.760013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.460309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.250827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:10.093742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.086283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.615595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:16.054150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.599355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.450757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.201434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.931088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.721530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:04.894196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.599486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.425515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:10.256517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.223097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.745125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:16.263276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.767835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.608271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.352284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.063281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:02.883266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.099564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.761931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.612912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:10.489682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.376173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:14.363024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:16.438826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:55.907187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.744464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.488845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.187730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.017428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.243487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:06.934473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.790668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:10.698436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.501508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:14.486571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:16.702133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.048438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:57.888783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.630413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.320166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.136933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.367465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.063143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:08.924409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:10.879249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.607196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:14.610979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:16.965526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.218518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.047661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.792942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.453560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.280841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.508957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.242712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.086880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.072710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.719480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:14.750936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:17.140263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.402723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.222648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:59.948739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.597657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.423900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.673669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.416001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.235988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.237779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.874419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:14.906958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:17.302106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.569792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.357748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.092750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.735705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.570192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.824238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.532164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.367635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.388096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:12.984959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:15.019814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:17.454237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:56.709376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:22:58.492385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:00.212354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:01.898439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:03.696332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:05.968447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:07.649470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:09.525055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:11.531476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:13.096755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:23:15.145936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:23:25.476106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역1월2월3월4월5월6월7월8월9월10월11월12월
지역1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1월1.0001.0000.8210.8190.8110.8720.7950.6081.0001.0001.0001.0001.000
2월1.0000.8211.0000.7310.6980.9170.7980.6670.9810.9770.8270.8010.976
3월1.0000.8190.7311.0000.9080.6250.8210.6950.7980.9450.8830.9560.977
4월1.0000.8110.6980.9081.0000.7260.7220.6490.6880.6750.6190.6710.666
5월1.0000.8720.9170.6250.7261.0000.9200.7611.0001.0000.8841.0001.000
6월1.0000.7950.7980.8210.7220.9201.0000.8760.7100.8930.6940.8840.890
7월1.0000.6080.6670.6950.6490.7610.8761.0000.7410.7320.7440.7590.710
8월1.0001.0000.9810.7980.6881.0000.7100.7411.0001.0001.0001.0000.984
9월1.0001.0000.9770.9450.6751.0000.8930.7321.0001.0001.0001.0000.984
10월1.0001.0000.8270.8830.6190.8840.6940.7441.0001.0001.0001.0000.985
11월1.0001.0000.8010.9560.6711.0000.8840.7591.0001.0001.0001.0001.000
12월1.0001.0000.9760.9770.6661.0000.8900.7100.9840.9840.9851.0001.000
2023-12-12T20:23:25.722909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0000.4680.2470.4860.0540.439-0.094-0.0720.1070.0760.2040.061
2월0.4681.0000.3900.4680.2790.3430.1250.0380.190-0.160-0.144-0.187
3월0.2470.3901.0000.3110.4190.1750.185-0.2510.168-0.130-0.1820.095
4월0.4860.4680.3111.0000.7010.225-0.033-0.1460.230-0.1220.5280.231
5월0.0540.2790.4190.7011.0000.1490.1250.0110.5450.1730.6560.556
6월0.4390.3430.1750.2250.1491.0000.620-0.1730.040-0.0780.0790.085
7월-0.0940.1250.185-0.0330.1250.6201.0000.0500.1490.012-0.058-0.037
8월-0.0720.038-0.251-0.1460.011-0.1730.0501.0000.2290.147-0.044-0.033
9월0.1070.1900.1680.2300.5450.0400.1490.2291.0000.5300.5330.567
10월0.076-0.160-0.130-0.1220.173-0.0780.0120.1470.5301.0000.4230.284
11월0.204-0.144-0.1820.5280.6560.079-0.058-0.0440.5330.4231.0000.698
12월0.061-0.1870.0950.2310.5560.085-0.037-0.0330.5670.2840.6981.000

Missing values

2023-12-12T20:23:17.689618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:23:18.002051image/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.
2023-12-12T20:23:18.295527image/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

지역1월2월3월4월5월6월7월8월9월10월11월12월
0강릉<NA><NA><NA><NA><NA>18.512.523.4<NA>2.3<NA><NA>
1거제16.019.018.1<NA><NA><NA><NA>25.3<NA>1.6<NA><NA>
2경주<NA><NA><NA><NA><NA><NA>22.8<NA><NA>2.616.630.4
3경주강동3.517.119.619.620.420.523.124.326.027.233.635.2
4고양63.911.97.912.87.025.2<NA><NA><NA>511.1<NA><NA>
5고흥16.922.720.118.718.325.342.023.517.018.110.214.9
6광주14.512.511.019.436.58.59.6365.4392.8418.5431.5373.6
7구좌27.618.523.027.022.315.2<NA>28.6<NA>15.1<NA><NA>
8군산<NA><NA><NA>5.05.6<NA>10.413.711.815.012.116.6
9금정산<NA>26.364.317.9<NA><NA>21.326.716.738.712.913.3
지역1월2월3월4월5월6월7월8월9월10월11월12월
38진영16.018.820.218.117.411.512.523.415.225.414.115.4
39진주19.618.521.617.517.311.415.924.819.220.613.520.2
40창원<NA><NA>82.018.519.0<NA><NA>22.7<NA>2.2<NA><NA>
41초란도28.825.224.926.7<NA><NA>10.923.520.030.327.4<NA>
42춘천<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
43통영18.418.228.224.820.7<NA><NA>23.5<NA>3.4<NA><NA>
44포항27.529.833.430.328.620.3<NA>14.7<NA>6.6<NA><NA>
45하동19.417.224.222.719.614.018.021.014.418.316.021.1
46함백산<NA><NA><NA><NA><NA><NA><NA>18.5<NA>2.6<NA><NA>
47해남27.427.019.224.820.017.27.824.55.14.914.219.6