Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory82.4 B

Variable types

Numeric6
Categorical2
Text1

Dataset

Description경남도내 연도별 해수욕장 방문 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3034255

Alerts

연번 is highly overall correlated with 시군High correlation
규모(㎡) is highly overall correlated with 2012년 방문객 수(명) and 3 other fieldsHigh correlation
2012년 방문객 수(명) is highly overall correlated with 규모(㎡) and 3 other fieldsHigh correlation
‘13년 is highly overall correlated with 규모(㎡) and 3 other fieldsHigh correlation
‘14년 is highly overall correlated with 규모(㎡) and 3 other fieldsHigh correlation
‘15년 is highly overall correlated with 규모(㎡) and 3 other fieldsHigh correlation
시군 is highly overall correlated with 연번High correlation
비고 is highly imbalanced (64.7%)Imbalance
연번 has unique valuesUnique
해수욕장명 has unique valuesUnique
2012년 방문객 수(명) has unique valuesUnique
‘13년 has unique valuesUnique
‘14년 has unique valuesUnique
‘15년 has 2 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:46:29.936873
Analysis finished2023-12-10 23:46:34.132255
Duration4.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:34.185719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-11T08:46:34.294596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

시군
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
거제
17 
통영
6 
남해
6 
사천
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
거제 17
56.7%
통영 6
 
20.0%
남해 6
 
20.0%
사천 1
 
3.3%

Length

2023-12-11T08:46:34.412102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:34.764751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거제 17
56.7%
통영 6
 
20.0%
남해 6
 
20.0%
사천 1
 
3.3%

해수욕장명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T08:46:34.953377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length3.6333333
Min length2

Characters and Unicode

Total characters109
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row통영공설
2nd row비진도 산호빛해변
3rd row사량 대항
4th row덕동
5th row봉암 몽돌
ValueCountFrequency (%)
통영공설 1
 
2.7%
황포 1
 
2.7%
덕원 1
 
2.7%
여차 1
 
2.7%
함목 1
 
2.7%
죽림 1
 
2.7%
옥계 1
 
2.7%
사곡 1
 
2.7%
구영 1
 
2.7%
망치 1
 
2.7%
Other values (27) 27
73.0%
2023-12-11T08:46:35.297256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
6.4%
해 4
 
3.7%
사 4
 
3.7%
변 4
 
3.7%
포 3
 
2.8%
대 3
 
2.8%
구 3
 
2.8%
덕 3
 
2.8%
래 2
 
1.8%
몽 2
 
1.8%
Other values (63) 74
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
91.7%
Space Separator 7
 
6.4%
Close Punctuation 1
 
0.9%
Open Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 4
 
4.0%
사 4
 
4.0%
변 4
 
4.0%
포 3
 
3.0%
대 3
 
3.0%
구 3
 
3.0%
덕 3
 
3.0%
래 2
 
2.0%
몽 2
 
2.0%
곡 2
 
2.0%
Other values (60) 70
70.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
91.7%
Common 9
 
8.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 4
 
4.0%
사 4
 
4.0%
변 4
 
4.0%
포 3
 
3.0%
대 3
 
3.0%
구 3
 
3.0%
덕 3
 
3.0%
래 2
 
2.0%
몽 2
 
2.0%
곡 2
 
2.0%
Other values (60) 70
70.0%
Common
ValueCountFrequency (%)
7
77.8%
) 1
 
11.1%
( 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
91.7%
ASCII 9
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7
77.8%
) 1
 
11.1%
( 1
 
11.1%
Hangul
ValueCountFrequency (%)
해 4
 
4.0%
사 4
 
4.0%
변 4
 
4.0%
포 3
 
3.0%
대 3
 
3.0%
구 3
 
3.0%
덕 3
 
3.0%
래 2
 
2.0%
몽 2
 
2.0%
곡 2
 
2.0%
Other values (60) 70
70.0%

규모(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44851.367
Minimum1000
Maximum546392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:35.411251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile2090
Q16150
median14250
Q330375
95-th percentile128537.05
Maximum546392
Range545392
Interquartile range (IQR)24225

Descriptive statistics

Standard deviation101105.67
Coefficient of variation (CV)2.2542383
Kurtosis22.416836
Mean44851.367
Median Absolute Deviation (MAD)8750
Skewness4.5277945
Sum1345541
Variance1.0222356 × 1010
MonotonicityNot monotonic
2023-12-11T08:46:35.529867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
6000 3
 
10.0%
10500 2
 
6.7%
22000 1
 
3.3%
9000 1
 
3.3%
15000 1
 
3.3%
27000 1
 
3.3%
13000 1
 
3.3%
104749 1
 
3.3%
546392 1
 
3.3%
4000 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
1000 1
 
3.3%
1100 1
 
3.3%
3300 1
 
3.3%
4000 1
 
3.3%
5000 1
 
3.3%
6000 3
10.0%
6600 1
 
3.3%
7500 1
 
3.3%
9000 1
 
3.3%
10500 2
6.7%
ValueCountFrequency (%)
546392 1
3.3%
148000 1
3.3%
104749 1
3.3%
92700 1
3.3%
80000 1
3.3%
66000 1
3.3%
36000 1
3.3%
31500 1
3.3%
27000 1
3.3%
24000 1
3.3%

2012년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46166.433
Minimum2480
Maximum393267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:35.651989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2480
5-th percentile3693.75
Q18827.25
median12428.5
Q343167.25
95-th percentile176177.05
Maximum393267
Range390787
Interquartile range (IQR)34340

Descriptive statistics

Standard deviation80391.331
Coefficient of variation (CV)1.7413373
Kurtosis12.057942
Mean46166.433
Median Absolute Deviation (MAD)6641.5
Skewness3.2453244
Sum1384993
Variance6.4627662 × 109
MonotonicityNot monotonic
2023-12-11T08:46:35.765006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
13000 1
 
3.3%
9865 1
 
3.3%
8400 1
 
3.3%
7200 1
 
3.3%
65602 1
 
3.3%
33052 1
 
3.3%
163389 1
 
3.3%
393267 1
 
3.3%
8936 1
 
3.3%
6789 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2480 1
3.3%
3525 1
3.3%
3900 1
3.3%
6789 1
3.3%
7200 1
3.3%
7330 1
3.3%
8400 1
3.3%
8791 1
3.3%
8936 1
3.3%
9130 1
3.3%
ValueCountFrequency (%)
393267 1
3.3%
186640 1
3.3%
163389 1
3.3%
118287 1
3.3%
78365 1
3.3%
65602 1
3.3%
61210 1
3.3%
46539 1
3.3%
33052 1
3.3%
32220 1
3.3%

‘13년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37825.867
Minimum1277
Maximum321541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:35.871334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1277
5-th percentile2076.4
Q15844
median16076.5
Q341323.5
95-th percentile124464.95
Maximum321541
Range320264
Interquartile range (IQR)35479.5

Descriptive statistics

Standard deviation62542.389
Coefficient of variation (CV)1.6534291
Kurtosis14.986533
Mean37825.867
Median Absolute Deviation (MAD)11990
Skewness3.57889
Sum1134776
Variance3.9115504 × 109
MonotonicityNot monotonic
2023-12-11T08:46:35.981716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
24875 1
 
3.3%
4200 1
 
3.3%
2579 1
 
3.3%
1277 1
 
3.3%
43781 1
 
3.3%
22884 1
 
3.3%
129329 1
 
3.3%
321541 1
 
3.3%
42608 1
 
3.3%
2008 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1277 1
3.3%
2008 1
3.3%
2160 1
3.3%
2579 1
3.3%
4000 1
3.3%
4200 1
3.3%
5000 1
3.3%
5292 1
3.3%
7500 1
3.3%
8100 1
3.3%
ValueCountFrequency (%)
321541 1
3.3%
129329 1
3.3%
118520 1
3.3%
70835 1
3.3%
62155 1
3.3%
58500 1
3.3%
43781 1
3.3%
42608 1
3.3%
37470 1
3.3%
34003 1
3.3%

‘14년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18653.467
Minimum507
Maximum115473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:36.099187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum507
5-th percentile776.7
Q13068.5
median8169.5
Q314347.75
95-th percentile70330.4
Maximum115473
Range114966
Interquartile range (IQR)11279.25

Descriptive statistics

Standard deviation27074.847
Coefficient of variation (CV)1.4514646
Kurtosis5.1953165
Mean18653.467
Median Absolute Deviation (MAD)5876.5
Skewness2.2735263
Sum559604
Variance7.3304733 × 108
MonotonicityNot monotonic
2023-12-11T08:46:36.213732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6965 1
 
3.3%
3670 1
 
3.3%
1364 1
 
3.3%
765 1
 
3.3%
6376 1
 
3.3%
10380 1
 
3.3%
50908 1
 
3.3%
115473 1
 
3.3%
13365 1
 
3.3%
2258 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
507 1
3.3%
765 1
3.3%
791 1
3.3%
1364 1
3.3%
1807 1
3.3%
2090 1
3.3%
2258 1
3.3%
2868 1
3.3%
3670 1
3.3%
4492 1
3.3%
ValueCountFrequency (%)
115473 1
3.3%
80450 1
3.3%
57962 1
3.3%
50908 1
3.3%
49650 1
3.3%
42242 1
3.3%
17446 1
3.3%
14460 1
3.3%
14011 1
3.3%
13443 1
3.3%

‘15년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25935.1
Minimum0
Maximum202066
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T08:46:36.330768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile566.1
Q15317.75
median8737
Q331200
95-th percentile81830.5
Maximum202066
Range202066
Interquartile range (IQR)25882.25

Descriptive statistics

Standard deviation40525.501
Coefficient of variation (CV)1.5625735
Kurtosis12.255724
Mean25935.1
Median Absolute Deviation (MAD)7275.5
Skewness3.187087
Sum778053
Variance1.6423162 × 109
MonotonicityNot monotonic
2023-12-11T08:46:36.449265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.7%
7311 1
 
3.3%
1448 1
 
3.3%
4200 1
 
3.3%
14594 1
 
3.3%
24798 1
 
3.3%
59975 1
 
3.3%
202066 1
 
3.3%
14895 1
 
3.3%
6266 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0 2
6.7%
1258 1
3.3%
1448 1
3.3%
1475 1
3.3%
4173 1
3.3%
4200 1
3.3%
5135 1
3.3%
5866 1
3.3%
6266 1
3.3%
6920 1
3.3%
ValueCountFrequency (%)
202066 1
3.3%
93130 1
3.3%
68020 1
3.3%
59975 1
3.3%
48990 1
3.3%
46675 1
3.3%
43400 1
3.3%
33334 1
3.3%
24798 1
3.3%
21790 1
3.3%

비고
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
-
28 
215 미개장
 
2

Length

Max length7
Median length1
Mean length1.4
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 28
93.3%
215 미개장 2
 
6.7%

Length

2023-12-11T08:46:36.570045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:36.663974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28
87.5%
215 2
 
6.2%
미개장 2
 
6.2%

Interactions

2023-12-11T08:46:33.428869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.264054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.855277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.537114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.205567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.817728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.515658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.368804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.950802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.651690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.309071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.924550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.593643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.481621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.094919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.750023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.394099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.023235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.687928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.580182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.231177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.871159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.494913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.133771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.768543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.659922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.333406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.964765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.597568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.235228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.847651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:30.756802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:31.430830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.079309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:32.705764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:33.322477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:46:36.733524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군해수욕장명규모(㎡)2012년 방문객 수(명)‘13년‘14년‘15년비고
연번1.0000.9181.0000.0000.5230.3500.0000.1840.000
시군0.9181.0001.0000.4600.1820.1910.3810.0000.134
해수욕장명1.0001.0001.0001.0001.0001.0001.0001.0001.000
규모(㎡)0.0000.4601.0001.0000.7940.8130.9930.9750.000
2012년 방문객 수(명)0.5230.1821.0000.7941.0000.9940.8090.8750.000
‘13년0.3500.1911.0000.8130.9941.0000.7930.8640.000
‘14년0.0000.3811.0000.9930.8090.7931.0000.9620.000
‘15년0.1840.0001.0000.9750.8750.8640.9621.0000.000
비고0.0000.1341.0000.0000.0000.0000.0000.0001.000
2023-12-11T08:46:36.838538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군비고
시군1.0000.062
비고0.0621.000
2023-12-11T08:46:36.919703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번규모(㎡)2012년 방문객 수(명)‘13년‘14년‘15년시군비고
연번1.0000.0200.043-0.103-0.0090.0120.7120.000
규모(㎡)0.0201.0000.7990.6690.5420.6000.2000.000
2012년 방문객 수(명)0.0430.7991.0000.8360.8190.7920.1590.000
‘13년-0.1030.6690.8361.0000.8330.8850.0500.000
‘14년-0.0090.5420.8190.8331.0000.9040.2560.000
‘15년0.0120.6000.7920.8850.9041.0000.0000.000
시군0.7120.2000.1590.0500.2560.0001.0000.062
비고0.0000.0000.0000.0000.0000.0000.0621.000

Missing values

2023-12-11T08:46:33.972009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:46:34.088293image/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

연번시군해수욕장명규모(㎡)2012년 방문객 수(명)‘13년‘14년‘15년비고
01통영통영공설22000130002487569657311-
12통영비진도 산호빛해변750091301622083028373-
23통영사량 대항600094401261720904173-
34통영덕동3300390040007911258-
45통영봉암 몽돌240007330750018077868-
56통영연대1000248050005070215 미개장
67사천남일대6600078365585004965043400-
78거제명사3150046539187831744618988-
89거제학동 흑진주몽돌해변1480001866401185208045093130-
910거제구조라92700118287708355796268020-
연번시군해수욕장명규모(㎡)2012년 방문객 수(명)‘13년‘14년‘15년비고
2021거제옥계11003525529298208354-
2122거제사곡500011986810084856920-
2223거제구영40006789200822586266-
2324거제망치60008936426081336514895-
2425남해상주 은모래비치546392393267321541115473202066-
2526남해송정 솔바람해변1047491633891293295090859975-
2627남해사촌1300033052228841038024798-
2728남해두곡월포270006560243781637614594-
2829남해선구10500720012777650215 미개장
2930남해설리150008400257913644200-