Overview

Dataset statistics

Number of variables10
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory91.7 B

Variable types

Numeric7
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 4 other fieldsHigh correlation
2012년 방문객 수(명) is highly overall correlated with 규모(㎡) and 4 other fieldsHigh correlation
2013년 방문객 수(명) is highly overall correlated with 규모(㎡) and 4 other fieldsHigh correlation
2014년 방문객 수(명) is highly overall correlated with 규모(㎡) and 4 other fieldsHigh correlation
2015년 방문객 수(명) is highly overall correlated with 규모(㎡) and 4 other fieldsHigh correlation
2016년 방문객 수(명) is highly overall correlated with 규모(㎡) and 4 other fieldsHigh correlation
시군 is highly overall correlated with 연번High correlation
비고 is highly imbalanced (52.6%)Imbalance
연번 has unique valuesUnique
해수욕장명 has unique valuesUnique
2012년 방문객 수(명) has unique valuesUnique
2013년 방문객 수(명) has unique valuesUnique
2014년 방문객 수(명) has unique valuesUnique
2015년 방문객 수(명) has unique valuesUnique
2016년 방문객 수(명) has unique valuesUnique
2016년 방문객 수(명) has 1 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-10 23:46:38.090866
Analysis finished2023-12-10 23:46:43.192731
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:43.247060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-11T08:46:43.355161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

시군
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
거제시
17 
통영시
5 
남해군
5 
사천시
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
거제시 17
60.7%
통영시 5
 
17.9%
남해군 5
 
17.9%
사천시 1
 
3.6%

Length

2023-12-11T08:46:43.471872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:43.565987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거제시 17
60.7%
통영시 5
 
17.9%
남해군 5
 
17.9%
사천시 1
 
3.6%

해수욕장명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T08:46:43.785224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length3.7857143
Min length2

Characters and Unicode

Total characters106
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row통영공설
2nd row비진도 산호빛해변
3rd row사량 대항
4th row덕동
5th row봉암 몽돌
ValueCountFrequency (%)
통영공설 1
 
2.9%
구영 1
 
2.9%
덕원 1
 
2.9%
여차 1
 
2.9%
함목 1
 
2.9%
죽림 1
 
2.9%
옥계 1
 
2.9%
사곡 1
 
2.9%
망치 1
 
2.9%
황포 1
 
2.9%
Other values (25) 25
71.4%
2023-12-11T08:46:44.118706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
6.6%
해 4
 
3.8%
변 4
 
3.8%
사 4
 
3.8%
포 3
 
2.8%
덕 3
 
2.8%
돌 2
 
1.9%
주 2
 
1.9%
치 2
 
1.9%
남 2
 
1.9%
Other values (62) 73
68.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
90.6%
Space Separator 7
 
6.6%
Other Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%
Open Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
해 4
 
4.2%
변 4
 
4.2%
사 4
 
4.2%
포 3
 
3.1%
덕 3
 
3.1%
돌 2
 
2.1%
주 2
 
2.1%
치 2
 
2.1%
남 2
 
2.1%
모 2
 
2.1%
Other values (58) 68
70.8%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
? 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
90.6%
Common 10
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
해 4
 
4.2%
변 4
 
4.2%
사 4
 
4.2%
포 3
 
3.1%
덕 3
 
3.1%
돌 2
 
2.1%
주 2
 
2.1%
치 2
 
2.1%
남 2
 
2.1%
모 2
 
2.1%
Other values (58) 68
70.8%
Common
ValueCountFrequency (%)
7
70.0%
? 1
 
10.0%
) 1
 
10.0%
( 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
90.6%
ASCII 10
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7
70.0%
? 1
 
10.0%
) 1
 
10.0%
( 1
 
10.0%
Hangul
ValueCountFrequency (%)
해 4
 
4.2%
변 4
 
4.2%
사 4
 
4.2%
포 3
 
3.1%
덕 3
 
3.1%
돌 2
 
2.1%
주 2
 
2.1%
치 2
 
2.1%
남 2
 
2.1%
모 2
 
2.1%
Other values (58) 68
70.8%

규모(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47630.321
Minimum1100
Maximum546000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:44.275830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile3545
Q16450
median15150
Q332625
95-th percentile132862.15
Maximum546000
Range544900
Interquartile range (IQR)26175

Descriptive statistics

Standard deviation104125.29
Coefficient of variation (CV)2.1861136
Kurtosis20.978751
Mean47630.321
Median Absolute Deviation (MAD)9150
Skewness4.3827959
Sum1333649
Variance1.0842077 × 1010
MonotonicityNot monotonic
2023-12-11T08:46:44.404679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
6000 3
 
10.7%
7500 1
 
3.6%
9000 1
 
3.6%
15000 1
 
3.6%
27000 1
 
3.6%
13000 1
 
3.6%
104749 1
 
3.6%
546000 1
 
3.6%
4000 1
 
3.6%
5000 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1100 1
 
3.6%
3300 1
 
3.6%
4000 1
 
3.6%
5000 1
 
3.6%
6000 3
10.7%
6600 1
 
3.6%
7500 1
 
3.6%
9000 1
 
3.6%
10500 1
 
3.6%
13000 1
 
3.6%
ValueCountFrequency (%)
546000 1
3.6%
148000 1
3.6%
104749 1
3.6%
92700 1
3.6%
80000 1
3.6%
66000 1
3.6%
36000 1
3.6%
31500 1
3.6%
27000 1
3.6%
24000 1
3.6%

2012년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49118.321
Minimum3525
Maximum393267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:44.529259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3525
5-th percentile4911.15
Q19081.5
median12935.5
Q350206.75
95-th percentile178502.15
Maximum393267
Range389742
Interquartile range (IQR)41125.25

Descriptive statistics

Standard deviation82495.645
Coefficient of variation (CV)1.679529
Kurtosis11.242162
Mean49118.321
Median Absolute Deviation (MAD)6641.5
Skewness3.1367352
Sum1375313
Variance6.8055315 × 109
MonotonicityNot monotonic
2023-12-11T08:46:44.690337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13000 1
 
3.6%
9865 1
 
3.6%
8400 1
 
3.6%
65602 1
 
3.6%
33052 1
 
3.6%
163389 1
 
3.6%
393267 1
 
3.6%
8936 1
 
3.6%
6789 1
 
3.6%
11986 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
3525 1
3.6%
3900 1
3.6%
6789 1
3.6%
7330 1
3.6%
8400 1
3.6%
8791 1
3.6%
8936 1
3.6%
9130 1
3.6%
9440 1
3.6%
9865 1
3.6%
ValueCountFrequency (%)
393267 1
3.6%
186640 1
3.6%
163389 1
3.6%
118287 1
3.6%
78365 1
3.6%
65602 1
3.6%
61210 1
3.6%
46539 1
3.6%
33052 1
3.6%
32220 1
3.6%

2013년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40303.536
Minimum2008
Maximum321541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:44.873967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2306.65
Q17950
median17501.5
Q342901.25
95-th percentile125545.85
Maximum321541
Range319533
Interquartile range (IQR)34951.25

Descriptive statistics

Standard deviation64074.528
Coefficient of variation (CV)1.5897992
Kurtosis14.133673
Mean40303.536
Median Absolute Deviation (MAD)13401.5
Skewness3.4808877
Sum1128499
Variance4.1055451 × 109
MonotonicityNot monotonic
2023-12-11T08:46:45.028610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
24875 1
 
3.6%
4200 1
 
3.6%
2579 1
 
3.6%
43781 1
 
3.6%
22884 1
 
3.6%
129329 1
 
3.6%
321541 1
 
3.6%
42608 1
 
3.6%
2008 1
 
3.6%
8100 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
2008 1
3.6%
2160 1
3.6%
2579 1
3.6%
4000 1
3.6%
4200 1
3.6%
5292 1
3.6%
7500 1
3.6%
8100 1
3.6%
9533 1
3.6%
9693 1
3.6%
ValueCountFrequency (%)
321541 1
3.6%
129329 1
3.6%
118520 1
3.6%
70835 1
3.6%
62155 1
3.6%
58500 1
3.6%
43781 1
3.6%
42608 1
3.6%
37470 1
3.6%
34003 1
3.6%

2014년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19940.429
Minimum791
Maximum115473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:45.184820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum791
5-th percentile1519.05
Q14286.5
median8393.5
Q315206.5
95-th percentile72579.2
Maximum115473
Range114682
Interquartile range (IQR)10920

Descriptive statistics

Standard deviation27596.769
Coefficient of variation (CV)1.3839607
Kurtosis4.750961
Mean19940.429
Median Absolute Deviation (MAD)5571.5
Skewness2.1892411
Sum558332
Variance7.6158169 × 108
MonotonicityNot monotonic
2023-12-11T08:46:45.332707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
6965 1
 
3.6%
3670 1
 
3.6%
1364 1
 
3.6%
6376 1
 
3.6%
10380 1
 
3.6%
50908 1
 
3.6%
115473 1
 
3.6%
13365 1
 
3.6%
2258 1
 
3.6%
8485 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
791 1
3.6%
1364 1
3.6%
1807 1
3.6%
2090 1
3.6%
2258 1
3.6%
2868 1
3.6%
3670 1
3.6%
4492 1
3.6%
4838 1
3.6%
6376 1
3.6%
ValueCountFrequency (%)
115473 1
3.6%
80450 1
3.6%
57962 1
3.6%
50908 1
3.6%
49650 1
3.6%
42242 1
3.6%
17446 1
3.6%
14460 1
3.6%
14011 1
3.6%
13443 1
3.6%

2015년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27787.607
Minimum1258
Maximum202066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:45.488818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1258
5-th percentile1457.45
Q16166
median9375.5
Q335850.5
95-th percentile84341.5
Maximum202066
Range200808
Interquartile range (IQR)29684.5

Descriptive statistics

Standard deviation41359.231
Coefficient of variation (CV)1.4884056
Kurtosis11.636589
Mean27787.607
Median Absolute Deviation (MAD)6710
Skewness3.1116726
Sum778053
Variance1.710586 × 109
MonotonicityNot monotonic
2023-12-11T08:46:45.634449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
7311 1
 
3.6%
1448 1
 
3.6%
4200 1
 
3.6%
14594 1
 
3.6%
24798 1
 
3.6%
59975 1
 
3.6%
202066 1
 
3.6%
14895 1
 
3.6%
6266 1
 
3.6%
6920 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1258 1
3.6%
1448 1
3.6%
1475 1
3.6%
4173 1
3.6%
4200 1
3.6%
5135 1
3.6%
5866 1
3.6%
6266 1
3.6%
6920 1
3.6%
7311 1
3.6%
ValueCountFrequency (%)
202066 1
3.6%
93130 1
3.6%
68020 1
3.6%
59975 1
3.6%
48990 1
3.6%
46675 1
3.6%
43400 1
3.6%
33334 1
3.6%
24798 1
3.6%
21790 1
3.6%

2016년 방문객 수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29838.929
Minimum0
Maximum193816
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T08:46:45.796916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2503.7
Q15936.5
median11181.5
Q341883.5
95-th percentile104913.3
Maximum193816
Range193816
Interquartile range (IQR)35947

Descriptive statistics

Standard deviation43226.8
Coefficient of variation (CV)1.4486713
Kurtosis7.155663
Mean29838.929
Median Absolute Deviation (MAD)7306
Skewness2.5186909
Sum835490
Variance1.8685562 × 109
MonotonicityNot monotonic
2023-12-11T08:46:45.961719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12457 1
 
3.6%
3231 1
 
3.6%
43364 1
 
3.6%
16980 1
 
3.6%
12504 1
 
3.6%
109492 1
 
3.6%
193816 1
 
3.6%
9045 1
 
3.6%
2377 1
 
3.6%
4918 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
2377 1
3.6%
2739 1
3.6%
3231 1
3.6%
3809 1
3.6%
3942 1
3.6%
4918 1
3.6%
6276 1
3.6%
6595 1
3.6%
7700 1
3.6%
ValueCountFrequency (%)
193816 1
3.6%
109492 1
3.6%
96410 1
3.6%
77460 1
3.6%
48460 1
3.6%
43893 1
3.6%
43364 1
3.6%
41390 1
3.6%
33060 1
3.6%
16980 1
3.6%

비고
Categorical

IMBALANCE 

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
-
23 
<NA>
 
2
비지정
 
2
미개장
 
1

Length

Max length4
Median length1
Mean length1.4285714
Min length1

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
- 23
82.1%
<NA> 2
 
7.1%
비지정 2
 
7.1%
미개장 1
 
3.6%

Length

2023-12-11T08:46:46.129538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:46:46.298612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
23
82.1%
na 2
 
7.1%
비지정 2
 
7.1%
미개장 1
 
3.6%

Interactions

2023-12-11T08:46:42.351114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.500048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.188437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.755111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.385484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.269443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.789137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.423736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.604589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.262238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.833281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.450887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.342105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.860457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.490643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.707165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.337865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.914937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.527845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.411704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.935898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.578016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.813341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.428156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.000797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.633874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.495239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.017742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.658149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.908998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.510356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.094794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.711061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.567875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.090309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.741631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:38.997050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.600893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.192995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.795175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.635343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.167659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.832567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.096796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:39.677929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:40.298289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.183664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:41.710639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:46:42.268449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:46:46.394642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군해수욕장명규모(㎡)2012년 방문객 수(명)2013년 방문객 수(명)2014년 방문객 수(명)2015년 방문객 수(명)2016년 방문객 수(명)비고
연번1.0000.8661.0000.0000.0000.3580.3720.0000.0000.710
시군0.8661.0001.0000.4950.2290.1200.3550.0000.1960.492
해수욕장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규모(㎡)0.0000.4951.0001.0000.7900.8090.9920.9730.9430.000
2012년 방문객 수(명)0.0000.2291.0000.7901.0000.9940.8010.8700.9040.000
2013년 방문객 수(명)0.3580.1201.0000.8090.9941.0000.7860.8580.9150.000
2014년 방문객 수(명)0.3720.3551.0000.9920.8010.7861.0000.9600.8560.000
2015년 방문객 수(명)0.0000.0001.0000.9730.8700.8580.9601.0000.9150.000
2016년 방문객 수(명)0.0000.1961.0000.9430.9040.9150.8560.9151.0000.000
비고0.7100.4921.0000.0000.0000.0000.0000.0000.0001.000
2023-12-11T08:46:46.884022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군비고
시군1.0000.475
비고0.4751.000
2023-12-11T08:46:47.010046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번규모(㎡)2012년 방문객 수(명)2013년 방문객 수(명)2014년 방문객 수(명)2015년 방문객 수(명)2016년 방문객 수(명)시군비고
연번1.000-0.0360.045-0.0570.0010.0350.0260.6210.464
규모(㎡)-0.0361.0000.7480.6650.5120.5870.7270.2100.000
2012년 방문객 수(명)0.0450.7481.0000.8280.7900.7560.7330.1830.000
2013년 방문객 수(명)-0.0570.6650.8281.0000.8120.8710.8240.0830.000
2014년 방문객 수(명)0.0010.5120.7900.8121.0000.8820.6860.2420.000
2015년 방문객 수(명)0.0350.5870.7560.8710.8821.0000.8330.0000.000
2016년 방문객 수(명)0.0260.7270.7330.8240.6860.8331.0000.0860.000
시군0.6210.2100.1830.0830.2420.0000.0861.0000.475
비고0.4640.0000.0000.0000.0000.0000.0000.4751.000

Missing values

2023-12-11T08:46:42.986800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:46:43.143333image/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)2013년 방문객 수(명)2014년 방문객 수(명)2015년 방문객 수(명)2016년 방문객 수(명)비고
01통영시통영공설750013000248756965731112457-
12통영시비진도 산호빛해변220009130162208302837316085<NA>
23통영시사량 대항6000944012617209041732739<NA>
34통영시덕동33003900400079112583942비지정
45통영시봉암 몽돌2400073307500180778687958비지정
56사천시남일대660007836558500496504340043893-
67거제시명사315004653918783174461898813635-
78거제시학동 흑진주몽돌해변148000186640118520804509313096410-
89거제시구조라9270011828770835579626802077460-
910거제시와현 모래숲해변153006121062155422424899048460-
연번시군해수욕장명규모(㎡)2012년 방문객 수(명)2013년 방문객 수(명)2014년 방문객 수(명)2015년 방문객 수(명)2016년 방문객 수(명)비고
1819거제시죽림15900145102160637914750미개장
1920거제시옥계110035255292982083543809-
2021거제시사곡5000119868100848569204918-
2122거제시구영400067892008225862662377-
2223거제시망치600089364260813365148959045-
2324남해군상주 은모래비치546000393267321541115473202066193816-
2425남해군송정 솔바람해변1047491633891293295090859975109492-
2526남해군사촌130003305222884103802479812504-
2627남해군두곡?월포27000656024378163761459416980-
2728남해군설리15000840025791364420043364-