Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells5
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory98.1 B

Variable types

Categorical2
Text2
Numeric6
DateTime1

Dataset

Description관광지란 "자연적 또는 문화적 관광자원을 갖추고 관광객을 위한 기본적인 편의시설을 설치하는 지역"을 의미함(관광진흥법 제2조)관광지 지정 요건은 자연적 또는 문화적 관광자원을 갖추고 관광 및 휴식에 적합한 지역으 대상으로 시·도지사가 지정함관광지, 관광단지, 관광특구 관련자료는 관광산업 및 관련산업의 계획수립 및 연구를 위한 기초자료로 활용될 수 있도록 이에 대한 현황 자료를 연도별로 서비스함
Author경상북도
URLhttps://www.data.go.kr/data/15069224/fileData.do

Alerts

면적(제곱미터) is highly overall correlated with 재원별(억원) 계 and 1 other fieldsHigh correlation
사업기간(시작) is highly overall correlated with 재원별(억원)공 공High correlation
재원별(억원) 계 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
재원별(억원)공 공 is highly overall correlated with 사업기간(시작)High correlation
재원별(억원)민 자 is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
재원별(억원)공 공 has 1 (3.1%) missing valuesMissing
재원별(억원)민 자 has 4 (12.5%) missing valuesMissing
관광지명 has unique valuesUnique
위 치 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
재원별(억원) 계 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:59:39.921351
Analysis finished2023-12-12 17:59:44.684415
Duration4.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
북부자원권
18 
동부연안권
7 
남부도시권
7 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북부자원권 18
56.2%
동부연안권 7
 
21.9%
남부도시권 7
 
21.9%

Length

2023-12-13T02:59:44.738132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:59:44.832076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북부자원권 18
56.2%
동부연안권 7
 
21.9%
남부도시권 7
 
21.9%

관광지명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T02:59:45.013236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.8125
Min length2

Characters and Unicode

Total characters122
Distinct characters69
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

Unique32 ?
Unique (%)100.0%

Sample

1st row안동하회
2nd row예안현
3rd row영주순흥
4th row풍기온천
5th row부석사
ValueCountFrequency (%)
안동하회 1
 
3.1%
예안현 1
 
3.1%
가산산성 1
 
3.1%
청도신화랑 1
 
3.1%
청도용암온천 1
 
3.1%
청도온천 1
 
3.1%
경산온천 1
 
3.1%
치산 1
 
3.1%
개척사 1
 
3.1%
울릉도 1
 
3.1%
Other values (22) 22
68.8%
2023-12-13T02:59:45.425023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 11
 
9.0%
온 8
 
6.6%
산 6
 
4.9%
수 4
 
3.3%
청 4
 
3.3%
도 4
 
3.3%
장 3
 
2.5%
사 3
 
2.5%
경 3
 
2.5%
문 3
 
2.5%
Other values (59) 73
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 11
 
9.0%
온 8
 
6.6%
산 6
 
4.9%
수 4
 
3.3%
청 4
 
3.3%
도 4
 
3.3%
장 3
 
2.5%
사 3
 
2.5%
경 3
 
2.5%
문 3
 
2.5%
Other values (59) 73
59.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 11
 
9.0%
온 8
 
6.6%
산 6
 
4.9%
수 4
 
3.3%
청 4
 
3.3%
도 4
 
3.3%
장 3
 
2.5%
사 3
 
2.5%
경 3
 
2.5%
문 3
 
2.5%
Other values (59) 73
59.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 11
 
9.0%
온 8
 
6.6%
산 6
 
4.9%
수 4
 
3.3%
청 4
 
3.3%
도 4
 
3.3%
장 3
 
2.5%
사 3
 
2.5%
경 3
 
2.5%
문 3
 
2.5%
Other values (59) 73
59.8%

위 치
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T02:59:45.721506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.96875
Min length6

Characters and Unicode

Total characters287
Distinct characters93
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row안동 풍천 하회
2nd row안동 도산 서부
3rd row영주 순흥 내죽·청구
4th row영주 풍기 창락
5th row영주 부석 북지
ValueCountFrequency (%)
문경 4
 
4.0%
영주 4
 
4.0%
상주 3
 
3.0%
청도 3
 
3.0%
안동 2
 
2.0%
봉화 2
 
2.0%
울진 2
 
2.0%
예천 2
 
2.0%
영덕 2
 
2.0%
우곡 2
 
2.0%
Other values (73) 73
73.7%
2023-12-13T02:59:46.121412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
23.3%
영 9
 
3.1%
천 7
 
2.4%
주 7
 
2.4%
문 6
 
2.1%
· 6
 
2.1%
산 6
 
2.1%
상 6
 
2.1%
경 5
 
1.7%
곡 5
 
1.7%
Other values (83) 163
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 212
73.9%
Space Separator 67
 
23.3%
Other Punctuation 8
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 9
 
4.2%
천 7
 
3.3%
주 7
 
3.3%
문 6
 
2.8%
산 6
 
2.8%
상 6
 
2.8%
경 5
 
2.4%
곡 5
 
2.4%
도 5
 
2.4%
동 5
 
2.4%
Other values (80) 151
71.2%
Other Punctuation
ValueCountFrequency (%)
· 6
75.0%
, 2
 
25.0%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 212
73.9%
Common 75
 
26.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 9
 
4.2%
천 7
 
3.3%
주 7
 
3.3%
문 6
 
2.8%
산 6
 
2.8%
상 6
 
2.8%
경 5
 
2.4%
곡 5
 
2.4%
도 5
 
2.4%
동 5
 
2.4%
Other values (80) 151
71.2%
Common
ValueCountFrequency (%)
67
89.3%
· 6
 
8.0%
, 2
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 212
73.9%
ASCII 69
 
24.0%
None 6
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
97.1%
, 2
 
2.9%
Hangul
ValueCountFrequency (%)
영 9
 
4.2%
천 7
 
3.3%
주 7
 
3.3%
문 6
 
2.8%
산 6
 
2.8%
상 6
 
2.8%
경 5
 
2.4%
곡 5
 
2.4%
도 5
 
2.4%
동 5
 
2.4%
Other values (80) 151
71.2%
None
ValueCountFrequency (%)
· 6
100.0%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282394
Minimum35298
Maximum1060290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:46.312926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35298
5-th percentile47468
Q1118241
median206977.5
Q3275267
95-th percentile920404
Maximum1060290
Range1024992
Interquartile range (IQR)157026

Descriptive statistics

Standard deviation275142.32
Coefficient of variation (CV)0.9743207
Kurtosis2.5482995
Mean282394
Median Absolute Deviation (MAD)84076.5
Skewness1.8660039
Sum9036608
Variance7.5703297 × 1010
MonotonicityNot monotonic
2023-12-13T02:59:46.480969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
184000 1
 
3.1%
88720 1
 
3.1%
37040 1
 
3.1%
392000 1
 
3.1%
291068 1
 
3.1%
891280 1
 
3.1%
473000 1
 
3.1%
262060 1
 
3.1%
270000 1
 
3.1%
152244 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
35298 1
3.1%
37040 1
3.1%
56000 1
3.1%
76419 1
3.1%
88720 1
3.1%
94913 1
3.1%
96310 1
3.1%
104219 1
3.1%
122915 1
3.1%
137000 1
3.1%
ValueCountFrequency (%)
1060290 1
3.1%
956000 1
3.1%
891280 1
3.1%
880400 1
3.1%
473000 1
3.1%
392000 1
3.1%
357132 1
3.1%
291068 1
3.1%
270000 1
3.1%
262060 1
3.1%

지정일
Date

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum1979-12-31 00:00:00
Maximum2014-10-16 00:00:00
2023-12-13T02:59:46.643762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:46.826091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

사업기간(시작)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.2812
Minimum1980
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:46.971280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1986
Q11989.5
median2000
Q32008
95-th percentile2012
Maximum2012
Range32
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation9.6861537
Coefficient of variation (CV)0.0048448179
Kurtosis-1.13869
Mean1999.2812
Median Absolute Deviation (MAD)9
Skewness-0.24185757
Sum63977
Variance93.821573
MonotonicityNot monotonic
2023-12-13T02:59:47.136801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2012 4
12.5%
1998 3
9.4%
2001 3
9.4%
1987 3
9.4%
2000 3
9.4%
2011 3
9.4%
1988 2
 
6.2%
2008 2
 
6.2%
1986 2
 
6.2%
2003 1
 
3.1%
Other values (6) 6
18.8%
ValueCountFrequency (%)
1980 1
 
3.1%
1986 2
6.2%
1987 3
9.4%
1988 2
6.2%
1990 1
 
3.1%
1992 1
 
3.1%
1997 1
 
3.1%
1998 3
9.4%
2000 3
9.4%
2001 3
9.4%
ValueCountFrequency (%)
2012 4
12.5%
2011 3
9.4%
2008 2
6.2%
2007 1
 
3.1%
2005 1
 
3.1%
2003 1
 
3.1%
2001 3
9.4%
2000 3
9.4%
1998 3
9.4%
1997 1
 
3.1%

사업기간(완료)
Real number (ℝ)

Distinct13
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.0312
Minimum2009
Maximum2026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:47.286551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2013.1
Q12015
median2022
Q32023.25
95-th percentile2025
Maximum2026
Range17
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation4.5329217
Coefficient of variation (CV)0.0022439859
Kurtosis-0.59478977
Mean2020.0312
Median Absolute Deviation (MAD)2.5
Skewness-0.68888268
Sum64641
Variance20.547379
MonotonicityNot monotonic
2023-12-13T02:59:47.442121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2015 6
18.8%
2023 5
15.6%
2022 4
12.5%
2025 4
12.5%
2020 3
9.4%
2024 3
9.4%
2009 1
 
3.1%
2014 1
 
3.1%
2012 1
 
3.1%
2021 1
 
3.1%
Other values (3) 3
9.4%
ValueCountFrequency (%)
2009 1
 
3.1%
2012 1
 
3.1%
2014 1
 
3.1%
2015 6
18.8%
2016 1
 
3.1%
2018 1
 
3.1%
2020 3
9.4%
2021 1
 
3.1%
2022 4
12.5%
2023 5
15.6%
ValueCountFrequency (%)
2026 1
 
3.1%
2025 4
12.5%
2024 3
9.4%
2023 5
15.6%
2022 4
12.5%
2021 1
 
3.1%
2020 3
9.4%
2018 1
 
3.1%
2016 1
 
3.1%
2015 6
18.8%

재원별(억원) 계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean928.84375
Minimum93
Maximum4280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:47.631135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile186.15
Q1363
median513.5
Q31181
95-th percentile3056.2
Maximum4280
Range4187
Interquartile range (IQR)818

Descriptive statistics

Standard deviation1004.956
Coefficient of variation (CV)1.081943
Kurtosis5.9522252
Mean928.84375
Median Absolute Deviation (MAD)317
Skewness2.3825566
Sum29723
Variance1009936.6
MonotonicityNot monotonic
2023-12-13T02:59:47.808501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
422 1
 
3.1%
179 1
 
3.1%
93 1
 
3.1%
201 1
 
3.1%
610 1
 
3.1%
4165 1
 
3.1%
1142 1
 
3.1%
951 1
 
3.1%
469 1
 
3.1%
451 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
93 1
3.1%
179 1
3.1%
192 1
3.1%
201 1
3.1%
207 1
3.1%
246 1
3.1%
295 1
3.1%
330 1
3.1%
374 1
3.1%
398 1
3.1%
ValueCountFrequency (%)
4280 1
3.1%
4165 1
3.1%
2149 1
3.1%
1672 1
3.1%
1559 1
3.1%
1534 1
3.1%
1358 1
3.1%
1298 1
3.1%
1142 1
3.1%
951 1
3.1%

재원별(억원)공 공
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)93.5%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean248
Minimum2
Maximum778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:47.973550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile31
Q1108.5
median174
Q3325.5
95-th percentile579
Maximum778
Range776
Interquartile range (IQR)217

Descriptive statistics

Standard deviation193.79938
Coefficient of variation (CV)0.78144912
Kurtosis0.43431464
Mean248
Median Absolute Deviation (MAD)133
Skewness0.96540863
Sum7688
Variance37558.2
MonotonicityNot monotonic
2023-12-13T02:59:48.137406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
174 2
 
6.2%
31 2
 
6.2%
262 1
 
3.1%
38 1
 
3.1%
35 1
 
3.1%
89 1
 
3.1%
610 1
 
3.1%
508 1
 
3.1%
116 1
 
3.1%
126 1
 
3.1%
Other values (19) 19
59.4%
ValueCountFrequency (%)
2 1
3.1%
31 2
6.2%
35 1
3.1%
38 1
3.1%
89 1
3.1%
100 1
3.1%
101 1
3.1%
116 1
3.1%
122 1
3.1%
126 1
3.1%
ValueCountFrequency (%)
778 1
3.1%
610 1
3.1%
548 1
3.1%
508 1
3.1%
483 1
3.1%
481 1
3.1%
401 1
3.1%
330 1
3.1%
321 1
3.1%
318 1
3.1%

재원별(억원)민 자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing4
Missing (%)12.5%
Infinite0
Infinite (%)0.0%
Mean786.96429
Minimum11
Maximum4106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T02:59:48.309711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile64.65
Q1156.25
median367.5
Q31014.75
95-th percentile2960.15
Maximum4106
Range4095
Interquartile range (IQR)858.5

Descriptive statistics

Standard deviation1000.8303
Coefficient of variation (CV)1.2717608
Kurtosis5.5188942
Mean786.96429
Median Absolute Deviation (MAD)276
Skewness2.3091615
Sum22035
Variance1001661.4
MonotonicityNot monotonic
2023-12-13T02:59:48.482575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1011 1
 
3.1%
58 1
 
3.1%
112 1
 
3.1%
3657 1
 
3.1%
1026 1
 
3.1%
920 1
 
3.1%
343 1
 
3.1%
277 1
 
3.1%
392 1
 
3.1%
161 1
 
3.1%
Other values (18) 18
56.2%
(Missing) 4
 
12.5%
ValueCountFrequency (%)
11 1
3.1%
58 1
3.1%
77 1
3.1%
106 1
3.1%
112 1
3.1%
141 1
3.1%
145 1
3.1%
160 1
3.1%
161 1
3.1%
203 1
3.1%
ValueCountFrequency (%)
4106 1
3.1%
3657 1
3.1%
1666 1
3.1%
1532 1
3.1%
1365 1
3.1%
1298 1
3.1%
1026 1
3.1%
1011 1
3.1%
920 1
3.1%
877 1
3.1%

비고
Categorical

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
<NA>
21 
협의예정
5 
준공
3 
민원
 
2
행정소송
 
1

Length

Max length4
Median length4
Mean length3.6875
Min length2

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row협의예정
2nd row준공
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
65.6%
협의예정 5
 
15.6%
준공 3
 
9.4%
민원 2
 
6.2%
행정소송 1
 
3.1%

Length

2023-12-13T02:59:49.030173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:59:49.155236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
65.6%
협의예정 5
 
15.6%
준공 3
 
9.4%
민원 2
 
6.2%
행정소송 1
 
3.1%

Interactions

2023-12-13T02:59:43.925294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:40.410372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.143408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.254435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.951059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.386378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.995879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:40.507262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.650462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.412656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.024539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.469967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:44.071146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:40.632791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.765239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.530165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.099692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.561006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:44.145520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:40.764863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.892401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.650824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.173348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.663590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:44.210634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:40.918871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.995576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.753622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.242498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.747410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:44.300077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:41.042457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.120656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:42.870982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.319186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:59:43.849535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:59:49.260460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관광지명위 치면적(제곱미터)지정일사업기간(시작)사업기간(완료)재원별(억원) 계재원별(억원)공 공재원별(억원)민 자비고
구분1.0001.0001.0000.4720.9380.2610.5140.3510.2310.2320.000
관광지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위 치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
면적(제곱미터)0.4721.0001.0001.0000.8820.5350.5050.5410.0000.5970.000
지정일0.9381.0001.0000.8821.0000.8970.0000.8340.9220.8710.781
사업기간(시작)0.2611.0001.0000.5350.8971.0000.4360.0000.3330.0000.586
사업기간(완료)0.5141.0001.0000.5050.0000.4361.0000.0000.0000.3400.450
재원별(억원) 계0.3511.0001.0000.5410.8340.0000.0001.0000.0000.8990.586
재원별(억원)공 공0.2311.0001.0000.0000.9220.3330.0000.0001.0000.1490.520
재원별(억원)민 자0.2321.0001.0000.5970.8710.0000.3400.8990.1491.0000.122
비고0.0001.0001.0000.0000.7810.5860.4500.5860.5200.1221.000
2023-12-13T02:59:49.443158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분비고
구분1.0000.000
비고0.0001.000
2023-12-13T02:59:49.556747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)사업기간(시작)사업기간(완료)재원별(억원) 계재원별(억원)공 공재원별(억원)민 자구분비고
면적(제곱미터)1.000-0.3230.0520.5770.1250.5620.3380.000
사업기간(시작)-0.3231.0000.372-0.1150.505-0.2230.2000.267
사업기간(완료)0.0520.3721.0000.2640.4640.1690.1760.089
재원별(억원) 계0.577-0.1150.2641.0000.3830.9200.1280.433
재원별(억원)공 공0.1250.5050.4640.3831.0000.1960.0000.000
재원별(억원)민 자0.562-0.2230.1690.9200.1961.0000.1140.000
구분0.3380.2000.1760.1280.0000.1141.0000.000
비고0.0000.2670.0890.4330.0000.0000.0001.000

Missing values

2023-12-13T02:59:44.390284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:59:44.518493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T02:59:44.628240image/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

구분관광지명위 치면적(제곱미터)지정일사업기간(시작)사업기간(완료)재원별(억원) 계재원별(억원)공 공재원별(억원)민 자비고
0북부자원권안동하회안동 풍천 하회1840002001-03-0820032022422262160협의예정
1북부자원권예안현안동 도산 서부352982014-10-1620122020330330<NA>준공
2북부자원권영주순흥영주 순흥 내죽·청구1229151998-02-1219982009401401<NA><NA>
3북부자원권풍기온천영주 풍기 창락963102000-06-2620012023881318563<NA>
4북부자원권부석사영주 부석 북지2248852012-06-1120122024295295<NA><NA>
5북부자원권문수영주 문수 탄산764192014-02-272012202224623511협의예정
6북부자원권경천대상주 사벌 삼덕2090001987-06-2019872014207101106<NA>
7북부자원권문장대온천상주 화북 운흥·중벌9560001987-06-2019872015153421532행정소송
8북부자원권회상나루상주 중동 회상1640512013-12-3020122023649130519<NA>
9북부자원권문경온천문경 문경 하리·마원·진안1530771998-01-261998202442801744106<NA>
구분관광지명위 치면적(제곱미터)지정일사업기간(시작)사업기간(완료)재원별(억원) 계재원별(억원)공 공재원별(억원)민 자비고
22동부연안권성류굴울진 근남 노음560001981-01-181987202319231161<NA>
23동부연안권울릉도울릉군 일원10602901987-12-3119882015550158392<NA>
24동부연안권개척사울릉 서면 태하1522442012-12-3120082015451174277협의예정
25남부도시권치산영천 신령 치산2700001990-12-3119902026469126343<NA>
26남부도시권경산온천경산 남산 상대2620601987-12-151988201595131920협의예정
27남부도시권청도온천청도 금천 사전4730001989-06-031992201611421161026<NA>
28남부도시권청도용암온천청도 화양 삼신8912801996-10-212000202541655083657협의예정
29남부도시권청도신화랑청도 운문 방지2910682012-07-2320112025610610<NA>준공
30남부도시권가산산성칠곡 동명 득명3920001986-10-241986201520189112<NA>
31남부도시권고령부례고령 우곡 예곡370402013-12-3020112018933558준공