Overview

Dataset statistics

Number of variables10
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory91.1 B

Variable types

Text3
Categorical1
Numeric6

Dataset

Description치유의 숲은 향기, 경관 등 자연의 다양한 요소를 활용하여 인체의 면역력을 높이고 건강을 증진시키도록 조성한 산림시설을 말하며, 산림욕장은 국민의 건강증진을 위하여 산림 안에서 맑은 공기를 호흡하고 접촉하며 산책 및 체력단령 등을 할 수 있도록 조성한 산림시설을 말한다.
Author경상남도
URLhttps://www.data.go.kr/data/15108989/fileData.do

Alerts

조성년도 is highly overall correlated with 사업비-합계(백만원) and 3 other fieldsHigh correlation
사업비-합계(백만원) is highly overall correlated with 조성년도 and 4 other fieldsHigh correlation
사업비-국비(백만원) is highly overall correlated with 조성년도 and 4 other fieldsHigh correlation
사업비-도비(백만원) is highly overall correlated with 조성년도 and 3 other fieldsHigh correlation
사업비-시군비(백만원) is highly overall correlated with 조성년도 and 3 other fieldsHigh correlation
소유구분명 is highly overall correlated with 사업비-합계(백만원) and 3 other fieldsHigh correlation
소유구분명 is highly imbalanced (75.8%)Imbalance
치유의숲 또는 산림욕장명 has unique valuesUnique
소재지위치정보 has unique valuesUnique
사업비-도비(백만원) has 3 (12.0%) zerosZeros

Reproduction

Analysis started2024-04-21 09:50:34.793230
Analysis finished2024-04-21 09:50:44.114263
Duration9.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct14
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-04-21T18:50:44.503204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75
Distinct characters24
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

Unique6 ?
Unique (%)24.0%

Sample

1st row창원시
2nd row산청군
3rd row함양군
4th row거창군
5th row합천군
ValueCountFrequency (%)
창원시 4
16.0%
진주시 3
12.0%
산청군 2
8.0%
함양군 2
8.0%
창녕군 2
8.0%
거제시 2
8.0%
의령군 2
8.0%
고성군 2
8.0%
거창군 1
 
4.0%
합천군 1
 
4.0%
Other values (4) 4
16.0%
2024-04-21T18:50:45.446490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 14
18.7%
시 11
14.7%
창 7
 
9.3%
원 4
 
5.3%
진 3
 
4.0%
주 3
 
4.0%
함 3
 
4.0%
양 3
 
4.0%
거 3
 
4.0%
령 2
 
2.7%
Other values (14) 22
29.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 14
18.7%
시 11
14.7%
창 7
 
9.3%
원 4
 
5.3%
진 3
 
4.0%
주 3
 
4.0%
함 3
 
4.0%
양 3
 
4.0%
거 3
 
4.0%
령 2
 
2.7%
Other values (14) 22
29.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 14
18.7%
시 11
14.7%
창 7
 
9.3%
원 4
 
5.3%
진 3
 
4.0%
주 3
 
4.0%
함 3
 
4.0%
양 3
 
4.0%
거 3
 
4.0%
령 2
 
2.7%
Other values (14) 22
29.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 14
18.7%
시 11
14.7%
창 7
 
9.3%
원 4
 
5.3%
진 3
 
4.0%
주 3
 
4.0%
함 3
 
4.0%
양 3
 
4.0%
거 3
 
4.0%
령 2
 
2.7%
Other values (14) 22
29.3%

치유의숲 또는 산림욕장명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-04-21T18:50:46.063380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.2
Min length7

Characters and Unicode

Total characters205
Distinct characters57
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row창원편백치유의숲
2nd row산청 치유의 숲
3rd row함양 대봉산 치유의 숲
4th row거창 치유의 숲
5th row합천 오도산 치유의 숲
ValueCountFrequency (%)
산림욕장 20
36.4%
치유의 4
 
7.3%
숲 4
 
7.3%
대봉산 2
 
3.6%
창원편백치유의숲 1
 
1.8%
무학산 1
 
1.8%
능포 1
 
1.8%
한우산 1
 
1.8%
당항포 1
 
1.8%
왕산한방 1
 
1.8%
Other values (19) 19
34.5%
2024-04-21T18:50:46.767259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 34
16.6%
30
14.6%
장 20
 
9.8%
욕 20
 
9.8%
림 20
 
9.8%
봉 5
 
2.4%
치 5
 
2.4%
유 5
 
2.4%
의 5
 
2.4%
숲 5
 
2.4%
Other values (47) 56
27.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 175
85.4%
Space Separator 30
 
14.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 34
19.4%
장 20
 
11.4%
욕 20
 
11.4%
림 20
 
11.4%
봉 5
 
2.9%
치 5
 
2.9%
유 5
 
2.9%
의 5
 
2.9%
숲 5
 
2.9%
천 3
 
1.7%
Other values (46) 53
30.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 175
85.4%
Common 30
 
14.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 34
19.4%
장 20
 
11.4%
욕 20
 
11.4%
림 20
 
11.4%
봉 5
 
2.9%
치 5
 
2.9%
유 5
 
2.9%
의 5
 
2.9%
숲 5
 
2.9%
천 3
 
1.7%
Other values (46) 53
30.3%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 175
85.4%
ASCII 30
 
14.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 34
19.4%
장 20
 
11.4%
욕 20
 
11.4%
림 20
 
11.4%
봉 5
 
2.9%
치 5
 
2.9%
유 5
 
2.9%
의 5
 
2.9%
숲 5
 
2.9%
천 3
 
1.7%
Other values (46) 53
30.3%
ASCII
ValueCountFrequency (%)
30
100.0%

소재지위치정보
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-04-21T18:50:47.595235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length15.04
Min length10

Characters and Unicode

Total characters376
Distinct characters86
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

Unique25 ?
Unique (%)100.0%

Sample

1st row창원시 진해구 장복산길 47
2nd row산청군 금서면 특리 산73-2
3rd row함양군 병곡면 광평리 산1번지
4th row거창군 가조면 수월리 산19번지
5th row합천군 봉산면 압곡리 산150번지
ValueCountFrequency (%)
창원시 4
 
4.3%
산1 3
 
3.2%
진주시 3
 
3.2%
진해구 2
 
2.1%
금서면 2
 
2.1%
특리 2
 
2.1%
창녕군 2
 
2.1%
함양군 2
 
2.1%
병곡면 2
 
2.1%
광평리 2
 
2.1%
Other values (65) 70
74.5%
2024-04-21T18:50:48.863376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69
 
18.4%
산 31
 
8.2%
1 19
 
5.1%
리 16
 
4.3%
군 14
 
3.7%
시 11
 
2.9%
면 11
 
2.9%
창 9
 
2.4%
동 9
 
2.4%
- 9
 
2.4%
Other values (76) 178
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 237
63.0%
Space Separator 69
 
18.4%
Decimal Number 61
 
16.2%
Dash Punctuation 9
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 31
 
13.1%
리 16
 
6.8%
군 14
 
5.9%
시 11
 
4.6%
면 11
 
4.6%
창 9
 
3.8%
동 9
 
3.8%
원 6
 
2.5%
곡 5
 
2.1%
진 5
 
2.1%
Other values (64) 120
50.6%
Decimal Number
ValueCountFrequency (%)
1 19
31.1%
2 8
13.1%
0 8
13.1%
7 7
 
11.5%
6 5
 
8.2%
8 4
 
6.6%
3 4
 
6.6%
5 3
 
4.9%
9 2
 
3.3%
4 1
 
1.6%
Space Separator
ValueCountFrequency (%)
69
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 237
63.0%
Common 139
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 31
 
13.1%
리 16
 
6.8%
군 14
 
5.9%
시 11
 
4.6%
면 11
 
4.6%
창 9
 
3.8%
동 9
 
3.8%
원 6
 
2.5%
곡 5
 
2.1%
진 5
 
2.1%
Other values (64) 120
50.6%
Common
ValueCountFrequency (%)
69
49.6%
1 19
 
13.7%
- 9
 
6.5%
2 8
 
5.8%
0 8
 
5.8%
7 7
 
5.0%
6 5
 
3.6%
8 4
 
2.9%
3 4
 
2.9%
5 3
 
2.2%
Other values (2) 3
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 237
63.0%
ASCII 139
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
69
49.6%
1 19
 
13.7%
- 9
 
6.5%
2 8
 
5.8%
0 8
 
5.8%
7 7
 
5.0%
6 5
 
3.6%
8 4
 
2.9%
3 4
 
2.9%
5 3
 
2.2%
Other values (2) 3
 
2.2%
Hangul
ValueCountFrequency (%)
산 31
 
13.1%
리 16
 
6.8%
군 14
 
5.9%
시 11
 
4.6%
면 11
 
4.6%
창 9
 
3.8%
동 9
 
3.8%
원 6
 
2.5%
곡 5
 
2.1%
진 5
 
2.1%
Other values (64) 120
50.6%

소유구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
공유림
24 
국유림
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row국유림
2nd row공유림
3rd row공유림
4th row공유림
5th row공유림

Common Values

ValueCountFrequency (%)
공유림 24
96.0%
국유림 1
 
4.0%

Length

2024-04-21T18:50:49.079115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:50:49.236552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공유림 24
96.0%
국유림 1
 
4.0%

조성년도
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.48
Minimum1994
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:49.390455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile1995.2
Q12000
median2004
Q32012
95-th percentile2020.8
Maximum2022
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8134367
Coefficient of variation (CV)0.0043924867
Kurtosis-1.0261587
Mean2006.48
Median Absolute Deviation (MAD)5
Skewness0.49621349
Sum50162
Variance77.676667
MonotonicityNot monotonic
2024-04-21T18:50:49.579946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2018 2
 
8.0%
2005 2
 
8.0%
2004 2
 
8.0%
1999 2
 
8.0%
2001 2
 
8.0%
2019 1
 
4.0%
2002 1
 
4.0%
2012 1
 
4.0%
2011 1
 
4.0%
2009 1
 
4.0%
Other values (10) 10
40.0%
ValueCountFrequency (%)
1994 1
4.0%
1995 1
4.0%
1996 1
4.0%
1997 1
4.0%
1999 2
8.0%
2000 1
4.0%
2001 2
8.0%
2002 1
4.0%
2003 1
4.0%
2004 2
8.0%
ValueCountFrequency (%)
2022 1
4.0%
2021 1
4.0%
2020 1
4.0%
2019 1
4.0%
2018 2
8.0%
2012 1
4.0%
2011 1
4.0%
2009 1
4.0%
2007 1
4.0%
2005 2
8.0%

조성면적(헥타르)
Real number (ℝ)

Distinct21
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.84
Minimum5
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:49.779558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q124
median60
Q3100
95-th percentile200
Maximum200
Range195
Interquartile range (IQR)76

Descriptive statistics

Standard deviation62.883941
Coefficient of variation (CV)0.84024507
Kurtosis-0.048446442
Mean74.84
Median Absolute Deviation (MAD)40
Skewness1.0263458
Sum1871
Variance3954.39
MonotonicityNot monotonic
2024-04-21T18:50:49.992260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
200 3
 
12.0%
60 2
 
8.0%
10 2
 
8.0%
58 1
 
4.0%
180 1
 
4.0%
14 1
 
4.0%
5 1
 
4.0%
29 1
 
4.0%
20 1
 
4.0%
70 1
 
4.0%
Other values (11) 11
44.0%
ValueCountFrequency (%)
5 1
4.0%
10 2
8.0%
14 1
4.0%
18 1
4.0%
20 1
4.0%
24 1
4.0%
29 1
4.0%
45 1
4.0%
50 1
4.0%
55 1
4.0%
ValueCountFrequency (%)
200 3
12.0%
180 1
 
4.0%
140 1
 
4.0%
111 1
 
4.0%
100 1
 
4.0%
75 1
 
4.0%
72 1
 
4.0%
70 1
 
4.0%
65 1
 
4.0%
60 2
8.0%

사업비-합계(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1304.24
Minimum200
Maximum5500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:50.191782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile225.8
Q1400
median400
Q3400
95-th percentile5000
Maximum5500
Range5300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1888.0245
Coefficient of variation (CV)1.4476051
Kurtosis0.69035809
Mean1304.24
Median Absolute Deviation (MAD)0
Skewness1.610742
Sum32606
Variance3564636.5
MonotonicityNot monotonic
2024-04-21T18:50:50.376831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
400 14
56.0%
5000 2
 
8.0%
200 2
 
8.0%
4662 1
 
4.0%
5500 1
 
4.0%
4800 1
 
4.0%
329 1
 
4.0%
346 1
 
4.0%
618 1
 
4.0%
351 1
 
4.0%
ValueCountFrequency (%)
200 2
 
8.0%
329 1
 
4.0%
346 1
 
4.0%
351 1
 
4.0%
400 14
56.0%
618 1
 
4.0%
4662 1
 
4.0%
4800 1
 
4.0%
5000 2
 
8.0%
5500 1
 
4.0%
ValueCountFrequency (%)
5500 1
 
4.0%
5000 2
 
8.0%
4800 1
 
4.0%
4662 1
 
4.0%
618 1
 
4.0%
400 14
56.0%
351 1
 
4.0%
346 1
 
4.0%
329 1
 
4.0%
200 2
 
8.0%

사업비-국비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean644.84
Minimum100
Maximum2750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:50.554678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median200
Q3200
95-th percentile2500
Maximum2750
Range2650
Interquartile range (IQR)0

Descriptive statistics

Standard deviation947.56788
Coefficient of variation (CV)1.469462
Kurtosis0.69176148
Mean644.84
Median Absolute Deviation (MAD)0
Skewness1.6117984
Sum16121
Variance897884.89
MonotonicityNot monotonic
2024-04-21T18:50:50.739399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
200 14
56.0%
100 3
 
12.0%
2500 2
 
8.0%
2331 1
 
4.0%
2750 1
 
4.0%
2400 1
 
4.0%
156 1
 
4.0%
209 1
 
4.0%
175 1
 
4.0%
ValueCountFrequency (%)
100 3
 
12.0%
156 1
 
4.0%
175 1
 
4.0%
200 14
56.0%
209 1
 
4.0%
2331 1
 
4.0%
2400 1
 
4.0%
2500 2
 
8.0%
2750 1
 
4.0%
ValueCountFrequency (%)
2750 1
 
4.0%
2500 2
 
8.0%
2400 1
 
4.0%
2331 1
 
4.0%
209 1
 
4.0%
200 14
56.0%
175 1
 
4.0%
156 1
 
4.0%
100 3
 
12.0%

사업비-도비(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.24
Minimum0
Maximum825
Zeros3
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:50.917481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median60
Q3100
95-th percentile750
Maximum825
Range825
Interquartile range (IQR)40

Descriptive statistics

Standard deviation284.88233
Coefficient of variation (CV)1.4666512
Kurtosis0.64717205
Mean194.24
Median Absolute Deviation (MAD)20
Skewness1.5823412
Sum4856
Variance81157.94
MonotonicityNot monotonic
2024-04-21T18:50:51.099036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
60 10
40.0%
0 3
 
12.0%
750 2
 
8.0%
40 2
 
8.0%
100 2
 
8.0%
699 1
 
4.0%
825 1
 
4.0%
720 1
 
4.0%
76 1
 
4.0%
103 1
 
4.0%
ValueCountFrequency (%)
0 3
 
12.0%
40 2
 
8.0%
53 1
 
4.0%
60 10
40.0%
76 1
 
4.0%
100 2
 
8.0%
103 1
 
4.0%
699 1
 
4.0%
720 1
 
4.0%
750 2
 
8.0%
ValueCountFrequency (%)
825 1
 
4.0%
750 2
 
8.0%
720 1
 
4.0%
699 1
 
4.0%
103 1
 
4.0%
100 2
 
8.0%
76 1
 
4.0%
60 10
40.0%
53 1
 
4.0%
40 2
 
8.0%

사업비-시군비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465.16
Minimum60
Maximum1925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-04-21T18:50:51.280430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile100
Q1140
median140
Q3200
95-th percentile1750
Maximum1925
Range1865
Interquartile range (IQR)60

Descriptive statistics

Standard deviation657.4631
Coefficient of variation (CV)1.4134128
Kurtosis0.67027679
Mean465.16
Median Absolute Deviation (MAD)40
Skewness1.5988292
Sum11629
Variance432257.72
MonotonicityNot monotonic
2024-04-21T18:50:51.486174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
140 10
40.0%
100 3
 
12.0%
1750 2
 
8.0%
200 2
 
8.0%
1632 1
 
4.0%
1925 1
 
4.0%
1680 1
 
4.0%
189 1
 
4.0%
60 1
 
4.0%
114 1
 
4.0%
Other values (2) 2
 
8.0%
ValueCountFrequency (%)
60 1
 
4.0%
100 3
 
12.0%
114 1
 
4.0%
123 1
 
4.0%
140 10
40.0%
189 1
 
4.0%
200 2
 
8.0%
306 1
 
4.0%
1632 1
 
4.0%
1680 1
 
4.0%
ValueCountFrequency (%)
1925 1
 
4.0%
1750 2
 
8.0%
1680 1
 
4.0%
1632 1
 
4.0%
306 1
 
4.0%
200 2
 
8.0%
189 1
 
4.0%
140 10
40.0%
123 1
 
4.0%
114 1
 
4.0%

Interactions

2024-04-21T18:50:41.793131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:35.336882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.198747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:37.419698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:38.885823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:40.348417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:42.046613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:35.472784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.349005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:37.662246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:39.127589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:40.586735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:42.315314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:35.632023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.516843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:37.920321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:39.384784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:40.840708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:42.565928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:35.769128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.669405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:38.158564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:39.624640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:41.079587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:42.812572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:35.908042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.907017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:38.395802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:39.859604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:41.312843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:43.058694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:36.039965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:37.151887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:38.628994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:40.092253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:50:41.539999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:50:51.657070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명치유의숲 또는 산림욕장명소재지위치정보소유구분명조성년도조성면적(헥타르)사업비-합계(백만원)사업비-국비(백만원)사업비-도비(백만원)사업비-시군비(백만원)
시군명1.0001.0001.0000.0000.0000.4250.5770.6000.6740.000
치유의숲 또는 산림욕장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지위치정보1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소유구분명0.0001.0001.0001.0000.0000.0000.4170.4210.8420.842
조성년도0.0001.0001.0000.0001.0000.0000.9830.9850.8450.813
조성면적(헥타르)0.4251.0001.0000.0000.0001.0000.0000.0000.5410.695
사업비-합계(백만원)0.5771.0001.0000.4170.9830.0001.0001.0001.0001.000
사업비-국비(백만원)0.6001.0001.0000.4210.9850.0001.0001.0001.0001.000
사업비-도비(백만원)0.6741.0001.0000.8420.8450.5411.0001.0001.0000.989
사업비-시군비(백만원)0.0001.0001.0000.8420.8130.6951.0001.0000.9891.000
2024-04-21T18:50:51.880369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조성년도조성면적(헥타르)사업비-합계(백만원)사업비-국비(백만원)사업비-도비(백만원)사업비-시군비(백만원)소유구분명
조성년도1.000-0.3950.7690.7720.5240.6440.000
조성면적(헥타르)-0.3951.000-0.035-0.0410.007-0.0570.000
사업비-합계(백만원)0.769-0.0351.0000.9990.7900.7990.643
사업비-국비(백만원)0.772-0.0410.9991.0000.7890.7860.643
사업비-도비(백만원)0.5240.0070.7900.7891.0000.4490.608
사업비-시군비(백만원)0.644-0.0570.7990.7860.4491.0000.608
소유구분명0.0000.0000.6430.6430.6080.6081.000

Missing values

2024-04-21T18:50:43.428202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:50:43.920107image/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

시군명치유의숲 또는 산림욕장명소재지위치정보소유구분명조성년도조성면적(헥타르)사업비-합계(백만원)사업비-국비(백만원)사업비-도비(백만원)사업비-시군비(백만원)
0창원시창원편백치유의숲창원시 진해구 장복산길 47국유림201958466223316991632
1산청군산청 치유의 숲산청군 금서면 특리 산73-2공유림202255500025007501750
2함양군함양 대봉산 치유의 숲함양군 병곡면 광평리 산1번지공유림202065500025007501750
3거창군거창 치유의 숲거창군 가조면 수월리 산19번지공유림202150550027508251925
4합천군합천 오도산 치유의 숲합천군 봉산면 압곡리 산150번지공유림201860480024007201680
5김해시신어산 산림욕장김해시 삼방동 산120-3공유림199410032910040189
6창녕군자하곡 산림욕장창녕군 창녕읍 송현리 산1공유림1995752001004060
7진주시진양호 산림욕장진주시 판문동 산150-1공유림19961834615676114
8창원시천자봉 산림욕장창원시 진해구 장천동 산1-300공유림199772400200100100
9창원시천주산 산림욕장창원시 의창구 북면 외감리 산62공유림1999140618209103306
시군명치유의숲 또는 산림욕장명소재지위치정보소유구분명조성년도조성면적(헥타르)사업비-합계(백만원)사업비-국비(백만원)사업비-도비(백만원)사업비-시군비(백만원)
15함안군입곡 산림욕장함안군 산인면 입곡리 산61-1공유림2003454002000200
16의령군남산 산림욕장의령군 의령읍 중동리 산6공유림200420040020060140
17남해군망운산 산림욕장남해군 아산리 산106-1공유림200420040020060140
18고성군갈모봉 산림욕장고성군 고성읍 이당리 산183공유림20057040020060140
19산청군왕산한방 산림욕장산청군 금서면 특리 산72공유림20051040020060140
20고성군당항포 산림욕장고성군 회화면 당항리 산1공유림20071040020060140
21의령군한우산 산림욕장의령군 궁류면 벽계리 산200공유림20092040020060140
22거제시능포 산림욕장거제시 능포동 산1공유림20112940020060140
23진주시비봉산 산림욕장진주시 상봉동 972-1공유림2012540020060140
24함양군대봉산 산림욕장함양군 병곡면 광평리 산17공유림20181440020060140