Overview

Dataset statistics

Number of variables7
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory62.7 B

Variable types

Categorical3
Numeric1
Text3

Dataset

Description농촌체험휴양마을 으뜸촌 현황(경관 및 서비스, 체험, 숙박, 음식 4개분야 모두 1등급 마을)
Author농림축산식품부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220217000000002075

Alerts

CTRD_CODE is highly overall correlated with CTRD_NMHigh correlation
CTRD_NM is highly overall correlated with CTRD_CODEHigh correlation
SIGNGU_CODE is highly imbalanced (81.7%)Imbalance
VILAGE_NM has unique valuesUnique
VILAGE_DETAIL_ADRES has unique valuesUnique

Reproduction

Analysis started2024-07-06 10:05:24.257598
Analysis finished2024-07-06 10:05:25.038943
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

SLCTN_YEAR
Categorical

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
2016
28 
2015

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 28
77.8%
2015 8
 
22.2%

Length

2024-07-06T19:05:25.090928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T19:05:25.174852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 28
77.8%
2015 8
 
22.2%

CTRD_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
전북
경남
강원
전남
충북
Other values (6)

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)8.3%

Sample

1st row인천
2nd row대전
3rd row경기
4th row강원
5th row강원

Common Values

ValueCountFrequency (%)
전북 7
19.4%
경남 7
19.4%
강원 6
16.7%
전남 4
11.1%
충북 3
8.3%
경기 2
 
5.6%
충남 2
 
5.6%
경북 2
 
5.6%
인천 1
 
2.8%
대전 1
 
2.8%

Length

2024-07-06T19:05:25.265108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 7
19.4%
경남 7
19.4%
강원 6
16.7%
전남 4
11.1%
충북 3
8.3%
경기 2
 
5.6%
충남 2
 
5.6%
경북 2
 
5.6%
인천 1
 
2.8%
대전 1
 
2.8%

CTRD_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.111111
Minimum28
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-07-06T19:05:25.360533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile38.25
Q142
median45
Q347
95-th percentile48
Maximum50
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4257544
Coefficient of variation (CV)0.10033196
Kurtosis6.4791544
Mean44.111111
Median Absolute Deviation (MAD)2.5
Skewness-2.2392182
Sum1588
Variance19.587302
MonotonicityNot monotonic
2024-07-06T19:05:25.452608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
45 7
19.4%
48 7
19.4%
42 6
16.7%
46 4
11.1%
43 3
8.3%
41 2
 
5.6%
44 2
 
5.6%
47 2
 
5.6%
28 1
 
2.8%
30 1
 
2.8%
ValueCountFrequency (%)
28 1
 
2.8%
30 1
 
2.8%
41 2
 
5.6%
42 6
16.7%
43 3
8.3%
44 2
 
5.6%
45 7
19.4%
46 4
11.1%
47 2
 
5.6%
48 7
19.4%
ValueCountFrequency (%)
50 1
 
2.8%
48 7
19.4%
47 2
 
5.6%
46 4
11.1%
45 7
19.4%
44 2
 
5.6%
43 3
8.3%
42 6
16.7%
41 2
 
5.6%
30 1
 
2.8%
Distinct27
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-07-06T19:05:25.625610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters72
Distinct characters32
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

Unique19 ?
Unique (%)52.8%

Sample

1st row강화
2nd row중구
3rd row양평
4th row정선
5th row화천
ValueCountFrequency (%)
완주 3
 
8.3%
인제 2
 
5.6%
옥천 2
 
5.6%
익산 2
 
5.6%
남원 2
 
5.6%
남해 2
 
5.6%
양평 2
 
5.6%
담양 2
 
5.6%
거창 1
 
2.8%
봉화 1
 
2.8%
Other values (17) 17
47.2%
2024-07-06T19:05:25.921957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
11.1%
5
 
6.9%
4
 
5.6%
4
 
5.6%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
Other values (22) 33
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
 
11.1%
5
 
6.9%
4
 
5.6%
4
 
5.6%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
Other values (22) 33
45.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
 
11.1%
5
 
6.9%
4
 
5.6%
4
 
5.6%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
Other values (22) 33
45.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
 
11.1%
5
 
6.9%
4
 
5.6%
4
 
5.6%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
3
 
4.2%
Other values (22) 33
45.8%

SIGNGU_CODE
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
<NA>
35 
30140
 
1

Length

Max length5
Median length4
Mean length4.0277778
Min length4

Unique

Unique1 ?
Unique (%)2.8%

Sample

1st row<NA>
2nd row30140
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 35
97.2%
30140 1
 
2.8%

Length

2024-07-06T19:05:26.045392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-06T19:05:26.133861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
97.2%
30140 1
 
2.8%

VILAGE_NM
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-07-06T19:05:26.323267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length5.3611111
Min length4

Characters and Unicode

Total characters193
Distinct characters93
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row도래미마을
2nd row무수천하마을
3rd row여물리체험마을
4th row개미들마을
5th row토고미마을
ValueCountFrequency (%)
도래미마을 1
 
2.8%
무수천하마을 1
 
2.8%
숲옛마을(갈계 1
 
2.8%
개실마을 1
 
2.8%
청량산비나리마을 1
 
2.8%
두모마을 1
 
2.8%
해바리마을 1
 
2.8%
의신마을 1
 
2.8%
마근담마을 1
 
2.8%
아홉굿마을 1
 
2.8%
Other values (26) 26
72.2%
2024-07-06T19:05:26.655777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35
 
18.1%
34
 
17.6%
5
 
2.6%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
( 2
 
1.0%
2
 
1.0%
Other values (83) 99
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
97.9%
Open Punctuation 2
 
1.0%
Close Punctuation 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
35
 
18.5%
34
 
18.0%
5
 
2.6%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
2
 
1.1%
2
 
1.1%
Other values (81) 95
50.3%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
97.9%
Common 4
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
35
 
18.5%
34
 
18.0%
5
 
2.6%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
2
 
1.1%
2
 
1.1%
Other values (81) 95
50.3%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
97.9%
ASCII 4
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
35
 
18.5%
34
 
18.0%
5
 
2.6%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
2
 
1.1%
2
 
1.1%
Other values (81) 95
50.3%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

VILAGE_DETAIL_ADRES
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2024-07-06T19:05:26.961101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length13.222222
Min length10

Characters and Unicode

Total characters476
Distinct characters116
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row선원면 해안동로 1129-19
2nd row운남로85번길 5 (무수동)
3rd row청운면 청운체육공원길 26
4th row남면 광락로 354
5th row상서면 토고미길 22-8
ValueCountFrequency (%)
청성면 2
 
1.8%
42 2
 
1.8%
선원면 1
 
0.9%
상주면 1
 
0.9%
600 1
 
0.9%
마근담길 1
 
0.9%
시천면 1
 
0.9%
27-2 1
 
0.9%
의신길 1
 
0.9%
화개면 1
 
0.9%
Other values (98) 98
89.1%
2024-07-06T19:05:27.387442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
20.8%
34
 
7.1%
2 24
 
5.0%
23
 
4.8%
18
 
3.8%
5 17
 
3.6%
1 16
 
3.4%
- 13
 
2.7%
4 13
 
2.7%
7 12
 
2.5%
Other values (106) 207
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 242
50.8%
Decimal Number 120
25.2%
Space Separator 99
20.8%
Dash Punctuation 13
 
2.7%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
34
 
14.0%
23
 
9.5%
18
 
7.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (92) 133
55.0%
Decimal Number
ValueCountFrequency (%)
2 24
20.0%
5 17
14.2%
1 16
13.3%
4 13
10.8%
7 12
10.0%
3 11
9.2%
8 9
 
7.5%
0 7
 
5.8%
9 6
 
5.0%
6 5
 
4.2%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 242
50.8%
Common 234
49.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
34
 
14.0%
23
 
9.5%
18
 
7.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (92) 133
55.0%
Common
ValueCountFrequency (%)
99
42.3%
2 24
 
10.3%
5 17
 
7.3%
1 16
 
6.8%
- 13
 
5.6%
4 13
 
5.6%
7 12
 
5.1%
3 11
 
4.7%
8 9
 
3.8%
0 7
 
3.0%
Other values (4) 13
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 242
50.8%
ASCII 234
49.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
42.3%
2 24
 
10.3%
5 17
 
7.3%
1 16
 
6.8%
- 13
 
5.6%
4 13
 
5.6%
7 12
 
5.1%
3 11
 
4.7%
8 9
 
3.8%
0 7
 
3.0%
Other values (4) 13
 
5.6%
Hangul
ValueCountFrequency (%)
34
 
14.0%
23
 
9.5%
18
 
7.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (92) 133
55.0%

Interactions

2024-07-06T19:05:24.559993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-07-06T19:05:27.482092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SLCTN_YEARCTRD_NMCTRD_CODESIGNGU_NMVILAGE_NMVILAGE_DETAIL_ADRES
SLCTN_YEAR1.0000.0000.0000.2201.0001.000
CTRD_NM0.0001.0001.0001.0001.0001.000
CTRD_CODE0.0001.0001.0001.0001.0001.000
SIGNGU_NM0.2201.0001.0001.0001.0001.000
VILAGE_NM1.0001.0001.0001.0001.0001.000
VILAGE_DETAIL_ADRES1.0001.0001.0001.0001.0001.000
2024-07-06T19:05:27.585283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/