Overview

Dataset statistics

Number of variables5
Number of observations50
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory44.6 B

Variable types

Text2
Numeric2
Categorical1

Dataset

Description제주특별자치도 서귀포시에서 조성한 숲길 산책로 현황에 관한 데이터로 산책로의 명칭, 구간, 연장 정보를 제공합니다.
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/3082951/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
조성완료년도 has 3 (6.0%) missing valuesMissing
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:41:49.684174
Analysis finished2023-12-16 15:41:55.016721
Duration5.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명칭
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-16T15:41:55.354365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.06
Min length4

Characters and Unicode

Total characters303
Distinct characters88
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

Unique50 ?
Unique (%)100.0%

Sample

1st row서홍추억의숲길
2nd row호근산책로
3rd row머체왓숲길
4th row송악산숲길
5th row섯알오름숲길
ValueCountFrequency (%)
숲길 37
41.6%
오름 2
 
2.2%
서홍추억의숲길 1
 
1.1%
좌보미 1
 
1.1%
해맞이길 1
 
1.1%
대병악 1
 
1.1%
남송악 1
 
1.1%
광해악 1
 
1.1%
왕이메 1
 
1.1%
따라비오름 1
 
1.1%
Other values (42) 42
47.2%
2023-12-16T15:41:56.732412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 49
16.2%
숲 48
15.8%
39
12.9%
산 16
 
5.3%
악 14
 
4.6%
오 12
 
4.0%
름 12
 
4.0%
봉 11
 
3.6%
대 5
 
1.7%
이 4
 
1.3%
Other values (78) 93
30.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
87.1%
Space Separator 39
 
12.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 49
18.6%
숲 48
18.2%
산 16
 
6.1%
악 14
 
5.3%
오 12
 
4.5%
름 12
 
4.5%
봉 11
 
4.2%
대 5
 
1.9%
이 4
 
1.5%
영 3
 
1.1%
Other values (77) 90
34.1%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
87.1%
Common 39
 
12.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 49
18.6%
숲 48
18.2%
산 16
 
6.1%
악 14
 
5.3%
오 12
 
4.5%
름 12
 
4.5%
봉 11
 
4.2%
대 5
 
1.9%
이 4
 
1.5%
영 3
 
1.1%
Other values (77) 90
34.1%
Common
ValueCountFrequency (%)
39
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
87.1%
ASCII 39
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 49
18.6%
숲 48
18.2%
산 16
 
6.1%
악 14
 
5.3%
오 12
 
4.5%
름 12
 
4.5%
봉 11
 
4.2%
대 5
 
1.9%
이 4
 
1.5%
영 3
 
1.1%
Other values (77) 90
34.1%
ASCII
ValueCountFrequency (%)
39
100.0%

구간
Text

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-16T15:41:58.070063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length3
Mean length4.04
Min length2

Characters and Unicode

Total characters202
Distinct characters91
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

Unique44 ?
Unique (%)88.0%

Sample

1st row제2산록도로-둘레길
2nd row제2산록도로-시오름
3rd row머체왓일대(순환)
4th row송악산
5th row섯알오름
ValueCountFrequency (%)
독자봉 2
 
4.0%
유건에 2
 
4.0%
월라산 2
 
4.0%
법정악 1
 
2.0%
매봉 1
 
2.0%
붉은오름휴양림-말찻오름 1
 
2.0%
제2산록도로-둘레길 1
 
2.0%
좌보미오름 1
 
2.0%
광해악 1
 
2.0%
왕이메 1
 
2.0%
Other values (37) 37
74.0%
2023-12-16T15:42:00.241421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 16
 
7.9%
름 16
 
7.9%
오 16
 
7.9%
악 12
 
5.9%
봉 11
 
5.4%
대 5
 
2.5%
라 4
 
2.0%
- 4
 
2.0%
이 4
 
2.0%
은 3
 
1.5%
Other values (81) 111
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 192
95.0%
Dash Punctuation 4
 
2.0%
Close Punctuation 2
 
1.0%
Open Punctuation 2
 
1.0%
Decimal Number 2
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 16
 
8.3%
름 16
 
8.3%
오 16
 
8.3%
악 12
 
6.2%
봉 11
 
5.7%
대 5
 
2.6%
라 4
 
2.1%
이 4
 
2.1%
은 3
 
1.6%
록 3
 
1.6%
Other values (77) 102
53.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 192
95.0%
Common 10
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 16
 
8.3%
름 16
 
8.3%
오 16
 
8.3%
악 12
 
6.2%
봉 11
 
5.7%
대 5
 
2.6%
라 4
 
2.1%
이 4
 
2.1%
은 3
 
1.6%
록 3
 
1.6%
Other values (77) 102
53.1%
Common
ValueCountFrequency (%)
- 4
40.0%
) 2
20.0%
( 2
20.0%
2 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 192
95.0%
ASCII 10
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 16
 
8.3%
름 16
 
8.3%
오 16
 
8.3%
악 12
 
6.2%
봉 11
 
5.7%
대 5
 
2.6%
라 4
 
2.1%
이 4
 
2.1%
은 3
 
1.6%
록 3
 
1.6%
Other values (77) 102
53.1%
ASCII
ValueCountFrequency (%)
- 4
40.0%
) 2
20.0%
( 2
20.0%
2 2
20.0%

연장(Km)
Real number (ℝ)

Distinct29
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.566
Minimum0.3
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-16T15:42:01.313994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.4
Q11.025
median1.85
Q32.725
95-th percentile7.855
Maximum15
Range14.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation2.7781663
Coefficient of variation (CV)1.0826837
Kurtosis8.9191399
Mean2.566
Median Absolute Deviation (MAD)0.85
Skewness2.7951267
Sum128.3
Variance7.7182082
MonotonicityNot monotonic
2023-12-16T15:42:02.189311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.0 5
 
10.0%
1.5 4
 
8.0%
2.1 3
 
6.0%
3.0 3
 
6.0%
1.2 3
 
6.0%
1.0 3
 
6.0%
0.4 3
 
6.0%
2.2 2
 
4.0%
0.8 2
 
4.0%
0.9 2
 
4.0%
Other values (19) 20
40.0%
ValueCountFrequency (%)
0.3 1
 
2.0%
0.4 3
6.0%
0.5 1
 
2.0%
0.7 1
 
2.0%
0.8 2
4.0%
0.9 2
4.0%
1.0 3
6.0%
1.1 1
 
2.0%
1.2 3
6.0%
1.4 2
4.0%
ValueCountFrequency (%)
15.0 1
 
2.0%
11.0 1
 
2.0%
8.8 1
 
2.0%
6.7 1
 
2.0%
6.3 1
 
2.0%
5.7 1
 
2.0%
4.5 1
 
2.0%
4.2 1
 
2.0%
3.1 1
 
2.0%
3.0 3
6.0%

조성완료년도
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)17.0%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean2011.1915
Minimum1997
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-16T15:42:03.077924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2008.3
Q12011
median2012
Q32012.5
95-th percentile2013
Maximum2013
Range16
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.5421516
Coefficient of variation (CV)0.0012640028
Kurtosis21.209183
Mean2011.1915
Median Absolute Deviation (MAD)1
Skewness-4.0301989
Sum94526
Variance6.4625347
MonotonicityNot monotonic
2023-12-16T15:42:03.644901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2012 16
32.0%
2013 12
24.0%
2011 8
16.0%
2010 6
 
12.0%
2009 2
 
4.0%
2007 1
 
2.0%
2008 1
 
2.0%
1997 1
 
2.0%
(Missing) 3
 
6.0%
ValueCountFrequency (%)
1997 1
 
2.0%
2007 1
 
2.0%
2008 1
 
2.0%
2009 2
 
4.0%
2010 6
 
12.0%
2011 8
16.0%
2012 16
32.0%
2013 12
24.0%
ValueCountFrequency (%)
2013 12
24.0%
2012 16
32.0%
2011 8
16.0%
2010 6
 
12.0%
2009 2
 
4.0%
2008 1
 
2.0%
2007 1
 
2.0%
1997 1
 
2.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-14
50 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-14
2nd row2023-12-14
3rd row2023-12-14
4th row2023-12-14
5th row2023-12-14

Common Values

ValueCountFrequency (%)
2023-12-14 50
100.0%

Length

2023-12-16T15:42:04.988996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:42:05.704149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-14 50
100.0%

Interactions

2023-12-16T15:41:51.835402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:41:50.889194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:41:52.413709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:41:51.400951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:42:06.290562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명칭구간연장(Km)조성완료년도
명칭1.0001.0001.0001.000
구간1.0001.0000.9890.000
연장(Km)1.0000.9891.0000.000
조성완료년도1.0000.0000.0001.000
2023-12-16T15:42:06.804395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장(Km)조성완료년도
연장(Km)1.0000.017
조성완료년도0.0171.000

Missing values

2023-12-16T15:41:53.726223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:41:54.658822image/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

명칭구간연장(Km)조성완료년도데이터기준일자
0서홍추억의숲길제2산록도로-둘레길11.020122023-12-14
1호근산책로제2산록도로-시오름4.520122023-12-14
2머체왓숲길머체왓일대(순환)6.720122023-12-14
3송악산숲길송악산1.220122023-12-14
4섯알오름숲길섯알오름1.920122023-12-14
5가시악숲길가시오름1.720112023-12-14
6녹남봉숲길녹남봉1.120132023-12-14
7물영아리오름 숲길물영아리오름2.020122023-12-14
8이승악 숲길이승악(순환)8.820112023-12-14
9자배봉숲길자배봉1.820132023-12-14
명칭구간연장(Km)조성완료년도데이터기준일자
40붉은오름 숲길붉은오름3.020132023-12-14
41제지오름 숲길제지오름0.520112023-12-14
42월라산 숲길월라산0.7<NA>2023-12-14
43칡오름 숲길월라산2.220122023-12-14
44영천악 숲길영천악6.320132023-12-14
45미악산 숲길미악산4.220112023-12-14
46고근산 숲길고근산2.820122023-12-14
47법정악 숲길법정악1.5<NA>2023-12-14
48해맞이길붉은오름휴양림-말찻오름5.720132023-12-14
49사려니 숲길사려니오름-붉은오름15.019972023-12-14