Overview

Dataset statistics

Number of variables4
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory38.1 B

Variable types

Numeric2
Text2

Dataset

Description전라남도 함평군 관내 임도현황에 대한 자료입니다. 이 자료는 전라남도 함평군 관내 임도의 위치, 구간, 거리 등의 자료를 포함합니다.
Author전라남도 함평군
URLhttps://www.data.go.kr/data/15061696/fileData.do

Alerts

연번 has unique valuesUnique
구간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:46:10.947202
Analysis finished2023-12-12 22:46:11.599565
Duration0.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:46:11.652916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q18.75
median16.5
Q324.25
95-th percentile30.45
Maximum32
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.56853524
Kurtosis-1.2
Mean16.5
Median Absolute Deviation (MAD)8
Skewness0
Sum528
Variance88
MonotonicityStrictly increasing
2023-12-13T07:46:11.780975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1
 
3.1%
18 1
 
3.1%
32 1
 
3.1%
31 1
 
3.1%
30 1
 
3.1%
29 1
 
3.1%
28 1
 
3.1%
27 1
 
3.1%
26 1
 
3.1%
25 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
3 1
3.1%
4 1
3.1%
5 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
10 1
3.1%
ValueCountFrequency (%)
32 1
3.1%
31 1
3.1%
30 1
3.1%
29 1
3.1%
28 1
3.1%
27 1
3.1%
26 1
3.1%
25 1
3.1%
24 1
3.1%
23 1
3.1%

위치
Text

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:46:11.994773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length7.125
Min length7

Characters and Unicode

Total characters228
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 (%)78.1%

Sample

1st row함평읍 자풍리
2nd row손불면 북성리
3rd row손불면 북성리
4th row손불면 양재리
5th row손불면 월천리
ValueCountFrequency (%)
나산면 7
 
10.8%
손불면 6
 
9.2%
학교면 5
 
7.7%
대동면 4
 
6.2%
해보면 4
 
6.2%
원선리 4
 
6.2%
월야면 3
 
4.6%
북성리 2
 
3.1%
죽정리 2
 
3.1%
신광면 2
 
3.1%
Other values (26) 26
40.0%
2023-12-13T07:46:12.312398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
33
 
14.5%
리 32
 
14.0%
면 31
 
13.6%
산 10
 
4.4%
나 7
 
3.1%
교 7
 
3.1%
손 6
 
2.6%
불 6
 
2.6%
월 6
 
2.6%
대 5
 
2.2%
Other values (47) 85
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 195
85.5%
Space Separator 33
 
14.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 32
 
16.4%
면 31
 
15.9%
산 10
 
5.1%
나 7
 
3.6%
교 7
 
3.6%
손 6
 
3.1%
불 6
 
3.1%
월 6
 
3.1%
대 5
 
2.6%
학 5
 
2.6%
Other values (46) 80
41.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 195
85.5%
Common 33
 
14.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 32
 
16.4%
면 31
 
15.9%
산 10
 
5.1%
나 7
 
3.6%
교 7
 
3.6%
손 6
 
3.1%
불 6
 
3.1%
월 6
 
3.1%
대 5
 
2.6%
학 5
 
2.6%
Other values (46) 80
41.0%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 195
85.5%
ASCII 33
 
14.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
33
100.0%
Hangul
ValueCountFrequency (%)
리 32
 
16.4%
면 31
 
15.9%
산 10
 
5.1%
나 7
 
3.6%
교 7
 
3.6%
손 6
 
3.1%
불 6
 
3.1%
월 6
 
3.1%
대 5
 
2.6%
학 5
 
2.6%
Other values (46) 80
41.0%

구간
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T07:46:12.553408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length16.71875
Min length9

Characters and Unicode

Total characters535
Distinct characters98
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row함장로 - 금곡제
2nd row손불 북성(저수지) - 손불 양재(호암)
3rd row손불 북성(저수지) - 연흥사
4th row손불 북성 사기 - 손불 양재 모량
5th row손불 대전(대전제) - 손불 북성(승선)
ValueCountFrequency (%)
30
21.3%
나산 11
 
7.8%
손불 11
 
7.8%
학교 7
 
5.0%
대동 5
 
3.5%
원선 3
 
2.1%
신광 3
 
2.1%
북성(저수지 2
 
1.4%
외치 2
 
1.4%
해보 2
 
1.4%
Other values (65) 65
46.1%
2023-12-13T07:46:12.963102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
20.4%
- 30
 
5.6%
( 27
 
5.0%
) 27
 
5.0%
산 21
 
3.9%
나 12
 
2.2%
리 11
 
2.1%
손 11
 
2.1%
불 11
 
2.1%
원 10
 
1.9%
Other values (88) 266
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
63.6%
Space Separator 109
 
20.4%
Dash Punctuation 30
 
5.6%
Open Punctuation 27
 
5.0%
Close Punctuation 27
 
5.0%
Decimal Number 1
 
0.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 21
 
6.2%
나 12
 
3.5%
리 11
 
3.2%
손 11
 
3.2%
불 11
 
3.2%
원 10
 
2.9%
동 10
 
2.9%
대 10
 
2.9%
교 10
 
2.9%
선 9
 
2.6%
Other values (82) 225
66.2%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
63.6%
Common 195
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 21
 
6.2%
나 12
 
3.5%
리 11
 
3.2%
손 11
 
3.2%
불 11
 
3.2%
원 10
 
2.9%
동 10
 
2.9%
대 10
 
2.9%
교 10
 
2.9%
선 9
 
2.6%
Other values (82) 225
66.2%
Common
ValueCountFrequency (%)
109
55.9%
- 30
 
15.4%
( 27
 
13.8%
) 27
 
13.8%
2 1
 
0.5%
/ 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
63.6%
ASCII 195
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
55.9%
- 30
 
15.4%
( 27
 
13.8%
) 27
 
13.8%
2 1
 
0.5%
/ 1
 
0.5%
Hangul
ValueCountFrequency (%)
산 21
 
6.2%
나 12
 
3.5%
리 11
 
3.2%
손 11
 
3.2%
불 11
 
3.2%
원 10
 
2.9%
동 10
 
2.9%
대 10
 
2.9%
교 10
 
2.9%
선 9
 
2.6%
Other values (82) 225
66.2%

거리(킬로미터)
Real number (ℝ)

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.695625
Minimum0.82
Maximum10.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T07:46:13.126198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.82
5-th percentile1.0875
Q11.3125
median2.04
Q32.9525
95-th percentile5.9355
Maximum10.26
Range9.44
Interquartile range (IQR)1.64

Descriptive statistics

Standard deviation1.995726
Coefficient of variation (CV)0.74035742
Kurtosis5.6971323
Mean2.695625
Median Absolute Deviation (MAD)0.84
Skewness2.145577
Sum86.26
Variance3.9829222
MonotonicityNot monotonic
2023-12-13T07:46:13.265831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.36 2
 
6.2%
1.2 2
 
6.2%
1.5 1
 
3.1%
2.02 1
 
3.1%
1.62 1
 
3.1%
1.13 1
 
3.1%
2.62 1
 
3.1%
1.11 1
 
3.1%
1.06 1
 
3.1%
3.77 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
0.82 1
3.1%
1.06 1
3.1%
1.11 1
3.1%
1.13 1
3.1%
1.14 1
3.1%
1.2 2
6.2%
1.26 1
3.1%
1.33 1
3.1%
1.5 1
3.1%
1.56 1
3.1%
ValueCountFrequency (%)
10.26 1
3.1%
6.04 1
3.1%
5.85 1
3.1%
5.59 1
3.1%
4.54 1
3.1%
4.3 1
3.1%
3.77 1
3.1%
3.08 1
3.1%
2.91 1
3.1%
2.9 1
3.1%

Interactions

2023-12-13T07:46:11.286061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:11.141156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:11.368797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:46:11.216904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:46:13.626902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위치구간거리(킬로미터)
연번1.0000.9711.0000.455
위치0.9711.0001.0000.856
구간1.0001.0001.0001.000
거리(킬로미터)0.4550.8561.0001.000
2023-12-13T07:46:13.740414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번거리(킬로미터)
연번1.000-0.187
거리(킬로미터)-0.1871.000

Missing values

2023-12-13T07:46:11.473238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:46:11.564921image/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

연번위치구간거리(킬로미터)
01함평읍 자풍리함장로 - 금곡제1.5
12손불면 북성리손불 북성(저수지) - 손불 양재(호암)4.3
23손불면 북성리손불 북성(저수지) - 연흥사2.36
34손불면 양재리손불 북성 사기 - 손불 양재 모량1.2
45손불면 월천리손불 대전(대전제) - 손불 북성(승선)2.28
56손불면 산남리손불 석창(대계) - 손불 산남(조내)1.94
67손불면 궁산리손불 궁산 - 손불 산남1.33
78신광면 삼덕리신광 삼덕(월암제) - 신광 삼덕(덕천)2.0
89신광면 월암리신광 월암(신천) - 대동 연암(상하금)4.54
910학교면 곡창리학교 곡창(곡창) - 학교 곡창(서당매)1.14
연번위치구간거리(킬로미터)
2223나산면 원선리원선저수지위(작업임도)1.26
2324나산면 신평리나산 신평 - 나산 나산2.36
2425나산면 원선리나산 원선리(유촌제) - 나산 구산리(국유임도)1.61
2526해보면 광암리금계 금계제 - 광암리 용천사3.77
2627해보면 금계리산내 원산 - 연동골1.06
2728해보면 상곡리해보 상곡리 - 나산 우치리1.11
2829해보면 대각리나산 원선 - 해보 대각2.62
2930월야면 외치리월야 외치제 - 외치 백야1.13
3031월야면 덕령리외치2제 - 덕령1.62
3132월야면 백야리외치 저수지 - 자혜사1.2