Overview

Dataset statistics

Number of variables5
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory993.0 B
Average record size in memory47.3 B

Variable types

Categorical2
Text1
Numeric1
DateTime1

Dataset

Description인천광역시 연수구 등산로 현황 데이터로서 구분, 법정동, 노선, 연장거리, 데이터 기준일자 등의 항목으로 이루어져 있습니다.
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15116386&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
연장거리(km) is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연장거리(km)High correlation

Reproduction

Analysis started2024-01-28 10:42:03.718365
Analysis finished2024-01-28 10:42:04.090601
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
문학산
11 
청량산
9 
봉재산
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.8%

Sample

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

Common Values

ValueCountFrequency (%)
문학산 11
52.4%
청량산 9
42.9%
봉재산 1
 
4.8%

Length

2024-01-28T19:42:04.136490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:42:04.211501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문학산 11
52.4%
청량산 9
42.9%
봉재산 1
 
4.8%

법정동
Categorical

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
청학동
8 
동춘동
4 
옥련동
4 
선학동
3 
연수동
2 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동춘동
2nd row옥련동
3rd row청학동
4th row청학동
5th row청학동

Common Values

ValueCountFrequency (%)
청학동 8
38.1%
동춘동 4
19.0%
옥련동 4
19.0%
선학동 3
 
14.3%
연수동 2
 
9.5%

Length

2024-01-28T19:42:04.290292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:42:04.378209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청학동 8
38.1%
동춘동 4
19.0%
옥련동 4
19.0%
선학동 3
 
14.3%
연수동 2
 
9.5%

노선
Text

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-01-28T19:42:04.508584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length12.095238
Min length8

Characters and Unicode

Total characters254
Distinct characters88
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row영일정씨묘 ∼ 시립박물관
2nd row 청룡공원 ∼ 청봉교
3rd row함박중학교 ∼ 병풍바위약수터
4th row청량산교회 ∼ 송도선원 상단
5th row서해아파트 ∼ 청량터널 상단
ValueCountFrequency (%)
∼ 18
25.7%
길마산 3
 
4.3%
상단 3
 
4.3%
정상 3
 
4.3%
문학산 2
 
2.9%
가지등산로 2
 
2.9%
삼호현 2
 
2.9%
노적봉 2
 
2.9%
서해아파트 2
 
2.9%
기타 2
 
2.9%
Other values (30) 31
44.3%
2024-01-28T19:42:04.738108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
20.1%
∼ 18
 
7.1%
산 10
 
3.9%
청 8
 
3.1%
터 7
 
2.8%
상 6
 
2.4%
학 5
 
2.0%
수 5
 
2.0%
아 4
 
1.6%
트 4
 
1.6%
Other values (78) 136
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
72.0%
Space Separator 51
 
20.1%
Math Symbol 19
 
7.5%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 10
 
5.5%
청 8
 
4.4%
터 7
 
3.8%
상 6
 
3.3%
학 5
 
2.7%
수 5
 
2.7%
아 4
 
2.2%
트 4
 
2.2%
파 4
 
2.2%
약 4
 
2.2%
Other values (74) 126
68.9%
Math Symbol
ValueCountFrequency (%)
∼ 18
94.7%
~ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
51
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
72.0%
Common 71
 
28.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 10
 
5.5%
청 8
 
4.4%
터 7
 
3.8%
상 6
 
3.3%
학 5
 
2.7%
수 5
 
2.7%
아 4
 
2.2%
트 4
 
2.2%
파 4
 
2.2%
약 4
 
2.2%
Other values (74) 126
68.9%
Common
ValueCountFrequency (%)
51
71.8%
∼ 18
 
25.4%
3 1
 
1.4%
~ 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
72.0%
ASCII 53
 
20.9%
Math Operators 18
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
96.2%
3 1
 
1.9%
~ 1
 
1.9%
Math Operators
ValueCountFrequency (%)
∼ 18
100.0%
Hangul
ValueCountFrequency (%)
산 10
 
5.5%
청 8
 
4.4%
터 7
 
3.8%
상 6
 
3.3%
학 5
 
2.7%
수 5
 
2.7%
아 4
 
2.2%
트 4
 
2.2%
파 4
 
2.2%
약 4
 
2.2%
Other values (74) 126
68.9%

연장거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2952381
Minimum0.3
Maximum5.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-01-28T19:42:04.836631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.6
median0.8
Q31.7
95-th percentile3
Maximum5.3
Range5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.1556281
Coefficient of variation (CV)0.89221283
Kurtosis6.7305567
Mean1.2952381
Median Absolute Deviation (MAD)0.2
Skewness2.4081161
Sum27.2
Variance1.3354762
MonotonicityNot monotonic
2024-01-28T19:42:04.921194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.8 4
19.0%
0.9 3
14.3%
0.6 3
14.3%
0.5 2
9.5%
2.3 1
 
4.8%
2.2 1
 
4.8%
0.3 1
 
4.8%
0.7 1
 
4.8%
5.3 1
 
4.8%
1.8 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
0.3 1
 
4.8%
0.5 2
9.5%
0.6 3
14.3%
0.7 1
 
4.8%
0.8 4
19.0%
0.9 3
14.3%
1.2 1
 
4.8%
1.7 1
 
4.8%
1.8 1
 
4.8%
2.2 1
 
4.8%
ValueCountFrequency (%)
5.3 1
 
4.8%
3.0 1
 
4.8%
2.3 1
 
4.8%
2.2 1
 
4.8%
1.8 1
 
4.8%
1.7 1
 
4.8%
1.2 1
 
4.8%
0.9 3
14.3%
0.8 4
19.0%
0.7 1
 
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2023-07-07 00:00:00
Maximum2023-07-07 00:00:00
2024-01-28T19:42:05.001609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T19:42:05.068148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T19:42:03.890515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T19:42:05.121624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법정동노선연장거리(km)
구분1.0000.4650.8470.716
법정동0.4651.0000.8780.413
노선0.8470.8781.0000.281
연장거리(km)0.7160.4130.2811.000
2024-01-28T19:42:05.204849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동구분
법정동1.0000.362
구분0.3621.000
2024-01-28T19:42:05.263816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장거리(km)구분법정동
연장거리(km)1.0000.6440.081
구분0.6441.0000.362
법정동0.0810.3621.000

Missing values

2024-01-28T19:42:03.986066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T19:42:04.060159image/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

구분법정동노선연장거리(km)데이터기준일자
0청량산동춘동영일정씨묘 ∼ 시립박물관2.32023-07-07
1청량산옥련동청룡공원 ∼ 청봉교2.22023-07-07
2청량산청학동함박중학교 ∼ 병풍바위약수터0.32023-07-07
3청량산청학동청량산교회 ∼ 송도선원 상단0.82023-07-07
4청량산청학동서해아파트 ∼ 청량터널 상단0.92023-07-07
5청량산청학동자연학습장 ∼ 흥륜사0.72023-07-07
6청량산청학동청량산 정상 ∼ 서해아파트0.92023-07-07
7청량산동춘동산우물약수터 ∼ 청량터널 상단0.52023-07-07
8청량산동춘동기타 가지등산로0.82023-07-07
9문학산옥련동법주사 ∼ 시립사격장5.32023-07-07
구분법정동노선연장거리(km)데이터기준일자
11문학산선학동희영아파트 ∼ 길마산 전 3거리0.62023-07-07
12문학산선학동베갯골약수터 ∼ 길마산0.62023-07-07
13문학산연수동문수암 ∼ 문학산 정상0.92023-07-07
14문학산연수동장미공원 ∼ 문학산 정상0.82023-07-07
15문학산청학동삼호현 ∼ 동굴0.82023-07-07
16문학산청학동청학풀장 ∼ 삼호현0.62023-07-07
17문학산청학동청우약수터 ∼ 노적봉0.52023-07-07
18문학산옥련동송도역 뒤 ∼ 노적봉1.22023-07-07
19문학산옥련동기타 가지등산로1.72023-07-07
20봉재산동춘동환경공단 ~ 동춘터널3.02023-07-07