Overview

Dataset statistics

Number of variables4
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory37.8 B

Variable types

Numeric2
Text2

Dataset

Description충청북도 보은군의 임도현황에 대한 데이터로 보은군에 있는 임도현황(노선명, 거리, 조성년도 등) 대한 정보를 제공합니다.
Author충청북도 보은군
URLhttps://www.data.go.kr/data/15054744/fileData.do

Alerts

연 번 is highly overall correlated with 거 리 (km)High correlation
거 리 (km) is highly overall correlated with 연 번High correlation
연 번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:12:31.857686
Analysis finished2023-12-12 10:12:32.462211
Duration0.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T19:12:32.516798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2023-12-12T19:12:32.641389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
27 1
 
2.9%
28 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
35 1
2.9%
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%

노 선 명
Text

Distinct33
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T19:12:32.815747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.0571429
Min length5

Characters and Unicode

Total characters247
Distinct characters77
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

Unique31 ?
Unique (%)88.6%

Sample

1st row내속 중판-중판
2nd row보은 노티-내북 하궁
3rd row산외 아시-백석
4th row산외 백석-백석
5th row내북 도원
ValueCountFrequency (%)
산외 8
 
10.7%
내북 5
 
6.7%
회남 5
 
6.7%
보은 4
 
5.3%
수한 4
 
5.3%
탄부 3
 
4.0%
속리 2
 
2.7%
중판 2
 
2.7%
남대문 2
 
2.7%
분저-용호 1
 
1.3%
Other values (39) 39
52.0%
2023-12-12T19:12:33.164252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
16.2%
- 18
 
7.3%
산 11
 
4.5%
외 9
 
3.6%
내 8
 
3.2%
남 7
 
2.8%
북 7
 
2.8%
회 6
 
2.4%
수 6
 
2.4%
대 5
 
2.0%
Other values (67) 130
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
76.5%
Space Separator 40
 
16.2%
Dash Punctuation 18
 
7.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 11
 
5.8%
외 9
 
4.8%
내 8
 
4.2%
남 7
 
3.7%
북 7
 
3.7%
회 6
 
3.2%
수 6
 
3.2%
대 5
 
2.6%
한 5
 
2.6%
은 5
 
2.6%
Other values (65) 120
63.5%
Space Separator
ValueCountFrequency (%)
40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
76.5%
Common 58
 
23.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 11
 
5.8%
외 9
 
4.8%
내 8
 
4.2%
남 7
 
3.7%
북 7
 
3.7%
회 6
 
3.2%
수 6
 
3.2%
대 5
 
2.6%
한 5
 
2.6%
은 5
 
2.6%
Other values (65) 120
63.5%
Common
ValueCountFrequency (%)
40
69.0%
- 18
31.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
76.5%
ASCII 58
 
23.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
69.0%
- 18
31.0%
Hangul
ValueCountFrequency (%)
산 11
 
5.8%
외 9
 
4.8%
내 8
 
4.2%
남 7
 
3.7%
북 7
 
3.7%
회 6
 
3.2%
수 6
 
3.2%
대 5
 
2.6%
한 5
 
2.6%
은 5
 
2.6%
Other values (65) 120
63.5%

거 리 (km)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8877143
Minimum0.3
Maximum9.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T19:12:33.292333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.485
Q11.5
median3
Q33.35
95-th percentile7.849
Maximum9.77
Range9.47
Interquartile range (IQR)1.85

Descriptive statistics

Standard deviation2.1978266
Coefficient of variation (CV)0.76109558
Kurtosis2.7378896
Mean2.8877143
Median Absolute Deviation (MAD)1.11
Skewness1.5577531
Sum101.07
Variance4.8304417
MonotonicityNot monotonic
2023-12-12T19:12:33.416245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.0 4
 
11.4%
2.3 2
 
5.7%
2.02 2
 
5.7%
3.36 1
 
2.9%
3.34 1
 
2.9%
0.45 1
 
2.9%
0.3 1
 
2.9%
0.6 1
 
2.9%
0.54 1
 
2.9%
0.5 1
 
2.9%
Other values (20) 20
57.1%
ValueCountFrequency (%)
0.3 1
2.9%
0.45 1
2.9%
0.5 1
2.9%
0.54 1
2.9%
0.6 1
2.9%
0.74 1
2.9%
0.91 1
2.9%
1.3 1
2.9%
1.46 1
2.9%
1.54 1
2.9%
ValueCountFrequency (%)
9.77 1
2.9%
8.5 1
2.9%
7.57 1
2.9%
5.24 1
2.9%
5.2 1
2.9%
4.0 1
2.9%
3.6 1
2.9%
3.48 1
2.9%
3.36 1
2.9%
3.34 1
2.9%

조 성 년 도
Text

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T19:12:33.595027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length4.1428571
Min length4

Characters and Unicode

Total characters145
Distinct characters11
Distinct categories2 ?
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 (%)54.3%

Sample

1st row1987
2nd row1988
3rd row1989
4th row1990
5th row1991
ValueCountFrequency (%)
2019 3
 
8.6%
1995 3
 
8.6%
2018 2
 
5.7%
2017 2
 
5.7%
1997 2
 
5.7%
1998 2
 
5.7%
2010 2
 
5.7%
2005 1
 
2.9%
2003 1
 
2.9%
2016 1
 
2.9%
Other values (16) 16
45.7%
2023-12-12T19:12:33.906560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 34
23.4%
1 33
22.8%
0 30
20.7%
2 22
15.2%
8 8
 
5.5%
7 6
 
4.1%
5 4
 
2.8%
3 3
 
2.1%
6 3
 
2.1%
4 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144
99.3%
Math Symbol 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 34
23.6%
1 33
22.9%
0 30
20.8%
2 22
15.3%
8 8
 
5.6%
7 6
 
4.2%
5 4
 
2.8%
3 3
 
2.1%
6 3
 
2.1%
4 1
 
0.7%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 34
23.4%
1 33
22.8%
0 30
20.7%
2 22
15.2%
8 8
 
5.5%
7 6
 
4.1%
5 4
 
2.8%
3 3
 
2.1%
6 3
 
2.1%
4 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 34
23.4%
1 33
22.8%
0 30
20.7%
2 22
15.2%
8 8
 
5.5%
7 6
 
4.1%
5 4
 
2.8%
3 3
 
2.1%
6 3
 
2.1%
4 1
 
0.7%

Interactions

2023-12-12T19:12:32.203621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:32.031049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:32.283612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:12:32.123566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:12:34.031820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번노 선 명거 리 (km)조 성 년 도
연 번1.0000.9630.4610.979
노 선 명0.9631.0001.0000.972
거 리 (km)0.4611.0001.0000.945
조 성 년 도0.9790.9720.9451.000
2023-12-12T19:12:34.133335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번거 리 (km)
연 번1.000-0.557
거 리 (km)-0.5571.000

Missing values

2023-12-12T19:12:32.369606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:12:32.434208image/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

연 번노 선 명거 리 (km)조 성 년 도
01내속 중판-중판3.361987
12보은 노티-내북 하궁3.481988
23산외 아시-백석8.51989
34산외 백석-백석3.121990
45내북 도원7.571991
56산외 신정3.01993
67산외 원평5.21994
78탄부 상장-벽지3.01995
89산외 길탕3.081995
910산외 대원0.741995
연 번노 선 명거 리 (km)조 성 년 도
2526내북 화전3.62012
2627속리산 갈목9.772013~2016
2728회남 남대문0.52016
2829회남 남대문0.542017
2930수한 교암2.022017
3031회인 쌍암2.32018
3132속리 중판0.62018
3233속리 중판0.32019
3334수한 병원0.452019
3435내북 세촌-용수2.32019