Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells38
Missing cells (%)20.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory74.7 B

Variable types

Categorical3
Numeric3
Text1
Unsupported1

Dataset

Description경상북도 내 위임국도의 노선, 구간별 거리, 차로별 거리, 미개통 구간 거리, 구간 정보, 관리 사업소 등에 관한 정보입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15107635/fileData.do

Alerts

노선 is highly overall correlated with 미개통(km) and 1 other fieldsHigh correlation
미개통(km) is highly overall correlated with 구간별(km) and 4 other fieldsHigh correlation
관리사업소 is highly overall correlated with 노선 and 1 other fieldsHigh correlation
구간별(km) is highly overall correlated with 2차로(km) and 1 other fieldsHigh correlation
2차로(km) is highly overall correlated with 구간별(km) and 2 other fieldsHigh correlation
4차로(km) is highly overall correlated with 2차로(km) and 1 other fieldsHigh correlation
미개통(km) is highly imbalanced (61.7%)Imbalance
4차로(km) has 15 (65.2%) missing valuesMissing
비고 has 23 (100.0%) missing valuesMissing
2차로(km) has unique valuesUnique
대상 구간 has unique valuesUnique
비고 is an unsupported type, check if it needs cleaning or further analysisUnsupported
2차로(km) has 1 (4.3%) zerosZeros

Reproduction

Analysis started2023-12-11 23:32:08.471778
Analysis finished2023-12-11 23:32:09.815272
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Memory size316.0 B
59호선
7 
28호선
3 
31호선
3 
67호선
3 
14호선
2 
Other values (3)
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row14호선
2nd row14호선
3rd row25호선
4th row28호선
5th row28호선

Common Values

ValueCountFrequency (%)
59호선 7
30.4%
28호선 3
13.0%
31호선 3
13.0%
67호선 3
13.0%
14호선 2
 
8.7%
34호선 2
 
8.7%
88호선 2
 
8.7%
25호선 1
 
4.3%

Length

2023-12-12T08:32:09.871861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:09.984333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
59호선 7
30.4%
28호선 3
13.0%
31호선 3
13.0%
67호선 3
13.0%
14호선 2
 
8.7%
34호선 2
 
8.7%
88호선 2
 
8.7%
25호선 1
 
4.3%

구간별(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.930435
Minimum3.1
Maximum51.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T08:32:10.114141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile3.63
Q112.45
median20.9
Q325.35
95-th percentile45.42
Maximum51.4
Range48.3
Interquartile range (IQR)12.9

Descriptive statistics

Standard deviation12.535021
Coefficient of variation (CV)0.59888968
Kurtosis0.67881244
Mean20.930435
Median Absolute Deviation (MAD)6.7
Skewness0.84497356
Sum481.4
Variance157.12676
MonotonicityNot monotonic
2023-12-12T08:32:10.231853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20.9 2
 
8.7%
23.8 2
 
8.7%
30.8 1
 
4.3%
22.1 1
 
4.3%
14.7 1
 
4.3%
3.1 1
 
4.3%
9.2 1
 
4.3%
12.7 1
 
4.3%
51.4 1
 
4.3%
11.1 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
3.1 1
4.3%
3.5 1
4.3%
4.8 1
4.3%
9.2 1
4.3%
11.1 1
4.3%
12.2 1
4.3%
12.7 1
4.3%
14.7 1
4.3%
17.4 1
4.3%
17.7 1
4.3%
ValueCountFrequency (%)
51.4 1
4.3%
46.0 1
4.3%
40.2 1
4.3%
30.8 1
4.3%
27.6 1
4.3%
26.9 1
4.3%
23.8 2
8.7%
22.6 1
4.3%
22.1 1
4.3%
20.9 2
8.7%

2차로(km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.978261
Minimum0
Maximum51.4
Zeros1
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T08:32:10.381730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.83
Q111.65
median16.8
Q323.2
95-th percentile45.42
Maximum51.4
Range51.4
Interquartile range (IQR)11.55

Descriptive statistics

Standard deviation13.281428
Coefficient of variation (CV)0.69982323
Kurtosis0.76306257
Mean18.978261
Median Absolute Deviation (MAD)5.8
Skewness0.91555902
Sum436.5
Variance176.39632
MonotonicityNot monotonic
2023-12-12T08:32:10.511676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
20.8 1
 
4.3%
14.6 1
 
4.3%
23.8 1
 
4.3%
14.7 1
 
4.3%
3.1 1
 
4.3%
7.2 1
 
4.3%
12.7 1
 
4.3%
51.4 1
 
4.3%
11.1 1
 
4.3%
3.5 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
0.0 1
4.3%
2.8 1
4.3%
3.1 1
4.3%
3.5 1
4.3%
7.2 1
4.3%
11.1 1
4.3%
12.2 1
4.3%
12.7 1
4.3%
14.6 1
4.3%
14.7 1
4.3%
ValueCountFrequency (%)
51.4 1
4.3%
46.0 1
4.3%
40.2 1
4.3%
27.6 1
4.3%
26.9 1
4.3%
23.8 1
4.3%
22.6 1
4.3%
22.4 1
4.3%
22.1 1
4.3%
20.8 1
4.3%

4차로(km)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing15
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean4.375
Minimum0.8
Maximum20.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T08:32:10.704593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile0.975
Q11.375
median1.85
Q33.125
95-th percentile15.02
Maximum20.9
Range20.1
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation6.7561083
Coefficient of variation (CV)1.5442533
Kurtosis7.440518
Mean4.375
Median Absolute Deviation (MAD)0.75
Skewness2.7022785
Sum35
Variance45.645
MonotonicityNot monotonic
2023-12-12T08:32:10.823242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.8 1
 
4.3%
2.8 1
 
4.3%
20.9 1
 
4.3%
1.7 1
 
4.3%
4.1 1
 
4.3%
2.0 1
 
4.3%
1.4 1
 
4.3%
1.3 1
 
4.3%
(Missing) 15
65.2%
ValueCountFrequency (%)
0.8 1
4.3%
1.3 1
4.3%
1.4 1
4.3%
1.7 1
4.3%
2.0 1
4.3%
2.8 1
4.3%
4.1 1
4.3%
20.9 1
4.3%
ValueCountFrequency (%)
20.9 1
4.3%
4.1 1
4.3%
2.8 1
4.3%
2.0 1
4.3%
1.7 1
4.3%
1.4 1
4.3%
1.3 1
4.3%
0.8 1
4.3%

미개통(km)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
20 
9.2
 
1
34.8
 
1
0.7
 
1

Length

Max length4
Median length4
Mean length3.9130435
Min length3

Unique

Unique3 ?
Unique (%)13.0%

Sample

1st row9.2
2nd row34.8
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 20
87.0%
9.2 1
 
4.3%
34.8 1
 
4.3%
0.7 1
 
4.3%

Length

2023-12-12T08:32:11.256327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:11.360634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 20
87.0%
9.2 1
 
4.3%
34.8 1
 
4.3%
0.7 1
 
4.3%

대상 구간
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T08:32:11.611485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length31
Mean length31
Min length30

Characters and Unicode

Total characters713
Distinct characters104
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row경북 경주시 외동읍 녹동리 ～ 경북 포항시 오천읍 구정리
2nd row경북 포항시 오천읍 세계리 ~ 경북 포항시 오천읍 구정리
3rd row경북 상주시 내서면 능암리 ~ 경북 상주시 화남면 평온리　
4th row경북 영천시 신녕면 화서리 ～ 경북 영천시 청통면 호당리
5th row경북 군위군 우보면 이화리 ～ 경북 군위군 고로면 화수리
ValueCountFrequency (%)
경북 46
22.2%
17
 
8.2%
～ 6
 
2.9%
영양군 6
 
2.9%
상주시 4
 
1.9%
의성군 4
 
1.9%
구미시 4
 
1.9%
군위군 4
 
1.9%
진보면 4
 
1.9%
청송군 4
 
1.9%
Other values (88) 108
52.2%
2023-12-12T08:32:12.012605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
25.8%
경 49
 
6.9%
리 47
 
6.6%
북 46
 
6.5%
면 34
 
4.8%
군 34
 
4.8%
시 18
 
2.5%
~ 17
 
2.4%
영 13
 
1.8%
성 12
 
1.7%
Other values (94) 259
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 505
70.8%
Space Separator 185
 
25.9%
Math Symbol 23
 
3.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 49
 
9.7%
리 47
 
9.3%
북 46
 
9.1%
면 34
 
6.7%
군 34
 
6.7%
시 18
 
3.6%
영 13
 
2.6%
성 12
 
2.4%
천 12
 
2.4%
읍 12
 
2.4%
Other values (90) 228
45.1%
Space Separator
ValueCountFrequency (%)
184
99.5%
　 1
 
0.5%
Math Symbol
ValueCountFrequency (%)
~ 17
73.9%
～ 6
 
26.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 505
70.8%
Common 208
29.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 49
 
9.7%
리 47
 
9.3%
북 46
 
9.1%
면 34
 
6.7%
군 34
 
6.7%
시 18
 
3.6%
영 13
 
2.6%
성 12
 
2.4%
천 12
 
2.4%
읍 12
 
2.4%
Other values (90) 228
45.1%
Common
ValueCountFrequency (%)
184
88.5%
~ 17
 
8.2%
～ 6
 
2.9%
　 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 505
70.8%
ASCII 201
 
28.2%
None 7
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
91.5%
~ 17
 
8.5%
Hangul
ValueCountFrequency (%)
경 49
 
9.7%
리 47
 
9.3%
북 46
 
9.1%
면 34
 
6.7%
군 34
 
6.7%
시 18
 
3.6%
영 13
 
2.6%
성 12
 
2.4%
천 12
 
2.4%
읍 12
 
2.4%
Other values (90) 228
45.1%
None
ValueCountFrequency (%)
～ 6
85.7%
　 1
 
14.3%

관리사업소
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
북부사업소
13 
남부사업소
10 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남부사업소
2nd row남부사업소
3rd row북부사업소
4th row남부사업소
5th row남부사업소

Common Values

ValueCountFrequency (%)
북부사업소 13
56.5%
남부사업소 10
43.5%

Length

2023-12-12T08:32:12.161603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:32:12.254353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북부사업소 13
56.5%
남부사업소 10
43.5%

비고
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing23
Missing (%)100.0%
Memory size339.0 B

Interactions

2023-12-12T08:32:09.312071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:08.774254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:09.034196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:09.396790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:08.839427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:09.130007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:09.486088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:08.919972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:32:09.220988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:32:12.333648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선구간별(km)2차로(km)4차로(km)미개통(km)대상 구간관리사업소
노선1.0000.6270.2680.0001.0001.0000.807
구간별(km)0.6271.0000.9710.0001.0001.0000.000
2차로(km)0.2680.9711.0000.0001.0001.0000.000
4차로(km)0.0000.0000.0001.000NaN1.0000.138
미개통(km)1.0001.0001.000NaN1.0001.000NaN
대상 구간1.0001.0001.0001.0001.0001.0001.000
관리사업소0.8070.0000.0000.138NaN1.0001.000
2023-12-12T08:32:12.450290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선미개통(km)관리사업소
노선1.0001.0000.522
미개통(km)1.0001.0001.000
관리사업소0.5221.0001.000
2023-12-12T08:32:12.544342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간별(km)2차로(km)4차로(km)노선미개통(km)관리사업소
구간별(km)1.0000.899-0.1800.3281.0000.000
2차로(km)0.8991.000-0.5000.0001.0000.000
4차로(km)-0.180-0.5001.0000.0001.0000.061
노선0.3280.0000.0001.0001.0000.522
미개통(km)1.0001.0001.0001.0001.0001.000
관리사업소0.0000.0000.0610.5221.0001.000

Missing values

2023-12-12T08:32:09.638797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:32:09.769168image/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

노선구간별(km)2차로(km)4차로(km)미개통(km)대상 구간관리사업소비고
014호선30.820.80.89.2경북 경주시 외동읍 녹동리 ～ 경북 포항시 오천읍 구정리남부사업소<NA>
114호선17.414.62.834.8경북 포항시 오천읍 세계리 ~ 경북 포항시 오천읍 구정리남부사업소<NA>
225호선27.627.6<NA><NA>경북 상주시 내서면 능암리 ~ 경북 상주시 화남면 평온리북부사업소<NA>
328호선20.90.020.9<NA>경북 영천시 신녕면 화서리 ～ 경북 영천시 청통면 호당리남부사업소<NA>
428호선18.016.31.7<NA>경북 군위군 우보면 이화리 ～ 경북 군위군 고로면 화수리남부사업소<NA>
528호선20.916.84.1<NA>경북 의성군 의성읍 비봉리 ~ 경북 의성군 금성면 개일리북부사업소<NA>
631호선4.82.82.0<NA>경북 청송군 진보면 진안리 ～ 경북 청송군 진보면 월전리북부사업소<NA>
731호선46.046.0<NA><NA>경북 영양군 영양읍 서부리 ～ 경북 영양군 일월면 용화리북부사업소<NA>
831호선17.717.7<NA><NA>경북 봉화군 소천면 임기리 ~ 경북 봉화군 법전면 어지리북부사업소<NA>
934호선23.822.41.4<NA>경북 청송군 진보면 추현리 ~ 경북 청송군 진보면 진안리북부사업소<NA>
노선구간별(km)2차로(km)4차로(km)미개통(km)대상 구간관리사업소비고
1359호선22.122.1<NA><NA>경북 구미시 선산읍 완전리 ~ 경북 구미시 옥성면 구봉리남부사업소<NA>
1459호선12.212.2<NA><NA>경북 상주시 낙동면 장곡리 ~ 경북 상주시 중동면 금당리북부사업소<NA>
1559호선3.53.5<NA><NA>경북 의성군 다인면 덕지리 ~ 경북 의성군 다인면 덕미리북부사업소<NA>
1659호선11.111.1<NA><NA>경북 예천군 풍양면 낙상리 ~ 경북 예천군 풍양면 삼강리북부사업소<NA>
1759호선51.451.4<NA><NA>경북 문경시 영순면 달지리 ~ 경북 문경시 동로면 적성리북부사업소<NA>
1867호선12.712.7<NA><NA>경북 칠곡군 왜관읍 왜관리 ~ 경북 칠곡군 석적읍 중리남부사업소<NA>
1967호선9.27.21.30.7경북 구미시 산동면 성수리 ~ 경북 구미시 장천면 오로리남부사업소<NA>
2067호선3.13.1<NA><NA>경북 군위군 군위읍 수서리 ~ 경북 군위군 군위읍 금구리남부사업소<NA>
2188호선14.714.7<NA><NA>경북 영양군 수비면 발리리 ~ 경북 영양군 수비면 본신리북부사업소<NA>
2288호선23.823.8<NA><NA>경북 영양군 일원면 문암리 ～ 경북 영양군 수비면 본신리북부사업소<NA>