Overview

Dataset statistics

Number of variables3
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory27.3 B

Variable types

Numeric2
Categorical1

Dataset

Description국토교통부에서 관할하는 일반국도에 대한 도로관리시스템 구축을 위해 정의한 도로관리청 코드로서, 18객 국토관리사무소와 5개 지방국토관리청 그리고 관리기관코드를 그룹핑한 별도 기관코드로 구성
Author국토교통부
URLhttps://www.data.go.kr/data/15122489/fileData.do

Alerts

관리기관코드 is highly overall correlated with 명칭High correlation
명칭 is highly overall correlated with 관리기관코드High correlation
기관코드 has 32 (33.7%) zerosZeros
관리기관코드 has 1 (1.1%) zerosZeros

Reproduction

Analysis started2024-04-20 21:52:03.410601
Analysis finished2024-04-20 21:52:04.298634
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관코드
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.852632
Minimum0
Maximum91
Zeros32
Zeros (%)33.7%
Negative0
Negative (%)0.0%
Memory size983.0 B
2024-04-21T06:52:04.412739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q350.5
95-th percentile81.3
Maximum91
Range91
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation28.815247
Coefficient of variation (CV)0.9987043
Kurtosis-1.0973454
Mean28.852632
Median Absolute Deviation (MAD)20
Skewness0.56830804
Sum2741
Variance830.31848
MonotonicityNot monotonic
2024-04-21T06:52:04.632032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 32
33.7%
70 6
 
6.3%
30 6
 
6.3%
50 6
 
6.3%
10 5
 
5.3%
20 4
 
4.2%
82 3
 
3.2%
11 3
 
3.2%
61 2
 
2.1%
71 2
 
2.1%
Other values (24) 26
27.4%
ValueCountFrequency (%)
0 32
33.7%
10 5
 
5.3%
11 3
 
3.2%
12 2
 
2.1%
15 1
 
1.1%
16 1
 
1.1%
20 4
 
4.2%
21 1
 
1.1%
22 1
 
1.1%
23 1
 
1.1%
ValueCountFrequency (%)
91 1
 
1.1%
84 1
 
1.1%
82 3
3.2%
81 1
 
1.1%
76 1
 
1.1%
73 1
 
1.1%
72 1
 
1.1%
71 2
 
2.1%
70 6
6.3%
64 1
 
1.1%

관리기관코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.757895
Minimum0
Maximum91
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size983.0 B
2024-04-21T06:52:04.854235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q122
median43
Q371
95-th percentile82.3
Maximum91
Range91
Interquartile range (IQR)49

Descriptive statistics

Standard deviation25.33676
Coefficient of variation (CV)0.55371341
Kurtosis-1.3607975
Mean45.757895
Median Absolute Deviation (MAD)22
Skewness0.043583807
Sum4347
Variance641.9514
MonotonicityNot monotonic
2024-04-21T06:52:05.078250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
13 4
 
4.2%
62 3
 
3.2%
63 3
 
3.2%
43 3
 
3.2%
14 3
 
3.2%
11 3
 
3.2%
82 3
 
3.2%
81 3
 
3.2%
73 3
 
3.2%
72 3
 
3.2%
Other values (31) 64
67.4%
ValueCountFrequency (%)
0 1
 
1.1%
10 2
2.1%
11 3
3.2%
12 3
3.2%
13 4
4.2%
14 3
3.2%
15 1
 
1.1%
16 1
 
1.1%
20 2
2.1%
21 3
3.2%
ValueCountFrequency (%)
91 2
2.1%
84 1
 
1.1%
83 2
2.1%
82 3
3.2%
81 3
3.2%
76 1
 
1.1%
75 2
2.1%
74 2
2.1%
73 3
3.2%
72 3
3.2%

명칭
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Memory size888.0 B
서울
 
4
광주
 
3
홍천
 
3
의정부
 
3
정선
 
3
Other values (36)
79 

Length

Max length10
Median length2
Mean length2.5578947
Min length2

Unique

Unique10 ?
Unique (%)10.5%

Sample

1st row의정부
2nd row수원
3rd row의정부
4th row홍천
5th row수원

Common Values

ValueCountFrequency (%)
서울 4
 
4.2%
광주 3
 
3.2%
홍천 3
 
3.2%
의정부 3
 
3.2%
정선 3
 
3.2%
충주 3
 
3.2%
보은 3
 
3.2%
논산 3
 
3.2%
예산 3
 
3.2%
전주 3
 
3.2%
Other values (31) 64
67.4%

Length

2024-04-21T06:52:05.331823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 4
 
4.2%
진영 3
 
3.2%
순천 3
 
3.2%
광주 3
 
3.2%
대전시 3
 
3.2%
인천 3
 
3.2%
수원 3
 
3.2%
진주 3
 
3.2%
영주 3
 
3.2%
포항 3
 
3.2%
Other values (31) 64
67.4%

Interactions

2024-04-21T06:52:03.827015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T06:52:03.571558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T06:52:03.953054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T06:52:03.696253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T06:52:05.501998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드관리기관코드명칭
기관코드1.0000.9410.747
관리기관코드0.9411.0001.000
명칭0.7471.0001.000
2024-04-21T06:52:05.870960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드관리기관코드명칭
기관코드1.0000.4280.284
관리기관코드0.4281.0000.797
명칭0.2840.7971.000

Missing values

2024-04-21T06:52:04.121084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T06:52:04.248345image/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

기관코드관리기관코드명칭
01012의정부
11111수원
21212의정부
32021홍천
41011수원
52022강릉
62023정선
72121홍천
82222강릉
92323정선
기관코드관리기관코드명칭
857070부산청
861515경기도건설본부
871616경기도도로관리사업소
882424강원도
893333충청북도
904444충청남도
915353전라북도
926464전라남도
937676경상북도
948484경상남도