Overview

Dataset statistics

Number of variables4
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory35.6 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description한국도로공사 고속도로 상습정체구간에 대한 데이터 정리로 정체구분, 노선, 정체구간, 연장(km) 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15045636/fileData.do

Alerts

정체구간 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:57:39.966383
Analysis finished2023-12-12 20:57:40.438362
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정체구분
Categorical

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
평일정체
22 
평일주말정체
22 
주말정체
6 

Length

Max length6
Median length4
Mean length4.88
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row평일정체
2nd row평일정체
3rd row주말정체
4th row평일주말정체
5th row평일주말정체

Common Values

ValueCountFrequency (%)
평일정체 22
44.0%
평일주말정체 22
44.0%
주말정체 6
 
12.0%

Length

2023-12-13T05:57:40.536898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:57:40.667017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평일정체 22
44.0%
평일주말정체 22
44.0%
주말정체 6
 
12.0%

노선
Categorical

Distinct9
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
수도권제1순환선
15 
경부선
8 
영동선
8 
서해안선
4 
중부선
4 
Other values (4)
11 

Length

Max length8
Median length3
Mean length4.74
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
수도권제1순환선 15
30.0%
경부선 8
16.0%
영동선 8
16.0%
서해안선 4
 
8.0%
중부선 4
 
8.0%
제2경인선 4
 
8.0%
남해선 3
 
6.0%
호남선 3
 
6.0%
중앙선 1
 
2.0%

Length

2023-12-13T05:57:40.788960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:57:40.910233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권제1순환선 15
30.0%
경부선 8
16.0%
영동선 8
16.0%
서해안선 4
 
8.0%
중부선 4
 
8.0%
제2경인선 4
 
8.0%
남해선 3
 
6.0%
호남선 3
 
6.0%
중앙선 1
 
2.0%

정체구간
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-13T05:57:41.180440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length12.16
Min length11

Characters and Unicode

Total characters608
Distinct characters89
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st row동탄Jct → 안성Jct
2nd row서울TG → 판교IC
3rd row청주IC → 목천IC
4th row서울TG → 수원신갈IC
5th row천안Jct → 옥산Jct
ValueCountFrequency (%)
→ 50
33.3%
서창jct 4
 
2.7%
송파ic 4
 
2.7%
하남jct 4
 
2.7%
호법jct 3
 
2.0%
성남ic 3
 
2.0%
서울tg 3
 
2.0%
도리jct 2
 
1.3%
덕평ic 2
 
1.3%
일직jct 2
 
1.3%
Other values (63) 73
48.7%
2023-12-13T05:57:41.613051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
16.6%
I 53
 
8.7%
C 53
 
8.7%
→ 50
 
8.2%
J 39
 
6.4%
c 39
 
6.4%
t 39
 
6.4%
남 13
 
2.1%
서 12
 
2.0%
천 9
 
1.5%
Other values (79) 200
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 220
36.2%
Uppercase Letter 159
26.2%
Space Separator 101
16.6%
Lowercase Letter 78
 
12.8%
Math Symbol 50
 
8.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 13
 
5.9%
서 12
 
5.5%
천 9
 
4.1%
동 7
 
3.2%
성 7
 
3.2%
평 6
 
2.7%
창 6
 
2.7%
하 6
 
2.7%
산 6
 
2.7%
송 5
 
2.3%
Other values (70) 143
65.0%
Uppercase Letter
ValueCountFrequency (%)
I 53
33.3%
C 53
33.3%
J 39
24.5%
G 7
 
4.4%
T 7
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
c 39
50.0%
t 39
50.0%
Space Separator
ValueCountFrequency (%)
101
100.0%
Math Symbol
ValueCountFrequency (%)
→ 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 237
39.0%
Hangul 220
36.2%
Common 151
24.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 13
 
5.9%
서 12
 
5.5%
천 9
 
4.1%
동 7
 
3.2%
성 7
 
3.2%
평 6
 
2.7%
창 6
 
2.7%
하 6
 
2.7%
산 6
 
2.7%
송 5
 
2.3%
Other values (70) 143
65.0%
Latin
ValueCountFrequency (%)
I 53
22.4%
C 53
22.4%
J 39
16.5%
c 39
16.5%
t 39
16.5%
G 7
 
3.0%
T 7
 
3.0%
Common
ValueCountFrequency (%)
101
66.9%
→ 50
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338
55.6%
Hangul 220
36.2%
Arrows 50
 
8.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101
29.9%
I 53
15.7%
C 53
15.7%
J 39
 
11.5%
c 39
 
11.5%
t 39
 
11.5%
G 7
 
2.1%
T 7
 
2.1%
Arrows
ValueCountFrequency (%)
→ 50
100.0%
Hangul
ValueCountFrequency (%)
남 13
 
5.9%
서 12
 
5.5%
천 9
 
4.1%
동 7
 
3.2%
성 7
 
3.2%
평 6
 
2.7%
창 6
 
2.7%
하 6
 
2.7%
산 6
 
2.7%
송 5
 
2.3%
Other values (70) 143
65.0%

연장(km)
Real number (ℝ)

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.288
Minimum0.7
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-13T05:57:41.782343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1
Q12.425
median4.45
Q39
95-th percentile16.765
Maximum23.6
Range22.9
Interquartile range (IQR)6.575

Descriptive statistics

Standard deviation5.2634915
Coefficient of variation (CV)0.83706926
Kurtosis1.8423751
Mean6.288
Median Absolute Deviation (MAD)2.5
Skewness1.4321564
Sum314.4
Variance27.704343
MonotonicityNot monotonic
2023-12-13T05:57:41.953410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3.7 3
 
6.0%
1.0 3
 
6.0%
4.2 3
 
6.0%
12.5 2
 
4.0%
5.0 2
 
4.0%
1.2 2
 
4.0%
2.0 2
 
4.0%
7.6 1
 
2.0%
4.8 1
 
2.0%
2.4 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
0.7 1
 
2.0%
1.0 3
6.0%
1.2 2
4.0%
1.4 1
 
2.0%
1.9 1
 
2.0%
2.0 2
4.0%
2.1 1
 
2.0%
2.2 1
 
2.0%
2.4 1
 
2.0%
2.5 1
 
2.0%
ValueCountFrequency (%)
23.6 1
2.0%
20.3 1
2.0%
16.9 1
2.0%
16.6 1
2.0%
12.5 2
4.0%
12.3 1
2.0%
12.1 1
2.0%
11.7 1
2.0%
11.1 1
2.0%
10.4 1
2.0%

Interactions

2023-12-13T05:57:40.167670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:57:42.052524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정체구분노선정체구간연장(km)
정체구분1.0000.3511.0000.258
노선0.3511.0001.0000.000
정체구간1.0001.0001.0001.000
연장(km)0.2580.0001.0001.000
2023-12-13T05:57:42.164116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선정체구분
노선1.0000.143
정체구분0.1431.000
2023-12-13T05:57:42.247099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연장(km)정체구분노선
연장(km)1.0000.1340.000
정체구분0.1341.0000.143
노선0.0000.1431.000

Missing values

2023-12-13T05:57:40.297657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:57:40.396508image/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

정체구분노선정체구간연장(km)
0평일정체경부선동탄Jct → 안성Jct12.5
1평일정체경부선서울TG → 판교IC2.1
2주말정체경부선청주IC → 목천IC16.6
3평일주말정체경부선서울TG → 수원신갈IC6.5
4평일주말정체경부선천안Jct → 옥산Jct9.2
5평일주말정체경부선안성Jct → 동탄Jct12.5
6평일주말정체경부선기흥동탄IC → 서울TG10.4
7평일주말정체경부선금토Jct → 양재IC5.0
8평일정체남해선대저Jct → 덕천IC4.6
9평일주말정체남해선북창원IC → 칠원Jct1.2
정체구분노선정체구간연장(km)
40평일주말정체수도권제1순환선구리IC → 하남Jct8.4
41평일주말정체수도권제1순환선하남Jct → 송파IC7.6
42평일주말정체수도권제1순환선시흥IC → 자유로IC20.3
43평일주말정체수도권제1순환선일산IC → 송내IC16.9
44평일주말정체수도권제1순환선판교Jct → 성남IC3.4
45평일주말정체수도권제1순환선하남Jct → 구리IC9.3
46평일정체제2경인선서창Jct → 인천시점8.3
47평일정체제2경인선학익Jct → 서창Jct7.1
48평일정체제2경인선신천IC → 일직Jct4.9
49평일주말정체제2경인선일직Jct → 광명IC2.2