Overview

Dataset statistics

Number of variables7
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory58.4 B

Variable types

Text1
Categorical6

Dataset

Description노선별(지방도, 국지도) 과속 방지턱 개수 현황(방지턱 종류에 따른 상태 안내 포함)(가상 방지턱 / 과속 방지턱)에 관한 데이터입니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055642/fileData.do

Alerts

과속방지턱 2 is highly overall correlated with 과속방지턱 1High correlation
과속방지턱 1 is highly overall correlated with 과속방지턱 2High correlation
가상방지턱 1 is highly overall correlated with 가상방지턱 3High correlation
가상방지턱 3 is highly overall correlated with 가상방지턱 1High correlation
과속방지턱 3 is highly imbalanced (77.1%)Imbalance
현황 has unique valuesUnique

Reproduction

Analysis started2024-03-15 00:13:46.040573
Analysis finished2024-03-15 00:13:47.562271
Duration1.52 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

현황
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size560.0 B
2024-03-15T09:13:48.306988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.9259259
Min length7

Characters and Unicode

Total characters428
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st row지방도635호선
2nd row지방도643호선
3rd row지방도697호선
4th row지방도701호선
5th row지방도702호선
ValueCountFrequency (%)
지방도635호선 1
 
1.9%
지방도749호선 1
 
1.9%
국지도55호선 1
 
1.9%
지방도734호선 1
 
1.9%
지방도735호선 1
 
1.9%
지방도736호선 1
 
1.9%
지방도740호선 1
 
1.9%
지방도741호선 1
 
1.9%
지방도742호선 1
 
1.9%
지방도743호선 1
 
1.9%
Other values (44) 44
81.5%
2024-03-15T09:13:49.740108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 54
12.6%
도 54
12.6%
호 54
12.6%
선 54
12.6%
7 50
11.7%
방 49
11.4%
1 15
 
3.5%
3 14
 
3.3%
0 14
 
3.3%
4 14
 
3.3%
Other values (6) 56
13.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
63.1%
Decimal Number 158
36.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 50
31.6%
1 15
 
9.5%
3 14
 
8.9%
0 14
 
8.9%
4 14
 
8.9%
2 13
 
8.2%
9 13
 
8.2%
5 11
 
7.0%
6 9
 
5.7%
8 5
 
3.2%
Other Letter
ValueCountFrequency (%)
지 54
20.0%
도 54
20.0%
호 54
20.0%
선 54
20.0%
방 49
18.1%
국 5
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
63.1%
Common 158
36.9%

Most frequent character per script

Common
ValueCountFrequency (%)
7 50
31.6%
1 15
 
9.5%
3 14
 
8.9%
0 14
 
8.9%
4 14
 
8.9%
2 13
 
8.2%
9 13
 
8.2%
5 11
 
7.0%
6 9
 
5.7%
8 5
 
3.2%
Hangul
ValueCountFrequency (%)
지 54
20.0%
도 54
20.0%
호 54
20.0%
선 54
20.0%
방 49
18.1%
국 5
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
63.1%
ASCII 158
36.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 54
20.0%
도 54
20.0%
호 54
20.0%
선 54
20.0%
방 49
18.1%
국 5
 
1.9%
ASCII
ValueCountFrequency (%)
7 50
31.6%
1 15
 
9.5%
3 14
 
8.9%
0 14
 
8.9%
4 14
 
8.9%
2 13
 
8.2%
9 13
 
8.2%
5 11
 
7.0%
6 9
 
5.7%
8 5
 
3.2%

가상방지턱 1
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
9 
1
4 
2
4 
5
 
3
22
 
3
Other values (19)
31 

Length

Max length2
Median length2
Mean length1.5925926
Min length1

Unique

Unique9 ?
Unique (%)16.7%

Sample

1st row2
2nd row6
3rd row
4th row39
5th row23

Common Values

ValueCountFrequency (%)
9
16.7%
1 4
 
7.4%
2 4
 
7.4%
5 3
 
5.6%
22 3
 
5.6%
6 3
 
5.6%
13 3
 
5.6%
3 2
 
3.7%
4 2
 
3.7%
9 2
 
3.7%
Other values (14) 19
35.2%

Length

2024-03-15T09:13:50.161519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 4
 
8.9%
2 4
 
8.9%
5 3
 
6.7%
22 3
 
6.7%
6 3
 
6.7%
13 3
 
6.7%
29 2
 
4.4%
14 2
 
4.4%
16 2
 
4.4%
18 2
 
4.4%
Other values (13) 17
37.8%

가상방지턱 2
Categorical

Distinct17
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size560.0 B
16 
7
6 
1
6 
2
5 
3
4 
Other values (12)
17 

Length

Max length2
Median length1
Mean length1.4814815
Min length1

Unique

Unique9 ?
Unique (%)16.7%

Sample

1st row
2nd row
3rd row
4th row24
5th row2

Common Values

ValueCountFrequency (%)
16
29.6%
7 6
 
11.1%
1 6
 
11.1%
2 5
 
9.3%
3 4
 
7.4%
12 3
 
5.6%
9 3
 
5.6%
10 2
 
3.7%
8 1
 
1.9%
18 1
 
1.9%
Other values (7) 7
13.0%

Length

2024-03-15T09:13:50.599213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7 6
15.8%
1 6
15.8%
2 5
13.2%
3 4
10.5%
12 3
7.9%
9 3
7.9%
10 2
 
5.3%
8 1
 
2.6%
18 1
 
2.6%
13 1
 
2.6%
Other values (6) 6
15.8%

가상방지턱 3
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size560.0 B
19 
4
5 
3
4 
1
4 
11
4 
Other values (10)
18 

Length

Max length2
Median length2
Mean length1.5925926
Min length1

Unique

Unique4 ?
Unique (%)7.4%

Sample

1st row2
2nd row6
3rd row
4th row15
5th row21

Common Values

ValueCountFrequency (%)
19
35.2%
4 5
 
9.3%
3 4
 
7.4%
1 4
 
7.4%
11 4
 
7.4%
2 3
 
5.6%
6 3
 
5.6%
15 2
 
3.7%
20 2
 
3.7%
10 2
 
3.7%
Other values (5) 6
 
11.1%

Length

2024-03-15T09:13:51.041605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 5
14.3%
3 4
11.4%
1 4
11.4%
11 4
11.4%
2 3
8.6%
6 3
8.6%
15 2
 
5.7%
20 2
 
5.7%
10 2
 
5.7%
9 2
 
5.7%
Other values (4) 4
11.4%

과속방지턱 1
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
2
6 
11
4 
3
4 
5
4 
12
4 
Other values (19)
32 

Length

Max length2
Median length2
Mean length1.5185185
Min length1

Unique

Unique9 ?
Unique (%)16.7%

Sample

1st row12
2nd row5
3rd row2
4th row11
5th row34

Common Values

ValueCountFrequency (%)
2 6
 
11.1%
11 4
 
7.4%
3 4
 
7.4%
5 4
 
7.4%
12 4
 
7.4%
3
 
5.6%
9 3
 
5.6%
1 3
 
5.6%
7 2
 
3.7%
15 2
 
3.7%
Other values (14) 19
35.2%

Length

2024-03-15T09:13:51.273020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 6
 
11.8%
3 4
 
7.8%
5 4
 
7.8%
12 4
 
7.8%
11 4
 
7.8%
9 3
 
5.9%
1 3
 
5.9%
13 2
 
3.9%
16 2
 
3.9%
10 2
 
3.9%
Other values (13) 17
33.3%

과속방지턱 2
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size560.0 B
2
6 
4 
11
4 
3
4 
7
 
3
Other values (19)
33 

Length

Max length2
Median length2
Mean length1.5185185
Min length1

Unique

Unique9 ?
Unique (%)16.7%

Sample

1st row7
2nd row
3rd row2
4th row11
5th row34

Common Values

ValueCountFrequency (%)
2 6
 
11.1%
4
 
7.4%
11 4
 
7.4%
3 4
 
7.4%
7 3
 
5.6%
12 3
 
5.6%
9 3
 
5.6%
1 3
 
5.6%
5 3
 
5.6%
20 2
 
3.7%
Other values (14) 19
35.2%

Length

2024-03-15T09:13:51.620092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 6
 
12.0%
11 4
 
8.0%
3 4
 
8.0%
7 3
 
6.0%
12 3
 
6.0%
9 3
 
6.0%
1 3
 
6.0%
5 3
 
6.0%
6 2
 
4.0%
13 2
 
4.0%
Other values (13) 17
34.0%

과속방지턱 3
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size560.0 B
52 
5
 
2

Length

Max length2
Median length2
Mean length1.962963
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
52
96.3%
5 2
 
3.7%

Length

2024-03-15T09:13:51.871187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T09:13:52.133706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 2
100.0%

Correlations

2024-03-15T09:13:52.328104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현황가상방지턱 1가상방지턱 2가상방지턱 3과속방지턱 1과속방지턱 2과속방지턱 3
현황1.0001.0001.0001.0001.0001.0001.000
가상방지턱 11.0001.0000.8820.9250.8030.8170.000
가상방지턱 21.0000.8821.0000.5210.8040.7690.000
가상방지턱 31.0000.9250.5211.0000.7850.7900.280
과속방지턱 11.0000.8030.8040.7851.0001.0000.000
과속방지턱 21.0000.8170.7690.7901.0001.0000.000
과속방지턱 31.0000.0000.0000.2800.0000.0001.000
2024-03-15T09:13:52.623505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가상방지턱 2과속방지턱 2과속방지턱 1과속방지턱 3가상방지턱 3가상방지턱 1
가상방지턱 21.0000.2890.3250.0000.1750.433
과속방지턱 20.2891.0000.9630.0000.3150.253
과속방지턱 10.3250.9631.0000.0000.3100.239
과속방지턱 30.0000.0000.0001.0000.2110.000
가상방지턱 30.1750.3150.3100.2111.0000.526
가상방지턱 10.4330.2530.2390.0000.5261.000
2024-03-15T09:13:52.912387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가상방지턱 1가상방지턱 2가상방지턱 3과속방지턱 1과속방지턱 2과속방지턱 3
가상방지턱 11.0000.4330.5260.2390.2530.000
가상방지턱 20.4331.0000.1750.3250.2890.000
가상방지턱 30.5260.1751.0000.3100.3150.211
과속방지턱 10.2390.3250.3101.0000.9630.000
과속방지턱 20.2530.2890.3150.9631.0000.000
과속방지턱 30.0000.0000.2110.0000.0001.000

Missing values

2024-03-15T09:13:46.811140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:13:47.414941image/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

현황가상방지턱 1가상방지턱 2가상방지턱 3과속방지턱 1과속방지턱 2과속방지턱 3
0지방도635호선221275
1지방도643호선6655
2지방도697호선22
3지방도701호선3924151111
4지방도702호선232213434
5지방도703호선42255
6지방도705호선2118377
7지방도706호선9812323
8지방도707호선771010
9지방도708호선41411616
현황가상방지턱 1가상방지턱 2가상방지턱 3과속방지턱 1과속방지턱 2과속방지턱 3
44지방도796호선33
45지방도799호선12121111
46지방도893호선22
47지방도897호선33
48지방도1089호선51499
49국지도15호선22
50국지도37호선5511
51국지도49호선3010201111
52국지도55호선9955
53국지도60호선167911