Overview

Dataset statistics

Number of variables6
Number of observations30
Missing cells60
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory52.4 B

Variable types

Text5
Unsupported1

Dataset

Description창원시 관내 임도현황(완공, 계획)
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3080599

Alerts

범 례 완공임도(97.33km) 계획임도(약35.5km) has 25 (83.3%) missing valuesMissing
Unnamed: 1 has 5 (16.7%) missing valuesMissing
Unnamed: 2 has 9 (30.0%) missing valuesMissing
Unnamed: 5 has 21 (70.0%) missing valuesMissing
Unnamed: 3 has unique valuesUnique
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:48:00.598292
Analysis finished2023-12-10 22:48:01.068177
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

범 례 완공임도(97.33km) 계획임도(약35.5km)
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing25
Missing (%)83.3%
Memory size372.0 B
2023-12-11T07:48:01.154643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.4
Min length3

Characters and Unicode

Total characters17
Distinct characters13
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

Unique5 ?
Unique (%)100.0%

Sample

1st row행정 구역
2nd row의창구
3rd row합포구
4th row회원구
5th row진해구
ValueCountFrequency (%)
행정 1
16.7%
구역 1
16.7%
의창구 1
16.7%
합포구 1
16.7%
회원구 1
16.7%
진해구 1
16.7%
2023-12-11T07:48:01.415249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 5
29.4%
행 1
 
5.9%
정 1
 
5.9%
1
 
5.9%
역 1
 
5.9%
의 1
 
5.9%
창 1
 
5.9%
합 1
 
5.9%
포 1
 
5.9%
회 1
 
5.9%
Other values (3) 3
17.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16
94.1%
Control 1
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5
31.2%
행 1
 
6.2%
정 1
 
6.2%
역 1
 
6.2%
의 1
 
6.2%
창 1
 
6.2%
합 1
 
6.2%
포 1
 
6.2%
회 1
 
6.2%
원 1
 
6.2%
Other values (2) 2
 
12.5%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16
94.1%
Common 1
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5
31.2%
행 1
 
6.2%
정 1
 
6.2%
역 1
 
6.2%
의 1
 
6.2%
창 1
 
6.2%
합 1
 
6.2%
포 1
 
6.2%
회 1
 
6.2%
원 1
 
6.2%
Other values (2) 2
 
12.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
94.1%
ASCII 1
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 5
31.2%
행 1
 
6.2%
정 1
 
6.2%
역 1
 
6.2%
의 1
 
6.2%
창 1
 
6.2%
합 1
 
6.2%
포 1
 
6.2%
회 1
 
6.2%
원 1
 
6.2%
Other values (2) 2
 
12.5%
ASCII
ValueCountFrequency (%)
1
100.0%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5
Missing (%)16.7%
Memory size372.0 B

Unnamed: 2
Text

MISSING 

Distinct19
Distinct (%)90.5%
Missing9
Missing (%)30.0%
Memory size372.0 B
2023-12-11T07:48:01.572701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length7.0952381
Min length4

Characters and Unicode

Total characters149
Distinct characters15
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

Unique18 ?
Unique (%)85.7%

Sample

1st row개설년도
2nd row1995~1996년
3rd row1996년
4th row1995년
5th row1996~1997년
ValueCountFrequency (%)
1996년 3
 
14.3%
1992년 1
 
4.8%
개설년도 1
 
4.8%
1989년 1
 
4.8%
1998~2011년 1
 
4.8%
2006~2008년 1
 
4.8%
2001~2002년 1
 
4.8%
1994~2005년 1
 
4.8%
1993년 1
 
4.8%
1990~1999년 1
 
4.8%
Other values (9) 9
42.9%
2023-12-11T07:48:01.858376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 42
28.2%
1 27
18.1%
년 21
14.1%
0 14
 
9.4%
~ 9
 
6.0%
2 9
 
6.0%
6 7
 
4.7%
8 7
 
4.7%
5 4
 
2.7%
7 3
 
2.0%
Other values (5) 6
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116
77.9%
Other Letter 24
 
16.1%
Math Symbol 9
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 42
36.2%
1 27
23.3%
0 14
 
12.1%
2 9
 
7.8%
6 7
 
6.0%
8 7
 
6.0%
5 4
 
3.4%
7 3
 
2.6%
4 2
 
1.7%
3 1
 
0.9%
Other Letter
ValueCountFrequency (%)
년 21
87.5%
개 1
 
4.2%
설 1
 
4.2%
도 1
 
4.2%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125
83.9%
Hangul 24
 
16.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 42
33.6%
1 27
21.6%
0 14
 
11.2%
~ 9
 
7.2%
2 9
 
7.2%
6 7
 
5.6%
8 7
 
5.6%
5 4
 
3.2%
7 3
 
2.4%
4 2
 
1.6%
Hangul
ValueCountFrequency (%)
년 21
87.5%
개 1
 
4.2%
설 1
 
4.2%
도 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125
83.9%
Hangul 24
 
16.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 42
33.6%
1 27
21.6%
0 14
 
11.2%
~ 9
 
7.2%
2 9
 
7.2%
6 7
 
5.6%
8 7
 
5.6%
5 4
 
3.2%
7 3
 
2.4%
4 2
 
1.6%
Hangul
ValueCountFrequency (%)
년 21
87.5%
개 1
 
4.2%
설 1
 
4.2%
도 1
 
4.2%

Unnamed: 3
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T07:48:02.089095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25.5
Mean length18.533333
Min length2

Characters and Unicode

Total characters556
Distinct characters69
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row위 치 (시점~종점)
2nd row북면 외감리 산112-2 ~ 북면 외감리 산68
3rd row북면 무곡리 산117-9 ~ 북면 내곡리 산230-2
4th row동읍 금산리 산197-1 ~ 동읍 봉곡리 산187-2
5th row동읍 봉곡리 산79-4 ~ 동읍 봉곡리 산151
ValueCountFrequency (%)
진전면 9
 
7.7%
창포리 4
 
3.4%
동읍 4
 
3.4%
북면 4
 
3.4%
4
 
3.4%
평암리 4
 
3.4%
여양리 4
 
3.4%
영학리 3
 
2.6%
봉곡리 3
 
2.6%
금산리 3
 
2.6%
Other values (67) 75
64.1%
2023-12-11T07:48:02.424118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90
16.2%
산 45
 
8.1%
1 36
 
6.5%
리 34
 
6.1%
면 28
 
5.0%
~ 27
 
4.9%
진 26
 
4.7%
- 21
 
3.8%
전 18
 
3.2%
2 17
 
3.1%
Other values (59) 214
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 296
53.2%
Decimal Number 120
21.6%
Space Separator 90
 
16.2%
Math Symbol 27
 
4.9%
Dash Punctuation 21
 
3.8%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 45
15.2%
리 34
 
11.5%
면 28
 
9.5%
진 26
 
8.8%
전 18
 
6.1%
동 15
 
5.1%
북 8
 
2.7%
곡 7
 
2.4%
읍 6
 
2.0%
포 6
 
2.0%
Other values (44) 103
34.8%
Decimal Number
ValueCountFrequency (%)
1 36
30.0%
2 17
14.2%
9 13
 
10.8%
7 12
 
10.0%
8 12
 
10.0%
3 11
 
9.2%
4 6
 
5.0%
5 6
 
5.0%
6 4
 
3.3%
0 3
 
2.5%
Space Separator
ValueCountFrequency (%)
90
100.0%
Math Symbol
ValueCountFrequency (%)
~ 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 296
53.2%
Common 260
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 45
15.2%
리 34
 
11.5%
면 28
 
9.5%
진 26
 
8.8%
전 18
 
6.1%
동 15
 
5.1%
북 8
 
2.7%
곡 7
 
2.4%
읍 6
 
2.0%
포 6
 
2.0%
Other values (44) 103
34.8%
Common
ValueCountFrequency (%)
90
34.6%
1 36
 
13.8%
~ 27
 
10.4%
- 21
 
8.1%
2 17
 
6.5%
9 13
 
5.0%
7 12
 
4.6%
8 12
 
4.6%
3 11
 
4.2%
4 6
 
2.3%
Other values (5) 15
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 296
53.2%
ASCII 260
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90
34.6%
1 36
 
13.8%
~ 27
 
10.4%
- 21
 
8.1%
2 17
 
6.5%
9 13
 
5.0%
7 12
 
4.6%
8 12
 
4.6%
3 11
 
4.2%
4 6
 
2.3%
Other values (5) 15
 
5.8%
Hangul
ValueCountFrequency (%)
산 45
15.2%
리 34
 
11.5%
면 28
 
9.5%
진 26
 
8.8%
전 18
 
6.1%
동 15
 
5.1%
북 8
 
2.7%
곡 7
 
2.4%
읍 6
 
2.0%
포 6
 
2.0%
Other values (44) 103
34.8%

Unnamed: 4
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T07:48:02.585562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.1333333
Min length2

Characters and Unicode

Total characters124
Distinct characters15
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row연장
2nd row3km
3rd row1km
4th row2km
5th row3km
ValueCountFrequency (%)
2km 5
16.7%
3km 4
 
13.3%
1km 3
 
10.0%
약1.5km 2
 
6.7%
약8km 1
 
3.3%
연장 1
 
3.3%
약3km 1
 
3.3%
26.58km 1
 
3.3%
6km 1
 
3.3%
약9km 1
 
3.3%
Other values (10) 10
33.3%
2023-12-11T07:48:02.875334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k 29
23.4%
m 29
23.4%
. 10
 
8.1%
2 9
 
7.3%
약 8
 
6.5%
1 7
 
5.6%
5 7
 
5.6%
3 6
 
4.8%
8 6
 
4.8%
6 4
 
3.2%
Other values (5) 9
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58
46.8%
Decimal Number 46
37.1%
Other Punctuation 10
 
8.1%
Other Letter 10
 
8.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9
19.6%
1 7
15.2%
5 7
15.2%
3 6
13.0%
8 6
13.0%
6 4
8.7%
4 3
 
6.5%
0 2
 
4.3%
9 2
 
4.3%
Other Letter
ValueCountFrequency (%)
약 8
80.0%
연 1
 
10.0%
장 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
k 29
50.0%
m 29
50.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 58
46.8%
Common 56
45.2%
Hangul 10
 
8.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 10
17.9%
2 9
16.1%
1 7
12.5%
5 7
12.5%
3 6
10.7%
8 6
10.7%
6 4
 
7.1%
4 3
 
5.4%
0 2
 
3.6%
9 2
 
3.6%
Hangul
ValueCountFrequency (%)
약 8
80.0%
연 1
 
10.0%
장 1
 
10.0%
Latin
ValueCountFrequency (%)
k 29
50.0%
m 29
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114
91.9%
Hangul 10
 
8.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 29
25.4%
m 29
25.4%
. 10
 
8.8%
2 9
 
7.9%
1 7
 
6.1%
5 7
 
6.1%
3 6
 
5.3%
8 6
 
5.3%
6 4
 
3.5%
4 3
 
2.6%
Other values (2) 4
 
3.5%
Hangul
ValueCountFrequency (%)
약 8
80.0%
연 1
 
10.0%
장 1
 
10.0%

Unnamed: 5
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing21
Missing (%)70.0%
Memory size372.0 B
2023-12-11T07:48:03.042337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5555556
Min length3

Characters and Unicode

Total characters50
Distinct characters16
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st row총연장
2nd row9km
3rd row약1.5km
4th row50.4km
5th row약18.5km
ValueCountFrequency (%)
총연장 1
11.1%
9km 1
11.1%
약1.5km 1
11.1%
50.4km 1
11.1%
약18.5km 1
11.1%
11.35km 1
11.1%
약15km 1
11.1%
26.58km 1
11.1%
약0.5km 1
11.1%
2023-12-11T07:48:03.438741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k 8
16.0%
m 8
16.0%
5 7
14.0%
. 6
12.0%
1 5
10.0%
약 4
8.0%
0 2
 
4.0%
8 2
 
4.0%
총 1
 
2.0%
연 1
 
2.0%
Other values (6) 6
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21
42.0%
Lowercase Letter 16
32.0%
Other Letter 7
 
14.0%
Other Punctuation 6
 
12.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 7
33.3%
1 5
23.8%
0 2
 
9.5%
8 2
 
9.5%
9 1
 
4.8%
4 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
6 1
 
4.8%
Other Letter
ValueCountFrequency (%)
약 4
57.1%
총 1
 
14.3%
연 1
 
14.3%
장 1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
k 8
50.0%
m 8
50.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27
54.0%
Latin 16
32.0%
Hangul 7
 
14.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 7
25.9%
. 6
22.2%
1 5
18.5%
0 2
 
7.4%
8 2
 
7.4%
9 1
 
3.7%
4 1
 
3.7%
3 1
 
3.7%
2 1
 
3.7%
6 1
 
3.7%
Hangul
ValueCountFrequency (%)
약 4
57.1%
총 1
 
14.3%
연 1
 
14.3%
장 1
 
14.3%
Latin
ValueCountFrequency (%)
k 8
50.0%
m 8
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43
86.0%
Hangul 7
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 8
18.6%
m 8
18.6%
5 7
16.3%
. 6
14.0%
1 5
11.6%
0 2
 
4.7%
8 2
 
4.7%
9 1
 
2.3%
4 1
 
2.3%
3 1
 
2.3%
Other values (2) 2
 
4.7%
Hangul
ValueCountFrequency (%)
약 4
57.1%
총 1
 
14.3%
연 1
 
14.3%
장 1
 
14.3%

Correlations

2023-12-11T07:48:03.650396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
범 례 완공임도(97.33km) 계획임도(약35.5km)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
범 례\n\n 완공임도(97.33km) 계획임도(약35.5km)1.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0000.9221.000
Unnamed: 31.0001.0001.0001.0001.000
Unnamed: 41.0000.9221.0001.0001.000
Unnamed: 51.0001.0001.0001.0001.000

Missing values

2023-12-11T07:48:00.821058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:48:00.914107image/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.
2023-12-11T07:48:01.011685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

범 례 완공임도(97.33km) 계획임도(약35.5km)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
0행정 구역순번개설년도위 치 (시점~종점)연장총연장
1의창구11995~1996년북면 외감리 산112-2 ~ 북면 외감리 산683km9km
2<NA>21996년북면 무곡리 산117-9 ~ 북면 내곡리 산230-21km<NA>
3<NA>31995년동읍 금산리 산197-1 ~ 동읍 봉곡리 산187-22km<NA>
4<NA>41996~1997년동읍 봉곡리 산79-4 ~ 동읍 봉곡리 산1513km<NA>
5<NA>계획<NA>봉곡~금산약1.5km약1.5km
6합포구51988년진전면 창포리 산26~진전면 창포리 산292km50.4km
7<NA>61991년진전면 시락리 산138~진전면 시락리 산982km<NA>
8<NA>71994년진전면 창포리 산49-3~진전면 창포리 산781km<NA>
9<NA>81986~1995년진북면 금산리 산35~진북면 금산리 산1-18km<NA>
범 례 완공임도(97.33km) 계획임도(약35.5km)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
20<NA>계획<NA>금산~정현약4km약18.5km
21<NA>NaN<NA>상평~평암약3km<NA>
22<NA>NaN<NA>여항약8km<NA>
23<NA>NaN<NA>시락~창포약1.5km<NA>
24<NA>NaN<NA>난포~옥계약2km<NA>
25회원구191998~2011년내서읍 감천리 산148~내서읍 신감리 산17911.35km11.35km
26<NA>계획<NA>감천~두척약9km약15km
27<NA>NaN<NA>신감~삼계6km<NA>
28진해구201997~2010년진해구 태백동 산84-1 진해구 소사동 산1526.58km26.58km
29<NA>계획<NA>백일약0.5km약0.5km