Overview

Dataset statistics

Number of variables7
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory61.4 B

Variable types

Categorical4
Text2
DateTime1

Dataset

Description대구광역시_북구_자동차공회전제한구역_20190901
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3037270&dataSetDetailId=30372702dddce0fbcb0b_201909111345&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
표지판(고정식) is highly overall correlated with 구분 and 1 other fieldsHigh correlation
표지판 설치위치 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
표지판(부착식) is highly overall correlated with 구분 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 표지판(고정식) and 2 other fieldsHigh correlation
표지판(고정식) is highly imbalanced (52.3%)Imbalance

Reproduction

Analysis started2024-04-19 05:46:25.820674
Analysis finished2024-04-19 05:46:26.325634
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
주차장
26 
차고지(화물운송 사업소)
5 
차고지(일반택시)
4 
차고지(전세버스)
 
2
터미널
 
1

Length

Max length13
Median length3
Mean length5.3589744
Min length3

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row터미널
2nd row주차장
3rd row주차장
4th row주차장
5th row주차장

Common Values

ValueCountFrequency (%)
주차장 26
66.7%
차고지(화물운송 사업소) 5
 
12.8%
차고지(일반택시) 4
 
10.3%
차고지(전세버스) 2
 
5.1%
터미널 1
 
2.6%
차고지(시내버스) 1
 
2.6%

Length

2024-04-19T14:46:26.396874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:46:26.508538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주차장 26
59.1%
차고지(화물운송 5
 
11.4%
사업소 5
 
11.4%
차고지(일반택시 4
 
9.1%
차고지(전세버스 2
 
4.5%
터미널 1
 
2.3%
차고지(시내버스 1
 
2.3%

상호
Text

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-04-19T14:46:26.691091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length7.5128205
Min length3

Characters and Unicode

Total characters293
Distinct characters85
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)59.0%

Sample

1st row서대구고속터미널
2nd row노상공영주차장
3rd row노상공영주차장
4th row노상공영주차장
5th row노상공영주차장
ValueCountFrequency (%)
노상공영주차장 16
32.7%
농산물도매시장 2
 
4.1%
은행가 2
 
4.1%
칠곡2지구 2
 
4.1%
남편 2
 
4.1%
북편 2
 
4.1%
주)한진택배대구지점 1
 
2.0%
건영화물(주 1
 
2.0%
주)세운고속관광 1
 
2.0%
홈센터 1
 
2.0%
Other values (19) 19
38.8%
2024-04-19T14:46:27.020111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 24
 
8.2%
장 21
 
7.2%
영 19
 
6.5%
차 19
 
6.5%
공 18
 
6.1%
노 16
 
5.5%
상 16
 
5.5%
10
 
3.4%
구 6
 
2.0%
) 5
 
1.7%
Other values (75) 139
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
92.2%
Space Separator 10
 
3.4%
Close Punctuation 5
 
1.7%
Open Punctuation 5
 
1.7%
Decimal Number 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 24
 
8.9%
장 21
 
7.8%
영 19
 
7.0%
차 19
 
7.0%
공 18
 
6.7%
노 16
 
5.9%
상 16
 
5.9%
구 6
 
2.2%
시 5
 
1.9%
지 5
 
1.9%
Other values (70) 121
44.8%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
6 1
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
92.2%
Common 23
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 24
 
8.9%
장 21
 
7.8%
영 19
 
7.0%
차 19
 
7.0%
공 18
 
6.7%
노 16
 
5.9%
상 16
 
5.9%
구 6
 
2.2%
시 5
 
1.9%
지 5
 
1.9%
Other values (70) 121
44.8%
Common
ValueCountFrequency (%)
10
43.5%
) 5
21.7%
( 5
21.7%
2 2
 
8.7%
6 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
92.2%
ASCII 23
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 24
 
8.9%
장 21
 
7.8%
영 19
 
7.0%
차 19
 
7.0%
공 18
 
6.7%
노 16
 
5.9%
상 16
 
5.9%
구 6
 
2.2%
시 5
 
1.9%
지 5
 
1.9%
Other values (70) 121
44.8%
ASCII
ValueCountFrequency (%)
10
43.5%
) 5
21.7%
( 5
21.7%
2 2
 
8.7%
6 1
 
4.3%

소재지
Text

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Memory size444.0 B
2024-04-19T14:46:27.241963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length23.435897
Min length21

Characters and Unicode

Total characters914
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)94.9%

Sample

1st row대구광역시 북구 팔달로 103(노원동3가)
2nd row대구광역시 북구 칠성시장로 15-1(칠성동1가)
3rd row대구광역시 북구 공평로 153(칠성동2가)
4th row대구광역시 북구 칠성남로26길 32(칠성동2가)
5th row대구광역시 북구 오봉로 13(노원동2가)
ValueCountFrequency (%)
대구광역시 39
24.4%
북구 39
24.4%
고성로 3
 
1.9%
노원로 3
 
1.9%
칠성시장로 3
 
1.9%
103(노원동3가 3
 
1.9%
오봉로 3
 
1.9%
매천로18길 2
 
1.2%
공평로 2
 
1.2%
구암로 2
 
1.2%
Other values (58) 61
38.1%
2024-04-19T14:46:27.604050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
 
13.6%
구 82
 
9.0%
대 44
 
4.8%
1 43
 
4.7%
시 43
 
4.7%
동 40
 
4.4%
북 40
 
4.4%
역 39
 
4.3%
광 39
 
4.3%
로 37
 
4.0%
Other values (47) 383
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 562
61.5%
Decimal Number 150
 
16.4%
Space Separator 124
 
13.6%
Open Punctuation 37
 
4.0%
Close Punctuation 37
 
4.0%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 82
14.6%
대 44
 
7.8%
시 43
 
7.7%
동 40
 
7.1%
북 40
 
7.1%
역 39
 
6.9%
광 39
 
6.9%
로 37
 
6.6%
가 24
 
4.3%
성 21
 
3.7%
Other values (33) 153
27.2%
Decimal Number
ValueCountFrequency (%)
1 43
28.7%
3 26
17.3%
2 23
15.3%
0 13
 
8.7%
5 11
 
7.3%
7 9
 
6.0%
8 7
 
4.7%
4 7
 
4.7%
6 6
 
4.0%
9 5
 
3.3%
Space Separator
ValueCountFrequency (%)
124
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 562
61.5%
Common 352
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 82
14.6%
대 44
 
7.8%
시 43
 
7.7%
동 40
 
7.1%
북 40
 
7.1%
역 39
 
6.9%
광 39
 
6.9%
로 37
 
6.6%
가 24
 
4.3%
성 21
 
3.7%
Other values (33) 153
27.2%
Common
ValueCountFrequency (%)
124
35.2%
1 43
 
12.2%
( 37
 
10.5%
) 37
 
10.5%
3 26
 
7.4%
2 23
 
6.5%
0 13
 
3.7%
5 11
 
3.1%
7 9
 
2.6%
8 7
 
2.0%
Other values (4) 22
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 562
61.5%
ASCII 352
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
35.2%
1 43
 
12.2%
( 37
 
10.5%
) 37
 
10.5%
3 26
 
7.4%
2 23
 
6.5%
0 13
 
3.7%
5 11
 
3.1%
7 9
 
2.6%
8 7
 
2.0%
Other values (4) 22
 
6.2%
Hangul
ValueCountFrequency (%)
구 82
14.6%
대 44
 
7.8%
시 43
 
7.7%
동 40
 
7.1%
북 40
 
7.1%
역 39
 
6.9%
광 39
 
6.9%
로 37
 
6.6%
가 24
 
4.3%
성 21
 
3.7%
Other values (33) 153
27.2%

표지판(고정식)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
<NA>
35 
1
4 

Length

Max length4
Median length4
Mean length3.6923077
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 35
89.7%
1 4
 
10.3%

Length

2024-04-19T14:46:27.745484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:46:27.852708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 35
89.7%
1 4
 
10.3%

표지판(부착식)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
<NA>
29 
1
10 

Length

Max length4
Median length4
Mean length3.2307692
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 29
74.4%
1 10
 
25.6%

Length

2024-04-19T14:46:27.963569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:46:28.069841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
74.4%
1 10
 
25.6%

표지판 설치위치
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size444.0 B
<NA>
25 
차량 출입구 근처
6 
주차장 안
6 
터미널 입구 근처
 
1
1층 주차장 출입구 근처
 
1

Length

Max length13
Median length4
Mean length5.2820513
Min length4

Unique

Unique2 ?
Unique (%)5.1%

Sample

1st row터미널 입구 근처
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 25
64.1%
차량 출입구 근처 6
 
15.4%
주차장 안 6
 
15.4%
터미널 입구 근처 1
 
2.6%
1층 주차장 출입구 근처 1
 
2.6%

Length

2024-04-19T14:46:28.172604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-19T14:46:28.353413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 25
40.3%
근처 8
 
12.9%
출입구 7
 
11.3%
주차장 7
 
11.3%
차량 6
 
9.7%
안 6
 
9.7%
터미널 1
 
1.6%
입구 1
 
1.6%
1층 1
 
1.6%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
Minimum2019-09-01 00:00:00
Maximum2019-09-01 00:00:00
2024-04-19T14:46:28.451932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:46:28.535564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Correlations

2024-04-19T14:46:28.600117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상호소재지표지판 설치위치
구분1.0001.0000.0000.784
상호1.0001.0000.8701.000
소재지0.0000.8701.0001.000
표지판 설치위치0.7841.0001.0001.000
2024-04-19T14:46:28.997427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표지판(고정식)표지판 설치위치표지판(부착식)구분
표지판(고정식)1.0001.000NaN1.000
표지판 설치위치1.0001.0001.0000.543
표지판(부착식)NaN1.0001.0001.000
구분1.0000.5431.0001.000
2024-04-19T14:46:29.088691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분표지판(고정식)표지판(부착식)표지판 설치위치
구분1.0001.0001.0000.543
표지판(고정식)1.0001.0000.0001.000
표지판(부착식)1.0000.0001.0001.000
표지판 설치위치0.5431.0001.0001.000

Missing values

2024-04-19T14:46:26.142900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:46:26.263871image/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

구분상호소재지표지판(고정식)표지판(부착식)표지판 설치위치데이터기준일자
0터미널서대구고속터미널대구광역시 북구 팔달로 103(노원동3가)1<NA>터미널 입구 근처2019-09-01
1주차장노상공영주차장대구광역시 북구 칠성시장로 15-1(칠성동1가)<NA><NA><NA>2019-09-01
2주차장노상공영주차장대구광역시 북구 공평로 153(칠성동2가)<NA><NA><NA>2019-09-01
3주차장노상공영주차장대구광역시 북구 칠성남로26길 32(칠성동2가)<NA><NA><NA>2019-09-01
4주차장노상공영주차장대구광역시 북구 오봉로 13(노원동2가)<NA><NA><NA>2019-09-01
5주차장대구체육회맞은편대구광역시 북구 고성로 178(고성동)<NA><NA><NA>2019-09-01
6주차장만평네거리 교통섬대구광역시 북구 팔달로 103(노원동3가)<NA><NA><NA>2019-09-01
7주차장노상공영주차장대구광역시 북구 대학로 139(산격동)<NA><NA><NA>2019-09-01
8주차장코트랜스대구지사대구광역시 북구 유통단지로 115(산격동)1<NA>차량 출입구 근처2019-09-01
9주차장노상공영주차장대구광역시 북구 오봉로 124(침산동)<NA><NA><NA>2019-09-01
구분상호소재지표지판(고정식)표지판(부착식)표지판 설치위치데이터기준일자
29차고지(전세버스)(주)세운고속관광대구광역시 북구 태전동 503-2외 2<NA>1차량 출입구 근처2019-09-01
30차고지(일반택시)금성운수대구광역시 북구 노원로17길 22(노원동3가)<NA>1주차장 안2019-09-01
31차고지(일반택시)삼익택시대구광역시 북구 검단공단로17길 8(검단동)<NA>1주차장 안2019-09-01
32차고지(일반택시)합동택시대구광역시 북구 원대로 101(노원동1가)<NA>1주차장 안2019-09-01
33차고지(일반택시)매일자동차(주)대구광역시 북구 동암로7길 51-6(읍내동)<NA>1주차장 안2019-09-01
34차고지(화물운송 사업소)삼화운수대구광역시 북구 오봉로 103(노원동3가)<NA>1주차장 안2019-09-01
35차고지(화물운송 사업소)(주)한진택배대구지점대구광역시 북구 유통단지로7길 60(산격동)1<NA>차량 출입구 근처2019-09-01
36차고지(화물운송 사업소)건영화물(주)대구광역시 북구 노원로 125(노원동3가)<NA>1차량 출입구 근처2019-09-01
37차고지(화물운송 사업소)홈센터대구광역시 북구 노원로25길 65 (노원동3가)<NA><NA><NA>2019-09-01
38차고지(화물운송 사업소)동주운수 외 6개소대구광역시 북구 칠곡중앙대로 77 (매천동)<NA><NA><NA>2019-09-01