Overview

Dataset statistics

Number of variables3
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory26.3 B

Variable types

Categorical1
Text2

Dataset

Description대구광역시 내 형성된 먹거리골목 관련 자료로 먹거리골목 명칭, 위치, 형성된 먹거리골목의 주 취급음식에 대한 자료입니다
URLhttps://www.data.go.kr/data/15115596/fileData.do

Alerts

골목명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:07:55.855945
Analysis finished2023-12-12 20:07:56.147433
Duration0.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구군
Categorical

Distinct8
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
달서구
12 
북구
10 
동구
9 
중구
7 
서구
5 
Other values (3)
14 

Length

Max length3
Median length2
Mean length2.3684211
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
달서구 12
21.1%
북구 10
17.5%
동구 9
15.8%
중구 7
12.3%
서구 5
8.8%
남구 5
8.8%
수성구 5
8.8%
달성군 4
 
7.0%

Length

2023-12-13T05:07:56.204128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:07:56.325502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
달서구 12
21.1%
북구 10
17.5%
동구 9
15.8%
중구 7
12.3%
서구 5
8.8%
남구 5
8.8%
수성구 5
8.8%
달성군 4
 
7.0%

골목명칭
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-13T05:07:56.606550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.5087719
Min length3

Characters and Unicode

Total characters428
Distinct characters142
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

Unique57 ?
Unique (%)100.0%

Sample

1st row동인동찜갈비골목
2nd row남산동보쌈골목
3rd row진골목
4th row종로골목
5th row대봉동 봉리단길
ValueCountFrequency (%)
먹거리타운 8
 
9.9%
먹거리촌 5
 
6.2%
먹골촌 2
 
2.5%
동인동찜갈비골목 1
 
1.2%
수성둘레맛길 1
 
1.2%
두류 1
 
1.2%
술골 1
 
1.2%
상인동 1
 
1.2%
웰빙음식거리 1
 
1.2%
수밭골 1
 
1.2%
Other values (59) 59
72.8%
2023-12-13T05:07:57.153756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 32
 
7.5%
거 28
 
6.5%
먹 24
 
5.6%
24
 
5.6%
골 19
 
4.4%
동 18
 
4.2%
촌 16
 
3.7%
목 14
 
3.3%
운 11
 
2.6%
타 9
 
2.1%
Other values (132) 233
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 402
93.9%
Space Separator 24
 
5.6%
Decimal Number 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 32
 
8.0%
거 28
 
7.0%
먹 24
 
6.0%
골 19
 
4.7%
동 18
 
4.5%
촌 16
 
4.0%
목 14
 
3.5%
운 11
 
2.7%
타 9
 
2.2%
길 9
 
2.2%
Other values (129) 222
55.2%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
0 1
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 402
93.9%
Common 26
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 32
 
8.0%
거 28
 
7.0%
먹 24
 
6.0%
골 19
 
4.7%
동 18
 
4.5%
촌 16
 
4.0%
목 14
 
3.5%
운 11
 
2.7%
타 9
 
2.2%
길 9
 
2.2%
Other values (129) 222
55.2%
Common
ValueCountFrequency (%)
24
92.3%
3 1
 
3.8%
0 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 402
93.9%
ASCII 26
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 32
 
8.0%
거 28
 
7.0%
먹 24
 
6.0%
골 19
 
4.7%
동 18
 
4.5%
촌 16
 
4.0%
목 14
 
3.5%
운 11
 
2.7%
타 9
 
2.2%
길 9
 
2.2%
Other values (129) 222
55.2%
ASCII
ValueCountFrequency (%)
24
92.3%
3 1
 
3.8%
0 1
 
3.8%

주 취급음식
Text

Distinct42
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
2023-12-13T05:07:57.415255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length11
Mean length6.8596491
Min length2

Characters and Unicode

Total characters391
Distinct characters89
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

Unique37 ?
Unique (%)64.9%

Sample

1st row찜갈비
2nd row보쌈 등
3rd row한식 등
4th row회+막창+삼겹살 등
5th row식육+일식 등
ValueCountFrequency (%)
등 43
43.0%
한식 11
 
11.0%
삼겹살+막창 4
 
4.0%
막창+삼겹살 2
 
2.0%
한식+숯불구이 2
 
2.0%
감자탕 2
 
2.0%
한식+치킨 2
 
2.0%
찐빵+만두 1
 
1.0%
국+탕+전골류 1
 
1.0%
메기매운탕 1
 
1.0%
Other values (31) 31
31.0%
2023-12-13T05:07:57.857379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
+ 44
 
11.3%
44
 
11.3%
등 43
 
11.0%
식 31
 
7.9%
한 25
 
6.4%
창 12
 
3.1%
막 10
 
2.6%
삼 10
 
2.6%
살 9
 
2.3%
겹 9
 
2.3%
Other values (79) 154
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
77.5%
Math Symbol 44
 
11.3%
Space Separator 44
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 43
 
14.2%
식 31
 
10.2%
한 25
 
8.3%
창 12
 
4.0%
막 10
 
3.3%
삼 10
 
3.3%
살 9
 
3.0%
겹 9
 
3.0%
탕 6
 
2.0%
치 5
 
1.7%
Other values (77) 143
47.2%
Math Symbol
ValueCountFrequency (%)
+ 44
100.0%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
77.5%
Common 88
 
22.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 43
 
14.2%
식 31
 
10.2%
한 25
 
8.3%
창 12
 
4.0%
막 10
 
3.3%
삼 10
 
3.3%
살 9
 
3.0%
겹 9
 
3.0%
탕 6
 
2.0%
치 5
 
1.7%
Other values (77) 143
47.2%
Common
ValueCountFrequency (%)
+ 44
50.0%
44
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
77.5%
ASCII 88
 
22.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
+ 44
50.0%
44
50.0%
Hangul
ValueCountFrequency (%)
등 43
 
14.2%
식 31
 
10.2%
한 25
 
8.3%
창 12
 
4.0%
막 10
 
3.3%
삼 10
 
3.3%
살 9
 
3.0%
겹 9
 
3.0%
탕 6
 
2.0%
치 5
 
1.7%
Other values (77) 143
47.2%

Correlations

2023-12-13T05:07:57.986041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구군골목명칭주 취급음식
구군1.0001.0000.927
골목명칭1.0001.0001.000
주 취급음식0.9271.0001.000

Missing values

2023-12-13T05:07:56.059733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:07:56.123178image/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

구군골목명칭주 취급음식
0중구동인동찜갈비골목찜갈비
1중구남산동보쌈골목보쌈 등
2중구진골목한식 등
3중구종로골목회+막창+삼겹살 등
4중구대봉동 봉리단길식육+일식 등
5중구방천시장식육+다류 등
6중구김광석길다류 등
7동구평화시장닭똥집골목닭똥집+찜닭 등
8동구동촌유원지먹거리촌막창+삼겹살 등
9동구갓바위먹거리촌산채비빔밥+촌두부 등
구군골목명칭주 취급음식
47달서구상화로 먹거리촌감자탕 등
48달서구감새미 먹거리촌한식 등
49달서구장기동 먹거리촌삼겹살 등
50달서구신당동 로데오거리한식 등
51달서구이곡으뜸 먹거리촌감자탕 등
52달서구동산 먹거리촌한식 등
53달성군가창우록리흑염소마을염소불고기+촌닭
54달성군가창찐빵골목찐빵+만두
55달성군부곡리매운탕마을메기매운탕
56달성군비슬산참맛길오리불고기+한방백숙