Overview

Dataset statistics

Number of variables5
Number of observations125
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory41.1 B

Variable types

Categorical2
Text3

Dataset

Description대구광역시 남구_ 먹거리 골목 업소현황_20190630
Author대구광역시 남구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=3069689&dataSetDetailId=30696892d208d9cc2e51&provdMethod=FILE

Alerts

골목명 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 골목명High correlation

Reproduction

Analysis started2024-04-18 04:22:10.330565
Analysis finished2024-04-18 04:22:12.360498
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

골목명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
안지랑 곱창골목
66 
앞산 맛둘레길
46 
바다맛길
13 

Length

Max length8
Median length8
Mean length7.216
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row앞산 맛둘레길
2nd row앞산 맛둘레길
3rd row앞산 맛둘레길
4th row앞산 맛둘레길
5th row앞산 맛둘레길

Common Values

ValueCountFrequency (%)
안지랑 곱창골목 66
52.8%
앞산 맛둘레길 46
36.8%
바다맛길 13
 
10.4%

Length

2024-04-18T13:22:12.442344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T13:22:12.546430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안지랑 66
27.8%
곱창골목 66
27.8%
앞산 46
19.4%
맛둘레길 46
19.4%
바다맛길 13
 
5.5%

업소명
Text

Distinct124
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-18T13:22:12.734834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length13
Mean length5.72
Min length2

Characters and Unicode

Total characters715
Distinct characters267
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)98.4%

Sample

1st row울진대게
2nd row왕손짜장
3rd row청심장식당
4th row리얼키친더홍(REAL KITCHEN THE HONG)
5th row김치단지
ValueCountFrequency (%)
앞산자락카페마실 2
 
1.6%
대덕골민물장어한방보쌈 1
 
0.8%
안지랑포차 1
 
0.8%
장원곱창 1
 
0.8%
호식이두마리치킨대명2호점 1
 
0.8%
홍림곱창 1
 
0.8%
또또식당 1
 
0.8%
홍림2호점곱창막창 1
 
0.8%
엘리펀트 1
 
0.8%
무상육회전문점 1
 
0.8%
Other values (118) 118
91.5%
2024-04-18T13:22:13.073314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
창 37
 
5.2%
곱 25
 
3.5%
당 22
 
3.1%
식 22
 
3.1%
막 14
 
2.0%
산 14
 
2.0%
지 13
 
1.8%
마 12
 
1.7%
어 11
 
1.5%
대 9
 
1.3%
Other values (257) 536
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 668
93.4%
Uppercase Letter 19
 
2.7%
Decimal Number 10
 
1.4%
Close Punctuation 5
 
0.7%
Open Punctuation 5
 
0.7%
Space Separator 4
 
0.6%
Lowercase Letter 3
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
창 37
 
5.5%
곱 25
 
3.7%
당 22
 
3.3%
식 22
 
3.3%
막 14
 
2.1%
산 14
 
2.1%
지 13
 
1.9%
마 12
 
1.8%
어 11
 
1.6%
대 9
 
1.3%
Other values (232) 489
73.2%
Uppercase Letter
ValueCountFrequency (%)
E 3
15.8%
H 3
15.8%
N 2
10.5%
T 2
10.5%
G 1
 
5.3%
O 1
 
5.3%
C 1
 
5.3%
I 1
 
5.3%
K 1
 
5.3%
Z 1
 
5.3%
Other values (3) 3
15.8%
Decimal Number
ValueCountFrequency (%)
2 4
40.0%
0 3
30.0%
5 1
 
10.0%
1 1
 
10.0%
9 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
n 1
33.3%
o 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 668
93.4%
Common 25
 
3.5%
Latin 22
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
창 37
 
5.5%
곱 25
 
3.7%
당 22
 
3.3%
식 22
 
3.3%
막 14
 
2.1%
산 14
 
2.1%
지 13
 
1.9%
마 12
 
1.8%
어 11
 
1.6%
대 9
 
1.3%
Other values (232) 489
73.2%
Latin
ValueCountFrequency (%)
E 3
13.6%
H 3
13.6%
N 2
 
9.1%
T 2
 
9.1%
G 1
 
4.5%
O 1
 
4.5%
C 1
 
4.5%
I 1
 
4.5%
K 1
 
4.5%
e 1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
) 5
20.0%
( 5
20.0%
2 4
16.0%
4
16.0%
0 3
12.0%
5 1
 
4.0%
1 1
 
4.0%
9 1
 
4.0%
· 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 668
93.4%
ASCII 46
 
6.4%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
창 37
 
5.5%
곱 25
 
3.7%
당 22
 
3.3%
식 22
 
3.3%
막 14
 
2.1%
산 14
 
2.1%
지 13
 
1.9%
마 12
 
1.8%
어 11
 
1.6%
대 9
 
1.3%
Other values (232) 489
73.2%
ASCII
ValueCountFrequency (%)
) 5
 
10.9%
( 5
 
10.9%
2 4
 
8.7%
4
 
8.7%
0 3
 
6.5%
E 3
 
6.5%
H 3
 
6.5%
N 2
 
4.3%
T 2
 
4.3%
5 1
 
2.2%
Other values (14) 14
30.4%
None
ValueCountFrequency (%)
· 1
100.0%

업소소재지
Text

Distinct104
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-18T13:22:13.381692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length32
Mean length25.616
Min length21

Characters and Unicode

Total characters3202
Distinct characters39
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

Unique92 ?
Unique (%)73.6%

Sample

1st row대구광역시 남구 앞산순환로 559-1 (대명동)
2nd row대구광역시 남구 앞산순환로 539 (대명동)
3rd row대구광역시 남구 앞산순환로 497 (대명동)
4th row대구광역시 남구 앞산순환로 473, 2층 (대명동)
5th row대구광역시 남구 앞산순환로 473 (대명동)
ValueCountFrequency (%)
대구광역시 125
19.0%
남구 125
19.0%
대명동 123
18.7%
대명로36길 56
8.5%
앞산순환로 34
 
5.2%
1층 23
 
3.5%
63 12
 
1.8%
대명복개로 11
 
1.7%
13 6
 
0.9%
2층 4
 
0.6%
Other values (101) 138
21.0%
2024-04-18T13:22:13.766453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
532
16.6%
대 327
 
10.2%
구 250
 
7.8%
명 202
 
6.3%
남 129
 
4.0%
) 125
 
3.9%
광 125
 
3.9%
역 125
 
3.9%
시 125
 
3.9%
로 125
 
3.9%
Other values (29) 1137
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1846
57.7%
Space Separator 532
 
16.6%
Decimal Number 501
 
15.6%
Close Punctuation 125
 
3.9%
Open Punctuation 125
 
3.9%
Other Punctuation 37
 
1.2%
Dash Punctuation 36
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 327
17.7%
구 250
13.5%
명 202
10.9%
남 129
 
7.0%
광 125
 
6.8%
역 125
 
6.8%
시 125
 
6.8%
로 125
 
6.8%
동 125
 
6.8%
길 72
 
3.9%
Other values (14) 241
13.1%
Decimal Number
ValueCountFrequency (%)
3 118
23.6%
6 94
18.8%
1 93
18.6%
2 48
9.6%
4 40
 
8.0%
7 26
 
5.2%
9 24
 
4.8%
8 23
 
4.6%
5 21
 
4.2%
0 14
 
2.8%
Space Separator
ValueCountFrequency (%)
532
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Open Punctuation
ValueCountFrequency (%)
( 125
100.0%
Other Punctuation
ValueCountFrequency (%)
, 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1846
57.7%
Common 1356
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 327
17.7%
구 250
13.5%
명 202
10.9%
남 129
 
7.0%
광 125
 
6.8%
역 125
 
6.8%
시 125
 
6.8%
로 125
 
6.8%
동 125
 
6.8%
길 72
 
3.9%
Other values (14) 241
13.1%
Common
ValueCountFrequency (%)
532
39.2%
) 125
 
9.2%
( 125
 
9.2%
3 118
 
8.7%
6 94
 
6.9%
1 93
 
6.9%
2 48
 
3.5%
4 40
 
2.9%
, 37
 
2.7%
- 36
 
2.7%
Other values (5) 108
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1846
57.7%
ASCII 1356
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
532
39.2%
) 125
 
9.2%
( 125
 
9.2%
3 118
 
8.7%
6 94
 
6.9%
1 93
 
6.9%
2 48
 
3.5%
4 40
 
2.9%
, 37
 
2.7%
- 36
 
2.7%
Other values (5) 108
 
8.0%
Hangul
ValueCountFrequency (%)
대 327
17.7%
구 250
13.5%
명 202
10.9%
남 129
 
7.0%
광 125
 
6.8%
역 125
 
6.8%
시 125
 
6.8%
로 125
 
6.8%
동 125
 
6.8%
길 72
 
3.9%
Other values (14) 241
13.1%

소재지전화
Text

Distinct111
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-18T13:22:14.011995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)87.2%

Sample

1st row053-625-5970
2nd row053-625-0555
3rd row053-626-6690
4th row053-628-2929
5th row053-656-8949
ValueCountFrequency (%)
053-000-0000 14
 
11.2%
053-624-6789 2
 
1.6%
053-246-2897 1
 
0.8%
053-656-3346 1
 
0.8%
053-623-7098 1
 
0.8%
053-627-2229 1
 
0.8%
053-654-0486 1
 
0.8%
053-629-6325 1
 
0.8%
053-621-1023 1
 
0.8%
053-621-5503 1
 
0.8%
Other values (101) 101
80.8%
2024-04-18T13:22:14.373606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 271
18.1%
- 250
16.7%
5 228
15.2%
3 184
12.3%
6 167
11.1%
2 121
8.1%
8 73
 
4.9%
9 62
 
4.1%
7 54
 
3.6%
1 46
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1250
83.3%
Dash Punctuation 250
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 271
21.7%
5 228
18.2%
3 184
14.7%
6 167
13.4%
2 121
9.7%
8 73
 
5.8%
9 62
 
5.0%
7 54
 
4.3%
1 46
 
3.7%
4 44
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 271
18.1%
- 250
16.7%
5 228
15.2%
3 184
12.3%
6 167
11.1%
2 121
8.1%
8 73
 
4.9%
9 62
 
4.1%
7 54
 
3.6%
1 46
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 271
18.1%
- 250
16.7%
5 228
15.2%
3 184
12.3%
6 167
11.1%
2 121
8.1%
8 73
 
4.9%
9 62
 
4.1%
7 54
 
3.6%
1 46
 
3.1%

구분
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
곱창,막창
42 
회집
8 
경양식
7 
한식
7 
호프/통닭
 
4
Other values (38)
57 

Length

Max length10
Median length8
Mean length4.08
Min length2

Unique

Unique24 ?
Unique (%)19.2%

Sample

1st row대게찜
2nd row중국식
3rd row청국장
4th row경양식
5th row묵은김치찜

Common Values

ValueCountFrequency (%)
곱창,막창 42
33.6%
회집 8
 
6.4%
경양식 7
 
5.6%
한식 7
 
5.6%
호프/통닭 4
 
3.2%
메밀묵밥 3
 
2.4%
한정식 3
 
2.4%
식육(숯불구이) 3
 
2.4%
통닭(치킨) 3
 
2.4%
식육취급 3
 
2.4%
Other values (33) 42
33.6%

Length

2024-04-18T13:22:14.524519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
곱창,막창 42
32.6%
회집 8
 
6.2%
경양식 7
 
5.4%
한식 7
 
5.4%
호프/통닭 4
 
3.1%
커피 4
 
3.1%
메밀묵밥 3
 
2.3%
한정식 3
 
2.3%
식육(숯불구이 3
 
2.3%
통닭(치킨 3
 
2.3%
Other values (34) 45
34.9%

Correlations

2024-04-18T13:22:14.609132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
골목명구분
골목명1.0000.967
구분0.9671.000
2024-04-18T13:22:14.690039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분골목명
구분1.0000.715
골목명0.7151.000
2024-04-18T13:22:14.765108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
골목명구분
골목명1.0000.715
구분0.7151.000

Missing values

2024-04-18T13:22:12.324382image/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

골목명업소명업소소재지소재지전화구분
0앞산 맛둘레길울진대게대구광역시 남구 앞산순환로 559-1 (대명동)053-625-5970대게찜
1앞산 맛둘레길왕손짜장대구광역시 남구 앞산순환로 539 (대명동)053-625-0555중국식
2앞산 맛둘레길청심장식당대구광역시 남구 앞산순환로 497 (대명동)053-626-6690청국장
3앞산 맛둘레길리얼키친더홍(REAL KITCHEN THE HONG)대구광역시 남구 앞산순환로 473, 2층 (대명동)053-628-2929경양식
4앞산 맛둘레길김치단지대구광역시 남구 앞산순환로 473 (대명동)053-656-8949묵은김치찜
5앞산 맛둘레길콩닭콩닭대구광역시 남구 앞산순환로 473 (대명동)053-627-8833찜닭
6앞산 맛둘레길곤지곤지식당대구광역시 남구 앞산순환로 465-1 (대명동)053-656-8872한식
7앞산 맛둘레길돌솥식당대구광역시 남구 앞산순환로 459 (대명동)053-652-7686돌솥밥
8앞산 맛둘레길전라도밥상대구광역시 남구 앞산순환로93길 20-1, 1층 (대명동)053-793-9741한정식
9앞산 맛둘레길고령촌돼지찌개대구광역시 남구 앞산순환로 440-1 (대명동)053-626-0780돼지찌게
골목명업소명업소소재지소재지전화구분
115안지랑 곱창골목한바가지대구광역시 남구 대명로36길 81-2, 1층 (대명동)053-000-0000곱창,막창
116안지랑 곱창골목더좋은곱창막창대구광역시 남구 대명로36길 82 (대명동)053-000-0000곱창,막창
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123안지랑 곱창골목불한닭대구광역시 남구 대명남로 141, 2층 (대명동)053-623-4832기타
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