Overview

Dataset statistics

Number of variables16
Number of observations27
Missing cells32
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory138.9 B

Variable types

Numeric6
Text6
Categorical2
Boolean2

Dataset

Description대구광역시 달서구의 전통시장의 현황 정보를 조회하는 서비스로 도로명주소, 시장개설주기, 영업시간, 취급품목, 주차장 보유 여부 등을 알 수 있다. 대구광역시 달서구 전통시장 지도 서비스 : https://www.dalseo.daegu.kr/dmap/market/
Author대구광역시 달서구
URLhttps://www.data.go.kr/data/15109953/fileData.do

Alerts

시장유형 has constant value ""Constant
개설년도 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 개설년도High correlation
공중화장실 보유여부 is highly overall correlated with 개설년도 and 1 other fieldsHigh correlation
주차장 보유여부 is highly overall correlated with 개설년도 and 1 other fieldsHigh correlation
시장개설주기 is highly imbalanced (66.0%)Imbalance
영업시간 has 7 (25.9%) missing valuesMissing
개설년도 has 2 (7.4%) missing valuesMissing
홈페이지 주소 has 23 (85.2%) missing valuesMissing
시장코드 has unique valuesUnique
전통시장명 has unique valuesUnique
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique
경도 has unique valuesUnique
위도 has unique valuesUnique
빈 점포수 has 3 (11.1%) zerosZeros

Reproduction

Analysis started2023-12-12 18:18:00.894886
Analysis finished2023-12-12 18:18:04.870056
Duration3.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장코드
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:04.922812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2023-12-13T03:18:05.022740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

전통시장명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T03:18:05.198413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length6.0740741
Min length4

Characters and Unicode

Total characters164
Distinct characters58
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

Unique27 ?
Unique (%)100.0%

Sample

1st row달서송림시장
2nd row달서시장
3rd row대곡시장
4th row대구예전우시장
5th row대동시장
ValueCountFrequency (%)
달서송림시장 1
 
3.7%
서대구시장 1
 
3.7%
월성청구시장 1
 
3.7%
월배신시장 1
 
3.7%
월배시장 1
 
3.7%
용산종합큰시장 1
 
3.7%
신내당시장상점가 1
 
3.7%
신내당시장 1
 
3.7%
송경종합시장 1
 
3.7%
성서용산시장 1
 
3.7%
Other values (17) 17
63.0%
2023-12-13T03:18:05.465697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 24
 
14.6%
시 23
 
14.0%
서 9
 
5.5%
성 8
 
4.9%
상 7
 
4.3%
가 6
 
3.7%
신 5
 
3.0%
대 4
 
2.4%
월 4
 
2.4%
두 3
 
1.8%
Other values (48) 71
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
97.6%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 24
 
15.0%
시 23
 
14.4%
서 9
 
5.6%
성 8
 
5.0%
상 7
 
4.4%
가 6
 
3.8%
신 5
 
3.1%
대 4
 
2.5%
월 4
 
2.5%
두 3
 
1.9%
Other values (46) 67
41.9%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
97.6%
Common 4
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 24
 
15.0%
시 23
 
14.4%
서 9
 
5.6%
성 8
 
5.0%
상 7
 
4.4%
가 6
 
3.8%
신 5
 
3.1%
대 4
 
2.5%
월 4
 
2.5%
두 3
 
1.9%
Other values (46) 67
41.9%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
97.6%
ASCII 4
 
2.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 24
 
15.0%
시 23
 
14.4%
서 9
 
5.6%
성 8
 
5.0%
상 7
 
4.4%
가 6
 
3.8%
신 5
 
3.1%
대 4
 
2.5%
월 4
 
2.5%
두 3
 
1.9%
Other values (46) 67
41.9%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

시장유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
상설
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상설 27
100.0%

Length

2023-12-13T03:18:05.576416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:18:05.648383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상설 27
100.0%

도로명주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T03:18:05.810864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length19
Min length15

Characters and Unicode

Total characters513
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row대구광역시 달서구 구마로 176
2nd row대구광역시 달서구 당산로 35
3rd row대구광역시 달서구 도원로 24
4th row대구광역시 달서구 야외음악당로47길 111
5th row대구광역시 달서구 상원로 22-24
ValueCountFrequency (%)
대구광역시 27
25.0%
달서구 27
25.0%
9 2
 
1.9%
17 2
 
1.9%
달구벌대로329길 2
 
1.9%
당산동길 2
 
1.9%
13 1
 
0.9%
월배로24길 1
 
0.9%
송현로12안길 1
 
0.9%
학산로 1
 
0.9%
Other values (42) 42
38.9%
2023-12-13T03:18:06.097135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
15.8%
구 60
 
11.7%
대 34
 
6.6%
달 33
 
6.4%
서 31
 
6.0%
광 27
 
5.3%
역 27
 
5.3%
시 27
 
5.3%
로 25
 
4.9%
1 18
 
3.5%
Other values (40) 150
29.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 338
65.9%
Decimal Number 90
 
17.5%
Space Separator 81
 
15.8%
Dash Punctuation 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 60
17.8%
대 34
10.1%
달 33
9.8%
서 31
9.2%
광 27
8.0%
역 27
8.0%
시 27
8.0%
로 25
7.4%
길 15
 
4.4%
벌 5
 
1.5%
Other values (28) 54
16.0%
Decimal Number
ValueCountFrequency (%)
1 18
20.0%
2 14
15.6%
3 12
13.3%
7 10
11.1%
9 9
10.0%
4 9
10.0%
6 8
8.9%
0 4
 
4.4%
8 4
 
4.4%
5 2
 
2.2%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 338
65.9%
Common 175
34.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 60
17.8%
대 34
10.1%
달 33
9.8%
서 31
9.2%
광 27
8.0%
역 27
8.0%
시 27
8.0%
로 25
7.4%
길 15
 
4.4%
벌 5
 
1.5%
Other values (28) 54
16.0%
Common
ValueCountFrequency (%)
81
46.3%
1 18
 
10.3%
2 14
 
8.0%
3 12
 
6.9%
7 10
 
5.7%
9 9
 
5.1%
4 9
 
5.1%
6 8
 
4.6%
0 4
 
2.3%
8 4
 
2.3%
Other values (2) 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 338
65.9%
ASCII 175
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
46.3%
1 18
 
10.3%
2 14
 
8.0%
3 12
 
6.9%
7 10
 
5.7%
9 9
 
5.1%
4 9
 
5.1%
6 8
 
4.6%
0 4
 
2.3%
8 4
 
2.3%
Other values (2) 6
 
3.4%
Hangul
ValueCountFrequency (%)
구 60
17.8%
대 34
10.1%
달 33
9.8%
서 31
9.2%
광 27
8.0%
역 27
8.0%
시 27
8.0%
로 25
7.4%
길 15
 
4.4%
벌 5
 
1.5%
Other values (28) 54
16.0%

지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T03:18:06.283550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.037037
Min length16

Characters and Unicode

Total characters514
Distinct characters47
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대구광역시 달서구 본동 225-1
2nd row대구광역시 달서구 본리동 145-2,-5
3rd row대구광역시 달서구 도원동 1432
4th row대구광역시 달서구 두류동 136-6
5th row대구광역시 달서구 상인2동 1472-1
ValueCountFrequency (%)
대구광역시 27
25.5%
달서구 26
24.5%
두류동 3
 
2.8%
감삼동 2
 
1.9%
용산동 2
 
1.9%
진천동 2
 
1.9%
성당동 2
 
1.9%
도원동 2
 
1.9%
241-3 1
 
0.9%
421-1 1
 
0.9%
Other values (38) 38
35.8%
2023-12-13T03:18:06.615857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
15.4%
구 54
 
10.5%
1 32
 
6.2%
대 27
 
5.3%
광 27
 
5.3%
역 27
 
5.3%
시 27
 
5.3%
달 27
 
5.3%
서 26
 
5.1%
동 26
 
5.1%
Other values (37) 162
31.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 296
57.6%
Decimal Number 116
 
22.6%
Space Separator 79
 
15.4%
Dash Punctuation 22
 
4.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 54
18.2%
대 27
9.1%
광 27
9.1%
역 27
9.1%
시 27
9.1%
달 27
9.1%
서 26
8.8%
동 26
8.8%
두 5
 
1.7%
류 5
 
1.7%
Other values (24) 45
15.2%
Decimal Number
ValueCountFrequency (%)
1 32
27.6%
2 16
13.8%
4 15
12.9%
3 12
 
10.3%
5 10
 
8.6%
9 7
 
6.0%
7 7
 
6.0%
6 7
 
6.0%
8 6
 
5.2%
0 4
 
3.4%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 296
57.6%
Common 218
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 54
18.2%
대 27
9.1%
광 27
9.1%
역 27
9.1%
시 27
9.1%
달 27
9.1%
서 26
8.8%
동 26
8.8%
두 5
 
1.7%
류 5
 
1.7%
Other values (24) 45
15.2%
Common
ValueCountFrequency (%)
79
36.2%
1 32
14.7%
- 22
 
10.1%
2 16
 
7.3%
4 15
 
6.9%
3 12
 
5.5%
5 10
 
4.6%
9 7
 
3.2%
7 7
 
3.2%
6 7
 
3.2%
Other values (3) 11
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 296
57.6%
ASCII 218
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
36.2%
1 32
14.7%
- 22
 
10.1%
2 16
 
7.3%
4 15
 
6.9%
3 12
 
5.5%
5 10
 
4.6%
9 7
 
3.2%
7 7
 
3.2%
6 7
 
3.2%
Other values (3) 11
 
5.0%
Hangul
ValueCountFrequency (%)
구 54
18.2%
대 27
9.1%
광 27
9.1%
역 27
9.1%
시 27
9.1%
달 27
9.1%
서 26
8.8%
동 26
8.8%
두 5
 
1.7%
류 5
 
1.7%
Other values (24) 45
15.2%

시장개설주기
Categorical

IMBALANCE 

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
매일
24 
매주 일요일 휴무
 
1
공휴일 휴무
 
1
셋째주 일요일 휴무
 
1

Length

Max length10
Median length2
Mean length2.7037037
Min length2

Unique

Unique3 ?
Unique (%)11.1%

Sample

1st row매주 일요일 휴무
2nd row매일
3rd row매일
4th row매일
5th row매일

Common Values

ValueCountFrequency (%)
매일 24
88.9%
매주 일요일 휴무 1
 
3.7%
공휴일 휴무 1
 
3.7%
셋째주 일요일 휴무 1
 
3.7%

Length

2023-12-13T03:18:06.815586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:18:06.926816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매일 24
75.0%
휴무 3
 
9.4%
일요일 2
 
6.2%
매주 1
 
3.1%
공휴일 1
 
3.1%
셋째주 1
 
3.1%

영업시간
Text

MISSING 

Distinct12
Distinct (%)60.0%
Missing7
Missing (%)25.9%
Memory size348.0 B
2023-12-13T03:18:07.088192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters220
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)40.0%

Sample

1st row10:00~20:00
2nd row09:00~21:00
3rd row08:00~22:00
4th row08:00~21:00
5th row09:00~22:00
ValueCountFrequency (%)
09:00~22:00 5
25.0%
09:00~21:00 3
15.0%
08:00~22:00 2
 
10.0%
08:00~21:00 2
 
10.0%
10:00~20:00 1
 
5.0%
08:00~24:00 1
 
5.0%
08:00~19:00 1
 
5.0%
10:30~20:00 1
 
5.0%
09:00~18:00 1
 
5.0%
07:00~22:00 1
 
5.0%
Other values (2) 2
 
10.0%
2023-12-13T03:18:07.414572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103
46.8%
: 40
 
18.2%
2 26
 
11.8%
~ 20
 
9.1%
9 11
 
5.0%
1 9
 
4.1%
8 8
 
3.6%
4 1
 
0.5%
3 1
 
0.5%
7 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
72.7%
Other Punctuation 40
 
18.2%
Math Symbol 20
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103
64.4%
2 26
 
16.2%
9 11
 
6.9%
1 9
 
5.6%
8 8
 
5.0%
4 1
 
0.6%
3 1
 
0.6%
7 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
: 40
100.0%
Math Symbol
ValueCountFrequency (%)
~ 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103
46.8%
: 40
 
18.2%
2 26
 
11.8%
~ 20
 
9.1%
9 11
 
5.0%
1 9
 
4.1%
8 8
 
3.6%
4 1
 
0.5%
3 1
 
0.5%
7 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103
46.8%
: 40
 
18.2%
2 26
 
11.8%
~ 20
 
9.1%
9 11
 
5.0%
1 9
 
4.1%
8 8
 
3.6%
4 1
 
0.5%
3 1
 
0.5%
7 1
 
0.5%

개설년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)76.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean1988.36
Minimum1905
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:07.550108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1905
5-th percentile1972.2
Q11984
median1988
Q31996
95-th percentile2009.8
Maximum2011
Range106
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.192573
Coefficient of variation (CV)0.010155391
Kurtosis12.444065
Mean1988.36
Median Absolute Deviation (MAD)6
Skewness-2.9925502
Sum49709
Variance407.74
MonotonicityNot monotonic
2023-12-13T03:18:07.677797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1994 3
 
11.1%
1981 2
 
7.4%
1984 2
 
7.4%
1986 2
 
7.4%
1985 2
 
7.4%
1970 1
 
3.7%
1995 1
 
3.7%
2010 1
 
3.7%
1988 1
 
3.7%
2009 1
 
3.7%
Other values (9) 9
33.3%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
1905 1
3.7%
1970 1
3.7%
1981 2
7.4%
1983 1
3.7%
1984 2
7.4%
1985 2
7.4%
1986 2
7.4%
1987 1
3.7%
1988 1
3.7%
1993 1
3.7%
ValueCountFrequency (%)
2011 1
 
3.7%
2010 1
 
3.7%
2009 1
 
3.7%
2006 1
 
3.7%
2005 1
 
3.7%
1997 1
 
3.7%
1996 1
 
3.7%
1995 1
 
3.7%
1994 3
11.1%
1993 1
 
3.7%

점포수
Real number (ℝ)

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.481481
Minimum25
Maximum241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:07.817058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26.6
Q137.5
median64
Q3118.5
95-th percentile192.3
Maximum241
Range216
Interquartile range (IQR)81

Descriptive statistics

Standard deviation58.257631
Coefficient of variation (CV)0.68959055
Kurtosis0.65609862
Mean84.481481
Median Absolute Deviation (MAD)32
Skewness1.1344762
Sum2281
Variance3393.9516
MonotonicityNot monotonic
2023-12-13T03:18:07.987842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
35 2
 
7.4%
42 1
 
3.7%
34 1
 
3.7%
64 1
 
3.7%
50 1
 
3.7%
132 1
 
3.7%
54 1
 
3.7%
112 1
 
3.7%
25 1
 
3.7%
31 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
25 1
3.7%
26 1
3.7%
28 1
3.7%
31 1
3.7%
34 1
3.7%
35 2
7.4%
40 1
3.7%
42 1
3.7%
48 1
3.7%
50 1
3.7%
ValueCountFrequency (%)
241 1
3.7%
195 1
3.7%
186 1
3.7%
144 1
3.7%
140 1
3.7%
132 1
3.7%
124 1
3.7%
113 1
3.7%
112 1
3.7%
96 1
3.7%

빈 점포수
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6296296
Minimum0
Maximum20
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:08.145260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312
95-th percentile17.1
Maximum20
Range20
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.2766242
Coefficient of variation (CV)0.94675337
Kurtosis-0.95673159
Mean6.6296296
Median Absolute Deviation (MAD)3
Skewness0.64632098
Sum179
Variance39.396011
MonotonicityNot monotonic
2023-12-13T03:18:08.307686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 6
22.2%
14 3
11.1%
0 3
11.1%
4 2
 
7.4%
2 2
 
7.4%
12 2
 
7.4%
6 1
 
3.7%
18 1
 
3.7%
11 1
 
3.7%
3 1
 
3.7%
Other values (5) 5
18.5%
ValueCountFrequency (%)
0 3
11.1%
1 6
22.2%
2 2
 
7.4%
3 1
 
3.7%
4 2
 
7.4%
5 1
 
3.7%
6 1
 
3.7%
7 1
 
3.7%
10 1
 
3.7%
11 1
 
3.7%
ValueCountFrequency (%)
20 1
 
3.7%
18 1
 
3.7%
15 1
 
3.7%
14 3
11.1%
12 2
7.4%
11 1
 
3.7%
10 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%
5 1
 
3.7%

취급품목
Text

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T03:18:08.515423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7037037
Min length2

Characters and Unicode

Total characters127
Distinct characters27
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

Unique14 ?
Unique (%)51.9%

Sample

1st row잡화
2nd row농수산물
3rd row식품
4th row식품/미용
5th row식료품/한식
ValueCountFrequency (%)
식품 3
 
11.1%
교육서비스 2
 
7.4%
농수산물 2
 
7.4%
정육/식자재 2
 
7.4%
의류 2
 
7.4%
농산물/정육 2
 
7.4%
식품/의류 1
 
3.7%
잡화 1
 
3.7%
농산물 1
 
3.7%
생필품/잡화 1
 
3.7%
Other values (10) 10
37.0%
2023-12-13T03:18:08.930695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 15
 
11.8%
식 13
 
10.2%
품 11
 
8.7%
산 9
 
7.1%
물 9
 
7.1%
농 8
 
6.3%
육 8
 
6.3%
정 5
 
3.9%
수 4
 
3.1%
잡 4
 
3.1%
Other values (17) 41
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
88.2%
Other Punctuation 15
 
11.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 13
 
11.6%
품 11
 
9.8%
산 9
 
8.0%
물 9
 
8.0%
농 8
 
7.1%
육 8
 
7.1%
정 5
 
4.5%
수 4
 
3.6%
잡 4
 
3.6%
류 4
 
3.6%
Other values (16) 37
33.0%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
88.2%
Common 15
 
11.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 13
 
11.6%
품 11
 
9.8%
산 9
 
8.0%
물 9
 
8.0%
농 8
 
7.1%
육 8
 
7.1%
정 5
 
4.5%
수 4
 
3.6%
잡 4
 
3.6%
류 4
 
3.6%
Other values (16) 37
33.0%
Common
ValueCountFrequency (%)
/ 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
88.2%
ASCII 15
 
11.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 15
100.0%
Hangul
ValueCountFrequency (%)
식 13
 
11.6%
품 11
 
9.8%
산 9
 
8.0%
물 9
 
8.0%
농 8
 
7.1%
육 8
 
7.1%
정 5
 
4.5%
수 4
 
3.6%
잡 4
 
3.6%
류 4
 
3.6%
Other values (16) 37
33.0%

홈페이지 주소
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing23
Missing (%)85.2%
Memory size348.0 B
2023-12-13T03:18:09.115482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length41.5
Mean length42.25
Min length32

Characters and Unicode

Total characters169
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st rowhttps://www.facebook.com/newseonam
2nd rowhttps://blog.naver.com/bobby0691
3rd rowhttp://www.wrmarket.co.kr/m/intro/shop.php?chk_on=0101
4th rowhttps://www.instagram.com/wolchon_station_market/
ValueCountFrequency (%)
https://www.facebook.com/newseonam 1
25.0%
https://blog.naver.com/bobby0691 1
25.0%
http://www.wrmarket.co.kr/m/intro/shop.php?chk_on=0101 1
25.0%
https://www.instagram.com/wolchon_station_market 1
25.0%
2023-12-13T03:18:09.450818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 15
 
8.9%
/ 15
 
8.9%
t 14
 
8.3%
w 12
 
7.1%
. 10
 
5.9%
h 8
 
4.7%
a 8
 
4.7%
n 8
 
4.7%
m 8
 
4.7%
s 7
 
4.1%
Other values (20) 64
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127
75.1%
Other Punctuation 30
 
17.8%
Decimal Number 8
 
4.7%
Connector Punctuation 3
 
1.8%
Math Symbol 1
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 15
11.8%
t 14
11.0%
w 12
 
9.4%
h 8
 
6.3%
a 8
 
6.3%
n 8
 
6.3%
m 8
 
6.3%
s 7
 
5.5%
p 7
 
5.5%
c 7
 
5.5%
Other values (10) 33
26.0%
Other Punctuation
ValueCountFrequency (%)
/ 15
50.0%
. 10
33.3%
: 4
 
13.3%
? 1
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
0 3
37.5%
9 1
 
12.5%
6 1
 
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 127
75.1%
Common 42
 
24.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 15
11.8%
t 14
11.0%
w 12
 
9.4%
h 8
 
6.3%
a 8
 
6.3%
n 8
 
6.3%
m 8
 
6.3%
s 7
 
5.5%
p 7
 
5.5%
c 7
 
5.5%
Other values (10) 33
26.0%
Common
ValueCountFrequency (%)
/ 15
35.7%
. 10
23.8%
: 4
 
9.5%
1 3
 
7.1%
_ 3
 
7.1%
0 3
 
7.1%
? 1
 
2.4%
9 1
 
2.4%
6 1
 
2.4%
= 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 15
 
8.9%
/ 15
 
8.9%
t 14
 
8.3%
w 12
 
7.1%
. 10
 
5.9%
h 8
 
4.7%
a 8
 
4.7%
n 8
 
4.7%
m 8
 
4.7%
s 7
 
4.1%
Other values (20) 64
37.9%

공중화장실 보유여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size159.0 B
True
22 
False
5 
ValueCountFrequency (%)
True 22
81.5%
False 5
 
18.5%
2023-12-13T03:18:09.568676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

주차장 보유여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size159.0 B
True
21 
False
6 
ValueCountFrequency (%)
True 21
77.8%
False 6
 
22.2%
2023-12-13T03:18:09.662657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.53856
Minimum128.49137
Maximum128.57069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:10.145298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.49137
5-th percentile128.50055
Q1128.53113
median128.54424
Q3128.54974
95-th percentile128.5598
Maximum128.57069
Range0.079319
Interquartile range (IQR)0.0186135

Descriptive statistics

Standard deviation0.018314292
Coefficient of variation (CV)0.00014248092
Kurtosis1.1856331
Mean128.53856
Median Absolute Deviation (MAD)0.008995
Skewness-1.0318544
Sum3470.541
Variance0.00033541328
MonotonicityNot monotonic
2023-12-13T03:18:10.298480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
128.547069 1
 
3.7%
128.544242 1
 
3.7%
128.548177 1
 
3.7%
128.531097 1
 
3.7%
128.528026 1
 
3.7%
128.527403 1
 
3.7%
128.531162 1
 
3.7%
128.554575 1
 
3.7%
128.553784 1
 
3.7%
128.551649 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
128.491368 1
3.7%
128.49932 1
3.7%
128.50342 1
3.7%
128.521367 1
3.7%
128.527403 1
3.7%
128.528026 1
3.7%
128.531097 1
3.7%
128.531162 1
3.7%
128.535247 1
3.7%
128.535467 1
3.7%
ValueCountFrequency (%)
128.570687 1
3.7%
128.562043 1
3.7%
128.554575 1
3.7%
128.553784 1
3.7%
128.553772 1
3.7%
128.551649 1
3.7%
128.551309 1
3.7%
128.548177 1
3.7%
128.547069 1
3.7%
128.546228 1
3.7%

위도
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.837275
Minimum35.807578
Maximum35.860598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T03:18:10.489570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.807578
5-th percentile35.808012
Q135.820212
median35.840195
Q335.855468
95-th percentile35.858385
Maximum35.860598
Range0.05302
Interquartile range (IQR)0.0352555

Descriptive statistics

Standard deviation0.019105183
Coefficient of variation (CV)0.00053310926
Kurtosis-1.5493424
Mean35.837275
Median Absolute Deviation (MAD)0.015819
Skewness-0.32035672
Sum967.60641
Variance0.00036500802
MonotonicityNot monotonic
2023-12-13T03:18:10.672439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
35.836834 1
 
3.7%
35.840195 1
 
3.7%
35.822741 1
 
3.7%
35.827537 1
 
3.7%
35.813499 1
 
3.7%
35.814086 1
 
3.7%
35.858048 1
 
3.7%
35.855796 1
 
3.7%
35.854687 1
 
3.7%
35.827077 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
35.807578 1
3.7%
35.80758 1
3.7%
35.809021 1
3.7%
35.811671 1
3.7%
35.813499 1
3.7%
35.814086 1
3.7%
35.817683 1
3.7%
35.822741 1
3.7%
35.822887 1
3.7%
35.827077 1
3.7%
ValueCountFrequency (%)
35.860598 1
3.7%
35.85853 1
3.7%
35.858048 1
3.7%
35.856459 1
3.7%
35.856014 1
3.7%
35.855826 1
3.7%
35.855796 1
3.7%
35.855139 1
3.7%
35.854687 1
3.7%
35.85435 1
3.7%

Interactions

2023-12-13T03:18:04.024382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.464993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.889526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.609360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.013401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.531799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:04.103292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.529912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.967495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.668448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.084996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.609050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:04.179146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.594303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.043520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.731991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.158035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.686095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:04.249949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.662166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.116363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.792668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.233103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.763216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:04.333169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.736347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.210356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.866508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.322317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.851831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:04.421508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:01.817289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.295192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:02.944599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.433896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:18:03.939158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:18:10.819491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드전통시장명도로명주소지번주소시장개설주기영업시간개설년도점포수빈 점포수취급품목홈페이지 주소공중화장실 보유여부주차장 보유여부경도위도
시장코드1.0001.0001.0001.0000.0000.6690.3200.5150.3350.5941.0000.6600.5740.2520.134
전통시장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시장개설주기0.0001.0001.0001.0001.0000.8930.0000.0000.8760.9001.0000.0000.0000.0000.431
영업시간0.6691.0001.0001.0000.8931.0000.8560.4050.7780.7731.0000.8050.5580.7570.737
개설년도0.3201.0001.0001.0000.0000.8561.0000.0000.6570.8301.0000.8840.7330.9400.000
점포수0.5151.0001.0001.0000.0000.4050.0001.0000.0000.0001.0000.3960.0000.0000.262
빈 점포수0.3351.0001.0001.0000.8760.7780.6570.0001.0000.2021.0000.0000.6090.7260.000
취급품목0.5941.0001.0001.0000.9000.7730.8300.0000.2021.0001.0001.0000.4980.7150.695
홈페이지 주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaNNaN1.0001.000
공중화장실 보유여부0.6601.0001.0001.0000.0000.8050.8840.3960.0001.000NaN1.0000.7320.0000.000
주차장 보유여부0.5741.0001.0001.0000.0000.5580.7330.0000.6090.498NaN0.7321.0000.0000.000
경도0.2521.0001.0001.0000.0000.7570.9400.0000.7260.7151.0000.0000.0001.0000.225
위도0.1341.0001.0001.0000.4310.7370.0000.2620.0000.6951.0000.0000.0000.2251.000
2023-12-13T03:18:10.999743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공중화장실 보유여부주차장 보유여부시장개설주기
공중화장실 보유여부1.0000.5220.000
주차장 보유여부0.5221.0000.000
시장개설주기0.0000.0001.000
2023-12-13T03:18:11.115921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드개설년도점포수빈 점포수경도위도시장개설주기공중화장실 보유여부주차장 보유여부
시장코드1.000-0.059-0.1470.038-0.2250.1170.0000.4120.351
개설년도-0.0591.0000.2970.074-0.517-0.1700.0000.7050.565
점포수-0.1470.2971.0000.369-0.262-0.0620.0000.2410.000
빈 점포수0.0380.0740.3691.0000.1320.2220.1730.0000.135
경도-0.225-0.517-0.2620.1321.0000.2280.0000.0000.000
위도0.117-0.170-0.0620.2220.2281.0000.2320.0000.000
시장개설주기0.0000.0000.0000.1730.0000.2321.0000.0000.000
공중화장실 보유여부0.4120.7050.2410.0000.0000.0000.0001.0000.522
주차장 보유여부0.3510.5650.0000.1350.0000.0000.0000.5221.000

Missing values

2023-12-13T03:18:04.539676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:18:04.717665image/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.
2023-12-13T03:18:04.821419image/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

시장코드전통시장명시장유형도로명주소지번주소시장개설주기영업시간개설년도점포수빈 점포수취급품목홈페이지 주소공중화장실 보유여부주차장 보유여부경도위도
01달서송림시장상설대구광역시 달서구 구마로 176대구광역시 달서구 본동 225-1매주 일요일 휴무10:00~20:001983426잡화<NA>YY128.54706935.836834
12달서시장상설대구광역시 달서구 당산로 35대구광역시 달서구 본리동 145-2,-5매일09:00~21:0019862414농수산물<NA>YY128.54424235.840195
23대곡시장상설대구광역시 달서구 도원로 24대구광역시 달서구 도원동 1432매일08:00~22:001997961식품<NA>YY128.53546735.807578
34대구예전우시장상설대구광역시 달서구 야외음악당로47길 111대구광역시 달서구 두류동 136-6매일08:00~21:0020118018식품/미용<NA>YN128.56204335.85853
45대동시장상설대구광역시 달서구 상원로 22-24대구광역시 달서구 상인2동 1472-1매일09:00~22:00198712414식료품/한식<NA>YY128.537835.811671
56도원시장상설대구광역시 달서구 도원로동길 8대구광역시 달서구 도원동 1437-1매일08:00~22:001996591농수산물<NA>YY128.53671935.80758
67두류신시장상설대구광역시 달서구 당산동길 27대구광역시 달서구 성당동 706-21매일<NA>200611311농수산물/정육<NA>NN128.54622835.845281
78두류젊음의광장상설대구광역시 달서구 달구벌대로344길 1대구광역시 달서구 두류동 490-5매일<NA>1905684주류<NA>NN128.55377235.856014
89두류종합시장상설대구광역시 달서구 당산동길 9대구광역시 달서구 성당동 704-5매일08:00~24:001984263의류<NA>YY128.54514435.8455
910보성은하상가상설대구광역시 달서구 월곡로 320대구광역시 달서구 상인1동42매일<NA>1994351교육서비스/미용<NA>YY128.53524735.822887
시장코드전통시장명시장유형도로명주소지번주소시장개설주기영업시간개설년도점포수빈 점포수취급품목홈페이지 주소공중화장실 보유여부주차장 보유여부경도위도
1718성서와룡시장상설대구광역시 달서구 성서서로69길 7대구광역시 달서구 신당동 1788-3공휴일 휴무09:00~18:00199419512정육/식자재http://www.wrmarket.co.kr/m/intro/shop.php?chk_on=0101YY128.4993235.855826
1819성서용산시장상설대구광역시 달서구 성지로 66-1대구광역시 달서구 용산동 421-1매일09:00~21:00200918614농산물/정육<NA>NY128.52136735.855139
1920송경종합시장상설대구광역시 달서구 앞산순환로49길 73대구광역시 달서구 송현1동 1941-3매일07:00~22:0019883110식품<NA>YN128.55164935.827077
2021신내당시장상설대구광역시 달서구 야외음악당로39길 62대구광역시 달서구 두류3동 486-1매일09:00~20:001981255식품/생필품<NA>YY128.55378435.854687
2122신내당시장상점가상설대구광역시 달서구 달구벌대로 1726-16대구광역시 달서구 두류동 489-6매일09:00~22:0019811127정육/식자재<NA>YY128.55457535.855796
2223용산종합큰시장상설대구광역시 달서구 달구벌대로301길 187-11대구광역시 달서구 용산동 933매일09:00~21:002010542농산물/정육<NA>NN128.53116235.858048
2324월배시장상설대구광역시 달서구 월배로24길 13대구광역시 달서구 진천동 241-3매일09:00~22:00198513215농산물/수산물<NA>YY128.52740335.814086
2425월배신시장상설대구광역시 달서구 월배로28안길 54대구광역시 달서구 진천동 239-23매일08:00~20:00<NA>501식품/잡화<NA>NN128.52802635.813499
2526월성청구시장상설대구광역시 달서구 학산로 17대구광역시 달서구 학산로 17매일<NA>1995350생필품/잡화<NA>YY128.53109735.827537
2627월촌역시장(송현주공시장)상설대구광역시 달서구 송현로12안길 38대구광역시 달서구 송현동 1948-3셋째주 일요일 휴무09:00~22:0019866412식자재/잡화https://www.instagram.com/wolchon_station_market/YY128.54817735.822741