Overview

Dataset statistics

Number of variables17
Number of observations28
Missing cells5
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory146.7 B

Variable types

Numeric6
Text6
Categorical3
Boolean2

Dataset

Description대구 북구 28개의 전통시장의 현황정보에 대한 csv 파일이다. 각 시장의 주차장 보유 유무, 주소 등의 정보를 파악 할 수 있다. 대구광역시 북구 전통시장 지도 서비스 : http://opendata1.buk.daegu.kr:8080/market
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15109867/fileData.do

Alerts

전통시장 홈페이지 주소 is highly overall correlated with 점포 수 and 5 other fieldsHigh correlation
시장유형 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
개설년도 is highly overall correlated with 빈 점포 수 and 1 other fieldsHigh correlation
점포 수 is highly overall correlated with 빈 점포 수 and 2 other fieldsHigh correlation
빈 점포 수 is highly overall correlated with 개설년도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 개설년도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시장유형 and 1 other fieldsHigh correlation
시장개설주기 is highly overall correlated with 점포 수 and 1 other fieldsHigh correlation
시장유형 is highly imbalanced (62.9%)Imbalance
시장개설주기 is highly imbalanced (50.0%)Imbalance
공중화장실 보유 여부 is highly imbalanced (77.8%)Imbalance
개설년도 has 3 (10.7%) missing valuesMissing
전통시장 대표 전화번호 has 2 (7.1%) missing valuesMissing
시장코드 has unique valuesUnique
시장명 has unique valuesUnique
점포 수 has unique valuesUnique
취급품목 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
빈 점포 수 has 9 (32.1%) zerosZeros

Reproduction

Analysis started2023-12-12 15:01:38.351426
Analysis finished2023-12-12 15:01:43.431054
Duration5.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장코드
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:43.490312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-13T00:01:43.608562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

시장명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T00:01:43.813414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.1071429
Min length3

Characters and Unicode

Total characters143
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)100.0%

Sample

1st row산격종합시장
2nd row대구능금시장
3rd row대구청과시장
4th row동대구시장
5th row삼성시장
ValueCountFrequency (%)
산격종합시장 1
 
3.6%
대구능금시장 1
 
3.6%
칠성원시장 1
 
3.6%
경명시장 1
 
3.6%
관음시장 1
 
3.6%
복현종합시장 1
 
3.6%
팔달시장 1
 
3.6%
구암시장 1
 
3.6%
칠성상가시장 1
 
3.6%
칠성전자주방시장 1
 
3.6%
Other values (18) 18
64.3%
2023-12-13T00:01:44.148541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 22
 
15.4%
장 22
 
15.4%
전 7
 
4.9%
칠 7
 
4.9%
성 7
 
4.9%
관 6
 
4.2%
구 5
 
3.5%
대 4
 
2.8%
신 3
 
2.1%
자 3
 
2.1%
Other values (42) 57
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 22
 
15.4%
장 22
 
15.4%
전 7
 
4.9%
칠 7
 
4.9%
성 7
 
4.9%
관 6
 
4.2%
구 5
 
3.5%
대 4
 
2.8%
신 3
 
2.1%
자 3
 
2.1%
Other values (42) 57
39.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 22
 
15.4%
장 22
 
15.4%
전 7
 
4.9%
칠 7
 
4.9%
성 7
 
4.9%
관 6
 
4.2%
구 5
 
3.5%
대 4
 
2.8%
신 3
 
2.1%
자 3
 
2.1%
Other values (42) 57
39.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 22
 
15.4%
장 22
 
15.4%
전 7
 
4.9%
칠 7
 
4.9%
성 7
 
4.9%
관 6
 
4.2%
구 5
 
3.5%
대 4
 
2.8%
신 3
 
2.1%
자 3
 
2.1%
Other values (42) 57
39.9%

시장유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
상설
26 
상설+정기
 
2

Length

Max length5
Median length2
Mean length2.2142857
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상설 26
92.9%
상설+정기 2
 
7.1%

Length

2023-12-13T00:01:44.279596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:44.379148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상설 26
92.9%
상설+정기 2
 
7.1%

소재지 도로명 주소
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T00:01:44.554256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length17.392857
Min length15

Characters and Unicode

Total characters487
Distinct characters45
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

Unique26 ?
Unique (%)92.9%

Sample

1st row대구광역시 북구 대동로1길 34
2nd row대구광역시 북구 칠성남로 197-15
3rd row대구광역시 북구 칠성시장로 34
4th row대구광역시 북구 대현로20길 30
5th row대구광역시 북구 칠성시장로 42
ValueCountFrequency (%)
대구광역시 28
25.0%
북구 28
25.0%
유통단지로 6
 
5.4%
칠성시장로 5
 
4.5%
칠성남로 3
 
2.7%
25 2
 
1.8%
경진로1길 2
 
1.8%
팔달로 2
 
1.8%
34 2
 
1.8%
19 2
 
1.8%
Other values (32) 32
28.6%
2023-12-13T00:01:44.924007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
17.2%
구 56
 
11.5%
시 34
 
7.0%
대 32
 
6.6%
로 28
 
5.7%
광 28
 
5.7%
역 28
 
5.7%
북 28
 
5.7%
1 16
 
3.3%
2 12
 
2.5%
Other values (35) 141
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 322
66.1%
Space Separator 84
 
17.2%
Decimal Number 78
 
16.0%
Dash Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
17.4%
시 34
10.6%
대 32
9.9%
로 28
8.7%
광 28
8.7%
역 28
8.7%
북 28
8.7%
칠 11
 
3.4%
성 10
 
3.1%
길 8
 
2.5%
Other values (23) 59
18.3%
Decimal Number
ValueCountFrequency (%)
1 16
20.5%
2 12
15.4%
5 10
12.8%
0 9
11.5%
3 7
9.0%
4 6
 
7.7%
7 5
 
6.4%
9 5
 
6.4%
6 4
 
5.1%
8 4
 
5.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 322
66.1%
Common 165
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
17.4%
시 34
10.6%
대 32
9.9%
로 28
8.7%
광 28
8.7%
역 28
8.7%
북 28
8.7%
칠 11
 
3.4%
성 10
 
3.1%
길 8
 
2.5%
Other values (23) 59
18.3%
Common
ValueCountFrequency (%)
84
50.9%
1 16
 
9.7%
2 12
 
7.3%
5 10
 
6.1%
0 9
 
5.5%
3 7
 
4.2%
4 6
 
3.6%
7 5
 
3.0%
9 5
 
3.0%
6 4
 
2.4%
Other values (2) 7
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 322
66.1%
ASCII 165
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
50.9%
1 16
 
9.7%
2 12
 
7.3%
5 10
 
6.1%
0 9
 
5.5%
3 7
 
4.2%
4 6
 
3.6%
7 5
 
3.0%
9 5
 
3.0%
6 4
 
2.4%
Other values (2) 7
 
4.2%
Hangul
ValueCountFrequency (%)
구 56
17.4%
시 34
10.6%
대 32
9.9%
로 28
8.7%
광 28
8.7%
역 28
8.7%
북 28
8.7%
칠 11
 
3.4%
성 10
 
3.1%
길 8
 
2.5%
Other values (23) 59
18.3%

소재지 지번 주소
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T00:01:45.105255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length18.214286
Min length16

Characters and Unicode

Total characters510
Distinct characters37
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

Unique26 ?
Unique (%)92.9%

Sample

1st row대구광역시 북구 산격동 1258
2nd row대구광역시 북구 칠성동1가 234-9
3rd row대구광역시 북구 칠성동1가 276-102
4th row대구광역시 북구 대현동 390
5th row대구광역시 북구 칠성동1가 276-108
ValueCountFrequency (%)
대구광역시 28
25.0%
북구 28
25.0%
칠성동1가 9
 
8.0%
산격동 6
 
5.4%
대현동 2
 
1.8%
복현동 2
 
1.8%
89 2
 
1.8%
81 1
 
0.9%
구암동 1
 
0.9%
1626 1
 
0.9%
Other values (32) 32
28.6%
2023-12-13T00:01:45.436612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
16.5%
구 57
 
11.2%
1 33
 
6.5%
대 30
 
5.9%
동 28
 
5.5%
광 28
 
5.5%
역 28
 
5.5%
시 28
 
5.5%
북 28
 
5.5%
2 20
 
3.9%
Other values (27) 146
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
57.3%
Decimal Number 121
23.7%
Space Separator 84
 
16.5%
Dash Punctuation 13
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 57
19.5%
대 30
10.3%
동 28
9.6%
광 28
9.6%
역 28
9.6%
시 28
9.6%
북 28
9.6%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (15) 33
11.3%
Decimal Number
ValueCountFrequency (%)
1 33
27.3%
2 20
16.5%
6 18
14.9%
7 9
 
7.4%
8 8
 
6.6%
9 8
 
6.6%
0 7
 
5.8%
5 7
 
5.8%
3 6
 
5.0%
4 5
 
4.1%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
57.3%
Common 218
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 57
19.5%
대 30
10.3%
동 28
9.6%
광 28
9.6%
역 28
9.6%
시 28
9.6%
북 28
9.6%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (15) 33
11.3%
Common
ValueCountFrequency (%)
84
38.5%
1 33
 
15.1%
2 20
 
9.2%
6 18
 
8.3%
- 13
 
6.0%
7 9
 
4.1%
8 8
 
3.7%
9 8
 
3.7%
0 7
 
3.2%
5 7
 
3.2%
Other values (2) 11
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
57.3%
ASCII 218
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
38.5%
1 33
 
15.1%
2 20
 
9.2%
6 18
 
8.3%
- 13
 
6.0%
7 9
 
4.1%
8 8
 
3.7%
9 8
 
3.7%
0 7
 
3.2%
5 7
 
3.2%
Other values (2) 11
 
5.0%
Hangul
ValueCountFrequency (%)
구 57
19.5%
대 30
10.3%
동 28
9.6%
광 28
9.6%
역 28
9.6%
시 28
9.6%
북 28
9.6%
가 12
 
4.1%
성 10
 
3.4%
칠 10
 
3.4%
Other values (15) 33
11.3%

시장개설주기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
매일
22 
매주 일요일 휴무
 
2
셋째주 수요일 휴무
 
2
첫째주, 셋째주 일요일 휴무
 
1
공휴일 휴무
 
1

Length

Max length15
Median length2
Mean length3.6785714
Min length2

Unique

Unique2 ?
Unique (%)7.1%

Sample

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

Common Values

ValueCountFrequency (%)
매일 22
78.6%
매주 일요일 휴무 2
 
7.1%
셋째주 수요일 휴무 2
 
7.1%
첫째주, 셋째주 일요일 휴무 1
 
3.6%
공휴일 휴무 1
 
3.6%

Length

2023-12-13T00:01:45.566482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:45.677265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매일 22
55.0%
휴무 6
 
15.0%
일요일 3
 
7.5%
셋째주 3
 
7.5%
매주 2
 
5.0%
수요일 2
 
5.0%
첫째주 1
 
2.5%
공휴일 1
 
2.5%

영업시간
Text

Distinct18
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T00:01:45.858659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters308
Distinct characters12
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

Unique11 ?
Unique (%)39.3%

Sample

1st row09:00~21:00
2nd row05:00~20:00
3rd row05:00~19:00
4th row09:00~22:00
5th row05:00~20:00
ValueCountFrequency (%)
10:00~20:00 3
 
10.7%
03:00~20:00 3
 
10.7%
07:00~21:00 3
 
10.7%
08:00~19:00 2
 
7.1%
09:00~23:00 2
 
7.1%
09:00~22:00 2
 
7.1%
05:00~20:00 2
 
7.1%
09:00~19:00 1
 
3.6%
06:00~22:00 1
 
3.6%
07:00~17:00 1
 
3.6%
Other values (8) 8
28.6%
2023-12-13T00:01:46.211364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 146
47.4%
: 56
 
18.2%
~ 28
 
9.1%
2 24
 
7.8%
1 18
 
5.8%
9 13
 
4.2%
7 6
 
1.9%
3 6
 
1.9%
8 5
 
1.6%
5 3
 
1.0%
Other values (2) 3
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 224
72.7%
Other Punctuation 56
 
18.2%
Math Symbol 28
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146
65.2%
2 24
 
10.7%
1 18
 
8.0%
9 13
 
5.8%
7 6
 
2.7%
3 6
 
2.7%
8 5
 
2.2%
5 3
 
1.3%
6 2
 
0.9%
4 1
 
0.4%
Other Punctuation
ValueCountFrequency (%)
: 56
100.0%
Math Symbol
ValueCountFrequency (%)
~ 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146
47.4%
: 56
 
18.2%
~ 28
 
9.1%
2 24
 
7.8%
1 18
 
5.8%
9 13
 
4.2%
7 6
 
1.9%
3 6
 
1.9%
8 5
 
1.6%
5 3
 
1.0%
Other values (2) 3
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146
47.4%
: 56
 
18.2%
~ 28
 
9.1%
2 24
 
7.8%
1 18
 
5.8%
9 13
 
4.2%
7 6
 
1.9%
3 6
 
1.9%
8 5
 
1.6%
5 3
 
1.0%
Other values (2) 3
 
1.0%

개설년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)64.0%
Missing3
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean1979.32
Minimum1953
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:46.349214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1953
5-th percentile1955.2
Q11970
median1976
Q31993
95-th percentile1999
Maximum2010
Range57
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.76121
Coefficient of variation (CV)0.007457718
Kurtosis-0.50489388
Mean1979.32
Median Absolute Deviation (MAD)7
Skewness0.18008139
Sum49483
Variance217.89333
MonotonicityNot monotonic
2023-12-13T00:01:46.488637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1974 4
14.3%
1999 3
10.7%
1983 2
 
7.1%
1969 2
 
7.1%
1993 2
 
7.1%
1976 2
 
7.1%
1965 1
 
3.6%
1953 1
 
3.6%
1954 1
 
3.6%
1995 1
 
3.6%
Other values (6) 6
21.4%
(Missing) 3
10.7%
ValueCountFrequency (%)
1953 1
 
3.6%
1954 1
 
3.6%
1960 1
 
3.6%
1965 1
 
3.6%
1969 2
7.1%
1970 1
 
3.6%
1972 1
 
3.6%
1974 4
14.3%
1976 2
7.1%
1981 1
 
3.6%
ValueCountFrequency (%)
2010 1
 
3.6%
1999 3
10.7%
1995 1
 
3.6%
1993 2
7.1%
1988 1
 
3.6%
1983 2
7.1%
1981 1
 
3.6%
1976 2
7.1%
1974 4
14.3%
1972 1
 
3.6%

점포 수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.14286
Minimum40
Maximum1757
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:46.611363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42.4
Q162.5
median77
Q3281.75
95-th percentile910.9
Maximum1757
Range1717
Interquartile range (IQR)219.25

Descriptive statistics

Standard deviation375.06194
Coefficient of variation (CV)1.5237572
Kurtosis9.8380918
Mean246.14286
Median Absolute Deviation (MAD)31.5
Skewness2.9723674
Sum6892
Variance140671.46
MonotonicityNot monotonic
2023-12-13T00:01:46.760276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
165 1
 
3.6%
84 1
 
3.6%
58 1
 
3.6%
119 1
 
3.6%
45 1
 
3.6%
41 1
 
3.6%
265 1
 
3.6%
108 1
 
3.6%
67 1
 
3.6%
70 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
40 1
3.6%
41 1
3.6%
45 1
3.6%
48 1
3.6%
54 1
3.6%
55 1
3.6%
58 1
3.6%
64 1
3.6%
65 1
3.6%
66 1
3.6%
ValueCountFrequency (%)
1757 1
3.6%
1025 1
3.6%
699 1
3.6%
522 1
3.6%
368 1
3.6%
360 1
3.6%
332 1
3.6%
265 1
3.6%
179 1
3.6%
165 1
3.6%

빈 점포 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.892857
Minimum0
Maximum247
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:46.881889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.5
Q317.75
95-th percentile176.95
Maximum247
Range247
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation62.700546
Coefficient of variation (CV)2.1701054
Kurtosis8.7519834
Mean28.892857
Median Absolute Deviation (MAD)7.5
Skewness3.0404679
Sum809
Variance3931.3585
MonotonicityNot monotonic
2023-12-13T00:01:46.994305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
32.1%
1 3
 
10.7%
11 2
 
7.1%
10 2
 
7.1%
43 1
 
3.6%
71 1
 
3.6%
247 1
 
3.6%
16 1
 
3.6%
20 1
 
3.6%
5 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
0 9
32.1%
1 3
 
10.7%
3 1
 
3.6%
5 1
 
3.6%
10 2
 
7.1%
11 2
 
7.1%
13 1
 
3.6%
16 1
 
3.6%
17 1
 
3.6%
20 1
 
3.6%
ValueCountFrequency (%)
247 1
3.6%
234 1
3.6%
71 1
3.6%
67 1
3.6%
43 1
3.6%
28 1
3.6%
20 1
3.6%
17 1
3.6%
16 1
3.6%
13 1
3.6%

취급품목
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-13T00:01:47.185325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.25
Min length2

Characters and Unicode

Total characters175
Distinct characters50
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

Unique28 ?
Unique (%)100.0%

Sample

1st row생필품, 한식
2nd row과일,농산물
3rd row농산물
4th row음식점,수선
5th row주방,야채
ValueCountFrequency (%)
전자제품 4
 
9.5%
잡화 3
 
7.1%
음식점 2
 
4.8%
농산품 2
 
4.8%
반찬류 2
 
4.8%
농수산물 1
 
2.4%
떡 1
 
2.4%
건어물,농수산물 1
 
2.4%
의류 1
 
2.4%
통신제품 1
 
2.4%
Other values (24) 24
57.1%
2023-12-13T00:01:47.554960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 23
 
13.1%
14
 
8.0%
산 12
 
6.9%
품 10
 
5.7%
농 10
 
5.7%
물 10
 
5.7%
식 8
 
4.6%
제 6
 
3.4%
화 5
 
2.9%
잡 5
 
2.9%
Other values (40) 72
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 138
78.9%
Other Punctuation 23
 
13.1%
Space Separator 14
 
8.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 12
 
8.7%
품 10
 
7.2%
농 10
 
7.2%
물 10
 
7.2%
식 8
 
5.8%
제 6
 
4.3%
화 5
 
3.6%
잡 5
 
3.6%
찬 5
 
3.6%
반 5
 
3.6%
Other values (38) 62
44.9%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 138
78.9%
Common 37
 
21.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 12
 
8.7%
품 10
 
7.2%
농 10
 
7.2%
물 10
 
7.2%
식 8
 
5.8%
제 6
 
4.3%
화 5
 
3.6%
잡 5
 
3.6%
찬 5
 
3.6%
반 5
 
3.6%
Other values (38) 62
44.9%
Common
ValueCountFrequency (%)
, 23
62.2%
14
37.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 138
78.9%
ASCII 37
 
21.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 23
62.2%
14
37.8%
Hangul
ValueCountFrequency (%)
산 12
 
8.7%
품 10
 
7.2%
농 10
 
7.2%
물 10
 
7.2%
식 8
 
5.8%
제 6
 
4.3%
화 5
 
3.6%
잡 5
 
3.6%
찬 5
 
3.6%
반 5
 
3.6%
Other values (38) 62
44.9%

전통시장 홈페이지 주소
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
15 
http://www.7stamall.kr
8 
http://dgtool.com
 
1
http://dgemc.com
 
1
http://dgelc.com
 
1
Other values (2)
2 

Length

Max length22
Median length4
Mean length11.5
Min length4

Unique

Unique5 ?
Unique (%)17.9%

Sample

1st row<NA>
2nd rowhttp://www.7stamall.kr
3rd rowhttp://www.7stamall.kr
4th row<NA>
5th rowhttp://www.7stamall.kr

Common Values

ValueCountFrequency (%)
<NA> 15
53.6%
http://www.7stamall.kr 8
28.6%
http://dgtool.com 1
 
3.6%
http://dgemc.com 1
 
3.6%
http://dgelc.com 1
 
3.6%
http://www.ezone.or.kr 1
 
3.6%
http://전자상가.com 1
 
3.6%

Length

2023-12-13T00:01:47.737183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:01:47.872909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
53.6%
http://www.7stamall.kr 8
28.6%
http://dgtool.com 1
 
3.6%
http://dgemc.com 1
 
3.6%
http://dgelc.com 1
 
3.6%
http://www.ezone.or.kr 1
 
3.6%
http://전자상가.com 1
 
3.6%

공중화장실 보유 여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size160.0 B
True
27 
False
 
1
ValueCountFrequency (%)
True 27
96.4%
False 1
 
3.6%
2023-12-13T00:01:48.026266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

주차장 보유 여부
Boolean

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size160.0 B
True
19 
False
9 
ValueCountFrequency (%)
True 19
67.9%
False 9
32.1%
2023-12-13T00:01:48.117183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

전통시장 대표 전화번호
Text

MISSING 

Distinct25
Distinct (%)96.2%
Missing2
Missing (%)7.1%
Memory size356.0 B
2023-12-13T00:01:48.323369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters312
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

Unique24 ?
Unique (%)92.3%

Sample

1st row053-959-8596
2nd row053-424-8510
3rd row053-255-8707
4th row053-939-7721
5th row053-425-4073
ValueCountFrequency (%)
053-939-7721 2
 
7.7%
054-604-2000 1
 
3.8%
053-959-8596 1
 
3.8%
053-604-6114 1
 
3.8%
053-426-0946 1
 
3.8%
053-322-2456 1
 
3.8%
053-665-2651 1
 
3.8%
053-323-9069 1
 
3.8%
053-423-3889 1
 
3.8%
053-425-4572 1
 
3.8%
Other values (15) 15
57.7%
2023-12-13T00:01:48.653359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 49
15.7%
3 41
13.1%
4 24
7.7%
2 21
 
6.7%
6 17
 
5.4%
9 16
 
5.1%
7 15
 
4.8%
1 14
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
83.3%
Dash Punctuation 52
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53
20.4%
5 49
18.8%
3 41
15.8%
4 24
9.2%
2 21
 
8.1%
6 17
 
6.5%
9 16
 
6.2%
7 15
 
5.8%
1 14
 
5.4%
8 10
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 49
15.7%
3 41
13.1%
4 24
7.7%
2 21
 
6.7%
6 17
 
5.4%
9 16
 
5.1%
7 15
 
4.8%
1 14
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53
17.0%
- 52
16.7%
5 49
15.7%
3 41
13.1%
4 24
7.7%
2 21
 
6.7%
6 17
 
5.4%
9 16
 
5.1%
7 15
 
4.8%
1 14
 
4.5%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.895863
Minimum35.875421
Maximum35.944906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:48.805964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.875421
5-th percentile35.875535
Q135.877087
median35.891753
Q335.905895
95-th percentile35.936888
Maximum35.944906
Range0.069485
Interquartile range (IQR)0.02880825

Descriptive statistics

Standard deviation0.020775147
Coefficient of variation (CV)0.0005787616
Kurtosis0.0647031
Mean35.895863
Median Absolute Deviation (MAD)0.0145765
Skewness0.92307586
Sum1005.0842
Variance0.00043160674
MonotonicityNot monotonic
2023-12-13T00:01:48.964384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
35.898616 1
 
3.6%
35.905696 1
 
3.6%
35.893265 1
 
3.6%
35.875421 1
 
3.6%
35.875653 1
 
3.6%
35.944906 1
 
3.6%
35.892579 1
 
3.6%
35.890927 1
 
3.6%
35.926491 1
 
3.6%
35.875886 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
35.875421 1
3.6%
35.875471 1
3.6%
35.875653 1
3.6%
35.875701 1
3.6%
35.875886 1
3.6%
35.876282 1
3.6%
35.87703 1
3.6%
35.877106 1
3.6%
35.877819 1
3.6%
35.878575 1
3.6%
ValueCountFrequency (%)
35.944906 1
3.6%
35.942486 1
3.6%
35.926491 1
3.6%
35.921662 1
3.6%
35.921003 1
3.6%
35.907191 1
3.6%
35.906259 1
3.6%
35.905774 1
3.6%
35.905696 1
3.6%
35.904972 1
3.6%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.59564
Minimum128.54491
Maximum128.61988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-13T00:01:49.125366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.54491
5-th percentile128.54863
Q1128.59982
median128.60424
Q3128.60786
95-th percentile128.6161
Maximum128.61988
Range0.074971
Interquartile range (IQR)0.008038

Descriptive statistics

Standard deviation0.022375373
Coefficient of variation (CV)0.00017399792
Kurtosis0.61538791
Mean128.59564
Median Absolute Deviation (MAD)0.003762
Skewness-1.4355666
Sum3600.6779
Variance0.00050065732
MonotonicityNot monotonic
2023-12-13T00:01:49.262432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
128.610349 1
 
3.6%
128.608275 1
 
3.6%
128.619192 1
 
3.6%
128.603244 1
 
3.6%
128.604036 1
 
3.6%
128.547237 1
 
3.6%
128.619878 1
 
3.6%
128.573872 1
 
3.6%
128.554148 1
 
3.6%
128.605146 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
128.544907 1
3.6%
128.547237 1
3.6%
128.55122 1
3.6%
128.554148 1
3.6%
128.561073 1
3.6%
128.573872 1
3.6%
128.596172 1
3.6%
128.60104 1
3.6%
128.601943 1
3.6%
128.603244 1
3.6%
ValueCountFrequency (%)
128.619878 1
3.6%
128.619192 1
3.6%
128.610349 1
3.6%
128.609784 1
3.6%
128.609613 1
3.6%
128.609091 1
3.6%
128.608275 1
3.6%
128.607723 1
3.6%
128.606993 1
3.6%
128.605909 1
3.6%

Interactions

2023-12-13T00:01:42.040823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.107994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.687307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.348288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.912655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.476996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:42.126844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.185195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.784393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.439524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.989688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.564147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:42.217862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.274482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.886196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.533327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.077091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.649789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:42.672235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.369760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.003769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.625248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.182076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.737072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:42.756774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.470536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.110647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.728952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.259762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.817743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:42.863720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:39.571926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.235757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:40.819155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.371068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:01:41.923336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:01:49.372775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드시장명시장유형소재지 도로명 주소소재지 지번 주소시장개설주기영업시간개설년도점포 수빈 점포 수취급품목전통시장 홈페이지 주소공중화장실 보유 여부주차장 보유 여부전통시장 대표 전화번호위도경도
시장코드1.0001.0000.3550.9320.9320.0000.0000.0000.0000.0001.0000.0000.6270.6270.9160.5860.461
시장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시장유형0.3551.0001.0001.0001.0000.0001.0000.0000.0000.0001.000NaN0.0000.0001.0000.5220.946
소재지 도로명 주소0.9321.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.9881.0001.000
소재지 지번 주소0.9321.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.9881.0001.000
시장개설주기0.0001.0000.0001.0001.0001.0000.9590.0000.6610.3721.0001.0000.0000.0001.0000.5160.000
영업시간0.0001.0001.0001.0001.0000.9591.0000.6080.0000.0001.0000.6051.0000.0000.9790.8720.707
개설년도0.0001.0000.0001.0001.0000.0000.6081.0000.0000.0001.0000.0000.0000.4321.0000.8740.819
점포 수0.0001.0000.0001.0001.0000.6610.0000.0001.0000.8271.0000.9690.0000.1601.0000.0000.333
빈 점포 수0.0001.0000.0001.0001.0000.3720.0000.0000.8271.0001.0001.0000.0000.0001.0000.4450.446
취급품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전통시장 홈페이지 주소0.0001.000NaN1.0001.0001.0000.6050.0000.9691.0001.0001.0000.0000.0001.0001.0000.920
공중화장실 보유 여부0.6271.0000.0001.0001.0000.0001.0000.0000.0000.0001.0000.0001.0000.0001.0000.0000.000
주차장 보유 여부0.6271.0000.0000.0000.0000.0000.0000.4320.1600.0001.0000.0000.0001.0001.0000.3160.512
전통시장 대표 전화번호0.9161.0001.0000.9880.9881.0000.9791.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도0.5861.0000.5221.0001.0000.5160.8720.8740.0000.4451.0001.0000.0000.3161.0001.0000.921
경도0.4611.0000.9461.0001.0000.0000.7070.8190.3330.4461.0000.9200.0000.5121.0000.9211.000
2023-12-13T00:01:49.549828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장개설주기주차장 보유 여부전통시장 홈페이지 주소시장유형공중화장실 보유 여부
시장개설주기1.0000.0000.8820.0000.000
주차장 보유 여부0.0001.0000.0000.0000.000
전통시장 홈페이지 주소0.8820.0001.0001.0000.000
시장유형0.0000.0001.0001.0000.000
공중화장실 보유 여부0.0000.0000.0000.0001.000
2023-12-13T00:01:49.656372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드개설년도점포 수빈 점포 수위도경도시장유형시장개설주기전통시장 홈페이지 주소공중화장실 보유 여부주차장 보유 여부
시장코드1.0000.232-0.0850.1110.0070.0180.2000.0000.0000.3920.392
개설년도0.2321.0000.3550.5810.6280.2200.0000.0000.0000.0000.288
점포 수-0.0850.3551.0000.5230.1570.3350.0000.5050.7740.0000.067
빈 점포 수0.1110.5810.5231.0000.6650.2360.0000.2960.8370.0000.000
위도0.0070.6280.1570.6651.000-0.1410.4990.3360.7980.0000.292
경도0.0180.2200.3350.236-0.1411.0000.6300.0000.5840.0000.312
시장유형0.2000.0000.0000.0000.4990.6301.0000.0001.0000.0000.000
시장개설주기0.0000.0000.5050.2960.3360.0000.0001.0000.8820.0000.000
전통시장 홈페이지 주소0.0000.0000.7740.8370.7980.5841.0000.8821.0000.0000.000
공중화장실 보유 여부0.3920.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
주차장 보유 여부0.3920.2880.0670.0000.2920.3120.0000.0000.0000.0001.000

Missing values

2023-12-13T00:01:43.023158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:01:43.220722image/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.
2023-12-13T00:01:43.362926image/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

시장코드시장명시장유형소재지 도로명 주소소재지 지번 주소시장개설주기영업시간개설년도점포 수빈 점포 수취급품목전통시장 홈페이지 주소공중화장실 보유 여부주차장 보유 여부전통시장 대표 전화번호위도경도
01산격종합시장상설대구광역시 북구 대동로1길 34대구광역시 북구 산격동 1258매일09:00~21:00198116543생필품, 한식<NA>YN053-959-859635.898616128.610349
12대구능금시장상설대구광역시 북구 칠성남로 197-15대구광역시 북구 칠성동1가 234-9매일05:00~20:0019541790과일,농산물http://www.7stamall.krYY053-424-851035.877106128.601943
23대구청과시장상설대구광역시 북구 칠성시장로 34대구광역시 북구 칠성동1가 276-102매일05:00~19:001970650농산물http://www.7stamall.krYY053-255-870735.87703128.603913
34동대구시장상설대구광역시 북구 대현로20길 30대구광역시 북구 대현동 390매일09:00~22:001960481음식점,수선<NA>YY053-939-772135.881079128.609091
45삼성시장상설대구광역시 북구 칠성시장로 42대구광역시 북구 칠성동1가 276-108매일05:00~20:001976550주방,야채http://www.7stamall.krYN053-425-407335.877819128.60348
56칠성시장상설대구광역시 북구 칠성남로 229대구광역시 북구 칠성동1가 276-163매일03:00~20:0019743320농축산물, 수산물http://www.7stamall.krYY053-423-348035.876282128.605427
67팔달신시장상설대구광역시 북구 팔달로 105대구광역시 북구 노원동3가 252-3매일03:00~20:00197652267농산물,한식<NA>YY053-357-495635.889841128.561073
78태전중앙시장상설대구광역시 북구 태전로 15대구광역시 북구 태전동 156-1첫째주, 셋째주 일요일 휴무10:20~18:00<NA>5413반찬류<NA>YY053-311-580335.921662128.544907
89칠곡시장상설+정기대구광역시 북구 칠곡중앙대로 526대구광역시 북구 읍내동 1201-1매일06:00~21:0019656811농산물,반찬<NA>YY053-321-807035.942486128.55122
910서변중앙시장상설+정기대구광역시 북구 호국로43길 27대구광역시 북구 서변동 1751매일08:00~22:00<NA>661반찬,잡화<NA>YN053-953-059035.921003128.596172
시장코드시장명시장유형소재지 도로명 주소소재지 지번 주소시장개설주기영업시간개설년도점포 수빈 점포 수취급품목전통시장 홈페이지 주소공중화장실 보유 여부주차장 보유 여부전통시장 대표 전화번호위도경도
1819칠성본시장상설대구광역시 북구 칠성시장로 15-1대구광역시 북구 칠성동1가 97-1매일07:00~17:001974640분식, 농산품http://www.7stamall.krNY053-253-770535.875471128.604419
1920칠성전자주방시장상설대구광역시 북구 칠성로 107대구광역시 북구 칠성동2가 409-71매일06:00~22:001974690가전제품, 주방용품http://www.7stamall.krYY053-425-457235.878575128.60104
2021칠성상가시장상설대구광역시 북구 칠성남로 230대구광역시 북구 칠성동1가 150-2매일03:00~20:001974705수산, 잡화<NA>YY053-423-388935.875886128.605146
2122구암시장상설대구광역시 북구 팔거천동로4길 25대구광역시 북구 구암동 693-2매일07:00~21:0020106720잡화, 반찬류<NA>YN053-323-906935.926491128.554148
2223팔달시장상설대구광역시 북구 팔달로 179-3대구광역시 북구 노원동2가 1매일07:00~21:00196910816의류, 음식점<NA>YN053-665-265135.890927128.573872
2324복현종합시장상설대구광역시 북구 경진로1길 78대구광역시 북구 복현동 462매일09:00~23:001983265247떡, 음식점<NA>YY<NA>35.892579128.619878
2425관음시장상설대구광역시 북구 관음중앙로28길 1대구광역시 북구 관음동 1284매일09:00~22:0019884110농수산물<NA>YN053-322-245635.944906128.547237
2526경명시장상설대구광역시 북구 칠성시장로 19대구광역시 북구 칠성동1가 89매일08:00~19:001972450건어물,농수산물<NA>YN053-426-094635.875653128.604036
2627칠성원시장상설대구광역시 북구 칠성시장로5길 22대구광역시 북구 칠성동1가 81매일04:00~19:0019691190농산물,잡화http://www.7stamall.krYN053-425-789435.875421128.603244
2728복현전통신시장상설대구광역시 북구 경진로1길 68대구광역시 북구 복현동 457-8매일09:00~23:0019835811식사류, 카페<NA>YN<NA>35.893265128.619192