Overview

Dataset statistics

Number of variables12
Number of observations397
Missing cells871
Missing cells (%)18.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.1 KiB
Average record size in memory98.3 B

Variable types

Unsupported6
Text3
Numeric1
Categorical2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1176/S/1/datasetView.do

Alerts

Unnamed: 2 is highly overall correlated with Unnamed: 4High correlation
Unnamed: 4 is highly overall correlated with Unnamed: 2High correlation
Unnamed: 0 has 397 (100.0%) missing valuesMissing
Unnamed: 1 has 393 (99.0%) missing valuesMissing
Unnamed: 2 has 29 (7.3%) missing valuesMissing
Unnamed: 9 has 33 (8.3%) missing valuesMissing
Unnamed: 10 has 11 (2.8%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
서울시 자치구별 전통시장 현황(2022년 상반기) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 06:40:34.497062
Analysis finished2023-12-11 06:40:35.720050
Duration1.22 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing397
Missing (%)100.0%
Memory size3.6 KiB

Unnamed: 1
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing393
Missing (%)99.0%
Memory size3.2 KiB
2023-12-11T15:40:35.820546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length11.25
Min length7

Characters and Unicode

Total characters45
Distinct characters29
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

Unique4 ?
Unique (%)100.0%

Sample

1st row도시재개발지구
2nd row2019.1월~(재개발중)
3rd row재개발로 시장기능 상실
4th row재정비사업(2016~)
ValueCountFrequency (%)
도시재개발지구 1
16.7%
2019.1월~(재개발중 1
16.7%
재개발로 1
16.7%
시장기능 1
16.7%
상실 1
16.7%
재정비사업(2016 1
16.7%
2023-12-11T15:40:36.170422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
재 4
 
8.9%
1 3
 
6.7%
개 3
 
6.7%
발 3
 
6.7%
0 2
 
4.4%
) 2
 
4.4%
시 2
 
4.4%
~ 2
 
4.4%
( 2
 
4.4%
2 2
 
4.4%
Other values (19) 20
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27
60.0%
Decimal Number 9
 
20.0%
Close Punctuation 2
 
4.4%
Math Symbol 2
 
4.4%
Open Punctuation 2
 
4.4%
Space Separator 2
 
4.4%
Other Punctuation 1
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 4
14.8%
개 3
 
11.1%
발 3
 
11.1%
시 2
 
7.4%
기 1
 
3.7%
비 1
 
3.7%
정 1
 
3.7%
실 1
 
3.7%
사 1
 
3.7%
상 1
 
3.7%
Other values (9) 9
33.3%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
0 2
22.2%
2 2
22.2%
9 1
 
11.1%
6 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27
60.0%
Common 18
40.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 4
14.8%
개 3
 
11.1%
발 3
 
11.1%
시 2
 
7.4%
기 1
 
3.7%
비 1
 
3.7%
정 1
 
3.7%
실 1
 
3.7%
사 1
 
3.7%
상 1
 
3.7%
Other values (9) 9
33.3%
Common
ValueCountFrequency (%)
1 3
16.7%
0 2
11.1%
) 2
11.1%
~ 2
11.1%
( 2
11.1%
2 2
11.1%
2
11.1%
. 1
 
5.6%
9 1
 
5.6%
6 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27
60.0%
ASCII 18
40.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
재 4
14.8%
개 3
 
11.1%
발 3
 
11.1%
시 2
 
7.4%
기 1
 
3.7%
비 1
 
3.7%
정 1
 
3.7%
실 1
 
3.7%
사 1
 
3.7%
상 1
 
3.7%
Other values (9) 9
33.3%
ASCII
ValueCountFrequency (%)
1 3
16.7%
0 2
11.1%
) 2
11.1%
~ 2
11.1%
( 2
11.1%
2 2
11.1%
2
11.1%
. 1
 
5.6%
9 1
 
5.6%
6 1
 
5.6%

Unnamed: 2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)11.4%
Missing29
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean11.698913
Minimum1.1
Maximum25.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-11T15:40:36.370067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.1
Q15.1
median11.55
Q319.025
95-th percentile23.1
Maximum25.1
Range24
Interquartile range (IQR)13.925

Descriptive statistics

Standard deviation7.3503674
Coefficient of variation (CV)0.6282949
Kurtosis-1.2573817
Mean11.698913
Median Absolute Deviation (MAD)6.45
Skewness0.12325349
Sum4305.2
Variance54.027901
MonotonicityNot monotonic
2023-12-11T15:40:36.568089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2.2 39
 
9.8%
1.1 27
 
6.8%
19.1 21
 
5.3%
21.1 20
 
5.0%
6.1 19
 
4.8%
9.1 18
 
4.5%
14.1 15
 
3.8%
15.1 15
 
3.8%
7.1 15
 
3.8%
5.1 15
 
3.8%
Other values (32) 164
41.3%
(Missing) 29
 
7.3%
ValueCountFrequency (%)
1.1 27
6.8%
2.2 39
9.8%
3.1 7
 
1.8%
4.1 9
 
2.3%
5.1 15
 
3.8%
6.1 19
4.8%
7.1 15
 
3.8%
8.1 10
 
2.5%
9.0 9
 
2.3%
9.1 18
4.5%
ValueCountFrequency (%)
25.1 8
 
2.0%
25.0 1
 
0.3%
24.1 7
 
1.8%
24.0 1
 
0.3%
23.1 10
2.5%
23.0 1
 
0.3%
22.1 6
 
1.5%
22.0 1
 
0.3%
21.1 20
5.0%
21.0 1
 
0.3%

서울시 자치구별 전통시장 현황(2022년 상반기)
Unsupported

REJECTED  UNSUPPORTED 

Missing2
Missing (%)0.5%
Memory size3.2 KiB

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
중구
44 
종로구
28 
영등포구
 
24
관악구
 
24
동대문
 
21
Other values (22)
256 

Length

Max length4
Median length3
Mean length3.209068
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
중구 44
 
11.1%
종로구 28
 
7.1%
영등포구 24
 
6.0%
관악구 24
 
6.0%
동대문 21
 
5.3%
강북구 20
 
5.0%
양천구 18
 
4.5%
동작구 17
 
4.3%
마포구 17
 
4.3%
은평구 16
 
4.0%
Other values (17) 168
42.3%

Length

2023-12-11T15:40:36.743497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 44
 
11.1%
종로구 28
 
7.1%
영등포구 24
 
6.0%
관악구 24
 
6.0%
동대문 21
 
5.3%
강북구 20
 
5.0%
양천구 18
 
4.5%
동작구 17
 
4.3%
마포구 17
 
4.3%
은평구 16
 
4.0%
Other values (17) 168
42.3%

Unnamed: 5
Unsupported

REJECTED  UNSUPPORTED 

Missing2
Missing (%)0.5%
Memory size3.2 KiB

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.2 KiB

Unnamed: 7
Categorical

Distinct8
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
골목형
153 
건물형
142 
상점가
51 
25 
지하도상가
22 
Other values (3)
 
4

Length

Max length5
Median length3
Mean length2.9899244
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
골목형 153
38.5%
건물형 142
35.8%
상점가 51
 
12.8%
25
 
6.3%
지하도상가 22
 
5.5%
<NA> 2
 
0.5%
형태 1
 
0.3%
혼합형 1
 
0.3%

Length

2023-12-11T15:40:36.909489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:40:37.058952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목형 153
41.1%
건물형 142
38.2%
상점가 51
 
13.7%
지하도상가 22
 
5.9%
na 2
 
0.5%
형태 1
 
0.3%
혼합형 1
 
0.3%

Unnamed: 8
Text

Distinct369
Distinct (%)93.4%
Missing2
Missing (%)0.5%
Memory size3.2 KiB
2023-12-11T15:40:37.380143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length133
Median length54
Mean length31.086076
Min length1

Characters and Unicode

Total characters12279
Distinct characters276
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique366 ?
Unique (%)92.7%

Sample

1st row신주소 (구주소)
2nd row
3rd row종로구 창경궁로 88 (종로구 예지동 6-1)
4th row종로구 종로 266 (종로구 종로6가 289-42)
5th row종로구 종로272 (종로구 종로6가 289-57)
ValueCountFrequency (%)
종로구 54
 
2.8%
일대 44
 
2.3%
중구 44
 
2.3%
영등포구 23
 
1.2%
관악구 22
 
1.2%
동대문구 20
 
1.0%
강북구 18
 
0.9%
양천구 17
 
0.9%
광진구 15
 
0.8%
중랑구 15
 
0.8%
Other values (1070) 1638
85.8%
2023-12-11T15:40:37.918985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3741
30.5%
1 599
 
4.9%
로 440
 
3.6%
동 427
 
3.5%
2 425
 
3.5%
구 410
 
3.3%
( 358
 
2.9%
) 357
 
2.9%
3 327
 
2.7%
- 321
 
2.6%
Other values (266) 4874
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4231
34.5%
Space Separator 3741
30.5%
Decimal Number 2856
23.3%
Open Punctuation 365
 
3.0%
Close Punctuation 363
 
3.0%
Dash Punctuation 321
 
2.6%
Control 312
 
2.5%
Other Punctuation 53
 
0.4%
Math Symbol 22
 
0.2%
Uppercase Letter 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 440
 
10.4%
동 427
 
10.1%
구 410
 
9.7%
길 253
 
6.0%
대 137
 
3.2%
일 89
 
2.1%
중 83
 
2.0%
종 73
 
1.7%
포 68
 
1.6%
가 67
 
1.6%
Other values (238) 2184
51.6%
Decimal Number
ValueCountFrequency (%)
1 599
21.0%
2 425
14.9%
3 327
11.4%
4 267
9.3%
5 247
8.6%
6 237
 
8.3%
7 204
 
7.1%
0 190
 
6.7%
8 185
 
6.5%
9 175
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
25.0%
C 1
12.5%
A 1
12.5%
T 1
12.5%
K 1
12.5%
S 1
12.5%
G 1
12.5%
Open Punctuation
ValueCountFrequency (%)
( 358
98.1%
[ 7
 
1.9%
Close Punctuation
ValueCountFrequency (%)
) 357
98.3%
] 6
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 51
96.2%
/ 2
 
3.8%
Space Separator
ValueCountFrequency (%)
3741
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 321
100.0%
Control
ValueCountFrequency (%)
312
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8033
65.4%
Hangul 4231
34.5%
Latin 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 440
 
10.4%
동 427
 
10.1%
구 410
 
9.7%
길 253
 
6.0%
대 137
 
3.2%
일 89
 
2.1%
중 83
 
2.0%
종 73
 
1.7%
포 68
 
1.6%
가 67
 
1.6%
Other values (238) 2184
51.6%
Common
ValueCountFrequency (%)
3741
46.6%
1 599
 
7.5%
2 425
 
5.3%
( 358
 
4.5%
) 357
 
4.4%
3 327
 
4.1%
- 321
 
4.0%
312
 
3.9%
4 267
 
3.3%
5 247
 
3.1%
Other values (10) 1079
 
13.4%
Latin
ValueCountFrequency (%)
m 7
46.7%
B 2
 
13.3%
C 1
 
6.7%
A 1
 
6.7%
T 1
 
6.7%
K 1
 
6.7%
S 1
 
6.7%
G 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8048
65.5%
Hangul 4231
34.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3741
46.5%
1 599
 
7.4%
2 425
 
5.3%
( 358
 
4.4%
) 357
 
4.4%
3 327
 
4.1%
- 321
 
4.0%
312
 
3.9%
4 267
 
3.3%
5 247
 
3.1%
Other values (18) 1094
 
13.6%
Hangul
ValueCountFrequency (%)
로 440
 
10.4%
동 427
 
10.1%
구 410
 
9.7%
길 253
 
6.0%
대 137
 
3.2%
일 89
 
2.1%
중 83
 
2.0%
종 73
 
1.7%
포 68
 
1.6%
가 67
 
1.6%
Other values (238) 2184
51.6%

Unnamed: 9
Text

MISSING 

Distinct300
Distinct (%)82.4%
Missing33
Missing (%)8.3%
Memory size3.2 KiB
2023-12-11T15:40:38.265636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length7.6648352
Min length1

Characters and Unicode

Total characters2790
Distinct characters28
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique295 ?
Unique (%)81.0%

Sample

1st row
2nd row전화번호
3rd row
4th row2272-0967
5th row2262-0211
ValueCountFrequency (%)
535-8182 3
 
1.0%
3
 
1.0%
2232-9559 2
 
0.6%
989-4730 2
 
0.6%
325-7447 1
 
0.3%
2693-1241 1
 
0.3%
2698-6057 1
 
0.3%
2644-3238 1
 
0.3%
2647-9191 1
 
0.3%
2693-9686 1
 
0.3%
Other values (293) 293
94.8%
2023-12-11T15:40:38.792276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 367
13.2%
- 349
12.5%
0 256
9.2%
5 248
8.9%
3 244
8.7%
6 228
8.2%
4 223
8.0%
7 205
7.3%
9 199
7.1%
1 190
6.8%
Other values (18) 281
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2346
84.1%
Dash Punctuation 349
 
12.5%
Space Separator 72
 
2.6%
Other Letter 9
 
0.3%
Control 4
 
0.1%
Math Symbol 3
 
0.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 367
15.6%
0 256
10.9%
5 248
10.6%
3 244
10.4%
6 228
9.7%
4 223
9.5%
7 205
8.7%
9 199
8.5%
1 190
8.1%
8 186
7.9%
Other Letter
ValueCountFrequency (%)
동 2
22.2%
중 1
11.1%
화 1
11.1%
설 1
11.1%
개 1
11.1%
호 1
11.1%
번 1
11.1%
전 1
11.1%
Space Separator
ValueCountFrequency (%)
71
98.6%
  1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 349
100.0%
Control
ValueCountFrequency (%)
4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2779
99.6%
Hangul 9
 
0.3%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 367
13.2%
- 349
12.6%
0 256
9.2%
5 248
8.9%
3 244
8.8%
6 228
8.2%
4 223
8.0%
7 205
7.4%
9 199
7.2%
1 190
6.8%
Other values (8) 270
9.7%
Hangul
ValueCountFrequency (%)
동 2
22.2%
중 1
11.1%
화 1
11.1%
설 1
11.1%
개 1
11.1%
호 1
11.1%
번 1
11.1%
전 1
11.1%
Latin
ValueCountFrequency (%)
C 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2780
99.6%
Hangul 9
 
0.3%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 367
13.2%
- 349
12.6%
0 256
9.2%
5 248
8.9%
3 244
8.8%
6 228
8.2%
4 223
8.0%
7 205
7.4%
9 199
7.2%
1 190
6.8%
Other values (9) 271
9.7%
Hangul
ValueCountFrequency (%)
동 2
22.2%
중 1
11.1%
화 1
11.1%
설 1
11.1%
개 1
11.1%
호 1
11.1%
번 1
11.1%
전 1
11.1%
None
ValueCountFrequency (%)
  1
100.0%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11
Missing (%)2.8%
Memory size3.2 KiB

Unnamed: 11
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.3%
Memory size3.2 KiB

Interactions

2023-12-11T15:40:35.083874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:40:38.909280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 4Unnamed: 7
Unnamed: 11.0001.0001.0001.000
Unnamed: 21.0001.0000.9990.404
Unnamed: 41.0000.9991.0000.771
Unnamed: 71.0000.4040.7711.000
2023-12-11T15:40:39.014873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 7Unnamed: 4
Unnamed: 71.0000.452
Unnamed: 40.4521.000
2023-12-11T15:40:39.101831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 4Unnamed: 7
Unnamed: 21.0000.9560.230
Unnamed: 40.9561.0000.452
Unnamed: 70.2300.4521.000

Missing values

2023-12-11T15:40:35.224755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:40:35.403594image/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.
2023-12-11T15:40:35.594844image/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

Unnamed: 0Unnamed: 1Unnamed: 2서울시 자치구별 전통시장 현황(2022년 상반기)Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
0<NA><NA><NA>NaN<NA>NaN시장별 기본 현황<NA><NA><NA>면적 및 점포NaN
1<NA><NA><NA>NaN<NA>NaNNaN<NA><NA>면적(㎡)점포 상세현황
2<NA><NA><NA>정렬자치구연번시장명형태신주소 (구주소)전화번호건물형\n(연면적)점포수\n(빈점포제외)
3<NA><NA>9.0총종로구계271004
4<NA><NA>1.11종로구1광장시장건물형종로구 창경궁로 88 (종로구 예지동 6-1)2272-096718975723
5<NA><NA>1.11종로구2동대문종합시장건물형종로구 종로 266 (종로구 종로6가 289-42)2262-0211614763287
6<NA><NA>1.11종로구3동대문종합시장 신관건물형종로구 종로272 (종로구 종로6가 289-57)13533290
7<NA><NA>1.11종로구4동대문종합시장D동상가건물형종로구 종로 266 (종로구 종로6가 262-1)2279-8751~2124521048
8<NA><NA>1.11종로구5신설종합시장건물형종로구 난계로27길 51 (종로구 숭인동 206-9)2234-71511691.18141
9<NA><NA>1.11종로구6종각지하쇼핑센터지하도상가종로구 종로 지하 73 (종로구 종로2가 11-1 일대)733-0330472369
Unnamed: 0Unnamed: 1Unnamed: 2서울시 자치구별 전통시장 현황(2022년 상반기)Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11
387<NA><NA>25.0총강동구계9999
388<NA><NA>25.125강동구1암사종합시장골목형강동구 성암로11길 25 (암사동 501-17)442-104017394113
389<NA><NA>25.125강동구2길동복조리시장골목형강동구 천호대로187길 61 (길동 397-3)070-4896-25442050144
390<NA><NA>25.125강동구3둔촌역전통시장골목형강동구 풍성로58길 34 (성내동 428-9)<NA>395588
391<NA><NA>25.125강동구4성내전통시장골목형강동구 천호대로162길 65 (성내동 144-16)475-8272270076
392<NA><NA>25.125강동구5명일전통시장골목형강동구 양재대로138길 21 (명일동 326-11)475-7095186260
393<NA><NA>25.125강동구6고분다리전통시장골목형강동구 구천면로34길 10 (천호동 393-33)6227-0045146885
394<NA><NA>25.125강동구7로데오거리상점가상점가강동구 천호대로157길 14 (천호동 454-50)478-71566273159
395<NA><NA>25.125강동구8장신구 특화거리 상점가상점가강동구 성내로 83 (성내동 545-6)475-91403366170
396<NA><NA><NA>25강동구9고덕 골목형상점가상점가강동구 고덕로83길 18일대429-13766699104