Overview

Dataset statistics

Number of variables14
Number of observations1812
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory216.0 KiB
Average record size in memory122.1 B

Variable types

Numeric10
Text3
Categorical1

Dataset

Description전통시장 실태조사 기준 전통시장 데이터로 전통시장 면적, 전체점포 수, 빈점포, 기타점포, 노점수와 함께 점포상인 수, 종업원 수, 노점상인 수의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15117652/fileData.do

Alerts

순번 is highly overall correlated with 시장면적 and 4 other fieldsHigh correlation
시장면적 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
전체점포 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
노점수 is highly overall correlated with 노점상인High correlation
점포상인 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
종업원 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
노점상인 is highly overall correlated with 노점수High correlation
총시장상인 is highly overall correlated with 순번 and 4 other fieldsHigh correlation
순번 has unique valuesUnique
빈점포 has 563 (31.1%) zerosZeros
기타점포 has 1274 (70.3%) zerosZeros
노점수 has 910 (50.2%) zerosZeros
종업원 has 234 (12.9%) zerosZeros
노점상인 has 910 (50.2%) zerosZeros

Reproduction

Analysis started2023-12-12 21:08:40.556151
Analysis finished2023-12-12 21:08:51.800270
Duration11.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1812
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean906.5
Minimum1
Maximum1812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:52.135654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile91.55
Q1453.75
median906.5
Q31359.25
95-th percentile1721.45
Maximum1812
Range1811
Interquartile range (IQR)905.5

Descriptive statistics

Standard deviation523.22366
Coefficient of variation (CV)0.57719102
Kurtosis-1.2
Mean906.5
Median Absolute Deviation (MAD)453
Skewness0
Sum1642578
Variance273763
MonotonicityStrictly increasing
2023-12-13T06:08:52.298073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1192 1
 
0.1%
1218 1
 
0.1%
1217 1
 
0.1%
1216 1
 
0.1%
1215 1
 
0.1%
1214 1
 
0.1%
1213 1
 
0.1%
1212 1
 
0.1%
1211 1
 
0.1%
Other values (1802) 1802
99.4%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1812 1
0.1%
1811 1
0.1%
1810 1
0.1%
1809 1
0.1%
1808 1
0.1%
1807 1
0.1%
1806 1
0.1%
1805 1
0.1%
1804 1
0.1%
1803 1
0.1%
Distinct1769
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2023-12-13T06:08:52.539569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.5
Min length2

Characters and Unicode

Total characters11778
Distinct characters435
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1738 ?
Unique (%)95.9%

Sample

1st row서울남대문시장
2nd row인천산업용품유통센터
3rd row동대문종합시장
4th row르네시떼
5th row평화시장
ValueCountFrequency (%)
상점가 46
 
2.2%
상인회 15
 
0.7%
중앙시장 14
 
0.7%
골목형 11
 
0.5%
전통시장 11
 
0.5%
시장 10
 
0.5%
골목형상점가 8
 
0.4%
거리 8
 
0.4%
종합시장 7
 
0.3%
지하상가 6
 
0.3%
Other values (1857) 1951
93.5%
2023-12-13T06:08:52.985224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1386
 
11.8%
1365
 
11.6%
527
 
4.5%
473
 
4.0%
275
 
2.3%
237
 
2.0%
224
 
1.9%
206
 
1.7%
193
 
1.6%
179
 
1.5%
Other values (425) 6713
57.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11326
96.2%
Space Separator 275
 
2.3%
Decimal Number 85
 
0.7%
Close Punctuation 40
 
0.3%
Open Punctuation 38
 
0.3%
Uppercase Letter 7
 
0.1%
Lowercase Letter 4
 
< 0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1386
 
12.2%
1365
 
12.1%
527
 
4.7%
473
 
4.2%
237
 
2.1%
224
 
2.0%
206
 
1.8%
193
 
1.7%
179
 
1.6%
149
 
1.3%
Other values (402) 6387
56.4%
Decimal Number
ValueCountFrequency (%)
5 32
37.6%
1 18
21.2%
2 13
15.3%
3 10
 
11.8%
4 5
 
5.9%
6 3
 
3.5%
7 2
 
2.4%
9 1
 
1.2%
0 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
T 1
14.3%
D 1
14.3%
A 1
14.3%
S 1
14.3%
K 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
s 1
25.0%
m 1
25.0%
a 1
25.0%
e 1
25.0%
Space Separator
ValueCountFrequency (%)
275
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11326
96.2%
Common 441
 
3.7%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1386
 
12.2%
1365
 
12.1%
527
 
4.7%
473
 
4.2%
237
 
2.1%
224
 
2.0%
206
 
1.8%
193
 
1.7%
179
 
1.6%
149
 
1.3%
Other values (402) 6387
56.4%
Common
ValueCountFrequency (%)
275
62.4%
) 40
 
9.1%
( 38
 
8.6%
5 32
 
7.3%
1 18
 
4.1%
2 13
 
2.9%
3 10
 
2.3%
4 5
 
1.1%
- 3
 
0.7%
6 3
 
0.7%
Other values (3) 4
 
0.9%
Latin
ValueCountFrequency (%)
B 2
18.2%
s 1
9.1%
m 1
9.1%
a 1
9.1%
e 1
9.1%
T 1
9.1%
D 1
9.1%
A 1
9.1%
S 1
9.1%
K 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11326
96.2%
ASCII 452
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1386
 
12.2%
1365
 
12.1%
527
 
4.7%
473
 
4.2%
237
 
2.1%
224
 
2.0%
206
 
1.8%
193
 
1.7%
179
 
1.6%
149
 
1.3%
Other values (402) 6387
56.4%
ASCII
ValueCountFrequency (%)
275
60.8%
) 40
 
8.8%
( 38
 
8.4%
5 32
 
7.1%
1 18
 
4.0%
2 13
 
2.9%
3 10
 
2.2%
4 5
 
1.1%
- 3
 
0.7%
6 3
 
0.7%
Other values (13) 15
 
3.3%

주소
Text

Distinct1796
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2023-12-13T06:08:53.391922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length33
Mean length20.128035
Min length11

Characters and Unicode

Total characters36472
Distinct characters332
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1784 ?
Unique (%)98.5%

Sample

1st row서울특별시 중구 남창동 49-1 일대
2nd row인천광역시 동구 송림동 294
3rd row서울특별시 종로구 종로6가 289-3 동대
4th row부산광역시 사상구 괘법동 529-1
5th row서울특별시 중구 을지로6가 17-48 일대
ValueCountFrequency (%)
서울특별시 297
 
3.7%
경기도 221
 
2.8%
부산광역시 186
 
2.3%
경상남도 173
 
2.2%
경상북도 150
 
1.9%
중구 136
 
1.7%
대구광역시 118
 
1.5%
전라남도 115
 
1.4%
인천광역시 80
 
1.0%
전라북도 76
 
0.9%
Other values (3449) 6484
80.7%
2023-12-13T06:08:53.893040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6270
 
17.2%
1 1618
 
4.4%
1603
 
4.4%
1582
 
4.3%
- 1391
 
3.8%
1224
 
3.4%
1060
 
2.9%
2 992
 
2.7%
3 829
 
2.3%
4 702
 
1.9%
Other values (322) 19201
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21401
58.7%
Decimal Number 7402
 
20.3%
Space Separator 6270
 
17.2%
Dash Punctuation 1391
 
3.8%
Math Symbol 4
 
< 0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1603
 
7.5%
1582
 
7.4%
1224
 
5.7%
1060
 
5.0%
629
 
2.9%
614
 
2.9%
584
 
2.7%
578
 
2.7%
537
 
2.5%
495
 
2.3%
Other values (307) 12495
58.4%
Decimal Number
ValueCountFrequency (%)
1 1618
21.9%
2 992
13.4%
3 829
11.2%
4 702
9.5%
5 629
 
8.5%
6 614
 
8.3%
7 554
 
7.5%
8 516
 
7.0%
9 485
 
6.6%
0 463
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%
Space Separator
ValueCountFrequency (%)
6270
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1391
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21401
58.7%
Common 15067
41.3%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1603
 
7.5%
1582
 
7.4%
1224
 
5.7%
1060
 
5.0%
629
 
2.9%
614
 
2.9%
584
 
2.7%
578
 
2.7%
537
 
2.5%
495
 
2.3%
Other values (307) 12495
58.4%
Common
ValueCountFrequency (%)
6270
41.6%
1 1618
 
10.7%
- 1391
 
9.2%
2 992
 
6.6%
3 829
 
5.5%
4 702
 
4.7%
5 629
 
4.2%
6 614
 
4.1%
7 554
 
3.7%
8 516
 
3.4%
Other values (3) 952
 
6.3%
Latin
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21401
58.7%
ASCII 15071
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6270
41.6%
1 1618
 
10.7%
- 1391
 
9.2%
2 992
 
6.6%
3 829
 
5.5%
4 702
 
4.7%
5 629
 
4.2%
6 614
 
4.1%
7 554
 
3.7%
8 516
 
3.4%
Other values (5) 956
 
6.3%
Hangul
ValueCountFrequency (%)
1603
 
7.5%
1582
 
7.4%
1224
 
5.7%
1060
 
5.0%
629
 
2.9%
614
 
2.9%
584
 
2.7%
578
 
2.7%
537
 
2.5%
495
 
2.3%
Other values (307) 12495
58.4%

시도
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
서울
299 
경기
221 
부산
186 
경남
173 
경북
150 
Other values (12)
783 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row인천
3rd row서울
4th row부산
5th row서울

Common Values

ValueCountFrequency (%)
서울 299
16.5%
경기 221
12.2%
부산 186
10.3%
경남 173
9.5%
경북 150
8.3%
대구 118
 
6.5%
전남 116
 
6.4%
인천 80
 
4.4%
전북 76
 
4.2%
강원 73
 
4.0%
Other values (7) 320
17.7%

Length

2023-12-13T06:08:54.016657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 299
16.5%
경기 221
12.2%
부산 186
10.3%
경남 173
9.5%
경북 150
8.3%
대구 118
 
6.5%
전남 116
 
6.4%
인천 80
 
4.4%
전북 76
 
4.2%
강원 73
 
4.0%
Other values (7) 320
17.7%
Distinct206
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
2023-12-13T06:08:54.334048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8774834
Min length2

Characters and Unicode

Total characters5214
Distinct characters133
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

Unique19 ?
Unique (%)1.0%

Sample

1st row중구
2nd row동구
3rd row종로구
4th row사상구
5th row중구
ValueCountFrequency (%)
중구 136
 
7.5%
창원시 75
 
4.1%
동구 69
 
3.8%
서구 52
 
2.9%
북구 44
 
2.4%
남구 39
 
2.2%
성남시 32
 
1.8%
포항시 28
 
1.5%
부산진구 27
 
1.5%
달서구 26
 
1.4%
Other values (195) 1284
70.9%
2023-12-13T06:08:54.852670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
835
 
16.0%
730
 
14.0%
295
 
5.7%
177
 
3.4%
164
 
3.1%
149
 
2.9%
145
 
2.8%
132
 
2.5%
120
 
2.3%
119
 
2.3%
Other values (123) 2348
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5213
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
835
 
16.0%
730
 
14.0%
295
 
5.7%
177
 
3.4%
164
 
3.1%
149
 
2.9%
145
 
2.8%
132
 
2.5%
120
 
2.3%
119
 
2.3%
Other values (122) 2347
45.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5213
> 99.9%
Common 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
835
 
16.0%
730
 
14.0%
295
 
5.7%
177
 
3.4%
164
 
3.1%
149
 
2.9%
145
 
2.8%
132
 
2.5%
120
 
2.3%
119
 
2.3%
Other values (122) 2347
45.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5213
> 99.9%
ASCII 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
835
 
16.0%
730
 
14.0%
295
 
5.7%
177
 
3.4%
164
 
3.1%
149
 
2.9%
145
 
2.8%
132
 
2.5%
120
 
2.3%
119
 
2.3%
Other values (122) 2347
45.0%
ASCII
ValueCountFrequency (%)
1
100.0%

시장면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1693
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10549.103
Minimum139
Maximum328300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:54.979154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139
5-th percentile1391.85
Q12933.75
median5561.5
Q311190
95-th percentile32531.55
Maximum328300
Range328161
Interquartile range (IQR)8256.25

Descriptive statistics

Standard deviation19048.089
Coefficient of variation (CV)1.8056595
Kurtosis95.17609
Mean10549.103
Median Absolute Deviation (MAD)3283
Skewness8.0818121
Sum19114975
Variance3.6282968 × 108
MonotonicityNot monotonic
2023-12-13T06:08:55.098624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2970 4
 
0.2%
5122 4
 
0.2%
2600 3
 
0.2%
1813 3
 
0.2%
3927 3
 
0.2%
2645 3
 
0.2%
1500 3
 
0.2%
2286 3
 
0.2%
2946 3
 
0.2%
3313 3
 
0.2%
Other values (1683) 1780
98.2%
ValueCountFrequency (%)
139 1
0.1%
208 1
0.1%
310 1
0.1%
392 1
0.1%
395 1
0.1%
416 1
0.1%
492 1
0.1%
510 1
0.1%
546 1
0.1%
557 1
0.1%
ValueCountFrequency (%)
328300 1
0.1%
269893 1
0.1%
235226 1
0.1%
234776 1
0.1%
202744 1
0.1%
148543 1
0.1%
141176 1
0.1%
137417 1
0.1%
136965 1
0.1%
128324 1
0.1%

전체점포
Real number (ℝ)

HIGH CORRELATION 

Distinct413
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.12141
Minimum0
Maximum5111
Zeros9
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:55.252848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23
Q155
median90
Q3153
95-th percentile490.9
Maximum5111
Range5111
Interquartile range (IQR)98

Descriptive statistics

Standard deviation279.82337
Coefficient of variation (CV)1.7809372
Kurtosis128.75073
Mean157.12141
Median Absolute Deviation (MAD)42
Skewness9.2823459
Sum284704
Variance78301.121
MonotonicityDecreasing
2023-12-13T06:08:55.417820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 30
 
1.7%
70 28
 
1.5%
65 25
 
1.4%
40 25
 
1.4%
62 22
 
1.2%
53 22
 
1.2%
90 22
 
1.2%
100 21
 
1.2%
80 20
 
1.1%
55 19
 
1.0%
Other values (403) 1578
87.1%
ValueCountFrequency (%)
0 9
0.5%
1 1
 
0.1%
2 1
 
0.1%
5 3
 
0.2%
6 2
 
0.1%
7 1
 
0.1%
8 3
 
0.2%
9 5
0.3%
10 2
 
0.1%
11 3
 
0.2%
ValueCountFrequency (%)
5111 1
0.1%
4735 1
0.1%
4300 1
0.1%
2620 1
0.1%
2070 1
0.1%
1860 1
0.1%
1816 1
0.1%
1775 1
0.1%
1747 1
0.1%
1650 1
0.1%

빈점포
Real number (ℝ)

ZEROS 

Distinct119
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.096578
Minimum0
Maximum1100
Zeros563
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:55.558726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile64
Maximum1100
Range1100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation56.689678
Coefficient of variation (CV)3.5218465
Kurtosis208.92482
Mean16.096578
Median Absolute Deviation (MAD)4
Skewness12.743687
Sum29167
Variance3213.7195
MonotonicityNot monotonic
2023-12-13T06:08:55.698036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 563
31.1%
1 116
 
6.4%
2 114
 
6.3%
3 106
 
5.8%
10 99
 
5.5%
5 97
 
5.4%
4 78
 
4.3%
6 56
 
3.1%
20 51
 
2.8%
7 46
 
2.5%
Other values (109) 486
26.8%
ValueCountFrequency (%)
0 563
31.1%
1 116
 
6.4%
2 114
 
6.3%
3 106
 
5.8%
4 78
 
4.3%
5 97
 
5.4%
6 56
 
3.1%
7 46
 
2.5%
8 24
 
1.3%
9 29
 
1.6%
ValueCountFrequency (%)
1100 1
0.1%
1048 1
0.1%
1000 1
0.1%
800 1
0.1%
450 2
0.1%
350 1
0.1%
316 1
0.1%
280 1
0.1%
250 1
0.1%
237 1
0.1%

기타점포
Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3636865
Minimum0
Maximum380
Zeros1274
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:55.863381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile18.45
Maximum380
Range380
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.860242
Coefficient of variation (CV)4.4178439
Kurtosis296.1545
Mean3.3636865
Median Absolute Deviation (MAD)0
Skewness14.426126
Sum6095
Variance220.8268
MonotonicityNot monotonic
2023-12-13T06:08:56.016107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1274
70.3%
1 118
 
6.5%
2 77
 
4.2%
5 49
 
2.7%
3 41
 
2.3%
4 34
 
1.9%
10 32
 
1.8%
20 21
 
1.2%
6 21
 
1.2%
11 12
 
0.7%
Other values (45) 133
 
7.3%
ValueCountFrequency (%)
0 1274
70.3%
1 118
 
6.5%
2 77
 
4.2%
3 41
 
2.3%
4 34
 
1.9%
5 49
 
2.7%
6 21
 
1.2%
7 10
 
0.6%
8 12
 
0.7%
9 7
 
0.4%
ValueCountFrequency (%)
380 1
0.1%
260 1
0.1%
160 1
0.1%
156 1
0.1%
149 1
0.1%
100 1
0.1%
72 2
0.1%
70 1
0.1%
61 1
0.1%
58 2
0.1%

노점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct121
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.937638
Minimum0
Maximum890
Zeros910
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:56.188565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile99.45
Maximum890
Range890
Interquartile range (IQR)13

Descriptive statistics

Standard deviation52.641259
Coefficient of variation (CV)2.7797162
Kurtosis66.144907
Mean18.937638
Median Absolute Deviation (MAD)0
Skewness6.5858253
Sum34315
Variance2771.1021
MonotonicityNot monotonic
2023-12-13T06:08:56.389675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 910
50.2%
2 71
 
3.9%
10 63
 
3.5%
1 54
 
3.0%
20 53
 
2.9%
5 46
 
2.5%
50 38
 
2.1%
4 37
 
2.0%
3 37
 
2.0%
30 36
 
2.0%
Other values (111) 467
25.8%
ValueCountFrequency (%)
0 910
50.2%
1 54
 
3.0%
2 71
 
3.9%
3 37
 
2.0%
4 37
 
2.0%
5 46
 
2.5%
6 28
 
1.5%
7 26
 
1.4%
8 22
 
1.2%
9 14
 
0.8%
ValueCountFrequency (%)
890 1
 
0.1%
514 1
 
0.1%
500 1
 
0.1%
450 2
0.1%
440 1
 
0.1%
400 3
0.2%
376 1
 
0.1%
340 2
0.1%
300 4
0.2%
272 1
 
0.1%

점포상인
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.66115
Minimum0
Maximum4638
Zeros11
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:56.520041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q149
median78.5
Q3139
95-th percentile408
Maximum4638
Range4638
Interquartile range (IQR)90

Descriptive statistics

Standard deviation248.7131
Coefficient of variation (CV)1.8067051
Kurtosis129.91055
Mean137.66115
Median Absolute Deviation (MAD)38.5
Skewness9.2974156
Sum249442
Variance61858.208
MonotonicityNot monotonic
2023-12-13T06:08:56.650528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 42
 
2.3%
60 38
 
2.1%
30 32
 
1.8%
40 30
 
1.7%
80 29
 
1.6%
100 25
 
1.4%
55 24
 
1.3%
65 23
 
1.3%
51 23
 
1.3%
120 20
 
1.1%
Other values (356) 1526
84.2%
ValueCountFrequency (%)
0 11
0.6%
3 1
 
0.1%
4 1
 
0.1%
5 3
 
0.2%
6 8
0.4%
7 3
 
0.2%
8 9
0.5%
9 3
 
0.2%
10 11
0.6%
11 4
 
0.2%
ValueCountFrequency (%)
4638 1
0.1%
4111 1
0.1%
3850 1
0.1%
1970 1
0.1%
1745 1
0.1%
1737 1
0.1%
1660 1
0.1%
1630 1
0.1%
1572 1
0.1%
1560 1
0.1%

종업원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct314
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.604857
Minimum0
Maximum11150
Zeros234
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:56.771841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median21
Q368
95-th percentile332.8
Maximum11150
Range11150
Interquartile range (IQR)61

Descriptive statistics

Standard deviation408.90494
Coefficient of variation (CV)4.1893913
Kurtosis348.05323
Mean97.604857
Median Absolute Deviation (MAD)19
Skewness15.953599
Sum176860
Variance167203.25
MonotonicityNot monotonic
2023-12-13T06:08:56.908475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 234
 
12.9%
10 112
 
6.2%
20 60
 
3.3%
15 51
 
2.8%
5 49
 
2.7%
3 43
 
2.4%
4 43
 
2.4%
30 37
 
2.0%
6 35
 
1.9%
8 34
 
1.9%
Other values (304) 1114
61.5%
ValueCountFrequency (%)
0 234
12.9%
1 14
 
0.8%
2 29
 
1.6%
3 43
 
2.4%
4 43
 
2.4%
5 49
 
2.7%
6 35
 
1.9%
7 23
 
1.3%
8 34
 
1.9%
9 17
 
0.9%
ValueCountFrequency (%)
11150 1
0.1%
5487 1
0.1%
5138 1
0.1%
5100 1
0.1%
4317 1
0.1%
3611 1
0.1%
3478 1
0.1%
1983 1
0.1%
1975 1
0.1%
1787 1
0.1%

노점상인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct121
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.746137
Minimum0
Maximum890
Zeros910
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:57.066613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile97.9
Maximum890
Range890
Interquartile range (IQR)13

Descriptive statistics

Standard deviation52.469692
Coefficient of variation (CV)2.7989603
Kurtosis67.110775
Mean18.746137
Median Absolute Deviation (MAD)0
Skewness6.6456611
Sum33968
Variance2753.0686
MonotonicityNot monotonic
2023-12-13T06:08:57.208330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 910
50.2%
2 71
 
3.9%
10 65
 
3.6%
1 54
 
3.0%
20 52
 
2.9%
5 45
 
2.5%
4 39
 
2.2%
3 37
 
2.0%
30 35
 
1.9%
50 33
 
1.8%
Other values (111) 471
26.0%
ValueCountFrequency (%)
0 910
50.2%
1 54
 
3.0%
2 71
 
3.9%
3 37
 
2.0%
4 39
 
2.2%
5 45
 
2.5%
6 31
 
1.7%
7 27
 
1.5%
8 18
 
1.0%
9 15
 
0.8%
ValueCountFrequency (%)
890 1
 
0.1%
514 1
 
0.1%
500 1
 
0.1%
450 2
0.1%
440 1
 
0.1%
400 3
0.2%
376 1
 
0.1%
340 2
0.1%
300 4
0.2%
272 1
 
0.1%

총시장상인
Real number (ℝ)

HIGH CORRELATION 

Distinct543
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.01214
Minimum3
Maximum15000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.1 KiB
2023-12-13T06:08:57.351154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile25
Q169
median124.5
Q3231
95-th percentile776
Maximum15000
Range14997
Interquartile range (IQR)162

Descriptive statistics

Standard deviation617.58349
Coefficient of variation (CV)2.4313148
Kurtosis235.12778
Mean254.01214
Median Absolute Deviation (MAD)67.5
Skewness12.744777
Sum460270
Variance381409.37
MonotonicityNot monotonic
2023-12-13T06:08:57.494739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 25
 
1.4%
80 18
 
1.0%
56 18
 
1.0%
115 16
 
0.9%
105 16
 
0.9%
130 15
 
0.8%
69 15
 
0.8%
40 15
 
0.8%
78 14
 
0.8%
37 13
 
0.7%
Other values (533) 1647
90.9%
ValueCountFrequency (%)
3 1
 
0.1%
6 2
 
0.1%
7 2
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
10 8
0.4%
11 4
0.2%
12 8
0.4%
13 3
 
0.2%
14 1
 
0.1%
ValueCountFrequency (%)
15000 1
0.1%
9776 1
0.1%
6730 1
0.1%
6464 1
0.1%
6287 1
0.1%
5767 1
0.1%
5271 1
0.1%
4429 1
0.1%
3543 1
0.1%
3200 1
0.1%

Interactions

2023-12-13T06:08:50.435506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:41.779460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.690974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.732825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.857792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.908646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.002088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.690829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.544694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.401699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.564479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:41.876786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.828719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.832451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.983413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.019384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.073894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.790895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.625729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.518191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.655176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:41.975696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.919727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.949570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.109011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.369398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.144096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.882721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.708270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.620184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.749143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.056482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.011435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.106173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.234372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.457731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.208675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.966352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.786965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.718006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.872803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.132008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.120600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.217738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.337295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.545021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.274026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.029204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.883032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.801519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:51.007431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.226447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.226838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.327740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.446990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.639750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.346218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.103627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.989721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.929573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:51.109673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.313642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.323470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.426178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.536811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.712324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.417871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.179140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.070969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.047643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:51.199196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.411479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.415900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.544406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.620307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.786794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.482400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.251008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.151712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.146804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:51.302244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.521668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.527766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.668768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.722961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.858159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.550750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.343613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.228050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.251182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:51.397397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:42.601243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:43.637503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:44.751119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:45.804084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:46.928275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:47.613226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:48.438470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:49.310228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:08:50.337168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:08:57.604897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시도시장면적전체점포빈점포기타점포노점수점포상인종업원노점상인총시장상인
순번1.0000.2090.2470.4600.2300.1100.0940.4540.3540.0940.305
시도0.2091.0000.2050.0000.0000.0000.1730.0000.0000.1730.000
시장면적0.2470.2051.0000.5550.6380.6580.2070.5900.6200.2070.699
전체점포0.4600.0000.5551.0000.6950.4300.0700.9320.8180.0700.860
빈점포0.2300.0000.6380.6951.0000.6810.0000.6930.5330.0000.747
기타점포0.1100.0000.6580.4300.6811.0000.0000.3680.3840.0000.582
노점수0.0940.1730.2070.0700.0000.0001.0000.2030.0001.0000.000
점포상인0.4540.0000.5900.9320.6930.3680.2031.0000.7410.2030.954
종업원0.3540.0000.6200.8180.5330.3840.0000.7411.0000.0000.883
노점상인0.0940.1730.2070.0700.0000.0001.0000.2030.0001.0000.000
총시장상인0.3050.0000.6990.8600.7470.5820.0000.9540.8830.0001.000
2023-12-13T06:08:57.989034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시장면적전체점포빈점포기타점포노점수점포상인종업원노점상인총시장상인시도
순번1.000-0.517-1.000-0.363-0.121-0.007-0.953-0.603-0.007-0.8540.083
시장면적-0.5171.0000.5170.0390.0260.1830.5530.4070.1830.5780.083
전체점포-1.0000.5171.0000.3630.1220.0060.9530.6020.0060.8540.000
빈점포-0.3630.0390.3631.0000.151-0.1180.1880.040-0.1190.1030.000
기타점포-0.1210.0260.1220.1511.0000.0750.035-0.0200.0740.0190.000
노점수-0.0070.1830.006-0.1180.0751.0000.018-0.0501.0000.1810.080
점포상인-0.9530.5530.9530.1880.0350.0181.0000.6520.0190.9120.000
종업원-0.6030.4070.6020.040-0.020-0.0500.6521.000-0.0500.7790.000
노점상인-0.0070.1830.006-0.1190.0741.0000.019-0.0501.0000.1810.080
총시장상인-0.8540.5780.8540.1030.0190.1810.9120.7790.1811.0000.000
시도0.0830.0830.0000.0000.0000.0800.0000.0000.0800.0001.000

Missing values

2023-12-13T06:08:51.559142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:08:51.734640image/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

순번시장명주소시도시군구시장면적전체점포빈점포기타점포노점수점포상인종업원노점상인총시장상인
01서울남대문시장서울특별시 중구 남창동 49-1 일대서울중구244665111100001241111644125767
12인천산업용품유통센터인천광역시 동구 송림동 294인천동구2352264735475004638513809776
23동대문종합시장서울특별시 종로구 종로6가 289-3 동대서울종로구14244430045000385011150015000
34르네시떼부산광역시 사상구 괘법동 529-1부산사상구242452620104800157231401886
45평화시장서울특별시 중구 을지로6가 17-48 일대서울중구80782070100001970431706287
56충장로상점가광주광역시 동구 충장로 4가 46광주동구20274418603501605013501389502789
67산본로데오거리상인회경기도 군포시 산본동 1131-2경기군포시3283001816015601660361105271
78종로광장전통시장서울특별시 종로구 예지동 2-1서울종로구330801775030210174512282103183
89성안길상점가충청북도 청주시 상당구 서문동 109-6충북청주시10480174710001737146303200
910방산시장서울특별시 중구 주교동 250-1 일대서울중구74623165002001630510006730
순번시장명주소시도시군구시장면적전체점포빈점포기타점포노점수점포상인종업원노점상인총시장상인
18021803좌일5일시장전라남도 해남군 북일면 흥촌리 161전남해남군443210112001212
18031804모란민속5일장경기도 성남시 중원구 성남동 4214경기성남시269000051400514514
18041805예산시장충청남도 예산군 예산읍 예산리 333-1충남예산군2026400025000250250
18051806곡천공설시장울산광역시 울주군 웅촌면 곡천리320-2울산울주군181300080088
18061807금곡시장경상남도 진주시 금곡면 두문리 639-1경남진주시153800021002121
18071808웅천시장경상남도 창원시 진해구 성내동 327경남창원시39500011001111
18081809진례전통시장경상남도 김해시 진례면 송정리 239-3경남김해시172900010001010
18091810세지5일시장전라남도 나주시 세지면 오봉리 246-3전남나주시524600021002121
18101811용산시장전라남도 장흥군 용산면 인암리 800-1전남장흥군359000070077
18111812장평시장전라남도 장흥군 장평면 양촌리 224전남장흥군263200011001111