Overview

Dataset statistics

Number of variables30
Number of observations100
Missing cells175
Missing cells (%)5.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.8 KiB
Average record size in memory254.3 B

Variable types

Text9
Categorical7
Numeric12
Boolean2

Alerts

file_name has constant value ""Constant
base_ymd has constant value ""Constant
pbctlt_pos_yn is highly imbalanced (63.4%)Imbalance
hmpg has 94 (94.0%) missing valuesMissing
opnng_yy has 16 (16.0%) missing valuesMissing
telno has 64 (64.0%) missing valuesMissing
id has unique valuesUnique
rdnm_addr has unique valuesUnique
x_cd has unique valuesUnique
y_cd has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:46:20.419112
Analysis finished2023-12-10 09:46:21.459964
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:21.988914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKC603PO20N000000001
2nd rowKC603PO20N000002705
3rd rowKC603PO20N000000003
4th rowKC603PO20N000000004
5th rowKC603PO20N000000005
ValueCountFrequency (%)
kc603po20n000000001 1
 
1.0%
kc603po20n000000063 1
 
1.0%
kc603po20n000000074 1
 
1.0%
kc603po20n000000073 1
 
1.0%
kc603po20n000000072 1
 
1.0%
kc603po20n000000071 1
 
1.0%
kc603po20n000000070 1
 
1.0%
kc603po20n000000069 1
 
1.0%
kc603po20n000000068 1
 
1.0%
kc603po20n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:46:22.490111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 914
48.1%
6 121
 
6.4%
2 121
 
6.4%
3 119
 
6.3%
K 100
 
5.3%
C 100
 
5.3%
P 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
7 24
 
1.3%
Other values (5) 101
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1400
73.7%
Uppercase Letter 500
 
26.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 914
65.3%
6 121
 
8.6%
2 121
 
8.6%
3 119
 
8.5%
7 24
 
1.7%
1 21
 
1.5%
5 21
 
1.5%
4 20
 
1.4%
9 20
 
1.4%
8 19
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
K 100
20.0%
C 100
20.0%
P 100
20.0%
O 100
20.0%
N 100
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1400
73.7%
Latin 500
 
26.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 914
65.3%
6 121
 
8.6%
2 121
 
8.6%
3 119
 
8.5%
7 24
 
1.7%
1 21
 
1.5%
5 21
 
1.5%
4 20
 
1.4%
9 20
 
1.4%
8 19
 
1.4%
Latin
ValueCountFrequency (%)
K 100
20.0%
C 100
20.0%
P 100
20.0%
O 100
20.0%
N 100
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 914
48.1%
6 121
 
6.4%
2 121
 
6.4%
3 119
 
6.3%
K 100
 
5.3%
C 100
 
5.3%
P 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
7 24
 
1.3%
Other values (5) 101
 
5.3%
Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:22.872996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length6.26
Min length4

Characters and Unicode

Total characters626
Distinct characters155
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row서대신1동골목시장
2nd row분당종합시장
3rd row남리5일시장
4th row광안시장
5th row원통시장
ValueCountFrequency (%)
중앙시장 2
 
2.0%
창원캔버라타운 1
 
1.0%
동성올림픽타운 1
 
1.0%
무풍시장(대덕산장터 1
 
1.0%
무주시장(반딧불장터 1
 
1.0%
조암시장 1
 
1.0%
대방덕산1차상가 1
 
1.0%
상남시장 1
 
1.0%
성원그랜드쇼핑상가 1
 
1.0%
창원코아상가 1
 
1.0%
Other values (91) 91
89.2%
2023-12-10T18:46:23.526343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
14.9%
85
 
13.6%
23
 
3.7%
22
 
3.5%
16
 
2.6%
15
 
2.4%
11
 
1.8%
( 10
 
1.6%
10
 
1.6%
) 10
 
1.6%
Other values (145) 331
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
95.2%
Open Punctuation 10
 
1.6%
Close Punctuation 10
 
1.6%
Decimal Number 5
 
0.8%
Space Separator 2
 
0.3%
Lowercase Letter 2
 
0.3%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
93
 
15.6%
85
 
14.3%
23
 
3.9%
22
 
3.7%
16
 
2.7%
15
 
2.5%
11
 
1.8%
10
 
1.7%
8
 
1.3%
7
 
1.2%
Other values (136) 306
51.3%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
5 2
40.0%
3 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
50.0%
a 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
95.2%
Common 27
 
4.3%
Latin 3
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
93
 
15.6%
85
 
14.3%
23
 
3.9%
22
 
3.7%
16
 
2.7%
15
 
2.5%
11
 
1.8%
10
 
1.7%
8
 
1.3%
7
 
1.2%
Other values (136) 306
51.3%
Common
ValueCountFrequency (%)
( 10
37.0%
) 10
37.0%
2
 
7.4%
1 2
 
7.4%
5 2
 
7.4%
3 1
 
3.7%
Latin
ValueCountFrequency (%)
B 1
33.3%
b 1
33.3%
a 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
95.2%
ASCII 30
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
93
 
15.6%
85
 
14.3%
23
 
3.9%
22
 
3.7%
16
 
2.7%
15
 
2.5%
11
 
1.8%
10
 
1.7%
8
 
1.3%
7
 
1.2%
Other values (136) 306
51.3%
ASCII
ValueCountFrequency (%)
( 10
33.3%
) 10
33.3%
2
 
6.7%
1 2
 
6.7%
5 2
 
6.7%
B 1
 
3.3%
3 1
 
3.3%
b 1
 
3.3%
a 1
 
3.3%

ctprvn_nm
Categorical

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경상남도
36 
경기도
10 
서울특별시
10 
전라북도
부산광역시
Other values (8)
27 

Length

Max length5
Median length4
Mean length4.2
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row부산광역시
2nd row경기도
3rd row전라남도
4th row부산광역시
5th row강원도

Common Values

ValueCountFrequency (%)
경상남도 36
36.0%
경기도 10
 
10.0%
서울특별시 10
 
10.0%
전라북도 9
 
9.0%
부산광역시 8
 
8.0%
인천광역시 8
 
8.0%
전라남도 6
 
6.0%
경상북도 5
 
5.0%
강원도 2
 
2.0%
대구광역시 2
 
2.0%
Other values (3) 4
 
4.0%

Length

2023-12-10T18:46:23.795356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 36
36.0%
경기도 10
 
10.0%
서울특별시 10
 
10.0%
전라북도 9
 
9.0%
부산광역시 8
 
8.0%
인천광역시 8
 
8.0%
전라남도 6
 
6.0%
경상북도 5
 
5.0%
강원도 2
 
2.0%
대구광역시 2
 
2.0%
Other values (3) 4
 
4.0%

sgnr_nm
Categorical

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
창원시 의창구
18 
창원시 성산구
12 
창원시 마산합포구
수영구
 
5
무주군
 
4
Other values (39)
55 

Length

Max length9
Median length7
Mean length4.75
Min length2

Unique

Unique27 ?
Unique (%)27.0%

Sample

1st row서구
2nd row성남시 분당구
3rd row해남군
4th row수영구
5th row인제군

Common Values

ValueCountFrequency (%)
창원시 의창구 18
18.0%
창원시 성산구 12
 
12.0%
창원시 마산합포구 6
 
6.0%
수영구 5
 
5.0%
무주군 4
 
4.0%
성남시 분당구 3
 
3.0%
장수군 3
 
3.0%
중구 3
 
3.0%
동구 3
 
3.0%
서구 2
 
2.0%
Other values (34) 41
41.0%

Length

2023-12-10T18:46:24.056097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 36
25.2%
의창구 18
 
12.6%
성산구 12
 
8.4%
마산합포구 6
 
4.2%
수영구 5
 
3.5%
성남시 5
 
3.5%
무주군 4
 
2.8%
분당구 3
 
2.1%
장수군 3
 
2.1%
중구 3
 
2.1%
Other values (38) 48
33.6%

legaldong_cd
Real number (ℝ)

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8852486 × 109
Minimum1.1110158 × 109
Maximum4.8125158 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:24.481279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110158 × 109
5-th percentile1.1375609 × 109
Q12.8243106 × 109
median4.5735295 × 109
Q34.8121127 × 109
95-th percentile4.8125111 × 109
Maximum4.8125158 × 109
Range3.7015 × 109
Interquartile range (IQR)1.9878022 × 109

Descriptive statistics

Standard deviation1.2133832 × 109
Coefficient of variation (CV)0.31230516
Kurtosis0.074139993
Mean3.8852486 × 109
Median Absolute Deviation (MAD)2.3888238 × 108
Skewness-1.1565901
Sum3.8852486 × 1011
Variance1.4722988 × 1018
MonotonicityNot monotonic
2023-12-10T18:46:24.852191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4812112700 4
 
4.0%
4812312700 4
 
4.0%
4812312500 4
 
4.0%
4113510200 3
 
3.0%
4812112400 2
 
2.0%
2871025021 2
 
2.0%
2650010100 2
 
2.0%
4812112800 2
 
2.0%
2814010700 2
 
2.0%
4812110700 2
 
2.0%
Other values (68) 73
73.0%
ValueCountFrequency (%)
1111015800 1
1.0%
1111016300 1
1.0%
1120012200 1
1.0%
1129013800 1
1.0%
1129013900 1
1.0%
1138010700 1
1.0%
1147010200 1
1.0%
1154510300 1
1.0%
1159010200 1
1.0%
1165010200 1
1.0%
ValueCountFrequency (%)
4812515800 2
2.0%
4812511900 1
 
1.0%
4812511600 1
 
1.0%
4812511100 2
2.0%
4812312700 4
4.0%
4812312600 1
 
1.0%
4812312500 4
4.0%
4812312400 1
 
1.0%
4812312300 1
 
1.0%
4812310500 1
 
1.0%
Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:25.319530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters300
Distinct characters93
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

Unique60 ?
Unique (%)60.0%

Sample

1st row서대신동1가
2nd row수내동
3rd row황산면
4th row광안동
5th row북면
ValueCountFrequency (%)
상남동 5
 
5.0%
봉곡동 4
 
4.0%
중앙동 4
 
4.0%
수내동 3
 
3.0%
망미동 2
 
2.0%
동성동 2
 
2.0%
팔용동 2
 
2.0%
북동 2
 
2.0%
소계동 2
 
2.0%
용호동 2
 
2.0%
Other values (66) 72
72.0%
2023-12-10T18:46:26.144474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
26.0%
12
 
4.0%
11
 
3.7%
7
 
2.3%
7
 
2.3%
7
 
2.3%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (83) 158
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 296
98.7%
Decimal Number 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
78
26.4%
12
 
4.1%
11
 
3.7%
7
 
2.4%
7
 
2.4%
7
 
2.4%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (80) 154
52.0%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
5 1
25.0%
1 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 296
98.7%
Common 4
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
78
26.4%
12
 
4.1%
11
 
3.7%
7
 
2.4%
7
 
2.4%
7
 
2.4%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (80) 154
52.0%
Common
ValueCountFrequency (%)
2 2
50.0%
5 1
25.0%
1 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 296
98.7%
ASCII 4
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
78
26.4%
12
 
4.1%
11
 
3.7%
7
 
2.4%
7
 
2.4%
7
 
2.4%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (80) 154
52.0%
ASCII
ValueCountFrequency (%)
2 2
50.0%
5 1
25.0%
1 1
25.0%

adstrd_cd
Real number (ℝ)

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8852847 × 109
Minimum1.1110615 × 109
Maximum4.812563 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:26.494660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110615 × 109
5-th percentile1.137611 × 109
Q12.8243675 × 109
median4.5735295 × 109
Q34.8121532 × 109
95-th percentile4.812553 × 109
Maximum4.812563 × 109
Range3.7015015 × 109
Interquartile range (IQR)1.9877858 × 109

Descriptive statistics

Standard deviation1.213374 × 109
Coefficient of variation (CV)0.3122999
Kurtosis0.074152318
Mean3.8852847 × 109
Median Absolute Deviation (MAD)2.389255 × 108
Skewness-1.1565908
Sum3.8852847 × 1011
Variance1.4722765 × 1018
MonotonicityNot monotonic
2023-12-10T18:46:26.866768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4812352000 6
 
6.0%
4812151000 4
 
4.0%
4812154000 4
 
4.0%
4812153000 3
 
3.0%
4812155000 3
 
3.0%
4812152000 2
 
2.0%
4812351000 2
 
2.0%
4113356000 2
 
2.0%
2871025000 2
 
2.0%
4812353000 2
 
2.0%
Other values (66) 70
70.0%
ValueCountFrequency (%)
1111061500 1
1.0%
1111063000 1
1.0%
1120079000 1
1.0%
1129077000 1
1.0%
1129081000 1
1.0%
1138060000 1
1.0%
1147053000 1
1.0%
1154567000 1
1.0%
1159053000 1
1.0%
1165065100 1
1.0%
ValueCountFrequency (%)
4812563000 2
 
2.0%
4812561000 1
 
1.0%
4812556500 1
 
1.0%
4812553000 2
 
2.0%
4812357000 1
 
1.0%
4812354000 1
 
1.0%
4812353000 2
 
2.0%
4812352000 6
6.0%
4812351000 2
 
2.0%
4812155000 3
3.0%
Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:27.342977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.43
Min length2

Characters and Unicode

Total characters343
Distinct characters99
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

Unique60 ?
Unique (%)60.0%

Sample

1st row서대신1동
2nd row수내3동
3rd row황산면
4th row광안4동
5th row북면
ValueCountFrequency (%)
중앙동 6
 
6.0%
봉림동 4
 
4.0%
의창동 4
 
4.0%
용지동 3
 
3.0%
명곡동 3
 
3.0%
반송동 2
 
2.0%
상남동 2
 
2.0%
은행2동 2
 
2.0%
월영동 2
 
2.0%
망미1동 2
 
2.0%
Other values (65) 70
70.0%
2023-12-10T18:46:28.068203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
23.3%
2 14
 
4.1%
12
 
3.5%
11
 
3.2%
1 9
 
2.6%
8
 
2.3%
7
 
2.0%
7
 
2.0%
3 7
 
2.0%
6
 
1.7%
Other values (89) 182
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 300
87.5%
Decimal Number 38
 
11.1%
Other Punctuation 5
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
80
26.7%
12
 
4.0%
11
 
3.7%
8
 
2.7%
7
 
2.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
5
 
1.7%
4
 
1.3%
Other values (82) 155
51.7%
Decimal Number
ValueCountFrequency (%)
2 14
36.8%
1 9
23.7%
3 7
18.4%
4 4
 
10.5%
6 3
 
7.9%
5 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 300
87.5%
Common 43
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
80
26.7%
12
 
4.0%
11
 
3.7%
8
 
2.7%
7
 
2.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
5
 
1.7%
4
 
1.3%
Other values (82) 155
51.7%
Common
ValueCountFrequency (%)
2 14
32.6%
1 9
20.9%
3 7
16.3%
. 5
 
11.6%
4 4
 
9.3%
6 3
 
7.0%
5 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 300
87.5%
ASCII 43
 
12.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
80
26.7%
12
 
4.0%
11
 
3.7%
8
 
2.7%
7
 
2.3%
7
 
2.3%
6
 
2.0%
5
 
1.7%
5
 
1.7%
4
 
1.3%
Other values (82) 155
51.7%
ASCII
ValueCountFrequency (%)
2 14
32.6%
1 9
20.9%
3 7
16.3%
. 5
 
11.6%
4 4
 
9.3%
6 3
 
7.0%
5 1
 
2.3%

rdnmaddr_cd
Real number (ℝ)

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8852684 × 1011
Minimum1.1110301 × 1011
Maximum4.8125479 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:28.492522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110301 × 1011
5-th percentile1.1375801 × 1011
Q12.8243343 × 1011
median4.5735315 × 1011
Q34.8121333 × 1011
95-th percentile4.8125333 × 1011
Maximum4.8125479 × 1011
Range3.7015178 × 1011
Interquartile range (IQR)1.987799 × 1011

Descriptive statistics

Standard deviation1.2133809 × 1011
Coefficient of variation (CV)0.31230298
Kurtosis0.074148965
Mean3.8852684 × 1011
Median Absolute Deviation (MAD)2.3890911 × 1010
Skewness-1.1565918
Sum3.8852684 × 1013
Variance1.4722932 × 1022
MonotonicityNot monotonic
2023-12-10T18:46:28.777968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
481212327001 3
 
3.0%
411353180003 2
 
2.0%
287103150025 2
 
2.0%
481233327026 2
 
2.0%
481213327028 2
 
2.0%
481212327004 2
 
2.0%
481233328034 1
 
1.0%
481233328012 1
 
1.0%
457303275050 1
 
1.0%
481213327022 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
111103005008 1
1.0%
111104100328 1
1.0%
112004109475 1
1.0%
112903005038 1
1.0%
112903107012 1
1.0%
113803005055 1
1.0%
114704142194 1
1.0%
115453117004 1
1.0%
115903119005 1
1.0%
116504163088 1
1.0%
ValueCountFrequency (%)
481254787578 1
1.0%
481254787437 1
1.0%
481254787258 1
1.0%
481254787217 1
1.0%
481253329021 1
1.0%
481253329015 1
1.0%
481234784239 1
1.0%
481234784180 1
1.0%
481233328034 1
1.0%
481233328024 1
1.0%

rdnm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:29.422014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27.5
Mean length23.18
Min length15

Characters and Unicode

Total characters2318
Distinct characters161
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

Unique100 ?
Unique (%)100.0%

Sample

1st row부산광역시 서구 대영로 30번길 19-1(서대신동1가)
2nd row경기도 성남시 분당구 돌마로366번길42(수내동)
3rd row전라남도 해남군 황산면 시등로 111-11
4th row부산광역시 수영구 수영로603번길 18
5th row강원도 인제군 북면 원통로 178번길 7-1
ValueCountFrequency (%)
창원시 36
 
7.5%
경상남도 36
 
7.5%
의창구 18
 
3.7%
성산구 12
 
2.5%
서울특별시 10
 
2.1%
경기도 10
 
2.1%
전라북도 9
 
1.9%
부산광역시 8
 
1.7%
인천광역시 8
 
1.7%
전라남도 6
 
1.2%
Other values (276) 330
68.3%
2023-12-10T18:46:30.354637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
383
 
16.5%
97
 
4.2%
87
 
3.8%
1 85
 
3.7%
76
 
3.3%
74
 
3.2%
65
 
2.8%
59
 
2.5%
58
 
2.5%
56
 
2.4%
Other values (151) 1278
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1475
63.6%
Space Separator 383
 
16.5%
Decimal Number 351
 
15.1%
Open Punctuation 41
 
1.8%
Close Punctuation 41
 
1.8%
Dash Punctuation 25
 
1.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
97
 
6.6%
87
 
5.9%
76
 
5.2%
74
 
5.0%
65
 
4.4%
59
 
4.0%
58
 
3.9%
56
 
3.8%
53
 
3.6%
49
 
3.3%
Other values (136) 801
54.3%
Decimal Number
ValueCountFrequency (%)
1 85
24.2%
2 44
12.5%
3 41
11.7%
6 35
10.0%
5 32
 
9.1%
8 30
 
8.5%
4 25
 
7.1%
0 25
 
7.1%
7 21
 
6.0%
9 13
 
3.7%
Space Separator
ValueCountFrequency (%)
383
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1475
63.6%
Common 843
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
97
 
6.6%
87
 
5.9%
76
 
5.2%
74
 
5.0%
65
 
4.4%
59
 
4.0%
58
 
3.9%
56
 
3.8%
53
 
3.6%
49
 
3.3%
Other values (136) 801
54.3%
Common
ValueCountFrequency (%)
383
45.4%
1 85
 
10.1%
2 44
 
5.2%
( 41
 
4.9%
) 41
 
4.9%
3 41
 
4.9%
6 35
 
4.2%
5 32
 
3.8%
8 30
 
3.6%
- 25
 
3.0%
Other values (5) 86
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1475
63.6%
ASCII 843
36.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
383
45.4%
1 85
 
10.1%
2 44
 
5.2%
( 41
 
4.9%
) 41
 
4.9%
3 41
 
4.9%
6 35
 
4.2%
5 32
 
3.8%
8 30
 
3.6%
- 25
 
3.0%
Other values (5) 86
 
10.2%
Hangul
ValueCountFrequency (%)
97
 
6.6%
87
 
5.9%
76
 
5.2%
74
 
5.0%
65
 
4.4%
59
 
4.0%
58
 
3.9%
56
 
3.8%
53
 
3.6%
49
 
3.3%
Other values (136) 801
54.3%

zip_cd
Real number (ℝ)

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39742.18
Minimum2769
Maximum61924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:30.661208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2769
5-th percentile4738.65
Q122540.5
median51135.5
Q351517.5
95-th percentile57730.35
Maximum61924
Range59155
Interquartile range (IQR)28977

Descriptive statistics

Standard deviation17916.948
Coefficient of variation (CV)0.45082952
Kurtosis-0.7898464
Mean39742.18
Median Absolute Deviation (MAD)4452
Skewness-0.87768702
Sum3974218
Variance3.2101703 × 108
MonotonicityNot monotonic
2023-12-10T18:46:30.927119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51181 3
 
3.0%
51522 3
 
3.0%
13157 2
 
2.0%
51721 2
 
2.0%
51174 2
 
2.0%
51183 2
 
2.0%
13599 2
 
2.0%
51504 2
 
2.0%
51496 1
 
1.0%
55535 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
2769 1
1.0%
2784 1
1.0%
3195 1
1.0%
3197 1
1.0%
3478 1
1.0%
4805 1
1.0%
6738 1
1.0%
6978 1
1.0%
7950 1
1.0%
8625 1
1.0%
ValueCountFrequency (%)
61924 1
1.0%
59041 1
1.0%
59007 1
1.0%
58922 1
1.0%
57908 1
1.0%
57721 1
1.0%
57043 1
1.0%
55647 1
1.0%
55632 1
1.0%
55616 1
1.0%
Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:31.423521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters17
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

Unique95 ?
Unique (%)95.0%

Sample

1st row마라380803
2nd row다사669298
3rd row다라020206
4th row마라468861
5th row라아617138
ValueCountFrequency (%)
마라054950 3
 
3.0%
다사703398 2
 
2.0%
라마074481 1
 
1.0%
다마905320 1
 
1.0%
라마314748 1
 
1.0%
라마138786 1
 
1.0%
다바392983 1
 
1.0%
마라098915 1
 
1.0%
마라076925 1
 
1.0%
마라084922 1
 
1.0%
Other values (87) 87
87.0%
2023-12-10T18:46:32.174447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85
10.6%
9 80
10.0%
69
8.6%
3 66
8.2%
62
 
7.8%
8 56
 
7.0%
7 56
 
7.0%
2 55
 
6.9%
6 55
 
6.9%
4 54
 
6.8%
Other values (7) 162
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Letter 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
14.2%
9 80
13.3%
3 66
11.0%
8 56
9.3%
7 56
9.3%
2 55
9.2%
6 55
9.2%
4 54
9.0%
5 50
8.3%
1 43
7.2%
Other Letter
ValueCountFrequency (%)
69
34.5%
62
31.0%
37
18.5%
28
14.0%
2
 
1.0%
1
 
0.5%
1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 600
75.0%
Hangul 200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85
14.2%
9 80
13.3%
3 66
11.0%
8 56
9.3%
7 56
9.3%
2 55
9.2%
6 55
9.2%
4 54
9.0%
5 50
8.3%
1 43
7.2%
Hangul
ValueCountFrequency (%)
69
34.5%
62
31.0%
37
18.5%
28
14.0%
2
 
1.0%
1
 
0.5%
1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
75.0%
Hangul 200
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85
14.2%
9 80
13.3%
3 66
11.0%
8 56
9.3%
7 56
9.3%
2 55
9.2%
6 55
9.2%
4 54
9.0%
5 50
8.3%
1 43
7.2%
Hangul
ValueCountFrequency (%)
69
34.5%
62
31.0%
37
18.5%
28
14.0%
2
 
1.0%
1
 
0.5%
1
 
0.5%

x_cd
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.95762
Minimum126.25769
Maximum129.35543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:32.451534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.25769
5-th percentile126.59245
Q1127.05246
median128.48935
Q3128.67279
95-th percentile129.11261
Maximum129.35543
Range3.0977388
Interquartile range (IQR)1.6203343

Descriptive statistics

Standard deviation0.90412862
Coefficient of variation (CV)0.0070658443
Kurtosis-1.4718438
Mean127.95762
Median Absolute Deviation (MAD)0.62895535
Skewness-0.28293556
Sum12795.762
Variance0.81744857
MonotonicityNot monotonic
2023-12-10T18:46:32.841295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0144325 1
 
1.0%
128.6768275 1
 
1.0%
127.788019 1
 
1.0%
127.8487939 1
 
1.0%
127.653586 1
 
1.0%
126.8170415 1
 
1.0%
128.7068915 1
 
1.0%
128.68338 1
 
1.0%
128.6915928 1
 
1.0%
128.6795415 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.2576888 1
1.0%
126.4324836 1
1.0%
126.4904067 1
1.0%
126.4928692 1
1.0%
126.5030195 1
1.0%
126.5971566 1
1.0%
126.6452481 1
1.0%
126.64683 1
1.0%
126.654796 1
1.0%
126.7266188 1
1.0%
ValueCountFrequency (%)
129.3554276 1
1.0%
129.3373619 1
1.0%
129.3073562 1
1.0%
129.1212751 1
1.0%
129.1153256 1
1.0%
129.1124667 1
1.0%
129.1035626 1
1.0%
129.1008259 1
1.0%
129.0737929 1
1.0%
129.0480431 1
1.0%

y_cd
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.005231
Minimum34.481103
Maximum38.122581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:33.201751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.481103
5-th percentile35.103886
Q135.221035
median35.343054
Q337.370139
95-th percentile37.588993
Maximum38.122581
Range3.6414782
Interquartile range (IQR)2.1491038

Descriptive statistics

Standard deviation1.0361007
Coefficient of variation (CV)0.028776393
Kurtosis-1.243884
Mean36.005231
Median Absolute Deviation (MAD)0.31793865
Skewness0.65784594
Sum3600.5231
Variance1.0735047
MonotonicityNot monotonic
2023-12-10T18:46:33.562269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1086576 1
 
1.0%
35.221233 1
 
1.0%
36.0099046 1
 
1.0%
35.9694532 1
 
1.0%
36.004648 1
 
1.0%
37.082198 1
 
1.0%
35.213619 1
 
1.0%
35.2222149 1
 
1.0%
35.2193735 1
 
1.0%
35.2204424 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.4811029 1
1.0%
34.568785 1
1.0%
34.5753237 1
1.0%
34.9614459 1
1.0%
35.0132251 1
1.0%
35.1086576 1
1.0%
35.1282584 1
1.0%
35.1461444 1
1.0%
35.1535989 1
1.0%
35.1591235 1
1.0%
ValueCountFrequency (%)
38.1225811 1
1.0%
37.7414215 1
1.0%
37.7397767 1
1.0%
37.6120877 1
1.0%
37.6095024 1
1.0%
37.5879137 1
1.0%
37.5702085 1
1.0%
37.5701125 1
1.0%
37.563518 1
1.0%
37.5481845 1
1.0%

mrkt_ty
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
상설장
75 
상설장+5일장
15 
5일장
10 

Length

Max length7
Median length3
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row상설장
2nd row상설장
3rd row5일장
4th row상설장
5th row상설장+5일장

Common Values

ValueCountFrequency (%)
상설장 75
75.0%
상설장+5일장 15
 
15.0%
5일장 10
 
10.0%

Length

2023-12-10T18:46:33.840397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:34.067597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상설장 75
75.0%
상설장+5일장 15
 
15.0%
5일장 10
 
10.0%

mrkt_opn_cycle
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-
40 
매일
33 
5일
23 
1일+6일
 
1
매일+2일+7일
 
1
Other values (2)
 
2

Length

Max length8
Median length2
Mean length1.81
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row-
2nd row매일
3rd row5일
4th row-
5th row5일

Common Values

ValueCountFrequency (%)
- 40
40.0%
매일 33
33.0%
5일 23
23.0%
1일+6일 1
 
1.0%
매일+2일+7일 1
 
1.0%
매일+1일+6일 1
 
1.0%
매일+4일+9일 1
 
1.0%

Length

2023-12-10T18:46:34.284705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:34.878782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
40
40.0%
매일 33
33.0%
5일 23
23.0%
1일+6일 1
 
1.0%
매일+2일+7일 1
 
1.0%
매일+1일+6일 1
 
1.0%
매일+4일+9일 1
 
1.0%

stor_cnt
Real number (ℝ)

Distinct81
Distinct (%)81.8%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean179.11111
Minimum5
Maximum4735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:35.149144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile17.7
Q149.5
median84
Q3131.5
95-th percentile517.8
Maximum4735
Range4730
Interquartile range (IQR)82

Descriptive statistics

Standard deviation511.56652
Coefficient of variation (CV)2.8561406
Kurtosis66.261989
Mean179.11111
Median Absolute Deviation (MAD)42
Skewness7.7311895
Sum17732
Variance261700.3
MonotonicityNot monotonic
2023-12-10T18:46:35.549154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 3
 
3.0%
115 3
 
3.0%
54 3
 
3.0%
87 2
 
2.0%
34 2
 
2.0%
12 2
 
2.0%
67 2
 
2.0%
68 2
 
2.0%
100 2
 
2.0%
53 2
 
2.0%
Other values (71) 76
76.0%
ValueCountFrequency (%)
5 1
 
1.0%
12 2
2.0%
13 1
 
1.0%
15 1
 
1.0%
18 1
 
1.0%
21 2
2.0%
25 1
 
1.0%
30 3
3.0%
31 2
2.0%
33 1
 
1.0%
ValueCountFrequency (%)
4735 1
1.0%
1742 1
1.0%
1179 1
1.0%
554 1
1.0%
543 1
1.0%
515 1
1.0%
257 1
1.0%
253 1
1.0%
240 1
1.0%
224 1
1.0%
Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:35.991822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length40
Mean length26.41
Min length3

Characters and Unicode

Total characters2641
Distinct characters55
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

Unique38 ?
Unique (%)38.0%

Sample

1st row농산물,가공식품,의류 및 신발,음식점업,기타소매업,근린생활서비스,
2nd row농수산물, 생필품,잡화 등
3rd row농산물,축산물,수산물,가공식품,의류 및 신발,음식점업,기타소매업
4th row농산물,축산물,수산물,가공식품,의류 및 신발,가정용품,음식점업,근린생활서비스,
5th row농산물,축산물,수산물,의류 및 신발,가정용품,음식점업,기타소매업
ValueCountFrequency (%)
48
20.8%
농산물,축산물,수산물,가공식품,의류 27
 
11.7%
생필품 24
 
10.4%
신발,가정용품,음식점업,기타소매업근린생활서비스 18
 
7.8%
먹거리 12
 
5.2%
신발,가정용품,음식점업,기타소매업,근린생활서비스 11
 
4.8%
5
 
2.2%
신발,가정용품,음식점업,기타소매업 5
 
2.2%
신발,가정용품,음식점업,근린생활서비스 4
 
1.7%
과일 4
 
1.7%
Other values (46) 73
31.6%
2023-12-10T18:46:36.697884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 417
 
15.8%
140
 
5.3%
140
 
5.3%
131
 
5.0%
129
 
4.9%
118
 
4.5%
111
 
4.2%
101
 
3.8%
83
 
3.1%
60
 
2.3%
Other values (45) 1211
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2093
79.3%
Other Punctuation 417
 
15.8%
Space Separator 131
 
5.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
140
 
6.7%
140
 
6.7%
129
 
6.2%
118
 
5.6%
111
 
5.3%
101
 
4.8%
83
 
4.0%
60
 
2.9%
60
 
2.9%
57
 
2.7%
Other values (43) 1094
52.3%
Other Punctuation
ValueCountFrequency (%)
, 417
100.0%
Space Separator
ValueCountFrequency (%)
131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2093
79.3%
Common 548
 
20.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
140
 
6.7%
140
 
6.7%
129
 
6.2%
118
 
5.6%
111
 
5.3%
101
 
4.8%
83
 
4.0%
60
 
2.9%
60
 
2.9%
57
 
2.7%
Other values (43) 1094
52.3%
Common
ValueCountFrequency (%)
, 417
76.1%
131
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2093
79.3%
ASCII 548
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 417
76.1%
131
 
23.9%
Hangul
ValueCountFrequency (%)
140
 
6.7%
140
 
6.7%
129
 
6.2%
118
 
5.6%
111
 
5.3%
101
 
4.8%
83
 
4.0%
60
 
2.9%
60
 
2.9%
57
 
2.7%
Other values (43) 1094
52.3%

use_psbl_gcct
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지역사랑상품권
37 
온누리상품권
27 
온누리+지역사랑상품권
25 
<NA>
온누리상품권, 성남사랑상품권
 
3

Length

Max length15
Median length11
Mean length7.73
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지역사랑상품권
2nd row온누리상품권, 성남사랑상품권
3rd row온누리+지역사랑상품권
4th row지역사랑상품권
5th row온누리+지역사랑상품권

Common Values

ValueCountFrequency (%)
지역사랑상품권 37
37.0%
온누리상품권 27
27.0%
온누리+지역사랑상품권 25
25.0%
<NA> 8
 
8.0%
온누리상품권, 성남사랑상품권 3
 
3.0%

Length

2023-12-10T18:46:36.999430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:37.223781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역사랑상품권 37
35.9%
온누리상품권 30
29.1%
온누리+지역사랑상품권 25
24.3%
na 8
 
7.8%
성남사랑상품권 3
 
2.9%

hmpg
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing94
Missing (%)94.0%
Memory size932.0 B
2023-12-10T18:46:37.500466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22.5
Mean length23.333333
Min length18

Characters and Unicode

Total characters140
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowwww.taepyung-market.kr
2nd rowdolgjimarket.co.kr
3rd rowhttp://namhansansungsj.modoo.at
4th rowhttp://koung1709.modoo.at/
5th rowblog.naver.com/mrn61261
ValueCountFrequency (%)
www.taepyung-market.kr 1
16.7%
dolgjimarket.co.kr 1
16.7%
http://namhansansungsj.modoo.at 1
16.7%
http://koung1709.modoo.at 1
16.7%
blog.naver.com/mrn61261 1
16.7%
jinanmarket.modoo.at 1
16.7%
2023-12-10T18:46:38.029729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 14
 
10.0%
a 12
 
8.6%
. 12
 
8.6%
t 11
 
7.9%
n 10
 
7.1%
m 9
 
6.4%
r 7
 
5.0%
/ 6
 
4.3%
k 6
 
4.3%
e 5
 
3.6%
Other values (22) 48
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 110
78.6%
Other Punctuation 20
 
14.3%
Decimal Number 9
 
6.4%
Dash Punctuation 1
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 14
12.7%
a 12
10.9%
t 11
10.0%
n 10
 
9.1%
m 9
 
8.2%
r 7
 
6.4%
k 6
 
5.5%
e 5
 
4.5%
g 5
 
4.5%
d 4
 
3.6%
Other values (12) 27
24.5%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
6 2
22.2%
7 1
 
11.1%
0 1
 
11.1%
9 1
 
11.1%
2 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 12
60.0%
/ 6
30.0%
: 2
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 110
78.6%
Common 30
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 14
12.7%
a 12
10.9%
t 11
10.0%
n 10
 
9.1%
m 9
 
8.2%
r 7
 
6.4%
k 6
 
5.5%
e 5
 
4.5%
g 5
 
4.5%
d 4
 
3.6%
Other values (12) 27
24.5%
Common
ValueCountFrequency (%)
. 12
40.0%
/ 6
20.0%
1 3
 
10.0%
: 2
 
6.7%
6 2
 
6.7%
- 1
 
3.3%
7 1
 
3.3%
0 1
 
3.3%
9 1
 
3.3%
2 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 14
 
10.0%
a 12
 
8.6%
. 12
 
8.6%
t 11
 
7.9%
n 10
 
7.1%
m 9
 
6.4%
r 7
 
5.0%
/ 6
 
4.3%
k 6
 
4.3%
e 5
 
3.6%
Other values (22) 48
34.3%

pbctlt_pos_yn
Boolean

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
93 
False
 
7
ValueCountFrequency (%)
True 93
93.0%
False 7
 
7.0%
2023-12-10T18:46:38.259904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
73 
False
27 
ValueCountFrequency (%)
True 73
73.0%
False 27
 
27.0%
2023-12-10T18:46:38.415987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

opnng_yy
Real number (ℝ)

MISSING 

Distinct45
Distinct (%)53.6%
Missing16
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean1981.2738
Minimum1780
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:38.622716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1780
5-th percentile1929
Q11976.5
median1988.5
Q31996
95-th percentile2013.85
Maximum2015
Range235
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation31.77153
Coefficient of variation (CV)0.016035911
Kurtosis18.720738
Mean1981.2738
Median Absolute Deviation (MAD)9.5
Skewness-3.419464
Sum166427
Variance1009.4301
MonotonicityNot monotonic
2023-12-10T18:46:38.956555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1996 5
 
5.0%
1993 5
 
5.0%
1994 4
 
4.0%
2014 4
 
4.0%
1981 4
 
4.0%
1980 4
 
4.0%
1992 3
 
3.0%
1929 3
 
3.0%
1995 3
 
3.0%
1964 3
 
3.0%
Other values (35) 46
46.0%
(Missing) 16
 
16.0%
ValueCountFrequency (%)
1780 1
 
1.0%
1918 1
 
1.0%
1920 1
 
1.0%
1929 3
3.0%
1931 1
 
1.0%
1950 2
2.0%
1953 2
2.0%
1955 1
 
1.0%
1959 1
 
1.0%
1964 3
3.0%
ValueCountFrequency (%)
2015 1
 
1.0%
2014 4
4.0%
2013 2
2.0%
2012 1
 
1.0%
2011 1
 
1.0%
2010 1
 
1.0%
2007 2
2.0%
2006 1
 
1.0%
2005 2
2.0%
2001 1
 
1.0%

telno
Text

MISSING 

Distinct35
Distinct (%)97.2%
Missing64
Missing (%)64.0%
Memory size932.0 B
2023-12-10T18:46:39.395545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.027778
Min length12

Characters and Unicode

Total characters433
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

Unique34 ?
Unique (%)94.4%

Sample

1st row031-716-9814
2nd row031-713-1071
3rd row031-786-0704
4th row055-212-4413
5th row055-212-4413
ValueCountFrequency (%)
055-212-4413 2
 
5.6%
055-282-0435 1
 
2.8%
055-287-4404 1
 
2.8%
055-277-0999 1
 
2.8%
055-275-0449 1
 
2.8%
055-282-7009 1
 
2.8%
055-268-5605 1
 
2.8%
055-268-5015 1
 
2.8%
055-245-7054 1
 
2.8%
055-287-6101 1
 
2.8%
Other values (25) 25
69.4%
2023-12-10T18:46:40.055185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 86
19.9%
- 72
16.6%
0 70
16.2%
2 49
11.3%
1 26
 
6.0%
4 24
 
5.5%
3 23
 
5.3%
7 23
 
5.3%
8 22
 
5.1%
6 21
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 361
83.4%
Dash Punctuation 72
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 86
23.8%
0 70
19.4%
2 49
13.6%
1 26
 
7.2%
4 24
 
6.6%
3 23
 
6.4%
7 23
 
6.4%
8 22
 
6.1%
6 21
 
5.8%
9 17
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 433
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 86
19.9%
- 72
16.6%
0 70
16.2%
2 49
11.3%
1 26
 
6.0%
4 24
 
5.5%
3 23
 
5.3%
7 23
 
5.3%
8 22
 
5.1%
6 21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 86
19.9%
- 72
16.6%
0 70
16.2%
2 49
11.3%
1 26
 
6.0%
4 24
 
5.5%
3 23
 
5.3%
7 23
 
5.3%
8 22
 
5.1%
6 21
 
4.8%

residnt_cnt_sum
Real number (ℝ)

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242913.69
Minimum22099
Maximum853106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:40.335209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22099
5-th percentile24031
Q1174933
median218096
Q3276386.25
95-th percentile483277.4
Maximum853106
Range831007
Interquartile range (IQR)101453.25

Descriptive statistics

Standard deviation153616.04
Coefficient of variation (CV)0.63238939
Kurtosis3.0585664
Mean242913.69
Median Absolute Deviation (MAD)55341.5
Skewness1.2137037
Sum24291369
Variance2.3597888 × 1010
MonotonicityNot monotonic
2023-12-10T18:46:40.740650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
262743 18
18.0%
215143 12
 
12.0%
174933 6
 
6.0%
176885 5
 
5.0%
24031 4
 
4.0%
22099 3
 
3.0%
482614 3
 
3.0%
69074 2
 
2.0%
114951 2
 
2.0%
218096 2
 
2.0%
Other values (37) 43
43.0%
ValueCountFrequency (%)
22099 3
3.0%
24031 4
4.0%
25351 1
 
1.0%
30748 1
 
1.0%
31623 1
 
1.0%
53183 1
 
1.0%
62747 2
2.0%
68966 2
2.0%
69074 2
2.0%
89031 1
 
1.0%
ValueCountFrequency (%)
853106 1
 
1.0%
819990 1
 
1.0%
526337 1
 
1.0%
497925 1
 
1.0%
495882 1
 
1.0%
482614 3
3.0%
480617 1
 
1.0%
455058 1
 
1.0%
438051 2
2.0%
425431 1
 
1.0%

mkt_cnt_sum
Real number (ℝ)

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.66
Minimum3
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:41.044746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.95
Q18
median18
Q319.25
95-th percentile36.1
Maximum38
Range35
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation9.1212041
Coefficient of variation (CV)0.54749124
Kurtosis0.32610073
Mean16.66
Median Absolute Deviation (MAD)3
Skewness0.63796146
Sum1666
Variance83.196364
MonotonicityNot monotonic
2023-12-10T18:46:41.325740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
18 18
18.0%
19 16
16.0%
20 9
9.0%
8 7
 
7.0%
6 6
 
6.0%
4 6
 
6.0%
38 5
 
5.0%
3 5
 
5.0%
15 5
 
5.0%
36 4
 
4.0%
Other values (11) 19
19.0%
ValueCountFrequency (%)
3 5
5.0%
4 6
6.0%
6 6
6.0%
7 2
 
2.0%
8 7
7.0%
9 3
3.0%
11 2
 
2.0%
14 2
 
2.0%
15 5
5.0%
16 1
 
1.0%
ValueCountFrequency (%)
38 5
 
5.0%
36 4
 
4.0%
33 1
 
1.0%
31 1
 
1.0%
28 1
 
1.0%
22 1
 
1.0%
21 3
 
3.0%
20 9
9.0%
19 16
16.0%
18 18
18.0%

stor_cnt_sum
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2477.56
Minimum168
Maximum20299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:41.614124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile185
Q11169.5
median1587
Q32837
95-th percentile6253.95
Maximum20299
Range20131
Interquartile range (IQR)1667.5

Descriptive statistics

Standard deviation3166.6333
Coefficient of variation (CV)1.2781258
Kurtosis19.498904
Mean2477.56
Median Absolute Deviation (MAD)1151
Skewness4.0155826
Sum247756
Variance10027566
MonotonicityNot monotonic
2023-12-10T18:46:41.905641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1587 18
18.0%
2837 12
 
12.0%
4773 6
 
6.0%
2851 5
 
5.0%
204 4
 
4.0%
1322 3
 
3.0%
175 3
 
3.0%
946 2
 
2.0%
2289 2
 
2.0%
20299 2
 
2.0%
Other values (35) 43
43.0%
ValueCountFrequency (%)
168 1
 
1.0%
175 3
3.0%
185 2
2.0%
204 4
4.0%
218 1
 
1.0%
266 1
 
1.0%
286 1
 
1.0%
332 1
 
1.0%
378 2
2.0%
409 1
 
1.0%
ValueCountFrequency (%)
20299 2
 
2.0%
10525 2
 
2.0%
7374 1
 
1.0%
6195 1
 
1.0%
5256 1
 
1.0%
4773 6
6.0%
4106 2
 
2.0%
3240 1
 
1.0%
2851 5
5.0%
2837 12
12.0%

person_mkt_cnt
Real number (ℝ)

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.51
Minimum6
Maximum2300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:42.182792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile35.75
Q176
median135
Q3197.5
95-th percentile573.95
Maximum2300
Range2294
Interquartile range (IQR)121.5

Descriptive statistics

Standard deviation325.98245
Coefficient of variation (CV)1.505623
Kurtosis23.080056
Mean216.51
Median Absolute Deviation (MAD)59
Skewness4.470856
Sum21651
Variance106264.56
MonotonicityNot monotonic
2023-12-10T18:46:42.456166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
166 18
18.0%
76 12
 
12.0%
37 6
 
6.0%
62 5
 
5.0%
118 4
 
4.0%
126 3
 
3.0%
365 3
 
3.0%
80 2
 
2.0%
304 2
 
2.0%
53 2
 
2.0%
Other values (37) 43
43.0%
ValueCountFrequency (%)
6 2
 
2.0%
7 2
 
2.0%
12 1
 
1.0%
37 6
6.0%
41 1
 
1.0%
49 2
 
2.0%
52 1
 
1.0%
53 2
 
2.0%
62 5
5.0%
76 12
12.0%
ValueCountFrequency (%)
2300 1
1.0%
1771 1
1.0%
1500 1
1.0%
712 1
1.0%
592 1
1.0%
573 1
1.0%
449 2
2.0%
429 1
1.0%
422 1
1.0%
418 1
1.0%

file_name
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_603_TRD_MART_CTL_STATN_BIZAEA_2020
100 

Length

Max length37
Median length37
Mean length37
Min length37

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_603_TRD_MART_CTL_STATN_BIZAEA_2020
2nd rowKC_603_TRD_MART_CTL_STATN_BIZAEA_2020
3rd rowKC_603_TRD_MART_CTL_STATN_BIZAEA_2020
4th rowKC_603_TRD_MART_CTL_STATN_BIZAEA_2020
5th rowKC_603_TRD_MART_CTL_STATN_BIZAEA_2020

Common Values

ValueCountFrequency (%)
KC_603_TRD_MART_CTL_STATN_BIZAEA_2020 100
100.0%

Length

2023-12-10T18:46:42.676613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:42.861781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_603_trd_mart_ctl_statn_bizaea_2020 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T18:46:43.053626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:43.231527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 100
100.0%

Sample

idfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdmrkt_tymrkt_opn_cyclestor_cnttrt_itmuse_psbl_gccthmpgpbctlt_pos_ynprklt_pos_ynopnng_yytelnoresidnt_cnt_summkt_cnt_sumstor_cnt_sumperson_mkt_cntfile_namebase_ymd
0KC603PO20N000000001서대신1동골목시장부산광역시서구2614010400서대신동1가2614054000서대신1동261403125009부산광역시 서구 대영로 30번길 19-1(서대신동1가)49228마라380803129.01443235.108658상설장-50농산물,가공식품,의류 및 신발,음식점업,기타소매업,근린생활서비스,지역사랑상품권<NA>NN1950<NA>10811919206052KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
1KC603PO20N000002705분당종합시장경기도성남시 분당구4113510200수내동4113554000수내3동411353180007경기도 성남시 분당구 돌마로366번길42(수내동)13600다사669298127.12674937.36718상설장매일40농수산물, 생필품,잡화 등온누리상품권, 성남사랑상품권<NA>YY1996031-716-9814482614151322365KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
2KC603PO20N000000003남리5일시장전라남도해남군4682040021황산면4682040000황산면468203294013전라남도 해남군 황산면 시등로 111-1159007다라020206126.43248434.5753245일장5일15농산물,축산물,수산물,가공식품,의류 및 신발,음식점업,기타소매업온누리+지역사랑상품권<NA>YY1964<NA>6896620141849KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
3KC603PO20N000000004광안시장부산광역시수영구2650010400광안동2650079000광안4동265004214281부산광역시 수영구 수영로603번길 1848254마라468861129.11246735.160118상설장-60농산물,축산물,수산물,가공식품,의류 및 신발,가정용품,음식점업,근린생활서비스,지역사랑상품권<NA>YY1955<NA>17688538285162KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
4KC603PO20N000000005원통시장강원도인제군4281032021북면4281032000북면428103233019강원도 인제군 북면 원통로 178번길 7-124617라아617138128.20461738.122581상설장+5일장5일58농산물,축산물,수산물,의류 및 신발,가정용품,음식점업,기타소매업온누리+지역사랑상품권<NA>YY<NA><NA>316233185171KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
5KC603PO20N000000006승주시장전라남도순천시4615025021승주읍4615025000승주읍461504649579전라남도 승주읍 승주장길 1457908다라897687127.38751935.0132255일장5일21농산물,수산물,의류 및 신발,가정용품,음식점업,기타소매업,근린생활서비스온누리+지역사랑상품권<NA>YN<NA><NA>282618171197236KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
6KC603PO20N000000007서면시장경상북도경주시4713035021서면4713035000서면471303305071경상북도 경주시 서면 내서로 401-538053마마397676129.04804335.8950635일장5일5가공식품,음식점업,기타소매업,근린생활서비스,지역사랑상품권<NA>YY1929<NA>25346831619541KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
7KC603PO20N000002706코끼리시장경기도성남시 분당구4113510200수내동4113553000수내2동411353180003경기도 성남시 분당구 내정로166번길7-6(수내동,파크타운)13599다사663305127.12042237.373112상설장매일92농수산물, 생필품,잡화 등온누리상품권, 성남사랑상품권<NA>YY1996031-713-1071482614151322365KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
8KC603PO20N000000009영광터미널시장전라남도영광군4687025021영광읍4687025000영광읍468704694222전라남도 영광군 영광읍 신남로3길 5-357043다라093982126.50301935.275536상설장+5일장5일90농산물,축산물,수산물,가공식품,의류 및 신발,가정용품,음식점업,기타소매업근린생활서비스온누리+지역사랑상품권<NA>YY1993<NA>531834286186KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
9KC603PO20N000000010지산목련시장(지산종합시장)대구광역시수성구2726011200지산동2726065200지산2동272603146020대구광역시 수성구 용학로42길 942212마마022588128.63186335.820797상설장-33농산물,축산물,가공식품,음식점업,근린생활서비스지역사랑상품권<NA>YY<NA><NA>42494719946449KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
idfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdmrkt_tymrkt_opn_cyclestor_cnttrt_itmuse_psbl_gccthmpgpbctlt_pos_ynprklt_pos_ynopnng_yytelnoresidnt_cnt_summkt_cnt_sumstor_cnt_sumperson_mkt_cntfile_namebase_ymd
90KC603PO20N000000091장승백이전통시장인천광역시남동구2820010300만수동2820058300만수6동282004259644인천광역시 남동구 인주대로888번길 3521591다사323388126.73522737.446564상설장-113농산물,축산물,수산물,가공식품,의류 및 신발,가정용품,음식점업,기타소매업근린생활서비스온누리+지역사랑상품권<NA>YY1995<NA>52633781284410KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
91KC603PO20N000000092강남시장서울특별시동작구1159010200상도동1159053000상도1동115903119005서울특별시 동작구 상도로 357 아파트 시장6978다사516443126.95354137.49694상설장-21가공식품,가정용품,음식점업,기타소매업,근린생활서비스지역사랑상품권<NA>YN<NA><NA>391896221372286KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
92KC603PO20N000000093연수구옥련시장인천광역시연수구2818510100옥련동2818564000옥련2동281853008017인천광역시 연수구 독배로 40번길 3521949다사245365126.6468337.424622상설장-61농산물,축산물,가공식품,의류 및 신발,음식점업,근린생활서비스,온누리+지역사랑상품권<NA>NN<NA><NA>38597332181771KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
93KC603PO20N000000094광장시장서울특별시종로구1111015800예지동1111061500종로1.2.3.4가동111103005008서울특별시 종로구 창경궁로 883195다사558524126.99970737.570113상설장-1742농산물,축산물,수산물,가공식품,의류 및 신발,가정용품,음식점업,기타소매업근린생활서비스<NA><NA>YN<NA><NA>14954936202997KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
94KC603PO20N000000095수성시장대구광역시수성구2726010400수성동2가2726058000수성2.3가동272603146008대구광역시 수성구 들안로 285-5642124마마008627128.61700635.855655상설장-100농산물,축산물,가공식품,의류 및 신발,가정용품,음식점업,기타소매업,근린생활서비스,지역사랑상품권<NA>YY<NA><NA>42494719946449KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
95KC603PO20N000000096신마산시장경상남도창원시 마산합포구4812515800해운동4812553000월영동481254787217경상남도 창원시 마산합포구 문화동15길13 (해운동)51744라라966878128.56170235.181097상설장매일85실비집, 활어, 분식온누리상품권<NA>YN1998055-223-032517493320477337KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
96KC603PO20N000000097월영대시장경상남도창원시 마산합포구4812515800해운동4812553000월영동481254787578경상남도 창원시 마산합포구 월영남1길13 (월영동)51769라라963876128.5577635.179275상설장매일108먹거리, 생필품<NA><NA>NN2013<NA>17493320477337KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
97KC603PO20N000000098신마산반월시장경상남도창원시 마산합포구4812511600반월동4812556500반월중앙동481254787258경상남도 창원시 마산합포구 반월남1길18-16 (반월동)51761라라966887128.56118335.189659상설장매일115먹거리온누리상품권<NA>YN1980055-221-666617493320477337KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
98KC603PO20N000000099정우새어시장경상남도창원시 마산합포구4812511100동성동4812563000오동동481254787437경상남도 창원시 마산합포구 어시장7길 126(동성동)51721라라981903128.57871135.204104상설장매일97수산물온누리상품권<NA>YY1996055-245-705417493320477337KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201
99KC603PO20N000000100마산어시장경상남도창원시 마산합포구4812511100동성동4812563000오동동481253329015경상남도 창원시 마산합포구 복요리로 7(동성동)51721라라982903128.57895635.203762상설장매일1179수산물온누리상품권<NA>YY1780055-224-000917493320477337KC_603_TRD_MART_CTL_STATN_BIZAEA_202020210201