Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory81.4 B

Variable types

Categorical3
Numeric3
Text3

Dataset

Description샘플 데이터
Author신용보증재단
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=69

Alerts

기준_년_코드(STDR_YY_CODE_SE) has constant value ""Constant
기준_분기_코드(STDR_QU_CODE_SE) has constant value ""Constant
매출액_합계(SUM_SELNG_AMT) has unique valuesUnique
매출액_합계(SUM_SELNG_AMT) has 1 (3.3%) zerosZeros
매출건수_합계(SUM_SELNG_CO) has 3 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:51:51.019194
Analysis finished2023-12-10 14:51:52.887809
Duration1.87 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년_코드(STDR_YY_CODE_SE)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2019
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 30
100.0%

Length

2023-12-10T23:51:52.967256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:53.079607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 30
100.0%

기준_분기_코드(STDR_QU_CODE_SE)
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
3
30 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 30
100.0%

Length

2023-12-10T23:51:53.187408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:53.288621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 30
100.0%

행정동코드(ADSTRD_CD)
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11538612
Minimum11110615
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:51:53.384717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110615
5-th percentile11224616
Q111418128
median11590618
Q311650608
95-th percentile11710670
Maximum11740700
Range630085
Interquartile range (IQR)232480

Descriptive statistics

Standard deviation167441.98
Coefficient of variation (CV)0.014511449
Kurtosis0.3050832
Mean11538612
Median Absolute Deviation (MAD)89938
Skewness-1.0610438
Sum3.4615835 × 108
Variance2.8036817 × 1010
MonotonicityNot monotonic
2023-12-10T23:51:53.524871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
11710670 2
 
6.7%
11650520 2
 
6.7%
11590510 1
 
3.3%
11560700 1
 
3.3%
11680590 1
 
3.3%
11590650 1
 
3.3%
11290575 1
 
3.3%
11650600 1
 
3.3%
11740700 1
 
3.3%
11680521 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
11110615 1
3.3%
11170650 1
3.3%
11290575 1
3.3%
11305630 1
3.3%
11350621 1
3.3%
11380520 1
3.3%
11380552 1
3.3%
11410640 1
3.3%
11440590 1
3.3%
11530560 1
3.3%
ValueCountFrequency (%)
11740700 1
3.3%
11710670 2
6.7%
11680740 1
3.3%
11680630 1
3.3%
11680590 1
3.3%
11680521 1
3.3%
11650610 1
3.3%
11650600 1
3.3%
11650570 1
3.3%
11650560 1
3.3%

행정동명(ADSTRD_NM)
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:51:53.737585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7333333
Min length3

Characters and Unicode

Total characters112
Distinct characters49
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

Unique26 ?
Unique (%)86.7%

Sample

1st row미성동
2nd row남영동
3rd row구의3동
4th row사근동
5th row이태원1동
ValueCountFrequency (%)
개봉1동 2
 
6.7%
영등포동 2
 
6.7%
미성동 1
 
3.3%
고척1동 1
 
3.3%
반포1동 1
 
3.3%
양평2동 1
 
3.3%
약수동 1
 
3.3%
구로1동 1
 
3.3%
잠실본동 1
 
3.3%
영등포본동 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:51:54.114121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 30
26.8%
1 10
 
8.9%
2 4
 
3.6%
영 4
 
3.6%
등 4
 
3.6%
포 4
 
3.6%
4 3
 
2.7%
개 2
 
1.8%
본 2
 
1.8%
목 2
 
1.8%
Other values (39) 47
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
83.9%
Decimal Number 18
 
16.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 30
31.9%
영 4
 
4.3%
등 4
 
4.3%
포 4
 
4.3%
개 2
 
2.1%
본 2
 
2.1%
목 2
 
2.1%
촌 2
 
2.1%
당 2
 
2.1%
봉 2
 
2.1%
Other values (35) 40
42.6%
Decimal Number
ValueCountFrequency (%)
1 10
55.6%
2 4
 
22.2%
4 3
 
16.7%
3 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
83.9%
Common 18
 
16.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 30
31.9%
영 4
 
4.3%
등 4
 
4.3%
포 4
 
4.3%
개 2
 
2.1%
본 2
 
2.1%
목 2
 
2.1%
촌 2
 
2.1%
당 2
 
2.1%
봉 2
 
2.1%
Other values (35) 40
42.6%
Common
ValueCountFrequency (%)
1 10
55.6%
2 4
 
22.2%
4 3
 
16.7%
3 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
83.9%
ASCII 18
 
16.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 30
31.9%
영 4
 
4.3%
등 4
 
4.3%
포 4
 
4.3%
개 2
 
2.1%
본 2
 
2.1%
목 2
 
2.1%
촌 2
 
2.1%
당 2
 
2.1%
봉 2
 
2.1%
Other values (35) 40
42.6%
ASCII
ValueCountFrequency (%)
1 10
55.6%
2 4
 
22.2%
4 3
 
16.7%
3 1
 
5.6%

서비스업종코드(SVC_INDUTY_CD)
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:51:54.300991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters240
Distinct characters10
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

Unique13 ?
Unique (%)43.3%

Sample

1st rowCS200002
2nd rowCS100006
3rd rowCS200018
4th rowCS300011
5th rowCS200008
ValueCountFrequency (%)
cs100010 3
 
10.0%
cs300004 2
 
6.7%
cs200004 2
 
6.7%
cs100006 2
 
6.7%
cs200011 2
 
6.7%
cs200018 2
 
6.7%
cs200002 2
 
6.7%
cs300017 2
 
6.7%
cs300010 1
 
3.3%
cs100003 1
 
3.3%
Other values (11) 11
36.7%
2023-12-10T23:51:54.631781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 110
45.8%
C 30
 
12.5%
S 30
 
12.5%
1 29
 
12.1%
2 15
 
6.2%
3 10
 
4.2%
4 6
 
2.5%
8 4
 
1.7%
7 3
 
1.2%
6 3
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
75.0%
Uppercase Letter 60
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 110
61.1%
1 29
 
16.1%
2 15
 
8.3%
3 10
 
5.6%
4 6
 
3.3%
8 4
 
2.2%
7 3
 
1.7%
6 3
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 30
50.0%
S 30
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
75.0%
Latin 60
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 110
61.1%
1 29
 
16.1%
2 15
 
8.3%
3 10
 
5.6%
4 6
 
3.3%
8 4
 
2.2%
7 3
 
1.7%
6 3
 
1.7%
Latin
ValueCountFrequency (%)
C 30
50.0%
S 30
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 110
45.8%
C 30
 
12.5%
S 30
 
12.5%
1 29
 
12.1%
2 15
 
6.2%
3 10
 
4.2%
4 6
 
2.5%
8 4
 
1.7%
7 3
 
1.2%
6 3
 
1.2%

서비스업종코드명(SVC_INDUTY_CD_NM)
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:51:54.834385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.8666667
Min length3

Characters and Unicode

Total characters146
Distinct characters77
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)50.0%

Sample

1st row의류점
2nd row치과의원
3rd row패스트푸드점
4th row당구장
5th row패스트푸드점
ValueCountFrequency (%)
패스트푸드점 3
 
10.0%
한의원 2
 
6.7%
일반교습학원 2
 
6.7%
분식전문점 2
 
6.7%
치과의원 2
 
6.7%
의류점 2
 
6.7%
통신판매업 2
 
6.7%
치킨전문점 1
 
3.3%
자동차수리·세차 1
 
3.3%
섬유제품 1
 
3.3%
Other values (12) 12
40.0%
2023-12-10T23:51:55.165088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 10
 
6.8%
원 7
 
4.8%
의 6
 
4.1%
· 5
 
3.4%
식 4
 
2.7%
패 4
 
2.7%
치 3
 
2.1%
푸 3
 
2.1%
스 3
 
2.1%
문 3
 
2.1%
Other values (67) 98
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
95.2%
Other Punctuation 5
 
3.4%
Open Punctuation 1
 
0.7%
Close Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 10
 
7.2%
원 7
 
5.0%
의 6
 
4.3%
식 4
 
2.9%
패 4
 
2.9%
치 3
 
2.2%
푸 3
 
2.2%
스 3
 
2.2%
문 3
 
2.2%
전 3
 
2.2%
Other values (64) 93
66.9%
Other Punctuation
ValueCountFrequency (%)
· 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
95.2%
Common 7
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 10
 
7.2%
원 7
 
5.0%
의 6
 
4.3%
식 4
 
2.9%
패 4
 
2.9%
치 3
 
2.2%
푸 3
 
2.2%
스 3
 
2.2%
문 3
 
2.2%
전 3
 
2.2%
Other values (64) 93
66.9%
Common
ValueCountFrequency (%)
· 5
71.4%
( 1
 
14.3%
) 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
95.2%
None 5
 
3.4%
ASCII 2
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 10
 
7.2%
원 7
 
5.0%
의 6
 
4.3%
식 4
 
2.9%
패 4
 
2.9%
치 3
 
2.2%
푸 3
 
2.2%
스 3
 
2.2%
문 3
 
2.2%
전 3
 
2.2%
Other values (64) 93
66.9%
None
ValueCountFrequency (%)
· 5
100.0%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

연령대_10세단위(AGE)
Categorical

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
60_ABOVE
7 
40
5 
10
5 
30
5 
50
5 

Length

Max length8
Median length2
Mean length3.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row40
3rd row60_ABOVE
4th row60_ABOVE
5th row10

Common Values

ValueCountFrequency (%)
60_ABOVE 7
23.3%
40 5
16.7%
10 5
16.7%
30 5
16.7%
50 5
16.7%
20 3
10.0%

Length

2023-12-10T23:51:55.320379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:51:55.442752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60_above 7
23.3%
40 5
16.7%
10 5
16.7%
30 5
16.7%
50 5
16.7%
20 3
10.0%

매출액_합계(SUM_SELNG_AMT)
Real number (ℝ)

UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3371571 × 108
Minimum0
Maximum2.8604138 × 109
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:51:55.570483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile176193.55
Q18964809.8
median14376536
Q31.4129602 × 108
95-th percentile9.8445436 × 108
Maximum2.8604138 × 109
Range2.8604138 × 109
Interquartile range (IQR)1.3233121 × 108

Descriptive statistics

Standard deviation5.6572038 × 108
Coefficient of variation (CV)2.4205492
Kurtosis16.708925
Mean2.3371571 × 108
Median Absolute Deviation (MAD)14112100
Skewness3.8466096
Sum7.0114713 × 109
Variance3.2003954 × 1017
MonotonicityNot monotonic
2023-12-10T23:51:55.706669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 1
 
3.3%
179372 1
 
3.3%
13040166 1
 
3.3%
10270014 1
 
3.3%
2860413836 1
 
3.3%
406315 1
 
3.3%
820575079 1
 
3.3%
146414281 1
 
3.3%
8884998 1
 
3.3%
13679057 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0 1
3.3%
173593 1
3.3%
179372 1
3.3%
349500 1
3.3%
406315 1
3.3%
3595786 1
3.3%
5306591 1
3.3%
8884998 1
3.3%
9204245 1
3.3%
10270014 1
3.3%
ValueCountFrequency (%)
2860413836 1
3.3%
1118537404 1
3.3%
820575079 1
3.3%
538758823 1
3.3%
535590632 1
3.3%
400876910 1
3.3%
170960024 1
3.3%
146414281 1
3.3%
125941219 1
3.3%
56429211 1
3.3%

매출건수_합계(SUM_SELNG_CO)
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22517.4
Minimum0
Maximum354550
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:51:55.829470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1311.75
median2076
Q38608.5
95-th percentile106408.35
Maximum354550
Range354550
Interquartile range (IQR)8296.75

Descriptive statistics

Standard deviation68384.682
Coefficient of variation (CV)3.0369706
Kurtosis20.589883
Mean22517.4
Median Absolute Deviation (MAD)2014
Skewness4.390218
Sum675522
Variance4.6764647 × 109
MonotonicityNot monotonic
2023-12-10T23:51:55.958337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 3
 
10.0%
392 1
 
3.3%
914 1
 
3.3%
2804 1
 
3.3%
68856 1
 
3.3%
295 1
 
3.3%
354550 1
 
3.3%
7003 1
 
3.3%
8875 1
 
3.3%
238 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0 3
10.0%
10 1
 
3.3%
114 1
 
3.3%
145 1
 
3.3%
238 1
 
3.3%
295 1
 
3.3%
362 1
 
3.3%
392 1
 
3.3%
429 1
 
3.3%
834 1
 
3.3%
ValueCountFrequency (%)
354550 1
3.3%
137133 1
3.3%
68856 1
3.3%
26327 1
3.3%
20569 1
3.3%
10225 1
3.3%
9696 1
3.3%
8875 1
3.3%
7809 1
3.3%
7003 1
3.3%

Interactions

2023-12-10T23:51:51.991833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.322473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.636621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:52.091360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.415645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.762745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:52.530667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.524130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:51:51.875125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:51:56.060570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(ADSTRD_CD)행정동명(ADSTRD_NM)서비스업종코드(SVC_INDUTY_CD)서비스업종코드명(SVC_INDUTY_CD_NM)연령대_10세단위(AGE)매출액_합계(SUM_SELNG_AMT)매출건수_합계(SUM_SELNG_CO)
행정동코드(ADSTRD_CD)1.0000.9460.5760.0000.3980.7250.000
행정동명(ADSTRD_NM)0.9461.0000.9130.9250.7440.9130.000
서비스업종코드(SVC_INDUTY_CD)0.5760.9131.0000.7670.6880.8170.000
서비스업종코드명(SVC_INDUTY_CD_NM)0.0000.9250.7671.0000.0000.8570.856
연령대_10세단위(AGE)0.3980.7440.6880.0001.0000.2460.103
매출액_합계(SUM_SELNG_AMT)0.7250.9130.8170.8570.2461.0000.000
매출건수_합계(SUM_SELNG_CO)0.0000.0000.0000.8560.1030.0001.000
2023-12-10T23:51:56.198613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드(ADSTRD_CD)매출액_합계(SUM_SELNG_AMT)매출건수_합계(SUM_SELNG_CO)연령대_10세단위(AGE)
행정동코드(ADSTRD_CD)1.0000.1560.1270.188
매출액_합계(SUM_SELNG_AMT)0.1561.000-0.1890.145
매출건수_합계(SUM_SELNG_CO)0.127-0.1891.0000.000
연령대_10세단위(AGE)0.1880.1450.0001.000

Missing values

2023-12-10T23:51:52.663657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:51:52.813663image/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

기준_년_코드(STDR_YY_CODE_SE)기준_분기_코드(STDR_QU_CODE_SE)행정동코드(ADSTRD_CD)행정동명(ADSTRD_NM)서비스업종코드(SVC_INDUTY_CD)서비스업종코드명(SVC_INDUTY_CD_NM)연령대_10세단위(AGE)매출액_합계(SUM_SELNG_AMT)매출건수_합계(SUM_SELNG_CO)
02019311590510미성동CS200002의류점200392
12019311650520남영동CS100006치과의원405387588234026
22019311440590구의3동CS200018패스트푸드점60_ABOVE318998564566
32019311680740사근동CS300011당구장60_ABOVE1114563120569
42019311590630이태원1동CS200008패스트푸드점105306591145
52019311170650일원2동CS100010화장품60_ABOVE359578610225
62019311710670상도1동CS100006세탁소(가정)30154234093761
72019311650560자양4동CS100003호프·간이주점60_ABOVE5355906321437
82019311680630염리동CS100010의류점60_ABOVE150740160
92019311650570수서동CS300017패션용품50535900579696
기준_년_코드(STDR_YY_CODE_SE)기준_분기_코드(STDR_QU_CODE_SE)행정동코드(ADSTRD_CD)행정동명(ADSTRD_NM)서비스업종코드(SVC_INDUTY_CD)서비스업종코드명(SVC_INDUTY_CD_NM)연령대_10세단위(AGE)매출액_합계(SUM_SELNG_AMT)매출건수_합계(SUM_SELNG_CO)
202019311410640등촌2동CS200016슈퍼마켓5011185374040
212019311350621장안1동CS100001한의원20173593429
222019311110615개봉1동CS200004통신판매업5013679057238
232019311680521영등포본동CS300008통신판매업4088849988875
242019311740700잠실본동CS300004일반교습학원101464142810
252019311650600구로1동CS300004컴퓨터·주변기기308205750797003
262019311710670약수동CS200001섬유제품30406315354550
272019311290575양평2동CS200014한의원402860413836295
282019311590650반포1동CS200002치과의원501027001468856
292019311680590목2동CS200018한식음식점50130401662804