Overview

Dataset statistics

Number of variables9
Number of observations8500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory647.6 KiB
Average record size in memory78.0 B

Variable types

Numeric5
Text1
Categorical3

Dataset

Description기준_년분기_코드,행정동_코드,행정동_코드_명,상권_변화_지표,상권_변화_지표_명,운영_영업_개월_평균,폐업_영업_개월_평균,서울_운영_영업_개월_평균,서울_폐업_영업_개월_평균
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-15575/S/1/datasetView.do

Alerts

상권_변화_지표_명 is highly overall correlated with 상권_변화_지표High correlation
상권_변화_지표 is highly overall correlated with 상권_변화_지표_명High correlation
기준_년분기_코드 is highly overall correlated with 서울_운영_영업_개월_평균 and 1 other fieldsHigh correlation
운영_영업_개월_평균 is highly overall correlated with 폐업_영업_개월_평균High correlation
폐업_영업_개월_평균 is highly overall correlated with 운영_영업_개월_평균High correlation
서울_운영_영업_개월_평균 is highly overall correlated with 기준_년분기_코드 and 1 other fieldsHigh correlation
서울_폐업_영업_개월_평균 is highly overall correlated with 기준_년분기_코드 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-05-04 00:39:41.112918
Analysis finished2024-05-04 00:39:54.278948
Duration13.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20212.5
Minimum20191
Maximum20234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.8 KiB
2024-05-04T00:39:54.560022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191
5-th percentile20191.95
Q120201.75
median20212.5
Q320223.25
95-th percentile20233.05
Maximum20234
Range43
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation14.187096
Coefficient of variation (CV)0.00070189712
Kurtosis-1.2840029
Mean20212.5
Median Absolute Deviation (MAD)11
Skewness0
Sum1.7180625 × 108
Variance201.27368
MonotonicityNot monotonic
2024-05-04T00:39:55.124116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20231 425
 
5.0%
20204 425
 
5.0%
20224 425
 
5.0%
20223 425
 
5.0%
20222 425
 
5.0%
20221 425
 
5.0%
20214 425
 
5.0%
20213 425
 
5.0%
20212 425
 
5.0%
20211 425
 
5.0%
Other values (10) 4250
50.0%
ValueCountFrequency (%)
20191 425
5.0%
20192 425
5.0%
20193 425
5.0%
20194 425
5.0%
20201 425
5.0%
20202 425
5.0%
20203 425
5.0%
20204 425
5.0%
20211 425
5.0%
20212 425
5.0%
ValueCountFrequency (%)
20234 425
5.0%
20233 425
5.0%
20232 425
5.0%
20231 425
5.0%
20224 425
5.0%
20223 425
5.0%
20222 425
5.0%
20221 425
5.0%
20214 425
5.0%
20213 425
5.0%

행정동_코드
Real number (ℝ)

Distinct425
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433425
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.8 KiB
2024-05-04T00:39:55.643704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140580
Q111260655
median11440630
Q311590680
95-th percentile11710680
Maximum11740700
Range630185
Interquartile range (IQR)330025

Descriptive statistics

Standard deviation191511.86
Coefficient of variation (CV)0.016750174
Kurtosis-1.2631187
Mean11433425
Median Absolute Deviation (MAD)179940
Skewness-0.01466905
Sum9.7184111 × 1010
Variance3.6676792 × 1010
MonotonicityNot monotonic
2024-05-04T00:39:56.133753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11650600 20
 
0.2%
11590530 20
 
0.2%
11590510 20
 
0.2%
11560720 20
 
0.2%
11560710 20
 
0.2%
11560700 20
 
0.2%
11560690 20
 
0.2%
11560680 20
 
0.2%
11560670 20
 
0.2%
11560660 20
 
0.2%
Other values (415) 8300
97.6%
ValueCountFrequency (%)
11110515 20
0.2%
11110530 20
0.2%
11110540 20
0.2%
11110550 20
0.2%
11110560 20
0.2%
11110570 20
0.2%
11110580 20
0.2%
11110600 20
0.2%
11110615 20
0.2%
11110630 20
0.2%
ValueCountFrequency (%)
11740700 20
0.2%
11740690 20
0.2%
11740685 20
0.2%
11740660 20
0.2%
11740650 20
0.2%
11740640 20
0.2%
11740620 20
0.2%
11740610 20
0.2%
11740600 20
0.2%
11740590 20
0.2%

행정동_코드_명
Text

Distinct424
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
2024-05-04T00:39:56.989689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7882353
Min length2

Characters and Unicode

Total characters32200
Distinct characters188
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

Unique0 ?
Unique (%)0.0%

Sample

1st row방배1동
2nd row청운효자동
3rd row청운효자동
4th row사직동
5th row사직동
ValueCountFrequency (%)
신사동 40
 
0.5%
노량진1동 20
 
0.2%
대림3동 20
 
0.2%
대림2동 20
 
0.2%
대림1동 20
 
0.2%
신길7동 20
 
0.2%
신길6동 20
 
0.2%
신길5동 20
 
0.2%
신길4동 20
 
0.2%
신길3동 20
 
0.2%
Other values (414) 8280
97.4%
2024-05-04T00:39:58.623615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8540
26.5%
1 1940
 
6.0%
2 1940
 
6.0%
3 860
 
2.7%
신 760
 
2.4%
4 520
 
1.6%
가 460
 
1.4%
곡 360
 
1.1%
계 340
 
1.1%
화 340
 
1.1%
Other values (178) 16140
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26180
81.3%
Decimal Number 5840
 
18.1%
Other Punctuation 180
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8540
32.6%
신 760
 
2.9%
가 460
 
1.8%
곡 360
 
1.4%
계 340
 
1.3%
화 340
 
1.3%
상 320
 
1.2%
성 320
 
1.2%
방 320
 
1.2%
산 320
 
1.2%
Other values (167) 14100
53.9%
Decimal Number
ValueCountFrequency (%)
1 1940
33.2%
2 1940
33.2%
3 860
14.7%
4 520
 
8.9%
5 220
 
3.8%
6 140
 
2.4%
7 120
 
2.1%
8 60
 
1.0%
9 20
 
0.3%
0 20
 
0.3%
Other Punctuation
ValueCountFrequency (%)
? 180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26180
81.3%
Common 6020
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8540
32.6%
신 760
 
2.9%
가 460
 
1.8%
곡 360
 
1.4%
계 340
 
1.3%
화 340
 
1.3%
상 320
 
1.2%
성 320
 
1.2%
방 320
 
1.2%
산 320
 
1.2%
Other values (167) 14100
53.9%
Common
ValueCountFrequency (%)
1 1940
32.2%
2 1940
32.2%
3 860
14.3%
4 520
 
8.6%
5 220
 
3.7%
? 180
 
3.0%
6 140
 
2.3%
7 120
 
2.0%
8 60
 
1.0%
9 20
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26180
81.3%
ASCII 6020
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8540
32.6%
신 760
 
2.9%
가 460
 
1.8%
곡 360
 
1.4%
계 340
 
1.3%
화 340
 
1.3%
상 320
 
1.2%
성 320
 
1.2%
방 320
 
1.2%
산 320
 
1.2%
Other values (167) 14100
53.9%
ASCII
ValueCountFrequency (%)
1 1940
32.2%
2 1940
32.2%
3 860
14.3%
4 520
 
8.6%
5 220
 
3.7%
? 180
 
3.0%
6 140
 
2.3%
7 120
 
2.0%
8 60
 
1.0%
9 20
 
0.3%

상권_변화_지표
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
LL
3773 
HH
1978 
LH
1503 
HL
1246 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHH
2nd rowLH
3rd rowLH
4th rowHH
5th rowHH

Common Values

ValueCountFrequency (%)
LL 3773
44.4%
HH 1978
23.3%
LH 1503
 
17.7%
HL 1246
 
14.7%

Length

2024-05-04T00:39:59.203076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:39:59.884465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ll 3773
44.4%
hh 1978
23.3%
lh 1503
 
17.7%
hl 1246
 
14.7%

상권_변화_지표_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
다이나믹
3773 
정체
1978 
상권확장
1503 
상권축소
1246 

Length

Max length4
Median length4
Mean length3.5345882
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정체
2nd row상권확장
3rd row상권확장
4th row정체
5th row정체

Common Values

ValueCountFrequency (%)
다이나믹 3773
44.4%
정체 1978
23.3%
상권확장 1503
 
17.7%
상권축소 1246
 
14.7%

Length

2024-05-04T00:40:00.268090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:00.757333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다이나믹 3773
44.4%
정체 1978
23.3%
상권확장 1503
 
17.7%
상권축소 1246
 
14.7%

운영_영업_개월_평균
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.628471
Minimum33
Maximum167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.8 KiB
2024-05-04T00:40:01.505238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile77
Q189
median95
Q3103
95-th percentile123
Maximum167
Range134
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.837127
Coefficient of variation (CV)0.15354819
Kurtosis3.2443864
Mean96.628471
Median Absolute Deviation (MAD)7
Skewness0.65155452
Sum821342
Variance220.14034
MonotonicityNot monotonic
2024-05-04T00:40:01.942016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 371
 
4.4%
91 365
 
4.3%
92 345
 
4.1%
93 333
 
3.9%
95 330
 
3.9%
96 327
 
3.8%
97 308
 
3.6%
89 285
 
3.4%
90 284
 
3.3%
98 283
 
3.3%
Other values (123) 5269
62.0%
ValueCountFrequency (%)
33 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
37 1
 
< 0.1%
38 4
< 0.1%
39 3
 
< 0.1%
40 8
0.1%
41 2
 
< 0.1%
42 3
 
< 0.1%
ValueCountFrequency (%)
167 2
< 0.1%
164 2
< 0.1%
163 3
< 0.1%
162 2
< 0.1%
161 4
< 0.1%
160 4
< 0.1%
159 2
< 0.1%
158 4
< 0.1%
157 3
< 0.1%
156 3
< 0.1%

폐업_영업_개월_평균
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.384235
Minimum27
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.8 KiB
2024-05-04T00:40:02.422704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile44
Q147
median50
Q352
95-th percentile60
Maximum105
Range78
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.2422556
Coefficient of variation (CV)0.10404555
Kurtosis15.795378
Mean50.384235
Median Absolute Deviation (MAD)3
Skewness2.1521871
Sum428266
Variance27.481243
MonotonicityNot monotonic
2024-05-04T00:40:02.993646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 963
11.3%
49 951
11.2%
48 882
10.4%
47 849
10.0%
51 776
9.1%
46 623
 
7.3%
52 613
 
7.2%
45 418
 
4.9%
53 381
 
4.5%
54 302
 
3.6%
Other values (45) 1742
20.5%
ValueCountFrequency (%)
27 15
 
0.2%
28 3
 
< 0.1%
29 2
 
< 0.1%
37 6
 
0.1%
38 9
 
0.1%
39 13
 
0.2%
40 13
 
0.2%
41 25
 
0.3%
42 48
0.6%
43 83
1.0%
ValueCountFrequency (%)
105 1
 
< 0.1%
104 2
< 0.1%
103 1
 
< 0.1%
102 1
 
< 0.1%
101 1
 
< 0.1%
100 1
 
< 0.1%
99 3
< 0.1%
98 1
 
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%

서울_운영_영업_개월_평균
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98
Minimum93
Maximum109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.8 KiB
2024-05-04T00:40:03.378094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93
5-th percentile93
Q194
median96
Q3101.5
95-th percentile107.1
Maximum109
Range16
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.0202555
Coefficient of variation (CV)0.051227097
Kurtosis-0.59507124
Mean98
Median Absolute Deviation (MAD)3
Skewness0.87997636
Sum833000
Variance25.202965
MonotonicityNot monotonic
2024-05-04T00:40:03.859861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
93 1700
20.0%
96 1700
20.0%
94 850
10.0%
95 850
10.0%
104 425
 
5.0%
107 425
 
5.0%
109 425
 
5.0%
106 425
 
5.0%
97 425
 
5.0%
99 425
 
5.0%
Other values (2) 850
10.0%
ValueCountFrequency (%)
93 1700
20.0%
94 850
10.0%
95 850
10.0%
96 1700
20.0%
97 425
 
5.0%
99 425
 
5.0%
101 425
 
5.0%
103 425
 
5.0%
104 425
 
5.0%
106 425
 
5.0%
ValueCountFrequency (%)
109 425
 
5.0%
107 425
 
5.0%
106 425
 
5.0%
104 425
 
5.0%
103 425
 
5.0%
101 425
 
5.0%
99 425
 
5.0%
97 425
 
5.0%
96 1700
20.0%
95 850
10.0%

서울_폐업_영업_개월_평균
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
51
2550 
50
2125 
52
1700 
49
1700 
48
425 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row52
2nd row52
3rd row52
4th row52
5th row52

Common Values

ValueCountFrequency (%)
51 2550
30.0%
50 2125
25.0%
52 1700
20.0%
49 1700
20.0%
48 425
 
5.0%

Length

2024-05-04T00:40:04.474455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T00:40:04.978662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
51 2550
30.0%
50 2125
25.0%
52 1700
20.0%
49 1700
20.0%
48 425
 
5.0%

Interactions

2024-05-04T00:39:49.509495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:43.006181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:44.391369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:45.913269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:47.541120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:50.171869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:43.266531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:44.681858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:46.203969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:47.819004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:50.866331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:43.567679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:45.068059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:46.498086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:48.117661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:51.736872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:43.848214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:45.337973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:46.953432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:48.603726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:52.352129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:44.108787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:45.607818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:47.244811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T00:39:49.025975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T00:40:05.337650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드행정동_코드상권_변화_지표상권_변화_지표_명운영_영업_개월_평균폐업_영업_개월_평균서울_운영_영업_개월_평균서울_폐업_영업_개월_평균
기준_년분기_코드1.0000.0000.0570.0570.2880.1690.9660.999
행정동_코드0.0001.0000.4450.4450.4340.5020.0000.000
상권_변화_지표0.0570.4451.0001.0000.6280.6880.0620.043
상권_변화_지표_명0.0570.4451.0001.0000.6280.6880.0620.043
운영_영업_개월_평균0.2880.4340.6280.6281.0000.8290.2950.406
폐업_영업_개월_평균0.1690.5020.6880.6880.8291.0000.1760.283
서울_운영_영업_개월_평균0.9660.0000.0620.0620.2950.1761.0000.828
서울_폐업_영업_개월_평균0.9990.0000.0430.0430.4060.2830.8281.000
2024-05-04T00:40:05.694762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울_폐업_영업_개월_평균상권_변화_지표_명상권_변화_지표
서울_폐업_영업_개월_평균1.0000.0350.035
상권_변화_지표_명0.0351.0001.000
상권_변화_지표0.0351.0001.000
2024-05-04T00:40:05.969530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드행정동_코드운영_영업_개월_평균폐업_영업_개월_평균서울_운영_영업_개월_평균상권_변화_지표상권_변화_지표_명서울_폐업_영업_개월_평균
기준_년분기_코드1.0000.0000.3470.2730.9730.0300.0300.831
행정동_코드0.0001.000-0.051-0.2470.0000.2810.2810.000
운영_영업_개월_평균0.347-0.0511.0000.5580.3550.4300.4300.180
폐업_영업_개월_평균0.273-0.2470.5581.0000.2650.4890.4890.121
서울_운영_영업_개월_평균0.9730.0000.3550.2651.0000.0280.0280.701
상권_변화_지표0.0300.2810.4300.4890.0281.0001.0000.035
상권_변화_지표_명0.0300.2810.4300.4890.0281.0001.0000.035
서울_폐업_영업_개월_평균0.8310.0000.1800.1210.7010.0350.0351.000

Missing values

2024-05-04T00:39:53.109827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T00:39:53.815113image/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_년분기_코드행정동_코드행정동_코드_명상권_변화_지표상권_변화_지표_명운영_영업_개월_평균폐업_영업_개월_평균서울_운영_영업_개월_평균서울_폐업_영업_개월_평균
02023111650600방배1동HH정체1065210452
12023311110515청운효자동LH상권확장965610752
22023411110515청운효자동LH상권확장985510952
32023311110530사직동HH정체1245910752
42023411110530사직동HH정체1266010952
52023311110540삼청동LH상권확장1005510752
62023411110540삼청동LH상권확장1015610952
72023311110550부암동LH상권확장1025710752
82023411110550부암동LH상권확장1045710952
92023311110560평창동HH정체1145910752
기준_년분기_코드행정동_코드행정동_코드_명상권_변화_지표상권_변화_지표_명운영_영업_개월_평균폐업_영업_개월_평균서울_운영_영업_개월_평균서울_폐업_영업_개월_평균
84902023211590540상도2동HH정체1145610652
84912023211590550상도3동LL다이나믹994810652
84922023211590620사당1동LL다이나믹1005010652
84932023211590660대방동HH정체1065310652
84942023211650550반포본동HH정체1526410652
84952023211710562방이2동LL다이나믹954910652
84962023211680565청담동LL다이나믹1015110652
84972023211710520풍납2동HH정체1215410652
84982023211740550고덕1동LL다이나믹824910652
84992023211740660성내3동HL상권축소1114710652