Overview

Dataset statistics

Number of variables8
Number of observations517
Missing cells274
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 KiB
Average record size in memory67.3 B

Variable types

Numeric2
Text4
Categorical2

Dataset

Description시군구코드,인허가번호,업소번호,사업종류,상호,전화번호,허가일자,주소
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13652/S/1/datasetView.do

Alerts

업소번호 is highly imbalanced (96.2%)Imbalance
사업종류 is highly imbalanced (50.9%)Imbalance
전화번호 has 249 (48.2%) missing valuesMissing
주소 has 25 (4.8%) missing valuesMissing

Reproduction

Analysis started2023-12-11 05:58:25.742318
Analysis finished2023-12-11 05:58:27.375864
Duration1.63 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

Distinct25
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3130193.4
Minimum3000000
Maximum3240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-11T14:58:27.464694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010000
Q13080000
median3140000
Q33180000
95-th percentile3240000
Maximum3240000
Range240000
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation68717.214
Coefficient of variation (CV)0.021953025
Kurtosis-0.85701579
Mean3130193.4
Median Absolute Deviation (MAD)50000
Skewness-0.28433745
Sum1.61831 × 109
Variance4.7220555 × 109
MonotonicityNot monotonic
2023-12-11T14:58:27.663045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3150000 54
 
10.4%
3160000 44
 
8.5%
3240000 30
 
5.8%
3130000 29
 
5.6%
3110000 27
 
5.2%
3140000 26
 
5.0%
3220000 26
 
5.0%
3200000 25
 
4.8%
3000000 25
 
4.8%
3030000 24
 
4.6%
Other values (15) 207
40.0%
ValueCountFrequency (%)
3000000 25
4.8%
3010000 12
2.3%
3020000 12
2.3%
3030000 24
4.6%
3040000 8
 
1.5%
3050000 17
3.3%
3060000 13
2.5%
3070000 13
2.5%
3080000 14
2.7%
3090000 14
2.7%
ValueCountFrequency (%)
3240000 30
5.8%
3230000 14
 
2.7%
3220000 26
5.0%
3210000 11
 
2.1%
3200000 25
4.8%
3190000 12
 
2.3%
3180000 17
 
3.3%
3170000 20
 
3.9%
3160000 44
8.5%
3150000 54
10.4%

인허가번호
Text

Distinct507
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T14:58:27.990591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters9823
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

Unique501 ?
Unique (%)96.9%

Sample

1st row2019318022104100001
2nd row2017315015104100002
3rd row2017323023104100001
4th row2017315015104100001
5th row2016315015104100001
ValueCountFrequency (%)
2001324007304100033 3
 
0.6%
1999700000004100003 3
 
0.6%
1999700000004100001 3
 
0.6%
1999700000004100002 3
 
0.6%
2001322000004100053 2
 
0.4%
2001324007304100070 2
 
0.4%
2001315007404120030 1
 
0.2%
1987314007004100004 1
 
0.2%
1986323007804100028 1
 
0.2%
1986305008204108610 1
 
0.2%
Other values (498) 498
96.1%
2023-12-11T14:58:28.486547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4302
43.8%
1 1660
 
16.9%
3 749
 
7.6%
4 737
 
7.5%
2 683
 
7.0%
7 487
 
5.0%
9 418
 
4.3%
8 350
 
3.6%
6 244
 
2.5%
5 192
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9822
> 99.9%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4302
43.8%
1 1660
 
16.9%
3 749
 
7.6%
4 737
 
7.5%
2 683
 
7.0%
7 487
 
5.0%
9 418
 
4.3%
8 350
 
3.6%
6 244
 
2.5%
5 192
 
2.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4302
43.8%
1 1660
 
16.9%
3 749
 
7.6%
4 737
 
7.5%
2 683
 
7.0%
7 487
 
5.0%
9 418
 
4.3%
8 350
 
3.6%
6 244
 
2.5%
5 192
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4302
43.8%
1 1660
 
16.9%
3 749
 
7.6%
4 737
 
7.5%
2 683
 
7.0%
7 487
 
5.0%
9 418
 
4.3%
8 350
 
3.6%
6 244
 
2.5%
5 192
 
2.0%

업소번호
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
1
513 
2
 
2
5
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 513
99.2%
2 2
 
0.4%
5 1
 
0.2%
4 1
 
0.2%

Length

2023-12-11T14:58:28.663843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:58:28.803896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 513
99.2%
2 2
 
0.4%
5 1
 
0.2%
4 1
 
0.2%

사업종류
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
판매사업
359 
충전사업
131 
가스용품제조사업
 
21
저장소설치
 
4
집단공급사업
 
2

Length

Max length8
Median length4
Mean length4.1779497
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row판매사업
2nd row충전사업
3rd row충전사업
4th row충전사업
5th row충전사업

Common Values

ValueCountFrequency (%)
판매사업 359
69.4%
충전사업 131
 
25.3%
가스용품제조사업 21
 
4.1%
저장소설치 4
 
0.8%
집단공급사업 2
 
0.4%

Length

2023-12-11T14:58:28.976341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:58:29.138424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
판매사업 359
69.4%
충전사업 131
 
25.3%
가스용품제조사업 21
 
4.1%
저장소설치 4
 
0.8%
집단공급사업 2
 
0.4%

상호
Text

Distinct360
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size4.2 KiB
2023-12-11T14:58:29.511220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length5.6827853
Min length3

Characters and Unicode

Total characters2938
Distinct characters231
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

Unique279 ?
Unique (%)54.0%

Sample

1st row동진가스
2nd row방화동LPG충전소
3rd row복지송파충전소
4th row활주로충전소
5th row한경에너지
ValueCountFrequency (%)
동양가스 10
 
1.8%
현대가스 10
 
1.8%
유공가스 8
 
1.5%
대성가스 6
 
1.1%
중앙가스 6
 
1.1%
삼표가스 5
 
0.9%
제일가스 5
 
0.9%
대흥가스 5
 
0.9%
우리가스 4
 
0.7%
린나이가스 4
 
0.7%
Other values (368) 485
88.5%
2023-12-11T14:58:30.179096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 387
 
13.2%
가 381
 
13.0%
주 82
 
2.8%
동 79
 
2.7%
) 78
 
2.7%
( 78
 
2.7%
전 78
 
2.7%
지 74
 
2.5%
소 71
 
2.4%
대 70
 
2.4%
Other values (221) 1560
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2691
91.6%
Close Punctuation 78
 
2.7%
Open Punctuation 78
 
2.7%
Uppercase Letter 37
 
1.3%
Space Separator 31
 
1.1%
Decimal Number 20
 
0.7%
Lowercase Letter 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 387
 
14.4%
가 381
 
14.2%
주 82
 
3.0%
동 79
 
2.9%
전 78
 
2.9%
지 74
 
2.7%
소 71
 
2.6%
대 70
 
2.6%
충 69
 
2.6%
에 52
 
1.9%
Other values (201) 1348
50.1%
Uppercase Letter
ValueCountFrequency (%)
S 8
21.6%
G 7
18.9%
L 7
18.9%
P 6
16.2%
K 6
16.2%
I 2
 
5.4%
N 1
 
2.7%
Decimal Number
ValueCountFrequency (%)
2 5
25.0%
1 4
20.0%
3 4
20.0%
4 3
15.0%
6 2
 
10.0%
9 1
 
5.0%
8 1
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
50.0%
s 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2691
91.6%
Common 208
 
7.1%
Latin 39
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 387
 
14.4%
가 381
 
14.2%
주 82
 
3.0%
동 79
 
2.9%
전 78
 
2.9%
지 74
 
2.7%
소 71
 
2.6%
대 70
 
2.6%
충 69
 
2.6%
에 52
 
1.9%
Other values (201) 1348
50.1%
Common
ValueCountFrequency (%)
) 78
37.5%
( 78
37.5%
31
 
14.9%
2 5
 
2.4%
1 4
 
1.9%
3 4
 
1.9%
4 3
 
1.4%
6 2
 
1.0%
9 1
 
0.5%
8 1
 
0.5%
Latin
ValueCountFrequency (%)
S 8
20.5%
G 7
17.9%
L 7
17.9%
P 6
15.4%
K 6
15.4%
I 2
 
5.1%
k 1
 
2.6%
s 1
 
2.6%
N 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2691
91.6%
ASCII 247
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 387
 
14.4%
가 381
 
14.2%
주 82
 
3.0%
동 79
 
2.9%
전 78
 
2.9%
지 74
 
2.7%
소 71
 
2.6%
대 70
 
2.6%
충 69
 
2.6%
에 52
 
1.9%
Other values (201) 1348
50.1%
ASCII
ValueCountFrequency (%)
) 78
31.6%
( 78
31.6%
31
 
12.6%
S 8
 
3.2%
G 7
 
2.8%
L 7
 
2.8%
P 6
 
2.4%
K 6
 
2.4%
2 5
 
2.0%
1 4
 
1.6%
Other values (10) 17
 
6.9%

전화번호
Text

MISSING 

Distinct255
Distinct (%)95.1%
Missing249
Missing (%)48.2%
Memory size4.2 KiB
2023-12-11T14:58:30.558701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.343284
Min length7

Characters and Unicode

Total characters2772
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

Unique248 ?
Unique (%)92.5%

Sample

1st row02 449 9123
2nd row000226021632
3rd row0220690845
4th row0226454600
5th row02 9885565
ValueCountFrequency (%)
02 186
38.2%
222222222222 7
 
1.4%
9130168 3
 
0.6%
804 3
 
0.6%
445 2
 
0.4%
807 2
 
0.4%
803 2
 
0.4%
547 2
 
0.4%
9941181 2
 
0.4%
3553440 2
 
0.4%
Other values (272) 276
56.7%
2023-12-11T14:58:31.080215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 547
19.7%
0 477
17.2%
282
10.2%
1 235
8.5%
3 224
8.1%
4 198
 
7.1%
6 194
 
7.0%
8 188
 
6.8%
5 161
 
5.8%
7 153
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2490
89.8%
Space Separator 282
 
10.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 547
22.0%
0 477
19.2%
1 235
9.4%
3 224
9.0%
4 198
 
8.0%
6 194
 
7.8%
8 188
 
7.6%
5 161
 
6.5%
7 153
 
6.1%
9 113
 
4.5%
Space Separator
ValueCountFrequency (%)
282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2772
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 547
19.7%
0 477
17.2%
282
10.2%
1 235
8.5%
3 224
8.1%
4 198
 
7.1%
6 194
 
7.0%
8 188
 
6.8%
5 161
 
5.8%
7 153
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 547
19.7%
0 477
17.2%
282
10.2%
1 235
8.5%
3 224
8.1%
4 198
 
7.1%
6 194
 
7.0%
8 188
 
6.8%
5 161
 
5.8%
7 153
 
5.5%

허가일자
Real number (ℝ)

Distinct409
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19951224
Minimum19691230
Maximum20191224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2023-12-11T14:58:31.288707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19691230
5-th percentile19761207
Q119820908
median20010829
Q320040610
95-th percentile20092924
Maximum20191224
Range499994
Interquartile range (IQR)219702

Descriptive statistics

Standard deviation116925.78
Coefficient of variation (CV)0.0058605821
Kurtosis-1.2352724
Mean19951224
Median Absolute Deviation (MAD)50196
Skewness-0.4072576
Sum1.0314783 × 1010
Variance1.3671639 × 1010
MonotonicityDecreasing
2023-12-11T14:58:31.500353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20060714 14
 
2.7%
20070326 9
 
1.7%
19740311 7
 
1.4%
19991028 6
 
1.2%
20021029 6
 
1.2%
20020222 5
 
1.0%
19820305 5
 
1.0%
20030929 4
 
0.8%
20020125 4
 
0.8%
19820326 4
 
0.8%
Other values (399) 453
87.6%
ValueCountFrequency (%)
19691230 1
 
0.2%
19700224 1
 
0.2%
19701120 2
 
0.4%
19720428 1
 
0.2%
19730614 1
 
0.2%
19730724 1
 
0.2%
19740223 2
 
0.4%
19740311 7
1.4%
19750201 1
 
0.2%
19750509 1
 
0.2%
ValueCountFrequency (%)
20191224 1
0.2%
20171204 1
0.2%
20170425 1
0.2%
20170116 1
0.2%
20160322 1
0.2%
20150916 1
0.2%
20150824 1
0.2%
20150323 1
0.2%
20141212 1
0.2%
20140528 1
0.2%

주소
Text

MISSING 

Distinct433
Distinct (%)88.0%
Missing25
Missing (%)4.8%
Memory size4.2 KiB
2023-12-11T14:58:31.940409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length32
Mean length25.563008
Min length16

Characters and Unicode

Total characters12577
Distinct characters196
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)77.6%

Sample

1st row서울특별시 영등포구 도림동 187번지 80호
2nd row서울특별시 강서구 개화동 565번지 4호
3rd row서울특별시 송파구 장지동 907번지
4th row서울특별시 강서구 외발산동 384번지 4호
5th row서울특별시 양천구 신월동 988번지 3호
ValueCountFrequency (%)
서울특별시 491
 
20.1%
강서구 48
 
2.0%
1호 43
 
1.8%
구로구 41
 
1.7%
2호 33
 
1.4%
강동구 29
 
1.2%
은평구 27
 
1.1%
강남구 26
 
1.1%
마포구 26
 
1.1%
양천구 26
 
1.1%
Other values (659) 1651
67.6%
2023-12-11T14:58:32.601575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3425
27.2%
동 569
 
4.5%
서 565
 
4.5%
구 546
 
4.3%
지 498
 
4.0%
시 498
 
4.0%
번 495
 
3.9%
특 491
 
3.9%
별 491
 
3.9%
울 491
 
3.9%
Other values (186) 4508
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6974
55.5%
Space Separator 3425
27.2%
Decimal Number 2161
 
17.2%
Dash Punctuation 12
 
0.1%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 569
 
8.2%
서 565
 
8.1%
구 546
 
7.8%
지 498
 
7.1%
시 498
 
7.1%
번 495
 
7.1%
특 491
 
7.0%
별 491
 
7.0%
울 491
 
7.0%
호 451
 
6.5%
Other values (170) 1879
26.9%
Decimal Number
ValueCountFrequency (%)
1 416
19.3%
2 320
14.8%
3 249
11.5%
4 217
10.0%
5 213
9.9%
0 178
8.2%
6 157
 
7.3%
9 148
 
6.8%
7 143
 
6.6%
8 120
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
G 2
40.0%
L 1
20.0%
P 1
20.0%
S 1
20.0%
Space Separator
ValueCountFrequency (%)
3425
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6974
55.5%
Common 5598
44.5%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 569
 
8.2%
서 565
 
8.1%
구 546
 
7.8%
지 498
 
7.1%
시 498
 
7.1%
번 495
 
7.1%
특 491
 
7.0%
별 491
 
7.0%
울 491
 
7.0%
호 451
 
6.5%
Other values (170) 1879
26.9%
Common
ValueCountFrequency (%)
3425
61.2%
1 416
 
7.4%
2 320
 
5.7%
3 249
 
4.4%
4 217
 
3.9%
5 213
 
3.8%
0 178
 
3.2%
6 157
 
2.8%
9 148
 
2.6%
7 143
 
2.6%
Other values (2) 132
 
2.4%
Latin
ValueCountFrequency (%)
G 2
40.0%
L 1
20.0%
P 1
20.0%
S 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6974
55.5%
ASCII 5603
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3425
61.1%
1 416
 
7.4%
2 320
 
5.7%
3 249
 
4.4%
4 217
 
3.9%
5 213
 
3.8%
0 178
 
3.2%
6 157
 
2.8%
9 148
 
2.6%
7 143
 
2.6%
Other values (6) 137
 
2.4%
Hangul
ValueCountFrequency (%)
동 569
 
8.2%
서 565
 
8.1%
구 546
 
7.8%
지 498
 
7.1%
시 498
 
7.1%
번 495
 
7.1%
특 491
 
7.0%
별 491
 
7.0%
울 491
 
7.0%
호 451
 
6.5%
Other values (170) 1879
26.9%

Interactions

2023-12-11T14:58:26.567980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:58:26.271489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:58:26.727781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:58:26.420166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:58:32.733627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드업소번호사업종류허가일자
시군구코드1.0000.0000.3220.442
업소번호0.0001.0000.0000.000
사업종류0.3220.0001.0000.524
허가일자0.4420.0000.5241.000
2023-12-11T14:58:32.877097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소번호사업종류
업소번호1.0000.000
사업종류0.0001.000
2023-12-11T14:58:33.016213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드허가일자업소번호사업종류
시군구코드1.000-0.0750.0000.146
허가일자-0.0751.0000.0000.245
업소번호0.0000.0001.0000.000
사업종류0.1460.2450.0001.000

Missing values

2023-12-11T14:58:26.951083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:58:27.164944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T14:58:27.303503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드인허가번호업소번호사업종류상호전화번호허가일자주소
0318000020193180221041000011판매사업동진가스<NA>20191224서울특별시 영등포구 도림동 187번지 80호
1315000020173150151041000021충전사업방화동LPG충전소<NA>20171204서울특별시 강서구 개화동 565번지 4호
2323000020173230231041000011충전사업복지송파충전소02 449 912320170425서울특별시 송파구 장지동 907번지
3315000020173150151041000011충전사업활주로충전소<NA>20170116서울특별시 강서구 외발산동 384번지 4호
4315000020163150151041000011충전사업한경에너지<NA>20160322<NA>
5315000020153150151041000021충전사업복지마곡충전소<NA>20150916<NA>
6314000020153140179041000011가스용품제조사업초이스하이테크(주)00022602163220150824서울특별시 양천구 신월동 988번지 3호
7315000020153150151041000011충전사업한경에너지<NA>20150323서울특별시 강서구 개화동 215번지 6호
8315000020143150151041000011충전사업대영가스충전소<NA>20141212서울특별시 강서구 마곡동 761번지 2호 대영가스충전소
9316000020143160203041000011가스용품제조사업(주)소시오텍022069084520140528서울특별시 구로구 신도림동 291번지 20호
시군구코드인허가번호업소번호사업종류상호전화번호허가일자주소
507318000019743180076041000881충전사업기린에너지<NA>19740311서울특별시 영등포구 양평동4가 158번지
508307000019743070000041000121충전사업우일에너지(주)02 913 872219740223서울특별시 성북구 하월곡동 88번지 681호
509308000019743080075041100011충전사업국제가스공업(주)<NA>19740223서울특별시 강북구 수유동 48번지 23호
510316000019733160071041000011충전사업한일가스산업(주)02 2675261619730724서울특별시 구로구 구로동 600번지 6호
511318000020023180076041000011충전사업양평동충전소<NA>19730614서울특별시 영등포구 양평동3가 59번지
512303000019723030074041043021판매사업고려가스상사<NA>19720428서울특별시 성동구 홍익동 77번지
513308000019703080075041000011충전사업서울와사공업(주)02 902111219701120서울특별시 강북구 번동 446번지 31호
514309000019703090087041000061충전사업주식회사 행복에너지02 3491667119701120서울특별시 도봉구 방학동 707번지 2호
515316000019703160071041000041충전사업대성산업(주)오류동충전소02 2612003919700224서울특별시 종로구 관훈동 155번지 2호
516315000020013150074041000011충전사업공항충전소02 661516119691230서울특별시 강서구 외발산동 142번지 1호