Overview

Dataset statistics

Number of variables18
Number of observations601
Missing cells4631
Missing cells (%)42.8%
Duplicate rows13
Duplicate rows (%)2.2%
Total size in memory91.1 KiB
Average record size in memory155.2 B

Variable types

Categorical3
Text3
DateTime2
Unsupported4
Numeric6

Dataset

Description용기냉동기특정설비 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=KGML4VNR3O2A2WYWYAP814104298&infSeq=1

Alerts

Dataset has 13 (2.2%) duplicate rowsDuplicates
영업상태명 is highly overall correlated with 영업상태구분코드High correlation
영업상태구분코드 is highly overall correlated with 영업상태명High correlation
도로명우편번호 is highly overall correlated with 소재지우편번호 and 5 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 도로명우편번호 and 4 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 도로명우편번호 and 4 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 도로명우편번호 and 3 other fieldsHigh correlation
X좌표값 is highly overall correlated with 도로명우편번호 and 5 other fieldsHigh correlation
Y좌표값 is highly overall correlated with 도로명우편번호 and 5 other fieldsHigh correlation
시군명 is highly overall correlated with 도로명우편번호 and 5 other fieldsHigh correlation
영업상태구분코드 is highly imbalanced (72.2%)Imbalance
인허가취소일자 has 601 (100.0%) missing valuesMissing
폐업일자 has 455 (75.7%) missing valuesMissing
소재지시설전화번호 has 601 (100.0%) missing valuesMissing
소재지면적정보 has 601 (100.0%) missing valuesMissing
도로명우편번호 has 570 (94.8%) missing valuesMissing
소재지도로명주소 has 44 (7.3%) missing valuesMissing
소재지우편번호 has 16 (2.7%) missing valuesMissing
WGS84위도 has 22 (3.7%) missing valuesMissing
WGS84경도 has 22 (3.7%) missing valuesMissing
업태구분명정보 has 601 (100.0%) missing valuesMissing
X좌표값 has 547 (91.0%) missing valuesMissing
Y좌표값 has 547 (91.0%) missing valuesMissing
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지시설전화번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:28:17.032847
Analysis finished2023-12-10 22:28:21.900281
Duration4.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
화성시
125 
시흥시
73 
김포시
65 
안산시
58 
평택시
57 
Other values (20)
223 

Length

Max length4
Median length3
Mean length3.0099834
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
화성시 125
20.8%
시흥시 73
12.1%
김포시 65
10.8%
안산시 58
9.7%
평택시 57
9.5%
파주시 46
 
7.7%
안성시 29
 
4.8%
부천시 21
 
3.5%
광주시 21
 
3.5%
양주시 16
 
2.7%
Other values (15) 90
15.0%

Length

2023-12-11T07:28:21.971564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화성시 125
20.8%
시흥시 73
12.1%
김포시 65
10.8%
안산시 58
9.7%
평택시 57
9.5%
파주시 46
 
7.7%
안성시 29
 
4.8%
부천시 21
 
3.5%
광주시 21
 
3.5%
양주시 16
 
2.7%
Other values (15) 90
15.0%

사업장명
Text

Distinct485
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T07:28:22.246504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length17
Mean length8.0366057
Min length2

Characters and Unicode

Total characters4830
Distinct characters332
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique392 ?
Unique (%)65.2%

Sample

1st row(주)엠디텍
2nd row(주)한라산업기술
3rd row세명에너지(주)
4th row수성밸브공업(주)
5th row(주)엠디텍
ValueCountFrequency (%)
주식회사 10
 
1.6%
케이피피(주 5
 
0.8%
주)유한엔지니어링 4
 
0.6%
우양에이치씨(주 4
 
0.6%
주)대흥티엔씨 4
 
0.6%
아이펙이엔지(주 3
 
0.5%
주)삼정이엔씨 3
 
0.5%
주)삼화에이스 3
 
0.5%
하이리움산업(주 3
 
0.5%
주)에이알 3
 
0.5%
Other values (499) 600
93.5%
2023-12-11T07:28:22.666412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 522
 
10.8%
( 511
 
10.6%
) 511
 
10.6%
이 200
 
4.1%
지 126
 
2.6%
에 118
 
2.4%
스 106
 
2.2%
엔 98
 
2.0%
기 75
 
1.6%
성 60
 
1.2%
Other values (322) 2503
51.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3676
76.1%
Open Punctuation 511
 
10.6%
Close Punctuation 511
 
10.6%
Uppercase Letter 59
 
1.2%
Space Separator 41
 
0.8%
Dash Punctuation 12
 
0.2%
Decimal Number 9
 
0.2%
Other Symbol 6
 
0.1%
Other Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 522
 
14.2%
이 200
 
5.4%
지 126
 
3.4%
에 118
 
3.2%
스 106
 
2.9%
엔 98
 
2.7%
기 75
 
2.0%
성 60
 
1.6%
어 60
 
1.6%
산 57
 
1.6%
Other values (294) 2254
61.3%
Uppercase Letter
ValueCountFrequency (%)
E 9
15.3%
C 7
11.9%
G 6
10.2%
D 6
10.2%
N 5
8.5%
S 4
6.8%
T 4
6.8%
I 3
 
5.1%
O 3
 
5.1%
H 3
 
5.1%
Other values (7) 9
15.3%
Decimal Number
ValueCountFrequency (%)
2 5
55.6%
0 2
 
22.2%
3 1
 
11.1%
1 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
, 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 511
100.0%
Close Punctuation
ValueCountFrequency (%)
) 511
100.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3682
76.2%
Common 1089
 
22.5%
Latin 59
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 522
 
14.2%
이 200
 
5.4%
지 126
 
3.4%
에 118
 
3.2%
스 106
 
2.9%
엔 98
 
2.7%
기 75
 
2.0%
성 60
 
1.6%
어 60
 
1.6%
산 57
 
1.5%
Other values (295) 2260
61.4%
Latin
ValueCountFrequency (%)
E 9
15.3%
C 7
11.9%
G 6
10.2%
D 6
10.2%
N 5
8.5%
S 4
6.8%
T 4
6.8%
I 3
 
5.1%
O 3
 
5.1%
H 3
 
5.1%
Other values (7) 9
15.3%
Common
ValueCountFrequency (%)
( 511
46.9%
) 511
46.9%
41
 
3.8%
- 12
 
1.1%
2 5
 
0.5%
. 4
 
0.4%
0 2
 
0.2%
3 1
 
0.1%
1 1
 
0.1%
, 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3676
76.1%
ASCII 1148
 
23.8%
None 6
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 522
 
14.2%
이 200
 
5.4%
지 126
 
3.4%
에 118
 
3.2%
스 106
 
2.9%
엔 98
 
2.7%
기 75
 
2.0%
성 60
 
1.6%
어 60
 
1.6%
산 57
 
1.6%
Other values (294) 2254
61.3%
ASCII
ValueCountFrequency (%)
( 511
44.5%
) 511
44.5%
41
 
3.6%
- 12
 
1.0%
E 9
 
0.8%
C 7
 
0.6%
G 6
 
0.5%
D 6
 
0.5%
2 5
 
0.4%
N 5
 
0.4%
Other values (17) 35
 
3.0%
None
ValueCountFrequency (%)
㈜ 6
100.0%

인허가일자
Date

Distinct517
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Minimum1981-08-07 00:00:00
Maximum2023-11-23 00:00:00
2023-12-11T07:28:22.816491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:22.988680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing601
Missing (%)100.0%
Memory size5.4 KiB

영업상태구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
<NA>
541 
1
 
48
3
 
9
2
 
3

Length

Max length4
Median length4
Mean length3.7004992
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 541
90.0%
1 48
 
8.0%
3 9
 
1.5%
2 3
 
0.5%

Length

2023-12-11T07:28:23.136256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:28:23.258674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 541
90.0%
1 48
 
8.0%
3 9
 
1.5%
2 3
 
0.5%

영업상태명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
운영중
393 
폐업 등
136 
영업중
48 
휴업 등
 
12
폐업
 
9

Length

Max length4
Median length3
Mean length3.2262895
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row운영중
3rd row운영중
4th row운영중
5th row폐업 등

Common Values

ValueCountFrequency (%)
운영중 393
65.4%
폐업 등 136
 
22.6%
영업중 48
 
8.0%
휴업 등 12
 
2.0%
폐업 9
 
1.5%
휴업 3
 
0.5%

Length

2023-12-11T07:28:23.391565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:28:23.551997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 393
52.5%
등 148
 
19.8%
폐업 145
 
19.4%
영업중 48
 
6.4%
휴업 15
 
2.0%

폐업일자
Date

MISSING 

Distinct114
Distinct (%)78.1%
Missing455
Missing (%)75.7%
Memory size4.8 KiB
Minimum2007-10-15 00:00:00
Maximum2023-09-25 00:00:00
2023-12-11T07:28:23.719726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:23.865225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소재지시설전화번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing601
Missing (%)100.0%
Memory size5.4 KiB

소재지면적정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing601
Missing (%)100.0%
Memory size5.4 KiB

도로명우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)80.6%
Missing570
Missing (%)94.8%
Infinite0
Infinite (%)0.0%
Mean16049.323
Minimum10040
Maximum18623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:24.038228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10040
5-th percentile10048.5
Q115078
median17599
Q318102
95-th percentile18578.5
Maximum18623
Range8583
Interquartile range (IQR)3024

Descriptive statistics

Standard deviation2964.8544
Coefficient of variation (CV)0.18473393
Kurtosis0.27333448
Mean16049.323
Median Absolute Deviation (MAD)975
Skewness-1.2527788
Sum497529
Variance8790361.7
MonotonicityNot monotonic
2023-12-11T07:28:24.168011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15078 3
 
0.5%
17963 3
 
0.5%
18102 2
 
0.3%
10049 2
 
0.3%
16201 1
 
0.2%
10048 1
 
0.2%
18531 1
 
0.2%
18269 1
 
0.2%
18583 1
 
0.2%
18523 1
 
0.2%
Other values (15) 15
 
2.5%
(Missing) 570
94.8%
ValueCountFrequency (%)
10040 1
 
0.2%
10048 1
 
0.2%
10049 2
0.3%
10117 1
 
0.2%
14119 1
 
0.2%
15078 3
0.5%
15416 1
 
0.2%
15426 1
 
0.2%
15599 1
 
0.2%
16201 1
 
0.2%
ValueCountFrequency (%)
18623 1
 
0.2%
18583 1
 
0.2%
18574 1
 
0.2%
18543 1
 
0.2%
18531 1
 
0.2%
18523 1
 
0.2%
18269 1
 
0.2%
18102 2
0.3%
17963 3
0.5%
17951 1
 
0.2%

소재지도로명주소
Text

MISSING 

Distinct493
Distinct (%)88.5%
Missing44
Missing (%)7.3%
Memory size4.8 KiB
2023-12-11T07:28:24.485011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length43
Mean length24.228007
Min length14

Characters and Unicode

Total characters13495
Distinct characters300
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)78.1%

Sample

1st row경기도 고양시 일산서구 구산로135번길 66-21 (구산동)
2nd row경기도 고양시 일산동구 성현로 532-29
3rd row경기도 고양시 일산서구 구산로 135 (구산동)
4th row경기도 고양시 일산동구 지영로229번길 31-1 (지영동)
5th row경기도 고양시 일산서구 구산로135번길 66-21 (구산동)
ValueCountFrequency (%)
경기도 557
 
19.2%
화성시 117
 
4.0%
시흥시 69
 
2.4%
김포시 59
 
2.0%
안산시 56
 
1.9%
평택시 54
 
1.9%
단원구 51
 
1.8%
정왕동 37
 
1.3%
파주시 32
 
1.1%
포승읍 28
 
1.0%
Other values (934) 1838
63.4%
2023-12-11T07:28:25.111766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2403
 
17.8%
시 653
 
4.8%
도 594
 
4.4%
기 586
 
4.3%
경 570
 
4.2%
1 554
 
4.1%
로 470
 
3.5%
2 353
 
2.6%
길 336
 
2.5%
3 261
 
1.9%
Other values (290) 6715
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7943
58.9%
Decimal Number 2527
 
18.7%
Space Separator 2403
 
17.8%
Dash Punctuation 186
 
1.4%
Open Punctuation 185
 
1.4%
Close Punctuation 185
 
1.4%
Other Punctuation 52
 
0.4%
Uppercase Letter 11
 
0.1%
Lowercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 653
 
8.2%
도 594
 
7.5%
기 586
 
7.4%
경 570
 
7.2%
로 470
 
5.9%
길 336
 
4.2%
번 234
 
2.9%
동 224
 
2.8%
성 186
 
2.3%
면 182
 
2.3%
Other values (266) 3908
49.2%
Decimal Number
ValueCountFrequency (%)
1 554
21.9%
2 353
14.0%
3 261
10.3%
4 230
9.1%
5 211
 
8.3%
8 201
 
8.0%
6 195
 
7.7%
7 189
 
7.5%
0 180
 
7.1%
9 153
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
B 4
36.4%
K 2
18.2%
S 2
18.2%
D 1
 
9.1%
T 1
 
9.1%
I 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 50
96.2%
. 2
 
3.8%
Space Separator
ValueCountFrequency (%)
2403
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 186
100.0%
Open Punctuation
ValueCountFrequency (%)
( 185
100.0%
Close Punctuation
ValueCountFrequency (%)
) 185
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 2
100.0%
Math Symbol
ValueCountFrequency (%)
∼ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7943
58.9%
Common 5539
41.0%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 653
 
8.2%
도 594
 
7.5%
기 586
 
7.4%
경 570
 
7.2%
로 470
 
5.9%
길 336
 
4.2%
번 234
 
2.9%
동 224
 
2.8%
성 186
 
2.3%
면 182
 
2.3%
Other values (266) 3908
49.2%
Common
ValueCountFrequency (%)
2403
43.4%
1 554
 
10.0%
2 353
 
6.4%
3 261
 
4.7%
4 230
 
4.2%
5 211
 
3.8%
8 201
 
3.6%
6 195
 
3.5%
7 189
 
3.4%
- 186
 
3.4%
Other values (7) 756
 
13.6%
Latin
ValueCountFrequency (%)
B 4
30.8%
n 2
15.4%
K 2
15.4%
S 2
15.4%
D 1
 
7.7%
T 1
 
7.7%
I 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7943
58.9%
ASCII 5551
41.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2403
43.3%
1 554
 
10.0%
2 353
 
6.4%
3 261
 
4.7%
4 230
 
4.1%
5 211
 
3.8%
8 201
 
3.6%
6 195
 
3.5%
7 189
 
3.4%
- 186
 
3.4%
Other values (13) 768
 
13.8%
Hangul
ValueCountFrequency (%)
시 653
 
8.2%
도 594
 
7.5%
기 586
 
7.4%
경 570
 
7.2%
로 470
 
5.9%
길 336
 
4.2%
번 234
 
2.9%
동 224
 
2.8%
성 186
 
2.3%
면 182
 
2.3%
Other values (266) 3908
49.2%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

소재지지번주소
Text

Distinct540
Distinct (%)90.5%
Missing4
Missing (%)0.7%
Memory size4.8 KiB
2023-12-11T07:28:25.450792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length42
Mean length24.056951
Min length11

Characters and Unicode

Total characters14362
Distinct characters268
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique488 ?
Unique (%)81.7%

Sample

1st row경기도 고양시 일산서구 구산동 413-5번지
2nd row경기도 고양시 일산동구 사리현동 478-30
3rd row경기도 고양시 일산서구 구산동 355-1번지
4th row경기도 고양시 일산동구 지영동 260-2
5th row경기도 고양시 일산서구 구산동 413-5번지
ValueCountFrequency (%)
경기도 597
 
19.4%
화성시 125
 
4.1%
시흥시 73
 
2.4%
정왕동 70
 
2.3%
김포시 65
 
2.1%
안산시 58
 
1.9%
평택시 57
 
1.9%
단원구 53
 
1.7%
파주시 45
 
1.5%
시화공단 43
 
1.4%
Other values (1007) 1892
61.5%
2023-12-11T07:28:26.024523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2541
 
17.7%
시 732
 
5.1%
도 634
 
4.4%
기 613
 
4.3%
경 598
 
4.2%
지 561
 
3.9%
1 558
 
3.9%
번 522
 
3.6%
- 513
 
3.6%
2 377
 
2.6%
Other values (258) 6713
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8429
58.7%
Decimal Number 2814
 
19.6%
Space Separator 2541
 
17.7%
Dash Punctuation 513
 
3.6%
Uppercase Letter 18
 
0.1%
Close Punctuation 17
 
0.1%
Open Punctuation 17
 
0.1%
Other Punctuation 10
 
0.1%
Lowercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 732
 
8.7%
도 634
 
7.5%
기 613
 
7.3%
경 598
 
7.1%
지 561
 
6.7%
번 522
 
6.2%
리 363
 
4.3%
동 294
 
3.5%
면 207
 
2.5%
성 196
 
2.3%
Other values (229) 3709
44.0%
Decimal Number
ValueCountFrequency (%)
1 558
19.8%
2 377
13.4%
3 273
9.7%
4 264
9.4%
5 258
9.2%
0 254
9.0%
6 250
8.9%
7 222
 
7.9%
8 207
 
7.4%
9 151
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
L 4
22.2%
B 3
16.7%
K 2
11.1%
D 2
11.1%
S 2
11.1%
G 1
 
5.6%
C 1
 
5.6%
T 1
 
5.6%
A 1
 
5.6%
I 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
/ 1
 
10.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
2541
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 513
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 2
100.0%
Math Symbol
ValueCountFrequency (%)
∼ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8429
58.7%
Common 5913
41.2%
Latin 20
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 732
 
8.7%
도 634
 
7.5%
기 613
 
7.3%
경 598
 
7.1%
지 561
 
6.7%
번 522
 
6.2%
리 363
 
4.3%
동 294
 
3.5%
면 207
 
2.5%
성 196
 
2.3%
Other values (229) 3709
44.0%
Common
ValueCountFrequency (%)
2541
43.0%
1 558
 
9.4%
- 513
 
8.7%
2 377
 
6.4%
3 273
 
4.6%
4 264
 
4.5%
5 258
 
4.4%
0 254
 
4.3%
6 250
 
4.2%
7 222
 
3.8%
Other values (8) 403
 
6.8%
Latin
ValueCountFrequency (%)
L 4
20.0%
B 3
15.0%
K 2
10.0%
D 2
10.0%
n 2
10.0%
S 2
10.0%
G 1
 
5.0%
C 1
 
5.0%
T 1
 
5.0%
A 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8429
58.7%
ASCII 5932
41.3%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2541
42.8%
1 558
 
9.4%
- 513
 
8.6%
2 377
 
6.4%
3 273
 
4.6%
4 264
 
4.5%
5 258
 
4.3%
0 254
 
4.3%
6 250
 
4.2%
7 222
 
3.7%
Other values (18) 422
 
7.1%
Hangul
ValueCountFrequency (%)
시 732
 
8.7%
도 634
 
7.5%
기 613
 
7.3%
경 598
 
7.1%
지 561
 
6.7%
번 522
 
6.2%
리 363
 
4.3%
동 294
 
3.5%
면 207
 
2.5%
성 196
 
2.3%
Other values (229) 3709
44.0%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct304
Distinct (%)52.0%
Missing16
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean15083.368
Minimum10003
Maximum18635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:26.193526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10003
5-th percentile10038
Q112634
median15416
Q317960
95-th percentile18581
Maximum18635
Range8632
Interquartile range (IQR)5326

Descriptive statistics

Standard deviation3040.9525
Coefficient of variation (CV)0.20160965
Kurtosis-1.2328847
Mean15083.368
Median Absolute Deviation (MAD)2686
Skewness-0.43206273
Sum8823770
Variance9247392.1
MonotonicityNot monotonic
2023-12-11T07:28:26.387163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17960 16
 
2.7%
10048 8
 
1.3%
18581 7
 
1.2%
18487 7
 
1.2%
15078 6
 
1.0%
17502 6
 
1.0%
10839 6
 
1.0%
18574 5
 
0.8%
18608 5
 
0.8%
18623 5
 
0.8%
Other values (294) 514
85.5%
(Missing) 16
 
2.7%
ValueCountFrequency (%)
10003 3
0.5%
10011 5
0.8%
10012 3
0.5%
10013 1
 
0.2%
10016 1
 
0.2%
10019 1
 
0.2%
10024 1
 
0.2%
10025 1
 
0.2%
10026 2
 
0.3%
10027 2
 
0.3%
ValueCountFrequency (%)
18635 1
 
0.2%
18631 2
 
0.3%
18628 4
0.7%
18625 4
0.7%
18624 2
 
0.3%
18623 5
0.8%
18608 5
0.8%
18589 2
 
0.3%
18586 1
 
0.2%
18585 1
 
0.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct482
Distinct (%)83.2%
Missing22
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean37.348513
Minimum36.935132
Maximum38.017557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:26.547780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.935132
5-th percentile36.97581
Q137.141027
median37.320791
Q337.522446
95-th percentile37.829106
Maximum38.017557
Range1.0824254
Interquartile range (IQR)0.38141906

Descriptive statistics

Standard deviation0.26108822
Coefficient of variation (CV)0.0069905922
Kurtosis-0.64760653
Mean37.348513
Median Absolute Deviation (MAD)0.18725881
Skewness0.55501161
Sum21624.789
Variance0.068167058
MonotonicityNot monotonic
2023-12-11T07:28:26.730328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6165068253 5
 
0.8%
37.1402791055 3
 
0.5%
37.0578505825 3
 
0.5%
36.9717692215 3
 
0.5%
36.9582196721 3
 
0.5%
37.071686519 3
 
0.5%
37.3405482955 3
 
0.5%
37.3347766891 3
 
0.5%
37.3318044337 3
 
0.5%
37.7252441258 3
 
0.5%
Other values (472) 547
91.0%
(Missing) 22
 
3.7%
ValueCountFrequency (%)
36.9351315046 1
 
0.2%
36.9479858681 1
 
0.2%
36.9561776632 1
 
0.2%
36.9582196721 3
0.5%
36.960577095 1
 
0.2%
36.9624855257 2
0.3%
36.966009155 1
 
0.2%
36.9697934271 1
 
0.2%
36.9717692215 3
0.5%
36.9723848921 1
 
0.2%
ValueCountFrequency (%)
38.0175569531 1
0.2%
38.0173222116 1
0.2%
37.9746474113 2
0.3%
37.9489581506 2
0.3%
37.9238866284 1
0.2%
37.9238489343 1
0.2%
37.9068551388 1
0.2%
37.9066113417 2
0.3%
37.8962465269 1
0.2%
37.8883869355 1
0.2%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct482
Distinct (%)83.2%
Missing22
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean126.88802
Minimum126.54607
Maximum127.70144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:26.911140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.54607
5-th percentile126.59648
Q1126.73227
median126.84199
Q3126.98892
95-th percentile127.29499
Maximum127.70144
Range1.1553751
Interquartile range (IQR)0.25664857

Descriptive statistics

Standard deviation0.21559066
Coefficient of variation (CV)0.0016990624
Kurtosis0.93418815
Mean126.88802
Median Absolute Deviation (MAD)0.11814909
Skewness1.0306678
Sum73468.164
Variance0.046479333
MonotonicityNot monotonic
2023-12-11T07:28:27.059485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.621303491 5
 
0.8%
126.8922498063 3
 
0.5%
126.7804911704 3
 
0.5%
126.8533160934 3
 
0.5%
126.8715938687 3
 
0.5%
127.2105495233 3
 
0.5%
126.7266399535 3
 
0.5%
126.7373102173 3
 
0.5%
126.7293145372 3
 
0.5%
126.5907783245 3
 
0.5%
Other values (472) 547
91.0%
(Missing) 22
 
3.7%
ValueCountFrequency (%)
126.5460655202 1
0.2%
126.5494942369 1
0.2%
126.5533792377 1
0.2%
126.5568364683 1
0.2%
126.5570145405 1
0.2%
126.5573797844 1
0.2%
126.5649401684 1
0.2%
126.5697811697 1
0.2%
126.5711126054 1
0.2%
126.5744561732 2
0.3%
ValueCountFrequency (%)
127.7014405714 1
0.2%
127.6688775578 1
0.2%
127.6610453985 1
0.2%
127.6499016636 1
0.2%
127.6241058427 1
0.2%
127.5672827061 1
0.2%
127.5561376168 1
0.2%
127.519346433 1
0.2%
127.5143342423 1
0.2%
127.4705337652 1
0.2%

업태구분명정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing601
Missing (%)100.0%
Memory size5.4 KiB

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)85.2%
Missing547
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean187660.07
Minimum162384.98
Maximum223055.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:27.214386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum162384.98
5-th percentile165088.04
Q1176267.05
median183995.16
Q3198971.59
95-th percentile220704
Maximum223055.7
Range60670.717
Interquartile range (IQR)22704.537

Descriptive statistics

Standard deviation16423.567
Coefficient of variation (CV)0.087517642
Kurtosis-0.52073255
Mean187660.07
Median Absolute Deviation (MAD)10268.423
Skewness0.53352631
Sum10133644
Variance2.6973354 × 108
MonotonicityNot monotonic
2023-12-11T07:28:27.352283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
188534.977569293 3
 
0.5%
166479.424635633 3
 
0.5%
176652.611934545 3
 
0.5%
180418.004683172 2
 
0.3%
175935.051895075 2
 
0.3%
206095.945352272 1
 
0.2%
186649.643181364 1
 
0.2%
187061.614266999 1
 
0.2%
193144.244700623 1
 
0.2%
191161.052466703 1
 
0.2%
Other values (36) 36
 
6.0%
(Missing) 547
91.0%
ValueCountFrequency (%)
162384.98391092 1
 
0.2%
164718.66661375 1
 
0.2%
165023.797079448 1
 
0.2%
165122.626762242 1
 
0.2%
166479.424635633 3
0.5%
167773.959047313 1
 
0.2%
171529.392796363 1
 
0.2%
174890.763423218 1
 
0.2%
174954.095350123 1
 
0.2%
175935.051895075 2
0.3%
ValueCountFrequency (%)
223055.700701008 1
0.2%
222555.383597393 1
0.2%
221076.915144882 1
0.2%
220503.196245264 1
0.2%
215486.530053716 1
0.2%
207765.63854761 1
0.2%
207638.120993433 1
0.2%
207587.594116755 1
0.2%
206095.945352272 1
0.2%
204803.930817199 1
0.2%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)85.2%
Missing547
Missing (%)91.0%
Infinite0
Infinite (%)0.0%
Mean418949.76
Minimum381549.93
Maximum480705.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T07:28:27.512774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum381549.93
5-th percentile384077.49
Q1398144.59
median409078.8
Q3426413.41
95-th percentile463645.84
Maximum480705.11
Range99155.172
Interquartile range (IQR)28268.824

Descriptive statistics

Standard deviation26528.941
Coefficient of variation (CV)0.063322487
Kurtosis-0.4878582
Mean418949.76
Median Absolute Deviation (MAD)15741.952
Skewness0.67063214
Sum22623287
Variance7.0378468 × 108
MonotonicityNot monotonic
2023-12-11T07:28:27.652346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
384077.485746701 3
 
0.5%
457257.804995428 3
 
0.5%
425878.917073303 3
 
0.5%
395131.958368796 2
 
0.3%
425553.021318322 2
 
0.3%
393128.519008197 1
 
0.2%
388144.565212656 1
 
0.2%
386081.493428542 1
 
0.2%
395954.334993415 1
 
0.2%
398082.889784302 1
 
0.2%
Other values (36) 36
 
6.0%
(Missing) 547
91.0%
ValueCountFrequency (%)
381549.933865891 1
 
0.2%
384077.485746701 3
0.5%
386081.493428542 1
 
0.2%
386410.977115116 1
 
0.2%
388144.565212656 1
 
0.2%
393128.519008197 1
 
0.2%
394642.378913036 1
 
0.2%
395131.958368796 2
0.3%
395954.334993415 1
 
0.2%
396099.373165483 1
 
0.2%
ValueCountFrequency (%)
480705.106236449 1
 
0.2%
477982.25813074 1
 
0.2%
467414.944932442 1
 
0.2%
461616.328440725 1
 
0.2%
459013.864680559 1
 
0.2%
457257.804995428 3
0.5%
456347.845428895 1
 
0.2%
455876.803049448 1
 
0.2%
455594.353022338 1
 
0.2%
446632.251138388 1
 
0.2%

Interactions

2023-12-11T07:28:20.247370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:17.873334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.345188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.801289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.241819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.715312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.359105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:17.938543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.413992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.870450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.308967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.798497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.453317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.007185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.489413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.946929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.378628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.880455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.556079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.107968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.579138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.021682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.461560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.973858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.670140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.192715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.660863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.103317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.542636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.062369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.782762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.268695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:18.732278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.174433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:19.634510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:20.147706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:28:27.751087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태구분코드영업상태명도로명우편번호소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값
시군명1.0000.6060.1740.9860.9970.9660.9420.8270.927
영업상태구분코드0.6061.0001.0000.5340.3480.5490.0000.0000.183
영업상태명0.1741.0001.0000.5340.1410.1890.0000.0000.183
도로명우편번호0.9860.5340.5341.0001.0000.8480.8940.8430.743
소재지우편번호0.9970.3480.1411.0001.0000.9280.8830.7590.901
WGS84위도0.9660.5490.1890.8480.9281.0000.8170.6640.965
WGS84경도0.9420.0000.0000.8940.8830.8171.0000.9270.677
X좌표값0.8270.0000.0000.8430.7590.6640.9271.0000.638
Y좌표값0.9270.1830.1830.7430.9010.9650.6770.6381.000
2023-12-11T07:28:27.873077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명영업상태구분코드
시군명1.0000.0770.383
영업상태명0.0771.0001.000
영업상태구분코드0.3831.0001.000
2023-12-11T07:28:28.000871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명우편번호소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값시군명영업상태구분코드영업상태명
도로명우편번호1.0001.000-0.7470.5160.589-0.8090.9100.3920.392
소재지우편번호1.0001.000-0.8780.3470.535-0.7670.9400.2210.074
WGS84위도-0.747-0.8781.000-0.355-0.6181.0000.7790.2680.100
WGS84경도0.5160.347-0.3551.0001.000-0.6180.6940.0000.000
X좌표값0.5890.535-0.6181.0001.000-0.6180.5170.0000.000
Y좌표값-0.809-0.7671.000-0.618-0.6181.0000.7240.1150.115
시군명0.9100.9400.7790.6940.5170.7241.0000.3830.077
영업상태구분코드0.3920.2210.2680.0000.0000.1150.3831.0001.000
영업상태명0.3920.0740.1000.0000.0000.1150.0771.0001.000

Missing values

2023-12-11T07:28:20.968776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:28:21.279349image/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.
2023-12-11T07:28:21.775660image/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

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값
0고양시(주)엠디텍20121126<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산서구 구산로135번길 66-21 (구산동)경기도 고양시 일산서구 구산동 413-5번지1020137.693681126.70299<NA><NA><NA>
1고양시(주)한라산업기술20090320<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산동구 성현로 532-29경기도 고양시 일산동구 사리현동 478-301025937.69487126.839941<NA><NA><NA>
2고양시세명에너지(주)20170515<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산서구 구산로 135 (구산동)경기도 고양시 일산서구 구산동 355-1번지1020137.695473126.705432<NA><NA><NA>
3고양시수성밸브공업(주)20091207<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산동구 지영로229번길 31-1 (지영동)경기도 고양시 일산동구 지영동 260-21025437.717945126.827449<NA><NA><NA>
4고양시(주)엠디텍20121123<NA><NA>폐업 등20121129<NA><NA><NA>경기도 고양시 일산서구 구산로135번길 66-21 (구산동)경기도 고양시 일산서구 구산동 413-5번지1020137.693681126.70299<NA><NA><NA>
5고양시(주)엠디텍20121123<NA><NA>폐업 등20121129<NA><NA><NA>경기도 고양시 일산서구 구산로135번길 66-21 (구산동)경기도 고양시 일산서구 구산동 413-5번지1020137.693681126.70299<NA><NA><NA>
6고양시(주)세항20100705<NA><NA>폐업 등20150930<NA><NA><NA>경기도 고양시 일산동구 지영로147번길 81-18경기도 고양시 일산동구 지영동 413-15번지1025437.716102126.821928<NA><NA><NA>
7고양시(주)세항20100705<NA><NA>폐업 등20150930<NA><NA><NA>경기도 고양시 일산동구 지영로147번길 81-18경기도 고양시 일산동구 지영동 413-15번지1025437.716102126.821928<NA><NA><NA>
8광명시(주)도전과기술20050317<NA><NA>운영중<NA><NA><NA><NA><NA>경기도 광명시 광명동 353-16번지1429537.473267126.847905<NA><NA><NA>
9광명시대조산업20050704<NA><NA>운영중<NA><NA><NA><NA>경기도 광명시 오리로 505-1 (소하동)경기도 광명시 소하동 208번지1433137.4478126.880896<NA><NA><NA>
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값
591화성시(주)티원엔지니어링20151007<NA><NA>폐업 등20160609<NA><NA><NA>경기도 화성시 팔탄면 터넉골로 16-43경기도 화성시 팔탄면 덕천리 312-5번지1857737.140163126.873335<NA><NA><NA>
592화성시(주)오일금속19950206<NA><NA>폐업 등20170321<NA><NA><NA>경기도 화성시 봉담읍 수영로82번길 22-9경기도 화성시 봉담읍 수영리 299-3번지 외3필지1829737.238271126.953472<NA><NA><NA>
593화성시퓨텍20091208<NA><NA>폐업 등20091208<NA><NA><NA><NA>경기도 화성시 동탄면 산척리 20-1번지<NA><NA><NA><NA><NA><NA>
594화성시(주)캔두2120040402<NA><NA>폐업 등20081218<NA><NA><NA>경기도 화성시 마도면 마도로 747-34경기도 화성시 마도면 쌍송리 266-8번지1854137.193194126.781519<NA><NA><NA>
595화성시(주)천우기계공업20070215<NA><NA>폐업 등20150930<NA><NA><NA>경기도 화성시 남양읍 현대기아로 720-14경기도 화성시 북양동 527-7번지1825537.211724126.85798<NA><NA><NA>
596화성시(주)신우전자20160329<NA><NA>폐업 등20180516<NA><NA><NA>경기도 화성시 팔탄면 푸른들판로 641경기도 화성시 팔탄면 구장리 113-12번지1852937.165487126.894853<NA><NA><NA>
597화성시알파냉동2023-05-18<NA>2휴업<NA><NA><NA>18531경기도 화성시 팔탄면 터넉골로165번길 70-1경기도 화성시 팔탄면 지월리 761-71853137.140279126.89225<NA>190355.862205404298.171169
598화성시(주)그린에너텍20111012<NA><NA>휴업 등<NA><NA><NA><NA>경기도 화성시 봉담읍 창작마을길 27-8경기도 화성시 봉담읍 당하리 16-11번지1833537.188368126.945426<NA><NA><NA>
599화성시지쓰리20111122<NA><NA>휴업 등<NA><NA><NA><NA>경기도 화성시 정남면 보통내길253번길 43-30경기도 화성시 정남면 보통리 732-1번지1851637.193846126.962389<NA><NA><NA>
600화성시(주)명보에어로켐20120802<NA><NA>휴업 등<NA><NA><NA><NA>경기도 화성시 양감면 제약단지로 154경기도 화성시 양감면 대양리 698-20번지1863137.08919126.927224<NA><NA><NA>

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자영업상태구분코드영업상태명폐업일자도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값# duplicates
0고양시(주)세항20100705<NA>폐업 등20150930<NA>경기도 고양시 일산동구 지영로147번길 81-18경기도 고양시 일산동구 지영동 413-15번지1025437.716102126.821928<NA><NA>2
1고양시(주)엠디텍20121123<NA>폐업 등20121129<NA>경기도 고양시 일산서구 구산로135번길 66-21 (구산동)경기도 고양시 일산서구 구산동 413-5번지1020137.693681126.70299<NA><NA>2
2광명시(주)삼정이엔씨 광명지점20120611<NA>폐업 등20131001<NA>경기도 광명시 노리실로25번길 12 (가학동)경기도 광명시 가학동 937-41번지1433937.406508126.862174<NA><NA>2
3김포시(주)한국공조20080123<NA>폐업 등20100405<NA>경기도 김포시 하성면 석평로110번길 11-1경기도 김포시 하성면 석탄리 704번지1001237.722912126.646577<NA><NA>2
4김포시백웅산업설비(주)20000107<NA>폐업 등20090903<NA><NA>경기도 김포시 운양동 503-2번지<NA>37.653765126.684281<NA><NA>2
5양주시(주)에이씨알텍20070409<NA>폐업 등20171206<NA>경기도 양주시 은현면 봉암12길 39-6경기도 양주시 은현면 봉암리 182-2번지1142537.906611127.009105<NA><NA>2
6평택시(주)귀뚜라미 범양냉방20050211<NA>폐업 등20090526<NA>경기도 평택시 포승읍 평택항로156번길 82경기도 평택시 포승읍 내기리 680-2번지1796036.975058126.854636<NA><NA>2
7평택시(주)대흥티엔씨20100705<NA>폐업 등20170119<NA>경기도 평택시 청원로 1512-18경기도 평택시 가재동 242-19번지1774237.04668127.090753<NA><NA>2
8평택시(주)이수에어텍20040227<NA>폐업 등20151202<NA>경기도 평택시 포승읍 평택항로156번길 74경기도 평택시 포승읍 내기리 680-4번지1796036.975818126.851832<NA><NA>2
9평택시범양월드에어컨(주)20050527<NA>폐업 등20080602<NA>경기도 평택시 포승읍 포승공단로118번길 77경기도 평택시 포승읍 내기리 680-9번지1796036.974234126.852318<NA><NA>2