Overview

Dataset statistics

Number of variables13
Number of observations3854
Missing cells8424
Missing cells (%)16.8%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory399.1 KiB
Average record size in memory106.0 B

Variable types

Categorical4
DateTime1
Text6
Numeric2

Dataset

Description화물자동차운송주선사업 허가업체 현황
Author가평군
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=2JTC5S2FO0L58TH4IM4I31297462&infSeq=1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
데이터기준일자 is highly overall correlated with 정제WGS84위도 and 4 other fieldsHigh correlation
영업상태 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 정제WGS84위도 and 4 other fieldsHigh correlation
정제WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
정제WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
업종구분 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
인허가번호 has 1057 (27.4%) missing valuesMissing
지번주소 has 1478 (38.3%) missing valuesMissing
연락처 has 2555 (66.3%) missing valuesMissing
사업자등록번호 has 3167 (82.2%) missing valuesMissing
정제WGS84위도 has 68 (1.8%) missing valuesMissing
정제WGS84경도 has 68 (1.8%) missing valuesMissing

Reproduction

Analysis started2024-04-14 04:46:29.294714
Analysis finished2024-04-14 04:46:32.341278
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
수원시
387 
안산시
341 
용인시
279 
성남시
277 
화성시
259 
Other values (26)
2311 

Length

Max length4
Median length3
Mean length3.0498184
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 387
 
10.0%
안산시 341
 
8.8%
용인시 279
 
7.2%
성남시 277
 
7.2%
화성시 259
 
6.7%
시흥시 246
 
6.4%
평택시 194
 
5.0%
고양시 168
 
4.4%
이천시 142
 
3.7%
김포시 136
 
3.5%
Other values (21) 1425
37.0%

Length

2024-04-14T13:46:32.404042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 387
 
10.0%
안산시 341
 
8.8%
용인시 279
 
7.2%
성남시 277
 
7.2%
화성시 259
 
6.7%
시흥시 246
 
6.4%
평택시 194
 
5.0%
고양시 168
 
4.4%
이천시 142
 
3.7%
김포시 136
 
3.5%
Other values (21) 1425
37.0%

인허가일자
Date

Distinct2479
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
Minimum1962-06-21 00:00:00
Maximum2024-02-27 00:00:00
2024-04-14T13:46:32.496635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:46:32.602156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가번호
Text

MISSING 

Distinct2431
Distinct (%)86.9%
Missing1057
Missing (%)27.4%
Memory size30.2 KiB
2024-04-14T13:46:32.797166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length12.772614
Min length2

Characters and Unicode

Total characters35725
Distinct characters48
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2100 ?
Unique (%)75.1%

Sample

1st row주선2023-06
2nd row22-Jul
3rd row17-Feb
4th row1994-30002
5th row11-Jan
ValueCountFrequency (%)
주선 15
 
0.5%
17-may 4
 
0.1%
주선2019-13 4
 
0.1%
주선2020-12 4
 
0.1%
19-apr 4
 
0.1%
주선2020-33 4
 
0.1%
22-jan 3
 
0.1%
주선2020-37 3
 
0.1%
22-nov 3
 
0.1%
주선2020-28 3
 
0.1%
Other values (2416) 2765
98.3%
2024-04-14T13:46:33.093839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13500
37.8%
- 4954
 
13.9%
2 3767
 
10.5%
3 2831
 
7.9%
1 2790
 
7.8%
9 1675
 
4.7%
4 1288
 
3.6%
5 825
 
2.3%
8 634
 
1.8%
7 570
 
1.6%
Other values (38) 2891
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28386
79.5%
Dash Punctuation 4954
 
13.9%
Other Letter 1623
 
4.5%
Lowercase Letter 470
 
1.3%
Uppercase Letter 235
 
0.7%
Close Punctuation 20
 
0.1%
Open Punctuation 20
 
0.1%
Space Separator 15
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 67
14.3%
u 62
13.2%
e 50
10.6%
r 48
10.2%
p 40
8.5%
n 39
8.3%
c 31
6.6%
y 26
 
5.5%
b 24
 
5.1%
g 22
 
4.7%
Other values (4) 61
13.0%
Other Letter
ValueCountFrequency (%)
선 541
33.3%
주 541
33.3%
호 121
 
7.5%
일 115
 
7.1%
반 115
 
7.1%
사 91
 
5.6%
이 91
 
5.6%
제 4
 
0.2%
영 2
 
0.1%
동 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 13500
47.6%
2 3767
 
13.3%
3 2831
 
10.0%
1 2790
 
9.8%
9 1675
 
5.9%
4 1288
 
4.5%
5 825
 
2.9%
8 634
 
2.2%
7 570
 
2.0%
6 506
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
J 59
25.1%
M 48
20.4%
A 48
20.4%
F 24
10.2%
O 19
 
8.1%
S 14
 
6.0%
D 12
 
5.1%
N 11
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 4954
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33397
93.5%
Hangul 1623
 
4.5%
Latin 705
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 67
 
9.5%
u 62
 
8.8%
J 59
 
8.4%
e 50
 
7.1%
M 48
 
6.8%
r 48
 
6.8%
A 48
 
6.8%
p 40
 
5.7%
n 39
 
5.5%
c 31
 
4.4%
Other values (12) 213
30.2%
Common
ValueCountFrequency (%)
0 13500
40.4%
- 4954
 
14.8%
2 3767
 
11.3%
3 2831
 
8.5%
1 2790
 
8.4%
9 1675
 
5.0%
4 1288
 
3.9%
5 825
 
2.5%
8 634
 
1.9%
7 570
 
1.7%
Other values (5) 563
 
1.7%
Hangul
ValueCountFrequency (%)
선 541
33.3%
주 541
33.3%
호 121
 
7.5%
일 115
 
7.1%
반 115
 
7.1%
사 91
 
5.6%
이 91
 
5.6%
제 4
 
0.2%
영 2
 
0.1%
동 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34102
95.5%
Hangul 1623
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13500
39.6%
- 4954
 
14.5%
2 3767
 
11.0%
3 2831
 
8.3%
1 2790
 
8.2%
9 1675
 
4.9%
4 1288
 
3.8%
5 825
 
2.4%
8 634
 
1.9%
7 570
 
1.7%
Other values (27) 1268
 
3.7%
Hangul
ValueCountFrequency (%)
선 541
33.3%
주 541
33.3%
호 121
 
7.5%
일 115
 
7.1%
반 115
 
7.1%
사 91
 
5.6%
이 91
 
5.6%
제 4
 
0.2%
영 2
 
0.1%
동 1
 
0.1%

영업상태
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
운영중
1507 
영업중
867 
영업
690 
신규
533 
<NA>
184 
Other values (2)
 
73

Length

Max length4
Median length3
Mean length2.7114686
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 1507
39.1%
영업중 867
22.5%
영업 690
17.9%
신규 533
 
13.8%
<NA> 184
 
4.8%
정상 62
 
1.6%
운영 11
 
0.3%

Length

2024-04-14T13:46:33.200939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T13:46:33.290768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 1507
39.1%
영업중 867
22.5%
영업 690
17.9%
신규 533
 
13.8%
na 184
 
4.8%
정상 62
 
1.6%
운영 11
 
0.3%

업종구분
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
화물운송주선업
2409 
일반화물
444 
일반
 
240
이사화물
 
235
화물자동차 운송주선사업
 
168
Other values (9)
358 

Length

Max length12
Median length7
Mean length6.1144266
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
화물운송주선업 2409
62.5%
일반화물 444
 
11.5%
일반 240
 
6.2%
이사화물 235
 
6.1%
화물자동차 운송주선사업 168
 
4.4%
화물운송주선 144
 
3.7%
주선 116
 
3.0%
이사 32
 
0.8%
일반주선 24
 
0.6%
이사주선 20
 
0.5%
Other values (4) 22
 
0.6%

Length

2024-04-14T13:46:33.382712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화물운송주선업 2409
59.9%
일반화물 444
 
11.0%
일반 240
 
6.0%
이사화물 235
 
5.8%
화물자동차 168
 
4.2%
운송주선사업 168
 
4.2%
화물운송주선 144
 
3.6%
주선 116
 
2.9%
이사 32
 
0.8%
일반주선 24
 
0.6%
Other values (5) 42
 
1.0%

업체명
Text

Distinct3271
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
2024-04-14T13:46:33.562586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length6.7664764
Min length2

Characters and Unicode

Total characters26078
Distinct characters569
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2786 ?
Unique (%)72.3%

Sample

1st row케이씨로지스
2nd row안성전국화물
3rd row중부물류(주)
4th row안성고속화물
5th row(주)대평엘에스
ValueCountFrequency (%)
주식회사 111
 
2.7%
익스프레스 19
 
0.5%
통인익스프레스 13
 
0.3%
전국화물 11
 
0.3%
제일익스프레스 5
 
0.1%
한솔익스프레스 5
 
0.1%
물류 5
 
0.1%
종합물류 5
 
0.1%
개미익스프레스 5
 
0.1%
행운익스프레스 5
 
0.1%
Other values (3316) 3920
95.5%
2024-04-14T13:46:33.855327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 2053
 
7.9%
물 1560
 
6.0%
주 1261
 
4.8%
) 1144
 
4.4%
( 1144
 
4.4%
류 901
 
3.5%
로 738
 
2.8%
화 729
 
2.8%
지 711
 
2.7%
이 631
 
2.4%
Other values (559) 15206
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22423
86.0%
Close Punctuation 1144
 
4.4%
Open Punctuation 1144
 
4.4%
Other Symbol 427
 
1.6%
Uppercase Letter 319
 
1.2%
Space Separator 250
 
1.0%
Decimal Number 241
 
0.9%
Lowercase Letter 69
 
0.3%
Other Punctuation 53
 
0.2%
Dash Punctuation 6
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 2053
 
9.2%
물 1560
 
7.0%
주 1261
 
5.6%
류 901
 
4.0%
로 738
 
3.3%
화 729
 
3.3%
지 711
 
3.2%
이 631
 
2.8%
레 498
 
2.2%
프 491
 
2.2%
Other values (501) 12850
57.3%
Uppercase Letter
ValueCountFrequency (%)
K 54
16.9%
G 38
11.9%
S 34
10.7%
B 27
8.5%
O 25
 
7.8%
L 18
 
5.6%
J 16
 
5.0%
C 16
 
5.0%
N 15
 
4.7%
T 12
 
3.8%
Other values (13) 64
20.1%
Lowercase Letter
ValueCountFrequency (%)
s 17
24.6%
o 9
13.0%
c 8
11.6%
p 7
10.1%
a 6
 
8.7%
e 5
 
7.2%
k 5
 
7.2%
g 4
 
5.8%
t 3
 
4.3%
i 2
 
2.9%
Other values (2) 3
 
4.3%
Decimal Number
ValueCountFrequency (%)
2 81
33.6%
4 58
24.1%
1 18
 
7.5%
0 18
 
7.5%
5 14
 
5.8%
8 14
 
5.8%
3 14
 
5.8%
6 12
 
5.0%
9 7
 
2.9%
7 5
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 21
39.6%
& 18
34.0%
, 9
17.0%
? 2
 
3.8%
* 2
 
3.8%
; 1
 
1.9%
Close Punctuation
ValueCountFrequency (%)
) 1144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1144
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 427
100.0%
Space Separator
ValueCountFrequency (%)
250
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22848
87.6%
Common 2840
 
10.9%
Latin 388
 
1.5%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 2053
 
9.0%
물 1560
 
6.8%
주 1261
 
5.5%
류 901
 
3.9%
로 738
 
3.2%
화 729
 
3.2%
지 711
 
3.1%
이 631
 
2.8%
레 498
 
2.2%
프 491
 
2.1%
Other values (500) 13275
58.1%
Latin
ValueCountFrequency (%)
K 54
13.9%
G 38
 
9.8%
S 34
 
8.8%
B 27
 
7.0%
O 25
 
6.4%
L 18
 
4.6%
s 17
 
4.4%
J 16
 
4.1%
C 16
 
4.1%
N 15
 
3.9%
Other values (25) 128
33.0%
Common
ValueCountFrequency (%)
) 1144
40.3%
( 1144
40.3%
250
 
8.8%
2 81
 
2.9%
4 58
 
2.0%
. 21
 
0.7%
1 18
 
0.6%
0 18
 
0.6%
& 18
 
0.6%
5 14
 
0.5%
Other values (12) 74
 
2.6%
Han
ValueCountFrequency (%)
華 1
50.0%
信 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22421
86.0%
ASCII 3228
 
12.4%
None 427
 
1.6%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 2053
 
9.2%
물 1560
 
7.0%
주 1261
 
5.6%
류 901
 
4.0%
로 738
 
3.3%
화 729
 
3.3%
지 711
 
3.2%
이 631
 
2.8%
레 498
 
2.2%
프 491
 
2.2%
Other values (499) 12848
57.3%
ASCII
ValueCountFrequency (%)
) 1144
35.4%
( 1144
35.4%
250
 
7.7%
2 81
 
2.5%
4 58
 
1.8%
K 54
 
1.7%
G 38
 
1.2%
S 34
 
1.1%
B 27
 
0.8%
O 25
 
0.8%
Other values (47) 373
 
11.6%
None
ValueCountFrequency (%)
㈜ 427
100.0%
CJK
ValueCountFrequency (%)
華 1
50.0%
信 1
50.0%

도로명주소
Text

Distinct3402
Distinct (%)89.0%
Missing31
Missing (%)0.8%
Memory size30.2 KiB
2024-04-14T13:46:34.100973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length47
Mean length30.229139
Min length13

Characters and Unicode

Total characters115566
Distinct characters579
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3021 ?
Unique (%)79.0%

Sample

1st row경기도 안성시 고수2로 27, 대우아파트상가 215호 (당왕동)
2nd row경기도 안성시 금광면 신양복길 4, 203호
3rd row경기도 안성시 일죽면 서동대로 7785
4th row경기도 안성시 서운면 제3공단3길 34-3
5th row경기도 안성시 원곡면 새울길 9-9
ValueCountFrequency (%)
경기도 3819
 
16.1%
수원시 381
 
1.6%
안산시 339
 
1.4%
성남시 277
 
1.2%
용인시 273
 
1.1%
화성시 250
 
1.1%
시흥시 246
 
1.0%
단원구 226
 
0.9%
2층 193
 
0.8%
평택시 193
 
0.8%
Other values (5815) 17596
74.0%
2024-04-14T13:46:34.470601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19991
 
17.3%
1 4537
 
3.9%
시 4219
 
3.7%
기 4036
 
3.5%
도 3983
 
3.4%
경 3961
 
3.4%
동 3610
 
3.1%
로 3432
 
3.0%
2 3301
 
2.9%
, 2649
 
2.3%
Other values (569) 61847
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65676
56.8%
Decimal Number 20505
 
17.7%
Space Separator 19991
 
17.3%
Other Punctuation 2665
 
2.3%
Close Punctuation 2622
 
2.3%
Open Punctuation 2621
 
2.3%
Dash Punctuation 1068
 
0.9%
Uppercase Letter 381
 
0.3%
Lowercase Letter 30
 
< 0.1%
Other Symbol 3
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 4219
 
6.4%
기 4036
 
6.1%
도 3983
 
6.1%
경 3961
 
6.0%
동 3610
 
5.5%
로 3432
 
5.2%
호 1830
 
2.8%
구 1807
 
2.8%
길 1541
 
2.3%
원 1177
 
1.8%
Other values (511) 36080
54.9%
Uppercase Letter
ValueCountFrequency (%)
A 69
18.1%
B 58
15.2%
I 33
 
8.7%
C 32
 
8.4%
T 30
 
7.9%
D 19
 
5.0%
E 16
 
4.2%
S 14
 
3.7%
L 14
 
3.7%
O 12
 
3.1%
Other values (15) 84
22.0%
Decimal Number
ValueCountFrequency (%)
1 4537
22.1%
2 3301
16.1%
3 2321
11.3%
0 2297
11.2%
4 1719
 
8.4%
5 1491
 
7.3%
6 1348
 
6.6%
7 1334
 
6.5%
8 1090
 
5.3%
9 1067
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
e 12
40.0%
a 3
 
10.0%
r 3
 
10.0%
l 2
 
6.7%
n 2
 
6.7%
k 2
 
6.7%
t 2
 
6.7%
c 2
 
6.7%
o 1
 
3.3%
w 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 2649
99.4%
. 12
 
0.5%
* 4
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 2619
99.9%
] 3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2618
99.9%
[ 3
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%
Space Separator
ValueCountFrequency (%)
19991
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1068
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65679
56.8%
Common 49473
42.8%
Latin 414
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 4219
 
6.4%
기 4036
 
6.1%
도 3983
 
6.1%
경 3961
 
6.0%
동 3610
 
5.5%
로 3432
 
5.2%
호 1830
 
2.8%
구 1807
 
2.8%
길 1541
 
2.3%
원 1177
 
1.8%
Other values (512) 36083
54.9%
Latin
ValueCountFrequency (%)
A 69
16.7%
B 58
14.0%
I 33
 
8.0%
C 32
 
7.7%
T 30
 
7.2%
D 19
 
4.6%
E 16
 
3.9%
S 14
 
3.4%
L 14
 
3.4%
O 12
 
2.9%
Other values (27) 117
28.3%
Common
ValueCountFrequency (%)
19991
40.4%
1 4537
 
9.2%
2 3301
 
6.7%
, 2649
 
5.4%
) 2619
 
5.3%
( 2618
 
5.3%
3 2321
 
4.7%
0 2297
 
4.6%
4 1719
 
3.5%
5 1491
 
3.0%
Other values (10) 5930
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65676
56.8%
ASCII 49884
43.2%
None 3
 
< 0.1%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19991
40.1%
1 4537
 
9.1%
2 3301
 
6.6%
, 2649
 
5.3%
) 2619
 
5.3%
( 2618
 
5.2%
3 2321
 
4.7%
0 2297
 
4.6%
4 1719
 
3.4%
5 1491
 
3.0%
Other values (45) 6341
 
12.7%
Hangul
ValueCountFrequency (%)
시 4219
 
6.4%
기 4036
 
6.1%
도 3983
 
6.1%
경 3961
 
6.0%
동 3610
 
5.5%
로 3432
 
5.2%
호 1830
 
2.8%
구 1807
 
2.8%
길 1541
 
2.3%
원 1177
 
1.8%
Other values (511) 36080
54.9%
None
ValueCountFrequency (%)
㈜ 3
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
66.7%
Ⅱ 1
33.3%

지번주소
Text

MISSING 

Distinct2037
Distinct (%)85.7%
Missing1478
Missing (%)38.3%
Memory size30.2 KiB
2024-04-14T13:46:34.721137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length47
Mean length23.140993
Min length10

Characters and Unicode

Total characters54983
Distinct characters449
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

Unique1803 ?
Unique (%)75.9%

Sample

1st row경기도 안성시 당왕동 534 대우아파트상가 215호
2nd row경기도 안성시 금광면 상중리 111-1
3rd row경기도 안성시 일죽면 당촌리 115
4th row경기도 안성시 서운면 신능리 223-12
5th row경기도 안성시 원곡면 내가천리 162-5
ValueCountFrequency (%)
경기도 2376
 
18.7%
수원시 385
 
3.0%
안산시 336
 
2.6%
용인시 267
 
2.1%
화성시 255
 
2.0%
단원구 225
 
1.8%
평택시 193
 
1.5%
권선구 177
 
1.4%
처인구 148
 
1.2%
이천시 143
 
1.1%
Other values (3205) 8216
64.6%
2024-04-14T13:46:35.109057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10345
 
18.8%
기 2496
 
4.5%
도 2449
 
4.5%
시 2409
 
4.4%
경 2396
 
4.4%
1 2112
 
3.8%
- 1840
 
3.3%
동 1678
 
3.1%
2 1391
 
2.5%
4 1195
 
2.2%
Other values (439) 26672
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31647
57.6%
Decimal Number 10879
 
19.8%
Space Separator 10345
 
18.8%
Dash Punctuation 1840
 
3.3%
Uppercase Letter 134
 
0.2%
Other Punctuation 60
 
0.1%
Open Punctuation 32
 
0.1%
Close Punctuation 31
 
0.1%
Lowercase Letter 13
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2496
 
7.9%
도 2449
 
7.7%
시 2409
 
7.6%
경 2396
 
7.6%
동 1678
 
5.3%
구 1058
 
3.3%
리 1012
 
3.2%
원 858
 
2.7%
산 602
 
1.9%
안 584
 
1.8%
Other values (394) 16105
50.9%
Uppercase Letter
ValueCountFrequency (%)
T 16
11.9%
A 15
11.2%
E 13
 
9.7%
B 12
 
9.0%
I 9
 
6.7%
R 9
 
6.7%
S 8
 
6.0%
U 7
 
5.2%
K 6
 
4.5%
O 6
 
4.5%
Other values (11) 33
24.6%
Decimal Number
ValueCountFrequency (%)
1 2112
19.4%
2 1391
12.8%
4 1195
11.0%
5 1006
9.2%
3 976
9.0%
0 946
8.7%
6 902
8.3%
7 856
7.9%
9 828
 
7.6%
8 667
 
6.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
38.5%
o 2
 
15.4%
w 2
 
15.4%
r 2
 
15.4%
k 1
 
7.7%
a 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 56
93.3%
* 4
 
6.7%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
10345
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1840
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31647
57.6%
Common 23187
42.2%
Latin 149
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2496
 
7.9%
도 2449
 
7.7%
시 2409
 
7.6%
경 2396
 
7.6%
동 1678
 
5.3%
구 1058
 
3.3%
리 1012
 
3.2%
원 858
 
2.7%
산 602
 
1.9%
안 584
 
1.8%
Other values (394) 16105
50.9%
Latin
ValueCountFrequency (%)
T 16
 
10.7%
A 15
 
10.1%
E 13
 
8.7%
B 12
 
8.1%
I 9
 
6.0%
R 9
 
6.0%
S 8
 
5.4%
U 7
 
4.7%
K 6
 
4.0%
O 6
 
4.0%
Other values (19) 48
32.2%
Common
ValueCountFrequency (%)
10345
44.6%
1 2112
 
9.1%
- 1840
 
7.9%
2 1391
 
6.0%
4 1195
 
5.2%
5 1006
 
4.3%
3 976
 
4.2%
0 946
 
4.1%
6 902
 
3.9%
7 856
 
3.7%
Other values (6) 1618
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31647
57.6%
ASCII 23334
42.4%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10345
44.3%
1 2112
 
9.1%
- 1840
 
7.9%
2 1391
 
6.0%
4 1195
 
5.1%
5 1006
 
4.3%
3 976
 
4.2%
0 946
 
4.1%
6 902
 
3.9%
7 856
 
3.7%
Other values (33) 1765
 
7.6%
Hangul
ValueCountFrequency (%)
기 2496
 
7.9%
도 2449
 
7.7%
시 2409
 
7.6%
경 2396
 
7.6%
동 1678
 
5.3%
구 1058
 
3.3%
리 1012
 
3.2%
원 858
 
2.7%
산 602
 
1.9%
안 584
 
1.8%
Other values (394) 16105
50.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

연락처
Text

MISSING 

Distinct1069
Distinct (%)82.3%
Missing2555
Missing (%)66.3%
Memory size30.2 KiB
2024-04-14T13:46:35.292857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.805235
Min length9

Characters and Unicode

Total characters15335
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

Unique852 ?
Unique (%)65.6%

Sample

1st row031-864-9001
2nd row031-859-9000
3rd row031-851-6605
4th row031-000-0000
5th row031-000-0000
ValueCountFrequency (%)
031-000-0000 7
 
0.5%
031-608-0070 4
 
0.3%
031-232-3611 4
 
0.3%
031-728-0703 4
 
0.3%
031-242-2424 3
 
0.2%
031-460-2352 3
 
0.2%
031-757-5524 2
 
0.2%
031-748-2424 2
 
0.2%
031-753-6575 2
 
0.2%
031-704-2424 2
 
0.2%
Other values (1059) 1266
97.5%
2024-04-14T13:46:35.580400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2522
16.4%
0 2367
15.4%
1 1957
12.8%
3 1923
12.5%
2 1378
9.0%
4 1268
8.3%
7 905
 
5.9%
8 857
 
5.6%
5 793
 
5.2%
6 776
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12813
83.6%
Dash Punctuation 2522
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2367
18.5%
1 1957
15.3%
3 1923
15.0%
2 1378
10.8%
4 1268
9.9%
7 905
 
7.1%
8 857
 
6.7%
5 793
 
6.2%
6 776
 
6.1%
9 589
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 2522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2522
16.4%
0 2367
15.4%
1 1957
12.8%
3 1923
12.5%
2 1378
9.0%
4 1268
8.3%
7 905
 
5.9%
8 857
 
5.6%
5 793
 
5.2%
6 776
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2522
16.4%
0 2367
15.4%
1 1957
12.8%
3 1923
12.5%
2 1378
9.0%
4 1268
8.3%
7 905
 
5.9%
8 857
 
5.6%
5 793
 
5.2%
6 776
 
5.1%

사업자등록번호
Text

MISSING 

Distinct51
Distinct (%)7.4%
Missing3167
Missing (%)82.2%
Memory size30.2 KiB
2024-04-14T13:46:35.746797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.7714702
Min length1

Characters and Unicode

Total characters1217
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)7.3%

Sample

1st row1138607943
2nd row1238151422
3rd row4778701253
4th row2298125479
5th row1381152982
ValueCountFrequency (%)
n 637
92.7%
124-48-86567 1
 
0.1%
1238151422 1
 
0.1%
124-49-95345 1
 
0.1%
124-81-41761 1
 
0.1%
135-25-23146 1
 
0.1%
124-81-55381 1
 
0.1%
124-86-43070 1
 
0.1%
135-81-15263 1
 
0.1%
124-86-25882 1
 
0.1%
Other values (41) 41
 
6.0%
2024-04-14T13:46:36.164510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 637
52.3%
1 101
 
8.3%
- 80
 
6.6%
2 73
 
6.0%
4 61
 
5.0%
8 52
 
4.3%
3 47
 
3.9%
5 42
 
3.5%
0 37
 
3.0%
7 34
 
2.8%
Other values (2) 53
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 637
52.3%
Decimal Number 500
41.1%
Dash Punctuation 80
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 101
20.2%
2 73
14.6%
4 61
12.2%
8 52
10.4%
3 47
9.4%
5 42
8.4%
0 37
 
7.4%
7 34
 
6.8%
9 27
 
5.4%
6 26
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
N 637
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 637
52.3%
Common 580
47.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 101
17.4%
- 80
13.8%
2 73
12.6%
4 61
10.5%
8 52
9.0%
3 47
8.1%
5 42
7.2%
0 37
 
6.4%
7 34
 
5.9%
9 27
 
4.7%
Latin
ValueCountFrequency (%)
N 637
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1217
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 637
52.3%
1 101
 
8.3%
- 80
 
6.6%
2 73
 
6.0%
4 61
 
5.0%
8 52
 
4.3%
3 47
 
3.9%
5 42
 
3.5%
0 37
 
3.0%
7 34
 
2.8%
Other values (2) 53
 
4.4%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
2024-02-23
341 
2023-04-25
279 
2023-05-23
259 
2023-06-08
 
246
2024-03-04
 
194
Other values (28)
2535 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-11
2nd row2024-04-11
3rd row2024-04-11
4th row2024-04-11
5th row2024-04-11

Common Values

ValueCountFrequency (%)
2024-02-23 341
 
8.8%
2023-04-25 279
 
7.2%
2023-05-23 259
 
6.7%
2023-06-08 246
 
6.4%
2024-03-04 194
 
5.0%
2021-05-10 194
 
5.0%
2022-05-06 193
 
5.0%
2023-05-26 168
 
4.4%
2024-03-06 157
 
4.1%
2023-05-18 142
 
3.7%
Other values (23) 1681
43.6%

Length

2024-04-14T13:46:36.264270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-02-23 341
 
8.8%
2023-04-25 279
 
7.2%
2023-05-23 259
 
6.7%
2023-06-08 246
 
6.4%
2024-03-04 194
 
5.0%
2021-05-10 194
 
5.0%
2022-05-06 193
 
5.0%
2023-05-26 168
 
4.4%
2024-03-06 157
 
4.1%
2023-05-18 142
 
3.7%
Other values (23) 1681
43.6%

정제WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2828
Distinct (%)74.7%
Missing68
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean37.384641
Minimum36.919965
Maximum38.097532
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2024-04-14T13:46:36.353847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.919965
5-th percentile37.034373
Q137.254267
median37.337504
Q337.518647
95-th percentile37.795437
Maximum38.097532
Range1.1775671
Interquartile range (IQR)0.26438017

Descriptive statistics

Standard deviation0.21741809
Coefficient of variation (CV)0.0058157063
Kurtosis-0.2601462
Mean37.384641
Median Absolute Deviation (MAD)0.10448777
Skewness0.45438028
Sum141538.25
Variance0.047270628
MonotonicityNot monotonic
2024-04-14T13:46:36.461553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3309479641 21
 
0.5%
37.3233710989 20
 
0.5%
37.3485653023 16
 
0.4%
37.2521927214 14
 
0.4%
37.3716127805 14
 
0.4%
37.3340818773 11
 
0.3%
37.2555893887 11
 
0.3%
37.3167242742 10
 
0.3%
37.3283551697 9
 
0.2%
37.322214387 9
 
0.2%
Other values (2818) 3651
94.7%
(Missing) 68
 
1.8%
ValueCountFrequency (%)
36.919965215 1
< 0.1%
36.9254362323 1
< 0.1%
36.9360125233 1
< 0.1%
36.9382474952 1
< 0.1%
36.9390230646 1
< 0.1%
36.9400315719 1
< 0.1%
36.9420914023 1
< 0.1%
36.9438761335 1
< 0.1%
36.9439205767 1
< 0.1%
36.9441503552 1
< 0.1%
ValueCountFrequency (%)
38.0975323133 1
 
< 0.1%
38.0966264721 1
 
< 0.1%
38.0928321665 1
 
< 0.1%
38.0191220285 1
 
< 0.1%
37.9769894098 1
 
< 0.1%
37.9696176208 4
0.1%
37.9685700664 1
 
< 0.1%
37.9413277115 1
 
< 0.1%
37.9401867091 1
 
< 0.1%
37.9383652685 1
 
< 0.1%

정제WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2828
Distinct (%)74.7%
Missing68
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean127.00475
Minimum126.54693
Maximum127.69505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2024-04-14T13:46:36.567443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.54693
5-th percentile126.72036
Q1126.83405
median127.00224
Q3127.13815
95-th percentile127.38362
Maximum127.69505
Range1.1481226
Interquartile range (IQR)0.30410136

Descriptive statistics

Standard deviation0.2082089
Coefficient of variation (CV)0.0016393789
Kurtosis-0.0062591326
Mean127.00475
Median Absolute Deviation (MAD)0.14988875
Skewness0.42982311
Sum480839.97
Variance0.043350945
MonotonicityNot monotonic
2024-04-14T13:46:36.670516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9372235089 21
 
0.5%
126.788892882 20
 
0.5%
126.7203592701 16
 
0.4%
127.0056361699 14
 
0.4%
126.9514327046 14
 
0.4%
126.9617863047 11
 
0.3%
126.9862067447 11
 
0.3%
126.9847413331 10
 
0.3%
126.95328247 9
 
0.2%
126.7349541609 9
 
0.2%
Other values (2818) 3651
94.7%
(Missing) 68
 
1.8%
ValueCountFrequency (%)
126.5469264474 1
< 0.1%
126.5524473353 1
< 0.1%
126.5574154248 1
< 0.1%
126.557634593 1
< 0.1%
126.5588119746 1
< 0.1%
126.5636127391 1
< 0.1%
126.5730784473 1
< 0.1%
126.5744174689 1
< 0.1%
126.575114269 1
< 0.1%
126.5755073861 1
< 0.1%
ValueCountFrequency (%)
127.6950490915 1
< 0.1%
127.686843709 2
0.1%
127.6855443842 1
< 0.1%
127.6757917358 1
< 0.1%
127.6706945499 1
< 0.1%
127.6683604459 1
< 0.1%
127.6619422692 1
< 0.1%
127.6618110691 1
< 0.1%
127.6610210051 1
< 0.1%
127.6578279756 1
< 0.1%

Interactions

2024-04-14T13:46:31.683758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:46:31.504363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:46:31.762239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-14T13:46:31.615306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-14T13:46:36.740708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태업종구분사업자등록번호데이터기준일자정제WGS84위도정제WGS84경도
시군명1.0001.0000.9520.8940.9990.9610.943
영업상태1.0001.0000.6560.8981.0000.6920.540
업종구분0.9520.6561.0000.7080.9530.6800.710
사업자등록번호0.8940.8980.7081.0000.9230.6540.691
데이터기준일자0.9991.0000.9530.9231.0000.9340.939
정제WGS84위도0.9610.6920.6800.6540.9341.0000.639
정제WGS84경도0.9430.5400.7100.6910.9390.6391.000
2024-04-14T13:46:36.829823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일자영업상태시군명업종구분
데이터기준일자1.0000.9960.9660.693
영업상태0.9961.0000.9970.395
시군명0.9660.9971.0000.695
업종구분0.6930.3950.6951.000
2024-04-14T13:46:36.899956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정제WGS84위도정제WGS84경도시군명영업상태업종구분데이터기준일자
정제WGS84위도1.000-0.1760.7710.4550.3550.686
정제WGS84경도-0.1761.0000.7080.3210.3830.699
시군명0.7710.7081.0000.9970.6950.966
영업상태0.4550.3210.9971.0000.3950.996
업종구분0.3550.3830.6950.3951.0000.693
데이터기준일자0.6860.6990.9660.9960.6931.000

Missing values

2024-04-14T13:46:31.893400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T13:46:32.080785image/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.
2024-04-14T13:46:32.231006image/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

시군명인허가일자인허가번호영업상태업종구분업체명도로명주소지번주소연락처사업자등록번호데이터기준일자정제WGS84위도정제WGS84경도
0안성시1995-11-21주선2023-06신규일반화물케이씨로지스경기도 안성시 고수2로 27, 대우아파트상가 215호 (당왕동)경기도 안성시 당왕동 534 대우아파트상가 215호<NA><NA>2024-04-1137.015687127.257942
1안성시1994-01-3122-Jul신규일반화물안성전국화물경기도 안성시 금광면 신양복길 4, 203호경기도 안성시 금광면 상중리 111-1<NA><NA>2024-04-1137.008109127.326497
2안성시1994-07-2117-Feb신규일반화물중부물류(주)경기도 안성시 일죽면 서동대로 7785경기도 안성시 일죽면 당촌리 115<NA><NA>2024-04-1137.104527127.505188
3안성시1994-01-051994-30002신규일반화물안성고속화물경기도 안성시 서운면 제3공단3길 34-3경기도 안성시 서운면 신능리 223-12<NA><NA>2024-04-1136.973196127.26839
4안성시1993-02-1511-Jan신규일반화물(주)대평엘에스경기도 안성시 원곡면 새울길 9-9경기도 안성시 원곡면 내가천리 162-5<NA><NA>2024-04-1137.038127.1208
5안성시1993-06-161996-30002신규일반화물제삼특장(주)경기도 안성시 죽산면 하구산길 26경기도 안성시 죽산면 매산리 340-1<NA><NA>2024-04-1137.08861127.438339
6안성시1992-12-31주선2020-08신규일반화물이권섭(이오화물)경기도 안성시 일죽면 장암로 276-44, 나동 1층경기도 안성시 일죽면 죽림리 703-6<NA><NA>2024-04-1137.073931127.448723
7안성시1992-12-23주선2020-05신규일반화물주식회사 와이씨로지스경기도 안성시 대덕면 무능로 18경기도 안성시 대덕면 소내리 161<NA><NA>2024-04-1137.035607127.227755
8안성시1992-01-3118-Jan신규일반화물25시퀵전국화물경기도 안성시 중앙3길 82 (봉남동)경기도 안성시 봉남동 28-2<NA><NA>2024-04-1137.007788127.274984
9안성시1989-04-0722-Aug신규일반화물와이지로지스틱스경기도 안성시 대덕면 서동대로 4721, 301호경기도 안성시 대덕면 신령리 500-23<NA><NA>2024-04-1137.013047127.225351
시군명인허가일자인허가번호영업상태업종구분업체명도로명주소지번주소연락처사업자등록번호데이터기준일자정제WGS84위도정제WGS84경도
3844성남시2020-03-23<NA>영업중일반화물지니물류경기도 성남시 분당구 벌말로 47, 3층 301-1호(야탑동, 명지프라자)<NA><NA>N2021-04-1637.412163127.1382
3845성남시2020-06-29<NA>영업중일반화물한국통운 익스프레스경기도 성남시 수정구 사송로 63(사송동)<NA><NA>N2021-04-1637.414229127.113069
3846성남시2020-08-03<NA>영업중일반화물동원익스프레스경기도 성남시 중원구 금빛로112번길 32(은행동)<NA><NA>N2021-04-1637.452742127.1686
3847성남시2020-08-24<NA>영업중일반화물㈜이사로경기도 성남시 분당구 판교로 253, 비동 304-1호(삼평동, 이노밸리)<NA><NA>N2021-04-1637.403282127.100923
3848성남시2014-12-05<NA>영업중일반화물+이사화물㈜에스피씨지에프에스경기도 성남시 중원구 둔촌대로457번길 13(상대원동)<NA>031-737-8482N2021-04-1637.433569127.168334
3849성남시1993-04-13<NA>영업중일반화물+이사화물정운종합물류경기도 성남시 중원구 금상로 77-1, 402호(금광동)<NA>031-744-2424N2021-04-1637.441922127.163979
3850성남시2001-12-19<NA>영업중일반화물화성냉동㈜경기도 성남시 분당구 정자일로 1, A동 1904호(금곡동, 코오롱트리폴리스)<NA>031-728-0703N2021-04-1637.350792127.105401
3851성남시2011-08-08<NA>영업중일반화물㈜씨앤에이비즈경기도 성남시 분당구 황새울로360번길 42, 지하2층(서현동, 플라자빌딩)<NA>031-779-3958N2021-04-1637.385101127.122708
3852성남시2013-02-26<NA>영업중일반화물유로통운㈜경기도 성남시 분당구 야탑로95, 604호(야탑동, 세신옴니코어빌딩)<NA>031-701-2278N2021-04-1637.41018127.129294
3853성남시2014-02-18<NA>영업중일반화물㈜한국종합물류경기도 성남시 분당구 방아로 82, 1층(이매동)<NA><NA>N2021-04-1637.397023127.136205

Duplicate rows

Most frequently occurring

시군명인허가일자인허가번호영업상태업종구분업체명도로명주소지번주소연락처사업자등록번호데이터기준일자정제WGS84위도정제WGS84경도# duplicates
0포천시1995-01-17<NA><NA>일반신용운수경기도 포천시 송선로 464 (선단동)경기도 포천시 선단동 209-18<NA><NA>2024-03-0637.863965127.1670172