Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells44872
Missing cells (%)26.4%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1.4 MiB
Average record size in memory151.0 B

Variable types

Categorical3
Text6
DateTime1
Unsupported2
Numeric5

Dataset

Description담배 소매업 현황_인허가
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=I7ZQ87RW9649Z3EOE7A126852371&infSeq=1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
소재지우편번호 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with X좌표값 and 1 other fieldsHigh correlation
X좌표값 is highly overall correlated with WGS84경도 and 1 other fieldsHigh correlation
Y좌표값 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
영업상태명 is highly imbalanced (52.8%)Imbalance
인허가취소일자 has 9000 (90.0%) missing valuesMissing
소재지시설전화번호 has 4428 (44.3%) missing valuesMissing
소재지면적정보 has 10000 (100.0%) missing valuesMissing
도로명우편번호 has 6138 (61.4%) missing valuesMissing
소재지도로명주소 has 1336 (13.4%) missing valuesMissing
소재지우편번호 has 725 (7.2%) missing valuesMissing
업태구분명정보 has 10000 (100.0%) missing valuesMissing
X좌표값 has 1553 (15.5%) missing valuesMissing
Y좌표값 has 1553 (15.5%) missing valuesMissing
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 22:57:55.228169
Analysis finished2023-12-10 22:58:01.257925
Duration6.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부천시
1977 
고양시
1874 
성남시
1445 
남양주시
1110 
김포시
860 
Other values (7)
2734 

Length

Max length4
Median length3
Mean length3.1406
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광명시
2nd row군포시
3rd row부천시
4th row부천시
5th row부천시

Common Values

ValueCountFrequency (%)
부천시 1977
19.8%
고양시 1874
18.7%
성남시 1445
14.4%
남양주시 1110
11.1%
김포시 860
8.6%
광주시 622
 
6.2%
군포시 491
 
4.9%
광명시 444
 
4.4%
구리시 424
 
4.2%
가평군 360
 
3.6%
Other values (2) 393
 
3.9%

Length

2023-12-11T07:58:01.327366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부천시 1977
19.8%
고양시 1874
18.7%
성남시 1445
14.4%
남양주시 1110
11.1%
김포시 860
8.6%
광주시 622
 
6.2%
군포시 491
 
4.9%
광명시 444
 
4.4%
구리시 424
 
4.2%
가평군 360
 
3.6%
Other values (2) 393
 
3.9%
Distinct8226
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T07:58:01.606526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length6.9652
Min length1

Characters and Unicode

Total characters69652
Distinct characters848
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7430 ?
Unique (%)74.3%

Sample

1st row코리아 마트
2nd row피오토
3rd row심곡할인마트
4th row한양철물건재
5th row동원쌀슈퍼
ValueCountFrequency (%)
씨유 395
 
3.1%
세븐일레븐 290
 
2.3%
gs25 266
 
2.1%
이마트24 133
 
1.0%
지에스25 115
 
0.9%
주)코리아세븐 98
 
0.8%
지에스(gs)25 78
 
0.6%
미니스톱 67
 
0.5%
훼미리마트 62
 
0.5%
주식회사 59
 
0.5%
Other values (8446) 11273
87.8%
2023-12-11T07:58:02.133327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3095
 
4.4%
2845
 
4.1%
2307
 
3.3%
2084
 
3.0%
1531
 
2.2%
1443
 
2.1%
2 1256
 
1.8%
1087
 
1.6%
5 976
 
1.4%
974
 
1.4%
Other values (838) 52054
74.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 59423
85.3%
Space Separator 2846
 
4.1%
Decimal Number 2844
 
4.1%
Uppercase Letter 2469
 
3.5%
Close Punctuation 804
 
1.2%
Open Punctuation 796
 
1.1%
Lowercase Letter 335
 
0.5%
Other Punctuation 93
 
0.1%
Dash Punctuation 40
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3095
 
5.2%
2307
 
3.9%
2084
 
3.5%
1531
 
2.6%
1443
 
2.4%
1087
 
1.8%
974
 
1.6%
963
 
1.6%
954
 
1.6%
942
 
1.6%
Other values (760) 44043
74.1%
Uppercase Letter
ValueCountFrequency (%)
S 739
29.9%
G 719
29.1%
C 197
 
8.0%
U 128
 
5.2%
K 82
 
3.3%
L 79
 
3.2%
A 67
 
2.7%
E 45
 
1.8%
D 45
 
1.8%
T 43
 
1.7%
Other values (15) 325
13.2%
Lowercase Letter
ValueCountFrequency (%)
e 50
14.9%
s 27
 
8.1%
o 27
 
8.1%
i 25
 
7.5%
a 24
 
7.2%
r 20
 
6.0%
t 18
 
5.4%
m 18
 
5.4%
n 17
 
5.1%
y 16
 
4.8%
Other values (13) 93
27.8%
Other Punctuation
ValueCountFrequency (%)
. 37
39.8%
& 18
19.4%
, 12
 
12.9%
* 8
 
8.6%
/ 5
 
5.4%
: 3
 
3.2%
3
 
3.2%
' 2
 
2.2%
? 2
 
2.2%
! 2
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 1256
44.2%
5 976
34.3%
4 259
 
9.1%
1 109
 
3.8%
3 85
 
3.0%
0 55
 
1.9%
6 47
 
1.7%
8 26
 
0.9%
7 17
 
0.6%
9 14
 
0.5%
Space Separator
ValueCountFrequency (%)
2845
> 99.9%
  1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 803
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 795
99.9%
[ 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 59424
85.3%
Common 7424
 
10.7%
Latin 2804
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3095
 
5.2%
2307
 
3.9%
2084
 
3.5%
1531
 
2.6%
1443
 
2.4%
1087
 
1.8%
974
 
1.6%
963
 
1.6%
954
 
1.6%
942
 
1.6%
Other values (761) 44044
74.1%
Latin
ValueCountFrequency (%)
S 739
26.4%
G 719
25.6%
C 197
 
7.0%
U 128
 
4.6%
K 82
 
2.9%
L 79
 
2.8%
A 67
 
2.4%
e 50
 
1.8%
E 45
 
1.6%
D 45
 
1.6%
Other values (38) 653
23.3%
Common
ValueCountFrequency (%)
2845
38.3%
2 1256
16.9%
5 976
 
13.1%
) 803
 
10.8%
( 795
 
10.7%
4 259
 
3.5%
1 109
 
1.5%
3 85
 
1.1%
0 55
 
0.7%
6 47
 
0.6%
Other values (19) 194
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 59423
85.3%
ASCII 10223
 
14.7%
None 5
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3095
 
5.2%
2307
 
3.9%
2084
 
3.5%
1531
 
2.6%
1443
 
2.4%
1087
 
1.8%
974
 
1.6%
963
 
1.6%
954
 
1.6%
942
 
1.6%
Other values (760) 44043
74.1%
ASCII
ValueCountFrequency (%)
2845
27.8%
2 1256
12.3%
5 976
 
9.5%
) 803
 
7.9%
( 795
 
7.8%
S 739
 
7.2%
G 719
 
7.0%
4 259
 
2.5%
C 197
 
1.9%
U 128
 
1.3%
Other values (64) 1506
14.7%
None
ValueCountFrequency (%)
3
60.0%
1
 
20.0%
  1
 
20.0%
Punctuation
ValueCountFrequency (%)
1
100.0%
Distinct5658
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T07:58:02.437582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.0983
Min length4

Characters and Unicode

Total characters80983
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3187 ?
Unique (%)31.9%

Sample

1st row20081031
2nd row20030417
3rd row20030513
4th row20210201
5th row19880729
ValueCountFrequency (%)
19890510 38
 
0.4%
19920201 21
 
0.2%
20000918 21
 
0.2%
20000421 20
 
0.2%
20020823 17
 
0.2%
19880101 15
 
0.1%
20000420 14
 
0.1%
19981209 13
 
0.1%
19981215 13
 
0.1%
19981231 12
 
0.1%
Other values (5650) 9818
98.2%
2023-12-11T07:58:02.876439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25363
31.3%
2 16065
19.8%
1 14994
18.5%
9 6080
 
7.5%
3 3355
 
4.1%
8 3339
 
4.1%
7 2792
 
3.4%
4 2712
 
3.3%
5 2692
 
3.3%
6 2593
 
3.2%
Other values (2) 998
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79985
98.8%
Dash Punctuation 996
 
1.2%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25363
31.7%
2 16065
20.1%
1 14994
18.7%
9 6080
 
7.6%
3 3355
 
4.2%
8 3339
 
4.2%
7 2792
 
3.5%
4 2712
 
3.4%
5 2692
 
3.4%
6 2593
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 996
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25363
31.3%
2 16065
19.8%
1 14994
18.5%
9 6080
 
7.5%
3 3355
 
4.1%
8 3339
 
4.1%
7 2792
 
3.4%
4 2712
 
3.3%
5 2692
 
3.3%
6 2593
 
3.2%
Other values (2) 998
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25363
31.3%
2 16065
19.8%
1 14994
18.5%
9 6080
 
7.5%
3 3355
 
4.1%
8 3339
 
4.1%
7 2792
 
3.4%
4 2712
 
3.3%
5 2692
 
3.3%
6 2593
 
3.2%
Other values (2) 998
 
1.2%

인허가취소일자
Date

MISSING 

Distinct480
Distinct (%)48.0%
Missing9000
Missing (%)90.0%
Memory size156.2 KiB
Minimum1992-10-27 00:00:00
Maximum2023-11-27 00:00:00
2023-12-11T07:58:03.056544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:03.460862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
폐업처리
6892 
정상영업
1985 
지정취소
723 
직권취소
 
326
임시소매기간만료
 
63
Other values (2)
 
11

Length

Max length8
Median length4
Mean length4.0252
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업처리
2nd row지정취소
3rd row폐업처리
4th row정상영업
5th row지정취소

Common Values

ValueCountFrequency (%)
폐업처리 6892
68.9%
정상영업 1985
 
19.9%
지정취소 723
 
7.2%
직권취소 326
 
3.3%
임시소매기간만료 63
 
0.6%
휴업처리 9
 
0.1%
영업정지 2
 
< 0.1%

Length

2023-12-11T07:58:03.601191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:03.757316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업처리 6892
68.9%
정상영업 1985
 
19.9%
지정취소 723
 
7.2%
직권취소 326
 
3.3%
임시소매기간만료 63
 
0.6%
휴업처리 9
 
0.1%
영업정지 2
 
< 0.1%
Distinct5237
Distinct (%)94.0%
Missing4428
Missing (%)44.3%
Memory size156.2 KiB
2023-12-11T07:58:04.134937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length10.777818
Min length1

Characters and Unicode

Total characters60054
Distinct characters15
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5043 ?
Unique (%)90.5%

Sample

1st row2687 8452
2nd row031 3910220
3rd row632 6665329
4th row032
5th row031 5603294
ValueCountFrequency (%)
031 2019
 
23.7%
032 460
 
5.4%
02 108
 
1.3%
0356 80
 
0.9%
0342 77
 
0.9%
0344 74
 
0.9%
0 22
 
0.3%
20
 
0.2%
1577-0711 18
 
0.2%
0000000 14
 
0.2%
Other values (5344) 5641
66.1%
2023-12-11T07:58:04.708289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8778
14.6%
3 7901
13.2%
1 7028
11.7%
2 4901
8.2%
5 4754
7.9%
7 4546
7.6%
6 4266
7.1%
9 4205
7.0%
8 3735
6.2%
4 3614
6.0%
Other values (5) 6326
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53728
89.5%
Dash Punctuation 3311
 
5.5%
Space Separator 2984
 
5.0%
Close Punctuation 26
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8778
16.3%
3 7901
14.7%
1 7028
13.1%
2 4901
9.1%
5 4754
8.8%
7 4546
8.5%
6 4266
7.9%
9 4205
7.8%
8 3735
7.0%
4 3614
6.7%
Math Symbol
ValueCountFrequency (%)
~ 4
80.0%
+ 1
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 3311
100.0%
Space Separator
ValueCountFrequency (%)
2984
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60054
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8778
14.6%
3 7901
13.2%
1 7028
11.7%
2 4901
8.2%
5 4754
7.9%
7 4546
7.6%
6 4266
7.1%
9 4205
7.0%
8 3735
6.2%
4 3614
6.0%
Other values (5) 6326
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8778
14.6%
3 7901
13.2%
1 7028
11.7%
2 4901
8.2%
5 4754
7.9%
7 4546
7.6%
6 4266
7.1%
9 4205
7.0%
8 3735
6.2%
4 3614
6.0%
Other values (5) 6326
10.5%

소재지면적정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

도로명우편번호
Text

MISSING 

Distinct1818
Distinct (%)47.1%
Missing6138
Missing (%)61.4%
Memory size156.2 KiB
2023-12-11T07:58:05.116184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.341015
Min length5

Characters and Unicode

Total characters20627
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

Unique933 ?
Unique (%)24.2%

Sample

1st row420824
2nd row14668
3rd row412500
4th row10067
5th row10335
ValueCountFrequency (%)
471030 31
 
0.8%
10071 25
 
0.6%
10113 17
 
0.4%
471010 16
 
0.4%
12248 15
 
0.4%
472501 15
 
0.4%
463400 15
 
0.4%
471020 15
 
0.4%
415060 14
 
0.4%
483030 14
 
0.4%
Other values (1808) 3685
95.4%
2023-12-11T07:58:05.670953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4589
22.2%
4 3265
15.8%
0 2799
13.6%
2 2394
11.6%
3 1522
 
7.4%
8 1499
 
7.3%
5 1392
 
6.7%
7 1270
 
6.2%
6 1170
 
5.7%
9 684
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20584
99.8%
Dash Punctuation 43
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4589
22.3%
4 3265
15.9%
0 2799
13.6%
2 2394
11.6%
3 1522
 
7.4%
8 1499
 
7.3%
5 1392
 
6.8%
7 1270
 
6.2%
6 1170
 
5.7%
9 684
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20627
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4589
22.2%
4 3265
15.8%
0 2799
13.6%
2 2394
11.6%
3 1522
 
7.4%
8 1499
 
7.3%
5 1392
 
6.7%
7 1270
 
6.2%
6 1170
 
5.7%
9 684
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4589
22.2%
4 3265
15.8%
0 2799
13.6%
2 2394
11.6%
3 1522
 
7.4%
8 1499
 
7.3%
5 1392
 
6.7%
7 1270
 
6.2%
6 1170
 
5.7%
9 684
 
3.3%
Distinct8054
Distinct (%)93.0%
Missing1336
Missing (%)13.4%
Memory size156.2 KiB
2023-12-11T07:58:06.081117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length58
Mean length30.416551
Min length11

Characters and Unicode

Total characters263529
Distinct characters633
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7506 ?
Unique (%)86.6%

Sample

1st row경기도 광명시 모세로 6 (철산동,7단지상가 다동 119-120)
2nd row경기도 부천시 부천로65번길 34 (심곡동)
3rd row경기도 부천시 역곡로 40-1 (역곡동)
4th row경기도 구리시 아치울길22번길 14-13 (아천동)
5th row경기도 고양시 덕양구 호국로 1809 (고양동)
ValueCountFrequency (%)
경기도 8664
 
16.0%
부천시 1810
 
3.3%
고양시 1621
 
3.0%
성남시 1322
 
2.4%
1층 1288
 
2.4%
남양주시 902
 
1.7%
김포시 675
 
1.2%
덕양구 636
 
1.2%
일산동구 570
 
1.1%
광주시 563
 
1.0%
Other values (8525) 36062
66.6%
2023-12-11T07:58:06.630710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45498
 
17.3%
1 12794
 
4.9%
9255
 
3.5%
9210
 
3.5%
9192
 
3.5%
8805
 
3.3%
8586
 
3.3%
8431
 
3.2%
) 7707
 
2.9%
( 7702
 
2.9%
Other values (623) 136349
51.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 150582
57.1%
Space Separator 45498
 
17.3%
Decimal Number 43951
 
16.7%
Close Punctuation 7707
 
2.9%
Open Punctuation 7702
 
2.9%
Other Punctuation 5815
 
2.2%
Dash Punctuation 1678
 
0.6%
Uppercase Letter 481
 
0.2%
Math Symbol 73
 
< 0.1%
Lowercase Letter 37
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9255
 
6.1%
9210
 
6.1%
9192
 
6.1%
8805
 
5.8%
8586
 
5.7%
8431
 
5.6%
3713
 
2.5%
3642
 
2.4%
3583
 
2.4%
3290
 
2.2%
Other values (564) 82875
55.0%
Uppercase Letter
ValueCountFrequency (%)
B 167
34.7%
A 91
18.9%
C 34
 
7.1%
S 32
 
6.7%
K 22
 
4.6%
D 22
 
4.6%
L 16
 
3.3%
G 14
 
2.9%
I 13
 
2.7%
T 12
 
2.5%
Other values (14) 58
 
12.1%
Decimal Number
ValueCountFrequency (%)
1 12794
29.1%
2 5445
12.4%
0 4998
 
11.4%
3 4096
 
9.3%
4 3420
 
7.8%
5 3031
 
6.9%
6 2819
 
6.4%
7 2749
 
6.3%
8 2411
 
5.5%
9 2188
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
e 13
35.1%
b 9
24.3%
c 4
 
10.8%
a 3
 
8.1%
n 2
 
5.4%
l 2
 
5.4%
i 2
 
5.4%
k 1
 
2.7%
p 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 5763
99.1%
. 20
 
0.3%
@ 13
 
0.2%
: 9
 
0.2%
/ 5
 
0.1%
& 3
 
0.1%
? 1
 
< 0.1%
1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Space Separator
ValueCountFrequency (%)
45498
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7707
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1678
100.0%
Math Symbol
ValueCountFrequency (%)
~ 73
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 150582
57.1%
Common 112425
42.7%
Latin 522
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9255
 
6.1%
9210
 
6.1%
9192
 
6.1%
8805
 
5.8%
8586
 
5.7%
8431
 
5.6%
3713
 
2.5%
3642
 
2.4%
3583
 
2.4%
3290
 
2.2%
Other values (564) 82875
55.0%
Latin
ValueCountFrequency (%)
B 167
32.0%
A 91
17.4%
C 34
 
6.5%
S 32
 
6.1%
K 22
 
4.2%
D 22
 
4.2%
L 16
 
3.1%
G 14
 
2.7%
e 13
 
2.5%
I 13
 
2.5%
Other values (25) 98
18.8%
Common
ValueCountFrequency (%)
45498
40.5%
1 12794
 
11.4%
) 7707
 
6.9%
( 7702
 
6.9%
, 5763
 
5.1%
2 5445
 
4.8%
0 4998
 
4.4%
3 4096
 
3.6%
4 3420
 
3.0%
5 3031
 
2.7%
Other values (14) 11971
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 150582
57.1%
ASCII 112941
42.9%
Number Forms 4
 
< 0.1%
None 1
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45498
40.3%
1 12794
 
11.3%
) 7707
 
6.8%
( 7702
 
6.8%
, 5763
 
5.1%
2 5445
 
4.8%
0 4998
 
4.4%
3 4096
 
3.6%
4 3420
 
3.0%
5 3031
 
2.7%
Other values (45) 12487
 
11.1%
Hangul
ValueCountFrequency (%)
9255
 
6.1%
9210
 
6.1%
9192
 
6.1%
8805
 
5.8%
8586
 
5.7%
8431
 
5.6%
3713
 
2.5%
3642
 
2.4%
3583
 
2.4%
3290
 
2.2%
Other values (564) 82875
55.0%
Number Forms
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
None
ValueCountFrequency (%)
1
100.0%
CJK Compat
ValueCountFrequency (%)
1
100.0%
Distinct9625
Distinct (%)96.3%
Missing7
Missing (%)0.1%
Memory size156.2 KiB
2023-12-11T07:58:06.985554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length63
Median length50
Mean length25.381967
Min length11

Characters and Unicode

Total characters253642
Distinct characters602
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9276 ?
Unique (%)92.8%

Sample

1st row경기도 광명시 철산동 233번지 1호 7단지상가 다동 119-120
2nd row경기도 군포시 산본동 호 1096-2 디퍼아울렛타운 236호
3rd row경기도 부천시 심곡동 367번지 2호
4th row경기도 부천시 역곡동 86-13
5th row경기도 부천시약대동 156번지 5 호
ValueCountFrequency (%)
경기도 9993
 
17.9%
남양주시 1110
 
2.0%
939
 
1.7%
부천시 915
 
1.6%
고양시 890
 
1.6%
1호 887
 
1.6%
김포시 860
 
1.5%
1층 675
 
1.2%
광주시 606
 
1.1%
성남시 548
 
1.0%
Other values (8570) 38294
68.7%
2023-12-11T07:58:07.548005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47413
 
18.7%
1 12284
 
4.8%
10516
 
4.1%
10108
 
4.0%
10101
 
4.0%
9801
 
3.9%
9800
 
3.9%
9159
 
3.6%
7970
 
3.1%
6928
 
2.7%
Other values (592) 119562
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 153587
60.6%
Decimal Number 49418
 
19.5%
Space Separator 47413
 
18.7%
Dash Punctuation 1910
 
0.8%
Uppercase Letter 466
 
0.2%
Other Punctuation 412
 
0.2%
Open Punctuation 170
 
0.1%
Close Punctuation 169
 
0.1%
Math Symbol 55
 
< 0.1%
Lowercase Letter 38
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10516
 
6.8%
10108
 
6.6%
10101
 
6.6%
9801
 
6.4%
9800
 
6.4%
9159
 
6.0%
7970
 
5.2%
6928
 
4.5%
4146
 
2.7%
4027
 
2.6%
Other values (533) 71031
46.2%
Uppercase Letter
ValueCountFrequency (%)
B 120
25.8%
A 116
24.9%
C 31
 
6.7%
S 28
 
6.0%
K 23
 
4.9%
L 23
 
4.9%
D 23
 
4.9%
T 18
 
3.9%
G 16
 
3.4%
I 12
 
2.6%
Other values (13) 56
12.0%
Lowercase Letter
ValueCountFrequency (%)
e 11
28.9%
b 6
15.8%
a 5
13.2%
c 5
13.2%
i 3
 
7.9%
s 2
 
5.3%
l 2
 
5.3%
k 1
 
2.6%
p 1
 
2.6%
n 1
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 12284
24.9%
2 5856
11.8%
3 5062
10.2%
0 5061
10.2%
4 4223
 
8.5%
5 4019
 
8.1%
6 3683
 
7.5%
7 3459
 
7.0%
8 3093
 
6.3%
9 2678
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 349
84.7%
@ 24
 
5.8%
. 22
 
5.3%
/ 10
 
2.4%
& 4
 
1.0%
2
 
0.5%
: 1
 
0.2%
Letter Number
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
47413
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1910
100.0%
Open Punctuation
ValueCountFrequency (%)
( 170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 169
100.0%
Math Symbol
ValueCountFrequency (%)
~ 55
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 153587
60.6%
Common 99548
39.2%
Latin 507
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10516
 
6.8%
10108
 
6.6%
10101
 
6.6%
9801
 
6.4%
9800
 
6.4%
9159
 
6.0%
7970
 
5.2%
6928
 
4.5%
4146
 
2.7%
4027
 
2.6%
Other values (533) 71031
46.2%
Latin
ValueCountFrequency (%)
B 120
23.7%
A 116
22.9%
C 31
 
6.1%
S 28
 
5.5%
K 23
 
4.5%
L 23
 
4.5%
D 23
 
4.5%
T 18
 
3.6%
G 16
 
3.2%
I 12
 
2.4%
Other values (26) 97
19.1%
Common
ValueCountFrequency (%)
47413
47.6%
1 12284
 
12.3%
2 5856
 
5.9%
3 5062
 
5.1%
0 5061
 
5.1%
4 4223
 
4.2%
5 4019
 
4.0%
6 3683
 
3.7%
7 3459
 
3.5%
8 3093
 
3.1%
Other values (13) 5395
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 153587
60.6%
ASCII 100049
39.4%
Number Forms 3
 
< 0.1%
None 2
 
< 0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47413
47.4%
1 12284
 
12.3%
2 5856
 
5.9%
3 5062
 
5.1%
0 5061
 
5.1%
4 4223
 
4.2%
5 4019
 
4.0%
6 3683
 
3.7%
7 3459
 
3.5%
8 3093
 
3.1%
Other values (45) 5896
 
5.9%
Hangul
ValueCountFrequency (%)
10516
 
6.8%
10108
 
6.6%
10101
 
6.6%
9801
 
6.4%
9800
 
6.4%
9159
 
6.0%
7970
 
5.2%
6928
 
4.5%
4146
 
2.7%
4027
 
2.6%
Other values (533) 71031
46.2%
None
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
2
66.7%
1
33.3%
CJK Compat
ValueCountFrequency (%)
1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1954
Distinct (%)21.1%
Missing725
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean12602.744
Minimum10002
Maximum15890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:58:07.694749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile10077
Q110530
median12734
Q314333
95-th percentile14757
Maximum15890
Range5888
Interquartile range (IQR)3803

Descriptive statistics

Standard deviation1731.3663
Coefficient of variation (CV)0.13738011
Kurtosis-1.2053398
Mean12602.744
Median Absolute Deviation (MAD)1687
Skewness-0.034845473
Sum1.1689045 × 108
Variance2997629.4
MonotonicityNot monotonic
2023-12-11T07:58:07.828260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10401 40
 
0.4%
14548 39
 
0.4%
10403 29
 
0.3%
10414 28
 
0.3%
12771 27
 
0.3%
10071 27
 
0.3%
14637 27
 
0.3%
10113 26
 
0.3%
11932 26
 
0.3%
14623 26
 
0.3%
Other values (1944) 8980
89.8%
(Missing) 725
 
7.2%
ValueCountFrequency (%)
10002 3
 
< 0.1%
10003 2
 
< 0.1%
10006 1
 
< 0.1%
10007 4
 
< 0.1%
10009 2
 
< 0.1%
10010 6
 
0.1%
10011 19
0.2%
10012 11
0.1%
10013 7
 
0.1%
10014 2
 
< 0.1%
ValueCountFrequency (%)
15890 2
 
< 0.1%
15889 1
 
< 0.1%
15888 4
< 0.1%
15887 4
< 0.1%
15886 6
0.1%
15885 3
< 0.1%
15884 3
< 0.1%
15882 1
 
< 0.1%
15880 6
0.1%
15876 2
 
< 0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct8130
Distinct (%)81.8%
Missing66
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean37.558638
Minimum37.280076
Maximum38.022535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:58:07.988297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.280076
5-th percentile37.359287
Q137.448857
median37.527856
Q337.656176
95-th percentile37.75993
Maximum38.022535
Range0.74245907
Interquartile range (IQR)0.20731894

Descriptive statistics

Standard deviation0.13423433
Coefficient of variation (CV)0.0035739935
Kurtosis-0.19125892
Mean37.558638
Median Absolute Deviation (MAD)0.11064335
Skewness0.41298885
Sum373107.51
Variance0.018018854
MonotonicityNot monotonic
2023-12-11T07:58:08.154176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.379504 36
 
0.4%
37.3691892 22
 
0.2%
37.3576249 21
 
0.2%
37.3595502 20
 
0.2%
37.4547083 17
 
0.2%
37.447797 15
 
0.1%
37.6665317 12
 
0.1%
37.6491272 12
 
0.1%
37.488759 10
 
0.1%
37.7005084 9
 
0.1%
Other values (8120) 9760
97.6%
(Missing) 66
 
0.7%
ValueCountFrequency (%)
37.2800760334 1
< 0.1%
37.2816314 1
< 0.1%
37.283714 1
< 0.1%
37.2867044489 1
< 0.1%
37.2885754 1
< 0.1%
37.2885777 1
< 0.1%
37.2886319428 1
< 0.1%
37.2955709 1
< 0.1%
37.2964626747 1
< 0.1%
37.2985424611 1
< 0.1%
ValueCountFrequency (%)
38.0225351 1
< 0.1%
37.9865702 1
< 0.1%
37.986018 1
< 0.1%
37.9793578 1
< 0.1%
37.9762683 1
< 0.1%
37.974698079 1
< 0.1%
37.9736252 1
< 0.1%
37.9730765575 1
< 0.1%
37.970879 1
< 0.1%
37.9707336495 1
< 0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct8130
Distinct (%)81.8%
Missing66
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean126.96632
Minimum126.52788
Maximum127.57443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:58:08.329017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.52788
5-th percentile126.67264
Q1126.78128
median126.89001
Q3127.14502
95-th percentile127.32486
Maximum127.57443
Range1.0465546
Interquartile range (IQR)0.36374012

Descriptive statistics

Standard deviation0.22088595
Coefficient of variation (CV)0.0017397208
Kurtosis-0.84094154
Mean126.96632
Median Absolute Deviation (MAD)0.1636896
Skewness0.39122101
Sum1261283.4
Variance0.048790601
MonotonicityNot monotonic
2023-12-11T07:58:08.493399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9422848 36
 
0.4%
126.9458943 22
 
0.2%
126.9568067 21
 
0.2%
126.949841 20
 
0.2%
126.8642971 17
 
0.2%
127.0167256 15
 
0.1%
127.2359001 12
 
0.1%
127.2502353 12
 
0.1%
126.7553017 10
 
0.1%
126.7694989 9
 
0.1%
Other values (8120) 9760
97.6%
(Missing) 66
 
0.7%
ValueCountFrequency (%)
126.5278752 1
< 0.1%
126.5335369 1
< 0.1%
126.5342837 1
< 0.1%
126.5345348 1
< 0.1%
126.5357373 1
< 0.1%
126.5359323 1
< 0.1%
126.5372094 1
< 0.1%
126.537561 1
< 0.1%
126.5408398 1
< 0.1%
126.542231 1
< 0.1%
ValueCountFrequency (%)
127.5744298236 1
< 0.1%
127.5656538 1
< 0.1%
127.5512077 1
< 0.1%
127.5500969 1
< 0.1%
127.5495498 1
< 0.1%
127.5495448 1
< 0.1%
127.5493669158 1
< 0.1%
127.5493541 2
< 0.1%
127.549073 1
< 0.1%
127.5489622 1
< 0.1%

업태구분명정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6851
Distinct (%)81.1%
Missing1553
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean196871.59
Minimum158310.27
Maximum250387.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:58:08.630888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum158310.27
5-th percentile173373.74
Q1180637.02
median188973.38
Q3212712.19
95-th percentile228941.13
Maximum250387.29
Range92077.023
Interquartile range (IQR)32075.162

Descriptive statistics

Standard deviation19475.942
Coefficient of variation (CV)0.098927136
Kurtosis-0.83330657
Mean196871.59
Median Absolute Deviation (MAD)12440.915
Skewness0.44604206
Sum1.6629743 × 109
Variance3.7931232 × 108
MonotonicityNot monotonic
2023-12-11T07:58:08.767837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201414.290589323 11
 
0.1%
178293.171578447 9
 
0.1%
209507.949102502 7
 
0.1%
213284.742417906 7
 
0.1%
213244.090085184 6
 
0.1%
181216.34241993 6
 
0.1%
188396.13131342 6
 
0.1%
179708.826286267 6
 
0.1%
180724.082487894 6
 
0.1%
212910.769639922 5
 
0.1%
Other values (6841) 8378
83.8%
(Missing) 1553
 
15.5%
ValueCountFrequency (%)
158310.268026326 1
< 0.1%
158794.762453556 1
< 0.1%
158837.769766282 1
< 0.1%
159001.64458176 1
< 0.1%
159023.779136838 1
< 0.1%
159164.290905365 1
< 0.1%
159410.224590453 1
< 0.1%
159552.651267724 1
< 0.1%
159560.013611818 1
< 0.1%
159586.506578232 1
< 0.1%
ValueCountFrequency (%)
250387.290841452 1
< 0.1%
249705.81396726 1
< 0.1%
248327.72224235 1
< 0.1%
248290.779568677 1
< 0.1%
248278.802925855 1
< 0.1%
248263.307374245 2
< 0.1%
248260.454500232 1
< 0.1%
248230.553929964 1
< 0.1%
248217.44178914 1
< 0.1%
248055.879400322 1
< 0.1%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6852
Distinct (%)81.1%
Missing1553
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean450875.85
Minimum419854.17
Maximum498303.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:58:08.899623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum419854.17
5-th percentile428777.22
Q1439455.69
median447197.16
Q3461522.53
95-th percentile473121.76
Maximum498303.12
Range78448.947
Interquartile range (IQR)22066.835

Descriptive statistics

Standard deviation14581.485
Coefficient of variation (CV)0.032340355
Kurtosis-0.098290702
Mean450875.85
Median Absolute Deviation (MAD)11449.456
Skewness0.44839465
Sum3.8085483 × 109
Variance2.1261971 × 108
MonotonicityNot monotonic
2023-12-11T07:58:09.027893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
438412.742054509 11
 
0.1%
442988.573961638 9
 
0.1%
426499.956841716 7
 
0.1%
433565.737288175 7
 
0.1%
442449.515665699 6
 
0.1%
437927.010144634 6
 
0.1%
441481.49797558 6
 
0.1%
461404.803949418 6
 
0.1%
442960.539943679 6
 
0.1%
437281.378806078 5
 
0.1%
Other values (6842) 8378
83.8%
(Missing) 1553
 
15.5%
ValueCountFrequency (%)
419854.174481953 1
< 0.1%
420019.03312919 1
< 0.1%
420255.365130952 1
< 0.1%
420571.356014539 1
< 0.1%
420785.746224581 1
< 0.1%
420788.975855547 2
< 0.1%
421658.637355512 1
< 0.1%
421885.08914283 1
< 0.1%
422128.601496172 1
< 0.1%
422294.516253207 1
< 0.1%
ValueCountFrequency (%)
498303.121270599 1
< 0.1%
497504.874918587 1
< 0.1%
497161.352711689 1
< 0.1%
496983.161577219 1
< 0.1%
496846.663332551 1
< 0.1%
496780.121860624 1
< 0.1%
496474.137690836 1
< 0.1%
496318.804185629 1
< 0.1%
496261.208127778 1
< 0.1%
496248.988519468 1
< 0.1%

민원종류명
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5446 
신규
2491 
제7조의3제2항에따른경우
1492 
2009년11월법개정전자료
 
306
변경
 
174

Length

Max length14
Median length4
Mean length5.1977
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row제7조의3제2항에따른경우
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5446
54.5%
신규 2491
24.9%
제7조의3제2항에따른경우 1492
 
14.9%
2009년11월법개정전자료 306
 
3.1%
변경 174
 
1.7%
제7조의3제3항에따른경우 91
 
0.9%

Length

2023-12-11T07:58:09.156591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:58:09.245818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5446
54.5%
신규 2491
24.9%
제7조의3제2항에따른경우 1492
 
14.9%
2009년11월법개정전자료 306
 
3.1%
변경 174
 
1.7%
제7조의3제3항에따른경우 91
 
0.9%

Interactions

2023-12-11T07:58:00.178140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.256295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.751694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.266009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.745651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:00.267429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.343434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.840988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.370199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.826995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:00.375804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.448394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.940136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.482633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.910795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:00.468965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.548678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.033780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.573589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.992338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:00.566616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:58.641860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.145479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:57:59.651530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:58:00.084649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:58:09.317967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값민원종류명
시군명1.0000.2500.9560.8700.9060.9100.8710.259
영업상태명0.2501.0000.1780.1570.2240.2350.1620.252
소재지우편번호0.9560.1781.0000.9310.8960.8960.9300.297
WGS84위도0.8700.1570.9311.0000.7870.7880.9900.186
WGS84경도0.9060.2240.8960.7871.0001.0000.7710.218
X좌표값0.9100.2350.8960.7881.0001.0000.7720.220
Y좌표값0.8710.1620.9300.9900.7710.7721.0000.183
민원종류명0.2590.2520.2970.1860.2180.2200.1831.000
2023-12-11T07:58:09.411517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
민원종류명영업상태명시군명
민원종류명1.0000.1640.145
영업상태명0.1641.0000.124
시군명0.1450.1241.000
2023-12-11T07:58:09.507257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값시군명영업상태명민원종류명
소재지우편번호1.000-0.7030.1220.074-0.6980.8280.0910.128
WGS84위도-0.7031.000-0.198-0.1771.0000.6130.0800.078
WGS84경도0.122-0.1981.0001.000-0.1780.6880.1150.092
X좌표값0.074-0.1771.0001.000-0.1770.6980.1210.093
Y좌표값-0.6981.000-0.178-0.1771.0000.6140.0820.077
시군명0.8280.6130.6880.6980.6141.0000.1240.145
영업상태명0.0910.0800.1150.1210.0820.1241.0000.164
민원종류명0.1280.0780.0920.0930.0770.1450.1641.000

Missing values

2023-12-11T07:58:00.710227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:58:00.913736image/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.
2023-12-11T07:58:01.115625image/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

시군명사업장명인허가일자인허가취소일자영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값민원종류명
12520광명시코리아 마트20081031<NA>폐업처리2687 8452<NA><NA>경기도 광명시 모세로 6 (철산동,7단지상가 다동 119-120)경기도 광명시 철산동 233번지 1호 7단지상가 다동 119-1201423337.480901126.865232<NA>188014.277016442097.152384<NA>
19127군포시피오토2003041720040608지정취소031 3910220<NA><NA><NA>경기도 군포시 산본동 호 1096-2 디퍼아울렛타운 236호<NA>37.379504126.942285<NA><NA><NA><NA>
34915부천시심곡할인마트20030513<NA>폐업처리632 6665329<NA>420824경기도 부천시 부천로65번길 34 (심곡동)경기도 부천시 심곡동 367번지 2호1463037.491147126.782714<NA>180719.247591443249.358287<NA>
32295부천시한양철물건재20210201<NA>정상영업032<NA>14668경기도 부천시 역곡로 40-1 (역곡동)경기도 부천시 역곡동 86-131466837.488772126.810302<NA>183154.777442970.841제7조의3제2항에따른경우
36783부천시동원쌀슈퍼1988072920110309지정취소<NA><NA><NA><NA>경기도 부천시약대동 156번지 5 호1451837.511093126.772386<NA><NA><NA><NA>
18362구리시연희슈퍼19990115<NA>폐업처리031 5603294<NA><NA>경기도 구리시 아치울길22번길 14-13 (아천동)경기도 구리시 아천동 86호1195637.574923127.120396<NA>210570.200216452531.458567<NA>
5893고양시일품양평해장국2014051520200903지정취소<NA><NA>412500경기도 고양시 덕양구 호국로 1809 (고양동)경기도 고양시 덕양구 고양동 608번지1027037.712128126.907419<NA>191775.0467756.0제7조의3제2항에따른경우
30646동두천시동광종합설비20050930<NA>폐업처리031 8675080<NA><NA>경기도 동두천시 큰시장로 6 (생연동)경기도 동두천시 생연동 725번지 3 호1131637.900665127.057093<NA>204958.863682488680.971178<NA>
22989김포시씨유마산에뜰점20171030<NA>폐업처리<NA><NA>10067경기도 김포시 김포한강8로 281, 1층 103호 (마산동, 한강신도시레이크에일린의뜰)경기도 김포시 마산동 616번지 3호 한강신도시레이크에일린의뜰1006737.644399126.637944<NA>167971.850385460231.89522제7조의3제2항에따른경우
29348남양주시삼신슈퍼19980103<NA>폐업처리5930188<NA><NA>경기도 남양주시 화도읍 비룡로 185-20 상가 B102호 (묵현리,삼신푸른솔아파트)경기도 남양주시 화도읍 묵현리 147-3호 삼신상가 B1021215837.667831127.300269<NA><NA><NA><NA>
시군명사업장명인허가일자인허가취소일자영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값민원종류명
43836성남시물가안정할인매장20120120<NA>폐업처리<NA><NA>461807경기도 성남시 수정구 성남대로 1215, 104호 (수진동, 밀란체 1)경기도 성남시 수정구 수진2동 4557번지 밀란체1 104호1331337.438055127.12738<NA>211208.739935437339.915754신규
48259성남시씨유미금헤리츠점20150722<NA>정상영업<NA><NA>463808경기도 성남시 분당구 성남대로 151, 1층 110호 (구미동)경기도 성남시 분당구 구미동 9번지 1호1363037.349198127.108272<NA>209526.984726427477.243753변경
26386남양주시씨유 별내로데오점20140501<NA>폐업처리<NA><NA>472501경기도 남양주시 별내중앙로 55 (별내동, 예스프라자 1동 104호)경기도 남양주시 별내동 1019번지 예스프라자 1동 104호1211237.647224127.12332<NA>210818.297934460553.402025신규
27672남양주시CU별내쌍용예가20160819<NA>폐업처리<NA><NA>12111경기도 남양주시 별내3로 63, 1동 107호 (별내동, 별내신도시 쌍용예가근린생활시설)경기도 남양주시 별내동 1056번지 별내신도시 쌍용예가근린생활시설 1동 107호1211137.646831127.119399<NA>210472.334613460510.475793신규
3503고양시훼미리마트 원당어울림20090924<NA>폐업처리<NA><NA><NA>경기도 고양시 덕양구 고양대로1384번길 15, 102호 (성사동,정암코아)경기도 고양시 덕양구 성사2동 722번지 4호 정암코아 102호1046837.653242126.837571<NA>185600.790872461228.641446<NA>
20251군포시베스트식당20070703<NA>폐업처리031 4566911<NA><NA>경기도 군포시 공단로 215 (금정동)경기도 군포시 금정동 185번지 4 호1584137.364478126.947591<NA>195291.319708429167.306188<NA>
25387남양주시우리동네슈퍼20041118<NA>폐업처리031 5721392<NA><NA>경기도 남양주시 진접읍 장현로147번길 9-1 (장현리)경기도 남양주시 진접읍 장현리 203-4호1200637.72213127.186358<NA><NA><NA><NA>
23288김포시GS25김포장기20120620<NA>정상영업<NA><NA>415060경기도 김포시 김포한강1로78번길 50, 1층 101호 (장기동)경기도 김포시 장기동 1458번지 1층 101호1007937.643434126.672134<NA>170998.493301460179.800479신규
36156부천시이편한마트20121130<NA>정상영업032-342-9778<NA>14754경기도 부천시 경인로 306-22 (소사본동)경기도 부천시 소사본동 88번지 23호1475437.482043126.790294<NA>181387.751834442234.819492신규
33173부천시나래정19981215<NA>폐업처리<NA><NA><NA>경기도 부천시 부천로 210 (춘의동)경기도 부천시춘의동 163번지 8 호1455537.502932126.787464<NA>181135.505758444579.271346<NA>

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자인허가취소일자영업상태명소재지시설전화번호도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값민원종류명# duplicates
0부천시지역경제과2001041220020103직권취소003206802273<NA>경기도 부천시 옥산로 255 (도당동,오정구청)경기도 부천시도당동 10호 오정구청1451937.516261126.779824180469.11286446035.22474<NA>2