Overview

Dataset statistics

Number of variables19
Number of observations811
Missing cells2435
Missing cells (%)15.8%
Duplicate rows4
Duplicate rows (%)0.5%
Total size in memory126.1 KiB
Average record size in memory159.2 B

Variable types

Categorical5
Text6
DateTime2
Numeric6

Alerts

Dataset has 4 (0.5%) duplicate rowsDuplicates
영업상태구분코드 is highly overall correlated with 인허가취소일자 and 1 other fieldsHigh correlation
영업상태명 is highly overall correlated with 인허가취소일자 and 1 other fieldsHigh correlation
인허가취소일자 is highly overall correlated with WGS84경도 and 4 other fieldsHigh correlation
WGS84위도 is highly overall correlated with Y좌표값 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 인허가취소일자 and 2 other fieldsHigh correlation
X좌표값 is highly overall correlated with 인허가취소일자 and 2 other fieldsHigh correlation
Y좌표값 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with WGS84위도 and 3 other fieldsHigh correlation
업태구분명정보 is highly overall correlated with 인허가취소일자 and 1 other fieldsHigh correlation
점포구분명 is highly overall correlated with 업태구분명정보High correlation
인허가취소일자 has 802 (98.9%) missing valuesMissing
폐업일자 has 678 (83.6%) missing valuesMissing
소재지시설전화번호 has 71 (8.8%) missing valuesMissing
소재지면적정보 has 60 (7.4%) missing valuesMissing
도로명우편번호 has 397 (49.0%) missing valuesMissing
소재지도로명주소 has 84 (10.4%) missing valuesMissing
소재지우편번호 has 46 (5.7%) missing valuesMissing
WGS84위도 has 48 (5.9%) missing valuesMissing
WGS84경도 has 48 (5.9%) missing valuesMissing
X좌표값 has 100 (12.3%) missing valuesMissing
Y좌표값 has 100 (12.3%) missing valuesMissing
소재지면적정보 has 56 (6.9%) zerosZeros

Reproduction

Analysis started2023-12-10 21:49:20.072888
Analysis finished2023-12-10 21:49:25.803385
Duration5.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
수원시
112 
성남시
109 
고양시
72 
부천시
71 
용인시
44 
Other values (26)
403 

Length

Max length4
Median length3
Mean length3.0579531
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수원시 112
13.8%
성남시 109
13.4%
고양시 72
 
8.9%
부천시 71
 
8.8%
용인시 44
 
5.4%
안양시 44
 
5.4%
안산시 42
 
5.2%
광명시 30
 
3.7%
구리시 24
 
3.0%
파주시 23
 
2.8%
Other values (21) 240
29.6%

Length

2023-12-11T06:49:25.864775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 112
13.8%
성남시 109
13.4%
고양시 72
 
8.9%
부천시 71
 
8.8%
용인시 44
 
5.4%
안양시 44
 
5.4%
안산시 42
 
5.2%
광명시 30
 
3.7%
구리시 24
 
3.0%
파주시 23
 
2.8%
Other values (21) 240
29.6%
Distinct753
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
2023-12-11T06:49:26.129519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length20
Mean length10.567201
Min length2

Characters and Unicode

Total characters8570
Distinct characters380
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique702 ?
Unique (%)86.6%

Sample

1st row라 몬테 이탈리아노
2nd rowGIFC MALL
3rd row고양삼송현대헤리엇
4th row스타필드 고양
5th row(주)이마트에브리데이 신원당점
ValueCountFrequency (%)
롯데쇼핑(주 51
 
3.6%
홈플러스(주 38
 
2.7%
롯데슈퍼 31
 
2.2%
롯데마트 25
 
1.7%
주)이마트 25
 
1.7%
익스프레스 24
 
1.7%
이마트 18
 
1.3%
주)지에스리테일 18
 
1.3%
주)이마트에브리데이 16
 
1.1%
the 16
 
1.1%
Other values (773) 1167
81.7%
2023-12-11T06:49:26.604481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
619
 
7.2%
482
 
5.6%
329
 
3.8%
306
 
3.6%
) 305
 
3.6%
( 304
 
3.5%
229
 
2.7%
202
 
2.4%
184
 
2.1%
184
 
2.1%
Other values (370) 5426
63.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6863
80.1%
Space Separator 619
 
7.2%
Uppercase Letter 350
 
4.1%
Close Punctuation 305
 
3.6%
Open Punctuation 304
 
3.5%
Decimal Number 68
 
0.8%
Lowercase Letter 52
 
0.6%
Dash Punctuation 5
 
0.1%
Other Punctuation 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
482
 
7.0%
329
 
4.8%
306
 
4.5%
229
 
3.3%
202
 
2.9%
184
 
2.7%
184
 
2.7%
182
 
2.7%
122
 
1.8%
114
 
1.7%
Other values (315) 4529
66.0%
Uppercase Letter
ValueCountFrequency (%)
S 60
17.1%
E 40
11.4%
G 40
11.4%
A 29
8.3%
H 27
 
7.7%
T 24
 
6.9%
R 18
 
5.1%
F 17
 
4.9%
C 14
 
4.0%
L 13
 
3.7%
Other values (14) 68
19.4%
Lowercase Letter
ValueCountFrequency (%)
e 12
23.1%
t 5
9.6%
a 5
9.6%
r 4
 
7.7%
s 3
 
5.8%
n 3
 
5.8%
u 3
 
5.8%
i 3
 
5.8%
h 3
 
5.8%
l 3
 
5.8%
Other values (7) 8
15.4%
Decimal Number
ValueCountFrequency (%)
2 26
38.2%
0 14
20.6%
9 12
17.6%
1 9
 
13.2%
3 5
 
7.4%
4 1
 
1.5%
8 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
619
100.0%
Close Punctuation
ValueCountFrequency (%)
) 305
100.0%
Open Punctuation
ValueCountFrequency (%)
( 304
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6863
80.1%
Common 1305
 
15.2%
Latin 402
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
482
 
7.0%
329
 
4.8%
306
 
4.5%
229
 
3.3%
202
 
2.9%
184
 
2.7%
184
 
2.7%
182
 
2.7%
122
 
1.8%
114
 
1.7%
Other values (315) 4529
66.0%
Latin
ValueCountFrequency (%)
S 60
14.9%
E 40
 
10.0%
G 40
 
10.0%
A 29
 
7.2%
H 27
 
6.7%
T 24
 
6.0%
R 18
 
4.5%
F 17
 
4.2%
C 14
 
3.5%
L 13
 
3.2%
Other values (31) 120
29.9%
Common
ValueCountFrequency (%)
619
47.4%
) 305
23.4%
( 304
23.3%
2 26
 
2.0%
0 14
 
1.1%
9 12
 
0.9%
1 9
 
0.7%
3 5
 
0.4%
- 5
 
0.4%
, 2
 
0.2%
Other values (4) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6863
80.1%
ASCII 1707
 
19.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
619
36.3%
) 305
17.9%
( 304
17.8%
S 60
 
3.5%
E 40
 
2.3%
G 40
 
2.3%
A 29
 
1.7%
H 27
 
1.6%
2 26
 
1.5%
T 24
 
1.4%
Other values (45) 233
 
13.6%
Hangul
ValueCountFrequency (%)
482
 
7.0%
329
 
4.8%
306
 
4.5%
229
 
3.3%
202
 
2.9%
184
 
2.7%
184
 
2.7%
182
 
2.7%
122
 
1.8%
114
 
1.7%
Other values (315) 4529
66.0%
Distinct657
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Minimum1961-11-06 00:00:00
Maximum2023-12-04 00:00:00
2023-12-11T06:49:26.739994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:27.145948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing802
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean20141499
Minimum20060621
Maximum20220105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:27.289058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060621
5-th percentile20072425
Q120110520
median20130319
Q320200428
95-th percentile20216109
Maximum20220105
Range159484
Interquartile range (IQR)89908

Descriptive statistics

Standard deviation56524.671
Coefficient of variation (CV)0.0028063785
Kurtosis-1.3186619
Mean20141499
Median Absolute Deviation (MAD)40189
Skewness0.25726929
Sum1.8127349 × 108
Variance3.1950384 × 109
MonotonicityNot monotonic
2023-12-11T06:49:27.393915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20130319 1
 
0.1%
20210115 1
 
0.1%
20110929 1
 
0.1%
20090130 1
 
0.1%
20200428 1
 
0.1%
20140324 1
 
0.1%
20110520 1
 
0.1%
20060621 1
 
0.1%
20220105 1
 
0.1%
(Missing) 802
98.9%
ValueCountFrequency (%)
20060621 1
0.1%
20090130 1
0.1%
20110520 1
0.1%
20110929 1
0.1%
20130319 1
0.1%
20140324 1
0.1%
20200428 1
0.1%
20210115 1
0.1%
20220105 1
0.1%
ValueCountFrequency (%)
20220105 1
0.1%
20210115 1
0.1%
20200428 1
0.1%
20140324 1
0.1%
20130319 1
0.1%
20110929 1
0.1%
20110520 1
0.1%
20090130 1
0.1%
20060621 1
0.1%

영업상태구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
1
480 
3
133 
2
116 
4
52 
5
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
1 480
59.2%
3 133
 
16.4%
2 116
 
14.3%
4 52
 
6.4%
5 30
 
3.7%

Length

2023-12-11T06:49:27.532292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:27.629776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 480
59.2%
3 133
 
16.4%
2 116
 
14.3%
4 52
 
6.4%
5 30
 
3.7%

영업상태명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
정상영업
480 
폐업처리
133 
휴업처리
116 
직권취소
52 
영업개시전
 
30

Length

Max length5
Median length4
Mean length4.0369914
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상영업 480
59.2%
폐업처리 133
 
16.4%
휴업처리 116
 
14.3%
직권취소 52
 
6.4%
영업개시전 30
 
3.7%

Length

2023-12-11T06:49:27.749961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:27.849504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상영업 480
59.2%
폐업처리 133
 
16.4%
휴업처리 116
 
14.3%
직권취소 52
 
6.4%
영업개시전 30
 
3.7%

폐업일자
Date

MISSING 

Distinct117
Distinct (%)88.0%
Missing678
Missing (%)83.6%
Memory size6.5 KiB
Minimum2001-08-16 00:00:00
Maximum2023-09-06 00:00:00
2023-12-11T06:49:27.959531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:28.106943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct696
Distinct (%)94.1%
Missing71
Missing (%)8.8%
Memory size6.5 KiB
2023-12-11T06:49:28.389050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length11.45
Min length1

Characters and Unicode

Total characters8473
Distinct characters16
Distinct categories7 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique664 ?
Unique (%)89.7%

Sample

1st row031-947-9603
2nd row955-9445
3rd row031-901-6611
4th row070-7896-7801
5th row031-994-0274
ValueCountFrequency (%)
031 146
 
14.0%
0342 23
 
2.2%
032 22
 
2.1%
02 20
 
1.9%
02-2006-2678 7
 
0.7%
0344 5
 
0.5%
650 5
 
0.5%
02-380-9396 4
 
0.4%
1234 4
 
0.4%
8000 4
 
0.4%
Other values (753) 801
76.9%
2023-12-11T06:49:28.812423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1731
20.4%
1 1055
12.5%
3 1023
12.1%
- 792
9.3%
2 759
9.0%
5 573
 
6.8%
8 490
 
5.8%
4 487
 
5.7%
7 457
 
5.4%
6 432
 
5.1%
Other values (6) 674
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7348
86.7%
Dash Punctuation 792
 
9.3%
Space Separator 318
 
3.8%
Close Punctuation 6
 
0.1%
Other Punctuation 4
 
< 0.1%
Math Symbol 3
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1731
23.6%
1 1055
14.4%
3 1023
13.9%
2 759
10.3%
5 573
 
7.8%
8 490
 
6.7%
4 487
 
6.6%
7 457
 
6.2%
6 432
 
5.9%
9 341
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 792
100.0%
Space Separator
ValueCountFrequency (%)
318
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8473
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1731
20.4%
1 1055
12.5%
3 1023
12.1%
- 792
9.3%
2 759
9.0%
5 573
 
6.8%
8 490
 
5.8%
4 487
 
5.7%
7 457
 
5.4%
6 432
 
5.1%
Other values (6) 674
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1731
20.4%
1 1055
12.5%
3 1023
12.1%
- 792
9.3%
2 759
9.0%
5 573
 
6.8%
8 490
 
5.8%
4 487
 
5.7%
7 457
 
5.4%
6 432
 
5.1%
Other values (6) 674
 
8.0%

소재지면적정보
Real number (ℝ)

MISSING  ZEROS 

Distinct640
Distinct (%)85.2%
Missing60
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean10937.367
Minimum0
Maximum214876.79
Zeros56
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:28.952264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1725.365
median6027
Q313725.655
95-th percentile41622.46
Maximum214876.79
Range214876.79
Interquartile range (IQR)13000.29

Descriptive statistics

Standard deviation17018.389
Coefficient of variation (CV)1.5559859
Kurtosis37.610081
Mean10937.367
Median Absolute Deviation (MAD)5640.5
Skewness4.699569
Sum8213962.4
Variance2.8962555 × 108
MonotonicityNot monotonic
2023-12-11T06:49:29.089558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 56
 
6.9%
41622.46 4
 
0.5%
10035.66 3
 
0.4%
10611.17 3
 
0.4%
23740.74 3
 
0.4%
13659.57 2
 
0.2%
17585.32 2
 
0.2%
8889.61 2
 
0.2%
1838.4 2
 
0.2%
13960.94 2
 
0.2%
Other values (630) 672
82.9%
(Missing) 60
 
7.4%
ValueCountFrequency (%)
0.0 56
6.9%
16.0 1
 
0.1%
26.7 1
 
0.1%
44.23 1
 
0.1%
61.0 1
 
0.1%
96.9 1
 
0.1%
98.0 1
 
0.1%
100.7 1
 
0.1%
110.9 1
 
0.1%
118.0 1
 
0.1%
ValueCountFrequency (%)
214876.79 1
0.1%
133515.61 1
0.1%
129225.0 1
0.1%
108067.0 1
0.1%
106011.0 1
0.1%
103145.33 1
0.1%
76978.9 1
0.1%
74247.63 1
0.1%
69930.0 1
0.1%
67922.98 1
0.1%

도로명우편번호
Text

MISSING 

Distinct361
Distinct (%)87.2%
Missing397
Missing (%)49.0%
Memory size6.5 KiB
2023-12-11T06:49:29.405770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.7391304
Min length5

Characters and Unicode

Total characters2376
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

Unique316 ?
Unique (%)76.3%

Sample

1st row10393
2nd row10391
3rd row10592
4th row412090
5th row10468
ValueCountFrequency (%)
11915 4
 
1.0%
10391 4
 
1.0%
435045 3
 
0.7%
449925 3
 
0.7%
446902 3
 
0.7%
411827 3
 
0.7%
16508 2
 
0.5%
17359 2
 
0.5%
480849 2
 
0.5%
422819 2
 
0.5%
Other values (351) 386
93.2%
2023-12-11T06:49:29.854147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 418
17.6%
1 413
17.4%
0 289
12.2%
8 221
9.3%
3 198
8.3%
5 186
7.8%
2 180
7.6%
6 164
 
6.9%
7 144
 
6.1%
9 112
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2325
97.9%
Dash Punctuation 51
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 418
18.0%
1 413
17.8%
0 289
12.4%
8 221
9.5%
3 198
8.5%
5 186
8.0%
2 180
7.7%
6 164
 
7.1%
7 144
 
6.2%
9 112
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 418
17.6%
1 413
17.4%
0 289
12.2%
8 221
9.3%
3 198
8.3%
5 186
7.8%
2 180
7.6%
6 164
 
6.9%
7 144
 
6.1%
9 112
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 418
17.6%
1 413
17.4%
0 289
12.2%
8 221
9.3%
3 198
8.3%
5 186
7.8%
2 180
7.6%
6 164
 
6.9%
7 144
 
6.1%
9 112
 
4.7%
Distinct615
Distinct (%)84.6%
Missing84
Missing (%)10.4%
Memory size6.5 KiB
2023-12-11T06:49:30.082759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length46
Mean length26.396149
Min length8

Characters and Unicode

Total characters19190
Distinct characters377
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique535 ?
Unique (%)73.6%

Sample

1st row경기도 고양시 일산동구 연리지로 51, 라몬테이탈리아노 (장항동)
2nd row경기도 고양시 일산서구 킨텍스로 240 (대화동)
3rd row경기도 고양시 덕양구 삼송로 222, 고양삼송현대헤리엇 (삼송동)
4th row370, 동산동동
5th row경기도 고양시 덕양구 고양대로1384번길 6, 지하층 1~3호 (성사동)
ValueCountFrequency (%)
경기도 724
 
17.2%
수원시 98
 
2.3%
성남시 95
 
2.3%
분당구 75
 
1.8%
고양시 66
 
1.6%
부천시 63
 
1.5%
안산시 41
 
1.0%
용인시 39
 
0.9%
안양시 39
 
0.9%
단원구 32
 
0.8%
Other values (1218) 2932
69.7%
2023-12-11T06:49:30.471290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3486
 
18.2%
785
 
4.1%
773
 
4.0%
772
 
4.0%
766
 
4.0%
749
 
3.9%
718
 
3.7%
) 674
 
3.5%
( 674
 
3.5%
1 563
 
2.9%
Other values (367) 9230
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11500
59.9%
Space Separator 3486
 
18.2%
Decimal Number 2569
 
13.4%
Close Punctuation 676
 
3.5%
Open Punctuation 676
 
3.5%
Other Punctuation 156
 
0.8%
Dash Punctuation 57
 
0.3%
Uppercase Letter 41
 
0.2%
Lowercase Letter 19
 
0.1%
Math Symbol 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
785
 
6.8%
773
 
6.7%
772
 
6.7%
766
 
6.7%
749
 
6.5%
718
 
6.2%
443
 
3.9%
225
 
2.0%
212
 
1.8%
204
 
1.8%
Other values (317) 5853
50.9%
Uppercase Letter
ValueCountFrequency (%)
B 9
22.0%
A 4
9.8%
C 3
 
7.3%
K 3
 
7.3%
E 3
 
7.3%
S 3
 
7.3%
L 2
 
4.9%
U 2
 
4.9%
P 2
 
4.9%
D 1
 
2.4%
Other values (9) 9
22.0%
Lowercase Letter
ValueCountFrequency (%)
o 3
15.8%
c 3
15.8%
e 2
10.5%
a 2
10.5%
m 2
10.5%
t 2
10.5%
s 1
 
5.3%
r 1
 
5.3%
l 1
 
5.3%
i 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 563
21.9%
2 341
13.3%
3 277
10.8%
0 259
10.1%
4 238
9.3%
7 206
 
8.0%
6 192
 
7.5%
5 182
 
7.1%
8 161
 
6.3%
9 150
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 152
97.4%
* 2
 
1.3%
. 2
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 674
99.7%
] 2
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 674
99.7%
[ 2
 
0.3%
Space Separator
ValueCountFrequency (%)
3486
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11500
59.9%
Common 7630
39.8%
Latin 60
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
785
 
6.8%
773
 
6.7%
772
 
6.7%
766
 
6.7%
749
 
6.5%
718
 
6.2%
443
 
3.9%
225
 
2.0%
212
 
1.8%
204
 
1.8%
Other values (317) 5853
50.9%
Latin
ValueCountFrequency (%)
B 9
 
15.0%
A 4
 
6.7%
o 3
 
5.0%
C 3
 
5.0%
K 3
 
5.0%
E 3
 
5.0%
S 3
 
5.0%
c 3
 
5.0%
L 2
 
3.3%
U 2
 
3.3%
Other values (20) 25
41.7%
Common
ValueCountFrequency (%)
3486
45.7%
) 674
 
8.8%
( 674
 
8.8%
1 563
 
7.4%
2 341
 
4.5%
3 277
 
3.6%
0 259
 
3.4%
4 238
 
3.1%
7 206
 
2.7%
6 192
 
2.5%
Other values (10) 720
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11500
59.9%
ASCII 7690
40.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3486
45.3%
) 674
 
8.8%
( 674
 
8.8%
1 563
 
7.3%
2 341
 
4.4%
3 277
 
3.6%
0 259
 
3.4%
4 238
 
3.1%
7 206
 
2.7%
6 192
 
2.5%
Other values (40) 780
 
10.1%
Hangul
ValueCountFrequency (%)
785
 
6.8%
773
 
6.7%
772
 
6.7%
766
 
6.7%
749
 
6.5%
718
 
6.2%
443
 
3.9%
225
 
2.0%
212
 
1.8%
204
 
1.8%
Other values (317) 5853
50.9%
Distinct763
Distinct (%)94.2%
Missing1
Missing (%)0.1%
Memory size6.5 KiB
2023-12-11T06:49:30.772225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length42
Mean length22.807407
Min length7

Characters and Unicode

Total characters18474
Distinct characters325
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)89.6%

Sample

1st row경기도 고양시 일산동구 장항동 1800번지 라몬테이탈리아노
2nd row경기도 고양시 일산서구 대화동 2603번지
3rd row경기도 고양시 덕양구 삼송동 293
4th row경기도 고양시 덕양구 동산동 370번지
5th row경기도 고양시 덕양구 성사동 726
ValueCountFrequency (%)
경기도 806
 
19.8%
1호 88
 
2.2%
수원시 54
 
1.3%
성남시 53
 
1.3%
성남시분당구 42
 
1.0%
분당구 40
 
1.0%
고양시 40
 
1.0%
36
 
0.9%
3호 31
 
0.8%
2호 31
 
0.8%
Other values (1223) 2841
69.9%
2023-12-11T06:49:31.207084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3759
20.3%
841
 
4.6%
839
 
4.5%
823
 
4.5%
820
 
4.4%
812
 
4.4%
1 742
 
4.0%
617
 
3.3%
563
 
3.0%
533
 
2.9%
Other values (315) 8125
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11289
61.1%
Space Separator 3759
 
20.3%
Decimal Number 3220
 
17.4%
Dash Punctuation 115
 
0.6%
Other Punctuation 26
 
0.1%
Uppercase Letter 26
 
0.1%
Lowercase Letter 14
 
0.1%
Close Punctuation 9
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
841
 
7.4%
839
 
7.4%
823
 
7.3%
820
 
7.3%
812
 
7.2%
617
 
5.5%
563
 
5.0%
533
 
4.7%
478
 
4.2%
207
 
1.8%
Other values (273) 4756
42.1%
Uppercase Letter
ValueCountFrequency (%)
B 6
23.1%
C 2
 
7.7%
K 2
 
7.7%
L 2
 
7.7%
E 2
 
7.7%
P 2
 
7.7%
S 2
 
7.7%
I 1
 
3.8%
A 1
 
3.8%
F 1
 
3.8%
Other values (5) 5
19.2%
Decimal Number
ValueCountFrequency (%)
1 742
23.0%
3 389
12.1%
2 353
11.0%
5 284
 
8.8%
4 278
 
8.6%
0 269
 
8.4%
6 262
 
8.1%
7 226
 
7.0%
9 214
 
6.6%
8 203
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
e 3
21.4%
m 2
14.3%
a 2
14.3%
r 1
 
7.1%
o 1
 
7.1%
c 1
 
7.1%
l 1
 
7.1%
t 1
 
7.1%
i 1
 
7.1%
u 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 22
84.6%
. 4
 
15.4%
Space Separator
ValueCountFrequency (%)
3759
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11289
61.1%
Common 7145
38.7%
Latin 40
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
841
 
7.4%
839
 
7.4%
823
 
7.3%
820
 
7.3%
812
 
7.2%
617
 
5.5%
563
 
5.0%
533
 
4.7%
478
 
4.2%
207
 
1.8%
Other values (273) 4756
42.1%
Latin
ValueCountFrequency (%)
B 6
 
15.0%
e 3
 
7.5%
C 2
 
5.0%
m 2
 
5.0%
a 2
 
5.0%
K 2
 
5.0%
L 2
 
5.0%
E 2
 
5.0%
P 2
 
5.0%
S 2
 
5.0%
Other values (15) 15
37.5%
Common
ValueCountFrequency (%)
3759
52.6%
1 742
 
10.4%
3 389
 
5.4%
2 353
 
4.9%
5 284
 
4.0%
4 278
 
3.9%
0 269
 
3.8%
6 262
 
3.7%
7 226
 
3.2%
9 214
 
3.0%
Other values (7) 369
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11289
61.1%
ASCII 7185
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3759
52.3%
1 742
 
10.3%
3 389
 
5.4%
2 353
 
4.9%
5 284
 
4.0%
4 278
 
3.9%
0 269
 
3.7%
6 262
 
3.6%
7 226
 
3.1%
9 214
 
3.0%
Other values (32) 409
 
5.7%
Hangul
ValueCountFrequency (%)
841
 
7.4%
839
 
7.4%
823
 
7.3%
820
 
7.3%
812
 
7.2%
617
 
5.5%
563
 
5.0%
533
 
4.7%
478
 
4.2%
207
 
1.8%
Other values (273) 4756
42.1%

소재지우편번호
Text

MISSING 

Distinct556
Distinct (%)72.7%
Missing46
Missing (%)5.7%
Memory size6.5 KiB
2023-12-11T06:49:31.556151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.4196078
Min length5

Characters and Unicode

Total characters4146
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

Unique416 ?
Unique (%)54.4%

Sample

1st row10391
2nd row10592
3rd row10595
4th row10468
5th row10450
ValueCountFrequency (%)
13497 9
 
1.2%
463050 9
 
1.2%
11915 7
 
0.9%
14637 6
 
0.8%
10500 5
 
0.7%
14548 5
 
0.7%
14237 4
 
0.5%
463020 4
 
0.5%
16426 4
 
0.5%
13599 4
 
0.5%
Other values (546) 708
92.5%
2023-12-11T06:49:32.085750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 848
20.5%
4 623
15.0%
0 496
12.0%
3 386
9.3%
6 357
8.6%
5 336
 
8.1%
2 330
 
8.0%
8 289
 
7.0%
7 232
 
5.6%
9 206
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4103
99.0%
Dash Punctuation 43
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 848
20.7%
4 623
15.2%
0 496
12.1%
3 386
9.4%
6 357
8.7%
5 336
 
8.2%
2 330
 
8.0%
8 289
 
7.0%
7 232
 
5.7%
9 206
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4146
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 848
20.5%
4 623
15.0%
0 496
12.0%
3 386
9.3%
6 357
8.6%
5 336
 
8.1%
2 330
 
8.0%
8 289
 
7.0%
7 232
 
5.6%
9 206
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 848
20.5%
4 623
15.0%
0 496
12.0%
3 386
9.3%
6 357
8.6%
5 336
 
8.1%
2 330
 
8.0%
8 289
 
7.0%
7 232
 
5.6%
9 206
 
5.0%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct664
Distinct (%)87.0%
Missing48
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean37.429788
Minimum36.985283
Maximum38.090166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:32.243371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.985283
5-th percentile37.191959
Q137.295261
median37.395446
Q337.539471
95-th percentile37.746951
Maximum38.090166
Range1.1048826
Interquartile range (IQR)0.24420975

Descriptive statistics

Standard deviation0.18597207
Coefficient of variation (CV)0.0049685581
Kurtosis0.20949593
Mean37.429788
Median Absolute Deviation (MAD)0.10887075
Skewness0.40641741
Sum28558.928
Variance0.034585613
MonotonicityNot monotonic
2023-12-11T06:49:32.397187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4130257 5
 
0.6%
37.280846809 4
 
0.5%
37.502485 4
 
0.5%
37.6131390076 4
 
0.5%
37.4751822 4
 
0.5%
37.4747894 3
 
0.4%
37.2504974396 3
 
0.4%
37.2518134073 3
 
0.4%
37.2662986444 3
 
0.4%
37.3399823 3
 
0.4%
Other values (654) 727
89.6%
(Missing) 48
 
5.9%
ValueCountFrequency (%)
36.9852830383 1
0.1%
36.9883462779 1
0.1%
36.9886134 1
0.1%
36.9902412081 1
0.1%
36.9919006082 1
0.1%
36.9942489 1
0.1%
36.9945235041 1
0.1%
36.9949133 1
0.1%
36.9953821 1
0.1%
36.995560236 1
0.1%
ValueCountFrequency (%)
38.0901656506 1
0.1%
38.0282034881 1
0.1%
38.0270658075 1
0.1%
37.9596084172 1
0.1%
37.9587528035 1
0.1%
37.8999697271 1
0.1%
37.8996382 2
0.2%
37.893021736 1
0.1%
37.8920935484 1
0.1%
37.8808927551 1
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct664
Distinct (%)87.0%
Missing48
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean126.98559
Minimum126.59743
Maximum127.63744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:32.551420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59743
5-th percentile126.74316
Q1126.81439
median127.00524
Q3127.11853
95-th percentile127.25649
Maximum127.63744
Range1.0400137
Interquartile range (IQR)0.30413874

Descriptive statistics

Standard deviation0.17934196
Coefficient of variation (CV)0.0014123017
Kurtosis0.20842099
Mean126.98559
Median Absolute Deviation (MAD)0.13038629
Skewness0.40726356
Sum96890.003
Variance0.032163539
MonotonicityNot monotonic
2023-12-11T06:49:32.698593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1272138 5
 
0.6%
126.976018037 4
 
0.5%
126.7751605 4
 
0.5%
127.1409110928 4
 
0.5%
126.8670629 4
 
0.5%
126.8703195 3
 
0.4%
127.0208246964 3
 
0.4%
127.0726078415 3
 
0.4%
127.0343149778 3
 
0.4%
127.1069049 3
 
0.4%
Other values (654) 727
89.6%
(Missing) 48
 
5.9%
ValueCountFrequency (%)
126.597429208 1
0.1%
126.5984311406 1
0.1%
126.6195226475 1
0.1%
126.6282980439 1
0.1%
126.6331667826 1
0.1%
126.6698315 1
0.1%
126.6774205372 1
0.1%
126.6774947 1
0.1%
126.6789187057 1
0.1%
126.6855310518 1
0.1%
ValueCountFrequency (%)
127.6374429 1
0.1%
127.635666647 1
0.1%
127.6350809464 1
0.1%
127.6122725791 1
0.1%
127.5025567 1
0.1%
127.5013348437 1
0.1%
127.4745350978 1
0.1%
127.459112025 1
0.1%
127.4588801 1
0.1%
127.4570188069 1
0.1%

업태구분명정보
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
그 밖의 대규모점포
243 
대형마트
188 
구분없음
141 
쇼핑센터
72 
백화점
42 
Other values (4)
125 

Length

Max length10
Median length4
Mean length5.6991369
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row그 밖의 대규모점포
2nd row그 밖의 대규모점포
3rd row그 밖의 대규모점포
4th row복합쇼핑몰
5th row구분없음

Common Values

ValueCountFrequency (%)
그 밖의 대규모점포 243
30.0%
대형마트 188
23.2%
구분없음 141
17.4%
쇼핑센터 72
 
8.9%
백화점 42
 
5.2%
복합쇼핑몰 41
 
5.1%
전문점 33
 
4.1%
<NA> 28
 
3.5%
시장 23
 
2.8%

Length

2023-12-11T06:49:32.857925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:33.001781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
243
18.7%
밖의 243
18.7%
대규모점포 243
18.7%
대형마트 188
14.5%
구분없음 141
10.9%
쇼핑센터 72
 
5.6%
백화점 42
 
3.2%
복합쇼핑몰 41
 
3.2%
전문점 33
 
2.5%
na 28
 
2.2%

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct587
Distinct (%)82.6%
Missing100
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean198635.33
Minimum164429.33
Maximum256448.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:33.160099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum164429.33
5-th percentile177401.83
Q1183445.43
median200590.51
Q3210321.69
95-th percentile222876.4
Maximum256448.25
Range92018.923
Interquartile range (IQR)26876.262

Descriptive statistics

Standard deviation15901.718
Coefficient of variation (CV)0.080054831
Kurtosis0.23917695
Mean198635.33
Median Absolute Deviation (MAD)11488.542
Skewness0.42605577
Sum1.4122972 × 108
Variance2.5286462 × 108
MonotonicityNot monotonic
2023-12-11T06:49:33.316268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211202.148849893 6
 
0.7%
180053.344768654 5
 
0.6%
212369.786160091 5
 
0.6%
188175.811151552 4
 
0.5%
209409.160207927 4
 
0.5%
197816.821324019 4
 
0.5%
210421.232418185 3
 
0.4%
222711.793919182 3
 
0.4%
213383.977793755 3
 
0.4%
206365.525998588 3
 
0.4%
Other values (577) 671
82.7%
(Missing) 100
 
12.3%
ValueCountFrequency (%)
164429.325739178 1
0.1%
164515.425324902 1
0.1%
167119.660833957 1
0.1%
167542.995851124 1
0.1%
170799.828335658 1
0.1%
171450.600413716 1
0.1%
171471.604963288 1
0.1%
172118.0 1
0.1%
172364.612934904 1
0.1%
172472.74587 1
0.1%
ValueCountFrequency (%)
256448.248498212 1
0.1%
256310.12023 1
0.1%
256295.540566504 1
0.1%
254263.945020146 1
0.1%
244381.267486222 1
0.1%
244268.697648456 1
0.1%
242016.421941572 1
0.1%
240622.258190291 2
0.2%
240460.438731023 1
0.1%
240306.158458325 1
0.1%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct588
Distinct (%)82.7%
Missing100
Missing (%)12.3%
Infinite0
Infinite (%)0.0%
Mean436827.23
Minimum387072.99
Maximum560834.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2023-12-11T06:49:33.461824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum387072.99
5-th percentile409670.99
Q1421482.99
median432568.8
Q3448681.62
95-th percentile472025.61
Maximum560834.91
Range173761.91
Interquartile range (IQR)27198.637

Descriptive statistics

Standard deviation22168.207
Coefficient of variation (CV)0.050748225
Kurtosis3.4263942
Mean436827.23
Median Absolute Deviation (MAD)12137.34
Skewness0.99123818
Sum3.1058416 × 108
Variance4.9142938 × 108
MonotonicityNot monotonic
2023-12-11T06:49:33.613231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
434560.911834998 6
 
0.7%
444507.864924505 5
 
0.6%
456717.132615198 5
 
0.6%
419948.630301716 4
 
0.5%
426451.358939753 4
 
0.5%
441461.453719641 4
 
0.5%
462865.812502378 3
 
0.4%
442988.573961638 3
 
0.4%
441418.706981087 3
 
0.4%
442564.317899051 3
 
0.4%
Other values (578) 671
82.7%
(Missing) 100
 
12.3%
ValueCountFrequency (%)
387072.994089902 1
0.1%
387425.098329575 1
0.1%
387484.797282739 1
0.1%
387625.950361082 1
0.1%
387674.877266816 1
0.1%
388080.83595037 1
0.1%
388089.490851808 1
0.1%
388208.048761482 1
0.1%
388254.423734324 1
0.1%
389422.608470627 1
0.1%
ValueCountFrequency (%)
560834.907896845 1
0.1%
558895.2705657 2
0.2%
509737.664329872 1
0.1%
502827.430494496 1
0.1%
502699.168905672 1
0.1%
495257.859851673 1
0.1%
495167.132130244 1
0.1%
488583.056118126 1
0.1%
488565.873404122 2
0.2%
487822.390972397 1
0.1%

점포구분명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
대규모점포
401 
<NA>
227 
준대규모점포
183 

Length

Max length6
Median length5
Mean length4.945746
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대규모점포
2nd row대규모점포
3rd row대규모점포
4th row대규모점포
5th row준대규모점포

Common Values

ValueCountFrequency (%)
대규모점포 401
49.4%
<NA> 227
28.0%
준대규모점포 183
22.6%

Length

2023-12-11T06:49:33.774028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:33.891613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대규모점포 401
49.4%
na 227
28.0%
준대규모점포 183
22.6%

Interactions

2023-12-11T06:49:24.683913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:21.757980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.384119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.949325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.515907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.135898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.764913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:21.911916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.472341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.033605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.612523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.223313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.838869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.014682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.547226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.112898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.690041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.305708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.925083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.100002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.644107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.213286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.781387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.398968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:25.011627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.196876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.742188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.308389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.885447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.481971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:25.096177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.287947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:22.848974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:23.420867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.008548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:49:24.587552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:49:33.986893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명인허가취소일자영업상태구분코드영업상태명소재지면적정보WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값점포구분명
시군명1.0000.0000.4320.4320.2010.9930.9780.4930.9760.9580.325
인허가취소일자0.0001.000NaNNaNNaN0.0000.3560.8160.3560.0000.000
영업상태구분코드0.432NaN1.0001.0000.1610.3010.2010.2680.1610.2000.226
영업상태명0.432NaN1.0001.0000.1610.3010.2010.2680.1610.2000.226
소재지면적정보0.201NaN0.1610.1611.0000.0580.2460.4320.2780.2880.363
WGS84위도0.9930.0000.3010.3010.0581.0000.7080.1660.7000.9290.077
WGS84경도0.9780.3560.2010.2010.2460.7081.0000.2691.0000.5740.204
업태구분명정보0.4930.8160.2680.2680.4320.1660.2691.0000.2610.1940.954
X좌표값0.9760.3560.1610.1610.2780.7001.0000.2611.0000.5720.229
Y좌표값0.9580.0000.2000.2000.2880.9290.5740.1940.5721.0000.102
점포구분명0.3250.0000.2260.2260.3630.0770.2040.9540.2290.1021.000
2023-12-11T06:49:34.383670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명점포구분명영업상태구분코드영업상태명업태구분명정보
시군명1.0000.2520.1980.1980.215
점포구분명0.2521.0000.2750.2750.808
영업상태구분코드0.1980.2751.0001.0000.167
영업상태명0.1980.2751.0001.0000.167
업태구분명정보0.2150.8080.1670.1671.000
2023-12-11T06:49:34.485090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가취소일자소재지면적정보WGS84위도WGS84경도X좌표값Y좌표값시군명영업상태구분코드영업상태명업태구분명정보점포구분명
인허가취소일자1.000-0.1020.2330.6830.6830.2330.0001.0001.0000.5770.000
소재지면적정보-0.1021.000-0.051-0.029-0.053-0.0440.0780.0990.0990.2480.271
WGS84위도0.233-0.0511.000-0.314-0.3200.9990.8500.1290.1290.0790.058
WGS84경도0.683-0.029-0.3141.0001.000-0.3200.7600.0840.0840.1310.155
X좌표값0.683-0.053-0.3201.0001.000-0.3250.7520.0660.0660.1270.171
Y좌표값0.233-0.0440.999-0.320-0.3251.0000.7670.1150.1150.0960.097
시군명0.0000.0780.8500.7600.7520.7671.0000.1980.1980.2150.252
영업상태구분코드1.0000.0990.1290.0840.0660.1150.1981.0001.0000.1670.275
영업상태명1.0000.0990.1290.0840.0660.1150.1981.0001.0000.1670.275
업태구분명정보0.5770.2480.0790.1310.1270.0960.2150.1670.1671.0000.808
점포구분명0.0000.2710.0580.1550.1710.0970.2520.2750.2750.8081.000

Missing values

2023-12-11T06:49:25.221582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:49:25.433762image/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.
2023-12-11T06:49:25.647559image/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

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값점포구분명
0고양시라 몬테 이탈리아노20190122<NA>5영업개시전<NA>031-947-96036030.8510393경기도 고양시 일산동구 연리지로 51, 라몬테이탈리아노 (장항동)경기도 고양시 일산동구 장항동 1800번지 라몬테이탈리아노<NA><NA><NA>그 밖의 대규모점포178307.044705461974.801225대규모점포
1고양시GIFC MALL20190123<NA>5영업개시전<NA>955-94459385.0210391경기도 고양시 일산서구 킨텍스로 240 (대화동)경기도 고양시 일산서구 대화동 2603번지1039137.666098126.75052그 밖의 대규모점포177924.792392462674.148709대규모점포
2고양시고양삼송현대헤리엇20210702<NA>5영업개시전<NA>031-901-661115789.010592경기도 고양시 덕양구 삼송로 222, 고양삼송현대헤리엇 (삼송동)경기도 고양시 덕양구 삼송동 2931059237.652896126.899229그 밖의 대규모점포<NA><NA>대규모점포
3고양시스타필드 고양20160425<NA>5영업개시전<NA>070-7896-7801129225.0412090370, 동산동동경기도 고양시 덕양구 동산동 370번지1059537.646939126.895611복합쇼핑몰190715.654303560834.907897대규모점포
4고양시(주)이마트에브리데이 신원당점2023-03-29<NA>5영업개시전<NA>031-994-0274643.6310468경기도 고양시 덕양구 고양대로1384번길 6, 지하층 1~3호 (성사동)경기도 고양시 덕양구 성사동 7261046837.653997126.837152구분없음185439.51931461265.376606준대규모점포
5고양시일산 벨라씨타(Bella Citta)20160826<NA>5영업개시전<NA>02-3438-948042599.4410450경기도 고양시 일산동구 강송로 33 (백석동, 일산요진와이시티)경기도 고양시 일산동구 백석동 1237번지1045037.642359126.792682쇼핑센터181637.189249460029.0134대규모점포
6고양시고양 일산 호수공원 가로수길20170825<NA>5영업개시전<NA><NA>16505.8210391경기도 고양시 일산서구 주엽로 80 (대화동)경기도 고양시 일산서구 대화동 1050번지 185호1039237.665939126.756403그 밖의 대규모점포178443.77289462653.992628대규모점포
7고양시(주)이마트에브리데이 고양덕이점20220810<NA>5영업개시전<NA>02-380-50421111.3210231경기도 고양시 일산서구 하이파크2로 65, 1층 (덕이동)경기도 고양시 일산서구 덕이동 373-211023137.699825126.756295구분없음178438.201603466393.045837준대규모점포
8고양시두진레이크20150306<NA>1정상영업<NA>031 912 60114162.0411742경기도 고양시 일산서구 중앙로 1391-0 (주엽동)경기도 고양시 일산서구 주엽동 83번지41229037.66774126.764546그 밖의 대규모점포179162.766413462851.250567대규모점포
9고양시일산시장19811020<NA>1정상영업<NA>975-39030.0<NA>경기도 고양시 일산서구 일청로12번길 9 (일산동)경기도 고양시일산서구 일산동 617-20호1034237.685654126.770872시장<NA><NA><NA>
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값점포구분명
801화성시마도공구유통상가20070118<NA>4직권취소<NA>02 575 54340.0<NA>경기도 화성시 마도면 마도공단로1길 8경기도 화성시 마도면 쌍송리 662호1854237.186269126.790169전문점181324.988087409385.427774준대규모점포
802화성시롯데쇼핑(주)롯데슈퍼신향남점20111125<NA>3폐업처리20181231031-8059-5601313.4445926경기도 화성시 향남읍 행정중앙1로 14경기도 화성시 향남읍 행정리 462번지 5호44592637.125878126.916332그 밖의 대규모점포192470.655401402684.622342준대규모점포
803화성시화성공구유통밸리(주)20051020<NA>2휴업처리<NA>031230 94420.0<NA>경기도 화성시 봉담읍 와우안길 109경기도 화성시 봉담읍 동화리 139-1호1829837.221472126.972011그 밖의 대규모점포<NA><NA><NA>
804화성시롯데쇼핑(주)VIC마켓 신영통점2004-08-05<NA>2휴업처리<NA>031695252210066.0445-330경기도 화성시 삼성1로 333 (반월동)경기도 화성시 반월동 123-5호1838237.230437127.066472대형마트205829.064604414278.001961대규모점포
805화성시(주)평해종합건설20050502<NA>2휴업처리<NA>031 421550419250.39<NA>경기도 화성시 봉담읍 와우안길 109경기도 화성시 봉담읍 동화리 139-1호1829837.221472126.972011대형마트<NA><NA><NA>
806<NA>(주)이마트 봉담점20120420<NA>1정상영업<NA>031-228-125210611.17<NA>효행로 278133번지 1호<NA><NA><NA>대형마트197532.418006413710.967302대규모점포
807<NA>이마트 화성봉담점2012-04-20<NA>1정상영업<NA>031228123410611.17<NA>효행로 278133번지 1호<NA><NA><NA>대형마트<NA><NA>대규모점포
808<NA>롯데쇼핑(주)롯데슈퍼 의정부2동점20120514<NA>3폐업처리20170417031)876-5601692.6480849경기도 범골로 146-23경기도 의정부시 의정부동 559-1번지 대산빌라3차1165337.734735127.041623그 밖의 대규모점포<NA><NA>준대규모점포
809<NA>안양롯데프라자20031212<NA>2휴업처리<NA>031 4640282<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
810<NA>엘지마트시화점19971231<NA>2휴업처리<NA><NA><NA><NA><NA><NA><NA><NA>그 밖의 대규모점포<NA><NA><NA>

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값점포구분명# duplicates
1부천시(주)GS리테일GS스퀘어부천점20050601<NA>2휴업처리<NA><NA>41622.46<NA>경기도 부천시 길주로 300 (중동)경기도 부천시중동 1140호1454837.502485126.77516백화점180053.344769444507.864925<NA>4
0광명시파보레쇼핑몰20040909<NA>2휴업처리<NA>02 206770008812.68<NA>경기도 광명시 철산로 4 (철산동)경기도 광명시 철산동 261호1423737.475182126.867063그 밖의 대규모점포188175.811152441461.45372<NA>2
2수원시수원축산농협 하나로마트20191126<NA>3폐업처리20220713031-267-38716332.0416670경기도 수원시 권선구 곡반정로 121 (곡반정동)[*미고시]경기도 수원시 권선구 곡반정동 555번지1667037.238981127.030717그 밖의 대규모점포202671.615398415224.539996대규모점포2
3시흥시시화유통상가사업협동조합20041203<NA>2휴업처리<NA>031 430 363623740.74<NA><NA>경기도 시흥시 정왕동 호 시화공단 3다 3다<NA><NA><NA>그 밖의 대규모점포<NA><NA><NA>2