Overview

Dataset statistics

Number of variables11
Number of observations51
Missing cells14
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory93.6 B

Variable types

Categorical1
Text6
Numeric3
DateTime1

Dataset

Description성남시 대규모 점포시장 현황 데이터로, 구분,상호,소재지지번주소, 소재지도로명주소, ,법인명,규모(지상/지하, 연면적㎡, 매장면적㎡),점포수,등록일시,전화번호 등의 항목으로 구성되어 있습니다.
URLhttps://www.data.go.kr/data/3073720/fileData.do

Alerts

규모_연면적(제곱미터) is highly overall correlated with 규모_매장면적(제곱미터) and 1 other fieldsHigh correlation
규모_매장면적(제곱미터) is highly overall correlated with 규모_연면적(제곱미터)High correlation
구분 is highly overall correlated with 규모_연면적(제곱미터)High correlation
규모_지상_지하 has 9 (17.6%) missing valuesMissing
전화번호 has 5 (9.8%) missing valuesMissing
규모_매장면적(제곱미터) has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:34:25.762526
Analysis finished2023-12-12 04:34:28.058639
Duration2.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size540.0 B
전통시장
28 
그밖의 대규모점포
6 
대형마트
5 
골목형상점가
5 
백화점
4 
Other values (2)
3 

Length

Max length9
Median length4
Mean length4.745098
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
전통시장 28
54.9%
그밖의 대규모점포 6
 
11.8%
대형마트 5
 
9.8%
골목형상점가 5
 
9.8%
백화점 4
 
7.8%
쇼핑센터 2
 
3.9%
종합유통센터 1
 
2.0%

Length

2023-12-12T13:34:28.129243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:34:28.239859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전통시장 28
49.1%
그밖의 6
 
10.5%
대규모점포 6
 
10.5%
대형마트 5
 
8.8%
골목형상점가 5
 
8.8%
백화점 4
 
7.0%
쇼핑센터 2
 
3.5%
종합유통센터 1
 
1.8%

상호
Text

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T13:34:28.491221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length8.0392157
Min length4

Characters and Unicode

Total characters410
Distinct characters137
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)96.1%

Sample

1st row이마트 분당점
2nd row홈플러스(주) 분당오리점
3rd row홈플러스(주) 야탑점
4th row이천일아울렛 분당점
5th row롯데마트 판교점
ValueCountFrequency (%)
분당점 4
 
5.3%
테마폴리스 3
 
4.0%
골목형상점가 3
 
4.0%
현대시장 2
 
2.7%
성남점 2
 
2.7%
판교점 2
 
2.7%
이마트 2
 
2.7%
야탑점 2
 
2.7%
홈플러스(주 2
 
2.7%
금호행복시장 1
 
1.3%
Other values (52) 52
69.3%
2023-12-12T13:34:28.853424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
 
7.6%
장 30
 
7.3%
24
 
5.9%
점 21
 
5.1%
성 8
 
2.0%
스 8
 
2.0%
대 8
 
2.0%
당 7
 
1.7%
마 7
 
1.7%
분 7
 
1.7%
Other values (127) 259
63.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 356
86.8%
Space Separator 24
 
5.9%
Uppercase Letter 13
 
3.2%
Decimal Number 5
 
1.2%
Lowercase Letter 5
 
1.2%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
 
8.7%
장 30
 
8.4%
점 21
 
5.9%
성 8
 
2.2%
스 8
 
2.2%
대 8
 
2.2%
당 7
 
2.0%
마 7
 
2.0%
분 7
 
2.0%
리 7
 
2.0%
Other values (106) 222
62.4%
Uppercase Letter
ValueCountFrequency (%)
A 3
23.1%
C 2
15.4%
K 1
 
7.7%
S 1
 
7.7%
V 1
 
7.7%
G 1
 
7.7%
N 1
 
7.7%
Z 1
 
7.7%
L 1
 
7.7%
P 1
 
7.7%
Lowercase Letter
ValueCountFrequency (%)
q 1
20.0%
u 1
20.0%
a 1
20.0%
r 1
20.0%
e 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
1 2
40.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
86.8%
Common 36
 
8.8%
Latin 18
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
 
8.7%
장 30
 
8.4%
점 21
 
5.9%
성 8
 
2.2%
스 8
 
2.2%
대 8
 
2.2%
당 7
 
2.0%
마 7
 
2.0%
분 7
 
2.0%
리 7
 
2.0%
Other values (106) 222
62.4%
Latin
ValueCountFrequency (%)
A 3
16.7%
C 2
 
11.1%
K 1
 
5.6%
S 1
 
5.6%
V 1
 
5.6%
G 1
 
5.6%
N 1
 
5.6%
Z 1
 
5.6%
L 1
 
5.6%
P 1
 
5.6%
Other values (5) 5
27.8%
Common
ValueCountFrequency (%)
24
66.7%
( 3
 
8.3%
) 3
 
8.3%
2 3
 
8.3%
1 2
 
5.6%
+ 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 356
86.8%
ASCII 54
 
13.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
 
8.7%
장 30
 
8.4%
점 21
 
5.9%
성 8
 
2.2%
스 8
 
2.2%
대 8
 
2.2%
당 7
 
2.0%
마 7
 
2.0%
분 7
 
2.0%
리 7
 
2.0%
Other values (106) 222
62.4%
ASCII
ValueCountFrequency (%)
24
44.4%
A 3
 
5.6%
( 3
 
5.6%
) 3
 
5.6%
2 3
 
5.6%
1 2
 
3.7%
C 2
 
3.7%
K 1
 
1.9%
S 1
 
1.9%
V 1
 
1.9%
Other values (11) 11
20.4%

소재지지번주소
Text

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T13:34:29.155168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length20.784314
Min length18

Characters and Unicode

Total characters1060
Distinct characters51
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)84.3%

Sample

1st row경기도 성남시 분당구 정자동 211
2nd row경기도 성남시 분당구 구미동 159
3rd row경기도 성남시 분당구 야탑동 341
4th row경기도 성남시 분당구 구미동 11-1
5th row경기도 성남시 분당구 삼평동 741
ValueCountFrequency (%)
경기도 51
19.0%
성남시 51
19.0%
분당구 31
 
11.6%
수정구 10
 
3.7%
중원구 9
 
3.4%
수내동 8
 
3.0%
서현동 7
 
2.6%
등 6
 
2.2%
야탑동 6
 
2.2%
구미동 5
 
1.9%
Other values (67) 84
31.3%
2023-12-12T13:34:29.545821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
218
20.6%
구 56
 
5.3%
성 55
 
5.2%
남 55
 
5.2%
시 52
 
4.9%
경 51
 
4.8%
기 51
 
4.8%
도 51
 
4.8%
동 51
 
4.8%
1 36
 
3.4%
Other values (41) 384
36.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 638
60.2%
Space Separator 218
 
20.6%
Decimal Number 188
 
17.7%
Dash Punctuation 16
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
 
8.8%
성 55
 
8.6%
남 55
 
8.6%
시 52
 
8.2%
경 51
 
8.0%
기 51
 
8.0%
도 51
 
8.0%
동 51
 
8.0%
당 31
 
4.9%
분 31
 
4.9%
Other values (29) 154
24.1%
Decimal Number
ValueCountFrequency (%)
1 36
19.1%
3 30
16.0%
2 27
14.4%
4 25
13.3%
5 18
9.6%
6 14
 
7.4%
9 12
 
6.4%
8 10
 
5.3%
7 10
 
5.3%
0 6
 
3.2%
Space Separator
ValueCountFrequency (%)
218
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 638
60.2%
Common 422
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
 
8.8%
성 55
 
8.6%
남 55
 
8.6%
시 52
 
8.2%
경 51
 
8.0%
기 51
 
8.0%
도 51
 
8.0%
동 51
 
8.0%
당 31
 
4.9%
분 31
 
4.9%
Other values (29) 154
24.1%
Common
ValueCountFrequency (%)
218
51.7%
1 36
 
8.5%
3 30
 
7.1%
2 27
 
6.4%
4 25
 
5.9%
5 18
 
4.3%
- 16
 
3.8%
6 14
 
3.3%
9 12
 
2.8%
8 10
 
2.4%
Other values (2) 16
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 638
60.2%
ASCII 422
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218
51.7%
1 36
 
8.5%
3 30
 
7.1%
2 27
 
6.4%
4 25
 
5.9%
5 18
 
4.3%
- 16
 
3.8%
6 14
 
3.3%
9 12
 
2.8%
8 10
 
2.4%
Other values (2) 16
 
3.8%
Hangul
ValueCountFrequency (%)
구 56
 
8.8%
성 55
 
8.6%
남 55
 
8.6%
시 52
 
8.2%
경 51
 
8.0%
기 51
 
8.0%
도 51
 
8.0%
동 51
 
8.0%
당 31
 
4.9%
분 31
 
4.9%
Other values (29) 154
24.1%

소재지도로명주소
Text

Distinct47
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T13:34:29.874149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length35
Mean length29.568627
Min length23

Characters and Unicode

Total characters1508
Distinct characters106
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)88.2%

Sample

1st row경기도 성남시 분당구 불정로 134 (정자동)
2nd row경기도 성남시 분당구 탄천상로151번길 20 (구미동)
3rd row경기도 성남시 분당구 성남대로925번길 16 (야탑동)
4th row경기도 성남시 분당구 미금일로154번길 20 (구미동)
5th row경기도 성남시 분당구 대왕판교로606번길 58 (삼평동)
ValueCountFrequency (%)
경기도 51
16.9%
성남시 51
16.9%
분당구 31
 
10.3%
수정구 10
 
3.3%
중원구 10
 
3.3%
야탑동 5
 
1.7%
산성대로 5
 
1.7%
돌마로 4
 
1.3%
16 4
 
1.3%
성남대로925번길 4
 
1.3%
Other values (105) 127
42.1%
2023-12-12T13:34:30.357279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
251
 
16.6%
성 66
 
4.4%
남 62
 
4.1%
구 55
 
3.6%
시 54
 
3.6%
경 51
 
3.4%
도 51
 
3.4%
기 51
 
3.4%
로 51
 
3.4%
) 50
 
3.3%
Other values (96) 766
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 937
62.1%
Space Separator 251
 
16.6%
Decimal Number 211
 
14.0%
Close Punctuation 50
 
3.3%
Open Punctuation 50
 
3.3%
Other Punctuation 7
 
0.5%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 66
 
7.0%
남 62
 
6.6%
구 55
 
5.9%
시 54
 
5.8%
경 51
 
5.4%
도 51
 
5.4%
기 51
 
5.4%
로 51
 
5.4%
동 50
 
5.3%
번 35
 
3.7%
Other values (81) 411
43.9%
Decimal Number
ValueCountFrequency (%)
1 46
21.8%
6 32
15.2%
2 26
12.3%
4 22
10.4%
3 20
9.5%
5 17
 
8.1%
0 16
 
7.6%
8 14
 
6.6%
7 10
 
4.7%
9 8
 
3.8%
Space Separator
ValueCountFrequency (%)
251
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 937
62.1%
Common 571
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 66
 
7.0%
남 62
 
6.6%
구 55
 
5.9%
시 54
 
5.8%
경 51
 
5.4%
도 51
 
5.4%
기 51
 
5.4%
로 51
 
5.4%
동 50
 
5.3%
번 35
 
3.7%
Other values (81) 411
43.9%
Common
ValueCountFrequency (%)
251
44.0%
) 50
 
8.8%
( 50
 
8.8%
1 46
 
8.1%
6 32
 
5.6%
2 26
 
4.6%
4 22
 
3.9%
3 20
 
3.5%
5 17
 
3.0%
0 16
 
2.8%
Other values (5) 41
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 937
62.1%
ASCII 571
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
251
44.0%
) 50
 
8.8%
( 50
 
8.8%
1 46
 
8.1%
6 32
 
5.6%
2 26
 
4.6%
4 22
 
3.9%
3 20
 
3.5%
5 17
 
3.0%
0 16
 
2.8%
Other values (5) 41
 
7.2%
Hangul
ValueCountFrequency (%)
성 66
 
7.0%
남 62
 
6.6%
구 55
 
5.9%
시 54
 
5.8%
경 51
 
5.4%
도 51
 
5.4%
기 51
 
5.4%
로 51
 
5.4%
동 50
 
5.3%
번 35
 
3.7%
Other values (81) 411
43.9%

법인명
Text

Distinct32
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Memory size540.0 B
2023-12-12T13:34:30.554143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.6862745
Min length3

Characters and Unicode

Total characters341
Distinct characters112
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)51.0%

Sample

1st row㈜이마트
2nd row㈜홈플러스
3rd row㈜홈플러스
4th row(주)이랜드리테일
5th row㈜롯데쇼핑
ValueCountFrequency (%)
상인회 15
25.9%
외3개법인 2
 
3.4%
주)대창기업 2
 
3.4%
㈜홈플러스 2
 
3.4%
㈜롯데쇼핑 2
 
3.4%
㈜이마트 2
 
3.4%
주)대림산업 2
 
3.4%
주)광통부도(상인회 1
 
1.7%
주)연신유통 1
 
1.7%
주)고려상사 1
 
1.7%
Other values (28) 28
48.3%
2023-12-12T13:34:30.897023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 23
 
6.7%
주 23
 
6.7%
) 23
 
6.7%
상 19
 
5.6%
회 19
 
5.6%
인 19
 
5.6%
이 12
 
3.5%
㈜ 11
 
3.2%
대 7
 
2.1%
7
 
2.1%
Other values (102) 178
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 271
79.5%
Open Punctuation 23
 
6.7%
Close Punctuation 23
 
6.7%
Other Symbol 11
 
3.2%
Space Separator 7
 
2.1%
Decimal Number 3
 
0.9%
Uppercase Letter 2
 
0.6%
Other Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 23
 
8.5%
상 19
 
7.0%
회 19
 
7.0%
인 19
 
7.0%
이 12
 
4.4%
대 7
 
2.6%
시 6
 
2.2%
스 6
 
2.2%
업 6
 
2.2%
개 5
 
1.8%
Other values (93) 149
55.0%
Decimal Number
ValueCountFrequency (%)
3 2
66.7%
1 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
R 1
50.0%
D 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 11
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 282
82.7%
Common 57
 
16.7%
Latin 2
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 23
 
8.2%
상 19
 
6.7%
회 19
 
6.7%
인 19
 
6.7%
이 12
 
4.3%
㈜ 11
 
3.9%
대 7
 
2.5%
시 6
 
2.1%
스 6
 
2.1%
업 6
 
2.1%
Other values (94) 154
54.6%
Common
ValueCountFrequency (%)
( 23
40.4%
) 23
40.4%
7
 
12.3%
3 2
 
3.5%
& 1
 
1.8%
1 1
 
1.8%
Latin
ValueCountFrequency (%)
R 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 271
79.5%
ASCII 59
 
17.3%
None 11
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 23
39.0%
) 23
39.0%
7
 
11.9%
3 2
 
3.4%
R 1
 
1.7%
& 1
 
1.7%
D 1
 
1.7%
1 1
 
1.7%
Hangul
ValueCountFrequency (%)
주 23
 
8.5%
상 19
 
7.0%
회 19
 
7.0%
인 19
 
7.0%
이 12
 
4.4%
대 7
 
2.6%
시 6
 
2.2%
스 6
 
2.2%
업 6
 
2.2%
개 5
 
1.8%
Other values (93) 149
55.0%
None
ValueCountFrequency (%)
㈜ 11
100.0%

규모_지상_지하
Text

MISSING 

Distinct26
Distinct (%)61.9%
Missing9
Missing (%)17.6%
Memory size540.0 B
2023-12-12T13:34:31.120353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.8809524
Min length4

Characters and Unicode

Total characters205
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)42.9%

Sample

1st row4층/1층
2nd row0층/4층
3rd row4층/4층
4th row7층/6층
5th row0/1층
ValueCountFrequency (%)
2층/1층 8
19.0%
2층/0 3
 
7.1%
5층/1층 3
 
7.1%
3층/2층 2
 
4.8%
6층/1층 2
 
4.8%
0층/1층 2
 
4.8%
골목시장 2
 
4.8%
2층/0층 2
 
4.8%
5층/4층 1
 
2.4%
8층/7층 1
 
2.4%
Other values (16) 16
38.1%
2023-12-12T13:34:31.677724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
층 76
37.1%
/ 40
19.5%
1 23
 
11.2%
2 19
 
9.3%
0 12
 
5.9%
3 6
 
2.9%
4 6
 
2.9%
5 4
 
2.0%
6 4
 
2.0%
7 4
 
2.0%
Other values (5) 11
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
41.0%
Decimal Number 81
39.5%
Other Punctuation 40
19.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 23
28.4%
2 19
23.5%
0 12
14.8%
3 6
 
7.4%
4 6
 
7.4%
5 4
 
4.9%
6 4
 
4.9%
7 4
 
4.9%
8 3
 
3.7%
Other Letter
ValueCountFrequency (%)
층 76
90.5%
골 2
 
2.4%
목 2
 
2.4%
시 2
 
2.4%
장 2
 
2.4%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
59.0%
Hangul 84
41.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 40
33.1%
1 23
19.0%
2 19
15.7%
0 12
 
9.9%
3 6
 
5.0%
4 6
 
5.0%
5 4
 
3.3%
6 4
 
3.3%
7 4
 
3.3%
8 3
 
2.5%
Hangul
ValueCountFrequency (%)
층 76
90.5%
골 2
 
2.4%
목 2
 
2.4%
시 2
 
2.4%
장 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
59.0%
Hangul 84
41.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
층 76
90.5%
골 2
 
2.4%
목 2
 
2.4%
시 2
 
2.4%
장 2
 
2.4%
ASCII
ValueCountFrequency (%)
/ 40
33.1%
1 23
19.0%
2 19
15.7%
0 12
 
9.9%
3 6
 
5.0%
4 6
 
5.0%
5 4
 
3.3%
6 4
 
3.3%
7 4
 
3.3%
8 3
 
2.5%

규모_연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49186.129
Minimum2143.7
Maximum256416.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T13:34:31.875813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2143.7
5-th percentile2986.505
Q17783.405
median12667.12
Q366069.64
95-th percentile205754.26
Maximum256416.98
Range254273.28
Interquartile range (IQR)58286.235

Descriptive statistics

Standard deviation70681.936
Coefficient of variation (CV)1.4370299
Kurtosis1.93678
Mean49186.129
Median Absolute Deviation (MAD)7972.85
Skewness1.7631579
Sum2508492.6
Variance4.995936 × 109
MonotonicityNot monotonic
2023-12-12T13:34:32.113254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
205754.26 4
 
7.8%
37609.0 1
 
2.0%
10775.62 1
 
2.0%
8768.92 1
 
2.0%
9551.77 1
 
2.0%
9225.05 1
 
2.0%
20556.91 1
 
2.0%
3083.77 1
 
2.0%
21490.86 1
 
2.0%
6478.32 1
 
2.0%
Other values (38) 38
74.5%
ValueCountFrequency (%)
2143.7 1
2.0%
2467.31 1
2.0%
2889.24 1
2.0%
3083.77 1
2.0%
3290.66 1
2.0%
3433.9 1
2.0%
4727.0 1
2.0%
5696.88 1
2.0%
5699.01 1
2.0%
5854.0 1
2.0%
ValueCountFrequency (%)
256416.98 1
 
2.0%
237090.0 1
 
2.0%
205754.26 4
7.8%
132124.39 1
 
2.0%
119383.61 1
 
2.0%
105578.79 1
 
2.0%
91167.98 1
 
2.0%
79633.93 1
 
2.0%
76939.29 1
 
2.0%
75066.55 1
 
2.0%

규모_매장면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9081.6871
Minimum1567.84
Maximum59887.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T13:34:32.341049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1567.84
5-th percentile2312.66
Q13850.435
median5540.28
Q310220.88
95-th percentile23959.765
Maximum59887.88
Range58320.04
Interquartile range (IQR)6370.445

Descriptive statistics

Standard deviation9955.4653
Coefficient of variation (CV)1.0962132
Kurtosis13.790686
Mean9081.6871
Median Absolute Deviation (MAD)2249.62
Skewness3.3229196
Sum463166.04
Variance99111289
MonotonicityNot monotonic
2023-12-12T13:34:32.545009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7772.51 1
 
2.0%
8854.0 1
 
2.0%
5207.5 1
 
2.0%
5279.02 1
 
2.0%
4017.97 1
 
2.0%
4128.21 1
 
2.0%
4337.89 1
 
2.0%
5955.0 1
 
2.0%
3083.77 1
 
2.0%
2871.78 1
 
2.0%
Other values (41) 41
80.4%
ValueCountFrequency (%)
1567.84 1
2.0%
2077.05 1
2.0%
2287.0 1
2.0%
2338.32 1
2.0%
2871.78 1
2.0%
2889.24 1
2.0%
3012.91 1
2.0%
3083.77 1
2.0%
3290.66 1
2.0%
3433.9 1
2.0%
ValueCountFrequency (%)
59887.88 1
2.0%
36532.47 1
2.0%
25571.68 1
2.0%
22347.85 1
2.0%
20698.77 1
2.0%
19968.31 1
2.0%
14172.39 1
2.0%
13912.0 1
2.0%
13715.2 1
2.0%
13385.2 1
2.0%

규모_점포수
Real number (ℝ)

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.33333
Minimum1
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T13:34:32.741358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.5
Q156
median85
Q3148
95-th percentile553
Maximum809
Range808
Interquartile range (IQR)92

Descriptive statistics

Standard deviation184.37252
Coefficient of variation (CV)1.1946384
Kurtosis4.1303132
Mean154.33333
Median Absolute Deviation (MAD)33
Skewness2.1321402
Sum7871
Variance33993.227
MonotonicityNot monotonic
2023-12-12T13:34:33.217213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
85 3
 
5.9%
84 2
 
3.9%
52 2
 
3.9%
63 2
 
3.9%
7 1
 
2.0%
91 1
 
2.0%
88 1
 
2.0%
40 1
 
2.0%
92 1
 
2.0%
140 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
1 1
2.0%
6 1
2.0%
7 1
2.0%
10 1
2.0%
14 1
2.0%
15 1
2.0%
19 1
2.0%
40 1
2.0%
45 1
2.0%
50 1
2.0%
ValueCountFrequency (%)
809 1
2.0%
732 1
2.0%
583 1
2.0%
523 1
2.0%
518 1
2.0%
376 1
2.0%
365 1
2.0%
328 1
2.0%
293 1
2.0%
245 1
2.0%

등록일시
Date

Distinct47
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Memory size540.0 B
Minimum1974-10-18 00:00:00
Maximum2022-04-19 00:00:00
2023-12-12T13:34:33.365779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:33.510217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

전화번호
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing5
Missing (%)9.8%
Memory size540.0 B
2023-12-12T13:34:33.790823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.065217
Min length12

Characters and Unicode

Total characters555
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

Unique46 ?
Unique (%)100.0%

Sample

1st row031-710-1234
2nd row031-884-2080
3rd row031-702-2080
4th row031-786-2001
5th row031-701-2500
ValueCountFrequency (%)
031-5170-2233 1
 
2.2%
031-755-9222 1
 
2.2%
031-707-7315 1
 
2.2%
031-704-8151 1
 
2.2%
031-755-0670 1
 
2.2%
031-701-4309 1
 
2.2%
031-717-3044 1
 
2.2%
031-716-9814 1
 
2.2%
031-713-1071 1
 
2.2%
031-786-0704 1
 
2.2%
Other values (36) 36
78.3%
2023-12-12T13:34:34.289977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 103
18.6%
- 92
16.6%
1 87
15.7%
3 63
11.4%
7 59
10.6%
2 34
 
6.1%
5 31
 
5.6%
8 31
 
5.6%
4 23
 
4.1%
9 17
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 463
83.4%
Dash Punctuation 92
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103
22.2%
1 87
18.8%
3 63
13.6%
7 59
12.7%
2 34
 
7.3%
5 31
 
6.7%
8 31
 
6.7%
4 23
 
5.0%
9 17
 
3.7%
6 15
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103
18.6%
- 92
16.6%
1 87
15.7%
3 63
11.4%
7 59
10.6%
2 34
 
6.1%
5 31
 
5.6%
8 31
 
5.6%
4 23
 
4.1%
9 17
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103
18.6%
- 92
16.6%
1 87
15.7%
3 63
11.4%
7 59
10.6%
2 34
 
6.1%
5 31
 
5.6%
8 31
 
5.6%
4 23
 
4.1%
9 17
 
3.1%

Interactions

2023-12-12T13:34:27.274886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:26.536083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:26.863950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:27.403120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:26.632869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:26.980104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:27.513747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:26.733464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:34:27.128685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:34:34.440282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분상호소재지지번주소소재지도로명주소법인명규모_지상_지하규모_연면적(제곱미터)규모_매장면적(제곱미터)규모_점포수등록일시전화번호
구분1.0001.0000.8760.7950.8970.8800.7580.8350.4341.0001.000
상호1.0001.0000.9860.9870.9191.0001.0001.0000.9870.9871.000
소재지지번주소0.8760.9861.0001.0000.8060.0000.9000.9230.0000.8711.000
소재지도로명주소0.7950.9871.0001.0000.0000.0000.8410.9250.0000.9371.000
법인명0.8970.9190.8060.0001.0000.8620.9630.9620.8440.9931.000
규모_지상_지하0.8801.0000.0000.0000.8621.0000.9040.9530.6770.9771.000
규모_연면적(제곱미터)0.7581.0000.9000.8410.9630.9041.0000.7770.7031.0001.000
규모_매장면적(제곱미터)0.8351.0000.9230.9250.9620.9530.7771.0000.6300.9901.000
규모_점포수0.4340.9870.0000.0000.8440.6770.7030.6301.0000.7221.000
등록일시1.0000.9870.8710.9370.9930.9771.0000.9900.7221.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T13:34:34.617445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
규모_연면적(제곱미터)규모_매장면적(제곱미터)규모_점포수구분
규모_연면적(제곱미터)1.0000.7580.3230.532
규모_매장면적(제곱미터)0.7581.0000.4060.439
규모_점포수0.3230.4061.0000.241
구분0.5320.4390.2411.000

Missing values

2023-12-12T13:34:27.720976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:34:27.891660image/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.
2023-12-12T13:34:28.007890image/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

구분상호소재지지번주소소재지도로명주소법인명규모_지상_지하규모_연면적(제곱미터)규모_매장면적(제곱미터)규모_점포수등록일시전화번호
0대형마트이마트 분당점경기도 성남시 분당구 정자동 211경기도 성남시 분당구 불정로 134 (정자동)㈜이마트4층/1층37609.07772.5171996-11-02031-710-1234
1대형마트홈플러스(주) 분당오리점경기도 성남시 분당구 구미동 159경기도 성남시 분당구 탄천상로151번길 20 (구미동)㈜홈플러스0층/4층14882.288854.0192009-03-12031-884-2080
2대형마트홈플러스(주) 야탑점경기도 성남시 분당구 야탑동 341경기도 성남시 분당구 성남대로925번길 16 (야탑동)㈜홈플러스4층/4층205754.2610370.012000-04-01031-702-2080
3대형마트이천일아울렛 분당점경기도 성남시 분당구 구미동 11-1경기도 성남시 분당구 미금일로154번길 20 (구미동)(주)이랜드리테일7층/6층57072.7320698.771962002-04-20031-786-2001
4대형마트롯데마트 판교점경기도 성남시 분당구 삼평동 741경기도 성남시 분당구 대왕판교로606번길 58 (삼평동)㈜롯데쇼핑0/1층75066.556214.8862013-01-15031-701-2500
5쇼핑센터이마트 성남점경기도 성남시 수정구 태평동7336경기도 성남시 수정구 수정로 201 (태평동)㈜이마트1층/2층91167.9814172.39142010-08-31031-8023-1234
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