Overview

Dataset statistics

Number of variables39
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory320.4 B

Variable types

DateTime4
Categorical15
Text17
Numeric3

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://www.bigdata-region.kr/#/dataset/46215a77-0fea-489d-8b3c-63b290619a15

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
기타업종명 has constant value ""Constant
기타제품명 has constant value ""Constant
업체형태명 is highly imbalanced (78.9%)Imbalance
기업규모명 is highly imbalanced (78.9%)Imbalance
사업자등록번호 has unique valuesUnique
대표자주소 has unique valuesUnique
대표자거주지우편번호주소 has unique valuesUnique
본사주소 has unique valuesUnique
주사업장우편번호 has unique valuesUnique
주사업장우편번호주소 has unique valuesUnique
기업번호 has unique valuesUnique
종업원수 has 12 (40.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:59:52.982058
Analysis finished2023-12-10 13:59:54.395890
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-04-01 00:00:00
Maximum2023-04-01 00:00:00
2023-12-10T22:59:54.895278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:55.046737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
18 
F
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 18
60.0%
F 12
40.0%

Length

2023-12-10T22:59:55.284601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:55.436245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 18
60.0%
f 12
40.0%

연령대코드
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
60
16 
50
70
80
40
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row50
2nd row50
3rd row70
4th row50
5th row50

Common Values

ValueCountFrequency (%)
60 16
53.3%
50 8
26.7%
70 3
 
10.0%
80 2
 
6.7%
40 1
 
3.3%

Length

2023-12-10T22:59:55.614246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:55.846074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 16
53.3%
50 8
26.7%
70 3
 
10.0%
80 2
 
6.7%
40 1
 
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:59:56.159164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters300
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

Unique30 ?
Unique (%)100.0%

Sample

1st row12913*****
2nd row12729*****
3rd row13026*****
4th row13303*****
5th row12411*****
ValueCountFrequency (%)
12913 1
 
3.3%
12729 1
 
3.3%
13708 1
 
3.3%
12430 1
 
3.3%
13425 1
 
3.3%
31302 1
 
3.3%
12515 1
 
3.3%
12509 1
 
3.3%
12608 1
 
3.3%
30704 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:59:56.691979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 150
50.0%
1 37
 
12.3%
2 29
 
9.7%
3 21
 
7.0%
0 21
 
7.0%
4 11
 
3.7%
5 10
 
3.3%
9 9
 
3.0%
7 6
 
2.0%
8 4
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 150
50.0%
Decimal Number 150
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
24.7%
2 29
19.3%
3 21
14.0%
0 21
14.0%
4 11
 
7.3%
5 10
 
6.7%
9 9
 
6.0%
7 6
 
4.0%
8 4
 
2.7%
6 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
* 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 150
50.0%
1 37
 
12.3%
2 29
 
9.7%
3 21
 
7.0%
0 21
 
7.0%
4 11
 
3.7%
5 10
 
3.3%
9 9
 
3.0%
7 6
 
2.0%
8 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 150
50.0%
1 37
 
12.3%
2 29
 
9.7%
3 21
 
7.0%
0 21
 
7.0%
4 11
 
3.7%
5 10
 
3.3%
9 9
 
3.0%
7 6
 
2.0%
8 4
 
1.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:59:56.892452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:57.011674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%
Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:59:57.252363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.9333333
Min length3

Characters and Unicode

Total characters118
Distinct characters38
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row광주시
2nd row의정부시
3rd row포천시
4th row광명시
5th row화성시
ValueCountFrequency (%)
평택시 3
 
8.3%
안성시 3
 
8.3%
김포시 3
 
8.3%
화성시 2
 
5.6%
시흥시 2
 
5.6%
양평군 2
 
5.6%
남양주시 2
 
5.6%
하남시 1
 
2.8%
수원시 1
 
2.8%
광주시 1
 
2.8%
Other values (16) 16
44.4%
2023-12-10T22:59:57.778471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
25.4%
7
 
5.9%
6
 
5.1%
6
 
5.1%
6
 
5.1%
6
 
5.1%
5
 
4.2%
4
 
3.4%
4
 
3.4%
3
 
2.5%
Other values (28) 41
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
94.9%
Space Separator 6
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
26.8%
7
 
6.2%
6
 
5.4%
6
 
5.4%
6
 
5.4%
5
 
4.5%
4
 
3.6%
4
 
3.6%
3
 
2.7%
3
 
2.7%
Other values (27) 38
33.9%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
94.9%
Common 6
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
26.8%
7
 
6.2%
6
 
5.4%
6
 
5.4%
6
 
5.4%
5
 
4.5%
4
 
3.6%
4
 
3.6%
3
 
2.7%
3
 
2.7%
Other values (27) 38
33.9%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
94.9%
ASCII 6
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
30
26.8%
7
 
6.2%
6
 
5.4%
6
 
5.4%
6
 
5.4%
5
 
4.5%
4
 
3.6%
4
 
3.6%
3
 
2.7%
3
 
2.7%
Other values (27) 38
33.9%
ASCII
ValueCountFrequency (%)
6
100.0%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:59:58.121094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters87
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row목동
2nd row신곡동
3rd row가산면
4th row철산동
5th row팔탄면
ValueCountFrequency (%)
지산동 2
 
6.7%
목동 1
 
3.3%
사노동 1
 
3.3%
고촌읍 1
 
3.3%
세류동 1
 
3.3%
이동 1
 
3.3%
진건읍 1
 
3.3%
구포동 1
 
3.3%
봉남동 1
 
3.3%
감북동 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T22:59:58.777018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
24.1%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
2
 
2.3%
2
 
2.3%
Other values (36) 38
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
21
24.1%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
2
 
2.3%
2
 
2.3%
Other values (36) 38
43.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
21
24.1%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
2
 
2.3%
2
 
2.3%
Other values (36) 38
43.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
21
24.1%
5
 
5.7%
5
 
5.7%
4
 
4.6%
3
 
3.4%
3
 
3.4%
2
 
2.3%
2
 
2.3%
2
 
2.3%
2
 
2.3%
Other values (36) 38
43.7%

업체형태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
개인기업
29 
주식회사
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
개인기업 29
96.7%
주식회사 1
 
3.3%

Length

2023-12-10T22:59:59.040051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:59.278191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인기업 29
96.7%
주식회사 1
 
3.3%
Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:59:59.532356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.5
Min length3

Characters and Unicode

Total characters105
Distinct characters31
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

Unique16 ?
Unique (%)53.3%

Sample

1st row광주시
2nd row의정부
3rd row포천시(의정부)
4th row광명시
5th row화성시(화성)
ValueCountFrequency (%)
평택시 3
 
10.0%
안성시 3
 
10.0%
양평군 2
 
6.7%
남양주시 2
 
6.7%
시흥시 2
 
6.7%
김포시 2
 
6.7%
화성시 1
 
3.3%
수원시 1
 
3.3%
안산시 1
 
3.3%
하남시 1
 
3.3%
Other values (12) 12
40.0%
2023-12-10T23:00:00.059243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
27.6%
7
 
6.7%
6
 
5.7%
5
 
4.8%
5
 
4.8%
4
 
3.8%
3
 
2.9%
3
 
2.9%
3
 
2.9%
3
 
2.9%
Other values (21) 37
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
94.3%
Close Punctuation 3
 
2.9%
Open Punctuation 3
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
29.3%
7
 
7.1%
6
 
6.1%
5
 
5.1%
5
 
5.1%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (19) 31
31.3%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
94.3%
Common 6
 
5.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
29.3%
7
 
7.1%
6
 
6.1%
5
 
5.1%
5
 
5.1%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (19) 31
31.3%
Common
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
94.3%
ASCII 6
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
29.3%
7
 
7.1%
6
 
6.1%
5
 
5.1%
5
 
5.1%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (19) 31
31.3%
ASCII
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2000-10-09 00:00:00
Maximum2023-06-13 00:00:00
2023-12-10T23:00:00.397120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.749890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum1986-04-01 00:00:00
Maximum2022-08-01 00:00:00
2023-12-10T23:00:00.958425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.305554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:01.613448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters150
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

Unique26 ?
Unique (%)86.7%

Sample

1st row12784
2nd row*****
3rd row01087
4th row18409
5th row18290
ValueCountFrequency (%)
17729 2
 
6.7%
16671 2
 
6.7%
12784 1
 
3.3%
02118 1
 
3.3%
10093 1
 
3.3%
15487 1
 
3.3%
12241 1
 
3.3%
17569 1
 
3.3%
17589 1
 
3.3%
05507 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:00:02.126495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 35
23.3%
0 24
16.0%
7 16
10.7%
2 15
10.0%
5 13
 
8.7%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 6
 
4.0%
3 6
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145
96.7%
Other Punctuation 5
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
24.1%
0 24
16.6%
7 16
11.0%
2 15
10.3%
5 13
 
9.0%
9 10
 
6.9%
6 10
 
6.9%
8 10
 
6.9%
4 6
 
4.1%
3 6
 
4.1%
Other Punctuation
ValueCountFrequency (%)
* 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
23.3%
0 24
16.0%
7 16
10.7%
2 15
10.0%
5 13
 
8.7%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 6
 
4.0%
3 6
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
23.3%
0 24
16.0%
7 16
10.7%
2 15
10.0%
5 13
 
8.7%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 6
 
4.0%
3 6
 
4.0%

대표자주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:02.604528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.6
Min length10

Characters and Unicode

Total characters408
Distinct characters82
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

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 태봉로
2nd row****************
3rd row서울특별시 강북구 삼양로
4th row경기도 화성시 병점
5th row경기도 화성시 매송고색로
ValueCountFrequency (%)
경기도 23
22.8%
서울특별시 4
 
4.0%
안성시 3
 
3.0%
화성시 3
 
3.0%
수원시 3
 
3.0%
남양주시 2
 
2.0%
경기 2
 
2.0%
권선구 2
 
2.0%
양평군 2
 
2.0%
평택시 2
 
2.0%
Other values (52) 55
54.5%
2023-12-10T23:00:03.320451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
17.4%
28
 
6.9%
26
 
6.4%
25
 
6.1%
23
 
5.6%
23
 
5.6%
* 16
 
3.9%
13
 
3.2%
9
 
2.2%
8
 
2.0%
Other values (72) 166
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 321
78.7%
Space Separator 71
 
17.4%
Other Punctuation 16
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
8
 
2.5%
7
 
2.2%
Other values (70) 151
47.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
* 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 321
78.7%
Common 87
 
21.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
8
 
2.5%
7
 
2.2%
Other values (70) 151
47.0%
Common
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 321
78.7%
ASCII 87
 
21.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%
Hangul
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
8
 
2.5%
7
 
2.2%
Other values (70) 151
47.0%
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:03.734048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters150
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

Unique26 ?
Unique (%)86.7%

Sample

1st row12784
2nd row*****
3rd row01087
4th row18409
5th row18290
ValueCountFrequency (%)
17729 2
 
6.7%
16671 2
 
6.7%
12784 1
 
3.3%
02118 1
 
3.3%
10093 1
 
3.3%
15487 1
 
3.3%
12241 1
 
3.3%
17569 1
 
3.3%
17589 1
 
3.3%
05507 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:00:04.238794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 35
23.3%
0 25
16.7%
7 16
10.7%
2 14
 
9.3%
5 12
 
8.0%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 7
 
4.7%
3 6
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145
96.7%
Other Punctuation 5
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
24.1%
0 25
17.2%
7 16
11.0%
2 14
 
9.7%
5 12
 
8.3%
9 10
 
6.9%
6 10
 
6.9%
8 10
 
6.9%
4 7
 
4.8%
3 6
 
4.1%
Other Punctuation
ValueCountFrequency (%)
* 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
23.3%
0 25
16.7%
7 16
10.7%
2 14
 
9.3%
5 12
 
8.0%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 7
 
4.7%
3 6
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
23.3%
0 25
16.7%
7 16
10.7%
2 14
 
9.3%
5 12
 
8.0%
9 10
 
6.7%
6 10
 
6.7%
8 10
 
6.7%
4 7
 
4.7%
3 6
 
4.0%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:04.619355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.566667
Min length10

Characters and Unicode

Total characters407
Distinct characters82
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

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 태봉로
2nd row****************
3rd row서울특별시 강북구 삼양로
4th row경기도 화성시 병점
5th row경기도 화성시 매송고색로
ValueCountFrequency (%)
경기도 23
22.8%
서울특별시 4
 
4.0%
안성시 3
 
3.0%
수원시 3
 
3.0%
상록구 2
 
2.0%
경기 2
 
2.0%
화성시 2
 
2.0%
남양주시 2
 
2.0%
김포시 2
 
2.0%
권선구 2
 
2.0%
Other values (53) 56
55.4%
2023-12-10T23:00:05.351354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
17.4%
28
 
6.9%
26
 
6.4%
25
 
6.1%
23
 
5.7%
23
 
5.7%
* 16
 
3.9%
13
 
3.2%
9
 
2.2%
8
 
2.0%
Other values (72) 165
40.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
78.6%
Space Separator 71
 
17.4%
Other Punctuation 16
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
28
 
8.8%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.1%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (70) 151
47.2%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
* 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
78.6%
Common 87
 
21.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
28
 
8.8%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.1%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (70) 151
47.2%
Common
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
78.6%
ASCII 87
 
21.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%
Hangul
ValueCountFrequency (%)
28
 
8.8%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.1%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (70) 151
47.2%
Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
임차
14 
자가
13 
기타
가족소유
 
1

Length

Max length4
Median length2
Mean length2.0666667
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row자가
2nd row임차
3rd row임차
4th row자가
5th row임차

Common Values

ValueCountFrequency (%)
임차 14
46.7%
자가 13
43.3%
기타 2
 
6.7%
가족소유 1
 
3.3%

Length

2023-12-10T23:00:05.618046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:05.812526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임차 14
46.7%
자가 13
43.3%
기타 2
 
6.7%
가족소유 1
 
3.3%
Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
18 
본인(대표이사 및 법인포함)
배우자

Length

Max length15
Median length2
Mean length6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row기타
3rd row기타
4th row본인(대표이사 및 법인포함)
5th row기타

Common Values

ValueCountFrequency (%)
기타 18
60.0%
본인(대표이사 및 법인포함) 9
30.0%
배우자 3
 
10.0%

Length

2023-12-10T23:00:05.985162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:06.145599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 18
37.5%
본인(대표이사 9
18.8%
9
18.8%
법인포함 9
18.8%
배우자 3
 
6.2%
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:06.397380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters150
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

Unique26 ?
Unique (%)86.7%

Sample

1st row12784
2nd row*****
3rd row01087
4th row18409
5th row18290
ValueCountFrequency (%)
17729 2
 
6.7%
16671 2
 
6.7%
12784 1
 
3.3%
02118 1
 
3.3%
10093 1
 
3.3%
15487 1
 
3.3%
12241 1
 
3.3%
17569 1
 
3.3%
17589 1
 
3.3%
05507 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:00:06.878891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 35
23.3%
0 26
17.3%
7 16
10.7%
2 15
10.0%
5 12
 
8.0%
9 10
 
6.7%
8 10
 
6.7%
6 9
 
6.0%
4 7
 
4.7%
3 5
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145
96.7%
Other Punctuation 5
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35
24.1%
0 26
17.9%
7 16
11.0%
2 15
10.3%
5 12
 
8.3%
9 10
 
6.9%
8 10
 
6.9%
6 9
 
6.2%
4 7
 
4.8%
3 5
 
3.4%
Other Punctuation
ValueCountFrequency (%)
* 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35
23.3%
0 26
17.3%
7 16
10.7%
2 15
10.0%
5 12
 
8.0%
9 10
 
6.7%
8 10
 
6.7%
6 9
 
6.0%
4 7
 
4.7%
3 5
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35
23.3%
0 26
17.3%
7 16
10.7%
2 15
10.0%
5 12
 
8.0%
9 10
 
6.7%
8 10
 
6.7%
6 9
 
6.0%
4 7
 
4.7%
3 5
 
3.3%

본사주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:07.234167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length13.6
Min length10

Characters and Unicode

Total characters408
Distinct characters83
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

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 태봉로
2nd row****************
3rd row서울특별시 강북구 삼양로
4th row경기도 화성시 병점
5th row경기도 화성시 매송고색로
ValueCountFrequency (%)
경기도 23
22.8%
서울특별시 4
 
4.0%
안성시 3
 
3.0%
수원시 3
 
3.0%
상록구 2
 
2.0%
경기 2
 
2.0%
화성시 2
 
2.0%
남양주시 2
 
2.0%
김포시 2
 
2.0%
권선구 2
 
2.0%
Other values (53) 56
55.4%
2023-12-10T23:00:07.840095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
17.4%
28
 
6.9%
26
 
6.4%
25
 
6.1%
23
 
5.6%
23
 
5.6%
* 16
 
3.9%
13
 
3.2%
9
 
2.2%
8
 
2.0%
Other values (73) 166
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 321
78.7%
Space Separator 71
 
17.4%
Other Punctuation 16
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (71) 152
47.4%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
* 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 321
78.7%
Common 87
 
21.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (71) 152
47.4%
Common
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 321
78.7%
ASCII 87
 
21.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
81.6%
* 16
 
18.4%
Hangul
ValueCountFrequency (%)
28
 
8.7%
26
 
8.1%
25
 
7.8%
23
 
7.2%
23
 
7.2%
13
 
4.0%
9
 
2.8%
8
 
2.5%
7
 
2.2%
7
 
2.2%
Other values (71) 152
47.4%
Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
임차
14 
자가
13 
기타
가족소유
 
1

Length

Max length4
Median length2
Mean length2.0666667
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row자가
2nd row임차
3rd row임차
4th row자가
5th row임차

Common Values

ValueCountFrequency (%)
임차 14
46.7%
자가 13
43.3%
기타 2
 
6.7%
가족소유 1
 
3.3%

Length

2023-12-10T23:00:08.142264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:08.328313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임차 14
46.7%
자가 13
43.3%
기타 2
 
6.7%
가족소유 1
 
3.3%
Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
16 
본인(대표이사 및 법인포함)
12 
배우자

Length

Max length15
Median length2
Mean length7.2666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row본인(대표이사 및 법인포함)
2nd row기타
3rd row기타
4th row본인(대표이사 및 법인포함)
5th row기타

Common Values

ValueCountFrequency (%)
기타 16
53.3%
본인(대표이사 및 법인포함) 12
40.0%
배우자 2
 
6.7%

Length

2023-12-10T23:00:08.531868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:08.702358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 16
29.6%
본인(대표이사 12
22.2%
12
22.2%
법인포함 12
22.2%
배우자 2
 
3.7%

주사업장우편번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29034.1
Minimum10029
Maximum456030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:08.892094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10029
5-th percentile10061.65
Q112238
median14110
Q317588.5
95-th percentile18442.85
Maximum456030
Range446001
Interquartile range (IQR)5350.5

Descriptive statistics

Standard deviation80696.722
Coefficient of variation (CV)2.7793774
Kurtosis29.917641
Mean29034.1
Median Absolute Deviation (MAD)2649.5
Skewness5.4663116
Sum871023
Variance6.5119609 × 109
MonotonicityNot monotonic
2023-12-10T23:00:09.203492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12770 1
 
3.3%
16859 1
 
3.3%
10029 1
 
3.3%
10093 1
 
3.3%
16660 1
 
3.3%
15487 1
 
3.3%
12241 1
 
3.3%
17587 1
 
3.3%
17589 1
 
3.3%
12988 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10029 1
3.3%
10036 1
3.3%
10093 1
3.3%
10364 1
3.3%
11164 1
3.3%
11775 1
3.3%
11905 1
3.3%
12237 1
3.3%
12241 1
3.3%
12522 1
3.3%
ValueCountFrequency (%)
456030 1
3.3%
18527 1
3.3%
18340 1
3.3%
18139 1
3.3%
18017 1
3.3%
17757 1
3.3%
17756 1
3.3%
17589 1
3.3%
17587 1
3.3%
16859 1
3.3%
Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:09.509330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length12.566667
Min length10

Characters and Unicode

Total characters377
Distinct characters79
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 광주시 광남안로
2nd row경기도 의정부시 장곡로
3rd row경기도 포천시 가산면 가산로
4th row경기 광명시 철산동
5th row경기도 화성시 푸른들판로
ValueCountFrequency (%)
경기도 25
24.8%
경기 5
 
5.0%
평택시 3
 
3.0%
김포시 3
 
3.0%
안성시 3
 
3.0%
화성시 2
 
2.0%
양평군 2
 
2.0%
통진읍 2
 
2.0%
시흥시 2
 
2.0%
남양주시 2
 
2.0%
Other values (52) 52
51.5%
2023-12-10T23:00:10.008459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
18.8%
30
 
8.0%
30
 
8.0%
30
 
8.0%
26
 
6.9%
21
 
5.6%
11
 
2.9%
9
 
2.4%
8
 
2.1%
7
 
1.9%
Other values (69) 134
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
81.2%
Space Separator 71
 
18.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
 
9.8%
30
 
9.8%
30
 
9.8%
26
 
8.5%
21
 
6.9%
11
 
3.6%
9
 
2.9%
8
 
2.6%
7
 
2.3%
6
 
2.0%
Other values (68) 128
41.8%
Space Separator
ValueCountFrequency (%)
71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
81.2%
Common 71
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
 
9.8%
30
 
9.8%
30
 
9.8%
26
 
8.5%
21
 
6.9%
11
 
3.6%
9
 
2.9%
8
 
2.6%
7
 
2.3%
6
 
2.0%
Other values (68) 128
41.8%
Common
ValueCountFrequency (%)
71
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
81.2%
ASCII 71
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
100.0%
Hangul
ValueCountFrequency (%)
30
 
9.8%
30
 
9.8%
30
 
9.8%
26
 
8.5%
21
 
6.9%
11
 
3.6%
9
 
2.9%
8
 
2.6%
7
 
2.3%
6
 
2.0%
Other values (68) 128
41.8%
Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
24 
본인(대표이사 및 법인포함)

Length

Max length15
Median length2
Mean length4.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row본인(대표이사 및 법인포함)
2nd row기타
3rd row기타
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
기타 24
80.0%
본인(대표이사 및 법인포함) 6
 
20.0%

Length

2023-12-10T23:00:10.200812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:10.384090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 24
57.1%
본인(대표이사 6
 
14.3%
6
 
14.3%
법인포함 6
 
14.3%
Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
임차
24 
자가

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자가
2nd row임차
3rd row임차
4th row임차
5th row임차

Common Values

ValueCountFrequency (%)
임차 24
80.0%
자가 6
 
20.0%

Length

2023-12-10T23:00:10.525173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:10.681069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임차 24
80.0%
자가 6
 
20.0%

종업원수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5333333
Minimum0
Maximum16
Zeros12
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:10.847321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.55
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.956388
Coefficient of variation (CV)1.9280791
Kurtosis21.049862
Mean1.5333333
Median Absolute Deviation (MAD)1
Skewness4.3007545
Sum46
Variance8.7402299
MonotonicityNot monotonic
2023-12-10T23:00:11.049261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 12
40.0%
1 9
30.0%
2 4
 
13.3%
3 3
 
10.0%
4 1
 
3.3%
16 1
 
3.3%
ValueCountFrequency (%)
0 12
40.0%
1 9
30.0%
2 4
 
13.3%
3 3
 
10.0%
4 1
 
3.3%
16 1
 
3.3%
ValueCountFrequency (%)
16 1
 
3.3%
4 1
 
3.3%
3 3
 
10.0%
2 4
 
13.3%
1 9
30.0%
0 12
40.0%

기업규모명
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
소상공인
29 
소기업
 
1

Length

Max length4
Median length4
Mean length3.9666667
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
소상공인 29
96.7%
소기업 1
 
3.3%

Length

2023-12-10T23:00:11.264383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:11.436207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소상공인 29
96.7%
소기업 1
 
3.3%

기타업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 30
100.0%

Length

2023-12-10T23:00:11.619177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:11.788910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 30
100.0%

기타제품명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
30 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 30
100.0%

Length

2023-12-10T23:00:11.938635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:12.105275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 30
100.0%
Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
기타
22 
중소기업은행
 
2
국민은행
 
2
농협은행
 
1
새마을금고
 
1
Other values (2)
 
2

Length

Max length6
Median length2
Mean length2.7333333
Min length2

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row중소기업은행
2nd row농협은행
3rd row기타
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
기타 22
73.3%
중소기업은행 2
 
6.7%
국민은행 2
 
6.7%
농협은행 1
 
3.3%
새마을금고 1
 
3.3%
지역농축협 1
 
3.3%
신한은행 1
 
3.3%

Length

2023-12-10T23:00:12.373298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:12.730606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 22
73.3%
중소기업은행 2
 
6.7%
국민은행 2
 
6.7%
농협은행 1
 
3.3%
새마을금고 1
 
3.3%
지역농축협 1
 
3.3%
신한은행 1
 
3.3%
Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:13.395080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.4333333
Min length4

Characters and Unicode

Total characters133
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)50.0%

Sample

1st row채권관리센터
2nd row의정부지점
3rd row채권관리센터
4th row광명지점
5th row화성지점
ValueCountFrequency (%)
채권관리센터 4
 
13.3%
평택지점 4
 
13.3%
안성지점 3
 
10.0%
시흥지점 2
 
6.7%
남양주지점 2
 
6.7%
용인지점 1
 
3.3%
동탄지점 1
 
3.3%
기술평가센터 1
 
3.3%
수원지점 1
 
3.3%
안산지점 1
 
3.3%
Other values (10) 10
33.3%
2023-12-10T23:00:13.913814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
18.8%
25
18.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (28) 46
34.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
25
18.8%
25
18.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (28) 46
34.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
25
18.8%
25
18.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (28) 46
34.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
25
18.8%
25
18.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
5
 
3.8%
4
 
3.0%
4
 
3.0%
4
 
3.0%
Other values (28) 46
34.6%
Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2007-07-02 00:00:00
Maximum2023-05-06 00:00:00
2023-12-10T23:00:14.139858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:14.345746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:14.630420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters180
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st rowG46332
2nd rowM73201
3rd rowC28302
4th rowI56123
5th rowC22259
ValueCountFrequency (%)
i56111 6
20.0%
g47212 3
 
10.0%
i56194 1
 
3.3%
g46332 1
 
3.3%
c29294 1
 
3.3%
c24222 1
 
3.3%
g46692 1
 
3.3%
h49309 1
 
3.3%
i56193 1
 
3.3%
g46433 1
 
3.3%
Other values (13) 13
43.3%
2023-12-10T23:00:15.141016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36
20.0%
2 28
15.6%
6 17
9.4%
4 17
9.4%
5 15
8.3%
G 11
 
6.1%
9 11
 
6.1%
I 10
 
5.6%
3 10
 
5.6%
7 8
 
4.4%
Other values (6) 17
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
83.3%
Uppercase Letter 30
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
24.0%
2 28
18.7%
6 17
11.3%
4 17
11.3%
5 15
10.0%
9 11
 
7.3%
3 10
 
6.7%
7 8
 
5.3%
0 5
 
3.3%
8 3
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
G 11
36.7%
I 10
33.3%
C 6
20.0%
M 1
 
3.3%
P 1
 
3.3%
H 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 150
83.3%
Latin 30
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 36
24.0%
2 28
18.7%
6 17
11.3%
4 17
11.3%
5 15
10.0%
9 11
 
7.3%
3 10
 
6.7%
7 8
 
5.3%
0 5
 
3.3%
8 3
 
2.0%
Latin
ValueCountFrequency (%)
G 11
36.7%
I 10
33.3%
C 6
20.0%
M 1
 
3.3%
P 1
 
3.3%
H 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 36
20.0%
2 28
15.6%
6 17
9.4%
4 17
9.4%
5 15
8.3%
G 11
 
6.1%
9 11
 
6.1%
I 10
 
5.6%
3 10
 
5.6%
7 8
 
4.4%
Other values (6) 17
9.4%
Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
G도매및소매업(45~47)
11 
I숙박및음식점업(55~56)
10 
C제조업(10~33)
M전문;과학및기술서비스업(70~73)
 
1
P교육서비스업(85)
 
1

Length

Max length20
Median length15
Mean length13.733333
Min length11

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st rowG도매및소매업(45~47)
2nd rowM전문;과학및기술서비스업(70~73)
3rd rowC제조업(10~33)
4th rowI숙박및음식점업(55~56)
5th rowC제조업(10~33)

Common Values

ValueCountFrequency (%)
G도매및소매업(45~47) 11
36.7%
I숙박및음식점업(55~56) 10
33.3%
C제조업(10~33) 6
20.0%
M전문;과학및기술서비스업(70~73) 1
 
3.3%
P교육서비스업(85) 1
 
3.3%
H운수업(49~52) 1
 
3.3%

Length

2023-12-10T23:00:15.393077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:15.600892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g도매및소매업(45~47 11
36.7%
i숙박및음식점업(55~56 10
33.3%
c제조업(10~33 6
20.0%
m전문;과학및기술서비스업(70~73 1
 
3.3%
p교육서비스업(85 1
 
3.3%
h운수업(49~52 1
 
3.3%
Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:15.990816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length7.8333333
Min length4

Characters and Unicode

Total characters235
Distinct characters77
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)56.7%

Sample

1st row음·식료품및담배도매업
2nd row전문디자인업
3rd row절연선및케이블제조업
4th row음식점업
5th row플라스틱제품제조업
ValueCountFrequency (%)
음식점업 10
33.3%
음·식료품및담배소매업 3
 
10.0%
기타교육기관 1
 
3.3%
음·식료품및담배도매업 1
 
3.3%
특수목적용기계제조업 1
 
3.3%
1차비철금속제조업 1
 
3.3%
건축자재;철물및난방장치도매업 1
 
3.3%
도로화물운송업 1
 
3.3%
가정용품도매업 1
 
3.3%
섬유;의복;신발및가죽제품소매업 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T23:00:16.559990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
12.3%
14
 
6.0%
14
 
6.0%
11
 
4.7%
11
 
4.7%
10
 
4.3%
9
 
3.8%
9
 
3.8%
7
 
3.0%
6
 
2.6%
Other values (67) 115
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 227
96.6%
Other Punctuation 7
 
3.0%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
 
12.8%
14
 
6.2%
14
 
6.2%
11
 
4.8%
11
 
4.8%
10
 
4.4%
9
 
4.0%
9
 
4.0%
7
 
3.1%
6
 
2.6%
Other values (64) 107
47.1%
Other Punctuation
ValueCountFrequency (%)
· 4
57.1%
; 3
42.9%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 227
96.6%
Common 8
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
 
12.8%
14
 
6.2%
14
 
6.2%
11
 
4.8%
11
 
4.8%
10
 
4.4%
9
 
4.0%
9
 
4.0%
7
 
3.1%
6
 
2.6%
Other values (64) 107
47.1%
Common
ValueCountFrequency (%)
· 4
50.0%
; 3
37.5%
1 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 227
96.6%
None 4
 
1.7%
ASCII 4
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
 
12.8%
14
 
6.2%
14
 
6.2%
11
 
4.8%
11
 
4.8%
10
 
4.4%
9
 
4.0%
9
 
4.0%
7
 
3.1%
6
 
2.6%
Other values (64) 107
47.1%
None
ValueCountFrequency (%)
· 4
100.0%
ASCII
ValueCountFrequency (%)
; 3
75.0%
1 1
 
25.0%
Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:17.001789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14.5
Mean length8.9333333
Min length4

Characters and Unicode

Total characters268
Distinct characters99
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st row비알콜음료도매업
2nd row인테리어디자인업
3rd row기타절연선및케이블제조업
4th row서양식음식점업
5th row플라스틱발포성형제품제조업
ValueCountFrequency (%)
한식음식점업 6
20.0%
육류소매업 3
 
10.0%
분식및김밥전문점 1
 
3.3%
비알콜음료도매업 1
 
3.3%
주형및금형제조업 1
 
3.3%
알루미늄압연;압출및연신제품제조업 1
 
3.3%
벽지및장판류도매업 1
 
3.3%
기타도로화물운송업 1
 
3.3%
치킨전문점 1
 
3.3%
가정용요업제품;비전기식주방용품및날붙이도매업 1
 
3.3%
Other values (13) 13
43.3%
2023-12-10T23:00:17.667421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27
 
10.1%
18
 
6.7%
11
 
4.1%
11
 
4.1%
10
 
3.7%
10
 
3.7%
10
 
3.7%
9
 
3.4%
6
 
2.2%
6
 
2.2%
Other values (89) 150
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 266
99.3%
Other Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27
 
10.2%
18
 
6.8%
11
 
4.1%
11
 
4.1%
10
 
3.8%
10
 
3.8%
10
 
3.8%
9
 
3.4%
6
 
2.3%
6
 
2.3%
Other values (88) 148
55.6%
Other Punctuation
ValueCountFrequency (%)
; 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
27
 
10.2%
18
 
6.8%
11
 
4.1%
11
 
4.1%
10
 
3.8%
10
 
3.8%
10
 
3.8%
9
 
3.4%
6
 
2.3%
6
 
2.3%
Other values (88) 148
55.6%
Common
ValueCountFrequency (%)
; 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
27
 
10.2%
18
 
6.8%
11
 
4.1%
11
 
4.1%
10
 
3.8%
10
 
3.8%
10
 
3.8%
9
 
3.4%
6
 
2.3%
6
 
2.3%
Other values (88) 148
55.6%
ASCII
ValueCountFrequency (%)
; 2
100.0%
Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:17.972688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length4.3666667
Min length2

Characters and Unicode

Total characters131
Distinct characters78
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

Unique26 ?
Unique (%)86.7%

Sample

1st row음료
2nd row내부수리
3rd row전선케이블(피복)
4th row도넛
5th row플라스틱성형
ValueCountFrequency (%)
한식 4
 
13.3%
음료 1
 
3.3%
정육점 1
 
3.3%
전자부품부분품;원료또는보조용품제조 1
 
3.3%
인테리어등 1
 
3.3%
이사 1
 
3.3%
영양탕 1
 
3.3%
음식업 1
 
3.3%
기타 1
 
3.3%
주방용품 1
 
3.3%
Other values (17) 17
56.7%
2023-12-10T23:00:18.500952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9
 
6.9%
7
 
5.3%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
3
 
2.3%
Other values (68) 89
67.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
96.2%
Other Punctuation 3
 
2.3%
Close Punctuation 1
 
0.8%
Open Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9
 
7.1%
7
 
5.6%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (65) 84
66.7%
Other Punctuation
ValueCountFrequency (%)
; 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
96.2%
Common 5
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9
 
7.1%
7
 
5.6%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (65) 84
66.7%
Common
ValueCountFrequency (%)
; 3
60.0%
) 1
 
20.0%
( 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
96.2%
ASCII 5
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9
 
7.1%
7
 
5.6%
4
 
3.2%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
Other values (65) 84
66.7%
ASCII
ValueCountFrequency (%)
; 3
60.0%
) 1
 
20.0%
( 1
 
20.0%
Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:00:18.767952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters180
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st rowG46332
2nd rowM73201
3rd rowC28302
4th rowI56114
5th rowC22250
ValueCountFrequency (%)
i56111 6
20.0%
g47212 3
 
10.0%
i56194 1
 
3.3%
g46332 1
 
3.3%
c29294 1
 
3.3%
c24222 1
 
3.3%
g46692 1
 
3.3%
h49390 1
 
3.3%
i56193 1
 
3.3%
g46433 1
 
3.3%
Other values (13) 13
43.3%
2023-12-10T23:00:19.228587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 37
20.6%
2 26
14.4%
4 19
10.6%
6 17
9.4%
5 14
 
7.8%
G 11
 
6.1%
I 10
 
5.6%
3 10
 
5.6%
9 10
 
5.6%
7 8
 
4.4%
Other values (6) 18
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
83.3%
Uppercase Letter 30
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
24.7%
2 26
17.3%
4 19
12.7%
6 17
11.3%
5 14
 
9.3%
3 10
 
6.7%
9 10
 
6.7%
7 8
 
5.3%
0 6
 
4.0%
8 3
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
G 11
36.7%
I 10
33.3%
C 6
20.0%
M 1
 
3.3%
P 1
 
3.3%
H 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 150
83.3%
Latin 30
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37
24.7%
2 26
17.3%
4 19
12.7%
6 17
11.3%
5 14
 
9.3%
3 10
 
6.7%
9 10
 
6.7%
7 8
 
5.3%
0 6
 
4.0%
8 3
 
2.0%
Latin
ValueCountFrequency (%)
G 11
36.7%
I 10
33.3%
C 6
20.0%
M 1
 
3.3%
P 1
 
3.3%
H 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37
20.6%
2 26
14.4%
4 19
10.6%
6 17
9.4%
5 14
 
7.8%
G 11
 
6.1%
I 10
 
5.6%
3 10
 
5.6%
9 10
 
5.6%
7 8
 
4.4%
Other values (6) 18
10.0%

기업번호
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95116.133
Minimum10161
Maximum101996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:00:19.497089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10161
5-th percentile50432.8
Q1100569.75
median101377.5
Q3101634.75
95-th percentile101926.75
Maximum101996
Range91835
Interquartile range (IQR)1065

Descriptive statistics

Standard deviation23101.517
Coefficient of variation (CV)0.24287695
Kurtosis12.183607
Mean95116.133
Median Absolute Deviation (MAD)411
Skewness-3.6552518
Sum2853484
Variance5.3368008 × 108
MonotonicityNot monotonic
2023-12-10T23:00:19.674227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
100329 1
 
3.3%
101518 1
 
3.3%
101996 1
 
3.3%
101947 1
 
3.3%
101902 1
 
3.3%
101797 1
 
3.3%
101780 1
 
3.3%
101777 1
 
3.3%
101672 1
 
3.3%
10165 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
10161 1
3.3%
10165 1
3.3%
99649 1
3.3%
99689 1
3.3%
100329 1
3.3%
100445 1
3.3%
100452 1
3.3%
100557 1
3.3%
100608 1
3.3%
100641 1
3.3%
ValueCountFrequency (%)
101996 1
3.3%
101947 1
3.3%
101902 1
3.3%
101797 1
3.3%
101780 1
3.3%
101777 1
3.3%
101672 1
3.3%
101639 1
3.3%
101622 1
3.3%
101601 1
3.3%

Sample

기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명업체형태명시군명고객신규등록일자기업설립년월대표자현주소우편번호대표자주소대표자거주지우편번호대표자거주지우편번호주소대표자현주소소유구분명대표자현주소소유자관계명본사우편번호본사주소본사소유구분명본사소유자관계명주사업장우편번호주사업장우편번호주소주사업장소유자관계명주사업장소유구분명종업원수기업규모명기타업종명기타제품명주요거래은행명관할구역명등록일자업종코드업종대분류명업종중분류명업종소분류명주요제품코드명표준산업분류코드기업번호
02023-04M5012913*****경기도광주시목동개인기업광주시2009-12-162007-0312784경기도 광주시 태봉로12784경기도 광주시 태봉로자가기타12784경기도 광주시 태봉로자가본인(대표이사 및 법인포함)12770경기도 광주시 광남안로본인(대표이사 및 법인포함)자가3소상공인기타기타중소기업은행채권관리센터2007-07-02G46332G도매및소매업(45~47)음·식료품및담배도매업비알콜음료도매업음료G46332100329
12023-04M5012729*****경기도의정부시신곡동개인기업의정부2021-04-142007-08******************************************임차기타*********************임차기타11775경기도 의정부시 장곡로기타임차0소상공인기타기타농협은행의정부지점2021-06-19M73201M전문;과학및기술서비스업(70~73)전문디자인업인테리어디자인업내부수리M7320199649
22023-04M7013026*****경기도포천시가산면개인기업포천시(의정부)2008-04-152007-0701087서울특별시 강북구 삼양로01087서울특별시 강북구 삼양로임차기타01087서울특별시 강북구 삼양로임차기타11164경기도 포천시 가산면 가산로기타임차3소상공인기타기타기타채권관리센터2008-04-15C28302C제조업(10~33)절연선및케이블제조업기타절연선및케이블제조업전선케이블(피복)C28302100445
32023-04F5013303*****경기도광명시철산동개인기업광명시2012-04-132008-0218409경기도 화성시 병점18409경기도 화성시 병점자가본인(대표이사 및 법인포함)18409경기도 화성시 병점자가본인(대표이사 및 법인포함)14237경기 광명시 철산동기타임차2소상공인기타기타기타광명지점2018-04-07I56123I숙박및음식점업(55~56)음식점업서양식음식점업도넛I56114100452
42023-04M5012411*****경기도화성시팔탄면개인기업화성시(화성)2013-03-272012-1118290경기도 화성시 매송고색로18290경기도 화성시 매송고색로임차기타18290경기도 화성시 매송고색로임차기타18527경기도 화성시 푸른들판로기타임차0소상공인기타기타기타화성지점2018-03-23C22259C제조업(10~33)플라스틱제품제조업플라스틱발포성형제품제조업플라스틱성형C22250100557
52023-04M5020357*****경기도고양시 일산동구장항동개인기업고양시2008-05-162008-0110407경기도 고양시 일산동구 일산로10407경기도 고양시 일산동구 일산로자가본인(대표이사 및 법인포함)10407경기도 고양시 일산동구 일산로자가본인(대표이사 및 법인포함)10364경기도 고양시 일산동구 고봉로기타임차0소상공인기타기타기타고양지점2008-05-16I56121I숙박및음식점업(55~56)음식점업중식음식점업중식I56112100608
62023-04F7012332*****경기도안양시 만안구박달동개인기업안양시2008-05-172007-0608621서울특별시 금천구 독산로08621서울특별시 금천구 독산로임차기타08621서울특별시 금천구 독산로기타기타13983경기도 안양시 만안구 박달로본인(대표이사 및 법인포함)자가0소상공인기타기타기타안양지점2008-05-17G47121G도매및소매업(45~47)종합소매업슈퍼마켓슈퍼마켓G47121100641
72023-04F6012510*****경기도안성시봉산동개인기업안성시2013-04-301986-0417590경기도 안성시 중앙로17590경기도 안성시 중앙로자가배우자17590경기도 안성시 중앙로자가배우자456030경기 안성시 봉산동기타임차1소상공인기타기타기타안성지점2018-04-06G46595G도매및소매업(45~47)기계장비및관련물품도매업전기용기계장비및관련기자재도매업전기재료G4659499689
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