Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory154.4 B

Variable types

Categorical11
Text4
Numeric3

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/b00491be-42d9-48f1-baa5-ab6aee726f35

Alerts

기준년도 has constant value ""Constant
등록일자 has constant value ""Constant
작업자명 has constant value ""Constant
가공기업구분 is highly overall correlated with 평균액 and 2 other fieldsHigh correlation
업종대분류코드 is highly overall correlated with 업종대분류명High correlation
가공기업구분코드 is highly overall correlated with 평균액 and 2 other fieldsHigh correlation
업종대분류명 is highly overall correlated with 업종대분류코드High correlation
업력구간코드 is highly overall correlated with 업력구간High correlation
평균액 is highly overall correlated with 중위액 and 2 other fieldsHigh correlation
중위액 is highly overall correlated with 평균액 and 2 other fieldsHigh correlation
업력구간 is highly overall correlated with 업력구간코드High correlation
행정동명 has unique valuesUnique
평균액 has unique valuesUnique
중위액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:17:25.589561
Analysis finished2023-12-10 14:17:28.960912
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 30
100.0%

Length

2023-12-10T23:17:29.072049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:29.217309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 30
100.0%

시도명
Categorical

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기
7 
서울
7 
부산
3 
전남
2 
경남
2 
Other values (7)
9 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row전남
2nd row경남
3rd row경기
4th row서울
5th row경기

Common Values

ValueCountFrequency (%)
경기 7
23.3%
서울 7
23.3%
부산 3
10.0%
전남 2
 
6.7%
경남 2
 
6.7%
충남 2
 
6.7%
충북 2
 
6.7%
대전 1
 
3.3%
전북 1
 
3.3%
인천 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:17:29.367692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 7
23.3%
서울 7
23.3%
부산 3
10.0%
전남 2
 
6.7%
경남 2
 
6.7%
충남 2
 
6.7%
충북 2
 
6.7%
대전 1
 
3.3%
전북 1
 
3.3%
인천 1
 
3.3%
Other values (2) 2
 
6.7%

시군구명
Text

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:17:29.636045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5
Min length2

Characters and Unicode

Total characters105
Distinct characters46
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

Unique25 ?
Unique (%)83.3%

Sample

1st row여수시
2nd row양산시
3rd row성남시 분당구
4th row광진구
5th row성남시 중원구
ValueCountFrequency (%)
강남구 3
 
9.1%
성남시 2
 
6.1%
양산시 2
 
6.1%
미추홀구 1
 
3.0%
여수시 1
 
3.0%
화성시 1
 
3.0%
경산시 1
 
3.0%
태백시 1
 
3.0%
송파구 1
 
3.0%
해운대구 1
 
3.0%
Other values (19) 19
57.6%
2023-12-10T23:17:30.481989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 15
 
14.3%
시 15
 
14.3%
산 5
 
4.8%
남 5
 
4.8%
성 5
 
4.8%
군 4
 
3.8%
양 4
 
3.8%
안 4
 
3.8%
포 3
 
2.9%
강 3
 
2.9%
Other values (36) 42
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
97.1%
Space Separator 3
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 15
 
14.7%
시 15
 
14.7%
산 5
 
4.9%
남 5
 
4.9%
성 5
 
4.9%
군 4
 
3.9%
양 4
 
3.9%
안 4
 
3.9%
포 3
 
2.9%
강 3
 
2.9%
Other values (35) 39
38.2%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
97.1%
Common 3
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 15
 
14.7%
시 15
 
14.7%
산 5
 
4.9%
남 5
 
4.9%
성 5
 
4.9%
군 4
 
3.9%
양 4
 
3.9%
안 4
 
3.9%
포 3
 
2.9%
강 3
 
2.9%
Other values (35) 39
38.2%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
97.1%
ASCII 3
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 15
 
14.7%
시 15
 
14.7%
산 5
 
4.9%
남 5
 
4.9%
성 5
 
4.9%
군 4
 
3.9%
양 4
 
3.9%
안 4
 
3.9%
포 3
 
2.9%
강 3
 
2.9%
Other values (35) 39
38.2%
ASCII
ValueCountFrequency (%)
3
100.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:17:30.753501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.7
Min length3

Characters and Unicode

Total characters111
Distinct characters60
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 row삼일동
2nd row상북면
3rd row삼평동
4th row구의2동
5th row상대원1동
ValueCountFrequency (%)
삼일동 1
 
3.3%
상북면 1
 
3.3%
종로1.2.3.4가동 1
 
3.3%
역삼1동 1
 
3.3%
자인면 1
 
3.3%
철암동 1
 
3.3%
대치2동 1
 
3.3%
석촌동 1
 
3.3%
재송1동 1
 
3.3%
동이면 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:17:31.100519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
21.6%
2 7
 
6.3%
1 6
 
5.4%
면 5
 
4.5%
삼 3
 
2.7%
. 3
 
2.7%
대 3
 
2.7%
안 2
 
1.8%
포 2
 
1.8%
학 2
 
1.8%
Other values (50) 54
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
83.8%
Decimal Number 15
 
13.5%
Other Punctuation 3
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
25.8%
면 5
 
5.4%
삼 3
 
3.2%
대 3
 
3.2%
안 2
 
2.2%
포 2
 
2.2%
학 2
 
2.2%
일 2
 
2.2%
읍 2
 
2.2%
의 2
 
2.2%
Other values (45) 46
49.5%
Decimal Number
ValueCountFrequency (%)
2 7
46.7%
1 6
40.0%
3 1
 
6.7%
4 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
83.8%
Common 18
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
25.8%
면 5
 
5.4%
삼 3
 
3.2%
대 3
 
3.2%
안 2
 
2.2%
포 2
 
2.2%
학 2
 
2.2%
일 2
 
2.2%
읍 2
 
2.2%
의 2
 
2.2%
Other values (45) 46
49.5%
Common
ValueCountFrequency (%)
2 7
38.9%
1 6
33.3%
. 3
16.7%
3 1
 
5.6%
4 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
83.8%
ASCII 18
 
16.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
25.8%
면 5
 
5.4%
삼 3
 
3.2%
대 3
 
3.2%
안 2
 
2.2%
포 2
 
2.2%
학 2
 
2.2%
일 2
 
2.2%
읍 2
 
2.2%
의 2
 
2.2%
Other values (45) 46
49.5%
ASCII
ValueCountFrequency (%)
2 7
38.9%
1 6
33.3%
. 3
16.7%
3 1
 
5.6%
4 1
 
5.6%

업종대분류코드
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
14 
H
2 
G
2 
N
2 
A
2 
Other values (7)
8 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st rowH
2nd rowG
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 14
46.7%
H 2
 
6.7%
G 2
 
6.7%
N 2
 
6.7%
A 2
 
6.7%
J 2
 
6.7%
K 1
 
3.3%
F 1
 
3.3%
R 1
 
3.3%
L 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:17:31.270200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 14
46.7%
h 2
 
6.7%
g 2
 
6.7%
n 2
 
6.7%
a 2
 
6.7%
j 2
 
6.7%
k 1
 
3.3%
f 1
 
3.3%
r 1
 
3.3%
l 1
 
3.3%
Other values (2) 2
 
6.7%

업종중분류코드
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:17:31.467009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters22
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

Unique16 ?
Unique (%)53.3%

Sample

1st rowH52
2nd rowG46
3rd rowC21
4th rowC14
5th rowC21
ValueCountFrequency (%)
c20 4
 
13.3%
c21 2
 
6.7%
c22 2
 
6.7%
g46 2
 
6.7%
h52 2
 
6.7%
a03 2
 
6.7%
c26 1
 
3.3%
j60 1
 
3.3%
c29 1
 
3.3%
j58 1
 
3.3%
Other values (12) 12
40.0%
2023-12-10T23:17:31.772058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15
16.7%
C 14
15.6%
0 8
8.9%
1 8
8.9%
4 6
 
6.7%
6 6
 
6.7%
3 5
 
5.6%
7 4
 
4.4%
5 4
 
4.4%
J 2
 
2.2%
Other values (12) 18
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Uppercase Letter 30
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 14
46.7%
J 2
 
6.7%
N 2
 
6.7%
H 2
 
6.7%
G 2
 
6.7%
A 2
 
6.7%
K 1
 
3.3%
F 1
 
3.3%
L 1
 
3.3%
R 1
 
3.3%
Other values (2) 2
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 15
25.0%
0 8
13.3%
1 8
13.3%
4 6
 
10.0%
6 6
 
10.0%
3 5
 
8.3%
7 4
 
6.7%
5 4
 
6.7%
9 2
 
3.3%
8 2
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 60
66.7%
Latin 30
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 14
46.7%
J 2
 
6.7%
N 2
 
6.7%
H 2
 
6.7%
G 2
 
6.7%
A 2
 
6.7%
K 1
 
3.3%
F 1
 
3.3%
L 1
 
3.3%
R 1
 
3.3%
Other values (2) 2
 
6.7%
Common
ValueCountFrequency (%)
2 15
25.0%
0 8
13.3%
1 8
13.3%
4 6
 
10.0%
6 6
 
10.0%
3 5
 
8.3%
7 4
 
6.7%
5 4
 
6.7%
9 2
 
3.3%
8 2
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15
16.7%
C 14
15.6%
0 8
8.9%
1 8
8.9%
4 6
 
6.7%
6 6
 
6.7%
3 5
 
5.6%
7 4
 
4.4%
5 4
 
4.4%
J 2
 
2.2%
Other values (12) 18
20.0%

업종대분류명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
14 
운수 및 창고업
2 
도매 및 소매업
2 
사업시설 관리; 사업 지원 및 임대 서비스업
2 
농업; 임업 및 어업
2 
Other values (7)
8 

Length

Max length24
Median length23.5
Mean length7.5666667
Min length3

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row운수 및 창고업
2nd row도매 및 소매업
3rd row제조업
4th row제조업
5th row제조업

Common Values

ValueCountFrequency (%)
제조업 14
46.7%
운수 및 창고업 2
 
6.7%
도매 및 소매업 2
 
6.7%
사업시설 관리; 사업 지원 및 임대 서비스업 2
 
6.7%
농업; 임업 및 어업 2
 
6.7%
정보통신업 2
 
6.7%
금융 및 보험업 1
 
3.3%
건설업 1
 
3.3%
예술; 스포츠 및 여가관련 서비스업 1
 
3.3%
부동산업 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:17:31.954445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조업 14
19.4%
및 12
16.7%
서비스업 4
 
5.6%
창고업 2
 
2.8%
임대 2
 
2.8%
운수 2
 
2.8%
정보통신업 2
 
2.8%
어업 2
 
2.8%
도매 2
 
2.8%
농업 2
 
2.8%
Other values (22) 28
38.9%

업종중분류명
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:17:32.209036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length16.5
Mean length12.9
Min length2

Characters and Unicode

Total characters387
Distinct characters88
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 row창고 및 운송관련 서비스업
2nd row도매 및 상품 중개업
3rd row의료용 물질 및 의약품 제조업
4th row의복; 의복 액세서리 및 모피제품 제조업
5th row의료용 물질 및 의약품 제조업
ValueCountFrequency (%)
및 18
 
16.4%
제조업 14
 
12.7%
서비스업 6
 
5.5%
의약품 6
 
5.5%
제외 4
 
3.6%
화학물질 4
 
3.6%
화학제품 4
 
3.6%
어업 2
 
1.8%
중개업 2
 
1.8%
의복 2
 
1.8%
Other values (40) 48
43.6%
2023-12-10T23:17:32.606734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
20.7%
업 32
 
8.3%
제 28
 
7.2%
품 19
 
4.9%
및 18
 
4.7%
조 15
 
3.9%
의 11
 
2.8%
; 10
 
2.6%
스 9
 
2.3%
비 9
 
2.3%
Other values (78) 156
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
76.7%
Space Separator 80
 
20.7%
Other Punctuation 10
 
2.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 32
 
10.8%
제 28
 
9.4%
품 19
 
6.4%
및 18
 
6.1%
조 15
 
5.1%
의 11
 
3.7%
스 9
 
3.0%
비 9
 
3.0%
학 8
 
2.7%
화 8
 
2.7%
Other values (76) 140
47.1%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
; 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 297
76.7%
Common 90
 
23.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 32
 
10.8%
제 28
 
9.4%
품 19
 
6.4%
및 18
 
6.1%
조 15
 
5.1%
의 11
 
3.7%
스 9
 
3.0%
비 9
 
3.0%
학 8
 
2.7%
화 8
 
2.7%
Other values (76) 140
47.1%
Common
ValueCountFrequency (%)
80
88.9%
; 10
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 297
76.7%
ASCII 90
 
23.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
88.9%
; 10
 
11.1%
Hangul
ValueCountFrequency (%)
업 32
 
10.8%
제 28
 
9.4%
품 19
 
6.4%
및 18
 
6.1%
조 15
 
5.1%
의 11
 
3.7%
스 9
 
3.0%
비 9
 
3.0%
학 8
 
2.7%
화 8
 
2.7%
Other values (76) 140
47.1%

가공기업구분코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
3
17 
2
5 
4
5 
1
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 17
56.7%
2 5
 
16.7%
4 5
 
16.7%
1 3
 
10.0%

Length

2023-12-10T23:17:32.801955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:32.908479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 17
56.7%
2 5
 
16.7%
4 5
 
16.7%
1 3
 
10.0%

가공기업구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
중기업
17 
중견기업
5 
소기업
5 
대기업
3 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중견기업
2nd row중기업
3rd row소기업
4th row중기업
5th row중견기업

Common Values

ValueCountFrequency (%)
중기업 17
56.7%
중견기업 5
 
16.7%
소기업 5
 
16.7%
대기업 3
 
10.0%

Length

2023-12-10T23:17:33.049465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:33.187704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중기업 17
56.7%
중견기업 5
 
16.7%
소기업 5
 
16.7%
대기업 3
 
10.0%

업력구간코드
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.166667
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:17:33.310247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q320
95-th percentile30
Maximum50
Range49
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.88841
Coefficient of variation (CV)0.76859364
Kurtosis2.5943532
Mean14.166667
Median Absolute Deviation (MAD)6.5
Skewness1.3837777
Sum425
Variance118.55747
MonotonicityNot monotonic
2023-12-10T23:17:33.488507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 9
30.0%
20 8
26.7%
5 6
20.0%
30 3
 
10.0%
2 2
 
6.7%
1 1
 
3.3%
50 1
 
3.3%
ValueCountFrequency (%)
1 1
 
3.3%
2 2
 
6.7%
5 6
20.0%
10 9
30.0%
20 8
26.7%
30 3
 
10.0%
50 1
 
3.3%
ValueCountFrequency (%)
50 1
 
3.3%
30 3
 
10.0%
20 8
26.7%
10 9
30.0%
5 6
20.0%
2 2
 
6.7%
1 1
 
3.3%

업력구간
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
10년 이상 20년 미만
9 
20년 이상 30년 미만
8 
5년 이상 10년 미만
6 
30년 이상 40년 미만
3 
2년 이상 5년 미만
2 
Other values (2)
2 

Length

Max length13
Median length13
Mean length12.6
Min length11

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row10년 이상 20년 미만
2nd row20년 이상 30년 미만
3rd row10년 이상 20년 미만
4th row30년 이상 40년 미만
5th row20년 이상 30년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 9
30.0%
20년 이상 30년 미만 8
26.7%
5년 이상 10년 미만 6
20.0%
30년 이상 40년 미만 3
 
10.0%
2년 이상 5년 미만 2
 
6.7%
1년 이상 2년 미만 1
 
3.3%
50년 이상 60년 미만 1
 
3.3%

Length

2023-12-10T23:17:33.725687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:33.892668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 30
25.0%
미만 30
25.0%
20년 17
14.2%
10년 15
12.5%
30년 11
 
9.2%
5년 8
 
6.7%
40년 3
 
2.5%
2년 3
 
2.5%
1년 1
 
0.8%
50년 1
 
0.8%

항목명
Categorical

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
당기순이익
6 
매출액
4 
자산총계
4 
자본총계
4 
자본금
4 
Other values (2)
8 

Length

Max length5
Median length4
Mean length3.9333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매출액
2nd row자산총계
3rd row매출액
4th row자본총계
5th row자본금

Common Values

ValueCountFrequency (%)
당기순이익 6
20.0%
매출액 4
13.3%
자산총계 4
13.3%
자본총계 4
13.3%
자본금 4
13.3%
영업이익 4
13.3%
부채총계 4
13.3%

Length

2023-12-10T23:17:34.086777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:34.272045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당기순이익 6
20.0%
매출액 4
13.3%
자산총계 4
13.3%
자본총계 4
13.3%
자본금 4
13.3%
영업이익 4
13.3%
부채총계 4
13.3%

총기업수
Categorical

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
23 
2
6 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 23
76.7%
2 6
 
20.0%
3 1
 
3.3%

Length

2023-12-10T23:17:34.474039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:34.603733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 23
76.7%
2 6
 
20.0%
3 1
 
3.3%

평균액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49704820
Minimum-10534874
Maximum6.1162187 × 108
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-10T23:17:34.766800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10534874
5-th percentile-416462.65
Q11764085.2
median7781685.8
Q315755007
95-th percentile2.293672 × 108
Maximum6.1162187 × 108
Range6.2215674 × 108
Interquartile range (IQR)13990922

Descriptive statistics

Standard deviation1.2310388 × 108
Coefficient of variation (CV)2.476699
Kurtosis15.507163
Mean49704820
Median Absolute Deviation (MAD)6989503.5
Skewness3.724664
Sum1.4911446 × 109
Variance1.5154565 × 1016
MonotonicityNot monotonic
2023-12-10T23:17:34.944530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
75929198.0 1
 
3.3%
16124361.0 1
 
3.3%
215822419.5 1
 
3.3%
3703954.0 1
 
3.3%
36200.0 1
 
3.3%
667938.0 1
 
3.3%
3502920.0 1
 
3.3%
1716919.0 1
 
3.3%
8000000.0 1
 
3.3%
14210029.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-10534874.0 1
3.3%
-786823.0 1
3.3%
36200.0 1
3.3%
429423.0 1
3.3%
532148.0 1
3.3%
582706.0 1
3.3%
667938.0 1
3.3%
1716919.0 1
3.3%
1905584.0 1
3.3%
2850000.0 1
3.3%
ValueCountFrequency (%)
611621866.0 1
3.3%
240449293.0 1
3.3%
215822419.5 1
3.3%
158318884.0 1
3.3%
75929198.0 1
3.3%
48095926.0 1
3.3%
21661152.66667 1
3.3%
16124361.0 1
3.3%
14646945.0 1
3.3%
14210029.0 1
3.3%

중위액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49618290
Minimum-10534874
Maximum6.1162187 × 108
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-10T23:17:35.135395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10534874
5-th percentile-416462.65
Q11764085.2
median7781685.8
Q315755007
95-th percentile2.293672 × 108
Maximum6.1162187 × 108
Range6.2215674 × 108
Interquartile range (IQR)13990922

Descriptive statistics

Standard deviation1.2312518 × 108
Coefficient of variation (CV)2.4814475
Kurtosis15.506193
Mean49618290
Median Absolute Deviation (MAD)6989503.5
Skewness3.7248572
Sum1.4885487 × 109
Variance1.5159811 × 1016
MonotonicityNot monotonic
2023-12-10T23:17:35.330146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
75929198.0 1
 
3.3%
16124361.0 1
 
3.3%
215822419.5 1
 
3.3%
3703954.0 1
 
3.3%
36200.0 1
 
3.3%
667938.0 1
 
3.3%
3502920.0 1
 
3.3%
1716919.0 1
 
3.3%
8000000.0 1
 
3.3%
14210029.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-10534874.0 1
3.3%
-786823.0 1
3.3%
36200.0 1
3.3%
429423.0 1
3.3%
532148.0 1
3.3%
582706.0 1
3.3%
667938.0 1
3.3%
1716919.0 1
3.3%
1905584.0 1
3.3%
2850000.0 1
3.3%
ValueCountFrequency (%)
611621866.0 1
3.3%
240449293.0 1
3.3%
215822419.5 1
3.3%
158318884.0 1
3.3%
75929198.0 1
3.3%
48095926.0 1
3.3%
19065231.0 1
3.3%
16124361.0 1
3.3%
14646945.0 1
3.3%
14210029.0 1
3.3%

등록일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2021-10-19
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-19
2nd row2021-10-19
3rd row2021-10-19
4th row2021-10-19
5th row2021-10-19

Common Values

ValueCountFrequency (%)
2021-10-19 30
100.0%

Length

2023-12-10T23:17:35.568471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:35.694878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-10-19 30
100.0%

작업자명
Categorical

CONSTANT 

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

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYS 30
100.0%

Length

2023-12-10T23:17:35.818415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:17:35.961005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:17:27.986423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.116919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.585647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:28.110647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.279594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.709294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:28.255046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.448113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:17:27.848175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:17:36.064168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명가공기업구분코드가공기업구분업력구간코드업력구간항목명총기업수평균액중위액
시도명1.0001.0001.0000.0000.7450.0000.7450.0000.0000.0000.0000.1340.0000.0000.000
시군구명1.0001.0001.0000.9070.8310.9070.8310.9180.9180.6350.6230.9640.9040.0000.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업종대분류코드0.0000.9071.0001.0001.0001.0001.0000.0000.0000.0000.6870.4060.0000.4960.496
업종중분류코드0.7450.8311.0001.0001.0001.0001.0000.0000.0000.4250.7950.0000.0000.0000.000
업종대분류명0.0000.9071.0001.0001.0001.0001.0000.0000.0000.0000.6870.4060.0000.4960.496
업종중분류명0.7450.8311.0001.0001.0001.0001.0000.0000.0000.4250.7950.0000.0000.0000.000
가공기업구분코드0.0000.9181.0000.0000.0000.0000.0001.0001.0000.3800.4240.3810.0000.6900.690
가공기업구분0.0000.9181.0000.0000.0000.0000.0001.0001.0000.3800.4240.3810.0000.6900.690
업력구간코드0.0000.6351.0000.0000.4250.0000.4250.3800.3801.0001.0000.3610.0000.0000.000
업력구간0.0000.6231.0000.6870.7950.6870.7950.4240.4241.0001.0000.3040.0000.0000.000
항목명0.1340.9641.0000.4060.0000.4060.0000.3810.3810.3610.3041.0000.0000.2360.236
총기업수0.0000.9041.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
평균액0.0000.0001.0000.4960.0000.4960.0000.6900.6900.0000.0000.2360.0001.0001.000
중위액0.0000.0001.0000.4960.0000.4960.0000.6900.6900.0000.0000.2360.0001.0001.000
2023-12-10T23:17:36.299461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총기업수가공기업구분시도명업종대분류코드업력구간항목명가공기업구분코드업종대분류명
총기업수1.0000.0000.0000.0000.0000.0000.0000.000
가공기업구분0.0001.0000.0000.0000.2740.2411.0000.000
시도명0.0000.0001.0000.0000.0000.0000.0000.000
업종대분류코드0.0000.0000.0001.0000.3570.1400.0001.000
업력구간0.0000.2740.0000.3571.0000.0590.2740.357
항목명0.0000.2410.0000.1400.0591.0000.2410.140
가공기업구분코드0.0001.0000.0000.0000.2740.2411.0000.000
업종대분류명0.0000.0000.0001.0000.3570.1400.0001.000
2023-12-10T23:17:36.480514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업력구간코드평균액중위액시도명업종대분류코드업종대분류명가공기업구분코드가공기업구분업력구간항목명총기업수
업력구간코드1.000-0.116-0.1160.0000.0000.0000.3050.3050.9590.2140.000
평균액-0.1161.0001.0000.0000.1820.1820.5010.5010.0000.1440.000
중위액-0.1161.0001.0000.0000.1820.1820.5010.5010.0000.1440.000
시도명0.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
업종대분류코드0.0000.1820.1820.0001.0001.0000.0000.0000.3570.1400.000
업종대분류명0.0000.1820.1820.0001.0001.0000.0000.0000.3570.1400.000
가공기업구분코드0.3050.5010.5010.0000.0000.0001.0001.0000.2740.2410.000
가공기업구분0.3050.5010.5010.0000.0000.0001.0001.0000.2740.2410.000
업력구간0.9590.0000.0000.0000.3570.3570.2740.2741.0000.0590.000
항목명0.2140.1440.1440.0000.1400.1400.2410.2410.0591.0000.000
총기업수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T23:17:28.464643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:17:28.837986image/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.

Sample

기준년도시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명가공기업구분코드가공기업구분업력구간코드업력구간항목명총기업수평균액중위액등록일자작업자명
02020전남여수시삼일동HH52운수 및 창고업창고 및 운송관련 서비스업2중견기업1010년 이상 20년 미만매출액175929198.075929198.02021-10-19KEDSYS
12020경남양산시상북면GG46도매 및 소매업도매 및 상품 중개업3중기업2020년 이상 30년 미만자산총계113070974.013070974.02021-10-19KEDSYS
22020경기성남시 분당구삼평동CC21제조업의료용 물질 및 의약품 제조업4소기업1010년 이상 20년 미만매출액27563371.57563371.52021-10-19KEDSYS
32020서울광진구구의2동CC14제조업의복; 의복 액세서리 및 모피제품 제조업3중기업3030년 이상 40년 미만자본총계1-10534874.0-10534874.02021-10-19KEDSYS
42020경기성남시 중원구상대원1동CC21제조업의료용 물질 및 의약품 제조업2중견기업2020년 이상 30년 미만자본금23407860.53407860.52021-10-19KEDSYS
52020대전대덕구대화동CC10제조업식료품 제조업1대기업55년 이상 10년 미만영업이익113345618.013345618.02021-10-19KEDSYS
62020서울영등포구여의동CC20제조업화학물질 및 화학제품 제조업; 의약품 제외1대기업1010년 이상 20년 미만자본금2240449293.0240449293.02021-10-19KEDSYS
72020경기군포시군포1동CC22제조업고무 및 플라스틱제품 제조업3중기업55년 이상 10년 미만영업이익16981153.06981153.02021-10-19KEDSYS
82020경기안양시 동안구호계2동KK64금융 및 보험업금융업1대기업55년 이상 10년 미만자산총계1611621866.0611621866.02021-10-19KEDSYS
92020경기안성시일죽면HH52운수 및 창고업창고 및 운송관련 서비스업4소기업22년 이상 5년 미만당기순이익148095926.048095926.02021-10-19KEDSYS
기준년도시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명가공기업구분코드가공기업구분업력구간코드업력구간항목명총기업수평균액중위액등록일자작업자명
202020경기김포시사우동LL68부동산업부동산업3중기업11년 이상 2년 미만당기순이익1-786823.0-786823.02021-10-19KEDSYS
212020충북옥천군동이면CC13제조업섬유제품 제조업; 의복제외4소기업55년 이상 10년 미만당기순이익1429423.0429423.02021-10-19KEDSYS
222020부산해운대구재송1동EE37수도; 하수 및 폐기물 처리; 원료 재생업하수; 폐수 및 분뇨 처리업3중기업1010년 이상 20년 미만자본총계114210029.014210029.02021-10-19KEDSYS
232020서울송파구석촌동AA03농업; 임업 및 어업어업2중견기업5050년 이상 60년 미만자본금18000000.08000000.02021-10-19KEDSYS
242020서울강남구대치2동JJ58정보통신업출판업3중기업22년 이상 5년 미만부채총계11716919.01716919.02021-10-19KEDSYS
252020강원태백시철암동CC20제조업화학물질 및 화학제품 제조업; 의약품 제외3중기업1010년 이상 20년 미만영업이익13502920.03502920.02021-10-19KEDSYS
262020경북경산시자인면CC29제조업기타 기계 및 장비 제조업3중기업2020년 이상 30년 미만당기순이익1667938.0667938.02021-10-19KEDSYS
272020서울강남구역삼1동JJ60정보통신업방송업3중기업1010년 이상 20년 미만부채총계136200.036200.02021-10-19KEDSYS
282020서울종로구종로1.2.3.4가동MM71전문; 과학 및 기술 서비스업전문 서비스업4소기업55년 이상 10년 미만당기순이익23703954.03703954.02021-10-19KEDSYS
292020서울강남구도곡2동GG46도매 및 소매업도매 및 상품 중개업2중견기업2020년 이상 30년 미만부채총계2215822419.5215822419.52021-10-19KEDSYS