Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory154.4 B

Variable types

Categorical10
Text4
Numeric3
DateTime1

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/8bcda50b-5254-4a67-b0f8-031076adc144

Alerts

기준년도 has constant value ""Constant
등록일자 has constant value ""Constant
작업자명 has constant value ""Constant
업종대분류코드 is highly overall correlated with 업종대분류명High correlation
업종대분류명 is highly overall correlated with 업종대분류코드High correlation
가공기업구분코드 is highly overall correlated with 평균액 and 2 other fieldsHigh correlation
가공기업구분 is highly overall correlated with 평균액 and 2 other fieldsHigh 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
총기업수 is highly imbalanced (78.9%)Imbalance
시군구명 has 1 (3.3%) missing valuesMissing
행정동명 has unique valuesUnique
평균액 has unique valuesUnique
중위액 has unique valuesUnique
업력구간코드 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:20:27.504540
Analysis finished2023-12-10 14:20:29.397206
Duration1.89 second
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
2017
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 30
100.0%

Length

2023-12-10T23:20:29.454954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:29.552042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 30
100.0%

시도명
Categorical

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기
8 
서울
4 
충남
4 
울산
4 
부산
2 
Other values (8)
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row경기
2nd row서울
3rd row대전
4th row경북
5th row충남

Common Values

ValueCountFrequency (%)
경기 8
26.7%
서울 4
13.3%
충남 4
13.3%
울산 4
13.3%
부산 2
 
6.7%
대전 1
 
3.3%
경북 1
 
3.3%
세종 1
 
3.3%
강원 1
 
3.3%
경남 1
 
3.3%
Other values (3) 3
 
10.0%

Length

2023-12-10T23:20:29.646423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 8
26.7%
서울 4
13.3%
충남 4
13.3%
울산 4
13.3%
부산 2
 
6.7%
대전 1
 
3.3%
경북 1
 
3.3%
세종 1
 
3.3%
강원 1
 
3.3%
경남 1
 
3.3%
Other values (3) 3
 
10.0%

시군구명
Text

MISSING 

Distinct24
Distinct (%)82.8%
Missing1
Missing (%)3.3%
Memory size372.0 B
2023-12-10T23:20:29.855322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.6551724
Min length2

Characters and Unicode

Total characters106
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

Unique20 ?
Unique (%)69.0%

Sample

1st row부천시
2nd row관악구
3rd row대덕구
4th row경주시
5th row예산군
ValueCountFrequency (%)
울주군 3
 
8.8%
화성시 2
 
5.9%
강남구 2
 
5.9%
서산시 2
 
5.9%
은평구 1
 
2.9%
부천시 1
 
2.9%
덕양구 1
 
2.9%
고양시 1
 
2.9%
수영구 1
 
2.9%
서구 1
 
2.9%
Other values (19) 19
55.9%
2023-12-10T23:20:30.170662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 15
 
14.2%
시 14
 
13.2%
산 6
 
5.7%
주 6
 
5.7%
5
 
4.7%
군 5
 
4.7%
서 5
 
4.7%
성 4
 
3.8%
남 4
 
3.8%
덕 3
 
2.8%
Other values (28) 39
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
95.3%
Space Separator 5
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 15
14.9%
시 14
 
13.9%
산 6
 
5.9%
주 6
 
5.9%
군 5
 
5.0%
서 5
 
5.0%
성 4
 
4.0%
남 4
 
4.0%
덕 3
 
3.0%
양 3
 
3.0%
Other values (27) 36
35.6%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101
95.3%
Common 5
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 15
14.9%
시 14
 
13.9%
산 6
 
5.9%
주 6
 
5.9%
군 5
 
5.0%
서 5
 
5.0%
성 4
 
4.0%
남 4
 
4.0%
덕 3
 
3.0%
양 3
 
3.0%
Other values (27) 36
35.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
95.3%
ASCII 5
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 15
14.9%
시 14
 
13.9%
산 6
 
5.9%
주 6
 
5.9%
군 5
 
5.0%
서 5
 
5.0%
성 4
 
4.0%
남 4
 
4.0%
덕 3
 
3.0%
양 3
 
3.0%
Other values (27) 36
35.6%
ASCII
ValueCountFrequency (%)
5
100.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:30.379182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4
Min length2

Characters and Unicode

Total characters102
Distinct characters51
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 row오정동
2nd row남현동
3rd row회덕동
4th row현곡면
5th row고덕면
ValueCountFrequency (%)
오정동 1
 
3.3%
남현동 1
 
3.3%
초월읍 1
 
3.3%
범서읍 1
 
3.3%
청담동 1
 
3.3%
수영동 1
 
3.3%
서신면 1
 
3.3%
지곡면 1
 
3.3%
대산읍 1
 
3.3%
온산읍 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:20:30.936839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 20
19.6%
읍 6
 
5.9%
면 6
 
5.9%
산 4
 
3.9%
서 3
 
2.9%
현 3
 
2.9%
곡 3
 
2.9%
2 3
 
2.9%
1 3
 
2.9%
정 2
 
2.0%
Other values (41) 49
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
91.2%
Decimal Number 8
 
7.8%
Other Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
21.5%
읍 6
 
6.5%
면 6
 
6.5%
산 4
 
4.3%
서 3
 
3.2%
현 3
 
3.2%
곡 3
 
3.2%
정 2
 
2.2%
성 2
 
2.2%
삼 2
 
2.2%
Other values (36) 42
45.2%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
1 3
37.5%
3 1
 
12.5%
6 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
91.2%
Common 9
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
21.5%
읍 6
 
6.5%
면 6
 
6.5%
산 4
 
4.3%
서 3
 
3.2%
현 3
 
3.2%
곡 3
 
3.2%
정 2
 
2.2%
성 2
 
2.2%
삼 2
 
2.2%
Other values (36) 42
45.2%
Common
ValueCountFrequency (%)
2 3
33.3%
1 3
33.3%
. 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
91.2%
ASCII 9
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 20
21.5%
읍 6
 
6.5%
면 6
 
6.5%
산 4
 
4.3%
서 3
 
3.2%
현 3
 
3.2%
곡 3
 
3.2%
정 2
 
2.2%
성 2
 
2.2%
삼 2
 
2.2%
Other values (36) 42
45.2%
ASCII
ValueCountFrequency (%)
2 3
33.3%
1 3
33.3%
. 1
 
11.1%
3 1
 
11.1%
6 1
 
11.1%

업종대분류코드
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
17 
F
6 
I
2 
K
2 
M
 
1
Other values (2)
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
C 17
56.7%
F 6
 
20.0%
I 2
 
6.7%
K 2
 
6.7%
M 1
 
3.3%
E 1
 
3.3%
G 1
 
3.3%

Length

2023-12-10T23:20:31.093832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:31.216730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 17
56.7%
f 6
 
20.0%
i 2
 
6.7%
k 2
 
6.7%
m 1
 
3.3%
e 1
 
3.3%
g 1
 
3.3%

업종중분류코드
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:31.345527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters17
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

Unique13 ?
Unique (%)43.3%

Sample

1st rowC29
2nd rowM72
3rd rowC12
4th rowC30
5th rowC25
ValueCountFrequency (%)
f41 5
16.7%
c29 3
 
10.0%
c30 3
 
10.0%
c25 2
 
6.7%
c20 2
 
6.7%
k64 2
 
6.7%
c19 1
 
3.3%
c13 1
 
3.3%
g46 1
 
3.3%
c27 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T23:20:31.645525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 17
18.9%
2 14
15.6%
4 11
12.2%
1 9
10.0%
F 6
 
6.7%
5 5
 
5.6%
0 5
 
5.6%
3 5
 
5.6%
9 4
 
4.4%
6 4
 
4.4%
Other values (7) 10
11.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
23.3%
4 11
18.3%
1 9
15.0%
5 5
 
8.3%
0 5
 
8.3%
3 5
 
8.3%
9 4
 
6.7%
6 4
 
6.7%
7 2
 
3.3%
8 1
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 17
56.7%
F 6
 
20.0%
K 2
 
6.7%
I 2
 
6.7%
M 1
 
3.3%
E 1
 
3.3%
G 1
 
3.3%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
23.3%
4 11
18.3%
1 9
15.0%
5 5
 
8.3%
0 5
 
8.3%
3 5
 
8.3%
9 4
 
6.7%
6 4
 
6.7%
7 2
 
3.3%
8 1
 
1.7%
Latin
ValueCountFrequency (%)
C 17
56.7%
F 6
 
20.0%
K 2
 
6.7%
I 2
 
6.7%
M 1
 
3.3%
E 1
 
3.3%
G 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 17
18.9%
2 14
15.6%
4 11
12.2%
1 9
10.0%
F 6
 
6.7%
5 5
 
5.6%
0 5
 
5.6%
3 5
 
5.6%
9 4
 
4.4%
6 4
 
4.4%
Other values (7) 10
11.1%

업종대분류명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
17 
건설업
6 
숙박 및 음식점업
2 
금융 및 보험업
2 
전문; 과학 및 기술 서비스업
 
1
Other values (2)
2 

Length

Max length23
Median length3
Mean length5
Min length3

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row제조업
2nd row전문; 과학 및 기술 서비스업
3rd row제조업
4th row제조업
5th row제조업

Common Values

ValueCountFrequency (%)
제조업 17
56.7%
건설업 6
 
20.0%
숙박 및 음식점업 2
 
6.7%
금융 및 보험업 2
 
6.7%
전문; 과학 및 기술 서비스업 1
 
3.3%
수도; 하수 및 폐기물 처리; 원료 재생업 1
 
3.3%
도매 및 소매업 1
 
3.3%

Length

2023-12-10T23:20:31.787909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:31.946497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 17
34.0%
및 7
14.0%
건설업 6
 
12.0%
숙박 2
 
4.0%
음식점업 2
 
4.0%
금융 2
 
4.0%
보험업 2
 
4.0%
폐기물 1
 
2.0%
도매 1
 
2.0%
재생업 1
 
2.0%
Other values (9) 9
18.0%

업종중분류명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:32.220137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length13.2
Min length3

Characters and Unicode

Total characters396
Distinct characters97
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

Unique13 ?
Unique (%)43.3%

Sample

1st row기타 기계 및 장비 제조업
2nd row건축기술; 엔지니어링 및 기타 과학기술 서비스업
3rd row담배 제조업
4th row자동차 및 트레일러 제조업
5th row금속가공제품 제조업; 기계 및 가구 제외
ValueCountFrequency (%)
및 18
 
15.7%
제조업 17
 
14.8%
종합 5
 
4.3%
기계 5
 
4.3%
건설업 5
 
4.3%
제외 4
 
3.5%
기타 4
 
3.5%
장비 3
 
2.6%
트레일러 3
 
2.6%
자동차 3
 
2.6%
Other values (41) 48
41.7%
2023-12-10T23:20:32.570492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
21.5%
업 30
 
7.6%
제 30
 
7.6%
및 18
 
4.5%
조 17
 
4.3%
기 14
 
3.5%
; 12
 
3.0%
품 11
 
2.8%
계 6
 
1.5%
학 6
 
1.5%
Other values (87) 167
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
75.3%
Space Separator 85
 
21.5%
Other Punctuation 12
 
3.0%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 30
 
10.1%
제 30
 
10.1%
및 18
 
6.0%
조 17
 
5.7%
기 14
 
4.7%
품 11
 
3.7%
계 6
 
2.0%
학 6
 
2.0%
의 6
 
2.0%
건 6
 
2.0%
Other values (84) 154
51.7%
Space Separator
ValueCountFrequency (%)
85
100.0%
Other Punctuation
ValueCountFrequency (%)
; 12
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
75.3%
Common 98
 
24.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 30
 
10.1%
제 30
 
10.1%
및 18
 
6.0%
조 17
 
5.7%
기 14
 
4.7%
품 11
 
3.7%
계 6
 
2.0%
학 6
 
2.0%
의 6
 
2.0%
건 6
 
2.0%
Other values (84) 154
51.7%
Common
ValueCountFrequency (%)
85
86.7%
; 12
 
12.2%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
75.3%
ASCII 98
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
86.7%
; 12
 
12.2%
1 1
 
1.0%
Hangul
ValueCountFrequency (%)
업 30
 
10.1%
제 30
 
10.1%
및 18
 
6.0%
조 17
 
5.7%
기 14
 
4.7%
품 11
 
3.7%
계 6
 
2.0%
학 6
 
2.0%
의 6
 
2.0%
건 6
 
2.0%
Other values (84) 154
51.7%

가공기업구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
3
15 
2
6 
1
4 
4
4 
98
 
1

Length

Max length2
Median length1
Mean length1.0333333
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row3
2nd row3
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 15
50.0%
2 6
 
20.0%
1 4
 
13.3%
4 4
 
13.3%
98 1
 
3.3%

Length

2023-12-10T23:20:32.697714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:32.796877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 15
50.0%
2 6
 
20.0%
1 4
 
13.3%
4 4
 
13.3%
98 1
 
3.3%

가공기업구분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
중기업
15 
중견기업
6 
대기업
4 
소기업
4 
판단제외
 
1

Length

Max length4
Median length3
Mean length3.2333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
중기업 15
50.0%
중견기업 6
 
20.0%
대기업 4
 
13.3%
소기업 4
 
13.3%
판단제외 1
 
3.3%

Length

2023-12-10T23:20:32.931506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:33.040792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중기업 15
50.0%
중견기업 6
 
20.0%
대기업 4
 
13.3%
소기업 4
 
13.3%
판단제외 1
 
3.3%

업력구간코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.4
Minimum0
Maximum40
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:33.143403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.45
Q15
median10
Q330
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)25

Descriptive statistics

Standard deviation13.981762
Coefficient of variation (CV)0.85254643
Kurtosis-1.0687049
Mean16.4
Median Absolute Deviation (MAD)8
Skewness0.66769862
Sum492
Variance195.48966
MonotonicityNot monotonic
2023-12-10T23:20:33.283275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 8
26.7%
40 5
16.7%
5 5
16.7%
30 4
13.3%
20 3
 
10.0%
2 3
 
10.0%
1 1
 
3.3%
0 1
 
3.3%
ValueCountFrequency (%)
0 1
 
3.3%
1 1
 
3.3%
2 3
 
10.0%
5 5
16.7%
10 8
26.7%
20 3
 
10.0%
30 4
13.3%
40 5
16.7%
ValueCountFrequency (%)
40 5
16.7%
30 4
13.3%
20 3
 
10.0%
10 8
26.7%
5 5
16.7%
2 3
 
10.0%
1 1
 
3.3%
0 1
 
3.3%

업력구간
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
10년 이상 20년 미만
8 
40년 이상 50년 미만
5 
5년 이상 10년 미만
5 
30년 이상 40년 미만
4 
20년 이상 30년 미만
3 
Other values (3)
5 

Length

Max length13
Median length13
Mean length12.3
Min length5

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row10년 이상 20년 미만
2nd row10년 이상 20년 미만
3rd row40년 이상 50년 미만
4th row5년 이상 10년 미만
5th row10년 이상 20년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 8
26.7%
40년 이상 50년 미만 5
16.7%
5년 이상 10년 미만 5
16.7%
30년 이상 40년 미만 4
13.3%
20년 이상 30년 미만 3
 
10.0%
2년 이상 5년 미만 3
 
10.0%
1년 이상 2년 미만 1
 
3.3%
1년 미만 1
 
3.3%

Length

2023-12-10T23:20:33.468988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:33.641472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미만 30
25.4%
이상 29
24.6%
10년 13
11.0%
20년 11
 
9.3%
40년 9
 
7.6%
5년 8
 
6.8%
30년 7
 
5.9%
50년 5
 
4.2%
2년 4
 
3.4%
1년 2
 
1.7%

항목명
Categorical

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
부채총계
10 
자본총계
6 
매출액
5 
자산총계
3 
영업이익
2 
Other values (2)
4 

Length

Max length5
Median length4
Mean length3.8333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매출액
2nd row자본총계
3rd row자본총계
4th row영업이익
5th row당기순이익

Common Values

ValueCountFrequency (%)
부채총계 10
33.3%
자본총계 6
20.0%
매출액 5
16.7%
자산총계 3
 
10.0%
영업이익 2
 
6.7%
당기순이익 2
 
6.7%
자본금 2
 
6.7%

Length

2023-12-10T23:20:33.795681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:33.946326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부채총계 10
33.3%
자본총계 6
20.0%
매출액 5
16.7%
자산총계 3
 
10.0%
영업이익 2
 
6.7%
당기순이익 2
 
6.7%
자본금 2
 
6.7%

총기업수
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
29 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
1 29
96.7%
2 1
 
3.3%

Length

2023-12-10T23:20:34.082415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:34.190619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
96.7%
2 1
 
3.3%

평균액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1555451 × 108
Minimum-398708
Maximum4.1996098 × 109
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-10T23:20:34.310592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-398708
5-th percentile11404.3
Q15475317.5
median23304068
Q347648735
95-th percentile8.1353667 × 108
Maximum4.1996098 × 109
Range4.2000085 × 109
Interquartile range (IQR)42173417

Descriptive statistics

Standard deviation7.9012233 × 108
Coefficient of variation (CV)3.6655337
Kurtosis24.257446
Mean2.1555451 × 108
Median Absolute Deviation (MAD)19777812
Skewness4.8262774
Sum6.4666354 × 109
Variance6.2429329 × 1017
MonotonicityNot monotonic
2023-12-10T23:20:34.456936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11528824.5 1
 
3.3%
5079000.0 1
 
3.3%
179487145.0 1
 
3.3%
25870725.0 1
 
3.3%
354799.0 1
 
3.3%
34559687.0 1
 
3.3%
19020972.0 1
 
3.3%
8315627.0 1
 
3.3%
49813647.0 1
 
3.3%
1332304466.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-398708.0 1
3.3%
-269555.0 1
3.3%
354799.0 1
3.3%
402824.0 1
3.3%
404884.0 1
3.3%
3074277.0 1
3.3%
3978235.0 1
3.3%
5079000.0 1
3.3%
6664270.0 1
3.3%
8315627.0 1
3.3%
ValueCountFrequency (%)
4199609788.0 1
3.3%
1332304466.0 1
3.3%
179487145.0 1
3.3%
163571555.0 1
3.3%
104287861.0 1
3.3%
49961155.0 1
3.3%
49813647.0 1
3.3%
47843935.0 1
3.3%
47063134.0 1
3.3%
41235236.0 1
3.3%

중위액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1555451 × 108
Minimum-398708
Maximum4.1996098 × 109
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-10T23:20:34.622096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-398708
5-th percentile11404.3
Q15475317.5
median23304068
Q347648735
95-th percentile8.1353667 × 108
Maximum4.1996098 × 109
Range4.2000085 × 109
Interquartile range (IQR)42173417

Descriptive statistics

Standard deviation7.9012233 × 108
Coefficient of variation (CV)3.6655337
Kurtosis24.257446
Mean2.1555451 × 108
Median Absolute Deviation (MAD)19777812
Skewness4.8262774
Sum6.4666354 × 109
Variance6.2429329 × 1017
MonotonicityNot monotonic
2023-12-10T23:20:34.776548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11528824.5 1
 
3.3%
5079000.0 1
 
3.3%
179487145.0 1
 
3.3%
25870725.0 1
 
3.3%
354799.0 1
 
3.3%
34559687.0 1
 
3.3%
19020972.0 1
 
3.3%
8315627.0 1
 
3.3%
49813647.0 1
 
3.3%
1332304466.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-398708.0 1
3.3%
-269555.0 1
3.3%
354799.0 1
3.3%
402824.0 1
3.3%
404884.0 1
3.3%
3074277.0 1
3.3%
3978235.0 1
3.3%
5079000.0 1
3.3%
6664270.0 1
3.3%
8315627.0 1
3.3%
ValueCountFrequency (%)
4199609788.0 1
3.3%
1332304466.0 1
3.3%
179487145.0 1
3.3%
163571555.0 1
3.3%
104287861.0 1
3.3%
49961155.0 1
3.3%
49813647.0 1
3.3%
47843935.0 1
3.3%
47063134.0 1
3.3%
41235236.0 1
3.3%

등록일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2021-10-19 00:00:00
Maximum2021-10-19 00:00:00
2023-12-10T23:20:34.887206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:34.994517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T23:20:35.151288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:35.266187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:20:28.810962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.343900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.598380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.894294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.436589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.674050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.965997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.519628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:28.739253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:20:35.338294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명가공기업구분코드가공기업구분업력구간코드업력구간항목명총기업수평균액중위액
시도명1.0001.0001.0000.3840.8480.3840.8480.2450.2450.6180.7160.0000.0000.0000.000
시군구명1.0001.0001.0000.7750.9210.7750.9210.8670.8670.9630.9500.0001.0000.0000.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업종대분류코드0.3840.7751.0001.0001.0001.0001.0000.1460.1460.1780.0000.0000.0000.3420.342
업종중분류코드0.8480.9211.0001.0001.0001.0001.0000.0000.0000.5650.6860.0000.0000.7550.755
업종대분류명0.3840.7751.0001.0001.0001.0001.0000.1460.1460.1780.0000.0000.0000.3420.342
업종중분류명0.8480.9211.0001.0001.0001.0001.0000.0000.0000.5650.6860.0000.0000.7550.755
가공기업구분코드0.2450.8671.0000.1460.0000.1460.0001.0001.0000.4720.5010.0000.0000.7430.743
가공기업구분0.2450.8671.0000.1460.0000.1460.0001.0001.0000.4720.5010.0000.0000.7430.743
업력구간코드0.6180.9631.0000.1780.5650.1780.5650.4720.4721.0001.0000.1490.0000.3790.379
업력구간0.7160.9501.0000.0000.6860.0000.6860.5010.5011.0001.0000.0000.0000.2710.271
항목명0.0000.0001.0000.0000.0000.0000.0000.0000.0000.1490.0001.0000.0000.0000.000
총기업수0.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
평균액0.0000.0001.0000.3420.7550.3420.7550.7430.7430.3790.2710.0000.0001.0001.000
중위액0.0000.0001.0000.3420.7550.3420.7550.7430.7430.3790.2710.0000.0001.0001.000
2023-12-10T23:20:35.487469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류코드업종대분류명가공기업구분코드시도명총기업수항목명가공기업구분업력구간
업종대분류코드1.0001.0000.0070.0990.0000.0000.0070.000
업종대분류명1.0001.0000.0070.0990.0000.0000.0070.000
가공기업구분코드0.0070.0071.0000.0000.0000.0001.0000.302
시도명0.0990.0990.0001.0000.0000.0000.0000.355
총기업수0.0000.0000.0000.0001.0000.0000.0000.000
항목명0.0000.0000.0000.0000.0001.0000.0000.000
가공기업구분0.0070.0071.0000.0000.0000.0001.0000.302
업력구간0.0000.0000.3020.3550.0000.0000.3021.000
2023-12-10T23:20:35.637071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업력구간코드평균액중위액시도명업종대분류코드업종대분류명가공기업구분코드가공기업구분업력구간항목명총기업수
업력구간코드1.0000.1250.1250.2820.0510.0510.3280.3280.9570.0000.000
평균액0.1251.0001.0000.0000.2240.2240.7130.7130.0000.0000.000
중위액0.1251.0001.0000.0000.2240.2240.7130.7130.0000.0000.000
시도명0.2820.0000.0001.0000.0990.0990.0000.0000.3550.0000.000
업종대분류코드0.0510.2240.2240.0991.0001.0000.0070.0070.0000.0000.000
업종대분류명0.0510.2240.2240.0991.0001.0000.0070.0070.0000.0000.000
가공기업구분코드0.3280.7130.7130.0000.0070.0071.0001.0000.3020.0000.000
가공기업구분0.3280.7130.7130.0000.0070.0071.0001.0000.3020.0000.000
업력구간0.9570.0000.0000.3550.0000.0000.3020.3021.0000.0000.000
항목명0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
총기업수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T23:20:29.077847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:20:29.296855image/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
02017경기부천시오정동CC29제조업기타 기계 및 장비 제조업3중기업1010년 이상 20년 미만매출액211528824.511528824.52021-10-19KEDSYS
12017서울관악구남현동MM72전문; 과학 및 기술 서비스업건축기술; 엔지니어링 및 기타 과학기술 서비스업3중기업1010년 이상 20년 미만자본총계127579368.027579368.02021-10-19KEDSYS
22017대전대덕구회덕동CC12제조업담배 제조업1대기업4040년 이상 50년 미만자본총계111528347.011528347.02021-10-19KEDSYS
32017경북경주시현곡면CC30제조업자동차 및 트레일러 제조업3중기업55년 이상 10년 미만영업이익1-398708.0-398708.02021-10-19KEDSYS
42017충남예산군고덕면CC25제조업금속가공제품 제조업; 기계 및 가구 제외4소기업1010년 이상 20년 미만당기순이익1-269555.0-269555.02021-10-19KEDSYS
52017경기광명시철산3동FF41건설업종합 건설업2중견기업3030년 이상 40년 미만부채총계1163571555.0163571555.02021-10-19KEDSYS
62017세종<NA>연동면CC20제조업화학물질 및 화학제품 제조업; 의약품 제외1대기업11년 이상 2년 미만자본총계121328607.021328607.02021-10-19KEDSYS
72017경기성남시 분당구서현1동II56숙박 및 음식점업음식점 및 주점업2중견기업4040년 이상 50년 미만매출액147843935.047843935.02021-10-19KEDSYS
82017강원고성군죽왕면II55숙박 및 음식점업숙박업3중기업1010년 이상 20년 미만부채총계1104287861.0104287861.02021-10-19KEDSYS
92017경남양산시삼성동CC24제조업1차 금속 제조업3중기업3030년 이상 40년 미만매출액125018974.025018974.02021-10-19KEDSYS
기준년도시도명시군구명행정동명업종대분류코드업종중분류코드업종대분류명업종중분류명가공기업구분코드가공기업구분업력구간코드업력구간항목명총기업수평균액중위액등록일자작업자명
202017인천서구오류왕길동EE38수도; 하수 및 폐기물 처리; 원료 재생업폐기물 수집; 운반; 처리 및 원료 재생업3중기업2020년 이상 30년 미만매출액141235236.041235236.02021-10-19KEDSYS
212017울산울주군온산읍CC25제조업금속가공제품 제조업; 기계 및 가구 제외4소기업1010년 이상 20년 미만매출액13978235.03978235.02021-10-19KEDSYS
222017충남서산시대산읍CC19제조업코크스; 연탄 및 석유정제품 제조업1대기업22년 이상 5년 미만부채총계11332304466.01332304466.02021-10-19KEDSYS
232017충남서산시지곡면CC30제조업자동차 및 트레일러 제조업1대기업22년 이상 5년 미만자산총계149813647.049813647.02021-10-19KEDSYS
242017경기화성시서신면CC27제조업의료; 정밀; 광학기기 및 시계 제조업3중기업1010년 이상 20년 미만자본총계18315627.08315627.02021-10-19KEDSYS
252017부산수영구수영동FF41건설업종합 건설업2중견기업55년 이상 10년 미만부채총계119020972.019020972.02021-10-19KEDSYS
262017서울강남구청담동GG46도매 및 소매업도매 및 상품 중개업3중기업4040년 이상 50년 미만자산총계134559687.034559687.02021-10-19KEDSYS
272017울산울주군범서읍FF41건설업종합 건설업3중기업1010년 이상 20년 미만당기순이익1354799.0354799.02021-10-19KEDSYS
282017경기광주시초월읍CC22제조업고무 및 플라스틱제품 제조업3중기업55년 이상 10년 미만자산총계125870725.025870725.02021-10-19KEDSYS
292017서울강남구대치2동CC29제조업기타 기계 및 장비 제조업2중견기업4040년 이상 50년 미만자본총계1179487145.0179487145.02021-10-19KEDSYS