Overview

Dataset statistics

Number of variables19
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory158.3 B

Variable types

Categorical14
Numeric3
Text1
Boolean1

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/99240270-38ba-4c08-8f26-afaddb833afc

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
XPORT_AT has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
SIGNGU_NM is highly overall correlated with ADSTRD_NM and 2 other fieldsHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with ADSTRD_NM and 6 other fieldsHigh correlation
INDUTY_MLSFC_NM is highly overall correlated with PDSMLPZ_SCTN_CODE and 9 other fieldsHigh correlation
INDUTY_MLSFC_CODE is highly overall correlated with PDSMLPZ_SCTN_CODE and 9 other fieldsHigh correlation
PRCSS_ENTRPRS_SE_CODE is highly overall correlated with PDSMLPZ_SCTN_CODE and 9 other fieldsHigh correlation
ADSTRD_NM is highly overall correlated with SIGNGU_NM and 7 other fieldsHigh correlation
PRCSS_ENTRPRS_SE is highly overall correlated with PDSMLPZ_SCTN_CODE and 9 other fieldsHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with ADSTRD_NM and 6 other fieldsHigh correlation
PDSMLPZ_SCTN_CODE is highly overall correlated with INDUTY_MLSFC_CODE and 4 other fieldsHigh correlation
SELNG_AM is highly overall correlated with BSN_PROFIT and 3 other fieldsHigh correlation
BSN_PROFIT is highly overall correlated with SELNG_AM and 2 other fieldsHigh correlation
PDSMLPZ_SCTN_NM is highly overall correlated with PDSMLPZ_SCTN_CODE and 5 other fieldsHigh correlation
TOT_ENTRPRS_CO is highly overall correlated with SELNG_AM and 4 other fieldsHigh correlation
TOT_ENTRPRS_CO is highly imbalanced (80.6%)Imbalance
PDSMLPZ_SCTN_CODE has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:11:56.637684
Analysis finished2023-12-10 14:12:01.609355
Duration4.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2016-01
100 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-01
2nd row2016-01
3rd row2016-01
4th row2016-01
5th row2016-01

Common Values

ValueCountFrequency (%)
2016-01 100
100.0%

Length

2023-12-10T23:12:01.757146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:01.923218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-01 100
100.0%

CTPRVN_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 100
100.0%

Length

2023-12-10T23:12:02.082957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:02.238503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 100
100.0%

SIGNGU_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광명시
41 
과천시
36 
가평군
23 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광명시 41
41.0%
과천시 36
36.0%
가평군 23
23.0%

Length

2023-12-10T23:12:02.402664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:02.585879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광명시 41
41.0%
과천시 36
36.0%
가평군 23
23.0%

ADSTRD_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
과천동
27 
소하1동
20 
광명6동
12 
갈현동
9 
상면
6 
Other values (9)
26 

Length

Max length4
Median length3.5
Mean length3.3
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row학온동
2nd row광명2동
3rd row광명2동
4th row광명4동
5th row광명6동

Common Values

ValueCountFrequency (%)
과천동 27
27.0%
소하1동 20
20.0%
광명6동 12
12.0%
갈현동 9
 
9.0%
상면 6
 
6.0%
광명7동 5
 
5.0%
설악면 5
 
5.0%
북면 4
 
4.0%
조종면 4
 
4.0%
가평읍 3
 
3.0%
Other values (4) 5
 
5.0%

Length

2023-12-10T23:12:02.807296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과천동 27
27.0%
소하1동 20
20.0%
광명6동 12
12.0%
갈현동 9
 
9.0%
상면 6
 
6.0%
광명7동 5
 
5.0%
설악면 5
 
5.0%
북면 4
 
4.0%
조종면 4
 
4.0%
가평읍 3
 
3.0%
Other values (4) 5
 
5.0%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
C
67 
G
17 
E
 
6
F
 
2
R
 
2
Other values (4)
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
C 67
67.0%
G 17
 
17.0%
E 6
 
6.0%
F 2
 
2.0%
R 2
 
2.0%
L 2
 
2.0%
A 2
 
2.0%
N 1
 
1.0%
M 1
 
1.0%

Length

2023-12-10T23:12:03.072474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:03.321978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 67
67.0%
g 17
 
17.0%
e 6
 
6.0%
f 2
 
2.0%
r 2
 
2.0%
l 2
 
2.0%
a 2
 
2.0%
n 1
 
1.0%
m 1
 
1.0%

INDUTY_MLSFC_CODE
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
C14
18 
G46
17 
C29
8 
C26
8 
E38
6 
Other values (18)
43 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st rowN76
2nd rowG46
3rd rowG46
4th rowC14
5th rowC17

Common Values

ValueCountFrequency (%)
C14 18
18.0%
G46 17
17.0%
C29 8
 
8.0%
C26 8
 
8.0%
E38 6
 
6.0%
C28 6
 
6.0%
C15 5
 
5.0%
C13 4
 
4.0%
C10 4
 
4.0%
C25 2
 
2.0%
Other values (13) 22
22.0%

Length

2023-12-10T23:12:03.594864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c14 18
18.0%
g46 17
17.0%
c29 8
 
8.0%
c26 8
 
8.0%
e38 6
 
6.0%
c28 6
 
6.0%
c15 5
 
5.0%
c13 4
 
4.0%
c10 4
 
4.0%
a01 2
 
2.0%
Other values (13) 22
22.0%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제조업
67 
도매 및 소매업
17 
수도; 하수 및 폐기물 처리; 원료 재생업
 
6
건설업
 
2
예술; 스포츠 및 여가관련 서비스업
 
2
Other values (4)
 
6

Length

Max length24
Median length3
Mean length5.89
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row사업시설 관리; 사업 지원 및 임대 서비스업
2nd row도매 및 소매업
3rd row도매 및 소매업
4th row제조업
5th row제조업

Common Values

ValueCountFrequency (%)
제조업 67
67.0%
도매 및 소매업 17
 
17.0%
수도; 하수 및 폐기물 처리; 원료 재생업 6
 
6.0%
건설업 2
 
2.0%
예술; 스포츠 및 여가관련 서비스업 2
 
2.0%
부동산업 2
 
2.0%
농업; 임업 및 어업 2
 
2.0%
사업시설 관리; 사업 지원 및 임대 서비스업 1
 
1.0%
전문; 과학 및 기술 서비스업 1
 
1.0%

Length

2023-12-10T23:12:03.828835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:04.102740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 67
34.5%
및 29
14.9%
소매업 17
 
8.8%
도매 17
 
8.8%
수도 6
 
3.1%
하수 6
 
3.1%
폐기물 6
 
3.1%
처리 6
 
3.1%
원료 6
 
3.1%
재생업 6
 
3.1%
Other values (17) 28
14.4%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
의복; 의복 액세서리 및 모피제품 제조업
18 
도매 및 상품 중개업
17 
기타 기계 및 장비 제조업
8 
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업
8 
폐기물 수집; 운반; 처리 및 원료 재생업
6 
Other values (18)
43 

Length

Max length28
Median length21.5
Mean length15.79
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row임대업; 부동산 제외
2nd row도매 및 상품 중개업
3rd row도매 및 상품 중개업
4th row의복; 의복 액세서리 및 모피제품 제조업
5th row펄프; 종이 및 종이제품 제조업

Common Values

ValueCountFrequency (%)
의복; 의복 액세서리 및 모피제품 제조업 18
18.0%
도매 및 상품 중개업 17
17.0%
기타 기계 및 장비 제조업 8
 
8.0%
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업 8
 
8.0%
폐기물 수집; 운반; 처리 및 원료 재생업 6
 
6.0%
전기장비 제조업 6
 
6.0%
가죽; 가방 및 신발 제조업 5
 
5.0%
섬유제품 제조업; 의복제외 4
 
4.0%
식료품 제조업 4
 
4.0%
금속가공제품 제조업; 기계 및 가구 제외 2
 
2.0%
Other values (13) 22
22.0%

Length

2023-12-10T23:12:04.381537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 74
16.1%
제조업 67
 
14.6%
의복 36
 
7.8%
액세서리 18
 
3.9%
모피제품 18
 
3.9%
도매 17
 
3.7%
상품 17
 
3.7%
중개업 17
 
3.7%
기타 11
 
2.4%
기계 10
 
2.2%
Other values (51) 175
38.0%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4
73 
3
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 73
73.0%
3 27
 
27.0%

Length

2023-12-10T23:12:04.672667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:04.938725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 73
73.0%
3 27
 
27.0%

PRCSS_ENTRPRS_SE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
소기업
73 
중기업
27 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소기업 73
73.0%
중기업 27
 
27.0%

Length

2023-12-10T23:12:05.097198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:05.248908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소기업 73
73.0%
중기업 27
 
27.0%

PDSMLPZ_SCTN_CODE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.47
Minimum0
Maximum30
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:05.385481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q12
median10
Q310
95-th percentile20
Maximum30
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.3316664
Coefficient of variation (CV)0.74754031
Kurtosis1.5172541
Mean8.47
Median Absolute Deviation (MAD)5
Skewness1.1093602
Sum847
Variance40.09
MonotonicityNot monotonic
2023-12-10T23:12:05.559754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 45
45.0%
2 23
23.0%
5 14
 
14.0%
20 11
 
11.0%
0 4
 
4.0%
30 2
 
2.0%
1 1
 
1.0%
ValueCountFrequency (%)
0 4
 
4.0%
1 1
 
1.0%
2 23
23.0%
5 14
 
14.0%
10 45
45.0%
20 11
 
11.0%
30 2
 
2.0%
ValueCountFrequency (%)
30 2
 
2.0%
20 11
 
11.0%
10 45
45.0%
5 14
 
14.0%
2 23
23.0%
1 1
 
1.0%
0 4
 
4.0%

PDSMLPZ_SCTN_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
10년 이상 20년 미만
45 
2년 이상 5년 미만
23 
5년 이상 10년 미만
14 
20년 이상 30년 미만
11 
1년 미만
 
4
Other values (2)
 
3

Length

Max length13
Median length13
Mean length12.06
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row10년 이상 20년 미만
2nd row2년 이상 5년 미만
3rd row2년 이상 5년 미만
4th row5년 이상 10년 미만
5th row10년 이상 20년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 45
45.0%
2년 이상 5년 미만 23
23.0%
5년 이상 10년 미만 14
 
14.0%
20년 이상 30년 미만 11
 
11.0%
1년 미만 4
 
4.0%
30년 이상 40년 미만 2
 
2.0%
1년 이상 2년 미만 1
 
1.0%

Length

2023-12-10T23:12:05.766464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:06.309036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미만 100
25.5%
이상 96
24.5%
10년 59
15.1%
20년 56
14.3%
5년 37
 
9.4%
2년 24
 
6.1%
30년 13
 
3.3%
1년 5
 
1.3%
40년 2
 
0.5%

PRDUCT_NM
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T23:12:06.731857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11.5
Mean length5.1
Min length1

Characters and Unicode

Total characters510
Distinct characters181
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row건설장비
2nd row시약장
3rd row과학기자재
4th row의류 외
5th row지함
ValueCountFrequency (%)
외 11
 
7.9%
의류 4
 
2.9%
등 3
 
2.2%
장비 2
 
1.4%
치과 2
 
1.4%
산소발생기 2
 
1.4%
제조 2
 
1.4%
케이스 2
 
1.4%
티셔츠 2
 
1.4%
rfid 2
 
1.4%
Other values (102) 107
77.0%
2023-12-10T23:12:07.429846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
7.8%
기 16
 
3.1%
외 12
 
2.4%
조 11
 
2.2%
비 10
 
2.0%
용 10
 
2.0%
화 10
 
2.0%
장 9
 
1.8%
품 9
 
1.8%
스 8
 
1.6%
Other values (171) 375
73.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 448
87.8%
Space Separator 40
 
7.8%
Uppercase Letter 22
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 16
 
3.6%
외 12
 
2.7%
조 11
 
2.5%
비 10
 
2.2%
용 10
 
2.2%
화 10
 
2.2%
장 9
 
2.0%
품 9
 
2.0%
스 8
 
1.8%
업 8
 
1.8%
Other values (156) 345
77.0%
Uppercase Letter
ValueCountFrequency (%)
D 3
13.6%
R 3
13.6%
I 3
13.6%
F 2
9.1%
E 2
9.1%
T 1
 
4.5%
U 1
 
4.5%
C 1
 
4.5%
L 1
 
4.5%
V 1
 
4.5%
Other values (4) 4
18.2%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 448
87.8%
Common 40
 
7.8%
Latin 22
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 16
 
3.6%
외 12
 
2.7%
조 11
 
2.5%
비 10
 
2.2%
용 10
 
2.2%
화 10
 
2.2%
장 9
 
2.0%
품 9
 
2.0%
스 8
 
1.8%
업 8
 
1.8%
Other values (156) 345
77.0%
Latin
ValueCountFrequency (%)
D 3
13.6%
R 3
13.6%
I 3
13.6%
F 2
9.1%
E 2
9.1%
T 1
 
4.5%
U 1
 
4.5%
C 1
 
4.5%
L 1
 
4.5%
V 1
 
4.5%
Other values (4) 4
18.2%
Common
ValueCountFrequency (%)
40
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 448
87.8%
ASCII 62
 
12.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40
64.5%
D 3
 
4.8%
R 3
 
4.8%
I 3
 
4.8%
F 2
 
3.2%
E 2
 
3.2%
T 1
 
1.6%
U 1
 
1.6%
C 1
 
1.6%
L 1
 
1.6%
Other values (5) 5
 
8.1%
Hangul
ValueCountFrequency (%)
기 16
 
3.6%
외 12
 
2.7%
조 11
 
2.5%
비 10
 
2.2%
용 10
 
2.2%
화 10
 
2.2%
장 9
 
2.0%
품 9
 
2.0%
스 8
 
1.8%
업 8
 
1.8%
Other values (156) 345
77.0%

XPORT_AT
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2023-12-10T23:12:07.625220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

TOT_ENTRPRS_CO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
97 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 97
97.0%
2 3
 
3.0%

Length

2023-12-10T23:12:07.781006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:07.934527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 97
97.0%
2 3
 
3.0%

SELNG_AM
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9552711.7
Minimum27000
Maximum55340957
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:08.100804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27000
5-th percentile121053
Q1984656.5
median3386896
Q37269643.2
95-th percentile55340957
Maximum55340957
Range55313957
Interquartile range (IQR)6284986.8

Descriptive statistics

Standard deviation14847143
Coefficient of variation (CV)1.5542333
Kurtosis4.1574362
Mean9552711.7
Median Absolute Deviation (MAD)2710807
Skewness2.2661446
Sum9.5527117 × 108
Variance2.2043765 × 1014
MonotonicityNot monotonic
2023-12-10T23:12:08.476912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2310951 7
 
7.0%
980371 6
 
6.0%
55340957 6
 
6.0%
6097703 5
 
5.0%
6209786 4
 
4.0%
1966089 4
 
4.0%
4917318 4
 
4.0%
928301 3
 
3.0%
14032000 3
 
3.0%
805438 3
 
3.0%
Other values (33) 55
55.0%
ValueCountFrequency (%)
27000 2
 
2.0%
78687 2
 
2.0%
121053 2
 
2.0%
181856 2
 
2.0%
340160 1
 
1.0%
517604 2
 
2.0%
805438 3
3.0%
854567 2
 
2.0%
928301 3
3.0%
980371 6
6.0%
ValueCountFrequency (%)
55340957 6
6.0%
49788720 2
 
2.0%
34989524 2
 
2.0%
22629668 1
 
1.0%
22139616 2
 
2.0%
21741213 2
 
2.0%
14738620 2
 
2.0%
14032000 3
3.0%
13217436 1
 
1.0%
13132161 2
 
2.0%

BSN_PROFIT
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371772
Minimum-2565077
Maximum5806717
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)19.0%
Memory size1.0 KiB
2023-12-10T23:12:08.734225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2565077
5-th percentile-273065
Q17630
median79704.5
Q3418314.75
95-th percentile1908000
Maximum5806717
Range8371794
Interquartile range (IQR)410684.75

Descriptive statistics

Standard deviation1042560.2
Coefficient of variation (CV)2.8042999
Kurtosis14.76045
Mean371772
Median Absolute Deviation (MAD)106680.5
Skewness2.7637964
Sum37177200
Variance1.0869317 × 1012
MonotonicityNot monotonic
2023-12-10T23:12:08.969487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
7630 7
 
7.0%
19390 6
 
6.0%
925676 6
 
6.0%
186385 5
 
5.0%
370950 4
 
4.0%
50043 4
 
4.0%
163367 4
 
4.0%
-191457 3
 
3.0%
1908000 3
 
3.0%
53899 3
 
3.0%
Other values (33) 55
55.0%
ValueCountFrequency (%)
-2565077 2
2.0%
-372566 2
2.0%
-273065 2
2.0%
-191457 3
3.0%
-37000 2
2.0%
-36523 2
2.0%
-19613 2
2.0%
-15390 2
2.0%
-8627 2
2.0%
2536 2
2.0%
ValueCountFrequency (%)
5806717 2
 
2.0%
2583619 2
 
2.0%
1908000 3
3.0%
1604607 1
 
1.0%
1559507 1
 
1.0%
925676 6
6.0%
786422 2
 
2.0%
664227 2
 
2.0%
657215 3
3.0%
516855 3
3.0%

REGIST_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020-12-01
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-01
2nd row2020-12-01
3rd row2020-12-01
4th row2020-12-01
5th row2020-12-01

Common Values

ValueCountFrequency (%)
2020-12-01 100
100.0%

Length

2023-12-10T23:12:09.260819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:09.493018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-01 100
100.0%

OPERTOR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KEDSYS
100 

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 100
100.0%

Length

2023-12-10T23:12:09.661186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:09.825440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 100
100.0%

Interactions

2023-12-10T23:12:00.325297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:59.364209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:59.828091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.546591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:59.516855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.007408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.757886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:11:59.666085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:00.191798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:09.997886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMPRDUCT_NMTOT_ENTRPRS_COSELNG_AMBSN_PROFIT
SIGNGU_NM1.0001.0000.6510.8850.6510.8850.1940.1940.4710.5710.9240.0000.5990.607
ADSTRD_NM1.0001.0000.8710.9520.8710.9520.8770.8770.7360.8940.9990.1300.7590.590
INDUTY_LCLAS_CODE0.6510.8711.0001.0001.0001.0000.5530.5530.5060.6921.0000.7760.8480.606
INDUTY_MLSFC_CODE0.8850.9521.0001.0001.0001.0000.8120.8120.9290.9390.9960.8360.7790.616
INDUTY_LCLAS_NM0.6510.8711.0001.0001.0001.0000.5530.5530.5060.6921.0000.7760.8480.606
INDUTY_MLSFC_NM0.8850.9521.0001.0001.0001.0000.8120.8120.9290.9390.9960.8360.7790.616
PRCSS_ENTRPRS_SE_CODE0.1940.8770.5530.8120.5530.8121.0000.9990.4800.5431.0000.0000.9420.737
PRCSS_ENTRPRS_SE0.1940.8770.5530.8120.5530.8120.9991.0000.4800.5431.0000.0000.9420.737
PDSMLPZ_SCTN_CODE0.4710.7360.5060.9290.5060.9290.4800.4801.0001.0000.6150.0000.5620.448
PDSMLPZ_SCTN_NM0.5710.8940.6920.9390.6920.9390.5430.5431.0001.0000.9000.0000.4810.431
PRDUCT_NM0.9240.9991.0000.9961.0000.9961.0001.0000.6150.9001.0000.0000.9960.937
TOT_ENTRPRS_CO0.0000.1300.7760.8360.7760.8360.0000.0000.0000.0000.0001.0000.6910.000
SELNG_AM0.5990.7590.8480.7790.8480.7790.9420.9420.5620.4810.9960.6911.0000.907
BSN_PROFIT0.6070.5900.6060.6160.6060.6160.7370.7370.4480.4310.9370.0000.9071.000
2023-12-10T23:12:10.306780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_NMINDUTY_MLSFC_CODEPRCSS_ENTRPRS_SE_CODETOT_ENTRPRS_COADSTRD_NMPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_NMINDUTY_LCLAS_NM
SIGNGU_NM1.0000.3540.6550.6550.3170.0000.9420.3170.4510.354
INDUTY_LCLAS_CODE0.3541.0000.9200.9200.5350.7660.5990.5350.4531.000
INDUTY_MLSFC_NM0.6550.9201.0001.0000.6530.6770.6760.6530.7030.920
INDUTY_MLSFC_CODE0.6550.9201.0001.0000.6530.6770.6760.6530.7030.920
PRCSS_ENTRPRS_SE_CODE0.3170.5350.6530.6531.0000.0000.6800.9740.5680.535
TOT_ENTRPRS_CO0.0000.7660.6770.6770.0001.0000.0880.0000.0000.766
ADSTRD_NM0.9420.5990.6760.6760.6800.0881.0000.6800.5370.599
PRCSS_ENTRPRS_SE0.3170.5350.6530.6530.9740.0000.6801.0000.5680.535
PDSMLPZ_SCTN_NM0.4510.4530.7030.7030.5680.0000.5370.5681.0000.453
INDUTY_LCLAS_NM0.3541.0000.9200.9200.5350.7660.5990.5350.4531.000
2023-12-10T23:12:10.582911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDSMLPZ_SCTN_CODESELNG_AMBSN_PROFITSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_NMTOT_ENTRPRS_CO
PDSMLPZ_SCTN_CODE1.0000.4800.1770.3990.4730.3140.6990.3140.6990.5740.5740.9890.000
SELNG_AM0.4801.0000.7500.4240.4520.4330.4140.4330.4140.7670.7670.2860.512
BSN_PROFIT0.1770.7501.0000.3120.2720.3000.4590.3000.4590.5600.5600.2640.000
SIGNGU_NM0.3990.4240.3121.0000.9420.3540.6550.3540.6550.3170.3170.4510.000
ADSTRD_NM0.4730.4520.2720.9421.0000.5990.6760.5990.6760.6800.6800.5370.088
INDUTY_LCLAS_CODE0.3140.4330.3000.3540.5991.0000.9201.0000.9200.5350.5350.4530.766
INDUTY_MLSFC_CODE0.6990.4140.4590.6550.6760.9201.0000.9201.0000.6530.6530.7030.677
INDUTY_LCLAS_NM0.3140.4330.3000.3540.5991.0000.9201.0000.9200.5350.5350.4530.766
INDUTY_MLSFC_NM0.6990.4140.4590.6550.6760.9201.0000.9201.0000.6530.6530.7030.677
PRCSS_ENTRPRS_SE_CODE0.5740.7670.5600.3170.6800.5350.6530.5350.6531.0000.9740.5680.000
PRCSS_ENTRPRS_SE0.5740.7670.5600.3170.6800.5350.6530.5350.6530.9741.0000.5680.000
PDSMLPZ_SCTN_NM0.9890.2860.2640.4510.5370.4530.7030.4530.7030.5680.5681.0000.000
TOT_ENTRPRS_CO0.0000.5120.0000.0000.0880.7660.6770.7660.6770.0000.0000.0001.000

Missing values

2023-12-10T23:12:00.968952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:01.420330image/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

STDR_YMCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMPRDUCT_NMXPORT_ATTOT_ENTRPRS_COSELNG_AMBSN_PROFITREGIST_DEOPERTOR_NM
02016-01경기광명시학온동NN76사업시설 관리; 사업 지원 및 임대 서비스업임대업; 부동산 제외3중기업1010년 이상 20년 미만건설장비Y169957152736332020-12-01KEDSYS
12016-01경기광명시광명2동GG46도매 및 소매업도매 및 상품 중개업4소기업22년 이상 5년 미만시약장Y1517604-365232020-12-01KEDSYS
22016-01경기광명시광명2동GG46도매 및 소매업도매 및 상품 중개업4소기업22년 이상 5년 미만과학기자재Y1517604-365232020-12-01KEDSYS
32016-01경기광명시광명4동CC14제조업의복; 의복 액세서리 및 모피제품 제조업4소기업55년 이상 10년 미만의류 외Y135214251323052020-12-01KEDSYS
42016-01경기광명시광명6동CC17제조업펄프; 종이 및 종이제품 제조업4소기업1010년 이상 20년 미만지함Y11571720705022020-12-01KEDSYS
52016-01경기광명시광명6동CC27제조업의료; 정밀; 광학기기 및 시계 제조업4소기업55년 이상 10년 미만공장자동화설비Y126601691153162020-12-01KEDSYS
62016-01경기광명시광명6동CC27제조업의료; 정밀; 광학기기 및 시계 제조업4소기업55년 이상 10년 미만공장자동화설비 외Y126601691153162020-12-01KEDSYS
72016-01경기광명시광명6동CC29제조업기타 기계 및 장비 제조업4소기업1010년 이상 20년 미만절단기Y11966089500432020-12-01KEDSYS
82016-01경기광명시광명6동CC29제조업기타 기계 및 장비 제조업4소기업1010년 이상 20년 미만크러셔Y11966089500432020-12-01KEDSYS
92016-01경기광명시광명6동CC29제조업기타 기계 및 장비 제조업4소기업1010년 이상 20년 미만소형절단기Y11966089500432020-12-01KEDSYS
STDR_YMCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMPRDUCT_NMXPORT_ATTOT_ENTRPRS_COSELNG_AMBSN_PROFITREGIST_DEOPERTOR_NM
902016-01경기과천시과천동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업1010년 이상 20년 미만산업용모니터Y160977031863852020-12-01KEDSYS
912016-01경기과천시과천동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업1010년 이상 20년 미만컴퓨터주변기기Y160977031863852020-12-01KEDSYS
922016-01경기과천시과천동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업1010년 이상 20년 미만산업용모니터컴퓨터제조Y160977031863852020-12-01KEDSYS
932016-01경기과천시과천동CC28제조업전기장비 제조업4소기업1010년 이상 20년 미만LED조명Y1980371193902020-12-01KEDSYS
942016-01경기과천시과천동CC28제조업전기장비 제조업4소기업1010년 이상 20년 미만망원렌즈Y1980371193902020-12-01KEDSYS
952016-01경기과천시과천동CC28제조업전기장비 제조업4소기업1010년 이상 20년 미만모션보드Y1980371193902020-12-01KEDSYS
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