Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory163.4 B

Variable types

Categorical14
Text1
Numeric4

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/8da58b6d-4c5f-41b6-b26c-1684c0e4155e

Alerts

STDR_YEAR has constant value ""Constant
CTPRVN_NM has constant value ""Constant
IEM_NM has constant value ""Constant
TOT_ENTRPRS_CO has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
INDUTY_MLSFC_NM is highly overall correlated with INDUTY_LCLAS_CODE and 2 other fieldsHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with INDUTY_MLSFC_CODE and 2 other fieldsHigh correlation
INDUTY_MLSFC_CODE is highly overall correlated with INDUTY_LCLAS_CODE and 2 other fieldsHigh correlation
PRCSS_ENTRPRS_SE_CODE is highly overall correlated with TOTAMT and 3 other fieldsHigh correlation
PRCSS_ENTRPRS_SE is highly overall correlated with TOTAMT and 3 other fieldsHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_LCLAS_CODE and 2 other fieldsHigh correlation
PDSMLPZ_SCTN_CODE is highly overall correlated with PDSMLPZ_SCTNHigh correlation
TOTAMT is highly overall correlated with AVRG_AM and 3 other fieldsHigh correlation
AVRG_AM is highly overall correlated with TOTAMT and 3 other fieldsHigh correlation
FSTLTN_AM is highly overall correlated with TOTAMT and 3 other fieldsHigh correlation
PDSMLPZ_SCTN is highly overall correlated with PDSMLPZ_SCTN_CODEHigh correlation
TOTAMT has 6 (20.0%) zerosZeros
AVRG_AM has 6 (20.0%) zerosZeros
FSTLTN_AM has 6 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:13:23.253782
Analysis finished2023-12-10 14:13:29.219161
Duration5.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YEAR
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

2023-12-10T23:13:29.774158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:30.080068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 30
100.0%

CTPRVN_NM
Categorical

CONSTANT 

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

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 30
100.0%

Length

2023-12-10T23:13:30.378470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:30.647360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 30
100.0%

SIGNGU_NM
Categorical

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
광주시
6 
성남시분당구
5 
성남시중원구
4 
고양시일산동구
3 
군포시
2 
Other values (7)
10 

Length

Max length7
Median length6.5
Mean length4.9333333
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row고양시덕양구
2nd row고양시일산동구
3rd row고양시일산동구
4th row고양시일산동구
5th row광주시

Common Values

ValueCountFrequency (%)
광주시 6
20.0%
성남시분당구 5
16.7%
성남시중원구 4
13.3%
고양시일산동구 3
10.0%
군포시 2
 
6.7%
김포시 2
 
6.7%
수원시권선구 2
 
6.7%
수원시영통구 2
 
6.7%
고양시덕양구 1
 
3.3%
구리시 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:13:30.937768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광주시 6
20.0%
성남시분당구 5
16.7%
성남시중원구 4
13.3%
고양시일산동구 3
10.0%
군포시 2
 
6.7%
김포시 2
 
6.7%
수원시권선구 2
 
6.7%
수원시영통구 2
 
6.7%
고양시덕양구 1
 
3.3%
구리시 1
 
3.3%
Other values (2) 2
 
6.7%

ADSTRD_NM
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:13:31.359785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4.5
Mean length3.5
Min length2

Characters and Unicode

Total characters105
Distinct characters34
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

Unique9 ?
Unique (%)30.0%

Sample

1st row고양동
2nd row고봉동
3rd row백석2동
4th row장항2동
5th row광남동
ValueCountFrequency (%)
상대원1동 4
13.3%
오포읍 3
 
10.0%
양촌읍 2
 
6.7%
군포1동 2
 
6.7%
평동 2
 
6.7%
야탑3동 2
 
6.7%
삼평동 2
 
6.7%
광교1동 2
 
6.7%
광남동 2
 
6.7%
동구동 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T23:13:32.278440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
22.9%
1 9
 
8.6%
읍 7
 
6.7%
포 5
 
4.8%
상 4
 
3.8%
광 4
 
3.8%
원 4
 
3.8%
평 4
 
3.8%
대 4
 
3.8%
오 3
 
2.9%
Other values (24) 37
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
87.6%
Decimal Number 13
 
12.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
26.1%
읍 7
 
7.6%
포 5
 
5.4%
상 4
 
4.3%
광 4
 
4.3%
원 4
 
4.3%
평 4
 
4.3%
대 4
 
4.3%
오 3
 
3.3%
양 3
 
3.3%
Other values (21) 30
32.6%
Decimal Number
ValueCountFrequency (%)
1 9
69.2%
2 2
 
15.4%
3 2
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
87.6%
Common 13
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
26.1%
읍 7
 
7.6%
포 5
 
5.4%
상 4
 
4.3%
광 4
 
4.3%
원 4
 
4.3%
평 4
 
4.3%
대 4
 
4.3%
오 3
 
3.3%
양 3
 
3.3%
Other values (21) 30
32.6%
Common
ValueCountFrequency (%)
1 9
69.2%
2 2
 
15.4%
3 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
87.6%
ASCII 13
 
12.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
26.1%
읍 7
 
7.6%
포 5
 
5.4%
상 4
 
4.3%
광 4
 
4.3%
원 4
 
4.3%
평 4
 
4.3%
대 4
 
4.3%
오 3
 
3.3%
양 3
 
3.3%
Other values (21) 30
32.6%
ASCII
ValueCountFrequency (%)
1 9
69.2%
2 2
 
15.4%
3 2
 
15.4%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
15 
G
7 
F
2 
M
2 
J
2 
Other values (2)
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
C 15
50.0%
G 7
23.3%
F 2
 
6.7%
M 2
 
6.7%
J 2
 
6.7%
I 1
 
3.3%
N 1
 
3.3%

Length

2023-12-10T23:13:32.546670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:32.775182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 15
50.0%
g 7
23.3%
f 2
 
6.7%
m 2
 
6.7%
j 2
 
6.7%
i 1
 
3.3%
n 1
 
3.3%

INDUTY_MLSFC_CODE
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
G46
6 
C26
4 
C10
3 
C29
3 
F42
2 
Other values (9)
12 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st rowG45
2nd rowG46
3rd rowF42
4th rowG46
5th rowG46

Common Values

ValueCountFrequency (%)
G46 6
20.0%
C26 4
13.3%
C10 3
10.0%
C29 3
10.0%
F42 2
 
6.7%
M72 2
 
6.7%
J59 2
 
6.7%
C27 2
 
6.7%
G45 1
 
3.3%
C22 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:13:33.076881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g46 6
20.0%
c26 4
13.3%
c10 3
10.0%
c29 3
10.0%
f42 2
 
6.7%
m72 2
 
6.7%
j59 2
 
6.7%
c27 2
 
6.7%
g45 1
 
3.3%
c22 1
 
3.3%
Other values (4) 4
13.3%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
15 
도매 및 소매업
7 
건설업
2 
전문; 과학 및 기술 서비스업
2 
정보통신업
2 
Other values (2)
2 

Length

Max length24
Median length3
Mean length6.0666667
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row도매 및 소매업
2nd row도매 및 소매업
3rd row건설업
4th row도매 및 소매업
5th row도매 및 소매업

Common Values

ValueCountFrequency (%)
제조업 15
50.0%
도매 및 소매업 7
23.3%
건설업 2
 
6.7%
전문; 과학 및 기술 서비스업 2
 
6.7%
정보통신업 2
 
6.7%
숙박 및 음식점업 1
 
3.3%
사업시설 관리; 사업 지원 및 임대 서비스업 1
 
3.3%

Length

2023-12-10T23:13:33.435070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:33.659164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 15
25.0%
및 11
18.3%
도매 7
11.7%
소매업 7
11.7%
서비스업 3
 
5.0%
전문 2
 
3.3%
과학 2
 
3.3%
기술 2
 
3.3%
건설업 2
 
3.3%
정보통신업 2
 
3.3%
Other values (7) 7
11.7%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
도매 및 상품 중개업
6 
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업
4 
식료품 제조업
3 
기타 기계 및 장비 제조업
3 
전문직별 공사업
2 
Other values (9)
12 

Length

Max length28
Median length19
Mean length15.566667
Min length7

Unique

Unique6 ?
Unique (%)20.0%

Sample

1st row자동차 및 부품 판매업
2nd row도매 및 상품 중개업
3rd row전문직별 공사업
4th row도매 및 상품 중개업
5th row도매 및 상품 중개업

Common Values

ValueCountFrequency (%)
도매 및 상품 중개업 6
20.0%
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업 4
13.3%
식료품 제조업 3
10.0%
기타 기계 및 장비 제조업 3
10.0%
전문직별 공사업 2
 
6.7%
건축기술; 엔지니어링 및 기타 과학기술 서비스업 2
 
6.7%
영상ㆍ오디오 기록물 제작 및 배급업 2
 
6.7%
의료; 정밀; 광학기기 및 시계 제조업 2
 
6.7%
자동차 및 부품 판매업 1
 
3.3%
고무 및 플라스틱제품 제조업 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:13:33.987472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 24
17.9%
제조업 15
 
11.2%
도매 6
 
4.5%
상품 6
 
4.5%
중개업 6
 
4.5%
기타 5
 
3.7%
전자부품 4
 
3.0%
컴퓨터 4
 
3.0%
영상 4
 
3.0%
음향 4
 
3.0%
Other values (32) 56
41.8%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
3
18 
4
9 
2
2 
98
 
1

Length

Max length2
Median length1
Mean length1.0333333
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
3 18
60.0%
4 9
30.0%
2 2
 
6.7%
98 1
 
3.3%

Length

2023-12-10T23:13:34.195383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:34.386813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 18
60.0%
4 9
30.0%
2 2
 
6.7%
98 1
 
3.3%

PRCSS_ENTRPRS_SE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
중기업
18 
소기업
9 
중견기업
2 
판단제외
 
1

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
중기업 18
60.0%
소기업 9
30.0%
중견기업 2
 
6.7%
판단제외 1
 
3.3%

Length

2023-12-10T23:13:34.639589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:34.925358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중기업 18
60.0%
소기업 9
30.0%
중견기업 2
 
6.7%
판단제외 1
 
3.3%

PDSMLPZ_SCTN_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.866667
Minimum2
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:35.093190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q110
median10
Q320
95-th percentile45.5
Maximum60
Range58
Interquartile range (IQR)10

Descriptive statistics

Standard deviation14.001806
Coefficient of variation (CV)0.78368318
Kurtosis1.9948854
Mean17.866667
Median Absolute Deviation (MAD)8
Skewness1.4055546
Sum536
Variance196.05057
MonotonicityNot monotonic
2023-12-10T23:13:35.302901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 11
36.7%
20 7
23.3%
30 4
 
13.3%
2 3
 
10.0%
5 2
 
6.7%
40 1
 
3.3%
50 1
 
3.3%
60 1
 
3.3%
ValueCountFrequency (%)
2 3
 
10.0%
5 2
 
6.7%
10 11
36.7%
20 7
23.3%
30 4
 
13.3%
40 1
 
3.3%
50 1
 
3.3%
60 1
 
3.3%
ValueCountFrequency (%)
60 1
 
3.3%
50 1
 
3.3%
40 1
 
3.3%
30 4
 
13.3%
20 7
23.3%
10 11
36.7%
5 2
 
6.7%
2 3
 
10.0%

PDSMLPZ_SCTN
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
10년 이상 20년 미만
11 
20년 이상 30년 미만
7 
30년 이상 40년 미만
4 
2년 이상 5년 미만
3 
5년 이상 10년 미만
2 
Other values (3)
3 

Length

Max length13
Median length13
Mean length12.733333
Min length11

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row10년 이상 20년 미만
2nd row10년 이상 20년 미만
3rd row2년 이상 5년 미만
4th row2년 이상 5년 미만
5th row20년 이상 30년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 11
36.7%
20년 이상 30년 미만 7
23.3%
30년 이상 40년 미만 4
 
13.3%
2년 이상 5년 미만 3
 
10.0%
5년 이상 10년 미만 2
 
6.7%
40년 이상 50년 미만 1
 
3.3%
50년 이상 60년 미만 1
 
3.3%
60년 이상 70년 미만 1
 
3.3%

Length

2023-12-10T23:13:35.616973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:35.832199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 30
25.0%
미만 30
25.0%
20년 18
15.0%
10년 13
10.8%
30년 11
 
9.2%
40년 5
 
4.2%
5년 5
 
4.2%
2년 3
 
2.5%
50년 2
 
1.7%
60년 2
 
1.7%

IEM_NM
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
연구개발비 30
100.0%

Length

2023-12-10T23:13:36.119615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:36.355246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연구개발비 30
100.0%

TOT_ENTRPRS_CO
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
30 

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 30
100.0%

Length

2023-12-10T23:13:36.561180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:36.772559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
100.0%

TOTAMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338041.7
Minimum0
Maximum1727103
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:36.950986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112033.5
median114796
Q3480358
95-th percentile1318359.5
Maximum1727103
Range1727103
Interquartile range (IQR)468324.5

Descriptive statistics

Standard deviation463849.88
Coefficient of variation (CV)1.3721676
Kurtosis2.5137984
Mean338041.7
Median Absolute Deviation (MAD)114796
Skewness1.7192831
Sum10141251
Variance2.1515671 × 1011
MonotonicityNot monotonic
2023-12-10T23:13:37.170215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
20.0%
128368 1
 
3.3%
1727103 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
340363 1
 
3.3%
81362 1
 
3.3%
430517 1
 
3.3%
1035505 1
 
3.3%
1520140 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0 6
20.0%
2513 1
 
3.3%
9361 1
 
3.3%
20051 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
29871 1
 
3.3%
35314 1
 
3.3%
81362 1
 
3.3%
101224 1
 
3.3%
ValueCountFrequency (%)
1727103 1
3.3%
1520140 1
3.3%
1071739 1
3.3%
1035505 1
3.3%
676642 1
3.3%
607571 1
3.3%
588300 1
3.3%
496515 1
3.3%
431887 1
3.3%
430517 1
3.3%

AVRG_AM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338041.7
Minimum0
Maximum1727103
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:37.406711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112033.5
median114796
Q3480358
95-th percentile1318359.5
Maximum1727103
Range1727103
Interquartile range (IQR)468324.5

Descriptive statistics

Standard deviation463849.88
Coefficient of variation (CV)1.3721676
Kurtosis2.5137984
Mean338041.7
Median Absolute Deviation (MAD)114796
Skewness1.7192831
Sum10141251
Variance2.1515671 × 1011
MonotonicityNot monotonic
2023-12-10T23:13:37.629902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
20.0%
128368 1
 
3.3%
1727103 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
340363 1
 
3.3%
81362 1
 
3.3%
430517 1
 
3.3%
1035505 1
 
3.3%
1520140 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0 6
20.0%
2513 1
 
3.3%
9361 1
 
3.3%
20051 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
29871 1
 
3.3%
35314 1
 
3.3%
81362 1
 
3.3%
101224 1
 
3.3%
ValueCountFrequency (%)
1727103 1
3.3%
1520140 1
3.3%
1071739 1
3.3%
1035505 1
3.3%
676642 1
3.3%
607571 1
3.3%
588300 1
3.3%
496515 1
3.3%
431887 1
3.3%
430517 1
3.3%

FSTLTN_AM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338041.7
Minimum0
Maximum1727103
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:13:37.864649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112033.5
median114796
Q3480358
95-th percentile1318359.5
Maximum1727103
Range1727103
Interquartile range (IQR)468324.5

Descriptive statistics

Standard deviation463849.88
Coefficient of variation (CV)1.3721676
Kurtosis2.5137984
Mean338041.7
Median Absolute Deviation (MAD)114796
Skewness1.7192831
Sum10141251
Variance2.1515671 × 1011
MonotonicityNot monotonic
2023-12-10T23:13:38.093533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 6
 
20.0%
128368 1
 
3.3%
1727103 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
340363 1
 
3.3%
81362 1
 
3.3%
430517 1
 
3.3%
1035505 1
 
3.3%
1520140 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0 6
20.0%
2513 1
 
3.3%
9361 1
 
3.3%
20051 1
 
3.3%
20532 1
 
3.3%
26880 1
 
3.3%
29871 1
 
3.3%
35314 1
 
3.3%
81362 1
 
3.3%
101224 1
 
3.3%
ValueCountFrequency (%)
1727103 1
3.3%
1520140 1
3.3%
1071739 1
3.3%
1035505 1
3.3%
676642 1
3.3%
607571 1
3.3%
588300 1
3.3%
496515 1
3.3%
431887 1
3.3%
430517 1
3.3%

REGIST_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-10-24
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-24
2nd row2020-10-24
3rd row2020-10-24
4th row2020-10-24
5th row2020-10-24

Common Values

ValueCountFrequency (%)
2020-10-24 30
100.0%

Length

2023-12-10T23:13:38.350507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:38.511799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-10-24 30
100.0%

OPERTOR_NM
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

2023-12-10T23:13:38.697912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:13:38.883304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:13:27.682480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:24.804497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:25.902977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:26.736066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:27.869127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:25.181895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:26.076826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:26.965997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:28.065091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:25.396113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:26.262073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:27.171419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:28.242626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:25.648421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:26.459028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:13:27.431415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:13:39.135701image/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_SCTNTOTAMTAVRG_AMFSTLTN_AM
SIGNGU_NM1.0001.0000.6240.7640.6240.7640.0000.0000.0000.0000.6420.6420.642
ADSTRD_NM1.0001.0000.6590.4010.6590.4010.0000.0000.0000.4970.0000.0000.000
INDUTY_LCLAS_CODE0.6240.6591.0001.0001.0001.0000.6420.6420.0000.0000.6140.6140.614
INDUTY_MLSFC_CODE0.7640.4011.0001.0001.0001.0000.6380.6380.8590.7430.8530.8530.853
INDUTY_LCLAS_NM0.6240.6591.0001.0001.0001.0000.6420.6420.0000.0000.6140.6140.614
INDUTY_MLSFC_NM0.7640.4011.0001.0001.0001.0000.6380.6380.8590.7430.8530.8530.853
PRCSS_ENTRPRS_SE_CODE0.0000.0000.6420.6380.6420.6381.0001.0000.3870.4140.9310.9310.931
PRCSS_ENTRPRS_SE0.0000.0000.6420.6380.6420.6381.0001.0000.3870.4140.9310.9310.931
PDSMLPZ_SCTN_CODE0.0000.0000.0000.8590.0000.8590.3870.3871.0001.0000.6340.6340.634
PDSMLPZ_SCTN0.0000.4970.0000.7430.0000.7430.4140.4141.0001.0000.7590.7590.759
TOTAMT0.6420.0000.6140.8530.6140.8530.9310.9310.6340.7591.0001.0001.000
AVRG_AM0.6420.0000.6140.8530.6140.8530.9310.9310.6340.7591.0001.0001.000
FSTLTN_AM0.6420.0000.6140.8530.6140.8530.9310.9310.6340.7591.0001.0001.000
2023-12-10T23:13:39.397656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMINDUTY_MLSFC_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTNINDUTY_LCLAS_NM
SIGNGU_NM1.0000.3660.3010.3660.0000.0000.0000.301
INDUTY_MLSFC_NM0.3661.0000.8341.0000.3060.3060.3590.834
INDUTY_LCLAS_CODE0.3010.8341.0000.8340.4660.4660.0001.000
INDUTY_MLSFC_CODE0.3661.0000.8341.0000.3060.3060.3590.834
PRCSS_ENTRPRS_SE_CODE0.0000.3060.4660.3061.0001.0000.1530.466
PRCSS_ENTRPRS_SE0.0000.3060.4660.3061.0001.0000.1530.466
PDSMLPZ_SCTN0.0000.3590.0000.3590.1530.1531.0000.000
INDUTY_LCLAS_NM0.3010.8341.0000.8340.4660.4660.0001.000
2023-12-10T23:13:39.673971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDSMLPZ_SCTN_CODETOTAMTAVRG_AMFSTLTN_AMSIGNGU_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN
PDSMLPZ_SCTN_CODE1.0000.1420.1420.1420.0000.0000.4040.0000.4040.2450.2450.978
TOTAMT0.1421.0001.0001.0000.2780.3680.4980.3680.4980.6020.6020.332
AVRG_AM0.1421.0001.0001.0000.2780.3680.4980.3680.4980.6020.6020.332
FSTLTN_AM0.1421.0001.0001.0000.2780.3680.4980.3680.4980.6020.6020.332
SIGNGU_NM0.0000.2780.2780.2781.0000.3010.3660.3010.3660.0000.0000.000
INDUTY_LCLAS_CODE0.0000.3680.3680.3680.3011.0000.8341.0000.8340.4660.4660.000
INDUTY_MLSFC_CODE0.4040.4980.4980.4980.3660.8341.0000.8341.0000.3060.3060.359
INDUTY_LCLAS_NM0.0000.3680.3680.3680.3011.0000.8341.0000.8340.4660.4660.000
INDUTY_MLSFC_NM0.4040.4980.4980.4980.3660.8341.0000.8341.0000.3060.3060.359
PRCSS_ENTRPRS_SE_CODE0.2450.6020.6020.6020.0000.4660.3060.4660.3061.0001.0000.153
PRCSS_ENTRPRS_SE0.2450.6020.6020.6020.0000.4660.3060.4660.3061.0001.0000.153
PDSMLPZ_SCTN0.9780.3320.3320.3320.0000.0000.3590.0000.3590.1530.1531.000

Missing values

2023-12-10T23:13:28.578103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:13:29.047655image/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_YEARCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNIEM_NMTOT_ENTRPRS_COTOTAMTAVRG_AMFSTLTN_AMREGIST_DEOPERTOR_NM
02020경기고양시덕양구고양동GG45도매 및 소매업자동차 및 부품 판매업3중기업1010년 이상 20년 미만연구개발비10002020-10-24KEDSYS
12020경기고양시일산동구고봉동GG46도매 및 소매업도매 및 상품 중개업3중기업1010년 이상 20년 미만연구개발비10002020-10-24KEDSYS
22020경기고양시일산동구백석2동FF42건설업전문직별 공사업3중기업22년 이상 5년 미만연구개발비11283681283681283682020-10-24KEDSYS
32020경기고양시일산동구장항2동GG46도매 및 소매업도매 및 상품 중개업4소기업22년 이상 5년 미만연구개발비13531435314353142020-10-24KEDSYS
42020경기광주시광남동GG46도매 및 소매업도매 및 상품 중개업3중기업2020년 이상 30년 미만연구개발비10002020-10-24KEDSYS
52020경기광주시광남동GG46도매 및 소매업도매 및 상품 중개업4소기업1010년 이상 20년 미만연구개발비19361936193612020-10-24KEDSYS
62020경기광주시오포읍CC10제조업식료품 제조업4소기업1010년 이상 20년 미만연구개발비12005120051200512020-10-24KEDSYS
72020경기광주시오포읍CC22제조업고무 및 플라스틱제품 제조업3중기업2020년 이상 30년 미만연구개발비14965154965154965152020-10-24KEDSYS
82020경기광주시오포읍FF42건설업전문직별 공사업3중기업3030년 이상 40년 미만연구개발비10002020-10-24KEDSYS
92020경기광주시초월읍GG46도매 및 소매업도매 및 상품 중개업3중기업55년 이상 10년 미만연구개발비11647741647741647742020-10-24KEDSYS
STDR_YEARCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNIEM_NMTOT_ENTRPRS_COTOTAMTAVRG_AMFSTLTN_AMREGIST_DEOPERTOR_NM
202020경기성남시분당구야탑3동CC29제조업기타 기계 및 장비 제조업2중견기업3030년 이상 40년 미만연구개발비11071739107173910717392020-10-24KEDSYS
212020경기성남시수정구복정동MM72전문; 과학 및 기술 서비스업건축기술; 엔지니어링 및 기타 과학기술 서비스업3중기업1010년 이상 20년 미만연구개발비15883005883005883002020-10-24KEDSYS
222020경기성남시중원구상대원1동CC21제조업의료용 물질 및 의약품 제조업3중기업6060년 이상 70년 미만연구개발비11520140152014015201402020-10-24KEDSYS
232020경기성남시중원구상대원1동CC27제조업의료; 정밀; 광학기기 및 시계 제조업3중기업2020년 이상 30년 미만연구개발비11035505103550510355052020-10-24KEDSYS
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