Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells18
Missing cells (%)3.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory163.4 B

Variable types

DateTime2
Categorical12
Numeric5

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/abbcd5a3-2044-43f9-88a0-9aef50e44c00

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
INDUTY_LCLAS_CODE has constant value ""Constant
INDUTY_LCLAS_NM has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
INDUTY_MLSFC_CODE is highly overall correlated with INDUTY_MLSFC_NMHigh correlation
INDUTY_MLSFC_NM is highly overall correlated with INDUTY_MLSFC_CODEHigh correlation
PDSMLPZ_SCTN_CODE is highly overall correlated with SELNG_AVRG_AM and 1 other fieldsHigh correlation
SELNG_AVRG_AM is highly overall correlated with PDSMLPZ_SCTN_CODE and 4 other fieldsHigh correlation
BSN_PROFIT_AVRG_AM is highly overall correlated with SELNG_AVRG_AM and 3 other fieldsHigh correlation
NPN_EMPLY_AVRG_CO is highly overall correlated with SELNG_AVRG_AM and 3 other fieldsHigh correlation
SIGNGU_NM is highly overall correlated with ADSTRD_NMHigh correlation
ADSTRD_NM is highly overall correlated with SIGNGU_NMHigh correlation
PRCSS_ENTRPRS_SE_CODE is highly overall correlated with SELNG_AVRG_AM and 3 other fieldsHigh correlation
PRCSS_ENTRPRS_SE is highly overall correlated with SELNG_AVRG_AM and 3 other fieldsHigh correlation
PDSMLPZ_SCTN is highly overall correlated with PDSMLPZ_SCTN_CODEHigh correlation
PRCSS_ENTRPRS_SE_CODE is highly imbalanced (53.1%)Imbalance
PRCSS_ENTRPRS_SE is highly imbalanced (53.1%)Imbalance
SELNG_AVRG_AM has 4 (13.3%) missing valuesMissing
BSN_PROFIT_AVRG_AM has 4 (13.3%) missing valuesMissing
RSDV_AVRG_AM has 8 (26.7%) missing valuesMissing
NPN_EMPLY_AVRG_CO has 2 (6.7%) missing valuesMissing
RSDV_AVRG_AM has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:19:28.559870
Analysis finished2023-12-10 14:19:35.011725
Duration6.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YM
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2017-04-01 00:00:00
Maximum2017-04-01 00:00:00
2023-12-10T23:19:35.078349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:35.212376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T23:19:35.367157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:35.511767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 30
100.0%

SIGNGU_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
광주시
17 
군포시
9 
광명시
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주시 17
56.7%
군포시 9
30.0%
광명시 4
 
13.3%

Length

2023-12-10T23:19:35.676609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:35.864057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 17
56.7%
군포시 9
30.0%
광명시 4
 
13.3%

ADSTRD_NM
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
군포1동
7 
오포읍
5 
초월읍
5 
도척면
3 
곤지암읍
3 
Other values (6)
7 

Length

Max length4
Median length3
Mean length3.4333333
Min length3

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row학온동
2nd row광명7동
3rd row소하1동
4th row하안3동
5th row광남동

Common Values

ValueCountFrequency (%)
군포1동 7
23.3%
오포읍 5
16.7%
초월읍 5
16.7%
도척면 3
10.0%
곤지암읍 3
10.0%
금정동 2
 
6.7%
학온동 1
 
3.3%
광명7동 1
 
3.3%
소하1동 1
 
3.3%
하안3동 1
 
3.3%

Length

2023-12-10T23:19:36.076070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군포1동 7
23.3%
오포읍 5
16.7%
초월읍 5
16.7%
도척면 3
10.0%
곤지암읍 3
10.0%
금정동 2
 
6.7%
학온동 1
 
3.3%
광명7동 1
 
3.3%
소하1동 1
 
3.3%
하안3동 1
 
3.3%

INDUTY_LCLAS_CODE
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
C 30
100.0%

Length

2023-12-10T23:19:36.270800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:36.424928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 30
100.0%

INDUTY_MLSFC_CODE
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
C26
7 
C29
6 
C28
4 
C24
2 
C20
2 
Other values (7)
9 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st rowC29
2nd rowC29
3rd rowC27
4th rowC29
5th rowC28

Common Values

ValueCountFrequency (%)
C26 7
23.3%
C29 6
20.0%
C28 4
13.3%
C24 2
 
6.7%
C20 2
 
6.7%
C25 2
 
6.7%
C23 2
 
6.7%
C27 1
 
3.3%
C13 1
 
3.3%
C17 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:19:36.584590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c26 7
23.3%
c29 6
20.0%
c28 4
13.3%
c24 2
 
6.7%
c20 2
 
6.7%
c25 2
 
6.7%
c23 2
 
6.7%
c27 1
 
3.3%
c13 1
 
3.3%
c17 1
 
3.3%
Other values (2) 2
 
6.7%

INDUTY_LCLAS_NM
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제조업 30
100.0%

Length

2023-12-10T23:19:36.734111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:36.852778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 30
100.0%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업
7 
기타 기계 및 장비 제조업
6 
전기장비 제조업
4 
1차 금속 제조업
2 
화학물질 및 화학제품 제조업; 의약품 제외
2 
Other values (7)
9 

Length

Max length28
Median length22
Mean length17.333333
Min length8

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row기타 기계 및 장비 제조업
2nd row기타 기계 및 장비 제조업
3rd row의료; 정밀; 광학기기 및 시계 제조업
4th row기타 기계 및 장비 제조업
5th row전기장비 제조업

Common Values

ValueCountFrequency (%)
전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업 7
23.3%
기타 기계 및 장비 제조업 6
20.0%
전기장비 제조업 4
13.3%
1차 금속 제조업 2
 
6.7%
화학물질 및 화학제품 제조업; 의약품 제외 2
 
6.7%
금속가공제품 제조업; 기계 및 가구 제외 2
 
6.7%
비금속 광물제품 제조업 2
 
6.7%
의료; 정밀; 광학기기 및 시계 제조업 1
 
3.3%
섬유제품 제조업; 의복제외 1
 
3.3%
펄프; 종이 및 종이제품 제조업 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:19:37.076754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조업 30
20.8%
및 20
13.9%
기계 8
 
5.6%
전자부품 7
 
4.9%
컴퓨터 7
 
4.9%
영상 7
 
4.9%
음향 7
 
4.9%
통신장비 7
 
4.9%
기타 7
 
4.9%
장비 6
 
4.2%
Other values (23) 38
26.4%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
4
27 
3
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 27
90.0%
3 3
 
10.0%

Length

2023-12-10T23:19:37.298374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:37.486690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 27
90.0%
3 3
 
10.0%

PRCSS_ENTRPRS_SE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
소기업
27 
중기업
3 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소기업 27
90.0%
중기업 3
 
10.0%

Length

2023-12-10T23:19:37.657464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:37.803871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소기업 27
90.0%
중기업 3
 
10.0%

PDSMLPZ_SCTN_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0333333
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:37.924798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q310
95-th percentile20
Maximum30
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.8806743
Coefficient of variation (CV)0.76169826
Kurtosis1.7655456
Mean9.0333333
Median Absolute Deviation (MAD)5
Skewness1.3291953
Sum271
Variance47.343678
MonotonicityNot monotonic
2023-12-10T23:19:38.053452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
10 11
36.7%
5 8
26.7%
2 5
16.7%
20 4
 
13.3%
1 1
 
3.3%
30 1
 
3.3%
ValueCountFrequency (%)
1 1
 
3.3%
2 5
16.7%
5 8
26.7%
10 11
36.7%
20 4
 
13.3%
30 1
 
3.3%
ValueCountFrequency (%)
30 1
 
3.3%
20 4
 
13.3%
10 11
36.7%
5 8
26.7%
2 5
16.7%
1 1
 
3.3%

PDSMLPZ_SCTN
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
10년 이상 20년 미만
11 
5년 이상 10년 미만
8 
2년 이상 5년 미만
5 
20년 이상 30년 미만
4 
1년 이상 2년 미만
 
1

Length

Max length13
Median length13
Mean length12.333333
Min length11

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row2년 이상 5년 미만
2nd row5년 이상 10년 미만
3rd row5년 이상 10년 미만
4th row2년 이상 5년 미만
5th row20년 이상 30년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 11
36.7%
5년 이상 10년 미만 8
26.7%
2년 이상 5년 미만 5
16.7%
20년 이상 30년 미만 4
 
13.3%
1년 이상 2년 미만 1
 
3.3%
30년 이상 40년 미만 1
 
3.3%

Length

2023-12-10T23:19:38.257785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:38.449979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 30
25.0%
미만 30
25.0%
10년 19
15.8%
20년 15
12.5%
5년 13
10.8%
2년 6
 
5.0%
30년 5
 
4.2%
1년 1
 
0.8%
40년 1
 
0.8%

TOT_ENTRPRS_CO
Categorical

Distinct3
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
1
24 
2
4 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 24
80.0%
2 4
 
13.3%
3 2
 
6.7%

Length

2023-12-10T23:19:38.686078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:38.852988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 24
80.0%
2 4
 
13.3%
3 2
 
6.7%

SELNG_AVRG_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean6100699.7
Minimum534880
Maximum46217035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:39.008224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum534880
5-th percentile614129.5
Q11542049.8
median2844507
Q34498411
95-th percentile29497132
Maximum46217035
Range45682155
Interquartile range (IQR)2956361.2

Descriptive statistics

Standard deviation10620877
Coefficient of variation (CV)1.7409277
Kurtosis9.9292081
Mean6100699.7
Median Absolute Deviation (MAD)1641202
Skewness3.1983257
Sum1.5861819 × 108
Variance1.1280302 × 1014
MonotonicityNot monotonic
2023-12-10T23:19:39.209008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
682000.0 1
 
3.3%
2554819.0 1
 
3.3%
4741158.0 1
 
3.3%
1709311.0 1
 
3.3%
2082652.0 1
 
3.3%
4506860.0 1
 
3.3%
534880.0 1
 
3.3%
4473064.0 1
 
3.3%
4032456.0 1
 
3.3%
3168781.0 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 4
 
13.3%
ValueCountFrequency (%)
534880.0 1
3.3%
591506.0 1
3.3%
682000.0 1
3.3%
909305.0 1
3.3%
1017060.0 1
3.3%
1190660.0 1
3.3%
1486296.0 1
3.3%
1709311.0 1
3.3%
2082652.0 1
3.3%
2199966.0 1
3.3%
ValueCountFrequency (%)
46217035.0 1
3.3%
35386166.0 1
3.3%
11830030.0 1
3.3%
6924455.0 1
3.3%
6824000.0 1
3.3%
4741158.0 1
3.3%
4506860.0 1
3.3%
4473064.0 1
3.3%
4307330.0 1
3.3%
4032456.0 1
3.3%

BSN_PROFIT_AVRG_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean348126.26
Minimum-125054
Maximum4034190
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)10.0%
Memory size402.0 B
2023-12-10T23:19:39.434850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-125054
5-th percentile-87120.625
Q141627.25
median182805
Q3274853.12
95-th percentile1075889.8
Maximum4034190
Range4159244
Interquartile range (IQR)233225.88

Descriptive statistics

Standard deviation798490.37
Coefficient of variation (CV)2.2936804
Kurtosis19.816346
Mean348126.26
Median Absolute Deviation (MAD)141151.5
Skewness4.2859518
Sum9051282.7
Variance6.3758687 × 1011
MonotonicityNot monotonic
2023-12-10T23:19:39.629270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
65000.0 1
 
3.3%
348264.33 1
 
3.3%
369023.33 1
 
3.3%
33107.0 1
 
3.3%
189347.0 1
 
3.3%
181202.0 1
 
3.3%
-125054.0 1
 
3.3%
276925.5 1
 
3.3%
218819.0 1
 
3.3%
268636.0 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 4
 
13.3%
ValueCountFrequency (%)
-125054.0 1
3.3%
-112671.5 1
3.3%
-10468.0 1
3.3%
27688.0 1
3.3%
29104.0 1
3.3%
33107.0 1
3.3%
41601.0 1
3.3%
41706.0 1
3.3%
42085.0 1
3.3%
49590.0 1
3.3%
ValueCountFrequency (%)
4034190.0 1
3.3%
1265443.0 1
3.3%
507230.0 1
3.3%
443000.0 1
3.3%
369023.33 1
3.3%
348264.33 1
3.3%
276925.5 1
3.3%
268636.0 1
3.3%
265663.0 1
3.3%
250052.0 1
3.3%

RSDV_AVRG_AM
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)100.0%
Missing8
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean162631.17
Minimum0
Maximum385914
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:39.795840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15839.7
Q184970
median116747.5
Q3272304.75
95-th percentile345125.55
Maximum385914
Range385914
Interquartile range (IQR)187334.75

Descriptive statistics

Standard deviation116995.19
Coefficient of variation (CV)0.71938972
Kurtosis-0.99638347
Mean162631.17
Median Absolute Deviation (MAD)63713
Skewness0.58320148
Sum3577885.8
Variance1.3687876 × 1010
MonotonicityNot monotonic
2023-12-10T23:19:39.960010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
345390.0 1
 
3.3%
109966.0 1
 
3.3%
101055.33 1
 
3.3%
284826.0 1
 
3.3%
89602.0 1
 
3.3%
73858.0 1
 
3.3%
146987.0 1
 
3.3%
91028.0 1
 
3.3%
133651.0 1
 
3.3%
211252.0 1
 
3.3%
Other values (12) 12
40.0%
(Missing) 8
26.7%
ValueCountFrequency (%)
0.0 1
3.3%
14350.0 1
3.3%
44144.0 1
3.3%
61925.0 1
3.3%
73858.0 1
3.3%
83426.0 1
3.3%
89602.0 1
3.3%
91028.0 1
3.3%
97211.5 1
3.3%
101055.33 1
3.3%
ValueCountFrequency (%)
385914.0 1
3.3%
345390.0 1
3.3%
340101.0 1
3.3%
316636.0 1
3.3%
288293.0 1
3.3%
284826.0 1
3.3%
234741.0 1
3.3%
211252.0 1
3.3%
146987.0 1
3.3%
133651.0 1
3.3%

NPN_EMPLY_AVRG_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)78.6%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean18.488214
Minimum4
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:19:40.138209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.35
Q19.875
median15.335
Q320.5
95-th percentile36.025
Maximum96
Range92
Interquartile range (IQR)10.625

Descriptive statistics

Standard deviation17.239506
Coefficient of variation (CV)0.93245921
Kurtosis15.741082
Mean18.488214
Median Absolute Deviation (MAD)5.585
Skewness3.5987798
Sum517.67
Variance297.20056
MonotonicityNot monotonic
2023-12-10T23:19:40.293419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7.0 3
 
10.0%
20.0 2
 
6.7%
17.0 2
 
6.7%
12.0 2
 
6.7%
16.0 2
 
6.7%
25.0 1
 
3.3%
23.0 1
 
3.3%
14.67 1
 
3.3%
22.0 1
 
3.3%
13.0 1
 
3.3%
Other values (12) 12
40.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
4.0 1
 
3.3%
5.0 1
 
3.3%
6.0 1
 
3.3%
7.0 3
10.0%
9.5 1
 
3.3%
10.0 1
 
3.3%
11.0 1
 
3.3%
12.0 2
6.7%
13.0 1
 
3.3%
14.5 1
 
3.3%
ValueCountFrequency (%)
96.0 1
3.3%
39.0 1
3.3%
30.5 1
3.3%
26.0 1
3.3%
25.0 1
3.3%
23.0 1
3.3%
22.0 1
3.3%
20.0 2
6.7%
17.5 1
3.3%
17.0 2
6.7%

REGIST_DE
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-10-28 00:00:00
Maximum2020-10-28 00:00:00
2023-12-10T23:19:40.402736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:40.498400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T23:19:40.648319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:19:40.739243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:19:31.771709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.629688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.183527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.739046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.221494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.881779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.748724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.294375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.839029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.333143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.375293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.837873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.425366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.921811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.431532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.477559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:29.932491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.517442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.018158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.536076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:32.775346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.068944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:30.620592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.136821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:19:31.646186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:19:40.830620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMADSTRD_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COSELNG_AVRG_AMBSN_PROFIT_AVRG_AMRSDV_AVRG_AMNPN_EMPLY_AVRG_CO
SIGNGU_NM1.0001.0000.7710.7710.0840.0840.1270.3030.6840.0000.0000.2270.000
ADSTRD_NM1.0001.0000.0000.0000.0000.0000.2090.0000.4720.0000.0000.6930.000
INDUTY_MLSFC_CODE0.7710.0001.0001.0000.3500.3500.3900.6250.0000.5980.6610.4480.568
INDUTY_MLSFC_NM0.7710.0001.0001.0000.3500.3500.3900.6250.0000.5980.6610.4480.568
PRCSS_ENTRPRS_SE_CODE0.0840.0000.3500.3501.0000.9550.3520.5380.0001.0000.9610.0000.650
PRCSS_ENTRPRS_SE0.0840.0000.3500.3500.9551.0000.3520.5380.0001.0000.9610.0000.650
PDSMLPZ_SCTN_CODE0.1270.2090.3900.3900.3520.3521.0001.0000.0000.5980.3980.4380.608
PDSMLPZ_SCTN0.3030.0000.6250.6250.5380.5381.0001.0000.0000.2110.3880.3800.294
TOT_ENTRPRS_CO0.6840.4720.0000.0000.0000.0000.0000.0001.0000.0000.3880.0000.000
SELNG_AVRG_AM0.0000.0000.5980.5981.0001.0000.5980.2110.0001.0000.8650.2830.947
BSN_PROFIT_AVRG_AM0.0000.0000.6610.6610.9610.9610.3980.3880.3880.8651.0000.0000.843
RSDV_AVRG_AM0.2270.6930.4480.4480.0000.0000.4380.3800.0000.2830.0001.0000.000
NPN_EMPLY_AVRG_CO0.0000.0000.5680.5680.6500.6500.6080.2940.0000.9470.8430.0001.000
2023-12-10T23:19:40.983364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRCSS_ENTRPRS_SEINDUTY_MLSFC_CODETOT_ENTRPRS_COINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPDSMLPZ_SCTNSIGNGU_NMADSTRD_NM
PRCSS_ENTRPRS_SE1.0000.1880.0000.1880.8070.3530.1290.000
INDUTY_MLSFC_CODE0.1881.0000.0001.0000.1880.2260.3880.000
TOT_ENTRPRS_CO0.0000.0001.0000.0000.0000.0000.3380.242
INDUTY_MLSFC_NM0.1881.0000.0001.0000.1880.2260.3880.000
PRCSS_ENTRPRS_SE_CODE0.8070.1880.0000.1881.0000.3530.1290.000
PDSMLPZ_SCTN0.3530.2260.0000.2260.3531.0000.0960.000
SIGNGU_NM0.1290.3880.3380.3880.1290.0961.0000.839
ADSTRD_NM0.0000.0000.2420.0000.0000.0000.8391.000
2023-12-10T23:19:41.105131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDSMLPZ_SCTN_CODESELNG_AVRG_AMBSN_PROFIT_AVRG_AMRSDV_AVRG_AMNPN_EMPLY_AVRG_COSIGNGU_NMADSTRD_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTNTOT_ENTRPRS_CO
PDSMLPZ_SCTN_CODE1.0000.5050.3110.0900.1640.0600.0000.1530.1530.4010.4010.9800.000
SELNG_AVRG_AM0.5051.0000.7700.0010.5930.0000.0000.3250.3250.9350.9350.1020.000
BSN_PROFIT_AVRG_AM0.3110.7701.0000.1440.5310.0000.0000.3590.3590.7890.7890.2310.369
RSDV_AVRG_AM0.0900.0010.1441.0000.2820.0000.3540.0900.0900.0000.0000.1430.000
NPN_EMPLY_AVRG_CO0.1640.5930.5310.2821.0000.0000.0000.2840.2840.7320.7320.1800.000
SIGNGU_NM0.0600.0000.0000.0000.0001.0000.8390.3880.3880.1290.1290.0960.338
ADSTRD_NM0.0000.0000.0000.3540.0000.8391.0000.0000.0000.0000.0000.0000.242
INDUTY_MLSFC_CODE0.1530.3250.3590.0900.2840.3880.0001.0001.0000.1880.1880.2260.000
INDUTY_MLSFC_NM0.1530.3250.3590.0900.2840.3880.0001.0001.0000.1880.1880.2260.000
PRCSS_ENTRPRS_SE_CODE0.4010.9350.7890.0000.7320.1290.0000.1880.1881.0000.8070.3530.000
PRCSS_ENTRPRS_SE0.4010.9350.7890.0000.7320.1290.0000.1880.1880.8071.0000.3530.000
PDSMLPZ_SCTN0.9800.1020.2310.1430.1800.0960.0000.2260.2260.3530.3531.0000.000
TOT_ENTRPRS_CO0.0000.0000.3690.0000.0000.3380.2420.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T23:19:33.899915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:19:34.705446image/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.
2023-12-10T23:19:34.918823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

STDR_YMCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COSELNG_AVRG_AMBSN_PROFIT_AVRG_AMRSDV_AVRG_AMNPN_EMPLY_AVRG_COREGIST_DEOPERTOR_NM
02017-04경기광명시학온동CC29제조업기타 기계 및 장비 제조업4소기업22년 이상 5년 미만22648098.5184408.097211.517.52020-10-28KEDSYS
12017-04경기광명시광명7동CC29제조업기타 기계 및 장비 제조업4소기업55년 이상 10년 미만1909305.029104.0234741.07.02020-10-28KEDSYS
22017-04경기광명시소하1동CC27제조업의료; 정밀; 광학기기 및 시계 제조업4소기업55년 이상 10년 미만23040915.5265663.0316636.09.52020-10-28KEDSYS
32017-04경기광명시하안3동CC29제조업기타 기계 및 장비 제조업4소기업22년 이상 5년 미만11017060.027688.0<NA>11.02020-10-28KEDSYS
42017-04경기광주시광남동CC28제조업전기장비 제조업4소기업2020년 이상 30년 미만12271783.0167392.0385914.020.02020-10-28KEDSYS
52017-04경기광주시도척면CC24제조업1차 금속 제조업4소기업1010년 이상 20년 미만13287604.041706.0123529.07.02020-10-28KEDSYS
62017-04경기광주시도척면CC28제조업전기장비 제조업4소기업1010년 이상 20년 미만16924455.0250052.044144.026.02020-10-28KEDSYS
72017-04경기광주시도척면CC29제조업기타 기계 및 장비 제조업4소기업1010년 이상 20년 미만1591506.041601.083426.05.02020-10-28KEDSYS
82017-04경기광주시오포읍CC13제조업섬유제품 제조업; 의복제외3중기업2020년 이상 30년 미만146217035.04034190.0340101.096.02020-10-28KEDSYS
92017-04경기광주시오포읍CC17제조업펄프; 종이 및 종이제품 제조업4소기업1010년 이상 20년 미만16824000.0443000.00.017.02020-10-28KEDSYS
STDR_YMCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COSELNG_AVRG_AMBSN_PROFIT_AVRG_AMRSDV_AVRG_AMNPN_EMPLY_AVRG_COREGIST_DEOPERTOR_NM
202017-04경기광주시곤지암읍CC33제조업기타 제품 제조업4소기업22년 이상 5년 미만1<NA><NA><NA><NA>2020-10-28KEDSYS
212017-04경기군포시금정동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업1010년 이상 20년 미만1534880.0-125054.073858.04.02020-10-28KEDSYS
222017-04경기군포시금정동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업2020년 이상 30년 미만1<NA><NA><NA><NA>2020-10-28KEDSYS
232017-04경기군포시군포1동CC20제조업화학물질 및 화학제품 제조업; 의약품 제외4소기업3030년 이상 40년 미만14506860.0181202.0<NA>10.02020-10-28KEDSYS
242017-04경기군포시군포1동CC22제조업고무 및 플라스틱제품 제조업4소기업22년 이상 5년 미만12082652.0189347.089602.013.02020-10-28KEDSYS
252017-04경기군포시군포1동CC23제조업비금속 광물제품 제조업4소기업55년 이상 10년 미만11709311.033107.0284826.022.02020-10-28KEDSYS
262017-04경기군포시군포1동CC25제조업금속가공제품 제조업; 기계 및 가구 제외4소기업1010년 이상 20년 미만1<NA><NA><NA>12.02020-10-28KEDSYS
272017-04경기군포시군포1동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업22년 이상 5년 미만1<NA><NA><NA>16.02020-10-28KEDSYS
282017-04경기군포시군포1동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업55년 이상 10년 미만34741158.0369023.33101055.3314.672020-10-28KEDSYS
292017-04경기군포시군포1동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업1010년 이상 20년 미만32554819.0348264.33109966.023.02020-10-28KEDSYS