Overview

Dataset statistics

Number of variables20
Number of observations30
Missing cells36
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory172.4 B

Variable types

DateTime2
Categorical11
Text2
Numeric5

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/e2b69e54-d7d1-4bac-a134-d7602e607273

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_LCLAS_CODEHigh correlation
PDSMLPZ_SCTN_NM is highly overall correlated with PDSMLPZ_SCTN_CODEHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with INDUTY_LCLAS_NMHigh correlation
PDSMLPZ_SCTN_CODE is highly overall correlated with PDSMLPZ_SCTN_NMHigh correlation
TOT_EMPLY_CO is highly overall correlated with EMPLY_AVRG_CO and 4 other fieldsHigh correlation
EMPLY_AVRG_CO is highly overall correlated with TOT_EMPLY_CO and 1 other fieldsHigh correlation
ANSLRY_AVRG_AM is highly overall correlated with ANSLRY_FSTLTN_AMHigh correlation
EMPLY_FSTLTN_CO is highly overall correlated with TOT_EMPLY_CO and 1 other fieldsHigh correlation
ANSLRY_FSTLTN_AM is highly overall correlated with ANSLRY_AVRG_AMHigh 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 TOT_EMPLY_CO and 1 other fieldsHigh correlation
PRCSS_ENTRPRS_SE_NM is highly overall correlated with TOT_EMPLY_CO and 1 other fieldsHigh correlation
NRMLT_BSN_ENTRPRS_CO is highly overall correlated with TOT_EMPLY_COHigh correlation
PRCSS_ENTRPRS_SE_CODE is highly imbalanced (64.7%)Imbalance
PRCSS_ENTRPRS_SE_NM is highly imbalanced (64.7%)Imbalance
NRMLT_BSN_ENTRPRS_CO is highly imbalanced (78.9%)Imbalance
EMPLY_AVRG_CO has 9 (30.0%) missing valuesMissing
ANSLRY_AVRG_AM has 9 (30.0%) missing valuesMissing
EMPLY_FSTLTN_CO has 9 (30.0%) missing valuesMissing
ANSLRY_FSTLTN_AM has 9 (30.0%) missing valuesMissing
TOT_EMPLY_CO has 9 (30.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:46:52.673355
Analysis finished2023-12-10 13:46:59.932258
Duration7.26 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
Minimum2020-01-01 00:00:00
Maximum2020-01-01 00:00:00
2023-12-10T22:47:00.002646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:00.156387image/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

2023-12-10T22:47:00.322545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:00.480510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 30
100.0%

SIGNGU_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
고양시덕양구
16 
가평군
14 

Length

Max length6
Median length6
Mean length4.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시덕양구 16
53.3%
가평군 14
46.7%

Length

2023-12-10T22:47:00.663940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:00.831521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시덕양구 16
53.3%
가평군 14
46.7%

ADSTRD_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
관산동
7 
능곡동
6 
가평읍
5 
상면
3 
설악면
3 
Other values (3)
6 

Length

Max length3
Median length3
Mean length2.9
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
관산동 7
23.3%
능곡동 6
20.0%
가평읍 5
16.7%
상면 3
10.0%
설악면 3
10.0%
대덕동 3
10.0%
청평면 2
 
6.7%
조종면 1
 
3.3%

Length

2023-12-10T22:47:01.002118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:01.258585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관산동 7
23.3%
능곡동 6
20.0%
가평읍 5
16.7%
상면 3
10.0%
설악면 3
10.0%
대덕동 3
10.0%
청평면 2
 
6.7%
조종면 1
 
3.3%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
16 
F
3 
G
3 
I
2 
A
2 
Other values (3)
4 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
C 16
53.3%
F 3
 
10.0%
G 3
 
10.0%
I 2
 
6.7%
A 2
 
6.7%
M 2
 
6.7%
J 1
 
3.3%
R 1
 
3.3%

Length

2023-12-10T22:47:01.489214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:01.686330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 16
53.3%
f 3
 
10.0%
g 3
 
10.0%
i 2
 
6.7%
a 2
 
6.7%
m 2
 
6.7%
j 1
 
3.3%
r 1
 
3.3%

INDUTY_MLSFC_CODE
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:47:01.984788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters18
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

Unique14 ?
Unique (%)46.7%

Sample

1st rowC11
2nd rowC26
3rd rowC33
4th rowF42
5th rowI55
ValueCountFrequency (%)
f42 3
 
10.0%
c10 3
 
10.0%
c11 2
 
6.7%
i55 2
 
6.7%
a01 2
 
6.7%
g46 2
 
6.7%
c26 2
 
6.7%
c32 1
 
3.3%
c20 1
 
3.3%
m73 1
 
3.3%
Other values (11) 11
36.7%
2023-12-10T22:47:02.489242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 16
17.8%
2 11
12.2%
1 11
12.2%
0 8
8.9%
5 6
 
6.7%
4 6
 
6.7%
6 5
 
5.6%
3 5
 
5.6%
7 4
 
4.4%
F 3
 
3.3%
Other values (8) 15
16.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11
18.3%
1 11
18.3%
0 8
13.3%
5 6
10.0%
4 6
10.0%
6 5
8.3%
3 5
8.3%
7 4
 
6.7%
8 2
 
3.3%
9 2
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 16
53.3%
F 3
 
10.0%
G 3
 
10.0%
A 2
 
6.7%
I 2
 
6.7%
M 2
 
6.7%
J 1
 
3.3%
R 1
 
3.3%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
2 11
18.3%
1 11
18.3%
0 8
13.3%
5 6
10.0%
4 6
10.0%
6 5
8.3%
3 5
8.3%
7 4
 
6.7%
8 2
 
3.3%
9 2
 
3.3%
Latin
ValueCountFrequency (%)
C 16
53.3%
F 3
 
10.0%
G 3
 
10.0%
A 2
 
6.7%
I 2
 
6.7%
M 2
 
6.7%
J 1
 
3.3%
R 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 16
17.8%
2 11
12.2%
1 11
12.2%
0 8
8.9%
5 6
 
6.7%
4 6
 
6.7%
6 5
 
5.6%
3 5
 
5.6%
7 4
 
4.4%
F 3
 
3.3%
Other values (8) 15
16.7%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
16 
건설업
3 
도매 및 소매업
3 
숙박 및 음식점업
2 
농업; 임업 및 어업
2 
Other values (3)
4 

Length

Max length19
Median length3
Mean length5.9
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row제조업
2nd row제조업
3rd row제조업
4th row건설업
5th row숙박 및 음식점업

Common Values

ValueCountFrequency (%)
제조업 16
53.3%
건설업 3
 
10.0%
도매 및 소매업 3
 
10.0%
숙박 및 음식점업 2
 
6.7%
농업; 임업 및 어업 2
 
6.7%
전문; 과학 및 기술 서비스업 2
 
6.7%
정보통신업 1
 
3.3%
예술; 스포츠 및 여가관련 서비스업 1
 
3.3%

Length

2023-12-10T22:47:02.774889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:03.034969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 16
27.6%
및 10
17.2%
건설업 3
 
5.2%
도매 3
 
5.2%
소매업 3
 
5.2%
서비스업 3
 
5.2%
기술 2
 
3.4%
과학 2
 
3.4%
전문 2
 
3.4%
어업 2
 
3.4%
Other values (8) 12
20.7%

INDUTY_MLSFC_NM
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:47:03.373758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19.5
Mean length11.833333
Min length2

Characters and Unicode

Total characters355
Distinct characters84
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

Unique14 ?
Unique (%)46.7%

Sample

1st row음료 제조업
2nd row전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업
3rd row기타 제품 제조업
4th row전문직별 공사업
5th row숙박업
ValueCountFrequency (%)
제조업 16
 
15.5%
및 14
 
13.6%
기타 4
 
3.9%
전문직별 3
 
2.9%
서비스업 3
 
2.9%
공사업 3
 
2.9%
식료품 3
 
2.9%
농업 2
 
1.9%
통신장비 2
 
1.9%
자동차 2
 
1.9%
Other values (40) 51
49.5%
2023-12-10T22:47:03.971424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
73
20.6%
업 30
 
8.5%
제 22
 
6.2%
조 16
 
4.5%
; 14
 
3.9%
및 14
 
3.9%
품 13
 
3.7%
기 11
 
3.1%
비 7
 
2.0%
전 7
 
2.0%
Other values (74) 148
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 268
75.5%
Space Separator 73
 
20.6%
Other Punctuation 14
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 30
 
11.2%
제 22
 
8.2%
조 16
 
6.0%
및 14
 
5.2%
품 13
 
4.9%
기 11
 
4.1%
비 7
 
2.6%
전 7
 
2.6%
료 6
 
2.2%
학 5
 
1.9%
Other values (72) 137
51.1%
Space Separator
ValueCountFrequency (%)
73
100.0%
Other Punctuation
ValueCountFrequency (%)
; 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 268
75.5%
Common 87
 
24.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 30
 
11.2%
제 22
 
8.2%
조 16
 
6.0%
및 14
 
5.2%
품 13
 
4.9%
기 11
 
4.1%
비 7
 
2.6%
전 7
 
2.6%
료 6
 
2.2%
학 5
 
1.9%
Other values (72) 137
51.1%
Common
ValueCountFrequency (%)
73
83.9%
; 14
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 268
75.5%
ASCII 87
 
24.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73
83.9%
; 14
 
16.1%
Hangul
ValueCountFrequency (%)
업 30
 
11.2%
제 22
 
8.2%
조 16
 
6.0%
및 14
 
5.2%
품 13
 
4.9%
기 11
 
4.1%
비 7
 
2.6%
전 7
 
2.6%
료 6
 
2.2%
학 5
 
1.9%
Other values (72) 137
51.1%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
4
28 
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 28
93.3%
3 2
 
6.7%

Length

2023-12-10T22:47:04.301845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:04.486050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 28
93.3%
3 2
 
6.7%

PRCSS_ENTRPRS_SE_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
소기업
28 
중기업
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소기업 28
93.3%
중기업 2
 
6.7%

Length

2023-12-10T22:47:04.671412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:04.827660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소기업 28
93.3%
중기업 2
 
6.7%

PDSMLPZ_SCTN_CODE
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2
11 
5
9 
10
5 
1
4 
20
 
1

Length

Max length2
Median length1
Mean length1.2
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row10
2nd row2
3rd row5
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 11
36.7%
5 9
30.0%
10 5
16.7%
1 4
 
13.3%
20 1
 
3.3%

Length

2023-12-10T22:47:05.010065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:05.210916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 11
36.7%
5 9
30.0%
10 5
16.7%
1 4
 
13.3%
20 1
 
3.3%

PDSMLPZ_SCTN_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2년 이상 5년 미만
11 
5년 이상 10년 미만
9 
10년 이상 20년 미만
5 
1년 이상 2년 미만
4 
20년 이상 30년 미만
 
1

Length

Max length13
Median length12.5
Mean length11.7
Min length11

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
2년 이상 5년 미만 11
36.7%
5년 이상 10년 미만 9
30.0%
10년 이상 20년 미만 5
16.7%
1년 이상 2년 미만 4
 
13.3%
20년 이상 30년 미만 1
 
3.3%

Length

2023-12-10T22:47:05.457445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:05.677311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 30
25.0%
미만 30
25.0%
5년 20
16.7%
2년 15
12.5%
10년 14
11.7%
20년 6
 
5.0%
1년 4
 
3.3%
30년 1
 
0.8%

NRMLT_BSN_ENTRPRS_CO
Categorical

HIGH CORRELATION  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 row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

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

Length

2023-12-10T22:47:05.879440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:06.033857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
96.7%
2 1
 
3.3%

TOT_EMPLY_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4
Minimum0
Maximum42
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:06.603763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q314.5
95-th percentile33.15
Maximum42
Range42
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation11.174478
Coefficient of variation (CV)1.1887743
Kurtosis2.3517216
Mean9.4
Median Absolute Deviation (MAD)6
Skewness1.6195639
Sum282
Variance124.86897
MonotonicityNot monotonic
2023-12-10T22:47:06.761624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
30.0%
6 3
 
10.0%
7 3
 
10.0%
4 2
 
6.7%
18 2
 
6.7%
21 1
 
3.3%
26 1
 
3.3%
8 1
 
3.3%
5 1
 
3.3%
10 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
0 9
30.0%
3 1
 
3.3%
4 2
 
6.7%
5 1
 
3.3%
6 3
 
10.0%
7 3
 
10.0%
8 1
 
3.3%
9 1
 
3.3%
10 1
 
3.3%
16 1
 
3.3%
ValueCountFrequency (%)
42 1
3.3%
39 1
3.3%
26 1
3.3%
21 1
3.3%
20 1
3.3%
18 2
6.7%
16 1
3.3%
10 1
3.3%
9 1
3.3%
8 1
3.3%

EMPLY_AVRG_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)66.7%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean13.047619
Minimum3
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:06.943686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q318
95-th percentile39
Maximum42
Range39
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.191408
Coefficient of variation (CV)0.85773567
Kurtosis1.7994321
Mean13.047619
Median Absolute Deviation (MAD)3
Skewness1.5739267
Sum274
Variance125.24762
MonotonicityNot monotonic
2023-12-10T22:47:07.138742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 3
 
10.0%
6 3
 
10.0%
4 2
 
6.7%
18 2
 
6.7%
8 2
 
6.7%
21 1
 
3.3%
20 1
 
3.3%
39 1
 
3.3%
42 1
 
3.3%
9 1
 
3.3%
Other values (4) 4
13.3%
(Missing) 9
30.0%
ValueCountFrequency (%)
3 1
 
3.3%
4 2
6.7%
5 1
 
3.3%
6 3
10.0%
7 3
10.0%
8 2
6.7%
9 1
 
3.3%
10 1
 
3.3%
18 2
6.7%
20 1
 
3.3%
ValueCountFrequency (%)
42 1
 
3.3%
39 1
 
3.3%
26 1
 
3.3%
21 1
 
3.3%
20 1
 
3.3%
18 2
6.7%
10 1
 
3.3%
9 1
 
3.3%
8 2
6.7%
7 3
10.0%

ANSLRY_AVRG_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean29887605
Minimum22200000
Maximum40985778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:07.365010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22200000
5-th percentile23031556
Q125560410
median29427556
Q333097143
95-th percentile37684000
Maximum40985778
Range18785778
Interquartile range (IQR)7536733

Descriptive statistics

Standard deviation5019103.6
Coefficient of variation (CV)0.16793262
Kurtosis-0.46036737
Mean29887605
Median Absolute Deviation (MAD)3867146
Skewness0.38296183
Sum6.276397 × 108
Variance2.5191401 × 1013
MonotonicityNot monotonic
2023-12-10T22:47:07.578896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
33097143.0 1
 
3.3%
25475333.0 1
 
3.3%
32989867.0 1
 
3.3%
30814400.0 1
 
3.3%
40985778.0 1
 
3.3%
37684000.0 1
 
3.3%
27496889.0 1
 
3.3%
32270844.5 1
 
3.3%
22200000.0 1
 
3.3%
35353524.0 1
 
3.3%
Other values (11) 11
36.7%
(Missing) 9
30.0%
ValueCountFrequency (%)
22200000.0 1
3.3%
23031556.0 1
3.3%
24227048.0 1
3.3%
25348133.0 1
3.3%
25475333.0 1
3.3%
25560410.0 1
3.3%
26305778.0 1
3.3%
26318667.0 1
3.3%
27496889.0 1
3.3%
29222074.0 1
3.3%
ValueCountFrequency (%)
40985778.0 1
3.3%
37684000.0 1
3.3%
35353524.0 1
3.3%
33513778.0 1
3.3%
33388034.0 1
3.3%
33097143.0 1
3.3%
32989867.0 1
3.3%
32928889.0 1
3.3%
32270844.5 1
3.3%
30814400.0 1
3.3%

EMPLY_FSTLTN_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)66.7%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean13.047619
Minimum3
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:07.836351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q318
95-th percentile39
Maximum42
Range39
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.191408
Coefficient of variation (CV)0.85773567
Kurtosis1.7994321
Mean13.047619
Median Absolute Deviation (MAD)3
Skewness1.5739267
Sum274
Variance125.24762
MonotonicityNot monotonic
2023-12-10T22:47:08.088160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 3
 
10.0%
6 3
 
10.0%
4 2
 
6.7%
18 2
 
6.7%
8 2
 
6.7%
21 1
 
3.3%
20 1
 
3.3%
39 1
 
3.3%
42 1
 
3.3%
9 1
 
3.3%
Other values (4) 4
13.3%
(Missing) 9
30.0%
ValueCountFrequency (%)
3 1
 
3.3%
4 2
6.7%
5 1
 
3.3%
6 3
10.0%
7 3
10.0%
8 2
6.7%
9 1
 
3.3%
10 1
 
3.3%
18 2
6.7%
20 1
 
3.3%
ValueCountFrequency (%)
42 1
 
3.3%
39 1
 
3.3%
26 1
 
3.3%
21 1
 
3.3%
20 1
 
3.3%
18 2
6.7%
10 1
 
3.3%
9 1
 
3.3%
8 2
6.7%
7 3
10.0%

ANSLRY_FSTLTN_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing9
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean29887605
Minimum22200000
Maximum40985778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:47:08.321770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22200000
5-th percentile23031556
Q125560410
median29427556
Q333097143
95-th percentile37684000
Maximum40985778
Range18785778
Interquartile range (IQR)7536733

Descriptive statistics

Standard deviation5019103.6
Coefficient of variation (CV)0.16793262
Kurtosis-0.46036737
Mean29887605
Median Absolute Deviation (MAD)3867146
Skewness0.38296183
Sum6.276397 × 108
Variance2.5191401 × 1013
MonotonicityNot monotonic
2023-12-10T22:47:08.551979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
33097143.0 1
 
3.3%
25475333.0 1
 
3.3%
32989867.0 1
 
3.3%
30814400.0 1
 
3.3%
40985778.0 1
 
3.3%
37684000.0 1
 
3.3%
27496889.0 1
 
3.3%
32270844.5 1
 
3.3%
22200000.0 1
 
3.3%
35353524.0 1
 
3.3%
Other values (11) 11
36.7%
(Missing) 9
30.0%
ValueCountFrequency (%)
22200000.0 1
3.3%
23031556.0 1
3.3%
24227048.0 1
3.3%
25348133.0 1
3.3%
25475333.0 1
3.3%
25560410.0 1
3.3%
26305778.0 1
3.3%
26318667.0 1
3.3%
27496889.0 1
3.3%
29222074.0 1
3.3%
ValueCountFrequency (%)
40985778.0 1
3.3%
37684000.0 1
3.3%
35353524.0 1
3.3%
33513778.0 1
3.3%
33388034.0 1
3.3%
33097143.0 1
3.3%
32989867.0 1
3.3%
32928889.0 1
3.3%
32270844.5 1
3.3%
30814400.0 1
3.3%

REGIST_DE
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-10-18 00:00:00
Maximum2020-10-18 00:00:00
2023-12-10T22:47:08.729749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:47:08.885890image/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

2023-12-10T22:47:09.159733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:47:09.366199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T22:46:58.076401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:54.408511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:55.135829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.257841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.128771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.231415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:54.547193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:55.611364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.442876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.393086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.392543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:54.691675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:55.747901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.645612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.602844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.555323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:54.842202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:55.895506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.807741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.771414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:58.710849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:54.988776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.050033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:56.965221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:46:57.934215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:47:09.531414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLSFC_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SE_NMPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMNRMLT_BSN_ENTRPRS_COTOT_EMPLY_COEMPLY_AVRG_COANSLRY_AVRG_AMEMPLY_FSTLTN_COANSLRY_FSTLTN_AM
SIGNGU_NM1.0001.0000.0000.4090.0000.4090.0000.0000.0000.0000.0000.2090.6110.0000.6110.000
ADSTRD_NM1.0001.0000.6900.5690.6900.5690.0000.0000.4800.4800.0000.0000.5070.0000.5070.000
INDUTY_LCLAS_CODE0.0000.6901.0001.0001.0001.0000.0000.0000.4190.4190.0000.0000.0000.0000.0000.000
INDUTY_MLSFC_CODE0.4090.5691.0001.0001.0001.0000.0000.0000.8390.8391.0000.4230.0000.0000.0000.000
INDUTY_LCLAS_NM0.0000.6901.0001.0001.0001.0000.0000.0000.4190.4190.0000.0000.0000.0000.0000.000
INDUTY_MLSFC_NM0.4090.5691.0001.0001.0001.0000.0000.0000.8390.8391.0000.4230.0000.0000.0000.000
PRCSS_ENTRPRS_SE_CODE0.0000.0000.0000.0000.0000.0001.0000.9060.0000.0000.0000.9020.7110.0000.7110.000
PRCSS_ENTRPRS_SE_NM0.0000.0000.0000.0000.0000.0000.9061.0000.0000.0000.0000.9020.7110.0000.7110.000
PDSMLPZ_SCTN_CODE0.0000.4800.4190.8390.4190.8390.0000.0001.0001.0000.0000.3320.2990.0000.2990.000
PDSMLPZ_SCTN_NM0.0000.4800.4190.8390.4190.8390.0000.0001.0001.0000.0000.3320.2990.0000.2990.000
NRMLT_BSN_ENTRPRS_CO0.0000.0000.0001.0000.0001.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.000
TOT_EMPLY_CO0.2090.0000.0000.4230.0000.4230.9020.9020.3320.3321.0001.0000.9090.0000.9090.000
EMPLY_AVRG_CO0.6110.5070.0000.0000.0000.0000.7110.7110.2990.2990.0000.9091.0000.0001.0000.000
ANSLRY_AVRG_AM0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.000
EMPLY_FSTLTN_CO0.6110.5070.0000.0000.0000.0000.7110.7110.2990.2990.0000.9091.0000.0001.0000.000
ANSLRY_FSTLTN_AM0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.000
2023-12-10T22:47:09.900141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRCSS_ENTRPRS_SE_NMINDUTY_LCLAS_NMADSTRD_NMPDSMLPZ_SCTN_NMPRCSS_ENTRPRS_SE_CODEINDUTY_LCLAS_CODEPDSMLPZ_SCTN_CODESIGNGU_NMNRMLT_BSN_ENTRPRS_CO
PRCSS_ENTRPRS_SE_NM1.0000.0000.0000.0000.7210.0000.0000.0000.000
INDUTY_LCLAS_NM0.0001.0000.2760.2380.0001.0000.2380.0000.000
ADSTRD_NM0.0000.2761.0000.2860.0000.2760.2860.8860.000
PDSMLPZ_SCTN_NM0.0000.2380.2861.0000.0000.2381.0000.0000.000
PRCSS_ENTRPRS_SE_CODE0.7210.0000.0000.0001.0000.0000.0000.0000.000
INDUTY_LCLAS_CODE0.0001.0000.2760.2380.0001.0000.2380.0000.000
PDSMLPZ_SCTN_CODE0.0000.2380.2861.0000.0000.2381.0000.0000.000
SIGNGU_NM0.0000.0000.8860.0000.0000.0000.0001.0000.000
NRMLT_BSN_ENTRPRS_CO0.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-10T22:47:10.198671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TOT_EMPLY_COEMPLY_AVRG_COANSLRY_AVRG_AMEMPLY_FSTLTN_COANSLRY_FSTLTN_AMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_LCLAS_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SE_NMPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMNRMLT_BSN_ENTRPRS_CO
TOT_EMPLY_CO1.0000.9940.0830.9940.0830.0270.1090.0000.0000.5160.5160.2060.2060.906
EMPLY_AVRG_CO0.9941.0000.0691.0000.0690.3820.2530.0000.0000.4560.4560.1610.1610.000
ANSLRY_AVRG_AM0.0830.0691.0000.0691.0000.0000.0000.0350.0350.0000.0000.0000.0000.000
EMPLY_FSTLTN_CO0.9941.0000.0691.0000.0690.3820.2530.0000.0000.4560.4560.1610.1610.000
ANSLRY_FSTLTN_AM0.0830.0691.0000.0691.0000.0000.0000.0350.0350.0000.0000.0000.0000.000
SIGNGU_NM0.0270.3820.0000.3820.0001.0000.8860.0000.0000.0000.0000.0000.0000.000
ADSTRD_NM0.1090.2530.0000.2530.0000.8861.0000.2760.2760.0000.0000.2860.2860.000
INDUTY_LCLAS_CODE0.0000.0000.0350.0000.0350.0000.2761.0001.0000.0000.0000.2380.2380.000
INDUTY_LCLAS_NM0.0000.0000.0350.0000.0350.0000.2761.0001.0000.0000.0000.2380.2380.000
PRCSS_ENTRPRS_SE_CODE0.5160.4560.0000.4560.0000.0000.0000.0000.0001.0000.7210.0000.0000.000
PRCSS_ENTRPRS_SE_NM0.5160.4560.0000.4560.0000.0000.0000.0000.0000.7211.0000.0000.0000.000
PDSMLPZ_SCTN_CODE0.2060.1610.0000.1610.0000.0000.2860.2380.2380.0000.0001.0001.0000.000
PDSMLPZ_SCTN_NM0.2060.1610.0000.1610.0000.0000.2860.2380.2380.0000.0001.0001.0000.000
NRMLT_BSN_ENTRPRS_CO0.9060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:46:59.008638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:46:59.498343image/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.
2023-12-10T22:46:59.809030image/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_SE_NMPDSMLPZ_SCTN_CODEPDSMLPZ_SCTN_NMNRMLT_BSN_ENTRPRS_COTOT_EMPLY_COEMPLY_AVRG_COANSLRY_AVRG_AMEMPLY_FSTLTN_COANSLRY_FSTLTN_AMREGIST_DEOPERTOR_NM
02020-01경기가평군가평읍CC11제조업음료 제조업4소기업1010년 이상 20년 미만1212133513778.02133513778.02020-10-18KEDSYS
12020-01경기가평군가평읍CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4소기업22년 이상 5년 미만17733097143.0733097143.02020-10-18KEDSYS
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