Overview

Dataset statistics

Number of variables20
Number of observations30
Missing cells16
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory172.4 B

Variable types

Categorical13
Text2
Numeric5

Dataset

Description샘플 데이터
Author한국기업데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/8e8cc5ee-5aa3-495e-970a-af8d09b62f8e

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
NRMLT_BSN_ENTRPRS_CO has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
PRCSS_ENTRPRS_SE_CODE is highly overall correlated with ADSTRD_NM and 3 other fieldsHigh correlation
PDSMLPZ_SCTN_NM is highly overall correlated with PDSMLPZ_SCTN_CODEHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_LCLAS_CODE and 2 other fieldsHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with INDUTY_LCLAS_NM and 2 other fieldsHigh correlation
PRCSS_ENTRPRS_SE_NM is highly overall correlated with ADSTRD_NM and 3 other fieldsHigh 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 3 other fieldsHigh correlation
EMPLY_AVRG_CO is highly overall correlated with TOT_EMPLY_CO and 3 other fieldsHigh correlation
ANSLRY_AVRG_AM is highly overall correlated with TOT_EMPLY_CO and 3 other fieldsHigh correlation
EMPLY_FSTLTN_CO is highly overall correlated with TOT_EMPLY_CO and 3 other fieldsHigh correlation
ANSLRY_FSTLTN_AM is highly overall correlated with TOT_EMPLY_CO and 3 other fieldsHigh correlation
SIGNGU_NM is highly overall correlated with ADSTRD_NMHigh correlation
ADSTRD_NM is highly overall correlated with SIGNGU_NM and 2 other fieldsHigh correlation
EMPLY_AVRG_CO has 4 (13.3%) missing valuesMissing
ANSLRY_AVRG_AM has 4 (13.3%) missing valuesMissing
EMPLY_FSTLTN_CO has 4 (13.3%) missing valuesMissing
ANSLRY_FSTLTN_AM has 4 (13.3%) missing valuesMissing
TOT_EMPLY_CO has 4 (13.3%) zerosZeros

Reproduction

Analysis started2023-12-10 13:49:34.780247
Analysis finished2023-12-10 13:49:40.937292
Duration6.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2020-01 30
100.0%

Length

2023-12-10T22:49:41.169014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:41.353702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01 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

2023-12-10T22:49:41.543030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:41.780934image/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
22 
가평군
8 

Length

Max length6
Median length6
Mean length5.2
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시덕양구 22
73.3%
가평군 8
 
26.7%

Length

2023-12-10T22:49:41.944898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:42.111239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시덕양구 22
73.3%
가평군 8
 
26.7%

ADSTRD_NM
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
창릉동
7 
능곡동
3 
성사1동
3 
원신동
3 
가평읍
2 
Other values (9)
12 

Length

Max length4
Median length3
Mean length2.9666667
Min length2

Unique

Unique6 ?
Unique (%)20.0%

Sample

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

Common Values

ValueCountFrequency (%)
창릉동 7
23.3%
능곡동 3
10.0%
성사1동 3
10.0%
원신동 3
10.0%
가평읍 2
 
6.7%
북면 2
 
6.7%
상면 2
 
6.7%
주교동 2
 
6.7%
설악면 1
 
3.3%
청평면 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T22:49:42.432286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창릉동 7
23.3%
능곡동 3
10.0%
성사1동 3
10.0%
원신동 3
10.0%
가평읍 2
 
6.7%
북면 2
 
6.7%
상면 2
 
6.7%
주교동 2
 
6.7%
설악면 1
 
3.3%
청평면 1
 
3.3%
Other values (4) 4
13.3%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
13 
G
7 
F
5 
H
2 
J
 
1
Other values (2)
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
C 13
43.3%
G 7
23.3%
F 5
 
16.7%
H 2
 
6.7%
J 1
 
3.3%
L 1
 
3.3%
N 1
 
3.3%

Length

2023-12-10T22:49:43.094660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:43.291915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 13
43.3%
g 7
23.3%
f 5
 
16.7%
h 2
 
6.7%
j 1
 
3.3%
l 1
 
3.3%
n 1
 
3.3%

INDUTY_MLSFC_CODE
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:49:43.641436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st rowC23
2nd rowG47
3rd rowC10
4th rowC10
5th rowC10
ValueCountFrequency (%)
c10 5
16.7%
f42 4
13.3%
g46 4
13.3%
c28 2
 
6.7%
g47 2
 
6.7%
c18 1
 
3.3%
c23 1
 
3.3%
c33 1
 
3.3%
c27 1
 
3.3%
c21 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T22:49:44.375561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 14
15.6%
C 13
14.4%
2 11
12.2%
1 8
8.9%
G 7
7.8%
0 5
 
5.6%
6 5
 
5.6%
F 5
 
5.6%
5 4
 
4.4%
8 4
 
4.4%
Other values (7) 14
15.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 14
23.3%
2 11
18.3%
1 8
13.3%
0 5
 
8.3%
6 5
 
8.3%
5 4
 
6.7%
8 4
 
6.7%
7 4
 
6.7%
3 3
 
5.0%
9 2
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 13
43.3%
G 7
23.3%
F 5
 
16.7%
H 2
 
6.7%
J 1
 
3.3%
L 1
 
3.3%
N 1
 
3.3%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
4 14
23.3%
2 11
18.3%
1 8
13.3%
0 5
 
8.3%
6 5
 
8.3%
5 4
 
6.7%
8 4
 
6.7%
7 4
 
6.7%
3 3
 
5.0%
9 2
 
3.3%
Latin
ValueCountFrequency (%)
C 13
43.3%
G 7
23.3%
F 5
 
16.7%
H 2
 
6.7%
J 1
 
3.3%
L 1
 
3.3%
N 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 14
15.6%
C 13
14.4%
2 11
12.2%
1 8
8.9%
G 7
7.8%
0 5
 
5.6%
6 5
 
5.6%
F 5
 
5.6%
5 4
 
4.4%
8 4
 
4.4%
Other values (7) 14
15.6%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
13 
도매 및 소매업
7 
건설업
5 
운수 및 창고업
2 
정보통신업
 
1
Other values (2)
2 

Length

Max length24
Median length3
Mean length5.3
Min length3

Unique

Unique3 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
제조업 13
43.3%
도매 및 소매업 7
23.3%
건설업 5
 
16.7%
운수 및 창고업 2
 
6.7%
정보통신업 1
 
3.3%
부동산업 1
 
3.3%
사업시설 관리; 사업 지원 및 임대 서비스업 1
 
3.3%

Length

2023-12-10T22:49:44.646975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:44.943550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 13
24.1%
및 10
18.5%
도매 7
13.0%
소매업 7
13.0%
건설업 5
 
9.3%
운수 2
 
3.7%
창고업 2
 
3.7%
정보통신업 1
 
1.9%
부동산업 1
 
1.9%
사업시설 1
 
1.9%
Other values (5) 5
 
9.3%

INDUTY_MLSFC_NM
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:49:45.285835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length11
Min length4

Characters and Unicode

Total characters330
Distinct characters78
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row비금속 광물제품 제조업
2nd row소매업; 자동차 제외
3rd row식료품 제조업
4th row식료품 제조업
5th row식료품 제조업
ValueCountFrequency (%)
및 13
 
13.5%
제조업 12
 
12.5%
식료품 5
 
5.2%
도매 4
 
4.2%
상품 4
 
4.2%
중개업 4
 
4.2%
전문직별 4
 
4.2%
공사업 4
 
4.2%
자동차 3
 
3.1%
제외 3
 
3.1%
Other values (37) 40
41.7%
2023-12-10T22:49:46.036087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
20.0%
업 31
 
9.4%
제 20
 
6.1%
품 14
 
4.2%
조 13
 
3.9%
및 13
 
3.9%
기 8
 
2.4%
매 8
 
2.4%
료 7
 
2.1%
상 6
 
1.8%
Other values (68) 144
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
78.5%
Space Separator 66
 
20.0%
Other Punctuation 5
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 31
 
12.0%
제 20
 
7.7%
품 14
 
5.4%
조 13
 
5.0%
및 13
 
5.0%
기 8
 
3.1%
매 8
 
3.1%
료 7
 
2.7%
상 6
 
2.3%
전 6
 
2.3%
Other values (66) 133
51.4%
Space Separator
ValueCountFrequency (%)
66
100.0%
Other Punctuation
ValueCountFrequency (%)
; 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
78.5%
Common 71
 
21.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 31
 
12.0%
제 20
 
7.7%
품 14
 
5.4%
조 13
 
5.0%
및 13
 
5.0%
기 8
 
3.1%
매 8
 
3.1%
료 7
 
2.7%
상 6
 
2.3%
전 6
 
2.3%
Other values (66) 133
51.4%
Common
ValueCountFrequency (%)
66
93.0%
; 5
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 258
78.2%
ASCII 71
 
21.5%
Compat Jamo 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
66
93.0%
; 5
 
7.0%
Hangul
ValueCountFrequency (%)
업 31
 
12.0%
제 20
 
7.8%
품 14
 
5.4%
조 13
 
5.0%
및 13
 
5.0%
기 8
 
3.1%
매 8
 
3.1%
료 7
 
2.7%
상 6
 
2.3%
전 6
 
2.3%
Other values (65) 132
51.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
4
21 
3
9 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
4 21
70.0%
3 9
30.0%

Length

2023-12-10T22:49:46.273506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:46.446502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 21
70.0%
3 9
30.0%

PRCSS_ENTRPRS_SE_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
소기업
21 
중기업
9 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소기업 21
70.0%
중기업 9
30.0%

Length

2023-12-10T22:49:46.626624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:46.794153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소기업 21
70.0%
중기업 9
30.0%

PDSMLPZ_SCTN_CODE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
10
13 
5
11 
20
3 
2
3 

Length

Max length2
Median length2
Mean length1.5333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row5
3rd row10
4th row5
5th row10

Common Values

ValueCountFrequency (%)
10 13
43.3%
5 11
36.7%
20 3
 
10.0%
2 3
 
10.0%

Length

2023-12-10T22:49:46.987476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:47.179070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 13
43.3%
5 11
36.7%
20 3
 
10.0%
2 3
 
10.0%

PDSMLPZ_SCTN_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
10년 이상 20년 미만
13 
5년 이상 10년 미만
11 
20년 이상 30년 미만
3 
2년 이상 5년 미만
3 

Length

Max length13
Median length13
Mean length12.433333
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20년 이상 30년 미만
2nd row5년 이상 10년 미만
3rd row10년 이상 20년 미만
4th row5년 이상 10년 미만
5th row10년 이상 20년 미만

Common Values

ValueCountFrequency (%)
10년 이상 20년 미만 13
43.3%
5년 이상 10년 미만 11
36.7%
20년 이상 30년 미만 3
 
10.0%
2년 이상 5년 미만 3
 
10.0%

Length

2023-12-10T22:49:47.384796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:47.621989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이상 30
25.0%
미만 30
25.0%
10년 24
20.0%
20년 16
13.3%
5년 14
11.7%
30년 3
 
2.5%
2년 3
 
2.5%

NRMLT_BSN_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

2023-12-10T22:49:47.872531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:48.046865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
100.0%

TOT_EMPLY_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.4
Minimum0
Maximum55
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:48.193400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median11
Q318.25
95-th percentile51.1
Maximum55
Range55
Interquartile range (IQR)13

Descriptive statistics

Standard deviation16.228115
Coefficient of variation (CV)0.98951922
Kurtosis0.5674141
Mean16.4
Median Absolute Deviation (MAD)6
Skewness1.2831088
Sum492
Variance263.35172
MonotonicityNot monotonic
2023-12-10T22:49:48.382511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
13.3%
11 3
 
10.0%
5 3
 
10.0%
10 2
 
6.7%
6 2
 
6.7%
15 2
 
6.7%
9 1
 
3.3%
50 1
 
3.3%
13 1
 
3.3%
8 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
0 4
13.3%
4 1
 
3.3%
5 3
10.0%
6 2
6.7%
8 1
 
3.3%
9 1
 
3.3%
10 2
6.7%
11 3
10.0%
13 1
 
3.3%
14 1
 
3.3%
ValueCountFrequency (%)
55 1
3.3%
52 1
3.3%
50 1
3.3%
45 1
3.3%
35 1
3.3%
32 1
3.3%
30 1
3.3%
19 1
3.3%
16 1
3.3%
15 2
6.7%

EMPLY_AVRG_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)73.1%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean18.923077
Minimum4
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:48.550823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q18.25
median12
Q327.25
95-th percentile51.5
Maximum55
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.994807
Coefficient of variation (CV)0.84525402
Kurtosis0.22989164
Mean18.923077
Median Absolute Deviation (MAD)6
Skewness1.2415191
Sum492
Variance255.83385
MonotonicityNot monotonic
2023-12-10T22:49:48.767603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 3
 
10.0%
11 3
 
10.0%
15 2
 
6.7%
10 2
 
6.7%
6 2
 
6.7%
16 1
 
3.3%
35 1
 
3.3%
30 1
 
3.3%
45 1
 
3.3%
4 1
 
3.3%
Other values (9) 9
30.0%
(Missing) 4
13.3%
ValueCountFrequency (%)
4 1
 
3.3%
5 3
10.0%
6 2
6.7%
8 1
 
3.3%
9 1
 
3.3%
10 2
6.7%
11 3
10.0%
13 1
 
3.3%
14 1
 
3.3%
15 2
6.7%
ValueCountFrequency (%)
55 1
3.3%
52 1
3.3%
50 1
3.3%
45 1
3.3%
35 1
3.3%
32 1
3.3%
30 1
3.3%
19 1
3.3%
16 1
3.3%
15 2
6.7%

ANSLRY_AVRG_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean30604056
Minimum16660800
Maximum42448000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:49.042603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16660800
5-th percentile20994667
Q126724696
median30125292
Q334530867
95-th percentile41112445
Maximum42448000
Range25787200
Interquartile range (IQR)7806171.2

Descriptive statistics

Standard deviation6721929.5
Coefficient of variation (CV)0.21964179
Kurtosis-0.49818232
Mean30604056
Median Absolute Deviation (MAD)3988642
Skewness0.039270473
Sum7.9570545 × 108
Variance4.5184336 × 1013
MonotonicityNot monotonic
2023-12-10T22:49:49.384652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20834667 1
 
3.3%
42448000 1
 
3.3%
38808000 1
 
3.3%
34916800 1
 
3.3%
25450971 1
 
3.3%
27000978 1
 
3.3%
25596606 1
 
3.3%
21474667 1
 
3.3%
33373067 1
 
3.3%
41154667 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 4
 
13.3%
ValueCountFrequency (%)
16660800 1
3.3%
20834667 1
3.3%
21474667 1
3.3%
23140622 1
3.3%
25450971 1
3.3%
25596606 1
3.3%
26676693 1
3.3%
26868703 1
3.3%
27000978 1
3.3%
27034462 1
3.3%
ValueCountFrequency (%)
42448000 1
3.3%
41154667 1
3.3%
40985778 1
3.3%
40096000 1
3.3%
38808000 1
3.3%
34986909 1
3.3%
34916800 1
3.3%
33373067 1
3.3%
32943758 1
3.3%
32864667 1
3.3%

EMPLY_FSTLTN_CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)73.1%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean18.923077
Minimum4
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:49.644112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q18.25
median12
Q327.25
95-th percentile51.5
Maximum55
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.994807
Coefficient of variation (CV)0.84525402
Kurtosis0.22989164
Mean18.923077
Median Absolute Deviation (MAD)6
Skewness1.2415191
Sum492
Variance255.83385
MonotonicityNot monotonic
2023-12-10T22:49:49.826375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 3
 
10.0%
11 3
 
10.0%
15 2
 
6.7%
10 2
 
6.7%
6 2
 
6.7%
16 1
 
3.3%
35 1
 
3.3%
30 1
 
3.3%
45 1
 
3.3%
4 1
 
3.3%
Other values (9) 9
30.0%
(Missing) 4
13.3%
ValueCountFrequency (%)
4 1
 
3.3%
5 3
10.0%
6 2
6.7%
8 1
 
3.3%
9 1
 
3.3%
10 2
6.7%
11 3
10.0%
13 1
 
3.3%
14 1
 
3.3%
15 2
6.7%
ValueCountFrequency (%)
55 1
3.3%
52 1
3.3%
50 1
3.3%
45 1
3.3%
35 1
3.3%
32 1
3.3%
30 1
3.3%
19 1
3.3%
16 1
3.3%
15 2
6.7%

ANSLRY_FSTLTN_AM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing4
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean30604056
Minimum16660800
Maximum42448000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:49:50.028544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16660800
5-th percentile20994667
Q126724696
median30125292
Q334530867
95-th percentile41112445
Maximum42448000
Range25787200
Interquartile range (IQR)7806171.2

Descriptive statistics

Standard deviation6721929.5
Coefficient of variation (CV)0.21964179
Kurtosis-0.49818232
Mean30604056
Median Absolute Deviation (MAD)3988642
Skewness0.039270473
Sum7.9570545 × 108
Variance4.5184336 × 1013
MonotonicityNot monotonic
2023-12-10T22:49:50.239273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20834667 1
 
3.3%
42448000 1
 
3.3%
38808000 1
 
3.3%
34916800 1
 
3.3%
25450971 1
 
3.3%
27000978 1
 
3.3%
25596606 1
 
3.3%
21474667 1
 
3.3%
33373067 1
 
3.3%
41154667 1
 
3.3%
Other values (16) 16
53.3%
(Missing) 4
 
13.3%
ValueCountFrequency (%)
16660800 1
3.3%
20834667 1
3.3%
21474667 1
3.3%
23140622 1
3.3%
25450971 1
3.3%
25596606 1
3.3%
26676693 1
3.3%
26868703 1
3.3%
27000978 1
3.3%
27034462 1
3.3%
ValueCountFrequency (%)
42448000 1
3.3%
41154667 1
3.3%
40985778 1
3.3%
40096000 1
3.3%
38808000 1
3.3%
34986909 1
3.3%
34916800 1
3.3%
33373067 1
3.3%
32943758 1
3.3%
32864667 1
3.3%

REGIST_DE
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2020-10-18
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-18
2nd row2020-10-18
3rd row2020-10-18
4th row2020-10-18
5th row2020-10-18

Common Values

ValueCountFrequency (%)
2020-10-18 30
100.0%

Length

2023-12-10T22:49:50.432787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:50.570584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-10-18 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

2023-12-10T22:49:50.777438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:49:50.941226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T22:49:39.206987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.313848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.960501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.651583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.366917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:39.431522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.432307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.100168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.811940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.493006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:39.566561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.545107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.258169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.972857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.615979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:39.706665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.683428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.399157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.098859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.858471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:39.861247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:36.803258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:37.520625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:38.229980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:49:39.005075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:49:51.135548image/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_NMTOT_EMPLY_COEMPLY_AVRG_COANSLRY_AVRG_AMEMPLY_FSTLTN_COANSLRY_FSTLTN_AM
SIGNGU_NM1.0001.0000.0800.7140.0800.7140.0000.0000.0000.0000.3130.0000.0000.0000.000
ADSTRD_NM1.0001.0000.5560.0000.5560.0000.8800.8800.0000.0000.0000.0000.0000.0000.000
INDUTY_LCLAS_CODE0.0800.5561.0001.0001.0001.0000.6520.6520.0000.0000.0000.0000.4440.0000.444
INDUTY_MLSFC_CODE0.7140.0001.0001.0001.0001.0000.6670.6670.7390.7390.3460.6820.5880.6820.588
INDUTY_LCLAS_NM0.0800.5561.0001.0001.0001.0000.6520.6520.0000.0000.0000.0000.4440.0000.444
INDUTY_MLSFC_NM0.7140.0001.0001.0001.0001.0000.6670.6670.7390.7390.3460.6820.5880.6820.588
PRCSS_ENTRPRS_SE_CODE0.0000.8800.6520.6670.6520.6671.0000.9920.2630.2630.5360.2440.3490.2440.349
PRCSS_ENTRPRS_SE_NM0.0000.8800.6520.6670.6520.6670.9921.0000.2630.2630.5360.2440.3490.2440.349
PDSMLPZ_SCTN_CODE0.0000.0000.0000.7390.0000.7390.2630.2631.0001.0000.0000.3190.7120.3190.712
PDSMLPZ_SCTN_NM0.0000.0000.0000.7390.0000.7390.2630.2631.0001.0000.0000.3190.7120.3190.712
TOT_EMPLY_CO0.3130.0000.0000.3460.0000.3460.5360.5360.0000.0001.0000.9470.3530.9470.353
EMPLY_AVRG_CO0.0000.0000.0000.6820.0000.6820.2440.2440.3190.3190.9471.0000.4521.0000.452
ANSLRY_AVRG_AM0.0000.0000.4440.5880.4440.5880.3490.3490.7120.7120.3530.4521.0000.4521.000
EMPLY_FSTLTN_CO0.0000.0000.0000.6820.0000.6820.2440.2440.3190.3190.9471.0000.4521.0000.452
ANSLRY_FSTLTN_AM0.0000.0000.4440.5880.4440.5880.3490.3490.7120.7120.3530.4521.0000.4521.000
2023-12-10T22:49:51.418221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRCSS_ENTRPRS_SE_CODEPDSMLPZ_SCTN_NMINDUTY_LCLAS_NMINDUTY_LCLAS_CODEPRCSS_ENTRPRS_SE_NMADSTRD_NMPDSMLPZ_SCTN_CODESIGNGU_NM
PRCSS_ENTRPRS_SE_CODE1.0000.1580.6340.6340.9180.5450.1580.000
PDSMLPZ_SCTN_NM0.1581.0000.0000.0000.1580.0001.0000.000
INDUTY_LCLAS_NM0.6340.0001.0001.0000.6340.1480.0000.000
INDUTY_LCLAS_CODE0.6340.0001.0001.0000.6340.1480.0000.000
PRCSS_ENTRPRS_SE_NM0.9180.1580.6340.6341.0000.5450.1580.000
ADSTRD_NM0.5450.0000.1480.1480.5451.0000.0000.756
PDSMLPZ_SCTN_CODE0.1581.0000.0000.0000.1580.0001.0000.000
SIGNGU_NM0.0000.0000.0000.0000.0000.7560.0001.000
2023-12-10T22:49:51.757366image/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_NM
TOT_EMPLY_CO1.0001.000-0.7031.000-0.7030.1650.0000.0000.0000.2930.2930.0000.000
EMPLY_AVRG_CO1.0001.000-0.7031.000-0.7030.0000.0000.0000.0000.2120.2120.1820.182
ANSLRY_AVRG_AM-0.703-0.7031.000-0.7031.0000.0000.0000.3730.3730.2350.2350.4430.443
EMPLY_FSTLTN_CO1.0001.000-0.7031.000-0.7030.0000.0000.0000.0000.2120.2120.1820.182
ANSLRY_FSTLTN_AM-0.703-0.7031.000-0.7031.0000.0000.0000.3730.3730.2350.2350.4430.443
SIGNGU_NM0.1650.0000.0000.0000.0001.0000.7560.0000.0000.0000.0000.0000.000
ADSTRD_NM0.0000.0000.0000.0000.0000.7561.0000.1480.1480.5450.5450.0000.000
INDUTY_LCLAS_CODE0.0000.0000.3730.0000.3730.0000.1481.0001.0000.6340.6340.0000.000
INDUTY_LCLAS_NM0.0000.0000.3730.0000.3730.0000.1481.0001.0000.6340.6340.0000.000
PRCSS_ENTRPRS_SE_CODE0.2930.2120.2350.2120.2350.0000.5450.6340.6341.0000.9180.1580.158
PRCSS_ENTRPRS_SE_NM0.2930.2120.2350.2120.2350.0000.5450.6340.6340.9181.0000.1580.158
PDSMLPZ_SCTN_CODE0.0000.1820.4430.1820.4430.0000.0000.0000.0000.1580.1581.0001.000
PDSMLPZ_SCTN_NM0.0000.1820.4430.1820.4430.0000.0000.0000.0000.1580.1581.0001.000

Missing values

2023-12-10T22:49:40.104666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:49:40.570394image/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.
2023-12-10T22:49:40.819353image/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
02020-01경기가평군가평읍CC23제조업비금속 광물제품 제조업4소기업2020년 이상 30년 미만111113294375811329437582020-10-18KEDSYS
12020-01경기가평군가평읍GG47도매 및 소매업소매업; 자동차 제외3중기업55년 이상 10년 미만119192775312319277531232020-10-18KEDSYS
22020-01경기가평군북면CC10제조업식료품 제조업3중기업1010년 이상 20년 미만155552686870355268687032020-10-18KEDSYS
32020-01경기가평군북면CC10제조업식료품 제조업3중기업55년 이상 10년 미만115152314062215231406222020-10-18KEDSYS
42020-01경기가평군상면CC10제조업식료품 제조업4소기업1010년 이상 20년 미만150502667669350266766932020-10-18KEDSYS
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