Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells69
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory148.0 B

Variable types

Numeric11
Text3
Categorical2

Dataset

Description수출지원사업에 참여한 기업의 현황에 대한 데이터로 지원사업 형태, 기업 소재지, 업종, 업력, 기업 재무, 규모 등의 항목을 포함합니다.
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15073345/fileData.do

Alerts

번호 is highly overall correlated with 지원년도High correlation
종업원수 is highly overall correlated with 지원전년도 자산(백만원) and 3 other fieldsHigh correlation
업력 is highly overall correlated with 지원전년도 자산(백만원) and 2 other fieldsHigh correlation
지원전년도 자산(백만원) is highly overall correlated with 종업원수 and 6 other fieldsHigh correlation
지원전년도 매출(백만원) is highly overall correlated with 종업원수 and 7 other fieldsHigh correlation
지원전년도 영업이익 is highly overall correlated with 지원전년도 자산(백만원) and 5 other fieldsHigh correlation
지원전년도 순이익 is highly overall correlated with 지원전년도 매출(백만원) and 4 other fieldsHigh correlation
지원년도 자산(백만원) is highly overall correlated with 종업원수 and 6 other fieldsHigh correlation
지원년도 매출(백만원) is highly overall correlated with 종업원수 and 6 other fieldsHigh correlation
지원년도 영업이익 is highly overall correlated with 지원전년도 자산(백만원) and 6 other fieldsHigh correlation
지원년도 순이익 is highly overall correlated with 지원전년도 순이익 and 1 other fieldsHigh correlation
지원년도 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
종업원수 has 526 (5.3%) zerosZeros
지원전년도 매출(백만원) has 135 (1.4%) zerosZeros
지원전년도 순이익 has 1435 (14.3%) zerosZeros
지원년도 순이익 has 1449 (14.5%) zerosZeros

Reproduction

Analysis started2023-12-12 19:24:26.990265
Analysis finished2023-12-12 19:24:46.241205
Duration19.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5874.4137
Minimum1
Maximum11760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:46.325924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile608.95
Q12944.5
median5850.5
Q38808.25
95-th percentile11172.05
Maximum11760
Range11759
Interquartile range (IQR)5863.75

Descriptive statistics

Standard deviation3389.8449
Coefficient of variation (CV)0.57705246
Kurtosis-1.1997491
Mean5874.4137
Median Absolute Deviation (MAD)2933.5
Skewness0.0090107886
Sum58744137
Variance11491048
MonotonicityNot monotonic
2023-12-13T04:24:46.475748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5435 1
 
< 0.1%
6695 1
 
< 0.1%
7213 1
 
< 0.1%
11211 1
 
< 0.1%
7791 1
 
< 0.1%
5698 1
 
< 0.1%
10738 1
 
< 0.1%
7879 1
 
< 0.1%
2879 1
 
< 0.1%
2600 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
11760 1
< 0.1%
11759 1
< 0.1%
11758 1
< 0.1%
11757 1
< 0.1%
11756 1
< 0.1%
11755 1
< 0.1%
11754 1
< 0.1%
11753 1
< 0.1%
11752 1
< 0.1%
11751 1
< 0.1%

사업자번호
Text

Distinct735
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:24:46.952272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99 ?
Unique (%)1.0%

Sample

1st row718*******
2nd row825*******
3rd row606*******
4th row134*******
5th row201*******
ValueCountFrequency (%)
606 198
 
2.0%
314 184
 
1.8%
134 162
 
1.6%
514 161
 
1.6%
503 156
 
1.6%
119 149
 
1.5%
130 148
 
1.5%
137 142
 
1.4%
131 141
 
1.4%
615 138
 
1.4%
Other values (725) 8421
84.2%
2023-12-13T04:24:47.527805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 70000
70.0%
1 6970
 
7.0%
2 3838
 
3.8%
0 3677
 
3.7%
3 3393
 
3.4%
6 2887
 
2.9%
4 2876
 
2.9%
5 2537
 
2.5%
7 1499
 
1.5%
8 1196
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 70000
70.0%
Decimal Number 30000
30.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6970
23.2%
2 3838
12.8%
0 3677
12.3%
3 3393
11.3%
6 2887
9.6%
4 2876
9.6%
5 2537
 
8.5%
7 1499
 
5.0%
8 1196
 
4.0%
9 1127
 
3.8%
Other Punctuation
ValueCountFrequency (%)
* 70000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 70000
70.0%
1 6970
 
7.0%
2 3838
 
3.8%
0 3677
 
3.7%
3 3393
 
3.4%
6 2887
 
2.9%
4 2876
 
2.9%
5 2537
 
2.5%
7 1499
 
1.5%
8 1196
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 70000
70.0%
1 6970
 
7.0%
2 3838
 
3.8%
0 3677
 
3.7%
3 3393
 
3.4%
6 2887
 
2.9%
4 2876
 
2.9%
5 2537
 
2.5%
7 1499
 
1.5%
8 1196
 
1.2%

지원사업
Text

Distinct265
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:24:47.744981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length41
Mean length9.1842
Min length2

Characters and Unicode

Total characters91842
Distinct characters73
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)0.9%

Sample

1st row수출바우처
2nd row수출바우처 지역중소기업수출마케팅 해외유통망진출지원
3rd row지역중소기업수출마케팅
4th row수출바우처 지역중소기업수출마케팅
5th row수출바우처
ValueCountFrequency (%)
수출바우처 4274
33.6%
융자 2225
17.5%
지역중소기업수출마케팅 876
 
6.9%
온라인수출플랫폼 762
 
6.0%
전자상거래활용수출지원사업 520
 
4.1%
온라인수출지원 479
 
3.8%
수출마케팅 438
 
3.4%
지역중소기업 433
 
3.4%
해외유통망진출지원 385
 
3.0%
글로벌바이어구매알선 356
 
2.8%
Other values (78) 1986
15.6%
2023-12-13T04:24:48.090764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
출 9135
 
9.9%
8853
 
9.6%
수 8743
 
9.5%
바 5094
 
5.5%
우 4738
 
5.2%
처 4738
 
5.2%
지 3470
 
3.8%
자 3087
 
3.4%
융 2527
 
2.8%
업 2368
 
2.6%
Other values (63) 39089
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81297
88.5%
Space Separator 8853
 
9.6%
Other Punctuation 974
 
1.1%
Uppercase Letter 608
 
0.7%
Open Punctuation 55
 
0.1%
Close Punctuation 55
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
출 9135
 
11.2%
수 8743
 
10.8%
바 5094
 
6.3%
우 4738
 
5.8%
처 4738
 
5.8%
지 3470
 
4.3%
자 3087
 
3.8%
융 2527
 
3.1%
업 2368
 
2.9%
마 2315
 
2.8%
Other values (54) 35082
43.2%
Uppercase Letter
ValueCountFrequency (%)
D 221
36.3%
G 166
27.3%
M 166
27.3%
R 55
 
9.0%
Other Punctuation
ValueCountFrequency (%)
, 919
94.4%
& 55
 
5.6%
Space Separator
ValueCountFrequency (%)
8853
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81297
88.5%
Common 9937
 
10.8%
Latin 608
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
출 9135
 
11.2%
수 8743
 
10.8%
바 5094
 
6.3%
우 4738
 
5.8%
처 4738
 
5.8%
지 3470
 
4.3%
자 3087
 
3.8%
융 2527
 
3.1%
업 2368
 
2.9%
마 2315
 
2.8%
Other values (54) 35082
43.2%
Common
ValueCountFrequency (%)
8853
89.1%
, 919
 
9.2%
& 55
 
0.6%
( 55
 
0.6%
) 55
 
0.6%
Latin
ValueCountFrequency (%)
D 221
36.3%
G 166
27.3%
M 166
27.3%
R 55
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81297
88.5%
ASCII 10545
 
11.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
출 9135
 
11.2%
수 8743
 
10.8%
바 5094
 
6.3%
우 4738
 
5.8%
처 4738
 
5.8%
지 3470
 
4.3%
자 3087
 
3.8%
융 2527
 
3.1%
업 2368
 
2.9%
마 2315
 
2.8%
Other values (54) 35082
43.2%
ASCII
ValueCountFrequency (%)
8853
84.0%
, 919
 
8.7%
D 221
 
2.1%
G 166
 
1.6%
M 166
 
1.6%
& 55
 
0.5%
R 55
 
0.5%
( 55
 
0.5%
) 55
 
0.5%

지원년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2019
3244 
2017
2584 
2018
2152 
2020
1694 
2021
326 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2017
3rd row2019
4th row2017
5th row2019

Common Values

ValueCountFrequency (%)
2019 3244
32.4%
2017 2584
25.8%
2018 2152
21.5%
2020 1694
16.9%
2021 326
 
3.3%

Length

2023-12-13T04:24:48.299328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:24:48.400436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 3244
32.4%
2017 2584
25.8%
2018 2152
21.5%
2020 1694
16.9%
2021 326
 
3.3%

업종
Text

Distinct795
Distinct (%)8.0%
Missing18
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T04:24:48.687968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length14.816069
Min length3

Characters and Unicode

Total characters147894
Distinct characters382
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166 ?
Unique (%)1.7%

Sample

1st row철물, 금속 파스너 및 수공구 도매업
2nd row치약, 비누 및 기타 세제 제조업
3rd row전기회로 개폐, 보호장치 제조업
4th row탭, 밸브 및 유사 장치 제조업
5th row화장품 및 화장용품 도매업
ValueCountFrequency (%)
제조업 7331
 
16.4%
및 4203
 
9.4%
기타 3055
 
6.8%
외 1480
 
3.3%
그 1477
 
3.3%
도매업 1061
 
2.4%
화장품 802
 
1.8%
기기 695
 
1.6%
플라스틱 470
 
1.1%
기계 399
 
0.9%
Other values (1051) 23741
53.1%
2023-12-13T04:24:49.189214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34732
23.5%
업 10343
 
7.0%
제 9437
 
6.4%
조 8251
 
5.6%
기 7249
 
4.9%
및 4203
 
2.8%
품 4154
 
2.8%
용 3095
 
2.1%
타 3093
 
2.1%
장 2753
 
1.9%
Other values (372) 60584
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111862
75.6%
Space Separator 34732
 
23.5%
Other Punctuation 1164
 
0.8%
Decimal Number 98
 
0.1%
Open Punctuation 19
 
< 0.1%
Close Punctuation 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 10343
 
9.2%
제 9437
 
8.4%
조 8251
 
7.4%
기 7249
 
6.5%
및 4203
 
3.8%
품 4154
 
3.7%
용 3095
 
2.8%
타 3093
 
2.8%
장 2753
 
2.5%
외 1564
 
1.4%
Other values (365) 57720
51.6%
Other Punctuation
ValueCountFrequency (%)
, 1112
95.5%
· 51
 
4.4%
; 1
 
0.1%
Space Separator
ValueCountFrequency (%)
34732
100.0%
Decimal Number
ValueCountFrequency (%)
1 98
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111862
75.6%
Common 36032
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 10343
 
9.2%
제 9437
 
8.4%
조 8251
 
7.4%
기 7249
 
6.5%
및 4203
 
3.8%
품 4154
 
3.7%
용 3095
 
2.8%
타 3093
 
2.8%
장 2753
 
2.5%
외 1564
 
1.4%
Other values (365) 57720
51.6%
Common
ValueCountFrequency (%)
34732
96.4%
, 1112
 
3.1%
1 98
 
0.3%
· 51
 
0.1%
( 19
 
0.1%
) 19
 
0.1%
; 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111637
75.5%
ASCII 35981
 
24.3%
Compat Jamo 225
 
0.2%
None 51
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34732
96.5%
, 1112
 
3.1%
1 98
 
0.3%
( 19
 
0.1%
) 19
 
0.1%
; 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
업 10343
 
9.3%
제 9437
 
8.5%
조 8251
 
7.4%
기 7249
 
6.5%
및 4203
 
3.8%
품 4154
 
3.7%
용 3095
 
2.8%
타 3093
 
2.8%
장 2753
 
2.5%
외 1564
 
1.4%
Other values (364) 57495
51.5%
Compat Jamo
ValueCountFrequency (%)
ㆍ 225
100.0%
None
ValueCountFrequency (%)
· 51
100.0%

소재지
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기
2320 
서울
1696 
경남
711 
부산
621 
경북
596 
Other values (14)
4056 

Length

Max length4
Median length2
Mean length2.0034
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row전북
2nd row전남
3rd row부산
4th row충남
5th row서울

Common Values

ValueCountFrequency (%)
경기 2320
23.2%
서울 1696
17.0%
경남 711
 
7.1%
부산 621
 
6.2%
경북 596
 
6.0%
인천 553
 
5.5%
대구 516
 
5.2%
전북 450
 
4.5%
충북 432
 
4.3%
충남 387
 
3.9%
Other values (9) 1718
17.2%

Length

2023-12-13T04:24:49.414380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 2320
23.2%
서울 1696
17.0%
경남 711
 
7.1%
부산 621
 
6.2%
경북 596
 
6.0%
인천 553
 
5.5%
대구 516
 
5.2%
전북 450
 
4.5%
충북 432
 
4.3%
충남 387
 
3.9%
Other values (9) 1718
17.2%

종업원수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct245
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.2135
Minimum0
Maximum1014
Zeros526
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:49.569608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median12
Q328
95-th percentile85.05
Maximum1014
Range1014
Interquartile range (IQR)23

Descriptive statistics

Standard deviation39.439674
Coefficient of variation (CV)1.6288299
Kurtosis101.57509
Mean24.2135
Median Absolute Deviation (MAD)9
Skewness7.0202718
Sum242135
Variance1555.4879
MonotonicityNot monotonic
2023-12-13T04:24:49.755337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 526
 
5.3%
4 517
 
5.2%
5 475
 
4.8%
3 459
 
4.6%
6 422
 
4.2%
2 394
 
3.9%
7 392
 
3.9%
8 382
 
3.8%
9 345
 
3.5%
1 308
 
3.1%
Other values (235) 5780
57.8%
ValueCountFrequency (%)
0 526
5.3%
1 308
3.1%
2 394
3.9%
3 459
4.6%
4 517
5.2%
5 475
4.8%
6 422
4.2%
7 392
3.9%
8 382
3.8%
9 345
3.5%
ValueCountFrequency (%)
1014 1
< 0.1%
880 1
< 0.1%
810 1
< 0.1%
522 1
< 0.1%
519 1
< 0.1%
518 1
< 0.1%
498 1
< 0.1%
415 1
< 0.1%
410 2
< 0.1%
390 1
< 0.1%

업력
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.6%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.510353
Minimum0
Maximum78
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:49.939695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median10
Q317
95-th percentile27
Maximum78
Range78
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9313669
Coefficient of variation (CV)0.63398425
Kurtosis2.0814513
Mean12.510353
Median Absolute Deviation (MAD)5
Skewness1.1472115
Sum125066
Variance62.90658
MonotonicityNot monotonic
2023-12-13T04:24:50.172346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 712
 
7.1%
6 663
 
6.6%
9 653
 
6.5%
8 646
 
6.5%
10 626
 
6.3%
5 508
 
5.1%
11 452
 
4.5%
4 429
 
4.3%
13 424
 
4.2%
12 406
 
4.1%
Other values (46) 4478
44.8%
ValueCountFrequency (%)
0 24
 
0.2%
1 120
 
1.2%
2 284
 
2.8%
3 373
3.7%
4 429
4.3%
5 508
5.1%
6 663
6.6%
7 712
7.1%
8 646
6.5%
9 653
6.5%
ValueCountFrequency (%)
78 1
 
< 0.1%
70 1
 
< 0.1%
65 1
 
< 0.1%
55 2
 
< 0.1%
53 1
 
< 0.1%
52 1
 
< 0.1%
50 2
 
< 0.1%
49 3
< 0.1%
47 2
 
< 0.1%
46 5
0.1%

지원전년도 자산(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct6538
Distinct (%)65.5%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7062.0141
Minimum0
Maximum767787
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:50.350859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile169
Q1957
median3076
Q38053.25
95-th percentile26691.05
Maximum767787
Range767787
Interquartile range (IQR)7096.25

Descriptive statistics

Standard deviation14450.708
Coefficient of variation (CV)2.0462587
Kurtosis792.96543
Mean7062.0141
Median Absolute Deviation (MAD)2565
Skewness17.929019
Sum70535397
Variance2.0882296 × 108
MonotonicityNot monotonic
2023-12-13T04:24:50.544339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
0.4%
169 9
 
0.1%
125 9
 
0.1%
450 8
 
0.1%
824 8
 
0.1%
112 8
 
0.1%
180 8
 
0.1%
355 8
 
0.1%
162 8
 
0.1%
2118 8
 
0.1%
Other values (6528) 9873
98.7%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
0 41
0.4%
1 1
 
< 0.1%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 3
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
11 2
 
< 0.1%
14 4
 
< 0.1%
ValueCountFrequency (%)
767787 1
< 0.1%
211522 1
< 0.1%
206681 1
< 0.1%
195941 1
< 0.1%
175275 1
< 0.1%
155685 1
< 0.1%
155370 1
< 0.1%
129638 1
< 0.1%
128593 1
< 0.1%
128547 1
< 0.1%

지원전년도 매출(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6490
Distinct (%)65.0%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7233.7489
Minimum-491
Maximum446968
Zeros135
Zeros (%)1.4%
Negative1
Negative (%)< 0.1%
Memory size166.0 KiB
2023-12-13T04:24:50.726016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-491
5-th percentile73
Q1893
median2859
Q37719.5
95-th percentile29344.6
Maximum446968
Range447459
Interquartile range (IQR)6826.5

Descriptive statistics

Standard deviation13482.322
Coefficient of variation (CV)1.8638084
Kurtosis138.92208
Mean7233.7489
Median Absolute Deviation (MAD)2444
Skewness7.3853788
Sum72250684
Variance1.8177301 × 108
MonotonicityNot monotonic
2023-12-13T04:24:50.883269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
 
1.4%
3 10
 
0.1%
1 9
 
0.1%
37 9
 
0.1%
14 9
 
0.1%
5 8
 
0.1%
203 8
 
0.1%
119 8
 
0.1%
139 8
 
0.1%
913 8
 
0.1%
Other values (6480) 9776
97.8%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
-491 1
 
< 0.1%
0 135
1.4%
1 9
 
0.1%
2 6
 
0.1%
3 10
 
0.1%
4 6
 
0.1%
5 8
 
0.1%
6 5
 
0.1%
7 4
 
< 0.1%
8 7
 
0.1%
ValueCountFrequency (%)
446968 1
< 0.1%
192882 1
< 0.1%
187663 1
< 0.1%
151709 1
< 0.1%
149578 1
< 0.1%
149412 1
< 0.1%
142493 1
< 0.1%
138151 1
< 0.1%
138095 1
< 0.1%
133891 1
< 0.1%

지원전년도 영업이익
Real number (ℝ)

HIGH CORRELATION 

Distinct2482
Distinct (%)24.8%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean278.52683
Minimum-15386
Maximum52232
Zeros63
Zeros (%)0.6%
Negative2175
Negative (%)21.8%
Memory size166.0 KiB
2023-12-13T04:24:51.053661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-15386
5-th percentile-496.65
Q16
median115
Q3369
95-th percentile1554.65
Maximum52232
Range67618
Interquartile range (IQR)363

Descriptive statistics

Standard deviation1297.9552
Coefficient of variation (CV)4.6600724
Kurtosis396.36655
Mean278.52683
Median Absolute Deviation (MAD)150
Skewness11.999051
Sum2781926
Variance1684687.7
MonotonicityNot monotonic
2023-12-13T04:24:51.231989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
0.6%
1 54
 
0.5%
3 47
 
0.5%
2 47
 
0.5%
9 46
 
0.5%
5 40
 
0.4%
11 40
 
0.4%
10 38
 
0.4%
4 38
 
0.4%
36 37
 
0.4%
Other values (2472) 9538
95.4%
ValueCountFrequency (%)
-15386 1
< 0.1%
-12773 1
< 0.1%
-12233 1
< 0.1%
-12186 1
< 0.1%
-11449 1
< 0.1%
-11099 1
< 0.1%
-10735 1
< 0.1%
-9813 1
< 0.1%
-9351 1
< 0.1%
-9167 1
< 0.1%
ValueCountFrequency (%)
52232 1
< 0.1%
39088 1
< 0.1%
27018 1
< 0.1%
25594 1
< 0.1%
17906 1
< 0.1%
16228 1
< 0.1%
16134 1
< 0.1%
15116 1
< 0.1%
14004 1
< 0.1%
13327 1
< 0.1%

지원전년도 순이익
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1801
Distinct (%)18.0%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean137.21996
Minimum-25222
Maximum23414
Zeros1435
Zeros (%)14.3%
Negative1103
Negative (%)11.0%
Memory size166.0 KiB
2023-12-13T04:24:51.445264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-25222
5-th percentile-218.65
Q10
median36
Q3186
95-th percentile907.65
Maximum23414
Range48636
Interquartile range (IQR)186

Descriptive statistics

Standard deviation842.56234
Coefficient of variation (CV)6.1402315
Kurtosis226.35887
Mean137.21996
Median Absolute Deviation (MAD)44
Skewness-1.0924768
Sum1370553
Variance709911.29
MonotonicityNot monotonic
2023-12-13T04:24:51.623711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1435
 
14.3%
1 278
 
2.8%
3 160
 
1.6%
2 157
 
1.6%
5 121
 
1.2%
4 119
 
1.2%
6 98
 
1.0%
9 84
 
0.8%
7 82
 
0.8%
8 80
 
0.8%
Other values (1791) 7374
73.7%
ValueCountFrequency (%)
-25222 1
< 0.1%
-17139 1
< 0.1%
-13747 1
< 0.1%
-13547 1
< 0.1%
-11385 1
< 0.1%
-11197 1
< 0.1%
-10318 1
< 0.1%
-9584 1
< 0.1%
-8815 1
< 0.1%
-8612 1
< 0.1%
ValueCountFrequency (%)
23414 1
< 0.1%
17075 1
< 0.1%
13081 1
< 0.1%
12107 1
< 0.1%
9550 1
< 0.1%
9051 1
< 0.1%
8839 1
< 0.1%
8668 1
< 0.1%
7874 1
< 0.1%
7815 1
< 0.1%

지원년도 자산(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct6893
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8020.7507
Minimum0
Maximum866656
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:52.166441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile289.95
Q11279.75
median3686
Q38939.25
95-th percentile29225.1
Maximum866656
Range866656
Interquartile range (IQR)7659.5

Descriptive statistics

Standard deviation16184.255
Coefficient of variation (CV)2.0177981
Kurtosis816.89814
Mean8020.7507
Median Absolute Deviation (MAD)2901
Skewness18.296323
Sum80207507
Variance2.6193012 × 108
MonotonicityNot monotonic
2023-12-13T04:24:52.356010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
275 9
 
0.1%
1074 8
 
0.1%
723 8
 
0.1%
450 7
 
0.1%
260 7
 
0.1%
900 7
 
0.1%
389 7
 
0.1%
1317 7
 
0.1%
1411 6
 
0.1%
223 6
 
0.1%
Other values (6883) 9928
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
2 1
< 0.1%
6 1
< 0.1%
9 1
< 0.1%
17 2
< 0.1%
20 1
< 0.1%
22 2
< 0.1%
23 2
< 0.1%
24 1
< 0.1%
29 1
< 0.1%
ValueCountFrequency (%)
866656 1
< 0.1%
259209 1
< 0.1%
216866 1
< 0.1%
201389 1
< 0.1%
185263 1
< 0.1%
171105 1
< 0.1%
165803 1
< 0.1%
159184 1
< 0.1%
156448 1
< 0.1%
155829 1
< 0.1%

지원년도 매출(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct6698
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7877.6296
Minimum0
Maximum450343
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:24:52.509873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile156
Q11042.75
median3211
Q38451
95-th percentile31606.35
Maximum450343
Range450343
Interquartile range (IQR)7408.25

Descriptive statistics

Standard deviation14458.021
Coefficient of variation (CV)1.8353263
Kurtosis116.00974
Mean7877.6296
Median Absolute Deviation (MAD)2633
Skewness6.9538203
Sum78776296
Variance2.0903437 × 108
MonotonicityNot monotonic
2023-12-13T04:24:52.655412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
0.2%
90 10
 
0.1%
9 9
 
0.1%
752 9
 
0.1%
307 8
 
0.1%
156 8
 
0.1%
15 8
 
0.1%
683 7
 
0.1%
11 7
 
0.1%
51 7
 
0.1%
Other values (6688) 9907
99.1%
ValueCountFrequency (%)
0 20
0.2%
1 2
 
< 0.1%
2 3
 
< 0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 9
0.1%
ValueCountFrequency (%)
450343 1
< 0.1%
227490 1
< 0.1%
184668 1
< 0.1%
180410 1
< 0.1%
179387 1
< 0.1%
170611 1
< 0.1%
153174 1
< 0.1%
147590 1
< 0.1%
138095 1
< 0.1%
134811 1
< 0.1%

지원년도 영업이익
Real number (ℝ)

HIGH CORRELATION 

Distinct2655
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.9502
Minimum-13958
Maximum43250
Zeros21
Zeros (%)0.2%
Negative2281
Negative (%)22.8%
Memory size166.0 KiB
2023-12-13T04:24:52.793980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13958
5-th percentile-623.05
Q16
median121
Q3388
95-th percentile1727.05
Maximum43250
Range57208
Interquartile range (IQR)382

Descriptive statistics

Standard deviation1445.0421
Coefficient of variation (CV)4.6027747
Kurtosis192.05645
Mean313.9502
Median Absolute Deviation (MAD)168
Skewness9.7134636
Sum3139502
Variance2088146.5
MonotonicityNot monotonic
2023-12-13T04:24:52.940410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 49
 
0.5%
26 48
 
0.5%
9 44
 
0.4%
2 44
 
0.4%
7 40
 
0.4%
31 39
 
0.4%
12 38
 
0.4%
1 38
 
0.4%
5 37
 
0.4%
11 37
 
0.4%
Other values (2645) 9586
95.9%
ValueCountFrequency (%)
-13958 1
< 0.1%
-9582 1
< 0.1%
-8879 1
< 0.1%
-8727 1
< 0.1%
-8032 1
< 0.1%
-7935 1
< 0.1%
-7488 1
< 0.1%
-6860 1
< 0.1%
-6412 1
< 0.1%
-6371 1
< 0.1%
ValueCountFrequency (%)
43250 1
< 0.1%
31706 1
< 0.1%
31497 1
< 0.1%
28936 1
< 0.1%
24809 1
< 0.1%
24089 1
< 0.1%
23837 1
< 0.1%
20322 1
< 0.1%
20088 1
< 0.1%
19746 1
< 0.1%

지원년도 순이익
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1945
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.9015
Minimum-12306
Maximum22787
Zeros1449
Zeros (%)14.5%
Negative1124
Negative (%)11.2%
Memory size166.0 KiB
2023-12-13T04:24:53.078934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12306
5-th percentile-293.05
Q10
median37
Q3194.25
95-th percentile1042.05
Maximum22787
Range35093
Interquartile range (IQR)194.25

Descriptive statistics

Standard deviation911.35268
Coefficient of variation (CV)5.4604223
Kurtosis161.28482
Mean166.9015
Median Absolute Deviation (MAD)49
Skewness7.1973797
Sum1669015
Variance830563.71
MonotonicityNot monotonic
2023-12-13T04:24:53.210399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1449
 
14.5%
1 268
 
2.7%
2 207
 
2.1%
3 137
 
1.4%
4 120
 
1.2%
5 110
 
1.1%
6 99
 
1.0%
10 87
 
0.9%
7 82
 
0.8%
9 75
 
0.8%
Other values (1935) 7366
73.7%
ValueCountFrequency (%)
-12306 1
< 0.1%
-10957 1
< 0.1%
-10432 1
< 0.1%
-9418 1
< 0.1%
-9222 1
< 0.1%
-9134 1
< 0.1%
-7902 1
< 0.1%
-7096 1
< 0.1%
-5924 1
< 0.1%
-5851 1
< 0.1%
ValueCountFrequency (%)
22787 1
< 0.1%
20020 1
< 0.1%
18786 1
< 0.1%
18173 1
< 0.1%
17345 1
< 0.1%
15580 1
< 0.1%
15516 1
< 0.1%
13516 1
< 0.1%
13029 1
< 0.1%
12870 1
< 0.1%

Interactions

2023-12-13T04:24:44.253906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:31.499129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.807888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.160939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.389472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.699262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.988424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.213522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.462130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.793490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.122290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.412632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:31.624173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.938106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.265947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.497166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.785460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.076739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.313653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.584023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.912930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.243871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.527913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:31.731598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.060772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.379357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.606622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.868198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.165962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.403753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.706401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.038736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.333420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.631932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:31.870389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.222459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.498420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.756523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.977607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.258010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.510275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.838359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.187877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.450243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.745540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.015351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.344130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.605184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.923195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.082110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.375396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.627612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.971787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.311378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.555804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.842346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.116992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.459387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.711002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.044740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.445344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.478881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.747614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.096592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.406282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.642740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:45.248107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.220161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.581398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.849718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.163520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.553916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.576578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.866993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.219192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.504287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.741699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:45.343396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.314060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.697985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.950648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.278572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.657239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.702997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.976957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.322071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.632215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.838764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:45.433514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.417067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.820537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.060243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.393097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.738773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.814411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.081026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.436353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.747218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:43.945594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:45.540287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.530719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:33.930968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.166888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.492773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.818199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:38.938296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.193010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.549414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.858420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.042771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:45.636322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:32.693701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:34.036583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:35.285315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:36.600095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:37.900788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:39.062058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:40.335514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:41.659327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:42.998338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:24:44.149847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:24:53.306704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지원년도소재지종업원수업력지원전년도 자산(백만원)지원전년도 매출(백만원)지원전년도 영업이익지원전년도 순이익지원년도 자산(백만원)지원년도 매출(백만원)지원년도 영업이익지원년도 순이익
번호1.0000.9870.4520.0490.4150.0630.0600.0460.0780.0550.0590.0530.143
지원년도0.9871.0000.0820.0000.3720.0170.0080.0380.0970.0000.0300.0470.156
소재지0.4520.0821.0000.0500.1280.0000.0180.0000.0000.0000.0000.0000.045
종업원수0.0490.0000.0501.0000.2740.7710.7700.6590.7900.7780.7090.8060.538
업력0.4150.3720.1280.2741.0000.1820.1780.0390.0490.1630.1630.0860.114
지원전년도 자산(백만원)0.0630.0170.0000.7710.1821.0000.8310.9180.7510.9820.7800.7720.626
지원전년도 매출(백만원)0.0600.0080.0180.7700.1780.8311.0000.7490.7560.8220.8900.7600.625
지원전년도 영업이익0.0460.0380.0000.6590.0390.9180.7491.0000.8290.9310.6860.8080.673
지원전년도 순이익0.0780.0970.0000.7900.0490.7510.7560.8291.0000.8080.6700.8740.739
지원년도 자산(백만원)0.0550.0000.0000.7780.1630.9820.8220.9310.8081.0000.7830.8030.670
지원년도 매출(백만원)0.0590.0300.0000.7090.1630.7800.8900.6860.6700.7831.0000.7500.685
지원년도 영업이익0.0530.0470.0000.8060.0860.7720.7600.8080.8740.8030.7501.0000.801
지원년도 순이익0.1430.1560.0450.5380.1140.6260.6250.6730.7390.6700.6850.8011.000
2023-12-13T04:24:53.446192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지원년도소재지
지원년도1.0000.042
소재지0.0421.000
2023-12-13T04:24:53.541110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호종업원수업력지원전년도 자산(백만원)지원전년도 매출(백만원)지원전년도 영업이익지원전년도 순이익지원년도 자산(백만원)지원년도 매출(백만원)지원년도 영업이익지원년도 순이익지원년도소재지
번호1.000-0.104-0.328-0.076-0.085-0.113-0.223-0.060-0.069-0.085-0.1790.8360.191
종업원수-0.1041.0000.4730.7580.7110.4780.3830.7770.7260.4660.3550.0000.017
업력-0.3280.4731.0000.6130.5340.3900.3670.5610.4750.3310.3010.1630.049
지원전년도 자산(백만원)-0.0760.7580.6131.0000.8660.6120.4350.9710.8310.5460.3560.0140.000
지원전년도 매출(백만원)-0.0850.7110.5340.8661.0000.7160.5390.8610.9490.6230.4390.0060.007
지원전년도 영업이익-0.1130.4780.3900.6120.7161.0000.7170.6230.6700.6820.4970.0230.000
지원전년도 순이익-0.2230.3830.3670.4350.5390.7171.0000.4500.5040.5030.6490.0560.000
지원년도 자산(백만원)-0.0600.7770.5610.9710.8610.6230.4501.0000.8560.5900.3970.0000.000
지원년도 매출(백만원)-0.0690.7260.4750.8310.9490.6700.5040.8561.0000.6900.4980.0190.000
지원년도 영업이익-0.0850.4660.3310.5460.6230.6820.5030.5900.6901.0000.7100.0270.000
지원년도 순이익-0.1790.3550.3010.3560.4390.4970.6490.3970.4980.7101.0000.0660.016
지원년도0.8360.0000.1630.0140.0060.0230.0560.0000.0190.0270.0661.0000.042
소재지0.1910.0170.0490.0000.0070.0000.0000.0000.0000.0000.0160.0421.000

Missing values

2023-12-13T04:24:45.776218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:24:45.978468image/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.
2023-12-13T04:24:46.143327image/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

번호사업자번호지원사업지원년도업종소재지종업원수업력지원전년도 자산(백만원)지원전년도 매출(백만원)지원전년도 영업이익지원전년도 순이익지원년도 자산(백만원)지원년도 매출(백만원)지원년도 영업이익지원년도 순이익
54345435718*******수출바우처2018철물, 금속 파스너 및 수공구 도매업전북17227395383421035011
30003001825*******수출바우처 지역중소기업수출마케팅 해외유통망진출지원2017치약, 비누 및 기타 세제 제조업전남36225578120228386046513968416967
59385939606*******지역중소기업수출마케팅2019전기회로 개폐, 보호장치 제조업부산4619172597663883618693102191012182
793794134*******수출바우처 지역중소기업수출마케팅2017탭, 밸브 및 유사 장치 제조업충남9018502642179930152888559402822533983338
75967597201*******수출바우처2019화장품 및 화장용품 도매업서울285498110308-434759261463310710
155156113*******수출바우처2017기어 및 동력전달장치 제조업경기1722331918691345433542713307208
18121813402*******수출바우처 지역중소기업수출마케팅2017그 외 기타 의료용 기기 제조업전북10111105849-2241011829053427
33293330123*******수출바우처 후속마케팅지원2018화장품 제조업경기33151059289321360119511134860310681047
41294130301*******융자2018전기용 기계ㆍ장비 및 관련 기자재 도매업경기41320501180342243717141756
93899390105*******수출바우처2020그 외 기타 특수 목적용 기계 제조업인천2924955894125245739498767512191
번호사업자번호지원사업지원년도업종소재지종업원수업력지원전년도 자산(백만원)지원전년도 매출(백만원)지원전년도 영업이익지원전년도 순이익지원년도 자산(백만원)지원년도 매출(백만원)지원년도 영업이익지원년도 순이익
75197520134*******수출바우처2019일반용 전기 조명장치 제조업경기362176155852126692432891
38533854208*******GMD 후속마케팅지원2018그 외 기타 달리 분류되지 않은 제품 제조업서울382790959162537424100099314563289
52735274617*******수출바우처2018의료용품 및 기타 의약 관련제품 제조업부산1617645796-17133999022359
83268327195*******후속마케팅2019남녀용 겉옷 및 셔츠 도매업서울0371446301625105856202156
86178618144*******수출바우처2019응용 소프트웨어 개발 및 공급업경기22550222659-2091429753076-20150
490491126*******지역중소기업수출마케팅2017기타 곡물 가공품 제조업충남972464306505125986463818612769
29532954717*******온라인수출지원2017기타 가공식품 도매업부산1724129538382392362929
55595560214*******수출인큐베이터,후속마케팅2019그 외 기타 기계 및 장비 도매업경기1281721574516824999107629609652765452733
50835084608*******지역중소기업 수출마케팅2018유압 기기 제조업경남1616826214028344151872913334449306
58845885140*******융자2019탭, 밸브 및 유사장치 제조업경기271312061973655301224795476080