Overview

Dataset statistics

Number of variables17
Number of observations5468
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory779.7 KiB
Average record size in memory146.0 B

Variable types

Numeric10
Categorical7

Dataset

Description중소벤처기업진흥공단 정책자금(창업기반지원(일반)) 자금의 2022년 12월 31일 기준, 지원 현황 데이터
URLhttps://www.data.go.kr/data/15120231/fileData.do

Alerts

사업1 has constant value ""Constant
사업2 has constant value ""Constant
신청금액(시설_백만원) is highly overall correlated with 신청금액(운전_백만원) and 7 other fieldsHigh correlation
신청금액(운전_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
신청금액(합계_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
추천금액(시설_백만원) is highly overall correlated with 신청금액(시설_백만원) and 7 other fieldsHigh correlation
추천금액(운전_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
추천금액(합계_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
대여금액(시설_백만원) is highly overall correlated with 신청금액(시설_백만원) and 7 other fieldsHigh correlation
대여금액(운전_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
대여금액(합계_백만원) is highly overall correlated with 신청금액(시설_백만원) and 4 other fieldsHigh correlation
일련번호 has unique valuesUnique
신청금액(시설_백만원) has 4616 (84.4%) zerosZeros
신청금액(운전_백만원) has 852 (15.6%) zerosZeros
추천금액(시설_백만원) has 4616 (84.4%) zerosZeros
추천금액(운전_백만원) has 852 (15.6%) zerosZeros
대여금액(시설_백만원) has 4616 (84.4%) zerosZeros
대여금액(운전_백만원) has 852 (15.6%) zerosZeros

Reproduction

Analysis started2023-12-12 17:48:08.862535
Analysis finished2023-12-12 17:48:24.167791
Duration15.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct5468
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2734.5
Minimum1
Maximum5468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:24.272444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile274.35
Q11367.75
median2734.5
Q34101.25
95-th percentile5194.65
Maximum5468
Range5467
Interquartile range (IQR)2733.5

Descriptive statistics

Standard deviation1578.62
Coefficient of variation (CV)0.57729748
Kurtosis-1.2
Mean2734.5
Median Absolute Deviation (MAD)1367
Skewness0
Sum14952246
Variance2492041
MonotonicityStrictly increasing
2023-12-13T02:48:24.448897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3645 1
 
< 0.1%
3653 1
 
< 0.1%
3652 1
 
< 0.1%
3651 1
 
< 0.1%
3650 1
 
< 0.1%
3649 1
 
< 0.1%
3648 1
 
< 0.1%
3647 1
 
< 0.1%
3646 1
 
< 0.1%
Other values (5458) 5458
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5468 1
< 0.1%
5467 1
< 0.1%
5466 1
< 0.1%
5465 1
< 0.1%
5464 1
< 0.1%
5463 1
< 0.1%
5462 1
< 0.1%
5461 1
< 0.1%
5460 1
< 0.1%
5459 1
< 0.1%

사업1
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
창업기반지원(일반)
5468 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row창업기반지원(일반)
2nd row창업기반지원(일반)
3rd row창업기반지원(일반)
4th row창업기반지원(일반)
5th row창업기반지원(일반)

Common Values

ValueCountFrequency (%)
창업기반지원(일반) 5468
100.0%

Length

2023-12-13T02:48:24.611929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:24.705440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창업기반지원(일반 5468
100.0%

사업2
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
혁신창업사업화
5468 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
혁신창업사업화 5468
100.0%

Length

2023-12-13T02:48:24.811719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:24.936030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
혁신창업사업화 5468
100.0%

자산규모
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
10억미만
2502 
<NA>
1745 
30억미만
864 
70억미만
286 
100억미만
 
35
Other values (2)
 
36

Length

Max length6
Median length5
Mean length4.6938552
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row10억미만
3rd row100억미만
4th row70억미만
5th row30억미만

Common Values

ValueCountFrequency (%)
10억미만 2502
45.8%
<NA> 1745
31.9%
30억미만 864
 
15.8%
70억미만 286
 
5.2%
100억미만 35
 
0.6%
200억미만 24
 
0.4%
200억이상 12
 
0.2%

Length

2023-12-13T02:48:25.075147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:25.216765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10억미만 2502
45.8%
na 1745
31.9%
30억미만 864
 
15.8%
70억미만 286
 
5.2%
100억미만 35
 
0.6%
200억미만 24
 
0.4%
200억이상 12
 
0.2%

매출규모
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
<NA>
1744 
50억미만
1373 
5억미만
1255 
10억미만
725 
100억미만
242 
Other values (2)
 
129

Length

Max length6
Median length4
Mean length4.5193855
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row10억미만
3rd row50억미만
4th row300억미만
5th row5억미만

Common Values

ValueCountFrequency (%)
<NA> 1744
31.9%
50억미만 1373
25.1%
5억미만 1255
23.0%
10억미만 725
13.3%
100억미만 242
 
4.4%
300억미만 113
 
2.1%
300억이상 16
 
0.3%

Length

2023-12-13T02:48:25.379828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:25.504372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1744
31.9%
50억미만 1373
25.1%
5억미만 1255
23.0%
10억미만 725
13.3%
100억미만 242
 
4.4%
300억미만 113
 
2.1%
300억이상 16
 
0.3%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
3년미만
1980 
1년미만
1301 
5년미만
1216 
7년미만
971 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년미만
2nd row5년미만
3rd row7년미만
4th row7년미만
5th row7년미만

Common Values

ValueCountFrequency (%)
3년미만 1980
36.2%
1년미만 1301
23.8%
5년미만 1216
22.2%
7년미만 971
17.8%

Length

2023-12-13T02:48:25.632612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:48:25.761815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3년미만 1980
36.2%
1년미만 1301
23.8%
5년미만 1216
22.2%
7년미만 971
17.8%
Distinct17
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
경기
1434 
서울
684 
경남
528 
경북
437 
충북
300 
Other values (12)
2085 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산
2nd row경남
3rd row대전
4th row경기
5th row경북

Common Values

ValueCountFrequency (%)
경기 1434
26.2%
서울 684
12.5%
경남 528
 
9.7%
경북 437
 
8.0%
충북 300
 
5.5%
부산 292
 
5.3%
인천 277
 
5.1%
충남 255
 
4.7%
대구 237
 
4.3%
전남 201
 
3.7%
Other values (7) 823
15.1%

Length

2023-12-13T02:48:25.872061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 1434
26.2%
서울 684
12.5%
경남 528
 
9.7%
경북 437
 
8.0%
충북 300
 
5.5%
부산 292
 
5.3%
인천 277
 
5.1%
충남 255
 
4.7%
대구 237
 
4.3%
전남 201
 
3.7%
Other values (7) 823
15.1%

신청금액(시설_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct196
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.27688
Minimum0
Maximum9000
Zeros4616
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:26.026444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1200
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation575.69345
Coefficient of variation (CV)3.3416755
Kurtosis32.759235
Mean172.27688
Median Absolute Deviation (MAD)0
Skewness4.9701235
Sum942010
Variance331422.95
MonotonicityNot monotonic
2023-12-13T02:48:26.157811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4616
84.4%
1000 41
 
0.7%
500 41
 
0.7%
1500 38
 
0.7%
300 31
 
0.6%
2000 28
 
0.5%
400 28
 
0.5%
200 27
 
0.5%
1200 25
 
0.5%
700 22
 
0.4%
Other values (186) 571
 
10.4%
ValueCountFrequency (%)
0 4616
84.4%
28 1
 
< 0.1%
50 1
 
< 0.1%
69 1
 
< 0.1%
70 2
 
< 0.1%
74 1
 
< 0.1%
75 1
 
< 0.1%
80 5
 
0.1%
85 1
 
< 0.1%
90 2
 
< 0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
5500 1
 
< 0.1%
5000 4
 
0.1%
4900 1
 
< 0.1%
4800 1
 
< 0.1%
4750 1
 
< 0.1%
4500 6
0.1%
4100 2
 
< 0.1%
4000 14
0.3%
3840 1
 
< 0.1%

신청금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.16331
Minimum0
Maximum1000
Zeros852
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:26.310499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median100
Q3200
95-th percentile350
Maximum1000
Range1000
Interquartile range (IQR)150

Descriptive statistics

Standard deviation118.55071
Coefficient of variation (CV)0.85804765
Kurtosis3.567638
Mean138.16331
Median Absolute Deviation (MAD)50
Skewness1.5106604
Sum755477
Variance14054.27
MonotonicityNot monotonic
2023-12-13T02:48:26.513477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
100 1823
33.3%
200 935
17.1%
0 852
15.6%
50 521
 
9.5%
300 501
 
9.2%
150 353
 
6.5%
500 219
 
4.0%
80 48
 
0.9%
400 39
 
0.7%
70 38
 
0.7%
Other values (34) 139
 
2.5%
ValueCountFrequency (%)
0 852
15.6%
10 12
 
0.2%
20 4
 
0.1%
30 18
 
0.3%
32 1
 
< 0.1%
38 1
 
< 0.1%
40 3
 
0.1%
48 1
 
< 0.1%
50 521
9.5%
55 1
 
< 0.1%
ValueCountFrequency (%)
1000 4
 
0.1%
700 1
 
< 0.1%
650 1
 
< 0.1%
500 219
4.0%
450 4
 
0.1%
400 39
 
0.7%
350 9
 
0.2%
300 501
9.2%
266 1
 
< 0.1%
252 1
 
< 0.1%

신청금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.4402
Minimum10
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:26.689415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile50
Q1100
median150
Q3300
95-th percentile1200
Maximum9000
Range8990
Interquartile range (IQR)200

Descriptive statistics

Standard deviation545.76902
Coefficient of variation (CV)1.7580488
Kurtosis35.720886
Mean310.4402
Median Absolute Deviation (MAD)50
Skewness5.0951878
Sum1697487
Variance297863.82
MonotonicityNot monotonic
2023-12-13T02:48:26.873253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1838
33.6%
200 962
17.6%
300 532
 
9.7%
50 522
 
9.5%
150 373
 
6.8%
500 260
 
4.8%
400 67
 
1.2%
80 53
 
1.0%
250 48
 
0.9%
1000 45
 
0.8%
Other values (203) 768
14.0%
ValueCountFrequency (%)
10 12
 
0.2%
20 4
 
0.1%
28 1
 
< 0.1%
30 18
 
0.3%
32 1
 
< 0.1%
38 1
 
< 0.1%
40 3
 
0.1%
48 1
 
< 0.1%
50 522
9.5%
55 1
 
< 0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
5500 1
 
< 0.1%
5000 4
 
0.1%
4900 1
 
< 0.1%
4800 1
 
< 0.1%
4750 1
 
< 0.1%
4500 6
0.1%
4100 2
 
< 0.1%
4000 14
0.3%
3840 1
 
< 0.1%

추천금액(시설_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.69971
Minimum0
Maximum9000
Zeros4616
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:27.075810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1096.5
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation536.92856
Coefficient of variation (CV)3.404753
Kurtosis38.24319
Mean157.69971
Median Absolute Deviation (MAD)0
Skewness5.2789483
Sum862302
Variance288292.27
MonotonicityNot monotonic
2023-12-13T02:48:27.276029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4616
84.4%
1000 51
 
0.9%
1500 33
 
0.6%
200 28
 
0.5%
500 24
 
0.4%
300 24
 
0.4%
400 23
 
0.4%
800 22
 
0.4%
600 21
 
0.4%
150 21
 
0.4%
Other values (200) 605
 
11.1%
ValueCountFrequency (%)
0 4616
84.4%
28 1
 
< 0.1%
35 1
 
< 0.1%
50 2
 
< 0.1%
69 1
 
< 0.1%
70 2
 
< 0.1%
74 1
 
< 0.1%
75 1
 
< 0.1%
80 7
 
0.1%
85 1
 
< 0.1%
ValueCountFrequency (%)
9000 1
 
< 0.1%
5200 1
 
< 0.1%
5000 2
 
< 0.1%
4900 1
 
< 0.1%
4800 1
 
< 0.1%
4500 6
0.1%
4100 2
 
< 0.1%
4000 11
0.2%
3840 1
 
< 0.1%
3500 2
 
< 0.1%

추천금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.919166
Minimum0
Maximum1000
Zeros852
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:27.470418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median100
Q3100
95-th percentile200
Maximum1000
Range1000
Interquartile range (IQR)50

Descriptive statistics

Standard deviation72.065614
Coefficient of variation (CV)0.75131611
Kurtosis6.7979314
Mean95.919166
Median Absolute Deviation (MAD)50
Skewness1.394614
Sum524486
Variance5193.4528
MonotonicityNot monotonic
2023-12-13T02:48:27.653658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
100 2042
37.3%
50 1012
18.5%
0 852
15.6%
200 655
 
12.0%
150 396
 
7.2%
300 142
 
2.6%
80 110
 
2.0%
70 90
 
1.6%
30 48
 
0.9%
250 21
 
0.4%
Other values (36) 100
 
1.8%
ValueCountFrequency (%)
0 852
15.6%
10 13
 
0.2%
20 8
 
0.1%
30 48
 
0.9%
32 1
 
< 0.1%
38 1
 
< 0.1%
40 8
 
0.1%
48 1
 
< 0.1%
50 1012
18.5%
55 1
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
500 13
 
0.2%
400 1
 
< 0.1%
300 142
2.6%
266 1
 
< 0.1%
252 1
 
< 0.1%
251 1
 
< 0.1%
250 21
 
0.4%
220 2
 
< 0.1%
213 1
 
< 0.1%

추천금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct233
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.61887
Minimum10
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:27.835067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile50
Q1100
median100
Q3200
95-th percentile1096.5
Maximum9000
Range8990
Interquartile range (IQR)100

Descriptive statistics

Standard deviation513.05686
Coefficient of variation (CV)2.0229443
Kurtosis42.134406
Mean253.61887
Median Absolute Deviation (MAD)50
Skewness5.5070591
Sum1386788
Variance263227.34
MonotonicityNot monotonic
2023-12-13T02:48:28.006176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2057
37.6%
50 1014
18.5%
200 683
 
12.5%
150 417
 
7.6%
300 166
 
3.0%
80 117
 
2.1%
70 92
 
1.7%
1000 52
 
1.0%
30 48
 
0.9%
250 37
 
0.7%
Other values (223) 785
 
14.4%
ValueCountFrequency (%)
10 13
 
0.2%
20 8
 
0.1%
28 1
 
< 0.1%
30 48
 
0.9%
32 1
 
< 0.1%
35 1
 
< 0.1%
38 1
 
< 0.1%
40 8
 
0.1%
48 1
 
< 0.1%
50 1014
18.5%
ValueCountFrequency (%)
9000 1
 
< 0.1%
5200 1
 
< 0.1%
5000 2
 
< 0.1%
4900 1
 
< 0.1%
4800 1
 
< 0.1%
4500 6
0.1%
4100 2
 
< 0.1%
4000 11
0.2%
3840 1
 
< 0.1%
3500 2
 
< 0.1%

대여금액(시설_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct338
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.94056
Minimum0
Maximum9000
Zeros4616
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:28.188573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile700
Maximum9000
Range9000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation383.13665
Coefficient of variation (CV)3.6861129
Kurtosis91.010043
Mean103.94056
Median Absolute Deviation (MAD)0
Skewness7.3234113
Sum568347
Variance146793.69
MonotonicityNot monotonic
2023-12-13T02:48:28.361095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4616
84.4%
1000 35
 
0.6%
200 30
 
0.5%
500 28
 
0.5%
400 26
 
0.5%
300 25
 
0.5%
150 23
 
0.4%
100 17
 
0.3%
1500 16
 
0.3%
800 15
 
0.3%
Other values (328) 637
 
11.6%
ValueCountFrequency (%)
0 4616
84.4%
1 1
 
< 0.1%
5 1
 
< 0.1%
10 1
 
< 0.1%
28 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
37 2
 
< 0.1%
ValueCountFrequency (%)
9000 1
< 0.1%
5200 1
< 0.1%
5000 1
< 0.1%
4900 1
< 0.1%
4500 1
< 0.1%
4000 1
< 0.1%
3940 1
< 0.1%
3840 1
< 0.1%
3400 1
< 0.1%
3300 1
< 0.1%

대여금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.401061
Minimum0
Maximum1000
Zeros852
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:28.495547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median100
Q3100
95-th percentile200
Maximum1000
Range1000
Interquartile range (IQR)50

Descriptive statistics

Standard deviation71.638272
Coefficient of variation (CV)0.75091693
Kurtosis6.907297
Mean95.401061
Median Absolute Deviation (MAD)50
Skewness1.4025509
Sum521653
Variance5132.042
MonotonicityNot monotonic
2023-12-13T02:48:28.687035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
100 2050
37.5%
50 1019
18.6%
0 852
15.6%
200 636
 
11.6%
150 396
 
7.2%
300 139
 
2.5%
80 111
 
2.0%
70 91
 
1.7%
30 48
 
0.9%
250 21
 
0.4%
Other values (38) 105
 
1.9%
ValueCountFrequency (%)
0 852
15.6%
10 13
 
0.2%
20 9
 
0.2%
30 48
 
0.9%
32 1
 
< 0.1%
38 1
 
< 0.1%
40 8
 
0.1%
48 1
 
< 0.1%
50 1019
18.6%
55 1
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
500 12
 
0.2%
400 2
 
< 0.1%
300 139
2.5%
291 1
 
< 0.1%
266 1
 
< 0.1%
252 1
 
< 0.1%
251 1
 
< 0.1%
250 21
 
0.4%
220 2
 
< 0.1%

대여금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct350
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.34162
Minimum1
Maximum9000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2023-12-13T02:48:29.171274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q180
median100
Q3200
95-th percentile700
Maximum9000
Range8999
Interquartile range (IQR)120

Descriptive statistics

Standard deviation363.44192
Coefficient of variation (CV)1.8232114
Kurtosis104.4924
Mean199.34162
Median Absolute Deviation (MAD)50
Skewness7.805176
Sum1090000
Variance132090.03
MonotonicityNot monotonic
2023-12-13T02:48:29.356433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 2067
37.8%
50 1021
18.7%
200 666
 
12.2%
150 419
 
7.7%
300 164
 
3.0%
80 118
 
2.2%
70 93
 
1.7%
30 49
 
0.9%
500 40
 
0.7%
1000 36
 
0.7%
Other values (340) 795
 
14.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
5 1
 
< 0.1%
10 14
 
0.3%
20 9
 
0.2%
28 1
 
< 0.1%
30 49
0.9%
32 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
ValueCountFrequency (%)
9000 1
< 0.1%
5200 1
< 0.1%
5000 1
< 0.1%
4900 1
< 0.1%
4500 1
< 0.1%
4000 1
< 0.1%
3940 1
< 0.1%
3840 1
< 0.1%
3400 1
< 0.1%
3300 1
< 0.1%

업종
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size42.8 KiB
금속
886 
유통
755 
기계
718 
기타
668 
식료
557 
Other values (6)
1884 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row식료
3rd row화공
4th row식료
5th row기계

Common Values

ValueCountFrequency (%)
금속 886
16.2%
유통 755
13.8%
기계 718
13.1%
기타 668
12.2%
식료 557
10.2%
화공 455
8.3%
정보 410
7.5%
잡화 391
7.2%
전자 248
 
4.5%
전기 200
 
3.7%

Length

2023-12-13T02:48:29.510031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금속 886
16.2%
유통 755
13.8%
기계 718
13.1%
기타 668
12.2%
식료 557
10.2%
화공 455
8.3%
정보 410
7.5%
잡화 391
7.2%
전자 248
 
4.5%
전기 200
 
3.7%

Interactions

2023-12-13T02:48:22.420413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:11.638902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.672777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.659314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.268589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.496694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.733909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.967814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.138842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.372094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.511848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:11.744708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.761887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.762750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.406934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.620905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.873592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.071902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.239654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.477274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.603609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:11.859893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.856317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.252563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.521214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.738619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.997560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.191027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.362378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.592433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.693670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:11.980439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.952848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.354473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.648363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.877489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.137171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.330687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.494012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.713794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.800331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.072648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.056912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.465350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.760439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.982955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.263260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.468489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.615352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.819016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.880822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.181576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.155542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.603013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.872813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.093652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.387286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.591633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.747923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.915852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.966438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.274971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.270317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.765447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.006992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.190847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.501620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.716936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.893655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.012677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:23.368242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.357426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.367449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:14.881129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.133450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.315065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.599544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.821493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.046540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.104874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:23.480109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.445836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.472025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.007526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.257877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.438596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.724107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:19.938390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.165541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.204928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:23.584147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:12.543745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:13.571913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:15.139216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:16.390576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:17.587742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:18.845348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:20.041221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:21.288163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:48:22.312569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:48:29.617032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호자산규모매출규모업력구분(중진공)지역구분(중진공)신청금액(시설_백만원)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(시설_백만원)추천금액(운전_백만원)추천금액(합계_백만원)대여금액(시설_백만원)대여금액(운전_백만원)대여금액(합계_백만원)업종
일련번호1.0000.1110.1080.1560.1860.2070.1530.2070.1990.1240.1990.1060.1200.1060.084
자산규모0.1111.0000.6470.2210.1540.2750.1510.2750.3290.2810.3290.2430.2750.2430.152
매출규모0.1080.6471.0000.3280.1840.1070.1860.1080.1530.2470.1530.0980.2400.0980.276
업력구분(중진공)0.1560.2210.3281.0000.1080.1100.0910.1080.0590.1160.0580.0330.1160.0310.139
지역구분(중진공)0.1860.1540.1840.1081.0000.1660.2470.1640.1650.1320.1640.0430.1490.0420.438
신청금액(시설_백만원)0.2070.2750.1070.1100.1661.0000.3221.0000.9430.2620.9430.8440.2610.8440.090
신청금액(운전_백만원)0.1530.1510.1860.0910.2470.3221.0000.3330.4440.6570.4450.3410.6520.3430.151
신청금액(합계_백만원)0.2070.2750.1080.1080.1641.0000.3331.0000.9430.2650.9430.8440.2640.8440.091
추천금액(시설_백만원)0.1990.3290.1530.0590.1650.9430.4440.9431.0000.2311.0000.9660.2300.9660.107
추천금액(운전_백만원)0.1240.2810.2470.1160.1320.2620.6570.2650.2311.0000.2360.1720.9990.1820.119
추천금액(합계_백만원)0.1990.3290.1530.0580.1640.9430.4450.9431.0000.2361.0000.9660.2350.9660.108
대여금액(시설_백만원)0.1060.2430.0980.0330.0430.8440.3410.8440.9660.1720.9661.0000.1711.0000.059
대여금액(운전_백만원)0.1200.2750.2400.1160.1490.2610.6520.2640.2300.9990.2350.1711.0000.1810.114
대여금액(합계_백만원)0.1060.2430.0980.0310.0420.8440.3430.8440.9660.1820.9661.0000.1811.0000.060
업종0.0840.1520.2760.1390.4380.0900.1510.0910.1070.1190.1080.0590.1140.0601.000
2023-12-13T02:48:29.789646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역구분(중진공)자산규모매출규모업종업력구분(중진공)
지역구분(중진공)1.0000.0730.0870.1790.060
자산규모0.0731.0000.2820.0780.144
매출규모0.0870.2821.0000.1450.217
업종0.1790.0780.1451.0000.084
업력구분(중진공)0.0600.1440.2170.0841.000
2023-12-13T02:48:29.902122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호신청금액(시설_백만원)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(시설_백만원)추천금액(운전_백만원)추천금액(합계_백만원)대여금액(시설_백만원)대여금액(운전_백만원)대여금액(합계_백만원)자산규모매출규모업력구분(중진공)지역구분(중진공)업종
일련번호1.000-0.194-0.060-0.307-0.1930.001-0.245-0.189-0.007-0.2350.0580.0570.0930.0730.036
신청금액(시설_백만원)-0.1941.000-0.6410.5501.000-0.6460.5930.997-0.6470.5240.1670.0630.0500.0700.042
신청금액(운전_백만원)-0.060-0.6411.0000.274-0.6410.8150.037-0.6410.8110.0860.0930.1310.0670.1140.063
신청금액(합계_백만원)-0.3070.5500.2741.0000.5500.0690.7910.5460.0650.7560.1670.0640.0490.0690.043
추천금액(시설_백만원)-0.1931.000-0.6410.5501.000-0.6460.5930.997-0.6470.5250.1240.0560.0410.0750.053
추천금액(운전_백만원)0.001-0.6460.8150.069-0.6461.0000.220-0.6460.9940.2710.2120.1890.0820.0730.060
추천금액(합계_백만원)-0.2450.5930.0370.7910.5930.2201.0000.5910.2140.9650.1240.0560.0400.0740.053
대여금액(시설_백만원)-0.1890.997-0.6410.5460.997-0.6460.5911.000-0.6470.5340.0900.0360.0220.0190.029
대여금액(운전_백만원)-0.007-0.6470.8110.065-0.6470.9940.214-0.6471.0000.2770.2070.1840.0850.0780.057
대여금액(합계_백만원)-0.2350.5240.0860.7560.5250.2710.9650.5340.2771.0000.0900.0360.0210.0190.030
자산규모0.0580.1670.0930.1670.1240.2120.1240.0900.2070.0901.0000.2820.1440.0730.078
매출규모0.0570.0630.1310.0640.0560.1890.0560.0360.1840.0360.2821.0000.2170.0870.145
업력구분(중진공)0.0930.0500.0670.0490.0410.0820.0400.0220.0850.0210.1440.2171.0000.0600.084
지역구분(중진공)0.0730.0700.1140.0690.0750.0730.0740.0190.0780.0190.0730.0870.0601.0000.179
업종0.0360.0420.0630.0430.0530.0600.0530.0290.0570.0300.0780.1450.0840.1791.000

Missing values

2023-12-13T02:48:23.762442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:48:24.031775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

일련번호사업1사업2자산규모매출규모업력구분(중진공)지역구분(중진공)신청금액(시설_백만원)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(시설_백만원)추천금액(운전_백만원)추천금액(합계_백만원)대여금액(시설_백만원)대여금액(운전_백만원)대여금액(합계_백만원)업종
01창업기반지원(일반)혁신창업사업화<NA><NA>1년미만부산1360013601360013603170317기타
12창업기반지원(일반)혁신창업사업화10억미만10억미만5년미만경남1126011261100011009700970식료
23창업기반지원(일반)혁신창업사업화100억미만50억미만7년미만대전400004000300003000150001500화공
34창업기반지원(일반)혁신창업사업화70억미만300억미만7년미만경기500050050005002600260식료
45창업기반지원(일반)혁신창업사업화30억미만5억미만7년미만경북9300930782078278078기계
56창업기반지원(일반)혁신창업사업화30억미만100억미만3년미만인천400040040004004000400식료
67창업기반지원(일반)혁신창업사업화70억미만50억미만7년미만충남500050030003003000300식료
78창업기반지원(일반)혁신창업사업화100억미만100억미만5년미만광주410004100410004100185001850화공
89창업기반지원(일반)혁신창업사업화100억미만100억미만5년미만광주4100041004100041002000200화공
910창업기반지원(일반)혁신창업사업화200억이상100억미만5년미만전북204002040204002040154001540화공
일련번호사업1사업2자산규모매출규모업력구분(중진공)지역구분(중진공)신청금액(시설_백만원)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(시설_백만원)추천금액(운전_백만원)추천금액(합계_백만원)대여금액(시설_백만원)대여금액(운전_백만원)대여금액(합계_백만원)업종
54585459창업기반지원(일반)혁신창업사업화30억미만50억미만5년미만충북010010001001000100100식료
54595460창업기반지원(일반)혁신창업사업화70억미만50억미만5년미만전남015015001501500150150기계
54605461창업기반지원(일반)혁신창업사업화10억미만10억미만3년미만강원065650656506565잡화
54615462창업기반지원(일반)혁신창업사업화70억미만300억미만5년미만경기030030003003000291291유통
54625463창업기반지원(일반)혁신창업사업화10억미만10억미만5년미만경기010010001001000100100화공
54635464창업기반지원(일반)혁신창업사업화70억미만300억미만5년미만전북050050005005000400400유통
54645465창업기반지원(일반)혁신창업사업화10억미만50억미만3년미만강원020020002002000200200유통
54655466창업기반지원(일반)혁신창업사업화10억미만10억미만3년미만경기010010001001000100100기계
54665467창업기반지원(일반)혁신창업사업화70억미만100억미만7년미만충북022022002202200220220잡화
54675468창업기반지원(일반)혁신창업사업화<NA><NA>1년미만전남055550555505555기계