Overview

Dataset statistics

Number of variables17
Number of observations210
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.1 KiB
Average record size in memory146.6 B

Variable types

Numeric7
Categorical10

Dataset

Description중소벤처기업진흥공단의 정책자금 이차보전(Net_Zero 유망기업 지원) 자금의 2023년 12월 31일 기준, 지원 현황 데이터
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15119756/fileData.do

Alerts

사업1 has constant value ""Constant
사업2 has constant value ""Constant
신청금액(시설_백만원) has constant value ""Constant
추천금액(시설_백만원) has constant value ""Constant
공급금액(시설_백만원) has constant value ""Constant
신청금액(운전_백만원) is highly overall correlated with 신청금액(합계_백만원) and 4 other fieldsHigh correlation
신청금액(합계_백만원) is highly overall correlated with 신청금액(운전_백만원) and 4 other fieldsHigh correlation
추천금액(운전_백만원) is highly overall correlated with 신청금액(운전_백만원) and 4 other fieldsHigh correlation
추천금액(합계_백만원) is highly overall correlated with 신청금액(운전_백만원) and 4 other fieldsHigh correlation
공급금액(운전_백만원) is highly overall correlated with 신청금액(운전_백만원) and 4 other fieldsHigh correlation
공급금액(합계_백만원) is highly overall correlated with 신청금액(운전_백만원) and 4 other fieldsHigh correlation
일련번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 09:22:33.645920
Analysis finished2024-03-14 09:22:42.865768
Duration9.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

UNIQUE 

Distinct210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.5
Minimum1
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:43.061114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.45
Q153.25
median105.5
Q3157.75
95-th percentile199.55
Maximum210
Range209
Interquartile range (IQR)104.5

Descriptive statistics

Standard deviation60.765944
Coefficient of variation (CV)0.57598052
Kurtosis-1.2
Mean105.5
Median Absolute Deviation (MAD)52.5
Skewness0
Sum22155
Variance3692.5
MonotonicityStrictly increasing
2024-03-14T18:22:43.305863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
159 1
 
0.5%
135 1
 
0.5%
136 1
 
0.5%
137 1
 
0.5%
138 1
 
0.5%
139 1
 
0.5%
140 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
Other values (200) 200
95.2%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
210 1
0.5%
209 1
0.5%
208 1
0.5%
207 1
0.5%
206 1
0.5%
205 1
0.5%
204 1
0.5%
203 1
0.5%
202 1
0.5%
201 1
0.5%

사업1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
Net-Zero 유망기업 지원(이차보전)
210 

Length

Max length22
Median length22
Mean length22
Min length22

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNet-Zero 유망기업 지원(이차보전)
2nd rowNet-Zero 유망기업 지원(이차보전)
3rd rowNet-Zero 유망기업 지원(이차보전)
4th rowNet-Zero 유망기업 지원(이차보전)
5th rowNet-Zero 유망기업 지원(이차보전)

Common Values

ValueCountFrequency (%)
Net-Zero 유망기업 지원(이차보전) 210
100.0%

Length

2024-03-14T18:22:43.524634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:43.692092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
net-zero 210
33.3%
유망기업 210
33.3%
지원(이차보전 210
33.3%

사업2
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
신성장기반
210 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신성장기반 210
100.0%

Length

2024-03-14T18:22:43.863165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:44.054754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신성장기반 210
100.0%

자산규모
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
70억미만
75 
200억미만
49 
100억미만
31 
200억이상
25 
30억미만
19 
Other values (2)
11 

Length

Max length6
Median length5.5
Mean length5.4952381
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row100억미만
2nd row200억이상
3rd row200억미만
4th row70억미만
5th row70억미만

Common Values

ValueCountFrequency (%)
70억미만 75
35.7%
200억미만 49
23.3%
100억미만 31
14.8%
200억이상 25
 
11.9%
30억미만 19
 
9.0%
10억미만 10
 
4.8%
<NA> 1
 
0.5%

Length

2024-03-14T18:22:44.316374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:44.545070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
70억미만 75
35.7%
200억미만 49
23.3%
100억미만 31
14.8%
200억이상 25
 
11.9%
30억미만 19
 
9.0%
10억미만 10
 
4.8%
na 1
 
0.5%

매출규모
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
50억미만
79 
300억미만
56 
100억미만
49 
300억이상
20 
5억미만
 
4
Other values (2)
 
2

Length

Max length6
Median length6
Mean length5.5714286
Min length4

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row100억미만
2nd row300억이상
3rd row300억미만
4th row100억미만
5th row100억미만

Common Values

ValueCountFrequency (%)
50억미만 79
37.6%
300억미만 56
26.7%
100억미만 49
23.3%
300억이상 20
 
9.5%
5억미만 4
 
1.9%
<NA> 1
 
0.5%
10억미만 1
 
0.5%

Length

2024-03-14T18:22:44.793938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:45.021814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50억미만 79
37.6%
300억미만 56
26.7%
100억미만 49
23.3%
300억이상 20
 
9.5%
5억미만 4
 
1.9%
na 1
 
0.5%
10억미만 1
 
0.5%

업력구분(중진공)
Categorical

Distinct8
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
20년이상
66 
20년미만
45 
15년미만
45 
10년미만
23 
7년미만
17 
Other values (3)
14 

Length

Max length5
Median length5
Mean length4.852381
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row10년미만
2nd row20년미만
3rd row20년미만
4th row20년이상
5th row7년미만

Common Values

ValueCountFrequency (%)
20년이상 66
31.4%
20년미만 45
21.4%
15년미만 45
21.4%
10년미만 23
 
11.0%
7년미만 17
 
8.1%
5년미만 10
 
4.8%
3년미만 3
 
1.4%
1년미만 1
 
0.5%

Length

2024-03-14T18:22:45.296163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:45.563177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20년이상 66
31.4%
20년미만 45
21.4%
15년미만 45
21.4%
10년미만 23
 
11.0%
7년미만 17
 
8.1%
5년미만 10
 
4.8%
3년미만 3
 
1.4%
1년미만 1
 
0.5%

지역구분(중진공)
Categorical

Distinct17
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
경기
45 
경북
23 
서울
22 
경남
19 
부산
15 
Other values (12)
86 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row경기
2nd row서울
3rd row경기
4th row인천
5th row충북

Common Values

ValueCountFrequency (%)
경기 45
21.4%
경북 23
11.0%
서울 22
10.5%
경남 19
9.0%
부산 15
 
7.1%
인천 12
 
5.7%
대구 10
 
4.8%
울산 9
 
4.3%
전남 9
 
4.3%
충남 9
 
4.3%
Other values (7) 37
17.6%

Length

2024-03-14T18:22:45.789620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 45
21.4%
경북 23
11.0%
서울 22
10.5%
경남 19
9.0%
부산 15
 
7.1%
인천 12
 
5.7%
대구 10
 
4.8%
충남 9
 
4.3%
충북 9
 
4.3%
전남 9
 
4.3%
Other values (7) 37
17.6%

신청금액(시설_백만원)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
210 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 210
100.0%

Length

2024-03-14T18:22:45.980598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:46.146739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 210
100.0%

신청금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413.7619
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:46.299344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile200
Q1300
median500
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation121.55547
Coefficient of variation (CV)0.2937812
Kurtosis0.086321128
Mean413.7619
Median Absolute Deviation (MAD)0
Skewness-1.1232104
Sum86890
Variance14775.733
MonotonicityNot monotonic
2024-03-14T18:22:46.562406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
500 127
60.5%
300 38
 
18.1%
200 16
 
7.6%
400 15
 
7.1%
100 5
 
2.4%
350 2
 
1.0%
150 2
 
1.0%
450 2
 
1.0%
40 1
 
0.5%
70 1
 
0.5%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 5
 
2.4%
150 2
 
1.0%
200 16
7.6%
280 1
 
0.5%
300 38
18.1%
350 2
 
1.0%
400 15
 
7.1%
450 2
 
1.0%
ValueCountFrequency (%)
500 127
60.5%
450 2
 
1.0%
400 15
 
7.1%
350 2
 
1.0%
300 38
 
18.1%
280 1
 
0.5%
200 16
 
7.6%
150 2
 
1.0%
100 5
 
2.4%
70 1
 
0.5%

신청금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean413.7619
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:46.762874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile200
Q1300
median500
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation121.55547
Coefficient of variation (CV)0.2937812
Kurtosis0.086321128
Mean413.7619
Median Absolute Deviation (MAD)0
Skewness-1.1232104
Sum86890
Variance14775.733
MonotonicityNot monotonic
2024-03-14T18:22:46.949857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
500 127
60.5%
300 38
 
18.1%
200 16
 
7.6%
400 15
 
7.1%
100 5
 
2.4%
350 2
 
1.0%
150 2
 
1.0%
450 2
 
1.0%
40 1
 
0.5%
70 1
 
0.5%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 5
 
2.4%
150 2
 
1.0%
200 16
7.6%
280 1
 
0.5%
300 38
18.1%
350 2
 
1.0%
400 15
 
7.1%
450 2
 
1.0%
ValueCountFrequency (%)
500 127
60.5%
450 2
 
1.0%
400 15
 
7.1%
350 2
 
1.0%
300 38
 
18.1%
280 1
 
0.5%
200 16
 
7.6%
150 2
 
1.0%
100 5
 
2.4%
70 1
 
0.5%

추천금액(시설_백만원)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
210 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 210
100.0%

Length

2024-03-14T18:22:47.152763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:47.317255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 210
100.0%

추천금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.57143
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:47.470232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile122.5
Q1300
median435
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation130.62477
Coefficient of variation (CV)0.3379059
Kurtosis-0.69253954
Mean386.57143
Median Absolute Deviation (MAD)65
Skewness-0.73905876
Sum81180
Variance17062.83
MonotonicityNot monotonic
2024-03-14T18:22:47.734393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
500 103
49.0%
300 44
21.0%
200 22
 
10.5%
400 20
 
9.5%
100 9
 
4.3%
350 2
 
1.0%
150 2
 
1.0%
450 2
 
1.0%
40 1
 
0.5%
420 1
 
0.5%
Other values (4) 4
 
1.9%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 9
 
4.3%
150 2
 
1.0%
200 22
10.5%
210 1
 
0.5%
260 1
 
0.5%
280 1
 
0.5%
300 44
21.0%
350 2
 
1.0%
ValueCountFrequency (%)
500 103
49.0%
450 2
 
1.0%
420 1
 
0.5%
400 20
 
9.5%
350 2
 
1.0%
300 44
21.0%
280 1
 
0.5%
260 1
 
0.5%
210 1
 
0.5%
200 22
 
10.5%

추천금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.57143
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:47.951675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile122.5
Q1300
median435
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation130.62477
Coefficient of variation (CV)0.3379059
Kurtosis-0.69253954
Mean386.57143
Median Absolute Deviation (MAD)65
Skewness-0.73905876
Sum81180
Variance17062.83
MonotonicityNot monotonic
2024-03-14T18:22:48.141442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
500 103
49.0%
300 44
21.0%
200 22
 
10.5%
400 20
 
9.5%
100 9
 
4.3%
350 2
 
1.0%
150 2
 
1.0%
450 2
 
1.0%
40 1
 
0.5%
420 1
 
0.5%
Other values (4) 4
 
1.9%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 9
 
4.3%
150 2
 
1.0%
200 22
10.5%
210 1
 
0.5%
260 1
 
0.5%
280 1
 
0.5%
300 44
21.0%
350 2
 
1.0%
ValueCountFrequency (%)
500 103
49.0%
450 2
 
1.0%
420 1
 
0.5%
400 20
 
9.5%
350 2
 
1.0%
300 44
21.0%
280 1
 
0.5%
260 1
 
0.5%
210 1
 
0.5%
200 22
 
10.5%

공급금액(시설_백만원)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
0
210 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 210
100.0%

Length

2024-03-14T18:22:48.338686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:22:48.506295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 210
100.0%

공급금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.95238
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:48.661148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile122.5
Q1300
median400
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation130.89332
Coefficient of variation (CV)0.34359495
Kurtosis-0.80162699
Mean380.95238
Median Absolute Deviation (MAD)100
Skewness-0.65903616
Sum80000
Variance17133.06
MonotonicityNot monotonic
2024-03-14T18:22:48.892549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
500 97
46.2%
300 45
21.4%
200 23
 
11.0%
400 21
 
10.0%
100 9
 
4.3%
450 3
 
1.4%
350 2
 
1.0%
150 2
 
1.0%
280 1
 
0.5%
210 1
 
0.5%
Other values (6) 6
 
2.9%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 9
 
4.3%
150 2
 
1.0%
180 1
 
0.5%
200 23
11.0%
210 1
 
0.5%
260 1
 
0.5%
280 1
 
0.5%
290 1
 
0.5%
ValueCountFrequency (%)
500 97
46.2%
450 3
 
1.4%
420 1
 
0.5%
400 21
 
10.0%
350 2
 
1.0%
300 45
21.4%
290 1
 
0.5%
280 1
 
0.5%
260 1
 
0.5%
210 1
 
0.5%

공급금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.95238
Minimum40
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-14T18:22:49.127698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile122.5
Q1300
median400
Q3500
95-th percentile500
Maximum500
Range460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation130.89332
Coefficient of variation (CV)0.34359495
Kurtosis-0.80162699
Mean380.95238
Median Absolute Deviation (MAD)100
Skewness-0.65903616
Sum80000
Variance17133.06
MonotonicityNot monotonic
2024-03-14T18:22:49.319916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
500 97
46.2%
300 45
21.4%
200 23
 
11.0%
400 21
 
10.0%
100 9
 
4.3%
450 3
 
1.4%
350 2
 
1.0%
150 2
 
1.0%
280 1
 
0.5%
210 1
 
0.5%
Other values (6) 6
 
2.9%
ValueCountFrequency (%)
40 1
 
0.5%
70 1
 
0.5%
100 9
 
4.3%
150 2
 
1.0%
180 1
 
0.5%
200 23
11.0%
210 1
 
0.5%
260 1
 
0.5%
280 1
 
0.5%
290 1
 
0.5%
ValueCountFrequency (%)
500 97
46.2%
450 3
 
1.4%
420 1
 
0.5%
400 21
 
10.0%
350 2
 
1.0%
300 45
21.4%
290 1
 
0.5%
280 1
 
0.5%
260 1
 
0.5%
210 1
 
0.5%

업종
Categorical

Distinct12
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
기계
38 
금속
37 
기타
37 
화공
32 
유통
21 
Other values (7)
45 

Length

Max length4
Median length2
Mean length2.0190476
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화공
2nd row유통
3rd row금속
4th row기계
5th row화공

Common Values

ValueCountFrequency (%)
기계 38
18.1%
금속 37
17.6%
기타 37
17.6%
화공 32
15.2%
유통 21
10.0%
전기 10
 
4.8%
잡화 10
 
4.8%
정보 7
 
3.3%
전자 7
 
3.3%
섬유 5
 
2.4%
Other values (2) 6
 
2.9%

Length

2024-03-14T18:22:49.539018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기계 38
18.1%
금속 37
17.6%
기타 37
17.6%
화공 32
15.2%
유통 21
10.0%
전기 10
 
4.8%
잡화 10
 
4.8%
정보 7
 
3.3%
전자 7
 
3.3%
섬유 5
 
2.4%
Other values (2) 6
 
2.9%

Interactions

2024-03-14T18:22:40.971300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:34.775656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.853613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.799640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.852583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.900139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.934637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:41.103727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:34.957652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.986431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.000785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.988515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.034383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.068647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:41.238437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.094905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.121751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.156418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.191891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.172580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.206466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:41.576410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.230271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.257496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.291448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.347162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.363206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.342923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:41.780077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.366382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.394562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.428987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.486595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.524536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.515828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:41.929837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.500152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.528677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.563060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.625408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.661439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.697956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:42.065450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:35.635401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:36.664154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:37.715030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:38.761632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:39.796664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:22:40.831487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:22:49.706481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호자산규모매출규모업력구분(중진공)지역구분(중진공)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(운전_백만원)추천금액(합계_백만원)공급금액(운전_백만원)공급금액(합계_백만원)업종
일련번호1.0000.1850.0000.1510.3120.2270.2270.2630.2630.2190.2190.187
자산규모0.1851.0000.8320.3150.0000.3880.3880.3730.3730.3680.3680.000
매출규모0.0000.8321.0000.2320.0000.5400.5400.5230.5230.5190.5190.372
업력구분(중진공)0.1510.3150.2321.0000.0000.3750.3750.2800.2800.2760.2760.260
지역구분(중진공)0.3120.0000.0000.0001.0000.3480.3480.2250.2250.1460.1460.460
신청금액(운전_백만원)0.2270.3880.5400.3750.3481.0001.0000.9610.9610.9510.9510.000
신청금액(합계_백만원)0.2270.3880.5400.3750.3481.0001.0000.9610.9610.9510.9510.000
추천금액(운전_백만원)0.2630.3730.5230.2800.2250.9610.9611.0001.0001.0001.0000.000
추천금액(합계_백만원)0.2630.3730.5230.2800.2250.9610.9611.0001.0001.0001.0000.000
공급금액(운전_백만원)0.2190.3680.5190.2760.1460.9510.9511.0001.0001.0001.0000.000
공급금액(합계_백만원)0.2190.3680.5190.2760.1460.9510.9511.0001.0001.0001.0000.000
업종0.1870.0000.3720.2600.4600.0000.0000.0000.0000.0000.0001.000
2024-03-14T18:22:49.950154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매출규모지역구분(중진공)업력구분(중진공)업종자산규모
매출규모1.0000.0000.1390.1980.447
지역구분(중진공)0.0001.0000.0000.1860.000
업력구분(중진공)0.1390.0001.0000.1230.192
업종0.1980.1860.1231.0000.000
자산규모0.4470.0000.1920.0001.000
2024-03-14T18:22:50.392187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호신청금액(운전_백만원)신청금액(합계_백만원)추천금액(운전_백만원)추천금액(합계_백만원)공급금액(운전_백만원)공급금액(합계_백만원)자산규모매출규모업력구분(중진공)지역구분(중진공)업종
일련번호1.000-0.228-0.228-0.180-0.180-0.169-0.1690.0960.0000.0700.1230.079
신청금액(운전_백만원)-0.2281.0001.0000.8250.8250.7950.7950.2020.3000.1930.1420.000
신청금액(합계_백만원)-0.2281.0001.0000.8250.8250.7950.7950.2020.3000.1930.1420.000
추천금액(운전_백만원)-0.1800.8250.8251.0001.0000.9600.9600.2040.3050.1360.0850.000
추천금액(합계_백만원)-0.1800.8250.8251.0001.0000.9600.9600.2040.3050.1360.0850.000
공급금액(운전_백만원)-0.1690.7950.7950.9600.9601.0001.0000.2010.3020.1340.0530.000
공급금액(합계_백만원)-0.1690.7950.7950.9600.9601.0001.0000.2010.3020.1340.0530.000
자산규모0.0960.2020.2020.2040.2040.2010.2011.0000.4470.1920.0000.000
매출규모0.0000.3000.3000.3050.3050.3020.3020.4471.0000.1390.0000.198
업력구분(중진공)0.0700.1930.1930.1360.1360.1340.1340.1920.1391.0000.0000.123
지역구분(중진공)0.1230.1420.1420.0850.0850.0530.0530.0000.0000.0001.0000.186
업종0.0790.0000.0000.0000.0000.0000.0000.0000.1980.1230.1861.000

Missing values

2024-03-14T18:22:42.290522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:22:42.677490image/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)공급금액(시설_백만원)공급금액(운전_백만원)공급금액(합계_백만원)업종
01Net-Zero 유망기업 지원(이차보전)신성장기반100억미만100억미만10년미만경기050050005005000500500화공
12Net-Zero 유망기업 지원(이차보전)신성장기반200억이상300억이상20년미만서울040040004004000400400유통
23Net-Zero 유망기업 지원(이차보전)신성장기반200억미만300억미만20년미만경기050050005005000500500금속
34Net-Zero 유망기업 지원(이차보전)신성장기반70억미만100억미만20년이상인천050050005005000500500기계
45Net-Zero 유망기업 지원(이차보전)신성장기반70억미만100억미만7년미만충북050050005005000500500화공
56Net-Zero 유망기업 지원(이차보전)신성장기반10억미만50억미만20년이상전북050050005005000500500기타
67Net-Zero 유망기업 지원(이차보전)신성장기반200억이상300억미만20년미만충남050050005005000500500금속
78Net-Zero 유망기업 지원(이차보전)신성장기반30억미만50억미만20년이상서울050050005005000500500유통
89Net-Zero 유망기업 지원(이차보전)신성장기반70억미만50억미만15년미만인천050050004004000400400섬유
910Net-Zero 유망기업 지원(이차보전)신성장기반70억미만100억미만20년이상울산030030002002000200200화공
일련번호사업1사업2자산규모매출규모업력구분(중진공)지역구분(중진공)신청금액(시설_백만원)신청금액(운전_백만원)신청금액(합계_백만원)추천금액(시설_백만원)추천금액(운전_백만원)추천금액(합계_백만원)공급금액(시설_백만원)공급금액(운전_백만원)공급금액(합계_백만원)업종
200201Net-Zero 유망기업 지원(이차보전)신성장기반200억미만100억미만20년미만전북040040004004000400400기계
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