Overview

Dataset statistics

Number of variables7
Number of observations332
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.6 KiB
Average record size in memory60.4 B

Variable types

Numeric4
Categorical2
DateTime1

Dataset

Description중소벤처기업진흥공단의 정책자금 중 시니어창업자금을 받은 업체의 업종별, 지역별, 지원금액 등의 데이터를 통계 자료로 활용할 수 있도록 개방
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15108046/fileData.do

Alerts

지원금액(시설_백만원) is highly overall correlated with 지원금액(운전_백만원) and 1 other fieldsHigh correlation
지원금액(운전_백만원) is highly overall correlated with 지원금액(시설_백만원)High correlation
지원금액(합계_백만원) is highly overall correlated with 지원금액(시설_백만원)High correlation
순번 has unique valuesUnique
지원금액(시설_백만원) has 267 (80.4%) zerosZeros
지원금액(운전_백만원) has 65 (19.6%) zerosZeros

Reproduction

Analysis started2023-12-12 22:39:31.909963
Analysis finished2023-12-12 22:39:33.997607
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct332
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.5
Minimum1
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:39:34.057940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.55
Q183.75
median166.5
Q3249.25
95-th percentile315.45
Maximum332
Range331
Interquartile range (IQR)165.5

Descriptive statistics

Standard deviation95.984374
Coefficient of variation (CV)0.57648273
Kurtosis-1.2
Mean166.5
Median Absolute Deviation (MAD)83
Skewness0
Sum55278
Variance9213
MonotonicityStrictly increasing
2023-12-13T07:39:34.433144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
230 1
 
0.3%
228 1
 
0.3%
227 1
 
0.3%
226 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
Other values (322) 322
97.0%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
332 1
0.3%
331 1
0.3%
330 1
0.3%
329 1
0.3%
328 1
0.3%
327 1
0.3%
326 1
0.3%
325 1
0.3%
324 1
0.3%
323 1
0.3%

업력
Categorical

Distinct8
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
3년미만
86 
5년미만
81 
7년미만
80 
1년미만
41 
10년미만
21 
Other values (3)
23 

Length

Max length5
Median length4
Mean length4.1325301
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5년미만
2nd row20년이상
3rd row3년미만
4th row20년이상
5th row5년미만

Common Values

ValueCountFrequency (%)
3년미만 86
25.9%
5년미만 81
24.4%
7년미만 80
24.1%
1년미만 41
12.3%
10년미만 21
 
6.3%
15년미만 9
 
2.7%
20년이상 7
 
2.1%
20년미만 7
 
2.1%

Length

2023-12-13T07:39:34.563546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:34.662919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3년미만 86
25.9%
5년미만 81
24.4%
7년미만 80
24.1%
1년미만 41
12.3%
10년미만 21
 
6.3%
15년미만 9
 
2.7%
20년이상 7
 
2.1%
20년미만 7
 
2.1%

업종
Categorical

Distinct11
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
기타
64 
정보
52 
기계
43 
전자
41 
금속
23 
Other values (6)
109 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전자
2nd row정보
3rd row전자
4th row전기
5th row기계

Common Values

ValueCountFrequency (%)
기타 64
19.3%
정보 52
15.7%
기계 43
13.0%
전자 41
12.3%
금속 23
 
6.9%
화공 23
 
6.9%
식료 22
 
6.6%
전기 21
 
6.3%
유통 18
 
5.4%
섬유 13
 
3.9%

Length

2023-12-13T07:39:34.786324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 64
19.3%
정보 52
15.7%
기계 43
13.0%
전자 41
12.3%
금속 23
 
6.9%
화공 23
 
6.9%
식료 22
 
6.6%
전기 21
 
6.3%
유통 18
 
5.4%
섬유 13
 
3.9%

지원일자
Date

Distinct141
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum2023-01-17 00:00:00
Maximum2023-10-31 00:00:00
2023-12-13T07:39:34.888761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:35.027757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지원금액(시설_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.9006
Minimum0
Maximum2500
Zeros267
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:39:35.165204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1345
Maximum2500
Range2500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation436.49752
Coefficient of variation (CV)2.799845
Kurtosis10.902822
Mean155.9006
Median Absolute Deviation (MAD)0
Skewness3.343139
Sum51759
Variance190530.08
MonotonicityNot monotonic
2023-12-13T07:39:35.384979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 267
80.4%
2000 4
 
1.2%
400 4
 
1.2%
700 3
 
0.9%
220 2
 
0.6%
500 2
 
0.6%
600 2
 
0.6%
900 2
 
0.6%
800 2
 
0.6%
1800 2
 
0.6%
Other values (41) 42
 
12.7%
ValueCountFrequency (%)
0 267
80.4%
55 1
 
0.3%
85 1
 
0.3%
90 1
 
0.3%
98 1
 
0.3%
100 1
 
0.3%
110 1
 
0.3%
116 1
 
0.3%
120 1
 
0.3%
130 1
 
0.3%
ValueCountFrequency (%)
2500 1
 
0.3%
2200 1
 
0.3%
2150 1
 
0.3%
2000 4
1.2%
1980 1
 
0.3%
1800 2
0.6%
1670 1
 
0.3%
1630 1
 
0.3%
1530 1
 
0.3%
1500 1
 
0.3%

지원금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.94578
Minimum0
Maximum2000
Zeros65
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:39:35.521982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median100
Q3200
95-th percentile500
Maximum2000
Range2000
Interquartile range (IQR)150

Descriptive statistics

Standard deviation193.95028
Coefficient of variation (CV)1.3021535
Kurtosis30.212704
Mean148.94578
Median Absolute Deviation (MAD)100
Skewness4.4343041
Sum49450
Variance37616.71
MonotonicityNot monotonic
2023-12-13T07:39:35.660561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 96
28.9%
0 65
19.6%
200 53
16.0%
50 34
 
10.2%
300 26
 
7.8%
150 21
 
6.3%
500 11
 
3.3%
1000 6
 
1.8%
80 6
 
1.8%
250 3
 
0.9%
Other values (7) 11
 
3.3%
ValueCountFrequency (%)
0 65
19.6%
40 1
 
0.3%
50 34
 
10.2%
60 1
 
0.3%
70 3
 
0.9%
80 6
 
1.8%
100 96
28.9%
120 3
 
0.9%
150 21
 
6.3%
200 53
16.0%
ValueCountFrequency (%)
2000 1
 
0.3%
1000 6
 
1.8%
800 1
 
0.3%
500 11
 
3.3%
400 1
 
0.3%
300 26
7.8%
250 3
 
0.9%
200 53
16.0%
150 21
 
6.3%
120 3
 
0.9%

지원금액(합계_백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.84639
Minimum40
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-13T07:39:35.803458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile50
Q1100
median150
Q3300
95-th percentile1400
Maximum2500
Range2460
Interquartile range (IQR)200

Descriptive statistics

Standard deviation426.10446
Coefficient of variation (CV)1.3977678
Kurtosis8.7526706
Mean304.84639
Median Absolute Deviation (MAD)50
Skewness2.9530468
Sum101209
Variance181565.01
MonotonicityNot monotonic
2023-12-13T07:39:35.952068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 97
29.2%
200 54
16.3%
50 34
 
10.2%
300 27
 
8.1%
150 21
 
6.3%
500 13
 
3.9%
80 6
 
1.8%
1000 6
 
1.8%
400 5
 
1.5%
2000 5
 
1.5%
Other values (47) 64
19.3%
ValueCountFrequency (%)
40 1
 
0.3%
50 34
 
10.2%
55 1
 
0.3%
60 1
 
0.3%
70 3
 
0.9%
80 6
 
1.8%
85 1
 
0.3%
90 1
 
0.3%
98 1
 
0.3%
100 97
29.2%
ValueCountFrequency (%)
2500 1
 
0.3%
2200 1
 
0.3%
2150 1
 
0.3%
2000 5
1.5%
1980 1
 
0.3%
1800 2
 
0.6%
1670 1
 
0.3%
1630 1
 
0.3%
1530 1
 
0.3%
1500 1
 
0.3%

Interactions

2023-12-13T07:39:33.468832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.203810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.658043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.084928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.560687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.302985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.767471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.169725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.638862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.419966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.880736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.254516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.724230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.555976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.003271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:33.370511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:39:36.048769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업력업종지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
순번1.0000.4790.3490.2750.2750.321
업력0.4791.0000.2340.0000.3450.170
업종0.3490.2341.0000.0000.0000.000
지원금액(시설_백만원)0.2750.0000.0001.0000.0000.998
지원금액(운전_백만원)0.2750.3450.0000.0001.0000.717
지원금액(합계_백만원)0.3210.1700.0000.9980.7171.000
2023-12-13T07:39:36.197192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종업력
업종1.0000.111
업력0.1111.000
2023-12-13T07:39:36.299060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)업력업종
순번1.0000.094-0.272-0.1890.2510.156
지원금액(시설_백만원)0.0941.000-0.6950.5320.0000.000
지원금액(운전_백만원)-0.272-0.6951.0000.2150.1990.000
지원금액(합계_백만원)-0.1890.5320.2151.0000.0810.000
업력0.2510.0000.1990.0811.0000.111
업종0.1560.0000.0000.0000.1111.000

Missing values

2023-12-13T07:39:33.837915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:39:33.957774image/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

순번업력업종지원일자지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
015년미만전자2023-05-02020002000
1220년이상정보2023-03-080400400
233년미만전자2023-02-160150150
3420년이상전기2023-02-070100100
455년미만기계2023-02-090100100
565년미만전기2023-01-3105050
675년미만전자2023-01-3105050
7810년미만정보2023-02-210200200
897년미만기타2023-02-020100100
91010년미만기계2023-01-3105050
순번업력업종지원일자지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
3223233년미만화공2023-06-290100100
3233241년미만기타2023-07-200100100
3243253년미만기계2023-07-170100100
3253261년미만기타2023-08-110100100
3263273년미만화공2023-08-3005050
3273281년미만식료2023-09-220100100
3283295년미만기계2023-08-140150150
3293303년미만기계2023-09-180100100
3303313년미만식료2023-10-100100100
3313323년미만식료2023-09-270100100