Overview

Dataset statistics

Number of variables5
Number of observations3070
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.0 KiB
Average record size in memory44.0 B

Variable types

Numeric4
Categorical1

Dataset

Description중소벤처기업진흥공단의 여성기업 지원실적을 개방하여 여성기업 지원 정보 활용 및 금융 소외계층 중점지원을 통한 사회안전망 제고
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15107259/fileData.do

Alerts

지원금액(시설_백만원) is highly overall correlated with 지원금액(운전_백만원)High correlation
지원금액(운전_백만원) is highly overall correlated with 지원금액(시설_백만원)High correlation
순번 has unique valuesUnique
지원금액(시설_백만원) has 2709 (88.2%) zerosZeros
지원금액(운전_백만원) has 359 (11.7%) zerosZeros

Reproduction

Analysis started2024-03-14 12:31:19.401267
Analysis finished2024-03-14 12:31:24.413892
Duration5.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct3070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1535.5
Minimum1
Maximum3070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-03-14T21:31:24.626531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile154.45
Q1768.25
median1535.5
Q32302.75
95-th percentile2916.55
Maximum3070
Range3069
Interquartile range (IQR)1534.5

Descriptive statistics

Standard deviation886.37699
Coefficient of variation (CV)0.57725626
Kurtosis-1.2
Mean1535.5
Median Absolute Deviation (MAD)767.5
Skewness0
Sum4713985
Variance785664.17
MonotonicityStrictly increasing
2024-03-14T21:31:25.067319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2052 1
 
< 0.1%
2043 1
 
< 0.1%
2044 1
 
< 0.1%
2045 1
 
< 0.1%
2046 1
 
< 0.1%
2047 1
 
< 0.1%
2048 1
 
< 0.1%
2049 1
 
< 0.1%
2050 1
 
< 0.1%
Other values (3060) 3060
99.7%
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 (%)
3070 1
< 0.1%
3069 1
< 0.1%
3068 1
< 0.1%
3067 1
< 0.1%
3066 1
< 0.1%
3065 1
< 0.1%
3064 1
< 0.1%
3063 1
< 0.1%
3062 1
< 0.1%
3061 1
< 0.1%

업종
Categorical

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
식료
475 
기타
470 
잡화
326 
금속
309 
화공
294 
Other values (6)
1196 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기계
2nd row기계
3rd row잡화
4th row잡화
5th row잡화

Common Values

ValueCountFrequency (%)
식료 475
15.5%
기타 470
15.3%
잡화 326
10.6%
금속 309
10.1%
화공 294
9.6%
유통 265
8.6%
정보 241
7.9%
기계 239
7.8%
섬유 239
7.8%
전기 110
 
3.6%

Length

2024-03-14T21:31:25.486234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
식료 475
15.5%
기타 470
15.3%
잡화 326
10.6%
금속 309
10.1%
화공 294
9.6%
유통 265
8.6%
정보 241
7.9%
기계 239
7.8%
섬유 239
7.8%
전기 110
 
3.6%

지원금액(시설_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)5.8%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean79.48631
Minimum0
Maximum4500
Zeros2709
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-03-14T21:31:25.867879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile556.5
Maximum4500
Range4500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation311.9901
Coefficient of variation (CV)3.9250796
Kurtosis42.782509
Mean79.48631
Median Absolute Deviation (MAD)0
Skewness5.7834268
Sum243864
Variance97337.821
MonotonicityNot monotonic
2024-03-14T21:31:26.513392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2709
88.2%
1000 21
 
0.7%
500 18
 
0.6%
300 12
 
0.4%
200 10
 
0.3%
600 10
 
0.3%
400 9
 
0.3%
800 8
 
0.3%
100 8
 
0.3%
350 7
 
0.2%
Other values (167) 256
 
8.3%
ValueCountFrequency (%)
0 2709
88.2%
10 1
 
< 0.1%
15 1
 
< 0.1%
27 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
40 2
 
0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
50 4
 
0.1%
ValueCountFrequency (%)
4500 1
 
< 0.1%
3300 1
 
< 0.1%
3000 3
0.1%
2700 2
0.1%
2500 1
 
< 0.1%
2400 2
0.1%
2300 2
0.1%
2268 1
 
< 0.1%
2200 2
0.1%
2100 1
 
< 0.1%

지원금액(운전_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.18857
Minimum0
Maximum1499.85
Zeros359
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-03-14T21:31:26.926550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median100
Q3150
95-th percentile300
Maximum1499.85
Range1499.85
Interquartile range (IQR)100

Descriptive statistics

Standard deviation103.29263
Coefficient of variation (CV)0.8594214
Kurtosis22.963016
Mean120.18857
Median Absolute Deviation (MAD)50
Skewness3.2051578
Sum368978.9
Variance10669.366
MonotonicityNot monotonic
2024-03-14T21:31:27.371404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 1303
42.4%
200.0 411
 
13.4%
0.0 359
 
11.7%
50.0 354
 
11.5%
150.0 185
 
6.0%
300.0 160
 
5.2%
70.0 56
 
1.8%
500.0 41
 
1.3%
30.0 36
 
1.2%
250.0 35
 
1.1%
Other values (52) 130
 
4.2%
ValueCountFrequency (%)
0.0 359
11.7%
10.0 1
 
< 0.1%
19.0 1
 
< 0.1%
20.0 6
 
0.2%
27.0 1
 
< 0.1%
30.0 36
 
1.2%
33.0 1
 
< 0.1%
40.0 10
 
0.3%
41.0 1
 
< 0.1%
42.0 1
 
< 0.1%
ValueCountFrequency (%)
1499.85 1
 
< 0.1%
1076.923077 1
 
< 0.1%
1000.0 3
 
0.1%
999.999429 1
 
< 0.1%
923.0769231 1
 
< 0.1%
700.0 3
 
0.1%
600.0 1
 
< 0.1%
599.946 1
 
< 0.1%
578.1275092 1
 
< 0.1%
500.0 41
1.3%

지원금액(합계_백만원)
Real number (ℝ)

Distinct205
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.62309
Minimum10
Maximum4500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-03-14T21:31:27.825927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile50
Q1100
median100
Q3200
95-th percentile600
Maximum4500
Range4490
Interquartile range (IQR)100

Descriptive statistics

Standard deviation298.07282
Coefficient of variation (CV)1.493178
Kurtosis42.399129
Mean199.62309
Median Absolute Deviation (MAD)50
Skewness5.5562783
Sum612842.9
Variance88847.404
MonotonicityNot monotonic
2024-03-14T21:31:28.274041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 1311
42.7%
200.0 421
 
13.7%
50.0 358
 
11.7%
150.0 188
 
6.1%
300.0 172
 
5.6%
500.0 59
 
1.9%
70.0 58
 
1.9%
250.0 40
 
1.3%
30.0 37
 
1.2%
80.0 31
 
1.0%
Other values (195) 395
 
12.9%
ValueCountFrequency (%)
10.0 2
 
0.1%
15.0 1
 
< 0.1%
19.0 1
 
< 0.1%
20.0 6
 
0.2%
27.0 2
 
0.1%
30.0 37
1.2%
33.0 2
 
0.1%
40.0 12
 
0.4%
41.0 1
 
< 0.1%
42.0 1
 
< 0.1%
ValueCountFrequency (%)
4500.0 1
 
< 0.1%
3300.0 1
 
< 0.1%
3000.0 3
0.1%
2700.0 2
0.1%
2500.0 1
 
< 0.1%
2400.0 2
0.1%
2300.0 2
0.1%
2268.0 1
 
< 0.1%
2200.0 2
0.1%
2100.0 1
 
< 0.1%

Interactions

2024-03-14T21:31:22.842884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:19.661378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:20.721752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:21.748981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:23.105680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:19.934067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:20.983535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:22.024222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:23.357653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:20.190643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:21.231880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:22.290821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:23.628731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:20.463961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:21.500689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:31:22.572505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:31:28.535394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
순번1.0000.2450.1720.2330.179
업종0.2451.0000.0870.1050.090
지원금액(시설_백만원)0.1720.0871.0000.0641.000
지원금액(운전_백만원)0.2330.1050.0641.0000.494
지원금액(합계_백만원)0.1790.0901.0000.4941.000
2024-03-14T21:31:28.808368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)업종
순번1.0000.020-0.132-0.1250.106
지원금액(시설_백만원)0.0201.000-0.5800.4480.039
지원금액(운전_백만원)-0.132-0.5801.0000.4440.049
지원금액(합계_백만원)-0.1250.4480.4441.0000.041
업종0.1060.0390.0490.0411.000

Missing values

2024-03-14T21:31:23.955676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:31:24.281741image/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

순번업종지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
01기계0300.0300.0
12기계14000.01400.0
23잡화1690.0169.0
34잡화5000.0500.0
45잡화3310.0331.0
56잡화2650.0265.0
67식료2500.0250.0
78잡화050.050.0
89식료01499.851499.85
910전자0100.0100.0
순번업종지원금액(시설_백만원)지원금액(운전_백만원)지원금액(합계_백만원)
30603061전자0250.0250.0
30613062기타0500.0500.0
30623063기타0300.0300.0
30633064기타0300.0300.0
30643065기타0400.0400.0
30653066화공7000.0700.0
30663067유통0500.0500.0
30673068금속0400.0400.0
30683069전자0180.0180.0
30693070식료0100.0100.0