Overview

Dataset statistics

Number of variables5
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory47.2 B

Variable types

Numeric4
Text1

Dataset

Description중소벤처기업진흥공단이 중소기업을 대상으로 제공하는 정책자금의 업종별 융자제한 부채비율을 개방하여, 중소기업이 정책자금 신청 시 활용할 수 있도록 개방
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15069482/fileData.do

Alerts

평균부채비율(퍼센트) is highly overall correlated with 제한부채비율(퍼센트) and 1 other fieldsHigh correlation
제한부채비율(퍼센트) is highly overall correlated with 평균부채비율(퍼센트) and 1 other fieldsHigh correlation
사업전환융자 is highly overall correlated with 평균부채비율(퍼센트) and 1 other fieldsHigh correlation
번호 has unique valuesUnique
업종(KSIC-10) has unique valuesUnique
평균부채비율(퍼센트) has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:03:37.297425
Analysis finished2024-04-06 08:03:41.141706
Duration3.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-04-06T17:03:41.285186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2024-04-06T17:03:41.697709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

업종(KSIC-10)
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2024-04-06T17:03:42.137998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length21
Mean length14.707317
Min length4

Characters and Unicode

Total characters603
Distinct characters152
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st rowA01(농업)
2nd rowA03(어업)
3rd rowB(광업)
4th rowC(제조업)
5th rowC10(식료품)
ValueCountFrequency (%)
및 24
 
20.2%
서비스업 3
 
2.5%
제외 3
 
2.5%
a01(농업 1
 
0.8%
제조업 1
 
0.8%
e(하수·폐기물 1
 
0.8%
공급업 1
 
0.8%
공기조절 1
 
0.8%
증기 1
 
0.8%
가스 1
 
0.8%
Other values (82) 82
68.9%
2024-04-06T17:03:42.972347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
78
 
12.9%
( 44
 
7.3%
) 44
 
7.3%
C 25
 
4.1%
및 24
 
4.0%
업 23
 
3.8%
제 18
 
3.0%
기 16
 
2.7%
품 15
 
2.5%
1 14
 
2.3%
Other values (142) 302
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 327
54.2%
Space Separator 78
 
12.9%
Decimal Number 55
 
9.1%
Open Punctuation 44
 
7.3%
Close Punctuation 44
 
7.3%
Uppercase Letter 40
 
6.6%
Other Punctuation 15
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
및 24
 
7.3%
업 23
 
7.0%
제 18
 
5.5%
기 16
 
4.9%
품 15
 
4.6%
비 10
 
3.1%
가 8
 
2.4%
스 8
 
2.4%
의 7
 
2.1%
서 5
 
1.5%
Other values (112) 193
59.0%
Uppercase Letter
ValueCountFrequency (%)
C 25
62.5%
A 2
 
5.0%
I 1
 
2.5%
H 1
 
2.5%
G 1
 
2.5%
F 1
 
2.5%
J 1
 
2.5%
L 1
 
2.5%
M 1
 
2.5%
R 1
 
2.5%
Other values (5) 5
 
12.5%
Decimal Number
ValueCountFrequency (%)
1 14
25.5%
2 12
21.8%
3 10
18.2%
0 5
 
9.1%
4 3
 
5.5%
5 3
 
5.5%
7 2
 
3.6%
8 2
 
3.6%
6 2
 
3.6%
9 2
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 14
93.3%
· 1
 
6.7%
Space Separator
ValueCountFrequency (%)
78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 327
54.2%
Common 236
39.1%
Latin 40
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
및 24
 
7.3%
업 23
 
7.0%
제 18
 
5.5%
기 16
 
4.9%
품 15
 
4.6%
비 10
 
3.1%
가 8
 
2.4%
스 8
 
2.4%
의 7
 
2.1%
서 5
 
1.5%
Other values (112) 193
59.0%
Common
ValueCountFrequency (%)
78
33.1%
( 44
18.6%
) 44
18.6%
1 14
 
5.9%
, 14
 
5.9%
2 12
 
5.1%
3 10
 
4.2%
0 5
 
2.1%
4 3
 
1.3%
5 3
 
1.3%
Other values (5) 9
 
3.8%
Latin
ValueCountFrequency (%)
C 25
62.5%
A 2
 
5.0%
I 1
 
2.5%
H 1
 
2.5%
G 1
 
2.5%
F 1
 
2.5%
J 1
 
2.5%
L 1
 
2.5%
M 1
 
2.5%
R 1
 
2.5%
Other values (5) 5
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 327
54.2%
ASCII 275
45.6%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
78
28.4%
( 44
16.0%
) 44
16.0%
C 25
 
9.1%
1 14
 
5.1%
, 14
 
5.1%
2 12
 
4.4%
3 10
 
3.6%
0 5
 
1.8%
4 3
 
1.1%
Other values (19) 26
 
9.5%
Hangul
ValueCountFrequency (%)
및 24
 
7.3%
업 23
 
7.0%
제 18
 
5.5%
기 16
 
4.9%
품 15
 
4.6%
비 10
 
3.1%
가 8
 
2.4%
스 8
 
2.4%
의 7
 
2.1%
서 5
 
1.5%
Other values (112) 193
59.0%
None
ValueCountFrequency (%)
· 1
100.0%

평균부채비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.4878
Minimum71.5
Maximum469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-04-06T17:03:43.577665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.5
5-th percentile97.8
Q1116.5
median131.5
Q3180
95-th percentile385
Maximum469
Range397.5
Interquartile range (IQR)63.5

Descriptive statistics

Standard deviation87.629995
Coefficient of variation (CV)0.53274463
Kurtosis6.119906
Mean164.4878
Median Absolute Deviation (MAD)26.9
Skewness2.4426715
Sum6744
Variance7679.0161
MonotonicityNot monotonic
2024-04-06T17:03:43.833823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
182.8 1
 
2.4%
137.0 1
 
2.4%
158.4 1
 
2.4%
236.8 1
 
2.4%
169.7 1
 
2.4%
122.0 1
 
2.4%
187.6 1
 
2.4%
469.0 1
 
2.4%
115.8 1
 
2.4%
110.9 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
71.5 1
2.4%
92.6 1
2.4%
97.8 1
2.4%
98.6 1
2.4%
99.0 1
2.4%
107.2 1
2.4%
110.9 1
2.4%
115.8 1
2.4%
115.9 1
2.4%
116.4 1
2.4%
ValueCountFrequency (%)
469.0 1
2.4%
461.1 1
2.4%
385.0 1
2.4%
238.1 1
2.4%
236.8 1
2.4%
211.0 1
2.4%
208.2 1
2.4%
199.9 1
2.4%
187.6 1
2.4%
182.8 1
2.4%

제한부채비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean410.54146
Minimum214.5
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-04-06T17:03:44.049467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum214.5
5-th percentile293.4
Q1349.5
median394.5
Q3500
95-th percentile500
Maximum500
Range285.5
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation81.354139
Coefficient of variation (CV)0.19816303
Kurtosis-0.94528596
Mean410.54146
Median Absolute Deviation (MAD)80.7
Skewness-0.31224795
Sum16832.2
Variance6618.496
MonotonicityNot monotonic
2024-04-06T17:03:44.278543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
500.0 14
34.1%
393.3 1
 
2.4%
394.5 1
 
2.4%
365.1 1
 
2.4%
411.0 1
 
2.4%
332.7 1
 
2.4%
347.4 1
 
2.4%
366.0 1
 
2.4%
475.2 1
 
2.4%
349.2 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
214.5 1
2.4%
277.8 1
2.4%
293.4 1
2.4%
295.8 1
2.4%
297.0 1
2.4%
321.6 1
2.4%
332.7 1
2.4%
347.4 1
2.4%
347.7 1
2.4%
349.2 1
2.4%
ValueCountFrequency (%)
500.0 14
34.1%
485.7 1
 
2.4%
475.2 1
 
2.4%
462.3 1
 
2.4%
420.9 1
 
2.4%
411.0 1
 
2.4%
402.6 1
 
2.4%
394.5 1
 
2.4%
393.3 1
 
2.4%
390.3 1
 
2.4%

사업전환융자
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean821.08293
Minimum429
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-04-06T17:03:44.491924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum429
5-th percentile586.8
Q1699
median789
Q31000
95-th percentile1000
Maximum1000
Range571
Interquartile range (IQR)301

Descriptive statistics

Standard deviation162.70828
Coefficient of variation (CV)0.19816303
Kurtosis-0.94528596
Mean821.08293
Median Absolute Deviation (MAD)161.4
Skewness-0.31224795
Sum33664.4
Variance26473.984
MonotonicityNot monotonic
2024-04-06T17:03:44.672493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1000.0 14
34.1%
786.6 1
 
2.4%
789.0 1
 
2.4%
730.2 1
 
2.4%
822.0 1
 
2.4%
665.4 1
 
2.4%
694.8 1
 
2.4%
732.0 1
 
2.4%
950.4 1
 
2.4%
698.4 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
429.0 1
2.4%
555.6 1
2.4%
586.8 1
2.4%
591.6 1
2.4%
594.0 1
2.4%
643.2 1
2.4%
665.4 1
2.4%
694.8 1
2.4%
695.4 1
2.4%
698.4 1
2.4%
ValueCountFrequency (%)
1000.0 14
34.1%
971.4 1
 
2.4%
950.4 1
 
2.4%
924.6 1
 
2.4%
841.8 1
 
2.4%
822.0 1
 
2.4%
805.2 1
 
2.4%
789.0 1
 
2.4%
786.6 1
 
2.4%
780.6 1
 
2.4%

Interactions

2024-04-06T17:03:39.949017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:37.679900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.448974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:39.174947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:40.108164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:37.878772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.627769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:39.354822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:40.267350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.044395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.814848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:39.531291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:40.433406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.283928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:38.968044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:39.794701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:03:44.799139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호업종(KSIC-10)평균부채비율(퍼센트)제한부채비율(퍼센트)사업전환융자
번호1.0001.0000.5630.5330.533
업종(KSIC-10)1.0001.0001.0001.0001.000
평균부채비율(퍼센트)0.5631.0001.0000.6370.637
제한부채비율(퍼센트)0.5331.0000.6371.0001.000
사업전환융자0.5331.0000.6371.0001.000
2024-04-06T17:03:44.983109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호평균부채비율(퍼센트)제한부채비율(퍼센트)사업전환융자
번호1.0000.2650.2590.259
평균부채비율(퍼센트)0.2651.0000.9800.980
제한부채비율(퍼센트)0.2590.9801.0001.000
사업전환융자0.2590.9801.0001.000

Missing values

2024-04-06T17:03:40.730324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:03:41.012249image/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

번호업종(KSIC-10)평균부채비율(퍼센트)제한부채비율(퍼센트)사업전환융자
01A01(농업)182.8500.01000.0
12A03(어업)238.1500.01000.0
23B(광업)180.0500.01000.0
34C(제조업)119.7359.1718.2
45C10(식료품)154.1462.3924.6
56C11(음료)140.3420.9841.8
67C13(섬유제품(의복제외))129.2387.6775.2
78C14(의복, 의복액세서리 및 모피제품)130.1390.3780.6
89C15(가죽, 가방 및 신발)115.9347.7695.4
910C16(목재 및 나무제품(가구 제외))161.9485.7971.4
번호업종(KSIC-10)평균부채비율(퍼센트)제한부채비율(퍼센트)사업전환융자
3132G(도매 및 소매업)137.0411.0822.0
3233H(운수 및 창고업)170.5500.01000.0
3334I(숙박 및 음식점업)385.0500.01000.0
3435J(정보통신업)121.7365.1730.2
3536L(부동산업)461.1500.01000.0
3637M(전문, 과학 및 기술 서비스업)131.5394.5789.0
3738N(사업시설관리 및 사업지원 및 임대서비스업)211.0500.01000.0
3839P(교육 서비스업)166.9500.01000.0
3940R(예술, 스포츠 및 여가관련 서비스업)208.2500.01000.0
4041기타산업199.9500.01000.0