Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory75.7 B

Variable types

Text1
Numeric7

Dataset

Description중소벤처기업진흥공단의 정책자금 융자 금액을 기업 매출액 규모별로 구분하여 개방하여 데이터를 활용 가능하도록 함.
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15107593/fileData.do

Alerts

5억미만 대여금액(단위_백만원) is highly overall correlated with 10억미만 대여금액(단위_백만원) and 4 other fieldsHigh correlation
10억미만 대여금액(단위_백만원) is highly overall correlated with 5억미만 대여금액(단위_백만원) and 5 other fieldsHigh correlation
50억미만 대여금액(단위_백만원) is highly overall correlated with 5억미만 대여금액(단위_백만원) and 5 other fieldsHigh correlation
100억미만 대여금액(단위_백만원) is highly overall correlated with 5억미만 대여금액(단위_백만원) and 5 other fieldsHigh correlation
300억미만 대여금액(단위_백만원) is highly overall correlated with 5억미만 대여금액(단위_백만원) and 4 other fieldsHigh correlation
300억이상 대여금액(단위_백만원) is highly overall correlated with 10억미만 대여금액(단위_백만원) and 3 other fieldsHigh correlation
재무제표미등록 대여금액(단위_백만원) is highly overall correlated with 5억미만 대여금액(단위_백만원) and 3 other fieldsHigh correlation
10억미만 대여금액(단위_백만원) has unique valuesUnique
50억미만 대여금액(단위_백만원) has unique valuesUnique
300억미만 대여금액(단위_백만원) has unique valuesUnique
5억미만 대여금액(단위_백만원) has 3 (11.1%) zerosZeros
10억미만 대여금액(단위_백만원) has 1 (3.7%) zerosZeros
50억미만 대여금액(단위_백만원) has 1 (3.7%) zerosZeros
100억미만 대여금액(단위_백만원) has 2 (7.4%) zerosZeros
300억미만 대여금액(단위_백만원) has 1 (3.7%) zerosZeros
300억이상 대여금액(단위_백만원) has 3 (11.1%) zerosZeros
재무제표미등록 대여금액(단위_백만원) has 2 (7.4%) zerosZeros

Reproduction

Analysis started2024-03-14 12:39:11.015918
Analysis finished2024-03-14 12:39:23.867912
Duration12.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size344.0 B
2024-03-14T21:39:24.371979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.5925926
Min length2

Characters and Unicode

Total characters178
Distinct characters76
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)88.9%

Sample

1st row혁신창업사업화
2nd row창업기반지원
3rd row일반
4th row 대환대출
5th row청년전용창업(일반)
ValueCountFrequency (%)
일반 3
 
10.0%
스케일업금융 1
 
3.3%
신시장진출지원자금 1
 
3.3%
재창업 1
 
3.3%
무역조정 1
 
3.3%
사업전환 1
 
3.3%
재도약지원자금 1
 
3.3%
포항피해기업지원 1
 
3.3%
재해중소기업 1
 
3.3%
일시적경영애로 1
 
3.3%
Other values (18) 18
60.0%
2024-03-14T21:39:25.502103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 15
 
8.4%
기 9
 
5.1%
일 6
 
3.4%
반 6
 
3.4%
6
 
3.4%
화 6
 
3.4%
창 6
 
3.4%
원 5
 
2.8%
금 5
 
2.8%
지 5
 
2.8%
Other values (66) 109
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
89.9%
Space Separator 6
 
3.4%
Lowercase Letter 5
 
2.8%
Close Punctuation 2
 
1.1%
Open Punctuation 2
 
1.1%
Uppercase Letter 2
 
1.1%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 15
 
9.4%
기 9
 
5.6%
일 6
 
3.8%
반 6
 
3.8%
화 6
 
3.8%
창 6
 
3.8%
원 5
 
3.1%
금 5
 
3.1%
지 5
 
3.1%
전 4
 
2.5%
Other values (56) 93
58.1%
Lowercase Letter
ValueCountFrequency (%)
e 2
40.0%
t 1
20.0%
r 1
20.0%
o 1
20.0%
Uppercase Letter
ValueCountFrequency (%)
N 1
50.0%
Z 1
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 160
89.9%
Common 11
 
6.2%
Latin 7
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 15
 
9.4%
기 9
 
5.6%
일 6
 
3.8%
반 6
 
3.8%
화 6
 
3.8%
창 6
 
3.8%
원 5
 
3.1%
금 5
 
3.1%
지 5
 
3.1%
전 4
 
2.5%
Other values (56) 93
58.1%
Latin
ValueCountFrequency (%)
e 2
28.6%
N 1
14.3%
t 1
14.3%
Z 1
14.3%
r 1
14.3%
o 1
14.3%
Common
ValueCountFrequency (%)
6
54.5%
) 2
 
18.2%
( 2
 
18.2%
- 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
89.9%
ASCII 18
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 15
 
9.4%
기 9
 
5.6%
일 6
 
3.8%
반 6
 
3.8%
화 6
 
3.8%
창 6
 
3.8%
원 5
 
3.1%
금 5
 
3.1%
지 5
 
3.1%
전 4
 
2.5%
Other values (56) 93
58.1%
ASCII
ValueCountFrequency (%)
6
33.3%
e 2
 
11.1%
) 2
 
11.1%
( 2
 
11.1%
N 1
 
5.6%
t 1
 
5.6%
- 1
 
5.6%
Z 1
 
5.6%
r 1
 
5.6%
o 1
 
5.6%

5억미만 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38495.889
Minimum0
Maximum288916
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:25.869195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12084.5
median7960
Q321104.5
95-th percentile260109
Maximum288916
Range288916
Interquartile range (IQR)19020

Descriptive statistics

Standard deviation82241.162
Coefficient of variation (CV)2.1363622
Kurtosis5.4282993
Mean38495.889
Median Absolute Deviation (MAD)6280
Skewness2.5981683
Sum1039389
Variance6.7636088 × 109
MonotonicityNot monotonic
2024-03-14T21:39:26.245702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 3
 
11.1%
2339 2
 
7.4%
1830 2
 
7.4%
288916 1
 
3.7%
274074 1
 
3.7%
1680 1
 
3.7%
16285 1
 
3.7%
20304 1
 
3.7%
7758 1
 
3.7%
9588 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
0 3
11.1%
1680 1
 
3.7%
1820 1
 
3.7%
1830 2
7.4%
2339 2
7.4%
3116 1
 
3.7%
6163 1
 
3.7%
7410 1
 
3.7%
7758 1
 
3.7%
7960 1
 
3.7%
ValueCountFrequency (%)
288916 1
3.7%
274074 1
3.7%
227524 1
3.7%
37975 1
3.7%
37298 1
3.7%
28068 1
3.7%
21905 1
3.7%
20304 1
3.7%
16285 1
3.7%
14842 1
3.7%

10억미만 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28939.074
Minimum0
Maximum196770
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:26.612613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile190.9
Q12992.5
median7421
Q317901
95-th percentile182946
Maximum196770
Range196770
Interquartile range (IQR)14908.5

Descriptive statistics

Standard deviation57446.705
Coefficient of variation (CV)1.9850913
Kurtosis4.9699126
Mean28939.074
Median Absolute Deviation (MAD)5431
Skewness2.5210311
Sum781355
Variance3.3001239 × 109
MonotonicityNot monotonic
2024-03-14T21:39:27.028399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
196770 1
 
3.7%
185850 1
 
3.7%
2670 1
 
3.7%
9645 1
 
3.7%
550 1
 
3.7%
5050 1
 
3.7%
5600 1
 
3.7%
17915 1
 
3.7%
37 1
 
3.7%
1990 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
37 1
3.7%
550 1
3.7%
1700 1
3.7%
1990 1
3.7%
2521 1
3.7%
2670 1
3.7%
3315 1
3.7%
3350 1
3.7%
4700 1
3.7%
ValueCountFrequency (%)
196770 1
3.7%
185850 1
3.7%
176170 1
3.7%
37829 1
3.7%
25708 1
3.7%
23187 1
3.7%
17915 1
3.7%
17887 1
3.7%
15860 1
3.7%
10920 1
3.7%

50억미만 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146221.07
Minimum0
Maximum826326
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:27.419098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile562.5
Q111684
median58777
Q3116369
95-th percentile718416
Maximum826326
Range826326
Interquartile range (IQR)104685

Descriptive statistics

Standard deviation232029.87
Coefficient of variation (CV)1.5868428
Kurtosis3.8052556
Mean146221.07
Median Absolute Deviation (MAD)47362
Skewness2.1917197
Sum3947969
Variance5.3837858 × 1010
MonotonicityNot monotonic
2024-03-14T21:39:27.802321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
826326 1
 
3.7%
722013 1
 
3.7%
29920 1
 
3.7%
24780 1
 
3.7%
3650 1
 
3.7%
70075 1
 
3.7%
73725 1
 
3.7%
128425 1
 
3.7%
375 1
 
3.7%
11953 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
375 1
3.7%
1000 1
3.7%
3650 1
3.7%
8954 1
3.7%
10990 1
3.7%
11415 1
3.7%
11953 1
3.7%
24780 1
3.7%
29920 1
3.7%
ValueCountFrequency (%)
826326 1
3.7%
722013 1
3.7%
710023 1
3.7%
310677 1
3.7%
240485 1
3.7%
202979 1
3.7%
128425 1
3.7%
104313 1
3.7%
99138 1
3.7%
86830 1
3.7%

100억미만 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71383.852
Minimum0
Maximum298090
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:28.157930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile180
Q15516
median50808
Q389183.5
95-th percentile241501.9
Maximum298090
Range298090
Interquartile range (IQR)83667.5

Descriptive statistics

Standard deviation87244.009
Coefficient of variation (CV)1.2221813
Kurtosis0.96113193
Mean71383.852
Median Absolute Deviation (MAD)44512
Skewness1.4205769
Sum1927364
Variance7.6115171 × 109
MonotonicityNot monotonic
2024-03-14T21:39:28.394531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2
 
7.4%
298090 1
 
3.7%
50810 1
 
3.7%
28920 1
 
3.7%
9190 1
 
3.7%
2500 1
 
3.7%
50808 1
 
3.7%
53308 1
 
3.7%
91418 1
 
3.7%
4736 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 2
7.4%
600 1
3.7%
769 1
3.7%
2500 1
3.7%
3071 1
3.7%
4736 1
3.7%
6296 1
3.7%
9190 1
3.7%
15450 1
3.7%
19850 1
3.7%
ValueCountFrequency (%)
298090 1
3.7%
245104 1
3.7%
233097 1
3.7%
232497 1
3.7%
151090 1
3.7%
135640 1
3.7%
91418 1
3.7%
86949 1
3.7%
64993 1
3.7%
57977 1
3.7%

300억미만 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80649.815
Minimum0
Maximum379920
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:28.609531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile165
Q110637
median42110
Q3127408.5
95-th percentile237922.8
Maximum379920
Range379920
Interquartile range (IQR)116771.5

Descriptive statistics

Standard deviation94491.518
Coefficient of variation (CV)1.1716272
Kurtosis2.5296669
Mean80649.815
Median Absolute Deviation (MAD)41130
Skewness1.5721808
Sum2177545
Variance8.9286471 × 109
MonotonicityNot monotonic
2024-03-14T21:39:29.006613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
247452 1
 
3.7%
166669 1
 
3.7%
29710 1
 
3.7%
4400 1
 
3.7%
700 1
 
3.7%
83188 1
 
3.7%
83888 1
 
3.7%
117998 1
 
3.7%
200 1
 
3.7%
9880 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0 1
3.7%
150 1
3.7%
200 1
3.7%
700 1
3.7%
980 1
3.7%
4400 1
3.7%
9880 1
3.7%
11394 1
3.7%
15840 1
3.7%
16019 1
3.7%
ValueCountFrequency (%)
379920 1
3.7%
247452 1
3.7%
215688 1
3.7%
187138 1
3.7%
166669 1
3.7%
166519 1
3.7%
136819 1
3.7%
117998 1
3.7%
83888 1
3.7%
83188 1
3.7%

300억이상 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31174.222
Minimum0
Maximum237908
Zeros3
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:29.316462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12487.5
median21229
Q333540
95-th percentile108818.3
Maximum237908
Range237908
Interquartile range (IQR)31052.5

Descriptive statistics

Standard deviation49766.577
Coefficient of variation (CV)1.5964016
Kurtosis11.917683
Mean31174.222
Median Absolute Deviation (MAD)18704
Skewness3.2291553
Sum841704
Variance2.4767122 × 109
MonotonicityNot monotonic
2024-03-14T21:39:29.681704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.1%
21545 2
 
7.4%
42774 1
 
3.7%
28029 1
 
3.7%
4800 1
 
3.7%
600 1
 
3.7%
2600 1
 
3.7%
28240 1
 
3.7%
30840 1
 
3.7%
36240 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
0 3
11.1%
196 1
 
3.7%
600 1
 
3.7%
1700 1
 
3.7%
2450 1
 
3.7%
2525 1
 
3.7%
2600 1
 
3.7%
4800 1
 
3.7%
5000 1
 
3.7%
17240 1
 
3.7%
ValueCountFrequency (%)
237908 1
3.7%
131330 1
3.7%
56291 1
3.7%
51291 1
3.7%
47837 1
3.7%
42774 1
3.7%
36240 1
3.7%
30840 1
3.7%
29729 1
3.7%
28240 1
3.7%

재무제표미등록 대여금액(단위_백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52749.481
Minimum0
Maximum429673
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:39:30.035177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1930
median3140
Q320842.5
95-th percentile372444
Maximum429673
Range429673
Interquartile range (IQR)19912.5

Descriptive statistics

Standard deviation121148.02
Coefficient of variation (CV)2.2966675
Kurtosis5.9347047
Mean52749.481
Median Absolute Deviation (MAD)2840
Skewness2.6139153
Sum1424236
Variance1.4676843 × 1010
MonotonicityNot monotonic
2024-03-14T21:39:30.406845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
300 3
 
11.1%
0 2
 
7.4%
429673 1
 
3.7%
10100 1
 
3.7%
10700 1
 
3.7%
1000 1
 
3.7%
845 1
 
3.7%
860 1
 
3.7%
2705 1
 
3.7%
1711 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
0 2
7.4%
300 3
11.1%
845 1
 
3.7%
860 1
 
3.7%
1000 1
 
3.7%
1127 1
 
3.7%
1711 1
 
3.7%
2300 1
 
3.7%
2705 1
 
3.7%
2920 1
 
3.7%
ValueCountFrequency (%)
429673 1
3.7%
426753 1
3.7%
245723 1
3.7%
162205 1
3.7%
36264 1
3.7%
27670 1
3.7%
22860 1
3.7%
18825 1
3.7%
10700 1
3.7%
10100 1
3.7%

Interactions

2024-03-14T21:39:21.436538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:11.306579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:13.061825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:14.866622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:16.779770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:18.123150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:19.742357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:21.682244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:11.560577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:13.323014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:15.114812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.028496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:18.280204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:19.984579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:21.939449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:11.825957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:13.597852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:15.373703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.295061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:18.489253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:20.242678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:22.174752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:12.065552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:13.844997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:15.603432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.519399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:18.739974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:20.472264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:22.421557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:12.315559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:14.107943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:15.848126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.670014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:18.993632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:20.716958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:22.680564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:12.573497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:14.373257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:16.318393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.842401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:19.254426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:20.974408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:22.915097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:12.821790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:14.620409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:16.547992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:17.983472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:19.494754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:39:21.206205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:39:30.645572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분5억미만 대여금액(단위_백만원)10억미만 대여금액(단위_백만원)50억미만 대여금액(단위_백만원)100억미만 대여금액(단위_백만원)300억미만 대여금액(단위_백만원)300억이상 대여금액(단위_백만원)재무제표미등록 대여금액(단위_백만원)
구분1.0000.0000.0000.0000.0000.9060.7140.000
5억미만 대여금액(단위_백만원)0.0001.0000.9820.8360.7840.8740.2570.994
10억미만 대여금액(단위_백만원)0.0000.9821.0000.9360.8890.9360.4400.977
50억미만 대여금액(단위_백만원)0.0000.8360.9361.0000.9920.9260.7170.709
100억미만 대여금액(단위_백만원)0.0000.7840.8890.9921.0000.9580.8810.607
300억미만 대여금액(단위_백만원)0.9060.8740.9360.9260.9581.0000.8140.635
300억이상 대여금액(단위_백만원)0.7140.2570.4400.7170.8810.8141.0000.000
재무제표미등록 대여금액(단위_백만원)0.0000.9940.9770.7090.6070.6350.0001.000
2024-03-14T21:39:30.962372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
5억미만 대여금액(단위_백만원)10억미만 대여금액(단위_백만원)50억미만 대여금액(단위_백만원)100억미만 대여금액(단위_백만원)300억미만 대여금액(단위_백만원)300억이상 대여금액(단위_백만원)재무제표미등록 대여금액(단위_백만원)
5억미만 대여금액(단위_백만원)1.0000.8890.7530.6710.5650.2970.917
10억미만 대여금액(단위_백만원)0.8891.0000.9260.8840.7920.5070.744
50억미만 대여금액(단위_백만원)0.7530.9261.0000.9720.9200.6560.599
100억미만 대여금액(단위_백만원)0.6710.8840.9721.0000.9680.7600.542
300억미만 대여금액(단위_백만원)0.5650.7920.9200.9681.0000.8620.464
300억이상 대여금액(단위_백만원)0.2970.5070.6560.7600.8621.0000.213
재무제표미등록 대여금액(단위_백만원)0.9170.7440.5990.5420.4640.2131.000

Missing values

2024-03-14T21:39:23.249639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:39:23.685853image/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

구분5억미만 대여금액(단위_백만원)10억미만 대여금액(단위_백만원)50억미만 대여금액(단위_백만원)100억미만 대여금액(단위_백만원)300억미만 대여금액(단위_백만원)300억이상 대여금액(단위_백만원)재무제표미등록 대여금액(단위_백만원)
0혁신창업사업화28891619677082632629809024745242774429673
1창업기반지원27407418585072201323309716666921545426753
2일반22752417617071002323249716651921545245723
3대환대출31163315895430719801961127
4청년전용창업(일반)379757980109906001500162205
5청년전용창업(창성패)85751700100000018825
6개발기술사업화14842109201043136499380783212292920
7신성장기반자금372983782931067724510437992023790836264
8혁신성장지원28068257082404851510902156885629127670
9일반21905231872029791356401871385129122860
구분5억미만 대여금액(단위_백만원)10억미만 대여금액(단위_백만원)50억미만 대여금액(단위_백만원)100억미만 대여금액(단위_백만원)300억미만 대여금액(단위_백만원)300억이상 대여금액(단위_백만원)재무제표미등록 대여금액(단위_백만원)
17긴급경영안정자금958817887991385797751840197652705
18일시적경영애로77581586086810532414176017240860
19재해중소기업1830199011953473698802525845
20포항피해기업지원037375020001000
21재도약지원자금2030417915128425914181179983624010700
22사업전환2339560073725533088388830840300
23일반2339505070075508088318828240300
24무역조정05503650250070026000
25재창업162859645247809190440060010100
26구조개선전용168026702992028920297104800300