Overview

Dataset statistics

Number of variables15
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory138.7 B

Variable types

Text1
Numeric14

Dataset

Description중소벤처기업진흥공단이 중소기업을 대상으로 제공하는 정책자금의 규모별, 사업별, 업력별, 업종별 집행실적을 개방하여, 중소기업이 정책자금 신청 시 활용할 수 있도록 개방
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15070080/fileData.do

Alerts

10억미만 건수 is highly overall correlated with 10억미만 금액(백만원) and 9 other fieldsHigh correlation
10억미만 금액(백만원) is highly overall correlated with 10억미만 건수 and 10 other fieldsHigh correlation
30억미만 건수 is highly overall correlated with 10억미만 건수 and 12 other fieldsHigh correlation
30억미만 금액(백만원) is highly overall correlated with 10억미만 건수 and 12 other fieldsHigh correlation
70억미만 건수 is highly overall correlated with 10억미만 건수 and 11 other fieldsHigh correlation
70억미만 금액(백만원) is highly overall correlated with 10억미만 건수 and 11 other fieldsHigh correlation
100억미만 건수 is highly overall correlated with 10억미만 건수 and 11 other fieldsHigh correlation
100억미만 금액(백만원) is highly overall correlated with 10억미만 건수 and 10 other fieldsHigh correlation
200억미만 건수 is highly overall correlated with 10억미만 건수 and 10 other fieldsHigh correlation
200억미만 금액(백만원) is highly overall correlated with 10억미만 금액(백만원) and 9 other fieldsHigh correlation
200억이상 건수 is highly overall correlated with 30억미만 건수 and 8 other fieldsHigh correlation
200억이상 금액(백만원) is highly overall correlated with 30억미만 건수 and 8 other fieldsHigh correlation
재무제표미등록 건수 is highly overall correlated with 10억미만 건수 and 4 other fieldsHigh correlation
재무제표미등록 금액(백만원) is highly overall correlated with 10억미만 건수 and 7 other fieldsHigh correlation
10억미만 금액(백만원) has unique valuesUnique
30억미만 금액(백만원) has unique valuesUnique
10억미만 건수 has 1 (3.7%) zerosZeros
10억미만 금액(백만원) has 1 (3.7%) zerosZeros
30억미만 건수 has 1 (3.7%) zerosZeros
30억미만 금액(백만원) has 1 (3.7%) zerosZeros
70억미만 건수 has 2 (7.4%) zerosZeros
70억미만 금액(백만원) has 2 (7.4%) zerosZeros
100억미만 건수 has 5 (18.5%) zerosZeros
100억미만 금액(백만원) has 5 (18.5%) zerosZeros
200억미만 건수 has 2 (7.4%) zerosZeros
200억미만 금액(백만원) has 2 (7.4%) zerosZeros
200억이상 건수 has 5 (18.5%) zerosZeros
200억이상 금액(백만원) has 5 (18.5%) zerosZeros
재무제표미등록 건수 has 2 (7.4%) zerosZeros
재무제표미등록 금액(백만원) has 2 (7.4%) zerosZeros

Reproduction

Analysis started2024-03-14 12:51:27.668851
Analysis finished2024-03-14 12:52:15.938201
Duration48.27 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
2024-03-14T21:52:16.429055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length6.5185185
Min length2

Characters and Unicode

Total characters176
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 row대환대출
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%
2024-03-14T21:52:17.613271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
8.5%
9
 
5.1%
6
 
3.4%
6
 
3.4%
6
 
3.4%
6
 
3.4%
5
 
2.8%
5
 
2.8%
5
 
2.8%
4
 
2.3%
Other values (66) 109
61.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 160
90.9%
Lowercase Letter 5
 
2.8%
Space Separator 4
 
2.3%
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 (%)
15
 
9.4%
9
 
5.6%
6
 
3.8%
6
 
3.8%
6
 
3.8%
6
 
3.8%
5
 
3.1%
5
 
3.1%
5
 
3.1%
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 (%)
4
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
90.9%
Common 9
 
5.1%
Latin 7
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
 
9.4%
9
 
5.6%
6
 
3.8%
6
 
3.8%
6
 
3.8%
6
 
3.8%
5
 
3.1%
5
 
3.1%
5
 
3.1%
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 (%)
4
44.4%
) 2
22.2%
( 2
22.2%
- 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 160
90.9%
ASCII 16
 
9.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
15
 
9.4%
9
 
5.6%
6
 
3.8%
6
 
3.8%
6
 
3.8%
6
 
3.8%
5
 
3.1%
5
 
3.1%
5
 
3.1%
4
 
2.5%
Other values (56) 93
58.1%
ASCII
ValueCountFrequency (%)
4
25.0%
) 2
12.5%
( 2
12.5%
e 2
12.5%
N 1
 
6.2%
t 1
 
6.2%
- 1
 
6.2%
Z 1
 
6.2%
r 1
 
6.2%
o 1
 
6.2%

10억미만 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean554.51852
Minimum0
Maximum4118
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:17.992569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q139
median164
Q3276.5
95-th percentile3785.4
Maximum4118
Range4118
Interquartile range (IQR)237.5

Descriptive statistics

Standard deviation1188.4245
Coefficient of variation (CV)2.1431646
Kurtosis5.3823574
Mean554.51852
Median Absolute Deviation (MAD)124
Skewness2.6052719
Sum14972
Variance1412352.7
MonotonicityNot monotonic
2024-03-14T21:52:18.369237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
261 2
 
7.4%
4118 1
 
3.7%
51 1
 
3.7%
270 1
 
3.7%
1 1
 
3.7%
40 1
 
3.7%
41 1
 
3.7%
362 1
 
3.7%
2 1
 
3.7%
25 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 1
3.7%
1 1
3.7%
2 1
3.7%
8 1
3.7%
9 1
3.7%
25 1
3.7%
38 1
3.7%
40 1
3.7%
41 1
3.7%
51 1
3.7%
ValueCountFrequency (%)
4118 1
3.7%
3954 1
3.7%
3392 1
3.7%
456 1
3.7%
362 1
3.7%
288 1
3.7%
283 1
3.7%
270 1
3.7%
266 1
3.7%
261 2
7.4%

10억미만 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85016.68
Minimum0
Maximum623144.12
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:18.727840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile103.6
Q16156
median16980
Q348272.5
95-th percentile584787.13
Maximum623144.12
Range623144.12
Interquartile range (IQR)42116.5

Descriptive statistics

Standard deviation183375.02
Coefficient of variation (CV)2.1569299
Kurtosis5.1924217
Mean85016.68
Median Absolute Deviation (MAD)12150
Skewness2.5738091
Sum2295450.4
Variance3.3626397 × 1010
MonotonicityNot monotonic
2024-03-14T21:52:19.121532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
623144.1194 1
 
3.7%
603492.1264 1
 
3.7%
4830.0 1
 
3.7%
33205.0 1
 
3.7%
100.0 1
 
3.7%
6565.0 1
 
3.7%
6665.0 1
 
3.7%
44700.0 1
 
3.7%
112.0 1
 
3.7%
3612.0 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.0 1
3.7%
100.0 1
3.7%
112.0 1
3.7%
3612.0 1
3.7%
4830.0 1
3.7%
5476.0 1
3.7%
5747.0 1
3.7%
6565.0 1
3.7%
6665.0 1
3.7%
7225.0 1
3.7%
ValueCountFrequency (%)
623144.1194 1
3.7%
603492.1264 1
3.7%
541142.1264 1
3.7%
81735.0 1
3.7%
69034.0 1
3.7%
58931.0 1
3.7%
51845.0 1
3.7%
44700.0 1
3.7%
33205.0 1
3.7%
28147.0 1
3.7%

30억미만 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385.40741
Minimum0
Maximum2284
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:19.496859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q140.5
median127
Q3407
95-th percentile1991.8
Maximum2284
Range2284
Interquartile range (IQR)366.5

Descriptive statistics

Standard deviation635.60433
Coefficient of variation (CV)1.6491752
Kurtosis4.4289351
Mean385.40741
Median Absolute Deviation (MAD)120
Skewness2.3377202
Sum10406
Variance403992.87
MonotonicityNot monotonic
2024-03-14T21:52:19.867828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7 2
 
7.4%
2284 1
 
3.7%
314 1
 
3.7%
88 1
 
3.7%
73 1
 
3.7%
120 1
 
3.7%
127 1
 
3.7%
288 1
 
3.7%
1 1
 
3.7%
43 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 1
3.7%
1 1
3.7%
7 2
7.4%
10 1
3.7%
27 1
3.7%
38 1
3.7%
43 1
3.7%
59 1
3.7%
73 1
3.7%
81 1
3.7%
ValueCountFrequency (%)
2284 1
3.7%
2002 1
3.7%
1968 1
3.7%
538 1
3.7%
490 1
3.7%
431 1
3.7%
429 1
3.7%
385 1
3.7%
314 1
3.7%
288 1
3.7%

30억미만 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95462.653
Minimum0
Maximum575727.45
Zeros1
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:20.216507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile371
Q16937
median34460
Q360249.5
95-th percentile523827.45
Maximum575727.45
Range575727.45
Interquartile range (IQR)53312.5

Descriptive statistics

Standard deviation167082.23
Coefficient of variation (CV)1.7502366
Kurtosis4.1342115
Mean95462.653
Median Absolute Deviation (MAD)27585
Skewness2.2998533
Sum2577491.6
Variance2.791647 × 1010
MonotonicityNot monotonic
2024-03-14T21:52:20.602341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
575727.447 1
 
3.7%
525303.4499 1
 
3.7%
10800.0 1
 
3.7%
15165.0 1
 
3.7%
1300.0 1
 
3.7%
36020.0 1
 
3.7%
37320.0 1
 
3.7%
63285.0 1
 
3.7%
200.0 1
 
3.7%
6999.0 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
0.0 1
3.7%
200.0 1
3.7%
770.0 1
3.7%
1300.0 1
3.7%
4150.0 1
3.7%
5274.0 1
3.7%
6875.0 1
3.7%
6999.0 1
3.7%
10800.0 1
3.7%
15165.0 1
3.7%
ValueCountFrequency (%)
575727.447 1
3.7%
525303.4499 1
3.7%
520383.4499 1
3.7%
187858.1 1
3.7%
148124.1 1
3.7%
127097.1 1
3.7%
63285.0 1
3.7%
57214.0 1
3.7%
50423.99707 1
3.7%
50015.0 1
3.7%

70억미만 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.96296
Minimum0
Maximum1255
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:20.979669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q133.5
median146
Q3379.5
95-th percentile946
Maximum1255
Range1255
Interquartile range (IQR)346

Descriptive statistics

Standard deviation326.7273
Coefficient of variation (CV)1.2238675
Kurtosis2.6641853
Mean266.96296
Median Absolute Deviation (MAD)141
Skewness1.7264371
Sum7208
Variance106750.73
MonotonicityNot monotonic
2024-03-14T21:52:21.373880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 2
 
7.4%
1255 1
 
3.7%
163 1
 
3.7%
99 1
 
3.7%
28 1
 
3.7%
14 1
 
3.7%
146 1
 
3.7%
160 1
 
3.7%
287 1
 
3.7%
1 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0 2
7.4%
1 1
3.7%
10 1
3.7%
13 1
3.7%
14 1
3.7%
28 1
3.7%
39 1
3.7%
54 1
3.7%
62 1
3.7%
72 1
3.7%
ValueCountFrequency (%)
1255 1
3.7%
949 1
3.7%
939 1
3.7%
536 1
3.7%
461 1
3.7%
410 1
3.7%
389 1
3.7%
370 1
3.7%
306 1
3.7%
304 1
3.7%

70억미만 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83495.738
Minimum0
Maximum371225.09
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:21.734857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q18648.5
median53194
Q390072.662
95-th percentile294506.76
Maximum371225.09
Range371225.09
Interquartile range (IQR)81424.162

Descriptive statistics

Standard deviation105010.97
Coefficient of variation (CV)1.2576806
Kurtosis1.4234135
Mean83495.738
Median Absolute Deviation (MAD)44730
Skewness1.5492867
Sum2254384.9
Variance1.1027303 × 1010
MonotonicityNot monotonic
2024-03-14T21:52:22.129581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 2
 
7.4%
371225.0865 1
 
3.7%
25860.0 1
 
3.7%
23250.0 1
 
3.7%
16230.0 1
 
3.7%
4400.0 1
 
3.7%
59997.0 1
 
3.7%
64397.0 1
 
3.7%
103877.0 1
 
3.7%
300.0 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0.0 2
7.4%
300.0 1
3.7%
1500.0 1
3.7%
4400.0 1
3.7%
5136.0 1
3.7%
8464.0 1
3.7%
8833.0 1
3.7%
16230.0 1
3.7%
23250.0 1
3.7%
25860.0 1
3.7%
ValueCountFrequency (%)
371225.0865 1
3.7%
294956.7633 1
3.7%
293456.7633 1
3.7%
247341.0 1
3.7%
185683.0 1
3.7%
158203.0 1
3.7%
103877.0 1
3.7%
76268.32319 1
3.7%
74493.0 1
3.7%
65360.0 1
3.7%

100억미만 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.7037
Minimum0
Maximum404
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:22.506498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.5
median72
Q3160.5
95-th percentile268.1
Maximum404
Range404
Interquartile range (IQR)150

Descriptive statistics

Standard deviation105.2838
Coefficient of variation (CV)1.0352012
Kurtosis1.0243536
Mean101.7037
Median Absolute Deviation (MAD)66
Skewness1.1297792
Sum2746
Variance11084.678
MonotonicityNot monotonic
2024-03-14T21:52:22.909303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 5
18.5%
231 2
 
7.4%
404 1
 
3.7%
81 1
 
3.7%
60 1
 
3.7%
6 1
 
3.7%
72 1
 
3.7%
78 1
 
3.7%
138 1
 
3.7%
10 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0 5
18.5%
6 1
 
3.7%
10 1
 
3.7%
11 1
 
3.7%
12 1
 
3.7%
23 1
 
3.7%
56 1
 
3.7%
60 1
 
3.7%
67 1
 
3.7%
72 1
 
3.7%
ValueCountFrequency (%)
404 1
3.7%
284 1
3.7%
231 2
7.4%
217 1
3.7%
194 1
3.7%
173 1
3.7%
148 1
3.7%
138 1
3.7%
130 1
3.7%
120 1
3.7%

100억미만 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41312.03
Minimum0
Maximum178140
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:23.279882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12900.5
median23840
Q359927.5
95-th percentile133290.86
Maximum178140
Range178140
Interquartile range (IQR)57027

Descriptive statistics

Standard deviation49076.665
Coefficient of variation (CV)1.1879509
Kurtosis1.1489894
Mean41312.03
Median Absolute Deviation (MAD)22742
Skewness1.3630838
Sum1115424.8
Variance2.408519 × 109
MonotonicityNot monotonic
2024-03-14T21:52:23.666701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 5
18.5%
99500.94058 2
 
7.4%
144026.9386 1
 
3.7%
19130.0 1
 
3.7%
19750.0 1
 
3.7%
1600.0 1
 
3.7%
32950.0 1
 
3.7%
34550.0 1
 
3.7%
54300.0 1
 
3.7%
4201.0 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0.0 5
18.5%
1098.0 1
 
3.7%
1600.0 1
 
3.7%
4201.0 1
 
3.7%
4345.0 1
 
3.7%
9150.0 1
 
3.7%
12380.0 1
 
3.7%
19130.0 1
 
3.7%
19750.0 1
 
3.7%
23840.0 1
 
3.7%
ValueCountFrequency (%)
178140.0 1
3.7%
144026.9386 1
3.7%
108240.0 1
3.7%
99500.94058 2
7.4%
99090.0 1
3.7%
65555.0 1
3.7%
54300.0 1
3.7%
44525.998 1
3.7%
34550.0 1
3.7%
32950.0 1
3.7%

200억미만 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.07407
Minimum0
Maximum338
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:24.031878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q19
median86
Q3166.5
95-th percentile308.9
Maximum338
Range338
Interquartile range (IQR)157.5

Descriptive statistics

Standard deviation100.07802
Coefficient of variation (CV)0.95245206
Kurtosis0.19689256
Mean105.07407
Median Absolute Deviation (MAD)78
Skewness0.87399139
Sum2837
Variance10015.61
MonotonicityNot monotonic
2024-03-14T21:52:24.411239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
338 2
 
7.4%
8 2
 
7.4%
1 2
 
7.4%
0 2
 
7.4%
105 1
 
3.7%
86 1
 
3.7%
3 1
 
3.7%
84 1
 
3.7%
87 1
 
3.7%
174 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
0 2
7.4%
1 2
7.4%
3 1
3.7%
8 2
7.4%
10 1
3.7%
14 1
3.7%
39 1
3.7%
63 1
3.7%
79 1
3.7%
84 1
3.7%
ValueCountFrequency (%)
338 2
7.4%
241 1
3.7%
203 1
3.7%
202 1
3.7%
174 1
3.7%
168 1
3.7%
165 1
3.7%
151 1
3.7%
135 1
3.7%
134 1
3.7%

200억미만 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47347.136
Minimum0
Maximum216803.07
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:24.985837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q15108.0326
median36571
Q365146.891
95-th percentile123055.15
Maximum216803.07
Range216803.07
Interquartile range (IQR)60038.859

Descriptive statistics

Standard deviation50724.458
Coefficient of variation (CV)1.0713311
Kurtosis3.4712635
Mean47347.136
Median Absolute Deviation (MAD)30748.935
Skewness1.5992772
Sum1278372.7
Variance2.5729707 × 109
MonotonicityNot monotonic
2024-03-14T21:52:25.346362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 2
 
7.4%
130293.7828 1
 
3.7%
44960.0 1
 
3.7%
34760.0 1
 
3.7%
300.0 1
 
3.7%
800.0 1
 
3.7%
56441.0 1
 
3.7%
57241.0 1
 
3.7%
92301.0 1
 
3.7%
4394.0 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
0.0 2
7.4%
200.0 1
3.7%
300.0 1
3.7%
776.0 1
3.7%
800.0 1
3.7%
4394.0 1
3.7%
5822.0652 1
3.7%
10196.0 1
3.7%
13090.0 1
3.7%
17680.0 1
3.7%
ValueCountFrequency (%)
216803.0652 1
3.7%
130293.7828 1
3.7%
106165.0 1
3.7%
94620.0 1
3.7%
92301.0 1
3.7%
88485.0 1
3.7%
66748.81 1
3.7%
63544.97278 1
3.7%
63344.97278 1
3.7%
57241.0 1
3.7%

200억이상 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.518519
Minimum0
Maximum341
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:25.689841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median50
Q396
95-th percentile129.1
Maximum341
Range341
Interquartile range (IQR)85

Descriptive statistics

Standard deviation71.810311
Coefficient of variation (CV)1.1865841
Kurtosis8.2117115
Mean60.518519
Median Absolute Deviation (MAD)44
Skewness2.391862
Sum1634
Variance5156.7208
MonotonicityNot monotonic
2024-03-14T21:52:26.068179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
18.5%
68 2
 
7.4%
11 2
 
7.4%
26 2
 
7.4%
120 1
 
3.7%
16 1
 
3.7%
15 1
 
3.7%
3 1
 
3.7%
50 1
 
3.7%
53 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
0 5
18.5%
3 1
 
3.7%
11 2
 
7.4%
15 1
 
3.7%
16 1
 
3.7%
18 1
 
3.7%
26 2
 
7.4%
50 1
 
3.7%
53 1
 
3.7%
57 1
 
3.7%
ValueCountFrequency (%)
341 1
3.7%
133 1
3.7%
120 1
3.7%
115 1
3.7%
114 1
3.7%
111 1
3.7%
98 1
3.7%
94 1
3.7%
86 1
3.7%
68 2
7.4%

200억이상 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44301.827
Minimum0
Maximum337358.83
Zeros5
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:26.430267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14055
median22460
Q351682
95-th percentile151731.57
Maximum337358.83
Range337358.83
Interquartile range (IQR)47627

Descriptive statistics

Standard deviation70572.931
Coefficient of variation (CV)1.5930027
Kurtosis11.672565
Mean44301.827
Median Absolute Deviation (MAD)22460
Skewness3.1632313
Sum1196149.3
Variance4.9805386 × 109
MonotonicityNot monotonic
2024-03-14T21:52:26.816386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 5
18.5%
16599.0 2
 
7.4%
56059.8784 1
 
3.7%
36329.0 1
 
3.7%
4310.0 1
 
3.7%
1800.0 1
 
3.7%
47727.0 1
 
3.7%
49527.0 1
 
3.7%
53837.0 1
 
3.7%
4875.0 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
0.0 5
18.5%
1800.0 1
 
3.7%
3800.0 1
 
3.7%
4310.0 1
 
3.7%
4875.0 1
 
3.7%
8945.0 1
 
3.7%
10250.0 1
 
3.7%
16599.0 2
 
7.4%
22460.0 1
 
3.7%
27335.0 1
 
3.7%
ValueCountFrequency (%)
337358.8348 1
3.7%
173652.0 1
3.7%
100583.9 1
3.7%
90333.9 1
3.7%
56059.8784 1
3.7%
54177.9348 1
3.7%
53837.0 1
3.7%
49527.0 1
3.7%
47727.0 1
3.7%
40129.0 1
3.7%

재무제표미등록 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.96296
Minimum0
Maximum2789
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:27.170048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14
median16
Q376
95-th percentile2399.1
Maximum2789
Range2789
Interquartile range (IQR)72

Descriptive statistics

Standard deviation787.07924
Coefficient of variation (CV)2.4072428
Kurtosis6.0986539
Mean326.96296
Median Absolute Deviation (MAD)15
Skewness2.6439726
Sum8828
Variance619493.73
MonotonicityNot monotonic
2024-03-14T21:52:27.520447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 3
 
11.1%
4 2
 
7.4%
0 2
 
7.4%
2772 1
 
3.7%
2 1
 
3.7%
104 1
 
3.7%
107 1
 
3.7%
10 1
 
3.7%
15 1
 
3.7%
13 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
0 2
7.4%
1 3
11.1%
2 1
 
3.7%
4 2
7.4%
7 1
 
3.7%
10 1
 
3.7%
11 1
 
3.7%
13 1
 
3.7%
15 1
 
3.7%
16 1
 
3.7%
ValueCountFrequency (%)
2789 1
3.7%
2772 1
3.7%
1529 1
3.7%
1063 1
3.7%
180 1
3.7%
107 1
3.7%
104 1
3.7%
48 1
3.7%
44 1
3.7%
35 1
3.7%

재무제표미등록 금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52677.231
Minimum0
Maximum429522.75
Zeros2
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-14T21:52:27.814733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90
Q1930
median3140
Q320842.5
95-th percentile372293.75
Maximum429522.75
Range429522.75
Interquartile range (IQR)19912.5

Descriptive statistics

Standard deviation121117.31
Coefficient of variation (CV)2.2992345
Kurtosis5.9362002
Mean52677.231
Median Absolute Deviation (MAD)2840
Skewness2.6143612
Sum1422285.2
Variance1.4669402 × 1010
MonotonicityNot monotonic
2024-03-14T21:52:28.084729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
300.0 3
 
11.1%
0.0 2
 
7.4%
429522.7464 1
 
3.7%
10100.0 1
 
3.7%
10700.0 1
 
3.7%
1000.0 1
 
3.7%
845.0 1
 
3.7%
860.0 1
 
3.7%
2705.0 1
 
3.7%
1711.0 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
0.0 2
7.4%
300.0 3
11.1%
845.0 1
 
3.7%
860.0 1
 
3.7%
1000.0 1
 
3.7%
1127.0 1
 
3.7%
1711.0 1
 
3.7%
2300.0 1
 
3.7%
2705.0 1
 
3.7%
2920.0 1
 
3.7%
ValueCountFrequency (%)
429522.7464 1
3.7%
426602.7464 1
3.7%
245572.7464 1
3.7%
162205.0 1
3.7%
35764.0 1
3.7%
27170.0 1
3.7%
22860.0 1
3.7%
18825.0 1
3.7%
10700.0 1
3.7%
10100.0 1
3.7%

Interactions

2024-03-14T21:52:12.953997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:28.326700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.263132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:34.044950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:37.632780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:41.168947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:44.713871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:48.032404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:51.623283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:55.191231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:58.717669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:02.302230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:05.842162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:09.405719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.086212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:28.554246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.402445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:34.277233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:37.878800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:41.458593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:44.954530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:48.272366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:51.869468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:55.433395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:58.947813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:02.546746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:06.083359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:09.636715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.228717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:28.799620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.550232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:34.716348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:38.131879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:41.717303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:45.206768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:48.530520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:52.124625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:55.687956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:59.188395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:02.806284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:06.336027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:09.883186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.365942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:29.025482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.782320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:34.941473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:38.369567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:41.930231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:45.447216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:48.774537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:52.367480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:55.927739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:59.609570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:03.047019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:06.576692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:10.115734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.509192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:29.273487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.933572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:35.187693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:38.621796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:42.179464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:45.701961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:49.033153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:52.624701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:56.181312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:59.851879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:03.302577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:06.831307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:10.365761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.655884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:29.512775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:32.090177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:35.429471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:38.880026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:42.429579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:45.909684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:49.293963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:52.883878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:56.436595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:00.095086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:03.559151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:07.093599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:10.612292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:13.805283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:29.764595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:32.248601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:35.680561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:39.138019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:42.695822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:46.068025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:49.561910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:53.143816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:56.694590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:00.341881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:03.819846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:07.354345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:10.864058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.050955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.018076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:32.415324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:35.933821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:39.399516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:42.957807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:46.238380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:49.825612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:53.411788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:56.957408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:00.597074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:04.084838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:07.626440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:11.121714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.201504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.268209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:32.615776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:36.185761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:39.659104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:43.222686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:46.401753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:50.088661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:53.668136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:57.215748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:00.843591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:04.342479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:07.891085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:11.373077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.352885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.515602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:32.808081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:36.434179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:39.915622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:43.479525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:46.576792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:50.351073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:53.928445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:57.473947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:01.098735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:04.602454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:08.153478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:11.813039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.483371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.683028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:33.047377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:36.663887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:40.158542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:43.718190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:46.821275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:50.599339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:54.168372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:57.717391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:01.328188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:04.840343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:08.393503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:12.043572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.635662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.833617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:33.306744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:36.910474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:40.421534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:43.973089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:47.084050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:50.862600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:54.432618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:57.973750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:01.578335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:05.095772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:08.652613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:12.294909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.788924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:30.991258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:33.563102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:37.162718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:40.679635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:44.233514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:47.344209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:51.126413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:54.694980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:58.232899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:01.827928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:05.354253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:08.911516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:12.551062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:14.926650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:31.131500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:33.807660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:37.395457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:40.924532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:44.474772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:47.784159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:51.378116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:54.946169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:51:58.477762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:02.067082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:05.600565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:09.158844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:52:12.818995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:52:28.276563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분10억미만 건수10억미만 금액(백만원)30억미만 건수30억미만 금액(백만원)70억미만 건수70억미만 금액(백만원)100억미만 건수100억미만 금액(백만원)200억미만 건수200억미만 금액(백만원)200억이상 건수200억이상 금액(백만원)재무제표미등록 건수재무제표미등록 금액(백만원)
구분1.0000.0000.0000.0000.0000.0000.0000.6670.9490.7760.9310.8380.0000.0000.000
10억미만 건수0.0001.0000.9760.6390.5430.6570.8110.6510.6420.0000.3380.0000.0001.0001.000
10억미만 금액(백만원)0.0000.9761.0000.8580.7600.8170.9810.9180.9590.6910.6730.3660.4230.9760.976
30억미만 건수0.0000.6390.8581.0000.9280.9430.9630.9160.8980.8910.8070.7890.7270.6390.639
30억미만 금액(백만원)0.0000.5430.7600.9281.0000.8700.9240.8240.8840.8080.7870.8840.9430.5430.543
70억미만 건수0.0000.6570.8170.9430.8701.0000.9180.9000.8770.8070.9480.7410.7770.6570.657
70억미만 금액(백만원)0.0000.8110.9810.9630.9240.9181.0000.9700.9820.8790.8730.8240.8350.8110.811
100억미만 건수0.6670.6510.9180.9160.8240.9000.9701.0000.9660.8730.8830.7690.7060.6510.651
100억미만 금액(백만원)0.9490.6420.9590.8980.8840.8770.9820.9661.0000.9070.9490.8090.8370.6420.642
200억미만 건수0.7760.0000.6910.8910.8080.8070.8790.8730.9071.0000.8630.6990.6590.0000.000
200억미만 금액(백만원)0.9310.3380.6730.8070.7870.9480.8730.8830.9490.8631.0000.7990.7610.3380.338
200억이상 건수0.8380.0000.3660.7890.8840.7410.8240.7690.8090.6990.7991.0000.9520.0000.000
200억이상 금액(백만원)0.0000.0000.4230.7270.9430.7770.8350.7060.8370.6590.7610.9521.0000.0000.000
재무제표미등록 건수0.0001.0000.9760.6390.5430.6570.8110.6510.6420.0000.3380.0000.0001.0001.000
재무제표미등록 금액(백만원)0.0001.0000.9760.6390.5430.6570.8110.6510.6420.0000.3380.0000.0001.0001.000
2024-03-14T21:52:28.563913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
10억미만 건수10억미만 금액(백만원)30억미만 건수30억미만 금액(백만원)70억미만 건수70억미만 금액(백만원)100억미만 건수100억미만 금액(백만원)200억미만 건수200억미만 금액(백만원)200억이상 건수200억이상 금액(백만원)재무제표미등록 건수재무제표미등록 금액(백만원)
10억미만 건수1.0000.9350.7830.7220.7000.6650.6330.5050.5510.4290.2620.1420.8840.785
10억미만 금액(백만원)0.9351.0000.8260.8180.7670.7740.7290.6550.6500.5930.4560.3480.8660.886
30억미만 건수0.7830.8261.0000.9610.9830.9410.9530.8490.8830.7750.6350.5150.5700.562
30억미만 금액(백만원)0.7220.8180.9611.0000.9740.9930.9580.9320.9020.8840.7000.6390.5280.591
70억미만 건수0.7000.7670.9830.9741.0000.9660.9710.8970.9060.8300.6770.5730.4720.503
70억미만 금액(백만원)0.6650.7740.9410.9930.9661.0000.9600.9470.9080.9040.7110.6650.4560.541
100억미만 건수0.6330.7290.9530.9580.9710.9601.0000.9390.9480.8870.7610.6660.4480.502
100억미만 금액(백만원)0.5050.6550.8490.9320.8970.9470.9391.0000.9010.9720.7850.7760.3430.491
200억미만 건수0.5510.6500.8830.9020.9060.9080.9480.9011.0000.9100.8650.7720.3370.392
200억미만 금액(백만원)0.4290.5930.7750.8840.8300.9040.8870.9720.9101.0000.8630.8750.2700.428
200억이상 건수0.2620.4560.6350.7000.6770.7110.7610.7850.8650.8631.0000.9450.1300.272
200억이상 금액(백만원)0.1420.3480.5150.6390.5730.6650.6660.7760.7720.8750.9451.0000.0060.198
재무제표미등록 건수0.8840.8660.5700.5280.4720.4560.4480.3430.3370.2700.1300.0061.0000.913
재무제표미등록 금액(백만원)0.7850.8860.5620.5910.5030.5410.5020.4910.3920.4280.2720.1980.9131.000

Missing values

2024-03-14T21:52:15.215512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:52:15.763191image/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

구분10억미만 건수10억미만 금액(백만원)30억미만 건수30억미만 금액(백만원)70억미만 건수70억미만 금액(백만원)100억미만 건수100억미만 금액(백만원)200억미만 건수200억미만 금액(백만원)200억이상 건수200억이상 금액(백만원)재무제표미등록 건수재무제표미등록 금액(백만원)
0혁신창업사업화4118623144.11942284575727.4471255371225.0865404144026.9386338130293.782812056059.87842789429522.7464
1창업기반지원3954603492.12642002525303.4499949294956.763323199500.9405813563544.972782616599.02772426602.7464
2일반3392541142.12641968520383.4499939293456.763323199500.9405813463344.972782616599.01063245572.7464
3대환대출875747.0816875.0545136.0121098.08776.000.0161127.0
4청년전용창업(일반)45651845.0274150.0101500.000.01200.000.01529162205.0
5청년전용창업(창성패)10610505.07770.000.000.000.000.018018825.0
6개발기술사업화16419651.99328250423.9970730676268.3231917344525.99820366748.819439460.8784172920.0
7신성장기반자금28381735.0538187858.1536247341.0284178140.0338216803.0652341337358.83484435764.0
8혁신성장지원26669034.0490148124.1461185683.0217108240.0241106165.0133100583.93327170.0
9일반22858931.0431127097.1389158203.019499090.020288485.011590333.92222860.0
구분10억미만 건수10억미만 금액(백만원)30억미만 건수30억미만 금액(백만원)70억미만 건수70억미만 금액(백만원)100억미만 건수100억미만 금액(백만원)200억미만 건수200억미만 금액(백만원)200억이상 건수200억이상 금액(백만원)재무제표미등록 건수재무제표미등록 금액(백만원)
17긴급경영안정자금28828147.042957214.041074493.013028041.016540965.06827335.0152705.0
18일시적경영애로26124423.038550015.037065360.012023840.015136571.05722460.010860.0
19재해중소기업253612.0436999.0398833.0104201.0144394.0114875.04845.0
20포항피해기업지원2112.01200.01300.000.000.000.011000.0
21재도약지원자금36244700.028863285.0287103877.013854300.017492301.06853837.010710700.0
22사업전환416665.012737320.016064397.07834550.08757241.05349527.01300.0
23일반406565.012036020.014659997.07232950.08456441.05047727.01300.0
24무역조정1100.071300.0144400.061600.03800.031800.000.0
25재창업27033205.07315165.02816230.000.01300.000.010410100.0
26구조개선전용514830.08810800.09923250.06019750.08634760.0154310.02300.0