Overview

Dataset statistics

Number of variables13
Number of observations306
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory114.4 B

Variable types

Categorical3
Numeric10

Dataset

Description기술보증기금이 보유한 지역별, 대출종류별(법인, 개인사업자) 대출 보증 건수 및 잔액, 사고건수 및 사고액, 사고율 현황
Author기술보증기금
URLhttps://www.data.go.kr/data/15048923/fileData.do

Alerts

법인보증건수 is highly overall correlated with 법인보증잔액(억원) and 9 other fieldsHigh correlation
법인보증잔액(억원) is highly overall correlated with 법인보증건수 and 9 other fieldsHigh correlation
개인보증건수 is highly overall correlated with 법인보증건수 and 9 other fieldsHigh correlation
개인보증잔액(억원) is highly overall correlated with 법인보증건수 and 9 other fieldsHigh correlation
법인사고건수 is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
법인사고금액(억원) is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
법인사고율 is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
개인사고건수 is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
개인사고금액(억원) is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
개인사고율 is highly overall correlated with 법인보증건수 and 8 other fieldsHigh correlation
보증종류 is highly overall correlated with 법인보증건수 and 3 other fieldsHigh correlation
법인보증건수 has 8 (2.6%) zerosZeros
법인보증잔액(억원) has 8 (2.6%) zerosZeros
개인보증건수 has 30 (9.8%) zerosZeros
개인보증잔액(억원) has 44 (14.4%) zerosZeros
법인사고건수 has 104 (34.0%) zerosZeros
법인사고금액(억원) has 103 (33.7%) zerosZeros
법인사고율 has 103 (33.7%) zerosZeros
개인사고건수 has 141 (46.1%) zerosZeros
개인사고금액(억원) has 146 (47.7%) zerosZeros
개인사고율 has 146 (47.7%) zerosZeros

Reproduction

Analysis started2023-12-12 09:04:24.016516
Analysis finished2023-12-12 09:04:38.088412
Duration14.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도구분
Categorical

Distinct17
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2015년1분기
 
18
2015년2분기
 
18
2015년3분기
 
18
2015년4분기
 
18
2016년1분기
 
18
Other values (12)
216 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015년1분기
2nd row2015년1분기
3rd row2015년1분기
4th row2015년1분기
5th row2015년1분기

Common Values

ValueCountFrequency (%)
2015년1분기 18
 
5.9%
2015년2분기 18
 
5.9%
2015년3분기 18
 
5.9%
2015년4분기 18
 
5.9%
2016년1분기 18
 
5.9%
2016년2분기 18
 
5.9%
2016년3분기 18
 
5.9%
2016년4분기 18
 
5.9%
2017년1분기 18
 
5.9%
2017년2분기 18
 
5.9%
Other values (7) 126
41.2%

Length

2023-12-12T18:04:38.180903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015년1분기 18
 
5.9%
2017년2분기 18
 
5.9%
2018년4분기 18
 
5.9%
2018년3분기 18
 
5.9%
2018년2분기 18
 
5.9%
2018년1분기 18
 
5.9%
2017년4분기 18
 
5.9%
2017년3분기 18
 
5.9%
2017년1분기 18
 
5.9%
2015년2분기 18
 
5.9%
Other values (7) 126
41.2%

지역구분
Categorical

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
수도권
51 
영남권
51 
충청권
51 
호남권
51 
강원도
51 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수도권
2nd row수도권
3rd row수도권
4th row영남권
5th row영남권

Common Values

ValueCountFrequency (%)
수도권 51
16.7%
영남권 51
16.7%
충청권 51
16.7%
호남권 51
16.7%
강원도 51
16.7%
제주도 51
16.7%

Length

2023-12-12T18:04:38.303800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:04:38.415928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권 51
16.7%
영남권 51
16.7%
충청권 51
16.7%
호남권 51
16.7%
강원도 51
16.7%
제주도 51
16.7%

보증종류
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
대출보증
102 
비은행대출보증
102 
기타
102 

Length

Max length7
Median length4
Mean length4.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대출보증
2nd row비은행대출보증
3rd row기타
4th row대출보증
5th row비은행대출보증

Common Values

ValueCountFrequency (%)
대출보증 102
33.3%
비은행대출보증 102
33.3%
기타 102
33.3%

Length

2023-12-12T18:04:38.537972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:04:38.652019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대출보증 102
33.3%
비은행대출보증 102
33.3%
기타 102
33.3%

법인보증건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct239
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3896.7778
Minimum0
Maximum41864
Zeros8
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T18:04:38.762243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q165.25
median345.5
Q31359.5
95-th percentile34663.25
Maximum41864
Range41864
Interquartile range (IQR)1294.25

Descriptive statistics

Standard deviation9072.2685
Coefficient of variation (CV)2.3281462
Kurtosis8.7166644
Mean3896.7778
Median Absolute Deviation (MAD)329
Skewness3.063023
Sum1192414
Variance82306056
MonotonicityNot monotonic
2023-12-12T18:04:38.882741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 10
 
3.3%
0 8
 
2.6%
2 6
 
2.0%
31 5
 
1.6%
21 4
 
1.3%
48 3
 
1.0%
22 3
 
1.0%
855 3
 
1.0%
113 3
 
1.0%
1 3
 
1.0%
Other values (229) 258
84.3%
ValueCountFrequency (%)
0 8
2.6%
1 3
 
1.0%
2 6
2.0%
7 1
 
0.3%
9 3
 
1.0%
10 10
3.3%
11 1
 
0.3%
12 2
 
0.7%
21 4
 
1.3%
22 3
 
1.0%
ValueCountFrequency (%)
41864 1
0.3%
41428 1
0.3%
40646 1
0.3%
40294 1
0.3%
39460 1
0.3%
39254 1
0.3%
39022 1
0.3%
38775 1
0.3%
37423 1
0.3%
37207 1
0.3%

법인보증잔액(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9219.8595
Minimum0
Maximum93411
Zeros8
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T18:04:39.024953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q1365.5
median769.5
Q32921.25
95-th percentile81361
Maximum93411
Range93411
Interquartile range (IQR)2555.75

Descriptive statistics

Standard deviation21141.462
Coefficient of variation (CV)2.2930352
Kurtosis7.9659947
Mean9219.8595
Median Absolute Deviation (MAD)747
Skewness2.9490902
Sum2821277
Variance4.4696143 × 108
MonotonicityNot monotonic
2023-12-12T18:04:39.182209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
2.6%
12 7
 
2.3%
8 5
 
1.6%
44 3
 
1.0%
2 3
 
1.0%
25 3
 
1.0%
446 2
 
0.7%
234 2
 
0.7%
22 2
 
0.7%
9 2
 
0.7%
Other values (252) 269
87.9%
ValueCountFrequency (%)
0 8
2.6%
2 3
 
1.0%
4 1
 
0.3%
6 1
 
0.3%
7 2
 
0.7%
8 5
1.6%
9 2
 
0.7%
11 2
 
0.7%
12 7
2.3%
13 1
 
0.3%
ValueCountFrequency (%)
93411 1
0.3%
93257 1
0.3%
91962 1
0.3%
91867 1
0.3%
90665 1
0.3%
90361 1
0.3%
90344 1
0.3%
90177 1
0.3%
86462 1
0.3%
86407 1
0.3%

개인보증건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201.7288
Minimum0
Maximum17522
Zeros30
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T18:04:39.358139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median133.5
Q3716.5
95-th percentile15808.25
Maximum17522
Range17522
Interquartile range (IQR)710.25

Descriptive statistics

Standard deviation4909.4035
Coefficient of variation (CV)2.2297949
Kurtosis3.9145638
Mean2201.7288
Median Absolute Deviation (MAD)133.5
Skewness2.3743948
Sum673729
Variance24102243
MonotonicityNot monotonic
2023-12-12T18:04:39.573935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33
 
10.8%
0 30
 
9.8%
8 12
 
3.9%
9 9
 
2.9%
7 8
 
2.6%
2 5
 
1.6%
6 5
 
1.6%
11 5
 
1.6%
4 4
 
1.3%
116 4
 
1.3%
Other values (168) 191
62.4%
ValueCountFrequency (%)
0 30
9.8%
1 33
10.8%
2 5
 
1.6%
4 4
 
1.3%
6 5
 
1.6%
7 8
 
2.6%
8 12
 
3.9%
9 9
 
2.9%
10 1
 
0.3%
11 5
 
1.6%
ValueCountFrequency (%)
17522 1
0.3%
17374 1
0.3%
17291 1
0.3%
17098 1
0.3%
17007 1
0.3%
16813 1
0.3%
16809 1
0.3%
16615 1
0.3%
16411 1
0.3%
16160 1
0.3%

개인보증잔액(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct179
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.8922
Minimum0
Maximum18891
Zeros44
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2023-12-12T18:04:39.739157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median118.5
Q3792
95-th percentile16811.75
Maximum18891
Range18891
Interquartile range (IQR)785

Descriptive statistics

Standard deviation5219.7868
Coefficient of variation (CV)2.2656385
Kurtosis4.0415765
Mean2303.8922
Median Absolute Deviation (MAD)118.5
Skewness2.4001639
Sum704991
Variance27246174
MonotonicityNot monotonic
2023-12-12T18:04:39.922429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
 
14.4%
1 17
 
5.6%
7 10
 
3.3%
16 10
 
3.3%
9 8
 
2.6%
8 7
 
2.3%
4 5
 
1.6%
5 5
 
1.6%
77 3
 
1.0%
19 3
 
1.0%
Other values (169) 194
63.4%
ValueCountFrequency (%)
0 44
14.4%
1 17
 
5.6%
2 2
 
0.7%
4 5
 
1.6%
5 5
 
1.6%
6 3
 
1.0%
7 10
 
3.3%
8 7
 
2.3%
9 8
 
2.6%
11 1
 
0.3%
ValueCountFrequency (%)
18891 1
0.3%
18747 1
0.3%
18706 1
0.3%
18464 1
0.3%
18450 1
0.3%
18372 1
0.3%
18294 1
0.3%
18232 1
0.3%
17666 1
0.3%
17482 1
0.3%

법인사고건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.068627
Minimum-3
Maximum559
Zeros104
Zeros (%)34.0%
Negative7
Negative (%)2.3%
Memory size2.8 KiB
2023-12-12T18:04:40.082429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile0
Q10
median3
Q316
95-th percentile343.75
Maximum559
Range562
Interquartile range (IQR)16

Descriptive statistics

Standard deviation112.04442
Coefficient of variation (CV)2.3309262
Kurtosis8.2826302
Mean48.068627
Median Absolute Deviation (MAD)3
Skewness2.9739119
Sum14709
Variance12553.953
MonotonicityNot monotonic
2023-12-12T18:04:40.256099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 104
34.0%
1 23
 
7.5%
3 15
 
4.9%
2 12
 
3.9%
11 10
 
3.3%
4 10
 
3.3%
7 7
 
2.3%
9 7
 
2.3%
6 6
 
2.0%
16 6
 
2.0%
Other values (75) 106
34.6%
ValueCountFrequency (%)
-3 2
 
0.7%
-1 5
 
1.6%
0 104
34.0%
1 23
 
7.5%
2 12
 
3.9%
3 15
 
4.9%
4 10
 
3.3%
5 3
 
1.0%
6 6
 
2.0%
7 7
 
2.3%
ValueCountFrequency (%)
559 1
0.3%
528 1
0.3%
525 1
0.3%
510 1
0.3%
489 1
0.3%
481 1
0.3%
463 1
0.3%
459 1
0.3%
451 1
0.3%
441 1
0.3%

법인사고금액(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct115
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.96078
Minimum-19
Maximum1349
Zeros103
Zeros (%)33.7%
Negative9
Negative (%)2.9%
Memory size2.8 KiB
2023-12-12T18:04:40.408007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19
5-th percentile0
Q10
median5
Q349.75
95-th percentile777.25
Maximum1349
Range1368
Interquartile range (IQR)49.75

Descriptive statistics

Standard deviation246.72227
Coefficient of variation (CV)2.3506139
Kurtosis8.2136677
Mean104.96078
Median Absolute Deviation (MAD)5
Skewness2.9475535
Sum32118
Variance60871.88
MonotonicityNot monotonic
2023-12-12T18:04:40.593751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 103
33.7%
2 13
 
4.2%
3 9
 
2.9%
1 9
 
2.9%
5 8
 
2.6%
4 6
 
2.0%
24 5
 
1.6%
18 4
 
1.3%
16 4
 
1.3%
12 4
 
1.3%
Other values (105) 141
46.1%
ValueCountFrequency (%)
-19 1
 
0.3%
-18 1
 
0.3%
-15 1
 
0.3%
-4 1
 
0.3%
-3 2
 
0.7%
-2 2
 
0.7%
-1 1
 
0.3%
0 103
33.7%
1 9
 
2.9%
2 13
 
4.2%
ValueCountFrequency (%)
1349 1
0.3%
1243 1
0.3%
1078 1
0.3%
1041 1
0.3%
1034 1
0.3%
1014 1
0.3%
1007 1
0.3%
1002 1
0.3%
978 1
0.3%
961 1
0.3%

법인사고율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67189542
Minimum-33.3
Maximum27.3
Zeros103
Zeros (%)33.7%
Negative9
Negative (%)2.9%
Memory size2.8 KiB
2023-12-12T18:04:40.776227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-33.3
5-th percentile0
Q10
median0.6
Q31.2
95-th percentile2.2
Maximum27.3
Range60.6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation3.0790984
Coefficient of variation (CV)4.5827048
Kurtosis78.315533
Mean0.67189542
Median Absolute Deviation (MAD)0.6
Skewness-3.5295629
Sum205.6
Variance9.4808469
MonotonicityNot monotonic
2023-12-12T18:04:40.925875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 103
33.7%
1.2 22
 
7.2%
1.1 16
 
5.2%
0.4 15
 
4.9%
1.0 12
 
3.9%
0.8 11
 
3.6%
1.4 11
 
3.6%
0.9 10
 
3.3%
0.7 10
 
3.3%
1.6 8
 
2.6%
Other values (33) 88
28.8%
ValueCountFrequency (%)
-33.3 1
 
0.3%
-23.1 1
 
0.3%
-5.3 1
 
0.3%
-2.0 1
 
0.3%
-1.1 1
 
0.3%
-0.9 1
 
0.3%
-0.4 2
 
0.7%
-0.2 1
 
0.3%
0.0 103
33.7%
0.1 1
 
0.3%
ValueCountFrequency (%)
27.3 1
0.3%
13.6 1
0.3%
7.7 1
0.3%
5.3 1
0.3%
3.8 1
0.3%
3.6 2
0.7%
3.1 1
0.3%
2.9 1
0.3%
2.7 1
0.3%
2.6 1
0.3%

개인사고건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.388889
Minimum-1
Maximum304
Zeros141
Zeros (%)46.1%
Negative2
Negative (%)0.7%
Memory size2.8 KiB
2023-12-12T18:04:41.112271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median1
Q312
95-th percentile187.25
Maximum304
Range305
Interquartile range (IQR)12

Descriptive statistics

Standard deviation59.279021
Coefficient of variation (CV)2.3348411
Kurtosis5.8611298
Mean25.388889
Median Absolute Deviation (MAD)1
Skewness2.6519562
Sum7769
Variance3514.0024
MonotonicityNot monotonic
2023-12-12T18:04:41.617423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
46.1%
1 26
 
8.5%
2 11
 
3.6%
4 9
 
2.9%
5 7
 
2.3%
12 7
 
2.3%
7 7
 
2.3%
8 6
 
2.0%
6 6
 
2.0%
9 5
 
1.6%
Other values (60) 81
26.5%
ValueCountFrequency (%)
-1 2
 
0.7%
0 141
46.1%
1 26
 
8.5%
2 11
 
3.6%
3 4
 
1.3%
4 9
 
2.9%
5 7
 
2.3%
6 6
 
2.0%
7 7
 
2.3%
8 6
 
2.0%
ValueCountFrequency (%)
304 1
0.3%
254 1
0.3%
240 1
0.3%
229 1
0.3%
225 1
0.3%
224 1
0.3%
220 1
0.3%
219 1
0.3%
216 2
0.7%
207 1
0.3%

개인사고금액(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct68
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.77451
Minimum-4
Maximum330
Zeros146
Zeros (%)47.7%
Negative5
Negative (%)1.6%
Memory size2.8 KiB
2023-12-12T18:04:41.823664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q10
median1
Q310.75
95-th percentile176.5
Maximum330
Range334
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation59.288996
Coefficient of variation (CV)2.3931451
Kurtosis7.8518475
Mean24.77451
Median Absolute Deviation (MAD)1
Skewness2.8770274
Sum7581
Variance3515.1851
MonotonicityNot monotonic
2023-12-12T18:04:42.040205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146
47.7%
1 21
 
6.9%
2 13
 
4.2%
5 8
 
2.6%
6 7
 
2.3%
7 6
 
2.0%
24 5
 
1.6%
8 5
 
1.6%
4 5
 
1.6%
9 5
 
1.6%
Other values (58) 85
27.8%
ValueCountFrequency (%)
-4 1
 
0.3%
-1 4
 
1.3%
0 146
47.7%
1 21
 
6.9%
2 13
 
4.2%
3 5
 
1.6%
4 5
 
1.6%
5 8
 
2.6%
6 7
 
2.3%
7 6
 
2.0%
ValueCountFrequency (%)
330 2
0.7%
257 1
0.3%
243 1
0.3%
237 1
0.3%
234 1
0.3%
226 1
0.3%
213 1
0.3%
209 1
0.3%
207 1
0.3%
200 1
0.3%

개인사고율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75686275
Minimum-4.2
Maximum25
Zeros146
Zeros (%)47.7%
Negative5
Negative (%)1.6%
Memory size2.8 KiB
2023-12-12T18:04:42.266297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.2
5-th percentile0
Q10
median0.2
Q31.1
95-th percentile2.4
Maximum25
Range29.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation1.8665393
Coefficient of variation (CV)2.466153
Kurtosis102.17187
Mean0.75686275
Median Absolute Deviation (MAD)0.2
Skewness8.6638851
Sum231.6
Variance3.4839691
MonotonicityNot monotonic
2023-12-12T18:04:42.459597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 146
47.7%
1.0 14
 
4.6%
1.1 14
 
4.6%
0.9 13
 
4.2%
1.2 11
 
3.6%
0.8 11
 
3.6%
0.4 10
 
3.3%
1.3 8
 
2.6%
0.7 8
 
2.6%
1.4 7
 
2.3%
Other values (31) 64
20.9%
ValueCountFrequency (%)
-4.2 1
 
0.3%
-1.1 1
 
0.3%
-0.7 1
 
0.3%
-0.4 1
 
0.3%
-0.1 1
 
0.3%
0.0 146
47.7%
0.1 1
 
0.3%
0.2 3
 
1.0%
0.3 4
 
1.3%
0.4 10
 
3.3%
ValueCountFrequency (%)
25.0 1
 
0.3%
14.3 1
 
0.3%
5.9 1
 
0.3%
5.4 1
 
0.3%
5.2 1
 
0.3%
4.9 1
 
0.3%
4.1 1
 
0.3%
2.9 1
 
0.3%
2.8 1
 
0.3%
2.6 5
1.6%

Interactions

2023-12-12T18:04:36.439549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:24.865789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.995870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.251132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:28.780356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.141894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.387569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.643623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.067904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.034170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:36.566847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:24.987145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.101242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.397952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:28.912871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.258619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.498022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.762869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.155829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.134434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:36.688519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.082919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.193436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.507671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.033339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.371670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.614995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.889048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.244290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.237223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:36.791025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.201923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.329134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.611681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.158880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.503194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.766000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.058850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.342601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.338280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:36.909900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.310244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.463598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.727622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.268917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.672895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.883397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.245429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.450513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.441115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:37.033197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.404146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.601442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.838504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.383129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.788835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.012067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.418386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.531545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.565995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:37.170030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.545044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.726837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.971766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.523720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.940234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.143368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.547323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.634752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.684242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:37.358858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.664953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.838873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:28.379570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.657178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.067993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.273996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.687903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.754719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.782010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:37.475163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.764970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:26.957726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:28.491961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:29.843502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.170301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.391921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.824521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.843292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:35.881101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:37.618822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:25.878715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:27.105101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:28.627094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:30.002439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:31.267666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:32.514448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:33.957567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:34.932631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:04:36.017374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:04:42.588471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분지역구분보증종류법인보증건수법인보증잔액(억원)개인보증건수개인보증잔액(억원)법인사고건수법인사고금액(억원)법인사고율개인사고건수개인사고금액(억원)개인사고율
연도구분1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0590.0000.0000.000
지역구분0.0001.0000.0000.5740.5790.5710.5330.5680.5390.0000.4880.4960.190
보증종류0.0000.0001.0000.5780.6240.5250.5780.6490.5980.1390.5700.6740.465
법인보증건수0.0000.5740.5781.0000.9380.8890.9850.9890.9750.0000.9550.8430.000
법인보증잔액(억원)0.0000.5790.6240.9381.0000.9290.9180.8900.8590.0000.8310.7960.000
개인보증건수0.0000.5710.5250.8890.9291.0000.9010.9200.8850.0000.9010.8590.000
개인보증잔액(억원)0.0000.5330.5780.9850.9180.9011.0000.9760.9660.0000.9520.8480.000
법인사고건수0.0000.5680.6490.9890.8900.9200.9761.0000.9800.0000.9250.8170.000
법인사고금액(억원)0.0000.5390.5980.9750.8590.8850.9660.9801.0000.0000.9100.7930.000
법인사고율0.0590.0000.1390.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
개인사고건수0.0000.4880.5700.9550.8310.9010.9520.9250.9100.0001.0000.9210.000
개인사고금액(억원)0.0000.4960.6740.8430.7960.8590.8480.8170.7930.0000.9211.0000.000
개인사고율0.0000.1900.4650.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T18:04:42.756712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분보증종류지역구분
연도구분1.0000.0000.000
보증종류0.0001.0000.000
지역구분0.0000.0001.000
2023-12-12T18:04:42.888504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인보증건수법인보증잔액(억원)개인보증건수개인보증잔액(억원)법인사고건수법인사고금액(억원)법인사고율개인사고건수개인사고금액(억원)개인사고율연도구분지역구분보증종류
법인보증건수1.0000.9810.9690.9660.9280.8980.6420.9040.8730.6870.0000.4340.524
법인보증잔액(억원)0.9811.0000.9430.9430.9090.8890.6260.8810.8550.6460.0000.3920.517
개인보증건수0.9690.9431.0000.9950.9310.9020.6580.9110.8820.6990.0000.4030.527
개인보증잔액(억원)0.9660.9430.9951.0000.9310.9030.6630.9090.8810.6940.0000.3960.524
법인사고건수0.9280.9090.9310.9311.0000.9530.7510.8970.8650.6640.0000.3410.492
법인사고금액(억원)0.8980.8890.9020.9030.9531.0000.8410.8670.8360.6260.0000.3180.437
법인사고율0.6420.6260.6580.6630.7510.8411.0000.6050.5680.5120.0230.0000.092
개인사고건수0.9040.8810.9110.9090.8970.8670.6051.0000.9670.8220.0000.2820.409
개인사고금액(억원)0.8730.8550.8820.8810.8650.8360.5680.9671.0000.8720.0000.2710.381
개인사고율0.6870.6460.6990.6940.6640.6260.5120.8220.8721.0000.0000.0700.216
연도구분0.0000.0000.0000.0000.0000.0000.0230.0000.0000.0001.0000.0000.000
지역구분0.4340.3920.4030.3960.3410.3180.0000.2820.2710.0700.0001.0000.000
보증종류0.5240.5170.5270.5240.4920.4370.0920.4090.3810.2160.0000.0001.000

Missing values

2023-12-12T18:04:37.801240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:04:38.017127image/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

연도구분지역구분보증종류법인보증건수법인보증잔액(억원)개인보증건수개인보증잔액(억원)법인사고건수법인사고금액(억원)법인사고율개인사고건수개인사고금액(억원)개인사고율
02015년1분기수도권대출보증343648159514274146304599781.21671771.2
12015년1분기수도권비은행대출보증82617204415099201.2681.6
22015년1분기수도권기타170654891142.1000.0
32015년1분기영남권대출보증132143651914615157611443230.91191310.8
42015년1분기영남권비은행대출보증835209666070011241.1310.1
52015년1분기영남권기타1115101319000.0000.0
62015년1분기충청권대출보증64941695228752991872121.319190.6
72015년1분기충청권비은행대출보증4581065296250230.3000.0
82015년1분기충청권기타7645025000.0000.0
92015년1분기호남권대출보증4670102372076189154750.712100.5
연도구분지역구분보증종류법인보증건수법인보증잔액(억원)개인보증건수개인보증잔액(억원)법인사고건수법인사고금액(억원)법인사고율개인사고건수개인사고금액(억원)개인사고율
2962019년1분기충청권기타5133800-1-18-5.3000.0
2972019년1분기호남권대출보증58101193324712268691521.327190.8
2982019년1분기호남권비은행대출보증5361019194161570.7221.2
2992019년1분기호남권기타3123010000.0000.0
3002019년1분기강원도대출보증1602336881781029611.81181.0
3012019년1분기강원도비은행대출보증224479114106791.9000.0
3022019년1분기강원도기타7411000.0000.0
3032019년1분기제주도대출보증2875618490320.4100.0
3042019년1분기제주도비은행대출보증38501811000.0000.0
3052019년1분기제주도기타0000000.0000.0