Overview

Dataset statistics

Number of variables19
Number of observations30
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory164.4 B

Variable types

Categorical8
Text4
Numeric7

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://www.bigdata-region.kr/#/dataset/9c3721a9-7bca-46ee-b5bd-c98738bae91a

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
회계년도 is highly overall correlated with 연령대코드 and 1 other fieldsHigh correlation
자기자본금액 is highly overall correlated with 당기순이익금액 and 3 other fieldsHigh correlation
당기순이익금액 is highly overall correlated with 자기자본금액 and 3 other fieldsHigh correlation
당기매출금액 is highly overall correlated with 자기자본금액 and 2 other fieldsHigh correlation
총차입금액 is highly overall correlated with 자기자본금액 and 3 other fieldsHigh correlation
운영자금차입금액 is highly overall correlated with 자기자본금액 and 1 other fieldsHigh correlation
부채비율 is highly overall correlated with 시군구명 and 1 other fieldsHigh correlation
연령대코드 is highly overall correlated with 회계년도 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 당기순이익금액 and 2 other fieldsHigh correlation
부실구분명 is highly overall correlated with 회계년도 and 4 other fieldsHigh correlation
대위변제구분명 is highly overall correlated with 연령대코드 and 1 other fieldsHigh correlation
성별코드 is highly imbalanced (53.1%)Imbalance
대위변제구분명 is highly imbalanced (53.1%)Imbalance
운영자금차입금액 has 1 (3.3%) missing valuesMissing
보증번호 has unique valuesUnique
자기자본금액 has 9 (30.0%) zerosZeros
당기매출금액 has 9 (30.0%) zerosZeros
총차입금액 has 7 (23.3%) zerosZeros
운영자금차입금액 has 7 (23.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:02:09.407592
Analysis finished2023-12-10 14:02:18.002405
Duration8.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-04
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04
2nd row2023-04
3rd row2023-04
4th row2023-04
5th row2023-04

Common Values

ValueCountFrequency (%)
2023-04 30
100.0%

Length

2023-12-10T23:02:18.094210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:18.264559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04 30
100.0%

성별코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
M
27 
F
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 27
90.0%
F 3
 
10.0%

Length

2023-12-10T23:02:18.412044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:18.557866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 27
90.0%
f 3
 
10.0%

연령대코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
60
15 
50
7 
40
5 
70
3 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row70

Common Values

ValueCountFrequency (%)
60 15
50.0%
50 7
23.3%
40 5
 
16.7%
70 3
 
10.0%

Length

2023-12-10T23:02:18.697649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:18.853277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 15
50.0%
50 7
23.3%
40 5
 
16.7%
70 3
 
10.0%

사업자등록번호
Text

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:19.038785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters300
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row12481*****
2nd row12481*****
3rd row12481*****
4th row12481*****
5th row31286*****
ValueCountFrequency (%)
12481 10
33.3%
12413 3
 
10.0%
13209 2
 
6.7%
12386 2
 
6.7%
60524 1
 
3.3%
12086 1
 
3.3%
20631 1
 
3.3%
20686 1
 
3.3%
13281 1
 
3.3%
12708 1
 
3.3%
Other values (7) 7
23.3%
2023-12-10T23:02:19.451904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 150
50.0%
1 46
 
15.3%
2 28
 
9.3%
8 24
 
8.0%
4 16
 
5.3%
3 13
 
4.3%
6 9
 
3.0%
0 8
 
2.7%
9 2
 
0.7%
7 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 150
50.0%
Decimal Number 150
50.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 46
30.7%
2 28
18.7%
8 24
16.0%
4 16
 
10.7%
3 13
 
8.7%
6 9
 
6.0%
0 8
 
5.3%
9 2
 
1.3%
7 2
 
1.3%
5 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
* 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 150
50.0%
1 46
 
15.3%
2 28
 
9.3%
8 24
 
8.0%
4 16
 
5.3%
3 13
 
4.3%
6 9
 
3.0%
0 8
 
2.7%
9 2
 
0.7%
7 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 150
50.0%
1 46
 
15.3%
2 28
 
9.3%
8 24
 
8.0%
4 16
 
5.3%
3 13
 
4.3%
6 9
 
3.0%
0 8
 
2.7%
9 2
 
0.7%
7 2
 
0.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T23:02:19.651948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:20.083325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
평택시
9 
화성시
5 
남양주시
5 
군포시
4 
광명시
1 
Other values (6)
6 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique7 ?
Unique (%)23.3%

Sample

1st row화성시
2nd row화성시
3rd row화성시
4th row화성시
5th row군포시

Common Values

ValueCountFrequency (%)
평택시 9
30.0%
화성시 5
16.7%
남양주시 5
16.7%
군포시 4
13.3%
광명시 1
 
3.3%
김포시 1
 
3.3%
양주시 1
 
3.3%
포천시 1
 
3.3%
오산시 1
 
3.3%
구리시 1
 
3.3%

Length

2023-12-10T23:02:20.247758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
평택시 9
30.0%
화성시 5
16.7%
남양주시 5
16.7%
군포시 4
13.3%
광명시 1
 
3.3%
김포시 1
 
3.3%
양주시 1
 
3.3%
포천시 1
 
3.3%
오산시 1
 
3.3%
구리시 1
 
3.3%

행정동명
Text

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:20.432647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row봉담읍
2nd row봉담읍
3rd row봉담읍
4th row봉담읍
5th row당동
ValueCountFrequency (%)
서탄면 6
20.0%
봉담읍 5
16.7%
산본동 3
10.0%
안중읍 3
10.0%
화도읍 2
 
6.7%
일패동 2
 
6.7%
당동 1
 
3.3%
일직동 1
 
3.3%
양촌읍 1
 
3.3%
광적면 1
 
3.3%
Other values (5) 5
16.7%
2023-12-10T23:02:20.842902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
읍 13
14.6%
동 10
 
11.2%
면 7
 
7.9%
서 6
 
6.7%
탄 6
 
6.7%
봉 5
 
5.6%
담 5
 
5.6%
일 3
 
3.4%
산 3
 
3.4%
본 3
 
3.4%
Other values (21) 28
31.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
읍 13
14.6%
동 10
 
11.2%
면 7
 
7.9%
서 6
 
6.7%
탄 6
 
6.7%
봉 5
 
5.6%
담 5
 
5.6%
일 3
 
3.4%
산 3
 
3.4%
본 3
 
3.4%
Other values (21) 28
31.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
읍 13
14.6%
동 10
 
11.2%
면 7
 
7.9%
서 6
 
6.7%
탄 6
 
6.7%
봉 5
 
5.6%
담 5
 
5.6%
일 3
 
3.4%
산 3
 
3.4%
본 3
 
3.4%
Other values (21) 28
31.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
읍 13
14.6%
동 10
 
11.2%
면 7
 
7.9%
서 6
 
6.7%
탄 6
 
6.7%
봉 5
 
5.6%
담 5
 
5.6%
일 3
 
3.4%
산 3
 
3.4%
본 3
 
3.4%
Other values (21) 28
31.5%

조사종류명
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
정식심사
23 
소액심사
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소액심사
2nd row정식심사
3rd row정식심사
4th row정식심사
5th row정식심사

Common Values

ValueCountFrequency (%)
정식심사 23
76.7%
소액심사 7
 
23.3%

Length

2023-12-10T23:02:21.057326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:21.223082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정식심사 23
76.7%
소액심사 7
 
23.3%

부실구분명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
정상해지
17 
정상
6 
특수채권
5 
구상채권
2 

Length

Max length4
Median length4
Mean length3.6
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상해지 17
56.7%
정상 6
 
20.0%
특수채권 5
 
16.7%
구상채권 2
 
6.7%

Length

2023-12-10T23:02:21.391479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:21.587000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상해지 17
56.7%
정상 6
 
20.0%
특수채권 5
 
16.7%
구상채권 2
 
6.7%

대위변제구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
대위변제해제
27 
대위변제
3 

Length

Max length6
Median length6
Mean length5.8
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대위변제
2nd row대위변제해제
3rd row대위변제해제
4th row대위변제해제
5th row대위변제

Common Values

ValueCountFrequency (%)
대위변제해제 27
90.0%
대위변제 3
 
10.0%

Length

2023-12-10T23:02:21.783083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:21.955214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대위변제해제 27
90.0%
대위변제 3
 
10.0%

회계년도
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.8
Minimum2003
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:22.125203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12004.25
median2012
Q32016
95-th percentile2020
Maximum2021
Range18
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.222318
Coefficient of variation (CV)0.003094449
Kurtosis-1.6145924
Mean2010.8
Median Absolute Deviation (MAD)6
Skewness0.08494849
Sum60324
Variance38.717241
MonotonicityNot monotonic
2023-12-10T23:02:22.400666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2004 5
16.7%
2016 3
10.0%
2017 3
10.0%
2003 3
10.0%
2005 3
10.0%
2014 2
 
6.7%
2020 2
 
6.7%
2013 2
 
6.7%
2008 2
 
6.7%
2021 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
2003 3
10.0%
2004 5
16.7%
2005 3
10.0%
2006 1
 
3.3%
2008 2
 
6.7%
2011 1
 
3.3%
2013 2
 
6.7%
2014 2
 
6.7%
2015 1
 
3.3%
2016 3
10.0%
ValueCountFrequency (%)
2021 1
 
3.3%
2020 2
6.7%
2018 1
 
3.3%
2017 3
10.0%
2016 3
10.0%
2015 1
 
3.3%
2014 2
6.7%
2013 2
6.7%
2011 1
 
3.3%
2008 2
6.7%

자기자본금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.446 × 108
Minimum0
Maximum2.1 × 109
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:22.679716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.05 × 108
Q35.8375 × 108
95-th percentile1.1055 × 109
Maximum2.1 × 109
Range2.1 × 109
Interquartile range (IQR)5.8375 × 108

Descriptive statistics

Standard deviation4.7356947 × 108
Coefficient of variation (CV)1.3742585
Kurtosis6.1979423
Mean3.446 × 108
Median Absolute Deviation (MAD)1.05 × 108
Skewness2.2431804
Sum1.0338 × 1010
Variance2.2426804 × 1017
MonotonicityNot monotonic
2023-12-10T23:02:22.858786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9
30.0%
610000000 4
13.3%
50000000 2
 
6.7%
500000000 1
 
3.3%
102000000 1
 
3.3%
600000000 1
 
3.3%
2100000000 1
 
3.3%
461000000 1
 
3.3%
430000000 1
 
3.3%
1470000000 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0 9
30.0%
48000000 1
 
3.3%
50000000 2
 
6.7%
51000000 1
 
3.3%
60000000 1
 
3.3%
102000000 1
 
3.3%
108000000 1
 
3.3%
248000000 1
 
3.3%
425000000 1
 
3.3%
430000000 1
 
3.3%
ValueCountFrequency (%)
2100000000 1
 
3.3%
1470000000 1
 
3.3%
660000000 1
 
3.3%
610000000 4
13.3%
600000000 1
 
3.3%
535000000 1
 
3.3%
500000000 1
 
3.3%
461000000 1
 
3.3%
430000000 1
 
3.3%
425000000 1
 
3.3%

당기순이익금액
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3616667 × 108
Minimum7000000
Maximum3.83 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:23.038014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7000000
5-th percentile32000000
Q139750000
median90000000
Q31.8175 × 108
95-th percentile1.6191 × 109
Maximum3.83 × 109
Range3.823 × 109
Interquartile range (IQR)1.42 × 108

Descriptive statistics

Standard deviation7.8313125 × 108
Coefficient of variation (CV)2.3295922
Kurtosis14.895711
Mean3.3616667 × 108
Median Absolute Deviation (MAD)53000000
Skewness3.7679949
Sum1.0085 × 1010
Variance6.1329456 × 1017
MonotonicityNot monotonic
2023-12-10T23:02:23.226347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
32000000 4
 
13.3%
37000000 3
 
10.0%
104000000 3
 
10.0%
49000000 2
 
6.7%
48000000 2
 
6.7%
503000000 2
 
6.7%
7000000 1
 
3.3%
82000000 1
 
3.3%
98000000 1
 
3.3%
112000000 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
7000000 1
 
3.3%
32000000 4
13.3%
37000000 3
10.0%
48000000 2
6.7%
49000000 2
6.7%
54000000 1
 
3.3%
68000000 1
 
3.3%
82000000 1
 
3.3%
98000000 1
 
3.3%
104000000 3
10.0%
ValueCountFrequency (%)
3830000000 1
3.3%
2214000000 1
3.3%
892000000 1
3.3%
503000000 2
6.7%
319000000 1
3.3%
212000000 1
3.3%
193000000 1
3.3%
148000000 1
3.3%
112000000 1
3.3%
105000000 1
3.3%

당기매출금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.818 × 109
Minimum0
Maximum4.3693 × 1010
Zeros9
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:23.458502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.4395 × 109
Q34.73925 × 109
95-th percentile2.33535 × 1010
Maximum4.3693 × 1010
Range4.3693 × 1010
Interquartile range (IQR)4.73925 × 109

Descriptive statistics

Standard deviation9.7060179 × 109
Coefficient of variation (CV)2.0145326
Kurtosis10.933875
Mean4.818 × 109
Median Absolute Deviation (MAD)1.4395 × 109
Skewness3.2965147
Sum1.4454 × 1011
Variance9.4206783 × 1019
MonotonicityNot monotonic
2023-12-10T23:02:23.647390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 9
30.0%
4056000000 2
 
6.7%
4967000000 1
 
3.3%
1322000000 1
 
3.3%
1389000000 1
 
3.3%
649000000 1
 
3.3%
2411000000 1
 
3.3%
414000000 1
 
3.3%
10731000000 1
 
3.3%
5830000000 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 9
30.0%
414000000 1
 
3.3%
649000000 1
 
3.3%
932000000 1
 
3.3%
1196000000 1
 
3.3%
1322000000 1
 
3.3%
1389000000 1
 
3.3%
1490000000 1
 
3.3%
1622000000 1
 
3.3%
2411000000 1
 
3.3%
ValueCountFrequency (%)
43693000000 1
3.3%
33681000000 1
3.3%
10731000000 1
3.3%
7942000000 1
3.3%
6980000000 1
3.3%
5957000000 1
3.3%
5830000000 1
3.3%
4967000000 1
3.3%
4056000000 2
6.7%
2747000000 1
3.3%

총차입금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2576333 × 109
Minimum0
Maximum6.701 × 109
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:23.828271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170250000
median3.685 × 108
Q31.294 × 109
95-th percentile5.1813 × 109
Maximum6.701 × 109
Range6.701 × 109
Interquartile range (IQR)1.22375 × 109

Descriptive statistics

Standard deviation1.8366082 × 109
Coefficient of variation (CV)1.4603686
Kurtosis2.5729351
Mean1.2576333 × 109
Median Absolute Deviation (MAD)3.685 × 108
Skewness1.8013721
Sum3.7729 × 1010
Variance3.3731297 × 1018
MonotonicityNot monotonic
2023-12-10T23:02:24.030504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 7
23.3%
3059000000 2
 
6.7%
398000000 1
 
3.3%
174000000 1
 
3.3%
250000000 1
 
3.3%
339000000 1
 
3.3%
1291000000 1
 
3.3%
176000000 1
 
3.3%
131000000 1
 
3.3%
6701000000 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0 7
23.3%
50000000 1
 
3.3%
131000000 1
 
3.3%
168000000 1
 
3.3%
174000000 1
 
3.3%
176000000 1
 
3.3%
200000000 1
 
3.3%
250000000 1
 
3.3%
339000000 1
 
3.3%
398000000 1
 
3.3%
ValueCountFrequency (%)
6701000000 1
3.3%
6093000000 1
3.3%
4067000000 1
3.3%
3865000000 1
3.3%
3059000000 2
6.7%
2394000000 1
3.3%
1295000000 1
3.3%
1291000000 1
3.3%
1199000000 1
3.3%
858000000 1
3.3%

운영자금차입금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)72.4%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean744.72414
Minimum0
Maximum5203
Zeros7
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:24.188639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median250
Q3785
95-th percentile2667.6
Maximum5203
Range5203
Interquartile range (IQR)735

Descriptive statistics

Standard deviation1152.8081
Coefficient of variation (CV)1.5479666
Kurtosis7.5273722
Mean744.72414
Median Absolute Deviation (MAD)250
Skewness2.5635595
Sum21597
Variance1328966.6
MonotonicityNot monotonic
2023-12-10T23:02:24.336842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 7
23.3%
2109 2
 
6.7%
200 2
 
6.7%
174 1
 
3.3%
250 1
 
3.3%
339 1
 
3.3%
1291 1
 
3.3%
176 1
 
3.3%
131 1
 
3.3%
5203 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
0 7
23.3%
50 1
 
3.3%
131 1
 
3.3%
168 1
 
3.3%
174 1
 
3.3%
176 1
 
3.3%
200 2
 
6.7%
250 1
 
3.3%
339 1
 
3.3%
358 1
 
3.3%
ValueCountFrequency (%)
5203 1
3.3%
3040 1
3.3%
2109 2
6.7%
1763 1
3.3%
1291 1
3.3%
849 1
3.3%
785 1
3.3%
707 1
3.3%
642 1
3.3%
608 1
3.3%

부채비율
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.145
Minimum7.15
Maximum731.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:24.479372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.15
5-th percentile23.8
Q172.94
median199.56
Q3262.505
95-th percentile476.64
Maximum731.37
Range724.22
Interquartile range (IQR)189.565

Descriptive statistics

Standard deviation178.18453
Coefficient of variation (CV)0.82437497
Kurtosis0.87653326
Mean216.145
Median Absolute Deviation (MAD)126.62
Skewness1.0354127
Sum6484.35
Variance31749.726
MonotonicityNot monotonic
2023-12-10T23:02:24.627786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
476.64 4
 
13.3%
23.8 3
 
10.0%
250.7 3
 
10.0%
72.94 2
 
6.7%
199.56 2
 
6.7%
239.86 2
 
6.7%
168.75 1
 
3.3%
41.56 1
 
3.3%
58.87 1
 
3.3%
348.42 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
7.15 1
 
3.3%
23.8 3
10.0%
38.71 1
 
3.3%
41.56 1
 
3.3%
58.87 1
 
3.3%
72.94 2
6.7%
75.12 1
 
3.3%
95.0 1
 
3.3%
131.42 1
 
3.3%
164.41 1
 
3.3%
ValueCountFrequency (%)
731.37 1
 
3.3%
476.64 4
13.3%
387.96 1
 
3.3%
348.42 1
 
3.3%
266.44 1
 
3.3%
250.7 3
10.0%
239.86 2
6.7%
214.39 1
 
3.3%
199.56 2
6.7%
168.75 1
 
3.3%

기업번호
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:24.871010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)43.3%

Sample

1st row+/XL9iUFcIVa/jyWgAdFmQ==
2nd row+/XL9iUFcIVa/jyWgAdFmQ==
3rd row+/XL9iUFcIVa/jyWgAdFmQ==
4th row+/XL9iUFcIVa/jyWgAdFmQ==
5th row+/cMgw7hdmH3xh1vh0Ix7A==
ValueCountFrequency (%)
1za20wpbxzyxgmv9jwhxw 6
20.0%
xl9iufciva/jywgadfmq 4
13.3%
23djrrgv8ixoopjvvnpmg 3
 
10.0%
2sdviucto3mocdj8i4beq 2
 
6.7%
0yndvww2pl+snrq7ouhuq 2
 
6.7%
15uzgttkp2so1bvyaoqba 1
 
3.3%
1biokfunwgfmifv24eg2w 1
 
3.3%
2gc9/q4px+fjhkpvsiekg 1
 
3.3%
29wybmlrqt+uvuxoazilw 1
 
3.3%
1ea6unn0h7qdtiflz84ng 1
 
3.3%
Other values (8) 8
26.7%
2023-12-10T23:02:25.338665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
+ 34
 
4.7%
A 22
 
3.1%
2 22
 
3.1%
h 21
 
2.9%
W 20
 
2.8%
X 19
 
2.6%
g 18
 
2.5%
I 18
 
2.5%
d 18
 
2.5%
Other values (55) 468
65.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 261
36.2%
Uppercase Letter 249
34.6%
Decimal Number 101
 
14.0%
Math Symbol 94
 
13.1%
Other Punctuation 15
 
2.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 22
 
8.8%
W 20
 
8.0%
X 19
 
7.6%
I 18
 
7.2%
V 17
 
6.8%
J 16
 
6.4%
O 15
 
6.0%
U 12
 
4.8%
F 12
 
4.8%
Q 12
 
4.8%
Other values (16) 86
34.5%
Lowercase Letter
ValueCountFrequency (%)
h 21
 
8.0%
g 18
 
6.9%
d 18
 
6.9%
z 17
 
6.5%
m 16
 
6.1%
v 15
 
5.7%
x 13
 
5.0%
w 13
 
5.0%
y 13
 
5.0%
b 12
 
4.6%
Other values (16) 105
40.2%
Decimal Number
ValueCountFrequency (%)
2 22
21.8%
0 14
13.9%
1 13
12.9%
9 13
12.9%
3 9
8.9%
8 8
 
7.9%
7 7
 
6.9%
4 7
 
6.9%
6 5
 
5.0%
5 3
 
3.0%
Math Symbol
ValueCountFrequency (%)
= 60
63.8%
+ 34
36.2%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 510
70.8%
Common 210
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 22
 
4.3%
h 21
 
4.1%
W 20
 
3.9%
X 19
 
3.7%
g 18
 
3.5%
I 18
 
3.5%
d 18
 
3.5%
V 17
 
3.3%
z 17
 
3.3%
J 16
 
3.1%
Other values (42) 324
63.5%
Common
ValueCountFrequency (%)
= 60
28.6%
+ 34
16.2%
2 22
 
10.5%
/ 15
 
7.1%
0 14
 
6.7%
1 13
 
6.2%
9 13
 
6.2%
3 9
 
4.3%
8 8
 
3.8%
7 7
 
3.3%
Other values (3) 15
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
+ 34
 
4.7%
A 22
 
3.1%
2 22
 
3.1%
h 21
 
2.9%
W 20
 
2.8%
X 19
 
2.6%
g 18
 
2.5%
I 18
 
2.5%
d 18
 
2.5%
Other values (55) 468
65.0%

보증번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:25.683168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters720
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row4j4tDBaloZG+0o6AEQhKpQ==
2nd rowQctIebSyiWiSkx3n1+PcTA==
3rd rowjq1pECgC0UVHeYLSSF+/1w==
4th rowsi+aH8mHVNVmQG5fF9iR/A==
5th roweHG86NtLavZo7kawyjPaaA==
ValueCountFrequency (%)
4j4tdbalozg+0o6aeqhkpq 1
 
3.3%
qctiebsyiwiskx3n1+pcta 1
 
3.3%
sbltrok2ularq4oqmfcnhq 1
 
3.3%
jkbjjvfnsrsqpxqx3izr2a 1
 
3.3%
nr69/8n/yhbhtfph3jfr2q 1
 
3.3%
oijvgg/qdzqheuz3pt7cja 1
 
3.3%
i5k+2t/fc9dhvxzcookpwg 1
 
3.3%
hejsigsnvdzxjj4w3hb+vq 1
 
3.3%
2e+eh3shw29dnxifmjsnaq 1
 
3.3%
rl7tiuwjlsea31mak7znra 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:02:26.329694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
o 22
 
3.1%
w 18
 
2.5%
A 18
 
2.5%
H 17
 
2.4%
S 17
 
2.4%
+ 17
 
2.4%
Q 17
 
2.4%
g 16
 
2.2%
r 15
 
2.1%
Other values (55) 503
69.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 279
38.8%
Uppercase Letter 253
35.1%
Decimal Number 102
 
14.2%
Math Symbol 77
 
10.7%
Other Punctuation 9
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 22
 
7.9%
w 18
 
6.5%
g 16
 
5.7%
r 15
 
5.4%
p 14
 
5.0%
x 14
 
5.0%
j 14
 
5.0%
s 12
 
4.3%
e 12
 
4.3%
a 11
 
3.9%
Other values (16) 131
47.0%
Uppercase Letter
ValueCountFrequency (%)
A 18
 
7.1%
H 17
 
6.7%
S 17
 
6.7%
Q 17
 
6.7%
N 14
 
5.5%
F 11
 
4.3%
Z 11
 
4.3%
V 11
 
4.3%
T 11
 
4.3%
C 10
 
4.0%
Other values (16) 116
45.8%
Decimal Number
ValueCountFrequency (%)
2 15
14.7%
3 14
13.7%
5 14
13.7%
6 12
11.8%
8 10
9.8%
7 8
7.8%
4 8
7.8%
1 8
7.8%
0 7
6.9%
9 6
 
5.9%
Math Symbol
ValueCountFrequency (%)
= 60
77.9%
+ 17
 
22.1%
Other Punctuation
ValueCountFrequency (%)
/ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532
73.9%
Common 188
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 22
 
4.1%
w 18
 
3.4%
A 18
 
3.4%
H 17
 
3.2%
S 17
 
3.2%
Q 17
 
3.2%
g 16
 
3.0%
r 15
 
2.8%
p 14
 
2.6%
x 14
 
2.6%
Other values (42) 364
68.4%
Common
ValueCountFrequency (%)
= 60
31.9%
+ 17
 
9.0%
2 15
 
8.0%
3 14
 
7.4%
5 14
 
7.4%
6 12
 
6.4%
8 10
 
5.3%
/ 9
 
4.8%
7 8
 
4.3%
4 8
 
4.3%
Other values (3) 21
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
o 22
 
3.1%
w 18
 
2.5%
A 18
 
2.5%
H 17
 
2.4%
S 17
 
2.4%
+ 17
 
2.4%
Q 17
 
2.4%
g 16
 
2.2%
r 15
 
2.1%
Other values (55) 503
69.9%

Interactions

2023-12-10T23:02:16.331275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:10.652281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:11.577632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.817151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.697387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.467329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.385378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.446731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:10.813878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.027045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.970482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.828660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.579416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.537635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.580838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:10.933569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.141150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.096605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.927047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.724668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.670682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.798054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:11.074950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.280954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.226739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.057260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.862017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.817317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.934827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:11.193727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.402667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.324833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.160684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.986074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.950697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:17.085155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:11.329830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.537888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.434688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.279938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.113395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.092679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:17.244905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:11.446202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:12.667122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:13.559077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:14.375212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.241936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:16.215432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:02:26.495873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드연령대코드사업자등록번호시군구명행정동명조사종류명부실구분명대위변제구분명회계년도자기자본금액당기순이익금액당기매출금액총차입금액운영자금차입금액부채비율기업번호보증번호
성별코드1.0000.5711.0000.0000.7780.0000.0000.0000.0000.0000.0000.0000.0000.0000.6281.0001.000
연령대코드0.5711.0001.0000.7430.9160.0000.8600.7930.7280.1890.0000.0000.0000.0000.8511.0001.000
사업자등록번호1.0001.0001.0000.9840.9900.0000.8970.0000.9490.8500.9050.7770.6610.0000.9611.0001.000
시군구명0.0000.7430.9841.0001.0000.0000.7860.0000.7900.3410.9210.5370.5970.0000.8321.0001.000
행정동명0.7780.9160.9901.0001.0000.0000.8560.0000.8850.8110.8550.6820.6900.6060.9611.0001.000
조사종류명0.0000.0000.0000.0000.0001.0000.0000.1660.0000.0000.0000.0000.0560.0000.0000.0001.000
부실구분명0.0000.8600.8970.7860.8560.0001.0000.7560.8750.0000.0000.0000.0000.0000.9051.0001.000
대위변제구분명0.0000.7930.0000.0000.0000.1660.7561.0000.0000.6460.0000.0000.0000.0000.1310.0001.000
회계년도0.0000.7280.9490.7900.8850.0000.8750.0001.0000.3280.6400.3870.4080.1690.7670.9971.000
자기자본금액0.0000.1890.8500.3410.8110.0000.0000.6460.3281.0000.6610.8050.5880.8120.0000.9371.000
당기순이익금액0.0000.0000.9050.9210.8550.0000.0000.0000.6400.6611.0000.9770.8600.6250.0000.9151.000
당기매출금액0.0000.0000.7770.5370.6820.0000.0000.0000.3870.8050.9771.0000.8130.7860.1490.8021.000
총차입금액0.0000.0000.6610.5970.6900.0560.0000.0000.4080.5880.8600.8131.0000.9710.4190.7251.000
운영자금차입금액0.0000.0000.0000.0000.6060.0000.0000.0000.1690.8120.6250.7860.9711.0000.5630.0001.000
부채비율0.6280.8510.9610.8320.9610.0000.9050.1310.7670.0000.0000.1490.4190.5631.0001.0001.000
기업번호1.0001.0001.0001.0001.0000.0001.0000.0000.9970.9370.9150.8020.7250.0001.0001.0001.000
보증번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:02:26.744500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드시군구명부실구분명대위변제구분명조사종류명연령대코드
성별코드1.0000.0000.0000.0000.0000.373
시군구명0.0001.0000.5160.0000.0000.467
부실구분명0.0000.5161.0000.5260.0000.517
대위변제구분명0.0000.0000.5261.0000.1000.561
조사종류명0.0000.0000.0000.1001.0000.000
연령대코드0.3730.4670.5170.5610.0001.000
2023-12-10T23:02:26.922106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년도자기자본금액당기순이익금액당기매출금액총차입금액운영자금차입금액부채비율성별코드연령대코드시군구명조사종류명부실구분명대위변제구분명
회계년도1.0000.1470.4350.2450.078-0.025-0.2590.0000.5380.4210.0000.6110.000
자기자본금액0.1471.0000.6290.8410.7290.723-0.0640.0000.0880.1140.0000.0000.431
당기순이익금액0.4350.6291.0000.7740.5640.287-0.1010.0000.0000.5250.0000.0000.000
당기매출금액0.2450.8410.7741.0000.7170.493-0.0480.0000.0000.2680.0000.0000.000
총차입금액0.0780.7290.5640.7171.0000.842-0.0680.0000.0000.2950.0000.0000.000
운영자금차입금액-0.0250.7230.2870.4930.8421.0000.2050.0000.0000.0000.0000.0000.000
부채비율-0.259-0.064-0.101-0.048-0.0680.2051.0000.4150.4770.5430.0000.5540.000
성별코드0.0000.0000.0000.0000.0000.0000.4151.0000.3730.0000.0000.0000.000
연령대코드0.5380.0880.0000.0000.0000.0000.4770.3731.0000.4670.0000.5170.561
시군구명0.4210.1140.5250.2680.2950.0000.5430.0000.4671.0000.0000.5160.000
조사종류명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.100
부실구분명0.6110.0000.0000.0000.0000.0000.5540.0000.5170.5160.0001.0000.526
대위변제구분명0.0000.4310.0000.0000.0000.0000.0000.0000.5610.0000.1000.5261.000

Missing values

2023-12-10T23:02:17.475622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:02:17.866349image/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

기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명조사종류명부실구분명대위변제구분명회계년도자기자본금액당기순이익금액당기매출금액총차입금액운영자금차입금액부채비율기업번호보증번호
02023-04M6012481*****경기도화성시봉담읍소액심사특수채권대위변제2004032000000000476.64+/XL9iUFcIVa/jyWgAdFmQ==4j4tDBaloZG+0o6AEQhKpQ==
12023-04M6012481*****경기도화성시봉담읍정식심사특수채권대위변제해제20040320000000398000000358476.64+/XL9iUFcIVa/jyWgAdFmQ==QctIebSyiWiSkx3n1+PcTA==
22023-04M6012481*****경기도화성시봉담읍정식심사특수채권대위변제해제200403200000000<NA>476.64+/XL9iUFcIVa/jyWgAdFmQ==jq1pECgC0UVHeYLSSF+/1w==
32023-04M6012481*****경기도화성시봉담읍정식심사특수채권대위변제해제20040320000000200000000200476.64+/XL9iUFcIVa/jyWgAdFmQ==si+aH8mHVNVmQG5fF9iR/A==
42023-04M7031286*****경기도군포시당동정식심사특수채권대위변제2014147000000021200000049670000001199000000849131.42+/cMgw7hdmH3xh1vh0Ix7A==eHG86NtLavZo7kawyjPaaA==
52023-04M4013812*****경기도광명시일직동정식심사정상해지대위변제해제20201080000001050000001490000000535000000445387.96+/doougv6AtOqIaKgIIebg==UPNgZSx52ws85KW3Fgi66w==
62023-04M6013781*****경기도김포시양촌읍정식심사정상해지대위변제해제20145000000008920000006980000000406700000020095.0+/rorhv4KCOOATlt6FGDAA==xfWj8jHTTyYl5CArpfxKxw==
72023-04F5080541*****경기도군포시산본동정식심사정상대위변제해제20211020000001480000005957000000168000000168731.37+07Ioh3ox7mz/5UXpq22sQ==kTtccx95oEDjm3SvBlG85A==
82023-04M5014381*****경기도화성시봉담읍정식심사정상대위변제해제201860000000068000000274700000064200000064275.12+0BO3eJdUfHBAhvhXheOLw==Dtg84u5qHYXH0sbuadS04w==
92023-04M6012281*****경기도양주시광적면정식심사정상해지대위변제해제201621000000003830000000436930000006093000000176338.71+0NzEYyKu9IIvAtzfhHT0w==5of2Ppn0YXYIHrN+TCsF3A==
기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명조사종류명부실구분명대위변제구분명회계년도자기자본금액당기순이익금액당기매출금액총차입금액운영자금차입금액부채비율기업번호보증번호
202023-04M6012481*****경기도평택시서탄면정식심사정상해지대위변제해제2003610000000104000000583000000038650000003040250.7+1zA20WPbXzyXGmV9JWhxw==qIs2ts2+nVwFeG/+VfJtqA==
212023-04M6012481*****경기도평택시서탄면정식심사정상해지대위변제해제20086600000005030000001073100000067010000005203239.86+1zA20WPbXzyXGmV9JWhxw==rL7TiUwJLSEA31Mak7ZnrA==
222023-04M5012413*****경기도평택시안중읍정식심사정상해지대위변제해제2005510000003700000041400000013100000013123.8+23dJRrgv8IxOoPJvvnpmg==2E+Eh3SHW29dNxIFmJSNAQ==
232023-04M5012413*****경기도평택시안중읍소액심사정상해지대위변제해제200503700000000023.8+23dJRrgv8IxOoPJvvnpmg==HejSIGsNvdzxjj4W3Hb+vQ==
242023-04M5012413*****경기도평택시안중읍정식심사정상해지대위변제해제2005037000000017600000017623.8+23dJRrgv8IxOoPJvvnpmg==i5k+2t/FC9dhVxZcooKPwg==
252023-04M6020686*****경기도남양주시진건읍정식심사정상해지대위변제해제201750000000112000000241100000012910000001291348.42+29WYBmlrQt+uVUxOAZilw==oIjVgg/qDzQHeUZ3pT7CJA==
262023-04M5020631*****경기도남양주시화도읍정식심사정상대위변제해제20202480000009800000064900000033900000033958.87+2GC9/q4pX+FjhKpvsIeKg==nR69/8N/YHBHtfph3JFR2Q==
272023-04F4013209*****경기도남양주시일패동정식심사정상해지대위변제해제201642500000049000000138900000025000000025072.94+2SdVIuCTO3MOcdJ8i4beQ==jkbjjvfNSrSQPXqX3IzR2A==
282023-04F4013209*****경기도남양주시일패동소액심사정상해지대위변제해제201604900000000072.94+2SdVIuCTO3MOcdJ8i4beQ==sBltroK2Ularq4oQMfCnHQ==
292023-04M5012886*****경기도부천시원미동정식심사정상대위변제해제20176000000082000000132200000017400000017441.56+2aNhPkaG2udsDhEKCkH8A==3ANLgyywRsm2EgE1ytm7Tw==