Overview

Dataset statistics

Number of variables18
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory154.4 B

Variable types

Categorical7
Text4
Numeric5
DateTime2

Dataset

Description샘플 데이터
Author경기신용보증재단
URLhttps://bigdata-region.kr/#/dataset/7165cd5a-0ad1-46a1-b725-f75c73536e10

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
최초적용금리 is highly overall correlated with 최종적용금리 and 1 other fieldsHigh correlation
최종적용금리 is highly overall correlated with 최초적용금리High correlation
혜택금리 is highly overall correlated with 최초적용금리High correlation
업체형태명 is highly overall correlated with 은행명 and 1 other fieldsHigh correlation
은행명 is highly overall correlated with 업체형태명 and 1 other fieldsHigh correlation
사업기간내용 is highly overall correlated with 업체형태명 and 1 other fieldsHigh correlation
업체형태명 is highly imbalanced (78.9%)Imbalance

Reproduction

Analysis started2023-12-10 14:02:04.908822
Analysis finished2023-12-10 14:02:09.939427
Duration5.03 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

2023-12-10T23:02:10.090025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:10.226381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04 30
100.0%

성별코드
Categorical

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
F
15 
M
15 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 15
50.0%
M 15
50.0%

Length

2023-12-10T23:02:10.372039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:10.514649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 15
50.0%
m 15
50.0%

연령대코드
Categorical

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
60
11 
50
10 
40
5 
30
2 
70
2 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row50
3rd row40
4th row60
5th row30

Common Values

ValueCountFrequency (%)
60 11
36.7%
50 10
33.3%
40 5
16.7%
30 2
 
6.7%
70 2
 
6.7%

Length

2023-12-10T23:02:10.682454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:10.875793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 11
36.7%
50 10
33.3%
40 5
16.7%
30 2
 
6.7%
70 2
 
6.7%

사업자등록번호
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:11.123676image/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

Unique28 ?
Unique (%)93.3%

Sample

1st row13433*****
2nd row72228*****
3rd row12746*****
4th row81771*****
5th row50350*****
ValueCountFrequency (%)
81771 2
 
6.7%
13433 1
 
3.3%
14105 1
 
3.3%
12792 1
 
3.3%
12691 1
 
3.3%
13530 1
 
3.3%
23101 1
 
3.3%
89218 1
 
3.3%
13410 1
 
3.3%
13601 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:02:11.534785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 150
50.0%
1 37
 
12.3%
2 23
 
7.7%
3 22
 
7.3%
7 17
 
5.7%
0 14
 
4.7%
4 11
 
3.7%
5 10
 
3.3%
9 8
 
2.7%
8 5
 
1.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37
24.7%
2 23
15.3%
3 22
14.7%
7 17
11.3%
0 14
 
9.3%
4 11
 
7.3%
5 10
 
6.7%
9 8
 
5.3%
8 5
 
3.3%
6 3
 
2.0%
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 37
 
12.3%
2 23
 
7.7%
3 22
 
7.3%
7 17
 
5.7%
0 14
 
4.7%
4 11
 
3.7%
5 10
 
3.3%
9 8
 
2.7%
8 5
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 150
50.0%
1 37
 
12.3%
2 23
 
7.7%
3 22
 
7.3%
7 17
 
5.7%
0 14
 
4.7%
4 11
 
3.7%
5 10
 
3.3%
9 8
 
2.7%
8 5
 
1.7%

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

2023-12-10T23:02:11.719119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:11.850278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:12.082098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.8666667
Min length3

Characters and Unicode

Total characters116
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)33.3%

Sample

1st row안산시 단원구
2nd row화성시
3rd row양주시
4th row가평군
5th row부천시
ValueCountFrequency (%)
안산시 3
 
8.3%
김포시 3
 
8.3%
오산시 3
 
8.3%
평택시 3
 
8.3%
단원구 3
 
8.3%
양주시 2
 
5.6%
가평군 2
 
5.6%
포천시 2
 
5.6%
화성시 2
 
5.6%
안양시 2
 
5.6%
Other values (11) 11
30.6%
2023-12-10T23:02:12.558229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
25.0%
안 7
 
6.0%
6
 
5.2%
산 6
 
5.2%
구 6
 
5.2%
포 5
 
4.3%
평 5
 
4.3%
양 4
 
3.4%
천 4
 
3.4%
오 3
 
2.6%
Other values (22) 41
35.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
94.8%
Space Separator 6
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
26.4%
안 7
 
6.4%
산 6
 
5.5%
구 6
 
5.5%
포 5
 
4.5%
평 5
 
4.5%
양 4
 
3.6%
천 4
 
3.6%
오 3
 
2.7%
주 3
 
2.7%
Other values (21) 38
34.5%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 110
94.8%
Common 6
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
26.4%
안 7
 
6.4%
산 6
 
5.5%
구 6
 
5.5%
포 5
 
4.5%
평 5
 
4.5%
양 4
 
3.6%
천 4
 
3.6%
오 3
 
2.7%
주 3
 
2.7%
Other values (21) 38
34.5%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 110
94.8%
ASCII 6
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
26.4%
안 7
 
6.4%
산 6
 
5.5%
구 6
 
5.5%
포 5
 
4.5%
평 5
 
4.5%
양 4
 
3.6%
천 4
 
3.6%
오 3
 
2.7%
주 3
 
2.7%
Other values (21) 38
34.5%
ASCII
ValueCountFrequency (%)
6
100.0%

행정동명
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:12.861762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters87
Distinct characters47
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

Unique22 ?
Unique (%)73.3%

Sample

1st row초지동
2nd row향남읍
3rd row고암동
4th row설악면
5th row중동
ValueCountFrequency (%)
선부동 2
 
6.7%
비전동 2
 
6.7%
설악면 2
 
6.7%
대곶면 2
 
6.7%
신곡동 1
 
3.3%
초지동 1
 
3.3%
동교동 1
 
3.3%
덕풍동 1
 
3.3%
원동 1
 
3.3%
소흘읍 1
 
3.3%
Other values (16) 16
53.3%
2023-12-10T23:02:13.282727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
24.1%
면 5
 
5.7%
읍 5
 
5.7%
양 3
 
3.4%
선 2
 
2.3%
악 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
교 2
 
2.3%
설 2
 
2.3%
Other values (37) 41
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
24.1%
면 5
 
5.7%
읍 5
 
5.7%
양 3
 
3.4%
선 2
 
2.3%
악 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
교 2
 
2.3%
설 2
 
2.3%
Other values (37) 41
47.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
24.1%
면 5
 
5.7%
읍 5
 
5.7%
양 3
 
3.4%
선 2
 
2.3%
악 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
교 2
 
2.3%
설 2
 
2.3%
Other values (37) 41
47.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
24.1%
면 5
 
5.7%
읍 5
 
5.7%
양 3
 
3.4%
선 2
 
2.3%
악 2
 
2.3%
안 2
 
2.3%
부 2
 
2.3%
교 2
 
2.3%
설 2
 
2.3%
Other values (37) 41
47.1%

업체형태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
개인기업
29 
개인
 
1

Length

Max length4
Median length4
Mean length3.9333333
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
개인기업 29
96.7%
개인 1
 
3.3%

Length

2023-12-10T23:02:13.442757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:13.579602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인기업 29
96.7%
개인 1
 
3.3%

보증금액
Real number (ℝ)

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2402833.3
Minimum1300000
Maximum3000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:13.710774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300000
5-th percentile1651500
Q12005000
median2500000
Q32700000
95-th percentile3000000
Maximum3000000
Range1700000
Interquartile range (IQR)695000

Descriptive statistics

Standard deviation470996.16
Coefficient of variation (CV)0.19601699
Kurtosis-0.53394159
Mean2402833.3
Median Absolute Deviation (MAD)380000
Skewness-0.62502928
Sum72085000
Variance2.2183739 × 1011
MonotonicityIncreasing
2023-12-10T23:02:13.928658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2700000 7
23.3%
1800000 3
10.0%
3000000 3
10.0%
2500000 3
10.0%
2400000 2
 
6.7%
2000000 2
 
6.7%
2600000 1
 
3.3%
2975000 1
 
3.3%
2900000 1
 
3.3%
2860000 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
1300000 1
 
3.3%
1530000 1
 
3.3%
1800000 3
10.0%
1900000 1
 
3.3%
2000000 2
6.7%
2020000 1
 
3.3%
2100000 1
 
3.3%
2300000 1
 
3.3%
2400000 2
6.7%
2500000 3
10.0%
ValueCountFrequency (%)
3000000 3
10.0%
2975000 1
 
3.3%
2900000 1
 
3.3%
2860000 1
 
3.3%
2700000 7
23.3%
2600000 1
 
3.3%
2500000 3
10.0%
2400000 2
 
6.7%
2300000 1
 
3.3%
2100000 1
 
3.3%

은행명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
농협은행
22 
하나은행
 
2
국민은행
 
2
중소기업은행
 
2
우리은행
 
1

Length

Max length6
Median length4
Mean length4.1666667
Min length4

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
농협은행 22
73.3%
하나은행 2
 
6.7%
국민은행 2
 
6.7%
중소기업은행 2
 
6.7%
우리은행 1
 
3.3%
새마을금고 1
 
3.3%

Length

2023-12-10T23:02:14.132764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:02:14.289274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농협은행 22
73.3%
하나은행 2
 
6.7%
국민은행 2
 
6.7%
중소기업은행 2
 
6.7%
우리은행 1
 
3.3%
새마을금고 1
 
3.3%

사업기간내용
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
1년 미만
14 
6년
4 
4년
3 
12년
 
1
10년
 
1
Other values (7)
7 

Length

Max length5
Median length3
Mean length3.5333333
Min length2

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row1년 미만
2nd row1년 미만
3rd row1년 미만
4th row1년 미만
5th row1년 미만

Common Values

ValueCountFrequency (%)
1년 미만 14
46.7%
6년 4
 
13.3%
4년 3
 
10.0%
12년 1
 
3.3%
10년 1
 
3.3%
5년 1
 
3.3%
9년 1
 
3.3%
17년 1
 
3.3%
2년 1
 
3.3%
8년 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:02:14.455417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1년 14
31.8%
미만 14
31.8%
6년 4
 
9.1%
4년 3
 
6.8%
12년 1
 
2.3%
10년 1
 
2.3%
5년 1
 
2.3%
9년 1
 
2.3%
17년 1
 
2.3%
2년 1
 
2.3%
Other values (3) 3
 
6.8%

CB평가등급명
Real number (ℝ)

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3333333
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:14.591507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q37
95-th percentile8.55
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3242846
Coefficient of variation (CV)0.43580336
Kurtosis-0.66259458
Mean5.3333333
Median Absolute Deviation (MAD)1.5
Skewness-0.41716271
Sum160
Variance5.4022989
MonotonicityNot monotonic
2023-12-10T23:02:14.749586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
7 6
20.0%
6 5
16.7%
5 4
13.3%
8 3
10.0%
1 3
10.0%
4 3
10.0%
3 3
10.0%
9 2
 
6.7%
2 1
 
3.3%
ValueCountFrequency (%)
1 3
10.0%
2 1
 
3.3%
3 3
10.0%
4 3
10.0%
5 4
13.3%
6 5
16.7%
7 6
20.0%
8 3
10.0%
9 2
 
6.7%
ValueCountFrequency (%)
9 2
 
6.7%
8 3
10.0%
7 6
20.0%
6 5
16.7%
5 4
13.3%
4 3
10.0%
3 3
10.0%
2 1
 
3.3%
1 3
10.0%

최초적용일자
Date

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2012-04-20 00:00:00
Maximum2020-10-20 00:00:00
2023-12-10T23:02:14.928297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.116973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

최초적용금리
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.001
Minimum3.1
Maximum7.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:15.316274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile3.767
Q14.415
median5.005
Q35.3475
95-th percentile6.673
Maximum7.3
Range4.2
Interquartile range (IQR)0.9325

Descriptive statistics

Standard deviation0.91527403
Coefficient of variation (CV)0.1830182
Kurtosis0.62681477
Mean5.001
Median Absolute Deviation (MAD)0.495
Skewness0.52962356
Sum150.03
Variance0.83772655
MonotonicityNot monotonic
2023-12-10T23:02:15.506274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4.22 3
 
10.0%
4.52 2
 
6.7%
4.7 1
 
3.3%
7.3 1
 
3.3%
4.24 1
 
3.3%
5.11 1
 
3.3%
6.2 1
 
3.3%
5.16 1
 
3.3%
5.22 1
 
3.3%
3.1 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
3.1 1
 
3.3%
3.74 1
 
3.3%
3.8 1
 
3.3%
4.22 3
10.0%
4.24 1
 
3.3%
4.38 1
 
3.3%
4.52 2
6.7%
4.58 1
 
3.3%
4.59 1
 
3.3%
4.7 1
 
3.3%
ValueCountFrequency (%)
7.3 1
3.3%
6.7 1
3.3%
6.64 1
3.3%
6.2 1
3.3%
5.77 1
3.3%
5.61 1
3.3%
5.51 1
3.3%
5.37 1
3.3%
5.28 1
3.3%
5.25 1
3.3%

최종적용일자
Date

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2013-04-18 00:00:00
Maximum2021-10-14 00:00:00
2023-12-10T23:02:15.712235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:15.921269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

최종적용금리
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3063333
Minimum2.86
Maximum6.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:16.105983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile2.9985
Q13.8575
median4.19
Q34.79
95-th percentile5.974
Maximum6.88
Range4.02
Interquartile range (IQR)0.9325

Descriptive statistics

Standard deviation0.88825083
Coefficient of variation (CV)0.20626616
Kurtosis2.2729939
Mean4.3063333
Median Absolute Deviation (MAD)0.53
Skewness1.0984775
Sum129.19
Variance0.78898954
MonotonicityNot monotonic
2023-12-10T23:02:16.322299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.18 2
 
6.7%
4.12 1
 
3.3%
4.45 1
 
3.3%
4.14 1
 
3.3%
3.94 1
 
3.3%
4.82 1
 
3.3%
4.94 1
 
3.3%
3.47 1
 
3.3%
2.94 1
 
3.3%
3.6 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
2.86 1
3.3%
2.94 1
3.3%
3.07 1
3.3%
3.47 1
3.3%
3.5 1
3.3%
3.6 1
3.3%
3.64 1
3.3%
3.83 1
3.3%
3.94 1
3.3%
4.0 1
3.3%
ValueCountFrequency (%)
6.88 1
3.3%
6.55 1
3.3%
5.27 1
3.3%
4.95 1
3.3%
4.94 1
3.3%
4.87 1
3.3%
4.84 1
3.3%
4.82 1
3.3%
4.7 1
3.3%
4.45 1
3.3%

혜택금리
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69466667
Minimum0.02
Maximum2.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:02:16.490545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.0345
Q10.1625
median0.5
Q31.1675
95-th percentile1.6375
Maximum2.39
Range2.37
Interquartile range (IQR)1.005

Descriptive statistics

Standard deviation0.63368508
Coefficient of variation (CV)0.91221461
Kurtosis-0.032240709
Mean0.69466667
Median Absolute Deviation (MAD)0.405
Skewness0.83875077
Sum20.84
Variance0.40155678
MonotonicityNot monotonic
2023-12-10T23:02:16.671999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.04 2
 
6.7%
0.3 2
 
6.7%
0.1 2
 
6.7%
0.58 1
 
3.3%
0.83 1
 
3.3%
1.17 1
 
3.3%
1.38 1
 
3.3%
0.22 1
 
3.3%
1.75 1
 
3.3%
0.16 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.02 1
3.3%
0.03 1
3.3%
0.04 2
6.7%
0.09 1
3.3%
0.1 2
6.7%
0.16 1
3.3%
0.17 1
3.3%
0.22 1
3.3%
0.23 1
3.3%
0.3 2
6.7%
ValueCountFrequency (%)
2.39 1
3.3%
1.75 1
3.3%
1.5 1
3.3%
1.45 1
3.3%
1.43 1
3.3%
1.38 1
3.3%
1.37 1
3.3%
1.17 1
3.3%
1.16 1
3.3%
1.04 1
3.3%

기업번호
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:02:17.029893image/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

Unique28 ?
Unique (%)93.3%

Sample

1st rowaSw2vkZiUXXR8Lh02ATGyA==
2nd rowA8fV6ZT1ZlMjnp9fnW2r8Q==
3rd rowVnPnUhubMSAyI6w76MA1Rg==
4th rowsPLTgQ1PwSIYUlzKUHbd7w==
5th rowxEVqtQpk/BapMW2cPKOuQQ==
ValueCountFrequency (%)
spltgq1pwsiyulzkuhbd7w 2
 
6.7%
asw2vkziuxxr8lh02atgya 1
 
3.3%
19qyanozrlf5yur1bzplsg 1
 
3.3%
plo+1lidcwllcewdoewiha 1
 
3.3%
5dcsqb+m0l1eicgzltskrq 1
 
3.3%
2xqyjurmtg/pqjhsaorhoq 1
 
3.3%
9ft6r2ygotvtglbps+paza 1
 
3.3%
f6bv+hn+wqomebty1td8xq 1
 
3.3%
xwj9sp1gvjei3yx+7rrwkq 1
 
3.3%
x+tuefug7piqnmt5gspz0q 1
 
3.3%
Other values (19) 19
63.3%
2023-12-10T23:02:17.627302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 60
 
8.3%
Q 27
 
3.8%
w 18
 
2.5%
L 16
 
2.2%
F 16
 
2.2%
P 16
 
2.2%
T 15
 
2.1%
A 15
 
2.1%
M 14
 
1.9%
n 14
 
1.9%
Other values (55) 509
70.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 293
40.7%
Lowercase Letter 256
35.6%
Decimal Number 91
 
12.6%
Math Symbol 71
 
9.9%
Other Punctuation 9
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Q 27
 
9.2%
L 16
 
5.5%
F 16
 
5.5%
P 16
 
5.5%
T 15
 
5.1%
A 15
 
5.1%
M 14
 
4.8%
R 14
 
4.8%
K 12
 
4.1%
Y 11
 
3.8%
Other values (16) 137
46.8%
Lowercase Letter
ValueCountFrequency (%)
w 18
 
7.0%
n 14
 
5.5%
s 13
 
5.1%
p 13
 
5.1%
y 13
 
5.1%
g 13
 
5.1%
b 12
 
4.7%
z 12
 
4.7%
u 12
 
4.7%
q 11
 
4.3%
Other values (16) 125
48.8%
Decimal Number
ValueCountFrequency (%)
7 12
13.2%
9 12
13.2%
1 12
13.2%
6 12
13.2%
2 10
11.0%
0 9
9.9%
8 7
7.7%
5 7
7.7%
3 6
6.6%
4 4
 
4.4%
Math Symbol
ValueCountFrequency (%)
= 60
84.5%
+ 11
 
15.5%
Other Punctuation
ValueCountFrequency (%)
/ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 549
76.2%
Common 171
 
23.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
Q 27
 
4.9%
w 18
 
3.3%
L 16
 
2.9%
F 16
 
2.9%
P 16
 
2.9%
T 15
 
2.7%
A 15
 
2.7%
M 14
 
2.6%
n 14
 
2.6%
R 14
 
2.6%
Other values (42) 384
69.9%
Common
ValueCountFrequency (%)
= 60
35.1%
7 12
 
7.0%
9 12
 
7.0%
1 12
 
7.0%
6 12
 
7.0%
+ 11
 
6.4%
2 10
 
5.8%
/ 9
 
5.3%
0 9
 
5.3%
8 7
 
4.1%
Other values (3) 17
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 720
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 60
 
8.3%
Q 27
 
3.8%
w 18
 
2.5%
L 16
 
2.2%
F 16
 
2.2%
P 16
 
2.2%
T 15
 
2.1%
A 15
 
2.1%
M 14
 
1.9%
n 14
 
1.9%
Other values (55) 509
70.7%

Interactions

2023-12-10T23:02:08.336920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:05.772872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.388264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.046484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.614192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.467522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:05.895756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.537192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.165903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.749002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.605453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.028197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.666775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.314069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.899518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.733846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.129253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.789342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.410510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.034435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.905734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.249024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:06.919030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:07.520676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:02:08.186166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:02:17.813637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드연령대코드사업자등록번호시군구명행정동명업체형태명보증금액은행명사업기간내용CB평가등급명최초적용일자최초적용금리최종적용일자최종적용금리혜택금리기업번호
성별코드1.0000.0001.0000.0000.5590.0000.0000.5930.2810.4011.0000.3671.0000.0000.4811.000
연령대코드0.0001.0001.0000.6810.8670.0000.0000.0000.5120.2961.0000.7111.0000.1310.0001.000
사업자등록번호1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명0.0000.6811.0001.0001.0000.0000.8540.3460.6720.6861.0000.8451.0000.7000.7861.000
행정동명0.5590.8671.0001.0001.0000.0000.8140.0000.6820.8441.0000.9221.0000.9040.8671.000
업체형태명0.0000.0001.0000.0000.0001.0000.0000.8121.0000.2441.0000.0001.0000.1620.2441.000
보증금액0.0000.0000.0000.8540.8140.0001.0000.0000.0000.4930.0000.4220.0000.0790.0000.000
은행명0.5930.0001.0000.3460.0000.8120.0001.0000.9300.6891.0000.6941.0000.5630.0001.000
사업기간내용0.2810.5121.0000.6720.6821.0000.0000.9301.0000.7331.0000.6321.0000.0000.0001.000
CB평가등급명0.4010.2961.0000.6860.8440.2440.4930.6890.7331.0001.0000.0001.0000.0000.0001.000
최초적용일자1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최초적용금리0.3670.7111.0000.8450.9220.0000.4220.6940.6320.0001.0001.0001.0000.7230.3351.000
최종적용일자1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
최종적용금리0.0000.1311.0000.7000.9040.1620.0790.5630.0000.0001.0000.7231.0001.0000.5341.000
혜택금리0.4810.0001.0000.7860.8670.2440.0000.0000.0000.0001.0000.3351.0000.5341.0001.000
기업번호1.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T23:02:18.043211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행명연령대코드성별코드업체형태명사업기간내용
은행명1.0000.0000.3930.5670.544
연령대코드0.0001.0000.0000.0000.240
성별코드0.3930.0001.0000.0000.135
업체형태명0.5670.0000.0001.0000.802
사업기간내용0.5440.2400.1350.8021.000
2023-12-10T23:02:18.202258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보증금액CB평가등급명최초적용금리최종적용금리혜택금리성별코드연령대코드업체형태명은행명사업기간내용
보증금액1.000-0.343-0.062-0.0140.0160.0000.0000.0000.0000.000
CB평가등급명-0.3431.0000.377-0.1040.4310.3350.1250.1890.3870.371
최초적용금리-0.0620.3771.0000.5810.5560.2160.3180.0000.4060.283
최종적용금리-0.014-0.1040.5811.000-0.2490.0000.0000.1340.3610.000
혜택금리0.0160.4310.556-0.2491.0000.4090.0000.1890.0000.000
성별코드0.0000.3350.2160.0000.4091.0000.0000.0000.3930.135
연령대코드0.0000.1250.3180.0000.0000.0001.0000.0000.0000.240
업체형태명0.0000.1890.0000.1340.1890.0000.0001.0000.5670.802
은행명0.0000.3870.4060.3610.0000.3930.0000.5671.0000.544
사업기간내용0.0000.3710.2830.0000.0000.1350.2400.8020.5441.000

Missing values

2023-12-10T23:02:09.419254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:02:09.765814image/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

기준년월성별코드연령대코드사업자등록번호시도명시군구명행정동명업체형태명보증금액은행명사업기간내용CB평가등급명최초적용일자최초적용금리최종적용일자최종적용금리혜택금리기업번호
02023-04F4013433*****경기도안산시 단원구초지동개인기업1300000농협은행1년 미만72014-11-284.72015-11-274.120.58aSw2vkZiUXXR8Lh02ATGyA==
12023-04F5072228*****경기도화성시향남읍개인기업1530000농협은행1년 미만82012-06-136.642013-06-056.550.09A8fV6ZT1ZlMjnp9fnW2r8Q==
22023-04F4012746*****경기도양주시고암동개인기업1800000농협은행1년 미만72013-05-064.872019-05-023.831.04VnPnUhubMSAyI6w76MA1Rg==
32023-04M6081771*****경기도가평군설악면개인기업1800000농협은행1년 미만12015-09-034.222018-09-044.180.04sPLTgQ1PwSIYUlzKUHbd7w==
42023-04F3050350*****경기도부천시중동개인기업1800000농협은행1년 미만82018-05-294.522020-07-063.071.45xEVqtQpk/BapMW2cPKOuQQ==
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