Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.9 KiB
Average record size in memory108.3 B

Variable types

Text4
DateTime3
Numeric3
Categorical2
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 채권관리일반유동화회사기업개요공통품의에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093241/fileData.do

Alerts

부실처리일자 has constant value ""Constant
발행일자 has constant value ""Constant
만기일자 has constant value ""Constant
최종수정수 is highly overall correlated with 삭제여부High correlation
삭제여부 is highly overall correlated with 최종수정수High correlation
기업편입금액 is highly overall correlated with 기업부실잔액High correlation
기업부실잔액 is highly overall correlated with 기업편입금액High correlation
삭제여부 is highly imbalanced (72.2%)Imbalance
최종수정수 is highly imbalanced (72.2%)Imbalance

Reproduction

Analysis started2024-04-17 13:02:31.811531
Analysis finished2024-04-17 13:02:33.083206
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

채권관리ID
Text

Distinct468
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T22:02:33.242312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique443 ?
Unique (%)88.6%

Sample

1st row9dnUacTFpN
2nd row9dnLb8Xvaq
3rd row9dnMY7ifHX
4th row9dnLc1xRhx
5th row9dktYQkKVR
ValueCountFrequency (%)
9dmq5fgrph 4
 
0.8%
9dk5c3brng 3
 
0.6%
9dk5dbtayg 3
 
0.6%
9dmle16fyv 3
 
0.6%
9dmxz00axm 3
 
0.6%
9dm1oyoubt 3
 
0.6%
9dntezeenz 2
 
0.4%
9dntjyotks 2
 
0.4%
9dltzse9bq 2
 
0.4%
9dl1sipuqh 2
 
0.4%
Other values (458) 473
94.6%
2024-04-17T22:02:33.574641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 563
 
11.3%
9 542
 
10.8%
m 234
 
4.7%
k 223
 
4.5%
l 169
 
3.4%
n 105
 
2.1%
B 78
 
1.6%
t 77
 
1.5%
0 74
 
1.5%
y 72
 
1.4%
Other values (52) 2863
57.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2503
50.1%
Uppercase Letter 1477
29.5%
Decimal Number 1020
20.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 563
22.5%
m 234
 
9.3%
k 223
 
8.9%
l 169
 
6.8%
n 105
 
4.2%
t 77
 
3.1%
y 72
 
2.9%
o 70
 
2.8%
b 67
 
2.7%
v 66
 
2.6%
Other values (16) 857
34.2%
Uppercase Letter
ValueCountFrequency (%)
B 78
 
5.3%
T 72
 
4.9%
Z 67
 
4.5%
Q 65
 
4.4%
R 64
 
4.3%
S 61
 
4.1%
G 61
 
4.1%
C 61
 
4.1%
A 59
 
4.0%
U 59
 
4.0%
Other values (16) 830
56.2%
Decimal Number
ValueCountFrequency (%)
9 542
53.1%
0 74
 
7.3%
1 63
 
6.2%
2 60
 
5.9%
5 52
 
5.1%
7 51
 
5.0%
4 50
 
4.9%
6 48
 
4.7%
8 44
 
4.3%
3 36
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3980
79.6%
Common 1020
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 563
 
14.1%
m 234
 
5.9%
k 223
 
5.6%
l 169
 
4.2%
n 105
 
2.6%
B 78
 
2.0%
t 77
 
1.9%
y 72
 
1.8%
T 72
 
1.8%
o 70
 
1.8%
Other values (42) 2317
58.2%
Common
ValueCountFrequency (%)
9 542
53.1%
0 74
 
7.3%
1 63
 
6.2%
2 60
 
5.9%
5 52
 
5.1%
7 51
 
5.0%
4 50
 
4.9%
6 48
 
4.7%
8 44
 
4.3%
3 36
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 563
 
11.3%
9 542
 
10.8%
m 234
 
4.7%
k 223
 
4.5%
l 169
 
3.4%
n 105
 
2.1%
B 78
 
1.6%
t 77
 
1.5%
0 74
 
1.5%
y 72
 
1.4%
Other values (52) 2863
57.3%

고객ID
Text

Distinct91
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T22:02:33.805864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)4.0%

Sample

1st row9cRcPMzXyv
2nd row9ci4NyLwhS
3rd row9dbIKowD01
4th row9dairBsmKB
5th row9c5xqAbLiP
ValueCountFrequency (%)
9dj3djgtcd 26
 
5.2%
9c2yqvailj 24
 
4.8%
9c4fynpdm9 20
 
4.0%
9c5xqablip 17
 
3.4%
9dairbsmkb 16
 
3.2%
9cslxzcqpe 15
 
3.0%
9cr5mikzt9 15
 
3.0%
9da0nfsmxd 15
 
3.0%
9dbikowd01 14
 
2.8%
9bqwqlhftc 14
 
2.8%
Other values (81) 324
64.8%
2024-04-17T22:02:34.106035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 561
 
11.2%
c 437
 
8.7%
d 201
 
4.0%
j 114
 
2.3%
D 111
 
2.2%
q 102
 
2.0%
w 98
 
2.0%
J 96
 
1.9%
B 96
 
1.9%
b 95
 
1.9%
Other values (52) 3089
61.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2276
45.5%
Uppercase Letter 1636
32.7%
Decimal Number 1088
21.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 437
19.2%
d 201
 
8.8%
j 114
 
5.0%
q 102
 
4.5%
w 98
 
4.3%
b 95
 
4.2%
l 94
 
4.1%
a 93
 
4.1%
m 89
 
3.9%
z 84
 
3.7%
Other values (16) 869
38.2%
Uppercase Letter
ValueCountFrequency (%)
D 111
 
6.8%
J 96
 
5.9%
B 96
 
5.9%
X 82
 
5.0%
I 81
 
5.0%
T 77
 
4.7%
G 75
 
4.6%
P 73
 
4.5%
Q 72
 
4.4%
C 71
 
4.3%
Other values (16) 802
49.0%
Decimal Number
ValueCountFrequency (%)
9 561
51.6%
0 86
 
7.9%
2 82
 
7.5%
1 71
 
6.5%
4 64
 
5.9%
6 64
 
5.9%
3 54
 
5.0%
5 46
 
4.2%
8 35
 
3.2%
7 25
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3912
78.2%
Common 1088
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 437
 
11.2%
d 201
 
5.1%
j 114
 
2.9%
D 111
 
2.8%
q 102
 
2.6%
w 98
 
2.5%
J 96
 
2.5%
B 96
 
2.5%
b 95
 
2.4%
l 94
 
2.4%
Other values (42) 2468
63.1%
Common
ValueCountFrequency (%)
9 561
51.6%
0 86
 
7.9%
2 82
 
7.5%
1 71
 
6.5%
4 64
 
5.9%
6 64
 
5.9%
3 54
 
5.0%
5 46
 
4.2%
8 35
 
3.2%
7 25
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 561
 
11.2%
c 437
 
8.7%
d 201
 
4.0%
j 114
 
2.3%
D 111
 
2.2%
q 102
 
2.0%
w 98
 
2.0%
J 96
 
1.9%
B 96
 
1.9%
b 95
 
1.9%
Other values (52) 3089
61.8%

기업명
Text

Distinct91
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T22:02:34.311314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length18.384
Min length16

Characters and Unicode

Total characters9192
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)4.0%

Sample

1st row신보2018제1차유동화전문(유한)
2nd row신보2014제5차유동화전문(유한)
3rd row신보2020제10차유동화전문(유한)
4th row신보2020제4차유동화전문(유한)
5th row신보2019제10차유동화전문(유한)
ValueCountFrequency (%)
신보2021제10차유동화전문(유한 26
 
5.2%
신보2019제7차유동화전문(유한 24
 
4.8%
신보2019제8차유동화전문(유한 20
 
4.0%
신보2019제10차유동화전문(유한 17
 
3.4%
신보2020제4차유동화전문(유한 16
 
3.2%
신보2018제3차유동화전문(유한 15
 
3.0%
신보2018제2차유동화전문(유한 15
 
3.0%
신보2020제7차유동화전문(유한 15
 
3.0%
신보2020제10차유동화전문(유한 14
 
2.8%
2011신보그레이트제1차유동화전문(유한 14
 
2.8%
Other values (81) 324
64.8%
2024-04-17T22:02:34.635810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 1000
 
10.9%
2 683
 
7.4%
0 654
 
7.1%
1 572
 
6.2%
제 500
 
5.4%
동 500
 
5.4%
화 500
 
5.4%
전 500
 
5.4%
문 500
 
5.4%
( 500
 
5.4%
Other values (42) 3283
35.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5620
61.1%
Decimal Number 2572
28.0%
Open Punctuation 500
 
5.4%
Close Punctuation 500
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 1000
17.8%
제 500
8.9%
동 500
8.9%
화 500
8.9%
전 500
8.9%
문 500
8.9%
한 500
8.9%
차 498
8.9%
신 497
8.8%
보 496
8.8%
Other values (30) 129
 
2.3%
Decimal Number
ValueCountFrequency (%)
2 683
26.6%
0 654
25.4%
1 572
22.2%
9 133
 
5.2%
7 112
 
4.4%
8 105
 
4.1%
4 102
 
4.0%
6 93
 
3.6%
5 72
 
2.8%
3 46
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 500
100.0%
Close Punctuation
ValueCountFrequency (%)
) 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5620
61.1%
Common 3572
38.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 1000
17.8%
제 500
8.9%
동 500
8.9%
화 500
8.9%
전 500
8.9%
문 500
8.9%
한 500
8.9%
차 498
8.9%
신 497
8.8%
보 496
8.8%
Other values (30) 129
 
2.3%
Common
ValueCountFrequency (%)
2 683
19.1%
0 654
18.3%
1 572
16.0%
( 500
14.0%
) 500
14.0%
9 133
 
3.7%
7 112
 
3.1%
8 105
 
2.9%
4 102
 
2.9%
6 93
 
2.6%
Other values (2) 118
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5620
61.1%
ASCII 3572
38.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 1000
17.8%
제 500
8.9%
동 500
8.9%
화 500
8.9%
전 500
8.9%
문 500
8.9%
한 500
8.9%
차 498
8.9%
신 497
8.8%
보 496
8.8%
Other values (30) 129
 
2.3%
ASCII
ValueCountFrequency (%)
2 683
19.1%
0 654
18.3%
1 572
16.0%
( 500
14.0%
) 500
14.0%
9 133
 
3.7%
7 112
 
3.1%
8 105
 
2.9%
4 102
 
2.9%
6 93
 
2.6%
Other values (2) 118
 
3.3%

부실처리일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-17 00:00:00
Maximum2024-04-17 00:00:00
2024-04-17T22:02:34.735474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:34.805094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

기업편입금액
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1460938 × 109
Minimum1.53 × 108
Maximum1 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T22:02:34.903208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.53 × 108
5-th percentile2.8 × 108
Q15 × 108
median9.55 × 108
Q31.35 × 109
95-th percentile2.705 × 109
Maximum1 × 1010
Range9.847 × 109
Interquartile range (IQR)8.5 × 108

Descriptive statistics

Standard deviation1.1526211 × 109
Coefficient of variation (CV)1.0056953
Kurtosis23.362405
Mean1.1460938 × 109
Median Absolute Deviation (MAD)4.55 × 108
Skewness4.1420218
Sum5.730469 × 1011
Variance1.3285355 × 1018
MonotonicityNot monotonic
2024-04-17T22:02:35.021271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000000 74
 
14.8%
500000000 51
 
10.2%
1200000000 24
 
4.8%
1500000000 21
 
4.2%
375000000 15
 
3.0%
560000000 14
 
2.8%
2000000000 14
 
2.8%
800000000 13
 
2.6%
1940000000 11
 
2.2%
700000000 11
 
2.2%
Other values (88) 252
50.4%
ValueCountFrequency (%)
153000000 1
 
0.2%
180000000 2
 
0.4%
196000000 2
 
0.4%
200000000 2
 
0.4%
220000000 1
 
0.2%
230400000 1
 
0.2%
238000000 1
 
0.2%
240000000 9
1.8%
262500000 1
 
0.2%
263000000 1
 
0.2%
ValueCountFrequency (%)
10000000000 2
0.4%
8000000000 4
0.8%
5800000000 1
 
0.2%
5000000000 3
0.6%
4500000000 2
0.4%
4000000000 1
 
0.2%
3600000000 2
0.4%
3500000000 3
0.6%
3200000000 4
0.8%
3000000000 1
 
0.2%

기업부실잔액
Real number (ℝ)

HIGH CORRELATION 

Distinct119
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.110788 × 109
Minimum0
Maximum1 × 1010
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T22:02:35.143906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.3762 × 108
Q15 × 108
median9.5 × 108
Q31.2555 × 109
95-th percentile2.557 × 109
Maximum1 × 1010
Range1 × 1010
Interquartile range (IQR)7.555 × 108

Descriptive statistics

Standard deviation1.1330263 × 109
Coefficient of variation (CV)1.0200204
Kurtosis22.781808
Mean1.110788 × 109
Median Absolute Deviation (MAD)4.5 × 108
Skewness4.0124468
Sum5.5539398 × 1011
Variance1.2837487 × 1018
MonotonicityNot monotonic
2024-04-17T22:02:35.258817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000000 72
 
14.4%
500000000 39
 
7.8%
1200000000 23
 
4.6%
1500000000 19
 
3.8%
2000000000 14
 
2.8%
375000000 14
 
2.8%
560000000 14
 
2.8%
800000000 12
 
2.4%
1940000000 11
 
2.2%
700000000 11
 
2.2%
Other values (109) 271
54.2%
ValueCountFrequency (%)
0 1
 
0.2%
19518750 2
 
0.4%
34446384 1
 
0.2%
45443064 1
 
0.2%
75000000 8
1.6%
90160000 2
 
0.4%
119850000 1
 
0.2%
153000000 1
 
0.2%
180000000 1
 
0.2%
196000000 2
 
0.4%
ValueCountFrequency (%)
10000000000 2
0.4%
8000000000 2
0.4%
6803601178 2
0.4%
5800000000 1
 
0.2%
5000000000 3
0.6%
4500000000 2
0.4%
3600000000 2
0.4%
3500000000 3
0.6%
3417956887 1
 
0.2%
3200000000 4
0.8%

발행일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-17 00:00:00
Maximum2024-04-17 00:00:00
2024-04-17T22:02:35.343920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:35.639423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

만기일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-17 00:00:00
Maximum2024-04-17 00:00:00
2024-04-17T22:02:35.704011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:35.774113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대물변제일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
336 
00:00.0
164 

Length

Max length26
Median length26
Mean length19.768
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 336
67.2%
00:00.0 164
32.8%

Length

2024-04-17T22:02:35.868477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:02:35.960264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 336
40.2%
00:00:00.000000 336
40.2%
00:00.0 164
19.6%

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
476 
True
 
24
ValueCountFrequency (%)
False 476
95.2%
True 24
 
4.8%
2024-04-17T22:02:36.036314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
476 
2
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 476
95.2%
2 24
 
4.8%

Length

2024-04-17T22:02:36.126000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:02:36.208460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 476
95.2%
2 24
 
4.8%

처리시각
Text

Distinct463
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T22:02:36.477276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
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

Unique433 ?
Unique (%)86.6%

Sample

1st row49:17.4
2nd row26:45.1
3rd row42:11.5
4th row28:03.9
5th row09:37.6
ValueCountFrequency (%)
00:03.1 4
 
0.8%
02:27.2 3
 
0.6%
10:53.8 3
 
0.6%
41:52.2 3
 
0.6%
16:21.5 3
 
0.6%
24:42.2 3
 
0.6%
00:18.8 2
 
0.4%
52:28.9 2
 
0.4%
24:31.8 2
 
0.4%
31:26.4 2
 
0.4%
Other values (453) 473
94.6%
2024-04-17T22:02:36.875399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 352
10.1%
1 325
9.3%
4 322
9.2%
0 316
9.0%
3 300
8.6%
5 294
8.4%
8 162
 
4.6%
9 154
 
4.4%
Other values (2) 275
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 352
14.1%
1 325
13.0%
4 322
12.9%
0 316
12.6%
3 300
12.0%
5 294
11.8%
8 162
6.5%
9 154
6.2%
6 153
6.1%
7 122
 
4.9%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 352
10.1%
1 325
9.3%
4 322
9.2%
0 316
9.0%
3 300
8.6%
5 294
8.4%
8 162
 
4.6%
9 154
 
4.4%
Other values (2) 275
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 352
10.1%
1 325
9.3%
4 322
9.2%
0 316
9.0%
3 300
8.6%
5 294
8.4%
8 162
 
4.6%
9 154
 
4.4%
Other values (2) 275
7.9%

처리직원번호
Real number (ℝ)

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5294.738
Minimum3719
Maximum5878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T22:02:36.974615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3719
5-th percentile4662
Q14955
median5125
Q35692
95-th percentile5878
Maximum5878
Range2159
Interquartile range (IQR)737

Descriptive statistics

Standard deviation435.96815
Coefficient of variation (CV)0.082339891
Kurtosis-0.80462907
Mean5294.738
Median Absolute Deviation (MAD)463
Skewness-0.11441568
Sum2647369
Variance190068.23
MonotonicityNot monotonic
2024-04-17T22:02:37.058020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5878 85
17.0%
5125 84
16.8%
5692 70
14.0%
4955 66
13.2%
5003 64
12.8%
4662 61
12.2%
5449 25
 
5.0%
5814 24
 
4.8%
5588 16
 
3.2%
3719 2
 
0.4%
Other values (2) 3
 
0.6%
ValueCountFrequency (%)
3719 2
 
0.4%
4044 1
 
0.2%
4662 61
12.2%
4955 66
13.2%
5003 64
12.8%
5125 84
16.8%
5272 2
 
0.4%
5449 25
 
5.0%
5588 16
 
3.2%
5692 70
14.0%
ValueCountFrequency (%)
5878 85
17.0%
5814 24
 
4.8%
5692 70
14.0%
5588 16
 
3.2%
5449 25
 
5.0%
5272 2
 
0.4%
5125 84
16.8%
5003 64
12.8%
4955 66
13.2%
4662 61
12.2%

Interactions

2024-04-17T22:02:32.614339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.174376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.374031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.679996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.240921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.465527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.756738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.309632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:32.549585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:02:37.135427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객ID기업명기업편입금액기업부실잔액대물변제일자삭제여부최종수정수처리직원번호
고객ID1.0001.0000.8540.8860.9920.0000.0000.712
기업명1.0001.0000.8540.8860.9920.0000.0000.712
기업편입금액0.8540.8541.0000.9940.3360.0000.0000.254
기업부실잔액0.8860.8860.9941.0000.2470.0000.0000.243
대물변제일자0.9920.9920.3360.2471.0000.0000.0000.286
삭제여부0.0000.0000.0000.0000.0001.0000.9990.107
최종수정수0.0000.0000.0000.0000.0000.9991.0000.107
처리직원번호0.7120.7120.2540.2430.2860.1070.1071.000
2024-04-17T22:02:37.237010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대물변제일자최종수정수삭제여부
대물변제일자1.0000.0000.000
최종수정수0.0001.0000.978
삭제여부0.0000.9781.000
2024-04-17T22:02:37.319695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기업편입금액기업부실잔액처리직원번호대물변제일자삭제여부최종수정수
기업편입금액1.0000.957-0.1090.2510.0000.000
기업부실잔액0.9571.000-0.0980.2450.0000.000
처리직원번호-0.109-0.0981.0000.3010.1050.105
대물변제일자0.2510.2450.3011.0000.0000.000
삭제여부0.0000.0000.1050.0001.0000.978
최종수정수0.0000.0000.1050.0000.9781.000

Missing values

2024-04-17T22:02:32.859999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:02:33.020088image/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

채권관리ID고객ID기업명부실처리일자기업편입금액기업부실잔액발행일자만기일자대물변제일자삭제여부최종수정수처리시각처리직원번호
09dnUacTFpN9cRcPMzXyv신보2018제1차유동화전문(유한)00:00.056000000056000000000:00.000:00.000:00.0N149:17.45003
19dnLb8Xvaq9ci4NyLwhS신보2014제5차유동화전문(유한)00:00.060000000060000000000:00.000:00.000:00.0N126:45.15878
29dnMY7ifHX9dbIKowD01신보2020제10차유동화전문(유한)00:00.044100000044100000000:00.000:00.00001-01-01 00:00:00.000000N142:11.55878
39dnLc1xRhx9dairBsmKB신보2020제4차유동화전문(유한)00:00.01105000000110500000000:00.000:00.00001-01-01 00:00:00.000000N128:03.94662
49dktYQkKVR9c5xqAbLiP신보2019제10차유동화전문(유한)00:00.064000000064000000000:00.000:00.00001-01-01 00:00:00.000000N109:37.65588
59dnDWvTnlJ9dkOCXT6Bc신보2021제13차유동화전문(유한)00:00.067000000067000000000:00.000:00.00001-01-01 00:00:00.000000N107:06.65449
69dnDWmx7Vs9dkOCXT6Bc신보2021제13차유동화전문(유한)00:00.067000000067000000000:00.000:00.00001-01-01 00:00:00.000000N106:46.85449
79dnM09xXx89cqDhKS3IN신보2015제2차유동화전문(유한)00:00.02400000000240000000000:00.000:00.000:00.0N104:21.55003
89dnM02LGIN9cqDhKS3IN신보2015제2차유동화전문(유한)00:00.02400000000240000000000:00.000:00.000:00.0N102:41.35003
99dnLBwTTfH9cv1NLbyn2신보2015제11차유동화전문(유한)00:00.050000000050000000000:00.000:00.000:00.0N157:07.25878
채권관리ID고객ID기업명부실처리일자기업편입금액기업부실잔액발행일자만기일자대물변제일자삭제여부최종수정수처리시각처리직원번호
4909dkoMqMBZ79cAzH0zXtl신보2016제6차유동화전문(유한)00:00.050000000050000000000:00.000:00.000:00.0N101:29.95692
4919djKkSnITZ9cAzH0zXtl신보2016제6차유동화전문(유한)00:00.050000000050000000000:00.000:00.000:00.0N159:05.85692
4929dkhrflpZq9c1SltqMi2신보2019제6차유동화전문(유한)00:00.01800000000180000000000:00.000:00.00001-01-01 00:00:00.000000N131:08.75449
4939dkhripcsQ9c2yQvaIlj신보2019제7차유동화전문(유한)00:00.01860000000186000000000:00.000:00.00001-01-01 00:00:00.000000N101:40.05449
4949dko34BgNq9dbIJyohOs신보2020제8차유동화전문(유한)00:00.01000000000100000000000:00.000:00.00001-01-01 00:00:00.000000N128:07.25878
4959dko3yVaId9dbIJyohOs신보2020제8차유동화전문(유한)00:00.01000000000100000000000:00.000:00.00001-01-01 00:00:00.000000N120:18.95878
4969dko2MSrz89dbIJyohOs신보2020제8차유동화전문(유한)00:00.01000000000100000000000:00.000:00.00001-01-01 00:00:00.000000N109:34.65878
4979dkoL1FzYG9cKnJ7nCgu신보2017제4차유동화전문(유한)00:00.01000000000100000000000:00.000:00.00001-01-01 00:00:00.000000N159:56.85003
4989dkkAjGCTa9c0TGcVUOw신보2019제4차유동화전문(유한)00:00.040000000040000000000:00.000:00.00001-01-01 00:00:00.000000N147:05.05692
4999dkg3yAA4V9bQwqLHFTC2011신보그레이트제1차유동화전문(유한)00:00.01000000000100000000000:00.000:00.000:00.0Y234:11.85003