Overview

Dataset statistics

Number of variables10
Number of observations71
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory83.9 B

Variable types

Text1
Categorical9

Dataset

Description해당 파일 데이터는 신용보증기금의 결산 예외 전표처리 구분정보를 확인하실 수 있는 자료이니 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092672/fileData.do

Alerts

유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
최초처리시각 is highly overall correlated with 처리시각 and 2 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 이력일련번호 and 3 other fieldsHigh correlation
처리시각 is highly overall correlated with 이력일련번호 and 4 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 이력일련번호 and 4 other fieldsHigh correlation
이력일련번호 is highly overall correlated with 처리시각 and 2 other fieldsHigh correlation
회계구분코드 is highly overall correlated with 처리시각 and 1 other fieldsHigh correlation
이력일련번호 is highly imbalanced (89.3%)Imbalance
최종수정수 is highly imbalanced (67.0%)Imbalance
최초처리시각 is highly imbalanced (51.1%)Imbalance

Reproduction

Analysis started2024-04-18 01:01:26.476277
Analysis finished2024-04-18 01:01:27.956007
Duration1.48 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

예외전표처리구분ID
Text

Distinct70
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
2024-04-18T10:01:28.098410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters710
Distinct characters58
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

Unique69 ?
Unique (%)97.2%

Sample

1st row9dap4lJTiO
2nd row9dap4jo7Y9
3rd row9dap4gxjXa
4th row9dap4eBvem
5th row9dap4ajkoM
ValueCountFrequency (%)
9b5fnursth 2
 
2.8%
a000000021 1
 
1.4%
a000000027 1
 
1.4%
a000000026 1
 
1.4%
a000000025 1
 
1.4%
a000000024 1
 
1.4%
a000000023 1
 
1.4%
a000000032 1
 
1.4%
a000000020 1
 
1.4%
a000000028 1
 
1.4%
Other values (60) 60
84.5%
2024-04-18T10:01:28.403762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 365
51.4%
A 54
 
7.6%
9 28
 
3.9%
4 24
 
3.4%
3 18
 
2.5%
1 16
 
2.3%
2 16
 
2.3%
c 12
 
1.7%
a 11
 
1.5%
d 10
 
1.4%
Other values (48) 156
22.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 497
70.0%
Uppercase Letter 115
 
16.2%
Lowercase Letter 98
 
13.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 54
47.0%
Y 7
 
6.1%
X 6
 
5.2%
P 6
 
5.2%
M 4
 
3.5%
O 4
 
3.5%
B 4
 
3.5%
C 3
 
2.6%
D 3
 
2.6%
F 3
 
2.6%
Other values (14) 21
 
18.3%
Lowercase Letter
ValueCountFrequency (%)
c 12
12.2%
a 11
 
11.2%
d 10
 
10.2%
p 9
 
9.2%
b 8
 
8.2%
f 5
 
5.1%
e 4
 
4.1%
u 4
 
4.1%
h 4
 
4.1%
j 3
 
3.1%
Other values (14) 28
28.6%
Decimal Number
ValueCountFrequency (%)
0 365
73.4%
9 28
 
5.6%
4 24
 
4.8%
3 18
 
3.6%
1 16
 
3.2%
2 16
 
3.2%
5 9
 
1.8%
8 8
 
1.6%
7 8
 
1.6%
6 5
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 497
70.0%
Latin 213
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 54
25.4%
c 12
 
5.6%
a 11
 
5.2%
d 10
 
4.7%
p 9
 
4.2%
b 8
 
3.8%
Y 7
 
3.3%
X 6
 
2.8%
P 6
 
2.8%
f 5
 
2.3%
Other values (38) 85
39.9%
Common
ValueCountFrequency (%)
0 365
73.4%
9 28
 
5.6%
4 24
 
4.8%
3 18
 
3.6%
1 16
 
3.2%
2 16
 
3.2%
5 9
 
1.8%
8 8
 
1.6%
7 8
 
1.6%
6 5
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365
51.4%
A 54
 
7.6%
9 28
 
3.9%
4 24
 
3.4%
3 18
 
2.5%
1 16
 
2.3%
2 16
 
2.3%
c 12
 
1.7%
a 11
 
1.5%
d 10
 
1.4%
Other values (48) 156
22.0%

이력일련번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
1
70 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 70
98.6%
2 1
 
1.4%

Length

2024-04-18T10:01:28.517819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:28.600723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 70
98.6%
2 1
 
1.4%

회계구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size700.0 B
G
59 
S
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 59
83.1%
S 12
 
16.9%

Length

2024-04-18T10:01:28.685489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:28.775993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 59
83.1%
s 12
 
16.9%

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
00:00.0
71 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 71
100.0%

Length

2024-04-18T10:01:28.884203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:28.983039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 71
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size700.0 B
00:00.0
71 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 71
100.0%

Length

2024-04-18T10:01:29.067810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:29.144820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 71
100.0%

최종수정수
Categorical

IMBALANCE 

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size700.0 B
1
64 
2
 
6
51
 
1

Length

Max length2
Median length1
Mean length1.0140845
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 64
90.1%
2 6
 
8.5%
51 1
 
1.4%

Length

2024-04-18T10:01:29.228499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:29.318102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 64
90.1%
2 6
 
8.5%
51 1
 
1.4%

처리시각
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
00:00.0
46 
02:40.3
 
1
01:57.9
 
1
01:29.3
 
1
00:25.9
 
1
Other values (21)
21 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique25 ?
Unique (%)35.2%

Sample

1st row03:14.8
2nd row02:40.3
3rd row01:57.9
4th row01:29.3
5th row00:25.9

Common Values

ValueCountFrequency (%)
00:00.0 46
64.8%
02:40.3 1
 
1.4%
01:57.9 1
 
1.4%
01:29.3 1
 
1.4%
00:25.9 1
 
1.4%
59:53.0 1
 
1.4%
59:09.8 1
 
1.4%
58:34.8 1
 
1.4%
53:08.8 1
 
1.4%
52:34.8 1
 
1.4%
Other values (16) 16
 
22.5%

Length

2024-04-18T10:01:29.405005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00.0 46
64.8%
02:40.3 1
 
1.4%
32:18.9 1
 
1.4%
52:58.5 1
 
1.4%
10:38.3 1
 
1.4%
27:19.3 1
 
1.4%
35:13.9 1
 
1.4%
30:03.4 1
 
1.4%
31:09.9 1
 
1.4%
24:11.1 1
 
1.4%
Other values (16) 16
 
22.5%

처리직원번호
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
test1
44 
4645
8 
5196
8 
5207
6 
4416
 
2
Other values (2)
 
3

Length

Max length5
Median length5
Mean length4.6478873
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row4645
2nd row4645
3rd row4645
4th row4645
5th row4645

Common Values

ValueCountFrequency (%)
test1 44
62.0%
4645 8
 
11.3%
5196 8
 
11.3%
5207 6
 
8.5%
4416 2
 
2.8%
TEST1 2
 
2.8%
4572 1
 
1.4%

Length

2024-04-18T10:01:29.511742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:29.605977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
test1 46
64.8%
4645 8
 
11.3%
5196 8
 
11.3%
5207 6
 
8.5%
4416 2
 
2.8%
4572 1
 
1.4%

최초처리시각
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size700.0 B
00:00.0
50 
32:18.9
 
2
02:40.3
 
1
01:57.9
 
1
01:29.3
 
1
Other values (16)
16 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique19 ?
Unique (%)26.8%

Sample

1st row03:14.8
2nd row02:40.3
3rd row01:57.9
4th row01:29.3
5th row00:25.9

Common Values

ValueCountFrequency (%)
00:00.0 50
70.4%
32:18.9 2
 
2.8%
02:40.3 1
 
1.4%
01:57.9 1
 
1.4%
01:29.3 1
 
1.4%
00:25.9 1
 
1.4%
59:53.0 1
 
1.4%
59:09.8 1
 
1.4%
58:34.8 1
 
1.4%
53:08.8 1
 
1.4%
Other values (11) 11
 
15.5%

Length

2024-04-18T10:01:29.710462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00.0 50
70.4%
32:18.9 2
 
2.8%
03:14.8 1
 
1.4%
51:28.0 1
 
1.4%
10:38.3 1
 
1.4%
27:19.3 1
 
1.4%
35:13.9 1
 
1.4%
30:03.4 1
 
1.4%
31:09.9 1
 
1.4%
24:11.1 1
 
1.4%
Other values (11) 11
 
15.5%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size700.0 B
test1
48 
4645
8 
5207
6 
5196
 
4
4416
 
2
Other values (2)
 
3

Length

Max length5
Median length5
Mean length4.7042254
Min length4

Unique

Unique1 ?
Unique (%)1.4%

Sample

1st row4645
2nd row4645
3rd row4645
4th row4645
5th row4645

Common Values

ValueCountFrequency (%)
test1 48
67.6%
4645 8
 
11.3%
5207 6
 
8.5%
5196 4
 
5.6%
4416 2
 
2.8%
TEST1 2
 
2.8%
4572 1
 
1.4%

Length

2024-04-18T10:01:29.816987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T10:01:29.923652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
test1 50
70.4%
4645 8
 
11.3%
5207 6
 
8.5%
5196 4
 
5.6%
4416 2
 
2.8%
4572 1
 
1.4%

Correlations

2024-04-18T10:01:29.995972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예외전표처리구분ID이력일련번호회계구분코드최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
예외전표처리구분ID1.0000.0001.0000.0000.0001.0001.0001.000
이력일련번호0.0001.0000.0000.0001.0000.6200.6110.620
회계구분코드1.0000.0001.0000.2290.7990.7170.2830.484
최종수정수0.0000.0000.2291.0000.7010.5670.0000.296
처리시각0.0001.0000.7990.7011.0000.9701.0000.970
처리직원번호1.0000.6200.7170.5670.9701.0000.9810.999
최초처리시각1.0000.6110.2830.0001.0000.9811.0000.993
최초처리직원번호1.0000.6200.4840.2960.9700.9990.9931.000
2024-04-18T10:01:30.101202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초처리시각이력일련번호최초처리직원번호회계구분코드처리시각처리직원번호최종수정수
최초처리시각1.0000.4580.7740.2020.9490.7070.000
이력일련번호0.4581.0000.6430.0000.8080.6430.000
최초처리직원번호0.7740.6431.0000.4990.7220.9490.199
회계구분코드0.2020.0000.4991.0000.5300.7470.371
처리시각0.9490.8080.7220.5301.0000.7220.388
처리직원번호0.7070.6430.9490.7470.7221.0000.442
최종수정수0.0000.0000.1990.3710.3880.4421.000
2024-04-18T10:01:30.195496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호회계구분코드최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
이력일련번호1.0000.0000.0000.8080.6430.4580.643
회계구분코드0.0001.0000.3710.5300.7470.2020.499
최종수정수0.0000.3711.0000.3880.4420.0000.199
처리시각0.8080.5300.3881.0000.7220.9490.722
처리직원번호0.6430.7470.4420.7221.0000.7070.949
최초처리시각0.4580.2020.0000.9490.7071.0000.774
최초처리직원번호0.6430.4990.1990.7220.9490.7741.000

Missing values

2024-04-18T10:01:27.907900image/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

예외전표처리구분ID이력일련번호회계구분코드유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dap4lJTiO1G00:00.000:00.0103:14.8464503:14.84645
19dap4jo7Y91G00:00.000:00.0102:40.3464502:40.34645
29dap4gxjXa1G00:00.000:00.0101:57.9464501:57.94645
39dap4eBvem1G00:00.000:00.0101:29.3464501:29.34645
49dap4ajkoM1G00:00.000:00.0100:25.9464500:25.94645
59dap374ZVs1G00:00.000:00.0159:53.0464559:53.04645
69dap349RIK1G00:00.000:00.0159:09.8464559:09.84645
79dap32MUuP1G00:00.000:00.0158:34.8464558:34.84645
89cYXfEXR8B1S00:00.000:00.0153:08.8519653:08.85196
99cYXfCE4Dz1S00:00.000:00.0152:34.8519652:34.85196
예외전표처리구분ID이력일련번호회계구분코드유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
61A0000000371S00:00.000:00.0100:00.0test100:00.0test1
62A0000000381S00:00.000:00.0100:00.0test100:00.0test1
63A0000000391G00:00.000:00.0100:00.0test100:00.0test1
64A0000000401G00:00.000:00.0100:00.0test100:00.0test1
65A0000000411G00:00.000:00.0100:00.0test100:00.0test1
66A0000000421G00:00.000:00.0100:00.0test100:00.0test1
67A0000000471G00:00.000:00.05100:00.0test100:00.0test1
68A0000000481G00:00.000:00.0100:00.0test100:00.0test1
69A0000000491G00:00.000:00.0100:00.0TEST100:00.0TEST1
70A0000000501G00:00.000:00.0100:00.0TEST100:00.0TEST1