Overview

Dataset statistics

Number of variables15
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory60.7 KiB
Average record size in memory124.3 B

Variable types

Numeric2
Categorical9
DateTime1
Boolean1
Text2

Dataset

Description해당 파일 데이터는 신용보증기금의 결산 마감 정보에 관련된 내용을 확인하실 수 있는 자료이니 활용에 참고하시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092676/fileData.do

Alerts

마감일자 has constant value ""Constant
삭제여부 has constant value ""Constant
배치성공여부 is highly overall correlated with 결산마감구분코드 and 2 other fieldsHigh correlation
최종수정수 is highly overall correlated with 결산마감구분코드 and 2 other fieldsHigh correlation
배치오류내용 is highly overall correlated with 결산마감구분코드 and 4 other fieldsHigh correlation
마감취소사유내용 is highly overall correlated with 결산마감구분코드 and 4 other fieldsHigh correlation
배치프로그램명 is highly overall correlated with 결산마감구분코드 and 3 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 마감취소사유내용 and 1 other fieldsHigh correlation
결산업무구분코드 is highly overall correlated with 결산마감구분코드 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 마감취소사유내용 and 1 other fieldsHigh correlation
결산마감구분코드 is highly overall correlated with 결산업무구분코드 and 5 other fieldsHigh correlation
회계구분코드 is highly overall correlated with 마감취소사유내용 and 1 other fieldsHigh correlation
마감취소사유내용 is highly imbalanced (71.5%)Imbalance
배치성공여부 is highly imbalanced (59.1%)Imbalance
배치오류내용 is highly imbalanced (58.8%)Imbalance
배치프로그램명 is highly imbalanced (54.2%)Imbalance
최종수정수 is highly imbalanced (59.1%)Imbalance
처리직원번호 is highly imbalanced (53.5%)Imbalance
최초처리직원번호 is highly imbalanced (53.5%)Imbalance

Reproduction

Analysis started2024-04-17 10:57:27.330479
Analysis finished2024-04-17 10:57:28.430120
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

회계년월
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202107.1
Minimum202104
Maximum202109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T19:57:28.471084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202104
5-th percentile202105
Q1202106
median202107
Q3202108
95-th percentile202109
Maximum202109
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2481586
Coefficient of variation (CV)6.1757286 × 10-6
Kurtosis-0.47793864
Mean202107.1
Median Absolute Deviation (MAD)1
Skewness-0.1203725
Sum1.0105355 × 108
Variance1.5578998
MonotonicityNot monotonic
2024-04-17T19:57:28.561692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
202107 142
28.4%
202106 141
28.2%
202108 99
19.8%
202109 85
17.0%
202105 19
 
3.8%
202104 14
 
2.8%
ValueCountFrequency (%)
202104 14
 
2.8%
202105 19
 
3.8%
202106 141
28.2%
202107 142
28.4%
202108 99
19.8%
202109 85
17.0%
ValueCountFrequency (%)
202109 85
17.0%
202108 99
19.8%
202107 142
28.4%
202106 141
28.2%
202105 19
 
3.8%
202104 14
 
2.8%

회계구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
319 
S
90 
I
32 
C
 
31
R
 
28

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 319
63.8%
S 90
 
18.0%
I 32
 
6.4%
C 31
 
6.2%
R 28
 
5.6%

Length

2024-04-17T19:57:28.656910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:28.739775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 319
63.8%
s 90
 
18.0%
i 32
 
6.4%
c 31
 
6.2%
r 28
 
5.6%

결산업무구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
317 
3
121 
2
62 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 317
63.4%
3 121
 
24.2%
2 62
 
12.4%

Length

2024-04-17T19:57:28.822794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:28.892768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 317
63.4%
3 121
 
24.2%
2 62
 
12.4%

결산마감구분코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1905.4
Minimum1000
Maximum3900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-17T19:57:28.966424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q11200
median1500
Q32300
95-th percentile3300
Maximum3900
Range2900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation845.58907
Coefficient of variation (CV)0.4437856
Kurtosis-0.71875113
Mean1905.4
Median Absolute Deviation (MAD)400
Skewness0.80060046
Sum952700
Variance715020.88
MonotonicityNot monotonic
2024-04-17T19:57:29.050966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1100 55
11.0%
1200 48
9.6%
1900 46
9.2%
1300 45
9.0%
1400 43
8.6%
1500 42
8.4%
1000 38
 
7.6%
3100 29
 
5.8%
3300 28
 
5.6%
3200 26
 
5.2%
Other values (6) 100
20.0%
ValueCountFrequency (%)
1000 38
7.6%
1100 55
11.0%
1200 48
9.6%
1300 45
9.0%
1400 43
8.6%
1500 42
8.4%
1900 46
9.2%
2000 11
 
2.2%
2100 16
 
3.2%
2200 16
 
3.2%
ValueCountFrequency (%)
3900 15
 
3.0%
3300 28
5.6%
3200 26
5.2%
3100 29
5.8%
3000 23
4.6%
2300 19
3.8%
2200 16
 
3.2%
2100 16
 
3.2%
2000 11
 
2.2%
1900 46
9.2%

마감일자
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
2024-04-17T19:57:29.125939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:57:29.193782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

마감취소사유내용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
403 
1
63 
국가결산서생성완료
 
23
결산보정 입력
 
2
가결산 수정
 
2
Other values (7)
 
7

Length

Max length9
Median length4
Mean length3.908
Min length1

Unique

Unique7 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row역분개 입력예정
3rd row국가결산서생성완료
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 403
80.6%
1 63
 
12.6%
국가결산서생성완료 23
 
4.6%
결산보정 입력 2
 
0.4%
가결산 수정 2
 
0.4%
역분개 입력예정 1
 
0.2%
결산 보정 1
 
0.2%
결산보정 1
 
0.2%
결산보정 수정 1
 
0.2%
보정기표 입력 1
 
0.2%
Other values (2) 2
 
0.4%

Length

2024-04-17T19:57:29.284922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 403
79.2%
1 63
 
12.4%
국가결산서생성완료 23
 
4.5%
결산보정 4
 
0.8%
수정 4
 
0.8%
입력 3
 
0.6%
가결산 2
 
0.4%
역분개 1
 
0.2%
입력예정 1
 
0.2%
결산 1
 
0.2%
Other values (4) 4
 
0.8%

배치성공여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Y
459 
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 459
91.8%
41
 
8.2%

Length

2024-04-17T19:57:29.380598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:29.451991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 459
100.0%

배치오류내용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
437 
보고서생성배치 완료
46 
배치수행 완료
 
17

Length

Max length10
Median length4
Mean length4.654
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row보고서생성배치 완료
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 437
87.4%
보고서생성배치 완료 46
 
9.2%
배치수행 완료 17
 
3.4%

Length

2024-04-17T19:57:29.539603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:29.622372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 437
77.6%
완료 63
 
11.2%
보고서생성배치 46
 
8.2%
배치수행 17
 
3.0%

배치프로그램명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
414 
AND301_00JO
44 
AAD201_00NBO
 
25
AAD301_00NBO
 
17

Length

Max length12
Median length4
Mean length5.288
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd rowAND301_00JO
4th rowAND301_00JO
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 414
82.8%
AND301_00JO 44
 
8.8%
AAD201_00NBO 25
 
5.0%
AAD301_00NBO 17
 
3.4%

Length

2024-04-17T19:57:29.731665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:29.828587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 414
82.8%
and301_00jo 44
 
8.8%
aad201_00nbo 25
 
5.0%
aad301_00nbo 17
 
3.4%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2024-04-17T19:57:29.892696image/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
2
459 
1
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 459
91.8%
1 41
 
8.2%

Length

2024-04-17T19:57:29.970447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:30.043595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 459
91.8%
1 41
 
8.2%

처리시각
Text

Distinct470
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T19:57:30.301506image/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

Unique441 ?
Unique (%)88.2%

Sample

1st row46:23.3
2nd row46:21.7
3rd row33:57.0
4th row08:01.5
5th row01:41.8
ValueCountFrequency (%)
45:30.0 3
 
0.6%
18:15.3 2
 
0.4%
45:22.0 2
 
0.4%
44:30.3 2
 
0.4%
17:01.8 2
 
0.4%
00:23.5 2
 
0.4%
59:51.0 2
 
0.4%
40:44.3 2
 
0.4%
00:37.6 2
 
0.4%
40:48.5 2
 
0.4%
Other values (460) 479
95.8%
2024-04-17T19:57:30.704986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 377
10.8%
4 340
9.7%
5 337
9.6%
0 322
9.2%
1 268
7.7%
2 250
7.1%
9 163
 
4.7%
7 154
 
4.4%
Other values (2) 289
8.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 377
15.1%
4 340
13.6%
5 337
13.5%
0 322
12.9%
1 268
10.7%
2 250
10.0%
9 163
6.5%
7 154
6.2%
6 150
 
6.0%
8 139
 
5.6%
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%
3 377
10.8%
4 340
9.7%
5 337
9.6%
0 322
9.2%
1 268
7.7%
2 250
7.1%
9 163
 
4.7%
7 154
 
4.4%
Other values (2) 289
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 377
10.8%
4 340
9.7%
5 337
9.6%
0 322
9.2%
1 268
7.7%
2 250
7.1%
9 163
 
4.7%
7 154
 
4.4%
Other values (2) 289
8.3%

처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
5803
360 
BATCH
65 
5314
 
28
5538
 
18
4645
 
16
Other values (4)
 
13

Length

Max length5
Median length4
Mean length4.13
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4936
2nd row4936
3rd rowBATCH
4th row5314
5th row5314

Common Values

ValueCountFrequency (%)
5803 360
72.0%
BATCH 65
 
13.0%
5314 28
 
5.6%
5538 18
 
3.6%
4645 16
 
3.2%
5595 6
 
1.2%
5255 3
 
0.6%
4936 2
 
0.4%
5032 2
 
0.4%

Length

2024-04-17T19:57:30.844065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:30.945473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5803 360
72.0%
batch 65
 
13.0%
5314 28
 
5.6%
5538 18
 
3.6%
4645 16
 
3.2%
5595 6
 
1.2%
5255 3
 
0.6%
4936 2
 
0.4%
5032 2
 
0.4%

최초처리시각
Text

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-17T19:57:31.245940image/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

Unique494 ?
Unique (%)98.8%

Sample

1st row46:23.3
2nd row33:57.0
3rd row08:01.5
4th row01:42.1
5th row01:40.5
ValueCountFrequency (%)
33:57.0 2
 
0.4%
00:37.6 2
 
0.4%
45:30.0 2
 
0.4%
42:51.7 1
 
0.2%
22:07.8 1
 
0.2%
48:24.4 1
 
0.2%
03:08.8 1
 
0.2%
06:28.2 1
 
0.2%
03:59.1 1
 
0.2%
48:35.9 1
 
0.2%
Other values (487) 487
97.4%
2024-04-17T19:57:31.618020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 372
10.6%
5 343
9.8%
4 339
9.7%
0 329
9.4%
1 266
7.6%
2 257
7.3%
7 160
 
4.6%
9 160
 
4.6%
Other values (2) 274
7.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 372
14.9%
5 343
13.7%
4 339
13.6%
0 329
13.2%
1 266
10.6%
2 257
10.3%
7 160
6.4%
9 160
6.4%
6 137
 
5.5%
8 137
 
5.5%
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%
3 372
10.6%
5 343
9.8%
4 339
9.7%
0 329
9.4%
1 266
7.6%
2 257
7.3%
7 160
 
4.6%
9 160
 
4.6%
Other values (2) 274
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 372
10.6%
5 343
9.8%
4 339
9.7%
0 329
9.4%
1 266
7.6%
2 257
7.3%
7 160
 
4.6%
9 160
 
4.6%
Other values (2) 274
7.8%

최초처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
5803
360 
BATCH
65 
5314
 
28
5538
 
18
4645
 
16
Other values (4)
 
13

Length

Max length5
Median length4
Mean length4.13
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4936
2nd row4936
3rd rowBATCH
4th row5314
5th row5314

Common Values

ValueCountFrequency (%)
5803 360
72.0%
BATCH 65
 
13.0%
5314 28
 
5.6%
5538 18
 
3.6%
4645 16
 
3.2%
5595 6
 
1.2%
5255 3
 
0.6%
4936 2
 
0.4%
5032 2
 
0.4%

Length

2024-04-17T19:57:31.728176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T19:57:31.818463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5803 360
72.0%
batch 65
 
13.0%
5314 28
 
5.6%
5538 18
 
3.6%
4645 16
 
3.2%
5595 6
 
1.2%
5255 3
 
0.6%
4936 2
 
0.4%
5032 2
 
0.4%

Interactions

2024-04-17T19:57:28.050015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:57:27.919557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:57:28.117110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T19:57:27.983799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T19:57:31.891561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년월회계구분코드결산업무구분코드결산마감구분코드마감취소사유내용배치성공여부배치오류내용배치프로그램명최종수정수처리직원번호최초처리직원번호
회계년월1.0000.3590.1300.0000.2180.0000.0000.0000.0000.3700.370
회계구분코드0.3591.0000.3040.2230.7960.0710.4520.5030.0710.5440.544
결산업무구분코드0.1300.3041.0000.9060.5310.0380.5541.0000.0380.3410.341
결산마감구분코드0.0000.2230.9061.0000.9790.4671.0001.0000.4670.3700.370
마감취소사유내용0.2180.7960.5310.9791.0000.000NaNNaN0.0000.9530.953
배치성공여부0.0000.0710.0380.4670.0001.000NaN0.0001.0000.1270.127
배치오류내용0.0000.4520.5541.000NaNNaN1.0001.000NaN0.0000.000
배치프로그램명0.0000.5031.0001.000NaN0.0001.0001.0000.0000.7420.742
최종수정수0.0000.0710.0380.4670.0001.000NaN0.0001.0000.1270.127
처리직원번호0.3700.5440.3410.3700.9530.1270.0000.7420.1271.0001.000
최초처리직원번호0.3700.5440.3410.3700.9530.1270.0000.7420.1271.0001.000
2024-04-17T19:57:32.021635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배치성공여부최종수정수배치오류내용마감취소사유내용배치프로그램명최초처리직원번호회계구분코드결산업무구분코드처리직원번호
배치성공여부1.0000.9871.0000.0000.0000.1260.0870.0630.126
최종수정수0.9871.0001.0000.0000.0000.1260.0870.0630.126
배치오류내용1.0001.0001.000NaN0.9920.0000.5360.3740.000
마감취소사유내용0.0000.000NaN1.0001.0000.8390.5770.3490.839
배치프로그램명0.0000.0000.9921.0001.0000.3990.4330.9940.399
최초처리직원번호0.1260.1260.0000.8390.3991.0000.3510.1591.000
회계구분코드0.0870.0870.5360.5770.4330.3511.0000.2400.351
결산업무구분코드0.0630.0630.3740.3490.9940.1590.2401.0000.159
처리직원번호0.1260.1260.0000.8390.3991.0000.3510.1591.000
2024-04-17T19:57:32.152979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
회계년월결산마감구분코드회계구분코드결산업무구분코드마감취소사유내용배치성공여부배치오류내용배치프로그램명최종수정수처리직원번호최초처리직원번호
회계년월1.000-0.0040.1470.1070.0000.0000.0000.0000.0000.1950.195
결산마감구분코드-0.0041.0000.1300.9100.5320.5050.9921.0000.5050.2080.208
회계구분코드0.1470.1301.0000.2400.5770.0870.5360.4330.0870.3510.351
결산업무구분코드0.1070.9100.2401.0000.3490.0630.3740.9940.0630.1590.159
마감취소사유내용0.0000.5320.5770.3491.0000.0000.0001.0000.0000.8390.839
배치성공여부0.0000.5050.0870.0630.0001.0001.0000.0000.9870.1260.126
배치오류내용0.0000.9920.5360.3740.0001.0001.0000.9921.0000.0000.000
배치프로그램명0.0001.0000.4330.9941.0000.0000.9921.0000.0000.3990.399
최종수정수0.0000.5050.0870.0630.0000.9871.0000.0001.0000.1260.126
처리직원번호0.1950.2080.3510.1590.8390.1260.0000.3990.1261.0001.000
최초처리직원번호0.1950.2080.3510.1590.8390.1260.0000.3990.1261.0001.000

Missing values

2024-04-17T19:57:28.218562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T19:57:28.371077image/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

회계년월회계구분코드결산업무구분코드결산마감구분코드마감일자마감취소사유내용배치성공여부배치오류내용배치프로그램명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0202109G3310000:00.0<NA><NA><NA>N146:23.3493646:23.34936
1202109G3300000:00.0역분개 입력예정Y<NA><NA>N246:21.7493633:57.04936
2202109G3330000:00.0국가결산서생성완료Y<NA>AND301_00JON233:57.0BATCH08:01.5BATCH
3202109G3320000:00.0<NA>Y보고서생성배치 완료AND301_00JON208:01.5531401:42.15314
4202109G3310000:00.0<NA>Y<NA><NA>N201:41.8531401:40.55314
5202109G3300000:00.01Y<NA><NA>N201:35.6531434:17.75314
6202109G1190000:00.0<NA><NA><NA>N101:33.0531401:33.05314
7202109G1150000:00.0<NA>Y<NA><NA>N201:33.0BATCH51:23.3BATCH
8202109G1140000:00.0<NA>Y보고서생성배치 완료AAD201_00NBON251:23.3531434:43.95314
9202109G1130000:00.0<NA>Y<NA><NA>N234:43.9531434:41.75314
회계년월회계구분코드결산업무구분코드결산마감구분코드마감일자마감취소사유내용배치성공여부배치오류내용배치프로그램명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490202106I1120000:00.0<NA>Y배치수행 완료AAD301_00NBON247:56.0580359:25.25803
491202106C1120000:00.0<NA>Y<NA><NA>N244:42.4559531:07.45595
492202106R1120000:00.0<NA>Y<NA><NA>N220:36.3503236:15.95032
493202106I1110000:00.0<NA>Y<NA><NA>N259:25.2580359:24.55803
494202106I1100000:00.01Y<NA><NA>N259:24.5580359:15.75803
495202106I1190000:00.0<NA>Y<NA><NA>N259:15.7580329:20.05803
496202106R1110000:00.0결산보정수정Y<NA><NA>N236:15.9503234:20.55032
497202106R1190000:00.0<NA>Y<NA><NA>N234:20.5580329:07.95803
498202106C1110000:00.0결산보정분개 수정Y<NA><NA>N231:07.4559531:02.65595
499202106C1130000:00.0<NA>Y<NA><NA>N231:02.6559530:35.85595