Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells500
Missing cells (%)7.7%
Duplicate rows6
Duplicate rows (%)1.2%
Total size in memory52.9 KiB
Average record size in memory108.3 B

Variable types

Categorical5
Unsupported1
Boolean2
Numeric1
Text4

Dataset

Description해당 파일 데이터는 신용보증기금의 전자문서 관리시스템 마스터에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093081/fileData.do

Alerts

삭제여부 has constant value ""Constant
Dataset has 6 (1.2%) duplicate rowsDuplicates
조사기준일자 is highly overall correlated with 전자문서업무구분코드High correlation
전자문서업무구분코드 is highly overall correlated with 조사기준일자High correlation
반송일자 is highly imbalanced (97.9%)Imbalance
조사기준일자 is highly imbalanced (88.2%)Imbalance
부실처리일자 is highly imbalanced (61.9%)Imbalance
이행접수번호 is highly imbalanced (97.9%)Imbalance
부실통지처명 has 500 (100.0%) missing valuesMissing
부실통지처명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 14:52:42.117875
Analysis finished2023-12-12 14:52:43.078154
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

반송일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
499 
00:00.0
 
1

Length

Max length26
Median length26
Mean length25.962
Min length7

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
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 499
99.8%
00:00.0 1
 
0.2%

Length

2023-12-12T23:52:43.159516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:52:43.271286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 499
49.9%
00:00:00.000000 499
49.9%
00:00.0 1
 
0.1%

조사기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
492 
00:00.0
 
8

Length

Max length26
Median length26
Mean length25.696
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
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 492
98.4%
00:00.0 8
 
1.6%

Length

2023-12-12T23:52:43.493350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:52:43.647490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 492
49.6%
00:00:00.000000 492
49.6%
00:00.0 8
 
0.8%

부실처리일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
463 
00:00.0
 
37

Length

Max length26
Median length26
Mean length24.594
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 463
92.6%
00:00.0 37
 
7.4%

Length

2023-12-12T23:52:43.780924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:52:43.923437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 463
48.1%
00:00:00.000000 463
48.1%
00:00.0 37
 
3.8%

부실통지처명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

이행접수번호
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
499 
20211175
 
1

Length

Max length8
Median length4
Mean length4.008
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 499
99.8%
20211175 1
 
0.2%

Length

2023-12-12T23:52:44.093866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:52:44.254765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 499
99.8%
20211175 1
 
0.2%

이미지유무
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
277 
True
223 
ValueCountFrequency (%)
False 277
55.4%
True 223
44.6%
2023-12-12T23:52:44.364432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

전자문서업무구분코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
347 
2
109 
3
 
32
6
 
8
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 347
69.4%
2 109
 
21.8%
3 32
 
6.4%
6 8
 
1.6%
4 4
 
0.8%

Length

2023-12-12T23:52:44.492507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:52:44.600789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 347
69.4%
2 109
 
21.8%
3 32
 
6.4%
6 8
 
1.6%
4 4
 
0.8%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T23:52:44.690042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.962
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:52:44.822173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile12
Maximum78
Range77
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.0902165
Coefficient of variation (CV)1.5371571
Kurtosis69.19213
Mean3.962
Median Absolute Deviation (MAD)1
Skewness7.1497763
Sum1981
Variance37.090737
MonotonicityNot monotonic
2023-12-12T23:52:44.968774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 147
29.4%
2 114
22.8%
4 70
14.0%
3 66
13.2%
5 22
 
4.4%
8 16
 
3.2%
6 15
 
3.0%
9 9
 
1.8%
11 8
 
1.6%
12 5
 
1.0%
Other values (15) 28
 
5.6%
ValueCountFrequency (%)
1 147
29.4%
2 114
22.8%
3 66
13.2%
4 70
14.0%
5 22
 
4.4%
6 15
 
3.0%
7 3
 
0.6%
8 16
 
3.2%
9 9
 
1.8%
10 4
 
0.8%
ValueCountFrequency (%)
78 1
0.2%
60 1
0.2%
55 1
0.2%
40 1
0.2%
21 2
0.4%
20 1
0.2%
19 2
0.4%
18 1
0.2%
17 2
0.4%
16 1
0.2%

처리시각
Text

Distinct438
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:52:45.340731image/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

Unique385 ?
Unique (%)77.0%

Sample

1st row55:03.4
2nd row55:01.3
3rd row55:00.5
4th row54:58.8
5th row54:57.5
ValueCountFrequency (%)
42:49.0 3
 
0.6%
43:51.3 3
 
0.6%
42:50.1 3
 
0.6%
42:49.8 3
 
0.6%
42:49.5 3
 
0.6%
42:49.3 3
 
0.6%
43:51.2 3
 
0.6%
42:50.4 3
 
0.6%
51:48.3 3
 
0.6%
43:51.9 2
 
0.4%
Other values (428) 471
94.2%
2023-12-12T23:52:45.874909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 471
13.5%
3 360
10.3%
5 356
10.2%
2 302
8.6%
0 244
7.0%
1 206
5.9%
6 150
 
4.3%
8 148
 
4.2%
Other values (2) 263
7.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 471
18.8%
3 360
14.4%
5 356
14.2%
2 302
12.1%
0 244
9.8%
1 206
8.2%
6 150
 
6.0%
8 148
 
5.9%
9 139
 
5.6%
7 124
 
5.0%
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%
4 471
13.5%
3 360
10.3%
5 356
10.2%
2 302
8.6%
0 244
7.0%
1 206
5.9%
6 150
 
4.3%
8 148
 
4.2%
Other values (2) 263
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 471
13.5%
3 360
10.3%
5 356
10.2%
2 302
8.6%
0 244
7.0%
1 206
5.9%
6 150
 
4.3%
8 148
 
4.2%
Other values (2) 263
7.5%

처리직원번호
Text

Distinct286
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:52:46.285494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.304
Min length4

Characters and Unicode

Total characters2152
Distinct characters15
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

Unique196 ?
Unique (%)39.2%

Sample

1st row4010
2nd row3606
3rd row5634
4th row5600
5th row5600
ValueCountFrequency (%)
esb 97
 
19.4%
5267 8
 
1.6%
9c798 4
 
0.8%
5484 4
 
0.8%
95980 4
 
0.8%
5636 4
 
0.8%
9c667 3
 
0.6%
5895 3
 
0.6%
5342 3
 
0.6%
5631 3
 
0.6%
Other values (276) 367
73.4%
2023-12-12T23:52:46.870132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 270
12.5%
6 217
10.1%
194
9.0%
4 190
8.8%
9 169
 
7.9%
0 156
 
7.2%
1 132
 
6.1%
3 130
 
6.0%
8 128
 
5.9%
2 118
 
5.5%
Other values (5) 448
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1628
75.7%
Uppercase Letter 330
 
15.3%
Space Separator 194
 
9.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 270
16.6%
6 217
13.3%
4 190
11.7%
9 169
10.4%
0 156
9.6%
1 132
8.1%
3 130
8.0%
8 128
7.9%
2 118
7.2%
7 118
7.2%
Uppercase Letter
ValueCountFrequency (%)
E 97
29.4%
S 97
29.4%
B 97
29.4%
C 39
11.8%
Space Separator
ValueCountFrequency (%)
194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1822
84.7%
Latin 330
 
15.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 270
14.8%
6 217
11.9%
194
10.6%
4 190
10.4%
9 169
9.3%
0 156
8.6%
1 132
7.2%
3 130
7.1%
8 128
7.0%
2 118
6.5%
Latin
ValueCountFrequency (%)
E 97
29.4%
S 97
29.4%
B 97
29.4%
C 39
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 270
12.5%
6 217
10.1%
194
9.0%
4 190
8.8%
9 169
 
7.9%
0 156
 
7.2%
1 132
 
6.1%
3 130
 
6.0%
8 128
 
5.9%
2 118
 
5.5%
Other values (5) 448
20.8%

최초처리시각
Text

Distinct479
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:52:47.338408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length7.304
Min length7

Characters and Unicode

Total characters3652
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique466 ?
Unique (%)93.2%

Sample

1st row48:03.0
2nd row55:01.3
3rd row55:00.5
4th row29:46.3
5th row51:02.8
ValueCountFrequency (%)
0001-01-01 8
 
1.6%
00:00:00.000000 8
 
1.6%
39:44.6 3
 
0.6%
14:18.0 3
 
0.6%
52:28.8 2
 
0.4%
39:24.2 2
 
0.4%
24:29.0 2
 
0.4%
31:03.9 2
 
0.4%
51:01.2 2
 
0.4%
53:23.2 2
 
0.4%
Other values (470) 474
93.3%
2023-12-12T23:52:47.891608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 508
13.9%
. 500
13.7%
0 418
11.4%
4 360
9.9%
1 325
8.9%
5 316
8.7%
2 312
8.5%
3 311
8.5%
8 166
 
4.5%
6 149
 
4.1%
Other values (4) 287
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2620
71.7%
Other Punctuation 1008
 
27.6%
Dash Punctuation 16
 
0.4%
Space Separator 8
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 418
16.0%
4 360
13.7%
1 325
12.4%
5 316
12.1%
2 312
11.9%
3 311
11.9%
8 166
 
6.3%
6 149
 
5.7%
9 136
 
5.2%
7 127
 
4.8%
Other Punctuation
ValueCountFrequency (%)
: 508
50.4%
. 500
49.6%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3652
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 508
13.9%
. 500
13.7%
0 418
11.4%
4 360
9.9%
1 325
8.9%
5 316
8.7%
2 312
8.5%
3 311
8.5%
8 166
 
4.5%
6 149
 
4.1%
Other values (4) 287
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3652
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 508
13.9%
. 500
13.7%
0 418
11.4%
4 360
9.9%
1 325
8.9%
5 316
8.7%
2 312
8.5%
3 311
8.5%
8 166
 
4.5%
6 149
 
4.1%
Other values (4) 287
7.9%

최초처리직원번호
Text

Distinct292
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:52:48.382658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.152
Min length4

Characters and Unicode

Total characters2076
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)39.2%

Sample

1st row4010
2nd row3606
3rd row5634
4th row5600
5th row5600
ValueCountFrequency (%)
5413 52
 
10.4%
5267 8
 
1.6%
batch 8
 
1.6%
5937 8
 
1.6%
5484 7
 
1.4%
9c646 6
 
1.2%
pdf01 6
 
1.2%
5974 5
 
1.0%
4525 5
 
1.0%
95980 4
 
0.8%
Other values (282) 391
78.2%
2023-12-12T23:52:48.961411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 363
17.5%
4 272
13.1%
6 225
10.8%
3 196
9.4%
9 196
9.4%
1 192
9.2%
0 152
7.3%
7 134
 
6.5%
8 125
 
6.0%
2 116
 
5.6%
Other values (8) 105
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1971
94.9%
Uppercase Letter 105
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 363
18.4%
4 272
13.8%
6 225
11.4%
3 196
9.9%
9 196
9.9%
1 192
9.7%
0 152
7.7%
7 134
 
6.8%
8 125
 
6.3%
2 116
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 55
52.4%
B 8
 
7.6%
T 8
 
7.6%
H 8
 
7.6%
A 8
 
7.6%
P 6
 
5.7%
D 6
 
5.7%
F 6
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1971
94.9%
Latin 105
 
5.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 363
18.4%
4 272
13.8%
6 225
11.4%
3 196
9.9%
9 196
9.9%
1 192
9.7%
0 152
7.7%
7 134
 
6.8%
8 125
 
6.3%
2 116
 
5.9%
Latin
ValueCountFrequency (%)
C 55
52.4%
B 8
 
7.6%
T 8
 
7.6%
H 8
 
7.6%
A 8
 
7.6%
P 6
 
5.7%
D 6
 
5.7%
F 6
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2076
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 363
17.5%
4 272
13.1%
6 225
10.8%
3 196
9.4%
9 196
9.4%
1 192
9.2%
0 152
7.3%
7 134
 
6.5%
8 125
 
6.0%
2 116
 
5.6%
Other values (8) 105
 
5.1%

Interactions

2023-12-12T23:52:42.582980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:52:49.086331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반송일자조사기준일자부실처리일자이미지유무전자문서업무구분코드최종수정수
반송일자1.0000.0000.0000.0000.0000.000
조사기준일자0.0001.0000.0000.1371.0000.000
부실처리일자0.0000.0001.0000.0980.0000.000
이미지유무0.0000.1370.0981.0000.1360.000
전자문서업무구분코드0.0001.0000.0000.1361.0000.000
최종수정수0.0000.0000.0000.0000.0001.000
2023-12-12T23:52:49.556381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사기준일자전자문서업무구분코드이미지유무반송일자부실처리일자이행접수번호
조사기준일자1.0000.9970.0880.0000.000NaN
전자문서업무구분코드0.9971.0000.1650.0000.000NaN
이미지유무0.0880.1651.0000.0000.062NaN
반송일자0.0000.0000.0001.0000.000NaN
부실처리일자0.0000.0000.0620.0001.000NaN
이행접수번호NaNNaNNaNNaNNaN1.000
2023-12-12T23:52:49.672108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수반송일자조사기준일자부실처리일자이행접수번호이미지유무전자문서업무구분코드
최종수정수1.0000.0000.0000.000NaN0.0000.000
반송일자0.0001.0000.0000.000NaN0.0000.000
조사기준일자0.0000.0001.0000.000NaN0.0880.997
부실처리일자0.0000.0000.0001.000NaN0.0620.000
이행접수번호NaNNaNNaNNaN1.000NaNNaN
이미지유무0.0000.0000.0880.062NaN1.0000.165
전자문서업무구분코드0.0000.0000.9970.000NaN0.1651.000

Missing values

2023-12-12T23:52:42.748846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:52:42.983741image/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

반송일자조사기준일자부실처리일자부실통지처명이행접수번호이미지유무전자문서업무구분코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
00001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N555:03.4401048:03.04010
10001-01-01 00:00:00.0000000001-01-01 00:00:00.00000000:00.0<NA><NA>N1N155:01.3360655:01.33606
20001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N2N155:00.5563455:00.55634
30001-01-01 00:00:00.0000000001-01-01 00:00:00.00000000:00.0<NA><NA>Y1N454:58.8560029:46.35600
40001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y1N454:57.5560051:02.85600
50001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N2154:54.139530001-01-01 00:00:00.000000BATCH
60001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N454:52.69266829:05.492668
70001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N454:51.59266845:54.592668
80001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N154:50.5472054:50.54720
90001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N654:44.9620626:35.56206
반송일자조사기준일자부실처리일자부실통지처명이행접수번호이미지유무전자문서업무구분코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4900001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N926:00.9489149:27.43953
4910001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N225:58.8554146:43.45541
4920001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N325:57.69070614:29.190706
4930001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N3N125:57.4467925:57.44679
4940001-01-01 00:00:00.0000000001-01-01 00:00:00.00000000:00.0<NA><NA>N1N425:56.9546710:10.55467
4950001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N425:55.2571550:49.55715
4960001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N425:54.5534245:50.75342
4970001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N825:53.7534252:16.15342
4980001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>Y2N525:53.1534212:08.75342
4990001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA><NA>N1N125:52.59C62925:52.59C629

Duplicate rows

Most frequently occurring

반송일자조사기준일자부실처리일자이행접수번호이미지유무전자문서업무구분코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호# duplicates
30001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>N1N451:48.39C79814:18.09C7983
00001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>N1N142:21.1541142:21.154112
10001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>N1N153:23.2609753:23.260972
20001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>N1N239:54.3559039:44.655902
40001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>N1N842:30.7560109:20.656012
50001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000<NA>Y1N233:47.5ESB24:29.057552