Overview

Dataset statistics

Number of variables11
Number of observations500
Missing cells502
Missing cells (%)9.1%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory46.0 KiB
Average record size in memory94.3 B

Variable types

Categorical6
Text2
Unsupported1
Numeric1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템관리전자문서첨부파일내역에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093327/fileData.do

Alerts

전자문서문서ID has constant value ""Constant
전자문서파일ID has constant value ""Constant
Dataset has 1 (0.2%) duplicate rowsDuplicates
파일타입명 is highly overall correlated with 처리직원번호High correlation
삭제여부 is highly overall correlated with 최종수정수 and 1 other fieldsHigh correlation
최종수정수 is highly overall correlated with 삭제여부 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 파일타입명 and 2 other fieldsHigh correlation
첨부파일순번 is highly imbalanced (77.9%)Imbalance
파일타입명 is highly imbalanced (71.8%)Imbalance
삭제여부 is highly imbalanced (96.2%)Imbalance
최종수정수 is highly imbalanced (96.2%)Imbalance
처리직원번호 is highly imbalanced (55.8%)Imbalance
첨부파일내용 has 500 (100.0%) missing valuesMissing
첨부파일내용 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 17:08:40.539305
Analysis finished2023-12-12 17:08:41.390398
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전자문서문서ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1630000000000000000
500 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1630000000000000000
2nd row1630000000000000000
3rd row1630000000000000000
4th row1630000000000000000
5th row1630000000000000000

Common Values

ValueCountFrequency (%)
1630000000000000000 500
100.0%

Length

2023-12-13T02:08:41.481324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:08:41.584994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1630000000000000000 500
100.0%

전자문서파일ID
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1630000000000000000
500 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1630000000000000000
2nd row1630000000000000000
3rd row1630000000000000000
4th row1630000000000000000
5th row1630000000000000000

Common Values

ValueCountFrequency (%)
1630000000000000000 500
100.0%

Length

2023-12-13T02:08:41.693562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:08:41.795999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1630000000000000000 500
100.0%

첨부파일순번
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
465 
2
 
26
3
 
5
4
 
4

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 (%)
1 465
93.0%
2 26
 
5.2%
3 5
 
1.0%
4 4
 
0.8%

Length

2023-12-13T02:08:41.911680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:08:42.029907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 465
93.0%
2 26
 
5.2%
3 5
 
1.0%
4 4
 
0.8%

첨부파일명
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T02:08:42.192748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length65
Mean length46
Min length9

Characters and Unicode

Total characters23000
Distinct characters221
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique492 ?
Unique (%)98.4%

Sample

1st row20211019101728_납부내역증명(국세)_대표자.pdf
2nd row9dnUeQsR4H_aaaaadQO9r.pdf
3rd row20211019101728_국세납세증명서_대표자.pdf
4th row9dnUf26E2g_9cQXpVhhHQ.pdf
5th row20211019101728_주민등록초본.pdf
ValueCountFrequency (%)
컨설팅 17
 
2.8%
별첨 14
 
2.3%
7 14
 
2.3%
수행일지 14
 
2.3%
경영진단 3
 
0.5%
애드원경영진단체크리스트.hwp 2
 
0.3%
사회보험료 2
 
0.3%
9월 2
 
0.3%
완납증명.pdf 2
 
0.3%
노우현 2
 
0.3%
Other values (530) 538
88.2%
2023-12-13T02:08:42.581737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4948
21.5%
1 2774
12.1%
_ 2152
 
9.4%
2 1914
 
8.3%
9 1153
 
5.0%
3 716
 
3.1%
8 695
 
3.0%
d 669
 
2.9%
4 572
 
2.5%
6 545
 
2.4%
Other values (211) 6862
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14263
62.0%
Lowercase Letter 2976
 
12.9%
Connector Punctuation 2152
 
9.4%
Uppercase Letter 1718
 
7.5%
Other Letter 1164
 
5.1%
Other Punctuation 511
 
2.2%
Space Separator 110
 
0.5%
Close Punctuation 52
 
0.2%
Open Punctuation 52
 
0.2%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 64
 
5.5%
부 57
 
4.9%
서 52
 
4.5%
가 48
 
4.1%
증 44
 
3.8%
신 42
 
3.6%
표 41
 
3.5%
고 40
 
3.4%
명 38
 
3.3%
원 33
 
2.8%
Other values (139) 705
60.6%
Lowercase Letter
ValueCountFrequency (%)
d 669
22.5%
p 508
17.1%
f 504
16.9%
n 196
 
6.6%
a 138
 
4.6%
c 114
 
3.8%
b 72
 
2.4%
t 72
 
2.4%
i 68
 
2.3%
e 63
 
2.1%
Other values (16) 572
19.2%
Uppercase Letter
ValueCountFrequency (%)
K 272
15.8%
E 218
12.7%
D 201
11.7%
A 127
 
7.4%
H 107
 
6.2%
T 107
 
6.2%
U 89
 
5.2%
S 50
 
2.9%
J 46
 
2.7%
W 44
 
2.6%
Other values (16) 457
26.6%
Decimal Number
ValueCountFrequency (%)
0 4948
34.7%
1 2774
19.4%
2 1914
 
13.4%
9 1153
 
8.1%
3 716
 
5.0%
8 695
 
4.9%
4 572
 
4.0%
6 545
 
3.8%
5 524
 
3.7%
7 422
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 507
99.2%
· 3
 
0.6%
, 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 36
69.2%
] 16
30.8%
Open Punctuation
ValueCountFrequency (%)
( 36
69.2%
[ 16
30.8%
Connector Punctuation
ValueCountFrequency (%)
_ 2152
100.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17142
74.5%
Latin 4694
 
20.4%
Hangul 1164
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 64
 
5.5%
부 57
 
4.9%
서 52
 
4.5%
가 48
 
4.1%
증 44
 
3.8%
신 42
 
3.6%
표 41
 
3.5%
고 40
 
3.4%
명 38
 
3.3%
원 33
 
2.8%
Other values (139) 705
60.6%
Latin
ValueCountFrequency (%)
d 669
 
14.3%
p 508
 
10.8%
f 504
 
10.7%
K 272
 
5.8%
E 218
 
4.6%
D 201
 
4.3%
n 196
 
4.2%
a 138
 
2.9%
A 127
 
2.7%
c 114
 
2.4%
Other values (42) 1747
37.2%
Common
ValueCountFrequency (%)
0 4948
28.9%
1 2774
16.2%
_ 2152
12.6%
2 1914
 
11.2%
9 1153
 
6.7%
3 716
 
4.2%
8 695
 
4.1%
4 572
 
3.3%
6 545
 
3.2%
5 524
 
3.1%
Other values (10) 1149
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21833
94.9%
Hangul 1164
 
5.1%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4948
22.7%
1 2774
12.7%
_ 2152
 
9.9%
2 1914
 
8.8%
9 1153
 
5.3%
3 716
 
3.3%
8 695
 
3.2%
d 669
 
3.1%
4 572
 
2.6%
6 545
 
2.5%
Other values (61) 5695
26.1%
Hangul
ValueCountFrequency (%)
세 64
 
5.5%
부 57
 
4.9%
서 52
 
4.5%
가 48
 
4.1%
증 44
 
3.8%
신 42
 
3.6%
표 41
 
3.5%
고 40
 
3.4%
명 38
 
3.3%
원 33
 
2.8%
Other values (139) 705
60.6%
None
ValueCountFrequency (%)
· 3
100.0%

첨부파일내용
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

파일크기값
Real number (ℝ)

Distinct461
Distinct (%)92.6%
Missing2
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean888021.74
Minimum19472
Maximum17696794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T02:08:42.734936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19472
5-th percentile128795.5
Q1206723.75
median454832
Q31546524
95-th percentile2838665.6
Maximum17696794
Range17677322
Interquartile range (IQR)1339800.2

Descriptive statistics

Standard deviation1142222
Coefficient of variation (CV)1.2862546
Kurtosis93.562251
Mean888021.74
Median Absolute Deviation (MAD)276180
Skewness6.917507
Sum4.4223482 × 108
Variance1.3046711 × 1012
MonotonicityNot monotonic
2023-12-13T02:08:42.927855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206352 4
 
0.8%
206384 4
 
0.8%
206704 2
 
0.4%
2837792 2
 
0.4%
2837424 2
 
0.4%
165712 2
 
0.4%
27152 2
 
0.4%
204880 2
 
0.4%
206656 2
 
0.4%
204848 2
 
0.4%
Other values (451) 474
94.8%
ValueCountFrequency (%)
19472 2
0.4%
23568 1
0.2%
23847 1
0.2%
24052 1
0.2%
27152 2
0.4%
42340 1
0.2%
42462 1
0.2%
68128 1
0.2%
73232 1
0.2%
77383 1
0.2%
ValueCountFrequency (%)
17696794 1
0.2%
3760999 1
0.2%
3615904 1
0.2%
3552494 1
0.2%
3112176 1
0.2%
3103552 1
0.2%
3009584 1
0.2%
3008384 1
0.2%
3007936 1
0.2%
3007104 1
0.2%

파일타입명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
pdf
435 
tif
 
32
hwp
 
20
jpg
 
8
png
 
2
Other values (2)
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
pdf 435
87.0%
tif 32
 
6.4%
hwp 20
 
4.0%
jpg 8
 
1.6%
png 2
 
0.4%
JPG 2
 
0.4%
wav 1
 
0.2%

Length

2023-12-13T02:08:43.098826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:08:43.212835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pdf 435
87.0%
tif 32
 
6.4%
hwp 20
 
4.0%
jpg 10
 
2.0%
png 2
 
0.4%
wav 1
 
0.2%

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
498 
True
 
2
ValueCountFrequency (%)
False 498
99.6%
True 2
 
0.4%
2023-12-13T02:08:43.308901image/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
498 
2
 
2

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 (%)
1 498
99.6%
2 2
 
0.4%

Length

2023-12-13T02:08:43.408105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:08:43.506798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 498
99.6%
2 2
 
0.4%

처리시각
Text

Distinct262
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T02:08:43.865942image/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

Unique225 ?
Unique (%)45.0%

Sample

1st row19:13.7
2nd row19:07.6
3rd row18:52.4
4th row18:41.0
5th row18:13.8
ValueCountFrequency (%)
00:15.7 63
 
12.6%
10:13.1 44
 
8.8%
30:12.8 20
 
4.0%
40:14.0 14
 
2.8%
30:13.6 12
 
2.4%
15:11.5 12
 
2.4%
40:10.1 12
 
2.4%
15:10.6 12
 
2.4%
50:13.4 10
 
2.0%
45:11.3 8
 
1.6%
Other values (252) 293
58.6%
2023-12-13T02:08:44.348348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 572
16.3%
0 505
14.4%
: 500
14.3%
. 500
14.3%
5 302
8.6%
3 254
7.3%
4 247
7.1%
2 178
 
5.1%
7 143
 
4.1%
6 116
 
3.3%
Other values (2) 183
 
5.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 572
22.9%
0 505
20.2%
5 302
12.1%
3 254
10.2%
4 247
9.9%
2 178
 
7.1%
7 143
 
5.7%
6 116
 
4.6%
8 105
 
4.2%
9 78
 
3.1%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 572
16.3%
0 505
14.4%
: 500
14.3%
. 500
14.3%
5 302
8.6%
3 254
7.3%
4 247
7.1%
2 178
 
5.1%
7 143
 
4.1%
6 116
 
3.3%
Other values (2) 183
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 572
16.3%
0 505
14.4%
: 500
14.3%
. 500
14.3%
5 302
8.6%
3 254
7.3%
4 247
7.1%
2 178
 
5.1%
7 143
 
4.1%
6 116
 
3.3%
Other values (2) 183
 
5.2%

처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
ACI01
217 
ACUHP
171 
ACR01
82 
2897
 
3
6010
 
3
Other values (17)
24 

Length

Max length5
Median length5
Mean length4.942
Min length4

Unique

Unique11 ?
Unique (%)2.2%

Sample

1st rowACR01
2nd rowACUHP
3rd rowACR01
4th rowACUHP
5th rowACR01

Common Values

ValueCountFrequency (%)
ACI01 217
43.4%
ACUHP 171
34.2%
ACR01 82
 
16.4%
2897 3
 
0.6%
6010 3
 
0.6%
5566 3
 
0.6%
5130 2
 
0.4%
5138 2
 
0.4%
5600 2
 
0.4%
4926 2
 
0.4%
Other values (12) 13
 
2.6%

Length

2023-12-13T02:08:44.495039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aci01 217
43.4%
acuhp 171
34.2%
acr01 82
 
16.4%
2897 3
 
0.6%
6010 3
 
0.6%
5566 3
 
0.6%
5130 2
 
0.4%
5138 2
 
0.4%
5600 2
 
0.4%
4926 2
 
0.4%
Other values (12) 13
 
2.6%

Interactions

2023-12-13T02:08:40.991094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:08:44.574093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
첨부파일순번파일크기값파일타입명삭제여부최종수정수처리직원번호
첨부파일순번1.0000.1130.3110.0000.0000.392
파일크기값0.1131.0000.0690.0000.0000.400
파일타입명0.3110.0691.0000.0950.0950.933
삭제여부0.0000.0000.0951.0000.9230.764
최종수정수0.0000.0000.0950.9231.0000.764
처리직원번호0.3920.4000.9330.7640.7641.000
2023-12-13T02:08:44.975720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부최종수정수첨부파일순번파일타입명처리직원번호
삭제여부1.0000.7480.0000.1010.611
최종수정수0.7481.0000.0000.1010.611
첨부파일순번0.0000.0001.0000.2170.214
파일타입명0.1010.1010.2171.0000.735
처리직원번호0.6110.6110.2140.7351.000
2023-12-13T02:08:45.082124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
파일크기값첨부파일순번파일타입명삭제여부최종수정수처리직원번호
파일크기값1.0000.0450.0500.0000.0000.222
첨부파일순번0.0451.0000.2170.0000.0000.214
파일타입명0.0500.2171.0000.1010.1010.735
삭제여부0.0000.0000.1011.0000.7480.611
최종수정수0.0000.0000.1010.7481.0000.611
처리직원번호0.2220.2140.7350.6110.6111.000

Missing values

2023-12-13T02:08:41.165364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:08:41.321135image/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

전자문서문서ID전자문서파일ID첨부파일순번첨부파일명첨부파일내용파일크기값파일타입명삭제여부최종수정수처리시각처리직원번호
016300000000000000001630000000000000000120211019101728_납부내역증명(국세)_대표자.pdf<NA>287376pdfN119:13.7ACR01
11630000000000000000163000000000000000019dnUeQsR4H_aaaaadQO9r.pdf<NA>1739136pdfN119:07.6ACUHP
216300000000000000001630000000000000000120211019101728_국세납세증명서_대표자.pdf<NA>275904pdfN118:52.4ACR01
31630000000000000000163000000000000000019dnUf26E2g_9cQXpVhhHQ.pdf<NA>1329296pdfN118:41.0ACUHP
416300000000000000001630000000000000000120211019101728_주민등록초본.pdf<NA>323024pdfN118:13.8ACR01
516300000000000000001630000000000000000120211019101728_주민등록등본.pdf<NA>199168pdfN117:54.2ACR01
61630000000000000000163000000000000000019dnUcXsMjm_aaaaadL8X0.pdf<NA>1548896pdfN117:40.7ACUHP
716300000000000000001630000000000000000120211019101527_부가세과세표준증명원_201801_202007.pdf<NA>284592pdfN117:37.0ACR01
816300000000000000001630000000000000000120211019101518_납부내역증명(국세)_대표자.pdf<NA>287360pdfN117:09.2ACR01
916300000000000000001630000000000000000120211019101518_국세납세증명서_대표자.pdf<NA>275872pdfN116:48.7ACR01
전자문서문서ID전자문서파일ID첨부파일순번첨부파일명첨부파일내용파일크기값파일타입명삭제여부최종수정수처리시각처리직원번호
490163000000000000000016300000000000000001KED_20211018181711_20201231_I01_0_00000000000000_1_1_1138177594000.pdf<NA>453038pdfN130:13.6ACI01
491163000000000000000016300000000000000001KED_20211018181933_20200630_K01_0_00000000000000_1_1_1138177594000.pdf<NA>158118pdfN130:13.6ACI01
492163000000000000000016300000000000000001KED_20211018181202_20210630_H04_1_68558881366930_1_1_1138177594000.pdf<NA>274464pdfN130:13.6ACI01
493163000000000000000016300000000000000001KED_20211018182226_20190331_K01_0_00000000000000_1_1_1138177594000.pdf<NA>179321pdfN130:13.6ACI01
494163000000000000000016300000000000000001KED_20211018181802_20210331_K01_0_00000000000000_1_1_1138177594000.pdf<NA>166387pdfN130:13.6ACI01
495163000000000000000016300000000000000001KED_20211018181908_20200930_K01_0_00000000000000_1_1_1138177594000.pdf<NA>166655pdfN130:13.6ACI01
496163000000000000000016300000000000000001KED_20211018182157_20190630_K01_0_00000000000000_1_1_1138177594000.pdf<NA>177983pdfN130:13.6ACI01
497163000000000000000016300000000000000001KED_20211018182022_20200331_K01_0_00000000000000_1_1_1138177594000.pdf<NA>170211pdfN130:13.6ACI01
498163000000000000000016300000000000000001KED_20211018182050_20190930_K01_0_00000000000000_1_1_1138177594000.pdf<NA>170730pdfN130:13.6ACI01
499163000000000000000016300000000000000001KED_20211018181143_20211018_H02_1_66975754199490_1_1_1138177594000.pdf<NA>264381pdfN130:13.6ACI01

Duplicate rows

Most frequently occurring

전자문서문서ID전자문서파일ID첨부파일순번첨부파일명파일크기값파일타입명삭제여부최종수정수처리시각처리직원번호# duplicates
01630000000000000000163000000000000000019dnOa0nlWq_aaaaacWAaS.pdf1595600pdfN101:01.6ACUHP2