Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows27
Duplicate rows (%)5.4%
Total size in memory49.4 KiB
Average record size in memory101.3 B

Variable types

Categorical4
DateTime1
Numeric3
Text3
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증심사 감액 기적립 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093273/fileData.do

Alerts

업무구분코드 has constant value ""Constant
실행해지기표일자 has constant value ""Constant
통화코드 has constant value ""Constant
최종환율 has constant value ""Constant
삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
Dataset has 27 (5.4%) duplicate rowsDuplicates
차감통화별금액 is highly overall correlated with 차감환산금액High correlation
차감환산금액 is highly overall correlated with 차감통화별금액High correlation

Reproduction

Analysis started2023-12-11 23:19:37.470500
Analysis finished2023-12-11 23:19:39.123781
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무구분코드
Categorical

CONSTANT 

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

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 500
100.0%

Length

2023-12-12T08:19:39.195024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:19:39.310598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

실행해지기표일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T08:19:39.395141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:39.538711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

실행해지일련번호
Real number (ℝ)

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.762
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T08:19:39.680260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum19
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1247082
Coefficient of variation (CV)1.2058503
Kurtosis20.410883
Mean1.762
Median Absolute Deviation (MAD)0
Skewness4.0443872
Sum881
Variance4.5143848
MonotonicityNot monotonic
2023-12-12T08:19:39.828287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 398
79.6%
2 33
 
6.6%
4 19
 
3.8%
3 13
 
2.6%
5 9
 
1.8%
7 6
 
1.2%
10 5
 
1.0%
8 4
 
0.8%
6 4
 
0.8%
9 3
 
0.6%
Other values (4) 6
 
1.2%
ValueCountFrequency (%)
1 398
79.6%
2 33
 
6.6%
3 13
 
2.6%
4 19
 
3.8%
5 9
 
1.8%
6 4
 
0.8%
7 6
 
1.2%
8 4
 
0.8%
9 3
 
0.6%
10 5
 
1.0%
ValueCountFrequency (%)
19 1
 
0.2%
18 1
 
0.2%
12 1
 
0.2%
11 3
 
0.6%
10 5
1.0%
9 3
 
0.6%
8 4
0.8%
7 6
1.2%
6 4
0.8%
5 9
1.8%

통화코드
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KRW 500
100.0%

Length

2023-12-12T08:19:40.011033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:19:40.105233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
krw 500
100.0%

심사서ID
Text

Distinct178
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:19:40.415671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)26.6%

Sample

1st row9dnSDcU2K5
2nd row
3rd row
4th row9dnAFYEdEx
5th row9dnp4MsFwb
ValueCountFrequency (%)
9dhyos6b1i 15
 
4.8%
9dme4f5hhu 14
 
4.4%
9dixpascs6 12
 
3.8%
9ddje5pirx 10
 
3.2%
9dhozllvpy 10
 
3.2%
9dmqoy8ell 9
 
2.9%
9dih9pevwz 8
 
2.5%
9didqc1hkx 6
 
1.9%
9djieklhiz 6
 
1.9%
9di4gkiv4i 5
 
1.6%
Other values (167) 220
69.8%
2023-12-12T08:19:40.898815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1850
37.0%
d 377
 
7.5%
9 343
 
6.9%
i 106
 
2.1%
H 82
 
1.6%
m 81
 
1.6%
e 72
 
1.4%
l 70
 
1.4%
h 70
 
1.4%
j 62
 
1.2%
Other values (53) 1887
37.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 1850
37.0%
Lowercase Letter 1578
31.6%
Uppercase Letter 917
18.3%
Decimal Number 655
 
13.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 377
23.9%
i 106
 
6.7%
m 81
 
5.1%
e 72
 
4.6%
l 70
 
4.4%
h 70
 
4.4%
j 62
 
3.9%
f 61
 
3.9%
p 59
 
3.7%
c 48
 
3.0%
Other values (16) 572
36.2%
Uppercase Letter
ValueCountFrequency (%)
H 82
 
8.9%
S 54
 
5.9%
E 54
 
5.9%
Y 53
 
5.8%
O 52
 
5.7%
B 45
 
4.9%
K 40
 
4.4%
Z 40
 
4.4%
V 39
 
4.3%
A 36
 
3.9%
Other values (16) 422
46.0%
Decimal Number
ValueCountFrequency (%)
9 343
52.4%
4 50
 
7.6%
6 49
 
7.5%
5 47
 
7.2%
7 35
 
5.3%
8 33
 
5.0%
1 32
 
4.9%
0 28
 
4.3%
3 19
 
2.9%
2 19
 
2.9%
Space Separator
ValueCountFrequency (%)
1850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2505
50.1%
Latin 2495
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 377
 
15.1%
i 106
 
4.2%
H 82
 
3.3%
m 81
 
3.2%
e 72
 
2.9%
l 70
 
2.8%
h 70
 
2.8%
j 62
 
2.5%
f 61
 
2.4%
p 59
 
2.4%
Other values (42) 1455
58.3%
Common
ValueCountFrequency (%)
1850
73.9%
9 343
 
13.7%
4 50
 
2.0%
6 49
 
2.0%
5 47
 
1.9%
7 35
 
1.4%
8 33
 
1.3%
1 32
 
1.3%
0 28
 
1.1%
3 19
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1850
37.0%
d 377
 
7.5%
9 343
 
6.9%
i 106
 
2.1%
H 82
 
1.6%
m 81
 
1.6%
e 72
 
1.4%
l 70
 
1.4%
h 70
 
1.4%
j 62
 
1.2%
Other values (53) 1887
37.7%

최종환율
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T08:19:41.087917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:19:41.180143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

차감통화별금액
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11689482
Minimum1
Maximum2.5875 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T08:19:41.284938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile239475
Q11040000
median4000000
Q310000000
95-th percentile48000000
Maximum2.5875 × 108
Range2.5875 × 108
Interquartile range (IQR)8960000

Descriptive statistics

Standard deviation23983197
Coefficient of variation (CV)2.0516903
Kurtosis38.497405
Mean11689482
Median Absolute Deviation (MAD)3200000
Skewness5.2838259
Sum5.8447411 × 109
Variance5.7519374 × 1014
MonotonicityNot monotonic
2023-12-12T08:19:41.479034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850000 24
 
4.8%
1821428 18
 
3.6%
8500000 12
 
2.4%
499999 12
 
2.4%
4250000 12
 
2.4%
2125000 11
 
2.2%
1275000 10
 
2.0%
170000 10
 
2.0%
9000000 10
 
2.0%
2550000 8
 
1.6%
Other values (229) 373
74.6%
ValueCountFrequency (%)
1 1
 
0.2%
3225 1
 
0.2%
4000 1
 
0.2%
45000 1
 
0.2%
90000 1
 
0.2%
100187 1
 
0.2%
127500 1
 
0.2%
142500 2
 
0.4%
154273 1
 
0.2%
170000 10
2.0%
ValueCountFrequency (%)
258750000 1
0.2%
200000000 1
0.2%
187200000 1
0.2%
145350000 1
0.2%
112914562 1
0.2%
110000000 1
0.2%
100000000 1
0.2%
96000000 1
0.2%
93600000 1
0.2%
85000000 1
0.2%

차감환산금액
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11689482
Minimum1
Maximum2.5875 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T08:19:41.653003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile239475
Q11040000
median4000000
Q310000000
95-th percentile48000000
Maximum2.5875 × 108
Range2.5875 × 108
Interquartile range (IQR)8960000

Descriptive statistics

Standard deviation23983197
Coefficient of variation (CV)2.0516903
Kurtosis38.497405
Mean11689482
Median Absolute Deviation (MAD)3200000
Skewness5.2838259
Sum5.8447411 × 109
Variance5.7519374 × 1014
MonotonicityNot monotonic
2023-12-12T08:19:42.104272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850000 24
 
4.8%
1821428 18
 
3.6%
8500000 12
 
2.4%
499999 12
 
2.4%
4250000 12
 
2.4%
2125000 11
 
2.2%
1275000 10
 
2.0%
170000 10
 
2.0%
9000000 10
 
2.0%
2550000 8
 
1.6%
Other values (229) 373
74.6%
ValueCountFrequency (%)
1 1
 
0.2%
3225 1
 
0.2%
4000 1
 
0.2%
45000 1
 
0.2%
90000 1
 
0.2%
100187 1
 
0.2%
127500 1
 
0.2%
142500 2
 
0.4%
154273 1
 
0.2%
170000 10
2.0%
ValueCountFrequency (%)
258750000 1
0.2%
200000000 1
0.2%
187200000 1
0.2%
145350000 1
0.2%
112914562 1
0.2%
110000000 1
0.2%
100000000 1
0.2%
96000000 1
0.2%
93600000 1
0.2%
85000000 1
0.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T08:19:42.266198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T08:19:42.373608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:19:42.468451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리시각
Text

Distinct334
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:19:42.872249image/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

Unique281 ?
Unique (%)56.2%

Sample

1st row43:47.5
2nd row47:37.9
3rd row16:51.1
4th row24:30.9
5th row12:25.4
ValueCountFrequency (%)
15:45.3 15
 
3.0%
43:54.1 14
 
2.8%
27:20.2 12
 
2.4%
44:27.0 12
 
2.4%
28:53.0 11
 
2.2%
47:52.8 10
 
2.0%
28:36.4 10
 
2.0%
57:57.7 9
 
1.8%
01:23.1 8
 
1.6%
05:06.8 6
 
1.2%
Other values (324) 393
78.6%
2023-12-12T08:19:43.372297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 359
10.3%
4 341
9.7%
0 325
9.3%
2 303
8.7%
1 295
8.4%
3 287
8.2%
8 172
 
4.9%
7 165
 
4.7%
Other values (2) 253
7.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 359
14.4%
4 341
13.6%
0 325
13.0%
2 303
12.1%
1 295
11.8%
3 287
11.5%
8 172
6.9%
7 165
6.6%
6 141
 
5.6%
9 112
 
4.5%
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%
5 359
10.3%
4 341
9.7%
0 325
9.3%
2 303
8.7%
1 295
8.4%
3 287
8.2%
8 172
 
4.9%
7 165
 
4.7%
Other values (2) 253
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 359
10.3%
4 341
9.7%
0 325
9.3%
2 303
8.7%
1 295
8.4%
3 287
8.2%
8 172
 
4.9%
7 165
 
4.7%
Other values (2) 253
7.2%

처리직원번호
Text

Distinct230
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T08:19:43.775699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.056
Min length4

Characters and Unicode

Total characters2028
Distinct characters12
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

Unique141 ?
Unique (%)28.2%

Sample

1st row5009
2nd row6058
3rd row5201
4th row5088
5th row5317
ValueCountFrequency (%)
5456 24
 
4.8%
5630 23
 
4.6%
4447 15
 
3.0%
5009 12
 
2.4%
4062 12
 
2.4%
4375 10
 
2.0%
6016 10
 
2.0%
4481 10
 
2.0%
4162 8
 
1.6%
4993 7
 
1.4%
Other values (220) 369
73.8%
2023-12-12T08:19:44.262002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 373
18.4%
4 354
17.5%
6 233
11.5%
0 182
9.0%
9 167
8.2%
3 158
7.8%
2 149
 
7.3%
1 142
 
7.0%
7 136
 
6.7%
8 106
 
5.2%
Other values (2) 28
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
98.6%
Uppercase Letter 28
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 373
18.6%
4 354
17.7%
6 233
11.7%
0 182
9.1%
9 167
8.3%
3 158
7.9%
2 149
 
7.4%
1 142
 
7.1%
7 136
 
6.8%
8 106
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 21
75.0%
B 7
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
98.6%
Latin 28
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 373
18.6%
4 354
17.7%
6 233
11.7%
0 182
9.1%
9 167
8.3%
3 158
7.9%
2 149
 
7.4%
1 142
 
7.1%
7 136
 
6.8%
8 106
 
5.3%
Latin
ValueCountFrequency (%)
C 21
75.0%
B 7
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 373
18.4%
4 354
17.5%
6 233
11.5%
0 182
9.0%
9 167
8.2%
3 158
7.8%
2 149
 
7.3%
1 142
 
7.0%
7 136
 
6.7%
8 106
 
5.2%
Other values (2) 28
 
1.4%

Interactions

2023-12-12T08:19:38.429214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:37.732408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:38.040593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:38.553352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:37.837575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:38.197042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:38.664385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:37.949485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:19:38.291975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:19:44.352730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실행해지일련번호차감통화별금액차감환산금액
실행해지일련번호1.0000.0000.000
차감통화별금액0.0001.0001.000
차감환산금액0.0001.0001.000
2023-12-12T08:19:44.431496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실행해지일련번호차감통화별금액차감환산금액
실행해지일련번호1.000-0.120-0.120
차감통화별금액-0.1201.0001.000
차감환산금액-0.1201.0001.000

Missing values

2023-12-12T08:19:38.824393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:19:39.040528image/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

업무구분코드실행해지기표일자실행해지일련번호통화코드심사서ID최종환율차감통화별금액차감환산금액삭제여부최종수정수처리시각처리직원번호
0G00:00.01KRW9dnSDcU2K514500000045000000N143:47.55009
1G00:00.03KRW143000004300000N147:37.96058
2G00:00.01KRW111250001125000N116:51.15201
3G00:00.01KRW9dnAFYEdEx117000001700000N124:30.95088
4G00:00.01KRW9dnp4MsFwb11600000016000000N112:25.45317
5G00:00.01KRW9dnormtXLn1425000425000N119:51.45400
6G00:00.01KRW9dnoeSdtH4142500004250000N102:14.35935
7G00:00.01KRW9dnoeSdtH4142500004250000N102:14.35935
8G00:00.01KRW9dnoeSdtH4159500005950000N102:14.35935
9G00:00.01KRW9dnoeSdtH4142500004250000N102:14.35935
업무구분코드실행해지기표일자실행해지일련번호통화코드심사서ID최종환율차감통화별금액차감환산금액삭제여부최종수정수처리시각처리직원번호
490G00:00.011KRW9dc7hv8JHE155625005562500N128:09.84517
491G00:00.010KRW9dc7hv8JHE155625005562500N128:09.84517
492G00:00.09KRW9dc7hv8JHE155625005562500N128:09.84517
493G00:00.01KRW16885000068850000N114:05.54257
494G00:00.01KRW12040000020400000N152:57.15211
495G00:00.01KRW152500005250000N153:48.24559
496G00:00.02KRW9dc5wjtlUK11728000017280000N140:01.55701
497G00:00.01KRW9dc5rS9mgf139100003910000N156:26.54367
498G00:00.01KRW9dc4c8kAz9121845002184500N153:54.75606
499G00:00.01KRW9dc37zJHMX185000008500000N135:06.35347

Duplicate rows

Most frequently occurring

업무구분코드실행해지기표일자실행해지일련번호통화코드심사서ID최종환율차감통화별금액차감환산금액삭제여부최종수정수처리시각처리직원번호# duplicates
14G00:00.01KRW9dixpAScS61499999499999N144:27.040629
7G00:00.01KRW9ddje5piRX118214281821428N147:52.844816
0G00:00.01KRW1170000170000N127:20.256305
1G00:00.01KRW1170000170000N128:53.056304
11G00:00.01KRW9di4GkIV4i142500004250000N142:11.851324
12G00:00.01KRW9diH9pEvWz118214281821428N101:23.160164
15G00:00.01KRW9dmE4f5HHu1790000790000N143:54.144474
18G00:00.01KRW9dnoeSdtH4142500004250000N102:14.359354
19G00:00.02KRW9ddje5piRX118214281821428N147:52.844814
16G00:00.01KRW9dmQOY8ell112750001275000N157:57.754563