Overview

Dataset statistics

Number of variables24
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.3 KiB
Average record size in memory201.3 B

Variable types

Text7
Categorical11
DateTime3
Numeric2
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 거래공통내역에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092771/fileData.do

Alerts

거래일자 has constant value ""Constant
전표구분코드 has constant value ""Constant
고액현금대상여부 has constant value ""Constant
출납확인시간 has constant value ""Constant
회계구분코드 is highly imbalanced (68.9%)Imbalance
책임자직원번호 is highly imbalanced (92.3%)Imbalance
출납직원번호 is highly imbalanced (95.3%)Imbalance
거래공통내역ID has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:36:40.774957
Analysis finished2024-04-21 02:36:41.431673
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

거래공통내역ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:36:42.174976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
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

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnSYBTTVn
2nd row9dnSYBOY3n
3rd row9dnSYBNet1
4th row9dnSYBHxEV
5th row9dnSYBHhgC
ValueCountFrequency (%)
9dnsybttvn 1
 
0.2%
9dnsx93cdo 1
 
0.2%
9dnsx9g4yw 1
 
0.2%
9dnsx9lekn 1
 
0.2%
9dnsx7amay 1
 
0.2%
9dnsx9uckg 1
 
0.2%
9dnsx9vdjd 1
 
0.2%
9dnsx9venv 1
 
0.2%
9dnsx90oht 1
 
0.2%
9dnsx9xxfx 1
 
0.2%
Other values (490) 490
98.0%
2024-04-21T11:36:43.311651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 565
 
11.3%
d 545
 
10.9%
S 539
 
10.8%
n 534
 
10.7%
Y 308
 
6.2%
X 213
 
4.3%
m 58
 
1.2%
1 58
 
1.2%
h 57
 
1.1%
b 55
 
1.1%
Other values (52) 2068
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2128
42.6%
Uppercase Letter 1927
38.5%
Decimal Number 945
18.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 545
25.6%
n 534
25.1%
m 58
 
2.7%
h 57
 
2.7%
b 55
 
2.6%
y 54
 
2.5%
x 50
 
2.3%
r 48
 
2.3%
q 47
 
2.2%
t 45
 
2.1%
Other values (16) 635
29.8%
Uppercase Letter
ValueCountFrequency (%)
S 539
28.0%
Y 308
16.0%
X 213
 
11.1%
W 50
 
2.6%
Z 49
 
2.5%
J 47
 
2.4%
H 46
 
2.4%
A 44
 
2.3%
P 43
 
2.2%
F 43
 
2.2%
Other values (16) 545
28.3%
Decimal Number
ValueCountFrequency (%)
9 565
59.8%
1 58
 
6.1%
3 53
 
5.6%
5 45
 
4.8%
8 43
 
4.6%
0 43
 
4.6%
7 40
 
4.2%
4 34
 
3.6%
6 33
 
3.5%
2 31
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4055
81.1%
Common 945
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 545
 
13.4%
S 539
 
13.3%
n 534
 
13.2%
Y 308
 
7.6%
X 213
 
5.3%
m 58
 
1.4%
h 57
 
1.4%
b 55
 
1.4%
y 54
 
1.3%
W 50
 
1.2%
Other values (42) 1642
40.5%
Common
ValueCountFrequency (%)
9 565
59.8%
1 58
 
6.1%
3 53
 
5.6%
5 45
 
4.8%
8 43
 
4.6%
0 43
 
4.6%
7 40
 
4.2%
4 34
 
3.6%
6 33
 
3.5%
2 31
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 565
 
11.3%
d 545
 
10.9%
S 539
 
10.8%
n 534
 
10.7%
Y 308
 
6.2%
X 213
 
4.3%
m 58
 
1.2%
1 58
 
1.2%
h 57
 
1.1%
b 55
 
1.1%
Other values (52) 2068
41.4%

회계구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
472 
I
 
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 472
94.4%
I 28
 
5.6%

Length

2024-04-21T11:36:43.718543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:44.011717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 472
94.4%
i 28
 
5.6%

부점코드
Text

Distinct79
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:36:44.822696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st rowTAH
2nd rowTHA
3rd rowTHA
4th rowADD
5th rowWAA
ValueCountFrequency (%)
add 46
 
9.2%
tph 25
 
5.0%
tie 22
 
4.4%
vao 18
 
3.6%
tcd 14
 
2.8%
toi 14
 
2.8%
thi 14
 
2.8%
tme 13
 
2.6%
taz 13
 
2.6%
tle 12
 
2.4%
Other values (69) 309
61.8%
2024-04-21T11:36:46.084169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 351
23.4%
A 221
14.7%
H 141
9.4%
D 134
 
8.9%
I 90
 
6.0%
P 63
 
4.2%
N 62
 
4.1%
E 51
 
3.4%
J 48
 
3.2%
O 48
 
3.2%
Other values (14) 291
19.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 351
23.4%
A 221
14.7%
H 141
9.4%
D 134
 
8.9%
I 90
 
6.0%
P 63
 
4.2%
N 62
 
4.1%
E 51
 
3.4%
J 48
 
3.2%
O 48
 
3.2%
Other values (14) 291
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 351
23.4%
A 221
14.7%
H 141
9.4%
D 134
 
8.9%
I 90
 
6.0%
P 63
 
4.2%
N 62
 
4.1%
E 51
 
3.4%
J 48
 
3.2%
O 48
 
3.2%
Other values (14) 291
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 351
23.4%
A 221
14.7%
H 141
9.4%
D 134
 
8.9%
I 90
 
6.0%
P 63
 
4.2%
N 62
 
4.1%
E 51
 
3.4%
J 48
 
3.2%
O 48
 
3.2%
Other values (14) 291
19.4%

거래일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-21 00:00:00
Maximum2024-04-21 00:00:00
2024-04-21T11:36:46.422705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:36:46.710894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

거래일시
Text

Distinct446
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:36:47.949849image/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

Unique410 ?
Unique (%)82.0%

Sample

1st row05:33.1
2nd row05:32.1
3rd row05:30.9
4th row05:30.5
5th row05:30.4
ValueCountFrequency (%)
56:49.0 4
 
0.8%
00:21.4 3
 
0.6%
59:42.5 3
 
0.6%
02:49.9 3
 
0.6%
57:13.5 3
 
0.6%
58:50.2 3
 
0.6%
03:38.6 3
 
0.6%
58:41.3 3
 
0.6%
59:33.9 3
 
0.6%
02:26.2 3
 
0.6%
Other values (436) 469
93.8%
2024-04-21T11:36:49.668147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456
18.2%
5 426
17.0%
2 273
10.9%
3 265
10.6%
4 262
10.5%
1 217
8.7%
7 170
 
6.8%
8 154
 
6.2%
6 147
 
5.9%
9 130
 
5.2%
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%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

거래유형코드
Real number (ℝ)

Distinct48
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17791.56
Minimum2052
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T11:36:50.093760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2052
5-th percentile2201
Q12401
median2961
Q32984
95-th percentile99999
Maximum99999
Range97947
Interquartile range (IQR)583

Descriptive statistics

Standard deviation35263.317
Coefficient of variation (CV)1.982025
Kurtosis1.6231313
Mean17791.56
Median Absolute Deviation (MAD)560
Skewness1.9015382
Sum8895780
Variance1.2435015 × 109
MonotonicityNot monotonic
2024-04-21T11:36:50.521899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2401 76
15.2%
2982 72
14.4%
99999 56
11.2%
2961 56
11.2%
2301 39
 
7.8%
2201 23
 
4.6%
2501 16
 
3.2%
3013 14
 
2.8%
2423 13
 
2.6%
99045 11
 
2.2%
Other values (38) 124
24.8%
ValueCountFrequency (%)
2052 8
 
1.6%
2054 1
 
0.2%
2057 2
 
0.4%
2201 23
4.6%
2202 7
 
1.4%
2210 3
 
0.6%
2301 39
7.8%
2302 6
 
1.2%
2306 2
 
0.4%
2310 3
 
0.6%
ValueCountFrequency (%)
99999 56
11.2%
99049 4
 
0.8%
99045 11
 
2.2%
99035 2
 
0.4%
99034 1
 
0.2%
99001 4
 
0.8%
4005 4
 
0.8%
3200 4
 
0.8%
3153 2
 
0.4%
3149 2
 
0.4%

삭제일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
312 
00:00.0
188 

Length

Max length26
Median length26
Mean length18.856
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 312
62.4%
00:00.0 188
37.6%

Length

2024-04-21T11:36:51.155431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:51.476403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 312
38.4%
00:00:00.000000 312
38.4%
00:00.0 188
23.2%

전표구분코드
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

2024-04-21T11:36:51.798985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:52.083160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

거래업무구분코드
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
334 
4
66 
3
41 
27
 
30
99
 
29

Length

Max length2
Median length1
Mean length1.118
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row27
5th row27

Common Values

ValueCountFrequency (%)
2 334
66.8%
4 66
 
13.2%
3 41
 
8.2%
27 30
 
6.0%
99 29
 
5.8%

Length

2024-04-21T11:36:52.392195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:52.710719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 334
66.8%
4 66
 
13.2%
3 41
 
8.2%
27 30
 
6.0%
99 29
 
5.8%

거래유형일련번호
Real number (ℝ)

Distinct48
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean591.56
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-04-21T11:36:53.079931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.5
Q1301
median501
Q3982
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)681

Descriptive statistics

Standard deviation354.00621
Coefficient of variation (CV)0.59842825
Kurtosis-1.5304706
Mean591.56
Median Absolute Deviation (MAD)449
Skewness-0.070936784
Sum295780
Variance125320.4
MonotonicityNot monotonic
2024-04-21T11:36:53.519623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
401 76
15.2%
982 72
14.4%
999 56
11.2%
961 56
11.2%
301 39
 
7.8%
201 23
 
4.6%
501 16
 
3.2%
13 14
 
2.8%
423 13
 
2.6%
45 11
 
2.2%
Other values (38) 124
24.8%
ValueCountFrequency (%)
1 4
 
0.8%
5 4
 
0.8%
13 14
2.8%
34 1
 
0.2%
35 2
 
0.4%
45 11
2.2%
49 4
 
0.8%
52 8
1.6%
54 1
 
0.2%
57 2
 
0.4%
ValueCountFrequency (%)
999 56
11.2%
992 7
 
1.4%
990 7
 
1.4%
984 7
 
1.4%
982 72
14.4%
961 56
11.2%
867 1
 
0.2%
703 5
 
1.0%
701 6
 
1.2%
626 1
 
0.2%

고액현금대상여부
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2024-04-21T11:36:53.920875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:54.207336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

책임자확인시간
Date

Distinct234
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-21 00:00:00
Maximum2024-04-21 14:05:33
2024-04-21T11:36:54.525008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:36:54.956244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

책임자직원번호
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
490 
1886
 
4
3516
 
3
3882
 
2
3531
 
1

Length

Max length4
Median length4
Mean length4
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> 490
98.0%
1886 4
 
0.8%
3516 3
 
0.6%
3882 2
 
0.4%
3531 1
 
0.2%

Length

2024-04-21T11:36:55.354130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:55.668214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 490
98.0%
1886 4
 
0.8%
3516 3
 
0.6%
3882 2
 
0.4%
3531 1
 
0.2%

출납확인시간
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-21 00:00:00
Maximum2024-04-21 00:00:00
2024-04-21T11:36:55.957571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:36:56.245624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

출납직원번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
496 
92980
 
3
5353
 
1

Length

Max length5
Median length4
Mean length4.006
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> 496
99.2%
92980 3
 
0.6%
5353 1
 
0.2%

Length

2024-04-21T11:36:56.602680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:56.909668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 496
99.2%
92980 3
 
0.6%
5353 1
 
0.2%

거래유형구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
444 
3
56 

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 (%)
1 444
88.8%
3 56
 
11.2%

Length

2024-04-21T11:36:57.239132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:57.539355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 444
88.8%
3 56
 
11.2%

거래삭제구분코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
312 
4
151 
3
37 

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 (%)
1 312
62.4%
4 151
30.2%
3 37
 
7.4%

Length

2024-04-21T11:36:57.853164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:58.156638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 312
62.4%
4 151
30.2%
3 37
 
7.4%

기산일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
369 
00:00.0
131 

Length

Max length26
Median length26
Mean length21.022
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row00:00.0
3rd row00:00.0
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 369
73.8%
00:00.0 131
 
26.2%

Length

2024-04-21T11:36:58.521875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:58.849544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 369
42.5%
00:00:00.000000 369
42.5%
00:00.0 131
 
15.1%

삭제여부
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size628.0 B
False
312 
True
188 
ValueCountFrequency (%)
False 312
62.4%
True 188
37.6%
2024-04-21T11:36:59.109235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
310 
2
188 
4
 
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 (%)
1 310
62.0%
2 188
37.6%
4 2
 
0.4%

Length

2024-04-21T11:36:59.429747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:36:59.730797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 310
62.0%
2 188
37.6%
4 2
 
0.4%

처리시각
Text

Distinct312
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:37:01.004407image/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

Unique246 ?
Unique (%)49.2%

Sample

1st row05:33.1
2nd row05:32.1
3rd row05:30.9
4th row05:30.5
5th row05:30.4
ValueCountFrequency (%)
57:16.7 6
 
1.2%
57:45.6 6
 
1.2%
02:32.8 6
 
1.2%
58:13.9 6
 
1.2%
04:40.6 6
 
1.2%
57:01.4 5
 
1.0%
02:30.7 5
 
1.0%
58:50.2 5
 
1.0%
02:26.2 5
 
1.0%
56:45.2 5
 
1.0%
Other values (302) 445
89.0%
2024-04-21T11:37:02.432053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 498
14.2%
5 404
11.5%
2 266
7.6%
3 264
7.5%
4 249
7.1%
1 205
5.9%
7 185
 
5.3%
8 161
 
4.6%
Other values (2) 268
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 498
19.9%
5 404
16.2%
2 266
10.6%
3 264
10.6%
4 249
10.0%
1 205
8.2%
7 185
 
7.4%
8 161
 
6.4%
6 137
 
5.5%
9 131
 
5.2%
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%
0 498
14.2%
5 404
11.5%
2 266
7.6%
3 264
7.5%
4 249
7.1%
1 205
5.9%
7 185
 
5.3%
8 161
 
4.6%
Other values (2) 268
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 498
14.2%
5 404
11.5%
2 266
7.6%
3 264
7.5%
4 249
7.1%
1 205
5.9%
7 185
 
5.3%
8 161
 
4.6%
Other values (2) 268
7.7%

처리직원번호
Text

Distinct113
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:37:03.398206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.27
Min length4

Characters and Unicode

Total characters2135
Distinct characters11
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

Unique8 ?
Unique (%)1.6%

Sample

1st row99021
2nd row9C734
3rd row9C734
4th row99001
5th row99001
ValueCountFrequency (%)
4351 17
 
3.4%
4456 10
 
2.0%
9c766 10
 
2.0%
99005 10
 
2.0%
5755 10
 
2.0%
9c748 10
 
2.0%
6130 10
 
2.0%
4067 9
 
1.8%
5132 8
 
1.6%
6061 8
 
1.6%
Other values (103) 398
79.6%
2024-04-21T11:37:04.572010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 305
14.3%
9 273
12.8%
4 269
12.6%
6 258
12.1%
0 205
9.6%
1 178
8.3%
8 163
7.6%
7 152
7.1%
3 140
6.6%
2 103
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2046
95.8%
Uppercase Letter 89
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 305
14.9%
9 273
13.3%
4 269
13.1%
6 258
12.6%
0 205
10.0%
1 178
8.7%
8 163
8.0%
7 152
7.4%
3 140
6.8%
2 103
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
C 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2046
95.8%
Latin 89
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 305
14.9%
9 273
13.3%
4 269
13.1%
6 258
12.6%
0 205
10.0%
1 178
8.7%
8 163
8.0%
7 152
7.4%
3 140
6.8%
2 103
 
5.0%
Latin
ValueCountFrequency (%)
C 89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 305
14.3%
9 273
12.8%
4 269
12.6%
6 258
12.1%
0 205
9.6%
1 178
8.3%
8 163
7.6%
7 152
7.1%
3 140
6.6%
2 103
 
4.8%

최초처리시각
Text

Distinct446
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:37:05.876281image/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

Unique410 ?
Unique (%)82.0%

Sample

1st row05:33.1
2nd row05:32.1
3rd row05:30.9
4th row05:30.5
5th row05:30.4
ValueCountFrequency (%)
56:49.0 4
 
0.8%
00:21.4 3
 
0.6%
59:42.5 3
 
0.6%
02:49.9 3
 
0.6%
57:13.5 3
 
0.6%
58:50.2 3
 
0.6%
03:38.6 3
 
0.6%
58:41.3 3
 
0.6%
59:33.9 3
 
0.6%
02:26.2 3
 
0.6%
Other values (436) 469
93.8%
2024-04-21T11:37:07.531986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456
18.2%
5 426
17.0%
2 273
10.9%
3 265
10.6%
4 262
10.5%
1 217
8.7%
7 170
 
6.8%
8 154
 
6.2%
6 147
 
5.9%
9 130
 
5.2%
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%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 456
13.0%
5 426
12.2%
2 273
7.8%
3 265
7.6%
4 262
7.5%
1 217
6.2%
7 170
 
4.9%
8 154
 
4.4%
Other values (2) 277
7.9%

최초처리직원번호
Text

Distinct114
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-04-21T11:37:08.555138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.27
Min length4

Characters and Unicode

Total characters2135
Distinct characters11
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

Unique8 ?
Unique (%)1.6%

Sample

1st row99021
2nd row9C734
3rd row9C734
4th row99001
5th row99001
ValueCountFrequency (%)
4351 17
 
3.4%
9c766 10
 
2.0%
9c748 10
 
2.0%
99005 10
 
2.0%
5755 10
 
2.0%
6130 10
 
2.0%
4456 10
 
2.0%
4067 9
 
1.8%
5691 8
 
1.6%
4940 8
 
1.6%
Other values (104) 398
79.6%
2024-04-21T11:37:09.770953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 307
14.4%
9 275
12.9%
4 271
12.7%
6 258
12.1%
0 213
10.0%
1 176
8.2%
7 152
7.1%
8 151
7.1%
3 142
6.7%
2 101
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2046
95.8%
Uppercase Letter 89
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 307
15.0%
9 275
13.4%
4 271
13.2%
6 258
12.6%
0 213
10.4%
1 176
8.6%
7 152
7.4%
8 151
7.4%
3 142
6.9%
2 101
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
C 89
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2046
95.8%
Latin 89
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 307
15.0%
9 275
13.4%
4 271
13.2%
6 258
12.6%
0 213
10.4%
1 176
8.6%
7 152
7.4%
8 151
7.4%
3 142
6.9%
2 101
 
4.9%
Latin
ValueCountFrequency (%)
C 89
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 307
14.4%
9 275
12.9%
4 271
12.7%
6 258
12.1%
0 213
10.0%
1 176
8.2%
7 152
7.1%
8 151
7.1%
3 142
6.7%
2 101
 
4.7%

Sample

거래공통내역ID회계구분코드부점코드거래일자거래일시거래유형코드삭제일자전표구분코드거래업무구분코드거래유형일련번호고액현금대상여부책임자확인시간책임자직원번호출납확인시간출납직원번호거래유형구분코드거래삭제구분코드기산일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnSYBTTVnGTAH00:00.005:33.123060001-01-01 00:00:00.0000001230614:05:33<NA>0:00:00<NA>110001-01-01 00:00:00.000000N105:33.19902105:33.199021
19dnSYBOY3nGTHA00:00.005:32.129820001-01-01 00:00:00.0000001298214:05:32<NA>0:00:00<NA>1100:00.0N105:32.19C73405:32.19C734
29dnSYBNet1GTHA00:00.005:30.924010001-01-01 00:00:00.0000001240114:05:30<NA>0:00:00<NA>1100:00.0N105:30.99C73405:30.99C734
39dnSYBHxEVGADD00:00.005:30.529900001-01-01 00:00:00.00000012799014:05:30<NA>0:00:00<NA>110001-01-01 00:00:00.000000N105:30.59900105:30.599001
49dnSYBHhgCGWAA00:00.005:30.429920001-01-01 00:00:00.00000012799214:05:30<NA>0:00:00<NA>110001-01-01 00:00:00.000000N105:30.49900105:30.499001
59dnSYBzEAwGWAA00:00.005:28.622010001-01-01 00:00:00.00000012720114:05:28<NA>0:00:00<NA>110001-01-01 00:00:00.000000N105:28.69900105:28.699001
69dnSYzbUFRGTIE00:00.004:53.2298200:00.01298214:04:53<NA>0:00:00<NA>1300:00.0Y205:22.6445604:53.24456
79dnSYy96wMGTIE00:00.004:52.2240100:00.01240114:04:52<NA>0:00:00<NA>1300:00.0Y205:22.6445604:52.24456
89dnSYA5b0OGTND00:00.005:21.1999990001-01-01 00:00:00.000000129990:00:00<NA>0:00:00<NA>310001-01-01 00:00:00.000000N105:21.1451005:21.14510
99dnSYA3B3NGTND00:00.005:21.0296100:00.0149610:00:00<NA>0:00:00<NA>140001-01-01 00:00:00.000000Y205:21.1451005:21.04510
거래공통내역ID회계구분코드부점코드거래일자거래일시거래유형코드삭제일자전표구분코드거래업무구분코드거래유형일련번호고액현금대상여부책임자확인시간책임자직원번호출납확인시간출납직원번호거래유형구분코드거래삭제구분코드기산일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnSXbEJB1GTQB00:00.043:49.4240100:00.01240113:43:49<NA>0:00:00<NA>140001-01-01 00:00:00.000000Y256:04.5602943:49.46029
4919dnSXdE4CaGTQB00:00.044:19.1298200:00.01298213:44:19<NA>0:00:00<NA>1400:00.0Y256:04.5602944:19.16029
4929dnSXZpZJcGTQB00:00.056:04.5999990001-01-01 00:00:00.000000129990:00:00<NA>0:00:00<NA>310001-01-01 00:00:00.000000N156:04.5602956:04.56029
4939dnSXZovcXGTQB00:00.056:04.5296100:00.0149610:00:00<NA>0:00:00<NA>140001-01-01 00:00:00.000000Y256:04.5602956:04.56029
4949dnSXZop9uGADD00:00.056:04.529610001-01-01 00:00:00.000000149610:00:00<NA>0:00:00<NA>110001-01-01 00:00:00.000000N156:04.5602956:04.56029
4959dnSIuzRhGGTMA00:00.059:27.0242300:00.0124239:59:26<NA>0:00:00<NA>130001-01-01 00:00:00.000000Y256:03.39C77859:27.09C778
4969dnSIuxZ7VGTMA00:00.059:26.5242300:00.0124239:59:26<NA>0:00:00<NA>130001-01-01 00:00:00.000000Y256:03.39C77859:26.59C778
4979dnSXYQ7o9GQAC00:00.055:56.220520001-01-01 00:00:00.000000125213:55:56<NA>0:00:00<NA>110001-01-01 00:00:00.000000N155:56.2461555:56.24615
4989dnSXYJcrXGTIA00:00.055:54.529820001-01-01 00:00:00.0000001298213:55:54<NA>0:00:00<NA>1100:00.0N155:54.59C72355:54.59C723
4999dnSXoOaXmGTCD00:00.047:03.7298200:00.01298213:47:03<NA>0:00:00<NA>1300:00.0Y255:54.3613047:03.76130