Overview

Dataset statistics

Number of variables23
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)2.2%
Total size in memory92.9 KiB
Average record size in memory190.3 B

Variable types

Categorical15
DateTime1
Text5
Boolean1
Numeric1

Dataset

Description해당 파일 데이터는 신용보증기금 보증 부문의 조건변경 원장과 관련된 정보를 확인하실 수 있으니 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092662/fileData.do

Alerts

업무구분코드 has constant value ""Constant
조변접수번호종류코드 has constant value ""Constant
접수일자 has constant value ""Constant
Dataset has 11 (2.2%) duplicate rowsDuplicates
조변보증서형태코드 is highly imbalanced (83.7%)Imbalance
조변종류코드 is highly imbalanced (73.2%)Imbalance
조변시기구분코드 is highly imbalanced (93.3%)Imbalance
전결구분코드 is highly imbalanced (70.5%)Imbalance
불승인사유단순내용 is highly imbalanced (79.6%)Imbalance
삭제여부 is highly imbalanced (91.9%)Imbalance

Reproduction

Analysis started2023-12-12 12:47:30.887769
Analysis finished2023-12-12 12:47:31.255707
Duration0.37 seconds
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

2023-12-12T21:47:31.328436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:31.434135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

조변접수번호종류코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 500
100.0%

Length

2023-12-12T21:47:31.617173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:31.712221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 500
100.0%

접수일자
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
2023-12-12T21:47:31.790625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:47:31.885374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

원장진행상태코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
6
322 
1
129 
2
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
6 322
64.4%
1 129
25.8%
2 49
 
9.8%

Length

2023-12-12T21:47:32.032443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:32.170517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6 322
64.4%
1 129
25.8%
2 49
 
9.8%

전자보증발급상태코드
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
264 
12
93 
11
77 
21
64 
13
 
2

Length

Max length2
Median length1
Mean length1.472
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
264
52.8%
12 93
 
18.6%
11 77
 
15.4%
21 64
 
12.8%
13 2
 
0.4%

Length

2023-12-12T21:47:32.647021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:32.757292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12 93
39.4%
11 77
32.6%
21 64
27.1%
13 2
 
0.8%

조변보증서형태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
488 
1
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 488
97.6%
1 12
 
2.4%

Length

2023-12-12T21:47:32.867272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:32.956049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 488
97.6%
1 12
 
2.4%

조변종류코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11
462 
14
 
34
21
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 462
92.4%
14 34
 
6.8%
21 4
 
0.8%

Length

2023-12-12T21:47:33.065443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:33.160079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 462
92.4%
14 34
 
6.8%
21 4
 
0.8%

조변시기구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
15
496 
14
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15
2nd row15
3rd row15
4th row15
5th row15

Common Values

ValueCountFrequency (%)
15 496
99.2%
14 4
 
0.8%

Length

2023-12-12T21:47:33.262872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:33.385641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
15 496
99.2%
14 4
 
0.8%

전결구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
474 
11
 
26

Length

Max length2
Median length1
Mean length1.052
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
474
94.8%
11 26
 
5.2%

Length

2023-12-12T21:47:33.477556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:33.559214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 26
100.0%

승인일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
372 
0001-01-01 00:00:00.000000
128 

Length

Max length26
Median length7
Mean length11.864
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 row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 372
74.4%
0001-01-01 00:00:00.000000 128
 
25.6%

Length

2023-12-12T21:47:33.685955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:33.779168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 372
59.2%
0001-01-01 128
 
20.4%
00:00:00.000000 128
 
20.4%

승인구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
372 
128 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 372
74.4%
128
 
25.6%

Length

2023-12-12T21:47:33.878576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:33.991298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 372
100.0%

불승인사유단순내용
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
484 
 
16

Length

Max length4
Median length4
Mean length3.904
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 484
96.8%
16
 
3.2%

Length

2023-12-12T21:47:34.088433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:34.170324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 484
100.0%

실행일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
322 
0001-01-01 00:00:00.000000
178 

Length

Max length26
Median length7
Mean length13.764
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 (%)
00:00.0 322
64.4%
0001-01-01 00:00:00.000000 178
35.6%

Length

2023-12-12T21:47:34.261160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:34.358850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 322
47.5%
0001-01-01 178
26.3%
00:00:00.000000 178
26.3%

사이버연장접수채널구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
349 
1
151 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
349
69.8%
1 151
30.2%

Length

2023-12-12T21:47:34.463760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:34.557680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 151
100.0%

사이버연장구분코드
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
349 
3
151 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
349
69.8%
3 151
30.2%

Length

2023-12-12T21:47:34.670493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:34.790475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 151
100.0%

접수팀코드
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
213 
2
168 
3
87 
4
32 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 213
42.6%
2 168
33.6%
3 87
17.4%
4 32
 
6.4%

Length

2023-12-12T21:47:34.913913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:47:35.034098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 213
42.6%
2 168
33.6%
3 87
17.4%
4 32
 
6.4%

자동연결처리시각
Text

Distinct322
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:47:35.427678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length13.764
Min length7

Characters and Unicode

Total characters6882
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

Unique320 ?
Unique (%)64.0%

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
ValueCountFrequency (%)
0001-01-01 178
26.3%
00:00:00.000000 178
26.3%
45:07.1 2
 
0.3%
11:33.4 1
 
0.1%
41:49.6 1
 
0.1%
33:04.9 1
 
0.1%
42:00.8 1
 
0.1%
27:03.0 1
 
0.1%
59:32.4 1
 
0.1%
12:17.7 1
 
0.1%
Other values (313) 313
46.2%
2023-12-12T21:47:35.982581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3255
47.3%
1 746
 
10.8%
: 678
 
9.9%
. 500
 
7.3%
- 356
 
5.2%
4 213
 
3.1%
3 198
 
2.9%
2 194
 
2.8%
5 180
 
2.6%
178
 
2.6%
Other values (4) 384
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5170
75.1%
Other Punctuation 1178
 
17.1%
Dash Punctuation 356
 
5.2%
Space Separator 178
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3255
63.0%
1 746
 
14.4%
4 213
 
4.1%
3 198
 
3.8%
2 194
 
3.8%
5 180
 
3.5%
8 107
 
2.1%
6 99
 
1.9%
9 93
 
1.8%
7 85
 
1.6%
Other Punctuation
ValueCountFrequency (%)
: 678
57.6%
. 500
42.4%
Dash Punctuation
ValueCountFrequency (%)
- 356
100.0%
Space Separator
ValueCountFrequency (%)
178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6882
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3255
47.3%
1 746
 
10.8%
: 678
 
9.9%
. 500
 
7.3%
- 356
 
5.2%
4 213
 
3.1%
3 198
 
2.9%
2 194
 
2.8%
5 180
 
2.6%
178
 
2.6%
Other values (4) 384
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6882
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3255
47.3%
1 746
 
10.8%
: 678
 
9.9%
. 500
 
7.3%
- 356
 
5.2%
4 213
 
3.1%
3 198
 
2.9%
2 194
 
2.8%
5 180
 
2.6%
178
 
2.6%
Other values (4) 384
 
5.6%

삭제여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
495 
True
 
5
ValueCountFrequency (%)
False 495
99.0%
True 5
 
1.0%
2023-12-12T21:47:36.113129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.756
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:47:36.231030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.204128
Coefficient of variation (CV)0.58682855
Kurtosis0.57272116
Mean3.756
Median Absolute Deviation (MAD)2
Skewness0.64897356
Sum1878
Variance4.8581804
MonotonicityNot monotonic
2023-12-12T21:47:36.352572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 115
23.0%
5 86
17.2%
3 80
16.0%
4 74
14.8%
6 67
13.4%
2 41
 
8.2%
7 13
 
2.6%
9 9
 
1.8%
8 8
 
1.6%
10 3
 
0.6%
Other values (3) 4
 
0.8%
ValueCountFrequency (%)
1 115
23.0%
2 41
 
8.2%
3 80
16.0%
4 74
14.8%
5 86
17.2%
6 67
13.4%
7 13
 
2.6%
8 8
 
1.6%
9 9
 
1.8%
10 3
 
0.6%
ValueCountFrequency (%)
13 1
 
0.2%
12 1
 
0.2%
11 2
 
0.4%
10 3
 
0.6%
9 9
 
1.8%
8 8
 
1.6%
7 13
 
2.6%
6 67
13.4%
5 86
17.2%
4 74
14.8%

처리시각
Text

Distinct479
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:47:36.711725image/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

Unique466 ?
Unique (%)93.2%

Sample

1st row12:27.6
2nd row12:20.8
3rd row12:17.9
4th row12:17.3
5th row12:11.7
ValueCountFrequency (%)
49:15.1 6
 
1.2%
00:39.4 4
 
0.8%
58:50.2 3
 
0.6%
38:33.1 3
 
0.6%
57:56.7 2
 
0.4%
21:17.7 2
 
0.4%
11:38.2 2
 
0.4%
30:28.3 2
 
0.4%
57:17.7 2
 
0.4%
45:05.8 2
 
0.4%
Other values (469) 472
94.4%
2023-12-12T21:47:37.245145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 363
10.4%
3 323
9.2%
4 319
9.1%
5 310
8.9%
2 302
8.6%
1 288
8.2%
9 155
 
4.4%
6 155
 
4.4%
Other values (2) 285
8.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 363
14.5%
3 323
12.9%
4 319
12.8%
5 310
12.4%
2 302
12.1%
1 288
11.5%
9 155
6.2%
6 155
6.2%
8 145
 
5.8%
7 140
 
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%
0 363
10.4%
3 323
9.2%
4 319
9.1%
5 310
8.9%
2 302
8.6%
1 288
8.2%
9 155
 
4.4%
6 155
 
4.4%
Other values (2) 285
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 363
10.4%
3 323
9.2%
4 319
9.1%
5 310
8.9%
2 302
8.6%
1 288
8.2%
9 155
 
4.4%
6 155
 
4.4%
Other values (2) 285
8.1%

처리직원번호
Text

Distinct256
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:47:37.612733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.48
Min length4

Characters and Unicode

Total characters2240
Distinct characters16
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

Unique154 ?
Unique (%)30.8%

Sample

1st row9C669
2nd row4492
3rd row5463
4th row9C669
5th row6178
ValueCountFrequency (%)
2555 25
 
5.0%
99001 14
 
2.8%
99016 12
 
2.4%
99002 11
 
2.2%
99023 10
 
2.0%
exi62 7
 
1.4%
4510 7
 
1.4%
99006 6
 
1.2%
exi61 6
 
1.2%
9c624 6
 
1.2%
Other values (246) 396
79.2%
2023-12-12T21:47:38.156072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 375
16.7%
5 250
11.2%
6 233
10.4%
0 230
10.3%
7 194
8.7%
4 181
8.1%
2 157
7.0%
3 150
 
6.7%
1 147
 
6.6%
C 147
 
6.6%
Other values (6) 176
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2008
89.6%
Uppercase Letter 232
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 375
18.7%
5 250
12.5%
6 233
11.6%
0 230
11.5%
7 194
9.7%
4 181
9.0%
2 157
7.8%
3 150
 
7.5%
1 147
 
7.3%
8 91
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
C 147
63.4%
E 29
 
12.5%
X 29
 
12.5%
I 13
 
5.6%
H 7
 
3.0%
J 7
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2008
89.6%
Latin 232
 
10.4%

Most frequent character per script

Common
ValueCountFrequency (%)
9 375
18.7%
5 250
12.5%
6 233
11.6%
0 230
11.5%
7 194
9.7%
4 181
9.0%
2 157
7.8%
3 150
 
7.5%
1 147
 
7.3%
8 91
 
4.5%
Latin
ValueCountFrequency (%)
C 147
63.4%
E 29
 
12.5%
X 29
 
12.5%
I 13
 
5.6%
H 7
 
3.0%
J 7
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 375
16.7%
5 250
11.2%
6 233
10.4%
0 230
10.3%
7 194
8.7%
4 181
8.1%
2 157
7.0%
3 150
 
6.7%
1 147
 
6.6%
C 147
 
6.6%
Other values (6) 176
7.9%

최초처리시각
Text

Distinct474
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:47:38.509851image/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

Unique455 ?
Unique (%)91.0%

Sample

1st row12:27.6
2nd row12:20.8
3rd row12:17.9
4th row12:17.3
5th row10:42.2
ValueCountFrequency (%)
49:15.1 5
 
1.0%
00:39.4 4
 
0.8%
58:50.2 3
 
0.6%
38:33.1 3
 
0.6%
30:27.2 2
 
0.4%
54:44.0 2
 
0.4%
14:03.8 2
 
0.4%
59:08.0 2
 
0.4%
45:05.8 2
 
0.4%
13:20.2 2
 
0.4%
Other values (464) 473
94.6%
2023-12-12T21:47:39.003747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 337
9.6%
2 329
9.4%
4 316
9.0%
0 312
8.9%
1 309
8.8%
5 304
8.7%
7 165
 
4.7%
9 150
 
4.3%
Other values (2) 278
7.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 337
13.5%
2 329
13.2%
4 316
12.6%
0 312
12.5%
1 309
12.4%
5 304
12.2%
7 165
6.6%
9 150
6.0%
8 150
6.0%
6 128
 
5.1%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 337
9.6%
2 329
9.4%
4 316
9.0%
0 312
8.9%
1 309
8.8%
5 304
8.7%
7 165
 
4.7%
9 150
 
4.3%
Other values (2) 278
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 337
9.6%
2 329
9.4%
4 316
9.0%
0 312
8.9%
1 309
8.8%
5 304
8.7%
7 165
 
4.7%
9 150
 
4.3%
Other values (2) 278
7.9%

최초처리직원번호
Text

Distinct283
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:47:39.359107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.436
Min length4

Characters and Unicode

Total characters2218
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

Unique159 ?
Unique (%)31.8%

Sample

1st row9C669
2nd row4492
3rd row5463
4th row9C669
5th row6178
ValueCountFrequency (%)
4510 6
 
1.2%
9c624 6
 
1.2%
5149 6
 
1.2%
9c764 5
 
1.0%
9c725 5
 
1.0%
9c681 5
 
1.0%
9c716 5
 
1.0%
4960 5
 
1.0%
9c759 5
 
1.0%
5089 4
 
0.8%
Other values (273) 448
89.6%
2023-12-12T21:47:39.963669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 342
15.4%
6 324
14.6%
4 230
10.4%
5 230
10.4%
7 221
10.0%
C 218
9.8%
1 158
7.1%
0 150
6.8%
8 117
 
5.3%
2 116
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
90.2%
Uppercase Letter 218
 
9.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 342
17.1%
6 324
16.2%
4 230
11.5%
5 230
11.5%
7 221
11.1%
1 158
7.9%
0 150
7.5%
8 117
 
5.9%
2 116
 
5.8%
3 112
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
C 218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
90.2%
Latin 218
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
9 342
17.1%
6 324
16.2%
4 230
11.5%
5 230
11.5%
7 221
11.1%
1 158
7.9%
0 150
7.5%
8 117
 
5.9%
2 116
 
5.8%
3 112
 
5.6%
Latin
ValueCountFrequency (%)
C 218
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 342
15.4%
6 324
14.6%
4 230
10.4%
5 230
10.4%
7 221
10.0%
C 218
9.8%
1 158
7.1%
0 150
6.8%
8 117
 
5.3%
2 116
 
5.2%

Sample

업무구분코드조변접수번호종류코드접수일자원장진행상태코드전자보증발급상태코드조변보증서형태코드조변종류코드조변시기구분코드전결구분코드승인일자승인구분코드불승인사유단순내용실행일자사이버연장접수채널구분코드사이버연장구분코드접수팀코드자동연결처리시각삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N112:27.69C66912:27.69C669
1GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000030001-01-01 00:00:00.000000N112:20.8449212:20.84492
2GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000020001-01-01 00:00:00.000000N112:17.9546312:17.95463
3GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N112:17.39C66912:17.39C669
4GA00:00.022111500:00.01<NA>0001-01-01 00:00:00.00000010001-01-01 00:00:00.000000N212:11.7617810:42.26178
5GA00:00.062111500:00.01<NA>00:00.0112:11.0N312:11.0496006:43.24960
6GA00:00.01214150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000020001-01-01 00:00:00.000000N112:07.1468512:07.14685
7GA00:00.06112111500:00.01<NA>00:00.013211:41.1N412:03.3EXJ8047:44.96186
8GA00:00.06214151100:00.01<NA>00:00.0111:57.3N311:57.3611359:07.36113
9GA00:00.022111500:00.01<NA>0001-01-01 00:00:00.00000030001-01-01 00:00:00.000000N211:53.5449210:18.04492
업무구분코드조변접수번호종류코드접수일자원장진행상태코드전자보증발급상태코드조변보증서형태코드조변종류코드조변시기구분코드전결구분코드승인일자승인구분코드불승인사유단순내용실행일자사이버연장접수채널구분코드사이버연장구분코드접수팀코드자동연결처리시각삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001330001-01-01 00:00:00.000000N122:45.09C73922:45.09C739
491GA00:00.062111500:00.01<NA>00:00.0122:39.9N322:39.9495158:11.44951
492GA00:00.02214151100:00.01<NA>0001-01-01 00:00:00.00000030001-01-01 00:00:00.000000N522:37.8607847:07.76078
493GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001310001-01-01 00:00:00.000000N122:33.3517022:33.35170
494GA00:00.062111500:00.01<NA>00:00.0222:31.0N322:31.0531324:03.15313
495GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000020001-01-01 00:00:00.000000N122:11.59C75722:11.59C757
496GA00:00.06112111500:00.01<NA>00:00.013421:16.9N421:57.8EXI6249:11.29C663
497GA00:00.01214150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000040001-01-01 00:00:00.000000N121:43.3512321:43.35123
498GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N121:17.7480321:17.74803
499GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N121:17.7480321:17.74803

Duplicate rows

Most frequently occurring

업무구분코드조변접수번호종류코드접수일자원장진행상태코드전자보증발급상태코드조변보증서형태코드조변종류코드조변시기구분코드전결구분코드승인일자승인구분코드불승인사유단순내용실행일자사이버연장접수채널구분코드사이버연장구분코드접수팀코드자동연결처리시각삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호# duplicates
3GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001310001-01-01 00:00:00.000000N149:15.1451049:15.145105
4GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N100:39.4484300:39.448434
0GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.00000010001-01-01 00:00:00.000000N138:33.1613638:33.161363
10GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001330001-01-01 00:00:00.000000N158:50.29C72958:50.29C7293
1GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001310001-01-01 00:00:00.000000N125:03.9532425:03.953242
2GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001310001-01-01 00:00:00.000000N145:05.89C70545:05.89C7052
5GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N121:17.7480321:17.748032
6GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N130:28.39C72530:28.39C7252
7GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001320001-01-01 00:00:00.000000N157:56.7449057:56.744902
8GA00:00.01211150001-01-01 00:00:00.000000<NA>0001-01-01 00:00:00.0000001330001-01-01 00:00:00.000000N111:38.2601011:38.260102