Overview

Dataset statistics

Number of variables16
Number of observations500
Missing cells500
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.5 KiB
Average record size in memory136.3 B

Variable types

Categorical7
Numeric1
Text5
Unsupported1
DateTime2

Dataset

Description해당 파일 데이터는 신용보증기금의 보증사업부문 제수입금 중 신용조사 수수료에 대한 정보를 확인하실 수 있는 자료입니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092613/fileData.do

Alerts

회계취소처리시각 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
수수료 is highly overall correlated with 기준금액수수료High correlation
기준금액수수료 is highly overall correlated with 수수료High correlation
이력일련번호 is highly imbalanced (89.8%)Imbalance
신용조사수수료구분코드 is highly imbalanced (53.0%)Imbalance
기준금액수수료 is highly imbalanced (69.2%)Imbalance
할인비율 is highly imbalanced (96.3%)Imbalance
최종수정수 is highly imbalanced (63.2%)Imbalance
취소처리자직원번호 has 500 (100.0%) missing valuesMissing
취소처리자직원번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
수수료 has 17 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-12 09:51:58.851442
Analysis finished2023-12-12 09:51:59.996098
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이력일련번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
489 
2
 
10
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 489
97.8%
2 10
 
2.0%
3 1
 
0.2%

Length

2023-12-12T18:52:00.068797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:00.162786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 489
97.8%
2 10
 
2.0%
3 1
 
0.2%

신용조사수수료구분코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4
395 
1
73 
3
 
31
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 395
79.0%
1 73
 
14.6%
3 31
 
6.2%
2 1
 
0.2%

Length

2023-12-12T18:52:00.252006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:00.338823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 395
79.0%
1 73
 
14.6%
3 31
 
6.2%
2 1
 
0.2%

기준금액수수료
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
442 
100000
 
26
150000
 
21
250000
 
6
200000
 
5

Length

Max length6
Median length1
Mean length1.58
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 442
88.4%
100000 26
 
5.2%
150000 21
 
4.2%
250000 6
 
1.2%
200000 5
 
1.0%

Length

2023-12-12T18:52:00.434285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:00.519466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 442
88.4%
100000 26
 
5.2%
150000 21
 
4.2%
250000 6
 
1.2%
200000 5
 
1.0%

할인비율
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
497 
100
 
2
25
 
1

Length

Max length3
Median length1
Mean length1.01
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 497
99.4%
100 2
 
0.4%
25 1
 
0.2%

Length

2023-12-12T18:52:00.629345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:00.720905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 497
99.4%
100 2
 
0.4%
25 1
 
0.2%

수수료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28810
Minimum0
Maximum250000
Zeros17
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T18:52:00.804740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15000
Q115000
median15000
Q315000
95-th percentile150000
Maximum250000
Range250000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42479.913
Coefficient of variation (CV)1.474485
Kurtosis9.8465944
Mean28810
Median Absolute Deviation (MAD)0
Skewness3.1440502
Sum14405000
Variance1.804543 × 109
MonotonicityNot monotonic
2023-12-12T18:52:00.911721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
15000 391
78.2%
20000 31
 
6.2%
100000 27
 
5.4%
150000 21
 
4.2%
0 17
 
3.4%
250000 5
 
1.0%
200000 4
 
0.8%
5000 4
 
0.8%
ValueCountFrequency (%)
0 17
 
3.4%
5000 4
 
0.8%
15000 391
78.2%
20000 31
 
6.2%
100000 27
 
5.4%
150000 21
 
4.2%
200000 4
 
0.8%
250000 5
 
1.0%
ValueCountFrequency (%)
250000 5
 
1.0%
200000 4
 
0.8%
150000 21
 
4.2%
100000 27
 
5.4%
20000 31
 
6.2%
15000 391
78.2%
5000 4
 
0.8%
0 17
 
3.4%

회계처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:52:01.350344image/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

Unique490 ?
Unique (%)98.0%

Sample

1st row18:25.8
2nd row19:57.1
3rd row20:02.8
4th row19:42.7
5th row18:58.2
ValueCountFrequency (%)
14:39.1 2
 
0.4%
38:15.5 2
 
0.4%
11:37.0 2
 
0.4%
05:45.8 2
 
0.4%
12:16.2 2
 
0.4%
31:18.2 1
 
0.2%
34:04.5 1
 
0.2%
33:26.7 1
 
0.2%
30:31.6 1
 
0.2%
30:48.8 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T18:52:02.005780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 372
10.6%
0 350
10.0%
2 321
9.2%
3 310
8.9%
5 310
8.9%
4 282
8.1%
6 153
 
4.4%
7 137
 
3.9%
Other values (2) 265
7.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 372
14.9%
0 350
14.0%
2 321
12.8%
3 310
12.4%
5 310
12.4%
4 282
11.3%
6 153
6.1%
7 137
 
5.5%
8 136
 
5.4%
9 129
 
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%
1 372
10.6%
0 350
10.0%
2 321
9.2%
3 310
8.9%
5 310
8.9%
4 282
8.1%
6 153
 
4.4%
7 137
 
3.9%
Other values (2) 265
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 372
10.6%
0 350
10.0%
2 321
9.2%
3 310
8.9%
5 310
8.9%
4 282
8.1%
6 153
 
4.4%
7 137
 
3.9%
Other values (2) 265
7.6%

회계취소처리시각
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

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 500
100.0%

Length

2023-12-12T18:52:02.207861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:02.309161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

취소처리자직원번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

수수료처리코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
10
387 
4
110 
5
 
3

Length

Max length2
Median length2
Mean length1.774
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 387
77.4%
4 110
 
22.0%
5 3
 
0.6%

Length

2023-12-12T18:52:02.432757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:02.545493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 387
77.4%
4 110
 
22.0%
5 3
 
0.6%

유효개시일자
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
2023-12-12T18:52:02.658893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:02.796140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
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
2023-12-12T18:52:02.917647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:52:03.036351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
438 
2
58 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 438
87.6%
2 58
 
11.6%
3 4
 
0.8%

Length

2023-12-12T18:52:03.171781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:52:03.300296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 438
87.6%
2 58
 
11.6%
3 4
 
0.8%

처리시각
Text

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:52:03.725129image/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

Unique492 ?
Unique (%)98.4%

Sample

1st row20:01.9
2nd row19:50.2
3rd row19:48.4
4th row19:38.3
5th row18:55.3
ValueCountFrequency (%)
17:27.0 2
 
0.4%
59:26.2 2
 
0.4%
59:57.4 2
 
0.4%
03:00.9 2
 
0.4%
30:52.9 1
 
0.2%
31:56.2 1
 
0.2%
32:24.5 1
 
0.2%
32:34.9 1
 
0.2%
32:39.2 1
 
0.2%
33:11.1 1
 
0.2%
Other values (486) 486
97.2%
2023-12-12T18:52:04.328124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 366
10.5%
0 343
9.8%
2 316
9.0%
5 303
8.7%
4 294
8.4%
3 278
7.9%
8 158
 
4.5%
9 150
 
4.3%
Other values (2) 292
8.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 366
14.6%
0 343
13.7%
2 316
12.6%
5 303
12.1%
4 294
11.8%
3 278
11.1%
8 158
6.3%
9 150
6.0%
7 149
6.0%
6 143
 
5.7%
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%
1 366
10.5%
0 343
9.8%
2 316
9.0%
5 303
8.7%
4 294
8.4%
3 278
7.9%
8 158
 
4.5%
9 150
 
4.3%
Other values (2) 292
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 366
10.5%
0 343
9.8%
2 316
9.0%
5 303
8.7%
4 294
8.4%
3 278
7.9%
8 158
 
4.5%
9 150
 
4.3%
Other values (2) 292
8.3%

처리직원번호
Text

Distinct338
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:52:04.748369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.292
Min length4

Characters and Unicode

Total characters2146
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

Unique222 ?
Unique (%)44.4%

Sample

1st row5931
2nd row4615
3rd row4872
4th row5570
5th row9C784
ValueCountFrequency (%)
4532 6
 
1.2%
4456 5
 
1.0%
9c671 5
 
1.0%
9c629 4
 
0.8%
6113 4
 
0.8%
5063 4
 
0.8%
4758 4
 
0.8%
6140 4
 
0.8%
6167 3
 
0.6%
6130 3
 
0.6%
Other values (328) 458
91.6%
2023-12-12T18:52:05.274274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 320
14.9%
9 282
13.1%
5 256
11.9%
4 219
10.2%
7 188
8.8%
3 159
7.4%
1 157
7.3%
8 157
7.3%
0 156
7.3%
C 146
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.2%
Uppercase Letter 146
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 320
16.0%
9 282
14.1%
5 256
12.8%
4 219
10.9%
7 188
9.4%
3 159
8.0%
1 157
7.8%
8 157
7.8%
0 156
7.8%
2 106
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.2%
Latin 146
 
6.8%

Most frequent character per script

Common
ValueCountFrequency (%)
6 320
16.0%
9 282
14.1%
5 256
12.8%
4 219
10.9%
7 188
9.4%
3 159
8.0%
1 157
7.8%
8 157
7.8%
0 156
7.8%
2 106
 
5.3%
Latin
ValueCountFrequency (%)
C 146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 320
14.9%
9 282
13.1%
5 256
11.9%
4 219
10.2%
7 188
8.8%
3 159
7.4%
1 157
7.3%
8 157
7.3%
0 156
7.3%
C 146
6.8%

최초처리시각
Text

Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:52:05.631257image/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

Unique471 ?
Unique (%)94.2%

Sample

1st row18:23.5
2nd row19:50.2
3rd row19:48.4
4th row19:38.3
5th row18:55.3
ValueCountFrequency (%)
47:00.8 3
 
0.6%
34:06.2 2
 
0.4%
04:54.2 2
 
0.4%
17:27.0 2
 
0.4%
59:57.4 2
 
0.4%
26:25.0 2
 
0.4%
01:10.3 2
 
0.4%
53:20.4 2
 
0.4%
59:26.2 2
 
0.4%
06:02.6 2
 
0.4%
Other values (475) 479
95.8%
2023-12-12T18:52:06.103198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 368
10.5%
0 352
10.1%
2 318
9.1%
5 296
8.5%
4 290
8.3%
3 275
7.9%
8 161
 
4.6%
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 (%)
1 368
14.7%
0 352
14.1%
2 318
12.7%
5 296
11.8%
4 290
11.6%
3 275
11.0%
8 161
6.4%
6 155
6.2%
9 146
 
5.8%
7 139
 
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%
1 368
10.5%
0 352
10.1%
2 318
9.1%
5 296
8.5%
4 290
8.3%
3 275
7.9%
8 161
 
4.6%
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%
1 368
10.5%
0 352
10.1%
2 318
9.1%
5 296
8.5%
4 290
8.3%
3 275
7.9%
8 161
 
4.6%
6 155
 
4.4%
Other values (2) 285
8.1%

최초처리직원번호
Text

Distinct338
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T18:52:06.449326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.292
Min length4

Characters and Unicode

Total characters2146
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

Unique222 ?
Unique (%)44.4%

Sample

1st row5931
2nd row4615
3rd row4872
4th row5570
5th row9C784
ValueCountFrequency (%)
4532 6
 
1.2%
4456 5
 
1.0%
9c671 5
 
1.0%
9c629 4
 
0.8%
6113 4
 
0.8%
5063 4
 
0.8%
4758 4
 
0.8%
6140 4
 
0.8%
6167 3
 
0.6%
6130 3
 
0.6%
Other values (328) 458
91.6%
2023-12-12T18:52:06.977714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 320
14.9%
9 282
13.1%
5 256
11.9%
4 219
10.2%
7 188
8.8%
3 159
7.4%
1 157
7.3%
8 157
7.3%
0 156
7.3%
C 146
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
93.2%
Uppercase Letter 146
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 320
16.0%
9 282
14.1%
5 256
12.8%
4 219
10.9%
7 188
9.4%
3 159
8.0%
1 157
7.8%
8 157
7.8%
0 156
7.8%
2 106
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
C 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
93.2%
Latin 146
 
6.8%

Most frequent character per script

Common
ValueCountFrequency (%)
6 320
16.0%
9 282
14.1%
5 256
12.8%
4 219
10.9%
7 188
9.4%
3 159
8.0%
1 157
7.8%
8 157
7.8%
0 156
7.8%
2 106
 
5.3%
Latin
ValueCountFrequency (%)
C 146
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 320
14.9%
9 282
13.1%
5 256
11.9%
4 219
10.2%
7 188
8.8%
3 159
7.4%
1 157
7.3%
8 157
7.3%
0 156
7.3%
C 146
6.8%

Interactions

2023-12-12T18:51:59.341394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:52:07.087845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호신용조사수수료구분코드기준금액수수료할인비율수수료수수료처리코드최종수정수
이력일련번호1.0000.0660.1050.0000.0990.7690.224
신용조사수수료구분코드0.0661.0000.5730.1150.5710.0640.052
기준금액수수료0.1050.5731.0000.4480.9980.1010.114
할인비율0.0000.1150.4481.0000.1630.0000.000
수수료0.0990.5710.9980.1631.0000.0810.125
수수료처리코드0.7690.0640.1010.0000.0811.0000.286
최종수정수0.2240.0520.1140.0000.1250.2861.000
2023-12-12T18:52:07.206536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신용조사수수료구분코드할인비율이력일련번호기준금액수수료최종수정수수수료처리코드
신용조사수수료구분코드1.0000.1080.0620.4990.0490.060
할인비율0.1081.0000.0000.3790.0000.000
이력일련번호0.0620.0001.0000.0790.0710.429
기준금액수수료0.4990.3790.0791.0000.0850.075
최종수정수0.0490.0000.0710.0851.0000.095
수수료처리코드0.0600.0000.4290.0750.0951.000
2023-12-12T18:52:07.317073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수수료이력일련번호신용조사수수료구분코드기준금액수수료할인비율수수료처리코드최종수정수
수수료1.0000.0740.4970.9360.1230.0600.094
이력일련번호0.0741.0000.0620.0790.0000.4290.071
신용조사수수료구분코드0.4970.0621.0000.4990.1080.0600.049
기준금액수수료0.9360.0790.4991.0000.3790.0750.085
할인비율0.1230.0000.1080.3791.0000.0000.000
수수료처리코드0.0600.4290.0600.0750.0001.0000.095
최종수정수0.0940.0710.0490.0850.0000.0951.000

Missing values

2023-12-12T18:51:59.453988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:51:59.923580image/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

이력일련번호신용조사수수료구분코드기준금액수수료할인비율수수료회계처리시각회계취소처리시각취소처리자직원번호수수료처리코드유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
014001500018:25.80001-01-01 00:00:00.000000<NA>1000:00.000:00.0220:01.9593118:23.55931
114001500019:57.10001-01-01 00:00:00.000000<NA>1000:00.000:00.0119:50.2461519:50.24615
214001500020:02.80001-01-01 00:00:00.000000<NA>1000:00.000:00.0119:48.4487219:48.44872
314001500019:42.70001-01-01 00:00:00.000000<NA>1000:00.000:00.0119:38.3557019:38.35570
414001500018:58.20001-01-01 00:00:00.000000<NA>1000:00.000:00.0118:55.39C78418:55.39C784
513002000018:59.10001-01-01 00:00:00.000000<NA>400:00.000:00.0118:54.8606318:54.86063
614001500003:53.20001-01-01 00:00:00.000000<NA>1000:00.000:00.0218:53.6609403:49.86094
714001500018:31.60001-01-01 00:00:00.000000<NA>1000:00.000:00.0118:28.19C70618:28.19C706
814001500018:18.40001-01-01 00:00:00.000000<NA>1000:00.000:00.0118:14.39C73918:14.39C739
914001500018:14.30001-01-01 00:00:00.000000<NA>400:00.000:00.0118:07.7590318:07.75903
이력일련번호신용조사수수료구분코드기준금액수수료할인비율수수료회계처리시각회계취소처리시각취소처리자직원번호수수료처리코드유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
49014001500053:53.60001-01-01 00:00:00.000000<NA>1000:00.000:00.0153:49.0616553:49.06165
49114001500053:49.10001-01-01 00:00:00.000000<NA>1000:00.000:00.0153:45.0506953:45.05069
49224001500053:23.00001-01-01 00:00:00.000000<NA>400:00.000:00.0153:20.4360753:20.43607
49314001500053:28.50001-01-01 00:00:00.000000<NA>1000:00.000:00.0153:19.3484853:19.34848
49413002000053:11.20001-01-01 00:00:00.000000<NA>1000:00.000:00.0153:07.19C79153:07.19C791
49514001500052:58.60001-01-01 00:00:00.000000<NA>1000:00.000:00.0152:55.1562052:55.15620
49613002000052:48.90001-01-01 00:00:00.000000<NA>400:00.000:00.0152:43.0617752:43.06177
49714001500052:38.70001-01-01 00:00:00.000000<NA>1000:00.000:00.0152:34.7602552:34.76025
49814001500002:07.60001-01-01 00:00:00.000000<NA>1000:00.000:00.0252:34.4484801:57.24848
49914001500052:14.40001-01-01 00:00:00.000000<NA>1000:00.000:00.0152:11.19C63452:11.19C634