Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.8 KiB
Average record size in memory110.3 B

Variable types

Text4
Numeric4
Categorical3
DateTime2

Dataset

Description해당 파일 데이터는 신용보증기금의 보증상품 마스터이력에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093170/fileData.do

Alerts

유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
레벨순서값 is highly overall correlated with 상품구분코드 and 1 other fieldsHigh correlation
노드구분코드 is highly overall correlated with 상품구분코드 and 1 other fieldsHigh correlation
상품구분코드 is highly overall correlated with 노드구분코드 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation

Reproduction

Analysis started2023-12-12 18:26:00.490908
Analysis finished2023-12-12 18:26:03.248988
Duration2.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상품코드
Text

Distinct374
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T03:26:03.594152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2500
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

Unique290 ?
Unique (%)58.0%

Sample

1st row13522
2nd row13521
3rd row13468
4th row13520
5th row11291
ValueCountFrequency (%)
13289 6
 
1.2%
13287 5
 
1.0%
13288 5
 
1.0%
12907 5
 
1.0%
13286 5
 
1.0%
13279 5
 
1.0%
c0125 4
 
0.8%
13285 4
 
0.8%
14001 4
 
0.8%
13312 4
 
0.8%
Other values (364) 453
90.6%
2023-12-13T03:26:04.196764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 686
27.4%
3 591
23.6%
2 223
 
8.9%
4 203
 
8.1%
0 161
 
6.4%
5 139
 
5.6%
8 126
 
5.0%
7 107
 
4.3%
9 103
 
4.1%
6 97
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2436
97.4%
Uppercase Letter 64
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 686
28.2%
3 591
24.3%
2 223
 
9.2%
4 203
 
8.3%
0 161
 
6.6%
5 139
 
5.7%
8 126
 
5.2%
7 107
 
4.4%
9 103
 
4.2%
6 97
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
C 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2436
97.4%
Latin 64
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 686
28.2%
3 591
24.3%
2 223
 
9.2%
4 203
 
8.3%
0 161
 
6.6%
5 139
 
5.7%
8 126
 
5.2%
7 107
 
4.4%
9 103
 
4.2%
6 97
 
4.0%
Latin
ValueCountFrequency (%)
C 64
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 686
27.4%
3 591
23.6%
2 223
 
8.9%
4 203
 
8.1%
0 161
 
6.4%
5 139
 
5.6%
8 126
 
5.0%
7 107
 
4.3%
9 103
 
4.1%
6 97
 
3.9%

이력일련번호
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.456
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T03:26:04.349244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.99902959
Coefficient of variation (CV)0.6861467
Kurtosis10.658271
Mean1.456
Median Absolute Deviation (MAD)0
Skewness2.9850821
Sum728
Variance0.99806012
MonotonicityNot monotonic
2023-12-13T03:26:04.507815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 374
74.8%
2 72
 
14.4%
3 28
 
5.6%
4 14
 
2.8%
5 6
 
1.2%
6 3
 
0.6%
7 2
 
0.4%
8 1
 
0.2%
ValueCountFrequency (%)
1 374
74.8%
2 72
 
14.4%
3 28
 
5.6%
4 14
 
2.8%
5 6
 
1.2%
6 3
 
0.6%
7 2
 
0.4%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
7 2
 
0.4%
6 3
 
0.6%
5 6
 
1.2%
4 14
 
2.8%
3 28
 
5.6%
2 72
 
14.4%
1 374
74.8%

상품구분코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
3
352 
2
85 
49 
1
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 352
70.4%
2 85
 
17.0%
49
 
9.8%
1 14
 
2.8%

Length

2023-12-13T03:26:04.663590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:04.778199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 352
78.0%
2 85
 
18.8%
1 14
 
3.1%

상위상품코드
Text

Distinct71
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T03:26:04.994996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length3.536
Min length1

Characters and Unicode

Total characters1768
Distinct characters12
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

Unique5 ?
Unique (%)1.0%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
c0125 29
 
9.1%
c0045 24
 
7.6%
c0172 19
 
6.0%
c0137 16
 
5.0%
c0168 11
 
3.5%
c0127 9
 
2.8%
c0052 9
 
2.8%
c0140 8
 
2.5%
c0149 8
 
2.5%
c0163 8
 
2.5%
Other values (60) 176
55.5%
2023-12-13T03:26:05.405832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 415
23.5%
C 317
17.9%
1 277
15.7%
183
10.4%
2 108
 
6.1%
5 101
 
5.7%
4 89
 
5.0%
6 71
 
4.0%
3 66
 
3.7%
7 59
 
3.3%
Other values (2) 82
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1268
71.7%
Uppercase Letter 317
 
17.9%
Space Separator 183
 
10.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 415
32.7%
1 277
21.8%
2 108
 
8.5%
5 101
 
8.0%
4 89
 
7.0%
6 71
 
5.6%
3 66
 
5.2%
7 59
 
4.7%
8 43
 
3.4%
9 39
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
C 317
100.0%
Space Separator
ValueCountFrequency (%)
183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1451
82.1%
Latin 317
 
17.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 415
28.6%
1 277
19.1%
183
12.6%
2 108
 
7.4%
5 101
 
7.0%
4 89
 
6.1%
6 71
 
4.9%
3 66
 
4.5%
7 59
 
4.1%
8 43
 
3.0%
Latin
ValueCountFrequency (%)
C 317
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 415
23.5%
C 317
17.9%
1 277
15.7%
183
10.4%
2 108
 
6.1%
5 101
 
5.7%
4 89
 
5.0%
6 71
 
4.0%
3 66
 
3.7%
7 59
 
3.3%
Other values (2) 82
 
4.6%

노드구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
436 
1
64 

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 (%)
2 436
87.2%
1 64
 
12.8%

Length

2023-12-13T03:26:05.550176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:05.656584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 436
87.2%
1 64
 
12.8%

레벨순서값
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
317 
1
119 
0
64 

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 (%)
2 317
63.4%
1 119
 
23.8%
0 64
 
12.8%

Length

2023-12-13T03:26:05.751289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:26:05.863370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 317
63.4%
1 119
 
23.8%
0 64
 
12.8%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T03:26:05.957237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:06.053985image/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
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T03:26:06.157320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:06.243981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Real number (ℝ)

Distinct10
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.632
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T03:26:06.340513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2711442
Coefficient of variation (CV)0.77888739
Kurtosis16.798778
Mean1.632
Median Absolute Deviation (MAD)0
Skewness3.5080618
Sum816
Variance1.6158076
MonotonicityNot monotonic
2023-12-13T03:26:06.444610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 330
66.0%
2 105
 
21.0%
3 31
 
6.2%
4 16
 
3.2%
5 7
 
1.4%
6 4
 
0.8%
7 3
 
0.6%
11 2
 
0.4%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
1 330
66.0%
2 105
 
21.0%
3 31
 
6.2%
4 16
 
3.2%
5 7
 
1.4%
6 4
 
0.8%
7 3
 
0.6%
8 1
 
0.2%
9 1
 
0.2%
11 2
 
0.4%
ValueCountFrequency (%)
11 2
 
0.4%
9 1
 
0.2%
8 1
 
0.2%
7 3
 
0.6%
6 4
 
0.8%
5 7
 
1.4%
4 16
 
3.2%
3 31
 
6.2%
2 105
 
21.0%
1 330
66.0%

처리시각
Text

Distinct408
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T03:26:06.760043image/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

Unique371 ?
Unique (%)74.2%

Sample

1st row58:48.1
2nd row58:10.2
3rd row20:40.9
4th row32:15.5
5th row36:11.5
ValueCountFrequency (%)
44:47.4 16
 
3.2%
53:09.6 8
 
1.6%
32:47.1 8
 
1.6%
17:11.6 7
 
1.4%
42:01.9 7
 
1.4%
04:39.4 6
 
1.2%
40:44.6 5
 
1.0%
55:31.7 5
 
1.0%
41:15.3 4
 
0.8%
34:42.5 4
 
0.8%
Other values (398) 430
86.0%
2023-12-13T03:26:07.224326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 397
11.3%
5 317
9.1%
1 305
8.7%
3 299
8.5%
2 299
8.5%
0 278
7.9%
7 168
 
4.8%
6 157
 
4.5%
Other values (2) 280
8.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 397
15.9%
5 317
12.7%
1 305
12.2%
3 299
12.0%
2 299
12.0%
0 278
11.1%
7 168
6.7%
6 157
 
6.3%
9 143
 
5.7%
8 137
 
5.5%
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%
4 397
11.3%
5 317
9.1%
1 305
8.7%
3 299
8.5%
2 299
8.5%
0 278
7.9%
7 168
 
4.8%
6 157
 
4.5%
Other values (2) 280
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 397
11.3%
5 317
9.1%
1 305
8.7%
3 299
8.5%
2 299
8.5%
0 278
7.9%
7 168
 
4.8%
6 157
 
4.5%
Other values (2) 280
8.0%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5060.242
Minimum4256
Maximum6105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T03:26:07.366349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4256
5-th percentile4432
Q14432
median5193
Q35573
95-th percentile6105
Maximum6105
Range1849
Interquartile range (IQR)1141

Descriptive statistics

Standard deviation624.62563
Coefficient of variation (CV)0.1234379
Kurtosis-1.5849732
Mean5060.242
Median Absolute Deviation (MAD)697
Skewness0.22853359
Sum2530121
Variance390157.18
MonotonicityNot monotonic
2023-12-13T03:26:07.477998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4432 206
41.2%
5823 54
 
10.8%
5220 50
 
10.0%
5552 38
 
7.6%
6105 33
 
6.6%
5573 29
 
5.8%
4496 23
 
4.6%
5873 19
 
3.8%
5193 13
 
2.6%
5472 7
 
1.4%
Other values (12) 28
 
5.6%
ValueCountFrequency (%)
4256 1
 
0.2%
4432 206
41.2%
4444 2
 
0.4%
4496 23
 
4.6%
4606 1
 
0.2%
4685 1
 
0.2%
5032 1
 
0.2%
5037 1
 
0.2%
5099 7
 
1.4%
5152 1
 
0.2%
ValueCountFrequency (%)
6105 33
6.6%
5873 19
 
3.8%
5823 54
10.8%
5797 4
 
0.8%
5637 7
 
1.4%
5573 29
5.8%
5561 1
 
0.2%
5552 38
7.6%
5472 7
 
1.4%
5264 1
 
0.2%

최초처리시각
Text

Distinct276
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T03:26:07.787922image/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

Unique189 ?
Unique (%)37.8%

Sample

1st row58:48.1
2nd row58:10.2
3rd row58:11.5
4th row32:15.5
5th row18:43.9
ValueCountFrequency (%)
04:39.4 29
 
5.8%
44:47.4 16
 
3.2%
42:01.9 14
 
2.8%
53:09.6 10
 
2.0%
21:43.6 9
 
1.8%
32:47.1 8
 
1.6%
17:11.6 7
 
1.4%
55:31.7 6
 
1.2%
32:19.0 6
 
1.2%
18:24.5 5
 
1.0%
Other values (266) 390
78.0%
2023-12-13T03:26:08.365570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 417
11.9%
3 317
9.1%
2 314
9.0%
5 296
8.5%
1 292
8.3%
0 284
8.1%
9 162
 
4.6%
7 162
 
4.6%
Other values (2) 256
7.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 417
16.7%
3 317
12.7%
2 314
12.6%
5 296
11.8%
1 292
11.7%
0 284
11.4%
9 162
 
6.5%
7 162
 
6.5%
6 132
 
5.3%
8 124
 
5.0%
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%
4 417
11.9%
3 317
9.1%
2 314
9.0%
5 296
8.5%
1 292
8.3%
0 284
8.1%
9 162
 
4.6%
7 162
 
4.6%
Other values (2) 256
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 417
11.9%
3 317
9.1%
2 314
9.0%
5 296
8.5%
1 292
8.3%
0 284
8.1%
9 162
 
4.6%
7 162
 
4.6%
Other values (2) 256
7.3%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4982.69
Minimum4026
Maximum6105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T03:26:08.530490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4026
5-th percentile4432
Q14432
median4496
Q35573
95-th percentile6105
Maximum6105
Range2079
Interquartile range (IQR)1141

Descriptive statistics

Standard deviation621.2915
Coefficient of variation (CV)0.12468998
Kurtosis-1.3253235
Mean4982.69
Median Absolute Deviation (MAD)175
Skewness0.50285692
Sum2491345
Variance386003.13
MonotonicityNot monotonic
2023-12-13T03:26:08.678916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4432 220
44.0%
5220 76
 
15.2%
5823 56
 
11.2%
6105 35
 
7.0%
5573 27
 
5.4%
4496 21
 
4.2%
5873 16
 
3.2%
4444 8
 
1.6%
5099 7
 
1.4%
4917 7
 
1.4%
Other values (10) 27
 
5.4%
ValueCountFrequency (%)
4026 2
 
0.4%
4256 4
 
0.8%
4432 220
44.0%
4444 8
 
1.6%
4496 21
 
4.2%
4606 1
 
0.2%
4773 3
 
0.6%
4917 7
 
1.4%
4927 1
 
0.2%
5053 4
 
0.8%
ValueCountFrequency (%)
6105 35
7.0%
5873 16
 
3.2%
5823 56
11.2%
5797 5
 
1.0%
5573 27
 
5.4%
5561 1
 
0.2%
5552 1
 
0.2%
5220 76
15.2%
5213 5
 
1.0%
5099 7
 
1.4%

Interactions

2023-12-13T03:26:02.432273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:00.992892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.504346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.961080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.586328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.118846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.630983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.087110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.717762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.242865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.748755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.177441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.848728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.375844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:01.867740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:26:02.287629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:26:08.845831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호상품구분코드상위상품코드노드구분코드레벨순서값최종수정수처리직원번호최초처리직원번호
이력일련번호1.0000.0000.6400.0000.0000.8110.3760.649
상품구분코드0.0001.0000.6120.9960.6700.0000.2570.401
상위상품코드0.6400.6121.0000.4430.8580.7380.8870.930
노드구분코드0.0000.9960.4431.0000.7290.0000.0210.216
레벨순서값0.0000.6700.8580.7291.0000.0050.4740.283
최종수정수0.8110.0000.7380.0000.0051.0000.3970.592
처리직원번호0.3760.2570.8870.0210.4740.3971.0000.892
최초처리직원번호0.6490.4010.9300.2160.2830.5920.8921.000
2023-12-13T03:26:08.998090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
레벨순서값노드구분코드상품구분코드
레벨순서값1.0000.9630.698
노드구분코드0.9631.0000.939
상품구분코드0.6980.9391.000
2023-12-13T03:26:09.152453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수처리직원번호최초처리직원번호상품구분코드노드구분코드레벨순서값
이력일련번호1.0000.200-0.124-0.1490.0000.0000.000
최종수정수0.2001.0000.010-0.1210.0000.0000.000
처리직원번호-0.1240.0101.0000.8770.1640.0140.233
최초처리직원번호-0.149-0.1210.8771.0000.1830.1560.183
상품구분코드0.0000.0000.1640.1831.0000.9390.698
노드구분코드0.0000.0000.0140.1560.9391.0000.963
레벨순서값0.0000.0000.2330.1830.6980.9631.000

Missing values

2023-12-13T03:26:02.982356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:26:03.180314image/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

상품코드이력일련번호상품구분코드상위상품코드노드구분코드레벨순서값유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
013522132100:00.000:00.0158:48.1509958:48.15099
113521132100:00.000:00.0158:10.2509958:10.25099
213468132100:00.000:00.0220:40.9557358:11.55873
313520132100:00.000:00.0132:15.5509932:15.55099
411291122100:00.000:00.0336:11.5587318:43.94432
511290122100:00.000:00.0335:53.5587318:43.94432
611291322100:00.000:00.0218:48.4587318:43.94432
711290322100:00.000:00.0218:25.9587318:43.94432
813519112100:00.000:00.0132:28.7587332:28.75873
91351813C01722200:00.000:00.0251:36.5555224:34.25823
상품코드이력일련번호상품구분코드상위상품코드노드구분코드레벨순서값유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
49013271232100:00.000:00.0122:40.4522022:40.45220
4911323613C01202200:00.000:00.0220:01.6522050:35.75220
4921323513C01202200:00.000:00.0219:42.7522050:35.75220
4931323413C01202200:00.000:00.0219:20.0522050:35.75220
4941323313C01202200:00.000:00.0218:55.3522049:18.05220
4951323213C01202200:00.000:00.0218:11.0522049:18.05220
4961315413C00452200:00.000:00.0126:20.4444426:20.44444
4971315513C00452200:00.000:00.0126:20.4444426:20.44444
4981126712C01212200:00.000:00.0233:17.5522023:10.75220
4991126812C01212200:00.000:00.0233:08.4522023:10.75220