Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.7 KiB
Average record size in memory83.3 B

Variable types

Categorical5
Numeric2
Text2
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템메뉴 자주가기 정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093189/fileData.do

Alerts

삭제여부 is highly overall correlated with 직원번호 and 3 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 직원번호 and 3 other fieldsHigh correlation
처리시각 is highly overall correlated with 직원번호 and 3 other fieldsHigh correlation
직원번호 is highly overall correlated with 삭제여부 and 3 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 직원번호 and 3 other fieldsHigh correlation
자주가기그룹일련번호 is highly overall correlated with 정렬순서값High correlation
정렬순서값 is highly overall correlated with 자주가기그룹일련번호High correlation
삭제여부 is highly imbalanced (90.6%)Imbalance
최종수정수 is highly imbalanced (68.3%)Imbalance
자주가기그룹일련번호 has 181 (36.2%) zerosZeros

Reproduction

Analysis started2023-12-12 03:22:35.636041
Analysis finished2023-12-12 03:22:37.192029
Duration1.56 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직원번호
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4531
216 
5616
98 
5901
70 
9C715
 
20
6207
 
19
Other values (31)
77 

Length

Max length5
Median length4
Mean length4.14
Min length4

Unique

Unique26 ?
Unique (%)5.2%

Sample

1st row95987
2nd row6152
3rd row9C637
4th row6121
5th row9C781

Common Values

ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9C715 20
 
4.0%
6207 19
 
3.8%
9C734 18
 
3.6%
9C798 15
 
3.0%
6124 12
 
2.4%
9C691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

Length

2023-12-12T12:22:37.286978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9c715 20
 
4.0%
6207 19
 
3.8%
9c734 18
 
3.6%
9c798 15
 
3.0%
6124 12
 
2.4%
9c691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

자주가기그룹일련번호
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.966
Minimum0
Maximum22
Zeros181
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:22:37.482320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile20
Maximum22
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.5426644
Coefficient of variation (CV)1.3174918
Kurtosis0.76172298
Mean4.966
Median Absolute Deviation (MAD)2
Skewness1.3956227
Sum2483
Variance42.806457
MonotonicityNot monotonic
2023-12-12T12:22:37.639556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 181
36.2%
2 55
 
11.0%
3 39
 
7.8%
1 37
 
7.4%
7 22
 
4.4%
6 22
 
4.4%
10 20
 
4.0%
9 18
 
3.6%
22 16
 
3.2%
18 14
 
2.8%
Other values (11) 76
15.2%
ValueCountFrequency (%)
0 181
36.2%
1 37
 
7.4%
2 55
 
11.0%
3 39
 
7.8%
4 11
 
2.2%
5 13
 
2.6%
6 22
 
4.4%
7 22
 
4.4%
8 2
 
0.4%
9 18
 
3.6%
ValueCountFrequency (%)
22 16
3.2%
21 7
1.4%
20 9
1.8%
19 13
2.6%
18 14
2.8%
15 4
 
0.8%
14 2
 
0.4%
13 7
1.4%
12 1
 
0.2%
11 7
1.4%

자주가기메뉴ID
Text

Distinct262
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:22:37.966655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.18
Min length9

Characters and Unicode

Total characters6090
Distinct characters27
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

Unique152 ?
Unique (%)30.4%

Sample

1st rowBCRMN120170110
2nd rowGRN380210100
3rd rowGRN440130120
4th rowISU300400
5th rowGRN100120100
ValueCountFrequency (%)
grn100120100 10
 
2.0%
grn390100100 10
 
2.0%
grn100130170 8
 
1.6%
grn380130100 7
 
1.4%
grn100180100 7
 
1.4%
grn440210 7
 
1.4%
grn440140120 7
 
1.4%
grn380160110 7
 
1.4%
grn380210100 7
 
1.4%
grn380160140 7
 
1.4%
Other values (252) 423
84.6%
2023-12-12T12:22:38.478433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1944
31.9%
1 1268
20.8%
N 366
 
6.0%
R 310
 
5.1%
G 302
 
5.0%
3 267
 
4.4%
4 216
 
3.5%
2 216
 
3.5%
C 169
 
2.8%
S 142
 
2.3%
Other values (17) 890
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4314
70.8%
Uppercase Letter 1776
29.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 366
20.6%
R 310
17.5%
G 302
17.0%
C 169
9.5%
S 142
 
8.0%
U 103
 
5.8%
T 94
 
5.3%
A 59
 
3.3%
B 59
 
3.3%
M 59
 
3.3%
Other values (7) 113
 
6.4%
Decimal Number
ValueCountFrequency (%)
0 1944
45.1%
1 1268
29.4%
3 267
 
6.2%
4 216
 
5.0%
2 216
 
5.0%
8 131
 
3.0%
5 113
 
2.6%
6 69
 
1.6%
9 53
 
1.2%
7 37
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4314
70.8%
Latin 1776
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 366
20.6%
R 310
17.5%
G 302
17.0%
C 169
9.5%
S 142
 
8.0%
U 103
 
5.8%
T 94
 
5.3%
A 59
 
3.3%
B 59
 
3.3%
M 59
 
3.3%
Other values (7) 113
 
6.4%
Common
ValueCountFrequency (%)
0 1944
45.1%
1 1268
29.4%
3 267
 
6.2%
4 216
 
5.0%
2 216
 
5.0%
8 131
 
3.0%
5 113
 
2.6%
6 69
 
1.6%
9 53
 
1.2%
7 37
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1944
31.9%
1 1268
20.8%
N 366
 
6.0%
R 310
 
5.1%
G 302
 
5.0%
3 267
 
4.4%
4 216
 
3.5%
2 216
 
3.5%
C 169
 
2.8%
S 142
 
2.3%
Other values (17) 890
14.6%

정렬순서값
Real number (ℝ)

HIGH CORRELATION 

Distinct224
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.454
Minimum2
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:22:38.678694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q119
median63
Q3118.25
95-th percentile206.05
Maximum234
Range232
Interquartile range (IQR)99.25

Descriptive statistics

Standard deviation63.681257
Coefficient of variation (CV)0.83293558
Kurtosis-0.46621742
Mean76.454
Median Absolute Deviation (MAD)47
Skewness0.74775046
Sum38227
Variance4055.3025
MonotonicityNot monotonic
2023-12-12T12:22:38.888837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 9
 
1.8%
2 9
 
1.8%
12 8
 
1.6%
6 8
 
1.6%
21 8
 
1.6%
7 8
 
1.6%
4 8
 
1.6%
13 8
 
1.6%
10 8
 
1.6%
3 7
 
1.4%
Other values (214) 419
83.8%
ValueCountFrequency (%)
2 9
1.8%
3 7
1.4%
4 8
1.6%
5 9
1.8%
6 8
1.6%
7 8
1.6%
8 7
1.4%
9 7
1.4%
10 8
1.6%
11 6
1.2%
ValueCountFrequency (%)
234 1
0.2%
233 1
0.2%
231 1
0.2%
229 1
0.2%
228 1
0.2%
227 1
0.2%
226 1
0.2%
225 1
0.2%
224 1
0.2%
223 1
0.2%

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
494 
True
 
6
ValueCountFrequency (%)
False 494
98.8%
True 6
 
1.2%
2023-12-12T12:22:39.023000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
432 
2
49 
3
 
13
5
 
4
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 432
86.4%
2 49
 
9.8%
3 13
 
2.6%
5 4
 
0.8%
4 2
 
0.4%

Length

2023-12-12T12:22:39.148094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:22:39.290151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 432
86.4%
2 49
 
9.8%
3 13
 
2.6%
5 4
 
0.8%
4 2
 
0.4%

처리시각
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11:47.0
216 
12:17.9
98 
14:37.6
70 
47:37.0
 
20
33:56.8
 
18
Other values (40)
78 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique36 ?
Unique (%)7.2%

Sample

1st row50:25.4
2nd row39:07.6
3rd row39:05.4
4th row32:05.6
5th row29:12.6

Common Values

ValueCountFrequency (%)
11:47.0 216
43.2%
12:17.9 98
19.6%
14:37.6 70
 
14.0%
47:37.0 20
 
4.0%
33:56.8 18
 
3.6%
24:21.1 13
 
2.6%
00:25.0 12
 
2.4%
36:33.5 12
 
2.4%
32:59.5 5
 
1.0%
07:39.9 1
 
0.2%
Other values (35) 35
 
7.0%

Length

2023-12-12T12:22:39.417732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11:47.0 216
43.2%
12:17.9 98
19.6%
14:37.6 70
 
14.0%
47:37.0 20
 
4.0%
33:56.8 18
 
3.6%
24:21.1 13
 
2.6%
00:25.0 12
 
2.4%
36:33.5 12
 
2.4%
32:59.5 5
 
1.0%
19:01.1 1
 
0.2%
Other values (35) 35
 
7.0%

처리직원번호
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4531
216 
5616
98 
5901
70 
9C715
 
20
6207
 
19
Other values (31)
77 

Length

Max length5
Median length4
Mean length4.14
Min length4

Unique

Unique26 ?
Unique (%)5.2%

Sample

1st row95987
2nd row6152
3rd row9C637
4th row6121
5th row9C781

Common Values

ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9C715 20
 
4.0%
6207 19
 
3.8%
9C734 18
 
3.6%
9C798 15
 
3.0%
6124 12
 
2.4%
9C691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

Length

2023-12-12T12:22:39.565516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9c715 20
 
4.0%
6207 19
 
3.8%
9c734 18
 
3.6%
9c798 15
 
3.0%
6124 12
 
2.4%
9c691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

최초처리시각
Text

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:22:39.963328image/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

Unique494 ?
Unique (%)98.8%

Sample

1st row50:25.4
2nd row45:09.6
3rd row39:05.4
4th row32:05.6
5th row29:12.6
ValueCountFrequency (%)
16:53.0 2
 
0.4%
38:16.3 2
 
0.4%
22:53.9 2
 
0.4%
29:51.0 1
 
0.2%
05:35.9 1
 
0.2%
31:49.9 1
 
0.2%
53:00.9 1
 
0.2%
33:48.0 1
 
0.2%
33:29.1 1
 
0.2%
50:25.4 1
 
0.2%
Other values (487) 487
97.4%
2023-12-12T12:22:40.580937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 345
9.9%
2 345
9.9%
0 324
9.3%
4 306
8.7%
1 287
8.2%
5 279
8.0%
8 175
 
5.0%
6 154
 
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 (%)
3 345
13.8%
2 345
13.8%
0 324
13.0%
4 306
12.2%
1 287
11.5%
5 279
11.2%
8 175
7.0%
6 154
6.2%
7 143
5.7%
9 142
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%
3 345
9.9%
2 345
9.9%
0 324
9.3%
4 306
8.7%
1 287
8.2%
5 279
8.0%
8 175
 
5.0%
6 154
 
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%
3 345
9.9%
2 345
9.9%
0 324
9.3%
4 306
8.7%
1 287
8.2%
5 279
8.0%
8 175
 
5.0%
6 154
 
4.4%
Other values (2) 285
8.1%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4531
216 
5616
98 
5901
70 
9C715
 
20
6207
 
19
Other values (31)
77 

Length

Max length5
Median length4
Mean length4.14
Min length4

Unique

Unique26 ?
Unique (%)5.2%

Sample

1st row95987
2nd row6152
3rd row9C637
4th row6121
5th row9C781

Common Values

ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9C715 20
 
4.0%
6207 19
 
3.8%
9C734 18
 
3.6%
9C798 15
 
3.0%
6124 12
 
2.4%
9C691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

Length

2023-12-12T12:22:40.811606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4531 216
43.2%
5616 98
19.6%
5901 70
 
14.0%
9c715 20
 
4.0%
6207 19
 
3.8%
9c734 18
 
3.6%
9c798 15
 
3.0%
6124 12
 
2.4%
9c691 4
 
0.8%
6140 2
 
0.4%
Other values (26) 26
 
5.2%

Interactions

2023-12-12T12:22:36.654487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:22:36.408599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:22:36.768422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:22:36.531387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:22:40.924872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직원번호자주가기그룹일련번호정렬순서값삭제여부최종수정수처리시각처리직원번호최초처리직원번호
직원번호1.0000.4670.5790.6490.1561.0001.0001.000
자주가기그룹일련번호0.4671.0000.8160.0000.1290.3510.4670.467
정렬순서값0.5790.8161.0000.1290.0830.5270.5790.579
삭제여부0.6490.0000.1291.0000.2311.0000.6490.649
최종수정수0.1560.1290.0830.2311.0000.4360.1560.156
처리시각1.0000.3510.5271.0000.4361.0001.0001.000
처리직원번호1.0000.4670.5790.6490.1561.0001.0001.000
최초처리직원번호1.0000.4670.5790.6490.1561.0001.0001.000
2023-12-12T12:22:41.471644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부최종수정수처리직원번호처리시각직원번호최초처리직원번호
삭제여부1.0000.2810.5060.9560.5060.506
최종수정수0.2811.0000.0700.1960.0700.070
처리직원번호0.5060.0701.0000.9901.0001.000
처리시각0.9560.1960.9901.0000.9900.990
직원번호0.5060.0701.0000.9901.0001.000
최초처리직원번호0.5060.0701.0000.9901.0001.000
2023-12-12T12:22:41.612185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자주가기그룹일련번호정렬순서값직원번호삭제여부최종수정수처리시각처리직원번호최초처리직원번호
자주가기그룹일련번호1.0000.7120.1700.0000.0700.1200.1700.170
정렬순서값0.7121.0000.2360.0980.0440.1980.2360.236
직원번호0.1700.2361.0000.5060.0700.9901.0001.000
삭제여부0.0000.0980.5061.0000.2810.9560.5060.506
최종수정수0.0700.0440.0700.2811.0000.1960.0700.070
처리시각0.1200.1980.9900.9560.1961.0000.9900.990
처리직원번호0.1700.2361.0000.5060.0700.9901.0001.000
최초처리직원번호0.1700.2361.0000.5060.0700.9901.0001.000

Missing values

2023-12-12T12:22:36.916660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:22:37.110708image/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

직원번호자주가기그룹일련번호자주가기메뉴ID정렬순서값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0959870BCRMN1201701103N150:25.49598750:25.495987
161520GRN38021010030N239:07.6615245:09.66152
29C6370GRN44013012017N139:05.49C63739:05.49C637
361218ISU30040054N132:05.6612132:05.66121
49C7813GRN10012010021N129:12.69C78129:12.69C781
59C7400GRN38018012021N124:27.89C74024:27.89C740
656160GRN3901001003N112:17.9561655:10.55616
756167ISU400500550104N112:17.9561636:20.75616
856167ISU300100105N112:17.9561606:29.95616
956167ISU160200106N112:17.9561633:52.05616
직원번호자주가기그룹일련번호자주가기메뉴ID정렬순서값삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
49059010GRN10013017077N114:37.6590119:58.15901
49159012BCRMN14023010096N114:37.6590130:51.15901
49259012GRN380150100102N114:37.6590132:10.95901
49359012BCRMN10010010088N214:37.6590142:01.35901
49459010GRN10013010076N114:37.6590138:16.35901
49559011ISU500500100127N114:37.6590141:44.05901
49659010GRN10013013059N214:37.6590148:12.85901
49759010GRN39010010072N114:37.6590122:32.75901
49859011ISU200100100118N314:37.6590138:40.45901
49959011ISU100200110N314:37.6590138:22.25901