Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells110
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.3 KiB
Average record size in memory74.3 B

Variable types

Text6
Numeric1
Boolean1
Categorical1

Dataset

Description해당 파일 데이터는 신용보증기금의 시스템메뉴 나의메뉴 정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093176/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
메뉴ID has 110 (22.0%) missing valuesMissing
정렬순서값 has 56 (11.2%) zerosZeros

Reproduction

Analysis started2023-12-11 22:47:03.104253
Analysis finished2023-12-11 22:47:03.901855
Duration0.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

직원번호
Text

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:47:04.074100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.378
Min length4

Characters and Unicode

Total characters2189
Distinct characters12
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9C639
2nd row9C639
3rd row9C639
4th row9C639
5th row9C639
ValueCountFrequency (%)
9c639 9
 
1.8%
5170 9
 
1.8%
6174 9
 
1.8%
5348 9
 
1.8%
3564 9
 
1.8%
4056 9
 
1.8%
9c634 9
 
1.8%
9c768 9
 
1.8%
6132 9
 
1.8%
9c732 9
 
1.8%
Other values (46) 410
82.0%
2023-12-12T07:47:04.479876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
91.4%
Uppercase Letter 189
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
91.4%
Latin 189
 
8.6%

Most frequent character per script

Common
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Latin
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

정렬순서값
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.98
Minimum0
Maximum8
Zeros56
Zeros (%)11.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T07:47:04.617363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5832046
Coefficient of variation (CV)0.64904638
Kurtosis-1.2282882
Mean3.98
Median Absolute Deviation (MAD)2
Skewness0.011648217
Sum1990
Variance6.6729459
MonotonicityNot monotonic
2023-12-12T07:47:04.766159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 56
11.2%
4 56
11.2%
3 56
11.2%
2 56
11.2%
1 56
11.2%
8 55
11.0%
7 55
11.0%
6 55
11.0%
5 55
11.0%
ValueCountFrequency (%)
0 56
11.2%
1 56
11.2%
2 56
11.2%
3 56
11.2%
4 56
11.2%
5 55
11.0%
6 55
11.0%
7 55
11.0%
8 55
11.0%
ValueCountFrequency (%)
8 55
11.0%
7 55
11.0%
6 55
11.0%
5 55
11.0%
4 56
11.2%
3 56
11.2%
2 56
11.2%
1 56
11.2%
0 56
11.2%

메뉴ID
Text

MISSING 

Distinct98
Distinct (%)25.1%
Missing110
Missing (%)22.0%
Memory size4.0 KiB
2023-12-12T07:47:05.014903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.948718
Min length9

Characters and Unicode

Total characters4660
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

Unique52 ?
Unique (%)13.3%

Sample

1st rowGRN390100100
2nd rowGRN440130120
3rd rowGRN380120150
4th rowGRN380120140
5th rowGRN100130130
ValueCountFrequency (%)
grn390100100 43
 
11.0%
grn100180100 27
 
6.9%
cust120100 24
 
6.2%
grn100120100 20
 
5.1%
grn380160110 18
 
4.6%
grn440130120 17
 
4.4%
bcrmn100100100 17
 
4.4%
grn440140100 15
 
3.8%
grn380120100 14
 
3.6%
otevl100130120 12
 
3.1%
Other values (88) 183
46.9%
2023-12-12T07:47:05.407744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1562
33.5%
1 966
20.7%
N 312
 
6.7%
G 281
 
6.0%
R 278
 
6.0%
3 234
 
5.0%
2 155
 
3.3%
4 150
 
3.2%
8 123
 
2.6%
C 97
 
2.1%
Other values (17) 502
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3333
71.5%
Uppercase Letter 1327
 
28.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 312
23.5%
G 281
21.2%
R 278
20.9%
C 97
 
7.3%
S 64
 
4.8%
U 63
 
4.7%
T 58
 
4.4%
A 30
 
2.3%
I 27
 
2.0%
M 26
 
2.0%
Other values (7) 91
 
6.9%
Decimal Number
ValueCountFrequency (%)
0 1562
46.9%
1 966
29.0%
3 234
 
7.0%
2 155
 
4.7%
4 150
 
4.5%
8 123
 
3.7%
9 49
 
1.5%
6 40
 
1.2%
5 39
 
1.2%
7 15
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3333
71.5%
Latin 1327
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 312
23.5%
G 281
21.2%
R 278
20.9%
C 97
 
7.3%
S 64
 
4.8%
U 63
 
4.7%
T 58
 
4.4%
A 30
 
2.3%
I 27
 
2.0%
M 26
 
2.0%
Other values (7) 91
 
6.9%
Common
ValueCountFrequency (%)
0 1562
46.9%
1 966
29.0%
3 234
 
7.0%
2 155
 
4.7%
4 150
 
4.5%
8 123
 
3.7%
9 49
 
1.5%
6 40
 
1.2%
5 39
 
1.2%
7 15
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1562
33.5%
1 966
20.7%
N 312
 
6.7%
G 281
 
6.0%
R 278
 
6.0%
3 234
 
5.0%
2 155
 
3.3%
4 150
 
3.2%
8 123
 
2.6%
C 97
 
2.1%
Other values (17) 502
 
10.8%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T07:47:05.535113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
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

2023-12-12T07:47:05.622705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:47:05.712327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리시각
Text

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:47:05.899603image/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

Unique0 ?
Unique (%)0.0%

Sample

1st row41:56.1
2nd row41:56.1
3rd row41:56.1
4th row41:56.1
5th row41:56.1
ValueCountFrequency (%)
41:56.1 9
 
1.8%
38:15.4 9
 
1.8%
47:04.9 9
 
1.8%
47:39.0 9
 
1.8%
45:58.7 9
 
1.8%
29:57.4 9
 
1.8%
10:02.4 9
 
1.8%
04:00.6 9
 
1.8%
03:59.5 9
 
1.8%
58:19.7 9
 
1.8%
Other values (46) 410
82.0%
2023-12-12T07:47:06.240976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
15.1%
4 352
14.1%
1 329
13.2%
2 311
12.4%
5 279
11.2%
3 248
9.9%
6 189
7.6%
8 171
6.8%
9 135
 
5.4%
7 108
 
4.3%
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%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

처리직원번호
Text

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:47:06.456980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.378
Min length4

Characters and Unicode

Total characters2189
Distinct characters12
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9C639
2nd row9C639
3rd row9C639
4th row9C639
5th row9C639
ValueCountFrequency (%)
9c639 9
 
1.8%
5170 9
 
1.8%
6174 9
 
1.8%
5348 9
 
1.8%
3564 9
 
1.8%
4056 9
 
1.8%
9c634 9
 
1.8%
9c768 9
 
1.8%
6132 9
 
1.8%
9c732 9
 
1.8%
Other values (46) 410
82.0%
2023-12-12T07:47:06.837713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
91.4%
Uppercase Letter 189
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
91.4%
Latin 189
 
8.6%

Most frequent character per script

Common
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Latin
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

최초처리시각
Text

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:47:07.075091image/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

Unique0 ?
Unique (%)0.0%

Sample

1st row41:56.1
2nd row41:56.1
3rd row41:56.1
4th row41:56.1
5th row41:56.1
ValueCountFrequency (%)
41:56.1 9
 
1.8%
38:15.4 9
 
1.8%
47:04.9 9
 
1.8%
47:39.0 9
 
1.8%
45:58.7 9
 
1.8%
29:57.4 9
 
1.8%
10:02.4 9
 
1.8%
04:00.6 9
 
1.8%
03:59.5 9
 
1.8%
58:19.7 9
 
1.8%
Other values (46) 410
82.0%
2023-12-12T07:47:07.409766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 378
15.1%
4 352
14.1%
1 329
13.2%
2 311
12.4%
5 279
11.2%
3 248
9.9%
6 189
7.6%
8 171
6.8%
9 135
 
5.4%
7 108
 
4.3%
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%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
0 378
10.8%
4 352
10.1%
1 329
9.4%
2 311
8.9%
5 279
8.0%
3 248
7.1%
6 189
 
5.4%
8 171
 
4.9%
Other values (2) 243
6.9%

최초처리직원번호
Text

Distinct56
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T07:47:07.628428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.378
Min length4

Characters and Unicode

Total characters2189
Distinct characters12
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

Unique0 ?
Unique (%)0.0%

Sample

1st row9C639
2nd row9C639
3rd row9C639
4th row9C639
5th row9C639
ValueCountFrequency (%)
9c639 9
 
1.8%
5170 9
 
1.8%
6174 9
 
1.8%
5348 9
 
1.8%
3564 9
 
1.8%
4056 9
 
1.8%
9c634 9
 
1.8%
9c768 9
 
1.8%
6132 9
 
1.8%
9c732 9
 
1.8%
Other values (46) 410
82.0%
2023-12-12T07:47:07.965486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
91.4%
Uppercase Letter 189
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
91.4%
Latin 189
 
8.6%

Most frequent character per script

Common
ValueCountFrequency (%)
6 388
19.4%
9 293
14.6%
3 207
10.3%
5 207
10.3%
7 171
8.6%
1 167
8.3%
0 162
8.1%
8 144
 
7.2%
4 144
 
7.2%
2 117
 
5.9%
Latin
ValueCountFrequency (%)
C 180
95.2%
A 9
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 388
17.7%
9 293
13.4%
3 207
9.5%
5 207
9.5%
C 180
8.2%
7 171
7.8%
1 167
7.6%
0 162
7.4%
8 144
 
6.6%
4 144
 
6.6%
Other values (2) 126
 
5.8%

Interactions

2023-12-12T07:47:03.545842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:47:08.051646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직원번호정렬순서값메뉴ID처리시각처리직원번호최초처리시각최초처리직원번호
직원번호1.0000.0000.0001.0001.0001.0001.000
정렬순서값0.0001.0000.7280.0000.0000.0000.000
메뉴ID0.0000.7281.0000.0000.0000.0000.000
처리시각1.0000.0000.0001.0001.0001.0001.000
처리직원번호1.0000.0000.0001.0001.0001.0001.000
최초처리시각1.0000.0000.0001.0001.0001.0001.000
최초처리직원번호1.0000.0000.0001.0001.0001.0001.000

Missing values

2023-12-12T07:47:03.705369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:47:03.850607image/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

직원번호정렬순서값메뉴ID삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09C6390GRN390100100N141:56.19C63941:56.19C639
19C6398GRN440130120N141:56.19C63941:56.19C639
29C6397GRN380120150N141:56.19C63941:56.19C639
39C6396GRN380120140N141:56.19C63941:56.19C639
49C6395GRN100130130N141:56.19C63941:56.19C639
59C6394GRN380120100N141:56.19C63941:56.19C639
69C6393GRN380130100N141:56.19C63941:56.19C639
79C6392GRN100180120N141:56.19C63941:56.19C639
89C6391CUST120100N141:56.19C63941:56.19C639
951700<NA>N138:15.4517038:15.45170
직원번호정렬순서값메뉴ID삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
49058256ACNG150200100N119:45.0582519:45.05825
49158255ACNG100110150N119:45.0582519:45.05825
49258254GRN110120N119:45.0582519:45.05825
49358253GRN100180100N119:45.0582519:45.05825
49458252<NA>N119:45.0582519:45.05825
49561690GRN390100100N113:42.4616913:42.46169
49661694<NA>N113:42.4616913:42.46169
49761693GRN440130120N113:42.4616913:42.46169
49861692<NA>N113:42.4616913:42.46169
49961691<NA>N113:42.4616913:42.46169