Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.4 KiB
Average record size in memory66.3 B

Variable types

Categorical2
Numeric1
Boolean1
Text4

Dataset

Description해당 파일 데이터는 신용보증기금의 전자결재 연결정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092967/fileData.do

Alerts

삭제여부 is highly overall correlated with 최종수정수High correlation
최종수정수 is highly overall correlated with 삭제여부High correlation

Reproduction

Analysis started2023-12-12 13:50:14.736375
Analysis finished2023-12-12 13:50:15.364919
Duration0.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무팀구분코드
Categorical

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
B
175 
G
151 
F
41 
I
41 
J
32 
Other values (6)
60 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowU

Common Values

ValueCountFrequency (%)
B 175
35.0%
G 151
30.2%
F 41
 
8.2%
I 41
 
8.2%
J 32
 
6.4%
D 22
 
4.4%
Z 18
 
3.6%
K 11
 
2.2%
U 6
 
1.2%
A 2
 
0.4%

Length

2023-12-12T22:50:15.442300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 175
35.0%
g 151
30.2%
f 41
 
8.2%
i 41
 
8.2%
j 32
 
6.4%
d 22
 
4.4%
z 18
 
3.6%
k 11
 
2.2%
u 6
 
1.2%
a 2
 
0.4%

문서종류코드
Real number (ℝ)

Distinct67
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.928
Minimum101
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:50:15.593080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median216
Q3330
95-th percentile619.1
Maximum809
Range708
Interquartile range (IQR)228

Descriptive statistics

Standard deviation171.95101
Coefficient of variation (CV)0.65899791
Kurtosis-0.3151057
Mean260.928
Median Absolute Deviation (MAD)114
Skewness0.80186036
Sum130464
Variance29567.149
MonotonicityNot monotonic
2023-12-12T22:50:15.800703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 113
22.6%
102 48
 
9.6%
330 41
 
8.2%
325 31
 
6.2%
113 23
 
4.6%
305 21
 
4.2%
521 19
 
3.8%
501 16
 
3.2%
123 15
 
3.0%
623 11
 
2.2%
Other values (57) 162
32.4%
ValueCountFrequency (%)
101 113
22.6%
102 48
9.6%
105 5
 
1.0%
107 1
 
0.2%
110 1
 
0.2%
111 1
 
0.2%
113 23
 
4.6%
119 8
 
1.6%
120 8
 
1.6%
123 15
 
3.0%
ValueCountFrequency (%)
809 1
 
0.2%
807 3
 
0.6%
702 1
 
0.2%
626 7
1.4%
623 11
2.2%
621 2
 
0.4%
619 2
 
0.4%
541 1
 
0.2%
533 2
 
0.4%
532 1
 
0.2%

삭제여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
413 
True
87 
ValueCountFrequency (%)
False 413
82.6%
True 87
 
17.4%
2023-12-12T22:50:16.280542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
413 
2
87 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 413
82.6%
2 87
 
17.4%

Length

2023-12-12T22:50:16.414293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:50:16.551830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 413
82.6%
2 87
 
17.4%

처리시각
Text

Distinct484
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:50:16.924384image/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

Unique469 ?
Unique (%)93.8%

Sample

1st row02:57.7
2nd row02:47.2
3rd row02:47.2
4th row02:46.3
5th row02:45.0
ValueCountFrequency (%)
08:14.8 3
 
0.6%
44:29.3 2
 
0.4%
14:20.8 2
 
0.4%
01:05.6 2
 
0.4%
38:54.7 2
 
0.4%
16:49.9 2
 
0.4%
33:57.4 2
 
0.4%
50:23.9 2
 
0.4%
13:57.5 2
 
0.4%
45:57.9 2
 
0.4%
Other values (474) 479
95.8%
2023-12-12T22:50:17.433798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 348
9.9%
4 336
9.6%
5 325
9.3%
2 301
8.6%
0 295
8.4%
1 287
8.2%
6 164
 
4.7%
7 159
 
4.5%
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 (%)
3 348
13.9%
4 336
13.4%
5 325
13.0%
2 301
12.0%
0 295
11.8%
1 287
11.5%
6 164
6.6%
7 159
6.4%
8 156
6.2%
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%
3 348
9.9%
4 336
9.6%
5 325
9.3%
2 301
8.6%
0 295
8.4%
1 287
8.2%
6 164
 
4.7%
7 159
 
4.5%
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%
3 348
9.9%
4 336
9.6%
5 325
9.3%
2 301
8.6%
0 295
8.4%
1 287
8.2%
6 164
 
4.7%
7 159
 
4.5%
Other values (2) 285
8.1%

처리직원번호
Text

Distinct300
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:50:17.880295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.05
Min length4

Characters and Unicode

Total characters2025
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

Unique186 ?
Unique (%)37.2%

Sample

1st row1936
2nd row5082
3rd row5082
4th row4400
5th row5331
ValueCountFrequency (%)
5354 14
 
2.8%
5472 8
 
1.6%
5685 8
 
1.6%
5918 6
 
1.2%
4946 5
 
1.0%
6125 5
 
1.0%
5170 5
 
1.0%
5082 5
 
1.0%
95980 4
 
0.8%
9c786 4
 
0.8%
Other values (290) 436
87.2%
2023-12-12T22:50:18.473824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 411
20.3%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2017
99.6%
Uppercase Letter 8
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 411
20.4%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
C 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2017
99.6%
Latin 8
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 411
20.4%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%
Latin
ValueCountFrequency (%)
C 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 411
20.3%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%

최초처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:50:18.912157image/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 row02:57.7
2nd row02:47.2
3rd row50:26.3
4th row02:46.3
5th row02:45.0
ValueCountFrequency (%)
01:05.6 2
 
0.4%
34:41.5 2
 
0.4%
36:58.8 2
 
0.4%
38:54.7 2
 
0.4%
08:14.8 2
 
0.4%
25:53.6 1
 
0.2%
27:22.5 1
 
0.2%
24:58.7 1
 
0.2%
25:14.3 1
 
0.2%
10:31.5 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T22:50:19.507211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 349
10.0%
5 335
9.6%
4 324
9.3%
1 302
8.6%
2 295
8.4%
0 292
8.3%
6 163
 
4.7%
7 157
 
4.5%
Other values (2) 283
8.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 349
14.0%
5 335
13.4%
4 324
13.0%
1 302
12.1%
2 295
11.8%
0 292
11.7%
6 163
6.5%
7 157
6.3%
8 153
6.1%
9 130
 
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%
3 349
10.0%
5 335
9.6%
4 324
9.3%
1 302
8.6%
2 295
8.4%
0 292
8.3%
6 163
 
4.7%
7 157
 
4.5%
Other values (2) 283
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 349
10.0%
5 335
9.6%
4 324
9.3%
1 302
8.6%
2 295
8.4%
0 292
8.3%
6 163
 
4.7%
7 157
 
4.5%
Other values (2) 283
8.1%

최초처리직원번호
Text

Distinct300
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:50:19.926174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.05
Min length4

Characters and Unicode

Total characters2025
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

Unique186 ?
Unique (%)37.2%

Sample

1st row1936
2nd row5082
3rd row5082
4th row4400
5th row5331
ValueCountFrequency (%)
5354 14
 
2.8%
5472 8
 
1.6%
5685 8
 
1.6%
5918 6
 
1.2%
4946 5
 
1.0%
6125 5
 
1.0%
5170 5
 
1.0%
5082 5
 
1.0%
95980 4
 
0.8%
9c786 4
 
0.8%
Other values (290) 436
87.2%
2023-12-12T22:50:20.480247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 411
20.3%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2017
99.6%
Uppercase Letter 8
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 411
20.4%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
C 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2017
99.6%
Latin 8
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 411
20.4%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%
Latin
ValueCountFrequency (%)
C 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 411
20.3%
4 253
12.5%
6 214
10.6%
0 186
9.2%
3 180
8.9%
9 180
8.9%
1 170
8.4%
8 148
 
7.3%
7 138
 
6.8%
2 137
 
6.8%

Interactions

2023-12-12T22:50:14.992846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:50:20.597965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무팀구분코드문서종류코드삭제여부최종수정수
업무팀구분코드1.0000.7140.2110.211
문서종류코드0.7141.0000.0000.000
삭제여부0.2110.0001.0001.000
최종수정수0.2110.0001.0001.000
2023-12-12T22:50:20.715163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부최종수정수업무팀구분코드
삭제여부1.0000.9930.200
최종수정수0.9931.0000.200
업무팀구분코드0.2000.2001.000
2023-12-12T22:50:20.835865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문서종류코드업무팀구분코드삭제여부최종수정수
문서종류코드1.0000.4040.0000.000
업무팀구분코드0.4041.0000.2000.200
삭제여부0.0000.2001.0000.993
최종수정수0.0000.2000.9931.000

Missing values

2023-12-12T22:50:15.148527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:50:15.309946image/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

업무팀구분코드문서종류코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0G211N102:57.7193602:57.71936
1G101N102:47.2508202:47.25082
2G101Y202:47.2508250:26.35082
3G404N102:46.3440002:46.34400
4U105N102:45.0533102:45.05331
5G123Y202:41.8544702:28.55447
6B101N102:28.8281002:28.82810
7B101N102:26.7500502:26.75005
8Z220N102:16.6535402:16.65354
9F102N102:16.2612502:16.26125
업무팀구분코드문서종류코드삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490B470N106:20.3508006:20.35080
491G501N106:08.89070606:08.890706
492G501N105:53.99598705:53.995987
493J101N105:50.9443105:50.94431
494J101N105:46.29C69005:46.29C690
495D113N105:35.4492005:35.44920
496G305N105:17.2543005:17.25430
497B325Y205:08.0495704:53.64957
498I521N105:05.6561705:05.65617
499G301N105:00.5563405:00.55634