Overview

Dataset statistics

Number of variables19
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.8 KiB
Average record size in memory157.3 B

Variable types

Categorical10
Numeric3
Text5
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 전자결재정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092969/fileData.do

Alerts

최종수정수 is highly overall correlated with 영업점신청일자 and 2 other fieldsHigh correlation
업무팀구분코드 is highly overall correlated with 심사자료등급코드High correlation
팀코드 is highly overall correlated with 전자결재기안문종류코드High correlation
심사자료등급코드 is highly overall correlated with 업무팀구분코드High correlation
최종결재부점구분코드 is highly overall correlated with 본부심사부점코드High correlation
본부심사부점코드 is highly overall correlated with 최종결재부점구분코드High correlation
전자결재기안문종류코드 is highly overall correlated with 팀코드High correlation
영업점신청일자 is highly overall correlated with 최종수정수High correlation
영업점결재일자 is highly overall correlated with 최종수정수High correlation
삭제여부 is highly overall correlated with 최종수정수High correlation
전자결재양식종류코드 is highly imbalanced (94.7%)Imbalance
심사자료등급코드 is highly imbalanced (97.9%)Imbalance
최종결재부점구분코드 is highly imbalanced (96.3%)Imbalance
본부심사부점코드 is highly imbalanced (97.4%)Imbalance
전자결재기안문종류코드 is highly imbalanced (95.3%)Imbalance
전자결재전자수기구분코드 is highly imbalanced (91.9%)Imbalance
영업점신청일자 is highly imbalanced (84.7%)Imbalance

Reproduction

Analysis started2023-12-12 02:51:07.861767
Analysis finished2023-12-12 02:51:10.413016
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무팀구분코드
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
B
168 
G
147 
I
39 
D
38 
F
37 
Other values (6)
71 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowZ
2nd rowB
3rd rowF
4th rowB
5th rowG

Common Values

ValueCountFrequency (%)
B 168
33.6%
G 147
29.4%
I 39
 
7.8%
D 38
 
7.6%
F 37
 
7.4%
J 29
 
5.8%
Z 14
 
2.8%
U 14
 
2.8%
K 11
 
2.2%
A 2
 
0.4%

Length

2023-12-12T11:51:10.481868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b 168
33.6%
g 147
29.4%
i 39
 
7.8%
d 38
 
7.6%
f 37
 
7.4%
j 29
 
5.8%
z 14
 
2.8%
u 14
 
2.8%
k 11
 
2.2%
a 2
 
0.4%

문서종류코드
Real number (ℝ)

Distinct62
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260.06
Minimum101
Maximum809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T11:51:10.594169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1102
median217.5
Q3337.25
95-th percentile533.4
Maximum809
Range708
Interquartile range (IQR)235.25

Descriptive statistics

Standard deviation169.54495
Coefficient of variation (CV)0.65194553
Kurtosis-0.66079678
Mean260.06
Median Absolute Deviation (MAD)115.5
Skewness0.68683494
Sum130030
Variance28745.491
MonotonicityNot monotonic
2023-12-12T11:51:10.723939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 105
21.0%
102 44
 
8.8%
330 43
 
8.6%
113 36
 
7.2%
325 30
 
6.0%
305 21
 
4.2%
521 17
 
3.4%
501 16
 
3.2%
502 15
 
3.0%
105 13
 
2.6%
Other values (52) 160
32.0%
ValueCountFrequency (%)
101 105
21.0%
102 44
8.8%
105 13
 
2.6%
107 1
 
0.2%
110 1
 
0.2%
111 1
 
0.2%
113 36
 
7.2%
116 3
 
0.6%
119 9
 
1.8%
120 9
 
1.8%
ValueCountFrequency (%)
809 1
 
0.2%
807 1
 
0.2%
626 5
 
1.0%
623 11
2.2%
621 2
 
0.4%
619 4
 
0.8%
541 1
 
0.2%
533 1
 
0.2%
521 17
3.4%
515 1
 
0.2%

팀코드
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
212 
2
144 
3
76 
4
24 
9
 
17
Other values (7)
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row6
2nd row3
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 212
42.4%
2 144
28.8%
3 76
 
15.2%
4 24
 
4.8%
9 17
 
3.4%
6 14
 
2.8%
5 4
 
0.8%
A 3
 
0.6%
D 2
 
0.4%
B 2
 
0.4%
Other values (2) 2
 
0.4%

Length

2023-12-12T11:51:10.883127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 212
42.4%
2 144
28.8%
3 76
 
15.2%
4 24
 
4.8%
9 17
 
3.4%
6 14
 
2.8%
5 4
 
0.8%
a 3
 
0.6%
d 2
 
0.4%
b 2
 
0.4%
Other values (2) 2
 
0.4%

전자결재양식종류코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
497 
1
 
3

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 (%)
2 497
99.4%
1 3
 
0.6%

Length

2023-12-12T11:51:11.046935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:11.132349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 497
99.4%
1 3
 
0.6%

전결구분코드
Real number (ℝ)

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.568
Minimum5
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T11:51:11.222526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q17
median11
Q311
95-th percentile11
Maximum55
Range50
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.4402118
Coefficient of variation (CV)0.60940687
Kurtosis21.278064
Mean10.568
Median Absolute Deviation (MAD)0
Skewness4.4700711
Sum5284
Variance41.476329
MonotonicityNot monotonic
2023-12-12T11:51:11.341840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
11 290
58.0%
7 190
38.0%
37 8
 
1.6%
47 4
 
0.8%
41 3
 
0.6%
5 2
 
0.4%
46 2
 
0.4%
55 1
 
0.2%
ValueCountFrequency (%)
5 2
 
0.4%
7 190
38.0%
11 290
58.0%
37 8
 
1.6%
41 3
 
0.6%
46 2
 
0.4%
47 4
 
0.8%
55 1
 
0.2%
ValueCountFrequency (%)
55 1
 
0.2%
47 4
 
0.8%
46 2
 
0.4%
41 3
 
0.6%
37 8
 
1.6%
11 290
58.0%
7 190
38.0%
5 2
 
0.4%

심사자료등급코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
499 
 
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 (%)
1 499
99.8%
1
 
0.2%

Length

2023-12-12T11:51:11.471095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:11.584479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 499
100.0%

최종결재부점구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
2
 
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 (%)
1 497
99.4%
2 2
 
0.4%
1
 
0.2%

Length

2023-12-12T11:51:11.683295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:11.782700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.6%
2 2
 
0.4%

본부심사부점코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
498 
TMD
 
1
THE
 
1

Length

Max length3
Median length1
Mean length1.008
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
498
99.6%
TMD 1
 
0.2%
THE 1
 
0.2%

Length

2023-12-12T11:51:11.893228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:12.028904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
tmd 1
50.0%
the 1
50.0%

전자결재기안문종류코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
496 
2
 
2
 
2

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 (%)
1 496
99.2%
2 2
 
0.4%
2
 
0.4%

Length

2023-12-12T11:51:12.159083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:12.279379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 496
99.6%
2 2
 
0.4%

전자결재전자수기구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
495 
3
 
5

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 (%)
1 495
99.0%
3 5
 
1.0%

Length

2023-12-12T11:51:12.416893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:12.570825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 495
99.0%
3 5
 
1.0%

기안직원번호
Text

Distinct306
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:51:12.977646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.056
Min length4

Characters and Unicode

Total characters2028
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

Unique198 ?
Unique (%)39.6%

Sample

1st row5354
2nd row5005
3rd row6125
4th row4836
5th row5565
ValueCountFrequency (%)
5354 13
 
2.6%
5342 8
 
1.6%
5267 7
 
1.4%
5472 7
 
1.4%
6125 6
 
1.2%
5685 6
 
1.2%
6150 5
 
1.0%
4946 5
 
1.0%
5918 5
 
1.0%
3671 4
 
0.8%
Other values (296) 434
86.8%
2023-12-12T11:51:13.573108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 392
19.3%
4 245
12.1%
6 226
11.1%
0 182
9.0%
3 181
8.9%
1 179
8.8%
9 167
8.2%
2 157
7.7%
8 148
 
7.3%
7 145
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022
99.7%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 392
19.4%
4 245
12.1%
6 226
11.2%
0 182
9.0%
3 181
9.0%
1 179
8.9%
9 167
8.3%
2 157
7.8%
8 148
 
7.3%
7 145
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
C 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2022
99.7%
Latin 6
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 392
19.4%
4 245
12.1%
6 226
11.2%
0 182
9.0%
3 181
9.0%
1 179
8.9%
9 167
8.3%
2 157
7.8%
8 148
 
7.3%
7 145
 
7.2%
Latin
ValueCountFrequency (%)
C 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 392
19.3%
4 245
12.1%
6 226
11.1%
0 182
9.0%
3 181
8.9%
1 179
8.8%
9 167
8.2%
2 157
7.7%
8 148
 
7.3%
7 145
 
7.1%

영업점신청일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
489 
0001-01-01 00:00:00.000000
 
11

Length

Max length26
Median length7
Mean length7.418
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 489
97.8%
0001-01-01 00:00:00.000000 11
 
2.2%

Length

2023-12-12T11:51:13.753907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:13.917515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 489
95.7%
0001-01-01 11
 
2.2%
00:00:00.000000 11
 
2.2%

영업점결재일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
297 
0001-01-01 00:00:00.000000
203 

Length

Max length26
Median length7
Mean length14.714
Min length7

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 (%)
00:00.0 297
59.4%
0001-01-01 00:00:00.000000 203
40.6%

Length

2023-12-12T11:51:14.080400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:51:14.206028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 297
42.2%
0001-01-01 203
28.9%
00:00:00.000000 203
28.9%

삭제여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
434 
True
66 
ValueCountFrequency (%)
False 434
86.8%
True 66
 
13.2%
2023-12-12T11:51:14.292123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.588
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T11:51:14.391254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q33
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.67762405
Coefficient of variation (CV)0.26183309
Kurtosis16.347258
Mean2.588
Median Absolute Deviation (MAD)0
Skewness1.4237403
Sum1294
Variance0.45917435
MonotonicityNot monotonic
2023-12-12T11:51:14.524796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 292
58.4%
2 179
35.8%
1 21
 
4.2%
4 6
 
1.2%
6 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
1 21
 
4.2%
2 179
35.8%
3 292
58.4%
4 6
 
1.2%
6 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
6 1
 
0.2%
4 6
 
1.2%
3 292
58.4%
2 179
35.8%
1 21
 
4.2%

처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:51:14.918712image/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 row02:27.4
2nd row02:26.7
3rd row02:16.2
4th row02:14.8
5th row02:14.4
ValueCountFrequency (%)
44:52.3 2
 
0.4%
56:45.5 2
 
0.4%
25:53.7 2
 
0.4%
38:32.0 2
 
0.4%
50:34.7 2
 
0.4%
37:21.8 1
 
0.2%
38:05.8 1
 
0.2%
37:55.1 1
 
0.2%
36:50.4 1
 
0.2%
37:01.4 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T11:51:15.701148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 377
10.8%
4 367
10.5%
3 345
9.9%
2 337
9.6%
0 283
8.1%
1 247
7.1%
8 155
 
4.4%
6 138
 
3.9%
Other values (2) 251
7.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 377
15.1%
4 367
14.7%
3 345
13.8%
2 337
13.5%
0 283
11.3%
1 247
9.9%
8 155
6.2%
6 138
 
5.5%
7 135
 
5.4%
9 116
 
4.6%
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%
5 377
10.8%
4 367
10.5%
3 345
9.9%
2 337
9.6%
0 283
8.1%
1 247
7.1%
8 155
 
4.4%
6 138
 
3.9%
Other values (2) 251
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 377
10.8%
4 367
10.5%
3 345
9.9%
2 337
9.6%
0 283
8.1%
1 247
7.1%
8 155
 
4.4%
6 138
 
3.9%
Other values (2) 251
7.2%

처리직원번호
Text

Distinct305
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:51:16.123197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.056
Min length4

Characters and Unicode

Total characters2028
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

Unique198 ?
Unique (%)39.6%

Sample

1st row5354
2nd row5005
3rd row6125
4th row4836
5th row5565
ValueCountFrequency (%)
5354 15
 
3.0%
5342 8
 
1.6%
5267 7
 
1.4%
5472 7
 
1.4%
6125 6
 
1.2%
5685 6
 
1.2%
5918 5
 
1.0%
4946 5
 
1.0%
6150 5
 
1.0%
5810 4
 
0.8%
Other values (295) 432
86.4%
2023-12-12T11:51:16.710673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 394
19.4%
4 247
12.2%
6 226
11.1%
3 183
9.0%
0 182
9.0%
1 175
8.6%
9 167
8.2%
2 155
 
7.6%
8 148
 
7.3%
7 145
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022
99.7%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 394
19.5%
4 247
12.2%
6 226
11.2%
3 183
9.1%
0 182
9.0%
1 175
8.7%
9 167
8.3%
2 155
 
7.7%
8 148
 
7.3%
7 145
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
C 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2022
99.7%
Latin 6
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 394
19.5%
4 247
12.2%
6 226
11.2%
3 183
9.1%
0 182
9.0%
1 175
8.7%
9 167
8.3%
2 155
 
7.7%
8 148
 
7.3%
7 145
 
7.2%
Latin
ValueCountFrequency (%)
C 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 394
19.4%
4 247
12.2%
6 226
11.1%
3 183
9.0%
0 182
9.0%
1 175
8.6%
9 167
8.2%
2 155
 
7.6%
8 148
 
7.3%
7 145
 
7.1%

최초처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:51:17.131528image/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 row02:16.6
2nd row02:26.7
3rd row02:16.2
4th row02:14.8
5th row02:14.4
ValueCountFrequency (%)
34:41.5 2
 
0.4%
55:28.2 2
 
0.4%
01:05.6 2
 
0.4%
36:58.8 2
 
0.4%
38:54.7 2
 
0.4%
36:00.2 1
 
0.2%
07:56.4 1
 
0.2%
54:38.4 1
 
0.2%
55:33.9 1
 
0.2%
28:07.7 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T11:51:17.632773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 378
10.8%
4 344
9.8%
5 342
9.8%
2 308
8.8%
0 287
8.2%
1 256
7.3%
8 153
 
4.4%
6 153
 
4.4%
Other values (2) 279
8.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 378
15.1%
4 344
13.8%
5 342
13.7%
2 308
12.3%
0 287
11.5%
1 256
10.2%
8 153
6.1%
6 153
6.1%
7 142
 
5.7%
9 137
 
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%
3 378
10.8%
4 344
9.8%
5 342
9.8%
2 308
8.8%
0 287
8.2%
1 256
7.3%
8 153
 
4.4%
6 153
 
4.4%
Other values (2) 279
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 378
10.8%
4 344
9.8%
5 342
9.8%
2 308
8.8%
0 287
8.2%
1 256
7.3%
8 153
 
4.4%
6 153
 
4.4%
Other values (2) 279
8.0%

최초처리직원번호
Text

Distinct306
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:51:18.073357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.056
Min length4

Characters and Unicode

Total characters2028
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

Unique198 ?
Unique (%)39.6%

Sample

1st row5354
2nd row5005
3rd row6125
4th row4836
5th row5565
ValueCountFrequency (%)
5354 13
 
2.6%
5342 8
 
1.6%
5267 7
 
1.4%
5472 7
 
1.4%
6125 6
 
1.2%
5685 6
 
1.2%
6150 5
 
1.0%
4946 5
 
1.0%
5918 5
 
1.0%
3671 4
 
0.8%
Other values (296) 434
86.8%
2023-12-12T11:51:18.764940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 392
19.3%
4 245
12.1%
6 226
11.1%
0 182
9.0%
3 181
8.9%
1 179
8.8%
9 167
8.2%
2 157
7.7%
8 148
 
7.3%
7 145
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2022
99.7%
Uppercase Letter 6
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 392
19.4%
4 245
12.1%
6 226
11.2%
0 182
9.0%
3 181
9.0%
1 179
8.9%
9 167
8.3%
2 157
7.8%
8 148
 
7.3%
7 145
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
C 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2022
99.7%
Latin 6
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 392
19.4%
4 245
12.1%
6 226
11.2%
0 182
9.0%
3 181
9.0%
1 179
8.9%
9 167
8.3%
2 157
7.8%
8 148
 
7.3%
7 145
 
7.2%
Latin
ValueCountFrequency (%)
C 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 392
19.3%
4 245
12.1%
6 226
11.1%
0 182
9.0%
3 181
8.9%
1 179
8.8%
9 167
8.2%
2 157
7.7%
8 148
 
7.3%
7 145
 
7.1%

Interactions

2023-12-12T11:51:09.662541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.079576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.388309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.770744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.181217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.483320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.888812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.267584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:51:09.558881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:51:18.936733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무팀구분코드문서종류코드팀코드전자결재양식종류코드전결구분코드심사자료등급코드최종결재부점구분코드본부심사부점코드전자결재기안문종류코드전자결재전자수기구분코드영업점신청일자영업점결재일자삭제여부최종수정수
업무팀구분코드1.0000.7290.7240.0810.6610.7150.0000.0000.4960.0690.0000.3180.2430.056
문서종류코드0.7291.0000.3120.0660.4090.0000.0000.0000.3310.1660.0000.1640.0840.000
팀코드0.7240.3121.0000.4960.4660.0000.0000.0000.8360.0000.0000.2320.0750.000
전자결재양식종류코드0.0810.0660.4961.0000.2530.0000.0000.0000.0000.0000.0000.0800.2410.040
전결구분코드0.6610.4090.4660.2531.0000.0000.0000.0000.0000.0000.1910.0730.1380.269
심사자료등급코드0.7150.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
최종결재부점구분코드0.0000.0000.0000.0000.0000.0001.0000.9420.0000.0000.0000.0000.0200.000
본부심사부점코드0.0000.0000.0000.0000.0000.0000.9421.0000.0000.0000.0000.0100.0590.000
전자결재기안문종류코드0.4960.3310.8360.0000.0000.0000.0000.0001.0000.1860.0000.0000.0000.097
전자결재전자수기구분코드0.0690.1660.0000.0000.0000.0000.0000.0000.1861.0000.0000.0690.0000.126
영업점신청일자0.0000.0000.0000.0000.1910.0000.0000.0000.0000.0001.0000.2510.2440.910
영업점결재일자0.3180.1640.2320.0800.0730.0000.0000.0100.0000.0690.2511.0000.6520.994
삭제여부0.2430.0840.0750.2410.1380.0000.0200.0590.0000.0000.2440.6521.0000.717
최종수정수0.0560.0000.0000.0400.2690.0000.0000.0000.0970.1260.9100.9940.7171.000
2023-12-12T11:51:19.114589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
삭제여부업무팀구분코드심사자료등급코드영업점신청일자팀코드전자결재전자수기구분코드전자결재기안문종류코드영업점결재일자본부심사부점코드최종결재부점구분코드전자결재양식종류코드
삭제여부1.0000.2310.0000.1570.0570.0000.0000.4520.0970.0320.155
업무팀구분코드0.2311.0000.6930.0000.3930.0650.3320.3020.0000.0000.077
심사자료등급코드0.0000.6931.0000.0000.0000.0000.0000.0000.0000.0000.000
영업점신청일자0.1570.0000.0001.0000.0000.0000.0000.1620.0000.0000.000
팀코드0.0570.3930.0000.0001.0000.0000.5600.1780.0000.0000.382
전자결재전자수기구분코드0.0000.0650.0000.0000.0001.0000.3060.0440.0000.0000.000
전자결재기안문종류코드0.0000.3320.0000.0000.5600.3061.0000.0000.0000.0000.000
영업점결재일자0.4520.3020.0000.1620.1780.0440.0001.0000.0170.0000.051
본부심사부점코드0.0970.0000.0000.0000.0000.0000.0000.0171.0000.7060.000
최종결재부점구분코드0.0320.0000.0000.0000.0000.0000.0000.0000.7061.0000.000
전자결재양식종류코드0.1550.0770.0000.0000.3820.0000.0000.0510.0000.0001.000
2023-12-12T11:51:19.306165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문서종류코드전결구분코드최종수정수업무팀구분코드팀코드전자결재양식종류코드심사자료등급코드최종결재부점구분코드본부심사부점코드전자결재기안문종류코드전자결재전자수기구분코드영업점신청일자영업점결재일자삭제여부
문서종류코드1.000-0.0390.1390.4400.1370.0650.0000.0000.0000.1530.1640.0000.1630.084
전결구분코드-0.0391.000-0.0750.4130.1990.1810.0000.0000.0000.0000.0000.1360.0520.098
최종수정수0.139-0.0751.0000.0270.0000.0280.0000.0000.0000.0400.0900.7310.9260.527
업무팀구분코드0.4400.4130.0271.0000.3930.0770.6930.0000.0000.3320.0650.0000.3020.231
팀코드0.1370.1990.0000.3931.0000.3820.0000.0000.0000.5600.0000.0000.1780.057
전자결재양식종류코드0.0650.1810.0280.0770.3821.0000.0000.0000.0000.0000.0000.0000.0510.155
심사자료등급코드0.0000.0000.0000.6930.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
최종결재부점구분코드0.0000.0000.0000.0000.0000.0000.0001.0000.7060.0000.0000.0000.0000.032
본부심사부점코드0.0000.0000.0000.0000.0000.0000.0000.7061.0000.0000.0000.0000.0170.097
전자결재기안문종류코드0.1530.0000.0400.3320.5600.0000.0000.0000.0001.0000.3060.0000.0000.000
전자결재전자수기구분코드0.1640.0000.0900.0650.0000.0000.0000.0000.0000.3061.0000.0000.0440.000
영업점신청일자0.0000.1360.7310.0000.0000.0000.0000.0000.0000.0000.0001.0000.1620.157
영업점결재일자0.1630.0520.9260.3020.1780.0510.0000.0000.0170.0000.0440.1621.0000.452
삭제여부0.0840.0980.5270.2310.0570.1550.0000.0320.0970.0000.0000.1570.4521.000

Missing values

2023-12-12T11:51:10.051891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:51:10.311420image/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

업무팀구분코드문서종류코드팀코드전자결재양식종류코드전결구분코드심사자료등급코드최종결재부점구분코드본부심사부점코드전자결재기안문종류코드전자결재전자수기구분코드기안직원번호영업점신청일자영업점결재일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0Z22062371111535400:00.00001-01-01 00:00:00.000000N202:27.4535402:16.65354
1B1013271111500500:00.00001-01-01 00:00:00.000000N102:26.7500502:26.75005
2F10222111111612500:00.00001-01-01 00:00:00.000000N102:16.2612502:16.26125
3B32542111111483600:00.00001-01-01 00:00:00.000000N102:14.8483602:14.84836
4G4041271111556500:00.00001-01-01 00:00:00.000000N102:14.4556502:14.45565
5G30512111111586600:00.00001-01-01 00:00:00.000000N202:12.3586601:25.95866
6B10122111111536500:00.00001-01-01 00:00:00.000000N102:10.6536502:10.65365
7B32532111111502600:00.00001-01-01 00:00:00.000000N202:07.8502601:45.35026
8J10152111111239400:00.00001-01-01 00:00:00.000000N102:03.8239402:03.82394
9I52142111111549200:00.00001-01-01 00:00:00.000000N102:03.3549202:03.35492
업무팀구분코드문서종류코드팀코드전자결재양식종류코드전결구분코드심사자료등급코드최종결재부점구분코드본부심사부점코드전자결재기안문종류코드전자결재전자수기구분코드기안직원번호영업점신청일자영업점결재일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
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491A1191211111602700:00.000:00.0N323:37.3602704:49.06027
492D1133271111361300:00.000:00.0N323:34.2361311:45.23613
493B32522111111590300:00.000:00.0N323:26.7590319:07.75903
494D1133271111361300:00.000:00.0N323:26.4361311:19.43613
495D1133271111361300:00.000:00.0N323:18.1361310:53.33613
496G1202271111581000:00.000:00.0N323:17.8581058:29.85810
497I62322111111562100:00.00001-01-01 00:00:00.000000N223:15.5562122:50.45621
498I52122111111487700:00.00001-01-01 00:00:00.000000N122:57.0487722:57.04877
499B1013271111424100:00.00001-01-01 00:00:00.000000Y222:46.7424122:30.54241