Overview

Dataset statistics

Number of variables12
Number of observations500
Missing cells1383
Missing cells (%)23.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.0 KiB
Average record size in memory100.3 B

Variable types

Text6
Numeric2
Categorical4

Dataset

Description해당 파일 데이터는 신용보증기금의 공통RM요구사항인증서발급상세에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093200/fileData.do

Alerts

최종수정수 is highly overall correlated with 삭제여부High correlation
인증서용도코드 is highly overall correlated with 인증만료일자High correlation
인증만료일자 is highly overall correlated with 인증서신청사유코드 and 1 other fieldsHigh correlation
삭제여부 is highly overall correlated with 최종수정수High correlation
인증서신청사유코드 is highly overall correlated with 인증만료일자High correlation
삭제여부 is highly imbalanced (52.5%)Imbalance
최종수정수 is highly imbalanced (74.8%)Imbalance
사용자직원번호 has 29 (5.8%) missing valuesMissing
계약직원주민등록번호 has 447 (89.4%) missing valuesMissing
계약직원업무영역내용 has 448 (89.6%) missing valuesMissing
기타사유내용 has 459 (91.8%) missing valuesMissing
요구사항ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:59:50.427944
Analysis finished2023-12-12 23:59:51.562999
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

요구사항ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:59:51.779865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
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

Unique500 ?
Unique (%)100.0%

Sample

1st row9dhhD7TKxi
2nd row9dgHyPkBSk
3rd row9dgHyMLExL
4th row9dgHyDr0ps
5th row9dgHyAkbXv
ValueCountFrequency (%)
9dhhd7tkxi 1
 
0.2%
9dfdhkxun1 1
 
0.2%
9dfdffqqt4 1
 
0.2%
9dfdfiqvwh 1
 
0.2%
9dfdfjiw4w 1
 
0.2%
9dfdfo3clp 1
 
0.2%
9dfdfwrgzb 1
 
0.2%
9dfdfzcc20 1
 
0.2%
9dfdfayeni 1
 
0.2%
9dfdfd62ri 1
 
0.2%
Other values (490) 490
98.0%
2023-12-13T08:59:52.148189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 557
 
11.1%
d 551
 
11.0%
f 427
 
8.5%
D 201
 
4.0%
g 123
 
2.5%
e 98
 
2.0%
I 82
 
1.6%
E 80
 
1.6%
x 77
 
1.5%
F 77
 
1.5%
Other values (52) 2727
54.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2335
46.7%
Uppercase Letter 1597
31.9%
Decimal Number 1068
21.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 551
23.6%
f 427
18.3%
g 123
 
5.3%
e 98
 
4.2%
x 77
 
3.3%
z 69
 
3.0%
o 62
 
2.7%
p 60
 
2.6%
t 57
 
2.4%
l 56
 
2.4%
Other values (16) 755
32.3%
Uppercase Letter
ValueCountFrequency (%)
D 201
 
12.6%
I 82
 
5.1%
E 80
 
5.0%
F 77
 
4.8%
G 72
 
4.5%
A 66
 
4.1%
R 63
 
3.9%
J 63
 
3.9%
B 61
 
3.8%
P 59
 
3.7%
Other values (16) 773
48.4%
Decimal Number
ValueCountFrequency (%)
9 557
52.2%
0 69
 
6.5%
6 67
 
6.3%
5 64
 
6.0%
2 62
 
5.8%
7 54
 
5.1%
8 54
 
5.1%
3 51
 
4.8%
1 49
 
4.6%
4 41
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3932
78.6%
Common 1068
 
21.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 551
 
14.0%
f 427
 
10.9%
D 201
 
5.1%
g 123
 
3.1%
e 98
 
2.5%
I 82
 
2.1%
E 80
 
2.0%
x 77
 
2.0%
F 77
 
2.0%
G 72
 
1.8%
Other values (42) 2144
54.5%
Common
ValueCountFrequency (%)
9 557
52.2%
0 69
 
6.5%
6 67
 
6.3%
5 64
 
6.0%
2 62
 
5.8%
7 54
 
5.1%
8 54
 
5.1%
3 51
 
4.8%
1 49
 
4.6%
4 41
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 557
 
11.1%
d 551
 
11.0%
f 427
 
8.5%
D 201
 
4.0%
g 123
 
2.5%
e 98
 
2.0%
I 82
 
1.6%
E 80
 
1.6%
x 77
 
1.5%
F 77
 
1.5%
Other values (52) 2727
54.5%

사용자직원번호
Text

MISSING 

Distinct381
Distinct (%)80.9%
Missing29
Missing (%)5.8%
Memory size4.0 KiB
2023-12-13T08:59:52.682846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.2462845
Min length4

Characters and Unicode

Total characters2000
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)65.6%

Sample

1st rowEXI76
2nd rowEXI18
3rd rowEXI17
4th rowEXI16
5th rowEXI15
ValueCountFrequency (%)
4207 6
 
1.3%
4845 4
 
0.8%
3376 3
 
0.6%
4751 3
 
0.6%
9a363 3
 
0.6%
5116 3
 
0.6%
4441 3
 
0.6%
4199 3
 
0.6%
3149 3
 
0.6%
5405 3
 
0.6%
Other values (371) 437
92.8%
2023-12-13T08:59:53.080565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 253
12.7%
5 243
12.2%
3 206
10.3%
0 168
8.4%
1 162
8.1%
9 157
7.8%
2 156
7.8%
6 125
6.2%
7 123
6.2%
8 117
5.9%
Other values (26) 290
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1710
85.5%
Uppercase Letter 287
 
14.3%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 56
19.5%
X 49
17.1%
I 42
14.6%
A 34
11.8%
T 27
9.4%
J 17
 
5.9%
B 9
 
3.1%
O 8
 
2.8%
C 7
 
2.4%
N 7
 
2.4%
Other values (13) 31
10.8%
Decimal Number
ValueCountFrequency (%)
4 253
14.8%
5 243
14.2%
3 206
12.0%
0 168
9.8%
1 162
9.5%
9 157
9.2%
2 156
9.1%
6 125
7.3%
7 123
7.2%
8 117
6.8%
Lowercase Letter
ValueCountFrequency (%)
n 1
50.0%
a 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1711
85.5%
Latin 289
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 56
19.4%
X 49
17.0%
I 42
14.5%
A 34
11.8%
T 27
9.3%
J 17
 
5.9%
B 9
 
3.1%
O 8
 
2.8%
C 7
 
2.4%
N 7
 
2.4%
Other values (15) 33
11.4%
Common
ValueCountFrequency (%)
4 253
14.8%
5 243
14.2%
3 206
12.0%
0 168
9.8%
1 162
9.5%
9 157
9.2%
2 156
9.1%
6 125
7.3%
7 123
7.2%
8 117
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 253
12.7%
5 243
12.2%
3 206
10.3%
0 168
8.4%
1 162
8.1%
9 157
7.8%
2 156
7.8%
6 125
6.2%
7 123
6.2%
8 117
5.9%
Other values (26) 290
14.5%

계약직원주민등록번호
Real number (ℝ)

MISSING 

Distinct45
Distinct (%)84.9%
Missing447
Missing (%)89.4%
Infinite0
Infinite (%)0.0%
Mean814163.43
Minimum600226
Maximum970702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:59:53.191795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600226
5-th percentile632875.4
Q1721027
median800817
Q3930108
95-th percentile960743.4
Maximum970702
Range370476
Interquartile range (IQR)209081

Descriptive statistics

Standard deviation106167.78
Coefficient of variation (CV)0.13040107
Kurtosis-1.082771
Mean814163.43
Median Absolute Deviation (MAD)90115
Skewness-0.079516816
Sum43150662
Variance1.1271598 × 1010
MonotonicityNot monotonic
2023-12-13T08:59:53.295793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
771016 3
 
0.6%
710702 2
 
0.4%
730317 2
 
0.4%
830531 2
 
0.4%
970702 2
 
0.4%
790616 2
 
0.4%
960419 2
 
0.4%
600226 1
 
0.2%
760303 1
 
0.2%
931102 1
 
0.2%
Other values (35) 35
 
7.0%
(Missing) 447
89.4%
ValueCountFrequency (%)
600226 1
0.2%
620315 1
0.2%
620525 1
0.2%
641109 1
0.2%
690812 1
0.2%
700203 1
0.2%
701123 1
0.2%
710113 1
0.2%
710620 1
0.2%
710702 2
0.4%
ValueCountFrequency (%)
970702 2
0.4%
961230 1
0.2%
960419 2
0.4%
950630 1
0.2%
950612 1
0.2%
950121 1
0.2%
940305 1
0.2%
931102 1
0.2%
930506 1
0.2%
930201 1
0.2%

계약직원업무영역내용
Text

MISSING 

Distinct30
Distinct (%)57.7%
Missing448
Missing (%)89.6%
Memory size4.0 KiB
2023-12-13T08:59:53.457363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length16
Mean length10.096154
Min length2

Characters and Unicode

Total characters525
Distinct characters89
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)34.6%

Sample

1st row업무시스템 유지관리
2nd row업무시스템 유지관리
3rd row자산운용시스템 유지관리
4th row정책연구
5th row출납
ValueCountFrequency (%)
구축 18
 
15.7%
출납 9
 
7.8%
기술평가 5
 
4.3%
협업시스템 5
 
4.3%
용역 5
 
4.3%
서비스 5
 
4.3%
데이터통장 4
 
3.5%
비대면 4
 
3.5%
사업(중소기업 3
 
2.6%
데이터플래그십 3
 
2.6%
Other values (36) 54
47.0%
2023-12-13T08:59:53.735224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
63
 
12.0%
업 26
 
5.0%
스 20
 
3.8%
구 19
 
3.6%
축 18
 
3.4%
이 16
 
3.0%
비 13
 
2.5%
기 12
 
2.3%
데 12
 
2.3%
터 12
 
2.3%
Other values (79) 314
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 449
85.5%
Space Separator 63
 
12.0%
Open Punctuation 6
 
1.1%
Close Punctuation 6
 
1.1%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 26
 
5.8%
스 20
 
4.5%
구 19
 
4.2%
축 18
 
4.0%
이 16
 
3.6%
비 13
 
2.9%
기 12
 
2.7%
데 12
 
2.7%
터 12
 
2.7%
시 12
 
2.7%
Other values (75) 289
64.4%
Space Separator
ValueCountFrequency (%)
63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 449
85.5%
Common 76
 
14.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 26
 
5.8%
스 20
 
4.5%
구 19
 
4.2%
축 18
 
4.0%
이 16
 
3.6%
비 13
 
2.9%
기 12
 
2.7%
데 12
 
2.7%
터 12
 
2.7%
시 12
 
2.7%
Other values (75) 289
64.4%
Common
ValueCountFrequency (%)
63
82.9%
( 6
 
7.9%
) 6
 
7.9%
2 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 449
85.5%
ASCII 76
 
14.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63
82.9%
( 6
 
7.9%
) 6
 
7.9%
2 1
 
1.3%
Hangul
ValueCountFrequency (%)
업 26
 
5.8%
스 20
 
4.5%
구 19
 
4.2%
축 18
 
4.0%
이 16
 
3.6%
비 13
 
2.9%
기 12
 
2.7%
데 12
 
2.7%
터 12
 
2.7%
시 12
 
2.7%
Other values (75) 289
64.4%

인증서용도코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
370 
5
77 
3
 
31
2
 
19
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
1 370
74.0%
5 77
 
15.4%
3 31
 
6.2%
2 19
 
3.8%
4 3
 
0.6%

Length

2023-12-13T08:59:53.843335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:59:53.917690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 370
74.0%
5 77
 
15.4%
3 31
 
6.2%
2 19
 
3.8%
4 3
 
0.6%

인증서신청사유코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.762
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T08:59:53.984987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median5
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4413574
Coefficient of variation (CV)0.30267899
Kurtosis1.971828
Mean4.762
Median Absolute Deviation (MAD)0
Skewness-1.3922903
Sum2381
Variance2.077511
MonotonicityNot monotonic
2023-12-13T08:59:54.069393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 335
67.0%
7 42
 
8.4%
1 42
 
8.4%
6 41
 
8.2%
4 22
 
4.4%
2 18
 
3.6%
ValueCountFrequency (%)
1 42
 
8.4%
2 18
 
3.6%
4 22
 
4.4%
5 335
67.0%
6 41
 
8.2%
7 42
 
8.4%
ValueCountFrequency (%)
7 42
 
8.4%
6 41
 
8.2%
5 335
67.0%
4 22
 
4.4%
2 18
 
3.6%
1 42
 
8.4%

기타사유내용
Text

MISSING 

Distinct29
Distinct (%)70.7%
Missing459
Missing (%)91.8%
Memory size4.0 KiB
2023-12-13T08:59:54.241848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length9.2195122
Min length2

Characters and Unicode

Total characters378
Distinct characters95
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)53.7%

Sample

1st rowPC고장
2nd row재택근무 중 결재오류로 인한 재발급
3rd row재택근무 중 결재오류
4th rowotp카드로 로그인후 결재처리
5th row재택근무 중 USB내 인증서 폐기
ValueCountFrequency (%)
재택근무 12
 
14.0%
인증서 9
 
10.5%
재택근무용 5
 
5.8%
중 4
 
4.7%
유효기간만료 3
 
3.5%
21.1.31까지 3
 
3.5%
연장요청 3
 
3.5%
사용 3
 
3.5%
재발급 3
 
3.5%
폐지 3
 
3.5%
Other values (32) 38
44.2%
2023-12-13T08:59:54.515681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
12.4%
재 26
 
6.9%
무 19
 
5.0%
근 18
 
4.8%
택 18
 
4.8%
인 14
 
3.7%
증 12
 
3.2%
서 12
 
3.2%
1 9
 
2.4%
오 9
 
2.4%
Other values (85) 194
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 288
76.2%
Space Separator 47
 
12.4%
Decimal Number 15
 
4.0%
Uppercase Letter 13
 
3.4%
Other Punctuation 12
 
3.2%
Lowercase Letter 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
재 26
 
9.0%
무 19
 
6.6%
근 18
 
6.2%
택 18
 
6.2%
인 14
 
4.9%
증 12
 
4.2%
서 12
 
4.2%
오 9
 
3.1%
용 8
 
2.8%
류 7
 
2.4%
Other values (68) 145
50.3%
Uppercase Letter
ValueCountFrequency (%)
P 3
23.1%
S 2
15.4%
C 2
15.4%
U 2
15.4%
B 2
15.4%
O 1
 
7.7%
T 1
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 9
60.0%
2 3
 
20.0%
3 3
 
20.0%
Other Punctuation
ValueCountFrequency (%)
. 6
50.0%
' 3
25.0%
, 3
25.0%
Lowercase Letter
ValueCountFrequency (%)
o 1
33.3%
t 1
33.3%
p 1
33.3%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 288
76.2%
Common 74
 
19.6%
Latin 16
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
재 26
 
9.0%
무 19
 
6.6%
근 18
 
6.2%
택 18
 
6.2%
인 14
 
4.9%
증 12
 
4.2%
서 12
 
4.2%
오 9
 
3.1%
용 8
 
2.8%
류 7
 
2.4%
Other values (68) 145
50.3%
Latin
ValueCountFrequency (%)
P 3
18.8%
S 2
12.5%
C 2
12.5%
U 2
12.5%
B 2
12.5%
o 1
 
6.2%
t 1
 
6.2%
p 1
 
6.2%
O 1
 
6.2%
T 1
 
6.2%
Common
ValueCountFrequency (%)
47
63.5%
1 9
 
12.2%
. 6
 
8.1%
' 3
 
4.1%
, 3
 
4.1%
2 3
 
4.1%
3 3
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 288
76.2%
ASCII 90
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
47
52.2%
1 9
 
10.0%
. 6
 
6.7%
' 3
 
3.3%
, 3
 
3.3%
P 3
 
3.3%
2 3
 
3.3%
3 3
 
3.3%
S 2
 
2.2%
C 2
 
2.2%
Other values (7) 9
 
10.0%
Hangul
ValueCountFrequency (%)
재 26
 
9.0%
무 19
 
6.6%
근 18
 
6.2%
택 18
 
6.2%
인 14
 
4.9%
증 12
 
4.2%
서 12
 
4.2%
오 9
 
3.1%
용 8
 
2.8%
류 7
 
2.4%
Other values (68) 145
50.3%

인증만료일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
423 
00:00.0
77 

Length

Max length26
Median length26
Mean length23.074
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 (%)
0001-01-01 00:00:00.000000 423
84.6%
00:00.0 77
 
15.4%

Length

2023-12-13T08:59:54.616691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:59:54.689027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 423
45.8%
00:00:00.000000 423
45.8%
00:00.0 77
 
8.3%

삭제여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
449 
N
51 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
449
89.8%
N 51
 
10.2%

Length

2023-12-13T08:59:54.761569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:59:54.831241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 51
100.0%

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
449 
2
 
39
3
 
8
5
 
2
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 449
89.8%
2 39
 
7.8%
3 8
 
1.6%
5 2
 
0.4%
4 2
 
0.4%

Length

2023-12-13T08:59:54.901745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:59:54.975403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 449
89.8%
2 39
 
7.8%
3 8
 
1.6%
5 2
 
0.4%
4 2
 
0.4%

처리시각
Text

Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:59:55.255298image/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

Unique498 ?
Unique (%)99.6%

Sample

1st row44:31.2
2nd row40:56.0
3rd row40:10.1
4th row38:49.2
5th row38:11.1
ValueCountFrequency (%)
15:12.6 2
 
0.4%
09:16.1 1
 
0.2%
03:45.2 1
 
0.2%
41:20.8 1
 
0.2%
41:58.9 1
 
0.2%
42:11.8 1
 
0.2%
43:36.8 1
 
0.2%
45:25.9 1
 
0.2%
46:12.9 1
 
0.2%
46:32.9 1
 
0.2%
Other values (489) 489
97.8%
2023-12-13T08:59:55.631063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 340
9.7%
3 322
9.2%
1 316
9.0%
5 302
8.6%
0 301
8.6%
4 284
8.1%
9 173
 
4.9%
7 158
 
4.5%
Other values (2) 304
8.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 340
13.6%
3 322
12.9%
1 316
12.6%
5 302
12.1%
0 301
12.0%
4 284
11.4%
9 173
6.9%
7 158
6.3%
6 156
6.2%
8 148
5.9%
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%
2 340
9.7%
3 322
9.2%
1 316
9.0%
5 302
8.6%
0 301
8.6%
4 284
8.1%
9 173
 
4.9%
7 158
 
4.5%
Other values (2) 304
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 340
9.7%
3 322
9.2%
1 316
9.0%
5 302
8.6%
0 301
8.6%
4 284
8.1%
9 173
 
4.9%
7 158
 
4.5%
Other values (2) 304
8.7%

처리직원번호
Text

Distinct305
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T08:59:55.961765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.016
Min length4

Characters and Unicode

Total characters2008
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

Unique202 ?
Unique (%)40.4%

Sample

1st row5807
2nd row4892
3rd row4892
4th row4892
5th row4892
ValueCountFrequency (%)
4597 18
 
3.6%
4892 17
 
3.4%
5807 8
 
1.6%
4067 7
 
1.4%
5743 7
 
1.4%
5365 6
 
1.2%
5940 5
 
1.0%
5575 5
 
1.0%
5908 5
 
1.0%
5752 5
 
1.0%
Other values (295) 417
83.4%
2023-12-13T08:59:56.373391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 492
24.5%
4 287
14.3%
7 187
 
9.3%
8 175
 
8.7%
9 172
 
8.6%
2 160
 
8.0%
3 153
 
7.6%
6 134
 
6.7%
0 125
 
6.2%
1 121
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2006
99.9%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 492
24.5%
4 287
14.3%
7 187
 
9.3%
8 175
 
8.7%
9 172
 
8.6%
2 160
 
8.0%
3 153
 
7.6%
6 134
 
6.7%
0 125
 
6.2%
1 121
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2006
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 492
24.5%
4 287
14.3%
7 187
 
9.3%
8 175
 
8.7%
9 172
 
8.6%
2 160
 
8.0%
3 153
 
7.6%
6 134
 
6.7%
0 125
 
6.2%
1 121
 
6.0%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 492
24.5%
4 287
14.3%
7 187
 
9.3%
8 175
 
8.7%
9 172
 
8.6%
2 160
 
8.0%
3 153
 
7.6%
6 134
 
6.7%
0 125
 
6.2%
1 121
 
6.0%

Interactions

2023-12-13T08:59:51.079012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:59:50.917710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:59:51.155037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:59:50.998375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:59:56.459622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약직원주민등록번호계약직원업무영역내용인증서용도코드인증서신청사유코드기타사유내용인증만료일자삭제여부최종수정수
계약직원주민등록번호1.0000.8730.6180.4751.0000.5110.0000.000
계약직원업무영역내용0.8731.0001.0000.9321.0001.0000.7840.960
인증서용도코드0.6181.0001.0000.5750.9531.0000.2800.451
인증서신청사유코드0.4750.9320.5751.000NaN0.9630.6740.356
기타사유내용1.0001.0000.953NaN1.0001.0001.0000.790
인증만료일자0.5111.0001.0000.9631.0001.0000.5070.298
삭제여부0.0000.7840.2800.6741.0000.5071.0001.000
최종수정수0.0000.9600.4510.3560.7900.2981.0001.000
2023-12-13T08:59:56.554245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수인증서용도코드인증만료일자삭제여부
최종수정수1.0000.1820.3630.997
인증서용도코드0.1821.0000.9970.341
인증만료일자0.3630.9971.0000.339
삭제여부0.9970.3410.3391.000
2023-12-13T08:59:56.626591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약직원주민등록번호인증서신청사유코드인증서용도코드인증만료일자삭제여부최종수정수
계약직원주민등록번호1.000-0.1840.3990.3770.0000.000
인증서신청사유코드-0.1841.0000.4350.8240.4920.250
인증서용도코드0.3990.4351.0000.9970.3410.182
인증만료일자0.3770.8240.9971.0000.3390.363
삭제여부0.0000.4920.3410.3391.0000.997
최종수정수0.0000.2500.1820.3630.9971.000

Missing values

2023-12-13T08:59:51.261464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:59:51.397336image/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.
2023-12-13T08:59:51.511471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

요구사항ID사용자직원번호계약직원주민등록번호계약직원업무영역내용인증서용도코드인증서신청사유코드기타사유내용인증만료일자삭제여부최종수정수처리시각처리직원번호
09dhhD7TKxiEXI76<NA><NA>57<NA>00:00.0144:31.25807
19dgHyPkBSkEXI18<NA><NA>57<NA>00:00.0N240:56.04892
29dgHyMLExLEXI17<NA><NA>57<NA>00:00.0N240:10.14892
39dgHyDr0psEXI16<NA><NA>57<NA>00:00.0N338:49.24892
49dgHyAkbXvEXI15<NA><NA>57<NA>00:00.0N238:11.14892
59dgHynnSDUEXI14<NA><NA>57<NA>00:00.0N337:21.84892
69dgHydkypIEXI12<NA><NA>57<NA>00:00.0N536:35.04892
79dgHx8JVBBEXI13<NA><NA>57<NA>00:00.0N335:40.04892
89dg8vixf41EXJ50<NA><NA>57<NA>00:00.0N229:54.94892
99dg8vgK6XJEXI55<NA><NA>57<NA>00:00.0129:14.84892
요구사항ID사용자직원번호계약직원주민등록번호계약직원업무영역내용인증서용도코드인증서신청사유코드기타사유내용인증만료일자삭제여부최종수정수처리시각처리직원번호
4909devY15tJi3594<NA><NA>15<NA>0001-01-01 00:00:00.000000116:23.65881
4919devTJgADUEXI72<NA><NA>57<NA>00:00.0155:24.84293
4929devTErzegEXI71<NA><NA>57<NA>00:00.0154:13.54293
4939devTfv4kgEXI70<NA><NA>57<NA>00:00.0148:05.24293
4949det70tKPAEX499<NA><NA>35<NA>0001-01-01 00:00:00.000000100:04.55637
4959desOR3ZYu1906<NA><NA>14<NA>0001-01-01 00:00:00.000000121:13.41906
4969derlj0dwGVAO01<NA><NA>34<NA>0001-01-01 00:00:00.000000103:22.04920
4979deq7iCGeM2981<NA><NA>15<NA>0001-01-01 00:00:00.000000129:15.85629
4989deq3NCvJW4218<NA><NA>15<NA>0001-01-01 00:00:00.000000135:49.35442
4999degSfCO2t9C139961230인턴41<NA>0001-01-01 00:00:00.000000152:47.15848