Overview

Dataset statistics

Number of variables28
Number of observations500
Missing cells594
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.3 KiB
Average record size in memory240.3 B

Variable types

Categorical15
Text5
Numeric6
Unsupported1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보험인수해지보험증권마스터에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093302/fileData.do

Alerts

업무구분코드 has constant value ""Constant
발급특약매수 has constant value ""Constant
발급일자 has constant value ""Constant
증서종류코드 has constant value ""Constant
품의일자 has constant value ""Constant
품의결재상태코드 has constant value ""Constant
결재자결재상태코드 has constant value ""Constant
결재일자 has constant value ""Constant
삭제여부 has constant value ""Constant
발급일련번호 is highly imbalanced (88.7%)Imbalance
발급사유코드 is highly imbalanced (90.6%)Imbalance
결재상태코드 is highly imbalanced (62.0%)Imbalance
결재자변경사유내용 is highly imbalanced (87.0%)Imbalance
결재자직원번호 has 47 (9.4%) missing valuesMissing
당초결재직원번호 has 500 (100.0%) missing valuesMissing
확인자직원번호 has 47 (9.4%) missing valuesMissing
당초결재직원번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 12:31:46.577064
Analysis finished2023-12-12 12:31:46.983190
Duration0.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업무구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 500
100.0%

Length

2023-12-12T21:31:47.068117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:47.203255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 500
100.0%

발급일련번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
488 
2
 
10
3
 
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 (%)
1 488
97.6%
2 10
 
2.0%
3 2
 
0.4%

Length

2023-12-12T21:31:47.335792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:47.473784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 488
97.6%
2 10
 
2.0%
3 2
 
0.4%

신청기업고객ID
Text

Distinct431
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:31:47.827775image/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

Unique398 ?
Unique (%)79.6%

Sample

1st row9cujqkCTb7
2nd row9c5yomYkBH
3rd row9cWtHaeylo
4th row9c5yHjOV0o
5th row9cxtDcbPqD
ValueCountFrequency (%)
9blaumhlqn 11
 
2.2%
9dae8kwhui 5
 
1.0%
9dihqbu7y1 5
 
1.0%
9cv6su02cj 4
 
0.8%
9ddsbaydw4 4
 
0.8%
aaaaadhk0o 4
 
0.8%
9dng1ikf6r 4
 
0.8%
9dnmmch1t5 4
 
0.8%
9cozbdrx2m 4
 
0.8%
9c0fpowitc 3
 
0.6%
Other values (421) 452
90.4%
2023-12-12T21:31:48.376101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 475
 
9.5%
a 461
 
9.2%
c 285
 
5.7%
d 202
 
4.0%
b 174
 
3.5%
M 90
 
1.8%
U 81
 
1.6%
n 81
 
1.6%
L 77
 
1.5%
Q 76
 
1.5%
Other values (52) 2998
60.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2427
48.5%
Uppercase Letter 1570
31.4%
Decimal Number 1003
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 461
19.0%
c 285
 
11.7%
d 202
 
8.3%
b 174
 
7.2%
n 81
 
3.3%
j 73
 
3.0%
q 72
 
3.0%
o 71
 
2.9%
z 68
 
2.8%
m 66
 
2.7%
Other values (16) 874
36.0%
Uppercase Letter
ValueCountFrequency (%)
M 90
 
5.7%
U 81
 
5.2%
L 77
 
4.9%
Q 76
 
4.8%
H 71
 
4.5%
I 70
 
4.5%
A 69
 
4.4%
V 66
 
4.2%
D 65
 
4.1%
S 62
 
3.9%
Other values (16) 843
53.7%
Decimal Number
ValueCountFrequency (%)
9 475
47.4%
4 73
 
7.3%
2 71
 
7.1%
8 66
 
6.6%
5 63
 
6.3%
1 59
 
5.9%
0 56
 
5.6%
7 50
 
5.0%
3 46
 
4.6%
6 44
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 3997
79.9%
Common 1003
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 461
 
11.5%
c 285
 
7.1%
d 202
 
5.1%
b 174
 
4.4%
M 90
 
2.3%
U 81
 
2.0%
n 81
 
2.0%
L 77
 
1.9%
Q 76
 
1.9%
j 73
 
1.8%
Other values (42) 2397
60.0%
Common
ValueCountFrequency (%)
9 475
47.4%
4 73
 
7.3%
2 71
 
7.1%
8 66
 
6.6%
5 63
 
6.3%
1 59
 
5.9%
0 56
 
5.6%
7 50
 
5.0%
3 46
 
4.6%
6 44
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 475
 
9.5%
a 461
 
9.2%
c 285
 
5.7%
d 202
 
4.0%
b 174
 
3.5%
M 90
 
1.8%
U 81
 
1.6%
n 81
 
1.6%
L 77
 
1.5%
Q 76
 
1.5%
Other values (52) 2998
60.0%

발급특약매수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 500
100.0%

Length

2023-12-12T21:31:48.588788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:48.715137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

발급일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
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 (%)
00:00.0 500
100.0%

Length

2023-12-12T21:31:48.834487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:48.988781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

발급일시
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:31:49.435931image/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 row37:22.4
2nd row36:36.0
3rd row37:18.2
4th row40:38.5
5th row21:29.8
ValueCountFrequency (%)
15:28.2 2
 
0.4%
37:19.5 2
 
0.4%
53:46.4 2
 
0.4%
56:01.2 2
 
0.4%
24:26.7 2
 
0.4%
10:51.9 1
 
0.2%
00:30.6 1
 
0.2%
33:00.3 1
 
0.2%
55:11.2 1
 
0.2%
44:56.4 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T21:31:50.146924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 334
9.5%
3 323
9.2%
5 314
9.0%
0 312
8.9%
2 305
8.7%
1 289
8.3%
7 169
 
4.8%
6 154
 
4.4%
Other values (2) 300
8.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 334
13.4%
3 323
12.9%
5 314
12.6%
0 312
12.5%
2 305
12.2%
1 289
11.6%
7 169
6.8%
6 154
6.2%
9 152
6.1%
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%
4 334
9.5%
3 323
9.2%
5 314
9.0%
0 312
8.9%
2 305
8.7%
1 289
8.3%
7 169
 
4.8%
6 154
 
4.4%
Other values (2) 300
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 334
9.5%
3 323
9.2%
5 314
9.0%
0 312
8.9%
2 305
8.7%
1 289
8.3%
7 169
 
4.8%
6 154
 
4.4%
Other values (2) 300
8.6%

발급사유코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
488 
99
 
9
3
 
2
2
 
1

Length

Max length2
Median length1
Mean length1.018
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 488
97.6%
99 9
 
1.8%
3 2
 
0.4%
2 1
 
0.2%

Length

2023-12-12T21:31:50.356083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:50.499802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 488
97.6%
99 9
 
1.8%
3 2
 
0.4%
2 1
 
0.2%

보험증권형태코드
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
270 
2
174 
3
56 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 270
54.0%
2 174
34.8%
3 56
 
11.2%

Length

2023-12-12T21:31:50.661096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:50.786340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 270
54.0%
2 174
34.8%
3 56
 
11.2%

증서종류코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
53
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row53
2nd row53
3rd row53
4th row53
5th row53

Common Values

ValueCountFrequency (%)
53 500
100.0%

Length

2023-12-12T21:31:50.925374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:51.038127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
53 500
100.0%

파손여부
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
252 
N
243 
Y
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
252
50.4%
N 243
48.6%
Y 5
 
1.0%

Length

2023-12-12T21:31:51.155328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:51.275321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 243
98.0%
y 5
 
2.0%

품의직원번호
Real number (ℝ)

Distinct229
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5257.358
Minimum2398
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:51.423104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2398
5-th percentile3765.35
Q14977
median5406
Q35723.5
95-th percentile6072.05
Maximum6200
Range3802
Interquartile range (IQR)746.5

Descriptive statistics

Standard deviation637.2886
Coefficient of variation (CV)0.12121841
Kurtosis1.2381375
Mean5257.358
Median Absolute Deviation (MAD)367
Skewness-1.1019273
Sum2628679
Variance406136.76
MonotonicityNot monotonic
2023-12-12T21:31:51.586900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5406 25
 
5.0%
5608 15
 
3.0%
3620 14
 
2.8%
5074 10
 
2.0%
5621 10
 
2.0%
5617 9
 
1.8%
5040 9
 
1.8%
5495 8
 
1.6%
5076 7
 
1.4%
5838 7
 
1.4%
Other values (219) 386
77.2%
ValueCountFrequency (%)
2398 1
 
0.2%
3294 1
 
0.2%
3447 2
 
0.4%
3590 1
 
0.2%
3593 1
 
0.2%
3594 1
 
0.2%
3611 1
 
0.2%
3613 2
 
0.4%
3616 1
 
0.2%
3620 14
2.8%
ValueCountFrequency (%)
6200 1
 
0.2%
6197 1
 
0.2%
6187 1
 
0.2%
6179 2
0.4%
6166 2
0.4%
6154 1
 
0.2%
6147 4
0.8%
6139 1
 
0.2%
6131 1
 
0.2%
6121 1
 
0.2%

품의일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
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 (%)
00:00.0 500
100.0%

Length

2023-12-12T21:31:51.760033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:51.865405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

결재상태코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
13
436 
11
59 
12
 
5

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row12
3rd row13
4th row13
5th row11

Common Values

ValueCountFrequency (%)
13 436
87.2%
11 59
 
11.8%
12 5
 
1.0%

Length

2023-12-12T21:31:51.967609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:52.085532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 436
87.2%
11 59
 
11.8%
12 5
 
1.0%

품의결재상태코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 500
100.0%

Length

2023-12-12T21:31:52.230384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:52.740729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 500
100.0%

결재자직원번호
Real number (ℝ)

MISSING 

Distinct154
Distinct (%)34.0%
Missing47
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean3893.3554
Minimum2710
Maximum5617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:52.876748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2710
5-th percentile3117
Q13628
median3965
Q34184
95-th percentile4406
Maximum5617
Range2907
Interquartile range (IQR)556

Descriptive statistics

Standard deviation392.55947
Coefficient of variation (CV)0.10082806
Kurtosis0.31719524
Mean3893.3554
Median Absolute Deviation (MAD)238
Skewness-0.30685583
Sum1763690
Variance154102.94
MonotonicityNot monotonic
2023-12-12T21:31:53.025306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307 34
 
6.8%
3882 25
 
5.0%
3471 16
 
3.2%
4184 16
 
3.2%
3773 15
 
3.0%
4287 10
 
2.0%
3071 9
 
1.8%
3965 9
 
1.8%
4136 9
 
1.8%
4203 9
 
1.8%
Other values (144) 301
60.2%
(Missing) 47
 
9.4%
ValueCountFrequency (%)
2710 1
 
0.2%
2962 1
 
0.2%
2981 2
 
0.4%
3055 1
 
0.2%
3060 1
 
0.2%
3071 9
1.8%
3082 4
0.8%
3117 5
1.0%
3136 2
 
0.4%
3151 2
 
0.4%
ValueCountFrequency (%)
5617 1
 
0.2%
4992 1
 
0.2%
4586 2
0.4%
4577 2
0.4%
4563 1
 
0.2%
4554 1
 
0.2%
4536 3
0.6%
4512 4
0.8%
4489 1
 
0.2%
4475 1
 
0.2%

결재자결재상태코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
11
500 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 500
100.0%

Length

2023-12-12T21:31:53.195645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:53.307961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 500
100.0%

결재일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
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 (%)
00:00.0 500
100.0%

Length

2023-12-12T21:31:53.419738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:53.537262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

당초결재직원번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

결재자변경사유내용
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
491 
휴가
 
9

Length

Max length4
Median length4
Mean length3.964
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 491
98.2%
휴가 9
 
1.8%

Length

2023-12-12T21:31:53.677404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:53.808518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 491
98.2%
휴가 9
 
1.8%

발급시점관할부점코드
Text

Distinct85
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:31:54.089626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)5.0%

Sample

1st rowNHN
2nd rowNIN
3rd rowNPN
4th rowNMN
5th rowTIK
ValueCountFrequency (%)
nbn 67
 
13.4%
npn 36
 
7.2%
nin 33
 
6.6%
ndn 29
 
5.8%
ncn 29
 
5.8%
nhn 28
 
5.6%
nkn 25
 
5.0%
nmn 25
 
5.0%
nan 17
 
3.4%
non 11
 
2.2%
Other values (75) 200
40.0%
2023-12-12T21:31:54.565993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 616
41.1%
T 198
 
13.2%
B 96
 
6.4%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 41
 
2.7%
M 41
 
2.7%
Other values (15) 164
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 616
41.1%
T 198
 
13.2%
B 96
 
6.4%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 41
 
2.7%
M 41
 
2.7%
Other values (15) 164
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 616
41.1%
T 198
 
13.2%
B 96
 
6.4%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 41
 
2.7%
M 41
 
2.7%
Other values (15) 164
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 616
41.1%
T 198
 
13.2%
B 96
 
6.4%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 41
 
2.7%
M 41
 
2.7%
Other values (15) 164
 
10.9%

발급부점코드
Text

Distinct86
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:31:54.918474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)5.2%

Sample

1st rowNHN
2nd rowNIN
3rd rowNPN
4th rowNMN
5th rowTIK
ValueCountFrequency (%)
nbn 66
 
13.2%
npn 36
 
7.2%
nin 33
 
6.6%
ndn 29
 
5.8%
ncn 29
 
5.8%
nhn 28
 
5.6%
nkn 25
 
5.0%
nmn 25
 
5.0%
nan 17
 
3.4%
non 11
 
2.2%
Other values (76) 201
40.2%
2023-12-12T21:31:55.392611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 614
40.9%
T 198
 
13.2%
B 95
 
6.3%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 42
 
2.8%
M 41
 
2.7%
Other values (15) 166
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 614
40.9%
T 198
 
13.2%
B 95
 
6.3%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 42
 
2.8%
M 41
 
2.7%
Other values (15) 166
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 614
40.9%
T 198
 
13.2%
B 95
 
6.3%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 42
 
2.8%
M 41
 
2.7%
Other values (15) 166
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 614
40.9%
T 198
 
13.2%
B 95
 
6.3%
A 85
 
5.7%
H 79
 
5.3%
P 67
 
4.5%
I 63
 
4.2%
D 50
 
3.3%
C 42
 
2.8%
M 41
 
2.7%
Other values (15) 166
 
11.1%

발급팀코드
Categorical

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
182 
2
156 
3
88 
4
74 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 182
36.4%
2 156
31.2%
3 88
17.6%
4 74
14.8%

Length

2023-12-12T21:31:55.573917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:31:55.711010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 182
36.4%
2 156
31.2%
3 88
17.6%
4 74
14.8%

발급자직원번호
Real number (ℝ)

Distinct229
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5257.358
Minimum2398
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:55.893495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2398
5-th percentile3765.35
Q14977
median5406
Q35723.5
95-th percentile6072.05
Maximum6200
Range3802
Interquartile range (IQR)746.5

Descriptive statistics

Standard deviation637.2886
Coefficient of variation (CV)0.12121841
Kurtosis1.2381375
Mean5257.358
Median Absolute Deviation (MAD)367
Skewness-1.1019273
Sum2628679
Variance406136.76
MonotonicityNot monotonic
2023-12-12T21:31:56.109276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5406 25
 
5.0%
5608 15
 
3.0%
3620 14
 
2.8%
5074 10
 
2.0%
5621 10
 
2.0%
5617 9
 
1.8%
5040 9
 
1.8%
5495 8
 
1.6%
5076 7
 
1.4%
5838 7
 
1.4%
Other values (219) 386
77.2%
ValueCountFrequency (%)
2398 1
 
0.2%
3294 1
 
0.2%
3447 2
 
0.4%
3590 1
 
0.2%
3593 1
 
0.2%
3594 1
 
0.2%
3611 1
 
0.2%
3613 2
 
0.4%
3616 1
 
0.2%
3620 14
2.8%
ValueCountFrequency (%)
6200 1
 
0.2%
6197 1
 
0.2%
6187 1
 
0.2%
6179 2
0.4%
6166 2
0.4%
6154 1
 
0.2%
6147 4
0.8%
6139 1
 
0.2%
6131 1
 
0.2%
6121 1
 
0.2%

확인자직원번호
Real number (ℝ)

MISSING 

Distinct154
Distinct (%)34.0%
Missing47
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean3893.3554
Minimum2710
Maximum5617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:56.311638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2710
5-th percentile3117
Q13628
median3965
Q34184
95-th percentile4406
Maximum5617
Range2907
Interquartile range (IQR)556

Descriptive statistics

Standard deviation392.55947
Coefficient of variation (CV)0.10082806
Kurtosis0.31719524
Mean3893.3554
Median Absolute Deviation (MAD)238
Skewness-0.30685583
Sum1763690
Variance154102.94
MonotonicityNot monotonic
2023-12-12T21:31:56.504896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4307 34
 
6.8%
3882 25
 
5.0%
3471 16
 
3.2%
4184 16
 
3.2%
3773 15
 
3.0%
4287 10
 
2.0%
3071 9
 
1.8%
3965 9
 
1.8%
4136 9
 
1.8%
4203 9
 
1.8%
Other values (144) 301
60.2%
(Missing) 47
 
9.4%
ValueCountFrequency (%)
2710 1
 
0.2%
2962 1
 
0.2%
2981 2
 
0.4%
3055 1
 
0.2%
3060 1
 
0.2%
3071 9
1.8%
3082 4
0.8%
3117 5
1.0%
3136 2
 
0.4%
3151 2
 
0.4%
ValueCountFrequency (%)
5617 1
 
0.2%
4992 1
 
0.2%
4586 2
0.4%
4577 2
0.4%
4563 1
 
0.2%
4554 1
 
0.2%
4536 3
0.6%
4512 4
0.8%
4489 1
 
0.2%
4475 1
 
0.2%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T21:31:56.648523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.716
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:56.774621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median5
Q35
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4040251
Coefficient of variation (CV)0.29771525
Kurtosis3.5341257
Mean4.716
Median Absolute Deviation (MAD)0
Skewness-1.3612178
Sum2358
Variance1.9712866
MonotonicityNot monotonic
2023-12-12T21:31:56.923373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 385
77.0%
1 47
 
9.4%
6 37
 
7.4%
2 10
 
2.0%
7 9
 
1.8%
4 5
 
1.0%
8 4
 
0.8%
9 2
 
0.4%
11 1
 
0.2%
ValueCountFrequency (%)
1 47
 
9.4%
2 10
 
2.0%
4 5
 
1.0%
5 385
77.0%
6 37
 
7.4%
7 9
 
1.8%
8 4
 
0.8%
9 2
 
0.4%
11 1
 
0.2%
ValueCountFrequency (%)
11 1
 
0.2%
9 2
 
0.4%
8 4
 
0.8%
7 9
 
1.8%
6 37
 
7.4%
5 385
77.0%
4 5
 
1.0%
2 10
 
2.0%
1 47
 
9.4%

처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T21:31:57.350953image/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 row44:00.9
2nd row40:35.6
3rd row39:47.2
4th row30:21.3
5th row21:29.8
ValueCountFrequency (%)
47:10.1 2
 
0.4%
40:50.8 2
 
0.4%
11:27.8 2
 
0.4%
12:58.1 2
 
0.4%
53:32.7 2
 
0.4%
14:36.3 1
 
0.2%
50:59.2 1
 
0.2%
55:55.7 1
 
0.2%
57:44.0 1
 
0.2%
58:01.4 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T21:31:57.966575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 327
9.3%
0 326
9.3%
3 325
9.3%
4 314
9.0%
5 310
8.9%
1 304
8.7%
8 155
 
4.4%
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 (%)
2 327
13.1%
0 326
13.0%
3 325
13.0%
4 314
12.6%
5 310
12.4%
1 304
12.2%
8 155
6.2%
6 154
6.2%
7 153
6.1%
9 132
5.3%
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%
2 327
9.3%
0 326
9.3%
3 325
9.3%
4 314
9.0%
5 310
8.9%
1 304
8.7%
8 155
 
4.4%
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%
2 327
9.3%
0 326
9.3%
3 325
9.3%
4 314
9.0%
5 310
8.9%
1 304
8.7%
8 155
 
4.4%
6 154
 
4.4%
Other values (2) 285
8.1%

처리직원번호
Real number (ℝ)

Distinct230
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5255.612
Minimum2398
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T21:31:58.162915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2398
5-th percentile3765.35
Q14977
median5406
Q35721.5
95-th percentile6071.05
Maximum6200
Range3802
Interquartile range (IQR)744.5

Descriptive statistics

Standard deviation635.97332
Coefficient of variation (CV)0.12100842
Kurtosis1.2539225
Mean5255.612
Median Absolute Deviation (MAD)366
Skewness-1.1060258
Sum2627806
Variance404462.07
MonotonicityNot monotonic
2023-12-12T21:31:58.369679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5406 25
 
5.0%
5608 15
 
3.0%
3620 14
 
2.8%
5074 10
 
2.0%
5621 10
 
2.0%
5617 9
 
1.8%
5040 9
 
1.8%
5495 8
 
1.6%
5076 7
 
1.4%
5838 7
 
1.4%
Other values (220) 386
77.2%
ValueCountFrequency (%)
2398 1
 
0.2%
3294 1
 
0.2%
3447 2
 
0.4%
3590 1
 
0.2%
3593 1
 
0.2%
3594 1
 
0.2%
3611 1
 
0.2%
3613 2
 
0.4%
3616 1
 
0.2%
3620 14
2.8%
ValueCountFrequency (%)
6200 1
 
0.2%
6197 1
 
0.2%
6187 1
 
0.2%
6179 2
0.4%
6166 2
0.4%
6154 1
 
0.2%
6147 4
0.8%
6139 1
 
0.2%
6131 1
 
0.2%
6121 1
 
0.2%

Sample

업무구분코드발급일련번호신청기업고객ID발급특약매수발급일자발급일시발급사유코드보험증권형태코드증서종류코드파손여부품의직원번호품의일자결재상태코드품의결재상태코드결재자직원번호결재자결재상태코드결재일자당초결재직원번호결재자변경사유내용발급시점관할부점코드발급부점코드발급팀코드발급자직원번호확인자직원번호삭제여부최종수정수처리시각처리직원번호
0A19cujqkCTb7000:00.037:22.41153N501200:00.0121144061100:00.0<NA><NA>NHNNHN250124406N444:00.95012
1A19c5yomYkBH000:00.036:36.01253590900:00.0121141471100:00.0<NA><NA>NINNIN459094147N440:35.65909
2A19cWtHaeylo000:00.037:18.21253500100:00.0131138821100:00.0<NA><NA>NPNNPN150013882N539:47.25001
3A19c5yHjOV0o000:00.040:38.51153N456100:00.0131141031100:00.0<NA><NA>NMNNMN145614103N530:21.34561
4A19cxtDcbPqD000:00.021:29.81153499300:00.01111<NA>1100:00.0<NA><NA>TIKTIK24993<NA>N121:29.84993
5A19cUAIsgnk4000:00.003:27.91153N473600:00.0131136661100:00.0<NA><NA>THDTHD247363666N916:36.84736
6A19cVtdC8LqB000:00.021:22.21153N549200:00.0131132711100:00.0<NA><NA>NANNAN454923271N545:54.35492
7A19bTLjn85dE000:00.056:23.41253540600:00.0131143071100:00.0<NA><NA>NBNNBN454064307N539:44.85406
8A19ddjkBHdAK000:00.047:55.71353540600:00.0131143071100:00.0<NA><NA>NBNNBN454064307N539:31.05406
9A19bkeFvE7vq000:00.058:25.31253540600:00.0131143071100:00.0<NA><NA>NBNNBN454064307N739:04.05406
업무구분코드발급일련번호신청기업고객ID발급특약매수발급일자발급일시발급사유코드보험증권형태코드증서종류코드파손여부품의직원번호품의일자결재상태코드품의결재상태코드결재자직원번호결재자결재상태코드결재일자당초결재직원번호결재자변경사유내용발급시점관할부점코드발급부점코드발급팀코드발급자직원번호확인자직원번호삭제여부최종수정수처리시각처리직원번호
490A19cSHreoWM7000:00.059:39.31153Y610700:00.0131134841100:00.0<NA><NA>TPLTPL261073484N651:41.55354
491A19crm7t3Ijs000:00.048:36.81253562100:00.0131134711100:00.0<NA><NA>NINNIN256213471N551:24.65621
492A19cyrcUKpzb000:00.027:07.01153N537600:00.0131137731100:00.0<NA><NA>NCNNCN253763773N546:21.45376
493A19c6VZ87CEx000:00.031:46.51353452600:00.0131137971100:00.0<NA><NA>TIHTIH245263797N542:58.64526
494A1aaaaacgDUf000:00.003:58.41253464600:00.0131136571100:00.0<NA><NA>NHNNHN146463657N542:41.44646
495A1aaaaabWSz0000:00.027:56.41253571400:00.0131139771100:00.0<NA><NA>NCNNCN157143977N530:30.85714
496A19clSdmLsta000:00.000:25.61353452600:00.0131137971100:00.0<NA><NA>TIHTIH245263797N521:13.94526
497A19dmHLGYGNX000:00.007:43.01153N515000:00.0131145361100:00.0<NA><NA>TQATQA151504536N515:53.85150
498A19cy6do1cIr000:00.048:48.41353576200:00.0131140531100:00.0<NA><NA>NHNNHN357624053N508:51.85762
499A39diGdmfRWj000:00.056:34.299153N484400:00.0111145121100:00.0<NA><NA>TQATQA348444512N208:47.24844