Overview

Dataset statistics

Number of variables26
Number of observations500
Missing cells2291
Missing cells (%)17.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory105.1 KiB
Average record size in memory215.3 B

Variable types

Text6
Numeric4
Categorical12
Unsupported3
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 공통RM요구사항마스터에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093191/fileData.do

Alerts

요청일자 has constant value ""Constant
희망완료일자 has constant value ""Constant
오류발생일자 has constant value ""Constant
삭제여부 has constant value ""Constant
반송일자 is highly imbalanced (93.3%)Imbalance
기타비고내용 is highly imbalanced (68.6%)Imbalance
일괄진행전자결재ID is highly imbalanced (71.4%)Imbalance
문서경로명 has 500 (100.0%) missing valuesMissing
사전협의직원번호 has 348 (69.6%) missing valuesMissing
프로그램명 has 500 (100.0%) missing valuesMissing
오류입력직원번호 has 500 (100.0%) missing valuesMissing
통계보고서요구사항ID has 443 (88.6%) missing valuesMissing
요구사항ID has unique valuesUnique
문서경로명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
프로그램명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
오류입력직원번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 13:07:25.017323
Analysis finished2023-12-12 13:07:25.409842
Duration0.39 seconds
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
2023-12-12T22:07:25.662628image/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 row9dnS0KQ3U4
2nd row9djxNDNtYG
3rd row9dnMCYAXBI
4th row9dnzmqfw0u
5th row9dnSYWdWxX
ValueCountFrequency (%)
9dns0kq3u4 1
 
0.2%
9dmgzg571w 1
 
0.2%
9dnsmobqoc 1
 
0.2%
9dmgblg7dq 1
 
0.2%
9dmyckchyy 1
 
0.2%
9dnogxuihf 1
 
0.2%
9dnofluxd6 1
 
0.2%
9dnspp294l 1
 
0.2%
9dnspp25vg 1
 
0.2%
9dnogxuhae 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T22:07:26.182327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 550
 
11.0%
9 547
 
10.9%
n 422
 
8.4%
m 163
 
3.3%
o 98
 
2.0%
L 84
 
1.7%
f 78
 
1.6%
S 73
 
1.5%
p 73
 
1.5%
l 72
 
1.4%
Other values (52) 2840
56.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2488
49.8%
Uppercase Letter 1455
29.1%
Decimal Number 1057
21.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 550
22.1%
n 422
17.0%
m 163
 
6.6%
o 98
 
3.9%
f 78
 
3.1%
p 73
 
2.9%
l 72
 
2.9%
z 71
 
2.9%
g 66
 
2.7%
s 64
 
2.6%
Other values (16) 831
33.4%
Uppercase Letter
ValueCountFrequency (%)
L 84
 
5.8%
S 73
 
5.0%
D 71
 
4.9%
C 69
 
4.7%
J 67
 
4.6%
M 67
 
4.6%
O 66
 
4.5%
Y 65
 
4.5%
K 62
 
4.3%
A 59
 
4.1%
Other values (16) 772
53.1%
Decimal Number
ValueCountFrequency (%)
9 547
51.8%
3 68
 
6.4%
5 62
 
5.9%
1 60
 
5.7%
4 59
 
5.6%
0 57
 
5.4%
2 54
 
5.1%
6 52
 
4.9%
7 50
 
4.7%
8 48
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 3943
78.9%
Common 1057
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 550
 
13.9%
n 422
 
10.7%
m 163
 
4.1%
o 98
 
2.5%
L 84
 
2.1%
f 78
 
2.0%
S 73
 
1.9%
p 73
 
1.9%
l 72
 
1.8%
D 71
 
1.8%
Other values (42) 2259
57.3%
Common
ValueCountFrequency (%)
9 547
51.8%
3 68
 
6.4%
5 62
 
5.9%
1 60
 
5.7%
4 59
 
5.6%
0 57
 
5.4%
2 54
 
5.1%
6 52
 
4.9%
7 50
 
4.7%
8 48
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 550
 
11.0%
9 547
 
10.9%
n 422
 
8.4%
m 163
 
3.3%
o 98
 
2.0%
L 84
 
1.7%
f 78
 
1.6%
S 73
 
1.5%
p 73
 
1.5%
l 72
 
1.4%
Other values (52) 2840
56.8%

요청부점코드
Text

Distinct101
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:07:26.492803image/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

Unique46 ?
Unique (%)9.2%

Sample

1st rowAAY
2nd rowAAY
3rd rowAAU
4th rowAAR
5th rowAAU
ValueCountFrequency (%)
aar 70
14.0%
aay 54
 
10.8%
acr 45
 
9.0%
aau 42
 
8.4%
aci 33
 
6.6%
ace 26
 
5.2%
abp 24
 
4.8%
abm 16
 
3.2%
abj 10
 
2.0%
abe 7
 
1.4%
Other values (91) 173
34.6%
2023-12-12T22:07:26.980730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 594
39.6%
C 121
 
8.1%
R 116
 
7.7%
T 112
 
7.5%
B 69
 
4.6%
Y 56
 
3.7%
U 52
 
3.5%
I 48
 
3.2%
E 45
 
3.0%
P 38
 
2.5%
Other values (15) 249
16.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 594
39.6%
C 121
 
8.1%
R 116
 
7.7%
T 112
 
7.5%
B 69
 
4.6%
Y 56
 
3.7%
U 52
 
3.5%
I 48
 
3.2%
E 45
 
3.0%
P 38
 
2.5%
Other values (15) 249
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 594
39.6%
C 121
 
8.1%
R 116
 
7.7%
T 112
 
7.5%
B 69
 
4.6%
Y 56
 
3.7%
U 52
 
3.5%
I 48
 
3.2%
E 45
 
3.0%
P 38
 
2.5%
Other values (15) 249
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 594
39.6%
C 121
 
8.1%
R 116
 
7.7%
T 112
 
7.5%
B 69
 
4.6%
Y 56
 
3.7%
U 52
 
3.5%
I 48
 
3.2%
E 45
 
3.0%
P 38
 
2.5%
Other values (15) 249
16.6%

요청자직원번호
Text

Distinct219
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:07:27.391022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.002
Min length4

Characters and Unicode

Total characters2001
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)25.0%

Sample

1st row5589
2nd row5560
3rd row5221
4th row4767
5th row5322
ValueCountFrequency (%)
5589 13
 
2.6%
5637 11
 
2.2%
5595 11
 
2.2%
5563 11
 
2.2%
5797 9
 
1.8%
5422 8
 
1.6%
5802 8
 
1.6%
5640 8
 
1.6%
5552 8
 
1.6%
5544 7
 
1.4%
Other values (209) 406
81.2%
2023-12-12T22:07:27.958896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 547
27.3%
4 248
12.4%
2 177
 
8.8%
8 164
 
8.2%
7 163
 
8.1%
6 162
 
8.1%
0 151
 
7.5%
3 145
 
7.2%
9 130
 
6.5%
1 112
 
5.6%
Other values (2) 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1999
99.9%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 547
27.4%
4 248
12.4%
2 177
 
8.9%
8 164
 
8.2%
7 163
 
8.2%
6 162
 
8.1%
0 151
 
7.6%
3 145
 
7.3%
9 130
 
6.5%
1 112
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
E 1
50.0%
X 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1999
99.9%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 547
27.4%
4 248
12.4%
2 177
 
8.9%
8 164
 
8.2%
7 163
 
8.2%
6 162
 
8.1%
0 151
 
7.6%
3 145
 
7.3%
9 130
 
6.5%
1 112
 
5.6%
Latin
ValueCountFrequency (%)
E 1
50.0%
X 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 547
27.3%
4 248
12.4%
2 177
 
8.8%
8 164
 
8.2%
7 163
 
8.1%
6 162
 
8.1%
0 151
 
7.5%
3 145
 
7.2%
9 130
 
6.5%
1 112
 
5.6%
Other values (2) 2
 
0.1%

요구사항요청구분코드
Real number (ℝ)

Distinct13
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.888
Minimum100
Maximum690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:07:28.096346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median300
Q3400
95-th percentile500
Maximum690
Range590
Interquartile range (IQR)200

Descriptive statistics

Standard deviation131.31136
Coefficient of variation (CV)0.44833301
Kurtosis0.98859024
Mean292.888
Median Absolute Deviation (MAD)100
Skewness0.92296555
Sum146444
Variance17242.673
MonotonicityNot monotonic
2023-12-12T22:07:28.214434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
200 181
36.2%
400 111
22.2%
300 60
 
12.0%
100 46
 
9.2%
301 43
 
8.6%
500 25
 
5.0%
690 16
 
3.2%
302 7
 
1.4%
303 4
 
0.8%
630 4
 
0.8%
Other values (3) 3
 
0.6%
ValueCountFrequency (%)
100 46
 
9.2%
200 181
36.2%
300 60
 
12.0%
301 43
 
8.6%
302 7
 
1.4%
303 4
 
0.8%
304 1
 
0.2%
305 1
 
0.2%
306 1
 
0.2%
400 111
22.2%
ValueCountFrequency (%)
690 16
 
3.2%
630 4
 
0.8%
500 25
 
5.0%
400 111
22.2%
306 1
 
0.2%
305 1
 
0.2%
304 1
 
0.2%
303 4
 
0.8%
302 7
 
1.4%
301 43
 
8.6%

문서번호
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
348 
[IT요구현황]
104 
48 

Length

Max length8
Median length4
Mean length4.544
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 348
69.6%
[IT요구현황] 104
 
20.8%
48
 
9.6%

Length

2023-12-12T22:07:28.362183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:28.487707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 348
77.0%
it요구현황 104
 
23.0%

문서경로명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

요청일자
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

2023-12-12T22:07:28.591963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:28.694380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

반송일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
496 
00:00.0
 
4

Length

Max length26
Median length26
Mean length25.848
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 (%)
0001-01-01 00:00:00.000000 496
99.2%
00:00.0 4
 
0.8%

Length

2023-12-12T22:07:28.799477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:28.902785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 496
49.8%
00:00:00.000000 496
49.8%
00:00.0 4
 
0.4%

희망완료일자
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

2023-12-12T22:07:29.003188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:29.102668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

완료예정일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
254 
00:00.0
246 

Length

Max length26
Median length26
Mean length16.652
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 254
50.8%
00:00.0 246
49.2%

Length

2023-12-12T22:07:29.209188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:29.308292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 254
33.7%
00:00:00.000000 254
33.7%
00:00.0 246
32.6%

완료일자
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
357 
0001-01-01 00:00:00.000000
143 

Length

Max length26
Median length7
Mean length12.434
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 row00:00.0
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
00:00.0 357
71.4%
0001-01-01 00:00:00.000000 143
28.6%

Length

2023-12-12T22:07:29.435950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:29.546939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 357
55.5%
0001-01-01 143
22.2%
00:00:00.000000 143
22.2%

요구사항처리절차코드값
Real number (ℝ)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean668.18
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:07:29.654129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1100
median900
Q3900
95-th percentile900
Maximum999
Range998
Interquartile range (IQR)800

Descriptive statistics

Standard deviation386.2119
Coefficient of variation (CV)0.57800578
Kurtosis-0.82976399
Mean668.18
Median Absolute Deviation (MAD)0
Skewness-1.0720228
Sum334090
Variance149159.63
MonotonicityNot monotonic
2023-12-12T22:07:29.777679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
900 355
71.0%
1 51
 
10.2%
11 29
 
5.8%
13 11
 
2.2%
50 8
 
1.6%
100 7
 
1.4%
12 6
 
1.2%
90 5
 
1.0%
70 5
 
1.0%
999 4
 
0.8%
Other values (10) 19
 
3.8%
ValueCountFrequency (%)
1 51
10.2%
11 29
5.8%
12 6
 
1.2%
13 11
 
2.2%
14 2
 
0.4%
50 8
 
1.6%
65 1
 
0.2%
70 5
 
1.0%
80 3
 
0.6%
90 5
 
1.0%
ValueCountFrequency (%)
999 4
 
0.8%
998 2
 
0.4%
900 355
71.0%
770 2
 
0.4%
760 2
 
0.4%
710 3
 
0.6%
190 1
 
0.2%
160 1
 
0.2%
120 2
 
0.4%
100 7
 
1.4%

담당자직원번호
Categorical

Distinct38
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
4496
73 
4509
61 
<NA>
60 
5113
36 
5573
31 
Other values (33)
239 

Length

Max length5
Median length4
Mean length4.004
Min length4

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st row<NA>
2nd row4168
3rd row4168
4th row6105
5th row<NA>

Common Values

ValueCountFrequency (%)
4496 73
14.6%
4509 61
 
12.2%
<NA> 60
 
12.0%
5113 36
 
7.2%
5573 31
 
6.2%
5544 21
 
4.2%
6105 20
 
4.0%
5423 18
 
3.6%
5873 14
 
2.8%
5354 13
 
2.6%
Other values (28) 153
30.6%

Length

2023-12-12T22:07:29.909303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4496 73
14.6%
4509 61
12.2%
na 60
 
12.0%
5113 36
 
7.2%
5573 31
 
6.2%
5544 21
 
4.2%
6105 20
 
4.0%
5423 18
 
3.6%
5873 14
 
2.8%
5354 13
 
2.6%
Other values (27) 152
30.5%

사전협의직원번호
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)19.1%
Missing348
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean5138.4013
Minimum4168
Maximum6105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:07:30.025932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4168
5-th percentile4168
Q14496
median5222
Q35741
95-th percentile6036.05
Maximum6105
Range1937
Interquartile range (IQR)1245

Descriptive statistics

Standard deviation620.98248
Coefficient of variation (CV)0.1208513
Kurtosis-1.4289329
Mean5138.4013
Median Absolute Deviation (MAD)606
Skewness-0.15694876
Sum781037
Variance385619.24
MonotonicityNot monotonic
2023-12-12T22:07:30.145074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4496 42
 
8.4%
4168 15
 
3.0%
5423 10
 
2.0%
5573 9
 
1.8%
5113 9
 
1.8%
5222 9
 
1.8%
6053 5
 
1.0%
5828 5
 
1.0%
5742 5
 
1.0%
5220 5
 
1.0%
Other values (19) 38
 
7.6%
(Missing) 348
69.6%
ValueCountFrequency (%)
4168 15
 
3.0%
4496 42
8.4%
5113 9
 
1.8%
5176 1
 
0.2%
5220 5
 
1.0%
5222 9
 
1.8%
5314 2
 
0.4%
5354 2
 
0.4%
5423 10
 
2.0%
5470 1
 
0.2%
ValueCountFrequency (%)
6105 2
 
0.4%
6053 5
1.0%
6052 1
 
0.2%
6023 1
 
0.2%
6009 1
 
0.2%
6000 2
 
0.4%
5921 2
 
0.4%
5873 2
 
0.4%
5870 1
 
0.2%
5828 5
1.0%

기타비고내용
Categorical

IMBALANCE 

Distinct12
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
317 
<NA>
173 
중복건은 업무대행으로 처리하여 해소된 상태임(요청자 전달 및 확인 필)
 
1
경영지원개발팀 업무 소관으로 별도 재접수 후 처리할 예정
 
1
보험부 테스트 중
 
1
Other values (7)
 
7

Length

Max length55
Median length1
Mean length2.566
Min length1

Unique

Unique10 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
317
63.4%
<NA> 173
34.6%
중복건은 업무대행으로 처리하여 해소된 상태임(요청자 전달 및 확인 필) 1
 
0.2%
경영지원개발팀 업무 소관으로 별도 재접수 후 처리할 예정 1
 
0.2%
보험부 테스트 중 1
 
0.2%
기업개선부 담당자 요청에 따른 전산화 반려 1
 
0.2%
21.9월 재단 정보 수신 후 테스트 가능하여 완료 예정일 조정 1
 
0.2%
OLAP 리포트 사용 안내(QAU_0008) 1
 
0.2%
시행일 연기에 따른 완료일 연기(개발완료는 7.8일) 1
 
0.2%
완료-시행일정만 조정 1
 
0.2%
Other values (2) 2
 
0.4%

Length

2023-12-12T22:07:30.301569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 173
73.6%
테스트 2
 
0.9%
조정 2
 
0.9%
따른 2
 
0.9%
후 2
 
0.9%
협약보증 1
 
0.4%
지원 1
 
0.4%
예정일 1
 
0.4%
포함하여 1
 
0.4%
olap 1
 
0.4%
Other values (49) 49
 
20.9%

프로그램명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

오류입력직원번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

오류발생일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

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 (%)
0001-01-01 00:00:00.000000 500
100.0%

Length

2023-12-12T22:07:30.410735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:30.484907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

통계보고서요구사항ID
Text

MISSING 

Distinct43
Distinct (%)75.4%
Missing443
Missing (%)88.6%
Memory size4.0 KiB
2023-12-12T22:07:30.654584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters570
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

Unique36 ?
Unique (%)63.2%

Sample

1st row9dnSHY3XN8
2nd row9dnSHY3XN8
3rd row9dnM25gc2h
4th row9dnJCyBcwJ
5th row9dnLppbSsL
ValueCountFrequency (%)
9dnlbl2wfv 6
 
10.5%
9dnasaedic 3
 
5.3%
9dnpmcg59j 3
 
5.3%
9dnlppbssl 3
 
5.3%
9dnshy3xn8 2
 
3.5%
9dnb1wotpm 2
 
3.5%
9dnsnu2nrs 2
 
3.5%
9dnod81umm 1
 
1.8%
9dnluhyo3o 1
 
1.8%
9dnngchr5o 1
 
1.8%
Other values (33) 33
57.9%
2023-12-12T22:07:31.158040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 67
 
11.8%
d 63
 
11.1%
n 59
 
10.4%
l 16
 
2.8%
v 15
 
2.6%
b 14
 
2.5%
S 13
 
2.3%
p 12
 
2.1%
F 11
 
1.9%
N 11
 
1.9%
Other values (52) 289
50.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292
51.2%
Uppercase Letter 152
26.7%
Decimal Number 126
22.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 63
21.6%
n 59
20.2%
l 16
 
5.5%
v 15
 
5.1%
b 14
 
4.8%
p 12
 
4.1%
w 11
 
3.8%
s 11
 
3.8%
m 10
 
3.4%
o 8
 
2.7%
Other values (16) 73
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 13
 
8.6%
F 11
 
7.2%
N 11
 
7.2%
A 10
 
6.6%
C 10
 
6.6%
J 8
 
5.3%
L 8
 
5.3%
U 7
 
4.6%
E 7
 
4.6%
M 7
 
4.6%
Other values (16) 60
39.5%
Decimal Number
ValueCountFrequency (%)
9 67
53.2%
2 11
 
8.7%
5 9
 
7.1%
0 8
 
6.3%
1 8
 
6.3%
7 7
 
5.6%
3 6
 
4.8%
8 5
 
4.0%
4 3
 
2.4%
6 2
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 444
77.9%
Common 126
 
22.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 63
 
14.2%
n 59
 
13.3%
l 16
 
3.6%
v 15
 
3.4%
b 14
 
3.2%
S 13
 
2.9%
p 12
 
2.7%
F 11
 
2.5%
N 11
 
2.5%
w 11
 
2.5%
Other values (42) 219
49.3%
Common
ValueCountFrequency (%)
9 67
53.2%
2 11
 
8.7%
5 9
 
7.1%
0 8
 
6.3%
1 8
 
6.3%
7 7
 
5.6%
3 6
 
4.8%
8 5
 
4.0%
4 3
 
2.4%
6 2
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 67
 
11.8%
d 63
 
11.1%
n 59
 
10.4%
l 16
 
2.8%
v 15
 
2.6%
b 14
 
2.5%
S 13
 
2.3%
p 12
 
2.1%
F 11
 
1.9%
N 11
 
1.9%
Other values (52) 289
50.7%

지연여부
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
387 
N
113 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
387
77.4%
N 113
 
22.6%

Length

2023-12-12T22:07:31.270480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:31.344344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 113
100.0%

지연사유내용
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
325 
<NA>
174 
완료예정일에 운영서버에 반영은 했으나, 오픈 후 모니터링 중 긴급수정사항이 있을 수 있기에 요청서를 남겨 둠.
 
1

Length

Max length61
Median length1
Mean length2.164
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
325
65.0%
<NA> 174
34.8%
완료예정일에 운영서버에 반영은 했으나, 오픈 후 모니터링 중 긴급수정사항이 있을 수 있기에 요청서를 남겨 둠. 1
 
0.2%

Length

2023-12-12T22:07:31.429506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:31.523311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 174
92.1%
완료예정일에 1
 
0.5%
운영서버에 1
 
0.5%
반영은 1
 
0.5%
했으나 1
 
0.5%
오픈 1
 
0.5%
후 1
 
0.5%
모니터링 1
 
0.5%
중 1
 
0.5%
긴급수정사항이 1
 
0.5%
Other values (6) 6
 
3.2%

일괄진행전자결재ID
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
443 
9dnfrmOzqH
 
19
9dnsNeX0iU
 
18
9dnCv1ba51
 
12
9dnM25gc2h
 
7

Length

Max length10
Median length4
Mean length4.684
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 443
88.6%
9dnfrmOzqH 19
 
3.8%
9dnsNeX0iU 18
 
3.6%
9dnCv1ba51 12
 
2.4%
9dnM25gc2h 7
 
1.4%
9dkFyyQr1h 1
 
0.2%

Length

2023-12-12T22:07:31.619880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:07:31.703975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 443
88.6%
9dnfrmozqh 19
 
3.8%
9dnsnex0iu 18
 
3.6%
9dncv1ba51 12
 
2.4%
9dnm25gc2h 7
 
1.4%
9dkfyyqr1h 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%
2023-12-12T22:07:31.776868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct33
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.456
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T22:07:31.857358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median10
Q313
95-th percentile23
Maximum41
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.2749353
Coefficient of variation (CV)0.60012771
Kurtosis2.8297288
Mean10.456
Median Absolute Deviation (MAD)4
Skewness1.2784687
Sum5228
Variance39.374814
MonotonicityNot monotonic
2023-12-12T22:07:31.969203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
10 63
 
12.6%
9 50
 
10.0%
11 40
 
8.0%
4 38
 
7.6%
3 32
 
6.4%
12 31
 
6.2%
7 24
 
4.8%
5 23
 
4.6%
6 22
 
4.4%
13 18
 
3.6%
Other values (23) 159
31.8%
ValueCountFrequency (%)
1 16
 
3.2%
2 7
 
1.4%
3 32
6.4%
4 38
7.6%
5 23
 
4.6%
6 22
 
4.4%
7 24
 
4.8%
8 17
 
3.4%
9 50
10.0%
10 63
12.6%
ValueCountFrequency (%)
41 1
 
0.2%
38 1
 
0.2%
35 2
 
0.4%
33 1
 
0.2%
32 2
 
0.4%
31 1
 
0.2%
27 3
0.6%
26 2
 
0.4%
25 6
1.2%
24 3
0.6%

처리시각
Text

Distinct444
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:07:32.319357image/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

Unique436 ?
Unique (%)87.2%

Sample

1st row38:36.0
2nd row16:55.7
3rd row16:09.3
4th row14:30.5
5th row12:45.7
ValueCountFrequency (%)
14:39.4 19
 
3.8%
37:29.0 18
 
3.6%
13:12.5 12
 
2.4%
55:26.0 7
 
1.4%
43:59.2 2
 
0.4%
44:20.2 2
 
0.4%
20:23.1 2
 
0.4%
44:01.2 2
 
0.4%
50:01.5 1
 
0.2%
59:52.9 1
 
0.2%
Other values (434) 434
86.8%
2023-12-12T22:07:32.779875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 363
10.4%
1 352
10.1%
2 312
8.9%
4 307
8.8%
0 307
8.8%
5 287
8.2%
9 176
 
5.0%
7 140
 
4.0%
Other values (2) 256
7.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 363
14.5%
1 352
14.1%
2 312
12.5%
4 307
12.3%
0 307
12.3%
5 287
11.5%
9 176
7.0%
7 140
 
5.6%
6 138
 
5.5%
8 118
 
4.7%
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%
3 363
10.4%
1 352
10.1%
2 312
8.9%
4 307
8.8%
0 307
8.8%
5 287
8.2%
9 176
 
5.0%
7 140
 
4.0%
Other values (2) 256
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 363
10.4%
1 352
10.1%
2 312
8.9%
4 307
8.8%
0 307
8.8%
5 287
8.2%
9 176
 
5.0%
7 140
 
4.0%
Other values (2) 256
7.3%

처리직원번호
Text

Distinct81
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T22:07:33.010308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.002
Min length4

Characters and Unicode

Total characters2001
Distinct characters13
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

Unique42 ?
Unique (%)8.4%

Sample

1st row5589
2nd row4168
3rd row4168
4th row6105
5th row5322
ValueCountFrequency (%)
4496 75
 
15.0%
4509 63
 
12.6%
5113 37
 
7.4%
5573 33
 
6.6%
5544 21
 
4.2%
6105 19
 
3.8%
5423 19
 
3.8%
5803 14
 
2.8%
5873 13
 
2.6%
5354 12
 
2.4%
Other values (71) 194
38.8%
2023-12-12T22:07:33.363614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 484
24.2%
4 364
18.2%
9 201
10.0%
0 198
9.9%
3 158
 
7.9%
6 155
 
7.7%
1 142
 
7.1%
8 113
 
5.6%
2 92
 
4.6%
7 91
 
4.5%
Other values (3) 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1998
99.9%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 484
24.2%
4 364
18.2%
9 201
10.1%
0 198
9.9%
3 158
 
7.9%
6 155
 
7.8%
1 142
 
7.1%
8 113
 
5.7%
2 92
 
4.6%
7 91
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
E 1
33.3%
X 1
33.3%
G 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1998
99.9%
Latin 3
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 484
24.2%
4 364
18.2%
9 201
10.1%
0 198
9.9%
3 158
 
7.9%
6 155
 
7.8%
1 142
 
7.1%
8 113
 
5.7%
2 92
 
4.6%
7 91
 
4.6%
Latin
ValueCountFrequency (%)
E 1
33.3%
X 1
33.3%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2001
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 484
24.2%
4 364
18.2%
9 201
10.0%
0 198
9.9%
3 158
 
7.9%
6 155
 
7.7%
1 142
 
7.1%
8 113
 
5.6%
2 92
 
4.6%
7 91
 
4.5%
Other values (3) 3
 
0.1%

Sample

요구사항ID요청부점코드요청자직원번호요구사항요청구분코드문서번호문서경로명요청일자반송일자희망완료일자완료예정일자완료일자요구사항처리절차코드값담당자직원번호사전협의직원번호기타비고내용프로그램명오류입력직원번호오류발생일자통계보고서요구사항ID지연여부지연사유내용일괄진행전자결재ID삭제여부최종수정수처리시각처리직원번호
09dnS0KQ3U4AAY5589200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000011<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N238:36.05589
19djxNDNtYGAAY5560100<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.00001-01-01 00:00:00.000000141684168<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N1316:55.74168
29dnMCYAXBIAAU5221400[IT요구현황]<NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.090041684168<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N1616:09.34168
39dnzmqfw0uAAR4767400[IT요구현황]<NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.00001-01-01 00:00:00.0000009061054496<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N2414:30.56105
49dnSYWdWxXAAU5322100<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000011<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N312:45.75322
59dnOv3Yf2jABP5834200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000011<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N811:32.75834
69dnSX9nKUjJHA5755200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000013<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N607:48.75755
79dnOayu7wfABM5489200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.0000001<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N707:39.65423
89dnK7foBrcAAY4978100<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.00001-01-01 00:00:00.000000144964496<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N707:17.65742
99dnSYcYcBsACR5797200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000012<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N301:53.25797
요구사항ID요청부점코드요청자직원번호요구사항요청구분코드문서번호문서경로명요청일자반송일자희망완료일자완료예정일자완료일자요구사항처리절차코드값담당자직원번호사전협의직원번호기타비고내용프로그램명오류입력직원번호오류발생일자통계보고서요구사항ID지연여부지연사유내용일괄진행전자결재ID삭제여부최종수정수처리시각처리직원번호
4909dm6noxU6JAAY5730301<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.00000000:00.09004509<NA><NA><NA><NA>0001-01-01 00:00:00.0000009dm57F2h5b<NA><NA>N447:38.74509
4919dm5ZvcVU3ABP5802301<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.00000000:00.09004509<NA><NA><NA><NA>0001-01-01 00:00:00.0000009dm1s9vN6g<NA><NA>N433:37.34509
4929dmT4pPUTWACR5563400[IT요구현황]<NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.090044964496<NA><NA>0001-01-01 00:00:00.000000<NA>N<NA>N1958:56.14496
4939dmZ0NLw5sACR5563400[IT요구현황]<NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.090044964496<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N1858:11.54496
4949dne9CS0ckTHZ5904200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.00001-01-01 00:00:00.0000000001-01-01 00:00:00.00000011<NA><NA><NA><NA><NA>0001-01-01 00:00:00.000000<NA><NA><NA>N347:38.25904
4959dmPFKhsZNAAN5336100<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.00001-01-01 00:00:00.0000008054235423<NA><NA>0001-01-01 00:00:00.000000<NA><NA>N1045:42.15423
4969dm6icQp3ATIG6080200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.09005573<NA><NA><NA>0001-01-01 00:00:00.000000<NA>N<NA>N1040:15.45573
4979dm3wtnjvbTAM6033200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.09005573<NA><NA><NA>0001-01-01 00:00:00.000000<NA>N<NA>N1040:03.35573
4989dm53Y2qztTAL4968200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.09005573<NA><NA><NA>0001-01-01 00:00:00.000000<NA>N<NA>N1139:47.75573
4999dm51QY5M5THZ6045200<NA><NA>00:00.00001-01-01 00:00:00.00000000:00.000:00.000:00.09005573<NA><NA><NA>0001-01-01 00:00:00.000000<NA>N<NA>N1039:12.45573