Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells376
Missing cells (%)9.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.8 KiB
Average record size in memory67.3 B

Variable types

Text3
Numeric3
Categorical1
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 공통RM요구사항업무대행상세에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093195/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 is highly overall correlated with 전자결재상태코드High correlation
전자결재상태코드 is highly overall correlated with 최종수정수High correlation
전자결재상태코드 is highly imbalanced (69.0%)Imbalance
원장번호 has 350 (70.0%) missing valuesMissing
현재책임자직원번호 has 13 (2.6%) missing valuesMissing
현재담당자직원번호 has 13 (2.6%) missing valuesMissing
요구사항ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:15:37.575411
Analysis finished2023-12-12 14:15:39.299594
Duration1.72 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
2023-12-12T23:15:39.508521image/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 row9dnOayu7wf
2nd row9dnSYcYcBs
3rd row9dnSXv3Udz
4th row9dmH2zYSOu
5th row9dnObjpe1F
ValueCountFrequency (%)
9dnoayu7wf 1
 
0.2%
9dmgdt7cr4 1
 
0.2%
9dmetaiwib 1
 
0.2%
9dmgdp3a45 1
 
0.2%
9dmgdhoujr 1
 
0.2%
9dmgqickof 1
 
0.2%
9dmgremdqn 1
 
0.2%
9dmgucbkc3 1
 
0.2%
9dmguu0nxt 1
 
0.2%
9dmgszts0h 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T23:15:39.907105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 565
 
11.3%
9 548
 
11.0%
m 381
 
7.6%
n 208
 
4.2%
l 92
 
1.8%
J 76
 
1.5%
v 74
 
1.5%
1 72
 
1.4%
G 72
 
1.4%
H 71
 
1.4%
Other values (52) 2841
56.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2508
50.2%
Uppercase Letter 1446
28.9%
Decimal Number 1046
20.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 565
22.5%
m 381
15.2%
n 208
 
8.3%
l 92
 
3.7%
v 74
 
3.0%
z 71
 
2.8%
p 70
 
2.8%
u 68
 
2.7%
o 68
 
2.7%
k 68
 
2.7%
Other values (16) 843
33.6%
Uppercase Letter
ValueCountFrequency (%)
J 76
 
5.3%
G 72
 
5.0%
H 71
 
4.9%
Z 67
 
4.6%
P 67
 
4.6%
A 67
 
4.6%
X 64
 
4.4%
D 64
 
4.4%
S 64
 
4.4%
M 60
 
4.1%
Other values (16) 774
53.5%
Decimal Number
ValueCountFrequency (%)
9 548
52.4%
1 72
 
6.9%
3 62
 
5.9%
0 57
 
5.4%
5 56
 
5.4%
6 53
 
5.1%
2 53
 
5.1%
8 52
 
5.0%
4 51
 
4.9%
7 42
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3954
79.1%
Common 1046
 
20.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 565
 
14.3%
m 381
 
9.6%
n 208
 
5.3%
l 92
 
2.3%
J 76
 
1.9%
v 74
 
1.9%
G 72
 
1.8%
H 71
 
1.8%
z 71
 
1.8%
p 70
 
1.8%
Other values (42) 2274
57.5%
Common
ValueCountFrequency (%)
9 548
52.4%
1 72
 
6.9%
3 62
 
5.9%
0 57
 
5.4%
5 56
 
5.4%
6 53
 
5.1%
2 53
 
5.1%
8 52
 
5.0%
4 51
 
4.9%
7 42
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 565
 
11.3%
9 548
 
11.0%
m 381
 
7.6%
n 208
 
4.2%
l 92
 
1.8%
J 76
 
1.5%
v 74
 
1.5%
1 72
 
1.4%
G 72
 
1.4%
H 71
 
1.4%
Other values (52) 2841
56.8%

원장번호
Text

MISSING 

Distinct145
Distinct (%)96.7%
Missing350
Missing (%)70.0%
Memory size4.0 KiB
2023-12-12T23:15:40.153298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1800
Distinct characters35
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

Unique140 ?
Unique (%)93.3%

Sample

1st rowQAC202107107
2nd rowTHZ202102554
3rd rowTBC202000054
4th rowTAO202100393
5th rowTIG201600818
ValueCountFrequency (%)
tif202102026 2
 
1.3%
tpq202101569 2
 
1.3%
jac201800020 2
 
1.3%
tao202100349 2
 
1.3%
tal202003546 2
 
1.3%
tib202101836 1
 
0.7%
ndn202100768 1
 
0.7%
qac201601543 1
 
0.7%
tib202101837 1
 
0.7%
tqa20213683 1
 
0.7%
Other values (134) 134
89.9%
2023-12-12T23:15:40.497484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 432
24.0%
2 302
16.8%
1 203
11.3%
T 134
 
7.4%
5 92
 
5.1%
3 64
 
3.6%
A 52
 
2.9%
7 51
 
2.8%
6 51
 
2.8%
4 50
 
2.8%
Other values (25) 369
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1334
74.1%
Uppercase Letter 447
 
24.8%
Space Separator 19
 
1.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 134
30.0%
A 52
 
11.6%
H 29
 
6.5%
I 24
 
5.4%
B 24
 
5.4%
N 23
 
5.1%
Q 19
 
4.3%
L 18
 
4.0%
P 17
 
3.8%
J 16
 
3.6%
Other values (14) 91
20.4%
Decimal Number
ValueCountFrequency (%)
0 432
32.4%
2 302
22.6%
1 203
15.2%
5 92
 
6.9%
3 64
 
4.8%
7 51
 
3.8%
6 51
 
3.8%
4 50
 
3.7%
9 46
 
3.4%
8 43
 
3.2%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1353
75.2%
Latin 447
 
24.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 134
30.0%
A 52
 
11.6%
H 29
 
6.5%
I 24
 
5.4%
B 24
 
5.4%
N 23
 
5.1%
Q 19
 
4.3%
L 18
 
4.0%
P 17
 
3.8%
J 16
 
3.6%
Other values (14) 91
20.4%
Common
ValueCountFrequency (%)
0 432
31.9%
2 302
22.3%
1 203
15.0%
5 92
 
6.8%
3 64
 
4.7%
7 51
 
3.8%
6 51
 
3.8%
4 50
 
3.7%
9 46
 
3.4%
8 43
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 432
24.0%
2 302
16.8%
1 203
11.3%
T 134
 
7.4%
5 92
 
5.1%
3 64
 
3.6%
A 52
 
2.9%
7 51
 
2.8%
6 51
 
2.8%
4 50
 
2.8%
Other values (25) 369
20.5%

현재책임자직원번호
Real number (ℝ)

MISSING 

Distinct250
Distinct (%)51.3%
Missing13
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean3932.4928
Minimum2522
Maximum4647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:15:40.638494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2522
5-th percentile3173
Q13650
median4005
Q34231
95-th percentile4521
Maximum4647
Range2125
Interquartile range (IQR)581

Descriptive statistics

Standard deviation416.17292
Coefficient of variation (CV)0.10582929
Kurtosis0.14722348
Mean3932.4928
Median Absolute Deviation (MAD)276
Skewness-0.63473776
Sum1915124
Variance173199.9
MonotonicityNot monotonic
2023-12-12T23:15:40.762930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4261 23
 
4.6%
4517 14
 
2.8%
4554 8
 
1.6%
4033 7
 
1.4%
4521 7
 
1.4%
4213 6
 
1.2%
3984 6
 
1.2%
4391 5
 
1.0%
3650 5
 
1.0%
3979 4
 
0.8%
Other values (240) 402
80.4%
(Missing) 13
 
2.6%
ValueCountFrequency (%)
2522 1
 
0.2%
2689 4
0.8%
2710 3
0.6%
2895 2
0.4%
2981 1
 
0.2%
3055 3
0.6%
3059 1
 
0.2%
3060 1
 
0.2%
3082 1
 
0.2%
3094 1
 
0.2%
ValueCountFrequency (%)
4647 3
 
0.6%
4596 1
 
0.2%
4577 1
 
0.2%
4563 1
 
0.2%
4558 2
 
0.4%
4554 8
1.6%
4536 1
 
0.2%
4524 2
 
0.4%
4521 7
1.4%
4517 14
2.8%

현재담당자직원번호
Text

MISSING 

Distinct334
Distinct (%)68.6%
Missing13
Missing (%)2.6%
Memory size4.0 KiB
2023-12-12T23:15:41.105474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0082136
Min length4

Characters and Unicode

Total characters1952
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

Unique241 ?
Unique (%)49.5%

Sample

1st row5489
2nd row5797
3rd row4366
4th row5241
5th row6067
ValueCountFrequency (%)
5736 20
 
4.1%
5797 12
 
2.5%
5340 5
 
1.0%
5636 5
 
1.0%
6045 5
 
1.0%
4334 4
 
0.8%
4767 4
 
0.8%
5554 4
 
0.8%
5236 4
 
0.8%
5609 4
 
0.8%
Other values (324) 420
86.2%
2023-12-12T23:15:41.640103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 405
20.7%
4 251
12.9%
6 232
11.9%
0 180
9.2%
3 177
9.1%
7 154
 
7.9%
1 146
 
7.5%
9 143
 
7.3%
8 138
 
7.1%
2 123
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1949
99.8%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 405
20.8%
4 251
12.9%
6 232
11.9%
0 180
9.2%
3 177
9.1%
7 154
 
7.9%
1 146
 
7.5%
9 143
 
7.3%
8 138
 
7.1%
2 123
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1949
99.8%
Latin 3
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 405
20.8%
4 251
12.9%
6 232
11.9%
0 180
9.2%
3 177
9.1%
7 154
 
7.9%
1 146
 
7.5%
9 143
 
7.3%
8 138
 
7.1%
2 123
 
6.3%
Latin
ValueCountFrequency (%)
A 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1952
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 405
20.7%
4 251
12.9%
6 232
11.9%
0 180
9.2%
3 177
9.1%
7 154
 
7.9%
1 146
 
7.5%
9 143
 
7.3%
8 138
 
7.1%
2 123
 
6.3%

전자결재상태코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
13
436 
58 
11
 
4
12
 
2

Length

Max length2
Median length2
Mean length1.884
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
13 436
87.2%
58
 
11.6%
11 4
 
0.8%
12 2
 
0.4%

Length

2023-12-12T23:15:41.807394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:15:41.928815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 436
98.6%
11 4
 
0.9%
12 2
 
0.5%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T23:15:42.067803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.384
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:15:42.171583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q37
95-th percentile11
Maximum18
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.5926797
Coefficient of variation (CV)0.40612151
Kurtosis2.3462113
Mean6.384
Median Absolute Deviation (MAD)1
Skewness-0.021985457
Sum3192
Variance6.721988
MonotonicityNot monotonic
2023-12-12T23:15:42.295293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 214
42.8%
7 106
21.2%
1 58
 
11.6%
8 47
 
9.4%
9 26
 
5.2%
10 14
 
2.8%
11 11
 
2.2%
12 7
 
1.4%
5 5
 
1.0%
14 4
 
0.8%
Other values (4) 8
 
1.6%
ValueCountFrequency (%)
1 58
 
11.6%
2 3
 
0.6%
4 1
 
0.2%
5 5
 
1.0%
6 214
42.8%
7 106
21.2%
8 47
 
9.4%
9 26
 
5.2%
10 14
 
2.8%
11 11
 
2.2%
ValueCountFrequency (%)
18 2
 
0.4%
14 4
 
0.8%
13 2
 
0.4%
12 7
 
1.4%
11 11
 
2.2%
10 14
 
2.8%
9 26
 
5.2%
8 47
 
9.4%
7 106
21.2%
6 214
42.8%

처리직원번호
Real number (ℝ)

Distinct71
Distinct (%)14.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5278.712
Minimum2522
Maximum6156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:15:42.475353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2522
5-th percentile4496
Q14496
median5544
Q35803
95-th percentile6023
Maximum6156
Range3634
Interquartile range (IQR)1307

Descriptive statistics

Standard deviation606.90309
Coefficient of variation (CV)0.11497181
Kurtosis-0.54211291
Mean5278.712
Median Absolute Deviation (MAD)279
Skewness-0.62450281
Sum2639356
Variance368331.36
MonotonicityNot monotonic
2023-12-12T23:15:42.658712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4496 146
29.2%
5573 52
 
10.4%
5823 46
 
9.2%
5544 36
 
7.2%
5803 35
 
7.0%
5804 28
 
5.6%
5354 22
 
4.4%
6105 15
 
3.0%
5423 12
 
2.4%
5741 9
 
1.8%
Other values (61) 99
19.8%
ValueCountFrequency (%)
2522 1
 
0.2%
3368 1
 
0.2%
4040 1
 
0.2%
4168 6
 
1.2%
4334 2
 
0.4%
4496 146
29.2%
4596 1
 
0.2%
4761 1
 
0.2%
4793 1
 
0.2%
4835 2
 
0.4%
ValueCountFrequency (%)
6156 1
 
0.2%
6105 15
3.0%
6099 1
 
0.2%
6096 1
 
0.2%
6078 1
 
0.2%
6067 1
 
0.2%
6045 3
 
0.6%
6035 1
 
0.2%
6023 6
 
1.2%
6000 6
 
1.2%

Interactions

2023-12-12T23:15:38.576752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:37.848459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.176842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.670786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:37.951053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.287664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.798894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.072819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:15:38.431930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:15:42.803938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현재책임자직원번호전자결재상태코드최종수정수처리직원번호
현재책임자직원번호1.0000.0000.0000.248
전자결재상태코드0.0001.0000.8250.329
최종수정수0.0000.8251.0000.356
처리직원번호0.2480.3290.3561.000
2023-12-12T23:15:42.903665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현재책임자직원번호최종수정수처리직원번호전자결재상태코드
현재책임자직원번호1.000-0.0220.0900.000
최종수정수-0.0221.0000.0820.695
처리직원번호0.0900.0821.0000.236
전자결재상태코드0.0000.6950.2361.000

Missing values

2023-12-12T23:15:38.956969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:15:39.087974image/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.
2023-12-12T23:15:39.218552image/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

요구사항ID원장번호현재책임자직원번호현재담당자직원번호전자결재상태코드삭제여부최종수정수처리직원번호
09dnOayu7wf<NA>4197548911N75423
19dnSYcYcBs<NA>45175797N15797
29dnSXv3Udz<NA>36424366N15901
39dmH2zYSOu<NA>4391524113N85573
49dnObjpe1F<NA>3570606713N95573
59dnLzEL1AAQAC2021071073981479913N64496
69dnOv8nYGJ<NA>45175797N15797
79dnOf1QkCGTHZ2021025544554604513N64496
89dnJSAkYTg<NA>3581592613N95544
99dnOboJYJp<NA>4521524313N64496
요구사항ID원장번호현재책임자직원번호현재담당자직원번호전자결재상태코드삭제여부최종수정수처리직원번호
4909dl2HI1dRj<NA>3553452213N64496
4919dl9bO11x1TOI20160051542135636N15636
4929dl2ztoOLxTBI2021022784475541113N85823
4939dl1dQQ6VhTAV2015008133418601213N65823
4949dl2P5bsbXJPA2021000534020402013N75573
4959dl1u0vVnJTOA2015005994497610912N45823
4969dl2KXvBpv<NA>41245445N15445
4979dl1oTQrCpTIG2021035773982471013N75573
4989dlTCiFEDl<NA>4261476713N85544
4999dl1n3j1mw<NA>3149479413N65823