Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.6 KiB
Average record size in memory85.3 B

Variable types

Text2
Categorical5
DateTime2
Numeric1

Dataset

Description해당 파일 데이터는 신용보증기금의 보험청약접수상담관계에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093278/fileData.do

Alerts

상담원장역할관계코드 has constant value ""Constant
이력일련번호 has constant value ""Constant
관련금액 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
업무구분코드 is highly imbalanced (97.9%)Imbalance
최종수정수 is highly imbalanced (76.7%)Imbalance

Reproduction

Analysis started2023-12-12 14:29:15.849941
Analysis finished2023-12-12 14:29:16.533883
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상담ID
Text

Distinct493
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:29:16.740117image/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

Unique486 ?
Unique (%)97.2%

Sample

1st row9dnUdPUmC5
2nd row9dnKeoQ51w
3rd row9dnUdfmQiM
4th row9dnUcQglnN
5th row9dnTcb0H7K
ValueCountFrequency (%)
9dnmgzdqmb 2
 
0.4%
9dnlapbvgn 2
 
0.4%
9dnsgzgxmd 2
 
0.4%
9dnng0huqf 2
 
0.4%
9dnorne09l 2
 
0.4%
9dnlblku7i 2
 
0.4%
9dnlyc5cqi 2
 
0.4%
9dnmgzsnku 1
 
0.2%
9dnmkftog3 1
 
0.2%
9dnmfnnswe 1
 
0.2%
Other values (483) 483
96.6%
2023-12-12T23:29:17.152108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 551
 
11.0%
9 539
 
10.8%
n 536
 
10.7%
M 179
 
3.6%
S 155
 
3.1%
L 154
 
3.1%
O 132
 
2.6%
K 83
 
1.7%
N 69
 
1.4%
q 67
 
1.3%
Other values (52) 2535
50.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2238
44.8%
Uppercase Letter 1745
34.9%
Decimal Number 1017
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 551
24.6%
n 536
23.9%
q 67
 
3.0%
c 62
 
2.8%
b 60
 
2.7%
r 58
 
2.6%
a 57
 
2.5%
m 55
 
2.5%
x 53
 
2.4%
v 49
 
2.2%
Other values (16) 690
30.8%
Uppercase Letter
ValueCountFrequency (%)
M 179
 
10.3%
S 155
 
8.9%
L 154
 
8.8%
O 132
 
7.6%
K 83
 
4.8%
N 69
 
4.0%
T 63
 
3.6%
Y 60
 
3.4%
F 57
 
3.3%
B 55
 
3.2%
Other values (16) 738
42.3%
Decimal Number
ValueCountFrequency (%)
9 539
53.0%
0 58
 
5.7%
1 57
 
5.6%
8 56
 
5.5%
6 55
 
5.4%
5 55
 
5.4%
4 54
 
5.3%
2 50
 
4.9%
7 49
 
4.8%
3 44
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3983
79.7%
Common 1017
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 551
 
13.8%
n 536
 
13.5%
M 179
 
4.5%
S 155
 
3.9%
L 154
 
3.9%
O 132
 
3.3%
K 83
 
2.1%
N 69
 
1.7%
q 67
 
1.7%
T 63
 
1.6%
Other values (42) 1994
50.1%
Common
ValueCountFrequency (%)
9 539
53.0%
0 58
 
5.7%
1 57
 
5.6%
8 56
 
5.5%
6 55
 
5.4%
5 55
 
5.4%
4 54
 
5.3%
2 50
 
4.9%
7 49
 
4.8%
3 44
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 551
 
11.0%
9 539
 
10.8%
n 536
 
10.7%
M 179
 
3.6%
S 155
 
3.1%
L 154
 
3.1%
O 132
 
2.6%
K 83
 
1.7%
N 69
 
1.4%
q 67
 
1.3%
Other values (52) 2535
50.7%

상담원장역할관계코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T23:29:17.289114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:17.379184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

업무구분코드
Categorical

IMBALANCE 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
A 499
99.8%
N 1
 
0.2%

Length

2023-12-12T23:29:17.473351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:17.560852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 499
99.8%
n 1
 
0.2%

이력일련번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-12T23:29:17.662484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:17.756706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

관련금액
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

2023-12-12T23:29:17.860437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:17.974640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T23:29:18.055085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.198662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T23:29:18.302780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.395249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
481 
2
 
19

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 (%)
1 481
96.2%
2 19
 
3.8%

Length

2023-12-12T23:29:18.519029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:18.632427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 481
96.2%
2 19
 
3.8%

처리시각
Text

Distinct495
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:29:18.991195image/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 row48:35.0
2nd row38:02.2
3rd row36:37.0
4th row31:04.0
5th row30:22.0
ValueCountFrequency (%)
15:43.6 2
 
0.4%
54:54.0 2
 
0.4%
17:38.4 2
 
0.4%
44:51.5 2
 
0.4%
36:11.6 2
 
0.4%
43:10.1 1
 
0.2%
22:35.3 1
 
0.2%
37:42.6 1
 
0.2%
39:21.7 1
 
0.2%
41:58.6 1
 
0.2%
Other values (485) 485
97.0%
2023-12-12T23:29:19.885490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 345
9.9%
2 332
9.5%
1 329
9.4%
0 325
9.3%
5 296
8.5%
4 294
8.4%
9 152
 
4.3%
6 148
 
4.2%
Other values (2) 279
8.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 345
13.8%
2 332
13.3%
1 329
13.2%
0 325
13.0%
5 296
11.8%
4 294
11.8%
9 152
6.1%
6 148
5.9%
8 146
5.8%
7 133
 
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%
3 345
9.9%
2 332
9.5%
1 329
9.4%
0 325
9.3%
5 296
8.5%
4 294
8.4%
9 152
 
4.3%
6 148
 
4.2%
Other values (2) 279
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 345
9.9%
2 332
9.5%
1 329
9.4%
0 325
9.3%
5 296
8.5%
4 294
8.4%
9 152
 
4.3%
6 148
 
4.2%
Other values (2) 279
8.0%

처리직원번호
Real number (ℝ)

Distinct237
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5231.634
Minimum3071
Maximum6197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:29:20.065886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3071
5-th percentile3620
Q14944.25
median5378
Q35757.5
95-th percentile6120.05
Maximum6197
Range3126
Interquartile range (IQR)813.25

Descriptive statistics

Standard deviation688.61901
Coefficient of variation (CV)0.13162599
Kurtosis0.34579131
Mean5231.634
Median Absolute Deviation (MAD)395.5
Skewness-0.89482658
Sum2615817
Variance474196.14
MonotonicityNot monotonic
2023-12-12T23:29:20.253309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3620 19
 
3.8%
5173 17
 
3.4%
5074 11
 
2.2%
5495 11
 
2.2%
5608 7
 
1.4%
6147 7
 
1.4%
5076 7
 
1.4%
4258 7
 
1.4%
5684 7
 
1.4%
5040 7
 
1.4%
Other values (227) 400
80.0%
ValueCountFrequency (%)
3071 1
 
0.2%
3290 1
 
0.2%
3447 1
 
0.2%
3548 2
0.4%
3555 1
 
0.2%
3590 4
0.8%
3593 2
0.4%
3598 1
 
0.2%
3608 1
 
0.2%
3611 1
 
0.2%
ValueCountFrequency (%)
6197 2
 
0.4%
6195 1
 
0.2%
6192 1
 
0.2%
6183 1
 
0.2%
6179 4
0.8%
6175 3
0.6%
6147 7
1.4%
6139 2
 
0.4%
6137 1
 
0.2%
6129 2
 
0.4%

Interactions

2023-12-12T23:29:16.085456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:29:20.378104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분코드최종수정수처리직원번호
업무구분코드1.0000.1540.000
최종수정수0.1541.0000.000
처리직원번호0.0000.0001.000
2023-12-12T23:29:20.477782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최종수정수업무구분코드
최종수정수1.0000.099
업무구분코드0.0991.000
2023-12-12T23:29:20.576805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호업무구분코드최종수정수
처리직원번호1.0000.0000.000
업무구분코드0.0001.0000.099
최종수정수0.0000.0991.000

Missing values

2023-12-12T23:29:16.288575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:29:16.468329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

상담ID상담원장역할관계코드업무구분코드이력일련번호관련금액유효개시일자유효종료일자최종수정수처리시각처리직원번호
09dnUdPUmC51A1000:00.000:00.0148:35.05928
19dnKeoQ51w1A1000:00.000:00.0138:02.26065
29dnUdfmQiM1A1000:00.000:00.0136:37.05460
39dnUcQglnN1A1000:00.000:00.0131:04.06088
49dnTcb0H7K1A1000:00.000:00.0130:22.04679
59dnUcliwGu1A1000:00.000:00.0122:44.14877
69dnUb13qfF1A1000:00.000:00.0119:57.05900
79dnTfrPBTd1A1000:00.000:00.0126:00.95074
89dnS8IypEV1A1000:00.000:00.0117:10.96139
99dnSNLV1Aa1N1000:00.000:00.0201:07.65406
상담ID상담원장역할관계코드업무구분코드이력일련번호관련금액유효개시일자유효종료일자최종수정수처리시각처리직원번호
4909dnKgJeMOH1A1000:00.000:00.0154:09.36179
4919dnKfOx8K21A1000:00.000:00.0131:12.25928
4929dnJ3Fp6oF1A1000:00.000:00.0107:01.06058
4939dnKdMSDtG1A1000:00.000:00.0104:02.05237
4949dnKdsb7TK1A1000:00.000:00.0153:30.05112
4959dnKc9I8wu1A1000:00.000:00.0151:33.25408
4969dnKcTWOye1A1000:00.000:00.0146:03.06018
4979dnKcsqzn71A1000:00.000:00.0139:53.45093
4989dnKci9cM21A1000:00.000:00.0136:16.44565
4999dnKbClrdF1A1000:00.000:00.0135:09.66179