Overview

Dataset statistics

Number of variables5
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.1 KiB
Average record size in memory43.3 B

Variable types

Text2
Numeric3

Dataset

Description해당 파일 데이터는 신용보증기금의 보증 투자 상담 관계에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093226/fileData.do

Alerts

이력일련번호 is highly overall correlated with 최종수정수High correlation
최종수정수 is highly overall correlated with 이력일련번호High correlation

Reproduction

Analysis started2023-12-12 00:37:01.593532
Analysis finished2023-12-12 00:37:02.848053
Duration1.25 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

투자상담아이디
Text

Distinct173
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:37:03.002288image/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

Unique80 ?
Unique (%)16.0%

Sample

1st row9dnDG8YFss
2nd row9dnDG8YFss
3rd row9dnDG8YFss
4th row9dnDG8YFss
5th row9dnDC6WRiT
ValueCountFrequency (%)
9djibxn3jg 27
 
5.4%
9dikqpunfn 18
 
3.6%
9degwm5riu 17
 
3.4%
9c0oeiemgi 16
 
3.2%
9cswqbgpa9 14
 
2.8%
9deohakhoc 11
 
2.2%
9da1hxi92r 10
 
2.0%
9diup8ifew 10
 
2.0%
9dfg33a3dw 9
 
1.8%
9dbrpcceen 9
 
1.8%
Other values (163) 359
71.8%
2023-12-12T09:37:03.325934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 566
 
11.3%
c 329
 
6.6%
d 284
 
5.7%
N 136
 
2.7%
e 132
 
2.6%
i 118
 
2.4%
j 110
 
2.2%
3 102
 
2.0%
R 97
 
1.9%
b 96
 
1.9%
Other values (52) 3030
60.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2116
42.3%
Uppercase Letter 1688
33.8%
Decimal Number 1196
23.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 329
15.5%
d 284
 
13.4%
e 132
 
6.2%
i 118
 
5.6%
j 110
 
5.2%
b 96
 
4.5%
g 84
 
4.0%
p 84
 
4.0%
w 78
 
3.7%
h 77
 
3.6%
Other values (16) 724
34.2%
Uppercase Letter
ValueCountFrequency (%)
N 136
 
8.1%
R 97
 
5.7%
M 87
 
5.2%
I 87
 
5.2%
W 86
 
5.1%
X 85
 
5.0%
Q 80
 
4.7%
V 78
 
4.6%
B 75
 
4.4%
U 74
 
4.4%
Other values (16) 803
47.6%
Decimal Number
ValueCountFrequency (%)
9 566
47.3%
3 102
 
8.5%
5 91
 
7.6%
1 90
 
7.5%
2 86
 
7.2%
0 68
 
5.7%
6 55
 
4.6%
4 55
 
4.6%
8 51
 
4.3%
7 32
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 3804
76.1%
Common 1196
 
23.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 329
 
8.6%
d 284
 
7.5%
N 136
 
3.6%
e 132
 
3.5%
i 118
 
3.1%
j 110
 
2.9%
R 97
 
2.5%
b 96
 
2.5%
M 87
 
2.3%
I 87
 
2.3%
Other values (42) 2328
61.2%
Common
ValueCountFrequency (%)
9 566
47.3%
3 102
 
8.5%
5 91
 
7.6%
1 90
 
7.5%
2 86
 
7.2%
0 68
 
5.7%
6 55
 
4.6%
4 55
 
4.6%
8 51
 
4.3%
7 32
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 566
 
11.3%
c 329
 
6.6%
d 284
 
5.7%
N 136
 
2.7%
e 132
 
2.6%
i 118
 
2.4%
j 110
 
2.2%
3 102
 
2.0%
R 97
 
1.9%
b 96
 
1.9%
Other values (52) 3030
60.6%

보증상담아이디
Text

Distinct195
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:37:03.732512image/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

Unique107 ?
Unique (%)21.4%

Sample

1st row9cXswMumF2
2nd row9dl8Orgg24
3rd row9cXswMumF2
4th row9dl8Orgg24
5th row9dl8Orgg24
ValueCountFrequency (%)
9c5mw3mph1 17
 
3.4%
9dd4joetwj 11
 
2.2%
9c1ubql8jk 10
 
2.0%
9dagqkjrnr 10
 
2.0%
9dl8orgg24 9
 
1.8%
9c8ilclard 9
 
1.8%
9djuh7lelz 9
 
1.8%
9dfmvtdskt 9
 
1.8%
9djrwe5iup 9
 
1.8%
9djrv76yc7 9
 
1.8%
Other values (185) 398
79.6%
2023-12-12T09:37:04.018851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 562
 
11.2%
c 435
 
8.7%
d 213
 
4.3%
1 117
 
2.3%
r 113
 
2.3%
R 103
 
2.1%
Z 100
 
2.0%
t 97
 
1.9%
f 94
 
1.9%
l 88
 
1.8%
Other values (52) 3078
61.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2171
43.4%
Uppercase Letter 1540
30.8%
Decimal Number 1289
25.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 435
20.0%
d 213
 
9.8%
r 113
 
5.2%
t 97
 
4.5%
f 94
 
4.3%
l 88
 
4.1%
g 83
 
3.8%
j 81
 
3.7%
w 76
 
3.5%
n 71
 
3.3%
Other values (16) 820
37.8%
Uppercase Letter
ValueCountFrequency (%)
R 103
 
6.7%
Z 100
 
6.5%
J 87
 
5.6%
M 86
 
5.6%
L 83
 
5.4%
I 79
 
5.1%
Q 76
 
4.9%
E 63
 
4.1%
U 63
 
4.1%
O 60
 
3.9%
Other values (16) 740
48.1%
Decimal Number
ValueCountFrequency (%)
9 562
43.6%
1 117
 
9.1%
5 88
 
6.8%
7 87
 
6.7%
6 84
 
6.5%
8 77
 
6.0%
0 75
 
5.8%
4 73
 
5.7%
2 64
 
5.0%
3 62
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 3711
74.2%
Common 1289
 
25.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 435
 
11.7%
d 213
 
5.7%
r 113
 
3.0%
R 103
 
2.8%
Z 100
 
2.7%
t 97
 
2.6%
f 94
 
2.5%
l 88
 
2.4%
J 87
 
2.3%
M 86
 
2.3%
Other values (42) 2295
61.8%
Common
ValueCountFrequency (%)
9 562
43.6%
1 117
 
9.1%
5 88
 
6.8%
7 87
 
6.7%
6 84
 
6.5%
8 77
 
6.0%
0 75
 
5.8%
4 73
 
5.7%
2 64
 
5.0%
3 62
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 562
 
11.2%
c 435
 
8.7%
d 213
 
4.3%
1 117
 
2.3%
r 113
 
2.3%
R 103
 
2.1%
Z 100
 
2.0%
t 97
 
1.9%
f 94
 
1.9%
l 88
 
1.8%
Other values (52) 3078
61.6%

이력일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.982
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:37:04.130210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile15
Maximum33
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.3683624
Coefficient of variation (CV)1.0775517
Kurtosis3.7307745
Mean4.982
Median Absolute Deviation (MAD)2
Skewness1.7788619
Sum2491
Variance28.819315
MonotonicityNot monotonic
2023-12-12T09:37:04.249527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 211
42.2%
3 81
 
16.2%
5 50
 
10.0%
7 35
 
7.0%
9 30
 
6.0%
11 23
 
4.6%
13 20
 
4.0%
15 18
 
3.6%
17 12
 
2.4%
2 9
 
1.8%
Other values (8) 11
 
2.2%
ValueCountFrequency (%)
1 211
42.2%
2 9
 
1.8%
3 81
 
16.2%
5 50
 
10.0%
7 35
 
7.0%
9 30
 
6.0%
11 23
 
4.6%
13 20
 
4.0%
15 18
 
3.6%
17 12
 
2.4%
ValueCountFrequency (%)
33 1
 
0.2%
31 1
 
0.2%
29 1
 
0.2%
27 1
 
0.2%
25 1
 
0.2%
23 1
 
0.2%
21 2
 
0.4%
19 3
 
0.6%
17 12
2.4%
15 18
3.6%

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.618
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:37:04.363453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile16
Maximum33
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5020561
Coefficient of variation (CV)0.97936207
Kurtosis3.2714051
Mean5.618
Median Absolute Deviation (MAD)3
Skewness1.6755177
Sum2809
Variance30.272621
MonotonicityNot monotonic
2023-12-12T09:37:04.478747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2 110
22.0%
1 109
21.8%
4 52
10.4%
6 39
 
7.8%
8 31
 
6.2%
3 30
 
6.0%
10 25
 
5.0%
12 20
 
4.0%
14 19
 
3.8%
16 13
 
2.6%
Other values (18) 52
10.4%
ValueCountFrequency (%)
1 109
21.8%
2 110
22.0%
3 30
 
6.0%
4 52
10.4%
5 11
 
2.2%
6 39
 
7.8%
7 4
 
0.8%
8 31
 
6.2%
9 5
 
1.0%
10 25
 
5.0%
ValueCountFrequency (%)
33 1
0.2%
32 1
0.2%
30 1
0.2%
28 1
0.2%
26 1
0.2%
24 1
0.2%
22 1
0.2%
21 1
0.2%
20 2
0.4%
19 1
0.2%

처리직원번호
Real number (ℝ)

Distinct116
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4725.938
Minimum2790
Maximum5933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:37:04.593758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2790
5-th percentile3670
Q14304
median4522
Q35259
95-th percentile5761
Maximum5933
Range3143
Interquartile range (IQR)955

Descriptive statistics

Standard deviation659.7005
Coefficient of variation (CV)0.13959144
Kurtosis-0.45322759
Mean4725.938
Median Absolute Deviation (MAD)432
Skewness-0.083174098
Sum2362969
Variance435204.76
MonotonicityNot monotonic
2023-12-12T09:37:04.724679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4304 27
 
5.4%
5236 24
 
4.8%
5761 20
 
4.0%
5583 18
 
3.6%
4360 14
 
2.8%
4050 14
 
2.8%
4417 13
 
2.6%
4389 13
 
2.6%
4334 12
 
2.4%
5756 12
 
2.4%
Other values (106) 333
66.6%
ValueCountFrequency (%)
2790 5
1.0%
3253 3
0.6%
3335 2
 
0.4%
3398 2
 
0.4%
3401 1
 
0.2%
3424 2
 
0.4%
3427 1
 
0.2%
3485 1
 
0.2%
3526 1
 
0.2%
3557 1
 
0.2%
ValueCountFrequency (%)
5933 1
 
0.2%
5868 11
2.2%
5761 20
4.0%
5756 12
2.4%
5712 8
 
1.6%
5642 8
 
1.6%
5583 18
3.6%
5578 3
 
0.6%
5569 3
 
0.6%
5477 4
 
0.8%

Interactions

2023-12-12T09:37:02.478606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:01.834142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.178173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.556426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:01.958213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.276595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.631538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.089769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:37:02.389061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:37:04.810808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수처리직원번호
이력일련번호1.0000.9640.172
최종수정수0.9641.0000.206
처리직원번호0.1720.2061.000
2023-12-12T09:37:04.908076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호최종수정수처리직원번호
이력일련번호1.0000.6610.318
최종수정수0.6611.0000.367
처리직원번호0.3180.3671.000

Missing values

2023-12-12T09:37:02.734251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:37:02.814089image/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

투자상담아이디보증상담아이디이력일련번호최종수정수처리직원번호
09dnDG8YFss9cXswMumF2135868
19dnDG8YFss9dl8Orgg24325868
29dnDG8YFss9cXswMumF2325868
39dnDG8YFss9dl8Orgg24135868
49dnDC6WRiT9dl8Orgg241145868
59dnDC6WRiT9dl8Orgg2413125868
69dnDC6WRiT9dl8Orgg2411105868
79dnDC6WRiT9dl8Orgg24985868
89dnDC6WRiT9dl8Orgg24765868
99dnDC6WRiT9dl8Orgg24545868
투자상담아이디보증상담아이디이력일련번호최종수정수처리직원번호
4909cJKsyOlF69cJKttuOWS322790
4919cJKsyOlF69cJKttuOWS212790
4929cJWA0ihaW9cJKttuOWS122790
4939cJWjnbMzN9cJynRkpOz114403
4949cJVupBNA29cJA27Qwx2114161
4959cJTzjdRhE9cByaPe0zV114132
4969cJTzjdRhE9csY9axL6g114132
4979cJTzjdRhE9cJTueLRsL114132
4989cJTzjdRhE9cJTt9jROL114132
4999cJQDQdd749cIx7sDaj8124609