Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.2 KiB
Average record size in memory76.3 B

Variable types

Text3
Numeric3
Boolean2
Categorical1

Dataset

Description해당 파일 데이터는 신용보증기금의 전자결재 집단 결재정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092964/fileData.do

Alerts

삭제여부 has constant value ""Constant
최종수정수 has constant value ""Constant
참여인원수 is highly overall correlated with 처리직원번호 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 참여인원수 and 1 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 참여인원수 and 1 other fieldsHigh correlation
가결여부 is highly imbalanced (77.6%)Imbalance
전자결재ID has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:59:51.134449
Analysis finished2023-12-12 22:59:52.649137
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전자결재ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T07:59:52.829083image/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 row9dnUh8DfQ7
2nd row9dnS9zeRzI
3rd row9dnS9GI9g1
4th row9dnS7BomGH
5th row9dnS1lMCjm
ValueCountFrequency (%)
9dnuh8dfq7 1
 
0.2%
9dnlc3pe3i 1
 
0.2%
9dng82mzuh 1
 
0.2%
9dng9que1v 1
 
0.2%
9dnglujyn9 1
 
0.2%
9dnfdkwrai 1
 
0.2%
9dnlkjbeh6 1
 
0.2%
9dnlugovfy 1
 
0.2%
9dnlkfgacx 1
 
0.2%
9dnlwju84m 1
 
0.2%
Other values (490) 490
98.0%
2023-12-13T07:59:53.182240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 558
 
11.2%
d 544
 
10.9%
n 385
 
7.7%
m 252
 
5.0%
o 103
 
2.1%
l 93
 
1.9%
5 84
 
1.7%
1 82
 
1.6%
4 82
 
1.6%
A 77
 
1.5%
Other values (52) 2740
54.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2442
48.8%
Uppercase Letter 1384
27.7%
Decimal Number 1174
23.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 544
22.3%
n 385
15.8%
m 252
 
10.3%
o 103
 
4.2%
l 93
 
3.8%
e 64
 
2.6%
z 62
 
2.5%
s 61
 
2.5%
t 58
 
2.4%
g 57
 
2.3%
Other values (16) 763
31.2%
Uppercase Letter
ValueCountFrequency (%)
A 77
 
5.6%
L 65
 
4.7%
M 64
 
4.6%
S 62
 
4.5%
D 60
 
4.3%
O 59
 
4.3%
J 59
 
4.3%
B 59
 
4.3%
C 57
 
4.1%
U 56
 
4.0%
Other values (16) 766
55.3%
Decimal Number
ValueCountFrequency (%)
9 558
47.5%
5 84
 
7.2%
1 82
 
7.0%
4 82
 
7.0%
6 73
 
6.2%
0 71
 
6.0%
2 68
 
5.8%
7 59
 
5.0%
3 55
 
4.7%
8 42
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 3826
76.5%
Common 1174
 
23.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 544
 
14.2%
n 385
 
10.1%
m 252
 
6.6%
o 103
 
2.7%
l 93
 
2.4%
A 77
 
2.0%
L 65
 
1.7%
M 64
 
1.7%
e 64
 
1.7%
z 62
 
1.6%
Other values (42) 2117
55.3%
Common
ValueCountFrequency (%)
9 558
47.5%
5 84
 
7.2%
1 82
 
7.0%
4 82
 
7.0%
6 73
 
6.2%
0 71
 
6.0%
2 68
 
5.8%
7 59
 
5.0%
3 55
 
4.7%
8 42
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 558
 
11.2%
d 544
 
10.9%
n 385
 
7.7%
m 252
 
5.0%
o 103
 
2.1%
l 93
 
1.9%
5 84
 
1.7%
1 82
 
1.6%
4 82
 
1.6%
A 77
 
1.5%
Other values (52) 2740
54.8%

참여인원수
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.786
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T07:59:53.305651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5339198
Coefficient of variation (CV)0.55058139
Kurtosis0.20872921
Mean2.786
Median Absolute Deviation (MAD)1
Skewness1.166074
Sum1393
Variance2.3529098
MonotonicityNot monotonic
2023-12-13T07:59:53.408296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 243
48.6%
3 89
 
17.8%
6 75
 
15.0%
1 61
 
12.2%
4 31
 
6.2%
5 1
 
0.2%
ValueCountFrequency (%)
1 61
 
12.2%
2 243
48.6%
3 89
 
17.8%
4 31
 
6.2%
5 1
 
0.2%
6 75
 
15.0%
ValueCountFrequency (%)
6 75
 
15.0%
5 1
 
0.2%
4 31
 
6.2%
3 89
 
17.8%
2 243
48.6%
1 61
 
12.2%

가결여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
482 
False
 
18
ValueCountFrequency (%)
True 482
96.4%
False 18
 
3.6%
2023-12-13T07:59:53.506770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-13T07:59:53.573286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
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

2023-12-13T07:59:53.665750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:59:53.764287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

처리시각
Text

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T07:59:54.033307image/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

Unique496 ?
Unique (%)99.2%

Sample

1st row58:26.6
2nd row38:25.2
3rd row34:15.2
4th row36:16.6
5th row34:25.4
ValueCountFrequency (%)
18:31.9 2
 
0.4%
33:20.2 2
 
0.4%
28:19.8 1
 
0.2%
26:27.1 1
 
0.2%
58:26.6 1
 
0.2%
23:46.5 1
 
0.2%
45:07.8 1
 
0.2%
14:20.0 1
 
0.2%
38:13.3 1
 
0.2%
09:44.4 1
 
0.2%
Other values (488) 488
97.6%
2023-12-13T07:59:54.449306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 334
13.4%
5 331
13.2%
2 323
12.9%
4 317
12.7%
0 305
12.2%
1 302
12.1%
6 161
6.4%
9 158
6.3%
8 137
5.5%
7 132
 
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%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3750.99
Minimum1886
Maximum5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T07:59:54.594087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1886
5-th percentile3150.25
Q13353
median3708
Q34058.25
95-th percentile4577.35
Maximum5848
Range3962
Interquartile range (IQR)705.25

Descriptive statistics

Standard deviation485.4212
Coefficient of variation (CV)0.12941149
Kurtosis0.89917525
Mean3750.99
Median Absolute Deviation (MAD)351
Skewness0.56760428
Sum1875495
Variance235633.74
MonotonicityNot monotonic
2023-12-13T07:59:54.738476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3264 74
 
14.8%
3564 13
 
2.6%
3857 11
 
2.2%
3485 9
 
1.8%
3415 7
 
1.4%
3368 6
 
1.2%
3353 6
 
1.2%
3504 6
 
1.2%
3550 5
 
1.0%
3423 5
 
1.0%
Other values (189) 358
71.6%
ValueCountFrequency (%)
1886 1
 
0.2%
2710 2
0.4%
2738 2
0.4%
2908 1
 
0.2%
2962 2
0.4%
3038 4
0.8%
3044 3
0.6%
3059 4
0.8%
3060 1
 
0.2%
3078 1
 
0.2%
ValueCountFrequency (%)
5848 1
0.2%
5596 1
0.2%
5389 1
0.2%
5093 2
0.4%
5073 1
0.2%
5030 1
0.2%
5025 1
0.2%
4872 1
0.2%
4870 1
0.2%
4755 2
0.4%

최초처리시각
Text

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T07:59:55.092077image/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

Unique496 ?
Unique (%)99.2%

Sample

1st row58:26.6
2nd row38:25.2
3rd row34:15.2
4th row36:16.6
5th row34:25.4
ValueCountFrequency (%)
18:31.9 2
 
0.4%
33:20.2 2
 
0.4%
28:19.8 1
 
0.2%
26:27.1 1
 
0.2%
58:26.6 1
 
0.2%
23:46.5 1
 
0.2%
45:07.8 1
 
0.2%
14:20.0 1
 
0.2%
38:13.3 1
 
0.2%
09:44.4 1
 
0.2%
Other values (488) 488
97.6%
2023-12-13T07:59:55.574861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 334
13.4%
5 331
13.2%
2 323
12.9%
4 317
12.7%
0 305
12.2%
1 302
12.1%
6 161
6.4%
9 158
6.3%
8 137
5.5%
7 132
 
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%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 334
9.5%
5 331
9.5%
2 323
9.2%
4 317
9.1%
0 305
8.7%
1 302
8.6%
6 161
 
4.6%
9 158
 
4.5%
Other values (2) 269
7.7%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3750.99
Minimum1886
Maximum5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T07:59:55.730869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1886
5-th percentile3150.25
Q13353
median3708
Q34058.25
95-th percentile4577.35
Maximum5848
Range3962
Interquartile range (IQR)705.25

Descriptive statistics

Standard deviation485.4212
Coefficient of variation (CV)0.12941149
Kurtosis0.89917525
Mean3750.99
Median Absolute Deviation (MAD)351
Skewness0.56760428
Sum1875495
Variance235633.74
MonotonicityNot monotonic
2023-12-13T07:59:55.906122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3264 74
 
14.8%
3564 13
 
2.6%
3857 11
 
2.2%
3485 9
 
1.8%
3415 7
 
1.4%
3368 6
 
1.2%
3353 6
 
1.2%
3504 6
 
1.2%
3550 5
 
1.0%
3423 5
 
1.0%
Other values (189) 358
71.6%
ValueCountFrequency (%)
1886 1
 
0.2%
2710 2
0.4%
2738 2
0.4%
2908 1
 
0.2%
2962 2
0.4%
3038 4
0.8%
3044 3
0.6%
3059 4
0.8%
3060 1
 
0.2%
3078 1
 
0.2%
ValueCountFrequency (%)
5848 1
0.2%
5596 1
0.2%
5389 1
0.2%
5093 2
0.4%
5073 1
0.2%
5030 1
0.2%
5025 1
0.2%
4872 1
0.2%
4870 1
0.2%
4755 2
0.4%

Interactions

2023-12-13T07:59:52.140605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:51.601644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:51.854647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:52.232534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:51.672048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:51.949656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:52.361932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:51.756077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:59:52.050623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:59:56.006757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참여인원수가결여부처리직원번호최초처리직원번호
참여인원수1.0000.0880.5700.570
가결여부0.0881.0000.0000.000
처리직원번호0.5700.0001.0001.000
최초처리직원번호0.5700.0001.0001.000
2023-12-13T07:59:56.097196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참여인원수처리직원번호최초처리직원번호가결여부
참여인원수1.000-0.515-0.5150.063
처리직원번호-0.5151.0001.0000.000
최초처리직원번호-0.5151.0001.0000.000
가결여부0.0630.0000.0001.000

Missing values

2023-12-13T07:59:52.482208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:59:52.603414image/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

전자결재ID참여인원수가결여부삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnUh8DfQ72YN158:26.6447558:26.64475
19dnS9zeRzI2YN138:25.2271038:25.22710
29dnS9GI9g14YN134:15.2390634:15.23906
39dnS7BomGH3YN136:16.6365036:16.63650
49dnS1lMCjm3YN134:25.4357934:25.43579
59dnS3Yj0Z73YN133:03.7357933:03.73579
69dnS6hSdSI4YN128:10.4371828:10.43718
79dnS0wjt9d2YN108:15.9403908:15.94039
89dnSNZcZLV3YN131:16.3348531:16.33485
99dnS17DmYf2YN103:36.0422303:36.04223
전자결재ID참여인원수가결여부삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dmQ1fgSq63YN159:32.8348559:32.83485
4919dmRaZzSlY3YN157:34.3348557:34.33485
4929dmQ0WXU3u3YN157:13.6348557:13.63485
4939dm0i1xLlv3YN150:43.2377650:43.23776
4949dm0gUGpzx2YN144:34.1339744:34.13397
4959dm0iPaCIk1YN134:47.0188634:47.01886
4969dm0htjykD2YN116:29.7396016:29.73960
4979dmZ5EnpwQ1YN108:54.7296208:54.72962
4989dmZ0xK4Dj1YN136:05.3402436:05.34024
4999dmZ0AQq9a2YN154:44.1342354:44.13423