Overview

Dataset statistics

Number of variables11
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory93.9 B

Variable types

Categorical7
DateTime2
Text2

Dataset

Description해당 파일 데이터는 신용보증기금의 기타 감리개선 현황정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093066/fileData.do

Alerts

유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
조치시작일자 is highly overall correlated with 조치종료일자 and 2 other fieldsHigh correlation
조치종료일자 is highly overall correlated with 조치시작일자 and 2 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
진행율 is highly overall correlated with 조치시작일자 and 1 other fieldsHigh correlation
최종수정수 is highly overall correlated with 조치시작일자 and 1 other fieldsHigh correlation
이력일련번호 is highly imbalanced (59.1%)Imbalance
진행율 is highly imbalanced (81.0%)Imbalance
최종수정수 is highly imbalanced (59.1%)Imbalance
처리시각 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:53:13.394738
Analysis finished2023-12-12 18:53:14.895679
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

이력일련번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
130 
2
17 
3
 
3

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 (%)
1 130
86.7%
2 17
 
11.3%
3 3
 
2.0%

Length

2023-12-13T03:53:15.029794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:15.231558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 130
86.7%
2 17
 
11.3%
3 3
 
2.0%

조치시작일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0001-01-01 00:00:00.000000
133 
00:00.0
17 

Length

Max length26
Median length26
Mean length23.846667
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 133
88.7%
00:00.0 17
 
11.3%

Length

2023-12-13T03:53:15.476560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:15.680113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 133
47.0%
00:00:00.000000 133
47.0%
00:00.0 17
 
6.0%

조치종료일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0001-01-01 00:00:00.000000
133 
00:00.0
17 

Length

Max length26
Median length26
Mean length23.846667
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 133
88.7%
00:00.0 17
 
11.3%

Length

2023-12-13T03:53:15.874450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:16.077961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 133
47.0%
00:00:00.000000 133
47.0%
00:00.0 17
 
6.0%

진행율
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
142 
90
 
4
100
 
2
50
 
2

Length

Max length3
Median length1
Mean length1.0666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 142
94.7%
90 4
 
2.7%
100 2
 
1.3%
50 2
 
1.3%

Length

2023-12-13T03:53:16.303735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:16.537734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 142
94.7%
90 4
 
2.7%
100 2
 
1.3%
50 2
 
1.3%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T03:53:16.721709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:16.916282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T03:53:17.145539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:53:17.346152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
130 
2
17 
3
 
3

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 (%)
1 130
86.7%
2 17
 
11.3%
3 3
 
2.0%

Length

2023-12-13T03:53:17.600358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:17.795406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 130
86.7%
2 17
 
11.3%
3 3
 
2.0%

처리시각
Text

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:53:18.399786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1050
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

Unique150 ?
Unique (%)100.0%

Sample

1st row07:23.7
2nd row06:58.6
3rd row35:48.8
4th row53:50.9
5th row53:56.5
ValueCountFrequency (%)
07:23.7 1
 
0.7%
25:33.5 1
 
0.7%
22:13.9 1
 
0.7%
19:28.7 1
 
0.7%
29:38.6 1
 
0.7%
56:58.2 1
 
0.7%
22:38.0 1
 
0.7%
14:12.7 1
 
0.7%
31:08.2 1
 
0.7%
09:35.1 1
 
0.7%
Other values (140) 140
93.3%
2023-12-13T03:53:19.323608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 117
11.1%
4 100
9.5%
1 97
9.2%
3 92
8.8%
2 86
8.2%
0 77
7.3%
8 59
 
5.6%
9 45
 
4.3%
Other values (2) 77
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
71.4%
Other Punctuation 300
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 117
15.6%
4 100
13.3%
1 97
12.9%
3 92
12.3%
2 86
11.5%
0 77
10.3%
8 59
7.9%
9 45
 
6.0%
7 39
 
5.2%
6 38
 
5.1%
Other Punctuation
ValueCountFrequency (%)
: 150
50.0%
. 150
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 117
11.1%
4 100
9.5%
1 97
9.2%
3 92
8.8%
2 86
8.2%
0 77
7.3%
8 59
 
5.6%
9 45
 
4.3%
Other values (2) 77
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 117
11.1%
4 100
9.5%
1 97
9.2%
3 92
8.8%
2 86
8.2%
0 77
7.3%
8 59
 
5.6%
9 45
 
4.3%
Other values (2) 77
7.3%

처리직원번호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
3559
84 
4042
50 
4597
15 
4001
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row4597
2nd row4597
3rd row4597
4th row4597
5th row4597

Common Values

ValueCountFrequency (%)
3559 84
56.0%
4042 50
33.3%
4597 15
 
10.0%
4001 1
 
0.7%

Length

2023-12-13T03:53:19.602013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:19.798106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3559 84
56.0%
4042 50
33.3%
4597 15
 
10.0%
4001 1
 
0.7%

최초처리시각
Text

Distinct130
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T03:53:20.359254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1050
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

Unique113 ?
Unique (%)75.3%

Sample

1st row07:23.7
2nd row06:58.6
3rd row35:48.8
4th row53:50.9
5th row53:56.5
ValueCountFrequency (%)
55:31.8 3
 
2.0%
57:42.5 3
 
2.0%
26:32.1 3
 
2.0%
22:26.1 2
 
1.3%
24:44.3 2
 
1.3%
08:35.9 2
 
1.3%
52:46.8 2
 
1.3%
12:39.4 2
 
1.3%
10:25.8 2
 
1.3%
13:41.7 2
 
1.3%
Other values (120) 127
84.7%
2023-12-13T03:53:21.209002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 119
11.3%
4 102
9.7%
1 96
9.1%
3 93
8.9%
2 89
8.5%
0 76
7.2%
8 59
 
5.6%
6 41
 
3.9%
Other values (2) 75
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
71.4%
Other Punctuation 300
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 119
15.9%
4 102
13.6%
1 96
12.8%
3 93
12.4%
2 89
11.9%
0 76
10.1%
8 59
7.9%
6 41
 
5.5%
9 40
 
5.3%
7 35
 
4.7%
Other Punctuation
ValueCountFrequency (%)
: 150
50.0%
. 150
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1050
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 119
11.3%
4 102
9.7%
1 96
9.1%
3 93
8.9%
2 89
8.5%
0 76
7.2%
8 59
 
5.6%
6 41
 
3.9%
Other values (2) 75
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 150
14.3%
. 150
14.3%
5 119
11.3%
4 102
9.7%
1 96
9.1%
3 93
8.9%
2 89
8.5%
0 76
7.2%
8 59
 
5.6%
6 41
 
3.9%
Other values (2) 75
7.1%

최초처리직원번호
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
3559
84 
4042
50 
4597
15 
4001
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row4597
2nd row4597
3rd row4597
4th row4597
5th row4597

Common Values

ValueCountFrequency (%)
3559 84
56.0%
4042 50
33.3%
4597 15
 
10.0%
4001 1
 
0.7%

Length

2023-12-13T03:53:21.448864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:53:21.658075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3559 84
56.0%
4042 50
33.3%
4597 15
 
10.0%
4001 1
 
0.7%

Correlations

2023-12-13T03:53:21.798084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호조치시작일자조치종료일자진행율최종수정수처리직원번호최초처리직원번호
이력일련번호1.0000.0000.0000.0000.6030.2140.214
조치시작일자0.0001.0000.9990.7890.3330.4260.426
조치종료일자0.0000.9991.0000.7890.3330.4260.426
진행율0.0000.7890.7891.0000.2300.0000.000
최종수정수0.6030.3330.3330.2301.0000.2140.214
처리직원번호0.2140.4260.4260.0000.2141.0001.000
최초처리직원번호0.2140.4260.4260.0000.2141.0001.000
2023-12-13T03:53:22.013040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초처리직원번호조치시작일자이력일련번호조치종료일자진행율처리직원번호최종수정수
최초처리직원번호1.0000.2840.2020.2840.0001.0000.202
조치시작일자0.2841.0000.0000.9670.5760.2840.533
이력일련번호0.2020.0001.0000.0000.0000.2020.274
조치종료일자0.2840.9670.0001.0000.5760.2840.533
진행율0.0000.5760.0000.5761.0000.0000.218
처리직원번호1.0000.2840.2020.2840.0001.0000.202
최종수정수0.2020.5330.2740.5330.2180.2021.000
2023-12-13T03:53:22.212331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호조치시작일자조치종료일자진행율최종수정수처리직원번호최초처리직원번호
이력일련번호1.0000.0000.0000.0000.2740.2020.202
조치시작일자0.0001.0000.9670.5760.5330.2840.284
조치종료일자0.0000.9671.0000.5760.5330.2840.284
진행율0.0000.5760.5761.0000.2180.0000.000
최종수정수0.2740.5330.5330.2181.0000.2020.202
처리직원번호0.2020.2840.2840.0000.2021.0001.000
최초처리직원번호0.2020.2840.2840.0000.2021.0001.000

Missing values

2023-12-13T03:53:14.436518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:53:14.778109image/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

이력일련번호조치시작일자조치종료일자진행율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0107:23.7459707:23.74597
110001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0106:58.6459706:58.64597
210001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0135:48.8459735:48.84597
310001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0153:50.9459753:50.94597
410001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0153:56.5459753:56.54597
510001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0142:08.9459742:08.94597
610001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0119:25.1459719:25.14597
710001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0139:17.6459739:17.64597
810001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0156:47.3459756:47.34597
910001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0156:17.4459756:17.44597
이력일련번호조치시작일자조치종료일자진행율유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
14020001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0120:35.2355920:35.23559
14110001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0118:06.6355918:06.63559
14210001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0117:02.8355917:02.83559
14320001-01-01 00:00:00.0000000001-01-01 00:00:00.000000000:00.000:00.0112:39.4355912:39.43559
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