Overview

Dataset statistics

Number of variables12
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory101.9 B

Variable types

Categorical5
Numeric1
DateTime4
Text2

Dataset

Description해당 파일 데이터는 신용보증기금의 기타 감리대상 사업관리정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093067/fileData.do

Alerts

사업시작일자 has constant value ""Constant
사업종료일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
처리직원번호 is highly overall correlated with 감리년도 and 1 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 감리년도 and 1 other fieldsHigh correlation
감리년도 is highly overall correlated with 처리직원번호 and 1 other fieldsHigh correlation
이력일련번호 is highly imbalanced (59.1%)Imbalance
최종수정수 is highly imbalanced (59.1%)Imbalance
처리시각 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:13:11.989842
Analysis finished2023-12-12 05:13:12.765977
Duration0.78 seconds
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

2023-12-12T14:13:12.828667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:12.922694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 130
86.7%
2 17
 
11.3%
3 3
 
2.0%

감리년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014
Minimum2012
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:13:13.019133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12012
median2012
Q32016
95-th percentile2020
Maximum2021
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7660453
Coefficient of variation (CV)0.0013734088
Kurtosis-0.15800354
Mean2014
Median Absolute Deviation (MAD)0
Skewness1.1223793
Sum302100
Variance7.6510067
MonotonicityDecreasing
2023-12-12T14:13:13.124191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2012 81
54.0%
2013 12
 
8.0%
2018 11
 
7.3%
2014 11
 
7.3%
2017 9
 
6.0%
2020 7
 
4.7%
2015 7
 
4.7%
2019 5
 
3.3%
2016 4
 
2.7%
2021 3
 
2.0%
ValueCountFrequency (%)
2012 81
54.0%
2013 12
 
8.0%
2014 11
 
7.3%
2015 7
 
4.7%
2016 4
 
2.7%
2017 9
 
6.0%
2018 11
 
7.3%
2019 5
 
3.3%
2020 7
 
4.7%
2021 3
 
2.0%
ValueCountFrequency (%)
2021 3
 
2.0%
2020 7
 
4.7%
2019 5
 
3.3%
2018 11
 
7.3%
2017 9
 
6.0%
2016 4
 
2.7%
2015 7
 
4.7%
2014 11
 
7.3%
2013 12
 
8.0%
2012 81
54.0%

감리종류구분코드
Categorical

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Q1
98 
A3
21 
A1
18 
Q2
 
9
A2
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q1 98
65.3%
A3 21
 
14.0%
A1 18
 
12.0%
Q2 9
 
6.0%
A2 4
 
2.7%

Length

2023-12-12T14:13:13.251975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:13.660181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
q1 98
65.3%
a3 21
 
14.0%
a1 18
 
12.0%
q2 9
 
6.0%
a2 4
 
2.7%

사업시작일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T14:13:13.751889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:13.833997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

사업종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T14:13:13.913434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:13.991516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T14:13:14.068800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:14.152325image/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
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T14:13:14.243876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:13:14.318696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
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

2023-12-12T14:13:14.409493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:14.499983image/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
2023-12-12T14:13:14.875902image/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%
2023-12-12T14:13:15.390155image/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

2023-12-12T14:13:15.599653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:15.743850image/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
2023-12-12T14:13:16.065699image/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%
2023-12-12T14:13:16.565439image/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

2023-12-12T14:13:16.710935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:13:16.823198image/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%

Interactions

2023-12-12T14:13:12.398640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:13:16.903017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호감리년도감리종류구분코드최종수정수처리직원번호최초처리직원번호
이력일련번호1.000NaN0.4300.6030.2140.214
감리년도NaN1.0000.000NaN0.9630.963
감리종류구분코드0.4300.0001.0000.4300.4200.420
최종수정수0.603NaN0.4301.0000.2140.214
처리직원번호0.2140.9630.4200.2141.0001.000
최초처리직원번호0.2140.9630.4200.2141.0001.000
2023-12-12T14:13:17.028084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호최종수정수최초처리직원번호이력일련번호감리종류구분코드
처리직원번호1.0000.2021.0000.2020.353
최종수정수0.2021.0000.2020.2740.358
최초처리직원번호1.0000.2021.0000.2020.353
이력일련번호0.2020.2740.2021.0000.358
감리종류구분코드0.3530.3580.3530.3581.000
2023-12-12T14:13:17.135098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
감리년도이력일련번호감리종류구분코드최종수정수처리직원번호최초처리직원번호
감리년도1.0000.0720.2300.0720.7680.768
이력일련번호0.0721.0000.3580.2740.2020.202
감리종류구분코드0.2300.3581.0000.3580.3530.353
최종수정수0.0720.2740.3581.0000.2020.202
처리직원번호0.7680.2020.3530.2021.0001.000
최초처리직원번호0.7680.2020.3530.2021.0001.000

Missing values

2023-12-12T14:13:12.514459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:13:12.705508image/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

이력일련번호감리년도감리종류구분코드사업시작일자사업종료일자유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
012021Q100:00.000:00.000:00.000:00.0107:23.7459707:23.74597
112021Q100:00.000:00.000:00.000:00.0106:58.6459706:58.64597
212021Q100:00.000:00.000:00.000:00.0135:48.8459735:48.84597
312020Q100:00.000:00.000:00.000:00.0153:50.9459753:50.94597
412020Q100:00.000:00.000:00.000:00.0153:56.5459753:56.54597
512020Q100:00.000:00.000:00.000:00.0142:08.9459742:08.94597
612020Q100:00.000:00.000:00.000:00.0119:25.1459719:25.14597
712020Q100:00.000:00.000:00.000:00.0139:17.6459739:17.64597
812020Q100:00.000:00.000:00.000:00.0156:47.3459756:47.34597
912020Q100:00.000:00.000:00.000:00.0156:17.4459756:17.44597
이력일련번호감리년도감리종류구분코드사업시작일자사업종료일자유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
14022012A100:00.000:00.000:00.000:00.0120:35.2355920:35.23559
14112012Q200:00.000:00.000:00.000:00.0118:06.6355918:06.63559
14212012A100:00.000:00.000:00.000:00.0117:02.8355917:02.83559
14322012A300:00.000:00.000:00.000:00.0112:39.4355912:39.43559
14412012Q200:00.000:00.000:00.000:00.0111:41.0355911:41.03559
14522012A100:00.000:00.000:00.000:00.0110:25.8355910:25.83559
14622012A100:00.000:00.000:00.000:00.0108:35.9355908:35.93559
14712012Q100:00.000:00.000:00.000:00.0105:42.5355905:42.53559
14822012Q100:00.000:00.000:00.000:00.0101:55.2355901:55.23559
14912012A100:00.000:00.000:00.000:00.0138:09.2355938:09.23559