Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory109.3 B

Variable types

Categorical5
DateTime3
Boolean1
Text2
Numeric2

Dataset

Description한국주택금융공사 유동화자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072965/fileData.do

Alerts

자료제공목적코드 has constant value ""Constant
반복제공회차 has constant value ""Constant
대외계전문여부 has constant value ""Constant
오류유형코드 has constant value ""Constant
면제관리일련번호 is highly overall correlated with 면제금액High correlation
면제금액 is highly overall correlated with 면제관리일련번호 and 1 other fieldsHigh correlation
대출기관코드 is highly overall correlated with 면제금액High correlation
대출기관코드 is highly imbalanced (56.4%)Imbalance
순번 is highly imbalanced (55.1%)Imbalance
면제금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:51:47.791854
Analysis finished2023-12-12 18:51:49.655190
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대출기관코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
SC은행
91 
기업은행
 
9

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC은행
2nd rowSC은행
3rd rowSC은행
4th row기업은행
5th rowSC은행

Common Values

ValueCountFrequency (%)
SC은행 91
91.0%
기업은행 9
 
9.0%

Length

2023-12-13T03:51:49.740197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:51:49.862230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sc은행 91
91.0%
기업은행 9
 
9.0%

수신일자
Date

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-16 00:00:00
Maximum2020-10-19 00:00:00
2023-12-13T03:51:49.987814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:50.146300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

자료작성기준일자
Date

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-16 00:00:00
Maximum2020-10-19 00:00:00
2023-12-13T03:51:50.318918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:50.518456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

자료제공목적코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
사후관리자료
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사후관리자료 100
100.0%

Length

2023-12-13T03:51:50.697494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:51:50.831516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사후관리자료 100
100.0%

반복제공회차
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

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 (%)
1 100
100.0%

Length

2023-12-13T03:51:50.965739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:51:51.097850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

대외계전문여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2023-12-13T03:51:51.179329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

순번
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
77 
2
17 
3
 
4
5
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 77
77.0%
2 17
 
17.0%
3 4
 
4.0%
5 1
 
1.0%
4 1
 
1.0%

Length

2023-12-13T03:51:51.759248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:51:51.910532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 77
77.0%
2 17
 
17.0%
3 4
 
4.0%
5 1
 
1.0%
4 1
 
1.0%

오류유형코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정상
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 100
100.0%

Length

2023-12-13T03:51:52.100597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:51:52.262135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 100
100.0%

유동화계획코드
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T03:51:52.520539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1400
Distinct characters18
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

Unique31 ?
Unique (%)31.0%

Sample

1st rowKHFCMB2018S-27
2nd rowKHFCMB2018S-31
3rd rowKHFCMB2019S-03
4th rowKHFCMB2019S-23
5th rowKHFCMB2018S-24
ValueCountFrequency (%)
khfcmb2018s-31 13
 
13.0%
khfcmb2017s-23 8
 
8.0%
khfcmb2017s-29 5
 
5.0%
khfcmb2019s-02 5
 
5.0%
khfcmb2018s-12 4
 
4.0%
khfcmb2018s-21 4
 
4.0%
khfcmb2018s-02 3
 
3.0%
khfcmb2018s-26 3
 
3.0%
khfcmb2018s-20 3
 
3.0%
khfcmb2019s-08 3
 
3.0%
Other values (40) 49
49.0%
2023-12-13T03:51:53.029525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 162
11.6%
0 138
9.9%
1 133
9.5%
B 101
 
7.2%
K 100
 
7.1%
- 100
 
7.1%
F 100
 
7.1%
C 100
 
7.1%
M 100
 
7.1%
H 100
 
7.1%
Other values (8) 266
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 700
50.0%
Decimal Number 600
42.9%
Dash Punctuation 100
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 162
27.0%
0 138
23.0%
1 133
22.2%
8 56
 
9.3%
9 38
 
6.3%
3 28
 
4.7%
7 28
 
4.7%
4 7
 
1.2%
6 6
 
1.0%
5 4
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
B 101
14.4%
K 100
14.3%
F 100
14.3%
C 100
14.3%
M 100
14.3%
H 100
14.3%
S 99
14.1%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
50.0%
Latin 700
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 162
23.1%
0 138
19.7%
1 133
19.0%
- 100
14.3%
8 56
 
8.0%
9 38
 
5.4%
3 28
 
4.0%
7 28
 
4.0%
4 7
 
1.0%
6 6
 
0.9%
Latin
ValueCountFrequency (%)
B 101
14.4%
K 100
14.3%
F 100
14.3%
C 100
14.3%
M 100
14.3%
H 100
14.3%
S 99
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 162
11.6%
0 138
9.9%
1 133
9.5%
B 101
 
7.2%
K 100
 
7.1%
- 100
 
7.1%
F 100
 
7.1%
C 100
 
7.1%
M 100
 
7.1%
H 100
 
7.1%
Other values (8) 266
19.0%

인수코드
Text

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-13T03:51:53.339489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1400
Distinct characters12
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

Unique36 ?
Unique (%)36.0%

Sample

1st rowB023-2018-0066
2nd rowB023-2018-0081
3rd rowB023-2019-0005
4th rowB003-2019-0082
5th rowB023-2018-0061
ValueCountFrequency (%)
b023-2018-0081 13
 
13.0%
b023-2017-0062 8
 
8.0%
b023-2017-0082 5
 
5.0%
b023-2018-0032 4
 
4.0%
b023-2019-0003 4
 
4.0%
b023-2018-0008 3
 
3.0%
b023-2018-0053 3
 
3.0%
b023-2018-0063 3
 
3.0%
b023-2019-0020 3
 
3.0%
b023-2019-0042 2
 
2.0%
Other values (44) 52
52.0%
2023-12-13T03:51:53.881205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 435
31.1%
2 231
16.5%
- 200
14.3%
3 121
 
8.6%
1 119
 
8.5%
B 100
 
7.1%
8 78
 
5.6%
7 32
 
2.3%
9 27
 
1.9%
6 23
 
1.6%
Other values (2) 34
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
78.6%
Dash Punctuation 200
 
14.3%
Uppercase Letter 100
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 435
39.5%
2 231
21.0%
3 121
 
11.0%
1 119
 
10.8%
8 78
 
7.1%
7 32
 
2.9%
9 27
 
2.5%
6 23
 
2.1%
5 20
 
1.8%
4 14
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 200
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1300
92.9%
Latin 100
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 435
33.5%
2 231
17.8%
- 200
15.4%
3 121
 
9.3%
1 119
 
9.2%
8 78
 
6.0%
7 32
 
2.5%
9 27
 
2.1%
6 23
 
1.8%
5 20
 
1.5%
Latin
ValueCountFrequency (%)
B 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 435
31.1%
2 231
16.5%
- 200
14.3%
3 121
 
8.6%
1 119
 
8.5%
B 100
 
7.1%
8 78
 
5.6%
7 32
 
2.3%
9 27
 
1.9%
6 23
 
1.6%
Other values (2) 34
 
2.4%

면제관리일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.46
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T03:51:54.084540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile10.05
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.1890596
Coefficient of variation (CV)1.2963657
Kurtosis5.0793399
Mean2.46
Median Absolute Deviation (MAD)0
Skewness2.4110767
Sum246
Variance10.170101
MonotonicityNot monotonic
2023-12-13T03:51:54.262585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 72
72.0%
2 8
 
8.0%
3 3
 
3.0%
9 2
 
2.0%
8 2
 
2.0%
7 2
 
2.0%
6 2
 
2.0%
5 2
 
2.0%
15 1
 
1.0%
14 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
1 72
72.0%
2 8
 
8.0%
3 3
 
3.0%
4 1
 
1.0%
5 2
 
2.0%
6 2
 
2.0%
7 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
10 1
 
1.0%
ValueCountFrequency (%)
15 1
1.0%
14 1
1.0%
13 1
1.0%
12 1
1.0%
11 1
1.0%
10 1
1.0%
9 2
2.0%
8 2
2.0%
7 2
2.0%
6 2
2.0%

발생일자
Date

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-15 00:00:00
Maximum2020-10-16 00:00:00
2023-12-13T03:51:54.538495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:54.760676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

면제금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334516.17
Minimum49
Maximum2403065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T03:51:55.026411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile385.65
Q112430.75
median127760.5
Q3464298.75
95-th percentile1591310.9
Maximum2403065
Range2403016
Interquartile range (IQR)451868

Descriptive statistics

Standard deviation487193.02
Coefficient of variation (CV)1.456411
Kurtosis4.3518695
Mean334516.17
Median Absolute Deviation (MAD)125397
Skewness2.0989823
Sum33451617
Variance2.3735704 × 1011
MonotonicityNot monotonic
2023-12-13T03:51:55.290028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
467886 1
 
1.0%
113173 1
 
1.0%
130056 1
 
1.0%
117061 1
 
1.0%
28105 1
 
1.0%
46351 1
 
1.0%
94945 1
 
1.0%
1844 1
 
1.0%
128045 1
 
1.0%
764340 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
49 1
1.0%
65 1
1.0%
143 1
1.0%
254 1
1.0%
265 1
1.0%
392 1
1.0%
481 1
1.0%
656 1
1.0%
1040 1
1.0%
1429 1
1.0%
ValueCountFrequency (%)
2403065 1
1.0%
1849280 1
1.0%
1723440 1
1.0%
1632112 1
1.0%
1617948 1
1.0%
1589909 1
1.0%
1456736 1
1.0%
1271188 1
1.0%
1116255 1
1.0%
1054527 1
1.0%

Interactions

2023-12-13T03:51:48.997510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:48.701680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:49.122051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:51:48.856056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:51:55.457371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대출기관코드수신일자자료작성기준일자순번유동화계획코드인수코드면제관리일련번호발생일자면제금액
대출기관코드1.0000.9470.7910.0001.0001.0000.0000.5640.556
수신일자0.9471.0001.0000.0000.4190.0000.3281.0000.721
자료작성기준일자0.7911.0001.0000.0000.6890.4720.0001.0000.000
순번0.0000.0000.0001.0000.0000.8470.0000.0000.000
유동화계획코드1.0000.4190.6890.0001.0001.0000.0000.0000.934
인수코드1.0000.0000.4720.8471.0001.0000.0000.0000.940
면제관리일련번호0.0000.3280.0000.0000.0000.0001.0000.6830.000
발생일자0.5641.0001.0000.0000.0000.0000.6831.0000.000
면제금액0.5560.7210.0000.0000.9340.9400.0000.0001.000
2023-12-13T03:51:55.636825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번대출기관코드
순번1.0000.000
대출기관코드0.0001.000
2023-12-13T03:51:55.763517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면제관리일련번호면제금액대출기관코드순번
면제관리일련번호1.000-0.5210.0000.000
면제금액-0.5211.0000.5380.000
대출기관코드0.0000.5381.0000.000
순번0.0000.0000.0001.000

Missing values

2023-12-13T03:51:49.296531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:51:49.557575image/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

대출기관코드수신일자자료작성기준일자자료제공목적코드반복제공회차대외계전문여부순번오류유형코드유동화계획코드인수코드면제관리일련번호발생일자면제금액
0SC은행2020-10-192020-10-19사후관리자료1Y1정상KHFCMB2018S-27B023-2018-006612020-10-16467886
1SC은행2020-10-132020-10-13사후관리자료1Y1정상KHFCMB2018S-31B023-2018-0081152020-10-127784
2SC은행2020-10-122020-10-12사후관리자료1Y1정상KHFCMB2019S-03B023-2019-000512020-10-08277083
3기업은행2020-10-092020-10-08사후관리자료1Y1정상KHFCMB2019S-23B003-2019-008212020-10-08830036
4SC은행2020-09-292020-09-29사후관리자료1Y1정상KHFCMB2018S-24B023-2018-006122020-09-28312974
5SC은행2020-09-252020-09-25사후관리자료1Y1정상KHFCMB2018S-12B023-2018-003212020-09-2442480
6SC은행2020-09-242020-09-24사후관리자료1Y1정상KHFCMB2019S-04B023-2019-000812020-09-23631026
7SC은행2020-09-282020-09-28사후관리자료1Y1정상KHFCMB2018S-24B023-2018-006112020-09-2517627
8SC은행2020-09-212020-09-21사후관리자료1Y1정상KHFCMB2020S-01B023-2020-000212020-09-18541134
9SC은행2020-09-172020-09-17사후관리자료1Y1정상KHFCMB2018S-21B023-2018-005312020-09-161992
대출기관코드수신일자자료작성기준일자자료제공목적코드반복제공회차대외계전문여부순번오류유형코드유동화계획코드인수코드면제관리일련번호발생일자면제금액
90SC은행2020-02-052020-02-05사후관리자료1Y1정상KHFCMB2018S-31B023-2018-008152020-02-0421643
91SC은행2020-01-312020-01-31사후관리자료1Y1정상KHFCMB2018S-20B023-2018-005012020-01-30656
92SC은행2020-01-282020-01-28사후관리자료1Y1정상KHFCMB2018S-31B023-2018-008132020-01-2327016
93SC은행2020-01-232020-01-23사후관리자료1Y2정상KHFCMB2018S-03B023-2018-001012020-01-22298599
94SC은행2020-01-232020-01-23사후관리자료1Y1정상KHFCMB2018S-31B023-2018-008122020-01-2236076
95SC은행2020-01-222020-01-22사후관리자료1Y1정상KHFCMB2018S-12B023-2018-003212020-01-21463103
96SC은행2020-01-212020-01-21사후관리자료1Y1정상KHFCMB2017S-23B023-2017-006222020-01-202507
97SC은행2020-01-162020-01-16사후관리자료1Y5정상KHFCMB2018S-09B023-2018-002612020-01-15191414
98SC은행2020-01-162020-01-16사후관리자료1Y4정상KHFCMB2018S-20B023-2018-005112020-01-1528795
99SC은행2020-01-162020-01-16사후관리자료1Y3정상KHFCMB2018S-09B023-2018-002712020-01-15138517