Overview

Dataset statistics

Number of variables7
Number of observations562
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.9 KiB
Average record size in memory56.2 B

Variable types

Text3
DateTime3
Categorical1

Dataset

Description주택금융공사 유동화계획코드정보에 대한 정보로 유동화증권부 데이터입니다.(유동화계획코드, 유동화계획명, 유동화대상코드 등을 포함한 파일데이터를 제공합니다.)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15049740/fileData.do

Alerts

유동화계획코드 has unique valuesUnique
유동화계획명 has unique valuesUnique
유동화대상코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:12:50.266811
Analysis finished2023-12-12 18:12:50.645629
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유동화계획코드
Text

UNIQUE 

Distinct562
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-13T03:12:50.824355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters7868
Distinct characters21
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

Unique562 ?
Unique (%)100.0%

Sample

1st rowKOMOCO2000S-01
2nd rowKOMOCO2000S-02
3rd rowKOMOCO2000S-03
4th rowKOMOCO2001S-01
5th rowKOMOCO2001S-02
ValueCountFrequency (%)
komoco2000s-01 1
 
0.2%
khfcmb2015s-23 1
 
0.2%
khfcmb2015s-15 1
 
0.2%
khfcmb2015s-27 1
 
0.2%
khfcmb2015s-26 1
 
0.2%
khfcmb2015b-01 1
 
0.2%
khfcmb2015s-25 1
 
0.2%
khfcmb2015s-24 1
 
0.2%
khfcmb2015l-03 1
 
0.2%
khfcmb2016l-04 1
 
0.2%
Other values (552) 552
98.2%
2023-12-13T03:12:51.271691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1019
13.0%
2 788
10.0%
1 737
9.4%
B 567
7.2%
K 562
7.1%
M 562
7.1%
C 562
7.1%
- 562
7.1%
H 553
 
7.0%
F 553
 
7.0%
Other values (11) 1403
17.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3976
50.5%
Decimal Number 3330
42.3%
Dash Punctuation 562
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1019
30.6%
2 788
23.7%
1 737
22.1%
3 148
 
4.4%
9 119
 
3.6%
5 112
 
3.4%
8 105
 
3.2%
4 104
 
3.1%
7 102
 
3.1%
6 96
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 567
14.3%
K 562
14.1%
M 562
14.1%
C 562
14.1%
H 553
13.9%
F 553
13.9%
S 348
8.8%
L 200
 
5.0%
A 42
 
1.1%
O 27
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3976
50.5%
Common 3892
49.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1019
26.2%
2 788
20.2%
1 737
18.9%
- 562
14.4%
3 148
 
3.8%
9 119
 
3.1%
5 112
 
2.9%
8 105
 
2.7%
4 104
 
2.7%
7 102
 
2.6%
Latin
ValueCountFrequency (%)
B 567
14.3%
K 562
14.1%
M 562
14.1%
C 562
14.1%
H 553
13.9%
F 553
13.9%
S 348
8.8%
L 200
 
5.0%
A 42
 
1.1%
O 27
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1019
13.0%
2 788
10.0%
1 737
9.4%
B 567
7.2%
K 562
7.1%
M 562
7.1%
C 562
7.1%
- 562
7.1%
H 553
 
7.0%
F 553
 
7.0%
Other values (11) 1403
17.8%

유동화계획명
Text

UNIQUE 

Distinct562
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-13T03:12:51.590724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5620
Distinct characters16
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

Unique562 ?
Unique (%)100.0%

Sample

1st rowMBS2000-01
2nd rowMBS2000-02
3rd rowMBS2000-03
4th rowMBS2001-01
5th rowMBS2001-02
ValueCountFrequency (%)
mbs2000-01 1
 
0.2%
mbs2015-23 1
 
0.2%
mbs2015-15 1
 
0.2%
mbs2015-27 1
 
0.2%
mbs2015-26 1
 
0.2%
mbb2015-01 1
 
0.2%
mbs2015-25 1
 
0.2%
mbs2015-24 1
 
0.2%
mbl2015-03 1
 
0.2%
mbl2016-04 1
 
0.2%
Other values (552) 552
98.2%
2023-12-13T03:12:52.001288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1019
18.1%
2 788
14.0%
1 737
13.1%
B 618
11.0%
M 562
10.0%
- 562
10.0%
S 348
 
6.2%
L 158
 
2.8%
3 148
 
2.6%
9 119
 
2.1%
Other values (6) 561
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3330
59.3%
Uppercase Letter 1728
30.7%
Dash Punctuation 562
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1019
30.6%
2 788
23.7%
1 737
22.1%
3 148
 
4.4%
9 119
 
3.6%
5 112
 
3.4%
8 105
 
3.2%
4 104
 
3.1%
7 102
 
3.1%
6 96
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B 618
35.8%
M 562
32.5%
S 348
20.1%
L 158
 
9.1%
A 42
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3892
69.3%
Latin 1728
30.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1019
26.2%
2 788
20.2%
1 737
18.9%
- 562
14.4%
3 148
 
3.8%
9 119
 
3.1%
5 112
 
2.9%
8 105
 
2.7%
4 104
 
2.7%
7 102
 
2.6%
Latin
ValueCountFrequency (%)
B 618
35.8%
M 562
32.5%
S 348
20.1%
L 158
 
9.1%
A 42
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1019
18.1%
2 788
14.0%
1 737
13.1%
B 618
11.0%
M 562
10.0%
- 562
10.0%
S 348
 
6.2%
L 158
 
2.8%
3 148
 
2.6%
9 119
 
2.1%
Other values (6) 561
10.0%

유동화대상코드
Text

UNIQUE 

Distinct562
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2023-12-13T03:12:52.259374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters7868
Distinct characters19
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

Unique562 ?
Unique (%)100.0%

Sample

1st rowKOMOCO2000M-01
2nd rowKOMOCO2000M-02
3rd rowKOMOCO2000M-03
4th rowKOMOCO2001M-01
5th rowKOMOCO2001M-02
ValueCountFrequency (%)
komoco2000m-01 1
 
0.2%
khfcmb2015m-28 1
 
0.2%
khfcmb2015m-20 1
 
0.2%
khfcmb2015m-33 1
 
0.2%
khfcmb2015m-32 1
 
0.2%
khfcmb2015m-29 1
 
0.2%
khfcmb2015m-31 1
 
0.2%
khfcmb2015m-30 1
 
0.2%
khfcmb2015m-19 1
 
0.2%
khfcmb2016m-34 1
 
0.2%
Other values (552) 552
98.2%
2023-12-13T03:12:52.613850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1082
13.8%
0 957
12.2%
2 810
10.3%
1 710
9.0%
K 562
7.1%
C 562
7.1%
- 562
7.1%
B 553
7.0%
F 553
7.0%
H 553
7.0%
Other values (9) 964
12.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3934
50.0%
Decimal Number 3372
42.9%
Dash Punctuation 562
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 957
28.4%
2 810
24.0%
1 710
21.1%
3 193
 
5.7%
4 139
 
4.1%
5 125
 
3.7%
9 124
 
3.7%
8 109
 
3.2%
7 105
 
3.1%
6 100
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
M 1082
27.5%
K 562
14.3%
C 562
14.3%
B 553
14.1%
F 553
14.1%
H 553
14.1%
P 42
 
1.1%
O 27
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3934
50.0%
Common 3934
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 957
24.3%
2 810
20.6%
1 710
18.0%
- 562
14.3%
3 193
 
4.9%
4 139
 
3.5%
5 125
 
3.2%
9 124
 
3.2%
8 109
 
2.8%
7 105
 
2.7%
Latin
ValueCountFrequency (%)
M 1082
27.5%
K 562
14.3%
C 562
14.3%
B 553
14.1%
F 553
14.1%
H 553
14.1%
P 42
 
1.1%
O 27
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1082
13.8%
0 957
12.2%
2 810
10.3%
1 710
9.0%
K 562
7.1%
C 562
7.1%
- 562
7.1%
B 553
7.0%
F 553
7.0%
H 553
7.0%
Other values (9) 964
12.3%

유동화시작일자
Date

Distinct483
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2000-04-07 00:00:00
Maximum2020-02-05 00:00:00
2023-12-13T03:12:52.764927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:12:52.921712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

자산선정기준일자
Date

Distinct469
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2000-03-15 00:00:00
Maximum2020-01-21 00:00:00
2023-12-13T03:12:53.072351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:12:53.240732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

자산양수일자
Date

Distinct483
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2000-04-07 00:00:00
Maximum2025-02-05 00:00:00
2023-12-13T03:12:53.433707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:12:53.629847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보유목적코드
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
신탁유동화
348 
매입보유
158 
고유유동화
56 

Length

Max length5
Median length5
Mean length4.7188612
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신탁유동화 348
61.9%
매입보유 158
28.1%
고유유동화 56
 
10.0%

Length

2023-12-13T03:12:53.802820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:12:53.930434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신탁유동화 348
61.9%
매입보유 158
28.1%
고유유동화 56
 
10.0%

Missing values

2023-12-13T03:12:50.465566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:12:50.594162image/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

유동화계획코드유동화계획명유동화대상코드유동화시작일자자산선정기준일자자산양수일자보유목적코드
0KOMOCO2000S-01MBS2000-01KOMOCO2000M-012000-04-072000-03-152000-04-07신탁유동화
1KOMOCO2000S-02MBS2000-02KOMOCO2000M-022000-09-012000-08-142000-09-01신탁유동화
2KOMOCO2000S-03MBS2000-03KOMOCO2000M-032000-12-082000-11-222000-12-08신탁유동화
3KOMOCO2001S-01MBS2001-01KOMOCO2001M-012001-05-182001-04-302001-05-18신탁유동화
4KOMOCO2001S-02MBS2001-02KOMOCO2001M-022001-09-202001-08-312001-09-20신탁유동화
5KOMOCO2002S-01MBS2002-01KOMOCO2002M-012002-01-232002-01-052002-01-23신탁유동화
6KOMOCO2002S-02MBS2002-02KOMOCO2002M-022002-02-212002-01-312002-02-21신탁유동화
7KOMOCO2003S-01MBS2003-01KOMOCO2003M-012003-04-022003-03-142003-04-02신탁유동화
8KOMOCO2003S-02MBS2003-02KOMOCO2003M-022003-08-042003-07-022003-08-04신탁유동화
9KHFCMB2004S-01MBS2004-01KHFCMB2004M-012004-06-152004-05-282004-06-15신탁유동화
유동화계획코드유동화계획명유동화대상코드유동화시작일자자산선정기준일자자산양수일자보유목적코드
552KHFCMB2019L-12MBL2019-12KHFCMB2019M-452019-12-202019-12-202019-12-20매입보유
553KHFCMB2019S-26MBS2019-26KHFCMB2019M-462019-12-202019-11-302019-12-20신탁유동화
554KHFCMB2019S-27MBS2019-27KHFCMB2019M-472019-12-242019-12-052019-12-24신탁유동화
555KHFCMB2019S-28MBS2019-28KHFCMB2019M-502019-12-272019-12-162019-12-27신탁유동화
556KHFCMB2020L-01MBL2020-01KHFCMB2020M-022020-01-082020-01-072020-01-08매입보유
557KHFCMB2020S-01MBS2020-01KHFCMB2020M-042020-01-102019-12-192020-01-10신탁유동화
558KHFCMB2020S-02MBS2020-02KHFCMB2020M-052020-01-172019-12-302020-01-17신탁유동화
559KHFCMB2020S-03MBS2020-03KHFCMB2020M-062020-01-212019-12-312020-01-21신탁유동화
560KHFCMB2020L-02MBL2020-02KHFCMB2020M-032020-01-222020-01-212020-01-22매입보유
561KHFCMB2020B-01MBB2020-01KHFCMB2020M-012020-02-052019-12-162025-02-05고유유동화