Overview

Dataset statistics

Number of variables8
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.5 KiB
Average record size in memory68.1 B

Variable types

Categorical6
Numeric1
DateTime1

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073292/fileData.do

Alerts

LIQD_PLAN_CD has constant value ""Constant
PROD_DIV_CD has constant value ""Constant
MNG_FEE_CD is highly overall correlated with LOAN_ACC_NO and 3 other fieldsHigh correlation
HOLD_CD is highly overall correlated with LOAN_ACC_NO and 3 other fieldsHigh correlation
FEE_RAT is highly overall correlated with LOAN_ACC_NO and 2 other fieldsHigh correlation
REG_ENO is highly overall correlated with HOLD_CD and 1 other fieldsHigh correlation
LOAN_ACC_NO is highly overall correlated with HOLD_CD and 2 other fieldsHigh correlation
MNG_FEE_CD is highly imbalanced (54.7%)Imbalance
FEE_RAT is highly imbalanced (90.6%)Imbalance

Reproduction

Analysis started2023-12-11 23:23:33.366819
Analysis finished2023-12-11 23:23:34.193946
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2020S-34
1000 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2020S-34
2nd rowKHFCMB2020S-34
3rd rowKHFCMB2020S-34
4th rowKHFCMB2020S-34
5th rowKHFCMB2020S-34

Common Values

ValueCountFrequency (%)
KHFCMB2020S-34 1000
100.0%

Length

2023-12-12T08:23:34.246410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:34.344742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2020s-34 1000
100.0%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B10-2015-0037
256 
B023-2020-0038
202 
B10-2015-0035
196 
B10-2015-0039
187 
B10-2015-0047
47 
Other values (5)
112 

Length

Max length14
Median length13
Mean length13.226
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB035-2020-0016
2nd rowB035-2020-0016
3rd rowB035-2020-0016
4th rowB035-2020-0016
5th rowB035-2020-0016

Common Values

ValueCountFrequency (%)
B10-2015-0037 256
25.6%
B023-2020-0038 202
20.2%
B10-2015-0035 196
19.6%
B10-2015-0039 187
18.7%
B10-2015-0047 47
 
4.7%
B10-2015-0043 41
 
4.1%
B10-2015-0045 39
 
3.9%
B035-2020-0016 22
 
2.2%
B10-2015-0041 8
 
0.8%
B020-2020-0103 2
 
0.2%

Length

2023-12-12T08:23:34.433658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:34.533103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b10-2015-0037 256
25.6%
b023-2020-0038 202
20.2%
b10-2015-0035 196
19.6%
b10-2015-0039 187
18.7%
b10-2015-0047 47
 
4.7%
b10-2015-0043 41
 
4.1%
b10-2015-0045 39
 
3.9%
b035-2020-0016 22
 
2.2%
b10-2015-0041 8
 
0.8%
b020-2020-0103 2
 
0.2%

LOAN_ACC_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct988
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6933338 × 1011
Minimum6.5073017 × 1010
Maximum3.3328404 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T08:23:34.699873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.5073017 × 1010
5-th percentile6.7573096 × 1010
Q11.1080372 × 1011
median1.1420241 × 1011
Q31.1810412 × 1011
95-th percentile1.198034 × 1011
Maximum3.3328404 × 1012
Range3.2677674 × 1012
Interquartile range (IQR)7.3003934 × 109

Descriptive statistics

Standard deviation4.2542157 × 1011
Coefficient of variation (CV)2.5123314
Kurtosis46.136138
Mean1.6933338 × 1011
Median Absolute Deviation (MAD)3.6980504 × 109
Skewness6.8590427
Sum1.6933338 × 1014
Variance1.8098351 × 1023
MonotonicityNot monotonic
2023-12-12T08:23:34.837672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3332840400010 2
 
0.2%
3132123900175 2
 
0.2%
3332832700011 2
 
0.2%
1203101819074 2
 
0.2%
832639800020 2
 
0.2%
2932224700033 2
 
0.2%
1832694100039 2
 
0.2%
3132593900027 2
 
0.2%
3132661200013 2
 
0.2%
3332499300011 2
 
0.2%
Other values (978) 980
98.0%
ValueCountFrequency (%)
65073016915 1
0.1%
65073017331 1
0.1%
65073018014 1
0.1%
65073018995 1
0.1%
65073019009 1
0.1%
65273127130 1
0.1%
65273127453 1
0.1%
65273127743 1
0.1%
65273128051 1
0.1%
65273128062 1
0.1%
ValueCountFrequency (%)
3332840400010 2
0.2%
3332832700011 2
0.2%
3332816900027 2
0.2%
3332672400031 2
0.2%
3332499300011 2
0.2%
3132661200013 2
0.2%
3132593900027 2
0.2%
3132123900175 2
0.2%
2932224700033 2
0.2%
1832694100039 2
0.2%

MNG_FEE_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
PL
774 
QB
202 
CL
 
12
C1
 
12

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCL
2nd rowC1
3rd rowCL
4th rowC1
5th rowCL

Common Values

ValueCountFrequency (%)
PL 774
77.4%
QB 202
 
20.2%
CL 12
 
1.2%
C1 12
 
1.2%

Length

2023-12-12T08:23:34.967344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:35.096964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pl 774
77.4%
qb 202
 
20.2%
cl 12
 
1.2%
c1 12
 
1.2%

FEE_RAT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0.1
988 
0.15
 
12

Length

Max length4
Median length3
Mean length3.012
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1
2nd row0.15
3rd row0.1
4th row0.15
5th row0.1

Common Values

ValueCountFrequency (%)
0.1 988
98.8%
0.15 12
 
1.2%

Length

2023-12-12T08:23:35.222487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:35.340909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.1 988
98.8%
0.15 12
 
1.2%

PROD_DIV_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
1000 

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 1000
100.0%

Length

2023-12-12T08:23:35.461309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:35.559696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000
100.0%

REG_ENO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1478
798 
6045
202 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1478
2nd row1478
3rd row1478
4th row1478
5th row1478

Common Values

ValueCountFrequency (%)
1478 798
79.8%
6045 202
 
20.2%

Length

2023-12-12T08:23:35.655767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:23:35.778944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1478 798
79.8%
6045 202
 
20.2%

REG_DT
Date

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2020-10-27 17:05:05
Maximum2020-10-28 11:04:42
2023-12-12T08:23:35.871403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:23:35.977359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

Interactions

2023-12-12T08:23:33.617536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:23:36.067521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDLOAN_ACC_NOMNG_FEE_CDFEE_RATREG_ENOREG_DT
HOLD_CD1.0000.8280.9180.8661.0001.000
LOAN_ACC_NO0.8281.0000.7390.8870.0480.828
MNG_FEE_CD0.9180.7391.0001.0001.0000.918
FEE_RAT0.8660.8871.0001.0000.0480.866
REG_ENO1.0000.0481.0000.0481.0001.000
REG_DT1.0000.8280.9180.8661.0001.000
2023-12-12T08:23:36.192585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MNG_FEE_CDHOLD_CDFEE_RATREG_ENO
MNG_FEE_CD1.0000.8120.9990.999
HOLD_CD0.8121.0000.6970.996
FEE_RAT0.9990.6971.0000.031
REG_ENO0.9990.9960.0311.000
2023-12-12T08:23:36.281754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ACC_NOHOLD_CDMNG_FEE_CDFEE_RATREG_ENO
LOAN_ACC_NO1.0000.6270.5740.7000.035
HOLD_CD0.6271.0000.8120.6970.996
MNG_FEE_CD0.5740.8121.0000.9990.999
FEE_RAT0.7000.6970.9991.0000.031
REG_ENO0.0350.9960.9990.0311.000

Missing values

2023-12-12T08:23:34.013194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:23:34.148510image/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

LIQD_PLAN_CDHOLD_CDLOAN_ACC_NOMNG_FEE_CDFEE_RATPROD_DIV_CDREG_ENOREG_DT
0KHFCMB2020S-34B035-2020-00163332840400010CL0.1114782020/10/28 11:04:42
1KHFCMB2020S-34B035-2020-00163332840400010C10.15114782020/10/28 11:04:42
2KHFCMB2020S-34B035-2020-00163332832700011CL0.1114782020/10/28 11:04:42
3KHFCMB2020S-34B035-2020-00163332832700011C10.15114782020/10/28 11:04:42
4KHFCMB2020S-34B035-2020-00163332816900027CL0.1114782020/10/28 11:04:42
5KHFCMB2020S-34B035-2020-00163332816900027C10.15114782020/10/28 11:04:42
6KHFCMB2020S-34B035-2020-00163332672400031CL0.1114782020/10/28 11:04:42
7KHFCMB2020S-34B035-2020-00163332672400031C10.15114782020/10/28 11:04:42
8KHFCMB2020S-34B035-2020-00163332499300011CL0.1114782020/10/28 11:04:42
9KHFCMB2020S-34B035-2020-00163332499300011C10.15114782020/10/28 11:04:42
LIQD_PLAN_CDHOLD_CDLOAN_ACC_NOMNG_FEE_CDFEE_RATPROD_DIV_CDREG_ENOREG_DT
990KHFCMB2020S-34B10-2015-0041119303623151PL0.1114782020/10/28 10:32:16
991KHFCMB2020S-34B10-2015-0047117001724511PL0.1114782020/10/28 10:32:30
992KHFCMB2020S-34B10-2015-0047117001723911PL0.1114782020/10/28 10:32:30
993KHFCMB2020S-34B10-2015-0047117001723421PL0.1114782020/10/28 10:32:30
994KHFCMB2020S-34B10-2015-0047116603947471PL0.1114782020/10/28 10:32:30
995KHFCMB2020S-34B10-2015-0047116603915691PL0.1114782020/10/28 10:32:30
996KHFCMB2020S-34B10-2015-0047116504746381PL0.1114782020/10/28 10:32:30
997KHFCMB2020S-34B10-2015-0047116504702381PL0.1114782020/10/28 10:32:30
998KHFCMB2020S-34B10-2015-0047116402240871PL0.1114782020/10/28 10:32:30
999KHFCMB2020S-34B10-2015-0047116402229391PL0.1114782020/10/28 10:32:30