Overview

Dataset statistics

Number of variables13
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.5 KiB
Average record size in memory114.1 B

Variable types

Text1
Categorical9
Numeric3

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073207/fileData.do

Alerts

ASSESS_DY has constant value ""Constant
CLOSE_DY has constant value ""Constant
NPAY_BASIS_DY has constant value ""Constant
REG_ENO has constant value ""Constant
REG_DT has constant value ""Constant
BPAY_BASIS_DY is highly overall correlated with CALC_DCNTHigh correlation
CALC_DCNT is highly overall correlated with BPAY_BASIS_DYHigh correlation
AVG_RAMT is highly overall correlated with UN_PAY_AMTHigh correlation
UN_PAY_AMT is highly overall correlated with AVG_RAMTHigh correlation
CALC_DCNT is highly imbalanced (93.2%)Imbalance
BPAY_BASIS_DY is highly imbalanced (93.2%)Imbalance
BEF_MM_UNTR_AMT is highly skewed (γ1 = 26.90330582)Skewed
AVG_RAMT has unique valuesUnique
UN_PAY_AMT has unique valuesUnique
BEF_MM_UNTR_AMT has 985 (98.5%) zerosZeros

Reproduction

Analysis started2023-12-12 01:16:55.611052
Analysis finished2023-12-12 01:16:57.611880
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Text

Distinct161
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T10:16:57.800949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14000
Distinct characters20
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

Unique13 ?
Unique (%)1.3%

Sample

1st rowKHFCMB2020S-33
2nd rowKHFCMB2020S-33
3rd rowKHFCMB2020S-33
4th rowKHFCMB2020S-33
5th rowKHFCMB2020S-33
ValueCountFrequency (%)
khfcmb2020l-09 14
 
1.4%
khfcmb2020s-06 13
 
1.3%
khfcmb2017s-23 13
 
1.3%
khfcmb2020s-03 12
 
1.2%
khfcmb2018s-14 12
 
1.2%
khfcmb2017s-26 12
 
1.2%
khfcmb2017s-10 12
 
1.2%
khfcmb2019s-14 12
 
1.2%
khfcmb2019s-25 11
 
1.1%
khfcmb2018s-26 11
 
1.1%
Other values (151) 878
87.8%
2023-12-12T10:16:58.246294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1753
12.5%
2 1688
12.1%
1 1116
8.0%
B 1006
7.2%
K 1000
7.1%
F 1000
7.1%
C 1000
7.1%
M 1000
7.1%
- 1000
7.1%
H 1000
7.1%
Other values (10) 2437
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7020
50.1%
Decimal Number 5980
42.7%
Dash Punctuation 1000
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1753
29.3%
2 1688
28.2%
1 1116
18.7%
9 359
 
6.0%
8 317
 
5.3%
7 256
 
4.3%
3 179
 
3.0%
5 110
 
1.8%
4 104
 
1.7%
6 98
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 1006
14.3%
K 1000
14.2%
F 1000
14.2%
C 1000
14.2%
M 1000
14.2%
H 1000
14.2%
S 712
10.1%
L 282
 
4.0%
A 20
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7020
50.1%
Common 6980
49.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1753
25.1%
2 1688
24.2%
1 1116
16.0%
- 1000
14.3%
9 359
 
5.1%
8 317
 
4.5%
7 256
 
3.7%
3 179
 
2.6%
5 110
 
1.6%
4 104
 
1.5%
Latin
ValueCountFrequency (%)
B 1006
14.3%
K 1000
14.2%
F 1000
14.2%
C 1000
14.2%
M 1000
14.2%
H 1000
14.2%
S 712
10.1%
L 282
 
4.0%
A 20
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1753
12.5%
2 1688
12.1%
1 1116
8.0%
B 1006
7.2%
K 1000
7.1%
F 1000
7.1%
C 1000
7.1%
M 1000
7.1%
- 1000
7.1%
H 1000
7.1%
Other values (10) 2437
17.4%

FEE_CD
Categorical

Distinct22
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B02003
122 
B00403
110 
B02303
109 
B01003
101 
B00303
92 
Other values (17)
466 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowB08803
2nd rowB03903
3rd rowB03203
4th rowB02303
5th rowB02003

Common Values

ValueCountFrequency (%)
B02003 122
12.2%
B00403 110
11.0%
B02303 109
10.9%
B01003 101
10.1%
B00303 92
9.2%
B08103 86
8.6%
B08803 71
7.1%
B03903 71
7.1%
B03203 56
5.6%
B02703 52
 
5.2%
Other values (12) 130
13.0%

Length

2023-12-12T10:16:58.423282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b02003 122
12.2%
b00403 110
11.0%
b02303 109
10.9%
b01003 101
10.1%
b00303 92
9.2%
b08103 86
8.6%
b08803 71
7.1%
b03903 71
7.1%
b03203 56
5.6%
b02703 52
 
5.2%
Other values (12) 130
13.0%

ASSESS_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201023
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201023
2nd row20201023
3rd row20201023
4th row20201023
5th row20201023

Common Values

ValueCountFrequency (%)
20201023 1000
100.0%

Length

2023-12-12T10:16:58.585519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:16:58.694001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201023 1000
100.0%

AVG_RAMT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5644981 × 1011
Minimum50
Maximum7.833694 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T10:16:58.841374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1.4130477 × 108
Q14.2397138 × 109
median4.0223846 × 1010
Q32.1128908 × 1011
95-th percentile1.029652 × 1012
Maximum7.833694 × 1012
Range7.833694 × 1012
Interquartile range (IQR)2.0704937 × 1011

Descriptive statistics

Standard deviation6.8848246 × 1011
Coefficient of variation (CV)2.6846675
Kurtosis47.878078
Mean2.5644981 × 1011
Median Absolute Deviation (MAD)3.9882882 × 1010
Skewness6.1534929
Sum2.5644981 × 1014
Variance4.740081 × 1023
MonotonicityNot monotonic
2023-12-12T10:16:59.041922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2058594402012 1
 
0.1%
121908736673 1
 
0.1%
696068125 1
 
0.1%
20155258979 1
 
0.1%
37165305273 1
 
0.1%
153212561509 1
 
0.1%
29516141541 1
 
0.1%
43704572739 1
 
0.1%
52986272076 1
 
0.1%
1404459242527 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
50 1
0.1%
872736 1
0.1%
2560434 1
0.1%
4935179 1
0.1%
8007244 1
0.1%
8224450 1
0.1%
11780852 1
0.1%
12326084 1
0.1%
14308521 1
0.1%
20745739 1
0.1%
ValueCountFrequency (%)
7833693983342 1
0.1%
7118822878108 1
0.1%
6445070303038 1
0.1%
6224629459131 1
0.1%
5788462648059 1
0.1%
5319999834171 1
0.1%
4319210201245 1
0.1%
4146536591238 1
0.1%
3720490733596 1
0.1%
3715842859656 1
0.1%

CLOSE_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201005
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201005
2nd row20201005
3rd row20201005
4th row20201005
5th row20201005

Common Values

ValueCountFrequency (%)
20201005 1000
100.0%

Length

2023-12-12T10:16:59.192358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:16:59.302799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201005 1000
100.0%

CALC_DCNT
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
23
985 
4
 
6
12
 
6
11
 
3

Length

Max length2
Median length2
Mean length1.994
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
23 985
98.5%
4 6
 
0.6%
12 6
 
0.6%
11 3
 
0.3%

Length

2023-12-12T10:16:59.758720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:16:59.891435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
23 985
98.5%
4 6
 
0.6%
12 6
 
0.6%
11 3
 
0.3%

BPAY_BASIS_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201001
985 
20201020
 
6
20201012
 
6
20201013
 
3

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201020
2nd row20201020
3rd row20201020
4th row20201020
5th row20201020

Common Values

ValueCountFrequency (%)
20201001 985
98.5%
20201020 6
 
0.6%
20201012 6
 
0.6%
20201013 3
 
0.3%

Length

2023-12-12T10:17:00.006428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:17:00.119117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201001 985
98.5%
20201020 6
 
0.6%
20201012 6
 
0.6%
20201013 3
 
0.3%

NPAY_BASIS_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201031
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201031
2nd row20201031
3rd row20201031
4th row20201031
5th row20201031

Common Values

ValueCountFrequency (%)
20201031 1000
100.0%

Length

2023-12-12T10:17:00.241720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:17:00.347060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201031 1000
100.0%

UN_PAY_AMT
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72806120
Minimum0
Maximum2.6329991 × 109
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T10:17:00.480627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16167.6
Q1529112
median11247068
Q358191746
95-th percentile3.0107354 × 108
Maximum2.6329991 × 109
Range2.6329991 × 109
Interquartile range (IQR)57662634

Descriptive statistics

Standard deviation2.1284748 × 108
Coefficient of variation (CV)2.9234834
Kurtosis66.609816
Mean72806120
Median Absolute Deviation (MAD)11207536
Skewness7.235962
Sum7.280612 × 1010
Variance4.5304051 × 1016
MonotonicityNot monotonic
2023-12-12T10:17:00.700052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129106000 1
 
0.1%
43670730 1
 
0.1%
109655 1
 
0.1%
7641793 1
 
0.1%
10892285 1
 
0.1%
49968590 1
 
0.1%
11586584 1
 
0.1%
15316581 1
 
0.1%
19738042 1
 
0.1%
502059086 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
0 1
0.1%
301 1
0.1%
505 1
0.1%
1049 1
0.1%
1244 1
0.1%
1307 1
0.1%
1379 1
0.1%
1671 1
0.1%
2073 1
0.1%
3607 1
0.1%
ValueCountFrequency (%)
2632999146 1
0.1%
2496134086 1
0.1%
2313733266 1
0.1%
2083081976 1
0.1%
1856751172 1
0.1%
1227463109 1
0.1%
1205111659 1
0.1%
1204830904 1
0.1%
1188635143 1
0.1%
1127835969 1
0.1%

ACCT_DV_CD
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
712 
1
288 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 712
71.2%
1 288
28.8%

Length

2023-12-12T10:17:00.869340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:17:00.996047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 712
71.2%
1 288
28.8%

REG_ENO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1390
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1390
2nd row1390
3rd row1390
4th row1390
5th row1390

Common Values

ValueCountFrequency (%)
1390 1000
100.0%

Length

2023-12-12T10:17:01.108946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:17:01.214541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1390 1000
100.0%

REG_DT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2020/10/24 14:50:00
1000 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020/10/24 14:50:00
2nd row2020/10/24 14:50:00
3rd row2020/10/24 14:50:00
4th row2020/10/24 14:50:00
5th row2020/10/24 14:50:00

Common Values

ValueCountFrequency (%)
2020/10/24 14:50:00 1000
100.0%

Length

2023-12-12T10:17:01.348827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:17:01.490388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020/10/24 1000
50.0%
14:50:00 1000
50.0%

BEF_MM_UNTR_AMT
Real number (ℝ)

SKEWED  ZEROS 

Distinct16
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59513.354
Minimum0
Maximum35223480
Zeros985
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T10:17:01.611920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum35223480
Range35223480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1187481.1
Coefficient of variation (CV)19.953188
Kurtosis777.75724
Mean59513.354
Median Absolute Deviation (MAD)0
Skewness26.903306
Sum59513354
Variance1.4101114 × 1012
MonotonicityNot monotonic
2023-12-12T10:17:01.782349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 985
98.5%
2706443 1
 
0.1%
8367315 1
 
0.1%
35223480 1
 
0.1%
9482416 1
 
0.1%
37576 1
 
0.1%
39430 1
 
0.1%
86168 1
 
0.1%
249876 1
 
0.1%
1346108 1
 
0.1%
Other values (6) 6
 
0.6%
ValueCountFrequency (%)
0 985
98.5%
2210 1
 
0.1%
9996 1
 
0.1%
10021 1
 
0.1%
37576 1
 
0.1%
39430 1
 
0.1%
78181 1
 
0.1%
86168 1
 
0.1%
249876 1
 
0.1%
367298 1
 
0.1%
ValueCountFrequency (%)
35223480 1
0.1%
9482416 1
0.1%
8367315 1
0.1%
2706443 1
0.1%
1506836 1
0.1%
1346108 1
0.1%
367298 1
0.1%
249876 1
0.1%
86168 1
0.1%
78181 1
0.1%

Interactions

2023-12-12T10:16:56.878649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.099432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.495689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.998959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.218463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.616035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:57.138155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.359687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:16:56.759352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:17:01.924058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FEE_CDAVG_RAMTCALC_DCNTBPAY_BASIS_DYUN_PAY_AMTACCT_DV_CDBEF_MM_UNTR_AMT
FEE_CD1.0000.2480.0000.0000.3270.3110.000
AVG_RAMT0.2481.0000.0840.0840.8880.0980.000
CALC_DCNT0.0000.0841.0001.0000.0000.1880.000
BPAY_BASIS_DY0.0000.0841.0001.0000.0000.1880.000
UN_PAY_AMT0.3270.8880.0000.0001.0000.1850.000
ACCT_DV_CD0.3110.0980.1880.1880.1851.0000.000
BEF_MM_UNTR_AMT0.0000.0000.0000.0000.0000.0001.000
2023-12-12T10:17:02.055463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BPAY_BASIS_DYACCT_DV_CDCALC_DCNTFEE_CD
BPAY_BASIS_DY1.0000.1251.0000.000
ACCT_DV_CD0.1251.0000.1250.243
CALC_DCNT1.0000.1251.0000.000
FEE_CD0.0000.2430.0001.000
2023-12-12T10:17:02.180722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AVG_RAMTUN_PAY_AMTBEF_MM_UNTR_AMTFEE_CDCALC_DCNTBPAY_BASIS_DYACCT_DV_CD
AVG_RAMT1.0000.980-0.1030.0930.0500.0500.075
UN_PAY_AMT0.9801.000-0.0860.1380.0000.0000.139
BEF_MM_UNTR_AMT-0.103-0.0861.0000.0000.0000.0000.000
FEE_CD0.0930.1380.0001.0000.0000.0000.243
CALC_DCNT0.0500.0000.0000.0001.0001.0000.125
BPAY_BASIS_DY0.0500.0000.0000.0001.0001.0000.125
ACCT_DV_CD0.0750.1390.0000.2430.1250.1251.000

Missing values

2023-12-12T10:16:57.327085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:16:57.521218image/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_CDFEE_CDASSESS_DYAVG_RAMTCLOSE_DYCALC_DCNTBPAY_BASIS_DYNPAY_BASIS_DYUN_PAY_AMTACCT_DV_CDREG_ENOREG_DTBEF_MM_UNTR_AMT
0KHFCMB2020S-33B088032020102320585944020122020100542020102020201031129106000313902020/10/24 14:50:000
1KHFCMB2020S-33B039032020102371017958982020100542020102020201031365665313902020/10/24 14:50:000
2KHFCMB2020S-33B032032020102315055697742020201005420201020202010316028450313902020/10/24 14:50:000
3KHFCMB2020S-33B0230320201023625784302568202010054202010202020103140193873313902020/10/24 14:50:000
4KHFCMB2020S-33B020032020102312549573619720201005420201020202010318474993313902020/10/24 14:50:000
5KHFCMB2020S-33B003032020102337158428596562020100542020102020201031223732331313902020/10/24 14:50:000
6KHFCMB2020S-32I00103202010236196895121202010051120201013202010311138809313902020/10/24 14:50:000
7KHFCMB2020S-32B02003202010233216893602752020100511202010132020103161029561313902020/10/24 14:50:000
8KHFCMB2020S-32B0100320201023821901980562020100511202010132020103115584486313902020/10/24 14:50:000
9KHFCMB2020S-31B039032020102310378524114182020100523202010012020103190442540313902020/10/24 14:50:000
LIQD_PLAN_CDFEE_CDASSESS_DYAVG_RAMTCLOSE_DYCALC_DCNTBPAY_BASIS_DYNPAY_BASIS_DYUN_PAY_AMTACCT_DV_CDREG_ENOREG_DTBEF_MM_UNTR_AMT
990KHFCMB2017S-03B0230320201023633584917412020100523202010012020103137030001313902020/10/24 14:50:000
991KHFCMB2017S-03B0200320201023420704863562020100523202010012020103117231610313902020/10/24 14:50:008367315
992KHFCMB2017S-03B010032020102326863981634420201005232020100120201031111236085313902020/10/24 14:50:000
993KHFCMB2017S-03B00303202010231242159074742020100523202010012020103150907819313902020/10/24 14:50:000
994KHFCMB2017S-02B0880320201023102922037598120201005232020100120201031422895643313902020/10/24 14:50:000
995KHFCMB2017S-02B08103202010232446545091232020100523202010012020103198948289313902020/10/24 14:50:000
996KHFCMB2017S-02B039032020102313201316229202010052320201001202010315375206313902020/10/24 14:50:000
997KHFCMB2017S-02B03203202010236044754167202010052320201001202010312368059313902020/10/24 14:50:000
998KHFCMB2017S-02B0270320201023158811725620201005232020100120201031237674313902020/10/24 14:50:000
999KHFCMB2017S-02B02003202010231382380540492020100523202010012020103155834159313902020/10/24 14:50:000