Overview

Dataset statistics

Number of variables10
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.1 KiB
Average record size in memory86.1 B

Variable types

Categorical7
Numeric3

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073064/fileData.do

Alerts

BASIS_DY has constant value ""Constant
STS has constant value ""Constant
REG_ENO has constant value ""Constant
LOAN_ORG_CD is highly overall correlated with LOAN_ACC_NO and 3 other fieldsHigh correlation
HOLD_CD is highly overall correlated with LOAN_ORG_CD and 2 other fieldsHigh correlation
REG_DT is highly overall correlated with LOAN_ACC_NO and 3 other fieldsHigh correlation
LIQD_PLAN_CD is highly overall correlated with LOAN_ORG_CD and 2 other fieldsHigh correlation
LOAN_ACC_NO is highly overall correlated with LOAN_ORG_CD and 1 other fieldsHigh correlation
LOAN_ACC_NO has unique valuesUnique
LOAN_RAMT has 97 (9.7%) zerosZeros

Reproduction

Analysis started2023-12-12 09:58:54.915596
Analysis finished2023-12-12 09:58:57.177716
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LOAN_ORG_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
I003
630 
I004
370 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI003
2nd rowI003
3rd rowI003
4th rowI003
5th rowI003

Common Values

ValueCountFrequency (%)
I003 630
63.0%
I004 370
37.0%

Length

2023-12-12T18:58:57.244524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:57.370569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i003 630
63.0%
i004 370
37.0%

LIQD_PLAN_CD
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2019S-01
349 
KHFCMB2019S-22
253 
KHFCMB2017S-24
215 
KHFCMB2018S-07
78 
KHFCMB2013S-33
 
34
Other values (3)
71 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2013S-11
2nd rowKHFCMB2013S-11
3rd rowKHFCMB2013S-11
4th rowKHFCMB2013S-11
5th rowKHFCMB2013S-11

Common Values

ValueCountFrequency (%)
KHFCMB2019S-01 349
34.9%
KHFCMB2019S-22 253
25.3%
KHFCMB2017S-24 215
21.5%
KHFCMB2018S-07 78
 
7.8%
KHFCMB2013S-33 34
 
3.4%
KHFCMB2018S-25 33
 
3.3%
KHFCMB2013S-11 32
 
3.2%
KHFCMB2017S-21 6
 
0.6%

Length

2023-12-12T18:58:57.515194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:57.674803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2019s-01 349
34.9%
khfcmb2019s-22 253
25.3%
khfcmb2017s-24 215
21.5%
khfcmb2018s-07 78
 
7.8%
khfcmb2013s-33 34
 
3.4%
khfcmb2018s-25 33
 
3.3%
khfcmb2013s-11 32
 
3.2%
khfcmb2017s-21 6
 
0.6%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
I003-2019-0001
349 
I004-2019-0001
253 
I003-2017-0001
215 
I004-2018-0001
78 
I003-2013-0003
 
34
Other values (3)
71 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI003-2013-0001
2nd rowI003-2013-0001
3rd rowI003-2013-0001
4th rowI003-2013-0001
5th rowI003-2013-0001

Common Values

ValueCountFrequency (%)
I003-2019-0001 349
34.9%
I004-2019-0001 253
25.3%
I003-2017-0001 215
21.5%
I004-2018-0001 78
 
7.8%
I003-2013-0003 34
 
3.4%
I004-2018-0002 33
 
3.3%
I003-2013-0001 32
 
3.2%
I004-2017-0002 6
 
0.6%

Length

2023-12-12T18:58:57.843828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:58.078400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i003-2019-0001 349
34.9%
i004-2019-0001 253
25.3%
i003-2017-0001 215
21.5%
i004-2018-0001 78
 
7.8%
i003-2013-0003 34
 
3.4%
i004-2018-0002 33
 
3.3%
i003-2013-0001 32
 
3.2%
i004-2017-0002 6
 
0.6%

LOAN_ACC_NO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4286176 × 1012
Minimum1.005501 × 1011
Maximum9.9667971 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:58:58.268698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.005501 × 1011
5-th percentile1.0300246 × 1011
Q11.0300254 × 1011
median2.7061047 × 1012
Q36.4349075 × 1012
95-th percentile9.2020358 × 1012
Maximum9.9667971 × 1012
Range9.866247 × 1012
Interquartile range (IQR)6.331905 × 1012

Descriptive statistics

Standard deviation3.3157322 × 1012
Coefficient of variation (CV)0.96707554
Kurtosis-1.22971
Mean3.4286176 × 1012
Median Absolute Deviation (MAD)2.6031022 × 1012
Skewness0.47401146
Sum3.4286176 × 1015
Variance1.099408 × 1025
MonotonicityNot monotonic
2023-12-12T18:58:58.445903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1523995100024 1
 
0.1%
103002542440 1
 
0.1%
103002543374 1
 
0.1%
103002543349 1
 
0.1%
103002543124 1
 
0.1%
103002543085 1
 
0.1%
103002543072 1
 
0.1%
103002542870 1
 
0.1%
103002542842 1
 
0.1%
103002542841 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
100550100051 1
0.1%
103002414151 1
0.1%
103002414163 1
0.1%
103002414179 1
0.1%
103002414211 1
0.1%
103002414265 1
0.1%
103002414593 1
0.1%
103002447781 1
0.1%
103002447788 1
0.1%
103002447790 1
0.1%
ValueCountFrequency (%)
9966797100020 1
0.1%
9964684200005 1
0.1%
9930135200008 1
0.1%
9924515200001 1
0.1%
9922905200001 1
0.1%
9920135200008 1
0.1%
9887874200015 1
0.1%
9874324200011 1
0.1%
9871874200006 1
0.1%
9863174200009 1
0.1%

BASIS_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20200901
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200901
2nd row20200901
3rd row20200901
4th row20200901
5th row20200901

Common Values

ValueCountFrequency (%)
20200901 1000
100.0%

Length

2023-12-12T18:58:58.601006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:58.705969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200901 1000
100.0%

LOAN_RAMT
Real number (ℝ)

ZEROS 

Distinct894
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4277166 × 108
Minimum0
Maximum4.750301 × 108
Zeros97
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:58:58.810327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q177407467
median1.3799062 × 108
Q31.9564387 × 108
95-th percentile3.183975 × 108
Maximum4.750301 × 108
Range4.750301 × 108
Interquartile range (IQR)1.182364 × 108

Descriptive statistics

Standard deviation91816299
Coefficient of variation (CV)0.6430989
Kurtosis0.38123324
Mean1.4277166 × 108
Median Absolute Deviation (MAD)59082585
Skewness0.60645011
Sum1.4277166 × 1011
Variance8.4302327 × 1015
MonotonicityNot monotonic
2023-12-12T18:58:58.971169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
 
9.7%
203308259 2
 
0.2%
135029961 2
 
0.2%
151985608 2
 
0.2%
230322110 2
 
0.2%
172849696 2
 
0.2%
143951312 2
 
0.2%
116926163 2
 
0.2%
123678497 2
 
0.2%
201991993 2
 
0.2%
Other values (884) 885
88.5%
ValueCountFrequency (%)
0 97
9.7%
9385707 1
 
0.1%
9609257 1
 
0.1%
14173667 1
 
0.1%
17588697 1
 
0.1%
17616083 1
 
0.1%
18632354 1
 
0.1%
19463214 1
 
0.1%
24741774 1
 
0.1%
27953414 1
 
0.1%
ValueCountFrequency (%)
475030095 1
0.1%
468990923 1
0.1%
466614152 1
0.1%
460871521 1
0.1%
428644701 1
0.1%
420785861 1
0.1%
409374441 1
0.1%
396567563 1
0.1%
396076360 1
0.1%
395306385 1
0.1%

APPLY_RAT
Real number (ℝ)

Distinct161
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.72102
Minimum2.32
Maximum5.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T18:58:59.112535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.32
5-th percentile2.8995
Q13.61
median3.81
Q33.92
95-th percentile4.17
Maximum5.72
Range3.4
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.39149793
Coefficient of variation (CV)0.10521253
Kurtosis2.9011069
Mean3.72102
Median Absolute Deviation (MAD)0.16
Skewness-0.65150703
Sum3721.02
Variance0.15327063
MonotonicityNot monotonic
2023-12-12T18:58:59.293391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.81 85
 
8.5%
3.91 46
 
4.6%
3.87 43
 
4.3%
3.92 40
 
4.0%
3.67 32
 
3.2%
4.02 28
 
2.8%
3.77 26
 
2.6%
3.74 26
 
2.6%
3.9 23
 
2.3%
3.73 21
 
2.1%
Other values (151) 630
63.0%
ValueCountFrequency (%)
2.32 1
 
0.1%
2.4 1
 
0.1%
2.48 1
 
0.1%
2.49 4
0.4%
2.5 3
0.3%
2.52 1
 
0.1%
2.54 4
0.4%
2.6 2
0.2%
2.64 2
0.2%
2.69 1
 
0.1%
ValueCountFrequency (%)
5.72 1
0.1%
5.46 1
0.1%
5.37 1
0.1%
5.29 1
0.1%
5.06 1
0.1%
4.88 1
0.1%
4.82 1
0.1%
4.66 1
0.1%
4.63 2
0.2%
4.62 1
0.1%

STS
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1000
100.0%

Length

2023-12-12T18:58:59.460779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:59.583319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1000
100.0%

REG_ENO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1769
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1769
2nd row1769
3rd row1769
4th row1769
5th row1769

Common Values

ValueCountFrequency (%)
1769 1000
100.0%

Length

2023-12-12T18:58:59.686432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:58:59.776977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1769 1000
100.0%

REG_DT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2020/10/13 20:19:14
630 
2020/10/13 15:17:01
370 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020/10/13 20:19:14
2nd row2020/10/13 20:19:14
3rd row2020/10/13 20:19:14
4th row2020/10/13 20:19:14
5th row2020/10/13 20:19:14

Common Values

ValueCountFrequency (%)
2020/10/13 20:19:14 630
63.0%
2020/10/13 15:17:01 370
37.0%

Length

2023-12-12T18:58:59.896932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:59:00.059885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020/10/13 1000
50.0%
20:19:14 630
31.5%
15:17:01 370
 
18.5%

Interactions

2023-12-12T18:58:56.398394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:55.320768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:56.049297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:56.532217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:55.460602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:56.161241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:56.675112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:55.579805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:58:56.281251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:59:00.156066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ORG_CDLIQD_PLAN_CDHOLD_CDLOAN_ACC_NOLOAN_RAMTAPPLY_RATREG_DT
LOAN_ORG_CD1.0001.0001.0000.9980.6370.4921.000
LIQD_PLAN_CD1.0001.0001.0000.6260.4490.6241.000
HOLD_CD1.0001.0001.0000.6260.4490.6241.000
LOAN_ACC_NO0.9980.6260.6261.0000.4620.3630.998
LOAN_RAMT0.6370.4490.4490.4621.0000.1990.637
APPLY_RAT0.4920.6240.6240.3630.1991.0000.492
REG_DT1.0001.0001.0000.9980.6370.4921.000
2023-12-12T18:59:00.285986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ORG_CDHOLD_CDREG_DTLIQD_PLAN_CD
LOAN_ORG_CD1.0000.9970.9980.997
HOLD_CD0.9971.0000.9971.000
REG_DT0.9980.9971.0000.997
LIQD_PLAN_CD0.9971.0000.9971.000
2023-12-12T18:59:00.422266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ACC_NOLOAN_RAMTAPPLY_RATLOAN_ORG_CDLIQD_PLAN_CDHOLD_CDREG_DT
LOAN_ACC_NO1.0000.4390.1520.9500.3600.3600.950
LOAN_RAMT0.4391.0000.1110.4920.2330.2330.492
APPLY_RAT0.1520.1111.0000.3830.3660.3660.383
LOAN_ORG_CD0.9500.4920.3831.0000.9970.9970.998
LIQD_PLAN_CD0.3600.2330.3660.9971.0001.0000.997
HOLD_CD0.3600.2330.3660.9971.0001.0000.997
REG_DT0.9500.4920.3830.9980.9970.9971.000

Missing values

2023-12-12T18:58:56.912327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:58:57.112940image/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

LOAN_ORG_CDLIQD_PLAN_CDHOLD_CDLOAN_ACC_NOBASIS_DYLOAN_RAMTAPPLY_RATSTSREG_ENOREG_DT
0I003KHFCMB2013S-11I003-2013-0001152399510002420200901411235022.7217692020/10/13 20:19:14
1I003KHFCMB2013S-11I003-2013-0001718028200007120200901820309195.46217692020/10/13 20:19:14
2I003KHFCMB2013S-11I003-2013-0001627761120000420200901332261615.06217692020/10/13 20:19:14
3I003KHFCMB2013S-11I003-2013-0001659886020001020200901698149333.1217692020/10/13 20:19:14
4I003KHFCMB2013S-11I003-2013-0001566077610003220200901721229915.29217692020/10/13 20:19:14
5I003KHFCMB2013S-11I003-2013-00012465190200014202009012280491882.72217692020/10/13 20:19:14
6I003KHFCMB2013S-11I003-2013-0001157945500004320200901635977672.64217692020/10/13 20:19:14
7I003KHFCMB2013S-11I003-2013-00015750536000009202009011813251532.5217692020/10/13 20:19:14
8I003KHFCMB2013S-11I003-2013-00016354310200010202009012079182552.64217692020/10/13 20:19:14
9I003KHFCMB2013S-11I003-2013-0001723490020000620200901318377842.4217692020/10/13 20:19:14
LOAN_ORG_CDLIQD_PLAN_CDHOLD_CDLOAN_ACC_NOBASIS_DYLOAN_RAMTAPPLY_RATSTSREG_ENOREG_DT
990I004KHFCMB2019S-22I004-2019-00011030025350852020090103.59217692020/10/13 15:17:01
991I004KHFCMB2019S-22I004-2019-000110300253503620200901756250013.62217692020/10/13 15:17:01
992I004KHFCMB2019S-22I004-2019-000110300253466820200901873282853.7217692020/10/13 15:17:01
993I004KHFCMB2019S-22I004-2019-00011030025346412020090103.63217692020/10/13 15:17:01
994I004KHFCMB2019S-22I004-2019-00011030025343882020090103.7217692020/10/13 15:17:01
995I004KHFCMB2019S-22I004-2019-000110300253438520200901941350203.73217692020/10/13 15:17:01
996I004KHFCMB2019S-22I004-2019-0001103002534364202009011324892473.74217692020/10/13 15:17:01
997I004KHFCMB2019S-22I004-2019-0001103002534342202009011503987113.7217692020/10/13 15:17:01
998I004KHFCMB2019S-22I004-2019-000110300253433720200901528798633.59217692020/10/13 15:17:01
999I004KHFCMB2019S-22I004-2019-0001103002534325202009012373613913.89217692020/10/13 15:17:01