Overview

Dataset statistics

Number of variables5
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.1 KiB
Average record size in memory43.1 B

Variable types

Categorical4
Numeric1

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073111/fileData.do

Alerts

RCV_DY has constant value ""Constant
DWRT_BASIS_DY has constant value ""Constant
HOLD_CD is highly overall correlated with SEQ and 1 other fieldsHigh correlation
LIQD_PLAN_CD is highly overall correlated with SEQ and 1 other fieldsHigh correlation
SEQ is highly overall correlated with LIQD_PLAN_CD and 1 other fieldsHigh correlation
LIQD_PLAN_CD is highly imbalanced (69.7%)Imbalance
HOLD_CD is highly imbalanced (69.7%)Imbalance
SEQ has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:28:58.491274
Analysis finished2023-12-12 22:28:58.957537
Duration0.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2014S-04
946 
KHFCMB2012S-36
 
54

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2014S-04
2nd rowKHFCMB2014S-04
3rd rowKHFCMB2014S-04
4th rowKHFCMB2014S-04
5th rowKHFCMB2014S-04

Common Values

ValueCountFrequency (%)
KHFCMB2014S-04 946
94.6%
KHFCMB2012S-36 54
 
5.4%

Length

2023-12-13T07:28:59.039968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:59.147520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2014s-04 946
94.6%
khfcmb2012s-36 54
 
5.4%

HOLD_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
F001-2014-0001
946 
F001-2012-0004
 
54

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF001-2014-0001
2nd rowF001-2014-0001
3rd rowF001-2014-0001
4th rowF001-2014-0001
5th rowF001-2014-0001

Common Values

ValueCountFrequency (%)
F001-2014-0001 946
94.6%
F001-2012-0004 54
 
5.4%

Length

2023-12-13T07:28:59.276377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:59.389705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f001-2014-0001 946
94.6%
f001-2012-0004 54
 
5.4%

RCV_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20140203
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20140203
2nd row20140203
3rd row20140203
4th row20140203
5th row20140203

Common Values

ValueCountFrequency (%)
20140203 1000
100.0%

Length

2023-12-13T07:28:59.529298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:28:59.927629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20140203 1000
100.0%

DWRT_BASIS_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20140131
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20140131
2nd row20140131
3rd row20140131
4th row20140131
5th row20140131

Common Values

ValueCountFrequency (%)
20140131 1000
100.0%

Length

2023-12-13T07:29:00.039621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:29:00.139191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20140131 1000
100.0%

SEQ
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4248.5
Minimum3749
Maximum4748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T07:29:00.251631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3749
5-th percentile3798.95
Q13998.75
median4248.5
Q34498.25
95-th percentile4698.05
Maximum4748
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.067981508
Kurtosis-1.2
Mean4248.5
Median Absolute Deviation (MAD)250
Skewness0
Sum4248500
Variance83416.667
MonotonicityStrictly decreasing
2023-12-13T07:29:00.404994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4748 1
 
0.1%
4076 1
 
0.1%
4089 1
 
0.1%
4088 1
 
0.1%
4087 1
 
0.1%
4086 1
 
0.1%
4085 1
 
0.1%
4084 1
 
0.1%
4083 1
 
0.1%
4082 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
3749 1
0.1%
3750 1
0.1%
3751 1
0.1%
3752 1
0.1%
3753 1
0.1%
3754 1
0.1%
3755 1
0.1%
3756 1
0.1%
3757 1
0.1%
3758 1
0.1%
ValueCountFrequency (%)
4748 1
0.1%
4747 1
0.1%
4746 1
0.1%
4745 1
0.1%
4744 1
0.1%
4743 1
0.1%
4742 1
0.1%
4741 1
0.1%
4740 1
0.1%
4739 1
0.1%

Interactions

2023-12-13T07:28:58.626382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:29:00.512408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIQD_PLAN_CDHOLD_CDSEQ
LIQD_PLAN_CD1.0001.0000.876
HOLD_CD1.0001.0000.876
SEQ0.8760.8761.000
2023-12-13T07:29:00.610683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDLIQD_PLAN_CD
HOLD_CD1.0000.990
LIQD_PLAN_CD0.9901.000
2023-12-13T07:29:00.706067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQLIQD_PLAN_CDHOLD_CD
SEQ1.0000.7110.711
LIQD_PLAN_CD0.7111.0000.990
HOLD_CD0.7110.9901.000

Missing values

2023-12-13T07:28:58.750043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:28:58.909097image/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_CDRCV_DYDWRT_BASIS_DYSEQ
0KHFCMB2014S-04F001-2014-000120140203201401314748
1KHFCMB2014S-04F001-2014-000120140203201401314747
2KHFCMB2014S-04F001-2014-000120140203201401314746
3KHFCMB2014S-04F001-2014-000120140203201401314745
4KHFCMB2014S-04F001-2014-000120140203201401314744
5KHFCMB2014S-04F001-2014-000120140203201401314743
6KHFCMB2014S-04F001-2014-000120140203201401314742
7KHFCMB2014S-04F001-2014-000120140203201401314741
8KHFCMB2014S-04F001-2014-000120140203201401314740
9KHFCMB2014S-04F001-2014-000120140203201401314739
LIQD_PLAN_CDHOLD_CDRCV_DYDWRT_BASIS_DYSEQ
990KHFCMB2012S-36F001-2012-000420140203201401313758
991KHFCMB2012S-36F001-2012-000420140203201401313757
992KHFCMB2012S-36F001-2012-000420140203201401313756
993KHFCMB2012S-36F001-2012-000420140203201401313755
994KHFCMB2012S-36F001-2012-000420140203201401313754
995KHFCMB2012S-36F001-2012-000420140203201401313753
996KHFCMB2012S-36F001-2012-000420140203201401313752
997KHFCMB2012S-36F001-2012-000420140203201401313751
998KHFCMB2012S-36F001-2012-000420140203201401313750
999KHFCMB2012S-36F001-2012-000420140203201401313749