Overview

Dataset statistics

Number of variables10
Number of observations119
Missing cells357
Missing cells (%)30.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory89.1 B

Variable types

Numeric5
Unsupported3
DateTime1
Categorical1

Dataset

Description한국주택금융공사 유동화자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072905/fileData.do

Alerts

ACPT_PTNO is highly overall correlated with TENDER_TDYHigh correlation
TENDER_TDY is highly overall correlated with ACPT_PTNOHigh correlation
REG_ENO is highly overall correlated with REG_BRCDHigh correlation
REG_BRCD is highly overall correlated with REG_ENOHigh correlation
UPDT_TS has 119 (100.0%) missing valuesMissing
UPDT_ENO has 119 (100.0%) missing valuesMissing
UPDT_BRCD has 119 (100.0%) missing valuesMissing
UPDT_TS is an unsupported type, check if it needs cleaning or further analysisUnsupported
UPDT_ENO is an unsupported type, check if it needs cleaning or further analysisUnsupported
UPDT_BRCD is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 20:59:02.772632
Analysis finished2023-12-12 20:59:05.424686
Duration2.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ACPT_PTNO
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0172381 × 1010
Minimum2.01107 × 1010
Maximum2.02007 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T05:59:05.509833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.01107 × 1010
5-th percentile2.01307 × 1010
Q12.01707 × 1010
median2.01807 × 1010
Q32.01807 × 1010
95-th percentile2.01907 × 1010
Maximum2.02007 × 1010
Range90000026
Interquartile range (IQR)10000073

Descriptive statistics

Standard deviation17865770
Coefficient of variation (CV)0.000885655
Kurtosis0.97107138
Mean2.0172381 × 1010
Median Absolute Deviation (MAD)9999993
Skewness-1.1917859
Sum2.4005133 × 1012
Variance3.1918573 × 1014
MonotonicityNot monotonic
2023-12-13T05:59:05.655408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180700051 7
 
5.9%
20180700075 4
 
3.4%
20190700037 4
 
3.4%
20190700098 4
 
3.4%
20180700036 4
 
3.4%
20180700061 4
 
3.4%
20170700031 4
 
3.4%
20170700034 4
 
3.4%
20140700028 4
 
3.4%
20180700042 4
 
3.4%
Other values (40) 76
63.9%
ValueCountFrequency (%)
20110700001 1
 
0.8%
20130700001 2
1.7%
20130700007 2
1.7%
20130700008 2
1.7%
20140700005 1
 
0.8%
20140700009 2
1.7%
20140700012 2
1.7%
20140700028 4
3.4%
20150700027 2
1.7%
20160700004 1
 
0.8%
ValueCountFrequency (%)
20200700027 2
1.7%
20190700098 4
3.4%
20190700072 2
1.7%
20190700064 1
 
0.8%
20190700061 1
 
0.8%
20190700056 2
1.7%
20190700037 4
3.4%
20190700031 1
 
0.8%
20190700023 1
 
0.8%
20190700022 3
2.5%

TENDER_TDY
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20182794
Minimum20110706
Maximum20210208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T05:59:05.832202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110706
5-th percentile20140884
Q120180116
median20190329
Q320191221
95-th percentile20201103
Maximum20210208
Range99502
Interquartile range (IQR)11105.5

Descriptive statistics

Standard deviation18346.94
Coefficient of variation (CV)0.00090903867
Kurtosis2.0556839
Mean20182794
Median Absolute Deviation (MAD)9877
Skewness-1.3670671
Sum2.4017525 × 109
Variance3.3661021 × 108
MonotonicityNot monotonic
2023-12-13T05:59:05.967275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191105 2
 
1.7%
20180618 2
 
1.7%
20191128 2
 
1.7%
20200102 2
 
1.7%
20200121 2
 
1.7%
20210125 1
 
0.8%
20180122 1
 
0.8%
20180523 1
 
0.8%
20171226 1
 
0.8%
20190529 1
 
0.8%
Other values (104) 104
87.4%
ValueCountFrequency (%)
20110706 1
0.8%
20131209 1
0.8%
20140116 1
0.8%
20140617 1
0.8%
20140722 1
0.8%
20140728 1
0.8%
20140901 1
0.8%
20140915 1
0.8%
20141020 1
0.8%
20150520 1
0.8%
ValueCountFrequency (%)
20210208 1
0.8%
20210125 1
0.8%
20210111 1
0.8%
20201221 1
0.8%
20201207 1
0.8%
20201116 1
0.8%
20201102 1
0.8%
20201012 1
0.8%
20200818 1
0.8%
20200728 1
0.8%

MDBTR_CUST_NO
Real number (ℝ)

Distinct50
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78401138
Minimum8583309
Maximum1.1958889 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T05:59:06.128706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8583309
5-th percentile16272880
Q173993536
median80730454
Q390295974
95-th percentile99779749
Maximum1.1958889 × 108
Range1.1100558 × 108
Interquartile range (IQR)16302438

Descriptive statistics

Standard deviation21725633
Coefficient of variation (CV)0.27710865
Kurtosis3.1644773
Mean78401138
Median Absolute Deviation (MAD)9180600
Skewness-1.6066459
Sum9.3297354 × 109
Variance4.7200314 × 1014
MonotonicityNot monotonic
2023-12-13T05:59:06.282835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97837492 7
 
5.9%
78780869 4
 
3.4%
16272880 4
 
3.4%
89670421 4
 
3.4%
65971649 4
 
3.4%
83969837 4
 
3.4%
98632120 4
 
3.4%
88828274 4
 
3.4%
77121131 4
 
3.4%
81490939 4
 
3.4%
Other values (40) 76
63.9%
ValueCountFrequency (%)
8583309 3
2.5%
16272880 4
3.4%
33209991 3
2.5%
52292904 1
 
0.8%
56094904 1
 
0.8%
65622011 3
2.5%
65806594 2
1.7%
65971649 4
3.4%
69459772 1
 
0.8%
69745017 2
1.7%
ValueCountFrequency (%)
119588894 2
 
1.7%
114440021 1
 
0.8%
108415732 1
 
0.8%
99779749 3
2.5%
98632120 4
3.4%
97837492 7
5.9%
96867418 1
 
0.8%
96743367 4
3.4%
94755247 2
 
1.7%
93915279 1
 
0.8%

AUCT_CNT_CD
Real number (ℝ)

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.092437
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T05:59:06.397767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1860174
Coefficient of variation (CV)0.56681152
Kurtosis2.0379546
Mean2.092437
Median Absolute Deviation (MAD)1
Skewness1.2755182
Sum249
Variance1.4066372
MonotonicityNot monotonic
2023-12-13T05:59:06.513754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 46
38.7%
2 38
31.9%
3 19
16.0%
4 13
 
10.9%
7 1
 
0.8%
6 1
 
0.8%
5 1
 
0.8%
ValueCountFrequency (%)
1 46
38.7%
2 38
31.9%
3 19
16.0%
4 13
 
10.9%
5 1
 
0.8%
6 1
 
0.8%
7 1
 
0.8%
ValueCountFrequency (%)
7 1
 
0.8%
6 1
 
0.8%
5 1
 
0.8%
4 13
 
10.9%
3 19
16.0%
2 38
31.9%
1 46
38.7%

UPDT_TS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing119
Missing (%)100.0%
Memory size1.2 KiB

UPDT_ENO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing119
Missing (%)100.0%
Memory size1.2 KiB

UPDT_BRCD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing119
Missing (%)100.0%
Memory size1.2 KiB

REG_TS
Date

Distinct50
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2011-08-23 18:22:45
Maximum2020-10-26 16:09:59
2023-12-13T05:59:06.669458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:06.808394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1596.3361
Minimum1127
Maximum2001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-13T05:59:07.212144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1127
5-th percentile1300
Q11467
median1598
Q31786
95-th percentile1968
Maximum2001
Range874
Interquartile range (IQR)319

Descriptive statistics

Standard deviation221.44626
Coefficient of variation (CV)0.13872157
Kurtosis-0.78511887
Mean1596.3361
Median Absolute Deviation (MAD)181
Skewness-0.14384652
Sum189964
Variance49038.445
MonotonicityNot monotonic
2023-12-13T05:59:07.347175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1304 11
 
9.2%
1300 9
 
7.6%
1598 8
 
6.7%
1799 8
 
6.7%
1779 7
 
5.9%
1689 7
 
5.9%
1557 7
 
5.9%
1475 6
 
5.0%
1859 6
 
5.0%
1610 4
 
3.4%
Other values (22) 46
38.7%
ValueCountFrequency (%)
1127 4
 
3.4%
1300 9
7.6%
1304 11
9.2%
1337 2
 
1.7%
1406 1
 
0.8%
1414 1
 
0.8%
1426 1
 
0.8%
1459 1
 
0.8%
1475 6
5.0%
1498 3
 
2.5%
ValueCountFrequency (%)
2001 3
 
2.5%
1968 4
3.4%
1917 2
 
1.7%
1912 2
 
1.7%
1859 6
5.0%
1825 3
 
2.5%
1799 8
6.7%
1793 2
 
1.7%
1779 7
5.9%
1753 1
 
0.8%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
TAB
30 
TBA
15 
TLA
11 
TBB
11 
TAA
8 
Other values (10)
44 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)1.7%

Sample

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

Common Values

ValueCountFrequency (%)
TAB 30
25.2%
TBA 15
12.6%
TLA 11
 
9.2%
TBB 11
 
9.2%
TAA 8
 
6.7%
TQA 7
 
5.9%
TLB 7
 
5.9%
THB 6
 
5.0%
QAD 6
 
5.0%
THO 6
 
5.0%
Other values (5) 12
 
10.1%

Length

2023-12-13T05:59:07.507185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tab 30
25.2%
tba 15
12.6%
tla 11
 
9.2%
tbb 11
 
9.2%
taa 8
 
6.7%
tqa 7
 
5.9%
tlb 7
 
5.9%
thb 6
 
5.0%
qad 6
 
5.0%
tho 6
 
5.0%
Other values (5) 12
 
10.1%

Interactions

2023-12-13T05:59:04.768480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.013800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.450680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.853041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.320640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.837969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.106150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.530473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.939160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.415930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.912797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.187583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.604377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.039524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.509377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:05.002146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.279996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.686772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.135022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.600436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:05.088972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.380280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:03.770862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.245616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:59:04.691398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:59:07.591503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOTENDER_TDYMDBTR_CUST_NOAUCT_CNT_CDREG_TSREG_ENOREG_BRCD
ACPT_PTNO1.0000.9090.4570.0001.0000.6360.726
TENDER_TDY0.9091.0000.5030.0000.9960.6780.729
MDBTR_CUST_NO0.4570.5031.0000.0001.0000.8070.781
AUCT_CNT_CD0.0000.0000.0001.0000.0000.0000.000
REG_TS1.0000.9961.0000.0001.0001.0001.000
REG_ENO0.6360.6780.8070.0001.0001.0000.878
REG_BRCD0.7260.7290.7810.0001.0000.8781.000
2023-12-13T05:59:07.738326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOTENDER_TDYMDBTR_CUST_NOAUCT_CNT_CDREG_ENOREG_BRCD
ACPT_PTNO1.0000.9230.1020.1050.4890.390
TENDER_TDY0.9231.0000.1360.1950.4830.393
MDBTR_CUST_NO0.1020.1361.0000.102-0.0280.431
AUCT_CNT_CD0.1050.1950.1021.000-0.0400.000
REG_ENO0.4890.483-0.028-0.0401.0000.605
REG_BRCD0.3900.3930.4310.0000.6051.000

Missing values

2023-12-13T05:59:05.206901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:59:05.361434image/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

ACPT_PTNOTENDER_TDYMDBTR_CUST_NOAUCT_CNT_CDUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCD
02018070007520210125787808694<NA><NA><NA>2020/09/24 09:31:091779TAB
12018070007520201221787808693<NA><NA><NA>2020/09/24 09:31:091779TAB
22018070007520201116787808692<NA><NA><NA>2020/09/24 09:31:091779TAB
32019070007220200612802810712<NA><NA><NA>2020/07/09 13:53:531689TAB
42019070007220200508802810711<NA><NA><NA>2020/07/09 13:53:531689TAB
52018070007520201012787808691<NA><NA><NA>2020/09/24 09:31:091779TAB
62019070003720200507162728804<NA><NA><NA>2020/06/24 10:43:021968TLA
72019070003720200326162728803<NA><NA><NA>2020/06/24 10:43:021968TLA
82019070003720200220162728802<NA><NA><NA>2020/06/24 10:43:021968TLA
92019070003720200116162728801<NA><NA><NA>2020/06/24 10:43:021968TLA
ACPT_PTNOTENDER_TDYMDBTR_CUST_NOAUCT_CNT_CDUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCD
1092014070002820150520771211311<NA><NA><NA>2015/07/13 11:45:031610TAA
110201607000312017072785833091<NA><NA><NA>2017/12/21 17:25:281300TBA
1112016070001020160411968674181<NA><NA><NA>2016/06/23 10:01:181598TAB
1122014070000920140901844648522<NA><NA><NA>2016/05/26 16:27:461598TAB
1132014070000920140728844648521<NA><NA><NA>2016/05/26 16:27:461598TAB
1142014070001220141020787191312<NA><NA><NA>2014/12/10 16:36:461503TJA
1152014070001220140915787191311<NA><NA><NA>2014/12/10 16:36:461503TJA
1162013070000720140116788490562<NA><NA><NA>2014/02/27 15:59:471337TAB
1172013070000720131209788490561<NA><NA><NA>2014/02/27 15:59:471337TAB
1182011070000120110706694597721<NA><NA><NA>2011/08/23 18:22:451459TPA