Overview

Dataset statistics

Number of variables12
Number of observations103
Missing cells136
Missing cells (%)11.0%
Duplicate rows6
Duplicate rows (%)5.8%
Total size in memory10.7 KiB
Average record size in memory106.3 B

Variable types

Categorical6
Boolean1
Numeric4
DateTime1

Dataset

Description한국주택금융공사 신탁자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072819/fileData.do

Alerts

Dataset has 6 (5.8%) duplicate rowsDuplicates
OMIT_STLE_DVCD is highly overall correlated with REG_ENO and 3 other fieldsHigh correlation
MSPRTC_SEQ is highly overall correlated with OMIT_STLE_DVCDHigh correlation
MSPRTC_KIND_CD is highly overall correlated with MSPRTC_REQ_DY and 5 other fieldsHigh correlation
REG_BRCD is highly overall correlated with MSPRTC_REQ_DY and 7 other fieldsHigh correlation
STLE_DVCD is highly overall correlated with REG_ENO and 3 other fieldsHigh correlation
RCV_REG_YN is highly overall correlated with MSPRTC_REQ_DY and 5 other fieldsHigh correlation
MSPRTC_REQ_DY is highly overall correlated with AFTFCT_PROOF_DY and 4 other fieldsHigh correlation
AFTFCT_PROOF_DY is highly overall correlated with MSPRTC_REQ_DY and 5 other fieldsHigh correlation
RLS_REQ_DY is highly overall correlated with MSPRTC_REQ_DY and 4 other fieldsHigh correlation
REG_ENO is highly overall correlated with STLE_DVCD and 2 other fieldsHigh correlation
RLS_STLE_DVCD is highly overall correlated with AFTFCT_PROOF_DY and 3 other fieldsHigh correlation
MSPRTC_SEQ is highly imbalanced (64.2%)Imbalance
MSPRTC_KIND_CD is highly imbalanced (51.0%)Imbalance
OMIT_STLE_DVCD is highly imbalanced (64.2%)Imbalance
RLS_STLE_DVCD is highly imbalanced (62.7%)Imbalance
MSPRTC_REQ_DY has 7 (6.8%) missing valuesMissing
AFTFCT_PROOF_DY has 37 (35.9%) missing valuesMissing
RLS_REQ_DY has 92 (89.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:11:23.095124
Analysis finished2023-12-12 19:11:26.267755
Duration3.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MSPRTC_SEQ
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
1
96 
2
 
7

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 96
93.2%
2 7
 
6.8%

Length

2023-12-13T04:11:26.369603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:26.500420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 96
93.2%
2 7
 
6.8%

RCV_REG_YN
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size235.0 B
False
81 
True
22 
ValueCountFrequency (%)
False 81
78.6%
True 22
 
21.4%
2023-12-13T04:11:26.605882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

MSPRTC_REQ_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)55.2%
Missing7
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean20077820
Minimum20050823
Maximum20090708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T04:11:26.766364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050823
5-th percentile20060273
Q120070700
median20081120
Q320081125
95-th percentile20090606
Maximum20090708
Range39885
Interquartile range (IQR)10424.75

Descriptive statistics

Standard deviation10578.766
Coefficient of variation (CV)0.00052688815
Kurtosis-0.1336956
Mean20077820
Median Absolute Deviation (MAD)9145
Skewness-0.81555002
Sum1.9274708 × 109
Variance1.1191028 × 108
MonotonicityNot monotonic
2023-12-13T04:11:26.944220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20081125 20
19.4%
20081120 18
17.5%
20090515 3
 
2.9%
20070827 2
 
1.9%
20090708 2
 
1.9%
20090605 2
 
1.9%
20090608 2
 
1.9%
20090511 2
 
1.9%
20080924 1
 
1.0%
20090430 1
 
1.0%
Other values (43) 43
41.7%
(Missing) 7
 
6.8%
ValueCountFrequency (%)
20050823 1
1.0%
20051211 1
1.0%
20051216 1
1.0%
20060110 1
1.0%
20060127 1
1.0%
20060322 1
1.0%
20060324 1
1.0%
20060403 1
1.0%
20060415 1
1.0%
20060710 1
1.0%
ValueCountFrequency (%)
20090708 2
1.9%
20090630 1
 
1.0%
20090608 2
1.9%
20090605 2
1.9%
20090603 1
 
1.0%
20090602 1
 
1.0%
20090601 1
 
1.0%
20090515 3
2.9%
20090514 1
 
1.0%
20090513 1
 
1.0%

MSPRTC_KIND_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
1
87 
6
9 
<NA>
 
7

Length

Max length4
Median length1
Mean length1.2038835
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 87
84.5%
6 9
 
8.7%
<NA> 7
 
6.8%

Length

2023-12-13T04:11:27.122183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:27.253543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 87
84.5%
6 9
 
8.7%
na 7
 
6.8%

STLE_DVCD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
1
63 
<NA>
33 
2
7 

Length

Max length4
Median length1
Mean length1.961165
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row<NA>
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 63
61.2%
<NA> 33
32.0%
2 7
 
6.8%

Length

2023-12-13T04:11:27.408311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:27.540398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 63
61.2%
na 33
32.0%
2 7
 
6.8%

AFTFCT_PROOF_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)50.0%
Missing37
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean20080460
Minimum20060210
Maximum20090806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T04:11:27.674850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060210
5-th percentile20060882
Q120081125
median20081128
Q320081206
95-th percentile20090681
Maximum20090806
Range30596
Interquartile range (IQR)81.25

Descriptive statistics

Standard deviation8102.6899
Coefficient of variation (CV)0.00040351117
Kurtosis1.1947108
Mean20080460
Median Absolute Deviation (MAD)38
Skewness-1.0888317
Sum1.3253104 × 109
Variance65653583
MonotonicityNot monotonic
2023-12-13T04:11:27.826777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
20081128 18
17.5%
20081125 14
 
13.6%
20090514 2
 
1.9%
20090525 2
 
1.9%
20081201 2
 
1.9%
20060525 1
 
1.0%
20061103 1
 
1.0%
20070809 1
 
1.0%
20070917 1
 
1.0%
20070808 1
 
1.0%
Other values (23) 23
22.3%
(Missing) 37
35.9%
ValueCountFrequency (%)
20060210 1
1.0%
20060428 1
1.0%
20060525 1
1.0%
20060808 1
1.0%
20061103 1
1.0%
20061117 1
1.0%
20070123 1
1.0%
20070808 1
1.0%
20070809 1
1.0%
20070913 1
1.0%
ValueCountFrequency (%)
20090806 1
1.0%
20090729 1
1.0%
20090702 1
1.0%
20090701 1
1.0%
20090622 1
1.0%
20090617 1
1.0%
20090609 1
1.0%
20090526 1
1.0%
20090525 2
1.9%
20090514 2
1.9%

OMIT_STLE_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
<NA>
96 
2
 
7

Length

Max length4
Median length4
Mean length3.7961165
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 96
93.2%
2 7
 
6.8%

Length

2023-12-13T04:11:27.968732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:28.092912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 96
93.2%
2 7
 
6.8%

RLS_REQ_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing92
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean20082568
Minimum20071127
Maximum20090713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T04:11:28.207728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071127
5-th percentile20075720
Q120080414
median20081202
Q320085864
95-th percentile20090713
Maximum20090713
Range19586
Interquartile range (IQR)5450.5

Descriptive statistics

Standard deviation5927.6159
Coefficient of variation (CV)0.00029516224
Kurtosis0.29378714
Mean20082568
Median Absolute Deviation (MAD)795
Skewness0.021562499
Sum2.2090825 × 108
Variance35136630
MonotonicityNot monotonic
2023-12-13T04:11:28.379075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
20090713 2
 
1.9%
20071127 1
 
1.0%
20081202 1
 
1.0%
20081204 1
 
1.0%
20081208 1
 
1.0%
20080407 1
 
1.0%
20090521 1
 
1.0%
20080425 1
 
1.0%
20080421 1
 
1.0%
20080312 1
 
1.0%
(Missing) 92
89.3%
ValueCountFrequency (%)
20071127 1
1.0%
20080312 1
1.0%
20080407 1
1.0%
20080421 1
1.0%
20080425 1
1.0%
20081202 1
1.0%
20081204 1
1.0%
20081208 1
1.0%
20090521 1
1.0%
20090713 2
1.9%
ValueCountFrequency (%)
20090713 2
1.9%
20090521 1
1.0%
20081208 1
1.0%
20081204 1
1.0%
20081202 1
1.0%
20080425 1
1.0%
20080421 1
1.0%
20080407 1
1.0%
20080312 1
1.0%
20071127 1
1.0%

RLS_STLE_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size956.0 B
<NA>
92 
2
 
7
1
 
4

Length

Max length4
Median length4
Mean length3.6796117
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 92
89.3%
2 7
 
6.8%
1 4
 
3.9%

Length

2023-12-13T04:11:28.519279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:11:28.627221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 92
89.3%
2 7
 
6.8%
1 4
 
3.9%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size956.0 B
BCC
43 
ACS
16 
TBA
11 
TAA
11 
BBC
8 
Other values (7)
14 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)2.9%

Sample

1st rowTBA
2nd rowACS
3rd rowBIC
4th rowACS
5th rowACS

Common Values

ValueCountFrequency (%)
BCC 43
41.7%
ACS 16
 
15.5%
TBA 11
 
10.7%
TAA 11
 
10.7%
BBC 8
 
7.8%
BIC 5
 
4.9%
THA 2
 
1.9%
TMA 2
 
1.9%
THO 2
 
1.9%
QAD 1
 
1.0%
Other values (2) 2
 
1.9%

Length

2023-12-13T04:11:28.757118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bcc 43
41.7%
acs 16
 
15.5%
tba 11
 
10.7%
taa 11
 
10.7%
bbc 8
 
7.8%
bic 5
 
4.9%
tha 2
 
1.9%
tma 2
 
1.9%
tho 2
 
1.9%
qad 1
 
1.0%
Other values (2) 2
 
1.9%

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1357.2039
Minimum1009
Maximum7244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-13T04:11:28.900669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile1037
Q11037
median1199
Q31256
95-th percentile1444
Maximum7244
Range6235
Interquartile range (IQR)219

Descriptive statistics

Standard deviation1032.7969
Coefficient of variation (CV)0.76097402
Kurtosis29.838596
Mean1357.2039
Median Absolute Deviation (MAD)144
Skewness5.5417609
Sum139792
Variance1066669.4
MonotonicityNot monotonic
2023-12-13T04:11:29.065062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1037 34
33.0%
1256 9
 
8.7%
1121 6
 
5.8%
1247 5
 
4.9%
1444 4
 
3.9%
1394 4
 
3.9%
1253 3
 
2.9%
1254 3
 
2.9%
7244 3
 
2.9%
1331 3
 
2.9%
Other values (21) 29
28.2%
ValueCountFrequency (%)
1009 1
 
1.0%
1037 34
33.0%
1091 1
 
1.0%
1095 2
 
1.9%
1121 6
 
5.8%
1157 2
 
1.9%
1174 1
 
1.0%
1178 1
 
1.0%
1179 2
 
1.9%
1191 1
 
1.0%
ValueCountFrequency (%)
7244 3
2.9%
1444 4
3.9%
1424 1
 
1.0%
1414 1
 
1.0%
1394 4
3.9%
1393 1
 
1.0%
1347 2
1.9%
1343 1
 
1.0%
1337 2
1.9%
1331 3
2.9%

REG_DT
Date

Distinct52
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Memory size956.0 B
Minimum2007-01-23 00:00:00
Maximum2009-07-08 00:00:00
2023-12-13T04:11:29.228885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:29.376162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T04:11:25.188610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:23.732898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.188280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.627984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:25.308918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:23.870294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.295347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.741349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:25.422700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:23.971170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.394605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.864024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:25.540818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.081197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:24.505932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:11:25.021710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:11:29.803808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MSPRTC_SEQRCV_REG_YNMSPRTC_REQ_DYMSPRTC_KIND_CDSTLE_DVCDAFTFCT_PROOF_DYRLS_REQ_DYRLS_STLE_DVCDREG_BRCDREG_ENOREG_DT
MSPRTC_SEQ1.0000.0000.2650.0000.0000.0000.0000.0000.0000.0000.401
RCV_REG_YN0.0001.0000.9500.751NaN0.8850.0000.0000.8240.3730.998
MSPRTC_REQ_DY0.2650.9501.0000.7030.2260.8710.2410.0000.8880.5230.992
MSPRTC_KIND_CD0.0000.7510.7031.000NaN0.562NaN0.0000.7090.0000.946
STLE_DVCD0.000NaN0.226NaN1.0000.0140.1320.0000.796NaN1.000
AFTFCT_PROOF_DY0.0000.8850.8710.5620.0141.0000.000NaN0.9220.5601.000
RLS_REQ_DY0.0000.0000.241NaN0.1320.0001.0001.0000.913NaN1.000
RLS_STLE_DVCD0.0000.0000.0000.0000.000NaN1.0001.0001.000NaN1.000
REG_BRCD0.0000.8240.8880.7090.7960.9220.9131.0001.0000.5130.998
REG_ENO0.0000.3730.5230.000NaN0.560NaNNaN0.5131.0001.000
REG_DT0.4010.9980.9920.9461.0001.0001.0001.0000.9981.0001.000
2023-12-13T04:11:29.944646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
OMIT_STLE_DVCDRLS_STLE_DVCDMSPRTC_SEQMSPRTC_KIND_CDREG_BRCDSTLE_DVCDRCV_REG_YN
OMIT_STLE_DVCD1.000NaN1.000NaN1.000NaN1.000
RLS_STLE_DVCDNaN1.0000.0000.0000.6670.0000.000
MSPRTC_SEQ1.0000.0001.0000.0000.0000.0000.000
MSPRTC_KIND_CDNaN0.0000.0001.0000.5291.0000.541
REG_BRCD1.0000.6670.0000.5291.0000.5890.632
STLE_DVCDNaN0.0000.0001.0000.5891.0001.000
RCV_REG_YN1.0000.0000.0000.5410.6321.0001.000
2023-12-13T04:11:30.086608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MSPRTC_REQ_DYAFTFCT_PROOF_DYRLS_REQ_DYREG_ENOMSPRTC_SEQRCV_REG_YNMSPRTC_KIND_CDSTLE_DVCDOMIT_STLE_DVCDRLS_STLE_DVCDREG_BRCD
MSPRTC_REQ_DY1.0000.9370.703-0.1490.1880.7920.5070.2680.0000.0000.538
AFTFCT_PROOF_DY0.9371.0000.5640.0690.0000.7240.5200.0000.0001.0000.765
RLS_REQ_DY0.7030.5641.000-0.1790.0000.2370.8820.0000.0000.8820.542
REG_ENO-0.1490.069-0.1791.0000.0000.2440.0001.0001.0001.0000.380
MSPRTC_SEQ0.1880.0000.0000.0001.0000.0000.0000.0001.0000.0000.000
RCV_REG_YN0.7920.7240.2370.2440.0001.0000.5411.0001.0000.0000.632
MSPRTC_KIND_CD0.5070.5200.8820.0000.0000.5411.0001.0000.0000.0000.529
STLE_DVCD0.2680.0000.0001.0000.0001.0001.0001.0000.0000.0000.589
OMIT_STLE_DVCD0.0000.0000.0001.0001.0001.0000.0000.0001.0000.0001.000
RLS_STLE_DVCD0.0001.0000.8821.0000.0000.0000.0000.0000.0001.0000.667
REG_BRCD0.5380.7650.5420.3800.0000.6320.5290.5891.0000.6671.000

Missing values

2023-12-13T04:11:25.737035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:11:25.974018image/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.
2023-12-13T04:11:26.136484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MSPRTC_SEQRCV_REG_YNMSPRTC_REQ_DYMSPRTC_KIND_CDSTLE_DVCDAFTFCT_PROOF_DYOMIT_STLE_DVCDRLS_REQ_DYRLS_STLE_DVCDREG_BRCDREG_ENOREG_DT
01N2009060311<NA><NA><NA><NA>TBA14142009/06/03 00:00:00
11N200903171120090331<NA><NA><NA>ACS11212009/03/17 00:00:00
21Y200902131<NA>20090217<NA><NA><NA>BIC10952009/04/08 00:00:00
31N200905111120090514<NA><NA><NA>ACS12532009/05/11 00:00:00
41N200906081120090702<NA><NA><NA>ACS11212009/06/08 00:00:00
51N200802221220080227<NA><NA><NA>ACS12532008/02/22 00:00:00
61Y200704026<NA><NA><NA><NA><NA>ACS12542008/03/12 00:00:00
71Y200603226<NA><NA><NA>200711271QAD14242007/12/11 00:00:00
81N2008111011<NA><NA><NA><NA>ACS14442008/11/10 00:00:00
92N200811201120081125<NA><NA><NA>BCC12562008/11/20 00:00:00
MSPRTC_SEQRCV_REG_YNMSPRTC_REQ_DYMSPRTC_KIND_CDSTLE_DVCDAFTFCT_PROOF_DYOMIT_STLE_DVCDRLS_REQ_DYRLS_STLE_DVCDREG_BRCDREG_ENOREG_DT
931Y200512111<NA><NA><NA><NA><NA>TAA11572007/04/10 00:00:00
941N2007050212<NA><NA><NA><NA>TAA12142007/05/02 00:00:00
951N<NA><NA><NA><NA>2<NA><NA>TMA13472007/04/10 00:00:00
961N200706291120070809<NA><NA><NA>TBA10092007/06/29 00:00:00
971N200610171120061103<NA>200804212THO12122007/07/09 00:00:00
981Y200601271<NA><NA><NA><NA><NA>THO12122007/04/06 00:00:00
991Y200704251<NA>20060525<NA><NA><NA>THA11742007/10/24 00:00:00
1001Y200607106<NA>20060808<NA><NA><NA>TPA10912007/07/05 00:00:00
1012N2007102512<NA><NA>200803122TAA12142007/10/25 00:00:00
1021N200707271120070808<NA><NA><NA>TAA11212007/07/27 00:00:00

Duplicate rows

Most frequently occurring

MSPRTC_SEQRCV_REG_YNMSPRTC_REQ_DYMSPRTC_KIND_CDSTLE_DVCDAFTFCT_PROOF_DYOMIT_STLE_DVCDRLS_REQ_DYRLS_STLE_DVCDREG_BRCDREG_ENOREG_DT# duplicates
21N200811251120081128<NA><NA><NA>BCC10372008/11/25 00:00:0017
01N200811201120081125<NA><NA><NA>BCC10372008/11/20 00:00:006
11N200811201120081125<NA><NA><NA>BCC12562008/11/20 00:00:005
31N200811251120081201<NA><NA><NA>BCC10372008/11/25 00:00:002
41N2009070811<NA><NA><NA><NA>ACS14442009/07/08 00:00:002
52N200811201120081125<NA><NA><NA>BCC12562008/11/20 00:00:002