Overview

Dataset statistics

Number of variables10
Number of observations1000
Missing cells1553
Missing cells (%)15.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.1 KiB
Average record size in memory86.1 B

Variable types

Numeric5
Categorical3
DateTime2

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072803/fileData.do

Alerts

UPDT_BRCD is highly overall correlated with REG_BR_CDHigh correlation
REG_BR_CD is highly overall correlated with ACPT_PTNO and 1 other fieldsHigh correlation
ACPT_PTNO is highly overall correlated with REG_BR_CDHigh correlation
UPDT_ENO is highly overall correlated with REG_ENOHigh correlation
REG_ENO is highly overall correlated with UPDT_ENOHigh correlation
AFTFCT_PROOF_DY has 940 (94.0%) missing valuesMissing
UPDT_ENO has 613 (61.3%) missing valuesMissing
ACPT_PTNO is highly skewed (γ1 = -31.25746325)Skewed
ACPT_PTNO has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:36:12.945803
Analysis finished2023-12-12 23:36:16.288982
Duration3.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ACPT_PTNO
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0199985 × 1010
Minimum2.0090102 × 1010
Maximum2.0200106 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T08:36:16.389939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0090102 × 1010
5-th percentile2.0200104 × 1010
Q12.0200105 × 1010
median2.0200105 × 1010
Q32.0200105 × 1010
95-th percentile2.0200106 × 1010
Maximum2.0200106 × 1010
Range1.1000389 × 108
Interquartile range (IQR)596.75

Descriptive statistics

Standard deviation3492635.8
Coefficient of variation (CV)0.00017290289
Kurtosis983.68919
Mean2.0199985 × 1010
Median Absolute Deviation (MAD)299
Skewness-31.257463
Sum2.0199985 × 1013
Variance1.2198505 × 1013
MonotonicityNot monotonic
2023-12-13T08:36:16.602480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200105556 1
 
0.1%
20200104757 1
 
0.1%
20200104773 1
 
0.1%
20200104777 1
 
0.1%
20200104779 1
 
0.1%
20200104778 1
 
0.1%
20200104776 1
 
0.1%
20200104758 1
 
0.1%
20200104772 1
 
0.1%
20200104763 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
20090101682 1
0.1%
20190105603 1
0.1%
20200100991 1
0.1%
20200102996 1
0.1%
20200103135 1
0.1%
20200103218 1
0.1%
20200104321 1
0.1%
20200104323 1
0.1%
20200104334 1
0.1%
20200104342 1
0.1%
ValueCountFrequency (%)
20200105576 1
0.1%
20200105574 1
0.1%
20200105571 1
0.1%
20200105570 1
0.1%
20200105567 1
0.1%
20200105563 1
0.1%
20200105561 1
0.1%
20200105560 1
0.1%
20200105558 1
0.1%
20200105557 1
0.1%

MDBTR_CUST_NO
Real number (ℝ)

Distinct914
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83930355
Minimum613099
Maximum1.3988455 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T08:36:16.804008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum613099
5-th percentile22805309
Q168908681
median88817864
Q31.0929834 × 108
95-th percentile1.238924 × 108
Maximum1.3988455 × 108
Range1.3927145 × 108
Interquartile range (IQR)40389656

Descriptive statistics

Standard deviation31505812
Coefficient of variation (CV)0.37538041
Kurtosis-0.31863185
Mean83930355
Median Absolute Deviation (MAD)20409610
Skewness-0.68778647
Sum8.3930355 × 1010
Variance9.9261616 × 1014
MonotonicityNot monotonic
2023-12-13T08:36:16.997412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4611644 13
 
1.3%
79469529 6
 
0.6%
128336338 3
 
0.3%
111019785 3
 
0.3%
78200349 3
 
0.3%
70083344 3
 
0.3%
106204822 3
 
0.3%
78148621 3
 
0.3%
85440558 3
 
0.3%
118598704 3
 
0.3%
Other values (904) 957
95.7%
ValueCountFrequency (%)
613099 1
 
0.1%
711726 1
 
0.1%
3664841 2
 
0.2%
4611644 13
1.3%
4638892 1
 
0.1%
5322026 1
 
0.1%
9357851 1
 
0.1%
12091722 1
 
0.1%
13977201 1
 
0.1%
14470383 1
 
0.1%
ValueCountFrequency (%)
139884549 1
0.1%
137655932 1
0.1%
137260291 1
0.1%
136202904 1
0.1%
131820679 1
0.1%
131765404 1
0.1%
130715235 1
0.1%
130687431 1
0.1%
130657348 1
0.1%
130301869 1
0.1%

MSPRTC_ORG_CD
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
3
349 
1
331 
<NA>
320 

Length

Max length4
Median length1
Mean length1.96
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 349
34.9%
1 331
33.1%
<NA> 320
32.0%

Length

2023-12-13T08:36:17.187211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:36:17.334912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 349
34.9%
1 331
33.1%
na 320
32.0%

AFTFCT_PROOF_DY
Real number (ℝ)

MISSING 

Distinct53
Distinct (%)88.3%
Missing940
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean20196495
Minimum20110927
Maximum20201026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T08:36:17.482780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110927
5-th percentile20180299
Q120200227
median20200519
Q320200720
95-th percentile20200921
Maximum20201026
Range90099
Interquartile range (IQR)493.25

Descriptive statistics

Standard deviation12758.034
Coefficient of variation (CV)0.00063169548
Kurtosis35.230239
Mean20196495
Median Absolute Deviation (MAD)203
Skewness-5.4898241
Sum1.2117897 × 109
Variance1.6276744 × 108
MonotonicityNot monotonic
2023-12-13T08:36:17.600176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200709 2
 
0.2%
20190909 2
 
0.2%
20200508 2
 
0.2%
20200616 2
 
0.2%
20200423 2
 
0.2%
20200907 2
 
0.2%
20200722 2
 
0.2%
20200511 1
 
0.1%
20180309 1
 
0.1%
20191116 1
 
0.1%
Other values (43) 43
 
4.3%
(Missing) 940
94.0%
ValueCountFrequency (%)
20110927 1
0.1%
20170901 1
0.1%
20180110 1
0.1%
20180309 1
0.1%
20190227 1
0.1%
20190626 1
0.1%
20190909 2
0.2%
20190919 1
0.1%
20190925 1
0.1%
20190930 1
0.1%
ValueCountFrequency (%)
20201026 1
0.1%
20201020 1
0.1%
20200922 1
0.1%
20200921 1
0.1%
20200918 1
0.1%
20200914 1
0.1%
20200907 2
0.2%
20200828 1
0.1%
20200825 1
0.1%
20200820 1
0.1%

UPDT_TS
Date

Distinct949
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2020-08-14 13:09:36
Maximum2020-10-30 09:32:49
2023-12-13T08:36:17.702942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:17.812680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

UPDT_ENO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)18.6%
Missing613
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean2483.4264
Minimum1118
Maximum53645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T08:36:17.921807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1118
5-th percentile1213
Q11590
median1690
Q31872
95-th percentile1976.5
Maximum53645
Range52527
Interquartile range (IQR)282

Descriptive statistics

Standard deviation6431.7691
Coefficient of variation (CV)2.5898771
Kurtosis60.182732
Mean2483.4264
Median Absolute Deviation (MAD)153
Skewness7.8617015
Sum961086
Variance41367653
MonotonicityNot monotonic
2023-12-13T08:36:18.033294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1605 32
 
3.2%
1690 27
 
2.7%
1487 24
 
2.4%
1872 19
 
1.9%
1592 17
 
1.7%
1590 14
 
1.4%
1686 12
 
1.2%
1842 12
 
1.2%
1883 12
 
1.2%
1696 12
 
1.2%
Other values (62) 206
 
20.6%
(Missing) 613
61.3%
ValueCountFrequency (%)
1118 3
0.3%
1138 3
0.3%
1142 5
0.5%
1160 1
 
0.1%
1166 4
0.4%
1183 1
 
0.1%
1185 1
 
0.1%
1195 2
 
0.2%
1255 1
 
0.1%
1339 7
0.7%
ValueCountFrequency (%)
53645 4
0.4%
53644 2
 
0.2%
2002 1
 
0.1%
1987 4
0.4%
1980 3
0.3%
1978 6
0.6%
1973 2
 
0.2%
1958 5
0.5%
1937 2
 
0.2%
1934 3
0.3%

UPDT_BRCD
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
<NA>
613 
QAD
 
58
TAA
 
50
TAC
 
37
TAD
 
28
Other values (21)
214 

Length

Max length4
Median length4
Mean length3.613
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 613
61.3%
QAD 58
 
5.8%
TAA 50
 
5.0%
TAC 37
 
3.7%
TAD 28
 
2.8%
THA 26
 
2.6%
TPA 22
 
2.2%
THO 20
 
2.0%
TBA 18
 
1.8%
THB 17
 
1.7%
Other values (16) 111
 
11.1%

Length

2023-12-13T08:36:18.131507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 613
61.3%
qad 58
 
5.8%
taa 50
 
5.0%
tac 37
 
3.7%
tad 28
 
2.8%
tha 26
 
2.6%
tpa 22
 
2.2%
tho 20
 
2.0%
tba 18
 
1.8%
thb 17
 
1.7%
Other values (16) 111
 
11.1%

REG_TS
Date

Distinct947
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2009-06-18 09:50:18
Maximum2020-10-30 09:32:49
2023-12-13T08:36:18.240639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:18.392554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2129.912
Minimum1007
Maximum53645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T08:36:18.554434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile1023
Q11136
median1215
Q31605
95-th percentile1926
Maximum53645
Range52638
Interquartile range (IQR)469

Descriptive statistics

Standard deviation6366.6833
Coefficient of variation (CV)2.9891767
Kurtosis61.73624
Mean2129.912
Median Absolute Deviation (MAD)134
Skewness7.9673001
Sum2129912
Variance40534657
MonotonicityNot monotonic
2023-12-13T08:36:18.671261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023 68
 
6.8%
1136 41
 
4.1%
1339 38
 
3.8%
1166 36
 
3.6%
1037 33
 
3.3%
1605 32
 
3.2%
1121 30
 
3.0%
1160 27
 
2.7%
1221 27
 
2.7%
1690 26
 
2.6%
Other values (104) 642
64.2%
ValueCountFrequency (%)
1007 8
 
0.8%
1021 7
 
0.7%
1023 68
6.8%
1032 1
 
0.1%
1037 33
3.3%
1081 8
 
0.8%
1086 13
 
1.3%
1088 8
 
0.8%
1098 24
 
2.4%
1103 7
 
0.7%
ValueCountFrequency (%)
53645 9
0.9%
53644 6
0.6%
2002 1
 
0.1%
1987 4
0.4%
1980 2
 
0.2%
1978 5
0.5%
1973 2
 
0.2%
1958 5
0.5%
1937 3
 
0.3%
1934 3
 
0.3%

REG_BR_CD
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
QAD
147 
TAC
101 
THO
88 
TAA
81 
TPA
63 
Other values (22)
520 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowACS
2nd rowACS
3rd rowACS
4th rowTQA
5th rowTAC

Common Values

ValueCountFrequency (%)
QAD 147
14.7%
TAC 101
 
10.1%
THO 88
 
8.8%
TAA 81
 
8.1%
TPA 63
 
6.3%
TAD 58
 
5.8%
THB 54
 
5.4%
THA 51
 
5.1%
ACS 50
 
5.0%
TAB 48
 
4.8%
Other values (17) 259
25.9%

Length

2023-12-13T08:36:18.774477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qad 147
14.7%
tac 101
 
10.1%
tho 88
 
8.8%
taa 81
 
8.1%
tpa 63
 
6.3%
tad 58
 
5.8%
thb 54
 
5.4%
tha 51
 
5.1%
acs 50
 
5.0%
tab 48
 
4.8%
Other values (17) 259
25.9%

Interactions

2023-12-13T08:36:15.548084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:13.428223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.000837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.496266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.191478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.621096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:13.549528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.124053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.609579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.262212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.703860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:13.695197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.222829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.692009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.353539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.769122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:13.784453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.307055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.764414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.417678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.830968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:13.877698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:14.409417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.130757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:36:15.482002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:36:18.846858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOMDBTR_CUST_NOMSPRTC_ORG_CDAFTFCT_PROOF_DYUPDT_ENOUPDT_BRCDREG_ENOREG_BR_CD
ACPT_PTNO1.000NaNNaNNaNNaNNaNNaNNaN
MDBTR_CUST_NONaN1.0000.0940.0000.0000.3770.0000.531
MSPRTC_ORG_CDNaN0.0941.0000.0000.0000.3610.2020.358
AFTFCT_PROOF_DYNaN0.0000.0001.000NaN0.3930.0000.393
UPDT_ENONaN0.0000.000NaN1.0000.1950.7800.202
UPDT_BRCDNaN0.3770.3610.3930.1951.0000.4861.000
REG_ENONaN0.0000.2020.0000.7800.4861.0000.294
REG_BR_CDNaN0.5310.3580.3930.2021.0000.2941.000
2023-12-13T08:36:18.952703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MSPRTC_ORG_CDUPDT_BRCDREG_BR_CD
MSPRTC_ORG_CD1.0000.3030.302
UPDT_BRCD0.3031.0000.999
REG_BR_CD0.3020.9991.000
2023-12-13T08:36:19.042415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOMDBTR_CUST_NOAFTFCT_PROOF_DYUPDT_ENOREG_ENOMSPRTC_ORG_CDUPDT_BRCDREG_BR_CD
ACPT_PTNO1.0000.0090.2790.0610.0970.0000.0460.987
MDBTR_CUST_NO0.0091.0000.1410.0430.1600.0640.1390.217
AFTFCT_PROOF_DY0.2790.1411.0000.1510.1480.0000.0680.068
UPDT_ENO0.0610.0430.1511.0000.6220.0000.1650.157
REG_ENO0.0970.1600.1480.6221.0000.1310.4090.250
MSPRTC_ORG_CD0.0000.0640.0000.0000.1311.0000.3030.302
UPDT_BRCD0.0460.1390.0680.1650.4090.3031.0000.999
REG_BR_CD0.9870.2170.0680.1570.2500.3020.9991.000

Missing values

2023-12-13T08:36:15.926756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:36:16.068227image/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.
2023-12-13T08:36:16.206796image/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

ACPT_PTNOMDBTR_CUST_NOMSPRTC_ORG_CDAFTFCT_PROOF_DYUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BR_CD
020200105556944245071<NA>2020/10/30 09:32:49<NA><NA>2020/10/30 09:32:491221ACS
120200105553294270621<NA>2020/10/30 09:27:05<NA><NA>2020/10/30 09:27:051221ACS
22020010313531637028<NA><NA>2020/10/30 09:26:23<NA><NA>2020/10/30 09:26:231221ACS
32020010557134016060<NA><NA>2020/10/30 09:09:25<NA><NA>2020/10/30 09:09:251365TQA
420200105560973079641<NA>2020/10/30 08:31:51<NA><NA>2020/10/30 08:31:511037TAC
52020010554887937182<NA><NA>2020/10/29 18:11:55<NA><NA>2020/10/29 18:11:551142QAD
6202001055761206249701<NA>2020/10/29 17:37:09<NA><NA>2020/10/29 17:37:091157THA
72020010557431721329<NA><NA>2020/10/29 17:17:47<NA><NA>2020/10/29 17:17:471104TAD
820200105546966081561<NA>2020/10/29 17:02:16<NA><NA>2020/10/29 17:02:161037TAC
920200105555700833441<NA>2020/10/29 16:42:17<NA><NA>2020/10/29 16:42:171339THO
ACPT_PTNOMDBTR_CUST_NOMSPRTC_ORG_CDAFTFCT_PROOF_DYUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BR_CD
99020200104407944763931<NA>2020/08/25 09:59:041883TAA2020/08/14 15:20:081121TAA
991202001044081173371313<NA>2020/08/14 15:11:521544TPA2020/08/14 15:11:521544TPA
99220200104404821580891<NA>2020/08/14 15:09:29<NA><NA>2020/08/14 15:09:291224TBA
993202001044031062406951<NA>2020/08/14 15:09:29<NA><NA>2020/08/14 15:09:291224TBA
99420200104400938395461<NA>2020/08/14 15:09:29<NA><NA>2020/08/14 15:09:291224TBA
995202001043941015315581<NA>2020/08/14 15:09:29<NA><NA>2020/08/14 15:09:291224TBA
996202001044061173371313<NA>2020/08/14 15:07:211544TPA2020/08/14 15:07:211544TPA
99720200104386967598981<NA>2020/08/14 13:50:25<NA><NA>2020/08/14 13:50:251098TPA
99820200104393120058148<NA><NA>2020/08/14 13:34:16<NA><NA>2020/08/14 13:34:161118TMA
99920200104399855657893202006222020/08/14 13:09:361867TPA2020/08/14 13:09:361867TPA