Overview

Dataset statistics

Number of variables11
Number of observations424
Missing cells433
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.1 KiB
Average record size in memory94.3 B

Variable types

Text2
Numeric4
Categorical4
DateTime1

Dataset

Description한국주택금융공사 채권관리부 시효내역 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15072964/fileData.do

Alerts

RDBTR_DVCD is highly overall correlated with REL_PTNOHigh correlation
REL_PTNO is highly overall correlated with RDBTR_CUST_NO and 6 other fieldsHigh correlation
UPDT_BRCD is highly overall correlated with REL_PTNO and 1 other fieldsHigh correlation
REG_BRCD is highly overall correlated with REL_PTNO and 1 other fieldsHigh correlation
RDBTR_CUST_NO is highly overall correlated with MDBTR_CUST_NO and 1 other fieldsHigh correlation
MDBTR_CUST_NO is highly overall correlated with RDBTR_CUST_NO and 1 other fieldsHigh correlation
UPDT_ENO is highly overall correlated with REL_PTNOHigh correlation
REG_ENO is highly overall correlated with REL_PTNOHigh correlation
RDBTR_DVCD is highly imbalanced (76.8%)Imbalance
REL_PTNO is highly imbalanced (97.0%)Imbalance
UPDT_TS has 215 (50.7%) missing valuesMissing
UPDT_ENO has 218 (51.4%) missing valuesMissing
REG_ENO is highly skewed (γ1 = 20.3556551)Skewed

Reproduction

Analysis started2023-12-12 14:56:07.263068
Analysis finished2023-12-12 14:56:09.711309
Duration2.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GUARNT_NO
Text

Distinct400
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T23:56:09.854575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters5936
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique378 ?
Unique (%)89.2%

Sample

1st rowRTHO2010000012
2nd rowRTPA2013000119
3rd rowRTBA2017000881
4th rowRTAB2016000788
5th rowRTAB2016000788
ValueCountFrequency (%)
rqad2012000810 3
 
0.7%
rqad2009000297 3
 
0.7%
rqad2010000266 2
 
0.5%
rtpa2012000136 2
 
0.5%
rqad2008000199 2
 
0.5%
rtpa2018000574 2
 
0.5%
rtad2017000312 2
 
0.5%
rtpa2007000018 2
 
0.5%
rqad2007000290 2
 
0.5%
rqad2009000240 2
 
0.5%
Other values (390) 402
94.8%
2023-12-12T23:56:10.180900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2099
35.4%
2 628
 
10.6%
1 568
 
9.6%
R 424
 
7.1%
A 398
 
6.7%
T 342
 
5.8%
3 166
 
2.8%
4 154
 
2.6%
9 134
 
2.3%
5 130
 
2.2%
Other values (14) 893
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4240
71.4%
Uppercase Letter 1696
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 424
25.0%
A 398
23.5%
T 342
20.2%
B 112
 
6.6%
Q 96
 
5.7%
D 88
 
5.2%
H 86
 
5.1%
O 57
 
3.4%
P 32
 
1.9%
M 21
 
1.2%
Other values (4) 40
 
2.4%
Decimal Number
ValueCountFrequency (%)
0 2099
49.5%
2 628
 
14.8%
1 568
 
13.4%
3 166
 
3.9%
4 154
 
3.6%
9 134
 
3.2%
5 130
 
3.1%
7 129
 
3.0%
8 120
 
2.8%
6 112
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4240
71.4%
Latin 1696
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 424
25.0%
A 398
23.5%
T 342
20.2%
B 112
 
6.6%
Q 96
 
5.7%
D 88
 
5.2%
H 86
 
5.1%
O 57
 
3.4%
P 32
 
1.9%
M 21
 
1.2%
Other values (4) 40
 
2.4%
Common
ValueCountFrequency (%)
0 2099
49.5%
2 628
 
14.8%
1 568
 
13.4%
3 166
 
3.9%
4 154
 
3.6%
9 134
 
3.2%
5 130
 
3.1%
7 129
 
3.0%
8 120
 
2.8%
6 112
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2099
35.4%
2 628
 
10.6%
1 568
 
9.6%
R 424
 
7.1%
A 398
 
6.7%
T 342
 
5.8%
3 166
 
2.8%
4 154
 
2.6%
9 134
 
2.3%
5 130
 
2.2%
Other values (14) 893
15.0%

RDBTR_CUST_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84769749
Minimum8032944
Maximum1.2861801 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T23:56:10.308348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8032944
5-th percentile52592195
Q174327768
median85639812
Q396846553
95-th percentile1.1570964 × 108
Maximum1.2861801 × 108
Range1.2058507 × 108
Interquartile range (IQR)22518784

Descriptive statistics

Standard deviation20125792
Coefficient of variation (CV)0.23741715
Kurtosis2.247875
Mean84769749
Median Absolute Deviation (MAD)11234821
Skewness-0.82890356
Sum3.5942373 × 1010
Variance4.0504752 × 1014
MonotonicityNot monotonic
2023-12-12T23:56:10.449521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64588826 3
 
0.7%
66081800 2
 
0.5%
83565477 2
 
0.5%
69908126 2
 
0.5%
66637296 2
 
0.5%
85863766 2
 
0.5%
89061797 2
 
0.5%
74329486 2
 
0.5%
73886438 2
 
0.5%
94232689 1
 
0.2%
Other values (404) 404
95.3%
ValueCountFrequency (%)
8032944 1
0.2%
8571201 1
0.2%
8583309 1
0.2%
8833855 1
0.2%
9255348 1
0.2%
16272880 1
0.2%
21615348 1
0.2%
23260050 1
0.2%
25547658 1
0.2%
30986754 1
0.2%
ValueCountFrequency (%)
128618014 1
0.2%
128197962 1
0.2%
126725747 1
0.2%
126546515 1
0.2%
124393524 1
0.2%
124393498 1
0.2%
123864494 1
0.2%
122844444 1
0.2%
121671867 1
0.2%
120992589 1
0.2%

RDBTR_DVCD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
1
408 
4
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row4
5th row1

Common Values

ValueCountFrequency (%)
1 408
96.2%
4 16
 
3.8%

Length

2023-12-12T23:56:10.559914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:56:10.640842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 408
96.2%
4 16
 
3.8%

MDBTR_CUST_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct400
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84769747
Minimum8032944
Maximum1.2861801 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T23:56:10.744014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8032944
5-th percentile52592195
Q174327768
median85639812
Q396846553
95-th percentile1.1570964 × 108
Maximum1.2861801 × 108
Range1.2058507 × 108
Interquartile range (IQR)22518784

Descriptive statistics

Standard deviation20125792
Coefficient of variation (CV)0.23741715
Kurtosis2.247875
Mean84769747
Median Absolute Deviation (MAD)11234821
Skewness-0.82890364
Sum3.5942373 × 1010
Variance4.050475 × 1014
MonotonicityNot monotonic
2023-12-12T23:56:10.873081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89061797 3
 
0.7%
64588826 3
 
0.7%
66637296 2
 
0.5%
69908126 2
 
0.5%
124393498 2
 
0.5%
115517542 2
 
0.5%
66081800 2
 
0.5%
85863766 2
 
0.5%
74426020 2
 
0.5%
88631380 2
 
0.5%
Other values (390) 402
94.8%
ValueCountFrequency (%)
8032944 1
0.2%
8571201 1
0.2%
8583309 1
0.2%
8833855 1
0.2%
9255348 1
0.2%
16272880 1
0.2%
21615348 1
0.2%
23260050 1
0.2%
25547658 1
0.2%
30986754 1
0.2%
ValueCountFrequency (%)
128618014 1
0.2%
128197962 1
0.2%
126725747 1
0.2%
126546515 1
0.2%
124393498 2
0.5%
123864494 1
0.2%
122844444 1
0.2%
121671867 1
0.2%
120992589 1
0.2%
119588894 1
0.2%

REL_PTNO
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
422 
20201600001
 
1
20191600001
 
1

Length

Max length11
Median length4
Mean length4.0330189
Min length4

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 422
99.5%
20201600001 1
 
0.2%
20191600001 1
 
0.2%

Length

2023-12-12T23:56:10.991928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:56:11.079536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 422
99.5%
20201600001 1
 
0.2%
20191600001 1
 
0.2%

UPDT_TS
Text

MISSING 

Distinct191
Distinct (%)91.4%
Missing215
Missing (%)50.7%
Memory size3.4 KiB
2023-12-12T23:56:11.327463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters3971
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)86.6%

Sample

1st row2020/10/15 17:13:54
2nd row2020/07/28 09:02:12
3rd row2020/09/24 12:50:27
4th row2020/08/18 17:20:30
5th row2020/06/18 17:26:18
ValueCountFrequency (%)
2017/12/14 10
 
2.4%
17:20:18 7
 
1.7%
2019/03/22 3
 
0.7%
17:12:52 3
 
0.7%
0001/01/01 3
 
0.7%
01:01:01 3
 
0.7%
2019/01/07 3
 
0.7%
14:36:20 3
 
0.7%
2018/01/17 3
 
0.7%
2019/03/26 3
 
0.7%
Other values (353) 377
90.2%
2023-12-12T23:56:11.702229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 681
17.1%
0 655
16.5%
2 503
12.7%
/ 418
10.5%
: 418
10.5%
209
 
5.3%
4 202
 
5.1%
3 192
 
4.8%
5 179
 
4.5%
7 146
 
3.7%
Other values (3) 368
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2926
73.7%
Other Punctuation 836
 
21.1%
Space Separator 209
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 681
23.3%
0 655
22.4%
2 503
17.2%
4 202
 
6.9%
3 192
 
6.6%
5 179
 
6.1%
7 146
 
5.0%
6 130
 
4.4%
8 125
 
4.3%
9 113
 
3.9%
Other Punctuation
ValueCountFrequency (%)
/ 418
50.0%
: 418
50.0%
Space Separator
ValueCountFrequency (%)
209
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3971
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 681
17.1%
0 655
16.5%
2 503
12.7%
/ 418
10.5%
: 418
10.5%
209
 
5.3%
4 202
 
5.1%
3 192
 
4.8%
5 179
 
4.5%
7 146
 
3.7%
Other values (3) 368
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 681
17.1%
0 655
16.5%
2 503
12.7%
/ 418
10.5%
: 418
10.5%
209
 
5.3%
4 202
 
5.1%
3 192
 
4.8%
5 179
 
4.5%
7 146
 
3.7%
Other values (3) 368
9.3%

UPDT_ENO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct85
Distinct (%)41.3%
Missing218
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean1747.9223
Minimum1020
Maximum51645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T23:56:11.822852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1127
Q11386
median1476
Q31625
95-th percentile1917
Maximum51645
Range50625
Interquartile range (IQR)239

Descriptive statistics

Standard deviation3500.2595
Coefficient of variation (CV)2.0025257
Kurtosis204.37882
Mean1747.9223
Median Absolute Deviation (MAD)122
Skewness14.2683
Sum360072
Variance12251816
MonotonicityNot monotonic
2023-12-12T23:56:11.939948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1598 18
 
4.2%
1464 14
 
3.3%
1917 10
 
2.4%
1475 10
 
2.4%
1425 10
 
2.4%
1127 8
 
1.9%
1656 8
 
1.9%
1625 5
 
1.2%
1386 4
 
0.9%
1348 4
 
0.9%
Other values (75) 115
27.1%
(Missing) 218
51.4%
ValueCountFrequency (%)
1020 4
0.9%
1023 1
 
0.2%
1081 1
 
0.2%
1087 1
 
0.2%
1127 8
1.9%
1149 2
 
0.5%
1152 1
 
0.2%
1159 2
 
0.5%
1160 1
 
0.2%
1163 1
 
0.2%
ValueCountFrequency (%)
51645 1
 
0.2%
1977 1
 
0.2%
1917 10
2.4%
1915 1
 
0.2%
1913 3
 
0.7%
1878 2
 
0.5%
1874 1
 
0.2%
1851 2
 
0.5%
1833 1
 
0.2%
1819 2
 
0.5%

UPDT_BRCD
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
218 
TAB
52 
THO
34 
QAD
 
19
TAC
 
19
Other values (15)
82 

Length

Max length4
Median length4
Mean length3.5141509
Min length3

Unique

Unique4 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 218
51.4%
TAB 52
 
12.3%
THO 34
 
8.0%
QAD 19
 
4.5%
TAC 19
 
4.5%
TPA 16
 
3.8%
TAA 14
 
3.3%
TAD 13
 
3.1%
THB 8
 
1.9%
TMA 6
 
1.4%
Other values (10) 25
 
5.9%

Length

2023-12-12T23:56:12.050237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 218
51.4%
tab 52
 
12.3%
tho 34
 
8.0%
qad 19
 
4.5%
tac 19
 
4.5%
tpa 16
 
3.8%
taa 14
 
3.3%
tad 13
 
3.1%
thb 8
 
1.9%
tma 6
 
1.4%
Other values (10) 25
 
5.9%

REG_TS
Date

Distinct408
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum2008-09-19 11:16:55
Maximum2020-10-15 15:11:45
2023-12-12T23:56:12.157334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:12.323225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

REG_ENO
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct155
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1688.184
Minimum1020
Maximum52549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T23:56:12.513279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1174
Q11425
median1557
Q31695.75
95-th percentile1918.7
Maximum52549
Range51529
Interquartile range (IQR)270.75

Descriptive statistics

Standard deviation2485.3281
Coefficient of variation (CV)1.4721903
Kurtosis417.52948
Mean1688.184
Median Absolute Deviation (MAD)132
Skewness20.355655
Sum715790
Variance6176855.7
MonotonicityNot monotonic
2023-12-12T23:56:12.653517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1598 20
 
4.7%
1641 14
 
3.3%
1656 13
 
3.1%
1917 12
 
2.8%
1475 12
 
2.8%
1302 11
 
2.6%
1425 11
 
2.6%
1532 11
 
2.6%
1300 10
 
2.4%
1650 8
 
1.9%
Other values (145) 302
71.2%
ValueCountFrequency (%)
1020 2
0.5%
1025 1
 
0.2%
1110 1
 
0.2%
1127 1
 
0.2%
1137 1
 
0.2%
1140 1
 
0.2%
1152 1
 
0.2%
1155 2
0.5%
1159 4
0.9%
1160 1
 
0.2%
ValueCountFrequency (%)
52549 1
 
0.2%
2003 1
 
0.2%
2002 2
0.5%
2001 1
 
0.2%
1980 1
 
0.2%
1977 3
0.7%
1970 1
 
0.2%
1968 3
0.7%
1938 1
 
0.2%
1937 3
0.7%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
TAB
80 
THO
47 
QAD
40 
TAA
29 
TAC
27 
Other values (17)
201 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowTHO
2nd rowTPA
3rd rowTBB
4th rowTAB
5th rowTAB

Common Values

ValueCountFrequency (%)
TAB 80
18.9%
THO 47
11.1%
QAD 40
9.4%
TAA 29
 
6.8%
TAC 27
 
6.4%
TPA 27
 
6.4%
THA 23
 
5.4%
TBA 23
 
5.4%
TAD 22
 
5.2%
THB 20
 
4.7%
Other values (12) 86
20.3%

Length

2023-12-12T23:56:12.773780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tab 80
18.9%
tho 47
11.1%
qad 40
9.4%
taa 29
 
6.8%
tac 27
 
6.4%
tpa 27
 
6.4%
tha 23
 
5.4%
tba 23
 
5.4%
tad 22
 
5.2%
thb 20
 
4.7%
Other values (12) 86
20.3%

Interactions

2023-12-12T23:56:08.783896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:07.733002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.127609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.490950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.886460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:07.826766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.228956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.565651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.979588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:07.928875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.323753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.642487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:09.050792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.039752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.400220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:56:08.711075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:56:12.854115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RDBTR_CUST_NORDBTR_DVCDMDBTR_CUST_NOREL_PTNOUPDT_ENOUPDT_BRCDREG_ENOREG_BRCD
RDBTR_CUST_NO1.0000.0001.0000.0000.0000.6040.0000.412
RDBTR_DVCD0.0001.0000.000NaN0.0000.0000.0000.000
MDBTR_CUST_NO1.0000.0001.0000.0000.0000.6040.0000.412
REL_PTNO0.000NaN0.0001.000NaN0.000NaN0.000
UPDT_ENO0.0000.0000.000NaN1.0000.0000.0000.000
UPDT_BRCD0.6040.0000.6040.0000.0001.0000.0000.998
REG_ENO0.0000.0000.000NaN0.0000.0001.0000.000
REG_BRCD0.4120.0000.4120.0000.0000.9980.0001.000
2023-12-12T23:56:13.004153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RDBTR_DVCDREL_PTNOUPDT_BRCDREG_BRCD
RDBTR_DVCD1.0001.0000.0000.000
REL_PTNO1.0001.0001.0001.000
UPDT_BRCD0.0001.0001.0000.972
REG_BRCD0.0001.0000.9721.000
2023-12-12T23:56:13.103574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
RDBTR_CUST_NOMDBTR_CUST_NOUPDT_ENOREG_ENORDBTR_DVCDREL_PTNOUPDT_BRCDREG_BRCD
RDBTR_CUST_NO1.0001.0000.2850.1170.0001.0000.2610.160
MDBTR_CUST_NO1.0001.0000.2850.1170.0001.0000.2610.160
UPDT_ENO0.2850.2851.0000.4220.0001.0000.0000.000
REG_ENO0.1170.1170.4221.0000.0001.0000.0000.000
RDBTR_DVCD0.0000.0000.0000.0001.0001.0000.0000.000
REL_PTNO1.0001.0001.0001.0001.0001.0001.0001.000
UPDT_BRCD0.2610.2610.0000.0000.0001.0001.0000.972
REG_BRCD0.1600.1600.0000.0000.0001.0000.9721.000

Missing values

2023-12-12T23:56:09.405125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:56:09.540651image/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.
2023-12-12T23:56:09.643094image/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

GUARNT_NORDBTR_CUST_NORDBTR_DVCDMDBTR_CUST_NOREL_PTNOUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCD
0RTHO201000001277928460177928460<NA><NA><NA><NA>2020/10/15 15:11:451917THO
1RTPA201300011992576635192576635<NA><NA><NA><NA>2020/10/08 10:17:201913TPA
2RTBA20170008811186488031118648803<NA><NA><NA><NA>2020/10/06 13:07:251304TBB
3RTAB20160007881127821054112781973<NA><NA><NA><NA>2020/09/29 11:07:381385TAB
4RTAB20160007881127819731112781973<NA><NA><NA><NA>2020/09/29 11:07:381385TAB
5RTAA200800002369048329169048329<NA><NA><NA><NA>2020/09/25 13:21:531970THA
6RQAD20160005341105797181110579718<NA><NA><NA><NA>2020/09/25 11:21:381406QAD
7RQAD200900027774544481474544449<NA><NA><NA><NA>2020/09/16 10:35:371977QAD
8RQAD200900027774544449174544449<NA><NA><NA><NA>2020/09/16 10:35:371977QAD
9RTHA20170003501157706194115770554<NA><NA><NA><NA>2020/09/10 13:15:211569THA
GUARNT_NORDBTR_CUST_NORDBTR_DVCDMDBTR_CUST_NOREL_PTNOUPDT_TSUPDT_ENOUPDT_BRCDREG_TSREG_ENOREG_BRCD
414RTAA200800008770572156470572059<NA>2014/06/25 15:09:211201TAA2012/02/15 14:23:201173TAA
415RTAA200800008770572059170572059<NA>2014/06/25 15:10:191201TAA2012/02/15 14:23:201173TAA
416RQAD201000041780228610180228610<NA>2013/10/18 14:10:391163QAD2011/11/24 11:44:211356QAD
417RTPA200800000869459772169459772<NA>2011/11/23 11:25:181459TPA2010/11/11 13:25:551359TPA
418RTOA200700000964966189164966189<NA>2011/04/13 11:21:531388TOA2010/10/13 12:33:191388TOA
419RTHO200800003969802125169802125<NA>2011/02/21 16:15:401152THO2010/04/05 11:51:421152THO
420RTHO200900001136420748136420748<NA>2011/02/22 17:49:031437THO2010/06/17 10:58:371384THO
421RQAD200700009065008415165008415<NA>2014/07/02 11:10:341177QAD2011/02/25 13:28:051406QAD
422RTPA200900002774322616174322616<NA>2011/06/08 16:34:011149TPA2010/09/30 11:46:301359TPA
423RTPA200700001866081800166081800<NA>2015/07/23 14:32:531375TPA2008/09/19 11:16:551137TPA