Overview

Dataset statistics

Number of variables14
Number of observations408
Missing cells2344
Missing cells (%)41.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.9 KiB
Average record size in memory125.3 B

Variable types

Numeric6
Unsupported5
Categorical3

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073222/fileData.do

Alerts

DISCHRG_MTHD_CD is highly overall correlated with HUSPCL_RSN_DCNTHigh correlation
REG_BRCD is highly overall correlated with HUSPCL_RSN_DCNTHigh correlation
HUSPCL_RSN_DCNT is highly overall correlated with ACPT_PTNO and 7 other fieldsHigh correlation
ACPT_PTNO is highly overall correlated with BBNAPPRV_NOTI_DY and 2 other fieldsHigh correlation
MDBTR_CUST_NO is highly overall correlated with HUSPCL_RSN_DCNTHigh correlation
SPCL_RSN_DCNT is highly overall correlated with HUSPCL_RSN_DCNTHigh correlation
BBNAPPRV_NOTI_DY is highly overall correlated with ACPT_PTNO and 1 other fieldsHigh correlation
RMT_BNK_CD is highly overall correlated with HUSPCL_RSN_DCNTHigh correlation
REG_ENO is highly overall correlated with ACPT_PTNO and 1 other fieldsHigh correlation
DISCHRG_MTHD_CD is highly imbalanced (90.5%)Imbalance
DEMND_DY has 408 (100.0%) missing valuesMissing
ACPT_DY has 408 (100.0%) missing valuesMissing
DISCHRG_APPRV_DY has 408 (100.0%) missing valuesMissing
BBNAPPRV_NOTI_DY has 297 (72.8%) missing valuesMissing
HQ_REQ_DY has 408 (100.0%) missing valuesMissing
RMT_BNK_CD has 7 (1.7%) missing valuesMissing
REG_TS has 408 (100.0%) missing valuesMissing
ACPT_PTNO has unique valuesUnique
DEMND_DY is an unsupported type, check if it needs cleaning or further analysisUnsupported
ACPT_DY is an unsupported type, check if it needs cleaning or further analysisUnsupported
DISCHRG_APPRV_DY is an unsupported type, check if it needs cleaning or further analysisUnsupported
HQ_REQ_DY is an unsupported type, check if it needs cleaning or further analysisUnsupported
REG_TS is an unsupported type, check if it needs cleaning or further analysisUnsupported
SPCL_RSN_DCNT has 374 (91.7%) zerosZeros

Reproduction

Analysis started2023-12-12 12:01:04.348376
Analysis finished2023-12-12 12:01:09.649449
Duration5.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ACPT_PTNO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.017388 × 1010
Minimum2.00804 × 1010
Maximum2.02004 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:09.774165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.00804 × 1010
5-th percentile2.01304 × 1010
Q12.01604 × 1010
median2.01804 × 1010
Q32.01904 × 1010
95-th percentile2.02004 × 1010
Maximum2.02004 × 1010
Range1.200001 × 108
Interquartile range (IQR)30000054

Descriptive statistics

Standard deviation24346904
Coefficient of variation (CV)0.0012068528
Kurtosis0.34934708
Mean2.017388 × 1010
Median Absolute Deviation (MAD)19999982
Skewness-0.9331284
Sum8.2309432 × 1012
Variance5.9277176 × 1014
MonotonicityNot monotonic
2023-12-12T21:01:09.980861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200400097 1
 
0.2%
20170400004 1
 
0.2%
20160400049 1
 
0.2%
20160400050 1
 
0.2%
20160400052 1
 
0.2%
20160400053 1
 
0.2%
20160400057 1
 
0.2%
20160400055 1
 
0.2%
20160400058 1
 
0.2%
20170400001 1
 
0.2%
Other values (398) 398
97.5%
ValueCountFrequency (%)
20080400001 1
0.2%
20100400002 1
0.2%
20100400003 1
0.2%
20100400004 1
0.2%
20100400005 1
0.2%
20100400006 1
0.2%
20110400001 1
0.2%
20110400003 1
0.2%
20120400001 1
0.2%
20120400002 1
0.2%
ValueCountFrequency (%)
20200400097 1
0.2%
20200400096 1
0.2%
20200400095 1
0.2%
20200400094 1
0.2%
20200400093 1
0.2%
20200400092 1
0.2%
20200400091 1
0.2%
20200400090 1
0.2%
20200400089 1
0.2%
20200400088 1
0.2%

MDBTR_CUST_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct406
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84909455
Minimum8032944
Maximum1.2861801 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:10.167183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8032944
5-th percentile52350345
Q174547150
median85793293
Q396846553
95-th percentile1.1558469 × 108
Maximum1.2861801 × 108
Range1.2058507 × 108
Interquartile range (IQR)22299403

Descriptive statistics

Standard deviation20162354
Coefficient of variation (CV)0.23745711
Kurtosis2.375146
Mean84909455
Median Absolute Deviation (MAD)11075132
Skewness-0.89544345
Sum3.4643058 × 1010
Variance4.0652051 × 1014
MonotonicityNot monotonic
2023-12-12T21:01:10.351344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84360781 2
 
0.5%
85863766 2
 
0.5%
79584640 1
 
0.2%
110476206 1
 
0.2%
88293210 1
 
0.2%
99459043 1
 
0.2%
66300280 1
 
0.2%
82978955 1
 
0.2%
75086896 1
 
0.2%
69898508 1
 
0.2%
Other values (396) 396
97.1%
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 1
0.2%
123864494 1
0.2%
122844444 1
0.2%
121671867 1
0.2%
120992589 1
0.2%
119588894 1
0.2%

DEMND_DY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing408
Missing (%)100.0%
Memory size3.7 KiB

ACPT_DY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing408
Missing (%)100.0%
Memory size3.7 KiB

SPCL_RSN_DCNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4926471
Minimum0
Maximum139
Zeros374
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:10.531029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum139
Range139
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.422992
Coefficient of variation (CV)6.9828913
Kurtosis138.87771
Mean1.4926471
Median Absolute Deviation (MAD)0
Skewness11.33164
Sum609
Variance108.63877
MonotonicityNot monotonic
2023-12-12T21:01:10.670208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 374
91.7%
6 3
 
0.7%
7 3
 
0.7%
17 3
 
0.7%
5 3
 
0.7%
3 3
 
0.7%
4 2
 
0.5%
11 2
 
0.5%
8 2
 
0.5%
13 2
 
0.5%
Other values (9) 11
 
2.7%
ValueCountFrequency (%)
0 374
91.7%
1 2
 
0.5%
2 1
 
0.2%
3 3
 
0.7%
4 2
 
0.5%
5 3
 
0.7%
6 3
 
0.7%
7 3
 
0.7%
8 2
 
0.5%
9 1
 
0.2%
ValueCountFrequency (%)
139 1
 
0.2%
132 1
 
0.2%
71 1
 
0.2%
26 1
 
0.2%
17 3
0.7%
16 2
0.5%
13 2
0.5%
11 2
0.5%
10 1
 
0.2%
9 1
 
0.2%

DISCHRG_APPRV_DY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing408
Missing (%)100.0%
Memory size3.7 KiB

BBNAPPRV_NOTI_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct104
Distinct (%)93.7%
Missing297
Missing (%)72.8%
Infinite0
Infinite (%)0.0%
Mean20168773
Minimum20100616
Maximum20200825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:10.867008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100616
5-th percentile20121074
Q120150916
median20171120
Q320190772
95-th percentile20200666
Maximum20200825
Range100209
Interquartile range (IQR)39856.5

Descriptive statistics

Standard deviation25902.148
Coefficient of variation (CV)0.0012842699
Kurtosis-0.57028073
Mean20168773
Median Absolute Deviation (MAD)19796
Skewness-0.61951302
Sum2.2387338 × 109
Variance6.7092128 × 108
MonotonicityNot monotonic
2023-12-12T21:01:11.116749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190909 3
 
0.7%
20180125 2
 
0.5%
20130411 2
 
0.5%
20200319 2
 
0.5%
20161121 2
 
0.5%
20200219 2
 
0.5%
20161215 1
 
0.2%
20161125 1
 
0.2%
20150810 1
 
0.2%
20151022 1
 
0.2%
Other values (94) 94
 
23.0%
(Missing) 297
72.8%
ValueCountFrequency (%)
20100616 1
0.2%
20101012 1
0.2%
20120531 1
0.2%
20120608 1
0.2%
20120613 1
0.2%
20121029 1
0.2%
20121119 1
0.2%
20130319 1
0.2%
20130411 2
0.5%
20130530 1
0.2%
ValueCountFrequency (%)
20200825 1
0.2%
20200805 1
0.2%
20200721 1
0.2%
20200720 1
0.2%
20200715 1
0.2%
20200703 1
0.2%
20200630 1
0.2%
20200629 1
0.2%
20200610 1
0.2%
20200527 1
0.2%

HQ_REQ_DY
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing408
Missing (%)100.0%
Memory size3.7 KiB

HUSPCL_RSN_DCNT
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
<NA>
282 
0
126 

Length

Max length4
Median length4
Mean length3.0735294
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> 282
69.1%
0 126
30.9%

Length

2023-12-12T21:01:11.318073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:11.471658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 282
69.1%
0 126
30.9%

RMT_BNK_CD
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)3.7%
Missing7
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean24.361596
Minimum3
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:11.616683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median11
Q331
95-th percentile88
Maximum88
Range85
Interquartile range (IQR)27

Descriptive statistics

Standard deviation30.167804
Coefficient of variation (CV)1.2383345
Kurtosis0.23528831
Mean24.361596
Median Absolute Deviation (MAD)7
Skewness1.3879865
Sum9769
Variance910.09642
MonotonicityNot monotonic
2023-12-12T21:01:11.767585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 150
36.8%
11 81
19.9%
88 44
 
10.8%
20 42
 
10.3%
81 30
 
7.4%
3 19
 
4.7%
31 11
 
2.7%
32 7
 
1.7%
39 7
 
1.7%
21 3
 
0.7%
Other values (5) 7
 
1.7%
(Missing) 7
 
1.7%
ValueCountFrequency (%)
3 19
 
4.7%
4 150
36.8%
5 1
 
0.2%
11 81
19.9%
12 1
 
0.2%
19 1
 
0.2%
20 42
 
10.3%
21 3
 
0.7%
31 11
 
2.7%
32 7
 
1.7%
ValueCountFrequency (%)
88 44
10.8%
81 30
7.4%
39 7
 
1.7%
37 2
 
0.5%
34 2
 
0.5%
32 7
 
1.7%
31 11
 
2.7%
21 3
 
0.7%
20 42
10.3%
19 1
 
0.2%

DISCHRG_MTHD_CD
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2
400 
<NA>
 
7
1
 
1

Length

Max length4
Median length1
Mean length1.0514706
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 400
98.0%
<NA> 7
 
1.7%
1 1
 
0.2%

Length

2023-12-12T21:01:11.957675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:12.116441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 400
98.0%
na 7
 
1.7%
1 1
 
0.2%

REG_TS
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing408
Missing (%)100.0%
Memory size3.7 KiB

REG_ENO
Real number (ℝ)

HIGH CORRELATION 

Distinct154
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1820.1618
Minimum1020
Maximum52549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2023-12-12T21:01:12.269262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1180.85
Q11425
median1557
Q31709.75
95-th percentile1918.3
Maximum52549
Range51529
Interquartile range (IQR)284.75

Descriptive statistics

Standard deviation3553.5463
Coefficient of variation (CV)1.9523244
Kurtosis200.06926
Mean1820.1618
Median Absolute Deviation (MAD)132
Skewness14.155016
Sum742626
Variance12627691
MonotonicityNot monotonic
2023-12-12T21:01:12.433619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1598 19
 
4.7%
1641 14
 
3.4%
1532 13
 
3.2%
1425 13
 
3.2%
1300 12
 
2.9%
1475 11
 
2.7%
1917 10
 
2.5%
1656 10
 
2.5%
1302 9
 
2.2%
1773 8
 
2.0%
Other values (144) 289
70.8%
ValueCountFrequency (%)
1020 2
0.5%
1025 1
 
0.2%
1110 1
 
0.2%
1117 1
 
0.2%
1127 1
 
0.2%
1152 1
 
0.2%
1155 1
 
0.2%
1159 3
0.7%
1160 1
 
0.2%
1163 1
 
0.2%
ValueCountFrequency (%)
52549 1
 
0.2%
52049 1
 
0.2%
2003 1
 
0.2%
2002 1
 
0.2%
2001 1
 
0.2%
1980 1
 
0.2%
1977 2
0.5%
1970 1
 
0.2%
1968 3
0.7%
1938 1
 
0.2%

REG_BRCD
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
TAB
77 
THO
44 
QAD
37 
TAA
28 
TPA
26 
Other values (17)
196 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
TAB 77
18.9%
THO 44
10.8%
QAD 37
9.1%
TAA 28
 
6.9%
TPA 26
 
6.4%
TBA 24
 
5.9%
TAC 23
 
5.6%
THA 22
 
5.4%
THB 21
 
5.1%
TAD 19
 
4.7%
Other values (12) 87
21.3%

Length

2023-12-12T21:01:12.622880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tab 77
18.9%
tho 44
10.8%
qad 37
9.1%
taa 28
 
6.9%
tpa 26
 
6.4%
tba 24
 
5.9%
tac 23
 
5.6%
tha 22
 
5.4%
thb 21
 
5.1%
tad 19
 
4.7%
Other values (12) 87
21.3%

Interactions

2023-12-12T21:01:08.242137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:04.834078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.495880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.180379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.962067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.646928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.343775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:04.960982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.610184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.316006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.073356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.751119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.435749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.082427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.721541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.440141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.185042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.868665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.540756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.190854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.848032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.572607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.308108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.976636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.623367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.283882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.981580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.700246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.421003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.059008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.701770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:05.371850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.076961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:06.836344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:07.528933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:08.145496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:01:12.744899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOMDBTR_CUST_NOSPCL_RSN_DCNTBBNAPPRV_NOTI_DYRMT_BNK_CDDISCHRG_MTHD_CDREG_ENOREG_BRCD
ACPT_PTNO1.0000.2830.0370.9280.0540.0000.0000.360
MDBTR_CUST_NO0.2831.0000.0000.4820.0000.0000.0000.376
SPCL_RSN_DCNT0.0370.0001.0000.0000.0000.0000.0000.000
BBNAPPRV_NOTI_DY0.9280.4820.0001.0000.0000.000NaN0.400
RMT_BNK_CD0.0540.0000.0000.0001.0000.8870.0000.667
DISCHRG_MTHD_CD0.0000.0000.0000.0000.8871.0000.0000.276
REG_ENO0.0000.0000.000NaN0.0000.0001.0000.000
REG_BRCD0.3600.3760.0000.4000.6670.2760.0001.000
2023-12-12T21:01:12.885186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DISCHRG_MTHD_CDREG_BRCDHUSPCL_RSN_DCNT
DISCHRG_MTHD_CD1.0000.2131.000
REG_BRCD0.2131.0001.000
HUSPCL_RSN_DCNT1.0001.0001.000
2023-12-12T21:01:12.992299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACPT_PTNOMDBTR_CUST_NOSPCL_RSN_DCNTBBNAPPRV_NOTI_DYRMT_BNK_CDREG_ENOHUSPCL_RSN_DCNTDISCHRG_MTHD_CDREG_BRCD
ACPT_PTNO1.0000.346-0.0991.0000.0120.5561.0000.0000.130
MDBTR_CUST_NO0.3461.000-0.0710.3560.0890.1241.0000.0000.143
SPCL_RSN_DCNT-0.099-0.0711.0000.032-0.044-0.0191.0000.0000.000
BBNAPPRV_NOTI_DY1.0000.3560.0321.0000.0710.4741.0000.0000.114
RMT_BNK_CD0.0120.089-0.0440.0711.000-0.0581.0000.0880.363
REG_ENO0.5560.124-0.0190.474-0.0581.0001.0000.0000.000
HUSPCL_RSN_DCNT1.0001.0001.0001.0001.0001.0001.0001.0001.000
DISCHRG_MTHD_CD0.0000.0000.0000.0000.0880.0001.0001.0000.213
REG_BRCD0.1300.1430.0000.1140.3630.0001.0000.2131.000

Missing values

2023-12-12T21:01:08.838688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:01:09.422740image/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.
2023-12-12T21:01:09.560567image/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_NODEMND_DYACPT_DYSPCL_RSN_DCNTDISCHRG_APPRV_DYBBNAPPRV_NOTI_DYHQ_REQ_DYHUSPCL_RSN_DCNTRMT_BNK_CDDISCHRG_MTHD_CDREG_TSREG_ENOREG_BRCD
02020040009779584640<NA><NA>0<NA><NA><NA><NA><NA><NA><NA>1385TAB
120200400096111686312<NA><NA>0<NA><NA><NA><NA><NA><NA><NA>1747TQC
22020040009577928460<NA><NA>0<NA><NA><NA><NA>882<NA>1917THO
32020040009392576635<NA><NA>0<NA><NA><NA><NA>312<NA>1913TPA
420200400094118648803<NA><NA>0<NA><NA><NA><NA>42<NA>1304TBB
520200400090112781973<NA><NA>0<NA><NA><NA><NA>202<NA>1385TAB
62020040009169048329<NA><NA>0<NA><NA><NA><NA>42<NA>1970THA
720200400092110579718<NA><NA>0<NA><NA><NA><NA>202<NA>1406QAD
82020040008974544449<NA><NA>0<NA><NA><NA><NA>882<NA>1977QAD
920200400087115770554<NA><NA>0<NA><NA><NA><NA>112<NA>1569THA
ACPT_PTNOMDBTR_CUST_NODEMND_DYACPT_DYSPCL_RSN_DCNTDISCHRG_APPRV_DYBBNAPPRV_NOTI_DYHQ_REQ_DYHUSPCL_RSN_DCNTRMT_BNK_CDDISCHRG_MTHD_CDREG_TSREG_ENOREG_BRCD
3982012040000264588826<NA><NA>0<NA>20120531<NA>042<NA>1495QAD
3992011040000380228610<NA><NA>4<NA><NA><NA><NA>112<NA>1356QAD
4002012040000170572059<NA><NA>0<NA><NA><NA><NA>202<NA>1173TAA
4012008040000166081800<NA><NA>0<NA><NA><NA>042<NA>1247TPA
4022010040000669459772<NA><NA>7<NA><NA><NA><NA>112<NA>1359TPA
4032011040000165008415<NA><NA>0<NA><NA><NA><NA>112<NA>1406QAD
4042010040000474322616<NA><NA>17<NA><NA><NA><NA>42<NA>1359TPA
4052010040000269802125<NA><NA>0<NA><NA><NA><NA>42<NA>1152THO
4062010040000564966189<NA><NA>0<NA>20101012<NA>042<NA>1388TOA
4072010040000336420748<NA><NA>0<NA>20100616<NA>0882<NA>1384THO