Overview

Dataset statistics

Number of variables13
Number of observations982
Missing cells1047
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.5 KiB
Average record size in memory112.1 B

Variable types

Text1
Categorical3
Numeric7
Boolean1
DateTime1

Dataset

Description신용평가수수료관리에 대한 데이터로, 유동화계획코드, 수수료코드, 결의일자, 결의번호, 기관코드, 정기평정기준일 등의 항목을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073007/fileData.do

Alerts

ORG_CD is highly overall correlated with DECIS_NO and 2 other fieldsHigh correlation
FEE_CD is highly overall correlated with ORG_CDHigh correlation
DECIS_DY is highly overall correlated with PAY_DY and 5 other fieldsHigh correlation
DECIS_NO is highly overall correlated with ORG_CD and 1 other fieldsHigh correlation
PAY_DY is highly overall correlated with DECIS_DY and 5 other fieldsHigh correlation
PAY_AMT is highly overall correlated with DECIS_DY and 5 other fieldsHigh correlation
RGLR_EVAL_BASIS_DY is highly overall correlated with DECIS_DY and 4 other fieldsHigh correlation
NOTICE_DY is highly overall correlated with DECIS_DY and 5 other fieldsHigh correlation
PAY_TRGT_YR is highly overall correlated with DECIS_DY and 5 other fieldsHigh correlation
CLOSE_YN is highly overall correlated with DECIS_NO and 1 other fieldsHigh correlation
REG_ENO is highly overall correlated with DECIS_DY and 4 other fieldsHigh correlation
CLOSE_YN is highly imbalanced (92.1%)Imbalance
RGLR_EVAL_BASIS_DY has 684 (69.7%) missing valuesMissing
CLOSE_YN has 361 (36.8%) missing valuesMissing

Reproduction

Analysis started2023-12-11 23:00:11.786633
Analysis finished2023-12-11 23:00:18.229114
Duration6.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Text

Distinct175
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2023-12-12T08:00:18.375702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters13748
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)4.7%

Sample

1st rowKHFCMB2013S-35
2nd rowKHFCMB2012S-31
3rd rowKHFCMB2007S-06
4th rowKHFCMB2013S-40
5th rowKHFCMB2012S-38
ValueCountFrequency (%)
khfcmb2004s-02 20
 
2.0%
khfcmb2004s-04 19
 
1.9%
khfcmb2004s-03 19
 
1.9%
khfcmb2005s-02 18
 
1.8%
khfcmb2004s-05 18
 
1.8%
khfcmb2005s-03 18
 
1.8%
khfcmb2005s-07 18
 
1.8%
khfcmb2005s-04 18
 
1.8%
khfcmb2004s-07 18
 
1.8%
khfcmb2005s-01 18
 
1.8%
Other values (165) 798
81.3%
2023-12-12T08:00:18.730864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2636
19.2%
2 1285
9.3%
K 982
 
7.1%
- 982
 
7.1%
C 982
 
7.1%
M 982
 
7.1%
S 973
 
7.1%
B 905
 
6.6%
H 896
 
6.5%
F 896
 
6.5%
Other values (9) 2229
16.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6874
50.0%
Decimal Number 5892
42.9%
Dash Punctuation 982
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2636
44.7%
2 1285
21.8%
1 597
 
10.1%
5 268
 
4.5%
4 252
 
4.3%
3 221
 
3.8%
6 171
 
2.9%
9 165
 
2.8%
7 162
 
2.7%
8 135
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
K 982
14.3%
C 982
14.3%
M 982
14.3%
S 973
14.2%
B 905
13.2%
H 896
13.0%
F 896
13.0%
O 258
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 982
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6874
50.0%
Latin 6874
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2636
38.3%
2 1285
18.7%
- 982
 
14.3%
1 597
 
8.7%
5 268
 
3.9%
4 252
 
3.7%
3 221
 
3.2%
6 171
 
2.5%
9 165
 
2.4%
7 162
 
2.4%
Latin
ValueCountFrequency (%)
K 982
14.3%
C 982
14.3%
M 982
14.3%
S 973
14.2%
B 905
13.2%
H 896
13.0%
F 896
13.0%
O 258
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2636
19.2%
2 1285
9.3%
K 982
 
7.1%
- 982
 
7.1%
C 982
 
7.1%
M 982
 
7.1%
S 973
 
7.1%
B 905
 
6.6%
H 896
 
6.5%
F 896
 
6.5%
Other values (9) 2229
16.2%

FEE_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
T01104
350 
T01204
331 
T01004
292 
T038B9
 
8
T011B9
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowT01004
2nd rowT01004
3rd rowT01004
4th rowT01104
5th rowT01104

Common Values

ValueCountFrequency (%)
T01104 350
35.6%
T01204 331
33.7%
T01004 292
29.7%
T038B9 8
 
0.8%
T011B9 1
 
0.1%

Length

2023-12-12T08:00:18.858452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:00:18.965285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t01104 350
35.6%
t01204 331
33.7%
t01004 292
29.7%
t038b9 8
 
0.8%
t011b9 1
 
0.1%

DECIS_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct204
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20112622
Minimum20051128
Maximum20150106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:19.091439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051128
5-th percentile20061106
Q120093367
median20120320
Q320131028
95-th percentile20140929
Maximum20150106
Range98978
Interquartile range (IQR)37661.25

Descriptive statistics

Standard deviation25316.616
Coefficient of variation (CV)0.0012587427
Kurtosis-0.60205616
Mean20112622
Median Absolute Deviation (MAD)19801.5
Skewness-0.6703489
Sum1.9750595 × 1010
Variance6.4093106 × 108
MonotonicityNot monotonic
2023-12-12T08:00:19.305680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110620 25
 
2.5%
20120712 19
 
1.9%
20141105 18
 
1.8%
20121127 16
 
1.6%
20140715 16
 
1.6%
20130521 15
 
1.5%
20130516 15
 
1.5%
20090108 15
 
1.5%
20140217 15
 
1.5%
20130214 15
 
1.5%
Other values (194) 813
82.8%
ValueCountFrequency (%)
20051128 3
0.3%
20051220 1
 
0.1%
20051221 3
0.3%
20051227 7
0.7%
20060105 3
0.3%
20060322 1
 
0.1%
20060403 1
 
0.1%
20060404 1
 
0.1%
20060405 1
 
0.1%
20060410 4
0.4%
ValueCountFrequency (%)
20150106 10
1.0%
20141218 1
 
0.1%
20141217 8
0.8%
20141121 11
1.1%
20141105 18
1.8%
20140929 5
 
0.5%
20140912 2
 
0.2%
20140911 11
1.1%
20140826 12
1.2%
20140825 7
 
0.7%

DECIS_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.743381
Minimum1
Maximum594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:19.786569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q315
95-th percentile37
Maximum594
Range593
Interquartile range (IQR)11

Descriptive statistics

Standard deviation25.692555
Coefficient of variation (CV)2.016149
Kurtosis320.46981
Mean12.743381
Median Absolute Deviation (MAD)5
Skewness15.730688
Sum12514
Variance660.10737
MonotonicityNot monotonic
2023-12-12T08:00:19.938418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 79
 
8.0%
3 74
 
7.5%
2 71
 
7.2%
6 68
 
6.9%
5 66
 
6.7%
1 62
 
6.3%
7 53
 
5.4%
8 50
 
5.1%
9 45
 
4.6%
10 43
 
4.4%
Other values (54) 371
37.8%
ValueCountFrequency (%)
1 62
6.3%
2 71
7.2%
3 74
7.5%
4 79
8.0%
5 66
6.7%
6 68
6.9%
7 53
5.4%
8 50
5.1%
9 45
4.6%
10 43
4.4%
ValueCountFrequency (%)
594 1
0.1%
401 1
0.1%
143 1
0.1%
107 1
0.1%
97 1
0.1%
86 1
0.1%
85 1
0.1%
83 1
0.1%
56 1
0.1%
55 1
0.1%

ORG_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
T011
352 
T012
331 
T010
291 
T038
 
8

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT010
2nd rowT010
3rd rowT010
4th rowT011
5th rowT011

Common Values

ValueCountFrequency (%)
T011 352
35.8%
T012 331
33.7%
T010 291
29.6%
T038 8
 
0.8%

Length

2023-12-12T08:00:20.069691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:00:20.192253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
t011 352
35.8%
t012 331
33.7%
t010 291
29.6%
t038 8
 
0.8%

PAY_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20112638
Minimum20051129
Maximum20150107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:20.317633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051129
5-th percentile20061109
Q120093370
median20120323
Q320131029
95-th percentile20141001
Maximum20150107
Range98978
Interquartile range (IQR)37659

Descriptive statistics

Standard deviation25324.591
Coefficient of variation (CV)0.0012591382
Kurtosis-0.60280987
Mean20112638
Median Absolute Deviation (MAD)19799
Skewness-0.6701806
Sum1.9750611 × 1010
Variance6.4133489 × 108
MonotonicityNot monotonic
2023-12-12T08:00:20.452359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110627 25
 
2.5%
20120717 20
 
2.0%
20080711 18
 
1.8%
20121204 17
 
1.7%
20110902 17
 
1.7%
20130227 16
 
1.6%
20100917 16
 
1.6%
20140221 15
 
1.5%
20090109 15
 
1.5%
20130605 15
 
1.5%
Other values (167) 808
82.3%
ValueCountFrequency (%)
20051129 3
0.3%
20051221 3
0.3%
20051222 1
 
0.1%
20051228 7
0.7%
20060106 3
0.3%
20060323 1
 
0.1%
20060405 1
 
0.1%
20060406 1
 
0.1%
20060407 1
 
0.1%
20060410 2
 
0.2%
ValueCountFrequency (%)
20150107 10
1.0%
20141222 9
0.9%
20141201 7
0.7%
20141125 4
 
0.4%
20141121 4
 
0.4%
20141111 9
0.9%
20141106 5
0.5%
20141001 5
0.5%
20140926 5
0.5%
20140925 6
0.6%

PAY_AMT
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2949169.1
Minimum1100000
Maximum85229204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:20.568869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1100000
5-th percentile1100000
Q11100000
median2200000
Q33300000
95-th percentile5500000
Maximum85229204
Range84129204
Interquartile range (IQR)2200000

Descriptive statistics

Standard deviation6571451.1
Coefficient of variation (CV)2.2282382
Kurtosis116.34673
Mean2949169.1
Median Absolute Deviation (MAD)1100000
Skewness10.55247
Sum2.896084 × 109
Variance4.318397 × 1013
MonotonicityNot monotonic
2023-12-12T08:00:20.710100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2200000 337
34.3%
1100000 265
27.0%
1650000 127
 
12.9%
3300000 96
 
9.8%
4400000 79
 
8.0%
5500000 49
 
5.0%
8250000 12
 
1.2%
2750000 6
 
0.6%
6600000 3
 
0.3%
68945500 1
 
0.1%
Other values (7) 7
 
0.7%
ValueCountFrequency (%)
1100000 265
27.0%
1650000 127
 
12.9%
2200000 337
34.3%
2750000 6
 
0.6%
3300000 96
 
9.8%
4400000 79
 
8.0%
5500000 49
 
5.0%
6600000 3
 
0.3%
8250000 12
 
1.2%
51407500 1
 
0.1%
ValueCountFrequency (%)
85229204 1
 
0.1%
83982600 1
 
0.1%
81321475 1
 
0.1%
73768500 1
 
0.1%
73027500 1
 
0.1%
68945500 1
 
0.1%
66751750 1
 
0.1%
51407500 1
 
0.1%
8250000 12
1.2%
6600000 3
 
0.3%

RGLR_EVAL_BASIS_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)16.1%
Missing684
Missing (%)69.7%
Infinite0
Infinite (%)0.0%
Mean20135231
Minimum20110601
Maximum20141218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:20.881642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110601
5-th percentile20120328
Q120130331
median20140264
Q320140731
95-th percentile20141031
Maximum20141218
Range30617
Interquartile range (IQR)10400

Descriptive statistics

Standard deviation8532.0875
Coefficient of variation (CV)0.00042373924
Kurtosis0.96820209
Mean20135231
Median Absolute Deviation (MAD)566.5
Skewness-1.4311901
Sum6.0002988 × 109
Variance72796518
MonotonicityNot monotonic
2023-12-12T08:00:21.062908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
20130125 17
 
1.7%
20140831 17
 
1.7%
20140630 16
 
1.6%
20140131 16
 
1.6%
20131130 15
 
1.5%
20140813 14
 
1.4%
20140117 12
 
1.2%
20121130 11
 
1.1%
20111031 11
 
1.1%
20120731 10
 
1.0%
Other values (38) 159
 
16.2%
(Missing) 684
69.7%
ValueCountFrequency (%)
20110601 1
 
0.1%
20110630 1
 
0.1%
20111031 11
1.1%
20120229 1
 
0.1%
20120313 1
 
0.1%
20120331 1
 
0.1%
20120531 2
 
0.2%
20120630 3
 
0.3%
20120731 10
1.0%
20120831 3
 
0.3%
ValueCountFrequency (%)
20141218 5
 
0.5%
20141130 2
 
0.2%
20141103 7
0.7%
20141031 3
 
0.3%
20141008 5
 
0.5%
20140930 9
0.9%
20140916 5
 
0.5%
20140831 17
1.7%
20140827 5
 
0.5%
20140813 14
1.4%

NOTICE_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct237
Distinct (%)24.2%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean20111973
Minimum20051101
Maximum20141219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:21.234588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051101
5-th percentile20061104
Q120091202
median20120313
Q320131017
95-th percentile20140916
Maximum20141219
Range90118
Interquartile range (IQR)39815.5

Descriptive statistics

Standard deviation25359.441
Coefficient of variation (CV)0.0012609126
Kurtosis-0.6703518
Mean20111973
Median Absolute Deviation (MAD)19804
Skewness-0.64642727
Sum1.9709734 × 1010
Variance6.4310125 × 108
MonotonicityNot monotonic
2023-12-12T08:00:21.381692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120703 21
 
2.1%
20130127 17
 
1.7%
20130510 15
 
1.5%
20140813 14
 
1.4%
20100630 13
 
1.3%
20131030 13
 
1.3%
20110601 13
 
1.3%
20110928 12
 
1.2%
20121025 12
 
1.2%
20130926 12
 
1.2%
Other values (227) 838
85.3%
ValueCountFrequency (%)
20051101 2
0.2%
20051205 2
0.2%
20051208 2
0.2%
20051209 1
 
0.1%
20051216 4
0.4%
20051222 2
0.2%
20051228 1
 
0.1%
20051229 2
0.2%
20060302 2
0.2%
20060324 1
 
0.1%
ValueCountFrequency (%)
20141219 2
 
0.2%
20141218 5
0.5%
20141215 3
 
0.3%
20141126 9
0.9%
20141104 7
0.7%
20141023 4
0.4%
20141021 4
0.4%
20141010 9
0.9%
20141008 5
0.5%
20140916 5
0.5%

PAY_TRGT_YR
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.0499
Minimum2005
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2023-12-12T08:00:21.505510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12009
median2012
Q32013
95-th percentile2014
Maximum2014
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5294504
Coefficient of variation (CV)0.001257776
Kurtosis-0.68195472
Mean2011.0499
Median Absolute Deviation (MAD)2
Skewness-0.60797454
Sum1974851
Variance6.3981193
MonotonicityNot monotonic
2023-12-12T08:00:21.609590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2014 202
20.6%
2013 161
16.4%
2012 135
13.7%
2011 122
12.4%
2010 96
9.8%
2009 79
 
8.0%
2008 68
 
6.9%
2007 56
 
5.7%
2006 46
 
4.7%
2005 17
 
1.7%
ValueCountFrequency (%)
2005 17
 
1.7%
2006 46
 
4.7%
2007 56
 
5.7%
2008 68
 
6.9%
2009 79
 
8.0%
2010 96
9.8%
2011 122
12.4%
2012 135
13.7%
2013 161
16.4%
2014 202
20.6%
ValueCountFrequency (%)
2014 202
20.6%
2013 161
16.4%
2012 135
13.7%
2011 122
12.4%
2010 96
9.8%
2009 79
 
8.0%
2008 68
 
6.9%
2007 56
 
5.7%
2006 46
 
4.7%
2005 17
 
1.7%

CLOSE_YN
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.3%
Missing361
Missing (%)36.8%
Memory size2.0 KiB
True
615 
False
 
6
(Missing)
361 
ValueCountFrequency (%)
True 615
62.6%
False 6
 
0.6%
(Missing) 361
36.8%
2023-12-12T08:00:21.726958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REG_ENO
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
1535
480 
1509
198 
7519
195 
1498
90 
1642
 
19

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1642
2nd row1642
3rd row1642
4th row1642
5th row1642

Common Values

ValueCountFrequency (%)
1535 480
48.9%
1509 198
20.2%
7519 195
19.9%
1498 90
 
9.2%
1642 19
 
1.9%

Length

2023-12-12T08:00:21.838729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:00:21.941944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1535 480
48.9%
1509 198
20.2%
7519 195
19.9%
1498 90
 
9.2%
1642 19
 
1.9%

REG_DT
Date

Distinct219
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
Minimum2013-02-22 16:18:47
Maximum2015-01-28 15:10:54
2023-12-12T08:00:22.067549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:22.213919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T08:00:17.097179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.387542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.006777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.941498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.796383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.518066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.342865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.193015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.470169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.097163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.087132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.910680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.658553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.462817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.304629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.558284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.220649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.224620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.050522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.771405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.584028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.418830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.639165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.376373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.333379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.167315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.894965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.684814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.515006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.721488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.510891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.417318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.249254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.977846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.790095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.628243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.812528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.706867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.516393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.337811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.072372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.894598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:17.726555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:12.894801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:13.848231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:14.651673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:15.425594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.181941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:00:16.990672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:00:22.336609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
FEE_CDDECIS_DYDECIS_NOORG_CDPAY_DYPAY_AMTRGLR_EVAL_BASIS_DYNOTICE_DYPAY_TRGT_YRCLOSE_YNREG_ENO
FEE_CD1.0000.0000.8530.9820.0000.6440.3460.0000.0000.0190.390
DECIS_DY0.0001.0000.0000.0001.0000.0000.9710.9830.9560.1040.916
DECIS_NO0.8530.0001.0000.5780.0000.9720.1700.0000.000NaN0.329
ORG_CD0.9820.0000.5781.0000.0000.6440.6350.0000.0000.0190.209
PAY_DY0.0001.0000.0000.0001.0000.0000.9710.9840.9580.1040.916
PAY_AMT0.6440.0000.9720.6440.0001.0000.0000.0000.000NaN0.427
RGLR_EVAL_BASIS_DY0.3460.9710.1700.6350.9710.0001.0000.9790.9620.1000.581
NOTICE_DY0.0000.9830.0000.0000.9840.0000.9791.0000.9960.2290.898
PAY_TRGT_YR0.0000.9560.0000.0000.9580.0000.9620.9961.0000.5810.781
CLOSE_YN0.0190.104NaN0.0190.104NaN0.1000.2290.5811.0000.171
REG_ENO0.3900.9160.3290.2090.9160.4270.5810.8980.7810.1711.000
2023-12-12T08:00:22.507226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
REG_ENOCLOSE_YNORG_CDFEE_CD
REG_ENO1.0000.2820.1720.154
CLOSE_YN0.2821.0000.0310.031
ORG_CD0.1720.0311.0000.998
FEE_CD0.1540.0310.9981.000
2023-12-12T08:00:22.636275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DECIS_DYDECIS_NOPAY_DYPAY_AMTRGLR_EVAL_BASIS_DYNOTICE_DYPAY_TRGT_YRFEE_CDORG_CDCLOSE_YNREG_ENO
DECIS_DY1.0000.2321.000-0.6360.9821.0000.9890.0000.0000.1230.616
DECIS_NO0.2321.0000.234-0.1690.0460.2330.2460.4900.5051.0000.128
PAY_DY1.0000.2341.000-0.6360.9821.0000.9890.0000.0000.1230.616
PAY_AMT-0.636-0.169-0.6361.000-0.084-0.636-0.6450.4970.5751.0000.128
RGLR_EVAL_BASIS_DY0.9820.0460.982-0.0841.0000.9840.8130.2830.2880.1660.508
NOTICE_DY1.0000.2331.000-0.6360.9841.0000.9890.0000.0000.2610.585
PAY_TRGT_YR0.9890.2460.989-0.6450.8130.9891.0000.0000.0000.3960.597
FEE_CD0.0000.4900.0000.4970.2830.0000.0001.0000.9980.0310.154
ORG_CD0.0000.5050.0000.5750.2880.0000.0000.9981.0000.0310.172
CLOSE_YN0.1231.0000.1231.0000.1660.2610.3960.0310.0311.0000.282
REG_ENO0.6160.1280.6160.1280.5080.5850.5970.1540.1720.2821.000

Missing values

2023-12-12T08:00:17.849151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:00:18.050985image/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.
2023-12-12T08:00:18.176996image/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

LIQD_PLAN_CDFEE_CDDECIS_DYDECIS_NOORG_CDPAY_DYPAY_AMTRGLR_EVAL_BASIS_DYNOTICE_DYPAY_TRGT_YRCLOSE_YNREG_ENOREG_DT
0KHFCMB2013S-35T01004201501063T01020150107220000020141031201412152014<NA>16422015/01/28 15:10:54
1KHFCMB2012S-31T01004201501062T01020150107220000020141031201412152014<NA>16422015/01/28 15:10:54
2KHFCMB2007S-06T01004201501061T01020150107110000020141031201412152014<NA>16422015/01/28 15:10:54
3KHFCMB2013S-40T011042015010610T01120150107165000020141218201412182014<NA>16422015/01/28 15:09:33
4KHFCMB2012S-38T01104201501069T01120150107110000020141218201412182014<NA>16422015/01/28 15:09:33
5KHFCMB2012S-35T01104201501068T01120150107220000020141218201412182014<NA>16422015/01/28 15:09:33
6KHFCMB2011S-18T01104201501067T01120150107165000020141218201412182014<NA>16422015/01/28 15:09:33
7KHFCMB2009S-11T01104201501066T01120150107110000020141218201412182014<NA>16422015/01/28 15:09:33
8KHFCMB2013S-36T01204201501065T01220150107220000020141130201412192014<NA>16422015/01/28 15:07:50
9KHFCMB2012S-24T01204201501064T01220150107220000020141130201412192014<NA>16422015/01/28 15:07:50
LIQD_PLAN_CDFEE_CDDECIS_DYDECIS_NOORG_CDPAY_DYPAY_AMTRGLR_EVAL_BASIS_DYNOTICE_DYPAY_TRGT_YRCLOSE_YNREG_ENOREG_DT
972KHFCMB2004S-01T01104200910084T011200910093300000<NA>200909232009Y15352013/02/22 16:18:47
973KHFCMB2004S-01T01004200908182T010200908183300000<NA>200908072009Y15352013/02/22 16:18:47
974KHFCMB2004S-01T01104200809225T011200809233300000<NA>200808062008Y15352013/02/22 16:18:47
975KHFCMB2004S-01T010042008070816T010200807113300000<NA>200806162008Y15352013/02/22 16:18:47
976KHFCMB2004S-01T011042007062912T011200707023300000<NA>200706282007Y15352013/02/22 16:18:47
977KHFCMB2004S-01T01004200706253T010200706273300000<NA>200706202007Y15352013/02/22 16:18:47
978KHFCMB2004S-01T01004200608292T010200608314400000<NA>200608222006Y15352013/02/22 16:19:13
979KHFCMB2004S-01T01104200607113T011200607144400000<NA>200606302006Y15352013/02/22 16:19:13
980KHFCMB2004S-01T01004200511287T010200511294400000<NA>200511012005N75192013/03/28 13:55:01
981KHFCMB2004S-02T01004200511286T010200511294400000<NA>200511012005Y15352013/02/22 16:27:29