Overview

Dataset statistics

Number of variables15
Number of observations1000
Missing cells2000
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory128.1 KiB
Average record size in memory131.1 B

Variable types

Categorical3
Numeric8
Unsupported2
Boolean1
DateTime1

Dataset

Description유동화변동금리이력(트렌치코드, 유동화계획코드, 시작일자 등)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073168/fileData.do

Alerts

CONFM_YN has constant value ""Constant
LIQD_PLAN_CD is highly overall correlated with BASIS_RAT and 3 other fieldsHigh correlation
TRNCH_CD is highly overall correlated with BASIS_RAT and 3 other fieldsHigh correlation
STRT_DY is highly overall correlated with END_DY and 4 other fieldsHigh correlation
END_DY is highly overall correlated with STRT_DY and 4 other fieldsHigh correlation
PAY_DY is highly overall correlated with STRT_DY and 4 other fieldsHigh correlation
RAT_STRT_DY is highly overall correlated with STRT_DY and 4 other fieldsHigh correlation
RAT_END_DY is highly overall correlated with STRT_DY and 4 other fieldsHigh correlation
BASIS_RAT is highly overall correlated with FACE_RAT and 2 other fieldsHigh correlation
FACE_RAT is highly overall correlated with BASIS_RAT and 2 other fieldsHigh correlation
IAPPLY_SPREAD_RAT is highly overall correlated with TRNCH_CD and 1 other fieldsHigh correlation
REG_ENO is highly overall correlated with STRT_DY and 4 other fieldsHigh correlation
DEBENTURE_BENIF_RAT has 1000 (100.0%) missing valuesMissing
RMK has 1000 (100.0%) missing valuesMissing
DEBENTURE_BENIF_RAT is an unsupported type, check if it needs cleaning or further analysisUnsupported
RMK is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 15:03:55.424898
Analysis finished2023-12-12 15:04:04.681036
Duration9.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

TRNCH_CD
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2010S-06-A420-1
 
25
KHFCMB2013S-13-A420-1
 
25
KHFCMB2017S-06-A420-1
 
25
KHFCMB2013S-11-A420-1
 
25
KHFCMB2014S-04-A420-1
 
25
Other values (36)
875 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2019S-01-A420-1
2nd rowKHFCMB2017S-24-A421-1
3rd rowKHFCMB2013S-33-A420-1
4th rowKHFCMB2013S-11-A420-1
5th rowKHFCMB2014S-04-A420-1

Common Values

ValueCountFrequency (%)
KHFCMB2010S-06-A420-1 25
 
2.5%
KHFCMB2013S-13-A420-1 25
 
2.5%
KHFCMB2017S-06-A420-1 25
 
2.5%
KHFCMB2013S-11-A420-1 25
 
2.5%
KHFCMB2014S-04-A420-1 25
 
2.5%
KHFCMB2012S-36-A421-1 25
 
2.5%
KHFCMB2012S-12-A420-1 25
 
2.5%
KHFCMB2010S-08-A420-1 25
 
2.5%
KHFCMB2009S-07-A360-1 25
 
2.5%
KHFCMB2011S-20-A421-1 25
 
2.5%
Other values (31) 750
75.0%

Length

2023-12-13T00:04:04.750921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
khfcmb2010s-06-a420-1 25
 
2.5%
khfcmb2013s-33-a420-1 25
 
2.5%
khfcmb2013s-13-a420-1 25
 
2.5%
khfcmb2013s-24-a420-1 25
 
2.5%
khfcmb2009s-13-a420-1 25
 
2.5%
khfcmb2012s-26-a420-1 25
 
2.5%
khfcmb2012s-38-a421-1 25
 
2.5%
khfcmb2010s-18-a420-1 25
 
2.5%
khfcmb2017s-21-a421-1 25
 
2.5%
khfcmb2018s-07-a420-1 25
 
2.5%
Other values (31) 750
75.0%

LIQD_PLAN_CD
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2010S-06
 
25
KHFCMB2013S-13
 
25
KHFCMB2017S-06
 
25
KHFCMB2013S-11
 
25
KHFCMB2014S-04
 
25
Other values (36)
875 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2019S-01
2nd rowKHFCMB2017S-24
3rd rowKHFCMB2013S-33
4th rowKHFCMB2013S-11
5th rowKHFCMB2014S-04

Common Values

ValueCountFrequency (%)
KHFCMB2010S-06 25
 
2.5%
KHFCMB2013S-13 25
 
2.5%
KHFCMB2017S-06 25
 
2.5%
KHFCMB2013S-11 25
 
2.5%
KHFCMB2014S-04 25
 
2.5%
KHFCMB2012S-36 25
 
2.5%
KHFCMB2012S-12 25
 
2.5%
KHFCMB2010S-08 25
 
2.5%
KHFCMB2009S-07 25
 
2.5%
KHFCMB2011S-20 25
 
2.5%
Other values (31) 750
75.0%

Length

2023-12-13T00:04:04.861447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
khfcmb2010s-06 25
 
2.5%
khfcmb2013s-33 25
 
2.5%
khfcmb2013s-13 25
 
2.5%
khfcmb2013s-24 25
 
2.5%
khfcmb2009s-13 25
 
2.5%
khfcmb2012s-26 25
 
2.5%
khfcmb2012s-38 25
 
2.5%
khfcmb2010s-18 25
 
2.5%
khfcmb2017s-21 25
 
2.5%
khfcmb2018s-07 25
 
2.5%
Other values (31) 750
75.0%

STRT_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct261
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20192639
Minimum20180815
Maximum20200930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:04.996711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180815
5-th percentile20181020
Q120190325
median20190925
Q320200325
95-th percentile20200825
Maximum20200930
Range20115
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation6758.1253
Coefficient of variation (CV)0.00033468262
Kurtosis-0.94347225
Mean20192639
Median Absolute Deviation (MAD)9200
Skewness-0.25707565
Sum2.0192639 × 1010
Variance45672258
MonotonicityNot monotonic
2023-12-13T00:04:05.157081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200725 25
 
2.5%
20200825 25
 
2.5%
20181225 25
 
2.5%
20200125 25
 
2.5%
20200625 25
 
2.5%
20200325 25
 
2.5%
20190725 25
 
2.5%
20191225 25
 
2.5%
20190825 25
 
2.5%
20190125 25
 
2.5%
Other values (251) 750
75.0%
ValueCountFrequency (%)
20180815 1
0.1%
20180816 1
0.1%
20180818 1
0.1%
20180820 1
0.1%
20180826 1
0.1%
20180827 1
0.1%
20180831 2
0.2%
20180915 1
0.1%
20180916 1
0.1%
20180918 1
0.1%
ValueCountFrequency (%)
20200930 1
 
0.1%
20200926 1
 
0.1%
20200925 25
2.5%
20200920 1
 
0.1%
20200901 1
 
0.1%
20200831 2
 
0.2%
20200830 1
 
0.1%
20200827 1
 
0.1%
20200826 3
 
0.3%
20200825 25
2.5%

END_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20193523
Minimum20180915
Maximum20201029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:05.295239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180915
5-th percentile20181124
Q120190424
median20191024
Q320200424
95-th percentile20200924
Maximum20201029
Range20114
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation6489.4928
Coefficient of variation (CV)0.00032136506
Kurtosis-0.8358201
Mean20193523
Median Absolute Deviation (MAD)9100
Skewness-0.35178685
Sum2.0193523 × 1010
Variance42113516
MonotonicityNot monotonic
2023-12-13T00:04:05.445938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190924 30
 
3.0%
20190324 30
 
3.0%
20181224 30
 
3.0%
20191124 30
 
3.0%
20190624 30
 
3.0%
20200424 30
 
3.0%
20190524 30
 
3.0%
20190724 30
 
3.0%
20200824 30
 
3.0%
20200224 30
 
3.0%
Other values (244) 700
70.0%
ValueCountFrequency (%)
20180915 1
 
0.1%
20180917 1
 
0.1%
20180918 1
 
0.1%
20180923 1
 
0.1%
20180930 1
 
0.1%
20181015 1
 
0.1%
20181017 1
 
0.1%
20181018 1
 
0.1%
20181023 1
 
0.1%
20181024 28
2.8%
ValueCountFrequency (%)
20201029 1
 
0.1%
20201025 1
 
0.1%
20201024 30
3.0%
20201019 1
 
0.1%
20200930 2
 
0.2%
20200929 1
 
0.1%
20200925 1
 
0.1%
20200924 30
3.0%
20200923 1
 
0.1%
20200919 1
 
0.1%

PAY_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20193667
Minimum20181025
Maximum20201030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:05.595934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20181025
5-th percentile20181126
Q120190425
median20191025
Q320200427
95-th percentile20200925
Maximum20201030
Range20005
Interquartile range (IQR)10002

Descriptive statistics

Standard deviation6448.5118
Coefficient of variation (CV)0.00031933338
Kurtosis-0.82304542
Mean20193667
Median Absolute Deviation (MAD)9103
Skewness-0.3668579
Sum2.0193667 × 1010
Variance41583305
MonotonicityNot monotonic
2023-12-13T00:04:05.727430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20201026 39
 
3.9%
20200727 39
 
3.9%
20200128 39
 
3.9%
20200427 39
 
3.9%
20190527 38
 
3.8%
20191226 38
 
3.8%
20200325 38
 
3.8%
20200525 38
 
3.8%
20200625 38
 
3.8%
20200225 38
 
3.8%
Other values (80) 616
61.6%
ValueCountFrequency (%)
20181025 33
3.3%
20181026 1
 
0.1%
20181030 1
 
0.1%
20181120 1
 
0.1%
20181126 36
3.6%
20181130 1
 
0.1%
20181220 1
 
0.1%
20181226 37
3.7%
20181231 1
 
0.1%
20190121 1
 
0.1%
ValueCountFrequency (%)
20201030 1
 
0.1%
20201026 39
3.9%
20201020 1
 
0.1%
20201005 1
 
0.1%
20200928 1
 
0.1%
20200925 38
3.8%
20200921 1
 
0.1%
20200831 1
 
0.1%
20200826 1
 
0.1%
20200825 38
3.8%

RAT_STRT_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct266
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20191666
Minimum20180625
Maximum20200825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:05.861112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180625
5-th percentile20180831
Q120190203
median20190825
Q320200225
95-th percentile20200725
Maximum20200825
Range20200
Interquartile range (IQR)10021.75

Descriptive statistics

Standard deviation6937.8995
Coefficient of variation (CV)0.00034360214
Kurtosis-1.0324376
Mean20191666
Median Absolute Deviation (MAD)9300
Skewness-0.13455763
Sum2.0191666 × 1010
Variance48134449
MonotonicityNot monotonic
2023-12-13T00:04:06.016224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190125 25
 
2.5%
20191225 25
 
2.5%
20181225 25
 
2.5%
20200325 25
 
2.5%
20190725 25
 
2.5%
20180825 25
 
2.5%
20200225 25
 
2.5%
20190525 25
 
2.5%
20200425 25
 
2.5%
20190825 25
 
2.5%
Other values (256) 750
75.0%
ValueCountFrequency (%)
20180625 1
0.1%
20180626 1
0.1%
20180630 1
0.1%
20180715 1
0.1%
20180716 1
0.1%
20180718 1
0.1%
20180720 2
0.2%
20180725 1
0.1%
20180726 2
0.2%
20180727 1
0.1%
ValueCountFrequency (%)
20200825 24
2.4%
20200801 1
 
0.1%
20200731 2
 
0.2%
20200727 1
 
0.1%
20200726 2
 
0.2%
20200725 24
2.4%
20200720 1
 
0.1%
20200718 2
 
0.2%
20200716 1
 
0.1%
20200715 2
 
0.2%

RAT_END_DY
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20192719
Minimum20180815
Maximum20200929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:06.534809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180815
5-th percentile20181024
Q120190324
median20190924
Q320200324
95-th percentile20200824
Maximum20200929
Range20114
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation6737.1693
Coefficient of variation (CV)0.00033364349
Kurtosis-0.93363732
Mean20192719
Median Absolute Deviation (MAD)9200
Skewness-0.26655842
Sum2.0192719 × 1010
Variance45389450
MonotonicityNot monotonic
2023-12-13T00:04:06.676437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190824 30
 
3.0%
20200924 30
 
3.0%
20191024 30
 
3.0%
20200624 30
 
3.0%
20200124 30
 
3.0%
20181224 30
 
3.0%
20191124 30
 
3.0%
20200524 30
 
3.0%
20200424 30
 
3.0%
20190124 30
 
3.0%
Other values (244) 700
70.0%
ValueCountFrequency (%)
20180815 1
 
0.1%
20180817 1
 
0.1%
20180818 1
 
0.1%
20180823 1
 
0.1%
20180831 1
 
0.1%
20180915 1
 
0.1%
20180917 1
 
0.1%
20180918 1
 
0.1%
20180923 1
 
0.1%
20180924 28
2.8%
ValueCountFrequency (%)
20200929 1
 
0.1%
20200925 1
 
0.1%
20200924 30
3.0%
20200919 1
 
0.1%
20200831 2
 
0.2%
20200829 1
 
0.1%
20200825 1
 
0.1%
20200824 30
3.0%
20200823 1
 
0.1%
20200819 1
 
0.1%

BASIS_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.26341
Minimum0.66
Maximum4.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:06.832833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile1.5
Q13.1175
median3.48
Q33.78
95-th percentile4.18
Maximum4.65
Range3.99
Interquartile range (IQR)0.6625

Descriptive statistics

Standard deviation0.78492941
Coefficient of variation (CV)0.2405243
Kurtosis1.5111993
Mean3.26341
Median Absolute Deviation (MAD)0.33
Skewness-1.365736
Sum3263.41
Variance0.61611419
MonotonicityNot monotonic
2023-12-13T00:04:07.007472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.52 31
 
3.1%
3.49 28
 
2.8%
3.57 26
 
2.6%
3.56 25
 
2.5%
3.4 24
 
2.4%
3.51 22
 
2.2%
3.87 21
 
2.1%
3.36 21
 
2.1%
3.53 21
 
2.1%
3.48 20
 
2.0%
Other values (209) 761
76.1%
ValueCountFrequency (%)
0.66 1
0.1%
0.68 1
0.1%
0.7 2
0.2%
0.71 2
0.2%
0.75 1
0.1%
0.77 2
0.2%
0.78 1
0.1%
0.79 1
0.1%
0.82 1
0.1%
0.86 1
0.1%
ValueCountFrequency (%)
4.65 5
0.5%
4.64 1
 
0.1%
4.63 2
 
0.2%
4.62 2
 
0.2%
4.61 1
 
0.1%
4.59 1
 
0.1%
4.56 1
 
0.1%
4.53 1
 
0.1%
4.48 1
 
0.1%
4.44 2
 
0.2%

FACE_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct195
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.02194
Minimum0.99
Maximum4.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-13T00:04:07.207383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.99
5-th percentile2.0795
Q12.79
median3.04
Q33.37
95-th percentile3.74
Maximum4.2
Range3.21
Interquartile range (IQR)0.58

Descriptive statistics

Standard deviation0.47413314
Coefficient of variation (CV)0.15689694
Kurtosis1.1657565
Mean3.02194
Median Absolute Deviation (MAD)0.29
Skewness-0.57376106
Sum3021.94
Variance0.22480224
MonotonicityNot monotonic
2023-12-13T00:04:07.369998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.07 32
 
3.2%
3.11 30
 
3.0%
3.04 29
 
2.9%
2.95 26
 
2.6%
3.12 25
 
2.5%
3.08 22
 
2.2%
2.91 21
 
2.1%
3.06 21
 
2.1%
3.03 21
 
2.1%
3.42 20
 
2.0%
Other values (185) 753
75.3%
ValueCountFrequency (%)
0.99 1
 
0.1%
1.04 1
 
0.1%
1.15 1
 
0.1%
1.27 1
 
0.1%
1.45 1
 
0.1%
1.58 1
 
0.1%
1.7 1
 
0.1%
1.79 1
 
0.1%
1.8 2
0.2%
1.81 3
0.3%
ValueCountFrequency (%)
4.2 5
0.5%
4.19 1
 
0.1%
4.18 2
 
0.2%
4.17 2
 
0.2%
4.16 1
 
0.1%
4.14 1
 
0.1%
4.11 1
 
0.1%
4.08 1
 
0.1%
4.03 1
 
0.1%
3.99 2
 
0.2%

IAPPLY_SPREAD_RAT
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.24147
Minimum-0.45
Maximum1.96
Zeros0
Zeros (%)0.0%
Negative877
Negative (%)87.7%
Memory size8.9 KiB
2023-12-13T00:04:07.515496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.45
5-th percentile-0.45
Q1-0.45
median-0.45
Q3-0.45
95-th percentile1.23
Maximum1.96
Range2.41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58048057
Coefficient of variation (CV)-2.4039449
Kurtosis5.8855934
Mean-0.24147
Median Absolute Deviation (MAD)0
Skewness2.7011794
Sum-241.47
Variance0.3369577
MonotonicityNot monotonic
2023-12-13T00:04:07.625658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-0.45 827
82.7%
-0.4 50
 
5.0%
1.96 25
 
2.5%
1.23 25
 
2.5%
1.09 25
 
2.5%
1.49 24
 
2.4%
0.33 24
 
2.4%
ValueCountFrequency (%)
-0.45 827
82.7%
-0.4 50
 
5.0%
0.33 24
 
2.4%
1.09 25
 
2.5%
1.23 25
 
2.5%
1.49 24
 
2.4%
1.96 25
 
2.5%
ValueCountFrequency (%)
1.96 25
 
2.5%
1.49 24
 
2.4%
1.23 25
 
2.5%
1.09 25
 
2.5%
0.33 24
 
2.4%
-0.4 50
 
5.0%
-0.45 827
82.7%

DEBENTURE_BENIF_RAT
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size8.9 KiB

CONFM_YN
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
True
1000 
ValueCountFrequency (%)
True 1000
100.0%
2023-12-13T00:04:07.746238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REG_ENO
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1769
289 
1841
279 
1438
264 
1628
168 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1769
2nd row1769
3rd row1769
4th row1769
5th row1769

Common Values

ValueCountFrequency (%)
1769 289
28.9%
1841 279
27.9%
1438 264
26.4%
1628 168
16.8%

Length

2023-12-13T00:04:07.828831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:04:07.918442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1769 289
28.9%
1841 279
27.9%
1438 264
26.4%
1628 168
16.8%

REG_DT
Date

Distinct378
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2018-10-02 16:41:27
Maximum2020-10-13 20:19:38
2023-12-13T00:04:08.027136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:08.158697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

RMK
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1000
Missing (%)100.0%
Memory size8.9 KiB

Interactions

2023-12-13T00:04:03.528122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.099148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.161706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.250750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.279182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:00.298563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.792490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.668720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.649413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.213833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.311968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.382226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.413615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:00.407466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.901289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.794421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.750168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.336422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.460594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.521277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.533110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:00.920272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.004433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.910965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.838558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.463974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.582023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.654302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.657883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.045984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.110842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.018742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.933690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.581478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.721345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.777949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.805216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.211583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.218073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.119540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.028959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.715193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.861499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.899304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.920483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.367785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.346301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.219807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.120046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:56.866232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.993190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.040334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:00.048587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.519806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.467521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.324677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:04.224237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:57.036145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:58.094506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:03:59.145107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:00.183030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:01.675671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:02.566579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:04:03.436556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:04:08.253123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
TRNCH_CDLIQD_PLAN_CDSTRT_DYEND_DYPAY_DYRAT_STRT_DYRAT_END_DYBASIS_RATFACE_RATIAPPLY_SPREAD_RATREG_ENO
TRNCH_CD1.0001.0000.0000.0000.0000.0000.0000.9370.9321.0000.000
LIQD_PLAN_CD1.0001.0000.0000.0000.0000.0000.0000.9370.9321.0000.000
STRT_DY0.0000.0001.0000.9900.9770.9901.0000.3500.2400.0000.965
END_DY0.0000.0000.9901.0000.9960.9700.9930.3180.2240.0000.945
PAY_DY0.0000.0000.9770.9961.0000.9510.9780.3000.2160.0000.924
RAT_STRT_DY0.0000.0000.9900.9700.9511.0000.9880.3390.2430.0000.970
RAT_END_DY0.0000.0001.0000.9930.9780.9881.0000.3420.2380.0000.965
BASIS_RAT0.9370.9370.3500.3180.3000.3390.3421.0000.9510.8350.388
FACE_RAT0.9320.9320.2400.2240.2160.2430.2380.9511.0000.6950.243
IAPPLY_SPREAD_RAT1.0001.0000.0000.0000.0000.0000.0000.8350.6951.0000.000
REG_ENO0.0000.0000.9650.9450.9240.9700.9650.3880.2430.0001.000
2023-12-13T00:04:08.370342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LIQD_PLAN_CDTRNCH_CDREG_ENO
LIQD_PLAN_CD1.0001.0000.000
TRNCH_CD1.0001.0000.000
REG_ENO0.0000.0001.000
2023-12-13T00:04:08.483350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STRT_DYEND_DYPAY_DYRAT_STRT_DYRAT_END_DYBASIS_RATFACE_RATIAPPLY_SPREAD_RATTRNCH_CDLIQD_PLAN_CDREG_ENO
STRT_DY1.0000.9990.9970.9970.999-0.200-0.2560.0160.0000.0000.749
END_DY0.9991.0000.9980.9961.000-0.194-0.2510.0090.0000.0000.686
PAY_DY0.9970.9981.0000.9950.998-0.182-0.2410.0010.0000.0000.648
RAT_STRT_DY0.9970.9960.9951.0000.996-0.158-0.239-0.0460.0000.0000.767
RAT_END_DY0.9991.0000.9980.9961.000-0.194-0.2510.0090.0000.0000.748
BASIS_RAT-0.200-0.194-0.182-0.158-0.1941.0000.855-0.4370.6710.6710.241
FACE_RAT-0.256-0.251-0.241-0.239-0.2510.8551.000-0.0620.6560.6560.147
IAPPLY_SPREAD_RAT0.0160.0090.001-0.0460.009-0.437-0.0621.0000.9820.9820.000
TRNCH_CD0.0000.0000.0000.0000.0000.6710.6560.9821.0001.0000.000
LIQD_PLAN_CD0.0000.0000.0000.0000.0000.6710.6560.9821.0001.0000.000
REG_ENO0.7490.6860.6480.7670.7480.2410.1470.0000.0000.0001.000

Missing values

2023-12-13T00:04:04.395679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:04:04.604463image/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.

Sample

TRNCH_CDLIQD_PLAN_CDSTRT_DYEND_DYPAY_DYRAT_STRT_DYRAT_END_DYBASIS_RATFACE_RATIAPPLY_SPREAD_RATDEBENTURE_BENIF_RATCONFM_YNREG_ENOREG_DTRMK
0KHFCMB2019S-01-A420-1KHFCMB2019S-0120200804202009022020102620200704202008023.863.41-0.45<NA>Y17692020/10/13 20:19:38<NA>
1KHFCMB2017S-24-A421-1KHFCMB2017S-2420200826202010242020102620200726202009243.783.33-0.45<NA>Y17692020/10/13 20:19:38<NA>
2KHFCMB2013S-33-A420-1KHFCMB2013S-3320200925202010242020102620200825202009243.513.06-0.45<NA>Y17692020/10/13 20:19:38<NA>
3KHFCMB2013S-11-A420-1KHFCMB2013S-1120200925202010242020102620200825202009243.092.64-0.45<NA>Y17692020/10/13 20:19:38<NA>
4KHFCMB2014S-04-A420-1KHFCMB2014S-0420200925202010242020102620200825202009242.992.54-0.45<NA>Y17692020/10/13 20:09:45<NA>
5KHFCMB2012S-36-A421-1KHFCMB2012S-3620200925202010242020102620200825202009242.752.3-0.45<NA>Y17692020/10/13 20:09:45<NA>
6KHFCMB2012S-12-A420-1KHFCMB2012S-1220200925202010242020102620200825202009243.092.64-0.45<NA>Y17692020/10/13 20:09:45<NA>
7KHFCMB2010S-08-A420-1KHFCMB2010S-0820200926202010252020102620200626202009250.72.661.96<NA>Y17692020/10/13 19:53:20<NA>
8KHFCMB2009S-02-A420-1KHFCMB2009S-0220200930202010292020103020200630202009290.71.931.23<NA>Y17692020/10/13 19:50:27<NA>
9KHFCMB2009S-03-A366-1KHFCMB2009S-0320200901202009302020102620200801202008310.682.171.49<NA>Y17692020/10/13 19:27:00<NA>
TRNCH_CDLIQD_PLAN_CDSTRT_DYEND_DYPAY_DYRAT_STRT_DYRAT_END_DYBASIS_RATFACE_RATIAPPLY_SPREAD_RATDEBENTURE_BENIF_RATCONFM_YNREG_ENOREG_DTRMK
990KHFCMB2012S-04-A421-1KHFCMB2012S-0420180925201810242018102520180825201809243.543.09-0.45<NA>Y14382018/10/04 10:45:21<NA>
991KHFCMB2011S-20-A421-1KHFCMB2011S-2020180925201810242018102520180825201809243.352.9-0.45<NA>Y14382018/10/04 10:45:21<NA>
992KHFCMB2011S-15-A421-1KHFCMB2011S-1520180925201810242018102520180825201809243.412.96-0.45<NA>Y14382018/10/04 10:45:21<NA>
993KHFCMB2011S-06-A421-1KHFCMB2011S-0620180925201810242018102520180825201809243.583.13-0.45<NA>Y14382018/10/04 10:45:21<NA>
994KHFCMB2010S-18-A420-1KHFCMB2010S-1820180925201810242018102520180825201809243.192.74-0.45<NA>Y14382018/10/04 10:45:21<NA>
995KHFCMB2010S-06-A420-1KHFCMB2010S-0620180925201810242018102520180825201809243.843.44-0.4<NA>Y14382018/10/04 10:45:21<NA>
996KHFCMB2009S-13-A420-1KHFCMB2009S-1320180925201810242018102520180825201809243.73.3-0.4<NA>Y14382018/10/04 10:45:21<NA>
997KHFCMB2010S-08-A420-1KHFCMB2010S-0820180926201810252018102620180626201809251.653.611.96<NA>Y14382018/10/02 17:05:02<NA>
998KHFCMB2009S-07-A360-1KHFCMB2009S-0720180925201810242018102520180625201809241.652.741.09<NA>Y14382018/10/02 17:04:30<NA>
999KHFCMB2009S-02-A420-1KHFCMB2009S-0220180930201810292018103020180630201809291.652.881.23<NA>Y14382018/10/02 16:41:27<NA>