Overview

Dataset statistics

Number of variables9
Number of observations87
Missing cells105
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory80.5 B

Variable types

Text1
Numeric4
Categorical3
DateTime1

Dataset

Description한국주택금융공사 주택연금부에서 제공하는 담보권리사항정보에 대한 데이터로, 보증번호, 처리순번, 담보권리순번, 담보권리구분코드 등의 항목을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073028/fileData.do

Alerts

MORT_START_DY is highly overall correlated with MORT_END_DYHigh correlation
MORT_END_DY is highly overall correlated with MORT_START_DY and 1 other fieldsHigh correlation
MORT_RIGHT_CD is highly overall correlated with MORT_RIGHT_CNFM_CDHigh correlation
MORT_RIGHT_CNFM_CD is highly overall correlated with MORT_END_DY and 1 other fieldsHigh correlation
MORT_START_DY has 40 (46.0%) missing valuesMissing
MORT_END_DY has 65 (74.7%) missing valuesMissing

Reproduction

Analysis started2023-12-13 00:30:09.180808
Analysis finished2023-12-13 00:30:10.856012
Duration1.68 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

GUARNT_NO
Text

Distinct60
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-13T09:30:11.011480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1218
Distinct characters22
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

Unique42 ?
Unique (%)48.3%

Sample

1st rowRTAC2017000653
2nd rowRTAC2019000548
3rd rowRTAA2017000172
4th rowRTPA2011000034
5th rowRTPA2011000034
ValueCountFrequency (%)
rtpa2013000096 6
 
6.9%
rqad2011000135 4
 
4.6%
rqad2008000253 3
 
3.4%
rqad2007000065 3
 
3.4%
rtpa2016000323 3
 
3.4%
rtaa2008000095 2
 
2.3%
rtha2010000333 2
 
2.3%
rtma2011000031 2
 
2.3%
rtab2012000034 2
 
2.3%
rtma2011000124 2
 
2.3%
Other values (50) 58
66.7%
2023-12-13T09:30:11.307505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 439
36.0%
1 122
 
10.0%
2 119
 
9.8%
R 88
 
7.2%
A 83
 
6.8%
T 74
 
6.1%
3 41
 
3.4%
6 35
 
2.9%
P 32
 
2.6%
9 31
 
2.5%
Other values (12) 154
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 870
71.4%
Uppercase Letter 348
 
28.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 88
25.3%
A 83
23.9%
T 74
21.3%
P 32
 
9.2%
M 14
 
4.0%
D 13
 
3.7%
Q 13
 
3.7%
H 10
 
2.9%
O 8
 
2.3%
B 6
 
1.7%
Other values (2) 7
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 439
50.5%
1 122
 
14.0%
2 119
 
13.7%
3 41
 
4.7%
6 35
 
4.0%
9 31
 
3.6%
5 26
 
3.0%
8 20
 
2.3%
7 19
 
2.2%
4 18
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 870
71.4%
Latin 348
 
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 88
25.3%
A 83
23.9%
T 74
21.3%
P 32
 
9.2%
M 14
 
4.0%
D 13
 
3.7%
Q 13
 
3.7%
H 10
 
2.9%
O 8
 
2.3%
B 6
 
1.7%
Other values (2) 7
 
2.0%
Common
ValueCountFrequency (%)
0 439
50.5%
1 122
 
14.0%
2 119
 
13.7%
3 41
 
4.7%
6 35
 
4.0%
9 31
 
3.6%
5 26
 
3.0%
8 20
 
2.3%
7 19
 
2.2%
4 18
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1218
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 439
36.0%
1 122
 
10.0%
2 119
 
9.8%
R 88
 
7.2%
A 83
 
6.8%
T 74
 
6.1%
3 41
 
3.4%
6 35
 
2.9%
P 32
 
2.6%
9 31
 
2.5%
Other values (12) 154
 
12.6%

PRCSS_SEQ
Real number (ℝ)

Distinct41
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.862069
Minimum2
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-13T09:30:11.437559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9.6
Q113
median17
Q357.5
95-th percentile91.1
Maximum111
Range109
Interquartile range (IQR)44.5

Descriptive statistics

Standard deviation29.141664
Coefficient of variation (CV)0.8359132
Kurtosis-0.024081339
Mean34.862069
Median Absolute Deviation (MAD)10
Skewness1.0501884
Sum3033
Variance849.23657
MonotonicityNot monotonic
2023-12-13T09:30:11.535458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
13 9
 
10.3%
12 9
 
10.3%
11 7
 
8.0%
15 6
 
6.9%
27 4
 
4.6%
17 3
 
3.4%
72 3
 
3.4%
14 3
 
3.4%
58 3
 
3.4%
61 3
 
3.4%
Other values (31) 37
42.5%
ValueCountFrequency (%)
2 1
 
1.1%
4 1
 
1.1%
7 2
 
2.3%
9 1
 
1.1%
11 7
8.0%
12 9
10.3%
13 9
10.3%
14 3
 
3.4%
15 6
6.9%
16 2
 
2.3%
ValueCountFrequency (%)
111 2
2.3%
109 1
 
1.1%
92 2
2.3%
89 1
 
1.1%
88 1
 
1.1%
87 1
 
1.1%
86 1
 
1.1%
81 1
 
1.1%
72 3
3.4%
68 1
 
1.1%

MORT_SEQ
Categorical

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size828.0 B
1
67 
2
16 
3
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 67
77.0%
2 16
 
18.4%
3 4
 
4.6%

Length

2023-12-13T09:30:11.648420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:30:11.729023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 67
77.0%
2 16
 
18.4%
3 4
 
4.6%

MORT_RIGHT_CD
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size828.0 B
1
63 
2
13 
<NA>
7 
99
 
2
4
 
1

Length

Max length4
Median length1
Mean length1.2643678
Min length1

Unique

Unique2 ?
Unique (%)2.3%

Sample

1st row2
2nd row2
3rd row4
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 63
72.4%
2 13
 
14.9%
<NA> 7
 
8.0%
99 2
 
2.3%
4 1
 
1.1%
3 1
 
1.1%

Length

2023-12-13T09:30:11.815819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:30:11.902427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 63
72.4%
2 13
 
14.9%
na 7
 
8.0%
99 2
 
2.3%
4 1
 
1.1%
3 1
 
1.1%

MORT_START_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)93.6%
Missing40
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean20116564
Minimum20010526
Maximum20200531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-13T09:30:11.999587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010526
5-th percentile20071005
Q120090910
median20110315
Q320135962
95-th percentile20187643
Maximum20200531
Range190005
Interquartile range (IQR)45052.5

Descriptive statistics

Standard deviation39385.22
Coefficient of variation (CV)0.0019578503
Kurtosis0.31185986
Mean20116564
Median Absolute Deviation (MAD)20395
Skewness0.19767979
Sum9.4547851 × 108
Variance1.5511956 × 109
MonotonicityNot monotonic
2023-12-13T09:30:12.101969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
20110309 2
 
2.3%
20071005 2
 
2.3%
20110307 2
 
2.3%
20110701 1
 
1.1%
20090915 1
 
1.1%
20090904 1
 
1.1%
20071203 1
 
1.1%
20110430 1
 
1.1%
20111015 1
 
1.1%
20081119 1
 
1.1%
Other values (34) 34
39.1%
(Missing) 40
46.0%
ValueCountFrequency (%)
20010526 1
1.1%
20060124 1
1.1%
20071005 2
2.3%
20071203 1
1.1%
20071207 1
1.1%
20080101 1
1.1%
20080401 1
1.1%
20081007 1
1.1%
20081119 1
1.1%
20090220 1
1.1%
ValueCountFrequency (%)
20200531 1
1.1%
20200128 1
1.1%
20190521 1
1.1%
20180928 1
1.1%
20170608 1
1.1%
20170519 1
1.1%
20170312 1
1.1%
20160704 1
1.1%
20151114 1
1.1%
20150825 1
1.1%

MORT_END_DY
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing65
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean23774766
Minimum20090220
Maximum99990909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-13T09:30:12.209951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090220
5-th percentile20091032
Q120123364
median20140307
Q320178335
95-th percentile20219025
Maximum99990909
Range79900689
Interquartile range (IQR)54970.75

Descriptive statistics

Standard deviation17023150
Coefficient of variation (CV)0.71601759
Kurtosis21.999785
Mean23774766
Median Absolute Deviation (MAD)30267.5
Skewness4.6903828
Sum5.2304485 × 108
Variance2.8978765 × 1014
MonotonicityNot monotonic
2023-12-13T09:30:12.308404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20140307 2
 
2.3%
20190311 1
 
1.1%
20090220 1
 
1.1%
20120201 1
 
1.1%
20121015 1
 
1.1%
20130412 1
 
1.1%
20130701 1
 
1.1%
20141119 1
 
1.1%
20131014 1
 
1.1%
20130430 1
 
1.1%
Other values (11) 11
 
12.6%
(Missing) 65
74.7%
ValueCountFrequency (%)
20090220 1
1.1%
20091028 1
1.1%
20091103 1
1.1%
20120201 1
1.1%
20120618 1
1.1%
20121015 1
1.1%
20130412 1
1.1%
20130430 1
1.1%
20130701 1
1.1%
20131014 1
1.1%
ValueCountFrequency (%)
99990909 1
1.1%
20220530 1
1.1%
20190426 1
1.1%
20190311 1
1.1%
20181113 1
1.1%
20180703 1
1.1%
20171231 1
1.1%
20170630 1
1.1%
20170519 1
1.1%
20141119 1
1.1%

MORT_RIGHT_CNFM_CD
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size828.0 B
<NA>
29 
99
21 
1
17 
2
13 
3
7 

Length

Max length4
Median length2
Mean length2.2413793
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
33.3%
99 21
24.1%
1 17
19.5%
2 13
14.9%
3 7
 
8.0%

Length

2023-12-13T09:30:12.419356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:30:12.521488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
33.3%
99 21
24.1%
1 17
19.5%
2 13
14.9%
3 7
 
8.0%

REG_ENO
Real number (ℝ)

Distinct21
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3672.4713
Minimum1059
Maximum51143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-13T09:30:12.615328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1059
5-th percentile1073
Q11160
median1652
Q31652
95-th percentile7486
Maximum51143
Range50084
Interquartile range (IQR)492

Descriptive statistics

Standard deviation7668.4044
Coefficient of variation (CV)2.0880774
Kurtosis32.984589
Mean3672.4713
Median Absolute Deviation (MAD)315
Skewness5.5846675
Sum319505
Variance58804426
MonotonicityNot monotonic
2023-12-13T09:30:12.701863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1652 27
31.0%
1073 16
18.4%
7486 12
13.8%
1160 4
 
4.6%
7398 3
 
3.4%
1410 3
 
3.4%
1059 3
 
3.4%
1788 2
 
2.3%
1547 2
 
2.3%
1566 2
 
2.3%
Other values (11) 13
14.9%
ValueCountFrequency (%)
1059 3
 
3.4%
1073 16
18.4%
1095 1
 
1.1%
1160 4
 
4.6%
1337 2
 
2.3%
1410 3
 
3.4%
1424 1
 
1.1%
1466 2
 
2.3%
1477 1
 
1.1%
1530 1
 
1.1%
ValueCountFrequency (%)
51143 1
 
1.1%
50785 1
 
1.1%
7486 12
13.8%
7406 1
 
1.1%
7398 3
 
3.4%
1818 1
 
1.1%
1788 2
 
2.3%
1652 27
31.0%
1574 1
 
1.1%
1566 2
 
2.3%

REG_TS
Date

Distinct67
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
Minimum2008-07-30 14:21:02
Maximum2020-10-22 10:37:20
2023-12-13T09:30:12.792592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:12.903014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T09:30:10.316771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.476288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.743278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.037765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.378197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.533735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.826226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.106406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.447661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.606008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.901205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.179261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.519587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.673932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:09.974589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:10.251456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:30:12.983063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GUARNT_NOPRCSS_SEQMORT_SEQMORT_RIGHT_CDMORT_START_DYMORT_END_DYMORT_RIGHT_CNFM_CDREG_ENOREG_TS
GUARNT_NO1.0000.9900.0000.9850.9951.0000.9981.0001.000
PRCSS_SEQ0.9901.0000.1660.5560.0000.0000.6500.1111.000
MORT_SEQ0.0000.1661.0000.0000.1520.0000.2240.0000.000
MORT_RIGHT_CD0.9850.5560.0001.0000.1780.6180.8550.0001.000
MORT_START_DY0.9950.0000.1520.1781.0000.6090.1640.3440.993
MORT_END_DY1.0000.0000.0000.6180.6091.000NaN0.0001.000
MORT_RIGHT_CNFM_CD0.9980.6500.2240.8550.164NaN1.0000.4330.994
REG_ENO1.0000.1110.0000.0000.3440.0000.4331.0001.000
REG_TS1.0001.0000.0001.0000.9931.0000.9941.0001.000
2023-12-13T09:30:13.076655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
MORT_RIGHT_CDMORT_RIGHT_CNFM_CDMORT_SEQ
MORT_RIGHT_CD1.0000.5110.000
MORT_RIGHT_CNFM_CD0.5111.0000.209
MORT_SEQ0.0000.2091.000
2023-12-13T09:30:13.152821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRCSS_SEQMORT_START_DYMORT_END_DYREG_ENOMORT_SEQMORT_RIGHT_CDMORT_RIGHT_CNFM_CD
PRCSS_SEQ1.0000.085-0.0890.0320.0890.2520.455
MORT_START_DY0.0851.0000.8110.3130.2110.0000.057
MORT_END_DY-0.0890.8111.0000.0460.0000.3971.000
REG_ENO0.0320.3130.0461.0000.0000.0000.431
MORT_SEQ0.0890.2110.0000.0001.0000.0000.209
MORT_RIGHT_CD0.2520.0000.3970.0000.0001.0000.511
MORT_RIGHT_CNFM_CD0.4550.0571.0000.4310.2090.5111.000

Missing values

2023-12-13T09:30:10.601300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:30:10.703202image/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.
2023-12-13T09:30:10.792884image/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_NOPRCSS_SEQMORT_SEQMORT_RIGHT_CDMORT_START_DYMORT_END_DYMORT_RIGHT_CNFM_CDREG_ENOREG_TS
0RTAC2017000653431220190521<NA><NA>17882020/10/22 10:37:20
1RTAC2019000548161220200128<NA>217882020/10/22 10:26:50
2RTAA201700017228142018092820190426<NA>14102019/04/26 15:07:43
3RTPA20110000349221<NA><NA><NA>16522017/08/31 14:19:46
4RTPA20110000349211<NA><NA><NA>16522017/08/31 14:19:46
5RTPA20150001313111<NA><NA><NA>16522017/08/29 16:52:48
6RTJA201100005387112017031220190311118182018/02/13 16:16:42
7RTJB20200000154112020053120220530115302020/07/01 15:30:48
8RTPA20160005661121<NA><NA><NA>16522017/08/18 16:52:12
9RTPA20160005661111<NA><NA><NA>16522017/08/18 16:52:12
GUARNT_NOPRCSS_SEQMORT_SEQMORT_RIGHT_CDMORT_START_DYMORT_END_DYMORT_RIGHT_CNFM_CDREG_ENOREG_TS
77RTMA200700000171<NA><NA><NA><NA>10952008/08/01 14:58:29
78RTAA2007000011121<NA>2009022020090220<NA>14102009/02/20 13:29:06
79RTMA2011000012719920110315<NA>214772011/09/09 11:33:27
80RTHO2009000009171<NA><NA><NA><NA>74062010/03/30 13:29:46
81RTPA2009000012191<NA><NA><NA><NA>73982010/03/29 16:13:28
82RTPA20090000132211<NA><NA>9973982010/03/29 16:04:38
83RTPA2008000004361<NA><NA><NA><NA>73982010/03/29 16:11:08
84RQAD200700006591220071005<NA>214242008/07/30 14:21:02
85RTAA2008000095211<NA><NA><NA>14102009/03/20 18:13:42
86RTHO20090000023511<NA><NA><NA>10732011/01/25 14:39:25