Overview

Dataset statistics

Number of variables7
Number of observations354
Missing cells101
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory62.4 B

Variable types

Text1
Numeric6

Dataset

Description한국주택금융공사에서 발행한 MBS 기초자산 상환 및 연체현황에 대한 공공데이터 자료입니다. 공공데이터 개방정책에 따라 등록되었습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073662/fileData.do

Alerts

전월대출잔액 is highly overall correlated with 조기상환액 and 3 other fieldsHigh correlation
조기상환액 is highly overall correlated with 전월대출잔액 and 3 other fieldsHigh correlation
기준월 대출잔액 is highly overall correlated with 전월대출잔액 and 3 other fieldsHigh correlation
연체금액 is highly overall correlated with 전월대출잔액 and 2 other fieldsHigh correlation
연체율 is highly overall correlated with 전월대출잔액 and 2 other fieldsHigh correlation
전월대출잔액 has 18 (5.1%) missing valuesMissing
조기상환액 has 34 (9.6%) missing valuesMissing
기준월 대출잔액 has 18 (5.1%) missing valuesMissing
연체금액 has 31 (8.8%) missing valuesMissing
상품명 has unique valuesUnique
조기상환율(퍼센트) has 34 (9.6%) zerosZeros
연체율 has 31 (8.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:23:19.992265
Analysis finished2023-12-12 21:23:24.240668
Duration4.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상품명
Text

UNIQUE 

Distinct354
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-13T06:23:24.386496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.675141
Min length10

Characters and Unicode

Total characters3779
Distinct characters14
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

Unique354 ?
Unique (%)100.0%

Sample

1st rowMBS 2004 1
2nd rowMBS 2004 2
3rd rowMBS 2004 3
4th rowMBS 2004 4
5th rowMBS 2004 5
ValueCountFrequency (%)
mbs 354
33.3%
2012 41
 
3.9%
2013 38
 
3.6%
2018 31
 
2.9%
2017 30
 
2.8%
2016 29
 
2.7%
2019 28
 
2.6%
2015 28
 
2.6%
2014 22
 
2.1%
2011 21
 
2.0%
Other values (58) 440
41.4%
2023-12-13T06:23:24.686374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
708
18.7%
2 530
14.0%
0 494
13.1%
1 463
12.3%
M 354
9.4%
B 354
9.4%
S 354
9.4%
3 97
 
2.6%
5 74
 
2.0%
8 71
 
1.9%
Other values (4) 280
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2009
53.2%
Uppercase Letter 1062
28.1%
Space Separator 708
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 530
26.4%
0 494
24.6%
1 463
23.0%
3 97
 
4.8%
5 74
 
3.7%
8 71
 
3.5%
4 70
 
3.5%
6 70
 
3.5%
7 70
 
3.5%
9 70
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
M 354
33.3%
B 354
33.3%
S 354
33.3%
Space Separator
ValueCountFrequency (%)
708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2717
71.9%
Latin 1062
 
28.1%

Most frequent character per script

Common
ValueCountFrequency (%)
708
26.1%
2 530
19.5%
0 494
18.2%
1 463
17.0%
3 97
 
3.6%
5 74
 
2.7%
8 71
 
2.6%
4 70
 
2.6%
6 70
 
2.6%
7 70
 
2.6%
Latin
ValueCountFrequency (%)
M 354
33.3%
B 354
33.3%
S 354
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
708
18.7%
2 530
14.0%
0 494
13.1%
1 463
12.3%
M 354
9.4%
B 354
9.4%
S 354
9.4%
3 97
 
2.6%
5 74
 
2.0%
8 71
 
1.9%
Other values (4) 280
 
7.4%

전월대출잔액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct335
Distinct (%)99.7%
Missing18
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean391930.73
Minimum481
Maximum3271402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:24.851973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum481
5-th percentile1584.75
Q117464.5
median99148.5
Q3650330.75
95-th percentile1495421.5
Maximum3271402
Range3270921
Interquartile range (IQR)632866.25

Descriptive statistics

Standard deviation528518.81
Coefficient of variation (CV)1.3485006
Kurtosis4.7070273
Mean391930.73
Median Absolute Deviation (MAD)97797.5
Skewness1.9315677
Sum1.3168873 × 108
Variance2.7933214 × 1011
MonotonicityNot monotonic
2023-12-13T06:23:25.025450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2493 2
 
0.6%
879893 1
 
0.3%
717714 1
 
0.3%
974052 1
 
0.3%
1033910 1
 
0.3%
1019197 1
 
0.3%
903884 1
 
0.3%
306065 1
 
0.3%
512847 1
 
0.3%
605538 1
 
0.3%
Other values (325) 325
91.8%
(Missing) 18
 
5.1%
ValueCountFrequency (%)
481 1
0.3%
844 1
0.3%
909 1
0.3%
938 1
0.3%
968 1
0.3%
975 1
0.3%
1004 1
0.3%
1020 1
0.3%
1023 1
0.3%
1059 1
0.3%
ValueCountFrequency (%)
3271402 1
0.3%
2911065 1
0.3%
2193355 1
0.3%
2162247 1
0.3%
1985207 1
0.3%
1930132 1
0.3%
1929739 1
0.3%
1891572 1
0.3%
1883499 1
0.3%
1865125 1
0.3%

조기상환액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct310
Distinct (%)96.9%
Missing34
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean5794.0437
Minimum0
Maximum32483
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:25.180005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.75
Q1430.5
median2421.5
Q39970.5
95-th percentile19956.95
Maximum32483
Range32483
Interquartile range (IQR)9540

Descriptive statistics

Standard deviation6736.0858
Coefficient of variation (CV)1.162588
Kurtosis1.0162055
Mean5794.0437
Median Absolute Deviation (MAD)2361.5
Skewness1.2656469
Sum1854094
Variance45374852
MonotonicityNot monotonic
2023-12-13T06:23:25.301491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 4
 
1.1%
1361 2
 
0.6%
160 2
 
0.6%
75 2
 
0.6%
5 2
 
0.6%
1 2
 
0.6%
122 2
 
0.6%
10 2
 
0.6%
15072 1
 
0.3%
13809 1
 
0.3%
Other values (300) 300
84.7%
(Missing) 34
 
9.6%
ValueCountFrequency (%)
0 1
0.3%
1 2
0.6%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 2
0.6%
9 1
0.3%
10 2
0.6%
11 1
0.3%
13 1
0.3%
ValueCountFrequency (%)
32483 1
0.3%
28821 1
0.3%
25334 1
0.3%
24392 1
0.3%
24192 1
0.3%
24137 1
0.3%
24098 1
0.3%
23683 1
0.3%
23450 1
0.3%
23251 1
0.3%

조기상환율(퍼센트)
Real number (ℝ)

ZEROS 

Distinct213
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.744548
Minimum0
Maximum14.09
Zeros34
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:25.423058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median1.73
Q32.4
95-th percentile3.452
Maximum14.09
Range14.09
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation1.3559774
Coefficient of variation (CV)0.77726574
Kurtosis19.53341
Mean1.744548
Median Absolute Deviation (MAD)0.805
Skewness2.6365637
Sum617.57
Variance1.8386747
MonotonicityNot monotonic
2023-12-13T06:23:25.548528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 34
 
9.6%
2.4 4
 
1.1%
1.3 4
 
1.1%
1.46 3
 
0.8%
2.39 3
 
0.8%
2.18 3
 
0.8%
2.9 3
 
0.8%
0.69 3
 
0.8%
2.65 3
 
0.8%
1.87 3
 
0.8%
Other values (203) 291
82.2%
ValueCountFrequency (%)
0.0 34
9.6%
0.01 1
 
0.3%
0.03 1
 
0.3%
0.09 1
 
0.3%
0.14 1
 
0.3%
0.16 1
 
0.3%
0.17 1
 
0.3%
0.19 2
 
0.6%
0.23 1
 
0.3%
0.26 2
 
0.6%
ValueCountFrequency (%)
14.09 1
0.3%
6.83 1
0.3%
6.69 1
0.3%
5.9 1
0.3%
5.64 1
0.3%
5.61 1
0.3%
5.13 1
0.3%
4.95 1
0.3%
4.77 1
0.3%
4.32 1
0.3%

기준월 대출잔액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct336
Distinct (%)100.0%
Missing18
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean385008.77
Minimum475
Maximum3245437
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:25.682692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum475
5-th percentile1517.5
Q116645.5
median96582.5
Q3632553.25
95-th percentile1483607
Maximum3245437
Range3244962
Interquartile range (IQR)615907.75

Descriptive statistics

Standard deviation521662.99
Coefficient of variation (CV)1.3549379
Kurtosis4.8445843
Mean385008.77
Median Absolute Deviation (MAD)95249
Skewness1.9548306
Sum1.2936295 × 108
Variance2.7213228 × 1011
MonotonicityNot monotonic
2023-12-13T06:23:25.815999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
863049 1
 
0.3%
704013 1
 
0.3%
954707 1
 
0.3%
1015777 1
 
0.3%
1001261 1
 
0.3%
885668 1
 
0.3%
299753 1
 
0.3%
503436 1
 
0.3%
595173 1
 
0.3%
155050 1
 
0.3%
Other values (326) 326
92.1%
(Missing) 18
 
5.1%
ValueCountFrequency (%)
475 1
0.3%
839 1
0.3%
875 1
0.3%
931 1
0.3%
962 1
0.3%
970 1
0.3%
974 1
0.3%
977 1
0.3%
980 1
0.3%
1016 1
0.3%
ValueCountFrequency (%)
3245437 1
0.3%
2892010 1
0.3%
2178289 1
0.3%
2145207 1
0.3%
1966745 1
0.3%
1897013 1
0.3%
1895445 1
0.3%
1858429 1
0.3%
1851011 1
0.3%
1832877 1
0.3%

연체금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct300
Distinct (%)92.9%
Missing31
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean1189.743
Minimum2
Maximum14327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:25.944741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile33.1
Q1277
median791
Q31702
95-th percentile3709.1
Maximum14327
Range14325
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation1362.5487
Coefficient of variation (CV)1.1452462
Kurtosis26.589065
Mean1189.743
Median Absolute Deviation (MAD)588
Skewness3.5970514
Sum384287
Variance1856538.9
MonotonicityNot monotonic
2023-12-13T06:23:26.067707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3
 
0.8%
84 3
 
0.8%
323 2
 
0.6%
1110 2
 
0.6%
398 2
 
0.6%
641 2
 
0.6%
215 2
 
0.6%
1454 2
 
0.6%
468 2
 
0.6%
144 2
 
0.6%
Other values (290) 301
85.0%
(Missing) 31
 
8.8%
ValueCountFrequency (%)
2 3
0.8%
5 1
 
0.3%
6 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
13 2
0.6%
18 1
 
0.3%
21 1
 
0.3%
24 1
 
0.3%
25 1
 
0.3%
ValueCountFrequency (%)
14327 1
0.3%
5153 1
0.3%
5137 1
0.3%
5027 1
0.3%
4792 1
0.3%
4760 1
0.3%
4656 1
0.3%
4651 1
0.3%
4425 1
0.3%
4401 1
0.3%

연체율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct186
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3601695
Minimum0
Maximum51.28
Zeros31
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T06:23:26.193202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1825
median0.54
Q31.7775
95-th percentile8.505
Maximum51.28
Range51.28
Interquartile range (IQR)1.595

Descriptive statistics

Standard deviation5.7172014
Coefficient of variation (CV)2.422369
Kurtosis33.191663
Mean2.3601695
Median Absolute Deviation (MAD)0.495
Skewness5.2303682
Sum835.5
Variance32.686392
MonotonicityNot monotonic
2023-12-13T06:23:26.332440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 31
 
8.8%
0.02 7
 
2.0%
0.01 7
 
2.0%
0.14 7
 
2.0%
0.21 6
 
1.7%
0.08 6
 
1.7%
0.2 6
 
1.7%
0.4 5
 
1.4%
0.62 5
 
1.4%
0.32 5
 
1.4%
Other values (176) 269
76.0%
ValueCountFrequency (%)
0.0 31
8.8%
0.01 7
 
2.0%
0.02 7
 
2.0%
0.03 3
 
0.8%
0.04 4
 
1.1%
0.05 3
 
0.8%
0.06 3
 
0.8%
0.07 3
 
0.8%
0.08 6
 
1.7%
0.1 4
 
1.1%
ValueCountFrequency (%)
51.28 1
0.3%
47.21 1
0.3%
37.61 1
0.3%
29.35 1
0.3%
29.3 1
0.3%
28.23 1
0.3%
22.37 1
0.3%
21.28 1
0.3%
19.81 1
0.3%
18.51 1
0.3%

Interactions

2023-12-13T06:23:23.435941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.235360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.801191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.390064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.034539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.899005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.515051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.348884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.911176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.505951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.150067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.990207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.594910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.448307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.997953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.616291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.272601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.071980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.681399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.550113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.090887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.709777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.365427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.170146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.767233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.634466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.182838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.802815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.448986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.262776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.858893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:20.716751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.282437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:21.933826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:22.532490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:23:23.353549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:23:26.425754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전월대출잔액조기상환액조기상환율(퍼센트)기준월 대출잔액연체금액연체율
전월대출잔액1.0000.7230.3101.0000.5320.000
조기상환액0.7231.0000.2260.7230.9190.000
조기상환율(퍼센트)0.3100.2261.0000.3100.1260.000
기준월 대출잔액1.0000.7230.3101.0000.5320.000
연체금액0.5320.9190.1260.5321.0000.000
연체율0.0000.0000.0000.0000.0001.000
2023-12-13T06:23:26.519273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전월대출잔액조기상환액조기상환율(퍼센트)기준월 대출잔액연체금액연체율
전월대출잔액1.0000.922-0.0341.0000.609-0.679
조기상환액0.9221.0000.1000.9200.700-0.596
조기상환율(퍼센트)-0.0340.1001.000-0.0380.1890.319
기준월 대출잔액1.0000.920-0.0381.0000.608-0.680
연체금액0.6090.7000.1890.6081.000-0.115
연체율-0.679-0.5960.319-0.680-0.1151.000

Missing values

2023-12-13T06:23:23.976033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:23:24.084734image/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.
2023-12-13T06:23:24.187551image/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

상품명전월대출잔액조기상환액조기상환율(퍼센트)기준월 대출잔액연체금액연체율
0MBS 2004 1<NA><NA>0.0<NA><NA>0.0
1MBS 2004 2<NA><NA>0.0<NA><NA>0.0
2MBS 2004 3<NA><NA>0.0<NA><NA>0.0
3MBS 2004 4<NA><NA>0.0<NA><NA>0.0
4MBS 2004 5<NA><NA>0.0<NA><NA>0.0
5MBS 2004 6<NA><NA>0.0<NA><NA>0.0
6MBS 2004 7<NA><NA>0.0<NA><NA>0.0
7MBS 2005 1<NA><NA>0.0<NA><NA>0.0
8MBS 2005 2303290.33001141747.21
9MBS 2005 32372251.06230545719.81
상품명전월대출잔액조기상환액조기상환율(퍼센트)기준월 대출잔액연체금액연체율
344MBS 2020 1279642339420.49790383850.01
345MBS 2020 1380128042390.537944273200.04
346MBS 2020 1440150911070.28399130<NA>0.0
347MBS 2020 1585258336120.428461446370.08
348MBS 2020 1649754534210.694930531440.03
349MBS 2020 17119999237800.3211919511530.01
350MBS 2020 18131436735470.2713031592060.02
351MBS 2020 19140255224060.1713940065150.04
352MBS 2020 20143581323680.1614274372180.02
353MBS 2020 2185767828800.348527053900.05