Overview

Dataset statistics

Number of variables11
Number of observations285
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory97.5 B

Variable types

Text1
DateTime1
Numeric9

Dataset

Description주택저당증권(MBS) 현황, 발행회차,발행일자,가중평균,1년,2년,3년,5년,7년,10년,15년,20년 칼럼이 포함되어있으며 관련 값을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15083275/fileData.do

Alerts

가중평균 is highly overall correlated with 1년 and 7 other fieldsHigh correlation
1년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
2년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
3년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
5년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
7년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
10년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
15년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
20년 is highly overall correlated with 가중평균 and 7 other fieldsHigh correlation
발행회차 has unique valuesUnique
1년 has 28 (9.8%) zerosZeros
2년 has 11 (3.9%) zerosZeros
3년 has 21 (7.4%) zerosZeros
5년 has 7 (2.5%) zerosZeros
7년 has 26 (9.1%) zerosZeros
10년 has 14 (4.9%) zerosZeros
15년 has 25 (8.8%) zerosZeros
20년 has 8 (2.8%) zerosZeros

Reproduction

Analysis started2023-12-11 23:53:46.586383
Analysis finished2023-12-11 23:53:56.392964
Duration9.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발행회차
Text

UNIQUE 

Distinct285
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-12T08:53:56.675915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters3135
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique285 ?
Unique (%)100.0%

Sample

1st rowMBS 2019-18
2nd rowMBS 2019-17
3rd rowMBS 2019-16
4th rowMBS 2019-15
5th rowMBS 2019-14
ValueCountFrequency (%)
mbs 285
50.0%
2012-24 1
 
0.2%
2012-19 1
 
0.2%
2012-20 1
 
0.2%
2012-21 1
 
0.2%
2012-23 1
 
0.2%
2012-22 1
 
0.2%
2012-15 1
 
0.2%
2012-28 1
 
0.2%
2012-16 1
 
0.2%
Other values (276) 276
48.4%
2023-12-12T08:53:57.191288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 484
15.4%
2 406
13.0%
1 387
12.3%
M 285
9.1%
B 285
9.1%
S 285
9.1%
285
9.1%
- 285
9.1%
3 78
 
2.5%
5 66
 
2.1%
Other values (5) 289
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1710
54.5%
Uppercase Letter 855
27.3%
Space Separator 285
 
9.1%
Dash Punctuation 285
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 484
28.3%
2 406
23.7%
1 387
22.6%
3 78
 
4.6%
5 66
 
3.9%
8 60
 
3.5%
6 60
 
3.5%
7 60
 
3.5%
4 58
 
3.4%
9 51
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
M 285
33.3%
B 285
33.3%
S 285
33.3%
Space Separator
ValueCountFrequency (%)
285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2280
72.7%
Latin 855
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 484
21.2%
2 406
17.8%
1 387
17.0%
285
12.5%
- 285
12.5%
3 78
 
3.4%
5 66
 
2.9%
8 60
 
2.6%
6 60
 
2.6%
7 60
 
2.6%
Other values (2) 109
 
4.8%
Latin
ValueCountFrequency (%)
M 285
33.3%
B 285
33.3%
S 285
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 484
15.4%
2 406
13.0%
1 387
12.3%
M 285
9.1%
B 285
9.1%
S 285
9.1%
285
9.1%
- 285
9.1%
3 78
 
2.5%
5 66
 
2.1%
Other values (5) 289
9.2%
Distinct284
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Minimum2004-06-15 00:00:00
Maximum2019-09-24 00:00:00
2023-12-12T08:53:57.396596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:57.842006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

가중평균
Real number (ℝ)

HIGH CORRELATION 

Distinct237
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2309474
Minimum0
Maximum6.64
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:57.997464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7224
Q12.29
median3.01
Q33.98
95-th percentile5.614
Maximum6.64
Range6.64
Interquartile range (IQR)1.69

Descriptive statistics

Standard deviation1.1980009
Coefficient of variation (CV)0.37078936
Kurtosis-0.29140721
Mean3.2309474
Median Absolute Deviation (MAD)0.77
Skewness0.65884632
Sum920.82
Variance1.4352062
MonotonicityNot monotonic
2023-12-12T08:53:58.146700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.11 5
 
1.8%
3.16 4
 
1.4%
3.12 4
 
1.4%
3.08 4
 
1.4%
2.29 3
 
1.1%
2.217 3
 
1.1%
2.17 3
 
1.1%
3.24 3
 
1.1%
3.78 3
 
1.1%
1.98 3
 
1.1%
Other values (227) 250
87.7%
ValueCountFrequency (%)
0.0 1
0.4%
1.361 1
0.4%
1.374 1
0.4%
1.506 1
0.4%
1.524 1
0.4%
1.525 1
0.4%
1.542 1
0.4%
1.588 1
0.4%
1.606 1
0.4%
1.636 1
0.4%
ValueCountFrequency (%)
6.64 1
0.4%
6.26 1
0.4%
5.88 1
0.4%
5.87 1
0.4%
5.86 1
0.4%
5.81 1
0.4%
5.75 1
0.4%
5.73 1
0.4%
5.72 1
0.4%
5.71 1
0.4%

1년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct199
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4348421
Minimum0
Maximum5.83
Zeros28
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:58.291116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.62
median2.29
Q33.22
95-th percentile4.83
Maximum5.83
Range5.83
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.2870445
Coefficient of variation (CV)0.52859464
Kurtosis0.086867272
Mean2.4348421
Median Absolute Deviation (MAD)0.71
Skewness0.13636254
Sum693.93
Variance1.6564835
MonotonicityNot monotonic
2023-12-12T08:53:58.426368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 28
 
9.8%
2.93 5
 
1.8%
2.74 5
 
1.8%
2.73 5
 
1.8%
2.77 3
 
1.1%
3.44 3
 
1.1%
3.05 3
 
1.1%
2.96 3
 
1.1%
1.68 3
 
1.1%
2.75 3
 
1.1%
Other values (189) 224
78.6%
ValueCountFrequency (%)
0.0 28
9.8%
1.275 1
 
0.4%
1.288 1
 
0.4%
1.291 1
 
0.4%
1.298 1
 
0.4%
1.338 1
 
0.4%
1.406 1
 
0.4%
1.411 1
 
0.4%
1.418 1
 
0.4%
1.433 1
 
0.4%
ValueCountFrequency (%)
5.83 1
0.4%
5.58 1
0.4%
5.57 1
0.4%
5.51 1
0.4%
5.5 1
0.4%
5.44 1
0.4%
5.38 2
0.7%
5.35 1
0.4%
5.17 1
0.4%
5.09 1
0.4%

2년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6946702
Minimum0
Maximum6.26
Zeros11
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:58.585699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3154
Q11.809
median2.39
Q33.52
95-th percentile5.068
Maximum6.26
Range6.26
Interquartile range (IQR)1.711

Descriptive statistics

Standard deviation1.2227478
Coefficient of variation (CV)0.45376527
Kurtosis0.22930107
Mean2.6946702
Median Absolute Deviation (MAD)0.64
Skewness0.44148864
Sum767.981
Variance1.4951121
MonotonicityNot monotonic
2023-12-12T08:53:58.724754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
3.9%
2.99 6
 
2.1%
3.02 5
 
1.8%
2.96 4
 
1.4%
1.74 4
 
1.4%
2.98 3
 
1.1%
3.0 3
 
1.1%
2.92 3
 
1.1%
2.78 3
 
1.1%
3.76 3
 
1.1%
Other values (201) 240
84.2%
ValueCountFrequency (%)
0.0 11
3.9%
1.282 1
 
0.4%
1.299 1
 
0.4%
1.312 1
 
0.4%
1.313 1
 
0.4%
1.325 1
 
0.4%
1.342 1
 
0.4%
1.414 1
 
0.4%
1.416 1
 
0.4%
1.451 1
 
0.4%
ValueCountFrequency (%)
6.26 1
0.4%
5.76 1
0.4%
5.75 1
0.4%
5.73 1
0.4%
5.65 1
0.4%
5.62 1
0.4%
5.5 1
0.4%
5.45 1
0.4%
5.4 1
0.4%
5.35 1
0.4%

3년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7772737
Minimum0
Maximum6.36
Zeros21
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:58.948772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.89
median2.79
Q33.72
95-th percentile5.162
Maximum6.36
Range6.36
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.357589
Coefficient of variation (CV)0.48882076
Kurtosis-0.080763739
Mean2.7772737
Median Absolute Deviation (MAD)0.913
Skewness0.068798772
Sum791.523
Variance1.843048
MonotonicityNot monotonic
2023-12-12T08:53:59.106753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 21
 
7.4%
3.01 4
 
1.4%
3.0 4
 
1.4%
3.89 4
 
1.4%
4.2 3
 
1.1%
3.04 3
 
1.1%
2.92 3
 
1.1%
3.84 3
 
1.1%
3.82 3
 
1.1%
3.03 3
 
1.1%
Other values (200) 234
82.1%
ValueCountFrequency (%)
0.0 21
7.4%
1.309 1
 
0.4%
1.311 1
 
0.4%
1.322 1
 
0.4%
1.346 1
 
0.4%
1.353 1
 
0.4%
1.418 1
 
0.4%
1.433 1
 
0.4%
1.47 1
 
0.4%
1.48 1
 
0.4%
ValueCountFrequency (%)
6.36 1
0.4%
5.97 1
0.4%
5.81 1
0.4%
5.75 1
0.4%
5.7 1
0.4%
5.63 1
0.4%
5.55 1
0.4%
5.48 1
0.4%
5.46 1
0.4%
5.42 1
0.4%

5년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct232
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1133895
Minimum0
Maximum6.67
Zeros7
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:59.258901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.62
Q12.24
median2.89
Q33.88
95-th percentile5.54
Maximum6.67
Range6.67
Interquartile range (IQR)1.64

Descriptive statistics

Standard deviation1.2503246
Coefficient of variation (CV)0.40159596
Kurtosis0.15417462
Mean3.1133895
Median Absolute Deviation (MAD)0.7
Skewness0.39855059
Sum887.316
Variance1.5633117
MonotonicityNot monotonic
2023-12-12T08:53:59.389235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
2.5%
2.97 5
 
1.8%
2.24 4
 
1.4%
3.76 3
 
1.1%
2.33 3
 
1.1%
3.03 3
 
1.1%
4.04 2
 
0.7%
2.82 2
 
0.7%
3.1 2
 
0.7%
2.555 2
 
0.7%
Other values (222) 252
88.4%
ValueCountFrequency (%)
0.0 7
2.5%
1.365 1
 
0.4%
1.372 1
 
0.4%
1.503 1
 
0.4%
1.568 1
 
0.4%
1.578 1
 
0.4%
1.588 1
 
0.4%
1.597 1
 
0.4%
1.606 1
 
0.4%
1.676 1
 
0.4%
ValueCountFrequency (%)
6.67 1
0.4%
6.28 1
0.4%
5.86 1
0.4%
5.85 1
0.4%
5.78 1
0.4%
5.76 1
0.4%
5.74 1
0.4%
5.73 1
0.4%
5.71 1
0.4%
5.69 1
0.4%

7년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.026207
Minimum0
Maximum6.69
Zeros26
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:59.537141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.293
median2.955
Q33.93
95-th percentile5.586
Maximum6.69
Range6.69
Interquartile range (IQR)1.637

Descriptive statistics

Standard deviation1.474575
Coefficient of variation (CV)0.48726838
Kurtosis-0.0080513391
Mean3.026207
Median Absolute Deviation (MAD)0.755
Skewness-0.11357544
Sum862.469
Variance2.1743714
MonotonicityNot monotonic
2023-12-12T08:53:59.755261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
9.1%
3.18 5
 
1.8%
3.71 4
 
1.4%
2.36 4
 
1.4%
2.38 3
 
1.1%
3.12 3
 
1.1%
2.33 3
 
1.1%
3.8 3
 
1.1%
2.94 3
 
1.1%
2.39 3
 
1.1%
Other values (200) 228
80.0%
ValueCountFrequency (%)
0.0 26
9.1%
1.375 1
 
0.4%
1.533 1
 
0.4%
1.598 1
 
0.4%
1.608 1
 
0.4%
1.627 1
 
0.4%
1.686 1
 
0.4%
1.71 1
 
0.4%
1.741 2
 
0.7%
1.77 1
 
0.4%
ValueCountFrequency (%)
6.69 1
0.4%
6.3 1
0.4%
5.9 1
0.4%
5.87 1
0.4%
5.86 1
0.4%
5.81 1
0.4%
5.77 1
0.4%
5.76 1
0.4%
5.72 1
0.4%
5.71 1
0.4%

10년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct228
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1992632
Minimum0
Maximum6.67
Zeros14
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:53:59.879240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3818
Q12.372
median3.03
Q34.02
95-th percentile5.71
Maximum6.67
Range6.67
Interquartile range (IQR)1.648

Descriptive statistics

Standard deviation1.3785133
Coefficient of variation (CV)0.43088464
Kurtosis0.085895577
Mean3.1992632
Median Absolute Deviation (MAD)0.766
Skewness0.063959792
Sum911.79
Variance1.900299
MonotonicityNot monotonic
2023-12-12T08:54:00.038623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
4.9%
3.82 4
 
1.4%
3.14 3
 
1.1%
3.32 3
 
1.1%
3.83 3
 
1.1%
4.12 3
 
1.1%
2.42 3
 
1.1%
2.61 3
 
1.1%
3.15 3
 
1.1%
3.16 3
 
1.1%
Other values (218) 243
85.3%
ValueCountFrequency (%)
0.0 14
4.9%
1.38 1
 
0.4%
1.389 1
 
0.4%
1.545 1
 
0.4%
1.574 1
 
0.4%
1.614 1
 
0.4%
1.623 1
 
0.4%
1.628 1
 
0.4%
1.637 1
 
0.4%
1.651 1
 
0.4%
ValueCountFrequency (%)
6.67 1
0.4%
6.31 1
0.4%
5.99 1
0.4%
5.95 1
0.4%
5.9 1
0.4%
5.88 1
0.4%
5.84 1
0.4%
5.82 1
0.4%
5.8 1
0.4%
5.79 1
0.4%

15년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1795789
Minimum0
Maximum10.38
Zeros25
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:54:00.221581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.4
median3.09
Q34.08
95-th percentile5.778
Maximum10.38
Range10.38
Interquartile range (IQR)1.68

Descriptive statistics

Standard deviation1.5709548
Coefficient of variation (CV)0.49407637
Kurtosis1.1309497
Mean3.1795789
Median Absolute Deviation (MAD)0.8
Skewness0.15663357
Sum906.18
Variance2.4678991
MonotonicityNot monotonic
2023-12-12T08:54:00.369385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
 
8.8%
2.47 4
 
1.4%
3.19 4
 
1.4%
3.2 4
 
1.4%
3.88 4
 
1.4%
5.16 4
 
1.4%
3.75 3
 
1.1%
3.72 3
 
1.1%
3.17 2
 
0.7%
5.77 2
 
0.7%
Other values (201) 230
80.7%
ValueCountFrequency (%)
0.0 25
8.8%
1.38 1
 
0.4%
1.535 1
 
0.4%
1.574 1
 
0.4%
1.624 1
 
0.4%
1.638 1
 
0.4%
1.667 1
 
0.4%
1.725 1
 
0.4%
1.745 1
 
0.4%
1.76 1
 
0.4%
ValueCountFrequency (%)
10.38 1
0.4%
6.68 1
0.4%
6.32 1
0.4%
6.04 1
0.4%
6.0 1
0.4%
5.92 1
0.4%
5.91 1
0.4%
5.9 1
0.4%
5.87 1
0.4%
5.85 1
0.4%

20년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct226
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3230982
Minimum0
Maximum6.69
Zeros8
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T08:54:00.524992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.644
Q12.456
median3.12
Q34.12
95-th percentile5.778
Maximum6.69
Range6.69
Interquartile range (IQR)1.664

Descriptive statistics

Standard deviation1.3246876
Coefficient of variation (CV)0.39863028
Kurtosis-0.012904695
Mean3.3230982
Median Absolute Deviation (MAD)0.759
Skewness0.21297704
Sum947.083
Variance1.7547972
MonotonicityNot monotonic
2023-12-12T08:54:00.707863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
2.8%
3.28 5
 
1.8%
3.9 4
 
1.4%
3.3 4
 
1.4%
3.21 3
 
1.1%
3.82 3
 
1.1%
4.5 3
 
1.1%
5.2 3
 
1.1%
3.25 2
 
0.7%
2.52 2
 
0.7%
Other values (216) 248
87.0%
ValueCountFrequency (%)
0.0 8
2.8%
1.361 1
 
0.4%
1.369 1
 
0.4%
1.535 1
 
0.4%
1.549 1
 
0.4%
1.551 1
 
0.4%
1.634 1
 
0.4%
1.638 1
 
0.4%
1.668 1
 
0.4%
1.677 1
 
0.4%
ValueCountFrequency (%)
6.69 1
0.4%
6.33 1
0.4%
6.09 1
0.4%
6.08 1
0.4%
5.94 1
0.4%
5.91 1
0.4%
5.89 1
0.4%
5.87 2
0.7%
5.86 1
0.4%
5.85 1
0.4%

Interactions

2023-12-12T08:53:55.033680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:46.894143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.742162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.712105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.789349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.973016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.013019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.032765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.067799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.148532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:46.991545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.832639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.833199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.902433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.074616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.119385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.129882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.160180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.258183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.078729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.923850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.983490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.027758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.187076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.234030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.242531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.265575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.371184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.167409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.012494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.099996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.115151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.301584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.353631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.359055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.373744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.482088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.259080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.103429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.229440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.200916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.440235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.462754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.474445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.491875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.598984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.351418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.209883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.350774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.294145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.565499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.585211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.581042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.614079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.729373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.482997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.328191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.447944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.389977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.702998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.704285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.701783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.713092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.847718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.569980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.474887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.564087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.754278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.811244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.813347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.828333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.829916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:55.960957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:47.653949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:48.596066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:49.673759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:50.849076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:51.907049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:52.903345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:53.960474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:53:54.933503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:54:00.850111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가중평균1년2년3년5년7년10년15년20년
가중평균1.0000.8830.9400.9500.9920.9920.9920.9060.975
1년0.8831.0000.9650.9600.8830.9130.8970.8280.875
2년0.9400.9651.0000.9850.9350.9330.9300.8370.918
3년0.9500.9600.9851.0000.9530.9690.9540.9000.928
5년0.9920.8830.9350.9531.0000.9920.9890.9150.980
7년0.9920.9130.9330.9690.9921.0000.9940.9470.971
10년0.9920.8970.9300.9540.9890.9941.0000.9290.987
15년0.9060.8280.8370.9000.9150.9470.9291.0000.894
20년0.9750.8750.9180.9280.9800.9710.9870.8941.000
2023-12-12T08:54:01.028672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가중평균1년2년3년5년7년10년15년20년
가중평균1.0000.8050.8400.9420.9410.8940.9200.9080.916
1년0.8051.0000.9390.8520.7420.7640.7560.7730.717
2년0.8400.9391.0000.8350.8040.7530.7730.7570.764
3년0.9420.8520.8351.0000.9120.9350.9300.9430.890
5년0.9410.7420.8040.9121.0000.9480.9700.9560.968
7년0.8940.7640.7530.9350.9481.0000.9690.9750.929
10년0.9200.7560.7730.9300.9700.9691.0000.9850.960
15년0.9080.7730.7570.9430.9560.9750.9851.0000.951
20년0.9160.7170.7640.8900.9680.9290.9600.9511.000

Missing values

2023-12-12T08:53:56.105075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:53:56.303050image/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

발행회차발행일자가중평균1년2년3년5년7년10년15년20년
0MBS 2019-182019-09-241.7941.5211.5751.61.741.811.8991.9491.693
1MBS 2019-172019-09-061.5880.01.5090.01.5880.01.6230.01.549
2MBS 2019-162019-08-231.3611.2751.2821.3091.3651.3751.381.381.361
3MBS 2019-152019-08-091.3740.01.3250.01.3720.01.3890.01.369
4MBS 2019-142019-07-261.5251.4111.4141.4181.5031.5331.5741.5741.551
5MBS 2019-132019-06-251.6360.01.5520.01.6060.01.6510.01.668
6MBS 2019-122019-06-141.6891.5751.581.5811.6761.6861.7151.7451.752
7MBS 2019-112019-05-241.881.7531.7461.7761.8441.8941.9251.9451.921
8MBS 2019-102019-05-101.980.01.8660.01.9460.02.0190.01.994
9MBS 2019-092019-04-231.9921.8431.8631.8771.9691.9992.0542.0242.01
발행회차발행일자가중평균1년2년3년5년7년10년15년20년
275MBS 2005-032005-04-284.710.00.03.964.34.564.824.894.93
276MBS 2005-022005-03-234.840.00.04.14.484.724.95.05.06
277MBS 2005-012005-02-245.180.00.04.424.835.055.215.325.43
278MBS 2004-072004-12-284.060.00.03.443.723.924.124.244.37
279MBS 2004-062004-11-304.090.00.03.433.773.924.164.284.45
280MBS 2004-052004-10-284.20.00.03.63.824.064.274.394.5
281MBS 2004-042004-09-234.260.00.03.653.894.134.344.454.49
282MBS 2004-032004-08-184.50.00.03.894.144.394.594.74.73
283MBS 2004-022004-07-285.00.00.04.284.634.885.085.175.2
284MBS 2004-012004-06-155.044.144.284.334.670.05.0410.385.26