Overview

Dataset statistics

Number of variables13
Number of observations5250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory569.2 KiB
Average record size in memory111.0 B

Variable types

DateTime2
Text3
Numeric7
Boolean1

Dataset

Description한국주택금융공사에서 발행하는 mbs의 증권표준코드정보, 종목명, 만기일, 평균수익률, 평균단가 등 주택저당증권 시세정보를 파악할 수 있는 주택저당증권 관련의 데이터들을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15088648/fileData.do

Alerts

평균수익률 is highly overall correlated with 최고수익률 and 1 other fieldsHigh correlation
최고수익률 is highly overall correlated with 평균수익률 and 1 other fieldsHigh correlation
최저수익률 is highly overall correlated with 평균수익률 and 1 other fieldsHigh correlation
거래량 is highly overall correlated with 거래대금 and 1 other fieldsHigh correlation
거래대금 is highly overall correlated with 거래량 and 1 other fieldsHigh correlation
거래건수 is highly overall correlated with 거래량 and 1 other fieldsHigh correlation
패스쓰루방식 여부 is highly imbalanced (82.6%)Imbalance

Reproduction

Analysis started2024-04-06 09:07:01.675413
Analysis finished2024-04-06 09:07:12.668605
Duration10.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

거래일
Date

Distinct1209
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
Minimum2015-11-05 00:00:00
Maximum2021-06-03 00:00:00
2024-04-06T18:07:12.778269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:13.011372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

증권표준코드
Text

Distinct1298
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2024-04-06T18:07:13.363725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters63000
Distinct characters16
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

Unique745 ?
Unique (%)14.2%

Sample

1st rowKR354401G5B9
2nd rowKR354402G4A3
3rd rowKR354402G5B8
4th rowKR354403G3B2
5th rowKR354403G5B7
ValueCountFrequency (%)
kr354411g578 78
 
1.5%
kr354426ga51 49
 
0.9%
kr354412g683 49
 
0.9%
kr354404g524 41
 
0.8%
kr354405g950 38
 
0.7%
kr354403g6a7 35
 
0.7%
kr354411g545 34
 
0.6%
kr354410g778 34
 
0.6%
kr354411g628 30
 
0.6%
kr354402g619 30
 
0.6%
Other values (1288) 4832
92.0%
2024-04-06T18:07:13.927510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 11973
19.0%
5 7533
12.0%
3 7100
11.3%
K 5250
8.3%
R 5250
8.3%
G 5224
8.3%
1 4120
 
6.5%
0 4024
 
6.4%
6 2590
 
4.1%
7 2090
 
3.3%
Other values (6) 7846
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45180
71.7%
Uppercase Letter 17820
 
28.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11973
26.5%
5 7533
16.7%
3 7100
15.7%
1 4120
 
9.1%
0 4024
 
8.9%
6 2590
 
5.7%
7 2090
 
4.6%
2 2039
 
4.5%
8 1919
 
4.2%
9 1792
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
K 5250
29.5%
R 5250
29.5%
G 5224
29.3%
A 997
 
5.6%
B 592
 
3.3%
C 507
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 45180
71.7%
Latin 17820
 
28.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 11973
26.5%
5 7533
16.7%
3 7100
15.7%
1 4120
 
9.1%
0 4024
 
8.9%
6 2590
 
5.7%
7 2090
 
4.6%
2 2039
 
4.5%
8 1919
 
4.2%
9 1792
 
4.0%
Latin
ValueCountFrequency (%)
K 5250
29.5%
R 5250
29.5%
G 5224
29.3%
A 997
 
5.6%
B 592
 
3.3%
C 507
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 11973
19.0%
5 7533
12.0%
3 7100
11.3%
K 5250
8.3%
R 5250
8.3%
G 5224
8.3%
1 4120
 
6.5%
0 4024
 
6.4%
6 2590
 
4.1%
7 2090
 
3.3%
Other values (6) 7846
12.5%

종목명
Text

Distinct1299
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2024-04-06T18:07:14.312880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters110250
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

Unique745 ?
Unique (%)14.2%

Sample

1st rowKHFCMB2015S-25-A012-1
2nd rowKHFCMB2014S-16-A024-1
3rd rowKHFCMB2015S-25-A024-1
4th rowKHFCMB2013S-36-A036-1
5th rowKHFCMB2015S-25-A036-1
ValueCountFrequency (%)
khfcmb2015s-17-a036-1 78
 
1.5%
khfcmb2020s-17-a012-1 49
 
0.9%
khfcmb2016s-17-a060-1 49
 
0.9%
khfcmb2015s-03-a060-1 41
 
0.8%
khfcmb2019s-11-a012-1 38
 
0.7%
khfcmb2016s-21-a036-1 35
 
0.7%
khfcmb2015s-07-a036-1 34
 
0.6%
khfcmb2017s-17-a024-1 34
 
0.6%
khfcmb2016s-05-a036-1 30
 
0.6%
khfcmb2016s-01-a024-1 30
 
0.6%
Other values (1289) 4832
92.0%
2024-04-06T18:07:14.903888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15750
14.3%
1 13655
12.4%
0 13437
12.2%
2 10822
9.8%
B 5252
 
4.8%
K 5250
 
4.8%
F 5250
 
4.8%
C 5250
 
4.8%
M 5250
 
4.8%
H 5250
 
4.8%
Other values (9) 25084
22.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52500
47.6%
Uppercase Letter 42000
38.1%
Dash Punctuation 15750
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13655
26.0%
0 13437
25.6%
2 10822
20.6%
6 4045
 
7.7%
4 2535
 
4.8%
3 2523
 
4.8%
5 1564
 
3.0%
7 1526
 
2.9%
8 1431
 
2.7%
9 962
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
B 5252
12.5%
K 5250
12.5%
F 5250
12.5%
C 5250
12.5%
M 5250
12.5%
H 5250
12.5%
S 5250
12.5%
A 5248
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 15750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68250
61.9%
Latin 42000
38.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 15750
23.1%
1 13655
20.0%
0 13437
19.7%
2 10822
15.9%
6 4045
 
5.9%
4 2535
 
3.7%
3 2523
 
3.7%
5 1564
 
2.3%
7 1526
 
2.2%
8 1431
 
2.1%
Latin
ValueCountFrequency (%)
B 5252
12.5%
K 5250
12.5%
F 5250
12.5%
C 5250
12.5%
M 5250
12.5%
H 5250
12.5%
S 5250
12.5%
A 5248
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 15750
14.3%
1 13655
12.4%
0 13437
12.2%
2 10822
9.8%
B 5252
 
4.8%
K 5250
 
4.8%
F 5250
 
4.8%
C 5250
 
4.8%
M 5250
 
4.8%
H 5250
 
4.8%
Other values (9) 25084
22.8%

만기일
Date

Distinct1229
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
Minimum2015-12-18 00:00:00
Maximum2054-11-25 00:00:00
2024-04-06T18:07:15.185357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:15.414391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

평균수익률
Real number (ℝ)

HIGH CORRELATION 

Distinct1658
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.640584
Minimum0.421
Maximum5.494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:15.674640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.421
5-th percentile0.77045
Q11.419
median1.647
Q31.89
95-th percentile2.423
Maximum5.494
Range5.073
Interquartile range (IQR)0.471

Descriptive statistics

Standard deviation0.46662954
Coefficient of variation (CV)0.28442893
Kurtosis1.2328333
Mean1.640584
Median Absolute Deviation (MAD)0.235
Skewness0.087951729
Sum8613.066
Variance0.21774313
MonotonicityNot monotonic
2024-04-06T18:07:15.920731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.873 52
 
1.0%
1.5 25
 
0.5%
1.84 24
 
0.5%
1.6 23
 
0.4%
2.16 19
 
0.4%
1.61 18
 
0.3%
1.7 17
 
0.3%
1.76 15
 
0.3%
1.616 14
 
0.3%
1.58 14
 
0.3%
Other values (1648) 5029
95.8%
ValueCountFrequency (%)
0.421 1
< 0.1%
0.48 1
< 0.1%
0.496 1
< 0.1%
0.524 1
< 0.1%
0.528 1
< 0.1%
0.53 1
< 0.1%
0.535 1
< 0.1%
0.537 1
< 0.1%
0.549 1
< 0.1%
0.55 1
< 0.1%
ValueCountFrequency (%)
5.494 1
 
< 0.1%
3.44 1
 
< 0.1%
3.42 1
 
< 0.1%
3.34 1
 
< 0.1%
3.16 1
 
< 0.1%
3.12 1
 
< 0.1%
3.09 3
0.1%
3.022 1
 
< 0.1%
3.006 1
 
< 0.1%
3.0 1
 
< 0.1%

평균단가
Real number (ℝ)

Distinct2338
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10029.05
Minimum9428.67
Maximum10547.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:16.159338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9428.67
5-th percentile9976
Q19999.4
median10021.345
Q310048
95-th percentile10116.758
Maximum10547.5
Range1118.83
Interquartile range (IQR)48.6

Descriptive statistics

Standard deviation53.233906
Coefficient of variation (CV)0.0053079709
Kurtosis19.63211
Mean10029.05
Median Absolute Deviation (MAD)21.995
Skewness1.1034545
Sum52652512
Variance2833.8487
MonotonicityNot monotonic
2024-04-06T18:07:16.389858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000.0 130
 
2.5%
9999.35 114
 
2.2%
9999.13 89
 
1.7%
9999.26 53
 
1.0%
9999.29 43
 
0.8%
9999.48 41
 
0.8%
9999.31 34
 
0.6%
9999.39 28
 
0.5%
9999.24 28
 
0.5%
9999.57 27
 
0.5%
Other values (2328) 4663
88.8%
ValueCountFrequency (%)
9428.67 1
< 0.1%
9618.75 1
< 0.1%
9628.5 1
< 0.1%
9643.33 1
< 0.1%
9654.67 1
< 0.1%
9673.5 1
< 0.1%
9725.67 1
< 0.1%
9743.0 1
< 0.1%
9749.0 1
< 0.1%
9755.0 1
< 0.1%
ValueCountFrequency (%)
10547.5 1
< 0.1%
10525.33 1
< 0.1%
10505.0 1
< 0.1%
10496.33 1
< 0.1%
10495.5 1
< 0.1%
10489.5 1
< 0.1%
10479.5 1
< 0.1%
10470.5 1
< 0.1%
10453.33 1
< 0.1%
10418.58 1
< 0.1%

최고수익률
Real number (ℝ)

HIGH CORRELATION 

Distinct1638
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6443672
Minimum0.421
Maximum5.494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:16.650702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.421
5-th percentile0.772
Q11.422
median1.65
Q31.893
95-th percentile2.423
Maximum5.494
Range5.073
Interquartile range (IQR)0.471

Descriptive statistics

Standard deviation0.46607551
Coefficient of variation (CV)0.2834376
Kurtosis1.2263246
Mean1.6443672
Median Absolute Deviation (MAD)0.236
Skewness0.082583793
Sum8632.928
Variance0.21722638
MonotonicityNot monotonic
2024-04-06T18:07:16.919286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.873 52
 
1.0%
1.84 29
 
0.6%
1.7 24
 
0.5%
1.5 23
 
0.4%
1.6 23
 
0.4%
2.16 19
 
0.4%
1.616 18
 
0.3%
1.58 18
 
0.3%
1.65 16
 
0.3%
1.618 16
 
0.3%
Other values (1628) 5012
95.5%
ValueCountFrequency (%)
0.421 1
< 0.1%
0.48 1
< 0.1%
0.496 1
< 0.1%
0.524 1
< 0.1%
0.53 1
< 0.1%
0.532 1
< 0.1%
0.537 1
< 0.1%
0.549 1
< 0.1%
0.55 1
< 0.1%
0.555 1
< 0.1%
ValueCountFrequency (%)
5.494 1
 
< 0.1%
3.44 1
 
< 0.1%
3.42 1
 
< 0.1%
3.34 1
 
< 0.1%
3.16 1
 
< 0.1%
3.12 1
 
< 0.1%
3.09 3
0.1%
3.022 1
 
< 0.1%
3.006 1
 
< 0.1%
3.0 1
 
< 0.1%

최저수익률
Real number (ℝ)

HIGH CORRELATION 

Distinct1653
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6365634
Minimum0.014
Maximum5.494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:17.189959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.014
5-th percentile0.76745
Q11.41325
median1.64
Q31.888
95-th percentile2.423
Maximum5.494
Range5.48
Interquartile range (IQR)0.47475

Descriptive statistics

Standard deviation0.46813641
Coefficient of variation (CV)0.28604843
Kurtosis1.2436648
Mean1.6365634
Median Absolute Deviation (MAD)0.238
Skewness0.086202353
Sum8591.958
Variance0.21915169
MonotonicityNot monotonic
2024-04-06T18:07:17.415612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.873 53
 
1.0%
1.5 32
 
0.6%
1.84 27
 
0.5%
1.6 25
 
0.5%
1.58 22
 
0.4%
2.16 21
 
0.4%
1.7 19
 
0.4%
1.65 16
 
0.3%
1.61 15
 
0.3%
1.55 15
 
0.3%
Other values (1643) 5005
95.3%
ValueCountFrequency (%)
0.014 1
< 0.1%
0.347 1
< 0.1%
0.421 1
< 0.1%
0.48 1
< 0.1%
0.484 1
< 0.1%
0.496 1
< 0.1%
0.516 1
< 0.1%
0.524 1
< 0.1%
0.529 1
< 0.1%
0.53 1
< 0.1%
ValueCountFrequency (%)
5.494 1
 
< 0.1%
3.44 1
 
< 0.1%
3.42 1
 
< 0.1%
3.34 1
 
< 0.1%
3.16 1
 
< 0.1%
3.12 1
 
< 0.1%
3.09 3
0.1%
3.022 1
 
< 0.1%
3.005 1
 
< 0.1%
3.0 1
 
< 0.1%

거래량
Real number (ℝ)

HIGH CORRELATION 

Distinct510
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2478388 × 1010
Minimum1000
Maximum1.12 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:17.664346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5 × 109
Q12 × 1010
median3 × 1010
Q39 × 1010
95-th percentile3.4 × 1011
Maximum1.12 × 1012
Range1.12 × 1012
Interquartile range (IQR)7 × 1010

Descriptive statistics

Standard deviation1.2360396 × 1011
Coefficient of variation (CV)1.4986225
Kurtosis13.120015
Mean8.2478388 × 1010
Median Absolute Deviation (MAD)2 × 1010
Skewness3.1714839
Sum4.3301154 × 1014
Variance1.527794 × 1022
MonotonicityNot monotonic
2024-04-06T18:07:18.320339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000000 1175
22.4%
30000000000 633
 
12.1%
10000000000 441
 
8.4%
40000000000 432
 
8.2%
60000000000 369
 
7.0%
80000000000 123
 
2.3%
50000000000 123
 
2.3%
100000000000 101
 
1.9%
90000000000 94
 
1.8%
120000000000 74
 
1.4%
Other values (500) 1685
32.1%
ValueCountFrequency (%)
1000 7
0.1%
5000 1
 
< 0.1%
10000 8
0.2%
20000 4
0.1%
33000 1
 
< 0.1%
35000 2
 
< 0.1%
36000 1
 
< 0.1%
49000 1
 
< 0.1%
50000 1
 
< 0.1%
52000 1
 
< 0.1%
ValueCountFrequency (%)
1120000000000 1
 
< 0.1%
1090000000000 1
 
< 0.1%
1040000000000 1
 
< 0.1%
990000000000 2
< 0.1%
970000000000 1
 
< 0.1%
950000000000 3
0.1%
930000000000 1
 
< 0.1%
880000000000 4
0.1%
860000000000 1
 
< 0.1%
850000000000 2
< 0.1%

거래대금
Real number (ℝ)

HIGH CORRELATION 

Distinct3930
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2602875 × 1010
Minimum1000
Maximum1.1199404 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:18.565713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile5.0049738 × 109
Q12.00305 × 1010
median3.0243125 × 1010
Q38.99948 × 1010
95-th percentile3.3998252 × 1011
Maximum1.1199404 × 1012
Range1.1199404 × 1012
Interquartile range (IQR)6.99643 × 1010

Descriptive statistics

Standard deviation1.2360382 × 1011
Coefficient of variation (CV)1.4963622
Kurtosis13.105432
Mean8.2602875 × 1010
Median Absolute Deviation (MAD)2.0195125 × 1010
Skewness3.1690734
Sum4.3366509 × 1014
Variance1.5277904 × 1022
MonotonicityNot monotonic
2024-04-06T18:07:18.848712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19998700000 44
 
0.8%
10000000000 33
 
0.6%
29998700000 16
 
0.3%
39997400000 15
 
0.3%
49997400000 14
 
0.3%
79996100000 11
 
0.2%
20000000000 10
 
0.2%
149993500000 10
 
0.2%
39998700000 10
 
0.2%
119992200000 9
 
0.2%
Other values (3920) 5078
96.7%
ValueCountFrequency (%)
1000 7
0.1%
5000 1
 
< 0.1%
10000 8
0.2%
20000 4
0.1%
33000 1
 
< 0.1%
35000 2
 
< 0.1%
36000 1
 
< 0.1%
49000 1
 
< 0.1%
50000 1
 
< 0.1%
52000 1
 
< 0.1%
ValueCountFrequency (%)
1119940400000 1
< 0.1%
1089918100000 1
< 0.1%
1039938900000 1
< 0.1%
989936300000 1
< 0.1%
989929800000 1
< 0.1%
970000000000 1
< 0.1%
949954000000 1
< 0.1%
949945400000 1
< 0.1%
949938900000 1
< 0.1%
929937600000 1
< 0.1%

거래건수
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.34
Minimum1
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.3 KiB
2024-04-06T18:07:19.096056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile22
Maximum69
Range68
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.270645
Coefficient of variation (CV)1.1467894
Kurtosis9.3265257
Mean6.34
Median Absolute Deviation (MAD)2
Skewness2.734469
Sum33285
Variance52.862279
MonotonicityNot monotonic
2024-04-06T18:07:19.358285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1003
19.1%
4 802
15.3%
3 785
15.0%
1 594
11.3%
5 442
8.4%
6 264
 
5.0%
7 166
 
3.2%
8 125
 
2.4%
9 104
 
2.0%
10 102
 
1.9%
Other values (44) 863
16.4%
ValueCountFrequency (%)
1 594
11.3%
2 1003
19.1%
3 785
15.0%
4 802
15.3%
5 442
8.4%
6 264
 
5.0%
7 166
 
3.2%
8 125
 
2.4%
9 104
 
2.0%
10 102
 
1.9%
ValueCountFrequency (%)
69 1
 
< 0.1%
63 1
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
48 1
 
< 0.1%
47 3
0.1%
46 1
 
< 0.1%
45 3
0.1%

유동화계획 코드
Text

Distinct229
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size41.1 KiB
2024-04-06T18:07:19.787054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters73500
Distinct characters18
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

Unique25 ?
Unique (%)0.5%

Sample

1st rowKHFCMB2015S-25
2nd rowKHFCMB2014S-16
3rd rowKHFCMB2015S-25
4th rowKHFCMB2013S-36
5th rowKHFCMB2015S-25
ValueCountFrequency (%)
khfcmb2015s-17 107
 
2.0%
khfcmb2016s-17 89
 
1.7%
khfcmb2015s-25 85
 
1.6%
khfcmb2016s-21 82
 
1.6%
khfcmb2016s-05 75
 
1.4%
khfcmb2016s-01 71
 
1.4%
khfcmb2016s-20 70
 
1.3%
khfcmb2016s-19 67
 
1.3%
khfcmb2016s-16 67
 
1.3%
khfcmb2015s-07 66
 
1.3%
Other values (219) 4471
85.2%
2024-04-06T18:07:20.400605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 7964
10.8%
0 7762
10.6%
1 7168
9.8%
K 5250
 
7.1%
F 5250
 
7.1%
C 5250
 
7.1%
M 5250
 
7.1%
B 5250
 
7.1%
H 5250
 
7.1%
S 5250
 
7.1%
Other values (8) 13856
18.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 36750
50.0%
Decimal Number 31500
42.9%
Dash Punctuation 5250
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7964
25.3%
0 7762
24.6%
1 7168
22.8%
6 1979
 
6.3%
5 1564
 
5.0%
7 1524
 
4.8%
8 1105
 
3.5%
9 962
 
3.1%
3 886
 
2.8%
4 586
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
K 5250
14.3%
F 5250
14.3%
C 5250
14.3%
M 5250
14.3%
B 5250
14.3%
H 5250
14.3%
S 5250
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 5250
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36750
50.0%
Latin 36750
50.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 7964
21.7%
0 7762
21.1%
1 7168
19.5%
- 5250
14.3%
6 1979
 
5.4%
5 1564
 
4.3%
7 1524
 
4.1%
8 1105
 
3.0%
9 962
 
2.6%
3 886
 
2.4%
Latin
ValueCountFrequency (%)
K 5250
14.3%
F 5250
14.3%
C 5250
14.3%
M 5250
14.3%
B 5250
14.3%
H 5250
14.3%
S 5250
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 7964
10.8%
0 7762
10.6%
1 7168
9.8%
K 5250
 
7.1%
F 5250
 
7.1%
C 5250
 
7.1%
M 5250
 
7.1%
B 5250
 
7.1%
H 5250
 
7.1%
S 5250
 
7.1%
Other values (8) 13856
18.9%

패스쓰루방식 여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
False
5113 
True
 
137
ValueCountFrequency (%)
False 5113
97.4%
True 137
 
2.6%
2024-04-06T18:07:20.575100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2024-04-06T18:07:10.937226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:03.760854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.964176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.141925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.189916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.271654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:09.744720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.114949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:03.935833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.148289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.248314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.359813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.405705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:09.888340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.288491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.109645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.317444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.397795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.507851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.562485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:10.076749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.456032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.266430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.478449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.534428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.651218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.713362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:10.235991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.628918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.417678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.625298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.675334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.805877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.877097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:10.378515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.790640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.587652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.796262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:06.843336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.979173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:09.420906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:10.572213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:11.978139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:04.758322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:05.952612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:07.017565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:08.107841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:09.581751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:07:10.788540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:07:20.677950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균수익률평균단가최고수익률최저수익률거래량거래대금거래건수패스쓰루방식 여부
평균수익률1.0000.2511.0000.8710.1560.1560.1800.124
평균단가0.2511.0000.2500.2230.1000.1000.0900.033
최고수익률1.0000.2501.0000.8700.1540.1540.1810.123
최저수익률0.8710.2230.8701.0000.1670.1660.1930.173
거래량0.1560.1000.1540.1671.0001.0000.8490.293
거래대금0.1560.1000.1540.1661.0001.0000.8490.293
거래건수0.1800.0900.1810.1930.8490.8491.0000.303
패스쓰루방식 여부0.1240.0330.1230.1730.2930.2930.3031.000
2024-04-06T18:07:20.823879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균수익률평균단가최고수익률최저수익률거래량거래대금거래건수패스쓰루방식 여부
평균수익률1.000-0.4690.9990.9990.0730.0330.1300.133
평균단가-0.4691.000-0.468-0.470-0.223-0.136-0.2710.030
최고수익률0.999-0.4681.0000.9970.0730.0340.1320.132
최저수익률0.999-0.4700.9971.0000.0720.0330.1280.130
거래량0.073-0.2230.0730.0721.0000.9930.7680.225
거래대금0.033-0.1360.0340.0330.9931.0000.7590.225
거래건수0.130-0.2710.1320.1280.7680.7591.0000.232
패스쓰루방식 여부0.1330.0300.1320.1300.2250.2250.2321.000

Missing values

2024-04-06T18:07:12.244878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:07:12.542486image/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

거래일증권표준코드종목명만기일평균수익률평균단가최고수익률최저수익률거래량거래대금거래건수유동화계획 코드패스쓰루방식 여부
02015-11-05KR354401G5B9KHFCMB2015S-25-A012-12016-11-061.6810000.01.681.6830000000000300000000003KHFCMB2015S-25N
12015-11-05KR354402G4A3KHFCMB2014S-16-A024-12016-10-021.7310075.421.8361.6910000000000100755000006KHFCMB2014S-16N
22015-11-05KR354402G5B8KHFCMB2015S-25-A024-12017-11-061.7610000.01.761.7620000000000200000000002KHFCMB2015S-25N
32015-11-05KR354403G3B2KHFCMB2013S-36-A036-12016-11-141.65710229.91.661.656600000000006137900000010KHFCMB2013S-36N
42015-11-05KR354403G5B7KHFCMB2015S-25-A036-12018-11-061.8110000.01.811.8110000000000100000000001KHFCMB2015S-25N
52015-11-05KR354410G5A0KHFCMB2015S-24-A024-12017-10-231.759997.51.7511.74920000000000199950000004KHFCMB2015S-24N
62015-11-06KR354401G5B9KHFCMB2015S-25-A012-12016-11-061.689999.291.681.6824000000000023998180000022KHFCMB2015S-25N
72015-11-06KR354402G5B8KHFCMB2015S-25-A024-12017-11-061.769999.291.761.7629000000000028998310000033KHFCMB2015S-25N
82015-11-06KR354403G5B7KHFCMB2015S-25-A036-12018-11-061.819999.411.811.8124000000000023998700000022KHFCMB2015S-25N
92015-11-06KR354404G5B6KHFCMB2015S-25-A060-12020-11-062.0919994.592.142.0841800000000041781470000033KHFCMB2015S-25N
거래일증권표준코드종목명만기일평균수익률평균단가최고수익률최저수익률거래량거래대금거래건수유동화계획 코드패스쓰루방식 여부
52402021-05-27KR354410G9C8KHFCMB2019S-26-A024-12021-12-200.70410075.750.7040.70490000000000906817500004KHFCMB2019S-26N
52412021-05-27KR354410GA26KHFCMB2020S-05-A024-12022-02-140.75510052.250.7550.75560000000000603135000004KHFCMB2020S-05N
52422021-05-31KR354401GB26KHFCMB2021S-03-A012-12022-02-050.77410012.20.7740.77490000000000901102500005KHFCMB2021S-03N
52432021-05-31KR354402GA34KHFCMB2020S-07-A024-12022-03-060.79410063.80.7990.78715000000000015095625000010KHFCMB2020S-07N
52442021-05-31KR354408G939KHFCMB2019S-07-A036-12022-03-220.81710131.30.8170.81760000000000607881000005KHFCMB2019S-07N
52452021-06-01KR354410G9A2KHFCMB2019S-20-A024-12021-10-250.64510050.30.6450.64530000000000301507500005KHFCMB2019S-20N
52462021-06-01KR354411G9B9KHFCMB2019S-23-A024-12021-11-190.66110051.750.6610.66120000000000201035000002KHFCMB2019S-23N
52472021-06-02KR354410GAC4KHFCMB2020S-39-A012-12021-12-110.69710030.170.6970.69780000000000802410000006KHFCMB2020S-39N
52482021-06-02KR354422G567KHFCMB2015S-13-A120-12025-06-161.71910405.561.7971.64760000000000624165000009KHFCMB2015S-13N
52492021-06-03KR354404G698KHFCMB2016S-19-A060-12021-09-020.66810027.90.6720.6622400000000002406690000005KHFCMB2016S-19N