Overview

Dataset statistics

Number of variables9
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory76.3 KiB
Average record size in memory78.1 B

Variable types

Categorical5
Numeric3
Text1

Dataset

Description한국주택금융공사 유동화자산부 업무 관련 공개 공공데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터)
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073309/fileData.do

Alerts

OBJECT_CODE has constant value ""Constant
OFFER_CNT has constant value ""Constant
DWRT_BASIS_DY is highly overall correlated with SEQ and 1 other fieldsHigh correlation
RCV_DY is highly overall correlated with SEQ and 1 other fieldsHigh correlation
SEQ is highly overall correlated with TRD_SEQ and 2 other fieldsHigh correlation
TRD_SEQ is highly overall correlated with SEQHigh correlation
PAY_INT_AMT has 89 (8.9%) zerosZeros

Reproduction

Analysis started2023-12-12 10:17:28.249665
Analysis finished2023-12-12 10:17:29.677002
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LOAN_ORG_CD
Categorical

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B088
229 
B020
223 
B081
206 
B004
162 
B010
79 
Other values (7)
101 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowB003
2nd rowB088
3rd rowB020
4th rowB081
5th rowB088

Common Values

ValueCountFrequency (%)
B088 229
22.9%
B020 223
22.3%
B081 206
20.6%
B004 162
16.2%
B010 79
 
7.9%
B003 67
 
6.7%
B039 12
 
1.2%
B032 9
 
0.9%
I001 6
 
0.6%
B005 4
 
0.4%
Other values (2) 3
 
0.3%

Length

2023-12-12T19:17:29.778359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b088 229
22.9%
b020 223
22.3%
b081 206
20.6%
b004 162
16.2%
b010 79
 
7.9%
b003 67
 
6.7%
b039 12
 
1.2%
b032 9
 
0.9%
i001 6
 
0.6%
b005 4
 
0.4%
Other values (2) 3
 
0.3%

RCV_DY
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201024
526 
20201025
403 
20201023
71 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201025
2nd row20201025
3rd row20201025
4th row20201025
5th row20201025

Common Values

ValueCountFrequency (%)
20201024 526
52.6%
20201025 403
40.3%
20201023 71
 
7.1%

Length

2023-12-12T19:17:29.907815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:30.017331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201024 526
52.6%
20201025 403
40.3%
20201023 71
 
7.1%

DWRT_BASIS_DY
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201024
526 
20201025
403 
20201023
71 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201025
2nd row20201025
3rd row20201025
4th row20201025
5th row20201025

Common Values

ValueCountFrequency (%)
20201024 526
52.6%
20201025 403
40.3%
20201023 71
 
7.1%

Length

2023-12-12T19:17:30.139338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:30.268817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201024 526
52.6%
20201025 403
40.3%
20201023 71
 
7.1%

OBJECT_CODE
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
US
1000 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
US 1000
100.0%

Length

2023-12-12T19:17:30.405285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:30.525227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
us 1000
100.0%

OFFER_CNT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 1000
100.0%

Length

2023-12-12T19:17:30.633827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:30.758944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000
100.0%

SEQ
Real number (ℝ)

HIGH CORRELATION 

Distinct597
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2104.924
Minimum1
Maximum27133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:17:30.912609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile26422.25
Maximum27133
Range27132
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation6763.3224
Coefficient of variation (CV)3.2130958
Kurtosis9.2216976
Mean2104.924
Median Absolute Deviation (MAD)125
Skewness3.3454616
Sum2104924
Variance45742530
MonotonicityNot monotonic
2023-12-12T19:17:31.067128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398 2
 
0.2%
140 2
 
0.2%
197 2
 
0.2%
146 2
 
0.2%
221 2
 
0.2%
55 2
 
0.2%
54 2
 
0.2%
120 2
 
0.2%
165 2
 
0.2%
36 2
 
0.2%
Other values (587) 980
98.0%
ValueCountFrequency (%)
1 2
0.2%
2 2
0.2%
3 2
0.2%
4 2
0.2%
5 2
0.2%
6 2
0.2%
7 2
0.2%
8 2
0.2%
9 2
0.2%
10 2
0.2%
ValueCountFrequency (%)
27133 1
0.1%
27131 1
0.1%
27118 1
0.1%
27114 1
0.1%
27075 1
0.1%
27055 1
0.1%
27024 1
0.1%
27019 1
0.1%
27014 1
0.1%
26996 1
0.1%

HOLD_CD
Text

Distinct569
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T19:17:31.371660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length13.92
Min length13

Characters and Unicode

Total characters13920
Distinct characters13
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

Unique370 ?
Unique (%)37.0%

Sample

1st rowB003-2020-0074
2nd rowB088-2020-0105
3rd rowB020-2020-0093
4th rowB081-2020-0097
5th rowB088-2020-0102
ValueCountFrequency (%)
b088-2020-0105 18
 
1.8%
b003-2020-0075 10
 
1.0%
b020-2019-0110 10
 
1.0%
b081-2020-0097 8
 
0.8%
b081-2015-0061 8
 
0.8%
b004-2020-0096 8
 
0.8%
b003-2020-0074 7
 
0.7%
b020-2020-0093 7
 
0.7%
b088-2020-0102 7
 
0.7%
b081-2016-0033 7
 
0.7%
Other values (559) 910
91.0%
2023-12-12T19:17:32.180279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4814
34.6%
- 2000
14.4%
2 1727
 
12.4%
1 1469
 
10.6%
B 994
 
7.1%
8 955
 
6.9%
9 367
 
2.6%
7 355
 
2.6%
4 341
 
2.4%
6 308
 
2.2%
Other values (3) 590
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10920
78.4%
Dash Punctuation 2000
 
14.4%
Uppercase Letter 1000
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4814
44.1%
2 1727
 
15.8%
1 1469
 
13.5%
8 955
 
8.7%
9 367
 
3.4%
7 355
 
3.3%
4 341
 
3.1%
6 308
 
2.8%
3 297
 
2.7%
5 287
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
B 994
99.4%
I 6
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12920
92.8%
Latin 1000
 
7.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4814
37.3%
- 2000
15.5%
2 1727
 
13.4%
1 1469
 
11.4%
8 955
 
7.4%
9 367
 
2.8%
7 355
 
2.7%
4 341
 
2.6%
6 308
 
2.4%
3 297
 
2.3%
Latin
ValueCountFrequency (%)
B 994
99.4%
I 6
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4814
34.6%
- 2000
14.4%
2 1727
 
12.4%
1 1469
 
10.6%
B 994
 
7.1%
8 955
 
6.9%
9 367
 
2.6%
7 355
 
2.6%
4 341
 
2.4%
6 308
 
2.2%
Other values (3) 590
 
4.2%

TRD_SEQ
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.778
Minimum3
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:17:32.383796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q116
median40
Q367
95-th percentile118.1
Maximum540
Range537
Interquartile range (IQR)51

Descriptive statistics

Standard deviation46.007972
Coefficient of variation (CV)0.94321152
Kurtosis29.409999
Mean48.778
Median Absolute Deviation (MAD)25
Skewness3.7798119
Sum48778
Variance2116.7334
MonotonicityNot monotonic
2023-12-12T19:17:32.554878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 31
 
3.1%
11 26
 
2.6%
13 26
 
2.6%
12 25
 
2.5%
10 24
 
2.4%
8 22
 
2.2%
9 22
 
2.2%
15 20
 
2.0%
14 19
 
1.9%
16 19
 
1.9%
Other values (145) 766
76.6%
ValueCountFrequency (%)
3 2
 
0.2%
4 8
 
0.8%
5 6
 
0.6%
6 17
1.7%
7 31
3.1%
8 22
2.2%
9 22
2.2%
10 24
2.4%
11 26
2.6%
12 25
2.5%
ValueCountFrequency (%)
540 1
0.1%
539 1
0.1%
355 1
0.1%
279 1
0.1%
278 1
0.1%
277 1
0.1%
253 1
0.1%
252 1
0.1%
203 1
0.1%
202 1
0.1%

PAY_INT_AMT
Real number (ℝ)

ZEROS 

Distinct877
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108671.12
Minimum0
Maximum1065902
Zeros89
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:17:32.740796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1763.5
median21458.5
Q3183223
95-th percentile410189.1
Maximum1065902
Range1065902
Interquartile range (IQR)182459.5

Descriptive statistics

Standard deviation147985.73
Coefficient of variation (CV)1.3617761
Kurtosis2.8828828
Mean108671.12
Median Absolute Deviation (MAD)21458.5
Skewness1.6004735
Sum1.0867112 × 108
Variance2.1899777 × 1010
MonotonicityNot monotonic
2023-12-12T19:17:32.898292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89
 
8.9%
65 3
 
0.3%
84 3
 
0.3%
1180 3
 
0.3%
1462 2
 
0.2%
3762 2
 
0.2%
1807 2
 
0.2%
4918 2
 
0.2%
90 2
 
0.2%
1724 2
 
0.2%
Other values (867) 890
89.0%
ValueCountFrequency (%)
0 89
8.9%
1 2
 
0.2%
3 1
 
0.1%
4 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
10 1
 
0.1%
11 2
 
0.2%
12 1
 
0.1%
15 1
 
0.1%
ValueCountFrequency (%)
1065902 1
0.1%
768301 1
0.1%
713536 1
0.1%
658388 1
0.1%
622030 1
0.1%
621779 1
0.1%
616380 1
0.1%
593259 1
0.1%
593053 1
0.1%
585532 1
0.1%

Interactions

2023-12-12T19:17:29.098095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:28.560853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:28.833923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:29.195078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:28.646983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:28.921540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:29.298170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:28.737947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:29.012527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:17:33.003504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LOAN_ORG_CDRCV_DYDWRT_BASIS_DYSEQTRD_SEQPAY_INT_AMT
LOAN_ORG_CD1.0000.2970.2970.2480.3360.124
RCV_DY0.2971.0001.0001.0000.2100.300
DWRT_BASIS_DY0.2971.0001.0001.0000.2100.300
SEQ0.2481.0001.0001.0000.2380.203
TRD_SEQ0.3360.2100.2100.2381.0000.126
PAY_INT_AMT0.1240.3000.3000.2030.1261.000
2023-12-12T19:17:33.151546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
DWRT_BASIS_DYLOAN_ORG_CDRCV_DY
DWRT_BASIS_DY1.0000.1391.000
LOAN_ORG_CD0.1391.0000.139
RCV_DY1.0000.1391.000
2023-12-12T19:17:33.283869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQTRD_SEQPAY_INT_AMTLOAN_ORG_CDRCV_DYDWRT_BASIS_DY
SEQ1.000-0.7130.1760.1920.9990.999
TRD_SEQ-0.7131.000-0.3890.1490.1350.135
PAY_INT_AMT0.176-0.3891.0000.0530.1380.138
LOAN_ORG_CD0.1920.1490.0531.0000.1390.139
RCV_DY0.9990.1350.1380.1391.0001.000
DWRT_BASIS_DY0.9990.1350.1380.1391.0001.000

Missing values

2023-12-12T19:17:29.440497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:17:29.604581image/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

LOAN_ORG_CDRCV_DYDWRT_BASIS_DYOBJECT_CODEOFFER_CNTSEQHOLD_CDTRD_SEQPAY_INT_AMT
0B0032020102520201025US1398B003-2020-00747494497
1B0882020102520201025US1397B088-2020-01056622030
2B0202020102520201025US1396B020-2020-00931499
3B0812020102520201025US1402B081-2020-00978242462
4B0882020102520201025US1392B088-2020-010210316471
5B0882020102520201025US1264B088-2020-0102311369
6B0202020102520201025US1394B020-2020-00936334086
7B0202020102520201025US1388B020-2020-008821593259
8B0202020102520201025US1403B020-2020-0085124467
9B0202020102520201025US1395B020-2020-008592704
LOAN_ORG_CDRCV_DYDWRT_BASIS_DYOBJECT_CODEOFFER_CNTSEQHOLD_CDTRD_SEQPAY_INT_AMT
990B0812020102320201023US126288B081-2020-00967585532
991B0812020102320201023US126138B081-2020-009613207
992B0202020102320201023US126497B020-2020-00936130583
993B0882020102320201023US126793B088-2020-0102152406
994B0812020102320201023US126100B081-2020-0097786000
995B0042020102320201023US126565B004-2020-00967302477
996B0882020102320201023US126689B088-2020-01027138634
997B0812020102320201023US126257B081-2020-0097770520
998B0042020102320201023US124937B004-2020-00978234959
999B0812020102320201023US126072B081-2020-00967390895