Overview

Dataset statistics

Number of variables7
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows5
Duplicate rows (%)0.5%
Total size in memory58.7 KiB
Average record size in memory60.1 B

Variable types

Categorical7

Dataset

Description화재보험대상채권정보에 대한 데이터로, 유동화계획코드, 인수코드, 변경일자, 보험가액 등의 항목을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073090/fileData.do

Alerts

LIQD_PLAN_CD has constant value ""Constant
UPDT_DY has constant value ""Constant
TREAT_ORG_CD has constant value ""Constant
INSURE_PRC_AMT has constant value ""Constant
Dataset has 5 (0.5%) duplicate rowsDuplicates
INSURE_STRT_DY is highly overall correlated with HOLD_CD and 1 other fieldsHigh correlation
HOLD_CD is highly overall correlated with INSURE_STRT_DYHigh correlation
INSURE_EXPIRE_DY is highly overall correlated with INSURE_STRT_DYHigh correlation
INSURE_STRT_DY is highly imbalanced (83.7%)Imbalance
INSURE_EXPIRE_DY is highly imbalanced (88.2%)Imbalance

Reproduction

Analysis started2023-12-13 00:12:47.238822
Analysis finished2023-12-13 00:12:47.556415
Duration0.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

LIQD_PLAN_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
KHFCMB2020S-34
1000 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKHFCMB2020S-34
2nd rowKHFCMB2020S-34
3rd rowKHFCMB2020S-34
4th rowKHFCMB2020S-34
5th rowKHFCMB2020S-34

Common Values

ValueCountFrequency (%)
KHFCMB2020S-34 1000
100.0%

Length

2023-12-13T09:12:47.602534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:47.668724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
khfcmb2020s-34 1000
100.0%

HOLD_CD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B081-2020-0101
778 
B081-2020-0102
115 
B081-2020-0100
107 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB081-2020-0102
2nd rowB081-2020-0102
3rd rowB081-2020-0102
4th rowB081-2020-0102
5th rowB081-2020-0102

Common Values

ValueCountFrequency (%)
B081-2020-0101 778
77.8%
B081-2020-0102 115
 
11.5%
B081-2020-0100 107
 
10.7%

Length

2023-12-13T09:12:47.736388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:47.805202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b081-2020-0101 778
77.8%
b081-2020-0102 115
 
11.5%
b081-2020-0100 107
 
10.7%

UPDT_DY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
20201027
1000 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20201027
2nd row20201027
3rd row20201027
4th row20201027
5th row20201027

Common Values

ValueCountFrequency (%)
20201027 1000
100.0%

Length

2023-12-13T09:12:47.893937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:47.975700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201027 1000
100.0%

TREAT_ORG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
B081
1000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB081
2nd rowB081
3rd rowB081
4th rowB081
5th rowB081

Common Values

ValueCountFrequency (%)
B081 1000
100.0%

Length

2023-12-13T09:12:48.061168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:48.145032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b081 1000
100.0%

INSURE_PRC_AMT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-12-13T09:12:48.209916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:48.271842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

INSURE_STRT_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
10101
976 
<NA>
 
24

Length

Max length5
Median length5
Mean length4.976
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10101
2nd row10101
3rd row10101
4th row10101
5th row10101

Common Values

ValueCountFrequency (%)
10101 976
97.6%
<NA> 24
 
2.4%

Length

2023-12-13T09:12:48.340737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:48.408232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10101 976
97.6%
na 24
 
2.4%

INSURE_EXPIRE_DY
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
10101
976 
<NA>
 
13
0
 
11

Length

Max length5
Median length5
Mean length4.943
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10101
2nd row10101
3rd row10101
4th row10101
5th row10101

Common Values

ValueCountFrequency (%)
10101 976
97.6%
<NA> 13
 
1.3%
0 11
 
1.1%

Length

2023-12-13T09:12:48.488437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:12:48.567218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10101 976
97.6%
na 13
 
1.3%
0 11
 
1.1%

Correlations

2023-12-13T09:12:48.611703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDINSURE_EXPIRE_DY
HOLD_CD1.0000.021
INSURE_EXPIRE_DY0.0211.000
2023-12-13T09:12:48.933368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INSURE_STRT_DYHOLD_CDINSURE_EXPIRE_DY
INSURE_STRT_DY1.0001.0001.000
HOLD_CD1.0001.0000.035
INSURE_EXPIRE_DY1.0000.0351.000
2023-12-13T09:12:48.998133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HOLD_CDINSURE_STRT_DYINSURE_EXPIRE_DY
HOLD_CD1.0001.0000.035
INSURE_STRT_DY1.0001.0001.000
INSURE_EXPIRE_DY0.0351.0001.000

Missing values

2023-12-13T09:12:47.414774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:12:47.519540image/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

LIQD_PLAN_CDHOLD_CDUPDT_DYTREAT_ORG_CDINSURE_PRC_AMTINSURE_STRT_DYINSURE_EXPIRE_DY
0KHFCMB2020S-34B081-2020-010220201027B08101010110101
1KHFCMB2020S-34B081-2020-010220201027B08101010110101
2KHFCMB2020S-34B081-2020-010220201027B08101010110101
3KHFCMB2020S-34B081-2020-010220201027B08101010110101
4KHFCMB2020S-34B081-2020-010220201027B08101010110101
5KHFCMB2020S-34B081-2020-010220201027B08101010110101
6KHFCMB2020S-34B081-2020-010220201027B08101010110101
7KHFCMB2020S-34B081-2020-010220201027B08101010110101
8KHFCMB2020S-34B081-2020-010220201027B08101010110101
9KHFCMB2020S-34B081-2020-010220201027B08101010110101
LIQD_PLAN_CDHOLD_CDUPDT_DYTREAT_ORG_CDINSURE_PRC_AMTINSURE_STRT_DYINSURE_EXPIRE_DY
990KHFCMB2020S-34B081-2020-010020201027B08101010110101
991KHFCMB2020S-34B081-2020-010020201027B08101010110101
992KHFCMB2020S-34B081-2020-010020201027B08101010110101
993KHFCMB2020S-34B081-2020-010020201027B08101010110101
994KHFCMB2020S-34B081-2020-010020201027B08101010110101
995KHFCMB2020S-34B081-2020-010020201027B08101010110101
996KHFCMB2020S-34B081-2020-010020201027B08101010110101
997KHFCMB2020S-34B081-2020-010020201027B08101010110101
998KHFCMB2020S-34B081-2020-010020201027B08101010110101
999KHFCMB2020S-34B081-2020-010020201027B08101010110101

Duplicate rows

Most frequently occurring

LIQD_PLAN_CDHOLD_CDUPDT_DYTREAT_ORG_CDINSURE_PRC_AMTINSURE_STRT_DYINSURE_EXPIRE_DY# duplicates
1KHFCMB2020S-34B081-2020-010120201027B08101010110101754
4KHFCMB2020S-34B081-2020-010220201027B08101010110101115
0KHFCMB2020S-34B081-2020-010020201027B08101010110101107
3KHFCMB2020S-34B081-2020-010120201027B0810<NA><NA>13
2KHFCMB2020S-34B081-2020-010120201027B0810<NA>011