Overview

Dataset statistics

Number of variables3
Number of observations127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory27.0 B

Variable types

DateTime1
Numeric2

Dataset

Description하우스푸어(3개월 이상 연체된 주택담보대출)에 대한 월별 채무조정액 및 약정건수 등 정보입니다.특히 2020년 3월 2일부터 제1금융권과 "주택담보대출 연체 서민을 위한 채무조정 및 주택 매각 후 임차 거주 지원 협약"을 체결해 기존의 채무조정 지원을 강화하였습니다.
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15045698/fileData.do

Alerts

약정건수 is highly overall correlated with 채무조정액(백만원)High correlation
채무조정액(백만원) is highly overall correlated with 약정건수High correlation
구분 has unique valuesUnique
약정건수 has 11 (8.7%) zerosZeros
채무조정액(백만원) has 11 (8.7%) zerosZeros

Reproduction

Analysis started2024-03-14 17:07:43.615835
Analysis finished2024-03-14 17:07:45.037295
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2013-06-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-15T02:07:45.225788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:45.667953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

약정건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1259843
Minimum0
Maximum49
Zeros11
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:07:46.059781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q310
95-th percentile17
Maximum49
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.8729885
Coefficient of variation (CV)0.96449672
Kurtosis10.930288
Mean7.1259843
Median Absolute Deviation (MAD)4
Skewness2.4861644
Sum905
Variance47.23797
MonotonicityNot monotonic
2024-03-15T02:07:46.399155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 13
 
10.2%
0 11
 
8.7%
1 11
 
8.7%
2 11
 
8.7%
9 10
 
7.9%
5 10
 
7.9%
3 9
 
7.1%
6 8
 
6.3%
8 6
 
4.7%
12 5
 
3.9%
Other values (12) 33
26.0%
ValueCountFrequency (%)
0 11
8.7%
1 11
8.7%
2 11
8.7%
3 9
7.1%
4 13
10.2%
5 10
7.9%
6 8
6.3%
7 5
 
3.9%
8 6
4.7%
9 10
7.9%
ValueCountFrequency (%)
49 1
 
0.8%
32 1
 
0.8%
23 2
 
1.6%
19 1
 
0.8%
18 1
 
0.8%
17 5
3.9%
16 3
2.4%
14 2
 
1.6%
13 5
3.9%
12 5
3.9%

채무조정액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071.4488
Minimum0
Maximum8799
Zeros11
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-15T02:07:46.798863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1376
median792
Q31472
95-th percentile2835.8
Maximum8799
Range8799
Interquartile range (IQR)1096

Descriptive statistics

Standard deviation1155.1233
Coefficient of variation (CV)1.0780947
Kurtosis16.235141
Mean1071.4488
Median Absolute Deviation (MAD)495
Skewness3.1473434
Sum136074
Variance1334309.9
MonotonicityNot monotonic
2024-03-15T02:07:47.220484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
8.7%
757 3
 
2.4%
856 2
 
1.6%
598 2
 
1.6%
1963 1
 
0.8%
3364 1
 
0.8%
2049 1
 
0.8%
396 1
 
0.8%
940 1
 
0.8%
1943 1
 
0.8%
Other values (103) 103
81.1%
ValueCountFrequency (%)
0 11
8.7%
29 1
 
0.8%
40 1
 
0.8%
46 1
 
0.8%
53 1
 
0.8%
70 1
 
0.8%
89 1
 
0.8%
90 1
 
0.8%
100 1
 
0.8%
115 1
 
0.8%
ValueCountFrequency (%)
8799 1
0.8%
5218 1
0.8%
4276 1
0.8%
3602 1
0.8%
3364 1
0.8%
3110 1
0.8%
2858 1
0.8%
2784 1
0.8%
2745 1
0.8%
2516 1
0.8%

Interactions

2024-03-15T02:07:44.131997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:43.710538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:44.376497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:07:43.996488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:07:47.497693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약정건수채무조정액(백만원)
약정건수1.0000.972
채무조정액(백만원)0.9721.000
2024-03-15T02:07:47.749417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
약정건수채무조정액(백만원)
약정건수1.0000.972
채무조정액(백만원)0.9721.000

Missing values

2024-03-15T02:07:44.688059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:07:44.933796image/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

구분약정건수채무조정액(백만원)
02013-06-01131963
12013-07-0191150
22013-08-01101641
32013-09-01142745
42013-10-01234276
52013-11-01498799
62013-12-01325218
72014-01-01173110
82014-02-01183602
92014-03-0191862
구분약정건수채무조정액(백만원)
1172023-03-012272
1182023-04-015822
1192023-05-014408
1202023-06-014581
1212023-07-013401
1222023-08-0151148
1232023-09-013297
1242023-10-0171070
1252023-11-0191113
1262023-12-01121809