Overview

Dataset statistics

Number of variables6
Number of observations183
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)1.6%
Total size in memory9.2 KiB
Average record size in memory51.7 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description주택도시보증공사가 주로 주택사업자에 대하여 보유하는 채권 중 제3자에게 매각하는 채권의 내역 (업체명(마스킹), 금액, 채권종류 등 의 정보)이며, 수요자들에게 투자기회 부여(예고) 및 예측가능성 제고 가능
URLhttps://www.data.go.kr/data/3047676/fileData.do

Alerts

환매금액 has constant value ""Constant
Dataset has 3 (1.6%) duplicate rowsDuplicates
매각금액 is highly overall correlated with 매각잔액High correlation
매각잔액 is highly overall correlated with 매각금액High correlation

Reproduction

Analysis started2023-12-12 19:36:22.953425
Analysis finished2023-12-12 19:36:23.718719
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관할영업점
Categorical

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
영남관리센터
67 
중부관리센터
52 
서울동부관리센터
49 
서울북부관리센터
15 

Length

Max length8
Median length6
Mean length6.6994536
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영남관리센터 67
36.6%
중부관리센터 52
28.4%
서울동부관리센터 49
26.8%
서울북부관리센터 15
 
8.2%

Length

2023-12-13T04:36:23.800646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:23.918101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영남관리센터 67
36.6%
중부관리센터 52
28.4%
서울동부관리센터 49
26.8%
서울북부관리센터 15
 
8.2%

채권구분
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
구상채권
108 
소송대지급금
68 
융자금
 
7

Length

Max length6
Median length4
Mean length4.704918
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소송대지급금
2nd row소송대지급금
3rd row소송대지급금
4th row구상채권
5th row구상채권

Common Values

ValueCountFrequency (%)
구상채권 108
59.0%
소송대지급금 68
37.2%
융자금 7
 
3.8%

Length

2023-12-13T04:36:24.073536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:24.206425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구상채권 108
59.0%
소송대지급금 68
37.2%
융자금 7
 
3.8%

채권번호
Text

Distinct117
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T04:36:24.417440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length15.174863
Min length9

Characters and Unicode

Total characters2777
Distinct characters29
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)46.4%

Sample

1st row2013가합13567
2nd row2015나2032279
3rd row2016차전230968
4th row12120071040001500
5th row12120072010002100
ValueCountFrequency (%)
200000000000 7
 
3.8%
12220052010005400 5
 
2.7%
12220062010007200 5
 
2.7%
12820102010006600 5
 
2.7%
12820062010000300 5
 
2.7%
12220072010004200 5
 
2.7%
12120072010002100 5
 
2.7%
34120082010002200 5
 
2.7%
34120062010002100 4
 
2.2%
12220052010005600 3
 
1.6%
Other values (107) 134
73.2%
2023-12-13T04:36:24.779958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1119
40.3%
2 476
17.1%
1 397
 
14.3%
118
 
4.2%
4 112
 
4.0%
3 91
 
3.3%
6 83
 
3.0%
7 74
 
2.7%
8 71
 
2.6%
5 68
 
2.4%
Other values (19) 168
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2553
91.9%
Space Separator 118
 
4.2%
Other Letter 88
 
3.2%
Uppercase Letter 18
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 16
18.2%
카 14
15.9%
단 12
13.6%
합 8
9.1%
전 7
8.0%
차 7
8.0%
확 4
 
4.5%
금 3
 
3.4%
타 3
 
3.4%
채 3
 
3.4%
Other values (7) 11
12.5%
Decimal Number
ValueCountFrequency (%)
0 1119
43.8%
2 476
18.6%
1 397
 
15.6%
4 112
 
4.4%
3 91
 
3.6%
6 83
 
3.3%
7 74
 
2.9%
8 71
 
2.8%
5 68
 
2.7%
9 62
 
2.4%
Space Separator
ValueCountFrequency (%)
118
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2671
96.2%
Hangul 88
 
3.2%
Latin 18
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 16
18.2%
카 14
15.9%
단 12
13.6%
합 8
9.1%
전 7
8.0%
차 7
8.0%
확 4
 
4.5%
금 3
 
3.4%
타 3
 
3.4%
채 3
 
3.4%
Other values (7) 11
12.5%
Common
ValueCountFrequency (%)
0 1119
41.9%
2 476
17.8%
1 397
 
14.9%
118
 
4.4%
4 112
 
4.2%
3 91
 
3.4%
6 83
 
3.1%
7 74
 
2.8%
8 71
 
2.7%
5 68
 
2.5%
Latin
ValueCountFrequency (%)
T 18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2689
96.8%
Hangul 88
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1119
41.6%
2 476
17.7%
1 397
 
14.8%
118
 
4.4%
4 112
 
4.2%
3 91
 
3.4%
6 83
 
3.1%
7 74
 
2.8%
8 71
 
2.6%
5 68
 
2.5%
Other values (2) 80
 
3.0%
Hangul
ValueCountFrequency (%)
가 16
18.2%
카 14
15.9%
단 12
13.6%
합 8
9.1%
전 7
8.0%
차 7
8.0%
확 4
 
4.5%
금 3
 
3.4%
타 3
 
3.4%
채 3
 
3.4%
Other values (7) 11
12.5%

매각금액
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5612626 × 108
Minimum9991
Maximum2.0470023 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T04:36:24.949843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9991
5-th percentile101945.1
Q11116756.5
median19169840
Q31.0392107 × 108
95-th percentile7.215149 × 108
Maximum2.0470023 × 1010
Range2.0470013 × 1010
Interquartile range (IQR)1.0280432 × 108

Descriptive statistics

Standard deviation1.5922837 × 109
Coefficient of variation (CV)6.2167919
Kurtosis145.76257
Mean2.5612626 × 108
Median Absolute Deviation (MAD)19037840
Skewness11.701953
Sum4.6871106 × 1010
Variance2.5353673 × 1018
MonotonicityNot monotonic
2023-12-13T04:36:25.092476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132000 9
 
4.9%
264000 3
 
1.6%
550000 2
 
1.1%
396000 2
 
1.1%
282370000 2
 
1.1%
42555000 2
 
1.1%
660000 2
 
1.1%
105922000 2
 
1.1%
70616000 1
 
0.5%
475700764 1
 
0.5%
Other values (157) 157
85.8%
ValueCountFrequency (%)
9991 1
0.5%
10760 1
0.5%
11551 1
0.5%
24800 1
0.5%
51170 1
0.5%
57600 1
0.5%
75150 1
0.5%
78700 1
0.5%
83100 1
0.5%
99910 1
0.5%
ValueCountFrequency (%)
20470023145 1
0.5%
6465522327 1
0.5%
1837149796 1
0.5%
1504576778 1
0.5%
882591096 1
0.5%
861628818 1
0.5%
788843494 1
0.5%
784718511 1
0.5%
766416426 1
0.5%
743079725 1
0.5%

환매금액
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
183 

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 (%)
0 183
100.0%

Length

2023-12-13T04:36:25.311296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:36:25.417863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 183
100.0%

매각잔액
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5612626 × 108
Minimum9991
Maximum2.0470023 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-13T04:36:25.581897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9991
5-th percentile101945.1
Q11116756.5
median19169840
Q31.0392107 × 108
95-th percentile7.215149 × 108
Maximum2.0470023 × 1010
Range2.0470013 × 1010
Interquartile range (IQR)1.0280432 × 108

Descriptive statistics

Standard deviation1.5922837 × 109
Coefficient of variation (CV)6.2167919
Kurtosis145.76257
Mean2.5612626 × 108
Median Absolute Deviation (MAD)19037840
Skewness11.701953
Sum4.6871106 × 1010
Variance2.5353673 × 1018
MonotonicityNot monotonic
2023-12-13T04:36:25.783479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132000 9
 
4.9%
264000 3
 
1.6%
550000 2
 
1.1%
396000 2
 
1.1%
282370000 2
 
1.1%
42555000 2
 
1.1%
660000 2
 
1.1%
105922000 2
 
1.1%
70616000 1
 
0.5%
475700764 1
 
0.5%
Other values (157) 157
85.8%
ValueCountFrequency (%)
9991 1
0.5%
10760 1
0.5%
11551 1
0.5%
24800 1
0.5%
51170 1
0.5%
57600 1
0.5%
75150 1
0.5%
78700 1
0.5%
83100 1
0.5%
99910 1
0.5%
ValueCountFrequency (%)
20470023145 1
0.5%
6465522327 1
0.5%
1837149796 1
0.5%
1504576778 1
0.5%
882591096 1
0.5%
861628818 1
0.5%
788843494 1
0.5%
784718511 1
0.5%
766416426 1
0.5%
743079725 1
0.5%

Interactions

2023-12-13T04:36:23.286470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:23.115200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:23.409322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:36:23.198470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:36:25.929450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관할영업점채권구분매각금액매각잔액
관할영업점1.0000.3770.1570.157
채권구분0.3771.0000.0000.000
매각금액0.1570.0001.0001.000
매각잔액0.1570.0001.0001.000
2023-12-13T04:36:26.061203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채권구분관할영업점
채권구분1.0000.366
관할영업점0.3661.000
2023-12-13T04:36:26.162478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매각금액매각잔액관할영업점채권구분
매각금액1.0001.0000.1480.000
매각잔액1.0001.0000.1480.000
관할영업점0.1480.1481.0000.366
채권구분0.0000.0000.3661.000

Missing values

2023-12-13T04:36:23.541433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:36:23.665905image/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

관할영업점채권구분채권번호매각금액환매금액매각잔액
0서울북부관리센터소송대지급금2013가합135672451830245183
1서울북부관리센터소송대지급금2015나2032279510941905109419
2서울북부관리센터소송대지급금2016차전230968113351301133513
3서울북부관리센터구상채권121200710400015007513000751300
4서울북부관리센터구상채권1212007201000210050065635050065635
5서울북부관리센터구상채권1212007201000210015254138015254138
6서울북부관리센터구상채권1212007201000210010747548010747548
7서울북부관리센터구상채권12120072010002100442751604427516
8서울북부관리센터구상채권1212007201000210074646772074646772
9서울북부관리센터구상채권121200610100074001248215800124821580
관할영업점채권구분채권번호매각금액환매금액매각잔액
173중부관리센터구상채권1222006201000720070616000070616000
174중부관리센터구상채권1222006201000720051243500051243500
175중부관리센터구상채권1222006201000720066264500066264500
176중부관리센터소송대지급금T2019008966600000660000
177중부관리센터구상채권5612004201000110058703734058703734
178중부관리센터소송대지급금T2018005102640000264000
179중부관리센터소송대지급금T2019010792640000264000
180중부관리센터구상채권6712009201000040010590290010590290
181중부관리센터구상채권671200920100004001277469990127746999
182중부관리센터구상채권671200920100004001243452490124345249

Duplicate rows

Most frequently occurring

관할영업점채권구분채권번호매각금액환매금액매각잔액# duplicates
0서울동부관리센터구상채권12220052010005400425550000425550002
1서울동부관리센터구상채권1222006201000040028237000002823700002
2서울동부관리센터구상채권1222007201000420010592200001059220002