Overview

Dataset statistics

Number of variables5
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory44.8 B

Variable types

Categorical3
Numeric2

Dataset

Description한국자산관리공사 공사채권 무담보 보유현황 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15069549/fileData.do

Alerts

차주수 is highly overall correlated with 관계인수 and 1 other fieldsHigh correlation
관계인수 is highly overall correlated with 차주수 and 1 other fieldsHigh correlation
채권구분명 is highly overall correlated with 차주수 and 1 other fieldsHigh correlation
차주수 has unique valuesUnique
관계인수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:42:22.999854
Analysis finished2023-12-12 06:42:23.736864
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준월
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
2016-11
24 
2016-12
24 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-11
2nd row2016-11
3rd row2016-11
4th row2016-11
5th row2016-11

Common Values

ValueCountFrequency (%)
2016-11 24
50.0%
2016-12 24
50.0%

Length

2023-12-12T15:42:23.792915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:42:23.876135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016-11 24
50.0%
2016-12 24
50.0%

채권구분명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
11차 채권
4 
14-1차 채권
4 
14차 채권
4 
15차 채권
4 
16차 채권
4 
Other values (7)
28 

Length

Max length16
Median length12
Mean length7
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11차 채권
2nd row11차 채권
3rd row14-1차 채권
4th row14-1차 채권
5th row14차 채권

Common Values

ValueCountFrequency (%)
11차 채권 4
8.3%
14-1차 채권 4
8.3%
14차 채권 4
8.3%
15차 채권 4
8.3%
16차 채권 4
8.3%
6차 채권 4
8.3%
7차 구채권 4
8.3%
7차 신채권 4
8.3%
9-2차 채권 4
8.3%
9-3차 채권 4
8.3%
Other values (2) 8
16.7%

Length

2023-12-12T15:42:23.967139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
채권 40
40.0%
7차 8
 
8.0%
11차 4
 
4.0%
14-1차 4
 
4.0%
14차 4
 
4.0%
15차 4
 
4.0%
16차 4
 
4.0%
6차 4
 
4.0%
구채권 4
 
4.0%
신채권 4
 
4.0%
Other values (5) 20
20.0%

위탁여부명
Categorical

Distinct2
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
비위탁
24 
위탁
24 

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비위탁
2nd row위탁
3rd row비위탁
4th row위탁
5th row비위탁

Common Values

ValueCountFrequency (%)
비위탁 24
50.0%
위탁 24
50.0%

Length

2023-12-12T15:42:24.075310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:42:24.168455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비위탁 24
50.0%
위탁 24
50.0%

차주수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24139.104
Minimum1398
Maximum170009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T15:42:24.263514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1398
5-th percentile1436.55
Q13189.25
median8515
Q317946.5
95-th percentile128830.15
Maximum170009
Range168611
Interquartile range (IQR)14757.25

Descriptive statistics

Standard deviation41130.594
Coefficient of variation (CV)1.7038989
Kurtosis6.5102855
Mean24139.104
Median Absolute Deviation (MAD)6954
Skewness2.6569974
Sum1158677
Variance1.6917257 × 109
MonotonicityNot monotonic
2023-12-12T15:42:24.403767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1520 1
 
2.1%
1398 1
 
2.1%
2124 1
 
2.1%
3811 1
 
2.1%
16300 1
 
2.1%
1578 1
 
2.1%
14282 1
 
2.1%
5599 1
 
2.1%
8791 1
 
2.1%
170009 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1398 1
2.1%
1399 1
2.1%
1411 1
2.1%
1484 1
2.1%
1487 1
2.1%
1519 1
2.1%
1520 1
2.1%
1544 1
2.1%
1578 1
2.1%
1645 1
2.1%
ValueCountFrequency (%)
170009 1
2.1%
169805 1
2.1%
128995 1
2.1%
128524 1
2.1%
54726 1
2.1%
54554 1
2.1%
42976 1
2.1%
42955 1
2.1%
41909 1
2.1%
41621 1
2.1%

관계인수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17977.396
Minimum1398
Maximum98084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T15:42:24.560453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1398
5-th percentile1436.55
Q13163
median8515
Q317706
95-th percentile70721.95
Maximum98084
Range96686
Interquartile range (IQR)14543

Descriptive statistics

Standard deviation24075.294
Coefficient of variation (CV)1.3391981
Kurtosis4.0460114
Mean17977.396
Median Absolute Deviation (MAD)6959
Skewness2.0934515
Sum862915
Variance5.796198 × 108
MonotonicityNot monotonic
2023-12-12T15:42:24.724411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1520 1
 
2.1%
1398 1
 
2.1%
2124 1
 
2.1%
3786 1
 
2.1%
16072 1
 
2.1%
1568 1
 
2.1%
14106 1
 
2.1%
5599 1
 
2.1%
8791 1
 
2.1%
98084 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1398 1
2.1%
1399 1
2.1%
1411 1
2.1%
1484 1
2.1%
1487 1
2.1%
1519 1
2.1%
1520 1
2.1%
1544 1
2.1%
1568 1
2.1%
1634 1
2.1%
ValueCountFrequency (%)
98084 1
2.1%
97979 1
2.1%
70821 1
2.1%
70538 1
2.1%
44468 1
2.1%
44322 1
2.1%
41908 1
2.1%
41620 1
2.1%
37544 1
2.1%
37533 1
2.1%

Interactions

2023-12-12T15:42:23.398322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:23.204090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:23.489838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:42:23.315429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:42:24.825063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월채권구분명위탁여부명차주수관계인수
기준월1.0000.0000.0000.0000.000
채권구분명0.0001.0000.0000.8980.912
위탁여부명0.0000.0001.0000.4120.536
차주수0.0000.8980.4121.0000.983
관계인수0.0000.9120.5360.9831.000
2023-12-12T15:42:24.927029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준월채권구분명위탁여부명
기준월1.0000.0000.000
채권구분명0.0001.0000.000
위탁여부명0.0000.0001.000
2023-12-12T15:42:25.034654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차주수관계인수기준월채권구분명위탁여부명
차주수1.0000.9980.0000.5340.267
관계인수0.9981.0000.0000.5570.353
기준월0.0000.0001.0000.0000.000
채권구분명0.5340.5570.0001.0000.000
위탁여부명0.2670.3530.0000.0001.000

Missing values

2023-12-12T15:42:23.620693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:42:23.704049image/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

기준월채권구분명위탁여부명차주수관계인수
02016-1111차 채권비위탁15201520
12016-1111차 채권위탁13991399
22016-1114-1차 채권비위탁14111411
32016-1114-1차 채권위탁22692269
42016-1114차 채권비위탁39033878
52016-1114차 채권위탁1635216122
62016-1115차 채권비위탁16451634
72016-1115차 채권위탁1431714140
82016-1116차 채권비위탁56055605
92016-1116차 채권위탁88158815
기준월채권구분명위탁여부명차주수관계인수
382016-127차 신채권비위탁35023467
392016-127차 신채권위탁64426352
402016-129-2차 채권비위탁1768017680
412016-129-2차 채권위탁1008210082
422016-129-3차 채권비위탁1432113601
432016-129-3차 채권위탁1868617724
442016-129차 채권비위탁37383738
452016-129차 채권위탁14841484
462016-12현대캐피탈(15년) 인수 채권비위탁82398239
472016-12현대캐피탈(15년) 인수 채권위탁4162141620