Overview

Dataset statistics

Number of variables3
Number of observations364
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory26.4 B

Variable types

Text1
Numeric2

Dataset

Description한국자산관리공사 공사채권 금융기관별 회수 내역("금융기관명","인수년도","회수처리건수") 데이터 제공
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15074403/fileData.do

Reproduction

Analysis started2023-12-12 12:43:33.951369
Analysis finished2023-12-12 12:43:34.817603
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

금융기관명
Text

Distinct159
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2023-12-12T21:43:35.051113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.6428571
Min length4

Characters and Unicode

Total characters2054
Distinct characters181
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)25.5%

Sample

1st row축협중앙
2nd row대구은행
3rd row농협중앙
4th row기업은행
5th row국민은행(주택)
ValueCountFrequency (%)
신한은행 14
 
3.8%
부산은행 12
 
3.3%
대구은행 11
 
3.0%
광주은행 10
 
2.7%
제주은행 10
 
2.7%
전북은행 10
 
2.7%
우리은행 9
 
2.5%
모아저축은행 9
 
2.5%
기술보증기금 6
 
1.6%
경기상호 6
 
1.6%
Other values (149) 267
73.4%
2023-12-12T21:43:35.475979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
은 171
 
8.3%
행 169
 
8.2%
호 91
 
4.4%
상 89
 
4.3%
축 59
 
2.9%
저 56
 
2.7%
주 47
 
2.3%
신 45
 
2.2%
한 36
 
1.8%
기 36
 
1.8%
Other values (171) 1255
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1948
94.8%
Uppercase Letter 45
 
2.2%
Open Punctuation 29
 
1.4%
Close Punctuation 29
 
1.4%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
은 171
 
8.8%
행 169
 
8.7%
호 91
 
4.7%
상 89
 
4.6%
축 59
 
3.0%
저 56
 
2.9%
주 47
 
2.4%
신 45
 
2.3%
한 36
 
1.8%
기 36
 
1.8%
Other values (158) 1149
59.0%
Uppercase Letter
ValueCountFrequency (%)
S 10
22.2%
C 9
20.0%
B 8
17.8%
K 7
15.6%
E 4
 
8.9%
I 3
 
6.7%
P 2
 
4.4%
N 1
 
2.2%
H 1
 
2.2%
Decimal Number
ValueCountFrequency (%)
2 2
66.7%
4 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1948
94.8%
Common 61
 
3.0%
Latin 45
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
은 171
 
8.8%
행 169
 
8.7%
호 91
 
4.7%
상 89
 
4.6%
축 59
 
3.0%
저 56
 
2.9%
주 47
 
2.4%
신 45
 
2.3%
한 36
 
1.8%
기 36
 
1.8%
Other values (158) 1149
59.0%
Latin
ValueCountFrequency (%)
S 10
22.2%
C 9
20.0%
B 8
17.8%
K 7
15.6%
E 4
 
8.9%
I 3
 
6.7%
P 2
 
4.4%
N 1
 
2.2%
H 1
 
2.2%
Common
ValueCountFrequency (%)
( 29
47.5%
) 29
47.5%
2 2
 
3.3%
4 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1948
94.8%
ASCII 106
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
은 171
 
8.8%
행 169
 
8.7%
호 91
 
4.7%
상 89
 
4.6%
축 59
 
3.0%
저 56
 
2.9%
주 47
 
2.4%
신 45
 
2.3%
한 36
 
1.8%
기 36
 
1.8%
Other values (158) 1149
59.0%
ASCII
ValueCountFrequency (%)
( 29
27.4%
) 29
27.4%
S 10
 
9.4%
C 9
 
8.5%
B 8
 
7.5%
K 7
 
6.6%
E 4
 
3.8%
I 3
 
2.8%
P 2
 
1.9%
2 2
 
1.9%
Other values (3) 3
 
2.8%

인수년도
Real number (ℝ)

Distinct18
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.283
Minimum1997
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T21:43:35.633645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1998
Q12003
median2009
Q32013
95-th percentile2016
Maximum2017
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8559039
Coefficient of variation (CV)0.0029158759
Kurtosis-0.85701776
Mean2008.283
Median Absolute Deviation (MAD)4
Skewness-0.52593056
Sum731015
Variance34.29161
MonotonicityIncreasing
2023-12-12T21:43:35.765861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2013 44
12.1%
2003 34
 
9.3%
1998 32
 
8.8%
2009 30
 
8.2%
2011 23
 
6.3%
2014 22
 
6.0%
2016 22
 
6.0%
2008 22
 
6.0%
2010 21
 
5.8%
2012 19
 
5.2%
Other values (8) 95
26.1%
ValueCountFrequency (%)
1997 11
 
3.0%
1998 32
8.8%
1999 17
4.7%
2003 34
9.3%
2004 5
 
1.4%
2005 6
 
1.6%
2006 12
 
3.3%
2007 18
4.9%
2008 22
6.0%
2009 30
8.2%
ValueCountFrequency (%)
2017 10
 
2.7%
2016 22
6.0%
2015 16
 
4.4%
2014 22
6.0%
2013 44
12.1%
2012 19
5.2%
2011 23
6.3%
2010 21
5.8%
2009 30
8.2%
2008 22
6.0%

회수처리건수
Real number (ℝ)

Distinct181
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1410.2692
Minimum1
Maximum92874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T21:43:35.942138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median32.5
Q3176
95-th percentile4984.95
Maximum92874
Range92873
Interquartile range (IQR)164

Descriptive statistics

Standard deviation7522.0677
Coefficient of variation (CV)5.3337814
Kurtosis86.021897
Mean1410.2692
Median Absolute Deviation (MAD)28.5
Skewness8.6843001
Sum513338
Variance56581503
MonotonicityNot monotonic
2023-12-12T21:43:36.090848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 19
 
5.2%
2 16
 
4.4%
4 13
 
3.6%
13 12
 
3.3%
1 11
 
3.0%
5 9
 
2.5%
6 9
 
2.5%
7 7
 
1.9%
3 7
 
1.9%
15 7
 
1.9%
Other values (171) 254
69.8%
ValueCountFrequency (%)
1 11
3.0%
2 16
4.4%
3 7
1.9%
4 13
3.6%
5 9
2.5%
6 9
2.5%
7 7
1.9%
8 5
 
1.4%
9 5
 
1.4%
10 2
 
0.5%
ValueCountFrequency (%)
92874 1
0.3%
69614 1
0.3%
47739 1
0.3%
43908 1
0.3%
35503 1
0.3%
31793 1
0.3%
13852 1
0.3%
13754 1
0.3%
13266 1
0.3%
10943 1
0.3%

Interactions

2023-12-12T21:43:34.333830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:43:34.083754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:43:34.474072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:43:34.213425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:43:36.173234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인수년도회수처리건수
인수년도1.0000.238
회수처리건수0.2381.000
2023-12-12T21:43:36.263154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인수년도회수처리건수
인수년도1.000-0.213
회수처리건수-0.2131.000

Missing values

2023-12-12T21:43:34.610673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:43:34.779638image/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

금융기관명인수년도회수처리건수
0축협중앙199736
1대구은행199712
2농협중앙199713
3기업은행199741
4국민은행(주택)1997308
5한길종금19971
6평화은행1997173
7제주은행19971
8조흥은행(강원)19972
9국민은행1997330
금융기관명인수년도회수처리건수
354신용보증기금20173
355전북은행201746
356한국주택금융공사201723
357화성수원오산산림조합20172
358거제수협20175
359성일새금20174
360신한은행201781
361현대캐피탈20178206
362이에이알제십오차유동화20173
363대흥새금20176