Overview

Dataset statistics

Number of variables8
Number of observations36
Missing cells7
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory68.7 B

Variable types

Numeric1
Categorical2
Text2
DateTime3

Dataset

Description(부보금융회사 종합정보)부보금융회사의 예금 등 채권의 지급정지(제1종 보험사고) 또는 부보금융회사의 영업인가·허가의 취소, 해산결의 또는 파산선고(제2종 보험사고) 등의 보험사고가 발생한 부보금융회사의 보험사고발생일자, 지급결정일, 지급공고일, 정리상태에 대한 정보
Author예금보험공사
URLhttps://www.data.go.kr/data/15083217/fileData.do

Alerts

상태 has constant value ""Constant
금융권역 is highly imbalanced (69.1%)Imbalance
지급결정일 has 3 (8.3%) missing valuesMissing
지급공고일 has 4 (11.1%) missing valuesMissing
번호 has unique valuesUnique
금융회사 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:22:51.472053
Analysis finished2023-12-12 23:22:52.171485
Duration0.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-13T08:22:52.264961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-13T08:22:52.438130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

금융권역
Categorical

IMBALANCE 

Distinct3
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size420.0 B
저축은행
33 
증권
 
2
보험
 
1

Length

Max length4
Median length4
Mean length3.8333333
Min length2

Unique

Unique1 ?
Unique (%)2.8%

Sample

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

Common Values

ValueCountFrequency (%)
저축은행 33
91.7%
증권 2
 
5.6%
보험 1
 
2.8%

Length

2023-12-13T08:22:52.592855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:22:52.733058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
저축은행 33
91.7%
증권 2
 
5.6%
보험 1
 
2.8%

금융회사
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T08:22:52.917973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9.5
Mean length7.0555556
Min length6

Characters and Unicode

Total characters254
Distinct characters74
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row전북저축은행
2nd row으뜸저축은행
3rd row전일저축은행
4th row삼화저축은행
5th row대전저축은행(대전)
ValueCountFrequency (%)
전북저축은행 1
 
2.8%
으뜸저축은행 1
 
2.8%
더블유저축은행 1
 
2.8%
미래저축은행(제주 1
 
2.8%
한국저축은행(서울 1
 
2.8%
와이즈에셋자산운용 1
 
2.8%
토마토2저축은행 1
 
2.8%
진흥저축은행(주 1
 
2.8%
경기저축은행 1
 
2.8%
서울저축은행 1
 
2.8%
Other values (26) 26
72.2%
2023-12-13T08:22:53.261190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
은 34
 
13.4%
저 33
 
13.0%
축 33
 
13.0%
행 33
 
13.0%
전 5
 
2.0%
( 5
 
2.0%
) 5
 
2.0%
주 4
 
1.6%
토 4
 
1.6%
산 4
 
1.6%
Other values (64) 94
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 242
95.3%
Open Punctuation 5
 
2.0%
Close Punctuation 5
 
2.0%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
은 34
14.0%
저 33
 
13.6%
축 33
 
13.6%
행 33
 
13.6%
전 5
 
2.1%
주 4
 
1.7%
토 4
 
1.7%
산 4
 
1.7%
울 4
 
1.7%
일 4
 
1.7%
Other values (61) 84
34.7%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 242
95.3%
Common 12
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
은 34
14.0%
저 33
 
13.6%
축 33
 
13.6%
행 33
 
13.6%
전 5
 
2.1%
주 4
 
1.7%
토 4
 
1.7%
산 4
 
1.7%
울 4
 
1.7%
일 4
 
1.7%
Other values (61) 84
34.7%
Common
ValueCountFrequency (%)
( 5
41.7%
) 5
41.7%
2 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 242
95.3%
ASCII 12
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
은 34
14.0%
저 33
 
13.6%
축 33
 
13.6%
행 33
 
13.6%
전 5
 
2.1%
주 4
 
1.7%
토 4
 
1.7%
산 4
 
1.7%
울 4
 
1.7%
일 4
 
1.7%
Other values (61) 84
34.7%
ASCII
ValueCountFrequency (%)
( 5
41.7%
) 5
41.7%
2 2
 
16.7%

상태
Categorical

CONSTANT 

Distinct1
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
파산
36 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파산 36
100.0%

Length

2023-12-13T08:22:53.375707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:22:53.460152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파산 36
100.0%

보험사고일
Date

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2008-12-26 00:00:00
Maximum2015-01-16 00:00:00
2023-12-13T08:22:53.534963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:53.638419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

지급결정일
Date

MISSING 

Distinct17
Distinct (%)51.5%
Missing3
Missing (%)8.3%
Memory size420.0 B
Minimum2009-02-25 00:00:00
Maximum2015-01-12 00:00:00
2023-12-13T08:22:53.740059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:53.835767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

지급공고일
Date

MISSING 

Distinct20
Distinct (%)62.5%
Missing4
Missing (%)11.1%
Memory size420.0 B
Minimum2009-04-03 00:00:00
Maximum2014-05-03 00:00:00
2023-12-13T08:22:53.933336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:22:54.029262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

연락처
Text

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T08:22:54.219328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.75
Min length9

Characters and Unicode

Total characters423
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)88.9%

Sample

1st row063-230-3400
2nd row064-750-3000
3rd row063-270-7700
4th row02-326-5701
5th row042-255-0900
ValueCountFrequency (%)
063-230-3400 2
 
5.6%
02-326-5701 2
 
5.6%
1600-1472 1
 
2.8%
051-250-8800 1
 
2.8%
02-786-3328 1
 
2.8%
063-840-5400 1
 
2.8%
02-2182-8816 1
 
2.8%
02-2268-2767 1
 
2.8%
042-252-1203 1
 
2.8%
02-6004-3200 1
 
2.8%
Other values (24) 24
66.7%
2023-12-13T08:22:54.518920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 121
28.6%
- 71
16.8%
2 55
13.0%
5 31
 
7.3%
1 30
 
7.1%
3 27
 
6.4%
6 26
 
6.1%
7 22
 
5.2%
4 17
 
4.0%
8 14
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 352
83.2%
Dash Punctuation 71
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121
34.4%
2 55
15.6%
5 31
 
8.8%
1 30
 
8.5%
3 27
 
7.7%
6 26
 
7.4%
7 22
 
6.2%
4 17
 
4.8%
8 14
 
4.0%
9 9
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 423
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 121
28.6%
- 71
16.8%
2 55
13.0%
5 31
 
7.3%
1 30
 
7.1%
3 27
 
6.4%
6 26
 
6.1%
7 22
 
5.2%
4 17
 
4.0%
8 14
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 121
28.6%
- 71
16.8%
2 55
13.0%
5 31
 
7.3%
1 30
 
7.1%
3 27
 
6.4%
6 26
 
6.1%
7 22
 
5.2%
4 17
 
4.0%
8 14
 
3.3%

Interactions

2023-12-13T08:22:51.749657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:22:54.606764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호금융권역금융회사보험사고일지급결정일지급공고일연락처
번호1.0000.0001.0000.9290.9140.9570.891
금융권역0.0001.0001.0001.000NaNNaN0.000
금융회사1.0001.0001.0001.0001.0001.0001.000
보험사고일0.9291.0001.0001.0000.9950.9890.737
지급결정일0.914NaN1.0000.9951.0000.9940.945
지급공고일0.957NaN1.0000.9890.9941.0000.960
연락처0.8910.0001.0000.7370.9450.9601.000
2023-12-13T08:22:54.699013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호금융권역
번호1.0000.159
금융권역0.1591.000

Missing values

2023-12-13T08:22:51.866536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:22:51.993557image/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.
2023-12-13T08:22:52.112966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

번호금융권역금융회사상태보험사고일지급결정일지급공고일연락처
01저축은행전북저축은행파산2008-12-262009-02-252009-04-03063-230-3400
12저축은행으뜸저축은행파산2009-08-112009-09-232009-11-20064-750-3000
23저축은행전일저축은행파산2009-12-312010-02-162010-04-09063-270-7700
34저축은행삼화저축은행파산2011-01-142011-03-092011-03-2402-326-5701
45저축은행대전저축은행(대전)파산2011-02-172011-04-132011-09-07042-255-0900
56저축은행부산저축은행파산2011-02-172011-04-082011-11-29051-290-7000
67저축은행부산2저축은행파산2011-02-192011-04-082011-08-30051-290-7500
78저축은행중앙부산저축은행파산2011-02-192011-04-082011-08-3002-540-1190
89저축은행보해저축은행파산2011-02-192011-04-082011-09-07061-260-8181
910저축은행전주저축은행파산2011-02-192011-04-082011-09-07063-230-3400
번호금융권역금융회사상태보험사고일지급결정일지급공고일연락처
2627저축은행더블유저축은행파산2012-12-282012-12-262012-12-2902-6015-6030
2728저축은행서울저축은행파산2013-02-152013-02-132013-02-1602-6004-3200
2829저축은행영남저축은행파산2013-02-152013-02-132013-02-16051-240-1800
2930저축은행신라저축은행파산2013-04-122013-02-132013-04-131600-1472
3031보험그린손해보험파산2013-05-03<NA><NA>02-2268-2767
3132저축은행스마일저축은행파산2013-11-012013-10-232013-11-0202-2182-8816
3233저축은행한울저축은행파산2013-12-272013-12-242013-12-28063-840-5400
3334증권한맥투자증권파산2014-01-15<NA><NA>02-786-3328
3435저축은행해솔저축은행파산2014-05-022014-04-292014-05-03051-250-8800
3536저축은행골든브릿지저축은행파산2015-01-162015-01-12<NA>061-660-0200