Overview

Dataset statistics

Number of variables15
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory140.0 B

Variable types

Text1
Numeric14

Dataset

Description1. 데이터 개요본 데이터는 예금보험공사의 부보금융회사 중 손해보험사의 주요 재무현황입니다.2. 데이터 단위(억원) : 보험손익, 투자손익, 당기순이익, 자산총계, 부채총계, 자본총계(%) : 지급여력비율, 부실자산비율, 신용시장리스크비율, 대손충당금적립률, 운용자산이익률, 유동성비율3. 데이터 주석- 지급여력비율 : 지급여력금액 ÷ 지급여력기준금액 x 100- 부실자산비율: 가중부실자산 ÷ 자산건전성 분류대상자산 × 100- 신용시장리스크비율: (신용위험액+일반시장위험액) ÷ (운용자산+신용리스크 측정대상 비운용자산+재보험거래 익스포져) × 100- 대손충당금 적립률: 대손충당금 및 대손준비금 합계 ÷ 고정이하 대출채권 × 100- 운용자산이익률: 직전 1년간 투자영업이익 ÷ 경과운용자산* × 100* (당기말 운용자산+전기말 운용자산-직전 1년간 투자영업이익)÷2- 유동성비율: 잔존만기 3개월 미만 가용 유동성자산 ÷ 3개월 평균 지급보험금* × 100* 직전 1년간 월평균 지급보험금의 3개월분1) 동 재무현황은 예금보험공사가 금융감독원으로부터 공유받은 손해보험사의 업무보고서를 바탕으로 작성한 것으로, 손해보험사가 업무보고서를 작성하는 과정에서의 착오 또는 오류 등으로 인해 일부 내용이 사실과 다를 수 있으며, 외부감사인 검토 또는 검사 결과 등에 따라 변경될 수 있습니다.2) 모든 통계는 반올림된 수치로 세부항목의 합계와 상위항목이 일치하지 않을 수도 있습니다.3) 주요 경영지표 비율 산출시 별도의 표시가 없는 경우 손익과목은 기중 누계액(회계연도 개시일부터 기준월 말일까지)이고 대차과목은 기준월말일 현재 잔액입니다.
Author예금보험공사
URLhttps://www.data.go.kr/data/15061505/fileData.do

Alerts

법인등록번호 is highly overall correlated with 점포수 and 8 other fieldsHigh correlation
점포수 is highly overall correlated with 법인등록번호 and 9 other fieldsHigh correlation
보험손익 is highly overall correlated with 법인등록번호 and 8 other fieldsHigh correlation
투자손익 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
당기순이익 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
자산총계 is highly overall correlated with 법인등록번호 and 8 other fieldsHigh correlation
부채총계 is highly overall correlated with 법인등록번호 and 8 other fieldsHigh correlation
자본 총계 is highly overall correlated with 법인등록번호 and 8 other fieldsHigh correlation
부실자산비율 is highly overall correlated with 법인등록번호 and 5 other fieldsHigh correlation
신용시장리스크비율 is highly overall correlated with 점포수High correlation
대손충당금적립률 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
구분 has unique valuesUnique
법인등록번호 has unique valuesUnique
보험손익 has unique valuesUnique
투자손익 has unique valuesUnique
당기순이익 has unique valuesUnique
자산총계 has unique valuesUnique
부채총계 has unique valuesUnique
자본 총계 has unique valuesUnique
지급여력비율 has unique valuesUnique
유동성비율 has unique valuesUnique
점포수 has 6 (27.3%) zerosZeros
투자손익 has 1 (4.5%) zerosZeros
부실자산비율 has 7 (31.8%) zerosZeros
대손충당금적립률 has 11 (50.0%) zerosZeros
유동성비율 has 1 (4.5%) zerosZeros

Reproduction

Analysis started2024-04-21 01:28:04.835615
Analysis finished2024-04-21 01:28:25.395344
Duration20.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2024-04-21T10:28:25.518046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.5
Min length2

Characters and Unicode

Total characters55
Distinct characters43
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row삼성
2nd row현대
3rd rowDB
4th rowKB
5th row메리츠
ValueCountFrequency (%)
삼성 1
 
4.5%
현대 1
 
4.5%
fm 1
 
4.5%
알리안츠 1
 
4.5%
fa 1
 
4.5%
미쓰이 1
 
4.5%
ace 1
 
4.5%
aig 1
 
4.5%
악사 1
 
4.5%
카카오페이 1
 
4.5%
Other values (12) 12
54.5%
2024-04-21T10:28:25.806752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3
 
5.5%
2
 
3.6%
2
 
3.6%
B 2
 
3.6%
G 2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
F 2
 
3.6%
M 2
 
3.6%
Other values (33) 34
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39
70.9%
Uppercase Letter 16
29.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (23) 23
59.0%
Uppercase Letter
ValueCountFrequency (%)
A 3
18.8%
B 2
12.5%
G 2
12.5%
F 2
12.5%
M 2
12.5%
C 1
 
6.2%
I 1
 
6.2%
E 1
 
6.2%
D 1
 
6.2%
K 1
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39
70.9%
Latin 16
29.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (23) 23
59.0%
Latin
ValueCountFrequency (%)
A 3
18.8%
B 2
12.5%
G 2
12.5%
F 2
12.5%
M 2
12.5%
C 1
 
6.2%
I 1
 
6.2%
E 1
 
6.2%
D 1
 
6.2%
K 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39
70.9%
ASCII 16
29.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3
18.8%
B 2
12.5%
G 2
12.5%
F 2
12.5%
M 2
12.5%
C 1
 
6.2%
I 1
 
6.2%
E 1
 
6.2%
D 1
 
6.2%
K 1
 
6.2%
Hangul
ValueCountFrequency (%)
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
2
 
5.1%
1
 
2.6%
1
 
2.6%
1
 
2.6%
1
 
2.6%
Other values (23) 23
59.0%

법인등록번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1108173 × 1012
Minimum1.10111 × 1012
Maximum1.3111106 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:25.922959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.10111 × 1012
5-th percentile1.10111 × 1012
Q11.10111 × 1012
median1.1011127 × 1012
Q31.1016368 × 1012
95-th percentile1.1018385 × 1012
Maximum1.3111106 × 1012
Range2.1000064 × 1011
Interquartile range (IQR)5.2676013 × 108

Descriptive statistics

Standard deviation4.4737157 × 1010
Coefficient of variation (CV)0.040274092
Kurtosis21.997724
Mean1.1108173 × 1012
Median Absolute Deviation (MAD)2698733.5
Skewness4.6900682
Sum2.443798 × 1013
Variance2.0014132 × 1021
MonotonicityNot monotonic
2024-04-21T10:28:26.025830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1101110005078 1
 
4.5%
1101112799299 1
 
4.5%
1101110099774 1
 
4.5%
1101840012723 1
 
4.5%
1101810057965 1
 
4.5%
1101810031894 1
 
4.5%
1101810034757 1
 
4.5%
1101810007423 1
 
4.5%
1101114725010 1
 
4.5%
1101111872236 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1101110005078 1
4.5%
1101110006456 1
4.5%
1101110013328 1
4.5%
1101110014459 1
4.5%
1101110016728 1
4.5%
1101110017859 1
4.5%
1101110035893 1
4.5%
1101110095285 1
4.5%
1101110099774 1
4.5%
1101111872236 1
4.5%
ValueCountFrequency (%)
1311110646900 1
4.5%
1101840012723 1
4.5%
1101810057965 1
4.5%
1101810034757 1
4.5%
1101810031894 1
4.5%
1101810007423 1
4.5%
1101117107728 1
4.5%
1101115065077 1
4.5%
1101114809369 1
4.5%
1101114725010 1
4.5%

점포수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)77.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.95455
Minimum0
Maximum534
Zeros6
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:26.147548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median54
Q3178.5
95-th percentile468.5
Maximum534
Range534
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation169.49293
Coefficient of variation (CV)1.3456675
Kurtosis0.7952828
Mean125.95455
Median Absolute Deviation (MAD)54
Skewness1.4028286
Sum2771
Variance28727.855
MonotonicityNot monotonic
2024-04-21T10:28:26.247605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
27.3%
534 1
 
4.5%
440 1
 
4.5%
100 1
 
4.5%
1 1
 
4.5%
36 1
 
4.5%
2 1
 
4.5%
9 1
 
4.5%
51 1
 
4.5%
63 1
 
4.5%
Other values (7) 7
31.8%
ValueCountFrequency (%)
0 6
27.3%
1 1
 
4.5%
2 1
 
4.5%
9 1
 
4.5%
36 1
 
4.5%
51 1
 
4.5%
57 1
 
4.5%
63 1
 
4.5%
100 1
 
4.5%
122 1
 
4.5%
ValueCountFrequency (%)
534 1
4.5%
470 1
4.5%
440 1
4.5%
331 1
4.5%
227 1
4.5%
193 1
4.5%
135 1
4.5%
122 1
4.5%
100 1
4.5%
63 1
4.5%

보험손익
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2215.4091
Minimum-163
Maximum12056
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)22.7%
Memory size330.0 B
2024-04-21T10:28:26.346362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-163
5-th percentile-158.35
Q112.25
median537
Q31976.25
95-th percentile9309.6
Maximum12056
Range12219
Interquartile range (IQR)1964

Descriptive statistics

Standard deviation3510.7836
Coefficient of variation (CV)1.5847112
Kurtosis2.238008
Mean2215.4091
Median Absolute Deviation (MAD)633.5
Skewness1.7725131
Sum48739
Variance12325602
MonotonicityNot monotonic
2024-04-21T10:28:26.459030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
12056 1
 
4.5%
-41 1
 
4.5%
1585 1
 
4.5%
-4 1
 
4.5%
28 1
 
4.5%
7 1
 
4.5%
404 1
 
4.5%
212 1
 
4.5%
670 1
 
4.5%
249 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
-163 1
4.5%
-159 1
4.5%
-146 1
4.5%
-41 1
4.5%
-4 1
4.5%
7 1
4.5%
28 1
4.5%
124 1
4.5%
212 1
4.5%
249 1
4.5%
ValueCountFrequency (%)
12056 1
4.5%
9367 1
4.5%
8219 1
4.5%
5291 1
4.5%
4995 1
4.5%
2005 1
4.5%
1890 1
4.5%
1585 1
4.5%
1121 1
4.5%
1029 1
4.5%

투자손익
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean787.04545
Minimum-420
Maximum3641
Zeros1
Zeros (%)4.5%
Negative4
Negative (%)18.2%
Memory size330.0 B
2024-04-21T10:28:26.584429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-420
5-th percentile-38.85
Q13.5
median87.5
Q3857.75
95-th percentile3105.7
Maximum3641
Range4061
Interquartile range (IQR)854.25

Descriptive statistics

Standard deviation1241.5545
Coefficient of variation (CV)1.5774877
Kurtosis0.32177936
Mean787.04545
Median Absolute Deviation (MAD)121.5
Skewness1.3450112
Sum17315
Variance1541457.7
MonotonicityNot monotonic
2024-04-21T10:28:26.916754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3641 1
 
4.5%
27 1
 
4.5%
878 1
 
4.5%
0 1
 
4.5%
-11 1
 
4.5%
1 1
 
4.5%
11 1
 
4.5%
40 1
 
4.5%
58 1
 
4.5%
117 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
-420 1
4.5%
-40 1
4.5%
-17 1
4.5%
-11 1
4.5%
0 1
4.5%
1 1
4.5%
11 1
4.5%
24 1
4.5%
27 1
4.5%
40 1
4.5%
ValueCountFrequency (%)
3641 1
4.5%
3114 1
4.5%
2948 1
4.5%
2789 1
4.5%
2087 1
4.5%
878 1
4.5%
797 1
4.5%
576 1
4.5%
492 1
4.5%
203 1
4.5%

당기순이익
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2252.5909
Minimum-322
Maximum11845
Zeros0
Zeros (%)0.0%
Negative6
Negative (%)27.3%
Memory size330.0 B
2024-04-21T10:28:27.017223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-322
5-th percentile-180.95
Q1-1.25
median489
Q31987.75
95-th percentile9297.25
Maximum11845
Range12167
Interquartile range (IQR)1989

Descriptive statistics

Standard deviation3546.0989
Coefficient of variation (CV)1.5742312
Kurtosis1.7239773
Mean2252.5909
Median Absolute Deviation (MAD)670.5
Skewness1.6593046
Sum49557
Variance12574818
MonotonicityNot monotonic
2024-04-21T10:28:27.116838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
11845 1
 
4.5%
-13 1
 
4.5%
1879 1
 
4.5%
-4 1
 
4.5%
63 1
 
4.5%
7 1
 
4.5%
415 1
 
4.5%
199 1
 
4.5%
563 1
 
4.5%
240 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
-322 1
4.5%
-181 1
4.5%
-180 1
4.5%
-165 1
4.5%
-13 1
4.5%
-4 1
4.5%
7 1
4.5%
63 1
4.5%
199 1
4.5%
240 1
4.5%
ValueCountFrequency (%)
11845 1
4.5%
9345 1
4.5%
8390 1
4.5%
5780 1
4.5%
5462 1
4.5%
2024 1
4.5%
1879 1
4.5%
1637 1
4.5%
1413 1
4.5%
1160 1
4.5%

자산총계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140366.68
Minimum168
Maximum799684
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:27.216674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum168
5-th percentile274.65
Q12477.5
median25280.5
Q3164436.75
95-th percentile440639.85
Maximum799684
Range799516
Interquartile range (IQR)161959.25

Descriptive statistics

Standard deviation208863.62
Coefficient of variation (CV)1.4879858
Kurtosis3.5742882
Mean140366.68
Median Absolute Deviation (MAD)25069
Skewness1.8839837
Sum3088067
Variance4.3624014 × 1010
MonotonicityNot monotonic
2024-04-21T10:28:27.338964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
799684 1
 
4.5%
2328 1
 
4.5%
86414 1
 
4.5%
168 1
 
4.5%
2160 1
 
4.5%
255 1
 
4.5%
1106 1
 
4.5%
11210 1
 
4.5%
9379 1
 
4.5%
9200 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
168 1
4.5%
255 1
4.5%
648 1
4.5%
1106 1
4.5%
2160 1
4.5%
2328 1
4.5%
2926 1
4.5%
9200 1
4.5%
9379 1
4.5%
11210 1
4.5%
ValueCountFrequency (%)
799684 1
4.5%
441474 1
4.5%
424791 1
4.5%
355743 1
4.5%
352489 1
4.5%
173839 1
4.5%
136230 1
4.5%
115122 1
4.5%
112340 1
4.5%
86414 1
4.5%

부채총계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114347.59
Minimum46
Maximum655662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:27.497430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile128.3
Q11292
median22181
Q3135234.75
95-th percentile353242.15
Maximum655662
Range655616
Interquartile range (IQR)133942.75

Descriptive statistics

Standard deviation172437.69
Coefficient of variation (CV)1.5080134
Kurtosis3.4201603
Mean114347.59
Median Absolute Deviation (MAD)22041
Skewness1.8542341
Sum2515647
Variance2.9734758 × 1010
MonotonicityNot monotonic
2024-04-21T10:28:27.641892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
655662 1
 
4.5%
974 1
 
4.5%
38356 1
 
4.5%
46 1
 
4.5%
1247 1
 
4.5%
127 1
 
4.5%
768 1
 
4.5%
5513 1
 
4.5%
3667 1
 
4.5%
5627 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
46 1
4.5%
127 1
4.5%
153 1
4.5%
768 1
4.5%
974 1
4.5%
1247 1
4.5%
1427 1
4.5%
3667 1
4.5%
5513 1
4.5%
5627 1
4.5%
ValueCountFrequency (%)
655662 1
4.5%
353764 1
4.5%
343327 1
4.5%
312885 1
4.5%
292041 1
4.5%
139752 1
4.5%
121683 1
4.5%
97703 1
4.5%
96563 1
4.5%
38356 1
4.5%

자본 총계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26019.091
Minimum122
Maximum144022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:27.761949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile137.55
Q11390.25
median5705
Q340664.5
95-th percentile87398.65
Maximum144022
Range143900
Interquartile range (IQR)39274.25

Descriptive statistics

Standard deviation37805.569
Coefficient of variation (CV)1.4529934
Kurtosis3.4600508
Mean26019.091
Median Absolute Deviation (MAD)5580.5
Skewness1.8764623
Sum572420
Variance1.429261 × 109
MonotonicityNot monotonic
2024-04-21T10:28:27.868832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
144022 1
 
4.5%
1354 1
 
4.5%
48058 1
 
4.5%
122 1
 
4.5%
913 1
 
4.5%
127 1
 
4.5%
338 1
 
4.5%
5697 1
 
4.5%
5713 1
 
4.5%
3573 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
122 1
4.5%
127 1
4.5%
338 1
4.5%
495 1
4.5%
913 1
4.5%
1354 1
4.5%
1499 1
4.5%
2216 1
4.5%
3573 1
4.5%
3984 1
4.5%
ValueCountFrequency (%)
144022 1
4.5%
87711 1
4.5%
81464 1
4.5%
60448 1
4.5%
48058 1
4.5%
42857 1
4.5%
34087 1
4.5%
18559 1
4.5%
14637 1
4.5%
14546 1
4.5%

지급여력비율
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean343.21364
Minimum62.1
Maximum2155.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:27.982371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62.1
5-th percentile131.6735
Q1186.61
median215.325
Q3292
95-th percentile646.296
Maximum2155.62
Range2093.52
Interquartile range (IQR)105.39

Descriptive statistics

Standard deviation424.50245
Coefficient of variation (CV)1.2368461
Kurtosis17.633976
Mean343.21364
Median Absolute Deviation (MAD)51.84
Skewness4.0531819
Sum7550.7
Variance180202.33
MonotonicityNot monotonic
2024-04-21T10:28:28.078510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
274.31 1
 
4.5%
654.1 1
 
4.5%
406.38 1
 
4.5%
498.02 1
 
4.5%
184.21 1
 
4.5%
130.0 1
 
4.5%
182.0 1
 
4.5%
297.01 1
 
4.5%
267.15 1
 
4.5%
276.97 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
62.1 1
4.5%
130.0 1
4.5%
163.47 1
4.5%
182.0 1
4.5%
184.21 1
4.5%
185.42 1
4.5%
190.18 1
4.5%
192.63 1
4.5%
201.18 1
4.5%
205.69 1
4.5%
ValueCountFrequency (%)
2155.62 1
4.5%
654.1 1
4.5%
498.02 1
4.5%
406.38 1
4.5%
332.71 1
4.5%
297.01 1
4.5%
276.97 1
4.5%
274.31 1
4.5%
267.15 1
4.5%
260.9 1
4.5%

부실자산비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10227273
Minimum0
Maximum0.54
Zeros7
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:28.188509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.07
Q30.14
95-th percentile0.332
Maximum0.54
Range0.54
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.13005244
Coefficient of variation (CV)1.2716238
Kurtosis5.5688764
Mean0.10227273
Median Absolute Deviation (MAD)0.07
Skewness2.1575009
Sum2.25
Variance0.016913636
MonotonicityNot monotonic
2024-04-21T10:28:28.290258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 7
31.8%
0.07 3
13.6%
0.18 3
13.6%
0.14 2
 
9.1%
0.1 1
 
4.5%
0.54 1
 
4.5%
0.03 1
 
4.5%
0.34 1
 
4.5%
0.09 1
 
4.5%
0.04 1
 
4.5%
ValueCountFrequency (%)
0.0 7
31.8%
0.03 1
 
4.5%
0.04 1
 
4.5%
0.07 3
13.6%
0.08 1
 
4.5%
0.09 1
 
4.5%
0.1 1
 
4.5%
0.14 2
 
9.1%
0.18 3
13.6%
0.34 1
 
4.5%
ValueCountFrequency (%)
0.54 1
 
4.5%
0.34 1
 
4.5%
0.18 3
13.6%
0.14 2
9.1%
0.1 1
 
4.5%
0.09 1
 
4.5%
0.08 1
 
4.5%
0.07 3
13.6%
0.04 1
 
4.5%
0.03 1
 
4.5%

신용시장리스크비율
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5690909
Minimum0.96
Maximum13.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:28.393044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.96
5-th percentile1.0355
Q13.1225
median5.72
Q37.15
95-th percentile9.4235
Maximum13.74
Range12.78
Interquartile range (IQR)4.0275

Descriptive statistics

Standard deviation3.0101668
Coefficient of variation (CV)0.54051313
Kurtosis1.194349
Mean5.5690909
Median Absolute Deviation (MAD)1.875
Skewness0.6419605
Sum122.52
Variance9.0611039
MonotonicityNot monotonic
2024-04-21T10:28:28.504653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.0 2
 
9.1%
7.08 1
 
4.5%
7.16 1
 
4.5%
9.5 1
 
4.5%
13.74 1
 
4.5%
0.96 1
 
4.5%
0.99 1
 
4.5%
4.86 1
 
4.5%
4.4 1
 
4.5%
7.97 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.96 1
4.5%
0.99 1
4.5%
1.9 1
4.5%
2.5 1
4.5%
3.0 2
9.1%
3.49 1
4.5%
4.4 1
4.5%
4.86 1
4.5%
5.37 1
4.5%
5.7 1
4.5%
ValueCountFrequency (%)
13.74 1
4.5%
9.5 1
4.5%
7.97 1
4.5%
7.65 1
4.5%
7.54 1
4.5%
7.16 1
4.5%
7.12 1
4.5%
7.08 1
4.5%
7.07 1
4.5%
5.78 1
4.5%

대손충당금적립률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.32182
Minimum0
Maximum1915.08
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:28.617861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.25
Q3107.2525
95-th percentile870.741
Maximum1915.08
Range1915.08
Interquartile range (IQR)107.2525

Descriptive statistics

Standard deviation436.52809
Coefficient of variation (CV)2.5332143
Kurtosis13.143228
Mean172.32182
Median Absolute Deviation (MAD)0.25
Skewness3.5349264
Sum3791.08
Variance190556.77
MonotonicityNot monotonic
2024-04-21T10:28:28.723280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 11
50.0%
1915.08 1
 
4.5%
179.78 1
 
4.5%
91.33 1
 
4.5%
269.41 1
 
4.5%
112.56 1
 
4.5%
205.9 1
 
4.5%
51.2 1
 
4.5%
42.16 1
 
4.5%
902.39 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0.0 11
50.0%
0.5 1
 
4.5%
20.77 1
 
4.5%
42.16 1
 
4.5%
51.2 1
 
4.5%
91.33 1
 
4.5%
112.56 1
 
4.5%
179.78 1
 
4.5%
205.9 1
 
4.5%
269.41 1
 
4.5%
ValueCountFrequency (%)
1915.08 1
4.5%
902.39 1
4.5%
269.41 1
4.5%
205.9 1
4.5%
179.78 1
4.5%
112.56 1
4.5%
91.33 1
4.5%
51.2 1
4.5%
42.16 1
4.5%
20.77 1
4.5%

운용자산이익률
Real number (ℝ)

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0254545
Minimum-5.26
Maximum5.02
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)9.1%
Memory size330.0 B
2024-04-21T10:28:28.859821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5.26
5-th percentile-1.373
Q11.1425
median2.665
Q33.21
95-th percentile3.9915
Maximum5.02
Range10.28
Interquartile range (IQR)2.0675

Descriptive statistics

Standard deviation2.1705074
Coefficient of variation (CV)1.0716149
Kurtosis5.3792574
Mean2.0254545
Median Absolute Deviation (MAD)0.76
Skewness-1.9904539
Sum44.56
Variance4.7111022
MonotonicityNot monotonic
2024-04-21T10:28:29.006910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3.0 2
 
9.1%
2.8 1
 
4.5%
2.76 1
 
4.5%
2.47 1
 
4.5%
-1.46 1
 
4.5%
2.68 1
 
4.5%
4.0 1
 
4.5%
1.78 1
 
4.5%
2.44 1
 
4.5%
0.73 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
-5.26 1
4.5%
-1.46 1
4.5%
0.28 1
4.5%
0.7 1
4.5%
0.73 1
4.5%
0.93 1
4.5%
1.78 1
4.5%
2.05 1
4.5%
2.44 1
4.5%
2.47 1
4.5%
ValueCountFrequency (%)
5.02 1
4.5%
4.0 1
4.5%
3.83 1
4.5%
3.58 1
4.5%
3.3 1
4.5%
3.28 1
4.5%
3.0 2
9.1%
2.8 1
4.5%
2.76 1
4.5%
2.68 1
4.5%

유동성비율
Real number (ℝ)

UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49571.21
Minimum0
Maximum1062586.9
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-04-21T10:28:29.123568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile269.0845
Q1416.465
median592.035
Q3958.155
95-th percentile9789.05
Maximum1062586.9
Range1062586.9
Interquartile range (IQR)541.69

Descriptive statistics

Standard deviation226271.29
Coefficient of variation (CV)4.5645706
Kurtosis21.995056
Mean49571.21
Median Absolute Deviation (MAD)291.495
Skewness4.6896633
Sum1090566.6
Variance5.1198695 × 1010
MonotonicityNot monotonic
2024-04-21T10:28:29.238656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
435.86 1
 
4.5%
5458.0 1
 
4.5%
1421.67 1
 
4.5%
0.0 1
 
4.5%
10017.0 1
 
4.5%
2043.0 1
 
4.5%
919.0 1
 
4.5%
834.29 1
 
4.5%
318.61 1
 
4.5%
636.27 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
268.38 1
4.5%
282.47 1
4.5%
318.61 1
4.5%
375.23 1
4.5%
410.0 1
4.5%
435.86 1
4.5%
456.1 1
4.5%
475.74 1
4.5%
509.08 1
4.5%
ValueCountFrequency (%)
1062586.95 1
4.5%
10017.0 1
4.5%
5458.0 1
4.5%
2043.0 1
4.5%
1421.67 1
4.5%
969.58 1
4.5%
923.88 1
4.5%
919.0 1
4.5%
834.29 1
4.5%
677.7 1
4.5%

Interactions

2024-04-21T10:28:23.551798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:06.709984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.920134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.026961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.280887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.803456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.000829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.324211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.522389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.959516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.211221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.405842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.912127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.366596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.632485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:06.831000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.005707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.104403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.363098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.881586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.083561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.423090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.629548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.047150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.287124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.488874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.993551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.432428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.801859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:06.904016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.087190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.175093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.447831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.960494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.177995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.510485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.924464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.175822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.354167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.569949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.077065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.503364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.009643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:06.983377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.176584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.274717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.533016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.040730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.280000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.589061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.004469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.277552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.426694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.652288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.168951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.572316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.175703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.059783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.260853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.361829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.619658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.124430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.401973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.677583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.089628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.369127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.544353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.742610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.285429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.656955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.274467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.150360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.332469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.450743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.702969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.198546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.514629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.768452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.167639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.455708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.630465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.827108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.379213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.743346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.375263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.250519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.411848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.533927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.795277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.296588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.613027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.860088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.255278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.551546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.722193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.923657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.493498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.854187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.475902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.341270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.500888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.612779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.152096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.386955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.699815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.937077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.337755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.641381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.794605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.032074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.626866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.949009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.562732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.439745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.592515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.716013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.241558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.464291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.789654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.043534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.438535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.741024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.875507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.130825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.762846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.050618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.640363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.525339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.667282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.802795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.338299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.541300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.878380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.122176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.519043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.811335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.947775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.208360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:21.923181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.126745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.726592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.596969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.746644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.890838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.419656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.626750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:13.969691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.199764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.603069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.893242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.032248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.298434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.026340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.205935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.827798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.674094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.814725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:09.986078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.522328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.704279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.064867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.279292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.691860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:17.992745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.129065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.397673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.120783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.292171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.908823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.755764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.891772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.083153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.615604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.791827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.156315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.362881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.793155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.072673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.249168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.492200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.214358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.396727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:24.980064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:07.830999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:08.958248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:10.184886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:11.709971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:12.911169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:14.234180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:15.429340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:16.868491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:18.138740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:19.327409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:20.571811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:22.287297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:28:23.474821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:28:29.358092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법인등록번호점포수보험손익투자손익당기순이익자산총계부채총계자본 총계지급여력비율부실자산비율신용시장리스크비율대손충당금적립률운용자산이익률유동성비율
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
법인등록번호1.0001.0000.000NaN0.4650.0000.0000.0000.0001.0000.0000.0000.0001.0000.628
점포수1.0000.0001.0000.9180.9550.9510.9930.9900.9800.0000.6510.0000.9800.0000.000
보험손익1.000NaN0.9181.0000.8730.9850.9670.9540.9430.0000.4870.0000.7600.000NaN
투자손익1.0000.4650.9550.8731.0000.9900.8600.8500.9850.0000.5910.4940.9450.0000.609
당기순이익1.0000.0000.9510.9850.9901.0000.9780.9680.9830.0000.4580.0000.8140.0000.000
자산총계1.0000.0000.9930.9670.8600.9781.0000.9990.9510.0000.3850.0000.8600.0000.000
부채총계1.0000.0000.9900.9540.8500.9680.9991.0000.9530.0000.4540.0000.8830.0000.000
자본 총계1.0000.0000.9800.9430.9850.9830.9510.9531.0000.0000.6380.6170.9550.0000.000
지급여력비율1.0001.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.7810.0000.7661.000
부실자산비율1.0000.0000.6510.4870.5910.4580.3850.4540.6380.0001.0000.3220.0000.0000.000
신용시장리스크비율1.0000.0000.0000.0000.4940.0000.0000.0000.6170.7810.3221.0000.0000.6070.000
대손충당금적립률1.0000.0000.9800.7600.9450.8140.8600.8830.9550.0000.0000.0001.0000.0000.000
운용자산이익률1.0001.0000.0000.0000.0000.0000.0000.0000.0000.7660.0000.6070.0001.0001.000
유동성비율1.0000.6280.000NaN0.6090.0000.0000.0000.0001.0000.0000.0000.0001.0001.000
2024-04-21T10:28:29.521476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인등록번호점포수보험손익투자손익당기순이익자산총계부채총계자본 총계지급여력비율부실자산비율신용시장리스크비율대손충당금적립률운용자산이익률유동성비율
법인등록번호1.000-0.816-0.787-0.788-0.758-0.868-0.880-0.8350.050-0.703-0.286-0.737-0.2720.365
점포수-0.8161.0000.7770.7400.7230.9050.9230.852-0.0820.6880.6050.8010.135-0.433
보험손익-0.7870.7771.0000.9030.9650.8670.8710.876-0.0330.6030.3120.8360.308-0.426
투자손익-0.7880.7400.9031.0000.9490.8270.8220.8830.2090.4800.2780.8330.367-0.473
당기순이익-0.7580.7230.9650.9491.0000.8080.8110.8540.0650.4730.2860.8380.382-0.450
자산총계-0.8680.9050.8670.8270.8081.0000.9940.960-0.0910.7290.3510.8140.214-0.422
부채총계-0.8800.9230.8710.8220.8110.9941.0000.953-0.1160.7480.3580.8070.210-0.412
자본 총계-0.8350.8520.8760.8830.8540.9600.9531.0000.0620.6520.3810.8020.207-0.403
지급여력비율0.050-0.082-0.0330.2090.065-0.091-0.1160.0621.000-0.2090.1760.002-0.229-0.062
부실자산비율-0.7030.6880.6030.4800.4730.7290.7480.652-0.2091.0000.3590.420-0.077-0.267
신용시장리스크비율-0.2860.6050.3120.2780.2860.3510.3580.3810.1760.3591.0000.334-0.406-0.316
대손충당금적립률-0.7370.8010.8360.8330.8380.8140.8070.8020.0020.4200.3341.0000.341-0.361
운용자산이익률-0.2720.1350.3080.3670.3820.2140.2100.207-0.229-0.077-0.4060.3411.000-0.019
유동성비율0.365-0.433-0.426-0.473-0.450-0.422-0.412-0.403-0.062-0.267-0.316-0.361-0.0191.000

Missing values

2024-04-21T10:28:25.093124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:28:25.308263image/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

구분법인등록번호점포수보험손익투자손익당기순이익자산총계부채총계자본 총계지급여력비율부실자산비율신용시장리스크비율대손충당금적립률운용자산이익률유동성비율
0삼성110111000507853412056364111845799684655662144022274.310.077.081915.082.8435.86
1현대110111003589344049952789578042479134332781464185.420.187.16179.782.76410.0
2DB110111009528547093672948934544147435376487711219.130.147.5491.333.58282.47
3KB110111001785933152912087546235248929204160448192.630.077.65269.412.05475.74
4메리츠110111001332822782193114839035574331288542857205.690.185.74112.563.83677.7
5한화11011100064561932005576202417383913975234087260.90.15.7205.93.3547.8
6흥국1101110016728122189020316371123409770314637211.520.185.3751.23.28509.08
7롯데11011100144591351029492116013623012168314546190.180.547.1242.160.28268.38
8농협110111480936957112179714131151229656318559332.710.033.49902.392.65456.1
9MG110111506507763124-420-3223545533239221662.10.345.780.00.7969.58
구분법인등록번호점포수보험손익투자손익당기순이익자산총계부채총계자본 총계지급여력비율부실자산비율신용시장리스크비율대손충당금적립률운용자산이익률유동성비율
12신한11011127992990-4127-1323289741354654.10.01.90.03.05458.0
13카카오페이13111106469002-163-17-1816481534952155.620.07.970.0-5.261062586.95
14악사110111187223636249117240920056273573276.970.094.40.00.73636.27
15AIG1101114725010067058563937936675713267.150.044.860.02.44318.61
16ACE11018100074230212401991121055135697297.010.080.990.01.78834.29
17미쓰이11018100347570404114151106768338182.00.03.00.03.0919.0
18FA11018100318940717255127127130.00.03.00.54.02043.0
19알리안츠1101810057965028-116321601247913184.210.00.960.02.6810017.0
20FM11018400127231-40-416846122498.020.013.740.0-1.460.0
21서울보증110111009977410015858781879864143835648058406.380.149.520.772.471421.67