Overview

Dataset statistics

Number of variables11
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory100.2 B

Variable types

Text1
Numeric10

Dataset

Description(부보금융회사 종합정보)[참고사항]1. 데이터 개요본 데이터는 예금보험공사의 부보금융회사 중 증권사의 주요 재무현황 자료임2. 데이터 단위(억원) : 총자산, 자기자본, 당기순이익, 영업용순자본, 총위험액, 필요유지자기자본(%) : ROE, 순자본비율, 레버리지비율3. 데이터 주석1) 당기순이익 : 당기 기중누적금액2) ROE산출 시 당기순이익(최근1년) : 손익계산서상 당기순이익에서 대손준비금전입액을 차감하거나 대손준비금환입액을 가산한 금액3) ROE산출 시 자기자본(최근1년평잔) : 최근 1년간 월말 자본총계에서 대손준비금을 차감한 금액의 평균4) 순자본비율 : (영업용순자본-총위험액)/필요유지자기자본 * 1005) 레버리지비율 : 총자산/자기자본*1004. 특이사항1) 순자본비율 : 2016년부터 본격 시행되어 2016년 3월 데이터부터 순자본비율이며, 그 전 데이터는 영업용순자본비율(영업용순자본/총위험액*100)임※ 9개 증권사(NH투자, 대우, 삼성, 한국투자, 현대, 미래에셋, HMC투자증권, 부국)는 순자본비율 조기도입하여 2015년 3월데이터부터 순자본비율로 제공2) 필요유지자기자본 : 순자본비율 본격도입된 2016년 3월 데이터부터 포함. 그 전 데이터에는 해당 항목 없음3) 사명변경 : 사명이 변경된 경우 변경 후 데이터에는 변경된 사명으로만 표시함
Author예금보험공사
URLhttps://www.data.go.kr/data/15061054/fileData.do

Alerts

법인등록번호 is highly overall correlated with 총자산 and 5 other fieldsHigh correlation
총자산 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
자기자본 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
당기순이익 is highly overall correlated with 총자산 and 5 other fieldsHigh correlation
자기자본이익률 is highly overall correlated with 당기순이익High correlation
영업용순자본 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
총위험액 is highly overall correlated with 법인등록번호 and 7 other fieldsHigh correlation
필요유지자기자본 is highly overall correlated with 법인등록번호 and 6 other fieldsHigh correlation
순자본비율 is highly overall correlated with 총자산 and 3 other fieldsHigh correlation
레버리지비율 is highly overall correlated with 법인등록번호 and 5 other fieldsHigh correlation
구분 has unique valuesUnique
총자산 has unique valuesUnique
자기자본 has unique valuesUnique
당기순이익 has unique valuesUnique
자기자본이익률 has unique valuesUnique
총위험액 has unique valuesUnique
순자본비율 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:25:32.209053
Analysis finished2024-04-21 01:25:44.029757
Duration11.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2024-04-21T10:25:44.462145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.2833333
Min length2

Characters and Unicode

Total characters197
Distinct characters104
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

Unique60 ?
Unique (%)100.0%

Sample

1st row미래에셋
2nd row한국
3rd rowNH
4th row삼성
5th rowKB
ValueCountFrequency (%)
미래에셋 1
 
1.7%
한국 1
 
1.7%
ds 1
 
1.7%
크레디트스위스 1
 
1.7%
한국sc 1
 
1.7%
메릴린치 1
 
1.7%
케이프 1
 
1.7%
유비에스 1
 
1.7%
비엔피파리바 1
 
1.7%
상상인 1
 
1.7%
Other values (50) 50
83.3%
2024-04-21T10:25:44.808011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
6.1%
8
 
4.1%
8
 
4.1%
7
 
3.6%
7
 
3.6%
6
 
3.0%
I 5
 
2.5%
K 5
 
2.5%
B 5
 
2.5%
4
 
2.0%
Other values (94) 130
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 159
80.7%
Uppercase Letter 38
 
19.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
 
7.5%
8
 
5.0%
8
 
5.0%
7
 
4.4%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (79) 97
61.0%
Uppercase Letter
ValueCountFrequency (%)
I 5
13.2%
K 5
13.2%
B 5
13.2%
C 4
10.5%
S 4
10.5%
N 3
7.9%
M 2
 
5.3%
D 2
 
5.3%
H 2
 
5.3%
G 1
 
2.6%
Other values (5) 5
13.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 159
80.7%
Latin 38
 
19.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
 
7.5%
8
 
5.0%
8
 
5.0%
7
 
4.4%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (79) 97
61.0%
Latin
ValueCountFrequency (%)
I 5
13.2%
K 5
13.2%
B 5
13.2%
C 4
10.5%
S 4
10.5%
N 3
7.9%
M 2
 
5.3%
D 2
 
5.3%
H 2
 
5.3%
G 1
 
2.6%
Other values (5) 5
13.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 159
80.7%
ASCII 38
 
19.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12
 
7.5%
8
 
5.0%
8
 
5.0%
7
 
4.4%
7
 
4.4%
6
 
3.8%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (79) 97
61.0%
ASCII
ValueCountFrequency (%)
I 5
13.2%
K 5
13.2%
B 5
13.2%
C 4
10.5%
S 4
10.5%
N 3
7.9%
M 2
 
5.3%
D 2
 
5.3%
H 2
 
5.3%
G 1
 
2.6%
Other values (5) 5
13.2%

법인등록번호
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1245853 × 1012
Minimum1.10111 × 1012
Maximum1.8011102 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:44.960895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.10111 × 1012
5-th percentile1.10111 × 1012
Q11.1011101 × 1012
median1.1011121 × 1012
Q31.1011171 × 1012
95-th percentile1.1018116 × 1012
Maximum1.8011102 × 1012
Range7.0000023 × 1011
Interquartile range (IQR)6960559.8

Descriptive statistics

Standard deviation1.26688 × 1011
Coefficient of variation (CV)0.11265308
Kurtosis27.359985
Mean1.1245853 × 1012
Median Absolute Deviation (MAD)2076711.5
Skewness5.3337079
Sum6.7475117 × 1013
Variance1.6049849 × 1022
MonotonicityNot monotonic
2024-04-21T10:25:45.107337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101111367740 2
 
3.3%
1101110011679 1
 
1.7%
1101114727719 1
 
1.7%
1101113915498 1
 
1.7%
1101810016987 1
 
1.7%
1101113916610 1
 
1.7%
1101810042081 1
 
1.7%
1101112582397 1
 
1.7%
1101110031560 1
 
1.7%
1101111911274 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
1101110003155 1
1.7%
1101110006612 1
1.7%
1101110007826 1
1.7%
1101110011679 1
1.7%
1101110018089 1
1.7%
1101110019962 1
1.7%
1101110026834 1
1.7%
1101110031510 1
1.7%
1101110031560 1
1.7%
1101110037112 1
1.7%
ValueCountFrequency (%)
1801110233188 1
1.7%
1801110093095 1
1.7%
1101840013119 1
1.7%
1101810058062 1
1.7%
1101810053301 1
1.7%
1101810042081 1
1.7%
1101810026861 1
1.7%
1101810020219 1
1.7%
1101810020152 1
1.7%
1101810019882 1
1.7%

총자산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114480.75
Minimum138
Maximum859645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:45.249002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138
5-th percentile236.55
Q13352.25
median17777
Q3103437.25
95-th percentile536726.3
Maximum859645
Range859507
Interquartile range (IQR)100085

Descriptive statistics

Standard deviation204707.57
Coefficient of variation (CV)1.7881396
Kurtosis3.8561948
Mean114480.75
Median Absolute Deviation (MAD)17199
Skewness2.1726157
Sum6868845
Variance4.1905188 × 1010
MonotonicityNot monotonic
2024-04-21T10:25:45.377025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
859645 1
 
1.7%
20602 1
 
1.7%
3380 1
 
1.7%
10558 1
 
1.7%
19953 1
 
1.7%
9620 1
 
1.7%
4999 1
 
1.7%
21733 1
 
1.7%
10703 1
 
1.7%
10175 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
138 1
1.7%
178 1
1.7%
190 1
1.7%
239 1
1.7%
404 1
1.7%
546 1
1.7%
610 1
1.7%
1499 1
1.7%
2081 1
1.7%
2105 1
1.7%
ValueCountFrequency (%)
859645 1
1.7%
757096 1
1.7%
578285 1
1.7%
534539 1
1.7%
509464 1
1.7%
509238 1
1.7%
500995 1
1.7%
459505 1
1.7%
433533 1
1.7%
159989 1
1.7%

자기자본
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14206.7
Minimum121
Maximum94683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:45.540761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum121
5-th percentile159.85
Q11166.25
median4729.5
Q312879
95-th percentile63761.45
Maximum94683
Range94562
Interquartile range (IQR)11712.75

Descriptive statistics

Standard deviation22774.398
Coefficient of variation (CV)1.6030744
Kurtosis3.3989401
Mean14206.7
Median Absolute Deviation (MAD)4108.5
Skewness2.0830622
Sum852402
Variance5.1867319 × 108
MonotonicityStrictly decreasing
2024-04-21T10:25:45.690371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94683 1
 
1.7%
3756 1
 
1.7%
3121 1
 
1.7%
3056 1
 
1.7%
2737 1
 
1.7%
2406 1
 
1.7%
2380 1
 
1.7%
2345 1
 
1.7%
2120 1
 
1.7%
1917 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
121 1
1.7%
131 1
1.7%
138 1
1.7%
161 1
1.7%
326 1
1.7%
406 1
1.7%
450 1
1.7%
485 1
1.7%
577 1
1.7%
665 1
1.7%
ValueCountFrequency (%)
94683 1
1.7%
81387 1
1.7%
71066 1
1.7%
63377 1
1.7%
61572 1
1.7%
57525 1
1.7%
56194 1
1.7%
52633 1
1.7%
42726 1
1.7%
28532 1
1.7%

당기순이익
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean945.5
Minimum-3187
Maximum19603
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)16.7%
Memory size672.0 B
2024-04-21T10:25:45.825025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3187
5-th percentile-88.4
Q114
median119.5
Q3513.75
95-th percentile4374.25
Maximum19603
Range22790
Interquartile range (IQR)499.75

Descriptive statistics

Standard deviation2869.7333
Coefficient of variation (CV)3.035149
Kurtosis30.949148
Mean945.5
Median Absolute Deviation (MAD)161
Skewness5.0217993
Sum56730
Variance8235369.5
MonotonicityNot monotonic
2024-04-21T10:25:45.961503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2379 1
 
1.7%
250 1
 
1.7%
100 1
 
1.7%
499 1
 
1.7%
154 1
 
1.7%
297 1
 
1.7%
-115 1
 
1.7%
7 1
 
1.7%
115 1
 
1.7%
-517 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
-3187 1
1.7%
-517 1
1.7%
-115 1
1.7%
-87 1
1.7%
-72 1
1.7%
-59 1
1.7%
-36 1
1.7%
-33 1
1.7%
-29 1
1.7%
-15 1
1.7%
ValueCountFrequency (%)
19603 1
1.7%
6855 1
1.7%
4835 1
1.7%
4350 1
1.7%
4242 1
1.7%
3524 1
1.7%
3384 1
1.7%
2379 1
1.7%
1435 1
1.7%
1083 1
1.7%

자기자본이익률
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5993333
Minimum-23.9
Maximum30.44
Zeros0
Zeros (%)0.0%
Negative11
Negative (%)18.3%
Memory size672.0 B
2024-04-21T10:25:46.107280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23.9
5-th percentile-11.925
Q10.7725
median3.135
Q37.63
95-th percentile17.626
Maximum30.44
Range54.34
Interquartile range (IQR)6.8575

Descriptive statistics

Standard deviation9.667848
Coefficient of variation (CV)2.6860107
Kurtosis2.3213434
Mean3.5993333
Median Absolute Deviation (MAD)3.58
Skewness-0.30864895
Sum215.96
Variance93.467284
MonotonicityNot monotonic
2024-04-21T10:25:46.293641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 1
 
1.7%
7.0 1
 
1.7%
3.25 1
 
1.7%
15.72 1
 
1.7%
6.85 1
 
1.7%
12.45 1
 
1.7%
-4.61 1
 
1.7%
0.32 1
 
1.7%
4.33 1
 
1.7%
-23.9 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
-23.9 1
1.7%
-23.69 1
1.7%
-19.43 1
1.7%
-11.53 1
1.7%
-9.79 1
1.7%
-7.55 1
1.7%
-6.76 1
1.7%
-4.61 1
1.7%
-4.44 1
1.7%
-2.08 1
1.7%
ValueCountFrequency (%)
30.44 1
1.7%
25.55 1
1.7%
24.01 1
1.7%
17.29 1
1.7%
15.72 1
1.7%
14.33 1
1.7%
13.52 1
1.7%
12.45 1
1.7%
10.93 1
1.7%
10.59 1
1.7%

영업용순자본
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11707.05
Minimum103
Maximum85043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:46.463287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile146.7
Q11068.75
median3949
Q39966.75
95-th percentile51252.1
Maximum85043
Range84940
Interquartile range (IQR)8898

Descriptive statistics

Standard deviation19311.138
Coefficient of variation (CV)1.6495307
Kurtosis4.9720659
Mean11707.05
Median Absolute Deviation (MAD)3308
Skewness2.344124
Sum702423
Variance3.7292006 × 108
MonotonicityNot monotonic
2024-04-21T10:25:46.603168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
765 2
 
3.3%
85043 1
 
1.7%
1123 1
 
1.7%
2913 1
 
1.7%
2884 1
 
1.7%
2879 1
 
1.7%
2249 1
 
1.7%
2289 1
 
1.7%
2224 1
 
1.7%
1511 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
103 1
1.7%
113 1
1.7%
122 1
1.7%
148 1
1.7%
205 1
1.7%
353 1
1.7%
406 1
1.7%
412 1
1.7%
418 1
1.7%
532 1
1.7%
ValueCountFrequency (%)
85043 1
1.7%
75962 1
1.7%
62217 1
1.7%
50675 1
1.7%
50578 1
1.7%
44478 1
1.7%
42752 1
1.7%
39213 1
1.7%
31384 1
1.7%
16097 1
1.7%

총위험액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5987.85
Minimum7
Maximum56287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:46.740684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.95
Q1126.25
median612
Q33970.25
95-th percentile29666.2
Maximum56287
Range56280
Interquartile range (IQR)3844

Descriptive statistics

Standard deviation12234.593
Coefficient of variation (CV)2.0432364
Kurtosis6.3320294
Mean5987.85
Median Absolute Deviation (MAD)587
Skewness2.5862782
Sum359271
Variance1.4968527 × 108
MonotonicityNot monotonic
2024-04-21T10:25:46.860852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56287 1
 
1.7%
697 1
 
1.7%
178 1
 
1.7%
254 1
 
1.7%
902 1
 
1.7%
175 1
 
1.7%
202 1
 
1.7%
631 1
 
1.7%
464 1
 
1.7%
188 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
7 1
1.7%
12 1
1.7%
14 1
1.7%
15 1
1.7%
20 1
1.7%
30 1
1.7%
36 1
1.7%
42 1
1.7%
70 1
1.7%
71 1
1.7%
ValueCountFrequency (%)
56287 1
1.7%
47551 1
1.7%
35731 1
1.7%
29347 1
1.7%
29343 1
1.7%
26821 1
1.7%
26710 1
1.7%
25716 1
1.7%
14752 1
1.7%
6511 1
1.7%

필요유지자기자본
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean778.35
Minimum7
Maximum1360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:46.996034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile24.8
Q1269.25
median1089.5
Q31304.75
95-th percentile1349
Maximum1360
Range1353
Interquartile range (IQR)1035.5

Descriptive statistics

Standard deviation522.66001
Coefficient of variation (CV)0.67149741
Kurtosis-1.729449
Mean778.35
Median Absolute Deviation (MAD)265
Skewness-0.21158039
Sum46701
Variance273173.49
MonotonicityNot monotonic
2024-04-21T10:25:47.145926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1342 9
 
15.0%
1349 4
 
6.7%
1113 2
 
3.3%
1300 2
 
3.3%
1120 2
 
3.3%
105 2
 
3.3%
1155 2
 
3.3%
546 1
 
1.7%
196 1
 
1.7%
264 1
 
1.7%
Other values (34) 34
56.7%
ValueCountFrequency (%)
7 1
1.7%
18 1
1.7%
21 1
1.7%
25 1
1.7%
56 1
1.7%
70 1
1.7%
91 1
1.7%
100 1
1.7%
105 2
3.3%
112 1
1.7%
ValueCountFrequency (%)
1360 1
 
1.7%
1349 4
6.7%
1342 9
15.0%
1307 1
 
1.7%
1304 1
 
1.7%
1300 2
 
3.3%
1272 1
 
1.7%
1223 1
 
1.7%
1167 1
 
1.7%
1155 2
 
3.3%

순자본비율
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean702.679
Minimum123.87
Maximum4872.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:47.281884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.87
5-th percentile182.1285
Q1280.475
median438.725
Q3862.7175
95-th percentile2040.376
Maximum4872.34
Range4748.47
Interquartile range (IQR)582.2425

Descriptive statistics

Standard deviation742.8551
Coefficient of variation (CV)1.0571756
Kurtosis16.349713
Mean702.679
Median Absolute Deviation (MAD)204.04
Skewness3.4399708
Sum42160.74
Variance551833.7
MonotonicityNot monotonic
2024-04-21T10:25:47.414349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2142.36 1
 
1.7%
227.47 1
 
1.7%
244.69 1
 
1.7%
235.87 1
 
1.7%
284.06 1
 
1.7%
431.46 1
 
1.7%
180.58 1
 
1.7%
207.0 1
 
1.7%
330.33 1
 
1.7%
1083.33 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
123.87 1
1.7%
151.54 1
1.7%
180.58 1
1.7%
182.21 1
1.7%
185.68 1
1.7%
189.58 1
1.7%
200.78 1
1.7%
207.0 1
1.7%
227.47 1
1.7%
235.87 1
1.7%
ValueCountFrequency (%)
4872.34 1
1.7%
2142.36 1
1.7%
2105.66 1
1.7%
2036.94 1
1.7%
1588.91 1
1.7%
1582.09 1
1.7%
1357.94 1
1.7%
1302.11 1
1.7%
1272.31 1
1.7%
1269.2 1
1.7%

레버리지비율
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.613
Minimum101.18
Maximum878.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-04-21T10:25:47.557098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101.18
5-th percentile108.231
Q1119.935
median374.665
Q3716.665
95-th percentile859.055
Maximum878.51
Range777.33
Interquartile range (IQR)596.73

Descriptive statistics

Standard deviation291.15032
Coefficient of variation (CV)0.69885077
Kurtosis-1.7110105
Mean416.613
Median Absolute Deviation (MAD)264.68
Skewness0.19580246
Sum24996.78
Variance84768.508
MonotonicityNot monotonic
2024-04-21T10:25:47.698575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108.3 2
 
3.3%
692.83 1
 
1.7%
660.21 1
 
1.7%
108.32 1
 
1.7%
715.45 1
 
1.7%
106.92 1
 
1.7%
110.7 1
 
1.7%
878.51 1
 
1.7%
472.43 1
 
1.7%
135.21 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
101.18 1
1.7%
105.77 1
1.7%
106.92 1
1.7%
108.3 2
3.3%
108.32 1
1.7%
109.27 1
1.7%
110.7 1
1.7%
112.16 1
1.7%
112.32 1
1.7%
113.72 1
1.7%
ValueCountFrequency (%)
878.51 1
1.7%
870.97 1
1.7%
864.85 1
1.7%
858.75 1
1.7%
830.23 1
1.7%
788.24 1
1.7%
770.4 1
1.7%
770.0 1
1.7%
757.59 1
1.7%
733.59 1
1.7%

Interactions

2024-04-21T10:25:42.827524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.221169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.321452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.231789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.283108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.338761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.245996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.294330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.096443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.018017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.929652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.378603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.426869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.341827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.422238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.441314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.328534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.390641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.197894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.118440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.013946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.462885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.497432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.433294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.523852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.525277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.628961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.459885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.303896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.201460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.098739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.548400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.576428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.521255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.614891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.613562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.697053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.539566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.381293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.279254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.191125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.633561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.659755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.605173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.718923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.703251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.779677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.619014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.460753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.356386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.312356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.741553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.750648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.690482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.809500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.798733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.861441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.699610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.550433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.432217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.400116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.867786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.846109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.783259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.903664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.886027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.933423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.776862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.643365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.502407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.484205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:34.977922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.966167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.905951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.055613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.974096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.005775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.854603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.738917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.580391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.571533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.088536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.068434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.031610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.160069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.065181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.101997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.936183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.834904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.661053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:43.672012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:35.206030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:36.142300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:37.140976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:38.242292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:39.148023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:40.203555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.003575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:41.922675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:25:42.742221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:25:47.802966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분법인등록번호총자산자기자본당기순이익자기자본이익률영업용순자본총위험액필요유지자기자본순자본비율레버리지비율
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
법인등록번호1.0001.0000.0000.3190.0000.0000.4590.0000.2630.0000.315
총자산1.0000.0001.0000.9870.7990.2900.9640.9370.2230.8750.603
자기자본1.0000.3190.9871.0000.9470.5730.9780.9720.0000.7890.761
당기순이익1.0000.0000.7990.9471.0000.6130.8580.9320.0000.5350.717
자기자본이익률1.0000.0000.2900.5730.6131.0000.0000.4400.3300.0000.000
영업용순자본1.0000.4590.9640.9780.8580.0001.0000.9920.0000.7650.721
총위험액1.0000.0000.9370.9720.9320.4400.9921.0000.0000.8190.672
필요유지자기자본1.0000.2630.2230.0000.0000.3300.0000.0001.0000.0000.557
순자본비율1.0000.0000.8750.7890.5350.0000.7650.8190.0001.0000.659
레버리지비율1.0000.3150.6030.7610.7170.0000.7210.6720.5570.6591.000
2024-04-21T10:25:48.208632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법인등록번호총자산자기자본당기순이익자기자본이익률영업용순자본총위험액필요유지자기자본순자본비율레버리지비율
법인등록번호1.000-0.582-0.567-0.429-0.085-0.568-0.648-0.508-0.336-0.609
총자산-0.5821.0000.9620.7000.2500.9560.9740.8580.5370.714
자기자본-0.5670.9621.0000.7140.2510.9960.9710.9060.5450.590
당기순이익-0.4290.7000.7141.0000.7450.7140.7110.6350.4180.363
자기자본이익률-0.0850.2500.2510.7451.0000.2480.2600.2200.0950.007
영업용순자본-0.5680.9560.9960.7140.2481.0000.9670.9160.5490.592
총위험액-0.6480.9740.9710.7110.2600.9671.0000.8720.5040.709
필요유지자기자본-0.5080.8580.9060.6350.2200.9160.8721.0000.2780.519
순자본비율-0.3360.5370.5450.4180.0950.5490.5040.2781.0000.304
레버리지비율-0.6090.7140.5900.3630.0070.5920.7090.5190.3041.000

Missing values

2024-04-21T10:25:43.807456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:25:43.960274image/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

구분법인등록번호총자산자기자본당기순이익자기자본이익률영업용순자본총위험액필요유지자기자본순자본비율레버리지비율
0미래에셋11011100116798596459468323792.1850435628713422142.36692.83
1한국1101110168769757096813871960325.55759624755113492105.66660.21
2NH11011100981305345397106643505.87622173573113002036.94551.34
3삼성11011103356495094646337748355.74444782682113001357.94642.88
4KB11011100424765782856157235246.86505782934313421582.09643.65
5하나110111020816945950557525-3187-6.76427522571613421269.2722.27
6메리츠11011101413015092385619442428.94506752934713421588.91870.97
7신한11011101434485009955263310831.5339213267101349926.63830.23
8키움11011118679484335334272633847.82313841475213071272.31733.59
9대신110111004295515123828532685530.441127765111342355.05438.81
구분법인등록번호총자산자기자본당기순이익자기자본이익률영업용순자본총위험액필요유지자기자본순자본비율레버리지비율
50홍콩상하이11018100071502838665385.7240670271123.87128.59
51크레디아그리콜11018100198826105776110.935323056895.47105.77
52한국포스증권11011152322042330485-59-11.5341220112349.94109.27
53에스아이1101111367740326945010.29418110130238.1139.02
54KR11011106168835464065213.5235342100312.57134.47
55나틱시스11018400131194043266924.012053691185.68123.83
56한국IMC1101117551181190161-33-23.691481570189.58118.15
57씨엠에스1101116283826178138-15-9.79113718603.52128.95
58CIMB1101810053301239131-29-19.431221425440.73112.32
59미즈호110181005806213812143.021031271302.11113.85