Overview

Dataset statistics

Number of variables11
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory98.6 B

Variable types

Text1
Categorical1
Numeric9

Dataset

Description저축은행명, 지역, 총자산, 기업대출, 가계담보대출, 가계신용대출, 기타대출, 당기순이익, BIS비율, 연체율, 고정이하여신비율
Author예금보험공사
URLhttps://www.data.go.kr/data/15120292/fileData.do

Alerts

총자산(억원) is highly overall correlated with 기업 대출금(억원) and 3 other fieldsHigh correlation
기업 대출금(억원) is highly overall correlated with 총자산(억원) and 3 other fieldsHigh correlation
가계 담보 대출금(억원) is highly overall correlated with 총자산(억원) and 2 other fieldsHigh correlation
가계 신용대출(억원) is highly overall correlated with 총자산(억원) and 3 other fieldsHigh correlation
기타 대출(억원) is highly overall correlated with 총자산(억원) and 2 other fieldsHigh correlation
연체율(퍼센트) is highly overall correlated with 고정이하여신비율(퍼센트)High correlation
고정이하여신비율(퍼센트) is highly overall correlated with 연체율(퍼센트)High correlation
저축은행명 has unique valuesUnique
총자산(억원) has unique valuesUnique
기업 대출금(억원) has 2 (2.5%) zerosZeros
가계 담보 대출금(억원) has 1 (1.3%) zerosZeros
가계 신용대출(억원) has 15 (19.0%) zerosZeros
기타 대출(억원) has 9 (11.4%) zerosZeros
연체율(퍼센트) has 1 (1.3%) zerosZeros

Reproduction

Analysis started2024-03-14 18:40:49.808750
Analysis finished2024-03-14 18:41:11.762580
Duration21.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

저축은행명
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size760.0 B
2024-03-15T03:41:12.781637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.4303797
Min length1

Characters and Unicode

Total characters192
Distinct characters105
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

Unique79 ?
Unique (%)100.0%

Sample

1st rowSBI
2nd rowOK
3rd row한국투자
4th row웰컴
5th row애큐온
ValueCountFrequency (%)
sbi 1
 
1.3%
유니온 1
 
1.3%
부림 1
 
1.3%
한성 1
 
1.3%
청주 1
 
1.3%
오투 1
 
1.3%
민국 1
 
1.3%
삼정 1
 
1.3%
조은 1
 
1.3%
융창 1
 
1.3%
Other values (69) 69
87.3%
2024-03-15T03:41:14.260101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
4.2%
B 7
 
3.6%
6
 
3.1%
K 5
 
2.6%
5
 
2.6%
5
 
2.6%
5
 
2.6%
4
 
2.1%
3
 
1.6%
3
 
1.6%
Other values (95) 141
73.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
84.4%
Uppercase Letter 30
 
15.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
 
4.9%
6
 
3.7%
5
 
3.1%
5
 
3.1%
5
 
3.1%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (84) 117
72.2%
Uppercase Letter
ValueCountFrequency (%)
B 7
23.3%
K 5
16.7%
H 3
10.0%
S 2
 
6.7%
O 2
 
6.7%
I 2
 
6.7%
J 2
 
6.7%
T 2
 
6.7%
D 2
 
6.7%
N 2
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
84.4%
Latin 30
 
15.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
 
4.9%
6
 
3.7%
5
 
3.1%
5
 
3.1%
5
 
3.1%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (84) 117
72.2%
Latin
ValueCountFrequency (%)
B 7
23.3%
K 5
16.7%
H 3
10.0%
S 2
 
6.7%
O 2
 
6.7%
I 2
 
6.7%
J 2
 
6.7%
T 2
 
6.7%
D 2
 
6.7%
N 2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
84.4%
ASCII 30
 
15.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
 
4.9%
6
 
3.7%
5
 
3.1%
5
 
3.1%
5
 
3.1%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (84) 117
72.2%
ASCII
ValueCountFrequency (%)
B 7
23.3%
K 5
16.7%
H 3
10.0%
S 2
 
6.7%
O 2
 
6.7%
I 2
 
6.7%
J 2
 
6.7%
T 2
 
6.7%
D 2
 
6.7%
N 2
 
6.7%

지역
Categorical

Distinct13
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Memory size760.0 B
서울
23 
경기
15 
부산
광주
대구
Other values (8)
22 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)2.5%

Sample

1st row서울
2nd row서울
3rd row경기
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 23
29.1%
경기 15
19.0%
부산 9
 
11.4%
광주 5
 
6.3%
대구 5
 
6.3%
경북 5
 
6.3%
인천 4
 
5.1%
충북 4
 
5.1%
경남 3
 
3.8%
충남 2
 
2.5%
Other values (3) 4
 
5.1%

Length

2024-03-15T03:41:14.550783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 23
29.1%
경기 15
19.0%
부산 9
 
11.4%
광주 5
 
6.3%
대구 5
 
6.3%
경북 5
 
6.3%
인천 4
 
5.1%
충북 4
 
5.1%
경남 3
 
3.8%
충남 2
 
2.5%
Other values (3) 4
 
5.1%

총자산(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17495.165
Minimum52
Maximum161468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:14.823301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile1529.1
Q13662
median6545
Q321907.5
95-th percentile59867.1
Maximum161468
Range161416
Interquartile range (IQR)18245.5

Descriptive statistics

Standard deviation27984.906
Coefficient of variation (CV)1.5995794
Kurtosis14.792683
Mean17495.165
Median Absolute Deviation (MAD)4316
Skewness3.580395
Sum1382118
Variance7.8315494 × 108
MonotonicityStrictly decreasing
2024-03-15T03:41:15.261237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161468 1
 
1.3%
150931 1
 
1.3%
3831 1
 
1.3%
3921 1
 
1.3%
3971 1
 
1.3%
4036 1
 
1.3%
4189 1
 
1.3%
4382 1
 
1.3%
4564 1
 
1.3%
4690 1
 
1.3%
Other values (69) 69
87.3%
ValueCountFrequency (%)
52 1
1.3%
90 1
1.3%
1291 1
1.3%
1467 1
1.3%
1536 1
1.3%
1565 1
1.3%
1636 1
1.3%
1746 1
1.3%
2229 1
1.3%
2284 1
1.3%
ValueCountFrequency (%)
161468 1
1.3%
150931 1
1.3%
93544 1
1.3%
64260 1
1.3%
59379 1
1.3%
57773 1
1.3%
47956 1
1.3%
31993 1
1.3%
31731 1
1.3%
31276 1
1.3%

기업 대출금(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7826.8734
Minimum0
Maximum62321
Zeros2
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:15.566077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile717.5
Q11809
median3129
Q310008
95-th percentile25148.2
Maximum62321
Range62321
Interquartile range (IQR)8199

Descriptive statistics

Standard deviation11583.216
Coefficient of variation (CV)1.4799288
Kurtosis11.788032
Mean7826.8734
Median Absolute Deviation (MAD)2175
Skewness3.2376247
Sum618323
Variance1.3417089 × 108
MonotonicityNot monotonic
2024-03-15T03:41:15.819804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.5%
62321 1
 
1.3%
2507 1
 
1.3%
2119 1
 
1.3%
1457 1
 
1.3%
1865 1
 
1.3%
2440 1
 
1.3%
2345 1
 
1.3%
2007 1
 
1.3%
3109 1
 
1.3%
Other values (68) 68
86.1%
ValueCountFrequency (%)
0 2
2.5%
573 1
1.3%
704 1
1.3%
719 1
1.3%
768 1
1.3%
925 1
1.3%
931 1
1.3%
954 1
1.3%
1104 1
1.3%
1142 1
1.3%
ValueCountFrequency (%)
62321 1
1.3%
60234 1
1.3%
47925 1
1.3%
28849 1
1.3%
24737 1
1.3%
21853 1
1.3%
21087 1
1.3%
18684 1
1.3%
15904 1
1.3%
15844 1
1.3%

가계 담보 대출금(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1463.8861
Minimum0
Maximum13650
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:16.317159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.5
Q1176.5
median712
Q31687
95-th percentile5491.2
Maximum13650
Range13650
Interquartile range (IQR)1510.5

Descriptive statistics

Standard deviation2145.4337
Coefficient of variation (CV)1.4655742
Kurtosis12.880986
Mean1463.8861
Median Absolute Deviation (MAD)549
Skewness3.0608328
Sum115647
Variance4602885.7
MonotonicityNot monotonic
2024-03-15T03:41:16.736379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
721 2
 
2.5%
4552 1
 
1.3%
372 1
 
1.3%
1800 1
 
1.3%
115 1
 
1.3%
382 1
 
1.3%
418 1
 
1.3%
569 1
 
1.3%
166 1
 
1.3%
120 1
 
1.3%
Other values (68) 68
86.1%
ValueCountFrequency (%)
0 1
1.3%
13 1
1.3%
17 1
1.3%
19 1
1.3%
44 1
1.3%
73 1
1.3%
90 1
1.3%
104 1
1.3%
115 1
1.3%
116 1
1.3%
ValueCountFrequency (%)
13650 1
1.3%
6360 1
1.3%
6160 1
1.3%
6087 1
1.3%
5425 1
1.3%
5346 1
1.3%
4706 1
1.3%
4552 1
1.3%
4211 1
1.3%
3884 1
1.3%

가계 신용대출(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3577.8354
Minimum0
Maximum62104
Zeros15
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:17.148709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median93
Q32369.5
95-th percentile16023.2
Maximum62104
Range62104
Interquartile range (IQR)2368.5

Descriptive statistics

Standard deviation9669.4785
Coefficient of variation (CV)2.7026057
Kurtosis24.16113
Mean3577.8354
Median Absolute Deviation (MAD)93
Skewness4.662012
Sum282649
Variance93498814
MonotonicityNot monotonic
2024-03-15T03:41:17.582274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
19.0%
1 7
 
8.9%
5 2
 
2.5%
4 2
 
2.5%
93 1
 
1.3%
40 1
 
1.3%
15 1
 
1.3%
1592 1
 
1.3%
1353 1
 
1.3%
158 1
 
1.3%
Other values (47) 47
59.5%
ValueCountFrequency (%)
0 15
19.0%
1 7
8.9%
3 1
 
1.3%
4 2
 
2.5%
5 2
 
2.5%
6 1
 
1.3%
12 1
 
1.3%
13 1
 
1.3%
15 1
 
1.3%
17 1
 
1.3%
ValueCountFrequency (%)
62104 1
1.3%
51653 1
1.3%
19171 1
1.3%
16844 1
1.3%
15932 1
1.3%
14438 1
1.3%
13565 1
1.3%
11353 1
1.3%
8396 1
1.3%
7917 1
1.3%

기타 대출(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.17722
Minimum0
Maximum7091
Zeros9
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:17.999488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112.5
median407
Q3929.5
95-th percentile3061.9
Maximum7091
Range7091
Interquartile range (IQR)817

Descriptive statistics

Standard deviation1257.1736
Coefficient of variation (CV)1.5555667
Kurtosis11.476624
Mean808.17722
Median Absolute Deviation (MAD)319
Skewness3.18098
Sum63846
Variance1580485.4
MonotonicityNot monotonic
2024-03-15T03:41:18.286658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
11.4%
407 2
 
2.5%
439 2
 
2.5%
1161 1
 
1.3%
395 1
 
1.3%
515 1
 
1.3%
347 1
 
1.3%
234 1
 
1.3%
390 1
 
1.3%
1 1
 
1.3%
Other values (59) 59
74.7%
ValueCountFrequency (%)
0 9
11.4%
1 1
 
1.3%
17 1
 
1.3%
27 1
 
1.3%
29 1
 
1.3%
51 1
 
1.3%
61 1
 
1.3%
82 1
 
1.3%
90 1
 
1.3%
103 1
 
1.3%
ValueCountFrequency (%)
7091 1
1.3%
5883 1
1.3%
5060 1
1.3%
3115 1
1.3%
3056 1
1.3%
2558 1
1.3%
2547 1
1.3%
1852 1
1.3%
1771 1
1.3%
1630 1
1.3%

당기순이익(억원)
Real number (ℝ)

Distinct71
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-17.898734
Minimum-677
Maximum704
Zeros0
Zeros (%)0.0%
Negative38
Negative (%)48.1%
Memory size839.0 B
2024-03-15T03:41:18.687802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-677
5-th percentile-300.3
Q1-39.5
median2
Q339
95-th percentile136.1
Maximum704
Range1381
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation183.76494
Coefficient of variation (CV)-10.266924
Kurtosis6.280726
Mean-17.898734
Median Absolute Deviation (MAD)40
Skewness0.22004721
Sum-1414
Variance33769.554
MonotonicityNot monotonic
2024-03-15T03:41:19.132197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 3
 
3.8%
27 2
 
2.5%
-2 2
 
2.5%
17 2
 
2.5%
61 2
 
2.5%
-32 2
 
2.5%
-7 2
 
2.5%
46 1
 
1.3%
8 1
 
1.3%
42 1
 
1.3%
Other values (61) 61
77.2%
ValueCountFrequency (%)
-677 1
1.3%
-480 1
1.3%
-458 1
1.3%
-375 1
1.3%
-292 1
1.3%
-288 1
1.3%
-281 1
1.3%
-266 1
1.3%
-233 1
1.3%
-189 1
1.3%
ValueCountFrequency (%)
704 1
1.3%
623 1
1.3%
358 1
1.3%
164 1
1.3%
133 1
1.3%
121 1
1.3%
114 1
1.3%
80 1
1.3%
68 1
1.3%
64 1
1.3%
Distinct57
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.783544
Minimum9.3
Maximum41.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:19.554871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile11.1
Q112.15
median14.3
Q317.8
95-th percentile34.33
Maximum41.4
Range32.1
Interquartile range (IQR)5.65

Descriptive statistics

Standard deviation7.2553681
Coefficient of variation (CV)0.43229058
Kurtosis3.2000403
Mean16.783544
Median Absolute Deviation (MAD)2.5
Skewness1.9125021
Sum1325.9
Variance52.640367
MonotonicityNot monotonic
2024-03-15T03:41:19.999191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.3 5
 
6.3%
15.7 3
 
3.8%
14.2 3
 
3.8%
11.1 3
 
3.8%
14.5 2
 
2.5%
13.0 2
 
2.5%
11.7 2
 
2.5%
13.3 2
 
2.5%
17.8 2
 
2.5%
23.1 2
 
2.5%
Other values (47) 53
67.1%
ValueCountFrequency (%)
9.3 1
 
1.3%
9.6 1
 
1.3%
10.6 1
 
1.3%
11.1 3
3.8%
11.3 5
6.3%
11.4 1
 
1.3%
11.5 2
 
2.5%
11.6 1
 
1.3%
11.7 2
 
2.5%
11.8 1
 
1.3%
ValueCountFrequency (%)
41.4 1
1.3%
40.1 1
1.3%
38.5 1
1.3%
35.5 1
1.3%
34.2 1
1.3%
32.8 1
1.3%
31.1 1
1.3%
28.0 1
1.3%
26.7 1
1.3%
23.1 2
2.5%

연체율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6734177
Minimum0
Maximum23.9
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:20.433890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.26
Q14.75
median5.7
Q38.15
95-th percentile12.72
Maximum23.9
Range23.9
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation3.6601705
Coefficient of variation (CV)0.54847017
Kurtosis5.7008165
Mean6.6734177
Median Absolute Deviation (MAD)1.5
Skewness1.7303615
Sum527.2
Variance13.396848
MonotonicityNot monotonic
2024-03-15T03:41:20.746893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 5
 
6.3%
5.3 4
 
5.1%
5.6 3
 
3.8%
6.3 3
 
3.8%
2.4 2
 
2.5%
8.1 2
 
2.5%
10.5 2
 
2.5%
2.3 2
 
2.5%
6.5 2
 
2.5%
4.9 2
 
2.5%
Other values (45) 52
65.8%
ValueCountFrequency (%)
0.0 1
1.3%
1.3 1
1.3%
1.5 1
1.3%
1.9 1
1.3%
2.3 2
2.5%
2.4 2
2.5%
3.3 1
1.3%
3.6 1
1.3%
3.7 1
1.3%
3.9 1
1.3%
ValueCountFrequency (%)
23.9 1
1.3%
17.2 1
1.3%
13.4 1
1.3%
12.9 1
1.3%
12.7 1
1.3%
11.5 1
1.3%
11.3 1
1.3%
11.1 1
1.3%
11.0 1
1.3%
10.9 1
1.3%

고정이하여신비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8037975
Minimum1.8
Maximum23.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T03:41:21.009484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.39
Q14.65
median6
Q38
95-th percentile13.53
Maximum23.1
Range21.3
Interquartile range (IQR)3.35

Descriptive statistics

Standard deviation3.7823288
Coefficient of variation (CV)0.55591437
Kurtosis5.3174699
Mean6.8037975
Median Absolute Deviation (MAD)1.7
Skewness1.9213496
Sum537.5
Variance14.306011
MonotonicityNot monotonic
2024-03-15T03:41:21.263498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8 3
 
3.8%
7.5 3
 
3.8%
6.3 3
 
3.8%
8.2 2
 
2.5%
6.9 2
 
2.5%
4.7 2
 
2.5%
5.2 2
 
2.5%
5.1 2
 
2.5%
4.6 2
 
2.5%
5.8 2
 
2.5%
Other values (53) 56
70.9%
ValueCountFrequency (%)
1.8 1
1.3%
2.0 1
1.3%
2.2 1
1.3%
2.3 1
1.3%
2.4 1
1.3%
2.5 1
1.3%
2.7 1
1.3%
2.9 1
1.3%
3.1 1
1.3%
3.3 1
1.3%
ValueCountFrequency (%)
23.1 1
1.3%
20.5 1
1.3%
15.7 1
1.3%
13.8 1
1.3%
13.5 1
1.3%
13.3 1
1.3%
12.5 1
1.3%
11.6 1
1.3%
10.4 1
1.3%
10.1 1
1.3%

Interactions

2024-03-15T03:41:08.312712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:50.550592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:52.558864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:54.564079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:56.662208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:58.600142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:00.562798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:03.215967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:05.697882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:08.618389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:50.826450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:52.781243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:54.876937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:56.934738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:58.880406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:01.020732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:03.484190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:06.044558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:08.910753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:51.091998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:53.018725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:55.093391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:57.137577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:59.141128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:01.289871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:03.736339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:06.322753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:09.171526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:51.425501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:53.277671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:55.333535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:57.298659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:59.335771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:01.612236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:03.997719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:06.616627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:09.388726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:51.688801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:53.430367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:55.500043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:57.450560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:59.492885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:01.871046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:04.251018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:06.882005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:09.669589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:51.924492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:53.590118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:55.664659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:57.608889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:59.653459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:02.132344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:04.509274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:07.189984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:09.921285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:52.094940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:53.833315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:55.902783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:57.862119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:59.824964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:02.387064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:04.854669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:07.459837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:10.191373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:52.260151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:54.095483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:56.167590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:58.118079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:00.073818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:02.641455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:05.113864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:07.720329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:10.472506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:52.418098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:54.326662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:56.421022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:40:58.363668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:00.325579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:02.982568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:05.452721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T03:41:07.977953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T03:41:21.540267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저축은행명지역총자산(억원)기업 대출금(억원)가계 담보 대출금(억원)가계 신용대출(억원)기타 대출(억원)당기순이익(억원)자기자본비율(BIS비율_퍼센트)연체율(퍼센트)고정이하여신비율(퍼센트)
저축은행명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지역1.0001.0000.0000.0000.0000.0000.0000.0000.1760.5820.633
총자산(억원)1.0000.0001.0000.9270.7270.9440.8140.7610.0000.0000.000
기업 대출금(억원)1.0000.0000.9271.0000.5430.7880.8390.7750.0000.0000.000
가계 담보 대출금(억원)1.0000.0000.7270.5431.0000.7610.5530.4830.0000.0000.000
가계 신용대출(억원)1.0000.0000.9440.7880.7611.0000.7300.6970.0000.0000.000
기타 대출(억원)1.0000.0000.8140.8390.5530.7301.0000.8770.0000.0000.000
당기순이익(억원)1.0000.0000.7610.7750.4830.6970.8771.0000.5340.4030.411
자기자본비율(BIS비율_퍼센트)1.0000.1760.0000.0000.0000.0000.0000.5341.0000.2340.303
연체율(퍼센트)1.0000.5820.0000.0000.0000.0000.0000.4030.2341.0000.849
고정이하여신비율(퍼센트)1.0000.6330.0000.0000.0000.0000.0000.4110.3030.8491.000
2024-03-15T03:41:21.998836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총자산(억원)기업 대출금(억원)가계 담보 대출금(억원)가계 신용대출(억원)기타 대출(억원)당기순이익(억원)자기자본비율(BIS비율_퍼센트)연체율(퍼센트)고정이하여신비율(퍼센트)지역
총자산(억원)1.0000.9730.6480.7780.731-0.096-0.256-0.108-0.1100.000
기업 대출금(억원)0.9731.0000.6070.7140.705-0.089-0.252-0.066-0.0950.000
가계 담보 대출금(억원)0.6480.6071.0000.5260.3800.024-0.211-0.115-0.2620.000
가계 신용대출(억원)0.7780.7140.5261.0000.523-0.262-0.333-0.107-0.0120.000
기타 대출(억원)0.7310.7050.3800.5231.000-0.108-0.184-0.012-0.0760.000
당기순이익(억원)-0.096-0.0890.024-0.262-0.1081.0000.452-0.231-0.2740.000
자기자본비율(BIS비율_퍼센트)-0.256-0.252-0.211-0.333-0.1840.4521.000-0.041-0.1920.055
연체율(퍼센트)-0.108-0.066-0.115-0.107-0.012-0.231-0.0411.0000.7190.296
고정이하여신비율(퍼센트)-0.110-0.095-0.262-0.012-0.076-0.274-0.1920.7191.0000.321
지역0.0000.0000.0000.0000.0000.0000.0550.2960.3211.000

Missing values

2024-03-15T03:41:10.858320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T03:41:11.478119image/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

저축은행명지역총자산(억원)기업 대출금(억원)가계 담보 대출금(억원)가계 신용대출(억원)기타 대출(억원)당기순이익(억원)자기자본비율(BIS비율_퍼센트)연체율(퍼센트)고정이하여신비율(퍼센트)
0SBI서울161468623214552621042962314.54.85.9
1OK서울15093160234388451653588370411.77.37.1
2한국투자경기9354447925534613565305611415.74.75.0
3웰컴서울6426024737616014438506035814.75.77.5
4애큐온서울5937928849160159327091-37511.34.56.0
5페퍼경기5777321853225619171365-67712.18.210.1
6다올서울47956158441040168441771-11112.25.64.9
7상상인경기319932108722341342728-48011.312.713.3
8신한서울31731717313650788115913316.63.63.9
9모아인천3127613964712508616306816.06.98.4
저축은행명지역총자산(억원)기업 대출금(억원)가계 담보 대출금(억원)가계 신용대출(억원)기타 대출(억원)당기순이익(억원)자기자본비율(BIS비율_퍼센트)연체율(퍼센트)고정이하여신비율(퍼센트)
69대백대구2284110435193256314.27.28.9
70국제부산2229126017001321028.06.33.6
71평택경기17467194411831432.86.56.9
72스타전북163676818715461938.55.35.8
73라온경북15659311164103-3811.812.913.8
74센트럴광주153670420300531.11.52.2
75에스앤티경남14679257300141.423.920.5
76오성경북12915731633143-721.68.25.0
77대아경북9001310-139.31.323.1
78대원경북520000-711.10.08.9