Overview

Dataset statistics

Number of variables15
Number of observations92
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.2 KiB
Average record size in memory135.4 B

Variable types

DateTime1
Numeric14

Dataset

Description주택담보노후연금보증 현황에 대한 데이터로, 금융기관(국민, 기업, 농협 등) 별 보증상세현황에 대한 자세한 내용을 포함하고 있습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/3045814/fileData.do

Alerts

합계 is highly overall correlated with 국민은행 and 11 other fieldsHigh correlation
국민은행 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
기업은행 is highly overall correlated with 합계 and 9 other fieldsHigh correlation
농협은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
신한은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
우리은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
하나은행 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
광주은행 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
대구은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
부산은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
전북은행 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
경남은행 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
외환은행 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
기준년 has unique valuesUnique
국민은행 has unique valuesUnique
광주은행 has 23 (25.0%) zerosZeros
대구은행 has 17 (18.5%) zerosZeros
부산은행 has 23 (25.0%) zerosZeros
전북은행 has 36 (39.1%) zerosZeros
경남은행 has 48 (52.2%) zerosZeros
외환은행 has 61 (66.3%) zerosZeros
기타 has 86 (93.5%) zerosZeros

Reproduction

Analysis started2023-12-12 23:48:37.817807
Analysis finished2023-12-12 23:48:53.172192
Duration15.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년
Date

UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size868.0 B
Minimum2008-01-01 00:00:00
Maximum2015-08-01 00:00:00
2023-12-13T08:48:53.237297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:53.365854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3575.8913
Minimum261
Maximum10779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:53.516935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum261
5-th percentile604.85
Q11893.75
median3549
Q35040.75
95-th percentile6478.55
Maximum10779
Range10518
Interquartile range (IQR)3147

Descriptive statistics

Standard deviation2116.6952
Coefficient of variation (CV)0.59193499
Kurtosis0.43486163
Mean3575.8913
Median Absolute Deviation (MAD)1572
Skewness0.54655916
Sum328982
Variance4480398.5
MonotonicityNot monotonic
2023-12-13T08:48:53.656788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002 2
 
2.2%
401 1
 
1.1%
6067 1
 
1.1%
3864 1
 
1.1%
5924 1
 
1.1%
5638 1
 
1.1%
3598 1
 
1.1%
3082 1
 
1.1%
3188 1
 
1.1%
4225 1
 
1.1%
Other values (81) 81
88.0%
ValueCountFrequency (%)
261 1
1.1%
401 1
1.1%
476 1
1.1%
568 1
1.1%
590 1
1.1%
617 1
1.1%
685 1
1.1%
689 1
1.1%
841 1
1.1%
860 1
1.1%
ValueCountFrequency (%)
10779 1
1.1%
8997 1
1.1%
8126 1
1.1%
7591 1
1.1%
6907 1
1.1%
6128 1
1.1%
6119 1
1.1%
6067 1
1.1%
6014 1
1.1%
5924 1
1.1%

국민은행
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct92
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1381
Minimum173
Maximum4594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:53.786910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173
5-th percentile293.5
Q1670
median1375.5
Q31858.75
95-th percentile2526.7
Maximum4594
Range4421
Interquartile range (IQR)1188.75

Descriptive statistics

Standard deviation790.33696
Coefficient of variation (CV)0.57229323
Kurtosis2.1657989
Mean1381
Median Absolute Deviation (MAD)572.5
Skewness0.94050273
Sum127052
Variance624632.51
MonotonicityNot monotonic
2023-12-13T08:48:53.924315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201 1
 
1.1%
2451 1
 
1.1%
1251 1
 
1.1%
2190 1
 
1.1%
2103 1
 
1.1%
1273 1
 
1.1%
1237 1
 
1.1%
1228 1
 
1.1%
1408 1
 
1.1%
3041 1
 
1.1%
Other values (82) 82
89.1%
ValueCountFrequency (%)
173 1
1.1%
190 1
1.1%
201 1
1.1%
275 1
1.1%
288 1
1.1%
298 1
1.1%
349 1
1.1%
404 1
1.1%
410 1
1.1%
429 1
1.1%
ValueCountFrequency (%)
4594 1
1.1%
3636 1
1.1%
3041 1
1.1%
2861 1
1.1%
2541 1
1.1%
2515 1
1.1%
2451 1
1.1%
2395 1
1.1%
2253 1
1.1%
2190 1
1.1%

기업은행
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.32609
Minimum7
Maximum388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:54.059836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile27.85
Q171.5
median140.5
Q3213.5
95-th percentile278.35
Maximum388
Range381
Interquartile range (IQR)142

Descriptive statistics

Standard deviation82.527
Coefficient of variation (CV)0.56787464
Kurtosis-0.47275257
Mean145.32609
Median Absolute Deviation (MAD)70.5
Skewness0.30427332
Sum13370
Variance6810.7057
MonotonicityNot monotonic
2023-12-13T08:48:54.190698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 2
 
2.2%
176 2
 
2.2%
72 2
 
2.2%
36 2
 
2.2%
261 2
 
2.2%
125 2
 
2.2%
7 2
 
2.2%
47 2
 
2.2%
267 2
 
2.2%
45 2
 
2.2%
Other values (71) 72
78.3%
ValueCountFrequency (%)
7 2
2.2%
11 1
1.1%
23 1
1.1%
24 1
1.1%
31 1
1.1%
36 2
2.2%
37 1
1.1%
38 1
1.1%
42 1
1.1%
45 2
2.2%
ValueCountFrequency (%)
388 1
1.1%
295 1
1.1%
292 1
1.1%
284 1
1.1%
280 1
1.1%
277 1
1.1%
267 2
2.2%
265 1
1.1%
261 2
2.2%
249 1
1.1%

농협은행
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean399.08696
Minimum13
Maximum945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:54.326246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile42.25
Q1155.75
median389.5
Q3579.25
95-th percentile854.45
Maximum945
Range932
Interquartile range (IQR)423.5

Descriptive statistics

Standard deviation264.56698
Coefficient of variation (CV)0.66293065
Kurtosis-1.0243832
Mean399.08696
Median Absolute Deviation (MAD)217.5
Skewness0.28412553
Sum36716
Variance69995.685
MonotonicityNot monotonic
2023-12-13T08:48:54.446847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755 2
 
2.2%
399 2
 
2.2%
528 2
 
2.2%
13 1
 
1.1%
558 1
 
1.1%
590 1
 
1.1%
342 1
 
1.1%
419 1
 
1.1%
577 1
 
1.1%
897 1
 
1.1%
Other values (79) 79
85.9%
ValueCountFrequency (%)
13 1
1.1%
14 1
1.1%
30 1
1.1%
31 1
1.1%
34 1
1.1%
49 1
1.1%
50 1
1.1%
54 1
1.1%
62 1
1.1%
67 1
1.1%
ValueCountFrequency (%)
945 1
1.1%
908 1
1.1%
897 1
1.1%
888 1
1.1%
866 1
1.1%
845 1
1.1%
840 1
1.1%
808 1
1.1%
761 1
1.1%
760 1
1.1%

신한은행
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.18478
Minimum6
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:54.554087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile46.95
Q1286.75
median590.5
Q3871.75
95-th percentile1267.8
Maximum2322
Range2316
Interquartile range (IQR)585

Descriptive statistics

Standard deviation417.72124
Coefficient of variation (CV)0.67030076
Kurtosis1.8047202
Mean623.18478
Median Absolute Deviation (MAD)295.5
Skewness0.89963625
Sum57333
Variance174491.03
MonotonicityNot monotonic
2023-12-13T08:48:54.678472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 2
 
2.2%
209 2
 
2.2%
1221 2
 
2.2%
257 2
 
2.2%
9 1
 
1.1%
1558 1
 
1.1%
883 1
 
1.1%
594 1
 
1.1%
1042 1
 
1.1%
643 1
 
1.1%
Other values (78) 78
84.8%
ValueCountFrequency (%)
6 1
1.1%
9 1
1.1%
29 2
2.2%
42 1
1.1%
51 1
1.1%
52 1
1.1%
89 1
1.1%
115 1
1.1%
126 1
1.1%
138 1
1.1%
ValueCountFrequency (%)
2322 1
1.1%
1558 1
1.1%
1548 1
1.1%
1371 1
1.1%
1325 1
1.1%
1221 2
2.2%
1183 1
1.1%
1179 1
1.1%
1075 1
1.1%
1060 1
1.1%

우리은행
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.48913
Minimum27
Maximum1443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:54.804814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile77.6
Q1235.75
median480.5
Q3679.75
95-th percentile1010.35
Maximum1443
Range1416
Interquartile range (IQR)444

Descriptive statistics

Standard deviation306.62066
Coefficient of variation (CV)0.62386052
Kurtosis0.40596678
Mean491.48913
Median Absolute Deviation (MAD)223
Skewness0.63605739
Sum45217
Variance94016.231
MonotonicityNot monotonic
2023-12-13T08:48:54.922998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
439 2
 
2.2%
85 2
 
2.2%
349 2
 
2.2%
663 2
 
2.2%
180 2
 
2.2%
1443 1
 
1.1%
780 1
 
1.1%
566 1
 
1.1%
656 1
 
1.1%
481 1
 
1.1%
Other values (77) 77
83.7%
ValueCountFrequency (%)
27 1
1.1%
39 1
1.1%
61 1
1.1%
64 1
1.1%
71 1
1.1%
83 1
1.1%
85 2
2.2%
90 1
1.1%
99 1
1.1%
106 1
1.1%
ValueCountFrequency (%)
1443 1
1.1%
1347 1
1.1%
1236 1
1.1%
1111 1
1.1%
1034 1
1.1%
991 1
1.1%
930 1
1.1%
888 1
1.1%
868 1
1.1%
846 1
1.1%

하나은행
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.52174
Minimum7
Maximum979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:55.042459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile27.2
Q1168.75
median332.5
Q3436.5
95-th percentile699.65
Maximum979
Range972
Interquartile range (IQR)267.75

Descriptive statistics

Standard deviation215.17228
Coefficient of variation (CV)0.65898301
Kurtosis0.66795994
Mean326.52174
Median Absolute Deviation (MAD)144
Skewness0.7624843
Sum30040
Variance46299.109
MonotonicityNot monotonic
2023-12-13T08:48:55.480658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
433 3
 
3.3%
333 2
 
2.2%
360 2
 
2.2%
11 2
 
2.2%
355 2
 
2.2%
959 1
 
1.1%
709 1
 
1.1%
212 1
 
1.1%
255 1
 
1.1%
350 1
 
1.1%
Other values (76) 76
82.6%
ValueCountFrequency (%)
7 1
1.1%
11 2
2.2%
20 1
1.1%
25 1
1.1%
29 1
1.1%
31 1
1.1%
32 1
1.1%
48 1
1.1%
58 1
1.1%
78 1
1.1%
ValueCountFrequency (%)
979 1
1.1%
959 1
1.1%
913 1
1.1%
717 1
1.1%
709 1
1.1%
692 1
1.1%
650 1
1.1%
644 1
1.1%
604 1
1.1%
586 1
1.1%

광주은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.815217
Minimum-5
Maximum49
Zeros23
Zeros (%)25.0%
Negative1
Negative (%)1.1%
Memory size960.0 B
2023-12-13T08:48:55.597334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile0
Q10
median10.5
Q322.25
95-th percentile42.9
Maximum49
Range54
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation14.036788
Coefficient of variation (CV)1.0160381
Kurtosis-0.047549227
Mean13.815217
Median Absolute Deviation (MAD)10.5
Skewness0.93837929
Sum1271
Variance197.03141
MonotonicityNot monotonic
2023-12-13T08:48:55.724098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 23
25.0%
5 5
 
5.4%
7 4
 
4.3%
14 4
 
4.3%
12 4
 
4.3%
6 4
 
4.3%
11 3
 
3.3%
24 3
 
3.3%
18 3
 
3.3%
8 3
 
3.3%
Other values (27) 36
39.1%
ValueCountFrequency (%)
-5 1
 
1.1%
0 23
25.0%
2 1
 
1.1%
3 2
 
2.2%
4 1
 
1.1%
5 5
 
5.4%
6 4
 
4.3%
7 4
 
4.3%
8 3
 
3.3%
9 1
 
1.1%
ValueCountFrequency (%)
49 1
1.1%
48 2
2.2%
47 1
1.1%
44 1
1.1%
42 1
1.1%
41 1
1.1%
37 1
1.1%
36 2
2.2%
34 1
1.1%
33 1
1.1%

대구은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.48913
Minimum0
Maximum220
Zeros17
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:55.839765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median53
Q3100.75
95-th percentile193
Maximum220
Range220
Interquartile range (IQR)86.75

Descriptive statistics

Standard deviation63.461071
Coefficient of variation (CV)0.92658603
Kurtosis-0.46966794
Mean68.48913
Median Absolute Deviation (MAD)43
Skewness0.79357048
Sum6301
Variance4027.3076
MonotonicityNot monotonic
2023-12-13T08:48:55.983757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
18.5%
14 3
 
3.3%
193 2
 
2.2%
79 2
 
2.2%
75 2
 
2.2%
143 2
 
2.2%
39 2
 
2.2%
66 2
 
2.2%
57 2
 
2.2%
148 2
 
2.2%
Other values (50) 56
60.9%
ValueCountFrequency (%)
0 17
18.5%
6 2
 
2.2%
7 1
 
1.1%
13 1
 
1.1%
14 3
 
3.3%
17 1
 
1.1%
19 1
 
1.1%
20 1
 
1.1%
22 1
 
1.1%
26 2
 
2.2%
ValueCountFrequency (%)
220 1
1.1%
215 1
1.1%
202 1
1.1%
197 1
1.1%
193 2
2.2%
192 1
1.1%
183 1
1.1%
182 1
1.1%
169 1
1.1%
159 1
1.1%

부산은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.695652
Minimum0
Maximum259
Zeros23
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:56.101296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median46
Q394.5
95-th percentile163.6
Maximum259
Range259
Interquartile range (IQR)90.75

Descriptive statistics

Standard deviation56.74693
Coefficient of variation (CV)0.95060407
Kurtosis0.61465052
Mean59.695652
Median Absolute Deviation (MAD)46
Skewness0.92164762
Sum5492
Variance3220.214
MonotonicityNot monotonic
2023-12-13T08:48:56.224749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
25.0%
46 3
 
3.3%
105 2
 
2.2%
91 2
 
2.2%
142 2
 
2.2%
87 2
 
2.2%
18 2
 
2.2%
90 2
 
2.2%
109 2
 
2.2%
17 2
 
2.2%
Other values (47) 50
54.3%
ValueCountFrequency (%)
0 23
25.0%
5 1
 
1.1%
10 2
 
2.2%
14 1
 
1.1%
17 2
 
2.2%
18 2
 
2.2%
21 1
 
1.1%
22 1
 
1.1%
25 1
 
1.1%
30 1
 
1.1%
ValueCountFrequency (%)
259 1
1.1%
203 1
1.1%
176 1
1.1%
175 1
1.1%
168 1
1.1%
160 1
1.1%
152 1
1.1%
151 1
1.1%
142 2
2.2%
133 1
1.1%

전북은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.23913
Minimum0
Maximum65
Zeros36
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:56.364927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q321
95-th percentile42.45
Maximum65
Range65
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.639039
Coefficient of variation (CV)1.1812739
Kurtosis1.0610069
Mean13.23913
Median Absolute Deviation (MAD)8.5
Skewness1.234059
Sum1218
Variance244.57955
MonotonicityNot monotonic
2023-12-13T08:48:56.474977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 36
39.1%
3 5
 
5.4%
28 4
 
4.3%
18 4
 
4.3%
21 3
 
3.3%
16 3
 
3.3%
42 2
 
2.2%
32 2
 
2.2%
19 2
 
2.2%
14 2
 
2.2%
Other values (24) 29
31.5%
ValueCountFrequency (%)
0 36
39.1%
3 5
 
5.4%
4 1
 
1.1%
5 2
 
2.2%
7 1
 
1.1%
8 1
 
1.1%
9 1
 
1.1%
10 2
 
2.2%
11 2
 
2.2%
13 2
 
2.2%
ValueCountFrequency (%)
65 1
1.1%
59 1
1.1%
55 1
1.1%
49 1
1.1%
43 1
1.1%
42 2
2.2%
38 1
1.1%
36 1
1.1%
35 1
1.1%
33 1
1.1%

경남은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.369565
Minimum0
Maximum105
Zeros48
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:56.589182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321.25
95-th percentile63
Maximum105
Range105
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation21.19751
Coefficient of variation (CV)1.5855048
Kurtosis4.7269189
Mean13.369565
Median Absolute Deviation (MAD)0
Skewness2.1103888
Sum1230
Variance449.33445
MonotonicityNot monotonic
2023-12-13T08:48:56.699192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 48
52.2%
5 3
 
3.3%
32 3
 
3.3%
11 3
 
3.3%
28 2
 
2.2%
14 2
 
2.2%
37 2
 
2.2%
63 2
 
2.2%
22 2
 
2.2%
9 1
 
1.1%
Other values (24) 24
26.1%
ValueCountFrequency (%)
0 48
52.2%
2 1
 
1.1%
4 1
 
1.1%
5 3
 
3.3%
6 1
 
1.1%
8 1
 
1.1%
9 1
 
1.1%
10 1
 
1.1%
11 3
 
3.3%
12 1
 
1.1%
ValueCountFrequency (%)
105 1
1.1%
81 1
1.1%
75 1
1.1%
68 1
1.1%
63 2
2.2%
59 1
1.1%
37 2
2.2%
36 1
1.1%
34 1
1.1%
33 1
1.1%

외환은행
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.119565
Minimum0
Maximum207
Zeros61
Zeros (%)66.3%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:56.808147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q383.25
95-th percentile168.45
Maximum207
Range207
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation61.954049
Coefficient of variation (CV)1.5442353
Kurtosis-0.14780634
Mean40.119565
Median Absolute Deviation (MAD)0
Skewness1.1747083
Sum3691
Variance3838.3042
MonotonicityNot monotonic
2023-12-13T08:48:56.910115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 61
66.3%
169 2
 
2.2%
168 2
 
2.2%
124 1
 
1.1%
207 1
 
1.1%
177 1
 
1.1%
57 1
 
1.1%
87 1
 
1.1%
70 1
 
1.1%
170 1
 
1.1%
Other values (20) 20
 
21.7%
ValueCountFrequency (%)
0 61
66.3%
27 1
 
1.1%
57 1
 
1.1%
62 1
 
1.1%
65 1
 
1.1%
70 1
 
1.1%
72 1
 
1.1%
80 1
 
1.1%
83 1
 
1.1%
84 1
 
1.1%
ValueCountFrequency (%)
207 1
1.1%
177 1
1.1%
170 1
1.1%
169 2
2.2%
168 2
2.2%
156 1
1.1%
152 1
1.1%
148 1
1.1%
144 1
1.1%
141 1
1.1%

기타
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68478261
Minimum0
Maximum20
Zeros86
Zeros (%)93.5%
Negative0
Negative (%)0.0%
Memory size960.0 B
2023-12-13T08:48:57.006231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.8
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1446076
Coefficient of variation (CV)4.5921254
Kurtosis26.59986
Mean0.68478261
Median Absolute Deviation (MAD)0
Skewness5.11195
Sum63
Variance9.8885571
MonotonicityNot monotonic
2023-12-13T08:48:57.100765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 86
93.5%
5 1
 
1.1%
7 1
 
1.1%
20 1
 
1.1%
1 1
 
1.1%
18 1
 
1.1%
12 1
 
1.1%
ValueCountFrequency (%)
0 86
93.5%
1 1
 
1.1%
5 1
 
1.1%
7 1
 
1.1%
12 1
 
1.1%
18 1
 
1.1%
20 1
 
1.1%
ValueCountFrequency (%)
20 1
 
1.1%
18 1
 
1.1%
12 1
 
1.1%
7 1
 
1.1%
5 1
 
1.1%
1 1
 
1.1%
0 86
93.5%

Interactions

2023-12-13T08:48:51.649973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.219320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.210875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.186729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.435372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.336489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.293019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.375432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.395089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.690260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.718150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.667107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.570684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.473801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.724165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.288251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.292610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.261067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.491521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.409290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.369306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.449580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.459154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.773473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.791693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.727775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.639512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.531522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.794257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.354657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.351037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.332759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.549888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.468740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.451057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.512295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.524430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.849881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.868211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.786712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.703467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.586941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.883722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.427816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.419771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.414503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.615576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.543096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.540954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.586153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.602544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.930074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.950884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.858166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.775657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.653884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.975328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.498426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.482884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.492679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.668282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.602530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.623415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.650817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.669241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.999113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.013027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.916895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.836931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.707664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.047093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.575113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.540392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.559259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.728115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.664468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.690200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.712043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.731842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.075402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.074481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.975845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.897446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.763802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.141984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.658633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.608856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.637432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.793360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.745804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.762317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.787984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.821976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.151108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.153174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.042880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.964686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.827046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.218973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.736135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.690114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.708873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.856240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.823962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.855842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.860533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.900091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.218383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.214410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.105298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.025105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.885522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.298364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.807529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.767023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.791906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.917750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.889290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.934819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.930989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.983678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.299256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.277549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.177203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.085773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.209514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.381721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.869545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.828895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.860966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.974697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.949888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.009953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.994565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.309001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.365625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.332746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.235152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.148467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.276793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.465516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.932047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.891797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.934553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.037031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.024976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.083544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.071194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.382841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.437402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.395665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.295525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.209866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.339786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.602898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:38.999652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.958913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.007440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.105965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.089807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.155862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.160089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.458249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.513477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.476652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.362182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.272910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.415114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.693266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.066234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.020722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.304526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.168919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.154775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.223838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.239685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.535264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.581805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.539392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.434104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.332922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.498190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:52.785349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:39.129824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:40.095691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:41.363822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:42.242889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:43.214863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:44.288650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:45.314472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:46.610528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:47.645803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:48.600461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:49.495969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:50.389040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:48:51.572611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:48:57.188025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년합계국민은행기업은행농협은행신한은행우리은행하나은행광주은행대구은행부산은행전북은행경남은행외환은행기타
기준년1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0000.9300.5780.8610.9120.9540.8060.7420.8010.7710.7760.7570.3170.000
국민은행1.0000.9301.0000.6480.6850.8960.8420.8980.5180.6240.8800.7060.7200.0000.000
기업은행1.0000.5780.6481.0000.5690.5700.5820.6830.4300.5400.5880.5220.3800.0000.000
농협은행1.0000.8610.6850.5691.0000.6690.8320.6470.6590.8220.5380.7050.5310.6650.478
신한은행1.0000.9120.8960.5700.6691.0000.8070.7160.6160.6250.7590.6470.8540.3310.000
우리은행1.0000.9540.8420.5820.8320.8071.0000.7940.6610.7800.7090.7790.6630.5010.447
하나은행1.0000.8060.8980.6830.6470.7160.7941.0000.5110.5840.7460.5520.5990.4520.491
광주은행1.0000.7420.5180.4300.6590.6160.6610.5111.0000.7580.4200.6300.4740.6320.508
대구은행1.0000.8010.6240.5400.8220.6250.7800.5840.7581.0000.6360.8260.4810.7300.686
부산은행1.0000.7710.8800.5880.5380.7590.7090.7460.4200.6361.0000.8000.7320.6380.725
전북은행1.0000.7760.7060.5220.7050.6470.7790.5520.6300.8260.8001.0000.5290.6730.820
경남은행1.0000.7570.7200.3800.5310.8540.6630.5990.4740.4810.7320.5291.0000.4960.221
외환은행1.0000.3170.0000.0000.6650.3310.5010.4520.6320.7300.6380.6730.4961.0000.915
기타1.0000.0000.0000.0000.4780.0000.4470.4910.5080.6860.7250.8200.2210.9151.000
2023-12-13T08:48:57.325314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계국민은행기업은행농협은행신한은행우리은행하나은행광주은행대구은행부산은행전북은행경남은행외환은행기타
합계1.0000.9680.7640.9250.9660.9480.9120.6210.8270.8190.7980.7520.5630.274
국민은행0.9681.0000.7430.8500.9350.8770.8770.5870.7500.7630.7630.6810.4340.198
기업은행0.7640.7431.0000.6890.7350.7120.6450.3530.6000.6220.5290.5500.3980.195
농협은행0.9250.8500.6891.0000.8640.8930.8270.6600.8550.8080.7910.7550.7040.350
신한은행0.9660.9350.7350.8641.0000.9190.8690.6110.7830.7690.7350.7190.5060.226
우리은행0.9480.8770.7120.8930.9191.0000.8760.5920.8120.7780.7490.7570.5770.285
하나은행0.9120.8770.6450.8270.8690.8761.0000.5880.7410.7160.7330.6470.4410.241
광주은행0.6210.5870.3530.6600.6110.5920.5881.0000.6770.5560.5880.4450.4750.300
대구은행0.8270.7500.6000.8550.7830.8120.7410.6771.0000.7930.7270.6400.6730.331
부산은행0.8190.7630.6220.8080.7690.7780.7160.5560.7931.0000.7650.6660.6150.356
전북은행0.7980.7630.5290.7910.7350.7490.7330.5880.7270.7651.0000.6130.5240.198
경남은행0.7520.6810.5500.7550.7190.7570.6470.4450.6400.6660.6131.0000.5340.239
외환은행0.5630.4340.3980.7040.5060.5770.4410.4750.6730.6150.5240.5341.0000.407
기타0.2740.1980.1950.3500.2260.2850.2410.3000.3310.3560.1980.2390.4071.000

Missing values

2023-12-13T08:48:52.925643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:48:53.102469image/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

기준년합계국민은행기업은행농협은행신한은행우리은행하나은행광주은행대구은행부산은행전북은행경남은행외환은행기타
02008-01-014012013713983580000000
12008-02-012611901114627110000000
22008-03-0147627531302985250000000
32008-04-0156828875315111670000000
42008-05-018604463649126611420000000
52008-06-019845825093115115290000000
62008-07-011002579246914999820000000
72008-08-015901733634209106320000000
82008-09-016894107505290810000000
92008-10-0110515427054194180110000000
기준년합계국민은행기업은행농협은행신한은행우리은행하나은행광주은행대구은행부산은행전북은행경남은행외환은행기타
822014-11-01463415661305078387104336193912841280
832014-12-016119209323776010328885224812020349201417
842015-01-01550720131595831043811447421598815361110
852015-02-0158182032295699102484647701971099111190
862015-03-01585020112188889128253874918217519141700
872015-04-01555318531467558337876923615815132217020
882015-05-014987151028494582769728917148110863871
892015-06-0156761935959088199915663298902859570
902015-07-0153701700235714105071539048125168102217718
912015-08-01550418491697558107606041419397191720712