Overview

Dataset statistics

Number of variables10
Number of observations52
Missing cells85
Missing cells (%)16.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory90.5 B

Variable types

DateTime1
Categorical1
Numeric8

Dataset

Description(부보금융회사 종합정보)예금보험에 가입되어 있는 개별 부보금융회사가 정기적으로 공사에 납부하는 "예금보험료"와 공적자금상환대책에 따라 예보채권상환기금 부담 부채의 원활한 상환을 위하여 부보금융회사에서 기존 보험료와는 별도로 공사에 납부하는 "특별기여금"을 수납한 정보- 단위 : 억원- 연단위 누적
Author예금보험공사
URLhttps://www.data.go.kr/data/3059359/fileData.do

Alerts

은행 is highly overall correlated with 금융투자 and 5 other fieldsHigh correlation
금융투자 is highly overall correlated with 은행 and 6 other fieldsHigh correlation
생보 is highly overall correlated with 은행 and 5 other fieldsHigh correlation
손보 is highly overall correlated with 은행 and 6 other fieldsHigh correlation
종금 is highly overall correlated with 금융투자 and 2 other fieldsHigh correlation
저축은행 is highly overall correlated with 은행 and 4 other fieldsHigh correlation
특별계정 is highly overall correlated with 은행 and 4 other fieldsHigh correlation
신협 is highly overall correlated with 생보 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 은행 and 5 other fieldsHigh correlation
금융투자 has 14 (26.9%) missing valuesMissing
종금 has 5 (9.6%) missing valuesMissing
저축은행 has 1 (1.9%) missing valuesMissing
특별계정 has 27 (51.9%) missing valuesMissing
신협 has 38 (73.1%) missing valuesMissing
은행 has unique valuesUnique

Reproduction

Analysis started2024-03-14 16:15:12.740022
Analysis finished2024-03-14 16:15:30.360201
Duration17.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일
Date

Distinct26
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size544.0 B
Minimum2011-06-30 00:00:00
Maximum2023-12-31 00:00:00
2024-03-15T01:15:30.561041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:31.046666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size544.0 B
예금보험료
26 
특별기여금
26 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
예금보험료 26
50.0%
특별기여금 26
50.0%

Length

2024-03-15T01:15:31.405535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:15:31.571407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예금보험료 26
50.0%
특별기여금 26
50.0%

은행
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6451.0385
Minimum1843
Maximum18037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:31.957220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1843
5-th percentile2118.75
Q13710.25
median5248
Q38400.25
95-th percentile14811.45
Maximum18037
Range16194
Interquartile range (IQR)4690

Descriptive statistics

Standard deviation3953.9771
Coefficient of variation (CV)0.61292103
Kurtosis1.1842674
Mean6451.0385
Median Absolute Deviation (MAD)2119.5
Skewness1.2597309
Sum335454
Variance15633935
MonotonicityNot monotonic
2024-03-15T01:15:32.405558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3040 1
 
1.9%
7789 1
 
1.9%
8518 1
 
1.9%
4439 1
 
1.9%
8973 1
 
1.9%
4708 1
 
1.9%
9590 1
 
1.9%
4938 1
 
1.9%
9786 1
 
1.9%
5246 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1843 1
1.9%
1953 1
1.9%
2072 1
1.9%
2157 1
1.9%
2284 1
1.9%
2464 1
1.9%
2631 1
1.9%
2692 1
1.9%
2868 1
1.9%
3040 1
1.9%
ValueCountFrequency (%)
18037 1
1.9%
17084 1
1.9%
15681 1
1.9%
14100 1
1.9%
12566 1
1.9%
12011 1
1.9%
11343 1
1.9%
10664 1
1.9%
9786 1
1.9%
9590 1
1.9%

금융투자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)81.6%
Missing14
Missing (%)26.9%
Infinite0
Infinite (%)0.0%
Mean296.28947
Minimum2
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:32.781145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.95
Q1185.75
median272
Q3401.75
95-th percentile696.35
Maximum755
Range753
Interquartile range (IQR)216

Descriptive statistics

Standard deviation201.49896
Coefficient of variation (CV)0.68007467
Kurtosis0.22636995
Mean296.28947
Median Absolute Deviation (MAD)92
Skewness0.74506628
Sum11259
Variance40601.833
MonotonicityNot monotonic
2024-03-15T01:15:33.073056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
227 2
 
3.8%
686 2
 
3.8%
755 2
 
3.8%
305 2
 
3.8%
272 2
 
3.8%
2 2
 
3.8%
283 2
 
3.8%
427 1
 
1.9%
428 1
 
1.9%
9 1
 
1.9%
Other values (21) 21
40.4%
(Missing) 14
26.9%
ValueCountFrequency (%)
2 2
3.8%
9 1
1.9%
26 1
1.9%
39 1
1.9%
41 1
1.9%
165 1
1.9%
166 1
1.9%
179 1
1.9%
181 1
1.9%
200 1
1.9%
ValueCountFrequency (%)
755 2
3.8%
686 2
3.8%
542 1
1.9%
538 1
1.9%
460 1
1.9%
456 1
1.9%
428 1
1.9%
427 1
1.9%
326 1
1.9%
307 1
1.9%

생보
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.5577
Minimum-14
Maximum3645
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.8%
Memory size596.0 B
2024-03-15T01:15:33.400372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile90.3
Q1892.5
median2323
Q33067.25
95-th percentile3584
Maximum3645
Range3659
Interquartile range (IQR)2174.75

Descriptive statistics

Standard deviation1150.3622
Coefficient of variation (CV)0.57849072
Kurtosis-1.217378
Mean1988.5577
Median Absolute Deviation (MAD)974
Skewness-0.24655941
Sum103405
Variance1323333.1
MonotonicityNot monotonic
2024-03-15T01:15:33.747704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3228 2
 
3.8%
2109 2
 
3.8%
3584 2
 
3.8%
2386 2
 
3.8%
3384 2
 
3.8%
1280 2
 
3.8%
1857 2
 
3.8%
2349 2
 
3.8%
3133 2
 
3.8%
2835 2
 
3.8%
Other values (32) 32
61.5%
ValueCountFrequency (%)
-14 1
1.9%
-5 1
1.9%
21 1
1.9%
147 1
1.9%
221 1
1.9%
397 1
1.9%
417 1
1.9%
501 1
1.9%
811 1
1.9%
812 1
1.9%
ValueCountFrequency (%)
3645 1
1.9%
3617 1
1.9%
3584 2
3.8%
3497 1
1.9%
3436 1
1.9%
3384 2
3.8%
3228 2
3.8%
3133 2
3.8%
3068 1
1.9%
3067 1
1.9%

손보
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean873.65385
Minimum2
Maximum1565
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:34.090950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12.75
Q1672.25
median935.5
Q31194
95-th percentile1518.45
Maximum1565
Range1563
Interquartile range (IQR)521.75

Descriptive statistics

Standard deviation443.41172
Coefficient of variation (CV)0.50753708
Kurtosis-0.46400774
Mean873.65385
Median Absolute Deviation (MAD)258.5
Skewness-0.51813682
Sum45430
Variance196613.96
MonotonicityNot monotonic
2024-03-15T01:15:34.330720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1194 2
 
3.8%
1093 2
 
3.8%
1565 2
 
3.8%
487 2
 
3.8%
790 2
 
3.8%
1449 2
 
3.8%
445 2
 
3.8%
1361 2
 
3.8%
806 2
 
3.8%
883 2
 
3.8%
Other values (28) 32
61.5%
ValueCountFrequency (%)
2 1
1.9%
9 1
1.9%
10 1
1.9%
15 1
1.9%
38 1
1.9%
99 1
1.9%
145 1
1.9%
445 2
3.8%
487 2
3.8%
488 1
1.9%
ValueCountFrequency (%)
1565 2
3.8%
1519 1
1.9%
1518 1
1.9%
1449 2
3.8%
1361 2
3.8%
1281 1
1.9%
1280 1
1.9%
1197 2
3.8%
1194 2
3.8%
1159 1
1.9%

종금
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)29.8%
Missing5
Missing (%)9.6%
Infinite0
Infinite (%)0.0%
Mean12.553191
Minimum4
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:34.657800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.3
Q18
median11
Q315.5
95-th percentile25
Maximum30
Range26
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.3170181
Coefficient of variation (CV)0.50322009
Kurtosis1.0689817
Mean12.553191
Median Absolute Deviation (MAD)3
Skewness1.2328823
Sum590
Variance39.904718
MonotonicityNot monotonic
2024-03-15T01:15:34.863129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7 6
11.5%
8 5
9.6%
10 5
9.6%
18 4
7.7%
9 4
7.7%
11 4
7.7%
14 4
7.7%
12 4
7.7%
4 2
 
3.8%
25 2
 
3.8%
Other values (4) 7
13.5%
(Missing) 5
9.6%
ValueCountFrequency (%)
4 2
 
3.8%
6 1
 
1.9%
7 6
11.5%
8 5
9.6%
9 4
7.7%
10 5
9.6%
11 4
7.7%
12 4
7.7%
14 4
7.7%
17 2
 
3.8%
ValueCountFrequency (%)
30 2
 
3.8%
25 2
 
3.8%
22 2
 
3.8%
18 4
7.7%
17 2
 
3.8%
14 4
7.7%
12 4
7.7%
11 4
7.7%
10 5
9.6%
9 4
7.7%

저축은행
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)72.5%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean394.43137
Minimum-2
Maximum2332
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.8%
Memory size596.0 B
2024-03-15T01:15:35.355549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile2
Q1201
median286
Q3530
95-th percentile992
Maximum2332
Range2334
Interquartile range (IQR)329

Descriptive statistics

Standard deviation396.95784
Coefficient of variation (CV)1.0064054
Kurtosis10.834181
Mean394.43137
Median Absolute Deviation (MAD)147
Skewness2.724299
Sum20116
Variance157575.53
MonotonicityNot monotonic
2024-03-15T01:15:35.780035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
253 3
 
5.8%
273 2
 
3.8%
539 2
 
3.8%
212 2
 
3.8%
216 2
 
3.8%
348 2
 
3.8%
286 2
 
3.8%
403 2
 
3.8%
469 2
 
3.8%
2 2
 
3.8%
Other values (27) 30
57.7%
ValueCountFrequency (%)
-2 1
1.9%
-1 1
1.9%
2 2
3.8%
3 1
1.9%
4 1
1.9%
33 1
1.9%
64 1
1.9%
69 1
1.9%
139 1
1.9%
141 1
1.9%
ValueCountFrequency (%)
2332 1
1.9%
1345 1
1.9%
992 2
3.8%
787 1
1.9%
786 1
1.9%
718 1
1.9%
651 2
3.8%
585 2
3.8%
539 2
3.8%
521 1
1.9%

특별계정
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing27
Missing (%)51.9%
Infinite0
Infinite (%)0.0%
Mean6930.96
Minimum1742
Maximum13739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:36.179667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1742
5-th percentile1803.2
Q14137
median6823
Q39360
95-th percentile12231
Maximum13739
Range11997
Interquartile range (IQR)5223

Descriptive statistics

Standard deviation3443.4057
Coefficient of variation (CV)0.49681512
Kurtosis-0.82915408
Mean6930.96
Median Absolute Deviation (MAD)2686
Skewness0.10139561
Sum173274
Variance11857043
MonotonicityNot monotonic
2024-03-15T01:15:36.606185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1770 1
 
1.9%
13739 1
 
1.9%
10766 1
 
1.9%
12472 1
 
1.9%
9700 1
 
1.9%
11267 1
 
1.9%
8456 1
 
1.9%
10259 1
 
1.9%
7737 1
 
1.9%
9360 1
 
1.9%
Other values (15) 15
28.8%
(Missing) 27
51.9%
ValueCountFrequency (%)
1742 1
1.9%
1770 1
1.9%
1936 1
1.9%
2688 1
1.9%
2909 1
1.9%
3374 1
1.9%
4137 1
1.9%
4866 1
1.9%
5816 1
1.9%
6158 1
1.9%
ValueCountFrequency (%)
13739 1
1.9%
12472 1
1.9%
11267 1
1.9%
10766 1
1.9%
10259 1
1.9%
9700 1
1.9%
9360 1
1.9%
9102 1
1.9%
8456 1
1.9%
8242 1
1.9%

신협
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)50.0%
Missing38
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean264.28571
Minimum205
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size596.0 B
2024-03-15T01:15:36.971306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum205
5-th percentile205
Q1233
median263
Q3293
95-th percentile332
Maximum332
Range127
Interquartile range (IQR)60

Descriptive statistics

Standard deviation41.144078
Coefficient of variation (CV)0.1556803
Kurtosis-0.71089868
Mean264.28571
Median Absolute Deviation (MAD)35
Skewness0.23985089
Sum3700
Variance1692.8352
MonotonicityIncreasing
2024-03-15T01:15:37.163910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
205 2
 
3.8%
228 2
 
3.8%
248 2
 
3.8%
263 2
 
3.8%
275 2
 
3.8%
299 2
 
3.8%
332 2
 
3.8%
(Missing) 38
73.1%
ValueCountFrequency (%)
205 2
3.8%
228 2
3.8%
248 2
3.8%
263 2
3.8%
275 2
3.8%
299 2
3.8%
332 2
3.8%
ValueCountFrequency (%)
332 2
3.8%
299 2
3.8%
275 2
3.8%
263 2
3.8%
248 2
3.8%
228 2
3.8%
205 2
3.8%

Interactions

2024-03-15T01:15:26.790463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:13.300573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:15.421593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:17.297063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:19.367268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:21.269126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.964261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:25.165789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:27.029742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:13.586672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:15.692327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:17.565815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:19.644486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:21.529727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:23.221577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:25.408300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:27.477442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:13.854368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:15.986289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:17.841409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:19.889384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:21.841766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:23.687671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:25.652021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:27.850698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:14.115392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:16.153907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:18.100816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:20.093798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.026314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:23.937023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:25.893108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:28.103930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:14.404142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:16.327154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:18.374212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:20.304518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.184773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:24.194918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:26.216051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:28.353597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:14.657057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:16.560503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:18.625047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:20.533298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.323524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:24.435605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:26.352547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:28.671982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:14.905079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:16.805442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:18.863834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:20.773652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.528938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:24.664150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:26.513677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:28.905324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:15.154608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:17.052508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:19.113332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:21.023284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:22.712350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:24.910213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:15:26.661811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:15:37.412378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일구분은행금융투자생보손보종금저축은행특별계정신협
기준일1.0000.0000.0000.0000.0000.0000.0000.4801.0001.000
구분0.0001.0000.7880.7560.9570.4780.4960.549NaNNaN
은행0.0000.7881.0000.6080.4000.5190.3350.4500.8460.000
금융투자0.0000.7560.6081.0000.8880.9510.8510.6650.6590.917
생보0.0000.9570.4000.8881.0000.8640.7110.5560.3880.944
손보0.0000.4780.5190.9510.8641.0000.7590.6350.5690.958
종금0.0000.4960.3350.8510.7110.7591.0000.7520.0001.000
저축은행0.4800.5490.4500.6650.5560.6350.7521.0000.4520.000
특별계정1.000NaN0.8460.6590.3880.5690.0000.4521.000NaN
신협1.000NaN0.0000.9170.9440.9581.0000.000NaN1.000
2024-03-15T01:15:37.757385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
은행금융투자생보손보종금저축은행특별계정신협구분
은행1.0000.5020.7230.6540.3360.6670.7270.4960.580
금융투자0.5021.0000.6580.8540.7770.5260.6200.4350.528
생보0.7230.6581.0000.7820.3060.6350.3150.9320.754
손보0.6540.8540.7821.0000.6440.5600.9110.9340.441
종금0.3360.7770.3060.6441.0000.3720.665-0.4000.461
저축은행0.6670.5260.6350.5600.3721.0000.1400.0970.557
특별계정0.7270.6200.3150.9110.6650.1401.000NaN1.000
신협0.4960.4350.9320.934-0.4000.097NaN1.0001.000
구분0.5800.5280.7540.4410.4610.5571.0001.0001.000

Missing values

2024-03-15T01:15:29.268481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:15:29.821215image/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.
2024-03-15T01:15:30.170488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준일구분은행금융투자생보손보종금저축은행특별계정신협
02011-06-30예금보험료304028323657001833<NA><NA>
12011-12-31예금보험료479528323626991823321742<NA>
22012-06-30예금보험료18432-5281561770<NA>
32012-12-31예금보험료37482-1499813455816<NA>
42013-06-30예금보험료1953<NA>219<NA>691936<NA>
52013-12-31예금보험료3948<NA>22148882536672<NA>
62014-06-30예금보험료2072<NA>812445<NA>43374<NA>
72014-12-31예금보험료4197<NA>81144562566158<NA>
82015-06-30예금보험료2157<NA>1280708424137<NA>
92015-12-31예금보험료4369<NA>12807104647120<NA>
기준일구분은행금융투자생보손보종금저축은행특별계정신협
422019-06-30특별기여금61823263228128012539<NA><NA>
432019-12-31특별기여금125663073228128112539<NA><NA>
442020-06-30특별기여금67953053384136114585<NA><NA>
452020-12-31특별기여금141003053384136114585<NA><NA>
462021-06-30특별기여금76495423497144917651<NA><NA>
472021-12-31특별기여금156815383436144917651<NA><NA>
482022-06-30특별기여금83617553584151822787<NA><NA>
492022-12-31특별기여금170847553584151922786<NA><NA>
502023-06-30특별기여금89786863645156530992<NA><NA>
512023-12-31특별기여금180376863617156530992<NA><NA>