Overview

Dataset statistics

Number of variables13
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory120.2 B

Variable types

Numeric13

Dataset

Description연도별 공무원연금기금운용수익(공공금융, 주택 및 시설투자, 유가증권 등) 현황에 대한 데이터입니다. 1982년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054071/fileData.do

Alerts

구분 is highly overall correlated with 기금회계기금운용수익(계) and 7 other fieldsHigh correlation
기금회계기금운용수익(계) is highly overall correlated with 구분 and 9 other fieldsHigh correlation
공공금융 is highly overall correlated with 구분 and 5 other fieldsHigh correlation
주택및시설운영등 is highly overall correlated with 기금회계기금운용수익(계) and 4 other fieldsHigh correlation
투자유가증권 is highly overall correlated with 구분 and 8 other fieldsHigh correlation
연금수입 is highly overall correlated with 구분 and 5 other fieldsHigh correlation
지불준비금등 is highly overall correlated with 구분 and 9 other fieldsHigh correlation
기금회계(비용) is highly overall correlated with 구분 and 9 other fieldsHigh correlation
순이익 is highly overall correlated with 투자유가증권High correlation
연금회계(결산상잉여금) is highly overall correlated with 기금회계기금운용수익(계) and 5 other fieldsHigh correlation
연금회계(적립금) is highly overall correlated with 구분 and 7 other fieldsHigh correlation
기금총액 is highly overall correlated with 구분 and 6 other fieldsHigh correlation
구분 has unique valuesUnique
기금회계기금운용수익(계) has unique valuesUnique
주택및시설운영등 has unique valuesUnique
투자유가증권 has unique valuesUnique
지불준비금등 has unique valuesUnique
기금회계(비용) has unique valuesUnique
순이익 has unique valuesUnique
기금총액 has unique valuesUnique
공공금융 has 2 (4.9%) zerosZeros
연금수입 has 31 (75.6%) zerosZeros
연금회계(결산상잉여금) has 21 (51.2%) zerosZeros
연금회계(적립금) has 30 (73.2%) zerosZeros
기타포괄손익누계액 has 29 (70.7%) zerosZeros

Reproduction

Analysis started2023-12-12 06:50:34.718396
Analysis finished2023-12-12 06:50:53.755635
Duration19.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002
Minimum1982
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:53.847255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1982
5-th percentile1984
Q11992
median2002
Q32012
95-th percentile2020
Maximum2022
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.0059835907
Kurtosis-1.2
Mean2002
Median Absolute Deviation (MAD)10
Skewness0
Sum82082
Variance143.5
MonotonicityStrictly increasing
2023-12-12T15:50:54.054458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1982 1
 
2.4%
2013 1
 
2.4%
2005 1
 
2.4%
2006 1
 
2.4%
2007 1
 
2.4%
2008 1
 
2.4%
2009 1
 
2.4%
2010 1
 
2.4%
2011 1
 
2.4%
2012 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1982 1
2.4%
1983 1
2.4%
1984 1
2.4%
1985 1
2.4%
1986 1
2.4%
1987 1
2.4%
1988 1
2.4%
1989 1
2.4%
1990 1
2.4%
1991 1
2.4%
ValueCountFrequency (%)
2022 1
2.4%
2021 1
2.4%
2020 1
2.4%
2019 1
2.4%
2018 1
2.4%
2017 1
2.4%
2016 1
2.4%
2015 1
2.4%
2014 1
2.4%
2013 1
2.4%

기금회계기금운용수익(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4572855.7
Minimum116012
Maximum21835332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:54.238341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116012
5-th percentile175313
Q1531510
median745338
Q31219793
95-th percentile19097216
Maximum21835332
Range21719320
Interquartile range (IQR)688283

Descriptive statistics

Standard deviation7180954.8
Coefficient of variation (CV)1.5703436
Kurtosis0.13031182
Mean4572855.7
Median Absolute Deviation (MAD)243906
Skewness1.378515
Sum1.8748708 × 108
Variance5.1566112 × 1013
MonotonicityNot monotonic
2023-12-12T15:50:54.405465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
116012 1
 
2.4%
11935651 1
 
2.4%
929586 1
 
2.4%
960388 1
 
2.4%
890641 1
 
2.4%
593514 1
 
2.4%
852869 1
 
2.4%
978976 1
 
2.4%
732451 1
 
2.4%
989244 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
116012 1
2.4%
130900 1
2.4%
175313 1
2.4%
227801 1
2.4%
268804 1
2.4%
283624 1
2.4%
352109 1
2.4%
385166 1
2.4%
451308 1
2.4%
518887 1
2.4%
ValueCountFrequency (%)
21835332 1
2.4%
20166109 1
2.4%
19097216 1
2.4%
17721976 1
2.4%
17084724 1
2.4%
16529124 1
2.4%
15154251 1
2.4%
14944311 1
2.4%
13505843 1
2.4%
11935651 1
2.4%

공공금융
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45282.829
Minimum0
Maximum187884
Zeros2
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:54.569526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119
Q1274
median2096
Q390223
95-th percentile178018
Maximum187884
Range187884
Interquartile range (IQR)89949

Descriptive statistics

Standard deviation68368.151
Coefficient of variation (CV)1.509803
Kurtosis-0.43760525
Mean45282.829
Median Absolute Deviation (MAD)1977
Skewness1.1588447
Sum1856596
Variance4.674204 × 109
MonotonicityNot monotonic
2023-12-12T15:50:54.720858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 2
 
4.9%
151 2
 
4.9%
390 2
 
4.9%
147 2
 
4.9%
280 1
 
2.4%
263 1
 
2.4%
359 1
 
2.4%
344 1
 
2.4%
345 1
 
2.4%
119 1
 
2.4%
Other values (27) 27
65.9%
ValueCountFrequency (%)
0 2
4.9%
119 1
2.4%
146 1
2.4%
147 2
4.9%
151 2
4.9%
152 1
2.4%
263 1
2.4%
274 1
2.4%
280 1
2.4%
344 1
2.4%
ValueCountFrequency (%)
187884 1
2.4%
178798 1
2.4%
178018 1
2.4%
169639 1
2.4%
166247 1
2.4%
160825 1
2.4%
154897 1
2.4%
147650 1
2.4%
132630 1
2.4%
112287 1
2.4%

주택및시설운영등
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264551.93
Minimum5265
Maximum659339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:54.911473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5265
5-th percentile33298
Q1148465
median234109
Q3324984
95-th percentile614276
Maximum659339
Range654074
Interquartile range (IQR)176519

Descriptive statistics

Standard deviation171733.27
Coefficient of variation (CV)0.64914768
Kurtosis-0.0030392802
Mean264551.93
Median Absolute Deviation (MAD)90875
Skewness0.75759192
Sum10846629
Variance2.9492316 × 1010
MonotonicityNot monotonic
2023-12-12T15:50:55.096923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5265 1
 
2.4%
621365 1
 
2.4%
463272 1
 
2.4%
614276 1
 
2.4%
304834 1
 
2.4%
249339 1
 
2.4%
319811 1
 
2.4%
341754 1
 
2.4%
303393 1
 
2.4%
548490 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
5265 1
2.4%
5854 1
2.4%
33298 1
2.4%
60372 1
2.4%
68495 1
2.4%
77680 1
2.4%
118762 1
2.4%
133345 1
2.4%
138482 1
2.4%
139608 1
2.4%
ValueCountFrequency (%)
659339 1
2.4%
621365 1
2.4%
614276 1
2.4%
601833 1
2.4%
548490 1
2.4%
463272 1
2.4%
461296 1
2.4%
403775 1
2.4%
387366 1
2.4%
341754 1
2.4%

투자유가증권
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263983.88
Minimum85873
Maximum687173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:55.234869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85873
5-th percentile119851
Q1161254
median246260
Q3316479
95-th percentile507456
Maximum687173
Range601300
Interquartile range (IQR)155225

Descriptive statistics

Standard deviation132538.19
Coefficient of variation (CV)0.50206926
Kurtosis1.5040284
Mean263983.88
Median Absolute Deviation (MAD)78112
Skewness1.1664813
Sum10823339
Variance1.7566372 × 1010
MonotonicityNot monotonic
2023-12-12T15:50:55.418194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
85873 1
 
2.4%
207228 1
 
2.4%
428199 1
 
2.4%
270286 1
 
2.4%
430359 1
 
2.4%
213619 1
 
2.4%
384889 1
 
2.4%
389774 1
 
2.4%
274467 1
 
2.4%
324372 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
85873 1
2.4%
99533 1
2.4%
119851 1
2.4%
120095 1
2.4%
120371 1
2.4%
127077 1
2.4%
128407 1
2.4%
140122 1
2.4%
156236 1
2.4%
160755 1
2.4%
ValueCountFrequency (%)
687173 1
2.4%
549861 1
2.4%
507456 1
2.4%
464699 1
2.4%
430359 1
2.4%
428199 1
2.4%
389774 1
2.4%
384889 1
2.4%
324372 1
2.4%
320510 1
2.4%

연금수입
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3848216.8
Minimum0
Maximum21018057
Zeros31
Zeros (%)75.6%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:55.564247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile18310885
Maximum21018057
Range21018057
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7017278.8
Coefficient of variation (CV)1.8235144
Kurtosis0.20327267
Mean3848216.8
Median Absolute Deviation (MAD)0
Skewness1.3977349
Sum1.5777689 × 108
Variance4.9242202 × 1013
MonotonicityIncreasing
2023-12-12T15:50:55.699887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 31
75.6%
10860598 1
 
2.4%
12646089 1
 
2.4%
13787053 1
 
2.4%
14141396 1
 
2.4%
15013054 1
 
2.4%
15877806 1
 
2.4%
17068950 1
 
2.4%
18310885 1
 
2.4%
19053002 1
 
2.4%
ValueCountFrequency (%)
0 31
75.6%
10860598 1
 
2.4%
12646089 1
 
2.4%
13787053 1
 
2.4%
14141396 1
 
2.4%
15013054 1
 
2.4%
15877806 1
 
2.4%
17068950 1
 
2.4%
18310885 1
 
2.4%
19053002 1
 
2.4%
ValueCountFrequency (%)
21018057 1
2.4%
19053002 1
2.4%
18310885 1
2.4%
17068950 1
2.4%
15877806 1
2.4%
15013054 1
2.4%
14141396 1
2.4%
13787053 1
2.4%
12646089 1
2.4%
10860598 1
2.4%

지불준비금등
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150820.22
Minimum9246
Maximum1501659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:55.835743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9246
5-th percentile12982
Q137725
median93972
Q3176075
95-th percentile357894
Maximum1501659
Range1492413
Interquartile range (IQR)138350

Descriptive statistics

Standard deviation236527.39
Coefficient of variation (CV)1.5682738
Kurtosis27.744874
Mean150820.22
Median Absolute Deviation (MAD)69923
Skewness4.8828284
Sum6183629
Variance5.5945207 × 1010
MonotonicityNot monotonic
2023-12-12T15:50:56.006143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
12982 1
 
2.4%
244364 1
 
2.4%
37725 1
 
2.4%
75467 1
 
2.4%
155104 1
 
2.4%
130211 1
 
2.4%
148050 1
 
2.4%
247168 1
 
2.4%
153906 1
 
2.4%
114281 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
9246 1
2.4%
11060 1
2.4%
12982 1
2.4%
13092 1
2.4%
15042 1
2.4%
15406 1
2.4%
19163 1
2.4%
19734 1
2.4%
23273 1
2.4%
24049 1
2.4%
ValueCountFrequency (%)
1501659 1
2.4%
362673 1
2.4%
357894 1
2.4%
286175 1
2.4%
277925 1
2.4%
247168 1
2.4%
244364 1
2.4%
222038 1
2.4%
191206 1
2.4%
181038 1
2.4%

기금회계(비용)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4202388.8
Minimum2007
Maximum21321155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:56.186045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile29199
Q1137928
median335563
Q3883453
95-th percentile18614151
Maximum21321155
Range21319148
Interquartile range (IQR)745525

Descriptive statistics

Standard deviation7111262.5
Coefficient of variation (CV)1.6921953
Kurtosis0.1191443
Mean4202388.8
Median Absolute Deviation (MAD)247591
Skewness1.3763865
Sum1.7229794 × 108
Variance5.0570055 × 1013
MonotonicityNot monotonic
2023-12-12T15:50:56.351192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2007 1
 
2.4%
11644780 1
 
2.4%
421919 1
 
2.4%
566936 1
 
2.4%
309237 1
 
2.4%
711726 1
 
2.4%
351637 1
 
2.4%
335563 1
 
2.4%
543393 1
 
2.4%
683409 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
2007 1
2.4%
3759 1
2.4%
29199 1
2.4%
51600 1
2.4%
60084 1
2.4%
65361 1
2.4%
87972 1
2.4%
106338 1
2.4%
116787 1
2.4%
129758 1
2.4%
ValueCountFrequency (%)
21321155 1
2.4%
19508259 1
2.4%
18614151 1
2.4%
17649998 1
2.4%
16249197 1
2.4%
15493392 1
2.4%
14792098 1
2.4%
14637481 1
2.4%
13293696 1
2.4%
11644780 1
2.4%

순이익
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371894.68
Minimum-118212
Maximum1591332
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)2.4%
Memory size501.0 B
2023-12-12T15:50:56.523157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-118212
5-th percentile92022
Q1218263
median334521
Q3501232
95-th percentile643413
Maximum1591332
Range1709544
Interquartile range (IQR)282969

Descriptive statistics

Standard deviation260344.17
Coefficient of variation (CV)0.7000481
Kurtosis11.422233
Mean371894.68
Median Absolute Deviation (MAD)145463
Skewness2.4925552
Sum15247682
Variance6.7779085 × 1010
MonotonicityNot monotonic
2023-12-12T15:50:56.672488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
114005 1
 
2.4%
290871 1
 
2.4%
507668 1
 
2.4%
393452 1
 
2.4%
581404 1
 
2.4%
-118212 1
 
2.4%
501232 1
 
2.4%
643413 1
 
2.4%
189058 1
 
2.4%
305835 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
-118212 1
2.4%
71978 1
2.4%
92022 1
2.4%
114005 1
2.4%
146114 1
2.4%
176201 1
2.4%
185679 1
2.4%
189058 1
2.4%
208720 1
2.4%
212147 1
2.4%
ValueCountFrequency (%)
1591332 1
2.4%
657850 1
2.4%
643413 1
2.4%
597434 1
2.4%
596521 1
2.4%
581404 1
2.4%
546610 1
2.4%
524222 1
2.4%
514177 1
2.4%
507668 1
2.4%

연금회계(결산상잉여금)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-134548.05
Minimum-2751971
Maximum377605
Zeros21
Zeros (%)51.2%
Negative7
Negative (%)17.1%
Memory size501.0 B
2023-12-12T15:50:56.815761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2751971
5-th percentile-945833
Q10
median0
Q333217
95-th percentile50845
Maximum377605
Range3129576
Interquartile range (IQR)33217

Descriptive statistics

Standard deviation534053.08
Coefficient of variation (CV)-3.9692369
Kurtosis16.183119
Mean-134548.05
Median Absolute Deviation (MAD)0
Skewness-3.8971986
Sum-5516470
Variance2.852127 × 1011
MonotonicityNot monotonic
2023-12-12T15:50:56.951032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 21
51.2%
55580 1
 
2.4%
41768 1
 
2.4%
377605 1
 
2.4%
-945833 1
 
2.4%
-2751971 1
 
2.4%
-1753460 1
 
2.4%
-76365 1
 
2.4%
43845 1
 
2.4%
-638460 1
 
2.4%
Other values (11) 11
26.8%
ValueCountFrequency (%)
-2751971 1
 
2.4%
-1753460 1
 
2.4%
-945833 1
 
2.4%
-638460 1
 
2.4%
-183146 1
 
2.4%
-76365 1
 
2.4%
-6522 1
 
2.4%
0 21
51.2%
25676 1
 
2.4%
32189 1
 
2.4%
ValueCountFrequency (%)
377605 1
2.4%
55580 1
2.4%
50845 1
2.4%
43845 1
2.4%
41768 1
2.4%
38365 1
2.4%
37211 1
2.4%
36399 1
2.4%
33351 1
2.4%
33236 1
2.4%

연금회계(적립금)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19894.78
Minimum0
Maximum150000
Zeros30
Zeros (%)73.2%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:57.058452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q330000
95-th percentile101200
Maximum150000
Range150000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation39138.165
Coefficient of variation (CV)1.967258
Kurtosis3.8455728
Mean19894.78
Median Absolute Deviation (MAD)0
Skewness2.1025427
Sum815686
Variance1.531796 × 109
MonotonicityNot monotonic
2023-12-12T15:50:57.172459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 30
73.2%
30000 2
 
4.9%
51758 1
 
2.4%
140000 1
 
2.4%
150000 1
 
2.4%
101200 1
 
2.4%
67028 1
 
2.4%
78800 1
 
2.4%
50000 1
 
2.4%
76400 1
 
2.4%
ValueCountFrequency (%)
0 30
73.2%
30000 2
 
4.9%
40500 1
 
2.4%
50000 1
 
2.4%
51758 1
 
2.4%
67028 1
 
2.4%
76400 1
 
2.4%
78800 1
 
2.4%
101200 1
 
2.4%
140000 1
 
2.4%
ValueCountFrequency (%)
150000 1
2.4%
140000 1
2.4%
101200 1
2.4%
78800 1
2.4%
76400 1
2.4%
67028 1
2.4%
51758 1
2.4%
50000 1
2.4%
40500 1
2.4%
30000 2
4.9%

기타포괄손익누계액
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319679.85
Minimum-961874
Maximum11970937
Zeros29
Zeros (%)70.7%
Negative7
Negative (%)17.1%
Memory size501.0 B
2023-12-12T15:50:57.306228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-961874
5-th percentile-392590
Q10
median0
Q30
95-th percentile1204728
Maximum11970937
Range12932811
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1897739.6
Coefficient of variation (CV)5.9363752
Kurtosis38.046239
Mean319679.85
Median Absolute Deviation (MAD)0
Skewness6.071365
Sum13106874
Variance3.6014154 × 1012
MonotonicityNot monotonic
2023-12-12T15:50:57.425105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 29
70.7%
-9320 1
 
2.4%
41323 1
 
2.4%
-34190 1
 
2.4%
-51906 1
 
2.4%
-79843 1
 
2.4%
1204728 1
 
2.4%
-961874 1
 
2.4%
-392590 1
 
2.4%
11970937 1
 
2.4%
Other values (3) 3
 
7.3%
ValueCountFrequency (%)
-961874 1
 
2.4%
-571801 1
 
2.4%
-392590 1
 
2.4%
-79843 1
 
2.4%
-51906 1
 
2.4%
-34190 1
 
2.4%
-9320 1
 
2.4%
0 29
70.7%
41323 1
 
2.4%
782698 1
 
2.4%
ValueCountFrequency (%)
11970937 1
 
2.4%
1208712 1
 
2.4%
1204728 1
 
2.4%
782698 1
 
2.4%
41323 1
 
2.4%
0 29
70.7%
-9320 1
 
2.4%
-34190 1
 
2.4%
-51906 1
 
2.4%
-79843 1
 
2.4%

기금총액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5601886.7
Minimum770409
Maximum15175240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T15:50:57.564104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum770409
5-th percentile1467187
Q12789334
median4784413
Q36357637
95-th percentile13308678
Maximum15175240
Range14404831
Interquartile range (IQR)3568303

Descriptive statistics

Standard deviation3779303.1
Coefficient of variation (CV)0.67464825
Kurtosis0.54303545
Mean5601886.7
Median Absolute Deviation (MAD)1995079
Skewness1.1213424
Sum2.2967736 × 108
Variance1.4283132 × 1013
MonotonicityNot monotonic
2023-12-12T15:50:57.692916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
770409 1
 
2.4%
8366994 1
 
2.4%
3829452 1
 
2.4%
4222904 1
 
2.4%
4804308 1
 
2.4%
4686096 1
 
2.4%
5187328 1
 
2.4%
5830741 1
 
2.4%
6010479 1
 
2.4%
6357637 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
770409 1
2.4%
1137856 1
2.4%
1467187 1
2.4%
1775152 1
2.4%
1782953 1
2.4%
2089636 1
2.4%
2095100 1
2.4%
2443008 1
2.4%
2628963 1
2.4%
2727627 1
2.4%
ValueCountFrequency (%)
15175240 1
2.4%
15117616 1
2.4%
13308678 1
2.4%
12042915 1
2.4%
10950561 1
2.4%
10837898 1
2.4%
10321103 1
2.4%
8754222 1
2.4%
8527235 1
2.4%
8366994 1
2.4%

Interactions

2023-12-12T15:50:51.744504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.090841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.432833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.704828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.079362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.645365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.089595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.354184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.718363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.005526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.710321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.050906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.528060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.840344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.187398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.527133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.835768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.171875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.753660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.195683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.453691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.827077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.126901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.829684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.179497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.654584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.918980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.268142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.628796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.947004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.265746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.843931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.271402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.551430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.923558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.234164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.935118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.270889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.746459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.015920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.364335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.737789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.060621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.375346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.960630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.367890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.670978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.036145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.349881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.056789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.384929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.834036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.112781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.475719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.853854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.164015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.462862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.079851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.463725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.771474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.141552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.782241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.148973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.496014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.917653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.220275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.582728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.952642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.280830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.569041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.208454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.572900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.888807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.253251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.878150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.257666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.607442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.005825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.320423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.676758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.031740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.383091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:39.652194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.305387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.675869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.990859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.335466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:46.986892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.354824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.716323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.089630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.423388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.797467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.118648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.489942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.025141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.427432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.790869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.083764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.439968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.103900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.460125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.851557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.186414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.511896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:35.897107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.194090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.584201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.109441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.525471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.880364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.165237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.524869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.200648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.562255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:49.945256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.267876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:52.615669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.024585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.288230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.682622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.208049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.629830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:42.971481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.265756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.607897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.289152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.674181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.062522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.357307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:53.038250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.128288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.400480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.765766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.319117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.729866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.067087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.367497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.710331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.402063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.767343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.190749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.456572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:53.149499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.241190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.523019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.866237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.411284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.870888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.161776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.487761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.810831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.521153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.856915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.304017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.548859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:53.263924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:36.333293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:37.625233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:38.977440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:40.527342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:41.974042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:43.261218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:44.602034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:45.909595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:47.614068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:48.948409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:50.411178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:50:51.649575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:50:57.803445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기금회계기금운용수익(계)공공금융주택및시설운영등투자유가증권연금수입지불준비금등기금회계(비용)순이익연금회계(결산상잉여금)연금회계(적립금)기타포괄손익누계액기금총액
구분1.0000.7410.5670.8860.6250.7410.6360.7410.7610.5380.6750.5070.810
기금회계기금운용수익(계)0.7411.0000.0000.0880.6711.0000.8341.0000.3740.0000.0000.8950.917
공공금융0.5670.0001.0000.0000.0000.0000.0000.0000.0000.0000.9050.0000.000
주택및시설운영등0.8860.0880.0001.0000.3300.0880.4310.0880.4780.2250.6350.0000.657
투자유가증권0.6250.6710.0000.3301.0000.6710.0000.6710.4740.0000.0000.6120.807
연금수입0.7411.0000.0000.0880.6711.0000.8361.0000.3800.0000.0000.8960.917
지불준비금등0.6360.8340.0000.4310.0000.8361.0000.8340.6320.0000.0000.6210.745
기금회계(비용)0.7411.0000.0000.0880.6711.0000.8341.0000.3740.0000.0000.8950.917
순이익0.7610.3740.0000.4780.4740.3800.6320.3741.0000.0000.0000.0000.663
연금회계(결산상잉여금)0.5380.0000.0000.2250.0000.0000.0000.0000.0001.0000.0000.0000.000
연금회계(적립금)0.6750.0000.9050.6350.0000.0000.0000.0000.0000.0001.0000.0000.000
기타포괄손익누계액0.5070.8950.0000.0000.6120.8960.6210.8950.0000.0000.0001.0000.990
기금총액0.8100.9170.0000.6570.8070.9170.7450.9170.6630.0000.0000.9901.000
2023-12-12T15:50:58.241573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기금회계기금운용수익(계)공공금융주택및시설운영등투자유가증권연금수입지불준비금등기금회계(비용)순이익연금회계(결산상잉여금)연금회계(적립금)기타포괄손익누계액기금총액
구분1.0000.907-0.7530.4800.7370.7540.8530.9400.293-0.439-0.774-0.0930.831
기금회계기금운용수익(계)0.9071.000-0.5160.5560.7300.7530.8570.9360.478-0.580-0.774-0.0640.877
공공금융-0.753-0.5161.000-0.127-0.556-0.588-0.551-0.620-0.0060.0330.4190.074-0.394
주택및시설운영등0.4800.556-0.1271.0000.4350.0150.5530.5400.326-0.598-0.737-0.1690.385
투자유가증권0.7370.730-0.5560.4351.0000.3350.5740.6680.576-0.531-0.7640.1310.541
연금수입0.7540.753-0.5880.0150.3351.0000.6680.7540.136-0.153-0.334-0.1270.753
지불준비금등0.8530.857-0.5510.5530.5740.6681.0000.8530.337-0.553-0.744-0.1790.780
기금회계(비용)0.9400.936-0.6200.5400.6680.7540.8531.0000.248-0.541-0.774-0.0860.803
순이익0.2930.478-0.0060.3260.5760.1360.3370.2481.000-0.414-0.4480.0070.456
연금회계(결산상잉여금)-0.439-0.5800.033-0.598-0.531-0.153-0.553-0.541-0.4141.0000.7220.029-0.398
연금회계(적립금)-0.774-0.7740.419-0.737-0.764-0.334-0.744-0.774-0.4480.7221.0000.063-0.632
기타포괄손익누계액-0.093-0.0640.074-0.1690.131-0.127-0.179-0.0860.0070.0290.0631.000-0.081
기금총액0.8310.877-0.3940.3850.5410.7530.7800.8030.456-0.398-0.632-0.0811.000

Missing values

2023-12-12T15:50:53.416881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:50:53.649721image/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

구분기금회계기금운용수익(계)공공금융주택및시설운영등투자유가증권연금수입지불준비금등기금회계(비용)순이익연금회계(결산상잉여금)연금회계(적립금)기타포괄손익누계액기금총액
0198211601211892526585873012982200711400555580517580770409
119831309001010758549953301540637591856794176814000001137856
21984175313126743329812009509246291991461143321715000001467187
319852278011421560372140122013092516001762013836510120001782953
41986268804240136849516125401504260084208720363996702802095100
51987283624578037768012840701973465361218263508457880002443008
619883521099022311876211985102327387972264137321895000002789334
71989385166112287133345120371019163106338278828333517640003177913
81990451308132630148465127077043136116787334521256764050003578610
91991531510147650156800156236070824129758401752332363000004043598
구분기금회계기금운용수익(계)공공금융주택및시설운영등투자유가증권연금수입지불준비금등기금회계(비용)순이익연금회계(결산상잉여금)연금회계(적립금)기타포괄손익누계액기금총액
312013119356512096621365207228108605982443641164478029087100-341908366994
322014135058431696387366192767126460892779251329369621214700-519068527235
33201514944311274601833197257137870533578941463748130683000-798438754222
34201615154251147403775246260141413963626731479209836215300120472810321103
3520171708472414725338531647915013054150165915493392159133200-96187410950561
36201816529124146160562268572158778062220381624919727992700-39259010837898
37201917721976151156382305287170689501912061764999871978001197093712042915
3820201909721615213848250745618310885140241186141514830650078269813308678
39202120166109151139608687173190530022861751950825965785000120871215175240
402022218353320176501464699210180571760752132115551417700-57180115117616