Overview

Dataset statistics

Number of variables15
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory141.3 B

Variable types

Numeric12
Categorical3

Dataset

Description공무원연금기금 재해보상특별회계(자산,부채,자본 등) 재무상태 데이터입니다. 2002년부터 2022년까지 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15052892/fileData.do

Alerts

자본 has constant value ""Constant
타회계자금수수(부채) is highly overall correlated with 유동자산 and 2 other fieldsHigh correlation
기금회계계정(부채) is highly overall correlated with 유동자산 and 2 other fieldsHigh correlation
연도 is highly overall correlated with 유동자산 and 4 other fieldsHigh correlation
유동자산 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
당좌자산 is highly overall correlated with 연도 and 3 other fieldsHigh correlation
기타유동자산 is highly overall correlated with 연도High correlation
비유동자산 is highly overall correlated with 연도 and 1 other fieldsHigh correlation
기타비유동자산 is highly overall correlated with 연도 and 1 other fieldsHigh correlation
타회계자금수수(자산) is highly overall correlated with 기금회계계정(자산) and 4 other fieldsHigh correlation
기금회계계정(자산) is highly overall correlated with 타회계자금수수(자산) and 4 other fieldsHigh correlation
자산 is highly overall correlated with 타회계자금수수(자산) and 4 other fieldsHigh correlation
유동부채 is highly overall correlated with 타회계자금수수(자산) and 3 other fieldsHigh correlation
비유동부채 is highly overall correlated with 타회계자금수수(자산) and 3 other fieldsHigh correlation
부채 is highly overall correlated with 타회계자금수수(자산) and 4 other fieldsHigh correlation
타회계자금수수(부채) is highly imbalanced (65.4%)Imbalance
기금회계계정(부채) is highly imbalanced (65.4%)Imbalance
연도 has unique valuesUnique
유동자산 has unique valuesUnique
당좌자산 has unique valuesUnique
자산 has unique valuesUnique
유동부채 has unique valuesUnique
부채 has unique valuesUnique
기타유동자산 has 10 (47.6%) zerosZeros
비유동자산 has 15 (71.4%) zerosZeros
기타비유동자산 has 15 (71.4%) zerosZeros
타회계자금수수(자산) has 2 (9.5%) zerosZeros
기금회계계정(자산) has 2 (9.5%) zerosZeros
비유동부채 has 2 (9.5%) zerosZeros

Reproduction

Analysis started2023-12-12 01:55:45.870994
Analysis finished2023-12-12 01:56:05.074898
Duration19.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012
Minimum2002
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:05.149423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2003
Q12007
median2012
Q32017
95-th percentile2021
Maximum2022
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.0030839149
Kurtosis-1.2
Mean2012
Median Absolute Deviation (MAD)5
Skewness0
Sum42252
Variance38.5
MonotonicityStrictly increasing
2023-12-12T10:56:05.307408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2002 1
 
4.8%
2003 1
 
4.8%
2022 1
 
4.8%
2021 1
 
4.8%
2020 1
 
4.8%
2019 1
 
4.8%
2018 1
 
4.8%
2017 1
 
4.8%
2016 1
 
4.8%
2015 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
2002 1
4.8%
2003 1
4.8%
2004 1
4.8%
2005 1
4.8%
2006 1
4.8%
2007 1
4.8%
2008 1
4.8%
2009 1
4.8%
2010 1
4.8%
2011 1
4.8%
ValueCountFrequency (%)
2022 1
4.8%
2021 1
4.8%
2020 1
4.8%
2019 1
4.8%
2018 1
4.8%
2017 1
4.8%
2016 1
4.8%
2015 1
4.8%
2014 1
4.8%
2013 1
4.8%

유동자산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1447736.8
Minimum13819
Maximum10863949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:05.488904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13819
5-th percentile50098
Q1159343
median352268
Q3914155
95-th percentile9607426
Maximum10863949
Range10850130
Interquartile range (IQR)754812

Descriptive statistics

Standard deviation2977377.7
Coefficient of variation (CV)2.0565739
Kurtosis7.0873751
Mean1447736.8
Median Absolute Deviation (MAD)290822
Skewness2.8321187
Sum30402473
Variance8.8647782 × 1012
MonotonicityNot monotonic
2023-12-12T10:56:05.679083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
61446 1
 
4.8%
50098 1
 
4.8%
914155 1
 
4.8%
487572 1
 
4.8%
904935 1
 
4.8%
2302140 1
 
4.8%
731510 1
 
4.8%
1006739 1
 
4.8%
159343 1
 
4.8%
540610 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
13819 1
4.8%
50098 1
4.8%
61446 1
4.8%
68852 1
4.8%
84767 1
4.8%
159343 1
4.8%
167168 1
4.8%
269401 1
4.8%
279441 1
4.8%
309154 1
4.8%
ValueCountFrequency (%)
10863949 1
4.8%
9607426 1
4.8%
2302140 1
4.8%
1227680 1
4.8%
1006739 1
4.8%
914155 1
4.8%
904935 1
4.8%
731510 1
4.8%
540610 1
4.8%
487572 1
4.8%

당좌자산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1445351.7
Minimum7876
Maximum10863918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:05.839418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7876
5-th percentile44645
Q1159343
median352268
Q3911955
95-th percentile9607426
Maximum10863918
Range10856042
Interquartile range (IQR)752612

Descriptive statistics

Standard deviation2978344.2
Coefficient of variation (CV)2.0606364
Kurtosis7.0849551
Mean1445351.7
Median Absolute Deviation (MAD)290822
Skewness2.8315349
Sum30352385
Variance8.8705344 × 1012
MonotonicityNot monotonic
2023-12-12T10:56:06.003784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
61446 1
 
4.8%
44645 1
 
4.8%
911955 1
 
4.8%
487572 1
 
4.8%
904935 1
 
4.8%
2302140 1
 
4.8%
731510 1
 
4.8%
1006739 1
 
4.8%
159343 1
 
4.8%
540344 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
7876 1
4.8%
44645 1
4.8%
61446 1
4.8%
62773 1
4.8%
78646 1
4.8%
159343 1
4.8%
161163 1
4.8%
263171 1
4.8%
273253 1
4.8%
309154 1
4.8%
ValueCountFrequency (%)
10863918 1
4.8%
9607426 1
4.8%
2302140 1
4.8%
1222108 1
4.8%
1006739 1
4.8%
911955 1
4.8%
904935 1
4.8%
731510 1
4.8%
540344 1
4.8%
487572 1
4.8%

기타유동자산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2385.0952
Minimum0
Maximum6229
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:06.146609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q35943
95-th percentile6188
Maximum6229
Range6229
Interquartile range (IQR)5943

Descriptive statistics

Standard deviation2907.9548
Coefficient of variation (CV)1.2192196
Kurtosis-1.896725
Mean2385.0952
Median Absolute Deviation (MAD)31
Skewness0.47151395
Sum50087
Variance8456201.3
MonotonicityNot monotonic
2023-12-12T10:56:06.291174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
47.6%
5452 1
 
4.8%
5573 1
 
4.8%
6229 1
 
4.8%
6005 1
 
4.8%
6121 1
 
4.8%
5943 1
 
4.8%
6079 1
 
4.8%
6188 1
 
4.8%
31 1
 
4.8%
Other values (2) 2
 
9.5%
ValueCountFrequency (%)
0 10
47.6%
31 1
 
4.8%
266 1
 
4.8%
2200 1
 
4.8%
5452 1
 
4.8%
5573 1
 
4.8%
5943 1
 
4.8%
6005 1
 
4.8%
6079 1
 
4.8%
6121 1
 
4.8%
ValueCountFrequency (%)
6229 1
4.8%
6188 1
4.8%
6121 1
4.8%
6079 1
4.8%
6005 1
4.8%
5943 1
4.8%
5573 1
4.8%
5452 1
4.8%
2200 1
4.8%
266 1
4.8%

비유동자산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean838696.52
Minimum0
Maximum13542519
Zeros15
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:06.436924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32631
95-th percentile2148064
Maximum13542519
Range13542519
Interquartile range (IQR)2631

Descriptive statistics

Standard deviation2953944.5
Coefficient of variation (CV)3.522066
Kurtosis19.606306
Mean838696.52
Median Absolute Deviation (MAD)0
Skewness4.3802171
Sum17612627
Variance8.7257882 × 1012
MonotonicityNot monotonic
2023-12-12T10:56:06.625389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 15
71.4%
486376 1
 
4.8%
2631 1
 
4.8%
2148064 1
 
4.8%
645644 1
 
4.8%
787393 1
 
4.8%
13542519 1
 
4.8%
ValueCountFrequency (%)
0 15
71.4%
2631 1
 
4.8%
486376 1
 
4.8%
645644 1
 
4.8%
787393 1
 
4.8%
2148064 1
 
4.8%
13542519 1
 
4.8%
ValueCountFrequency (%)
13542519 1
 
4.8%
2148064 1
 
4.8%
787393 1
 
4.8%
645644 1
 
4.8%
486376 1
 
4.8%
2631 1
 
4.8%
0 15
71.4%

기타비유동자산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean838696.52
Minimum0
Maximum13542519
Zeros15
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:06.769343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32631
95-th percentile2148064
Maximum13542519
Range13542519
Interquartile range (IQR)2631

Descriptive statistics

Standard deviation2953944.5
Coefficient of variation (CV)3.522066
Kurtosis19.606306
Mean838696.52
Median Absolute Deviation (MAD)0
Skewness4.3802171
Sum17612627
Variance8.7257882 × 1012
MonotonicityNot monotonic
2023-12-12T10:56:06.922115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 15
71.4%
486376 1
 
4.8%
2631 1
 
4.8%
2148064 1
 
4.8%
645644 1
 
4.8%
787393 1
 
4.8%
13542519 1
 
4.8%
ValueCountFrequency (%)
0 15
71.4%
2631 1
 
4.8%
486376 1
 
4.8%
645644 1
 
4.8%
787393 1
 
4.8%
2148064 1
 
4.8%
13542519 1
 
4.8%
ValueCountFrequency (%)
13542519 1
 
4.8%
2148064 1
 
4.8%
787393 1
 
4.8%
645644 1
 
4.8%
486376 1
 
4.8%
2631 1
 
4.8%
0 15
71.4%

타회계자금수수(자산)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16258352
Minimum0
Maximum37462002
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:07.045070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17008719
median14590013
Q324196055
95-th percentile35616285
Maximum37462002
Range37462002
Interquartile range (IQR)17187336

Descriptive statistics

Standard deviation12047401
Coefficient of variation (CV)0.74099767
Kurtosis-1.0732625
Mean16258352
Median Absolute Deviation (MAD)9606042
Skewness0.20500068
Sum3.4142539 × 108
Variance1.4513987 × 1014
MonotonicityNot monotonic
2023-12-12T10:56:07.183532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 2
 
9.5%
19915539 1
 
4.8%
1865821 1
 
4.8%
8238540 1
 
4.8%
33719445 1
 
4.8%
37462002 1
 
4.8%
18995867 1
 
4.8%
837221 1
 
4.8%
2586175 1
 
4.8%
7008719 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
0 2
9.5%
837221 1
4.8%
1865821 1
4.8%
2586175 1
4.8%
7008719 1
4.8%
8238540 1
4.8%
10253927 1
4.8%
13001628 1
4.8%
13554919 1
4.8%
14590013 1
4.8%
ValueCountFrequency (%)
37462002 1
4.8%
35616285 1
4.8%
33719445 1
4.8%
27736531 1
4.8%
26616029 1
4.8%
24196055 1
4.8%
23772373 1
4.8%
21458300 1
4.8%
19915539 1
4.8%
18995867 1
4.8%

기금회계계정(자산)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16258352
Minimum0
Maximum37462002
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:07.369350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17008719
median14590013
Q324196055
95-th percentile35616285
Maximum37462002
Range37462002
Interquartile range (IQR)17187336

Descriptive statistics

Standard deviation12047401
Coefficient of variation (CV)0.74099767
Kurtosis-1.0732625
Mean16258352
Median Absolute Deviation (MAD)9606042
Skewness0.20500068
Sum3.4142539 × 108
Variance1.4513987 × 1014
MonotonicityNot monotonic
2023-12-12T10:56:07.508313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 2
 
9.5%
19915539 1
 
4.8%
1865821 1
 
4.8%
8238540 1
 
4.8%
33719445 1
 
4.8%
37462002 1
 
4.8%
18995867 1
 
4.8%
837221 1
 
4.8%
2586175 1
 
4.8%
7008719 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
0 2
9.5%
837221 1
4.8%
1865821 1
4.8%
2586175 1
4.8%
7008719 1
4.8%
8238540 1
4.8%
10253927 1
4.8%
13001628 1
4.8%
13554919 1
4.8%
14590013 1
4.8%
ValueCountFrequency (%)
37462002 1
4.8%
35616285 1
4.8%
33719445 1
4.8%
27736531 1
4.8%
26616029 1
4.8%
24196055 1
4.8%
23772373 1
4.8%
21458300 1
4.8%
19915539 1
4.8%
18995867 1
4.8%

자산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18544785
Minimum1009370
Maximum38366937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:07.654783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1009370
5-th percentile3231894
Q111473247
median19976985
Q324246153
95-th percentile35701052
Maximum38366937
Range37357567
Interquartile range (IQR)12772906

Descriptive statistics

Standard deviation10783781
Coefficient of variation (CV)0.58149935
Kurtosis-0.75193196
Mean18544785
Median Absolute Deviation (MAD)7926714
Skewness0.18283186
Sum3.8944049 × 108
Variance1.1628992 × 1014
MonotonicityNot monotonic
2023-12-12T10:56:07.820170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19976985 1
 
4.8%
24246153 1
 
4.8%
22695214 1
 
4.8%
34994410 1
 
4.8%
38366937 1
 
4.8%
21943651 1
 
4.8%
3716795 1
 
4.8%
1009370 1
 
4.8%
3231894 1
 
4.8%
7549329 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1009370 1
4.8%
3231894 1
4.8%
3716795 1
4.8%
7549329 1
4.8%
10863949 1
4.8%
11473247 1
4.8%
11481608 1
4.8%
13070480 1
4.8%
13824320 1
4.8%
14899167 1
4.8%
ValueCountFrequency (%)
38366937 1
4.8%
35701052 1
4.8%
34994410 1
4.8%
27903699 1
4.8%
26968297 1
4.8%
24246153 1
4.8%
24051814 1
4.8%
22695214 1
4.8%
21943651 1
4.8%
21472119 1
4.8%

유동부채
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9123515.7
Minimum95326
Maximum20095979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:07.948853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95326
5-th percentile160703
Q14413382
median6485369
Q315596034
95-th percentile19442611
Maximum20095979
Range20000653
Interquartile range (IQR)11182652

Descriptive statistics

Standard deviation6732981.5
Coefficient of variation (CV)0.73798103
Kurtosis-1.253056
Mean9123515.7
Median Absolute Deviation (MAD)3450842
Skewness0.48850289
Sum1.9159383 × 108
Variance4.533304 × 1013
MonotonicityNot monotonic
2023-12-12T10:56:08.077696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2349480 1
 
4.8%
19430783 1
 
4.8%
14945647 1
 
4.8%
20095979 1
 
4.8%
18399184 1
 
4.8%
3749815 1
 
4.8%
160703 1
 
4.8%
95326 1
 
4.8%
3231894 1
 
4.8%
4413382 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
95326 1
4.8%
160703 1
4.8%
2349480 1
4.8%
3231894 1
4.8%
3749815 1
4.8%
4413382 1
4.8%
5035105 1
4.8%
5398574 1
4.8%
5565996 1
4.8%
6016023 1
4.8%
ValueCountFrequency (%)
20095979 1
4.8%
19442611 1
4.8%
19430783 1
4.8%
18399184 1
4.8%
16314713 1
4.8%
15596034 1
4.8%
14945647 1
4.8%
9936211 1
4.8%
7954498 1
4.8%
6976502 1
4.8%

비유동부채
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9377106.4
Minimum0
Maximum19967753
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:08.201305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14815370
median7504484
Q316258441
95-th percentile19949201
Maximum19967753
Range19967753
Interquartile range (IQR)11443071

Descriptive statistics

Standard deviation6579941.7
Coefficient of variation (CV)0.70170279
Kurtosis-1.2568791
Mean9377106.4
Median Absolute Deviation (MAD)3948392
Skewness0.39412909
Sum1.9691923 × 108
Variance4.3295633 × 1013
MonotonicityNot monotonic
2023-12-12T10:56:08.329460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 2
 
9.5%
17627505 1
 
4.8%
6438142 1
 
4.8%
7749567 1
 
4.8%
14898431 1
 
4.8%
19967753 1
 
4.8%
18193836 1
 
4.8%
3556092 1
 
4.8%
3135947 1
 
4.8%
4338197 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
0 2
9.5%
3135947 1
4.8%
3556092 1
4.8%
4338197 1
4.8%
4815370 1
4.8%
4962956 1
4.8%
5465585 1
4.8%
5903085 1
4.8%
6438142 1
4.8%
7504484 1
4.8%
ValueCountFrequency (%)
19967753 1
4.8%
19949201 1
4.8%
18193836 1
4.8%
17627505 1
4.8%
17075312 1
4.8%
16258441 1
4.8%
14898431 1
4.8%
10653584 1
4.8%
8425746 1
4.8%
7749567 1
4.8%

타회계자금수수(부채)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
19 
40383
 
1
914044
 
1

Length

Max length6
Median length1
Mean length1.4285714
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
90.5%
40383 1
 
4.8%
914044 1
 
4.8%

Length

2023-12-12T10:56:08.504110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:56:08.608577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
90.5%
40383 1
 
4.8%
914044 1
 
4.8%

기금회계계정(부채)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
19 
40383
 
1
914044
 
1

Length

Max length6
Median length1
Mean length1.4285714
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19
90.5%
40383 1
 
4.8%
914044 1
 
4.8%

Length

2023-12-12T10:56:08.715353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:56:08.824505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19
90.5%
40383 1
 
4.8%
914044 1
 
4.8%

부채
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18546071
Minimum1009370
Maximum38366937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T10:56:08.948711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1009370
5-th percentile3231894
Q111473247
median19976985
Q324246153
95-th percentile35701052
Maximum38366937
Range37357567
Interquartile range (IQR)12772906

Descriptive statistics

Standard deviation10784149
Coefficient of variation (CV)0.58147888
Kurtosis-0.75240105
Mean18546071
Median Absolute Deviation (MAD)7926714
Skewness0.18244869
Sum3.8946749 × 108
Variance1.1629786 × 1014
MonotonicityNot monotonic
2023-12-12T10:56:09.078594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
19976985 1
 
4.8%
24246153 1
 
4.8%
22695214 1
 
4.8%
34994410 1
 
4.8%
38366937 1
 
4.8%
21943651 1
 
4.8%
3716795 1
 
4.8%
1009370 1
 
4.8%
3231894 1
 
4.8%
7549329 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1009370 1
4.8%
3231894 1
4.8%
3716795 1
4.8%
7549329 1
4.8%
10863949 1
4.8%
11473247 1
4.8%
11481608 1
4.8%
13070480 1
4.8%
13824320 1
4.8%
14899167 1
4.8%
ValueCountFrequency (%)
38366937 1
4.8%
35701052 1
4.8%
34994410 1
4.8%
27903699 1
4.8%
26968297 1
4.8%
24246153 1
4.8%
24051814 1
4.8%
22695214 1
4.8%
21943651 1
4.8%
21499119 1
4.8%

자본
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
21 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
100.0%

Length

2023-12-12T10:56:09.229157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:56:09.322717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
100.0%

Interactions

2023-12-12T10:56:03.190629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:46.467472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.943056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.416702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.011736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.611710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.043000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.170954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.769497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.180561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.768101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.371293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.334683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:46.603964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.079113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.528996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.157913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.750014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.151571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.294408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.894182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.317501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.892498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.515681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.466776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:46.719505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.186901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.930856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.273982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.868115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.236094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.408749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.991907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.445785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.016277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.636435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.600048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:46.840082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.292959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.030560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.384594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.991748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.317976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.502757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.097615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.570765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.157862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.757255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.717873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:46.962282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.404212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.127805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.506853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.105108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.396614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.607259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.198000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.689204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.276951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.868129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.815409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.076039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.540872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.224042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.654684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.240901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.482187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.724769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.315239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.842806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.402072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.983589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:03.941848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.181796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.664721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.312179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.793120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.374128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.571688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.848191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.421050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.984696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.540826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.100878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:04.055564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.292925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.803745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.404059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:51.938339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.492940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.664329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.957775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.527734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.118423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.669564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.230019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:04.173344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.403351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:48.923737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.525531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.067797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.616739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.754102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.067281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.621974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.253886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.818892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.362893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:04.303953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.523986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.072753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.652562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.205702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.731874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.852482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.442476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.734833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.397601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:00.956450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.503398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:04.437994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.678025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.196310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.765364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.343230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.854856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:54.945399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.540915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:57.878850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.512173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.092225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.634721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:04.552051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:47.808007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:49.308125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:50.885252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:52.484124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:53.951113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:55.046720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:56.645423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:58.010117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:55:59.649249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:01.232625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:56:02.759085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:56:09.399385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도유동자산당좌자산기타유동자산비유동자산기타비유동자산타회계자금수수(자산)기금회계계정(자산)자산유동부채비유동부채타회계자금수수(부채)기금회계계정(부채)부채
연도1.0000.3140.3140.9200.3180.3180.4830.4830.5050.4330.5860.3180.3180.505
유동자산0.3141.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.6510.6510.000
당좌자산0.3141.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.6510.6510.000
기타유동자산0.9200.0000.0001.0000.6390.6390.7080.7080.0000.0000.0000.0000.0000.000
비유동자산0.3180.0000.0000.6391.0001.0000.0000.0000.0000.8300.2020.0000.0000.000
기타비유동자산0.3180.0000.0000.6391.0001.0000.0000.0000.0000.8300.2020.0000.0000.000
타회계자금수수(자산)0.4830.0000.0000.7080.0000.0001.0001.0000.9820.7350.0000.0000.0000.982
기금회계계정(자산)0.4830.0000.0000.7080.0000.0001.0001.0000.9820.7350.0000.0000.0000.982
자산0.5050.0000.0000.0000.0000.0000.9820.9821.0000.8390.1950.0000.0001.000
유동부채0.4330.0000.0000.0000.8300.8300.7350.7350.8391.0000.2820.0000.0000.839
비유동부채0.5860.0000.0000.0000.2020.2020.0000.0000.1950.2821.0000.0000.0000.195
타회계자금수수(부채)0.3180.6510.6510.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.000
기금회계계정(부채)0.3180.6510.6510.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.000
부채0.5050.0000.0000.0000.0000.0000.9820.9821.0000.8390.1950.0000.0001.000
2023-12-12T10:56:09.924154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
타회계자금수수(부채)기금회계계정(부채)
타회계자금수수(부채)1.0001.000
기금회계계정(부채)1.0001.000
2023-12-12T10:56:10.031514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도유동자산당좌자산기타유동자산비유동자산기타비유동자산타회계자금수수(자산)기금회계계정(자산)자산유동부채비유동부채부채타회계자금수수(부채)기금회계계정(부채)
연도1.0000.5910.591-0.5500.7280.728-0.237-0.237-0.092-0.064-0.108-0.0920.1620.162
유동자산0.5911.0001.000-0.4120.3170.317-0.478-0.478-0.291-0.256-0.123-0.2910.5820.582
당좌자산0.5911.0001.000-0.4120.3170.317-0.478-0.478-0.291-0.256-0.123-0.2910.5820.582
기타유동자산-0.550-0.412-0.4121.000-0.429-0.4290.2070.2070.2070.2690.1640.2070.0000.000
비유동자산0.7280.3170.317-0.4291.0001.000-0.293-0.293-0.179-0.243-0.177-0.1790.0000.000
기타비유동자산0.7280.3170.317-0.4291.0001.000-0.293-0.293-0.179-0.243-0.177-0.1790.0000.000
타회계자금수수(자산)-0.237-0.478-0.4780.207-0.293-0.2931.0001.0000.9410.7460.7580.9410.0000.000
기금회계계정(자산)-0.237-0.478-0.4780.207-0.293-0.2931.0001.0000.9410.7460.7580.9410.0000.000
자산-0.092-0.291-0.2910.207-0.179-0.1790.9410.9411.0000.8220.8051.0000.0000.000
유동부채-0.064-0.256-0.2560.269-0.243-0.2430.7460.7460.8221.0000.3900.8220.2000.200
비유동부채-0.108-0.123-0.1230.164-0.177-0.1770.7580.7580.8050.3901.0000.8050.0000.000
부채-0.092-0.291-0.2910.207-0.179-0.1790.9410.9411.0000.8220.8051.0000.0000.000
타회계자금수수(부채)0.1620.5820.5820.0000.0000.0000.0000.0000.0000.2000.0000.0001.0001.000
기금회계계정(부채)0.1620.5820.5820.0000.0000.0000.0000.0000.0000.2000.0000.0001.0001.000

Missing values

2023-12-12T10:56:04.719278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:56:04.990530image/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

연도유동자산당좌자산기타유동자산비유동자산기타비유동자산타회계자금수수(자산)기금회계계정(자산)자산유동부채비유동부채타회계자금수수(부채)기금회계계정(부채)부채자본
02002614466144600019915539199155391997698523494801762750500199769850
12003500984464554520024196055241960552424615319430783481537000242461530
22004122768012221085573001025392710253927114816086016023546558500114816080
320052694012631716229001355491913554919138243205398574842574600138243200
4200616716816116360050027736531277365312790369979544981994920100279036990
520078476778646612100356162853561628535701052194426111625844100357010520
6200813819787659430021458300214583002147211915596034590308500214991190
7200968852627736079001300162813001628130704805565996750448400130704800
8201027944127325361880023772373237723732405181469765021707531200240518140
92011352268352268000266160292661602926968297163147131065358400269682970
연도유동자산당좌자산기타유동자산비유동자산기타비유동자산타회계자금수수(자산)기금회계계정(자산)자산유동부채비유동부채타회계자금수수(부채)기금회계계정(부채)부채자본
1120139607426960742600018658211865821114732475035105643814200114732470
122014108639491086391831000010863949648536943381974038340383108639490
13201554061054034426600700871970087197549329441338231359470075493290
1420161593431593430486376486376258617525861753231894323189400032318940
1520171006739100673902631263100100937095326091404491404410093700
162018731510731510021480642148064837221837221371679516070335560920037167950
17201923021402302140064564464564418995867189958672194365137498151819383600219436510
182020904935904935000374620023746200238366937183991841996775300383669370
1920214875724875720787393787393337194453371944534994410200959791489843100349944100
20202291415591195522001354251913542519823854082385402269521414945647774956700226952140