Overview

Dataset statistics

Number of variables8
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory77.3 B

Variable types

Text1
Numeric7

Dataset

Description연도별 공무원연금공단 재무상태표(전체회계_자산, 부채 , 자본)에 대한 데이터입니다. 2016년부터 시작되며 연 단위로 구분됩니다.
URLhttps://www.data.go.kr/data/15054078/fileData.do

Alerts

2016 is highly overall correlated with 2017 and 5 other fieldsHigh correlation
2017 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2018 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2019 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2020 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2021 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
2022 is highly overall correlated with 2016 and 5 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:45:58.461267
Analysis finished2023-12-12 00:46:04.358631
Duration5.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T09:46:04.522809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.8571429
Min length4

Characters and Unicode

Total characters144
Distinct characters45
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st rowI.유동자산
2nd row1.당좌자산
3rd row2.재고자산
4th row3.기타유동자산
5th row4.대부자산
ValueCountFrequency (%)
i.유동자산 1
 
4.8%
자산 1
 
4.8%
3.이익잉여금 1
 
4.8%
2.기타포괄손익누계액 1
 
4.8%
1.자본금 1
 
4.8%
iv.공무원연금기금 1
 
4.8%
부채 1
 
4.8%
iii.대여학자금수탁금 1
 
4.8%
ii.비유동부채 1
 
4.8%
i.유동부채 1
 
4.8%
Other values (11) 11
52.4%
2023-12-12T09:46:04.969217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 18
 
12.5%
자 16
 
11.1%
산 12
 
8.3%
I 10
 
6.9%
유 7
 
4.9%
동 6
 
4.2%
금 6
 
4.2%
부 5
 
3.5%
기 4
 
2.8%
타 3
 
2.1%
Other values (35) 57
39.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
67.4%
Other Punctuation 18
 
12.5%
Decimal Number 12
 
8.3%
Uppercase Letter 11
 
7.6%
Close Punctuation 3
 
2.1%
Open Punctuation 3
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 16
16.5%
산 12
 
12.4%
유 7
 
7.2%
동 6
 
6.2%
금 6
 
6.2%
부 5
 
5.2%
기 4
 
4.1%
타 3
 
3.1%
채 3
 
3.1%
비 3
 
3.1%
Other values (25) 32
33.0%
Decimal Number
ValueCountFrequency (%)
3 3
25.0%
2 3
25.0%
1 3
25.0%
4 2
16.7%
5 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
I 10
90.9%
V 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
67.4%
Common 36
 
25.0%
Latin 11
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 16
16.5%
산 12
 
12.4%
유 7
 
7.2%
동 6
 
6.2%
금 6
 
6.2%
부 5
 
5.2%
기 4
 
4.1%
타 3
 
3.1%
채 3
 
3.1%
비 3
 
3.1%
Other values (25) 32
33.0%
Common
ValueCountFrequency (%)
. 18
50.0%
] 3
 
8.3%
[ 3
 
8.3%
3 3
 
8.3%
2 3
 
8.3%
1 3
 
8.3%
4 2
 
5.6%
5 1
 
2.8%
Latin
ValueCountFrequency (%)
I 10
90.9%
V 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
67.4%
ASCII 47
32.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 18
38.3%
I 10
21.3%
] 3
 
6.4%
[ 3
 
6.4%
3 3
 
6.4%
2 3
 
6.4%
1 3
 
6.4%
4 2
 
4.3%
5 1
 
2.1%
V 1
 
2.1%
Hangul
ValueCountFrequency (%)
자 16
16.5%
산 12
 
12.4%
유 7
 
7.2%
동 6
 
6.2%
금 6
 
6.2%
부 5
 
5.2%
기 4
 
4.1%
타 3
 
3.1%
채 3
 
3.1%
비 3
 
3.1%
Other values (25) 32
33.0%

2016
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6943354 × 109
Minimum7140053
Maximum1.7651988 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:05.136645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7140053
5-th percentile11708490
Q18.1317348 × 108
median3.4784829 × 109
Q35.7058945 × 109
95-th percentile1.4173505 × 1010
Maximum1.7651988 × 1010
Range1.7644848 × 1010
Interquartile range (IQR)4.892721 × 109

Descriptive statistics

Standard deviation4.8911388 × 109
Coefficient of variation (CV)1.0419236
Kurtosis1.3229417
Mean4.6943354 × 109
Median Absolute Deviation (MAD)2.6653094 × 109
Skewness1.3191855
Sum9.8581044 × 1010
Variance2.3923239 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:05.327243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10321103011 2
 
9.5%
3478482878 1
 
4.8%
17651988201 1
 
4.8%
1044243550 1
 
4.8%
3670062963 1
 
4.8%
5606796498 1
 
4.8%
7330885190 1
 
4.8%
4523078321 1
 
4.8%
1994633391 1
 
4.8%
813173478 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
7140053 1
4.8%
11708490 1
4.8%
114909616 1
4.8%
136011996 1
4.8%
524434251 1
4.8%
813173478 1
4.8%
1044243550 1
4.8%
1994633391 1
4.8%
2827430521 1
4.8%
2884844501 1
4.8%
ValueCountFrequency (%)
17651988201 1
4.8%
14173505323 1
4.8%
10321103011 2
9.5%
7330885190 1
4.8%
5705894515 1
4.8%
5606796498 1
4.8%
5439614258 1
4.8%
4523078321 1
4.8%
3670062963 1
4.8%
3478482878 1
4.8%

2017
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7490076 × 109
Minimum5710269
Maximum1.775572 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:05.496114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5710269
5-th percentile14081433
Q16.5354091 × 108
median3.2230508 × 109
Q36.7347259 × 109
95-th percentile1.3936964 × 1010
Maximum1.775572 × 1010
Range1.775001 × 1010
Interquartile range (IQR)6.081185 × 109

Descriptive statistics

Standard deviation4.9358882 × 109
Coefficient of variation (CV)1.0393515
Kurtosis1.2069581
Mean4.7490076 × 109
Median Absolute Deviation (MAD)2.7205116 × 109
Skewness1.3112915
Sum9.972916 × 1010
Variance2.4362992 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:05.690405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10950561197 2
 
9.5%
3818755548 1
 
4.8%
17755719837 1
 
4.8%
2635575338 1
 
4.8%
2708189361 1
 
4.8%
5606796498 1
 
4.8%
6805158640 1
 
4.8%
4223874077 1
 
4.8%
1927743656 1
 
4.8%
653540907 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
5710269 1
4.8%
14081433 1
4.8%
50347617 1
4.8%
79084146 1
4.8%
502539168 1
4.8%
653540907 1
4.8%
1927743656 1
4.8%
2635575338 1
4.8%
2708189361 1
4.8%
2734661896 1
4.8%
ValueCountFrequency (%)
17755719837 1
4.8%
13936964289 1
4.8%
10950561197 2
9.5%
6805158640 1
4.8%
6734725922 1
4.8%
5606796498 1
4.8%
4411518585 1
4.8%
4223874077 1
4.8%
3818755548 1
4.8%
3223050801 1
4.8%

2018
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8527028 × 109
Minimum4359356
Maximum1.8213772 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:05.855564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4359356
5-th percentile30194606
Q11.657648 × 109
median2.9155029 × 109
Q37.3758735 × 109
95-th percentile1.4783583 × 1010
Maximum1.8213772 × 1010
Range1.8209413 × 1010
Interquartile range (IQR)5.7182254 × 109

Descriptive statistics

Standard deviation5.0908754 × 109
Coefficient of variation (CV)1.0490804
Kurtosis1.2266427
Mean4.8527028 × 109
Median Absolute Deviation (MAD)2.6912936 × 109
Skewness1.3379397
Sum1.0190676 × 1011
Variance2.5917012 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:06.007093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
10837898452 2
 
9.5%
3430188890 1
 
4.8%
18213771908 1
 
4.8%
2915502906 1
 
4.8%
2315599048 1
 
4.8%
5606796498 1
 
4.8%
7375873456 1
 
4.8%
4003802964 1
 
4.8%
1657648045 1
 
4.8%
1714422447 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
4359356 1
4.8%
30194606 1
4.8%
64500968 1
4.8%
71101332 1
4.8%
460663200 1
4.8%
1657648045 1
4.8%
1714422447 1
4.8%
2315599048 1
4.8%
2582396466 1
4.8%
2868229752 1
4.8%
ValueCountFrequency (%)
18213771908 1
4.8%
14783583018 1
4.8%
10837898452 2
9.5%
7761376575 1
4.8%
7375873456 1
4.8%
5606796498 1
4.8%
4370949653 1
4.8%
4003802964 1
4.8%
3430188890 1
4.8%
2915502906 1
4.8%

2019
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0679898 × 109
Minimum6876022
Maximum1.8876974 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:06.166993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6876022
5-th percentile26119743
Q11.0407176 × 109
median3.2453585 × 109
Q36.8340593 × 109
95-th percentile1.5631616 × 1010
Maximum1.8876974 × 1010
Range1.8870098 × 1010
Interquartile range (IQR)5.7933417 × 109

Descriptive statistics

Standard deviation5.4010523 × 109
Coefficient of variation (CV)1.0657189
Kurtosis1.0210714
Mean5.0679898 × 109
Median Absolute Deviation (MAD)2.7971741 × 109
Skewness1.3247035
Sum1.0642779 × 1011
Variance2.9171366 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:06.334171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
12042914758 2
 
9.5%
3245358504 1
 
4.8%
18876974076 1
 
4.8%
2987481270 1
 
4.8%
3448636990 1
 
4.8%
5606796498 1
 
4.8%
6834059318 1
 
4.8%
3780238836 1
 
4.8%
1040717645 1
 
4.8%
2013102837 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
6876022 1
4.8%
26119743 1
4.8%
70985813 1
4.8%
155163021 1
4.8%
448184415 1
4.8%
1040717645 1
4.8%
2013102837 1
4.8%
2442259700 1
4.8%
2700068533 1
4.8%
2987481270 1
4.8%
ValueCountFrequency (%)
18876974076 1
4.8%
15631615572 1
4.8%
12042914758 2
9.5%
8061719111 1
4.8%
6834059318 1
4.8%
5606796498 1
4.8%
4965597718 1
4.8%
3780238836 1
4.8%
3448636990 1
4.8%
3245358504 1
4.8%

2020
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3497254 × 109
Minimum5559434
Maximum1.9807111 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:06.468752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5559434
5-th percentile21418456
Q11.1969604 × 109
median3.5123531 × 109
Q36.4984327 × 109
95-th percentile1.6168479 × 1010
Maximum1.9807111 × 1010
Range1.9801552 × 1010
Interquartile range (IQR)5.3014723 × 109

Descriptive statistics

Standard deviation5.6762973 × 109
Coefficient of variation (CV)1.0610446
Kurtosis1.0092321
Mean5.3497254 × 109
Median Absolute Deviation (MAD)2.6471095 × 109
Skewness1.3520475
Sum1.1234423 × 1011
Variance3.2220351 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:06.655112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13308678272 2
 
9.5%
3638632208 1
 
4.8%
19807110943 1
 
4.8%
3470546030 1
 
4.8%
4231335744 1
 
4.8%
5606796498 1
 
4.8%
6498432671 1
 
4.8%
3512353052 1
 
4.8%
1196960371 1
 
4.8%
1789119248 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
5559434 1
4.8%
21418456 1
4.8%
71026221 1
4.8%
429926921 1
4.8%
865243534 1
4.8%
1196960371 1
4.8%
1789119248 1
4.8%
2265624983 1
4.8%
3116260610 1
4.8%
3470546030 1
4.8%
ValueCountFrequency (%)
19807110943 1
4.8%
16168478735 1
4.8%
13308678272 2
9.5%
7722194964 1
4.8%
6498432671 1
4.8%
5606796498 1
4.8%
5309855820 1
4.8%
4231335744 1
4.8%
3638632208 1
4.8%
3512353052 1
4.8%

2021
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7963936 × 109
Minimum4252613
Maximum2.1309805 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:06.818081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4252613
5-th percentile28781124
Q11.0367639 × 109
median4.1152181 × 109
Q36.5301646 × 109
95-th percentile1.7194587 × 1010
Maximum2.1309805 × 1010
Range2.1305553 × 1010
Interquartile range (IQR)5.4934007 × 109

Descriptive statistics

Standard deviation6.2104933 × 109
Coefficient of variation (CV)1.0714409
Kurtosis0.85038385
Mean5.7963936 × 109
Median Absolute Deviation (MAD)3.0784542 × 109
Skewness1.3311772
Sum1.2172427 × 1011
Variance3.8570227 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:06.970774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15175239896 2
 
9.5%
4115218137 1
 
4.8%
21309805184 1
 
4.8%
4128395612 1
 
4.8%
5440047786 1
 
4.8%
5606796498 1
 
4.8%
6134565288 1
 
4.8%
3275660229 1
 
4.8%
1036763918 1
 
4.8%
1822141141 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
4252613 1
4.8%
28781124 1
4.8%
77189494 1
4.8%
388653427 1
4.8%
975413637 1
4.8%
1036763918 1
4.8%
1822141141 1
4.8%
2094314688 1
4.8%
3275660229 1
4.8%
3620594092 1
4.8%
ValueCountFrequency (%)
21309805184 1
4.8%
17194587047 1
4.8%
15175239896 2
9.5%
7590441535 1
4.8%
6530164574 1
4.8%
6134565288 1
4.8%
5606796498 1
4.8%
5440047786 1
4.8%
4128395612 1
4.8%
4115218137 1
4.8%

2022
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6804366 × 109
Minimum885422
Maximum2.083431 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T09:46:07.147314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum885422
5-th percentile22577191
Q11.2831715 × 109
median3.8098535 × 109
Q36.8053694 × 109
95-th percentile1.7024457 × 1010
Maximum2.083431 × 1010
Range2.0833425 × 1010
Interquartile range (IQR)5.5221978 × 109

Descriptive statistics

Standard deviation6.1353998 × 109
Coefficient of variation (CV)1.080093
Kurtosis0.80264846
Mean5.6804366 × 109
Median Absolute Deviation (MAD)2.6498993 × 109
Skewness1.3364036
Sum1.1928917 × 1011
Variance3.764313 × 1019
MonotonicityNot monotonic
2023-12-12T09:46:07.304041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15117615765 2
 
9.5%
3809853471 1
 
4.8%
20834310421 1
 
4.8%
4642573109 1
 
4.8%
4868246158 1
 
4.8%
5606796498 1
 
4.8%
5716694656 1
 
4.8%
3133555999 1
 
4.8%
1159954141 1
 
4.8%
1423184516 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
885422 1
4.8%
22577191 1
4.8%
30970294 1
4.8%
366107240 1
4.8%
1159954141 1
4.8%
1283171544 1
4.8%
1423184516 1
4.8%
1905485771 1
4.8%
3133555999 1
4.8%
3420283618 1
4.8%
ValueCountFrequency (%)
20834310421 1
4.8%
17024456950 1
4.8%
15117615765 2
9.5%
6999459963 1
4.8%
6805369378 1
4.8%
5716694656 1
4.8%
5606796498 1
4.8%
4868246158 1
4.8%
4642573109 1
4.8%
3809853471 1
4.8%

Interactions

2023-12-12T09:46:03.460489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:58.737512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.515708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.309990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.114046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.771419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.777848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.557154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:58.890221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.637422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.434141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.216378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.154566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.875039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.675142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:58.996224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.750051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.553400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.316087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.269161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.972724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.779860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.082752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.859204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.676895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.412495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.382009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.075059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.869276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.190395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.963145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.795889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.514873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.495827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.188309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.959038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.323355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.089797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.917992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.611431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.595608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.301450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:04.036561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:45:59.422036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:00.207683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.014938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:01.691387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:02.687240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:46:03.382515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:46:07.410032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2016201720182019202020212022
구분1.0001.0001.0001.0001.0001.0001.0001.000
20161.0001.0000.9160.9670.9830.9200.9070.898
20171.0000.9161.0000.9590.9010.9830.9760.973
20181.0000.9670.9591.0000.9860.9630.9140.898
20191.0000.9830.9010.9861.0000.9620.9240.905
20201.0000.9200.9830.9630.9621.0000.9890.980
20211.0000.9070.9760.9140.9240.9891.0000.999
20221.0000.8980.9730.8980.9050.9800.9991.000
2023-12-12T09:46:07.555753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016201720182019202020212022
20161.0000.9830.9640.9790.9740.9560.948
20170.9831.0000.9900.9820.9730.9530.938
20180.9640.9901.0000.9860.9750.9640.948
20190.9790.9820.9861.0000.9950.9810.969
20200.9740.9730.9750.9951.0000.9900.979
20210.9560.9530.9640.9810.9901.0000.992
20220.9480.9380.9480.9690.9790.9921.000

Missing values

2023-12-12T09:46:04.155056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:46:04.296088image/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

구분2016201720182019202020212022
0I.유동자산3478482878381875554834301888903245358504363863220841152181373809853471
11.당좌자산2827430521322305080128682297522700068533311626061036205940923420283618
22.재고자산1149096167908414671101332709858137102622177189494885422
33.기타유동자산11708490140814333019460626119743214184562878112422577191
44.대부자산524434251502539168460663200448184415429926921388653427366107240
5II.비유동자산14173505323139369642891478358301815631615572161684787351719458704717024456950
61.투자자산5439614258673472592277613765758061719111772219496475904415356805369378
72.대부자산2884844501273466189625823964662442259700226562498320943146881905485771
83.유형자산5705894515441151858543709496534965597718530985582065301645746999459963
94.무형자산71400535710269435935668760225559434425261330970294
구분2016201720182019202020212022
11[자산]17651988201177557198371821377190818876974076198071109432130980518420834310421
12I.유동부채81317347865354090717144224472013102837178911924818221411411423184516
13II.비유동부채1994633391192774365616576480451040717645119696037110367639181159954141
14III.대여학자금수탁금4523078321422387407740038029643780238836351235305232756602293133555999
15[부채]7330885190680515864073758734566834059318649843267161345652885716694656
16IV.공무원연금기금10321103011109505611971083789845212042914758133086782721517523989615117615765
171.자본금5606796498560679649856067964985606796498560679649856067964985606796498
182.기타포괄손익누계액3670062963270818936123155990483448636990423133574454400477864868246158
193.이익잉여금1044243550263557533829155029062987481270347054603041283956124642573109
20[자본]10321103011109505611971083789845212042914758133086782721517523989615117615765