Overview

Dataset statistics

Number of variables9
Number of observations123
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory81.1 B

Variable types

Numeric8
Categorical1

Dataset

Description회계별(일반회계, 통신사업, 철도사업, 전매사업 등 특별회계) 급여심의회 심의 추이(가결)에 대한 데이터입니다. 1983년부터 시작됩니다.
URLhttps://www.data.go.kr/data/15054088/fileData.do

Alerts

구분 is highly overall correlated with 철도사업특별회계 and 3 other fieldsHigh correlation
합계 is highly overall correlated with 일반회계 and 1 other fieldsHigh correlation
일반회계 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
통신사업특별회계 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
철도사업특별회계 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
전매사업특별회계 is highly overall correlated with 구분High correlation
지방자치단체회계 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
교육행정자치회계 is highly overall correlated with 구분 and 2 other fieldsHigh correlation
일반회계 has 2 (1.6%) zerosZeros
통신사업특별회계 has 7 (5.7%) zerosZeros
철도사업특별회계 has 42 (34.1%) zerosZeros
전매사업특별회계 has 108 (87.8%) zerosZeros
지방자치단체회계 has 58 (47.2%) zerosZeros
교육행정자치회계 has 59 (48.0%) zerosZeros

Reproduction

Analysis started2023-12-12 11:33:19.984808
Analysis finished2023-12-12 11:33:32.310838
Duration12.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.9512
Minimum1983
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T20:33:32.449625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile1985
Q11993
median2003
Q32013
95-th percentile2021
Maximum2022
Range39
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.802232
Coefficient of variation (CV)0.0058924211
Kurtosis-1.2230561
Mean2002.9512
Median Absolute Deviation (MAD)10
Skewness-0.019812735
Sum246363
Variance139.29268
MonotonicityIncreasing
2023-12-12T20:33:32.721827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2022 4
 
3.3%
2021 4
 
3.3%
2020 4
 
3.3%
1983 3
 
2.4%
2012 3
 
2.4%
2006 3
 
2.4%
2007 3
 
2.4%
2008 3
 
2.4%
2009 3
 
2.4%
2010 3
 
2.4%
Other values (30) 90
73.2%
ValueCountFrequency (%)
1983 3
2.4%
1984 3
2.4%
1985 3
2.4%
1986 3
2.4%
1987 3
2.4%
1988 3
2.4%
1989 3
2.4%
1990 3
2.4%
1991 3
2.4%
1992 3
2.4%
ValueCountFrequency (%)
2022 4
3.3%
2021 4
3.3%
2020 4
3.3%
2019 3
2.4%
2018 3
2.4%
2017 3
2.4%
2016 3
2.4%
2015 3
2.4%
2014 3
2.4%
2013 3
2.4%

항목구분
Categorical

Distinct5
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
공무상요양
40 
장해급여
40 
유족보상
34 
순직유족보상
6 
(공무수행사망)
 
3

Length

Max length8
Median length4
Mean length4.5203252
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유족보상
2nd row공무상요양
3rd row장해급여
4th row유족보상
5th row공무상요양

Common Values

ValueCountFrequency (%)
공무상요양 40
32.5%
장해급여 40
32.5%
유족보상 34
27.6%
순직유족보상 6
 
4.9%
(공무수행사망) 3
 
2.4%

Length

2023-12-12T20:33:32.984099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:33:33.181790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공무상요양 40
32.5%
장해급여 40
32.5%
유족보상 34
27.6%
순직유족보상 6
 
4.9%
공무수행사망 3
 
2.4%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1348.0976
Minimum-8
Maximum6213
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)2.4%
Memory size1.2 KiB
2023-12-12T20:33:33.433274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile63.2
Q1128.5
median252
Q32563
95-th percentile5289.9
Maximum6213
Range6221
Interquartile range (IQR)2434.5

Descriptive statistics

Standard deviation1868.1039
Coefficient of variation (CV)1.3857335
Kurtosis0.058454024
Mean1348.0976
Median Absolute Deviation (MAD)176
Skewness1.2614981
Sum165816
Variance3489812.3
MonotonicityNot monotonic
2023-12-12T20:33:33.699109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 3
 
2.4%
249 3
 
2.4%
65 2
 
1.6%
229 2
 
1.6%
123 2
 
1.6%
66 2
 
1.6%
130 2
 
1.6%
93 2
 
1.6%
73 2
 
1.6%
91 1
 
0.8%
Other values (102) 102
82.9%
ValueCountFrequency (%)
-8 1
0.8%
-4 1
0.8%
-1 1
0.8%
42 1
0.8%
47 1
0.8%
57 1
0.8%
63 1
0.8%
65 2
1.6%
66 2
1.6%
69 1
0.8%
ValueCountFrequency (%)
6213 1
0.8%
6055 1
0.8%
5855 1
0.8%
5648 1
0.8%
5377 1
0.8%
5366 1
0.8%
5297 1
0.8%
5226 1
0.8%
5089 1
0.8%
4874 1
0.8%

일반회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct113
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.96748
Minimum-1
Maximum3260
Zeros2
Zeros (%)1.6%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-12T20:33:33.956617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile23.1
Q158
median179
Q31896.5
95-th percentile2785.3
Maximum3260
Range3261
Interquartile range (IQR)1838.5

Descriptive statistics

Standard deviation1061.0895
Coefficient of variation (CV)1.311659
Kurtosis-0.77853844
Mean808.96748
Median Absolute Deviation (MAD)144
Skewness0.99470932
Sum99503
Variance1125910.8
MonotonicityNot monotonic
2023-12-12T20:33:34.245686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 3
 
2.4%
63 2
 
1.6%
51 2
 
1.6%
32 2
 
1.6%
28 2
 
1.6%
22 2
 
1.6%
43 2
 
1.6%
89 2
 
1.6%
0 2
 
1.6%
2286 1
 
0.8%
Other values (103) 103
83.7%
ValueCountFrequency (%)
-1 1
 
0.8%
0 2
1.6%
17 1
 
0.8%
22 2
1.6%
23 1
 
0.8%
24 1
 
0.8%
27 1
 
0.8%
28 2
1.6%
29 3
2.4%
32 2
1.6%
ValueCountFrequency (%)
3260 1
0.8%
2913 1
0.8%
2834 1
0.8%
2806 1
0.8%
2797 1
0.8%
2789 1
0.8%
2786 1
0.8%
2779 1
0.8%
2694 1
0.8%
2669 1
0.8%

통신사업특별회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.04878
Minimum0
Maximum715
Zeros7
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T20:33:34.511621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q17
median13
Q3195
95-th percentile384.1
Maximum715
Range715
Interquartile range (IQR)188

Descriptive statistics

Standard deviation164.44705
Coefficient of variation (CV)1.5804804
Kurtosis2.7302019
Mean104.04878
Median Absolute Deviation (MAD)9
Skewness1.779057
Sum12798
Variance27042.834
MonotonicityNot monotonic
2023-12-12T20:33:34.781733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 8
 
6.5%
0 7
 
5.7%
4 6
 
4.9%
11 5
 
4.1%
12 5
 
4.1%
13 4
 
3.3%
16 4
 
3.3%
3 4
 
3.3%
5 4
 
3.3%
15 4
 
3.3%
Other values (54) 72
58.5%
ValueCountFrequency (%)
0 7
5.7%
1 3
 
2.4%
2 3
 
2.4%
3 4
3.3%
4 6
4.9%
5 4
3.3%
6 3
 
2.4%
7 3
 
2.4%
8 4
3.3%
9 8
6.5%
ValueCountFrequency (%)
715 1
0.8%
708 1
0.8%
640 1
0.8%
549 1
0.8%
535 1
0.8%
389 1
0.8%
386 1
0.8%
367 1
0.8%
363 1
0.8%
353 1
0.8%

철도사업특별회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.593496
Minimum0
Maximum388
Zeros42
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T20:33:35.052754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q342.5
95-th percentile241.7
Maximum388
Range388
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation79.466339
Coefficient of variation (CV)1.5402395
Kurtosis3.1371329
Mean51.593496
Median Absolute Deviation (MAD)24
Skewness1.9147107
Sum6346
Variance6314.899
MonotonicityNot monotonic
2023-12-12T20:33:35.320923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
34.1%
31 5
 
4.1%
38 4
 
3.3%
36 3
 
2.4%
27 3
 
2.4%
1 3
 
2.4%
3 3
 
2.4%
34 3
 
2.4%
33 2
 
1.6%
24 2
 
1.6%
Other values (48) 53
43.1%
ValueCountFrequency (%)
0 42
34.1%
1 3
 
2.4%
2 2
 
1.6%
3 3
 
2.4%
6 2
 
1.6%
7 1
 
0.8%
11 1
 
0.8%
13 1
 
0.8%
15 1
 
0.8%
16 1
 
0.8%
ValueCountFrequency (%)
388 1
0.8%
293 1
0.8%
256 1
0.8%
253 1
0.8%
251 1
0.8%
246 1
0.8%
244 1
0.8%
221 1
0.8%
211 1
0.8%
208 1
0.8%

전매사업특별회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3414634
Minimum0
Maximum54
Zeros108
Zeros (%)87.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T20:33:35.566189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum54
Range54
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.2434612
Coefficient of variation (CV)4.6542165
Kurtosis46.769901
Mean1.3414634
Median Absolute Deviation (MAD)0
Skewness6.4521758
Sum165
Variance38.980808
MonotonicityNot monotonic
2023-12-12T20:33:35.760619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 108
87.8%
1 4
 
3.3%
4 3
 
2.4%
7 1
 
0.8%
31 1
 
0.8%
11 1
 
0.8%
17 1
 
0.8%
2 1
 
0.8%
24 1
 
0.8%
3 1
 
0.8%
ValueCountFrequency (%)
0 108
87.8%
1 4
 
3.3%
2 1
 
0.8%
3 1
 
0.8%
4 3
 
2.4%
7 1
 
0.8%
11 1
 
0.8%
17 1
 
0.8%
24 1
 
0.8%
31 1
 
0.8%
ValueCountFrequency (%)
54 1
 
0.8%
31 1
 
0.8%
24 1
 
0.8%
17 1
 
0.8%
11 1
 
0.8%
7 1
 
0.8%
4 3
2.4%
3 1
 
0.8%
2 1
 
0.8%
1 4
3.3%

지방자치단체회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.09756
Minimum-8
Maximum1742
Zeros58
Zeros (%)47.2%
Negative2
Negative (%)1.6%
Memory size1.2 KiB
2023-12-12T20:33:35.997850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile0
Q10
median26
Q364
95-th percentile1204.9
Maximum1742
Range1750
Interquartile range (IQR)64

Descriptive statistics

Standard deviation444.4461
Coefficient of variation (CV)2.0759045
Kurtosis2.9155108
Mean214.09756
Median Absolute Deviation (MAD)26
Skewness2.0567255
Sum26334
Variance197532.33
MonotonicityNot monotonic
2023-12-12T20:33:36.267665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58
47.2%
31 3
 
2.4%
26 3
 
2.4%
34 2
 
1.6%
56 2
 
1.6%
37 2
 
1.6%
64 2
 
1.6%
30 2
 
1.6%
1237 1
 
0.8%
998 1
 
0.8%
Other values (47) 47
38.2%
ValueCountFrequency (%)
-8 1
 
0.8%
-3 1
 
0.8%
0 58
47.2%
19 1
 
0.8%
26 3
 
2.4%
27 1
 
0.8%
28 1
 
0.8%
30 2
 
1.6%
31 3
 
2.4%
32 1
 
0.8%
ValueCountFrequency (%)
1742 1
0.8%
1690 1
0.8%
1673 1
0.8%
1483 1
0.8%
1477 1
0.8%
1237 1
0.8%
1217 1
0.8%
1096 1
0.8%
1065 1
0.8%
1015 1
0.8%

교육행정자치회계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.04878
Minimum-1
Maximum1226
Zeros59
Zeros (%)48.0%
Negative1
Negative (%)0.8%
Memory size1.2 KiB
2023-12-12T20:33:36.559416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median3
Q329
95-th percentile1049.6
Maximum1226
Range1227
Interquartile range (IQR)29

Descriptive statistics

Standard deviation358.53056
Coefficient of variation (CV)2.133491
Kurtosis1.9129909
Mean168.04878
Median Absolute Deviation (MAD)3
Skewness1.9042211
Sum20670
Variance128544.16
MonotonicityNot monotonic
2023-12-12T20:33:36.823562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 59
48.0%
4 3
 
2.4%
12 2
 
1.6%
9 2
 
1.6%
13 2
 
1.6%
18 2
 
1.6%
24 2
 
1.6%
14 2
 
1.6%
8 2
 
1.6%
29 2
 
1.6%
Other values (44) 45
36.6%
ValueCountFrequency (%)
-1 1
 
0.8%
0 59
48.0%
1 1
 
0.8%
3 1
 
0.8%
4 3
 
2.4%
5 1
 
0.8%
6 1
 
0.8%
7 1
 
0.8%
8 2
 
1.6%
9 2
 
1.6%
ValueCountFrequency (%)
1226 1
0.8%
1192 1
0.8%
1177 1
0.8%
1142 1
0.8%
1092 1
0.8%
1068 1
0.8%
1052 1
0.8%
1028 1
0.8%
961 1
0.8%
936 1
0.8%

Interactions

2023-12-12T20:33:30.552861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:20.527895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.996314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.361251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.566585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.809642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.357818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.669215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:30.740358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:20.739129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:22.176445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.531366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.723795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.972906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.520275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.837728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:30.877190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:20.911978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:22.343366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.674474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.878434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:26.129199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.664186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.994278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:31.044149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.082791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:22.513502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.815649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.029118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:26.284838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.826037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:29.159961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:31.191767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.272318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:22.677982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.978940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.184638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:26.516258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.994938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:29.339005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:31.365304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.458118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:22.856416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.128421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.338691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:26.762405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.188659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:29.528688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:31.532052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.622162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.014188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.275573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.484288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:26.995785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.336250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:30.218654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:31.695196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:21.816969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:23.188048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:24.424900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:25.650692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:27.183487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:28.510796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:33:30.379612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:33:36.996820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분항목구분합계일반회계통신사업특별회계철도사업특별회계전매사업특별회계지방자치단체회계교육행정자치회계
구분1.0000.3900.6990.6240.3770.5080.3600.3490.296
항목구분0.3901.0000.7750.7950.6110.4050.0000.3720.372
합계0.6990.7751.0000.9350.8810.8230.6030.8030.781
일반회계0.6240.7950.9351.0000.7680.7140.7880.6130.573
통신사업특별회계0.3770.6110.8810.7681.0000.7320.0000.9200.809
철도사업특별회계0.5080.4050.8230.7140.7321.0000.4600.0000.000
전매사업특별회계0.3600.0000.6030.7880.0000.4601.0000.0000.000
지방자치단체회계0.3490.3720.8030.6130.9200.0000.0001.0000.931
교육행정자치회계0.2960.3720.7810.5730.8090.0000.0000.9311.000
2023-12-12T20:33:37.226108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분합계일반회계통신사업특별회계철도사업특별회계전매사업특별회계지방자치단체회계교육행정자치회계항목구분
구분1.000-0.118-0.239-0.005-0.778-0.5590.6880.6790.172
합계-0.1181.0000.9660.9240.368-0.0660.2810.2710.421
일반회계-0.2390.9661.0000.8800.440-0.0280.1040.0950.441
통신사업특별회계-0.0050.9240.8801.0000.271-0.1120.3570.3400.404
철도사업특별회계-0.7780.3680.4400.2711.0000.279-0.545-0.5490.243
전매사업특별회계-0.559-0.066-0.028-0.1120.2791.000-0.337-0.3440.000
지방자치단체회계0.6880.2810.1040.357-0.545-0.3371.0000.9770.261
교육행정자치회계0.6790.2710.0950.340-0.549-0.3440.9771.0000.261
항목구분0.1720.4210.4410.4040.2430.0000.2610.2611.000

Missing values

2023-12-12T20:33:31.912059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:33:32.186456image/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

구분항목구분합계일반회계통신사업특별회계철도사업특별회계전매사업특별회계지방자치단체회계교육행정자치회계
01983유족보상2762201336700
11983공무상요양905576452533100
21983장해급여6340319100
31984유족보상2261801131400
41984공무상요양1090836412021100
51984장해급여4224215100
61985유족보상2491981238100
71985공무상요양13321093421801700
81985장해급여8051025400
91986유족보상249200938200
구분항목구분합계일반회계통신사업특별회계철도사업특별회계전매사업특별회계지방자치단체회계교육행정자치회계
1132020공무상요양621327796400017421052
1142020장해급여21664131011127
1152021순직유족보상7029000374
1162021(공무수행사망)-80000-80
1172021공무상요양53772034708101673961
1182021장해급여20772160010118
1192022순직유족보상109424005112
1202022(공무수행사망)-100000-1
1212022공무상요양564822157150016901028
1222022장해급여2048511009513