Overview

Dataset statistics

Number of variables8
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory73.5 B

Variable types

Numeric7
Text1

Dataset

Description농지관리기금을 2023년 한해 동안 운용하고 그에 대한 결산을 수행하였습니다. 결산 수행 후 생성한 결산보고서 중에서 손익계산서입니다.
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15063932/fileData.do

Alerts

당기 is highly overall correlated with 전기High correlation
2022 is highly overall correlated with 2021High correlation
전기 is highly overall correlated with 당기High correlation
2021 is highly overall correlated with 2022High correlation
증감 is highly overall correlated with 비율High correlation
비율 is highly overall correlated with 증감High correlation
순번 has unique valuesUnique
계정과목 has unique valuesUnique
당기 has 28 (53.8%) zerosZeros
2022 has 44 (84.6%) zerosZeros
전기 has 29 (55.8%) zerosZeros
2021 has 44 (84.6%) zerosZeros
증감 has 20 (38.5%) zerosZeros
비율 has 22 (42.3%) zerosZeros

Reproduction

Analysis started2024-03-30 03:10:15.899564
Analysis finished2024-03-30 03:10:30.248632
Duration14.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-30T03:10:30.529538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2024-03-30T03:10:30.961385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

계정과목
Text

UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
2024-03-30T03:10:31.534751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length10.346154
Min length6

Characters and Unicode

Total characters538
Distinct characters96
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)100.0%

Sample

1st rowI. 사업수익
2nd row1. 농지보전부담금수익
3rd row2. 조성토지매출액
4th row3. 대여금이자
5th row4. 매각대이자
ValueCountFrequency (%)
1 6
 
5.8%
2 6
 
5.8%
5 4
 
3.8%
6 4
 
3.8%
3 4
 
3.8%
4 4
 
3.8%
7 3
 
2.9%
9 3
 
2.9%
8 3
 
2.9%
11 2
 
1.9%
Other values (63) 65
62.5%
2024-03-30T03:10:32.485249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 52
 
9.7%
52
 
9.7%
손 18
 
3.3%
이 16
 
3.0%
1 15
 
2.8%
실 15
 
2.8%
익 15
 
2.8%
자 13
 
2.4%
금 11
 
2.0%
사 11
 
2.0%
Other values (86) 320
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 366
68.0%
Other Punctuation 52
 
9.7%
Space Separator 52
 
9.7%
Decimal Number 51
 
9.5%
Uppercase Letter 15
 
2.8%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
손 18
 
4.9%
이 16
 
4.4%
실 15
 
4.1%
익 15
 
4.1%
자 13
 
3.6%
금 11
 
3.0%
사 11
 
3.0%
지 10
 
2.7%
업 10
 
2.7%
가 9
 
2.5%
Other values (69) 238
65.0%
Decimal Number
ValueCountFrequency (%)
1 15
29.4%
2 8
15.7%
3 5
 
9.8%
4 4
 
7.8%
6 4
 
7.8%
5 4
 
7.8%
8 3
 
5.9%
7 3
 
5.9%
9 3
 
5.9%
0 2
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
I 10
66.7%
V 4
 
26.7%
X 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 52
100.0%
Space Separator
ValueCountFrequency (%)
52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 366
68.0%
Common 157
29.2%
Latin 15
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
손 18
 
4.9%
이 16
 
4.4%
실 15
 
4.1%
익 15
 
4.1%
자 13
 
3.6%
금 11
 
3.0%
사 11
 
3.0%
지 10
 
2.7%
업 10
 
2.7%
가 9
 
2.5%
Other values (69) 238
65.0%
Common
ValueCountFrequency (%)
. 52
33.1%
52
33.1%
1 15
 
9.6%
2 8
 
5.1%
3 5
 
3.2%
4 4
 
2.5%
6 4
 
2.5%
5 4
 
2.5%
8 3
 
1.9%
7 3
 
1.9%
Other values (4) 7
 
4.5%
Latin
ValueCountFrequency (%)
I 10
66.7%
V 4
 
26.7%
X 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 366
68.0%
ASCII 172
32.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 52
30.2%
52
30.2%
1 15
 
8.7%
I 10
 
5.8%
2 8
 
4.7%
3 5
 
2.9%
V 4
 
2.3%
4 4
 
2.3%
6 4
 
2.3%
5 4
 
2.3%
Other values (7) 14
 
8.1%
Hangul
ValueCountFrequency (%)
손 18
 
4.9%
이 16
 
4.4%
실 15
 
4.1%
익 15
 
4.1%
자 13
 
3.6%
금 11
 
3.0%
사 11
 
3.0%
지 10
 
2.7%
업 10
 
2.7%
가 9
 
2.5%
Other values (69) 238
65.0%

당기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2638149 × 1010
Minimum0
Maximum8.6354424 × 1011
Zeros28
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-30T03:10:33.042655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.7280643 × 109
95-th percentile1.3773024 × 1011
Maximum8.6354424 × 1011
Range8.6354424 × 1011
Interquartile range (IQR)7.7280643 × 109

Descriptive statistics

Standard deviation1.2358015 × 1011
Coefficient of variation (CV)3.7863714
Kurtosis41.960323
Mean3.2638149 × 1010
Median Absolute Deviation (MAD)0
Skewness6.2434998
Sum1.6971837 × 1012
Variance1.5272054 × 1022
MonotonicityNot monotonic
2024-03-30T03:10:33.675173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 28
53.8%
863544241014 1
 
1.9%
171797000000 1
 
1.9%
5163201645 1
 
1.9%
154973000230 1
 
1.9%
1851790 1
 
1.9%
252491570 1
 
1.9%
13476555698 1
 
1.9%
42105244301 1
 
1.9%
3892237531 1
 
1.9%
Other values (15) 15
28.8%
ValueCountFrequency (%)
0 28
53.8%
1851790 1
 
1.9%
252491570 1
 
1.9%
574291191 1
 
1.9%
658115592 1
 
1.9%
1261675640 1
 
1.9%
1612450030 1
 
1.9%
1754274348 1
 
1.9%
3892237531 1
 
1.9%
4744544090 1
 
1.9%
ValueCountFrequency (%)
863544241014 1
1.9%
171797000000 1
1.9%
154973000230 1
1.9%
123622531643 1
1.9%
89445798851 1
1.9%
58243082462 1
1.9%
55843295660 1
1.9%
42105244301 1
1.9%
41857573090 1
1.9%
34453191327 1
1.9%

2022
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.607403 × 1010
Minimum0
Maximum1.1056834 × 1012
Zeros44
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-30T03:10:34.171384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.678557 × 1011
Maximum1.1056834 × 1012
Range1.1056834 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3490133 × 1011
Coefficient of variation (CV)3.0877992
Kurtosis10.502499
Mean7.607403 × 1010
Median Absolute Deviation (MAD)0
Skewness3.3219458
Sum3.9558496 × 1012
Variance5.5178634 × 1022
MonotonicityNot monotonic
2024-03-30T03:10:34.689273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 44
84.6%
667855700171 2
 
3.8%
1105683401621 1
 
1.9%
180316577029 1
 
1.9%
2412389940 1
 
1.9%
922954434652 1
 
1.9%
76836319184 1
 
1.9%
331935053665 1
 
1.9%
ValueCountFrequency (%)
0 44
84.6%
2412389940 1
 
1.9%
76836319184 1
 
1.9%
180316577029 1
 
1.9%
331935053665 1
 
1.9%
667855700171 2
 
3.8%
922954434652 1
 
1.9%
1105683401621 1
 
1.9%
ValueCountFrequency (%)
1105683401621 1
 
1.9%
922954434652 1
 
1.9%
667855700171 2
 
3.8%
331935053665 1
 
1.9%
180316577029 1
 
1.9%
76836319184 1
 
1.9%
2412389940 1
 
1.9%
0 44
84.6%

전기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5351669 × 1010
Minimum0
Maximum9.2733302 × 1011
Zeros29
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-30T03:10:35.285704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.2634474 × 109
95-th percentile1.6509112 × 1011
Maximum9.2733302 × 1011
Range9.2733302 × 1011
Interquartile range (IQR)3.2634474 × 109

Descriptive statistics

Standard deviation1.341223 × 1011
Coefficient of variation (CV)3.7939454
Kurtosis40.06509
Mean3.5351669 × 1010
Median Absolute Deviation (MAD)0
Skewness6.0662775
Sum1.8382868 × 1012
Variance1.7988792 × 1022
MonotonicityNot monotonic
2024-03-30T03:10:36.146532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 29
55.8%
1170970189 1
 
1.9%
203533000000 1
 
1.9%
1921273534 1
 
1.9%
174279709056 1
 
1.9%
2041240 1
 
1.9%
829470730 1
 
1.9%
13351131137 1
 
1.9%
43617721900 1
 
1.9%
9781286 1
 
1.9%
Other values (14) 14
26.9%
ValueCountFrequency (%)
0 29
55.8%
2041240 1
 
1.9%
9781286 1
 
1.9%
22050066 1
 
1.9%
58556970 1
 
1.9%
829470730 1
 
1.9%
1021882741 1
 
1.9%
1170970189 1
 
1.9%
1204982340 1
 
1.9%
1754509689 1
 
1.9%
ValueCountFrequency (%)
927333020193 1
1.9%
203533000000 1
1.9%
174279709056 1
1.9%
157573180217 1
1.9%
109308214290 1
1.9%
87039031546 1
1.9%
44926800000 1
1.9%
44728240540 1
1.9%
43617721900 1
1.9%
13351131137 1
1.9%

2021
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9744931 × 1010
Minimum0
Maximum1.0932528 × 1012
Zeros44
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-30T03:10:36.526845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.9384167 × 1011
Maximum1.0932528 × 1012
Range1.0932528 × 1012
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1257507 × 1011
Coefficient of variation (CV)3.0478928
Kurtosis12.588905
Mean6.9744931 × 1010
Median Absolute Deviation (MAD)0
Skewness3.4827707
Sum3.6267364 × 1012
Variance4.5188162 × 1022
MonotonicityNot monotonic
2024-03-30T03:10:36.975443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 44
84.6%
493841668816 2
 
3.8%
1093252839000 1
 
1.9%
289561061829 1
 
1.9%
2925479878 1
 
1.9%
800766297293 1
 
1.9%
72811395353 1
 
1.9%
379736023830 1
 
1.9%
ValueCountFrequency (%)
0 44
84.6%
2925479878 1
 
1.9%
72811395353 1
 
1.9%
289561061829 1
 
1.9%
379736023830 1
 
1.9%
493841668816 2
 
3.8%
800766297293 1
 
1.9%
1093252839000 1
 
1.9%
ValueCountFrequency (%)
1093252839000 1
 
1.9%
800766297293 1
 
1.9%
493841668816 2
 
3.8%
379736023830 1
 
1.9%
289561061829 1
 
1.9%
72811395353 1
 
1.9%
2925479878 1
 
1.9%
0 44
84.6%

증감
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6155785 × 109
Minimum-1.2311999 × 1011
Maximum1.7401403 × 1011
Zeros20
Zeros (%)38.5%
Negative14
Negative (%)26.9%
Memory size600.0 B
2024-03-30T03:10:37.368657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2311999 × 1011
5-th percentile-5.4995484 × 1010
Q1-200922.75
median0
Q31.6291681 × 109
95-th percentile7.9470111 × 1010
Maximum1.7401403 × 1011
Range2.9713402 × 1011
Interquartile range (IQR)1.6293691 × 109

Descriptive statistics

Standard deviation4.7478785 × 1010
Coefficient of variation (CV)13.131726
Kurtosis6.9717261
Mean3.6155785 × 109
Median Absolute Deviation (MAD)5.1297227 × 108
Skewness1.4621673
Sum1.8801008 × 1011
Variance2.254235 × 1021
MonotonicityNot monotonic
2024-03-30T03:10:37.762703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 20
38.5%
174014031355 2
 
3.8%
-576979160 1
 
1.9%
4024923831 1
 
1.9%
-812087604 1
 
1.9%
2918587388 1
 
1.9%
3882456245 1
 
1.9%
-1512477599 1
 
1.9%
125424561 1
 
1.9%
12430562621 1
 
1.9%
Other values (22) 22
42.3%
ValueCountFrequency (%)
-123119988890 1
1.9%
-109244484800 1
1.9%
-63788779179 1
1.9%
-47800970165 1
1.9%
-31736000000 1
1.9%
-31195735886 1
1.9%
-19306708826 1
1.9%
-1512477599 1
1.9%
-812087604 1
1.9%
-576979160 1
1.9%
ValueCountFrequency (%)
174014031355 2
3.8%
122188137359 1
1.9%
44518998851 1
1.9%
41857573090 1
1.9%
14314317353 1
1.9%
13514841922 1
1.9%
12430562621 1
1.9%
4685987120 1
1.9%
4024923831 1
1.9%
3882456245 1
1.9%

비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean968.46154
Minimum-78
Maximum39693
Zeros22
Zeros (%)42.3%
Negative13
Negative (%)25.0%
Memory size600.0 B
2024-03-30T03:10:38.403347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-78
5-th percentile-40.7
Q1-1
median0
Q313
95-th percentile1220.2
Maximum39693
Range39771
Interquartile range (IQR)14

Descriptive statistics

Standard deviation5596.0981
Coefficient of variation (CV)5.778338
Kurtosis47.447491
Mean968.46154
Median Absolute Deviation (MAD)6.5
Skewness6.7929505
Sum50360
Variance31316314
MonotonicityNot monotonic
2024-03-30T03:10:38.967896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 22
42.3%
1 2
 
3.8%
35 2
 
3.8%
13 2
 
3.8%
-11 2
 
3.8%
15 1
 
1.9%
-16 1
 
1.9%
169 1
 
1.9%
-9 1
 
1.9%
-13 1
 
1.9%
Other values (17) 17
32.7%
ValueCountFrequency (%)
-78 1
1.9%
-70 1
1.9%
-44 1
1.9%
-38 1
1.9%
-36 1
1.9%
-18 1
1.9%
-16 1
1.9%
-13 1
1.9%
-11 2
3.8%
-9 1
1.9%
ValueCountFrequency (%)
39693 1
1.9%
8002 1
1.9%
2505 1
1.9%
169 1
1.9%
99 1
1.9%
40 1
1.9%
35 2
3.8%
34 1
1.9%
30 1
1.9%
24 1
1.9%

Interactions

2024-03-30T03:10:27.618109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:16.334718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:17.955028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:20.297863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:22.252491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:24.142690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:25.849055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:27.875725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:16.522956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:18.444533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:20.609266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:22.579993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:24.389807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:26.089985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:28.155947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:16.668649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:18.750029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:20.841394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:22.865564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:24.620766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:26.322555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:28.423009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:16.862302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:19.071850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:21.098921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:23.145299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:24.862760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:26.575150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:28.776704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:17.101853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:19.324087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:21.420459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:23.368094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:25.100226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:26.802280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:29.027801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:17.418607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:19.576935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:21.670393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:23.619009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:25.342978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:27.058101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:29.288530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:17.659764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:19.967556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:21.967398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:23.857787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:25.585223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T03:10:27.318861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T03:10:39.299993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번계정과목당기2022전기2021증감비율
순번1.0001.0000.0000.3310.0000.3310.2950.000
계정과목1.0001.0001.0001.0001.0001.0001.0001.000
당기0.0001.0001.0000.0000.8090.0000.8580.000
20220.3311.0000.0001.0000.0001.0000.9500.000
전기0.0001.0000.8090.0001.0000.0000.9220.000
20210.3311.0000.0001.0000.0001.0000.9500.000
증감0.2951.0000.8580.9500.9220.9501.0000.000
비율0.0001.0000.0000.0000.0000.0000.0001.000
2024-03-30T03:10:39.617991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번당기2022전기2021증감비율
순번1.000-0.385-0.017-0.340-0.017-0.143-0.147
당기-0.3851.000-0.3690.949-0.369-0.0400.027
2022-0.017-0.3691.000-0.3581.0000.2000.074
전기-0.3400.949-0.3581.000-0.358-0.151-0.024
2021-0.017-0.3691.000-0.3581.0000.2000.074
증감-0.143-0.0400.200-0.1510.2001.0000.917
비율-0.1470.0270.074-0.0240.0740.9171.000

Missing values

2024-03-30T03:10:29.678343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:10:30.090792image/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

순번계정과목당기2022전기2021증감비율
01I. 사업수익0110568340162101093252839000124305626211
121. 농지보전부담금수익86354424101409273330201930-63788779179-7
232. 조성토지매출액4744544090058556970046859871208002
343. 대여금이자123622531643010930821429001431431735313
454. 매각대이자126167564001021882741023979289924
565. 간척지사용료161245003001204982340040746769034
676. 수입이자5824308246204472824054001351484192230
787. 간척지임대료10797303652095979419260119936172613
898. 토지대여료(농지매입비축)000000
9109. 현재가치할인차금환입액41857573090000418575730900
순번계정과목당기2022전기2021증감비율
42438. 잡손실51632016450192127353403241928111169
43449. 특별손실000000
444510. 일반유형자산재평가손실000000
454611. 관유물처분손실000000
464712. 무상이전지출000000
474813. 기금전출금17179700000002035330000000-31736000000-16
4849VII. 경상이익0667855700171049384166881617401403135535
4950X. 당기순이익0667855700171049384166881617401403135535
50511. 전기오류수정이익000000
51522. 전기오류수정손실000000