Overview

Dataset statistics

Number of variables8
Number of observations108
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory72.2 B

Variable types

Numeric7
Categorical1

Dataset

Description지역별 재해구호기금 연말잔액, 수입액, 지출액, 적립률 등 통계정보를 제공하는 서비스
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=3008

Alerts

기준년도 is highly overall correlated with 당해연도 수입액(백만원) and 2 other fieldsHigh correlation
전년도 연말잔액(백만원) is highly overall correlated with 당해연도 연말잔액(백만원) and 2 other fieldsHigh correlation
당해연도 수입액(백만원) is highly overall correlated with 기준년도 and 2 other fieldsHigh correlation
당해연도 지출액(백만원) is highly overall correlated with 기준년도 and 2 other fieldsHigh correlation
당해연도 연말잔액(백만원) is highly overall correlated with 전년도 연말잔액(백만원) and 1 other fieldsHigh correlation
누적적립 기준액(백만원) is highly overall correlated with 전년도 연말잔액(백만원) and 2 other fieldsHigh correlation
적립률(%) is highly overall correlated with 기준년도 and 1 other fieldsHigh correlation
지역 is highly overall correlated with 전년도 연말잔액(백만원)High correlation
전년도 연말잔액(백만원) has unique valuesUnique
당해연도 수입액(백만원) has unique valuesUnique
당해연도 지출액(백만원) has unique valuesUnique
당해연도 연말잔액(백만원) has unique valuesUnique

Reproduction

Analysis started2024-01-09 23:18:09.710074
Analysis finished2024-01-09 23:18:14.203712
Duration4.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:14.247691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018.5
Q32020
95-th percentile2021
Maximum2021
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7157871
Coefficient of variation (CV)0.00085003075
Kurtosis-1.2716396
Mean2018.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum217998
Variance2.9439252
MonotonicityIncreasing
2024-01-10T08:18:14.343006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2016 18
16.7%
2017 18
16.7%
2018 18
16.7%
2019 18
16.7%
2020 18
16.7%
2021 18
16.7%
ValueCountFrequency (%)
2021 18
16.7%
2020 18
16.7%
2019 18
16.7%
2018 18
16.7%
2017 18
16.7%
2016 18
16.7%

지역
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size996.0 B
합계
 
6
서울
 
6
부산
 
6
대구
 
6
인천
 
6
Other values (13)
78 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row합계
2nd row서울
3rd row부산
4th row대구
5th row인천

Common Values

ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
부산 6
 
5.6%
대구 6
 
5.6%
인천 6
 
5.6%
광주 6
 
5.6%
대전 6
 
5.6%
울산 6
 
5.6%
세종 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

Length

2024-01-10T08:18:14.441878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
합계 6
 
5.6%
서울 6
 
5.6%
경남 6
 
5.6%
경북 6
 
5.6%
전남 6
 
5.6%
전북 6
 
5.6%
충남 6
 
5.6%
충북 6
 
5.6%
강원 6
 
5.6%
경기 6
 
5.6%
Other values (8) 48
44.4%

전년도 연말잔액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104368.91
Minimum1234
Maximum1245252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:14.547802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1234
5-th percentile5628.7
Q123360
median32052
Q350937.5
95-th percentile333735.15
Maximum1245252
Range1244018
Interquartile range (IQR)27577.5

Descriptive statistics

Standard deviation224698.3
Coefficient of variation (CV)2.1529237
Kurtosis14.437136
Mean104368.91
Median Absolute Deviation (MAD)12186
Skewness3.8002624
Sum11271842
Variance5.0489324 × 1010
MonotonicityNot monotonic
2024-01-10T08:18:14.671271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912259 1
 
0.9%
41030 1
 
0.9%
8691 1
 
0.9%
40544 1
 
0.9%
39659 1
 
0.9%
37767 1
 
0.9%
52004 1
 
0.9%
67417 1
 
0.9%
132037 1
 
0.9%
281495 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
1234 1
0.9%
2366 1
0.9%
3935 1
0.9%
4452 1
0.9%
4647 1
0.9%
5397 1
0.9%
6059 1
0.9%
7674 1
0.9%
7789 1
0.9%
8691 1
0.9%
ValueCountFrequency (%)
1245252 1
0.9%
1133268 1
0.9%
1031074 1
0.9%
956305 1
0.9%
912259 1
0.9%
357763 1
0.9%
289112 1
0.9%
281495 1
0.9%
279759 1
0.9%
277548 1
0.9%

당해연도 수입액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41801.019
Minimum86
Maximum943354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:14.802828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum86
5-th percentile483.1
Q12758.75
median5143
Q312510.5
95-th percentile224674.15
Maximum943354
Range943268
Interquartile range (IQR)9751.75

Descriptive statistics

Standard deviation136295.15
Coefficient of variation (CV)3.2605702
Kurtosis33.059871
Mean41801.019
Median Absolute Deviation (MAD)2965.5
Skewness5.5169024
Sum4514510
Variance1.8576369 × 1010
MonotonicityNot monotonic
2024-01-10T08:18:14.913293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53250 1
 
0.9%
7899 1
 
0.9%
2976 1
 
0.9%
4704 1
 
0.9%
376796 1
 
0.9%
2413 1
 
0.9%
21160 1
 
0.9%
21548 1
 
0.9%
8219 1
 
0.9%
256942 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
86 1
0.9%
105 1
0.9%
190 1
0.9%
295 1
0.9%
417 1
0.9%
481 1
0.9%
487 1
0.9%
652 1
0.9%
668 1
0.9%
691 1
0.9%
ValueCountFrequency (%)
943354 1
0.9%
914898 1
0.9%
376796 1
0.9%
270396 1
0.9%
256942 1
0.9%
255702 1
0.9%
167051 1
0.9%
140771 1
0.9%
117675 1
0.9%
95287 1
0.9%

당해연도 지출액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49021.574
Minimum24
Maximum1770477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:15.026575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile74.4
Q1330.5
median757
Q313467.5
95-th percentile240144.05
Maximum1770477
Range1770453
Interquartile range (IQR)13137

Descriptive statistics

Standard deviation198199.87
Coefficient of variation (CV)4.0431152
Kurtosis55.730641
Mean49021.574
Median Absolute Deviation (MAD)650
Skewness6.9657129
Sum5294330
Variance3.9283189 × 1010
MonotonicityNot monotonic
2024-01-10T08:18:15.145911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9204 1
 
0.9%
2494 1
 
0.9%
7215 1
 
0.9%
23872 1
 
0.9%
411809 1
 
0.9%
29345 1
 
0.9%
60146 1
 
0.9%
58390 1
 
0.9%
120053 1
 
0.9%
486424 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
24 1
0.9%
40 1
0.9%
52 1
0.9%
56 1
0.9%
64 1
0.9%
66 1
0.9%
90 1
0.9%
92 1
0.9%
97 1
0.9%
135 1
0.9%
ValueCountFrequency (%)
1770477 1
0.9%
813181 1
0.9%
486424 1
0.9%
411809 1
0.9%
267346 1
0.9%
249208 1
0.9%
223311 1
0.9%
149111 1
0.9%
131375 1
0.9%
120053 1
0.9%

당해연도 연말잔액(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct108
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97148.296
Minimum2366
Maximum1245303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:15.269754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2366
5-th percentile6357.6
Q120822.5
median32503
Q350937.5
95-th percentile372973.05
Maximum1245303
Range1242937
Interquartile range (IQR)30115

Descriptive statistics

Standard deviation213751.33
Coefficient of variation (CV)2.2002581
Kurtosis16.998183
Mean97148.296
Median Absolute Deviation (MAD)14356
Skewness4.0620618
Sum10492016
Variance4.5689629 × 1010
MonotonicityNot monotonic
2024-01-10T08:18:15.402948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
956305 1
 
0.9%
46435 1
 
0.9%
4452 1
 
0.9%
21376 1
 
0.9%
4646 1
 
0.9%
10835 1
 
0.9%
13018 1
 
0.9%
30575 1
 
0.9%
20203 1
 
0.9%
52013 1
 
0.9%
Other values (98) 98
90.7%
ValueCountFrequency (%)
2366 1
0.9%
3935 1
0.9%
4452 1
0.9%
4646 1
0.9%
5397 1
0.9%
6058 1
0.9%
6914 1
0.9%
7674 1
0.9%
7789 1
0.9%
8252 1
0.9%
ValueCountFrequency (%)
1245303 1
0.9%
1133921 1
0.9%
1032873 1
0.9%
956305 1
0.9%
459477 1
0.9%
418129 1
0.9%
289112 1
0.9%
281496 1
0.9%
279759 1
0.9%
277548 1
0.9%

누적적립 기준액(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135241.85
Minimum4443
Maximum1494647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:15.523707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4443
5-th percentile15783.8
Q124683.5
median37211
Q376144.25
95-th percentile769202.8
Maximum1494647
Range1490204
Interquartile range (IQR)51460.75

Descriptive statistics

Standard deviation284151.22
Coefficient of variation (CV)2.1010598
Kurtosis12.140198
Mean135241.85
Median Absolute Deviation (MAD)16630
Skewness3.5150946
Sum14606120
Variance8.0741915 × 1010
MonotonicityNot monotonic
2024-01-10T08:18:15.646012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514044 2
 
1.9%
906596 1
 
0.9%
25519 1
 
0.9%
24036 1
 
0.9%
37476 1
 
0.9%
37062 1
 
0.9%
85644 1
 
0.9%
64290 1
 
0.9%
103422 1
 
0.9%
1410558 1
 
0.9%
Other values (97) 97
89.8%
ValueCountFrequency (%)
4443 1
0.9%
6550 1
0.9%
10224 1
0.9%
12858 1
0.9%
15282 1
0.9%
15284 1
0.9%
16712 1
0.9%
17823 1
0.9%
18150 1
0.9%
18186 1
0.9%
ValueCountFrequency (%)
1494647 1
0.9%
1410558 1
0.9%
1288445 1
0.9%
1173263 1
0.9%
1029551 1
0.9%
906596 1
0.9%
514044 2
1.9%
380223 1
0.9%
341702 1
0.9%
301498 1
0.9%

적립률(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.717593
Minimum10.1
Maximum396.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-01-10T08:18:15.771302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile19.745
Q145.225
median99.9
Q3108.65
95-th percentile131.71
Maximum396.3
Range386.2
Interquartile range (IQR)63.425

Descriptive statistics

Standard deviation48.017043
Coefficient of variation (CV)0.56678951
Kurtosis15.273521
Mean84.717593
Median Absolute Deviation (MAD)15.85
Skewness2.2887203
Sum9149.5
Variance2305.6364
MonotonicityNot monotonic
2024-01-10T08:18:15.900816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102.6 2
 
1.9%
104.9 2
 
1.9%
96.6 2
 
1.9%
100.0 2
 
1.9%
19.3 2
 
1.9%
105.5 1
 
0.9%
112.4 1
 
0.9%
88.9 1
 
0.9%
12.4 1
 
0.9%
29.2 1
 
0.9%
Other values (93) 93
86.1%
ValueCountFrequency (%)
10.1 1
0.9%
12.4 1
0.9%
15.2 1
0.9%
19.3 2
1.9%
19.5 1
0.9%
20.2 1
0.9%
20.6 1
0.9%
21.1 1
0.9%
22.5 1
0.9%
24.5 1
0.9%
ValueCountFrequency (%)
396.3 1
0.9%
166.1 1
0.9%
145.6 1
0.9%
144.6 1
0.9%
133.5 1
0.9%
132.9 1
0.9%
129.5 1
0.9%
126.9 1
0.9%
125.0 1
0.9%
123.1 1
0.9%

Interactions

2024-01-10T08:18:13.194613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:09.974468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.485119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.018719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.524611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.099057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.639260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.500237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.048400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.560418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.085900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.600338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.174764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.717308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.574763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.121331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.633740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.155417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.685263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.251613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.796973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.644029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.184273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.703032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.222870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.761260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.320529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.875894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.722516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.260669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.780715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.296311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.840131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.401947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.958923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.802581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.331273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.859253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.372896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.921149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.477076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.034699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.884269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.406562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:10.939420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:11.447713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.002494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:12.557145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:18:13.114585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:18:15.989238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도지역전년도 연말잔액(백만원)당해연도 수입액(백만원)당해연도 지출액(백만원)당해연도 연말잔액(백만원)누적적립 기준액(백만원)적립률(%)
기준년도1.0000.0000.0000.0000.3450.0000.0000.737
지역0.0001.0000.8750.5470.0000.7690.7900.535
전년도 연말잔액(백만원)0.0000.8751.0000.5070.5120.8920.9280.000
당해연도 수입액(백만원)0.0000.5470.5071.0000.9590.7270.8340.000
당해연도 지출액(백만원)0.3450.0000.5120.9591.0000.6150.7280.000
당해연도 연말잔액(백만원)0.0000.7690.8920.7270.6151.0000.9070.320
누적적립 기준액(백만원)0.0000.7900.9280.8340.7280.9071.0000.135
적립률(%)0.7370.5350.0000.0000.0000.3200.1351.000
2024-01-10T08:18:16.097295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도전년도 연말잔액(백만원)당해연도 수입액(백만원)당해연도 지출액(백만원)당해연도 연말잔액(백만원)누적적립 기준액(백만원)적립률(%)지역
기준년도1.000-0.0260.5350.644-0.1780.169-0.5250.000
전년도 연말잔액(백만원)-0.0261.0000.3940.3890.8230.8320.0390.525
당해연도 수입액(백만원)0.5350.3941.0000.6710.3120.554-0.3480.293
당해연도 지출액(백만원)0.6440.3890.6711.0000.1060.488-0.5290.000
당해연도 연말잔액(백만원)-0.1780.8230.3120.1061.0000.7940.2920.453
누적적립 기준액(백만원)0.1690.8320.5540.4880.7941.000-0.2190.461
적립률(%)-0.5250.039-0.348-0.5290.292-0.2191.0000.225
지역0.0000.5250.2930.0000.4530.4610.2251.000

Missing values

2024-01-10T08:18:13.991056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:18:14.150744image/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

기준년도지역전년도 연말잔액(백만원)당해연도 수입액(백만원)당해연도 지출액(백만원)당해연도 연말잔액(백만원)누적적립 기준액(백만원)적립률(%)
02016합계912259532509204956305906596105.5
12016서울2720076187646277548272123102.0
22016부산125171191033412674776301166.1
32016대구2826210263739377864738779.7
42016인천32859295199329555894455.9
52016광주2560624841352795527936100.1
62016대전281891626642975129150102.1
72016울산2784920302635272442934092.9
82016세종12341172402366655036.1
92016경기17249137331164175060160920108.8
기준년도지역전년도 연말잔액(백만원)당해연도 수입액(백만원)당해연도 지출액(백만원)당해연도 연말잔액(백만원)누적적립 기준액(백만원)적립률(%)
982021세종4452311665469141818638.0
992021경기5058269671583956185829255121.1
1002021강원7789175871582695502826133.8
1012021충북25967505815660153642877153.4
1022021충남44862856016260371624259187.3
1032021전북5397493598393492804833.3
1042021전남76745261789121472930741.4
1052021경북103478074811176094443396.3
1062021경남131691364914255125636520219.3
1072021제주322653032732698298943961675.5