Overview

Dataset statistics

Number of variables11
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory98.0 B

Variable types

Categorical5
Numeric6

Dataset

Description2017년부터 2022년까지 서천군 지방세 부과액대비 징수현황에 대한 자료, 부과액, 수납금액, 환급금, 결손금액, 미수납금액 및 징수율 현황입니다
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=347&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080473

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 20 (29.9%) zerosZeros
결손금액 has 31 (46.3%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2024-01-09 23:11:58.558137
Analysis finished2024-01-09 23:12:02.153906
Duration3.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
충청남도
67 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 67
100.0%

Length

2024-01-10T08:12:02.214819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:02.299233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
서천군
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 67
100.0%

Length

2024-01-10T08:12:02.387030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:02.469666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
44770
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 67
100.0%

Length

2024-01-10T08:12:02.554062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:02.646838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 67
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
2017
14 
2018
14 
2019
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 14
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

Length

2024-01-10T08:12:02.737769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:02.848441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
20.9%
2018 14
20.9%
2019 13
19.4%
2020 13
19.4%
2021 13
19.4%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size668.0 B
레저세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (9)
42 

Length

Max length7
Median length5
Mean length4.4179104
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

Length

2024-01-10T08:12:02.999571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
자동차세 5
 
7.5%
과년도수입 5
 
7.5%
담배소비세 5
 
7.5%
도시계획세 5
 
7.5%
등록면허세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.563937 × 109
Minimum0
Maximum3.7975004 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:03.443933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.20872 × 108
median2.123743 × 109
Q36.9408155 × 109
95-th percentile1.3199943 × 1010
Maximum3.7975004 × 1010
Range3.7975004 × 1010
Interquartile range (IQR)6.0199435 × 109

Descriptive statistics

Standard deviation5.9499091 × 109
Coefficient of variation (CV)1.3036791
Kurtosis14.797316
Mean4.563937 × 109
Median Absolute Deviation (MAD)2.123743 × 109
Skewness3.1881547
Sum3.0578378 × 1011
Variance3.5401418 × 1019
MonotonicityNot monotonic
2024-01-10T08:12:03.568245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
7557100000 1
 
1.5%
7104039000 1
 
1.5%
869240000 1
 
1.5%
5926641000 1
 
1.5%
2009116000 1
 
1.5%
20889475000 1
 
1.5%
7464934000 1
 
1.5%
1840586000 1
 
1.5%
4605933000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
814323000 1
 
1.5%
869240000 1
 
1.5%
972504000 1
 
1.5%
1028149000 1
 
1.5%
1077374000 1
 
1.5%
1175249000 1
 
1.5%
1228950000 1
 
1.5%
1311099000 1
 
1.5%
1584474000 1
 
1.5%
ValueCountFrequency (%)
37975004000 1
1.5%
20889475000 1
1.5%
15665717000 1
1.5%
13333858000 1
1.5%
12887476000 1
1.5%
10784848000 1
1.5%
9240270000 1
1.5%
8665054000 1
1.5%
8501343000 1
1.5%
8381626000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3912505 × 109
Minimum0
Maximum3.7954719 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:03.699759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.990835 × 108
median1.985152 × 109
Q36.7491135 × 109
95-th percentile1.3122336 × 1010
Maximum3.7954719 × 1010
Range3.7954719 × 1010
Interquartile range (IQR)5.95003 × 109

Descriptive statistics

Standard deviation5.9442751 × 109
Coefficient of variation (CV)1.3536634
Kurtosis15.196814
Mean4.3912505 × 109
Median Absolute Deviation (MAD)1.985152 × 109
Skewness3.2478409
Sum2.9421378 × 1011
Variance3.5334407 × 1019
MonotonicityNot monotonic
2024-01-10T08:12:03.850417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
7557100000 1
 
1.5%
6891927000 1
 
1.5%
847548000 1
 
1.5%
5773770000 1
 
1.5%
1985152000 1
 
1.5%
20854906000 1
 
1.5%
7041026000 1
 
1.5%
860906000 1
 
1.5%
4605933000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
531251000 1
 
1.5%
789929000 1
 
1.5%
808238000 1
 
1.5%
847548000 1
 
1.5%
860906000 1
 
1.5%
867860000 1
 
1.5%
875701000 1
 
1.5%
947195000 1
 
1.5%
1023166000 1
 
1.5%
ValueCountFrequency (%)
37954719000 1
1.5%
20854906000 1
1.5%
15555617000 1
1.5%
13231546000 1
1.5%
12867514000 1
1.5%
10235638000 1
1.5%
8694205000 1
1.5%
8249005000 1
1.5%
8102173000 1
1.5%
8023373000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60214284
Minimum0
Maximum6.05998 × 108
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:04.019359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3302000
Q359240500
95-th percentile3.056364 × 108
Maximum6.05998 × 108
Range6.05998 × 108
Interquartile range (IQR)59240500

Descriptive statistics

Standard deviation1.1849433 × 108
Coefficient of variation (CV)1.9678775
Kurtosis8.331026
Mean60214284
Median Absolute Deviation (MAD)3302000
Skewness2.7822957
Sum4.034357 × 109
Variance1.4040907 × 1016
MonotonicityNot monotonic
2024-01-10T08:12:04.177622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
21096000 1
 
1.5%
18000 1
 
1.5%
2034000 1
 
1.5%
20173000 1
 
1.5%
143015000 1
 
1.5%
65520000 1
 
1.5%
341136000 1
 
1.5%
4354000 1
 
1.5%
3302000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
18000 1
 
1.5%
87000 1
 
1.5%
113000 1
 
1.5%
147000 1
 
1.5%
230000 1
 
1.5%
380000 1
 
1.5%
572000 1
 
1.5%
676000 1
 
1.5%
957000 1
 
1.5%
ValueCountFrequency (%)
605998000 1
1.5%
478545000 1
1.5%
341136000 1
1.5%
319596000 1
1.5%
273064000 1
1.5%
266997000 1
1.5%
256570000 1
1.5%
172168000 1
1.5%
163404000 1
1.5%
143015000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16347955
Minimum0
Maximum3.79367 × 108
Zeros31
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:04.312961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20000
Q3252500
95-th percentile1.549961 × 108
Maximum3.79367 × 108
Range3.79367 × 108
Interquartile range (IQR)252500

Descriptive statistics

Standard deviation59945712
Coefficient of variation (CV)3.666863
Kurtosis21.875601
Mean16347955
Median Absolute Deviation (MAD)20000
Skewness4.4383658
Sum1.095313 × 109
Variance3.5934884 × 1015
MonotonicityNot monotonic
2024-01-10T08:12:04.433546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 31
46.3%
118000 1
 
1.5%
69000 1
 
1.5%
6946000 1
 
1.5%
104000 1
 
1.5%
319000 1
 
1.5%
247000 1
 
1.5%
189515000 1
 
1.5%
40000 1
 
1.5%
153000 1
 
1.5%
Other values (27) 27
40.3%
ValueCountFrequency (%)
0 31
46.3%
8000 1
 
1.5%
15000 1
 
1.5%
20000 1
 
1.5%
25000 1
 
1.5%
40000 1
 
1.5%
41000 1
 
1.5%
46000 1
 
1.5%
58000 1
 
1.5%
69000 1
 
1.5%
ValueCountFrequency (%)
379367000 1
1.5%
189515000 1
1.5%
165967000 1
1.5%
158645000 1
1.5%
146482000 1
1.5%
30922000 1
1.5%
7052000 1
1.5%
6946000 1
1.5%
3232000 1
1.5%
1894000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5633849 × 108
Minimum0
Maximum9.99684 × 108
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:04.559905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24394000
Q31.907875 × 108
95-th percentile7.587493 × 108
Maximum9.99684 × 108
Range9.99684 × 108
Interquartile range (IQR)1.907875 × 108

Descriptive statistics

Standard deviation2.4997705 × 108
Coefficient of variation (CV)1.5989476
Kurtosis3.5668317
Mean1.5633849 × 108
Median Absolute Deviation (MAD)24394000
Skewness2.0256466
Sum1.0474679 × 1010
Variance6.2488526 × 1016
MonotonicityNot monotonic
2024-01-10T08:12:04.691126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
236323000 1
 
1.5%
205166000 1
 
1.5%
21692000 1
 
1.5%
152767000 1
 
1.5%
23645000 1
 
1.5%
34569000 1
 
1.5%
423661000 1
 
1.5%
790165000 1
 
1.5%
7234000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
3570000 1
 
1.5%
4937000 1
 
1.5%
6699000 1
 
1.5%
7234000 1
 
1.5%
19962000 1
 
1.5%
19967000 1
 
1.5%
20285000 1
 
1.5%
21692000 1
 
1.5%
22364000 1
 
1.5%
ValueCountFrequency (%)
999684000 1
1.5%
986492000 1
1.5%
875917000 1
1.5%
790165000 1
1.5%
685446000 1
1.5%
548693000 1
1.5%
545474000 1
1.5%
477760000 1
1.5%
423661000 1
1.5%
415788000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.773731
Minimum0
Maximum100
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T08:12:04.821745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q142.23
median96.85
Q398.875
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)56.645

Descriptive statistics

Standard deviation41.521663
Coefficient of variation (CV)0.5785078
Kurtosis-0.76777411
Mean71.773731
Median Absolute Deviation (MAD)2.67
Skewness-1.058473
Sum4808.84
Variance1724.0485
MonotonicityNot monotonic
2024-01-10T08:12:04.957282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.0 15
22.4%
100.0 7
 
10.4%
96.92 1
 
1.5%
97.01 1
 
1.5%
97.5 1
 
1.5%
97.42 1
 
1.5%
98.81 1
 
1.5%
99.83 1
 
1.5%
94.32 1
 
1.5%
46.77 1
 
1.5%
Other values (37) 37
55.2%
ValueCountFrequency (%)
0.0 15
22.4%
31.55 1
 
1.5%
38.62 1
 
1.5%
45.84 1
 
1.5%
46.77 1
 
1.5%
49.28 1
 
1.5%
94.09 1
 
1.5%
94.32 1
 
1.5%
94.38 1
 
1.5%
94.91 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.95 1
 
1.5%
99.85 1
 
1.5%
99.83 1
 
1.5%
99.72 1
 
1.5%
99.54 1
 
1.5%
99.52 1
 
1.5%
99.38 1
 
1.5%
99.3 1
 
1.5%
99.23 1
 
1.5%

Interactions

2024-01-10T08:12:01.473553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:58.849488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.477074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.012619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.498503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.993594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.557634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:58.936177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.585306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.095540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.580829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.074177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.635654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.051354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.685831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.176019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.665942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.156814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.713320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.164749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.770662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.257803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.746965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.232762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.792815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.278476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.853433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.339530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.832181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.322345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.868187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.380939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:11:59.933043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.424826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:00.907823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:01.396038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:12:05.046721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8690.8630.7980.6350.8390.888
부과금액0.0000.8691.0001.0000.7160.0000.4760.300
수납급액0.0000.8631.0001.0000.7200.0000.4610.300
환급금액0.0000.7980.7160.7201.0000.8100.8720.821
결손금액0.0000.6350.0000.0000.8101.0000.9430.941
미수납 금액0.0000.8390.4760.4610.8720.9431.0000.924
징수율0.0000.8880.3000.3000.8210.9410.9241.000
2024-01-10T08:12:05.157384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T08:12:05.243673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9840.6650.3100.5910.5040.0000.486
수납급액0.9841.0000.5920.2520.5110.5730.0000.478
환급금액0.6650.5921.0000.6640.8610.1700.0000.399
결손금액0.3100.2520.6641.0000.825-0.0710.0000.378
미수납 금액0.5910.5110.8610.8251.000-0.0390.0000.519
징수율0.5040.5730.170-0.071-0.0391.0000.0000.675
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.4860.4780.3990.3780.5190.6750.0001.000

Missing values

2024-01-10T08:12:01.971430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:12:02.103058image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0충청남도서천군447702017도축세000000.0
1충청남도서천군447702017레저세000000.0
2충청남도서천군447702017재산세49520860004796180000957000705200014885400096.85
3충청남도서천군447702017주민세161526700015783880005720002580003662100097.72
4충청남도서천군447702017취득세133338580001323154600094786000010231200099.23
5충청남도서천군447702017자동차세924027000086942050005178700059100054547400094.09
6충청남도서천군447702017과년도수입224691100086786000027306400037936700099968400038.62
7충청남도서천군447702017담배소비세41244950004124495000000100.0
8충청남도서천군447702017도시계획세000000.0
9충청남도서천군447702017등록면허세10773740001070660000516100015000669900099.38
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57충청남도서천군447702021취득세379750040003795471900011239800002028500099.95
58충청남도서천군447702021자동차세866505400082490050006008000026100041578800095.2
59충청남도서천군447702021과년도수입164016600080823800031959600014648200068544600049.28
60충청남도서천군447702021담배소비세432144800043214480008700000100.0
61충청남도서천군447702021도시계획세000000.0
62충청남도서천군447702021등록면허세13110990001307459000763400070000357000099.72
63충청남도서천군447702021지방교육세61187850005967534000308470007400015117700097.53
64충청남도서천군447702021지방소득세8265661000788925400017216800018700037622000095.45
65충청남도서천군447702021지방소비세76254550007625455000000100.0
66충청남도서천군447702021지역자원시설세212374300021012820004096900002246100098.94