Overview

Dataset statistics

Number of variables11
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory98.0 B

Variable types

Categorical5
Numeric6

Dataset

Description부산광역시 중구 지방세 징수현황에 관한 자료 (시도명, 시군구명, 자치단체코드, 과세년도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율 포함)
Author부산광역시 중구
URLhttps://www.data.go.kr/data/15078419/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액High correlation
세목명 is highly overall correlated with 미수납 금액High correlation
부과금액 has 13 (19.7%) zerosZeros
수납급액 has 13 (19.7%) zerosZeros
환급금액 has 21 (31.8%) zerosZeros
결손금액 has 50 (75.8%) zerosZeros
미수납 금액 has 21 (31.8%) zerosZeros
징수율 has 13 (19.7%) zerosZeros

Reproduction

Analysis started2024-04-29 22:43:07.656590
Analysis finished2024-04-29 22:43:13.544408
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
부산광역시
66 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 66
100.0%

Length

2024-04-30T07:43:13.605916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:43:13.694634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 66
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
중구
66 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 66
100.0%

Length

2024-04-30T07:43:13.790183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:43:13.880119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 66
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size660.0 B
26110
66 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26110
2nd row26110
3rd row26110
4th row26110
5th row26110

Common Values

ValueCountFrequency (%)
26110 66
100.0%

Length

2024-04-30T07:43:13.965750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:43:14.054445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26110 66
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size660.0 B
2018
14 
2019
13 
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 14
21.2%
2019 13
19.7%
2020 13
19.7%
2021 13
19.7%
2022 13
19.7%

Length

2024-04-30T07:43:14.149597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:43:14.248378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 14
21.2%
2019 13
19.7%
2020 13
19.7%
2021 13
19.7%
2022 13
19.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size660.0 B
레저세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (9)
41 

Length

Max length7
Median length5
Mean length4.4393939
Min length3

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 5
 
7.6%
재산세 5
 
7.6%
주민세 5
 
7.6%
취득세 5
 
7.6%
자동차세 5
 
7.6%
과년도수입 5
 
7.6%
담배소비세 5
 
7.6%
도시계획세 5
 
7.6%
등록면허세 5
 
7.6%
지방교육세 5
 
7.6%
Other values (4) 16
24.2%

Length

2024-04-30T07:43:14.369571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 5
 
7.6%
재산세 5
 
7.6%
주민세 5
 
7.6%
취득세 5
 
7.6%
자동차세 5
 
7.6%
과년도수입 5
 
7.6%
담배소비세 5
 
7.6%
도시계획세 5
 
7.6%
등록면허세 5
 
7.6%
지방교육세 5
 
7.6%
Other values (4) 16
24.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.067144 × 109
Minimum0
Maximum4.6322083 × 1010
Zeros13
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-04-30T07:43:14.516509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.8021325 × 108
median3.1380995 × 109
Q36.701024 × 109
95-th percentile3.5640581 × 1010
Maximum4.6322083 × 1010
Range4.6322083 × 1010
Interquartile range (IQR)6.1208108 × 109

Descriptive statistics

Standard deviation1.154743 × 1010
Coefficient of variation (CV)1.4314149
Kurtosis2.3060008
Mean8.067144 × 109
Median Absolute Deviation (MAD)2.943629 × 109
Skewness1.7933105
Sum5.324315 × 1011
Variance1.3334314 × 1020
MonotonicityNot monotonic
2024-04-30T07:43:14.658046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
19.7%
2594992000 1
 
1.5%
2536000000 1
 
1.5%
3585510000 1
 
1.5%
493138000 1
 
1.5%
22216338000 1
 
1.5%
4691175000 1
 
1.5%
27973028000 1
 
1.5%
2711238000 1
 
1.5%
43913000 1
 
1.5%
Other values (44) 44
66.7%
ValueCountFrequency (%)
0 13
19.7%
8035000 1
 
1.5%
43913000 1
 
1.5%
481666000 1
 
1.5%
493138000 1
 
1.5%
841439000 1
 
1.5%
1248016000 1
 
1.5%
1675782000 1
 
1.5%
1959345000 1
 
1.5%
2032558000 1
 
1.5%
ValueCountFrequency (%)
46322083000 1
1.5%
39814745000 1
1.5%
39337420000 1
1.5%
36000516000 1
1.5%
34560775000 1
1.5%
27973028000 1
1.5%
24559659000 1
1.5%
23440871000 1
1.5%
22216338000 1
1.5%
21179383000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8113171 × 109
Minimum-1.734268 × 109
Maximum4.5869222 × 1010
Zeros13
Zeros (%)19.7%
Negative4
Negative (%)6.1%
Memory size726.0 B
2024-04-30T07:43:14.781878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.734268 × 109
5-th percentile-1.06335 × 108
Q154457250
median3.121433 × 109
Q36.498305 × 109
95-th percentile3.51149 × 1010
Maximum4.5869222 × 1010
Range4.760349 × 1010
Interquartile range (IQR)6.4438478 × 109

Descriptive statistics

Standard deviation1.148881 × 1010
Coefficient of variation (CV)1.4707904
Kurtosis2.2797614
Mean7.8113171 × 109
Median Absolute Deviation (MAD)3.121433 × 109
Skewness1.7787365
Sum5.1554693 × 1011
Variance1.3199276 × 1020
MonotonicityNot monotonic
2024-04-30T07:43:14.942378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
19.7%
2594992000 1
 
1.5%
2536000000 1
 
1.5%
3530029000 1
 
1.5%
493138000 1
 
1.5%
21723852000 1
 
1.5%
4602031000 1
 
1.5%
27967022000 1
 
1.5%
2431428000 1
 
1.5%
-1734268000 1
 
1.5%
Other values (44) 44
66.7%
ValueCountFrequency (%)
-1734268000 1
 
1.5%
-1562151000 1
 
1.5%
-677527000 1
 
1.5%
-141780000 1
 
1.5%
0 13
19.7%
217829000 1
 
1.5%
481666000 1
 
1.5%
493138000 1
 
1.5%
1675782000 1
 
1.5%
2032558000 1
 
1.5%
ValueCountFrequency (%)
45869222000 1
1.5%
39459021000 1
1.5%
38794991000 1
1.5%
35552502000 1
1.5%
33802096000 1
1.5%
27967022000 1
1.5%
24188762000 1
1.5%
22973261000 1
1.5%
21723852000 1
1.5%
20692866000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8040955 × 108
Minimum0
Maximum2.884113 × 109
Zeros21
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-04-30T07:43:15.096192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4408000
Q332925500
95-th percentile1.8101 × 109
Maximum2.884113 × 109
Range2.884113 × 109
Interquartile range (IQR)32925500

Descriptive statistics

Standard deviation6.8094146 × 108
Coefficient of variation (CV)2.4283819
Kurtosis6.1370075
Mean2.8040955 × 108
Median Absolute Deviation (MAD)4408000
Skewness2.6358892
Sum1.850703 × 1010
Variance4.6368127 × 1017
MonotonicityNot monotonic
2024-04-30T07:43:15.251025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 21
31.8%
1858608000 1
 
1.5%
1664576000 1
 
1.5%
89000 1
 
1.5%
12550000 1
 
1.5%
3515000 1
 
1.5%
228287000 1
 
1.5%
33577000 1
 
1.5%
2884113000 1
 
1.5%
12738000 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
0 21
31.8%
89000 1
 
1.5%
158000 1
 
1.5%
467000 1
 
1.5%
893000 1
 
1.5%
965000 1
 
1.5%
1763000 1
 
1.5%
1939000 1
 
1.5%
1980000 1
 
1.5%
2181000 1
 
1.5%
ValueCountFrequency (%)
2884113000 1
1.5%
2670027000 1
1.5%
2491944000 1
1.5%
1858608000 1
1.5%
1664576000 1
1.5%
1553741000 1
1.5%
1532057000 1
1.5%
988763000 1
1.5%
902377000 1
1.5%
871167000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36033909
Minimum0
Maximum5.70097 × 108
Zeros50
Zeros (%)75.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-04-30T07:43:15.372839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.113455 × 108
Maximum5.70097 × 108
Range5.70097 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2566288 × 108
Coefficient of variation (CV)3.4873506
Kurtosis10.3652
Mean36033909
Median Absolute Deviation (MAD)0
Skewness3.4150501
Sum2.378238 × 109
Variance1.5791158 × 1016
MonotonicityNot monotonic
2024-04-30T07:43:15.475928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 50
75.8%
607000 1
 
1.5%
2000 1
 
1.5%
521171000 1
 
1.5%
10000 1
 
1.5%
3000 1
 
1.5%
42000 1
 
1.5%
570097000 1
 
1.5%
11000 1
 
1.5%
47014000 1
 
1.5%
Other values (7) 7
 
10.6%
ValueCountFrequency (%)
0 50
75.8%
2000 1
 
1.5%
3000 1
 
1.5%
5000 1
 
1.5%
10000 1
 
1.5%
11000 1
 
1.5%
42000 1
 
1.5%
183000 1
 
1.5%
479000 1
 
1.5%
607000 1
 
1.5%
ValueCountFrequency (%)
570097000 1
1.5%
521171000 1
1.5%
443731000 1
1.5%
426047000 1
1.5%
367241000 1
1.5%
47014000 1
1.5%
1595000 1
1.5%
607000 1
1.5%
479000 1
1.5%
183000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1979298 × 108
Minimum0
Maximum1.220345 × 109
Zeros21
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size726.0 B
2024-04-30T07:43:15.589936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60071500
Q33.092715 × 108
95-th percentile1.075328 × 109
Maximum1.220345 × 109
Range1.220345 × 109
Interquartile range (IQR)3.092715 × 108

Descriptive statistics

Standard deviation3.2002092 × 108
Coefficient of variation (CV)1.4560106
Kurtosis2.9743218
Mean2.1979298 × 108
Median Absolute Deviation (MAD)60071500
Skewness1.869972
Sum1.4506337 × 1010
Variance1.0241339 × 1017
MonotonicityNot monotonic
2024-04-30T07:43:15.748934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 21
31.8%
355724000 1
 
1.5%
711665000 1
 
1.5%
55481000 1
 
1.5%
492486000 1
 
1.5%
89144000 1
 
1.5%
6006000 1
 
1.5%
279799000 1
 
1.5%
1208084000 1
 
1.5%
16477000 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
0 21
31.8%
6006000 1
 
1.5%
11174000 1
 
1.5%
13458000 1
 
1.5%
16477000 1
 
1.5%
16814000 1
 
1.5%
17136000 1
 
1.5%
27175000 1
 
1.5%
38914000 1
 
1.5%
43427000 1
 
1.5%
ValueCountFrequency (%)
1220345000 1
1.5%
1208084000 1
1.5%
1126455000 1
1.5%
1092919000 1
1.5%
1022555000 1
1.5%
711665000 1
1.5%
576455000 1
1.5%
542424000 1
1.5%
492486000 1
1.5%
486517000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-284.57045
Minimum-19441.83
Maximum100
Zeros13
Zeros (%)19.7%
Negative4
Negative (%)6.1%
Memory size726.0 B
2024-04-30T07:43:15.894211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-19441.83
5-th percentile-8.52
Q12.78
median97.74
Q399.0875
95-th percentile100
Maximum100
Range19541.83
Interquartile range (IQR)96.3075

Descriptive statistics

Standard deviation2445.4125
Coefficient of variation (CV)-8.5933464
Kurtosis60.491207
Mean-284.57045
Median Absolute Deviation (MAD)2.06
Skewness-7.6778181
Sum-18781.65
Variance5980042.2
MonotonicityNot monotonic
2024-04-30T07:43:16.043501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 13
 
19.7%
100.0 8
 
12.1%
98.3 2
 
3.0%
99.02 1
 
1.5%
-3949.33 1
 
1.5%
99.46 1
 
1.5%
97.14 1
 
1.5%
97.8 1
 
1.5%
98.45 1
 
1.5%
97.78 1
 
1.5%
Other values (36) 36
54.5%
ValueCountFrequency (%)
-19441.83 1
 
1.5%
-3949.33 1
 
1.5%
-80.52 1
 
1.5%
-11.36 1
 
1.5%
0.0 13
19.7%
11.12 1
 
1.5%
88.62 1
 
1.5%
88.76 1
 
1.5%
89.24 1
 
1.5%
89.68 1
 
1.5%
ValueCountFrequency (%)
100.0 8
12.1%
99.98 1
 
1.5%
99.83 1
 
1.5%
99.77 1
 
1.5%
99.64 1
 
1.5%
99.54 1
 
1.5%
99.51 1
 
1.5%
99.48 1
 
1.5%
99.46 1
 
1.5%
99.11 1
 
1.5%

Interactions

2024-04-30T07:43:12.610222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:09.880459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.611436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.093782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.574887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.082242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.701476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.009303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.695468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.171553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.646468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.163234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.800165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.214634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.771467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.247409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.731814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.245672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.892208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.293393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.852118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.327539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.819368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.346431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.972594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.369014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.925711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.408544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.894274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.430398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:13.057518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:10.457116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.007114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.490858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:11.979182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:43:12.511325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:43:16.320064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.1220.0000.0000.026
세목명0.0001.0000.7510.7910.4880.6310.8700.280
부과금액0.0000.7511.0000.9970.7810.0000.7480.000
수납급액0.0000.7910.9971.0000.8550.0000.673NaN
환급금액0.1220.4880.7810.8551.0000.7980.7080.560
결손금액0.0000.6310.0000.0000.7981.0000.9430.878
미수납 금액0.0000.8700.7480.6730.7080.9431.0000.837
징수율0.0260.2800.000NaN0.5600.8780.8371.000
2024-04-30T07:43:16.429514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-04-30T07:43:16.510813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9770.5890.0280.5920.4920.0000.418
수납급액0.9771.0000.445-0.1080.4440.5440.0000.468
환급금액0.5890.4451.0000.5340.853-0.0220.0740.240
결손금액0.028-0.1080.5341.0000.548-0.3430.0000.374
미수납 금액0.5920.4440.8530.5481.000-0.1730.0000.594
징수율0.4920.544-0.022-0.343-0.1731.0000.0310.000
과세년도0.0000.0000.0740.0000.0000.0311.0000.000
세목명0.4180.4680.2400.3740.5940.0000.0001.000

Missing values

2024-04-30T07:43:13.186793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:43:13.488835image/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
0부산광역시중구261102018도축세000000.0
1부산광역시중구261102018레저세20325580002032558000000100.0
2부산광역시중구261102018재산세17752846000174473300003102000030551600098.28
3부산광역시중구261102018주민세432538300042146560002492000011072700097.44
4부산광역시중구261102018취득세16433972000164067970007479900002717500099.83
5부산광역시중구261102018자동차세276339200024526760002468600060700031010900088.76
6부산광역시중구261102018과년도수입1248016000-1417800009887630003672410001022555000-11.36
7부산광역시중구261102018담배소비세000000.0
8부산광역시중구261102018도시계획세000000.0
9부산광역시중구261102018등록면허세309609400030849200001900600001117400099.64
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
56부산광역시중구261102022취득세2091064500020334190000206783000057645500097.24
57부산광역시중구261102022자동차세2687972000242061800031004000026735400090.05
58부산광역시중구261102022과년도수입1959345000217829000871167000521171000122034500011.12
59부산광역시중구261102022담배소비세000000.0
60부산광역시중구261102022도시계획세000000.0
61부산광역시중구261102022등록면허세290830300028948450001427800001345800099.54
62부산광역시중구261102022지방교육세6715057000649924000021093000200021581500096.79
63부산광역시중구261102022지방소득세46322083000458692220002670027000045286100099.02
64부산광역시중구261102022지방소비세49194540004919454000000100.0
65부산광역시중구261102022지역자원시설세37315570003667956000530100006360100098.3