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 memory97.9 B

Variable types

Categorical5
Numeric6

Dataset

Description지방세 징수현황, 지방세 부과액에 대한 세목별 징수현황을 제공, 지자체의 재정자주도·재정자립도 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
Author강원특별자치도 양양군
URLhttps://www.data.go.kr/data/15079471/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 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 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 징수율High correlation
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 21 (31.3%) zerosZeros
결손금액 has 27 (40.3%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2024-03-14 09:47:34.556866
Analysis finished2024-03-14 09:47:41.244296
Duration6.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size664.0 B
강원도
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원도 67
100.0%

Length

2024-03-14T18:47:41.421157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:47:41.614504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원도 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size664.0 B
양양군
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양양군 67
100.0%

Length

2024-03-14T18:47:41.784715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:47:41.953358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양양군 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size664.0 B
42830
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42830
2nd row42830
3rd row42830
4th row42830
5th row42830

Common Values

ValueCountFrequency (%)
42830 67
100.0%

Length

2024-03-14T18:47:42.124641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:47:42.289903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42830 67
100.0%

과세년도
Categorical

Distinct5
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size664.0 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

2024-03-14T18:47:42.463181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:47:42.753626image/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 size664.0 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

2024-03-14T18:47:42.988185image/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%
Mean3.4725101 × 109
Minimum0
Maximum2.7804623 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:43.228981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.875365 × 108
median1.538213 × 109
Q34.2547325 × 109
95-th percentile1.6557139 × 1010
Maximum2.7804623 × 1010
Range2.7804623 × 1010
Interquartile range (IQR)3.667196 × 109

Descriptive statistics

Standard deviation5.3126099 × 109
Coefficient of variation (CV)1.5299048
Kurtosis9.1265482
Mean3.4725101 × 109
Median Absolute Deviation (MAD)1.538213 × 109
Skewness2.9172586
Sum2.3265818 × 1011
Variance2.8223824 × 1019
MonotonicityNot monotonic
2024-03-14T18:47:43.472897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
3648400000 1
 
1.5%
4173094000 1
 
1.5%
868830000 1
 
1.5%
4883584000 1
 
1.5%
644087000 1
 
1.5%
21916800000 1
 
1.5%
4098817000 1
 
1.5%
738588000 1
 
1.5%
3022807000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
532895000 1
 
1.5%
570884000 1
 
1.5%
604189000 1
 
1.5%
606264000 1
 
1.5%
614313000 1
 
1.5%
644087000 1
 
1.5%
738588000 1
 
1.5%
786849000 1
 
1.5%
789417000 1
 
1.5%
ValueCountFrequency (%)
27804623000 1
1.5%
21916800000 1
1.5%
19508497000 1
1.5%
17267159000 1
1.5%
14900426000 1
1.5%
6297604000 1
1.5%
5597492000 1
1.5%
5587142000 1
1.5%
5377439000 1
1.5%
5120202000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3686506 × 109
Minimum0
Maximum2.7785711 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:43.928086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.33126 × 108
median1.234547 × 109
Q34.1056045 × 109
95-th percentile1.6518224 × 1010
Maximum2.7785711 × 1010
Range2.7785711 × 1010
Interquartile range (IQR)3.7724785 × 109

Descriptive statistics

Standard deviation5.2931399 × 109
Coefficient of variation (CV)1.5712938
Kurtosis9.2511611
Mean3.3686506 × 109
Median Absolute Deviation (MAD)1.234547 × 109
Skewness2.9393505
Sum2.2569959 × 1011
Variance2.801733 × 1019
MonotonicityNot monotonic
2024-03-14T18:47:44.169693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
3648400000 1
 
1.5%
3985335000 1
 
1.5%
850014000 1
 
1.5%
4747050000 1
 
1.5%
622816000 1
 
1.5%
21433798000 1
 
1.5%
3805586000 1
 
1.5%
268782000 1
 
1.5%
3022807000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
243559000 1
 
1.5%
268782000 1
 
1.5%
397470000 1
 
1.5%
426655000 1
 
1.5%
496676000 1
 
1.5%
540447000 1
 
1.5%
577011000 1
 
1.5%
581188000 1
 
1.5%
622816000 1
 
1.5%
ValueCountFrequency (%)
27785711000 1
1.5%
21433798000 1
1.5%
19446107000 1
1.5%
17215279000 1
1.5%
14891761000 1
1.5%
6030226000 1
1.5%
5476734000 1
1.5%
5461662000 1
1.5%
5275322000 1
1.5%
4987744000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52073970
Minimum0
Maximum5.92306 × 108
Zeros21
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:44.415073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4823000
Q357081000
95-th percentile2.73619 × 108
Maximum5.92306 × 108
Range5.92306 × 108
Interquartile range (IQR)57081000

Descriptive statistics

Standard deviation1.0630554 × 108
Coefficient of variation (CV)2.0414334
Kurtosis11.038487
Mean52073970
Median Absolute Deviation (MAD)4823000
Skewness3.0755991
Sum3.488956 × 109
Variance1.1300868 × 1016
MonotonicityNot monotonic
2024-03-14T18:47:44.661362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 21
31.3%
1713000 1
 
1.5%
99014000 1
 
1.5%
10995000 1
 
1.5%
126842000 1
 
1.5%
19736000 1
 
1.5%
63461000 1
 
1.5%
282672000 1
 
1.5%
275650000 1
 
1.5%
2415000 1
 
1.5%
Other values (37) 37
55.2%
ValueCountFrequency (%)
0 21
31.3%
7000 1
 
1.5%
39000 1
 
1.5%
41000 1
 
1.5%
81000 1
 
1.5%
115000 1
 
1.5%
372000 1
 
1.5%
446000 1
 
1.5%
1713000 1
 
1.5%
1746000 1
 
1.5%
ValueCountFrequency (%)
592306000 1
1.5%
403361000 1
1.5%
282672000 1
1.5%
275650000 1
1.5%
268880000 1
1.5%
205987000 1
1.5%
166741000 1
1.5%
145533000 1
1.5%
131861000 1
1.5%
126842000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18582582
Minimum0
Maximum2.24863 × 108
Zeros27
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:44.994602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median703000
Q37051000
95-th percentile1.221621 × 108
Maximum2.24863 × 108
Range2.24863 × 108
Interquartile range (IQR)7051000

Descriptive statistics

Standard deviation46464187
Coefficient of variation (CV)2.5004161
Kurtosis9.1788573
Mean18582582
Median Absolute Deviation (MAD)703000
Skewness3.0733955
Sum1.245033 × 109
Variance2.1589206 × 1015
MonotonicityNot monotonic
2024-03-14T18:47:45.325811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 27
40.3%
53084000 1
 
1.5%
54513000 1
 
1.5%
6982000 1
 
1.5%
9692000 1
 
1.5%
1535000 1
 
1.5%
10559000 1
 
1.5%
194156000 1
 
1.5%
115000 1
 
1.5%
4955000 1
 
1.5%
Other values (31) 31
46.3%
ValueCountFrequency (%)
0 27
40.3%
10000 1
 
1.5%
21000 1
 
1.5%
115000 1
 
1.5%
158000 1
 
1.5%
185000 1
 
1.5%
233000 1
 
1.5%
703000 1
 
1.5%
752000 1
 
1.5%
971000 1
 
1.5%
ValueCountFrequency (%)
224863000 1
1.5%
194156000 1
1.5%
162162000 1
1.5%
126447000 1
1.5%
112164000 1
1.5%
101376000 1
1.5%
70935000 1
1.5%
54513000 1
1.5%
53084000 1
1.5%
18308000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85276910
Minimum0
Maximum4.83002 × 108
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:45.567717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22991000
Q31.155355 × 108
95-th percentile3.14078 × 108
Maximum4.83002 × 108
Range4.83002 × 108
Interquartile range (IQR)1.155355 × 108

Descriptive statistics

Standard deviation1.222091 × 108
Coefficient of variation (CV)1.4330855
Kurtosis1.7591332
Mean85276910
Median Absolute Deviation (MAD)22991000
Skewness1.5945257
Sum5.713553 × 109
Variance1.4935065 × 1016
MonotonicityNot monotonic
2024-03-14T18:47:45.824169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
66545000 1
 
1.5%
133246000 1
 
1.5%
11834000 1
 
1.5%
126842000 1
 
1.5%
19736000 1
 
1.5%
483002000 1
 
1.5%
282672000 1
 
1.5%
275650000 1
 
1.5%
2415000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
2415000 1
 
1.5%
2950000 1
 
1.5%
3024000 1
 
1.5%
3191000 1
 
1.5%
3754000 1
 
1.5%
8665000 1
 
1.5%
10713000 1
 
1.5%
11834000 1
 
1.5%
12778000 1
 
1.5%
ValueCountFrequency (%)
483002000 1
1.5%
456768000 1
1.5%
366242000 1
1.5%
320189000 1
1.5%
299819000 1
1.5%
287375000 1
1.5%
282672000 1
1.5%
280624000 1
1.5%
275650000 1
1.5%
266126000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.653433
Minimum0
Maximum100
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size731.0 B
2024-03-14T18:47:46.069159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.075
median96.2
Q398.5
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)59.425

Descriptive statistics

Standard deviation41.240902
Coefficient of variation (CV)0.57556073
Kurtosis-0.72574208
Mean71.653433
Median Absolute Deviation (MAD)3.6
Skewness-1.0768009
Sum4800.78
Variance1700.812
MonotonicityNot monotonic
2024-03-14T18:47:46.446089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 15
22.4%
100.0 7
 
10.4%
99.7 2
 
3.0%
97.8 2
 
3.0%
98.5 2
 
3.0%
99.8 2
 
3.0%
97.45 1
 
1.5%
95.5 1
 
1.5%
97.83 1
 
1.5%
97.2 1
 
1.5%
Other values (33) 33
49.3%
ValueCountFrequency (%)
0.0 15
22.4%
36.4 1
 
1.5%
38.5 1
 
1.5%
39.65 1
 
1.5%
45.18 1
 
1.5%
69.0 1
 
1.5%
91.17 1
 
1.5%
91.62 1
 
1.5%
92.21 1
 
1.5%
92.8 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.94 1
 
1.5%
99.9 1
 
1.5%
99.8 2
 
3.0%
99.7 2
 
3.0%
99.68 1
 
1.5%
99.63 1
 
1.5%
99.62 1
 
1.5%
98.5 2
 
3.0%
98.1 1
 
1.5%

Interactions

2024-03-14T18:47:39.882952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:34.949182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.233212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.159987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.125690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.996274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:40.023792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:35.192055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.373166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.309436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.264266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:39.183383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:40.225996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:35.667247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.536267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.454360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.401711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:39.324415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:40.396232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:35.815299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.744612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.603639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.547431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:39.470867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:40.531740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:35.952086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.880506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.792667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.677653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:39.605333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:40.672903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:36.093080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.021406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:37.985454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:38.820652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:47:39.743164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:47:46.709338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8210.8060.6190.4230.7830.808
부과금액0.0000.8211.0000.9920.5990.0000.5020.144
수납급액0.0000.8060.9921.0000.6220.0000.6290.144
환급금액0.0000.6190.5990.6221.0000.9250.8020.797
결손금액0.0000.4230.0000.0000.9251.0000.9450.866
미수납 금액0.0000.7830.5020.6290.8020.9451.0000.808
징수율0.0000.8080.1440.1440.7970.8660.8081.000
2024-03-14T18:47:46.996085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-14T18:47:47.242833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9920.6300.3850.5750.6590.0000.425
수납급액0.9921.0000.5750.3380.5210.6960.0000.408
환급금액0.6300.5751.0000.7450.8850.1570.0000.255
결손금액0.3850.3380.7451.0000.820-0.0130.0000.182
미수납 금액0.5750.5210.8850.8201.0000.0240.0000.466
징수율0.6590.6960.157-0.0130.0241.0000.0000.543
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.4250.4080.2550.1820.4660.5430.0001.000

Missing values

2024-03-14T18:47:40.872432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:47:41.141216image/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
0강원도양양군428302017도축세000000.0
1강원도양양군428302017레저세000000.0
2강원도양양군428302017재산세466563100045566720001713000125840009637500097.66
3강원도양양군428302017주민세5328950004966760001150009750003524400093.2
4강원도양양군428302017취득세1490042600014891761000634410000866500099.94
5강원도양양군428302017자동차세3321209000306250000025837000605900025265000092.21
6강원도양양군428302017과년도수입110828600042665500020598700022486300045676800038.5
7강원도양양군428302017담배소비세29776740002977674000000100.0
8강원도양양군428302017도시계획세000000.0
9강원도양양군428302017등록면허세8253450008222100001746000185000295000099.62
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57강원도양양군428302021취득세27804623000277857110005072100001891200099.9
58강원도양양군428302021자동차세3681483000341465900023540000712000025970400092.8
59강원도양양군428302021과년도수입1541865000106345900016674100011216400036624200069.0
60강원도양양군428302021담배소비세3142113000314211300044600000100.0
61강원도양양군428302021도시계획세000000.0
62강원도양양군428302021등록면허세153821300015350310007007000158000302400099.8
63강원도양양군428302021지방교육세537743900052753220001158700025440009957300098.1
64강원도양양군428302021지방소득세62976040006030226000268880000125200026612600095.8
65강원도양양군428302021지방소비세37164350003716435000000100.0
66강원도양양군428302021지역자원시설세975172000960613000700017810001277800098.5