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

Description지방세 부과액에 대한 세목별 징수현황을 제공 (2017~2021년도) - 활용업무 : 지자체의 재정자주도, 재정자립도를 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
URLhttps://www.data.go.kr/data/15080267/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 4 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 22 (32.8%) zerosZeros
결손금액 has 22 (32.8%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2023-12-12 00:28:11.000211
Analysis finished2023-12-12 00:28:14.927824
Duration3.93 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
대전광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대전광역시 67
100.0%

Length

2023-12-12T09:28:14.990871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:15.077740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
유성구 67
100.0%

Length

2023-12-12T09:28:15.171010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:15.265771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
30200
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30200
2nd row30200
3rd row30200
4th row30200
5th row30200

Common Values

ValueCountFrequency (%)
30200 67
100.0%

Length

2023-12-12T09:28:15.365016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:15.460999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30200 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

2023-12-12T09:28:15.573611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:28:15.680678image/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

2023-12-12T09:28:15.810776image/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%
Mean3.6190527 × 1010
Minimum0
Maximum2.1718038 × 1011
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T09:28:16.200610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0893225 × 109
median1.2424535 × 1010
Q33.8200758 × 1010
95-th percentile1.2888004 × 1011
Maximum2.1718038 × 1011
Range2.1718038 × 1011
Interquartile range (IQR)3.7111436 × 1010

Descriptive statistics

Standard deviation4.9472994 × 1010
Coefficient of variation (CV)1.367015
Kurtosis2.3293852
Mean3.6190527 × 1010
Median Absolute Deviation (MAD)1.2424535 × 1010
Skewness1.691748
Sum2.4247653 × 1012
Variance2.4475771 × 1021
MonotonicityNot monotonic
2023-12-12T09:28:16.360241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
3505000000 1
 
1.5%
121080385000 1
 
1.5%
11762362000 1
 
1.5%
1057750000 1
 
1.5%
88293277000 1
 
1.5%
23361764000 1
 
1.5%
155910966000 1
 
1.5%
36870801000 1
 
1.5%
324986000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
324986000 1
 
1.5%
1057750000 1
 
1.5%
1120895000 1
 
1.5%
3505000000 1
 
1.5%
3518957000 1
 
1.5%
3846079000 1
 
1.5%
4111251000 1
 
1.5%
4504797000 1
 
1.5%
4579086000 1
 
1.5%
ValueCountFrequency (%)
217180383000 1
1.5%
166780026000 1
1.5%
155910966000 1
1.5%
131999711000 1
1.5%
121600822000 1
1.5%
121080385000 1
1.5%
120916022000 1
1.5%
116235199000 1
1.5%
114964680000 1
1.5%
97896590000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5027346 × 1010
Minimum-4.612754 × 109
Maximum2.1702272 × 1011
Zeros15
Zeros (%)22.4%
Negative2
Negative (%)3.0%
Memory size735.0 B
2023-12-12T09:28:16.488856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.612754 × 109
5-th percentile0
Q11.618415 × 108
median1.2270326 × 1010
Q33.6035608 × 1010
95-th percentile1.2795846 × 1011
Maximum2.1702272 × 1011
Range2.2163547 × 1011
Interquartile range (IQR)3.5873767 × 1010

Descriptive statistics

Standard deviation4.9152434 × 1010
Coefficient of variation (CV)1.4032589
Kurtosis2.4428421
Mean3.5027346 × 1010
Median Absolute Deviation (MAD)1.2270326 × 1010
Skewness1.7132988
Sum2.3468322 × 1012
Variance2.4159618 × 1021
MonotonicityNot monotonic
2023-12-12T09:28:16.647070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
3505000000 1
 
1.5%
117950796000 1
 
1.5%
11625996000 1
 
1.5%
1057750000 1
 
1.5%
86972236000 1
 
1.5%
23067010000 1
 
1.5%
152908924000 1
 
1.5%
33611646000 1
 
1.5%
-4612754000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
-4612754000 1
 
1.5%
-1100032000 1
 
1.5%
0 15
22.4%
323683000 1
 
1.5%
1057750000 1
 
1.5%
1120895000 1
 
1.5%
2233689000 1
 
1.5%
3359706000 1
 
1.5%
3505000000 1
 
1.5%
3518957000 1
 
1.5%
ValueCountFrequency (%)
217022720000 1
1.5%
164407881000 1
1.5%
152908924000 1
1.5%
131808505000 1
1.5%
118975012000 1
1.5%
117950796000 1
1.5%
117564954000 1
1.5%
115934966000 1
1.5%
112365417000 1
1.5%
97662076000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.522343 × 108
Minimum0
Maximum1.0276506 × 1010
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T09:28:16.842054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38717000
Q35.380985 × 108
95-th percentile4.9682456 × 109
Maximum1.0276506 × 1010
Range1.0276506 × 1010
Interquartile range (IQR)5.380985 × 108

Descriptive statistics

Standard deviation2.0505065 × 109
Coefficient of variation (CV)2.1533634
Kurtosis7.5744505
Mean9.522343 × 108
Median Absolute Deviation (MAD)38717000
Skewness2.7142763
Sum6.3799698 × 1010
Variance4.204577 × 1018
MonotonicityNot monotonic
2023-12-12T09:28:17.065370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
5001260000 1
 
1.5%
3939299000 1
 
1.5%
2782000 1
 
1.5%
71549000 1
 
1.5%
25245000 1
 
1.5%
990940000 1
 
1.5%
623864000 1
 
1.5%
10276506000 1
 
1.5%
70279000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
2620000 1
 
1.5%
2782000 1
 
1.5%
6734000 1
 
1.5%
6828000 1
 
1.5%
14601000 1
 
1.5%
20690000 1
 
1.5%
20920000 1
 
1.5%
22809000 1
 
1.5%
25245000 1
 
1.5%
ValueCountFrequency (%)
10276506000 1
1.5%
7447291000 1
1.5%
6809017000 1
1.5%
5001260000 1
1.5%
4891212000 1
1.5%
4281913000 1
1.5%
3939299000 1
1.5%
3883168000 1
1.5%
3652768000 1
1.5%
3258528000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2471312 × 108
Minimum0
Maximum2.309427 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T09:28:17.253434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3972000
Q395412000
95-th percentile1.7631624 × 109
Maximum2.309427 × 109
Range2.309427 × 109
Interquartile range (IQR)95412000

Descriptive statistics

Standard deviation5.4347368 × 108
Coefficient of variation (CV)2.4185223
Kurtosis6.3025932
Mean2.2471312 × 108
Median Absolute Deviation (MAD)3972000
Skewness2.7116414
Sum1.5055779 × 1010
Variance2.9536364 × 1017
MonotonicityNot monotonic
2023-12-12T09:28:17.404883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
322431000 1
 
1.5%
1683578000 1
 
1.5%
6265000 1
 
1.5%
6206000 1
 
1.5%
4343000 1
 
1.5%
21381000 1
 
1.5%
136887000 1
 
1.5%
1117996000 1
 
1.5%
969000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
31000 1
 
1.5%
47000 1
 
1.5%
195000 1
 
1.5%
251000 1
 
1.5%
292000 1
 
1.5%
598000 1
 
1.5%
804000 1
 
1.5%
969000 1
 
1.5%
1226000 1
 
1.5%
ValueCountFrequency (%)
2309427000 1
1.5%
1986491000 1
1.5%
1950355000 1
1.5%
1797270000 1
1.5%
1683578000 1
1.5%
1149880000 1
1.5%
1117996000 1
1.5%
826196000 1
1.5%
419198000 1
1.5%
322431000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3846701 × 108
Minimum0
Maximum4.119915 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T09:28:17.564898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.82155 × 108
Q31.339706 × 109
95-th percentile3.7476222 × 109
Maximum4.119915 × 109
Range4.119915 × 109
Interquartile range (IQR)1.339706 × 109

Descriptive statistics

Standard deviation1.2959194 × 109
Coefficient of variation (CV)1.3808896
Kurtosis0.3087842
Mean9.3846701 × 108
Median Absolute Deviation (MAD)1.82155 × 108
Skewness1.2918994
Sum6.287729 × 1010
Variance1.679407 × 1018
MonotonicityNot monotonic
2023-12-12T09:28:17.703180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
2303379000 1
 
1.5%
1446011000 1
 
1.5%
130101000 1
 
1.5%
1314835000 1
 
1.5%
290411000 1
 
1.5%
2980661000 1
 
1.5%
3122268000 1
 
1.5%
3819744000 1
 
1.5%
33106000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
24218000 1
 
1.5%
29112000 1
 
1.5%
30034000 1
 
1.5%
33106000 1
 
1.5%
38903000 1
 
1.5%
129778000 1
 
1.5%
130101000 1
 
1.5%
153036000 1
 
1.5%
172938000 1
 
1.5%
ValueCountFrequency (%)
4119915000 1
1.5%
4062587000 1
1.5%
3877400000 1
1.5%
3819744000 1
1.5%
3579338000 1
1.5%
3483053000 1
1.5%
3449622000 1
1.5%
3122268000 1
1.5%
3108415000 1
1.5%
3090155000 1
1.5%

징수율
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.013134
Minimum-1419.37
Maximum100
Zeros15
Zeros (%)22.4%
Negative2
Negative (%)3.0%
Memory size735.0 B
2023-12-12T09:28:17.835059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1419.37
5-th percentile0
Q12.89
median97.84
Q398.795
95-th percentile100
Maximum100
Range1519.37
Interquartile range (IQR)95.905

Descriptive statistics

Standard deviation187.2021
Coefficient of variation (CV)3.898977
Kurtosis59.507949
Mean48.013134
Median Absolute Deviation (MAD)2.08
Skewness-7.5129883
Sum3216.88
Variance35044.628
MonotonicityNot monotonic
2023-12-12T09:28:18.007329image/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%
96.78 1
 
1.5%
97.42 1
 
1.5%
98.84 1
 
1.5%
98.5 1
 
1.5%
98.74 1
 
1.5%
98.07 1
 
1.5%
91.16 1
 
1.5%
-1419.37 1
 
1.5%
Other values (37) 37
55.2%
ValueCountFrequency (%)
-1419.37 1
 
1.5%
-28.6 1
 
1.5%
0.0 15
22.4%
5.78 1
 
1.5%
26.53 1
 
1.5%
35.85 1
 
1.5%
88.72 1
 
1.5%
89.24 1
 
1.5%
90.76 1
 
1.5%
91.16 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.93 1
 
1.5%
99.86 1
 
1.5%
99.8 1
 
1.5%
99.76 1
 
1.5%
99.75 1
 
1.5%
99.74 1
 
1.5%
99.73 1
 
1.5%
99.72 1
 
1.5%
99.71 1
 
1.5%

Interactions

2023-12-12T09:28:14.113468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.327149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.895469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.420130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.943331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.492438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.217630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.401122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.968295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.502067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.029452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.586535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.301505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.480182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.041044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.576843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.103303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.703085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.387012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.555108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.146676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.663926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.184418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.816824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.469551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.655991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.239335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.765261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.265181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.903658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.566889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:11.774653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.327203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:12.855756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:13.361878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:28:14.001155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:28:18.139277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.2400.000NaN
세목명0.0001.0000.8260.8180.7350.1080.931NaN
부과금액0.0000.8261.0000.9930.6710.0000.641NaN
수납급액0.0000.8180.9931.0000.7510.0000.643NaN
환급금액0.0000.7350.6710.7511.0000.9630.878NaN
결손금액0.2400.1080.0000.0000.9631.0000.815NaN
미수납 금액0.0000.9310.6410.6430.8780.8151.000NaN
징수율NaNNaNNaNNaNNaNNaNNaN1.000
2023-12-12T09:28:18.273556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T09:28:18.379121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9830.7380.6940.6840.4320.0000.523
수납급액0.9831.0000.6260.5870.5680.4850.0000.503
환급금액0.7380.6261.0000.9200.9050.0230.0000.340
결손금액0.6940.5870.9201.0000.934-0.0450.1480.000
미수납 금액0.6840.5680.9050.9341.000-0.0930.0000.591
징수율0.4320.4850.023-0.045-0.0931.0000.0490.000
과세년도0.0000.0000.0000.1480.0000.0491.0000.000
세목명0.5230.5030.3400.0000.5910.0000.0001.000

Missing values

2023-12-12T09:28:14.690973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:28:14.869009image/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
0대전광역시유성구302002017도축세000000.0
1대전광역시유성구302002017레저세45790860004579086000000100.0
2대전광역시유성구302002017재산세71994252000708739750003723200016102000110417500098.44
3대전광역시유성구302002017주민세2060515200020276387000290440001565400031311100098.4
4대전광역시유성구302002017취득세11623519900011593496600035578200012729500017293800099.74
5대전광역시유성구302002017자동차세3428404300030415223000533641000419198000344962200088.72
6대전광역시유성구302002017과년도수입8420516000223368900042819130002309427000387740000026.53
7대전광역시유성구302002017담배소비세000000.0
8대전광역시유성구302002017도시계획세000000.0
9대전광역시유성구302002017등록면허세1073746300010707137000471690002920003003400099.72
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57대전광역시유성구302002021취득세2171803830002170227200001785482000462700015303600099.93
58대전광역시유성구302002021자동차세3797667200034718506000642365000168011000309015500091.42
59대전광역시유성구302002021과년도수입3846079000-110003200074472910008261960004119915000-28.6
60대전광역시유성구302002021담배소비세000000.0
61대전광역시유성구302002021도시계획세000000.0
62대전광역시유성구302002021등록면허세1435391300014314815000631120001950003890300099.73
63대전광역시유성구302002021지방교육세434327440004224427200042606800048784000113968800097.26
64대전광역시유성구302002021지방소득세1667800260001644078810004891212000123760000224838500098.58
65대전광역시유성구302002021지방소비세35189570003518957000000100.0
66대전광역시유성구302002021지역자원시설세131741110001297282000067340004700020124400098.47