Overview

Dataset statistics

Number of variables11
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory97.1 B

Variable types

Categorical5
Numeric6

Dataset

Description경기도 안산시 지방세 징수현황을 세목별로 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율로 구분하여 제공합니다
URLhttps://www.data.go.kr/data/15080154/fileData.do

Alerts

시도명 has constant value ""Constant
자치단체코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 자치단체코드High correlation
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 1 other fieldsHigh correlation
부과금액 has 47 (40.2%) zerosZeros
수납급액 has 47 (40.2%) zerosZeros
환급금액 has 57 (48.7%) zerosZeros
결손금액 has 92 (78.6%) zerosZeros
미수납 금액 has 63 (53.8%) zerosZeros
징수율 has 47 (40.2%) zerosZeros

Reproduction

Analysis started2023-12-11 23:18:49.756298
Analysis finished2023-12-11 23:18:54.413378
Duration4.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
경기도
117 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 117
100.0%

Length

2023-12-12T08:18:54.478347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:18:54.563109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 117
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
안산시
39 
안산시 단원구
39 
안산시 상록구
39 

Length

Max length7
Median length7
Mean length5.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 39
33.3%
안산시 단원구 39
33.3%
안산시 상록구 39
33.3%

Length

2023-12-12T08:18:54.667604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:18:54.786196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안산시 117
60.0%
단원구 39
 
20.0%
상록구 39
 
20.0%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
41270
39 
41273
39 
41271
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41270
2nd row41270
3rd row41270
4th row41270
5th row41270

Common Values

ValueCountFrequency (%)
41270 39
33.3%
41273 39
33.3%
41271 39
33.3%

Length

2023-12-12T08:18:54.905208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:18:55.024384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41270 39
33.3%
41273 39
33.3%
41271 39
33.3%

과세년도
Categorical

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2022
39 
2021
39 
2020
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 39
33.3%
2021 39
33.3%
2020 39
33.3%

Length

2023-12-12T08:18:55.139906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:18:55.227529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 39
33.3%
2021 39
33.3%
2020 39
33.3%

세목명
Categorical

Distinct13
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
레저세
9 
재산세
9 
주민세
9 
취득세
9 
자동차세
9 
Other values (8)
72 

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 9
 
7.7%
재산세 9
 
7.7%
주민세 9
 
7.7%
취득세 9
 
7.7%
자동차세 9
 
7.7%
과년도수입 9
 
7.7%
담배소비세 9
 
7.7%
도시계획세 9
 
7.7%
등록면허세 9
 
7.7%
지방교육세 9
 
7.7%
Other values (3) 27
23.1%

Length

2023-12-12T08:18:55.354768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 9
 
7.7%
재산세 9
 
7.7%
주민세 9
 
7.7%
취득세 9
 
7.7%
자동차세 9
 
7.7%
과년도수입 9
 
7.7%
담배소비세 9
 
7.7%
도시계획세 9
 
7.7%
등록면허세 9
 
7.7%
지방교육세 9
 
7.7%
Other values (3) 27
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7024889 × 1010
Minimum-12483000
Maximum2.18 × 1011
Zeros47
Zeros (%)40.2%
Negative1
Negative (%)0.9%
Memory size1.2 KiB
2023-12-12T08:18:55.491349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12483000
5-th percentile0
Q10
median6.195102 × 109
Q33.0538022 × 1010
95-th percentile1.473194 × 1011
Maximum2.18 × 1011
Range2.1801248 × 1011
Interquartile range (IQR)3.0538022 × 1010

Descriptive statistics

Standard deviation4.5460625 × 1010
Coefficient of variation (CV)1.6821762
Kurtosis6.544164
Mean2.7024889 × 1010
Median Absolute Deviation (MAD)6.195102 × 109
Skewness2.5589233
Sum3.161912 × 1012
Variance2.0666685 × 1021
MonotonicityNot monotonic
2023-12-12T08:18:55.620320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
40.2%
34667133000 1
 
0.9%
1384370000 1
 
0.9%
5237000000 1
 
0.9%
24812271000 1
 
0.9%
56404346000 1
 
0.9%
-12483000 1
 
0.9%
26160197000 1
 
0.9%
5038122000 1
 
0.9%
47244656000 1
 
0.9%
Other values (61) 61
52.1%
ValueCountFrequency (%)
-12483000 1
 
0.9%
0 47
40.2%
325171000 1
 
0.9%
1384370000 1
 
0.9%
4734396000 1
 
0.9%
4839885000 1
 
0.9%
5038122000 1
 
0.9%
5237000000 1
 
0.9%
5254892000 1
 
0.9%
5527496000 1
 
0.9%
ValueCountFrequency (%)
218000000000 1
0.9%
210632000000 1
0.9%
182174000000 1
0.9%
173758000000 1
0.9%
170370000000 1
0.9%
148597000000 1
0.9%
147000000000 1
0.9%
110112000000 1
0.9%
101000000000 1
0.9%
100000000000 1
0.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5390809 × 1010
Minimum-12483000
Maximum2.17842 × 1011
Zeros47
Zeros (%)40.2%
Negative1
Negative (%)0.9%
Memory size1.2 KiB
2023-12-12T08:18:55.744161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12483000
5-th percentile0
Q10
median6.010465 × 109
Q32.6503355 × 1010
95-th percentile1.32918 × 1011
Maximum2.17842 × 1011
Range2.1785448 × 1011
Interquartile range (IQR)2.6503355 × 1010

Descriptive statistics

Standard deviation4.4715656 × 1010
Coefficient of variation (CV)1.7610962
Kurtosis6.9519317
Mean2.5390809 × 1010
Median Absolute Deviation (MAD)6.010465 × 109
Skewness2.6329033
Sum2.9707246 × 1012
Variance1.9994899 × 1021
MonotonicityNot monotonic
2023-12-12T08:18:55.868757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47
40.2%
34667133000 1
 
0.9%
1384370000 1
 
0.9%
5237000000 1
 
0.9%
24812271000 1
 
0.9%
56404346000 1
 
0.9%
-12483000 1
 
0.9%
26160197000 1
 
0.9%
4941284000 1
 
0.9%
43555899000 1
 
0.9%
Other values (61) 61
52.1%
ValueCountFrequency (%)
-12483000 1
 
0.9%
0 47
40.2%
325171000 1
 
0.9%
1384370000 1
 
0.9%
4631408000 1
 
0.9%
4635096000 1
 
0.9%
4757094000 1
 
0.9%
4894683000 1
 
0.9%
4941284000 1
 
0.9%
5095483000 1
 
0.9%
ValueCountFrequency (%)
217842000000 1
0.9%
208377000000 1
0.9%
181983000000 1
0.9%
173488000000 1
0.9%
164445000000 1
0.9%
140702000000 1
0.9%
130972000000 1
0.9%
108206000000 1
0.9%
100200000000 1
0.9%
99411105000 1
0.9%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.035644 × 108
Minimum0
Maximum7.113289 × 109
Zeros57
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T08:18:56.004267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median910000
Q31.41862 × 108
95-th percentile2.3750624 × 109
Maximum7.113289 × 109
Range7.113289 × 109
Interquartile range (IQR)1.41862 × 108

Descriptive statistics

Standard deviation1.1390975 × 109
Coefficient of variation (CV)2.8225917
Kurtosis15.771929
Mean4.035644 × 108
Median Absolute Deviation (MAD)910000
Skewness3.863335
Sum4.7217035 × 1010
Variance1.2975431 × 1018
MonotonicityNot monotonic
2023-12-12T08:18:56.146099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
48.7%
4963425000 1
 
0.9%
326454000 1
 
0.9%
1341057000 1
 
0.9%
26171000 1
 
0.9%
137605000 1
 
0.9%
2315234000 1
 
0.9%
10127000 1
 
0.9%
12483000 1
 
0.9%
61235000 1
 
0.9%
Other values (51) 51
43.6%
ValueCountFrequency (%)
0 57
48.7%
631000 1
 
0.9%
910000 1
 
0.9%
2467000 1
 
0.9%
2540000 1
 
0.9%
3653000 1
 
0.9%
3879000 1
 
0.9%
7240000 1
 
0.9%
8304000 1
 
0.9%
10127000 1
 
0.9%
ValueCountFrequency (%)
7113289000 1
0.9%
4963425000 1
0.9%
4882874000 1
0.9%
4681400000 1
0.9%
4344186000 1
0.9%
2614376000 1
0.9%
2315234000 1
0.9%
2271776000 1
0.9%
1823778000 1
0.9%
1444791000 1
0.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3427825 × 108
Minimum0
Maximum1.5211561 × 1010
Zeros92
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T08:18:56.261868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.90919 × 108
Maximum1.5211561 × 1010
Range1.5211561 × 1010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7101392 × 109
Coefficient of variation (CV)5.1159152
Kurtosis51.918493
Mean3.3427825 × 108
Median Absolute Deviation (MAD)0
Skewness6.7029149
Sum3.9110555 × 1010
Variance2.924576 × 1018
MonotonicityNot monotonic
2023-12-12T08:18:56.610787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 92
78.6%
3000 2
 
1.7%
10000 2
 
1.7%
68000 1
 
0.9%
28797000 1
 
0.9%
4420885000 1
 
0.9%
328000 1
 
0.9%
13678000 1
 
0.9%
1151000 1
 
0.9%
83000 1
 
0.9%
Other values (14) 14
 
12.0%
ValueCountFrequency (%)
0 92
78.6%
3000 2
 
1.7%
5000 1
 
0.9%
10000 2
 
1.7%
21000 1
 
0.9%
65000 1
 
0.9%
68000 1
 
0.9%
83000 1
 
0.9%
104000 1
 
0.9%
113000 1
 
0.9%
ValueCountFrequency (%)
15211561000 1
0.9%
6248338000 1
0.9%
6105060000 1
0.9%
4420885000 1
0.9%
3731300000 1
0.9%
3339407000 1
0.9%
28797000 1
0.9%
13678000 1
0.9%
8078000 1
0.9%
1151000 1
0.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.307282 × 109
Minimum0
Maximum1.7610711 × 1010
Zeros63
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T08:18:56.719619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.73277 × 108
95-th percentile6.261244 × 109
Maximum1.7610711 × 1010
Range1.7610711 × 1010
Interquartile range (IQR)9.73277 × 108

Descriptive statistics

Standard deviation3.1314896 × 109
Coefficient of variation (CV)2.3954202
Kurtosis13.833013
Mean1.307282 × 109
Median Absolute Deviation (MAD)0
Skewness3.5748072
Sum1.5295199 × 1011
Variance9.8062273 × 1018
MonotonicityNot monotonic
2023-12-12T08:18:56.835370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
53.8%
1906050000 1
 
0.9%
100017000 1
 
0.9%
4151821000 1
 
0.9%
6410848000 1
 
0.9%
32497000 1
 
0.9%
1419254000 1
 
0.9%
3688689000 1
 
0.9%
96838000 1
 
0.9%
2122469000 1
 
0.9%
Other values (45) 45
38.5%
ValueCountFrequency (%)
0 63
53.8%
32157000 1
 
0.9%
32497000 1
 
0.9%
37520000 1
 
0.9%
41186000 1
 
0.9%
51260000 1
 
0.9%
59586000 1
 
0.9%
82791000 1
 
0.9%
96838000 1
 
0.9%
99300000 1
 
0.9%
ValueCountFrequency (%)
17610711000 1
0.9%
15917291000 1
0.9%
15631585000 1
0.9%
12643716000 1
0.9%
7508491000 1
0.9%
6410848000 1
0.9%
6223843000 1
0.9%
6141166000 1
0.9%
5924197000 1
0.9%
4196841000 1
0.9%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.710342
Minimum0
Maximum100
Zeros47
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T08:18:56.952173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median88
Q398.29
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.29

Descriptive statistics

Standard deviation47.222482
Coefficient of variation (CV)0.86313631
Kurtosis-1.916174
Mean54.710342
Median Absolute Deviation (MAD)12
Skewness-0.24123295
Sum6401.11
Variance2229.9629
MonotonicityNot monotonic
2023-12-12T08:18:57.051867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.0 47
40.2%
100.0 23
19.7%
98.0 9
 
7.7%
92.0 3
 
2.6%
96.0 3
 
2.6%
88.0 3
 
2.6%
93.0 2
 
1.7%
95.0 1
 
0.9%
42.0 1
 
0.9%
85.0 1
 
0.9%
Other values (24) 24
20.5%
ValueCountFrequency (%)
0.0 47
40.2%
13.0 1
 
0.9%
27.42 1
 
0.9%
30.0 1
 
0.9%
42.0 1
 
0.9%
45.0 1
 
0.9%
47.25 1
 
0.9%
85.0 1
 
0.9%
86.0 1
 
0.9%
86.23 1
 
0.9%
ValueCountFrequency (%)
100.0 23
19.7%
99.84 1
 
0.9%
99.74 1
 
0.9%
99.63 1
 
0.9%
99.48 1
 
0.9%
99.0 1
 
0.9%
98.46 1
 
0.9%
98.29 1
 
0.9%
98.27 1
 
0.9%
98.2 1
 
0.9%

Interactions

2023-12-12T08:18:53.473642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.171402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.991289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.486010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.118940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.784737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.577446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.533147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.066888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.606860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.204408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.880063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.698302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.622733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.158630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.693780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.311320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.992555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.809462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.731492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.244460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.794529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.438719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.117950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.915519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.818765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.328274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.891060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.563242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.236119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:54.018048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:50.916917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:51.411415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.014484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:52.675113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:18:53.352401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:18:57.124994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
시군구명1.0001.0000.0000.0000.3790.4060.4360.1740.2960.382
자치단체코드1.0001.0000.0000.0000.3790.4060.4360.1740.2960.382
과세년도0.0000.0001.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0000.0001.0000.6650.7080.5010.5520.6140.678
부과금액0.3790.3790.0000.6651.0000.9900.6870.0000.5900.400
수납급액0.4060.4060.0000.7080.9901.0000.6810.0000.5900.356
환급금액0.4360.4360.0000.5010.6870.6811.0000.8800.8700.794
결손금액0.1740.1740.0000.5520.0000.0000.8801.0000.9931.000
미수납 금액0.2960.2960.0000.6140.5900.5900.8700.9931.0000.922
징수율0.3820.3820.0000.6780.4000.3560.7941.0000.9221.000
2023-12-12T08:18:57.220340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도자치단체코드시군구명세목명
과세년도1.0000.0000.0000.000
자치단체코드0.0001.0001.0000.000
시군구명0.0001.0001.0000.000
세목명0.0000.0000.0001.000
2023-12-12T08:18:57.303535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율시군구명자치단체코드과세년도세목명
부과금액1.0000.9860.7940.3960.7510.7190.2410.2410.0000.344
수납급액0.9861.0000.7450.3390.6990.7600.1930.1930.0000.394
환급금액0.7940.7451.0000.6080.9170.4500.1970.1970.0000.265
결손금액0.3960.3390.6081.0000.6360.1580.1630.1630.0000.340
미수납 금액0.7510.6990.9170.6361.0000.3410.1910.1910.0000.325
징수율0.7190.7600.4500.1580.3411.0000.2880.2880.0000.446
시군구명0.2410.1930.1970.1630.1910.2881.0001.0000.0000.000
자치단체코드0.2410.1930.1970.1630.1910.2881.0001.0000.0000.000
과세년도0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
세목명0.3440.3940.2650.3400.3250.4460.0000.0000.0001.000

Missing values

2023-12-12T08:18:54.175864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:18:54.354037image/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
0경기도안산시412702022레저세000000.0
1경기도안산시412702022재산세000000.0
2경기도안산시412702022주민세000000.0
3경기도안산시412702022취득세000000.0
4경기도안산시412702022자동차세3466713300034667133000000100.0
5경기도안산시412702022과년도수입000000.0
6경기도안산시412702022담배소비세5827551100058275511000000100.0
7경기도안산시412702022도시계획세000000.0
8경기도안산시412702022등록면허세000000.0
9경기도안산시412702022지방교육세2563539600025635396000000100.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
107경기도안산시 상록구412712020취득세1821740000001819830000005095530000191776000100.0
108경기도안산시 상록구412712020자동차세27393775000231969340002918410000419684100085.0
109경기도안산시 상록구412712020과년도수입20392371000846299500014447910004420885000750849100042.0
110경기도안산시 상록구412712020담배소비세000000.0
111경기도안산시 상록구412712020도시계획세000000.0
112경기도안산시 상록구412712020등록면허세9226794000918927400027449000037520000100.0
113경기도안산시 상록구412712020지방교육세27899902000264615220001296790003000143837700095.0
114경기도안산시 상록구412712020지방소득세4289808900039480722000182377800028797000338857000092.0
115경기도안산시 상록구412712020지방소비세000000.0
116경기도안산시 상록구412712020지역자원시설세4734396000463509600063100009930000098.0