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

Description인천광역시 서구 2017년도부터 2021년도까지 세목별 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율을 포함하고 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15078590&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 5 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 5 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
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 20 (29.9%) zerosZeros
수납급액 has 20 (29.9%) zerosZeros
환급금액 has 22 (32.8%) zerosZeros
결손금액 has 34 (50.7%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 20 (29.9%) zerosZeros

Reproduction

Analysis started2024-01-28 16:12:25.515309
Analysis finished2024-01-28 16:12:28.525151
Duration3.01 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
인천광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 67
100.0%

Length

2024-01-29T01:12:28.573853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:28.661160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 67
100.0%

Length

2024-01-29T01:12:28.755793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:28.839954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
28260
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28260
2nd row28260
3rd row28260
4th row28260
5th row28260

Common Values

ValueCountFrequency (%)
28260 67
100.0%

Length

2024-01-29T01:12:29.143626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:29.213579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28260 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

2024-01-29T01:12:29.299827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:12:29.399702image/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

2024-01-29T01:12:29.498627image/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 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5654343 × 1010
Minimum0
Maximum5.2762215 × 1011
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-29T01:12:29.603234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.3210756 × 1010
Q36.837986 × 1010
95-th percentile3.446985 × 1011
Maximum5.2762215 × 1011
Range5.2762215 × 1011
Interquartile range (IQR)6.837986 × 1010

Descriptive statistics

Standard deviation1.0665662 × 1011
Coefficient of variation (CV)1.6245174
Kurtosis6.5235961
Mean6.5654343 × 1010
Median Absolute Deviation (MAD)2.3210756 × 1010
Skewness2.510028
Sum4.3988409 × 1012
Variance1.1375635 × 1022
MonotonicityNot monotonic
2024-01-29T01:12:29.706992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
67565436000 1
 
1.5%
24311584000 1
 
1.5%
179960000000 1
 
1.5%
18528811000 1
 
1.5%
358680000000 1
 
1.5%
47111591000 1
 
1.5%
22239482000 1
 
1.5%
25096039000 1
 
1.5%
69194284000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
2483933000 1
 
1.5%
3456000000 1
 
1.5%
15966307000 1
 
1.5%
17004873000 1
 
1.5%
17662123000 1
 
1.5%
18402181000 1
 
1.5%
18528811000 1
 
1.5%
19005735000 1
 
1.5%
20274375000 1
 
1.5%
ValueCountFrequency (%)
527622151000 1
1.5%
383220000000 1
1.5%
358883000000 1
1.5%
358680000000 1
1.5%
312075000000 1
1.5%
189585026000 1
1.5%
179960000000 1
1.5%
177304000000 1
1.5%
155649000000 1
1.5%
146109000000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2580336 × 1010
Minimum-2.740936 × 109
Maximum5.2594306 × 1011
Zeros20
Zeros (%)29.9%
Negative1
Negative (%)1.5%
Memory size735.0 B
2024-01-29T01:12:29.812378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.740936 × 109
5-th percentile0
Q10
median2.0453534 × 1010
Q36.59141 × 1010
95-th percentile3.436378 × 1011
Maximum5.2594306 × 1011
Range5.2868399 × 1011
Interquartile range (IQR)6.59141 × 1010

Descriptive statistics

Standard deviation1.0656838 × 1011
Coefficient of variation (CV)1.7029053
Kurtosis6.7005658
Mean6.2580336 × 1010
Median Absolute Deviation (MAD)2.0453534 × 1010
Skewness2.5508463
Sum4.1928825 × 1012
Variance1.135682 × 1022
MonotonicityNot monotonic
2024-01-29T01:12:29.918673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
64972005000 1
 
1.5%
23701893000 1
 
1.5%
175701000000 1
 
1.5%
17997839000 1
 
1.5%
357925000000 1
 
1.5%
41253141000 1
 
1.5%
6700558000 1
 
1.5%
24994467000 1
 
1.5%
66856194000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
-2740936000 1
 
1.5%
0 20
29.9%
2483933000 1
 
1.5%
3456000000 1
 
1.5%
5348872000 1
 
1.5%
6206392000 1
 
1.5%
6700558000 1
 
1.5%
7911137000 1
 
1.5%
15184153000 1
 
1.5%
16406990000 1
 
1.5%
ValueCountFrequency (%)
525943055000 1
1.5%
380961000000 1
1.5%
358076000000 1
1.5%
357925000000 1
1.5%
310301000000 1
1.5%
185651712000 1
1.5%
175701000000 1
1.5%
173499000000 1
1.5%
150883000000 1
1.5%
142443000000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4470267 × 109
Minimum0
Maximum1.8283035 × 1010
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-29T01:12:30.021182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median86534000
Q37.3384 × 108
95-th percentile8.5896773 × 109
Maximum1.8283035 × 1010
Range1.8283035 × 1010
Interquartile range (IQR)7.3384 × 108

Descriptive statistics

Standard deviation3.1597626 × 109
Coefficient of variation (CV)2.1836242
Kurtosis12.526063
Mean1.4470267 × 109
Median Absolute Deviation (MAD)86534000
Skewness3.2431798
Sum9.695079 × 1010
Variance9.9840999 × 1018
MonotonicityNot monotonic
2024-01-29T01:12:30.127004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
5142215000 1
 
1.5%
3971587000 1
 
1.5%
28286000 1
 
1.5%
198514000 1
 
1.5%
22207000 1
 
1.5%
3736579000 1
 
1.5%
717594000 1
 
1.5%
9168555000 1
 
1.5%
95760000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
8689000 1
 
1.5%
9536000 1
 
1.5%
16439000 1
 
1.5%
22207000 1
 
1.5%
22707000 1
 
1.5%
24665000 1
 
1.5%
27596000 1
 
1.5%
28286000 1
 
1.5%
65015000 1
 
1.5%
ValueCountFrequency (%)
18283035000 1
1.5%
9602616000 1
1.5%
9168555000 1
1.5%
9058139000 1
1.5%
7496600000 1
1.5%
6196635000 1
1.5%
5142215000 1
1.5%
4950332000 1
1.5%
3971587000 1
1.5%
3736579000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7058994 × 108
Minimum0
Maximum5.731475 × 109
Zeros34
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-29T01:12:30.229192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35348000
95-th percentile9.804861 × 108
Maximum5.731475 × 109
Range5.731475 × 109
Interquartile range (IQR)5348000

Descriptive statistics

Standard deviation7.3906144 × 108
Coefficient of variation (CV)4.3323858
Kurtosis50.203993
Mean1.7058994 × 108
Median Absolute Deviation (MAD)0
Skewness6.7616911
Sum1.1429526 × 1010
Variance5.4621181 × 1017
MonotonicityNot monotonic
2024-01-29T01:12:30.326240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 34
50.7%
41000 2
 
3.0%
5474000 1
 
1.5%
1540000 1
 
1.5%
968304000 1
 
1.5%
107000 1
 
1.5%
3317000 1
 
1.5%
54236000 1
 
1.5%
4827000 1
 
1.5%
23575000 1
 
1.5%
Other values (23) 23
34.3%
ValueCountFrequency (%)
0 34
50.7%
41000 2
 
3.0%
76000 1
 
1.5%
87000 1
 
1.5%
107000 1
 
1.5%
125000 1
 
1.5%
254000 1
 
1.5%
1540000 1
 
1.5%
1541000 1
 
1.5%
1747000 1
 
1.5%
ValueCountFrequency (%)
5731475000 1
1.5%
1241367000 1
1.5%
1040998000 1
1.5%
985707000 1
1.5%
968304000 1
1.5%
699203000 1
1.5%
292495000 1
1.5%
170064000 1
1.5%
63532000 1
1.5%
59681000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9034323 × 109
Minimum0
Maximum1.9840751 × 1010
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-29T01:12:30.431485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.11079 × 108
Q34.0820235 × 109
95-th percentile1.4361076 × 1010
Maximum1.9840751 × 1010
Range1.9840751 × 1010
Interquartile range (IQR)4.0820235 × 109

Descriptive statistics

Standard deviation4.5891263 × 109
Coefficient of variation (CV)1.5805866
Kurtosis4.8247406
Mean2.9034323 × 109
Median Absolute Deviation (MAD)6.11079 × 108
Skewness2.2058815
Sum1.9452997 × 1011
Variance2.106008 × 1019
MonotonicityNot monotonic
2024-01-29T01:12:30.550304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
5911280000 1
 
1.5%
7692747000 1
 
1.5%
608150000 1
 
1.5%
4235560000 1
 
1.5%
530885000 1
 
1.5%
755067000 1
 
1.5%
5856910000 1
 
1.5%
14570620000 1
 
1.5%
101465000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
63055000 1
 
1.5%
78548000 1
 
1.5%
101465000 1
 
1.5%
112191000 1
 
1.5%
180955000 1
 
1.5%
491362000 1
 
1.5%
530885000 1
 
1.5%
557287000 1
 
1.5%
587257000 1
 
1.5%
ValueCountFrequency (%)
19840751000 1
1.5%
19362061000 1
1.5%
14570620000 1
1.5%
14372344000 1
1.5%
14334783000 1
1.5%
7692747000 1
1.5%
7566339000 1
1.5%
7105589000 1
1.5%
6724247000 1
1.5%
6575762000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.573134
Minimum-15.52
Maximum100
Zeros20
Zeros (%)29.9%
Negative1
Negative (%)1.5%
Memory size735.0 B
2024-01-29T01:12:30.658545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-15.52
5-th percentile0
Q10
median94.86
Q397.565
95-th percentile99.743
Maximum100
Range115.52
Interquartile range (IQR)97.565

Descriptive statistics

Standard deviation45.60179
Coefficient of variation (CV)0.74061179
Kurtosis-1.6335625
Mean61.573134
Median Absolute Deviation (MAD)4.91
Skewness-0.57741186
Sum4125.4
Variance2079.5232
MonotonicityNot monotonic
2024-01-29T01:12:30.760298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 20
29.9%
97.93 2
 
3.0%
97.49 2
 
3.0%
100.0 2
 
3.0%
99.61 1
 
1.5%
99.6 1
 
1.5%
96.16 1
 
1.5%
92.88 1
 
1.5%
97.63 1
 
1.5%
97.13 1
 
1.5%
Other values (35) 35
52.2%
ValueCountFrequency (%)
-15.52 1
 
1.5%
0.0 20
29.9%
21.01 1
 
1.5%
27.81 1
 
1.5%
28.83 1
 
1.5%
30.13 1
 
1.5%
83.54 1
 
1.5%
83.61 1
 
1.5%
85.65 1
 
1.5%
87.28 1
 
1.5%
ValueCountFrequency (%)
100.0 2
3.0%
99.79 1
1.5%
99.77 1
1.5%
99.68 1
1.5%
99.67 1
1.5%
99.66 1
1.5%
99.61 1
1.5%
99.6 1
1.5%
99.43 1
1.5%
99.41 1
1.5%

Interactions

2024-01-29T01:12:27.893723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:25.794559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.207467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.613410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.043921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.451838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.966807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:25.856910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.269937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.684350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.107355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.520837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:28.028807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:25.922986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.334158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.756868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.170590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.587998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:28.095178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:25.995345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.404082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.832412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.248598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.663170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:28.160388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.064008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.477442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.904581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.313354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.731610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:28.232612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.136688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.548470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:26.978817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.388076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:12:27.808403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:12:30.829427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8300.8080.7570.7190.8820.932
부과금액0.0000.8301.0001.0000.5760.0000.5880.000
수납급액0.0000.8081.0001.0000.5840.0000.5600.202
환급금액0.0000.7570.5760.5841.0000.7770.7680.790
결손금액0.0000.7190.0000.0000.7771.0000.7840.634
미수납 금액0.0000.8820.5880.5600.7680.7841.0000.806
징수율0.0000.9320.0000.2020.7900.6340.8061.000
2024-01-29T01:12:30.912521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-01-29T01:12:30.989862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9760.8260.6270.7680.6700.0000.530
수납급액0.9761.0000.7150.5150.6600.7280.0000.501
환급금액0.8260.7151.0000.8070.9230.3770.0000.359
결손금액0.6270.5150.8071.0000.7800.2160.0000.456
미수납 금액0.7680.6600.9230.7801.0000.2630.0000.642
징수율0.6700.7280.3770.2160.2631.0000.0000.751
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5300.5010.3590.4560.6420.7510.0001.000

Missing values

2024-01-29T01:12:28.350051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:12:28.479376image/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
0인천광역시서구282602017도축세000000.0
1인천광역시서구282602017레저세000000.0
2인천광역시서구282602017재산세1461090000001424430000002340420000366610300097.49
3인천광역시서구282602017주민세159663070001518415300016439000078215400095.1
4인천광역시서구282602017취득세3120750000003103010000003357926000292495000148172300099.43
5인천광역시서구282602017자동차세40858557000341343100005270900000672424700083.54
6인천광역시서구282602017과년도수입28451091000791113700061966350006992030001984075100027.81
7인천광역시서구282602017담배소비세000000.0
8인천광역시서구282602017도시계획세000000.0
9인천광역시서구282602017등록면허세19005735000189426800007067700006305500099.67
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57인천광역시서구282602021취득세527622151000525943055000188082400063532000161556400099.68
58인천광역시서구282602021자동차세50035197000436689770007500100001747000636447300087.28
59인천광역시서구282602021과년도수입21526882000620639200074966000009857070001433478300028.83
60인천광역시서구282602021담배소비세000000.0
61인천광역시서구282602021도시계획세000000.0
62인천광역시서구282602021등록면허세287440300002863183900094376000011219100099.61
63인천광역시서구282602021지방교육세85250498000826356990004110080006695000260810400096.93
64인천광역시서구282602021지방소득세14444820800013731078500049503320001241367000589605600095.06
65인천광역시서구282602021지방소비세24839330002483933000000100.0
66인천광역시서구282602021지역자원시설세284709700002788091400065015000279900058725700097.93