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

Description연도별 대구광역시 중구 지방세 부과액에 대한 세목별 징수 현황 데이터를 제공하며, 세목별 부과, 수납, 환급, 결손 금액 등의 자료를 포함합니다.
Author대구광역시 중구
URLhttps://www.data.go.kr/data/15079679/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 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
미수납금액 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 25 (37.3%) zerosZeros
미수납금액 has 22 (32.8%) zerosZeros
징수율 has 20 (29.9%) zerosZeros

Reproduction

Analysis started2023-12-12 05:10:18.407985
Analysis finished2023-12-12 05:10:22.964612
Duration4.56 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
대구광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 67
100.0%

Length

2023-12-12T14:10:23.016739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:23.091730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 67
100.0%

시군구명
Categorical

CONSTANT 

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

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 67
100.0%

Length

2023-12-12T14:10:23.171242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:23.250272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
27110
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27110
2nd row27110
3rd row27110
4th row27110
5th row27110

Common Values

ValueCountFrequency (%)
27110 67
100.0%

Length

2023-12-12T14:10:23.333614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:23.420119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27110 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

2023-12-12T14:10:23.506596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:23.607471image/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

2023-12-12T14:10:23.740221image/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%
Mean1.7666352 × 1010
Minimum0
Maximum1.20043 × 1011
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T14:10:23.923994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.981593 × 109
Q31.8829302 × 1010
95-th percentile8.1633029 × 1010
Maximum1.20043 × 1011
Range1.20043 × 1011
Interquartile range (IQR)1.8829302 × 1010

Descriptive statistics

Standard deviation2.7791237 × 1010
Coefficient of variation (CV)1.5731169
Kurtosis4.4702634
Mean1.7666352 × 1010
Median Absolute Deviation (MAD)4.981593 × 109
Skewness2.1758997
Sum1.1836456 × 1012
Variance7.7235284 × 1020
MonotonicityNot monotonic
2023-12-12T14:10:24.069124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
16212120000 1
 
1.5%
4981593000 1
 
1.5%
36286737000 1
 
1.5%
4166529000 1
 
1.5%
114807000000 1
 
1.5%
17243233000 1
 
1.5%
3042134000 1
 
1.5%
4414686000 1
 
1.5%
17706862000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
781774000 1
 
1.5%
2699114000 1
 
1.5%
2743688000 1
 
1.5%
3042134000 1
 
1.5%
3382116000 1
 
1.5%
4166529000 1
 
1.5%
4378848000 1
 
1.5%
4414686000 1
 
1.5%
4422498000 1
 
1.5%
ValueCountFrequency (%)
120043000000 1
1.5%
114807000000 1
1.5%
92935659000 1
1.5%
85219893000 1
1.5%
73263680000 1
1.5%
63768615000 1
1.5%
57085660000 1
1.5%
50005428000 1
1.5%
47112191000 1
1.5%
42419127000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7267189 × 1010
Minimum-1.450358 × 109
Maximum1.19529 × 1011
Zeros20
Zeros (%)29.9%
Negative1
Negative (%)1.5%
Memory size735.0 B
2023-12-12T14:10:24.274074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.450358 × 109
5-th percentile0
Q10
median4.897885 × 109
Q31.8066316 × 1010
95-th percentile8.1585172 × 1010
Maximum1.19529 × 1011
Range1.2097936 × 1011
Interquartile range (IQR)1.8066316 × 1010

Descriptive statistics

Standard deviation2.7723534 × 1010
Coefficient of variation (CV)1.6055616
Kurtosis4.5735935
Mean1.7267189 × 1010
Median Absolute Deviation (MAD)4.897885 × 109
Skewness2.1966856
Sum1.1569016 × 1012
Variance7.6859435 × 1020
MonotonicityNot monotonic
2023-12-12T14:10:24.429238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
15883973000 1
 
1.5%
4897885000 1
 
1.5%
35762827000 1
 
1.5%
4119272000 1
 
1.5%
114747000000 1
 
1.5%
16519769000 1
 
1.5%
754563000 1
 
1.5%
4382782000 1
 
1.5%
17416339000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
-1450358000 1
 
1.5%
0 20
29.9%
321937000 1
 
1.5%
724820000 1
 
1.5%
754563000 1
 
1.5%
1307511000 1
 
1.5%
4119272000 1
 
1.5%
4348088000 1
 
1.5%
4382782000 1
 
1.5%
4399750000 1
 
1.5%
ValueCountFrequency (%)
119529000000 1
1.5%
114747000000 1
1.5%
92726093000 1
1.5%
85174698000 1
1.5%
73209610000 1
1.5%
62563309000 1
1.5%
56088072000 1
1.5%
48998551000 1
1.5%
45828738000 1
1.5%
41436062000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0616881 × 108
Minimum0
Maximum3.399302 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T14:10:24.609257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20881000
Q32.84913 × 108
95-th percentile1.3324655 × 109
Maximum3.399302 × 109
Range3.399302 × 109
Interquartile range (IQR)2.84913 × 108

Descriptive statistics

Standard deviation5.8440019 × 108
Coefficient of variation (CV)1.9087516
Kurtosis11.778616
Mean3.0616881 × 108
Median Absolute Deviation (MAD)20881000
Skewness3.0385275
Sum2.051331 × 1010
Variance3.4152358 × 1017
MonotonicityNot monotonic
2023-12-12T14:10:24.784379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
1112029000 1
 
1.5%
924622000 1
 
1.5%
300000 1
 
1.5%
13769000 1
 
1.5%
76248000 1
 
1.5%
253878000 1
 
1.5%
534442000 1
 
1.5%
1253975000 1
 
1.5%
27835000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
78000 1
 
1.5%
300000 1
 
1.5%
473000 1
 
1.5%
1202000 1
 
1.5%
1352000 1
 
1.5%
1517000 1
 
1.5%
2009000 1
 
1.5%
2445000 1
 
1.5%
2950000 1
 
1.5%
ValueCountFrequency (%)
3399302000 1
1.5%
1958245000 1
1.5%
1446260000 1
1.5%
1335509000 1
1.5%
1325364000 1
1.5%
1253975000 1
1.5%
1112029000 1
1.5%
924622000 1
1.5%
877241000 1
1.5%
871642000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98576701
Minimum0
Maximum1.085664 × 109
Zeros25
Zeros (%)37.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T14:10:24.978866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median501000
Q311692000
95-th percentile7.005599 × 108
Maximum1.085664 × 109
Range1.085664 × 109
Interquartile range (IQR)11692000

Descriptive statistics

Standard deviation2.3674453 × 108
Coefficient of variation (CV)2.4016276
Kurtosis5.7152037
Mean98576701
Median Absolute Deviation (MAD)501000
Skewness2.5323688
Sum6.604639 × 109
Variance5.6047971 × 1016
MonotonicityNot monotonic
2023-12-12T14:10:25.188876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 25
37.3%
2755000 1
 
1.5%
2387000 1
 
1.5%
452665000 1
 
1.5%
1119000 1
 
1.5%
154000 1
 
1.5%
2066000 1
 
1.5%
712730000 1
 
1.5%
324000 1
 
1.5%
763000 1
 
1.5%
Other values (33) 33
49.3%
ValueCountFrequency (%)
0 25
37.3%
8000 1
 
1.5%
67000 1
 
1.5%
154000 1
 
1.5%
175000 1
 
1.5%
255000 1
 
1.5%
324000 1
 
1.5%
350000 1
 
1.5%
422000 1
 
1.5%
501000 1
 
1.5%
ValueCountFrequency (%)
1085664000 1
1.5%
777815000 1
1.5%
728380000 1
1.5%
712730000 1
1.5%
672163000 1
1.5%
543291000 1
1.5%
508323000 1
1.5%
466496000 1
1.5%
452665000 1
1.5%
248794000 1
1.5%

미수납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0057819 × 108
Minimum0
Maximum1.574841 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T14:10:25.366830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median59216000
Q35.23406 × 108
95-th percentile1.3431836 × 109
Maximum1.574841 × 109
Range1.574841 × 109
Interquartile range (IQR)5.23406 × 108

Descriptive statistics

Standard deviation4.1764439 × 108
Coefficient of variation (CV)1.38947
Kurtosis1.8873153
Mean3.0057819 × 108
Median Absolute Deviation (MAD)59216000
Skewness1.5912705
Sum2.0138739 × 1010
Variance1.7442684 × 1017
MonotonicityNot monotonic
2023-12-12T14:10:25.543272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
828649000 1
 
1.5%
544923000 1
 
1.5%
83708000 1
 
1.5%
522791000 1
 
1.5%
47103000 1
 
1.5%
59216000 1
 
1.5%
721398000 1
 
1.5%
1574841000 1
 
1.5%
31580000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
22493000 1
 
1.5%
23531000 1
 
1.5%
27955000 1
 
1.5%
30338000 1
 
1.5%
31580000 1
 
1.5%
44688000 1
 
1.5%
45628000 1
 
1.5%
45673000 1
 
1.5%
47103000 1
 
1.5%
ValueCountFrequency (%)
1574841000 1
1.5%
1465971000 1
1.5%
1454317000 1
1.5%
1346225000 1
1.5%
1336087000 1
1.5%
828649000 1
1.5%
814643000 1
1.5%
785547000 1
1.5%
778948000 1
1.5%
740162000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.328358
Minimum-186
Maximum100
Zeros20
Zeros (%)29.9%
Negative1
Negative (%)1.5%
Memory size735.0 B
2023-12-12T14:10:25.731038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-186
5-th percentile0
Q10
median97
Q398.5
95-th percentile100
Maximum100
Range286
Interquartile range (IQR)98.5

Descriptive statistics

Standard deviation54.906915
Coefficient of variation (CV)0.91013442
Kurtosis4.2388367
Mean60.328358
Median Absolute Deviation (MAD)2
Skewness-1.6274654
Sum4042
Variance3014.7693
MonotonicityNot monotonic
2023-12-12T14:10:25.865514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 20
29.9%
98 16
23.9%
99 9
13.4%
100 8
 
11.9%
97 4
 
6.0%
96 3
 
4.5%
95 2
 
3.0%
27 1
 
1.5%
39 1
 
1.5%
-186 1
 
1.5%
Other values (2) 2
 
3.0%
ValueCountFrequency (%)
-186 1
 
1.5%
0 20
29.9%
12 1
 
1.5%
25 1
 
1.5%
27 1
 
1.5%
39 1
 
1.5%
95 2
 
3.0%
96 3
 
4.5%
97 4
 
6.0%
98 16
23.9%
ValueCountFrequency (%)
100 8
11.9%
99 9
13.4%
98 16
23.9%
97 4
 
6.0%
96 3
 
4.5%
95 2
 
3.0%
39 1
 
1.5%
27 1
 
1.5%
25 1
 
1.5%
12 1
 
1.5%

Interactions

2023-12-12T14:10:22.333674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:18.789357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.522964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.270396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.293167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.826692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.409281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:18.935750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.617048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.399756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.378804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.917362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.481203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.051495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.733100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.546888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.470645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.006524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.555948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.161664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.855350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.684465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.560440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.095683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.623884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.276362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.995780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.787425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.637633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.171969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.701836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:19.420419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:20.124979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.189575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:21.741984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:22.251267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:10:26.313337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8410.8400.8000.4700.8490.935
부과금액0.0000.8411.0001.0000.3380.6000.6880.486
수납급액0.0000.8401.0001.0000.4690.6320.6860.486
환급금액0.0000.8000.3380.4691.0000.8730.7920.794
결손금액0.0000.4700.6000.6320.8731.0000.8770.590
미수납금액0.0000.8490.6880.6860.7920.8771.0000.785
징수율0.0000.9350.4860.4860.7940.5900.7851.000
2023-12-12T14:10:26.462163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-12T14:10:26.590752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납금액징수율과세년도세목명
부과금액1.0000.9910.6970.5870.6780.7490.0000.537
수납급액0.9911.0000.6490.5380.6300.7630.0000.537
환급금액0.6970.6491.0000.8320.9050.3910.0000.401
결손금액0.5870.5380.8321.0000.8890.2860.0000.209
미수납금액0.6780.6300.9050.8891.0000.2940.0000.559
징수율0.7490.7630.3910.2860.2941.0000.0000.616
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5370.5370.4010.2090.5590.6160.0001.000

Missing values

2023-12-12T14:10:22.797668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:10:22.918081image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납금액징수율
0대구광역시중구271102017도축세000000
1대구광역시중구271102017레저세000000
2대구광역시중구271102017재산세28322744000277959680002009000275500052402100098
3대구광역시중구271102017주민세49187730004773393000135200098900014439100097
4대구광역시중구271102017취득세73263680000732096100003146510006700054003000100
5대구광역시중구271102017자동차세1829500600017472208000773645000815500081464300096
6대구광역시중구271102017과년도수입26991140007248200001446260000508323000146597100027
7대구광역시중구271102017담배소비세000000
8대구광역시중구271102017도시계획세000000
9대구광역시중구271102017등록면허세43788480004348088000208810004220003033800099
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납금액징수율
57대구광역시중구271102021취득세12004300000011952900000012091100029971000484214000100
58대구광역시중구271102021자동차세1936359800018660425000701923000240800070076500096
59대구광역시중구271102021과년도수입274368800032193700019582450001085664000133608700012
60대구광역시중구271102021담배소비세000000
61대구광역시중구271102021도시계획세000000
62대구광역시중구271102021등록면허세545327200054290690007390900067200023531000100
63대구광역시중구271102021지방교육세20388307000200101050002385230003787300034032900098
64대구광역시중구271102021지방소득세6376861500062563309000132536400067216300053314300098
65대구광역시중구271102021지방소비세48585530004858553000000100
66대구광역시중구271102021지역자원시설세51549220005057179000473000520700004567300098