Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells33
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory96.3 B

Variable types

Categorical4
Numeric6
Text1

Dataset

Description지방세 부과액에 대한 세목별 징수현황 데이터로 세목별 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15078325/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
수납급액 is highly overall correlated with 환급금액 and 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
결손금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 징수율High correlation
징수율 has 33 (33.0%) missing valuesMissing
수납급액 has 20 (20.0%) zerosZeros
환급금액 has 22 (22.0%) zerosZeros
결손금액 has 23 (23.0%) zerosZeros
미수납 금액 has 22 (22.0%) zerosZeros
징수율 has 20 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 01:50:58.674941
Analysis finished2023-12-12 01:51:03.425747
Duration4.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대전광역시
100 

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 100
100.0%

Length

2023-12-12T10:51:03.492961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:51:03.605805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 100
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대덕구
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대덕구 100
100.0%

Length

2023-12-12T10:51:03.728538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:51:03.836673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대덕구 100
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
30230
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30230
2nd row30230
3rd row30230
4th row30230
5th row30230

Common Values

ValueCountFrequency (%)
30230 100
100.0%

Length

2023-12-12T10:51:03.938621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:51:04.035458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30230 100
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.96
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:04.131958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32022
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.8527621
Coefficient of variation (CV)0.00091722711
Kurtosis-1.3816641
Mean2019.96
Median Absolute Deviation (MAD)2
Skewness-0.30015859
Sum201996
Variance3.4327273
MonotonicityIncreasing
2023-12-12T10:51:04.269891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 33
33.0%
2017 14
14.0%
2018 14
14.0%
2019 13
 
13.0%
2020 13
 
13.0%
2021 13
 
13.0%
ValueCountFrequency (%)
2017 14
14.0%
2018 14
14.0%
2019 13
 
13.0%
2020 13
 
13.0%
2021 13
 
13.0%
2022 33
33.0%
ValueCountFrequency (%)
2022 33
33.0%
2021 13
 
13.0%
2020 13
 
13.0%
2019 13
 
13.0%
2018 14
14.0%
2017 14
14.0%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지방소득세
15 
재산세
13 
주민세
10 
취득세
9 
자동차세
9 
Other values (9)
44 

Length

Max length7
Median length5
Mean length4.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 15
15.0%
재산세 13
13.0%
주민세 10
10.0%
취득세 9
9.0%
자동차세 9
9.0%
등록면허세 6
 
6.0%
지역자원시설세 6
 
6.0%
레저세 5
 
5.0%
과년도수입 5
 
5.0%
담배소비세 5
 
5.0%
Other values (4) 17
17.0%

Length

2023-12-12T10:51:04.409969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세 15
15.0%
재산세 13
13.0%
주민세 10
10.0%
취득세 9
9.0%
자동차세 9
9.0%
등록면허세 6
 
6.0%
지역자원시설세 6
 
6.0%
레저세 5
 
5.0%
과년도수입 5
 
5.0%
담배소비세 5
 
5.0%
Other values (4) 17
17.0%

부과금액
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-12T10:51:04.677684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length8.56
Min length1

Characters and Unicode

Total characters856
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)50.0%

Sample

1st row0
2nd row0
3rd row27369120000
4th row11090150000
5th row28844724000
ValueCountFrequency (%)
0 20
 
18.9%
미만 6
 
5.7%
10만원 6
 
5.7%
10만원~30만원미만 5
 
4.7%
50만원~1백만원미만 5
 
4.7%
1백만원~3백만원미만 4
 
3.8%
30만원~50만원미만 4
 
3.8%
1천만원~3천만원미만 2
 
1.9%
3백만원~5백만원미만 2
 
1.9%
5백만원~1천만원미만 2
 
1.9%
Other values (50) 50
47.2%
2023-12-12T10:51:05.101595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 230
26.9%
만 89
 
10.4%
1 83
 
9.7%
원 60
 
7.0%
3 49
 
5.7%
5 46
 
5.4%
2 41
 
4.8%
6 40
 
4.7%
4 33
 
3.9%
미 33
 
3.9%
Other values (8) 152
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 610
71.3%
Other Letter 213
 
24.9%
Math Symbol 27
 
3.2%
Space Separator 6
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 230
37.7%
1 83
 
13.6%
3 49
 
8.0%
5 46
 
7.5%
2 41
 
6.7%
6 40
 
6.6%
4 33
 
5.4%
9 33
 
5.4%
8 29
 
4.8%
7 26
 
4.3%
Other Letter
ValueCountFrequency (%)
만 89
41.8%
원 60
28.2%
미 33
 
15.5%
백 19
 
8.9%
천 8
 
3.8%
억 4
 
1.9%
Math Symbol
ValueCountFrequency (%)
~ 27
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 643
75.1%
Hangul 213
 
24.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 230
35.8%
1 83
 
12.9%
3 49
 
7.6%
5 46
 
7.2%
2 41
 
6.4%
6 40
 
6.2%
4 33
 
5.1%
9 33
 
5.1%
8 29
 
4.5%
~ 27
 
4.2%
Other values (2) 32
 
5.0%
Hangul
ValueCountFrequency (%)
만 89
41.8%
원 60
28.2%
미 33
 
15.5%
백 19
 
8.9%
천 8
 
3.8%
억 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 643
75.1%
Hangul 213
 
24.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 230
35.8%
1 83
 
12.9%
3 49
 
7.6%
5 46
 
7.2%
2 41
 
6.4%
6 40
 
6.2%
4 33
 
5.1%
9 33
 
5.1%
8 29
 
4.5%
~ 27
 
4.2%
Other values (2) 32
 
5.0%
Hangul
ValueCountFrequency (%)
만 89
41.8%
원 60
28.2%
미 33
 
15.5%
백 19
 
8.9%
천 8
 
3.8%
억 4
 
1.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5411759 × 109
Minimum0
Maximum6.2271415 × 1010
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:05.277083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median1962.5
Q31.1452882 × 1010
95-th percentile4.6616067 × 1010
Maximum6.2271415 × 1010
Range6.2271415 × 1010
Interquartile range (IQR)1.1452882 × 1010

Descriptive statistics

Standard deviation1.528975 × 1010
Coefficient of variation (CV)1.7901223
Kurtosis4.6831242
Mean8.5411759 × 109
Median Absolute Deviation (MAD)1962.5
Skewness2.2813011
Sum8.5411759 × 1011
Variance2.3377644 × 1020
MonotonicityNot monotonic
2023-12-12T10:51:05.498369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
20.0%
1 5
 
5.0%
4 3
 
3.0%
2 3
 
3.0%
6 2
 
2.0%
13885577000 1
 
1.0%
2192223000 1
 
1.0%
6989841000 1
 
1.0%
12603859000 1
 
1.0%
62271415000 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0 20
20.0%
1 5
 
5.0%
2 3
 
3.0%
4 3
 
3.0%
6 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
10 1
 
1.0%
18 1
 
1.0%
19 1
 
1.0%
ValueCountFrequency (%)
62271415000 1
1.0%
61100425000 1
1.0%
59167074000 1
1.0%
58710639000 1
1.0%
58317719000 1
1.0%
46000191000 1
1.0%
40776649000 1
1.0%
32210525000 1
1.0%
30290314000 1
1.0%
29341763000 1
1.0%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8039721 × 108
Minimum0
Maximum4.97645 × 109
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:05.775091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1238622.5
median18214500
Q399171968
95-th percentile1.7814879 × 109
Maximum4.97645 × 109
Range4.97645 × 109
Interquartile range (IQR)98933345

Descriptive statistics

Standard deviation7.8832104 × 108
Coefficient of variation (CV)2.8114439
Kurtosis16.857908
Mean2.8039721 × 108
Median Absolute Deviation (MAD)18214500
Skewness3.9297582
Sum2.8039721 × 1010
Variance6.2145007 × 1017
MonotonicityNot monotonic
2023-12-12T10:51:05.963577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
22.0%
17173000 1
 
1.0%
27485050 1
 
1.0%
51283200 1
 
1.0%
21173750 1
 
1.0%
32387050 1
 
1.0%
10152410 1
 
1.0%
26200970 1
 
1.0%
148391720 1
 
1.0%
113240980 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0 22
22.0%
46340 1
 
1.0%
156000 1
 
1.0%
205000 1
 
1.0%
249830 1
 
1.0%
1126800 1
 
1.0%
1417470 1
 
1.0%
1814000 1
 
1.0%
1873000 1
 
1.0%
1955500 1
 
1.0%
ValueCountFrequency (%)
4976450000 1
1.0%
3559934000 1
1.0%
3186808000 1
1.0%
2334151000 1
1.0%
2299122000 1
1.0%
1754244000 1
1.0%
1418136000 1
1.0%
1407036000 1
1.0%
1387222000 1
1.0%
1006575000 1
1.0%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94115161
Minimum0
Maximum1.67334 × 109
Zeros23
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:06.156158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median1532.5
Q34765250
95-th percentile6.7605205 × 108
Maximum1.67334 × 109
Range1.67334 × 109
Interquartile range (IQR)4765247.5

Descriptive statistics

Standard deviation2.9860976 × 108
Coefficient of variation (CV)3.1728124
Kurtosis13.251758
Mean94115161
Median Absolute Deviation (MAD)1532.5
Skewness3.6001837
Sum9.4115161 × 109
Variance8.9167786 × 1016
MonotonicityNot monotonic
2023-12-12T10:51:06.693011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
23.0%
1 2
 
2.0%
10 2
 
2.0%
8 2
 
2.0%
7 2
 
2.0%
14 1
 
1.0%
20 1
 
1.0%
643839000 1
 
1.0%
1032000 1
 
1.0%
4314000 1
 
1.0%
Other values (64) 64
64.0%
ValueCountFrequency (%)
0 23
23.0%
1 2
 
2.0%
3 1
 
1.0%
4 1
 
1.0%
5 1
 
1.0%
6 1
 
1.0%
7 2
 
2.0%
8 2
 
2.0%
9 1
 
1.0%
10 2
 
2.0%
ValueCountFrequency (%)
1673340000 1
1.0%
1464177000 1
1.0%
1079600000 1
1.0%
1028026000 1
1.0%
830162000 1
1.0%
667941000 1
1.0%
643839000 1
1.0%
618477000 1
1.0%
565059000 1
1.0%
534111000 1
1.0%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5157836 × 108
Minimum0
Maximum2.451384 × 109
Zeros22
Zeros (%)22.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:06.867772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14277765
median55023540
Q33.861675 × 108
95-th percentile1.8638995 × 109
Maximum2.451384 × 109
Range2.451384 × 109
Interquartile range (IQR)3.8188974 × 108

Descriptive statistics

Standard deviation6.1149349 × 108
Coefficient of variation (CV)1.7392808
Kurtosis3.7312838
Mean3.5157836 × 108
Median Absolute Deviation (MAD)55023540
Skewness2.173539
Sum3.5157836 × 1010
Variance3.7392429 × 1017
MonotonicityNot monotonic
2023-12-12T10:51:07.055013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
22.0%
508763000 1
 
1.0%
45443190 1
 
1.0%
80051070 1
 
1.0%
29869370 1
 
1.0%
48487760 1
 
1.0%
41060040 1
 
1.0%
43355370 1
 
1.0%
232559700 1
 
1.0%
230773950 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
0 22
22.0%
171600 1
 
1.0%
3021770 1
 
1.0%
4215110 1
 
1.0%
4298650 1
 
1.0%
4480420 1
 
1.0%
5553810 1
 
1.0%
5709140 1
 
1.0%
9187000 1
 
1.0%
9985460 1
 
1.0%
ValueCountFrequency (%)
2451384000 1
1.0%
2395081000 1
1.0%
2154143000 1
1.0%
2088308000 1
1.0%
2077736000 1
1.0%
1852645000 1
1.0%
1843115000 1
1.0%
1810114000 1
1.0%
1739926000 1
1.0%
1734866000 1
1.0%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct46
Distinct (%)68.7%
Missing33
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean62.708806
Minimum0
Maximum100
Zeros20
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T10:51:07.266779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95.25
Q398.825
95-th percentile99.814
Maximum100
Range100
Interquartile range (IQR)98.825

Descriptive statistics

Standard deviation45.645458
Coefficient of variation (CV)0.72789551
Kurtosis-1.615401
Mean62.708806
Median Absolute Deviation (MAD)4.6
Skewness-0.60221321
Sum4201.49
Variance2083.5079
MonotonicityNot monotonic
2023-12-12T10:51:07.445480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 20
20.0%
100.0 2
 
2.0%
99.73 2
 
2.0%
98.4 1
 
1.0%
98.86 1
 
1.0%
98.65 1
 
1.0%
98.5 1
 
1.0%
99.14 1
 
1.0%
88.51 1
 
1.0%
0.57 1
 
1.0%
Other values (36) 36
36.0%
(Missing) 33
33.0%
ValueCountFrequency (%)
0.0 20
20.0%
0.57 1
 
1.0%
4.25 1
 
1.0%
28.77 1
 
1.0%
44.61 1
 
1.0%
50.41 1
 
1.0%
85.82 1
 
1.0%
86.18 1
 
1.0%
87.56 1
 
1.0%
88.51 1
 
1.0%
ValueCountFrequency (%)
100.0 2
2.0%
99.94 1
1.0%
99.85 1
1.0%
99.73 2
2.0%
99.69 1
1.0%
99.67 1
1.0%
99.64 1
1.0%
99.61 1
1.0%
99.59 1
1.0%
99.17 1
1.0%

Interactions

2023-12-12T10:51:02.401200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.035464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.704645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.451894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.126456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.760653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.526154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.129855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.816360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.561084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.214730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.854851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.643335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.242925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.936964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.695065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.315789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.979615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.771205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.358787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.055830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.795794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.415084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.099318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.880291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.486581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.182956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.897708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.543241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.212541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.969591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:50:59.595853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:00.329494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.017179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:01.642123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:51:02.313282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:51:07.582125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.9400.3370.1410.3380.1250.000
세목명0.0001.0000.0000.6790.2650.5010.7950.834
부과금액0.9400.0001.0001.0001.0001.0000.9831.000
수납급액0.3370.6791.0001.0000.2970.5090.6230.670
환급금액0.1410.2651.0000.2971.0000.8950.6810.555
결손금액0.3380.5011.0000.5090.8951.0000.7340.894
미수납 금액0.1250.7950.9830.6230.6810.7341.0000.739
징수율0.0000.8341.0000.6700.5550.8940.7391.000
2023-12-12T10:51:07.738866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.000-0.2120.168-0.178-0.0000.1810.000
수납급액-0.2121.0000.6150.8600.7090.6700.356
환급금액0.1680.6151.0000.7040.8760.3800.109
결손금액-0.1780.8600.7041.0000.7990.3950.260
미수납 금액-0.0000.7090.8760.7991.0000.2620.407
징수율0.1810.6700.3800.3950.2621.0000.565
세목명0.0000.3560.1090.2600.4070.5651.000

Missing values

2023-12-12T10:51:03.132065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:51:03.363342image/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
0대전광역시대덕구302302017도축세000000.0
1대전광역시대덕구302302017레저세000000.0
2대전광역시대덕구302302017재산세27369120000268597960001717300056100050876300098.14
3대전광역시대덕구302302017주민세11090150000108636990001359000011300022633800097.96
4대전광역시대덕구302302017취득세288447240002874086600063982000206600010179200099.64
5대전광역시대덕구302302017자동차세15208111000130519750002007080001993000215414300085.82
6대전광역시대덕구302302017과년도수입3257674000138349000355993400066794100024513840004.25
7대전광역시대덕구302302017담배소비세000000.0
8대전광역시대덕구302302017도시계획세000000.0
9대전광역시대덕구302302017등록면허세59778430005954498000107830001950002315000099.61
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
90대전광역시대덕구302302022지방소득세30만원~50만원미만803212806015462267570<NA>
91대전광역시대덕구302302022지방소득세3백만원~5백만원미만10378516801556799260<NA>
92대전광역시대덕구302302022지방소득세3천만원~5천만원미만41529344905195256740<NA>
93대전광역시대덕구302302022지방소득세50만원~1백만원미만7452846280190135072560<NA>
94대전광역시대덕구302302022지방소득세5백만원~1천만원미만1912707016020136690680<NA>
95대전광역시대덕구302302022지역자원시설세10만원 미만24634014171600<NA>
96대전광역시대덕구302302022취득세10만원~30만원미만1249830183021770<NA>
97대전광역시대덕구302302022취득세1백만원~3백만원미만1195550034298650<NA>
98대전광역시대덕구302302022취득세50만원~1백만원미만2141747064480420<NA>
99대전광역시대덕구302302022취득세5억원~10억원미만16724380001672438000<NA>