Overview

Dataset statistics

Number of variables13
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory114.7 B

Variable types

Numeric8
Categorical4
DateTime1

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공-항목 : 재산세, 주민세, 취득세, 자동차세, 담배소비세, 등록면허세, 지방교육세, 지방소득세, 지역자원시설세 등
Author인천광역시 옹진군
URLhttps://www.data.go.kr/data/15079399/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 순번High correlation
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
순번 has unique valuesUnique
부과금액 has 16 (20.0%) zerosZeros
수납급액 has 16 (20.0%) zerosZeros
환급금액 has 24 (30.0%) zerosZeros
결손금액 has 64 (80.0%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:29:57.895422
Analysis finished2023-12-12 13:30:06.414021
Duration8.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:06.505446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityStrictly increasing
2023-12-12T22:30:06.724339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
42 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
인천광역시
80 

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 80
100.0%

Length

2023-12-12T22:30:06.897635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:30:07.010359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 80
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
옹진군
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
옹진군 80
100.0%

Length

2023-12-12T22:30:07.119260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:30:07.230340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
옹진군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
28720
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28720
2nd row28720
3rd row28720
4th row28720
5th row28720

Common Values

ValueCountFrequency (%)
28720 80
100.0%

Length

2023-12-12T22:30:07.349373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:30:07.473122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28720 80
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:07.602679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2023-12-12T22:30:07.728885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

Length

2023-12-12T22:30:07.898119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7633288 × 109
Minimum0
Maximum1.3184667 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:08.086421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.44633 × 108
median2.5141515 × 109
Q34.6313195 × 109
95-th percentile1.1249972 × 1010
Maximum1.3184667 × 1010
Range1.3184667 × 1010
Interquartile range (IQR)3.7866865 × 109

Descriptive statistics

Standard deviation3.7918648 × 109
Coefficient of variation (CV)1.0075826
Kurtosis-0.060628257
Mean3.7633288 × 109
Median Absolute Deviation (MAD)1.7257475 × 109
Skewness1.0172599
Sum3.010663 × 1011
Variance1.4378238 × 1019
MonotonicityNot monotonic
2023-12-12T22:30:08.276644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
4130151000 1
 
1.2%
3618620000 1
 
1.2%
2399599000 1
 
1.2%
815359000 1
 
1.2%
3752244000 1
 
1.2%
4232522000 1
 
1.2%
3499500000 1
 
1.2%
11176478000 1
 
1.2%
8144142000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
11867000 1
 
1.2%
722924000 1
 
1.2%
781027000 1
 
1.2%
815359000 1
 
1.2%
854391000 1
 
1.2%
895199000 1
 
1.2%
896514000 1
 
1.2%
935169000 1
 
1.2%
939983000 1
 
1.2%
ValueCountFrequency (%)
13184667000 1
1.2%
13001296000 1
1.2%
12594705000 1
1.2%
12315435000 1
1.2%
11193895000 1
1.2%
11176478000 1
1.2%
9785412000 1
1.2%
9739805000 1
1.2%
9640833000 1
1.2%
9404818000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4423782 × 109
Minimum-6.37236 × 108
Maximum1.3047645 × 1010
Zeros16
Zeros (%)20.0%
Negative1
Negative (%)1.2%
Memory size852.0 B
2023-12-12T22:30:08.776091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.37236 × 108
5-th percentile0
Q15.3450075 × 108
median2.234938 × 109
Q34.1960082 × 109
95-th percentile1.1202574 × 1010
Maximum1.3047645 × 1010
Range1.3684881 × 1010
Interquartile range (IQR)3.6615075 × 109

Descriptive statistics

Standard deviation3.8550181 × 109
Coefficient of variation (CV)1.1198706
Kurtosis0.0356936
Mean3.4423782 × 109
Median Absolute Deviation (MAD)1.835446 × 109
Skewness1.1117058
Sum2.7539026 × 1011
Variance1.4861165 × 1019
MonotonicityNot monotonic
2023-12-12T22:30:08.935591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
4017490000 1
 
1.2%
346598000 1
 
1.2%
2399599000 1
 
1.2%
813192000 1
 
1.2%
3646397000 1
 
1.2%
3899934000 1
 
1.2%
3499500000 1
 
1.2%
11145444000 1
 
1.2%
7880951000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
-637236000 1
 
1.2%
0 16
20.0%
11867000 1
 
1.2%
147677000 1
 
1.2%
346598000 1
 
1.2%
597135000 1
 
1.2%
705111000 1
 
1.2%
718892000 1
 
1.2%
757726000 1
 
1.2%
777922000 1
 
1.2%
ValueCountFrequency (%)
13047645000 1
1.2%
12971005000 1
1.2%
12563650000 1
1.2%
12288044000 1
1.2%
11145444000 1
1.2%
11118237000 1
1.2%
9756829000 1
1.2%
9660632000 1
1.2%
9544761000 1
1.2%
9382603000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50673062
Minimum0
Maximum1.467624 × 109
Zeros24
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:09.111868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1947000
Q328061250
95-th percentile1.150498 × 108
Maximum1.467624 × 109
Range1.467624 × 109
Interquartile range (IQR)28061250

Descriptive statistics

Standard deviation1.9141369 × 108
Coefficient of variation (CV)3.777425
Kurtosis42.392865
Mean50673062
Median Absolute Deviation (MAD)1947000
Skewness6.2869751
Sum4.053845 × 109
Variance3.6639201 × 1016
MonotonicityNot monotonic
2023-12-12T22:30:09.325630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
30.0%
5167000 1
 
1.2%
50548000 1
 
1.2%
318364000 1
 
1.2%
2882000 1
 
1.2%
4001000 1
 
1.2%
22217000 1
 
1.2%
69217000 1
 
1.2%
261000 1
 
1.2%
3063000 1
 
1.2%
Other values (47) 47
58.8%
ValueCountFrequency (%)
0 24
30.0%
2000 1
 
1.2%
3000 1
 
1.2%
4000 1
 
1.2%
48000 1
 
1.2%
50000 1
 
1.2%
66000 1
 
1.2%
72000 1
 
1.2%
82000 1
 
1.2%
98000 1
 
1.2%
ValueCountFrequency (%)
1467624000 1
1.2%
863636000 1
1.2%
318364000 1
1.2%
138017000 1
1.2%
113841000 1
1.2%
112035000 1
1.2%
100197000 1
1.2%
84019000 1
1.2%
70373000 1
1.2%
69217000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20553875
Minimum0
Maximum4.38192 × 108
Zeros64
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:09.494013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.7070615 × 108
Maximum4.38192 × 108
Range4.38192 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74286252
Coefficient of variation (CV)3.6142213
Kurtosis16.909926
Mean20553875
Median Absolute Deviation (MAD)0
Skewness4.0595403
Sum1.64431 × 109
Variance5.5184473 × 1015
MonotonicityNot monotonic
2023-12-12T22:30:09.647166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 64
80.0%
61406000 1
 
1.2%
2459000 1
 
1.2%
142000 1
 
1.2%
885000 1
 
1.2%
268911000 1
 
1.2%
18000 1
 
1.2%
453000 1
 
1.2%
10000 1
 
1.2%
311754000 1
 
1.2%
Other values (7) 7
 
8.8%
ValueCountFrequency (%)
0 64
80.0%
10000 1
 
1.2%
18000 1
 
1.2%
139000 1
 
1.2%
142000 1
 
1.2%
453000 1
 
1.2%
885000 1
 
1.2%
1329000 1
 
1.2%
2459000 1
 
1.2%
22550000 1
 
1.2%
ValueCountFrequency (%)
438192000 1
1.2%
311754000 1
1.2%
268911000 1
1.2%
247735000 1
1.2%
166652000 1
1.2%
121675000 1
1.2%
61406000 1
1.2%
22550000 1
1.2%
2459000 1
1.2%
1329000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.003967 × 108
Minimum0
Maximum3.030072 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T22:30:09.791236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28384500
Q31.789865 × 108
95-th percentile2.7548039 × 109
Maximum3.030072 × 109
Range3.030072 × 109
Interquartile range (IQR)1.789865 × 108

Descriptive statistics

Standard deviation7.5667696 × 108
Coefficient of variation (CV)2.5189257
Kurtosis8.5231862
Mean3.003967 × 108
Median Absolute Deviation (MAD)28384500
Skewness3.1538695
Sum2.4031736 × 1010
Variance5.7256002 × 1017
MonotonicityNot monotonic
2023-12-12T22:30:09.983043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
295223000 1
 
1.2%
56623000 1
 
1.2%
168541000 1
 
1.2%
3024287000 1
 
1.2%
2167000 1
 
1.2%
104518000 1
 
1.2%
210913000 1
 
1.2%
31034000 1
 
1.2%
263191000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
2167000 1
 
1.2%
2220000 1
 
1.2%
3563000 1
 
1.2%
4032000 1
 
1.2%
10578000 1
 
1.2%
11730000 1
 
1.2%
15479000 1
 
1.2%
22215000 1
 
1.2%
22447000 1
 
1.2%
ValueCountFrequency (%)
3030072000 1
1.2%
3024287000 1
1.2%
3008244000 1
1.2%
2910793000 1
1.2%
2746594000 1
1.2%
2687700000 1
1.2%
615974000 1
1.2%
490624000 1
1.2%
410825000 1
1.2%
329911000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.794625
Minimum-30.17
Maximum100
Zeros16
Zeros (%)20.0%
Negative1
Negative (%)1.2%
Memory size852.0 B
2023-12-12T22:30:10.126469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-30.17
5-th percentile0
Q117.4225
median96.28
Q399.605
95-th percentile100
Maximum100
Range130.17
Interquartile range (IQR)82.1825

Descriptive statistics

Standard deviation43.236305
Coefficient of variation (CV)0.61072864
Kurtosis-0.79752036
Mean70.794625
Median Absolute Deviation (MAD)3.61
Skewness-1.0572963
Sum5663.57
Variance1869.3781
MonotonicityNot monotonic
2023-12-12T22:30:10.276502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
20.0%
100.0 10
 
12.5%
98.75 1
 
1.2%
99.19 1
 
1.2%
93.43 1
 
1.2%
9.58 1
 
1.2%
99.73 1
 
1.2%
97.18 1
 
1.2%
92.14 1
 
1.2%
99.72 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
-30.17 1
 
1.2%
0.0 16
20.0%
4.42 1
 
1.2%
9.58 1
 
1.2%
15.63 1
 
1.2%
18.02 1
 
1.2%
18.95 1
 
1.2%
85.81 1
 
1.2%
86.55 1
 
1.2%
87.51 1
 
1.2%
ValueCountFrequency (%)
100.0 10
12.5%
99.78 1
 
1.2%
99.77 1
 
1.2%
99.76 1
 
1.2%
99.75 1
 
1.2%
99.73 1
 
1.2%
99.72 1
 
1.2%
99.71 1
 
1.2%
99.7 1
 
1.2%
99.66 1
 
1.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
Minimum2023-08-31 00:00:00
Maximum2023-08-31 00:00:00
2023-12-12T22:30:10.426543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:10.544445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T22:30:05.091599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.319675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.241879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.198589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:01.460444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.398055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.261195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.141588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.223523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.432136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.387923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.316327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:01.605131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.521735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.363566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.268737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.335996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.558189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.494609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.423084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:01.726619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.630121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.460891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.366428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.449549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.669514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.620394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.536947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:01.839170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.731994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.567184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.483776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.571486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.776066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.736573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.639865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:01.958804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.863301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.664169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.600039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.697877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:58.895195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.854152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.773684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.063977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.967945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.762744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.726707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.816195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.023914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.961978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.880447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.163279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.054855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.862589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.831618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:05.949128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:29:59.136452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.083337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:00.986878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:02.278976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:03.167815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.044298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:04.959321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:30:10.811156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
순번1.0000.9300.0000.0000.0000.0600.0510.0000.000
과세년도0.9301.0000.0000.0000.0000.0000.0420.0000.000
세목명0.0000.0001.0000.8560.8790.0000.1250.7170.795
부과금액0.0000.0000.8561.0000.9910.0000.0000.4760.466
수납급액0.0000.0000.8790.9911.0000.0000.0000.4190.640
환급금액0.0600.0000.0000.0000.0001.0001.0000.7190.696
결손금액0.0510.0420.1250.0000.0001.0001.0000.8090.816
미수납 금액0.0000.0000.7170.4760.4190.7190.8091.0000.968
징수율0.0000.0000.7950.4660.6400.6960.8160.9681.000
2023-12-12T22:30:10.962686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
순번1.0000.9860.2020.1990.0690.288-0.0010.2680.000
과세년도0.9861.0000.1660.1590.0570.2970.0030.2330.000
부과금액0.2020.1661.0000.9230.5260.2430.6000.4980.566
수납급액0.1990.1590.9231.0000.351-0.0180.4000.6460.609
환급금액0.0690.0570.5260.3511.0000.5870.861-0.0330.000
결손금액0.2880.2970.243-0.0180.5871.0000.527-0.1930.023
미수납 금액-0.0010.0030.6000.4000.8610.5271.000-0.1370.445
징수율0.2680.2330.4980.646-0.033-0.193-0.1371.0000.534
세목명0.0000.0000.5660.6090.0000.0230.4450.5341.000

Missing values

2023-12-12T22:30:06.123977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:30:06.329511image/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

순번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일자
01인천광역시옹진군287202017도축세000000.02023-08-31
12인천광역시옹진군287202017레저세000000.02023-08-31
23인천광역시옹진군287202017재산세73403320007045109000383000029522300095.982023-08-31
34인천광역시옹진군287202017주민세89651400086186000022300003465400096.132023-08-31
45인천광역시옹진군287202017취득세964083300095447610003245100009607200099.02023-08-31
56인천광역시옹진군287202017자동차세2124551000182298700027484000030156400085.812023-08-31
67인천광역시옹진군287202017과년도수입2111870000-6372360001467624000614060002687700000-30.172023-08-31
78인천광역시옹진군287202017담배소비세24906620002490662000000100.02023-08-31
89인천광역시옹진군287202017도시계획세000000.02023-08-31
910인천광역시옹진군287202017등록면허세72292400071889200064430000403200099.442023-08-31
순번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일자
7071인천광역시옹진군287202022취득세1119389500011118237000297930004530007520500099.322023-08-31
7172인천광역시옹진군287202022자동차세23094990002120923000562660001800018855800091.832023-08-31
7273인천광역시옹진군287202022과년도수입372061600070511100037577000268911000274659400018.952023-08-31
7374인천광역시옹진군287202022담배소비세25029230002502923000300000100.02023-08-31
7475인천광역시옹진군287202022도시계획세000000.02023-08-31
7576인천광역시옹진군287202022등록면허세7810270007779220005799000885000222000099.62023-08-31
7677인천광역시옹진군287202022지방교육세399047000038657310001751600014200012459700096.872023-08-31
7778인천광역시옹진군287202022지방소득세45979050003979472000112035000245900061597400086.552023-08-31
7879인천광역시옹진군287202022지방소비세47315630004731563000000100.02023-08-31
7980인천광역시옹진군287202022지역자원시설세924426000092160740006600002818600099.72023-08-31