Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells123
Missing cells (%)14.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 알 수 있는 데이터로 지자체의 재정자주도, 재정자립도 산출하는 자료로 활용됩니다.
Author충청남도 아산시
URLhttps://www.data.go.kr/data/15079011/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 High correlation
수납급액 is highly overall correlated with 부과금액 High correlation
환급금액 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 환급금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
부과금액 has 16 (20.0%) missing valuesMissing
수납급액 has 16 (20.0%) missing valuesMissing
환급금액 has 23 (28.7%) missing valuesMissing
결손금액 has 42 (52.5%) missing valuesMissing
미수납 금액 has 26 (32.5%) missing valuesMissing
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2024-05-04 07:57:49.323148
Analysis finished2024-05-04 07:58:06.264062
Duration16.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 80
100.0%

Length

2024-05-04T07:58:06.564135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:58:06.988536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 80
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아산시 80
100.0%

Length

2024-05-04T07:58:07.434055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:58:07.925061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
44200
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44200
2nd row44200
3rd row44200
4th row44200
5th row44200

Common Values

ValueCountFrequency (%)
44200 80
100.0%

Length

2024-05-04T07:58:08.579474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:58:09.106378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 80
100.0%

과세년도
Real number (ℝ)

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
2024-05-04T07:58:09.810180image/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
2024-05-04T07:58:10.598304image/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 

Distinct27
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
지역자원시설세
 
4
등록면허세
 
4
담배소비세
 
4
재산세
 
4
주민세
 
4
Other values (22)
60 

Length

Max length9
Median length7
Mean length5.075
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역자원시설세 4
 
5.0%
등록면허세 4
 
5.0%
담배소비세 4
 
5.0%
재산세 4
 
5.0%
주민세 4
 
5.0%
취득세 4
 
5.0%
과년도수입 4
 
5.0%
자동차세 4
 
5.0%
도시계획세 4
 
5.0%
지방교육세 4
 
5.0%
Other values (17) 40
50.0%

Length

2024-05-04T07:58:11.620351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지역자원시설세 6
 
7.5%
등록면허세 6
 
7.5%
담배소비세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
과년도수입 6
 
7.5%
자동차세 6
 
7.5%
도시계획세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)100.0%
Missing16
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean6.5283984 × 1010
Minimum1.6695 × 108
Maximum2.59967 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:12.253725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6695 × 108
5-th percentile1.1034665 × 1010
Q11.7273103 × 1010
median3.2318205 × 1010
Q36.5219102 × 1010
95-th percentile2.2215 × 1011
Maximum2.59967 × 1011
Range2.5980005 × 1011
Interquartile range (IQR)4.7945999 × 1010

Descriptive statistics

Standard deviation7.0399223 × 1010
Coefficient of variation (CV)1.0783537
Kurtosis1.246994
Mean6.5283984 × 1010
Median Absolute Deviation (MAD)1.9654816 × 1010
Skewness1.5826809
Sum4.178175 × 1012
Variance4.9560507 × 1021
MonotonicityNot monotonic
2024-05-04T07:58:12.772423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53207635000 1
 
1.2%
28340750000 1
 
1.2%
12453829000 1
 
1.2%
45664091000 1
 
1.2%
160000000000 1
 
1.2%
17290000000 1
 
1.2%
15795746000 1
 
1.2%
74213617000 1
 
1.2%
32669389000 1
 
1.2%
252000000000 1
 
1.2%
Other values (54) 54
67.5%
(Missing) 16
 
20.0%
ValueCountFrequency (%)
166950000 1
1.2%
10186592000 1
1.2%
10951137000 1
1.2%
10969069000 1
1.2%
11406374000 1
1.2%
11538515000 1
1.2%
12453829000 1
1.2%
12872949000 1
1.2%
13039496000 1
1.2%
14054968000 1
1.2%
ValueCountFrequency (%)
259967000000 1
1.2%
252000000000 1
1.2%
244092000000 1
1.2%
225000000000 1
1.2%
206000000000 1
1.2%
205000000000 1
1.2%
199000000000 1
1.2%
195000000000 1
1.2%
166000000000 1
1.2%
164000000000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)98.4%
Missing16
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean6.2675964 × 1010
Minimum1.6695 × 108
Maximum2.56212 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:13.294075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.6695 × 108
5-th percentile4.4027562 × 109
Q11.5342334 × 1010
median3.1967884 × 1010
Q36.2692927 × 1010
95-th percentile2.1715 × 1011
Maximum2.56212 × 1011
Range2.5604505 × 1011
Interquartile range (IQR)4.7350593 × 1010

Descriptive statistics

Standard deviation7.0457407 × 1010
Coefficient of variation (CV)1.1241536
Kurtosis1.2347818
Mean6.2675964 × 1010
Median Absolute Deviation (MAD)2.0739988 × 1010
Skewness1.5713905
Sum4.0112617 × 1012
Variance4.9642462 × 1021
MonotonicityNot monotonic
2024-05-04T07:58:13.795808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201000000000 2
 
2.5%
191000000000 1
 
1.2%
12428549000 1
 
1.2%
43968120000 1
 
1.2%
156000000000 1
 
1.2%
17290000000 1
 
1.2%
15458159000 1
 
1.2%
72180810000 1
 
1.2%
32386613000 1
 
1.2%
251000000000 1
 
1.2%
Other values (53) 53
66.2%
(Missing) 16
 
20.0%
ValueCountFrequency (%)
166950000 1
1.2%
961366000 1
1.2%
2362609000 1
1.2%
4401337000 1
1.2%
4410798000 1
1.2%
9939245000 1
1.2%
10684995000 1
1.2%
10916731000 1
1.2%
11380253000 1
1.2%
11502458000 1
1.2%
ValueCountFrequency (%)
256212000000 1
1.2%
251000000000 1
1.2%
243300000000 1
1.2%
220000000000 1
1.2%
201000000000 2
2.5%
199000000000 1
1.2%
191000000000 1
1.2%
164000000000 1
1.2%
163000000000 1
1.2%
156000000000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)100.0%
Missing23
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean1.8577901 × 109
Minimum26000
Maximum2.1878313 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:14.143173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26000
5-th percentile3891800
Q137878000
median2.60966 × 108
Q31.270613 × 109
95-th percentile7.9445436 × 109
Maximum2.1878313 × 1010
Range2.1878287 × 1010
Interquartile range (IQR)1.232735 × 109

Descriptive statistics

Standard deviation4.3051251 × 109
Coefficient of variation (CV)2.3173366
Kurtosis13.73395
Mean1.8577901 × 109
Median Absolute Deviation (MAD)2.45918 × 108
Skewness3.5975806
Sum1.0589404 × 1011
Variance1.8534102 × 1019
MonotonicityNot monotonic
2024-05-04T07:58:14.498976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59175000 1
 
1.2%
20525260000 1
 
1.2%
30526000 1
 
1.2%
44255000 1
 
1.2%
264540000 1
 
1.2%
3718580000 1
 
1.2%
2422000 1
 
1.2%
37878000 1
 
1.2%
42865000 1
 
1.2%
2578584000 1
 
1.2%
Other values (47) 47
58.8%
(Missing) 23
28.7%
ValueCountFrequency (%)
26000 1
1.2%
2422000 1
1.2%
2891000 1
1.2%
4142000 1
1.2%
5113000 1
1.2%
6911000 1
1.2%
9601000 1
1.2%
11290000 1
1.2%
12732000 1
1.2%
15048000 1
1.2%
ValueCountFrequency (%)
21878313000 1
1.2%
20525260000 1
1.2%
11379522000 1
1.2%
7085799000 1
1.2%
6106821000 1
1.2%
5481956000 1
1.2%
5323006000 1
1.2%
3823356000 1
1.2%
3718580000 1
1.2%
2578584000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)100.0%
Missing42
Missing (%)52.5%
Infinite0
Infinite (%)0.0%
Mean6.6206426 × 108
Minimum2000
Maximum5.283178 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:14.764483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile49850
Q1119750
median624000
Q312601250
95-th percentile4.1931755 × 109
Maximum5.283178 × 109
Range5.283176 × 109
Interquartile range (IQR)12481500

Descriptive statistics

Standard deviation1.5461293 × 109
Coefficient of variation (CV)2.3353161
Kurtosis2.8292308
Mean6.6206426 × 108
Median Absolute Deviation (MAD)588500
Skewness2.1000104
Sum2.5158442 × 1010
Variance2.3905158 × 1018
MonotonicityNot monotonic
2024-05-04T07:58:15.115333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4111922000 1
 
1.2%
3721000 1
 
1.2%
9452000 1
 
1.2%
3945000 1
 
1.2%
15000 1
 
1.2%
62000 1
 
1.2%
58000 1
 
1.2%
601000 1
 
1.2%
139000 1
 
1.2%
4653612000 1
 
1.2%
Other values (28) 28
35.0%
(Missing) 42
52.5%
ValueCountFrequency (%)
2000 1
1.2%
15000 1
1.2%
56000 1
1.2%
58000 1
1.2%
62000 1
1.2%
72000 1
1.2%
73000 1
1.2%
82000 1
1.2%
114000 1
1.2%
119000 1
1.2%
ValueCountFrequency (%)
5283178000 1
1.2%
4653612000 1
1.2%
4111922000 1
1.2%
3914466000 1
1.2%
3802991000 1
1.2%
2962915000 1
1.2%
222317000 1
1.2%
117948000 1
1.2%
34376000 1
1.2%
13651000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing26
Missing (%)32.5%
Infinite0
Infinite (%)0.0%
Mean3.3634548 × 109
Minimum25075000
Maximum1.8589369 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:15.498915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25075000
5-th percentile32643250
Q13.54932 × 108
median1.715131 × 109
Q34.2700488 × 109
95-th percentile1.5944684 × 1010
Maximum1.8589369 × 1010
Range1.8564294 × 1010
Interquartile range (IQR)3.9151168 × 109

Descriptive statistics

Standard deviation4.8640858 × 109
Coefficient of variation (CV)1.4461576
Kurtosis3.4669642
Mean3.3634548 × 109
Median Absolute Deviation (MAD)1.429331 × 109
Skewness2.121039
Sum1.8162656 × 1011
Variance2.3659331 × 1019
MonotonicityNot monotonic
2024-05-04T07:58:15.965288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36639000 1
 
1.2%
4476787000 1
 
1.2%
16254702000 1
 
1.2%
25075000 1
 
1.2%
1692250000 1
 
1.2%
3776477000 1
 
1.2%
333642000 1
 
1.2%
2032792000 1
 
1.2%
282714000 1
 
1.2%
1121733000 1
 
1.2%
Other values (44) 44
55.0%
(Missing) 26
32.5%
ValueCountFrequency (%)
25075000 1
1.2%
26121000 1
1.2%
30313000 1
1.2%
33898000 1
1.2%
35938000 1
1.2%
36639000 1
1.2%
269150000 1
1.2%
273353000 1
1.2%
282714000 1
1.2%
288886000 1
1.2%
ValueCountFrequency (%)
18589369000 1
1.2%
16759665000 1
1.2%
16254702000 1
1.2%
15777751000 1
1.2%
15690250000 1
1.2%
14155980000 1
1.2%
5016600000 1
1.2%
4937081000 1
1.2%
4775591000 1
1.2%
4764494000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.07225
Minimum0
Maximum105
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-05-04T07:58:16.323125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q172
median97.785
Q399.7025
95-th percentile100
Maximum105
Range105
Interquartile range (IQR)27.7025

Descriptive statistics

Standard deviation41.716081
Coefficient of variation (CV)0.56318096
Kurtosis-0.58503957
Mean74.07225
Median Absolute Deviation (MAD)2.215
Skewness-1.1808373
Sum5925.78
Variance1740.2314
MonotonicityNot monotonic
2024-05-04T07:58:16.687756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
100.0 18
22.5%
0.0 16
20.0%
98.0 10
12.5%
97.0 7
 
8.8%
99.0 6
 
7.5%
96.0 4
 
5.0%
92.0 2
 
2.5%
93.0 2
 
2.5%
99.68 1
 
1.2%
98.39 1
 
1.2%
Other values (13) 13
16.2%
ValueCountFrequency (%)
0.0 16
20.0%
5.0 1
 
1.2%
11.0 1
 
1.2%
16.96 1
 
1.2%
18.0 1
 
1.2%
90.0 1
 
1.2%
91.0 1
 
1.2%
91.88 1
 
1.2%
92.0 2
 
2.5%
93.0 2
 
2.5%
ValueCountFrequency (%)
105.0 1
 
1.2%
100.0 18
22.5%
99.77 1
 
1.2%
99.68 1
 
1.2%
99.15 1
 
1.2%
99.0 6
 
7.5%
98.56 1
 
1.2%
98.39 1
 
1.2%
98.0 10
12.5%
97.57 1
 
1.2%

Interactions

2024-05-04T07:58:02.578356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:49.926869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:51.741332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:53.601144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:55.644568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:57.857765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:00.032045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:02.873995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:50.175187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:51.932444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:53.915159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:55.985913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:58.156845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:00.384444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:03.134878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:50.434974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:52.122728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:54.159219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:56.297486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:58.397617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:00.734427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:03.472626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:50.749611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:52.364276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:54.397456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:56.638936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:58.664609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:01.139422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:03.751709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:50.995721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:52.619107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:54.680480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:56.915810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:59.024867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:01.430272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:04.003479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:51.248741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:52.952603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:54.998294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:57.233436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:59.297374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:01.779315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:04.352189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:51.559471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:53.286513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:55.331360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:57.551937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:57:59.645130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:58:02.081540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:58:16.958964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.7570.7380.8110.0000.8890.965
부과금액0.0000.7571.0001.0000.2350.0000.3730.000
수납급액0.0000.7381.0001.0000.2350.0000.3220.000
환급금액0.0000.8110.2350.2351.0000.7670.8830.880
결손금액0.0000.0000.0000.0000.7671.0000.8370.652
미수납 금액0.0000.8890.3730.3220.8830.8371.0000.802
징수율0.0000.9650.0000.0000.8800.6520.8021.000
2024-05-04T07:58:17.313833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.000-0.0100.010-0.017-0.119-0.0100.2230.000
부과금액-0.0101.0000.9700.412-0.0690.374-0.2360.339
수납급액0.0100.9701.0000.286-0.1920.209-0.1130.320
환급금액-0.0170.4120.2861.0000.6810.739-0.3070.451
결손금액-0.119-0.069-0.1920.6811.0000.667-0.3610.000
미수납 금액-0.0100.3740.2090.7390.6671.000-0.6950.503
징수율0.223-0.236-0.113-0.307-0.361-0.6951.0000.726
세목명0.0000.3390.3200.4510.0000.5030.7261.000

Missing values

2024-05-04T07:58:04.845662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:58:05.514369image/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.
2024-05-04T07:58:05.969750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0충청남도아산시442002017도축세<NA><NA><NA><NA><NA>0.0
1충청남도아산시442002017레저세<NA><NA><NA><NA><NA>0.0
2충청남도아산시442002017재산세6009497600058246010000134852000<NA>184896600097.0
3충청남도아산시442002017주민세297775160002942164500026936000<NA>35587100099.0
4충청남도아산시442002017취득세1990000000001990000000001582656000<NA>842027000100.0
5충청남도아산시442002017자동차세5866316300054008234000342193000<NA>465492900092.0
6충청남도아산시442002017과년도수입238499560004410798000532300600052831780001415598000018.0
7충청남도아산시442002017담배소비세2969605600029696056000<NA><NA><NA>100.0
8충청남도아산시442002017도시계획세<NA><NA><NA><NA><NA>0.0
9충청남도아산시442002017등록면허세114063740001138025300073109000<NA>26121000100.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70충청남도아산시442002022취득세2440920000002433000000001270613000<NA>79234400099.68
71충청남도아산시442002022자동차세5866166500053896934000509217000237000476449400091.88
72충청남도아산시442002022과년도수입259536210004401337000610682100029629150001858936900016.96
73충청남도아산시442002022담배소비세304345920003043459200026000<NA><NA>100.0
74충청남도아산시442002022도시계획세<NA><NA><NA><NA><NA>0.0
75충청남도아산시442002022등록면허세130394960001300911000059345000730003031300099.77
76충청남도아산시442002022지방교육세546917450005295367700027212900056000173801200096.82
77충청남도아산시442002022지방소득세2599670000002562120000003823356000117948000363670100098.56
78충청남도아산시442002022지방소비세2680137600026801376000<NA><NA><NA>100.0
79충청남도아산시442002022지역자원시설세17020622000167472690006911000<NA>27335300098.39