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

Description지방세 부과액에 대한 세목별 징수현황을 알 수 있는 데이터로 지자체의 재정자주도, 재정자립도 산출하는 자료로 활용됩니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=349&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079011

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 20 (29.9%) zerosZeros
결손금액 has 34 (50.7%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2024-01-09 20:03:24.500366
Analysis finished2024-01-09 20:03:29.824692
Duration5.32 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
충청남도
67 

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 67
100.0%

Length

2024-01-10T05:03:29.913590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:03:30.049680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 67
100.0%

시군구명
Categorical

CONSTANT 

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

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 67
100.0%

Length

2024-01-10T05:03:30.197070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:03:30.327972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
44200
67 

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 67
100.0%

Length

2024-01-10T05:03:30.448684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:03:30.572890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 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

2024-01-10T05:03:30.689254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:03:30.811286image/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 

Distinct27
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Memory size668.0 B
지역자원시설세
 
3
등록면허세
 
3
담배소비세
 
3
재산세
 
3
주민세
 
3
Other values (22)
52 

Length

Max length9
Median length7
Mean length5.1940299
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지역자원시설세 3
 
4.5%
등록면허세 3
 
4.5%
담배소비세 3
 
4.5%
재산세 3
 
4.5%
주민세 3
 
4.5%
취득세 3
 
4.5%
과년도수입 3
 
4.5%
자동차세 3
 
4.5%
도시계획세 3
 
4.5%
지방교육세 3
 
4.5%
Other values (17) 37
55.2%

Length

2024-01-10T05:03:30.964317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지역자원시설세 5
 
7.5%
등록면허세 5
 
7.5%
담배소비세 5
 
7.5%
재산세 5
 
7.5%
주민세 5
 
7.5%
취득세 5
 
7.5%
과년도수입 5
 
7.5%
자동차세 5
 
7.5%
도시계획세 5
 
7.5%
지방교육세 5
 
7.5%
Other values (4) 17
25.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9632789 × 1010
Minimum0
Maximum2.5217629 × 1011
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:31.152776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0960103 × 1010
median2.7358684 × 1010
Q35.937907 × 1010
95-th percentile2.0322135 × 1011
Maximum2.5217629 × 1011
Range2.5217629 × 1011
Interquartile range (IQR)4.8418966 × 1010

Descriptive statistics

Standard deviation6.4529973 × 1010
Coefficient of variation (CV)1.300148
Kurtosis2.0640729
Mean4.9632789 × 1010
Median Absolute Deviation (MAD)2.715681 × 1010
Skewness1.7584266
Sum3.3253968 × 1012
Variance4.1641174 × 1021
MonotonicityNot monotonic
2024-01-10T05:03:31.369517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
17290000000 1
 
1.5%
205737800000 1
 
1.5%
15312960000 1
 
1.5%
70313609000 1
 
1.5%
31023253000 1
 
1.5%
165714002000 1
 
1.5%
53207635000 1
 
1.5%
10969069000 1
 
1.5%
28340750000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
10186592000 1
 
1.5%
10951137000 1
 
1.5%
10969069000 1
 
1.5%
11406374000 1
 
1.5%
11538515000 1
 
1.5%
12453829000 1
 
1.5%
12872949000 1
 
1.5%
14054968000 1
 
1.5%
14705184000 1
 
1.5%
ValueCountFrequency (%)
252176288000 1
1.5%
224841634000 1
1.5%
205737800000 1
1.5%
204873872000 1
1.5%
199365477000 1
1.5%
194543394000 1
1.5%
165714002000 1
1.5%
163507047000 1
1.5%
159853480000 1
1.5%
143999142000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7688049 × 1010
Minimum0
Maximum2.510545 × 1011
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:31.625074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.3867035 × 109
median2.7358684 × 1010
Q35.6127122 × 1010
95-th percentile2.0035304 × 1011
Maximum2.510545 × 1011
Range2.510545 × 1011
Interquartile range (IQR)5.2740418 × 1010

Descriptive statistics

Standard deviation6.4100396 × 1010
Coefficient of variation (CV)1.3441606
Kurtosis2.0982668
Mean4.7688049 × 1010
Median Absolute Deviation (MAD)2.6397318 × 1010
Skewness1.7714046
Sum3.1950993 × 1012
Variance4.1088608 × 1021
MonotonicityNot monotonic
2024-01-10T05:03:31.885204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
17290000000 1
 
1.5%
201137153000 1
 
1.5%
14994860000 1
 
1.5%
68277860000 1
 
1.5%
30668520000 1
 
1.5%
164445008000 1
 
1.5%
48722991000 1
 
1.5%
9939245000 1
 
1.5%
28340750000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
961366000 1
 
1.5%
2362609000 1
 
1.5%
4410798000 1
 
1.5%
9939245000 1
 
1.5%
10684995000 1
 
1.5%
10916731000 1
 
1.5%
11380253000 1
 
1.5%
11502458000 1
 
1.5%
12428549000 1
 
1.5%
ValueCountFrequency (%)
251054497000 1
1.5%
219890902000 1
1.5%
201426966000 1
1.5%
201137153000 1
1.5%
198523450000 1
1.5%
190563813000 1
1.5%
164445008000 1
1.5%
162716698000 1
1.5%
156067551000 1
1.5%
143162897000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3961609 × 109
Minimum0
Maximum2.1878313 × 1010
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:32.059752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44243000
Q34.677135 × 108
95-th percentile6.6046461 × 109
Maximum2.1878313 × 1010
Range2.1878313 × 1010
Interquartile range (IQR)4.677135 × 108

Descriptive statistics

Standard deviation3.9888043 × 109
Coefficient of variation (CV)2.8569804
Kurtosis17.938999
Mean1.3961609 × 109
Median Absolute Deviation (MAD)44243000
Skewness4.1329793
Sum9.354278 × 1010
Variance1.591056 × 1019
MonotonicityNot monotonic
2024-01-10T05:03:32.220521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
190605000 1
 
1.5%
9601000 1
 
1.5%
44568000 1
 
1.5%
17568000 1
 
1.5%
1251697000 1
 
1.5%
488345000 1
 
1.5%
20525260000 1
 
1.5%
30526000 1
 
1.5%
44255000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
2422000 1
 
1.5%
2891000 1
 
1.5%
4142000 1
 
1.5%
5113000 1
 
1.5%
9601000 1
 
1.5%
11290000 1
 
1.5%
12732000 1
 
1.5%
15048000 1
 
1.5%
17568000 1
 
1.5%
ValueCountFrequency (%)
21878313000 1
1.5%
20525260000 1
1.5%
11379522000 1
1.5%
7085799000 1
1.5%
5481956000 1
1.5%
5323006000 1
1.5%
3718580000 1
1.5%
2578584000 1
1.5%
2343599000 1
1.5%
2278440000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2951064 × 108
Minimum0
Maximum5.283178 × 109
Zeros34
Zeros (%)50.7%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:32.403937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3624000
95-th percentile3.8810235 × 109
Maximum5.283178 × 109
Range5.283178 × 109
Interquartile range (IQR)624000

Descriptive statistics

Standard deviation1.1614916 × 109
Coefficient of variation (CV)3.5248985
Kurtosis10.242321
Mean3.2951064 × 108
Median Absolute Deviation (MAD)0
Skewness3.4122117
Sum2.2077213 × 1010
Variance1.3490627 × 1018
MonotonicityNot monotonic
2024-01-10T05:03:32.602738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 34
50.7%
15000 1
 
1.5%
7857000 1
 
1.5%
4653612000 1
 
1.5%
205000 1
 
1.5%
3721000 1
 
1.5%
9452000 1
 
1.5%
3945000 1
 
1.5%
62000 1
 
1.5%
8551000 1
 
1.5%
Other values (24) 24
35.8%
ValueCountFrequency (%)
0 34
50.7%
2000 1
 
1.5%
15000 1
 
1.5%
58000 1
 
1.5%
62000 1
 
1.5%
72000 1
 
1.5%
82000 1
 
1.5%
114000 1
 
1.5%
119000 1
 
1.5%
122000 1
 
1.5%
ValueCountFrequency (%)
5283178000 1
1.5%
4653612000 1
1.5%
4111922000 1
1.5%
3914466000 1
1.5%
3802991000 1
1.5%
222317000 1
1.5%
34376000 1
1.5%
13651000 1
1.5%
9452000 1
1.5%
8551000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2308779 × 109
Minimum0
Maximum1.6759665 × 1010
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:33.277520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.55871 × 108
Q32.021805 × 109
95-th percentile1.5229969 × 1010
Maximum1.6759665 × 1010
Range1.6759665 × 1010
Interquartile range (IQR)2.021805 × 109

Descriptive statistics

Standard deviation4.1597443 × 109
Coefficient of variation (CV)1.8646221
Kurtosis6.3375433
Mean2.2308779 × 109
Median Absolute Deviation (MAD)3.55871 × 108
Skewness2.6436915
Sum1.4946882 × 1011
Variance1.7303472 × 1019
MonotonicityNot monotonic
2024-01-10T05:03:33.465923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
3776477000 1
 
1.5%
4378330000 1
 
1.5%
316714000 1
 
1.5%
2027198000 1
 
1.5%
354619000 1
 
1.5%
1268994000 1
 
1.5%
4476787000 1
 
1.5%
16254702000 1
 
1.5%
25075000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
25075000 1
 
1.5%
26121000 1
 
1.5%
33898000 1
 
1.5%
35938000 1
 
1.5%
36639000 1
 
1.5%
269150000 1
 
1.5%
282714000 1
 
1.5%
288886000 1
 
1.5%
316714000 1
 
1.5%
ValueCountFrequency (%)
16759665000 1
1.5%
16254702000 1
1.5%
15777751000 1
1.5%
15690250000 1
1.5%
14155980000 1
1.5%
5016600000 1
1.5%
4937081000 1
1.5%
4775591000 1
1.5%
4654929000 1
1.5%
4476787000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.044776
Minimum0
Maximum105
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-01-10T05:03:33.628425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.5
median97
Q399
95-th percentile100
Maximum105
Range105
Interquartile range (IQR)84.5

Descriptive statistics

Standard deviation42.979006
Coefficient of variation (CV)0.59655964
Kurtosis-0.85687019
Mean72.044776
Median Absolute Deviation (MAD)3
Skewness-1.0680606
Sum4827
Variance1847.1949
MonotonicityNot monotonic
2024-01-10T05:03:33.793101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 15
22.4%
100 15
22.4%
98 10
14.9%
97 7
10.4%
99 6
 
9.0%
96 4
 
6.0%
92 2
 
3.0%
93 2
 
3.0%
18 1
 
1.5%
90 1
 
1.5%
Other values (4) 4
 
6.0%
ValueCountFrequency (%)
0 15
22.4%
5 1
 
1.5%
11 1
 
1.5%
18 1
 
1.5%
90 1
 
1.5%
91 1
 
1.5%
92 2
 
3.0%
93 2
 
3.0%
96 4
 
6.0%
97 7
10.4%
ValueCountFrequency (%)
105 1
 
1.5%
100 15
22.4%
99 6
 
9.0%
98 10
14.9%
97 7
10.4%
96 4
 
6.0%
93 2
 
3.0%
92 2
 
3.0%
91 1
 
1.5%
90 1
 
1.5%

Interactions

2024-01-10T05:03:28.842969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.373683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.116409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.760150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.436975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.087499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.970898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.485381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.221375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.860131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.550657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.205723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:29.087047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.603965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.322516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.969006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.649136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.307447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:29.183940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.713572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.416443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.080080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.755903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.429071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:29.281505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.843405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.534091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.183612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.880162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.571961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:29.399378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:25.985204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:26.665702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.313800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:27.986132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:03:28.730173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:03:33.914699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8990.8910.7790.6380.9400.962
부과금액0.0000.8991.0001.0000.5050.0000.5270.625
수납급액0.0000.8911.0001.0000.5050.0000.5140.548
환급금액0.0000.7790.5050.5051.0000.8830.7470.661
결손금액0.0000.6380.0000.0000.8831.0000.8320.838
미수납 금액0.0000.9400.5270.5140.7470.8321.0000.648
징수율0.0000.9620.6250.5480.6610.8380.6481.000
2024-01-10T05:03:34.067905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T05:03:34.193413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9850.6930.3840.6800.4300.0000.528
수납급액0.9851.0000.6380.3340.6200.4900.0000.513
환급금액0.6930.6381.0000.6990.9000.2760.0000.383
결손금액0.3840.3340.6991.0000.7580.0900.0000.303
미수납 금액0.6800.6200.9000.7581.0000.0800.0000.637
징수율0.4300.4900.2760.0900.0801.0000.0000.686
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5280.5130.3830.3030.6370.6860.0001.000

Missing values

2024-01-10T05:03:29.563933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:03:29.746518image/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
0충청남도아산시442002017도축세000000
1충청남도아산시442002017레저세000000
2충청남도아산시442002017재산세60094976000582460100001348520000184896600097
3충청남도아산시442002017주민세297775160002942164500026936000035587100099
4충청남도아산시442002017취득세19936547700019852345000015826560000842027000100
5충청남도아산시442002017자동차세58663163000540082340003421930000465492900092
6충청남도아산시442002017과년도수입238499560004410798000532300600052831780001415598000018
7충청남도아산시442002017담배소비세2969605600029696056000000100
8충청남도아산시442002017도시계획세000000
9충청남도아산시442002017등록면허세114063740001138025300073109000026121000100
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57충청남도아산시442002021취득세2521762880002510544970002578584000580001121733000100
58충청남도아산시442002021자동차세6360393800059189181000498109000601000441415600093
59충청남도아산시442002021과년도수입101865920001068499500021878313000411192200016759665000105
60충청남도아산시442002021담배소비세2911849200029118492000414200000100
61충청남도아산시442002021도시계획세000000
62충청남도아산시442002021등록면허세14054968000140181900005917500013900036639000100
63충청남도아산시442002021지방교육세5451549400052860228000376505000184000165508200097
64충청남도아산시442002021지방소득세194543394000190563813000548195600034376000394520500098
65충청남도아산시442002021지방소비세1722241200017222412000000100
66충청남도아산시442002021지역자원시설세16576433000162875470005113000028888600098