Overview

Dataset statistics

Number of variables11
Number of observations67
Missing cells27
Missing cells (%)3.7%
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 제공지자체의 재정자주도 및 재정자립도를 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
Author전북특별자치도 완주군
URLhttps://www.data.go.kr/data/15078382/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 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 2 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 3 other fieldsHigh correlation
세목명 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
부과금액 has 3 (4.5%) missing valuesMissing
수납급액 has 3 (4.5%) missing valuesMissing
환급금액 has 4 (6.0%) missing valuesMissing
결손금액 has 9 (13.4%) missing valuesMissing
미수납 금액 has 5 (7.5%) missing valuesMissing
징수율 has 3 (4.5%) missing valuesMissing
부과금액 has 11 (16.4%) zerosZeros
수납급액 has 11 (16.4%) zerosZeros
환급금액 has 16 (23.9%) zerosZeros
결손금액 has 35 (52.2%) zerosZeros
미수납 금액 has 17 (25.4%) zerosZeros
징수율 has 11 (16.4%) zerosZeros

Reproduction

Analysis started2024-04-21 01:45:52.922791
Analysis finished2024-04-21 01:45:58.923190
Duration6 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
전라북도
67 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 67
100.0%

Length

2024-04-21T10:45:58.993506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:59.093315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완주군 67
100.0%

Length

2024-04-21T10:45:59.191653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:59.283616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완주군 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
45710
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45710
2nd row45710
3rd row45710
4th row45710
5th row45710

Common Values

ValueCountFrequency (%)
45710 67
100.0%

Length

2024-04-21T10:45:59.382318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:59.492838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45710 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

2024-04-21T10:45:59.611951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:45:59.718735image/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

2024-04-21T10:45:59.850815image/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  MISSING  ZEROS 

Distinct54
Distinct (%)84.4%
Missing3
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.155169 × 1010
Minimum-1.1042271 × 109
Maximum5.3127025 × 1010
Zeros11
Zeros (%)16.4%
Negative1
Negative (%)1.5%
Memory size735.0 B
2024-04-21T10:45:59.993287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1042271 × 109
5-th percentile0
Q12.6496152 × 109
median6.9593908 × 109
Q31.473213 × 1010
95-th percentile3.4203708 × 1010
Maximum5.3127025 × 1010
Range5.4231252 × 1010
Interquartile range (IQR)1.2082514 × 1010

Descriptive statistics

Standard deviation1.309998 × 1010
Coefficient of variation (CV)1.1340315
Kurtosis0.4719539
Mean1.155169 × 1010
Median Absolute Deviation (MAD)6.2485334 × 109
Skewness1.2227624
Sum7.3930818 × 1011
Variance1.7160949 × 1020
MonotonicityNot monotonic
2024-04-21T10:46:00.141160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
16.4%
6165982000 1
 
1.5%
9814525000 1
 
1.5%
3084647000 1
 
1.5%
14567097710 1
 
1.5%
7041527560 1
 
1.5%
37184025340 1
 
1.5%
32981141960 1
 
1.5%
-1104227100 1
 
1.5%
6960181540 1
 
1.5%
Other values (44) 44
65.7%
(Missing) 3
 
4.5%
ValueCountFrequency (%)
-1104227100 1
 
1.5%
0 11
16.4%
31000 1
 
1.5%
4089000 1
 
1.5%
1073578290 1
 
1.5%
2412028000 1
 
1.5%
2728811000 1
 
1.5%
2789708000 1
 
1.5%
2851531000 1
 
1.5%
2994476000 1
 
1.5%
ValueCountFrequency (%)
53127025290 1
1.5%
37184025340 1
1.5%
36857207420 1
1.5%
34318078000 1
1.5%
33555610000 1
1.5%
33084035550 1
1.5%
33008140000 1
1.5%
32981141960 1
1.5%
31984918000 1
1.5%
31822005000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct54
Distinct (%)84.4%
Missing3
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean1.1143628 × 1010
Minimum-4.3187048 × 109
Maximum5.2999636 × 1010
Zeros11
Zeros (%)16.4%
Negative3
Negative (%)4.5%
Memory size735.0 B
2024-04-21T10:46:00.287872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.3187048 × 109
5-th percentile0
Q11.2125298 × 109
median6.946283 × 109
Q31.4320627 × 1010
95-th percentile3.3622255 × 1010
Maximum5.2999636 × 1010
Range5.7318341 × 1010
Interquartile range (IQR)1.3108097 × 1010

Descriptive statistics

Standard deviation1.3055057 × 1010
Coefficient of variation (CV)1.1715266
Kurtosis0.54407083
Mean1.1143628 × 1010
Median Absolute Deviation (MAD)6.5829015 × 109
Skewness1.2141286
Sum7.131922 × 1011
Variance1.704345 × 1020
MonotonicityNot monotonic
2024-04-21T10:46:00.462645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
16.4%
6080841000 1
 
1.5%
9413300000 1
 
1.5%
3014595000 1
 
1.5%
14201174210 1
 
1.5%
6960759830 1
 
1.5%
36944307590 1
 
1.5%
31940133090 1
 
1.5%
-4318704840 1
 
1.5%
6960181540 1
 
1.5%
Other values (44) 44
65.7%
(Missing) 3
 
4.5%
ValueCountFrequency (%)
-4318704840 1
 
1.5%
-2237950050 1
 
1.5%
-1430147000 1
 
1.5%
0 11
16.4%
31000 1
 
1.5%
820567000 1
 
1.5%
1343184000 1
 
1.5%
2656004000 1
 
1.5%
2783323000 1
 
1.5%
2784385000 1
 
1.5%
ValueCountFrequency (%)
52999636290 1
1.5%
36944307590 1
1.5%
36343925760 1
1.5%
33640567000 1
1.5%
33518485000 1
1.5%
32582741000 1
1.5%
32516106710 1
1.5%
31940133090 1
1.5%
31537980000 1
1.5%
31214297000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct48
Distinct (%)76.2%
Missing4
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean3.6364322 × 108
Minimum0
Maximum6.4659296 × 109
Zeros16
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:46:00.606963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126500
median12685900
Q31.4299154 × 108
95-th percentile1.1674366 × 109
Maximum6.4659296 × 109
Range6.4659296 × 109
Interquartile range (IQR)1.4296504 × 108

Descriptive statistics

Standard deviation1.066105 × 109
Coefficient of variation (CV)2.9317335
Kurtosis20.648814
Mean3.6364322 × 108
Median Absolute Deviation (MAD)12685900
Skewness4.3846632
Sum2.2909523 × 1010
Variance1.1365799 × 1018
MonotonicityNot monotonic
2024-04-21T10:46:00.743314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 16
23.9%
845000 1
 
1.5%
195780 1
 
1.5%
17307190 1
 
1.5%
1647440 1
 
1.5%
488858880 1
 
1.5%
145580020 1
 
1.5%
6465929600 1
 
1.5%
15016750 1
 
1.5%
12435350 1
 
1.5%
Other values (38) 38
56.7%
(Missing) 4
 
6.0%
ValueCountFrequency (%)
0 16
23.9%
53000 1
 
1.5%
144530 1
 
1.5%
195780 1
 
1.5%
264000 1
 
1.5%
845000 1
 
1.5%
1647440 1
 
1.5%
3215000 1
 
1.5%
5542000 1
 
1.5%
5794000 1
 
1.5%
ValueCountFrequency (%)
6465929600 1
1.5%
4367186050 1
1.5%
3399682000 1
1.5%
1171493000 1
1.5%
1130929000 1
1.5%
1042259000 1
1.5%
860707000 1
1.5%
775917000 1
1.5%
666907090 1
1.5%
571395470 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)41.4%
Missing9
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean35540267
Minimum0
Maximum7.98272 × 108
Zeros35
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:46:00.880429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3233000
95-th percentile2.8945378 × 108
Maximum7.98272 × 108
Range7.98272 × 108
Interquartile range (IQR)233000

Descriptive statistics

Standard deviation1.2772071 × 108
Coefficient of variation (CV)3.5936902
Kurtosis23.369081
Mean35540267
Median Absolute Deviation (MAD)0
Skewness4.5532524
Sum2.0613355 × 109
Variance1.6312579 × 1016
MonotonicityNot monotonic
2024-04-21T10:46:01.026114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 35
52.2%
331935000 1
 
1.5%
363950700 1
 
1.5%
113490 1
 
1.5%
281957090 1
 
1.5%
424100 1
 
1.5%
10300 1
 
1.5%
4190 1
 
1.5%
591610 1
 
1.5%
5815580 1
 
1.5%
Other values (14) 14
 
20.9%
(Missing) 9
 
13.4%
ValueCountFrequency (%)
0 35
52.2%
4190 1
 
1.5%
10300 1
 
1.5%
28000 1
 
1.5%
52000 1
 
1.5%
74150 1
 
1.5%
113490 1
 
1.5%
133900 1
 
1.5%
200000 1
 
1.5%
244000 1
 
1.5%
ValueCountFrequency (%)
798272000 1
1.5%
363950700 1
1.5%
331935000 1
1.5%
281957090 1
1.5%
196441000 1
1.5%
71019760 1
1.5%
5815580 1
1.5%
4252780 1
1.5%
3280000 1
1.5%
1154220 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct46
Distinct (%)74.2%
Missing5
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean3.8797815 × 108
Minimum0
Maximum3.0295712 × 109
Zeros17
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:46:01.180554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median92809260
Q34.258685 × 108
95-th percentile1.1812403 × 109
Maximum3.0295712 × 109
Range3.0295712 × 109
Interquartile range (IQR)4.258685 × 108

Descriptive statistics

Standard deviation6.291599 × 108
Coefficient of variation (CV)1.6216375
Kurtosis8.1448846
Mean3.8797815 × 108
Median Absolute Deviation (MAD)92809260
Skewness2.6995016
Sum2.4054645 × 1010
Variance3.9584218 × 1017
MonotonicityNot monotonic
2024-04-21T10:46:01.507290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 17
25.4%
85141000 1
 
1.5%
400981000 1
 
1.5%
70052000 1
 
1.5%
364769280 1
 
1.5%
80633830 1
 
1.5%
168697990 1
 
1.5%
1040346240 1
 
1.5%
2850527040 1
 
1.5%
5831230 1
 
1.5%
Other values (36) 36
53.7%
(Missing) 5
 
7.5%
ValueCountFrequency (%)
0 17
25.4%
5068000 1
 
1.5%
5323000 1
 
1.5%
5831230 1
 
1.5%
6183000 1
 
1.5%
6315210 1
 
1.5%
37125000 1
 
1.5%
60575080 1
 
1.5%
68208000 1
 
1.5%
70052000 1
 
1.5%
ValueCountFrequency (%)
3029571250 1
1.5%
2850527040 1
1.5%
2178795000 1
1.5%
1185395000 1
1.5%
1102301000 1
1.5%
1095826000 1
1.5%
1046675000 1
1.5%
1040346240 1
1.5%
1033727270 1
1.5%
793189000 1
1.5%

징수율
Real number (ℝ)

MISSING  ZEROS 

Distinct45
Distinct (%)70.3%
Missing3
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean-468.88516
Minimum-34975.47
Maximum391.1
Zeros11
Zeros (%)16.4%
Negative2
Negative (%)3.0%
Memory size735.0 B
2024-04-21T10:46:01.652214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-34975.47
5-th percentile0
Q195.48
median97.45
Q399.5
95-th percentile100
Maximum391.1
Range35366.57
Interquartile range (IQR)4.02

Descriptive statistics

Standard deviation4382.2783
Coefficient of variation (CV)-9.3461655
Kurtosis63.970014
Mean-468.88516
Median Absolute Deviation (MAD)2.08
Skewness-7.9972315
Sum-30008.65
Variance19204363
MonotonicityNot monotonic
2024-04-21T10:46:01.788216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 11
 
16.4%
100.0 8
 
11.9%
96.4 2
 
3.0%
99.9 2
 
3.0%
99.89 1
 
1.5%
98.03 1
 
1.5%
97.73 1
 
1.5%
97.5 1
 
1.5%
97.4 1
 
1.5%
99.4 1
 
1.5%
Other values (35) 35
52.2%
(Missing) 3
 
4.5%
ValueCountFrequency (%)
-34975.47 1
 
1.5%
-208.5 1
 
1.5%
0.0 11
16.4%
34.02 1
 
1.5%
36.12 1
 
1.5%
95.24 1
 
1.5%
95.56 1
 
1.5%
95.91 1
 
1.5%
95.96 1
 
1.5%
96.21 1
 
1.5%
ValueCountFrequency (%)
391.1 1
 
1.5%
100.0 8
11.9%
99.9 2
 
3.0%
99.89 1
 
1.5%
99.83 1
 
1.5%
99.82 1
 
1.5%
99.81 1
 
1.5%
99.8 1
 
1.5%
99.4 1
 
1.5%
99.21 1
 
1.5%

Interactions

2024-04-21T10:45:57.747227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:54.572870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.318400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.987954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.596338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.185975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.836279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:54.719217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.429708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.084869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.696284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.269947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.937608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:54.937467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.537579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.202451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.787868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.356343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:58.041395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.014032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.675535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.313729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.893415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.449205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:58.142999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.127081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.784981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.414967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.992969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.548862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:58.254421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.217859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:55.884951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:56.499058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.089928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:45:57.650018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:46:01.891757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.3390.000NaN
세목명0.0001.0000.8130.8540.6010.3360.858NaN
부과금액0.0000.8131.0000.9840.0000.0000.618NaN
수납급액0.0000.8540.9841.0000.0000.0000.698NaN
환급금액0.0000.6010.0000.0001.0000.9660.765NaN
결손금액0.3390.3360.0000.0000.9661.0000.809NaN
미수납 금액0.0000.8580.6180.6980.7650.8091.000NaN
징수율NaNNaNNaNNaNNaNNaNNaN1.000
2024-04-21T10:46:02.009908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-04-21T10:46:02.101223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9900.6120.2240.5670.3200.0000.495
수납급액0.9901.0000.5370.1430.4830.3480.0000.558
환급금액0.6120.5371.0000.6100.8660.0870.0000.332
결손금액0.2240.1430.6101.0000.625-0.0570.1270.154
미수납 금액0.5670.4830.8660.6251.000-0.1150.0000.598
징수율0.3200.3480.087-0.057-0.1151.0000.0000.000
과세년도0.0000.0000.0000.1270.0000.0001.0000.000
세목명0.4950.5580.3320.1540.5980.0000.0001.000

Missing values

2024-04-21T10:45:58.571822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:45:58.713503image/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.
2024-04-21T10:45:58.835413image/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
0전라북도완주군457102017도축세000000.0
1전라북도완주군457102017레저세000000.0
2전라북도완주군457102017재산세117733820001140909800011740000036428400096.91
3전라북도완주군457102017주민세6165982000608084100084500008514100098.62
4전라북도완주군457102017취득세335556100003351848500019698300003712500099.89
5전라북도완주군457102017자동차세22000067000209533920001126750000104667500095.24
6전라북도완주군457102017과년도수입2412028000820567000104225900079827200079318900034.02
7전라북도완주군457102017담배소비세69006230006900623000000100.0
8전라북도완주군457102017도시계획세000000.0
9전라북도완주군457102017등록면허세2789708000278438500095100000532300099.81
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57전라북도완주군457102021취득세5312702529052999636290232971400<NA>12738900099.8
58전라북도완주군457102021자동차세2949564551028461494140143261070424100103372727096.5
59전라북도완주군457102021과년도수입1073578290-223795005043671860502819570903029571250-208.5
60전라북도완주군457102021담배소비세70088273607008827360144530<NA><NA>100.0
61전라북도완주군457102021도시계획세<NA><NA><NA><NA><NA><NA>
62전라북도완주군457102021등록면허세4284554130427823892012685900<NA>631521099.9
63전라북도완주군457102021지방교육세12129553270117322844106491318011349039715537096.7
64전라북도완주군457102021지방소득세3685720742036343925760571395470<NA>51328166098.6
65전라북도완주군457102021지방소비세70472410007047241000<NA><NA><NA>100.0
66전라북도완주군457102021지역자원시설세319348740031138999807789490<NA>7958742097.3