Overview

Dataset statistics

Number of variables12
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory108.2 B

Variable types

Numeric7
Categorical5

Dataset

Description전라남도 신안군 2017년 ~2019년 지방세 부과액에 대한 세목별 징수현황을 제공하는 자료로 과세년도, 세목명, 부과금액, 수납금액, 환그금액, 결손금액, 미수납 금액, 징수율 등을 포함하고 있습니다.
Author전라남도 신안군
URLhttps://www.data.go.kr/data/15079953/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
순번 is highly overall correlated with 과세년도High correlation
부과금액 is highly overall correlated with 수납급액 and 4 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 4 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 부과금액 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 순번High correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
순번 has unique valuesUnique
부과금액 has 11 (26.8%) zerosZeros
수납급액 has 11 (26.8%) zerosZeros
환급금액 has 14 (34.1%) zerosZeros
결손금액 has 26 (63.4%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-13 00:31:48.194209
Analysis finished2023-12-13 00:31:52.133241
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:52.184794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median21
Q331
95-th percentile39
Maximum41
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.979149
Coefficient of variation (CV)0.57043565
Kurtosis-1.2
Mean21
Median Absolute Deviation (MAD)10
Skewness0
Sum861
Variance143.5
MonotonicityStrictly increasing
2023-12-13T09:31:52.291236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 1
 
2.4%
32 1
 
2.4%
24 1
 
2.4%
25 1
 
2.4%
26 1
 
2.4%
27 1
 
2.4%
28 1
 
2.4%
29 1
 
2.4%
30 1
 
2.4%
31 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1 1
2.4%
2 1
2.4%
3 1
2.4%
4 1
2.4%
5 1
2.4%
6 1
2.4%
7 1
2.4%
8 1
2.4%
9 1
2.4%
10 1
2.4%
ValueCountFrequency (%)
41 1
2.4%
40 1
2.4%
39 1
2.4%
38 1
2.4%
37 1
2.4%
36 1
2.4%
35 1
2.4%
34 1
2.4%
33 1
2.4%
32 1
2.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
전라남도
41 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 41
100.0%

Length

2023-12-13T09:31:52.397688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:52.473406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
신안군
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신안군 41
100.0%

Length

2023-12-13T09:31:52.543166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:52.614352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신안군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
46910
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46910
2nd row46910
3rd row46910
4th row46910
5th row46910

Common Values

ValueCountFrequency (%)
46910 41
100.0%

Length

2023-12-13T09:31:52.683366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:52.751259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46910 41
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017
14 
2018
14 
2019
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
34.1%
2018 14
34.1%
2019 13
31.7%

Length

2023-12-13T09:31:52.824575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:52.898108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
26 

Length

Max length7
Median length5
Mean length4.3902439
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

Length

2023-12-13T09:31:52.986663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2477739 × 109
Minimum0
Maximum1.1256369 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:53.079738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.676769 × 109
Q32.915918 × 109
95-th percentile8.848333 × 109
Maximum1.1256369 × 1010
Range1.1256369 × 1010
Interquartile range (IQR)2.915918 × 109

Descriptive statistics

Standard deviation2.8084696 × 109
Coefficient of variation (CV)1.2494449
Kurtosis3.0143772
Mean2.2477739 × 109
Median Absolute Deviation (MAD)1.425052 × 109
Skewness1.7920815
Sum9.2158729 × 1010
Variance7.8875017 × 1018
MonotonicityNot monotonic
2023-12-13T09:31:53.181524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
1946044000 1
 
2.4%
265588000 1
 
2.4%
3487933000 1
 
2.4%
3294921000 1
 
2.4%
1396851000 1
 
2.4%
2585005000 1
 
2.4%
1676769000 1
 
2.4%
5238223000 1
 
2.4%
11256369000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
215040000 1
 
2.4%
251717000 1
 
2.4%
265588000 1
 
2.4%
467725000 1
 
2.4%
484142000 1
 
2.4%
484171000 1
 
2.4%
964914000 1
 
2.4%
1348425000 1
 
2.4%
1396851000 1
 
2.4%
ValueCountFrequency (%)
11256369000 1
2.4%
10155122000 1
2.4%
8848333000 1
2.4%
6386918000 1
2.4%
6254265000 1
2.4%
5238223000 1
2.4%
3487933000 1
2.4%
3294921000 1
2.4%
3085967000 1
2.4%
3038929000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1008244 × 109
Minimum0
Maximum1.1211546 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:53.300355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.018282 × 109
Q32.847376 × 109
95-th percentile8.832272 × 109
Maximum1.1211546 × 1010
Range1.1211546 × 1010
Interquartile range (IQR)2.847376 × 109

Descriptive statistics

Standard deviation2.7666278 × 109
Coefficient of variation (CV)1.3169248
Kurtosis3.5995577
Mean2.1008244 × 109
Median Absolute Deviation (MAD)1.018282 × 109
Skewness1.9335033
Sum8.6133799 × 1010
Variance7.6542293 × 1018
MonotonicityNot monotonic
2023-12-13T09:31:53.395586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
1757826000 1
 
2.4%
258772000 1
 
2.4%
3298810000 1
 
2.4%
3110631000 1
 
2.4%
1384276000 1
 
2.4%
2585005000 1
 
2.4%
753367000 1
 
2.4%
4704531000 1
 
2.4%
11211546000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
209754000 1
 
2.4%
245847000 1
 
2.4%
258772000 1
 
2.4%
402541000 1
 
2.4%
429575000 1
 
2.4%
440704000 1
 
2.4%
753367000 1
 
2.4%
955618000 1
 
2.4%
1001475000 1
 
2.4%
ValueCountFrequency (%)
11211546000 1
2.4%
10124324000 1
2.4%
8832272000 1
2.4%
5858128000 1
2.4%
5724213000 1
2.4%
4704531000 1
2.4%
3298810000 1
2.4%
3110631000 1
2.4%
2898502000 1
2.4%
2897430000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21800659
Minimum0
Maximum2.00907 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:53.490179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median750000
Q325897000
95-th percentile94928000
Maximum2.00907 × 108
Range2.00907 × 108
Interquartile range (IQR)25897000

Descriptive statistics

Standard deviation40982721
Coefficient of variation (CV)1.8798845
Kurtosis8.6141486
Mean21800659
Median Absolute Deviation (MAD)750000
Skewness2.705794
Sum8.93827 × 108
Variance1.6795834 × 1015
MonotonicityNot monotonic
2023-12-13T09:31:53.594899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
6308000 1
 
2.4%
26000 1
 
2.4%
86956000 1
 
2.4%
15437000 1
 
2.4%
8626000 1
 
2.4%
200907000 1
 
2.4%
39598000 1
 
2.4%
53353000 1
 
2.4%
750000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
19000 1
 
2.4%
26000 1
 
2.4%
50000 1
 
2.4%
89000 1
 
2.4%
525000 1
 
2.4%
728000 1
 
2.4%
750000 1
 
2.4%
962000 1
 
2.4%
1342000 1
 
2.4%
ValueCountFrequency (%)
200907000 1
2.4%
108639000 1
2.4%
94928000 1
2.4%
86956000 1
2.4%
78787000 1
2.4%
57054000 1
2.4%
53353000 1
2.4%
39598000 1
2.4%
33348000 1
2.4%
32327000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400634.1
Minimum0
Maximum1.16138 × 108
Zeros26
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:53.686126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q376000
95-th percentile3543000
Maximum1.16138 × 108
Range1.16138 × 108
Interquartile range (IQR)76000

Descriptive statistics

Standard deviation19930973
Coefficient of variation (CV)4.5291139
Kurtosis26.802255
Mean4400634.1
Median Absolute Deviation (MAD)0
Skewness5.0870487
Sum1.80426 × 108
Variance3.972437 × 1014
MonotonicityNot monotonic
2023-12-13T09:31:53.994086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 26
63.4%
794000 1
 
2.4%
13000 1
 
2.4%
11000 1
 
2.4%
193000 1
 
2.4%
389000 1
 
2.4%
56380000 1
 
2.4%
517000 1
 
2.4%
76000 1
 
2.4%
3543000 1
 
2.4%
Other values (6) 6
 
14.6%
ValueCountFrequency (%)
0 26
63.4%
9000 1
 
2.4%
10000 1
 
2.4%
11000 1
 
2.4%
13000 1
 
2.4%
76000 1
 
2.4%
135000 1
 
2.4%
193000 1
 
2.4%
389000 1
 
2.4%
448000 1
 
2.4%
ValueCountFrequency (%)
116138000 1
2.4%
56380000 1
2.4%
3543000 1
2.4%
1770000 1
2.4%
794000 1
2.4%
517000 1
2.4%
448000 1
2.4%
389000 1
2.4%
193000 1
2.4%
135000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4254888 × 108
Minimum0
Maximum8.67022 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:54.083170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16061000
Q31.84097 × 108
95-th percentile7.75713 × 108
Maximum8.67022 × 108
Range8.67022 × 108
Interquartile range (IQR)1.84097 × 108

Descriptive statistics

Standard deviation2.3901759 × 108
Coefficient of variation (CV)1.6767413
Kurtosis3.0587649
Mean1.4254888 × 108
Median Absolute Deviation (MAD)16061000
Skewness2.0046442
Sum5.844504 × 109
Variance5.712941 × 1016
MonotonicityNot monotonic
2023-12-13T09:31:54.188204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
9617000 1
 
2.4%
6803000 1
 
2.4%
189112000 1
 
2.4%
184097000 1
 
2.4%
12186000 1
 
2.4%
867022000 1
 
2.4%
533175000 1
 
2.4%
44823000 1
 
2.4%
53802000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
5286000 1
 
2.4%
5870000 1
 
2.4%
6803000 1
 
2.4%
9296000 1
 
2.4%
9617000 1
 
2.4%
12186000 1
 
2.4%
16061000 1
 
2.4%
30798000 1
 
2.4%
43428000 1
 
2.4%
ValueCountFrequency (%)
867022000 1
2.4%
807012000 1
2.4%
775713000 1
2.4%
533175000 1
2.4%
530052000 1
2.4%
528342000 1
2.4%
191553000 1
2.4%
189112000 1
2.4%
188218000 1
2.4%
187330000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.475366
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T09:31:54.274150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median91.72
Q397.54
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)97.54

Descriptive statistics

Standard deviation42.477312
Coefficient of variation (CV)0.63899329
Kurtosis-1.1361109
Mean66.475366
Median Absolute Deviation (MAD)7.57
Skewness-0.87950671
Sum2725.49
Variance1804.3221
MonotonicityNot monotonic
2023-12-13T09:31:54.367987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
99.29 1
 
2.4%
97.43 1
 
2.4%
94.58 1
 
2.4%
94.41 1
 
2.4%
99.1 1
 
2.4%
44.93 1
 
2.4%
89.81 1
 
2.4%
99.6 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0.0 11
26.8%
44.93 1
 
2.4%
52.9 1
 
2.4%
55.68 1
 
2.4%
86.06 1
 
2.4%
88.72 1
 
2.4%
89.81 1
 
2.4%
90.33 1
 
2.4%
91.03 1
 
2.4%
91.52 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.82 1
 
2.4%
99.7 1
 
2.4%
99.6 1
 
2.4%
99.29 1
 
2.4%
99.1 1
 
2.4%
99.04 1
 
2.4%
97.67 1
 
2.4%
97.54 1
 
2.4%
97.43 1
 
2.4%

Interactions

2023-12-13T09:31:51.506542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.497160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.960450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.448634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.124075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.606547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.073541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.572950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.565285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.030058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.732433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.197111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.675886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.149148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.632194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.631114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.102144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.792256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.263122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.738205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.208771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.694371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.696801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.170395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.855805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.327116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.801349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.269215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.760482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.770090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.238714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.931818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.406202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.870413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.334537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.823813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.835155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.308161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.005002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.478812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.937060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.397840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.878826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:48.897448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:49.381209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.065154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:50.543736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.007885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:51.450397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:31:54.439425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
순번1.0000.9560.0000.0000.0000.3020.1980.0000.362
과세년도0.9561.0000.0000.0000.0000.0000.1380.0000.000
세목명0.0000.0001.0000.8710.9530.8450.1900.9010.861
부과금액0.0000.0000.8711.0000.9780.6800.0000.7250.553
수납급액0.0000.0000.9530.9781.0000.7180.0000.6490.446
환급금액0.3020.0000.8450.6800.7181.0001.0000.8470.799
결손금액0.1980.1380.1900.0000.0001.0001.0000.9830.819
미수납 금액0.0000.0000.9010.7250.6490.8470.9831.0000.684
징수율0.3620.0000.8610.5530.4460.7990.8190.6841.000
2023-12-13T09:31:54.537324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T09:31:54.604250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
순번1.0000.0720.0700.1140.4220.0320.1330.8370.000
부과금액0.0721.0000.9960.7350.3010.6710.6770.0000.567
수납급액0.0700.9961.0000.7080.2770.6360.7090.0000.608
환급금액0.1140.7350.7081.0000.6020.8900.3610.0000.424
결손금액0.4220.3010.2770.6021.0000.6370.0490.0210.000
미수납 금액0.0320.6710.6360.8900.6371.0000.1780.0000.637
징수율0.1330.6770.7090.3610.0490.1781.0000.0000.577
과세년도0.8370.0000.0000.0000.0210.0000.0001.0000.000
세목명0.0000.5670.6080.4240.0000.6370.5770.0001.000

Missing values

2023-12-13T09:31:51.964437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:31:52.083452image/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 금액징수율
01전라남도신안군469102017도축세000000.0
12전라남도신안군469102017레저세000000.0
23전라남도신안군469102017재산세19460440001757826000962000018821800090.33
34전라남도신안군469102017주민세4677250004025410005000006518400086.06
45전라남도신안군469102017취득세884833300088322720002589700001606100099.82
56전라남도신안군469102017자동차세6254265000572421300025825000053005200091.52
67전라남도신안군469102017과년도수입1828837000101828200078787000354300080701200055.68
78전라남도신안군469102017담배소비세28974300002897430000000100.0
89전라남도신안군469102017도시계획세000000.0
910전라남도신안군469102017등록면허세96491400095561800013420000929600099.04
순번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
3132전라남도신안군469102019취득세11256369000112115460005335300004482300099.6
3233전라남도신안군469102019자동차세523822300047045310003959800051700053317500089.81
3334전라남도신안군469102019과년도수입16767690007533670002009070005638000086702200044.93
3435전라남도신안군469102019담배소비세25850050002585005000000100.0
3536전라남도신안군469102019도시계획세000000.0
3637전라남도신안군469102019등록면허세1396851000138427600086260003890001218600099.1
3738전라남도신안군469102019지방교육세329492100031106310001543700019300018409700094.41
3839전라남도신안군469102019지방소득세34879330003298810000869560001100018911200094.58
3940전라남도신안군469102019지방소비세000000.0
4041전라남도신안군469102019지역자원시설세2655880002587720002600013000680300097.43