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 제공하는 자료로 지자체의 재정자주도,재정자립도를 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
URLhttps://www.data.go.kr/data/15078899/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 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 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 has 15 (22.4%) zerosZeros
수납급액 has 15 (22.4%) zerosZeros
환급금액 has 20 (29.9%) zerosZeros
결손금액 has 23 (34.3%) zerosZeros
미수납 금액 has 22 (32.8%) zerosZeros
징수율 has 15 (22.4%) zerosZeros

Reproduction

Analysis started2023-12-12 19:39:45.071971
Analysis finished2023-12-12 19:39:49.658847
Duration4.59 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
전라남도
67 

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 67
100.0%

Length

2023-12-13T04:39:49.758983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:49.873840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 67
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영암군 67
100.0%

Length

2023-12-13T04:39:50.006019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:50.115360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영암군 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
46830
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46830
2nd row46830
3rd row46830
4th row46830
5th row46830

Common Values

ValueCountFrequency (%)
46830 67
100.0%

Length

2023-12-13T04:39:50.259733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:50.429402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46830 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

2023-12-13T04:39:50.558635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:50.709905image/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

2023-12-13T04:39:50.911917image/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  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2387851 × 109
Minimum0
Maximum2.0360907 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:51.141088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.966346 × 109
median4.464983 × 109
Q31.0030818 × 1010
95-th percentile1.6824346 × 1010
Maximum2.0360907 × 1010
Range2.0360907 × 1010
Interquartile range (IQR)8.064472 × 109

Descriptive statistics

Standard deviation5.8951378 × 109
Coefficient of variation (CV)0.94491759
Kurtosis-0.31795332
Mean6.2387851 × 109
Median Absolute Deviation (MAD)4.464983 × 109
Skewness0.84230652
Sum4.179986 × 1011
Variance3.475265 × 1019
MonotonicityNot monotonic
2023-12-13T04:39:51.372702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
8940300000 1
 
1.5%
15837881000 1
 
1.5%
2418445000 1
 
1.5%
10295779000 1
 
1.5%
4190058000 1
 
1.5%
20168694000 1
 
1.5%
10071313000 1
 
1.5%
3778515000 1
 
1.5%
5923119000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
1558914000 1
 
1.5%
1898822000 1
 
1.5%
2033870000 1
 
1.5%
2088280000 1
 
1.5%
2113773000 1
 
1.5%
2282947000 1
 
1.5%
2418445000 1
 
1.5%
2496722000 1
 
1.5%
2556713000 1
 
1.5%
ValueCountFrequency (%)
20360907000 1
1.5%
20168694000 1
1.5%
19854431000 1
1.5%
17226739000 1
1.5%
15885429000 1
1.5%
15884253000 1
1.5%
15837881000 1
1.5%
15110609000 1
1.5%
14838423000 1
1.5%
14808446000 1
1.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8732073 × 109
Minimum0
Maximum2.0182124 × 1010
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:51.918272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.426495 × 108
median4.169445 × 109
Q39.4919585 × 109
95-th percentile1.6634094 × 1010
Maximum2.0182124 × 1010
Range2.0182124 × 1010
Interquartile range (IQR)8.849309 × 109

Descriptive statistics

Standard deviation5.8045823 × 109
Coefficient of variation (CV)0.98831559
Kurtosis-0.24950378
Mean5.8732073 × 109
Median Absolute Deviation (MAD)4.169445 × 109
Skewness0.86307779
Sum3.9350489 × 1011
Variance3.3693176 × 1019
MonotonicityNot monotonic
2023-12-13T04:39:52.139801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
22.4%
8940300000 1
 
1.5%
14798847000 1
 
1.5%
2196115000 1
 
1.5%
9805160000 1
 
1.5%
4070828000 1
 
1.5%
19992177000 1
 
1.5%
9579217000 1
 
1.5%
1324023000 1
 
1.5%
5923119000 1
 
1.5%
Other values (43) 43
64.2%
ValueCountFrequency (%)
0 15
22.4%
429650000 1
 
1.5%
587031000 1
 
1.5%
698268000 1
 
1.5%
1075809000 1
 
1.5%
1324023000 1
 
1.5%
1554965000 1
 
1.5%
1801837000 1
 
1.5%
2014910000 1
 
1.5%
2106139000 1
 
1.5%
ValueCountFrequency (%)
20182124000 1
1.5%
19992177000 1
1.5%
19350522000 1
1.5%
17001242000 1
1.5%
15777417000 1
1.5%
14798847000 1
1.5%
14692170000 1
1.5%
14384905000 1
1.5%
14203924000 1
1.5%
13946102000 1
1.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1562437 × 108
Minimum0
Maximum8.5506 × 108
Zeros20
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:52.356749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4363000
Q389821500
95-th percentile7.091307 × 108
Maximum8.5506 × 108
Range8.5506 × 108
Interquartile range (IQR)89821500

Descriptive statistics

Standard deviation2.2576504 × 108
Coefficient of variation (CV)1.9525731
Kurtosis4.0905206
Mean1.1562437 × 108
Median Absolute Deviation (MAD)4363000
Skewness2.2434041
Sum7.746833 × 109
Variance5.0969854 × 1016
MonotonicityNot monotonic
2023-12-13T04:39:52.552453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 20
29.9%
38618000 1
 
1.5%
682000 1
 
1.5%
1695000 1
 
1.5%
3630000 1
 
1.5%
58864000 1
 
1.5%
203702000 1
 
1.5%
832733000 1
 
1.5%
689000 1
 
1.5%
14878000 1
 
1.5%
Other values (38) 38
56.7%
ValueCountFrequency (%)
0 20
29.9%
45000 1
 
1.5%
61000 1
 
1.5%
106000 1
 
1.5%
214000 1
 
1.5%
682000 1
 
1.5%
689000 1
 
1.5%
713000 1
 
1.5%
758000 1
 
1.5%
1695000 1
 
1.5%
ValueCountFrequency (%)
855060000 1
1.5%
832733000 1
1.5%
825012000 1
1.5%
716034000 1
1.5%
693023000 1
1.5%
569658000 1
1.5%
477756000 1
1.5%
363575000 1
1.5%
338099000 1
1.5%
266213000 1
1.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98145985
Minimum0
Maximum1.068081 × 109
Zeros23
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:52.747794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1441000
Q331580000
95-th percentile5.867749 × 108
Maximum1.068081 × 109
Range1.068081 × 109
Interquartile range (IQR)31580000

Descriptive statistics

Standard deviation2.2837239 × 108
Coefficient of variation (CV)2.3268644
Kurtosis6.9830662
Mean98145985
Median Absolute Deviation (MAD)1441000
Skewness2.694257
Sum6.575781 × 109
Variance5.215395 × 1016
MonotonicityNot monotonic
2023-12-13T04:39:52.951386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 23
34.3%
547850000 1
 
1.5%
6590000 1
 
1.5%
603457000 1
 
1.5%
14541000 1
 
1.5%
7366000 1
 
1.5%
288000 1
 
1.5%
1309000 1
 
1.5%
422365000 1
 
1.5%
159000 1
 
1.5%
Other values (35) 35
52.2%
ValueCountFrequency (%)
0 23
34.3%
155000 1
 
1.5%
159000 1
 
1.5%
167000 1
 
1.5%
185000 1
 
1.5%
288000 1
 
1.5%
454000 1
 
1.5%
502000 1
 
1.5%
609000 1
 
1.5%
776000 1
 
1.5%
ValueCountFrequency (%)
1068081000 1
1.5%
932237000 1
1.5%
680922000 1
1.5%
603457000 1
1.5%
547850000 1
1.5%
496117000 1
1.5%
455995000 1
1.5%
422365000 1
1.5%
380089000 1
1.5%
377816000 1
1.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6743187 × 108
Minimum0
Maximum2.032127 × 109
Zeros22
Zeros (%)32.8%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:53.137765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.18942 × 108
Q33.84616 × 108
95-th percentile1.1182573 × 109
Maximum2.032127 × 109
Range2.032127 × 109
Interquartile range (IQR)3.84616 × 108

Descriptive statistics

Standard deviation4.0692591 × 108
Coefficient of variation (CV)1.5216059
Kurtosis7.5983451
Mean2.6743187 × 108
Median Absolute Deviation (MAD)1.18942 × 108
Skewness2.5889146
Sum1.7917935 × 1010
Variance1.6558869 × 1017
MonotonicityNot monotonic
2023-12-13T04:39:53.368630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 22
32.8%
414898000 1
 
1.5%
435577000 1
 
1.5%
207789000 1
 
1.5%
483253000 1
 
1.5%
118942000 1
 
1.5%
176517000 1
 
1.5%
490787000 1
 
1.5%
2032127000 1
 
1.5%
5175000 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 22
32.8%
3794000 1
 
1.5%
4930000 1
 
1.5%
5175000 1
 
1.5%
7449000 1
 
1.5%
18793000 1
 
1.5%
78285000 1
 
1.5%
78692000 1
 
1.5%
78713000 1
 
1.5%
106334000 1
 
1.5%
ValueCountFrequency (%)
2032127000 1
1.5%
1794946000 1
1.5%
1364078000 1
1.5%
1162513000 1
1.5%
1014994000 1
1.5%
633723000 1
1.5%
618014000 1
1.5%
581521000 1
1.5%
557358000 1
1.5%
505776000 1
1.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.842836
Minimum0
Maximum100
Zeros15
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T04:39:53.569534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123.14
median95.11
Q397.68
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)74.54

Descriptive statistics

Standard deviation41.991451
Coefficient of variation (CV)0.60122775
Kurtosis-1.0005766
Mean69.842836
Median Absolute Deviation (MAD)4.01
Skewness-0.96855032
Sum4679.47
Variance1763.282
MonotonicityNot monotonic
2023-12-13T04:39:53.771836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.0 15
22.4%
100.0 7
 
10.4%
99.12 2
 
3.0%
95.72 2
 
3.0%
94.56 1
 
1.5%
96.88 1
 
1.5%
93.44 1
 
1.5%
90.81 1
 
1.5%
95.23 1
 
1.5%
97.15 1
 
1.5%
Other values (35) 35
52.2%
ValueCountFrequency (%)
0.0 15
22.4%
19.61 1
 
1.5%
20.57 1
 
1.5%
25.71 1
 
1.5%
35.04 1
 
1.5%
37.15 1
 
1.5%
90.81 1
 
1.5%
91.7 1
 
1.5%
92.49 1
 
1.5%
92.95 1
 
1.5%
ValueCountFrequency (%)
100.0 7
10.4%
99.8 1
 
1.5%
99.78 1
 
1.5%
99.75 1
 
1.5%
99.64 1
 
1.5%
99.33 1
 
1.5%
99.12 2
 
3.0%
99.07 1
 
1.5%
98.69 1
 
1.5%
97.9 1
 
1.5%

Interactions

2023-12-13T04:39:48.739675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:45.487604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.287848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.024841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.597074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.175906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.841599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:45.630090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.418395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.127424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.710667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.271662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.934961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:45.761947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.549301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.221898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.822176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.369503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:49.014780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:45.877381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.670039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.293195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.901888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.466146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:49.107788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.007236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.785038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.383181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.996317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.561245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:49.221181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.161983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:46.921021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:47.513191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.084599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:48.656575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:39:53.923061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8890.8650.7260.7090.6890.808
부과금액0.0000.8891.0000.9950.5830.2220.5730.745
수납급액0.0000.8650.9951.0000.5220.0000.2470.587
환급금액0.0000.7260.5830.5221.0000.8830.9360.729
결손금액0.0000.7090.2220.0000.8831.0000.8870.818
미수납 금액0.0000.6890.5730.2470.9360.8871.0000.941
징수율0.0000.8080.7450.5870.7290.8180.9411.000
2023-12-13T04:39:54.075224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T04:39:54.196755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9900.6260.4890.5920.5730.0000.609
수납급액0.9901.0000.5740.4380.5300.6210.0000.563
환급금액0.6260.5741.0000.7650.8650.2020.0000.392
결손금액0.4890.4380.7651.0000.8320.0190.0000.318
미수납 금액0.5920.5300.8650.8321.0000.0340.0000.356
징수율0.5730.6210.2020.0190.0341.0000.0000.543
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6090.5630.3920.3180.3560.5430.0001.000

Missing values

2023-12-13T04:39:49.356967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:39:49.594501image/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
0전라남도영암군468302017도축세000000.0
1전라남도영암군468302017레저세000000.0
2전라남도영암군468302017재산세877430000084258170007130003014100031834200096.03
3전라남도영암군468302017주민세41597080004072204000701100088120007869200097.9
4전라남도영암군468302017취득세158842530001577741700011919200050200010633400099.33
5전라남도영암군468302017자동차세1072102700010137593000204931000191300058152100094.56
6전라남도영암군468302017과년도수입2282947000587031000693023000680922000101499400025.71
7전라남도영암군468302017담배소비세54361110005436111000000100.0
8전라남도영암군468302017도시계획세000000.0
9전라남도영암군468302017등록면허세1558914000155496500014595000155000379400099.75
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
57전라남도영암군468302021취득세2036090700020182124000955400006735500011142800099.12
58전라남도영암군468302021자동차세1209357200011585863000256443000193300050577600095.8
59전라남도영암군468302021과년도수입35612950006982680008550600001068081000179494600019.61
60전라남도영암군468302021담배소비세615463900061546390004500000100.0
61전라남도영암군468302021도시계획세000000.0
62전라남도영암군468302021등록면허세2496722000249118300019626000609000493000099.78
63전라남도영암군468302021지방교육세73725420007152568000563260001264300020733100097.02
64전라남도영암군468302021지방소득세158854290001469217000036357500093223700026102200092.49
65전라남도영암군468302021지방소비세89036860008903686000000100.0
66전라남도영암군468302021지역자원시설세259408300024257030001060003301900013536100093.51