Overview

Dataset statistics

Number of variables11
Number of observations52
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory98.5 B

Variable types

Categorical5
Numeric6

Dataset

Description최근 4년간 지방세 부과 징수 자료에 따른 지방세 세목별 통계자료를 근거로 연도별 지방세 징수 현황 자료 추출한 자료에 해당됩니다
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15079473/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 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 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 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 has 8 (15.4%) zerosZeros
수납급액 has 8 (15.4%) zerosZeros
환급금액 has 13 (25.0%) zerosZeros
결손금액 has 27 (51.9%) zerosZeros
미수납 금액 has 16 (30.8%) zerosZeros
징수율 has 8 (15.4%) zerosZeros

Reproduction

Analysis started2024-03-23 05:33:07.104816
Analysis finished2024-03-23 05:33:15.070278
Duration7.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
충청북도
52 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 52
100.0%

Length

2024-03-23T14:33:15.184518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:33:15.354266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 52
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
진천군
52 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진천군 52
100.0%

Length

2024-03-23T14:33:15.573143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:33:15.753426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진천군 52
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
43750
52 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43750
2nd row43750
3rd row43750
4th row43750
5th row43750

Common Values

ValueCountFrequency (%)
43750 52
100.0%

Length

2024-03-23T14:33:15.931678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:33:16.056910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43750 52
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size548.0 B
2019
13 
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 13
25.0%
2020 13
25.0%
2021 13
25.0%
2022 13
25.0%

Length

2024-03-23T14:33:16.220168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T14:33:16.410982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13
25.0%
2020 13
25.0%
2021 13
25.0%
2022 13
25.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size548.0 B
레저세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (8)
32 

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 4
 
7.7%
재산세 4
 
7.7%
주민세 4
 
7.7%
취득세 4
 
7.7%
자동차세 4
 
7.7%
과년도수입 4
 
7.7%
담배소비세 4
 
7.7%
도시계획세 4
 
7.7%
등록면허세 4
 
7.7%
지방교육세 4
 
7.7%
Other values (3) 12
23.1%

Length

2024-03-23T14:33:16.646820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 4
 
7.7%
재산세 4
 
7.7%
주민세 4
 
7.7%
취득세 4
 
7.7%
자동차세 4
 
7.7%
과년도수입 4
 
7.7%
담배소비세 4
 
7.7%
도시계획세 4
 
7.7%
등록면허세 4
 
7.7%
지방교육세 4
 
7.7%
Other values (3) 12
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4751507 × 1010
Minimum0
Maximum6.3676996 × 1010
Zeros8
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T14:33:16.921994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.2697852 × 109
median9.1055395 × 109
Q31.7200863 × 1010
95-th percentile5.5057494 × 1010
Maximum6.3676996 × 1010
Range6.3676996 × 1010
Interquartile range (IQR)1.2931078 × 1010

Descriptive statistics

Standard deviation1.6857633 × 1010
Coefficient of variation (CV)1.1427736
Kurtosis1.7271215
Mean1.4751507 × 1010
Median Absolute Deviation (MAD)6.3156435 × 109
Skewness1.6122237
Sum7.6707837 × 1011
Variance2.841798 × 1020
MonotonicityNot monotonic
2024-03-23T14:33:17.173786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 8
 
15.4%
22480173000 1
 
1.9%
17081751000 1
 
1.9%
4709171000 1
 
1.9%
9134893000 1
 
1.9%
3894591000 1
 
1.9%
15191848000 1
 
1.9%
54334977000 1
 
1.9%
11374515000 1
 
1.9%
4812251000 1
 
1.9%
Other values (35) 35
67.3%
ValueCountFrequency (%)
0 8
15.4%
44969000 1
 
1.9%
255865000 1
 
1.9%
3474474000 1
 
1.9%
3894591000 1
 
1.9%
4164039000 1
 
1.9%
4305034000 1
 
1.9%
4343770000 1
 
1.9%
4709171000 1
 
1.9%
4812251000 1
 
1.9%
ValueCountFrequency (%)
63676996000 1
1.9%
59183338000 1
1.9%
55940570000 1
1.9%
54334977000 1
1.9%
44309413000 1
1.9%
43343699000 1
1.9%
41548547000 1
1.9%
40959773000 1
1.9%
22480173000 1
1.9%
20515825000 1
1.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4093274 × 1010
Minimum-3.818075 × 109
Maximum6.3533818 × 1010
Zeros8
Zeros (%)15.4%
Negative1
Negative (%)1.9%
Memory size600.0 B
2024-03-23T14:33:17.392323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.818075 × 109
5-th percentile0
Q12.880268 × 109
median9.1055395 × 109
Q31.6128657 × 1010
95-th percentile5.378884 × 1010
Maximum6.3533818 × 1010
Range6.7351893 × 1010
Interquartile range (IQR)1.3248389 × 1010

Descriptive statistics

Standard deviation1.6716544 × 1010
Coefficient of variation (CV)1.1861363
Kurtosis1.8082497
Mean1.4093274 × 1010
Median Absolute Deviation (MAD)7.139564 × 109
Skewness1.6027969
Sum7.3285023 × 1011
Variance2.7944285 × 1020
MonotonicityNot monotonic
2024-03-23T14:33:17.620706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 8
 
15.4%
22131861000 1
 
1.9%
16012210000 1
 
1.9%
1110685000 1
 
1.9%
9134893000 1
 
1.9%
3890294000 1
 
1.9%
14817932000 1
 
1.9%
52888017000 1
 
1.9%
11374515000 1
 
1.9%
4760842000 1
 
1.9%
Other values (35) 35
67.3%
ValueCountFrequency (%)
-3818075000 1
 
1.9%
0 8
15.4%
44969000 1
 
1.9%
52100000 1
 
1.9%
872992000 1
 
1.9%
1110685000 1
 
1.9%
3470129000 1
 
1.9%
3890294000 1
 
1.9%
4108746000 1
 
1.9%
4267169000 1
 
1.9%
ValueCountFrequency (%)
63533818000 1
1.9%
58726559000 1
1.9%
54889845000 1
1.9%
52888017000 1
1.9%
42695234000 1
1.9%
41383806000 1
1.9%
40233154000 1
1.9%
38765229000 1
1.9%
22131861000 1
1.9%
20217226000 1
1.9%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3827342 × 108
Minimum0
Maximum5.903168 × 109
Zeros13
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T14:33:17.837856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16000
median23453000
Q31.19205 × 108
95-th percentile1.4439482 × 109
Maximum5.903168 × 109
Range5.903168 × 109
Interquartile range (IQR)1.19199 × 108

Descriptive statistics

Standard deviation9.423865 × 108
Coefficient of variation (CV)2.7858721
Kurtosis24.92419
Mean3.3827342 × 108
Median Absolute Deviation (MAD)23453000
Skewness4.6603887
Sum1.7590218 × 1010
Variance8.8809232 × 1017
MonotonicityNot monotonic
2024-03-23T14:33:18.117428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 13
25.0%
16110000 1
 
1.9%
134109000 1
 
1.9%
1607103000 1
 
1.9%
1282000 1
 
1.9%
61147000 1
 
1.9%
98716000 1
 
1.9%
694397000 1
 
1.9%
119000 1
 
1.9%
3436000 1
 
1.9%
Other values (30) 30
57.7%
ValueCountFrequency (%)
0 13
25.0%
8000 1
 
1.9%
119000 1
 
1.9%
188000 1
 
1.9%
413000 1
 
1.9%
1233000 1
 
1.9%
1282000 1
 
1.9%
1491000 1
 
1.9%
1824000 1
 
1.9%
3436000 1
 
1.9%
ValueCountFrequency (%)
5903168000 1
1.9%
2878956000 1
1.9%
1607103000 1
1.9%
1310458000 1
1.9%
1111278000 1
1.9%
916363000 1
1.9%
833035000 1
1.9%
694397000 1
1.9%
555924000 1
1.9%
290326000 1
1.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54525538
Minimum0
Maximum9.47467 × 108
Zeros27
Zeros (%)51.9%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T14:33:18.496740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3873250
95-th percentile4.1776305 × 108
Maximum9.47467 × 108
Range9.47467 × 108
Interquartile range (IQR)873250

Descriptive statistics

Standard deviation1.9286304 × 108
Coefficient of variation (CV)3.537114
Kurtosis14.684808
Mean54525538
Median Absolute Deviation (MAD)0
Skewness3.9027582
Sum2.835328 × 109
Variance3.7196153 × 1016
MonotonicityNot monotonic
2024-03-23T14:33:18.708150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 27
51.9%
667000 1
 
1.9%
31033000 1
 
1.9%
411000 1
 
1.9%
31000 1
 
1.9%
591821000 1
 
1.9%
1501000 1
 
1.9%
258000 1
 
1.9%
6000 1
 
1.9%
1492000 1
 
1.9%
Other values (16) 16
30.8%
ValueCountFrequency (%)
0 27
51.9%
6000 1
 
1.9%
10000 1
 
1.9%
16000 1
 
1.9%
28000 1
 
1.9%
31000 1
 
1.9%
60000 1
 
1.9%
62000 1
 
1.9%
133000 1
 
1.9%
174000 1
 
1.9%
ValueCountFrequency (%)
947467000 1
1.9%
849327000 1
1.9%
591821000 1
1.9%
275352000 1
1.9%
63424000 1
1.9%
31033000 1
1.9%
28858000 1
1.9%
22424000 1
1.9%
8752000 1
1.9%
7650000 1
1.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0370792 × 108
Minimum0
Maximum5.544184 × 109
Zeros16
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size600.0 B
2024-03-23T14:33:18.947814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89451500
Q36.3420525 × 108
95-th percentile3.2635376 × 109
Maximum5.544184 × 109
Range5.544184 × 109
Interquartile range (IQR)6.3420525 × 108

Descriptive statistics

Standard deviation1.1423074 × 109
Coefficient of variation (CV)1.8921524
Kurtosis7.5537128
Mean6.0370792 × 108
Median Absolute Deviation (MAD)89451500
Skewness2.7135519
Sum3.1392812 × 1010
Variance1.3048662 × 1018
MonotonicityNot monotonic
2024-03-23T14:33:19.163538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 16
30.8%
348312000 1
 
1.9%
143178000 1
 
1.9%
1069513000 1
 
1.9%
3323134000 1
 
1.9%
4297000 1
 
1.9%
372424000 1
 
1.9%
1446960000 1
 
1.9%
51403000 1
 
1.9%
102969000 1
 
1.9%
Other values (27) 27
51.9%
ValueCountFrequency (%)
0 16
30.8%
3576000 1
 
1.9%
4297000 1
 
1.9%
4345000 1
 
1.9%
4933000 1
 
1.9%
47369000 1
 
1.9%
51403000 1
 
1.9%
55293000 1
 
1.9%
73047000 1
 
1.9%
73273000 1
 
1.9%
ValueCountFrequency (%)
5544184000 1
1.9%
3661113000 1
1.9%
3323134000 1
1.9%
3214777000 1
1.9%
3126473000 1
1.9%
1446960000 1
1.9%
1135396000 1
1.9%
1080203000 1
1.9%
1069513000 1
1.9%
1023155000 1
1.9%

징수율
Real number (ℝ)

ZEROS 

Distinct38
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.552308
Minimum-1492.22
Maximum100
Zeros8
Zeros (%)15.4%
Negative1
Negative (%)1.9%
Memory size600.0 B
2024-03-23T14:33:19.387083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1492.22
5-th percentile0
Q191.21
median98.235
Q399.645
95-th percentile100
Maximum100
Range1592.22
Interquartile range (IQR)8.435

Descriptive statistics

Standard deviation221.17866
Coefficient of variation (CV)4.6512708
Kurtosis48.625876
Mean47.552308
Median Absolute Deviation (MAD)1.73
Skewness-6.8723272
Sum2472.72
Variance48920
MonotonicityNot monotonic
2024-03-23T14:33:19.629171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.0 8
 
15.4%
100.0 8
 
15.4%
98.45 1
 
1.9%
99.78 1
 
1.9%
93.74 1
 
1.9%
23.59 1
 
1.9%
99.89 1
 
1.9%
97.54 1
 
1.9%
97.34 1
 
1.9%
98.93 1
 
1.9%
Other values (28) 28
53.8%
ValueCountFrequency (%)
-1492.22 1
 
1.9%
0.0 8
15.4%
1.21 1
 
1.9%
17.68 1
 
1.9%
23.59 1
 
1.9%
87.49 1
 
1.9%
92.45 1
 
1.9%
93.3 1
 
1.9%
93.74 1
 
1.9%
93.85 1
 
1.9%
ValueCountFrequency (%)
100.0 8
15.4%
99.93 1
 
1.9%
99.91 1
 
1.9%
99.89 1
 
1.9%
99.87 1
 
1.9%
99.78 1
 
1.9%
99.6 1
 
1.9%
99.23 1
 
1.9%
99.1 1
 
1.9%
99.09 1
 
1.9%

Interactions

2024-03-23T14:33:13.597416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:07.682700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:08.721217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.794098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:10.954838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.583749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:13.743423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:07.815574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:08.883154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.983816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:11.182758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.759538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:13.887312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:07.959209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.034362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:10.169989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:11.887536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.929929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:14.039301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:08.094570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.221219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:10.328823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.066159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:13.065004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:14.200952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:08.287474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.413547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:10.492607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.227445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:13.224769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:14.392110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:08.533981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:09.613919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:10.702826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:12.412064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T14:33:13.412081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T14:33:19.816439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.000NaN
세목명0.0001.0000.9350.9270.7950.2840.759NaN
부과금액0.0000.9351.0000.9940.4750.0000.501NaN
수납급액0.0000.9270.9941.0000.6060.0000.477NaN
환급금액0.0000.7950.4750.6061.0000.9900.840NaN
결손금액0.0000.2840.0000.0000.9901.0000.785NaN
미수납 금액0.0000.7590.5010.4770.8400.7851.000NaN
징수율NaNNaNNaNNaNNaNNaNNaN1.000
2024-03-23T14:33:20.007623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-23T14:33:20.160004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9920.5560.2490.5480.2980.0000.736
수납급액0.9921.0000.4980.1930.4820.3360.0000.718
환급금액0.5560.4981.0000.6650.898-0.1470.0000.536
결손금액0.2490.1930.6651.0000.651-0.2220.0000.126
미수납 금액0.5480.4820.8980.6511.000-0.3310.0000.462
징수율0.2980.336-0.147-0.222-0.3311.0000.0000.000
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.7360.7180.5360.1260.4620.0000.0001.000

Missing values

2024-03-23T14:33:14.662786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T14:33:14.958133image/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
0충청북도진천군437502019레저세000000.0
1충청북도진천군437502019재산세1930035300018957191000201730001600034314600098.22
2충청북도진천군437502019주민세80081660007935057000413000620007304700099.09
3충청북도진천군437502019취득세44309413000387652290001690780000554418400087.49
4충청북도진천군437502019자동차세1504327300013907703000108234000174000113539600092.45
5충청북도진천군437502019과년도수입49370960008729920001310458000849327000321477700017.68
6충청북도진천군437502019담배소비세85179260008517926000000100.0
7충청북도진천군437502019도시계획세000000.0
8충청북도진천군437502019등록면허세5643144000563807800032597000133000493300099.91
9충청북도진천군437502019지방교육세1313944400012480686000505040006000065869800094.99
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
42충청북도진천군437502022취득세5918333800058726559000201262000045677900099.23
43충청북도진천군437502022자동차세15292452000142677960001130780001501000102315500093.3
44충청북도진천군437502022과년도수입430503400052100000287895600059182100036611130001.21
45충청북도진천군437502022담배소비세96370740009637074000800000100.0
46충청북도진천군437502022도시계획세000000.0
47충청북도진천군437502022등록면허세521728100052136740002673300031000357600099.93
48충청북도진천군437502022지방교육세15549914000151838100004671900041100036569300097.65
49충청북도진천군437502022지방소득세559405700005488984500083303500031033000101969200098.12
50충청북도진천군437502022지방소비세1588342000015883420000000100.0
51충청북도진천군437502022지역자원시설세5132218000508484900018800004736900099.08