Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description지방세 부과액에 대한 세목별 징수현황에 대하여 과세연도, 세목명, 부과금액, 수납금액, 환급금액, 결손 금액, 미수납 급액, 징수율 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15079698/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 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 부과금액 and 2 other fieldsHigh correlation
부과금액 has 11 (13.8%) zerosZeros
수납급액 has 11 (13.8%) zerosZeros
환급금액 has 25 (31.2%) zerosZeros
결손금액 has 46 (57.5%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 11 (13.8%) zerosZeros

Reproduction

Analysis started2023-12-12 04:50:24.028487
Analysis finished2023-12-12 04:50:30.322674
Duration6.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
경상북도
80 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 80
100.0%

Length

2023-12-12T13:50:30.394652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:50:30.520054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 80
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
청도군
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청도군 80
100.0%

Length

2023-12-12T13:50:30.676198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:50:30.855614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청도군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
47820
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47820
2nd row47820
3rd row47820
4th row47820
5th row47820

Common Values

ValueCountFrequency (%)
47820 80
100.0%

Length

2023-12-12T13:50:31.003442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:50:31.111012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47820 80
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:31.228573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2023-12-12T13:50:31.421641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

Length

2023-12-12T13:50:31.612803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1714039 × 109
Minimum0
Maximum1.8166884 × 1010
Zeros11
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:31.780283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.2426596 × 108
median2.9781051 × 109
Q35.8720088 × 109
95-th percentile1.3686801 × 1010
Maximum1.8166884 × 1010
Range1.8166884 × 1010
Interquartile range (IQR)5.2477428 × 109

Descriptive statistics

Standard deviation4.2956315 × 109
Coefficient of variation (CV)1.0297808
Kurtosis1.9049491
Mean4.1714039 × 109
Median Absolute Deviation (MAD)2.5513735 × 109
Skewness1.4284199
Sum3.3371231 × 1011
Variance1.845245 × 1019
MonotonicityNot monotonic
2023-12-12T13:50:31.962521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
13.8%
461215150 1
 
1.2%
2426915580 1
 
1.2%
7698147490 1
 
1.2%
16576670970 1
 
1.2%
279731960 1
 
1.2%
5677227150 1
 
1.2%
2287456630 1
 
1.2%
8338600000 1
 
1.2%
1228314730 1
 
1.2%
Other values (60) 60
75.0%
ValueCountFrequency (%)
0 11
13.8%
258098430 1
 
1.2%
279731960 1
 
1.2%
295745670 1
 
1.2%
440500000 1
 
1.2%
461215150 1
 
1.2%
480984000 1
 
1.2%
541554000 1
 
1.2%
558094050 1
 
1.2%
573904820 1
 
1.2%
ValueCountFrequency (%)
18166884000 1
1.2%
16916799000 1
1.2%
16576670970 1
1.2%
14159600260 1
1.2%
13661916350 1
1.2%
13186065000 1
1.2%
11455165980 1
1.2%
10393907800 1
1.2%
8777923560 1
1.2%
8338600000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9057096 × 109
Minimum0
Maximum1.8143661 × 1010
Zeros11
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:32.164076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.9373644 × 108
median2.8630187 × 109
Q35.5253762 × 109
95-th percentile1.365285 × 1010
Maximum1.8143661 × 1010
Range1.8143661 × 1010
Interquartile range (IQR)4.9316398 × 109

Descriptive statistics

Standard deviation4.2759636 × 109
Coefficient of variation (CV)1.0947981
Kurtosis2.2449708
Mean3.9057096 × 109
Median Absolute Deviation (MAD)2.4141809 × 109
Skewness1.5608166
Sum3.1245677 × 1011
Variance1.8283864 × 1019
MonotonicityNot monotonic
2023-12-12T13:50:32.358262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
13.8%
439852660 1
 
1.2%
827616040 1
 
1.2%
7239081060 1
 
1.2%
16470438900 1
 
1.2%
279475370 1
 
1.2%
5450591390 1
 
1.2%
2287456630 1
 
1.2%
8338600000 1
 
1.2%
1225067960 1
 
1.2%
Other values (60) 60
75.0%
ValueCountFrequency (%)
0 11
13.8%
258098430 1
 
1.2%
279475370 1
 
1.2%
294216810 1
 
1.2%
421641000 1
 
1.2%
439852660 1
 
1.2%
457823000 1
 
1.2%
511005000 1
 
1.2%
533686640 1
 
1.2%
551488750 1
 
1.2%
ValueCountFrequency (%)
18143661000 1
1.2%
16763162000 1
1.2%
16470438900 1
1.2%
14038389320 1
1.2%
13632558780 1
1.2%
13106301000 1
1.2%
11455165980 1
1.2%
10122932550 1
1.2%
8338600000 1
1.2%
8336033000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52498195
Minimum0
Maximum5.9108328 × 108
Zeros25
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:32.548256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2754000
Q347250655
95-th percentile2.3198953 × 108
Maximum5.9108328 × 108
Range5.9108328 × 108
Interquartile range (IQR)47250655

Descriptive statistics

Standard deviation1.0109057 × 108
Coefficient of variation (CV)1.9256009
Kurtosis10.07768
Mean52498195
Median Absolute Deviation (MAD)2754000
Skewness2.8240173
Sum4.1998556 × 109
Variance1.0219303 × 1016
MonotonicityNot monotonic
2023-12-12T13:50:32.741046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
31.2%
46813260 1
 
1.2%
3241990 1
 
1.2%
5356510 1
 
1.2%
21714060 1
 
1.2%
344405680 1
 
1.2%
183580 1
 
1.2%
3414340 1
 
1.2%
361740 1
 
1.2%
198608360 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
0 25
31.2%
2710 1
 
1.2%
9000 1
 
1.2%
10850 1
 
1.2%
13470 1
 
1.2%
67000 1
 
1.2%
183580 1
 
1.2%
361740 1
 
1.2%
383000 1
 
1.2%
411000 1
 
1.2%
ValueCountFrequency (%)
591083280 1
1.2%
344405680 1
1.2%
289280520 1
1.2%
244705000 1
1.2%
231320300 1
1.2%
225724000 1
1.2%
222129000 1
1.2%
198608360 1
1.2%
189954140 1
1.2%
188230000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19747424
Minimum0
Maximum4.9626839 × 108
Zeros46
Zeros (%)57.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:32.944761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q379977.5
95-th percentile1.9454955 × 108
Maximum4.9626839 × 108
Range4.9626839 × 108
Interquartile range (IQR)79977.5

Descriptive statistics

Standard deviation73988417
Coefficient of variation (CV)3.7467377
Kurtosis23.846903
Mean19747424
Median Absolute Deviation (MAD)0
Skewness4.605291
Sum1.5797939 × 109
Variance5.4742858 × 1015
MonotonicityNot monotonic
2023-12-12T13:50:33.133705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 46
57.5%
61800 2
 
2.5%
1378470 1
 
1.2%
10740 1
 
1.2%
72342710 1
 
1.2%
10701460 1
 
1.2%
595240 1
 
1.2%
99958140 1
 
1.2%
64890 1
 
1.2%
2170 1
 
1.2%
Other values (24) 24
30.0%
ValueCountFrequency (%)
0 46
57.5%
2000 1
 
1.2%
2170 1
 
1.2%
7000 1
 
1.2%
9270 1
 
1.2%
10000 1
 
1.2%
10740 1
 
1.2%
22000 1
 
1.2%
32000 1
 
1.2%
48000 1
 
1.2%
ValueCountFrequency (%)
496268390 1
1.2%
263570000 1
1.2%
221925000 1
1.2%
214167940 1
1.2%
193517000 1
1.2%
99958140 1
1.2%
72342710 1
1.2%
10701460 1
1.2%
1559610 1
1.2%
1378470 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4360215 × 108
Minimum0
Maximum1.5203411 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:33.324250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26290285
Q32.6193894 × 108
95-th percentile1.4310022 × 109
Maximum1.5203411 × 109
Range1.5203411 × 109
Interquartile range (IQR)2.6193894 × 108

Descriptive statistics

Standard deviation4.2271096 × 108
Coefficient of variation (CV)1.7352514
Kurtosis3.2616524
Mean2.4360215 × 108
Median Absolute Deviation (MAD)26290285
Skewness2.0792964
Sum1.9488172 × 1010
Variance1.7868455 × 1017
MonotonicityNot monotonic
2023-12-12T13:50:33.867785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
992353000 1
 
1.2%
29357570 1
 
1.2%
466949270 1
 
1.2%
1434026340 1
 
1.2%
3130300 1
 
1.2%
175751410 1
 
1.2%
174876460 1
 
1.2%
21362490 1
 
1.2%
215934300 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
256590 1
 
1.2%
1528860 1
 
1.2%
1966000 1
 
1.2%
2265000 1
 
1.2%
2287000 1
 
1.2%
2746250 1
 
1.2%
3130300 1
 
1.2%
3181880 1
 
1.2%
18857000 1
 
1.2%
ValueCountFrequency (%)
1520341100 1
1.2%
1514376000 1
1.2%
1499341400 1
1.2%
1434026340 1
1.2%
1430843000 1
1.2%
1286811000 1
1.2%
1239270000 1
1.2%
1091427000 1
1.2%
992353000 1
1.2%
505956000 1
1.2%

징수율
Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.289875
Minimum0
Maximum100
Zeros11
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:50:34.065297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q188.7075
median95.67
Q399.7825
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)11.075

Descriptive statistics

Standard deviation34.959156
Coefficient of variation (CV)0.44090316
Kurtosis1.0320291
Mean79.289875
Median Absolute Deviation (MAD)4.13
Skewness-1.6436115
Sum6343.19
Variance1222.1426
MonotonicityNot monotonic
2023-12-12T13:50:34.265952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 15
 
18.8%
0.0 11
 
13.8%
99.81 2
 
2.5%
96.09 1
 
1.2%
99.78 1
 
1.2%
94.67 1
 
1.2%
47.54 1
 
1.2%
99.75 1
 
1.2%
96.18 1
 
1.2%
94.66 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0.0 11
13.8%
32.21 1
 
1.2%
34.1 1
 
1.2%
47.54 1
 
1.2%
49.64 1
 
1.2%
50.88 1
 
1.2%
53.2 1
 
1.2%
79.42 1
 
1.2%
81.12 1
 
1.2%
81.26 1
 
1.2%
ValueCountFrequency (%)
100.0 15
18.8%
99.9 1
 
1.2%
99.87 1
 
1.2%
99.81 2
 
2.5%
99.79 1
 
1.2%
99.78 1
 
1.2%
99.75 1
 
1.2%
99.74 1
 
1.2%
99.73 1
 
1.2%
99.48 1
 
1.2%

Interactions

2023-12-12T13:50:29.280090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.364544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.223181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.932600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.034690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.746574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.522685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.372648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.466496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.345962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.028352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.133433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.843659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.627556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.490925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.596683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.454450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.140947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.253567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.965885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.748442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.590693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.723089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.541570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.265870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.371323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.080418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.860195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.689883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.850722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.644694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.368810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.470466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.186659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.971189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.781961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:24.961758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.735303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.462736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.556653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.305050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.073111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.906898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.095785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:25.840271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:26.916676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:27.648599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:28.426427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:50:29.183775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:50:34.400483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0420.0000.000
세목명0.0001.0000.8630.8540.7060.4640.8170.867
부과금액0.0000.8631.0000.9950.6430.0000.5870.156
수납급액0.0000.8540.9951.0000.5610.0000.5020.000
환급금액0.0000.7060.6430.5611.0000.9540.4080.769
결손금액0.0420.4640.0000.0000.9541.0000.5810.932
미수납 금액0.0000.8170.5870.5020.4080.5811.0000.828
징수율0.0000.8670.1560.0000.7690.9320.8281.000
2023-12-12T13:50:34.560452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1460.1580.1300.083-0.0070.2260.000
부과금액0.1461.0000.9890.5900.2500.5300.3030.578
수납급액0.1580.9891.0000.5410.1990.4460.3840.563
환급금액0.1300.5900.5411.0000.5970.785-0.0990.321
결손금액0.0830.2500.1990.5971.0000.697-0.2840.174
미수납 금액-0.0070.5300.4460.7850.6971.000-0.3680.517
징수율0.2260.3030.384-0.099-0.284-0.3681.0000.489
세목명0.0000.5780.5630.3210.1740.5170.4891.000

Missing values

2023-12-12T13:50:30.047229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:50:30.241531image/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
0경상북도청도군478202017도축세000000.0
1경상북도청도군478202017레저세30410460003041046000000100.0
2경상북도청도군478202017재산세529480000043024250006810002200099235300081.26
3경상북도청도군478202017주민세6410530006078190000620003317200094.82
4경상북도청도군478202017취득세181668840001814366100018823000002322300099.87
5경상북도청도군478202017자동차세667231100061933760003598100021400047872100092.82
6경상북도청도군478202017과년도수입2901635000144044000075692000221925000123927000049.64
7경상북도청도군478202017담배소비세31720510003172051000000100.0
8경상북도청도군478202017도시계획세000000.0
9경상북도청도군478202017등록면허세956623000954625000568900032000196600099.79
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70경상북도청도군478202022취득세1415960026014038389320113358320012121094099.14
71경상북도청도군478202022자동차세665235732062070736904856284068110044460253093.3
72경상북도청도군478202022과년도수입2558692790824183750189954140214167940152034110032.21
73경상북도청도군478202022담배소비세30484952003048495200271000100.0
74경상북도청도군478202022도시계획세000000.0
75경상북도청도군478202022등록면허세112224158011194335301426986061800274625099.74
76경상북도청도군478202022지방교육세576369517055874857602246755021044017599897096.94
77경상북도청도군478202022지방소득세1039390780010122932550289280520799708331949097.39
78경상북도청도군478202022지방소비세1145516598011455165980000100.0
79경상북도청도군478202022지역자원시설세5739048205514887501085002241607096.09