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 데한 데이터로 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율 등을 제공합니다.
URLhttps://www.data.go.kr/data/15078423/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 3 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 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액High correlation
부과금액 has 16 (20.0%) zerosZeros
수납급액 has 16 (20.0%) zerosZeros
환급금액 has 22 (27.5%) zerosZeros
결손금액 has 40 (50.0%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-13 00:30:59.188389
Analysis finished2023-12-13 00:31:03.064275
Duration3.88 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
경기도
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 80
100.0%

Length

2023-12-13T09:31:03.111558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:03.180458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 80
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여주시 80
100.0%

Length

2023-12-13T09:31:03.253875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:03.323775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여주시 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
41670
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41670
2nd row41670
3rd row41670
4th row41670
5th row41670

Common Values

ValueCountFrequency (%)
41670 80
100.0%

Length

2023-12-13T09:31:03.396076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:31:03.469309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41670 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
2023-12-13T09:31:03.535202image/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
2023-12-13T09:31:03.623519image/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

2023-12-13T09:31:03.744221image/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 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9745402 × 1010
Minimum0
Maximum1.18124 × 1011
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:03.849018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.1926255 × 109
median9.0773215 × 109
Q32.8396606 × 1010
95-th percentile6.2437005 × 1010
Maximum1.18124 × 1011
Range1.18124 × 1011
Interquartile range (IQR)2.520398 × 1010

Descriptive statistics

Standard deviation2.5472272 × 1010
Coefficient of variation (CV)1.2900356
Kurtosis4.3292769
Mean1.9745402 × 1010
Median Absolute Deviation (MAD)9.0773215 × 109
Skewness1.9474641
Sum1.5796321 × 1012
Variance6.4883664 × 1020
MonotonicityNot monotonic
2023-12-13T09:31:03.963982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
26424189590 1
 
1.2%
8416123440 1
 
1.2%
9708586760 1
 
1.2%
6274821350 1
 
1.2%
22875521210 1
 
1.2%
24853157230 1
 
1.2%
9812900000 1
 
1.2%
3421075930 1
 
1.2%
48799151590 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
62061610 1
 
1.2%
2752932000 1
 
1.2%
3042754000 1
 
1.2%
3160356000 1
 
1.2%
3203382000 1
 
1.2%
3222357900 1
 
1.2%
3311052000 1
 
1.2%
3421075930 1
 
1.2%
3477864000 1
 
1.2%
ValueCountFrequency (%)
118124000000 1
1.2%
118019000000 1
1.2%
87710481470 1
1.2%
68044543000 1
1.2%
62141871000 1
1.2%
58316297000 1
1.2%
52486171640 1
1.2%
51508087000 1
1.2%
48799151590 1
1.2%
48367028640 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8905493 × 1010
Minimum0
Maximum1.17821 × 1011
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:04.073171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.4524138 × 109
median6.7693178 × 109
Q32.7597444 × 1010
95-th percentile6.1079724 × 1010
Maximum1.17821 × 1011
Range1.17821 × 1011
Interquartile range (IQR)2.5145031 × 1010

Descriptive statistics

Standard deviation2.5396169 × 1010
Coefficient of variation (CV)1.3433222
Kurtosis4.5295491
Mean1.8905493 × 1010
Median Absolute Deviation (MAD)6.7693178 × 109
Skewness1.9882794
Sum1.5124395 × 1012
Variance6.449654 × 1020
MonotonicityNot monotonic
2023-12-13T09:31:04.180369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
25927081280 1
 
1.2%
2686381850 1
 
1.2%
9708586760 1
 
1.2%
6258754750 1
 
1.2%
22396803750 1
 
1.2%
23892490290 1
 
1.2%
9812900000 1
 
1.2%
3329849290 1
 
1.2%
47819046910 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
62061610 1
 
1.2%
251109000 1
 
1.2%
1189294500 1
 
1.2%
2020296080 1
 
1.2%
2596453000 1
 
1.2%
2671174000 1
 
1.2%
2686381850 1
 
1.2%
2919171000 1
 
1.2%
3086290000 1
 
1.2%
ValueCountFrequency (%)
117821000000 1
1.2%
117530000000 1
1.2%
87501032120 1
1.2%
66838560000 1
1.2%
60776627000 1
1.2%
58049621000 1
1.2%
51307144900 1
1.2%
50030758000 1
1.2%
47819046910 1
1.2%
47306311480 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.541195 × 108
Minimum0
Maximum4.929197 × 109
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:04.287983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20079720
Q32.38518 × 108
95-th percentile2.4544208 × 109
Maximum4.929197 × 109
Range4.929197 × 109
Interquartile range (IQR)2.38518 × 108

Descriptive statistics

Standard deviation8.7678825 × 108
Coefficient of variation (CV)2.4759671
Kurtosis13.115047
Mean3.541195 × 108
Median Absolute Deviation (MAD)20079720
Skewness3.5272543
Sum2.832956 × 1010
Variance7.6875763 × 1017
MonotonicityNot monotonic
2023-12-13T09:31:04.399195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
27823000 1
 
1.2%
286284470 1
 
1.2%
1858561800 1
 
1.2%
10050550 1
 
1.2%
35633270 1
 
1.2%
179388430 1
 
1.2%
647125930 1
 
1.2%
885480 1
 
1.2%
28021990 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
8960 1
 
1.2%
21000 1
 
1.2%
230000 1
 
1.2%
248000 1
 
1.2%
624670 1
 
1.2%
686000 1
 
1.2%
885480 1
 
1.2%
1366370 1
 
1.2%
2131470 1
 
1.2%
ValueCountFrequency (%)
4929197000 1
1.2%
3946113000 1
1.2%
2848107830 1
1.2%
2757891000 1
1.2%
2438448650 1
1.2%
1858561800 1
1.2%
1283680660 1
1.2%
963059250 1
1.2%
886864900 1
1.2%
828160000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92133450
Minimum0
Maximum2.16309 × 109
Zeros40
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:04.507422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median515
Q31386500
95-th percentile5.9467601 × 108
Maximum2.16309 × 109
Range2.16309 × 109
Interquartile range (IQR)1386500

Descriptive statistics

Standard deviation3.7312234 × 108
Coefficient of variation (CV)4.0498032
Kurtosis21.782864
Mean92133450
Median Absolute Deviation (MAD)515
Skewness4.6207728
Sum7.370676 × 109
Variance1.3922028 × 1017
MonotonicityNot monotonic
2023-12-13T09:31:04.605101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 40
50.0%
61800 1
 
1.2%
2057350 1
 
1.2%
1184230480 1
 
1.2%
20810 1
 
1.2%
624230 1
 
1.2%
23041070 1
 
1.2%
10300 1
 
1.2%
4580 1
 
1.2%
341073970 1
 
1.2%
Other values (31) 31
38.8%
ValueCountFrequency (%)
0 40
50.0%
1030 1
 
1.2%
3000 1
 
1.2%
4580 1
 
1.2%
10300 1
 
1.2%
16000 1
 
1.2%
20600 1
 
1.2%
20810 1
 
1.2%
31000 1
 
1.2%
61800 1
 
1.2%
ValueCountFrequency (%)
2163090000 1
1.2%
2056713000 1
1.2%
1184230480 1
1.2%
957844000 1
1.2%
575561910 1
1.2%
341073970 1
1.2%
23041070 1
1.2%
12604400 1
1.2%
10329000 1
1.2%
6500350 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4778484 × 108
Minimum0
Maximum6.003561 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:04.700824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1447795 × 108
Q31.058804 × 109
95-th percentile4.3532926 × 109
Maximum6.003561 × 109
Range6.003561 × 109
Interquartile range (IQR)1.058804 × 109

Descriptive statistics

Standard deviation1.2871376 × 109
Coefficient of variation (CV)1.7212673
Kurtosis6.065461
Mean7.4778484 × 108
Median Absolute Deviation (MAD)1.1447795 × 108
Skewness2.5052057
Sum5.9822788 × 1010
Variance1.6567232 × 1018
MonotonicityNot monotonic
2023-12-13T09:31:04.802486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
2123920000 1
 
1.2%
209449350 1
 
1.2%
1058659810 1
 
1.2%
4545511110 1
 
1.2%
16045790 1
 
1.2%
478093230 1
 
1.2%
937625870 1
 
1.2%
91226640 1
 
1.2%
980104680 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
9889000 1
 
1.2%
10237050 1
 
1.2%
13509000 1
 
1.2%
14651930 1
 
1.2%
15941000 1
 
1.2%
16045790 1
 
1.2%
56782000 1
 
1.2%
66140980 1
 
1.2%
81758000 1
 
1.2%
ValueCountFrequency (%)
6003561000 1
1.2%
4981992810 1
1.2%
4545511110 1
1.2%
4520410000 1
1.2%
4344497000 1
1.2%
4323754670 1
1.2%
2123920000 1
1.2%
2007670000 1
1.2%
1520679160 1
1.2%
1477329000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.8
Minimum0
Maximum100
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T09:31:04.894181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.5
median97
Q398.4
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)66.9

Descriptive statistics

Standard deviation41.634431
Coefficient of variation (CV)0.57190152
Kurtosis-0.76144299
Mean72.8
Median Absolute Deviation (MAD)2.9
Skewness-1.0874188
Sum5824
Variance1733.4258
MonotonicityNot monotonic
2023-12-13T09:31:04.975709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
100.0 18
22.5%
0.0 16
20.0%
98.0 12
15.0%
97.0 12
15.0%
96.0 6
 
7.5%
95.0 4
 
5.0%
32.0 2
 
2.5%
96.8 2
 
2.5%
99.6 1
 
1.2%
99.8 1
 
1.2%
Other values (6) 6
 
7.5%
ValueCountFrequency (%)
0.0 16
20.0%
5.0 1
 
1.2%
17.6 1
 
1.2%
24.0 1
 
1.2%
30.0 1
 
1.2%
32.0 2
 
2.5%
95.0 4
 
5.0%
96.0 6
 
7.5%
96.6 1
 
1.2%
96.8 2
 
2.5%
ValueCountFrequency (%)
100.0 18
22.5%
99.8 1
 
1.2%
99.6 1
 
1.2%
98.0 12
15.0%
97.8 1
 
1.2%
97.0 12
15.0%
96.8 2
 
2.5%
96.6 1
 
1.2%
96.0 6
 
7.5%
95.0 4
 
5.0%

Interactions

2023-12-13T09:31:02.217923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.458293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.949382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.421661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.865029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.312938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.750343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.281143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.520215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.012384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.484532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.929277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.373442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.812218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.580163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.587026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.104623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.550104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.003258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.437561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.878089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.642243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.654990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.174088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.612271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.069051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.512010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.945694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.702492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.718427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.237375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.677861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.128128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.572983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.017379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.759031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.792599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.296983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.739093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.184673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.627546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.084019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.821718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:30:59.882745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.362839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:00.804693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.254017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:01.693005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:31:02.156736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:31:05.051048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0140.0000.000
세목명0.0001.0000.8090.7760.5960.4040.8600.767
부과금액0.0000.8091.0000.9990.2000.0000.5600.000
수납급액0.0000.7760.9991.0000.0000.0000.5640.000
환급금액0.0000.5960.2000.0001.0000.9670.7770.818
결손금액0.0140.4040.0000.0000.9671.0000.8160.845
미수납 금액0.0000.8600.5600.5640.7770.8161.0000.892
징수율0.0000.7670.0000.0000.8180.8450.8921.000
2023-12-13T09:31:05.145588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1580.1690.0810.0570.0040.2500.000
부과금액0.1581.0000.9690.7150.3540.6810.5200.506
수납급액0.1690.9691.0000.6030.2480.5660.6060.463
환급금액0.0810.7150.6031.0000.7130.9010.1490.295
결손금액0.0570.3540.2480.7131.0000.713-0.0540.195
미수납 금액0.0040.6810.5660.9010.7131.000-0.0240.480
징수율0.2500.5200.6060.149-0.054-0.0241.0000.500
세목명0.0000.5060.4630.2950.1950.4800.5001.000

Missing values

2023-12-13T09:31:02.904848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:31:03.017945image/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
0경기도여주시416702017도축세000000.0
1경기도여주시416702017레저세000000.0
2경기도여주시416702017재산세43704111000415801750002782300016000212392000095.0
3경기도여주시416702017주민세304275400029191710002767000012358300096.0
4경기도여주시416702017취득세62141871000607766270002048610000136524400098.0
5경기도여주시416702017자동차세51508087000500307580002338460000147732900097.0
6경기도여주시416702017과년도수입10763104000259645300027578910002163090000600356100024.0
7경기도여주시416702017담배소비세94814210009481421000000100.0
8경기도여주시416702017도시계획세000000.0
9경기도여주시416702017등록면허세4862598000484665700057234000015941000100.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70경기도여주시416702022취득세118019000000117530000000294388240048886405099.6
71경기도여주시416702022자동차세35883652570347482266202755985105938410112948754096.8
72경기도여주시416702022과년도수입674684922011892945002848107830575561910498199281017.6
73경기도여주시416702022담배소비세1027085727010270857270896000100.0
74경기도여주시416702022도시계획세000000.0
75경기도여주시416702022등록면허세6777073680676674857023277440880601023705099.8
76경기도여주시416702022지방교육세2711435769026559904010114610220227158055218210098.0
77경기도여주시416702022지방소득세436731172904227732903012836806606500350138928791096.8
78경기도여주시416702022지방소비세1490363567014903635670000100.0
79경기도여주시416702022지역자원시설세378547608036705498702131470011492621097.0