Overview

Dataset statistics

Number of variables12
Number of observations49
Missing cells37
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory106.7 B

Variable types

Numeric6
Categorical5
Text1

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공지자체의 재정자주도·재정자립도 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용
Author경상북도 경산시
URLhttps://www.data.go.kr/data/15079710/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
부과금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
결손금액 is highly overall correlated with 미수납 금액 High correlation
미수납 금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
환급금액 has 5 (10.2%) missing valuesMissing
결손금액 has 20 (40.8%) missing valuesMissing
미수납 금액 has 12 (24.5%) missing valuesMissing
연번 has unique valuesUnique
부과금액 has unique valuesUnique
수납급액 has unique valuesUnique

Reproduction

Analysis started2024-03-23 05:45:40.688673
Analysis finished2024-03-23 05:45:58.744474
Duration18.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:45:58.989297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4
Q113
median25
Q337
95-th percentile46.6
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.28869
Coefficient of variation (CV)0.57154761
Kurtosis-1.2
Mean25
Median Absolute Deviation (MAD)12
Skewness0
Sum1225
Variance204.16667
MonotonicityStrictly increasing
2024-03-23T05:45:59.435832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1 1
 
2.0%
38 1
 
2.0%
28 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%
40 1
2.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
경상북도
49 

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 49
100.0%

Length

2024-03-23T05:45:59.905788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:46:00.397424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 49
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
경산시
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경산시 49
100.0%

Length

2024-03-23T05:46:01.069042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:46:01.536785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경산시 49
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
47290
49 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47290
2nd row47290
3rd row47290
4th row47290
5th row47290

Common Values

ValueCountFrequency (%)
47290 49
100.0%

Length

2024-03-23T05:46:02.307413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:46:02.785060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47290 49
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size524.0 B
2020
13 
2021
12 
2022
12 
2023
12 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
26.5%
2021 12
24.5%
2022 12
24.5%
2023 12
24.5%

Length

2024-03-23T05:46:03.512035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T05:46:04.208325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
26.5%
2021 12
24.5%
2022 12
24.5%
2023 12
24.5%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size524.0 B
교육세
4 
담배소비세
4 
등록면허세
4 
등록세
4 
자동차세
4 
Other values (8)
29 

Length

Max length7
Median length5
Mean length4.3877551
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row교육세
2nd row담배소비세
3rd row등록면허세
4th row등록세
5th row면허세

Common Values

ValueCountFrequency (%)
교육세 4
8.2%
담배소비세 4
8.2%
등록면허세 4
8.2%
등록세 4
8.2%
자동차세 4
8.2%
재산세 4
8.2%
주민세 4
8.2%
지방소득세 4
8.2%
지방소비세 4
8.2%
지역자원시설세 4
8.2%
Other values (3) 9
18.4%

Length

2024-03-23T05:46:04.827069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육세 4
 
7.5%
담배소비세 4
 
7.5%
등록면허세 4
 
7.5%
등록세 4
 
7.5%
자동차세 4
 
7.5%
재산세 4
 
7.5%
주민세 4
 
7.5%
지방소득세 4
 
7.5%
지방소비세 4
 
7.5%
지역자원시설세 4
 
7.5%
Other values (4) 13
24.5%

부과금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1170478 × 1010
Minimum6180
Maximum1.44245 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:46:05.484046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6180
5-th percentile2.4322231 × 108
Q17.4764904 × 109
median2.0355498 × 1010
Q35.2834569 × 1010
95-th percentile8.8906693 × 1010
Maximum1.44245 × 1011
Range1.4424499 × 1011
Interquartile range (IQR)4.5358078 × 1010

Descriptive statistics

Standard deviation3.1758304 × 1010
Coefficient of variation (CV)1.0188584
Kurtosis3.2867495
Mean3.1170478 × 1010
Median Absolute Deviation (MAD)1.3438259 × 1010
Skewness1.6958212
Sum1.5273534 × 1012
Variance1.0085899 × 1021
MonotonicityNot monotonic
2024-03-23T05:46:06.105029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
33793756970 1
 
2.0%
30558471190 1
 
2.0%
6269322850 1
 
2.0%
424019220 1
 
2.0%
44197540960 1
 
2.0%
60161804260 1
 
2.0%
8682663490 1
 
2.0%
57837388810 1
 
2.0%
20355497700 1
 
2.0%
7182749380 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
6180 1
2.0%
102289500 1
2.0%
145102850 1
2.0%
390401500 1
2.0%
424019220 1
2.0%
6269322850 1
2.0%
6608676210 1
2.0%
6971116420 1
2.0%
6977076410 1
2.0%
6999104790 1
2.0%
ValueCountFrequency (%)
144245000000 1
2.0%
126026000000 1
2.0%
88991449930 1
2.0%
88779557640 1
2.0%
60161804260 1
2.0%
58128711530 1
2.0%
57837388810 1
2.0%
57815684410 1
2.0%
56511499990 1
2.0%
56051985550 1
2.0%

수납급액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.934488 × 1010
Minimum6180
Maximum1.44098 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:46:06.642467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6180
5-th percentile1.9880675 × 108
Q16.8450456 × 109
median2.0355498 × 1010
Q34.936307 × 1010
95-th percentile8.6188301 × 1010
Maximum1.44098 × 1011
Range1.4409799 × 1011
Interquartile range (IQR)4.2518024 × 1010

Descriptive statistics

Standard deviation3.1895082 × 1010
Coefficient of variation (CV)1.0869045
Kurtosis3.4656565
Mean2.934488 × 1010
Median Absolute Deviation (MAD)1.5821813 × 1010
Skewness1.7313194
Sum1.4378991 × 1012
Variance1.0172963 × 1021
MonotonicityNot monotonic
2024-03-23T05:46:07.122156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
32629446080 1
 
2.0%
29129700260 1
 
2.0%
6253759780 1
 
2.0%
424019220 1
 
2.0%
40986386070 1
 
2.0%
59237922260 1
 
2.0%
8549936330 1
 
2.0%
55789002270 1
 
2.0%
20355497700 1
 
2.0%
7069415440 1
 
2.0%
Other values (39) 39
79.6%
ValueCountFrequency (%)
6180 1
2.0%
102289500 1
2.0%
145102850 1
2.0%
279362600 1
2.0%
390401500 1
2.0%
424019220 1
2.0%
3294538130 1
2.0%
4153540220 1
2.0%
4533684650 1
2.0%
6253759780 1
2.0%
ValueCountFrequency (%)
144098000000 1
2.0%
125554000000 1
2.0%
88570617060 1
2.0%
82614826790 1
2.0%
59237922260 1
2.0%
57259359170 1
2.0%
55789002270 1
2.0%
55549943540 1
2.0%
55525845240 1
2.0%
54982975470 1
2.0%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)100.0%
Missing5
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean6.2113674 × 108
Minimum19030
Maximum5.9265171 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:46:07.750173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19030
5-th percentile439829.5
Q19002422.5
median70404750
Q33.7137104 × 108
95-th percentile3.0406985 × 109
Maximum5.9265171 × 109
Range5.9264981 × 109
Interquartile range (IQR)3.6236862 × 108

Descriptive statistics

Standard deviation1.2112367 × 109
Coefficient of variation (CV)1.9500323
Kurtosis8.1985026
Mean6.2113674 × 108
Median Absolute Deviation (MAD)69938695
Skewness2.7075951
Sum2.7330016 × 1010
Variance1.4670943 × 1018
MonotonicityNot monotonic
2024-03-23T05:46:08.236008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
19030 1
 
2.0%
1511080 1
 
2.0%
329524750 1
 
2.0%
49589840 1
 
2.0%
14892440 1
 
2.0%
1750200500 1
 
2.0%
6647640 1
 
2.0%
414238610 1
 
2.0%
2378007110 1
 
2.0%
128262850 1
 
2.0%
Other values (34) 34
69.4%
(Missing) 5
 
10.2%
ValueCountFrequency (%)
19030 1
2.0%
110490 1
2.0%
428590 1
2.0%
503520 1
2.0%
575780 1
2.0%
855650 1
2.0%
1511080 1
2.0%
1823690 1
2.0%
5041880 1
2.0%
5142030 1
2.0%
ValueCountFrequency (%)
5926517090 1
2.0%
3327017350 1
2.0%
3135196130 1
2.0%
2505212130 1
2.0%
2378007110 1
2.0%
2084328890 1
2.0%
1762526730 1
2.0%
1750200500 1
2.0%
610114100 1
2.0%
594629960 1
2.0%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing20
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean2.8344872 × 108
Minimum27810
Maximum2.3344147 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:46:08.724728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27810
5-th percentile31512
Q1478400
median3972460
Q374105030
95-th percentile1.8148545 × 109
Maximum2.3344147 × 109
Range2.3343869 × 109
Interquartile range (IQR)73626630

Descriptive statistics

Standard deviation6.3080115 × 108
Coefficient of variation (CV)2.2254507
Kurtosis5.2132997
Mean2.8344872 × 108
Median Absolute Deviation (MAD)3941560
Skewness2.4583225
Sum8.220013 × 109
Variance3.9791009 × 1017
MonotonicityNot monotonic
2024-03-23T05:46:09.219684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
32430 1
 
2.0%
2097518090 1
 
2.0%
8159790 1
 
2.0%
373609300 1
 
2.0%
3241900 1
 
2.0%
26792460 1
 
2.0%
882680 1
 
2.0%
114330 1
 
2.0%
516635220 1
 
2.0%
168295630 1
 
2.0%
Other values (19) 19
38.8%
(Missing) 20
40.8%
ValueCountFrequency (%)
27810 1
2.0%
30900 1
2.0%
32430 1
2.0%
78020 1
2.0%
114330 1
2.0%
309000 1
2.0%
389940 1
2.0%
478400 1
2.0%
882680 1
2.0%
1698120 1
2.0%
ValueCountFrequency (%)
2334414670 1
2.0%
2097518090 1
2.0%
1390859110 1
2.0%
1114561050 1
2.0%
516635220 1
2.0%
373609300 1
2.0%
168295630 1
2.0%
74105030 1
2.0%
63819030 1
2.0%
26792460 1
2.0%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)100.0%
Missing12
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean2.0333621 × 109
Minimum13000590
Maximum1.1262527 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2024-03-23T05:46:09.767820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13000590
5-th percentile15294766
Q11.3203083 × 108
median8.6927434 × 108
Q31.8921316 × 109
95-th percentile1.1155466 × 1010
Maximum1.1262527 × 1010
Range1.1249526 × 1010
Interquartile range (IQR)1.7601007 × 109

Descriptive statistics

Standard deviation3.3816195 × 109
Coefficient of variation (CV)1.663068
Kurtosis3.7913907
Mean2.0333621 × 109
Median Absolute Deviation (MAD)7.4483604 × 108
Skewness2.2299509
Sum7.5234397 × 1010
Variance1.143535 × 1019
MonotonicityNot monotonic
2024-03-23T05:46:10.189946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
383512910 1
 
2.0%
919909540 1
 
2.0%
132727160 1
 
2.0%
2048386540 1
 
2.0%
113333940 1
 
2.0%
404198010 1
 
2.0%
11100612270 1
 
2.0%
1260475300 1
 
2.0%
16654870 1
 
2.0%
13000590 1
 
2.0%
Other values (27) 27
55.1%
(Missing) 12
24.5%
ValueCountFrequency (%)
13000590 1
2.0%
14345150 1
2.0%
15532170 1
2.0%
16654870 1
2.0%
25780170 1
2.0%
113333940 1
2.0%
119564100 1
2.0%
124107110 1
2.0%
124438300 1
2.0%
132030830 1
2.0%
ValueCountFrequency (%)
11262526730 1
2.0%
11163402820 1
2.0%
11153481600 1
2.0%
11100612270 1
2.0%
3469800790 1
2.0%
3466865280 1
2.0%
3409246240 1
2.0%
3208619910 1
2.0%
2048386540 1
2.0%
1892131570 1
2.0%

징수율
Text

Distinct34
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size524.0 B
2024-03-23T05:46:10.607590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2244898
Min length5

Characters and Unicode

Total characters305
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)61.2%

Sample

1st row96.55%
2nd row100.00%
3rd row99.83%
4th row100.00%
5th row100.00%
ValueCountFrequency (%)
100.00 12
24.5%
98.50 3
 
6.1%
96.50 2
 
4.1%
99.63 2
 
4.1%
95.88 1
 
2.0%
92.70 1
 
2.0%
98.40 1
 
2.0%
99.50 1
 
2.0%
25.40 1
 
2.0%
99.77 1
 
2.0%
Other values (24) 24
49.0%
2024-03-23T05:46:11.573878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66
21.6%
. 49
16.1%
% 49
16.1%
9 46
15.1%
8 19
 
6.2%
1 18
 
5.9%
5 15
 
4.9%
6 12
 
3.9%
3 10
 
3.3%
7 9
 
3.0%
Other values (2) 12
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 207
67.9%
Other Punctuation 98
32.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
31.9%
9 46
22.2%
8 19
 
9.2%
1 18
 
8.7%
5 15
 
7.2%
6 12
 
5.8%
3 10
 
4.8%
7 9
 
4.3%
2 8
 
3.9%
4 4
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 49
50.0%
% 49
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 305
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
21.6%
. 49
16.1%
% 49
16.1%
9 46
15.1%
8 19
 
6.2%
1 18
 
5.9%
5 15
 
4.9%
6 12
 
3.9%
3 10
 
3.3%
7 9
 
3.0%
Other values (2) 12
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 305
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
21.6%
. 49
16.1%
% 49
16.1%
9 46
15.1%
8 19
 
6.2%
1 18
 
5.9%
5 15
 
4.9%
6 12
 
3.9%
3 10
 
3.3%
7 9
 
3.0%
Other values (2) 12
 
3.9%

Interactions

2024-03-23T05:45:54.795831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:41.334105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:42.807401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:44.376231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:46.340913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:47.974585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:55.611877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:41.556885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:43.110329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:44.728936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:46.585411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:48.298297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:56.084975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:41.794514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:43.400036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:45.071726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:46.849373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:48.664729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:56.448330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:42.051548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:43.636938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:45.472634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:47.165614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:49.376509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:56.704325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:42.270260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:43.917291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:45.888319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:47.420422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:51.169388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:56.993032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:42.509883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:44.114586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:46.094941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:47.708976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T05:45:52.891827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T05:46:11.821313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
연번1.0000.9560.0000.0000.0000.2410.0000.0000.560
과세년도0.9561.0000.0000.0000.0000.0000.0000.0000.647
세목명0.0000.0001.0000.8800.8380.6040.5310.9670.653
부과금액0.0000.0000.8801.0000.9500.5850.4450.5500.885
수납급액0.0000.0000.8380.9501.0000.4040.4220.0000.793
환급금액0.2410.0000.6040.5850.4041.0000.8930.5230.955
결손금액0.0000.0000.5310.4450.4220.8931.0000.4521.000
미수납 금액0.0000.0000.9670.5500.0000.5230.4521.0000.978
징수율0.5600.6470.6530.8850.7930.9551.0000.9781.000
2024-03-23T05:46:12.122286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-03-23T05:46:12.357275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부과금액수납급액환급금액결손금액미수납 금액과세년도세목명
연번1.0000.1160.0730.0640.3030.0770.8530.000
부과금액0.1161.0000.9450.6270.1790.4380.0000.567
수납급액0.0730.9451.0000.432-0.1300.1090.0000.512
환급금액0.0640.6270.4321.0000.3810.7330.0000.334
결손금액0.3030.179-0.1300.3811.0000.5680.0000.282
미수납 금액0.0770.4380.1090.7330.5681.0000.0000.688
과세년도0.8530.0000.0000.0000.0000.0001.0000.000
세목명0.0000.5670.5120.3340.2820.6880.0001.000

Missing values

2024-03-23T05:45:57.423782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T05:45:58.094437image/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.
2024-03-23T05:45:58.471210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
01경상북도경산시472902020교육세33793756970326294460801567792309587790115472310096.55%
12경상북도경산시472902020담배소비세206502294502065022945024451760<NA><NA>100.00%
23경상북도경산시472902020등록면허세7476490350746345733063053560324301300059099.83%
34경상북도경산시472902020등록세390401500390401500575780<NA><NA>100.00%
45경상북도경산시472902020면허세61806180<NA><NA><NA>100.00%
56경상북도경산시472902020자동차세42774867960393053902803121335402612400346686528091.89%
67경상북도경산시472902020재산세5494355717054055158900181783907410503081429324098.38%
78경상북도경산시472902020주민세699910479069733246205142030<NA>2578017099.63%
89경상북도경산시472902020지방소득세54161753260529451117101762526730389940121625161097.75%
910경상북도경산시472902020지방소비세1320180000013201800000<NA><NA><NA>100.00%
연번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
3940경상북도경산시472902023등록면허세73608807707344111570402281201143301665487099.77%
4041경상북도경산시472902023등록세102289500102289500110490<NA><NA>100.00%
4142경상북도경산시472902023자동차세4408937847040679249550338130250882680340924624092.27%
4243경상북도경산시472902023재산세5651149999055525845240777559402679246095882629098.26%
4344경상북도경산시472902023주민세1016083741099283661205041880324190022922939097.71%
4445경상북도경산시472902023지방소득세57815684410555499435402505212130373609300189213157096.08%
4546경상북도경산시472902023지방소비세2212005390022120053900<NA><NA><NA>100.00%
4647경상북도경산시472902023지역자원시설세697111642068385183301823690815979012443830098.10%
4748경상북도경산시472902023취득세8877955764082614826790336579430<NA>16473085093.06%
4849경상북도경산시472902023지난연도 체납1353036229027936260059265170902097518090111534816002.06%