Overview

Dataset statistics

Number of variables11
Number of observations69
Missing cells36
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory94.9 B

Variable types

Categorical4
Numeric4
Text3

Dataset

Description지방세 부과액에 대한 세목별 징수 현황을 제공2017년~2022년 주민세, 재산세, 자동차세, 담배소비세, 지방소득세, 취득세, 등록면허세, 지역자원시설세, 과년도수입, 지방교육세 등의 부과금액, 수납금액, 환급금액, 결손금액,미수납 금액,징수율에 대한 자료를 제공한다.
Author전라남도 장성군
URLhttps://www.data.go.kr/data/15080219/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액High correlation
수납급액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
징수율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
부과금액 has 4 (5.8%) missing valuesMissing
수납급액 has 4 (5.8%) missing valuesMissing
환급금액 has 6 (8.7%) missing valuesMissing
결손금액 has 14 (20.3%) missing valuesMissing
미수납 금액 has 8 (11.6%) missing valuesMissing
부과금액 has 1 (1.4%) zerosZeros
수납급액 has 1 (1.4%) zerosZeros
징수율 has 5 (7.2%) zerosZeros

Reproduction

Analysis started2024-03-14 16:17:55.145329
Analysis finished2024-03-14 16:18:01.371383
Duration6.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size680.0 B
전라남도
69 

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 69
100.0%

Length

2024-03-15T01:18:01.595547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:18:01.909551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 69
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size680.0 B
장성군
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장성군 69
100.0%

Length

2024-03-15T01:18:02.241672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:18:02.566313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장성군 69
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size680.0 B
46880
69 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46880
2nd row46880
3rd row46880
4th row46880
5th row46880

Common Values

ValueCountFrequency (%)
46880 69
100.0%

Length

2024-03-15T01:18:02.910628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:18:03.232136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46880 69
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6957
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T01:18:03.520372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7090103
Coefficient of variation (CV)0.00084617218
Kurtosis-1.221452
Mean2019.6957
Median Absolute Deviation (MAD)1
Skewness-0.18118628
Sum139359
Variance2.9207161
MonotonicityIncreasing
2024-03-15T01:18:03.894128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 13
18.8%
2021 13
18.8%
2022 13
18.8%
2017 10
14.5%
2018 10
14.5%
2019 10
14.5%
ValueCountFrequency (%)
2017 10
14.5%
2018 10
14.5%
2019 10
14.5%
2020 13
18.8%
2021 13
18.8%
2022 13
18.8%
ValueCountFrequency (%)
2022 13
18.8%
2021 13
18.8%
2020 13
18.8%
2019 10
14.5%
2018 10
14.5%
2017 10
14.5%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size680.0 B
주민세
6 
재산세
6 
자동차세
6 
담배소비세
6 
지방소득세
6 
Other values (8)
39 

Length

Max length7
Median length5
Mean length4.4782609
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주민세
2nd row재산세
3rd row자동차세
4th row담배소비세
5th row지방소득세

Common Values

ValueCountFrequency (%)
주민세 6
8.7%
재산세 6
8.7%
자동차세 6
8.7%
담배소비세 6
8.7%
지방소득세 6
8.7%
취득세 6
8.7%
등록면허세 6
8.7%
지역자원시설세 6
8.7%
과년도수입 6
8.7%
지방교육세 6
8.7%
Other values (3) 9
13.0%

Length

2024-03-15T01:18:04.361746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주민세 6
8.7%
재산세 6
8.7%
자동차세 6
8.7%
담배소비세 6
8.7%
지방소득세 6
8.7%
취득세 6
8.7%
등록면허세 6
8.7%
지역자원시설세 6
8.7%
과년도수입 6
8.7%
지방교육세 6
8.7%
Other values (3) 9
13.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct65
Distinct (%)100.0%
Missing4
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean5.9356423 × 109
Minimum0
Maximum3.3853028 × 1010
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T01:18:05.024148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.318584 × 108
Q11.471287 × 109
median3.501504 × 109
Q39.282159 × 109
95-th percentile1.5198195 × 1010
Maximum3.3853028 × 1010
Range3.3853028 × 1010
Interquartile range (IQR)7.810872 × 109

Descriptive statistics

Standard deviation6.1825841 × 109
Coefficient of variation (CV)1.0416032
Kurtosis5.6793376
Mean5.9356423 × 109
Median Absolute Deviation (MAD)2.348489 × 109
Skewness2.008126
Sum3.8581675 × 1011
Variance3.8224346 × 1019
MonotonicityNot monotonic
2024-03-15T01:18:05.487160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12128293000 1
 
1.4%
3292296000 1
 
1.4%
1684114000 1
 
1.4%
4985341000 1
 
1.4%
11576770000 1
 
1.4%
9303000000 1
 
1.4%
968245000 1
 
1.4%
5629551000 1
 
1.4%
1462948000 1
 
1.4%
21710652000 1
 
1.4%
Other values (55) 55
79.7%
(Missing) 4
 
5.8%
ValueCountFrequency (%)
0 1
1.4%
24406000 1
1.4%
697981000 1
1.4%
710715000 1
1.4%
816432000 1
1.4%
937366000 1
1.4%
968245000 1
1.4%
1029335000 1
1.4%
1093501000 1
1.4%
1153015000 1
1.4%
ValueCountFrequency (%)
33853028000 1
1.4%
21710652000 1
1.4%
19787376000 1
1.4%
15307692000 1
1.4%
14760205000 1
1.4%
14663888000 1
1.4%
13628756000 1
1.4%
13477657000 1
1.4%
12128293000 1
1.4%
11576770000 1
1.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct65
Distinct (%)100.0%
Missing4
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean5.7373628 × 109
Minimum0
Maximum3.3821322 × 1010
Zeros1
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T01:18:05.858604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.01794 × 108
Q11.32805 × 109
median3.48218 × 109
Q39.208225 × 109
95-th percentile1.5158145 × 1010
Maximum3.3821322 × 1010
Range3.3821322 × 1010
Interquartile range (IQR)7.880175 × 109

Descriptive statistics

Standard deviation6.1894245 × 109
Coefficient of variation (CV)1.0787926
Kurtosis5.8272504
Mean5.7373628 × 109
Median Absolute Deviation (MAD)2.675645 × 109
Skewness2.0253345
Sum3.7292858 × 1011
Variance3.8308975 × 1019
MonotonicityNot monotonic
2024-03-15T01:18:06.146446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11829553000 1
 
1.4%
3292296000 1
 
1.4%
1681777000 1
 
1.4%
4840312000 1
 
1.4%
11347574000 1
 
1.4%
9303000000 1
 
1.4%
950780000 1
 
1.4%
5479583000 1
 
1.4%
1412775000 1
 
1.4%
21543142000 1
 
1.4%
Other values (55) 55
79.7%
(Missing) 4
 
5.8%
ValueCountFrequency (%)
0 1
1.4%
24406000 1
1.4%
173948000 1
1.4%
281058000 1
1.4%
384738000 1
1.4%
456973000 1
1.4%
686377000 1
1.4%
690675000 1
1.4%
792066000 1
1.4%
806535000 1
1.4%
ValueCountFrequency (%)
33821322000 1
1.4%
21543142000 1
1.4%
19656789000 1
1.4%
15292755000 1
1.4%
14619706000 1
1.4%
14232696000 1
1.4%
13600385000 1
1.4%
12903503000 1
1.4%
11829553000 1
1.4%
11347574000 1
1.4%

환급금액
Text

MISSING 

Distinct59
Distinct (%)93.7%
Missing6
Missing (%)8.7%
Memory size680.0 B
2024-03-15T01:18:07.016405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.015873
Min length1

Characters and Unicode

Total characters442
Distinct characters11
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

Unique57 ?
Unique (%)90.5%

Sample

1st row254000
2nd row2083000
3rd row54301000
4th row
5th row318033000
ValueCountFrequency (%)
0 3
 
5.0%
77171000 1
 
1.7%
28789000 1
 
1.7%
254000 1
 
1.7%
502000 1
 
1.7%
1205658000 1
 
1.7%
3726000 1
 
1.7%
9576000 1
 
1.7%
30284000 1
 
1.7%
469681000 1
 
1.7%
Other values (48) 48
80.0%
2024-03-15T01:18:08.288715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 200
45.2%
2 35
 
7.9%
1 35
 
7.9%
8 29
 
6.6%
3 24
 
5.4%
5 24
 
5.4%
6 24
 
5.4%
7 22
 
5.0%
4 22
 
5.0%
9 21
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 436
98.6%
Space Separator 6
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 200
45.9%
2 35
 
8.0%
1 35
 
8.0%
8 29
 
6.7%
3 24
 
5.5%
5 24
 
5.5%
6 24
 
5.5%
7 22
 
5.0%
4 22
 
5.0%
9 21
 
4.8%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 200
45.2%
2 35
 
7.9%
1 35
 
7.9%
8 29
 
6.6%
3 24
 
5.4%
5 24
 
5.4%
6 24
 
5.4%
7 22
 
5.0%
4 22
 
5.0%
9 21
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 200
45.2%
2 35
 
7.9%
1 35
 
7.9%
8 29
 
6.6%
3 24
 
5.4%
5 24
 
5.4%
6 24
 
5.4%
7 22
 
5.0%
4 22
 
5.0%
9 21
 
4.8%

결손금액
Text

MISSING 

Distinct42
Distinct (%)76.4%
Missing14
Missing (%)20.3%
Memory size680.0 B
2024-03-15T01:18:09.006280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length5.2181818
Min length1

Characters and Unicode

Total characters287
Distinct characters11
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

Unique40 ?
Unique (%)72.7%

Sample

1st row737000
2nd row2618000
3rd row7105000
4th row
5th row127811000
ValueCountFrequency (%)
0 6
 
13.0%
97000 1
 
2.2%
737000 1
 
2.2%
332861000 1
 
2.2%
1115000 1
 
2.2%
201582000 1
 
2.2%
320000 1
 
2.2%
126000 1
 
2.2%
41000 1
 
2.2%
24353000 1
 
2.2%
Other values (31) 31
67.4%
2024-03-15T01:18:10.183936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 136
47.4%
1 36
 
12.5%
18
 
6.3%
2 17
 
5.9%
3 16
 
5.6%
7 14
 
4.9%
8 13
 
4.5%
5 11
 
3.8%
6 10
 
3.5%
4 8
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269
93.7%
Space Separator 18
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136
50.6%
1 36
 
13.4%
2 17
 
6.3%
3 16
 
5.9%
7 14
 
5.2%
8 13
 
4.8%
5 11
 
4.1%
6 10
 
3.7%
4 8
 
3.0%
9 8
 
3.0%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136
47.4%
1 36
 
12.5%
18
 
6.3%
2 17
 
5.9%
3 16
 
5.6%
7 14
 
4.9%
8 13
 
4.5%
5 11
 
3.8%
6 10
 
3.5%
4 8
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136
47.4%
1 36
 
12.5%
18
 
6.3%
2 17
 
5.9%
3 16
 
5.6%
7 14
 
4.9%
8 13
 
4.5%
5 11
 
3.8%
6 10
 
3.5%
4 8
 
2.8%

미수납 금액
Text

MISSING 

Distinct56
Distinct (%)91.8%
Missing8
Missing (%)11.6%
Memory size680.0 B
2024-03-15T01:18:11.057915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.704918
Min length1

Characters and Unicode

Total characters470
Distinct characters11
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

Unique54 ?
Unique (%)88.5%

Sample

1st row37753000
2nd row131496000
3rd row492566000
4th row
5th row190458000
ValueCountFrequency (%)
0 4
 
6.9%
155430000 1
 
1.7%
821078000 1
 
1.7%
130587000 1
 
1.7%
394982000 1
 
1.7%
777457000 1
 
1.7%
2337000 1
 
1.7%
144709000 1
 
1.7%
229196000 1
 
1.7%
17465000 1
 
1.7%
Other values (45) 45
77.6%
2024-03-15T01:18:12.117523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 188
40.0%
1 47
 
10.0%
4 38
 
8.1%
7 34
 
7.2%
3 33
 
7.0%
9 28
 
6.0%
5 27
 
5.7%
2 26
 
5.5%
6 24
 
5.1%
8 19
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 464
98.7%
Space Separator 6
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 188
40.5%
1 47
 
10.1%
4 38
 
8.2%
7 34
 
7.3%
3 33
 
7.1%
9 28
 
6.0%
5 27
 
5.8%
2 26
 
5.6%
6 24
 
5.2%
8 19
 
4.1%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 470
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 188
40.0%
1 47
 
10.0%
4 38
 
8.1%
7 34
 
7.2%
3 33
 
7.0%
9 28
 
6.0%
5 27
 
5.7%
2 26
 
5.5%
6 24
 
5.1%
8 19
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 188
40.0%
1 47
 
10.0%
4 38
 
8.1%
7 34
 
7.2%
3 33
 
7.0%
9 28
 
6.0%
5 27
 
5.7%
2 26
 
5.5%
6 24
 
5.1%
8 19
 
4.0%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.373333
Minimum0
Maximum100
Zeros5
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size749.0 B
2024-03-15T01:18:12.528784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q195
median97
Q399
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)4

Descriptive statistics

Standard deviation31.23723
Coefficient of variation (CV)0.37022633
Kurtosis2.4905375
Mean84.373333
Median Absolute Deviation (MAD)2
Skewness-2.040351
Sum5821.76
Variance975.76451
MonotonicityNot monotonic
2024-03-15T01:18:12.933055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
100.0 12
17.4%
97.0 8
 
11.6%
98.0 6
 
8.7%
0.0 5
 
7.2%
99.0 4
 
5.8%
96.0 4
 
5.8%
95.0 3
 
4.3%
94.0 3
 
4.3%
93.0 2
 
2.9%
99.91 2
 
2.9%
Other values (20) 20
29.0%
ValueCountFrequency (%)
0.0 5
7.2%
11.37 1
 
1.4%
16.0 1
 
1.4%
28.37 1
 
1.4%
30.0 1
 
1.4%
36.0 1
 
1.4%
40.0 1
 
1.4%
93.0 2
 
2.9%
94.0 3
4.3%
94.96 1
 
1.4%
ValueCountFrequency (%)
100.0 12
17.4%
99.91 2
 
2.9%
99.82 1
 
1.4%
99.23 1
 
1.4%
99.0 4
 
5.8%
98.25 1
 
1.4%
98.0 6
8.7%
97.69 1
 
1.4%
97.67 1
 
1.4%
97.63 1
 
1.4%

Interactions

2024-03-15T01:17:59.116517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:55.635340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:56.643526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:57.952900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:59.389787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:55.882443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:56.938695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:58.214164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:59.679249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:56.139159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:57.180621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:58.529272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:59.957286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:56.402307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:57.476964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:17:58.835409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:18:13.221236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.9750.9690.9730.000
세목명0.0001.0000.7780.8000.0000.0000.0000.796
부과금액0.0000.7781.0000.9990.9780.0000.9770.000
수납급액0.0000.8000.9991.0000.9700.0000.9690.000
환급금액0.9750.0000.9780.9701.0001.0001.0000.000
결손금액0.9690.0000.0000.0001.0001.0001.0000.692
미수납 금액0.9730.0000.9770.9691.0001.0001.0000.000
징수율0.0000.7960.0000.0000.0000.6920.0001.000
2024-03-15T01:18:13.431217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액징수율세목명
과세년도1.0000.0800.0660.1480.000
부과금액0.0801.0000.9740.1290.475
수납급액0.0660.9741.0000.2220.502
징수율0.1480.1290.2221.0000.553
세목명0.0000.4750.5020.5531.000

Missing values

2024-03-15T01:18:00.383041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:18:00.810326image/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.
2024-03-15T01:18:01.172359image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0전라남도장성군468802017주민세115301500011145250002540007370003775300097.0
1전라남도장성군468802017재산세350150400033673900002083000261800013149600096.0
2전라남도장성군468802017자동차세9994331000949466000054301000710500049256600095.0
3전라남도장성군468802017담배소비세27973120002797312000100.0
4전라남도장성군468802017지방소득세8695787000837751800031803300012781100019045800093.0
5전라남도장성군468802017취득세13628756000136003850001009680002837100099.0
6전라남도장성군468802017등록면허세13686760001366668000552600019000198900099.0
7전라남도장성군468802017지역자원시설세71071500068637700020000019780002236000097.0
8전라남도장성군468802017과년도수입150878500069067500023961800018909700062901300030.0
9전라남도장성군468802017지방교육세3950157000377946200023574000246100016823400095.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
59전라남도장성군468802022취득세33853028000338213220005643800003170600099.91
60전라남도장성군468802022자동차세9115256000865545100080870000101200045879300094.96
61전라남도장성군468802022과년도수입152929000017394800064126900055871000129947100011.37
62전라남도장성군468802022담배소비세34821800003482180000300000100.0
63전라남도장성군468802022도시계획세000000.0
64전라남도장성군468802022등록면허세1750873000174776200011172000111000300000099.82
65전라남도장성군468802022지방교육세677591500066150800002878900030100016053400097.63
66전라남도장성군468802022지방소득세1347765700012903503000405019000057415400095.74
67전라남도장성군468802022지방소비세1109571000011095710000000100.0
68전라남도장성군468802022지역자원시설세10935010001074391000726000350001907500098.25