Overview

Dataset statistics

Number of variables12
Number of observations42
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory106.1 B

Variable types

Categorical6
Numeric6

Dataset

Description미환급금 유형별 미환급금 건수, 금액, 납세자 유형, 연간 누적률, 증감 등을 제공함으로써 자치단체의 환급금 해소노력을 확인 가능합니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15078943/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
미환급유형 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액 and 2 other fieldsHigh correlation
당해미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급건수 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액 is highly overall correlated with 당해미환급건수 and 2 other fieldsHigh correlation
누적미환급금액증감 has 1 (2.4%) missing valuesMissing
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 3 (7.1%) zerosZeros

Reproduction

Analysis started2024-04-21 01:50:10.395826
Analysis finished2024-04-21 01:50:15.828786
Duration5.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
부산광역시
42 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 42
100.0%

Length

2024-04-21T10:50:15.889450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:15.982773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 42
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
해운대구
42 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 42
100.0%

Length

2024-04-21T10:50:16.075034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:16.172172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 42
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
26350
42 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 42
100.0%

Length

2024-04-21T10:50:16.270643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:16.368754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 42
100.0%

세목명
Categorical

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size468.0 B
자동차세
12 
지방소득세
12 
등록면허세
8 
재산세
6 
주민세
3 

Length

Max length5
Median length4
Mean length4.2380952
Min length3

Unique

Unique1 ?
Unique (%)2.4%

Sample

1st row자동차세
2nd row자동차세
3rd row지방소득세
4th row지방소득세
5th row자동차세

Common Values

ValueCountFrequency (%)
자동차세 12
28.6%
지방소득세 12
28.6%
등록면허세 8
19.0%
재산세 6
14.3%
주민세 3
 
7.1%
취득세 1
 
2.4%

Length

2024-04-21T10:50:16.484943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:16.599249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 12
28.6%
지방소득세 12
28.6%
등록면허세 8
19.0%
재산세 6
14.3%
주민세 3
 
7.1%
취득세 1
 
2.4%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.9524
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:16.730167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6520849
Coefficient of variation (CV)0.00081788308
Kurtosis-1.086271
Mean2019.9524
Median Absolute Deviation (MAD)1
Skewness-0.36353807
Sum84838
Variance2.7293844
MonotonicityIncreasing
2024-04-21T10:50:16.852949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 10
23.8%
2022 9
21.4%
2020 7
16.7%
2018 6
14.3%
2019 6
14.3%
2017 4
 
9.5%
ValueCountFrequency (%)
2017 4
 
9.5%
2018 6
14.3%
2019 6
14.3%
2020 7
16.7%
2021 10
23.8%
2022 9
21.4%
ValueCountFrequency (%)
2022 9
21.4%
2021 10
23.8%
2020 7
16.7%
2019 6
14.3%
2018 6
14.3%
2017 4
 
9.5%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
신규
42 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 42
100.0%

Length

2024-04-21T10:50:16.989889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:17.095027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 42
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
개인
23 
법인
19 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row개인
4th row법인
5th row개인

Common Values

ValueCountFrequency (%)
개인 23
54.8%
법인 19
45.2%

Length

2024-04-21T10:50:17.205846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:17.300278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 23
54.8%
법인 19
45.2%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.357143
Minimum1
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:17.410277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q367.75
95-th percentile410.4
Maximum712
Range711
Interquartile range (IQR)64.75

Descriptive statistics

Standard deviation147.18245
Coefficient of variation (CV)1.979399
Kurtosis8.9094213
Mean74.357143
Median Absolute Deviation (MAD)8
Skewness2.8855089
Sum3123
Variance21662.674
MonotonicityNot monotonic
2024-04-21T10:50:17.557316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 6
 
14.3%
2 4
 
9.5%
3 3
 
7.1%
6 2
 
4.8%
7 2
 
4.8%
9 2
 
4.8%
78 1
 
2.4%
29 1
 
2.4%
51 1
 
2.4%
414 1
 
2.4%
Other values (19) 19
45.2%
ValueCountFrequency (%)
1 6
14.3%
2 4
9.5%
3 3
7.1%
4 1
 
2.4%
5 1
 
2.4%
6 2
 
4.8%
7 2
 
4.8%
8 1
 
2.4%
9 2
 
4.8%
12 1
 
2.4%
ValueCountFrequency (%)
712 1
2.4%
436 1
2.4%
414 1
2.4%
342 1
2.4%
185 1
2.4%
180 1
2.4%
147 1
2.4%
142 1
2.4%
78 1
2.4%
71 1
2.4%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1661839
Minimum160
Maximum17571080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:17.702686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile4156.5
Q168807.5
median219595
Q31252395
95-th percentile6030995.5
Maximum17571080
Range17570920
Interquartile range (IQR)1183587.5

Descriptive statistics

Standard deviation3660058.8
Coefficient of variation (CV)2.2024147
Kurtosis12.232231
Mean1661839
Median Absolute Deviation (MAD)209195
Skewness3.4362323
Sum69797240
Variance1.3396031 × 1013
MonotonicityNot monotonic
2024-04-21T10:50:17.843456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
613030 1
 
2.4%
104640 1
 
2.4%
138770 1
 
2.4%
4710670 1
 
2.4%
1158360 1
 
2.4%
125640 1
 
2.4%
161330 1
 
2.4%
66340 1
 
2.4%
97970 1
 
2.4%
15172750 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
160 1
2.4%
3690 1
2.4%
4110 1
2.4%
5040 1
2.4%
7580 1
2.4%
13220 1
2.4%
38980 1
2.4%
39150 1
2.4%
41310 1
2.4%
63920 1
2.4%
ValueCountFrequency (%)
17571080 1
2.4%
15172750 1
2.4%
6043710 1
2.4%
5789420 1
2.4%
4710670 1
2.4%
2811060 1
2.4%
2476980 1
2.4%
2276000 1
2.4%
2147520 1
2.4%
1907820 1
2.4%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.45238
Minimum1
Maximum1166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:17.964830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q16
median30.5
Q3115.25
95-th percentile742.15
Maximum1166
Range1165
Interquartile range (IQR)109.25

Descriptive statistics

Standard deviation260.87603
Coefficient of variation (CV)1.8185549
Kurtosis6.0885849
Mean143.45238
Median Absolute Deviation (MAD)27.5
Skewness2.4843108
Sum6025
Variance68056.303
MonotonicityNot monotonic
2024-04-21T10:50:18.081162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3 4
 
9.5%
6 3
 
7.1%
8 3
 
7.1%
1 3
 
7.1%
26 2
 
4.8%
13 2
 
4.8%
746 1
 
2.4%
669 1
 
2.4%
58 1
 
2.4%
59 1
 
2.4%
Other values (21) 21
50.0%
ValueCountFrequency (%)
1 3
7.1%
2 1
 
2.4%
3 4
9.5%
4 1
 
2.4%
6 3
7.1%
8 3
7.1%
13 2
4.8%
20 1
 
2.4%
26 2
4.8%
30 1
 
2.4%
ValueCountFrequency (%)
1166 1
2.4%
822 1
2.4%
746 1
2.4%
669 1
2.4%
361 1
2.4%
360 1
2.4%
357 1
2.4%
330 1
2.4%
188 1
2.4%
180 1
2.4%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2849924.8
Minimum5040
Maximum25863330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:18.207260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5040
5-th percentile27195.5
Q1226472.5
median474850
Q33049822.5
95-th percentile10205161
Maximum25863330
Range25858290
Interquartile range (IQR)2823350

Descriptive statistics

Standard deviation5295409.1
Coefficient of variation (CV)1.8580873
Kurtosis10.442643
Mean2849924.8
Median Absolute Deviation (MAD)434760
Skewness3.0891014
Sum1.1969684 × 108
Variance2.8041357 × 1013
MonotonicityNot monotonic
2024-04-21T10:50:18.345664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1033970 1
 
2.4%
514400 1
 
2.4%
760590 1
 
2.4%
9048650 1
 
2.4%
1566960 1
 
2.4%
345750 1
 
2.4%
6205040 1
 
2.4%
66340 1
 
2.4%
97970 1
 
2.4%
20371350 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
5040 1
2.4%
7740 1
2.4%
26990 1
2.4%
31100 1
2.4%
49080 1
2.4%
66340 1
2.4%
83950 1
2.4%
97970 1
2.4%
105320 1
2.4%
216710 1
2.4%
ValueCountFrequency (%)
25863330 1
2.4%
20371350 1
2.4%
10266030 1
2.4%
9048650 1
2.4%
6205040 1
2.4%
6105490 1
2.4%
6102280 1
2.4%
5759940 1
2.4%
4665570 1
2.4%
4173120 1
2.4%

누적미환급금액증감
Real number (ℝ)

MISSING  ZEROS 

Distinct39
Distinct (%)95.1%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean1054.7817
Minimum0
Maximum30575
Zeros3
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T10:50:18.466490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134.26
median99.38
Q3308.05
95-th percentile1564.98
Maximum30575
Range30575
Interquartile range (IQR)273.79

Descriptive statistics

Standard deviation4769.3586
Coefficient of variation (CV)4.5216546
Kurtosis39.400444
Mean1054.7817
Median Absolute Deviation (MAD)77.54
Skewness6.229648
Sum43246.05
Variance22746781
MonotonicityNot monotonic
2024-04-21T10:50:18.600283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0.0 3
 
7.1%
68.67 1
 
2.4%
86.77 1
 
2.4%
10.16 1
 
2.4%
448.09 1
 
2.4%
92.09 1
 
2.4%
35.27 1
 
2.4%
175.19 1
 
2.4%
3746.18 1
 
2.4%
34.26 1
 
2.4%
Other values (29) 29
69.0%
ValueCountFrequency (%)
0.0 3
7.1%
1.02 1
 
2.4%
2.11 1
 
2.4%
4.75 1
 
2.4%
7.94 1
 
2.4%
10.16 1
 
2.4%
10.21 1
 
2.4%
21.9 1
 
2.4%
34.26 1
 
2.4%
35.27 1
 
2.4%
ValueCountFrequency (%)
30575.0 1
2.4%
3746.18 1
2.4%
1564.98 1
2.4%
1011.88 1
2.4%
995.34 1
2.4%
792.79 1
2.4%
656.69 1
2.4%
631.44 1
2.4%
448.09 1
2.4%
391.59 1
2.4%

Interactions

2024-04-21T10:50:14.743215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:11.917233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.574389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.114943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.626789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.218009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.845050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.051286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.656569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.196902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.698740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.307976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.950753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.247960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.758341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.279875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.782181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.411210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:15.103671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.330169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.873247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.368229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.869573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.505169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:15.204587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.421158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.961197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.449687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.996977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.583446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:15.301770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:12.496050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.036442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:13.538787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.128410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:14.662559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:50:18.692915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.0490.263
과세년도0.0001.0000.0000.0670.0000.3600.0000.184
납세자유형0.0000.0001.0000.2900.0000.4410.3550.024
당해미환급건수0.0000.0670.2901.0000.9470.9770.9080.000
당해미환급금액0.0000.0000.0000.9471.0000.9640.9780.000
누적미환급건수0.0000.3600.4410.9770.9641.0000.9400.000
누적미환급금액0.0490.0000.3550.9080.9780.9401.0000.000
누적미환급금액증감0.2630.1840.0240.0000.0000.0000.0001.000
2024-04-21T10:50:19.005196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형
세목명1.0000.000
납세자유형0.0001.000
2024-04-21T10:50:19.082746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명납세자유형
과세년도1.000-0.0170.059-0.0360.102-0.1820.0000.000
당해미환급건수-0.0171.0000.7050.9670.672-0.0630.0000.284
당해미환급금액0.0590.7051.0000.6820.885-0.3410.0000.000
누적미환급건수-0.0360.9670.6821.0000.6960.0590.0000.299
누적미환급금액0.1020.6720.8850.6961.0000.0270.0000.213
누적미환급금액증감-0.182-0.063-0.3410.0590.0271.0000.0920.017
세목명0.0000.0000.0000.0000.0000.0921.0000.000
납세자유형0.0000.2840.0000.2990.2130.0170.0001.000

Missing values

2024-04-21T10:50:15.593893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:50:15.757014image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0부산광역시해운대구26350자동차세2017신규개인78613030117103397068.67
1부산광역시해운대구26350자동차세2017신규법인171323802624724086.77
2부산광역시해운대구26350지방소득세2017신규개인611006850110200748099.38
3부산광역시해운대구26350지방소득세2017신규법인536901326990631.44
4부산광역시해운대구26350자동차세2018신규개인718631501881897120119.79
5부산광역시해운대구26350자동차세2018신규법인88026034327500308.05
6부산광역시해운대구26350재산세2018신규개인720689082167104.75
7부산광역시해운대구26350재산세2018신규법인16043710361054901.02
8부산광역시해운대구26350지방소득세2018신규개인7012837401803291220156.38
9부산광역시해운대구26350지방소득세2018신규법인741102031100656.69
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
32부산광역시해운대구26350지방소득세2021신규법인1410464042514400391.59
33부산광역시해운대구26350등록면허세2022신규개인92228201324556010.21
34부산광역시해운대구26350등록면허세2022신규법인3639208700140995.34
35부산광역시해운대구26350자동차세2022신규개인43657894208221026603077.32
36부산광역시해운대구26350자동차세2022신규법인34876320741924140119.57
37부산광역시해운대구26350재산세2022신규개인62286202637872065.65
38부산광역시해운대구26350주민세2022신규개인4389806105320170.19
39부산광역시해운대구26350지방소득세2022신규개인7121757108011662586333047.19
40부산광역시해운대구26350지방소득세2022신규법인31190782057232563021.9
41부산광역시해운대구26350취득세2022신규개인15040150400.0