Overview

Dataset statistics

Number of variables12
Number of observations80
Missing cells129
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory106.7 B

Variable types

Numeric8
Categorical4

Dataset

Description지방세 징수현황의 데이터로 과세년도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율 등의 데이터를 제공합니다.
Author전라남도 장흥군
URLhttps://www.data.go.kr/data/15078664/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
과세년도 is highly overall correlated with 연번High correlation
부과금액 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
환급금액 is highly overall correlated with 미수납 금액High correlation
결손금액 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
징수율 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 has 16 (20.0%) missing valuesMissing
수납급액 has 16 (20.0%) missing valuesMissing
환급금액 has 23 (28.7%) missing valuesMissing
결손금액 has 33 (41.2%) missing valuesMissing
미수납 금액 has 26 (32.5%) missing valuesMissing
징수율 has 15 (18.8%) missing valuesMissing
연번 has unique valuesUnique
징수율 has 1 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-21 01:48:24.173373
Analysis finished2024-04-21 01:48:31.677064
Duration7.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:31.906627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityStrictly increasing
2024-04-21T10:48:32.073352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
42 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%

시도명
Categorical

CONSTANT 

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

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 80
100.0%

Length

2024-04-21T10:48:32.239542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:32.324946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 80
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장흥군 80
100.0%

Length

2024-04-21T10:48:32.434226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:32.537987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장흥군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
46800
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46800
2nd row46800
3rd row46800
4th row46800
5th row46800

Common Values

ValueCountFrequency (%)
46800 80
100.0%

Length

2024-04-21T10:48:32.652617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:32.750492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46800 80
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

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
2024-04-21T10:48:32.833983image/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
2024-04-21T10:48:32.943337image/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

2024-04-21T10:48:33.076473image/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  MISSING 

Distinct64
Distinct (%)100.0%
Missing16
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean3.1165623 × 109
Minimum20763000
Maximum1.0054297 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:33.216202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20763000
5-th percentile3.664114 × 108
Q15.8313675 × 108
median2.702163 × 109
Q34.165307 × 109
95-th percentile8.5941674 × 109
Maximum1.0054297 × 1010
Range1.0033534 × 1010
Interquartile range (IQR)3.5821702 × 109

Descriptive statistics

Standard deviation2.8181358 × 109
Coefficient of variation (CV)0.90424496
Kurtosis0.094123662
Mean3.1165623 × 109
Median Absolute Deviation (MAD)2.1025665 × 109
Skewness1.006225
Sum1.9945999 × 1011
Variance7.9418892 × 1018
MonotonicityNot monotonic
2024-04-21T10:48:33.353494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8382543000 1
 
1.2%
2629571000 1
 
1.2%
862883000 1
 
1.2%
2990423000 1
 
1.2%
3784666000 1
 
1.2%
6895600000 1
 
1.2%
423784000 1
 
1.2%
3112590000 1
 
1.2%
615722000 1
 
1.2%
9791156000 1
 
1.2%
Other values (54) 54
67.5%
(Missing) 16
 
20.0%
ValueCountFrequency (%)
20763000 1
1.2%
235587000 1
1.2%
340284000 1
1.2%
360307000 1
1.2%
401003000 1
1.2%
421203000 1
1.2%
423784000 1
1.2%
458851000 1
1.2%
459647000 1
1.2%
477445000 1
1.2%
ValueCountFrequency (%)
10054297000 1
1.2%
9852726000 1
1.2%
9791156000 1
1.2%
8603002000 1
1.2%
8544105000 1
1.2%
8497572000 1
1.2%
8382543000 1
1.2%
6941549000 1
1.2%
6895600000 1
1.2%
6874825000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)100.0%
Missing16
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean3.0554018 × 109
Minimum20763000
Maximum9.869932 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:33.509835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20763000
5-th percentile3.1617655 × 108
Q15.6857925 × 108
median2.702163 × 109
Q34.0798802 × 109
95-th percentile8.5283526 × 109
Maximum9.869932 × 109
Range9.849169 × 109
Interquartile range (IQR)3.511301 × 109

Descriptive statistics

Standard deviation2.7979599 × 109
Coefficient of variation (CV)0.91574205
Kurtosis0.13446874
Mean3.0554018 × 109
Median Absolute Deviation (MAD)2.0933135 × 109
Skewness1.0142167
Sum1.9554572 × 1011
Variance7.8285797 × 1018
MonotonicityNot monotonic
2024-04-21T10:48:33.652803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8197789000 1
 
1.2%
2629571000 1
 
1.2%
859112000 1
 
1.2%
2913197000 1
 
1.2%
3713832000 1
 
1.2%
6895600000 1
 
1.2%
407781000 1
 
1.2%
3087956000 1
 
1.2%
605330000 1
 
1.2%
9766253000 1
 
1.2%
Other values (54) 54
67.5%
(Missing) 16
 
20.0%
ValueCountFrequency (%)
20763000 1
1.2%
43465000 1
1.2%
238549000 1
1.2%
316027000 1
1.2%
317024000 1
1.2%
322245000 1
1.2%
348859000 1
1.2%
365492000 1
1.2%
382458000 1
1.2%
407781000 1
1.2%
ValueCountFrequency (%)
9869932000 1
1.2%
9852726000 1
1.2%
9766253000 1
1.2%
8540375000 1
1.2%
8460226000 1
1.2%
8354143000 1
1.2%
8197789000 1
1.2%
6921701000 1
1.2%
6895600000 1
1.2%
6874825000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct57
Distinct (%)100.0%
Missing23
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean60252140
Minimum2000
Maximum1.071133 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:33.790510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile8800
Q11413000
median24477000
Q372646000
95-th percentile1.785048 × 108
Maximum1.071133 × 109
Range1.071131 × 109
Interquartile range (IQR)71233000

Descriptive statistics

Standard deviation1.4620721 × 108
Coefficient of variation (CV)2.4265895
Kurtosis42.332585
Mean60252140
Median Absolute Deviation (MAD)24133000
Skewness6.1439781
Sum3.434372 × 109
Variance2.1376549 × 1016
MonotonicityNot monotonic
2024-04-21T10:48:33.923645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9586000 1
 
1.2%
71940000 1
 
1.2%
4000 1
 
1.2%
7421000 1
 
1.2%
33636000 1
 
1.2%
122169000 1
 
1.2%
8000 1
 
1.2%
24208000 1
 
1.2%
572000 1
 
1.2%
114476000 1
 
1.2%
Other values (47) 47
58.8%
(Missing) 23
28.7%
ValueCountFrequency (%)
2000 1
1.2%
4000 1
1.2%
8000 1
1.2%
9000 1
1.2%
65000 1
1.2%
219000 1
1.2%
239000 1
1.2%
273000 1
1.2%
344000 1
1.2%
379000 1
1.2%
ValueCountFrequency (%)
1071133000 1
1.2%
234021000 1
1.2%
195292000 1
1.2%
174308000 1
1.2%
122169000 1
1.2%
114574000 1
1.2%
114476000 1
1.2%
106363000 1
1.2%
94227000 1
1.2%
92949000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)100.0%
Missing33
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean19071851
Minimum9000
Maximum1.762 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:34.055356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9000
5-th percentile32500
Q1259500
median2035000
Q39616000
95-th percentile1.031341 × 108
Maximum1.762 × 108
Range1.76191 × 108
Interquartile range (IQR)9356500

Descriptive statistics

Standard deviation39793993
Coefficient of variation (CV)2.0865302
Kurtosis7.0841774
Mean19071851
Median Absolute Deviation (MAD)1899000
Skewness2.6854942
Sum8.96377 × 108
Variance1.5835618 × 1015
MonotonicityNot monotonic
2024-04-21T10:48:34.218722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
762000 1
 
1.2%
31000 1
 
1.2%
511000 1
 
1.2%
65158000 1
 
1.2%
9000 1
 
1.2%
156000 1
 
1.2%
1071000 1
 
1.2%
230000 1
 
1.2%
2184000 1
 
1.2%
176200000 1
 
1.2%
Other values (37) 37
46.2%
(Missing) 33
41.2%
ValueCountFrequency (%)
9000 1
1.2%
13000 1
1.2%
31000 1
1.2%
36000 1
1.2%
52000 1
1.2%
108000 1
1.2%
110000 1
1.2%
136000 1
1.2%
145000 1
1.2%
156000 1
1.2%
ValueCountFrequency (%)
176200000 1
1.2%
152667000 1
1.2%
114853000 1
1.2%
75790000 1
1.2%
75062000 1
1.2%
65158000 1
1.2%
52083000 1
1.2%
36720000 1
1.2%
36349000 1
1.2%
29073000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct54
Distinct (%)100.0%
Missing26
Missing (%)32.5%
Infinite0
Infinite (%)0.0%
Mean64156685
Minimum1055000
Maximum2.57773 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:34.356841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1055000
5-th percentile1599550
Q114230500
median40775000
Q377219250
95-th percentile2.070787 × 108
Maximum2.57773 × 108
Range2.56718 × 108
Interquartile range (IQR)62988750

Descriptive statistics

Standard deviation68877697
Coefficient of variation (CV)1.0735857
Kurtosis1.1140718
Mean64156685
Median Absolute Deviation (MAD)31118500
Skewness1.3928401
Sum3.464461 × 109
Variance4.7441371 × 1015
MonotonicityNot monotonic
2024-04-21T10:48:34.495415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1055000 1
 
1.2%
184243000 1
 
1.2%
136717000 1
 
1.2%
3762000 1
 
1.2%
77070000 1
 
1.2%
70834000 1
 
1.2%
16003000 1
 
1.2%
23563000 1
 
1.2%
10162000 1
 
1.2%
24903000 1
 
1.2%
Other values (44) 44
55.0%
(Missing) 26
32.5%
ValueCountFrequency (%)
1055000 1
1.2%
1380000 1
1.2%
1582000 1
1.2%
1609000 1
1.2%
1665000 1
1.2%
1903000 1
1.2%
3730000 1
1.2%
3762000 1
1.2%
5589000 1
1.2%
10162000 1
1.2%
ValueCountFrequency (%)
257773000 1
1.2%
248859000 1
1.2%
216557000 1
1.2%
201975000 1
1.2%
197804000 1
1.2%
195952000 1
1.2%
184243000 1
1.2%
136717000 1
1.2%
132282000 1
1.2%
115441000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct15
Distinct (%)23.1%
Missing15
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean93.969231
Minimum0
Maximum153
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:34.616250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61.8
Q197
median98
Q3100
95-th percentile100
Maximum153
Range153
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.628558
Coefficient of variation (CV)0.20888282
Kurtosis12.418963
Mean93.969231
Median Absolute Deviation (MAD)2
Skewness-2.750491
Sum6108
Variance385.28029
MonotonicityNot monotonic
2024-04-21T10:48:34.906540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100 21
26.2%
98 13
16.2%
97 11
13.8%
96 5
 
6.2%
99 5
 
6.2%
65 1
 
1.2%
93 1
 
1.2%
74 1
 
1.2%
153 1
 
1.2%
95 1
 
1.2%
Other values (5) 5
 
6.2%
(Missing) 15
18.8%
ValueCountFrequency (%)
0 1
 
1.2%
18 1
 
1.2%
39 1
 
1.2%
61 1
 
1.2%
65 1
 
1.2%
74 1
 
1.2%
93 1
 
1.2%
94 1
 
1.2%
95 1
 
1.2%
96 5
6.2%
ValueCountFrequency (%)
153 1
 
1.2%
100 21
26.2%
99 5
 
6.2%
98 13
16.2%
97 11
13.8%
96 5
 
6.2%
95 1
 
1.2%
94 1
 
1.2%
93 1
 
1.2%
74 1
 
1.2%

Interactions

2024-04-21T10:48:30.656154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:25.677693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.546355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.224048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.886402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.548318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.373129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.037026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.733449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:25.960463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.661466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.320031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.983532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.650233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.454463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.121092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.814218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.049271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.745925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.397169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.064006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.732385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.529405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.203071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.891736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.137487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.835675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.469885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.151283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.809622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.606565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.282469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.967676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.218287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.907618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.544217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.225708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.879797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.685714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.360019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:31.040004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.295212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.981624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.614568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.302862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.951880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.763011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.436986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:31.124478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.378111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.060124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.694995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.389906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.042907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.846066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.515546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:31.198889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:26.456178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.132870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:27.778199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:28.463318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.302825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:29.938965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:30.583816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:48:34.998664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
연번1.0000.9300.0000.0000.0000.1460.0000.0000.000
과세년도0.9301.0000.0000.0000.0000.0680.0740.0000.000
세목명0.0000.0001.0000.8190.8110.3790.4700.7670.708
부과금액0.0000.0000.8191.0000.9890.0000.0000.7100.000
수납급액0.0000.0000.8110.9891.0000.0000.0000.8260.000
환급금액0.1460.0680.3790.0000.0001.0000.9920.0000.940
결손금액0.0000.0740.4700.0000.0000.9921.0000.6071.000
미수납 금액0.0000.0000.7670.7100.8260.0000.6071.0000.488
징수율0.0000.0000.7080.0000.0000.9401.0000.4881.000
2024-04-21T10:48:35.109271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
연번1.0000.9860.1180.1130.076-0.176-0.1060.1570.000
과세년도0.9861.0000.1340.1240.074-0.181-0.0940.1650.000
부과금액0.1180.1341.0000.9860.484-0.0030.4150.2590.542
수납급액0.1130.1240.9861.0000.442-0.0510.3510.3190.519
환급금액0.0760.0740.4840.4421.0000.4710.650-0.1190.223
결손금액-0.176-0.181-0.003-0.0510.4711.0000.602-0.5260.250
미수납 금액-0.106-0.0940.4150.3510.6500.6021.000-0.5310.345
징수율0.1570.1650.2590.319-0.119-0.526-0.5311.0000.401
세목명0.0000.0000.5420.5190.2230.2500.3450.4011.000

Missing values

2024-04-21T10:48:31.329094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:48:31.493542image/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.
2024-04-21T10:48:31.607352image/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
01전라남도장흥군468002017도축세<NA><NA><NA><NA><NA><NA>
12전라남도장흥군468002017레저세<NA><NA><NA><NA><NA><NA>
23전라남도장흥군468002017재산세23987050002347013000141300087570004293500098
34전라남도장흥군468002017주민세4588510004469000002190007620001118900097
45전라남도장흥군468002017취득세6941549000692170100034552000<NA>19848000100
56전라남도장흥군468002017자동차세5551262000533093500081716000377000021655700096
67전라남도장흥군468002017과년도수입48649400031702400058535000757900009368000065
78전라남도장흥군468002017담배소비세24559690002455969000<NA><NA><NA>100
89전라남도장흥군468002017도시계획세<NA><NA><NA><NA><NA><NA>
910전라남도장흥군468002017등록면허세5583780005567600001946000360001582000100
연번시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
7071전라남도장흥군468002022취득세8544105000854037500029120000<NA>3730000100
7172전라남도장흥군468002022자동차세4997164000488016800092949000155500011544100098
7273전라남도장흥군468002022과년도수입235587000434650001952920001148530007726900018
7374전라남도장흥군468002022담배소비세275529400027552940002000<NA><NA>100
7475전라남도장흥군468002022도시계획세<NA><NA><NA><NA><NA>0
7576전라남도장흥군468002022등록면허세95339200095187600039860001360001380000100
7677전라남도장흥군468002022지방교육세31693120003129777000259750009200003861500099
7778전라남도장흥군468002022지방소득세461039300043159000001743080003672000025777300094
7879전라남도장흥군468002022지방소비세98527260009852726000<NA><NA><NA>100
7980전라남도장흥군468002022지역자원시설세4774450004626830002390001100001465200097