Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory99.2 B

Variable types

Categorical5
Numeric6

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 표준지방세정보시스템을 이용하여 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 징수율을 열람할 수 있음
Author전라남도 고흥군
URLhttps://www.data.go.kr/data/15079038/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 4 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 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 has 11 (26.8%) zerosZeros
수납급액 has 11 (26.8%) zerosZeros
환급금액 has 13 (31.7%) zerosZeros
결손금액 has 20 (48.8%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-12 20:06:43.612651
Analysis finished2023-12-12 20:06:48.010015
Duration4.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
전라남도
41 

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 41
100.0%

Length

2023-12-13T05:06:48.089532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:48.214074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
고흥군
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고흥군 41
100.0%

Length

2023-12-13T05:06:48.330770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:48.437761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고흥군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
46770
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46770
2nd row46770
3rd row46770
4th row46770
5th row46770

Common Values

ValueCountFrequency (%)
46770 41
100.0%

Length

2023-12-13T05:06:48.541023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:48.892242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46770 41
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017
14 
2018
14 
2019
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

Length

2023-12-13T05:06:48.979902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:06:49.070395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
26 

Length

Max length7
Median length5
Mean length4.3902439
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

Length

2023-12-13T05:06:49.205889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0373025 × 109
Minimum0
Maximum1.3110485 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:49.320534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.210121 × 109
Q34.533127 × 109
95-th percentile1.2299808 × 1010
Maximum1.3110485 × 1010
Range1.3110485 × 1010
Interquartile range (IQR)4.533127 × 109

Descriptive statistics

Standard deviation3.6912308 × 109
Coefficient of variation (CV)1.2152991
Kurtosis1.6171983
Mean3.0373025 × 109
Median Absolute Deviation (MAD)1.210121 × 109
Skewness1.4782001
Sum1.245294 × 1011
Variance1.3625185 × 1019
MonotonicityNot monotonic
2023-12-13T05:06:49.460276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
3123390000 1
 
2.4%
560395000 1
 
2.4%
4758430000 1
 
2.4%
4682637000 1
 
2.4%
1445483000 1
 
2.4%
4330543000 1
 
2.4%
1328033000 1
 
2.4%
8879503000 1
 
2.4%
13110485000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
445556000 1
 
2.4%
524481000 1
 
2.4%
560395000 1
 
2.4%
798376000 1
 
2.4%
824475000 1
 
2.4%
835167000 1
 
2.4%
838580000 1
 
2.4%
961349000 1
 
2.4%
996693000 1
 
2.4%
ValueCountFrequency (%)
13110485000 1
2.4%
12993186000 1
2.4%
12299808000 1
2.4%
8879503000 1
2.4%
8306735000 1
2.4%
7376845000 1
2.4%
4758430000 1
2.4%
4682637000 1
2.4%
4593823000 1
2.4%
4539553000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9210924 × 109
Minimum0
Maximum1.2982782 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:49.577127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.51773 × 108
Q34.346904 × 109
95-th percentile1.2288582 × 1010
Maximum1.2982782 × 1010
Range1.2982782 × 1010
Interquartile range (IQR)4.346904 × 109

Descriptive statistics

Standard deviation3.6317328 × 109
Coefficient of variation (CV)1.243279
Kurtosis1.8868857
Mean2.9210924 × 109
Median Absolute Deviation (MAD)9.51773 × 108
Skewness1.5387331
Sum1.1976479 × 1011
Variance1.3189483 × 1019
MonotonicityNot monotonic
2023-12-13T05:06:49.713118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
3019978000 1
 
2.4%
534319000 1
 
2.4%
4582364000 1
 
2.4%
4496858000 1
 
2.4%
1436456000 1
 
2.4%
4330543000 1
 
2.4%
800528000 1
 
2.4%
8363518000 1
 
2.4%
12982782000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
429519000 1
 
2.4%
472274000 1
 
2.4%
483389000 1
 
2.4%
498649000 1
 
2.4%
534319000 1
 
2.4%
794707000 1
 
2.4%
800528000 1
 
2.4%
803119000 1
 
2.4%
939118000 1
 
2.4%
ValueCountFrequency (%)
12982782000 1
2.4%
12904515000 1
2.4%
12288582000 1
2.4%
8363518000 1
2.4%
7825082000 1
2.4%
6822257000 1
2.4%
4582364000 1
2.4%
4500293000 1
2.4%
4496858000 1
2.4%
4419758000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39040463
Minimum0
Maximum2.70254 × 108
Zeros13
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:49.834596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2506000
Q361136000
95-th percentile1.84588 × 108
Maximum2.70254 × 108
Range2.70254 × 108
Interquartile range (IQR)61136000

Descriptive statistics

Standard deviation65490451
Coefficient of variation (CV)1.6775019
Kurtosis3.334929
Mean39040463
Median Absolute Deviation (MAD)2506000
Skewness1.9313536
Sum1.600659 × 109
Variance4.2889991 × 1015
MonotonicityNot monotonic
2023-12-13T05:06:49.950518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 13
31.7%
1152000 1
 
2.4%
1159000 1
 
2.4%
120315000 1
 
2.4%
22681000 1
 
2.4%
8424000 1
 
2.4%
184588000 1
 
2.4%
61136000 1
 
2.4%
81625000 1
 
2.4%
899000 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0 13
31.7%
10000 1
 
2.4%
252000 1
 
2.4%
281000 1
 
2.4%
321000 1
 
2.4%
899000 1
 
2.4%
1152000 1
 
2.4%
1159000 1
 
2.4%
2506000 1
 
2.4%
4855000 1
 
2.4%
ValueCountFrequency (%)
270254000 1
2.4%
189804000 1
2.4%
184588000 1
2.4%
143868000 1
2.4%
133250000 1
2.4%
120315000 1
2.4%
117873000 1
2.4%
81625000 1
2.4%
73692000 1
2.4%
64053000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13722951
Minimum0
Maximum2.54662 × 108
Zeros20
Zeros (%)48.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:50.081186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21000
Q31057000
95-th percentile78833000
Maximum2.54662 × 108
Range2.54662 × 108
Interquartile range (IQR)1057000

Descriptive statistics

Standard deviation46281191
Coefficient of variation (CV)3.3725392
Kurtosis20.043757
Mean13722951
Median Absolute Deviation (MAD)21000
Skewness4.3474184
Sum5.62641 × 108
Variance2.1419487 × 1015
MonotonicityNot monotonic
2023-12-13T05:06:50.229691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 20
48.8%
30426000 1
 
2.4%
577000 1
 
2.4%
198000 1
 
2.4%
52000 1
 
2.4%
78833000 1
 
2.4%
828000 1
 
2.4%
21000 1
 
2.4%
20600000 1
 
2.4%
288000 1
 
2.4%
Other values (12) 12
29.3%
ValueCountFrequency (%)
0 20
48.8%
21000 1
 
2.4%
31000 1
 
2.4%
37000 1
 
2.4%
52000 1
 
2.4%
198000 1
 
2.4%
201000 1
 
2.4%
288000 1
 
2.4%
309000 1
 
2.4%
577000 1
 
2.4%
ValueCountFrequency (%)
254662000 1
2.4%
144008000 1
2.4%
78833000 1
2.4%
30426000 1
2.4%
20600000 1
2.4%
10643000 1
2.4%
9352000 1
2.4%
4870000 1
2.4%
3304000 1
2.4%
2344000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0248707 × 108
Minimum0
Maximum5.53531 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:50.373644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26076000
Q31.62166 × 108
95-th percentile4.76783 × 108
Maximum5.53531 × 108
Range5.53531 × 108
Interquartile range (IQR)1.62166 × 108

Descriptive statistics

Standard deviation1.5226227 × 108
Coefficient of variation (CV)1.4856729
Kurtosis2.6019588
Mean1.0248707 × 108
Median Absolute Deviation (MAD)26076000
Skewness1.8274775
Sum4.20197 × 109
Variance2.3183798 × 1016
MonotonicityNot monotonic
2023-12-13T05:06:50.487027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 14
34.1%
92769000 2
 
4.9%
26076000 1
 
2.4%
175489000 1
 
2.4%
185581000 1
 
2.4%
8975000 1
 
2.4%
448672000 1
 
2.4%
515157000 1
 
2.4%
127703000 1
 
2.4%
57554000 1
 
2.4%
Other values (17) 17
41.5%
ValueCountFrequency (%)
0 14
34.1%
6685000 1
 
2.4%
8975000 1
 
2.4%
9375000 1
 
2.4%
9988000 1
 
2.4%
10917000 1
 
2.4%
25832000 1
 
2.4%
26076000 1
 
2.4%
29704000 1
 
2.4%
43842000 1
 
2.4%
ValueCountFrequency (%)
553531000 1
2.4%
515157000 1
2.4%
476783000 1
2.4%
448672000 1
2.4%
295676000 1
2.4%
197078000 1
2.4%
192361000 1
2.4%
185581000 1
2.4%
175489000 1
2.4%
164171000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.035366
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T05:06:50.617583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95.07
Q396.69
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)96.69

Descriptive statistics

Standard deviation42.864122
Coefficient of variation (CV)0.63002706
Kurtosis-1.0475871
Mean68.035366
Median Absolute Deviation (MAD)4.1
Skewness-0.94017799
Sum2789.45
Variance1837.3329
MonotonicityNot monotonic
2023-12-13T05:06:50.738365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
96.4 2
 
4.9%
95.76 1
 
2.4%
95.35 1
 
2.4%
96.3 1
 
2.4%
96.03 1
 
2.4%
99.38 1
 
2.4%
60.28 1
 
2.4%
94.19 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0.0 11
26.8%
58.63 1
 
2.4%
59.15 1
 
2.4%
60.28 1
 
2.4%
92.48 1
 
2.4%
93.39 1
 
2.4%
94.19 1
 
2.4%
94.2 1
 
2.4%
94.22 1
 
2.4%
94.77 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.91 1
 
2.4%
99.38 1
 
2.4%
99.32 1
 
2.4%
99.17 1
 
2.4%
99.03 1
 
2.4%
99.0 1
 
2.4%
97.5 1
 
2.4%
96.69 1
 
2.4%
96.4 2
4.9%

Interactions

2023-12-13T05:06:47.155919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.059365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.774663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.428688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.025254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.592906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.253670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.184260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.889004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.533771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.126539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.671972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.343081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.292233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.996216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.644905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.221100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.760299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.449759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.405545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.109723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.731977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.328010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.857444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.549227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.524284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.226695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.828586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.417490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.949665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.632198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:44.647454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.328805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:45.922876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:46.513050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:06:47.044520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:06:50.831776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9740.9760.7430.2470.7790.876
부과금액0.0000.9741.0000.9990.7740.1570.8520.572
수납급액0.0000.9760.9991.0000.6960.0000.8090.085
환급금액0.0000.7430.7740.6961.0000.9450.8330.826
결손금액0.0000.2470.1570.0000.9451.0000.7250.827
미수납 금액0.0000.7790.8520.8090.8330.7251.0000.715
징수율0.0000.8760.5720.0850.8260.8270.7151.000
2023-12-13T05:06:50.955433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T05:06:51.038888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9910.7230.4130.6700.6920.0000.674
수납급액0.9911.0000.6620.3460.6150.7420.0000.679
환급금액0.7230.6621.0000.7280.8560.3660.0000.386
결손금액0.4130.3460.7281.0000.6770.2010.0000.065
미수납 금액0.6700.6150.8560.6771.0000.2140.0000.352
징수율0.6920.7420.3660.2010.2141.0000.0000.608
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6740.6790.3860.0650.3520.6080.0001.000

Missing values

2023-12-13T05:06:47.746516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:06:47.931683image/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
0전라남도고흥군467702017도축세000000.0
1전라남도고흥군467702017레저세000000.0
2전라남도고흥군467702017재산세312339000030199780001152000106430009276900096.69
3전라남도고흥군467702017주민세83516700080311900025200023440002970400096.16
4전라남도고흥군467702017취득세12299808000122885820001898040003090001091700099.91
5전라남도고흥군467702017자동차세8306735000782508200073692000487000047678300094.2
6전라남도고흥군467702017과년도수입82447500048338900013325000014400800019707800058.63
7전라남도고흥군467702017담배소비세45002930004500293000000100.0
8전라남도고흥군467702017도시계획세000000.0
9전라남도고흥군467702017등록면허세96134900095177300011240000201000937500099.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31전라남도고흥군467702019취득세131104850001298278200081625000012770300099.03
32전라남도고흥군467702019자동차세887950300083635180006113600082800051515700094.19
33전라남도고흥군467702019과년도수입13280330008005280001845880007883300044867200060.28
34전라남도고흥군467702019담배소비세43305430004330543000000100.0
35전라남도고흥군467702019도시계획세000000.0
36전라남도고흥군467702019등록면허세14454830001436456000842400052000897500099.38
37전라남도고흥군467702019지방교육세468263700044968580002268100019800018558100096.03
38전라남도고흥군467702019지방소득세4758430000458236400012031500057700017548900096.3
39전라남도고흥군467702019지방소비세000000.0
40전라남도고흥군467702019지역자원시설세560395000534319000115900002607600095.35