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

Description2017년부터 2022년까지 서천군 지방세 부과액대비 징수현황에 대한 자료, 부과액, 수납금액, 환급금, 결손금액, 미수납금액 및 징수율 현황입니다
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=347&beforeMenuCd=DOM_000000201001001000&publicdatapk=15080473

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 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 11 (26.8%) zerosZeros
수납급액 has 11 (26.8%) zerosZeros
환급금액 has 14 (34.1%) zerosZeros
결손금액 has 19 (46.3%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2024-01-09 23:12:05.947866
Analysis finished2024-01-09 23:12:09.789060
Duration3.84 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
충청남도
41 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 41
100.0%

Length

2024-01-10T08:12:09.848915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:09.934274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 41
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 41
100.0%

Length

2024-01-10T08:12:10.025648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:10.111597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
44770
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 41
100.0%

Length

2024-01-10T08:12:10.196874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:10.285610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 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

2024-01-10T08:12:10.391979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T08:12:10.513182image/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

2024-01-10T08:12:10.631947image/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%
Mean3.7313144 × 109
Minimum0
Maximum1.5665717 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:10.747585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.910263 × 109
Q35.248223 × 109
95-th percentile1.2887476 × 1010
Maximum1.5665717 × 1010
Range1.5665717 × 1010
Interquartile range (IQR)5.248223 × 109

Descriptive statistics

Standard deviation4.172665 × 109
Coefficient of variation (CV)1.1182829
Kurtosis0.88659703
Mean3.7313144 × 109
Median Absolute Deviation (MAD)1.910263 × 109
Skewness1.2479957
Sum1.5298389 × 1011
Variance1.7411133 × 1019
MonotonicityNot monotonic
2024-01-10T08:12:10.865994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
4952086000 1
 
2.4%
869240000 1
 
2.4%
7104039000 1
 
2.4%
5044420000 1
 
2.4%
1228950000 1
 
2.4%
4067123000 1
 
2.4%
1910263000 1
 
2.4%
8501343000 1
 
2.4%
15665717000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
814323000 1
 
2.4%
869240000 1
 
2.4%
1028149000 1
 
2.4%
1077374000 1
 
2.4%
1175249000 1
 
2.4%
1228950000 1
 
2.4%
1615267000 1
 
2.4%
1683710000 1
 
2.4%
1758263000 1
 
2.4%
ValueCountFrequency (%)
15665717000 1
2.4%
13333858000 1
2.4%
12887476000 1
2.4%
10784848000 1
2.4%
9240270000 1
2.4%
8501343000 1
2.4%
8381626000 1
2.4%
7325261000 1
2.4%
7104039000 1
2.4%
5551277000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5481185 × 109
Minimum0
Maximum1.5555617 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:10.991005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.578388 × 109
Q35.059648 × 109
95-th percentile1.2867514 × 1010
Maximum1.5555617 × 1010
Range1.5555617 × 1010
Interquartile range (IQR)5.059648 × 109

Descriptive statistics

Standard deviation4.1278365 × 109
Coefficient of variation (CV)1.1633874
Kurtosis1.0792179
Mean3.5481185 × 109
Median Absolute Deviation (MAD)1.578388 × 109
Skewness1.3035775
Sum1.4547286 × 1011
Variance1.7039034 × 1019
MonotonicityNot monotonic
2024-01-10T08:12:11.110323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
4796180000 1
 
2.4%
847548000 1
 
2.4%
6891927000 1
 
2.4%
4864162000 1
 
2.4%
1208925000 1
 
2.4%
4067123000 1
 
2.4%
875701000 1
 
2.4%
8023373000 1
 
2.4%
15555617000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
531251000 1
 
2.4%
789929000 1
 
2.4%
847548000 1
 
2.4%
867860000 1
 
2.4%
875701000 1
 
2.4%
1023166000 1
 
2.4%
1070660000 1
 
2.4%
1149653000 1
 
2.4%
1208925000 1
 
2.4%
ValueCountFrequency (%)
15555617000 1
2.4%
13231546000 1
2.4%
12867514000 1
2.4%
10235638000 1
2.4%
8694205000 1
2.4%
8102173000 1
2.4%
8023373000 1
2.4%
6980580000 1
2.4%
6891927000 1
2.4%
5369576000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57787024
Minimum0
Maximum6.05998 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:11.230662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median957000
Q352666000
95-th percentile2.73064 × 108
Maximum6.05998 × 108
Range6.05998 × 108
Interquartile range (IQR)52666000

Descriptive statistics

Standard deviation1.2952965 × 108
Coefficient of variation (CV)2.2415005
Kurtosis9.7776732
Mean57787024
Median Absolute Deviation (MAD)957000
Skewness3.0806275
Sum2.369268 × 109
Variance1.6777929 × 1016
MonotonicityNot monotonic
2024-01-10T08:12:11.341865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
6054000 1
 
2.4%
18000 1
 
2.4%
163404000 1
 
2.4%
21096000 1
 
2.4%
4591000 1
 
2.4%
478545000 1
 
2.4%
58401000 1
 
2.4%
70771000 1
 
2.4%
676000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
18000 1
 
2.4%
113000 1
 
2.4%
147000 1
 
2.4%
380000 1
 
2.4%
572000 1
 
2.4%
676000 1
 
2.4%
957000 1
 
2.4%
1406000 1
 
2.4%
1486000 1
 
2.4%
ValueCountFrequency (%)
605998000 1
2.4%
478545000 1
2.4%
273064000 1
2.4%
266997000 1
2.4%
163404000 1
2.4%
103071000 1
2.4%
94786000 1
2.4%
70771000 1
2.4%
66184000 1
2.4%
58401000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18478024
Minimum0
Maximum3.79367 × 108
Zeros19
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:11.456081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15000
Q3517000
95-th percentile1.58645 × 108
Maximum3.79367 × 108
Range3.79367 × 108
Interquartile range (IQR)517000

Descriptive statistics

Standard deviation67768757
Coefficient of variation (CV)3.6675326
Kurtosis21.311408
Mean18478024
Median Absolute Deviation (MAD)15000
Skewness4.4545061
Sum7.57599 × 108
Variance4.5926044 × 1015
MonotonicityNot monotonic
2024-01-10T08:12:11.577749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 19
46.3%
7052000 1
 
2.4%
6946000 1
 
2.4%
69000 1
 
2.4%
58000 1
 
2.4%
158645000 1
 
2.4%
210000 1
 
2.4%
41000 1
 
2.4%
8000 1
 
2.4%
1894000 1
 
2.4%
Other values (13) 13
31.7%
ValueCountFrequency (%)
0 19
46.3%
8000 1
 
2.4%
15000 1
 
2.4%
25000 1
 
2.4%
41000 1
 
2.4%
46000 1
 
2.4%
58000 1
 
2.4%
69000 1
 
2.4%
174000 1
 
2.4%
210000 1
 
2.4%
ValueCountFrequency (%)
379367000 1
2.4%
165967000 1
2.4%
158645000 1
2.4%
30922000 1
2.4%
7052000 1
2.4%
6946000 1
2.4%
3232000 1
2.4%
1894000 1
2.4%
1191000 1
2.4%
591000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6471788 × 108
Minimum0
Maximum9.99684 × 108
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:11.692870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24394000
Q31.93025 × 108
95-th percentile8.75917 × 108
Maximum9.99684 × 108
Range9.99684 × 108
Interquartile range (IQR)1.93025 × 108

Descriptive statistics

Standard deviation2.6938737 × 108
Coefficient of variation (CV)1.635447
Kurtosis3.7438187
Mean1.6471788 × 108
Median Absolute Deviation (MAD)24394000
Skewness2.0883056
Sum6.753433 × 109
Variance7.2569552 × 1016
MonotonicityNot monotonic
2024-01-10T08:12:11.819103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
4937000 1
 
2.4%
21692000 1
 
2.4%
205166000 1
 
2.4%
180189000 1
 
2.4%
19967000 1
 
2.4%
875917000 1
 
2.4%
477760000 1
 
2.4%
110100000 1
 
2.4%
28875000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
4937000 1
 
2.4%
6699000 1
 
2.4%
19962000 1
 
2.4%
19967000 1
 
2.4%
21692000 1
 
2.4%
22364000 1
 
2.4%
24394000 1
 
2.4%
28875000 1
 
2.4%
36621000 1
 
2.4%
ValueCountFrequency (%)
999684000 1
2.4%
986492000 1
2.4%
875917000 1
2.4%
548693000 1
2.4%
545474000 1
2.4%
477760000 1
2.4%
342787000 1
2.4%
248531000 1
2.4%
205166000 1
2.4%
195360000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.039756
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T08:12:11.932486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median96.41
Q397.86
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)97.86

Descriptive statistics

Standard deviation43.902138
Coefficient of variation (CV)0.6548672
Kurtosis-1.296907
Mean67.039756
Median Absolute Deviation (MAD)2.89
Skewness-0.81392904
Sum2748.63
Variance1927.3977
MonotonicityNot monotonic
2024-01-10T08:12:12.048415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
99.52 1
 
2.4%
97.5 1
 
2.4%
97.01 1
 
2.4%
96.43 1
 
2.4%
98.37 1
 
2.4%
45.84 1
 
2.4%
94.38 1
 
2.4%
99.3 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0.0 11
26.8%
31.55 1
 
2.4%
38.62 1
 
2.4%
45.84 1
 
2.4%
94.09 1
 
2.4%
94.38 1
 
2.4%
94.91 1
 
2.4%
95.29 1
 
2.4%
95.82 1
 
2.4%
95.93 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.85 1
 
2.4%
99.52 1
 
2.4%
99.38 1
 
2.4%
99.3 1
 
2.4%
99.23 1
 
2.4%
98.55 1
 
2.4%
98.37 1
 
2.4%
97.86 1
 
2.4%
97.82 1
 
2.4%

Interactions

2024-01-10T08:12:09.066146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.237988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.748714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.259804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.769739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.308516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:09.149899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.325714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.838676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.352575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.858012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.680511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:09.231106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.419375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.927610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.446754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.946177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.760408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:09.310063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.501799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.008135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.526895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.027511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.837196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:09.392378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.589705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.097654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.617144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.117675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.917492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:09.473394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:06.668017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.177876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:07.693797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.209551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T08:12:08.992060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T08:12:12.137121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9120.9120.6740.6970.8480.876
부과금액0.0000.9121.0000.9980.7470.4280.8030.552
수납급액0.0000.9120.9981.0000.6560.0000.7400.000
환급금액0.0000.6740.7470.6561.0000.9830.8630.869
결손금액0.0000.6970.4280.0000.9831.0000.8100.728
미수납 금액0.0000.8480.8030.7400.8630.8101.0000.985
징수율0.0000.8760.5520.0000.8690.7280.9851.000
2024-01-10T08:12:12.248019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T08:12:12.331202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9830.7410.4520.7130.5330.0000.644
수납급액0.9831.0000.6740.3980.6380.6020.0000.644
환급금액0.7410.6741.0000.7350.9010.2680.0000.352
결손금액0.4520.3980.7351.0000.8400.0920.0000.429
미수납 금액0.7130.6380.9010.8401.0000.1300.0000.529
징수율0.5330.6020.2680.0920.1301.0000.0000.608
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6440.6440.3520.4290.5290.6080.0001.000

Missing values

2024-01-10T08:12:09.577330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T08:12:09.730757image/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
0충청남도서천군447702017도축세000000.0
1충청남도서천군447702017레저세000000.0
2충청남도서천군447702017재산세49520860004796180000957000705200014885400096.85
3충청남도서천군447702017주민세161526700015783880005720002580003662100097.72
4충청남도서천군447702017취득세133338580001323154600094786000010231200099.23
5충청남도서천군447702017자동차세924027000086942050005178700059100054547400094.09
6충청남도서천군447702017과년도수입224691100086786000027306400037936700099968400038.62
7충청남도서천군447702017담배소비세41244950004124495000000100.0
8충청남도서천군447702017도시계획세000000.0
9충청남도서천군447702017등록면허세10773740001070660000516100015000669900099.38
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31충청남도서천군447702019취득세156657170001555561700070771000011010000099.3
32충청남도서천군447702019자동차세850134300080233730005840100021000047776000094.38
33충청남도서천군447702019과년도수입191026300087570100047854500015864500087591700045.84
34충청남도서천군447702019담배소비세40671230004067123000000100.0
35충청남도서천군447702019도시계획세000000.0
36충청남도서천군447702019등록면허세122895000012089250004591000580001996700098.37
37충청남도서천군447702019지방교육세50444200004864162000210960006900018018900096.43
38충청남도서천군447702019지방소득세71040390006891927000163404000694600020516600097.01
39충청남도서천군447702019지방소비세000000.0
40충청남도서천군447702019지역자원시설세8692400008475480001800002169200097.5