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

Description충청남도 논산시의 지방세 부과액에 대한 세목별 징수현황에 대한 데이터로 재산세,취득세,등록면허세 등에 대한 정보를 제공공한다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=350&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079059

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 5 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 부과금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 1 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 14 (34.1%) zerosZeros
결손금액 has 18 (43.9%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2024-01-09 20:47:05.779638
Analysis finished2024-01-09 20:47:09.564505
Duration3.78 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

2024-01-10T05:47:09.628550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:47:09.712617image/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
논산시
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
논산시 41
100.0%

Length

2024-01-10T05:47:09.808391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:47:09.891357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
논산시 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
44230
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44230
2nd row44230
3rd row44230
4th row44230
5th row44230

Common Values

ValueCountFrequency (%)
44230 41
100.0%

Length

2024-01-10T05:47:09.970840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:47:10.052386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44230 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

2024-01-10T05:47:10.132299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T05:47:10.214396image/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

2024-01-10T05:47:10.318511image/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%
Mean8.3233097 × 109
Minimum0
Maximum3.4279897 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:10.418772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.220228 × 109
Q31.19934 × 1010
95-th percentile2.918264 × 1010
Maximum3.4279897 × 1010
Range3.4279897 × 1010
Interquartile range (IQR)1.19934 × 1010

Descriptive statistics

Standard deviation9.1364821 × 109
Coefficient of variation (CV)1.0976982
Kurtosis1.0052876
Mean8.3233097 × 109
Median Absolute Deviation (MAD)6.220228 × 109
Skewness1.2321864
Sum3.412557 × 1011
Variance8.3475305 × 1019
MonotonicityNot monotonic
2024-01-10T05:47:10.533263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
11263928000 1
 
2.4%
2047285000 1
 
2.4%
16264563000 1
 
2.4%
11343364000 1
 
2.4%
2855806000 1
 
2.4%
9837427000 1
 
2.4%
6252187000 1
 
2.4%
20152258000 1
 
2.4%
34279897000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
1641673000 1
 
2.4%
1900518000 1
 
2.4%
2047285000 1
 
2.4%
2503815000 1
 
2.4%
2661817000 1
 
2.4%
2713814000 1
 
2.4%
2806229000 1
 
2.4%
2855806000 1
 
2.4%
2958201000 1
 
2.4%
ValueCountFrequency (%)
34279897000 1
2.4%
30358391000 1
2.4%
29182640000 1
2.4%
20848625000 1
2.4%
20669240000 1
2.4%
20152258000 1
2.4%
16264563000 1
2.4%
15072579000 1
2.4%
13760640000 1
2.4%
12712528000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7207807 × 109
Minimum0
Maximum3.4055997 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:10.651307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.705862 × 109
Q31.1577603 × 1010
95-th percentile2.8842521 × 1010
Maximum3.4055997 × 1010
Range3.4055997 × 1010
Interquartile range (IQR)1.1577603 × 1010

Descriptive statistics

Standard deviation9.0611719 × 109
Coefficient of variation (CV)1.1736083
Kurtosis1.2587321
Mean7.7207807 × 109
Median Absolute Deviation (MAD)2.705862 × 109
Skewness1.3291538
Sum3.1655201 × 1011
Variance8.2104837 × 1019
MonotonicityNot monotonic
2024-01-10T05:47:10.763880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
10859535000 1
 
2.4%
1976049000 1
 
2.4%
14834471000 1
 
2.4%
10925020000 1
 
2.4%
2846451000 1
 
2.4%
9837427000 1
 
2.4%
2100705000 1
 
2.4%
19004900000 1
 
2.4%
34055997000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
1112663000 1
 
2.4%
1584535000 1
 
2.4%
1690226000 1
 
2.4%
1838330000 1
 
2.4%
1976049000 1
 
2.4%
2100705000 1
 
2.4%
2472336000 1
 
2.4%
2558775000 1
 
2.4%
2692082000 1
 
2.4%
ValueCountFrequency (%)
34055997000 1
2.4%
30099045000 1
2.4%
28842521000 1
2.4%
19657549000 1
2.4%
19477169000 1
2.4%
19004900000 1
2.4%
14834471000 1
2.4%
14317106000 1
2.4%
13225594000 1
2.4%
12237702000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1830851 × 108
Minimum0
Maximum1.163298 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:10.877114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4370000
Q31.36647 × 108
95-th percentile4.39302 × 108
Maximum1.163298 × 109
Range1.163298 × 109
Interquartile range (IQR)1.36647 × 108

Descriptive statistics

Standard deviation2.4338078 × 108
Coefficient of variation (CV)2.0571705
Kurtosis9.7062545
Mean1.1830851 × 108
Median Absolute Deviation (MAD)4370000
Skewness2.9920868
Sum4.850649 × 109
Variance5.9234203 × 1016
MonotonicityNot monotonic
2024-01-10T05:47:10.988958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
20364000 1
 
2.4%
361000 1
 
2.4%
418455000 1
 
2.4%
54158000 1
 
2.4%
38489000 1
 
2.4%
400575000 1
 
2.4%
136969000 1
 
2.4%
151411000 1
 
2.4%
4525000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
243000 1
 
2.4%
361000 1
 
2.4%
856000 1
 
2.4%
1736000 1
 
2.4%
2535000 1
 
2.4%
2984000 1
 
2.4%
4370000 1
 
2.4%
4525000 1
 
2.4%
6994000 1
 
2.4%
ValueCountFrequency (%)
1163298000 1
2.4%
881175000 1
2.4%
439302000 1
2.4%
418455000 1
2.4%
400575000 1
2.4%
340199000 1
2.4%
254967000 1
2.4%
151411000 1
2.4%
144173000 1
2.4%
136969000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0862193 × 108
Minimum0
Maximum1.913059 × 109
Zeros18
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:11.105840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37000
Q33434000
95-th percentile9.83098 × 108
Maximum1.913059 × 109
Range1.913059 × 109
Interquartile range (IQR)3434000

Descriptive statistics

Standard deviation3.7009217 × 108
Coefficient of variation (CV)3.4071589
Kurtosis15.763327
Mean1.0862193 × 108
Median Absolute Deviation (MAD)37000
Skewness3.9201909
Sum4.453499 × 109
Variance1.3696821 × 1017
MonotonicityNot monotonic
2024-01-10T05:47:11.212919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 18
43.9%
6661000 1
 
2.4%
12000 1
 
2.4%
141333000 1
 
2.4%
1615000 1
 
2.4%
53000 1
 
2.4%
983098000 1
 
2.4%
5635000 1
 
2.4%
72000 1
 
2.4%
86000 1
 
2.4%
Other values (14) 14
34.1%
ValueCountFrequency (%)
0 18
43.9%
12000 1
 
2.4%
15000 1
 
2.4%
37000 1
 
2.4%
53000 1
 
2.4%
72000 1
 
2.4%
86000 1
 
2.4%
113000 1
 
2.4%
165000 1
 
2.4%
377000 1
 
2.4%
ValueCountFrequency (%)
1913059000 1
2.4%
1128288000 1
2.4%
983098000 1
2.4%
199532000 1
2.4%
141333000 1
2.4%
56609000 1
2.4%
8268000 1
2.4%
6661000 1
2.4%
5635000 1
2.4%
3593000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.939071 × 108
Minimum0
Maximum4.10196 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:11.324950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.00202 × 108
Q34.28101 × 108
95-th percentile3.168384 × 109
Maximum4.10196 × 109
Range4.10196 × 109
Interquartile range (IQR)4.28101 × 108

Descriptive statistics

Standard deviation9.3264648 × 108
Coefficient of variation (CV)1.8883035
Kurtosis7.3590375
Mean4.939071 × 108
Median Absolute Deviation (MAD)1.00202 × 108
Skewness2.746454
Sum2.0250191 × 1010
Variance8.6982946 × 1017
MonotonicityNot monotonic
2024-01-10T05:47:11.424327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
21695000 1
 
2.4%
71224000 1
 
2.4%
1288759000 1
 
2.4%
416729000 1
 
2.4%
9302000 1
 
2.4%
3168384000 1
 
2.4%
1141723000 1
 
2.4%
223900000 1
 
2.4%
108465000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
9302000 1
 
2.4%
21695000 1
 
2.4%
31479000 1
 
2.4%
57123000 1
 
2.4%
58754000 1
 
2.4%
71224000 1
 
2.4%
100202000 1
 
2.4%
102929000 1
 
2.4%
108465000 1
 
2.4%
ValueCountFrequency (%)
4101960000 1
2.4%
3194506000 1
2.4%
3168384000 1
2.4%
1288759000 1
2.4%
1191014000 1
2.4%
1187483000 1
2.4%
1141723000 1
2.4%
555941000 1
2.4%
535046000 1
2.4%
474740000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.665122
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T05:47:11.526516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median96.1
Q396.53
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)96.53

Descriptive statistics

Standard deviation44.415507
Coefficient of variation (CV)0.67639419
Kurtosis-1.4468008
Mean65.665122
Median Absolute Deviation (MAD)3.1
Skewness-0.74763865
Sum2692.27
Variance1972.7372
MonotonicityNot monotonic
2024-01-10T05:47:11.633482image/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%
96.52 2
 
4.9%
99.2 1
 
2.4%
91.21 1
 
2.4%
96.31 1
 
2.4%
99.67 1
 
2.4%
33.6 1
 
2.4%
94.31 1
 
2.4%
99.35 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0.0 11
26.8%
17.89 1
 
2.4%
24.42 1
 
2.4%
33.6 1
 
2.4%
91.21 1
 
2.4%
94.23 1
 
2.4%
94.29 1
 
2.4%
94.31 1
 
2.4%
94.99 1
 
2.4%
96.02 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.67 1
 
2.4%
99.35 1
 
2.4%
99.2 1
 
2.4%
99.15 1
 
2.4%
98.83 1
 
2.4%
98.74 1
 
2.4%
96.73 1
 
2.4%
96.53 1
 
2.4%
96.52 2
4.9%

Interactions

2024-01-10T05:47:08.835352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.059180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.503612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.198909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.740971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.299210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.933067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.134593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.581325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.269620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.840924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.389897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:09.018969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.208821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.660134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.358297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.949839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.483098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:09.097915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.283976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.734702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.453849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.044609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.574343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:09.175771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.365874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.816899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.561860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.149389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.679894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:09.242491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.430955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:06.891819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:07.644742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.221067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:47:08.756319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:47:11.707668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9240.9700.7370.6410.8670.753
부과금액0.0000.9241.0000.9490.8730.6620.9160.701
수납급액0.0000.9700.9491.0000.7190.2930.7420.000
환급금액0.0000.7370.8730.7191.0000.9010.9610.837
결손금액0.0000.6410.6620.2930.9011.0000.7810.827
미수납 금액0.0000.8670.9160.7420.9610.7811.0000.799
징수율0.0000.7530.7010.0000.8370.8270.7991.000
2024-01-10T05:47:11.802812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T05:47:11.884786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9780.7560.5220.7460.5320.0000.673
수납급액0.9781.0000.6800.4440.6590.6050.0000.659
환급금액0.7560.6801.0000.7480.9260.2330.0000.412
결손금액0.5220.4440.7481.0000.8420.1110.0000.350
미수납 금액0.7460.6590.9260.8421.0000.1370.0000.578
징수율0.5320.6050.2330.1110.1371.0000.0000.441
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6730.6590.4120.3500.5780.4410.0001.000

Missing values

2024-01-10T05:47:09.346315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:47:09.503685image/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
0충청남도논산시442302017도축세000000.0
1충청남도논산시442302017레저세000000.0
2충청남도논산시442302017재산세1126392800010859535000298400038700040400600096.41
3충청남도논산시442302017주민세26618170002558775000437000011300010292900096.13
4충청남도논산시442302017취득세3035839100030099045000105876000025934600099.15
5충청남도논산시442302017자동차세20669240000194771690001366470001057000119101400094.23
6충청남도논산시442302017과년도수입692047400016902260008811750001128288000410196000024.42
7충청남도논산시442302017담배소비세1022714400010227144000000100.0
8충청남도논산시442302017도시계획세000000.0
9충청남도논산시442302017등록면허세250381500024723360003224300003147900098.74
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31충청남도논산시442302019취득세3427989700034055997000151411000022390000099.35
32충청남도논산시442302019자동차세20152258000190049000001369690005635000114172300094.31
33충청남도논산시442302019과년도수입62521870002100705000400575000983098000316838400033.6
34충청남도논산시442302019담배소비세98374270009837427000000100.0
35충청남도논산시442302019도시계획세000000.0
36충청남도논산시442302019등록면허세285580600028464510003848900053000930200099.67
37충청남도논산시442302019지방교육세113433640001092502000054158000161500041672900096.31
38충청남도논산시442302019지방소득세1626456300014834471000418455000141333000128875900091.21
39충청남도논산시442302019지방소비세000000.0
40충청남도논산시442302019지역자원시설세20472850001976049000361000120007122400096.52