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

Description제공범위 : 지방세 부과액에 대한 세목별 징수현황을 제공. 관련 법령 : 지방세징수법. 소관기관 : 지방자치단체. 제공기관 : 시군구. 표준데이터 셋 제공시스템: 표준지방세시스템. 자료기준일 : 매년 12월 31일
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=351&beforeMenuCd=DOM_000000201001001000&publicdatapk=15078552

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 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 17 (41.5%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2024-01-09 22:48:18.144125
Analysis finished2024-01-09 22:48:21.882756
Duration3.74 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

2024-01-10T07:48:21.943945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:22.042811image/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
홍성군
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
홍성군 41
100.0%

Length

2024-01-10T07:48:22.147234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:22.247833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
홍성군 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
44800
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44800
2nd row44800
3rd row44800
4th row44800
5th row44800

Common Values

ValueCountFrequency (%)
44800 41
100.0%

Length

2024-01-10T07:48:22.357834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:22.458219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44800 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

2024-01-10T07:48:22.558905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:48:22.667144image/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

2024-01-10T07:48:22.779677image/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%
Mean7.5614859 × 109
Minimum0
Maximum3.5263392 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T07:48:22.883438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.548132 × 109
Q31.4839657 × 1010
95-th percentile2.6206656 × 1010
Maximum3.5263392 × 1010
Range3.5263392 × 1010
Interquartile range (IQR)1.4839657 × 1010

Descriptive statistics

Standard deviation8.8704648 × 109
Coefficient of variation (CV)1.1731113
Kurtosis1.3424746
Mean7.5614859 × 109
Median Absolute Deviation (MAD)2.548132 × 109
Skewness1.3175917
Sum3.1002092 × 1011
Variance7.8685146 × 1019
MonotonicityNot monotonic
2024-01-10T07:48:22.998859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
15129658000 1
 
2.4%
2108230000 1
 
2.4%
15675490000 1
 
2.4%
10011336000 1
 
2.4%
2548132000 1
 
2.4%
7160561000 1
 
2.4%
1536156000 1
 
2.4%
16342731000 1
 
2.4%
27986906000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
1536156000 1
 
2.4%
1760053000 1
 
2.4%
1789728000 1
 
2.4%
1907700000 1
 
2.4%
1931187000 1
 
2.4%
1987695000 1
 
2.4%
2108230000 1
 
2.4%
2383446000 1
 
2.4%
2395894000 1
 
2.4%
ValueCountFrequency (%)
35263392000 1
2.4%
27986906000 1
2.4%
26206656000 1
2.4%
17932083000 1
2.4%
16607849000 1
2.4%
16342731000 1
2.4%
16251813000 1
2.4%
15881572000 1
2.4%
15675490000 1
2.4%
15129658000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2661559 × 109
Minimum-1.43372 × 108
Maximum3.517806 × 1010
Zeros11
Zeros (%)26.8%
Negative1
Negative (%)2.4%
Memory size501.0 B
2024-01-10T07:48:23.119135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.43372 × 108
5-th percentile0
Q10
median2.366183 × 109
Q31.4344477 × 1010
95-th percentile2.612539 × 1010
Maximum3.517806 × 1010
Range3.5321432 × 1010
Interquartile range (IQR)1.4344477 × 1010

Descriptive statistics

Standard deviation8.8189242 × 109
Coefficient of variation (CV)1.2136987
Kurtosis1.5373363
Mean7.2661559 × 109
Median Absolute Deviation (MAD)2.366183 × 109
Skewness1.3644327
Sum2.9791239 × 1011
Variance7.7773425 × 1019
MonotonicityNot monotonic
2024-01-10T07:48:23.235099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
14830765000 1
 
2.4%
2068055000 1
 
2.4%
14993734000 1
 
2.4%
9752091000 1
 
2.4%
2542869000 1
 
2.4%
7160561000 1
 
2.4%
-143372000 1
 
2.4%
15692614000 1
 
2.4%
27781459000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
-143372000 1
 
2.4%
0 11
26.8%
1104942000 1
 
2.4%
1283242000 1
 
2.4%
1722512000 1
 
2.4%
1746280000 1
 
2.4%
1866942000 1
 
2.4%
1897082000 1
 
2.4%
1948260000 1
 
2.4%
2068055000 1
 
2.4%
ValueCountFrequency (%)
35178060000 1
2.4%
27781459000 1
2.4%
26125390000 1
2.4%
17177574000 1
2.4%
15934400000 1
2.4%
15876778000 1
2.4%
15692614000 1
2.4%
15558622000 1
2.4%
14993734000 1
2.4%
14830765000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0922456 × 108
Minimum0
Maximum1.732543 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T07:48:23.343725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5907000
Q390877000
95-th percentile4.23647 × 108
Maximum1.732543 × 109
Range1.732543 × 109
Interquartile range (IQR)90877000

Descriptive statistics

Standard deviation2.9039775 × 108
Coefficient of variation (CV)2.6587221
Kurtosis25.344865
Mean1.0922456 × 108
Median Absolute Deviation (MAD)5907000
Skewness4.7079039
Sum4.478207 × 109
Variance8.4330856 × 1016
MonotonicityNot monotonic
2024-01-10T07:48:23.452985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
18513000 1
 
2.4%
642000 1
 
2.4%
423647000 1
 
2.4%
39018000 1
 
2.4%
22988000 1
 
2.4%
1732543000 1
 
2.4%
115823000 1
 
2.4%
64681000 1
 
2.4%
7632000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
42000 1
 
2.4%
164000 1
 
2.4%
381000 1
 
2.4%
642000 1
 
2.4%
1169000 1
 
2.4%
1696000 1
 
2.4%
5907000 1
 
2.4%
7632000 1
 
2.4%
7970000 1
 
2.4%
ValueCountFrequency (%)
1732543000 1
2.4%
534033000 1
2.4%
423647000 1
2.4%
356090000 1
2.4%
355632000 1
2.4%
266998000 1
2.4%
129956000 1
2.4%
115823000 1
2.4%
112907000 1
2.4%
102316000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59121366
Minimum0
Maximum6.2079 × 108
Zeros17
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T07:48:23.556876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median69000
Q34591000
95-th percentile4.49642 × 108
Maximum6.2079 × 108
Range6.2079 × 108
Interquartile range (IQR)4591000

Descriptive statistics

Standard deviation1.5875429 × 108
Coefficient of variation (CV)2.6852271
Kurtosis7.1856344
Mean59121366
Median Absolute Deviation (MAD)69000
Skewness2.843021
Sum2.423976 × 109
Variance2.5202925 × 1016
MonotonicityNot monotonic
2024-01-10T07:48:23.660702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 17
41.5%
1716000 1
 
2.4%
2028000 1
 
2.4%
62590000 1
 
2.4%
2519000 1
 
2.4%
69000 1
 
2.4%
618046000 1
 
2.4%
4600000 1
 
2.4%
185000 1
 
2.4%
6987000 1
 
2.4%
Other values (15) 15
36.6%
ValueCountFrequency (%)
0 17
41.5%
5000 1
 
2.4%
31000 1
 
2.4%
36000 1
 
2.4%
69000 1
 
2.4%
72000 1
 
2.4%
185000 1
 
2.4%
561000 1
 
2.4%
671000 1
 
2.4%
1332000 1
 
2.4%
ValueCountFrequency (%)
620790000 1
2.4%
618046000 1
2.4%
449642000 1
2.4%
335771000 1
2.4%
250007000 1
2.4%
62590000 1
2.4%
50479000 1
2.4%
7229000 1
2.4%
6987000 1
2.4%
4600000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3620856 × 108
Minimum0
Maximum1.410488 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2024-01-10T07:48:23.773209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38147000
Q32.97177 × 108
95-th percentile1.061482 × 109
Maximum1.410488 × 109
Range1.410488 × 109
Interquartile range (IQR)2.97177 × 108

Descriptive statistics

Standard deviation3.7138243 × 108
Coefficient of variation (CV)1.5722649
Kurtosis3.3357527
Mean2.3620856 × 108
Median Absolute Deviation (MAD)38147000
Skewness1.9474964
Sum9.684551 × 109
Variance1.3792491 × 1017
MonotonicityNot monotonic
2024-01-10T07:48:23.906694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
17656000 1
 
2.4%
38147000 1
 
2.4%
619166000 1
 
2.4%
256726000 1
 
2.4%
5194000 1
 
2.4%
1061482000 1
 
2.4%
645517000 1
 
2.4%
205447000 1
 
2.4%
33920000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
5194000 1
 
2.4%
17263000 1
 
2.4%
17656000 1
 
2.4%
30787000 1
 
2.4%
33920000 1
 
2.4%
37510000 1
 
2.4%
38147000 1
 
2.4%
38764000 1
 
2.4%
40686000 1
 
2.4%
ValueCountFrequency (%)
1410488000 1
2.4%
1376648000 1
2.4%
1061482000 1
2.4%
753177000 1
2.4%
726480000 1
2.4%
645517000 1
2.4%
619166000 1
2.4%
529304000 1
2.4%
315721000 1
2.4%
310426000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.122439
Minimum-9.33
Maximum100
Zeros11
Zeros (%)26.8%
Negative1
Negative (%)2.4%
Memory size501.0 B
2024-01-10T07:48:24.042981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.33
5-th percentile0
Q10
median97.21
Q398.09
95-th percentile100
Maximum100
Range109.33
Interquartile range (IQR)98.09

Descriptive statistics

Standard deviation45.476597
Coefficient of variation (CV)0.68776345
Kurtosis-1.3919563
Mean66.122439
Median Absolute Deviation (MAD)2.48
Skewness-0.77640864
Sum2711.02
Variance2068.1208
MonotonicityNot monotonic
2024-01-10T07:48:24.173451image/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%
98.02 2
 
4.9%
99.26 1
 
2.4%
98.09 1
 
2.4%
95.65 1
 
2.4%
97.41 1
 
2.4%
99.79 1
 
2.4%
-9.33 1
 
2.4%
96.02 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
-9.33 1
 
2.4%
0.0 11
26.8%
37.27 1
 
2.4%
39.12 1
 
2.4%
93.54 1
 
2.4%
95.6 1
 
2.4%
95.65 1
 
2.4%
95.79 1
 
2.4%
96.02 1
 
2.4%
96.66 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.79 1
 
2.4%
99.76 1
 
2.4%
99.69 1
 
2.4%
99.28 1
 
2.4%
99.27 1
 
2.4%
99.26 1
 
2.4%
98.23 1
 
2.4%
98.09 1
 
2.4%
98.05 1
 
2.4%

Interactions

2024-01-10T07:48:20.981534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.440405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.931429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.385997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.946337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.509182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:21.054034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.525663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.008829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.464286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.041477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.587450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:21.129275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.609412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.082491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.569733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.138206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.674715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:21.436769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.703894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.161214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.663354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.239095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.756739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:21.506109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.781584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.236827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.762896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.330291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.833172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:21.583486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:18.861696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.315710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:19.857932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.425297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:48:20.909563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:48:24.266934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9260.8830.5480.6010.8630.919
부과금액0.0000.9261.0001.0000.5740.5470.7860.357
수납급액0.0000.8831.0001.0000.7060.7680.7670.576
환급금액0.0000.5480.5740.7061.0000.9320.8910.595
결손금액0.0000.6010.5470.7680.9321.0000.8610.863
미수납 금액0.0000.8630.7860.7670.8910.8611.0000.813
징수율0.0000.9190.3570.5760.5950.8630.8131.000
2024-01-10T07:48:24.375842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-01-10T07:48:24.456492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9700.7040.5370.6790.5910.0000.547
수납급액0.9701.0000.5730.4120.5480.6610.0000.609
환급금액0.7040.5731.0000.8100.8860.1700.0000.266
결손금액0.5370.4120.8101.0000.8690.0470.0000.293
미수납 금액0.6790.5480.8860.8691.0000.0870.0000.553
징수율0.5910.6610.1700.0470.0871.0000.0000.711
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5470.6090.2660.2930.5530.7110.0001.000

Missing values

2024-01-10T07:48:21.682461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:48:21.822131image/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
0충청남도홍성군448002017도축세000000.0
1충청남도홍성군448002017레저세000000.0
2충청남도홍성군448002017재산세151296580001483076500012378000171600029717700098.02
3충청남도홍성군448002017주민세17600530001722512000381000310003751000097.87
4충청남도홍성군448002017취득세352633920003517806000012995600008533200099.76
5충청남도홍성군448002017자동차세179320830001717757400090877000133200075317700095.79
6충청남도홍성군448002017과년도수입29650720001104942000534033000449642000141048800037.27
7충청남도홍성군448002017담배소비세74537730007453773000000100.0
8충청남도홍성군448002017도시계획세000000.0
9충청남도홍성군448002017등록면허세23834460002366183000797000001726300099.28
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31충청남도홍성군448002019취득세279869060002778145900064681000020544700099.27
32충청남도홍성군448002019자동차세1634273100015692614000115823000460000064551700096.02
33충청남도홍성군448002019과년도수입1536156000-14337200017325430006180460001061482000-9.33
34충청남도홍성군448002019담배소비세71605610007160561000000100.0
35충청남도홍성군448002019도시계획세000000.0
36충청남도홍성군448002019등록면허세254813200025428690002298800069000519400099.79
37충청남도홍성군448002019지방교육세10011336000975209100039018000251900025672600097.41
38충청남도홍성군448002019지방소득세15675490000149937340004236470006259000061916600095.65
39충청남도홍성군448002019지방소비세000000.0
40충청남도홍성군448002019지역자원시설세2108230000206805500064200020280003814700098.09