Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory101.1 B

Variable types

Categorical4
Text1
Numeric6

Dataset

Description충청남도 논산시의 지방세 부과액에 대한 세목별 징수현황에 대한 데이터로 재산세,취득세,등록면허세 등에 대한 정보를 제공공한다.
Author충청남도 논산시
URLhttps://www.data.go.kr/data/15079059/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 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 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 2 other fieldsHigh correlation
부과금액 has 4 (15.4%) zerosZeros
수납급액 has 4 (15.4%) zerosZeros
환급금액 has 6 (23.1%) zerosZeros
결손금액 has 13 (50.0%) zerosZeros
미수납 금액 has 8 (30.8%) zerosZeros
징수율 has 4 (15.4%) zerosZeros

Reproduction

Analysis started2024-04-17 16:38:50.496985
Analysis finished2024-04-17 16:38:53.660737
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
충청남도
26 

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 26
100.0%

Length

2024-04-18T01:38:53.706207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:38:53.774357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 26
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
논산시
26 

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 26
100.0%

Length

2024-04-18T01:38:53.842032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:38:53.908273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
논산시 26
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
44230
26 

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 26
100.0%

Length

2024-04-18T01:38:53.978853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:38:54.045561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44230 26
100.0%

과세년도
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

Length

2024-04-18T01:38:54.124311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:38:54.199661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

세목명
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-04-18T01:38:54.315531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Characters and Unicode

Total characters116
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row레저세
2nd row재산세
3rd row주민세
4th row취득세
5th row자동차세
ValueCountFrequency (%)
레저세 2
 
7.7%
재산세 2
 
7.7%
주민세 2
 
7.7%
취득세 2
 
7.7%
자동차세 2
 
7.7%
과년도수입 2
 
7.7%
담배소비세 2
 
7.7%
도시계획세 2
 
7.7%
등록면허세 2
 
7.7%
지방교육세 2
 
7.7%
Other values (3) 6
23.1%
2024-04-18T01:38:54.598550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0084682 × 1010
Minimum0
Maximum3.5329646 × 1010
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:54.691671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.4121745 × 109
median7.500686 × 109
Q31.362242 × 1010
95-th percentile3.1894422 × 1010
Maximum3.5329646 × 1010
Range3.5329646 × 1010
Interquartile range (IQR)1.1210246 × 1010

Descriptive statistics

Standard deviation9.9802391 × 109
Coefficient of variation (CV)0.9896434
Kurtosis1.2909826
Mean1.0084682 × 1010
Median Absolute Deviation (MAD)5.3413725 × 109
Skewness1.2792369
Sum2.6220174 × 1011
Variance9.9605172 × 1019
MonotonicityNot monotonic
2024-04-18T01:38:54.786426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
13259347000 1
 
3.8%
2205170000 1
 
3.8%
7462972000 1
 
3.8%
18968236000 1
 
3.8%
11831315000 1
 
3.8%
3010960000 1
 
3.8%
10241977000 1
 
3.8%
5304399000 1
 
3.8%
21800503000 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
2113457000 1
 
3.8%
2205170000 1
 
3.8%
2226903000 1
 
3.8%
2967989000 1
 
3.8%
3010960000 1
 
3.8%
3016365000 1
 
3.8%
5304399000 1
 
3.8%
5614511000 1
 
3.8%
7462972000 1
 
3.8%
ValueCountFrequency (%)
35329646000 1
3.8%
35259062000 1
3.8%
21800503000 1
3.8%
21672427000 1
3.8%
18968236000 1
3.8%
16204253000 1
3.8%
13743445000 1
3.8%
13259347000 1
3.8%
11943404000 1
3.8%
11831315000 1
3.8%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5075478 × 109
Minimum0
Maximum3.5110949 × 1010
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:54.888191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.049602 × 109
median7.500686 × 109
Q31.3039498 × 1010
95-th percentile3.141009 × 1010
Maximum3.5110949 × 1010
Range3.5110949 × 1010
Interquartile range (IQR)1.0989896 × 1010

Descriptive statistics

Standard deviation1.0009613 × 1010
Coefficient of variation (CV)1.052807
Kurtosis1.4073556
Mean9.5075478 × 109
Median Absolute Deviation (MAD)5.556075 × 109
Skewness1.3148692
Sum2.4719624 × 1011
Variance1.0019235 × 1020
MonotonicityNot monotonic
2024-04-18T01:38:55.007415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
12719511000 1
 
3.8%
2096375000 1
 
3.8%
7462972000 1
 
3.8%
18482470000 1
 
3.8%
11379281000 1
 
3.8%
2998262000 1
 
3.8%
10241977000 1
 
3.8%
1338418000 1
 
3.8%
20635240000 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
868286000 1
 
3.8%
1338418000 1
 
3.8%
2034011000 1
 
3.8%
2096375000 1
 
3.8%
2201367000 1
 
3.8%
2869735000 1
 
3.8%
2998262000 1
 
3.8%
3008405000 1
 
3.8%
7462972000 1
 
3.8%
ValueCountFrequency (%)
35110949000 1
3.8%
35001707000 1
3.8%
20635240000 1
3.8%
20624777000 1
3.8%
18482470000 1
3.8%
15408695000 1
3.8%
13146161000 1
3.8%
12719511000 1
3.8%
11542251000 1
3.8%
11379281000 1
3.8%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8755512 × 108
Minimum0
Maximum1.548804 × 109
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:55.126757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1178750
median8730500
Q31.52088 × 108
95-th percentile9.9442725 × 108
Maximum1.548804 × 109
Range1.548804 × 109
Interquartile range (IQR)1.5190925 × 108

Descriptive statistics

Standard deviation3.815949 × 108
Coefficient of variation (CV)2.0345747
Kurtosis7.0088106
Mean1.8755512 × 108
Median Absolute Deviation (MAD)8730500
Skewness2.6533471
Sum4.876433 × 109
Variance1.4561466 × 1017
MonotonicityNot monotonic
2024-04-18T01:38:55.220564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
23.1%
6181000 1
 
3.8%
117000 1
 
3.8%
474036000 1
 
3.8%
58771000 1
 
3.8%
22025000 1
 
3.8%
1503000 1
 
3.8%
1149331000 1
 
3.8%
167885000 1
 
3.8%
132516000 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 6
23.1%
117000 1
 
3.8%
364000 1
 
3.8%
428000 1
 
3.8%
1317000 1
 
3.8%
1503000 1
 
3.8%
6181000 1
 
3.8%
6391000 1
 
3.8%
11070000 1
 
3.8%
14756000 1
 
3.8%
ValueCountFrequency (%)
1548804000 1
3.8%
1149331000 1
3.8%
529716000 1
3.8%
495104000 1
3.8%
474036000 1
3.8%
167885000 1
3.8%
158612000 1
3.8%
132516000 1
3.8%
97506000 1
3.8%
58771000 1
3.8%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90751154
Minimum0
Maximum1.624178 × 109
Zeros13
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:55.311527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5500
Q31635000
95-th percentile4.994485 × 108
Maximum1.624178 × 109
Range1.624178 × 109
Interquartile range (IQR)1635000

Descriptive statistics

Standard deviation3.3713139 × 108
Coefficient of variation (CV)3.7148992
Kurtosis18.745966
Mean90751154
Median Absolute Deviation (MAD)5500
Skewness4.2483941
Sum2.35953 × 109
Variance1.1365757 × 1017
MonotonicityNot monotonic
2024-04-18T01:38:55.400138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 13
50.0%
11353000 1
 
3.8%
453000 1
 
3.8%
2029000 1
 
3.8%
1624178000 1
 
3.8%
253000 1
 
3.8%
2331000 1
 
3.8%
71335000 1
 
3.8%
5231000 1
 
3.8%
61000 1
 
3.8%
Other values (4) 4
 
15.4%
ValueCountFrequency (%)
0 13
50.0%
11000 1
 
3.8%
44000 1
 
3.8%
61000 1
 
3.8%
98000 1
 
3.8%
253000 1
 
3.8%
453000 1
 
3.8%
2029000 1
 
3.8%
2331000 1
 
3.8%
5231000 1
 
3.8%
ValueCountFrequency (%)
1624178000 1
3.8%
642153000 1
3.8%
71335000 1
3.8%
11353000 1
3.8%
5231000 1
3.8%
2331000 1
3.8%
2029000 1
3.8%
453000 1
3.8%
253000 1
3.8%
98000 1
3.8%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8638312 × 108
Minimum0
Maximum3.323828 × 109
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:55.495452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.03298 × 108
Q35.1780375 × 108
95-th percentile2.63284 × 109
Maximum3.323828 × 109
Range3.323828 × 109
Interquartile range (IQR)5.1780375 × 108

Descriptive statistics

Standard deviation8.720635 × 108
Coefficient of variation (CV)1.7929559
Kurtosis6.6365877
Mean4.8638312 × 108
Median Absolute Deviation (MAD)1.03298 × 108
Skewness2.6264011
Sum1.2645961 × 1010
Variance7.6049474 × 1017
MonotonicityNot monotonic
2024-04-18T01:38:55.585714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
528483000 1
 
3.8%
108795000 1
 
3.8%
485766000 1
 
3.8%
452023000 1
 
3.8%
12600000 1
 
3.8%
3323828000 1
 
3.8%
1165219000 1
 
3.8%
327939000 1
 
3.8%
25536000 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
7707000 1
 
3.8%
12600000 1
 
3.8%
25536000 1
 
3.8%
74215000 1
 
3.8%
97801000 1
 
3.8%
108795000 1
 
3.8%
148113000 1
 
3.8%
327939000 1
 
3.8%
398822000 1
 
3.8%
ValueCountFrequency (%)
3323828000 1
3.8%
3122047000 1
3.8%
1165219000 1
3.8%
1045621000 1
3.8%
724223000 1
3.8%
597223000 1
3.8%
528483000 1
3.8%
485766000 1
3.8%
452023000 1
3.8%
398822000 1
3.8%

징수율
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.625769
Minimum0
Maximum100
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-04-18T01:38:55.676923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194.755
median96.21
Q399.4525
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)4.6975

Descriptive statistics

Standard deviation39.358171
Coefficient of variation (CV)0.51364145
Kurtosis0.05716074
Mean76.625769
Median Absolute Deviation (MAD)3.115
Skewness-1.4038773
Sum1992.27
Variance1549.0657
MonotonicityNot monotonic
2024-04-18T01:38:55.762336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 4
15.4%
100.0 4
15.4%
99.58 2
 
7.7%
95.65 1
 
3.8%
95.07 1
 
3.8%
97.44 1
 
3.8%
96.18 1
 
3.8%
25.23 1
 
3.8%
94.65 1
 
3.8%
99.07 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0.0 4
15.4%
15.47 1
 
3.8%
25.23 1
 
3.8%
94.65 1
 
3.8%
95.07 1
 
3.8%
95.09 1
 
3.8%
95.17 1
 
3.8%
95.65 1
 
3.8%
95.93 1
 
3.8%
96.18 1
 
3.8%
ValueCountFrequency (%)
100.0 4
15.4%
99.74 1
 
3.8%
99.58 2
7.7%
99.07 1
 
3.8%
98.85 1
 
3.8%
97.44 1
 
3.8%
96.69 1
 
3.8%
96.64 1
 
3.8%
96.24 1
 
3.8%
96.18 1
 
3.8%

Interactions

2024-04-18T01:38:53.071842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:50.738876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.167532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.800924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.221825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.643603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:53.138824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:50.813926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.235970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.871770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.295444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.712141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:53.203016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:50.891698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.530797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.944782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.367166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.782222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:53.273851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:50.963564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.601237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.016648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.440793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.854376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:53.344384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.036962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.671791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.087979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.511243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.931687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:53.408811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.101253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:51.738010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.158583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.580765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:38:52.997245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:38:56.047982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9560.9570.8450.4260.9360.801
부과금액0.0000.9561.0001.0000.8680.6801.0000.798
수납급액0.0000.9571.0001.0000.5550.0000.8440.000
환급금액0.0000.8450.8680.5551.0001.0000.9590.824
결손금액0.0000.4260.6800.0001.0001.0000.6681.000
미수납 금액0.0000.9361.0000.8440.9590.6681.0000.575
징수율0.0000.8010.7980.0000.8241.0000.5751.000
2024-04-18T01:38:56.138235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도
부과금액1.0000.9660.6950.1600.5880.3550.000
수납급액0.9661.0000.5760.0670.4720.4470.000
환급금액0.6950.5761.0000.5270.8100.0470.000
결손금액0.1600.0670.5271.0000.693-0.2190.000
미수납 금액0.5880.4720.8100.6931.000-0.2730.000
징수율0.3550.4470.047-0.219-0.2731.0000.000
과세년도0.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-18T01:38:53.498713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:38:53.615818image/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
0충청남도논산시442302020레저세000000.0
1충청남도논산시442302020재산세132593470001271951100061810001135300052848300095.93
2충청남도논산시442302020주민세296798900028697350003640004530009780100096.69
3충청남도논산시442302020취득세3525906200035110949000529716000014811300099.58
4충청남도논산시442302020자동차세21672427000206247770001586120002029000104562100095.17
5충청남도논산시442302020과년도수입561451100086828600015488040001624178000312204700015.47
6충청남도논산시442302020담배소비세10486994000104869940001107000000100.0
7충청남도논산시442302020도시계획세000000.0
8충청남도논산시442302020등록면허세3016365000300840500014756000253000770700099.74
9충청남도논산시442302020지방교육세119434040001154225100097506000233100039882200096.64
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
16충청남도논산시442302021취득세3532964600035001707000132516000032793900099.07
17충청남도논산시442302021자동차세218005030002063524000016788500044000116521900094.65
18충청남도논산시442302021과년도수입530439900013384180001149331000642153000332382800025.23
19충청남도논산시442302021담배소비세1024197700010241977000150300000100.0
20충청남도논산시442302021도시계획세000000.0
21충청남도논산시442302021등록면허세3010960000299826200022025000980001260000099.58
22충청남도논산시442302021지방교육세1183131500011379281000587710001100045202300096.18
23충청남도논산시442302021지방소득세1896823600018482470000474036000048576600097.44
24충청남도논산시442302021지방소비세74629720007462972000000100.0
25충청남도논산시442302021지역자원시설세22051700002096375000117000010879500095.07