Overview

Dataset statistics

Number of variables12
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory107.2 B

Variable types

Categorical5
Numeric6
DateTime1

Dataset

Description과세년도 2017~2019에 대한 지방세 세목별 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 등에 대한 자료
Author충청남도 공주시
URLhttps://www.data.go.kr/data/15080734/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 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 13 (31.7%) zerosZeros
결손금액 has 15 (36.6%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-12 21:01:57.280151
Analysis finished2023-12-12 21:02:01.321150
Duration4.04 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

2023-12-13T06:02:01.374084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:02:01.449817image/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
공주시
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공주시 41
100.0%

Length

2023-12-13T06:02:01.528929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:02:01.610621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공주시 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
44150
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44150
2nd row44150
3rd row44150
4th row44150
5th row44150

Common Values

ValueCountFrequency (%)
44150 41
100.0%

Length

2023-12-13T06:02:01.699556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:02:01.793233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44150 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

2023-12-13T06:02:01.876253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:02:01.968554image/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

2023-12-13T06:02:02.096525image/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%
Mean9.6856665 × 109
Minimum0
Maximum4.0965732 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T06:02:02.234865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.252212 × 109
Q31.5135226 × 1010
95-th percentile3.2596802 × 1010
Maximum4.0965732 × 1010
Range4.0965732 × 1010
Interquartile range (IQR)1.5135226 × 1010

Descriptive statistics

Standard deviation1.1617186 × 1010
Coefficient of variation (CV)1.1994204
Kurtosis0.8745527
Mean9.6856665 × 109
Median Absolute Deviation (MAD)3.252212 × 109
Skewness1.3202468
Sum3.9711233 × 1011
Variance1.34959 × 1020
MonotonicityNot monotonic
2023-12-13T06:02:02.345204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
14475407000 1
 
2.4%
2091835000 1
 
2.4%
27279318000 1
 
2.4%
11170513000 1
 
2.4%
3051542000 1
 
2.4%
8285529000 1
 
2.4%
4097128000 1
 
2.4%
20393521000 1
 
2.4%
32596802000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
1833575000 1
 
2.4%
1863273000 1
 
2.4%
2091835000 1
 
2.4%
2443878000 1
 
2.4%
2825942000 1
 
2.4%
2979097000 1
 
2.4%
3033419000 1
 
2.4%
3051542000 1
 
2.4%
3109073000 1
 
2.4%
ValueCountFrequency (%)
40965732000 1
2.4%
39945547000 1
2.4%
32596802000 1
2.4%
29402098000 1
2.4%
27843560000 1
2.4%
27279318000 1
2.4%
20393521000 1
2.4%
19723811000 1
2.4%
16611009000 1
2.4%
16063630000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2594658 × 109
Minimum-2.86531 × 108
Maximum4.0915138 × 1010
Zeros11
Zeros (%)26.8%
Negative1
Negative (%)2.4%
Memory size501.0 B
2023-12-13T06:02:02.446093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.86531 × 108
5-th percentile0
Q10
median3.040799 × 109
Q31.4562344 × 1010
95-th percentile3.2512542 × 1010
Maximum4.0915138 × 1010
Range4.1201669 × 1010
Interquartile range (IQR)1.4562344 × 1010

Descriptive statistics

Standard deviation1.1593494 × 1010
Coefficient of variation (CV)1.2520694
Kurtosis1.0165535
Mean9.2594658 × 109
Median Absolute Deviation (MAD)3.040799 × 109
Skewness1.3609008
Sum3.796381 × 1011
Variance1.344091 × 1020
MonotonicityNot monotonic
2023-12-13T06:02:02.546093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
13997922000 1
 
2.4%
2034939000 1
 
2.4%
26464202000 1
 
2.4%
10788615000 1
 
2.4%
3040799000 1
 
2.4%
8285529000 1
 
2.4%
1238255000 1
 
2.4%
19459694000 1
 
2.4%
32512542000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
-286531000 1
 
2.4%
0 11
26.8%
1238255000 1
 
2.4%
1492502000 1
 
2.4%
1784612000 1
 
2.4%
1803692000 1
 
2.4%
2034939000 1
 
2.4%
2752353000 1
 
2.4%
2949257000 1
 
2.4%
2972353000 1
 
2.4%
ValueCountFrequency (%)
40915138000 1
2.4%
39821572000 1
2.4%
32512542000 1
2.4%
28834781000 1
2.4%
27223084000 1
2.4%
26464202000 1
2.4%
19459694000 1
2.4%
18836477000 1
2.4%
15751671000 1
2.4%
15456020000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7731954 × 108
Minimum0
Maximum2.297402 × 109
Zeros13
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T06:02:02.645000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8937000
Q31.99247 × 108
95-th percentile8.23925 × 108
Maximum2.297402 × 109
Range2.297402 × 109
Interquartile range (IQR)1.99247 × 108

Descriptive statistics

Standard deviation4.1067945 × 108
Coefficient of variation (CV)2.3160417
Kurtosis18.143907
Mean1.7731954 × 108
Median Absolute Deviation (MAD)8937000
Skewness3.9393134
Sum7.270101 × 109
Variance1.6865761 × 1017
MonotonicityNot monotonic
2023-12-13T06:02:02.743164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 13
31.7%
4006000 1
 
2.4%
7236000 1
 
2.4%
315336000 1
 
2.4%
90078000 1
 
2.4%
23762000 1
 
2.4%
823925000 1
 
2.4%
260281000 1
 
2.4%
160332000 1
 
2.4%
1177000 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0 13
31.7%
38000 1
 
2.4%
80000 1
 
2.4%
1177000 1
 
2.4%
1951000 1
 
2.4%
2495000 1
 
2.4%
4006000 1
 
2.4%
7236000 1
 
2.4%
8937000 1
 
2.4%
13558000 1
 
2.4%
ValueCountFrequency (%)
2297402000 1
2.4%
996655000 1
2.4%
823925000 1
2.4%
718691000 1
2.4%
336638000 1
2.4%
315336000 1
2.4%
288932000 1
2.4%
260281000 1
2.4%
259574000 1
2.4%
214305000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95514805
Minimum0
Maximum1.47515 × 109
Zeros15
Zeros (%)36.6%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T06:02:02.840456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median244000
Q36051000
95-th percentile6.35099 × 108
Maximum1.47515 × 109
Range1.47515 × 109
Interquartile range (IQR)6051000

Descriptive statistics

Standard deviation2.9158816 × 108
Coefficient of variation (CV)3.0528059
Kurtosis14.463485
Mean95514805
Median Absolute Deviation (MAD)244000
Skewness3.7411541
Sum3.916107 × 109
Variance8.5023654 × 1016
MonotonicityNot monotonic
2023-12-13T06:02:02.942436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 15
36.6%
8782000 1
 
2.4%
954000 1
 
2.4%
325578000 1
 
2.4%
13548000 1
 
2.4%
80000 1
 
2.4%
1007516000 1
 
2.4%
2202000 1
 
2.4%
28439000 1
 
2.4%
103000 1
 
2.4%
Other values (17) 17
41.5%
ValueCountFrequency (%)
0 15
36.6%
45000 1
 
2.4%
80000 1
 
2.4%
103000 1
 
2.4%
136000 1
 
2.4%
235000 1
 
2.4%
244000 1
 
2.4%
304000 1
 
2.4%
338000 1
 
2.4%
395000 1
 
2.4%
ValueCountFrequency (%)
1475150000 1
2.4%
1007516000 1
2.4%
635099000 1
2.4%
325578000 1
2.4%
243981000 1
2.4%
105356000 1
2.4%
52143000 1
2.4%
28439000 1
2.4%
13548000 1
2.4%
8782000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.306859 × 108
Minimum0
Maximum2.283561 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T06:02:03.039025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median55942000
Q34.61961 × 108
95-th percentile1.851357 × 109
Maximum2.283561 × 109
Range2.283561 × 109
Interquartile range (IQR)4.61961 × 108

Descriptive statistics

Standard deviation5.6601211 × 108
Coefficient of variation (CV)1.7116306
Kurtosis5.2911079
Mean3.306859 × 108
Median Absolute Deviation (MAD)55942000
Skewness2.3561934
Sum1.3558122 × 1010
Variance3.2036971 × 1017
MonotonicityNot monotonic
2023-12-13T06:02:03.142031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
8520000 1
 
2.4%
55942000 1
 
2.4%
489538000 1
 
2.4%
368350000 1
 
2.4%
10663000 1
 
2.4%
1851357000 1
 
2.4%
931625000 1
 
2.4%
55821000 1
 
2.4%
88345000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
6349000 1
 
2.4%
8520000 1
 
2.4%
10663000 1
 
2.4%
46032000 1
 
2.4%
50549000 1
 
2.4%
55821000 1
 
2.4%
55942000 1
 
2.4%
59445000 1
 
2.4%
71431000 1
 
2.4%
ValueCountFrequency (%)
2283561000 1
2.4%
2095310000 1
2.4%
1851357000 1
2.4%
931625000 1
2.4%
886590000 1
2.4%
853287000 1
2.4%
572578000 1
2.4%
555467000 1
2.4%
489538000 1
2.4%
468703000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.536341
Minimum-11.72
Maximum100
Zeros11
Zeros (%)26.8%
Negative1
Negative (%)2.4%
Memory size501.0 B
2023-12-13T06:02:03.263486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11.72
5-th percentile0
Q10
median96.7
Q397.77
95-th percentile100
Maximum100
Range111.72
Interquartile range (IQR)97.77

Descriptive statistics

Standard deviation45.81996
Coefficient of variation (CV)0.69915346
Kurtosis-1.4488547
Mean65.536341
Median Absolute Deviation (MAD)3.02
Skewness-0.7487976
Sum2686.99
Variance2099.4687
MonotonicityNot monotonic
2023-12-13T06:02:03.373540image/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%
97.28 2
 
4.9%
-11.72 1
 
2.4%
97.01 1
 
2.4%
96.58 1
 
2.4%
99.65 1
 
2.4%
30.22 1
 
2.4%
95.42 1
 
2.4%
99.74 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
-11.72 1
 
2.4%
0.0 11
26.8%
28.42 1
 
2.4%
30.22 1
 
2.4%
94.83 1
 
2.4%
95.42 1
 
2.4%
95.5 1
 
2.4%
96.21 1
 
2.4%
96.22 1
 
2.4%
96.58 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.88 1
 
2.4%
99.77 1
 
2.4%
99.74 1
 
2.4%
99.72 1
 
2.4%
99.69 1
 
2.4%
99.65 1
 
2.4%
98.07 1
 
2.4%
97.77 1
 
2.4%
97.4 1
 
2.4%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
Minimum2019-12-31 00:00:00
Maximum2019-12-31 00:00:00
2023-12-13T06:02:03.452329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:03.519558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T06:02:00.267870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.629814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.134705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.715085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.239197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.761259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.354207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.716727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.244754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.801246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.316336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.836231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.448957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.787204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.361064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.872531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.392880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.922369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.529245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.859956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.453784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.973535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.487995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.028237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.655916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.951421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.537437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.077207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.579238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.121865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.734410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.041269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:58.627932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.149113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:59.666360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:00.194505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:02:03.591731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.1130.0000.000
세목명0.0001.0000.9440.9690.5500.2690.9440.876
부과금액0.0000.9441.0000.9770.7290.7390.8210.895
수납급액0.0000.9690.9771.0000.6550.5120.8510.330
환급금액0.0000.5500.7290.6551.0000.8870.7560.791
결손금액0.1130.2690.7390.5120.8871.0000.7940.908
미수납 금액0.0000.9440.8210.8510.7560.7941.0000.746
징수율0.0000.8760.8950.3300.7910.9080.7461.000
2023-12-13T06:02:03.679258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T06:02:03.750057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9630.7630.5640.6700.6020.0000.724
수납급액0.9631.0000.6210.4370.5290.6810.0000.659
환급금액0.7630.6211.0000.8250.8720.2850.0000.268
결손금액0.5640.4370.8251.0000.8610.1740.0000.073
미수납 금액0.6700.5290.8720.8611.0000.0790.0000.586
징수율0.6020.6810.2850.1740.0791.0000.0000.608
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.7240.6590.2680.0730.5860.6080.0001.000

Missing values

2023-12-13T06:02:01.123619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:02:01.267349image/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 금액징수율데이터기준일
0충청남도공주시441502017도축세000000.02019-12-31
1충청남도공주시441502017레저세000000.02019-12-31
2충청남도공주시441502017재산세14475407000139979220004006000878200046870300096.72019-12-31
3충청남도공주시441502017주민세28259420002752353000249500021580007143100097.42019-12-31
4충청남도공주시441502017취득세4096573200040915138000336638000450005054900099.882019-12-31
5충청남도공주시441502017자동차세1661100900015751671000199247000605100085328700094.832019-12-31
6충청남도공주시441502017과년도수입525121300014925020009966550001475150000228356100028.422019-12-31
7충청남도공주시441502017담배소비세88379830008837983000000100.02019-12-31
8충청남도공주시441502017도시계획세000000.02019-12-31
9충청남도공주시441502017등록면허세2979097000297235300019543000395000634900099.772019-12-31
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
31충청남도공주시441502019취득세3259680200032512542000160332000284390005582100099.742019-12-31
32충청남도공주시441502019자동차세2039352100019459694000260281000220200093162500095.422019-12-31
33충청남도공주시441502019과년도수입409712800012382550008239250001007516000185135700030.222019-12-31
34충청남도공주시441502019담배소비세82855290008285529000000100.02019-12-31
35충청남도공주시441502019도시계획세000000.02019-12-31
36충청남도공주시441502019등록면허세3051542000304079900023762000800001066300099.652019-12-31
37충청남도공주시441502019지방교육세1117051300010788615000900780001354800036835000096.582019-12-31
38충청남도공주시441502019지방소득세272793180002646420200031533600032557800048953800097.012019-12-31
39충청남도공주시441502019지방소비세000000.02019-12-31
40충청남도공주시441502019지역자원시설세2091835000203493900072360009540005594200097.282019-12-31