Overview

Dataset statistics

Number of variables12
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory103.7 B

Variable types

Categorical4
Numeric5
Text2
DateTime1

Dataset

Description○ 지방세 부과액에 대한 세목별 징수현황을 제공(세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율)- 지자체의 재정자주도, 재정자립도, 산출하는 기초 및 납세협력도 및 조세 순응도를 확인하는 자료로 활용
Author세종특별자치시
URLhttps://www.data.go.kr/data/15080312/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
환급금액 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
징수율 is highly overall correlated with 세목명High correlation
세목명 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
환급금액 has 22 (27.5%) zerosZeros
결손금액 has 55 (68.8%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 13 (16.2%) zerosZeros

Reproduction

Analysis started2023-12-12 19:39:02.753095
Analysis finished2023-12-12 19:39:06.586260
Duration3.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
세종특별자치시
80 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종특별자치시 80
100.0%

Length

2023-12-13T04:39:06.642530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:06.735094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종특별자치시 80
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
세종특별자치시
80 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
세종특별자치시 80
100.0%

Length

2023-12-13T04:39:06.835155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:06.927540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
세종특별자치시 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
36110
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36110
2nd row36110
3rd row36110
4th row36110
5th row36110

Common Values

ValueCountFrequency (%)
36110 80
100.0%

Length

2023-12-13T04:39:07.017298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:39:07.109807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
36110 80
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T04:39:07.199983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2023-12-13T04:39:07.310157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

Length

2023-12-13T04:39:07.439692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Text

Distinct68
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-13T04:39:07.617296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.475
Min length2

Characters and Unicode

Total characters838
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)83.8%

Sample

1st row0
2nd row0
3rd row67251851000
4th row7760161000
5th row335693486000
ValueCountFrequency (%)
0 13
 
16.2%
87010241000 1
 
1.2%
60062089000 1
 
1.2%
11990660000 1
 
1.2%
16820137000 1
 
1.2%
15617666000 1
 
1.2%
54877647000 1
 
1.2%
67251851000 1
 
1.2%
13319426000 1
 
1.2%
51005484000 1
 
1.2%
Other values (58) 58
72.5%
2023-12-13T04:39:07.955410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 279
33.3%
79
 
9.4%
1 76
 
9.1%
6 54
 
6.4%
5 53
 
6.3%
4 52
 
6.2%
2 52
 
6.2%
8 51
 
6.1%
3 51
 
6.1%
7 46
 
5.5%
Other values (3) 45
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 757
90.3%
Space Separator 79
 
9.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 279
36.9%
1 76
 
10.0%
6 54
 
7.1%
5 53
 
7.0%
4 52
 
6.9%
2 52
 
6.9%
8 51
 
6.7%
3 51
 
6.7%
7 46
 
6.1%
9 43
 
5.7%
Space Separator
ValueCountFrequency (%)
79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 838
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 279
33.3%
79
 
9.4%
1 76
 
9.1%
6 54
 
6.4%
5 53
 
6.3%
4 52
 
6.2%
2 52
 
6.2%
8 51
 
6.1%
3 51
 
6.1%
7 46
 
5.5%
Other values (3) 45
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279
33.3%
79
 
9.4%
1 76
 
9.1%
6 54
 
6.4%
5 53
 
6.3%
4 52
 
6.2%
2 52
 
6.2%
8 51
 
6.1%
3 51
 
6.1%
7 46
 
5.5%
Other values (3) 45
 
5.4%

수납급액
Text

Distinct68
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-13T04:39:08.164685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.4
Min length2

Characters and Unicode

Total characters832
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)83.8%

Sample

1st row0
2nd row0
3rd row65970549000
4th row7615244000
5th row331831403000
ValueCountFrequency (%)
0 13
 
16.2%
84876833000 1
 
1.2%
56934656000 1
 
1.2%
1996785000 1
 
1.2%
16820137000 1
 
1.2%
15608144000 1
 
1.2%
53636253000 1
 
1.2%
65970549000 1
 
1.2%
13069528000 1
 
1.2%
49643406000 1
 
1.2%
Other values (58) 58
72.5%
2023-12-13T04:39:08.470873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 279
33.5%
1 79
 
9.5%
78
 
9.4%
6 56
 
6.7%
4 54
 
6.5%
5 54
 
6.5%
2 49
 
5.9%
3 48
 
5.8%
8 45
 
5.4%
7 45
 
5.4%
Other values (3) 45
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
90.1%
Space Separator 78
 
9.4%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 279
37.2%
1 79
 
10.5%
6 56
 
7.5%
4 54
 
7.2%
5 54
 
7.2%
2 49
 
6.5%
3 48
 
6.4%
8 45
 
6.0%
7 45
 
6.0%
9 41
 
5.5%
Space Separator
ValueCountFrequency (%)
78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 832
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 279
33.5%
1 79
 
9.5%
78
 
9.4%
6 56
 
6.7%
4 54
 
6.5%
5 54
 
6.5%
2 49
 
5.9%
3 48
 
5.8%
8 45
 
5.4%
7 45
 
5.4%
Other values (3) 45
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279
33.5%
1 79
 
9.5%
78
 
9.4%
6 56
 
6.7%
4 54
 
6.5%
5 54
 
6.5%
2 49
 
5.9%
3 48
 
5.8%
8 45
 
5.4%
7 45
 
5.4%
Other values (3) 45
 
5.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2114052 × 109
Minimum0
Maximum2.7631512 × 1010
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T04:39:08.603185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49709000
Q35.9335875 × 108
95-th percentile6.017014 × 109
Maximum2.7631512 × 1010
Range2.7631512 × 1010
Interquartile range (IQR)5.9335875 × 108

Descriptive statistics

Standard deviation3.6483766 × 109
Coefficient of variation (CV)3.0116898
Kurtosis35.787012
Mean1.2114052 × 109
Median Absolute Deviation (MAD)49709000
Skewness5.4646084
Sum9.6912414 × 1010
Variance1.3310652 × 1019
MonotonicityNot monotonic
2023-12-13T04:39:08.719829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
45704000 1
 
1.2%
583684000 1
 
1.2%
11097851000 1
 
1.2%
21646000 1
 
1.2%
137437000 1
 
1.2%
327837000 1
 
1.2%
3518097000 1
 
1.2%
6603000 1
 
1.2%
622383000 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
16000 1
 
1.2%
28000 1
 
1.2%
358000 1
 
1.2%
1657000 1
 
1.2%
2200000 1
 
1.2%
2566000 1
 
1.2%
3348000 1
 
1.2%
6603000 1
 
1.2%
6613000 1
 
1.2%
ValueCountFrequency (%)
27631512000 1
1.2%
11097851000 1
1.2%
8984946000 1
1.2%
8775568000 1
1.2%
5871827000 1
1.2%
5092308000 1
1.2%
4655111000 1
1.2%
3518097000 1
1.2%
3123906000 1
1.2%
2009978000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3562154 × 108
Minimum0
Maximum2.956355 × 109
Zeros55
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T04:39:08.847217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333250
95-th percentile5.6035575 × 108
Maximum2.956355 × 109
Range2.956355 × 109
Interquartile range (IQR)33250

Descriptive statistics

Standard deviation5.4486515 × 108
Coefficient of variation (CV)4.0175415
Kurtosis17.206479
Mean1.3562154 × 108
Median Absolute Deviation (MAD)0
Skewness4.2449234
Sum1.0849723 × 1010
Variance2.9687803 × 1017
MonotonicityNot monotonic
2023-12-13T04:39:08.966937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 55
68.8%
72000 2
 
2.5%
14000 1
 
1.2%
73052000 1
 
1.2%
4809000 1
 
1.2%
15000 1
 
1.2%
2144889000 1
 
1.2%
7436000 1
 
1.2%
16549000 1
 
1.2%
1327000 1
 
1.2%
Other values (15) 15
 
18.8%
ValueCountFrequency (%)
0 55
68.8%
5000 1
 
1.2%
7000 1
 
1.2%
14000 1
 
1.2%
15000 1
 
1.2%
25000 1
 
1.2%
58000 1
 
1.2%
72000 2
 
2.5%
110000 1
 
1.2%
147000 1
 
1.2%
ValueCountFrequency (%)
2956355000 1
1.2%
2601348000 1
1.2%
2144889000 1
1.2%
2088331000 1
1.2%
479936000 1
1.2%
469097000 1
1.2%
73052000 1
1.2%
16549000 1
1.2%
7436000 1
1.2%
4809000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7116444 × 109
Minimum0
Maximum1.2463099 × 1010
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T04:39:09.088347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.227375 × 108
Q32.184732 × 109
95-th percentile1.0298763 × 1010
Maximum1.2463099 × 1010
Range1.2463099 × 1010
Interquartile range (IQR)2.184732 × 109

Descriptive statistics

Standard deviation2.9339827 × 109
Coefficient of variation (CV)1.714131
Kurtosis5.7229064
Mean1.7116444 × 109
Median Absolute Deviation (MAD)2.227375 × 108
Skewness2.462989
Sum1.3693155 × 1011
Variance8.6082546 × 1018
MonotonicityNot monotonic
2023-12-13T04:39:09.256442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
1281302000 1
 
1.2%
732644000 1
 
1.2%
3127433000 1
 
1.2%
11031090000 1
 
1.2%
9522000 1
 
1.2%
1241336000 1
 
1.2%
2133408000 1
 
1.2%
249788000 1
 
1.2%
2910394000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
9522000 1
 
1.2%
12870000 1
 
1.2%
13236000 1
 
1.2%
13555000 1
 
1.2%
14940000 1
 
1.2%
16725000 1
 
1.2%
118898000 1
 
1.2%
144903000 1
 
1.2%
146044000 1
 
1.2%
ValueCountFrequency (%)
12463099000 1
1.2%
11938554000 1
1.2%
11119150000 1
1.2%
11031090000 1
1.2%
10260220000 1
1.2%
8778266000 1
1.2%
3862083000 1
1.2%
3856423000 1
1.2%
3705284000 1
1.2%
3640973000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.968125
Minimum-16.65
Maximum296.51
Zeros13
Zeros (%)16.2%
Negative1
Negative (%)1.2%
Memory size852.0 B
2023-12-13T04:39:09.392490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-16.65
5-th percentile0
Q192.8675
median97.62
Q399.865
95-th percentile100
Maximum296.51
Range313.16
Interquartile range (IQR)6.9975

Descriptive statistics

Standard deviation47.494259
Coefficient of variation (CV)0.60143582
Kurtosis4.7464167
Mean78.968125
Median Absolute Deviation (MAD)2.305
Skewness0.26501137
Sum6317.45
Variance2255.7046
MonotonicityNot monotonic
2023-12-13T04:39:09.583425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
16.2%
100.0 13
 
16.2%
99.9 3
 
3.8%
97.33 2
 
2.5%
97.59 1
 
1.2%
-16.65 1
 
1.2%
99.94 1
 
1.2%
97.74 1
 
1.2%
97.55 1
 
1.2%
98.12 1
 
1.2%
Other values (43) 43
53.8%
ValueCountFrequency (%)
-16.65 1
 
1.2%
0.0 13
16.2%
0.21 1
 
1.2%
0.75 1
 
1.2%
23.89 1
 
1.2%
27.08 1
 
1.2%
92.46 1
 
1.2%
92.86 1
 
1.2%
92.87 1
 
1.2%
93.62 1
 
1.2%
ValueCountFrequency (%)
296.51 1
 
1.2%
100.0 13
16.2%
99.94 1
 
1.2%
99.91 1
 
1.2%
99.9 3
 
3.8%
99.88 1
 
1.2%
99.86 1
 
1.2%
99.75 1
 
1.2%
99.63 1
 
1.2%
99.58 1
 
1.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2023-12-13T04:39:09.684271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:09.768084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T04:39:05.534757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.129575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.732759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.354130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.960384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.638454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.251495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.894248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.469270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.093026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.744529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.375026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.019482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.593652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.207129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:06.163657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.485368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.136169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.714660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.325307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:06.246451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:03.600073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.250791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:04.827165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:39:05.437676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:39:09.852922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.3560.3560.0000.0000.0000.000
세목명0.0001.0000.7570.7570.5130.5470.8840.788
부과금액0.3560.7571.0001.0001.0001.0001.0001.000
수납급액0.3560.7571.0001.0001.0001.0001.0001.000
환급금액0.0000.5131.0001.0001.0000.8840.7820.854
결손금액0.0000.5471.0001.0000.8841.0000.7860.769
미수납 금액0.0000.8841.0001.0000.7820.7861.0000.724
징수율0.0000.7881.0001.0000.8540.7690.7241.000
2023-12-13T04:39:09.977365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1200.2440.0810.0810.000
환급금액0.1201.0000.5450.911-0.0800.263
결손금액0.2440.5451.0000.642-0.2560.300
미수납 금액0.0810.9110.6421.000-0.2350.514
징수율0.081-0.080-0.256-0.2351.0000.542
세목명0.0000.2630.3000.5140.5421.000

Missing values

2023-12-13T04:39:06.360312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:39:06.522789image/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
0세종특별자치시세종특별자치시361102017도축세000000.02022-12-31
1세종특별자치시세종특별자치시361102017레저세000000.02022-12-31
2세종특별자치시세종특별자치시361102017재산세6725185100065970549000457040000128130200098.092022-12-31
3세종특별자치시세종특별자치시361102017주민세7760161000761524400022000001400014490300098.132022-12-31
4세종특별자치시세종특별자치시361102017취득세33569348600033183140300010972810000386208300098.852022-12-31
5세종특별자치시세종특별자치시361102017자동차세3868196200035765091000323767000590000291628100092.462022-12-31
6세종특별자치시세종특별자치시361102017과년도수입1268106800034337050004655111000469097000877826600027.082022-12-31
7세종특별자치시세종특별자치시361102017담배소비세1514602600015146026000000100.02022-12-31
8세종특별자치시세종특별자치시361102017도시계획세000000.02022-12-31
9세종특별자치시세종특별자치시361102017등록면허세13509452000134965820005120900001287000099.92022-12-31
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
70세종특별자치시세종특별자치시361102022취득세2289920000002262840000008645250000270868800098.822022-12-31
71세종특별자치시세종특별자치시361102022자동차세55442779000519078030006374570007436000352754000093.622022-12-31
72세종특별자치시세종특별자치시361102022과년도수입146394080003142000089849460002144889000124630990000.212022-12-31
73세종특별자치시세종특별자치시361102022담배소비세17558501000175585010001600000100.02022-12-31
74세종특별자치시세종특별자치시361102022도시계획세000000.02022-12-31
75세종특별자치시세종특별자치시361102022등록면허세1402241700014005677000100710000150001672500099.882022-12-31
76세종특별자치시세종특별자치시361102022지방교육세53495351000517388380002611680004809000175170400096.722022-12-31
77세종특별자치시세종특별자치시361102022지방소득세116806000000113505000000587182700073052000322739700097.172022-12-31
78세종특별자치시세종특별자치시361102022지방소비세233241000000233241000000000100.02022-12-31
79세종특별자치시세종특별자치시361102022지역자원시설세14813626000144173220007811000039630400097.322022-12-31