Overview

Dataset statistics

Number of variables11
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory97.5 B

Variable types

Categorical5
Numeric5
Text1

Dataset

Description경상북도 구미시의 지방세 부과액에 대한 세목별 징수현황에 대한 데이터로 지방자치단체코드, 과세연도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납 금액, 징수율를 제공합니다. ※ 매년 통계연감이 확정된 최근 3년간 자료를 연도별로 나타냄
URLhttps://www.data.go.kr/data/15078329/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 has 11 (20.8%) zerosZeros
환급금액 has 14 (26.4%) zerosZeros
결손금액 has 40 (75.5%) zerosZeros
미수납 금액 has 17 (32.1%) zerosZeros
징수율 has 11 (20.8%) zerosZeros

Reproduction

Analysis started2023-12-13 00:09:00.483936
Analysis finished2023-12-13 00:09:02.574622
Duration2.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
경상북도
53 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 53
100.0%

Length

2023-12-13T09:09:02.625789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:09:02.703706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 53
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
구미시
53 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구미시 53
100.0%

Length

2023-12-13T09:09:02.791789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:09:03.079672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구미시 53
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
47190
53 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47190
2nd row47190
3rd row47190
4th row47190
5th row47190

Common Values

ValueCountFrequency (%)
47190 53
100.0%

Length

2023-12-13T09:09:03.147006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:09:03.214058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47190 53
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size556.0 B
2018
14 
2019
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 14
26.4%
2019 13
24.5%
2020 13
24.5%
2021 13
24.5%

Length

2023-12-13T09:09:03.280663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T09:09:03.350703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 14
26.4%
2019 13
24.5%
2020 13
24.5%
2021 13
24.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
레저세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (9)
33 

Length

Max length7
Median length5
Mean length4.4339623
Min length3

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 4
 
7.5%
재산세 4
 
7.5%
주민세 4
 
7.5%
취득세 4
 
7.5%
자동차세 4
 
7.5%
과년도수입 4
 
7.5%
담배소비세 4
 
7.5%
도시계획세 4
 
7.5%
등록면허세 4
 
7.5%
지방교육세 4
 
7.5%
Other values (4) 13
24.5%

Length

2023-12-13T09:09:03.439403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 4
 
7.5%
재산세 4
 
7.5%
주민세 4
 
7.5%
취득세 4
 
7.5%
자동차세 4
 
7.5%
과년도수입 4
 
7.5%
담배소비세 4
 
7.5%
도시계획세 4
 
7.5%
등록면허세 4
 
7.5%
지방교육세 4
 
7.5%
Other values (4) 13
24.5%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9006675 × 1010
Minimum0
Maximum2.77132 × 1011
Zeros11
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T09:09:03.531838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0839984 × 1010
median2.9331299 × 1010
Q36.6298499 × 1010
95-th percentile2.013692 × 1011
Maximum2.77132 × 1011
Range2.77132 × 1011
Interquartile range (IQR)5.5458515 × 1010

Descriptive statistics

Standard deviation6.4103961 × 1010
Coefficient of variation (CV)1.3080659
Kurtosis4.4480012
Mean4.9006675 × 1010
Median Absolute Deviation (MAD)2.8462688 × 1010
Skewness2.1333722
Sum2.5973538 × 1012
Variance4.1093178 × 1021
MonotonicityNot monotonic
2023-12-13T09:09:03.626472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 11
 
20.8%
64935422000 1
 
1.9%
74981924000 1
 
1.9%
21450556000 1
 
1.9%
31342412000 1
 
1.9%
9436599000 1
 
1.9%
42960822000 1
 
1.9%
191448000000 1
 
1.9%
10979600000 1
 
1.9%
14167199000 1
 
1.9%
Other values (33) 33
62.3%
ValueCountFrequency (%)
0 11
20.8%
9436599000 1
 
1.9%
9525342000 1
 
1.9%
10839984000 1
 
1.9%
10873055000 1
 
1.9%
10979600000 1
 
1.9%
11220134000 1
 
1.9%
13298438000 1
 
1.9%
14003409000 1
 
1.9%
14167199000 1
 
1.9%
ValueCountFrequency (%)
277132000000 1
1.9%
256621000000 1
1.9%
216251000000 1
1.9%
191448000000 1
1.9%
135476000000 1
1.9%
123626000000 1
1.9%
116602000000 1
1.9%
109916000000 1
1.9%
74981924000 1
1.9%
74615492000 1
1.9%

수납금액
Text

Distinct43
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T09:09:03.778914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.018868
Min length2

Characters and Unicode

Total characters531
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

Unique42 ?
Unique (%)79.2%

Sample

1st row0
2nd row0
3rd row62846157000
4th row29523359000
5th row115522000000
ValueCountFrequency (%)
0 11
 
20.8%
26692404000 1
 
1.9%
70270712000 1
 
1.9%
8670991000 1
 
1.9%
31342412000 1
 
1.9%
9418157000 1
 
1.9%
41281432000 1
 
1.9%
189332000000 1
 
1.9%
10979600000 1
 
1.9%
13793040000 1
 
1.9%
Other values (33) 33
62.3%
2023-12-13T09:09:04.025190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 193
36.3%
50
 
9.4%
2 40
 
7.5%
1 39
 
7.3%
4 36
 
6.8%
9 33
 
6.2%
6 30
 
5.6%
8 28
 
5.3%
3 27
 
5.1%
5 27
 
5.1%
Other values (3) 28
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 475
89.5%
Space Separator 50
 
9.4%
Open Punctuation 3
 
0.6%
Close Punctuation 3
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 193
40.6%
2 40
 
8.4%
1 39
 
8.2%
4 36
 
7.6%
9 33
 
6.9%
6 30
 
6.3%
8 28
 
5.9%
3 27
 
5.7%
5 27
 
5.7%
7 22
 
4.6%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 531
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 193
36.3%
50
 
9.4%
2 40
 
7.5%
1 39
 
7.3%
4 36
 
6.8%
9 33
 
6.2%
6 30
 
5.6%
8 28
 
5.3%
3 27
 
5.1%
5 27
 
5.1%
Other values (3) 28
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 531
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 193
36.3%
50
 
9.4%
2 40
 
7.5%
1 39
 
7.3%
4 36
 
6.8%
9 33
 
6.2%
6 30
 
5.6%
8 28
 
5.3%
3 27
 
5.1%
5 27
 
5.1%
Other values (3) 28
 
5.3%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3947315 × 109
Minimum0
Maximum1.8753782 × 1010
Zeros14
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T09:09:04.132246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36866000
Q35.50513 × 108
95-th percentile1.0302486 × 1010
Maximum1.8753782 × 1010
Range1.8753782 × 1010
Interquartile range (IQR)5.50513 × 108

Descriptive statistics

Standard deviation3.9314193 × 109
Coefficient of variation (CV)2.8187643
Kurtosis11.809303
Mean1.3947315 × 109
Median Absolute Deviation (MAD)36866000
Skewness3.5127066
Sum7.3920768 × 1010
Variance1.5456058 × 1019
MonotonicityNot monotonic
2023-12-13T09:09:04.231624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 14
26.4%
11154000 1
 
1.9%
550513000 1
 
1.9%
18753782000 1
 
1.9%
43106000 1
 
1.9%
33587000 1
 
1.9%
257448000 1
 
1.9%
2843056000 1
 
1.9%
20375000 1
 
1.9%
26870000 1
 
1.9%
Other values (30) 30
56.6%
ValueCountFrequency (%)
0 14
26.4%
70000 1
 
1.9%
666000 1
 
1.9%
880000 1
 
1.9%
9261000 1
 
1.9%
11154000 1
 
1.9%
17129000 1
 
1.9%
20375000 1
 
1.9%
26870000 1
 
1.9%
28646000 1
 
1.9%
ValueCountFrequency (%)
18753782000 1
1.9%
15592251000 1
1.9%
14882023000 1
1.9%
7249462000 1
1.9%
4400731000 1
1.9%
2843056000 1
1.9%
1968344000 1
1.9%
1745559000 1
1.9%
889671000 1
1.9%
814450000 1
1.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6934504 × 108
Minimum0
Maximum5.08682 × 109
Zeros40
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T09:09:04.330900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5115732 × 109
Maximum5.08682 × 109
Range5.08682 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.935768 × 108
Coefficient of variation (CV)3.6888625
Kurtosis14.406712
Mean2.6934504 × 108
Median Absolute Deviation (MAD)0
Skewness3.8326827
Sum1.4275287 × 1010
Variance9.8719486 × 1017
MonotonicityNot monotonic
2023-12-13T09:09:04.413967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 40
75.5%
2769215000 1
 
1.9%
42129000 1
 
1.9%
226000 1
 
1.9%
126000 1
 
1.9%
2339812000 1
 
1.9%
43000 1
 
1.9%
56000 1
 
1.9%
10379000 1
 
1.9%
4019723000 1
 
1.9%
Other values (4) 4
 
7.5%
ValueCountFrequency (%)
0 40
75.5%
4000 1
 
1.9%
15000 1
 
1.9%
43000 1
 
1.9%
56000 1
 
1.9%
126000 1
 
1.9%
226000 1
 
1.9%
6739000 1
 
1.9%
10379000 1
 
1.9%
42129000 1
 
1.9%
ValueCountFrequency (%)
5086820000 1
1.9%
4019723000 1
1.9%
2769215000 1
1.9%
2339812000 1
1.9%
42129000 1
1.9%
10379000 1
1.9%
6739000 1
1.9%
226000 1
1.9%
126000 1
1.9%
56000 1
1.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9049763 × 109
Minimum0
Maximum2.6101824 × 1010
Zeros17
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T09:09:04.504324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.74159 × 108
Q32.007721 × 109
95-th percentile2.4153296 × 1010
Maximum2.6101824 × 1010
Range2.6101824 × 1010
Interquartile range (IQR)2.007721 × 109

Descriptive statistics

Standard deviation6.5869943 × 109
Coefficient of variation (CV)2.2674864
Kurtosis8.2158644
Mean2.9049763 × 109
Median Absolute Deviation (MAD)3.74159 × 108
Skewness3.0537782
Sum1.5396375 × 1011
Variance4.3388494 × 1019
MonotonicityNot monotonic
2023-12-13T09:09:04.603010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 17
32.1%
1660961000 1
 
1.9%
1867529000 1
 
1.9%
4711212000 1
 
1.9%
26101824000 1
 
1.9%
18442000 1
 
1.9%
1679390000 1
 
1.9%
2109395000 1
 
1.9%
374159000 1
 
1.9%
216990000 1
 
1.9%
Other values (27) 27
50.9%
ValueCountFrequency (%)
0 17
32.1%
18442000 1
 
1.9%
25336000 1
 
1.9%
43078000 1
 
1.9%
48400000 1
 
1.9%
216990000 1
 
1.9%
305311000 1
 
1.9%
352787000 1
 
1.9%
361514000 1
 
1.9%
367899000 1
 
1.9%
ValueCountFrequency (%)
26101824000 1
1.9%
25791817000 1
1.9%
25324651000 1
1.9%
23372392000 1
1.9%
5717279000 1
1.9%
5088394000 1
1.9%
4711212000 1
1.9%
4705979000 1
1.9%
3164428000 1
1.9%
3101472000 1
1.9%

징수율
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.726604
Minimum-40.42
Maximum100
Zeros11
Zeros (%)20.8%
Negative3
Negative (%)5.7%
Memory size609.0 B
2023-12-13T09:09:04.691895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-40.42
5-th percentile-8.256
Q10
median97.25
Q399.07
95-th percentile100
Maximum100
Range140.42
Interquartile range (IQR)99.07

Descriptive statistics

Standard deviation47.319613
Coefficient of variation (CV)0.68851958
Kurtosis-0.72243618
Mean68.726604
Median Absolute Deviation (MAD)2.52
Skewness-1.0695114
Sum3642.51
Variance2239.1457
MonotonicityNot monotonic
2023-12-13T09:09:04.787637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 11
20.8%
100.0 6
 
11.3%
99.55 2
 
3.8%
98.87 2
 
3.8%
99.24 1
 
1.9%
-40.42 1
 
1.9%
99.8 1
 
1.9%
96.09 1
 
1.9%
98.89 1
 
1.9%
97.36 1
 
1.9%
Other values (26) 26
49.1%
ValueCountFrequency (%)
-40.42 1
 
1.9%
-26.41 1
 
1.9%
-20.64 1
 
1.9%
0.0 11
20.8%
12.56 1
 
1.9%
90.11 1
 
1.9%
92.93 1
 
1.9%
93.69 1
 
1.9%
93.72 1
 
1.9%
95.26 1
 
1.9%
ValueCountFrequency (%)
100.0 6
11.3%
99.8 1
 
1.9%
99.77 1
 
1.9%
99.55 2
 
3.8%
99.52 1
 
1.9%
99.47 1
 
1.9%
99.24 1
 
1.9%
99.07 1
 
1.9%
98.89 1
 
1.9%
98.87 2
 
3.8%

Interactions

2023-12-13T09:09:02.059073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:00.741972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.058668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.401516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.732583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:02.119371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:00.809806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.120643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.467962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.793608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:02.187018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:00.877100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.189403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.535408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.867698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:02.249802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:00.935170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.257362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.594381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.934963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:02.321661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:00.995123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.322204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.653930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:09:01.993344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:09:04.862051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.3520.0000.0130.0000.000
세목명0.0001.0000.8700.5660.5600.1510.7980.776
부과금액0.0000.8701.0001.0000.6830.0000.4290.000
수납금액0.3520.5661.0001.0001.0001.0001.0001.000
환급금액0.0000.5600.6831.0001.0001.0000.8150.902
결손금액0.0130.1510.0001.0001.0001.0000.9480.990
미수납 금액0.0000.7980.4291.0000.8150.9481.0000.885
징수율0.0000.7760.0001.0000.9020.9900.8851.000
2023-12-13T09:09:04.945555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T09:09:05.016870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.7490.2840.7280.3500.0000.584
환급금액0.7491.0000.5870.8870.0540.0000.210
결손금액0.2840.5871.0000.593-0.2060.0000.000
미수납 금액0.7280.8870.5931.000-0.1630.0000.516
징수율0.3500.054-0.206-0.1631.0000.0000.489
과세년도0.0000.0000.0000.0000.0001.0000.000
세목명0.5840.2100.0000.5160.4890.0001.000

Missing values

2023-12-13T09:09:02.412997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:09:02.525994image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
0경상북도구미시471902018도축세000000.0
1경상북도구미시471902018레저세000000.0
2경상북도구미시471902018재산세6493542200062846157000357430000208926500096.78
3경상북도구미시471902018주민세300079690002952335900017129000048461000098.39
4경상북도구미시471902018취득세1166020000001155220000007057400000108061100099.07
5경상북도구미시471902018자동차세57793987000520767080004533160000571727900090.11
6경상북도구미시471902018과년도수입20680023000(5461584000)15592251000276921500023372392000-26.41
7경상북도구미시471902018담배소비세30792600000307926000007000000100.0
8경상북도구미시471902018도시계획세000000.0
9경상북도구미시471902018등록면허세10873055000108246550003686600004840000099.55
시도명시군구명자치단체코드과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
43경상북도구미시471902021취득세135476000000134829000000814450000064721100099.52
44경상북도구미시471902021자동차세746154920006990949800060509800015000470597900093.69
45경상북도구미시471902021과년도수입25208502000(5202969000)14882023000508682000025324651000-20.64
46경상북도구미시471902021담배소비세305722940003057229400066600000100.0
47경상북도구미시471902021도시계획세000000.0
48경상북도구미시471902021등록면허세10839984000108146480004852300002533600099.77
49경상북도구미시471902021지방교육세44493945000428561750002348080004000163776600096.32
50경상북도구미시471902021지방소득세21625100000021327000000044007310000298074800098.62
51경상북도구미시471902021지방소비세1122013400011220134000000100.0
52경상북도구미시471902021지역자원시설세141711980001380329900038920000036789900097.4