Overview

Dataset statistics

Number of variables11
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory98.4 B

Variable types

Categorical5
Numeric6

Dataset

Description부산광역시북구_지방세_징수현황_20220502
Author부산광역시 북구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079260

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 4 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 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 has 17 (31.5%) zerosZeros
수납급액 has 17 (31.5%) zerosZeros
환급금액 has 18 (33.3%) zerosZeros
결손금액 has 40 (74.1%) zerosZeros
미수납 금액 has 18 (33.3%) zerosZeros
징수율 has 17 (31.5%) zerosZeros

Reproduction

Analysis started2023-12-10 16:47:33.591954
Analysis finished2023-12-10 16:47:37.963953
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
부산광역시
54 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 54
100.0%

Length

2023-12-11T01:47:38.055101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:47:38.169135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 54
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
북구
54 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
북구 54
100.0%

Length

2023-12-11T01:47:38.271564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:47:38.382267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북구 54
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
26320
54 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26320
2nd row26320
3rd row26320
4th row26320
5th row26320

Common Values

ValueCountFrequency (%)
26320 54
100.0%

Length

2023-12-11T01:47:38.484516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:47:38.578732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26320 54
100.0%

과세년도
Categorical

Distinct4
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size564.0 B
2017
14 
2018
14 
2019
13 
2020
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
25.9%
2018 14
25.9%
2019 13
24.1%
2020 13
24.1%

Length

2023-12-11T01:47:38.696752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:47:38.816355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
25.9%
2018 14
25.9%
2019 13
24.1%
2020 13
24.1%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size564.0 B
레저세
4 
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
Other values (9)
34 

Length

Max length7
Median length5
Mean length4.4074074
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 4
 
7.4%
재산세 4
 
7.4%
주민세 4
 
7.4%
취득세 4
 
7.4%
자동차세 4
 
7.4%
과년도수입 4
 
7.4%
담배소비세 4
 
7.4%
도시계획세 4
 
7.4%
등록면허세 4
 
7.4%
지방교육세 4
 
7.4%
Other values (4) 14
25.9%

Length

2023-12-11T01:47:38.999375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 4
 
7.4%
재산세 4
 
7.4%
주민세 4
 
7.4%
취득세 4
 
7.4%
자동차세 4
 
7.4%
과년도수입 4
 
7.4%
담배소비세 4
 
7.4%
도시계획세 4
 
7.4%
등록면허세 4
 
7.4%
지방교육세 4
 
7.4%
Other values (4) 14
25.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0463862 × 1010
Minimum0
Maximum3.450685 × 1010
Zeros17
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:39.146188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.0328625 × 109
Q32.04008 × 1010
95-th percentile3.2623501 × 1010
Maximum3.450685 × 1010
Range3.450685 × 1010
Interquartile range (IQR)2.04008 × 1010

Descriptive statistics

Standard deviation1.2020009 × 1010
Coefficient of variation (CV)1.1487164
Kurtosis-0.90506596
Mean1.0463862 × 1010
Median Absolute Deviation (MAD)4.0328625 × 109
Skewness0.8486757
Sum5.6504854 × 1011
Variance1.4448062 × 1020
MonotonicityNot monotonic
2023-12-11T01:47:39.312159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 17
31.5%
3678653000 1
 
1.9%
20303234000 1
 
1.9%
4053104000 1
 
1.9%
4522002000 1
 
1.9%
13222713000 1
 
1.9%
26373783000 1
 
1.9%
3887080000 1
 
1.9%
34506850000 1
 
1.9%
33522120000 1
 
1.9%
Other values (28) 28
51.9%
ValueCountFrequency (%)
0 17
31.5%
2990886000 1
 
1.9%
3307950000 1
 
1.9%
3583176000 1
 
1.9%
3662294000 1
 
1.9%
3678653000 1
 
1.9%
3849350000 1
 
1.9%
3887080000 1
 
1.9%
3923441000 1
 
1.9%
4012000000 1
 
1.9%
ValueCountFrequency (%)
34506850000 1
1.9%
33522120000 1
1.9%
33035860000 1
1.9%
32401462000 1
1.9%
31770424000 1
1.9%
30093973000 1
1.9%
28714178000 1
1.9%
28540367000 1
1.9%
28176178000 1
1.9%
26605463000 1
1.9%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9531828 × 109
Minimum0
Maximum3.3951769 × 1010
Zeros17
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:39.492890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.848549 × 109
Q31.8403345 × 1010
95-th percentile3.2406865 × 1010
Maximum3.3951769 × 1010
Range3.3951769 × 1010
Interquartile range (IQR)1.8403345 × 1010

Descriptive statistics

Standard deviation1.175176 × 1010
Coefficient of variation (CV)1.1807037
Kurtosis-0.7607726
Mean9.9531828 × 109
Median Absolute Deviation (MAD)3.848549 × 109
Skewness0.90869973
Sum5.3747187 × 1011
Variance1.3810386 × 1020
MonotonicityNot monotonic
2023-12-11T01:47:39.664663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 17
31.5%
3529536000 1
 
1.9%
18328792000 1
 
1.9%
2136780000 1
 
1.9%
4508839000 1
 
1.9%
12539833000 1
 
1.9%
25126802000 1
 
1.9%
3833382000 1
 
1.9%
33951769000 1
 
1.9%
33495857000 1
 
1.9%
Other values (28) 28
51.9%
ValueCountFrequency (%)
0 17
31.5%
1062712000 1
 
1.9%
2136780000 1
 
1.9%
3119990000 1
 
1.9%
3142107000 1
 
1.9%
3274042000 1
 
1.9%
3413979000 1
 
1.9%
3504537000 1
 
1.9%
3529536000 1
 
1.9%
3797954000 1
 
1.9%
ValueCountFrequency (%)
33951769000 1
1.9%
33495857000 1
1.9%
32450219000 1
1.9%
32383520000 1
1.9%
31214287000 1
1.9%
29602855000 1
1.9%
28517804000 1
1.9%
27247990000 1
1.9%
26589893000 1
1.9%
26573433000 1
1.9%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4422631 × 108
Minimum0
Maximum3.679779 × 109
Zeros18
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:39.831812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8447500
Q31.231155 × 108
95-th percentile1.3693476 × 109
Maximum3.679779 × 109
Range3.679779 × 109
Interquartile range (IQR)1.231155 × 108

Descriptive statistics

Standard deviation6.5600885 × 108
Coefficient of variation (CV)2.6860695
Kurtosis16.248168
Mean2.4422631 × 108
Median Absolute Deviation (MAD)8447500
Skewness3.87611
Sum1.3188221 × 1010
Variance4.3034761 × 1017
MonotonicityNot monotonic
2023-12-11T01:47:40.233476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 18
33.3%
11556000 1
 
1.9%
135926000 1
 
1.9%
195530000 1
 
1.9%
1918662000 1
 
1.9%
36963000 1
 
1.9%
74388000 1
 
1.9%
3679779000 1
 
1.9%
434000 1
 
1.9%
16142000 1
 
1.9%
Other values (27) 27
50.0%
ValueCountFrequency (%)
0 18
33.3%
316000 1
 
1.9%
434000 1
 
1.9%
974000 1
 
1.9%
982000 1
 
1.9%
1485000 1
 
1.9%
1919000 1
 
1.9%
5327000 1
 
1.9%
5500000 1
 
1.9%
6527000 1
 
1.9%
ValueCountFrequency (%)
3679779000 1
1.9%
2419427000 1
1.9%
1918662000 1
1.9%
1073563000 1
1.9%
695682000 1
1.9%
695476000 1
1.9%
627704000 1
1.9%
457335000 1
1.9%
227086000 1
1.9%
195530000 1
1.9%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92700833
Minimum0
Maximum1.310358 × 109
Zeros40
Zeros (%)74.1%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:40.354098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319500
95-th percentile7.895498 × 108
Maximum1.310358 × 109
Range1.310358 × 109
Interquartile range (IQR)19500

Descriptive statistics

Standard deviation2.8635331 × 108
Coefficient of variation (CV)3.0890047
Kurtosis9.823891
Mean92700833
Median Absolute Deviation (MAD)0
Skewness3.232923
Sum5.005845 × 109
Variance8.1998218 × 1016
MonotonicityNot monotonic
2023-12-11T01:47:40.469509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 40
74.1%
29000 1
 
1.9%
30000 1
 
1.9%
51000 1
 
1.9%
1310358000 1
 
1.9%
26000 1
 
1.9%
466406000 1
 
1.9%
947375000 1
 
1.9%
704567000 1
 
1.9%
16656000 1
 
1.9%
Other values (5) 5
 
9.3%
ValueCountFrequency (%)
0 40
74.1%
26000 1
 
1.9%
29000 1
 
1.9%
30000 1
 
1.9%
51000 1
 
1.9%
379000 1
 
1.9%
2224000 1
 
1.9%
2493000 1
 
1.9%
16656000 1
 
1.9%
409011000 1
 
1.9%
ValueCountFrequency (%)
1310358000 1
1.9%
1146240000 1
1.9%
947375000 1
1.9%
704567000 1
1.9%
466406000 1
1.9%
409011000 1
1.9%
16656000 1
1.9%
2493000 1
1.9%
2224000 1
1.9%
379000 1
1.9%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1797819 × 108
Minimum0
Maximum2.128032 × 109
Zeros18
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:40.601665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52357500
Q36.802975 × 108
95-th percentile1.9049836 × 109
Maximum2.128032 × 109
Range2.128032 × 109
Interquartile range (IQR)6.802975 × 108

Descriptive statistics

Standard deviation6.1183087 × 108
Coefficient of variation (CV)1.4637866
Kurtosis1.4218443
Mean4.1797819 × 108
Median Absolute Deviation (MAD)52357500
Skewness1.5350464
Sum2.2570822 × 1010
Variance3.7433701 × 1017
MonotonicityNot monotonic
2023-12-11T01:47:40.731317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 18
33.3%
555081000 1
 
1.9%
22563000 1
 
1.9%
1974442000 1
 
1.9%
770084000 1
 
1.9%
13163000 1
 
1.9%
680387000 1
 
1.9%
837970000 1
 
1.9%
53319000 1
 
1.9%
149117000 1
 
1.9%
Other values (27) 27
50.0%
ValueCountFrequency (%)
0 18
33.3%
11797000 1
 
1.9%
12684000 1
 
1.9%
12707000 1
 
1.9%
13163000 1
 
1.9%
17942000 1
 
1.9%
22563000 1
 
1.9%
26263000 1
 
1.9%
32030000 1
 
1.9%
51396000 1
 
1.9%
ValueCountFrequency (%)
2128032000 1
1.9%
2013653000 1
1.9%
1974442000 1
1.9%
1867583000 1
1.9%
1710707000 1
1.9%
1419718000 1
1.9%
1129682000 1
1.9%
980799000 1
1.9%
928188000 1
1.9%
887147000 1
1.9%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.953704
Minimum0
Maximum100
Zeros17
Zeros (%)31.5%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-11T01:47:40.843584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.095
Q398.385
95-th percentile99.92
Maximum100
Range100
Interquartile range (IQR)98.385

Descriptive statistics

Standard deviation44.693265
Coefficient of variation (CV)0.70993861
Kurtosis-1.5259496
Mean62.953704
Median Absolute Deviation (MAD)5.66
Skewness-0.65337634
Sum3399.5
Variance1997.4879
MonotonicityNot monotonic
2023-12-11T01:47:40.962455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 17
31.5%
99.92 2
 
3.7%
98.39 1
 
1.9%
90.28 1
 
1.9%
52.72 1
 
1.9%
99.71 1
 
1.9%
94.84 1
 
1.9%
95.27 1
 
1.9%
98.62 1
 
1.9%
95.95 1
 
1.9%
Other values (27) 27
50.0%
ValueCountFrequency (%)
0.0 17
31.5%
35.53 1
 
1.9%
52.72 1
 
1.9%
59.84 1
 
1.9%
64.56 1
 
1.9%
89.59 1
 
1.9%
90.17 1
 
1.9%
90.28 1
 
1.9%
90.8 1
 
1.9%
92.6 1
 
1.9%
ValueCountFrequency (%)
100.0 1
1.9%
99.94 1
1.9%
99.92 2
3.7%
99.88 1
1.9%
99.76 1
1.9%
99.75 1
1.9%
99.71 1
1.9%
99.69 1
1.9%
98.66 1
1.9%
98.62 1
1.9%

Interactions

2023-12-11T01:47:36.943265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:33.921592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.371567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.786958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.329931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.342466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:37.118394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.013431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.443778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.865175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.674408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.455636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:37.258128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.091118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.513525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.948813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.880741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.556631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:37.390604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.163717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.585512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.041533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.044901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.654711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:37.485430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.233214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.653125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.137831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.148788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.750752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:37.581424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.302685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:34.714769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:35.206136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.243286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:47:36.850145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:47:41.063092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8970.9820.5090.4870.8600.757
부과금액0.0000.8971.0000.9690.3310.4800.9080.589
수납급액0.0000.9820.9691.0000.4190.3870.8040.548
환급금액0.0000.5090.3310.4191.0000.9750.6990.905
결손금액0.0000.4870.4800.3870.9751.0000.8140.918
미수납 금액0.0000.8600.9080.8040.6990.8141.0000.676
징수율0.0000.7570.5890.5480.9050.9180.6761.000
2023-12-11T01:47:41.174940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-11T01:47:41.271759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9810.7720.3240.7270.7100.0000.636
수납급액0.9811.0000.7060.2290.6710.7720.0000.733
환급금액0.7720.7061.0000.5500.8880.4410.0000.246
결손금액0.3240.2290.5501.0000.5860.0100.0000.231
미수납 금액0.7270.6710.8880.5861.0000.3380.0000.566
징수율0.7100.7720.4410.0100.3381.0000.0000.454
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6360.7330.2460.2310.5660.4540.0001.000

Missing values

2023-12-11T01:47:37.727534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:47:37.896817image/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
0부산광역시북구263202017도축세000000.0
1부산광역시북구263202017레저세000000.0
2부산광역시북구263202017재산세3009397300029602855000103680002900049108900098.37
3부산광역시북구263202017주민세3307950000314210700019190003000016581300094.99
4부산광역시북구263202017취득세32401462000323835200003780100001794200099.94
5부산광역시북구263202017자동차세204749040001846120000017963600051000201365300090.17
6부산광역시북구263202017과년도수입54715470003274042000627704000131035800088714700059.84
7부산광역시북구263202017담배소비세000000.0
8부산광역시북구263202017도시계획세000000.0
9부산광역시북구263202017등록면허세474844600047366490001792700001179700099.75
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
44부산광역시북구263202020취득세335221200003349585700017216300002626300099.92
45부산광역시북구263202020자동차세20295779000184281960002270860000186758300090.8
46부산광역시북구263202020과년도수입483292100031199900004573350002224000171070700064.56
47부산광역시북구263202020담배소비세000000.0
48부산광역시북구263202020도시계획세000000.0
49부산광역시북구263202020등록면허세537309400053603870002486400001270700099.76
50부산광역시북구263202020지방교육세141056000001346090400084684000064469600095.43
51부산광역시북구263202020지방소득세28176178000272479900001073563000092818800096.71
52부산광역시북구263202020지방소비세40120000004012000000000100.0
53부산광역시북구263202020지역자원시설세4012621000395533000031600005729100098.57