Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description부산광역시동래구_지방세징수현황_20221231
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15087155

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 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 2 other fieldsHigh correlation
세목명 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
부과금액 has 22 (27.5%) zerosZeros
수납급액 has 22 (27.5%) zerosZeros
환급금액 has 26 (32.5%) zerosZeros
결손금액 has 55 (68.8%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 22 (27.5%) zerosZeros

Reproduction

Analysis started2023-12-10 17:22:28.518798
Analysis finished2023-12-10 17:22:41.066787
Duration12.55 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
부산광역시
80 

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 80
100.0%

Length

2023-12-11T02:22:41.207840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:22:41.415895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 80
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래구 80
100.0%

Length

2023-12-11T02:22:41.641019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:22:41.951675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동래구 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
26260
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26260
2nd row26260
3rd row26260
4th row26260
5th row26260

Common Values

ValueCountFrequency (%)
26260 80
100.0%

Length

2023-12-11T02:22:42.164302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:22:42.371333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26260 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
2023-12-11T02:22:42.576027image/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
2023-12-11T02:22:42.814450image/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
지역자원시설세
6 
지방소비세
6 
지방교육세
6 
레저세
6 
재산세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지역자원시설세
2nd row지방소비세
3rd row도축세
4th row지방교육세
5th row레저세

Common Values

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

Length

2023-12-11T02:22:43.044600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지역자원시설세 6
 
7.5%
지방소비세 6
 
7.5%
지방교육세 6
 
7.5%
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8961669 × 1010
Minimum0
Maximum1.1608308 × 1011
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:43.300074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.7717635 × 109
Q32.2027417 × 1010
95-th percentile6.4729647 × 1010
Maximum1.1608308 × 1011
Range1.1608308 × 1011
Interquartile range (IQR)2.2027417 × 1010

Descriptive statistics

Standard deviation2.62485 × 1010
Coefficient of variation (CV)1.3842927
Kurtosis2.8539128
Mean1.8961669 × 1010
Median Absolute Deviation (MAD)6.7717635 × 109
Skewness1.7970135
Sum1.5169335 × 1012
Variance6.8898376 × 1020
MonotonicityNot monotonic
2023-12-11T02:22:44.145674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
4554473000 1
 
1.2%
7400273000 1
 
1.2%
9568423000 1
 
1.2%
22268547000 1
 
1.2%
48412610000 1
 
1.2%
3843000000 1
 
1.2%
5325747000 1
 
1.2%
54106717000 1
 
1.2%
97974242000 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
144806000 1
 
1.2%
3843000000 1
 
1.2%
3957351000 1
 
1.2%
4431710000 1
 
1.2%
4554473000 1
 
1.2%
4717851000 1
 
1.2%
4751779000 1
 
1.2%
4968850000 1
 
1.2%
5023859000 1
 
1.2%
ValueCountFrequency (%)
116083082000 1
1.2%
97974242000 1
1.2%
97431022000 1
1.2%
82808130000 1
1.2%
63778148000 1
1.2%
63580434000 1
1.2%
61828398000 1
1.2%
56261828000 1
1.2%
55840158000 1
1.2%
54106717000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8168817 × 1010
Minimum0
Maximum1.1537023 × 1011
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:44.453744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.2377515 × 109
Q32.1454905 × 1010
95-th percentile6.442733 × 1010
Maximum1.1537023 × 1011
Range1.1537023 × 1011
Interquartile range (IQR)2.1454905 × 1010

Descriptive statistics

Standard deviation2.610834 × 1010
Coefficient of variation (CV)1.4369862
Kurtosis3.0692103
Mean1.8168817 × 1010
Median Absolute Deviation (MAD)5.2377515 × 109
Skewness1.8510555
Sum1.4535053 × 1012
Variance6.8164541 × 1020
MonotonicityNot monotonic
2023-12-11T02:22:44.794504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
4419439000 1
 
1.2%
2907942000 1
 
1.2%
9537694000 1
 
1.2%
21669141000 1
 
1.2%
46052047000 1
 
1.2%
3843000000 1
 
1.2%
5224652000 1
 
1.2%
53445122000 1
 
1.2%
97924281000 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
144806000 1
 
1.2%
2031979000 1
 
1.2%
2589644000 1
 
1.2%
2907942000 1
 
1.2%
2970253000 1
 
1.2%
3463637000 1
 
1.2%
3514543000 1
 
1.2%
3843000000 1
 
1.2%
3957351000 1
 
1.2%
ValueCountFrequency (%)
115370231000 1
1.2%
97924281000 1
1.2%
97354899000 1
1.2%
82714137000 1
1.2%
63464866000 1
1.2%
62751249000 1
1.2%
61714826000 1
1.2%
54545954000 1
1.2%
53840028000 1
1.2%
53445122000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2706979 × 108
Minimum0
Maximum3.229301 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:45.130651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24240500
Q32.0378825 × 108
95-th percentile2.5802009 × 109
Maximum3.229301 × 109
Range3.229301 × 109
Interquartile range (IQR)2.0378825 × 108

Descriptive statistics

Standard deviation7.4412378 × 108
Coefficient of variation (CV)2.2751223
Kurtosis7.0361481
Mean3.2706979 × 108
Median Absolute Deviation (MAD)24240500
Skewness2.8265661
Sum2.6165583 × 1010
Variance5.537202 × 1017
MonotonicityNot monotonic
2023-12-11T02:22:45.442584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
5270000 1
 
1.2%
255580000 1
 
1.2%
245717000 1
 
1.2%
841656000 1
 
1.2%
52071000 1
 
1.2%
105814000 1
 
1.2%
3031708000 1
 
1.2%
4975000 1
 
1.2%
41628000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
1914000 1
 
1.2%
1937000 1
 
1.2%
2150000 1
 
1.2%
3173000 1
 
1.2%
4835000 1
 
1.2%
4975000 1
 
1.2%
5270000 1
 
1.2%
5340000 1
 
1.2%
6504000 1
 
1.2%
ValueCountFrequency (%)
3229301000 1
1.2%
3031708000 1
1.2%
2625484000 1
1.2%
2621676000 1
1.2%
2578018000 1
1.2%
2407471000 1
1.2%
1612851000 1
1.2%
850858000 1
1.2%
841656000 1
1.2%
770333000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.196936 × 108
Minimum0
Maximum2.417885 × 109
Zeros55
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:45.749943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324000
95-th percentile8.895915 × 108
Maximum2.417885 × 109
Range2.417885 × 109
Interquartile range (IQR)24000

Descriptive statistics

Standard deviation3.615171 × 108
Coefficient of variation (CV)3.0203545
Kurtosis21.68399
Mean1.196936 × 108
Median Absolute Deviation (MAD)0
Skewness4.2300767
Sum9.575488 × 109
Variance1.3069461 × 1017
MonotonicityNot monotonic
2023-12-11T02:22:46.036087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 55
68.8%
573095000 1
 
1.2%
289658000 1
 
1.2%
9000 1
 
1.2%
645516000 1
 
1.2%
64000 1
 
1.2%
31000 1
 
1.2%
11000 1
 
1.2%
437274000 1
 
1.2%
8000 1
 
1.2%
Other values (16) 16
 
20.0%
ValueCountFrequency (%)
0 55
68.8%
8000 1
 
1.2%
9000 1
 
1.2%
10000 1
 
1.2%
11000 1
 
1.2%
23000 1
 
1.2%
27000 1
 
1.2%
31000 1
 
1.2%
41000 1
 
1.2%
64000 1
 
1.2%
ValueCountFrequency (%)
2417885000 1
1.2%
1279980000 1
1.2%
928115000 1
1.2%
893781000 1
1.2%
889371000 1
1.2%
645516000 1
1.2%
573095000 1
1.2%
499636000 1
1.2%
437274000 1
1.2%
415486000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7315858 × 108
Minimum0
Maximum4.252398 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:46.315020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1457 × 108
Q37.3053 × 108
95-th percentile3.4174095 × 109
Maximum4.252398 × 109
Range4.252398 × 109
Interquartile range (IQR)7.3053 × 108

Descriptive statistics

Standard deviation1.0544537 × 109
Coefficient of variation (CV)1.5664269
Kurtosis3.4352923
Mean6.7315858 × 108
Median Absolute Deviation (MAD)1.1457 × 108
Skewness1.9752842
Sum5.3852686 × 1010
Variance1.1118726 × 1018
MonotonicityNot monotonic
2023-12-11T02:22:46.626897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
135034000 1
 
1.2%
76123000 1
 
1.2%
1649889000 1
 
1.2%
3919236000 1
 
1.2%
30729000 1
 
1.2%
599406000 1
 
1.2%
1466782000 1
 
1.2%
101095000 1
 
1.2%
661595000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
26029000 1
 
1.2%
26658000 1
 
1.2%
27423000 1
 
1.2%
28493000 1
 
1.2%
30729000 1
 
1.2%
30807000 1
 
1.2%
49961000 1
 
1.2%
76123000 1
 
1.2%
85425000 1
 
1.2%
ValueCountFrequency (%)
4252398000 1
1.2%
4168676000 1
1.2%
3919236000 1
1.2%
3574625000 1
1.2%
3409135000 1
1.2%
2705182000 1
1.2%
2122500000 1
1.2%
2038227000 1
1.2%
1833730000 1
1.2%
1833249000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.00975
Minimum0
Maximum100
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-11T02:22:46.976163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95.6
Q398.44
95-th percentile99.9525
Maximum100
Range100
Interquartile range (IQR)98.44

Descriptive statistics

Standard deviation43.651488
Coefficient of variation (CV)0.66128849
Kurtosis-1.3430906
Mean66.00975
Median Absolute Deviation (MAD)4.22
Skewness-0.76010854
Sum5280.78
Variance1905.4524
MonotonicityNot monotonic
2023-12-11T02:22:47.278207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 22
27.5%
100.0 4
 
5.0%
97.04 2
 
2.5%
99.62 2
 
2.5%
99.82 2
 
2.5%
98.44 2
 
2.5%
96.07 1
 
1.2%
39.3 1
 
1.2%
99.68 1
 
1.2%
97.31 1
 
1.2%
Other values (42) 42
52.5%
ValueCountFrequency (%)
0.0 22
27.5%
30.71 1
 
1.2%
34.59 1
 
1.2%
39.3 1
 
1.2%
39.75 1
 
1.2%
46.5 1
 
1.2%
47.34 1
 
1.2%
88.88 1
 
1.2%
90.06 1
 
1.2%
90.43 1
 
1.2%
ValueCountFrequency (%)
100.0 4
5.0%
99.95 1
 
1.2%
99.92 1
 
1.2%
99.89 1
 
1.2%
99.82 2
2.5%
99.68 1
 
1.2%
99.62 2
2.5%
99.61 1
 
1.2%
99.58 1
 
1.2%
99.49 1
 
1.2%

Interactions

2023-12-11T02:22:39.185633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:29.300347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:31.764382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:33.400163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:35.009099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:36.593706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.951027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:39.361775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:29.557853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:31.993469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:33.621623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:35.251458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:36.792386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:38.123831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:39.533289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:30.658685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:32.252568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:33.847530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:35.486711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.010336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:38.314281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:39.711415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:30.907389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:32.501083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:34.094588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:35.700094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.218537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:38.491100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:39.905499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:31.136156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:32.742654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:34.328007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:35.931569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.442900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:38.665546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:40.099863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:31.348248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:32.964021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:34.554993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:36.156721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.629987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:38.839513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:40.277109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:31.565551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:33.189592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:34.771113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:36.368007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:37.793875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:22:39.003967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:22:47.482661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8040.8040.7890.6750.8350.838
부과금액0.0000.8041.0000.9990.5450.1780.6060.000
수납급액0.0000.8040.9991.0000.5340.3010.6150.000
환급금액0.0000.7890.5450.5341.0000.7960.7370.612
결손금액0.0000.6750.1780.3010.7961.0000.8310.632
미수납 금액0.0000.8350.6060.6150.7370.8311.0000.812
징수율0.0000.8380.0000.0000.6120.6320.8121.000
2023-12-11T02:22:47.740306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1610.1560.054-0.0380.0290.2350.000
부과금액0.1611.0000.9670.8560.3830.7290.6430.487
수납급액0.1560.9671.0000.7510.2470.6160.7290.487
환급금액0.0540.8560.7511.0000.6640.8930.3540.396
결손금액-0.0380.3830.2470.6641.0000.728-0.0920.388
미수납 금액0.0290.7290.6160.8930.7281.0000.2060.544
징수율0.2350.6430.7290.354-0.0920.2061.0000.594
세목명0.0000.4870.4870.3960.3880.5440.5941.000

Missing values

2023-12-11T02:22:40.545792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:22:40.931261image/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
0부산광역시동래구262602017지역자원시설세455447300044194390005270000013503400097.04
1부산광역시동래구262602017지방소비세000000.0
2부산광역시동래구262602017도축세000000.0
3부산광역시동래구262602017지방교육세169194470001619030100010121800017100072897500095.69
4부산광역시동래구262602017레저세000000.0
5부산광역시동래구262602017재산세378920510003719588500052887000069616600098.16
6부산광역시동래구262602017주민세4431710000418406100053400004100024760800094.41
7부산광역시동래구262602017취득세6358043400063464866000423115000011556800099.82
8부산광역시동래구262602017자동차세1832955400016290728000176883000599000203822700088.88
9부산광역시동래구262602017담배소비세000000.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70부산광역시동래구262602022취득세116083082000115370231000770333000071285100099.39
71부산광역시동래구262602022자동차세199856180001833554400025543000064000165001000091.74
72부산광역시동래구262602022과년도수입74875580002589644000850858000645516000425239800034.59
73부산광역시동래구262602022담배소비세000000.0
74부산광역시동래구262602022도시계획세000000.0
75부산광역시동래구262602022등록면허세738382700073553340002143400002849300099.61
76부산광역시동래구262602022지방교육세2445476100023732115000145279000900072263700097.04
77부산광역시동래구262602022지방소득세56261828000545459540002407471000289658000142621600096.95
78부산광역시동래구262602022지방소비세93434630009343463000000100.0
79부산광역시동래구262602022지역자원시설세573742900056373200007375000010010900098.26