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 memory96.7 B

Variable types

Categorical4
Numeric6
Text1

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공하여 지자체의 재정 자주도를 산출하는 기초 및 납세 협력도, 조세 순응도를 확인하는 자료로 활용합니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15078897/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 3 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 미수납 금액 and 1 other fieldsHigh correlation
부과금액 has 22 (27.5%) zerosZeros
환급금액 has 25 (31.2%) zerosZeros
결손금액 has 37 (46.2%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 22 (27.5%) zerosZeros

Reproduction

Analysis started2024-04-21 01:48:52.327418
Analysis finished2024-04-21 01:48:57.377623
Duration5.05 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

2024-04-21T10:48:57.445625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:57.555084image/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
해운대구
80 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 80
100.0%

Length

2024-04-21T10:48:57.670624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:57.756095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
26350
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 80
100.0%

Length

2024-04-21T10:48:57.837186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:48:57.923407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 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
2024-04-21T10:48:58.013198image/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
2024-04-21T10:48:58.119700image/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

2024-04-21T10:48:58.232780image/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
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.514775 × 1010
Minimum0
Maximum4.1303 × 1011
Zeros22
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:58.355932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5700554 × 1010
Q34.9653747 × 1010
95-th percentile1.5678365 × 1011
Maximum4.1303 × 1011
Range4.1303 × 1011
Interquartile range (IQR)4.9653747 × 1010

Descriptive statistics

Standard deviation6.9986972 × 1010
Coefficient of variation (CV)1.5501763
Kurtosis9.5569792
Mean4.514775 × 1010
Median Absolute Deviation (MAD)1.5700554 × 1010
Skewness2.6697856
Sum3.61182 × 1012
Variance4.8981763 × 1021
MonotonicityNot monotonic
2024-04-21T10:48:58.680866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
27.5%
85642850000 1
 
1.2%
15591632000 1
 
1.2%
23799908000 1
 
1.2%
66602836000 1
 
1.2%
141427000000 1
 
1.2%
2881000000 1
 
1.2%
17937813000 1
 
1.2%
130688000000 1
 
1.2%
10672476000 1
 
1.2%
Other values (49) 49
61.3%
ValueCountFrequency (%)
0 22
27.5%
215368000 1
 
1.2%
2382596000 1
 
1.2%
2881000000 1
 
1.2%
7965582000 1
 
1.2%
8975520000 1
 
1.2%
9068398000 1
 
1.2%
9573594000 1
 
1.2%
10341831000 1
 
1.2%
10463821000 1
 
1.2%
ValueCountFrequency (%)
413030000000 1
1.2%
267823000000 1
1.2%
187764000000 1
1.2%
167930000000 1
1.2%
156197000000 1
1.2%
149778000000 1
1.2%
144652000000 1
1.2%
141427000000 1
1.2%
130688000000 1
1.2%
128547000000 1
1.2%

수납급액
Text

Distinct59
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Memory size772.0 B
2024-04-21T10:48:58.906651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.275
Min length2

Characters and Unicode

Total characters742
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

Unique58 ?
Unique (%)72.5%

Sample

1st row0
2nd row0
3rd row84235022000
4th row8568835000
5th row144227000000
ValueCountFrequency (%)
0 22
27.5%
412370000000 1
 
1.2%
5723441000 1
 
1.2%
23738495000 1
 
1.2%
65158016000 1
 
1.2%
134201000000 1
 
1.2%
2881000000 1
 
1.2%
17644982000 1
 
1.2%
128786000000 1
 
1.2%
10198087000 1
 
1.2%
Other values (49) 49
61.3%
2024-04-21T10:48:59.249947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 281
37.9%
79
 
10.6%
1 57
 
7.7%
5 56
 
7.5%
2 49
 
6.6%
4 44
 
5.9%
8 40
 
5.4%
9 36
 
4.9%
6 35
 
4.7%
3 34
 
4.6%
Other values (3) 31
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 661
89.1%
Space Separator 79
 
10.6%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 281
42.5%
1 57
 
8.6%
5 56
 
8.5%
2 49
 
7.4%
4 44
 
6.7%
8 40
 
6.1%
9 36
 
5.4%
6 35
 
5.3%
3 34
 
5.1%
7 29
 
4.4%
Space Separator
ValueCountFrequency (%)
79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 742
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 281
37.9%
79
 
10.6%
1 57
 
7.7%
5 56
 
7.5%
2 49
 
6.6%
4 44
 
5.9%
8 40
 
5.4%
9 36
 
4.9%
6 35
 
4.7%
3 34
 
4.6%
Other values (3) 31
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 281
37.9%
79
 
10.6%
1 57
 
7.7%
5 56
 
7.5%
2 49
 
6.6%
4 44
 
5.9%
8 40
 
5.4%
9 36
 
4.9%
6 35
 
4.7%
3 34
 
4.6%
Other values (3) 31
 
4.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4984612 × 108
Minimum0
Maximum1.046897 × 1010
Zeros25
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:59.396284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60285500
Q33.6597275 × 108
95-th percentile4.3690208 × 109
Maximum1.046897 × 1010
Range1.046897 × 1010
Interquartile range (IQR)3.6597275 × 108

Descriptive statistics

Standard deviation1.7586875 × 109
Coefficient of variation (CV)2.3453979
Kurtosis12.739028
Mean7.4984612 × 108
Median Absolute Deviation (MAD)60285500
Skewness3.2998524
Sum5.998769 × 1010
Variance3.0929818 × 1018
MonotonicityNot monotonic
2024-04-21T10:48:59.546356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
31.2%
7445000 1
 
1.2%
426277000 1
 
1.2%
2395936000 1
 
1.2%
143572000 1
 
1.2%
195753000 1
 
1.2%
4984033000 1
 
1.2%
22648000 1
 
1.2%
100000 1
 
1.2%
102356000 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
0 25
31.2%
100000 1
 
1.2%
4489000 1
 
1.2%
5636000 1
 
1.2%
6644000 1
 
1.2%
7445000 1
 
1.2%
11709000 1
 
1.2%
12358000 1
 
1.2%
13186000 1
 
1.2%
22648000 1
 
1.2%
ValueCountFrequency (%)
10468970000 1
1.2%
6591422000 1
1.2%
4984033000 1
1.2%
4722968000 1
1.2%
4350392000 1
1.2%
3924962000 1
1.2%
3452924000 1
1.2%
3432668000 1
1.2%
3006665000 1
1.2%
2801621000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7044325 × 108
Minimum0
Maximum2.492581 × 109
Zeros37
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:59.701985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28000
Q363174750
95-th percentile1.8305787 × 109
Maximum2.492581 × 109
Range2.492581 × 109
Interquartile range (IQR)63174750

Descriptive statistics

Standard deviation6.1989117 × 108
Coefficient of variation (CV)2.2921303
Kurtosis3.9846185
Mean2.7044325 × 108
Median Absolute Deviation (MAD)28000
Skewness2.3013877
Sum2.163546 × 1010
Variance3.8426507 × 1017
MonotonicityNot monotonic
2024-04-21T10:48:59.848310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 37
46.2%
28000 2
 
2.5%
3446000 1
 
1.2%
252000 1
 
1.2%
193493000 1
 
1.2%
1606240000 1
 
1.2%
56863000 1
 
1.2%
2492581000 1
 
1.2%
25000 1
 
1.2%
66147000 1
 
1.2%
Other values (33) 33
41.2%
ValueCountFrequency (%)
0 37
46.2%
8000 1
 
1.2%
25000 1
 
1.2%
28000 2
 
2.5%
55000 1
 
1.2%
175000 1
 
1.2%
180000 1
 
1.2%
211000 1
 
1.2%
252000 1
 
1.2%
297000 1
 
1.2%
ValueCountFrequency (%)
2492581000 1
1.2%
2193113000 1
1.2%
2095208000 1
1.2%
1929469000 1
1.2%
1825374000 1
1.2%
1785938000 1
1.2%
1606240000 1
1.2%
1471342000 1
1.2%
1369684000 1
1.2%
1365704000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6132014 × 109
Minimum0
Maximum8.908746 × 109
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2024-04-21T10:48:59.989549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.937445 × 108
Q32.09061 × 109
95-th percentile8.1094043 × 109
Maximum8.908746 × 109
Range8.908746 × 109
Interquartile range (IQR)2.09061 × 109

Descriptive statistics

Standard deviation2.4090294 × 109
Coefficient of variation (CV)1.4933222
Kurtosis2.3972751
Mean1.6132014 × 109
Median Absolute Deviation (MAD)3.937445 × 108
Skewness1.7838002
Sum1.2905611 × 1011
Variance5.8034227 × 1018
MonotonicityNot monotonic
2024-04-21T10:49:00.132264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
1407828000 1
 
1.2%
660067000 1
 
1.2%
3532555000 1
 
1.2%
8261951000 1
 
1.2%
61413000 1
 
1.2%
1387957000 1
 
1.2%
4732972000 1
 
1.2%
292806000 1
 
1.2%
1835018000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
53377000 1
 
1.2%
57160000 1
 
1.2%
61413000 1
 
1.2%
73287000 1
 
1.2%
78874000 1
 
1.2%
132221000 1
 
1.2%
222434000 1
 
1.2%
234219000 1
 
1.2%
246816000 1
 
1.2%
ValueCountFrequency (%)
8908746000 1
1.2%
8675227000 1
1.2%
8261951000 1
1.2%
8182370000 1
1.2%
8105564000 1
1.2%
8061100000 1
1.2%
5961363000 1
1.2%
4732972000 1
1.2%
4397038000 1
1.2%
4258598000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.526875
Minimum-25.14
Maximum100
Zeros22
Zeros (%)27.5%
Negative1
Negative (%)1.2%
Memory size852.0 B
2024-04-21T10:49:00.255206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-25.14
5-th percentile0
Q10
median96
Q398
95-th percentile100
Maximum100
Range125.14
Interquartile range (IQR)98

Descriptive statistics

Standard deviation45.088325
Coefficient of variation (CV)0.69875265
Kurtosis-1.4119483
Mean64.526875
Median Absolute Deviation (MAD)4
Skewness-0.71766846
Sum5162.15
Variance2032.9571
MonotonicityNot monotonic
2024-04-21T10:49:00.361849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 22
27.5%
100.0 13
16.2%
98.0 9
11.2%
96.0 7
 
8.8%
97.0 4
 
5.0%
95.0 3
 
3.8%
99.0 3
 
3.8%
87.0 2
 
2.5%
95.69 1
 
1.2%
96.29 1
 
1.2%
Other values (15) 15
18.8%
ValueCountFrequency (%)
-25.14 1
 
1.2%
0.0 22
27.5%
13.0 1
 
1.2%
33.0 1
 
1.2%
34.0 1
 
1.2%
36.0 1
 
1.2%
37.0 1
 
1.2%
87.0 2
 
2.5%
88.0 1
 
1.2%
89.0 1
 
1.2%
ValueCountFrequency (%)
100.0 13
16.2%
99.45 1
 
1.2%
99.0 3
 
3.8%
98.0 9
11.2%
97.84 1
 
1.2%
97.0 4
 
5.0%
96.83 1
 
1.2%
96.57 1
 
1.2%
96.34 1
 
1.2%
96.29 1
 
1.2%

Interactions

2024-04-21T10:48:56.656233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.253021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.763680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.236898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.688575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.156361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.726579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.386003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.856613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.321749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.763887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.230552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.801198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.454985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.921584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.395428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.846109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.303507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.879606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.529907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.998401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.468340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.932508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.393584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.966690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.603706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.080545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.545156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.006857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.494028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:57.041371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:54.676083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.153444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:55.612380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.083002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:48:56.578909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:49:00.438118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.3560.0000.0000.0000.000
세목명0.0001.0000.8140.5640.6750.6670.8580.806
부과금액0.0000.8141.0001.0000.2980.4130.6850.000
수납급액0.3560.5641.0001.0001.0001.0001.0001.000
환급금액0.0000.6750.2981.0001.0000.8960.7170.961
결손금액0.0000.6670.4131.0000.8961.0000.7860.752
미수납 금액0.0000.8580.6851.0000.7170.7861.0000.717
징수율0.0000.8060.0001.0000.9610.7520.7171.000
2024-04-21T10:49:00.550536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1180.073-0.0680.1140.1340.000
부과금액0.1181.0000.7990.5570.7420.6670.422
환급금액0.0730.7991.0000.7760.8940.3520.388
결손금액-0.0680.5570.7761.0000.8040.1420.341
미수납 금액0.1140.7420.8940.8041.0000.2090.581
징수율0.1340.6670.3520.1420.2091.0000.533
세목명0.0000.4220.3880.3410.5810.5331.000

Missing values

2024-04-21T10:48:57.169704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:48:57.309382image/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
0부산광역시해운대구263502017도축세000000.0
1부산광역시해운대구263502017레저세000000.0
2부산광역시해운대구263502017재산세8564285000084235022000535050000140782800098.0
3부산광역시해운대구263502017주민세897552000085688350004489000833300039835200095.0
4부산광역시해운대구263502017취득세144652000000144227000000500476000292852000132221000100.0
5부산광역시해운대구263502017자동차세3325228000028883060000326956000210627000415859300087.0
6부산광역시해운대구263502017과년도수입15896390000574008200024497900002095208000806110000036.0
7부산광역시해운대구263502017담배소비세000000.0
8부산광역시해운대구263502017도시계획세000000.0
9부산광역시해운대구263502017등록면허세1322352700013170150000164127000053377000100.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70부산광역시해운대구263502022취득세1877640000001818020000005635560000596136300096.83
71부산광역시해운대구263502022자동차세341222470003080425200044538700050858000326713700090.28
72부산광역시해운대구263502022과년도수입7965582000(2002726000)1046897000017859380008182370000-25.14
73부산광역시해운대구263502022담배소비세000000.0
74부산광역시해운대구263502022도시계획세000000.0
75부산광역시해운대구263502022등록면허세1423442800014155526000108399000280007887400099.45
76부산광역시해운대구263502022지방교육세482663160004649801900019360900014101000175419600096.34
77부산광역시해운대구263502022지방소득세16793000000016216400000047229680001369684000439703800096.57
78부산광역시해운대구263502022지방소비세90683980009068398000000100.0
79부산광역시해운대구263502022지역자원시설세184400130001775554600012358000068446700096.29