Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory101.1 B

Variable types

Categorical4
Text1
Numeric6

Dataset

Description부산광역시사상구_지방세징수현황_20211231
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079606

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 부과금액 and 3 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
부과금액 has 6 (23.1%) zerosZeros
수납급액 has 6 (23.1%) zerosZeros
환급금액 has 8 (30.8%) zerosZeros
결손금액 has 8 (30.8%) zerosZeros
미수납 금액 has 8 (30.8%) zerosZeros
징수율 has 6 (23.1%) zerosZeros

Reproduction

Analysis started2023-12-10 16:18:02.526376
Analysis finished2023-12-10 16:18:07.663110
Duration5.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
부산광역시
26 

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 26
100.0%

Length

2023-12-11T01:18:07.755412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:18:07.887848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 26
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
사상구
26 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 26
100.0%

Length

2023-12-11T01:18:08.019715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:18:08.156775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 26
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
26530
26 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 26
100.0%

Length

2023-12-11T01:18:08.291417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:18:08.420329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 26
100.0%

과세년도
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

Length

2023-12-11T01:18:08.573303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:18:08.752412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
50.0%
2021 13
50.0%

세목명
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T01:18:08.954252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Characters and Unicode

Total characters116
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row레저세
2nd row재산세
3rd row주민세
4th row취득세
5th row자동차세
ValueCountFrequency (%)
레저세 2
 
7.7%
재산세 2
 
7.7%
주민세 2
 
7.7%
취득세 2
 
7.7%
자동차세 2
 
7.7%
과년도수입 2
 
7.7%
담배소비세 2
 
7.7%
도시계획세 2
 
7.7%
등록면허세 2
 
7.7%
지방교육세 2
 
7.7%
Other values (3) 6
23.1%
2023-12-11T01:18:09.430762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 116
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 116
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 116
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 24
20.7%
지 8
 
6.9%
방 6
 
5.2%
소 6
 
5.2%
득 4
 
3.4%
자 4
 
3.4%
비 4
 
3.4%
도 4
 
3.4%
시 4
 
3.4%
역 2
 
1.7%
Other values (25) 50
43.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5728303 × 1010
Minimum0
Maximum5.6231227 × 1010
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:09.575329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.344036 × 109
median6.3451885 × 109
Q31.6797712 × 1010
95-th percentile5.0850777 × 1010
Maximum5.6231227 × 1010
Range5.6231227 × 1010
Interquartile range (IQR)1.3453676 × 1010

Descriptive statistics

Standard deviation1.9071472 × 1010
Coefficient of variation (CV)1.2125575
Kurtosis-0.26921025
Mean1.5728303 × 1010
Median Absolute Deviation (MAD)6.3451885 × 109
Skewness1.1633845
Sum4.0893588 × 1011
Variance3.6372104 × 1020
MonotonicityNot monotonic
2023-12-11T01:18:09.731478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
23.1%
49343418000 1
 
3.8%
5008133000 1
 
3.8%
4443121000 1
 
3.8%
44797244000 1
 
3.8%
17014458000 1
 
3.8%
6092820000 1
 
3.8%
4475037000 1
 
3.8%
15539346000 1
 
3.8%
56231227000 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 6
23.1%
3033048000 1
 
3.8%
4277000000 1
 
3.8%
4443121000 1
 
3.8%
4475037000 1
 
3.8%
4952494000 1
 
3.8%
5008133000 1
 
3.8%
6092820000 1
 
3.8%
6597557000 1
 
3.8%
7497706000 1
 
3.8%
ValueCountFrequency (%)
56231227000 1
3.8%
51353230000 1
3.8%
49343418000 1
3.8%
45196825000 1
3.8%
44797244000 1
3.8%
43482109000 1
3.8%
17014458000 1
3.8%
16147473000 1
3.8%
15619206000 1
3.8%
15539346000 1
3.8%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5178354 × 1010
Minimum0
Maximum5.6219946 × 1010
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:09.896917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.1310475 × 108
median6.326927 × 109
Q31.6271345 × 1010
95-th percentile5.0265658 × 1010
Maximum5.6219946 × 1010
Range5.6219946 × 1010
Interquartile range (IQR)1.545824 × 1010

Descriptive statistics

Standard deviation1.895023 × 1010
Coefficient of variation (CV)1.2485036
Kurtosis-0.19808177
Mean1.5178354 × 1010
Median Absolute Deviation (MAD)6.326927 × 109
Skewness1.187345
Sum3.9463721 × 1011
Variance3.5911123 × 1020
MonotonicityNot monotonic
2023-12-11T01:18:10.062384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 6
23.1%
48608171000 1
 
3.8%
4957099000 1
 
3.8%
4443121000 1
 
3.8%
43467304000 1
 
3.8%
16501922000 1
 
3.8%
6075259000 1
 
3.8%
1951085000 1
 
3.8%
14146100000 1
 
3.8%
56219946000 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 6
23.1%
433778000 1
 
3.8%
1951085000 1
 
3.8%
4277000000 1
 
3.8%
4443121000 1
 
3.8%
4865066000 1
 
3.8%
4957099000 1
 
3.8%
6075259000 1
 
3.8%
6578595000 1
 
3.8%
7128574000 1
 
3.8%
ValueCountFrequency (%)
56219946000 1
3.8%
50818154000 1
3.8%
48608171000 1
3.8%
45167861000 1
3.8%
43467304000 1
3.8%
41694050000 1
3.8%
16501922000 1
3.8%
15579614000 1
3.8%
14146100000 1
3.8%
14138370000 1
3.8%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8696608 × 108
Minimum0
Maximum2.571963 × 109
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:10.219234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25207000
Q31.6076275 × 108
95-th percentile1.4270245 × 109
Maximum2.571963 × 109
Range2.571963 × 109
Interquartile range (IQR)1.6076275 × 108

Descriptive statistics

Standard deviation6.1354127 × 108
Coefficient of variation (CV)2.1380272
Kurtosis7.5713488
Mean2.8696608 × 108
Median Absolute Deviation (MAD)25207000
Skewness2.719282
Sum7.461118 × 109
Variance3.7643289 × 1017
MonotonicityNot monotonic
2023-12-11T01:18:10.383926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
21940000 1
 
3.8%
777000 1
 
3.8%
1043202000 1
 
3.8%
62289000 1
 
3.8%
32446000 1
 
3.8%
1529007000 1
 
3.8%
162545000 1
 
3.8%
157552000 1
 
3.8%
53167000 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
777000 1
 
3.8%
4318000 1
 
3.8%
6780000 1
 
3.8%
13291000 1
 
3.8%
21940000 1
 
3.8%
28474000 1
 
3.8%
32446000 1
 
3.8%
53167000 1
 
3.8%
62289000 1
 
3.8%
ValueCountFrequency (%)
2571963000 1
3.8%
1529007000 1
3.8%
1121077000 1
3.8%
1043202000 1
3.8%
398475000 1
3.8%
162545000 1
3.8%
161833000 1
3.8%
157552000 1
3.8%
91982000 1
3.8%
62289000 1
3.8%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4976827 × 108
Minimum0
Maximum1.167492 × 109
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:10.527312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4859500
Q361675000
95-th percentile8.0159275 × 108
Maximum1.167492 × 109
Range1.167492 × 109
Interquartile range (IQR)61675000

Descriptive statistics

Standard deviation3.1478146 × 108
Coefficient of variation (CV)2.1017901
Kurtosis4.227191
Mean1.4976827 × 108
Median Absolute Deviation (MAD)4859500
Skewness2.2639452
Sum3.893975 × 109
Variance9.9087368 × 1016
MonotonicityNot monotonic
2023-12-11T01:18:10.685179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
194884000 1
 
3.8%
4795000 1
 
3.8%
607526000 1
 
3.8%
11919000 1
 
3.8%
714000 1
 
3.8%
808737000 1
 
3.8%
18481000 1
 
3.8%
439000 1
 
3.8%
4924000 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
254000 1
 
3.8%
439000 1
 
3.8%
714000 1
 
3.8%
3905000 1
 
3.8%
4795000 1
 
3.8%
4924000 1
 
3.8%
6996000 1
 
3.8%
11919000 1
 
3.8%
18481000 1
 
3.8%
ValueCountFrequency (%)
1167492000 1
3.8%
808737000 1
3.8%
780160000 1
3.8%
607526000 1
3.8%
194884000 1
3.8%
139490000 1
3.8%
66844000 1
3.8%
46168000 1
3.8%
30247000 1
3.8%
18481000 1
3.8%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0018058 × 108
Minimum0
Maximum1.81911 × 109
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:10.822723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median51710000
Q35.30526 × 108
95-th percentile1.6301025 × 109
Maximum1.81911 × 109
Range1.81911 × 109
Interquartile range (IQR)5.30526 × 108

Descriptive statistics

Standard deviation5.6268836 × 108
Coefficient of variation (CV)1.4060861
Kurtosis1.2038527
Mean4.0018058 × 108
Median Absolute Deviation (MAD)51710000
Skewness1.4870359
Sum1.0404695 × 1010
Variance3.1661819 × 1017
MonotonicityNot monotonic
2023-12-11T01:18:11.000889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8
30.8%
540363000 1
 
3.8%
46239000 1
 
3.8%
722414000 1
 
3.8%
500617000 1
 
3.8%
16847000 1
 
3.8%
1715215000 1
 
3.8%
1374765000 1
 
3.8%
10842000 1
 
3.8%
243363000 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 8
30.8%
10842000 1
 
3.8%
16847000 1
 
3.8%
18708000 1
 
3.8%
21968000 1
 
3.8%
46239000 1
 
3.8%
57181000 1
 
3.8%
243363000 1
 
3.8%
365227000 1
 
3.8%
488908000 1
 
3.8%
ValueCountFrequency (%)
1819110000 1
3.8%
1715215000 1
3.8%
1374765000 1
3.8%
1341346000 1
3.8%
722414000 1
3.8%
620567000 1
3.8%
540363000 1
3.8%
501015000 1
3.8%
500617000 1
3.8%
488908000 1
3.8%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.683462
Minimum0
Maximum100
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:11.136382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121.625
median96.655
Q398.975
95-th percentile99.995
Maximum100
Range100
Interquartile range (IQR)77.35

Descriptive statistics

Standard deviation43.307478
Coefficient of variation (CV)0.62148862
Kurtosis-1.0450093
Mean69.683462
Median Absolute Deviation (MAD)3.305
Skewness-0.97192222
Sum1811.77
Variance1875.5377
MonotonicityNot monotonic
2023-12-11T01:18:11.301769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0 6
23.1%
99.71 2
 
7.7%
100.0 2
 
7.7%
98.96 1
 
3.8%
98.98 1
 
3.8%
97.03 1
 
3.8%
96.99 1
 
3.8%
43.6 1
 
3.8%
91.03 1
 
3.8%
99.98 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0.0 6
23.1%
14.3 1
 
3.8%
43.6 1
 
3.8%
90.52 1
 
3.8%
91.03 1
 
3.8%
95.08 1
 
3.8%
95.89 1
 
3.8%
96.48 1
 
3.8%
96.83 1
 
3.8%
96.99 1
 
3.8%
ValueCountFrequency (%)
100.0 2
7.7%
99.98 1
3.8%
99.94 1
3.8%
99.71 2
7.7%
98.98 1
3.8%
98.96 1
3.8%
98.51 1
3.8%
98.23 1
3.8%
97.03 1
3.8%
96.99 1
3.8%

Interactions

2023-12-11T01:18:06.569188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:02.835333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.482731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.107044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.685639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:05.716883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.678933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:02.939032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.564764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.183550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.784217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:05.841438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.793789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.058793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.655281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.274738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.883379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.012287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.911673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.186761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.778424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.364694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:05.014496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.158664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:07.056056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.290175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.902967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.475307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:05.140993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.303538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:07.184996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:03.393729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.024362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:04.598135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:05.266506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:06.447365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:18:11.408147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0780.0000.000
세목명0.0001.0000.8940.9280.7570.7480.9620.801
부과금액0.0000.8941.0001.0000.6150.5680.7370.000
수납급액0.0000.9281.0001.0000.6360.5800.8890.000
환급금액0.0000.7570.6150.6361.0000.9340.7500.823
결손금액0.0780.7480.5680.5800.9341.0000.7980.561
미수납 금액0.0000.9620.7370.8890.7500.7981.0000.692
징수율0.0000.8010.0000.0000.8230.5610.6921.000
2023-12-11T01:18:11.525945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도
부과금액1.0000.9970.6360.6310.5730.5790.000
수납급액0.9971.0000.6080.6000.5450.6040.000
환급금액0.6360.6081.0000.8580.8590.1750.000
결손금액0.6310.6000.8581.0000.9490.1110.040
미수납 금액0.5730.5450.8590.9491.0000.0320.000
징수율0.5790.6040.1750.1110.0321.0000.000
과세년도0.0000.0000.0000.0400.0000.0001.000

Missing values

2023-12-11T01:18:07.348259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:18:07.569648image/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
0부산광역시사상구265302020레저세000000.0
1부산광역시사상구265302020재산세49343418000486081710002194000019488400054036300098.51
2부산광역시사상구265302020주민세749770600071285740004318000390500036522700095.08
3부산광역시사상구265302020취득세451968250004516786100039847500069960002196800099.94
4부산광역시사상구265302020자동차세1561920600014138370000161833000139490000134134600090.52
5부산광역시사상구265302020과년도수입30330480004337780002571963000780160000181911000014.3
6부산광역시사상구265302020담배소비세000000.0
7부산광역시사상구265302020도시계획세000000.0
8부산광역시사상구265302020등록면허세65975570006578595000284740002540001870800099.71
9부산광역시사상구265302020지방교육세1614747300015579614000919820006684400050101500096.48
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
16부산광역시사상구265302021취득세56231227000562199460001575520004390001084200099.98
17부산광역시사상구265302021자동차세155393460001414610000016254500018481000137476500091.03
18부산광역시사상구265302021과년도수입447503700019510850001529007000808737000171521500043.6
19부산광역시사상구265302021담배소비세000000.0
20부산광역시사상구265302021도시계획세000000.0
21부산광역시사상구265302021등록면허세60928200006075259000324460007140001684700099.71
22부산광역시사상구265302021지방교육세1701445800016501922000622890001191900050061700096.99
23부산광역시사상구265302021지방소득세4479724400043467304000104320200060752600072241400097.03
24부산광역시사상구265302021지방소비세44431210004443121000000100.0
25부산광역시사상구265302021지역자원시설세5008133000495709900077700047950004623900098.98