Overview

Dataset statistics

Number of variables8
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory70.3 B

Variable types

Categorical6
Numeric2

Dataset

Description부산광역시_세원유형별과세현황_20191231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15079367

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
부과건수 has 21 (36.8%) zerosZeros
부과금액 has 21 (36.8%) zerosZeros

Reproduction

Analysis started2024-03-13 13:20:42.388226
Analysis finished2024-03-13 13:20:43.268045
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
부산광역시
57 

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 57
100.0%

Length

2024-03-13T22:20:43.330472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:20:43.423084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 57
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
부산광역시
57 

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 57
100.0%

Length

2024-03-13T22:20:43.529634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:20:43.637272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 57
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
26000
57 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26000
2nd row26000
3rd row26000
4th row26000
5th row26000

Common Values

ValueCountFrequency (%)
26000 57
100.0%

Length

2024-03-13T22:20:43.769276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:20:43.880908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26000 57
100.0%

과세년도
Categorical

Distinct3
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
2017
19 
2018
19 
2019
19 

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 19
33.3%
2018 19
33.3%
2019 19
33.3%

Length

2024-03-13T22:20:44.010356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:20:44.143116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 19
33.3%
2018 19
33.3%
2019 19
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
취득세
21 
자동차세
21 
등록면허세
3 
교육세
3 
체납
3 
Other values (2)
6 

Length

Max length5
Median length4
Mean length3.6315789
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 21
36.8%
자동차세 21
36.8%
등록면허세 3
 
5.3%
교육세 3
 
5.3%
체납 3
 
5.3%
담배소비세 3
 
5.3%
지방소비세 3
 
5.3%

Length

2024-03-13T22:20:44.272005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:20:44.400450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
취득세 21
36.8%
자동차세 21
36.8%
등록면허세 3
 
5.3%
교육세 3
 
5.3%
체납 3
 
5.3%
담배소비세 3
 
5.3%
지방소비세 3
 
5.3%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
토지
 
3
건축물
 
3
선박
 
3
차량
 
3
기계장비
 
3
Other values (14)
42 

Length

Max length9
Median length8
Mean length3.4736842
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row건축물
3rd row선박
4th row차량
5th row기계장비

Common Values

ValueCountFrequency (%)
토지 3
 
5.3%
건축물 3
 
5.3%
선박 3
 
5.3%
차량 3
 
5.3%
기계장비 3
 
5.3%
항공기 3
 
5.3%
기타 3
 
5.3%
등록면허세(등록) 3
 
5.3%
교육세 3
 
5.3%
승용 3
 
5.3%
Other values (9) 27
47.4%

Length

2024-03-13T22:20:44.566908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
토지 3
 
5.3%
기타승용 3
 
5.3%
담배소비세 3
 
5.3%
체납 3
 
5.3%
자동차세(주행 3
 
5.3%
3륜이하 3
 
5.3%
특수 3
 
5.3%
화물 3
 
5.3%
승합 3
 
5.3%
승용 3
 
5.3%
Other values (9) 27
47.4%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30530.456
Minimum0
Maximum231896
Zeros21
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-03-13T22:20:44.713770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11
Q31709
95-th percentile209852.4
Maximum231896
Range231896
Interquartile range (IQR)1709

Descriptive statistics

Standard deviation69352.285
Coefficient of variation (CV)2.2715771
Kurtosis2.5568724
Mean30530.456
Median Absolute Deviation (MAD)11
Skewness2.0518274
Sum1740236
Variance4.8097394 × 109
MonotonicityNot monotonic
2024-03-13T22:20:44.850450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 21
36.8%
8 3
 
5.3%
12 3
 
5.3%
1 2
 
3.5%
1709 1
 
1.8%
22551 1
 
1.8%
2 1
 
1.8%
1192 1
 
1.8%
805 1
 
1.8%
635 1
 
1.8%
Other values (22) 22
38.6%
ValueCountFrequency (%)
0 21
36.8%
1 2
 
3.5%
2 1
 
1.8%
6 1
 
1.8%
8 3
 
5.3%
11 1
 
1.8%
12 3
 
5.3%
138 1
 
1.8%
293 1
 
1.8%
565 1
 
1.8%
ValueCountFrequency (%)
231896 1
1.8%
221633 1
1.8%
212218 1
1.8%
209261 1
1.8%
184517 1
1.8%
167302 1
1.8%
157212 1
1.8%
150735 1
1.8%
142635 1
1.8%
22551 1
1.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.8982921 × 1010
Minimum0
Maximum8.29812 × 1011
Zeros21
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-03-13T22:20:45.045256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24934000
Q39.6151816 × 1010
95-th percentile3.91435 × 1011
Maximum8.29812 × 1011
Range8.29812 × 1011
Interquartile range (IQR)9.6151816 × 1010

Descriptive statistics

Standard deviation1.7361295 × 1011
Coefficient of variation (CV)1.9510817
Kurtosis6.174738
Mean8.8982921 × 1010
Median Absolute Deviation (MAD)24934000
Skewness2.4234819
Sum5.0720265 × 1012
Variance3.0141455 × 1022
MonotonicityNot monotonic
2024-03-13T22:20:45.203909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 21
36.8%
39867305000 1
 
1.8%
296900000000 1
 
1.8%
36797124000 1
 
1.8%
197863000000 1
 
1.8%
567042000000 1
 
1.8%
340380000000 1
 
1.8%
5126716000 1
 
1.8%
96151816000 1
 
1.8%
9263000 1
 
1.8%
Other values (27) 27
47.4%
ValueCountFrequency (%)
0 21
36.8%
52000 1
 
1.8%
53000 1
 
1.8%
94000 1
 
1.8%
958000 1
 
1.8%
4961000 1
 
1.8%
9263000 1
 
1.8%
24527000 1
 
1.8%
24934000 1
 
1.8%
25949000 1
 
1.8%
ValueCountFrequency (%)
829812000000 1
1.8%
567042000000 1
1.8%
561679000000 1
1.8%
348874000000 1
1.8%
342573000000 1
1.8%
340380000000 1
1.8%
328310000000 1
1.8%
324142000000 1
1.8%
296900000000 1
1.8%
211147000000 1
1.8%

Interactions

2024-03-13T22:20:42.855038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:20:42.625547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:20:42.949176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:20:42.740505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:20:45.302823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.4910.958
세원 유형명0.0001.0001.0000.8230.966
부과건수0.0000.4910.8231.0000.738
부과금액0.0000.9580.9660.7381.000
2024-03-13T22:20:45.427250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세원 유형명과세년도세목명
세원 유형명1.0000.0000.872
과세년도0.0001.0000.000
세목명0.8720.0001.000
2024-03-13T22:20:45.526627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액과세년도세목명세원 유형명
부과건수1.0000.8110.0000.3470.517
부과금액0.8111.0000.0000.6860.757
과세년도0.0000.0001.0000.0000.000
세목명0.3470.6860.0001.0000.872
세원 유형명0.5170.7570.0000.8721.000

Missing values

2024-03-13T22:20:43.067406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:20:43.222028image/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

시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
0부산광역시부산광역시260002017취득세토지00
1부산광역시부산광역시260002017취득세건축물00
2부산광역시부산광역시260002017취득세선박00
3부산광역시부산광역시260002017취득세차량231896348874000000
4부산광역시부산광역시260002017취득세기계장비43587758906000
5부산광역시부산광역시260002017취득세항공기00
6부산광역시부산광역시260002017취득세기타00
7부산광역시부산광역시260002017등록면허세등록면허세(등록)00
8부산광역시부산광역시260002017교육세교육세142635101292000000
9부산광역시부산광역시260002017자동차세승용15073532951781000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
47부산광역시부산광역시260002019자동차세승용20926139867305000
48부산광역시부산광역시260002019자동차세기타승용2939263000
49부산광역시부산광역시260002019자동차세승합80524934000
50부산광역시부산광역시260002019자동차세화물119227315000
51부산광역시부산광역시260002019자동차세특수294000
52부산광역시부산광역시260002019자동차세3륜이하00
53부산광역시부산광역시260002019자동차세자동차세(주행)12328310000000
54부산광역시부산광역시260002019체납체납2255138743039000
55부산광역시부산광역시260002019담배소비세담배소비세1709191794000000
56부산광역시부산광역시260002019지방소비세지방소비세6829812000000