Overview

Dataset statistics

Number of variables8
Number of observations273
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.3 KiB
Average record size in memory68.5 B

Variable types

Categorical5
Numeric3

Dataset

Description부산광역시 동래구의 세원 유형별 과세현황(2017년~2022년)에 관한 데이터로 자치단체코드, 과세년도, 세목명, 세원 유형명, 부과건수, 부과금액등에 대한 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15086943/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
세목명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
부과건수 has 77 (28.2%) zerosZeros
부과금액 has 77 (28.2%) zerosZeros

Reproduction

Analysis started2023-12-12 22:34:45.414119
Analysis finished2023-12-12 22:34:46.620987
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
부산광역시
273 

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 273
100.0%

Length

2023-12-13T07:34:46.670863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:34:46.738226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 273
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
동래구
273 

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 273
100.0%

Length

2023-12-13T07:34:46.812028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:34:46.882678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동래구 273
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
26260
273 

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 273
100.0%

Length

2023-12-13T07:34:46.956853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:34:47.025589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26260 273
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5495
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T07:34:47.090917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6820663
Coefficient of variation (CV)0.00083289187
Kurtosis-1.2354191
Mean2019.5495
Median Absolute Deviation (MAD)1
Skewness-0.018542207
Sum551337
Variance2.8293471
MonotonicityIncreasing
2023-12-13T07:34:47.175478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 47
17.2%
2019 47
17.2%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
2017 40
14.7%
ValueCountFrequency (%)
2017 40
14.7%
2018 47
17.2%
2019 47
17.2%
2020 47
17.2%
2021 46
16.8%
2022 46
16.8%
ValueCountFrequency (%)
2022 46
16.8%
2021 46
16.8%
2020 47
17.2%
2019 47
17.2%
2018 47
17.2%
2017 40
14.7%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
지방소득세
24 
Other values (8)
73 

Length

Max length7
Median length3
Mean length3.7106227
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방소득세
2nd row지방소득세
3rd row지방소득세
4th row지방소득세
5th row교육세

Common Values

ValueCountFrequency (%)
취득세 54
19.8%
주민세 50
18.3%
자동차세 42
15.4%
재산세 30
11.0%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
체납 6
 
2.2%
Other values (3) 15
 
5.5%

Length

2023-12-13T07:34:47.268716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.8%
주민세 50
18.3%
자동차세 42
15.4%
재산세 30
11.0%
지방소득세 24
8.8%
레저세 20
 
7.3%
지역자원시설세 14
 
5.1%
등록면허세 12
 
4.4%
교육세 6
 
2.2%
체납 6
 
2.2%
Other values (3) 15
 
5.5%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
지방소득세(특별징수)
 
6
재산세(건축물)
 
6
지방소득세(양도소득)
 
6
지방소득세(종합소득)
 
6
교육세
 
6
Other values (45)
243 

Length

Max length11
Median length8
Mean length6.1025641
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방소득세(특별징수)
2nd row지방소득세(법인소득)
3rd row지방소득세(양도소득)
4th row지방소득세(종합소득)
5th row교육세

Common Values

ValueCountFrequency (%)
지방소득세(특별징수) 6
 
2.2%
재산세(건축물) 6
 
2.2%
지방소득세(양도소득) 6
 
2.2%
지방소득세(종합소득) 6
 
2.2%
교육세 6
 
2.2%
건축물 6
 
2.2%
자동차세(주행) 6
 
2.2%
재산세(선박) 6
 
2.2%
기타 6
 
2.2%
주택(단독) 6
 
2.2%
Other values (40) 213
78.0%

Length

2023-12-13T07:34:47.364379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세(특별징수 6
 
2.2%
승용 6
 
2.2%
특수 6
 
2.2%
3륜이하 6
 
2.2%
승합 6
 
2.2%
기타승용 6
 
2.2%
재산세(건축물 6
 
2.2%
주민세(종업원분 6
 
2.2%
주민세(특별징수 6
 
2.2%
화물 6
 
2.2%
Other values (40) 213
78.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31883.828
Minimum0
Maximum557292
Zeros77
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T07:34:47.456814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1327
Q318133
95-th percentile158475.4
Maximum557292
Range557292
Interquartile range (IQR)18133

Descriptive statistics

Standard deviation86945.022
Coefficient of variation (CV)2.7269317
Kurtosis22.395992
Mean31883.828
Median Absolute Deviation (MAD)1327
Skewness4.4823731
Sum8704285
Variance7.5594369 × 109
MonotonicityNot monotonic
2023-12-13T07:34:47.556422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
28.2%
20 2
 
0.7%
26 2
 
0.7%
3 2
 
0.7%
30814 1
 
0.4%
45145 1
 
0.4%
27216 1
 
0.4%
52697 1
 
0.4%
184166 1
 
0.4%
659 1
 
0.4%
Other values (184) 184
67.4%
ValueCountFrequency (%)
0 77
28.2%
2 1
 
0.4%
3 2
 
0.7%
5 1
 
0.4%
6 1
 
0.4%
7 1
 
0.4%
8 1
 
0.4%
9 1
 
0.4%
11 1
 
0.4%
13 1
 
0.4%
ValueCountFrequency (%)
557292 1
0.4%
548032 1
0.4%
536630 1
0.4%
527855 1
0.4%
515305 1
0.4%
512956 1
0.4%
198268 1
0.4%
187000 1
0.4%
184166 1
0.4%
180564 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct197
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5931763 × 109
Minimum0
Maximum6.2100559 × 1010
Zeros77
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-13T07:34:47.659227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.0583 × 108
Q38.902146 × 109
95-th percentile2.2320582 × 1010
Maximum6.2100559 × 1010
Range6.2100559 × 1010
Interquartile range (IQR)8.902146 × 109

Descriptive statistics

Standard deviation9.4611203 × 109
Coefficient of variation (CV)1.6915469
Kurtosis9.2941989
Mean5.5931763 × 109
Median Absolute Deviation (MAD)3.0583 × 108
Skewness2.5943837
Sum1.5269371 × 1012
Variance8.9512797 × 1019
MonotonicityNot monotonic
2023-12-13T07:34:47.766936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
28.2%
12502591000 1
 
0.4%
13460424000 1
 
0.4%
8509500000 1
 
0.4%
5265206000 1
 
0.4%
60541000 1
 
0.4%
8653335000 1
 
0.4%
16040165000 1
 
0.4%
9863477000 1
 
0.4%
17000203000 1
 
0.4%
Other values (187) 187
68.5%
ValueCountFrequency (%)
0 77
28.2%
704000 1
 
0.4%
3287000 1
 
0.4%
3591000 1
 
0.4%
5468000 1
 
0.4%
5963000 1
 
0.4%
6502000 1
 
0.4%
6890000 1
 
0.4%
8160000 1
 
0.4%
8235000 1
 
0.4%
ValueCountFrequency (%)
62100559000 1
0.4%
57008115000 1
0.4%
52018326000 1
0.4%
36178504000 1
0.4%
36085267000 1
0.4%
29322850000 1
0.4%
28253639000 1
0.4%
27966255000 1
0.4%
27956500000 1
0.4%
25303516000 1
0.4%

Interactions

2023-12-13T07:34:46.244337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.608746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.817055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:46.310974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.674296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.893575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:46.382618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.736864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:34:45.954021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:34:47.842055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.000
세목명0.0001.0001.0000.8150.615
세원 유형명0.0001.0001.0000.9850.835
부과건수0.0000.8150.9851.0000.464
부과금액0.0000.6150.8350.4641.000
2023-12-13T07:34:47.919163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.926
세원 유형명0.9261.000
2023-12-13T07:34:47.985847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.000-0.0320.0040.0000.000
부과건수-0.0321.0000.8650.6110.784
부과금액0.0040.8651.0000.3310.460
세목명0.0000.6110.3311.0000.926
세원 유형명0.0000.7840.4600.9261.000

Missing values

2023-12-13T07:34:46.488541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:34:46.583915image/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
0부산광역시동래구262602017지방소득세지방소득세(특별징수)3081412502591000
1부산광역시동래구262602017지방소득세지방소득세(법인소득)18817315107000
2부산광역시동래구262602017지방소득세지방소득세(양도소득)565912034859000
3부산광역시동래구262602017지방소득세지방소득세(종합소득)2723210984749000
4부산광역시동래구262602017교육세교육세51295616919447000
5부산광역시동래구262602017취득세건축물11779851196000
6부산광역시동래구262602017취득세주택(개별)199213008507000
7부산광역시동래구262602017취득세주택(단독)606924338886000
8부산광역시동래구262602017취득세기타368525000
9부산광역시동래구262602017취득세항공기00
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
263부산광역시동래구262602022주민세주민세(특별징수)00
264부산광역시동래구262602022주민세주민세(법인세분)00
265부산광역시동래구262602022주민세주민세(양도소득)00
266부산광역시동래구262602022주민세주민세(종합소득)00
267부산광역시동래구262602022등록면허세등록면허세(면허)296211092318000
268부산광역시동래구262602022등록면허세등록면허세(등록)411346291509000
269부산광역시동래구262602022지역자원시설세지역자원시설세(소방)1982685691051000
270부산광역시동래구262602022지역자원시설세지역자원시설세(시설)00
271부산광역시동래구262602022지역자원시설세지역자원시설세(특자)57746378000
272부산광역시동래구262602022체납체납15794710120291000