Overview

Dataset statistics

Number of variables9
Number of observations141
Missing cells84
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.6 KiB
Average record size in memory76.9 B

Variable types

Numeric2
Categorical6
Text1

Dataset

Description부산광역시남구_세원유형별과세현황_20191231
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078563

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
141 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 141High correlation
세목명 is highly overall correlated with 세원유형명High correlation
세원유형명 is highly overall correlated with 세목명High correlation
부과건수 has 42 (29.8%) missing valuesMissing
부과금액 has 42 (29.8%) missing valuesMissing
141 has unique valuesUnique

Reproduction

Analysis started2023-12-10 17:03:48.679445
Analysis finished2023-12-10 17:03:51.628887
Duration2.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

141
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct141
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71
Minimum1
Maximum141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T02:03:51.799431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q136
median71
Q3106
95-th percentile134
Maximum141
Range140
Interquartile range (IQR)70

Descriptive statistics

Standard deviation40.847277
Coefficient of variation (CV)0.57531375
Kurtosis-1.2
Mean71
Median Absolute Deviation (MAD)35
Skewness0
Sum10011
Variance1668.5
MonotonicityStrictly increasing
2023-12-11T02:03:52.145585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
98 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
97 1
 
0.7%
99 1
 
0.7%
90 1
 
0.7%
Other values (131) 131
92.9%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
141 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
부산광역시
141 

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 141
100.0%

Length

2023-12-11T02:03:52.455463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:03:52.646630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 141
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
남구
141 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남구 141
100.0%

Length

2023-12-11T02:03:52.868624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:03:53.054195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 141
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
26290
141 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26290
2nd row26290
3rd row26290
4th row26290
5th row26290

Common Values

ValueCountFrequency (%)
26290 141
100.0%

Length

2023-12-11T02:03:53.259239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:03:53.446346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26290 141
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2017
47 
2018
47 
2019
47 

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 47
33.3%
2018 47
33.3%
2019 47
33.3%

Length

2023-12-11T02:03:53.648636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:03:53.877598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 47
33.3%
2018 47
33.3%
2019 47
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
취득세
27 
주민세
27 
자동차세
21 
재산세
15 
지방소득세
12 
Other values (8)
39 

Length

Max length7
Median length3
Mean length3.6808511
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row취득세
5th row취득세

Common Values

ValueCountFrequency (%)
취득세 27
19.1%
주민세 27
19.1%
자동차세 21
14.9%
재산세 15
10.6%
지방소득세 12
8.5%
레저세 12
8.5%
등록면허세 6
 
4.3%
지역자원시설세 6
 
4.3%
담배소비세 3
 
2.1%
교육세 3
 
2.1%
Other values (3) 9
 
6.4%

Length

2023-12-11T02:03:54.066713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 27
19.1%
주민세 27
19.1%
자동차세 21
14.9%
재산세 15
10.6%
지방소득세 12
8.5%
레저세 12
8.5%
등록면허세 6
 
4.3%
지역자원시설세 6
 
4.3%
담배소비세 3
 
2.1%
교육세 3
 
2.1%
Other values (3) 9
 
6.4%

세원유형명
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
담배소비세
 
3
재산세(주택)
 
3
도시계획세
 
3
건축물
 
3
주택(개별)
 
3
Other values (42)
126 

Length

Max length11
Median length8
Mean length6.0425532
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row담배소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)

Common Values

ValueCountFrequency (%)
담배소비세 3
 
2.1%
재산세(주택) 3
 
2.1%
도시계획세 3
 
2.1%
건축물 3
 
2.1%
주택(개별) 3
 
2.1%
주택(단독) 3
 
2.1%
기타 3
 
2.1%
항공기 3
 
2.1%
기계장비 3
 
2.1%
차량 3
 
2.1%
Other values (37) 111
78.7%

Length

2023-12-11T02:03:54.285887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배소비세 3
 
2.1%
주민세(개인사업 3
 
2.1%
지방소득세(특별징수 3
 
2.1%
지방소득세(법인소득 3
 
2.1%
지방소득세(양도소득 3
 
2.1%
지방소득세(종합소득 3
 
2.1%
지방소비세 3
 
2.1%
등록면허세(면허 3
 
2.1%
등록면허세(등록 3
 
2.1%
지역자원시설세(소방 3
 
2.1%
Other values (37) 111
78.7%

부과건수
Text

MISSING 

Distinct98
Distinct (%)99.0%
Missing42
Missing (%)29.8%
Memory size1.2 KiB
2023-12-11T02:03:54.723094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.010101
Min length1

Characters and Unicode

Total characters496
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)98.0%

Sample

1st row533,827
2nd row849
3rd row2,092
4th row5,139
5th row14
ValueCountFrequency (%)
67 2
 
2.0%
1,771 1
 
1.0%
1,819 1
 
1.0%
7 1
 
1.0%
18 1
 
1.0%
5,924 1
 
1.0%
1,813 1
 
1.0%
1,471 1
 
1.0%
566,449 1
 
1.0%
205,790 1
 
1.0%
Other values (88) 88
88.9%
2023-12-11T02:03:55.484705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 81
16.3%
, 77
15.5%
2 45
9.1%
3 43
8.7%
4 41
8.3%
5 39
7.9%
0 37
7.5%
7 36
7.3%
8 35
7.1%
9 34
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 419
84.5%
Other Punctuation 77
 
15.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 81
19.3%
2 45
10.7%
3 43
10.3%
4 41
9.8%
5 39
9.3%
0 37
8.8%
7 36
8.6%
8 35
8.4%
9 34
8.1%
6 28
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 496
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 81
16.3%
, 77
15.5%
2 45
9.1%
3 43
8.7%
4 41
8.3%
5 39
7.9%
0 37
7.5%
7 36
7.3%
8 35
7.1%
9 34
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 81
16.3%
, 77
15.5%
2 45
9.1%
3 43
8.7%
4 41
8.3%
5 39
7.9%
0 37
7.5%
7 36
7.3%
8 35
7.1%
9 34
6.9%

부과금액
Real number (ℝ)

MISSING 

Distinct99
Distinct (%)100.0%
Missing42
Missing (%)29.8%
Infinite0
Infinite (%)0.0%
Mean8.1891914 × 109
Minimum3268000
Maximum7.7353424 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T02:03:55.737359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3268000
5-th percentile13453000
Q11.800985 × 108
median4.161858 × 109
Q31.1913035 × 1010
95-th percentile2.7282536 × 1010
Maximum7.7353424 × 1010
Range7.7350156 × 1010
Interquartile range (IQR)1.1732936 × 1010

Descriptive statistics

Standard deviation1.2768273 × 1010
Coefficient of variation (CV)1.5591616
Kurtosis13.68745
Mean8.1891914 × 109
Median Absolute Deviation (MAD)4.118685 × 109
Skewness3.1691253
Sum8.1072995 × 1011
Variance1.6302878 × 1020
MonotonicityNot monotonic
2023-12-11T02:03:56.065466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8034000 1
 
0.7%
104560000 1
 
0.7%
280341000 1
 
0.7%
19223000 1
 
0.7%
94315000 1
 
0.7%
23321852000 1
 
0.7%
9226344000 1
 
0.7%
14021952000 1
 
0.7%
18972587000 1
 
0.7%
8955460000 1
 
0.7%
Other values (89) 89
63.1%
(Missing) 42
29.8%
ValueCountFrequency (%)
3268000 1
0.7%
8034000 1
0.7%
9656000 1
0.7%
10836000 1
0.7%
13156000 1
0.7%
13486000 1
0.7%
19223000 1
0.7%
20724000 1
0.7%
20809000 1
0.7%
22833000 1
0.7%
ValueCountFrequency (%)
77353424000 1
0.7%
72278393000 1
0.7%
31909412000 1
0.7%
30855066000 1
0.7%
27580836000 1
0.7%
27249392000 1
0.7%
24447005000 1
0.7%
23321852000 1
0.7%
22468993000 1
0.7%
21841958000 1
0.7%

Interactions

2023-12-11T02:03:50.017717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:03:49.606644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:03:50.217311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:03:49.789623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:03:56.246028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
141과세년도세목명세원유형명부과건수부과금액
1411.0000.9420.6700.0000.9390.000
과세년도0.9421.0000.0000.0000.7470.000
세목명0.6700.0001.0001.0000.9810.479
세원유형명0.0000.0001.0001.0000.9880.826
부과건수0.9390.7470.9810.9881.0001.000
부과금액0.0000.0000.4790.8261.0001.000
2023-12-11T02:03:56.429285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세원유형명과세년도세목명
세원유형명1.0000.0000.857
과세년도0.0001.0000.000
세목명0.8570.0001.000
2023-12-11T02:03:56.636768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
141부과금액과세년도세목명세원유형명
1411.000-0.0130.9050.3540.000
부과금액-0.0131.0000.0000.2570.452
과세년도0.9050.0001.0000.0000.000
세목명0.3540.2570.0001.0000.857
세원유형명0.0000.4520.0000.8571.000

Missing values

2023-12-11T02:03:50.900445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:03:51.202938image/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.
2023-12-11T02:03:51.494330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

141시도명시군구명자치단체코드과세년도세목명세원유형명부과건수부과금액
01부산광역시남구262902017담배소비세담배소비세<NA><NA>
12부산광역시남구262902017교육세교육세533,82715430254000
23부산광역시남구262902017도시계획세도시계획세<NA><NA>
34부산광역시남구262902017취득세건축물8496293476000
45부산광역시남구262902017취득세주택(개별)2,0929265343000
56부산광역시남구262902017취득세주택(단독)5,13918335666000
67부산광역시남구262902017취득세기타1425032000
78부산광역시남구262902017취득세항공기<NA><NA>
89부산광역시남구262902017취득세기계장비620809000
910부산광역시남구262902017취득세차량1,830281740000
141시도명시군구명자치단체코드과세년도세목명세원유형명부과건수부과금액
131132부산광역시남구262902019레저세경륜<NA><NA>
132133부산광역시남구262902019레저세경마<NA><NA>
133134부산광역시남구262902019자동차세자동차세(주행)<NA><NA>
134135부산광역시남구262902019자동차세3륜이하1,20313156000
135136부산광역시남구262902019자동차세특수5,674176448000
136137부산광역시남구262902019자동차세화물11,830309337000
137138부산광역시남구262902019자동차세승합3,132159157000
138139부산광역시남구262902019자동차세기타승용22113486000
139140부산광역시남구262902019자동차세승용132,37918783043000
140141부산광역시남구262902019체납체납184,0669344665000