Overview

Dataset statistics

Number of variables8
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory70.9 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description부산광역시 수영구_세원유형별 과세현황(과세년도, 세목명, 세원 유형명, 부과건수, 부과금액) 관련 정보를 제공합니다
URLhttps://www.data.go.kr/data/15078627/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
부과건수 is highly overall correlated with 부과금액High correlation
부과금액 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 14 (30.4%) zerosZeros
부과금액 has 14 (30.4%) zerosZeros

Reproduction

Analysis started2023-12-12 21:47:04.564915
Analysis finished2023-12-12 21:47:05.371114
Duration0.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
부산광역시
46 

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 46
100.0%

Length

2023-12-13T06:47:05.429275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:47:05.510889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
수영구
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수영구 46
100.0%

Length

2023-12-13T06:47:05.595338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:47:05.677433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수영구 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
26500
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26500
2nd row26500
3rd row26500
4th row26500
5th row26500

Common Values

ValueCountFrequency (%)
26500 46
100.0%

Length

2023-12-13T06:47:05.792245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:47:05.930837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26500 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
2021
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 46
100.0%

Length

2023-12-13T06:47:06.030823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:47:06.117781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 46
100.0%

세목명
Categorical

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
취득세
9 
자동차세
7 
주민세
7 
재산세
5 
레저세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
레저세 4
8.7%
지방소득세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
지방소비세 1
 
2.2%
교육세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-13T06:47:06.211333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.6%
자동차세 7
15.2%
주민세 7
15.2%
재산세 5
10.9%
레저세 4
8.7%
지방소득세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
지방소비세 1
 
2.2%
교육세 1
 
2.2%
Other values (3) 3
 
6.5%

세원 유형명
Text

UNIQUE 

Distinct46
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T06:47:06.442935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length6.0217391
Min length2

Characters and Unicode

Total characters277
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row지방소비세
2nd row교육세
3rd row도시계획세
4th row건축물
5th row주택(개별)
ValueCountFrequency (%)
지방소비세 1
 
2.2%
경정 1
 
2.2%
지역자원시설세(특자 1
 
2.2%
주민세(개인분 1
 
2.2%
주민세(종업원분 1
 
2.2%
주민세(특별징수 1
 
2.2%
주민세(법인세분 1
 
2.2%
주민세(양도소득 1
 
2.2%
주민세(종합소득 1
 
2.2%
담배소비세 1
 
2.2%
Other values (36) 36
78.3%
2023-12-13T06:47:06.801440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 27
 
9.7%
) 24
 
8.7%
( 24
 
8.7%
소 14
 
5.1%
주 11
 
4.0%
지 10
 
3.6%
득 9
 
3.2%
민 7
 
2.5%
방 6
 
2.2%
산 5
 
1.8%
Other values (63) 140
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
82.3%
Close Punctuation 24
 
8.7%
Open Punctuation 24
 
8.7%
Decimal Number 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
산 5
 
2.2%
재 5
 
2.2%
기 5
 
2.2%
Other values (60) 129
56.6%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 228
82.3%
Common 49
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
산 5
 
2.2%
재 5
 
2.2%
기 5
 
2.2%
Other values (60) 129
56.6%
Common
ValueCountFrequency (%)
) 24
49.0%
( 24
49.0%
3 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 228
82.3%
ASCII 49
 
17.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 27
 
11.8%
소 14
 
6.1%
주 11
 
4.8%
지 10
 
4.4%
득 9
 
3.9%
민 7
 
3.1%
방 6
 
2.6%
산 5
 
2.2%
재 5
 
2.2%
기 5
 
2.2%
Other values (60) 129
56.6%
ASCII
ValueCountFrequency (%)
) 24
49.0%
( 24
49.0%
3 1
 
2.0%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23195.022
Minimum0
Maximum388426
Zeros14
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T06:47:06.932117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1051.5
Q313619.25
95-th percentile109343
Maximum388426
Range388426
Interquartile range (IQR)13619.25

Descriptive statistics

Standard deviation63544.635
Coefficient of variation (CV)2.7395808
Kurtosis24.936714
Mean23195.022
Median Absolute Deviation (MAD)1051.5
Skewness4.6334816
Sum1066971
Variance4.0379207 × 109
MonotonicityNot monotonic
2023-12-13T06:47:07.057248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
30.4%
3 1
 
2.2%
4386 1
 
2.2%
87614 1
 
2.2%
8961 1
 
2.2%
71664 1
 
2.2%
850 1
 
2.2%
26952 1
 
2.2%
1502 1
 
2.2%
32365 1
 
2.2%
Other values (23) 23
50.0%
ValueCountFrequency (%)
0 14
30.4%
2 1
 
2.2%
3 1
 
2.2%
24 1
 
2.2%
42 1
 
2.2%
215 1
 
2.2%
341 1
 
2.2%
393 1
 
2.2%
680 1
 
2.2%
850 1
 
2.2%
ValueCountFrequency (%)
388426 1
2.2%
135630 1
2.2%
116586 1
2.2%
87614 1
2.2%
82453 1
2.2%
71664 1
2.2%
33338 1
2.2%
32365 1
2.2%
26952 1
2.2%
20622 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2647895 × 109
Minimum0
Maximum4.007045 × 1010
Zeros14
Zeros (%)30.4%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T06:47:07.191138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.113915 × 108
Q37.5427775 × 109
95-th percentile2.2671056 × 1010
Maximum4.007045 × 1010
Range4.007045 × 1010
Interquartile range (IQR)7.5427775 × 109

Descriptive statistics

Standard deviation8.8016239 × 109
Coefficient of variation (CV)1.6717903
Kurtosis4.5466077
Mean5.2647895 × 109
Median Absolute Deviation (MAD)2.113915 × 108
Skewness2.0576439
Sum2.4218032 × 1011
Variance7.7468584 × 1019
MonotonicityNot monotonic
2023-12-13T06:47:07.397623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 14
30.4%
2854000000 1
 
2.2%
13894820000 1
 
2.2%
12341535000 1
 
2.2%
782359000 1
 
2.2%
719366000 1
 
2.2%
2001020000 1
 
2.2%
10401980000 1
 
2.2%
10403003000 1
 
2.2%
8006245000 1
 
2.2%
Other values (23) 23
50.0%
ValueCountFrequency (%)
0 14
30.4%
9157000 1
 
2.2%
14665000 1
 
2.2%
18203000 1
 
2.2%
18693000 1
 
2.2%
20304000 1
 
2.2%
41346000 1
 
2.2%
58855000 1
 
2.2%
104563000 1
 
2.2%
182141000 1
 
2.2%
ValueCountFrequency (%)
40070450000 1
2.2%
22899617000 1
2.2%
22789297000 1
2.2%
22316335000 1
2.2%
18579156000 1
2.2%
16968065000 1
2.2%
15394279000 1
2.2%
13894820000 1
2.2%
12341535000 1
2.2%
10403003000 1
2.2%

Interactions

2023-12-13T06:47:04.970456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:47:04.767205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:47:05.071791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:47:04.863153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:47:07.503787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.7550.583
세원 유형명1.0001.0001.0001.000
부과건수0.7551.0001.0000.495
부과금액0.5831.0000.4951.000
2023-12-13T06:47:07.615113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.8480.479
부과금액0.8481.0000.282
세목명0.4790.2821.000

Missing values

2023-12-13T06:47:05.212357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:47:05.327492image/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
0부산광역시수영구265002021지방소비세지방소비세32854000000
1부산광역시수영구265002021교육세교육세38842618579156000
2부산광역시수영구265002021도시계획세도시계획세00
3부산광역시수영구265002021취득세건축물206122316335000
4부산광역시수영구265002021취득세주택(개별)167916968065000
5부산광역시수영구265002021취득세주택(단독)505140070450000
6부산광역시수영구265002021취득세기타24309387000
7부산광역시수영구265002021취득세항공기00
8부산광역시수영구265002021취득세기계장비218203000
9부산광역시수영구265002021취득세차량1796240642000
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
36부산광역시수영구265002021지방소득세지방소득세(특별징수)2695210401980000
37부산광역시수영구265002021지방소득세지방소득세(법인소득)150210403003000
38부산광역시수영구265002021지방소득세지방소득세(양도소득)438613894820000
39부산광역시수영구265002021지방소득세지방소득세(종합소득)323658006245000
40부산광역시수영구265002021등록면허세등록면허세(면허)20622839567000
41부산광역시수영구265002021등록면허세등록면허세(등록)333386152375000
42부산광역시수영구265002021지역자원시설세지역자원시설세(소방)1356303845126000
43부산광역시수영구265002021지역자원시설세지역자원시설세(시설)00
44부산광역시수영구265002021지역자원시설세지역자원시설세(특자)39318693000
45부산광역시수영구265002021체납체납1165865915214000