Overview

Dataset statistics

Number of variables9
Number of observations46
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory78.9 B

Variable types

Categorical5
Text1
Numeric2
DateTime1

Dataset

Description경상남도 사천시 세원 유형별 과세현황(2018 ~ 2020년)에 대한 데이터로 지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공합니다.
URLhttps://www.data.go.kr/data/15079573/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
세목명 is highly overall correlated with 부과건수High correlation
세원 유형명 has unique valuesUnique
부과건수 has 10 (21.7%) zerosZeros
부과금액 has 10 (21.7%) zerosZeros

Reproduction

Analysis started2023-12-12 17:01:53.797326
Analysis finished2023-12-12 17:01:54.680790
Duration0.88 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
경상남도
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 46
100.0%

Length

2023-12-13T02:01:54.753741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:54.880888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 46
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 46
100.0%

Length

2023-12-13T02:01:54.992321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:55.078365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
48240
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48240
2nd row48240
3rd row48240
4th row48240
5th row48240

Common Values

ValueCountFrequency (%)
48240 46
100.0%

Length

2023-12-13T02:01:55.172819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:55.259438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48240 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

2023-12-13T02:01:55.365956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:55.470377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 46
100.0%

세목명
Categorical

HIGH CORRELATION 

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 row담배소비세
2nd row도시계획세
3rd row취득세
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%
담배소비세 1
 
2.2%
도시계획세 1
 
2.2%
Other values (3) 3
 
6.5%

Length

2023-12-13T02:01:55.582852image/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%
담배소비세 1
 
2.2%
도시계획세 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
2023-12-13T02:01:55.832400image/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 row담배소비세
2nd row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)
ValueCountFrequency (%)
담배소비세 1
 
2.2%
주민세(종합소득 1
 
2.2%
교육세 1
 
2.2%
기타승용 1
 
2.2%
승용 1
 
2.2%
주민세(사업소분 1
 
2.2%
주민세(개인분 1
 
2.2%
주민세(종업원분 1
 
2.2%
주민세(특별징수 1
 
2.2%
주민세(법인세분 1
 
2.2%
Other values (36) 36
78.3%
2023-12-13T02:01:56.286679image/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%
자 5
 
1.8%
Other values (63) 140
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 228
82.3%
Open Punctuation 24
 
8.7%
Close 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%
자 5
 
2.2%
산 5
 
2.2%
재 5
 
2.2%
Other values (60) 129
56.6%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close 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%
자 5
 
2.2%
산 5
 
2.2%
재 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%
자 5
 
2.2%
산 5
 
2.2%
재 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 

Distinct37
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16692.152
Minimum0
Maximum268994
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:01:56.440465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median872
Q311932.75
95-th percentile71130.75
Maximum268994
Range268994
Interquartile range (IQR)11927.25

Descriptive statistics

Standard deviation43448.329
Coefficient of variation (CV)2.6029195
Kurtosis25.95407
Mean16692.152
Median Absolute Deviation (MAD)872
Skewness4.7061548
Sum767839
Variance1.8877573 × 109
MonotonicityNot monotonic
2023-12-13T02:01:56.591241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
473 1
 
2.2%
1726 1
 
2.2%
297 1
 
2.2%
72883 1
 
2.2%
6107 1
 
2.2%
47423 1
 
2.2%
1064 1
 
2.2%
14716 1
 
2.2%
1708 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
1 1
 
2.2%
5 1
 
2.2%
7 1
 
2.2%
12 1
 
2.2%
26 1
 
2.2%
135 1
 
2.2%
244 1
 
2.2%
297 1
 
2.2%
372 1
 
2.2%
ValueCountFrequency (%)
268994 1
2.2%
83679 1
2.2%
72883 1
2.2%
65874 1
2.2%
49080 1
2.2%
47423 1
2.2%
45697 1
2.2%
27677 1
2.2%
20545 1
2.2%
14716 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6109615 × 109
Minimum0
Maximum1.9349543 × 1010
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T02:01:56.786384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17120500
median6.3064 × 108
Q36.092065 × 109
95-th percentile1.3184588 × 1010
Maximum1.9349543 × 1010
Range1.9349543 × 1010
Interquartile range (IQR)6.0849445 × 109

Descriptive statistics

Standard deviation4.8913377 × 109
Coefficient of variation (CV)1.3545804
Kurtosis1.2922815
Mean3.6109615 × 109
Median Absolute Deviation (MAD)6.3064 × 108
Skewness1.4004899
Sum1.6610423 × 1011
Variance2.3925185 × 1019
MonotonicityNot monotonic
2023-12-13T02:01:56.987158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
9957347000 1
 
2.2%
2625584000 1
 
2.2%
16179000 1
 
2.2%
9761053000 1
 
2.2%
897386000 1
 
2.2%
476407000 1
 
2.2%
5021059000 1
 
2.2%
13994882000 1
 
2.2%
12078290000 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
4092000 1
 
2.2%
5145000 1
 
2.2%
13047000 1
 
2.2%
16179000 1
 
2.2%
22314000 1
 
2.2%
45469000 1
 
2.2%
79395000 1
 
2.2%
100188000 1
 
2.2%
107075000 1
 
2.2%
ValueCountFrequency (%)
19349543000 1
2.2%
13994882000 1
2.2%
13553354000 1
2.2%
12078290000 1
2.2%
11405604000 1
2.2%
10557121000 1
2.2%
9957347000 1
2.2%
9761053000 1
2.2%
8134693000 1
2.2%
7424409000 1
2.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size500.0 B
Minimum2023-06-01 00:00:00
Maximum2023-06-01 00:00:00
2023-12-13T02:01:57.134408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:57.243046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T02:01:54.205416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:54.008219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:54.297307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:54.099796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:01:57.319174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8450.526
세원 유형명1.0001.0001.0001.000
부과건수0.8451.0001.0000.686
부과금액0.5261.0000.6861.000
2023-12-13T02:01:57.409730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7420.595
부과금액0.7421.0000.229
세목명0.5950.2291.000

Missing values

2023-12-13T02:01:54.470652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:01:54.616596image/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 유형명부과건수부과금액데이터기준일자
0경상남도사천시482402021담배소비세담배소비세47399573470002023-06-01
1경상남도사천시482402021도시계획세도시계획세002023-06-01
2경상남도사천시482402021취득세건축물89648556730002023-06-01
3경상남도사천시482402021취득세주택(개별)148029922020002023-06-01
4경상남도사천시482402021취득세주택(단독)279974244090002023-06-01
5경상남도사천시482402021취득세기타26793950002023-06-01
6경상남도사천시482402021취득세항공기140920002023-06-01
7경상남도사천시482402021취득세기계장비2442844370002023-06-01
8경상남도사천시482402021취득세차량835081346930002023-06-01
9경상남도사천시482402021취득세선박1357848730002023-06-01
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자
36경상남도사천시482402021지방소득세지방소득세(양도소득)172626255840002023-06-01
37경상남도사천시482402021지방소득세지방소득세(종합소득)1293228065480002023-06-01
38경상남도사천시482402021지방소비세지방소비세7105571210002023-06-01
39경상남도사천시482402021등록면허세등록면허세(면허)205453486120002023-06-01
40경상남도사천시482402021등록면허세등록면허세(등록)2767728881730002023-06-01
41경상남도사천시482402021지역자원시설세지역자원시설세(소방)4569732609660002023-06-01
42경상남도사천시482402021지역자원시설세지역자원시설세(시설)002023-06-01
43경상남도사천시482402021지역자원시설세지역자원시설세(특자)848454690002023-06-01
44경상남도사천시482402021교육세교육세268994135533540002023-06-01
45경상남도사천시482402021체납체납8367964490670002023-06-01