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 memory78.8 B

Variable types

Categorical5
Text1
Numeric2
DateTime1

Dataset

Description지방세 과세를 위해 세원이 되는 과세 대상 유형별 부과된 현황을 제공합니다. (물건 유형에 따른 세부담 수준의 형평성 검토 및 부동산 등 관련분야 규제정책 대상 확인시 기초자료 활용)
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15078876/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일 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 started2024-03-14 09:16:27.412122
Analysis finished2024-03-14 09:16:29.108799
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
전라남도
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 46
100.0%

Length

2024-03-14T18:16:29.213872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:16:29.363896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 46
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
광양시
46 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광양시 46
100.0%

Length

2024-03-14T18:16:29.523280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:16:29.673836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양시 46
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
46230
46 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46230
2nd row46230
3rd row46230
4th row46230
5th row46230

Common Values

ValueCountFrequency (%)
46230 46
100.0%

Length

2024-03-14T18:16:29.903233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:16:30.189498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46230 46
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
2022
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 46
100.0%

Length

2024-03-14T18:16:30.487649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:16:30.773423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 46
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Memory size496.0 B
취득세
9 
주민세
7 
자동차세
7 
재산세
5 
지방소득세
4 
Other values (8)
14 

Length

Max length7
Median length3
Mean length3.7826087
Min length2

Unique

Unique5 ?
Unique (%)10.9%

Sample

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

Common Values

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

Length

2024-03-14T18:16:31.102268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 9
19.6%
주민세 7
15.2%
자동차세 7
15.2%
재산세 5
10.9%
지방소득세 4
8.7%
레저세 4
8.7%
지역자원시설세 3
 
6.5%
등록면허세 2
 
4.3%
교육세 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 size496.0 B
2024-03-14T18:16:32.006295image/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 row교육세
2nd row도시계획세
3rd row건축물
4th row주택(개별)
5th row주택(단독)
ValueCountFrequency (%)
교육세 1
 
2.2%
지방소비세 1
 
2.2%
승용 1
 
2.2%
지방소득세(양도소득 1
 
2.2%
지방소득세(종합소득 1
 
2.2%
등록면허세(면허 1
 
2.2%
등록면허세(등록 1
 
2.2%
지역자원시설세(소방 1
 
2.2%
지역자원시설세(시설 1
 
2.2%
지역자원시설세(특자 1
 
2.2%
Other values (36) 36
78.3%
2024-03-14T18:16:33.212215image/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%
시 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%
시 5
 
2.2%
자 5
 
2.2%
산 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%
시 5
 
2.2%
자 5
 
2.2%
산 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%
시 5
 
2.2%
자 5
 
2.2%
산 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%
Mean19016.587
Minimum0
Maximum272485
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-14T18:16:33.513006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.25
median1208
Q313564.5
95-th percentile98661.75
Maximum272485
Range272485
Interquartile range (IQR)13553.25

Descriptive statistics

Standard deviation46503.594
Coefficient of variation (CV)2.4454227
Kurtosis20.012577
Mean19016.587
Median Absolute Deviation (MAD)1208
Skewness4.1171994
Sum874763
Variance2.1625843 × 109
MonotonicityNot monotonic
2024-03-14T18:16:33.768208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
272485 1
 
2.2%
34 1
 
2.2%
36027 1
 
2.2%
53577 1
 
2.2%
23 1
 
2.2%
134 1
 
2.2%
643 1
 
2.2%
9 1
 
2.2%
11 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
9 1
 
2.2%
11 1
 
2.2%
12 1
 
2.2%
23 1
 
2.2%
34 1
 
2.2%
56 1
 
2.2%
87 1
 
2.2%
134 1
 
2.2%
169 1
 
2.2%
ValueCountFrequency (%)
272485 1
2.2%
111130 1
2.2%
108271 1
2.2%
69834 1
2.2%
58061 1
2.2%
53577 1
2.2%
36027 1
2.2%
29960 1
2.2%
29721 1
2.2%
26106 1
2.2%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1696136 × 109
Minimum0
Maximum1.1231047 × 1011
Zeros10
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size542.0 B
2024-03-14T18:16:34.088346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19116500
median2.764655 × 109
Q31.191206 × 1010
95-th percentile2.9694031 × 1010
Maximum1.1231047 × 1011
Range1.1231047 × 1011
Interquartile range (IQR)1.1902943 × 1010

Descriptive statistics

Standard deviation1.8071316 × 1010
Coefficient of variation (CV)1.9707828
Kurtosis23.933204
Mean9.1696136 × 109
Median Absolute Deviation (MAD)2.764655 × 109
Skewness4.3906499
Sum4.2180222 × 1011
Variance3.2657248 × 1020
MonotonicityNot monotonic
2024-03-14T18:16:34.508416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 10
 
21.7%
23317844000 1
 
2.2%
56807000 1
 
2.2%
7576424000 1
 
2.2%
8049310000 1
 
2.2%
3758984000 1
 
2.2%
3019000 1
 
2.2%
12128469000 1
 
2.2%
16988031000 1
 
2.2%
22528000 1
 
2.2%
Other values (27) 27
58.7%
ValueCountFrequency (%)
0 10
21.7%
3019000 1
 
2.2%
4646000 1
 
2.2%
22528000 1
 
2.2%
40902000 1
 
2.2%
54985000 1
 
2.2%
56807000 1
 
2.2%
75643000 1
 
2.2%
145197000 1
 
2.2%
178284000 1
 
2.2%
ValueCountFrequency (%)
112310467000 1
2.2%
31409333000 1
2.2%
30332741000 1
2.2%
27777901000 1
2.2%
24079084000 1
2.2%
23317844000 1
2.2%
19813737000 1
2.2%
17109266000 1
2.2%
16988031000 1
2.2%
15954446000 1
2.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size496.0 B
Minimum2024-01-23 00:00:00
Maximum2024-01-23 00:00:00
2024-03-14T18:16:34.854070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:16:35.158931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T18:16:28.181102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:16:27.701326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:16:28.425511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:16:27.939286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:16:35.365100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명부과건수부과금액
세목명1.0001.0000.8830.363
세원 유형명1.0001.0001.0001.000
부과건수0.8831.0001.0000.308
부과금액0.3631.0000.3081.000
2024-03-14T18:16:35.619833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액세목명
부과건수1.0000.7230.630
부과금액0.7231.0000.178
세목명0.6300.1781.000

Missing values

2024-03-14T18:16:28.774115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:16:29.014886image/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 유형명부과건수부과금액데이터기준일
0전라남도광양시462302022교육세교육세272485233178440002024-01-23
1전라남도광양시462302022도시계획세도시계획세002024-01-23
2전라남도광양시462302022취득세건축물1610303327410002024-01-23
3전라남도광양시462302022취득세주택(개별)98123712610002024-01-23
4전라남도광양시462302022취득세주택(단독)3676142696040002024-01-23
5전라남도광양시462302022취득세기타874363040002024-01-23
6전라남도광양시462302022취득세항공기002024-01-23
7전라남도광양시462302022취득세기계장비99337052170002024-01-23
8전라남도광양시462302022취득세차량14191171092660002024-01-23
9전라남도광양시462302022취득세선박56549850002024-01-23
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일
36전라남도광양시462302022레저세경륜34568070002024-01-23
37전라남도광양시462302022레저세경마002024-01-23
38전라남도광양시462302022자동차세자동차세(주행)12198137370002024-01-23
39전라남도광양시462302022자동차세3륜이하44946460002024-01-23
40전라남도광양시462302022자동차세특수55411782840002024-01-23
41전라남도광양시462302022자동차세화물168635110380002024-01-23
42전라남도광양시462302022자동차세승합27541451970002024-01-23
43전라남도광양시462302022자동차세기타승용1423756430002024-01-23
44전라남도광양시462302022자동차세승용111130159544460002024-01-23
45전라남도광양시462302022체납체납108271105422250002024-01-23