Overview

Dataset statistics

Number of variables9
Number of observations279
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.1 KiB
Average record size in memory77.5 B

Variable types

Numeric4
Categorical5

Dataset

Description2017년, 2018년, 2019년, 2020년, 2021,2022년의 세원유형별로 세목명, 세원 유형명, 부과건수, 부과금액등의 데이터를 제공합니다.
Author전라남도 장흥군
URLhttps://www.data.go.kr/data/15078569/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
세원유형명 is highly overall correlated with 부과건수 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 연번High correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
부과건수 has 69 (24.7%) zerosZeros
부과금액 has 70 (25.1%) zerosZeros

Reproduction

Analysis started2024-04-21 01:47:43.093948
Analysis finished2024-04-21 01:47:46.315945
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct279
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140
Minimum1
Maximum279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T10:47:46.393708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.9
Q170.5
median140
Q3209.5
95-th percentile265.1
Maximum279
Range278
Interquartile range (IQR)139

Descriptive statistics

Standard deviation80.684571
Coefficient of variation (CV)0.57631836
Kurtosis-1.2
Mean140
Median Absolute Deviation (MAD)70
Skewness0
Sum39060
Variance6510
MonotonicityStrictly increasing
2024-04-21T10:47:46.524487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
185 1
 
0.4%
191 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
184 1
 
0.4%
193 1
 
0.4%
Other values (269) 269
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
279 1
0.4%
278 1
0.4%
277 1
0.4%
276 1
0.4%
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
전라남도
279 

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 279
100.0%

Length

2024-04-21T10:47:46.652277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:46.736875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 279
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
장흥군
279 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장흥군 279
100.0%

Length

2024-04-21T10:47:46.835105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:46.928913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장흥군 279
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
46800
279 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46800
2nd row46800
3rd row46800
4th row46800
5th row46800

Common Values

ValueCountFrequency (%)
46800 279
100.0%

Length

2024-04-21T10:47:47.014667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:47:47.095157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46800 279
100.0%

과세년도
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4839
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T10:47:47.176377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7108177
Coefficient of variation (CV)0.00084715591
Kurtosis-1.2694549
Mean2019.4839
Median Absolute Deviation (MAD)1
Skewness0.01465019
Sum563436
Variance2.9268972
MonotonicityIncreasing
2024-04-21T10:47:47.300529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 47
16.8%
2018 47
16.8%
2019 47
16.8%
2020 46
16.5%
2021 46
16.5%
2022 46
16.5%
ValueCountFrequency (%)
2017 47
16.8%
2018 47
16.8%
2019 47
16.8%
2020 46
16.5%
2021 46
16.5%
2022 46
16.5%
ValueCountFrequency (%)
2022 46
16.5%
2021 46
16.5%
2020 46
16.5%
2019 47
16.8%
2018 47
16.8%
2017 47
16.8%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
레저세
24 
Other values (8)
79 

Length

Max length7
Median length3
Mean length3.7096774
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 54
19.4%
주민세 50
17.9%
자동차세 42
15.1%
재산세 30
10.8%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.2%
교육세 6
 
2.2%
Other values (3) 17
 
6.1%

Length

2024-04-21T10:47:47.431926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
19.4%
주민세 50
17.9%
자동차세 42
15.1%
재산세 30
10.8%
레저세 24
8.6%
지방소득세 24
8.6%
지역자원시설세 14
 
5.0%
등록면허세 12
 
4.3%
담배소비세 6
 
2.2%
교육세 6
 
2.2%
Other values (3) 17
 
6.1%

세원유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
담배소비세
 
6
건축물
 
6
주택(단독)
 
6
3륜이하
 
6
기계장비
 
6
Other values (45)
249 

Length

Max length11
Median length8
Mean length6.0394265
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
담배소비세 6
 
2.2%
건축물 6
 
2.2%
주택(단독) 6
 
2.2%
3륜이하 6
 
2.2%
기계장비 6
 
2.2%
차량 6
 
2.2%
선박 6
 
2.2%
토지 6
 
2.2%
재산세(건축물) 6
 
2.2%
주택(개별) 6
 
2.2%
Other values (40) 219
78.5%

Length

2024-04-21T10:47:47.561171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담배소비세 6
 
2.2%
주민세(특별징수 6
 
2.2%
화물 6
 
2.2%
건축물 6
 
2.2%
기타승용 6
 
2.2%
승용 6
 
2.2%
주민세(종업원분 6
 
2.2%
체납 6
 
2.2%
주민세(법인세분 6
 
2.2%
주민세(양도소득 6
 
2.2%
Other values (40) 219
78.5%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5722.0036
Minimum0
Maximum101389
Zeros69
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T10:47:47.694116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median265
Q34676
95-th percentile18058.9
Maximum101389
Range101389
Interquartile range (IQR)4673

Descriptive statistics

Standard deviation15862.761
Coefficient of variation (CV)2.7722389
Kurtosis23.403888
Mean5722.0036
Median Absolute Deviation (MAD)265
Skewness4.6813113
Sum1596439
Variance2.5162719 × 108
MonotonicityNot monotonic
2024-04-21T10:47:48.141378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69
 
24.7%
12 6
 
2.2%
50 3
 
1.1%
9 3
 
1.1%
21 2
 
0.7%
9963 2
 
0.7%
63 2
 
0.7%
32 2
 
0.7%
8 2
 
0.7%
81 2
 
0.7%
Other values (182) 186
66.7%
ValueCountFrequency (%)
0 69
24.7%
3 2
 
0.7%
4 1
 
0.4%
5 1
 
0.4%
6 1
 
0.4%
7 2
 
0.7%
8 2
 
0.7%
9 3
 
1.1%
11 1
 
0.4%
12 6
 
2.2%
ValueCountFrequency (%)
101389 1
0.4%
100252 1
0.4%
100028 1
0.4%
97925 1
0.4%
95052 1
0.4%
92688 1
0.4%
49554 1
0.4%
49093 1
0.4%
48186 1
0.4%
47560 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct210
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9268093 × 108
Minimum0
Maximum9.852726 × 109
Zeros70
Zeros (%)25.1%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T10:47:48.335666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1119500
median1.77985 × 108
Q37.34831 × 108
95-th percentile2.8395183 × 109
Maximum9.852726 × 109
Range9.852726 × 109
Interquartile range (IQR)7.347115 × 108

Descriptive statistics

Standard deviation1.1900251 × 109
Coefficient of variation (CV)1.717999
Kurtosis15.110718
Mean6.9268093 × 108
Median Absolute Deviation (MAD)1.77985 × 108
Skewness3.1088763
Sum1.9325798 × 1011
Variance1.4161598 × 1018
MonotonicityNot monotonic
2024-04-21T10:47:48.508788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
25.1%
2455969000 1
 
0.4%
428071000 1
 
0.4%
2112000 1
 
0.4%
1697761000 1
 
0.4%
1069624000 1
 
0.4%
652254000 1
 
0.4%
365027000 1
 
0.4%
814684000 1
 
0.4%
2675536000 1
 
0.4%
Other values (200) 200
71.7%
ValueCountFrequency (%)
0 70
25.1%
239000 1
 
0.4%
258000 1
 
0.4%
282000 1
 
0.4%
287000 1
 
0.4%
344000 1
 
0.4%
380000 1
 
0.4%
394000 1
 
0.4%
624000 1
 
0.4%
632000 1
 
0.4%
ValueCountFrequency (%)
9852726000 1
0.4%
6874825000 1
0.4%
5390491000 1
0.4%
3846597000 1
0.4%
3812056000 1
0.4%
3462857000 1
0.4%
3290283000 1
0.4%
3278997000 1
0.4%
3169312000 1
0.4%
3159172000 1
0.4%

Interactions

2024-04-21T10:47:45.748413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:44.574972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.045732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.390760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.847418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:44.828751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.124622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.495715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.925722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:44.898061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.206858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.576690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.998809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:44.972821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.294465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:47:45.665464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:47:48.627306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명세원유형명부과건수부과금액
연번1.0000.9430.3520.0000.0000.000
과세년도0.9431.0000.0000.0000.0000.000
세목명0.3520.0001.0001.0000.8370.705
세원유형명0.0000.0001.0001.0000.9920.879
부과건수0.0000.0000.8370.9921.0000.572
부과금액0.0000.0000.7050.8790.5721.000
2024-04-21T10:47:48.734289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원유형명
세목명1.0000.928
세원유형명0.9281.000
2024-04-21T10:47:48.821109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도부과건수부과금액세목명세원유형명
연번1.0000.9860.0490.0530.1520.000
과세년도0.9861.0000.0180.0610.0000.000
부과건수0.0490.0181.0000.7690.6590.863
부과금액0.0530.0610.7691.0000.4190.543
세목명0.1520.0000.6590.4191.0000.928
세원유형명0.0000.0000.8630.5430.9281.000

Missing values

2024-04-21T10:47:46.105202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:47:46.247680image/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

연번시도명시군구명자치단체코드과세년도세목명세원유형명부과건수부과금액
01전라남도장흥군468002017담배소비세담배소비세1002455969000
12전라남도장흥군468002017교육세교육세926882621729000
23전라남도장흥군468002017도시계획세도시계획세00
34전라남도장흥군468002017취득세건축물890872270000
45전라남도장흥군468002017취득세주택(개별)857559957000
56전라남도장흥군468002017취득세주택(단독)204621358000
67전라남도장흥군468002017취득세기타317679000
78전라남도장흥군468002017취득세항공기00
89전라남도장흥군468002017취득세기계장비366170096000
910전라남도장흥군468002017취득세차량31492321399000
연번시도명시군구명자치단체코드과세년도세목명세원유형명부과건수부과금액
269270전라남도장흥군468002022등록면허세등록면허세(면허)16541191780000
270271전라남도장흥군468002022등록면허세등록면허세(등록)9305761612000
271272전라남도장흥군468002022지역자원시설세지역자원시설세(소방)8481475789000
272273전라남도장흥군468002022지역자원시설세지역자원시설세(시설)00
273274전라남도장흥군468002022지역자원시설세지역자원시설세(특자)321656000
274275전라남도장흥군468002022지방소득세지방소득세(특별징수)64962040217000
275276전라남도장흥군468002022지방소득세지방소득세(법인소득)8631403566000
276277전라남도장흥군468002022지방소득세지방소득세(양도소득)687664105000
277278전라남도장흥군468002022지방소득세지방소득세(종합소득)5907502505000
278279전라남도장흥군468002022체납체납5960549152000