Overview

Dataset statistics

Number of variables9
Number of observations253
Missing cells0
Missing cells (%)0.0%
Duplicate rows40
Duplicate rows (%)15.8%
Total size in memory18.9 KiB
Average record size in memory76.5 B

Variable types

Categorical7
Numeric2

Dataset

Description인천광역시 남동구 세원유형별과세현황에 대한 데이터로(과세년도, 세목명, 세원 유형명, 부과건수, 부과금액, 데이터기준일)등을 제공합니다.
Author인천광역시 남동구
URLhttps://www.data.go.kr/data/15079444/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 40 (15.8%) duplicate rowsDuplicates
세목명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
세원 유형명 is highly overall correlated with 부과건수 and 1 other fieldsHigh correlation
부과건수 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
부과건수 has 52 (20.6%) zerosZeros
부과금액 has 52 (20.6%) zerosZeros

Reproduction

Analysis started2024-03-14 22:46:39.573091
Analysis finished2024-03-14 22:46:41.909619
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
인천광역시
253 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 253
100.0%

Length

2024-03-15T07:46:42.118475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:46:42.431887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 253
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
남동구
253 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 253
100.0%

Length

2024-03-15T07:46:42.787595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:46:43.156223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 253
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
28200
253 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28200
2nd row28200
3rd row28200
4th row28200
5th row28200

Common Values

ValueCountFrequency (%)
28200 253
100.0%

Length

2024-03-15T07:46:43.495377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:46:43.848240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 253
100.0%

과세년도
Categorical

Distinct5
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2019
80 
2021
46 
2022
46 
2020
41 
2018
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2019 80
31.6%
2021 46
18.2%
2022 46
18.2%
2020 41
16.2%
2018 40
15.8%

Length

2024-03-15T07:46:44.189320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:46:44.546604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 80
31.6%
2021 46
18.2%
2022 46
18.2%
2020 41
16.2%
2018 40
15.8%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
취득세
54 
주민세
50 
자동차세
42 
재산세
30 
지방소득세
24 
Other values (8)
53 

Length

Max length7
Median length3
Mean length3.7035573
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방소득세
2nd row지방소득세
3rd row지방소득세
4th row지방소득세
5th row교육세

Common Values

ValueCountFrequency (%)
취득세 54
21.3%
주민세 50
19.8%
자동차세 42
16.6%
재산세 30
11.9%
지방소득세 24
9.5%
지역자원시설세 14
 
5.5%
등록면허세 12
 
4.7%
레저세 8
 
3.2%
교육세 6
 
2.4%
체납 6
 
2.4%
Other values (3) 7
 
2.8%

Length

2024-03-15T07:46:44.923034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 54
21.3%
주민세 50
19.8%
자동차세 42
16.6%
재산세 30
11.9%
지방소득세 24
9.5%
지역자원시설세 14
 
5.5%
등록면허세 12
 
4.7%
레저세 8
 
3.2%
교육세 6
 
2.4%
체납 6
 
2.4%
Other values (3) 7
 
2.8%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
지방소득세(특별징수)
 
6
재산세(건축물)
 
6
재산세(선박)
 
6
교육세
 
6
지방소득세(종합소득)
 
6
Other values (45)
223 

Length

Max length11
Median length8
Mean length6.3201581
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지방소득세(특별징수)
2nd row지방소득세(법인소득)
3rd row지방소득세(양도소득)
4th row지방소득세(종합소득)
5th row교육세

Common Values

ValueCountFrequency (%)
지방소득세(특별징수) 6
 
2.4%
재산세(건축물) 6
 
2.4%
재산세(선박) 6
 
2.4%
교육세 6
 
2.4%
지방소득세(종합소득) 6
 
2.4%
주택(단독) 6
 
2.4%
자동차세(주행) 6
 
2.4%
항공기 6
 
2.4%
차량 6
 
2.4%
건축물 6
 
2.4%
Other values (40) 193
76.3%

Length

2024-03-15T07:46:45.348713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세(특별징수 6
 
2.4%
승용 6
 
2.4%
특수 6
 
2.4%
3륜이하 6
 
2.4%
승합 6
 
2.4%
기타승용 6
 
2.4%
재산세(건축물 6
 
2.4%
주민세(종업원분 6
 
2.4%
주민세(특별징수 6
 
2.4%
화물 6
 
2.4%
Other values (40) 193
76.3%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct166
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74621.067
Minimum0
Maximum1101250
Zeros52
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:46:46.048710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q146
median5217
Q359207
95-th percentile350363.2
Maximum1101250
Range1101250
Interquartile range (IQR)59161

Descriptive statistics

Standard deviation182782.23
Coefficient of variation (CV)2.4494722
Kurtosis19.268195
Mean74621.067
Median Absolute Deviation (MAD)5217
Skewness4.1573483
Sum18879130
Variance3.3409342 × 1010
MonotonicityNot monotonic
2024-03-15T07:46:46.538080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
20.6%
46543 2
 
0.8%
39102 2
 
0.8%
186544 2
 
0.8%
4137 2
 
0.8%
46 2
 
0.8%
60717 2
 
0.8%
456 2
 
0.8%
71 2
 
0.8%
1444 2
 
0.8%
Other values (156) 183
72.3%
ValueCountFrequency (%)
0 52
20.6%
3 2
 
0.8%
7 2
 
0.8%
11 2
 
0.8%
34 1
 
0.4%
41 1
 
0.4%
43 1
 
0.4%
45 1
 
0.4%
46 2
 
0.8%
47 1
 
0.4%
ValueCountFrequency (%)
1101250 1
0.4%
1090111 1
0.4%
1072415 2
0.8%
1057618 1
0.4%
1051834 1
0.4%
426407 1
0.4%
416062 1
0.4%
407030 2
0.8%
395739 1
0.4%
394832 1
0.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct169
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.663717 × 1010
Minimum0
Maximum1.55 × 1011
Zeros52
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-03-15T07:46:46.953470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17298000
median1.948323 × 109
Q32.4917465 × 1010
95-th percentile5.9660838 × 1010
Maximum1.55 × 1011
Range1.55 × 1011
Interquartile range (IQR)2.4910167 × 1010

Descriptive statistics

Standard deviation2.6822105 × 1010
Coefficient of variation (CV)1.6121795
Kurtosis7.3442438
Mean1.663717 × 1010
Median Absolute Deviation (MAD)1.948323 × 109
Skewness2.428278
Sum4.209204 × 1012
Variance7.1942532 × 1020
MonotonicityNot monotonic
2024-03-15T07:46:47.300776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52
 
20.6%
16864000 2
 
0.8%
43095720000 2
 
0.8%
40248709000 2
 
0.8%
42597764000 2
 
0.8%
5664000 2
 
0.8%
120000000000 2
 
0.8%
655303000 2
 
0.8%
4131848000 2
 
0.8%
18966306000 2
 
0.8%
Other values (159) 183
72.3%
ValueCountFrequency (%)
0 52
20.6%
3138000 1
 
0.4%
3898000 1
 
0.4%
4192000 1
 
0.4%
4284000 1
 
0.4%
4702000 1
 
0.4%
4913000 2
 
0.8%
5664000 2
 
0.8%
6074000 1
 
0.4%
6821000 1
 
0.4%
ValueCountFrequency (%)
155000000000 1
0.4%
140000000000 1
0.4%
139000000000 1
0.4%
128000000000 1
0.4%
120000000000 2
0.8%
93153867000 1
0.4%
68536592000 1
0.4%
66837580000 1
0.4%
65160548000 1
0.4%
61638157000 1
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-01-08
253 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-08
2nd row2024-01-08
3rd row2024-01-08
4th row2024-01-08
5th row2024-01-08

Common Values

ValueCountFrequency (%)
2024-01-08 253
100.0%

Length

2024-03-15T07:46:47.768667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:46:48.082095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-08 253
100.0%

Interactions

2024-03-15T07:46:40.531465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:46:40.009823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:46:40.799758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T07:46:40.256390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T07:46:48.223542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.0000.191
세목명0.0001.0001.0000.8510.532
세원 유형명0.0001.0001.0000.9930.867
부과건수0.0000.8510.9931.0000.555
부과금액0.1910.5320.8670.5551.000
2024-03-15T07:46:48.440686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명과세년도
세목명1.0000.9200.000
세원 유형명0.9201.0000.000
과세년도0.0000.0001.000
2024-03-15T07:46:48.594044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과건수부과금액과세년도세목명세원 유형명
부과건수1.0000.8170.0000.6660.814
부과금액0.8171.0000.1100.2590.488
과세년도0.0000.1101.0000.0000.000
세목명0.6660.2590.0001.0000.920
세원 유형명0.8140.4880.0000.9201.000

Missing values

2024-03-15T07:46:41.204680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T07:46:41.688898image/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
0인천광역시남동구282002018지방소득세지방소득세(특별징수)114141399475730002024-01-08
1인천광역시남동구282002018지방소득세지방소득세(법인소득)7098560749940002024-01-08
2인천광역시남동구282002018지방소득세지방소득세(양도소득)617793963570002024-01-08
3인천광역시남동구282002018지방소득세지방소득세(종합소득)64145177191170002024-01-08
4인천광역시남동구282002018교육세교육세1057618436636470002024-01-08
5인천광역시남동구282002018취득세건축물5515393448300002024-01-08
6인천광역시남동구282002018취득세주택(개별)99775571710002024-01-08
7인천광역시남동구282002018취득세주택(단독)12921398126950002024-01-08
8인천광역시남동구282002018취득세기타6710314610002024-01-08
9인천광역시남동구282002018취득세항공기002024-01-08
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자
243인천광역시남동구282002022등록면허세등록면허세(등록)90537111948520002024-01-08
244인천광역시남동구282002022지역자원시설세지역자원시설세(소방)327987162003260002024-01-08
245인천광역시남동구282002022지역자원시설세지역자원시설세(시설)1187100002024-01-08
246인천광역시남동구282002022지역자원시설세지역자원시설세(특자)34347020002024-01-08
247인천광역시남동구282002022담배소비세담배소비세002024-01-08
248인천광역시남동구282002022레저세소싸움002024-01-08
249인천광역시남동구282002022레저세경정11793920002024-01-08
250인천광역시남동구282002022레저세경륜342001910002024-01-08
251인천광역시남동구282002022레저세경마002024-01-08
252인천광역시남동구282002022체납체납228421249174650002024-01-08

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액데이터기준일자# duplicates
0인천광역시남동구282002019교육세교육세1072415442006390002024-01-082
1인천광역시남동구282002019등록면허세등록면허세(등록)116044131716040002024-01-082
2인천광역시남동구282002019등록면허세등록면허세(면허)5994621908670002024-01-082
3인천광역시남동구282002019자동차세3륜이하1444168640002024-01-082
4인천광역시남동구282002019자동차세기타승용463296830002024-01-082
5인천광역시남동구282002019자동차세승용407030596608380002024-01-082
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