Overview

Dataset statistics

Number of variables8
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory69.6 B

Variable types

Categorical5
Numeric3

Dataset

Description대구광역시_세원유형별과세현황_20221231
Author대구광역시
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15079128&dataSetDetailId=150791281998ea87a542b&provdMethod=FILE

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
세원 유형명 is highly overall correlated with 세목명High correlation
부과건수 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 부과건수High correlation
부과건수 has 52 (63.4%) zerosZeros
부과금액 has 52 (63.4%) zerosZeros

Reproduction

Analysis started2023-12-10 18:16:54.127728
Analysis finished2023-12-10 18:16:56.933348
Duration2.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
대구광역시
82 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 82
100.0%

Length

2023-12-11T03:16:57.039920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:16:57.210377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 82
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
대구광역시
82 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 82
100.0%

Length

2023-12-11T03:16:57.418399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:16:57.686455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대구광역시 82
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
27000
82 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27000
2nd row27000
3rd row27000
4th row27000
5th row27000

Common Values

ValueCountFrequency (%)
27000 82
100.0%

Length

2023-12-11T03:16:57.885022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:16:58.036003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27000 82
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.1098
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T03:16:58.165131image/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 deviation1.7498225
Coefficient of variation (CV)0.00086663072
Kurtosis-1.2422199
Mean2019.1098
Median Absolute Deviation (MAD)1.5
Skewness0.338289
Sum165567
Variance3.061879
MonotonicityIncreasing
2023-12-11T03:16:58.394136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 19
23.2%
2018 19
23.2%
2019 11
13.4%
2020 11
13.4%
2021 11
13.4%
2022 11
13.4%
ValueCountFrequency (%)
2017 19
23.2%
2018 19
23.2%
2019 11
13.4%
2020 11
13.4%
2021 11
13.4%
2022 11
13.4%
ValueCountFrequency (%)
2022 11
13.4%
2021 11
13.4%
2020 11
13.4%
2019 11
13.4%
2018 19
23.2%
2017 19
23.2%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size788.0 B
자동차세
42 
취득세
14 
교육세
6 
체납
6 
담배소비세
6 
Other values (2)
8 

Length

Max length5
Median length4
Mean length3.7804878
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 42
51.2%
취득세 14
 
17.1%
교육세 6
 
7.3%
체납 6
 
7.3%
담배소비세 6
 
7.3%
지방소비세 6
 
7.3%
등록면허세 2
 
2.4%

Length

2023-12-11T03:16:58.623620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T03:16:58.871866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 42
51.2%
취득세 14
 
17.1%
교육세 6
 
7.3%
체납 6
 
7.3%
담배소비세 6
 
7.3%
지방소비세 6
 
7.3%
등록면허세 2
 
2.4%

세원 유형명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
승용
6 
특수
6 
교육세
6 
기타승용
6 
승합
6 
Other values (14)
52 

Length

Max length9
Median length8
Mean length3.5121951
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row건축물
3rd row선박
4th row차량
5th row기계장비

Common Values

ValueCountFrequency (%)
승용 6
 
7.3%
특수 6
 
7.3%
교육세 6
 
7.3%
기타승용 6
 
7.3%
승합 6
 
7.3%
화물 6
 
7.3%
지방소비세 6
 
7.3%
3륜이하 6
 
7.3%
자동차세(주행) 6
 
7.3%
체납 6
 
7.3%
Other values (9) 22
26.8%

Length

2023-12-11T03:16:59.110298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
승용 6
 
7.3%
지방소비세 6
 
7.3%
담배소비세 6
 
7.3%
특수 6
 
7.3%
자동차세(주행 6
 
7.3%
3륜이하 6
 
7.3%
체납 6
 
7.3%
화물 6
 
7.3%
승합 6
 
7.3%
기타승용 6
 
7.3%
Other values (9) 22
26.8%

부과건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.195122
Minimum0
Maximum642
Zeros52
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T03:16:59.286280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile324.95
Maximum642
Range642
Interquartile range (IQR)12

Descriptive statistics

Standard deviation139.00985
Coefficient of variation (CV)2.3093209
Kurtosis7.8256492
Mean60.195122
Median Absolute Deviation (MAD)0
Skewness2.8165633
Sum4936
Variance19323.739
MonotonicityNot monotonic
2023-12-11T03:16:59.452102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 52
63.4%
12 6
 
7.3%
130 2
 
2.4%
8 2
 
2.4%
103 2
 
2.4%
10 2
 
2.4%
111 2
 
2.4%
482 2
 
2.4%
9 1
 
1.2%
642 1
 
1.2%
Other values (10) 10
 
12.2%
ValueCountFrequency (%)
0 52
63.4%
6 1
 
1.2%
8 2
 
2.4%
9 1
 
1.2%
10 2
 
2.4%
12 6
 
7.3%
103 2
 
2.4%
106 1
 
1.2%
108 1
 
1.2%
111 2
 
2.4%
ValueCountFrequency (%)
642 1
1.2%
639 1
1.2%
482 2
2.4%
325 1
1.2%
324 1
1.2%
283 1
1.2%
279 1
1.2%
253 1
1.2%
202 1
1.2%
130 2
2.4%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.861153 × 1010
Minimum0
Maximum1.00658 × 1012
Zeros52
Zeros (%)63.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-11T03:16:59.681941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.036188 × 1010
95-th percentile3.7654685 × 1011
Maximum1.00658 × 1012
Range1.00658 × 1012
Interquartile range (IQR)6.036188 × 1010

Descriptive statistics

Standard deviation1.6777932 × 1011
Coefficient of variation (CV)2.4453517
Kurtosis15.469757
Mean6.861153 × 1010
Median Absolute Deviation (MAD)0
Skewness3.6993177
Sum5.6261455 × 1012
Variance2.8149901 × 1022
MonotonicityNot monotonic
2023-12-11T03:16:59.932591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 52
63.4%
61919886000 1
 
1.2%
1006580000000 1
 
1.2%
138319000000 1
 
1.2%
1317593000 1
 
1.2%
122355000000 1
 
1.2%
60846577000 1
 
1.2%
759788000000 1
 
1.2%
135678000000 1
 
1.2%
1182930000 1
 
1.2%
Other values (21) 21
25.6%
ValueCountFrequency (%)
0 52
63.4%
737925000 1
 
1.2%
881425000 1
 
1.2%
899250000 1
 
1.2%
1182930000 1
 
1.2%
1203971000 1
 
1.2%
1317593000 1
 
1.2%
57362197000 1
 
1.2%
59038497000 1
 
1.2%
59684587000 1
 
1.2%
ValueCountFrequency (%)
1006580000000 1
1.2%
759788000000 1
1.2%
585333000000 1
1.2%
415800000000 1
1.2%
376652000000 1
1.2%
374549000000 1
1.2%
179811000000 1
1.2%
172794000000 1
1.2%
160127000000 1
1.2%
157604000000 1
1.2%

Interactions

2023-12-11T03:16:56.158836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:55.247112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:55.734536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:56.304600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:55.440302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:55.870696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:56.444994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:55.581222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T03:16:56.014339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T03:17:00.146370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명세원 유형명부과건수부과금액
과세년도1.0000.0000.0000.4050.000
세목명0.0001.0001.0000.7440.833
세원 유형명0.0001.0001.0000.5850.677
부과건수0.4050.7440.5851.0000.000
부과금액0.0000.8330.6770.0001.000
2023-12-11T03:17:00.321694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명세원 유형명
세목명1.0000.917
세원 유형명0.9171.000
2023-12-11T03:17:00.488612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과건수부과금액세목명세원 유형명
과세년도1.0000.2120.1670.0000.000
부과건수0.2121.0000.8900.5160.264
부과금액0.1670.8901.0000.4340.346
세목명0.0000.5160.4341.0000.917
세원 유형명0.0000.2640.3460.9171.000

Missing values

2023-12-11T03:16:56.626228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T03:16:56.854047image/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
0대구광역시대구광역시270002017취득세토지00
1대구광역시대구광역시270002017취득세건축물00
2대구광역시대구광역시270002017취득세선박00
3대구광역시대구광역시270002017취득세차량00
4대구광역시대구광역시270002017취득세기계장비00
5대구광역시대구광역시270002017취득세항공기00
6대구광역시대구광역시270002017취득세기타00
7대구광역시대구광역시270002017등록면허세등록면허세(등록)00
8대구광역시대구광역시270002017교육세교육세11161919886000
9대구광역시대구광역시270002017자동차세승용00
시도명시군구명자치단체코드과세년도세목명세원 유형명부과건수부과금액
72대구광역시대구광역시270002022자동차세승용00
73대구광역시대구광역시270002022자동차세기타승용00
74대구광역시대구광역시270002022자동차세승합00
75대구광역시대구광역시270002022자동차세화물00
76대구광역시대구광역시270002022자동차세특수00
77대구광역시대구광역시270002022자동차세3륜이하00
78대구광역시대구광역시270002022자동차세자동차세(주행)12122355000000
79대구광역시대구광역시270002022체납체납2531317593000
80대구광역시대구광역시270002022담배소비세담배소비세642138319000000
81대구광역시대구광역시270002022지방소비세지방소비세101006580000000