Overview

Dataset statistics

Number of variables5
Number of observations198
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory42.6 B

Variable types

Categorical2
Numeric2
Text1

Dataset

Description지방자치단체의 지방세 전체 과세액 중 비과세와 감면액이 차지하는 비율 현황입니다. 비과세· 감면액, 지방세 징수액, 지방세 비과세 감면율을 제공합니다.
Author경상남도
URLhttps://www.data.go.kr/data/15067831/fileData.do

Alerts

부과세액 is highly overall correlated with 감면세액High correlation
감면세액 is highly overall correlated with 부과세액High correlation
부과세액 has 4 (2.0%) zerosZeros
감면세액 has 49 (24.7%) zerosZeros

Reproduction

Analysis started2024-03-14 23:05:52.919271
Analysis finished2024-03-14 23:05:54.720146
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct22
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
창원시 성산구
 
9
창원시 의창구
 
9
창원시 마산합포구
 
9
창원시 마산회원구
 
9
창원시 진해구
 
9
Other values (17)
153 

Length

Max length9
Median length3
Mean length4.0909091
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row창원시 성산구
2nd row창원시 성산구
3rd row창원시 성산구
4th row창원시 성산구
5th row창원시 성산구

Common Values

ValueCountFrequency (%)
창원시 성산구 9
 
4.5%
창원시 의창구 9
 
4.5%
창원시 마산합포구 9
 
4.5%
창원시 마산회원구 9
 
4.5%
창원시 진해구 9
 
4.5%
진주시 9
 
4.5%
통영시 9
 
4.5%
사천시 9
 
4.5%
김해시 9
 
4.5%
밀양시 9
 
4.5%
Other values (12) 108
54.5%

Length

2024-03-15T08:05:54.955254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원시 45
 
18.5%
양산시 9
 
3.7%
거창군 9
 
3.7%
함양군 9
 
3.7%
산청군 9
 
3.7%
하동군 9
 
3.7%
남해군 9
 
3.7%
고성군 9
 
3.7%
창녕군 9
 
3.7%
함안군 9
 
3.7%
Other values (13) 117
48.1%

세목
Categorical

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
101000(취득세)
22 
102000(등록세)
22 
104000(주민세)
22 
105000(재산세)
22 
106000(자동차세)
22 
Other values (4)
88 

Length

Max length14
Median length11
Mean length11.888889
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row101000(취득세)
2nd row102000(등록세)
3rd row104000(주민세)
4th row105000(재산세)
5th row106000(자동차세)

Common Values

ValueCountFrequency (%)
101000(취득세) 22
11.1%
102000(등록세) 22
11.1%
104000(주민세) 22
11.1%
105000(재산세) 22
11.1%
106000(자동차세) 22
11.1%
109000(레저세) 22
11.1%
114000(등록면허세) 22
11.1%
135000(지역자원시설세 22
11.1%
140000(지방소득세) 22
11.1%

Length

2024-03-15T08:05:55.371627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:05:55.745578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
101000(취득세 22
11.1%
102000(등록세 22
11.1%
104000(주민세 22
11.1%
105000(재산세 22
11.1%
106000(자동차세 22
11.1%
109000(레저세 22
11.1%
114000(등록면허세 22
11.1%
135000(지역자원시설세 22
11.1%
140000(지방소득세 22
11.1%

부과세액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct195
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18112526
Minimum0
Maximum1.8990102 × 108
Zeros4
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T08:05:56.183601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28561.45
Q1704489.25
median5489675.5
Q319528128
95-th percentile82160634
Maximum1.8990102 × 108
Range1.8990102 × 108
Interquartile range (IQR)18823638

Descriptive statistics

Standard deviation30602675
Coefficient of variation (CV)1.6895863
Kurtosis10.406718
Mean18112526
Median Absolute Deviation (MAD)5439795.5
Skewness2.9684752
Sum3.5862801 × 109
Variance9.3652371 × 1014
MonotonicityNot monotonic
2024-03-15T08:05:56.642876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
2.0%
148861801 1
 
0.5%
2487894 1
 
0.5%
2401286 1
 
0.5%
11528420 1
 
0.5%
8599430 1
 
0.5%
38732 1
 
0.5%
1841701 1
 
0.5%
1850381 1
 
0.5%
12706602 1
 
0.5%
Other values (185) 185
93.4%
ValueCountFrequency (%)
0 4
2.0%
16130 1
 
0.5%
17661 1
 
0.5%
23059 1
 
0.5%
24936 1
 
0.5%
27444 1
 
0.5%
27952 1
 
0.5%
28669 1
 
0.5%
28747 1
 
0.5%
33128 1
 
0.5%
ValueCountFrequency (%)
189901021 1
0.5%
172953022 1
0.5%
148861801 1
0.5%
128118251 1
0.5%
120801983 1
0.5%
117316228 1
0.5%
87245779 1
0.5%
86847881 1
0.5%
85064007 1
0.5%
82464831 1
0.5%

감면세액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct150
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3011992
Minimum0
Maximum47456527
Zeros49
Zeros (%)24.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-03-15T08:05:57.283288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q183.5
median113202.5
Q31030618
95-th percentile17799793
Maximum47456527
Range47456527
Interquartile range (IQR)1030534.5

Descriptive statistics

Standard deviation7699430.9
Coefficient of variation (CV)2.5562587
Kurtosis12.710084
Mean3011992
Median Absolute Deviation (MAD)113202.5
Skewness3.4530542
Sum5.9637442 × 108
Variance5.9281236 × 1013
MonotonicityNot monotonic
2024-03-15T08:05:57.720542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
24.7%
26228737 1
 
0.5%
3195 1
 
0.5%
15368 1
 
0.5%
3115 1
 
0.5%
8194252 1
 
0.5%
329264 1
 
0.5%
112464 1
 
0.5%
116518 1
 
0.5%
3980150 1
 
0.5%
Other values (140) 140
70.7%
ValueCountFrequency (%)
0 49
24.7%
57 1
 
0.5%
163 1
 
0.5%
232 1
 
0.5%
282 1
 
0.5%
446 1
 
0.5%
450 1
 
0.5%
855 1
 
0.5%
1924 1
 
0.5%
2148 1
 
0.5%
ValueCountFrequency (%)
47456527 1
0.5%
43164353 1
0.5%
33848532 1
0.5%
32495518 1
0.5%
32272556 1
0.5%
31073506 1
0.5%
26228737 1
0.5%
26052753 1
0.5%
23208633 1
0.5%
21599525 1
0.5%

감면비율
Text

Distinct143
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-03-15T08:05:59.025123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2929293
Min length5

Characters and Unicode

Total characters1048
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)68.2%

Sample

1st row14.98%
2nd row16.65%
3rd row0.11%
4th row26.95%
5th row1.76%
ValueCountFrequency (%)
0.00 49
 
24.7%
5.67 2
 
1.0%
1.00 2
 
1.0%
3.09 2
 
1.0%
20.59 2
 
1.0%
1.87 2
 
1.0%
2.81 2
 
1.0%
5.49 2
 
1.0%
5.75 1
 
0.5%
22.45 1
 
0.5%
Other values (133) 133
67.2%
2024-03-15T08:06:00.478332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 204
19.5%
. 198
18.9%
% 198
18.9%
2 73
 
7.0%
1 71
 
6.8%
3 56
 
5.3%
4 52
 
5.0%
5 51
 
4.9%
9 40
 
3.8%
6 39
 
3.7%
Other values (2) 66
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 652
62.2%
Other Punctuation 396
37.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 204
31.3%
2 73
 
11.2%
1 71
 
10.9%
3 56
 
8.6%
4 52
 
8.0%
5 51
 
7.8%
9 40
 
6.1%
6 39
 
6.0%
7 34
 
5.2%
8 32
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 198
50.0%
% 198
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 204
19.5%
. 198
18.9%
% 198
18.9%
2 73
 
7.0%
1 71
 
6.8%
3 56
 
5.3%
4 52
 
5.0%
5 51
 
4.9%
9 40
 
3.8%
6 39
 
3.7%
Other values (2) 66
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 204
19.5%
. 198
18.9%
% 198
18.9%
2 73
 
7.0%
1 71
 
6.8%
3 56
 
5.3%
4 52
 
5.0%
5 51
 
4.9%
9 40
 
3.8%
6 39
 
3.7%
Other values (2) 66
 
6.3%

Interactions

2024-03-15T08:05:53.708607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:05:53.193101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:05:53.968717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:05:53.458869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:06:00.704892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구세목부과세액감면세액
시군구1.0000.0000.5640.528
세목0.0001.0000.3550.543
부과세액0.5640.3551.0000.755
감면세액0.5280.5430.7551.000
2024-03-15T08:06:00.858829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구세목
시군구1.0000.000
세목0.0001.000
2024-03-15T08:06:01.092548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과세액감면세액시군구세목
부과세액1.0000.5560.2590.182
감면세액0.5561.0000.2270.200
시군구0.2590.2271.0000.000
세목0.1820.2000.0001.000

Missing values

2024-03-15T08:05:54.281586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:05:54.600374image/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

시군구세목부과세액감면세액감면비율
0창원시 성산구101000(취득세)1488618012622873714.98%
1창원시 성산구102000(등록세)33313665516.65%
2창원시 성산구104000(주민세)34151029382150.11%
3창원시 성산구105000(재산세)706024662605275326.95%
4창원시 성산구106000(자동차세)5820363810479071.76%
5창원시 성산구109000(레저세)1784914000.00%
6창원시 성산구114000(등록면허세)107802241403971.28%
7창원시 성산구135000(지역자원시설세111229675803844.95%
8창원시 성산구140000(지방소득세)17295302200.00%
9창원시 의창구101000(취득세)601687531626499321.27%
시군구세목부과세액감면세액감면비율
188거창군140000(지방소득세)816403600.00%
189합천군101000(취득세)9534641247311020.59%
190합천군102000(등록세)11637300.00%
191합천군104000(주민세)8090719533610.54%
192합천군105000(재산세)4303284333688743.67%
193합천군106000(자동차세)55880811837383.18%
194합천군109000(레저세)2874700.00%
195합천군114000(등록면허세)789303712738.28%
196합천군135000(지역자원시설세64988813124716.80%
197합천군140000(지방소득세)624349300.00%